From de6d4bdc96debed9876a8141caaf5efa227ef88c Mon Sep 17 00:00:00 2001 From: spjuhel Date: Fri, 15 Nov 2024 08:42:23 +0100 Subject: [PATCH 01/49] Revert "Revert "Merge branch 'feature/documentation-restructuring' into develop"" This reverts commit b8cc3c4da3ee3beb9028a5ffaeed6392c048e742. --- doc/_static/css/custom.css | 18 +++++ doc/{ => api}/climada/climada.engine.rst | 0 .../climada/climada.engine.unsequa.rst | 0 .../climada/climada.entity.disc_rates.rst | 0 .../climada.entity.exposures.litpop.rst | 0 .../climada/climada.entity.exposures.rst | 0 .../climada/climada.entity.impact_funcs.rst | 0 .../climada/climada.entity.measures.rst | 0 doc/{ => api}/climada/climada.entity.rst | 0 .../climada/climada.hazard.centroids.rst | 0 doc/{ => api}/climada/climada.hazard.rst | 0 .../climada/climada.hazard.trop_cyclone.rst | 0 doc/{ => api}/climada/climada.rst | 0 .../climada/climada.util.calibrate.rst | 0 doc/{ => api}/climada/climada.util.rst | 0 doc/api/index.rst | 1 + doc/conf.py | 22 +++++- doc/development/index.rst | 7 ++ doc/getting-started/index.rst | 60 +++++++++++++++ doc/index.rst | 74 ++----------------- doc/misc/AUTHORS.md | 2 +- doc/misc/CHANGELOG.md | 2 +- doc/misc/CONTRIBUTING.md | 2 +- doc/user-guide/index.rst | 7 ++ 24 files changed, 124 insertions(+), 71 deletions(-) create mode 100644 doc/_static/css/custom.css rename doc/{ => api}/climada/climada.engine.rst (100%) rename doc/{ => api}/climada/climada.engine.unsequa.rst (100%) rename doc/{ => api}/climada/climada.entity.disc_rates.rst (100%) rename doc/{ => api}/climada/climada.entity.exposures.litpop.rst (100%) rename doc/{ => api}/climada/climada.entity.exposures.rst (100%) rename doc/{ => api}/climada/climada.entity.impact_funcs.rst (100%) rename doc/{ => api}/climada/climada.entity.measures.rst (100%) rename doc/{ => api}/climada/climada.entity.rst (100%) rename doc/{ => api}/climada/climada.hazard.centroids.rst (100%) rename doc/{ => api}/climada/climada.hazard.rst (100%) rename doc/{ => api}/climada/climada.hazard.trop_cyclone.rst (100%) rename doc/{ => api}/climada/climada.rst (100%) rename doc/{ => api}/climada/climada.util.calibrate.rst (100%) rename doc/{ => api}/climada/climada.util.rst (100%) create mode 100644 doc/api/index.rst create mode 100644 doc/development/index.rst create mode 100644 doc/getting-started/index.rst create mode 100644 doc/user-guide/index.rst diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css new file mode 100644 index 0000000000..e894d682ee --- /dev/null +++ b/doc/_static/css/custom.css @@ -0,0 +1,18 @@ +:root { + + .navbar-brand { + height: 7rem; + max-height: 7rem; + } + +} + +.bd-page-width { + max-width: 98rem; +} + + +html { + --pst-font-size-base: 18px; + --pst-header-height: 7rem; +} diff --git a/doc/climada/climada.engine.rst b/doc/api/climada/climada.engine.rst similarity index 100% rename from doc/climada/climada.engine.rst rename to doc/api/climada/climada.engine.rst diff --git a/doc/climada/climada.engine.unsequa.rst b/doc/api/climada/climada.engine.unsequa.rst similarity index 100% rename from doc/climada/climada.engine.unsequa.rst rename to doc/api/climada/climada.engine.unsequa.rst diff --git a/doc/climada/climada.entity.disc_rates.rst b/doc/api/climada/climada.entity.disc_rates.rst similarity index 100% rename from doc/climada/climada.entity.disc_rates.rst rename to doc/api/climada/climada.entity.disc_rates.rst diff --git a/doc/climada/climada.entity.exposures.litpop.rst b/doc/api/climada/climada.entity.exposures.litpop.rst similarity index 100% rename from doc/climada/climada.entity.exposures.litpop.rst rename to doc/api/climada/climada.entity.exposures.litpop.rst diff --git a/doc/climada/climada.entity.exposures.rst b/doc/api/climada/climada.entity.exposures.rst similarity index 100% rename from doc/climada/climada.entity.exposures.rst rename to doc/api/climada/climada.entity.exposures.rst diff --git a/doc/climada/climada.entity.impact_funcs.rst b/doc/api/climada/climada.entity.impact_funcs.rst similarity index 100% rename from doc/climada/climada.entity.impact_funcs.rst rename to doc/api/climada/climada.entity.impact_funcs.rst diff --git a/doc/climada/climada.entity.measures.rst b/doc/api/climada/climada.entity.measures.rst similarity index 100% rename from doc/climada/climada.entity.measures.rst rename to doc/api/climada/climada.entity.measures.rst diff --git a/doc/climada/climada.entity.rst b/doc/api/climada/climada.entity.rst similarity index 100% rename from doc/climada/climada.entity.rst rename to doc/api/climada/climada.entity.rst diff --git a/doc/climada/climada.hazard.centroids.rst b/doc/api/climada/climada.hazard.centroids.rst similarity index 100% rename from doc/climada/climada.hazard.centroids.rst rename to doc/api/climada/climada.hazard.centroids.rst diff --git a/doc/climada/climada.hazard.rst b/doc/api/climada/climada.hazard.rst similarity index 100% rename from doc/climada/climada.hazard.rst rename to doc/api/climada/climada.hazard.rst diff --git a/doc/climada/climada.hazard.trop_cyclone.rst b/doc/api/climada/climada.hazard.trop_cyclone.rst similarity index 100% rename from doc/climada/climada.hazard.trop_cyclone.rst rename to doc/api/climada/climada.hazard.trop_cyclone.rst diff --git a/doc/climada/climada.rst b/doc/api/climada/climada.rst similarity index 100% rename from doc/climada/climada.rst rename to doc/api/climada/climada.rst diff --git a/doc/climada/climada.util.calibrate.rst b/doc/api/climada/climada.util.calibrate.rst similarity index 100% rename from doc/climada/climada.util.calibrate.rst rename to doc/api/climada/climada.util.calibrate.rst diff --git a/doc/climada/climada.util.rst b/doc/api/climada/climada.util.rst similarity index 100% rename from doc/climada/climada.util.rst rename to doc/api/climada/climada.util.rst diff --git a/doc/api/index.rst b/doc/api/index.rst new file mode 100644 index 0000000000..77248264ef --- /dev/null +++ b/doc/api/index.rst @@ -0,0 +1 @@ +Could be nice to have an API section homepage diff --git a/doc/conf.py b/doc/conf.py index b4ef1dc69d..9a9dfbd9d4 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.viewcode", "sphinx.ext.napoleon", "sphinx.ext.ifconfig", + "sphinx_design", "myst_nb", "sphinx_markdown_tables", "readthedocs_ext.readthedocs", @@ -123,12 +124,26 @@ # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. -html_theme = "sphinx_book_theme" +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} +html_theme_options = { + "header_links_before_dropdown": 7, + "icon_links": [ + { + # Label for this link + "name": "GitHub", + # URL where the link will redirect + "url": "https://github.com/CLIMADA-project", # required + # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local") + "icon": "fa-brands fa-square-github", + # The type of image to be used (see below for details) + "type": "fontawesome", + } + ], +} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] @@ -154,6 +169,9 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] +html_css_files = [ + "css/custom.css", +] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' diff --git a/doc/development/index.rst b/doc/development/index.rst new file mode 100644 index 0000000000..ff8730d4aa --- /dev/null +++ b/doc/development/index.rst @@ -0,0 +1,7 @@ +==================== +Developer guide +==================== + +Landing page of the Development section + +Introduce the section and links to all Development guides diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst new file mode 100644 index 0000000000..797f0f2d82 --- /dev/null +++ b/doc/getting-started/index.rst @@ -0,0 +1,60 @@ +=================== +Getting started +=================== + +Installation +------------------- + + + +Are you already working with conda ? proceed to install CLIMADA by executing the following line in the terminal:: + + conda create -n climada_env -c conda-forge climada + +Each time you will want to work with CLIMADA, simply activate the environnment:: + + conda activate climada_env + +You are good to go! + + +.. seealso:: + + You don't have conda installed or you are looking for advaced installation instructions ? Look up our `detailed instructions `__ on CLIMADA installation. + + +.. dropdown:: How does CLIMADA compute impacts ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do you create an Hazard ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do we define an exposure ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: How do we model vulnerability ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: Do you want to quantify the uncertainties ? + :color: primary + :icon: unlock + + And some content! + +.. dropdown:: Compare adaptation measures and assess their cost effectiveness + :color: primary + :icon: unlock + + And some content! diff --git a/doc/index.rst b/doc/index.rst index 4ad14dd788..eb8840627d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -47,73 +47,15 @@ Jump right in: with CLIMADA. If not, see https://www.gnu.org/licenses/. -.. toctree:: - :hidden: - - GitHub Repositories - CLIMADA Petals - Weather and Climate Risks Group - - .. toctree:: :maxdepth: 1 - :caption: User Guide - :hidden: - - guide/Guide_Introduction - Getting Started - guide/install - Running CLIMADA on Euler - - -.. toctree:: - :caption: API Reference - :hidden: - - Python Modules - -.. toctree:: - :maxdepth: 2 - :caption: Tutorials - :hidden: - - Overview - Python Introduction - Hazard - Exposures - Impact - Uncertainty Quantification - tutorial/climada_engine_Forecast - tutorial/climada_util_calibrate - Google Earth Engine - tutorial/climada_util_api_client - - -.. toctree:: - :maxdepth: 1 - :caption: Developer Guide - :hidden: - - Development with Git - guide/Guide_CLIMADA_Tutorial - guide/Guide_Configuration - guide/Guide_Testing - guide/Guide_continuous_integration_GitHub_actions - guide/Guide_Review - guide/Guide_PythonDos-n-Donts - guide/Guide_Exception_Logging - Performance and Best Practices - CLIMADA Coding Conventions - Building the Documentation - - -.. toctree:: - :caption: Miscellaneous - :hidden: - - README + Getting started + User Guide + Development + API + Authors Changelog - List of Authors - Contribution Guide - misc/citation + Contribute + CLIMADA Petals + Weather and Climate Risks Group diff --git a/doc/misc/AUTHORS.md b/doc/misc/AUTHORS.md index 561ed5cd36..2d2e8405f4 120000 --- a/doc/misc/AUTHORS.md +++ b/doc/misc/AUTHORS.md @@ -1 +1 @@ -../../AUTHORS.md +../../AUTHORS.md \ No newline at end of file diff --git a/doc/misc/CHANGELOG.md b/doc/misc/CHANGELOG.md index 03cb731062..699cc9e7b7 120000 --- a/doc/misc/CHANGELOG.md +++ b/doc/misc/CHANGELOG.md @@ -1 +1 @@ -../../CHANGELOG.md +../../CHANGELOG.md \ No newline at end of file diff --git a/doc/misc/CONTRIBUTING.md b/doc/misc/CONTRIBUTING.md index bcac999a8e..f939e75f21 120000 --- a/doc/misc/CONTRIBUTING.md +++ b/doc/misc/CONTRIBUTING.md @@ -1 +1 @@ -../../CONTRIBUTING.md +../../CONTRIBUTING.md \ No newline at end of file diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst new file mode 100644 index 0000000000..a2fd54a108 --- /dev/null +++ b/doc/user-guide/index.rst @@ -0,0 +1,7 @@ +=================== +User guide +=================== + +Landing page of User guide + +Introduce the tutorials and links to them From 1d05166158f53c5ee6c16c1041715b76859a25bd Mon Sep 17 00:00:00 2001 From: spjuhel Date: Fri, 15 Nov 2024 09:02:01 +0100 Subject: [PATCH 02/49] adds sphinx-design dependency --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 94514cf74c..0a1c3b69fd 100644 --- a/setup.py +++ b/setup.py @@ -19,6 +19,7 @@ "sphinx", "sphinx-book-theme", "sphinx-markdown-tables", + "sphinx-design", ] # Requirements for testing From 8b5227c39ac657baac6c394e23518c7839b25699 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Wed, 4 Dec 2024 18:58:06 +0100 Subject: [PATCH 03/49] fixes docstrings indentations errors --- climada/engine/unsequa/calc_base.py | 6 ++--- climada/engine/unsequa/calc_cost_benefit.py | 2 +- climada/engine/unsequa/calc_delta_climate.py | 2 +- climada/engine/unsequa/unc_output.py | 12 +--------- climada/hazard/base.py | 9 ++++---- climada/hazard/tc_clim_change.py | 14 ++++++------ climada/hazard/tc_tracks.py | 4 ++++ climada/hazard/trop_cyclone/trop_cyclone.py | 23 ++++++++++++-------- climada/util/api_client.py | 1 + climada/util/coordinates.py | 2 ++ 10 files changed, 39 insertions(+), 36 deletions(-) diff --git a/climada/engine/unsequa/calc_base.py b/climada/engine/unsequa/calc_base.py index 4ec8e55b06..a024d1e12f 100644 --- a/climada/engine/unsequa/calc_base.py +++ b/climada/engine/unsequa/calc_base.py @@ -207,7 +207,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): sampling_method : str, optional The sampling method as defined in SALib. Possible choices: 'saltelli', 'latin', 'morris', 'dgsm', 'fast_sampler', 'ff', 'finite_diff', - https://salib.readthedocs.io/en/latest/api.html + https://salib.readthedocs.io/en/latest/api.html The default is 'saltelli'. sampling_kwargs : kwargs, optional Optional keyword arguments passed on to the SALib sampling_method. @@ -223,7 +223,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): The 'ff' sampling method does not require a value for the N parameter. The inputed N value is hence ignored in the sampling process in the case of this method. - The 'ff' sampling method requires a number of uncerainty parameters to be + The 'ff' sampling method requires a number of uncertainty parameters to be a power of 2. The users can generate dummy variables to achieve this requirement. Please refer to https://salib.readthedocs.io/en/latest/api.html for more details. @@ -232,7 +232,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): See Also -------- SALib.sample: sampling methods from SALib SALib.sample - https://salib.readthedocs.io/en/latest/api.html + https://salib.readthedocs.io/en/latest/api.html """ diff --git a/climada/engine/unsequa/calc_cost_benefit.py b/climada/engine/unsequa/calc_cost_benefit.py index b42e76da11..3bdfe46bba 100644 --- a/climada/engine/unsequa/calc_cost_benefit.py +++ b/climada/engine/unsequa/calc_cost_benefit.py @@ -77,7 +77,7 @@ class CalcCostBenefit(Calc): _metric_names : tuple(str) Names of the cost benefit output metrics ('tot_climate_risk', 'benefit', 'cost_ben_ratio', - 'imp_meas_present', 'imp_meas_future') + 'imp_meas_present', 'imp_meas_future') """ diff --git a/climada/engine/unsequa/calc_delta_climate.py b/climada/engine/unsequa/calc_delta_climate.py index 0ec1fb3afc..93fdfec969 100644 --- a/climada/engine/unsequa/calc_delta_climate.py +++ b/climada/engine/unsequa/calc_delta_climate.py @@ -81,7 +81,7 @@ class CalcDeltaImpact(Calc): _input_var_names : tuple(str) Names of the required uncertainty input variables ('exp_initial_input_var', 'impf_initial_input_var', 'haz_initial_input_var', - 'exp_final_input_var', 'impf_final_input_var', 'haz_final_input_var'') + 'exp_final_input_var', 'impf_final_input_var', 'haz_final_input_var'') _metric_names : tuple(str) Names of the impact output metrics ('aai_agg', 'freq_curve', 'at_event', 'eai_exp') diff --git a/climada/engine/unsequa/unc_output.py b/climada/engine/unsequa/unc_output.py index d9c68fe69d..80a385395e 100644 --- a/climada/engine/unsequa/unc_output.py +++ b/climada/engine/unsequa/unc_output.py @@ -84,20 +84,9 @@ class UncOutput: samples_df : pandas.DataFrame Values of the sampled uncertainty parameters. It has n_samples rows and one column per uncertainty parameter. - sampling_method : str - Name of the sampling method from SAlib. - https://salib.readthedocs.io/en/latest/api.html# - n_samples : int - Effective number of samples (number of rows of samples_df) - param_labels : list - Name of all the uncertainty parameters distr_dict : dict Comon flattened dictionary of all the distr_dict of all input variables. It represents the distribution of all the uncertainty parameters. - problem_sa : dict - The description of the uncertainty variables and their - distribution as used in SALib. - https://salib.readthedocs.io/en/latest/basics.html. """ _metadata = [ @@ -192,6 +181,7 @@ def check_salib(self, sensitivity_method): def sampling_method(self): """ Returns the sampling method used to generate self.samples_df + See: https://salib.readthedocs.io/en/latest/api.html# Returns ------- diff --git a/climada/hazard/base.py b/climada/hazard/base.py index 51d90cbbf7..507b79312b 100644 --- a/climada/hazard/base.py +++ b/climada/hazard/base.py @@ -225,15 +225,16 @@ def check_matrices(self): -------- :py:func:`climada.util.checker.prune_csr_matrix` - Todo - ----- - * Check consistency with centroids - Raises ------ ValueError If matrices are ill-formed or ill-shaped in relation to each other """ + + # Todo (Previously in docstring) + # ----- + # * Check consistency with centroids + u_check.prune_csr_matrix(self.intensity) u_check.prune_csr_matrix(self.fraction) if self.fraction.nnz > 0: diff --git a/climada/hazard/tc_clim_change.py b/climada/hazard/tc_clim_change.py index 576cb38bde..9aad4b6032 100644 --- a/climada/hazard/tc_clim_change.py +++ b/climada/hazard/tc_clim_change.py @@ -71,11 +71,11 @@ def get_knutson_scaling_factor( in Jewson et al., (2021). Related publications: + - Knutson et al., (2020): Tropical cyclones and climate change assessment. Part II: Projected response to anthropogenic warming. Bull. Amer. Meteor. Soc., 101 (3), E303–E322, https://doi.org/10.1175/BAMS-D-18-0194.1. - - Jewson (2021): Conversion of the Knutson et al. (2020) Tropical Cyclone Climate Change Projections to Risk Model Baselines, https://doi.org/10.1175/JAMC-D-21-0102.1 @@ -94,15 +94,15 @@ def get_knutson_scaling_factor( the provided percentiles are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications for more details. possible percentiles: - '5/10' either the 5th or 10th percentile depending on variable (see text above) - '25' for the 25th percentile - '50' for the 50th percentile - '75' for the 75th percentile - '90/95' either the 90th or 95th percentile depending on variable (see text above) + - '5/10' either the 5th or 10th percentile depending on variable (see text above) + - '25' for the 25th percentile + - '50' for the 50th percentile + - '75' for the 75th percentile + - '90/95' either the 90th or 95th percentile depending on variable (see text above) Default: '50' basin : str region of interest, possible choices are: - 'NA', 'WP', 'EP', 'NI', 'SI', 'SP' + 'NA', 'WP', 'EP', 'NI', 'SI', 'SP' baseline : tuple of int the starting and ending years that define the historical baseline. The historical baseline period must fall within diff --git a/climada/hazard/tc_tracks.py b/climada/hazard/tc_tracks.py index 963d282cd3..4a6b03d2e3 100644 --- a/climada/hazard/tc_tracks.py +++ b/climada/hazard/tc_tracks.py @@ -198,6 +198,7 @@ class TCTracks: ---------- data : list(xarray.Dataset) List of tropical cyclone tracks. Each track contains following attributes: + - time (coords) - lat (coords) - lon (coords) @@ -216,9 +217,12 @@ class TCTracks: - data_provider (attrs) - id_no (attrs) - category (attrs) + Computed during processing: + - on_land (bool for each track position) - dist_since_lf (in km) + Additional data variables such as "nature" (specifiying, for each track position, whether a system is a disturbance, tropical storm, post-transition extratropical storm etc.) might be included, depending on the data source and on use cases. diff --git a/climada/hazard/trop_cyclone/trop_cyclone.py b/climada/hazard/trop_cyclone/trop_cyclone.py index ae01332ca0..f4f0a7b0d0 100644 --- a/climada/hazard/trop_cyclone/trop_cyclone.py +++ b/climada/hazard/trop_cyclone/trop_cyclone.py @@ -408,20 +408,25 @@ def apply_climate_scenario_knu( are the 10th, 25th, 50th, 75th and 90th. Please refer to the mentioned publications for more details. possible percentiles: - '5/10' either the 5th or 10th percentile depending on variable (see text above) - '25' for the 25th percentile - '50' for the 50th percentile - '75' for the 75th percentile - '90/95' either the 90th or 95th percentile depending on variable (see text above) + + - '5/10' either the 5th or 10th percentile depending on variable (see text above) + - '25' for the 25th percentile + - '50' for the 50th percentile + - '75' for the 75th percentile + - '90/95' either the 90th or 95th percentile depending on variable (see text above) + Default: '50' scenario : str possible scenarios: - '2.6' for RCP 2.6 - '4.5' for RCP 4.5 - '6.0' for RCP 6.0 - '8.5' for RCP 8.5 + + - '2.6' for RCP 2.6 + - '4.5' for RCP 4.5 + - '6.0' for RCP 6.0 + - '8.5' for RCP 8.5 + target_year : int future year to be simulated, between 2000 and 2100. Default: 2050. + Returns ------- haz_cc : climada.hazard.TropCyclone diff --git a/climada/util/api_client.py b/climada/util/api_client.py index 3857cf0d88..4fb1c92be5 100644 --- a/climada/util/api_client.py +++ b/climada/util/api_client.py @@ -1143,6 +1143,7 @@ def purge_cache(self, target_dir=SYSTEM_DIR, keep_testfiles=True): """Removes downloaded dataset files from the given directory if they have been downloaded with the API client, if they are beneath the given directory and if one of the following is the case: + - there status is neither 'active' nor 'test_dataset' - their status is 'test_dataset' and keep_testfiles is set to False - their status is 'active' and they are outdated, i.e., there is a dataset with the same diff --git a/climada/util/coordinates.py b/climada/util/coordinates.py index cec74b512c..5a13edf176 100644 --- a/climada/util/coordinates.py +++ b/climada/util/coordinates.py @@ -2940,9 +2940,11 @@ def set_df_geometry_points(df_val, scheduler=None, crs=None): contains latitude and longitude columns scheduler : str, optional Scheduler type for dask map_partitions. + .. deprecated:: 5.0 This function does not use dask features anymore. The parameter has no effect and will be removed in a future version. + crs : object (anything readable by pyproj4.CRS.from_user_input), optional Coordinate Reference System, if omitted or None: df_val.geometry.crs """ From 80c0694f7f591425aecc290bd7bb055669f40f28 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Wed, 4 Dec 2024 18:58:51 +0100 Subject: [PATCH 04/49] improves navbar header rendering --- doc/_static/css/custom.css | 8 ++++++-- doc/conf.py | 27 ++++++++++++++------------- 2 files changed, 20 insertions(+), 15 deletions(-) diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css index e894d682ee..aa76131f59 100644 --- a/doc/_static/css/custom.css +++ b/doc/_static/css/custom.css @@ -7,12 +7,16 @@ } +.bd-main .bd-content .bd-article-container { + max-width: 100%; /* default is 60em */ +} + .bd-page-width { - max-width: 98rem; + max-width: 100rem; } html { - --pst-font-size-base: 18px; + --pst-font-size-base: 16px; --pst-header-height: 7rem; } diff --git a/doc/conf.py b/doc/conf.py index 9a9dfbd9d4..195603a1fb 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -130,19 +130,20 @@ # further. For a list of options available for each theme, see the # documentation. html_theme_options = { - "header_links_before_dropdown": 7, - "icon_links": [ - { - # Label for this link - "name": "GitHub", - # URL where the link will redirect - "url": "https://github.com/CLIMADA-project", # required - # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local") - "icon": "fa-brands fa-square-github", - # The type of image to be used (see below for details) - "type": "fontawesome", - } - ], + "header_links_before_dropdown": 8, + "navbar_align": "left", + # "icon_links": [ + # { + # # Label for this link + # "name": "GitHub", + # # URL where the link will redirect + # "url": "https://github.com/CLIMADA-project", # required + # # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local") + # "icon": "fa-brands fa-square-github", + # # The type of image to be used (see below for details) + # "type": "fontawesome", + # } + # ], } # Add any paths that contain custom themes here, relative to this directory. From efef7610ee58d8bc810187da0664278f914af3bf Mon Sep 17 00:00:00 2001 From: spjuhel Date: Wed, 4 Dec 2024 19:02:07 +0100 Subject: [PATCH 05/49] creates the new toctrees to start seeing content --- doc/api/index.rst | 10 ++++ doc/development/index.rst | 22 +++++-- doc/getting-started/index.rst | 9 ++- doc/index.rst | 106 +++++++++++++++++++++++++++++----- doc/user-guide/index.rst | 21 +++++-- 5 files changed, 143 insertions(+), 25 deletions(-) diff --git a/doc/api/index.rst b/doc/api/index.rst index 77248264ef..562fd27de5 100644 --- a/doc/api/index.rst +++ b/doc/api/index.rst @@ -1 +1,11 @@ +============== +API Reference +============== + Could be nice to have an API section homepage + +.. toctree:: + :caption: API Reference + :hidden: + + Modules diff --git a/doc/development/index.rst b/doc/development/index.rst index ff8730d4aa..0291fe3bb8 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -1,7 +1,19 @@ -==================== -Developer guide -==================== +.. include:: ../misc/CONTRIBUTING.md + :parser: commonmark -Landing page of the Development section +.. toctree:: + :maxdepth: 1 + :caption: Developer Guide + :hidden: -Introduce the section and links to all Development guides + Development with Git + Guide_CLIMADA_Tutorial + Guide_Configuration + Guide_Testing + Guide_continuous_integration_GitHub_actions + Guide_Review + Guide_PythonDos-n-Donts + Guide_Exception_Logging + Performance and Best Practices + CLIMADA Coding Conventions + Building the Documentation <../misc/README> diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index 797f0f2d82..b4d45ca51b 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -20,7 +20,7 @@ You are good to go! .. seealso:: - You don't have conda installed or you are looking for advaced installation instructions ? Look up our `detailed instructions `__ on CLIMADA installation. + You don't have conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. .. dropdown:: How does CLIMADA compute impacts ? @@ -58,3 +58,10 @@ You are good to go! :icon: unlock And some content! + +.. toctree:: + :maxdepth: 1 + :hidden: + + install + Python Introduction <0_intro_python> diff --git a/doc/index.rst b/doc/index.rst index eb8840627d..e7df54dbea 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -6,22 +6,96 @@ Welcome to CLIMADA! :align: center :alt: CLIMADA Logo -CLIMADA stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.). +CLIMADA (CLIMate ADAptation) is a free and open-source software framework for +comprehensive climate risk assessment. Designed by a large scientific community, +CLIMADA offers a robust and flexible platform to analyse the impacts of natural +hazards and explore adaptation strategies, and it can be used by researchers, +policy and decision-makers. -CLIMADA is primarily developed and maintained by the `Weather and Climate Risks Group `_ at `ETH Zürich `_. +CLIMADA is primarily developed and maintained by the `Weather and Climate Risks +Group `_ at `ETH Zürich `_. -If you use CLIMADA for your own scientific work, please reference the appropriate publications according to the :doc:`misc/citation`. +If you use CLIMADA for your own scientific work, please reference the +appropriate publications according to the :doc:`misc/citation`. -This is the documentation of the CLIMADA core module which contains all functionalities necessary for performing climate risk analysis and appraisal of adaptation options. Modules for generating different types of hazards and other specialized applications can be found in the `CLIMADA Petals `_ module. +This is the documentation of the CLIMADA core module which contains all +functionalities necessary for performing climate risk analysis and appraisal of +adaptation options. Modules for generating different types of hazards and other +specialized applications can be found in the `CLIMADA Petals +`_ module. -Jump right in: +.. grid:: 1 2 2 2 + :gutter: 4 + :padding: 2 2 0 0 + :class-container: sd-text-center -* :doc:`README ` -* :doc:`Getting Started ` -* :doc:`Installation ` -* :doc:`Overview ` -* `GitHub Repository `_ -* :doc:`Module Reference ` + .. grid-item-card:: Getting Started + :shadow: md + + Getting started with CLIMADA: How to install? + What are the basic concepts and functionalities? + + +++ + + .. button-ref:: getting-started/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + + .. grid-item-card:: User Guide + :shadow: md + + Want to go more in depth? Check out the User guide. It contains detailed + tutorials on the different concepts, modules and possible usage of CLIMADA. + + +++ + + .. button-ref:: user-guide/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the user guide! + + + + .. grid-item-card:: Implementation API reference + :shadow: md + + The reference guide contains a detailed description of + the CLIMADA API. The API reference describes each module, class, + methods and functions. + + +++ + + .. button-ref:: api/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the reference guide! + + .. grid-item-card:: Developer guide + :shadow: md + + Saw a typo in the documentation? Want to improve + existing functionalities? Want to extend them? + The contributing guidelines will guide you through + the process of improving CLIMADA. + + +++ + + .. button-ref:: development/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the development guide! .. ifconfig:: readthedocs @@ -31,6 +105,8 @@ Jump right in: Use the drop-down menu on the bottom left to switch versions. ``stable`` refers to the most recent release, whereas ``latest`` refers to the latest development version. +**Date**: |today| **Version**: |version| + .. admonition:: Copyright Notice Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in :doc:`AUTHORS.md `. @@ -49,13 +125,13 @@ Jump right in: .. toctree:: :maxdepth: 1 + :hidden: Getting started User Guide Development - API - Authors + API Reference + About Changelog - Contribute CLIMADA Petals - Weather and Climate Risks Group + WCR Group diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index a2fd54a108..757568b049 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -1,7 +1,20 @@ -=================== +==================== User guide -=================== +==================== -Landing page of User guide +Landing page of the user guide -Introduce the tutorials and links to them +.. toctree:: + :maxdepth: 2 + :caption: User guides + :hidden: + + Overview <1_main_climada> + Hazard + Exposures + Impact + Uncertainty Quantification + climada_engine_Forecast + climada_util_calibrate + Google Earth Engine + climada_util_api_client From 567a492e51d05a742db0f93cc93e8ce32071f256 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Wed, 4 Dec 2024 19:02:44 +0100 Subject: [PATCH 06/49] renames folder to match new naming --- .../Guide_CLIMADA_Tutorial.ipynb | 0 .../Guide_CLIMADA_conventions.ipynb | 0 .../Guide_Configuration.ipynb | 0 doc/{guide => development}/Guide_Euler.ipynb | 0 .../Guide_Exception_Logging.ipynb | 0 .../Guide_Git_Development.ipynb | 0 doc/{guide => development}/Guide_Introduction.ipynb | 0 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100% rename from doc/tutorial/hazard.rst rename to doc/user-guide/hazard.rst diff --git a/doc/tutorial/img/UncertaintySensitivity.jpg b/doc/user-guide/img/UncertaintySensitivity.jpg similarity index 100% rename from doc/tutorial/img/UncertaintySensitivity.jpg rename to doc/user-guide/img/UncertaintySensitivity.jpg diff --git a/doc/tutorial/impact.rst b/doc/user-guide/impact.rst similarity index 100% rename from doc/tutorial/impact.rst rename to doc/user-guide/impact.rst diff --git a/doc/tutorial/unsequa.rst b/doc/user-guide/unsequa.rst similarity index 100% rename from doc/tutorial/unsequa.rst rename to doc/user-guide/unsequa.rst From 2dce78dab134f13c65eb59d770827f5364eacbb6 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Wed, 4 Dec 2024 19:18:28 +0100 Subject: [PATCH 07/49] changes conda to mamba --- doc/getting-started/index.rst | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index b4d45ca51b..e452d7cbde 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -2,25 +2,22 @@ Getting started =================== -Installation -------------------- +Quick installation +-------------------- +Are you already working with mamba or conda? proceed to install CLIMADA by executing the following line in the terminal:: - -Are you already working with conda ? proceed to install CLIMADA by executing the following line in the terminal:: - - conda create -n climada_env -c conda-forge climada + mamba create -n climada_env -c conda-forge climada Each time you will want to work with CLIMADA, simply activate the environnment:: - conda activate climada_env + mamba activate climada_env You are good to go! - .. seealso:: - You don't have conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. + You don't have mamba or conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. .. dropdown:: How does CLIMADA compute impacts ? From 62dfd2942fcecfd4d5d9f12824e0304d4adb59e6 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Mon, 6 Jan 2025 16:18:01 +0100 Subject: [PATCH 08/49] added draft for 10min intro --- doc/user-guide/0_10min_climada.ipynb | 2987 ++++++++++++++++++++++++++ doc/user-guide/index.rst | 1 + 2 files changed, 2988 insertions(+) create mode 100644 doc/user-guide/0_10min_climada.ipynb diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb new file mode 100644 index 0000000000..c802e8a469 --- /dev/null +++ b/doc/user-guide/0_10min_climada.ipynb @@ -0,0 +1,2987 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10 minutes CLIMADA\n", + "\n", + "This is a short introduction to the main building blocks of CLIMADA's impact calculation. For a more detailed impact calculation, please check out the more detailed [Impact Calculation](../tutorial/1_main_climada.ipynb). TBDnaming\n", + "\n", + "To get started, we import the CLIMADA package." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import climada as climada" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Key objects in a CLIMADA impact calculation\n", + "\n", + "For an impact calculation, we have to specify the following ingredients\n", + "1. Hazard: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard.\n", + "2. Exposures: The exposure information provides the location and the number/value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", + "3. ImpfFunc: The impact or vunerability functions models the average impact that is expected for a given exposure value and given hazard intensity.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Expsoure object\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Hazard object\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-12-19 18:11:22,924 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/vgebhart/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", + "2024-12-19 18:11:23,707 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2024-12-19 18:11:23,717 - climada.hazard.tc_tracks - INFO - Progress: 21%\n", + "2024-12-19 18:11:23,727 - climada.hazard.tc_tracks - INFO - Progress: 31%\n", + "2024-12-19 18:11:23,738 - climada.hazard.tc_tracks - INFO - Progress: 42%\n", + "2024-12-19 18:11:23,748 - climada.hazard.tc_tracks - INFO - Progress: 52%\n", + "2024-12-19 18:11:23,758 - climada.hazard.tc_tracks - INFO - Progress: 63%\n", + "2024-12-19 18:11:23,767 - climada.hazard.tc_tracks - INFO - Progress: 73%\n", + "2024-12-19 18:11:23,778 - climada.hazard.tc_tracks - INFO - Progress: 84%\n", + "2024-12-19 18:11:23,789 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", + "2024-12-19 18:11:23,794 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", + " if ibtracs_ds.dims['storm'] == 0:\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", + " return lib.intersection(a, b, **kwargs)\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n", + "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", + " return lib.buffer(\n" + ] + }, + { + "data": { + "image/png": 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a2pq6deuybds2tc+QkBAkSWLVqlU0b94cGxsbatSowZ49ewBITk7m77//RqPRWLhrHjhwgDZt2mBnZ4erqyudOnUiMTERgG3bttGyZUtcXFxwd3enW7du9+WVZGdnh4+PD4GBgTRt2pSPPvqIr776iqVLl7Jz5061XXh4OP3798fV1RV3d3d69uxJSEiIun3o0KH06tWL6dOn4+XlhZOTE6+//joGg0Ft07ZtW0aOHMnYsWPx8PBQQ2bOnz/PM888g4ODA97e3rz44otqqBPAb7/9Rq1atbC1tcXd3Z2nnnqK9PR0APbs2UPjxo2xt7fHxcWFFi1acOPGDSC/u2Zhv6e1a9fSrl077OzsqFOnDn///XeRr2tRECLvMUSWZf7++28+/vhjRo4cyZEjR3BxceHZZ58t7aEJbiHLZiV1+vRpwDyblpmZed9i5cyZM6oFr379+nz44YdF2j8qWWHESgPp2ebl9lU1vNvl7ob81NRUrl27ZhEjp9PpcHV1ve9kMnZ2dgQEBFC+fHlatGiBVqvFaDRStmxZGjduTEhICO+99x7Tpk2jWbNmDB8+nI8//rjA+D+BQCAQCJ4k3n33XUaPHs2FCxfo1KkTn332GfPmzeOTTz7h9OnTdOrUiR49enDlyhWL/caNG8fbb7/NyZMnad68OT169CA6OrrAY5w6dYoOHTpQo0YN/v77b/bv30/37t3V95P09HTGjh3L0aNH2bVrFxqNht69e6vvNQ/CkCFDcHV1Ze3atYA5Nr9du3Y4ODiwd+9e9u/fj4ODA507d7YQcbt27eLChQvs3r2bn3/+mXXr1uXzXFuxYgU6nY4DBw7w1VdfERkZSZs2bahbty7Hjh1j27ZtREdHqxPMkZGRDBw4kJdffpkLFy6wZ88e+vTpg6IoGI1GevXqRZs2bTh9+jR///03r7322h2toYX9niZPnsw777zDqVOnqFy5MgMHDsRoND7wdb0Twl3zMcNgMDB9+nR1xsXHx4datWrRs2fPYq9NJrh/HBwc0Gq1qrh7++23Lbb37t1bnWm6G1evXmXDhg1cu3aNihUrMnDgQGrWrFmksaRkKryx0kBMqnm5doDER8/q0Wru7LqRmprKlClTyMnJoXLlypQvXx43Nze8vb0tSic8CHq9nrJly3L9+nVu3LjBjRs3CA4OpmvXrvj6+qLVakWpD4FAIBAUiq8bQlrUwz+ugw+8dqx4+hozZoxFzNonn3zCu+++y4ABAwD46KOP2L17NwsWLOCLL75Q240cOZK+ffsCsGTJErZt28aCBQsYPnx4vmPMnTuXhg0bqvV8AWrUqKF+zu0nl2+//RYvLy/Onz9f5PeP29FoNFSuXFm11K1atQqNRsM333yjCqhly5bh4uLCnj176NixI2CO6/vuu++ws7OjRo0afPDBB4wbN44ZM2ao774VK1Zk7ty56rHef/996tevz+zZs9V13333HYGBgVy+fJm0tDSMRiN9+vRRy1nVqlULgISEBJKTk+nWrRsVKlQAoFq1anc8r8J+T++88w5du3YFYPr06dSoUYOrV68W2TOrsAiR9xiRlZXFlClTyMjIoFWrVvTr10+kxS1l/vzzT5KSkqhZs6aFdWvPnj2YTCYCAgLyCTyAdevW3VXknTlzhjVr1hATE4O3tzcvvPACTZs2LbKQN5oUpm00YHXLK7OMm8Si562w0d/dN1+WZXJycgCIj4/n8uXLgLlOXlEyeebt7/r16yQnJxMUFMS+ffu4du0a169fV9vY2try2muviaKtAoFAICgyaVGQGl7ao3gwGjZsqH5OSUkhIiIinydLixYt+OeffyzWNWvWTP2s0+lo2LAh165dQ6fT4eXlZdH21KlT9OvX745juHbtGu+99x6HDh0iLi5OteDdvHnzgUUemJO35Qq648ePc/Xq1Xwx/llZWRYuonXq1LF4N2jWrBlpaWmEhoaqAi3vtcvte/fu3QXGzl27do2OHTvSoUMHatWqRadOnejYsSPPPvssrq6uuLm5MXToUDp16sTTTz/NU089xXPPPYevr2++voryPeWduM7tKyYmRoi8/zpxcXF8/PHHZGRk0KtXL3V2Q1B6pKSk8NtvvwGwc+dOWrduTXp6OleuXCElJQUgn9WrXbt27N69G4ARI0YAMHjwYGrUqMH169fZt28fMTExxMXFUaZMGV599VXq1q1731bahbuM7DivoJGgSXmJ97vrcbO/d/B1rvjSarWMHz8eOzs7wsLCcHR0LHIZg9OnT/Prr78SHx9f4Pbhw4cTEBAgyiMIBAKB4L5x8Hn8j1tQsrbbXQTziqQ7YTKZ0Ov1uLq65nt/uFc8fffu3QkMDGTp0qX4+fkhyzI1a9a0cJ+8X0wmE1euXKFRo0aAeQK4QYMG/Pjjj/naenp63rO/vNfh9msnyzLdu3fno48+yrdfrrfQjh07OHjwINu3b2fRokVMnjyZw4cPU758eZYtW8bo0aPZtm0bq1evZsqUKezYseOOSeAK8z3lTaSTu6043GDvhBB5jzAhISHs2bOH69evq4GinTt3FgLvESE3O1T58uUJDg5m7969BbYrU6YMI0eOJD09HScnJ44ePUp6eroa65abWTKX8uXL06FDB1q1avVALrhbzphYdsDsY6+RYHhbPWXc797f+fPn2b59u2q5e/755zEYDDg6OlK2bFlkWSY4OJjo6Gi8vb0pV67cHX9sTCYTa9euZc+ePVSvXp2XX34ZjUZDQkIC6enpVK1aFTc3N+FmLBAIBIIHprhcJh8VnJyc8PPzY//+/bRu3Vpdf/DgQRo3bmzR9tChQ2obo9HIyZMneeGFFwr8fa1duza7du0qMBt7fHw8Fy5c4KuvvqJVq1YA7N+/v9jOacWKFSQmJqouofXr12f16tVqQpU78c8//5CZmakK1EOHDuHg4EBAQMAd96lfvz5r1qyhXLlyd/R6kySJFi1a0KJFC95//33Kli3LunXrGDt2LAD16tWjXr16TJw4kWbNmvHTTz/lE3lF+Z4eNkLkPUSSk5MJCQkhPDycqKgokpKSsLe3R6fT4ePjQ1hYGLIso9FouHHjhpoJKdeHuVOnTnf1CRY8HH799VcuX76sZn586623WLFiBcePHy+w/bvvvoskSarLwNy5c8nJycFkMnH06FH27dtH3bp1qVOnTrGlOb4QKTN1Q466PL6zjobl7i2mNm/eTExMDF26dGHv3r388MMPAHTs2JHy5cuzdetWbt68qbavVKkSrVq1yucmAfDLL79w4MAB+vTpQ7t27dQfm1zXCoFAIBAIBHdm3LhxTJ06lQoVKlC3bl2WLVvGqVOn8lm+vvjiCypVqkS1atX49NNPSUpKYsCAAaSlpeXrc+LEidSqVYsRI0YwfPhwrKys2L17N/369cPNzQ13d3e+/vprfH19uXnzpprVu6hkZGQQFRWF0WgkPDyctWvX8umnn/LGG2/Qrl07AAYNGsTHH39Mz5491eyUN2/eZO3atYwbN04VcQaDgVdeeYUpU6Zw48YNpk6dysiRI+86Sfy///2PpUuXMnDgQMaNG4eHhwdXr15l1apVLF26lGPHjrFr1y46duyIl5cXhw8fJjY2lmrVqhEcHMzXX39Njx498PPz49KlS1y+fJnBgwc/0Pf0sBEir4SZOnWqRXxTXiRJsshcmBetVkvDhg3p2bMn7u7uJT1MQSH54YcfLFLeent7o9PpaNOmjYXIyxU+np6eBYo2vV6PXq+nVatW6mxZcZGQrvDmzwaybt1yvetpGdD47qUSYmJiCA4OJiIigsaNG+Pu7k56ejqDBw8mOjqaP/74AzBbGUePHk1QUBDnzp3jjz/+4LvvvuPQoUO0aNGC2rVro9VqOXXqFPv27WPQoEEiM6ZAIBAIBPfB6NGjSUlJ4e233yYmJobq1auzceNGKlWqZNFuzpw5fPTRR5w8eZIKFSqwYcMG3NzcCszoXblyZbZv386kSZNo3Lgxtra2NGnShIEDB6LRaFi1ahWjR4+mZs2aVKlShYULF9K2bdsij33p0qUsXboUKysr3N3dadCgAatXr6Z3795qGzs7O/bu3cu7775Lnz59SE1Nxd/fnw4dOlhY9jp06EClSpVo3bo12dnZDBgwgGnTpt31+H5+fhw4cIB3332XTp06kZ2dTdmyZencuTMajQYnJyf27t3LggULSElJoWzZssybN48uXboQHR3NxYsXWbFiBfHx8fj6+jJy5Ehef/31Ao9V2O/pYSMpd1IZ98H48eP5+OOP1eXdu3ff88bYtm0bX3/9NUeOHCE2NhZPT08aN27Ma6+9RufOne95zIiICCZNmsTmzZtJT0+nUaNGvPfeezz11FMFtg8JCbGoXdakSRMOHTp012NMmzZNNWsHBwdTrly5e44rJSUFZ2dnRo4ciaenJ66urnh6euLj40OZMmUICAhQCzUajUZu3ryJm5sbLi4uqjXvSURRFJKTk9UaMY8qsixz9uxZQkJCsLOzIy4ujqioKNWN8dlnn8Xd3Z06derw3XffcezYv34iQUFBvP3226V2fgt25LDljInIZHMmzWUvWWGlu/NYFEXhf//7H1qtFhsbG9X//vjx49jZ2eHp6UlkZCQdOnRQs0Ll5eTJk2zbto3Q0FAcHByoVasWqampREZGMmPGjJI8VYHgkUSWZcLDw/H3939in+UCQWmSlZVFcHAw5cuXx8bGprSHU2rkvtOePHnSom4bQHR0NAaDoUSeQ4qiYDKZ0Gq1Jf6uM3ToUJKSkli/fn2JHudR4273eK7GSE5Ovquba7FZ8v755x8+/fTTQrdXFIXhw4fnq3ofHh7OunXrWLduHa+99hpffvnlHW+giIgImjRpYpHcYu/evXTq1IkVK1bwwgsv3HMchw8fZvPmzQW+vBYHs2bNuusXAOZMSEFBQeqyeCl4uKSlpeXLvrRixQqOHj1aYPuuXbvSvn17AFavXq0KvOHDh5d6yv99V0x8u9+ErR5q+Ut82v/uAg/MFuUqVarg5uZG9+7dWbx4MUePHuXNN99k7969nDx5Eo1GQ1ZWFhkZGfmyX+b6rIeGhnL8+HGOHDlCUlKSmopYIBAIBALBw8XJyYmYmBgSExOFR9h/lGIRebIsM2zYMIxGI15eXsTExNxznylTpqgCr169eowfP54KFSpw7do15s6dy8mTJ/n666/x9PRk5syZBfbx1ltvERYWRrNmzZg0aRKurq6sWbNGrQ3SpUuXQt3Y77//fomJPMGjy6JFi7hw4QIALi4uFrVUfHzM6bLc3Nzo0qULXl5eeHl5qTGUAOfOneOvv/4CYMGCBapltrTIMSrM3WYuqpmZAy800+HlVLgZtjfffFP9HBgYSFhYGAaDgUuXLgHmv/Fdu3aRlZV1xxIKgYGBBAYG0rFjR7Zv307z5s0f8IwEAoFAIBDcDzY2NkiSRFpamhB5/1GKReQtXLiQo0ePUrVqVXr37s2HH3541/ZXr15VCxY2bNiQvXv3qhlzGjVqRI8ePWjTpg3Hjh3jo48+4qWXXlKLEeaSnZ3Nhg0bCAwMZMeOHWrq1BYtWiDLMp999hmbN2++Y5AkgIeHB3FxcZw4cYJ169ZZ+AkLnmwiIyNVgQeQlJTEiBEjqFChApUqVSI1NRUrKysSEhIwGo0F+lX/8ssv6PV6pk2bVuoCD2DnBRMhcWbv63plJLrULJxFODY2loSEBMqWLYter+fYsWP4+/vz22+/kZGRQYUKFUhISMDDw4OOHTsWaPnMi52dHb169SqOUxIIBAKBQHAHypUrd8fcDpIk4ejoSEpKCgaD4ZF4T7kfli9fXtpDeGx5YL/A0NBQ3nvvPQCWLFlSqJvo008/xWg0WxwWLVqUr2aHnZ0dixYtAszxagsWLMjXR3x8PNnZ2TRu3DhfbYwOHToAqNkP78TQoUPVIpFTp0694x+K4MlClmU1VmzUqFHq/QvmApnbtm3jwIEDak2Y2ycYcskVgI/Cg9MkKyzYaaSGn4SfC7zbRV9oP/n09HT0ej05OTloNBr8/f0JDw8nLS2Nfv36UbZsWRITE7ly5QpTp05l/PjxZGRklOwJCQQCgUAgeCByw4WKo8ad4PHjgS15I0aMIC0tjSFDhtC2bVv27Nlz1/aKorBhwwYAqlateseigk2bNqVKlSpcunSJ9evXs3DhQouXVldXV3Q6HceOHcsXJ5Q7hlyXuzthb2/Pu+++y9tvv82ZM2f49ddfee655wpx1oLHlZSUFHXSwNHRkSpVqqDRaFi8eDFgLnNx/vx5ZFlm7dq19O7d+451WGrVqsX58+ctiluWFvuuyEQkQUSSQqtKEjX8Cj9/c3sioZEjR7J+/Xr279/Pr7/+WuA+9yqm+jAxyQoxKQoxqZCUoZCUCUnpCkmZCnotjGhX+t+PQCAQCAQPm+zsbIBH4j1F8PB5IJH3yy+/sGnTJtzc3Cyyat6N4OBg1cLWpk2bu7Zt06YNly5dIiwsLF9WTFtbWzp16sTmzZvp2LEjEydOxNXVlfXr17NgwQLs7Ox45pln7jmeN954g08++YTIyEimTZvGs88+KxKflALx8fFs3LgRGxsbateuzW+//UbVqlXp379/sfSfkJDAzp071QmAGjVq8L///S9fO2dnZ5o1awZw19T/iqIQEhKCp6fnI2HJ23jq3zTJAxo/2NyNnZ0d6enpd9xetWrVUskcqigKN+MVTocrBMfKhMQrBMcp3IhXqB0gcfxGfku8h4MQeQKBQCD4b5JrwdNq715GSfBkct9vg0lJSWqyho8++ghPT89C7Zc3Dqpq1ap3bZt3+4ULFyxEHpiTXRw6dIgDBw7QrVs3db0kSSxcuBBvb+97jsfW1paJEycyevRoLly4wE8//VSorJyC4mPbtm1s3LhRXd63bx9gTv+r0+no2bOnmuykqGRkZPDzzz9b1LBzc3MrUOAVBlmW+e233zh58iTJycn31Udxcz1WZud5mRp+Eq72Ei0q3v8khaIobN68WZ2I6dy5M9u2bbNoU1DNx5Iix6Rw/IbMrgsyuy+aKOchcfh6fjEXn1awq3VSpvmcHuVyHQKBQCAQFDe5JasATCbTfb9HCR5f7vsbHz9+PFFRUTRv3pxXXnml0PuFhoaqn+/kBpdLYGBggfvlUrFiRY4cOcLEiRPZvn07WVlZ1KtXjylTphTKipfLa6+9xty5cwkLC+ODDz5g4MCBYtbjIXH06FFV4L388svUq1ePkJAQUlJS+PPPP9m1axfR0dGMGDGiSP2GhITw008/WZTXqFOnDi+++GK+EgCFJVfg7dmzh7Jly9K6dWvV6leafP+3CQU4F6HwTkctWs39C5qNGzfyxx9/4OvryyuvvEL9+vWpWrUqPj4+aDQa/v77b9avX8+5c+eoUaNG8Z3EbdyMl/n+bxNbz5pIyfx3fVq2pZjTaaGsm0R5D2hRUYOznYSLLbjaSzjbgqudEHcCgUAg+O+Rmpqqfn4UPI4ED5/7Enn79+/nm2++QafT3bWOXUHkvenulqEPsEiokpaWVmCboKAgVq9eXejjF4S1tTWTJ0/mjTfe4MqVK3z//fe89NJLD9Sn4N6sXbuWnTt3UrFiRYYPH66Kr9xEJ9WqVWPs2LGcPXu20NYYWZb56aefOHjwIGD+buvUqcOzzz57z/vtThiNRrZu3crFixcJDg6mevXqvP7664+Ej3t8msLv/5hdNe2toU+DB5ucyP0OevfuTc2aNQGoXLmyur1Zs2bs3r2bL774gpdeeolGjRo90PFuJz7NnEBmwykTDcpKFgJPr4W6gRK1AzTU8NNQ3kPCz0VCpxVCTiAQCASCvGRlZaHVavH39xfeLP9RiuzXZTAYeO2111AUhbfeeqvIBY+zsrLUz/eaWbC2tlY/Z2Zm3qXlg/PKK6+oCShmzJjxUF3S/ksoikJ8fDybNm1i586dgDnDZUHWtZUrVwJmwVGYB1R8fDwTJ07k4MGD6HQ62rdvz8cff8zQoUPvW+ClpKQwd+5ctm7dSmRkJMOHD2fEiBGPhMAD2HrWRGVv87XpW1+Lo82DPcjbtm0LwPXr1wvc7uDgwMyZMylTpgzLli0rtr8Tk6zw82Ej3Rdls/6kCUWB0AQFeyvoUlPDx/307B1vzZcvWjOinZ42VbSUcdcIgScQCAQCQQFotVpMJhN//PEHkiSRlJRULP2GhISg0Wg4depUsfQnKDmKbMmbPXs2Fy5coEyZMkydOrXIB7SxsVE/3yula25WICj5bH56vZ733nuPV155heDgYL777jtef/31B+5XURRRmgFzbNyZM2dYu3YtGo2GzMxM6tWrR+fOndHpdBbXKCkpiS+//JLQ0FBq1qzJU089VeA1NJlMREdH4+fnh6IofPjhh2pdt9GjR6v+50W9/llZWSxfvpwzZ86o68qXL8+IESNUMfoofKdGk8KKAzlEp4CvMwxopHmgcR0/flwt7h4UFHTX2jvVq1fn5s2b/PXXX2rJkvslJkXhh4NGvj9ktkhKgIM1vNhEy7MNtdhZ/zsX9Shc9ycREbdYvOQ+98XzXyAoGXL/rh6nv7F7JfUbMmQIy5YtK7bjOTs7YzQaqVChApcuXcLJyalYrtXtfRTU5/Xr15kyZQp//fWXWme3QYMGzJ07l8qVKxMSEkJQUBAnTpygbt26DzymJ5G73eOF/R6LJPIuXryoFjpftGhRvvp0hcHR0VH9fCcXzFzyZvi7X0tMURg8eDAffvghV69eZdasWQwdOtTCmng/JCcnPzYPoJIiKipKLWbp7e1N3bp1qVmzpnpt8yYwOX78ODt27ADMbpvPP/98vgQn//zzD1u3blWXnZ2dSU5ORqfT4eTkRGpqKvv27aNOnTpFiq1UFIW//vqLQ4cOAeYSCba2tjRp0gRfX19ycnIemWQrAMdCTGhyjPjaQgM/DY5aPQ8yvBMnThAbG4uTkxNxcXF3PdfmzZtz7tw5zp49S8OGDe/7mOnZClM35pBlUAhyhkwDtK6iYVATHS52EjlZkJx1734E94eiKGxMSyPSaOR1Fxch9IoJWZbJzMwkOTlZZGsWCEoAg8GALMuYTCZMJtO9d3gEyJtb4pdffmH69OmcO3dOXWdra2txLjk5OQ/sNeTu7o5OpyMzM5OMjAwLQ8v9kjtGRVEKvPYGg4GOHTtSpUoVfvnlF3x9fQkLC2Pbtm0kJCRYfGfF8f0Vx3V6FDGZTMiyTGpqqoXRC8xeZoWhSCLv008/xWAwEBQUREZGBqtWrcrX5uzZs+rnP//8k6ioKAC6d++Ovb29RbKVvEkxCiLvH0TeJCwlhU6n4/3332fw4MGEhoaydOlSRo4c+UB9Ojs7q8Uo/4vcvHmThQsX4urqyqBBg6hatSrJyck4Ozvne6E8efIka9asAWD69Ol4eHio2zIzM9m1a5eFuMulcuXK2NnZYWVlxZkzZ0hJSeG3337jt99+IzAwkCpVqtC6dWvc3d3vOtbIyEi2b98OoCYdeZT57YyByEwZgE719Dg7P1g83ssvv8zRo0f5888/OX369D1LnGRnZxMaGoqdnd19PWCzchTeXGvgZJj5R6ecO3zYR0+9siLp0cMgR1GYHRfHb7c8KtwVhVddXEp3UE8IsiyTlpaGs7OzEHkCQQmQlZVFfHw8Wq32sUmU5+/vr352dXVFkiR1XUhICIGBgaxatYolS5Zw6NAhFi9ezJAhQ5g5cyZLly4lNjaWatWq8eGHH9K5c2d1v6CgIH766ScWLVrEiRMnqFChAp9//rkafnH69Gnat2/PxYsX1Rj7AwcOMHnyZI4ePYq1tTWNGzfm559/xtXVlW3btjFr1izOnj2LVqulWbNmLFiwQM2XkHu9JUkq8NpfunSJ69evs2vXLsqWLQuYvYNat26ttqlUqRKAGtffpk0bdu/ejSzLhTrf26/T3r17SUpKolGjRixcuJDs7GzGjBnD5MmTmThxIt999x12dnZMnz6dl19+uXi+0BJGq9Wi0WhwdHTMJ84LOyFbJJGXqySvX7/OwIED79l+xowZ6ufg4GDs7e2pXr26uu7ixYt33T/v9mrVqhVlqPfNoEGDmD17NhcvXmT27Nm8+uqrD9SfJEn/6dnxuXPnAuaac9WrV1fdwgq6Lt9//736ecaMGVSvXp1BgwaxY8cONX7P1tYWGxsbXnrpJcqXL5/vAfP7779bCMHQ0FBCQ0PR6/V07979rmPNLbPQqVMnGjRocP8n/RCITlFIz4ZaARqSMqBlJe0D32c6nY5mzZpx9OhRrKys7tlfTEwMYH7o5k3OUhiMJoUJa4ycuAEg4WYPiwZZUdZdvBA/DFJlmbHR0RzMzIRb37PpP/6sKk7yPuPENRUIip/cv6vH9W8s7/jz/j9hwgTmzZvHsmXLsLa2ZuHChcyfP5+vvvqKevXq8d1339GzZ0/OnTtHpUqV1P3Gjx/PggULqF69OvPnz6dnz54EBwfj7u6utjGZTEiSxKlTp3jqqad4+eWXWbhwITqdThVYkiSRkZHB2LFjqVWrFunp6bz//vv06dOHU6dOodFo8l3v25e9vLzQaDSsWbOGMWPGFCgEjxw5QuPGjdm5cyc1atRQ3zkKe763X6e9e/fy559/EhAQwN69ezlw4ACvvPIKhw4donXr1hw+fJjVq1fzxhtv0LFjx4diOHpQ7naPl4jIKw7Kly+Pn58fERERavzPndi7dy9gnv3ITYpS0mg0GqZNm8aAAQOIjIxkyZIlD+W4jzOhoaHs378fBwcHOnXqRExMDDExMXzzzTdqm8L4XLu5uamWX6PRyOnTpzl9+rS6XZIkXnvtNTw8PO5olWvatCkGg4HAwEC1nEZqauodE4nkJVccBgcHI8vyIz0Dv+OciVOhZjfg11prHqhsQl4uX77MxYsXefbZZ+/aLm/CldzZvaKw9YyJPZfMVkg7K1j8ghB4D4sIo5ERkZFcufUd6oGZXl50ewgu8QKBQFBS9P8qm7g71EwtSTwcJFa//mChPbmMGTOGPn36qMuffPIJ7777LgMGDADMdal3797NggUL+OKLL9R2I0eOpG/fvgAsWbKEbdu28e233zJ+/Hi1TW7o0Ny5c2nYsCGLFy9Wt+UtiZTbTy7ffvstXl5enD9/Xs26fTf8/f1ZuHAh48ePZ/r06TRs2JB27doxaNAggoKCANTa2u7u7vj4+BT5fG+/TmB+h1y4cCEajYYqVaowd+5cMjIymDRpEgATJ05kzpw5HDhwQO3/SadIb1XLly/PF1B++7+8yVh2796trs8VaZIk0bNnT8BsqcuNf7qdQ4cOqZa8nj17PtSZmueee07NGjpnzhyL2ECBJZcuXeLDDz9k37597Ny5k0mTJjF79mwLgTdnzhz8/Pzu2o/BYODpp5++4wMk1+3Vzc2NzZs35yvQnYunpyd9+/alcePGaDQatWTHkCFD7nkuTZo0AcxC59NPP32kM6xuP/+vD3uXWsU3V3Py5EmcnJzu6qqZkZHBm2++qS4X1VXmUpTMlPVGqvlK2FnBggF6avgJgfcwOJudzcDwcFXgOWs0fOvnJwSeQCB47IlLU4hJ4aH/K05hmTfGPSUlhYiICFq0aGHRpkWLFly4cMFiXd6avTqdjoYNG+ZrYzKZyM7O5tSpU3dNmHbt2jWef/55goKCcHJyonz58oA5/Kaw/O9//yMqKoqVK1fSrFkzfv31V2rUqKHmXCiIopxvQbkAatSoYTE57+3tbVEBQKvV4u7urnoh/Rd46JY8MCvwpUuXYjQaGTVqFHv37rXInpmZmcmoUaPMA9TpGDNmzEMdnyRJTJs2jb59+xITE6MmDRHkZ9OmTYD5D9pkMrFx40YyMjIAs/tAQECAmuXybixZsoRLly7lWz906FCqV6+Ovb29KvR9fHxwc3O7Z5+5EwjVqlUrVAKdIUOG0KtXL44cOcK6deuYN28ePXr0oFq1ao+UO0hEoszJm+YflQqeEhW9ikcg5eTkcOzYMZo0aXJX4ZZ3ImfmzJlFOoaiKMzdZkRWzMXb32irpVmFxyOe4nFnV3o642NiyLo1m1tGp+NLX1/KPoEB6wKB4L+Hh4MElI4lr7goKKHh7e8fhc2GXFCblJSUe2ar7969O4GBgSxduhQ/Pz9kWaZmzZr3zIh/O46OjvTo0YMePXowc+ZMOnXqxMyZM3n66aeLNO6Czreg63R7bgBJkgpcJ8tyUU7jsaZURF7lypV55513mDNnDseOHaNFixa8++67VKhQgWvXrvHRRx9x8uRJAMaNG6cGaD5MevfuTb169Th58iRxcXEP/fiPAykpKVy7do1evXqppv6aNWty5coVKlSoUChxl0ve5DROTk60aNGCp556qsCHUceOHe/ZX0xMDD/++CMAFy5c4O2332bixIl39MNWFEgKhvAjztSu/jTur7qzdu1aPv/8c1q2bMnzzz9f6HMpabacMeHrDIGuEq0qF49AkmWZJUuWkJWVRcuWLe/YLjk5WbVsz58/v8iZunZfkjkSbH7ABrhKvNqqVB5B/ykUReH75GQ+TkhQX3/q29iwyNsbl8ckYYFAIBDci+JymXxUcHJyws/Pj/3791skLTl48CCNGze2aJsbewbmcJfjx4/nSxzo4OBARkYGlStXZteuXUyfPj3fMePj47lw4QJfffUVrVq1AmD//v0PfC6SJFG1alUOHjwI/FsnO29mzaKcr6BwlNob1qxZs4iJieG7777j5MmTBfrHvvLKK0W2FBQXkiTxwQcf3DNZx38ZSZLQ6XQWJvxcX+iikJycTLdu3WjQoAE1atR44ExZ169fZ8GCBfnW3y3l7KWNsLqX+XPLidBhdn2MRiPLly9n//799OrVq8CC7Q8bRVHY8I9MZDJEJitM61k8VrwjR45w8eJF/ve//1n4x+c97o0bNyzcZIsq8HKMCsv3G9XltzvqsNI9OhbSJxGjojAnPp6f89z73RwcmOHpidUjZJ0WCAQCQX7GjRvH1KlTqVChAnXr1mXZsmWcOnVKncTO5YsvvqBSpUpUq1aNTz/9lMTExHxZJN3c3NDpdLz22mt07tyZN954gzfeeAMrKyt2795Nv379cHNzw93dna+//hpfX19u3rzJhAkTijTmU6dOMXXqVF588UWqV6+OlZUVf/31F9999x3vvvsuYE7OYmtry7Zt2wgICMDGxgZnZ+dCn6+gcJSayNNoNHz77bf07duXr7/+mqNHjxIXF4eHhweNGjXi9ddfp0uXLqU1PAC6detG48aNOXLkSKmO41HF0dGRPn368Msvv/D999/TqVMnvLy8kCSJrKwsdDrdPa15iYmJHDhwAG9vbzWV7oPi5eWF0WgWE/Pmzbuna8IPP/zA8d2XccWcDXbX9xdZl/K5hUl/06ZNPPfcc8Uyvgfhn1CFkDizPaZhOYlAt6KJvKSkJD7//HNq165Njx491PUHDhygRo0aFsHXedm8eTNbtmy5/4EDq46aOBeh0KCshI1eokM1EYdXkqTLMu9ER7M3M1Nd94aLC/+7lbpbIBAIBI82o0ePJiUlhbfffpuYmBiqV6/Oxo0b83m4zZkzR/WCq1ChAhs2bLAoQ5WLg4MDTZs25fvvv+fjjz9m2bJlaj3ggQMHotFoWLVqFaNHj6ZmzZpUqVKFhQsXquUYCkNAQADlypVj+vTphISEIEmSuvzWW28B5lCshQsX8sEHH/D+++/TqlUr9uzZU+jzFRQOSfmvV+ouIVJSUtQi3U9ynTxFUdixYwfr168HzNYdGxsbkpKSAHj++ect3P8URbGok/fnn3+SnJxM27ZtcXV1LZbxTJ48WT0+mGNAo6OjcXFxyReYu3z5co4cOYKzszO2SyajJDuATRb6KZ9i72BHr169uHDhAlu2bGHcuHGlmnZXURTeWp3DpSiZsESY1VtPj7qFt3rm5OTwzjvvqAllFi9ejCzLXL58mS+//JJOnToVOLFiMpkYM2YMJpOJkSNHsnfvXrp27Vqka5GcofDMwmxSbmXs/3mYnhr+wlWwpIgwGpkXH8+2W661OuADT096OjqW7sD+I8iyTHh4OP7+/o90ll6B4HElKyuL4OBgypcvXywFvh9XQkJCKF++PCdPnixUFvNcIiMj1Rg7BwcH0tLS8rXR6/V3TJqXWwhdq33w8k2CgrnbPV5YjSECYgQPhCRJdOzYkUaNGnHp0iVSUlLUYpXr16/np59+IjAwUC2ImZekpCSuXLlCfHw8DRs2LBaRFxoaaiHwgHyum4GBgej1ehISEkhKSsLb25vJkyezUa/hUpSJS04aJr38LpV9zS9nvr6+nDhxgiVLljBu3LhiGef98Mc5mV0XzNbF3vU0PFW9aC+PycnJqsDT6XScPn2aNWvWEBsbS6VKle6YUTMxMRGTycTw4cOpXr26Ra3LwnLipomsW8lKu9fRCIFXgvyTlcWo6GjiTSYa2NhwxWDgM29vGt/Doi0QCASC/x4FCTwwTwwXNsmL4NFEiDxBseDq6krTpk0t1jk7O7Ns2TI++ugjC7dJg8HA4sWLOXfunNr2ww8/pH79+tSoUYNGjRoVKWlLXnx9fSlXrhz29vYW/eclNDTUYnnChAnodDrSOhv56Q+zm+fRG7Iq8qysrBgxYgSffPIJ8+fP56233ipUds/iJD5NYdbmf0s6tKikxc6qaA/e3KynAP379+err76iWrVq9OvXj+rVq9/R4uDq6oq7uzsHDx6kdu3a9zX+JXtMeNib856Nai8eOyXFlrQ0JsfGYrjloJFgNPKTnx/lbwW5CwQCgUDg4+OD0WgkLS3NIl9BQECAWnxbCLzHH/G2JSgx9u/fj42NDYGBgWoylZycHE6cOKEKsDfeeAM/Pz/ee+89Tpw4wYkTJ/jhhx/UzKZgLswZGBiIm5tbgT7medHr9YwaNUq11K1cuZKrV69Svnx5unbtire3Nz/++KNagxHMliofHx+aBP0rco4EywzKo1ldXFwYM2YMM2fO5JdffmH48OF3HMOGDRvIyMigf//+xeaqtWy/EVs9JAFPV9fQqUbBljBZlomIiODQoUMcPnyYQYMGqS4cZcqUYfr06Tg6OrJkyRJsbGx47bXX1CxXd0Kr1VK1alWuXLlyX2M3mhSuxSoYjBDkKeHjLNzXihtFUfgiMZEleazYDW1s+Exk0BQIBIInlnLlynE/UVe55QVcXV0tRF5sbKyafE0IvMcfIfIEJUZCQgK1a9dmyJAhSJJEWloa06ZNQ6fT0ahRIwYMGKBa9z777DPOnTvH5s2bCQ8PVwUewJo1a9TPn3/++T2FU26fXl5ejB071mJbfHw8jRo1shB5586dw8fHh0peErUDJPRauJEgk2lQsM1jLfPw8KBfv378+OOP/P333xbFR/OSnJycbwbMYDCQlZV1X/GZh66bWPG3CSstNCkvMblrwXXNZFnmo48+srBUXrp0ycJPX5IkvvjiC4KDgxk9evQ9BV4utra2FqmOi0J0ioLm1qWo6CV+NIqbLFlmcmysGn8H0MfRkfc8PEQGTYFAIBDcFVtbWzJvJej6L9WQ+y8gRJ6gxKhWrRr79+8nNDQUR0dH4uPjycjIoFOnTvTo0cNCBOn1eurWrUvdunVJS0vDzs6OjIwMTCYTq1at4p9//gF4YMvYvn37sLa2pn79+pw4cQIwi8gOHTqg0UhU8NSw7qRZzOy+JPNMLUsrSPPmzTlz5gybN2+mYcOG+QptAgwePDjfuvT09PtKwpNjUpi92exCajBB19pa3O9QePXo0aMWAq9s2bL07t2bnJwczp49y86dO7l58yaurq6MHDmSypUrF3ocsbGxuLu7F2nsuSRlgJ0V6DTg9N+Njy8RYo1GRkZHczY7GwAJeNvNjaG3EhsJBAKBQHA37OzsyMzMxNXVFUeRnOuJQog8QYkxcOBA6tSpw6lTp8jIyMDd3Z0qVarcNRWuwWDg3Llz7Nu3j+vXr1ts69ev3wOPqXPnzowfP14tsZDL/PnzGTt2LN3rall30oReCweumPKJPEmSaNy4MadPn+bmzZtUqFChUMd1cXG5LytedLJMy4oakjJMeDlJ9LxLNs3br+ugQYNYtGgRN2/eJCcnh3LlytGjRw9atmx5z7ISebly5QqXL1++72KksgIJt4xMoi5e8XEhO5uRUVFE3bKw2koSc728aG9vX8ojEwgEAsHjgr29Penp6SQmJpKUlIS9vT1ubm5iovAJQIg8QYkhSVK+2mu5JRTyEhMTg7W1NadOnWL16tV37K9+/foPPKYrV67kE3iAKtYalJHoXEPD/qsyG/+RGdZappyHpfUwV0zFxsYWWuRJknRfRd7PRsAPh8wv8c811KDR3Pmhq9Vq0Wg0qrvF/PnzkWWZHj16UL16dXx9fYt8fIPBwNKlS3F0dKR9+/ZF3h9Al+e0jffn8Sm4jV3p6bwbE0PmrVgMH62WL3x8qGptXcojEwgEAsHjhCRJeHl5kZqaSmpqqupNVZTJYMGjiRB5glJl8+bNbN68Od/6/v3706ZNG1JSUoiOjqZ8+fL3nXEzL7klBHIZPHgw165do2fPngBoNBI1/DVsO2cWSj8fMTHxGU2BfTyMEpO2eijrLpGWraDcZVItOzubr776CgcHB8aPH8+xY8eIi4ujadOmBAUF3ffxjx49SlpaGm+//Taenp731Ye1DrydQNZAkj4HKDimUHBvFEVhWXIy8xMSyL376lhb85m3N57F8PchEAgEgv8ekiTh4OBAYmKiuix4/BFvBYKHjqIoJCUlYWNjYyHwxo0bR/ny5S3aOjk5FWsx+fr169O8eXMOHjwImC17L774okWb3vW1LN5txMNRIjhWJj1bwd763wde9q34p+Ka5crIyGDPnj1UrVo1nyDzd5WITlHIyoGb8QWLyoiICFauXElkZKRa3qFjx47FMrZDhw5RpUoVvL2977uPMh4Sun4xhBqNnNJqkZUyaMQPSJFJMpmYFBvLmawsnDQakmWZZ+ztmenpibUouC0QCASCByCvsPsvF5h/khBvBoKHzldffcXkyZN5++231XVvvvlmPoFXUjz//PNqQfPr169jMBgstjvbSjzXUEtogsLf1xU2n7b0MQwJCQEoFvEZExPDrFmz2LRpE5988km+7eXcJXQa8HUGmwIMYGfOnGHWrFncvHmT0aNHU6ZMmQceU15u3rx5X26eedFJEuVvJaiJMpk4c0skCwrP8aws+oaF8VdGBgmyTDm9nlEuLsz18hICTyAQCAR3Zfny5bi4uNy1Ta41D8zZ0R+Gt5KgZBFvB4KHTlKeWl4Azz33HFWqVHlox9doNKqlKzo6mqlTp+Zr07XOv4FkvxwzWTzsct01P/nkkwdON/zRRx+RmJiIg4MDnTp1yrddp5Uo5wGRybD+pEyO6d9xyLLMhg0bsLGx4aOPPioRkRwQEMCNGzce+Dw73vrhANieJ9V/SZKcqbD3sonE9Mf3h0pWFL5OTOSliAg1wYqbRsMbrq4MF4HxAoFA8FgxdOhQevXqlW/9nj17kCQp3/tRcdG/f38uX758z3Zubm64uLiQmpqqum4+bFJSUpg8eTJVq1bFxsYGHx8fnnrqKdauXVto4fkg1/Prr7+mbdu2ODk5leh38jAQIk/wUPnzzz/Vzy1btuSzzz6jbdu2D30cecsH3J4IBqCar4baAeYX6EtRCqfD/n2wNGnSBDDPej1oSYdhw4ZRo0YNxo0bp8YF5kWWZcq6ypR1U/BzzOGnXzdz/vx5wCxWIyIiyMzMLNYA6YyMDPVz165dCQ4O5scff3ygPtvZ2am+4dtS0zGZircWj6IohCXK/P6PiQ9+z6H3F9m0nJPN/37M4e/rj2fdnzijkdejovgsMZFcW3IjGxvWBATQys6uVMcmEAgEgkeL272ScsnJycHW1hYvL6979iFJEs7Ozjg5OalJWB4mSUlJNG/enO+//56JEydy4sQJ9u7dS//+/Rk/fnyB72vFTUZGBp07d2bSpEklfqySRog8wUMhPT2dsWPHqoXNZ82axfPPP19gnbmS5q+//uKHH35Ql994440C2/VraLbmBbrCgav/umzmFgVv3br1A43DYDAQExODwWBg9uzZ7N+/nxMnTnDx4kUOHTrEpk2bmDp1KieOHuRGgkREqp7tf1/k888/Z8SIEYwYMSLfmB6EhIQERowYwTvvvMP8+fOJjo7mp59+ArAoTn8/uGi19HZwpHmaE2lbXNl89sGFV2K62ZV20tpsen5uoMsCA5PW5vDrMRNXY/4V5SdvPn4i7++MDPqEh3PwVoFaCfifqyvf+vriJRKsCAQCwRPNtGnTqFu3rsW6BQsWUK5cOXU51yr44Ycf4ufnR+XKlQkJCUGSJH755Rfatm2LjY0NK1euLNBdc+PGjTRs2BAbGxs8PDzo06ePum3Tpk307NkTHx8fvL29ef7554mJiVG379mzB71ez65du2jYsCF2dnY0b96cS5cuWRzj999/p0GDBtjY2BAUFMT06dMLzHCey6RJkwgJCeHw4cMMGTKE6tWrU7lyZYYNG8apU6dUd9KVK1fSsGFDHB0d8fHxsRhfSEgI7dq1A8DV1RVJkhg6dGhhLz1jxoxhwoQJNG3atND7PKqItwVBiSPLMmvXriUrKwuNRsOwYcPu6RteklhbW6txdeXKlaNWrVoFtutUQ8PKQ3ApClYeMvFqKx1WOolly5YBPFD82/nz5/n8888t1uUKqlz0ej01atTA0bUMP5wFW52JZF05nHMs6wcOGzbsgcRyZmYmERERzJs3T12XmJjIjz/+iE6n4/XXX6ds2bL33X8uXTPceXmNeaZxwQ4jHapqLRLa3AtZVjgfqbDvssz+qybOhCvkem5U97XsR6uBqj4S9cpoaF358ZnLMioKixMT+TopSc2e6anV8pGXF01EOmuBQCAQ5GHXrl04OTmxY8cOC1fGd999l3nz5rFs2TKsra3Zvn27xX6bN2+mT58+TJ48mR9++AGDwWCRCC8nJ4dZs2bh4+PDzZs3mTFjBkOGDGHr1q0W/UyZMoV58+bh6enJ8OHDefnllzlw4AAAf/zxBy+88AILFy6kVatWXLt2jddeew2gwDAZWZZZtWoVgwYNws/PL992hzxhHwaDgRkzZlClShViYmJ46623GDp0KFu2bCEwMJA1a9bQt29fLl26hJOT03+2HIQQeYISZ/fu3fz999907dqVZ5555qGY2+9EWloax44dA0Cn0zF+/Pg7trXRS1Tx1nApSiY1C77/4wp+0lXOnTsH/Ou2WVRCQkJUgTdgwABatmzJjRs3SExMpGLFihgMBuzt7bG2tja7ZCbKnEgwcC5Ci33FrnQra4e3tzdubm6UK1cuX1xWTk7OXUWfLMsEBwejKAqpqaksXbo0X5vnnnuOJUuW0LZtW+rUqXNf53k7jcppaFdVw+6LMrGpsHSvkTFP312cKorCP2EKW08b2XtFJuwOIQK2VtCsgoZ6ZTTUKyNR21+DXREE5KNAhNHItNhYDtyy3gG0tLVltpcX7vdRY1EgEAj+SzwXFkZcMXi1FBUPrZZfAgIK3X7Tpk0WggXu3xvH3t6eb775BisrK+DfxHBjxoyxsMzdzqxZsxgwYADTp09X1+X9rX/55ZcB82+wn58f06ZNo2fPnqSmpuLo6Ki2mzlzJm3atAFgwoQJdO3alaysLGxsbJg1axYTJkxgyJAhAAQFBTFjxgzGjx9foMiLi4sjMTGRqlWr3vO8c8eX2+/ChQtp3LgxaWlpODg44ObmBoCXl1epGhVKGyHyBCXK4cOHWbNmDRUrVqRr166lnq0pr6gryGUgJiaGffv2sWvXLgASrKqB8zAAftobR9XUTQDMnj27yPF4iqKwd+9efvvtN7y9vXnhhRfUYurly5e/Y+IUP1cNsiIBCuejJCr37UxFr/zHjouL4/333wfMs2QFlT0wmUyMGjUq3/rx48czd+5cdXnJkiUAVKtWrUjneC/GddKx/4qBHBN8/7eJPvW1lHHPfy7RKQq//2NiwykTIXHme6Z2gERY4r/3T0UviVaVNLSspKFeoAa97vESdbkoisKGtDQ+jItTs5BqgTfd3HjJ2VmUmxAIBIJCEGcyEV0KIq+otGvXTv2NzeXw4cO88MILRe6rVq1aqsDLS8OGDe+636lTpxg2bNgdt588eZJp06Zx6tQpEhISVBF65coV6tevr7arXbu2+jk3E3dMTAxlypTh+PHjHD16lFmzZqltTCYTWVlZZGRkYHdbbHnu+2FhEordPr7c5HA3b96kevXq99z/v4IQeYIS5ezZswC0atWq1MagKAqSJBEeHo5Go7HIFPnZZ5/x5ptvkpKSwoQJE/Lt62K4jE7OwKixI13ni4yGT+bOyfdwuhcGg4HvvvuO06dP07hxY5577rl79hEfH8/Zs2eRZZmGnkFcjPSmsrfEz4cNvNc9fw2b2bNnq5+nT5+Op6cno0ePxt3dXV1/pwQqFy9eZNSoUSxatAiAbt260bp163yzjQ9KoJuGwc20fLvfRI4JPv7DyKLnzT9QOUaFfVdM/HJM5u9rMvJt8wGyAu2rmkVdq0pafJwff/ETbzIxLTaWP28luzljMPCUnR1DXVyoJ+oUCQQCQaHxKCWPh6Ie197enooVK1qsCwsLs1jWaDT5JsVzM3vf3tedjnE37ua+mJ6eTseOHenYsSMrV67E09OTq1ev0r17d7Kysiza5vUayhVnue9Ysiwzffr0Ai2KBdXh8/T0xNXVlQsXLtx17AWN7+bNm3Tq1OmOyWf+qwiRJyhRcv/gli1bRmhoKD169Hiox09MTOSPP/4gKCiIw4cP5ysFcOnSJXbt2qUmhLkdDSbcs/8hSV+JDK0nIycvZMuW39izZw8VK1Zk7Nixdz2+LMtMnTqVpKQkFEXhueeeu2s20aysLCIiIvLVzMuRrHFyGc6l6LJcjjLRyPUsnVvWVLenp6fne/jGxsby3nvv4eLigq2tLZGRkYA5YUxmZiZHjx4FzC4aJ06cIDExkRkzZpCSklKiNQuHtdax8R8TWgmux8qsOW4kKweWHzRS3l3i7+uWP2yNymnoWU/L09UePxfMu7EzPZ3psbEk5Lknezo4MNHDA0dR+04gEAiKRFFcJh91PD09iYqKUiepwWx9Ky5q167Nrl27eOmll/Jtu3jxInFxccyZM4fAwEAAjhw5AhTsAXUn6tevz6VLl/IJ2juh0Wjo378/P/zwA1OnTs0Xl5eeno61tXWB48sNw8kl17pZHEnpHmeEyBOUKEOGDCEuLo41a9awc+dOjh49Su3atXn66afx8PAo8eNbW1vj5ORExYoV8fX15fLly/n+6G8XeJIkYWdnR5cuXTAajZw31ODbo+bC59diFfbs2QOYa7kURFpaGlOmTLGYUapSpQq9e/emTJkyxMbGkpCQQMWKFZEkiW3btrFp06Y7nsPMmTOZMmUKTtmXSNaVRZG0rDxuT+eW/7axtbXF39+f8PBwi32bN2+Oo6MjaWlp6M41ICPJSLM3WmHtmU1kZCReXl40btyYixcv0q5dO9zd3S0sfyWBvbXErN56xq7OIS0Fpm3890dDvjVz6e8i0aOuhu51tAS6PVmCJ8Vk4sP4eDbmSU3tptEw1dOTp+4x+yoQCASCJ5+2bdsSGxvL3LlzefbZZ9m2bRtbt27FycmpWPqfOnUqHTp0oEKFCgwYMACj0cjWrVsZP348ZcqUwcrKikWLFjF8+HDOnj2rulympaURERFBQkICYM7KrdFoCrQcvv/++3Tr1o3AwED69euHRqPh9OnTnDlzhpkzZxY4rtmzZ7Nnzx6aNGnCrFmzaNiwIXq9nn379vHhhx9y9OjRAsc3Y8YMi37Kli2LJEls2rSJZ555BltbWxwcHPj8889Zt26dGpJTEFFRUURFRXH16lUAzpw5g6OjI2XKlFFj/R4XhMgTlCh2dnaUKVOGMWPGcOXKFU6dOsX58+fZt28f//vf/6hRo0aJH/+ZZ54BzEU+fX1987lF5KLX66lcuTL169enTJky+Pv7A2B/1cS3R81uEgdP3VDb38mKt2bNGguB16tXL7X4+pEjR1i+fHmRzmHKlCkABGTuIdK2OVbaHGLr2nEgI4MWdnYoisLBgwctBF7Lli15/vnnLfpZ/CEYzsGKHfBODGoNmGXLluHj44Onp2eRxnW/JGXIHAmWycrveUI1Hw3TumtpUVGDRvPkWO1y+TsjgymxsWphc4D2dnZM8/QUyVUEAoFAAJjj4RcvXszs2bOZMWMGffv25Z133uHrr78ulv7btm3Lr7/+yowZM5gzZw5OTk5qWShPT0+WL1/OpEmTWLhwIfXr1+eTTz6hR48e2NnZYWNjg+5WKZ/09HT0ej1JSUn5vIk6derEpk2b+OCDD5g7dy56vZ6qVavy6quv3nFcrq6uHDp0iDlz5jBz5kxu3LiBq6srtWrV4uOPP8bZ2RlJku44vlz8/f2ZPn06EyZM4KWXXmLw4MEsX76cuLg4rl27dtdr8+WXX1okpMm9LsuWLStSKYZHAUkp7UwYTygpKSk4OzuTnJxcbDMvTwKKohATE8NXX32Fi4sLo0ePLrFjHT9+HHt7e4tMTadPn8ZoNJKQkMDGjRsxGo3UqlWLatWqYTQaadOmTb7MlBcjTbyxMgeNBJXtbyKdWYCNjQ3z588v8PyWLl3KqVOnePrppzEYDOzdu5eAgACCgoJISkrin3/+ybefq6srQUFBHD9+nKCgIHr37s3u3bs5ceKE2sbR0ZFgK3+uDu5KtlbCXatlrb8/v//wg+p6mcu8efMsfO7jLsH6wRB7EbxqwCsH/20bGxvL2bNniYqKokePHvf05b9fsnIUVh4yseqIkRwTJKRbbn+6uob5/fMHkD8JZMoyXyQmsixPZlkHSWKihwc9HRwKFWgueHyQZZnw8HD8/f2LnKBJIBDcm6ysLIKDgylfvnyB8V2C4kdRFBISEjAajWg0GrKzszGZTDg6OpKamgqYJ8v1ej1arRYnJydVDAqKzt3u8cJqDHH1BQ8da2trmjZtyvr164mJicHLy6tEjuPl5aW6FOSSmwnKYDAgSRKenp54enqSkpJCWFgYqamp+czxtlYScbc86+r6+9O6Uyfat2/P999/j729PX379gXMfuyrV68mOjoagB07dqh9hIaGEhoaesexJiYmcvz4cTw9PXnjjTewt7enQoUKJCcnM3HiRABSU1Nx5yJW0jOcRCLeZOKD0FByjh1DwpxE5k6lE86ugvAjoLGCure54Ht6euLk5MS1a9cICwujSpUqd7+wRUSWFbaclVm4M4fIWxqnbqBESpZCuyoa9l6WyTbCjvMyW8+Y6FLrybJoncvO5t2YGIs4uyY2Nsz08sJP/AAKBAKB4DHBYDConkraW94nqamp+Pn5ERcXhyzLZGVloSgK2dnZuLq6ChFeiog3DEGp0KBBA9avX8+qVatKzJoXGBioBuXejpWVFe3btyc9PR07Ozt8fX3vKG4kwMMBNBI42VvRs0dPAA4dOgSYUxh/8cUXauar2rVrc/r0aTQaDS1btiQhIQEPDw/Cw8O5cuUKYLbcJSYm0r9/fwICAlAUBScnJ5ycnNQHYmpqKj///LM6jn79+vHrr79i8+WXBAwfjouNDbsMBgLatWNm2bJ3FHiyDGdv1VmXc6BSF8vtsbGxODk50ahRo2IvGHr8hswnf+RwNvxfhwGNBBW8JOY+q8fXRcP6k0beW2+Oy/vkjxwqeUNFr8df6BkVhW+TklicmEhu1GFDGxs62tsz0MlJlEYQCAQCwWODJEn4+vpiMBhIT0/HxsYGjUaDlZWVug3MFr/k5GSSk5OJjo7G1tYWNzc3YdUrBcQVF5QKbm5u1KxZk7Nnz5KYmIirq+tDO/aNGzfYtm0bV65cIeNW6voPP/wQZ2fnAttrNaiWvPRss1jZv3+/KtQWLFigth00aBAtWrRg165drFu3jr1796rbRo4cCZjrzCQmJtKkSROaN2+OXq8nIyOD7du3c+zYMaysrGjWrBl//vknycnJdOrUie7du6PRaAgMDGT+/Pl4/Pwz/wwYAFotYfXrk3QXa+jNvZAeC+XagoMfON2WgMzW1pZz587dNetnUYlLVVjxt5HlByyT3LSspOHtjjqLOn+96uk4GixzPlIhMUPhzZ+N/DRMg7Pd4yuCQnNymBATw6nsbHVdDSsrpnp4EFRATSOBQCAQCB4HrKys0Ov1mEwmtFptvnCDnJwcMjMz0Wq1mEwmsrPNid5cXV2LvSyT4O6IYAFBqfHiiy+i0WjYunXrQztmWloac+fO5Z9//rFIBRwXF1eo/aOio9m6dSs//fQTiYmJ6voKFSrwwQcf0KJFCwA6dOjAp59+Sr9+/dQ2v/32m2rJA6hcuTJ6vZ60tDTeeecdtm/fTtmyZXFzc2PdunUkJyczaNAgevbsqcb1VKxYkUmTJtGjcmUq79yJVpapa23Nu7GxXL1DfZjDCyErEUL2QJXu+bc7ODgUm8BTFIUNp0z0/CKb5QdM1PQ3P/wreUt8NVjPkhesCizkPrmrDhu9Qnwa3ExQePuXHHJMj1+4sKIorE1JoU9YmCrwNMBrLi6s9PcXAk8gEAgETwzZ2dnEx8db1PAzmUwYDAZMJhOurq74+/tja2tLfHw8CQkJ+er/CUoOIfIEpYajoyP+/v7s37+fixcvPpRjSpKkziQZDAbKlCnDsGHDqFChQqH2DwsNzVfuoFGjRly7do0FCxaotejAHICcGwMI5rS8lSpVUpe3bNmC0Whk/PjxgLncxLBhwxg5ciTPPPMMrq6uNGjQIN8YAgIC6N+/P2vfeIPnXVw4lZ1NtqIwKz6WHCwtZ2HBCpd/Nz9QHXyhWt87n1tsbCyff/45u3fvLtS1uJ2IJIU3VuYwZV0OKZnmdalZCtN66Ph1uBXNK9zZBdPOWsO856xxu5Xz5XCwzJwtxsfqxyDBZOLN6Gjei4sj49a4A3U6vvfz4003N6yEe6ZAIBAIniCMRiOZmZlkZmaq66ysrHB2dsba2lp16XR3d8fNzY3U1FTS8pQPEpQswl1TUKq8/vrrTJkyhT179lhkwSwp7O3tmTZtGiaTCTs7u0JlvlOAOgESISEh6OVUFEWhd+/erFu3DkDNbJmQkMAff/zB4MGD1X7d3d1ZvHgx3333HceOHbOw5MXHx6vlFNq1a0eTJk3Ubd26daNbt273HNtbbm4czsrCXmuigucl/iSJTtQgJVNBq4FPj+Zw4S2FbmF66jaQ0OrvLDSsra3JyMhQE8cUFllWWH3UxIKdRjLyGBOfqaVhQhc9rvaFEzd+LhKfDbDi5eUGckzwyzETFbwknm/y6D+m9t4qjRCfpzRCH0dHJri7Yy+yKwoEAoHgCcTe3h47OzsLl83U1FSSk5Oxs7NTcwVIkoSjoyOZmZmkpKTg6OhYWkP+TyHePgSlSm4sXkhIyEM7Zm5RzMKmNo8ID+efMIVkXVlyNI6ULVuWDh06FNj2yJEj7Ny5M996Hx8f9XNuUVFALZGQm6GzqFhrNCz0daWaz3lkXSY3SOBAfDxnwnJ4dUU228/JhNoqrK5roNb/7t6Xk5MT48aNY8CAAYU+flSywsifDKw4+K/A83KCz5/X89GzVoUWeLnULaNhWo9/E8jM3WbkwBXTXfYoXUzI/KFcYHb8vwLPVaNhobc3Mzw9hcATCAQCwRONRqOxEHm5Ai4jIwOTKf/vt9FoJCUlRV1WFAWj0UhWVhZpaWkkJycTHx9PbGwsUVFRREREEBERUWBfgrvz6E+RC55oct3xUlJS+PLLL+nXrx/u7u6lPCpLTp85A7QHwNvbm/FvjUeSJJo3b87Bgwfztd+5c6da/Bzg7NmzqovnRx99hKOjI6+++ir//PMPBoMBhweskxaotecpqnKeSEJJ5Og/yRz6y+z36OUIiRkw5mkdjjZ3P4Ysy1y8eJGqVasSlybhbAvWd7H8bT9nYvrvZtfMCp7mLKR9G2gZ2/Hex7obPepquRYr891+Ez5OMGdrDvOcJCp7P1qCKYkMfuMkEVISLTzd+TXCixa2tnzg6YmnyCImEAgEgv8gWq2WgIAATCZTvoya1tbWZGZmkpiYSFJSUpFCMrKzs7Gzsyvu4T7RiDcRwUMhLCyM2bNnU7FiRV588UV1vUajYf78+fz+++/s2bOH06dPM2nSJAICAu7S28Pl6ac6sDEiGUmjo2alQFWQderUiZycnHyFyPv372+xvHjxYgAmTpyoznDVr1+f+vXrF9sYG1OOEOIphzuXbVPR6mRcbTUkZcKaN6wo635v0ZWYmMiGDRtI13gxcbMDtQM0fNxPj1ZjuW96tsKcrUbWn/x3Vi0tG74arKfZXeLuisKbHXRkZMOm0ybSsuGNlQZ+fNUaH+dHI67tMtGs5x+yMAeba2wSme8fRAcrT1HYXCAQCAT/abRarVpHLy+2trakpqaqAlCr1aLRaNBqteqyTqdTPwPExMSQmZlJbGwskiRhbW2Ng4NDPjdRQX6KPDWekJDAypUrefPNN2nZsiVBQUE4OTlhbW2Nr68vHTt25Isvvih0YOW2bdvo06cPAQEBWFtbExAQQJ8+fdi2bVuh9o+IiGDo0KF4enpiZ2dHmzZtCnSXyyUkJARJktR/TZs2vecxpk2bprZ/mG6FTxJXr15V/1+7di2KonDhwgWOHz+OwWCgX79+vP322wB8/vnnJCcnl+ZwLbCzsSI4xYnrSXbEpP673tPTM5/VsVKlShbJUgwGA1ZWVnTo0OGONfuKAwmJrtQkjWyM0e6YjBriUmFCXwPlPTVoNPd+ELq7u/PUc2N4a70D0Snm4uRf7jFatDkTJtPvS4OFwOtYQ8OaEdbFJvAANBqJtzrqKOdhHndMCoz40UBqVukmYpGR2cVFVnFMFXiu2PEyzXnK2kv84AgEAoHgkWP58uW4uLiU9jDQ6XQ4OTkhSRJ6vR5vb288PT1xc3PDyckJe3t7rK2tLQSil5cX/v7+ODo6otFoyMrKIi4ujtDQUCIjI0lJSUGW5VI8q0eXIou8P//8kxdffJGFCxdy4MABgoODSU1NxWAwEBUVxY4dOxg5ciTVq1fPZ+HIi6IovP7663Tp0oV169YRHh6OwWAgPDycdevW0aVLF15//fW7mnIjIiJo0qQJK1asIC4ujszMTPbu3UunTp1YuXJloc7n8OHDbN68uaiXQVBEGjZsqGa1DA4OZuTIkSxatIhvv/2WCRMmcODAAcD8AMjIyGD69OlcuHChNIesYpunxnhWjuW2gIAAypQpoy7fXtj97NmzGAwGWrZsWSxjUWSIPAFnV8Hvr8OPXeCL6vDrAPixng22HVuSs9sBV3021joj5zyPcUGOKVTf+6+Y+N/PEsm3kmRV85Xo39hs7FcUhZV/G/lsZw6hCea/STsrmNFLxyf99DjbFr+4sbOS+Px5KwJczX1fiVZ4a3UOOcbSEXqpZPE9hznANXVdFbwZRkt8KbjGokAgEAgEuQwdOpRevXrlW79nzx4kSSIpKalEjtu/f38uX75cIn0XBY1Gg5OTE87OzmRkZFjE5uWSkpLC5MmTqVq1KjY2Nnh5edG5c2f27NmDv78/gYGBuLi4oNPpMBgMJCYmEhoaSnh4OImJiRiNxvu+ngkJCYwaNYoqVapgZ2dHmTJlGD169CNleCgK9+Wu6e/vT+vWrWnevDmBgYH4+fmRlZXFzZs3+fHHH9m2bRuhoaF07NiRc+fO4efnl6+PKVOm8PXXXwNQr149xo8fT4UKFbh27Rpz587l5MmTfP3113h6ejJz5swCx/HWW28RFhZGs2bNmDRpEq6urqxZs4YFCxYwfPhwunTpUqj4rvfff5+uXbvez6UQFBIHBwfmzp3LqVOnWLVqFWAuMZBbW+XHH39U29rY2JCVlcWqVauYOnVqoROklBS2VvBUNQ2hiQoJ6f/OFh04cIAff/xRvb8bNmzIlStXiI6OpmnTplhbW3Py5Em0Wi3e3t4PNIaIY3D1Dzj6OaRFmQubh+zJ00CCuPMAWmLK2ZKUo6AxguTTju+qGwgamEn39rb4N4GCjE2bT5uYsi4H463Ta1xew2cD9DjYSGRkK0zdmMO2s+aNtfwlJAk+7KOnjHvJfjfuDhJLXtDz4rcGkjLg8HWZqRtzmNVb/1CtZteJYx0nScecXUaDRAeq0pTySAjrnUAgEAhKl1zPodvJycnB1tYWW1vbUhhVwTg4OJCUlERiYiJWVlbY2NgAkJSURMuWLUlOTmbmzJmUL18ek8nE4cOHeeedd2jbtq1aVN3Z2RlFUUhPTyctLQ2DwUBKSgopKSnExsYC5mtSFHKTvHzyySdUr16dGzduMHz4cCIiIvjpp58KnX3c3t4eNze3ol2UEqDIb2i9evUiLCyMn376iZEjR9KzZ08aNWpEq1atGDRoEFu2bGHBggWA+cv65JNP8vVx9epV5s6dC5hfjA8cOMCAAQNo1KgRAwYMYP/+/TRs2BAwJ6q4du1avj6ys7PZsGEDgYGB7Nixg27dutGiRQvmz5/P6NGjSU9Pv6eFzsPDAzBnOMxNhy8oWerUqcOECRP44osv+Oyzzxg3blw+v+2srCzAHCP2KNRT0WokwhIVLkUpXI6GlAyz2FmzZg0AHTt2xMbGhmPHjrFw4UJWr17N4cOHiY6OJjg4+IH+0COOw8/dYWkjuLHXLPAAov75t43OBhQTaG5N2ci3LqfGBMgSDmetObdZ4dtmsKQW/P0pZNyq/Z5jUli4M4cJa/4VeE9Vk/j8eS0ONhLXY2UGLjWoAg+gUXmJ5S9blbjAy6Wch4ZFz1thfev8Nv0js+rIw8uydZZwNnBKFXiO2DCEpjQjSAg8gUAgEBQ706ZNo27duhbrFixYQLly5dTlXKvghx9+iJ+fH5UrV1ZDkn755Rfatm2LjY0NK1euLNBdc+PGjTRs2BAbGxs8PDzo06ePum3lypU0bNgQR0dHfHx8eP7554mJ+dcraM+ePej1enbt2kXDhg2xs7OjefPmXLp0yeIYv//+Ow0aNMDGxoagoCCmT5+O0WhUY+8AiwnbSZMmERISwuHDhxkyZAitW7cmKCiIgQMHsmnTJlJSUoiJiWHRokXUqVMHJycnKlasyNixY7GxscHb25vY2Fiee+45wJwsT5Ik+vfvb1HL707UrFmTNWvW0L17dypUqED79u2ZNWsWv//+O+np6apbqEajueM/RVFITU0t1PFKmiJb8m7PlFMQI0aMYPLkyaSlpbF379582z/99FOMRnOsz6JFi/LNLtjZ2bFo0SKaNWuG0WhkwYIFLFq0yKJNfHw82dnZNG7cGHt7e4ttHTp04LPPPiM8PPyu4xw6dCjff/89MTExTJ06lV69eomYmodM+fLlWbRoEYqi8PPPP7N//351m9Fo5MaNG9SqVasUR2imT30t+67IHL8hs+WMiQFNNIwcORK9Xk90dLQqTHPZtm2barF85plniny8zETYMR7SIuHKrbmKsL9BZwtBT0HQ0+DXENwqgp2H2TqnKJCVBBe+B1OWhHW2hKaWEdNZLS4XrAGIPQfbx8KZn0BXTWZz1RwuZytU9pa4HK3wXEMtbVwOs/+vTEy+7Xl/Qw6ZtybCHKxhZm89HaoVX+xdYakbqOGjZ/VMWJNDRS+J2VuM+DhLtKtasmM5xg22cBYvHEkjmyA86UUd7LEu0eMKBAKBQHAvdu3ahZOTEzt27LAIb3r33XeZN28ey5Ytw9ramu3bt1vst3nzZvr06cPkyZP54YcfMBgMFoYRg8HAjBkzqFKlCjExMbz11lsMHTqULVu2WPQzZcoU5s2bh6enJ8OHD+fll19Ww2/++OMPXnjhBRYuXEirVq24du0ar732GgBTp07F0dGRxMREEhMT8fb2RlEUVq1axaBBgyw8ALVaLfb29vj6+pKYmEhWVhY5OTm89dZbVKhQAYBx48ap46tXrx5r1qyhb9++HDp0CL1ej16vJyYmBkmSsLKyKjApzJ0ICwvD0dGRjIwMwGwguptV1Gg0EhERQUxMjBp3WJTjFSclkl1Tp9NhY2NDWloa2dnZFtsURWHDhg0AVK1a9Y6JT5o2bUqVKlW4dOkS69evZ+HChRYCzNXVFZ1Ox7Fjx8jIyLBIq7pnzx7AsjZZQdjb2/Puu+/y9ttvc+bMGX799VdV/QseLpIkMXDgQCpXrsyqVavIyMjA19eXmjVrlvbQAKgVoGH2FvPExG8nZJ6qrhAUFASYC3/eTq4fuJ2dHc2aNSvSsa5sgQ0vQ3q0WdTZuIGVHbScCLVeABungveTJJAcFM4nKigK1AyCKf9oWXwlmS1rDVT4zQn342aXiNAUGd0PGqrrrdA2N5L5jInJXXX0b6QlOCSQ7444EHLDqAq8yt4S8/vrKfuQrHcF0aGalv+1k5m33WzFm7Q2h1WvSyU2phPcZAtnAYghleYE0YGqwnonEAgEjyBL2U8a2fduWMw4YM0wCh93v2nTJjVHQS73WwPO3t6eb775RnXTzE0OOGbMGAvL3O3MmjWLAQMGMH36dHVdnTp11M8vv/yy+jkoKIiFCxfSuHFj0tLSLMY+c+ZM2rRpA8CECRPo2rUrWVlZ2NjYMGvWLCZMmMCQIUPUfmbMmMH48eOZOnUqTk5OWFlZERMTQ0JCAiaTicTERKpWrar2ryiKmonTysoKb29vTCYT77zzDqmpqSQmJgJmC2DPnj3V8eV6UFWpUgUXFxdMJhOpqamkp6fn0yV3IzExkXnz5jFw4EDVOHUvY5dOp8PHx4eEhASys7OJiIjA29u7QFfakqZERN6uXbuIizP7g+X9ssCcdCPXwpZ7Y9yJNm3acOnSJcLCwggJCaF8+fLqNltbWzp16sTmzZvp2LEjEydOxNXVlfXr17NgwQLs7OwKZUF54403+OSTT4iMjGTatGk8++yzpR4D9l9FkiQaNmxInTp1WLhwITExMWRlZT0SfuQ1/TUMa6XlSLDMP2EK438zML+/FS52EtWrV2fUqFHcvHlTncAAsxtnQQHWdyIrCbaMhIvrwfZWKKlGB23eh4bDQVcI49GNeJncybxy7uaMsCMqOZM+LIF9z8dy5BJU/MaFshvMD2ldjkSNv/T4WuuoXUEiuYbCnH2+/H1Nxlav4OcCDcpqeK+bHlur0hc3Q5rrOBOusP2cTFo2vLU6h5WvWmFXzGP7hzA2cUZdbkEF2lNFCDyBQCB4REkjm1Sy7t2wlGnXrh1LliyxWHf48GFeeOGFIvdVq1atAsVDbsjTnTh16hTDhg274/aTJ08ybdo0Tp06RUJCguqmePPmTapXr662q127tvrZ19cXMJc8KFOmDMePH+fo0aPMmjVLbWMymcjKylKNMzY2Ntjb25OWlqaeR16DTq51MiMjQ60prNVq840vVyTfPr5ctFotLi4uRcowmpKSQv/+/alVqxbz589Hr9ffe6dbWFlZ4ePjQ3JyMklJSURFReHl5aXGHj4sik3kpaamEhYWxq+//sq8efPU9bdnG8ybMfF2AXg7ebdfuHDBQuSB2T/50KFDHDhwgG7duqnrJUli4cKFhUp2YWtry8SJExk9ejQXLlzgp59+uq8/NMGDYzAY+Ouvvzh+/Dg3b94EzH+wLi4uxMXFUaNGjVId38AmOjadzibIQ+FMqIlhy2VerRuKkxRP06ZNqVatGp6enkRHR3Pjxo0iz8yt6mmOuwNwKQtVesIzn4NTEUoG7r0sU85dItBNonlFDRkGhRM3ZKTrjsQ6ZZPqZOLkjFjOD02h/kwvAg/r8KkFkdslft0OCTVkzrSRwQNyTPBKKx39GmgfGTdmSZL4oKeeqzEGrscqXIlWmL4xhzl9iy8Ry2nC2MC/QY9NKS8EnkAgEDziOJSSG31Rj2tvb0/FihUt1oWFhVks58Z25SU3Ud3tfd3pGHfjbpPn6enpdOzYkY4dO7Jy5Uo8PT25efMmnTp1ypfIJK/wyf0NzhWEsiwzffr0Ai2KecVObh+yLOPq6mqhE3KzcaakpKiT/gWN7+jRowwePLjIiVbuRGpqKp07d8bBwYF169YVSeDlxdnZWbVW5iZt8ff3L1ToW3HwQEf55JNPGDduXIHbtFot8+bNo1WrVhbrQ0ND1c/3Knidt65Y3v1yqVixIkeOHGHixIls376drKws6tWrx5QpU4oUB/Xaa68xd+5cwsLC+OCDDxg4cGCp+c/+FzGZTOzcuVP9Dhs0aEC9evVwd3encuXKfPHFFxgMBjw8PB44S+WD4Oko8c0QK8auNqBIGtJzJKbsCuTVBtY0uuVOUL9+fSIiIvj9998B6Nu3b6H7bzcDlrcFaydoNRlqPV+08SmKwu//yITEK4TEK8Slyby/wYjxltYM8HIhtWksZGjRlTfi9GsCbyR7sne6hqhT5jZOlzW0v2JNfE2ZZz+Qearhw3kQFQV7a4lP++sZ+LWBDANsOSNTO8DEoKYPPtbrpnA2aP8VeE0ox9NUEwJPIBAIHnGK4jL5qOPp6UlUVBSKoqji6dSpU8XWf+3atdm1axcvvfRSvm0XL14kLi6OOXPmqO/hx44dK/Ix6tevz6VLl/IJ2ttxcnJCp9MRGxtL3759+fHHH5k6daoal+fo6EhKSgqpqano9foCx7dr1y7gX8tfrlXwftxgU1JS6NSpE9bW1mzcuPGBrW+2trb4+voSHx+vlorz9vZ+KFa9EnmDa9u2LV988UWBJtO88Uu3+yTfTt6ZiDtlWQwKCmL16tX3OVIz1tbWTJ48mTfeeIMrV67w/fffF3jjC4qP7Oxs5s+fT1hYGJIkoSgKzs7ODBgwgIYNGyJJErIs880333D+/Hlat25dqgIvlzLuGj4baMWSPUY2nJJpUFbD54e9+W3PPpo7HMJGj+rvfa9JjNsp2xp6fGNOrOJc5t7tFUUhIknhYpTCpSiZkzdNBMf9u/1CpGX7iFgNL2Z5sNE3hgwUohQjW7yTyXjNgWAH8FmtI9lbxueaFt8TWi5NNhL2iYmmY7RU6Q7SI+TFHOSpYWZvPWNXm2c2F+4yUsVHomG5+5+cSc24Skb0L1QMaMYVfQYNKUtHqguBJxAIBIKHStu2bYmNjWXu3Lk8++yzbNu2ja1bt+LkdIeg/CIydepUOnToQIUKFRgwYABGo5GtW7cyfvx4ypQpg5WVFYsWLWL48OGcPXuWGTNmFPkY77//Pt26dSMwMJB+/fqh0Wg4ffo0Z86cyVcaLVfIjho1iv3799OkSRNmzZpFw4YN0el0bNy4kcWLF3PixIkCx/fZZ58BqPXsypYtiyRJbNq0iWeeeQZbW1scHBz4/PPPWbdunSoKbyc1NZWOHTuSkZHBypUr1ZIMYBbe92sAsrKywtfXl/T0dOLi4oiLiyvyO+L98ECvbS+99BJnzpzhzJkzHD58mBUrVtChQwf27NnDgAEDOHz4cL598mYhvFcQorX1vybwkk5F+sorr6ipaWfMmFGgWVxQPISFhTF27FjVPSF35iU5OZlly5axevVqNUHPyZMngaLXOilJ/F01TO6qZ2hzLadu3nKnsPdjZ1YvrofFqfdObrBxUaj3csECT1EUgmNl1p808tHWHF5alk2LOdl0XmBgzKocluwxcaaAZLJl3CT6NdTyyXN69oyzZkILe6Z7e1FOr+ef7Gy+Ck/jiytpBFc2cfidbNybK9i4KTj6y8SdtuLGHi0HPzaXXjj3q7kY+6PC09W1vNRCi5cjeDqa4/NuxN/fADOyQgmN/gVJyaFm6F46GfzoQg0h8AQCgUDw0KlWrRqLFy/miy++oE6dOhw5coR33nmn2Ppv27Ytv/76Kxs3bqRu3bq0b99efWf39PRk+fLl/Prrr1SvXp05c+YUWA7tXnTq1IlNmzaxY8cOGjVqRNOmTZk/fz5ly5bN1zZXDzg7O7N9+3ZeeOEFZs6cSb169WjdujUbN25k+vTpODs7Fzi+3DCxjIwMcnJy8Pf3Z/r06UyYMAFvb29GjhwJQFxcXIFl2XI5fvw4hw8f5syZM1SsWBFfX1/1X0EehUXF3t4ea2trTCZTgYXgixtJud3ptxiYM2cOEydOxMbGhg0bNtCxY0d128cff8z48eMB2Lp1K507d75jP1u3blXdLj/55BPefvvtBx5b3gQuU6dOZdq0aeq27777jldeeQWAL7/8ktdffx0w1yvJzUAUHBxsUafkTqSkpODs7ExSUlKxzbw8CSiKwr59+1i9ejXOzs7qrIuTkxMDBgwgMTGRX3/9FS8vL7UmiyRJvP76649EKYVccv9sVu2NZd1ZGy7FWqGRZBo7/sN7/cvh5eleLJmUwhNlfv9HZtNpExW8JPZcvLeIcbGF0R20NKuow8+lYJHyYWQ868MzyXAxgEFC+tONdxva83wTLTnpEufXwP7ZkHANPKpC3EXzft61ofV7ULXno2HZM5oUJq018Mc58/dR1l3i+1fMCXEKS5YhhpCI5Zhk8wSUk311Arz6ID0KJyh4LJFlmfDwcPz9/UUiL4GgBMjKyiIkJIRy5co99GQWAjMmk6nYQ5siIyPR6XR4enrmWy/LMl5eXneMj8stXeDi4vJIv3crikJoaCh6vV5NVlMQd7vHU1JScHFxITk5+a7nWiLumhMmTGDDhg0cOnSIYcOGce3aNTXI0NHRUW13r0LX6enp6ud7uXYWB4MHD+bDDz/k6tWrzJo1i6FDh1pYE++H5OTkfMGz/2UUxVx6YNasWUiSxN69e9FqtTRt2hStVku5cuU4c+YMERERFjfu+vXrKVOmED6MD4nw8HAkSSL86GYa2AWiuLbDxU7iRkIFDt7Q0ckh876tz1kGmUPBCnsumbgQ8e+9k54KvnlipV0doJy7hgBX2HNRJvWWkXxcZ53qtnhLQ1twNVrm1HYjztWycNYZkAwarNtFUN7DjZQU84OkfC8o1wNu7INTyyH9VsZhm7KwaTwc/ArqvQLlOkBph6+OaaOQmJRDaIKCIQOm/SYxpZsevfbeQi/HmEpE7AaMsgTYYmvth4N1e1JS8pfFEAgKiyzLZGZmkpycLESeQFACGAwGZFnGZDLdd/kBwYORm2ClONFoNBgMBoxGo0UyNRcXFxISEoiJicHd3b1AoSdJEjqdjtTU1HsmniltdDqdWh7iTphMJmRZ5uTJk+zcuZPMzEzc3d2xtrYudKK5Esuq0KNHDw4dOsTNmzc5cuQIzZs3ByzjlG7PJnQ7eU2jeZOwlBQ6nY7333+fwYMHExoaytKlS1UT7/3i7Oz8SM8oPGxyBa+zszOSJNG9e3eL7Uajke7du/Pxxx+r66ysrOjTpw/Ozs4Pdax3Q6/Xc/78eUwmE9UCbGlV3Z03VuZQ3kPicJhEr8Z6rHRFc/VLylD48ZCRg9dMnAuHvFMDkgQ+Won+zTVU9dNQxUeDm725/3nbDNg6KISkKDQsr6F97Ttnmtx82sTPh41cjLNF+dsR2xbJZJTNpLKVFaPS03nT2ppBzs5ob+3v0gNqd4drf8BfH0DqBcgMhvBgiN4HDr5QcyBU7wvedczjfNg4A9P7KbzwTTbxaRB5Hez2aJjV++4ZNw05ycREbUBrFY8WsLH2o5zvs2g1otC54MGQZZm0tDScnZ2FyBMISoCsrCzi4+PRarUiUV4pUtzX3tnZmZiYGOLi4vDw8FANRLa2tuh0OrKzs1WhZ29vn8+amJuNPff5+6hib29PcnIyCQkJeHh4FPiuotVqSU9P5/fff1e93lJSUlAUpdAhTCUm8jw8PNTPN27cUEVe3mQsFy9evGsfebdXq1atmEdYMIMGDWL27NlcvHiR2bNn8+qrrz5Qf5IkPTLp5x8Vcq9JQdflzTffzLdu7Nixj5QVD8xiNDExkZYtW9KypTmjV+eaMlvPylyPU+hYQ6ZzzcL9ecWlKqw4aGT1MdOt4uMSVXwkLkUplHOX6FFXS/c6Wnyc81+vU6EyPxxWkBUI8pCY0EVf4EulSVZYuMvId/tNOFiDn4uEt7OOWY28+Torge1paWRLEqtSU1mblkZvR0e6OjjgqdMhSVCpC1TsDFe3wd4PIOwQBDSBkD1w8hvYPwtcg8yCr0xLcxIZvd0DXOAi4u8qseh5a15aZiArBzadVvBxMvHm0wW7dRhyErgRuRKdzo4cI1jp3Snn+zw6rXD7ETw4eZ9x4vkvEBQ/uX9X4m+sdMjroVac19/GxgZXV1eSkpKIjIzEwcEBR0dH9Ho93t7epKSkkJGRQXx8PFlZWarHX2BgIBqNBnt7e7KyskhOTsba2vqRqLNcEC4uLqSlpZGZmUlYWBg+Pj75QnxkWVbF3OTJk/H391e3RUVF8e23397zOCUm8nILnoOlq2X58uXx8/MjIiKCv/7666597N1rLhrm7+9fqDi44kCj0TBt2jQGDBhAZGRkvoKVgofLK6+88sgJPDDfJ5UrV7aYzOjXUEtUikJShsKqI8Z7irzgOJnvD5q4FCVzJvzfB6ZOA3UDNbzXTUvtgDv/gGVky7y3Pgf51q496uoo55Ff4KVlKby7Joe9l82uFWnZ0KeeljFP69DrJKY4eGArSYTl5LAnMxM3jYaPExL4OCGBalZWVLO2ppmtLbWtranURU/FzhC8C86vMdf186oBwdGQeN28vG8WaK0gsIU5U2ilZx6Ola+mv4Y5ffW8tToHGx38ftpEShZM6GI+z1wys8K5EfUTJjmDHFMizg618HJtj077aLt3CAQCgUDwuJCTk4NGoymStU+SJJycnHBwcCApKYm0tDTS0tLw8PDAzs5O9Y5LTEy0yNYfFhaGn58fOp0OR0dH0tLSiImJeag16YqKv78/SUlJpKSkEBkZiUajwdnZGUdHRyRJIjY2FoAuXbpYCDwAO7vCzaKXiB+JLMusXbtWXc5bxFqSJHr27AmYLXWHDh0qsI9Dhw6plryePXs+1Jma5557Tk3yMWfOHIvYQEHJUZB/96Oa5dTR0ZFy5cpZTGA0LKchMQNMsgRIXI/N72utKArHQmRG/WSgxyIDvx03kZVzq66LDgY21rLlTWumdNNTJ1Bzx/teURTmbDHiZGMWhTX9JYY0z/8gvRkvM+gbgyrwtBqz6Hmn87/Cx0qSmOjhwTAXF+pYW9PgVoBvoE7HBYOBjampvBcTwyuRkSznb0KkOMo/pdBtCbwVBrUGQfn2IGkh3ZwrB5MBQnbDn5Nh1yRYUhOOLgHD3cNwH5gO1bS811VHrQCJ6BT45ZiJl5cbiEkxX+PUjMsER67AJGcAYK33xMutA1Z6l5IdmEAgEAgE/xFycnKIiIhQhUpR0Wg0uLm5ERAQgLW1NbGxsSQlJQFmHeHm5mZh+VIUhYwM8+963nwI8fHx938SJYwkSbi6uuLr64u1tTWyLJOYmEhoaChhYWFqvF7t2rXv+xhFFnnffffdXV+8ZVlm/PjxnDlzBoAWLVoQFBRk0WbMmDGqsh41alS+BBWZmZmMGjUKMMfJjRkzpqjDfCAkSVKzbsbExLB8+fKHevz/ImlpaflKbgQFBVGnTp1SGlHRkSSJgY213ExQiEpW2HTaUrQevm7ig99zeGmZgT2X/t0WkQyj2uv4Y4w1k7rq8b1DRsy8/HrMxLpTMqfDFGoHSMzspUN3W6KRI8EmPtySw/VYs8BxsoUlL+gZ1FRXoHisa2vLj35+vO7qymsuLjS2tUUHBOr1ZAFBtkZuksApwtjCWWRkHH3NZR8G74LxceaC7vVfA5fy//abEgax52HLCJgfANvegoSrhb+uRaVfIx3d62ixujV5dypUof9X2fwTcpybUatQFPPzy86mLOX9XsJK9+j67QsEAoFA8LiRGzaSWzf4QfrJrU+XnJxMXNy/xYBdXV0t2ppMJguxB+bYzZIuwfagWFlZ4ePjQ0BAAPb29hYJWZydnR8oe2yRbZhjx45l0qRJPPvsszRv3pxy5cphb29PUlISJ0+eZMWKFZw6dQowWzu++OKLfH1UrlyZd955hzlz5nDs2DFatGjBu+++S4UKFbh27RofffSRWh9t3LhxVKpU6b5P8H7p3bs39erV4+TJkxY3laBkuH79Oj/88IPFuuKsCfOweKamhh3nJI7dUNhzUeb5xiaSMiXmbzey74qMu4PZ8maUwdsJXmyqo08DLY42hbdU/3XJxEdbjepyv0Y6KnhZWvHWnTDywe9GZAWq+kgYTLBooJ4y7nef15EkiWrW1lS7lVV2krs757KzOZqVRapNKBqsOEM45XBnE2foSi20t+aKbFygRj/zPzCXX7iyBc7+/G//2clweAEc/gx6/wBVe4FVCXhJ9qqno5KXhjGrDUQlK7StcACdfEDd7mRfHX/P3mg0j6Ybx5OAokDcBdDbg0v+skgCgUAgeELRarXFlhVfo9Hg6+tLVFQU6enpqiXP2toarVarCiKdTkdMTEw+Q1RMTAx+fn53LL3wqKDVavHw8MDd3Z2srCxMJtMD1+Yrcp283LoM96JKlSr88MMPNGrUqMDtsiwzbNgwvvvuuzv28corr/D1118Xa3ayu9XJu51Nmzbly/5Y1Dp596ph8V9DURSSk5PV7Jp5ee+991TT+iuvvEKDBg1KY4iF4tq1ayxfvpxu3brRpEkTi21Lduew74rMzXgFXxe4HI0aNwfQtoqGTjW1dKqhKVSa/7zsumBi/G85lHOXCE9U6NNAy/jO/z64ZFlhwU4jyw786yraoZrEzF5WOBRBSBaEgsIJbnKacEJJxAVbtGjoQBUq44PmLoXDI0/Ckc/h7E9gzALXipAaDj514fnNYOt6x10fiPiUHLaf3kRtv9PqutORjehUpzNuDiLrYUkRfgR+6Wu24jYfD09/VNojergUpk5eVlaWqO8lENwnWVlZBAcHU758efF3VArkWpu0Wu1DC6eSZZmIiAhMJhOurq7qu3VOTo4a02YymbC2tsbKygpbW1sURSE2NhZra2vVIvi4cLd7vLAao8hvOYcOHWLRokX06dOHmjVr4unpiU6nw8nJiSpVqjBgwABWr17N6dOn7yjwwKzMv/32WzZv3kzPnj3x8/PDysoKPz8/evbsyZYtW/jmm29KNf10t27daNy4cakd/7/EvHnzLHynt27dWoqjuTNhYWGMGDFCHe/GjRuZPXs2V6/+63/Yt4EOvVYh2wjXYv4VeD7OMLuPns8G6OlWW1tkgbf3som3f8nBYITL0QrPNdIy9ul/LVEZBoW3VudYCLxBTbR80u/BBR6AhEQDytKIslTFm2QyScfAWk7xm3ICQ07SHff1rQc9vzXH8LWfDdkpYMyEsL9heRtIjXzg4eUnOxP3je/xtOaKuur7o+2Yvb09/b4ycDSk+Gv8CMy4BkHKrdxb17eX7lgeRVJTUzl9+jQRERHqOlmWiY6OLsVRCQQCQemTlpaGwWAgIiKChIQEdb1Go1ETkOR1wdTr9Tg7O6sWPS8vL9zc3LC1tcXOzg4PDw+ys7MJCwuzcOX8L1BkX6WqVatStWrVB64fl8szzzzDM888Uyx9FYZy5coVqTj57XFiguIjIiKCn3/+mf79+3Pt2jWLbU899VQpjSo/RpPC4esySLBzww4UJKRbVex69erFsmXLcHFxUdt+vTeHEzfN+zpYg1aG4W10DGqqxUZ/f2Jr1wUTY1fnUL+M2RW0ex0Nozr8G4eXnKHw/gYDp0LN48pNsDKgcfG7I9bEHy+cSMeAhMRNEvBLCScq8yr+Xj3Rau48q2rnDq0mmjNuruwE6dEQcwaWtYQXd5jFQbGQmgA/ToTIK3hcAVPfPoTYtebvkMoAxKTAq8sNfNBLR486D28m8r+CnYe5lIYxy5yQJzkMnAPuvd9/BQcHB5ycnPDx8VHXHT16lIMHD9K3b99HMqOwQCB4fFm+fDljxoxRk5c8ymi1WrKzs8nJySEnJwc3Nzd1W+77++0umc7OzsiyTEpKCkaj0SIpi729PdbW1oSHh5OQkICNjc1/pn5pkd01BYVDuGsWTF53zb/++otffvnFYru9vT1z5swpcZN6coZMaKLZypaerZCQrnAtViE7B0ITFTQSuNvJ3IzNIjTFhopeGkKjU4lKt8I2/Tw1s9ey4ONZJGZqCYlXCHSVSMqQmb05h5N5XKgdbeCTfjqaV7x/sXUpSub5pQYMt8LwhjbX8tbTOjQaszCJTVV47XsDV2MUyrpDWhbM6qOnRcWSvYYKCteJ41j2cepGX0aRc3B1qv9/9s47PIpy++Ofme2bTTa9J5DQe+/Sm4KFogiiiL1ey0+vXQEBxa5guZarqFiuKFhAQUVAQWkCSg8lvfeyybaZ+f0xZCGkkEAo6nyeZ5/sTnnfdyY7s3Pec873EBE8vFH7Fx2CD0dDSQoYAxS8c73cfr2eENtpGlz5abDkISjJUT+bbTBtLrTsRl6ZwsPLPGxJlkkIFcgtU5jUU8cDF9YtRqNx6qx7EtbPUt9fuBD6/evcjuds0phwzRPxeDxkZGTQsmVL7buooXES/qrhmjNnzqSkpIQvv/yyxvJ169YxfPhwiouLfZPGzUlVVRXl5eWEh4c3S3tnMlyzrKyMuXPnsmzZMjIzMwkMDKRz587cdttt9OrVy9dfixY1k71dLhc5OTkEBwfz+++/1zqfBQUFtdTyg4ODsdlsNY7hlltu4ccffyQrKwubzcbAgQN55plnaN++fbMe58lojnBNTXVA45wxZMgQn5FnMpmIj4/nyiuvPGMGnqIobE+VWfSTl8QwgRV/yEiKKoBSVAEONxh04Dka7dg50s3uHDOtw2B9kgyKBQSR2LiuZNKNuSsk1iWplpfNBOlFcPyMSYgfXNlHR+eY0zueZdu9dIsT2JqscHFXsYaBl1ksc9MHHtKL1J4dLnjjGgMdos583LkiK2z48FsGDe6BzpyPy51Lcdk2wgIGIupNJ90/uDVctwE+vERh0zAPOwtlDv1P5p1rjTXq2jWJ1F3wyWNQdbR+jj0crn4awtU83PAAgbdmGHhjnZePNklUumHJJgmXFx4bf+y8apw+HScdM/IOffvPMvJOBYPB4MsX19DQ0Ggqbre7VkFtUCeQLBbLeVsY/HhKSkq44IILKC0t5dFHH6V169bodDo2b97Mfffdx9dff43dripie73eGjXwqo+9pKSkzpJcNpsNk8lEcXGxzyNYVFSExWKp0U6vXr2YPn068fHxFBUVMXv2bMaMGUNycvJfKqcPzlCdPA2NxlBcXOx7f9111zFw4ECio6ObvR9JVvglycuNi90+g2j9AZkqD7i9EGUXcLjBbjlm4AFYzaqhEhEgcGEHLz2t2wkRskgrFjiUB8t2KCSEChQ5IK0Ijk+xC7LCf64x0C1eJMBy6oZDcoHMx5tltiarXronJxh8hsjhPIkZ77p9Bl50ILx/vfGsGHigeiusViuZ6cVYCCCwSKDVqj2IO39odBsBMXDFOsiKVm/I29MUnj5OObRJ7FkPH9x/zMCLbAU3vuoz8KrRiQJ3jjAc9d6py5Zuk3j8Kw+SrAU2NBdhnaDzNAjrCIdWQXHyuR6RhoaGxl+D2bNn07179xrLXn755RrCfzNnzmTChAk8/fTTREdH07ZtW1JSUhAEgc8++4xhw4ZhNptZsmQJixcvruUh/Prrr+nduzdms5nQ0FAmTZrkW7dkyRJ69+6Nv78/kZGRXHXVVeTl5fnWr1u3DoPBwJo1a+jduzdWq5WBAwdy4MCBGn1888039OrVC7PZTGJiInPmzMHrrf83/pFHHiElJYXNmzdzyy230KNHDxITE5k2bRpr1671ea2WL19Ov379aowvPz+foKAgUlNTGTlyJKCWWRAEgZkzZ2I2m/H39yc+Pp7Y2GP5AycWS7/55psZMmQILVu2pGfPnsybN4/09HRSUlLqHff5iubJ0zjruN1u7rjjDt8NZ9iwYfz444907NixWfs5nCexcpfE93sUUgsVusUJeCTVaCuuVLi0u4ifUaBthMCwdtAyRCA6kKPlDAQURUEUBd7+zyIykvdz24wZBLVswb3/8xJth33ZCn+mHzMKvIqaC6cT4L3rjLQKP/05lFW7j81GXdFb7xNr2ZMp89AXHp+eZUKowFszjETaz4wnSpblWmFner2eiy++mOLiYiKpgK8XqYZVUWaT2g4PEHhlmpFr31VDUpduk2gXIXBlU/IJt34NK19RdfsBWvWCKbPBXH99hok99ZgMAo8s8yDJ8PVOGbfXw1OTDE0WxdGojSBAeJdjJTR2Lobhc87pkDQ0NDT+VqxZs4aAgAB++OGHGnoTDz74IC+88ALvvfceJpOJ77+vqYC1cuVKJk2axKOPPsqHH36I2+1m5cqVvvVut5u5c+fSrl078vLyuPfee5k5cybffvttjXYee+wxXnjhBcLCwrj11lu5/vrr2bhRLVe0evVqrr76ahYuXMjgwYM5fPgwN998M6Cq25+ILMt8+umnTJ8+3TfhHxISgslk8uUSRkZG4vV6sVqtzJs3r87x9e7dm3feeYcbb7yR7du3Y7fba9XU0+l0vvILVVVV9Xo5HQ4H7733HgkJCcTFxTXmX3JeoRl5GmedLVu2APgu2lGjRqEoymm7wRVFYb/LxVfFDtZ96ofLCwUV0D1OILUQckoVOkYJXDNAx9hOugZD8xwOB06nU40Fb9cSV0UhPXv2xGjU8enNIhsPqbXqyo6LCOgRLzBvgoFIu4ChGZxpiqLwR5qMTgBJgbGd1Ea3JEv862MPlW6I8If+iSLPXG4g2O/MGCYFBQVs2rSJ8ePH14q9z8rK4tdff+Waq68GvyDIPghFWTDqJmhCYnPnGJHZlxp4ZJmaTP36Oi+RdhjarhG3qPVLYNcaMFrB5YDuY+HS+0B38n3HddFh1MG/P/fglVSjOi7Yyx3D9ei00M3TptsM+HmuWiqj7PTK/WhoaGg0yOGMt/BKFWe9X73ORqvYmxu9/YoVK2rVkKtWhmwqfn5+vPPOO75QxWpv0z333FPDM3ci8+fPZ+rUqcyZc2zmrVu3br73119/ve99YmIiCxcupG/fvlRUVNQY+7x58xg6dCgADz30EOPHj/eVh5k/fz4PPfQQ1157ra+duXPn8sADD9Rp5BUUFFBcXFwr981ms+Hn50daWho5OTnY7XZuvPFG38RzXeOrVuGsLjRutVpr9VdtFOfl5eHn54fRaMTf3x9BEHj99dd54IEHcDgctG/fnh9++KHOUNjzHc3I0zirSJLkm+UBuPzyy2soJ50qTlnm07IyFhUXk2AwIAt+JIQKFFQoeCQY3EZkci8dQ9sK6HUnNz5ef/11LBYLXbp0ISQkhPvuuw+j0ci+bJnXfvKyPkkmLggKHWpo5oLLDQxs1bxhkluSZTYelgm0woWdRCLtgq9OXrUIS2ywwItXGppUTL2p2Gw2evToUWdy9dq1a9m+fTuTJ0/G2qa3KnoiS1BWAIFNS/C+pJuOAzkyPyfJlFUp3PeZl9emC/RLbOC8rvsQ1r6nvg+Ng36TYMRMaEIi+KiOOl6ZCvd+6qFjtMDbP0tUueHBi87vwql/BQJiVG9exm/qa/SzqvKmhoaGRnPjlSrwSuXnehgnZfjw4bzxxhs1lm3evJmrr766yW116dKlTuOjd+/eDe63c+dObrrppnrX79ixg9mzZ7Nz506Kiop8OW5paWk1oq66du3qex8VFQWoRlN8fDy///47W7duZf78+b5tJEnC6XRSWVlZy/CqNrrqetYQBIHAwEAqKiooLS3l119/5dVXX2XPnj11js9kMvn6KykpqVNRMy4uDrfbTXFxMQ6HA4fDgSiK2Gw2pk+fzujRo8nOzub5559nypQpbNy48S8l8gOakadxltiwYQMff/wxgC+mun///owYMaJZ2jcKArtdTsw6NwWKl4h4J3GimeHt9VzYWUeYf+Mf+g8fPkx8fDxer5f//e9/DB8+AmN0P75a4+Grncdm2/zNMLCVwJzLzkyY5Meb1b5KKqFnC5EPc8t4dZnRZ+ANbSvy/BTDKZdlaCxms9k3K3YigwYNom3btlitVmSjGSH3iCp24ixHoOkqXveO1lNW5WH5DvVm/6+PPbx+tUDvlnUY5mvfh3XvH/vccxwMurLJfQIMaavjhSlwz6eqJ3HJJonYIIHp/bVb5OnSYjBkqc570n+Ddpec2/FoaGj8PdHrbCff6Dzo18/Pj9atW9dYlpGRUeOzKIq1yn2dWDaguq36+miIhkRYHA4HY8aMYcyYMSxZsoSwsDDS0tIYO3Ysbre7xrYGw7HJ0GrjrNrgkmWZOXPm1OlRrMtYCgsLIygoiH379tU5Lrvdjt1up6CggGuuuYbBgwfz4osvEhgYSFZWFjNmzPCN73hD0ePxkJ6eTnR0dI3xgurpi4iIwO12k52dTUlJCWVlZQQGBtKmTRvatGlD//79CQoKYvny5UybNq3e83Y+oj3BaJwVqg2845kxY0aztf+do5wMpRRR9BJlLWPsJdkMyUvCYoolwNQXaNhbmJGRgcvlolWrVvzyyy9s2bIFr2AmzzyQt5JHkbPbS+JxHoiIALi8t57JPRsO+zxVftwnsfaAeqMM84efQov41lFBx1H+HFrlz4WdRZ6ccO5zxxYtWgSoSql5vdtR2K4YBIGWdh0N/8TUjU4UePxiA8WVHtYdFce5/SM3b15jpEf8UUNPUWDtYlj/4bEdx94GA684rWMZ1l7HrEsVnvhKtaKfWeUlOlBgePu/lprW+UZoe9WbFxCrOnk1NDQ0zgRNCZk83wkLCyMnJwdFUXwGy86dO5ut/a5du7JmzRquu+66Wuv2799PQUEBCxYs8OWhbdu2rcl99OzZkwMHDtQyaOtDFEWuvPJKPvzwQ2bNmlVLiM/hcGAymUhNTaWoqIg5c+b4tB127doF4BN1qfZuHl9eIC8vj+jo6Do9hUaj0VeSQJIk8vPzaxiFiqLgcrmadgLOAzR1TY2zwomqTvfcc0+ztj/QpmNoZA4zYosYGSwR4yxBQEdR2Way8r8iLecTSir+xOM9Fq9fVFTEZ599RkZGBoqiYLVakSSJn7cd5IjfpWwJfgJP+BByHOqM05ECuKCNyEMX6Vl5l4kreje/5H6VW+HZVR7u/dSDnxG6J4C3nYNvq9Rx7w0r576rZeZPPPcGHsA111zji93XG+1YzS0wGELxUFu+uLEY9AIvTDEwuI16e6pyw21L3PyRLqsG3k/v1jTwLrzjtA28aib21HPTYNWoUxR48HMPe7JO/Vg0wBSgFrw/9B2UHDnXo9HQ0NA4/xk2bBj5+fk8++yzHD58mNdee43vvvuu2dqfNWsWn3zyCbNmzWLfvn3s2rWLZ599FoD4+HiMRiOLFi3iyJEjfP3118ydO7fJfTzxxBN88MEHzJ49mz179rBv3z7+97//8dhjj9W7z1NPPUVcXBz9+vXjgw8+YO/evRw8eJB3332X7t27U1FR4RvfRx99hMfjYefOnbz22msAFBYW+rYRBIF169ZRUVGBw+HA6/Xy0ksv+ZQ3TyQwMJCqqireffdddu3axdatW9mwYQNTpkzBYrEwbty4Jp+Dc41m5GmcUfbu3Ut+fn6NRNqOHTvSpk2bZu0nCD9u4gKuZQDDaEtLtw6nOw8BHU5PPuWVSeQVrSUjdymHM98mq2Al+YW7cTqr2LlzJ7GxsWz6I5n7Fqfye/CDZFqHIYlmnAY15LBXC4EFkw28fKWB6f1VVcbm5lCexFVvu/nwN9XdUeGC3MAqCjuWAmAVBF4MD+fqVtbzpp7bgAEDfLH/RkMQkjmUvTYrn7jLcNRRp6axGPUCL11pYGAr9RYlAC9976bom//CkR0gHL11XXQnDJh8uodRgztH6Lmoy1ED0wP3fuqmpFIz9E4V73GTn7qTl0/U0NDQ+MfToUMHXn/9dV577TW6devGli1buP/++5ut/WHDhrF06VK+/vprunfvzogRI9i8eTOgehEXL17M0qVL6dixIwsWLOD5559vch9jx45lxYoV/PDDD/Tp04f+/fvz4osv1ipifjxBQUFs2rSJq6++mnnz5tGjRw8GDx7MJ598wnPPPYfdbq8xvs6dO/PMM8/w4osvAqpqZmFhIZIk8fjjj/Pwww/TpUsXnnnmGQAyMzM5fPhwvf0HBASwdetWrrvuOoYNG8aUKVPw8/Pj119/bbZC8mcTQTkx6FejWWhsNfq/Mx6Ph7vvvrvGsvHjxzNw4EACAwPrdJk3BUVRKCgoICwsrM71XqmSsoo9FJRsQAG8UhlmYzQI4HRlIQh6RCEAP2tHVuzuwtu/2PDIIv7uZMqNCZj0cGl3HVP76mgb0fT5EKfTqZYXiIw86bGu3iPx6DIP3eMENicr6HQKnu5lmDs6CDbqsQoCL0dEkHAeqzt5kXhdWU9BWRS/lliZERjINQEBp/V/dnoU7v/Mw+F8mUuLl3AbR8N+Y9pDtzHQb0LzDP4E3F6Fm953cyBXoXW4gJ9R4D/XGE77O/tPZPs78M3R/P5L3oaeN57b8ZwNZFkmMzOTmJiYWsn+Z5q0tDRKS0vp0qXLWe1XQ+Ns4nQ6SU5OJiEh4S8nhvF3QFEUJElCp9OdV7+LiqJQVlZGRUUFkiQRHR3tq4OXlZXly2uMiIio93tTXl5OUVERAOHh4eesiHxD3/HG2hiaJ0/jjGEwGLjwwgtrLAsLC2u2G8KGDRvYuHEjFRV1SybrdVaC7X1oE383cRFTCLH3R6/399VSMxsjkOQiSss30Dl8Cf83/At6xB7ELyiSWwYLfP9/Jp64xHBKBh6oeYg//fRTg3HciqLwzs9e7v/Mg8sLm5MVguI9eC7Jg84OnCJcYLHwSUzMeW3gAejRcYl3AGuLLbgUhWcKC1lfWXlabZoNAs9ebuA678fHDDygquPoM2bggepJfHmqgRA/+CNd4dfDMl/u1BLKTgVHHviFQ3hX8I8++fYap4fZbK4lLtAQqampVFVVncERaWhoaJwdBEHAbrcTHh6OIAi+Ul1Qs+h5XcI21TidTkBV3xRFsd5nzL8CmpGn0Wy48HCEAn7jCBkUA3DJJZcQHx/v22bx4sU+5aXTQVEUsrOzURSlVr2ZExEEEas5hsiQsbSInEpC9HXEhF1BWlEIsiyQXBhBsLUcu7mSB0cu4z/Tv2dq7+3Yze4G2z0ZHTp0oGPHjg3OMlY54bvdx4yH2PZuyoYWgF1CBP4dHMwToaFYz7I34FRJMFh4MCSEYJ2ORIOB+YWFFB9NhD5VrJs+Ykr5Et/np7mFWw6Mw+k5s0EIQX5ijTIKz63yklemBT40lfJs1dDL+xPMQSffXuP0CA8Pr1VnqiGSk5MpKCg4gyPS0NDQOLsYDAYURcHhcPiMuWoxluqQzuLi4jr3rTby0tPTycnJobCwsF6D8Hznr/HkqPGXIIUilrCZH9hHErkoisLbb79NWlpaje0OHjx42n0JgsCkSZO46KKLTmFfPe9vbs1rG8Zxxxe38fPhTpRWBZAYDiDi9qRRWrGbg+mvUHVoDbhPbZZ7wIAB9OjRo8Ftlo4T6PWGEZsHQno4SOtbQBuzAbso8lZkJDObIay1uaioqCApKemks/7jbTbCdDryvF7MgsBdubkccribfpNUFPjpffh9hW/Rq8ab+JjL2JOp8N2uM+9ZG9JWx8Vd1dtkuRPmrvD8ZW/254qy4y5/e9y5G4dG3QwZMoTY2NhzPQwNDQ2NZqX6t7pacTMwMBCz2YwkSQ06G+pKAUpPTz8zgzzDaCUUNJqNCPx977O8xdxx1x21tjEajfXK6SqKwhtvvEFKSgrjxo1j6NChDRo4er2+hvu9sbz8o5d3N8gE+4FH8icurD992g2iynWI8sr9lJTvQFFsiOgwffosxHWFDhdAr/Gga75LRlEg5w/wFAsMKdWx4kZVYKVQkngvKop2pvNLpUKSpBp/60MQBOaEhfF4fj6HPB6yPR5u/K0UtvsT6i/QLlJgWl897SMF9PUphMoSfLsItn4N1kAIioLelzAicQpLl7gJMMMTX3npEivSOvzMzlU9eJGBXw+7KHLAugMya/dLjOig3Tobi9EGkT3AWQq2qHM9Go0TOds5gxoaGhpnA0EQUBQFne5YGaSwsDDS09Pxer0YjUa8Xm+t50iz2UyLFi1q5OYpikJqaioBAQHNoilxttDu7hrNhh0LxqPzBlnemm5wg8FAr169eOmll2pccKBePCtWrODee+9l9+7dVFRU8Nlnn3HXXXdRWlrarGP8M0Pmf1tUI6W4Eh68UM9dowwY9CIBfm2JCbuUxJhbMRkjCE1zI3q8UJIDK1+Bjx+Dg5ubbSzlmeA8eppKE9Vk4FCdjnfPQwMP1EKkbdu2PWl4LECMwcBdwcGMtloxiCKeP/2wmQUO5CjszlSY+a6by99ws/lIHQaj1w1L56oGHkBlCQycAhdMpWO0yLUDdKQUqqve+eX0QkEbQ6BV4NHxBox66N1S4NlVXirdmjevsRxZAzk7QHaDqJUc/FtTVlbG9u3bz/UwNDQ0/qIoitJsKT3VnrzjJ7JEUfQVii8sLPSFZtaFv78/LVq0IDY21iduUlZWRlpaWrOM8WygTUdrNBsCAhH4k04xTrOCYNajOL3Mnj0bf39/Xzz0iaxcuZJvv/2WYcOG0bZtW1q3bs0DDzyAJEkkJyfTvXv3UxpPdnY2S5cuJTQ0lKlTp+LyCvzrYzdRgZBTCveO1jOhZ+1LwGKKIC5iEohpUO4HW76EuE5waAvkp0BEAoy5FcLqlwFuDLn7ZarnWUrbugkWRf4bFUXL81hgZf/+/bRo0aJRalPdzWYSDQZGW/1YHKZHL0JmsYLTo+Dygk6EG9/3cEFriduH6+gSqwNXJXzyOCTvUBsRdTDh36qS5lGm9tXz3q8SJZWQUqCQnC+REHZmrYcxnXR8u8vLmn3qj8b7v0rcNky7fZ4MyQPOEvW9LfKcDkXjLHDirLmGhoZGU5BluVm8ZMcbb1VVVTWeWUJCQnA4HIBq6Pn5+TXYp06nIygoCEmSfPuVlpYSFHT+J5lrTykazUJGRgbPPPMMAZe2R2jhDyYdumgbQ1r2rFFb5MR8poyMDL799lvatWvHlClTaqwbMGBAkwy8oqIiLBaL72JOS0tj//79ACQmJpJp6E2RA4ocMKK9wJQ+J/n6h8XD+Lug6yhY/4G6zC8YkjZDbjK06gPDrgF702unKChs8TsEvcIhyY9Eo8KsqChan8cGXk5ODt999x1du3att5joiQTodPT3s9L/GvWzR1LYdETmP+u8lFSq34Uih8JVb3sYFprH3R9dT+jFQQQCXtHEum6Pk1HRl8AdXkx6gTYRArFBMLmXjpV/SOzJUnjxB4n740S2LBSI6gWVhdDlKohqOB2yydw9ysD6A268Mry3wcvknjrCA/4aIRvnitI0kFxgDYWY/ud6NBpnGrvdTrdu3c71MDQ0NP6iNKR62RQqj1P2zsvLIyoqyudoEASB8PBw8vLyMBgMSJLUqNSf0NBQn5FXVlb2lwjb1Iw8jdPmueeeIzk5GQBnYQX20S0BuPz2axhi7VDvfm63mzfffJPAwMBaBh7A9u3bueaaa2otM5lMdOrUCVANu/z8fA4dOsTKlSsZOXIkkyerxbH79euHv78/2dnZdO3alU+/lujdUiC9SGmaFyauI0x/Gvb+DKteV5f5h8L2lfDH9zDyOuh1MZhPHsZYzVqSOGTIh9/bAtDZZaH9eRiieTwRERGMHDmyScp9J2LQCQxuo2NQK4Hvdiv8Z50Xh0shpDKPu5++mNbZuyhMjeXQ9cOZrb+X3Ts7ICleEsMEjuQr9G4pkFqoEBEAOWVqm+sOyGS/4+GiKiO5u6DwAGRthV63QJdpzXTwQEKoyJQ+Oj7eLFHlgUU/eZk7ofFS9f9E8vdAYAKUpYPZfq5Ho6GhoaFxPiMIQrMYTkFBQTVKH2RnZxMUFOQLu6yul+fxeGrUND1ZjnJcXJxPhKVaVLCh4u7nGs3I0zhtqg08AG9mme99hbVhgY5vv/2WsrIy7r//fqKijikyVF84Y8eOrbG9LMvk5uYSHBystl9RwWOPPVZjm7Zt29b43LFjRzp27EhRhcza/W5kBVqFQfuoJoYUCQJ0Ggpt+8O2FbD2PXV5WDx8/xb8uhQuvAM6D1e3bYDtpLGBQ9Dm2OXnSjr/i7kKgkCHDh1wOBz1ht42FlEUGd8VRncUWL06nQHPjSE0O0ldWeHh/qK7ORzeAYMIkgRVR3PgqtzQMkQgu1T9LAAKcGiAzL71EoEpOhBVD9Kyq0ByQ/drT2uoNbhtmJ5v/pCocCrsSa9kR5qNHvFaeFp9ZG6FkmTQmTVPnoaGhobG2UEURWJjYykoKPCFbhYXF1NcXExcXFytZ5jc3Fy8Xi+hoaG+nL362o2JiSEzM9O3rC7xlvOF83NUGn8ZcnJyany+a+pNfMQeAPIor3e/jIwMfvrpJ4YOHUpcXE1d9c8//xybzcYFF1xQY7koijVKJoiiiF6vx+v1ctVVV6HT6ejcuXOd/X39h0SUHeKCRfolnsYskcEEAyZDt1Gw8TPYv1FdXlEMf/4IB36FS+8HY91G259ksIJdxBFEhr0YfZiEO19H4elXlTgrlJaW4nK5MJlMjcrLOxnGgjwumT8NMlUDzxMdy+H/fs/tcW2RFRBQEASBKrdCfgUk58t0iBbZlSEzrovCr4cUdmeBpIcfRnp4/EUF87t6fn9Tbd9V1kDnp0CgVeC+0SL/WVPBwQIrs77ysvRWEZPh/A7ZOFeUpEBALJRlQKQWxaehoaFxTli8eDH33HNPjeLgf3d0Oh0RERFUVVVRXl7uK/+UlZVFSEgIFovFt6y6zEJ1jl5D6PV6WrRoQWFhIRUVFeTk5BAcHIzVaj2zB3QKaEaeximhKApPvPI5h5IzEQyt0csOHn/wXyRG22nvzcDsrSLEVYASoNRyvSuKwrJlywgICKhV506WZQ4dOsS4ceNOquJotVpZuHDhScfqlRRW/imTWQKZJTL/vrAZ8t6sdhh9E/S5BFa9AbmHIWmTui4vBWY8C7bgGrvsJZuv+AOAdIoZTXv2tdGRng+eSvA6QX+eO/RCQ0Nxu92n7ckDwOmESy+FykoICoKgIAxr1tC3ZctGN3HzEIVHlnv4fo+MDDy50su/roK+Vh1R3YRm9eJVc1kPA59v9yOnUiG5QOGNdV7uGa2FbZ6IpxL2LlVz8mIHaDXyNDQ0NGbOnElJSQlffvlljeXr1q1j+PDhFBcXExgY2Oz9XnnllYwbN67Z2z0TlJWV8cwzz/DFF1+QkpJCYGAgnTt35vbbb2fixImNCuc88XxWT0p7vV4KCwvJy8sjJCQEnU5HZWUlgYGBNcolVJdfGDduHKtWrWL58uVMmDChRh8hISHY7XZf2lBgYCB2+/mVl6AZeRpNoqioiE2bNrFixQp2Bt5NeeAlAPSNczPpbSM2k4tR7QIIMJUyvM1uvH7DMeht/Pbbb5hMJnr06MH+/fs5cOAAN910U62Zj9TUVAA6dKg/l6+p/HJQJr1IoXucQESAQNuIZqwcEhgJU+fArp/g6xfUwulWO7x1O8x4DkLVJ9tkCthKCtXpxH1oQX8Safc+mALAGnbSKM/zhmYx8BQFbrkFtm5VP3fuDN99B00symwyCDx3uYFwfy9LNkn0bCGwcI2XMf1l5k0woAZ0Ni96ncCTlxmY8qYbrwRr98uM7CCp6qAaPtI2gM6oGnnhdTvYNTQ0NDSaifomYD0eTw1RuvOZkpISLrjgAkpLS5k3bx59+vRBr9ezfv16HnjgAUaMGHFaRrBeryc8PJzCwkKKiooIDAz05eOB+ozrcDiw2Wy8/PLLJzUo9Xo9YWFhlJaWUlJSgslkwmw+f2brtTp5Go2iWjr2scceY8WKFQAoxz1AuxRVNKTCBalFYXywdSR3fn4bn2yuICk5iw8//JB33nmHjIwMVq9eDUCPHrXlD/fv34/ZbKZlE7w5J2N3pozDDTvTFS7tfoYexLuMgJteg4Qeqvx/aR68939QmEEhFSzld1IpIgo73YnlQjohIBDcGvzC/zoGXrPxyivwwVHFUqsVlixpsoFXjSgKPHChnicv07M9TTWjv98jc+27bnJKz0w9uzYRIrcP09G7pUBKocLjX3pxebTaeceTtBLcDgjvCm0vOdej0firUFRUxK5duygvrz/cX0Pj787s2bNrqYu//PLLNZ6NZs6cyYQJE3j66aeJjo6mbdu2pKSkIAgCn332GcOGDcNsNrNkyRIWL15cyzj6+uuv6d27N2azmdDQUCZNmuRbt2TJEnr37o2/vz+RkZFcddVV5OXl+davW7cOg8HAmjVr6N27N1arlYEDB3LgwIEafXzzzTf06tULs9lMYmIic+bM8YVG1sUjjzxCSkoKmzdv5tprr6Vjx460bduWm266iZ07d/oivBoaX0pKCsOHDwdUARZBEJg5c6avD0EQCAkJwWazUVxcTFZWFl6vF5vNhl6vp7CwkI0bN/Liiy/y7rvvnvR/JQgCdrsdg8FAfn4+brf7pPucLTQjT6NRfP/99/z73/+usWx0awfX9nVz02Ad3eMELmgjEh0Ihwr8AdCLsPAnf679XxB/jJvGgdGj2V5R0WDxyeTkZOLj45u11tLXf0hEBECbcIGBrc7gVz68JVz+GEQkqp9DY1HevYdvC7/HiXpT88PIeLognAEP01+GjRvh0UePfX7vPThN2XVBEJjYU8+iaQb8joqU7stWmPaWi4O5Z6Zo6cxBeirdICtwOF/htbVnvjD7X4lD3wGyqrDZYsi5Ho3GX4WAgAAMBsN5md+ioXG+sWbNGvbt28cPP/zgm4AHePDBB7nrrrvYt29fLRE7UOsTT5o0ifHjx7Njxw6fsVaN2+1m7ty5/PHHH3z55ZckJyfXMJSqeeyxx3jhhRfYtm0ber2e66+/3rdu9erVXH311dx1113s3buXN998k8WLFzN//vw6j0WWZT799FOmT59OdHR0rfXVRtjJxhcXF8cXX3wBwIEDB8jOzuaVV16p0ZYgCAQHB/v6KSoqQhAEoqKicLvdzJw5k1dffZXIyMYVeBUEgYiICARB8Im4nA9o4Zr/EFJSUnjjjTcoLy9n0qRJjBo1qkn7r1mzxvf+wgsvpG3btvVK6e/JyOE/a9PIKAnhUEEMEeEKKR1iaGlowW6TidS+fQn68Uff9ooiIyteHI4yklP2MWb0xad2kHVQ5VbIKVXfR9nVULszii0IZr4IK16CPesRgC5rt5M7ric2ayiT6Ynunzy3kp0NkyZBy5ZQUAA33AB1lM84VYa207HkRoE7P/aQWayQECpwzX/dLLrKSJ+WzXveDTqBeRMMXPmmG48Eq3ZLDGsn0rOFFrZZdAT8o1Thm9D2WvkEjcaj1+tPuUyLy+VCFEUMBi1H9h/Jm7dCRdHZ79cWDLf8p9Gbr1ixopbmgCQ1rEZeH35+frzzzju+MM2UlBQA7rnnnhqeuROZP38+U6dOZc6cOb5lx9e4PN5YS0xMZOHChfTt25eKiooaY583bx5Dhw4F4KGHHmL8+PE4nU7MZjPz58/noYce4tprr/W1M3fuXB544AFmzZpVa0wFBQUUFxc36vo/2fiqVdjDw8MbDO80GAy+vDpJkpBlmTlz5tCnTx8uu+yyk47jeKqFXrKyssjJySE6OvqkJRnONJqRd4ZxOp2+uhzniqSkJF5++WXfZ5fLVed2iqLwyy+/sHr1akpKSrj66qvJyMhg7dq1vm3GjRvHuHHjGvzidogJ5bZBb5NT7s9nO8bQY0A8CwXI9Hr5xOOhZWQkwRO68R6/Ukwl/cuc2At/B2DmraEYddkcyngDP3ML/P06YjXHIgon/6rKioKsKOiPG1uW7KHdJWWYKwx08TcCZ6EWnTUALr6HipwjJCPSfdeveCudtLp6NibxH3zJeb0wdSrk5amvyy6DuXObvZvW4SKf3GRk3kpVkAXgtg/dvDTVwOA2zWuAtYkQuWO4jo2HZHZlKDz4hYfPbxWxW//Bnlog+UdI/Vl93++eczoUjX8QmZmZFBcX06tXr3M9FI1zQUURlBWc61GclOHDh/PGG2/UWLZ582auvvrqJrfVpUuXOvPwjvfK1cXOnTu56aab6l2/Y8cOZs+ezc6dOykqKkKW1d/StLQ0Onbs6Nuua9euvvfVpbDy8vKIj4/n999/Z+vWrTU8d5Ik4XQ6qaysrOWtry6C3hhhlcaOrzFU5yrm5+ezYsUKNm7cyNZqvYAmYjAYCA0NpbCwkJycHMLDw89peYV/8BPn2eGRRx7h9ddfbx6xilPkp59+qvH5+Fy4H3/8kWXLltW534cffljj87hx47j44pN72URBj92/GyZjLncN+ZIW8XdzpdCCT0pKWFZWhstuJyWoAxEUA1CuFzl+ot8j5aBILlzuPMorDyHLVdhtXbFZWqGzRKMTTZTjJIcy8mUnn+VayfB6yfB6ucjPj50uF52MRq4OMZDuFdkXXAHB0DswEGhYGre5KDbZuHXYHbz1zZNUGMwEtAhDV5UOfs0nKPOX4+GH4eejT/4xMfDOO9CMYbnHE+QnMH+iAafHw89JMi4v3PWJh2cvh9Edm7fPawfq+OWgjNMLOaXwxFceXp5qaJaCrn9Vko+75SQMP3fj0PhnER0dTewp5vZq/A04QdH6fO3Xz8+P1q1b11iWkZFR47Moij6jp5rqAt4ntlVfHw3RkAiLw+FgzJgxjBkzhiVLlhAWFkZaWhpjx46tlW92vNe8+jev2uCq9orV5VGsS5wkLCyMoKAg9u3b1+DYmzK+xqDX6/Hz88PhcLB582ZSU1NrhWlOnjyZwYMHs27dupO25+fnh16vJycnh8zMTGJjY5s1BakpaEbeGUav159TK16WZcLCwmosmzdvXqP2NZlMDB48mH79+hETE9OkfnWiBac7CwCvM5lAW2cut9splGUqqqrY5FWNXj/FiFdSyEz3YDCY8bfZMJpkdAY3ep0Nj1c1BKtc2RSVbWFXeAeSbMduXnpB5NeqtniP5rjtcblI9XhI9XgID02j0q+KAWEBBLsjGGw5e4pHi0tKCAiL48lhM7nWtgudv5OcwtXYLK0RxX9YKJGiwIIFsHQp+PtDVRV89hmEhp7Rbs0GgZevNPDQMtWjp8jwzR8Sdgv0TWi+G65eJ7JgspEr/uOipBJ+2i/z6RaJaf3+mbdXRQGvC+zx4CyBaM2ponGWOJ9U7TTOAU0ImTzfCQsLIycnxyfnD6r3rbno2rUra9as4brrrqu1bv/+/RQUFLBgwQJfHeNt27Y1uY+ePXty4MCBWgZtfYiiyJVXXsmHH37IrFmzauXlORwOTCZTo8ZX7VhpbBhsSEgIQUFBzJ07l7vvvrvGui5duvDSSy9xySWNVxAzmUw+j15mZiZxcXHnZOL3n/kUchaZMmVKjdDG/dkyc1d4GNBKZEArHV1jBQxnME/M6XTWyKerj5CQEKZNm0aHDh0oLy9Hr9efVuK7zZJIYemvAFRUHsFm64hdD4+EhnBEEEj9eDW57x6hS7tOVIaEsHmzk7lzH8XpdOJ2uwkNs1NRmUx55T7KHPuRZLVgZb7Zhih70Qs63AJ4BZlEi5c0p5FYvR6dIGAUBEL1UKVzIABhRjcW/wNsVbLR0ZKORGE8yVdfVhScioJZEBCbeGFme728X1qKB4iI6c0thkq87nSMhlBKK3YTFFBbVfRvi6LAQw/Bs8+qn3v0gHvugYEDz0r3Br3AM5MNmPQe9mYprN0vszNN5tNbRKIDm++6i7Sr+Xl3fqzOtL630cvQtiLRQf+8/MuydDjwpfq+1YXwT45Q1tDQ0DgVhg0bRn5+Ps8++yyXX345q1at4rvvvmu29J9Zs2YxcuRIWrVqxdSpU/F6vXz33Xc88MADxMfHYzQaWbRoEbfeeiu7d+9m7imkVjzxxBNcfPHFxMXFccUVVyCKIn/++Se7du2q19nw1FNPsW7dOvr168f8+fPp3bs3BoOBX375haeffpqtW7c2anwtWrRAEARWrFjBuHHjsFgs2Gw2Xn31VZYvX17ruVgQBHQ6HZGRkXWKrcTHx5OQkNCk4/fz80OSJIqLi3E6neekhMUp/fxu376dVatW8csvv7B7927y8vIwGAxER0czaNAgbrjhBi644IJGt7dq1SreeusttmzZQn5+PmFhYfTt25ebb76ZCy+88KT7Z2Vl8cgjj7By5UocDgd9+vTh8ccfr1dcJCUlpcY/q1+/fmzatKnBPmbPnu1LUE1OTm60xH9hYWGNzxsPyfyZofBnhsSb6yXGdBSJsAtM6a2jZWjzPBDKsqy6+lFYt2UdsfFGrH4iQSE6AoMMBG1PoU1lEVZBolTWERcWjBAVBjs/gvRYAqLaQXAU6BPA2PSZ0VSPhy3uIFaK42lnyccbJqHwHdHYyaKESJ2Z2ZeJuJxt2P3nEbIy9zO4XQIWsxmjQU9FTga6tBzsJTnYs7ORJQvK4Q3k9uvAcLZSaQzAz12GgsjB2MG0jaigv9CCOCEYHSIeReGQVM7PhfFkB2T6QgfyhDJ2kM4q9tBRiSLImUBvkw2LKFIly/xaVcXKigpsosiy8nIusFj4paqK7iYTBZJEmE5HK6MRgyAQJIoUSBIBosiaykq6GY3IgkBPs5ktVVVUB1VcGhBArHUUKVmLcVQdxuMpJtC/+z8nlO+RR47VwgM1J2/GjLM6BL1OYO4EA3d85OFwvkJxJdzzqZv3rzdiMTbf/2FoOx3T+8lsOiJTWqXw9HdeFl117sK0zxVZx02oRvU8d+PQ0GgISZL44osvWLduHeHh4dxzzz1npAi1hsap0KFDB15//XWeeuop5s6dy+TJk7n//vt56623mqX9YcOGsXTpUubOncuCBQsICAhgyBBVBjksLIzFixfzyCOPsHDhQnr27Mnzzz/PpZde2qQ+xo4dy4oVK3jyySd59tlnMRgMtG/fnhtvvLHefYKCgti0aRMLFixg3rx5pKamEhQURJcuXXjuueew2+0IgnDS8cXExDBnzhweeughrrvuOmbMmMHixYspKCjg8OHDp3bSTgF/f39KS0txu93nxMgTlBODfk/C0KFD+bk6r6YBrrnmmhqKP3WhKAq33nprg1/am2++mf/85z/1PhRnZWXRr1+/OuOZ33///ToTWU808kBVOxo/fny942iqkVdWVobdbueGG25gxIgRTJkyBb1ezxNfeli+Q3Uf60QwiApOr3psj10nMzbOROBxsbt/ZsjsyZT4ab9CmwiB7aky8SEC8cECbcIFIuwiLYIFFFcJLreLwxXlfL5pGW2GdaYkHDxF5VxReYCiQi/BIapN7/nhCN2z0gGQDBZ0niqIagvZSWqn8V0gbRfoDBDXEaLbQschEN0edDoUFBy4SXYq5EsSBZJEhsdDmsdDgSSxw+Win9nMZqeTXvoMWser9Y5CsFGolBNZbGRg8W/odRYkuYqcP0vos+sIAYFByAXppHrMJBiqIKY9ZO6HFt0g9Q9KhowgM0HCaAjB7VGN57XxvSnX69AhEI4/bYmgB3FYMPLcKg8fbXMR1zmXkRdlUWmqQI+IEy8Wxcj7yYn4CSJGQUACInQ6kjweeplM/O5y0cNkYofLRWejkd1H47w7Go3sPfq+pV5PsE7HdpcLC1B19H8WLIoUyTIGYFlsLIlGIylZH+BwJmM2RhMVOh6rubY88N+OZ55RvXgAvXvDTTfBzTefs+GUVilMe8tNepF6y7tlqI47RzRv6KzDKXPpq27yjpb4WnKjkW5x/yxv3q/Pw0+PguSGKV9Ah/oF3v62yLJMZmZmjSK7GucHeXl5LFmyhPj4+Br56tOnT2fQoEHncGQaTcHpdJKcnExCQoIWpnsOUBQFSZLQ6XT/nEnrUyQzMxOr1UpQUFCT9mvoO15tY5SWljbo3W2yJy8zMxNQE5yvuOIKBg8eTHx8PJIk8dtvv/HCCy+QmZnJhx9+iNfr5eOPP663rccee8xn4PXo0YMHHniAVq1acfjwYZ599ll27NjBW2+9RVhYWL2u3XvvvZeMjAwGDBjAI488QlBQEF988QUvv/wyt956KxdddBEhISEnPa4nnniiQSPvdNiwYQPp6enccccdPDnBxh0j9Gw6IrPliMQ3O10gGDCTz1xXFf/NNPNsWBhrV2VTKIXy1S4jg9uIbDoiU+4U2JOlsCdLfUjtES+wI02txaFTBKRLK5CDdVwwoiuF0TLu7HLyi1uyulLHp8GdaUExwVQS2rcnR/ZuZ0Tan9hNBvAYqFG2rULNg8MeBil/QMofKAd+pTI6jIqEFnzQox1VosSGvA5keqGNwcBBj4dQUcShKOhQvXkA+ZIffVx5WPRB2HRWzOgIULzoRAuiaEaSqwgrLkGyhYCfP2JBGhb/AHBWQWWZOg5PFRhMGGQDFlMEOtEPvc4Pj+ykUlQIxEI+FTjxkkExW0jhdvkC3JJI92gDSmEsVzgSEE3l/E4au8nC4g5HQaCl0cjuowbdHy4XQaJIjiTR1WQiQq+nKxCs0xHg9VImy5iPe2DrbDaz1uHALghUHTdXUnTUc+gBJmRk8Lwpms7mblS5c3C6syhz7Pn7G3mbNsHx+QM33nhODTwAu0Vg4TQDV73lJjFMYNVuiQs762gd3nwP4X5mkVuH6XnyG/W6XPSTl3eu/Wd58zI3gyxDQByEdTrXo9HQqMns2bMBSEhIQBRFX6RHXYItiqKQl5dHWFiYZqxraGg0iCzLlJeX4+/vX+N+YTQacTgc2O32s34fabIn7+KLL2bGjBlMnjy5TrWYgoICBg0aRFKS6hVav369zwV8PIcOHaJDhw54vV569+7Nzz//XMOVWVlZydChQ30FFvfv30+rVq1qtOFyubDb7YSHh7Nv374aakL33HMPr7zyCu+//z4zTggPO96TFxoaSkGBKrm7bNkyJk6cWOdxn6onb9euXbzzzjs+xR+9Xo/X68VgMODxePAIVnLMfcga2BVnDxOmjAo8B+IQskUUr4BZLxJg1ZNXrtZ5yy1Tiy8DhJtKyXMd1aVUZJSrMlGMOjrm7KJzfz07P4+mMDsUf4OZlLHl2EWFRIMOUdAjCCJG4LWoKLYLKfjJelqWg39hPuQmQ+5hqCxDSfoNR8s4ClrZcQXb8ZpEfglOJC84hMMZsWxz+9fweB1wu5no70+swUA7YwV7xK3kmgz0Floyjs4oikJpaSl2ux1ZceP2FKJsW4ulpAhBb4Dk7RDXWT2msBYQGgdh8WALgTpmi7xIHCafwxSwnTR0isigCpnI4iSeXH09f2QY0Ymw7TGTr0aeGy/bnVWsKndiE0VWOxxcarNRIEmM8/Ojp8WCoY6+XLJMviRRJEkUyzJRej0BokikXo9bUdjtcvFoXh5pxxXBjBT0OD4JIzEU/jVoIQEWJxZTNIkxNyHJCmlFCma9gJ8JAix/k9kwRYH+/WHLFmjVSg3RbKTYz9lg0Y8e3vpF9aZP76fjoXHN683zSAqXLnKTUaxeqO/ONNCnGYVeznfe6gXZ20EQ4VGnGhDwT0Pz5J2/fPnllxiNRi666CIqKys5ePBgnXk4aWlpLFy4kMrKSjp27Midd955jkasUReaJ+/connyauN0OsnNzQXUHL7q8+L1esnKysJisRAUFNRoMcZz4slbsWJFg+tDQ0N54YUXfCo0n3/+eZ1G3ksvveSrCL9o0aJasapWq5VFixYxYMAAvF4vL7/8MosWLaqxTWFhIS6Xi759+9aSix05ciSvvPKKz/NYHzNnzuSDDz4gLy+PWbNmMWHChGb9wsbHx/Piiy/y4YcfsnnzZt8xV0vhGpRK4qrWE7ZlOwcCxlJ+pANK1tF/piBQpZMQ2+TT0niEKkcBsWUViCUgFgsohXqidBFU6UKRBQOG7FzaJCbSM6QVvzy3l+zSAEwBgWQUGQjLljDHu3EoAgKgU2Tam838LiSzgcNYRD377QH0tregZUIPnO48yh37KB0UDp4q3KITS34FXouNjnmZ2IoqiMsvYGCxm7jQWG5r05s4PzvRBgP6o+evDBM/oXoxipWqmt5CQCeasJiiYdD0Uz6/enS0I5J2RDKIRLaU/0BIwU4UfSTx9v3odd2ICBBqFEE3oqe/2Z/+Zn8A7m+EpxfAJIrEiiKxdRTaNQoCiQYDWUf/v+E6HZf42fhqIzhcAmlFMPeH6xiUkILDLVLkdLIjDSrdMKi1wMZDCjFBAn1bCrQOFxnYWqRVmPDXvHl++61q4AFYrfDkk+d2PCcwY5Ce93+TcHnh6z8k7h6lb9bcPINO4MbBOp5Z5aVvgkhOWZPm0f7ylKqR4Njj/5kGnsb5zYQJE3zv/fz86N69e53b/fnnn1RWVgKwd+9ePvnkE6ZNm3YWRqihofFX5HhD7PgIAL1eT1BQEEVFRbhcrrNa5uWM6J4NGzbM976uBEdFUfjqq68AaN++Pf3796+znf79+9OuXTsOHDjAl19+ycKFC2s89FZbxNu2batVWLG6lkVdKjnH4+fnx4MPPsh9993Hrl27WLp0KVOmTGnsoTYKURTp1q0bmzdvrrH8iiuuYMCAAb4vxqvfZfFWtg0AQQJjpYIxwkNlnJccj5nKdsdUGUMOHMFlaEmbNIWesp0+QjDZ3rak7FPYmCFz2NsaQwCIOvj3hXqm949AEAS8isKOqio2OJ1M8vdnH7mAgg0TZVTxjfInvYryiXZVUelMxWgIwy06MRnDkSIDicnV0/FQJsLBtWCyQGg8bPkfdBoGsR2g5zh1OeCnmCgpas+uKoXfZAPT45t23txud5PqC9qxMsQ8iDR9Om5vDpd1+5k7P+tEt7izEy6XKuQxIdRJcqWVbkY//i84hPH9ZO5L9WDSKyTlBvBpsVo4tE24l0q3evntPRp+m1mskGSB5Tu8sBrC/aF1uECoTaBLrIjVKDCwlUio/3lu+K1aBaKoxuzNmaO+P4+wWwTGdhb5eqdMuRNW75aY0LN5b4XD24nM/hrWH5DxSnBJt2Zt/rzFUwWucrBFQvg/5Jg1/p6MGDGCb7/91vf5l19+YfTo0axcuRKdTkdYWBgtW7akXbt253CUGhoa5xNxcXG+4uwOhwN/f9WZ4O/vT2VlZaNLOjQXZ8TIO74YYV0hncnJyT4P29ChQxtsa+jQoRw4cICMjIxagikWi4WxY8eycuVKxowZw8MPP0xQUBBffvklL7/8MlarlXHjxp10vLfddhvPP/882dnZzJ49m8svv7zZQmz+7//+r5ahYrPZmDlzJh07dvQtKyhXeH9bEIJbILQIumwUCd1tBCxs8qukNDGAxB/8saUaiFpnxT81gdLWboL2mSgHfgK23V1Jil0dd5AVBrYWGdRa5JJux/7NekGgj9VKn6MGcRYWuhNLARVU4kYnCMiCWpcOBAw6G3qdhdDAC7BZEhFa6KCXFw5vg61fQ0muGk5ZmA6pf8Kq1yAiEcw2dC260F/WYwtpyff+kSwtLeXyRsr/ZmRksGrVKiZNmkRwcOMLjZqMoZiMYZRKHnKMNnpP2cW06L6N3v90OCTmoAvIonUAXI46cdEuUuTd6wzc+6mHvi3L2Jluxi0Z8DM6ibLb6BgtggIFDoWkHKWGPVTkgF8PK4BCXrnCpiMK94/VYzHAlD7nqS59UhK8+ipER0PnznDcrPn5xBW9dOzLUrCa4OudzW/kBVoFREENrS6p/Od48qoKQXJCRc55Z9traDQJq9XK7Nmz+fnnnykoKKBfv36sX7++xmStKIpMmzaN0NBQ2rRpo4Xmamj8wxFFkdDQUJxOZ41C8bIs4/V6z/o94ow8Ka5fv973vkOHDrXWH1/Nvn379g22dfz6ffv21VLFfPnll9m0aRMbN27k4osv9i0XBIGFCxcSERFx0vFaLBYefvhh7rrrLvbt28fHH39cpypnc1FRUUFZWVmNZQvXeAgpEsixQmSlSCtzKaWoRcw7fP4TVEVj39IBWSch6kXwCNgya8ZCuT0yoH6BhvaSaWXR1zDw6mIAiQBIyJRRxVZScVpkArwmrKZYbNY2GA32mjvp9NC2v/oqzoF1i+HIdjV/rqoMco+o2+WnMKWyjPioDjyWd4gd4a3Z2ncCbeO6nvQcHT58GI/Hg91uP+m2J2Lz60J55X5aU8Ygk41I/7o9xc2JjMxh8gEwoSeWYypK4f4ii683UlLhZenm7wm0OOgcG0DXhAk12pBkhUN5MluSFX49LOPxKmxOVg2EnFL17xvrvDhcEOovMKL9eZjn9cUX6t+sLLj33jrzKM8HusWJIMAf6ep5PZgr0yai+W6+oihgt0BxJZRUNluz5z1eF8QNhMoCMAee69FoaJwe4eHhXH755b7PCQkJ6PV6Vq9eDagPbh999BGgpqrceuuttQo4a2ho/PM4PnRTURSysrKQJInQ0NCzOo5mNyllWWbBggW+z1dccUWtbdLT033vTxabWl3N/sT9qmndujVbtmxhypQpBAYGYjabGTBgACtWrOCGG25o9Lhvvvlm31iefPLJM+JStdlsREVFMW/evBohqttTZZbvkMkMAFsxDMvWU7k/BEWQKO+9gg6TQA7bA4Ao6QhpKRDeBbqMEmh3p4TfI5UcerKIkpFlJF5Sijwxl2UtctjeqoAsj6e+4dRAh0ggVobSlsHmwcSGTyDY3ru2gXciQZEw4UGYNk8tvdC6L1gDwGjxqWMmyl6MkoeoymJ6ffUMzm9ego2fgcddZ5OSJPH777/j8Xjq9ATXhaIoJBfI5JXJCGJr9hNNNkF0lFOQFe/JGzhN9pCNDRN2LLQiDN0Jl5ZBJxAaEMGoLuW07uTHgSgTEnKNbXSiQLtIHdcM0PPG1UbevMbAT/eb+OpOI/ePNXBBGxGHC7rFCTzxpYc9WTX3Py84LryJyZMb3PTEiY6ziSAIXN7r2Hfri9+b93qvdCm4vNA5RmBKn3/O7L4gQPqvUJgEHse5Ho2GRvMSGBjImDFjfGkjx2sJFBQU8PXXX5+roWloaJyneDwen01RreZ7tmh2T95LL73ElqOiCxMnTqR37961tikvL/e9t9lsDbZ3vKBKRUVFndskJibyv//971SG68NkMvHoo49y2223cfDgQT744AOuu+6602rzeObNm1dn2KFXUnhjnWqECBKMTjKQt04koivk7ABTZjtKXd/giUqnvP9yek4K5sp7hh4VNBAAHWBFUiysrazkreJicKtfpjJZ5tKMDO4KDmZ6QAC6k3hVBARM6GsJpJwUQVBr6UW3PbasqhwKMyD7EGFZSZQ4inEKOkQU8iQvET+8BXvXQ+s+0H0sBB+b/RRFkT59+rBhwwY2b95Mv379Guw+KVdm+XaJ5dslhrYTCTBDQoydYHs+WUIgwZW7aOnXo8E2TgcvEutIophKDOgYTOs6txMEkd1R3dhNFiBzWC6krRhWb7s6nUiYP4T5CySGwZC2Is+s8vLRJvX/O+crDy9caSAu+DwxIioqIDVVrYkXGgoneN2PJyMjA7fbjSzL56wA8fiuOl783ovLC9/8KXHTUB0hfs1zLr/fK1Hpht2ZCu0aTgv+W2GLBP9oMNqg6IgqtHqeOnM1NE4Ji8XC/Pnz2b59O59//jmCIKAoComJiYwePfpcD09DQ+M8o6qqyvf++HS2s0GzGnnr16/noaPFj8PDw3njjTfq3M7pdPren0xYw2Qy+d4ff6LOBDfccAPPPPMMKSkpzJ07l6uvvrpGTO2p8MILLzQYcvi/rRLbU2X6hAiEvW4gIVYgWVINvNhrDrJd/ybFyZUEhgVS0uMHEgfPqFOxTicIjPLzY4TVyjcVFbxZUsJel4sqReGZwkJ2O53cGBRE2yYImZwWFn9ViCW2AwJglWUWbf+ZyUlr6Jp2EFd4DObM/Wqx899XqkZeu4Hq9lFtSE1NxeFw1JgQqIv0Ipnv/pRYuk0iPkQtFm82gIfOdPLfT4KYT27JeszWRCKFpod+NobNpOCPiSo8RBJAOP71bttOjibFrWNLiYklOoUn67fxaiEIAveN1vNnukywn8D6JJkr33Tz6jQDPVueB6Gbu3ZBerr6uummBjcNDw8nIyOjQenfM43dIjBzoI6kPIX1B2Qe/sLL69MNNZRYT5UvdxzzDE7ocR78b84SBiu0uhB2vgtia4X/fuflhov0f02VWA2NeggMDKR///5IkkSbNm2Ijo5GEASWL19OUlISY8aMaXQUioaGxt+X6rJhAMHBwbUqAZxpms3I27NnDxMnTsTr9WI2m1m6dGm9+XDHx6qezKp1uVy+9yeWWWhuDAYDjz/+ODfccAPJycm8++673HLLLafdbn2lCAsrFF5f6yH4gEDwKj2hAZC8FiK6KcSPrcIwLIPfv6lEEARfaNsHH3xASkoKY8eOrdMDIgCX2myMsFh4pbiYT8vKCNXpWFtRwfcVFUy327klMBD/s/wDZBAEbureieU9yjmU3JYuhw/QpyAbITwBcg6rBdiLcqCiEFr35rKyUtq270jvwYPqPX8Abq/Cr4e96ASocipE2aGgAr75PZ6u+taERRQRFFBIbvIiKk1xuNw52KxtUBQvwQF9sZrjT+sB1IvEbxymEjdm9IxBzUFVqHvMLZVQPs1yUKkopAgVPBwcUqPA+snQ6+C/M/XMfNeDgEKbcIEb33fzxCV6LutxjsVY8vNh4EA4fBjatFHdOPVgMBhISEigoKCArKwsunY9eZ7mmeCqfjomvuZCUWDTYYkXVsMDF53exM72VImiCgWLQSHSLtA1pv57wN+RztMgr5vEqwUeOmUIZHwt8/A4PUb9eeJxPgsoilLjpfH3w2KxMGrUKN/nlJQUn6r3999/z6BBg7jwwgvP+kPdP4Xq60q7xhrH4sWLuffeeykuLm72trXz3zABAQGUlpZitVp9nv/G0NB3vLFtNMtTYXJyMmPGjKG4uBidTsenn35aZ228aqolRaH+EMxqHI5jiR0nC+1sDmbMmMHTTz/NoUOHmD9/PjNnzqzhTWwqpaWl9f4z3n9NYvgvYDssIkRXYQ0QsLRwkd3uc1KEdFhPnZ6OnTt3kp2dfdJw0jsNBkbabHxTUcHWo97TQ14v1+Tm0kfnR8dSGyadgFeCYD81b6x1uNAsnoy6iENPayUQncmfX/oGcrBzX4aluLCJVpC9UFoAljAoLACng7ZhVZS99yAMnQFRdYdApuVIuCu9JNqhe7zILUMN/LRb4vDbAoU7uhP/5EoyyyMINZRR7nHj9QpI7hJc3gKqHCIIOwgLHIIonprRm0Qu4ZhwoyMYG2YUSiltcJ9LgXSvF7MosjEvj96nMHnx0kSF93/1sj5JJtwMRcU6DqSJRNrP4YN0airk5oLNpoZqljZ8HgAOHDiAw+GgRYsWZ2GAtRGBpy+RmbfCgyTDT39CiwA9F3Y+te+Dy6OwaJUHV6VC22C4YZCesjLnyXf8GxHSGww6iWGpEvtzFPL1Avd/BHeN0BMW8M8w9GRZpqqqitLSUk1x8R9CdVHitm3bEhgYyM6dO9m3bx8zZsw4JQExjYapDveXJOmsy9KfDtdffz2lpaV8US1SdpT169czatQo8vPzz0gKw+WXX87YsWOb9VydqfyysrIynnvuOZYvX05KSgqBgYF06tSJW2+9tdG1rE/nfI4cOZKff/65xrIpU6b4RJaaisVi8QkuHm//nAxJkpBlmfLy8hoOL2i8psFpG3lZWVmMGjWKrKwsBEHg3Xff5bLLLmtwn+PFVjIyMhrc9nixleNFWM4Uer2eJ554ghkzZpCens7bb7/NnXfeecrt2e32Og219EKZpG0y4et0VAmQOALcRTJ/xD2LrCuBMjVU1WAwYLVaueGGG1i3bh1hYWF8/fXXlJWVYbPZThoS0h/oGRbGO8XFfFFezn5FoVCvx1Nl4LNv/egaK/BnhkK/RJHNR2SsRpjeT0enGJHBbcRmN/guV/rzAetx2gXKAv35KMrJjC7XEOGJRDiyHbIOwJEd4CxA2fcTguyFZbNhxA3Q99Jauuz2OBdVbSuoOmxlZFcjAQEiEwYKrPwQClKDWHdZBC2v3EnMjRvQ63RYzBagAr2iwyPtwWKMpcixnBaRV6LXN30SIZ3DpKCGlI6hB3ZO/mM+WK/njtxcAPxFkZGn8ABgB/59qULBFx42JMm8/DNkV+l4ZPw5rD5dWAiZmeB2g9kMjTiugQMHcuDAASorK4mKijoLg6zNADtc7/Dy5AovPeIFnvpBwWTRn1JZhWe+9bA9W/0RDQ0WuKCT8YxNmpzPXDsMQv+UqPrDyy+HFSID4Lal8H+j9VzU5Twt/9GMyLJMRUUFdrtdM/L+AUiSxAcffIDNZmPq1KlYLBZGjBjBI488wquvvsqzzz57rof4t8PpdFJYWIhOp/tLhcaKooggCLXGXH2fOJ3jqa+2sMfjwWaznRFHSXOf+5KSEoYMGUJpaSlz586lT58+6PV61q9fz8MPP8yoUaMaZbSdzvkUBIEbb7yRJ5980rfMYrGc8rHqdDr8/f0pKSnBaDTWqOl9sv1EUcTf379GBGT1GBvDaf36FBQUMHr0aI4cUSXzFy1axIwZM0663/H14fbv39/gtsevr6scw5lg+vTpvtINTz31VI0cwqYiCEKdr80pCimtZfQuEf/RCtEj8tnR/W5k/xIAXnnlFV566SWeffZZZs+eTVxcHNdccw27du3ytV1VVVVv+8e/TKLIHSEhfBITwzCbDYMo0rk0AAUBk15AQSCvDBQEHG6BH/fL3P2pl9uWePlki4RXqv84mvoyCnouErrQQ4inQHDQo0qhuHA1WdJWpJ6jKB9+M9473uPggJsoNAYhoCBEJCKsWoTw+VwEj0v9cguQLZTyP9NOLJ2dVF1YQIFerUEiigIXLRSQnAJmUwgdLzOg1/uhN/jhZ4mjddwdhAcPw2wMw+XOweXJJKvgG5zu3CYdCwIUCpUgCBgFA7FCYKP262+1Yj56oy88OhN2KudSrxN5/GIjfRJ0tI4QWL5D5oe90lGxi+b5fzXpVVyM4HQiWCwIERGN2kcURUwmEyUlJedmzEdfl/cx8PA4PdvT1Ovgia8lPv9dalIbK3dJ/HxQRkHAqBeYO9GIQS+e0+M6l6+Lu+n51wgj8cEiFpNAfrnAq2sl7v7UQ27JOfqOai/tdQZeOp2OkJAQbDabLyQrMDCQoUOH4nA4WLZs2Tkf49/xdaq/ned6zPWN+/jlc+bMoUePHjXWv/LKKyQkJPg+X3fddUycOJEFCxYQExNDu3btSE1NRRRFli5dyvDhw7FYLHz00Ue8//77BAUF1Wjvm2++oU+fPlgsFsLCwpg8ebJv3UcffUSfPn0ICAggKiqK6dOnk5+f71u/bt06DAYDa9asoU+fPvj5+TFo0CCSkpJq9LFixQp69+6NxWKhVatWPvX6+s7Po48+SkpKCps3b2bmzJl06tSJdu3acfPNN7Nz5078/f1POr7U1FRGjBgBqHlwoihy3XXXNel/5OfnR1RUlO8VGNi457v6XgEBAVitVgoKCigvL2+W73hjOGUjr7S0lLFjx7J3714AFixYwB133NGofRMSEny1ZI6vqVcX1S7TmJgYWrZsearDbRKiKDJ79mwAsrOz6xWQOR3W7JPJ8VNYd5cL86VLWX54DgajngcffJDXX3+9TsGXkpISkpOTAdX4bIrbFyDCYGBOWBhr4uO5poWVxy/Wc1kPHY+N13NxN5FxXUTiggWOqOXe8MgKT3/r5eFlHranNp+L34aJMXRklNSS6NytmI0ROKoOcyj1f8ya/TD3//vf+Hcdwvex4yjvdyWkq+UjyD4EX8wHp4N1JLGavfhZC/EzuehpNzEg8tg505tg6pdw42ZI7NkBgyEQUTBS5kgiPe8z/K1tiQmbhE5nxWpuQUXVQZKz3mWX92C9+XQn4sBNDmXYMNGaUMRGXk4mUaS32YwR+LmqipLTCHkItArYrWDSCcgK3PeZl1s/9PDi9x6SC85yiYVqr7zDAWGNV5RJTEysMfFzrpjaV881A47N1D35jZdv/mhc+Y1VuyUeXeYlvRhGdRS5b4yexLC/vwfnwIEDNSaeTqRLrMgnNxtoGyHSP1EkvQg2H5Z5byhseAaksys0pqFxRhAEgVGjRpGSkkJBQQFer5fNmzf7hMNOJiCmoXGqrFmzhn379vHDDz+wYsUK3/IHH3zQV/t57NixtfZbuXIlkyZNYvz48ezYsYM1a9bUUMN3u93MnTuXP/74gy+//JLk5GRmzpxZq53HHnuMF154gW3btqHX67n++ut961avXs3VV1/NXXfdxd69e3nzzTdZvHgx8+fPr/NYZFnm008/Zfr06XXWm7TZbOj1+pOOLy4uzhcOe+DAAbKzs3nllVdqtFVaWlojHexEPvroI0JDQ+nUqRP333//aV/DgiAQGhqKwWCguLiY4uLis5LLeEpxM5WVlYwfP57t27cD8Oijj/Lggw82en9BELjssst444032L9/P5s2bapRN66aTZs2+Tx5l112WaMt1+ZgypQpzJ8/n127drFgwYJGeSibQlyQQGWcQFUE7N6+ERF4/vnn61Xz9Hg8PPLII4B6UZ1OzHawTkdwLHSpo0ShJCtsPCTz2VaJDQdlusQIrN4jk1Gs0Dpc5q6ROsKbIa9Gj46Buk5URJpJz1uGKBiQSGXCRVYKtjiIXPUcUxXQmTpBhwsgZSe4HHDgN5zfvYRx3GXkmsqwYuDOCJEoKRi7oaYr3eaTrg8jIng0aTmfYDQE4qg6QnreF0QEDScx5kbScz4DoMLellX6JHbIpXR3d6brCe7xEylCvUFU4MKPpuVthuv1VPuHs71egk4j5OHxiw188buX3d9JiAIczJP57QhsOCQT4S+QUaJgMUBskEB8sEDnGJEBrUT8TM18PbVvrwquZGSA/q8XkicIAv8eq0cvwnsbJbrHCTy23ItXhokNiNqs2i3x0Bce5KP36yCrwLR+f73jPxWSkpLIycmhS5cu9W7jbxF59nID3++VOJArM6hAj3uXwJqHYN/ncMEj0H6CVmpB469NdVpG9cRwdnY2AQEBjBgxggkTJpzbwf1T6N0bcnLOfr+RkbBtW6M3X7FiRa3QyVPNlfPz8+Odd97xhWmmpKQAcM899zBp0qR695s/fz5Tp05lzpw5vmXdunXzvT/eWEtMTGThwoX07duXioqKGmOfN28eQ4cOBeChhx5i/PjxOJ1OzGYz8+fP56GHHuLaa6/1tTN37lweeOABZs2aVWtMBQUFFBcX+yLpGuJk46suWRYeHl7n87Isy/XmFE6fPp2EhAQiIyPZvXs3Dz/8MH/88Qc//PDDScfVEIIgEBYWRm5uLmVlZVRUVCDLMhEREbXCMZuLJj+JuN1uJk6cyMaNGwG4++67mTdvXpM7vueee3j77bfxer3861//4ueff66hnllVVcW//vUvdZB6Pffcc0+T+zgdBEFg9uzZTJ48mby8PBYvXtys7R/Mk9mRpj4VXh4TT0FOWoPlGo6PBa5rhqO50IkCQ9rqGNJWx55MiedWe+kULbAnS2FPlkRSrsSoDnpmDNRhNpz+U5nN2oqE6GtJyfqAjFQXMS2tdCnyIuzdiQ5wlhVgLj7qIdIZQBAx7P4ZfSsvug5daGNoTT+xJYLY8Fhslha0jJpOeu5nmI2RVDlTScn+gNjwybSMvpac8s18GVAKKPxZ6sczxVlM8fdnut1O63rKTlTipiUhOPEQTNMU1OIlE50lLxa3jqISoG4h2kZhNghM7asnOlBk0xGZT7dI6ETILFLIK1UoPWpNOlzww14ZkNDrYExHkdbhIr1aiHSOUUMMT4uiIjh4UH2/eTPE1jGL0ACSJJ3z3ApBELh3tB6bSWDRT6oXb/ZXXiwGoU4xlp+TVANPOvpbMbmn6hn/p3DJJZc0ajtBEBjbSU//BB0bHoHfRVBkEA3w2SSI6AZDHleNvVPUQNLQOKcEBAQgCAJvvPEGer0alXOuBKX+seTkqHnh5znDhw+vFSG2efNmrr766ia31aVLlzrz8OqqUX08O3fu5KYGSh3t2LGD2bNns3PnToqKinwGUVpaWo3Im+OVsavz6vPy8oiPj+f3339n69atNTx3kiThdDqprKyslZtW7dlqjEOnseOri+ocxfqeuY8/L507d6ZNmzb07t2b7du307Nnz5OOrSEMBgMxMTFUVVXhcDiorKwkNzfXp9/R3DncTX4amTZtGt9//z0AI0aM4IYbbmD37t31bu/n50dCHUWR27Zty/3338+CBQvYtm0bgwYN4sEHH6RVq1YcPnyYZ555hh07dgDw73//mzZt2jR1qKfNxIkT6dGjBzt27KCgoKBZ204MFSgsVwjyA3uFCSUkpN5tJUnyzXqc6TISx9MpRsc714p8/YfE86u9tA4T2JGukFLg5cd9Xm4ZqmdEe91pe1jLSiAyaBqVle+x7dtsppYe8a0zVpYc29A/DKUki7QLOmLx0zE2fQ9muz9CSOP6t5rjSIy+mYz85b7PGXlLCQsaRlTgEKYJhWz0ZrCgWL1hbqqqYml5OTfZ7Uy124k4wTtVhIMUCgEY1ERPXmCGlb3fqP1cfInutIw8UI3z4e11DG+v4+YhOg7kKryw2ktBhYK/Gao84JGOhQZ4JcgpVfh2l2rImPTQK0xk3BYjpgB8L50R3A5wlakvTwVctrger8uNN6o/sl9+Ca+9BpMmNdo9s3XrVlJTUxk8eHC9pVfOFoIgcNMQHcWVCks2ScgKPPyFB4sBhrY7ZoFsS5F5+AsPHSIFdmcpTO6p44lL9IgnmXD4J2O3Cox/GXrOgN9ehF0fq8tz/4AfH4Tv7oLOU9VXdG/Nu6fx1yEmJoYHH3yQr776ihEjRmgG3rkgMvLk25wH/fr5+dG6dU3F8BNFCEVRrBXO5/F46myrvj4aoqFnSYfDwZgxYxgzZgxLliwhLCyMtLQ0xo4dW6vs2fGGUvWzYLXBJcsyc+bMqdOjWJfnKiwsjKCgIPbt29fg2JsyvhORZZns7GwURSE+vnEltHr27InBYODgwYOnbeSBep6sVitWqxWPx0N5eTllZWVUVlYSFhZ22vW5j6fJRt6yZct873/66aeT1rcaOnSor3bMicyfP5+8vDzeffddduzYwdSpU2ttc8MNN5ySp7A5EASBJ598stGz1U2hwgUGPaQWweBWfUlK+oDCwkJCTjD2cnNza7jT77777mYfS0PodQKTeqrG3Lu/ePkzU6JTtMC2VIX3N0p8tUPm3xfqiAtu+vR7aWkpS5YsYc8eNedObxDo328Q8rgbKLFHI8sSf276hdZBZmKNEiRtwuMpwh0RgE5QbyKe0t/IKykgNOEKRPHkF4bB4E/LqKvJK1pLQekGAIrKtmIyhJFo60icLgRjaDnrHA5+rqpCANY4HHxSXs7rERH0PO7G6I+ZOAIpw4WDpiUXBfkdu7EUVDRvXHaITWSgDfrfKtYwOBwume1pCj8nyfySJFNceaxflxcc5bDzvZpthXWC/D01l417HYx1/X60bw87doBOB+Xl8OKLcN99jRpzaGgoR44cabTq1JlGEAQeuFBPlRu+2C4RZRdYsMqD3SrQPU4kpUDmnk/dlDlhT5bC9P4iD4zVDLzGEtUTJn4IXa6CdbMhayv4R0HaBtj0Imx6CYJbQ/xgaDUaEkaBX+i5HrWGRsPEx8f7IpA0zgFNCJk83wkLCyMnJwdFUXyGyM6dO5ut/a5du7JmzZo6S3Ht37+fgoICFixY4FO133YK57Znz54cOHCglkFbH6IocuWVV/Lhhx8ya9asWlFrDocDk8nUqPFVezdPDIMVBAE/P78m5cPt2bMHj8dzRhTADQYDwcHB2Gw2cnNzycrKIjY2ttmims5pXJEoivz3v/9l8uTJvPXWW2zdupWCggJCQ0Pp06cPt9xyCxdddNG5HCIXX3wxffv2ZcuWLc3a7pQ+Om76QMYrwfrc1gQarDz++OPExcXRrl07YmJieP/992uMY+TIkadVs+90CLQK/N9YA5d0F3n5By9twlWvXs8WMPl1D/eMVriqiXlIu3btYs+ePcTHx9OlSxecTic///wzGzZsoGPHjoSHh7NunRrG+/TTT2McMBljaT72pMUUGcpRRAGDUyLfkoSyaQFh3e9EtAadtF9BEIkIGYlOZyW/eD2iYCQj73OMhpuwmKK4MiCAS2w2/ldWxk8OB9tdLiJ1OhYWF3Mn+OraBeGHn2KmxKtnr8tA3yaoEyeECAxsJZJSKHMw78wIpJxocPiZRAa3gcFtdCjjFNIKZX5PVfg9VWZbqkxbRawlOWOqXf0DV1k9Rp6fH7z3HjzwgPpju2OHmiNxNF6/IRISEur0+J9LBEHg8Uv06ESFlbtkHC7418duXrvKwMPLvJRWqdsNbC1y/xiDZuA1EUGANuOg9UWQsg62/UcN35Q9ENFV9e4VHYSd70LCSPA4oO0lkHCxQnTn2t9vDQ0Njb8Lw4YNIz8/n2effZbLL7+cVatW8d1339VZkutUmDVrFiNHjqRVq1ZMnToVr9fLd999xwMPPEB8fDxGo5FFixZx6623snv3bubOndvkPp544gkuvvhi4uLiuOKKKxBFkT///JNdu3bV67x56qmnWLduHf369WP+/Pn07t0bRVFYv349L730Elu3bm3U+Fq0aOFT9xw3bhwWiwWbzcZrr73G8uXLWbNmTZ39Hz58mI8++ohx48YRGhrK3r17ue++++jRoweDBg1q8jloLEajkfDwcPLy8igqKiIo6OTPso2hyUbemVCDGTduHOPGjWv2duujZcuWTTqOzZs3N/sYuseJ9EsQKa9S+CPTxsWD59KLlfz00081agNWc9FFF51V4Zn6aBOhY1o/hduXeDHoYHuqeh4/3SJRUK5w16jGu5l/++03dDod//73v32zFiNHjmTRokXs3bvXp9zqdDpxuVwYjUaUgFBcsTEIVekochWSXsBcWI5DhJAP/g/x0ochum2j+g8NHIDXW0Fh2a8Y9cHkFa0lLvJKREGHVRS5LjCQif7+PJafz2+VlZTIMsvLy+lqNmMUBCLw59ucYColA05ZYoafgtjI/1FcsMD2NBmnB2ikmmdzIggCLUJ1tAiFSb3UZY4SBcfYY6GZrlLQW0CRqBHC6deQcObw4TBqlGrktW8Pzz8PffpAIzx0X375pU+U6XxBJwo8dJGB1EIPm5NlSirh+sUeXEdFN9tECDx/heEfWQuvuRAESBiuvqqKYN8yyNsNhQfAezSftCQZio9AxiawbFb4oq+b9lECHSJF7FYBkx6MRyc+nV61KL0gQEEFOFwKMYECOWUKVqNAh3gFa6Kb3mYzYX9BgSANDY2/Px06dOD111/nqaeeYu7cuUyePJn777+ft956q1naHzZsGEuXLmXu3LksWLCAgIAAhgwZAqhexMWLF/PII4+wcOFCevbsyfPPP8+ll17apD7Gjh3LihUrePLJJ3n22WcxGAy0b9+eG2+8sd59goKC2LRpEwsWLGDevHmkpqb6iqE/88wz2O12BEE46fhiYmKYM2cODz30ENdddx0zZsxg8eLFFBQUcPjw4Xr7NxqNrFmzhldeeYWKigri4uIYP348s2bNOuOaASaTicDAQIqLi6msrEQQBKqqqiguLiYiIuKU8vUE5WxoeP4DKSsrw263U1paWu/My5F8malvuqnygCjAa0OMDBymuqQffPBBLBYL8+bNO6t5eCfju7tgfZHEylZqbHiIDQor1HVPTzZwcdeGLwJFUSgtLcXf359//etfdOvWjVtuuaXWdrIsU15ezsMPP8yQIUN8obxVrhwKSn6hzJFEkH83ArIrKclZR/SmJETRqKo5TJkF7QY06nhkRSIz70vKHHsABbutGzFhNZVcJUXh7txcdjmdFMgyN3pDubet+j9dVJJDhlKO3ljBXdY2RAiNd+fdsNhFTilEBMBTkwxE2v8mkvuSBFdeCUcljJk9G+pQ0joep9PJSy+9hMfj4YknnjjzY2wi5U6FGe+4OZR/7HYZaoNPbjYRadcMvDOBpwrSN8KRHyFpxbGw4dJ/e/jBoobgdIkR2JWp/k+CrFBceWz/dpECB3LUdb3iBX4/KnQ1oIfExq65AMTr9Uzw96e9ycQAiwVjM02kybJMZmYmMTExWjF0DY0zgNPpJDk5mYSEhDOmTKhRP4qi+MTSzgcHxN8JSZJwOBxUVFSQnJzMihUrkGWZdu3aUVFRQYsWLejbty/R0dEN2hhwmsXQNU6PxDCRJy41EGyByTsMrL9YpGCfWt3+9ddf54UXXjivDDyAMc+DVA72HAFzBRgr1Iu7ZSgnNfCOJzU1FYBevXrVuV4URV+S8fFKSaUVuylz7AUkTMZwbF2nECv2QpQBjxO8bvjiKUhvOHHX14+gIzRwIHqdHxZTDKUVf5Ce+xkuT5FvG50gcEtgICE6PW3+COHdjw2s/FN9yOwRWIEtMIlgcxm/OssaffwAXWIF0ooUtqYo7Mn6G8216HQwb96xMgrPPANZWQ3uYjabmT59Og888MBZGGDTkfMEBv/PSFjysR+zPi1FzcA7gxgskDgKRi2A23fDXUfgwoVg7weRdnWboOMcxKYTnHLG425HlcelzBaKx2ofpnm9/ORwcHtODkNSU3k4L48fHQ4cp1G7UkNDQ0ND41TR6XQEBAQQHBxMYGAg06ZNo3///qSmplJQUMBPP/3Eq6++2qi2tFiVM8zJHKUXd9UhfyCy42sBL7D0Crhpaz05T+cBrnLoWKzD9rWO+CGQskHB3Bn6P940I6U6gTg+Pr7+vlwuoGb5iCpXFgZ9EDrRgr9fB3XhwCmwbwMUqIYjIbHwwX1wy5sQGnfSsVhMUcSETyY1+wMM+kDKK/dTUXUYqzkef2s7/Cwt6GwKZXJWBAt2SnSNFXh0uQe3V2FI9zheyjaw3yURpfMwLk7G1MiZ+zbhIiATEVDzIfRvQfv28K9/wfLlEB4OTzwB77zT4C4Gg4GkpCQ6d+58Rr0fVUVgCW789slr4fMpUFkg0DnZwPqZbmQdbDoicyBHpl2kNld2NghKgH7/gn4YAANFDoXcUpmp/cB1NOzZLamhm2YDWI1qeRE/I+hEcEvqdZalg72GQLZWVbHP7WbfUTW2cllmtcPBtxVqaEJPs5mJ/v4MsFi0sE4NDQ0NjbOOKIq0atWKrl27MnHiREB1kjz99NON2/9MDk4Dfv3115Nuc9FcgYijIqUF++D7/zvDgzoNrCEwYw1c+ApkbgFRFnD/KfDzFSJfXQf5exvXTrWXzuv11rtNREQEVquV1157TZW89brxeIox6Pwx6u0Y9f5HB+UPka0grhP4BULWAXA7Yc1/wVtbcrgubJaWxEdehcUUA4DFFI2j6jClFX9wOOMNktJeYlrq09zZ/gi7M2QkGTYektl/RE+YaKS90YhNp+Orow+IjUF/1HYtrIBix9/Ik1fNI49ASQls2QKLF0NS0kl3qaioOCN5vwBZ22DJhfB2H5AaYVQriirz/+FoqDxaQaWVUeTOXnoiA8DPCI8u95yx8Wo0TLCfQIdoHYPb6BjVUceojnrGddExsoOOQa119IjX0SFKJD5EJCZIJCFUpFO0yOgIM3cHB7MkJoafW7Tg5YgILrHZsAkC7Y1GvIAX2OJ08kFpKSPT0rgjJ4dtTgfKOcif1dDQ0NDQqKZFixZMnz69UdtqRt4Z5osvvuDjjz/2eaXqwmCByz8Do00tCrx3GexcfPbG2FR0Buh3F9y4CVqoeboIOkhZD290gW9ugfLshtuorjVSXl5e7zZ6vZ7Ro0cDMHfuXIS8VNp8t5vojfuwlZ7w1Y1sBel7wFEC/iHQoivs/QXWLm70cflbWxMddgnhQcMRRVXFVBDUGXyjZELcs46b99/JlbZfaaXPYfUemUeXVjHFa2G/202y201eA0briRSUQ88WAh2iBML9G73bX4fQULj/fvW9JMGTTza4eV5eHmVlTQt5bQxlmfD1TaqBd3i1KuCx492T7JMFn0+F7+9ThWcAWo2Fm7fBjRN0BNsgowQO5ChsS9Ue/P+q+IkiI/z8WBAezi8tW3JXcDDTAgKI0+sJEEX2ud1IwLrKStZ403mP38ik5FwPW0NDQ0PjH0y3bt0atZ1m5J1hLr/8cn777TcWLlzY4Hah7dQi07l/QFUBrLxdVZhrKl6vl8rKypNv2AxEdoNr18HkT6HdpaoCniLD9rdgURu1/pWnqu59q0tB6E8SBjV27FhfUnXprg0IuSmYDu4mqPiEr66ggMkPYjvCkKshYy+gwJ8/wvdvqrl6jUAnmggLGkJ8xFQSY27G7teZAL8O+Beq+wvAw8JbRHozCKSUAGc2i97JITEnn0hnOSvzslB+/xbSdkNFseoOqoe0IoXtqQq7MhVahlYXEf2bGQx3360Wih0/Etq4oTCz3k07depEnz59mlXBKvUXeK0D7HgHwo6mdtpbgLUOhVDJowp8fH4lLEyE1PXH1g1+FK5aqYZ5CoLAjAGqiqxRB3sytfytvwNGQaC/xcJjoaF8FxfH4qgobg4MJEKnI0AUqPJLJoNi/stG1pNEFY2LEtDQ0DhzaJEUGn9XmuO7rRl5Z5iBAwfSq1cvkpOTefvtt3E5pHq37TgZet6svvdWqQ+b7sZH/wGqNyQpKems3fgEATpfCZOWwIj5YDzqkfI4GvaW7Nu3D0EQGiUs06tXL4xGI/5BoWAJAAQw11SxdFdkgcuhGnfB0TD6Fug5DsryYeP/4MvnoSCtCcclYDFFEWzvTVzEFEJ73QM3vQbDr0Ow+DGfF+isTyWZODKIJOOHDki/WcgtN7Ljt2Ww6nV4bjIsuAw+ehSWLYCfP4Z9v0BBOkgSJr2qTgpQXqVw9ydu3t3g5aNNXvZl/00MB5sNPlgAvWSQC2DT5/VuqtPpCA5uQrLcSUj+CRYPUb3joEryX/gK3HlAvdZAtcEzt6qqsS/GwCeXwJ7PQHJBaHuwRcKVy2HEPBCPsz3HdBRpFSZg0MMHvzXee6vx10AQBNqZTNwdHMwP8fG8EO1PuKDeqwyI7CCdN/mZIxSc45GeXUpLS9m+ffu5HoaGhm8y0O3+uyW0a2ioVDtsDIbGlyY7ES2b/AyTlZXFJZdcQmlpKTt27CB57n5iQlrR50YznabUrjl24cuQuQnKMtQaZd/eARPer7NpH0eOHCEkJAS73U5gYCCSJFFRUYHD4SAsLOyM1/YAMFhh8CPQ8yZY/yT8/h/V6DPUY8OtWLGCtm3bEh0d3WC7FRUVbNu2jcGDByMqXjBajr6Oyep5vBUcbutGFz+UcLk1gbEdoHUf2LQMdHqIbAO7foQDG+C6VyC6TdMPUBQhtoP6GnYNISW5PHwghbt/KiJayeZnVydy0+LROSq53fAkz7gWM5T94KwARzEk/XasrYAwqCzBZbqPQoca7/rBmiLWZQSwPgkkGcL84eOb/iby/IMmwub/qeqnO1ajDLsWwS+w1mYbNmxAlmWGNqJ4ekNkboH8fbD7E3XSIX2DGmo5+WPVE6coUHAA9n4Of36oGn8nYgmBqJ5w1bc1vmo+DHqBYD+Bw/kKDheUVCoEWv8G/yuNWugEgYHGUPoxhG2kspds0ikGYAmbudDTgt66dojiqf8Q/1X47LPPyM3NpWvXrieNwtDQOJPo9XqsViv5+fkYDAatVMlZRiuhcOZQFIXKykry8vIIDAw8rWd47S59hnn++efp27cvd999N08+8CJyRgcK0kW+uxO2vKoQ2k6g6zXQZpxqEFXn5300DrJ/V1+JY6BrPTmWLpcLl8tFWloaXbp0wWq1YrVakSQJt9t91m98fmEwbhEMuBcCW9Zc5/F42Lx5MyEhIUiS1GCeYjWzZs3C5XKpuXlp21VhFWcFHP4dOl6AoijkFH6HjAfZDJUBgQRWe/n6T4L4LvD1C+rnkDj4bA5cORuiWp/egQZGEN8vgs/7KCz4LgzzdgmdWcJRaMWBlUdaX8dNreKYlrQak950bD+9SfUuAqPNv5KNnrX0Z32GHxGl6RTaIkFnIL8cbnujkE9mCpgjQ05vrOcaawD0vAgpczdFnVqC8w/C/GoacuXl5eh0Olq1anXK3Xidai7o8hmq4RY3CNxHUz57XA+bF0H2NtVzZw09VnetGp0J2l0CXa+B1heCzthwf63DBbamqO8P5in0aan90P2d0SHSjwTaE8lX/EEKhVgUA8bs7zksrCc2fCIWU8OTVn91rr/+ekpLSzUDT+OcIwgCUVFRJCcn+0oyaZxdZFnWjOszSGBgIJGRkafVhlYM/QxRXQz9hhtuwGg00q5dO6644G4+mlJO5RF/vPZc9KURAIh68IuAhOHQ+SpIGAH7lsGKW8Aer0q+35Oulh87EUVR8Hq96PX683425bvvvuObb74hKCiI8vJyvF4vr776ap03CUVR+PXXX/noo48AeP3119Uwx0+fUL1zXi9K/wnkxRspcGzBao7H6c6jTdyd6HUn1J9wV6m18/ZvVD+HtYBb/gMGE81FWZXCpzvcvP6LF6lKRLTKSDYvfSc4GG0yMtldirEwDYqyITsJ8lIoMkQwPvsBRGTK8Kdb+m88tex6brv6G9KDExl4+AciyzKZvfcFGD1aLUswcCB07w7Gk1gg5xkeVyFJma8BCjqdH23j7q7h+Vi1ahVBQUGEh4eTkJBwSn3s/hS+uEoNIVZkMAaAux4dF2soOEtB9qjiQV2vgY6Xgzmw8f19ttXL3BVqqOYj4/RM66c9+P5TUFDYQgqekh0EFe06ulQgLHAIoYGDAUErhq6hcRaQZVkL2TwHKIpCeXk5/v7+5/2zZzWyopDv9ZLp9ZLl9eKQZLK2WHBL4PKqZXe8Ehj1EGoT8MpqOR6LQSDQChajgN0sYLdAoB9YjWfu3m4wGBr04FXbGCcrhq4ZeWeI6n/A3r17ef/99ykrK8NgMHD99deTtUNizRtpWHcPQ1cZSGRviZxt6j8zpi8U7Ifu10NJCpRnwYwfwdSA+mJRUREej4eIiIizc3CnyH//+19+//13AgICfCqKzz33HH5+NY2y5ORklixZQna2KtF5xRVXMHz4cDXO7tfPYPc6yD2CO8CPw+O7IusEQCQx+nos5pi6O/e64e07IecwtOoFrfrAoCua/RhLXV5GvOLC7dCht0m4L8mjh7+RLK+Xqa5QLgq1EBd89MbgdrF3XxZt3PtYtU1g6CcvEvD7r6SYo7jn2q857J8IwGufTGRIaCn8djTk02yGPn3g4ouhSxcYNAgauMjPF9JzP6fMobrP/CMuJd6vh2+dx+MhNzeXmJiYU/7BUGT45SlY+7j62d4CSuuY4DUHQnQf6Hq1auCd6HFuLFuOSLz0oxeLEQa31nHdBZqR90/D6c4nM285Trd6r7KaW6AoXqJCLqMg36kZeRoaGn9LFEWhtLQUu91+Xhl5siKTL8lker1kejxkejykeDwc9ng44vHQwWhkx9EoMrMg4FociYA6fpsJKo4GmHWMEtibfcw8CvWDAsexflqEQH45hAcIhPsLdI4RsFsEWoUJJIYJxAQKiOKZOy+NNfK0p5IzTExMDDfccAMvvfQSHo+HI0eOMHHmREZN78Ld/7oHY2Y7Yv0vxXSwJYoE2TtBdqvCK1csVZ1WdSHLMpWVldhsNjweD3q9HkVRzquL7USqa+NFR0dTVlZGdHR0LQOvoKCA559/noiICPR6PWPGjFENPFBdNP0mQWk+VJVhFARaJJtIbQPhwSPrN/AA9EaY+BB8+jgc3gbpu6HrSPBvPqEPALtJz019FT7bLpFfqsMvyUJKVyflssw738JaqwdZgVuG6BnU2kjHbglAApf0AW67CCSJlklJXLu+mCeOlqGYc/FrLNtxB/bqTpxO+OUXOHAA8vLUfMEePeDSS1Xjb8gQOOG8ng+EBg7EgZstgQHkmQq4Aw8WVG+ewWAgNjb2tNoXRFVB0xoGlfnQcpiadxfVUzXqontDTB8IaqV+lU6XsACB3Znqj0BkwN9EKEejSZiNYSTG3EB+8S+UVvxJpTMDkDic+RY66QIU5e8dvqmhoaFxtqnCQxEOiqnEgYs8yimhilIqceDmg7TWwLEf+Ri9nsyj5a2K5GO/1U5FweQv4ylXnSxVxwkmu6Wa/q/iE5TiSyqh0g0pBQopBQpJueqyam5cbSIsBOIuUCeTY/vXFG87W2hG3llg/XpViz04OJjLLrsMUB9qX174Evfeey8Z4e/z4NuPkrFBz+6PYf9X0GlK/QYeqDlMiqLgdrvPew9eNZdffjl//vknZrOZ1157rU6D1OVyoSgKERERPPHEE7Ub0Rvgojtg6NWwfgnW0Fa0iRuMXm+rve2JRCZCm36w5Uu8sa1xlOzA7j+yGY6sJp1iRPLXSsQqAi2fCMXez0nFKDcbc3ToW8GfqQqLv/PyWp7ANW10xEkiXpdARTbIkg7oQIBeYThe1uol8vyjeeLK93n+imUYftsIv/4KbjdkZKgdyjL8/ruqZDlrlhrKOXgwXHQRjBgB3bqphuA5xmKKZndEe1LIBtyskQ9xsdihWfvoMEl9VR2tXnHpf8/cjTXAfOz7W+48M31onP8Igo7w4GHYrG3IzF+G21OEongpKd9JdkEpkaEja4eQa2hoaGjUixeJIiopoIJynGRTSiEOKnBSyrEf3BD8KOQ4F5sAUQaFbM+x3+dQUSQbiDMYaGs0MspqJc5gIN5gIPBaAwEGHSYDgILbK+DyKlS5FSrdApVuhXKn+iqpFChyKBQ5FAorFPIqIL9cweWpaeAFWaBks0AJcPBbCEoEVxm0vVR9tk8YodabPhto4ZpniONdqaIo8tBDDyHLMl26dOG2227zbZeSksKzzz4LwJw5cwgLC8PrUvP0Gno4VRQFh8OBzdYI4+Y84vbbbycgIICnn366TiPvq6++YvXq1UyfPp1BgwY1e/9KeRHZ+16jOFRGrw8kJvpabPrAZu3D7VVIPwif9hTwOiGk3TEFR0VUEOTax21IVPAcERD0ilp8WxHo87jC8wEuEkIFtiQrDG4j8vwUA1ajAFlZsHEj/PwzrF8Pu3ZBz55wvLx5376wZQsEBcGwYTBypBre2akThJwbMZcyqnhT+QWcEXyaa+OtyBh6Ha2D+FfD7VXoNVeN7ejZQuD965svx1Pjr4ksu8kp+oHCkm24Ha0w+h3GaPAjJmwiNuupiwppaGhonE80R7imosh4vKW4PUV4vOU43Tm4PYW4PIV8E9sKx9G56RYEk0qRbz8rRipR8zCN6HAjHX2vJwgLpcWJSJKZaL2eGIOBeL2eeIMB8xma7C6rksksgcP5CkfyZMoywPSAgUpVY4/YAZBxnMB6+4kQ0hb63AH2uFPsUwvXPH+w2Wzcc889vPjii+zatYtly5YxadIkAFq2bInBYMDj8TBr1ixeeuklX6HwhhAE4S9n4B1PXaGlxcXFrF69mh49epwRAw9A8A/G0GYo1qojlEilfKD8TBBROHBhrYohqzKAbiYTbU0mWpxibRKjXiAyFLpdC4e+A+loTrjBCp7KY8cs+SvoytXP+RUygehQvMfWBwcLvDnNwJVvqjEEvxyUeWG1l/8bo8cvOhquuEJ9ARQWwrp18P33sHo1pKaquXsAxcWwfLlqGG7erC6LjYVWrdRXYqJq/CUkQNu20Ijv36kSgIWE8j7MKlAl6J8pKODj6Gh0gsDatWvx9/enT58+Z6z/5sSoFzDrOT4qROMfjigaiQ4dj5+5NQcOqhEcXslBas4SiLyQ1tZemLSfXQ0NjX8CXjeUF0JZPpKrlKIw8HhLcHtLUGQvla4MQA2fNBujcbqzfLtGuwUOmlUflMSxEMsAzMQTdNSg8yMYK3YsBOGHGb2aXxd0Vo+SAItIgAU6RAGo3hnlWihMgrQNkLEJ8nYdq3tdsB/2L4dfn1dr9g56CKJ61Nv8aaH92pwlWrduzfjx41m5ciUHDx6sse6VV17h9ttvByApKYkuXbqciyGeEm63m5KSEvR6faMKWV9//fV8/vnnFBQUEB4eXmOd2WxGFEXKyso4cOAAe/bs4YILLqi13ekS6N8dSdTzdUAVDsGNCTdZlOLxBLOsVGGgxcKveXlcbLMRLxroXxlA2zABf0vjZ4H8wuDi/4CnEna8p17gsgS5O9X6h0abQkWYwu9ZEpGtYE+xRGy0xOEShQ6RAln5MGOMgYQIHW9eA3d94iEuWOCL7RKZJQqLrjJg0B1nXYSEwOTJ6ktR4OBB1csXGgpr16qGnuO4kAazWfUAHg0lpnVrOHRIDeucNAkkCTp3Vr2D7dtDmzZ1y7ueAhP9A1laXolblqlSFL6sqCBq1y4+//xzWrVq9Zcx8gD8TFDogOwSLSBC4xj+1jZEBOuQ9ZupdB1CNEXxuSUfG+u5kI6051iyv4aGhsZfFUVR8HhLcLnzj3riinB7ijC6FGLe/8i3nWgPI29CR99nQdDDccabJB9LehMFI/GyBTOB/9/efYdHUa0PHP/O9t20TQcSSAglQOi9itgQpSg2LBfBghWuV7FfEBsier1YsHBt6FV/VkDAchFpSo303kIKCek92Trz+2PIJksSCKSH83meebI7c3b2bMrkvHPOeQ/B+BKKL0H4EIQPehphYtsFkCQIiVW3vnfDmLfg6C9w4DvY961aRnHDsVVweCW0HQqXzIKoEXVbDxHkNaCVK1cCMGbMmErHpk2bxqJFixq6SrWm1+ux2+0E13D4X1l6/E8//ZQJEybw5ptvzrFt5wAAUpxJREFU8vrrr2OxWDCbzUyaNIkvv/ySDz/8kOLiYn777TfP8Tqrs84f34B+XE42m0lAe7qxle5QLx65brXrP760lL/cNt5bbKJXpIbEbIWhHTTYXNArUkOAGYbEaGgTKFU7XEFvgYEPVXVEAiScLomkHJnY41pyixWSd8r8WaBgjoY/812E2HQMaK8Geg/814lbhj+Pyixa5+Khy6rpaZQktUeuc2e45x51zt7u3bBjhzqcc+9eMJvVoK7MiRPqV1lWewG3bVN7/y65RA0WTSY1gDSZoGdPdZ5fbCyEh593FhOtJPGPoCDuPp09dXF+PiM3b0an03HNNdec17kam9kgQbHiNWFbEAA0GiORrSaRW7SNny2FKJKDQmx8y3YuKfWlv9IWH3P7Jp0sSxAEoSJFcVNqP0Vx6Qmyc05yKi8Jt1yMjymGYttxTzmXFOD9wsIcUPCMfJHQYDa1Q6f1Qa8LxKgPxqAPxKAPQaf1bXHXRb0Zul6vblf9C+Lfh/j3IKgjJG+E46vUrdcU6H4LdBhdNwnixJy8enLmeFmXy8WMGTMAqlwbTlEUHnpIjQaqS0rSEjgcDmbPnu1ZQqHMjTfeyGWXXYbD4eCRRx7xOqbVapk6dSp9+/att3q5kUlzuUh2uNhhs7Hf4WBNSQkjigL483sf+kVJ/JWo0D1CzajYMQyOZkB0sDp09tLOEjod9IjQsGK3zIAoiS6+WrpEaM5r7bXsIoX7PnNwIlvBrQAKzBytY2SshowCuHexg7ZBEr4mhafHGIiLqMUY8/x8OH5cDfaOHIH9+9WtsLA8AOzTRw0OQR3Suef0mmCdOqmvsVphwgTQ69XjAwaoAWANgvIpqan8ZbPRUa9nVGEhXVJSuOqqqy788zSCG961czhdwaCDv2Y1z7mFDUlRFOx2O6ZmOg+zpmRZ9lonL4difmYfx8jERzFweVI8ercDoz6EIP+B+Pv2QKdt2d8TQRCaH1l2UGpLpdieSIktUc0grMjIioLdZsRoKkWSwGRo7VlKBkCv9afTqmNI/qHgFwL+oRR274bOFIReZ0WrMbfYdm5Nueyw+3P44xXIPQ5aIxj8oDQLwnvBiGehy3VVJ2kRc/KaiIKCAvbs2cNXX33l2VfVukmSJBESEkJWVhYulwv9Bc4Ha+r0ej3jxo3zLHIOEBAQwNKlSxkyZAhmsxmA0NBQMjMz6datm2fOYn3SoiFSZyBSZ2DI6QAlxelkf7JC634STrdCWr5CsUO9J5KaB4EWOJENncPhk40yob6wK1nhQJrCqn1wTa5E4H81+ITBzd9BSBc1oc7ZBPtK9G6nwS3LnCpQKLbD4o0u3lwN39xnYPIwLR+uV3sav9rq5qXraxHkBQSoQVyfMwaDu92QkKD2+B08qM7Z27ULkpLU45IEycnq47w8NVDcsEF9fuWV6hDQu++E2y+HLsMhuOqlLR4JCuKlrCwOOBycslj48bLLLvyzNJLIQAlfozpsU5ZlsSbaOWzYsIGdO3d6bnhdLILw4TYGcIBTFJUcRn96oq7dmUV67mpO5awmwKcLVr/eWExRF33jRxCEhueW7Tic2djsGZTaUyi1p2BzZGAxRlJiT/Yqa9SHYrcVodEY8TFFYja1JTRwBAZdEHp9IFqNAe7xPv9Zlnu+KOmM0Pce6D0F9nwJh1fA/tNDOdN3we//hP89CgOnQ//7wXgByyGLnrx6UhZl33333RgMBs/+wMBAXn755Spfs2XLFhYvXswDDzzQrOblnY+yjEyJiYl88MEHnv1hYWE8/fTTGI1G3nzzTQoLC7nnnnvw9/dn8eLF7Dndg/TQQw8RFxfXWNXHLStkFCgkZsvsS4UNR9xoNbDnpELbQMguUhv8STlw6wYDgVkaco7CZXNh8N9r1v3ucivotBLPfG9nV4pCUg7089cwbruey9+CaxbZKbSBQQe/PWok0KeBGoRFRbBvHxw4ADt3ql8PHlQXYt+7Vy3Tq5caEN41ESJzITQKDBaIHQI9Loeg1l6nnJmezs/FxWiBDgYDb4SF0b7C30tTN+EdO8czFSwG2PKs6Ik5l/z8fJxOJyEhIY1dlXp1Zk9eRYoiU1h8kOyCrZTYErGYoimxnfAc1+usBAcMJcA3Dp227oapC4JwEVAUcDvBUXp6s4HTBk67+tVeom4l+ae3AtK7hJBnKcDlVkdYmY2RlNpTPKc06kOxO9VUkTqtPz7mKCzG9jgdAYQGR4ubm3VEdsPBpfDnqyC74NTpQVQaHfz9BPhXuF9e0548EeTVk7IfwH333Yefnx9Wq5WTJ08yf/78arNiyrLMww8/THBwMC+++GID17hhVEy7+/PPP7NixQoiIyN5/PHHPb2Xmzdv5rPPPqv2HKNGjaKwsJDx48d7GosnT57k5MmTDBgwoMHvgqfnyxzNVNBrIdQPXG6J/WkKhs81FKVI5B6FuEkw+JHzXxul2K4w8zkXAQt1WEol7vgFvnc5+WyT2ps38yoddw5r5A75wkI1yNuzB+Lj4fffYXxnCDeq/0Qiu0HKfug8CPQmGDkZwttjs9l4+7PPWBkbS2hMDPudToySxIzAQG4LCMDQDHozLn/dRkYhhPnD6sdEkCeozhbkVWSznyKveC+5BfHIsroch14XiNOViyTpsPr2IjhgMEZDyw6KBUGonqIouNzFOF15OF35OF15nudutw23bMMtl+LjsBDxxTdqtFAmqick7i5/rjOoWS8ryLzqCjJal4+WspiiKLElAhJGQxg+pnaYjW2wmKLQ66xIklQnSygIVVMUNSvnxtfU3r0et8HE/3qXEcM1m4j58+fj7+/PG2+8AcD27du55JJLqiyr0WjQaDRkZ2dXucRAS5GXl4dGoyE0NBRQF3Y/efIk0dHR2Gw2srKyqnzd/fffz//+9z/WrFkDQHx8PIMHD+aaa67x9I4ePHiQyZMnN8wHOS08QEP4GXOMO4UDvdXHiqIuo3CuoZpV8TFK3N9Nz2/+YI6CrEMw6VYt6w7LBPnAH0fdjR/k+fnBkCHqNm2aui8zCRJ2wK5V6l1FjRaS9qqJXY5vh/Z9UDoPR19YyHWJifzvdEIegP/k5fFVQQHTAwO5xtcXTRP9OyhxKBTYoFMY9ItqHhm/hKbFZGxFK2MrwqwjKSw5RF7hLtxyKU5XLoriIrfwL3IL/8Lq14cAnzh8zDEt9v+CIFwsFBRKcFCKExtObLiw2Asx2XNwyzZk2abOX3Zk4nTl4nDlYdSHes15A5DQoeDyPHdKOu8A73QpLy4HSBpQyjNbGp06tBotBn0oRkMIZkMEYYGXYjK2UYddCg1KktQsm1EjIPuI+uO6UCLIayC33347zz//PFu2bKk2yAP1DjBAVlaWJwhqaT777DNOnTrF1KlTAXUI1/z583n00Uf5+OOPycvLq/J177//fqV9mzdvZvPmzZ55e5s3b+bWW29tUnMaJUkde32hDL5g8FH/0CWgbZAGXyPsSFI74dMLFML9m1jDL7Sdug2cAPmZsOt/sOUHCIqEpD3IuWmY/reQRwLCYfgo7ouMZEFODilOJ2tLS8mTZZ7NzOT3khLutVrpWo9r912ofakKNiccyYCekY1dG6E502j0BPh2J8C3O3ZHLrmFW8kt2I6sONBp/ckr3EFe4Q4M+hCC/QcS4NdLNL4EoQmz4yQfG7mUkEcJpThJI9/zvBX+pJDnKT/QpaVt1hbP8zPXjau4xEAZjcaIW3YBElqNCQ0maN0JDObyLbA1hLcHg0kdSaM3gjkAzD5g9gdLAH7+wcQaW15Gy5YguFPtXi+CvAYSHh5OUFAQCQkJ5ObmEhh49tUam1KQUtf69+/PihUriI+Pp2fPnhw6dAi73e7p7ezfvz9xcXF88cUXuFyuKs/RpUsXDh486HleMTGLto7Wc2sqchOgNAdKs9XsSwAjOmnYl6resfvziJuJ/Zrwn3JAKFxyOwy5Ef5aieIopTTtBDluHZH240j71mK2F/N01xHss9tx5OSwsbSUHkYjvxYX81txMVMCApgeEIhe13TG/u9OdtOnrYRThv7R4p+jUDeMhkBaBY8mNHAkuQU7KLYlUlSizpVxOLNIy/6JguL9GPRBWP16YzZGisaZINQxWXZ6hkGWSG4y9RpsOCnFCSjkY8OBCwduHKd748qO23ASjh8nyfecrx1BJJFT7fs5NWe2W9Qb/pKkQ6+zYtQH4WvpiF4XgEFnPZ2h0gedzoJGMpRfA+6/+7w/q7h6tFxNuGXY8tx6660sXLiQTZs2nXM9sB07djBq1KgGqlnDGjp0KIcPH/YkU6lo5MiRTJw4kVmzZnkCPJPJhM1mAyA6OpoTJ04QHh6Oy+Xi6NGjhISEEBwczPHjx7nhhhta3CRgSzCYg8AnDKJPJ6Ac1lHL++vcWPMljiyX+OYV6HkHdBxTu17DeqU3wuCJfLQrA0e+jRHGXCLNeohfAdknoeMg4oxG/tO6NZtLSngjR/2H6Ab2ZLu57Rsn/7pZT7vgpvHzXXtIZmey2pvaVwzXFOqYVmMixDqEYGUQhSWHyM7fQoktEUnSUmpPp9h2gtzC7Rj0IVh9exLg2wOD3trY1RaExudynk4wUlzh6+nHtmJ1qGJBpvrYVqT2eGWegNIisBXiDmnNwZHlI6kUnxh+CA/yPG9LIMnker2lER32CkMnddUs2q1DQyAWgvElEB/M6DGio5VWolVICFqtCY3GhFZjRqf1Raf1ETdxhAsmgrwGVBbUtGvX7pxlW3o+nJEjR3L48GEApk6dSnp6Or/88gvr1q0D1J7PsrX0br/9dj766CMArrjiCrZs2cL69euZPXs2zz//PFlZWWRlZRETE3PWobDNVe8p0OtOyNwHgaenrsW1khjzpgGffA1OIxyww4HvwRQI3W5SA752w2o3lru+OGWFvQ5fRtw1E2nnF1BaCCkHYO9q6DMGgMEWC/81m/kkL4//yytg31JfSosVbv7AwdzrdVzWtXEvXTangsOt0KedhEkHbazin7BQPyRJg79PV/x9umKzn6Kg+CDZ+Zs8xx3OLPKL9pKR+ztmY8Tpst0w6M8+WkQQmhSnozwYc5SqAZmjVM0I6bCpk9uL87yDN4MZshLVYM2zv1RdgKxMSFvI8k7/T7uekFQhGUmrDnDqmOepZPYFyoM8jey9hJMbmTMZ0SGjYEKPGT3B+BKAGSsWAk9vViz4YUSqqu/McHoThDokgrwGtHXrVgwGA927d6+2zFNPPcW8efM4depUA9as4fXu3ZuZM2fy8ccfs2bNGp544gmuueYaHn74YdatW8e0adPo3Lkz3bp1IzIykmHDhhEYGMiHH37oOce8efO8ztmSv2eSBGEVfm3suRKt/SVKXer/vjK2XNi+SN1irlQX1Iy7Gdr0r9nyDQ3hjjvu4PDhw8R17w6Bd8KHD4PJF5L3e4I8AIMkcV9gIJc6/Xnc7CKhWKHErq4PmJYPtw9uvMtXsV2hfYiGA6kK/kFN5BsrtHgmYytMxlYEW4dSULyfvMKdlNgS0WrN4IRS+0lK7SdJz/kNX3MnjIYQfM0dsJjaodG03CkAQhMly1CSBwVZUJgNRbmQdwoKs07vy1JTTmecUBN0lYnqBYm7vM/VNg6S93nva9cDks4YEWS0QMVZHk7vTJJA5X+GZdkmjRb1f5FfCBZTFFqNGa3GhGIM4lJaYUaPCT0WDOjRYkSHAR1GdOjRoq+m904QGosI8hpQTRZLbtWqFQB//PEHt956a4vupo+JiWHMmDF88cUX5OXlYbVaiYmJ4dSpU2RnZzNmzBjP9+v2228H1LmKS5YsAcBut3udz2S6eFLY+4bD34+rAZ4tD9K2w57/woEfwFkC5mA4/hscXwWbXofAGOh2sxrwterduAGfn58f/fr1U59EdoXOg+HoNvhrpTpvLzTKq3xsmJb/m6Zhzo9OErIUNh9X2HzchdMNUxops6gkSazcLaPVQLTcBLtLhRZNqzEQ6NebQL/eOJw55BcfwC3bsDvSAZAkPUWlRykqPUJ2/iYkSYu/T3cMugAspnaYTZFoNU11XLfQZCiKOtKiKEfdHKVQnF9hDbbTyUDKjpVt9hI1yUfqIe9sj2ExkHHc+z1Co7wDPPWNq6hL5d4zytpTGg0YfdQtIEx9vdFHDdosAeqyAUaLus/koyYeGTX59HPf8q+n5/NrgPZnvJXIrSU0RyLIa0CxsbHs2bMHh8PhtUB6RWf2TrV07du3R6PRsGTJEqZOnUp4eDjHjx+nZ8+efPXVV2zbto3hw4dz9dVXY7FYuPLKK9HpdHz77bf4+/t7hnQCBAQEsH79evz9/UlLSyM1NZWYmBhGjBiBTtfyftXLlmTwCYWOo9Xt2vfg4DJI3qj25smn72jmHoc/56lb5GDwiwBrezX40xmgMA3Sd8GNXzfwEE9Jgo4D1DkUWUmw9nO46Z+VilmMEvNu0PHO7y4OnlIbAP/6nwuzHm4Z2PA/2yAfiadkA8f+JaGUSOSOLx9KKwgNyaAPItQ6jFDrMOzObAqKD2B3ZJJfVD4cTVHclNpSyHeV9Y5I+PvEodP64GOOxscUpfYGCi2XoqhDGZ02cJSUD4e0FUFhTnkgV5Sj9q6l7Fd73ioGYJFdIOWg93n9QtQeuTO17lw5nb/hjBuxOgP4BqnvZ7SoAZjRB6yt1BuAelN5VkizH+j05cGb0QKG06/Rm5rOUBVBaEJaXsu3iTp+/Lin56mkpKTaIK/ikMOW2ItXWFjolQmzTZs2XHLJJaxdu5Y+ffqwadMmLBYLFouFP//8E4D169fz+++/0759ex5//HHP97EswNNoNMiyTEJCAgkJCV7v99dff3H48GHuu+++BvqEjcvgCz1vV7dRL6g9e/u/gYTfK9wI1ajz98oEdYKcI+rjrEMQ2rWBK93tEvj9YwhrD3vXqP+8R9+vfq1Ao9Ew4woDRr2Ld3530bedxEsrXZQ4YOrwhr+UtTNrOFoMOhPs/hxGzm7wKgiCF6M+mFDrcABaBY+muPQ4RaXHKbWnenr5VApOVz4FxXvJKVDTtpsMrbCY2mExRWI2RnoWPRaaIEWBkgIoyIDiXMjLgJJ89XFxnlomO6U8kCvbDGZ1X0WtO0HaEe99Ie0gP6OKN67i98Fpq7xPZwD/EPWfjn+IGgj6BUNQBFz6N/W5f4iawl/8jglCvbmgllF6ejpbtmzxbNu2bfM0uJ977jnmzJlzXuf75ZdfWLRoEVu3biUzM5PQ0FAGDhzItGnTuPrqq8/5+tTUVJ555hlWrlxJcXExAwYMYNasWVxxxRVVlj9x4gTtKyy+PGjQIDZv3nzW95gzZw7PP/88AAkJCURHR9f48y1YsMCTZMRgMGC1Ws/5mptuuqnG528unE4nTz31lKcHbu7cuVitVq644grWrl3LokWLAPD39+fYsfJJ0MOGDWPdunWcOHECWZYrLZYuyzK33nora9eupbCwkOnTp2MymbBYLDz++OPs2rWLf/3rXzz22GMN+nkbmyUY+t2rbsUZsP97NeCTzxj1EtKlPMhL3dYIQZ5fMNz5Bix6AKJ7wr41aoNl9AMQ2KpS8ftG6vA1wryf1W7KN1a5MOgafo5en7shcYPaa7rpXzDo72AKaNAqCEK1dFqLZ+09RVGDuhJ7MiW2JEptKThd3tkB3bKdnIKt5BRsBUCr9cHX3BGd1oxeF4hRH4RJ9kNnDAS9yBBR75x2yE1VMw/nnIScVLVnLHG3mhnSeXq6QrvukLTX+7WWAPUaeia9sXKQZ6iiB9dWBD6B4Buo9rT5BauP/UPVedPGCuuw6Y1q75rBrPasGUygFf0HgtAUXNBfYtm8sdpSFIX777/f07gvc/LkSZYsWcKSJUuYNm0a77//frV3FFNTUxk0aBApKSmefevXr2f06NEsXryYO+6445z12LJlCytXruTaa6+t3QeqwuOPP45Wq6Vt27aYzWZuuOGGGr2udevWdV6XxlZS4v3P5ZlnnuHdd98lKCiI2NhYjh49SlBQEDfffDPh4eGecrfccgv9+vVDq9Wi0WjYuHFjpXN/9dVX9OzZk7S0ND744ANuueUWevTowbPPPsvLL7/sFTRejHzCYMAD6ia7ID9ZHcKZexz8I9W5ehGDIKhjI1WwTSe49SX49gUIjlDn6AGMeVhdZ+8Mtw/WUWyHt39XA71v4t1oJIVbBzVccgnfcPCPAMfpEcPx78Hwpxrs7QWhxiRJwqC3YtBbsfr2AMDtLqXYlkhx6QlKbCfQaMxegZ/bXYzNnobdWd6j0yZRS+Da39UgIiBUTYahM0BIFIS2U3uALP4N/vmateI8yEyEzCQ1U2Rmkjp03WmvHKhF9VR76Co6M2gDdR4dEpgs5cGX0QwB4WqgWBagGS3qYtm9rqwQzAWBj1UEaoLQAtT6rzgmJobIyEjWr19/3q/95z//6Qnw+vTpwxNPPEGHDh04duwY8+fPZ8eOHSxatIjQ0FBeeumlKs/xj3/8g5SUFIYMGcIzzzxDYGAg33//PQsWLOD+++9nzJgxBAcHn7Mus2fPrpcgD+DSSy/l5ptvPme5ikHQ+fQUNhdnDlGtmChlxowZZGZmsmDBArZt20bHjmq00bFjRw4cOECXLl08gb7FYqGkpIRp06Z53SDo0KEDgwcPZtGiRbz33nuMHTuW0aNHA5w1o+nFRqNT548Ftgcub+zaVBA7GO55C755EazhcGCDOgdj7CPqpPgzTBupw+FWWHNQJjlHYe5PbpxuiclDG65xMuwJ2PmJmsk0quWt3iG0YFqtGX+fLvj7dAHA5Sql1HGSUluKJ0uny+0dQBhy89QHJfnqZrCoPUsVhUaDxU8dmufZ2qi9QBZ/qLTo80XCVqwGaNnJarr+9OPqJrvLh1hWFBZdOciT3WpPWUC4+v0MCFODtIHXqYGZj1XtgbMEqAFcC1szVhCE83NBraHZs2czaNAgBg0aRHBwMGvXrj3vhbuPHj3K/PnzAejfvz/r16/HbFaHDQwYMIDx48czcuRI4uPjefXVV5k6dSodOnTwOofdbmfZsmW0bduWVatW4ePjA6jD+2RZ5s0332TlypVMnjy52nqEhISQlZXF9u3bWbJkCddff/15fY5zefXVV2s0PBNg377y9MDVzdlrzsp+vqBmWJw2bZrnuSRJhIWF0bt3b4KDgz09b0ePHuXtt9+mT58+hIWF0aFDB2655RYiIiJo06YNs2fP5t133yUrK4vCwkKWLFmC1WolLy+PFStWeHqd9+49YziL0DS16ghT/gWfPqr2Euz5XZ1LctvLVRZ/aJQOjeTivbXqBP/XfnVR6lSYdomuQeYTBXeGGcfAGl3vb9Votm3bhtPpRKPRMHjw4MaujlBPdDozfrqO+FnUG2yKouByF+Bw5uJw5uBw5WD0PQBt7epwwcKsyok1QL0xk7hH3Soy+oK9SL1hY/FXe/+cNnW4n96kfg1srfZglSXgMPmAyU89ZjhdRm9Wh4vqDGqWKEkqzxYlSep8NVlW54MpijqN7MxySKcnKStqGUVWkz857d5z2FwOsBWqCUvOnNtmK1YD1rJNb1SPyW51uITbpSYuKciA3DR1Dp3eVPUcNpOvOkTS89wHwmPU62FQhDq6IShC/f5YAsQ8NkEQauSCgryyuWm18e9//xuXSx1q9fbbb3sFAKD21rz99tsMGTIEl8vFggULePvtt73KZGdnY7fbGThwoCfAK3P55Zfz5ptvcvLkybPWY8qUKXz22WdkZGTw3HPPcd1119Vp4/BcSyaUURSFTz75BIDrr7++xq9rDhRFYfPmzZ7e3pCQEGbMmFFlIFvW4+l0OpFlmR07djBu3Di+++47r3I+Pj689tprtGrVijlz5pCUlMTChQsByMvL85SLiYnxBH01WcJCaAL8Q9Sg7qPpakY1BdjxC/SpPD9XkiQeHKVHI0ksXOOiX5TEO7+7OXRK4aXr9FiM9d8YaskBHqjD891uNwcOHGjsqggNSJIk9LoA9LoAfMzR6s6rKsxzl91qsJedog4xzExUhxkazJUTeWh0aoAHajBjK1KDuDPLRfWs3DMY2U3N9FiRj7Vy71dgG3UOW0Vh7SHDOxkXRh910ewzBUWoc98qqiopCVS9wHZYFGQkeu8z+ajBYBmnDSxWde04UBOPhMeomSQDwk4PeY1S57+JQE4QhFpqlEHXiqKwbNkyALp06VLt3eHBgwcTGxvLoUOHWLp0KW+99ZZXABYYGIhOpyM+Pp6SkhIslvJsfGvXrgXOPX/Qx8eHJ598kscee4w9e/bw7bff1mhoZV1KTU31BHiRkZFceeWVDfr+9W3Pnj18/vnnnudXXnklev3Z507p9XrcbjdhYWFcdtllREdHs337drZs2UJxcTE9e/ZEURS++uor/P39yczMpLi48j/u//73vwwbNoyVK1eydOlSJk6cWOefT6gHIW1h3GPqHL3DmyA/HXqPrrbhc/+lOiID4Z9L1BtHq/bLFJQ6uOcSHYNjLtLhYXWkbdu2QPVDyB0OBw6HA1/fykNqhRZMo1VT3VtbQYf+3sfsJWqikJzTSUNK8tVAsLRA7dEqLaycTh+q7hmsaninroo1/qq6NlS1tlp1a8Toqzin21V5H4C7hvX0Dwd7gjq0MrCN2hPXprOaUCo8Rs0yKYI5QRDqSaMEeQkJCZ4etpEjR5617MiRIzl06BApKSmVsmKazWZGjx7NypUrueqqq3j66acJDAxk6dKlLFiwAIvFwjXXXHPO+jzwwAO8/vrrpKWlMWfOHG688cYG6fFRFIUVK1bw888/A3DVVVdxySUNM7HH6XRSWFhIQEAAWq0Wh8NBQUEBISEhdf5e77//PqD2as6dOxf5zPSO1XC5XJ4e2piYGHx9ffn9998BiIiIwO12s2XLFs+SDH379mX79u106NCB6OhoVq9ezf79+9m/X70L/NtvvzFu3LhzBphCE9FtBPaOXbH5GSntO4QQuRSd1lJt8bG9dPibJZ763klUsMSWBIUtCU6u6Obm2h5ahnbUYDGIBlVd++GHH8jIyGDGjBmNXRWhqTBaoHVHdauO2w1uhzpEsmxzlKq9bPZitQfMVqwGQe3iwGE7vcbb6SGeRTnlQzJlWR3G6B9SPixT0pxOEBNWXk6R1QDRaatQ7vRXayt1YW6DWQ1ADebTcwj13tkkDebygE52l2+SFlDKh29qdWpPnV+QOrRUEAShgTVKkFdx2E+XLl3OWrbi8QMHDngFeaAuT7B582b+/PNPxo4d69kvSRJvvfWWV5bG6pjNZp5++mlmzJjBgQMH+PLLL2uUlbM2tmzZwuLFiwHo168fEyZMqJcAqzoFBQXo9XrsdjsWiwW73U5+fr6nDoqiUFRUhNlsrtVC4l9++aXnsaIoKIpSqYyiKPz888/4+vp6glyXy0VCQoLn5+90Or2W5vjuu+/47rvvuPHGGz1DOQMC1Pz1x44d8/Q6xMbGYjQa2b17N35+frjdbhHkNSOnLhtEUelRcOwhhNHnLH9JZy1f3iuxcI2TvSdBI8HOJJnf9su08geHG3QaaBcs4XBBdIhETIiGu4ZrxZpgF2jkyJHk5uaeu6AgVKTVgtZcdQp/QRAEodYaZYJScnL5WPbIyMizli0bKnTm68p07NiRrVu3cvPNN2O1WjGZTAwZMoQVK1Zw991317hO06ZN89TlhRdewF3VcIwL8Oyzz/Lggw9SWloKQGZmJl988QWLFy+mTZs2TJ48mbvuuqtBAzyA4OBg/P39PUNc/fz8vBLbpKenk5yc7LU4+4Xw8/PzPFYUhd27d1NUVORVRlEUzzDMMsnJyeTl5eHj48N3333Ho48+Cqi9gW3btvUsRbFjxw7PawoKCjAYDBgMBlavXg2oPX73338/cXFxBAQEkJSUVKvPIzQshysXgz4EP0ssWk3NGoPRIRpevcHAC9fpuK6PhqzTv25tgyRyiiGjEDIKYHeKwo87Zb6Jd/H+WjeHTtWsh1nw1rp1a7p169bY1RAEQRAEoYJG6ckrLCz0PD7XPI6KCVXODA7KxMTE8PXXX9eqTkajkWeffZYHHniAI0eO8NlnnzF16tRanROgtLQUg8HAY489xsiRI1m/fj0Wi4WhQ4d6Le9QUFDAvn37iImJqVHvY11QFIXS0lKvuYxlgoKC0Ol0+Ptf+JpHDoeDX3/91Wtf2Ry6l19+Ga1WHfKi0WiYNWuW1xDZskQ8q1evxmg0eoJuWZYZP368Z/mF9PR0z2vS0tIwGo1ER0dz4MABOnbsyO+//85VV11FdHQ0+/fv9wo6haZNll2gyDhcuUgaPVJ1c2mqoNFIXN9Hx7U9FK7sJrPmoIyfCVLzZVxuBUuF0VP5pfDuWherD0p8da8Bva7qHr1t70H3SWAOrPo9FUUhLR+OZsjsO6lwJENGVhSMOomZo/VYLaDXit5CQRAEQRDqX6MEeTZbeQrhcy0VYDSWT4Yu6w2rL3fffTevvvoqJ06c4MUXX+SOO+6o06F969at44orruDaa6/1+lyApweqIbM/HjhwAFmWad++vSeYttlsFBcXExwcXKveRUVReOSRR6o8VraoeUVnDgl1uVzodDoGDhxIv379vDKrduvWjVdeeYWgoCBycnI8+//2t78RGhrK999/j8vl4uDBgwB8/fXX7Nixg/bt27fIReZbKodG4ad23bG5C+jg9uNC1mk36CSGd9IyvJN6Q+GRCjmNSh0KRzNknvzOSbEdjqQr/HZAZkyPygkUDi2Hnx6Etc9B7AQI7QbH/qdmZw/qqPBTNyc7C2TySmBAtMS2E+qw5OhgcCvwt48cvH6Tnu4RIsiriaysLNxud4Pd8BIEQRCEMhkZGRQWFlZauq25aZQgr+Ii2A6H46xl7Xa75/GZyyzUNb1ez6xZs7j77rtJSEjg448/5r777qv1ea+99locDgdXXXVVpaUeyphMJvr161fr96oJt9uNLMsYDAYURfHqyUtLS6OkpKRGC8ifzRNPPOH1fPTo0fTt25fIyEjy8/OreVW5yMhIXn/9dc9NgNdff52ffvqJNWvWUFBQQEpKitcNAh8fH9544w2cTmeljKplQzoTEhLIzMxEURTCwsJq9fmE+mdERxsCKNKayNA6UFCQqLsgyWyQ6BGp5V+3SMz40oG/GV5c4WRkZ43X0gtOl8IPrygUhICxN+z4UKNGd57ppRL5j0Pe6ctTUfklC7cCKTkKXVtLrD7gpnuEWMKjJoqKiirdCBMEQRCEhqDRaCgpKWnsatRaowR5FYfMVTcEs0zFeVoNkaJ78uTJvPLKKxw9epSXX36ZKVOm1LqxcfXVV3t6rqpKPNLQkpKS8Pf3JzIy0pNsoqxe0dHRyLJcq3r+73//8/q5tWvXjvHjx3vep7oELGfS6/UoisLGjRv5+uuvcblctG7dmlWrViFJkucGQdkyGyaTCafTyalTp2jfvj0JCer6SP7+/hQUFADqGo+yLPPOO++IRBvNQCkO7DgJx48i7PhS9w3/Lq0kBsVI/LhTnZP3yR9OHrysvAc/Pd/NHxOdFCbDiJUGDBoFSeOdnb29JJHko9CltYZeERL3jNDSKUwiMhDeWuVkd6rMyl0yN/bV0CZQBHrnEhUVBTSN6+WFqHida66fQRAEoTot/foWHBxMcHBwk/18Na1XowR5FZOtpKSknLVsxWQrFZOw1BedTsfs2bOZPHkyycnJ/Oc//+Hhhx++4PN17tzZaw5iU+Dr64vL5WLfvn3o9XratWtXZ+dOTExkzZo1nrl8N910E9HR0Z7eu7KsnUClIKu0tBS9Xl9p6Oby5cuxWCxce+21dOvWjYULFxIaGorNZsNkMpGWlsall17K8OHDeeuttwC44447cDgcbN68mT179uDv7+8V7B08eJA2bdrU2eduiYqLi8nJySEoKKjaHuj6ZkVLBoUUU0QSwURgrZf3ubGHzPajTtoFSizfprA/SUIrQUaRgiKDywXmVnD8aom+e/UoKRJBnSBqJLQbCj7tFB4JqPw7XVwEKZkOnKUKQXr48HcNl8Zq6NVWrN3XksmyTGlpKfn5+Q06BF8QBKEhnK0tJ9S/srbsuTRKkFcxE1vZvKnqVDzetWvXeqtTRbfffjtz587l4MGDzJ07l3vuueeCzzVt2jRPav+mprCwEFmW66x+J0+eZNGiRZ47DHq9nr59+3qVKTsWEBDgdWGQZZk33niD6OjoSglvyn6Ze/Xqha+vr1fGT51OR0FBAWvXrmXt2rUADB48mLCwMDQaDSaTiXXr1nmdJyoqitjY2Iuu8fXQQw8B6rIjNZlrWrZ8htVqPefc2frSnfYco4BCbGwkiZsII4C6H7bdKwCemuBmxpdOOoVLbDhRfpesb5TEgWwFgwyDFhnp/pCE3ANGPV99EpaKHhwts3KXi3WHZL7fA8v2wxf3GOjS+uL6/TuXtLQ0QkNDa7VkS1MhyzJFRUUEBARcdNcZQRBavurackLDqOn3vFH+m7Zv3542bdqQmprqaYBXZ/369YCaCr9s7bP6ptFomDNnDpMmTSItLY333nuvVudqqn8AHTteSCqLymRZZv/+/Xz88ceenrigoCBmzpxZ5WeXJMmzldFqtVx++eV079690muee+45Xn31VV566SVGjBjhdZ4uXbogyzLt2rVDr9fTsWNH/P39kWUZrVZLWFgYAQEBXvMAQ0JCKCwsxGq11snnbw4qLgly5ve+OkajsdETX4TgSxdacYQM0ihkGbtoRxBD6YCxji9fQztoeeRKyC+VOZ4lE+ILpQ5oG6QhLlhiWGstkWMkwntIGM8jSWuncC1/GypxPMvJ0SyFPm0lpn7q5Ov7DEQFiwAA1GtIRkYGdru90lqozVHFa1xTvf4LgiDUhrjGNZ6afs8bpYUhSRITJkwA1J66zZs3V1lu8+bNnp68CRMmNOgv0s0330yPHj0AmDdvntccM6Gcw+Hg/fff591338Vms+FwOFAUhalTp55378/IkSOrTPgSHh7O448/TkxMDD///LNnjbyy+Xq9e/fm0ksvZdiwYfj4+DBr1ixefvllHA4HycnJdOvWjeDgYGbNmgXAX3/9xbZt22r/4ZsRrVbLww8/zBtvvNGsFoP3x0w/onDgxoqZJHLYwFF+dyWS7HTW6XvptBJThul4cJSe3x41svQhI+ufNPHidQYen6Bn6EAN7YaeX4BXJthXw78nGRjTXcPWEwrFdnjmBycud9Mc79/QNBoNHTp0aLAbeYIgCILQ0jXabeRHHnnEMyxn+vTplZZHKC0tZfr06YA6JK+6dPz1RZIk5syZA6ipVD/99NMGff/mwOl08uabb7J3717Pvri4OGbOnFntUgWpqan8+eef5/1erVu35s4778RisfD999/TunVrT1bQitlB9Xo9JpOJzMxM5s6dy7x589i8eTMOh4PWrVvzzDPPAHDkyJHzrkNz161bN6/Mts1FGH48zKX0pi0SEmbZzJxkiRnp6ZTI517A3O12s3nzZjIyMmr0fnqtRKCPhNlQtzeVtBqJORP0tAtSc4TGKBpSLq57DWfl5+fnuZGXnZ19zszLgiAIgiBU74LGO/3xxx8cPXrU87zivLmdO3dWCoimTJlS6RydO3dm5syZzJs3j/j4eIYNG8aTTz5Jhw4dOHbsGK+++qon9f3jjz9Op06dLqSqtXL99dfTp08fduzYQVZW1gWd4+TJk7VaULypcrvdfPTRRyQkJBAeHk52djZRUVE8+OCDZ+1x/eabbyguLuaaa6457/c0m8088cQTZGZmEhAQQE5ODh06dPBKCmI0Grniiiv45ptvPI36uLg4hg0bBqhJf6Kioti7dy8FBQUt8mfTEpnQcwmdiFVa8/fMFIoUMEkS3xYUcGcNht22atWqSQS4FoPEk331fH8X6I5q+GsSRA9u7FrVLVmW2bVrF3FxcRc8l9PhcOB0OhttLqggCIIgNHcXFOR9+OGHLF68uMpjy5YtY9myZV77qgryAF5++WUyMjL4+OOP2bFjB5MmTapU5u677+all166kGrWmiRJvPDCC4wbN+6Cz7Fly5YGSxjTkN577z32798PQHp6OsOHD+eGG24455Dav/3tbyQmJl7w+1qtVhITE1m4cKFn37hx4xgzZgx79uxh2bJlpKamotPpePTRR7Hb7cTGxnrK7t271/P+TaHRL5yfcMmXuYExzHSks9tu54DdzrW+voRUkawjMzOTjRs3Mn78eKKjo0lKSuLEiRP07NmzEWpebvgQDRszwA4cW6UuxSC1oKl5LpcLs9mM3W6/4CDNarWKdfIEQRAEoRYatWmh0Wj46KOPWLlyJRMmTKBNmzYYDAbatGnDhAkT+Omnn/jwww8bNTvZ2LFjGThw4AW/fuLEiXVYm6ZBURRPgKfVarn//vu59dZba9QoCw4OpkOHDhf83t9++y2ffPKJ177ly5dTUlLC119/TWpqKqAO8Y2OjvYK8MB7jcYlS5ZccD2ExhNjMDDUYiFKr6ePycSSapYocblcxMXFeW48hIaGotU2/tIFGh20v1x9bA6CrLMnGG5WykY8dOnSxetv7XyZzWaRlVIQBEEQakFSmupKf81cQUGBJ6tjSxsSeODAAd5++21AHUp7PtnwFEUhPz//vNPu5ufnc+DAAT777DM6duzIww8/jMFg4MSJE8yfP9/zvZ4yZQpZWVkMHjyYoKCgKs+1c+dOFi1ahF6v580336xxHRqLoig4HI4679k4tgpirgDZCdpmNirupMPBC1lZJDpdGNONLO4ehtXSfDJ8bVoA6+aAPR/G/Qf6XvgqLU3K999/T69eveosc29zJcsyJ0+eJCIiQgSrgiC0OBfalhPqRk1jjOa/IJHQoDIzM3n77bfRarWEhIRUm2Clrhw9epTPPvvMa07koEGDMBgMlJaWMn/+fEANAvv370/v3r3POUSsLCitTU9DQzp8+DB5eXkMGjSoTs7ndsKv/4BtC6HTtZC2He76EwKbUeb6CIOBYZIvyT/rOJ6mYX6Kk+fH69Hrmsc/m1Y91QAPIGPv2cs2J9dcc02zyt4qCIIgCC2VCPKEGik4CYUn4URSEbqsSJBkHnzwnxQnQ6lWHYJWtkla0OpPPz/99UJu9OTn57No0SKCgoIYOHAgRUVF3HTTTbjdbnJycnj33XcBdRje7NmzazwUz9fXF1CHjiqKgqyomQ+bKrfbTVhYWJ2db81sNcADOLJS/frbE3DTt3X2Fg3iSrMfHxfY6dtOYuVuGYPOxXPjdM3irmJoXPnjzH2NV4+6ZjbX/UL1giAIgiCcPxHkCTWy81NY80+A9gSjLkPw7jfQqi+c2u5dVmsEt917n0YHUZdCajxYOoA7Qw0EfVuDoxB0JvV1OiPozOrXnEI7UvI4/GLasDdzN5ISxGtfbUDR21F0DmR9MHqDD7mZpSx86UvuuOdGrK3MaM4R62m1Wk/W1J/+OMx7O6J5coyOEZ0af77WmdLS0njnnXcYPnx4nSwSvedLSN8Jvq2gJFv9XjsKYP93cGIdRI+sfZ0bSmurxNu36ZnyiRNZga3H3azYJTGud9O/rPmEgTkYSrMh80Bj16bh/PXXXxQVFTFyZDP6RRMEQRCEZqjpt4aEJkFx13y/Rlc5yJNd6twvewFo8qD0JCCr+/OTvMvqzOAqBQjDQhhZ+8EYDob08oQtisaJJJcPC8tYl8jbc8wgKfiGSxgDwDccIgYCGgjvAU5zNp0v8ccvVM/kyZPZsW0vn38XTKJV4cH/Onn9Zhgd17QCvffeew+AwmqSi5yvvxZB4jr1cc/JgAK7P1efr34Kut4IbYdC5KDmkfGxR6SWOePhs40uknMU5ix3YXakMLR7mNf6iWdTVFTEqlWryM7Opnfv3vTv37+ea632bHceC8kbIS9RDbgtwfX+to1m2bJlxMbGYrVaOXToUGNXRxAEQRBaPBHkCTXSZgAMnA4H9h0k/VQmKBJ+PgFEDe1BWNzpIO70JmnAUawGdbJLnQMmO9Xeo5BY0ISAzq4GgsYAtRfPZQdOpwDSGcuCvHLWwABK0ivs0LvAXh7kGUxa9eWKhKJA9iF1Q1KDGku3dEr2h+Mbl4mfKRTfCB3Bv88m3OmPz1MKGcEKo2KbVlRz5MgRz1zEa6+9ttbnyzlWHuAhwe7PoMPV5cdTNqubJQRm1mzd8CZhXC8tu5PdHDyl/gKtOGChXdApOneMqdHrf/31V1avXo2vry+hoaENEuQB6H0g54j6OHMfRF3SIG/bKLKzs3nrrbcIDQ3l+eefr/FrTCaT1zqYgiAIgiDUjAjyhBrpNAYiRpawad6XFJ4OPLoPGcKYv/XwKpeYmMi6desYN24cgYGBlc6jKJCfDwEB3vP0FEUNBF12cJbChjUb+Wnp//A1BWLW+XHjbVM5eTyDnPQiIkKjcdtNlObKOIo0pJ3IZu/BgwT6yYT4tkVnkHCVqr2GLpt6/vzsYvRA4RFfihzADpAsgF1Pv+8UJv4BhiaUtENRFFatWgXAZZddRmRkZB2cFDqNhSMr1HmTigsyq0j6ETnkwuZQNqaZo/XsPulAUWD1cX9GdA+icw1fe91119G9e3cMBgNt27at13pWFNa9/HHG6SAvISGBoKAgAgICGqweDWHq1KkYjcbzGnI8a9Ysz+MhQ4Zw9OhR8vPz+ec//0lISEh9VFMQBEEQWgwR5Ak1tnTpUq8sl263m9LSUq9kCzt37uTw4cPs2bOHSy6pedeEJKlp/LUGMPpBu+4BuNdlkE8GV99yC+1HSrQfFQZUTECiRiKLF6+gNCyeeW++iaZCAhVnCeQly7w390vsKRqcISno06NB1qLLD8MY4mTwdIi7RaKV9QK/KfVk//79FBYW8re//Y0hQ4bUyTmDOsJN38BcC3S5HvxaQ+x4dV7kor5qz6rBD/o0w3T+Rr3EU1frmPyxE4D/2+pmYl9tjZKwaLXaSuspNoSwCslXMvaqmWTfeOMNAF555ZUWFehJksTtt99+Xq/5+9//zptvvonVamXTpk0A9O7du9qlUQRBEARBKCeCPKFGHA4HW7Zs8dq3detWtm7dyjvvvONZC2r8+PFceumlF7w2oKIoLF++HLvdjslkwuVyUVJSgizL1a435XQ6cbvdFBYWejWM9RbI1x7nlM9G7px7J4WFhWzc+CmnUrLR5ofw2FN/J7rPBVWz3oWHh3PZZZcxYMCAOj2v3qwOxbSEePfWxY5Tk684CkHbTK8KfaK09GnnorBUQbZlcyg1iC4RpsauVrUqZthM2+VCd+yY53lzyBBa32JjYz0ZdAFKSkpqPM9SEARBEC52TWsSktAkud1uFi9ejNOp9pJERkYyYcIEz/EXXngBt1vNwCJJUo0Xx1QUhaKiIq99TqeT5ORkNm3ahM1mw+VysWLFCvLz86s9T1kgVFVykrJG4eLFixk0aBCdOnUCnZOh13Ukuk/TXaQ+JCTkggK81O3wij98dgX8/HeIfx8OLFETfOQeV+dK+oRWHo7Z82/lj8sSsTRH/aM0HM2Ew3lB5JQ27WjVEgztRkDkUNBoNHTt0o1///vfLFiw4IJvkrRkIsATBEEQhJpr2q0godHJssz06dMBuPXWW+nZsye+vr5otVp69erFCy+8QEZGBsnJyURHR5/XudesWcMff/zBjBkzsFqtABgMBm666SYyMzP54IMPAJgyZUqV8/vKlC3IHh8fX2nuWps2bbj//vt5//332b59OzExMWzYsKHGa+o1J7v/C8d/U3vjElarW3Ds6QQ0FRh8wSdczT7auj9otODXRt3vtEH2ESjOBp9mmO1Rr4OurSWCfcCkb/q9YeZAOPQjgAZzcVuMxsaukSAIgiAILYEI8oSzWr58OQDt27dnxIgRXsdatWpFWFgYGRkZpKSk4Ha76dChQ1WnqVJaWhp2ux1Zlvnqq69wuVyMGDGCqKgodu3axaRJkxg4cCA63dl/TcPCwhg3bhzLly/n2LFjjB07ltjYWJKSkli+fDkTJkwgNjaWHTt28PDDD3PkyBGGDRt2/t+MJq7H7eryE0dWQsnpqZP2KlZecBSpW+4xNXX/mUFg2l/wRisYuwj6TK3/etcpReJohsIBN9zYT2ns2pxT22FlQR4k/Qmh3Rq3Pg2ttLQURVHO2UtXUFBAWloaCQkJxMXFNWiCHEEQBEFojkSQJ1SrLMNjUFAQjz/+eKXjSUlJZGSoufa//PJLAG677TYGDhyIwWA45/nLEjEsWLCAw4cPA7Bp0yYmT57MkiVLADw9h+dy9dVX07p1a1asWMGbb75JTEwMOTk55OXlERYW5pnPo9PpuOOOO2r2DWhmJAm63aBuxRnqItv5SVCQAsXpUHSq/GtROthy1XJVkV3qsM3c43DZiw37OWqjT7iGSANYQ8C3GfSKtRte/jj5D+h3b+PVpaEVFRXxxBNP0KFDBx577LGzlnW5XKSmpvLjjz+i0WhEkCdUsmfPHux2e4MtgSIIgtDUiSBPqMTtdpOdnc3GjRuRZZmJEydWWS45ObnSvi+//JJvv/2WiRMnMnLkyHO+l8PhwOFwYDQasdvtBAQE0KlTJ1577TXsdnuNAjxQ5wL27t2bHj16sHr1ag4ePEheXh4A69api8M98sgjNTpXS+ATpm5n47JDYSoUJKvB4Im1sPMTUGT1eGEadL+l3qtat3ZAm61aRhh1BEVIUPOO5QaVm5vLrl27GDJwBFqjFrdd7clrabKzswkKCvKao5uamkpgYCAHDx4EYPDgwec8T1BQEKNGjWLUqFH1VleheTt8+DB79+4VQZ4gCMJpkqIoTX9MUzNUUFBAQEAA+fn5zSqJgizLzJ07l9TUVHQ6HYMHD+a2224762vsdjuvvfYaubm5jBkzhh9++AEAq9XK5MmTCQoKIixMjTgURSE/P9+TnCUnJ4e3334bf39/Ro8eTWxsbJ3Nl3O5XLz00ktkZGRw1VVXcd1113kd3759OxEREYSHh9fJ+zV3zlJYEA0lp3v3zEHwRHajVum8Lb0LUreCpIFblkFQzZdla1BOp9NzE+Pbm9We1dxjcPP3EDmosWtXNwoLC/nuu++IjIzkyiuv9OzfsGEDVquVbt26cerUKSIiIhqxlvVDlmVOnjxJREREtVmBBUEQmqsz23JCw6ppjCF68gSPLVu28MUXX+ByuQB1Tapx48ad83VGo5F//vOfnucOh4P4+HhOnTrFW2+9hdFoZPbs2QQGBiLLMtu2bePAgQPce++9WK1WhgwZgp+fH9261e2EJJ1Ox6BBg0hISKhyrmDr1q0bPABPTU0lOzubHj16nLtwA8vYqw7hLFOaB+m7Ibxno1XpvMhuCO8BhSdBq6+fAC8vL4+dO3dy6aWX1uo8er0evV4PQNzN8O1N6v7fn4XJv9WykmdRVFTEF198QUREBGPHjq2/NwJ8fX3p3r07vXv39to/ZMgQzzzblhjgCYIgCEJTIII8AVDvPO/fv98T4JlMJu66667zPk9xcTFXXnkl11xzDdu2beP//u//KC0tpaSkhFOnTnl67QoKCjh+/DhxcXFcddVVdf1xPAYNGoSPj4+6dMIZyrJyNiStVttkU8G36gPtLoHEtepznzDY9i6Mfb9Rq1VjGi0M+Ye61df4hKSkJHx8fOr0nLHjITBGnf+YsBo2LYAhj9TpW3gUFxeTm5vbIDc3JEmqchmQcyVSEgRBEASh9sR/W4GDBw/y1ltvAXiyZZb1MpyPTz/9lK1bt3oSIyQlJQHqnfvExER27NjhVT4qKqr2lT+HoKAgLrnkknp/n5oKDw9vssNDtTqIuwk0kroUQ1Ea7PkKRv9bXUS9Oamv0SM9e9Z9t6bWAJe+ANsWqkly/vcomPyhz/nfYzmn8PBwnnzyybo/sSAIgiAITYoI8hpBSUkJq1evpnfv3o2aJc7lcvHbb7/x448/evZlZGQQFRXFgw8+eF7nKikpYdu2bQAEBwfj6+vLpEmT6NmzJ+np6SxYsIDOnTsTGxvLyJEj6dWrV5MZx11aWsqRI0eIiYmpcaKXlqrzWHXIYBlHARxaBt0nNV6dLgY9boP8ZPj9aTAFQlQ93pdorL+7sunfTeXvXrj4ZGZmotPpzrruqiAIQkshgrxGkJCQQFFREUVFRY3y/q+//jrHjx+v9niPHj3w8/M7r3NaLBYmTJjA0qVLyczMxGg0MmjQIAwGA4WF6mJtAwYMwGg0Npl5OC6Xi2XLlhEeHk7btm1xu92NXaVG5x8BE7+AH26D0hzwbQPG5pM3qNmSJBj+JMhOdVmFoI6NXaO65XA4mDt3Ln369GHChAmNXR3hIpWeno4kSSLIEwThoiCCvEYQFxdHhw4dMBobZyGv3Nzy7Bp33nkny5cvJycnx7PvyJEjFBQUMG/ePFwuFzqdjqKiIgwGA4GBgfj4+OBwOBg2bBgpKSkcPnyYKVOm0K5dO89SCCkpKfzxxx9cdtllRERE8MILLxASEuLJyNQU7Nu3j9WrVwPw7rvvNnJtmo6Oo2HaX/DxcLh/J1hCGrtGFwdJgpGzGrsW9UOj0eDv709QUFBjV0W4iHXv3r2xqyAIgtBgRJDXSGw2G6+++ioDBw5kzJgxDfa+ycnJ5OXl0b59exISEli8eLFnyYJ27dqRlJRETk4Os2bNwul0er3W5XJRUlKCJEkoisKJEyc8x1555RXP4+DgYEJDQxk+XF3pWZIkQkKaXqRQXFzseVy2WLqgskbDI4lqMhNBqC2dTsejjz7a2NUQBEEQhIuGCPIagdPpZN68eRQUFLB8+XK6d+/eIHPzcnNz+fDDD4mIiGDGjBksWbKE9evX43a7CQgI4KmnnkKWZVatWsWyZcuIjY1l1KhRdO/endTUVNq0aeNZ86mkpISZM2d6nX/KlCn079+/2awLtXbtWgBCQ0NFgFcFEeAJgtDSFRUVsXHjRq644opm879LEAShJsQVrRFoNBrPPDWA5cuX1/t7njhxgvnz5+N2u5k2bRpGo5FJkyZxyy230LVrV2bPnu2p28aNGwF44IEH6NmzJxqNhsjISK9/gBaLhcGDB3uet2nTpskGeCdOnPAajlomJSUFoNI6XoJwvo4dO8aKFSs8S5AIgtA8PPnkkyxdupQXX3yxsasiCIJQp0RPXj0rKCiocn9cXBzbt28HYNSoUdWWqws2m40PPvgAjUbDfffdh8Fg8Lxfnz596NOnD06n0zM802azIcsyNpsNm81W6XwLFy7k2LFjXvuysrJqlEhGURQKCgqQJKlBsuzZbDZeeuklAF5++WXM5vK1AO655x4+/fRTTp06Va/ff6HlO3bsGKWlpWRlZYleYQFZliksLKSgoKBJ3vgSyt1777288847dO3aVfwfEIQaaui2nOCtptcqSVHqa9ngi5vNZqN9+/acOnWqsasiCIIgCIIgCEIL0apVKxISEjCZTNWWEUFePbLZbDgcjsauhiAIgiAIgiAILYTBYDhrgAciyBMEQRAEQRAEQWhRxGQBQRAEQRAEQRCEFkQEeYIgCIIgCIIgCC2ICPIEQRAEQRAEQRBaEBHkCYIgCIIgCIIgtCAiyBMEQRAEQRAEQWhBRJAnCIIgCIIgCILQgoggr4VKT0/nxx9/5Nlnn+WKK64gICAASZKQJIk5c+bU6Bw2m41ly5Yxffp0Bg0aRFBQEHq9nuDgYIYMGcKcOXPOa7H3kpISXnvtNQYOHEhQUBC+vr507dqVmTNnkpSUVKNz/PLLLwwdOhSLxUJ4eDjTpk07ax3mzJnj+dw13dauXVvjzyQIQsOoi2tamcTERJ566in69euH1WpFr9cTFBTE0KFDefHFF8nMzKzReRrymrZ161bP533iiSfOes4///zT65qWkZFx1vLXX3+9p2xNP7sgCHXrYm+3iWtcPVCEFgmodnvuuefO+fpdu3Ypfn5+Zz0PoPj7+ytff/31Oc939OhRJTY2ttrzBAQEKCtXrjzrOT799FNFkqRKr23Xrp2Smppa5Wuee+65c36GM7c1a9ac8/MIgtCwantNK/PFF18oFovlrOcLDg5WVq9efdbzNPQ1zel0Kr6+vgqgDB48+KznnTdvntf5vvvuu7OWDwkJUQClS5cuZy0nCEL9udjbbeIaV/dET95FICYmhksuueS8XlNQUEBhYSEAw4YN45VXXmHVqlVs376dX3/9lfvuuw+tVktBQQG33XYbP//8c7XnKioqYuzYsRw6dAiAe++9l9WrV7Nx40ZefvllfH19yc/P56abbmL37t1VniMrK4uHHnoISZJ47LHH+OOPP/jxxx8ZNGgQSUlJ/OMf/zjnZ/r444/Zs2fPObcBAwac1/dKEISGdSHXNIBNmzYxefJkSkpK0Gg0TJ06laVLl7J161a+++47xo0bB0B2djbjx4/nxIkTVZ6nMa5pOp2OIUOGAPDXX39RUlJS7efcsGEDAFqt1ut5VQ4cOEBWVhbABX1PBUGoexdju01c4+pBY0eZQv2YPXu2snLlSiUrK0tRFEVZs2bNed0R+vPPP5Wbb75Z2bdvX7Vlli5d6rlD06FDB0WW5SrLVexNmz9/fqXjGzduVHQ6nQIoo0aNqvIcn376qQIojz76qNf+wsJCJTIyUjGZTIrdbj/re4seOkFovmp7TVMURRk7dqznNQsXLqyyzKOPPuopM3369CrLNNY17YUXXvC8b3U9jW63W7FarQqg3HLLLQqg9O3bt8qyiqIoH3zwgeec//3vf6stJwhC/RLtNnGNq2siyLtIXEiDqCZuuOEGz3n/+uuvSscdDofnj7Fr166K2+2u8jz33Xef5zzx8fGVjr/88ssKoCxfvrzSsYkTJypAlUM2RZAnCC3ThVzTAgMDPcMxq5OXl+c5b79+/Sodb8xr2tq1az3nnDNnTpXvu2vXLgVQNBqNEh8frwCKVqtVCgoKqix/xx13eM6ZmJhYZRlBEBrexdhuE9e4uiWGawq1MmrUKM/jY8eOVTq+du1a8vLyALjzzjvRaKr+lZsyZYrn8Q8//FDpeFhYGADr1q3z2l9cXEx8fDwGg4GgoKDzrb4gCBcRh8MBQPv27astExAQQEhICAB2u73S8ca8pg0aNAiDwQBUPzypbH+vXr3o168f4eHhuN1uNm7ceNbyUVFRtGvXrsoygiC0HE253SaucXVLBHlCrVRsBJWNja6o4h/pyJEjqz1P//798fHxAeCPP/6odHzMmDEYDAbeeOMNnnzySTZu3MjKlSu56qqrSEpKYty4cRiNxtp8FEEQWrhOnToBkJCQUG2ZgoICz/yNzp07VzremNc0k8nkmTO8adMmnE5ntfUbPnw4AEOHDq1U7zLJyckkJiYCF+FcFUG4SDXldpu4xtUtEeQJtVLxDk3Xrl0rHT9w4IDncZcuXao9j06no0OHDpVeUyYiIoJXX30VWZaZP38+w4YNY+zYsWzcuJE2bdqwYMGCWnwKQRAuBvfeey+gJlZ5//33qyzz4osveh4/8MADlY439jVtxIgRgJrafPv27ZWOlzW2yhpAZV+ragBV3HcxNoAE4WLU1Ntt4hpXd0SQJ1ywXbt2sXLlSgDi4uKqvFgkJycD4OPjg9VqPev52rZtC0BmZmaVw6QeeeQRvv32W/r164fRaCQ4OJjJkyezdetWIiMjz1nfhIQE9u7de9atqqELgiC0DNOmTWPSpEkAPPTQQ9x7770sX76c+Ph4fvjhByZOnMjrr78O4Fmr6kyNfU2r2FA5s1GTkJDAyZMngcoNoK1bt1aqQ8XXlzWsBEFouZpDu01c4+qOrrErIDRPdrude+65B7fbDcDcuXOrLFeWztfX1/ec5yzr9gc1fW9Vwy9vvPFGbrzxxgupMnfdddc5y/Tq1YudO3de0PkFQWjadDodX375JRMmTGDevHl8+OGHfPjhh15lRo0axdNPP82VV15Z5Tka+5o2bNgwNBoNsiyzYcMGZs6c6TlW1qCJiYmhTZs2APTp0weLxUJJSQnx8fEMGzasUvnw8HBiY2NrXAdBEJqf5tJuE9e4uiN68oQL8vDDDxMfHw+oE3PHjx9fZTmbzQbgmUh7NhUvDqWlpXVQS0EQBG+HDh3iq6++Yu/evVUe37RpE4sXLyYtLa3K4419TfP396dXr16AOmxJURTPsTPnqgDo9XoGDhzodRwgJyeH/fv3AxfnHW5BuNg0l3abuMbVHRHkCeftlVde8dz9HjBgAAsXLqy2rMlkAsqz2p1NxW52s9lcy1pWtmbNGhR12ZBqN9GLJwgt14YNGxgyZAg//vgjkZGRfP7555w6dQqHw0FycjILFy7EbDbzxRdfMHDgwCrnmTSFa1pZgyUnJ4d9+/Z59pc1cM5s0FQ1Z6Vi4+libQAJwsWiubXbxDWuboggTzgvH3zwAc888wygTsj96aefvLrrz+Tn5weo3fjnUlxc7Hlck2ECgiAINWW327n11lvJy8ujVatWbN68mTvuuIPw8HD0ej2RkZE8+OCDbNiwAZPJREpKCpMnT650nqZwTatqzkpmZiaHDh0CvO9yA57hS3/++SeyLHu97szzCYLQsjTHdpu4xtUNEeQJNfbVV1/x4IMPAup6I6tWrfKsJ1Wdsom1xcXFnnVXqlM22Tc0NFQshyAIQp365ZdfPBP2p0+fTqtWraosFxcXxx133AFAfHw8u3bt8jreFK5pFe9Kr1+/HijPOBcSElIpI97QoUPRaDTk5+ezZ88eoLwBFBAQQM+ePeusboIgNB3Ntd0mrnF1QwR5Qo38+OOPTJ48GVmWad26NatXr65RRstu3bp5Hh88eLDaci6Xy5PZsqpsT4IgCLVRcehl3759z1q2X79+nsdnXreawjUtLCzMk0SgrCFT1VyVMv7+/vTo0QNQG0wVU5MPHz682sWOBUFovppzu01c4+rGxfmphfOyevVqbr75ZlwuF8HBwaxatcqzNsq5VPxjrLg2y5ni4+M93f4VMyMJgiDUBZ2uPJm0y+U6a9mKC/BWfB00nWta2Z3ukydPkpCQcNYGUMU6bNiwwWuR4Yt1roogtGQtod0mrnG1J4I84aw2btzIhAkTsNvt+Pv78+uvvxIXF1fj11966aUEBAQAsHjxYq8sSRV9+umnnsfXX399reosCIJwpvbt23seV7VobkUVGzYVXwdN55pWcY7JTz/95EkaVV0DqGJiAjFXRRBarpbSbhPXuNoTQZ5QrZ07d3LttddSXFyMj48PP/30k9cwppowGAzMmDEDUIdLlS00XNGmTZv46KOPABg5ciQDBgyofeUFQRAquPzyy7FYLAC89957nnkbZ/r5559ZsmQJABEREfTu3dvreFO5plW8O/3GG2/gcrmwWCzVDkUtawCdOnWKzz//HFCz4fXv37/O6yYIQuNoSe02cY2rPbEYegv1xx9/cPToUc/ziuOqd+7c6XUHBmDKlClez48dO8bo0aM9k25feuklAgICql1bCtTJulartdL+xx9/nK+//prDhw/zxBNPcPToUSZNmoTZbGbNmjXMnTsXl8uF2WxmwYI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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.hazard import TCTracks, TropCyclone, Centroids\n", + "\n", + "TC_tracks_WP_2023 = TCTracks.from_ibtracs_netcdf(\n", + " provider=\"usa\", basin=\"WP\", year_range=(2023, 2023)\n", + ")\n", + "TC_tracks_WP_2023.plot();" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "min_lon, min_lat, max_lon, max_lat = 116.0, 4.6, 126.6, 21.2\n", + "centroids = Centroids.from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.05)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2024-12-19 18:14:30,677 - climada.util.coordinates - INFO - Sampling from /Users/vgebhart/climada/data/GMT_intermediate_coast_distance_01d.tif\n", + "2024-12-19 18:14:30,814 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 19 tracks to 70929 coastal centroids.\n", + "2024-12-19 18:14:31,008 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 10%\n", + "2024-12-19 18:14:31,127 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 21%\n", + "2024-12-19 18:14:31,393 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 31%\n", + "2024-12-19 18:14:31,454 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 42%\n", + "2024-12-19 18:14:31,876 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 52%\n", + "2024-12-19 18:14:31,996 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 63%\n", + "2024-12-19 18:14:32,061 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 73%\n", + "2024-12-19 18:14:32,282 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 84%\n", + "2024-12-19 18:14:32,340 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 94%\n", + "2024-12-19 18:14:32,432 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n" + ] + } + ], + "source": [ + "haz = TropCyclone.from_tracks(TC_tracks_WP_2023, centroids=centroids)\n", + "haz.check() # verifies that the necessary data for the Hazard object is correctly provided" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(19, 70929)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "haz.intensity.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an Impact Function object\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Calculate the impacts\n", + "\n", + "For an impact calculation, we have to specify the following ingredients\n", + "1. Exposures: A\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further possible continuations of the impact calculation" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "### What is CLIMADA?\n", + "\n", + "CLIMADA is a fully probabilistic climate risk assessment tool. It provides a framework for users to combine exposure, hazard and vulnerability or impact data to calculate risk. Users can create probabilistic impact data from event sets, look at how climate change affects these impacts, and see how effectively adaptation measures can change them. CLIMADA also allows for studies of individual events, historical event sets and forecasts.\n", + "\n", + "The model is highly customisable, meaning that users can work with out-of-the-box data provided for different hazards, population and economic exposure, or can provide their own data for part or all of the analysis. The pre-packaged data make CLIMADA particularly useful for users who focus on just one element of risk, since CLIMADA can 'fill in the gaps' for hazard, exposure or vulnerability in the rest of the analysis.\n", + "\n", + "The model core is designed to give as much flexibility as possible when describing the elements of risk, meaning that CLIMADA isn't limited to particular hazards, exposure types or impacts. We love to see the model applied to new problems and contexts.\n", + "\n", + "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#CLIMADA-features) section below.\n", + "\n", + "\n", + "### This tutorial\n", + "\n", + "This tutorial is for people new to CLIMADA who want to get a high level understanding of the model and work through an example risk analysis. It will list the current features of the model, and go through a complete CLIMADA analysis to give an idea of how the model works. Other tutorials go into more detail about different model components and individual hazards.\n", + "\n", + "### Resources beyond this tutorial\n", + "\n", + "- [Installation guide](../guide/install.rst) - go here if you've not installed the model yet\n", + "- [CLIMADA Read the Docs home page](https://climada-python.readthedocs.io) - for all other documentation\n", + "- [List of CLIMADA's features and associated tutorials](#CLIMADA-features)\n", + "- [CLIMADA GitHub develop branch documentation](https://github.com/CLIMADA-project/climada_python/tree/develop/doc) for the very latest versions of code and documentation\n", + "- [CLIMADA paper GitHub repository](https://github.com/CLIMADA-project/climada_papers) - for publications using CLIMADA\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CLIMADA features\n", + "\n", + "A risk analysis with CLIMADA can include\n", + "\n", + "1. the statistical risk to your exposure from a set of events,\n", + "2. how it changes under climate change, and\n", + "3. a cost-benefit analysis of adaptation measures.\n", + "\n", + "CLIMADA is flexible: the \"statistical risk\" above could be describing the annual expected insured flood losses to a property portfolio, the number of people displaced by an ensemble of typhoon forecasts, the annual disruption to a railway network from landslides, or changes to crop yields.\n", + "\n", + "Users from risk-analysis backgrounds will be familiar with describing the impact of events by combining exposure, hazard and an impact function (or vulnerability curve) that combines the two to describe a hazard's effects. A CLIMADA analysis uses the same approach but wraps the exposures and their impact functions into a single `Entity` class, along with discount rates and adaptation options (see the below tutorials for more on CLIMADA's financial model).\n", + "\n", + "CLIMADA's `Impact` object is used to analyse events and event sets, whether this is the impact of a single wildfire, or the global economic risk from tropical cyclones in 2100.\n", + "\n", + "CLIMADA is divided into two parts (two repositories): \n", + "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_util_api_client.ipynb). [Litpop](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", + "2. the petals [climada_petals](https://github.com/CLIMADA-project/climada_petals) contains all the modules for generating data (e.g., TC_Surge, WildFire, OpenStreeMap, ...). Most development is done here. The petals builds-upon the core and does not work as a stand-alone.\n", + "\n", + "### CLIMADA classes\n", + "\n", + "This is a full directory of tutorials for CLIMADA's classes to use as a reference. You don't need to read all this to do this tutorial, but it may be useful to refer back to.\n", + "\n", + "Core (climada_python):\n", + "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", + " - [Tropical cyclone wind](../tutorial/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", + " - [European windstorms](../tutorial/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", + "\n", + "- [**Entity**](#Entity): this is a container that groups CLIMADA's socio-economic models. It's is where the Exposures and Impact Functions are stored, which can then be combined with a hazard for a risk analysis (using the Engine's Impact class). It is also where Discount Rates and Measure Sets are stored, which are used in adaptation cost-benefit analyses (using the Engine's CostBenefit class):\n", + " - [Exposures](../tutorial/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", + " - [LitPop](../tutorial/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", + " - [Polygons_lines](../tutorial/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", + " - [ImpactFuncSet](../tutorial/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", + " - ImpactFunc: a basic adjustable impact function, specified by the user\n", + " - IFTropCyclone: impact functions for tropical cyclone winds\n", + " - IFRiverFlood: impact functions for river floods\n", + " - IFStormEurope: impact functions for European windstorms \n", + " - [DiscRates](../tutorial/climada_entity_DiscRates.ipynb): discount rates per year\n", + " - [MeasureSet](../tutorial/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", + " \n", + "- [**Engine**](../tutorial/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", + " - [Impact](../tutorial/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", + " - [Impact_data](../tutorial/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", + " - [CostBenefit](#Adaptation-options-appraisal): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", + " - [Unsequa](../tutorial/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", + " - [Unsequa_helper](../tutorial/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", + " - [Forecast](../tutorial/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", + "\n", + "climada_petals:\n", + "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb):\n", + " - [Storm surge](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCSurgeBathtub.html): Tropical cyclone surge from linear wind-surge relationship and a bathtub model.\n", + " - [River flooding](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_RiverFlood.html): global water depth hazard for flood, including methods to work with ISIMIP simulations.\n", + " - [Crop modelling](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_entity_Crop.html): combines ISIMIP crop simulations and UN Food and Agrigultre Organization data. The module uses crop production as exposure, with hydrometeorological 'hazard' increasing or decreasing production.\n", + " - [Wildfire (global)](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_Wildfire.html): This class is used to model the wildfire hazard using the historical data available and creating synthetic fires which are summarized into event years to establish a comprehensiv probabilistic risk assessment.\n", + " - [Landslide](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_Landslide.html): This class is able to handle two different types of landslide source files (in one case, already the finished product of some model output, in the other case just a historic data collection).\n", + " - [TCForecast](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCForecast.html): This class extends the TCTracks class with methods to download operational ECMWF ensemble tropical storm track forecasts, read the BUFR files they're contained in and produce a TCTracks object that can be used to generate TropCyclone hazard footprints.\n", + " - [Emulator](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_emulator.html):Given a database of hazard events, this module climada.hazard.emulator provides tools to subsample events (or time series of events) from that event database.\n", + " - Drought (global): tutorial under development\n", + "\n", + "- [**Entity**](#Entity): \n", + " - [Exposures](../tutorial/climada_entity_Exposures.ipynb):\n", + " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", + " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", + "\n", + "- [**Engine**](../tutorial/climada_engine_Impact.ipynb):\n", + " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", + "\n", + "This list will be updated periodically along with new CLIMADA releases. To see the latest, development version of all tutorials, see the [tutorials page on the CLIMADA GitHub](https://github.com/CLIMADA-project/climada_python/tree/develop/doc/tutorial)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tutorial: an example risk assessment\n", + "\n", + "This example will work through a risk assessment for tropical storm wind in Puerto Rico, constructing hazard, exposure and vulnerability and combining them to create an Impact object. Everything you need for this is included in the main CLIMADA installation and additional data will be downloaded by the scripts as required." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Hazard\n", + "\n", + "Hazards are characterized by their frequency of occurrence and the geographical distribution of their intensity. The `Hazard` class collects events of the same hazard type (e.g. tropical cyclone, flood, drought, ...) with intensity values over the same geographic centroids. They might be historical events or synthetic.\n", + "\n", + "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#CLIMADA-features) section of this document to explore tutorials for different hazards, including\n", + "[tropical cyclones](climada_hazard_TropCyclone.ipynb), as used here.\n", + "\n", + "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", + "\n", + "So before we create the hazard, we will create our storm tracks and define the geographic centroids for the locations we want to calculate hazard at.\n", + "\n", + "### Storm tracks\n", + "\n", + "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2022-03-09T16:14:07.505695Z", + "start_time": "2022-03-09T16:14:05.379337Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:31:20,322 - climada.hazard.tc_tracks - WARNING - 1122 storm events are discarded because no valid wind/pressure values have been found: 1851175N26270, 1851181N19275, 1851187N22262, 1851192N12300, 1851214N14321, ...\n", + "2022-03-21 14:31:20,345 - climada.hazard.tc_tracks - WARNING - 139 storm events are discarded because only one valid timestep has been found: 1852232N21293, 1853242N12336, 1855236N12304, 1856221N25277, 1856235N13302, ...\n", + "2022-03-21 14:31:22,766 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2022-03-21 14:31:25,059 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", + "2022-03-21 14:31:27,491 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", + "2022-03-21 14:31:30,067 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", + "2022-03-21 14:31:32,415 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", + "2022-03-21 14:31:34,829 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", + "2022-03-21 14:31:37,482 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", + "2022-03-21 14:31:39,976 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", + "2022-03-21 14:31:42,307 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", + "2022-03-21 14:31:44,580 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", + "2022-03-21 14:31:45,780 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", + "2022-03-21 14:31:45,833 - climada.hazard.tc_tracks - INFO - Progress: 21%\n", + "2022-03-21 14:31:45,886 - climada.hazard.tc_tracks - INFO - Progress: 31%\n", + "2022-03-21 14:31:45,939 - climada.hazard.tc_tracks - INFO - Progress: 42%\n", + "2022-03-21 14:31:45,992 - climada.hazard.tc_tracks - INFO - Progress: 52%\n", + "2022-03-21 14:31:46,048 - climada.hazard.tc_tracks - INFO - Progress: 63%\n", + "2022-03-21 14:31:46,100 - climada.hazard.tc_tracks - INFO - Progress: 73%\n", + "2022-03-21 14:31:46,150 - climada.hazard.tc_tracks - INFO - Progress: 84%\n", + "2022-03-21 14:31:46,203 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", + "2022-03-21 14:31:46,232 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from climada.hazard import TCTracks\n", + "import warnings # To hide the warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "tracks = TCTracks.from_ibtracs_netcdf(\n", + " provider=\"usa\", basin=\"NA\"\n", + ") # Here we download the full dataset for the analysis\n", + "# afterwards (e.g. return period), but you can also use \"year_range\" to adjust the range of the dataset to be downloaded.\n", + "# While doing that, you need to make sure that the year 2017 is included if you want to run the blocks with the codes\n", + "# subsetting a specific tropic cyclone, which happened in 2017. (Of course, you can also change the subsetting codes.)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This will load all historical tracks in the North Atlantic into the `tracks` object (since we set `basin='NA'`). The `TCTracks.plot` method will plot the downloaded tracks, though there are too many for the plot to be very useful:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plotting tracks can be very time consuming, depending on the number of tracks. So we choose only a few here, by limiting the time range to one year\n", + "tracks_2017 = TCTracks.from_ibtracs_netcdf(\n", + " provider=\"usa\", basin=\"NA\", year_range=(2017, 2017)\n", + ")\n", + "tracks_2017.plot(); # This may take a very long time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's also worth adding additional time steps to the tracks (though this can be memory intensive!). Most tracks are reported at 3-hourly intervals (plus a frame at landfall). Event footprints are calculated as the maximum wind from any time step. For a fast-moving storm these combined three-hourly footprints give quite a rough event footprint, and it's worth adding extra frames to smooth the footprint artificially (try running this notebook with and without this interpolation to see the effect): " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:32:39,466 - climada.hazard.tc_tracks - INFO - Interpolating 1049 tracks to 0.5h time steps.\n" + ] + } + ], + "source": [ + "tracks.equal_timestep(time_step_h=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Centroids\n", + "\n", + "A hazard's centroids can be any set of locations where we want the hazard to be evaluated. This could be the same as the locations of your exposure, though commonly it is on a regular lat-lon grid (with hazard being imputed to exposure between grid points).\n", + "\n", + "Here we'll set the centroids as a 0.1 degree grid covering Puerto Rico. Centroids are defined by a `Centroids` class, which has the `from_pnt_bounds` method for generating regular grids and a `plot` method to inspect the centroids." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.hazard import Centroids\n", + "\n", + "min_lat, max_lat, min_lon, max_lon = 17.5, 19.0, -68.0, -65.0\n", + "cent = Centroids.from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.05)\n", + "cent.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hazard footprint\n", + "\n", + "Now we're ready to create our hazard object. This will be a `TropCyclone` class, which inherits from the `Hazard` class, and has the `from_tracks` constructor method to create a hazard from a `TCTracks` object at given centroids." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:35:51,458 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", + "2022-03-21 14:35:52,496 - climada.util.coordinates - INFO - dist_to_coast: UTM 32619 (1/2)\n", + "2022-03-21 14:35:53,234 - climada.util.coordinates - INFO - dist_to_coast: UTM 32620 (2/2)\n", + "2022-03-21 14:35:53,706 - climada.hazard.trop_cyclone - INFO - Mapping 1049 tracks to 1891 coastal centroids.\n", + "2022-03-21 14:35:56,704 - climada.hazard.trop_cyclone - INFO - Progress: 10%\n", + "2022-03-21 14:36:00,561 - climada.hazard.trop_cyclone - INFO - Progress: 20%\n", + "2022-03-21 14:36:05,356 - climada.hazard.trop_cyclone - INFO - Progress: 30%\n", + "2022-03-21 14:36:09,524 - climada.hazard.trop_cyclone - INFO - Progress: 40%\n", + "2022-03-21 14:36:15,423 - climada.hazard.trop_cyclone - INFO - Progress: 50%\n", + "2022-03-21 14:36:20,307 - climada.hazard.trop_cyclone - INFO - Progress: 60%\n", + "2022-03-21 14:36:25,005 - climada.hazard.trop_cyclone - INFO - Progress: 70%\n", + "2022-03-21 14:36:30,606 - climada.hazard.trop_cyclone - INFO - Progress: 80%\n", + "2022-03-21 14:36:35,743 - climada.hazard.trop_cyclone - INFO - Progress: 90%\n", + "2022-03-21 14:36:41,322 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n" + ] + } + ], + "source": [ + "from climada.hazard import TropCyclone\n", + "\n", + "haz = TropCyclone.from_tracks(tracks, centroids=cent)\n", + "haz.check() # verifies that the necessary data for the Hazard object is correctly provided" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In 2017 Hurricane Maria devastated Puerto Rico. In the IBTRaCs event set, it has ID `2017260N12310` (we use this rather than the name, as IBTRaCS contains three North Atlantic storms called Maria). We can plot the track:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2022-03-09T16:16:32.680624Z", + "start_time": "2022-03-09T16:16:32.673915Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "tracks.subset(\n", + " {\"sid\": \"2017260N12310\"}\n", + ").plot(); # This is how we subset a TCTracks object" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And plot the hazard on our centroids for Puerto Rico:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_intensity(event=\"2017260N12310\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Hazard object also lets us plot the hazard at different return periods. The IBTRaCS archive produces footprints from 1980 onwards (CLIMADA discarded earlier events) and so the historical period is short. Therefore these plots don't make sense as 'real' return periods, but we're being irresponsible and demonstrating the functionality anyway." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-15 22:20:11,511 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 10 20 40]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_rp_intensity(return_periods=(5, 10, 20, 40));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See the [TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", + "\n", + "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](climada_hazard_TropCyclone.ipynb) for details.\n", + "\n", + ">**Exercise:** Extend this notebook's analysis to examine the effects of climate change in Puerto Rico. You'll need to extend the historical event set with stochastic tracks to create a robust statistical storm climatology - the `TCTracks` class has the functionality to do this. Then you can apply the `apply_climate_scenario_knu` method to the generated hazard object to create a second hazard climatology representing storm activity under climate change. See how the results change using the different hazard sets.\n", + "\n", + "Next we'll work on exposure and vulnerability, part of the Entity class." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Entity\n", + "\n", + "The entity class is a container class that stores exposures and impact functions (vulnerability curves) needed for a risk calculation, and the discount rates and adaptation measures for an adaptation cost-benefit analysis.\n", + "\n", + "As with Hazard objects, Entities can be read from files or created through code. The Excel template can be found in `climada_python/climada/data/system/entity_template.xlsx`.\n", + "\n", + "In this tutorial we will create an Exposure object using the LitPop economic exposure module, and load a pre-defined wind damage function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exposures\n", + "\n", + "The `Entity`'s `exposures` attribute contains geolocalized values of anything exposed to the hazard, whether monetary values of assets or number of human lives, for example. It is of type `Exposures`. \n", + "\n", + "See the [Exposures tutorial](climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", + "\n", + "LitPop is a module that allows CLIMADA to estimate exposed populations and economic assets at any point on the planet without additional information, and in a globally consistent way. Before we try it out with the next code block, we'll need to download a data set and put it into the right folder:\n", + "1. Go to the [download page](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download) on Socioeconomic Data and Applications Center (sedac).\n", + "2. You'll be asked to log in or register. Please register if you don't have an account.\n", + "3. Wait until several drop-down menus show up.\n", + "4. Choose in the drop-down menus: Temporal: single year, FileFormat: GeoTiff, Resolution: 30 seconds. Click “2020” and then \"create download\".\n", + "5. Copy the file \"gpw_v4_population_count_rev11_2020_30_sec.tif\" into the folder \"~/climada/data\". (Or you can run the block once to find the right path in the error message)\n", + "\n", + "Now we can create an economic Exposure dataset for Puerto Rico." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:37:03,770 - climada.entity.exposures.litpop.litpop - INFO - \n", + " LitPop: Init Exposure for country: PRI (630)...\n", + "\n", + "2022-03-21 14:37:03,773 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", + "2022-03-21 14:37:03,774 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", + "2022-03-21 14:37:03,824 - climada.entity.exposures.litpop.nightlight - INFO - No satellite files found locally in /home/yuyue/climada/data\n", + "2022-03-21 14:37:03,826 - climada.entity.exposures.litpop.nightlight - INFO - Attempting to download file from https://eoimages.gsfc.nasa.gov/images/imagerecords/144000/144897/BlackMarble_2016_B1_geo_gray.tif\n", + "2022-03-21 14:37:04,665 - climada.util.files_handler - INFO - Downloading https://eoimages.gsfc.nasa.gov/images/imagerecords/144000/144897/BlackMarble_2016_B1_geo_gray.tif to file /home/yuyue/climada/data/BlackMarble_2016_B1_geo_gray.tif\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "26.8kKB [00:02, 9.72kKB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:37:08,919 - climada.util.files_handler - INFO - Downloading https://databank.worldbank.org/data/download/Wealth-Accounts_CSV.zip to file /mnt/c/Users/yyljy/Documents/climada_main/doc/tutorial/results/Wealth-Accounts_CSV.zip\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "1.44kKB [00:03, 429KB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:37:12,440 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n", + "2022-03-21 14:37:13,356 - climada.util.finance - INFO - GDP PRI 2018: 1.009e+11.\n", + "2022-03-21 14:37:13,361 - climada.util.finance - WARNING - No data for country, using mean factor.\n", + "2022-03-21 14:37:13,378 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2022-03-21 14:37:13,380 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2022-03-21 14:37:13,381 - climada.entity.exposures.base - INFO - cover not set.\n", + "2022-03-21 14:37:13,383 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2022-03-21 14:37:13,384 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2022-03-21 14:37:13,387 - climada.util.coordinates - INFO - Setting geometry points.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity.exposures import LitPop\n", + "\n", + "exp_litpop = LitPop.from_countries(\n", + " \"Puerto Rico\", res_arcsec=120\n", + ") # We'll go lower resolution than default to keep it simple\n", + "\n", + "exp_litpop.plot_hexbin(pop_name=True, linewidth=4, buffer=0.1);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "LitPop's default exposure is measured in US Dollars, with a reference year depending on the most recent data available.\n", + "\n", + "Once we've created our impact function we will come back to this Exposure and give it the parameters needed to connect exposure to impacts. \n", + "\n", + "### Impact functions\n", + "\n", + "Impact functions describe a relationship between a hazard's intensity and your exposure in terms of a percentage loss. The impact is described through two terms. The Mean Degree of Damage (MDD) gives the percentage of an exposed asset's numerical value that's affected as a function of intensity, such as the damage to a building from wind in terms of its total worth. Then the Proportion of Assets Affected (PAA) gives the fraction of exposures that are affected, such as the mortality rate in a population from a heatwave. These multiply to give the Mean Damage Ratio (MDR), the average impact to an asset.\n", + "\n", + "Impact functions are stored as the Entity's `impact_funcs` attribute, in an instance of the `ImpactFuncSet` class which groups one or more `ImpactFunc` objects. They can be specified manually, read from a file, or you can use CLIMADA's pre-defined impact functions. We'll use a pre-defined function for tropical storm wind damage stored in the `IFTropCyclone` class. \n", + "\n", + "See the [Impact Functions tutorial](climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", + "\n", + "We initialise an Impact Function with the `IFTropCyclone` class, and use its `from_emanuel_usa` method to load the Emanuel (2011) impact function. (The class also contains regional impact functions for the full globe, but we'll won't use these for now.) The class's `plot` method visualises the function, which we can see is expressed just through the Mean Degree of Damage, with all assets affected." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity.impact_funcs import ImpactFuncSet, ImpfTropCyclone\n", + "\n", + "imp_fun = ImpfTropCyclone.from_emanuel_usa()\n", + "imp_fun.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot title also includes information about the function's ID, which were also set by the `from_emanuel_usa` class method. The hazard is \"TC\" and the function ID is 1. Since a study might use several impact functions - for different hazards, or for different types of exposure.\n", + "\n", + "We then create an `ImpactFuncSet` object to store the impact function. This is a container class, and groups a study's impact functions together. Studies will often have several impact functions, due to multiple hazards, multiple types of exposure that are impacted differently, or different adaptation scenarios. We add it to our Entity object." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "imp_fun_set = ImpactFuncSet([imp_fun])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can update our LitPop exposure to point to the TC 1 impact function. This is done by adding a column to the exposure:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:37:53,587 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2022-03-21 14:37:53,591 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2022-03-21 14:37:53,593 - climada.entity.exposures.base - INFO - cover not set.\n", + "2022-03-21 14:37:53,594 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2022-03-21 14:37:53,595 - climada.entity.exposures.base - INFO - centr_ not set.\n", + "2022-03-21 14:37:53,600 - climada.entity.impact_funcs.base - WARNING - For intensity = 0, mdd != 0 or paa != 0. Consider shifting the origin of the intensity scale. In impact.calc the impact is always null at intensity = 0.\n" + ] + } + ], + "source": [ + "exp_litpop.gdf[\"impf_TC\"] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here the `impf_TC` column tells the CLIMADA engine that for a tropical cyclone (TC) hazard, it should use the first impact function defined for TCs. We use the same impact function for all of our exposure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is now everything we need for a risk analysis, but while we're working on the Entity class, we can define the adaptation measures and discount rates needed for an adaptation analysis. If you're not interested in the cost-benefit analysis, you can skip ahead to the [Impact section](#Impact)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adaptation measures\n", + "\n", + "CLIMADA's adaptation measures describe possible interventions that would change event hazards and impacts, and the cost of these interventions.\n", + "\n", + "They are stored as `Measure` objects within a `MeasureSet` container class (similarly to `ImpactFuncSet` containing several `ImpactFunc`s), and are assigned to the `measures` attribute of the Entity.\n", + "\n", + "See the [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", + "\n", + "The best way to understand an adaptation measure is by an example. Here's a possible measure for the creation of coastal mangroves (ignore the exact numbers, they are just for illustration):" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.entity import Measure, MeasureSet\n", + "\n", + "meas_mangrove = Measure(\n", + " name=\"Mangrove\",\n", + " haz_type=\"TC\",\n", + " color_rgb=np.array([0.2, 0.2, 0.7]),\n", + " cost=500000000,\n", + " mdd_impact=(1, 0),\n", + " paa_impact=(1, -0.15),\n", + " hazard_inten_imp=(1, -10),\n", + ")\n", + "\n", + "meas_set = MeasureSet(measure_list=[meas_mangrove])\n", + "meas_set.check()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What values have we set here?\n", + "- The `haz_type` gives the hazard that this measure affects.\n", + "- The `cost` is a flat price that will be used in cost-benefit analyses.\n", + "- The `mdd_impact`, `paa_impact`, and `hazard_inten_imp` attributes are all tuples that describes a linear transformation to event hazard, the impact function's mean damage degree and the impact function's proportion of assets affected. The tuple `(a, b)` describes a scalar multiplication of the function and a constant to add. So `(1, 0)` is unchanged, `(1.1, 0)` increases values by 10%, and `(1, -10)` decreases all values by 10.\n", + "\n", + "So the Mangrove example above costs 50,000,000 USD, protects 15% of assets from any impact at all (`paa_impact = (1, -0.15)`) and decreases the (effective) hazard intensity by 10 m/s (`hazard_inten_imp = (1, -10)`.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can apply these measures to our existing Exposure, Hazard and Impact functions, and plot the old and new impact functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'TC: Modified impact function')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mangrove_exp, mangrove_imp_fun_set, mangrove_haz = meas_mangrove.apply(\n", + " exp_litpop, imp_fun_set, haz\n", + ")\n", + "axes1 = imp_fun_set.plot()\n", + "axes1.set_title(\"TC: Emanuel (2011) impact function\")\n", + "axes2 = mangrove_imp_fun_set.plot()\n", + "axes2.set_title(\"TC: Modified impact function\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's define a second measure. Again, the numbers here are made up, for illustration only." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:38:24,711 - climada.entity.exposures.base - INFO - Matching 691 exposures with 1891 centroids.\n", + "2022-03-21 14:38:24,716 - climada.engine.impact - INFO - Calculating damage for 661 assets (>0) and 1049 events.\n" + ] + } + ], + "source": [ + "meas_buildings = Measure(\n", + " name=\"Building code\",\n", + " haz_type=\"TC\",\n", + " color_rgb=np.array([0.2, 0.7, 0.5]),\n", + " cost=100000000,\n", + " hazard_freq_cutoff=0.1,\n", + ")\n", + "\n", + "meas_set.append(meas_buildings)\n", + "meas_set.check()\n", + "\n", + "buildings_exp, buildings_imp_fun_set, buildings_haz = meas_buildings.apply(\n", + " exp_litpop, imp_fun_set, haz\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This measure describes an upgrade to building codes to withstand 10-year events. The measure costs 100,000,000 USD and, through `hazard_freq_cutoff = 0.1`, removes events with calculated impacts below the 10-year return period. \n", + "\n", + "The [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) describes other parameters for describing adaptation measures, including risk transfer, assigning measures to subsets of exposure, and reassigning impact functions." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compare the 5- and 20-year return period hazard (remember: not a real return period due to the small event set!) compared to the adjusted hazard once low-impact events are removed." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-15 22:27:56,309 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 20]\n", + "2022-03-15 22:28:13,337 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 20]\n", + "2022-03-15 22:28:13,911 - climada.hazard.base - WARNING - Exceedance intenstiy values below 0 are set to 0. Reason: no negative intensity values were found in hazard.\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_rp_intensity(return_periods=(5, 20))\n", + "buildings_haz.plot_rp_intensity(return_periods=(5, 20));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It shows there are now very few events at the 5-year return period - the new building codes removed most of these from the event set." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Discount rates\n", + "\n", + "The `disc_rates` attribute is of type `DiscRates`. This class contains the discount rates for the following years and computes the net present value for given values.\n", + "\n", + "See the [Discount Rates tutorial](climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", + "\n", + "Here we will implement a simple, flat 2% discount rate." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity import DiscRates\n", + "\n", + "years = np.arange(1950, 2101)\n", + "rates = np.ones(years.size) * 0.02\n", + "disc = DiscRates(years=years, rates=rates)\n", + "disc.check()\n", + "disc.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now ready to move to the last part of the CLIMADA model for Impact and Cost Benefit analyses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Define Entity\n", + "\n", + "We are now ready to define our Entity object that contains the exposures, impact functions, discount rates and measures." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.entity import Entity\n", + "\n", + "ent = Entity(\n", + " exposures=exp_litpop,\n", + " disc_rates=disc,\n", + " impact_func_set=imp_fun_set,\n", + " measure_set=meas_set,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Engine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CLIMADA Engine is where the main risk calculations are done. It contains two classes, `Impact`, for risk assessments, and `CostBenefit`, to evaluate adaptation measures.\n", + "\n", + "### Impact\n", + "\n", + "Let us compute the impact of historical tropical cyclones in Puerto Rico.\n", + "\n", + "Our work above has given us everything we need for a risk analysis using the Impact class. By computing the impact for each historical event, the `Impact` class provides different risk measures, as the expected annual impact per exposure, the probable maximum impact for different return periods and the total average annual impact." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: the configurable parameter `CONFIG.maz_matrix_size` controls the maximum matrix size contained in a chunk. You can decrease its value if you are having memory issues when using the `Impact`'s `calc` method. A high value will make the computation fast, but increase the memory use.\n", + "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values.)\n", + "\n", + "CLIMADA calculates impacts by providing exposures, impact functions and hazard to an `Impact` object's `calc` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:38:36,337 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2022-03-21 14:38:36,343 - climada.engine.impact - INFO - Calculating damage for 661 assets (>0) and 1049 events.\n" + ] + } + ], + "source": [ + "from climada.engine import ImpactCalc\n", + "\n", + "imp = ImpactCalc(ent.exposures, ent.impact_funcs, haz).impact()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A useful parameter for the `calc` method is `save_mat`. When set to `True` (default is `False`), the `Impact` object saves the calculated impact for each event at each point of exposure, stored as a (large) sparse matrix in the `imp_mat` attribute. This allows for more detailed analysis at the event level.\n", + "\n", + "The `Impact` class includes a number of analysis tools. We can plot an exceedance frequency curve, showing us how often different damage thresholds are reached in our source data (remember this is only 40 years of storms, so not a full climatology!)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expected average annual impact: 9.068e+08 USD\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "freq_curve = imp.calc_freq_curve() # impact exceedance frequency curve\n", + "freq_curve.plot()\n", + "\n", + "print(\"Expected average annual impact: {:.3e} USD\".format(imp.aai_agg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can map the expected annual impact by exposure:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-21 14:38:43,047 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2022-03-21 14:38:43,151 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2022-03-21 14:38:46,480 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "imp.plot_basemap_eai_exposure(buffer=0.1); # average annual impact at each exposure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For additional functionality, including plotting the impacts of individual events, see the [Impact tutorial](climada_engine_Impact.ipynb).\n", + "\n", + ">**Exercise:** Plot the impacts of Hurricane Maria. To do this you'll need to set `save_mat=True` in the earlier `ImpactCalc.impact()`.\n", + "\n", + "We recommend to use CLIMADA's writers in `hdf5` or `csv` whenever possible. It is also possible to save our variables in pickle format using the `save` function and load them with `load`. This will save your results in the folder specified in the configuration file. The default folder is a `results` folder which is created in the current path (see default configuration file `climada/conf/defaults.conf`). The pickle format has a [transient format](saving-with-pickle) and should be avoided when possible." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from climada.util import save, load\n", + "\n", + "### Uncomment this to save - saves by default to ./results/\n", + "# save('impact_puerto_rico_tc.p', imp)\n", + "\n", + "### Uncomment this to read the saved data:\n", + "# abs_path = os.path.join(os.getcwd(), 'results/impact_puerto_rico_tc.p')\n", + "# data = load(abs_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](climada_engine_Impact.ipynb) to get more information about these functions and the class in general." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Adaptation options appraisal" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, let's look at a cost-benefit analysis. The adaptation measures defined with our `Entity` can be valued by estimating their cost-benefit ratio. This is done in the class `CostBenefit`.\n", + "\n", + "Let us suppose that the socioeconomic and climatoligical conditions remain the same in 2040. We then compute the cost and benefit of every adaptation measure from our Hazard and Entity (and plot them) as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2022-03-15 22:32:07,393 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2022-03-15 22:32:07,397 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", + "2022-03-15 22:32:07,406 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2022-03-15 22:32:07,408 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", + "2022-03-15 22:32:07,418 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2022-03-15 22:32:07,420 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", + "2022-03-15 22:32:07,437 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", + "2022-03-15 22:32:07,440 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", + "2022-03-15 22:32:07,452 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", + "\n", + "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", + "------------- --------------- ------------------ --------------\n", + "Mangrove 0.5 11.2129 22.4258\n", + "Building code 0.1 0.00761204 0.0761204\n", + "\n", + "-------------------- --------- --------\n", + "Total climate risk: 17.749 (USD bn)\n", + "Average annual risk: 0.951281 (USD bn)\n", + "Residual risk: 6.52855 (USD bn)\n", + "-------------------- --------- --------\n", + "Net Present Values\n" + ] + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from climada.engine import CostBenefit\n", + "\n", + "cost_ben = CostBenefit()\n", + "cost_ben.calc(haz, ent, future_year=2040) # prints costs and benefits\n", + "cost_ben.plot_cost_benefit()\n", + "# plot cost benefit ratio and averted damage of every exposure\n", + "cost_ben.plot_event_view(\n", + " return_per=(10, 20, 40)\n", + "); # plot averted damage of each measure for every return period" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is just the start. Analyses improve as we add more adaptation measures into the mix.\n", + "\n", + "Cost-benefit calculations can also include\n", + "- climate change, by specifying the `haz_future` parameter in `CostBenefit.calc()`\n", + "- changes to economic exposure over time (or to whatever exposure you're modelling) by specifying the `ent_future` parameter in `CostBenefit.calc()`\n", + "- different functions to calculate risk benefits. These are specified in `CostBenefit.calc()` and by default use changes to average annual impact\n", + "- linear, sublinear and superlinear evolution of impacts between the present and future, specified in the `imp_time_depen` parameter in `CostBenefit.calc()`\n", + "\n", + "And once future hazards and exposures are defined, we can express changes to impacts over time as waterfall diagrams. See the CostBenefit class for more details.\n", + "\n", + "> **Exercise:** repeat the above analysis, creating future climate hazards (see the first exercise), and future exposures based on projected economic growth. Visualise it with the `CostBenefit.plot_waterfall()` method." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## What next?\n", + "\n", + "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#CLIMADA-features) section.\n", + "\n", + "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#Resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "supply_chain", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index 757568b049..bf1a922e10 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -9,6 +9,7 @@ Landing page of the user guide :caption: User guides :hidden: + 10 minutes CLIMADA <0_10min_climada> Overview <1_main_climada> Hazard Exposures From bae24b56757327b5364fbbd572f3453a5bbe6a29 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Tue, 21 Jan 2025 15:52:06 +0100 Subject: [PATCH 09/49] first version of 10min CLIMADA intro --- doc/user-guide/0_10min_climada.ipynb | 2867 ++------------------------ 1 file changed, 166 insertions(+), 2701 deletions(-) diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb index c802e8a469..544e472aca 100644 --- a/doc/user-guide/0_10min_climada.ipynb +++ b/doc/user-guide/0_10min_climada.ipynb @@ -6,2960 +6,425 @@ "source": [ "# 10 minutes CLIMADA\n", "\n", - "This is a short introduction to the main building blocks of CLIMADA's impact calculation. For a more detailed impact calculation, please check out the more detailed [Impact Calculation](../tutorial/1_main_climada.ipynb). TBDnaming\n", + "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../tutorial/1_main_climada.ipynb). TBDnaming\n", "\n", - "To get started, we import the CLIMADA package." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "import climada as climada" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Key objects in a CLIMADA impact calculation\n", - "\n", - "For an impact calculation, we have to specify the following ingredients\n", - "1. Hazard: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard.\n", - "2. Exposures: The exposure information provides the location and the number/value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", - "3. ImpfFunc: The impact or vunerability functions models the average impact that is expected for a given exposure value and given hazard intensity.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an Expsoure object\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an Hazard object\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-12-19 18:11:22,924 - climada.hazard.tc_tracks - WARNING - The cached IBTrACS data set dates from 2023-06-07 23:07:38 (older than 180 days). Very likely, a more recent version is available. Consider manually removing the file /Users/vgebhart/climada/data/IBTrACS.ALL.v04r00.nc and re-running this function, which will download the most recent version of the IBTrACS data set from the official URL.\n", - "2024-12-19 18:11:23,707 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", - "2024-12-19 18:11:23,717 - climada.hazard.tc_tracks - INFO - Progress: 21%\n", - "2024-12-19 18:11:23,727 - climada.hazard.tc_tracks - INFO - Progress: 31%\n", - "2024-12-19 18:11:23,738 - climada.hazard.tc_tracks - INFO - Progress: 42%\n", - "2024-12-19 18:11:23,748 - climada.hazard.tc_tracks - INFO - Progress: 52%\n", - "2024-12-19 18:11:23,758 - climada.hazard.tc_tracks - INFO - Progress: 63%\n", - "2024-12-19 18:11:23,767 - climada.hazard.tc_tracks - INFO - Progress: 73%\n", - "2024-12-19 18:11:23,778 - climada.hazard.tc_tracks - INFO - Progress: 84%\n", - "2024-12-19 18:11:23,789 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", - "2024-12-19 18:11:23,794 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/climada/hazard/tc_tracks.py:614: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n", - " if ibtracs_ds.dims['storm'] == 0:\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", - " return lib.intersection(a, b, **kwargs)\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", - " return lib.intersection(a, b, **kwargs)\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", - " return lib.intersection(a, b, **kwargs)\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection\n", - " return lib.intersection(a, b, **kwargs)\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - 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"/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - 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"/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n", - "/Users/vgebhart/miniforge3/envs/supply_chain/lib/python3.11/site-packages/shapely/constructive.py:181: RuntimeWarning: invalid value encountered in buffer\n", - " return lib.buffer(\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from climada.hazard import TCTracks, TropCyclone, Centroids\n", - "\n", - "TC_tracks_WP_2023 = TCTracks.from_ibtracs_netcdf(\n", - " provider=\"usa\", basin=\"WP\", year_range=(2023, 2023)\n", - ")\n", - "TC_tracks_WP_2023.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "min_lon, min_lat, max_lon, max_lat = 116.0, 4.6, 126.6, 21.2\n", - "centroids = Centroids.from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2024-12-19 18:14:30,677 - climada.util.coordinates - INFO - Sampling from /Users/vgebhart/climada/data/GMT_intermediate_coast_distance_01d.tif\n", - "2024-12-19 18:14:30,814 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Mapping 19 tracks to 70929 coastal centroids.\n", - "2024-12-19 18:14:31,008 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 10%\n", - "2024-12-19 18:14:31,127 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 21%\n", - "2024-12-19 18:14:31,393 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 31%\n", - "2024-12-19 18:14:31,454 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 42%\n", - "2024-12-19 18:14:31,876 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 52%\n", - "2024-12-19 18:14:31,996 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 63%\n", - "2024-12-19 18:14:32,061 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 73%\n", - "2024-12-19 18:14:32,282 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 84%\n", - "2024-12-19 18:14:32,340 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 94%\n", - "2024-12-19 18:14:32,432 - climada.hazard.trop_cyclone.trop_cyclone - INFO - Progress: 100%\n" - ] - } - ], - "source": [ - "haz = TropCyclone.from_tracks(TC_tracks_WP_2023, centroids=centroids)\n", - "haz.check() # verifies that the necessary data for the Hazard object is correctly provided" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(19, 70929)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "haz.intensity.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create an Impact Function object\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate the impacts\n", - "\n", - "For an impact calculation, we have to specify the following ingredients\n", - "1. Exposures: A\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further possible continuations of the impact calculation" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "\n", - "### What is CLIMADA?\n", - "\n", - "CLIMADA is a fully probabilistic climate risk assessment tool. It provides a framework for users to combine exposure, hazard and vulnerability or impact data to calculate risk. Users can create probabilistic impact data from event sets, look at how climate change affects these impacts, and see how effectively adaptation measures can change them. CLIMADA also allows for studies of individual events, historical event sets and forecasts.\n", - "\n", - "The model is highly customisable, meaning that users can work with out-of-the-box data provided for different hazards, population and economic exposure, or can provide their own data for part or all of the analysis. The pre-packaged data make CLIMADA particularly useful for users who focus on just one element of risk, since CLIMADA can 'fill in the gaps' for hazard, exposure or vulnerability in the rest of the analysis.\n", - "\n", - "The model core is designed to give as much flexibility as possible when describing the elements of risk, meaning that CLIMADA isn't limited to particular hazards, exposure types or impacts. We love to see the model applied to new problems and contexts.\n", - "\n", - "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#CLIMADA-features) section below.\n", - "\n", - "\n", - "### This tutorial\n", - "\n", - "This tutorial is for people new to CLIMADA who want to get a high level understanding of the model and work through an example risk analysis. It will list the current features of the model, and go through a complete CLIMADA analysis to give an idea of how the model works. Other tutorials go into more detail about different model components and individual hazards.\n", - "\n", - "### Resources beyond this tutorial\n", - "\n", - "- [Installation guide](../guide/install.rst) - go here if you've not installed the model yet\n", - "- [CLIMADA Read the Docs home page](https://climada-python.readthedocs.io) - for all other documentation\n", - "- [List of CLIMADA's features and associated tutorials](#CLIMADA-features)\n", - "- [CLIMADA GitHub develop branch documentation](https://github.com/CLIMADA-project/climada_python/tree/develop/doc) for the very latest versions of code and documentation\n", - "- [CLIMADA paper GitHub repository](https://github.com/CLIMADA-project/climada_papers) - for publications using CLIMADA\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## CLIMADA features\n", - "\n", - "A risk analysis with CLIMADA can include\n", - "\n", - "1. the statistical risk to your exposure from a set of events,\n", - "2. how it changes under climate change, and\n", - "3. a cost-benefit analysis of adaptation measures.\n", - "\n", - "CLIMADA is flexible: the \"statistical risk\" above could be describing the annual expected insured flood losses to a property portfolio, the number of people displaced by an ensemble of typhoon forecasts, the annual disruption to a railway network from landslides, or changes to crop yields.\n", - "\n", - "Users from risk-analysis backgrounds will be familiar with describing the impact of events by combining exposure, hazard and an impact function (or vulnerability curve) that combines the two to describe a hazard's effects. A CLIMADA analysis uses the same approach but wraps the exposures and their impact functions into a single `Entity` class, along with discount rates and adaptation options (see the below tutorials for more on CLIMADA's financial model).\n", - "\n", - "CLIMADA's `Impact` object is used to analyse events and event sets, whether this is the impact of a single wildfire, or the global economic risk from tropical cyclones in 2100.\n", - "\n", - "CLIMADA is divided into two parts (two repositories): \n", - "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_util_api_client.ipynb). [Litpop](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", - "2. the petals [climada_petals](https://github.com/CLIMADA-project/climada_petals) contains all the modules for generating data (e.g., TC_Surge, WildFire, OpenStreeMap, ...). Most development is done here. The petals builds-upon the core and does not work as a stand-alone.\n", - "\n", - "### CLIMADA classes\n", - "\n", - "This is a full directory of tutorials for CLIMADA's classes to use as a reference. You don't need to read all this to do this tutorial, but it may be useful to refer back to.\n", - "\n", - "Core (climada_python):\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", - " - [Tropical cyclone wind](../tutorial/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", - " - [European windstorms](../tutorial/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", - "\n", - "- [**Entity**](#Entity): this is a container that groups CLIMADA's socio-economic models. It's is where the Exposures and Impact Functions are stored, which can then be combined with a hazard for a risk analysis (using the Engine's Impact class). It is also where Discount Rates and Measure Sets are stored, which are used in adaptation cost-benefit analyses (using the Engine's CostBenefit class):\n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", - " - [LitPop](../tutorial/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", - " - [Polygons_lines](../tutorial/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", - " - [ImpactFuncSet](../tutorial/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", - " - ImpactFunc: a basic adjustable impact function, specified by the user\n", - " - IFTropCyclone: impact functions for tropical cyclone winds\n", - " - IFRiverFlood: impact functions for river floods\n", - " - IFStormEurope: impact functions for European windstorms \n", - " - [DiscRates](../tutorial/climada_entity_DiscRates.ipynb): discount rates per year\n", - " - [MeasureSet](../tutorial/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", - " \n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", - " - [Impact](../tutorial/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", - " - [Impact_data](../tutorial/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", - " - [CostBenefit](#Adaptation-options-appraisal): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", - " - [Unsequa](../tutorial/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", - " - [Unsequa_helper](../tutorial/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", - " - [Forecast](../tutorial/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", - "\n", - "climada_petals:\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb):\n", - " - [Storm surge](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCSurgeBathtub.html): Tropical cyclone surge from linear wind-surge relationship and a bathtub model.\n", - " - [River flooding](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_RiverFlood.html): global water depth hazard for flood, including methods to work with ISIMIP simulations.\n", - " - [Crop modelling](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_entity_Crop.html): combines ISIMIP crop simulations and UN Food and Agrigultre Organization data. The module uses crop production as exposure, with hydrometeorological 'hazard' increasing or decreasing production.\n", - " - [Wildfire (global)](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_Wildfire.html): This class is used to model the wildfire hazard using the historical data available and creating synthetic fires which are summarized into event years to establish a comprehensiv probabilistic risk assessment.\n", - " - [Landslide](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_Landslide.html): This class is able to handle two different types of landslide source files (in one case, already the finished product of some model output, in the other case just a historic data collection).\n", - " - [TCForecast](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCForecast.html): This class extends the TCTracks class with methods to download operational ECMWF ensemble tropical storm track forecasts, read the BUFR files they're contained in and produce a TCTracks object that can be used to generate TropCyclone hazard footprints.\n", - " - [Emulator](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_emulator.html):Given a database of hazard events, this module climada.hazard.emulator provides tools to subsample events (or time series of events) from that event database.\n", - " - Drought (global): tutorial under development\n", - "\n", - "- [**Entity**](#Entity): \n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb):\n", - " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", - " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", - "\n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb):\n", - " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", - "\n", - "This list will be updated periodically along with new CLIMADA releases. To see the latest, development version of all tutorials, see the [tutorials page on the CLIMADA GitHub](https://github.com/CLIMADA-project/climada_python/tree/develop/doc/tutorial)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tutorial: an example risk assessment\n", - "\n", - "This example will work through a risk assessment for tropical storm wind in Puerto Rico, constructing hazard, exposure and vulnerability and combining them to create an Impact object. Everything you need for this is included in the main CLIMADA installation and additional data will be downloaded by the scripts as required." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Hazard\n", - "\n", - "Hazards are characterized by their frequency of occurrence and the geographical distribution of their intensity. The `Hazard` class collects events of the same hazard type (e.g. tropical cyclone, flood, drought, ...) with intensity values over the same geographic centroids. They might be historical events or synthetic.\n", - "\n", - "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#CLIMADA-features) section of this document to explore tutorials for different hazards, including\n", - "[tropical cyclones](climada_hazard_TropCyclone.ipynb), as used here.\n", - "\n", - "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", - "\n", - "So before we create the hazard, we will create our storm tracks and define the geographic centroids for the locations we want to calculate hazard at.\n", - "\n", - "### Storm tracks\n", - "\n", - "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2022-03-09T16:14:07.505695Z", - "start_time": "2022-03-09T16:14:05.379337Z" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:31:20,322 - climada.hazard.tc_tracks - WARNING - 1122 storm events are discarded because no valid wind/pressure values have been found: 1851175N26270, 1851181N19275, 1851187N22262, 1851192N12300, 1851214N14321, ...\n", - "2022-03-21 14:31:20,345 - climada.hazard.tc_tracks - WARNING - 139 storm events are discarded because only one valid timestep has been found: 1852232N21293, 1853242N12336, 1855236N12304, 1856221N25277, 1856235N13302, ...\n", - "2022-03-21 14:31:22,766 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", - "2022-03-21 14:31:25,059 - climada.hazard.tc_tracks - INFO - Progress: 20%\n", - "2022-03-21 14:31:27,491 - climada.hazard.tc_tracks - INFO - Progress: 30%\n", - "2022-03-21 14:31:30,067 - climada.hazard.tc_tracks - INFO - Progress: 40%\n", - "2022-03-21 14:31:32,415 - climada.hazard.tc_tracks - INFO - Progress: 50%\n", - "2022-03-21 14:31:34,829 - climada.hazard.tc_tracks - INFO - Progress: 60%\n", - "2022-03-21 14:31:37,482 - climada.hazard.tc_tracks - INFO - Progress: 70%\n", - "2022-03-21 14:31:39,976 - climada.hazard.tc_tracks - INFO - Progress: 80%\n", - "2022-03-21 14:31:42,307 - climada.hazard.tc_tracks - INFO - Progress: 90%\n", - "2022-03-21 14:31:44,580 - climada.hazard.tc_tracks - INFO - Progress: 100%\n", - "2022-03-21 14:31:45,780 - climada.hazard.tc_tracks - INFO - Progress: 10%\n", - "2022-03-21 14:31:45,833 - climada.hazard.tc_tracks - INFO - Progress: 21%\n", - "2022-03-21 14:31:45,886 - climada.hazard.tc_tracks - INFO - Progress: 31%\n", - "2022-03-21 14:31:45,939 - climada.hazard.tc_tracks - INFO - Progress: 42%\n", - "2022-03-21 14:31:45,992 - climada.hazard.tc_tracks - INFO - Progress: 52%\n", - "2022-03-21 14:31:46,048 - climada.hazard.tc_tracks - INFO - Progress: 63%\n", - "2022-03-21 14:31:46,100 - climada.hazard.tc_tracks - INFO - Progress: 73%\n", - "2022-03-21 14:31:46,150 - climada.hazard.tc_tracks - INFO - Progress: 84%\n", - "2022-03-21 14:31:46,203 - climada.hazard.tc_tracks - INFO - Progress: 94%\n", - "2022-03-21 14:31:46,232 - climada.hazard.tc_tracks - INFO - Progress: 100%\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from climada.hazard import TCTracks\n", - "import warnings # To hide the warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "tracks = TCTracks.from_ibtracs_netcdf(\n", - " provider=\"usa\", basin=\"NA\"\n", - ") # Here we download the full dataset for the analysis\n", - "# afterwards (e.g. return period), but you can also use \"year_range\" to adjust the range of the dataset to be downloaded.\n", - "# While doing that, you need to make sure that the year 2017 is included if you want to run the blocks with the codes\n", - "# subsetting a specific tropic cyclone, which happened in 2017. (Of course, you can also change the subsetting codes.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will load all historical tracks in the North Atlantic into the `tracks` object (since we set `basin='NA'`). The `TCTracks.plot` method will plot the downloaded tracks, though there are too many for the plot to be very useful:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plotting tracks can be very time consuming, depending on the number of tracks. So we choose only a few here, by limiting the time range to one year\n", - "tracks_2017 = TCTracks.from_ibtracs_netcdf(\n", - " provider=\"usa\", basin=\"NA\", year_range=(2017, 2017)\n", - ")\n", - "tracks_2017.plot(); # This may take a very long time" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It's also worth adding additional time steps to the tracks (though this can be memory intensive!). Most tracks are reported at 3-hourly intervals (plus a frame at landfall). Event footprints are calculated as the maximum wind from any time step. For a fast-moving storm these combined three-hourly footprints give quite a rough event footprint, and it's worth adding extra frames to smooth the footprint artificially (try running this notebook with and without this interpolation to see the effect): " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:32:39,466 - climada.hazard.tc_tracks - INFO - Interpolating 1049 tracks to 0.5h time steps.\n" - ] - } - ], - "source": [ - "tracks.equal_timestep(time_step_h=0.5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Centroids\n", - "\n", - "A hazard's centroids can be any set of locations where we want the hazard to be evaluated. This could be the same as the locations of your exposure, though commonly it is on a regular lat-lon grid (with hazard being imputed to exposure between grid points).\n", - "\n", - "Here we'll set the centroids as a 0.1 degree grid covering Puerto Rico. Centroids are defined by a `Centroids` class, which has the `from_pnt_bounds` method for generating regular grids and a `plot` method to inspect the centroids." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from climada.hazard import Centroids\n", - "\n", - "min_lat, max_lat, min_lon, max_lon = 17.5, 19.0, -68.0, -65.0\n", - "cent = Centroids.from_pnt_bounds((min_lon, min_lat, max_lon, max_lat), res=0.05)\n", - "cent.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Hazard footprint\n", - "\n", - "Now we're ready to create our hazard object. This will be a `TropCyclone` class, which inherits from the `Hazard` class, and has the `from_tracks` constructor method to create a hazard from a `TCTracks` object at given centroids." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:35:51,458 - climada.hazard.centroids.centr - INFO - Convert centroids to GeoSeries of Point shapes.\n", - "2022-03-21 14:35:52,496 - climada.util.coordinates - INFO - dist_to_coast: UTM 32619 (1/2)\n", - "2022-03-21 14:35:53,234 - climada.util.coordinates - INFO - dist_to_coast: UTM 32620 (2/2)\n", - "2022-03-21 14:35:53,706 - climada.hazard.trop_cyclone - INFO - Mapping 1049 tracks to 1891 coastal centroids.\n", - "2022-03-21 14:35:56,704 - climada.hazard.trop_cyclone - INFO - Progress: 10%\n", - "2022-03-21 14:36:00,561 - climada.hazard.trop_cyclone - INFO - Progress: 20%\n", - "2022-03-21 14:36:05,356 - climada.hazard.trop_cyclone - INFO - Progress: 30%\n", - "2022-03-21 14:36:09,524 - climada.hazard.trop_cyclone - INFO - Progress: 40%\n", - "2022-03-21 14:36:15,423 - climada.hazard.trop_cyclone - INFO - Progress: 50%\n", - "2022-03-21 14:36:20,307 - climada.hazard.trop_cyclone - INFO - Progress: 60%\n", - "2022-03-21 14:36:25,005 - climada.hazard.trop_cyclone - INFO - Progress: 70%\n", - "2022-03-21 14:36:30,606 - climada.hazard.trop_cyclone - INFO - Progress: 80%\n", - "2022-03-21 14:36:35,743 - climada.hazard.trop_cyclone - INFO - Progress: 90%\n", - "2022-03-21 14:36:41,322 - climada.hazard.trop_cyclone - INFO - Progress: 100%\n" - ] - } - ], - "source": [ - "from climada.hazard import TropCyclone\n", - "\n", - "haz = TropCyclone.from_tracks(tracks, centroids=cent)\n", - "haz.check() # verifies that the necessary data for the Hazard object is correctly provided" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In 2017 Hurricane Maria devastated Puerto Rico. In the IBTRaCs event set, it has ID `2017260N12310` (we use this rather than the name, as IBTRaCS contains three North Atlantic storms called Maria). We can plot the track:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "ExecuteTime": { - "end_time": "2022-03-09T16:16:32.680624Z", - "start_time": "2022-03-09T16:16:32.673915Z" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "tracks.subset(\n", - " {\"sid\": \"2017260N12310\"}\n", - ").plot(); # This is how we subset a TCTracks object" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And plot the hazard on our centroids for Puerto Rico:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "haz.plot_intensity(event=\"2017260N12310\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A Hazard object also lets us plot the hazard at different return periods. The IBTRaCS archive produces footprints from 1980 onwards (CLIMADA discarded earlier events) and so the historical period is short. Therefore these plots don't make sense as 'real' return periods, but we're being irresponsible and demonstrating the functionality anyway." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-15 22:20:11,511 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 10 20 40]\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "haz.plot_rp_intensity(return_periods=(5, 10, 20, 40));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See the [TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", - "\n", - "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](climada_hazard_TropCyclone.ipynb) for details.\n", - "\n", - ">**Exercise:** Extend this notebook's analysis to examine the effects of climate change in Puerto Rico. You'll need to extend the historical event set with stochastic tracks to create a robust statistical storm climatology - the `TCTracks` class has the functionality to do this. Then you can apply the `apply_climate_scenario_knu` method to the generated hazard object to create a second hazard climatology representing storm activity under climate change. See how the results change using the different hazard sets.\n", - "\n", - "Next we'll work on exposure and vulnerability, part of the Entity class." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Entity\n", - "\n", - "The entity class is a container class that stores exposures and impact functions (vulnerability curves) needed for a risk calculation, and the discount rates and adaptation measures for an adaptation cost-benefit analysis.\n", - "\n", - "As with Hazard objects, Entities can be read from files or created through code. The Excel template can be found in `climada_python/climada/data/system/entity_template.xlsx`.\n", - "\n", - "In this tutorial we will create an Exposure object using the LitPop economic exposure module, and load a pre-defined wind damage function." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exposures\n", - "\n", - "The `Entity`'s `exposures` attribute contains geolocalized values of anything exposed to the hazard, whether monetary values of assets or number of human lives, for example. It is of type `Exposures`. \n", - "\n", - "See the [Exposures tutorial](climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", - "\n", - "LitPop is a module that allows CLIMADA to estimate exposed populations and economic assets at any point on the planet without additional information, and in a globally consistent way. Before we try it out with the next code block, we'll need to download a data set and put it into the right folder:\n", - "1. Go to the [download page](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download) on Socioeconomic Data and Applications Center (sedac).\n", - "2. You'll be asked to log in or register. Please register if you don't have an account.\n", - "3. Wait until several drop-down menus show up.\n", - "4. Choose in the drop-down menus: Temporal: single year, FileFormat: GeoTiff, Resolution: 30 seconds. Click “2020” and then \"create download\".\n", - "5. Copy the file \"gpw_v4_population_count_rev11_2020_30_sec.tif\" into the folder \"~/climada/data\". (Or you can run the block once to find the right path in the error message)\n", - "\n", - "Now we can create an economic Exposure dataset for Puerto Rico." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:37:03,770 - climada.entity.exposures.litpop.litpop - INFO - \n", - " LitPop: Init Exposure for country: PRI (630)...\n", - "\n", - "2022-03-21 14:37:03,773 - climada.entity.exposures.litpop.gpw_population - WARNING - Reference year: 2018. Using nearest available year for GPW data: 2020\n", - "2022-03-21 14:37:03,774 - climada.entity.exposures.litpop.gpw_population - INFO - GPW Version v4.11\n", - "2022-03-21 14:37:03,824 - climada.entity.exposures.litpop.nightlight - INFO - No satellite files found locally in /home/yuyue/climada/data\n", - "2022-03-21 14:37:03,826 - climada.entity.exposures.litpop.nightlight - INFO - Attempting to download file from https://eoimages.gsfc.nasa.gov/images/imagerecords/144000/144897/BlackMarble_2016_B1_geo_gray.tif\n", - "2022-03-21 14:37:04,665 - climada.util.files_handler - INFO - Downloading https://eoimages.gsfc.nasa.gov/images/imagerecords/144000/144897/BlackMarble_2016_B1_geo_gray.tif to file /home/yuyue/climada/data/BlackMarble_2016_B1_geo_gray.tif\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "26.8kKB [00:02, 9.72kKB/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:37:08,919 - climada.util.files_handler - INFO - Downloading https://databank.worldbank.org/data/download/Wealth-Accounts_CSV.zip to file /mnt/c/Users/yyljy/Documents/climada_main/doc/tutorial/results/Wealth-Accounts_CSV.zip\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "1.44kKB [00:03, 429KB/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-21 14:37:12,440 - climada.util.finance - WARNING - No data available for country. Using non-financial wealth instead\n", - "2022-03-21 14:37:13,356 - climada.util.finance - INFO - GDP PRI 2018: 1.009e+11.\n", - "2022-03-21 14:37:13,361 - climada.util.finance - WARNING - No data for country, using mean factor.\n", - "2022-03-21 14:37:13,378 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2022-03-21 14:37:13,380 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2022-03-21 14:37:13,381 - climada.entity.exposures.base - INFO - cover not set.\n", - "2022-03-21 14:37:13,383 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2022-03-21 14:37:13,384 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2022-03-21 14:37:13,387 - climada.util.coordinates - INFO - Setting geometry points.\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from climada.entity.exposures import LitPop\n", - "\n", - "exp_litpop = LitPop.from_countries(\n", - " \"Puerto Rico\", res_arcsec=120\n", - ") # We'll go lower resolution than default to keep it simple\n", + "## Key ingredients in a CLIMADA impact calculation\n", "\n", - "exp_litpop.plot_hexbin(pop_name=True, linewidth=4, buffer=0.1);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "LitPop's default exposure is measured in US Dollars, with a reference year depending on the most recent data available.\n", + "For CLIMADA's impact calculation, we have to specify the following ingredients:\n", + "- **Hazard**: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard. It contains a probabilistic event set, meaning that is a set of several events, each of which is associated to a frequency corresponding to the estimated probability of the occurence of the event.\n", + "- **Exposure**: The exposure information provides the location and the number and/or value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", + "- **Vulnerability**: The impact or vunerability function models the average impact that is expected for a given exposure value and given hazard intensity.\n", "\n", - "Once we've created our impact function we will come back to this Exposure and give it the parameters needed to connect exposure to impacts. \n", + "## Exemplary impact calculation\n", "\n", - "### Impact functions\n", + "We exemplify the impact calculation and its key ingredients with an analysis of the risk of tropical cyclones on several assets in Florida.\n", "\n", - "Impact functions describe a relationship between a hazard's intensity and your exposure in terms of a percentage loss. The impact is described through two terms. The Mean Degree of Damage (MDD) gives the percentage of an exposed asset's numerical value that's affected as a function of intensity, such as the damage to a building from wind in terms of its total worth. Then the Proportion of Assets Affected (PAA) gives the fraction of exposures that are affected, such as the mortality rate in a population from a heatwave. These multiply to give the Mean Damage Ratio (MDR), the average impact to an asset.\n", "\n", - "Impact functions are stored as the Entity's `impact_funcs` attribute, in an instance of the `ImpactFuncSet` class which groups one or more `ImpactFunc` objects. They can be specified manually, read from a file, or you can use CLIMADA's pre-defined impact functions. We'll use a pre-defined function for tropical storm wind damage stored in the `IFTropCyclone` class. \n", + "### Hazard objects\n", "\n", - "See the [Impact Functions tutorial](climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", - "\n", - "We initialise an Impact Function with the `IFTropCyclone` class, and use its `from_emanuel_usa` method to load the Emanuel (2011) impact function. (The class also contains regional impact functions for the full globe, but we'll won't use these for now.) The class's `plot` method visualises the function, which we can see is expressed just through the Mean Degree of Damage, with all assets affected." + "First, we read a demo hazard file that includes information about several tropical cyclone events. " ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from climada.entity.impact_funcs import ImpactFuncSet, ImpfTropCyclone\n", + "from climada.hazard import Hazard\n", + "from climada.util import HAZ_DEMO_H5\n", "\n", - "imp_fun = ImpfTropCyclone.from_emanuel_usa()\n", - "imp_fun.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot title also includes information about the function's ID, which were also set by the `from_emanuel_usa` class method. The hazard is \"TC\" and the function ID is 1. Since a study might use several impact functions - for different hazards, or for different types of exposure.\n", + "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n", "\n", - "We then create an `ImpactFuncSet` object to store the impact function. This is a container class, and groups a study's impact functions together. Studies will often have several impact functions, due to multiple hazards, multiple types of exposure that are impacted differently, or different adaptation scenarios. We add it to our Entity object." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "imp_fun_set = ImpactFuncSet([imp_fun])" + "# to hide the warnings\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, we can update our LitPop exposure to point to the TC 1 impact function. This is done by adding a column to the exposure:" + "We can infer some information from the Hazard object. The central piece of the hazard object is a sparse matrix at `haz.intensity` that contains the hazard intensity values for each event (axis 0) and each location (axis 1). " ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-21 14:37:53,587 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2022-03-21 14:37:53,591 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2022-03-21 14:37:53,593 - climada.entity.exposures.base - INFO - cover not set.\n", - "2022-03-21 14:37:53,594 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2022-03-21 14:37:53,595 - climada.entity.exposures.base - INFO - centr_ not set.\n", - "2022-03-21 14:37:53,600 - climada.entity.impact_funcs.base - WARNING - For intensity = 0, mdd != 0 or paa != 0. Consider shifting the origin of the intensity scale. In impact.calc the impact is always null at intensity = 0.\n" + "The hazard object contains 216 events. \n", + "The maximal intensity contained in the Hazard object is 72.75 m/s. \n", + "The first event was observed in a time series of 185 years, \n", + "which is why CLIMADA estimates an annual probability of 0.0054 for the occurence of this event.\n" ] } ], "source": [ - "exp_litpop.gdf[\"impf_TC\"] = 1" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here the `impf_TC` column tells the CLIMADA engine that for a tropical cyclone (TC) hazard, it should use the first impact function defined for TCs. We use the same impact function for all of our exposure." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is now everything we need for a risk analysis, but while we're working on the Entity class, we can define the adaptation measures and discount rates needed for an adaptation analysis. If you're not interested in the cost-benefit analysis, you can skip ahead to the [Impact section](#Impact)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adaptation measures\n", - "\n", - "CLIMADA's adaptation measures describe possible interventions that would change event hazards and impacts, and the cost of these interventions.\n", - "\n", - "They are stored as `Measure` objects within a `MeasureSet` container class (similarly to `ImpactFuncSet` containing several `ImpactFunc`s), and are assigned to the `measures` attribute of the Entity.\n", - "\n", - "See the [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", - "\n", - "The best way to understand an adaptation measure is by an example. Here's a possible measure for the creation of coastal mangroves (ignore the exact numbers, they are just for illustration):" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from climada.entity import Measure, MeasureSet\n", - "\n", - "meas_mangrove = Measure(\n", - " name=\"Mangrove\",\n", - " haz_type=\"TC\",\n", - " color_rgb=np.array([0.2, 0.2, 0.7]),\n", - " cost=500000000,\n", - " mdd_impact=(1, 0),\n", - " paa_impact=(1, -0.15),\n", - " hazard_inten_imp=(1, -10),\n", - ")\n", - "\n", - "meas_set = MeasureSet(measure_list=[meas_mangrove])\n", - "meas_set.check()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What values have we set here?\n", - "- The `haz_type` gives the hazard that this measure affects.\n", - "- The `cost` is a flat price that will be used in cost-benefit analyses.\n", - "- The `mdd_impact`, `paa_impact`, and `hazard_inten_imp` attributes are all tuples that describes a linear transformation to event hazard, the impact function's mean damage degree and the impact function's proportion of assets affected. The tuple `(a, b)` describes a scalar multiplication of the function and a constant to add. So `(1, 0)` is unchanged, `(1.1, 0)` increases values by 10%, and `(1, -10)` decreases all values by 10.\n", - "\n", - "So the Mangrove example above costs 50,000,000 USD, protects 15% of assets from any impact at all (`paa_impact = (1, -0.15)`) and decreases the (effective) hazard intensity by 10 m/s (`hazard_inten_imp = (1, -10)`.\n", - "\n" + "print(\n", + " f\"The hazard object contains {haz.intensity.shape[0]} events. \\n\"\n", + " f\"The maximal intensity contained in the Hazard object is {haz.intensity.max():.2f} {haz.units}. \\n\"\n", + " f\"The first event was observed in a time series of {int(1/haz.frequency[0])} {haz.frequency_unit[2:]}s, \\n\"\n", + " f\"which is why CLIMADA estimates an annual probability of {haz.frequency[0]:.4f} for the occurence of this event.\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can apply these measures to our existing Exposure, Hazard and Impact functions, and plot the old and new impact functions:" + "The probabilistic event set and its single events can be plotted. For instance, below we plot maximal intensity per grid point over the whole event set." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "Text(0.5, 1.0, 'TC: Modified impact function')" + "
" ] }, - "execution_count": 14, "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, "output_type": "display_data" } ], "source": [ - "mangrove_exp, mangrove_imp_fun_set, mangrove_haz = meas_mangrove.apply(\n", - " exp_litpop, imp_fun_set, haz\n", - ")\n", - "axes1 = imp_fun_set.plot()\n", - "axes1.set_title(\"TC: Emanuel (2011) impact function\")\n", - "axes2 = mangrove_imp_fun_set.plot()\n", - "axes2.set_title(\"TC: Modified impact function\")" + "haz.plot_intensity(0, figsize=(6, 6));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's define a second measure. Again, the numbers here are made up, for illustration only." + "### Exposure objects\n", + "Now, we read a demo expopure file containing the location and value of a number of exposed assets in Florida." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-21 14:38:24,711 - climada.entity.exposures.base - INFO - Matching 691 exposures with 1891 centroids.\n", - "2022-03-21 14:38:24,716 - climada.engine.impact - INFO - Calculating damage for 661 assets (>0) and 1049 events.\n" + "2025-01-21 15:38:13,269 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/demo/data/exp_demo_today.h5\n" ] } ], "source": [ - "meas_buildings = Measure(\n", - " name=\"Building code\",\n", - " haz_type=\"TC\",\n", - " color_rgb=np.array([0.2, 0.7, 0.5]),\n", - " cost=100000000,\n", - " hazard_freq_cutoff=0.1,\n", - ")\n", + "from climada.entity import Exposures\n", + "from climada.util.constants import EXP_DEMO_H5\n", "\n", - "meas_set.append(meas_buildings)\n", - "meas_set.check()\n", - "\n", - "buildings_exp, buildings_imp_fun_set, buildings_haz = meas_buildings.apply(\n", - " exp_litpop, imp_fun_set, haz\n", - ")" + "exp = Exposures.from_hdf5(EXP_DEMO_H5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "This measure describes an upgrade to building codes to withstand 10-year events. The measure costs 100,000,000 USD and, through `hazard_freq_cutoff = 0.1`, removes events with calculated impacts below the 10-year return period. \n", - "\n", - "The [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) describes other parameters for describing adaptation measures, including risk transfer, assigning measures to subsets of exposure, and reassigning impact functions." + "We can print some basic information about the exposure object. The central information of the exposure object is contained in a geopandas.GeoDataFrame at `exp.gdf`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the exposure object, a total amount of USD 657.05B is distributed among 50 points.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"In the exposure object, a total amount of {exp.value_unit} {exp.gdf.value.sum() / 1_000_000_000:.2f}B\"\n", + " f\" is distributed among {exp.gdf.shape[0]} points.\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can compare the 5- and 20-year return period hazard (remember: not a real return period due to the small event set!) compared to the adjusted hazard once low-impact events are removed." + "We can plot the different exposure points on a map." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-15 22:27:56,309 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 20]\n", - "2022-03-15 22:28:13,337 - climada.hazard.base - INFO - Computing exceedance intenstiy map for return periods: [ 5 20]\n", - "2022-03-15 22:28:13,911 - climada.hazard.base - WARNING - Exceedance intenstiy values below 0 are set to 0. Reason: no negative intensity values were found in hazard.\n" + "2025-01-21 15:39:38,249 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:39:38,498 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" ] }, { "data": { - "image/png": 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "haz.plot_rp_intensity(return_periods=(5, 20))\n", - "buildings_haz.plot_rp_intensity(return_periods=(5, 20));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It shows there are now very few events at the 5-year return period - the new building codes removed most of these from the event set." + "exp.plot_basemap(figsize=(6, 6));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Discount rates\n", - "\n", - "The `disc_rates` attribute is of type `DiscRates`. This class contains the discount rates for the following years and computes the net present value for given values.\n", + "### Impact Functions\n", "\n", - "See the [Discount Rates tutorial](climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", + "To model the impact to the exposure that is caused by the hazard, CLIMADA makes use of an impact function. This function relates both percentage of assets affected (PAA, red line below) and the mean damage degree (MDD, blue line below), to the hazard intensity. The multiplication of PAA and MDD result in the mean damage ratio (MDR, black dashed line below), that relates the hazard intensity to corresponding relative impact values. Finally, a multiplication with the exposure values results in the total impact.\n", "\n", - "Here we will implement a simple, flat 2% discount rate." + "Below, we read and plot a standard impact function for tropical cyclones." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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RGxSSRERERGxQSBIRERGxQSFJRERExAaFJBEREREbFJJEREREbFBIEhEREbHBxdEFiIiIiKTErl1n+fTTn9i27Vyqfo5CkoiIiKRrZrPB/Pn7+OqrJfz222JiYnbc2eKdqp+rkCQiIiLpTnw8/PILLFkCP/ywjYiIiv/aasLH5xmefroW69YNSbUaFJJEREQkXfj778t8+ulyfvppCefP5yM+fvSdLeUxmfITFFSa2rUb0atXfUqVCiY6Ohp/f4UkERERyYQ2bPibceOWsGHDYq5e/RVIvLMllFy5RtKokRONGjlTpcoxfH2d07Q2hSQRERFJM2YzbNt2+zTa+PGNuHbtJ6vt7u4lKFeuIR06NKJdOxPOllyUtgEJFJJEREQklcXGJvDJJ2uYNWsxly+PIzLS7c6WIoAz2bOHUaVKI7p3b0iVKoUcWaoVhSQRERGxO7PZYN68PXzyyQz27JmN2Xz+zpbG+PnVpF49CAvrTe3aH1KwYHaH1novCkkiIiJiN3v2RNKv31TWr59BXNwBy3qTKZDixZvRs2duWrcGNzeAIIfVmRwKSSIiIvJIrl2DBQvgu+9g3brTQN87W9zJk6cR7dq1pl+/Onh5uTqyzBRTSBIREZEUi41NYPTo1UyePIPTp7ORmDjhzpayBAW1pXbtSgwZ0pT8+bM5ssxHopAkIiIiyWI2G8ydu5tPPpnBH3/M+dc8Ix8KF/6Etm29aNXKRIEC0xxZpt0oJImIiMh9nT4Nb789jZ9++iTJPKOSJZvz9tutadPGEycnBxaZChSSREREJImzZ6+xfLk7s2e7sX49GMZp4AC35xm9SPv2renbt3aGm2eUEgpJIiIiYrF48QH69v2cP/+cCUwDXgbg6afbULRocIafZ5QSCkkiIiJZXEKCmY8+Wsb48eO4fHmNZb2//1ree+9lWraEAgXyAa87rkgHUEgSERHJoq5eTeT11yewZMkX3Lp15M5aJ0JDX6Jv3x506VI5080zSgmFJBERkSzm779h/Hj49lsnrl2bARwBslG+/Ot89lkXKlUq4OAK0weFJBERkSzAbDYYN24Dn3wykbNnvwGyASZy5x5EpUrHGTu2DcHBPg6uMn1RSBIREcnErl6NpVev2cyZM47Y2L131j5D7drv0LMn1KpVL0ufUrsfhSQREZFMaNeus3TvPoEtW77GMC7eWetFsWJtGDWqPvXrO7S8DEEhSUREJBPZvh1Gj47ihx8KAzcBcHbOR+3aXRk//nUKFszu2AIzEIUkERGRDC4m5hYff7yJNWuqsnUrgD/wIn5+p3njjR4MHfoSHh76yk+pdH0WMiEhgQ8//JCCBQvi6elJoUKFGDJkCGaz2dLGMAwGDRpEaGgonp6eVKlShQMHDtxnryIiIplDbGwC7dpNxs+vMB99VI2tW//EzQ3atIGtW6cRFfUrn3zSVAHpIaXrkDRy5Ei++uorxo8fz6FDhxg1ahSjR4/miy++sLQZNWoUY8aMYfz48Wzfvp3g4GBq1qzJtWvXHFi5iIhI6klIMNOt2xz8/IoxffrrJCaexGTKRfPmxzhxAqZPh4oV3R1dZoZnMgzDcHQR99KgQQOCgoKYPHmyZV2TJk3w8vJixowZGIZBaGgoPXv2pE+fPgDExcURFBTEyJEj6dixY7I+Jzo6Gn9/f6KiovDz80uVYxEREXlUZrNB//5LGDOmP7Gx+wAwmXLSqFE/pkzpRI4cng6uMG2l9vd3uh5JqlSpEmvXruWvv/4C4I8//mDTpk3Uq1cPgPDwcCIiIqhVq5blPe7u7oSFhbFly5Z77jcuLo7o6Girl4iISHplGLBmDTz99HWGD+9wJyD5U736UM6c+ZtFi97OcgEpLaTrk5R9+vQhKiqKokWL4uzsTGJiIsOGDaN58+YAREREABAUFGT1vqCgIE6cOHHP/Y4YMYLBgwenXuEiIiJ2MnXqLqZPL8vGjSbAF1fXwZQvf5aZM9+jUCFdqZaa0vVI0rx585g5cyazZ89m165dTJ8+nU8++YTp06dbtTOZTFbLhmEkWfdv/fr1IyoqyvI6depUqtQvIiLysObO3U2uXPXp0KEcGzcuwc0NuneHU6e6smXLcAWkNJCuR5Lee+89+vbty6uvvgpAyZIlOXHiBCNGjKBt27YEBwcDt0eUQkJCLO+LjIxMMrr0b+7u7ri7a0KbiIikPz//fIhOnQZw+vSPd9Y4U778AebPf5F8+RxaWpaTrkeSYmJicPrPvdKdnZ0ttwAoWLAgwcHBrF692rI9Pj6ejRs38txzz6VprSIiIo/il1/CKVy4HQ0alLgTkEzkz9+CVasOsX37+wpIDpCuR5IaNmzIsGHDyJcvH8WLF2f37t2MGTOGDh06ALdPs/Xs2ZPhw4dTpEgRihQpwvDhw/Hy8qJFixYOrl5EROTBzp6FYcNg4sRmGMYOAIKDX2T8+I9o0qSkg6vL2tJ1SPriiy/o378/nTt3JjIyktDQUDp27MiAAQMsbXr37s3Nmzfp3LkzV65c4ZlnnmHVqlX4+vo6sHIREZH7O3z4IhMnevH1117ExgL0J0eO8XzyyVDat3/a0eUJ6fw+SWlF90kSEZG0cvp0NK1ajWHjxjHAh0BvnnsOhg41qFr13hcdSVJZ+j5JIiIimYXZbPD229+TP39RNm4cDFzDx2cVS5cabNqEAlI6pJAkIiKSyn75JZygoHqMHfsKZvM5XF2L0KvXD1y5sor69U3c56414kDpek6SiIhIRnbrFrRtO485c9oDNwE3qlR5n4UL+5Atm4ejy5MHUEgSERFJBZs3Q8eOcOBAKSCBbNmqMnv2ROrWfcLRpUky6XSbiIiIHR07dpnq1b+nUiU4cAACA5/ko4+2cenSWgWkDEYjSSIiInZgNht07Tqbr756G8O4BBTmtdeeYuRICAgo4+jy5CEoJImIiDyi1auP8Oqrnbl8eQ0Abm5P8sUXibz5poMLk0eikCQiIvKQoqPjeOmlkaxfPxyIAzyoWbM/Cxa8i4+Pm6PLk0ekkCQiIvIQNmwwqFu3CrGxvwEQEFCL77+fQLVqjzm4MrEXTdwWERFJgYsXoX372zd/jI1ti5NTEN26zSEycoUCUiajkSQREZFkMJsN3nhjGj/8EMq1a7UxmeDNN9+kX7/m5M/v7+jyJBUoJImIiDzAsmV/0qJFJ6KiNgL5KVHiAJMmeVOxohOggJRZKSSJiIjcQ3R0HA0aDOPXXz8GbgFe1KvXhR9+cMPLy9HVSWpTSBIREbFh7dqjNGz4Cjdv7gIgV676zJ//JZUq5XdwZZJWNHFbRETkP8aO/ZsaNZ7i5s1dmEwB9Or1A+fO/aSAlMVoJElEROSOmBjo0QO+/bYgUB9//7OsWTOb8uVzO7o0cQCNJImIiABLlhykXLkrfPstmEwm+vWbTETEWgWkLEwjSSIikqWZzQavvTaVadO6AnUICprPrFkmqlfXzOysTiFJRESyrLNnr/H8829x/PgsAHLkuM6WLTEUKuTt4MokPdDpNhERyZLmzt1NgQLl7gQkZ2rXHsH58ysUkMRCIUlERLIUs9nglVe+pHnzity6dQRn57xMmLCRFSv64uKir0X5h063iYhIlnH1KrRte40lS0YC8QQFNeTXX6dSpEiAo0uTdEghSUREsoTff4dXX4Xjx/1wdp5Lw4bbmD+/B05OJkeXJumUQpKIiGRqCQlm/ve/z1i2LAdmc3sKFYJ5856jfPnnHF2apHMKSSIikmkdPnyRypXbcuHCMsCTevWqMXt2fvz1TFpJBs1QExGRTOmLL36hWLEydwKSOy1afMZPP+VTQJJk00iSiIhkKvHxidSrN4K1awcCZtzcnmDGjHk0a1ba0aVJBqOQJCIimcaZM4mUKlWPy5dXAVCoUBs2b/6S4GAfB1cmGZFOt4mISKawYweUK+fM5cvPAF68/vo0jh2broAkD00hSUREMrzFixMJC4Pz56FYsQGsWLGXSZPaOrosyeAUkkREJENr0eIrXnopjJiYGGrVgq1bXahd+zFHlyWZgOYkiYhIhpSQYKZSpff5/feRADz77HcsXdoJV1cHFyaZhkKSiIhkONHRcZQq1Z4TJ+YAUK3aR6xe3REnnR8RO9KPk4iIZCjh4VfIl6/2nYDkwhtvTGft2g/1eBGxO4UkERHJMDZvPkHRos8TFbUR8GPkyBV8800bR5clmZROt4mISIawcye89NIt4uMv4OSUm++/X06TJiUdXZZkYgpJIiKS7i1bBs2awY0bhSlceAWzZwdRoUIeR5clmZxCkoiIpGutW09i9uzcmM31qFEDfvyxnJ6/JmlCIUlERNIls9mgcuUP2bJlOOBN48Z7mTOnEG5ujq5MsgqFJBERSXeuX4+nZMkOHD8+C4AXXniXH34oqEv8JU3px01ERNKVEyeukjdvnTsByYX27aewceMgXeIvaU4hSURE0o2tW0/yxBOVuHp1PeDDsGE/M2VKe0eXJVmUTreJiEi6sGcP1Kz5JXFxB3ByCmX27J955ZUyji5LsjCFJBERcbiVK6FpU7hxYxgBAbH8/PO7PPNMXkeXJVmcTreJiIhD9ey5nHr1Erh+HapVc+Ho0XEKSJIuKCSJiIhDmM0GL7wwgHHj6mE2d6FVK4PlyyFbNkdXJnKbTreJiEiaS0gwU7z4m/z112QAnn8+J9Ono0v8JV3Rj6OIiKSphAQzJUt2uhOQnGjd+hs2bRqqS/wl3VFIEhGRNGM2G5Qt250//5wEONG58wy+++4NR5clYpNCkoiIpAnDgKef7sP+/V8CJl5/fQpfftnC0WWJ3JNCkoiIpDrDgD59YOfOyoA7bdp8w6RJbR1dlsh9aeK2iIikKsOA/v1h9GiAhgwffpR+/fI4uiyRB1JIEhGRVFW//ucsX14feIzPP4du3RSQJGPQ6TYREUk1dep8zPLlPYAwhg69Srdujq5IJPkUkkREJFU0bPgpK1f2A6B27a588EE2xxYkkkIKSSIiYndNm37B0qXvAlC16mBWrOjr4IpEUi7dh6QzZ87QqlUrAgIC8PLyokyZMuzcudOy3TAMBg0aRGhoKJ6enlSpUoUDBw44sGIRkaytefOvmD+/OwCVKn3IunUDHFyRyMN5qInbp06d4vjx48TExJAzZ06KFy+Ou7u7vWvjypUrPP/881StWpXly5eTK1cujh07RrZ/Pdhn1KhRjBkzhmnTpvH4448zdOhQatasyeHDh/H19bV7TSIicm+dOv3A3LlvAfDMM33YuHGIgysSeXgmwzCM5DQ8ceIEX331FXPmzOHUqVP8+21ubm5UrlyZN998kyZNmuBkp4fv9O3bl82bN/Prr7/a3G4YBqGhofTs2ZM+ffoAEBcXR1BQECNHjqRjx47J+pzo6Gj8/f2JiorCz8/PLrWLiGQ106dDu3aXgJqULVuFHTs+1aNGJFWl9vd3stJMjx49KFmyJEeOHGHIkCEcOHCAqKgo4uPjiYiIYNmyZVSqVIn+/ftTqlQptm/fbpfilixZQvny5Xn55ZfJlSsXZcuWZdKkSZbt4eHhREREUKtWLcs6d3d3wsLC2LJlyz33GxcXR3R0tNVLREQe3uzZ0L49QABvvPGLApJkCsk63ebm5saxY8fImTNnkm25cuWiWrVqVKtWjYEDB7Js2TJOnDhBhQoVHrm4v//+m4kTJ9KrVy/ef/99tm3bRvfu3XF3d6dNmzZEREQAEBQUZPW+oKAgTpw4cc/9jhgxgsGDBz9yfSIiAr16/cDYsZcwjE68+SZMnOiDnU4oiDhUsk+3OYKbmxvly5e3GhXq3r0727dvZ+vWrWzZsoXnn3+es2fPEhISYmnzxhtvcOrUKVasWGFzv3FxccTFxVmWo6OjyZs3r063iYikUJ8+Cxk16mUgkdq1V7NsWQ0FJEkzqX267ZHuuH3x4kV+//13EhMTqVChglVQsYeQkBCKFStmte7JJ59k/vz5AAQHBwMQERFh9dmRkZFJRpf+zd3dPVUmmouIZCUDBixl1KhXgEQKFmzNkiVVFZAkU3noH+f58+dTuHBhBg8ezMCBA3nssceYOnWqPWvj+eef5/Dhw1br/vrrL/Lnzw9AwYIFCQ4OZvXq1Zbt8fHxbNy4keeee86utYiIyD+GDl3BRx81AW6RL9+rHDo0FTc3Z0eXJWJfRjJdu3bNarlkyZLG4cOHLctLly41QkJCkru7ZNm2bZvh4uJiDBs2zDhy5Igxa9Ysw8vLy5g5c6alzccff2z4+/sbCxYsMPbt22c0b97cCAkJMaKjo5P9OVFRUQZgREVF2bV+EZHMaOTI1Qa4G4CRO3cTIybmlqNLkiwqtb+/kz2SVK5cORYvXmxZdnFxITIy0rJ8/vx53Nzc7BjfoEKFCixcuJA5c+ZQokQJPvroI8aOHUvLli0tbXr37k3Pnj3p3Lkz5cuX58yZM6xatUr3SBIRSQVz5oTTp08jII7g4Bc5fHgOnp56VrpkTsmeuH38+HE6d+6Mu7s7X375JceOHePVV18lMTGRhIQEnJycmDZtGvXq1Uvtmu1O90kSEXmwTZugTh24ceMjcub8jaNHF+Dnp/md4jjpZuJ2gQIFWLZsGbNnzyYsLIwePXpw9OhRjh49SmJiIkWLFsXDw8PuBYqIiONt3w5168KNG1CzZn8WLEjAx0cjSJK5pXjidosWLdi2bRu7d++mSpUqmM1mypQpo4AkIpJJbd58ghdeaM/169epWhUWLUIBSbKEFP2UL1++nIMHD1K6dGkmT57Mhg0baNGiBfXq1WPIkCF4enqmVp0iIuIAJ09GUb16A+Li9pMtWwKLF8/Ay8vRVYmkjWSPJPXu3Zt27dqxfft2OnbsyEcffUSVKlXYvXs37u7ulClThuXLl6dmrSIikoZu3kzgqadeIS5uP05OIaxYMRxdEyNZSbInbgcGBrJy5UrKlSvH5cuXqVixIn/99Zdl+4EDB+jYsSObNm1KtWJTiyZui4hYM5sNSpbszMGDXwFezJjxC61alXN0WSJW0sUDbgG8vLwIDw8H4NSpU0nmIBUvXjxDBiQREUnqpZc+uxOQTPTrN1sBSbKkZIekESNG0KZNG0JDQwkLC+Ojjz5KzbpERMRB+vVbxE8/vQvAiy9+yvDhLzq4IhHHSNEDbi9dusTff/9NkSJFyJYtWyqWlbZ0uk1E5LYdO6BSpZ3ExTWgePH/sXfvlzg5mRxdlohN6eY+SQABAQEEBATYvQgREXG8kyehYUOIiytHlSq7WL48pwKSZGnJOt3WqVMnTp06lawdzps3j1mzZj1SUSIikrZOn46mWrVdRERAyZKweHEIHh66F5Jkbcn6F5AzZ05KlCjBc889R6NGjShfvjyhoaF4eHhw5coVDh48yKZNm5g7dy65c+fmm2++Se26RUTETmJjEyhb9hUuXtxAtmzzWLq0EZp5IJKCOUmRkZFMnjyZuXPnsn//fqttvr6+1KhRgzfffJNatWqlSqGpSXOSRCSrMpsNSpXqwoEDEwFPpk//hTZtyju6LJFkSe3v7xRN3L7r6tWrnDhxgps3bxIYGMhjjz2GyZRxz1srJIlIVvXSS5+xeHEvwESfPgv4+OOXHF2SSLKlq4nbd2XLli1TXd0mIpIVvf/+YhYvfgeABg1GKyCJ/EeKH3ArIiIZ38yZOxkxogVgUKxYpzujSSLybwpJIiJZzKlT8OabXwMxBATUZseOL3Spv4gNur5TRCQLuXYNGjSAmzcnEhRUmO3bO+Hpqa8CEVv0L0NEJIuIi0ukWTMn9u41ERTkzO+/9yZvXkdXJZJ+pfh0W7Vq1bh69WqS9dHR0VSrVs0eNYmIiJ2ZzQbly3dnxYr2eHjE89NPkD+/o6sSSd9SHJI2bNhAfHx8kvWxsbH8+uuvdilKRETsq0mTcezfPwH4jr59N1OhgqMrEkn/kn26be/evZY/Hzx4kIiICMtyYmIiK1asIHfu3PatTkREHtkHHyxh0aLbV6/Vrz+KgQOrOrgikYwh2SGpTJkymEwmTCaTzdNqnp6efPHFF3YtTkREHs2sWbsYPrw5YFC06JssWfKOo0sSyTCSHZLCw8MxDINChQqxbds2cubMadnm5uZGrly5cHZ2TpUiRUQk5bZvP02bNg2BGHLkqMnOneN1qb9ICiQ7JOW/M8PPbDanWjEiImIfUVFmwsL+h9l8Fnf34uze/QNeXq6OLkskQ0nxxO0RI0YwZcqUJOunTJnCyJEj7VKUiIg8vMREaNnSiZs3P8bZ+QnWrFlKvnz+ji5LJMNJcUj6+uuvKVq0aJL1xYsX56uvvrJLUSIi8vAGD4affwYPj+r88ssBKlUq4OiSRDKkFIekiIgIQkJCkqzPmTMn586ds0tRIiLycD7+eAMffXQUgEmT4LnnNFdU5GGlOCTlzZuXzZs3J1m/efNmQkND7VKUiIik3KZNx3n//cZAeZo23U2rVo6uSCRjS/FjSV5//XV69uzJrVu3LLcCWLt2Lb179+add3RpqYiII1y9Gkvt2k0xjCt4ez/N5MnFHF2SSIaX4pDUu3dvLl++TOfOnS133vbw8KBPnz7069fP7gWKiMiDPfdcT2JidmIyBbB69Q/4+bk7uiSRDM9kGIbxMG+8fv06hw4dwtPTkyJFiuDunnH/QUZHR+Pv709UVBR+fn6OLkdEJEXeeGM6337bDjAxdOhyPvigtqNLEkkTqf39neKRpLt8fHyooIf/iIg41A8/7OXbbzsBUKXKIAUkETt6qJC0fft2fvjhB06ePJnkYbcLFiywS2EiInJ/UVHQocNIIJbAwDqsXv2ho0sSyVRSfHXb3Llzef755zl48CALFy7k1q1bHDx4kHXr1uHvr5uViYikBcOAdu3g+vWp+Pn1Z8uWmbi4pPhXuojcR4r/RQ0fPpzPPvuMpUuX4ubmxrhx4zh06BDNmjUjX758qVGjiIj8xyefwKJFt5+duWbNEIoUCXB0SSKZTopD0rFjx6hfvz4A7u7u3LhxA5PJxNtvv80333xj9wJFRMTa2LEb6d27H5DAuHGg6aEiqSPFISlHjhxcu3YNgNy5c7N//34Arl69SkxMjH2rExERK3v2nOOdd14BPqZs2U/p2NHRFYlkXimeuF25cmVWr15NyZIladasGT169GDdunWsXr2a6tWrp0aNIiICxMTcIiysGWbzeTw8SrJqVTdMJkdXJZJ5pTgkjR8/ntjYWAD69euHq6srmzZtonHjxvTv39/uBYqIyG0vvNCP6OhNgB9LlswnMNDL0SWJZGoPfTPJzEQ3kxSR9O7dd+fz6adNAejdewEjR/7PwRWJOF66vJlkYmIiCxcu5NChQ5hMJp588klefPFFXFwe+t6UIiJyDytX/sWnn7YHoHz5dxWQRNJIilPN/v37efHFF4mIiOCJJ54A4K+//iJnzpwsWbKEkiVL2r1IEZGs6sYNePPNv4BE/P1f4NdfRzi6JJEsI8VXt73++usUL16c06dPs2vXLnbt2sWpU6coVaoUb775ZmrUKCKSJRkGdOoEJ082ICBgGxs2zMXDQyP2Imklxf/a/vjjD3bs2EH27Nkt67Jnz86wYcP0LDcRETv68stbzJzpirMzLFhQnDJlHF2RSNaS4pGkJ554gvPnzydZHxkZSeHChe1SlIhIVjd16ja6dXsc2MTHH8MLLzi6IpGs56EeS9K9e3d+/PFHTp8+zenTp/nxxx/p2bMnI0eOJDo62vISEZGU++uvS7zxxsvAcXLn/px33nF0RSJZU4pvAeDk9E+uMt25i9ndXfx72WQykZiYaK86U5VuASAi6UV8fCKhofW5dGklrq6FOXp0B/ny6eHhIraku1sArF+/3u5FiIjIbbVqDeXSpZWAJ7Nnz1dAEnGgFIeksLCw1KhDRCTLGzp0BRs3DgagY8evadq0lIMrEsnaHupa0tjYWPbu3UtkZCRms9lqW6NGjexSmIhIVrJ58wkGDGgJGBQr1omvvmrt6JJEsrwUh6QVK1bQpk0bLl68mGRbRpqHJCKSXsTFQbduOTCManh5HWfr1rGOLklEeIir27p27crLL7/MuXPnMJvNVi8FJBGRlOvVC3bv9iV79u/ZunUNfn7uji5JRHiIkBQZGUmvXr0ICgpKjXpERLKUiRPDmTDh9hXCs2aZKFVKE7VF0osUh6SmTZuyYcOGVChFRCRr2br1JF26PAU0pUePaOrWdXRFIvJvKb5PUkxMDC+//DI5c+akZMmSuLq6Wm3v3r27XQtMC7pPkoiktdjYBIKCqhEd/Sve3hWIiNiEj4+bo8sSyVDS3X2SZs+ezcqVK/H09GTDhg2WG0jC7YnbGTEkiYiktbp1hxEd/Svgy5IlcxSQRNKhFJ9u+/DDDxkyZAhRUVEcP36c8PBwy+vvv/9OjRotRowYgclkomfPnpZ1hmEwaNAgQkND8fT0pEqVKhw4cCBV6xAReRTjx//Khg1DAOjc+SuqVXvMwRWJiC0pDknx8fG88sorVo8nSQvbt2/nm2++oVQp65urjRo1ijFjxjB+/Hi2b99OcHAwNWvW5Nq1a2lan4hIchw7dpmePVsCZh57rC1fftnC0SWJyD2kOOm0bduWefPmpUYt93T9+nVatmzJpEmTyJ49u2W9YRiMHTuWDz74gMaNG1OiRAmmT59OTEwMs2fPTtMaRUQexDAgLKwTiYmncHUtwi+/fOHokkTkPlI8JykxMZFRo0axcuVKSpUqlWTi9pgxY+xW3F1dunShfv361KhRg6FDh1rWh4eHExERQa1atSzr3N3dCQsLY8uWLXTs2NHutYiIPKxvvoEzZ7oBu5gyZQ6hob6OLklE7iPFIWnfvn2ULVsWgP3791tt+/ckbnuZO3cuu3btYvv27Um2RUREACS5Z1NQUBAnTpy45z7j4uKIi4uzLEdHR9upWhER2/bvh9vTKSszatSftGr1UE+FEpE0lOJ/pevXr0+NOmw6deoUPXr0YNWqVXh4eNyz3X/DmWEY9w1sI0aMYPDgwXarU0Tkfi5fvsn//neK2NjHqVMH3nlHAUkkI0jb2dcptHPnTiIjIylXrhwuLi64uLiwceNGPv/8c1xcXCwjSHdHlO6KjIy87x3B+/XrR1RUlOV16tSpVD0OEcnaXnjhHY4eLYuf32ymTYM0vu5FRB5Ssv93pnHjxslqt2DBgocu5r+qV6/Ovn37rNa1b9+eokWL0qdPHwoVKkRwcDCrV6+2nAKMj49n48aNjBw58p77dXd3x91dz0YSkdTXp89CDhyYCEDfvoHoiU4iGUeyQ5K/f9o/T8jX15cSJUpYrfP29iYgIMCyvmfPngwfPpwiRYpQpEgRhg8fjpeXFy1a6LJaEXGs338/xejRrwFQocJ79OtX6wHvEJH0JNkhaerUqalZx0Pr3bs3N2/epHPnzly5coVnnnmGVatW4eurq0ZExHHi4xOpVaslhnEFL68KrFs39MFvEpF0JcXPbsuM9Ow2EbG3atWGsH79QMCXNWt2U7267qotYm+p/f2t6YMiInY2efJO1q+/fQXtW29NVEASyaB0HaqIiB1duQKDBpUG3uexx04zYUJLR5ckIg9JIUlExE4MA954A06fdqFw4Y/YuTPLz2YQydB0uk1ExE769NnC/PlxuLrC3Lng52f/pxCISNpRSBIRsYPFiw8wenR14Dk+/PAS5co5uiIReVQKSSIij+jy5Zu88sqrQCwBATl5//3sji5JROxAIUlE5BGFhb1LXNx+nJyC2LBhOi4u+tUqkhnoX7KIyCPo128R+/dPAOCjj76jRAk9d0Qks1BIEhF5SL//foqRIzsAtx878v77euyISGaikCQi8hASE6FevS53HjtSXo8dEcmEdJ8kEZGHMHw4XL48FmfnmyxZMhEfHzdHlyQidqaQJCKSQps3w6BBAIWYNm011as7uCARSRU63SYikgLh4Vdo3HgtZjO0bg2tWjm6IhFJLQpJIiLJZDYbVK78BpGRNQgM/Iwvv3R0RSKSmhSSRESSqV27bzlzZj7gwiefVMLX19EViUhqUkgSEUmGpUsPMWNGDwDq1RtO27YVHFyRiKQ2hSQRkQe4ejWWl19+FbhJjhw1Wbz4HUeXJCJpQCFJROQBqlTpQ2zsXkymnKxdq8eOiGQV+pcuInIf48bt5o8/Pgdg4MBplCkT4uCKRCSt6D5JIiL3cO4cDB1aFviOChX+ZODAeo4uSUTSkEKSiIgNZjO0aQMXL0KZMq359VdHVyQiaU2n20REbGjXbiFr1lzEywvmzAF3d0dXJCJpTSFJROQ/pk7dxowZzYDSDBlyjqJFHV2RiDiCQpKIyL+cORPNm282BxLIk6cSb78d7OiSRMRBFJJERP6lcuUuJCT8jbNzfjZt+honJ5OjSxIRB1FIEhG5o1OnGYSHzwSc+OKLWeTPn83RJYmIAykkiYgAa9ce5euvOwNQteog3nrreQdXJCKOppAkIllefDy8/HI/4Dr+/i+wYsX7ji5JRNIB3SdJRLK8AQPgypVvcXPLzsqV/XFzc3Z0SSKSDigkiUiWtmYNjBoF4M+cOd/wzDOOrkhE0gudbhORLOvQoQs0aTIZwzDo2BEaN3Z0RSKSnigkiUiWZDYbVKnSnujo18mR4z3GjHF0RSKS3igkiUiW1KzZeCIjfwbc+frrNnh5OboiEUlvFJJEJMv5/vs/mD//XQCaNv2Epk1LObgiEUmPFJJEJEuJjLxB69avAvEEBTVk3rwuji5JRNIphSQRyVLCwt4mPv5PnJxC2Lhxih47IiL3pJAkIlnGuHGH+fPPKYCJUaNm8sQTgY4uSUTSMd0nSUSyhJMnYdCgJ4CNVK/+O++8U83RJYlIOqeQJCKZXkICtGwJV6/CM888z/Llei6biDyYTreJSKbXuPG3bNp0EF9fmD0bXF0dXZGIZAQaSRKRTG3ChE389FNHwJ0hQ/ZTqFAhR5ckIhmERpJEJNM6cuQS3bu3BMwUKvQyPXsqIIlI8ikkiUimFB+fyHPPtSIx8SSuroX59dfxji5JRDIYhSQRyZRq1x7KxYsrAE9mz55PaKivo0sSkQxGIUlEMp2hQ1ewYcNgADp2/FqPHRGRh6KQJCKZyvHjMGTIt4BBsWKd+Oqr1o4uSUQyKF3dJiKZRmwsNG0Kt27NJV++iWzd+qajSxKRDEwhSUQyje7dYedOCAhw4ddfu+Hn5+iKRCQj0+k2EckUOnSYyqRJXYB4Zs+GfPkcXZGIZHQaSRKRDG/OnN1MndoZiOXFF8tSq9brji5JRDIBjSSJSIYWHn6FNm2aALHkylWfH3/s4OiSRCSTUEgSkQwrIcFMxYqtSUgIx8WlIL/9NgMXF/1aExH70G8TEcmw6tQZTmTkz4AH3303n4IFszu6JBHJRBSSRCRD+vjj1axdOwCA9u0n0Lx5WQdXJCKZjSZui0iGc/IkDB8eD/jyxBPNmDKlvaNLEpFMSCFJRDKUuLjbN4y8dq0+JUrs5tdfQx1dkohkUun6dNuIESOoUKECvr6+5MqVi5deeonDhw9btTEMg0GDBhEaGoqnpydVqlThwIEDDqpYRFLbW29dYft2yJ4dfvqpENmyeTi6JBHJpNJ1SNq4cSNdunTht99+Y/Xq1SQkJFCrVi1u3LhhaTNq1CjGjBnD+PHj2b59O8HBwdSsWZNr1645sHIRSQ0dO85g6tQiwGpmzYICBRxdkYhkZibDMAxHF5FcFy5cIFeuXGzcuJEXXngBwzAIDQ2lZ8+e9OnTB4C4uDiCgoIYOXIkHTt2TNZ+o6Oj8ff3JyoqCj89x0AkXfrxx728/HJF4CZhYQPZsGGQo0sSEQdL7e/vdD2S9F9RUVEA5MiRA4Dw8HAiIiKoVauWpY27uzthYWFs2bLFITWKiP2dOHGV5s0bAzcJDKzDmjUDHF2SiGQBGWbitmEY9OrVi0qVKlGiRAkAIiIiAAgKCrJqGxQUxIkTJ+65r7i4OOLi4izL0dHRqVCxiNjD7RtGtiMh4RjOzvnZsmWmbhgpImkiw/ym6dq1K3v37mXOnDlJtplMJqtlwzCSrPu3ESNG4O/vb3nlzZvX7vWKiH00aDCKiIjFgBtTpvxIkSIBji5JRLKIDBGSunXrxpIlS1i/fj158uSxrA8ODgb+GVG6KzIyMsno0r/169ePqKgoy+vUqVOpU7iIPJJx4zazcuUHALRuPZ42bco7uCIRyUrSdUgyDIOuXbuyYMEC1q1bR8GCBa22FyxYkODgYFavXm1ZFx8fz8aNG3nuuefuuV93d3f8/PysXiKSvpw+DUOHPgW0oXDh9kyb9rqjSxKRLCZdz0nq0qULs2fPZvHixfj6+lpGjPz9/fH09MRkMtGzZ0+GDx9OkSJFKFKkCMOHD8fLy4sWLVo4uHoReVjx8fDyy3DxoielSk1h06ZEnJzufQpdRCQ1pOuQNHHiRACqVKlitX7q1Km0a9cOgN69e3Pz5k06d+7MlStXeOaZZ1i1ahW+vr5pXK2I2EvTpiv47bda+Ps7sWCBCV/fdP2rSkQyqQx1n6TUovskiaQfXbrMYcKEFkB9Fi1azIsvOju6JBFJp3SfJBHJMhYu3M+ECbfnHj3/fBkFJBFxKIUkEUkX9uw5R7NmjYAYcuSowbp1gx1dkohkcQpJIuJwJ05c5dln65CQEI6LSyE2bZqNm5tGkUTEsTQbUkQc6vLlm5Qu/SKxsXtxcgpi5cpVPPlkTkeXJWIXiYmJ3Lp1y9FlZFiurq44Ozvuf5gUkkTEYRISoHHjfURFbQf8mD17BdWqPeboskQemWEYREREcPXqVUeXkuFly5aN4ODg+z5JI7UoJImIQxgGdOoEGzc+javrWkaNiueVV8o4uiwRu7gbkHLlyoWXl5dDvuAzOsMwiImJITIyEoCQkJA0r0EhSUQc4p13LjN5cg6cnGDevGf53/8cXZGIfSQmJloCUkCAnjX4KDw9PYHbjxvLlStXmp9608RtEUlzjRuP47PPngR28dVXKCBJpnJ3DpKXl5eDK8kc7vajI+Z2KSSJSJrq3HkWCxf2BCKpXXstb7zh6IpEUodOsdmHI/tRIUlE0szQoSuYOLEdAGXK9GDZsncdW5CIyH0oJIlImpg8+Xf6928CJJA/fwu2bx+jh9aKSLqmkCQiqW7p0kO88UY9IIaAgNrs3z8VFxf9+hFJb9q1a4fJZKJTp05JtnXu3BmTyWR5wPzdtiaTCVdXV4KCgqhZsyZTpkzBbDZbvbdAgQKWtp6enhQoUIBmzZqxbt26tDish6bfUiKSqk6dgmbN+mMYl/H2fpr9+3/Ex8fN0WWJyD3kzZuXuXPncvPmTcu62NhY5syZQ758+aza1qlTh3PnznH8+HGWL19O1apV6dGjBw0aNCAhIcGq7ZAhQzh37hyHDx/mu+++I1u2bNSoUYNhw4alyXE9DN0CQERSzaVLULs23Lw5nWzZgvjtt8EEB/s4uiyRNGcYEBPjmM/28oKUzH1+6qmn+Pvvv1mwYAEtW7YEYMGCBeTNm5dChQpZtXV3dyc4OBiA3Llz89RTT1GxYkWqV6/OtGnTeP311y1tfX19LW3z5cvHCy+8QEhICAMGDKBp06Y88cQTj3ik9qeRJBFJFdHRiTRoAIcOQe7c3vzxx5c88USgo8sScYiYGPDxcczrYcJZ+/btmTp1qmV5ypQpdOjQIVnvrVatGqVLl2bBggUPbNujRw8Mw2Dx4sUpLzINKCSJiN3FxNyicOGG/PbbR2TLZrByJfxnlF5E0rHWrVuzadMmjh8/zokTJ9i8eTOtWrVK9vuLFi3K8ePHH9guR44c5MqVK1ltHUGn20TErhISzJQo0YELF5YDG5g0qSXFixd64PtEMjMvL7h+3XGfnVKBgYHUr1+f6dOnYxgG9evXJzAw+SPBhmEk+/5GKWmb1hSSRMRuzGaDZ555l/DwmYAzgwb9SNOmCkgiJhN4ezu6ipTp0KEDXbt2BeDLL79M0XsPHTpEwYIFH9ju0qVLXLhwIVltHUGn20TEburXH8WuXZ8B0LHjVAYOrOfgikTkYdWpU4f4+Hji4+OpXbt2st+3bt069u3bR5MmTR7Ydty4cTg5OfHSSy89QqWpRyNJImIX7dtPYcWKvgA0avQpX33V2sEVicijcHZ25tChQ5Y/2xIXF0dERASJiYmcP3+eFStWMGLECBo0aECbNm2s2l67do2IiAhu3bpFeHg4M2fO5Ntvv2XEiBEULlw41Y/nYSgkicgj+/rrY0yb9iYAzzzTm8WLezm4IhGxBz8/v/tuX7FiBSEhIbi4uJA9e3ZKly7N559/Ttu2bXFysj5ZNWDAAAYMGICbmxvBwcFUrFiRtWvXUrVq1dQ8hEdiMgzDcHQRjhYdHY2/vz9RUVEP/IEQEWu//gq1akFs7GQef/w3Dh36Ro8bkSwtNjaW8PBwChYsiIeHh6PLyfDu15+p/f2tOUki8tD27oWGDSE2Fho1eo0DByYpIIlIpqGQJCIPZeXKv3j66UZERV2gUiWYOxdcdAJfRDIRhSQRSbHRo9dSt25F4uJ+ws+vE0uWgKeno6sSEbEv/X+fiKTIq69OYN687kAi3t7PsHnzBLJnd3RVIiL2p5AkIsly82YCTz/dk/37b99UrkCBluze/S3ZsmliqohkTjrdJiIPFB5+hTx56t4JSCZq1x7BsWMzFJBEJFNTSBKR+zp8GGrUgMuXTwDe9Ou3kBUr+uoqNhHJ9HS6TUTuadUqaNYMoqKyExLyE+PGxfHyy6UcXZaISJpQSBKRJMxmg2bNxrNggQuG8RbPPQcLFjxBUJCjKxMRSTs63SYiVmJiblGixFvMn98dw+hGw4b7WbcOBSSRLKBdu3aYTCZMJhOurq4UKlSId999lxs3bljavPnmmzg7OzN37tz77qtWrVo4Ozvz22+/pXbZqUYhSUQsjhy5RO7ctTh06GvARP36H7NoUXHc3R1dmYiklTp16nDu3Dn+/vtvhg4dyoQJE3j33XcBiImJYd68ebz33ntMnjz5nvs4efIkW7dupWvXrvdtl94pJIkIAEuXHqJYsWe4enUD4EP//ktYuvRdTdAWyWLc3d0JDg4mb968tGjRgpYtW7Jo0SIAfvjhB4oVK0a/fv3YvHkzx48ft7mPqVOn0qBBA9566y3mzZtnNRKVkSgkiQhDh66gYcOKJCQcw8WlAPPnb2XIkAaOLksk8zAMuHHDMa9HfI69p6cnt27dAmDy5Mm0atUKf39/6tWrx9SpU20cqsHUqVNp1aoVRYsW5fHHH+f7779/pBocRRO3RbIww4Bx42DAgH1ANH5+lfntt/k8+WROR5cmkrnExICPj2M++/p18PZ+qLdu27aN2bNnU716dY4cOcJvv/3GggULAGjVqhXdu3dn4MCBODn9M+ayZs0aYmJiqF27tqXd5MmTad++/aMfSxrTSJJIFhUfD2++CW+/DYbxLpUqTeHMmTUKSCJZ3NKlS/Hx8cHDw4Nnn32WF154gS+++ILJkydTu3ZtAgMDAahXrx43btxgzZo1Vu+fPHkyr7zyCi53nnjdvHlzfv/9dw4fPpzmx/KoNJIkkgUdPnyRKlXeJyLiU5ycfBk92sTbb7fHpOlHIqnDy+v2iI6jPjsFqlatysSJE3F1dSU0NBRXV1cSExP57rvviIiIsIQfgMTERCZPnkytWrUAuHz5MosWLeLWrVtMnDjRqt2UKVMYOXKkfY4pjSgkiWQxixcfoGnThiQkhOPicoPFi2dRr56jqxLJ5Eymhz7llda8vb0pXLiw1bply5Zx7do1du/ejbOzs2X9n3/+ScuWLbl06RIBAQHMmjWLPHnyWCZ637V27VpGjBjBsGHDrEJWeqfTbSJZyKBBP/PSS8/eCUiFmD//AwUkEXmgyZMnU79+fUqXLk2JEiUsryZNmpAzZ05mzpxpade0aVOrNiVKlKBDhw5cvXqVn3/+2cFHkjIKSSJZQGxsAnXrjmTw4IbANfz9wzhwYBuNGhVzdGkiks6dP3+en3/+mSZNmiTZZjKZaNy4MZMnT2bnzp388ccfNtv5+vpSq1atDHfPJJNhPOK1gZlAdHQ0/v7+REVF4efn5+hyROzGMODLL//knXdeIj7+9qTJokXfYPv28fj4uDm4OpHMKTY2lvDwcAoWLIiHh4ejy8nw7tefqf39rZEkkUxq82aoVAm6dctHfPw1TKZAWrb8mgMHvlZAEhFJBoUkkUxm6dJDPPbY21SqZGbLFvD09KJ9+8WcOHGMmTPf1B20RUSSKeNMMReR+9q16ywtWgzk8OEpgBmT6SneeKM1AwdCaGh5R5cnIpLhKCSJZHAnT0bRvPkotmz5DLgJQEjIS3z7bQVduSYi8gh0uk0kg4qJSeR//xtLgQKPsWXLcOAmvr7PMWHCJs6eXUi9ekUdXaKISIamkCSSwZjNMGsWFCvmxKJF8zCMS7i5FaVfv0VcvbqJt9563tEliohkCjrdJpKBjBy5htmzK7B3rz9gIiBgDPXq7eebb9rj4aF/ziIi9qTfqiIZwOzZu+nWrQ+XL68GPsDPbyh9+kCPHs/i7f2so8sTEcmUFJJE0rFffgmnTZsPOXFi9p01rpQvb2b5crjzIG4REUklmpMkkg5t336GsmV7Ehb2hCUg5c/fgvXr/2T79uEKSCIiaUAhSSQduH49nlWr4undG0qWhKef/pY9e8YBt8iRowYzZ+7k+PFZVKlSyNGlikgm1q5dO0wmE506dUqyrXPnzphMJtq1a2fV1mQy4erqSlBQEDVr1mTKlCmYzWar9xYoUMDS1tPTk6JFizJ69GjS+5PRFJJEHGTr1pO0bPk1ISEv4esbQO3aPzF6NOzfD1APX9/nGT58JZcuraZly6ccXa6IZBF58+Zl7ty53Lx507IuNjaWOXPmkC9fPqu2derU4dy5cxw/fpzly5dTtWpVevToQYMGDUhISLBqO2TIEM6dO8ehQ4d49913ef/99/nmm2/S5JgeluYkiaSRa9fimTjxF77/fjn79i0nPv6Q1XZPz400bdqEOnWgVq0KBAZuclClIpKVPfXUU/z9998sWLCAli1bArBgwQLy5s1LoULWo9nu7u4EBwcDkDt3bp566ikqVqxI9erVmTZtGq+//rqlra+vr6Xt66+/zsSJE1m1ahUdO3ZMoyNLOYUkkVR06NBN1q/3ZPlyWLPmLLGxNf+11Qlf32d5+um6tGtXh1dfLYuL/kWKZGo3bty45zZnZ2erp9zfr62TkxOenp4PbOvt7f0QVUL79u2ZOnWqJSRNmTKFDh06sGHDhge+t1q1apQuXZoFCxZYhaS7DMNg48aNHDp0iCJFijxUfWlFv5JF7Ojy5ZtMnPgLP/64nAMHlnPr1hPAkjtbC+DmVpV8+QrQoEFdunWrQaFC2R1ZroikMR8fn3tuq1evHj///LNlOVeuXMTExNhsGxYWZhVYChQowMWLF5O0e9g5P61bt6Zfv34cP34ck8nE5s2bmTt3brJCEkDRokXZu3ev1bo+ffrw4YcfEh8fz61bt/Dw8KB79+4PVV9ayTQhacKECYwePZpz585RvHhxxo4dS+XKlR1dlmRSCQlw/jycOwdr1mzi9993sHnzKi5c2MDd56fddp5KlRKoV8+FunWhVKl1OGkmoIikc4GBgdSvX5/p06djGAb169cnMAWX1RqGgclkslr33nvv0a5dOy5cuMAHH3xAtWrVeO655+xdul1lipA0b948evbsyYQJE3j++ef5+uuvqVu3LgcPHkwyyUzkfm7duh1+Nm06yN69RwgPP8uZM+c4f/4sV66c4/r1s8TFXcZsPg7c/QXwKbDIsg8np9wULlyXRo3q0q1bdfLlyxT/zETEDq5fv37Pbc7OzlbLkZGR92zr9J//2zp+/Pgj1WVLhw4d6Nq1KwBffvllit576NAhChYsaLUuMDCQwoULU7hwYebPn0/hwoWpWLEiNWrUsFvN9pYpfnuPGTOG1157zXLuc+zYsaxcuZKJEycyYsSIZO/nt99O4u3tm2S9t7cP2bMHAGA2mzl79tQ99+Hp6UVAQE7gdpI+c+bkPdt6eHgSGJjLsnz69Il7tnV39yBnziDL8tmzp5JcYnmXm5sbuXKFWJbPnTtNYmKizba3L9sMtSxHRJxJckXCXS4uLgQH57YsR0aeIz4+3mZbZ2dnQkLy/KttBPHxcTbbmkwmcuf+J8xeuHCeuLhYy3JiopmEBDO3biWSmGiQL98TJCZCYiKcOHGUK1cuk5CQyK1biXfa3P2vmVKlamIYTpjNcOjQ70RGniAhIZELF65w+vRZIiPPceXKOW7cuIC39zYuXTJxe3S6P7DAZr23XcHZOQfBwWAyPUdCgpmyZZ/ntdfq8r//lcDJyXSf94pIVpWSOUKp1Ta56tSpY/kdX7t27WS/b926dezbt4+33377nm2yZ89Ot27dePfdd9m9e3eSUaf0IsOHpPj4eHbu3Enfvn2t1teqVYstW7bYfE9cXBxxcf98YUdHRwNQu3bJe3zKa8C3d/58HShwn4peBebc+XPiA9o25J/5KgCPA7ZDB1QH1vxruTRw5R5tKwJb/7N85h5tSwL/Pm9cDfjrHm0fA47+a7k+sPsebYOBc/9abgpsvkdbXyD6X8ttgFX3aOsC3PrX8rvA4nu0BYgD3O78eRz//N0kdfPmZSAAFxfw9CyB2XwaX98QcuQIITg4lDx5QihUKJTHHw+hcmVfQkLg9v/4vXfnJSKSeTg7O3Po0CHLn22Ji4sjIiKCxMREzp8/z4oVKxgxYgQNGjSgTZs2991/ly5dGDlyJPPnz6dp06Z2r98eMnxIunjxIomJiQQFBVmtDwoKIiIiwuZ7RowYweDBg21sceefUyj/cHZ2xdX19p8NA+LiPJK0ucvJyRU3t+S2dbO0BYiN9eBet66y3db2vk0mN9zd/1mOi/PAMO7V1v0/bd3t1NbDqm18vBtm8736woN/XdBho60JcAacMZlcCQi4HU6cnSEqKojY2PyYTM42X0WLGri6gpMTnD1bjKioFzCZnPH09LeEn7x5QyhYMJQ6dbwpUOD24z6cnAYDtn5GRESyDj8/v/tuX7FiBSEhIbi4uJA9e3ZKly7N559/Ttu2bZOcEvyvnDlz0rp1awYNGkTjxo0f2N4RTEZ6v93lA5w9e5bcuXOzZcsWnn32nwd9Dhs2jBkzZvDnn38meY+tkaS8efMSFRX1wB8IERGR+4mNjSU8PJyCBQtaXdIvD+d+/RkdHY2/v3+qfX9n+JGkwMBAnJ2dk4waRUZGJhldusvd3R33fw9ziIiIiPxH+hvbSiE3NzfKlSvH6tWrrdavXr063V9aKCIiIulXhh9JAujVqxetW7emfPnyPPvss3zzzTecPHnS5gP6RERERJIjU4SkV155hUuXLlkenleiRAmWLVtG/vz5HV2aiIiIZFCZIiQBdO7cmc6dOzu6DBEREckkMvycJBERkfQog188nm44sh8VkkREROzI9c6N9e71cFpJmbv9eLdf01KmOd0mIiKSHjg7O5MtWzbLs9e8vLzS7WM30jPDMIiJiSEyMpJs2bLd867fqUkhSURExM6Cg4OB+z+kVpInW7Zslv5MawpJIiIidmYymQgJCSFXrlzcunXrwW8Qm1xdXR0ygnSXQpKIiEgqcXZ2duiXvDwaTdwWERERsUEhSURERMQGhSQRERERGzQniX9uVBUdHe3gSkRERCS57n5vp9YNJxWSgEuXLgGQN29eB1ciIiIiKXXp0iX8/f3tvl+FJCBHjhwAnDx5MlU6OSuJjo4mb968nDp1Cj8/P0eXk2GpH+1HfWk/6kv7UD/aT1RUFPny5bN8j9ubQhLg5HR7apa/v79+YO3Ez89PfWkH6kf7UV/aj/rSPtSP9nP3e9zu+02VvYqIiIhkcApJIiIiIjYoJAHu7u4MHDgQd3d3R5eS4akv7UP9aD/qS/tRX9qH+tF+UrsvTUZqXTcnIiIikoFpJElERETEBoUkERERERsUkkRERERsUEgSERERsSHLh6QJEyZQsGBBPDw8KFeuHL/++qujS0r3RowYQYUKFfD19SVXrly89NJLHD582KqNYRgMGjSI0NBQPD09qVKlCgcOHHBQxRnDiBEjMJlM9OzZ07JO/Zh8Z86coVWrVgQEBODl5UWZMmXYuXOnZbv6MnkSEhL48MMPKViwIJ6enhQqVIghQ4ZgNpstbdSXSf3yyy80bNiQ0NBQTCYTixYtstqenD6Li4ujW7duBAYG4u3tTaNGjTh9+nQaHkX6cL++vHXrFn369KFkyZJ4e3sTGhpKmzZtOHv2rNU+7NaXRhY2d+5cw9XV1Zg0aZJx8OBBo0ePHoa3t7dx4sQJR5eWrtWuXduYOnWqsX//fmPPnj1G/fr1jXz58hnXr1+3tPn4448NX19fY/78+ca+ffuMV155xQgJCTGio6MdWHn6tW3bNqNAgQJGqVKljB49eljWqx+T5/Lly0b+/PmNdu3aGb///rsRHh5urFmzxjh69KiljfoyeYYOHWoEBAQYS5cuNcLDw40ffvjB8PHxMcaOHWtpo75MatmyZcYHH3xgzJ8/3wCMhQsXWm1PTp916tTJyJ07t7F69Wpj165dRtWqVY3SpUsbCQkJaXw0jnW/vrx69apRo0YNY968ecaff/5pbN261XjmmWeMcuXKWe3DXn2ZpUPS008/bXTq1MlqXdGiRY2+ffs6qKKMKTIy0gCMjRs3GoZhGGaz2QgODjY+/vhjS5vY2FjD39/f+OqrrxxVZrp17do1o0iRIsbq1auNsLAwS0hSPyZfnz59jEqVKt1zu/oy+erXr2906NDBal3jxo2NVq1aGYahvkyO/36xJ6fPrl69ari6uhpz5861tDlz5ozh5ORkrFixIs1qT29sBc7/2rZtmwFYBjjs2ZdZ9nRbfHw8O3fupFatWlbra9WqxZYtWxxUVcYUFRUF/POg4PDwcCIiIqz61t3dnbCwMPWtDV26dKF+/frUqFHDar36MfmWLFlC+fLlefnll8mVKxdly5Zl0qRJlu3qy+SrVKkSa9eu5a+//gLgjz/+YNOmTdSrVw9QXz6M5PTZzp07uXXrllWb0NBQSpQooX59gKioKEwmE9myZQPs25dZ9gG3Fy9eJDExkaCgIKv1QUFBREREOKiqjMcwDHr16kWlSpUoUaIEgKX/bPXtiRMn0rzG9Gzu3Lns2rWL7du3J9mmfky+v//+m4kTJ9KrVy/ef/99tm3bRvfu3XF3d6dNmzbqyxTo06cPUVFRFC1aFGdnZxITExk2bBjNmzcH9HP5MJLTZxEREbi5uZE9e/YkbfSddG+xsbH07duXFi1aWB4WbM++zLIh6S6TyWS1bBhGknVyb127dmXv3r1s2rQpyTb17f2dOnWKHj16sGrVKjw8PO7ZTv34YGazmfLlyzN8+HAAypYty4EDB5g4cSJt2rSxtFNfPti8efOYOXMms2fPpnjx4uzZs4eePXsSGhpK27ZtLe3Ulyn3MH2mfr23W7du8eqrr2I2m5kwYcID2z9MX2bZ022BgYE4OzsnSZWRkZFJ0r7Y1q1bN5YsWcL69evJkyePZX1wcDCA+vYBdu7cSWRkJOXKlcPFxQUXFxc2btzI559/jouLi6Wv1I8PFhISQrFixazWPfnkk5w8eRLQz2RKvPfee/Tt25dXX32VkiVL0rp1a95++21GjBgBqC8fRnL6LDg4mPj4eK5cuXLPNvKPW7du0axZM8LDw1m9erVlFAns25dZNiS5ublRrlw5Vq9ebbV+9erVPPfccw6qKmMwDIOuXbuyYMEC1q1bR8GCBa22FyxYkODgYKu+jY+PZ+PGjerbf6levTr79u1jz549llf58uVp2bIle/bsoVChQurHZHr++eeT3Ibir7/+In/+/IB+JlMiJiYGJyfrrwZnZ2fLLQDUlymXnD4rV64crq6uVm3OnTvH/v371a//cTcgHTlyhDVr1hAQEGC13a59maJp3pnM3VsATJ482Th48KDRs2dPw9vb2zh+/LijS0vX3nrrLcPf39/YsGGDce7cOcsrJibG0ubjjz82/P39jQULFhj79u0zmjdvnuUvEU6Of1/dZhjqx+Tatm2b4eLiYgwbNsw4cuSIMWvWLMPLy8uYOXOmpY36Mnnatm1r5M6d23ILgAULFhiBgYFG7969LW3Ul0ldu3bN2L17t7F7924DMMaMGWPs3r3bcsVVcvqsU6dORp48eYw1a9YYu3btMqpVq5YlbwFwv768deuW0ahRIyNPnjzGnj17rL6D4uLiLPuwV19m6ZBkGIbx5ZdfGvnz5zfc3NyMp556ynIZu9wbYPM1depUSxuz2WwMHDjQCA4ONtzd3Y0XXnjB2Ldvn+OKziD+G5LUj8n3008/GSVKlDDc3d2NokWLGt98843VdvVl8kRHRxs9evQw8uXLZ3h4eBiFChUyPvjgA6svIPVlUuvXr7f5e7Ft27aGYSSvz27evGl07drVyJEjh+Hp6Wk0aNDAOHnypAOOxrHu15fh4eH3/A5av369ZR/26kuTYRhGysaeRERERDK/LDsnSUREROR+FJJEREREbFBIEhEREbFBIUlERETEBoUkERERERsUkkRERERsUEgSERERsUEhSUQyhePHj2MymdizZ0+q7H/y5MnUqlUrVfZ9L0uXLqVs2bKWR4KISNpSSBKR+2rXrh0vvfRSit5jMplYtGhRqtRzL3nz5uXcuXOUKFECgA0bNmAymbh69eoj7zsuLo4BAwbQv3//R94XwM2bN/Hy8uLPP/+8b7sGDRpgMpmYPXu2XT5XRFJGIUlEMgVnZ2eCg4NxcXGx+77nz5+Pj48PlStXtsv+Vq9eTd68eSlatOgD27Zv354vvvjCLp8rIimjkCQiKVKlShW6d+9O7969yZEjB8HBwQwaNMiyvUCBAgD873//w2QyWZYBfvrpJ8qVK4eHhweFChVi8ODBJCQkWLabTCa+/fZb/ve//+Hl5UWRIkVYsmSJZfuVK1do2bIlOXPmxNPTkyJFijB16lTA+nTb8ePHqVq1KgDZs2fHZDLRrl07vvvuOwICAoiLi7M6piZNmtCmTZt7HvPcuXNp1KiR1bq7I2zDhw8nKCiIbNmyWY7nvffeI0eOHOTJk4cpU6Yk2d/ixYst+/vjjz+oWrUqvr6++Pn5Ua5cOXbs2GFp26hRI7Zt28bff/99z/pEJHUoJIlIik2fPh1vb29+//13Ro0axZAhQ1i9ejUA27dvB2Dq1KmcO3fOsrxy5UpatWpF9+7dOXjwIF9//TXTpk1j2LBhVvsePHgwzZo1Y+/evdSrV4+WLVty+fJlAPr378/BgwdZvnw5hw4dYuLEiQQGBiapL2/evMyfPx+Aw4cPc+7cOcaNG8fLL79MYmKiVfC6ePEiS5cupX379vc83l9//ZXy5csnWb9u3TrOnj3LL7/8wpgxYxg0aBANGjQge/bs/P7773Tq1IlOnTpx6tQpy3vMZjNLly7lxRdfBKBly5bkyZOH7du3s3PnTvr27Yurq6ulff78+cmVKxe//vrrff5GRCRV2O2xvSKSKbVt29Z48cUXLcthYWFGpUqVrNpUqFDB6NOnj2UZMBYuXGjVpnLlysbw4cOt1s2YMcMICQmxet+HH35oWb5+/bphMpmM5cuXG4ZhGA0bNjTat29vs867TwffvXu3YRj/PEn8ypUrVu3eeusto27dupblsWPHGoUKFTLMZrPN/V65csUAjF9++cVqfdu2bY38+fMbiYmJlnVPPPGEUblyZctyQkKC4e3tbcyZM8eybvPmzUZgYKDlfb6+vsa0adNsfvZdZcuWNQYNGnTfNiJifxpJEpEUK1WqlNVySEgIkZGR933Pzp07GTJkCD4+PpbXG2+8wblz54iJibG5b29vb3x9fS37fuutt5g7dy5lypShd+/ebNmyJcW1v/HGG6xatYozZ84At0e82rVrh8lkstn+5s2bAHh4eCTZVrx4cZyc/vk1GhQURMmSJS3Lzs7OBAQEWPXN4sWLadCggeV9vXr14vXXX6dGjRp8/PHHHDt2LMnneHp6WvWRiKQNhSQRSbF/nw6C23OJHnSZutlsZvDgwezZs8fy2rdvH0eOHLEKIPfbd926dTlx4gQ9e/bk7NmzVK9enXfffTdFtZctW5bSpUvz3XffsWvXLvbt20e7du3u2T4gIACTycSVK1eSbLNV64P6ZsmSJZZTbQCDBg3iwIED1K9fn3Xr1lGsWDEWLlxotY/Lly+TM2fOlBymiNiBQpKI2J2rqyuJiYlW65566ikOHz5M4cKFk7z+PRrzIDlz5qRdu3bMnDmTsWPH8s0339hs5+bmBpCkDoDXX3+dqVOnMmXKFGrUqEHevHnv+Xlubm4UK1aMgwcPJrvGezly5AjHjx9Pcr+lxx9/nLfffptVq1bRuHFjy2R0gNjYWI4dO0bZsmUf+fNFJGUUkkTE7goUKMDatWuJiIiwjMAMGDCA7777zjJycujQIebNm8eHH36Y7P0OGDCAxYsXc/ToUQ4cOMDSpUt58sknbbbNnz8/JpOJpUuXcuHCBa5fv27Z1rJlS86cOcOkSZPo0KHDAz+3du3abNq0Kdl13svixYupUaMGXl5ewO1TeV27dmXDhg2cOHGCzZs3s337dqtj+u2333B3d+fZZ5995M8XkZRRSBIRu/v0008t9wK6OwJSu3Ztli5dyurVq6lQoQIVK1ZkzJgx5M+fP9n7dXNzo1+/fpQqVYoXXngBZ2dn5s6da7Nt7ty5GTx4MH379iUoKIiuXbtatvn5+dGkSRN8fHySdaPMN954g2XLlhEVFZXsWm1ZvHix1ak2Z2dnLl26RJs2bXj88cdp1qwZdevWZfDgwZY2c+bMoWXLlpZgJSJpx2QYhuHoIkRE0lrNmjV58skn+fzzz5PVvlmzZpQtW5Z+/fo91OddvHiRkJAQTp06RXBwcLLec+HCBYoWLcqOHTsoWLDgQ32uiDw8jSSJSJZy+fJl5s6dy7p16+jSpUuy3zd69Gh8fHwe6XPHjBmT7IAEEB4ezoQJExSQRBxEI0kikqUUKFCAK1eu0L9//xRfGSciWYtCkoiIiIgNOt0mIiIiYoNCkoiIiIgNCkkiIiIiNigkiYiIiNigkCQiIiJig0KSiIiIiA0KSSIiIiI2KCSJiIiI2KCQJCIiImLD/wGJ/Fje8TSl0gAAAABJRU5ErkJggg==", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from climada.entity import DiscRates\n", - "\n", - "years = np.arange(1950, 2101)\n", - "rates = np.ones(years.size) * 0.02\n", - "disc = DiscRates(years=years, rates=rates)\n", - "disc.check()\n", - "disc.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now ready to move to the last part of the CLIMADA model for Impact and Cost Benefit analyses." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Define Entity\n", - "\n", - "We are now ready to define our Entity object that contains the exposures, impact functions, discount rates and measures." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from climada.entity import Entity\n", - "\n", - "ent = Entity(\n", - " exposures=exp_litpop,\n", - " disc_rates=disc,\n", - " impact_func_set=imp_fun_set,\n", - " measure_set=meas_set,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Engine" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The CLIMADA Engine is where the main risk calculations are done. It contains two classes, `Impact`, for risk assessments, and `CostBenefit`, to evaluate adaptation measures.\n", - "\n", - "### Impact\n", + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", "\n", - "Let us compute the impact of historical tropical cyclones in Puerto Rico.\n", - "\n", - "Our work above has given us everything we need for a risk analysis using the Impact class. By computing the impact for each historical event, the `Impact` class provides different risk measures, as the expected annual impact per exposure, the probable maximum impact for different return periods and the total average annual impact." + "impf_tc = ImpfTropCyclone.from_emanuel_usa()\n", + "impf_set = ImpactFuncSet([impf_tc])\n", + "impf_set.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Note: the configurable parameter `CONFIG.maz_matrix_size` controls the maximum matrix size contained in a chunk. You can decrease its value if you are having memory issues when using the `Impact`'s `calc` method. A high value will make the computation fast, but increase the memory use.\n", - "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values.)\n", + "### Impact calculation \n", "\n", - "CLIMADA calculates impacts by providing exposures, impact functions and hazard to an `Impact` object's `calc` method:" + "Having defined hazard, exposure, and impact function, we can finally perform the impact calcuation. \n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-21 14:38:36,337 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-21 14:38:36,343 - climada.engine.impact - INFO - Calculating damage for 661 assets (>0) and 1049 events.\n" + "2025-01-21 15:43:22,682 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:43:22,683 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:43:22,686 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:43:22,687 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" ] } ], "source": [ "from climada.engine import ImpactCalc\n", "\n", - "imp = ImpactCalc(ent.exposures, ent.impact_funcs, haz).impact()" + "imp = ImpactCalc(exp, impf_set, haz).impact(save_mat=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "A useful parameter for the `calc` method is `save_mat`. When set to `True` (default is `False`), the `Impact` object saves the calculated impact for each event at each point of exposure, stored as a (large) sparse matrix in the `imp_mat` attribute. This allows for more detailed analysis at the event level.\n", - "\n", - "The `Impact` class includes a number of analysis tools. We can plot an exceedance frequency curve, showing us how often different damage thresholds are reached in our source data (remember this is only 40 years of storms, so not a full climatology!)" + "The Impact object contains the results of the impact calculation (including event- and location-wise impact information when `save_mat=True`)." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Expected average annual impact: 9.068e+08 USD\n" + "The total expected annual impact over all exposure points is USD 288.90 M. \n", + "The largest estimated single-event impact is USD 20.96 B. \n", + "The largest expected annual impact for a single location is USD 9.58 M. \n", + "\n" ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" } ], "source": [ - "freq_curve = imp.calc_freq_curve() # impact exceedance frequency curve\n", - "freq_curve.plot()\n", - "\n", - "print(\"Expected average annual impact: {:.3e} USD\".format(imp.aai_agg))" + "print(\n", + " f\"The total expected annual impact over all exposure points is {imp.unit} {imp.aai_agg / 1_000_000:.2f} M. \\n\"\n", + " f\"The largest estimated single-event impact is {imp.unit} {max(imp.at_event) / 1_000_000_000:.2f} B. \\n\"\n", + " f\"The largest expected annual impact for a single location is {imp.unit} {max(imp.eai_exp) / 1_000_000:.2f} M. \\n\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We can map the expected annual impact by exposure:" + "Several visualizations of impact objects are available. For instance, we can plot the expected annual impact per location on a map." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "2022-03-21 14:38:43,047 - climada.util.coordinates - INFO - Setting geometry points.\n", - "2022-03-21 14:38:43,151 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", - "2022-03-21 14:38:46,480 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + "2025-01-21 15:44:16,514 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2025-01-21 15:44:16,518 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:44:16,771 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" ] }, { "data": { - "image/png": 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1ik/Kszm6O8tIX4IwFM4tVVktBwjCWF4xOeigLFguac4thaQSLkM5l4nBBAnPolwPODFXo1QLySZtju7OMZjz8EPDmfkas2sNHEtxeCLL5FAKgLn1Bqfnq4RamBxMcmgiS8K1KFQDTsxUqDRD8mmH63fn6cs4NH3N6fkaC4XmU655v6X56F+e5b7PLFJYa+IlbHbtSvB1X5Xku74pScN4TDd2UQhzJF2LI7uzjPYlMUaYXmlwfqkGCkL99Bf06lKD/+977+EnfvFOrrtxu4fM1Y1n61nTw1Nj+9hvPJ9pW+KCVhXbcbFsC5RF4LdQSmEpCzfpbD6/o53YbfN4KctcAU7bAOC1NUMRtDaYoB79An8KWE9zy/pyQsvz3xXncvEA/gLwtyLym+3vjymlSsDnlVI/c6npUUTkjFJqlcga+LQgIieVUr8N/BLwpqc8X9lorn3fkmgbMsBxkuA4mMtk9dPKvSxyOqCim91OedQbNRzXQ0z0pHCSeUSpy9b+ZwLXhlffCJ4D1oZ1khBFgJscwLbZYrVMkc/lqNcDcrl0h+9MTjKslcsM5vZF/i4blkHJEgaCmCZ33nHrpo+giGAMKJVmcLB/86HWnxd2j/XxyLk1btmXw7Yu+MwM9ad5YrrGofE82ZSD3ZbVl08zMZpnfnmNg6MKx46O57LCxDA8eLbBrcMlbGvjCa0ZyJQ5ttLPDbtdEo612a58Fkb6ASfH+EBiU1Y+J+wZy/DIVMDLjg7i2FF7B/pgfCTPvSfWeel1A7iOtTmOA/0ZVtfL2KpFJr2lvVnhwLiiVlcMD2axt8iaHM3zwKkVXnLdGLZ9QdZQf5bcTIXJoST5tLtZf38+zd6xPqaW69y0L795vC8v7Bnr4wvH17jrhqGOcRwZzPHQmSLn5osXXfN/8ltPcOLRNb7le29i94E8XlBE5k6zuNQg51nk8BlKnOVY/TB33nYj3pZxHOjPsH88z9RinfNrTUSi9SVEW2J+cPEXiVaR1dfgPDv35RXGtdinqwUdY7/l0Rs9p5MEfosgBGNCbCeB40TnS3v9XhZse+QrO4El0uVa032ZurIKIC8cBTANXea0jd5f8htbKbUHGCLaEn4m+Fng24mYuV/Q2Ng2bTbquF4C23aeN1u+F4NSChEhmcrQatZx3QSWbT8v2r5vSHBttaloQHtxq7BD+YON7ROL7DblLyqz6M/u6VD+IPq16tghoZguWZYlJBOJjgeaUgrbEm7dn8Pa5jDt2Ipb9mdRyt5UpqLjFvmURWrMwt7SLEspsIRbR8vY2x7djgU3j7ewbKcjcMWxYCSv8FJex5hYSuHY8JLD/ZtK1tb6X3K4H8fqHEfHthgeSOO3wo72WpbCFsPwYH5T+dt6ze3XDeHY1ra+W9y4JxdZLLZcY9sWmaTDzfvz2NbWcVdgwUuvG8Cyto+jxYsO9HN+vsjF8MgXl/jabzvCba/YBQh39Z8h/+JkxznHTrZ43+8/wWNP3k+jEXLkyAA/+ZN38FVftY/+rMdU7Qzv/Pf38savHKda8fnXTyxj24pXvHEfX/ttR7HsS7sHVpfq/Lfv+Vd+/Bfv4vBNF1yqf/K7P8Erv3Iv/+ZbrgPgE393ls//yywrCzUSSYfrbh7km77nRgbaFtMTj63yaz/5Rf7zz76Mf/jzk0yfKTE8luabvudGbrp99JLa0sO1iY17xEsk2755PKf+x9Hz9akNOPYVVACvBmecS+UBzCqlbmszZW+yZrdz4AF8GPgupdR3KKUOKKVeBfwm8Dhwsi3jznagx53t74eUUj+jlLpDKbVPKfWVRIzb54G/fSadEZEV4BeB//JMrr/W0KzX8LzkVaP8bWDD9ySRTD9vlD+Itn3tmGgPy3ZjnZMty9pwBu6CbbmxEXWyw7bBTtF3G1sucWMUKZTd11nKxO6eWEphb9+qacNxnE0LX8c1lrPpv7gVtqVIuPEP6IRrdylzAFoHxO0N2bYdGzEN4Npu/PpQ0qHkbcCx42fEshRJz+5QSi+UsWNfNpDr93jigRVqlRYpr07WaXSdU60J3/gN+/noR9/CPfd8E294wx7e9rZ/4uTJAlprmrUSRmv+9m+mGegz/PZvvZi3f2uef/7QGR74zDSX7u8QnZdO2OzJC7vyduw6APjOH7iVD/7pV/H9P/VSCqtNfu9XHuo656//4Bhf8y3X8dO/8Rr2HOrjd3/5QRq1q+H11sNzAaWs52XwWRTIYl+xz9WAS521lwIPtT97gHe0/96gfXkv8PPATxLxBf4FEWv2m+XCGy0NHG3/C+ADryMKFjkJ/BZwH/BKEak/8y7xfmD1y7j+qkfkn9GMQvefRwrU08Wl0AA8l2j48VFoYnSsQhVF+ca/tEV0/BbGDoqeuYgvlDHxSqMxBq1jyhTEbdCIyI71i9Fxeh4YHTtHF4sUDLVgTJyi58QqumanuoFQ69h6FAodMy5G4pXfjXp2gjYhzkWe6d/2g7cye77Mj33bv/BT3/dFfuIXKnzsnlZH215xu8db3zLIkSP9XHddP//9v7+Mo0f7+fCHz2JZFslUEsd1uOnGDG94LXj2Ol/zpl3c+fIBZk/MM95fJ+GGKCXYlqCU4NidayzhWEwORha8oTQ0lk9jVk9zaMjGtmA463Jk1OPAgMV3/4cbefXtHvsGa7zh5YP8wI/exukn1imsNTrWx7952xFufskoY5NZvuE7rqdRCzl/urTzYPTQw/MACnCUc8U+VwMulQfwU1xkK7et5L23/bkkGSIyw5cZsSsi3xlzrAns/XLkXu0wWoNSWFeZ5e/5jvliwN5hu+tG0MYQ6hBnuzVKhFJtiXxmtINuR8RQqi/Ql5nokGXEYLRqKzvSEQUtIlTrJdLpZIcsYzRL61OMDe7Fti/czlprPve5L3HXXS9v83epzbpFhGbYIOEkO7aURaDcSpBxah2WThEolloMjXRGXouAJkDCJp6T7FDeBGg066RTmc4oQWOYXamydzTL1kdKGIZUGhpLNJ57wRInIhhRTK8W2DPcj72l76HWHJs5z017D+HYncfn11cY6RvA2mKdkLbfUK1VIp0cwFKd41iozjKY29NRhzYhy4VF9gyVyedGqTQ9Fgstqk3DBR1WceTmQd7zu3czd3qOqZOznHmixjveVeJ1r/D4/V/pQynFalH4hfc9xiOP3M/ycoMwNDSbmunpMqFuMTjpYtmKF902wq7xAW657SU0mjV2ja2ztFRlz2iJ4ZZLvemQ8AQRC88RwkrUiv6Mw0sODdIoRilJR/sSDAwOoY1hfeoJRIe0Sksk9S7qhVUeeaTAX/zlCrOzPtWa2VSyy6t1Jie8zdnZe7Bvczz6h6Jt7UqhiVKCiNqkQDIiNCsF/EaF3NAkyr46XoI9XKtQV3QL+GpA7w69xiAScf1Ztv28NMtfzWgGimKjTn8qB7qFshRYGVA5ms0miYTg2A4ghNpntXyOWr0CSpNL7UIpEFHUmutUGgv4YY2h3AFsK7oNm60qjbpNo96gv9/Ba287+mGT9eoc9VaLdNMhmxwCFEY0cyunmF87x0pxiaP7bmsrYuD7hlbgc3rqCQ7tP4LnpNqy6iyXTiNo+qwhElYGpSyazSYrK1XOnplh/94hxgZtbDeBCQNWCk3WK6CtkJEht03eKoRaU5cmQb1Af2qSlJdHofD9gOXVEvn+NMqySDgORjex7CSNpiHrwPp6hcHBHJalMMawtLzE4uoCmX6b4fwomUQSBCpNw/1nWpTrLRoHGxwa7kOCOrgZHpudYWp1gWbQ4NZ9R3AdG1DMrS3z6NQJsskEtx24ib50DoBqs8pK6Sy2HTDEPlJ2FqN9lJ2g2Jil0lilXPfZPbwHi8gCPV+YYXr5OGl7iLTbYijnMpzTzK6vUapp/JZLGKZIJxSpbIO+mx2OvqgP9S1p7v7XJr/w3jW+8Ci8/DabH/65MufnFT/ww0e46yUTpBPC977jC9TrTaZmTpHMGRBDPuNw800HCXVIvdFExBCGQtgwpO0QL9nCCBGXmqUY74+sw4fHEvQlLTY2n0VClAR4tsJ1HbQWlIqU3alza/zsz83xurv7ees39DMykmN5XfPTP3Wa5OJJ2LUbIVoztqMAoc+pI2210Ej0lwC5lMXkYJpSucrffuTvWJyb5tX/5lvZNTRMpjxDNTVGK7dr8z4Kg4CpM0/ieQl2HzhyST9Sk55FLuVQaYQ0/QuWc9tSDGRd/NBQrvcihnu4AAUdP+Z66EZPAbyGICL4rVZk+nZ6kXOXG6GxQfuYxipC235lNbFTGRw7BcZvh/4LYHDaW5q25SDiY0zbqqfCdoBCAqVUe+tRYSsX0PT153CcVnuLWOHYLolEkiAwOLaHEb3JN9ifnsAmS6hDEm4GECzLJpG0OHL9BF7SwlIWxgSgogeiY0cvzGRyBNeOLDpOQlhYrJDL9ZEfmiQ7OriZrSRwy8wtP4mXGiWZHW2/sC0crWlUW9gqJJnow7Y9EEUylUTrMs1GA1NbwLfC9i6loFWWUi3DyMgglhVZO5VSjIwM0JJlTAhpL8HGJnU64ZBO1KjWwVl5iPJ6E0FhIaRNmqFMP2P5FJb4BIGgUPSlLCYHE6QTKdKJBNpEikHC9XBtG2jRWj1JaCSaR4GWshDPwjY+QWMFI4KFwjE+gkXKyzM+ug8FDORhfHiMR07dy0qQ4c6jCTIJheChVB/H5oeYWlVcd2QBWKNQz2LnB3jo4SW+/jtu4LWvcpj0TlBvCOfPFTm4O2DxnMWhIwdAh1Cfx5+vIAKJ3NEoE4NlM57Lba7FAKGs66ScPBODGeAR0knBc5vUaxFjVqnY4PWv2w/A0lKTQuEYg6ODHDy8i5OnhFZrht/7/TdhJGRmZo5P/c+/BwYY/9ff5pb7pyiou4BX0OfUeP3IKdSm+gd9XmvTAlpuGPoaAfkE5PJ5apU+7jj554ye/jRieygdUNj9Uo6/8Wd46MH7+INf+5lNa2y2b4Af/On3M7HvUOw9pxS8/Ogge0fSaBFspZheqXHvyQL7R9O85LqBdtQ01JqaTz22Qr31/I++7OE5gOpZAJ8KPQXwGoKYaHvPS6audFOuSbhKyFqR8/umzcK00PXzJPsir4PImKFQ2PSnJ0l6/SS9fDviN7ok5eYJ3An602PRNmT7uOt65HMGoxttJUu167LpS4xjBS08Ox1t27avSafS+E3NxOhgh8XXtmxGhiZJJTq3jFE2I/lD+IHftW175PrraAY1km6y7dgdHe8fzHP7nTeTSqY7ZNm2xWBuEqUsHNuLZLXbdWD/BHPnn8BNhe1I6fY11BgZyJLPp9s+nlGhq5LsHj1KGDSwrU6/1Zdd18fi6XvJSbPttBxpHmN2g5HB/fQNjEVbve3z016a68YOkUoNbFpXNzA5dD3r8/dhm0iZ2WhbWgyOlSGbzkey2tXnUzmOjl1PKrGrw5rgWTYvPvIK1lenSHnwjd/8Rb7uLeO86NY+BgZcWjNL/OYHp+nL27zy5TmUgkMHkjzx+WkqrxvkSSO874NrGAO2BUcmwK0eb/dNQHT0Q65yHFcJlur0wXFF0e8Nk0oPUi3UN9eebQu7d6d56UsH+OBvneT66/sJAsN73vMoyaSN51l4rsfhw30oBR/84DG+8Rv386W//iK/+/fRuFp+EytskSpPAa/glr4pvG27CXtTK6Sc/TRCGxHFfNHHAm545Zv5hjN/z+A9H0HpAHR0vwzM3k/+oz/P7/3Jp/FbzU05rWaDX/up7+OX/uAjUSqwbTgykWPPSArbVmw4X+wZSWME9o2mO4JbcinF624d4SNfeirK2B5eCFCA27MAXhQ9BfAaQcQVZy4pl24Pzwx7+1uxxy3bBUzXVpZCkUkMdLPeWzZ9udEYehgFaIzRsfPoeW7Xtr5tWwwODXQdV0qRSWUwpjvQRKFIJTJd9VuWRcJNdh9XFulUFtkWOBJFINtYVnfwhlKKfFp3+UsqhGSq+wdKpHDaHf6Km2Vi6JPuoAMLIZPt6+L6UsoimcjFRmAb7eMY01ViKch6fV3jaCmL/uwQfhgX5WNIJVwspXj960b4mw/N8yvvO0W1GjI0aPOyl2b5tffuY3Agesy+7737eNd/P89bvn2akUGb//SdAzSa0fy4jiCtIK7FbX/IbWOiwHNSKBQbU+w40djl8xl+/f138OPvfIiv+Tf/wq5dKd79M7dx5mwFz4nyf9988yC/9Esv59d//VHe975HuTG/zDsPfJzvf+JbL/S9bTmNCxh68NEmn/vDezg/VWf33jzf8G1HueUVu1C2R98Df4kKmh3nW9rn45/65GZmh60I/SZPPvwFbn7pa7rK9o2lY6mE9o+lY+4rRcqzGcx5rFf8bZIExwoxYmHE6hrPHq49KNRVE417pdBTAK8hBEGLRCLVC/x4lpB0JDbn706Rsxebh52oW0RMrOJiWdZFKGLi65EdypSyLmxhby+7yIsxjlT1Yn3ciQrW2oG6RWIigyM5OxO+KnsnV4cdIsjb2+px0dk75ZZWyo6NHN46Vz/0A4f4oR+4sI0ZVBe7RvKGIyk+9Cf7YEu08b//ln4ATBigLMXnPtSZPlSheP8vXxdP89MO8lpejrz+xsZSGxdx5Egff/fh13ec/6Y37cZ2Epvfv/d7b+B7v/cGAOa/53XopVkefdWFOL47+qc58QvHSIxNdsj5jZ8b4Fc+WObH//Mo17/4dj77mfN88Lce4tvVbRy9cwzq5e7BAlaahjDs9tMTMaj6GvkE1ALQZmOGBGcH+p/tvJebshBSrsEiUvZQoERwVZOMO4cxSarBOBqHnhJ4bUNxZXkArwb0RucaQdBqbuZm7OHZwVLVJYzRRXRQj6Ui0VoTBN18aSJCo96Itc5ZlhMFl2yDEdNlgduQtZEovbvMxB8nnp7GGENo/Ni+BLpFqGP6Ejkjxtdhe13HAVqNUizlSsQp2H2+KBeJkSVAq17CxFLzhG0anu2VeLGUMiLQbDTj+x40idVNd6B60NrQbElsPaF4GIlRyi07nmIIFUvlE62hJqdOFfn/feBJDh/OMzkZMWwpui2G0TUmViUXEewXvQxirM56eqrrB84H/qDCL/3UADfenGdtcYrbbs3yI/9lP3/9p8exFRQnbtviLXgBd+0dIBVj/TXa8LIX30CfXmU82WRfv2Fvv2ZPn9Dy4/kGG75PGDMutgVhcIK+xCly7jn63Fn6UzNkM+fQ9iqWN89Qaob+VB3b2pmmqYerH0opXGVfsc/VgJ62cA1AIhp2bKdH+/JsYq6coO5bbN3FCrXh3PkV5ueWO17UWmuqlSpPPn4SrfWmsmeMwRjDl754f/t4dE20ha9ZLk1RqC5vBi5E12garQrza6fRWxSbjWseO3U/Qehv1m+MQZuQhbXT1FuVDmXLGE3DL1OozmDkAoeeMQYjIeuVqQ7FUcSgjeb4uQeoNcodSqAxmlpzlfXSXGcdYtA6IHTygNpUhKLUZrBamyfQrQ6ePmM0peoS5WoVvWWAjTE0mwFL/i6MXJBlBIxYnJkqEQbh5vhuuEKcO79AuVzqqENrTasVUlM5ZJssLbBer3XwF0ayhJm5Zeq1Wuf8hprVlTXm5iIS563j6PsBMwtB5M230XeJ6lmqZPG11aFQChY1laVqpbYpThY4SYqBj9nWLlDc84UlXv+Gf2Zlpcnv/PZd7ShzYWFhlXqj8weGMUKzEfD4w8e2tVci2V/57Ugqh2xQtygFiRSZr/gecPthS9rM0+dDDh5wmFso4Hke6XSKO186xMxUFcexOP/y70N7aUzb/1KUhbgJbnjrf2LPnj0kEheskOl0mre97Zu465V3sm//Plxdx24VSUmd6/f0o8TumBMjgjaGM/Or+IHfMb+hDjk1/yR1s0xLLaHtaYw7i7irhKrWXu8+iVSRkcwqk4N1skkh5UVZbaKctT2F8FqCrawr9rka0NsCvhYggtEa5cZbXHq4PDCi+MJUH7fuUUwOJbEUtBp18qM5luZXKRVKXH/T9SjLolyuMDMzj7JsTp44w6HDh3AcRaPhM3XuPF7SY27xPPm+LPn0MIHxmV06znJxBj/w2T92PeNDB1GWxWplntmVk7SamkZQ5brdt5H0MjRbDU7PPsp6ZZkvPvYxrj9wBwO5YRqtGifOPUQgVXKZFSaGDtOfi1J3VZorlBuRk7yIoT+zG6UcavUSxcYUIgGLxeMMZg/gOWkC3eL07CMsFeeYXpri5oN3sHvsIADlxgL11iq+L4jYDOajOhp+kUJthpQzSugdIOn62I5Nq1Wn2qpQb63w2JlPMzl0lKH+XYgYVgpztMwqCpuUOYLjJBEMtUaVcqVGcmiImk7Rn3JxvARh4LPeqpExsFScYjC/i6SXxRhhrbRMS9c5dbrC2EiN4cEMylL4gaHeDGmJDWqMgb4Mtu1QbxSZW1uk1lylFlQ5MH6IpNdHaJqUawukBzT1RpXlxTrZVLSFHkoav2UITYLllRbjk6M4tkOlUmVuvkLf4ASFujAyksZ1XXQYUqwYBkaSeIn9qLCE69kYrWm0AuqtGjWribgjDPcNoJRFrdZgtQa65qMbq+RmHsMqrqJG9lEYejF3vGScx54YI51K4zoJjDFU6iXW10tguSzMzrL/4AGUZbG2ssb0+TlEhNPHzzA2ZGMrTaWueeSxcwShYfTb/gdjJ/+JrCmgvDyJV347iZvvplou4ZpFVLiONoZDB9Y5V7iZG2+xGBkZxLZ9Pv/5ZQ4f7iPlWpQH9jD9Hf+b/fd+ALV2HpUbwn/Nd+MlJvnwh9/On/+3n8Xc+wgkPA5+xzfxxu/+d6yUGywuF5hwFmnVCpQqWWYFbMelVvLoqxyD0hz0TVIfvZ2BtMuTs+eYHB5gMDNEoH1OLhxjZvkUWgy5ZI59o4dxbI/5wiKF6gopL0HWS7J75BDpxAClasig52PZFsr2cL2AlOvQDBzOLAaUGgZjhEBvuA30cDVBoXCuUYOIUuooUSKNA0AKWAHuBz79dBJpqJ3Y+q91/M4/PHDNdDzwW4iAt+WX9bMJW4IXZIJ2BbzmlmGGcy6e66DbGUAKlXl002V4eKQd7RtloShX1zk/f4JbrntZO2+vgzYhWoccO3cfh3bfQiqRxbHddrYL4fFznyOb6mf/rptQloUCtNEsrp1nev4Md97yhsi52XYIdUDLb/DFxz7Oy255I0kvjWO7aB1Gss7ew8TIYYbyk+3o3ciSUqzN0woqjPUfjQjDlYU2IX7QYHrxCQ5OvhjLcrAtG2M0RjSff/QfObznRYz0T+A6XmSVwVCoTmO0YTC3dzNzizGaVlijUltnKL93M7+uMRpBOD13P2MDB8kk+3Bsd5OceqlwloSXYSi/h40Xroih6ddZr0wx2rcPUFiWFZFmm5CV8mmG84dwLA/LumAtWiqcxrM9Ml52009PgEYroFIP2Lf7YFffHzz+SW4/+gY8N8qdvdHemYXjWE1DJmVwHDuSpxSByeGk82RSqQt9F4MYqFR8+vpSKBVtRUXtgmKpQi6bwmlb6zesjOfOTaNs2LtnfNO/TWtNpVLn/MP3s/ux/41lQpQJMZaLsT0enXgLr3zT1+C4FrZtR1vhIkxPLZBIJJmYnOggwS4WSkyfOc7ekYgSyFKRVTII4cx0k0NLH8Pyy1gmAMsBZZN47X/BT7jYUmIj3fvf/sM6v/zr8/zSL7yMV92V4t4vrfOjP/4oP/HjR/nqr5lkdUUY+tAPYRplLO2D7aGVxdJrfgz+3+cpfvqLmEYTLAvLc9n9I99L69VHGG0+jEWUqcVgEdp5CrKfoSf/AGVCLAkjtwLbxX7tj+INjpLwvGidtq3WD5/9Io7tcdPe27GsiAIp0AErpWVWS1PctO9OFArLstFGI0ao1Vo4dhPLjnwLNyys1YbLWs3w6PlyrOvHtY6r4Tn/H9/8kh01vIyTlOvzVy4nxIOFUw+IyEsvp0yl1NuA/wq8BFgG5oAmMAgcav/9p8AvtJNtXFzepSiASqnXAD8K3EaUZeNnReTd2875PuAHiTTSEvAx4J0isnSJHXs38DPA74vI92w5Pgb8PlGHPwa8o53tA6XUHwLfAfyKiLxzyzW7gRngde0MJF24VhRAESEMfGzbwXqOIoCvhgfDs4G9IynuPDqIuy0qcWP709pGOaB1SCuok0pkO6JkjRgqtQLZVF9H9g6AIGxhWXYXfUmoA8rlCoMDg511mJBidZ3+zGCXrGarjut4Xcc3FJvtdWijqTcqZNpUKBsQEWqNMslEpGB2yGqntNseIGKMJgx9XDfZ4ZYgYmi0qnhOMrZd9gadzLY+tlplXCfRJcsPm3hOuit6NwybaL/aJUtE8NKjXf3QOqRSK5HL9neRxzYb65RWZkinOo+LKNJDh7v6vqHU2dvSMIoI2uhNxaSz7yGmXdbZd03xz34StXq2wwYlKHjFt9N3x5u6r9EG1+0eE2MM5bkvgemOZtfnn4DzD4LZFqQxfAD7Jf+mvT16AR/6+zU+8DvLnD5d5brDWf7zDx3m679uAhFoffT3CR//JGpL0JIA64Vhlj5Rj5S/LbDzKW7+o7ej6PTpE2XTXFwjubZBj7MBReLF30j6lq/uyt0cuU6orjkMdUiofRJuqmtO/FYNkVaX+4wxwsq65sRCibVGor2d3zkLlhXFFcX5PF7tuBqe8xdTALNOUm7u2/8ctqYT966fuKwKoFLqXqJUun8AfEhEzm0rTwGvBt4O/Fvge0Xkby4m81K3gLNEOX7/D/C+mIa9FfgN4PuAfwEmgQ8SaaJf8VTClVKvJ1LkHo0p/jngEeBdRErojwC/sKW8CfywUuo3RWTqEvtzTUGHYSyHVg+XF3tH0l3KH2zk0O0+37YdUlaum65CWeQyA7FRtdYOzsO25eA63RZe23IYyA7HZn3x3ERsbl2Ij/a1LbtL+YMNSpn8jjl/40KKLcvuUv4iWRbJRHaH16XVJobe3larS/nbkJVw0/F9NPHbdko5sWNs2w75XPycEDZj59dyk5Fz3/b2KoVtx9DZKNXFcXihufG5ky0xWGvnu/uBkNlzOHbeLSv+sS7aR4wfO/ay8CRqu/IH0D9CZPnrvOrrvnaUb/ymo1jW9j6CPvWFDuWP9tWN4+uYBl3I3DAWhf5umxYlGkoLdPvlCd7Q7i7lL6onPvbcsZ3YsY++hztEy0M6bTPeVyWXaTFXyOKHW7ggHcOuwRaLhSR+cO0pgFc/rjki6PeJyF/sVCgiDSIj2ceUUpPAvqcSeEmjIyIfFZF3icj/I1K4tuNVwKMi8nsicl5EPkekAL78qWS3LXx/DHw7UIg5ZQB4XEQeA062v2/F54kUxB3zEF/LiMifuznoerj8CHX8S3onxEXgXijc4bgitg4Rs6MbUlx08AbiImFlRxIYLkQtxFwVF7UcnR8fnbwjLlJHrKyL9G/HelQ81YtchFJmR+z0Eokbjy017VgS1+b4wF0EhexE8xPGR8gqJfFjdrGX4Q5KozJ6B8U/al1su6z4H6OWC3E8SibYeW2zA6vBTn2HeFEXvRcvcl8FugqqRjZRZiDbaKuXQsLRjA+0qDactvInpDxNPhXQCyR5fkApsJW6Yp/LjYspfzHnzonI55/qvMulHn8WuEEpdbeKMAZ8M/CRi12kop/ufwb8toh8dofT3gP8qlIqIDJtvn9buQA/BrxNKXVZ99uvFji94I9nGYJrKxqtMJ5GAxP7UjciVOprXZQnGxGvepvFRcTQajVjrXPRNmw3fUqoA+ZXp7ooWowxVGrFHZWtVgx1TagDlguzaN3ZLmM066WlHZRJOiIxt/al0Sp1XWOMZrW0EEspE4ZBfHNFqDaKXS9xbTTr5dWu+qOt1ni1UUTT8qtdymwYhtSqjS7KFTFRoEecMK2bBLqbNkfrkPXyUle7jNEUK8uxFDgbqdG6oCz8sVvQ26hjxHIoL57ukiUitPxG7Lwry0GsVFdRqA21oZu6aHsEhalUidWQTEiz0d13Y4Tw+teBs+2ZZNlkXnYY3G7lsHZiFWL4HEXZmMHDXcqpKIfm+tIOlD3hDjQ/Qitods175K/a7Br7KNd1wFplhrpfIQhajA/UOLiryIHRCvtHC6AMpZpDyoPJ4YD94xUG890WVqWinMU9PLdQgIN1xT7Pat8i3K6UeqtS6hvbfz/tRXZZWikifwX8MPBRwAcWgQbwnU9x6U8TbUO/5yKyHybyO9wD3CIiCzHnfAb4MPCrT7/1Vze00fGWmR4uAwTPMYwN+BweV/RnXIIgCtbQWqO1JtQBZ2cf5vz8I2gdbh43xrC8NsPp6YdpNKvtsrDta1ZkYe0U5doSxmi0Dgl1QBAGHDv/eY5P34s20bFQB2gT8uT5+zh+/j6afp0g9KPjOmSpMMuXTnySxbXpyM8pDAhCn5Zf577H/4UHj38GrcPomjC65onTX+LxU/fhh80LdeiQuZWzPHr6MxQqy22fqYAwDKg2ytx//JOcnH4QbSJZG5/7j32CLz3xCcIwaFPcRFbSQmWJ2dUnaLaqm4EvWoeU6+s8dOIezs092dFHP2zx+On7ODPzRMdYaRNyavYhTs4+RCtoRLLan2JlhcfOfJ614uLmOG6M5fTiaZaLK1GwiJg2zY1hqTTHqbn7CcOAMIzoY7TWrK2ucfbUGZYXFy/MbRjS8n3OT80yv1RDa4MxtP81nFo+z8Mn7yEIW4S6PSdGM7tyhkfOfIZyde3COOqQerPM4+c+z5nZx6J2hgFh6BNqnyfO3svJqUc2x2ND1lppGes134npG8HYLsZ2MLaDPzBGeXyccm19s3/Regw5NfUEx87ed2E9tp8RxXIB3xtDLCuyLLaziDxxYpYPPx4QjtyIWBHnotgJJJEnuOGbCdWuiMbGgNaC1sLimuLYk3OEOvJ3NCbSOStVTfWmr8feexs4CcRJIm4S+idpvPkHGPyPb0d5HiqVxMqksNIpDv3G/8Dd95WR5dByI35FZWMP3kht8vVIbgJsD7E9jHIxuXGC8TtoNIJNf0tjDIHvs7o+R7VRQIchYftjjOHM6TOcOXmWer2+ScWktaZULFFYbbK8vB7JEoORKPBpoXCWVlABZUjaA9SWA3blMoxnUwzkbHLpkMOTTQ4NtRhUTfpSaZqBS8Jz8ByLlOfg2IqJgSSHRxJkkm5PEXwOoVC46sp9nrV+KXUXcAL4EvAXwF+2/z6hlHrV05F1WWhglFKvJPLV+3HgHiIfwF8G/gj4lh2ueQ3w/cDtclH7PEiUAuGpEjz+BPCEUuprgQefss2isZ7JdtDzCCKCFbZwbBuH8DnbebAlxlfoKoQCDo2n2TeawrUt1is+x2aqVJua4ZzN0QmHpKcwYtEMmgStGuvrVVxXkfCSiBgq9TWaDZ8whDmZJpvJoBQ0Gi3qzRaunWJx9TyD+UlsyyEIA1YLMzQaMFWfZnxUkUpmETGsFucxoaLVajK/dJ6+zAigqNTXaTRrGKM4NfU4e3ddj2N7NFoVzk0fJ6VSTM+fxrVSpLwsWkKWVs+BKFbXlzk/e5aRgQlAWCst0mrVMATMLpxmuH8vlnKo1gvMLJxFjM3p6WMcmnRIehmCsMnZ2cfRgbCyvkguOUN/dgQRw+L6FMVKEcuymFo4w2j/XizLotmqs1ZZolxrcKZ5nN0jR3GdBL7f4PTsMYKmcG72HNnkMPnMEEY0c8tnKFYKtAIfS1xGh/eggEJlmYXlOVCaqYVzTA5fh217tFo1zs+fBbGZWzmPbaVJJfIYE7BemcdvWxNrvpDyBkBBubZGsVqj0aoytzjDYN8otu3QbDYoFNcJCVhZWyWZzuC6HsZoFhbncTyPQKepBjnS6RSCUKgsU6g20Trk7OwpJkcORePYWGV+eQYJbc7OHmf/rhvx3BRB6DO3doowgNPnTpGwsvRlBxExFKrLVMtNlBMyPX+W4f4JLGVTqq1Tqq0i2sZ7ww8w0rcL103h+zXq9WVsZVOvt1DUce0ERjRrpTmMCnBVirmFc2TTeRSKlh9SqVewHU2wXsZaeBLCBiQHSXoHeeNXvgrTWKHx0MOY5fOQyuK8/K2EJGmUmzizT2AVTkfRziM3kB6/iwPX7aX8hX+CRz8KrRpq12Hkzrez7rskX/sOnPJxlLRAOVhDN9FcCRl5y2sZfbHCFM9G/IDDN2Id2k8t9AjqGezlx6L8wfndmPwAxrJZt2/CfvQhKK+hswOY17yeETdNsxkpeJYVPQtnp6dRiZBW4FPQZXL5DJZSNBstqpU6SwsL1Ktl9u2fwLYVvh8yNbXE4OAo008+CPkCdrMEtos/vJtgYBzXSjPev5dsahAZFSqVOvV6nVotJJ+zSSd9lCOIcqhXDUMK+tx5hlM1bJpoHFbKDrUgwWgiy659g+QykbVzoRCwXBIqjZBWGJ/lJ59y8dzI77Dpa6rNnbe+Lzeuhee8fY1RHSul9gL/RBQv8f8RxWYA3EQUHfyPSqmbLzUe4mnTwCilTgN/ujUKWCl1D/CEiHzflmOvIPLPu0FEjsfIeTfw36FDC4u4KqJj+0Rk7ina8ofAbhF5Y/v7B4A3AG8CznGNRwGLCDoMUCoigX6ucCE6rDMy72rDy472s2c4uZlr1Ehk3Xjg9Bov2tf5a11ECLRNKpXpSENlRNP0K5iwhedcyE8aEewayvUCwwOTHYEHRjRr5SmG8vux1AXHdG00K2vz5LJ5UoncpoO/EUMQtJiaP8PhvTd1RM9qHXJ67kEOTb6oI6rXGM3S+nkG8hMktkSEGqPxwwaNVom+zK6OyOVQB5yYup/r993RUUeoA87MPcKB8ZtxnQtRulqHlGprhGGLob6JzWsiq0zI9NIx9u66qaNdWoc8fvaL3Lj/jihzTVtWqEOWCzOkEhny6QsRzZt9X3qSgxO34mzr+4mZ+zi6587OMTEhpcoSmfQArpPcDOwwxuCHDVZLc4wPHuwax5nl4+wdu6FjTLQJmV8+TdrNk81snV9NvVXF15qR/j0dfdcmmpPDk7d19t2EnJl/lN2DB3Bst2NOKvUiWBb5zODmNZFlTzO/cpbdY0c7IluNGE6dOMV1R67bpKDZkFVrVjF+SDp9IQgnYogxlI79K87cA50BH5YLu+6m8bs/Av4WF28vhfe6f4+VrSCNAmqDHFzZkB6Cqod+8O9ga95fN8XKm36C8YFqZwo/ZbMejNA3/XFUUN8M1RDLQbK7ESeFtXYCtVXpcJLU8neh/uSnOtplHI/cD36Agbu+KhKtNuZQUygUcGxDMult5tOOsuWEnDtzlv0HJjrybGttePTz93LYvx9HXWivWDbByEEGbnsrzpY1JEZoNpsszp5isN/F3hIUpo1QqykS/hRbY8W0gfk1i303vphkKrkZvKKN0GoFPDqtCcXGthRam8g44bjYlsVIShM2SlG0skpRCmzqvlCqX0wRlG3JDp/Zs/lqjwLud9Py6oGjz2VzOvAPKw8/GzQw7yOKrXj1dsOZUsomMsB9UUR+9FLkXS6tIQ1d5rSNnzQ7TdD/Av5q27E/AKaJ6GAuiT5mG36WKJjkPz6Da69KaK2xrK4AuucAQsoL6U+VWa9maOkEV5MSmPIs9o6kOpQ8Symw4EX7PLYH+yqlSLhOVw5SS9kk3CyazqhPy1JgFEN9E11Rp5ayGczt7coHbFs2o0MTkWV3i/O7pSwcx2Vi+FAXdYpl2RwYv6W7Dstm19BBzHZZlo3nJGOjZ23L5ro9t3fV4dguhydva4/DhWts26EvO7TJq7Z1rJRlsWfsxi6qGdt2uOHAHW3F94Isx3YYG9gT0dNsqd9SFrbtcGji1q522bbD4cnbY/rh0J/btXn9hb5bOHaCiaHu6FnLstg9crSLyse2HMaH91MuFbfNr00qkaM/0dchS6loLA5N3Nrdd8th/9j1GB12zUkuPYBlOx31W8oCJewePdJFa2Ipi4OHDnYof5vt8tKEqtlxXCkIgwB79oFOJQvABLT+4dcg2EYP4zcIH/8o3otv21T+INo9keoq+v57I2vdVoQ+I9YcilzncdH0lR9Bab8jTleZEFWdjfaXu9oVEnzsf+P5nXGHVujjlJcA6bx/bJuBgT7CoNERnRzNicXBQ7u7Alps2+KGgTJmudMeoIwmLTaOnehYQ8pSuJ7D0KDXFYVsKUiapTZlztb7GvbtGybhdV5jWwrPsxnrUxSXFkinElRrJcrlIgMDQ6QyWRpFn0Yj4vV1vTrB6iq5vhGa7iCtQGMp3c44Y3XUOdoXYClhsRSf+vCFAAU4V88r6VLxeuDn43ZNRUQrpd4P/NSlCrskBVAplQUOt796wC6l1G1AVUROE/nfvUspdR8XtoDfDzxOFLmLUupOomjffy8i94nIMhGR4dZ6akBBRB6/1A5shYisKKV+kci38AUBy7Jjc8c+F8h6dVwp4OoigdqPkasj/yFAPu2gtXT55FiWigtUjMrs+FR7YnRsXK1lWV0KxQZsy4mlLxGIzeccnd+9JaOUwrHdHaPAt/PdRRdZiEg33YqycLc777dh7UBfspPSbyl7x7zUru3FjyPEHt8gi46V5XTzBm62LOa4vUPOXaUsbDu+L3H8fEBbIY2hblFWO+S1G5aysOyYx+4O8xetn/j+OTukfox4GWNofnRzp+BoZGU+NnDEymVB+93n1ypRhO72nUvROOl4QnrVrKBMjOVKdohKNyFUirGyvN2HdlhDEnvcti1Cs8ONXV2OJY+x+3bFtisiq7bYPpBKKSwJYufSTmSxne5ngUKR8WC2VKBRVTTqVXK5PsrFNer1WjSPItiuQ7VcJN/XR61Zx0kP4wch+VQJmwqFxgRG3E2pzUAxkgtxLCHQxPbjWodC4V5jW8BEPMsPX6T8YWD/pQq71NF5KfBQ+7MHeEf7799rl78X+HngJ4n2pP8COAW8ue2/B5GV8Gj732cT7wdWn+U6nj9Q8ZxuzwXWalmWy/3Ugz6MXE03mtDwA+J4sy/mEiFbcuduxU6KzkZ2gjhoo2OjalXEA9MtKyZy9EI98e0CuiJ6IYps3UlxMDv4/WxsR3a3l9gXXtT3+DaHOoiXtYNyonUYZbmIgTbhjn2Pq38nyqSNwJ44KKzYSGezI0VKFEgQ3yaJlbWTmUYbvfMa0vEBYJZlxVPKOMlYOQBqYDT2uKnVECvmR0E6C3HRzCikFb+GJJFB4uhmdpp4ZUM6130cCBam4nkTd3il7bR+AEgPxhI568oKOuzui4lR/mgfkR22TI1fj50rAdZXVsjk+7G9NKOTB1ldXaF/aAyxPEYm9tE/thsvnSc7MEKgfZIDIzT9EMfRZFO1NgtBZ/tLdYczSykCHR23X6B5ju0r+HmWkAMqFymvEPE2XxIulQfwUyKiYj53t8u1iLxXRI6KSEpEJkTk7VsdEbfI+NRF6rl7axaQS2jXd274/2051hSRvU9V17UCMfEv5mcfCiM2TRnCOEM8v39hCkpFEb39GZ99o1X2j67SbJW7HsqCoRk0u97HRoRG0wc6uQCNRH5lgW52KHQiF9KuxdHALK/NdL30jdHUGgVC7Xe89KUdyVpprHbLEk2xOtvFb2eMZq083xUlbozQCkNWi4ux9CnrlenY9q4WZpGYvkdZOuoYs23ARCjU5rpkaR1ycvqhrnGPqFMWqbcqHe3aUKbOzT3eRR2jdcj86qkYepiQQmWhrRx2zokxUCpXuuddhJXCQjdNiNbU6i1AdfbdRKnoas1Cl7InIqwWu8dR65DZxak2d2enrGZQjel7RA2zuHY2llJmdulM7LwXK6ttxVE6ZGHZNEeOdPH0iXIo3f714G5TEN0kzsFXRxG4Hfe3hUr34dz6VeB2WvvEdlH9N0bKW0cdNuX8zYjaTuliI+lR1NB13VyElkPijd8GXqqzj06CYqtbnTFGCELp4uuMuLoVtbrpUgSNaVPgbLfUWw6BicIEO+4ficKd64HfrVQKFIM8XbcCimqr1HX/hGFIoxVyvpFnWYZZd8ZZCPLsvfFOgvQ4leQeThZszpY9FoIB1sTDGVGsBwEihr6MTyKhUHYG6VI5VHvO2r6h2xu6A+fmtYSIBkZdsc+ziKeauEuu/LmLHOjhWYHtuBit21t6V0AJe54TUNuWoT8TMJj3GcolcO2AMKxgMMyvnGSofy/9uVEU4IcNivUZMC5gRT5/losxIYVyEdtxaYVV0skcqUQGgFpzjZXSOUQMeW+cvuwwylLUmyVmV05hJKSZbzI6eADbcgi1z2rpPNVWgZnlGrsGD+G56SgatLxMuTmL74dMDF9HNjUIKGqNIsuFMzRbAcmkRV9mHKUsAt1ief0Mvi4T6CZDuQM4dgIRTam+QLkxR6G0xK7h68il+wBYrZR48NQq+XSBI5ZhMBfJ8sM665Xz1FtFRDSDuX3YloeRkGJlgUJ5iXJ1lcmxQ6QSfQhCo1mkWq9SrVYZGdhDLhf98GwFVdYq52n6FXy/yUj/AWzLJdQ+J2ceYmrhSVYK89x25NX0ZaMo4Nnl0xyfuR9b2dyw7+XsGo58JIuVNR45dQ+VepEgbHF49204jkurVefk7APUWwWafpU9ozeR9DJR3uT1MxQqMxSqacYGjmyOY7PV5B/+/uN84Qtf5J3v/M+Mjo1EEbJBg1MzD7FeXqIZFJkcOYLreG3leoFyY5lWwyBqjHw2SsXX8CvMr53FELJr8HB7HBVBGLCyNkc9WMf3A3YNH8Sxo76fm3uCpfUpFtdnOLL3ps0o4LXyCtPLT6IJ2Td6I2MDe1BKUSit8sCTnyPUTfZNFDm8+yYc2yY0IfNrZ1hcWaHSWOPg5E0k3IirsFhfZXb1FMPZQ2STGdIpF1CExqfQmEMO3IZ28tiLUdq3Ut1Q6L+N1sAewq/+UXJf+r94KQdp+VQnX0rwmv9AbX2B/OKnSLttkmNviNbBryI3OAaZAfTUl1BeEtMKWbzuzRzc+zJUYw/hwhegWUKcJM3sYaoMIkcnGFj4BBJWQAQrtxdz5N/S0kLy/Mcw6ycQpTAqiX3TN5HITmBZNq3P/jl2fgBdLmJe9CYWd91K8fGTHD68D8+L8jMHoeAHghgPrVskE5FSZMTQbEFobOqtKplkBkvZ+H7AarHESskhkbuL8eAkqWwe3Wqw5qdZ5wbsY6c4enQP6WRkiWz6ZVaKZwnFR3SCrNeHZbkEYYu672BnU1TDFjnxcb0sJmxS1k3KaILiGYZz+7DRiMDM/CqL9TU8J0nTz5JL5Ui4FmuNJqW6ix8CKDIJyKcAa4lqc5Wk55PP9JFLa1otGz94qk019YL0A1SA+7w2TDwjKGDucr3rewrgVYwLEX4vwLv7EjGU97l+UjHWfzDyD0IhCLPLx7EdTTbV1/bhExw7gWdnqAVFsskhXC+iZ7HtBLlMZM1LenkSbhLTjqJMujlELEQr+vK7Nn3FctlR0tUViuUl+rK7cGwPEcF1k2TTIxSrKwz1DeB5mchKpRT57BDFyjLpRIZ0sh8RgwikEnlSiT5q1XUSbi7a8jUhtuXQlx1jvVYnkxzCcZLtLU6bXGqUQDfQDmRSubYVQxjMZpgYnAXlkE8PtTOPGDwnRSY5TCuskE4ORoqvaJSyyKQGaWRqIDZJL7dp8UomcpSrdbQOSWeSm9Y2z03jWClarTXG+kewlI0xIZZlM9I/yfTCCYZyu8il+zeteiMDuzk9/ziW5TDcPx5t+7ZT5tlkCP0CY0P7UJYi1AGul2Cob5KVqXkmhvJ4bgqtI/+rofwElfoSrpMmnezftMwmvASDg/0MDQ2S78uBgMHgOQny6UEW1s6ScNM4toM2UXqwhJelWTqLbecZ6JtgIyNLLjtKqrqGsnxyqTzG+IDCthQDfWNQ0eTSw5H/IoJje4yP7qdcX6I/N0x/fhQjBmXZDPaNM7t6Egubwfw42kTWmf78MHvG97JSnGM4P4AOaoRB5H+WdvKYaoVdE3uwdANf11EokpYilxgkk8yT8CIrpVJRSizXShCYClZtGlwPEY+8a7ATVU5Wyxy8fi/OgX+HSBRYkg4tTp89zsTEMJmbX7apRFi2haZBYIrYr3oj9l13R68lEdylFuVygfTCvajCmchP0BhSrQC379UkEhbW/qORdUpAWTbJpIsdGsxgP2rgpSjAQvBNHb/RYOjuN5F+3VfS9hIlDIVE3cGyW1iqhe9H97XWhqnzSyQSSfbvH998Prq2olIr0/J98vnBNsWPkMqmGXINVqpGLnErfdnXbT4jMkZIF1YRVSWVSEdrXiDpZnGdFNrX9PUfwnWSiAgJZaOrazSqKwyPHCCbGo7uRRSOX2Xm1OdwcdDWAmHbaWesX6hrQ8qucNO+G3Btj2i3wuLs4iKPTzV58YEcY30eRgyWOkylMcr51TOkkjYt32Kl2E+tleLp7cCobd/aEdlXRFnaeHdd/roVVyI48pmjnRL348A5ETm8w2nfdVnrfKEqD9cCDQy0t48aNVLp7HNmAbwa6AE2MDkY8IobJrqiKLUJMSbEsTtzzBrR1Fsl0om+TuoWY6i3CqQT/R2BHSJCELawLKcrgEIbTaNVJpMc7AgiMEZTbayRTQ1tk2Xwgxa2bXe11xjD6toqw8PDHVGJxhh83SDhZruON5vNNu2EtU2WJtQ+rtOZq9cYQyuokPByHdeIGJp+rU0n09muUIeIaJxtgR3ahFTra+TSw520KjpkYW2aXYN7cLbkrxYx1Fs1XNvD27atGOqAteIiwwOd8xjqkIXVc4wP7+8IdtnYUk26ma72GmPwfZ9EonPeQx0wv3KOiZED22Rpqo0CyiQ3LZwX+hIQhPV2EM7W8RLqzRbZdK6jfmM0pdoKufRAR4SwiOAHTQTBc7a3K2R59SwD2e1ryFAqV8nnkh0BHyKC72u8ZKqDomRjjCsP/iV2eaUj6EGUg9p9BxKsobZEdRgBX2VJDQ51BUkICknko4CILcdbfoC/OE+qtC23sLJpZPaSGh6MkWUjRlDS6pAVamGZAxw8vLdjoyHyo3TwXLtr3EMd4nnZDqoXiOheyuUSuVy6616s+0Uyie33ouCHLTwnEbOGovs6lch11K91SKGyzFDfeNe8r67PkZRK970oBje/n4SX6iiL7pNZhnJ92NvmvdIssN5Yp1gOmFkZJJR4P8lLR7wS9tw85788BfBiNDCjbka+cfDGZyT3cuC3lu+/ZBqYdva0+4EngMMXUQAvK3oWwKscSikc17tyW8DPcwzl+3YMkrFjolEVFplEf1cEqWVZZJIDXc4XSils20HFuNMqpcimhrrqsCybbHq462WwEYUbGzyhFOlUtusay7JIqPjjqVQqsg5v674IXYrvpiw3F9uupJclzvUkotjojka1lE0uM9Ily7YdJob3dY2vUhaJ7T5om7IsRgYnu2Q5tsPE8IEYahyLpJvdMXhiu/IXyXKZHDkYS7OTTQ1Sq9VjJClsy43piyKbzsdQzdj0ZUa6/PaUUtiWExvBqpRiMBe3hiz6cqmYSG6F68W/tCVoYldWuxQwJSE051Hb+64gmYwPeIjuFNN1ZyU8F7tyulP5AxBNyvV3kKVREnZH0SsYGxuO76Nzwb9t63HH8WK9UpSCfD4Tey9uV/42ZHmOt6Oz1XblD6K1vV3526gjn0ziN6pdciw7QcLt/pFm2w7D+f5uqhnLIp8aZG5tnUI5hePkEU3bahzBsS0sS+EHG88RwbOF0CiMxD0Lr+R7I67uy8cva18FW8BbUuL+JpDkAuPKpV6fIEq9Owp8JI53eSf0FMCrHKpNEyDGINs4wXoAx7JjKTw2o21jXi47G8VVLL0GAnHP1e0P9a764wuIe+g9k1l9Jj8KLnZ+HK2Kgo2duW45O4yj2sZ/GCOxA5Zl76jM7RSBLZtUHd3t2tgmj2vXTk3asWyHmdmpf0oR+2NhJ0nRGnqac7jTFVo/bVlcZExiuY8gom+Jg7WDa/wO68SyFCYuVL/dgKezti92X+8kRyS+TCmFMd00Su3CHWTpHeZ3503Ki7E7pLyDuAkYTwktcVipBJvzMZZzsCVkqe5Qbwb4oSaXjPw312o7R4I/X+BYQjoZUmm4X5bvYuQDeFXgp4lm75eJkmPsCKXU/wAyG0TPbfLnzxAxtQC8Ryn1WhG591Iqvpq4O3rYAZZlE2wnce0BgPlCi0DHKA9KxdOXGE0QNrv8KqMtoWYsrQo7KN7ahPhBo+u4iMEP6ztSheyEOHcNYwz1Wi2WYmInmpAdZWmD7wexZUHY2rHvceOodUC9WY4ZR0OlVuyK6N0UF3MsCH2are5xNGKo1Aqx7TI6noolyhXcfb42IeVaITai3g+amz6f3a2NGUcx+H73GgKiNRQjS1nWDnQ6OrYfUd5aK7YOQWK5QcVLoe1uShdBEWgnngrFb8X+uhFj0DH3lQB+ciTezuf7O1ig4pUdgyJoNWLvxUA3Y+dKjN6RlqgrUr0tq+nXYmXtROOkjUbH8BlqHVKuFmLvOaPcWNonrZtdxyC6r4PQxM5vqxUykEqSqs2QosloMmDAn2OXU2C3V2bl9IPUV86TLJ9lLBVgWxalhkuleXWoQ0YUKS8g5YV8OZHKURTwlfsAw0qp+7d8uhJUKKVeB/wn4NufKiVuG18LfHHL928BbgFeDYwA/0qUFveS0FMArwHYjoNlOb1gkBgsFnxWSv6mEhhRgRhK1UXWylOYLTxrxmgC3WRh/UTHi3fjRbC4fpJGq7qpcETccSEnpx/m5LnH2/5w0RxoE9JoVZhdObatjkgxWS6dJNStzRfPBt1JoTxLpb7S8UKK6lG0/IirboOvboOKpFCbbvs06i191JTqM7SCcocsrUOWC7OcX3i8s11iMAQU6ufafTcX2mU000uPUWsWO/tuQtZKUywXznTI0iak3qryyKnPEOpgU+HaUL6+8MTHqdRLm0qgMYZQBzx8+vMcn36YUF9QQsMwYHFlnvseuaeDPzDUIUHo88ipe2j69W2yQh4981nmV84Rhhde1EEYMLdyjgePbcgybVkBLb/Bg8c/iR+2omASNnwlAx548pMUqyubdYgIYRhQLJVZWy92UO1oownDFuvF1fY8yIXxNZr18jRNv7Y5JhtraHbxNFOLT6K3rSE/aFBurWzSCkVjEkWRFkuCbq/nzXkXQ6m5SrVV6FA4jNE0ghpnc7ejsTdfq0ZZiOOx5h3E4KLNRmBZ9CI+s+KxVgrYqutpA8vrASsFC60vUKtE18Bq9maM8pCN9GkosFxWnSPUAwdh4zgIFoE7zqo/iJELm9MGhS8Wa5XV9thdWKcihrXqLH7Y7Lh/tAmZWTnN8tpMBw2ONiHNVpV6I5rzrfd7qAOmFh8n0K1NxdyYSNaJ6S8xu3KGYMsaCnXA0vosD576HKEON8c41AGNVoNPPfhP+GFzc36NGLQOeejMl1gtF9AdlDJCU1osFU9H929Hu3xq9SgIbOOxLhL5Ms7OLFOvllhdnGVxbopyYY2VhRmKK/OErToDgwOUi2tk0kmCyiqeaxMaha+vjte9ESjVEmSTDdKJAEtFAWyWMpt/XwqNzUYQyBXkAVwVkZdu+fxOR/uUGgb+FPgPIrJ4icOzjyjBxga+EviQiHxORNaI+JjvuERZvSCQawEigt9q4rgelnWx7bXLg6spCASiB8HukSSHdyXJpxwafpFS/TyCIZscpj8b5eqt1NdZKp7BmICEk2HX4GE8N0WoWxSqMzT8Ko1ak6G+Awz2jaJNwPTiSeaXz2ORYu/uvUyOHMZSDqXqIrOrJ1AK+jLDjA1eh+ck8cM6i2unUHaIMTDWf5ik1xcpctVZ6v46Iob+9CS59BhK2fh+iNYuzWaDtdUl8vkUAwMDaOOztH6W0NSwLZfRwcOkEjmC0KdYn6bllxGE/vRucukxjNGcmz/OuYXHcByHPSOH2D9+M47j0WiVWCycQhsfx0qya/AICTdLGLZYKp6hXFum1qize/gmRgZ3I0azXDzHWnmGhJtgMD/JYG4PluVQrCxybOp+ypUiSSfP7Te+mmyqj2qjxEMnPkuxvoayHF583SsZH9xLEPocO/8AU0snsZTi8J5bOTJ5M5blMLt0jo//60eY3D3J8OAINxx8MZl0llJtjcfPfIFmq4qlPG697i6G+nbR8hucmHqIpcIUOhRu2Psy9kwcAAUziyc5PvUAtmUzPLCbG/a/lISbZK20xCMnPovGx3PS3HrdXfTnRmi2ajxx5j4K1UWM73DzkduZGDmA1sLi0iKZTI5ypYSWOnvGD+C6LuX6KoXKLLnkLlp1m3xG4TmCQVGuB2BBpbFOLtPHUN84Rocsr89Sqi1TrTUZGRxnz65DOLZLpbFGpbEQKX9+jrGhSTzXxQ8CWkFIuVwl0CX6+zNkkwNoE1CoLhBIHREh7fSRTvSjFKyVVjg5t4axh5nIFBhPJEkks9QbJSpWgAmHKC4U2bc7RyabIfB9pmfXUV6eZqPO5KhHxgswYlhZ96nrHJVymd17Rhjoz+J5HrVmkbJfIGxmaZVb7BuzSCTz6KBJkxy1ls3q8gqj/TYDQ/2IMSyvFCmUDWEQMDKYYHg4g2U71Gs1llurNFoB+XSC0UwfNgqjoKxbNMIGiGJ88BCZ1CCh9jk/f5xKcxXXcRnM7mIoN4qlFIVKkeXiAiP9h1AIffk8npuk3ixyfvFxmq0GmVSWiZEj5FKDBKHP9PKTzK6cpR74HBq/hUPjN6CUYmr5JI+d+xJKKYaz49x2+OUkE2lWS0t86clPs7yywuDAEHdc/xp2DU3QChscO/Mlzk2fRqF4+YvuYmwwCmop1ZcoNVYplctkEn1MDu/Fth1qjSrlepGUM4ltJ8llIuUn1EKh0CSRTLEwN8fayjrpTJbA9/GDgKGRUVLZfqbPnqCvf4BqpUxqYILFIEsQPlVm1m5c/uf80wn4EIZyNQazNcqNDMvFDAOZKqIrVP0hJkfy7B9NcmTf4R2FjbsZ+e6BKxcE8p6ViweBKKXuBj5JZz6dDZ8PTZQ17f9su6YK3CIi59rfnwQ+ICK/2f6+FzghIp3kmTu1oacAXhvQYUDg+yRS6Z4CuAM8R/GiPWmSiQK+WSeT7GM4v58on6qFMZpWUOfs/CMc2XNHlLbLsjfJX+fXnmQgu4ekl8G2nU2C5oXV0yAek2OH2jluFVoHVBpFppee4KYDr0IpC6tdh2CYX3uc0f4juHZy08dNRFgpnSHp5cilRjedyUUMQWBYL1QYHYki/pRSkTXEaBYKxxkfuAHLcrbJOkkmOUI6MbApK9QB86tnKVVXufHAy1BYWJbVtjq0mF19gr2jL8JS9pa+G45PfYFdQ9eRzwxtpmYzxjC99CSWrdgzcsPmOIY6oFov8aXHP8ndd7wFy7KwLaed0cPwucc+yosOv4psug/Xdjetoo+dvZdMMsvBiZvaqe8UQeizuDzHzPJJ7rzl9ShlYVs2oQ7ROuBzj36UO298Iwkvjeu4mLY17rMP/TMTw/s4sOd63Ha0cRAGLK1NMb96jtuvv7vdR4tQR5a2+x7/OC+/9atxbQ/bdiJZYrjviY8zOXSEybF9OI67aY0rVQqUK2V2j++J5tey0DokCFvMzJ1irC8DKsoxvfGYXSvbjI4N4bpuFPjRttqtlaYQUYwM7N/MEb2xHmcXz3Jw981Y1sY6jawgy4Up+jIjOI6LvWWdrlWmsVBkt0SshzqkUCkxsxZy24ExNvLjah1Zus/NTHNo34HNujf6uFJYxWmVSbl6M0WiAE1xMMkcg9lRoC2rbblaWl1gcuwQth2NiYhBgOWlVVLJJJlMon08spAuL63TbDbZs3es3UfVjuSuMzV3kn35bOTXuNEuYCVssnvXbZuR6Rv34vzqKbTfZLRvbNPXVBtN029QrDfYP3E9G/matQnRRvPwyU9z6+FX4joJbMuJrIQYjp37IrnsGLuHD11YQzqyIk+vnOauG79iyxoKaPpNPvQvf8Y3vPHbSLgJnPZ61Mbwqfv+nqMHbmViZN9m9Ls2ISuFGdYL0+wZiZ5DlormREvIeilgbKi/7WWhCEONZSkqrTq1RgvbzrE6X4LsLqotQVk2TT/EtQRlO4gOqQVCtRHvbvFUeHYVQNny//hzXVszMbCK4ygqzSwpp4wJlrnx4MvIZwZwbIdcbnTHl92Em5F3XEEF8N1PrQBmiFK7bcX3A28GvgaYEZHStmseA94rIv9XKTUBTAMvFZGH2+UvB/5aRCYvpY2XpAAqpV4D/ChwG7AX+FkRefe2c74P+MF2h0rAx4B3isjSReT+EvAWYDcQEpk2f15E/mnLOWPA7wMvact8h4g022V/CHwH8Csi8s4t1+wGZoDX7ZQN5FpTAEWEwG9h2w628+zG9lytCiDAvhGPiXwFx9WMDx/sChDROqTRqpJOZrsoJvyggdNWDrYiyhXrdcmKtj99kl6m47i0s4dsKH/bZUVKg911TaVSJZfrlhWELTw33RWoEOpg8wW1vY9AbD9aQYOk1x0t2fRrOLbXlVtYm4jVrIseJgxo+nUyqXyHLBFDtV4ilcx2yQp1iFJ0UKQABIFPaIJN8u0L7TWsFVYY6BvCcbqvUZaFs62PG1u/26lmtNFU60Vy6f6uvjT9On4zJJ/Pd9UP0nW+1iGlwllcq9s3VKwMqcxg15wYo1GW3RU4pI1Gax/P7fxBL2Jotqq4jhdTf0AY1rtl6RBtFAmv0w/QGEOz1SSZ7I5GbTXL6Mp8V/CTESE5fLRrriIlMCSZ6F5DoQ4i8uftY9Le1t1+vjaa9cWzZD3ZtoYEKz1Eum9fN72T9mk11mKpjyyvj+T2NSSGejOidNkuK7p/nK77JGi7ImyPWtc6ZLWwwvDAaBcNTaNVx7XdDuqjjWvq1blu2icxaK1w3e6oZj9sMb1+nnItSSPYT7EWxPs5f5l4Np/ztmVwLKEVbg1y2m4hFHLJBv25KglPI2GdrJfkyL6XbD47LqYATroZ+b7+K6cA/vTqpdPAbEAp9W7g23aigVFK/RjwLiKd6LVEASE3byn/r8BXichXXUp9l+oUkCXK8ftOYC6mUW8FfoMoD++NwFuBFxPtb18Mx4AfAF4EvBz4LPD3Sqnbt5zzc8AjRHvdAvzINhlN4IeVUvsusS/XJDZ+0RsT7zjcQ4Slok/LOCScQeJ8SGzbIZ3spnhQSuG5qa6XQbu0i9oDImqRhNvN0q+Uheekul7cW+XFHo2NSLRilT8A27J3jGKMO9+y7FjlD2gTJMe8DCQ+oMRx3C7lb6O92XR/vCwkNurPdT2SXvc4WpbF0MBIl/K30Ze4KGzbsruUwo3jucxA7Jx4ThIrLoftDmGwtu3g2fGuGIlEKjYqXSA24MC2bNyYHL5KWXhe/BoSo2PdoyzLxnXjxsoimUzFjhdBIz561UnGHrctm2QifhfCgtgo+sjaGC/L3oEaJ5ka6lKaAMwOwUWW5ZCIW0PKIpPMx8rasOZvh2PZuDHr17Ydhge7lT+AhJuMfXYY7ce3V1m4rhsbVOzaHn6rQa2lWSk328rf1ZXaTRvVVv4uIG49VZopFtcHKJYdGo0G/dmxHZ4d3YiCQOSKfZ4lvI9I13ojsE5E/7IVdwF/canCLjUX8EdF5F0i8v+IFK7teBXwqIj8noicF5HPAR8kUuouJvePROQTInJGRI6LyP8HVIkiWjYwADwuIo8BJ9vft+LzRAriey+lL9cybMclDIOuXKM9XEAzEObWHRw3nj1/q7N9Ny443Hceldhn74aFKFbSxaYnLrLzIhfITnXscJ1wMeqLi9TzNNq1sQ399Mu6j0d5cC/9fIgCQeMjOA07KdgX63vsvF90rexUxw59lx3W0EUCA2WHCG9QO8y77EgtsiPNjh3/KouCL+JlxUXbXqhn53vr0s8FMWF8+Y7r+mL34k78MDutx53X0E5R99GzIyZqeaOiHa6JPypUG3VCc0GhtBTY1tX0zFdbPhGk4y8h4Wpc2xBoh3orjYjCIBe9JzogYF/BzzOBiLz7YiTQEuFnReR2EXmTiBzbVv5WEfn9S63vcoUFfRa4QSl1t4owRqSZfuRSBSilHKXUvyeyNn5mS9F7gF9VSgXA24msjFshwI8Bb1NKXbK5dcNSdq0pSolEklazmzKjhw0oVivCYrGCjnmJGKNZXJ1pKx0XoLVmZW2h60UZ+TGFO1B1GNZKC92yTMh6ea6LDsS0M27spND5Ya2r/ihTxWrXy0XE0PK7iWcheuTGWYqNMayvr3W9xIwxLC4sxNNbiMEPW13t0kaztD7VRbmijWZpbSa+760GRsdQ44hE47itLNQBC6tnuyhljNFU6x2uM1uFUaqud/VF65C10lwXpYzWmuX1eXZ6Gdfq1W5ZJqTqN7rm3RihXCnGrqHQBLH0MBFtTvf8hjpkaX2ma3wj9wI/dg2JCOVaMUaWZmV9pavvRgwNHa+wivZptLrph7TRrBbnu2UZQ73px8oyBoJAd5Vpo1mv1zsiZzfatVqY7rrnIv/AHZRfEcqV9dhxXF6fiVlDhlq9tMO9KKxXlrtk+YHPyaljBEGnVU9rzeLafLwsZVNv1rqUGiOGhl+JPV5tlEgmUmRTBtvaiI5mM4L76sWF9qc8zUh+HYvoGdYKk4T0s1JaufiP5y2wAE/kin2uBlwWZzER+SulVD/wUSLuRQf4R+A7n+papdSbgT8HUkAZ+HoReXCL7IfbkS0jwJLE3N0i8hml1IeBXwXuvpQ2t5p1LMuOto/aDtzt9lzK5c9LKKXAsnA8jzDwcb3EU1/0AoQ2wvRKEzjHgV37cKzIymEpxdT8CWaXz5JwE/T3jSDGoCyLSr3A7MpxfKkxMXQIY0w7EEN48uzDKGVz/f7bcVwHJJqL+ZUpVgsLeE6abDqPSLSdVaqs8+S5L3F4762MDRzYVBT8oMlDJz5JLjXATYdecWEtKsXS+imq9TKZTBrXjvKPKsui2lxltXQO6TPk0mORLxUR39zU4qO4do794ze1rZQCSjG7dJp8aoJ8Pr257aeURbFU4sypM1hHLfr6+jHGYFmKZrPF9PlpUqksuXwGlLBBNfPA8U+gTchLb3gjthUFSNiWzYmpB5laeJKX3vhGhvK7olymlk29UWV66QTVZpGDEzejjUYRBXt8+t6PoAOLr37d10WBE+12PfTkPaxV5rjjhq+gLzuEEYNt2cwsnOYz9/8zL7npVVx/6La28qhoBnWOTX2e4f4xDuy6rW1cix4b9z72cWqtIq960ZtJJjKRP5myWCvPMbN4ChPCyOAE2mgsy6JSK/LY2XsYzu/h1swrNuWICA8eu4eFxUW+4tVvIeEmESJZ82unKNYWGUoOkmnXoZSiVK1xZnaWXbvWOLzvlsjvD4U2IQ+c+CRKKV5y/es3/eqUsjg19Qj11iqHdt9ONj3Qnl+L1eIcx6ceoOnX2LfraJRXWEV5kr/46CfxHIfbb3h5B5H0+dnzrBSXufHQzWTS2c06VgvrnJh5jMAcYmJ43+Y6CcIW51dOocKAg/0j7btHgcC59SJuo8HByZtxiAh7LWWxXl1geuUUlm0xlN+12Xc/0FQqIXUVMDSU3tz21Vrz8AOP43oJbrz5IJYVBczYtsXU0ikWqqtkU0PkvMgCpJSwXq0wUyvQxG6v7WjNGzEsFk7QqNeZHNwXWTvbARQr5RXKrTX22beSTkb3omVZLK1P88Cxezg0eQs3HLwtGkcU9WaVex75Jwbz49xxQ7QhtWFB/dRD/8jCyhxvftU3kU3lMUZwbJsnzz7K/Sc/g+PaHJq8PppfpShVC3z6kX9kcmQvr7jpDdEPAIna+0/3/A1aN3jtS15DwvU23TNqQYVKq8CAskk6qWjVSeR/uFCYjbbZQ4VlSZui5+p9b3Ui6oc2EAQB4LW/W5QaQyirwuNTi9y0dxcXMTS3IVsIj64NKKVOEf9rtAkcB/5newf2knBZFECl1CuJfPV+HLgHmCRitf4jIqLCi+GTRMElA8A3AX+slHrjNiVQA0/Fk/MTwBNKqa8FHnyKc0klk2ACkJAwCDBK4bguF89ScHXAtgxhGKJ0t4P6ZZEvOzD9X0UoV4XzASwuP0EuEc15sbSCbUPCy1KqVPDcPLblYkyLSq1IKpmh1ayyvHhm0wcoNAqHHKIVU+emGJ8cB6Beq1Ne8SksBcyEC+zZ50bRgkHA1Ll5HPIsry7iWX0k3AzGaOZXzlFarxOmPZbXFshnhgFo+hXqjSqtRsDa+gq5zFgULWgarFdWCXxYKyyDTmFbHiKGQnWBYqkK1Ek6A/RlhwGhWFllcXGBRlrQeoRkIvIvq1ZrPPTQY+w/sI9SuYxltSM4EVaLizgZYWbxHBNmkkQijSAUyouEvmAEFpZmGe6fRCmLamONtfUVdGgxM3cOV6VwbA9tAqq1On3JCeq1KstrS2RTeUQMiytzJN0sKiXML00z1D+BQlGprbO4vIQow9zSFI6VwrZcWn6FheU5Em6amfkZRgb2kk5m2+M4R7MqrPpVUtYifblBQLFeWqTebNCoh8wsnGdi+CCWZdPy6ywsLVCv+iyoaVw7TcJLY0yLhZUplDg0Gy2W15fIpQYQEZbXZjk/NYVlK85OnWLf5BEsFSmMU7NTKDvAr9c4uHs/rpMkCFuslk4itma1sEJ/do1cZgBjDPPLU1QrdbyEx/LqAgP5ibYSUqRcKRPokMWVeSaGU1iWS6gbFEprpNwslVqFUrlKwksjGIrlVRzLo1qrM7+0zPDAKApotFoUyzVM4LJerOC6kd9bELRYLxWRULFWKNGfMZEs0aysL9JoBIBF3ZlkID8aReCuzbNaWiXlC6uZdYby0bw3ggZrxQLNpmZpZYWEPbiZ2rBYLbWtzj5LJx/DNnUUCt/KMzw6SctvsrK0SDYVvZICHVIqFlHGpRikyQ9dj+ckCEOfZuMUrtWkVCmxnlol4aYAoVJfp9FooQ0Uqi36cyOAotmq0Qx9/JZhYXmG0YG9OLaDHzSZmjlLpeiz7C0yPlQi4WUwopmam8FvKkqUmF2aZrhvHAWslBapNmokk0nOz5/j0MRRbMulVCkxuziN56SZX5lhMDNGKpEh1D5npk8j2mJ5eYW5oRmG+yPFeHl9Dtu28NwMlaYhkRwGZRHoBtX6KpVai7CxSL+Xb9/v0NAhnpOmFSrqNQdCjf0sWZuu5HNeB0KlNoCNYEsAKHRgsV7qo9Es02gu46okd9+xa0cZCnCuEkvc08BOcRUDRPx/n1JKvUZEvnApwp42DYxS6jTwp1ujgJVS9wBPiMj3bTn2CiL/vBvkaeSmU0r9M1AQkW+9hHP/ENgtIm9sf/8A8AbgTcA5LiEKeKP/Ogw3KSRc18Oy4x2ArxaEYUAYBCSSSXiaaZOeCldzFHAnpE0vYcAEKAnJpg0Hd2U5smdPV9L49coUCRNZCDbGM/JWcbG9QTzP3XQAFxHCMGRmapb9Bw90OIZrrZlZPM2e8UMdUZShDlkvLpFN50km0pvK+waZ7NLKHONjezsc1o3RLBfPMNp/qDsBfWmm7TSd2OyLMQY/aNBo1chnBjr66Ps+hUKZ4eH+juOhDjk3e5KDe27Asd3NvkfbemX8wKcvO9TRxyAMODn1AEf23dbhtB1trTVIp5Md/dA6ZL20RiqZJJ3q2yyLopx9phdPsH/ixg5ZoQ544PHP8pKbX90R2BHqkPnlc4wMTJDwLgRdGDG0Wg3mls+xf/Joh6yI0PtBjuy9vcNZP9QBU3MnGOrbTT53Ib+v1ppqo0S1VmRseE+HrCAMODX9ENftffEmfchGu05NP8LhPbd0jGMUPbqI67r054a30P9E7gVzK6eYHDnS0S5jNPOrJxgfOtw178XqMspokon05jgaMQSBz3Jxmb27DnfI0kYzu3yKvbtu7JiTUAcsrJxjdHAfnpvcsoY0jWadYnWZsaHdHWtY65Anzz/ADftf0lmHDllZn8JdeQwlF3IIGwGt0qjB/SRcC3vLutNGM7te5+jhF3XN1cLaacYG9nT1vdEqYzsJEk66cxxFs1qaZii/u7OPYcDjpx/kRdff1bW2ZpbOMzIwRjqRuXAvGkPTrzO3fp4DY0e75v2Bk5/jpUdfue24z+OnH+L6A7dEeX+3rKF6s0JgGvRnRrv6cmb6XkYzAx1jYkQotmrMlJPMr2UJ2+TO8Tlpvjxc6ee8UoKlpL21feH9ZVuavnQRS4p8w+u+eccX234nLT+du+65aGosvqf46NOOAv5yoZR6HxFP4Fde0vmXSQG8H7hXRH5gy7E7gXuBG0Xkyach/+NATUS+7hLO/UM6FcAR4DRRUuV38TRpYDZ8Av1mo51dw37WKVWeTQR+lL4rkby83IBX+sHw7EJ4853j5FLd/WvUVtB+uZvaQ7mksrti6FZ0m5uv29U2CFrYthtLB2LEdNOt6JB6vdVFAwMQah/bcrvaFVlcNHYX3Yq/yTnWfb6Jba8RaXPwbaf9iKeB0UZjdNBFe7FRT0R1s328ouPdVDNRHtU4a3YQ+NG92iUrykyyvf4gjFJjbZe1oWh6bnfErdYhtVq9iwYmCP3oGdFF5RNlknCd7rRrYRiglOqKCA3DAJTqilCOeCOla9yja1qb/IPbj/t+tZsCJwzxtigzHX00Bsf2uteQRNkots9JEPpYbeaB7X0PddBFswNQW3qS5uqTXY7nxkrgjR7pppoxBhLj5PP9MX1vQky+52htWTFzondc26ER3C0/kjawwZG4fR79oE231bWGBD9sddHDXExWqIPYiHVjDJX1U9gxj21thBPzFg1rkEItYLLfJeEo5kshxZqPQm1mZ/ly8Hx+zttWyFBmka977Vt2fLEdcNLyM9kd4ymedXxX6bEroQDeAnxcRHY2jW7BJWk3SqkssDGSHrBLKXUbUBWR08CHgXcppe7jwhbw+4l4/U62ZdwJ/DERu/V97UCR7yfyG1wE+oG3EVnw3nop7doOEVlRSv0iUXLlp42Nh18ilSYMg4jTqhngtbfJrjaLoON6gMJvNa/aPjz3UKQTO9wWEsaOnzbx0YKRRSx+vOOUP6DtMxVDX2I7xAfgs0maHNPgLuUPInoLI7qraRGNUPyLY4MYOqYKRHVfYykLFUuZs0FDsxPVTTx1y04/VB2nW/HdaJgVQ8fh2E5sMItSFk6MwtYujKXwsJQdReJuG5coG0/8i9O2d5qr+JViXaTvluXEBr0ao2MpXSJleOf1GNsu6VZKIZqTKD9yZz2WZeHu0HfjV2KjDi03EZkCt2k7lmWBFT8nUVvj6XTiehitq/hxdO1uDs9IVrzi7bmJ2PzTSqlYpX+znpgyhdX2uew8blkWMemcozIFKQcGMoq8Csh7MHPuJAP9u5gYH6JSKVEI01QaV7+rzk7QxqZY67/oOerZpWN5vqIJdP8C2QGXGgX8UuCh9mcP8I7237/XLn8vUQ66nyTi9vsL4BTw5rb/HkAaONr+F8An8v37UPvcfyYie/4aEfmbS+1ADN4PrD7TizdeTq7rRQz7tkOzUSfiKLu6FpNSCseJiExbzTpwcWqFHiJUd3hwKiteebCVjn15Rta8mKhWIgtKLGWEUrGUHGEYxM6diNDym7HXCNIV3Qi0Hd27sWEFi4M2YawsIJZaZCPPahyC0I9/ge7wxtNaIzsopmEQ7kC9obqihiHqR1znjRh8P17BFiOx7TWiYxVWY/SO4xhlMIlfXzvRrey8hlqxfbcsOzbiNIp6j6ez8f3mDusxfg1p003cvNFeP2jFtlcl8ug4BpywFUvfEm2B77QeTVeUdfuiWBqlyL3n6Y3jRoDOduzUP2NM+16Mu09NV3RwVEk3z+FGe8O4wQLC0DA6nuPUY19k7vSjLEyfodVqUVufx2mtsXr+GOnnp+HuMkLhm+7dkA4I2GKu2OcK4W6iXdBLwqXyAH5KRFTM5+52uRaR94rIURFJiciEiLxdRKZiZHyq/b0gIm9pn+uJyC4R+QrZkgXkEtr1nRvbv1uONUVk79a6niksy8J2HDzPo9looMMw2lYNw83tsue7QqUsC9uxcRy3rcj28FR4+FyRcBuzvhFN2S92cetFFB4hfljteLmadnL6c/OPxVCLhByfug8jndQXoQ6ZWz5NrVHuuMYYQ6BbzKyc7HoZaxNy7Nx9XS/DUAecm30ierltbZfRNFs15lfPdbfLGE6cf7DrpadNyMnphzZTuW3tR7G6wnJhprO9Ygh1yOnZh2NlPfzkF9DtLbmOPvoBzWbny1hEMNpw6uTJLoVOa82JJ4933YOhDji/cIxmq9oxLsZoWn6DM1PHuhQ0YzTHzt3XNb5B6PPYyXvb2R86+14orXFu9njHNZEbieHk1IPdc6VD5laOY9pp9La2d6Uwz8r6XAety0Z6s6X1M93jqEOeON29hrQJWS+t0tpGQxOl7tOcnjoWbTdvlWU0j5/6UpeiqU3I8vp5dEzfq7UyZ6dPdMkyYphePB67HmdrJcw21VSwaAU21Ybu2LaMKF2gVJvvUtyM0ZQqK5H1eWvfdcji6izrpdWO9bgRsX5+4VgsLdHp2Ye7+h7qgFPzT1BvVTtkaR3S9Bs8dOK+7jUkmgdOfrarDj/weeDYF/DDVkdfoh8KDSqNlY7jG+09szDdRYEjAksrDdYXC3hugoPX3UAqE+ViVspieuocyM58jy8kKMDFXLHPs9Inpe7a4fM17Swiv0rEwXxJuHod3J4jKKWwbAfPazv9S+R3ZFk2OgywHQe7vQ218Uvu+bbNqpSF3d7+8VsNvESqffz51c7nC+bXmnzu2BovPtRHNuUQ6hYrpSlqzTUs4zCYHiCd7EfEUG2VqIZ1rHKBgfReMslBUFCprfHk1H3UW1VqzSpH9txOwk1Rb1Z4/OwXWC3OUyyvccuRV9KfGUbrgLPzj3Nu4Ql0KNx44GXs2XUYpWBpbZpjZ++l0QzQpsXRfS/GdRJU6kUePv4ZVooLVOolbjv6KnKpfoIw4PTsQ5yceozzi8d50ZFXMpQfRxDml89zbuExWnWhWqtxcPcNOI5LubbGifMPsrq+RqADbjjwEpJehkC3ODP3CLPLZ1gtzXHj/pcx1DeOEcPs8klOzT6MGOHQ5K3s3XU9tmWzsDLLw8c/Qzrn4DgWk0PXk0ykqdYrPHbyXp448SjrpRXuevHryWUGMUYoFMo89NBj/PPHPsYP/dA72LNnNwpFsbLK0to0LZocP9Pkuv1Re5utOrPLp6lZq9x/bInDu29hoG+YUAecnn2ER49/Ccf2ePltr2fX0H4A5pbOcvzcIxTL61QbBW46fCeu41FpFHj8zOdZKy6xurbKHbe8jr7sAM1WnQePfZ712jTLxXledOQORgZ3g8DZmeM88MQ9KFtotKpcf+A2bNuhWi9wfuEx1krLrD24yu03voZMKkcraDC1+Bh+WKZ0usjusesY6BtB65Czc09ydu4RglBzw/6XsH/yRmzLolJfZ718Hl838JeajA8fwbUT+EGDUzMPUWos8aXHPsFNh+8km+5rR0CfZ2l1mlTGZjA3wVB+HKUsKo0Cq+VZakGV0zMh+yduxPOSVGtFPv35TxBQY7WwzCtuewP57ECUp7Z4ntXiDMuFaSZHrmcwvwsjwqlzx3j8+JcwKqQRVrhudxQ8UmtWmVo6jq1szk4fY9fwHlLJNEHY4tjZh1hem2U9lWXSE3LJBGKEcivJmj+MWm+RbzUZH57Ath1qjRLFoIkxPsvFU/QFoOoFyAxSthNUKj6V8jJjgylUax0sh6LWLK6dRRBa/mF2jezFUopKvcxS8RSrxUWaQZPDu2/BdZI0mlWm5k6xVl5mbe1fuOnIS+jPDhLogMfPPMBjZx9ANNxx9DXccOhmUIrzC6e594lPUSwWabbqvPTGu3DdBOvlVR48/VnWyssUK0VecdPr6M8N0mg2+OyD/8q5heOcmz/Oq2//CsaH9wJCsbrISvEslq1opKsM5vZgWw7VepET5x7CIKzW1xnO7cJz01QqZY6fmOXG/z97/x1tSZad94G/E+b652167yory3dXV6MtGkADNBCIgTgiJVLgDI0w4lCjRRIYUKJEiiSGTiJFYRZnQCOQi0OuRUAEgQYaaF/d5V167573711vwhwzf8S9N/O+iJuV1V2VmV1Ve61bWS/ujX3O2edExI599vftU89QqpSYPHSKdDZFdnQKna8RBJKM4yK0pBR8FO7t9x5jtAX80CJxH5S8TP9Mh3Xgf3ovRNAfO4D3IUKILhjExulCy103qryhlULKENuOIm1Yjx6VzJ0xGHyviZtKY/UpFfaxwHLRY6XYYsdYk4mhMmHQwBIwPXKI4aHoBg6CdG6CsHwNS6TJZ9pFaoxhIDfK+NBuFjducGDHSVw7hTYRMvPQrlOsbS6xd8cxhnKj7Xw8wf6dj7G6NU9+aIA904e6fGFTo3tZ3VxgI1znyJ4nsG0XYzSF7CD7d56k0tjk6N4nKGSGUDpKdN+/43GW1ucYHdzB6MB0NyKzY2I/y2vzNFWR/buOI0RUPnAwN8bE8F6uXrvBJ05+kXQq4olz7DT7pk6yurXA7qlDjLadP4xh9+RRNsvLCAv2Th8Doqjc1PguDu8/ydLGNXaMHYkoVQzksgWO7X+KpbU5HjvyJAP5sW7O48joEPkBhwP797Jr504wBoNmaGCMlY05NkpLfPLUlxGWhTGKdCrD8OA4NxfP8tiBTzE8ONbmZrQ4sONxbs/fYHJ8mh1j+6NoPbB76iCV+iaplM2xA09HOY9GUcgMsX/HSTa2Vnn8xHMM5AZRWuE4Lk8e/yQvvrXC2OA0E6O7uxGnA3uOsVaco96scWT/EyAE2mjy2WEcMczGxjW+8PxnyWYiShXXSbF78jhzq+cYHtnFyNBEt18Hdp1gbXMOK+ewf9cJaFc7GMiNEsg6teYa02MHcWwXQ1THeP+OE6xvLLP34GHy2YFuBGl6fC/l+iq5dIHpscPRGhKC0cE9hCrAcVx2j7dRyEA+N8yTJz/B+ZuvcOrYcwzkR9qcfg7jQ/tYWZ8nnx1gZHCqSx5+ZP8Jat4G5VKJPeO7CIM6UghcSzCUHmVlrcr0gQlcx0KGLYwx7J08yOLcCtlMnsGRAhiwLMFoQVDfVGQHhtm35zgRYwGM5CawW+uUNhex3/pNmo2I2FsIgciOkX/iP8NUb+HdWu5QE5JCMDE8jUgPMz22C2MkSkM+kyeXGkKrdfZPP4ZjpTHGkEkX2DG5l83aEvt2HWcwP4TSIQI4sf8Jbs5dYXBwlOOHTra3vGH/jkPMLd1EtiRPHvkElrBRSjEyMMrxPU/w6sVv8cShZxnMDSGlxLEdnj/1I9yev87UyG6mx/Z0UzCGCtMUa6topRgd2IMlrOi6zg2xZ8dB5leuMjl+nFxmCBAMuiMce2wEBExOjVPIFegECHdN5Hj50gbrW1FU0pC8df5REgHYHz4H8ECf4xVjTPm9KnvPKOAPiyShgL9f6dgwKsMWhe9TqTTiEXQEjYnymbSUIESXLPq99PP9Rod17Peo2QoMjm3YNbZFxi4znB9j/47HExCvEtpExz3HlaTaLDFcmOj5TmnJemmJieGdPehKYwwtr046Fa85rJRks7jBxNhUL0WLDNkoLzExsitGt1JrlihkhxIQpxIvaJLPFnpy2EIZsLa5wPTEvp7E/4haZIOhwngsKV6qEGM0rpOOHS9W1hgb3hGje9koLTM+siOGLA1CPyJnTxh7pV5keGCs145KslFeYnybHbXRNFpVculCoh3rzSoD+e1UNyGrm/NMj+3tQQ5rrdiqrJO28zEUsFQhfuCRyxR61q6UIWtbi216mF47NrwyucxwDEEajV3g2L1AAa0VXlAlkx7qAXYorShXVhkc6K2Ja4zBD1uk3ExsrrRWBLJJysn3jD2UIetby0yP7+kdu9G0vBqZdCHW31CGVCsLZLfV/VVa0/IMgwO9bWitqdW3cFrriG2gIa0hP/1UrKazNpr113+T9OoZxF1btEbYqKnHsfMuYtsDXtspBo//oURUeq1RZWibvaSSlKqrjA7tiF0/lXqJgfxID5UPRDtA5VqJseGJbXYMWNlaYMfY3p5zlFKsbiwzNTGdgHCXSBmScjPb7BilGIwMjvfQK0UIc42TQFMWSsVvvbaENndoYaJb6/d3X32UUcAd+fN/5Nm+gzvipM3/Wtj9ILvTI3+4cvuBo4Dfq3zsAL7PYozBaI3vtXBSqYgmIgGN+LBFa41WkjAISGUy7yka+H7cGLpOc3gHDJHOZH8gnR+MGHKpgMPTHvum95LPDCTQrSgMOoa8NEYjRDLqU2uVTMehJAYS6UCaTZ9CIV7QXmmVWMw+6hex76QK25Quyeck9cu06UBilCMqRBBHyUbzm4xo1jqZjqNTmm/7QzLKFUxGDt977HEUp1KKqGBOEhVKP12aRqMVo+DpR93SaT/ZjskvO/e2Y/LLkVJhMgq4Xc0iieqmnx21SUa8dupZb9cVhh6BV0qcR8vOJB73a8vIVjHujjgF8mNHYqhtYwxrX/l7ZGQxpstMHUQUhmPH7aFdZHc/h2XF7WglUCV1xpiE8lY6WvPbHUApZZvKJ/n6TdLVucclXT+WiNMSdc7pxxQA8TURSs2rVzYI5Aauq2h5NpXGQIftNKbn3eSH3QE86qTN/3twx4PsTo/8RGnufXUAhRBPGWPO3udv08B+Y8y1e/3u/aoF/LG0RQiBsCwyuTwYUDIk8FqPHFgkAri4pLNZQt9HhgFhGHS5ED9I6bThtRoRx1g7h7IfSu/hiqAZpGh4wzginewki2QkbJLz03NSv28S2riXrv4J3yLGrdb9fb857vsSEOd860gS4jQaQz+ql3uMPZnEo//v++iKmDWS7Phe2+6cd78H76Odfn1+z3r6ftXvjHt80++7ZJTqPVvp93udjD4XQiQORggBfRDQfQcvHN6rs3OvayuJTqffdRDp6tevftf1+7iGhMG2BF7g0mg5KNnCsRWO/Wg9ex6UCAy2pR/a5wOQrwgh/g8hxJdFn0UrhNgthPhlIiTwZ99N4ccO4AcgXSqZVCoqL2dZ+G0n8FFyBDt1J9PZNmu+MbSa9W5FlPdTOmOXYYgMg4ibMJXBTUVVVyzLQqt4QfhHQwTFikRrK7F//R6eSkn8oBE7RxtNpbHVh15D9aWxUDr59+XaZiJdRctvJlKhdNvZJlJJSpXNmCNujKHRqiX2t9/YpQqpNUuxdaS1ptooJlPERMV/Y4ejCg/1uB21olLbIpRJdulHm6MJEug4lJKUaxuJ9mp59cSXEwERD+A2CWXAZmm9BzHdadsPmolzFV2LscNdpOj2sRijafnJ/UrqU0eXH7Rix5WSVGpbifQ0fuAn2kQIO3ENaa1oes2E/hqMm0ebhEhmwjUCUaTNz0zEHGODQClIcidldY0kB7CDMo+vIUO1Wk0co5Q+JiGHTBtFIOOUQVJJtkrrMRRw9F2QSOcjEMmUPe05ia+hdgpPgu1tISg36tS9LNXmADV/ilA5SPWopdY8IBFgWeqhfT4AOQ5cIioHVxFCfE8I8e+EEP+7EOK3hRC3gDngx4D/zBjzz++lDD52AD9wsSwbx03hOA6+1+omUj8q0nFWbcfBcVNksrmuk+Z7LbRS31efOw6fMQYlJUpKfK+FUhLLdkhnMt1ye1H77gOLQH4/EmrwfIVSpkuWbIxp55vVaXXnNrphSxmyWV7hrUvfQqqw+9BXShJKnzNXv0OlXkS2qSSUVkgleefKi9xcPI9Ud6iGpApZL80zu3wZpWT3YSVViOc3OHf1dVp+o+tUKSUJw4Bvv/YVbs5HlCemTT0ileTW4gXevvztdr9Ut7+1RonXzn2DIPS79B5ShoQy4PXzX2d9a6nLR6jb/bo88ybnb7zc81CSMqRUXefs9ReRKuj2KwwDQunz1uVvslla6/KidSpnXF14k5tLd+hmjDHIdp7f6avfbY9d3hl70OLq3OtROa22HaWKcqpePf1NLlw9HVVg0O2xy5Cb85d462J77G07hmFArVHh2298hUar1nUopQwJQp9vv/47LKzMtO3YGbvk7KU3eemNb0Wk8erO2Kv1MqcvvkoQ+F1nQCmJVJLLN9+iWit3x2FMxGc3s3ye20vne6h2pAop1za4cutNtJbduYp0hcyvXuxxAqNxRqUBS/WldvUL06UVKVZWOHvpVUIZdvsVyhDP8zhz7eUeO3b6e/byK8yv3OrmenbW0NzqFa4unI8qvrQdTqUlDa/B5dvnIq7HzprX0ZhaUmDsLAaBMdG2cygV6xWL4mYNre9c+1JKpFTofc+Dk0GLaFtVWw7GSbE2vIeWcVBtZ6+dcEAtM8ra+gZK6bvGriltlbl86Vp3PURjjMB7M7dn8f0AKaP+dkovnr32CrNLl5FtGhzTPr60fou3Ln2bUIbdeVRKEoRNLtx4Fc9vdq8fpSRKS9ZKV6k21rppDp05KdUXWS/f6FYA6tirWi9xdfatnus9SsPQrBRvU2+Vu+uho2+zUsR17n65EXd9PnoihMFx1UP7vN9ijGkYY/4HYDfwp4B3iEifdwBV4FeBk8aYLxljXrkvGz2KD9sHIR9UDmA/6VzwMmxTx9yjIsDDFtMmU5VKopVECAulJK4bcU05SFSbob87BmPab7IRkbGS0XlShlhtB09YVrtKQj/SV0XgeWRy+UfONhlL8amjIwzk0qighGNF5LWBERiRpV73sWyP8dEpQLBWXODSrTcxaCZHd3J837OkUzlafp0bC2fYKK9gtM2TR36E8ZGdhKHP9fkzzK/ewLEdDu95iv07jgGC5c1b3Fo6h5YO0+O7ObjrJOlUhkp9k0szr9NqSLLuAI8dfZKhwjhe0OTMlVfYqixhWTZPHH2eXZNHwBjmV29y6fabKBmyb+dRThx4FtfJsFFa5vSVlxC2ZDA7xWMHn2WwMELLr3Pu+ivMLdzGtl1eePrH2TW5r8urdnX2DFpqjh58koM7T2JbDmvFRS7cfA3bMQzkxji25zkKuSHK1SKnr7yENHUa1YAXnv1xdk7sQ6mAW0vnmVu9hjGGQ7tPsW/6BLbtsLa5yNW5t/CCJjvG93Fs33Nk0zlqjSLvXH4J24ZsJsOhPU8yXJggDAPeOfcGb58+zdTkNJ9+7nNM75hCa8WN2au8/Oa3yebSHD9ygieOvEA6nWGrtMqZq29Sb1UYyI7w7MlPMzQwSq1e4XtvfoNUwUeHLsf2Pcm+XVFt0dtLl3n51e+Rzw9w5MhBHjv4HCk3zfrWCm+c+xbCUkyMTXHqyKfJZ4doeXWuzL5Jy6tRyA6wZ/IxRocnMGjWy3Msrd3C9wJ2Tx5m945j2JZNtbnBrflzUYk85XLs4BNk0lmaXp210gwtr0HKTbNnKkKKaiMp1ZeoNjeQSjI2uJeRgR0IBKXaGmtbt7BIY4sCu6f347oZKrUyF268RqPWIpPN8PiRZxgbniBUIWubC9Ra623bH2Z6fB9Ga+ZWrzO7epl0ymV8cBd7J4/gOC7F2jqvnP4ubtZhZGCYY7uOk0/nafkeq1vLDA9Nk7VtxBu/jzn/Io1A8Yo/yfGf+0vs2r0Hq3abbPUaJmxihg/h7HqKcmsLixoDokAmO4TvVdjwNtlqlHCFy87hyWjsKmSrWiK0FbadImtGyLoahCGQNr5MsbG+wo4dOxmfHMe2bJrNJuVymbW1Vaanp8nnsxQKuYhLsb7ERmmRjc0tdk8eYc/UPoQQbJRXuXzzLIEKGMxN8tTxT5HL5vGCBgvrl/D8JrlsnqnBXaTsFEorKq0tpIgcs5HCHgqZUQDq3iaV5jIA+fQ4w/nd2LZDtV7iyvVLpJwMNgMcO3aATCaL1prVrVtsVNao+2kyqT0cmBog67pIKSk1y6xW16k3sxRrnWjr938f/WHPATyRcs2/nB5+gL3plU8vbH4MAnlU5UE7gB1RMopiWJbVLtX2aEtnfRgdvf0iBEJ63RuDkiGOm4rekLXGcV20UliO0y2Fdr/8iNEWcQCIaOv8EXICU5bi5A7NzhGNMKq7XWcAL0yRyg6Tz2e7ydxSSZpejWuz7/DM8S9Ezq+wosiW1lyZe4eje57GddPd5HupQq7OnGFkYJKp8Tuo3igStMmt+as8e/Kz2JYVOeVaoZTk7Svf4NnjP4Zju9i23Y5UKM5ef4k9U0cYG57u0bW6Oc9meZnHD3+qW0ZOKUkgfV4//3U+8/Qfxrai0m9aa7RRvHr2D3j88PMMFkbu6JIhM4vXMCLk0O5T3eR31R77Gxe+wxee+6MR4MS229EixTde+S0++9yXyWYKXQCFlCHnr73B4MAw+3Ye7ZbNkiqk3qxybe5tnj3xpXbCvNXeEpdcn3+LI3uewbacbtk0pTU3b91kenKagYFCN1lfypCN4iqzy7f55KnPYtt2++VGEaqAl858nc8/85PYlo3dLhmnteLs1dfYOXGAyfFeO96cvcJmcZ1PPf35LnpWKonvN3nnyvf4kae+jGjXee3Y8fTl73Li4NNkUvluv7RWFGvLGCMYH97TXQ9KK4KwyUbpBtOjR6Bdv7lDQL9SvMHUyBEc2+2uO6Ulyxs3cawsk6O77+pXSLG0we3bM3ziuU/32FGqkBdf+yqfe/4nujVuO4TWq6WbDGQmyOeGe9ZpsbpKrbHJnqljUdk/EVXNCMKAN6++yHOHnsUW7dQOHdE/1+oe/PNfQmzOQ7uahrJTmCe+jH7iBQq1ywjT3j61HEgVCI7/n5icnAIj7tKlOXf1exzadQTXuVPGTRtNrVVEewGFdKZ7jSqlMcIiTDmMFvZ0U186dtyo3mIkvxMh7LZ9o7GvFedIu5kISd+1r6JSLTK/tsAzj30mSl8RVrfSyK3Fd9g5vLNrk050brW6ws6pY6SdO7WYtVbUW1uUKkX27XzszhwqhZSSG7duc+J4VCO5019tDBdmZ7i+5KC0hSUEk4OC/ZOGUsNnYQtG801aQZqtepaPtAOYdsyv7xrs9/UHLp+aKX04HEAhxOeAv0xUum0v8DeNMX9j229+AfiLRDw1FeDrwC8aY9b66BwG/kei/eqDQBn4JvDXjDFLd/1uCvgXRGXivg78BWOM1/7u14H/EvgHxphfvOuc3cAC8MV+1UAelgMIdzuBNo77aF9g28UY03NjiHjceq/BH8Rx69jGdVOIeyRbP2gRwnByqsW+MeI1OoVNdmhvbNyd7Z/UtuLwxhik1LhuvAapVDLir4pRt4Q0mk2GBod6jmujabaq5DIDcaSmjArDO9tRjH0Qp0pJWn6DfHYglhjvBU1sy4nVM1UqQkRub1uqkCD0yGUG2C7VWplsNpeoi6SxqzCiVtpmR200nlcnncrE2o+2+QTWtsmKkLtWQtuKWqvKYH4olvjv+x6+H8RpYPqggKWSBEGL7DZ6GICWX8ex7QSKFo1lxetDKy0Jw3qMHibaHg9xnfjYO1uG2+c9lAFCODFUq9aaWqNEPluIta91xFEZR1OHaB0mtl2tr5J10zE7eqe/g/eb/xgR9ubPmUyB9E/9dIzSBeHgPPtnGRybYLs0GpsIVLy/KkR51ZjdjTFkhvfi2OnY8SCsY1lOrL9KhWgVxudESdzUYMy+xmhqtWXshHuiEQ6Z3FjCOlUIy42hzzuOuevEgWe1ls/vvrne/duxDfsnfQIZsLgZ8XcKQH3UI4AZ2/zrvYUH2Z0e+eSN6iPvAN7vE7ZAVOP3F4Gl7V8KIX4O+CdEdXgfA34OeJooWbGf7CByFv8HIsfyPwVOAL+/DeHyt4BzwE8QBVz+2216POAvCSH23edYHrp0toCjLeHgkcx56yednL3Op7Ole/fnBxHbcdp5b/KRsosxkEslF2i3nGxiX23bifHjQWTDJOcP2rfrBBs6josl4vQSlrDIZQYTaSQ6kax4+1bic8G2nUTnDyDtZhKL2Rvu5ET29Nd2yaaTb74DhaFkXX3m27HdRDtawiKTziWOHZKBorbtJNvKthnMxZ0/iAjfk+2YjKZ2bCfR+YPIjkl0K0IkgwEsYWNb8YdwtIayyWMXychW10kl0txYlkU+N5C8HoWFJZLaSEbu2rZNzs0m2lFdfzvm/AFYY2PJc28kVjq53qtjO8kUKQkAGwBh9bejY6cS+9sPAW3bcUc90mXh9KHZcdx+61QkAqksy050/gAKmRSFrKKDG5cKNioCgUQIgzYWylh8VHP/OiIE2Cn10D4/DHJflUCMMV8FvgoghPg7CT/5DHD+LtTJrBDinwJ//x46rwA/c9ehG0KIvwi8ARwFrraPjwDfMcZcEEJcb/99t7xK5KD+CvCf3894HgXpVBbpRBEe5ZzABy2pdBa/1Yxuzs6j8gYqaIUCrWH7vT966CQhd6OIRnLOo8aY+MMiosPRkEQ23Wd5KC2xrKR0AoE2Cnub42j68It1+mzbSdxjYEyc286YZEerwx8nEpwHpaLIzfY+CCEiPrqE3wsr+UGpjcZOclDo8DDGIytWgiMA0fZeP7skoUS1iXM/dtqNIuMJPIAYMDqZeqQParmfGKMxCQ5Hf2SpSnRkoY0c7sNXmmjje7yc9atTZQ2OYSwHsc1JM4GfuIYMYHQAxLkvEQKjExw0YSW23w8Zfa/+RvnM8TaiijP9qHwi68e/UIm8ftokR2E629CJa8gYhgZ8Gl6WDtFz03fxwsF2v/qP6CMlwiDcD10lkPdV3q89tpeBE0KIL4hIpoA/Dvzee9TT2WMp33Xs7wD/UAgRAn+SKMp4txjgrwB/QgjxSIdbt4vtOLhuqhsFfJQiXg9bUpkMMgwTH7wPS2aL8RLfxkDgt3oQjHfL6spqjApFKcXS4lKcIsVoqvVKH/oSKJXjNCVShswsXIvRqkQVN5b6UqGU65sxGg+pQhbWrsd0SSVZ3phFJ1CRKB3iBY3EsSys3YxRYoRhwJkLb8VoLLTWNLwaWsuY02MwrG7NxWhKpApZXLsRa0NrTbm2GaPQ6EilvhFrP5QBV26fi1HEKCVZXJuNOVSdB3S9VYnPr9ZslBcSxqio1NZjEdMo4u23EaLxPleb8blSWlGpr8V+r42m5deAJOS+oVjZiPU3lAFL63H7aqNp+pVkV0IIvAS6l6iCRj22TrXRBKe+EHt7MoBptlBuOpHupVRcio1da0Wz5SXMCWhjY4yI09AA5VJ8rrTWVMq12HFjNKHySWRnFJqWX42tL6UUgQkTqVs8P06/EzmXmlC14mveaLaqKwlj16xXS9QaLmlXd/sXKoEf2ij9sePXESHAcvRD+/wwyPtSC9gY85vtnL6vAm5b7+8DP3+/OoQQOaKI4X8wxqzepfusEGIvMAGsmYQnmjHmJSHEbwP/EPjC9z+SBy/CsnDdNIHXIp1NeNP9CEr0FmvhOC5Kht2t5octNd/i7KLLkzsDBNGzrFb3+Ge//hV++md+lpMnD7QT6KMar5vFDdY2VknlLUYHIz4zYQSlSpGV0hwBTfbvjtC5wrKoNUu8fu5b5HMDPPf453GdVPRQ05I3LnyTSqVKNv8FRgYn0UohLJsb8+e5cvs0xg45vOuJdhTLplRd481L32R0cIJnT/xoN/IThAHfef13sFzFJx77MUaGxruRifPXXuPyrbP8yLOGvdPH2tEyh/XiEm+c/Q6HDx3jsf2fABNt/3l+k7evfBOlJJ88+WVymXy3CsXZ6y+xVVkinU4xMbw3ynUSFtdnLvHWxe+gCXjm1KcxJmq70ary9pVvkUqlePbYl0i5mS6FxzuXvst6eZHnn/gSE8O7o2oLls3s6hXOX32NRqPJsYNPRnYUgoZX4fLsqxRyQ5w88OkIHKIN2igu3HqFzeI6nzz14wwVxtv9Erx96RXevPAKgQx48ugnUErh2A4bpWXevvwtRgu7eOHpH+1GQEPpc/rat6k36nzqiS9TyA53+3Vz4Swr67M8ftRibHh3O5JjUa6tcXvhItn0GEf2PYYBLCEIlc/ixmUcx2bn2OO4nTw1AYvrl1kvzjM9cpjJ0V1de5Vra6yX5vADj8nR/d2x+2GThfVLKAUHdj5Fyo10GaN5+9L38PwqT5/4AkMDY9H8Coubc5dZ3rwBImD35BFMO47lhXXmVy9RyA2xc/x4NO+RNlbWlxFWJwcR0BJhpyiWK2yU15BqgLHB8W5ErOE12bAs7J/8cwx/49ex80Po0MfYDsGf+ItUcllGFi5FddbtFCaoszY4zdbWTdx8nsnR3RgtEZZLo1Wh0ZAoI8mlM5GhjMH3JStLZdbXlnj8sV24joVlR7V6W2GG+blb7Duwh/GJ8fbatlhcWGZ5YZn9B/cxOj7YnStlQor1OWSgmRzej92+/xgMm/VFgqDF9OhxsumBdlTXolLboOKvUXAHyNkFEBYYQ2gCWkriV3xGB3dF9zcRXdcb1VtIFTA5dIy0m+/afmFrkYWNDXZ6A+ybGIyisJag3Kpy9naZbFqh1N2R7DYdzscxhDsiQDgfXoMIIR4H/gJwBPgzxpgVIcTPAHPGmDP3o+N9cQCFED9ClKv3V4HvAbuInLl/Bfyf7+P8NPB/tPvzZ7d/b4xRwOr249vkl4BLQoifBk6/a5tGYT0ihaKNZbBdgfLrpNKZdz/hIYttkvNs3vd2bIPUEqE0lrEfCVCI70uqdY+UExVYqtY8crkBjFaUyxWy7XJ2XtBiaXWemlcjX8qScYajV1KjWd1aZLO6gDIBo4NTOE4KAZQrJcAi9A3lcolCbhiAequK1gbHcdkqrmDpaJtHKcnW1gZGWlTKVWpD9baDIqjWKgjt4AeKUqVMIRsF1yv1Io6bwpiQUrlE2o1AGlIFtFo+hfwgpXKF0UIDgQUEFEtFjHLwvZBKpUo6FY2xXCvj1y0gRblcQQw5GCAMWjQbAVo6lEplcs4YHXqgSrVKJl2g5XlUqxVcN4MANrY2CDyFMJpSucRgfhQDNJt1giDEwmVrs0jOHY1cCuNRKVeQgY0f+NTrzXZunaBSr2J0u7/VKpnUAGBotKo0Gh6+r9nY3MQVeQxt1HLDIyVybG2VqFZruI5DQEilWkGQRmsoV8pkMwPRvDdK+C1JGBjKpRKWyUQPe9mgUq3RakjKlTJpe6Sd3wfVao3QsxG6Sa3eIOVGdmx4dbxWiDEhRbYYKIwBECqfra0KjUZIxaqSTw3f0VWpoaVDo+7TzHhYwkUIaPothHFRgaZaaTBQcNpjr1EuVZHGY31zHcfKgolIjT3PRxiXarVOLdfs5glWGhVqVR/bBJSsjS7YxQ9CVOCQyWbYXJnBtcIuBVRTpnFcF2VSSApt5DtUG1XKtSLO9GFGfunf4aailAWlNcXyAqH0GDjxs+TyEUVKKAPk+hVEvcbmxjx26HXzJBstiWYIx3XxjGlvwwsqlRbz8wtks1kClUY4Dijw/ZDNYpVGo0mtWiOTjdo2JgKdFQaHWViYxWK8CxryQo+G9HHcNI2WR8qOIpRShngtTRAoarU6Rme6dizXSpSrDcxggczwaPt+JQj9IvXmClorNoobOFYWy5EEshldx9KwuvoqjltAM0apYeEFFlIOsby+ybkbJQpZCz9UaCPxw4hRQGmNnVg/5v2RB3Wf/8BEQGJmzIdAhBBfItphfRH4ItCpo3oY+NPAz96Xnve67SiEuAn8m7tRwEKI7wGXjDG/cNexF4jy804YY67GFN35XQ74j8AU8CVjzOZ76MuvA7uNMT/W/vtXgS8BPwXM8IiigJPEtIlqhRCPPD3Mg0SHGWMIfA/btrH6JH4/KMm5is8eqODc1YWIgDZFbuQAli16csKiLdXb7NtxrKe+rVQhN+bPc3Tvkz05WdG27QpDA2Ok3Tv1VI0xhDLg1twF9u3Y23OOlCHX5q7yxPFP97QREdbeZnp8L66T6uacaa0JpUelvsXY0I5eXSrkxsI5jux5sre/MmR25Tr7dx7tOa61pt6soJRicGCkB2AgVcit+Usc2nOyJ48zlAFXZ87z2MGnto1dUa5vkE7lyKV70aihDJhZvMqB3cd7wCNShVy+dZqTh57t1aUlm+VlRgYmcZw7Cf7aaKQMWCsusmNsX0+/pJJcvnGWE4eewHVTPceXNm4ynNvB4EAnxyoauxc0qTfLjA1Nx+bk+uw5jh7otaPSktX1eaYneudQa0XTr4EhhuZWWrK0cp2J0R09aGOtFZVmifHhvT2/j7bSK7h2hpSb7l1Doc/C6m327ToSW4+3F65wYPexnjaUVtSbW6RttwfgFUVmDdXNBVJ22JMzagy0nBzjowe3rQfJ/PI1pkcne+xujCFUAVgpcunB2NjnFi4xNTTcsx6MMQQqT2Eg15OL1xmj0X6bOuXOd9oYQuOQy+RjupqtGrZq9P5ea5pBk4GBHdi22zt2o/FlwGB+YpvtFZvlOcaH98WO11obrGxdjbanSeMHHlr5OHZE8VKvN6kGE5RbO9sxwM7m7sPb0v1hRwGfHLTMv//kw3uWPv4t/wNDAQshXgN+wxjzvwghasCTxpjb7TS4/2iM2X0/et6XCCBRlu72cFoneaHvBAkhCkRe7ADwo8aYrR+wH3+TiCH7z/+Aeh64RCTJNqqNfv0YEBKJEIJ0JovvteAe9WgfhOwb9mIoYAFYjoNliRggQAiL/TtPxJCXju1yZM8TMSCAbTtMjOzsbvPd0RM9zCZHdsaS+B3H5eSRT/Q80Du6dk8dRhvV004EvnAYH94ZA3TYls3h3U/EdDmOy4Gdx2P2sCyLbKYQEX1v02UJm8N7T3W57jriOilOHHgqNo+2bTNcmABM7DtLWBzeezLWX8d2OXHw6YRxOEwM74q2tu8eu4iusV0TB2NtOLYTc/46x/dMHqFeb/Zck5ZlkXLSjA/viLVvWTZH9z8ZnxPLYcdknKzAsmwyqUI7rWA72EIwNb4rtrYsy45InrfdJyzLIp8ZjAEOhBBYts2B3ccS7bh/19EYnY1t2eQzg0jp9bQjhMDokLSrEkEwY0O7E9a8w+6p/ahtJdSEELh2GjcVpzGyhMVQZjARLJTNJKFwBbZloUwceCUw5LP52DUnhMAxAXobmMmyLAq5ERBWbOzCWAwVJmO6LMtmbGhv4noYyE4wH17Glzbl5hB1b5S0E7BrtELaNYRZh0Z9DHNXZZOP5QcTIUA4H9rn6CkiTMR22QTG71fJfTmAbUftcPvPFDAthHgKqBtjbgK/DfyyEOJN7mwB/yPgInC9reOTwL8G/rQx5k0hxADwNWCUCA3sCiGm221UjDHxrNl3EWPMhhDi7wJ//b2e+yiI7TiEYRDlEn2MCu4Ry7aRMqqi8lDsYgyDmTCRBsa2k9+SbctORnsSPRQSaSwwiVvdTh+qEyCRUqWjbbsT0ulXUuRfCCvmBNzpr0gcixDJDyvLSq6bDLQJmOOGjFhVkmg3nHtQxCSvB2MMVgKq1bacvuCQDjI/LiLmyEb9TR5jv2o3nXP6I0jjYllW33rL0T0iCYVsEo87ttu3xne/eTdaYyXNVZ/0GSHA6nM9gEm0I33s2O/agej66YeuTwLBRmjafvcNE+OL7PQrycG1bLs/0rnPC6oBmkGeldIwrbCdJhJmkJtpUo7GtnxsS7XDJh/f998XEQLhPLyAwQcsVSJcxMy246eAlftVcr/WeQ440/7sIUo8PAN0aF9+BfjbwF8j4gv898AN4I+08/cgihIe4w6m/1nghfaxK+1Odz7vmjd4D/lHRF7wD6WkM1lkEPDxO2CvPGxKGMeSKKFIoLxDbYuQdESqEKmC+AmAH7RiCD+Ibv1JSFCpVF+akGYrjmKEaMuzU9e257jWidwtWitafjOxv3fXj93e46SxR7VR/WRdYdDHEUl+8EkpE9HgxphoK62Pru2oVqBd0zaZssfz42jm6DsZQ0YDGHRin5VS+EGc7w6isSfZsR9lb1TxJBkJH4ZeDGkMHcqehDUkQ7SJ/15rjR+0EscuLCcqRbddbKcvdUsYJq8hbUzinBijEtdQNPZkR1OpZP5U0edFSRvVl1LH4MQQ7gBGJaOytZKJt+coLcBP1KW1ZqnYcf6i2TZGUPcyFOtZtmoDBPL92pD7WIAIBJKyH9rnA5bfAv6WEKJDlGmEECeAv0fkf92X3JcDaIx50RgjEj5faH+vjDG/Yow5ZozJGmN2GmP+pDFmLkHHi++iUxhjfv0++/Xznfy/u455xpi9d7f1wyaO66Kk+pgW5i7RSj2crXFjECjGB+uolCIeoBIYkWo/QO88XLVWUb7b7bd7HnqdCg7nbrwSVRq46wEjVcj1uXMUy+s9ToLSilbQ4ObStZ7jpl327NUz30ZpGdN14cYbNJqVnnOkklQbRW4vnu9xarTWKK14+9zLMSdJqpAzV1+OORxSSTaKyyytzvS0obUikAGXbr+VQN0imV+5EZX0uqsNpRSLC4sUt7Z6nD2lFEop5mbmeo53EMI3Fs9GY7/LUZAy5Nz116nUi712VJJao8zrZ18klHcc82jsklfOfAultulSkmtz7xCGfo8jppRkfXOZs5fe6HGytYl03Zy5FBu7UpJrMxfaVTR67bi0Ns/q5kLPOZ1qELcXr/a0Hc27YnblUmzsoQy4MX+ejdJqz/wqpfCCBpdvvRlbQ1orZpcvYrZRx2itWFtfx/MDwrD3nFCG1GTY4+x0qKxWi/No3Xv/Uloxv7iCVKanv1E5tDLr5fltY49KTxYbxVjEVhtNqVqLrSGtNZVGmXKl3uO0Rnl7hrW1rR4Kns4a2iqrNrDo7jYMlUaDlucl2N6wvLzWM44OLdDF2VuJa/vqwiq+7FeaTaCNgzKZPt9/LN+XCIFw7If2+YDll4AMsEEUVHubaMd1majC2n3Jx68cj5gIIRCWhQzDe2xJffTEGJNIUPxBi20bhtIlxodbOLZDQ2QZdAoIwoj7T7gokWOrOE8uk2dkYAoElGrr3Jg/R7lcwfM9jh98lkw6T7NV5dbSWereFi+d+R0eP/wCo4NTUR3g2dPcmL2AsGweP/xJDu46gRCCmZXrnLv1GkbaBMrj1KFnyKRzVJpFTl9+ha3SJi++/Vs8dfSzDA+ME0iPSzffZKU4w1p5huN7P8GuiQMYY5hdvs71mUsI16fWqHF8/zOk3DSl6hoXbr5JudjilTe+y1OnniGfHcALGpy99iprW/NsFld56thnmBzfgTGa2eUrXLz5FqmUQ8MrcWD349iWw0ZxhTMX30CkPZRucnzfJ8mmC1TrZV49800GB7PUqg0mRvcyOREhL+sNDzeVYX5pBoRgeHgYYQnq9Qqb1TU84zG/dIsd03txbRcv8JhbvsZqcYbN4hJPHP0MQ4VxgtDn3LXXubV8lZsL1zix52mOHIzsuLQxyzuXX2aztIlxFM8cfYGUm6FY3eCNC99maWWJmlfl049/ibHhCbygxbXbF9iqLbC8vMHhA8fYNbUPrTU35y/x3Ve/Qa3apOU3eebxF3Adl63SJpevvYOxfZZfmef5p79APjuAH7a4MXeWarXG6cubHNp7itHBSbRWXLt9lnNX3yKdynDy2Cc4tOc4tmWzVVljbvUCTa+GWJbsm36MlJvFD5osbtyg1Fhj7u1Zjuw8xdToBEorbi3foNxYYb18m2r9EPt2HkUIWN1cZG7tMo1aA600R/c/TSqVptmqMrN0gVRGsLR1kYmhg6ScHFKGLK/PILVH2EyRcQYYGY4Qus2gQsVbRbiCljEUUgMIYePLJtWgTrVeR+vL7Bg/SMrNEYQBt28vUG0WKZXL7N09xdhIRD90e+kGm7UFWp7H/p0lDuyK1lC5us5mdQYpDJutTUZz07hOhiD0KNU3kGGW6uwc+/btxbaire311U1kqsmtS/Mc2L+PnbunsITF2uoqxWKTpYVFDhw+wMFD+7AdC98LmJtZpjA0yvXbNfbvHSKddtBG0vQ1whplfbPCUCFgMB+10fLh1q1Fdu85yNbWFoODg9i2je97rKyu4/iCK1dvcPDAPlJuFD2dm52jFmSi3Mkfsnf6bMpmz0QWSwiWtlrUWpGTblmwazRFPm1RrCvWK3deNiaG0owPpvACxfxGC9V2uvMZm91j0fb3wmaLpt8uWWgL9oznyKQsNio+m9U7L2c7RjKMFFzqnmRxs5W4A3MvEX3IzX/YxRhTBz4vhPgi0W6qBbxjjPnWe9HzsYfxCIpl2SjZQtnyh65W8AclVp+8sQ9aXFtRyNQxUmJhUUgXcJw0mDRWOyLphYpsJsXo4BSdLdHRgSlO7P8k6+UZDu16BstysC2bQn6Ixw5+ipn5W4yNjjE6ONnNZ3r80PP4YZNMeoCje0+184wEh3adoOnVWVpf5BMnv4hlWTi2y6iT4anjn+f3X/wNnjr+WYYL49iWQ0pkePL4Z2heqDI1soc9U1H6rgEO7XkMP2hSbVQ4deRTEThA2IwMTnHy0PN8d/MPeOLk0+SyBSzLJp3K8exjn+eVc7/LrrEjTI3vjHIUjeDQ7lOsba2gteTI3qeiebIsJsd28Pixp7m68CaPHXyBVLtebSE/yOc+8ZO8dfkbTE/vYGxorJsvNziQp1jcwFN1RkdHQUSjHygMsVVfYqN2myP7/3D0e0uQTqXYNb2Pxa0rPH7kBYYLExGnppPiuZOfpdn0yKUHOHH0VHcu904folTdIpde4pMnPo9lWdiWw+jgBM899nnWNn6DH3niS4wMjmPbDpl0llPHnuN3vzHHnl3j7J7a131BO3bgCRYXF6nWqjz/9OcQ7Xyx8dEJjhx6jNff/g4//oWfjexo26RFluMHnuPt86+wZ+oQY0NTXXDPicPPMrs4i+PaHN13smvH8ZFplPGZXb7Mod3P4tgulmWTs1z2Tj/O/Nk5Dk7tZ6yQQck6xhgOTu1idiNgoDDCromj0F5DOyf2kE8PsVVZ58CeY9iWjWXZ5HPDHN33SUqVImPDIzi2284Fhb07jrCwcYlcKsfo4DQdaOpAfoJQeQSqwUh+TxRpQeAwgh028IJZdk0dxXWiknBpYXP06EFOX7vN5PA+pqf2tyNuhuOHPsnpK01sPI7sfSYau7AYHdqBEYbFtZtMjx3HthwsYeOkCmRywywtLzA+PkkqFWUUWcawc88u1jaXGBsb58DhI90o3O59h7GcZQAOHNyD084LS6dT7N03TcNvcmB0grSbisBcxjDoQqUqGRlIk3WaqLCF0Ya0BRk3pF4tM73jUJejNJ3OMDUxyaUz32Bscorm1A5ef+cVlFQMDo8SmiauPUkgHx1i+3eTXaMZnj8ZpeULITi1f5ArCzVm1+p88dQQtgW2LVDKUG0pXrpU5TMnJxgbSGFbAqUNTx8a5sXzK0yPpHh8X4RNMMATB4Y4c7vMVjXgR5+cjOqft8/ZqPi8enWLH31ikoGs023jmcOGb5xZo+Hdnw2FJRCpR9fFEUL8VSL8w2NEZnkH+O+NMW/crw5jzHeA73zfffiobjM+ajQwd4sxBiUlWivcVHI9yIcpD4MewPda2Lb9EChyDGmrxb6JNabHhxjJTMXmwxhw0/FaqlprfE+SLwzEKCY8zyedTsWS4oMgwHHidDdKSTaKm0yOT/YkpocyZL20zNTozh7AhzGaRqtGJp2PJfgrJQllQCbdSzwuZchmeY2JkR09/dJaUalvUcgNxwAnoQwxRncJh+/WValvMTLYS5WhtKJS22SwMBoDqEgpEYIY0lmqkKZXo5DtpQNRSlKqrTM8MBkbY7NVI+VmYnmjoQxp+U0GcoM9cyJlyMrmIjsm9myjW9FsldZxnQxDgwMxOyqlSaezPcelDKnUigwPjfegYbXWNFplspnBWH/DMAAhcLf1V6qQllejkBvuAUVordncmiPjmBjiVhlNNj8ZQ6MqpQgCTT5fiK3HWrVKvpDrmXdjDIFsYYs4YEkpRRjWY3OotUJqyOfGes7pUAblcwOxWsiyvYbc7WtIhWwWVxgfGe8ZuzEaPwxIOZnY9aN0tJ2bdC026mWy2Wxs7EHYiq65bYCPwPfRfjEGENHa4Bb2k8320tAoKZm5NcPS0gojY2PM3rrJxOQ0tVqd/PhuNmQOPzToBKDToyZp1+LHHh9mcLC3FrNUmmqjTiFr94CDlDKUG4rx4UKXNBvaNDt+g0wqG1+nWuOHkly6954ilWaj4jM5lO7Z9dHasFn1+da5je6xe9HAnBpPmd/6Tybe48jfPznyL5fvSQMjhPgqEf/xW4Ak2tb9WeAJY8x2cMf2c//avb43xvzK/fTx0XWPP8LSoWxISpj+KEont8jqg1T8YEUQqDSKDFm3kOiMW1Zyv4QQMecv+r1FJpNORAzadh+ErmWRzwzEHlKu4zI9FqfdEMIilymQlFNkIOawQUT3Mjm6M07DYtkMD4z3gSXFaVs6ukaH4s6ybdkMD0wknmNZViJC1xI2A7mRuC7bYXRwOlFXh6x6uwghyGfi8+g4Ljsn98bpbCyLsZFx6o04sMG0z9sujuMyOjyRQBMSUYskvtB1a7j2im05iedYlkUhk2nXyt3Wvps8dsuyyOdzyboGCjFHJ6JocfugpnXMVpEum3xmKGENWQzkhxP7JUQHpbttHLZLLj2QQN1ikU5lExG60CnJGP9uu/PX6Zfrpvpk5oWJYCnLTpHNxHXZjsOu/XtooGjIEYYPjWHcFOlCi3JoEcjwPW9hPizZOZpNvN6FgIFsfDfGtgXjQ7ke5y/6vSCTyvbJbNRk3Pi907EtpoYzsfVoWYLxwTSOLZDqPgwpBDzCaVTGmD90999CiP8L8MeALwP/n3c5/c9t+zsFTAMtYJ0ImPuu8uha5yMuUb5IFAn8qG8DG2P60j48KEl+2ETyft7T+47x+2jkXiXhv58+97X++2aA70fReySyJ9nRevfzko+9v8/zJGqceF3bH0T6AanudWn1IbR5v7r0fem61xnvl7VEu8RcknH6XVtSa2a2BK0uAr7joKs+Zzxq0qafvkdX+4393uu0D2Rc9Dvn3e5eBteJI/O3N/mQcwDHhRBv3/X3rxljfu0ev88RldItv5tiY8yB7ceEEDuAXwd+9X47+KElyfkwSCqVjqHpPmoSOcEh8JDIsY0h7QRkMoZmUE+MhhidHKk1GDy/mViAPgiT6UuiqG8CjYTRNLw43YtUkpW1pRjdizGGerOWSPsBUcWE7SJlSLG6lliAvtooJ9PWCJFIeyFVSKmykTB2Ram6jkygp4m27+KitaLaKMV0KSVZ21pOpLoJpJ/oCBijE6luQhmysroQi7pro6nVk2l2ECKRhkWpaPt7+3daa0qVzcSxW0IkctFJFdJoVRLtGGiV2C8ZtBKpW4yJ6F6230+01vhhnAYmqtKRTJuDEIkULUpravVqvL9G4wf1RHt1vt8uoQyp1uNjj+awhkq47rTWyc4JBi9sxmhdtEkeOwB2ug9dUkgQxGlolFIsbRUJVIo7xD53f344RADLW80+91uBH+iENaRoepXE+fWCVnIUWUAg4xFspSTF2nrifajcaGDbNXKZLYYKs+8yEAEp9+F9YNMY89xdn3s5fwD/kCh697vv8rtEMcasAP8d8A/u95yPHcBHVKKC4T88N40PUqSUD6k8niHthEyNeQgLKs0qraCGNrpLIWGModrwuXjpKkEQIKXsUkJ4fpOZlXOE0u9Sq0gZ0PRqnL/1IvVm5FRF1DAR19xr5/+ANy+8iFQR912HCuTSrXd46+p3CaRPEPpdDrzN4jr/v//wL9korhKE0UMpCAOC0Oer3/0Nzl9/E9mmHVFKIZXkzfPf4xuv/DZSht1+BaHPenGZ185+nWaripRBu7RWgB+0eP3811hcuxnRpLSpTpSSvHX1RV6//I0252HYLrcWslac56W3vkHTaxB2dMkAz29x5trLrG0ttdvW3X68fflbnLn2vfbYI3qTiAblCt9786v4odejq9oo8/I7v8/a5lLXJmEYEIYBL7/zB5y++FKkqzN2GXJ17jTfe+t38X2P8C57bZXW+NpL/4Fqo9S1YxgGhEHIlatXWd/YRGvdpcvRWnFj9jyXbrzVzgWU3Xlc2Vzkd77+76nWKviB3573kEB6XJ17jYXVWxEnX7v8o1KShbWLzK9ejPqqI/45qUIWV2/z1sVvEUivnSsX0QgF0mOtMosvve461FqjjWZ2ZZnf/erXkFK2v9NorZhbusHpK98mlH7XjlJJml6d67fO0vJq7bYjahitJfPLl9koLXfXfOfTbLWotUpoo3var9dbLK5fww8ad9a8ClEq5PrCWyxv3O5S13Tsdu7my5y5/iKqfQ3o9tiLlRVmli4RSr9r32gNhyxsXKJS3+heP7ptt7PXX+b8zdei9alVd+zl2grLWze6c9c5R6qA5Y1bNL3GXccjO9Y9n0ZoR2j/7thhoyhZLy5jzB1dSimqrRbXFlso/cOPPPVCzdxKub3mTdcu62trLM0voO+6/2mtkTqg1JzFlw207sxVlGv81vXvcGXhTDTfbV5LqSSX5k7z5vXvRHWf71orxfomr1z+NvVW9c71rkJCGXBu9m1y2VUymWW0qN97EEIgbPuhfd6LCCH+ByL+459tI3y/X/GJCnHcl3y8BfwIixARma2tnI8sJYwx+qFU/xgdSHFq3yCjAw6h9tioLFNveGxU15gaGWAwP4rBUKlt0PDLjE6mqVSbjI+NIYRFs9Wg0QhJpxyWt64wNXqElJ2h4VW5cPM1Qt3g8uwrHN79DEOFCWSbv21ta4lcLsvM0kX27TgOCOZXbnBt7iyNesh33/4Dnn/882QzhpX1Jb79xh8Q4vON17/CT3z6pxkbnKDpNXn59DdZL60R3vQYyA2yb8cRDIab85dZL92m1fI5f/11Thx8Bm1rVtcXefmdb6BkyGtnvslTJ36E4cEx6o0aF2++Tugbzl5+GyEsdozvxxjNzMplltZnCPyA18IXefrE8ziOy9LaHG9feBFtNK+d/wbPnfwc+ewgjVaVqzNvE3iKS9fPIizBxMg0UkpuLp6jWFvBKLiaGuDwnpNYwmZlY4ErN8/hhXXevvBdnjz+KbKZPJVambfOvITSktfOfJMXnv4SE6M7CUKPi1fPsr6xSrGyxsjIKHunj2GMYWHtJrcXLlKp1nnpra/x/NNfII1hZW2Rb73ye2xubvHdN36PZx77DBOjk/iBx+mz57BFgWKxzuDgFkNDecBQrKzhh3WUaHHl9jsc3nsK13Ep1za4MX+awqDLWxe/w7OPfw7bGsILm9xePkegWsytXySbzTA6uDNy8lauU6ot0Wy2wNjsnDiMJSxmF29xa/4cXtjk3JWXOXXsBdIiooFZ3ryGZUOTJpn0OJn0EFpJtiqLCAdOPrWTcm2LsZEdgKBaX2erukIrqPHO5Rc5ceCT5LJ56s0KtxfO4qYEN2beZtfIBCk7evkseS2EJahUiqSsEEeLCKTjjmCJAdKpIfxQk88OYdk2zUYriszR4Mrsmxzc/ST5zCB+0OLK7fNU6ls0vDqpVIrhwjQGw+zyZW4vXMN1bc7zGkcPPI1ju5Sqa8yvXUTYiuuLZzm06ymy6TRSe6yWbtJsNbhZvcjx/c8wkBtDA3MrV6m1NhFCcHvpKvt3HAfLotbcYr04B5Zms7bAxNB+LNsllC02qvMYY7FVW8FJ7SeXKaC1ZKO0SKWxhQwVKj/BUGEIy4JKzaPmBYwN11gsniWfGkWbFOvlBjPrAS1/iEc12jcxlObxfYMM5hxK9ZCLsxWK9ZBsyubx/YPsGMkQSM2tpS1WN5Zp1ARLi5qp6Ukcx2F5cYkb124QyhCv5XHo8GGyuVxUW9xbxiDZKN9gdGA/mdQgQehzdfFMRP9T2ySXyrNn4iAGmFu/wa3lKyA0Xz/9W+we3082nWWrts7K1hJKa77xzn9kenQ3w/lRWkGTmeUlhHsKY9IUcqtYVjLh+B15tHMAOyKE+NvAXwR+whjzzn2e8+nth4CdwF8l4gS8v7Y/qtuLjzIKuCOdtyhjTKxG6cOUB4kCDoMArRXpTHJi+wchE0MpvnBqAtu6U+VCa8VS8TrTwwexLbeb5B4hZDcR2AwWRrvHO1GB5Y1b7J463EVkRqS0iqtzb3Bkz3M4d+lSSjK3doXB3FgPslUqSaVW5PLN83zqqc91kbhRhEzyH77+b/nZn/gTbZqQSFcoA9648D1OHHiCoYHRu3SFFCvrrG0tcvzAM13UaURcHXL60vf4xBNf7CljJ2XIW+e/x6ljnyCbyXVRuqEMmFm4ie83OXbwFKlUujsOL/A4d+1lPnnqS9iWcyenVUtOX3mRJ49+BtdJde0iVcj86jVSTpapsb3dfkklaTTrXJs9yzOP/UgXdRpFJhSvnv0Gzz/xo21dd8Z+5cZ5pid3Mjo00QVqSCUpVTdZWL7OqWOf6tpR6Sg6+Hvf+vf8oS/+DLZ9B4UtpeT69TkmJ0cZGRnomfdWUKfeajA5su+OHY1GqZArs29y8sALPbmrSknePP8dnjrxQgRiuGvspeoqlmUxXJjq2lcpRSh9ZpbOc3Tfc93jnQjzRuU640OHscSdudJaUaovk00XyLiD3Ta0VvhBi/nVGxza/XjXJh07Xp97lR3ZqK5zp7/GgC/SOAIc686GhDFQq/toe5ydu3Z2kbidqNrc2gX2TZ1qU/xYXdufv/E2x/efJJ3KdpHAUsmISNwE7J0+2rMepQq4cusdHj/yqa4dO2O/uXSa/dOncJxUNz9XaclGaR7HTjEyON1tI7pOfDYrM0yOHMQSd/prjGZx/Sa7Jg71zJXWipWt26Qsm3xmoAt40VoTqpCSt4RlW4ShwA+h4SkWt8bwwwd3n3ovsnM0w488NoZj33V/0oZXLm/x/LFRUo7VTUFQSrK0fouUNcjk+BSW3bk/KbxWi4X5RQ4fPYzj9K7HudXz7J16HHEXalyqkKsLZ9g1doDB3Eh3DUslKTe2eO3Kd5FqB4YMrrOJECFaNwlDH2NcoqYLyDDAsgoocRSlHRw7xBj4v/6hF/qjgHfkzO/8mWMflEnfVQ7+v87eEwUMIIT4n4Gf5z04f+3zNMmJkq8Af8ZEJXrfXc/9OIBCiM8Bfxl4CtgL/E1jzN/Y9ptfIPJiDwAV4OvALxpj1u6h988ThT2fIqoJ/MXt1TuEEFPAvyAiO/w68BeMMV77u18H/kvgHxhjfvGuc3YDC0n6OvJrv/u2uWO/5KwRITRCKLR2+/zmgxfV3sZxXPeRoYN5UA6gMYbA93Ac94FGQL/8zCSjA3GUrFQBlrBj9Bqd/KEY9YSJtt+2014ABGEL204lUCNEDn+MIkWFNBstBgcHY21XG1UG84Ox9sN27mQSdUvEgbedJkQSSp9Mupf6AcDzPew2YrKnXzJEJOiS7S2bbKKuJq6TitG9dF524tQtARhibRtjaHoNsulsbE6irWoTo1UJZUBSzWOtNbV6kVw2F7OjlJJWy6NQyG07R+E4uYT1oJA6JOVk7nvsndyp5HHoxHrPUvlYlhsDKGmjoQ2c6tGlJcbE6/5qrSlvXMHSfqzurxEWwk4htmVUSg1DY8di9Za11gTSI+3GUbJB6Hc5LHvHEWIJK7G/tVqd4aHh2NhD6UfcgAm2h7gdtQ5RWsauxWjb0ce2UzE7htIn8CpxexlFxdsgNB5BKCjVHLzAsFIaQ5tHbfs3mrc/8skdDGTj9+ymH5J27Rh6V2lFs9FiYKDQc7yz7budfieiDPJIOekYalspicbgxuZd8vLlZTbKIVK7CKFxHQ9blDFsIdVOLGxCNUBUc1xgjMXdz+J70sDszJvf+XOP38M2H6wc/J/efDcamF8l8l9+Djh311ctY0zlXrqFEPu2HdLARsc3ul+53xzAAlGN318ElhI683PAPyGqw/sY0YCeBv7Nu+jNAd8G/so9fvO3iIzzE0Sr+b/d9r0H/KUEg9xThgdWGB1aIpOqYwlJZD/T/mhSToOh3G2GCzM4tsdDq80rBDJMrif7UZAO8e+DlKF8crQ1ivwl3eA766ZXLGF1ow3bxXUyiTQaGBAJl2VE0JtEu2ElOn+d9pMcB9uyEusK27ZDOpWLHQdIpVLJUWgBOgFw4NgumT660qlszAGCNoowYapdJ5X4AiCEIJuOO2AdSbJvZMckKh+LXC7fl54m6bgQNiZp3i0b146/QMA9xt7n/mLbTsxh6n5nxZ2WSFkfXVac6y7qr4UjdMz5g2gtbnf+AFw3kzhXlmWRdjOJNnYcN3EsApEIwrAtB8dOvhYdO3WPazEuQjiJ16IQAsdOJ9rF6LDPdWVjCxeMwbU1+VQdo++FWn24ks+0KGSSX6CzKSfm/AF911B0LSSj1VNOJub8db5zEl6CLctmOD+IbAdYjLGQMoPSoxi9F6VGCNQwBhttnLbz9x5ECHDsh/d5d/mviXyrPwBW7vr8r+92ojFmbttn4b06f3CfOYDGmK8CXwUQQvydhJ98BjhvjPnn7b9nhRD/FPj776L3H7d17r7Hz0aA7xhjLgghrrf/vlteJTLirwD/+bsMpStpdxWDYShfApNBqhR+mMOyJI4d4thVtGm2H7Ar1JpThDLHg77ILcv6COf/GWQYPHAAiB8qcuk+D2ljEm9ySaJNB5GYgCQ0CgsrrkuINlJxe0StPyekVCEpK+5wCBF9Fyfr1YkPzwh4oGJRs+i7aPs2HrlKRmd3wAdJzo7Ssh1JTRi71tuH3o0yJl16SkmEiDt1QgiUVomE2kkP9ajPqi/XZJKDoo3CTuJZNBGIwBbJa8joBO7EPv5DBz0pEr6MxpJgR5IdyijSlvxg0ibptSMaSxhoUqlta0jJxA53gCVJLx6mXcN4e/RIG5PofHa2p5P72+f66XN/NkbRz8iRrjjNlCVsVMJLiTYabRQGEc2zbeHYNkKYhxYnuJdoI5FaxiJwEEX6ouhrnL8vUZfWfb9TWvZ5WYmuxTghvabp31lHAsikfEDhBQUMvdG+9/rsFQjEQ+GOvT8x5r0xgifk/d1L96v387v3CwX8MnBCCPEFEckU8MeB33sfdP8d4B8KIULgTxJFGe8WQxRB/BNCiHvut98tWocYrTDGA1HBsTfIpGZIuwu4zjqIVjvnxeA6JfKZJQQPp4yP0brvG9mHWbSS2AnOyActl+drSBWn3ag112L0B8ZoQtUiVHFaCIyhVFtPoANRlKrLMfoUbTS+34B2Ts32dtaLCzFHUKqQy7dOx6hQpJLMLd/EbCtMr7UmlCGl6lqc8kRrljduxI5LGXJr7moy9UOrTqVWRKn4GK/PnY+2b++SUIZcm78Qo6dRSrKxtULLb8btZQy3Zq8iZW+/wjDk5vzFGB1IRA+z2EYj3rFjxwGZW74Vo2KRKmRlazZmX2MMlWp8NybK9ZOUq1txugqj2SzNx+yotKRYWcSQQAskPQLpxeYdY6jUV2I2UVqysjET+73Simqj2EXTbh/L0lp8jFKGXJmfj43DGAhUJzpjeo4rFVCsrCFVrx2Vlly5fSbRvrNLN+PzrhVe0KTSKCbMu2Z1ay5uRxUyv3ItNj6tFV5QRyo/gXZEUG0mX4vV+mqcHkYrWmGzi36+M/boJdCXTWwyGG0RyCzr1WGUfjRJNQSS2bWbifeOmeXLCWPXlIqlLop8+3dbm1soGV+Pc+u3kWr7da1YKTbQuncNaWPQBhY3W91jBmj6WZp+ob2V/gMGWzpE0A/r8/7Ly8BL7X/v9XnpfhW+L700xvymEGKYKErotvX+PlFy4w+q+6wQYi8wAayZhKRFY8xLQojfJuLR+cL96M04OVpho/1OaGERbSc5tkvTrwF33oKM0Th2A9tqIPUgDzoK2IHaJ4bqP8TSqYjyoHMfbyzXSTmCE3sKGC2xbZuat0GpMU+ttcnE4OH2G78gkB7rletoJZgcPkQ2XYiSCLRks3aLRrOCMZqRgamu/kpziXqwjF+sMTF0CIFBWDZBELC6sk5xs8gTTz/VTbIOQ5/FjStUai3WVweYmp4iqnggWN9aQSnF7OINDuw+GtW/RFCplpiZu8l6eY6Th54nm4py8Sr1ImeuvcTmxgYvPP1j7Jra36VfuHD9depBmWbT48j+U0CE+lxcv8HN+XNUGis8dvB50qksQkCxvMG3XvtdGvUGP/65n2bX1F4MUe7U6avfZWl1ljCUPHb4aWgHRy7PvMNbF19hcWmGzz735XbOkGBuaYYXX/89CoU8n3n2J5kc3wEm4lH8+su/TamygVKaowdPdCk5Ll0/y5WZt1jfWuH5J7+IY6cQlmBpbZZXz36docFhXnjyJxjIDQNQb1Z448I3abRqWBbsnj6E0dHY377wEqEoU2tUOLL7FJbtIIBqvcbK1jKDubFu9RYhwA+aLG3OEiqJ7QgG8mPQjpLOLV9COC2smmB0YA8QreVSdZnVzRtsFJc5sOtUdzu60aqztH6FVNpm5/hxXCsVUYwYh62tTexsk1prg4HcBJ2AVKm6jKTK6tYMEyN726AdQa2xye3FswSh5LGDL5DPDgGGlt/g7LXv4fkeli2YHtvTBiRpzlx7jRtL1/GDKqcOHMF1cxgtqbU8ql4atMP4sIoqaRDlMV6av8rS6jovPP1j7Nt9BKMNSoe8ffG73F64iuPC0T3PRFQcwOzKDd688DJXF8/xuSe/TDadRwBb5U2+/ebvgq34/LM/xeTIruilKgy4NvcWi8vLCCE4uPdOMv/C2jXm1i7hyQqHdj6DbTvRmq9vcGvlNI7lcHj3J8ilo3zZUAWsl6/h+TUsIchnJ9qROsNacYZSfYVqvcyO8cNYlkAIm3qryOLaNYyBvZOHcNv5nFoZakER23JJO8MEsshGNYMXvg8OywckQhhuLF9GCNg/eYROmPLW0gWuz5+l1tri5P5P4TrRLsLayirzs/OESvHk00+Qy+UQCFpei5XFVW5cv8azn3yOsfGxtlMnuDi/ycLWGlXP4+Sek1giuk4WNj3evF6mkGnw6cdGGci4IKDaDHnl8ia979nvs/2EgA9XEYUY+fMPKu+LAyiE+BGiXL2/CnyPiIfm7wP/igjk8QOJieL3q+/ys18CLgkhfho4/W46nzv0RSqNClcXzmAJwfHdz5J2M1H0Bc3N5UuUm5vd3wsDLiWMyfIgL3RjDGkHbCERj0gU0DYPpkSdkhHwwH4Yww62KG8W6RBQt2QLPzTgaOqiSGdTThtN6AtkYPAzATJstKO1Br+lCQODSivqjc5xgedpAg+wfMrhQncDRGqo132UAq8V0KkioJQkbGSQzQBGNLVqCdUmvK1u1WhtWbhhSHPU6yIl/ZZEN9JkhofxPYkKG4AgDA2+b8BOYbRNo9HqRqSESSFI4dgZmg0Pg47yEbVLPpsnk8oThgoZNqJ2tMVgboTQMwjj3KXLIFQKR2SwSNFseNE2m7AJG1BabrJvLE3gSwJfYQmBLVwG8sNkMhkENs126TWpFH7DYJSLZUVtRLosXCdLysliiwy+H+ITIrDQUpB2C6jAIQwMDdPWJcGxsjgixCJFo9GMoutAITfEVrmJkWmCQGCItkuDQGNZDq2Gj1dQWJZpz4lDmkmCZhUVOjTqLUyUpo5tpSmVSzhKkm7nDwsEXksBKVw7RbPeecm0CFVA0HTQoaIU3riTdacNJjXE7LUS+/YPIVSDzjZmZcunXnNJZzQDmaCtSxAEhjAQhJ5FGGgauglEjl7KzaK1QQV050pg4ZBGqAwT4ydwC4eAaEvWEXUqq4ukMi5ufh/GsjGAZXzW188iTArXzdCoN7tzkkkXGB+bROgUzeadNWQCh1xmkJxbIAw0WjbauX9QyI4S6CYqFDTqze56rJZ96lUftB2tR6MRwsISaTLuIErad64TIQgCTaupcG0Lrxmgw3p77IZaJaTWaDGYActEnHNCCLwWbKzWUINpBjKl6FoUAj/wwLhkU1mkdFEqmkODQIYOSmts7RD6Lo2G/cDuie9dDH4rD+44V2bPcnX2HI6VptGs4To2KSfL6sYK5fLXmCgcolGukysUcFIpsm6ab/zBN9h/8DBBGJLLD5HP5Thw5BhXr90mv7yJDDzyIwMUa00sJVhZn2F9cxXMHhp+pl2yTVBvhHz9rSYZ18IAfhjN8QcKmRHAI7wF/F7FGDP3fut8zzQwQoibwL+5GwUshPgecMkY8wt3HXuBKD/vhDHm6rvofFfUbp/zfh3YbYz5sfbfvwp8CfgpYOZe+mq1daO1phU0sC2blJvpSQRWWvLG9W/RCiJORmOg2Rqi4R/hQfJnPywk7L3kQaCAu+N2U7GcoQ9ajkwZju0Q2HclO2utafgtBgvxIvdKK1LpgXZE5+7vFNXmBoO5iZ6cO60VxfICGdvqWXMGMMYmm5/Gdnojn0op1tYWGRsd7mlfKU253GJicqLHTkop6rU6hYFCz/EIredTqmwyMTLdk6OnlGRx/Ta7Jg/25OsoJVnbXGBqYk8PilIbjec1CcKAwcJQzxilDJlZusaBXcd6UL2hDLl8/Rwnjz7Zc1xrTbVeIp3KkElne+wYyoDFldvs2XmoJ8dIypCF9RvsmTrSc1wpRbGyxtDAWKzmcSgj0ueJ0Z09dpFKsllcY3x0skdXtK3oIwPJwMAdRHNEFB3i+yGFQmHbmlAsrc2wc3J/zL611jrZbcjhSJfEK8/i2KKH/90Y8K1pRsfGYvO7uLDEnr17e45rrWl6VdKpXAzwIlXIZmmF8eGd3ehy5/j88i327TwS01WuVBgcLMRyu/zAo94sMzw40QO2kTJkfv06e6eObpsTyfLmLDvG922zr8YLWoQyYCA32GOXUIZcv3WZY4dP9q5HLSlWlxkd3Nm7Httjt227DUS5+36uKFfXGR3a0dOGUpJiZY58ptB7LRqNH3qk3YGEnEVNpVHGtlKUGlUuL6Zp+o9ypMng2HVy7jV8WcX3Qhr1JqDJ5VKkXJdMNsNgfoyMk8UmBfYYrspRK1dQ+SnqvkaqKJqYT0OoIvqbkUKTbKpKuW7R8C2EaBHIAaTMtnP4Pli5Fwr4iX3D5nd+6fMfeB/6yYH/+nfelQbmB5V2+bd9RLWAu2KM+d79nP9+eRQ5YHvSRScZ4EHGxf8m8KeAP38/P7Ysi0y7cPp2FJhAsHN0P7dWL0U1Pw2JdB4PQj6qXI1CJKW+f/BycLLX+YNOMfuheKK4ZQF2m+suDmoYzE/G1pZl2eRTObTqLccmAMdNR9u4CdveSRQltm0xMTkee0jZts3g0GBs7QghEMJiYmRHwjkOuyYOxJK1bdthx+S+mC5LWKTcNCk3HQOVWJbNwd3HYyAQ13E5eeypBJtE9jUJIBuBxb5dR2NjdxyXPVNHE/prMzY8lQjcEEIwObYrrst2mBzbETtuWTbZdJpa0BvhEUJgWTaFQjohgd5i59SBGArZth3y6RGMDrf9XmARYm9z/joyNj4Ws69t2+zZuzehvxbZzACdyHVPOwgmR3cnjN1lz/Sh2HqwLIvhoaF4XiKRXUYGJ2L9chyXvVPHEtfQromDMXCKZUVI9XTCGrItu+30b9NlOYwN7mL7o8WyLDKpfBR1jK0hGBveGTtu2w4DuRHYlpsohEXazcfa6EgmlaPaLLNScgnVo56aI5CqQMM8RsrZQoUz+K0m+QEXpTV+GKKNRSoV0goGaHoT+DJDSmiEPYZXvHOfsoQhCE071UNgCUmxlicIXaS2gMFumw9dhAWPEH/u+ylCiJ3AvwU+2zlELwTpvqIm9+XRCCEKwOH2nylgWgjxFFBvEw7+NvDLQog3ubMF/I+Ai8D1to5PAv8a+NPGmDfbx6aBaWCyrfuwEKIMrBpj3m3LNybGmA0hxN8F/vp9nyOSl6pl2WTcXDtPRmBUHqmGeRgL27btCAX5ERP1kEAgqT6XjsBKphCxk+fGEtY9nPd+tY2TKUeSqBfubie5heQ2bGG1t9niA+0fbRV9qFAsEilwLIt+ILdOvlpiK4n0IU5fO/Ydu4lzwUVtO8l1Sfu0fUdXMn1KPz1Wn+iHwJDk5Zk+dZA7DnuSWH1eFKJ2EubddvqiavvNu0ngE4SISqjfnPTLVRaCvlQsSZosy+o7v0IkX4uIKF0nruteOXr9X7ATKU8QKKVZLWtWSln0ewNzPiQRKJ2jFWSwM+NM7PSx7SJGNVChj2XlqTfHCNVoF4Ah27sbd4s2EMg74621Mj1tPGryKKOAf0D5X4j8t08Q+Vw/TeRL/XXgv7lfJffrVTwHnGl/9gB/of3/HdqXXwH+NvDXiPgC/z1wA/gj7fw9iKKEx9r/duS/auv5Wvvvf9b++7+63wEkyD8CNt/1V20RfYigpZJs1VYBm1BOUm4eohmM/gDd+v5Ftgl9P0pitO6pyPAgpdynwpDahirtiNEq8aEuVUgQxqmZjDGEWiU6Ikp5iQ9WpaKanEm6mq0mKoGLz7Tro26Xfg6Q0oqW30gco1QBahviE6JhJzkoUoY0W8klLeuNagwl2taWqCuqEZs8Ds9rxR5Sd3+fpMskUbpoTRAkoUc73yfYvssduq0NrQhkK3YcQLXrx24X242TRkMHbezHjhtjCKXXx6EziTQwUc3VBDtqjed5iRHTTpWUpHOS2lBa0fTriWsoCH3CPpymSc6clCGe30j8vR80Y+hgiF7Skq7FqDZwfByd+r5J15zZhgC++5xKs85qOUXCsnjExQKRRZkhAnmA0JxEO08TcgxfTtwH+lbc4/OISVS38OF9Plj5PPCXjTGniXZfF4wx/xb4ZeC/v18l9/V0Nca8aIwRCZ8vtL9XxphfMcYcM8ZkjTE7jTF/8u6kxbt0vHjXsb/RR+/fuM9+/Xwn/++uY54xZu/2tpIkKip+g+ViLzWC0hIvaLBWXiaUI9RaO1E6zcNY6B3E48PZDH04YozpG716EHJxEZQ23Yd+hMI2nHn7MmGoepwtbTQNv0ixutTzQFJK0vIaXJ55o1vNIdKlUVpyffFSRLNw14NSa816cY1KtdLzMI6K00uq3noPLYVp08VU61sopbpULKZdkmutfJOWV+npVygDljbmefPSSz0ULVorlA65tXQWdZdzakzExTa3cp5Gq9bjDEgVslFeYKM82+NoKiUJpMe1ube61T064wjDkFff+B5S9VJMSCm5ef0GS4uLPZQyWmt8P+DyhUsopXp0aaXZ3NqK7HuXI6SUYn1zjXp9e38ltWaZ6zOXULpXl9KSteI8Rutuv0y7nF+x1CAMw1i/PK/BxlYvRYvSijD0mV+O6Gm223Fta5ZQBj3nRPQ0Lp7M9LA9aWPQGIq1pe59oKPLGM1q8TpKh9vGLplfvUK1vtEz71KGNL0aF2++gZRhrx21Znb5SttJurOGQhnw0ptfY7O02mPHUIasl5a5fPuNHhqYyFkMOHc9qtHcsaM2GqlC3rr0HUrVrR4nMJQhN2evcunm2z3HpZI0vQbvXHoFKcM7unREv3Nx5hX8sNU7RhVyc+E888u9lCdaK4LQY3nzGvquee/YcXOjFnMCjTGsbzTwwyB2jXqBh9I1hnLvVov2UZbOsyxKYaHLufches4IEYFAHtbng5UCd4CxJWC8/f9niaqm3Zd8aOOj7yZbtXXm16+zWVtFCCjXN9g9HiWZr5UWmN+8RSAL1Jo70frhmUkIEeVRfYiuy/uRwPdxU6mH4gRWWoKrs00O7Rkhn85Sr1W5eX2WdHaQ7734Jp94/lkGBgsIYai2Nqj7W/i+R9NrMD12AEtYlGprXJt/B89r8fbl7/D44edJp3J4fo3Z1UsIW3Nj7Qb7J49QyI9Eyei1FbZqWzQ9i2ppmF17dreRnQG3btxkcCJNtVVmKDeGsFykbFFtFHHTaaq1BrZlKAzk0Cak3FikFRTZKi8yMrCP8eHdaK25sXCJ87feotls0fKafOLkZ0insjSbRWZXLtEKGrx+9mscP/gsQwMj+KHH/PIVFlfnOH3uLM88/mn27joU1S1euc761izZbAY/aDE5sh/LsilWV5lfi3Jnby9eZM/UCRzHodVqcOnqRSbGdrOyusbU5BSplIPRmquXr5FK5zh/+hyO7TI6PgJAcbPIpQsXqdfrDBQGGZsYwHVtarUm62tF/NBnfv0SRw88zlB+BKkUK6srKG1oeU3KtUVGC8OARTNsUSzXsMiyuHiZYSdE6BDlZqjoAIPN/NoFRgamSbsZgiCkWmsxWJhmbnYJS0A2F0Xq6vUW506fYXR0jGq5zu49e7Bth7XNRS7deIvJiWHm184xPXaElJNFKUWjUccIxXLpOuMDe8nnxiKwgS/ZKpbxA8nYSIZCdhhL2NSaW2w110ijqdbLZDM5HNshkC02qzNU6ls0vbeYHj1CITtKqAKuz5zlyrULDA8PceTAk+ycPIAQgmJlmWJ9DmEHXJp5nYO7TpF28zQaNU5f+Q4r60ssrN7m5KHnGB2eotms893Xv8788i1K9SUeP/I8+3cdw2CYWbjKldtncFzY2izx3BOfIZXKUqyu8c6l76IJeO3CVzl54FMUciN4foPXzn6HC5fO88Ybb/LlH/1pDu87gdaKyzfPcHPhPEprNjY2+eRTn8GxXZbX5zh77WVkCK+c/SpPHfscuWweL6hzbe4tNiurfPf073Dq8PNMDO9G6ZD5tSssb96iXKlRa5Q5su8Utm1Trq+zunkTyxa06iWmjMQKGpjcMPV8gYZncfW7V/nEC8+SzWbxfZ+ZW3M4bhawsUSDwcE8xhiKW2WksiiVW5j8YL9byMfyKIgQH9ocQOAmcAiYAy4Bf0oIcZqoGMb974B+VAEG/+5bv21SThXbamGEQisVMdJbop28naXS2I8fFniY3pdWCt9rkcnlH1pEbLt80ChgYwxh4OO4qYeyBVzIWPzks1MRKMNxugTEr7/6Go8/cZJcLtvOS4u2wdZLt5HKsHvieHuOBForyrV1Lt54ix95+qfaFRvsKMqiJDOL59kxeYhsKhflZrXrt86vXaKQnWBs6A5QQWvN/OwcpVKFJ59+AohytpRSSKm4eP4CTzz1OBDV6NRGgYGlrcsUMhMMD+zo5lIprbg6d5bN8iqfefInEUQvGJ2o9/XZtzm+/5M4TgrHdqKIkNGcvvJd9kwdbwMo7G708fbCBRCaQ3ueopMnqJSk4ZWYmb/BY0c+gd2uVqG1QmvD2voWI8MDpFIutm11I62+LylubrBj147uLp7Wmmqlyvr6Knv3TgKm3YZGKcWN24scOrSPbCaP085xMwbmFhYYzDvk0nfy4QzgSwvpSVLVi9CmbTEItO3SGt7P6MAUVhvg0al6UKkKlPSYmBztmZMbV28QKs2pJ061+2UjZUi1XmR+9SInD3+mW6+2E8HaKC1QyA2SSQ1g2057q1SwVVojCJtMT+7rpqVoo6jUNtkoz3F4z3NRvMayIzsaxczy2+yaOEk6lY9yG7VCa81b515kYmySA3tO9vS30lynVF1k3/TT3dq7YRhQb9b4N7/5z/gvfu7Pks8OkEqlI2JxY3j5zO+xe+oQB3afwLKiuidKKy7fOEOlscELT/4EQljYto2UAbVmmdPXvsOnn/jDuE4ax3bb0V7F1176DU4ceoZ9u47g2E53DV289Qbraxt88YU/fJeukM3yKmcuv8aPfuqPYlsujuOglERpyavnf4+TBz/FyMAkjuNG847h6tybYARH9z7T5qKLqlBUauusLLzDjq2bgMYyJqK1sV3CI18il93DwMBAe21FUdDTb53Gsm3WVlc4+cSTLM0voLVCCMH6+jr5g59iqxbyYX07f1A1338QuScK+NCk+crf+4FZ6L5v2f+f/uoHhgIWQvwlwBhj/jchxGeJeJezRLe5/7sx5p/el56PqgP4a7/7trGEIpuukksvobSH1gbLSmPMCK1gBC94uM4fgAwDjAHHTa5h+jDkQTiAge+RSscLiz8I+cKpEaZHeh1uYzRe0MCx3Ri6UxuFZbmxBHelJH7YIpsuxHV5TVzXjZcq0yqq8RorYaYol6uMjY3EjlcqJYaGBmPrI1QBrp2JJfFHD1EVo0iJKE9qZFL5HrsbExEJd6hFtp9jiNfdVVri+U2y6W0UKUYThj5OQn5n5160fRxKKzxvC1v0gke00TRaPkMDozG0se83UV4plsSvlCZcu4xlevPBDBbu9JO4uTjSu9HwcdwshUK+53hUBs+OASiUkrT8OvnsYMyO/cq3dV4Ats+VVCFh6JFJWEP1ZoVcdiDGThDKAMuy4+tUK6QKSLWZD+7ub7G8wejwRMyOLb+B66Ri895xxFLbchelCqk1SgwVepHLxhgqtSL57ECspnQnTcDZBviSMmR9c53pyV50ttaaWqtEPjMY61e0Ja1jNlFaUXz9X2D71Z47ukGQPvYT5HY8EZsTKSUvfvt7jIyOIaUk8MOIB1AGOG6aZn4vpXpSLuuHQ37oHcDDU+Yr//BPPsju9Mj+P/aP33cHUAjx14B/YYxZ23Z8DxEg5IYx5sL96vvIbgFHb9gODW+EIMwgRNAuOO2i2sWpH7bz130g3gPt92EUozVaKR6W/SeHc3EKDWGRdjsvWNtFdPh5e8S2HbJWIVlXOl67F/qjWi0r+RzbthOdPwDX7u9AJ9XotSybTCqpv6LtyCXVD07Muce2HHKZgThtjrBw3VTizHaqCmzXJyDm/HV0DQ+OYiXQMwkjE5eP0AFJJR0FGicdn3eAfL6AH/RDDseP2bZDPht3JDvpHEltRHm+cYleOOLnCGFRyA0lzm/SPHXO6VS02N7fsZGpxGh7ys30ufcIbCvuHDi2y1BhPLG27GBhJHnsfZDRjuMyWBhJpLoZzI326ZchKaYhVIgT1BJGYUgPxul/ACzbZvjAk5QaEmOBSUUoWAGEQlBtJANaPpZHRDogkA+X/FXgbwghvgL8mjHmawDGmAUiLuX3JB9hB7AjglDl6AUnPzoShgHpdPbdf/ghEkMU8XxYrWsNfZhduhUE7kvTPaLr/RynOJ3TnX69V4ncqQTPNPHIu4iIolQxvkyRrO2eOwv9B0+iJ32Pl7F+zQiR7JQjrP4Vdfoc7xeE7mz391HW97v3bQ31b6HP0e8PTGa0SWAM0vTDECahgyGa8sSxm2T+w3vp6mtD0/1PvPF+0gdFDgZblNmqOehHtM7vx3IvscBJftH+IZYdRNXV/izw+0KIOSI2ln9pjFl5r8o+XtWPsBijP5IcgP3oFx5Ayzi2Zr28kVA0XlMqltFJdCsmmRIj2jauxigxImRrnO4lQoP6iU91YyAIWzGaEmMMQejH+hvVU20m2lEIiyD0E9uXMm57YwyNRjmR2qOfC6K0ZLO0EqNoUVqyXlpOpBbpR7MDEAQq5p8ZY/A8P0bRYowBK53M2eikwc3FZstg0ajVY3Q6Wmvq9aicWlyiGt3b7aW0pFJLWENGU29W+1DNmMQ2jNHUW6WE9aio1TdjVCjRHIaJjp7R0Go1Y3QvWivqza3ENV+rl/o4YQLPb8TGEsqA1Y2FGN2M1oqN4kqUnxpTZRIpXUIZsL653IO+hgghvLq5kLyGjE6eKsshzI7F511YNMrLCdePQakQL2xi9Edn9+VDJVES98P7fADSZjn5V8aYzwKPAf+BiPdvTgjxH4UQPyXew3bhR8uz+CGSLu3DQ8iBexSknyPwQYoABrM+68UZvKARUaMohZSSZqPJ7VuzrCyvdgEIus2zt1q8yVrpavfvCIigqTdqFOtzSOV36Ta01oRSUvdLeEGjTQ+i246EZqN6i63qfFv/ne8WFxZZWlgl8IMu5YuUknK5zOVL12j5dXRbl1KKMJTMLNxkbvnatn4ZSpUKl2+8TSh9lArRbbqSIJQEgUCpiPYmDCWe5xMEAYtrt1hdX+zqlzLqw+XLN7h9K+qvVCFSyahM2cos126dp96stPnnQqQKqTcrvHXh2yys3URpFXH8yZAg9Hnz4jd5/dy3uhQxHVDD6toGG1tNdJQm16XlCQKD52uCQKK1adO5aJTSlIoBFy/NRfyJphPcE0gKqKGTGOFgsNowEEFD5VjelEhpunaM7KYoVrao1prdsat2v4qlGuvr6+1+3vnUGptsVRcIwmbP8VB6rGxdZnXj9l3HJVpL1stXWStf7YKEOvO1Vpzh5tw7EeWJlt3vm36VN858i8WV2+11KpEqRGnJSvEyq8Wr7Zy/sEuTM78wz8LqTaQO7hqHouGVmVk+R6W+3p2PUAb4YYsrc69xu00NFMqAUAYoJTl77SW++/bv4fseQRgQymh+l1bnOHvxbbaK68j2sVAGNLwa33n9K5y9/BpSSUIZdHkBv/vG7/Ha2W93/+7wFd6YvcSN2YvUm9UeXaXyJi+9/fvcXroc1cmWAWEY6Xvx9d/h7Ssvdn8rZTT+azPnuO0O4wsHiUAh0Ag8J0/Jsqg2N7rXSYcz8PbiZep1r28U8mN5xOXDzQOIMeaqMeYvExXe+FNE25hfISqDe1/y8RbwIyyh7+M8JCqUhymWsB4Ej1JMDFBqZPEDh3JrkX2Towzm09GDJKgzvXOSVrOJVJJMOosxmkpjjXq9TiZrUW2uMZibQghB02uwMD/P1K5RVpbW2LlrN7ZtEwQBxWKJUrGGu8fFsVOk3BzaaGrNDaTysITL+vo6U1NTYKDRbLEwP0+tUmegkGfHrh24rkuz2eTs26cZGhnGCyukU3lcJ4PWiqXldVp1SGcD6q0ihewYYKg36vheSDqdY6O4xOTYHhzLxg9bNFs+aWeAwJcI4WPbgmajwdp6mWrNolEpk88OMzgwCGjWtpbxVJF6ySWzkmJ6R1T2ruU1MUYwNbafta1Z9k4fI5XK4gdNbi1dwLIE5do648O7KOSGUFpyY+4iW5U1fE+ysHqbfTuOIoRgs7TK6uYMMhSkRJMBpwYmROHgMUkI+F6JvN3CNj4GC21PICggpU2tmWZyahyEoFKucvqtdzDGcPT44+zavQvHdamUSqSl4GBhAKlqWHaWlJtBqpBSeY5mUI5od9QoKTeFMYbNzS1mZpcwWiOVZueOaSzLot5oMHN7A98TyOYqB/bvxXVTSBVSri9h2RrPX2Lj1k1cGYAQtJw0YWGAwBek7DWGB6YQWNSaRdY25vEDj8XVa+zbdRLHTuH5TW7PXcLzfTbLi0yMT5FND6C1olRfIpRNNCG15gYDuQkQgmarTuArcATF6jzjQ/uxRUQps1a8TctrceX2WU4c/ARDhRGCMODK7dP4sslWZYnB3DgTo7sxBpbWZ1haneXWzXmGc2/wzKlP4jouW+UNbs9fB7fJ7dVz5POfJpcZwJctXnv7RTaLRSqVt5ke38OOyT1oo7l84xwrG4vkM0PMzF9kz46ohN761iqLK7fBDbm5dIYT+z5BJp2n6de5OnOaIPS5vXiZkYEJxoanUFpxe+EyXlCnMl9mMHORQ/uOYQmL9eIKt+evks25FHe/wOE9p0ilsrRaFebnL+BtLbNZ2eL4gefI54ZQSrJZnmO9uEzYsrAYQfOh20r88Iv4UG4Bx8QYEwoh3gGeISrasfN+z/0Io4DfeaQHrpREhiGpdL8k7IcnHzQ6TMkoCvFwx26wBKRdRcrVTAwFTA5m2D2+B8uyusn3WivqjTKWbZHL3Clmb0wULWq2GuRzvQXllVLcunGLw8cOI7gD8NFG0fA2SbsDOFamm5hujMH3PFZW1tm3f09PwnqEDi4xMjrak5+ntWJ1dZ2x8VFSrtvtb4SwDvADn0Kh0NOGMQbfD7GtoCdlSmtNqVZifHRXu4xbm1JGSUrVTbQSjI9O9OjSxlCt1Bga6gWPKCW5MXeBw/se7wE3KCVZ3pglk84zOngHjaq1JpA+s7deZSqbQtxV58sgaDnD5Eyzp78GgRJDWJkxBgYHuv3qRC5XllfZu28fzl3bNFHEU5FK3Znbjh2X1+coZEcZGrwD7NBa44ctPK/F0MDIXfMeRRDXNxeYGt/TO3at2Ny6hbN5pR13bLdhIHRc8rteIJ8d6p7TiUIurd9gz/Sxbboks4sX2b/7cay7ADJaKyr1NbKZAVJuvrsmIl2Gaq3M8NDINlSt4ub8aQ7teboHHCRVyNzKNXaM7yOTynXbV0pSbZSZnb/JyWNP4d6F3pUy5NLtN3j88Kd7ELpBGPDOhZd48sTzZNK5u+ZEsllapdEos3Nyd/c60e0yZDfmr3LyyCd76gFLGfLmhRd57vHP4dhuj65idRVLCAbyw10UtNaaIPRZWJ3h0N5TPf1VWnJ19m2O7X22B4WslGS9uMj1xTnWartR5kPLJ9dXfuhRwMf3mq/8i7/yILvTI/s/8998YDQwAEKIFPBzwJ8DPgfMA/+SCCW8fF86PigHUAjxOeAvA08Be4G/ub3ChxDiF4C/CBwAKsDXgV+8G+IshPjviErDrQF/wRjzTvv4F4DvEA36mDHGu+ucbwKLxpif79e/R9kBNMagpMQQUSN81BxAGUbVDdxUch7Xg5VombiO4YXjaXaMTCZQeESI5e0AiU7uUlKd1SAIcBOofTr5j9uRnNoomg0vRkUSORwSS8SRop38tDhNScRllkTD4ns1bDsJPmtj2akYvYZUYZdTbruuaBwJZeKUbJ8Tp8DBmBhCOZQBtaU3sJJy5Kx0uxxd73E7NUR6eH9s7FppLDtOv3Ov/mqtqNdbDA4Wth2XIOL1aqNqLypGUQJQX7tMULwey9CzUgMMHPhCfN7b2/ZJqO1Q+jh2fIegH6VMv/VojEGqAMd22Y4q7lT0cJ1eBygMo5eEmH21JlSSTCoOXIsqz4geByzS5aNUK4FKSOH5MDw0HNPlBx6O7cTsEqU0BInUR7aTia1fgJbXJJ3KxK9rrXjp3Ous1na1S6R9tOSH3gE8sc/87v/+/3yQ3emRfS/83z4QB1AI8TiR0/dfAIPA7wG/Bvy+eY8O3Qe5z1Ygqgv8b4H/efuXQoifA/4J8AvAN4n2sf8p8G+AH2//5tPAzwB/jIj1+l8Bj29TNQn8P4C/+/4P4eFJGPiks8m0FB96EREFw6MhbRJhY8jdFbnokQ7yMEYDY/cFsyQ5f512EsELItkeQggskqlFhEimBIlImeM5lpFTmLzeLMtJfHiKNs44SVc/iaKIyRQeSZgSW9CnFRCij73cbOJcCcvqY6v+/Y3IiZNycUUb0Zzw+z52NEE1ETZjpQcTUcgd0u0kSXL+okaSUda2bfed9766EDHnr6srAdBhWRZpK3nbLSLFTrKjSRyjbdk4fe4DKTf55bBf7rBtO32voSTnD6JSfJl0HlNNPO1jeeTlw0cDI4R4nYjvbwH4x7yHaF+SfGAOoDHmq8BXAYQQfyfhJ58Bzhtj/nn771khxD8F/v5dvxkBVoALRBHC3vBHJP8Y+GUhxD83xtx3CZRHWQLf+0jm/nVEhmHEFfdIjD96MDm2odwIGcrp+MOiDxVKJzKYhMjURmFhxSIuCNF2Arf9vi9VRaTLFvFL2QBGq8ToXNIDz7TrHydFADvRrn5OYPz3ydFHiCJRlhWPAHY7vf33xvSlPDEmOrrdXkp6iX5QP2eqU5VC9HEStiONo66aRN7GKCqbPCdWqtAmY9mGHA4aJDEBRlHR5Ciybs9t7DrpQ4GT5PzdaUcm8g1CFO3bTt4cVcSI69EmAuFsJxnvfGeUjkXtREIUtfP7JLt3+mQ7TixqKISVeE7U3+SXBakkDnHnVAiL1eoQ5mOs5A+nCAvcOO/lD7msAn+U7yPalyQPc2W/DJwQQnxBRDIF/HGicGZHvkZ0K2sAZ4BfTtDz/yVyEv/HD7i/D0Q623P9CGM/7GK0BswjEAE0WJYhn1Xk0hrX0cyuVTHbohURerYRJd7fFX0wEVyVSmM5FinRRlFuLMbQhdooWn4JpYOeSEZEM6Np+JsxXREQZTHehlbUW2sYemlKOlvGftiIO0MCao16nCbEKFaLt7tI5bvb8EOPMAzikRchWN1somKUI5pSqcZ270RpRbm2QdOv9Ti7HZuWgwjN2zt2ga+HY7qk1FSrrRhFi9aRY1atF2NjNESAniQKnDCMyrX12lEjZUCjVY7PCZpidTGRbqVl52PIfgOEskWoWrE1ZLRhdW01RqejtaJUW4n1VxuF51dQOsp3vPN73a6gsZZIKbO6dTtG6SJlyK25S0gV9owlyssMWFq/HaNiMcZwY+Zc7HgYBtyevxi/frQiVD4Nrxp3UI2hWFuMUcRIFXLx2un2veLu41E+oR8m2BFDtV6OtaGUYmZ+LRZ8lUozv96i6Ts87IIAH8v3KUIgbPehfT4IMcb8jDHmq++H8wcPEQVsjPlNIcQwUZTQbffl94Gfv+s3EvhPhBATQO3uPL+7RAK/BPyGEOJ/M8Zc/6D7/kGJMQbddgA/is4fEG3/9qlk8GDEkHE1A7mQiRHDcN6h4QUEMiCV0iwVbzA1uJuUm8dgaHqblJrzYAQj+b3kM2OAoOVXmVu5hO0qtJEM53ZhWQ5KB2xWZvFkmVA2GCnsJ+XkIkeuvk6pMY9lW0wOHSKbGgYEXlBjq3abVssnnbEZzO5ACAulQ2qNLer+BqH2GM3vw7EzkYNZX6baWqbhlRgfPNJNom/6VYr12xhtMZI7QD4XVf4Iw5CllRUWVq6ze/cYO8b24zgZtJKslxZYLs1wY+4KTx37HCODExgD1XqJUnWVXLZAIT8albxD0PR9zt6qsbjlcbLuc2zfCI4d1e8tleukUjlqNZ9MxiaVimrCVutb3Jg/jdQhR/Y8zfjgThCGaqPM7YWrWDpDJpWl4GosJ4UMPXw9gkiPs1WGkXwLx7UxWuOHNlU/i16rMDaW71ZQ8X2fur9BICvYtk0uE1VQCUKPteIsYdhC6AwDheHuVngYKpSyaLUC0mkX143s2ArKrBVnkIFkemI/+ew4AkEoW2zVZqhW6zhWjqHCWHtVaUq1RVqyRWH8SajO4LhpjJa0ZJ1WfpBm+QqjA/soZMYBQSAbbBQX2dxs4XuSPXt24jg2oZSsbt6k5VfxfZ/RoSlc247Qvn6ZSnONfGo3KIehoQJCQKXS4ObN22QHBLt2wNDABELYBKHH3PJF6q0tGo06e6aPdRHuyxu3mFk6z/LmDU4d/TQjgxEqfXl9nldPf51Qhjx17JMcHMwjvDpqaBfr/jqhKTO7fJFdE8dIpVyUVly48jYrG7dZ31riyeOfppAfxhhNubbGevkWWmsmBvcyVJhAWBa+77FVW2SrvI7rWkyNHcS2HILQ58ylV1nfXCOgzLF9zzE8MIbSmptzl7k++za7d+1k79SJNppa4MsGxdoClh5AWJJCdjSqgywDbi0ucmnZYrG+wXOHRxjMuShtuLFc59xM5cHeej6W91kEJFSr+VjuyENzAIUQPwL8LaLSJt8jygH8+0R5fj0VnI0xG/fSZYz5bSHEa0R5gD97X+0bhcWD55rrJ9HbvkaogHQmC8h+hP4PXWwTJ219v8RohVI+trG+r6oFP4jYFkwPe4zkfSaHp0ilCmA0IymBH7bYqi9iWXkCX+D79Wgjz6SRvoPRoFNparIBJqoRa+k8jVqJkWyGBj7gRRvCOk/glUiLPIEPvhfpEiZLqx6V2JIZl1rQAEBgEzRT+F6IkVkazVa7DQsV2ngtTdrK4Xkao+vRYGQOv2HjZB18L8QnbEdebKRnU6vXGUy1SY5FO5fPChBuQNodQ8oUYSgBgWONIfQSo4MTOCJHrVYHLGyRxbbS1BoNNit1AhXSaNlsVLKEKpq/q4sht1ZqTAymmMwLhgYHCGWIAIJQ4/t+lHtoMtiqgBAKx7iUK1tANMaBzDiWyZIdHEM4TgSOyglSgaJSqeGkU5hMgRDAMrh5gWk10SZEKUGzGbS31S2CQBJIjZLQaERE2cKySDl5mo0qQRDQat0h6VZKUqk2aLUCGo3m/7+9M4+x5DgP++/r6x395s09e5HcFbk8JFLO2qIo0xFpgoJiJbaiyE6C2EYSAU4gAYlgR5ENxA6iIIkVB75ixI5tBQ4iIw5kJwbsIApMJZQoMHJEytLKEe9jucs9ZnbueWf3666q/NH9Zuddu7Pk7szsTP2Axsyr111dXfW97q+rvgO/IKR6BUNEmmiM9tGpT6Oe9buIQBqSdlro1KHRaND9IYsqkiZ1Op5HOPcgWmtEoGQ0zfpZRIFJCjTSbNwRB60dxiYNE5UK7XZEloFDcNQE60stSoc7NDeW0LnZgDGaJDW00yaV8hj1ei1f8hemDxVZXmyRdgIajSg/hTBeOYw2HarhJGmSUu9kRm8lf5xqeY7x6hS+hNRqWflYeZLbDt2Jai1RPv2HLKVJ9tKqNZ25O3GOv5uwMEUct2i3s3YdmjlGkraZmToMJqDeaAIGTyqZTLUb+FOTRLEBUYgUQJUI3DJFr0qr2d7sx0PTd7C+vsFE+SguRTZqNUSE2YmjnPWrJLGH74zTasaAIFLAJAVanTrteIOzzdcQDLHSLNSm0arAylqTJ77RxBE2Z5oP+sLvzbzP7wj5DKBlNLsZB/BfA39kjPmN/PN3RGQD+FMR+Ywx5qXrrO/TwDMi8v7t7GzERQ3mN9o1tFbEcYdieQx1C8z+3SzvMIWg3az+nZ0FNRyeNNw2m1ANZ6iWZ3pmIkNdplB0N2cPNo8yhnL4LjDg9RnLnwwfYKO2xER1treusEQYFigXBusqle/HcwsDtnZheB9r6yuMVyd77KXK5SKOZxgfm+mxhzImpFAMKBfDgRnVMLyfRMUEXrHHBrFUPkFQcDg8fazHTqtSKVMsn6Janhmw3wrDO/l/b7zKhZUAA7QiL3uAdsO7YPBcB1wf8RxiDb7rMDNR6nGUMMZw6v7vHeoNGpaLIGWCoNcsQilFp9NgrFLucdIwBkpFn7AyOyBDYeUukrRN4IU934XlIpWwRLk41eMskI1JwMLlVRyvRL0VkaYOgQ+dRDh5/D4Kffa65XKJcrFCJezN0WxMiXI5pFio9PS7MYawcj/A4LiX7yLqbFAMxnqOCcMSQaApFTSu01tXkAaUKzMDdoPl8DhHDykKffmey+UihaBIpTzWIytah7wreIhKODZQ1/tOPcbif/0Z/NZqjz1jYX4DffIhJiene2zqwrBEsVRkcnxucHzD7yJV6UDe4bB8JytrZSYmZvtkvkSxGDA3c1ufzGseec8P4PvBgA1iGN7DhcUXiFKoRUW0aqPcKs2kgtkqU2BXfLew172Ar4aIIENyX1uusJsKYBkGpuC6xinX/RM0xnxDRL4A/BLQeJtt21G01nQ6cT7zZ7lq3s6bhOvAzFgbJGGsPDegNDmOQ6U0NeC0ISIIDgzx+hSEyfFDA0qI47iUC9NDjM4F3y0xauq3EFQGjOUdx2VibGagvSIO5WJlZF39yh+A5/ocmTkx8IB2HJeJytzICemwXKZ1yaC04Jg8R+zmJQupMhR8zcuXE6KkzUP3Tg90l4jgSJadoR8RwQ8GbWJd12ViogB9NngiEARFtNZDnCeEwBv0rnccl3JxekiuY8H3iry+mFDrtEiVQQiZCAM8r4A7JEyT4zhUwvGBchGHQhDSf3sTEcQ4Q8VeBIpBdeg5xsIAozsDdRUKpeGezjCg/EHmbVspjw94gDuOUAkH9wdQtUX8aGPAmcXRKdXZOwZk23EcJquD/du9SN8bdPoScSgG1YFjXNfj0MxtQ36LDkFQHPHwEDxvjpfOtmjFLo6jMWaUb7llf2BnAK/FTVMARaQCnMw/BsBhETkFNIwxrwF/TOa9+yxXloB/FXgOeKt2fD8LvESmWP7BW2/9zqG1Ju3EuJ43MkTFQUJrjUrTAa/Dm40jBpEOBj38IQUjbTNHjZnjOCO9TkcP8/AQHtnDblRdo9s77F1KxBnqaXzlmKG14Qz5ThuhFRWAiMDpUJCIuqpufu86MDeuWW102GgLYPC94XKuzXBf36v+JkY9wYfEGcyuQkYeNMrkwBhDI4J2qjf3XKwFzI77wyK3XJOh1zPCkzxbiBzlAT7i5CIj5OtqoW5Glw9tbxohjgt6cJlQvFEP3RG/n1Eq21VD8wyva5iMQvZbjNIyzThbFlZ676z+WG4SIoh78AJ4Xw8308zhQTLP3dPA7cDH8/+7YV8+C/wrMqXtBTKF7VXgh4wZljH82hhjzgL/DrglptIyZSfBwJ4M+LwbuI7bk6Fhp0iUML/uESWKqFMb6g3a7jRQQx54jHgYZblY44FyYzRxJxoalkObzcS1A8cMCwVjjKaTtobGP9NaD3h2QrZ0Oiy/aapSNpprQ49pRY08kG8vIjC/3KLgxlSDNdK+W4o2hkRp6m1DV2F5c7FFqobY34o7VKXJZsgHz62NoR4JaljYD9UZeo1ZoO3hfRV1olwJ7aXd0UTJ1mMyxXq1ng5VnJTWpCoZKkNpmg59KRilBGXtSgbKs7A17lAF1KiEEUFzho6tMZpERSP6JR1a7k7dNjRsjXE8OuuLQ69R5TmeBxn+opIqTSse7C9jDFHcHvCMhtGKYZJqLixHQ8+zF8mCsrc3A4FbBrl2vwji+Lu23QrcNAXQGPOUMUaGbI/l3ytjzGeNMfcaY0rGmKPGmB8zxpy7zvov9JX/dF7+sRt/VTeOLLF8dkPci+nedov+MBE7yVqjwMKax6W1M5nClbdD58rXKxe/TTtubCpixhi0VqzWzrHWOI82V27WWYiUBsu11zazKXTLU6V55tUlGlG6qQhpbVBa89zZy7y5uEqqrtSVKs1Gu8GZxSWU3tIurVA64fLqK3mojq3t0ly8eJm1tayuLp1OwleeeprnXvoWSqvNdqUqoRU1ePrbT9BJYtI89IbSKUma8PR3vsTyxmVSlfRe+8YCRydWmK1ukOIQ6zJbH7LGCIsbLnGazUoJcGahQa3Ve+2p0px+vcbLFyNSZXquvdGMmV++jNJ6U9lLlaaTaJ59tU27Y0iV2WyXMRAnhlqz1htSRiuizjqrtTM9Y5UqTdRRfPX5FeKO3myXytv1zMurQ+VFacM3X90gVVfi1aVK02gr3lxa6FE2u+16+eWzxHFnUxHrZiBptuus1xd6HvhKa1YbCU8/v06SXrn2rgy0IhetZVMJNHkKvlZkaEdRT11aK6I4YW21gdamp1zplMW1NzI53dIurRUr9XO0442BfmxFTTj1oxjH31QEjeuji1WWIrUZdmbrtSeJQxwnKNV7jvXGJZbr57Pryq8xUZqNZsrp12skyqBUt73ZS8233lii1ko2ZVtrTaoUL5y9xItvrmdjkl9jkmqWazGXVoYFkdh7bEaDAKJWE62GvzQcZLbK1yhEHByvuGvbrYDNBbwLKKXQt7DydzNTBGmliOOI4o5nQTEU/A6V0jwTYYvx4gwT5WOE5TLNuMnFlTeBiLAYMD02R7k4hdIJG415oqSB57n4bki1fAhXPOrRMq1oFTGglcP42FFct8J6s86fv6FoxVm2iBOHQo5NF2l3FOcWGizWMgXr8GSRk0dKOCKsNtZpRuvMLxXwCiH3HqswU3UQaVCrLbNeX8fzPO44fC/lYpU0VSwurtJqtUlMg1LlGEfnqrgizC8s8rWvf5273jlDx7Q4cfhuAq/AhaWzvLHwMtooikGJ43P3MF2do95c58zCS7SSzFP59tk7OT53EhFoRXWWNxSrjYB6FJAoh1HvlI6Y3LsyW4L1HLh9NuT2mTJRonjtUoPVRnbts1WPE3MBngsXVhLacQdDitIFThwao1r2WdqIeX2+SSfVuA7cMeNydNLBcxzijscrl2PacczMeIk7j4SMFRWdzgr1Vp31losxZSar0/hewMJaxJmFJqky+J5w1+EKhyYK1Nopr15s0IjSq8r8ROhx99GQYuBycSXi3GKTSjHl/js6zE0cI/BLGA3f/NZz/Oa//y0+8pG/yiOPPkg5LGCMoBKfjfoya61l2mqaE4fGEXE5sxBxdrGFMUK54HDySMhExWetHtOOOxyu+hQ9n7GxLGOGNgkbjUXS1CNJO5SKhSzcC0IjWkMlAcXCBEaneL4GNPVojZXaMmknJfBdjs0ep1gYI007LCxfwPUMvhdQLJSplKYRHFpRjUZrFc8dI1o4S3Dx69Cu4Ry9j+S2e3D8IohL4BUJvAqtWLFaVxjjsjR/kVefP80jjz7M3JEJaq3LRGmbOC2yvD7FHbPjFHyXC8stzi22EJ3gFwrcfSRkcsynGUWcX6qztJGZERyfK3P7TIko0bw232C1nsvQeIGTR0I8Vzi32OL8UvuWsPfrxg+NozZ+/mxIk8yLvZsac6fui3shFVxXP+n2izaaNOlsZsj55F9//8jOOHXqAfO//9d/27G29jM7985rpoITkb9M5hD7TuAS8GvGmH+7A83Lzm8VwJ1F60yARRz84Na0T7ipCmC+LO7tcCYQEU3Rj9FETFXO4+uAQjEgVuMs16okqeB7mvFyyh0zLXwvQZts1o48s4bSGscRUqXQKnNAcMUhSYSVZpmF9ZCSv0EtmsxtkLrXd0UUr1ioXfnOEUO1FKNSQ72TWTccnfTwG2eZmpyiMuNzaPL2zVRb3RmXleU1wkqJQqGw6SWbKs3ltToXl19gpXGBNE3yoMYJrpfdVB1xSDoJjuPgBx4I2TXlgZUFh7AwR2reQb0d9rV3VP+afKbqZo6pwXcdfM+hFaue8nJBMxV2WG/5NCN36PLl1biazItApRhgMDSjBGOgWnL5gfccwnGyHNHdWdnPf/4LvPvd38Wx2w4RpcsgDpXCYbRoLqwvM7/q02y72+rTahE+cOoIvu/iOrIZ8HijPo/vFykXJjadg7KZPk2rpUmSmJQ12mmTRkexVvNxxOehe+7Ac9xchjTGwMLSAlMTkwR+sFmXMYZWu86lxfMUiy6Oq7KZOwHIckOnyuFyzefyWolmvMUWttPgxWe+xIl3zPLwo+8CFButAuvNEmv1YOCa94ISslN0+yhqtwiCAm5uCmOMQamUtNPB9X08zxtp93sj2cm+31T0dNfURUjTZPN+plRmF25MliSgG43g4x9+8CoK4LvNl5/8o51o/lCmZ05eVQEUkfcA7sh/zAAAClBJREFU/5csVe7vAu8Dfgv4pDHmP+xEG3fTC/jAYYwh6cQ4joPnH4yb2vVjrjm1f1POaoREeYiUSOJx/KBJKwlYXB8nUZmylihoxz6OwNGpVpY2zVF4WQxejBiSRBEnCQaDqzWB41OLqtTbRVLtUmtPok3/zfuKsjfMOkwbYb1VxDXJprOAidsUgxLTs7M4Ij15ViV3ApicquZZZa6cz3MdDk2Ocf6yotXIspe4roPg4IqH6zqExTJpqYzSGpEGnSTCcwDxUCpAU2WjfQJlSmxXoeumbLu5CIkyJKrfNkxoxQ6tuLj5+UYyXQnw628yOTNHLazSaCc8ePc47hanrmwcXH78x/8GURyT6oROJJhsbpNGW7O0HqBU/wvAaI7OVPC8K3mHM4/0LK4fIj3OTFnKOIfAVxjj0Y5T6m3D+aWQetvj1Dsmchnq1uXk8fsyL/atDjUiQqlYISx7IIpEdUjzdH2uQNwR5teKLNUClO5eR/43qHD/Ix+hGAidpsH3UppRCMZjIgTIXhSUMZlca2gplzh5S2bhtxxRq0lQKPZkQupmhnJLHp2oTaKzdJRu7mxzq60gdemaP3RnOEUEpRS+74OA53k4uaIXyJUl1e1er4iDu7eXYj8F/Jkxppvh7EURuZ/ML8IqgPuF7ttNHLVxXXfHZ7duJXSeCm7nEVLlIeJSj+dwWaelxjaVvy4GYX6txEo9wCD4riYsKMZKCYHXAVOn3vYATdGLSbzMWaGTuijlXGPm6VrfZVv+iEalmleef4nvee/3DPV4dUZ4lYvAeGWGhbV1xIRAiSCAYhAR+B6eW2BpdRqlIfAaBO4SnqOI0yqxmkKZgGyp91aS4ZvXVhdN3KozO/MuzMJFCjpmqjIztO+DwOe1+cuUgzZRGqEMzDfWWGn4pAqiZPszO7fPlrMYiwOMvlbHNSgNSxtFLqw6NKNM0Tg20xuXcXN/Z5QnOYTlKlHcoqM7JEpIlNBoBlxeK9JOugpM/7GCxgPxmH/zDKXAYSzs4EhmGhNFEb7vMTYxTdKJWFtZ5tDkUS4qf6izz34i6cR4foDjDuZ3FslmeINiCaVSjDbE7RZ+obg5RrfCM2VQ6QOQbHkb8IMrLzNvl+zFZU9PtLwf+J2+sj8BPi0it/X7N9wMDuwSsMVisVgslv2JiPwJMLOLTSgCWz2PPmeM+Vz3g4h0gH/YV3Y/WSi8h4wx37jZDbQzgBaLxWKxWPYVxpgP7XYb3gY7MjN30NMdWiwWi8Visew088DhvrLu54WdaIBVAC0Wi8VisVh2lq8B/bOUHwLO7YT9H1gF0GKxWCwWi2Wn+VXgvSLyWRG5T0T+LvBJ4Bd2qgHWCcRisVgsFotlhxGRHyQLBH0v2bLvrxljfmXHzm8VQIvFYrFYLJaDhV0C3meIyLSI/KaIXBKRSEReE5GPb/n+KRExQ7bmNeoddtxTffscEpH/ISLzIvJ5kSx6p4j8hIgkIjLWt/8LIhKLSLmv/BUR+Y233Rl7hBs1JiLycyJyXkT+LI8ij4iU8j78RN++P5PX8ff6yn9WRDZEZN9EALhW/+b7lEXkF0TkbN5fb4rIP79GvVbm3wY3alys3A+yjXvKx0bcU05co14r8wcIqwDuI0SkAjwNnAR+FLgP+NvAi1t2+2HgyJbtKHAB+MI2TvFf+o794b7v/yXw58BfInNj/0d5+ZNkIYce3dLWw8A9wCLwyJbyY8Dd+TG3PDdqTETk+4C/BnwU+EXg8wDGmDZZOqEP9J36ceDNEeVfNcakb/vi9gDb6V8RcYEvAh8EPpHv81Hg69s4hZX5t8CNGhcr94Ns854CoOiV3SPA+W2cwsr8AWFfvA1ZNvlpoAx82BjTDUB5dusOxpjVrZ9F5IPAbWQ5CK9F2xhzNff0SeArxpjviMgr+WeMMWdF5AzZTfmL+b6Pk91Ens3Ln8jLPwBo4KlttOdW4EaNySRZ2IDvABtAuOW7J4GfFBExxhgR8cmizP8U8PNbygvA9wH/hP3DNfsX+DvAe4CTxpjFvOyNbdZvZf6tcaPGxcr9INvpWwCuIbujsDJ/QLAzgPuLHyF7M/zFfHr+ZRH5ZREJr3LMJ4DT24w6/lERWRKRl0Tk10Vkqu/7nwd+SUQS4MfIvJy6PEnvW/njwJeBrwwpP92vFN3C3KgxeYLsbbsJnKb3YfYkMA2cyj+/D2iRzZaUgAfy8ofzz/vprXs7/fsjZA+gfyAi50TkjIj8johsJ0uAlfm3xo0aFyv3g2z3nuKKyBv5MvET+WzqdrAyf1Do5uaz262/AW2y1DP/GXgQ+DDZm+Hvj9j/MJAAH99G3X+fbKnmAbIlgReBbwJ+335uXq/0lf8tsje+2fzzGeCvAHNACkzl5eeAf7PbfblXxwSYBYp9ZR5QB/5x/vmfAX+Q//9F4Cfz//8FsLDbfbLT/ZvLagR8CfhesofPt8mWEOUqdVuZ3yPjYuX+uvv2YbJl4b8A/EXgP+Uy99A16rYyf4C2XW+A3W7gYEIMXNr6YyWznzHA3JD9fy6/gY69hXPdldf74W3uP5ffGP4mcIJMyank3z2X32zuzuv84G735a02JvkD73/m/38V+ET+/6eB/57//zXg93a7T3a6f4FX8v2mtuxzKt/nqg/EvnNZmd9j43IQ5f567ylb9vkq8IfXeS4r8/t4s0vA+4t54BVjTLKl7Pn87/GtO4qIQ/a293vGmPr1nsgY8zqwTGbgu539F8luAB/It28YYxr5193lgceBDvB/rrc9e5idGpMngUdFZJxsNuXLeflXgO/Py9/L/lkG67Kd/r0EzJve5aahY3A1rMxfFzs1LgdR7rd9T+njWbYpu12szO9vrAK4v3gaONkX6uDe/O/Zvn0/RHaz+O23ciIRuZ3M/mb+Og7r2od07UK6bL0x/KnJPPz2Czs1Jk+SGch/ClgyxrySl58meyP/FOCzvx6EsL3+fRo4kisDo/a5Jlbmr4udGpeDKPfXc0/ZyndzfbJrZX6/s9tTkHa7cRuZvUcMfI4sNMBjZMssA8sfwB8Dz46o5yHgJfJlGLJlgM+QvUkfJ3P/P01m31G+jvb9ENnUfw14fEv5NFnIghrwT3e7H/fimGzjPEIWaqEG/O6QemvAa7vdH7vRv2ShLNbyfnggl+9nyGYgusHwrczvwXHZxnkOnNxvs28/Q/ZCeVe+/6+TKcQ/uGUfK/MHfNv1BtjtBg9oPu1OZiR8FvhlIOzb5xiZQe5PjKjjsfwH/Fj++XYyd/3l/MZzhmyW6sh1tm2MzCYkYtCg+9v5OR/e7T7ci2OyzfP8ft6HH+sr/6m8/Ld3uy92sX+/m2wGokW29Pgfgekt31uZ34Pjss3zHDi5v1bfAr+Sl0fAEtlM3ON9dViZP+CbTQVnsVgsFovFcsCwNoAWi8VisVgsBwyrAFosFovFYrEcMKwCaLFYLBaLxXLAsAqgxWKxWCwWywHDKoAWi8VisVgsBwyrAFosFovFYrEcMKwCaLFYLBaLxXLAsAqgxWKxWCwWywHj/wMToxI77+jMpAAAAABJRU5ErkJggg==", "text/plain": [ - "
" + "" ] }, - "metadata": { - "needs_background": "light" + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "imp.plot_basemap_eai_exposure(buffer=0.1); # average annual impact at each exposure" + "imp.plot_basemap_eai_exposure(figsize=(6, 6))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For additional functionality, including plotting the impacts of individual events, see the [Impact tutorial](climada_engine_Impact.ipynb).\n", + "## Further CLIMADA features\n", + "\n", + "CLIMADA offers several additional features and modules that complement its basic impact and risk calculation, among which are\n", + "- uncertainty and sensitivity analysis\n", + "- adaptation option appraisal and cost benefit analysis\n", + "- several tools for providing hazard objects such as tropical cyclones, floods, or winter storms; and exposure objects such as Litpop, or open street maps\n", + "- impact function calibration methods\n", "\n", - ">**Exercise:** Plot the impacts of Hurricane Maria. To do this you'll need to set `save_mat=True` in the earlier `ImpactCalc.impact()`.\n", + "We end this introduction with a simple adaptation measure analysis. \n", "\n", - "We recommend to use CLIMADA's writers in `hdf5` or `csv` whenever possible. It is also possible to save our variables in pickle format using the `save` function and load them with `load`. This will save your results in the folder specified in the configuration file. The default folder is a `results` folder which is created in the current path (see default configuration file `climada/conf/defaults.conf`). The pickle format has a [transient format](saving-with-pickle) and should be avoided when possible." + "### Adaptation measure analysis\n", + "\n", + "Consider a simple adaptation measure that results in a 10% decrease in the percentage of affected assets (PAA) decreases and a 20% decrease in the mean damage degree (MDD). We apply this measure and recompute the impact." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:49:48,642 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:49:48,645 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], "source": [ - "import os\n", - "from climada.util import save, load\n", + "from climada.entity.measures import Measure\n", "\n", - "### Uncomment this to save - saves by default to ./results/\n", - "# save('impact_puerto_rico_tc.p', imp)\n", + "meas = Measure(haz_type=\"TC\", paa_impact=(0.9, 0), mdd_impact=(0.8, 0))\n", "\n", - "### Uncomment this to read the saved data:\n", - "# abs_path = os.path.join(os.getcwd(), 'results/impact_puerto_rico_tc.p')\n", - "# data = load(abs_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](climada_engine_Impact.ipynb) to get more information about these functions and the class in general." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adaptation options appraisal" + "new_exp, new_impfs, new_haz = meas.apply(exp, impf_set, haz)\n", + "new_imp = ImpactCalc(new_exp, new_impfs, new_haz).impact()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Finally, let's look at a cost-benefit analysis. The adaptation measures defined with our `Entity` can be valued by estimating their cost-benefit ratio. This is done in the class `CostBenefit`.\n", - "\n", - "Let us suppose that the socioeconomic and climatoligical conditions remain the same in 2040. We then compute the cost and benefit of every adaptation measure from our Hazard and Entity (and plot them) as follows:" + "To analyze the effect of the adaptation measure, we can, for instance, plot the impact exceedance frequency curves that describe, according to the given data, how frequent different impacts thresholds are expected to be exceeded." ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 32, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-03-15 22:32:07,393 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-15 22:32:07,397 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", - "2022-03-15 22:32:07,406 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-15 22:32:07,408 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", - "2022-03-15 22:32:07,418 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-15 22:32:07,420 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", - "2022-03-15 22:32:07,437 - climada.engine.impact - INFO - Exposures matching centroids found in centr_TC\n", - "2022-03-15 22:32:07,440 - climada.engine.impact - INFO - Calculating damage for 691 assets (>0) and 1040 events.\n", - "2022-03-15 22:32:07,452 - climada.engine.cost_benefit - INFO - Computing cost benefit from years 2018 to 2040.\n", - "\n", - "Measure Cost (USD bn) Benefit (USD bn) Benefit/Cost\n", - "------------- --------------- ------------------ --------------\n", - "Mangrove 0.5 11.2129 22.4258\n", - "Building code 0.1 0.00761204 0.0761204\n", - "\n", - "-------------------- --------- --------\n", - "Total climate risk: 17.749 (USD bn)\n", - "Average annual risk: 0.951281 (USD bn)\n", - "Residual risk: 6.52855 (USD bn)\n", - "-------------------- --------- --------\n", - "Net Present Values\n" - ] - }, { "data": { - "image/png": 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", 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", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "from climada.engine import CostBenefit\n", - "\n", - "cost_ben = CostBenefit()\n", - "cost_ben.calc(haz, ent, future_year=2040) # prints costs and benefits\n", - "cost_ben.plot_cost_benefit()\n", - "# plot cost benefit ratio and averted damage of every exposure\n", - "cost_ben.plot_event_view(\n", - " return_per=(10, 20, 40)\n", - "); # plot averted damage of each measure for every return period" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is just the start. Analyses improve as we add more adaptation measures into the mix.\n", - "\n", - "Cost-benefit calculations can also include\n", - "- climate change, by specifying the `haz_future` parameter in `CostBenefit.calc()`\n", - "- changes to economic exposure over time (or to whatever exposure you're modelling) by specifying the `ent_future` parameter in `CostBenefit.calc()`\n", - "- different functions to calculate risk benefits. These are specified in `CostBenefit.calc()` and by default use changes to average annual impact\n", - "- linear, sublinear and superlinear evolution of impacts between the present and future, specified in the `imp_time_depen` parameter in `CostBenefit.calc()`\n", - "\n", - "And once future hazards and exposures are defined, we can express changes to impacts over time as waterfall diagrams. See the CostBenefit class for more details.\n", - "\n", - "> **Exercise:** repeat the above analysis, creating future climate hazards (see the first exercise), and future exposures based on projected economic growth. Visualise it with the `CostBenefit.plot_waterfall()` method." + "ax = imp.calc_freq_curve().plot(label=\"Without measure\")\n", + "new_imp.calc_freq_curve().plot(axis=ax, label=\"With measure\")\n", + "ax.legend()" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, - "source": [ - "## What next?\n", - "\n", - "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#CLIMADA-features) section.\n", - "\n", - "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#Resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" - ] + "outputs": [], + "source": [] } ], "metadata": { From ac6b5405c276404c81a3d2ea83cf6bcd1caff441 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Mon, 3 Feb 2025 10:51:57 +0100 Subject: [PATCH 10/49] split climada dev and git intro --- .../Guide_CLIMADA_Development.ipynb | 445 ++++++++++++ .../Guide_CLIMADA_conventions.ipynb | 2 +- doc/development/Guide_Git_Development.ipynb | 673 +----------------- 3 files changed, 450 insertions(+), 670 deletions(-) create mode 100644 doc/development/Guide_CLIMADA_Development.ipynb diff --git a/doc/development/Guide_CLIMADA_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb new file mode 100644 index 0000000000..7de39dfe24 --- /dev/null +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CLIMADA Development\n", + "\n", + "This is a guide about how to contribute to the development of CLIMADA. We first explain some general guidelines about when and how one should contribute to CLIMADA, and then describe in detail the steps. We assume that you are familiar with Git and Github and its commands. If you are not familiar with these, you can refer to our instructions for [Development with Git](Guide_Git_Development.ipynb). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Is CLIMADA the right place for your contribution? \n", + "\n", + "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", + "\n", + "- How does the planned module fit into CLIMADA?\n", + "- What is an optimal architecture for the new module?\n", + "- What parts might already exist in other parts of the code?\n", + "\n", + "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Planning a new feature\n", + "\n", + "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", + "\n", + "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", + "\n", + "### Talk to the group\n", + " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", + " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", + " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", + " - Also, keep talking! Your plans _will_ change :)\n", + "\n", + "### Planning the work\n", + "\n", + "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository. The way this is done is slowly changing, so definitely discuss it with the group.\n", + "- Find a few people who will help to review your code.\n", + " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", + "- How can the work split into manageable chunks?\n", + " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", + " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", + " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", + "- Will there be any changes to the CLIMADA core? These should be planned carefully\n", + "- Will you need any new dependencies? Are you sure?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing CLIMADA for development\n", + "\n", + "See [Installation](install.rst) for instructions on how to install CLIMADA for developers. You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. Also see [Apps for working with CLIMADA](../guide/Guide_get_started.ipynb#apps-for-working-with-climada) for an overview of which tools are useful for CLIMADA developers. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pre-Commit Hooks\n", + "\n", + "Climada developer dependencies include pre-commit hooks to help ensure code linting and formatting.\n", + "See [Code Formatting](Guide_CLIMADA_conventions.ipynb#code-formatting) for our conventions regarding formatting.\n", + "These hooks will run on all staged files and verify:\n", + "\n", + "- the absence of trailing whitespace\n", + "- that files end in a newline and only a newline\n", + "- the correct sorting of imports using ``isort``\n", + "- the correct formatting of the code using ``black``\n", + "\n", + "If you have installed the pre-commit hooks (see [Install developer dependencies](install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", + "\n", + "If any check fails, you will be warned and these hooks **will apply** corrections (such as formatting the code with black if it is not).\n", + "As files are modified, you are required to stage them again (hooks cannot stage their modification, only you can) and commit again.\n", + "\n", + "As an exemple, suppose you made an improvement to Centroids and want to commit these changes, you would run:\n", + "\n", + "```console\n", + "$ git status\n", + "On branch feature/\n", + "Your branch is up-to-date with 'origin/'.\n", + "\n", + "Changes to be committed:\n", + " (use \"git restore --staged ...\" to unstage)\n", + "\tmodified: climada/hazard/centroids/centr.py\n", + "```\n", + "\n", + "Now trying to commit, and assuming that imports are not correctly sorted,\n", + "and some of the code is not correctly formatted:\n", + "\n", + "```console \n", + "$ git commit -m \"Add to centroids\"\n", + "Fix End of Files.........................................................Passed\n", + "Trim Trailing Whitespace.................................................Passed\n", + "isort....................................................................Failed\n", + "- hook id: isort\n", + "- files were modified by this hook\n", + "\n", + "Fixing [...]/climada_python/climada/hazard/centroids/centr.py\n", + "\n", + "black-jupyter............................................................Failed\n", + "- hook id: black-jupyter\n", + "- files were modified by this hook\n", + "\n", + "reformatted climada/hazard/centroids/centr.py\n", + "\n", + "All done! ✨ 🍰 ✨\n", + "```\n", + "\n", + "Note the commit was aborted, and the problems were fixed.\n", + "However, these changes added by the hooks are not *staged* yet.\n", + "You have to run ``git add`` again to stage them:\n", + "\n", + "```console\n", + "$ git status\n", + "On branch feature/\n", + "Your branch is up-to-date with 'origin/'.\n", + "\n", + "Changes to be committed:\n", + " (use \"git restore --staged ...\" to unstage)\n", + "\tmodified: climada/hazard/centroids/centr.py\n", + "\n", + "Changes not staged for commit:\n", + " (use \"git add ...\" to update what will be committed)\n", + " (use \"git restore ...\" to discard changes in working directory)\n", + "\tmodified: climada/hazard/centroids/centr.py\n", + "\n", + "$ git add climada/hazard/centroids/centr.py\n", + "```\n", + "\n", + "After that, you can execute the commit and the hooks should pass:\n", + "\n", + "```console\n", + "$ git commit -m \"Add to centroids\"\n", + "Fix End of Files.........................................................Passed\n", + "Trim Trailing Whitespace.................................................Passed\n", + "isort....................................................................Passed\n", + "black-jupyter............................................................Passed\n", + "\n", + "All done! ✨ 🍰 ✨\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working on feature branches\n", + "\n", + "When developing a big new feature, consider creating a feature branch and merging smaller branches into that feature branch with pull requests, keeping the whole process separate from `develop` until it's completed. This makes step-by-step code review nice and easy, and makes the final merge more easily tracked in the history.\n", + "\n", + "e.g. developing the big `feature/meteorite` module you might write `feature/meteorite-hazard` and merge it in, then `feature/meteorite-impact`, then `feature/meteorite-stochastic-events` etc... before finally merging `feature/meteorite` into `develop`. Each of these could be a reviewable pull request.\n", + "\n", + "### Make a new **branch**\n", + "\n", + "For new features in Git flow:\n", + "\n", + " git flow feature start feature_name\n", + " \n", + "Which is equivalent to (in vanilla git):\n", + "\n", + " git checkout -b feature/feature_name\n", + "\n", + "Or work on an existing branch:\n", + "\n", + " git checkout -b branch_name\n", + "\n", + "### Follow the [python do's and don't](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_PythonDos-n-Donts.ipynb) and [performance](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", + "\n", + "get the latest data from the remote repository and update your branch\n", + " \n", + " git pull\n", + "\n", + "see your locally modified files\n", + "\n", + " git status\n", + "\n", + "add changes you want to include in the commit\n", + "\n", + " git add climada/modified_file.py climada/test/test_modified_file.py\n", + "\n", + "commit the changes\n", + "\n", + " git commit -m \"new functionality of .. implemented\"\n", + " \n", + "### Make unit and integration tests on your code, preferably during development\n", + "see [Guide on unit and integration tests](../guide/Guide_Testing.ipynb)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pull requests\n", + "\n", + "We want every line of code that goes into the CLIMADA repository to be reviewed!\n", + "\n", + "Code review:\n", + "- catches bugs (there are _always_ bugs)\n", + "- lets you draw on the experience of the rest of the team\n", + "- makes sure that more than one person knows how your code works\n", + "- helps to unify and standardise CLIMADA's code, so new users find it easier to read and navigate\n", + "- creates an archived description and discussion of the changes you've made\n", + "\n", + "### When to make a pull request\n", + "\n", + "- When you've finished writing a big new class or method (and its tests)\n", + "- When you've fixed a bug or made an improvement you want to merge\n", + "- When you want to merge a change of code into `develop` or `main`\n", + "- When you want to _discuss_ a bit of code you've been working on - pull requests aren't only for merging branches\n", + "\n", + "Not all pull requests have to be into `develop` - you can make a pull request into any active branch that suits you.\n", + "\n", + "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases).\n", + "\n", + "### Step by step pull request!\n", + "\n", + "Let's suppose you've developed a cool new module on the `feature/meteorite` branch and you're ready to merge it into `develop`.\n", + "\n", + "### Checklist before you start\n", + "\n", + "- Documentation\n", + "- Tests\n", + "- Tutorial (if a complete new feature)\n", + "- Updated dependencies (if need be)\n", + "- Added your name to the AUTHORS file\n", + "- Added an entry to the ``CHANGELOG.md`` file. See for information on how this shoud look like.\n", + "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n", + "\n", + "### Steps\n", + "\n", + "1) Make sure the `develop` branch is up to date on your own machine\n", + " ```\n", + " git checkout develop\n", + " git pull\n", + " ```\n", + "\n", + "2) Merge `develop` into your feature branch and resolve any conflicts\n", + " ```\n", + " git checkout feature/meteorite\n", + " git merge develop\n", + " ```\n", + "\n", + "In the case of more complex conflicts, you may want to speak with others who worked on the same code. Your IDE should have a tool for conflict resolution.\n", + " \n", + "3) Check all the tests pass locally\n", + " ```\n", + " make unit_test\n", + " make integ_test\n", + " ```\n", + "\n", + "4) Perform a static code analysis using pylint with CLIMADA's configuration `.pylintrc` (in the climada root directory). Jenkins executes it after every push.\\\n", + " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`.\n", + "\n", + "5) Push to GitHub.\n", + " If you're pushing this branch for the first time, use\n", + " ```\n", + " git push -u origin feature/meteorite\n", + " ```\n", + " and if you're updating a branch that's already on GitHub:\n", + " ```\n", + " git push\n", + " ```\n", + "\n", + "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!\n", + "\n", + "7) Create the pull request!\n", + "\n", + " - On the CLIMADA GitHub page, navigate to your feature branch (there's a drop-down menu above the file structure, pointing by default to `main`).\n", + " - Above the file structure is a branch summary and an icon to the right labelled \"Pull request\".\n", + " - Choose which branch you want to merge with. This will usually be `develop`, but may be another feature branch for more complex feature development.\n", + " - Give your pull request an informative title (like a commit message).\n", + " - Write a description of the pull request. This can usually be adapted from your branch's commit messages (you wrote informative commit messages, didn't you?), and should give a high-level summary of the changes, specific points you want the reviewers' input on, and explanations for decisions you've made. The code documentation (and any references) should cover the more detailed stuff. \n", + " - Assign reviewers in the page's right hand sidebar. Tag anyone who might be interested in reading the code. You should already have found one or two people who are happy to read the whole request and\n", + " sign it off (they could also be added to 'Assignees').\n", + " - Create the pull request.\n", + " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!\n", + "\n", + "8) Talk with your reviewers\n", + "\n", + " - Use the comment/chat functionality within GitHub's pull requests - it's useful to have an archive of discussions and the decisions made.\n", + " - Take comments and suggestions on board, but you don't need to agree with everything and you don't need to implement everything.\n", + " - If you feel someone is asking for too many changes, prioritise, especially if you don't have time for complex rewrites.\n", + " - If the suggested changes and or features don't block functionality and you don't have time to fix them, they can be moved to Issues.\n", + " - Chase people up if they're slow. People are slow.\n", + "\n", + "\n", + "9) Once you implement the requested changes, respond to the comments with the corresponding commit implementing each requested change.\n", + "\n", + "10) If the review takes a while, remember to merge `develop` back into the feature branch every now and again\n", + " (and check the tests are still passing on Jenkins).\\\n", + " Anything pushed to the branch is added to the pull request.\n", + " \n", + "11) Once everyone reviewing has said they're satisfied with the code you can merge the pull request using the GitHub interface.\\\n", + " Delete the branch once it's merged, there's no reason to keep it. (Also try not to re-use that branch name later.)\n", + " \n", + "12) Update the `develop` branch on your local machine.\n", + "\n", + "Also see the [**Reviewer Guide**](../guide/Guide_Review.ipynb) and [**Reviewer Checklist**](../guide/Guide_Review.ipynb#reviewer-checklist)!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## General tips and tricks\n", + "\n", + "### Ask for help with Git\n", + "\n", + "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message. See also our instructions for [Development with Git](Guide_Git_Development.ipynb).\n", + "\n", + "### Don't push or commit to develop or main\n", + "\n", + "- Almost all new additions to CLIMADA should be merged into the `develop` branch with a pull request.\n", + "- You won't merge into the `main` branch, except for emergency hotfixes (which should be communicated to the team).\n", + "- You won't merge into the `develop` branch without a pull request, except for small documentation updates and typos.\n", + "- The above points mean you should never need to push the `main` or `develop` branches.\n", + "\n", + "So if you find yourself on the `main` or `develop` branches typing `git merge ...` or `git push` stop and think again - you should probably be making a pull request.\n", + "\n", + "This can be difficult to undo, so contact someone on the team if you're unsure!\n", + "\n", + "### Commit more often than you think, and use informative commit messages\n", + "\n", + "- Committing often makes mistakes less scary to undo\n", + "```\n", + "git reset --hard HEAD\n", + "```\n", + "- Detailed commit messages make writing pull requests really easy\n", + "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed\n", + "\n", + "### Commit message syntax guidelines\n", + "\n", + "Basic syntax guidelines taken from here (on 17.06.2020)\n", + "\n", + "- Limit the subject line to 50 characters\n", + "- Capitalize the subject line\n", + "- Do not end the subject line with a period\n", + "- Use the imperative mood in the subject line (e.g. \"Add new tests\")\n", + "- Wrap the body at 72 characters (most editors will do this automatically)\n", + "- Use the body to explain what and why vs. how\n", + "- Separate the subject from body with a blank line (This is best done with\n", + " a GUI. With the command line you have to use text editor, you cannot\n", + " do it directly with the git command)\n", + "- Put the name of the function/class/module/file that was edited\n", + "- When fixing an issue, add the reference gh-ISSUENUMBER to the commit message \n", + " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here .\n", + "\n", + "### What not to commit\n", + "\n", + "There are a lot of things that don't belong in the Git repository: \n", + "- Don't commit data, except for config files and very small files for tests.\n", + "- Don't commit anything containing passwords or authentication credentials or tokens. (These are annoying to remove from the Git history.) Contact the team if you need to manage authorisations within the code.\n", + "- Don't commit anything that can be created by the CLIMADA code itself\n", + "\n", + "If files like this are going to be present for other users as well, add them to the repository's `.gitignore`.\n", + "\n", + "#### Jupyter Notebook metadata\n", + "\n", + "Git compares file versions by text tokens. Jupyter Notebooks typically contain a lot of metadata, along with binary data like image files. Simply re-running a notebook can change this metadata, which will be reported as file changes by Git. This causes excessive Diff reports that cannot be reviewed conveniently.\n", + "\n", + "To avoid committing changes of unrelated metadata, open Jupyter Notebooks in a text editor instead of your browser renderer. When committing changes, make sure that you indeed only commit things you *did* change, and revert any changes to metadata that are not related to your code updates.\n", + "\n", + "Several code editors use plugins to render Jupyter Notebooks. Here we collect the instructions to inspect Jupyter Notebooks as plain text when using them:\n", + "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'\n", + "\n", + "### Log ideas and bugs as GitHub Issues\n", + "\n", + "If there's a change you might want to see in the code - something that generalises, something that's not quite right, or a cool new feature - it can be set up as a GitHub Issue. Issues are pages for conversations about changes to the codebase and for logging bugs, and act as a 'backlog' for the CLIMADA project.\n", + "\n", + "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue.\n", + "\n", + "### How not to mess up the timeline\n", + "\n", + "Git builds the repository through incremental edits. This means it's great at keeping track of its history. But there are a few commands that _edit_ this history, and if histories get out of sync on different copies of the repository you're going to have a bad time.\n", + "\n", + "- Don't rebase any commits that already exist remotely!\n", + "- Don't `--force` anything that exists remotely unless you know what you're doing!\n", + "- Otherwise, you're unlikely to do anything irreversible\n", + "- You can do what you like with commits that only exist on your machine.\n", + "\n", + "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)\n", + "\n", + "### Do not fast forward merges \n", + "\n", + "(This shouldn't be relevant - all your merges into `develop` should be through pull requests, which doesn't fast forward. But:)\n", + "\n", + "Don't fast forward your merges unless your branch is a single commit. Use\n", + "`git merge --no-ff ...`\n", + "\n", + "The exceptions is when you're merging `develop` into your feature branch.\n", + "\n", + "### Merge the remote develop branch into your feature branch every now and again\n", + "\n", + "- This way you'll find conflicts early\n", + "```\n", + "git checkout develop\n", + "git pull\n", + "git checkout feature/myfeature\n", + "git merge develop\n", + "```\n", + "\n", + "### Create frequent pull requests\n", + "\n", + "I said this already:\n", + "- It structures your workflow\n", + "- It's easier for reviewers\n", + "- If you're going to break something for other people you all know sooner\n", + "- It saves work for the rest of the team right before a release\n", + "\n", + "### Whenever you do something with CLIMADA, make a new local branch \n", + "\n", + "You never know when a quick experiment will become something you want to save for later.\n", + "\n", + "### But do not do everything in the CLIMADA repository\n", + "\n", + "- If you're running CLIMADA rather than developing it, create a new folder, initialise a new repository with `git init` and store your scripts and data there\n", + "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to.\n", + "\n", + "### Questions\n", + "\n", + "![Git and Github logos](img/xkcd_git.png)\\\n", + "" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/development/Guide_CLIMADA_conventions.ipynb b/doc/development/Guide_CLIMADA_conventions.ipynb index 6b4e4c2909..9370dce900 100644 --- a/doc/development/Guide_CLIMADA_conventions.ipynb +++ b/doc/development/Guide_CLIMADA_conventions.ipynb @@ -132,7 +132,7 @@ "\n", "Note that most text editors usually take care of 1. and 2. by default.\n", "\n", - "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](guide-pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", + "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](Guide_CLIMADA_Development.ipynb#pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", "Following the [advanced installation instructions](install.rst#advanced-instructions) will set up these hooks for you." ] }, diff --git a/doc/development/Guide_Git_Development.ipynb b/doc/development/Guide_Git_Development.ipynb index 08eb92c0cc..b71d408fe5 100644 --- a/doc/development/Guide_Git_Development.ipynb +++ b/doc/development/Guide_Git_Development.ipynb @@ -8,7 +8,10 @@ } }, "source": [ - "# Development and Git and CLIMADA" + "# Development with Git\n", + "\n", + " Here we provide a detailed instruction to the use of Git and GitHub and their workflows, which are essential to the code development of CLIMADA. \n", + "\n" ] }, { @@ -268,674 +271,6 @@ "- Hotfixes will occasionally be used to fix bugs on the `main` branch, in which case they will merge into both `main` and `develop`.\n", "- Some hotfixes are so simple - e.g. fixing a typo or a docstring - that they don't need a pull request. Use your judgement, but as a rule, if you change what the code does, or how, you should be merging with a pull request." ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Installing CLIMADA for development\n", - "\n", - "See [Installation](install.rst) for instructions on how to install CLIMADA for developers. You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. Also see [Apps for working with CLIMADA](../guide/Guide_get_started.ipynb#apps-for-working-with-climada) for an overview of which tools are useful for CLIMADA developers. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "(guide-pre-commit-hooks)=\n", - "### Pre-Commit Hooks\n", - "\n", - "Climada developer dependencies include pre-commit hooks to help ensure code linting and formatting.\n", - "See [Code Formatting](Guide_CLIMADA_conventions.ipynb#code-formatting) for our conventions regarding formatting.\n", - "These hooks will run on all staged files and verify:\n", - "\n", - "- the absence of trailing whitespace\n", - "- that files end in a newline and only a newline\n", - "- the correct sorting of imports using ``isort``\n", - "- the correct formatting of the code using ``black``\n", - "\n", - "If you have installed the pre-commit hooks (see [Install developer dependencies](install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", - "\n", - "If any check fails, you will be warned and these hooks **will apply** corrections (such as formatting the code with black if it is not).\n", - "As files are modified, you are required to stage them again (hooks cannot stage their modification, only you can) and commit again.\n", - "\n", - "As an exemple, suppose you made an improvement to Centroids and want to commit these changes, you would run:\n", - "\n", - "```console\n", - "$ git status\n", - "On branch feature/\n", - "Your branch is up-to-date with 'origin/'.\n", - "\n", - "Changes to be committed:\n", - " (use \"git restore --staged ...\" to unstage)\n", - "\tmodified: climada/hazard/centroids/centr.py\n", - "```\n", - "\n", - "Now trying to commit, and assuming that imports are not correctly sorted,\n", - "and some of the code is not correctly formatted:\n", - "\n", - "```console \n", - "$ git commit -m \"Add to centroids\"\n", - "Fix End of Files.........................................................Passed\n", - "Trim Trailing Whitespace.................................................Passed\n", - "isort....................................................................Failed\n", - "- hook id: isort\n", - "- files were modified by this hook\n", - "\n", - "Fixing [...]/climada_python/climada/hazard/centroids/centr.py\n", - "\n", - "black-jupyter............................................................Failed\n", - "- hook id: black-jupyter\n", - "- files were modified by this hook\n", - "\n", - "reformatted climada/hazard/centroids/centr.py\n", - "\n", - "All done! ✨ 🍰 ✨\n", - "```\n", - "\n", - "Note the commit was aborted, and the problems were fixed.\n", - "However, these changes added by the hooks are not *staged* yet.\n", - "You have to run ``git add`` again to stage them:\n", - "\n", - "```console\n", - "$ git status\n", - "On branch feature/\n", - "Your branch is up-to-date with 'origin/'.\n", - "\n", - "Changes to be committed:\n", - " (use \"git restore --staged ...\" to unstage)\n", - "\tmodified: climada/hazard/centroids/centr.py\n", - "\n", - "Changes not staged for commit:\n", - " (use \"git add ...\" to update what will be committed)\n", - " (use \"git restore ...\" to discard changes in working directory)\n", - "\tmodified: climada/hazard/centroids/centr.py\n", - "\n", - "$ git add climada/hazard/centroids/centr.py\n", - "```\n", - "\n", - "After that, you can execute the commit and the hooks should pass:\n", - "\n", - "```console\n", - "$ git commit -m \"Add to centroids\"\n", - "Fix End of Files.........................................................Passed\n", - "Trim Trailing Whitespace.................................................Passed\n", - "isort....................................................................Passed\n", - "black-jupyter............................................................Passed\n", - "\n", - "All done! ✨ 🍰 ✨\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Does it belong in CLIMADA? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", - "\n", - "- How does the planned module fit into CLIMADA?\n", - "- What is an optimal architecture for the new module?\n", - "- What parts might already exist in other parts of the code?\n", - "\n", - "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Features and branches" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning a new feature\n", - "\n", - "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", - "\n", - "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", - "\n", - "### Talk to the group\n", - " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", - " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", - " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", - " - Also, keep talking! Your plans _will_ change :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning the work\n", - "\n", - "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository?\n", - " - The way this is done is slowly changing, so definitely discuss it with the group.\n", - " - Chahan will discuss this later!\n", - "- Find a few people who will help to review your code.\n", - " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", - "- How can the work split into manageable chunks?\n", - " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", - " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", - " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", - "- Will there be any changes to the CLIMADA core?\n", - " - These should be planned carefully\n", - "- Will you need any new dependencies? Are you sure?\n", - " - Chahan will discuss this later!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Working on feature branches\n", - "\n", - "When developing a big new feature, consider creating a feature branch and merging smaller branches into that feature branch with pull requests, keeping the whole process separate from `develop` until it's completed. This makes step-by-step code review nice and easy, and makes the final merge more easily tracked in the history.\n", - "\n", - "e.g. developing the big `feature/meteorite` module you might write `feature/meteorite-hazard` and merge it in, then `feature/meteorite-impact`, then `feature/meteorite-stochastic-events` etc... before finally merging `feature/meteorite` into `develop`. Each of these could be a reviewable pull request.\n", - "\n", - "### Make a new **branch**\n", - "\n", - "For new features in Git flow:\n", - "\n", - " git flow feature start feature_name\n", - " \n", - "Which is equivalent to (in vanilla git):\n", - "\n", - " git checkout -b feature/feature_name\n", - "\n", - "Or work on an existing branch:\n", - "\n", - " git checkout -b branch_name\n", - "\n", - "### Follow the [python do's and don't](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_PythonDos-n-Donts.ipynb) and [performance](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", - "\n", - "get the latest data from the remote repository and update your branch\n", - " \n", - " git pull\n", - "\n", - "see your locally modified files\n", - "\n", - " git status\n", - "\n", - "add changes you want to include in the commit\n", - "\n", - " git add climada/modified_file.py climada/test/test_modified_file.py\n", - "\n", - "commit the changes\n", - "\n", - " git commit -m \"new functionality of .. implemented\"\n", - " \n", - "### Make unit and integration tests on your code, preferably during development\n", - "see [Guide on unit and integration tests](../guide/Guide_Testing.ipynb)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Pull requests" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "We want every line of code that goes into the CLIMADA repository to be reviewed!\n", - "\n", - "Code review:\n", - "- catches bugs (there are _always_ bugs)\n", - "- lets you draw on the experience of the rest of the team\n", - "- makes sure that more than one person knows how your code works\n", - "- helps to unify and standardise CLIMADA's code, so new users find it easier to read and navigate\n", - "- creates an archived description and discussion of the changes you've made" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### When to make a pull request\n", - "\n", - "- When you've finished writing a big new class or method (and its tests)\n", - "- When you've fixed a bug or made an improvement you want to merge\n", - "- When you want to merge a change of code into `develop` or `main`\n", - "- When you want to _discuss_ a bit of code you've been working on - pull requests aren't only for merging branches\n", - "\n", - "Not all pull requests have to be into `develop` - you can make a pull request into any active branch that suits you.\n", - "\n", - "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Step by step pull request!\n", - "\n", - "Let's suppose you've developed a cool new module on the `feature/meteorite` branch and you're ready to merge it into `develop`.\n", - "\n", - "### Checklist before you start\n", - "\n", - "- Documentation\n", - "- Tests\n", - "- Tutorial (if a complete new feature)\n", - "- Updated dependencies (if need be)\n", - "- Added your name to the AUTHORS file\n", - "- Added an entry to the ``CHANGELOG.md`` file. See for information on how this shoud look like.\n", - "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Steps\n", - "\n", - "1) Make sure the `develop` branch is up to date on your own machine\n", - " ```\n", - " git checkout develop\n", - " git pull\n", - " ```\n", - "\n", - "2) Merge `develop` into your feature branch and resolve any conflicts\n", - " ```\n", - " git checkout feature/meteorite\n", - " git merge develop\n", - " ```\n", - "\n", - "In the case of more complex conflicts, you may want to speak with others who worked on the same code. Your IDE should have a tool for conflict resolution.\n", - " \n", - "3) Check all the tests pass locally\n", - " ```\n", - " make unit_test\n", - " make integ_test\n", - " ```\n", - "\n", - "4) Perform a static code analysis using pylint with CLIMADA's configuration `.pylintrc` (in the climada root directory). Jenkins executes it after every push.\\\n", - " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "5) Push to GitHub.\n", - " If you're pushing this branch for the first time, use\n", - " ```\n", - " git push -u origin feature/meteorite\n", - " ```\n", - " and if you're updating a branch that's already on GitHub:\n", - " ```\n", - " git push\n", - " ```\n", - "\n", - "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "7) Create the pull request!\n", - "\n", - " - On the CLIMADA GitHub page, navigate to your feature branch (there's a drop-down menu above the file structure, pointing by default to `main`).\n", - " - Above the file structure is a branch summary and an icon to the right labelled \"Pull request\".\n", - " - Choose which branch you want to merge with. This will usually be `develop`, but may be another feature branch for more complex feature development.\n", - " - Give your pull request an informative title (like a commit message).\n", - " - Write a description of the pull request. This can usually be adapted from your branch's commit messages (you wrote informative commit messages, didn't you?), and should give a high-level summary of the changes, specific points you want the reviewers' input on, and explanations for decisions you've made. The code documentation (and any references) should cover the more detailed stuff. \n", - " - Assign reviewers in the page's right hand sidebar. Tag anyone who might be interested in reading the code. You should already have found one or two people who are happy to read the whole request and\n", - " sign it off (they could also be added to 'Assignees').\n", - " - Create the pull request.\n", - " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "8) Talk with your reviewers\n", - "\n", - " - Use the comment/chat functionality within GitHub's pull requests - it's useful to have an archive of discussions and the decisions made.\n", - " - Take comments and suggestions on board, but you don't need to agree with everything and you don't need to implement everything.\n", - " - If you feel someone is asking for too many changes, prioritise, especially if you don't have time for complex rewrites.\n", - " - If the suggested changes and or features don't block functionality and you don't have time to fix them, they can be moved to Issues.\n", - " - Chase people up if they're slow. People are slow." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "\n", - "9) Once you implement the requested changes, respond to the comments with the corresponding commit implementing each requested change.\n", - "\n", - "10) If the review takes a while, remember to merge `develop` back into the feature branch every now and again\n", - " (and check the tests are still passing on Jenkins).\\\n", - " Anything pushed to the branch is added to the pull request.\n", - " \n", - "11) Once everyone reviewing has said they're satisfied with the code you can merge the pull request using the GitHub interface.\\\n", - " Delete the branch once it's merged, there's no reason to keep it. (Also try not to re-use that branch name later.)\n", - " \n", - "12) Update the `develop` branch on your local machine.\n", - "\n", - "Also see the [**Reviewer Guide**](../guide/Guide_Review.ipynb) and [**Reviewer Checklist**](../guide/Guide_Review.ipynb#reviewer-checklist)!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## General tips and tricks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Ask for help with Git\n", - "\n", - "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Don't push or commit to develop or main\n", - "\n", - "- Almost all new additions to CLIMADA should be merged into the `develop` branch with a pull request.\n", - "- You won't merge into the `main` branch, except for emergency hotfixes (which should be communicated to the team).\n", - "- You won't merge into the `develop` branch without a pull request, except for small documentation updates and typos.\n", - "- The above points mean you should never need to push the `main` or `develop` branches.\n", - "\n", - "So if you find yourself on the `main` or `develop` branches typing `git merge ...` or `git push` stop and think again - you should probably be making a pull request.\n", - "\n", - "This can be difficult to undo, so contact someone on the team if you're unsure!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Commit more often than you think, and use informative commit messages\n", - "\n", - "- Committing often makes mistakes less scary to undo\n", - "```\n", - "git reset --hard HEAD\n", - "```\n", - "- Detailed commit messages make writing pull requests really easy\n", - "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Commit message syntax guidelines\n", - "\n", - "Basic syntax guidelines taken from here (on 17.06.2020)\n", - "\n", - "- Limit the subject line to 50 characters\n", - "- Capitalize the subject line\n", - "- Do not end the subject line with a period\n", - "- Use the imperative mood in the subject line (e.g. \"Add new tests\")\n", - "- Wrap the body at 72 characters (most editors will do this automatically)\n", - "- Use the body to explain what and why vs. how\n", - "- Separate the subject from body with a blank line (This is best done with\n", - " a GUI. With the command line you have to use text editor, you cannot\n", - " do it directly with the git command)\n", - "- Put the name of the function/class/module/file that was edited\n", - "- When fixing an issue, add the reference gh-ISSUENUMBER to the commit message \n", - " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here ." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### What not to commit\n", - "\n", - "There are a lot of things that don't belong in the Git repository: \n", - "- Don't commit data, except for config files and very small files for tests.\n", - "- Don't commit anything containing passwords or authentication credentials or tokens. (These are annoying to remove from the Git history.) Contact the team if you need to manage authorisations within the code.\n", - "- Don't commit anything that can be created by the CLIMADA code itself\n", - "\n", - "If files like this are going to be present for other users as well, add them to the repository's `.gitignore`.\n", - "\n", - "#### Jupyter Notebook metadata\n", - "\n", - "Git compares file versions by text tokens. Jupyter Notebooks typically contain a lot of metadata, along with binary data like image files. Simply re-running a notebook can change this metadata, which will be reported as file changes by Git. This causes excessive Diff reports that cannot be reviewed conveniently.\n", - "\n", - "To avoid committing changes of unrelated metadata, open Jupyter Notebooks in a text editor instead of your browser renderer. When committing changes, make sure that you indeed only commit things you *did* change, and revert any changes to metadata that are not related to your code updates.\n", - "\n", - "Several code editors use plugins to render Jupyter Notebooks. Here we collect the instructions to inspect Jupyter Notebooks as plain text when using them:\n", - "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Log ideas and bugs as GitHub Issues\n", - "\n", - "If there's a change you might want to see in the code - something that generalises, something that's not quite right, or a cool new feature - it can be set up as a GitHub Issue. Issues are pages for conversations about changes to the codebase and for logging bugs, and act as a 'backlog' for the CLIMADA project.\n", - "\n", - "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### How not to mess up the timeline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "Git builds the repository through incremental edits. This means it's great at keeping track of its history. But there are a few commands that _edit_ this history, and if histories get out of sync on different copies of the repository you're going to have a bad time.\n", - "\n", - "- Don't rebase any commits that already exist remotely!\n", - "- Don't `--force` anything that exists remotely unless you know what you're doing!\n", - "- Otherwise, you're unlikely to do anything irreversible\n", - "- You can do what you like with commits that only exist on your machine.\n", - "\n", - "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Do not fast forward merges \n", - "\n", - "(This shouldn't be relevant - all your merges into `develop` should be through pull requests, which doesn't fast forward. But:)\n", - "\n", - "Don't fast forward your merges unless your branch is a single commit. Use\n", - "`git merge --no-ff ...`\n", - "\n", - "The exceptions is when you're merging `develop` into your feature branch." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Merge the remote develop branch into your feature branch every now and again\n", - "\n", - "- This way you'll find conflicts early\n", - "```\n", - "git checkout develop\n", - "git pull\n", - "git checkout feature/myfeature\n", - "git merge develop\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Create frequent pull requests\n", - "\n", - "I said this already:\n", - "- It structures your workflow\n", - "- It's easier for reviewers\n", - "- If you're going to break something for other people you all know sooner\n", - "- It saves work for the rest of the team right before a release" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Whenever you do something with CLIMADA, make a new local branch \n", - "\n", - "You never know when a quick experiment will become something you want to save for later." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### But do not do everything in the CLIMADA repository\n", - "\n", - "- If you're running CLIMADA rather than developing it, create a new folder, initialise a new repository with `git init` and store your scripts and data there\n", - "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Git and Github logos](img/xkcd_git.png)\\\n", - "" - ] } ], "metadata": { From 225a734f73b7c27e9cbf2165bfc91577412a5370 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Mon, 3 Feb 2025 11:25:58 +0100 Subject: [PATCH 11/49] add data flow and workflow to dev intro --- doc/development/Guide_CLIMADA_Development.ipynb | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/development/Guide_CLIMADA_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb index 7de39dfe24..6e98a68ce5 100644 --- a/doc/development/Guide_CLIMADA_Development.ipynb +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -41,6 +41,12 @@ " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", " - Also, keep talking! Your plans _will_ change :)\n", "\n", + "### Formulate the feature's data flow and workflow\n", + "\n", + "To optimize implementation and usefulness of the new feature, first conceptualize its data flow and workflow. It makes sense to discuss these with a CLIMADA core developer before starting to work on the feature's implementation.\n", + "- **Data flow**: Outline of how data moves through the system — where it is created or input, how it is processed, and if and where it is stored. This helps to improve the computational efficiency and to identify potential bottlenecks. \n", + "- **Workflow**: Plan about where and how the user and other CLIMADA components can interact with the new feature. This ensures that the new feature couples seamlessly to the existing code base of CLIMADA and that the new feaute is easily and clearly accessible to users.\n", + "\n", "### Planning the work\n", "\n", "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository. The way this is done is slowly changing, so definitely discuss it with the group.\n", From 26c10633dec72390705bf929ca64e0d8e3a63241 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Mon, 3 Feb 2025 14:16:23 +0100 Subject: [PATCH 12/49] fixes ``sphinx.configuration`` key is missing see https://about.readthedocs.com/blog/2024/12/deprecate-config-files-without-sphinx-or-mkdocs-config/ --- .readthedocs.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.readthedocs.yml b/.readthedocs.yml index b0c36ed98b..eea4e96d22 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -22,3 +22,6 @@ python: formats: - pdf + +sphinx: + configuration: doc/conf.py From ac2c997decbb2cd0e0597f9ab53c44117eff345b Mon Sep 17 00:00:00 2001 From: spjuhel Date: Mon, 3 Feb 2025 14:42:26 +0100 Subject: [PATCH 13/49] moves pages around --- doc/development/index.rst | 1 + doc/{user-guide => getting-started}/0_10min_climada.ipynb | 0 doc/getting-started/index.rst | 1 + doc/user-guide/index.rst | 1 - 4 files changed, 2 insertions(+), 1 deletion(-) rename doc/{user-guide => getting-started}/0_10min_climada.ipynb (100%) diff --git a/doc/development/index.rst b/doc/development/index.rst index 0291fe3bb8..ccb9a30a1d 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -6,6 +6,7 @@ :caption: Developer Guide :hidden: + Developer guide Development with Git Guide_CLIMADA_Tutorial Guide_Configuration diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/getting-started/0_10min_climada.ipynb similarity index 100% rename from doc/user-guide/0_10min_climada.ipynb rename to doc/getting-started/0_10min_climada.ipynb diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index e452d7cbde..e625cdeeeb 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -62,3 +62,4 @@ You are good to go! install Python Introduction <0_intro_python> + 10 minutes CLIMADA <0_10min_climada> diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index bf1a922e10..757568b049 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -9,7 +9,6 @@ Landing page of the user guide :caption: User guides :hidden: - 10 minutes CLIMADA <0_10min_climada> Overview <1_main_climada> Hazard Exposures From 1125d36b9cda9f5d9512dd971804b364f7d0c8ce Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Mon, 3 Feb 2025 21:51:20 +0100 Subject: [PATCH 14/49] add some warnings and info to mamba installation --- doc/getting-started/install.rst | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 5e05d25ca1..44a70cfa82 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -24,13 +24,23 @@ Prerequisites * Ensure a **stable internet connection** for the installation procedure. All dependencies will be downloaded from the internet. Do **not** use a metered, mobile connection! -* Install the `Conda`_ environment management system. - We highly recommend you use `Miniforge`_, which includes the potent `Mamba`_ package manager. - Download the installer suitable for your system and follow the respective installation instructions. - We do **not** recommend using the ``conda`` command anymore, rather use ``mamba`` (see :ref:`conda-instead-of-mamba`). .. note:: When mentioning the terms "terminal" or "command line" in the following, we are referring to the "Terminal" apps on macOS or Linux and the "Miniforge Prompt" on Windows. +Install Mamba or Conda +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. +We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge. + +For the installation of Miniforge, plase scroll to the **Install** section of `Miniforge`_ and follow the respective installation instructions for your OS. + +.. attention:: After accepting the license terms and confirming the location, you are asked if you wish to update yor shell profile to automatically initialize conda. **Do not just hit ENTER but first type the choice 'yes'**. + +.. note:: If you later encounter ``command not found: conda``, open a new terminal window. + +.. note:: If you later encounter ``Run 'mamba init' to be able to run mamba activate/deactivate and start a new shell session. Or use conda to activate/deactivate.`, please run ``mamba init`` or ``mamba init zsh``. + .. _install-choice: Decide on Your Entry Level! From 17e1a96f7485f980344167a2399630b218642894 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Tue, 4 Feb 2025 09:23:22 +0100 Subject: [PATCH 15/49] fixed typos --- doc/development/Guide_CLIMADA_Development.ipynb | 2 +- doc/getting-started/install.rst | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/doc/development/Guide_CLIMADA_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb index 6e98a68ce5..a255d415f1 100644 --- a/doc/development/Guide_CLIMADA_Development.ipynb +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -6,7 +6,7 @@ "source": [ "# CLIMADA Development\n", "\n", - "This is a guide about how to contribute to the development of CLIMADA. We first explain some general guidelines about when and how one should contribute to CLIMADA, and then describe in detail the steps. We assume that you are familiar with Git and Github and its commands. If you are not familiar with these, you can refer to our instructions for [Development with Git](Guide_Git_Development.ipynb). " + "This is a guide about how to contribute to the development of CLIMADA. We first explain some general guidelines about when and how one can contribute to CLIMADA, and then describe the steps in detail. We assume that you are familiar with Git, Github and their commands. If you are not familiar with these, you can refer to our instructions for [Development with Git](Guide_Git_Development.ipynb). " ] }, { diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 44a70cfa82..0c341a171f 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -33,13 +33,13 @@ Install Mamba or Conda If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge. -For the installation of Miniforge, plase scroll to the **Install** section of `Miniforge`_ and follow the respective installation instructions for your OS. +For the installation of Miniforge, please scroll to the **Install** section of `Miniforge`_ and follow the respective installation instructions for your OS. -.. attention:: After accepting the license terms and confirming the location, you are asked if you wish to update yor shell profile to automatically initialize conda. **Do not just hit ENTER but first type the choice 'yes'**. +.. attention:: After accepting the license terms and confirming the location, you are asked if you wish to update your shell profile to automatically initialize conda. **Do not just hit ENTER but first type the choice 'yes'**. -.. note:: If you later encounter ``command not found: conda``, open a new terminal window. +.. note:: If you encounter ``command not found: mamba``, open a new terminal window. -.. note:: If you later encounter ``Run 'mamba init' to be able to run mamba activate/deactivate and start a new shell session. Or use conda to activate/deactivate.`, please run ``mamba init`` or ``mamba init zsh``. +.. note:: If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate and start a new shell session. Or use conda to activate/deactivate.``, please run ``mamba init zsh`` or ``mamba init``. .. _install-choice: From e534253c8063560bd0aac7012b779099b698d624 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 4 Feb 2025 13:37:50 +0100 Subject: [PATCH 16/49] Restructures development guide with subsections - Fixes links in development guide - Minor renaming --- doc/development/Guide_Euler.ipynb | 5 +- doc/development/coding-in-python.rst | 10 + doc/development/dev-git.rst | 10 + doc/development/index.rst | 23 +- doc/development/write-documentation.rst | 9 + doc/getting-started/0_10min_climada.ipynb | 452 ------------------ .../Guide_Introduction.ipynb | 4 +- .../Guide_get_started.ipynb | 31 +- doc/index.rst | 2 +- 9 files changed, 46 insertions(+), 500 deletions(-) create mode 100644 doc/development/coding-in-python.rst create mode 100644 doc/development/dev-git.rst create mode 100644 doc/development/write-documentation.rst delete mode 100644 doc/getting-started/0_10min_climada.ipynb rename doc/{development => getting-started}/Guide_Introduction.ipynb (98%) rename doc/{development => getting-started}/Guide_get_started.ipynb (63%) diff --git a/doc/development/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb index 2f2cfa7f47..326b4b8eb7 100644 --- a/doc/development/Guide_Euler.ipynb +++ b/doc/development/Guide_Euler.ipynb @@ -92,7 +92,7 @@ "\n", "### 3. Adjust the Climada configuration\n", "\n", - "Edit a configuration file according to your needs (see [Guide_Configuration](../guide/Guide_Configuration.ipynb)).\n", + "Edit a configuration file according to your needs (see [Guide_Configuration](Guide_Configuration.ipynb)).\n", "Create a climada.conf file e.g., in /cluster/home/$USER/.config with the following content:\n", "\n", "```json\n", @@ -140,7 +140,7 @@ "\n", "### 1. Load dependencies \n", "\n", - "See [Load dependencies](#1.-load-dependencies) above." + "See [Load dependencies](#load-dependencies) above." ] }, { @@ -159,7 +159,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", "### 3. Checkout sources\n", "\n", "```bash\n", diff --git a/doc/development/coding-in-python.rst b/doc/development/coding-in-python.rst new file mode 100644 index 0000000000..39912c73ec --- /dev/null +++ b/doc/development/coding-in-python.rst @@ -0,0 +1,10 @@ +################ +Coding in python +################ + +.. toctree:: + :maxdepth: 1 + + Guide_PythonDos-n-Donts + Guide_Exception_Logging + Performance and Best Practices diff --git a/doc/development/dev-git.rst b/doc/development/dev-git.rst new file mode 100644 index 0000000000..44ba858fc0 --- /dev/null +++ b/doc/development/dev-git.rst @@ -0,0 +1,10 @@ +############################### +Using git and GitHub to develop +############################### + +.. toctree:: + :maxdepth: 1 + :hidden: + + Development with Git + Guide_continuous_integration_GitHub_actions diff --git a/doc/development/index.rst b/doc/development/index.rst index ccb9a30a1d..177b169291 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -1,20 +1,15 @@ -.. include:: ../misc/CONTRIBUTING.md - :parser: commonmark +.. include:: ../../CONTRIBUTING.md + :parser: myst_parser.sphinx_ .. toctree:: - :maxdepth: 1 - :caption: Developer Guide + :maxdepth: 2 :hidden: Developer guide - Development with Git - Guide_CLIMADA_Tutorial - Guide_Configuration - Guide_Testing - Guide_continuous_integration_GitHub_actions - Guide_Review - Guide_PythonDos-n-Donts - Guide_Exception_Logging - Performance and Best Practices + Development with Git + Coding in python CLIMADA Coding Conventions - Building the Documentation <../misc/README> + Documenting your code + Writing tests for your code + Guide_Review + Guide_Euler diff --git a/doc/development/write-documentation.rst b/doc/development/write-documentation.rst new file mode 100644 index 0000000000..cfa4baa323 --- /dev/null +++ b/doc/development/write-documentation.rst @@ -0,0 +1,9 @@ +########################### +Documentation writing +########################### + +.. toctree:: + :maxdepth: 1 + + Guide_CLIMADA_Tutorial + Building the Documentation <../README> diff --git a/doc/getting-started/0_10min_climada.ipynb b/doc/getting-started/0_10min_climada.ipynb deleted file mode 100644 index 544e472aca..0000000000 --- a/doc/getting-started/0_10min_climada.ipynb +++ /dev/null @@ -1,452 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 10 minutes CLIMADA\n", - "\n", - "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../tutorial/1_main_climada.ipynb). TBDnaming\n", - "\n", - "## Key ingredients in a CLIMADA impact calculation\n", - "\n", - "For CLIMADA's impact calculation, we have to specify the following ingredients:\n", - "- **Hazard**: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard. It contains a probabilistic event set, meaning that is a set of several events, each of which is associated to a frequency corresponding to the estimated probability of the occurence of the event.\n", - "- **Exposure**: The exposure information provides the location and the number and/or value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", - "- **Vulnerability**: The impact or vunerability function models the average impact that is expected for a given exposure value and given hazard intensity.\n", - "\n", - "## Exemplary impact calculation\n", - "\n", - "We exemplify the impact calculation and its key ingredients with an analysis of the risk of tropical cyclones on several assets in Florida.\n", - "\n", - "\n", - "### Hazard objects\n", - "\n", - "First, we read a demo hazard file that includes information about several tropical cyclone events. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from climada.hazard import Hazard\n", - "from climada.util import HAZ_DEMO_H5\n", - "\n", - "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n", - "\n", - "# to hide the warnings\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can infer some information from the Hazard object. The central piece of the hazard object is a sparse matrix at `haz.intensity` that contains the hazard intensity values for each event (axis 0) and each location (axis 1). " - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The hazard object contains 216 events. \n", - "The maximal intensity contained in the Hazard object is 72.75 m/s. \n", - "The first event was observed in a time series of 185 years, \n", - "which is why CLIMADA estimates an annual probability of 0.0054 for the occurence of this event.\n" - ] - } - ], - "source": [ - "print(\n", - " f\"The hazard object contains {haz.intensity.shape[0]} events. \\n\"\n", - " f\"The maximal intensity contained in the Hazard object is {haz.intensity.max():.2f} {haz.units}. \\n\"\n", - " f\"The first event was observed in a time series of {int(1/haz.frequency[0])} {haz.frequency_unit[2:]}s, \\n\"\n", - " f\"which is why CLIMADA estimates an annual probability of {haz.frequency[0]:.4f} for the occurence of this event.\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The probabilistic event set and its single events can be plotted. For instance, below we plot maximal intensity per grid point over the whole event set." - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "haz.plot_intensity(0, figsize=(6, 6));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exposure objects\n", - "Now, we read a demo expopure file containing the location and value of a number of exposed assets in Florida." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-01-21 15:38:13,269 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/demo/data/exp_demo_today.h5\n" - ] - } - ], - "source": [ - "from climada.entity import Exposures\n", - "from climada.util.constants import EXP_DEMO_H5\n", - "\n", - "exp = Exposures.from_hdf5(EXP_DEMO_H5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can print some basic information about the exposure object. The central information of the exposure object is contained in a geopandas.GeoDataFrame at `exp.gdf`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "In the exposure object, a total amount of USD 657.05B is distributed among 50 points.\n" - ] - } - ], - "source": [ - "print(\n", - " f\"In the exposure object, a total amount of {exp.value_unit} {exp.gdf.value.sum() / 1_000_000_000:.2f}B\"\n", - " f\" is distributed among {exp.gdf.shape[0]} points.\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can plot the different exposure points on a map." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-01-21 15:39:38,249 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", - "2025-01-21 15:39:38,498 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "exp.plot_basemap(figsize=(6, 6));" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Impact Functions\n", - "\n", - "To model the impact to the exposure that is caused by the hazard, CLIMADA makes use of an impact function. This function relates both percentage of assets affected (PAA, red line below) and the mean damage degree (MDD, blue line below), to the hazard intensity. The multiplication of PAA and MDD result in the mean damage ratio (MDR, black dashed line below), that relates the hazard intensity to corresponding relative impact values. Finally, a multiplication with the exposure values results in the total impact.\n", - "\n", - "Below, we read and plot a standard impact function for tropical cyclones." - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", - "\n", - "impf_tc = ImpfTropCyclone.from_emanuel_usa()\n", - "impf_set = ImpactFuncSet([impf_tc])\n", - "impf_set.plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Impact calculation \n", - "\n", - "Having defined hazard, exposure, and impact function, we can finally perform the impact calcuation. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-01-21 15:43:22,682 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2025-01-21 15:43:22,683 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2025-01-21 15:43:22,686 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", - "2025-01-21 15:43:22,687 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" - ] - } - ], - "source": [ - "from climada.engine import ImpactCalc\n", - "\n", - "imp = ImpactCalc(exp, impf_set, haz).impact(save_mat=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The Impact object contains the results of the impact calculation (including event- and location-wise impact information when `save_mat=True`)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The total expected annual impact over all exposure points is USD 288.90 M. \n", - "The largest estimated single-event impact is USD 20.96 B. \n", - "The largest expected annual impact for a single location is USD 9.58 M. \n", - "\n" - ] - } - ], - "source": [ - "print(\n", - " f\"The total expected annual impact over all exposure points is {imp.unit} {imp.aai_agg / 1_000_000:.2f} M. \\n\"\n", - " f\"The largest estimated single-event impact is {imp.unit} {max(imp.at_event) / 1_000_000_000:.2f} B. \\n\"\n", - " f\"The largest expected annual impact for a single location is {imp.unit} {max(imp.eai_exp) / 1_000_000:.2f} M. \\n\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Several visualizations of impact objects are available. For instance, we can plot the expected annual impact per location on a map." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-01-21 15:44:16,514 - climada.util.coordinates - INFO - Setting geometry points.\n", - "2025-01-21 15:44:16,518 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", - "2025-01-21 15:44:16,771 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "imp.plot_basemap_eai_exposure(figsize=(6, 6))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Further CLIMADA features\n", - "\n", - "CLIMADA offers several additional features and modules that complement its basic impact and risk calculation, among which are\n", - "- uncertainty and sensitivity analysis\n", - "- adaptation option appraisal and cost benefit analysis\n", - "- several tools for providing hazard objects such as tropical cyclones, floods, or winter storms; and exposure objects such as Litpop, or open street maps\n", - "- impact function calibration methods\n", - "\n", - "We end this introduction with a simple adaptation measure analysis. \n", - "\n", - "### Adaptation measure analysis\n", - "\n", - "Consider a simple adaptation measure that results in a 10% decrease in the percentage of affected assets (PAA) decreases and a 20% decrease in the mean damage degree (MDD). We apply this measure and recompute the impact." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-01-21 15:49:48,642 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", - "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", - "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", - "2025-01-21 15:49:48,645 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", - "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" - ] - } - ], - "source": [ - "from climada.entity.measures import Measure\n", - "\n", - "meas = Measure(haz_type=\"TC\", paa_impact=(0.9, 0), mdd_impact=(0.8, 0))\n", - "\n", - "new_exp, new_impfs, new_haz = meas.apply(exp, impf_set, haz)\n", - "new_imp = ImpactCalc(new_exp, new_impfs, new_haz).impact()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To analyze the effect of the adaptation measure, we can, for instance, plot the impact exceedance frequency curves that describe, according to the given data, how frequent different impacts thresholds are expected to be exceeded." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ax = imp.calc_freq_curve().plot(label=\"Without measure\")\n", - "new_imp.calc_freq_curve().plot(axis=ax, label=\"With measure\")\n", - "ax.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "kernelspec": { - "display_name": "supply_chain", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/doc/development/Guide_Introduction.ipynb b/doc/getting-started/Guide_Introduction.ipynb similarity index 98% rename from doc/development/Guide_Introduction.ipynb rename to doc/getting-started/Guide_Introduction.ipynb index 3f3a9ff134..a88accdddf 100644 --- a/doc/development/Guide_Introduction.ipynb +++ b/doc/getting-started/Guide_Introduction.ipynb @@ -70,7 +70,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -84,7 +84,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/development/Guide_get_started.ipynb b/doc/getting-started/Guide_get_started.ipynb similarity index 63% rename from doc/development/Guide_get_started.ipynb rename to doc/getting-started/Guide_get_started.ipynb index 6fa55047b2..463d238377 100644 --- a/doc/development/Guide_get_started.ipynb +++ b/doc/getting-started/Guide_get_started.ipynb @@ -5,7 +5,7 @@ "id": "trying-bronze", "metadata": {}, "source": [ - "# Getting started with CLIMADA" + "# Climada documentation" ] }, { @@ -42,31 +42,6 @@ "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../tutorial/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../guide/Guide_Py_Performance.ipynb) for best practice tips." ] }, - { - "cell_type": "markdown", - "id": "c6ae7939", - "metadata": {}, - "source": [ - "## Apps for working with CLIMADA\n", - "\n", - "To work with CLIMADA, you will need an application that supports Jupyter Notebooks.\n", - "There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/).\n", - "It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what:" - ] - }, - { - "cell_type": "markdown", - "id": "25ab3b98", - "metadata": {}, - "source": [ - "| Use | Tools | Description | Useful for |\n", - "|:----------------------------------|:---------------------|:------------|:-----------|\n", - "| Distribution /
manage virtual environment
& packages | Recommended:
Mamba
Alternatives:
Anaconda|
  • Install climada, manage & use the climada virtual environment, install packages
  • Anaconda includes Anaconda navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder etc.
    • | Climada Users
    & Developers|\n", - "| IDE
    (Integrated Development Environment)|Recommended:
    VSCode
    Alternatives:
    Spyder
    JupyterLab
    PyCharm
    & many more|
  • Write and run code
    • Useful for Developers:
  • VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code)
  • VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon)
    • |Climada Users
    & Developers|\n", - "| Git GUI
    (Graphical User Interface)|GitHub Desktop
    Gitkraken|
  • Provides an interface which keeps track of the branch you’re working on, changes you made etc.
  • Allows you to commit changes, push to GitHub etc. without having to use command line
  • The code itself is not written using these applications but with your IDE of choice(see above)
    • |Climada Developers|\n", - "| Continuous integration
    (CI) server|Jenkins|
  • Automatically checks code changes in GitHub repositories, e.g. when you create a pull request for the develop branch
  • Performs static code analysis using pylint
  • you don't need to do any installations yourself, this runs automatically when you push new code to GitHub
  • see [Continuous Integration and GitHub Actions](../guide/Guide_continuous_integration_GitHub_actions.ipynb)
    • |Climada Developers|" - ] - }, { "cell_type": "markdown", "id": "touched-penetration", @@ -108,7 +83,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -122,7 +97,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:06) \n[Clang 14.0.6 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/index.rst b/doc/index.rst index e7df54dbea..cba11fa18d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -129,7 +129,7 @@ specialized applications can be found in the `CLIMADA Petals Getting started User Guide - Development + Developer Guide API Reference About Changelog From e958b7c17ee4084099183438550ce80a19b21c1e Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 4 Feb 2025 13:40:33 +0100 Subject: [PATCH 17/49] More linkref fixing --- CONTRIBUTING.md | 2 +- .../Guide_CLIMADA_Development.ipynb | 19 +++++++++++-------- .../Guide_CLIMADA_conventions.ipynb | 4 ++-- doc/development/Guide_Configuration.ipynb | 6 +++--- doc/development/Guide_Euler.ipynb | 18 +++++++++--------- 5 files changed, 26 insertions(+), 23 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f21b73e951..11a8ecc246 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -12,7 +12,7 @@ For orientation, these are some categories of possible contributions we can thin * **New Modules and Utility Functions:** Did you create a function or an entire module you find useful for your work? Maybe you are not the only one! Feel free to simply raise a pull request for functions that improve, e.g., plotting or data handling. As an entire module has to be carefully integrated into the framework, it might help if you talk to us first so we can design the module and plan the next steps. You can do that by raising an issue or starting a [discussion](https://github.com/CLIMADA-project/climada_python/discussions) on GitHub. A good place to start a personal discussion is our monthly CLIMADA developers call. -Please contact the [lead developers](https://wcr.ethz.ch/research/climada.html) if you want to join. +Please contact the [lead developers](https://climada.ethz.ch/team/) if you want to join. ## Why Should You Contribute? diff --git a/doc/development/Guide_CLIMADA_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb index a255d415f1..8e23160497 100644 --- a/doc/development/Guide_CLIMADA_Development.ipynb +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -66,8 +66,7 @@ "metadata": {}, "source": [ "## Installing CLIMADA for development\n", - "\n", - "See [Installation](install.rst) for instructions on how to install CLIMADA for developers. You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. Also see [Apps for working with CLIMADA](../guide/Guide_get_started.ipynb#apps-for-working-with-climada) for an overview of which tools are useful for CLIMADA developers. " + "\n" ] }, { @@ -85,7 +84,7 @@ "- the correct sorting of imports using ``isort``\n", "- the correct formatting of the code using ``black``\n", "\n", - "If you have installed the pre-commit hooks (see [Install developer dependencies](install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", + "If you have installed the pre-commit hooks (see [Install developer dependencies](../getting-started/install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", "\n", "If any check fails, you will be warned and these hooks **will apply** corrections (such as formatting the code with black if it is not).\n", "As files are modified, you are required to stage them again (hooks cannot stage their modification, only you can) and commit again.\n", @@ -182,8 +181,6 @@ "\n", " git checkout -b branch_name\n", "\n", - "### Follow the [python do's and don't](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_PythonDos-n-Donts.ipynb) and [performance](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", - "\n", "get the latest data from the remote repository and update your branch\n", " \n", " git pull\n", @@ -201,7 +198,7 @@ " git commit -m \"new functionality of .. implemented\"\n", " \n", "### Make unit and integration tests on your code, preferably during development\n", - "see [Guide on unit and integration tests](../guide/Guide_Testing.ipynb)\n" + "see [Guide on unit and integration tests](Guide_Testing.ipynb)\n" ] }, { @@ -313,7 +310,7 @@ " \n", "12) Update the `develop` branch on your local machine.\n", "\n", - "Also see the [**Reviewer Guide**](../guide/Guide_Review.ipynb) and [**Reviewer Checklist**](../guide/Guide_Review.ipynb#reviewer-checklist)!" + "Also see the [**Reviewer Guide**](Guide_Review.ipynb) and [**Reviewer Checklist**](Guide_Review.ipynb#reviewer-checklist)!" ] }, { @@ -322,6 +319,8 @@ "source": [ "## General tips and tricks\n", "\n", + "Follow the [python do's and don't](Guide_PythonDos-n-Donts) and [performance](Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", + "\n", "### Ask for help with Git\n", "\n", "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message. See also our instructions for [Development with Git](Guide_Git_Development.ipynb).\n", @@ -442,10 +441,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "name": "python" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/development/Guide_CLIMADA_conventions.ipynb b/doc/development/Guide_CLIMADA_conventions.ipynb index 9370dce900..28a6f18f67 100644 --- a/doc/development/Guide_CLIMADA_conventions.ipynb +++ b/doc/development/Guide_CLIMADA_conventions.ipynb @@ -133,7 +133,7 @@ "Note that most text editors usually take care of 1. and 2. by default.\n", "\n", "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](Guide_CLIMADA_Development.ipynb#pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", - "Following the [advanced installation instructions](install.rst#advanced-instructions) will set up these hooks for you." + "Following the [advanced installation instructions](../getting-started/install.rst#advanced-instructions) will set up these hooks for you." ] }, { @@ -505,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/development/Guide_Configuration.ipynb b/doc/development/Guide_Configuration.ipynb index 69056eba61..ad8ccb36f4 100644 --- a/doc/development/Guide_Configuration.ipynb +++ b/doc/development/Guide_Configuration.ipynb @@ -439,7 +439,7 @@ "source": [ "### Test Configuration \n", "\n", - "The configuration values for unit and integration tests are not part of the [default configuration](#Default-Configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", + "The configuration values for unit and integration tests are not part of the [default configuration](#default-configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", "The default test configuration is defined in the `climada.conf` file of the installation directory.\n", "This file contains paths to files that are read during tests. If they are part of the GitHub repository, their path i.g. starts with the `climada` folder within the installation directory:\n", "```json\n", @@ -509,7 +509,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -523,7 +523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) \n[Clang 12.0.1 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/development/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb index 326b4b8eb7..ad7e6b0a9f 100644 --- a/doc/development/Guide_Euler.ipynb +++ b/doc/development/Guide_Euler.ipynb @@ -207,7 +207,7 @@ "\n", "### 6. Adjust the Climada configuration\n", "\n", - "See [Adjust the Climada configuration](#3.-adjust-the-climada-configuration) above." + "See [Adjust the Climada configuration](#adjust-the-climada-configuration) above." ] }, { @@ -217,7 +217,7 @@ "\n", "### 7. Run a job\n", "\n", - "See [Run a job](#4.-run-a-job) above." + "See [Run a job](#run-a-job) above." ] }, { @@ -260,9 +260,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 2. Checkout sources \n", + "#### 2. Work with a different branch\n", "\n", - "See [Checkout sources](#3.-Checkout-sources) above." + "See [Checkout sources](#checkout-sources) above." ] }, { @@ -288,9 +288,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### 4. Adjust the Climada configuration\n", + "#### 4. Adjust configuration\n", "\n", - "See [Adjust the Climada configuration](#3.-Adjust-the-Climada-configuration) above." + "See [Adjust the Climada configuration](#adjust-the-climada-configuration) above." ] }, { @@ -358,7 +358,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Deinstallation" + "### Uninstallation" ] }, { @@ -468,7 +468,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -482,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) \n[Clang 12.0.1 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { From f93460910dc4c469da615443c15d6539de2226e8 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 4 Feb 2025 13:48:41 +0100 Subject: [PATCH 18/49] Reworks getting-started section --- doc/getting-started/index.rst | 8 +++-- doc/getting-started/install.rst | 53 ++++++++++++++++++++++++++++++++- 2 files changed, 57 insertions(+), 4 deletions(-) diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index e625cdeeeb..4d9829ef6d 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -60,6 +60,8 @@ You are good to go! :maxdepth: 1 :hidden: - install - Python Introduction <0_intro_python> - 10 minutes CLIMADA <0_10min_climada> + Introduction + Navigate this documentation + In depth installation instructions + How to cite CLIMADA <../misc/citation> + Python introduction <0_intro_python> diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 0c341a171f..f84f674bfe 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -14,6 +14,7 @@ All following instructions should work on any operating system (OS) that is supp .. hint:: If you need help with the vocabulary used on this page, refer to the :ref:`Glossary `. + ------------- Prerequisites ------------- @@ -41,6 +42,56 @@ For the installation of Miniforge, please scroll to the **Install** section of ` .. note:: If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate and start a new shell session. Or use conda to activate/deactivate.``, please run ``mamba init zsh`` or ``mamba init``. + +Apps for working with CLIMADA +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +To work with CLIMADA, you will need an application that supports Jupyter Notebooks. +There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). +It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: + +.. list-table:: + :header-rows: 1 + :widths: auto + + * - Use + - Tools + - Description + - Useful for + * - Distribution / manage virtual environment & packages + - **Recommended:** + Mamba + **Alternatives:** + Anaconda + - - Install climada, manage & use the climada virtual environment, install packages + - Anaconda includes Anaconda Navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder, etc. + - Climada Users + & Developers + * - IDE (Integrated Development Environment) + - **Recommended:** + VSCode + **Alternatives:** + Spyder, JupyterLab, PyCharm, & many more + - - Write and run code + - Useful for Developers: + - VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code) + - VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon) + - Climada Users + & Developers + * - Git GUI (Graphical User Interface) + - GitHub Desktop, GitKraken + - - Provides an interface which keeps track of the branch you’re working on, changes you made, etc. + - Allows you to commit changes, push to GitHub, etc. without having to use the command line + - The code itself is not written using these applications but with your IDE of choice (see above) + - Climada Developers + * - Continuous integration (CI) server + - Jenkins + - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch + - Performs static code analysis using pylint + - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub + - See `Continuous Integration and GitHub Actions <../guide/Guide_continuous_integration_GitHub_actions.ipynb>`_ + - Climada Developers + .. _install-choice: Decide on Your Entry Level! @@ -221,7 +272,7 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM If this test passes, great! You are good to go. -.. _install-dev: +.. _devdeps: Install Developer Dependencies (Optional) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ From c7f76c651dbfc466678b655dc960149c94328281 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 4 Feb 2025 13:43:15 +0100 Subject: [PATCH 19/49] Title level fixing and 10min to clim back in userguide --- doc/user-guide/1_main_climada.ipynb | 4 +-- doc/user-guide/climada_engine_Impact.ipynb | 10 +++---- .../climada_engine_impact_data.ipynb | 17 ++++++------ doc/user-guide/climada_entity_Exposures.ipynb | 13 +++------- .../climada_entity_ImpactFuncSet.ipynb | 26 +++++++++---------- doc/user-guide/climada_hazard_Hazard.ipynb | 18 +++++-------- .../climada_hazard_TropCyclone.ipynb | 13 +++++++--- .../climada_util_earth_engine.ipynb | 8 +++--- doc/user-guide/index.rst | 1 + 9 files changed, 54 insertions(+), 56 deletions(-) diff --git a/doc/user-guide/1_main_climada.ipynb b/doc/user-guide/1_main_climada.ipynb index 7a9b45ab83..1e5fee2732 100644 --- a/doc/user-guide/1_main_climada.ipynb +++ b/doc/user-guide/1_main_climada.ipynb @@ -1235,7 +1235,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1249,7 +1249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/user-guide/climada_engine_Impact.ipynb b/doc/user-guide/climada_engine_Impact.ipynb index a342a43b39..03683b3b3c 100644 --- a/doc/user-guide/climada_engine_Impact.ipynb +++ b/doc/user-guide/climada_engine_Impact.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Goal of this tutorial" + "## Goal of this tutorial" ] }, { @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an Impact?" + "## What is an Impact?" ] }, { @@ -44,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Impact class data structure" + "## Impact class data structure" ] }, { @@ -97,7 +97,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### How do I compute an impact in CLIMADA?" + "### How do I compute an impact in CLIMADA?" ] }, { @@ -2039,7 +2039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/user-guide/climada_engine_impact_data.ipynb b/doc/user-guide/climada_engine_impact_data.ipynb index 40ead3d807..6f6972f3b3 100644 --- a/doc/user-guide/climada_engine_impact_data.ipynb +++ b/doc/user-guide/climada_engine_impact_data.ipynb @@ -62,6 +62,7 @@ "metadata": {}, "source": [ "### clean_emdat_df()\n", + "\n", "read CSV from EM-DAT into a DataFrame and clean up.\n", "\n", "Use the parameters countries, hazard, and year_range to filter. These parameters are the same for most functions shown here." @@ -184,11 +185,11 @@ "### emdat_to_impact()\n", "function to load EM-DAT impact data and return impact set with impact per event\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "- hazard_type_climada (str): Hazard type abbreviation used in CLIMADA, e.g. 'TC'\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard_type_emdat (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -196,7 +197,7 @@ "- reference_year (int): reference year of exposures for normalization. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)\n", "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- impact_instance (instance of climada.engine.Impact):\n", " Impact() instance (same format as output from CLIMADA impact computations).\n", " Values are scaled with GDP to reference_year if reference_year not equal 0.\n", @@ -322,10 +323,10 @@ "\n", "function to load EM-DAT impact data and return DataFrame with impact summed per year and country\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -334,7 +335,7 @@ "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "- version (int): given EM-DAT data format version (i.e. year of download), changes naming of columns/variables (default: 2020)\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- pandas.DataFrame with impact per year and country" ] }, @@ -430,9 +431,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/user-guide/climada_entity_Exposures.ipynb b/doc/user-guide/climada_entity_Exposures.ipynb index a57079ef20..aa1b39fd38 100644 --- a/doc/user-guide/climada_entity_Exposures.ipynb +++ b/doc/user-guide/climada_entity_Exposures.ipynb @@ -12,21 +12,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### What is an exposure?\n", + "## What is an exposure?\n", "\n", "Exposure describes the set of assets, people, livelihoods, infrastructures, etc. within an area of interest in terms of their geographic location, their value etc.; in brief - everything potentially exposed to hazards. \n", "\n", - "\n", - "\n", - "### What options does CLIMADA offer for me to create an exposure?\n", + "## What options does CLIMADA offer for me to create an exposure?\n", "\n", "CLIMADA has an `Exposures` class for this purpuse. An `Exposures` instance can be filled with your own data, or loaded from available default sources implemented through some Exposures-type classes from CLIMADA.
    \n", "If you have your own data, they can be provided in the formats of a `pandas.DataFrame`, a `geopandas.GeoDataFrame` or simply an `Excel` file. \n", "If you didn't collect your own data, exposures can be generated on the fly using CLIMADA's [LitPop](climada_entity_LitPop.ipynb), [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html) or [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html) modules. See the respective tutorials to learn what exactly they contain and how to use them.\n", "\n", - "\n", - "\n", - "### What does an exposure look like in CLIMADA?\n", + "## What does an exposure look like in CLIMADA?\n", "\n", "An exposure is represented in the class `Exposures`, which contains a [geopandas](https://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html) [GeoDataFrame](https://geopandas.readthedocs.io/en/latest/docs/user_guide/data_structures.html#geodataframe) that is accessible through the `Exposures.data` attribute.\n", "A \"geometry\" column is initialized in the `GeoDataFrame` of the `Exposures` object, other columns are optional at first but some have to be present or make a difference when it comes to do calculations.\n", @@ -1689,7 +1685,6 @@ }, { "cell_type": "markdown", - "id": "5d078d09", "metadata": {}, "source": [ "Optionally use climada's save option to save it in pickle format. This allows fast to quickly restore the object in its current state and take up your work right were you left it the next time.\n", @@ -1727,7 +1722,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/user-guide/climada_entity_ImpactFuncSet.ipynb b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb index 6df482925f..fd349487cd 100644 --- a/doc/user-guide/climada_entity_ImpactFuncSet.ipynb +++ b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an impact function?\n", + "## What is an impact function?\n", "\n", "An impact function relates the percentage of damage in the exposure to the hazard intensity, also commonly referred to as a \"vulnerability curve\" in the modelling community. Every hazard and exposure types are characterized by an impact function." ] @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", + "## What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", "\n", "An `ImpactFunc` is a class for a single impact function. E.g. a function that relates the percentage of damage of a reinforced concrete building (exposure) to the wind speed of a tropical cyclone (hazard intensity). \n", "\n", @@ -31,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFunc` look like in CLIMADA?\n", + "### What does an `ImpactFunc` look like in CLIMADA?\n", "\n", "The `ImpactFunc` class requires users to define the following attributes.\n", "\n", @@ -52,7 +52,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFuncSet` look like in CLIMADA?\n", + "### What does an `ImpactFuncSet` look like in CLIMADA?\n", "\n", "The `ImpactFuncSet` class contains all the `ImpactFunc` classes. Users are not required to define any attributes in `ImpactFuncSet`. \n", "\n", @@ -77,7 +77,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Generate a dummy impact function from scratch.\n", + "### Generate a dummy impact function from scratch.\n", "\n", "Here we generate an impact function with random dummy data for illustrative reasons. Assuming this impact function is a function that relates building damage to tropical cyclone (TC) wind, with an arbitrary id 3." ] @@ -187,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Loading CLIMADA in-built impact function for tropical cyclones\n", + "### Loading CLIMADA in-built impact function for tropical cyclones\n", "\n", "`ImpfTropCyclone` is a derivated class of `ImpactFunc`. This in-built impact function estimates the insured property damages by tropical cyclone wind in USA, following the reference paper [Emanuel (2011)](https://doi.org/10.1175/WCAS-D-11-00007.1).
    \n", "\n", @@ -289,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plotting all the impact functions in an `ImpactFuncSet`\n", + "### Plotting all the impact functions in an `ImpactFuncSet`\n", "\n", "The method `plot()` in `ImpactFuncSet` also uses the the [matplotlib's axes plot function](https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.axes.Axes.plot.html) to visualise the impact functions, returning a figure with all the subplots of impact functions. Users may modify these plots." ] @@ -321,7 +321,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Retrieving an impact function from the `ImpactFuncSet`\n", + "### Retrieving an impact function from the `ImpactFuncSet`\n", "User may want to retrive a particular impact function from `ImpactFuncSet`. Using the method `get_func(haz_type, id)`, it returns an `ImpactFunc` class of the desired impact function. Below is an example of extracting the TC impact function with id 1, and using `plot()` to visualise the function." ] }, @@ -354,7 +354,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Removing an impact function from the `ImpactFuncSet`\n", + "### Removing an impact function from the `ImpactFuncSet`\n", "\n", "If there is an unwanted impact function from the `ImpactFuncSet`, we may remove it using the method `remove_func(haz_type, id)` to remove it from the set. \n", "\n", @@ -423,7 +423,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Reading impact functions from an Excel file\n", + "### Reading impact functions from an Excel file\n", "\n", "Impact functions defined in an excel file following the template provided in sheet `impact_functions` of `climada_python/climada/data/system/entity_template.xlsx` can be ingested directly using the method `from_excel()`." ] @@ -471,7 +471,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Write impact functions\n", + "### Write impact functions\n", "\n", "Users may write the impact functions in Excel format using `write_excel()` method." ] @@ -570,7 +570,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -584,7 +584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/user-guide/climada_hazard_Hazard.ipynb b/doc/user-guide/climada_hazard_Hazard.ipynb index aebd85792c..412346d041 100644 --- a/doc/user-guide/climada_hazard_Hazard.ipynb +++ b/doc/user-guide/climada_hazard_Hazard.ipynb @@ -6,17 +6,13 @@ "source": [ "# Hazard class\n", "\n", - "#### What is a hazard?\n", + "## What is a hazard?\n", "A hazard describes weather events such as storms, floods, droughts, or heat waves both in terms of probability of occurrence as well as physical intensity.\n", "\n", - "
    \n", - "\n", - "#### How are hazards embedded in the CLIMADA architecture?\n", + "## How are hazards embedded in the CLIMADA architecture?\n", "Hazards are defined by the base class `Hazard` which gathers the required attributes that enable the impact computation (such as centroids, frequency per event, and intensity per event and centroid) and common methods such as readers and visualization functions. Each hazard class collects historical data or model simulations and transforms them, if necessary, in order to construct a coherent event database. Stochastic events can be generated taking into account the frequency and main intensity characteristics (such as local water depth for floods or gust speed for storms) of historical events, producing an ensemble of probabilistic events for each historical event. CLIMADA provides therefore an event-based probabilistic approach which does not depend on a hypothesis of a priori general probability distribution choices. Note that one can also reduce the probabilistic approach to a deterministic approach (e.g., story-line or forecasting) by defining the frequency to be 1. The source of the historical data (e.g. inventories or satellite images) or model simulations (e.g. synthetic tropical cyclone tracks) and the methodologies used to compute the hazard attributes and its stochastic events depend on each hazard type and are defined in its corresponding Hazard-derived class (e.g. `TropCylcone` for tropical cyclones, explained in the tutorial [TropCyclone](climada_hazard_TropCyclone.ipynb)). This procedure provides a solid and homogeneous methodology to compute impacts worldwide. In the case where the risk analysis comprises a specific region where good quality data or models describing the hazard intensity and frequency are available, these can be directly ingested by the platform through the reader functions, skipping the hazard modelling part (in total or partially), and allowing us to easily and seamlessly combine CLIMADA with external sources. Hence the impact model can be used for a wide variety of applications, e.g. deterministically to assess the impact of a single (past or future) event or to quantify risk based on a (large) set of probabilistic events. Note that since the `Hazard` class is not an abstract class, any hazard that is not defined in CLIMADA can still be used by providing the `Hazard` attributes.\n", "\n", - "
    \n", - "\n", - "#### What do hazards look like in CLIMADA?\n", + "## What do hazards look like in CLIMADA?\n", "\n", "A `Hazard` contains events of some hazard type defined at `centroids`. There are certain variables in a `Hazard` instance that _are needed_ to compute the impact, while others are _descriptive_ and can therefore be set with default values. The full list of looks like this:\n", "\n", @@ -779,9 +775,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1009,7 +1003,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('climada_env')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1023,7 +1017,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/user-guide/climada_hazard_TropCyclone.ipynb b/doc/user-guide/climada_hazard_TropCyclone.ipynb index 47df87fb75..b4f1ef2ffb 100644 --- a/doc/user-guide/climada_hazard_TropCyclone.ipynb +++ b/doc/user-guide/climada_hazard_TropCyclone.ipynb @@ -13,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What do tropical cyclones look like in CLIMADA?\n", + "## What do tropical cyclones look like in CLIMADA?\n", "\n", "`TCTracks` reads and handles historical tropical cyclone tracks of the [IBTrACS](https://www.ncdc.noaa.gov/ibtracs/) repository or synthetic tropical cyclone tracks simulated using fully statistical or coupled statistical-dynamical modeling approaches. It also generates synthetic tracks from the historical ones using Wiener processes.\n", "\n", @@ -2183,7 +2183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### REFERENCES:\n", + "## REFERENCES:\n", "\n", "- Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0381-2\n", "\n", @@ -2195,6 +2195,13 @@ "\n", "- Lee, C. Y., Tippett, M. K., Sobel, A. H., & Camargo, S. J. (2018). An environmentally forced tropical cyclone hazard model. Journal of Advances in Modeling Earth Systems, 10(1), 223–241. https://doi.org/10.1002/2017MS001186" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -2213,7 +2220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" }, "toc": { "base_numbering": 1, diff --git a/doc/user-guide/climada_util_earth_engine.ipynb b/doc/user-guide/climada_util_earth_engine.ipynb index 10811ce4d7..bf773ef7d4 100644 --- a/doc/user-guide/climada_util_earth_engine.ipynb +++ b/doc/user-guide/climada_util_earth_engine.ipynb @@ -88,10 +88,10 @@ "metadata": {}, "source": [ "If you have a collection, specification of the time range and area of interest. Then, use methods of the series **obtain_image_type(collection,time_range,area)** depending the type of product needed.\n", - "#### Time range\n", + "### Time range\n", "It depends on the image acquisition period of the targeted satellite and type of images desired (without clouds, from a specific period...) \n", "\n", - "#### Area\n", + "### Area\n", "GEE needs a special format for defining an area of interest. It has to be a GeoJSON Polygon and the coordinates should be first defined in a list and then converted using ee.Geometry. It is possible to use data obtained via Exposure layer. Some examples are given below." ] }, @@ -558,9 +558,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index 757568b049..bf1a922e10 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -9,6 +9,7 @@ Landing page of the user guide :caption: User guides :hidden: + 10 minutes CLIMADA <0_10min_climada> Overview <1_main_climada> Hazard Exposures From ef1c45264e86d0877bf67d8970d28d3da41f5e74 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 4 Feb 2025 13:46:39 +0100 Subject: [PATCH 20/49] forgot moving 10min climada notebook file --- doc/misc/AUTHORS.md | 1 - doc/misc/CHANGELOG.md | 1 - doc/misc/CONTRIBUTING.md | 1 - doc/user-guide/0_10min_climada.ipynb | 452 +++++++++++++++++++++++++++ 4 files changed, 452 insertions(+), 3 deletions(-) delete mode 120000 doc/misc/AUTHORS.md delete mode 120000 doc/misc/CHANGELOG.md delete mode 120000 doc/misc/CONTRIBUTING.md create mode 100644 doc/user-guide/0_10min_climada.ipynb diff --git a/doc/misc/AUTHORS.md b/doc/misc/AUTHORS.md deleted file mode 120000 index 2d2e8405f4..0000000000 --- a/doc/misc/AUTHORS.md +++ /dev/null @@ -1 +0,0 @@ -../../AUTHORS.md \ No newline at end of file diff --git a/doc/misc/CHANGELOG.md b/doc/misc/CHANGELOG.md deleted file mode 120000 index 699cc9e7b7..0000000000 --- a/doc/misc/CHANGELOG.md +++ /dev/null @@ -1 +0,0 @@ -../../CHANGELOG.md \ No newline at end of file diff --git a/doc/misc/CONTRIBUTING.md b/doc/misc/CONTRIBUTING.md deleted file mode 120000 index f939e75f21..0000000000 --- a/doc/misc/CONTRIBUTING.md +++ /dev/null @@ -1 +0,0 @@ -../../CONTRIBUTING.md \ No newline at end of file diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb new file mode 100644 index 0000000000..705e0da0f2 --- /dev/null +++ b/doc/user-guide/0_10min_climada.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10 minutes CLIMADA\n", + "\n", + "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../tutorial/1_main_climada.ipynb). TBDnaming\n", + "\n", + "## Key ingredients in a CLIMADA impact calculation\n", + "\n", + "For CLIMADA's impact calculation, we have to specify the following ingredients:\n", + "- **Hazard**: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard. It contains a probabilistic event set, meaning that is a set of several events, each of which is associated to a frequency corresponding to the estimated probability of the occurence of the event.\n", + "- **Exposure**: The exposure information provides the location and the number and/or value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", + "- **Vulnerability**: The impact or vunerability function models the average impact that is expected for a given exposure value and given hazard intensity.\n", + "\n", + "## Exemplary impact calculation\n", + "\n", + "We exemplify the impact calculation and its key ingredients with an analysis of the risk of tropical cyclones on several assets in Florida.\n", + "\n", + "\n", + "### Hazard objects\n", + "\n", + "First, we read a demo hazard file that includes information about several tropical cyclone events. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.hazard import Hazard\n", + "from climada.util import HAZ_DEMO_H5\n", + "\n", + "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n", + "\n", + "# to hide the warnings\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can infer some information from the Hazard object. The central piece of the hazard object is a sparse matrix at `haz.intensity` that contains the hazard intensity values for each event (axis 0) and each location (axis 1). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The hazard object contains 216 events. \n", + "The maximal intensity contained in the Hazard object is 72.75 m/s. \n", + "The first event was observed in a time series of 185 years, \n", + "which is why CLIMADA estimates an annual probability of 0.0054 for the occurence of this event.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The hazard object contains {haz.intensity.shape[0]} events. \\n\"\n", + " f\"The maximal intensity contained in the Hazard object is {haz.intensity.max():.2f} {haz.units}. \\n\"\n", + " f\"The first event was observed in a time series of {int(1/haz.frequency[0])} {haz.frequency_unit[2:]}s, \\n\"\n", + " f\"which is why CLIMADA estimates an annual probability of {haz.frequency[0]:.4f} for the occurence of this event.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The probabilistic event set and its single events can be plotted. For instance, below we plot maximal intensity per grid point over the whole event set." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_intensity(0, figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exposure objects\n", + "Now, we read a demo expopure file containing the location and value of a number of exposed assets in Florida." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:38:13,269 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/demo/data/exp_demo_today.h5\n" + ] + } + ], + "source": [ + "from climada.entity import Exposures\n", + "from climada.util.constants import EXP_DEMO_H5\n", + "\n", + "exp = Exposures.from_hdf5(EXP_DEMO_H5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print some basic information about the exposure object. The central information of the exposure object is contained in a geopandas.GeoDataFrame at `exp.gdf`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the exposure object, a total amount of USD 657.05B is distributed among 50 points.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"In the exposure object, a total amount of {exp.value_unit} {exp.gdf.value.sum() / 1_000_000_000:.2f}B\"\n", + " f\" is distributed among {exp.gdf.shape[0]} points.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the different exposure points on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:39:38,249 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:39:38,498 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp.plot_basemap(figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact Functions\n", + "\n", + "To model the impact to the exposure that is caused by the hazard, CLIMADA makes use of an impact function. This function relates both percentage of assets affected (PAA, red line below) and the mean damage degree (MDD, blue line below), to the hazard intensity. The multiplication of PAA and MDD result in the mean damage ratio (MDR, black dashed line below), that relates the hazard intensity to corresponding relative impact values. Finally, a multiplication with the exposure values results in the total impact.\n", + "\n", + "Below, we read and plot a standard impact function for tropical cyclones." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "\n", + "impf_tc = ImpfTropCyclone.from_emanuel_usa()\n", + "impf_set = ImpactFuncSet([impf_tc])\n", + "impf_set.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact calculation \n", + "\n", + "Having defined hazard, exposure, and impact function, we can finally perform the impact calcuation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:43:22,682 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:43:22,683 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:43:22,686 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:43:22,687 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.engine import ImpactCalc\n", + "\n", + "imp = ImpactCalc(exp, impf_set, haz).impact(save_mat=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Impact object contains the results of the impact calculation (including event- and location-wise impact information when `save_mat=True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total expected annual impact over all exposure points is USD 288.90 M. \n", + "The largest estimated single-event impact is USD 20.96 B. \n", + "The largest expected annual impact for a single location is USD 9.58 M. \n", + "\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The total expected annual impact over all exposure points is {imp.unit} {imp.aai_agg / 1_000_000:.2f} M. \\n\"\n", + " f\"The largest estimated single-event impact is {imp.unit} {max(imp.at_event) / 1_000_000_000:.2f} B. \\n\"\n", + " f\"The largest expected annual impact for a single location is {imp.unit} {max(imp.eai_exp) / 1_000_000:.2f} M. \\n\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Several visualizations of impact objects are available. For instance, we can plot the expected annual impact per location on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:44:16,514 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2025-01-21 15:44:16,518 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:44:16,771 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imp.plot_basemap_eai_exposure(figsize=(6, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further CLIMADA features\n", + "\n", + "CLIMADA offers several additional features and modules that complement its basic impact and risk calculation, among which are\n", + "- uncertainty and sensitivity analysis\n", + "- adaptation option appraisal and cost benefit analysis\n", + "- several tools for providing hazard objects such as tropical cyclones, floods, or winter storms; and exposure objects such as Litpop, or open street maps\n", + "- impact function calibration methods\n", + "\n", + "We end this introduction with a simple adaptation measure analysis. \n", + "\n", + "### Adaptation measure analysis\n", + "\n", + "Consider a simple adaptation measure that results in a 10% decrease in the percentage of affected assets (PAA) decreases and a 20% decrease in the mean damage degree (MDD). We apply this measure and recompute the impact." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:49:48,642 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:49:48,645 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.entity.measures import Measure\n", + "\n", + "meas = Measure(haz_type=\"TC\", paa_impact=(0.9, 0), mdd_impact=(0.8, 0))\n", + "\n", + "new_exp, new_impfs, new_haz = meas.apply(exp, impf_set, haz)\n", + "new_imp = ImpactCalc(new_exp, new_impfs, new_haz).impact()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To analyze the effect of the adaptation measure, we can, for instance, plot the impact exceedance frequency curves that describe, according to the given data, how frequent different impacts thresholds are expected to be exceeded." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = imp.calc_freq_curve().plot(label=\"Without measure\")\n", + "new_imp.calc_freq_curve().plot(axis=ax, label=\"With measure\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From ca6b749075ba69ea589ed2bbd178c0fe71d6bf7d Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Fri, 7 Feb 2025 16:42:23 +0100 Subject: [PATCH 21/49] updated conda installation instructions for different OS --- doc/getting-started/install.rst | 138 ++++++++++++++++---------------- 1 file changed, 70 insertions(+), 68 deletions(-) diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index f84f674bfe..2c8d3b079a 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -14,88 +14,40 @@ All following instructions should work on any operating system (OS) that is supp .. hint:: If you need help with the vocabulary used on this page, refer to the :ref:`Glossary `. - ------------- -Prerequisites +Install Conda ------------- -* Make sure you are using the **latest version** of your OS. Install any outstanding **updates**. -* Free up at least 10 GB of **free storage space** on your machine. - Conda and the CLIMADA dependencies will require around 5 GB of free space, and you will need at least that much additional space for storing the input and output data of CLIMADA. -* Ensure a **stable internet connection** for the installation procedure. - All dependencies will be downloaded from the internet. - Do **not** use a metered, mobile connection! - -.. note:: When mentioning the terms "terminal" or "command line" in the following, we are referring to the "Terminal" apps on macOS or Linux and the "Miniforge Prompt" on Windows. - -Install Mamba or Conda -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. -We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge. - -For the installation of Miniforge, please scroll to the **Install** section of `Miniforge`_ and follow the respective installation instructions for your OS. - -.. attention:: After accepting the license terms and confirming the location, you are asked if you wish to update your shell profile to automatically initialize conda. **Do not just hit ENTER but first type the choice 'yes'**. +We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge, and can be installed as follows. -.. note:: If you encounter ``command not found: mamba``, open a new terminal window. +macOS and Linux +""""""""""" -.. note:: If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate and start a new shell session. Or use conda to activate/deactivate.``, please run ``mamba init zsh`` or ``mamba init``. +* Open the "Terminal" app, copy-paste the two commands below, and hit enter: + .. code-block:: shell -Apps for working with CLIMADA -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" + bash Miniforge3-$(uname)-$(uname -m).sh -To work with CLIMADA, you will need an application that supports Jupyter Notebooks. -There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). -It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: +* Accept the license terms. +* You can confirm the default location. +* Answer 'yes' when asked if if you wish to update your shell profile to automatically initialize conda. **Do not just hit ENTER but first type 'yes'** +* If at some point you encounter ``command not found: mamba``, open a new terminal window. +* If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate ...``, please run ``mamba init zsh`` or ``mamba init``. -.. list-table:: - :header-rows: 1 - :widths: auto +Windows +""""""""""" - * - Use - - Tools - - Description - - Useful for - * - Distribution / manage virtual environment & packages - - **Recommended:** - Mamba - **Alternatives:** - Anaconda - - - Install climada, manage & use the climada virtual environment, install packages - - Anaconda includes Anaconda Navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder, etc. - - Climada Users - & Developers - * - IDE (Integrated Development Environment) - - **Recommended:** - VSCode - **Alternatives:** - Spyder, JupyterLab, PyCharm, & many more - - - Write and run code - - Useful for Developers: - - VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code) - - VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon) - - Climada Users - & Developers - * - Git GUI (Graphical User Interface) - - GitHub Desktop, GitKraken - - - Provides an interface which keeps track of the branch you’re working on, changes you made, etc. - - Allows you to commit changes, push to GitHub, etc. without having to use the command line - - The code itself is not written using these applications but with your IDE of choice (see above) - - Climada Developers - * - Continuous integration (CI) server - - Jenkins - - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch - - Performs static code analysis using pylint - - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub - - See `Continuous Integration and GitHub Actions <../guide/Guide_continuous_integration_GitHub_actions.ipynb>`_ - - Climada Developers +* Download the Windows installer at the Install section from `Miniforge`_. +* Execute the installer. This will install Mamba and provide the "Miniforge Prompt" program as a command line replacement. .. _install-choice: - +------------- Decide on Your Entry Level! -^^^^^^^^^^^^^^^^^^^^^^^^^^^ +------------- + Depening on your level of expertise, we provide two different approaches: @@ -481,6 +433,56 @@ Therefore, we recommend installing Spyder in a *separate* environment, and then #. Set the Python interpreter used by Spyder to the one of ``climada_env``. Select *Preferences* > *Python Interpreter* > *Use the following interpreter* and paste the iterpreter path you copied from the ``climada_env``. +------------- +Apps for working with CLIMADA +------------- + +To work with CLIMADA, you will need an application that supports Jupyter Notebooks. +There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). +It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: + +.. list-table:: + :header-rows: 1 + :widths: auto + + * - Use + - Tools + - Description + - Useful for + * - Distribution / manage virtual environment & packages + - **Recommended:** + Mamba + **Alternatives:** + Anaconda + - - Install climada, manage & use the climada virtual environment, install packages + - Anaconda includes Anaconda Navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder, etc. + - Climada Users + & Developers + * - IDE (Integrated Development Environment) + - **Recommended:** + VSCode + **Alternatives:** + Spyder, JupyterLab, PyCharm, & many more + - - Write and run code + - Useful for Developers: + - VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code) + - VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon) + - Climada Users + & Developers + * - Git GUI (Graphical User Interface) + - GitHub Desktop, GitKraken + - - Provides an interface which keeps track of the branch you’re working on, changes you made, etc. + - Allows you to commit changes, push to GitHub, etc. without having to use the command line + - The code itself is not written using these applications but with your IDE of choice (see above) + - Climada Developers + * - Continuous integration (CI) server + - Jenkins + - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch + - Performs static code analysis using pylint + - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub + - See `Continuous Integration and GitHub Actions <../guide/Guide_continuous_integration_GitHub_actions.ipynb>`_ + - Climada Developers + ---- FAQs ---- From c0681bd6115f3f2648218be770bebbfb58da33c6 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Fri, 7 Feb 2025 16:58:56 +0100 Subject: [PATCH 22/49] fixed white space --- doc/getting-started/install.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 2c8d3b079a..38679655d1 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -44,6 +44,7 @@ Windows * Execute the installer. This will install Mamba and provide the "Miniforge Prompt" program as a command line replacement. .. _install-choice: + ------------- Decide on Your Entry Level! ------------- From eec248778e729f4e7627e917281df1c5e32724e7 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Fri, 7 Feb 2025 18:15:33 +0100 Subject: [PATCH 23/49] fixed compiling headers issues --- doc/getting-started/install.rst | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 38679655d1..28ec6377a8 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -22,7 +22,7 @@ If you haven't already installed an environment management system like `Mamba`_ We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge, and can be installed as follows. macOS and Linux -""""""""""" +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * Open the "Terminal" app, copy-paste the two commands below, and hit enter: @@ -38,16 +38,16 @@ macOS and Linux * If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate ...``, please run ``mamba init zsh`` or ``mamba init``. Windows -""""""""""" +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ * Download the Windows installer at the Install section from `Miniforge`_. * Execute the installer. This will install Mamba and provide the "Miniforge Prompt" program as a command line replacement. .. _install-choice: -------------- +--------------------------------------- Decide on Your Entry Level! -------------- +--------------------------------------- Depening on your level of expertise, we provide two different approaches: @@ -307,6 +307,10 @@ To install CLIMADA Petals, we assume you have already installed CLIMADA Core wit python -m pip install -e ./ +--------------------------------------- +Code Editors +--------------------------------------- + JupyterLab ^^^^^^^^^^ @@ -434,9 +438,9 @@ Therefore, we recommend installing Spyder in a *separate* environment, and then #. Set the Python interpreter used by Spyder to the one of ``climada_env``. Select *Preferences* > *Python Interpreter* > *Use the following interpreter* and paste the iterpreter path you copied from the ``climada_env``. -------------- +--------------------------------------- Apps for working with CLIMADA -------------- +--------------------------------------- To work with CLIMADA, you will need an application that supports Jupyter Notebooks. There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). From 993ec4af5ba1c3559f8b04e58cbc3e65bd580006 Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Mon, 24 Feb 2025 09:33:47 +0100 Subject: [PATCH 24/49] Update website link Co-authored-by: Emanuel Schmid <51439563+emanuel-schmid@users.noreply.github.com> --- doc/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/index.rst b/doc/index.rst index cba11fa18d..b36354746a 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -6,7 +6,7 @@ Welcome to CLIMADA! :align: center :alt: CLIMADA Logo -CLIMADA (CLIMate ADAptation) is a free and open-source software framework for +`CLIMADA `_ (CLIMate ADAptation) is a free and open-source software framework for comprehensive climate risk assessment. Designed by a large scientific community, CLIMADA offers a robust and flexible platform to analyse the impacts of natural hazards and explore adaptation strategies, and it can be used by researchers, From 029b81dcc10071e54f714ba7acfd714667485a19 Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 15:12:37 +0100 Subject: [PATCH 25/49] Adds link to new website in top bar --- doc/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/index.rst b/doc/index.rst index b36354746a..ac006e188e 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -135,3 +135,4 @@ specialized applications can be found in the `CLIMADA Petals Changelog CLIMADA Petals WCR Group + CLIMADA Website From 7c850a53b561f89526a814e7aea95923734e695b Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 16:17:25 +0100 Subject: [PATCH 26/49] Updates User Guide landing page --- doc/user-guide/index.rst | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index bf1a922e10..4ff7acf0c2 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -2,7 +2,13 @@ User guide ==================== -Landing page of the user guide +This user guide contains all the detailed tutorials about the different parts of CLIMADA. +If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada>`_ +which introduces the basics briefly, or the full `Overview <1_main_climada>`_ which goes more in depth. + +You can then go on to more specific tutorial about `Hazard `_, +`Exposures `_ or `Impact `_ or advanced usage such as +`Uncertainty Quantification `_ .. toctree:: :maxdepth: 2 From c95b7df87ea424ee97d72b3dcd475309a7d5aabb Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 16:19:50 +0100 Subject: [PATCH 27/49] Updates hazard.rst with some landing content --- doc/user-guide/hazard.rst | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/user-guide/hazard.rst b/doc/user-guide/hazard.rst index 248dacd828..8c22fdb45d 100644 --- a/doc/user-guide/hazard.rst +++ b/doc/user-guide/hazard.rst @@ -2,6 +2,9 @@ Hazard Tutorials ================ +These guides present the `Hazard` class as well as subclasses +that handle tropical cyclones and winter storms more specifically. + .. toctree:: :maxdepth: 1 From 754417f1a9ffd09146d85111381a94bdf08b8a76 Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 16:26:53 +0100 Subject: [PATCH 28/49] Updates exposures.rst with some landing content --- doc/user-guide/exposures.rst | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/doc/user-guide/exposures.rst b/doc/user-guide/exposures.rst index c47dda6fbb..17d74572fa 100644 --- a/doc/user-guide/exposures.rst +++ b/doc/user-guide/exposures.rst @@ -2,6 +2,10 @@ Exposures Tutorials =================== +These guides present the `Exposures` class, as the main object to handle exposure data, +as well as the `LitPop` subclass which allows to estimate exposure using nightlight intensity and +population count data. We also show how to handle polygons or lines with CLIMADA. + .. toctree:: :maxdepth: 1 From 2302e91f33f34fa45dc10456c85758ded6bf50bc Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 16:38:36 +0100 Subject: [PATCH 29/49] Updates impact.rst with landing content --- doc/user-guide/impact.rst | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/doc/user-guide/impact.rst b/doc/user-guide/impact.rst index 259cb0c9b1..d37454f084 100644 --- a/doc/user-guide/impact.rst +++ b/doc/user-guide/impact.rst @@ -2,6 +2,14 @@ Impact Tutorials ================ +These tutorials show how to compute impacts with CLIMADA, and all related aspects such +as impact functions, adaptation measures and discount rates and cost-benefit analysis. + +The first tutorial presents an end-to-end impact calculation, +and subsequent ones present each aspect in more details. + +Additionally you can find a guide on how to populate impact data from EM-DAT database. + .. toctree:: :maxdepth: 1 @@ -9,6 +17,6 @@ Impact Tutorials climada_entity_ImpactFuncSet climada_entity_MeasureSet Discount Rates - climada_engine_impact_data + Using EM-DAT data Cost Benefit Calculation Probabilistic Yearly Impacts From 68d2f3d739cb9616758c31ca8a7e207c4272c860 Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Wed, 5 Mar 2025 16:43:57 +0100 Subject: [PATCH 30/49] Updates index.rst install title --- doc/getting-started/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index 4d9829ef6d..98f50f916a 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -62,6 +62,6 @@ You are good to go! Introduction Navigate this documentation - In depth installation instructions + Installation instructions How to cite CLIMADA <../misc/citation> Python introduction <0_intro_python> From 97b108b2a988364da805fc1c5ebd379b9d3c8eec Mon Sep 17 00:00:00 2001 From: Nicolas Colombi Date: Mon, 10 Mar 2025 13:08:53 +0100 Subject: [PATCH 31/49] getting started --- doc/getting-started/index.rst | 76 +++++++++++++++++++++-------------- 1 file changed, 45 insertions(+), 31 deletions(-) diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index 98f50f916a..e34f644625 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -2,66 +2,80 @@ Getting started =================== -Quick installation --------------------- +Installation +------------------- -Are you already working with mamba or conda? proceed to install CLIMADA by executing the following line in the terminal:: - mamba create -n climada_env -c conda-forge climada + +Are you already working with conda ? proceed to install CLIMADA by executing the following line in the terminal:: + + conda create -n climada_env -c conda-forge climada Each time you will want to work with CLIMADA, simply activate the environnment:: - mamba activate climada_env + conda activate climada_env You are good to go! + .. seealso:: - You don't have mamba or conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. + You don't have conda installed or you are looking for advaced installation instructions ? Look up our `detailed instructions `__ on CLIMADA installation. .. dropdown:: How does CLIMADA compute impacts ? :color: primary - :icon: unlock - And some content! - -.. dropdown:: How do you create an Hazard ? + CLIMADA computes impacts following the IPCC risk framework by combining hazard intensity, exposure, and vulnerability + data. It models hazards intensity (e.g., tropical cyclones, floods) using + historical event sets or stochastic simulations, overlaying them with spatial exposure data + (e.g., population, infrastructure), and applies vulnerability functions that estimate damage or + loss, given the hazard intensity. By aggregating these results, CLIMADA calculates expected + impacts, such as economic losses or affected populations. +.. dropdown:: How do you create a Hazard ? :color: primary - :icon: unlock - And some content! + From a risk perspective, the intersting aspect of a natural hazard is its location and intensity. For such, + CLIMADA allows you to load your own hazard data or to directly define it using the platform. As an example, + Users can easily load historical tropical cyclone tracks (IBTracks) and apply stochastic methods to generate + a larger ensemble of tracks from the historical ones, from which they can easily compute the maximal windspeed. .. dropdown:: How do we define an exposure ? :color: primary - :icon: unlock - And some content! + Exposure is defined as the entity that could potentially be damaged by a hazard: it can be people, infrastructures, + assests, ecosystems or others. The CLIMADA user is given the option to load its own exposure data into the platform, + or to use CLIMADA to define it. One common way of defining assets' exposure is through LitPop (link). LitPop dissagrate a + financial index, as the GDP of a country for instance, to a much finer resolution proportionally to population + density and nighlight intensity. -.. dropdown:: How do we model vulnerability ? +.. dropdown:: What are centroids ? :color: primary - :icon: unlock - And some content! + How can you compute the impact of a hazard on an exposure if their locations differs ? Well, you can't. + This is what cetroids are for. Centroids are a grid of points defined by the users, in which both the exposure value + and hazard intensity are calculated, allowing you to obtain the asset value and the hazard intensity im those + defined points. -.. dropdown:: Do you want to quantify the uncertainties ? +.. dropdown:: How do we model vulnerability ? :color: primary - :icon: unlock - And some content! + Vulnerability curves, also known as impact functions, tie the link between hazard intensity and damage. + CLIMADA offers built-in sigmoidal or step-wise vulnerability curves, or allows you to calibrate your own + impact functions with damage and hazard data through the calibration module (link). + + (image many impact functions and optimal) -.. dropdown:: Compare adaptation measures and assess their cost effectiveness +.. dropdown:: Do you want to quantify the uncertainties ? :color: primary - :icon: unlock - And some content! + CLIMADA provides a dedicated module ([unsequa link]) for conducting uncertainty and sensitivity analyses. + This module allows you to define a range of input parameters and evaluate their influence on the output, + helping you quantify the sensitivity of the modeling chain as well as the uncertainties in your results. -.. toctree:: - :maxdepth: 1 - :hidden: +.. dropdown:: Compare adaptation measures and assess their cost-effectiveness + :color: primary - Introduction - Navigate this documentation - Installation instructions - How to cite CLIMADA <../misc/citation> - Python introduction <0_intro_python> + Is there an adaptation measure that will decrease the impact? Does the cost needed to implement such + measure outweight the gains? All these questions can be asnwered using the cost-benefit module (link adaptation). + With this module, users can define and compare adaptation measures to establish their cost-effectiveness. From e0d4e7e813104f604fe4c1a72c86d570c03118d0 Mon Sep 17 00:00:00 2001 From: Valentin Gebhart Date: Tue, 11 Mar 2025 16:44:31 +0100 Subject: [PATCH 32/49] readd the navigation in getting started --- doc/getting-started/index.rst | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index e34f644625..df368651d0 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -2,25 +2,24 @@ Getting started =================== -Installation -------------------- +Quick Installation +-------------------- -Are you already working with conda ? proceed to install CLIMADA by executing the following line in the terminal:: +Are you already working with mamba or conda? proceed to install CLIMADA by executing the following line in the terminal:: - conda create -n climada_env -c conda-forge climada + mamba create -n climada_env -c conda-forge climada Each time you will want to work with CLIMADA, simply activate the environnment:: - conda activate climada_env + mamba activate climada_env You are good to go! - .. seealso:: - You don't have conda installed or you are looking for advaced installation instructions ? Look up our `detailed instructions `__ on CLIMADA installation. + You don't have mamba or conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. .. dropdown:: How does CLIMADA compute impacts ? @@ -79,3 +78,13 @@ You are good to go! Is there an adaptation measure that will decrease the impact? Does the cost needed to implement such measure outweight the gains? All these questions can be asnwered using the cost-benefit module (link adaptation). With this module, users can define and compare adaptation measures to establish their cost-effectiveness. + +.. toctree:: + :maxdepth: 1 + :hidden: + + Introduction + Navigate this documentation + Installation instructions + How to cite CLIMADA <../misc/citation> + Python introduction <0_intro_python> From b4421676b9914c2db78efcbd0ac2d508329aacd5 Mon Sep 17 00:00:00 2001 From: Samuel Juhel <10011382+spjuhel@users.noreply.github.com> Date: Mon, 17 Mar 2025 09:22:27 +0100 Subject: [PATCH 33/49] Update urls in install.rst --- doc/getting-started/install.rst | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 28ec6377a8..1127361fc7 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -8,18 +8,18 @@ The following sections will guide you through the installation of CLIMADA and it CLIMADA has a complicated set of dependencies that cannot be installed with ``pip`` alone. Please follow the installation instructions carefully! - We recommend to use `Conda`_ for creating a suitable software environment to execute CLIMADA. + We recommend to use a ``conda``-based python environment manager such as `Mamba`_ or `Conda`_ for creating a suitable software environment to execute CLIMADA. -All following instructions should work on any operating system (OS) that is supported by `Conda`_, including in particular: **Windows**, **macOS**, and **Linux**. +All following instructions should work on any operating system (OS) that is supported by ``conda``, including in particular: **Windows**, **macOS**, and **Linux**. .. hint:: If you need help with the vocabulary used on this page, refer to the :ref:`Glossary `. -------------- -Install Conda -------------- +--------------------------- +Install environment manager +--------------------------- If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. -We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge, and can be installed as follows. +We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge (see below). macOS and Linux ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -49,7 +49,6 @@ Windows Decide on Your Entry Level! --------------------------------------- - Depening on your level of expertise, we provide two different approaches: * If you have never worked with a command line, or if you just want to give CLIMADA a try, follow the :ref:`simple instructions `. @@ -130,7 +129,7 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM .. warning:: - If you followed the :ref:`install-simple` before, make sure you **either** remove the environment with + If you followed the :ref:`install-simple` before, make sure you **either** remove the environment with: .. code-block:: shell @@ -443,7 +442,7 @@ Apps for working with CLIMADA --------------------------------------- To work with CLIMADA, you will need an application that supports Jupyter Notebooks. -There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/). +There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend `JupyterLab `_, `Visual Studio Code `_ or `Spyder `_. It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: .. list-table:: @@ -485,7 +484,7 @@ It is easy to get confused by all the different softwares and their uses so here - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch - Performs static code analysis using pylint - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub - - See `Continuous Integration and GitHub Actions <../guide/Guide_continuous_integration_GitHub_actions.ipynb>`_ + - See `Continuous Integration and GitHub Actions <../development/Guide_continuous_integration_GitHub_actions.ipynb>`_ - Climada Developers ---- @@ -604,12 +603,12 @@ the level set to ``WARNING``. If you prefer another logging configuration, e.g., for using Climada embedded in another application, you can opt out of the default pre-configuration by setting the config value for -``logging.climada_style`` to ``false`` in the :doc:`configuration file ` +``logging.climada_style`` to ``false`` in the :doc:`configuration file <../development/Guide_Configuration>` ``climada.conf``. Changing the logging level can be done in multiple ways: -* Adjust the :doc:`configuration file ` ``climada.conf`` by setting a the value of the ``global.log_level`` property. +* Adjust the :doc:`configuration file <../development/Guide_Configuration>` ``climada.conf`` by setting a the value of the ``global.log_level`` property. This only has an effect if the ``logging.climada_style`` is set to ``true`` though. * Set a global logging level in your Python script: @@ -708,6 +707,6 @@ IDE .. _Conda: https://docs.conda.io/en/latest/ -.. _Mamba: https://mamba.readthedocs.io/en/latest/ +.. _Mamba: https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html .. _Miniforge: https://github.com/conda-forge/miniforge .. _CLIMADA Petals: https://climada-petals.readthedocs.io/en/latest/ From 400eb739b8a7ec1bc9bc8d3c5aff31c54df636dc Mon Sep 17 00:00:00 2001 From: spjuhel Date: Mon, 17 Mar 2025 10:47:14 +0100 Subject: [PATCH 34/49] wip on urls --- doc/development/Guide_CLIMADA_conventions.ipynb | 2 +- doc/development/Guide_Euler.ipynb | 7 +++---- doc/development/Guide_Review.ipynb | 12 ++++++++---- doc/development/Guide_Testing.ipynb | 6 +++--- doc/development/index.rst | 1 + doc/getting-started/Guide_get_started.ipynb | 16 +++++++++------- doc/getting-started/install.rst | 6 +++--- 7 files changed, 28 insertions(+), 22 deletions(-) diff --git a/doc/development/Guide_CLIMADA_conventions.ipynb b/doc/development/Guide_CLIMADA_conventions.ipynb index 28a6f18f67..9ce5b3285e 100644 --- a/doc/development/Guide_CLIMADA_conventions.ipynb +++ b/doc/development/Guide_CLIMADA_conventions.ipynb @@ -49,7 +49,7 @@ " - Contact a [repository admin](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board) to get permission\n", " - Open an [issue](https://github.com/CLIMADA-project/climada_python/issues)\n", " \n", - "Hence, first try to solve your problem with the standard library and function/methods already implemented in CLIMADA (see in particular the [utility functions](#Utility-functions)) then use the packages included in CLIMADA, and if this is not enough, propose the addition of a new package. Do not hesitate to propose new packages if this is needed for your work!" + "Hence, first try to solve your problem with the standard library and function/methods already implemented in CLIMADA then use the packages included in CLIMADA, and if this is not enough, propose the addition of a new package. Do not hesitate to propose new packages if this is needed for your work!" ] }, { diff --git a/doc/development/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb index ad7e6b0a9f..92568b537d 100644 --- a/doc/development/Guide_Euler.ipynb +++ b/doc/development/Guide_Euler.ipynb @@ -14,7 +14,7 @@ "\n", "## Access to Euler\n", "\n", - "See https://scicomp.ethz.ch/wiki/Getting_started_with_clusters for details on how to register at and get started with Euler.\n", + "See [this page](https://scicomp.ethz.ch/wiki/Getting_started_with_clusters) for details on how to register at and get started with Euler.\n", "\n", "For all steps below, first enter the Cluster via SSH." ] @@ -28,7 +28,7 @@ "\n", "## Installation and working directories\n", "\n", - "Please, get familiar with the various Euler storage options: https://scicomp.ethz.ch/wiki/Storage_systems.
    \n", + "Please, get familiar with the various [Euler storage options](https://scicomp.ethz.ch/wiki/Storage_systems).
    \n", "As a general rule: use `/cluster/project` for installation and `/cluster/work` for data processing.\n", "\n", "For ETH WCR group members, the suggested installation and working directories are `/cluster/project/climate/$USER` and `/cluster/work/climate/$USER` respectively.
    \n", @@ -137,8 +137,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", - "### 1. Load dependencies \n", + "### 1. Load dependencies\n", "\n", "See [Load dependencies](#load-dependencies) above." ] diff --git a/doc/development/Guide_Review.ipynb b/doc/development/Guide_Review.ipynb index f996598b43..2ae95b7ed9 100644 --- a/doc/development/Guide_Review.ipynb +++ b/doc/development/Guide_Review.ipynb @@ -39,9 +39,9 @@ "At least one reviewer needs to\n", "- Review all the changes in the pull request. Read what it's supposed to do, check it does that, and make sure the logic is sound.\n", "- Check that the code follows the CLIMADA style guidelines \n", - "- [CLIMADA coding conventions](../guide/Guide_CLIMADA_conventions.ipynb) \n", - "- [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) \n", - " - [Python performance tips and best practice for CLIMADA developers](../guide/Guide_Py_Performance.ipynb) \n", + "- [CLIMADA coding conventions](../development/Guide_CLIMADA_conventions.ipynb) \n", + "- [Python Dos and Don't](../development/Guide_PythonDos-n-Donts.ipynb) \n", + "- [Python performance tips and best practice for CLIMADA developers](../development/Guide_Py_Performance.ipynb) \n", "- If the code is implementing an algorithm it should be referenced in the documentation. Check it's implemented correctly.\n", "- Try to think of edge cases and ways the code could break. See if there's appropriate error handling in cases where the function might behave unexpectedly.\n", "- (Optional) suggest easy ways to speed up the code, and more elegant ways to achieve the same goal.\n", @@ -126,10 +126,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "name": "python" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/development/Guide_Testing.ipynb b/doc/development/Guide_Testing.ipynb index 319d8ada55..ad27d64011 100644 --- a/doc/development/Guide_Testing.ipynb +++ b/doc/development/Guide_Testing.ipynb @@ -287,7 +287,7 @@ "## Testing CLIMADA\n", "\n", "Executing the entire test suite requires you to install the additional requirements for testing.\n", - "See the [installation instructions](install.rst) for [developer dependencies](install-dev) for further information.\n", + "See the [installation instructions for developer dependencies](../getting-started/install.rst#advanced-instructions) for further information.\n", "\n", "In general, you execute tests with\n", "```\n", @@ -334,7 +334,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_py38", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -348,7 +348,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/development/index.rst b/doc/development/index.rst index 177b169291..f64685e3ca 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -9,6 +9,7 @@ Development with Git Coding in python CLIMADA Coding Conventions + CLIMADA Configuration convention Documenting your code Writing tests for your code Guide_Review diff --git a/doc/getting-started/Guide_get_started.ipynb b/doc/getting-started/Guide_get_started.ipynb index 463d238377..47c7cf3e0c 100644 --- a/doc/getting-started/Guide_get_started.ipynb +++ b/doc/getting-started/Guide_get_started.ipynb @@ -14,7 +14,7 @@ "metadata": {}, "source": [ "This is a short summary of the guides to help you find the information that you need to get started.\n", - "To learn more about CLIMADA, have a look at the [introduction](../guide/Guide_Introduction.ipynb). You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects." + "To learn more about CLIMADA, have a look at the [introduction](../getting-started/Guide_Introduction.ipynb). You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects." ] }, { @@ -24,13 +24,15 @@ "metadata": {}, "source": [ "## Installation\n", + "\n", "The first step to getting started is installing CLIMADA. To do so you will need:\n", + "\n", "1. To get the lastest release from the git repository [CLIMADA releases](https://github.com/climada-project/climada_python/releases) or clone the project with git if you are interested in contributing to the development.\n", - "2. To build a conda environment with the dependencies needed by CLIMADA. \n", + "2. To build a conda environment with the dependencies needed by CLIMADA.\n", "\n", - "For details see the [Installation Instructions](install.rst).\n", + "For details see the [Installation Instructions](../getting-started/install.rst).\n", "\n", - "If you need to run a model on a computational cluster, have a look at [this guide](Guide_Euler.ipynb) to install CLIMADA and run your jobs." + "If you need to run a model on a computational cluster, have a look at [this guide](../development/Guide_Euler.ipynb) to install CLIMADA and run your jobs." ] }, { @@ -39,7 +41,7 @@ "metadata": {}, "source": [ "## Programming in Python\n", - "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../tutorial/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../guide/Guide_Py_Performance.ipynb) for best practice tips." + "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../getting-started/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../development/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../development/Guide_Py_Performance.ipynb) for best practice tips." ] }, { @@ -48,7 +50,7 @@ "metadata": {}, "source": [ "## Tutorials\n", - "A good way to start using CLIMADA is to have a look at the [Tutorials](https://github.com/CLIMADA-project/climada_python/tree/main/doc/tutorial). The [Main Tutorial](../tutorial/1_main_climada.ipynb) will introduce you the structure of CLIMADA and how to calculate you first impacts, as well as your first appraisal of adaptation options. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../tutorial/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../tutorial/climada_entity_LitPop.ipynb),...). " + "A good way to start using CLIMADA is to have a look at the [Tutorials](../user-guide/index.rst). The [10 minute climada](../user-guide/0_10min_climada.ipynb) tutorial will give you a quick introduction to CLIMADA, with a brief example on how to calculate you first impacts, as well as your first appraisal of adaptation options, while the [Overview](../user-guide/1_main_climada.ipynb) will present the whole structure of CLIMADA more in depth. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../user-guide/climada_entity_LitPop.ipynb),...). " ] }, { @@ -67,7 +69,7 @@ "metadata": {}, "source": [ "## Contributing\n", - "If you would like to participate in the development of CLIMADA, carefully read the [Git and Development Guide](../guide/Guide_Git_Development.ipynb). Before making a new feature, discuss with one of the repository admins (Now Chahan, Emmanuel and David). Every new feature or enhancement should be done on a separate branch, which will be merged in the develop branch after being reviewed (see [Checklist](../guide/Guide_Review.ipynb)). Finally, the develop branch is merged in the main branch in each CLIMADA release. Each new feature should come with a tutorial and with [Unit and Integration Tests](../guide/Guide_Testing.ipynb). " + "If you would like to participate in the development of CLIMADA, carefully read the [Git and Development Guide](../development/Guide_Git_Development.ipynb). Before making a new feature, discuss with one of the repository admins (Now Chahan, Emmanuel and David). Every new feature or enhancement should be done on a separate branch, which will be merged in the develop branch after being reviewed (see [Checklist](../development/Guide_Review.ipynb)). Finally, the develop branch is merged in the main branch in each CLIMADA release. Each new feature should come with a tutorial and with [Unit and Integration Tests](../development/Guide_Testing.ipynb). " ] }, { diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index 1127361fc7..af3f78b415 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -64,14 +64,14 @@ Notes on the CLIMADA Petals Package CLIMADA is divided into two packages, CLIMADA Core (`climada_python `_) and CLIMADA Petals (`climada_petals `_). The Core contains all the modules necessary for probabilistic impact, averted damage, uncertainty and forecast calculations. -Data for hazard, exposures and impact functions can be obtained from the :doc:`CLIMADA Data API `. +Data for hazard, exposures and impact functions can be obtained from the :doc:`CLIMADA Data API `. Hazard and Exposures subclasses are included as demonstrators only. .. attention:: CLIMADA Petals is **not** a standalone module and requires CLIMADA Core to be installed! CLIMADA Petals contains all the modules for generating data (e.g., ``TC_Surge``, ``WildFire``, ``OpenStreeMap``, ...). New modules are developed and tested here. -Some data created with modules from Petals is available to download from the :doc:`Data API `. +Some data created with modules from Petals is available to download from the :doc:`Data API `. This works with just CLIMADA Core installed. CLIMADA Petals can be used to generate additional data of this type, or to have a look at the tutorials for all data types available from the API. @@ -263,7 +263,7 @@ With the ``climada_env`` activated, execute pre-commit install -Please refer to the :ref:`guide on pre-commit hooks ` for information on how to use this tool. +Please refer to the :ref:`pre-commit-hooks` for information on how to use this tool. For executing the pre-defined test scripts in exactly the same way as they are executed by the automated CI pipeline, you will need ``make`` to be installed. On macOS and on Linux it is pre-installed. On Windows, it can easily be installed with Conda: From 410223260ed46c87ed1449f5fbcff4b77b28b10f Mon Sep 17 00:00:00 2001 From: spjuhel Date: Mon, 17 Mar 2025 17:50:35 +0100 Subject: [PATCH 35/49] url fixing WIP --- AUTHORS.md | 1 + CHANGELOG.md | 2 +- CONTRIBUTING.md | 2 +- doc/_static/css/custom.css | 17 ++++ doc/development/index.rst | 3 +- doc/getting-started/install.rst | 6 +- doc/index.rst | 22 +++--- doc/misc/AUTHORS.md | 1 + doc/misc/CHANGELOG.md | 1 + doc/misc/CONTRIBUTING.md | 1 + doc/misc/citation.rst | 14 ++-- doc/user-guide/0_10min_climada.ipynb | 2 +- doc/user-guide/1_main_climada.ipynb | 82 ++++++++++---------- doc/user-guide/climada_engine_Impact.ipynb | 2 +- doc/user-guide/climada_engine_unsequa.ipynb | 4 +- doc/user-guide/climada_entity_LitPop.ipynb | 10 +-- doc/user-guide/climada_util_api_client.ipynb | 6 +- doc/user-guide/climada_util_calibrate.ipynb | 15 ++-- 18 files changed, 107 insertions(+), 84 deletions(-) create mode 120000 doc/misc/AUTHORS.md create mode 120000 doc/misc/CHANGELOG.md create mode 120000 doc/misc/CONTRIBUTING.md diff --git a/AUTHORS.md b/AUTHORS.md index d76bfcb03a..6f961f235d 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -1,3 +1,4 @@ +(authors)= # CLIMADA List of Authors * Gabriela Aznar-Siguan diff --git a/CHANGELOG.md b/CHANGELOG.md index 28a5e2d311..395513c9d8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -203,7 +203,7 @@ Removed: ### Added - Convenience method `api_client.Client.get_dataset_file`, combining `get_dataset_info` and `download_dataset`, returning a single file objet. [#821](https://github.com/CLIMADA-project/climada_python/pull/821) -- Read and Write methods to and from csv files for the `DiscRates` class. [#818](ttps://github.com/CLIMADA-project/climada_python/pull/818) +- Read and Write methods to and from csv files for the `DiscRates` class. [#818](https://github.com/CLIMADA-project/climada_python/pull/818) - Add `CalcDeltaClimate` to unsequa module to allow uncertainty and sensitivity analysis of impact change calculations [#844](https://github.com/CLIMADA-project/climada_python/pull/844) - Add function `safe_divide` in util which handles division by zero and NaN values in the numerator or denominator [#844](https://github.com/CLIMADA-project/climada_python/pull/844) - Add reset_frequency option for the impact.select() function. [#847](https://github.com/CLIMADA-project/climada_python/pull/847) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 11a8ecc246..fee95be1a2 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ Please contact the [lead developers](https://climada.ethz.ch/team/) if you want ## Why Should You Contribute? -* You will be listed as author of the CLIMADA repository in the [AUTHORS](AUTHORS.md) file. +* You will be listed as author of the CLIMADA repository in the [AUTHORS](#authors) file. * You will improve the quality of the CLIMADA software for you and for everybody else using it. * You will gain insights into scientific software development. diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css index aa76131f59..18c791182b 100644 --- a/doc/_static/css/custom.css +++ b/doc/_static/css/custom.css @@ -20,3 +20,20 @@ html { --pst-font-size-base: 16px; --pst-header-height: 7rem; } + +.hero { + display: flex; + align-items: center; + justify-content: center; + text-align: center; /* Center-align text */ + background: linear-gradient(to right, #f39c12, #1abc9c, #bdc3c7); /* Orange, teal, gray gradient */ + color: white; /* Ensure text stands out */ + padding: 10px; + border-radius: 10px; /* Soft rounded corners */ + margin: 10px auto; /* Center the hero section horizontally */ + max-width: 980px; /* Restrict width to ensure readability */ + font-size: 1.1em; /* Slightly smaller text size */ + line-height: 1.8; /* Better line spacing for readability */ + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* Subtle shadow for depth */ + overflow-wrap: break-word; /* Ensure text doesn't overflow borders */ +} diff --git a/doc/development/index.rst b/doc/development/index.rst index f64685e3ca..bbb8e5c4f3 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -1,4 +1,4 @@ -.. include:: ../../CONTRIBUTING.md +.. include:: ../misc/CONTRIBUTING.md :parser: myst_parser.sphinx_ .. toctree:: @@ -14,3 +14,4 @@ Writing tests for your code Guide_Review Guide_Euler + Authors <../misc/AUTHORS> diff --git a/doc/getting-started/install.rst b/doc/getting-started/install.rst index af3f78b415..daac711911 100644 --- a/doc/getting-started/install.rst +++ b/doc/getting-started/install.rst @@ -263,7 +263,7 @@ With the ``climada_env`` activated, execute pre-commit install -Please refer to the :ref:`pre-commit-hooks` for information on how to use this tool. +Please refer to the `guide on pre-commit hooks <../development/Guide_CLIMADA_Development.html#pre-commit-hooks>`_ for information on how to use this tool. For executing the pre-defined test scripts in exactly the same way as they are executed by the automated CI pipeline, you will need ``make`` to be installed. On macOS and on Linux it is pre-installed. On Windows, it can easily be installed with Conda: @@ -272,7 +272,7 @@ On macOS and on Linux it is pre-installed. On Windows, it can easily be installe mamba install -n climada_env make -Instructions for running the test scripts can be found in the :doc:`Testing Guide `. +Instructions for running the test scripts can be found in the `Testing Guide <../development/Guide_Testing.html>`_. Install CLIMADA Petals (Optional) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -373,7 +373,7 @@ Test Explorer Setup After you set up a workspace, you might want to configure the test explorer for easily running the CLIMADA test suite within VSCode. -.. note:: Please install the additional :ref:`test dependencies ` before proceeding. +.. note:: Please install the additional :ref:`test dependencies ` before proceeding. #. In the left sidebar, select the "Testing" symbol, and click on *Configure Python Tests*. diff --git a/doc/index.rst b/doc/index.rst index ac006e188e..1c1192cb23 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,16 +1,20 @@ +:html_theme.sidebar_secondary.remove: true + =================== Welcome to CLIMADA! =================== -.. image:: guide/img/CLIMADA_logo_QR.png - :align: center - :alt: CLIMADA Logo - -`CLIMADA `_ (CLIMate ADAptation) is a free and open-source software framework for -comprehensive climate risk assessment. Designed by a large scientific community, -CLIMADA offers a robust and flexible platform to analyse the impacts of natural -hazards and explore adaptation strategies, and it can be used by researchers, -policy and decision-makers. +.. raw:: html + +
    + CLIMADA (CLIMate ADAptation) +

    + is a free and open-source software framework for climate risk assessment and + adaptation option appraisal. Designed by a large scientific community, it + helps reasearchers, policymakers, and businesses analyse the impacts of + natural hazards and explore adaptation strategies. +

    +
    CLIMADA is primarily developed and maintained by the `Weather and Climate Risks Group `_ at `ETH Zürich `_. diff --git a/doc/misc/AUTHORS.md b/doc/misc/AUTHORS.md new file mode 120000 index 0000000000..2d2e8405f4 --- /dev/null +++ b/doc/misc/AUTHORS.md @@ -0,0 +1 @@ +../../AUTHORS.md \ No newline at end of file diff --git a/doc/misc/CHANGELOG.md b/doc/misc/CHANGELOG.md new file mode 120000 index 0000000000..699cc9e7b7 --- /dev/null +++ b/doc/misc/CHANGELOG.md @@ -0,0 +1 @@ +../../CHANGELOG.md \ No newline at end of file diff --git a/doc/misc/CONTRIBUTING.md b/doc/misc/CONTRIBUTING.md new file mode 120000 index 0000000000..f939e75f21 --- /dev/null +++ b/doc/misc/CONTRIBUTING.md @@ -0,0 +1 @@ +../../CONTRIBUTING.md \ No newline at end of file diff --git a/doc/misc/citation.rst b/doc/misc/citation.rst index 91570dbe49..9f4610db7a 100644 --- a/doc/misc/citation.rst +++ b/doc/misc/citation.rst @@ -19,19 +19,19 @@ If you use specific tools and modules of CLIMADA, please cite the appropriate pu - Publication to cite * - *Any* - The `Zenodo archive `_ of the CLIMADA version you are using - * - :doc:`Impact calculations ` + * - :doc:`Impact calculations ` - Aznar-Siguan, G. and Bresch, D. N. (2019): CLIMADA v1: A global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-14-351-2021 - * - :doc:`Cost-benefit analysis ` + * - :doc:`Cost-benefit analysis ` - Bresch, D. N. and Aznar-Siguan, G. (2021): CLIMADA v1.4.1: Towards a globally consistent adaptation options appraisal tool, Geosci. Model Dev., 14, 351–363, https://doi.org/10.5194/gmd-14-351-2021 - * - :doc:`Uncertainty and sensitivity analysis ` + * - :doc:`Uncertainty and sensitivity analysis ` - Kropf, C. M. et al. (2022): Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geosci. Model Dev. 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022 - * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ + * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ - Mühlhofer, E., et al. (2024): OpenStreetMap for Multi-Faceted Climate Risk Assessments : Environ. Res. Commun. 6 015005, https://doi.org/10.1088/2515-7620/ad15ab - * - :doc:`LitPop exposures ` + * - :doc:`LitPop exposures ` - Eberenz, S., et al. (2020): Asset exposure data for global physical risk assessment. Earth System Science Data 12, 817–833, https://doi.org/10.3929/ethz-b-000409595 - * - :doc:`Impact function calibration ` + * - :doc:`Impact function calibration ` - Riedel, L., et al. (2024): A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA. Journal of Open Source Software, 9(99), 6755, https://doi.org/10.21105/joss.06755 - * - `GloFAS River Flood Module `_ + * - `GloFAS River Flood Module `_ - Riedel, L. et al. (2024): Fluvial flood inundation and socio-economic impact model based on open data, Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024 Please find the code to reprocduce selected CLIMADA-related scientific publications in our `repository of scientific publications `_. diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb index 705e0da0f2..1e575c19eb 100644 --- a/doc/user-guide/0_10min_climada.ipynb +++ b/doc/user-guide/0_10min_climada.ipynb @@ -6,7 +6,7 @@ "source": [ "# 10 minutes CLIMADA\n", "\n", - "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../tutorial/1_main_climada.ipynb). TBDnaming\n", + "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../user-guide/1_main_climada.ipynb).\n", "\n", "## Key ingredients in a CLIMADA impact calculation\n", "\n", diff --git a/doc/user-guide/1_main_climada.ipynb b/doc/user-guide/1_main_climada.ipynb index 1e5fee2732..ee18d9e062 100644 --- a/doc/user-guide/1_main_climada.ipynb +++ b/doc/user-guide/1_main_climada.ipynb @@ -22,7 +22,7 @@ "\n", "The model core is designed to give as much flexibility as possible when describing the elements of risk, meaning that CLIMADA isn't limited to particular hazards, exposure types or impacts. We love to see the model applied to new problems and contexts.\n", "\n", - "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#CLIMADA-features) section below.\n", + "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#climada-features) section below.\n", "\n", "\n", "### This tutorial\n", @@ -31,9 +31,9 @@ "\n", "### Resources beyond this tutorial\n", "\n", - "- [Installation guide](../guide/install.rst) - go here if you've not installed the model yet\n", + "- [Installation guide](../getting-started/install.rst) - go here if you've not installed the model yet\n", "- [CLIMADA Read the Docs home page](https://climada-python.readthedocs.io) - for all other documentation\n", - "- [List of CLIMADA's features and associated tutorials](#CLIMADA-features)\n", + "- [List of CLIMADA's features and associated tutorials](#climada-features)\n", "- [CLIMADA GitHub develop branch documentation](https://github.com/CLIMADA-project/climada_python/tree/develop/doc) for the very latest versions of code and documentation\n", "- [CLIMADA paper GitHub repository](https://github.com/CLIMADA-project/climada_papers) - for publications using CLIMADA\n" ] @@ -57,7 +57,7 @@ "CLIMADA's `Impact` object is used to analyse events and event sets, whether this is the impact of a single wildfire, or the global economic risk from tropical cyclones in 2100.\n", "\n", "CLIMADA is divided into two parts (two repositories): \n", - "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_util_api_client.ipynb). [Litpop](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", + "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](/user-guide/climada_util_api_client.ipynb). [Litpop](/user-guide/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](/user-guide/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", "2. the petals [climada_petals](https://github.com/CLIMADA-project/climada_petals) contains all the modules for generating data (e.g., TC_Surge, WildFire, OpenStreeMap, ...). Most development is done here. The petals builds-upon the core and does not work as a stand-alone.\n", "\n", "### CLIMADA classes\n", @@ -65,32 +65,32 @@ "This is a full directory of tutorials for CLIMADA's classes to use as a reference. You don't need to read all this to do this tutorial, but it may be useful to refer back to.\n", "\n", "Core (climada_python):\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", - " - [Tropical cyclone wind](../tutorial/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", - " - [European windstorms](../tutorial/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", + "- [**Hazard**](../user-guide/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", + " - [Tropical cyclone wind](../user-guide/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", + " - [European windstorms](../user-guide/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", "\n", "- [**Entity**](#Entity): this is a container that groups CLIMADA's socio-economic models. It's is where the Exposures and Impact Functions are stored, which can then be combined with a hazard for a risk analysis (using the Engine's Impact class). It is also where Discount Rates and Measure Sets are stored, which are used in adaptation cost-benefit analyses (using the Engine's CostBenefit class):\n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", - " - [LitPop](../tutorial/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", - " - [Polygons_lines](../tutorial/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", - " - [ImpactFuncSet](../tutorial/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", + " - [Exposures](../user-guide/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", + " - [LitPop](../user-guide/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", + " - [Polygons_lines](../user-guide/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", + " - [ImpactFuncSet](../user-guide/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", " - ImpactFunc: a basic adjustable impact function, specified by the user\n", " - IFTropCyclone: impact functions for tropical cyclone winds\n", " - IFRiverFlood: impact functions for river floods\n", " - IFStormEurope: impact functions for European windstorms \n", - " - [DiscRates](../tutorial/climada_entity_DiscRates.ipynb): discount rates per year\n", - " - [MeasureSet](../tutorial/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", + " - [DiscRates](../user-guide/climada_entity_DiscRates.ipynb): discount rates per year\n", + " - [MeasureSet](../user-guide/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", " \n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", - " - [Impact](../tutorial/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", - " - [Impact_data](../tutorial/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", - " - [CostBenefit](#Adaptation-options-appraisal): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", - " - [Unsequa](../tutorial/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", - " - [Unsequa_helper](../tutorial/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", - " - [Forecast](../tutorial/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", + "- [**Engine**](../user-guide/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", + " - [Impact](../user-guide/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", + " - [Impact_data](../user-guide/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", + " - [CostBenefit](../user-guide/climada_engine_CostBenefit.ipynb): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", + " - [Unsequa](../user-guide/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", + " - [Unsequa_helper](../user-guide/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", + " - [Forecast](../user-guide/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", "\n", "climada_petals:\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb):\n", + "- [**Hazard**](../user-guide/climada_hazard_Hazard.ipynb):\n", " - [Storm surge](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCSurgeBathtub.html): Tropical cyclone surge from linear wind-surge relationship and a bathtub model.\n", " - [River flooding](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_RiverFlood.html): global water depth hazard for flood, including methods to work with ISIMIP simulations.\n", " - [Crop modelling](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_entity_Crop.html): combines ISIMIP crop simulations and UN Food and Agrigultre Organization data. The module uses crop production as exposure, with hydrometeorological 'hazard' increasing or decreasing production.\n", @@ -101,12 +101,12 @@ " - Drought (global): tutorial under development\n", "\n", "- [**Entity**](#Entity): \n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb):\n", - " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", - " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", + " - [Exposures](../user-guide/climada_entity_Exposures.ipynb):\n", + " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", + " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", "\n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb):\n", - " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", + "- [**Engine**](../user-guide/climada_engine_Impact.ipynb):\n", + " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", "\n", "This list will be updated periodically along with new CLIMADA releases. To see the latest, development version of all tutorials, see the [tutorials page on the CLIMADA GitHub](https://github.com/CLIMADA-project/climada_python/tree/develop/doc/tutorial)." ] @@ -128,16 +128,16 @@ "\n", "Hazards are characterized by their frequency of occurrence and the geographical distribution of their intensity. The `Hazard` class collects events of the same hazard type (e.g. tropical cyclone, flood, drought, ...) with intensity values over the same geographic centroids. They might be historical events or synthetic.\n", "\n", - "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#CLIMADA-features) section of this document to explore tutorials for different hazards, including\n", - "[tropical cyclones](climada_hazard_TropCyclone.ipynb), as used here.\n", + "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#climada-features) section of this document to explore tutorials for different hazards, including\n", + "[tropical cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), as used here.\n", "\n", - "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", + "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](../user-guide/climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", "\n", "So before we create the hazard, we will create our storm tracks and define the geographic centroids for the locations we want to calculate hazard at.\n", "\n", "### Storm tracks\n", "\n", - "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." + "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." ] }, { @@ -257,7 +257,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." + "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." ] }, { @@ -444,9 +444,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See the [TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", + "See the [TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", "\n", - "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](climada_hazard_TropCyclone.ipynb) for details.\n", + "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for details.\n", "\n", ">**Exercise:** Extend this notebook's analysis to examine the effects of climate change in Puerto Rico. You'll need to extend the historical event set with stochastic tracks to create a robust statistical storm climatology - the `TCTracks` class has the functionality to do this. Then you can apply the `apply_climate_scenario_knu` method to the generated hazard object to create a second hazard climatology representing storm activity under climate change. See how the results change using the different hazard sets.\n", "\n", @@ -474,7 +474,7 @@ "\n", "The `Entity`'s `exposures` attribute contains geolocalized values of anything exposed to the hazard, whether monetary values of assets or number of human lives, for example. It is of type `Exposures`. \n", "\n", - "See the [Exposures tutorial](climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", + "See the [Exposures tutorial](../user-guide/climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](../user-guide/climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", "\n", "LitPop is a module that allows CLIMADA to estimate exposed populations and economic assets at any point on the planet without additional information, and in a globally consistent way. Before we try it out with the next code block, we'll need to download a data set and put it into the right folder:\n", "1. Go to the [download page](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download) on Socioeconomic Data and Applications Center (sedac).\n", @@ -578,7 +578,7 @@ "\n", "Impact functions are stored as the Entity's `impact_funcs` attribute, in an instance of the `ImpactFuncSet` class which groups one or more `ImpactFunc` objects. They can be specified manually, read from a file, or you can use CLIMADA's pre-defined impact functions. We'll use a pre-defined function for tropical storm wind damage stored in the `IFTropCyclone` class. \n", "\n", - "See the [Impact Functions tutorial](climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", + "See the [Impact Functions tutorial](../user-guide/climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", "\n", "We initialise an Impact Function with the `IFTropCyclone` class, and use its `from_emanuel_usa` method to load the Emanuel (2011) impact function. (The class also contains regional impact functions for the full globe, but we'll won't use these for now.) The class's `plot` method visualises the function, which we can see is expressed just through the Mean Degree of Damage, with all assets affected." ] @@ -679,7 +679,7 @@ "\n", "They are stored as `Measure` objects within a `MeasureSet` container class (similarly to `ImpactFuncSet` containing several `ImpactFunc`s), and are assigned to the `measures` attribute of the Entity.\n", "\n", - "See the [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", + "See the [Adaptation Measures tutorial](../user-guide/climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", "\n", "The best way to understand an adaptation measure is by an example. Here's a possible measure for the creation of coastal mangroves (ignore the exact numbers, they are just for illustration):" ] @@ -889,7 +889,7 @@ "\n", "The `disc_rates` attribute is of type `DiscRates`. This class contains the discount rates for the following years and computes the net present value for given values.\n", "\n", - "See the [Discount Rates tutorial](climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", + "See the [Discount Rates tutorial](../user-guide/climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", "\n", "Here we will implement a simple, flat 2% discount rate." ] @@ -979,7 +979,7 @@ "metadata": {}, "source": [ "Note: the configurable parameter `CONFIG.maz_matrix_size` controls the maximum matrix size contained in a chunk. You can decrease its value if you are having memory issues when using the `Impact`'s `calc` method. A high value will make the computation fast, but increase the memory use.\n", - "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values.)\n", + "(See the [config guide](../development/Guide_Configuration.ipynb) on how to set configuration values.)\n", "\n", "CLIMADA calculates impacts by providing exposures, impact functions and hazard to an `Impact` object's `calc` method:" ] @@ -1115,7 +1115,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](climada_engine_Impact.ipynb) to get more information about these functions and the class in general." + "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](../user-guide/climada_engine_Impact.ipynb) to get more information about these functions and the class in general." ] }, { @@ -1226,9 +1226,9 @@ "source": [ "## What next?\n", "\n", - "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#CLIMADA-features) section.\n", + "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#climada-features) section.\n", "\n", - "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#Resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" + "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" ] } ], diff --git a/doc/user-guide/climada_engine_Impact.ipynb b/doc/user-guide/climada_engine_Impact.ipynb index 03683b3b3c..150d76e0da 100644 --- a/doc/user-guide/climada_engine_Impact.ipynb +++ b/doc/user-guide/climada_engine_Impact.ipynb @@ -141,7 +141,7 @@ "By default it is set to 1e9 in the [default config file](https://github.com/CLIMADA-project/climada_python/blob/main/climada/conf/climada.conf).\n", "A high value makes the computation fast at the cost of increased memory consumption.\n", "You can decrease its value if you are having memory issues with the `ImpactCalc.impact()` method.\n", - "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values)." + "(See the [config guide](../development/Guide_Configuration.ipynb) on how to set configuration values)." ] }, { diff --git a/doc/user-guide/climada_engine_unsequa.ipynb b/doc/user-guide/climada_engine_unsequa.ipynb index a7f6fabd6c..d1f60722fe 100644 --- a/doc/user-guide/climada_engine_unsequa.ipynb +++ b/doc/user-guide/climada_engine_unsequa.ipynb @@ -3085,7 +3085,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For examples of how to use non-defaults please see the [impact example](###Compute-uncertainty-and-sensitivity-using-default-methods )" + "For examples of how to use non-defaults please see the [impact example](#compute-uncertainty-and-sensitivity-using-default-methods)" ] }, { @@ -5778,7 +5778,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/user-guide/climada_entity_LitPop.ipynb b/doc/user-guide/climada_entity_LitPop.ipynb index b41728bf2d..613d3b3e28 100644 --- a/doc/user-guide/climada_entity_LitPop.ipynb +++ b/doc/user-guide/climada_entity_LitPop.ipynb @@ -29,7 +29,7 @@ "\n", "*Note*: All required data except for the population data from Gridded Population of the World (GPW) is downloaded automatically when an `LitPop.set_*` method is called.\n", "\n", - "**Warning**: Processing the data for the first time can take up huge amounts of RAM (>10 GB), depending on country or region size. Consider using the [wrapper function](climada_util_api_client.ipynb#The-wrapper-functions-client.get_litpop()) of the [data API](climada_util_api_client.ipynb) to download readily computed LitPop exposure data for default values ($n = m = 1$) on demand.\n", + "**Warning**: Processing the data for the first time can take up huge amounts of RAM (>10 GB), depending on country or region size. Consider using the [wrapper function](climada_util_api_client.ipynb#the-wrapper-functions-client-get-litpop) of the [data API](climada_util_api_client.ipynb) to download readily computed LitPop exposure data for default values ($n = m = 1$) on demand.\n", "\n", "#### Nightlight intensity\n", "Black Marble annual composite of the VIIRS day-night band (Grayscale) at 15 arcsec resolution is downloaded from the NASA Earth Observatory: https://earthobservatory.nasa.gov/Features/NightLights (available for 2012 and 2016 at 15 arcsec resolution (~500m)).\n", @@ -50,7 +50,7 @@ "\n", "### Downloading existing LitPop asset exposure data\n", "\n", - "The easiest way to download existing data is using the [wrapper function](climada_util_api_client.ipynb#The-wrapper-functions-client.get_litpop()) of the [data API](climada_util_api_client.ipynb).\n", + "The easiest way to download existing data is using the [wrapper function](climada_util_api_client.ipynb#the-wrapper-functions-client-get-litpop) of the [data API](climada_util_api_client.ipynb).\n", "\n", "Readily computed LitPop asset exposure data based on $Lit^1Pop^1$ for 224 countries, distributing produced capital / non-financial wealth of 2014 at a resolution of 30 arcsec can also be downloaded from the ETH Research Repository: https://doi.org/10.3929/ethz-b-000331316.\n", "The dataset contains gridded data for more than 200 countries as CSV files." @@ -62,7 +62,7 @@ "source": [ "## Attributes\n", "\n", - "The `LitPop` class inherits from [`Exposures`](climada_entity_Exposures.ipynb#Exposures-class).\n", + "The `LitPop` class inherits from [`Exposures`](climada_entity_Exposures.ipynb).\n", "It adds the following attributes:\n", "\n", " exponents : Defining powers (m, n) with which nightlights and population go into Lit**m * Pop**n.\n", @@ -830,7 +830,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -844,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/user-guide/climada_util_api_client.ipynb b/doc/user-guide/climada_util_api_client.ipynb index 215f8b6d0f..29d6bf0a02 100644 --- a/doc/user-guide/climada_util_api_client.ipynb +++ b/doc/user-guide/climada_util_api_client.ipynb @@ -620,9 +620,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1331,7 +1329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/user-guide/climada_util_calibrate.ipynb b/doc/user-guide/climada_util_calibrate.ipynb index 0efefc5a2d..bd08946fff 100644 --- a/doc/user-guide/climada_util_calibrate.ipynb +++ b/doc/user-guide/climada_util_calibrate.ipynb @@ -7,17 +7,17 @@ "source": [ "# Impact Function Calibration\n", "\n", - "CLIMADA provides the [`climada.util.calibrate`](../climada/climada.util.calibrate) module for calibrating impact functions based on impact data.\n", + "CLIMADA provides the [`climada.util.calibrate`](../api/climada/climada.util.calibrate) module for calibrating impact functions based on impact data.\n", "This tutorial will guide through the usage of this module by calibrating an impact function for tropical cyclones (TCs).\n", "\n", - "For further information on the classes available from the module, see its [documentation](../climada/climada.util.calibrate).\n", + "For further information on the classes available from the module, see its [documentation](../api/climada/climada.util.calibrate).\n", "\n", "## Overview\n", "\n", "The basic idea of the calibration is to find a set of parameters for an impact function that minimizes the deviation between the calculated impact and some impact data.\n", "For setting up a calibration task, users have to supply the following information:\n", "\n", - "* Hazard and Exposure (as usual, see [the tutorial](../tutorial/1_main_climada.ipynb#tutorial-an-example-risk-assessment))\n", + "* Hazard and Exposure (as usual, see [the tutorial](../user-guide/1_main_climada.ipynb#tutorial-an-example-risk-assessment))\n", "* The impact data to calibrate the model to\n", "* An impact function definition depending on the calibrated parameters\n", "* Bounds and constraints of the calibrated parameters (depending on the calibration algorithm)\n", @@ -4049,7 +4049,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env_3.9", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -4063,10 +4063,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 + "version": "3.12.6" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } From 99625557c68af19c6fbdca3a7d8788fcd8e6794a Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 10:08:32 +0100 Subject: [PATCH 36/49] backtracking .md file hack --- CONTRIBUTING.md | 2 +- doc/index.rst | 1 - doc/misc/AUTHORS.md | 1 - doc/misc/CHANGELOG.md | 1 - doc/misc/CONTRIBUTING.md | 1 - 5 files changed, 1 insertion(+), 5 deletions(-) delete mode 120000 doc/misc/AUTHORS.md delete mode 120000 doc/misc/CHANGELOG.md delete mode 120000 doc/misc/CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index fee95be1a2..57e1baf812 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -83,4 +83,4 @@ It also contains a checklist for both pull request authors and reviewers to guid [docs]: https://climada-python.readthedocs.io/en/latest/ [devguide]: https://climada-python.readthedocs.io/en/latest/#developer-guide -[testing]: https://climada-python.readthedocs.io/en/latest/guide/Guide_Testing.html +[testing]: https://climada-python.readthedocs.io/en/latest/development/Guide_Testing.html diff --git a/doc/index.rst b/doc/index.rst index 1c1192cb23..a9357b76c7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -135,7 +135,6 @@ specialized applications can be found in the `CLIMADA Petals User Guide Developer Guide API Reference - About Changelog CLIMADA Petals WCR Group diff --git a/doc/misc/AUTHORS.md b/doc/misc/AUTHORS.md deleted file mode 120000 index 2d2e8405f4..0000000000 --- a/doc/misc/AUTHORS.md +++ /dev/null @@ -1 +0,0 @@ -../../AUTHORS.md \ No newline at end of file diff --git a/doc/misc/CHANGELOG.md b/doc/misc/CHANGELOG.md deleted file mode 120000 index 699cc9e7b7..0000000000 --- a/doc/misc/CHANGELOG.md +++ /dev/null @@ -1 +0,0 @@ -../../CHANGELOG.md \ No newline at end of file diff --git a/doc/misc/CONTRIBUTING.md b/doc/misc/CONTRIBUTING.md deleted file mode 120000 index f939e75f21..0000000000 --- a/doc/misc/CONTRIBUTING.md +++ /dev/null @@ -1 +0,0 @@ -../../CONTRIBUTING.md \ No newline at end of file From fb7cd37028f3638381c3189f04e926844d2291b0 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 11:41:35 +0100 Subject: [PATCH 37/49] Adds some near final touch --- CONTRIBUTING.md | 5 +++-- doc/api/index.rst | 3 ++- doc/development/index.rst | 6 +++--- doc/misc/AUTHORS.rst | 2 ++ 4 files changed, 10 insertions(+), 6 deletions(-) create mode 100644 doc/misc/AUTHORS.rst diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 57e1baf812..c4239a35fe 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ Please contact the [lead developers](https://climada.ethz.ch/team/) if you want ## Why Should You Contribute? -* You will be listed as author of the CLIMADA repository in the [AUTHORS](#authors) file. +* You will be listed as author of the CLIMADA repository in the [AUTHORS](authors) file. * You will improve the quality of the CLIMADA software for you and for everybody else using it. * You will gain insights into scientific software development. @@ -40,7 +40,7 @@ To contribute follow these steps: ```bash pylint ``` -6. Add your name to the [AUTHORS](AUTHORS.md) file. +6. Add your name to the [AUTHORS](authors) file. 7. Push your updates to the remote repository: ```bash @@ -84,3 +84,4 @@ It also contains a checklist for both pull request authors and reviewers to guid [docs]: https://climada-python.readthedocs.io/en/latest/ [devguide]: https://climada-python.readthedocs.io/en/latest/#developer-guide [testing]: https://climada-python.readthedocs.io/en/latest/development/Guide_Testing.html +[authors]: https://github.com/CLIMADA-project/climada_python/blob/main/AUTHORS.md diff --git a/doc/api/index.rst b/doc/api/index.rst index 562fd27de5..eabfe4a5ea 100644 --- a/doc/api/index.rst +++ b/doc/api/index.rst @@ -2,7 +2,8 @@ API Reference ============== -Could be nice to have an API section homepage +The API reference contains the whole specification of the code, that is, every modules, +classes (and their attributes), and functions that are available (and documented). .. toctree:: :caption: API Reference diff --git a/doc/development/index.rst b/doc/development/index.rst index bbb8e5c4f3..1c3c14ca35 100644 --- a/doc/development/index.rst +++ b/doc/development/index.rst @@ -1,12 +1,12 @@ -.. include:: ../misc/CONTRIBUTING.md +.. include:: ../../CONTRIBUTING.md :parser: myst_parser.sphinx_ .. toctree:: :maxdepth: 2 :hidden: - Developer guide - Development with Git + Overview + Developing with Git Coding in python CLIMADA Coding Conventions CLIMADA Configuration convention diff --git a/doc/misc/AUTHORS.rst b/doc/misc/AUTHORS.rst new file mode 100644 index 0000000000..02a6cf8607 --- /dev/null +++ b/doc/misc/AUTHORS.rst @@ -0,0 +1,2 @@ +.. include:: ../../AUTHORS.md + :parser: myst_parser.sphinx_ From 2d81ae19be487e6779e01ca2745b8561693005fa Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 11:42:20 +0100 Subject: [PATCH 38/49] Removes some errors messages from documentation build (see details) Several attributes of Centroids became properties and were doctringed twice, I removed the attribute docstring and improved the properties ones. --- climada/hazard/centroids/centr.py | 38 ++++++++++--------------------- 1 file changed, 12 insertions(+), 26 deletions(-) diff --git a/climada/hazard/centroids/centr.py b/climada/hazard/centroids/centr.py index b0c6365c7e..38a0f075e5 100644 --- a/climada/hazard/centroids/centr.py +++ b/climada/hazard/centroids/centr.py @@ -23,7 +23,7 @@ import logging import warnings from pathlib import Path -from typing import Any, Literal, Union +from typing import Any, Literal, Optional, Union import cartopy import cartopy.crs as ccrs @@ -52,21 +52,7 @@ class Centroids: - """Contains vector centroids as a GeoDataFrame - - Attributes - ---------- - lat : np.array - Latitudinal coordinates in the specified CRS (can be any unit). - lon : np.array - Longitudinal coordinates in the specified CRS (can be any unit). - crs : pyproj.CRS - Coordinate reference system. Default: EPSG:4326 (WGS84) - region_id : np.array, optional - Numeric country (or region) codes. Default: None - on_land : np.array, optional - Boolean array indicating on land (True) or off shore (False). Default: None - """ + """Contains vector centroids as a GeoDataFrame""" def __init__( self, @@ -116,13 +102,13 @@ def __init__( self.set_on_land(source=on_land, overwrite=True) @property - def lat(self): - """Return latitudes""" + def lat(self) -> np.array: + """Latitudinal coordinates in the specified CRS (can be any unit).""" return self.gdf.geometry.y.values @property - def lon(self): - """Return longitudes""" + def lon(self) -> np.array: + """Longitudinal coordinates in the specified CRS (can be any unit).""" return self.gdf.geometry.x.values @property @@ -131,8 +117,8 @@ def geometry(self): return self.gdf["geometry"] @property - def on_land(self): - """Get the on_land property""" + def on_land(self) -> Optional[np.array]: + """Boolean array indicating on land (True) or off shore (False). Default: None""" if "on_land" not in self.gdf: return None if self.gdf["on_land"].isna().all(): @@ -140,8 +126,8 @@ def on_land(self): return self.gdf["on_land"].values @property - def region_id(self): - """Get the assigned region_id""" + def region_id(self) -> Optional[np.array]: + """Numeric country (or region) codes. Default: None""" if "region_id" not in self.gdf: return None if self.gdf["region_id"].isna().all(): @@ -149,8 +135,8 @@ def region_id(self): return self.gdf["region_id"].values @property - def crs(self): - """Get the crs""" + def crs(self) -> CRS: + """Coordinate reference system. Default: EPSG:4326 (WGS84)""" return self.gdf.crs @property From f2c27a7dcc3b76f86aeb6b5928fea64356e03366 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 11:57:23 +0100 Subject: [PATCH 39/49] implements improvement --- climada/util/earth_engine.py | 296 ++++++++++++++++++----------------- 1 file changed, 154 insertions(+), 142 deletions(-) diff --git a/climada/util/earth_engine.py b/climada/util/earth_engine.py index 2a35755e52..c0aa1646ce 100644 --- a/climada/util/earth_engine.py +++ b/climada/util/earth_engine.py @@ -26,148 +26,160 @@ # That's why `earthengine-api` is not in the CLIMADA requirements. # See tutorial: climada_util_earth_engine.ipynb # pylint: disable=import-error -import ee - LOGGER = logging.getLogger(__name__) -ee.Initialize() - - -def obtain_image_landsat_composite(landsat_collection, time_range, area): - """Selection of Landsat cloud-free composites in the Earth Engine library - See also: https://developers.google.com/earth-engine/landsat - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_composite : ee.image.Image - """ - collection = ee.ImageCollection(landsat_collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) - return image_composite - - -def obtain_image_median(collection, time_range, area): - """Selection of median from a collection of images in the Earth Engine library - See also: https://developers.google.com/earth-engine/reducers_image_collection - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_median : ee.image.Image - """ - collection = ee.ImageCollection(collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_median = image_area.median() - return image_median - - -def obtain_image_sentinel(sentinel_collection, time_range, area): - """Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset - See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - sentinel_median : ee.image.Image - """ - - # First, method to remove cloud from the image - def maskclouds(image): - band_qa = image.select("QA60") - cloud_mask = ee.Number(2).pow(10).int() - cirrus_mask = ee.Number(2).pow(11).int() - mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and ( - band_qa.bitwiseAnd(cirrus_mask).eq(0) - ) - return image.updateMask(mask).divide(10000) - - sentinel_filtered = ( - ee.ImageCollection(sentinel_collection) - .filterBounds(area) - .filterDate(time_range[0], time_range[1]) - .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) - .map(maskclouds) + +try: + import ee + + LOGGER.info("Google Earth Engine API successfully imported.") + ee_available = True +except ImportError: + LOGGER.error( + "Google Earth Engine API not found. Please install it using 'pip install earthengine-api'." ) + ee_available = False + +if not ee_available: + LOGGER.error( + "Google Earth Engine API not found. Skipping the init of `earth_engine.py`." + ) +else: + ee.Initialize() + + def obtain_image_landsat_composite(landsat_collection, time_range, area): + """Selection of Landsat cloud-free composites in the Earth Engine library + See also: https://developers.google.com/earth-engine/landsat + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + image_composite : ee.image.Image + """ + collection = ee.ImageCollection(landsat_collection) + + # Filter by time range and location + collection_time = collection.filterDate(time_range[0], time_range[1]) + image_area = collection_time.filterBounds(area) + image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) + return image_composite + + def obtain_image_median(collection, time_range, area): + """Selection of median from a collection of images in the Earth Engine library + See also: https://developers.google.com/earth-engine/reducers_image_collection + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + image_median : ee.image.Image + """ + collection = ee.ImageCollection(collection) + + # Filter by time range and location + collection_time = collection.filterDate(time_range[0], time_range[1]) + image_area = collection_time.filterBounds(area) + image_median = image_area.median() + return image_median + + def obtain_image_sentinel(sentinel_collection, time_range, area): + """Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset + See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + sentinel_median : ee.image.Image + """ + + # First, method to remove cloud from the image + def maskclouds(image): + band_qa = image.select("QA60") + cloud_mask = ee.Number(2).pow(10).int() + cirrus_mask = ee.Number(2).pow(11).int() + mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and ( + band_qa.bitwiseAnd(cirrus_mask).eq(0) + ) + return image.updateMask(mask).divide(10000) + + sentinel_filtered = ( + ee.ImageCollection(sentinel_collection) + .filterBounds(area) + .filterDate(time_range[0], time_range[1]) + .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) + .map(maskclouds) + ) + + sentinel_median = sentinel_filtered.median() + return sentinel_median + + def get_region(geom): + """Get the region of a given geometry, needed for exporting tasks. + + Parameters + ---------- + geom : ee.Geometry, ee.Feature, ee.Image + region of interest + + Returns + ------- + region : list + """ + if isinstance(geom, ee.Geometry): + region = geom.getInfo()["coordinates"] + elif isinstance(geom, ee.Feature, ee.Image): + region = geom.geometry().getInfo()["coordinates"] + elif isinstance(geom, list): + condition = all([isinstance(item) == list for item in geom]) + if condition: + region = geom + return region + + def get_url(name, image, scale, region): + """It will open and download automatically a zip folder containing Geotiff data of 'image'. + If additional parameters are needed, see also: + https://github.com/google/earthengine-api/blob/master/python/ee/image.py + + Parameters + ---------- + name : str + name of the created folder + image : ee.image.Image + image to export + scale : int + resolution of export in meters (e.g: 30 for Landsat) + region : list + region of interest + + Returns + ------- + path : str + """ + path = image.getDownloadURL( + {"name": (name), "scale": scale, "region": (region)} + ) - sentinel_median = sentinel_filtered.median() - return sentinel_median - - -def get_region(geom): - """Get the region of a given geometry, needed for exporting tasks. - - Parameters - ---------- - geom : ee.Geometry, ee.Feature, ee.Image - region of interest - - Returns - ------- - region : list - """ - if isinstance(geom, ee.Geometry): - region = geom.getInfo()["coordinates"] - elif isinstance(geom, ee.Feature, ee.Image): - region = geom.geometry().getInfo()["coordinates"] - elif isinstance(geom, list): - condition = all([isinstance(item) == list for item in geom]) - if condition: - region = geom - return region - - -def get_url(name, image, scale, region): - """It will open and download automatically a zip folder containing Geotiff data of 'image'. - If additional parameters are needed, see also: - https://github.com/google/earthengine-api/blob/master/python/ee/image.py - - Parameters - ---------- - name : str - name of the created folder - image : ee.image.Image - image to export - scale : int - resolution of export in meters (e.g: 30 for Landsat) - region : list - region of interest - - Returns - ------- - path : str - """ - path = image.getDownloadURL({"name": (name), "scale": scale, "region": (region)}) - - webbrowser.open_new_tab(path) - return path + webbrowser.open_new_tab(path) + return path From a66697760e89fb13d9967bed0c4240fa36496f57 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 13:58:27 +0100 Subject: [PATCH 40/49] Final touch on urls --- CONTRIBUTING.md | 4 ++-- doc/getting-started/Guide_Introduction.ipynb | 4 ++-- doc/getting-started/index.rst | 2 +- doc/misc/CHANGELOG.rst | 2 ++ doc/user-guide/index.rst | 8 ++++---- 5 files changed, 11 insertions(+), 9 deletions(-) create mode 100644 doc/misc/CHANGELOG.rst diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c4239a35fe..2ee11385ff 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ Please contact the [lead developers](https://climada.ethz.ch/team/) if you want ## Why Should You Contribute? -* You will be listed as author of the CLIMADA repository in the [AUTHORS](authors) file. +* You will be listed as author of the CLIMADA repository in the [AUTHORS][authors] file. * You will improve the quality of the CLIMADA software for you and for everybody else using it. * You will gain insights into scientific software development. @@ -40,7 +40,7 @@ To contribute follow these steps: ```bash pylint ``` -6. Add your name to the [AUTHORS](authors) file. +6. Add your name to the [AUTHORS][authors] file. 7. Push your updates to the remote repository: ```bash diff --git a/doc/getting-started/Guide_Introduction.ipynb b/doc/getting-started/Guide_Introduction.ipynb index a88accdddf..aae2fa54d9 100644 --- a/doc/getting-started/Guide_Introduction.ipynb +++ b/doc/getting-started/Guide_Introduction.ipynb @@ -9,7 +9,7 @@ "# Introduction\n", "\n", "CLIMADA implements a fully probabilistic risk assessment model.\n", - "According to the IPCC [[1]](#1), natural risks emerge through the\n", + "According to the IPCC [[1](#references)], natural risks emerge through the\n", "interplay of climate and weather-related hazards, the exposure of goods\n", "or people to this hazard, and the specific vulnerability of exposed\n", "people, infrastructure and environment. \n", @@ -53,7 +53,7 @@ "

    \n", "\n", "## References\n", - "[1] \n", + "[IPCC] \n", "IPCC: Climate Change 2014: Impacts, Adaptation and Vulnerability.\n", " Part A: Global and Sectoral Aspects. Contribution of Working Group\n", " II to the Fifth Assessment Report of the Intergovernmental Panel on\n", diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index df368651d0..1c4ad0b8a2 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -83,8 +83,8 @@ You are good to go! :maxdepth: 1 :hidden: - Introduction Navigate this documentation + Introduction Installation instructions How to cite CLIMADA <../misc/citation> Python introduction <0_intro_python> diff --git a/doc/misc/CHANGELOG.rst b/doc/misc/CHANGELOG.rst new file mode 100644 index 0000000000..9f448a2b95 --- /dev/null +++ b/doc/misc/CHANGELOG.rst @@ -0,0 +1,2 @@ +.. include:: ../../CHANGELOG.md + :parser: myst_parser.sphinx_ diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst index 4ff7acf0c2..85c7d70328 100644 --- a/doc/user-guide/index.rst +++ b/doc/user-guide/index.rst @@ -3,16 +3,15 @@ User guide ==================== This user guide contains all the detailed tutorials about the different parts of CLIMADA. -If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada>`_ +If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada>`_ which introduces the basics briefly, or the full `Overview <1_main_climada>`_ which goes more in depth. -You can then go on to more specific tutorial about `Hazard `_, -`Exposures `_ or `Impact `_ or advanced usage such as +You can then go on to more specific tutorial about `Hazard `_, +`Exposures `_ or `Impact `_ or advanced usage such as `Uncertainty Quantification `_ .. toctree:: :maxdepth: 2 - :caption: User guides :hidden: 10 minutes CLIMADA <0_10min_climada> @@ -20,6 +19,7 @@ You can then go on to more specific tutorial about `Hazard `_, Hazard Exposures Impact + Compute local exceedance Uncertainty Quantification climada_engine_Forecast climada_util_calibrate From fdcb21519ccbb5cb4bc254832fe122a1c6b1efa9 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 13:59:41 +0100 Subject: [PATCH 41/49] Avoids section navigation in Changelog --- doc/conf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/doc/conf.py b/doc/conf.py index 195603a1fb..f17a510f51 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -146,6 +146,9 @@ # ], } +# Avoid section navigation sidebar in changelog page +html_sidebars = {"misc/CHANGELOG": []} + # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] From 2ef5bd73e7d263241d9f5ae20d27c79bc4e79909 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 14:00:01 +0100 Subject: [PATCH 42/49] This file is not used anymore --- doc/misc/README.md | 75 ---------------------------------------------- 1 file changed, 75 deletions(-) delete mode 100644 doc/misc/README.md diff --git a/doc/misc/README.md b/doc/misc/README.md deleted file mode 100644 index 58d9075952..0000000000 --- a/doc/misc/README.md +++ /dev/null @@ -1,75 +0,0 @@ -# CLIMADA - -CLIMADA stands for **CLIM**ate **ADA**ptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.). - -As of today, CLIMADA provides global coverage of major climate-related extreme-weather hazards at high resolution (4x4km) via a [data API](https://climada.ethz.ch/data-api/v1/docs) For select hazards, historic and probabilistic events sets, for past, present and future climate exist at distinct time horizons. -You will find a repository containing scientific peer-reviewed articles that explain software components implemented in CLIMADA [here](https://github.com/CLIMADA-project/climada_papers). - -CLIMADA is divided into two parts (two repositories): - -1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_util_api_client.ipynb). [Litpop](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. -2. the petals [climada_petals](https://github.com/CLIMADA-project/climada_petals) contains all the modules for generating data (e.g., TC_Surge, WildFire, OpenStreeMap, ...). Most development is done here. The petals builds-upon the core and does not work as a stand-alone. - -It is recommend for new users to begin with the core (1) and the [tutorials](https://github.com/CLIMADA-project/climada_python/tree/main/doc/tutorial) therein. - -This is the Python (3.9+) version of CLIMADA - please see [here](https://github.com/davidnbresch/climada) for backward compatibility with the MATLAB version. - -## Getting started - -CLIMADA runs on Windows, macOS and Linux. -The released versions of CLIMADA are available from [conda-forge](https://anaconda.org/conda-forge/climada). -Use the [Mamba](https://mamba.readthedocs.io/en/latest/) package manager to install it: - -```shell -mamba install -c conda-forge climada -``` - -It is **highly recommended** to install CLIMADA into a **separate** Conda environment. -See the [installation guide](https://climada-python.readthedocs.io/en/latest/guide/install.html) for further information. - -Follow the [tutorials](https://climada-python.readthedocs.io/en/stable/tutorial/1_main_climada.html) in a Jupyter Notebook to see what can be done with CLIMADA and how. - -## Documentation - -The online documentation is available on [Read the Docs](https://climada-python.readthedocs.io/en/stable/).The documentation of each release version of CLIMADA can be accessed separately through the drop-down menu at the bottom of the left sidebar. Additionally, the version 'stable' refers to the most recent release (installed via `conda`), and 'latest' refers to the latest unstable development version (the `develop` branch). - - -CLIMADA python: - -* [online (recommended)](https://climada-python.readthedocs.io/en/latest/) -* [PDF file](https://climada-python.readthedocs.io/_/downloads/en/stable/pdf/) -* [core Tutorials on GitHub](https://github.com/CLIMADA-project/climada_python/tree/main/doc/tutorial) - -CLIMADA petals: - -* [online (recommended)](https://climada-petals.readthedocs.io/en/latest/) -* [PDF file](https://climada-petals.readthedocs.io/_/downloads/en/stable/pdf/) -* [petals Tutorials on GitHub](https://github.com/CLIMADA-project/climada_petals/tree/main/doc/tutorial) - -The documentation can also be [built locally](https://climada-python.readthedocs.io/en/latest/README.html). - -## Citing CLIMADA - -See the [Citation Guide](https://climada-python.readthedocs.io/en/latest/misc/citation.html). - -Please use the following logo if you are presenting results obtained with or through CLIMADA: - -![https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/img/CLIMADA_logo_QR.png](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/img/CLIMADA_logo_QR.png?raw=true) - -## Contributing - -We welcome any contribution to this repository, be it bugfixes and other code changes and additions, documentation improvements, or tutorial updates. - -If you would like to contribute, please refer to our [Contribution Guide](CONTRIBUTING.md). - -## Versioning - -We use [SemVer](http://semver.org/) for versioning. For the versions available, see the [releases on this repository](https://github.com/CLIMADA-project/climada_python/releases). - -## License - -Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in AUTHORS. - -CLIMADA is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License Version 3, 29 June 2007 as published by the Free Software Foundation, https://www.gnu.org/licenses/gpl-3.0.html - -CLIMADA is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details: https://www.gnu.org/licenses/gpl-3.0.html From 6282eb7c3c07ee08e4ea3eaaa3bf8ad60b5f53c1 Mon Sep 17 00:00:00 2001 From: spjuhel Date: Tue, 18 Mar 2025 14:00:15 +0100 Subject: [PATCH 43/49] Improves How to navigate page --- doc/getting-started/Guide_get_started.ipynb | 60 ++++++++++++++------- 1 file changed, 40 insertions(+), 20 deletions(-) diff --git a/doc/getting-started/Guide_get_started.ipynb b/doc/getting-started/Guide_get_started.ipynb index 47c7cf3e0c..c4171d5005 100644 --- a/doc/getting-started/Guide_get_started.ipynb +++ b/doc/getting-started/Guide_get_started.ipynb @@ -5,7 +5,7 @@ "id": "trying-bronze", "metadata": {}, "source": [ - "# Climada documentation" + "# How to navigate this documentation" ] }, { @@ -13,26 +13,23 @@ "id": "multiple-radical", "metadata": {}, "source": [ - "This is a short summary of the guides to help you find the information that you need to get started.\n", - "To learn more about CLIMADA, have a look at the [introduction](../getting-started/Guide_Introduction.ipynb). You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects." + "This page is a short summary of the different sections and guides here, to help you find the information that you need to get started.\n", + "\n", + "Each top section has its own landing page, presenting the essential elements in brief, and often several subsections, which go more into details." ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "israeli-street", + "id": "68085d04-e4ee-45c8-8dbb-41e35bd072a1", "metadata": {}, "source": [ - "## Installation\n", - "\n", - "The first step to getting started is installing CLIMADA. To do so you will need:\n", + "## Getting started\n", "\n", - "1. To get the lastest release from the git repository [CLIMADA releases](https://github.com/climada-project/climada_python/releases) or clone the project with git if you are interested in contributing to the development.\n", - "2. To build a conda environment with the dependencies needed by CLIMADA.\n", + "The [Getting started](../getting-started/index.rst) section, where you are currently, presents the very basics of climada.\n", "\n", - "For details see the [Installation Instructions](../getting-started/install.rst).\n", + "For instance, to start learning about CLIMADA, you can have a look at the [introduction](../getting-started/Guide_Introduction.ipynb). \n", "\n", - "If you need to run a model on a computational cluster, have a look at [this guide](../development/Guide_Euler.ipynb) to install CLIMADA and run your jobs." + "You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects conducted with CLIMADA." ] }, { @@ -40,7 +37,8 @@ "id": "future-distinction", "metadata": {}, "source": [ - "## Programming in Python\n", + "### Programming in Python\n", + "\n", "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../getting-started/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../development/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../development/Guide_Py_Performance.ipynb) for best practice tips." ] }, @@ -50,26 +48,48 @@ "metadata": {}, "source": [ "## Tutorials\n", - "A good way to start using CLIMADA is to have a look at the [Tutorials](../user-guide/index.rst). The [10 minute climada](../user-guide/0_10min_climada.ipynb) tutorial will give you a quick introduction to CLIMADA, with a brief example on how to calculate you first impacts, as well as your first appraisal of adaptation options, while the [Overview](../user-guide/1_main_climada.ipynb) will present the whole structure of CLIMADA more in depth. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../user-guide/climada_entity_LitPop.ipynb),...). " + "A good way to start using CLIMADA is to have a look at the tutorials in the [User Guide](../user-guide/index.rst). The [10 minute climada](../user-guide/0_10min_climada.ipynb) tutorial will give you a quick introduction to CLIMADA, with a brief example on how to calculate you first impacts, as well as your first appraisal of adaptation options, while the [Overview](../user-guide/1_main_climada.ipynb) will present the whole structure of CLIMADA more in depth. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../user-guide/climada_entity_LitPop.ipynb),...). " ] }, { "cell_type": "markdown", - "id": "0cc77b19", + "id": "cd831a52-ea5f-48ee-bf6b-4a6c1a1cdf15", "metadata": {}, "source": [ - "## Documentation\n", + "## Contributing\n", "\n", - "You can find the documentation of CLIMADA on Read the Docs [online](https://climada-python.readthedocs.io/en/stable/index.html#). Note that the documentation has several versions: 'latest', 'stable' and explicit version numbers, such as 'v3.1.1', in the url path. 'latest' is created from the 'develop' branch and has the latest changes of the developers, 'stable' from the latest release. For more details about documentation versions, please have a look at [here](https://readthedocs.org/projects/climada-python/versions/)." + "If you would like to participate in the development of CLIMADA, carefully read the [Developer Guide](../development/index.rst). \n", + "Here you will find how to set up an environment to develop new features for CLIMADA, the workflow and rules to follow to make sure you can implement a valuable contribution!" ] }, { "cell_type": "markdown", - "id": "growing-religious", + "id": "dc0f6303-e02b-429f-acc3-dd1b23d49be3", "metadata": {}, "source": [ - "## Contributing\n", - "If you would like to participate in the development of CLIMADA, carefully read the [Git and Development Guide](../development/Guide_Git_Development.ipynb). Before making a new feature, discuss with one of the repository admins (Now Chahan, Emmanuel and David). Every new feature or enhancement should be done on a separate branch, which will be merged in the develop branch after being reviewed (see [Checklist](../development/Guide_Review.ipynb)). Finally, the develop branch is merged in the main branch in each CLIMADA release. Each new feature should come with a tutorial and with [Unit and Integration Tests](../development/Guide_Testing.ipynb). " + "## API Reference\n", + "\n", + "The [API reference](../api/index.rst) presents the documentation of the internal modules, classes, methods and function of CLIMADA." + ] + }, + { + "cell_type": "markdown", + "id": "6a25d260-336f-439a-8859-00b9b4c1251d", + "metadata": {}, + "source": [ + "## Changelog\n", + "\n", + "In the Changelog section, you can have a look at all the changes made between the different versions of CLIMADA" + ] + }, + { + "cell_type": "markdown", + "id": "efa8c4fc-706b-40e1-9567-c58dc9f59e0c", + "metadata": {}, + "source": [ + "## External links\n", + "\n", + "The top bar of this website also link to the documentation of [Climada Petals](https://climada-petals.readthedocs.io/en/stable/), the webpage of the [Weather and Climate Risk group](https://wcr.ethz.ch/) at ETH, and the official [CLIMADA website](https://climada.ethz.ch/)." ] }, { From 9dcc2a378e974bdf22beb178d77d50279d50b135 Mon Sep 17 00:00:00 2001 From: Nicolas Colombi Date: Wed, 19 Mar 2025 08:24:14 +0100 Subject: [PATCH 44/49] add links and images to getting started --- doc/getting-started/index.rst | 69 ++++++++++++++++--------- doc/user-guide/img/cost-benefit.png | Bin 0 -> 18708 bytes doc/user-guide/img/exposure.png | Bin 0 -> 172053 bytes doc/user-guide/img/impact-function.png | Bin 0 -> 85965 bytes doc/user-guide/img/risk_framework.png | Bin 0 -> 480216 bytes doc/user-guide/img/sensitivity.png | Bin 0 -> 144886 bytes doc/user-guide/img/tc-tracks.png | Bin 0 -> 68747 bytes 7 files changed, 45 insertions(+), 24 deletions(-) create mode 100644 doc/user-guide/img/cost-benefit.png create mode 100644 doc/user-guide/img/exposure.png create mode 100644 doc/user-guide/img/impact-function.png create mode 100644 doc/user-guide/img/risk_framework.png create mode 100644 doc/user-guide/img/sensitivity.png create mode 100644 doc/user-guide/img/tc-tracks.png diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst index 1c4ad0b8a2..1333466936 100644 --- a/doc/getting-started/index.rst +++ b/doc/getting-started/index.rst @@ -25,60 +25,81 @@ You are good to go! .. dropdown:: How does CLIMADA compute impacts ? :color: primary - CLIMADA computes impacts following the IPCC risk framework by combining hazard intensity, exposure, and vulnerability - data. It models hazards intensity (e.g., tropical cyclones, floods) using + CLIMADA follows the IPCC risk framework to compute impacts by combining hazard intensity, exposure, and vulnerability. + It models hazards intensity (e.g., tropical cyclones, floods) using historical event sets or stochastic simulations, overlaying them with spatial exposure data - (e.g., population, infrastructure), and applies vulnerability functions that estimate damage or - loss, given the hazard intensity. By aggregating these results, CLIMADA calculates expected - impacts, such as economic losses or affected populations. + (e.g., population, infrastructure), and applies vulnerability functions that estimate damage + given the hazard intensity. By aggregating these results, CLIMADA calculates expected + impacts, such as economic losses or affected populations. See the dedicated :doc:`impact tutorial     ` + for more informations. + + .. image:: /user-guide/img/risk_framework.png + :width: 400 + :alt: Alternative text + :align: center + .. dropdown:: How do you create a Hazard ? :color: primary From a risk perspective, the intersting aspect of a natural hazard is its location and intensity. For such, - CLIMADA allows you to load your own hazard data or to directly define it using the platform. As an example, - Users can easily load historical tropical cyclone tracks (IBTracks) and apply stochastic methods to generate - a larger ensemble of tracks from the historical ones, from which they can easily compute the maximal windspeed. + CLIMADA allows you to load your own :doc:`hazard ` data or to directly define it in the platform. As an example, + users can easily load historical tropical cyclone tracks (IBTracks) and apply stochastic methods to generate + a larger ensemble of tracks from the historical ones, from which they can easily compute the maximal windspeed, + the hazard intensity. + + .. image:: /user-guide/img/tc-tracks.png + :width: 500 + :alt: Alternative text + :align: center .. dropdown:: How do we define an exposure ? :color: primary Exposure is defined as the entity that could potentially be damaged by a hazard: it can be people, infrastructures, - assests, ecosystems or others. The CLIMADA user is given the option to load its own exposure data into the platform, - or to use CLIMADA to define it. One common way of defining assets' exposure is through LitPop (link). LitPop dissagrate a - financial index, as the GDP of a country for instance, to a much finer resolution proportionally to population + assests, ecosystems or more. A CLIMADA user is given the option to load its own exposure data into the platform, + or to use CLIMADA to define it. One common way of defining assets' exposure is through :doc:`LitPop `. LitPop dissagrate a + financial index, as the country GDP for instance, to a much finer resolution proportionally to population density and nighlight intensity. -.. dropdown:: What are centroids ? - :color: primary - - How can you compute the impact of a hazard on an exposure if their locations differs ? Well, you can't. - This is what cetroids are for. Centroids are a grid of points defined by the users, in which both the exposure value - and hazard intensity are calculated, allowing you to obtain the asset value and the hazard intensity im those - defined points. + .. image:: /user-guide/img/exposure.png + :width: 500 + :align: center .. dropdown:: How do we model vulnerability ? :color: primary Vulnerability curves, also known as impact functions, tie the link between hazard intensity and damage. - CLIMADA offers built-in sigmoidal or step-wise vulnerability curves, or allows you to calibrate your own - impact functions with damage and hazard data through the calibration module (link). + CLIMADA offers built-in sigmoidal or step-wise vulnerability curves, and allows you to calibrate your own + impact functions with damage and hazard data through the :doc:`calibration module `. + - (image many impact functions and optimal) + .. image:: /user-guide/img/impact-function.png + :width: 400 + :align: center -.. dropdown:: Do you want to quantify the uncertainties ? +.. dropdown:: Do you want to quantify uncertainties ? :color: primary - CLIMADA provides a dedicated module ([unsequa link]) for conducting uncertainty and sensitivity analyses. + CLIMADA provides a dedicated module :doc:`unsequa ` for conducting uncertainty and sensitivity analyses. This module allows you to define a range of input parameters and evaluate their influence on the output, helping you quantify the sensitivity of the modeling chain as well as the uncertainties in your results. + .. image:: /user-guide/img/sensitivity.png + :width: 500 + :align: center + .. dropdown:: Compare adaptation measures and assess their cost-effectiveness :color: primary Is there an adaptation measure that will decrease the impact? Does the cost needed to implement such - measure outweight the gains? All these questions can be asnwered using the cost-benefit module (link adaptation). + measure outweight the gains? All these questions can be asnwered using the :doc:`cost-benefit ` and + :doc:`adaptation module `. 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