From a62a695782f538137b030f086b23549127cab5fc Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Fri, 15 Sep 2023 11:44:09 +0100 Subject: [PATCH 01/11] add a notebook for comparing cbmr and cbma on neuroth --- .../12_compare_cbmr_and_cbma.py | 226 ++++++++++++++++++ 1 file changed, 226 insertions(+) create mode 100644 examples/02_meta-analyses/12_compare_cbmr_and_cbma.py diff --git a/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py new file mode 100644 index 000000000..f277f2d4c --- /dev/null +++ b/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py @@ -0,0 +1,226 @@ +""" + +.. _metas_cbmr_vs_cbma: + +================================================================ +Compare coordinate-based meta-regression and meta-analysis methods +================================================================ + +A comparison between coordinate-based meta-regression (CBMR) and +coordinate-based meta-analysis (CBMA) in NiMARE + +CBMR is a generative framework to approximate smooth activation intensity function and investigate +the effect of study-level moderators (e.g., year of pubilication, sample size, subtype of stimuli). +It allows flexible statistical inference for either spatial homogeneity tests or group comparison +tests. Additionally, it's a computationally efficient approach with good statistical +interpretability to model the locations of activation foci. + +This tutorial is intended to provide an intuitive comparison of CBMA and MKDA results on +neurosynth dataset. + +For more detailed introduction to CBMR implementation in NiMARE, see the `CBMR tutoral +`_ and +`documatation `_. + +""" +import os + +from nimare.extract import download_abstracts, fetch_neurosynth +from nimare.io import convert_neurosynth_to_dataset +from nimare.meta import models +from nilearn.plotting import plot_stat_map + +############################################################################### +# Download the Neurosynth Dataset +# ----------------------------------------------------------------------------- +# Neurosynth is a large-scale functional magnetic resonance imaing (fMRI) database. +# There are currently 507891 activations reported in 14371 studies in the Neurosynth +# database, with interactive, downloadable meta-analyses of 1334 terms. There is also +# a `platform `_ designed for automated synthesis of fMRI data. + +out_dir = os.path.abspath("../example_data/") +os.makedirs(out_dir, exist_ok=True) + +files = fetch_neurosynth( + data_dir=out_dir, + version="7", + overwrite=False, + source="abstract", + vocab="terms", +) +# Note that the files are saved to a new folder within "out_dir" named "neurosynth". +neurosynth_db = files[0] + +neurosynth_dset = convert_neurosynth_to_dataset( + coordinates_file=neurosynth_db["coordinates"], + metadata_file=neurosynth_db["metadata"], + annotations_files=neurosynth_db["features"], +) +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset.pkl.gz")) + +neurosynth_dset = download_abstracts(neurosynth_dset, "example@example.edu") +neurosynth_dset.save(os.path.join(out_dir, "neurosynth_dataset_with_abstracts.pkl.gz")) + +############################################################################### +# For term-based meta-analyses, we split the whole Neurosynth dataset into two subsets, +# one including all studies in the Neurosynth database whose abstracts include the term +# at least once, the other including all the remaining studies. Here, we will conduct +# meta-analyses based on the term "pain", and explore the spatial convergence between +# pain studies and other fMRI studies. + +# extract study_id for pain dataset and non-pain dataset +all_study_id = neurosynth_dset.annotations["id"] +pain_study_id = neurosynth_dset.get_studies_by_label(labels=["terms_abstract_tfidf__pain"]) +non_pain_study_id = list(set(list(all_study_id)) - set(pain_study_id)) # 13855 studies +# add an additional column for group +neurosynth_dset.annotations.loc[all_study_id.isin(pain_study_id), "group"] = "pain" +neurosynth_dset.annotations.loc[all_study_id.isin(non_pain_study_id), "group"] = "non_pain" + +############################################################################### +# Estimation of group-specific spatial intensity functions +# ----------------------------------------------------------------------------- +# Now we are going to run CBMR framework on the Neurosynth Dataset and estimate +# spatial intensity functions for both pain studies and non-pain fMRI studies. + +from nimare.meta.cbmr import CBMREstimator +cbmr = CBMREstimator( + group_categories="group", + moderators=None, + spline_spacing=10, # a reasonable choice is 10 or 5, 100 is for speed + model=models.PoissonEstimator, + penalty=False, + lr=1e-1, + tol=1e-2, # a reasonable choice is 1e-2, 1e3 is for speed + device="cpu", # "cuda" if you have GPU +) +results = cbmr.fit(dataset=neurosynth_dset) + +############################################################################### +# Now that we have fitted the model, we can plot the spatial intensity maps. + +plot_stat_map( + results.get_map("spatialIntensity_group-Pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Pain studies", + threshold=3e-4, + vmax=1e-3, +) +plot_stat_map( + results.get_map("spatialIntensity_group-Non_pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Non-pain fMRI studies", + threshold=3e-4, + vmax=1e-3, +) + +############################################################################### +# These two figures correspond to group-specific spatial intensity map of pain group +# and non-pain group. Areas with stronger spatial intensity are highlighted. + +############################################################################### +# Group-wise tests for spatial homogeneity +# ----------------------------------------------------------------------------- +# For group-wise spatial homogeneity test, we generate contrast matrix *t_con_groups* +# by specifying the group names in *create_contrast* function, and generate group-wise +# p-value and z-score maps for spatial homogeneity tests. +from nimare.meta.cbmr import CBMRInference + +inference = CBMRInference(device="cpu") +inference.fit(result=results) +t_con_groups = inference.create_contrast( + ["Pain", "Non_pain"], source="groups" +) +contrast_result = inference.transform(t_con_groups=t_con_groups) + +############################################################################### + +# generate z-score maps for group-wise spatial homogeneity test. +plot_stat_map( + contrast_result.get_map("z_group-Pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Z-score map for spatial homogeneity test on pain studies", + threshold=20, + vmax=30, +) + +plot_stat_map( + contrast_result.get_map("z_group-Non_pain"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="Z-score map for spatial homogeneity test on non-pain fMRI studies", + threshold=20, + vmax=30, +) + +############################################################################### +# Group comparison test between pain studies and non-pain fMRI studies +# ----------------------------------------------------------------------------- +# CBMR framework also allows flexible statistical inference for group comparison +# between any two or more groups. For example, it's straightforward to generate +# contrast matrix *t_con_groups* by specifying *contrast_name* as "group1-group2". + +inference = CBMRInference(device="cpu") +inference.fit(result=results) +t_con_groups = inference.create_contrast( + ["Pain-Non_pain"], source="groups" +) +contrast_result = inference.transform(t_con_groups=t_con_groups) + +############################################################################### + +# generate z-statistics maps for each group +plot_stat_map( + contrast_result.get_map("z_group-Pain-Non_pain"), + cut_coords=[0, 0, 0], + draw_cross=False, + cmap="RdBu_r", + title="Spatial convergence between pain studies and Non-pain fMRI studies", + threshold=6, + vmax=20, +) + +############################################################################### +# This figure (displayed as z-statistics map) shows CBMR group comparison test +# of spatial intensity between pain studies and non-pain studies in Neurosynth. +# The null hypothesis assumes spatial intensity estimations of two groups are equal +# at voxel level, $H_0: \mu_{1j}=\mu_{2j}, j=1,\cdots,N$, where $N$ is number of +# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules +# (siginificant difference in spatial intensity estimation between two groups) are +# highlighted. We found that estimated activation level are significantly different +# in ... between pain group and non-pain group. + +############################################################################### +# Run MKDA on Neurosynth dataset +# ----------------------------------------------------------------------------- +# For the purpose of justifying the validity of CBMR framework, we compare the estimated +# spatial covergence of activation regions between pain studies and non-pain fMRI studies +# with MKDA. + +from nimare.meta.cbma.mkda import MKDAChi2 + +pain_dset = neurosynth_dset.slice(ids=pain_study_id) +non_pain_dset = neurosynth_dset.slice(ids=pain_study_id) + +meta = MKDAChi2() +results = meta.fit(pain_dset, non_pain_dset) + +plot_stat_map( + results.get_map("z_desc-consistency"), + cut_coords=[0, 0, -8], + draw_cross=False, + cmap="RdBu_r", + title="MKDA Chi-square analysis between pain studies and non-pain studies", + threshold=5, +) + +############################################################################### +# This figure (displayed as z-statistics map) shows MKDA spatial covergence of +# activation between pain studies and non-pain fMRI studies. We found the results are +# very consistent with CBMR approach, with higher specificity but lower sensitivity. \ No newline at end of file From 47a3e16ae98a5c1ad46005d443e1b43f9bf6acb8 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 19 Sep 2023 22:56:43 +0800 Subject: [PATCH 02/11] fix some spelling error --- .../02_meta-analyses/12_compare_cbmr_and_cbma.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py index f277f2d4c..d1dff787f 100644 --- a/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py +++ b/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py @@ -10,7 +10,7 @@ coordinate-based meta-analysis (CBMA) in NiMARE CBMR is a generative framework to approximate smooth activation intensity function and investigate -the effect of study-level moderators (e.g., year of pubilication, sample size, subtype of stimuli). +the effect of study-level moderators (e.g., year of publication, sample size, subtype of stimuli). It allows flexible statistical inference for either spatial homogeneity tests or group comparison tests. Additionally, it's a computationally efficient approach with good statistical interpretability to model the locations of activation foci. @@ -191,16 +191,16 @@ # of spatial intensity between pain studies and non-pain studies in Neurosynth. # The null hypothesis assumes spatial intensity estimations of two groups are equal # at voxel level, $H_0: \mu_{1j}=\mu_{2j}, j=1,\cdots,N$, where $N$ is number of -# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-vaules -# (siginificant difference in spatial intensity estimation between two groups) are +# voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values +# (significant difference in spatial intensity estimation between two groups) are # highlighted. We found that estimated activation level are significantly different -# in ... between pain group and non-pain group. +# in ... between the pain group and non-pain group. ############################################################################### # Run MKDA on Neurosynth dataset # ----------------------------------------------------------------------------- # For the purpose of justifying the validity of CBMR framework, we compare the estimated -# spatial covergence of activation regions between pain studies and non-pain fMRI studies +# spatial convergence of activation regions between pain studies and non-pain fMRI studies # with MKDA. from nimare.meta.cbma.mkda import MKDAChi2 @@ -221,6 +221,6 @@ ) ############################################################################### -# This figure (displayed as z-statistics map) shows MKDA spatial covergence of +# This figure (displayed as a z-statistics map) shows MKDA spatial covergence of # activation between pain studies and non-pain fMRI studies. We found the results are -# very consistent with CBMR approach, with higher specificity but lower sensitivity. \ No newline at end of file +# very consistent with CBMR approach, with higher specificity but lower sensitivity. From 263f9e91c95103c27e17665475d062b01f655d4b Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 19 Sep 2023 23:58:11 +0800 Subject: [PATCH 03/11] change notebook name --- .../{12_compare_cbmr_and_cbma.py => 13_compare_cbmr_and_cbma.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/02_meta-analyses/{12_compare_cbmr_and_cbma.py => 13_compare_cbmr_and_cbma.py} (100%) diff --git a/examples/02_meta-analyses/12_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py similarity index 100% rename from examples/02_meta-analyses/12_compare_cbmr_and_cbma.py rename to examples/02_meta-analyses/13_compare_cbmr_and_cbma.py From d2cb58a60035e5b5b87717edd55924b8c625dfaf Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Wed, 20 Sep 2023 00:56:15 +0800 Subject: [PATCH 04/11] reformalize codes with black --- examples/02_meta-analyses/13_compare_cbmr_and_cbma.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py index d1dff787f..f9692ac72 100644 --- a/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py +++ b/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py @@ -83,6 +83,7 @@ # spatial intensity functions for both pain studies and non-pain fMRI studies. from nimare.meta.cbmr import CBMREstimator + cbmr = CBMREstimator( group_categories="group", moderators=None, @@ -90,7 +91,7 @@ model=models.PoissonEstimator, penalty=False, lr=1e-1, - tol=1e-2, # a reasonable choice is 1e-2, 1e3 is for speed + tol=1e-2, # a reasonable choice is 1e-2, 1e3 is for speed device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=neurosynth_dset) @@ -131,9 +132,7 @@ inference = CBMRInference(device="cpu") inference.fit(result=results) -t_con_groups = inference.create_contrast( - ["Pain", "Non_pain"], source="groups" -) +t_con_groups = inference.create_contrast(["Pain", "Non_pain"], source="groups") contrast_result = inference.transform(t_con_groups=t_con_groups) ############################################################################### @@ -168,9 +167,7 @@ inference = CBMRInference(device="cpu") inference.fit(result=results) -t_con_groups = inference.create_contrast( - ["Pain-Non_pain"], source="groups" -) +t_con_groups = inference.create_contrast(["Pain-Non_pain"], source="groups") contrast_result = inference.transform(t_con_groups=t_con_groups) ############################################################################### From a8612c345bffec9507059bd795fb76feba153b3c Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 19:44:31 +0800 Subject: [PATCH 05/11] add executed cbmr-pain notebook in misc-notebooks --- .../13_compare_cbmr_and_cbma.py | 2 +- .../misc-notebooks/neurosynth_pain-cbmr.ipynb | 607 ++++++++++++++++++ 2 files changed, 608 insertions(+), 1 deletion(-) create mode 100644 examples/misc-notebooks/neurosynth_pain-cbmr.ipynb diff --git a/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py b/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py index f9692ac72..644b831e9 100644 --- a/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py +++ b/examples/02_meta-analyses/13_compare_cbmr_and_cbma.py @@ -203,7 +203,7 @@ from nimare.meta.cbma.mkda import MKDAChi2 pain_dset = neurosynth_dset.slice(ids=pain_study_id) -non_pain_dset = neurosynth_dset.slice(ids=pain_study_id) +non_pain_dset = neurosynth_dset.slice(ids=non_pain_study_id) meta = MKDAChi2() results = meta.fit(pain_dset, non_pain_dset) diff --git a/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb b/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb new file mode 100644 index 000000000..97346c8da --- /dev/null +++ b/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb @@ -0,0 +1,607 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "# Compare coordinate-based meta-regression and meta-analysis methods\n", + "\n", + "A comparison between coordinate-based meta-regression (CBMR) and\n", + "coordinate-based meta-analysis (CBMA) in NiMARE\n", + "\n", + "CBMR is a generative framework to approximate smooth activation intensity function and investigate\n", + "the effect of study-level moderators (e.g., year of publication, sample size, subtype of stimuli).\n", + "It allows flexible statistical inference for either spatial homogeneity tests or group comparison\n", + "tests. Additionally, it's a computationally efficient approach with good statistical\n", + "interpretability to model the locations of activation foci.\n", + "\n", + "This tutorial is intended to provide an intuitive comparison of CBMA and MKDA results on\n", + "neurosynth dataset.\n", + "\n", + "For more detailed introduction to CBMR implementation in NiMARE, see the [CBMR tutoral](https://nimare.readthedocs.io/en/latest/auto_examples/02_meta-analyses/11_plot_cbmr.html) and\n", + "[documatation](https://nimare.readthedocs.io/en/latest/generated/nimare.meta.cbmr.html).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/yifan0330/NiMARE.git@fix_diagnostics_index\n", + " Cloning https://github.com/yifan0330/NiMARE.git (to revision fix_diagnostics_index) to /private/var/folders/6z/dr8b0msn34dczlnpt0gt5ymmvxq11c/T/pip-req-build-8q1g_sza\n", + " Running 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git+https://github.com/yifan0330/NiMARE.git@fix_diagnostics_index" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:numexpr.utils:NumExpr defaulting to 8 threads.\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "from nimare.extract import download_abstracts, fetch_neurosynth\n", + "from nimare.io import convert_neurosynth_to_dataset\n", + "from nimare.meta import models\n", + "from nilearn.plotting import plot_stat_map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download the Neurosynth Dataset\n", + "Neurosynth is a large-scale functional magnetic resonance imaing (fMRI) database.\n", + "There are currently 507891 activations reported in 14371 studies in the Neurosynth\n", + "database, with interactive, downloadable meta-analyses of 1334 terms. There is also\n", + "a [platform](https://neurosynth.org/) designed for automated synthesis of fMRI data.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.extract.utils:Dataset found in /Users/yifany/Documents/GitHub/NiMARE/examples/example_data/neurosynth\n", + "\n", + "INFO:nimare.extract.extract:Searching for any feature files matching the following criteria: [('source-abstract', 'vocab-terms', 'data-neurosynth', 'version-7')]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading data-neurosynth_version-7_coordinates.tsv.gz\n", + "File exists and overwrite is False. Skipping.\n", + "Downloading data-neurosynth_version-7_metadata.tsv.gz\n", + "File exists and overwrite is False. Skipping.\n", + "Downloading data-neurosynth_version-7_vocab-terms_source-abstract_type-tfidf_features.npz\n", + "File exists and overwrite is False. Skipping.\n", + "Downloading data-neurosynth_version-7_vocab-terms_vocabulary.txt\n", + "File exists and overwrite is False. Skipping.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:nimare.utils:Not applying transforms to coordinates in unrecognized space 'UNKNOWN'\n", + "INFO:nimare.extract.extract:Downloading chunk 1 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 2 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 3 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 4 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 5 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 6 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 7 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 8 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 9 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 10 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 11 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 12 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 13 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 14 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 15 of 16\n", + "INFO:nimare.extract.extract:Downloading chunk 16 of 16\n" + ] + } + ], + "source": [ + "out_dir = os.path.abspath(\"../example_data/\")\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "files = fetch_neurosynth(\n", + " data_dir=out_dir,\n", + " version=\"7\",\n", + " overwrite=False,\n", + " source=\"abstract\",\n", + " vocab=\"terms\",\n", + ")\n", + "# Note that the files are saved to a new folder within \"out_dir\" named \"neurosynth\".\n", + "neurosynth_db = files[0]\n", + "\n", + "neurosynth_dset = convert_neurosynth_to_dataset(\n", + " coordinates_file=neurosynth_db[\"coordinates\"],\n", + " metadata_file=neurosynth_db[\"metadata\"],\n", + " annotations_files=neurosynth_db[\"features\"],\n", + ")\n", + "neurosynth_dset.save(os.path.join(out_dir, \"neurosynth_dataset.pkl.gz\"))\n", + "\n", + "neurosynth_dset = download_abstracts(neurosynth_dset, \"example@example.edu\")\n", + "neurosynth_dset.save(os.path.join(out_dir, \"neurosynth_dataset_with_abstracts.pkl.gz\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For term-based meta-analyses, we split the whole Neurosynth dataset into two subsets,\n", + "one including all studies in the Neurosynth database whose abstracts include the term\n", + "at least once, the other including all the remaining studies. Here, we will conduct\n", + "meta-analyses based on the term \"pain\", and explore the spatial convergence between\n", + "pain studies and other fMRI studies.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# extract study_id for pain dataset and non-pain dataset\n", + "all_study_id = neurosynth_dset.annotations[\"id\"]\n", + "pain_study_id = neurosynth_dset.get_studies_by_label(labels=[\"terms_abstract_tfidf__pain\"])\n", + "non_pain_study_id = list(set(list(all_study_id)) - set(pain_study_id)) # 13855 studies\n", + "# add an additional column for group\n", + "neurosynth_dset.annotations.loc[all_study_id.isin(pain_study_id), \"group\"] = \"pain\"\n", + "neurosynth_dset.annotations.loc[all_study_id.isin(non_pain_study_id), \"group\"] = \"non_pain\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Estimation of group-specific spatial intensity functions\n", + "Now we are going to run CBMR framework on the Neurosynth Dataset and estimate\n", + "spatial intensity functions for both pain studies and non-pain fMRI studies.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.diagnostics:17510/507891 coordinates fall outside of the mask. Removing them.\n" + ] + } + ], + "source": [ + "from nimare.meta.cbmr import CBMREstimator\n", + "\n", + "cbmr = CBMREstimator(\n", + " group_categories=\"group\",\n", + " moderators=None,\n", + " spline_spacing=10, # a reasonable choice is 10 or 5, 100 is for speed\n", + " model=models.PoissonEstimator,\n", + " penalty=False,\n", + " lr=1,\n", + " tol=1e-9, # a reasonable choice is 1e-2, 1e3 is for speed\n", + " device=\"cpu\", # \"cuda\" if you have GPU\n", + ")\n", + "results = cbmr.fit(dataset=neurosynth_dset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fitted the model, we can plot the spatial intensity maps.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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n4r777quT0diKoiiKojRvrrrqKlx11VVxv6NboZeRI0diyZIlVW7znHPOwTnnnBP4/ahRo/aLGUh9ErISLPGSJUswePDgui6P0gRYvHhxg758KYqiKIqi1AdFRUXIy8vDleGuSA9VH4G+16rEI5XrsXPnTuTm5tZ4fw2muCuKoiiKoihKUyASCiGSgNVkBLWzo2yQwamKoiiK0pyZOXMmQqEQPv/884YuitJE4T3Gv5SUFHTs2BHnnXceVq1a1dDFU/YRVdwVRVEURVGaKDNmzEDfvn1RWlqKjz/+GH/+858xf/58rFixAq1atWro4jUZIiHzV+1ytdyPNtwVRVEURVGaKAMGDMCQIUMAmMGY0WgUU6ZMwWuvvZZQpnulcaENd0VRFEVRlGYCG/E///xzA5ekaaEx7oqiKIqiKMp+Zc2aNQDiZ3VXGj+quCuKoiiKojRRotEoKioqnBj3P/3pTzjmmGN8Gd2V2qMx7oqiKIqiKEqtOPLII32f+/Xrh9dffx0pKdoETEY0VEZRFEVRFKWJ8vTTT+Ozzz7D+++/jyuuuALLly/H+eef39DFanIwxj2Rv9qQ8OtWfn4+MjIyUFpaWqsdKk2bjIwM5OfnN3QxFEVRFEWBUdg5IPXYY49FNBrFk08+iZdffhnnnHNOA5dOqSkJN9y7du2KlStXoqCgoC7LoyQ5+fn56Nq1a0MXQ1EURVGUONx777145ZVXcOutt+Kss85COKzBF/uDEBILY6md3l7DGPeuXbtqo0xRFEVR9hPvv/8+1q5dGzP/1FNPRVZWVv0XSGnytGrVCpMmTcKNN96I559/Hr/5zW8aukhNgvqyg9SRCYqiKIrSQPzxj3+MO3/NmjXo3r17/RZGaTZcc801ePDBB3H77bfj/PPPRyRSW68Tpb4IWZZlNXQhFEVRFEVRFCXZKCoqQl5eHqZk9kRGqPpgmVKrEv9X8gN27tyJ3NzcGu9PA5sURVEURVEUJQnQUBlFURRFUeqNWbNmAQDatGkDAMjMzPR9z0CA4uJiAMCYMWMS3vbrr78OAMjOzgYAhETMcUlJCQBg27ZtAICLLrqoRmVXlCBMAqZEYtxrhyruiqIoiqIoipIEaIy7oiiKoij7ndmzZwMAOnToAABIT08HAMd+kFOq4pWVlb71+ZnTpUuXAgCuvPJKZ5lHHnkEADBw4MC42yb8zCaP3PbevXsBAJs3bwYAjBs3rkbHqjRfGOP+5+yeyAhVr6eXWlH8b7HGuCuKoiiKoihKk0Zj3BVFURRFqTUPPPAAADd2vUePHgCAtLQ033K0HmQcempqKgBXDSeMcS8qKgIAdOvWDQBw2223OcscccQRvnW5TU4JVf3y8nLftqPRqK8MzFXz/PPPA3Bj4a+55poqj11R6svHXRV3RVEURVEURUkCVHFXFEVRFKVKXnnlFQBAu3btALgKtTcuvWPHjr51qHJzSnWb61RUVAAAcnJyAAApKaZJUlpaCiA2Bp4x8lzeO4/LcB1uKyMjw7cvuspQeSfsBeB22EvAY1q4cKGzLPfBbWzZsgUAcPbZZ0NpvoRDxlmm2uVqu59arl9rZs6ciVAohM8//7yhi6I0QXh/8S8lJQUdO3bEeeedh1WrVjV08RRFURRFaQIwVCaRv9qgirvSLJgxYwb69u2L0tJSfPzxx/jzn/+M+fPnY8WKFWjVqlVDF09RFKVR8fLLLwMA8vLyALix31SbqVBTRQdc95iffvoJgKtuExnDThWcKje3uWfPHgCxyjtVcK83O+dxGa4j4+hZTu6TU8LvWWb2CnTq1AmAq+x7ty3j4ufNmwcA2LlzJwDgnHPOgaLsb7ThrjQLBgwYgCFDhgAARo0ahWg0iilTpuC1117DJZdc0sClUxRFURQlmYkkGCpT2wRM2nBXmiVsxP/8888NXBJFUZTGw4IFCwC46rlUu6kyc0p1HHDjyrks1Wsuy++pZnM5qtlUwemp7lXzgfh+7zIzKteR2+A+uE+q/zw+GQPP5VhmTgEgKysLgBvjzinVfWaC5bkcOXIkFGV/oQ13pVmyZs0aAEDv3r0buCSKoiiKoiQ7qrgryn4kGo2ioqLCiXH/05/+hGOOOQZnnHFGQxdNURSlwaFrCl1YqBpTTZZZTalUe2O/y8rKALhx8fRKJ1KR5/gixowzPp37pFouVXX52QvX4TaopLOc3CcVeZaZy/E4eQwsm/c4ZVZWrsNl2MNA9Z7n9qijjgost6IkijbclWbBkUce6fvcr18/vP76677uT0VRFEVRlH1BEzApyn7k6aefxmeffYb3338fV1xxBZYvX47zzz+/oYulKIqiKIqSMCo3Ks2Cfv36OQNSjz32WESjUTz55JN4+eWX1bJLUZRmy+uvvw4AaN++PQB3gGWLFi0AALt27QIQG0pCGBbiXZfLMqSEU36fn58PwA0t4TYZvsKBowyJ4Wf2kDJ8xTsvaB1uk6E/DAViYqWCggIAbsgMj5vhPCyz9zgJyy0TRHEbPO7du3cDcM/1mDFjYralJD8RJBjjblW/TFWo4q40S+699160atUKt956a8wPkaIoiqIoSmOk0Sju77//PtauXRsz/9RTT3WslxRlf9GqVStMmjQJN954I55//nn85je/aegiKYqi1Ds5OTkAYm0RqVi3adMGgN/2EXAVaO9ATSrPVME52JQqd7t27QC4irlUxbdv3w7AHVgqt1tZWYnBpd/jsEPbwKq0rSJ3L0cobCvwdlmWpPdyBBluk4p7kPIuB8jyezmgluWIB9sqPB7Z88BzrTRNwgnGuIebSubUP/7xj3Hnr1mzBt27d6/fwijNgmuuuQYPPvggbr/9dpx//vm+7ldFURRFUZRESdgOsnbtdoQsmYNYURRFUZQmzZtvvgnAVYmlwsz5bdu29c1PxIqRy1Bp5rr8zGYHl1+/fj0AoLCwEEMrzf/RHVvNtKgQAFC2yyj+leV2rH3UVb0jqXbse4adNMmeRnJMrHkow8Sph7PN55KBpzlCjUzqJGP5WdaqhB0uw3W3bjVlZy8F51PN5/zTTjstcJtK8lBUVIS8vDxMb9MbWeHqBcA9lVFcuu077Ny5E7m5uTXeX6NR3BVFURRFURQlGUnYDrKphMooiqIoilK3fPTRRwBcZxeqwTKunGo4lWfGtFOhrkp5TxQq0EVFRTi87AdTnp+N4r57o1GtS7bsAADsLTTOLBWlZTHboeKekm16DdJbmljyjFYtfJ9Tck1SpYxP/gEAKB/uWgKzN8Aby14dVNo5pTMNz508t9w2ezN4LY4++uiE96ko2nBXFEVRFEVRlFpQXzHu2nBXFEVRlCbOW2+9BQBo2bIlBhZ+BQD4rtORjkrMKZVnepJTJQ5S2r2uMkQuQwVaDqmjR3xZWRmiu4yyXrptJwBXad+9cZv5vMOo8+XFsYp7SobpDUjNLjH7s1V5GQ+fyeXtaeqi2Wabw8Y5ZZax7PGGAUr/dsJzxXNXUmLKw94Kfs8pY915bU4++eSYfSmKRBvuiqIoiqIoilILNMZdURRFUZT9Aj3EvRlAMzMzkZdn4r4Zj00Vmdk/q3OT8XqaJ6JWe+fvz3j5/UVQ2dhrEA/pX0/veyrrMvMrezcY+67+7kpN0Ia7oiiKojQjvm07eJ9s6OqUCtOIdcJbnDAX2zqyLOr7bEW9LwVm3UianUTKDpVxQmbKKuxtmGmEyZs0a7ayHwmHQgklV2oyCZgURVEURdm/PPjggwCA/v37A3DjrxlfTocTqr7MLCpdZPYF6YXObfEzy8J9NhShUMgpE8u8Lwn55PgAfmas++7dxhmHse3cFxV4Xqurr7665gehNBu04a4oiqIoSsNiJ65xkijR2jHXTC17gGlFqV+JB4BImlk3JcO2hWQCJnsaioT9UybJiTOwVlH2lVAkhFC4+hfd2oaGacNdURRFUZoo7dq1AwAUFxcDcOOsZfw1VeLs7Gzf+lKJrspVJigOPKihwvl5eXnApkSOpu4IcsKJR6UIsZE9CoTnhrHsvAb8TKWd83mtlOQkHAkhnEDDXUNlFEVRFEVJakJpRllPa2FCd7LatgLgJldKa2HCSyqE1SPgKulhe9nUbG7LvISk5WbZ8zN9+wqlJJ5sSVEaC9pwVxRFUZQmxj/+YbKDdurUCYCrtO/duxeAG3dNVZgx3TIOneow47IJ49K9mUZrGhfO5atStxsL3jJSKec5JBwvIMcHyOOU575t27YAXGcfXruxY8fu56NQ6pRIGKFEwq9C8d2WEkUDvBRFURRFaVDC2S0Qzm6BSF4bRPLaILNDPjI75CO7c1tkd26LFl3bo0XX9sjt0dH8dXf/WnRpjxZd2iOnc1vkdG6L7A5tkN2hDTLa5CKjTS7SW7ZAessWCGXZf5nZCGVmY++QX6H8iLMa+tCTnocffhg9evRARkYGBg8ejA8//LDK5RcsWIDBgwcjIyMDPXv2xKOPPhqzzCuvvIL+/fsjPT0d/fv3x5w5c2q831AoFPdv6tSpzjKjRo2K+f68887bxzNRP6jiriiKoihNDNo9Uv2lQs5YdsL50tmF6nBRUREAYOdOk9GU8d3cDj3LvduQ6r2E81k29gLUN1UNEmSPg1TJAWDbNpPNleWmck7FvGXLlr75PC/ymhCeL+6j0Vl1VsHs2bNx/fXX4+GHH8bw4cPx2GOP4ZRTTsGyZcvQtWvXmOXXrFmDU089FePHj8ezzz6Ljz/+GFdddRXatm2Ls88+GwCwaNEijBs3DnfccQfOPPNMzJkzB2PHjsVHH32EoUOHJrzfTZv8Ayf+/e9/47LLLnP2Q8aPH4/bb7/d+byvLkehcAihSAKDU1G7GPeQFZQhQVEURVGUpOStt94CALRqZWLF2RiRYSmczwY4G5VsoBcWFgLYvw13NjvY8F23bh0AYPv27c4+RoZ+NMvuLTXTMntq+71b5WWBxx7i/hnLzmmmiXnfPfB0p/zVlbEmDXcms5INd4YbcT0ZYiMb7jt27AAAnHzyyYHH2FgYOnQoBg0ahEceecSZ169fP/zqV7/CXXfdFbP8H//4R/zzn//E8uXLnXkTJkzAl19+iUWLFgEAxo0bh6KiIvz73/92ljn55JPRqlUrvPDCC/u0XwD41a9+hV27duG9995z5o0aNQoDBw7EtGnT9u0EwLzc5uXl4ZWehyA7gXCx4mgUZ//wFXbu3LlPL2mquCuKoihKE4ONRk7pFsMGKxvocjnpvU44n41QfmaDPt42ZaNYOtNwecaG8yUhEokAFagzvIo3yxQWsck8fqro3uNkA5uNeqmgSycebpsvOfJc8gVAvgg0dsrKyrB48WLcdNNNvvknnngiFi5cGHedRYsW4cQTT/TNO+mkk/DUU0+hvLwcqampWLRoEW644YaYZdi43pf9/vzzz/jXv/6FWbNmxXz33HPP4dlnn0X79u1xyimnYMqUKU7m4JoQjoQQTkBxD9dScdeGu6IoiqIojYqFKT2RkZEBmLxQGFz2AwBXcWemVQCwgmwoOUjUdo/5oePhzmDd+ibny38BAIoOPqVB9l8XFBQUIBqNon379r757du3x+bNm+Ous3nz5rjLV1RUoKCgAB07dgxchtvcl/3OmjULLVq0wFln+cc0XHDBBejRowc6dOiAb775BpMmTcKXX36JefPmVX8CGghtuDcAHGTBNzo54lwqH+w+rMkIc45Kb926ddxtyn0yi96ZZ55Z4+NRlGTixRdfBBDbFS5DCIKyPrIuXXTRRXVfWEWpAQ888IDzf69evQC4qi7VbH7mfcyMqTJURsZn00mFU0K3GiBYpZffSyWev1MsY1wlOzgyJmG4n3hlkmWV33uPU44ToLMOzxXPnVTtAWDr1q1OBlXuk8fJa8PlvdfzmmuuqeboGg55PS3LqnL8QLzl5fxEtlmT/U6fPh0XXHCBeRn0MH78eOf/AQMG4MADD8SQIUOwZMkSDBo0KPAY4hEKJ+YqE6plhLo23BVFURRFadR8ld3HJCpKcWPAATeGXyaOYtgJRaqePXvWU0n9ZP33ZQBApR2T32PNAgDA120HN0h59if5+fmIRCIxKveWLVti1HDSoUOHuMunpKQ4L49By3CbNd3vhx9+iJUrV2L27NnVHtOgQYOQmpqKVatW1bjhXl9ow11RFEVRmgBetVH2sjIumw1aqaBzOWbvpMJMdZle41KZ9u7Tq0p7v+M0qBeLinPnzp0BuE42nM+yx4tHl6o1VW+q19LJRfrUy540zueU63HAKeAOQiUypj+e0i7Ztm2bo9RLBT9ojEBjIy0tDYMHD8a8efN8vfXz5s3DmDFj4q4zbNgwvPHGG75577zzDoYMGeKc62HDhmHevHm+OPd33nkHRx111D7t96mnnsLgwYNx6KGHVntM3377LcrLy9GxY8dql5VojHsTgOEqrPDskuzSpQuA2AeEfAARdu/Nnz8fAHDssccG7pPLHHDAAb5tE9lNygcDy8iBHezKY9elJoJQkg26D5SWGjcK2WiQUyJDZoKMt7xuBvLH/3/+539qVXZFUZIbKu3RXaZ3gO44dLw5tlUEKAc+Tu3VMAXcT0ycOBEXXnghhgwZgmHDhuHxxx/H+vXrMWHCBADApEmTsHHjRjz99NMAjIPMgw8+iIkTJ2L8+PFYtGgRnnrqKed5DQDXXXcdjjnmGNxzzz0YM2YMXn/9dbz77rv46KOPEt4vKSoqwksvvYS//vWvMWVfvXo1nnvuOZx66qnIz8/HsmXL8Lvf/Q6HHXYYhg8fXuNzEYrUjx2kNtwVRVEURVGUGjNu3Dhs27YNt99+OzZt2oQBAwZg7ty56NatGwDjpb5+/Xpn+R49emDu3Lm44YYb8NBDD6FTp074+9//7vNWP+qoo/Diiy/illtuweTJk9GrVy/Mnj3b8XBPZL/kxRdfhGVZOP/882PKnpaWhvfeew/3338/du/ejS5duuCXv/wlpkyZUuMswPWJ+rjXAfQIZRcd1TgqeexO5FR2h8nuRnZlcv1ly5YBcFVxwFXz+/fvD8CN6/OmowZiu+5klx6nXJ/fs+vy+OOPDzxuRWkonn32WQD+gXMMCZAKOutXUPe2HHwne8SqeqBLFT/Iak/WL5bhyiuvrPpAFaUKHnzwQef/fv36AXCtFuWzfM+ePQBMPDHghmswRjgzMxPZX/zTbIze6XZdomqMFLq2mOmeQ38JILaO8P7m74vsoWIdZY+wDN9hTDt90GmmALgmD4yPZsw7t83fQPZks2yyB47PhaAeOO98WW+93/GcRXdsAQDsLTDmEuXFRnGPpJr9ZHbIBwB8mHdYTFuB18brdX711VdDaZzQx/3NgUOQHaleDy+OVuC0pZ/vs497cpiFKoqiKIqiKEozR0Nl9hNvvvmm878c3CMz0UnbRyoC8jPf4qkQUCnhICFvQgg5cIgKvMwOJ5UMfpbWXzLjG1UN73Gedtpp1ZwVRakbnnnmGQCugsf7lPHsQKzqLbM5BinuRPZOyZ4x71gU2XMlVX7Zk8XyEpaF9m9S0fP2wnEbGkevSGRvERDb48ueKGlH7L3H8r+ZCwCoLDa/OY53eqV9X6f4/dGZmTRz8WvONkoG/ypQ3Za9z4T1gHWL9Zn1Ra7vnSeXkbaWhGVhnZS9YfJ8xbOJ5LqyVy89PR2Z9jkr215ozsMW01tQVmRU9HCa/5hH5a0HioDPc/oH9rYryUF9DU5VxV1RFEVRFEVRkgBV3GsJYwoZWw4Ep3OWKjeXk4qHjH+VxIuxDYq7lSojy0TFQO5Tqv+MtePyPBbvsWvsnVJXUFmnmiaTJUmF26uOBSVYCqoT1SltQfXVuy8ZDy+3Ie3sguzepH2eV/1n+Vj/WA7ppqA0PyZOnOj8P3euUc3Z6yp7eeh77lWoe6xZAGwAokUmlrysqBgAUFlux6ZH7DEbdpx2arbpoXWUeA9U3/ce7maqlD2/QbHwRMa8V6W4cxmuw0Q7Qcl+uDx7meX3sg5TXQeAn3/+2TevsrISQ3absWdl2wsAAHs2m3O4e6P5XLrD/JZG0vzHnNnOxMKXpx/ozON2vddTafyEQiGEwgm4ylSq4q4oiqIoiqIoTR5V3BNkxowZAFz1QirRxcXFzrKML2fSCCpiVKtlTJ10mZHIuHQZP+udJ1V9r0Je1T5YJn7P4+MxUIXwHieP/cknn/Tti2rBJZdcEndfihIEFXYZ2yrVwqCY2XhIJV3Gtkq1XG5LOsFIxb4q5DJcVz4Dgo6rqn3IuHqvowigPWHNHY6Jkoq7vAdDoRAO3fElUAiUbfkJAFCyzaxbUWwnPoradSDN9E6lZvvTxjujPcKumkznmYzFr5ttDjrD+Y5l4G+FdD8jUv1mffCq30HJnYK2FaT2B7nJcEpHG8CfzGq4ZawOozvN93u2FAJwlfad68znPQXxFfesdsYFZ2+rvc4x8NopyUU4EkY4Ur0eHrZqp5mr4q4oiqIoiqIoSYAq7gFMnz4dABwz/8MOOwxArB/tqlWrAJgkA4SxdRy1T5WasbRUQGS8q4x75Vs93/hl+mivQiC/k764jOOTnrVy31QyWGZuh7653uOk/++BBx7o2yb3QT/7devWAQAuvfRSKEo8Zs2aBcC952Uvk1TcWP+qy4KaCNKnWbrRkKoyrEqVXpYzqL7J5aSv9YSDjRpniRjiEJVNe/knvimMKf/9998PwFUWVYFvXjDPhxzHRCoqKjCk1Px+lW4wz+hd603sdqmtuMvY9rQW/h5cxrrTmzyc4rqgWOW2Dk/HsqXGkWzPob90fpdYr/mbKX/rpCsN2b59u/M/U9PLnjHZW8W6JZ3UqPbLfbAsXJ690PxuaPkPZj1baS/dasrE2PaiDcb1bccPhebzTtOLmCZioLM7bAUAHNNzJbAXmFve2ZejRUkeEs6camnmVEVRFEVRFEVpMOqr4a6ZUwVU/rp37w4AyMvLAxCrlEn/aGYz9dKpUycA7uh9Zshq2bJl3G0SvvlT5ZYq5CFF39oLRr0rAXAz28WM8rcVOpnxbnv/k3z7LCwsBODG2FF5/+mnn2KOj0467EmgsiNVUvrNr127FgBw0UUXxWxLaZ489dRTANyeHtYreQ9JNU3Gq8s4VyA4u2GQMheUGVXGBAfF43qRKr7cF7fB5S7pZeqQtdeocvTNtipsBZPHxXrN+kw/bdtHO5Rupo8tL455rkhPeHWhaR48/vjjANx7jvdBdnY2Tij8AgCwY7mtuG80qnHpDnMfshGSlm3us8x8M34ro435LctsY34f01ua+RHPmKpQhvnf8XpP9Xu/U4n/Oqev8zvL7KcyL8PatWtxpPWjWS+Or7rskZL1xCmT/fnr3P7OeeDvNFV/OU6spKQEndcvdPdVZspUuasQALC3wM7m+qPprdix0vxWbl1mYtx/3GTU84Iy29XK3m8v+5x2H9EFAND1BNOr/1Koh+ZoSDKYOXXeiKOQHTCO0EtxRQVGf7hwnzOnquKuKIqiKIqiKLWgvganasPd5pVXXgEA/OIXvwDgxoRTnWJcOpUzxrtRUWOcHeCq0xyFTqWDqqJ0cCFUAFotewcA0NpW26QKF6UKV+E6bVBpt6LCe9q+iUKptuohFLrWdoa87QNO9ZWRigePgcdJdcJbfumaIzP08Vzy3PJcn3322VCaF08//TQAV0mTCnuQQ4SMP61JbLuMP5dx5EHuErIssr56vdWDXGDk/Egk4qjrAFDJsSR2PSu3XTwqis35idoxxpVl/iyuzL6YkmHqNV0+Iraj1RV9TGz8zLWVMbG9LPfDDz/s2+ZVV10FpenB6y5ju/fs2eOxg2k4KioqHF90/u5QeffV/9pFF8TsUzqo8beOPeLsSQ7KkVJXaLZUpTq04a4oiqIozYjf5poBkVsWrzHTr43YtNsO64iW2S+rmabRmt0u2/5smgwcpEqbSMcu0huyQmHJDk+xqDOJl/CBZd+48zkm0zgsIg9GlGoPwFkrTrKnmFAZAUNkLHs6sOwru2xhYLtnwXAE2OzZR2Ul8gBUlruN6cpS87JdvtOEk5ZsLQQA7NlkNrTLPoc7tpoXgh9LzIvy1r1mylAZDlJtuc6EkrbeaK7J5cf0Rum7xn464wS1VE4qEoxxhw5OrR1vvfUWAKBz586++TKTKD/zLZxx64zf9mZfa926NQA3XpzKs8ygKjOk5ix5DQAQLTEPBqvUVPwoPeNLzcMjyqlHhQtS2iO2MhehMpdFZc5eLtU/ol5miePbP4/Je5zeWEkgVpGUPQ1cjlOe+5NPPhlK02XmzJnO/3K8hsxeKtVxGSvOz1QNWYekmhgP6eQix2RUl8VU+r4nskw4HMYVA1qa/RQXAYg66jrgZqbcW7jbnppxLeW24i6VdyKV9gw71jijjTkP6fZyF/c0yuWz62PHB8heDCrw3vNw5ZVXxhyrkhw88sgjvs++Hpeah9UqitJIaPYNd0VRFEVpDkzoFQEQwZaPjcr902dG2t70nVGLOYAy1X55a5tuXvZSMuxwyHwjPkmhKB6OCu4o7/ZLeIVYUA42tdezPKp3vO8TwlHaxUBtx041IAxGlIHhqgBQtssIaXt3mJft4k324NRN5qV7109m/s97zbpU2svt9/1y+6V+Y4k5Lz02mZf3PZvNNYhu25z48SmNinAohHC4ejU9nEDyvqpodg33l156CYCrINOLPEgxk/P5mTHvVO2oGAKuEw0VLW8sbLx95Cx9wyxvK+3OaHVHhTNTqnNU4yrLYxV3R2kXilxaC6Nyh+hkk8VR9/GdbVhmjnjmMXmPU8b/S196qaZKv1yO4qffO715zz33XCjJD5V2rydxUEy6VNrlclIdlo5MvMfkcl7kdzKGVar5UtUPGpsSr/yRSASX97a9qW2f54pCc3+zPgPAni07ALi+2XsKTB2nq8feItNbFS33b182pFp0tHvnykxjgPU83XbwsKwcZ12ZtVU63nivDVVbVd6TD/62ERNHvjf+woqiJA3NruGuKIqiKM2RXatM0qAtX24AACwaMwYHH3wwlpxhBiaXRM1LW16q/dJqC4M5pUbIiZaJF+xopZi639MIwY0XF+sKRd2y48idbTF8U+wjEULC2YNOHyGpvIvl5L4ZjsrwVMAVzkq3mTDZvYX+l+3du82yO+2X7fKAcfO7K8w+tu0xL9sdNpmXdyZyAoDsoANUGiWhSDjmnoq7XKW6yiQE46nbt28PwM1qKr2ZgzK1BWVVZMw3XTIAV63O8njaArHZ4Bxli91xtj8sFTmqcRz8UlJgK/BFdqxiqesqQ8Kpdmx+jjmerDa2l659M6W3MsobuwrpLiNVN+kYw++9aiOPXbp0SDWVirxcTsbPUwHV2Pfkht7sdGvwqtFBirjs2QpSwaXbjLxf6UpR1b7k90HKOknEv91bfy4/wO5ZsnvOynYIVX2zG+O+e1OhPWU3u2kE7Cg0z4Id9o8/G1QkN8WUqf0Gsx4bDYyFZ0OFsfAXdskGYMo4Y038LK9B4wwA4NFHHzXbtc+F+kw3LtiT7HU3y8/PB+Be38zMTOCb5b710tLSqhwXojQsH330kfM/s5Zrj7TSbBruiqIoitLcGPTzJ87/G1ZvBOAmB9o8/35sBrCj3P+SS2W9gx2SRZcZq9IWsKp5KQbgKukB8ylYVdrhXY71qT2NUfMT2adNkLKeiBpqylThK5NXcWfIqhPrboezlRXbtpv2OYoGKO0SLl+22046ZSv5ADB4x1IAwOJWAxPbmNKghCMhhBNwlQlXaox7lcyfPx+Aq0RIxbxPwVIAwIZuwwHEKu5SlSNUMaiqM8spEKxSByl6DvYgHrcrzih0u+0utF0/UV3zPyi8pGXbSnu+UcMjdpdnRht/nD2V9qKDTOZUlohlpErO3gOpwHuPXbpxBMW4y9jh7us/8q23ot0QAG48Pa/dscceG3OcSuPjySefBOD2Pkk1HIgd70FYz6j2yt4b6fwSNPbEOwaDbkaSoEzFQRlT5TMhXpx+ZWUlJvQ3vVlU2iuKzA8w63GJ3YNGlR0AdtpWcIX2dF2xeQast7vPiyqqbqzk7zFl62c3tBxbvlS6SZl63MLT+3dpP+OgNX25eZ7Ic+g9P/KYeV2YjVOV94Zl+vTpAIDevXs3cEmUuoZtGF7zSy+9tCGLo8QhlKAdZEgb7oqiKIqixGNvgTc0y7xEbrFFH6m071eC3F+qUdqpdgfFuFcV6y4VdeezYylfteIu98UyVZS6IkClLK+Q1unPnmOHs2XbDblisVyeHdaaaX/P460ojfWMVxQvTbLh/tprrzn/d+/eHYCrJuXYmQU7rTDZSZmFtPMq42xSYGcQlYo7keoy1ThvPDsVR+mmQiVZujeEUvyxs87DYo+tvO8w3tfFW0zXXMEuU+biOA+wVuV0l7F7BPIzfd+HbSUulJ4R97hYRqqV8ni9xylj9eWyVFql0t7hu3fNcdrKJDPC9tlrFPhV3Y4xx9LKZH/0Xs9f/epXMcesNCyzZs0C4B/nAcTGjnvnScclOf5BIpV26ewSL8Y9qJdMTklQ9lXO57Zl78D/G2hiiY1PuztWhT1nzIbKrnXGowPA7p/ND/MGW2FfucvOXJxgNzvt+z7eZvZxpL1eqp0oJ4WuUrluvQ1nmCFvkYh/XA/xPvOCxhTw2B977DGzP/tZpypg/dKlSxcA7vXh2C0guMdJST5ycnKcXn1ec6XxoYNTFUVRFEWpFd74bCrJzN6ZYavDpZX+N0Wqxfw+kma/hKfGd2ehWuxVw0PihTBR/3U2fGoS0+7snyF1tFwW4pZVTaNKxtPTJce7Hary3EckzTaEsMNUc3LMS3h7+5wycoIuMjz3re31WuXQutlMfQ2/ilgDCkVpdg33zmv+A8DtgqLaawUlYqgHig4+BQCQtc3ErsmHYrmdMrnUnjLudbcn/pXdcxlh4Xdtd8e5vu5Ggd912Jj9dwA1xKJyafvWs2uQ0cl9tn/lLLu85YB6LZui1BRLJmsJ6DXgj7/XUq/CttnbafeUJaq0S1jrP9lulPfM5SY8go0Br+LO7Mm/7dkdAPDcujoMl1DqBPZ09OvXD4Db4+RV3OPlG1CSkxYtWjhZpzn+jPfAFVdc0WDlUvyEI0hwcGrt9tOkavYTTzwBABgyZEjMd9nZiTmissuXXY/syg/qwmd3vPeBSRs8+TDlVHbJV2dbVxfIQbeyq5VhAeyei/fDUF14gxzQGpRSvjq6devm/M9rPH78+H3alrL/4f0uiRduJsOmZFIu3iNBIWpym1w/3r0VZHEqkzUFJXeSxyFpKjZ6Qec23jIyZEY+J2fMmAEAuOSSS+qwxEoiHGgbL+z2KLh8kctvYaZdy/zZPSkAtU03daW1UJOpLgfhVcn5Ahv01KeyHHbCBsw+K2GHzznNEzuWXO7Lq+6H/Sp9kFovFflE8argtFjl/tNz/S44hOcq13adKRWKe2a2eQ616GTCdxnWmprlCW9NcQfbKwppUg33hKDC3gi7oOj0kpJpHgyMR2e8amrE370Z9bzZyQEx6blmGxl55u08Lde8uIRzWtZZ+ROFXahyAFClHSPdGK+NogTh5ESwx6pw/Egkw+5Jsnu5GG+ebtdJwG0QZe6OdYjaF9h0YMx71tdbzT5z3X2yYZDrjFdpu1/2rSiK0pwJhUMIhRNwlUlgmapoUg33Aw44AIBfCWOSIJkMqTqqSm8OxFrIeZOzMPEFycgwP9hBg98aI23atAEQ21PhPc6gdPTSBlOq+jUhKyvL6SIE3GusNDxMtES1Vare3oGiRCru8SwIgVglXtq4ygGmVfVasWuZUz4T5ADZoAGY0gqR7GsPUmOjul5FIPhY5fXkNVflvW6R9sbyWQsAm7ofDQBovWalMy+rXS4AoGU305Pax3Y6aV/ir6sUgHLamd9NvvhRRU7UD92HCEflHcUthez4ATq/MGupo8xLoSfqVfdFLHs1vdhByrv0e7f4vPHE60fD4bjLuknPzLXgOWOiRHrh0zhCWjdndzCJCJkkEXDFvHA47FxjJi70JttSmhdNquFeI1j5qJYJl5WGIJxpGsmpLeyHZUtTgTNamYypOe1Nha3cZH+Oel5Q7IdBdptMe1mzrcy2Lc20jXlgh1u0rKviJ4zzMLJfaMLl/gd0ooOYFKUx8MSyIqSkpOCS3v6XXNnJXRknmQsdo9rbmVK/t5X3fY11d7ZrD4z771YzjiT96y3Od+m5fqeZC7uZcs/c0HDjfBRFUZKdcDhcreUoAISj+/Di66FJNNyp/B188MEA/OqQVIKqg/G6jIOVSVikOhdPRaa6LxU8qQoGKdb1gYxxD0qiRHUy3jHIcyDVH36mQsBr0LIG5QyHw76ysQeA1/yyyy6rwdaU/Ql7QqjUyjEN8e5vaa0oe7Zksi6uy+XlPRWU3MuLrMOE68ryyh4jaf8oyw6wzvvtMJMNGbcOuOeCx85l5PgB2TOiMe91S+vWRp2V9cd77Zz7u2VrZ15OZxMWxZdHxrzn2halHDhNZZ2qcUYrO4zTHtgcTrPHLSWivEvjB9YbKZ7xTdcWbhgqKhV2R+kuc5O5WREO/PYneAtS3kMBqrlb5ODjiogY9xTH+MEOhbPPbaY95blm+R1lPsv/Ap3dwfRyp7du6ZYj2whulmXFPCN5DyjNjybRcFcURVEURVGUhiLhzKkJLFMVTaLhznhsmS4dcJPCUB1sZ4/SZrgGB5R91+EIs8Iufxpwvt1S6Q1KuV4VMj6XSBVrVcehAICeO42VW0Ybkwwpp6PpBaAawhg6Wsl55zFeLrereRvP7mjOTUprE3f/XZtDAQBtAxR2WWaJV/0OSmjD4youNt30MnHNyvyBAIAD7YQ1qQyNEYP8AHP9vNeT//OaK/UP090Tee/wM2PK410/GT8uFXWp7MoeM3mvUY3yquoyhp3lkSoxP8t9cGyKrOvcpzfhVCQSwZs7su1y5CEUCuHEPPtZY9/P8VQ8qol7i4wq18Ou09/tp8GqP+81ZV2xYZczL2ulPWDVDsWjUnhxT+PeNGO1ex5k3LvsVZG9hVxenntl/8BkZ7169QLg/j4xsaB3TJbT05TnPitbdPVn4kyzwzL3FprwS2YxdbZhq94RoSqnZJi6lEhYgINQ2GOUeCdEMtX3ORKxe7h4bzEG3vPcqRTllsq7JCg+3UlQyO/D8RV5wOv5bs4hM77S3phlkvawjJfnvqi4U2n3Xq8tvY8HYBpqvMasU0ywxXvioosuqvKYlbon4QRM+zJGxEPT8DNTFEVRFEVRlCZOUivu06ebhEWMbZdxsYCrtHPely36AwAOxTLfZ9jLBSl93HZubq7v+3jI2F65TTlfpoaPtGgFAMhqZ6sgpebNmm//e1sZ1cSbyCXFtozkINQsO14uq1M7s037LZ77qC62XcbyV9WzIOOMi4qMks7xAjJG2Nkm4/dsZYVKzNLsPu72Skp8+6bbSOfOnQG494CmWq97Zs6cCcCNLyfy3uA9xWvl/Z73Fbch6yc/y3hOTuXyVJ3ijTmRbjfcJ8sl6yPX5TY5vkPW9Xgx87y3vbH2b5e0tZ8fLVFaWoqz2xm1soXnfFCNK7MV9j4F5vjWFO+fQarkG9tLGgDa28mZMtsYpY9WsXn22JxL+5mU6tOXuyp9dT2LQR7w/KzJYvYPLVu2BBD7/K7KEWij7S6zYcMGHNHG3Fct7Gcte10cxd3+rZHKu5sl1B77leG3LHaUac994jixSKVdeJM7822l3fk9qBTKvG0THO9OdFxioiynKVeQ8k7FMyIUdh4P13cVeM8zIChZo13uyrJyX5kqpb+7vc2wfS5DGbZVc7bJkfLTAcc5zx6Ux/Z+yXYD7wml4QmFwwnlCKhpHgGJKu6KoiiKoiiKkgQkteLes2dPALFe6l7VR8bO8q11SZZRdcvtOFUZh81tMVNodb7uXuU6yHM6CH7PN+fvK48yx1cyDwCQZasGYVt1pM+r17uWI9upntEGMtLKuAissuPKW9v7qK5MQd7r8Y5TZqaUPR/MvkrFk99/kmpiao+0k7E6SotQFKi2eudxW7wHlLrj2WefBQDs3r27yuWko4j3uhF5TXmPMF5c9s7I3hwiY6el4h1v/9KRRsbFc3l+T+Vdus9weW/dKAnoseMypaWleG5TKtLS0jC2fWdnvRaMBbfjYktsd4/+u8y+v9zpKuX7C26z1UqjvGflm2cG1dfMDOa8SAvMkBqkrAc5ZfH6qPJeO+Q4DNYb3rPecRfMJ+Lt+fo8qy8KCwsxuq09L8s8fNNammd01O4l5f3oxHFLRxcRAx6J5zLDMUsipj3ms+jR4i8Me6OsijLffGc5z+8flXE37txfXln+sDgO9iA4ajjHt7B3wNNLEBijz7IIO2PZo+wcd6p/vN1nKd3RvXt3pMGtL9u3b3e2I3uwa+qYp9Q94UiCdpAa464oiqIoiqIoTZ+kVtyphlMJo2LtjffjWyrVMypGnAYpeVI1DorjlUqT9zupass4UKlKUdnr0KGD2edmczzplXSTMaqAo4ZUxiruETtOlTHtOw893WxTKIBV+dDHO86qPOelOi/PnTznMvb5i/Te5hjs65hmT+kC4s16SyWTSgTvAaXu4JgF2cMikfG28eoY1SF5L3DdoCymQWMugmLhvd/J+1Pel1xOxrSzrNVlDfUec1AvFO/TsrIyvLQ10znuM9qZ9VrYdZqxxr1sF5hldmz6/op1B4ACe2zMj3Yit9x1hQCAjDbGbYZOI5f0MD0DL25Oi6nDQVlkq+vJ47mnM9H//M//7PuBNENYF/lslG5n8dRX/p7QlaSkpAT/iXbEjz/+CCALWVlZ+JXd6xLOMYp9SqkZR2XZceVWuV+BlzgKfKrrCOb6tdMtLNX/WcTAx2yT/9ix7padWRXhqG+f5sBFOZjgJkhp51gUxrRLpT3Nr7j7EjSK41mWd5BT73k9duzYASC23bF1q6ljbJd06dIFKAfatm2LHLjPi82bNwMAdu7c6exWtjXo2sZ7QmkEJOgqA1XcFUVRFEVRFKXpk5SK+6OPPgoAGDrU+J5LlccZkQ3Xi5mxf4y35pswkU4YQd7NUqGOp0TLrIJS3ZaKu1QROd116GkAgBZfvmm2Z6sAkbLYDI2OQpBp3viLDvml2VaAYi6dbWK2JxQ0OUbAW055fDLemOqQVNqlWsrlqE5KH23AjeWU14f3xIQJE+Iej1Jz6NhDFY/XQ1536TJD4jld8FpLdZ73Y7y4eCBWHZf5FeLFwsvsx4RqV1APAteTdZ73p3SB8pZP1mE5zkVmZ32rzKihJ7Uz6lmLLkYFz+9rVLRDfzbzPy/c/1lZ1+4x17XLBrOv3F8UAgD2MH+E3dN1fteOANLw0k9h59zJayvrvszkWdVzRKmeRx55BIDb+8jrwN81OU4KcH/r+Dxl7gsqtL/4xS8AAOvXrwfyg13SlKqJRCJOe0L2NsrnFscJde3aFYD7TOG1oYrOa+ftQSksLAQQ+1vOZwnvkSuvvHL/HZxSI0LhBH3ca+kqk5QNd0VRFEVR9g/Pro3iwAMPBFJNY/PozI0AAMsWiZxQGTt0htaHMQMvvYRFKEx1ITJcvnLfX+rk4NQgYgbZcsrQGBEiQ2EMAL7K7uM0qBl+pCj1SVI23PmGKZVdvq16kXHSXJdKBafSpUIqe/HUX+++vUhVXqr2QWo2HwZSdVzeZiAAoP/Ob83yFbFxjAvDxqGFTgLRjebB26lTJ99y3IfsFQg6Lqmme483KO6f54rHy2sQdI65PKdcnnh7R3iNpftIVb76Ss149dVXAbiqXpCKTGR9lM5L3usuHVp4baXTC3vNZA+RrI+cxsvUKe9xmfUzCFkG6Uwl7z0vpcKlisiMsdJhKWznXcjuaGJaW/Yw9bjHDyZWdrntMlMc3X/B7tvtWPfibcYRZ882c72zCo3al97SKLNh22c6EmkT8ywLUs6DMkvL68SeMkB7y6qC9zkVdT4jeU/S1YhTwL2POR6oSxfjz9+6dWsAbrw1G5/8nJubm6Qtg/qDzyH2Iku3JdZ/1g/GvPNcc31eC35mbDvX815P1iU+l+TvbbzxZ0r9Ul8+7lo9FUVRFEVxWFDRwTQm083AyQMLlgLwiEYctMoXt3gquVDhpe1jDNwGQ0ijflWftpDcd6UnSRT/p+JeGWBj6ZZFDFbloFoq7fZnKu3f5vbX8C6lWkKRcPX3OYBQpHb3UlI23FmBtm0zPsRUmakQepFZDqlUcJqXZ+I5gzKEJpI5VBKkMlXn5MIyyjhuxiTOLzXHSZXyp59+ctbt1Mnvlc638rZt2/r2GaQ2yjIFZXdN5K2e++Y5DnLXCYqvl3Gw3p4U6WXLe0AfqvsPqkNUkajoEKpJjguQUJ2qUqa5jlehGj+gpVnP1yiQ11N0STvd73YMddi+r8OmLNOX74rp4ZKZVIOmsgdM9s4Rb12Q3u/chozFl71J3OaMVWZfF3UydTy7s6nz+X2MWnrkZlMH3tsa+4zbVyjel9j/MHtrWZHt5V1sypZqO4yc2TYbaBvBs2ujMWOCZO+n7H0J6tmTvXCKnyeffBJAbD4ROc6C59lb13iuqdryXmM8NWOm+Rvx3XffAdDwj3jk5OQ4cedAcP2WPW18RrA3o3fv3r71eC14bWQ+CG/vuxxjJOscowp4z1x++eX7drBKoycpG+6KoiiKotQPq/IHxoS57dixAwfvXmEWoDjlVd6ri1UXIpBU7x0rSqG0R0tti8VSd1B8pZ2kMGor7/wcg1TeHQGANpG2Wmor76vyBzovp4pSHaEE7SATsoysgqRsuEsVhyoX53tdLhKNgQ6K165OlYvn4y7nSZVRqsN8k2a5ZWxu3759fevxrX7w4MExxymdNILUfj6MpIooeyakSuk9zqAMsYn2XlTnIS/jgb3HLstVXdyyUj1z5swB4DofyPswyJFI9qxIp4t4dcPrLOQOgBM/1HGIzT5oPnIPXj03KNeCrH/8nj07vN9knKpU2bw9EfTKplNH+/btAcTGo8Ycjz3fiXlfYWdLtjMftzzAbLeLnVF16BITA/vpjtq7zLRNN+cwM2JfJ1t5Z9iBky9ib6lvGo2Gnesnx/5IxVDWU4m31+LBBx8EAFx99dW1O7AmBGPa5W+IdDqSLj5e+B2vDa8Z79H169cDADp27AggOEs4y8I47Jr0Qicrq1evxsEHHwzAVdUBt+eXz0rWfyrrPHd8hvDcEp57WW/kevHGlPEekE42vBd0vFfTR1s7iqIoiqLUmG9z+/sS5FEQ+vrrrwEAPXr0QB/Gx1cKRV0q7PzeUdrtWHYaSNgvkvLFEnAV9so4CagAIIxUe1e2haL4PkYQiOeSoyjVEA6HE3qhre1Lb1I23Pnm36pVKwDuW2q82GmptgXFWgZ9ltsMUu3i7VNeHL4RMy572bJlAICVK1cCAIYNGwYA6N+/PwD3LVyqKPHeqOU86Y9N5Y/7XLRoEQCgT58+vn0y5k4eV7xjkudClqGm4wOC/O6955b7kJkyNXtc7WEMJ6+nzGlAVbi6OiDvlXh1g9cxLS0NqLRVRCrtVY2jkJZy3Lc9/+nVbu+bdDCR8aft2rUD4N7zVNR43DLzqsw3EK+XR6rz0uO+ugyjBX1HAwBaF78CAMjpbJS9dqX+7bT+xih7y3eZ+fRkD8JbA7tmmePobk9z2pmGV3qe7VOf6j8uNpTC9vW5+EDz3H12TblzbnjOZFbHoDEx8ZRhdcWIJTc3F4B7X1HplS4mciwXEOuMxnV5nzOW3ev9DrjXhg1yLid7O7kdOQYGALp1My5nyR5m0q1bt5jeJS+8z6U9JNVwTjlGgeeay/H5IMfnST9477Zkz7uMcfe+SClNk6RsuCuKoiiK0vhZ0+kIRCIRdP3RiEUxyruIZWdIFhX1qFTaS/jZfVmNClcZImOJw2kpVX5PQWBNpyNiwkgVpTo0xj0OjIG8/ADzFr8i9QDf91Qb4sWFVedgEkSQQ4xUFeOpRVINkTH5zJ72888/AwDef/99AMDixYsBAKNGjQLgxs1KFd1btiDlZcOGDQCADz74AACwc6fxiWbMHcvAMgXF8Mv9xDt26UAT5AVPgjJXBm3He1yEahKdETROtubMnTsXgBuvGZT1k0hlXSpAEq8y7VWkf9MrHUAEVqkdB22r5vFqZMiJaRe2bfb0me/9cfVArNJOB6nDDjsMQGzvUtA9L78n8ZaT9251PX0kxivfzoCc0T7ft1xKtlH98roYJbbjT+aaFW8xyl/pTlOPK8v9tngpGe41SMuxew5y7RwX+bZS18ZMU+19xPy4iAGI0Wg0pkch3pifqj575/PYH3jgAQDANddcg+bKK6+YHhc6pknf/yC8vWDsQZNjq+hGxmc/461lxmCqw1TWGb/N3lv2eHuvIZVjlpt1Lyi7cmMlPT09RhX3KtkyzwR7fKWLm3T/oW87v+e14HmSfvxVXW/5zJAuX7yHzj777EQPu055+OGHMXXqVGzatAkHHXQQpk2bhhEjRgQuv2DBAkycOBHffvstOnXqhBtvvDEm18Mrr7yCyZMnY/Xq1ejVqxf+/Oc/48wzz6zRfi+++GLMmjXLt87QoUPxySef7Iejrhua/ugSRVEURVEalnDYly3Vqoyav4oyWBVlqNyzB5V79qC8uNT87dqD8l17UFZk/kp37ELpjl0o21Vs/orc7yqKS1FRXIpoaRmipWWoLKtAZVkFrGilT4WXn92yRew/fxmV/cPs2bNx/fXX43//93/xxRdfYMSIETjllFOcgdGSNWvW4NRTT8WIESPwxRdf4Oabb8a1117rvIwAJtx33LhxuPDCC/Hll1/iwgsvxNixY/Hpp5/WeL8nn3wyNm3a5PxRzKopVNwT+asNSaW48423ssSoxn03/9d8QfWNip3Xhsq1OgcA7Dr0NLNMNYqQJMhdJt4bcZB/tFTMyZAhQwAAX331FQAzkh0wNx3gvt3TA/aQQw4B4PfbpVrKbdCTV45W79Wrl28bhGVi92BQvLp3fpCqKNepzr+e83O//jcAoOjgUwDEd+8g0l2B50Lj+2qO9HkOcliSeQa4HHtreA/xevHaxMtmWlFRgVnf7UE4HMZvuoqU6HEGhoVSqbDbPu0r6e1vyhzPR5n7p9J+1FFH+ZaVsfpSHZPx6lKR9+5LngO5rnQ/ku5NvNdZhu0DTgUAtP7G/IBk2sed3tLc5zmdjbLXyvZcLy8215De644jjJPMxn3mOOndqdRl2DH9ttKemp1pT23faBHzzhCHUCjk3Au8xlL9k+eFVNUTqjkZYt2I5JiJoPFDXt9vfidzDDBunhlVqY5zSniPyph3lo3b89Zv2eMi7+tkITU1Naa+e+PQeX+z106OR+O5knkbeB4KCgoAuOeQij2vt1T0gdgMzvws7xG5jcbAfffdh8suu8zxlp82bRrefvttPPLII7jrrrtiln/00UfRtWtXTJs2DQDQr18/fP755/jLX/7i9CBMmzYNo0ePxqRJkwAAkyZNwoIFCzBt2jS88MILNdpvenq64wyUDCRXbVIURVEUJenY3GME1nU6wp1RWWn+KsqBinJUlJaZvz2lqNhT6ijvVNilAl++p9T9s5flNqLlFYiWu4p7pf3nKJ52avpQJGLi2lVprzPKysqwePFinHjiib75J554IhYuXBh3nUWLFsUsf9JJJ+Hzzz93XliCluE2a7LfDz74AO3atUPv3r0xfvx4bNmypeYHCiAU8txbVf2FmpHiTpxBLOW2ukB1iwtIeykPOZ8YFTuUbtSkXYeNqbuCKnHJ/fZtAEBlsYnBpI83rxavEQCEs3PREsDG7kfXYwmV+oQquoNHcaf6/tBSo1BRTYrXC9MU2XHwLwG4Sl1Lu+6k2M/ATKpw9MKXaeHLbC/6OOnhiUwLT4U9Yk+dcQXasFEUpYYUFBQgGo06YylI+/btnZwAks2bN8ddvqKiAgUFBejYsWPgMtxmovs95ZRTcO6556Jbt25Ys2YNJk+ejOOOOw6LFy+O6TGpDh2cGof6SLJTVQiM93tvl38QsntYDt6TXVxM9LBp0yYAbtcc12MYzDfffAPAvF2St99+27dPmQqbXXfchyxDUBnlct5j4v8yIZZcJ966NYUDbuTgYJ5/ebxK4tDSTCbxqm4gpQwxIbJ7nN3I3nX8Xf/VW8bJUAw5YCze4E/eCwyRkd3PchoEy8oU8dK6DXDvT2klyW3LQWfyuSFfSBjOw7CGxkg4HHaOg9dD2rTy+GV4VVUhhjzfzXmgOc8fn8cMqWA4m7Tgreq5x8aHDOPiNaku/JPLyWewvMcB99qxvN6kRUD8UEbn5dIO7yq3w73KisxzKchdptIT/hVm+FfUHwbGl9Jwog2lsGvuwOdivPMij53nRtYDmQhRWutK691EkhOyzvE5xH3wnEvL5MZCvFDaoN+YoOXl/ES2Wd0y48aNc/4fMGAAhgwZgm7duuFf//oXzjrrrKoOKbbM2nCvHmkr5bgeCFupeINRwmnmps5e+Lz5nG1+IHcPPL3OytvcyV70IgCgYnchAPfBzAcxb+a0Fu7Dnde47fcfAAB+/MWR9VFUpR55asVuu8Eathsq0ZgXM8Wwvd/oGPGA54o/1Dt27AAAzJtnsrBaloVL+7gNKDdTrf2ctNV52VNZXVIa4wqUjunLd+2fg1MUpcmRn5+PSCQSo65v2bIlRg0nHTp0iLt8SkqKM64gaBluc1/2CxjHvW7dumHVqlWJHWADkFQN9/pQU/kmTHWNCgeVA06louQdtCmVDeJ0d9s2W9yHHGzSvXt3AG72OWnxyDLGG7giE16wDNwmG0LyDZ9lkmoqiWe1KZNEsAxUKjilitgDtYfbijdAEFDFPVG8o+blgGSZYEiqRIR1gcsF3TPe7kbpjSzVPyLvKZZBWk9KFdCrVA0YMABA4gOWpaLDnq9vv/0WAJy4R5bBO/iLyZxos8qBftw3E7CwnKz7sreDg8w5ZbI2DrAFXBs+Is8N9zV27FgAwIcffghgZ0LnYF8Ih8MxA/JZT2XvjXx2ea+XnNecB6nKZz4H37PO0eqRqqtUz4HYBGLyGR6U2E9eS2kzSOIpoEEWlFJ5B+B5Uaz0TaO2Ai+VdmfAdTRO8i7Yv8Mc9ByRU/t8MgyMfu7C2ILToqIip+yy19p7LqSaKwcPS1MHOV9em6AeZe+2OY8NWNZ32TPQWOpPWloaBg8ejHnz5vmsGufNm4cxY+KHKg8bNgxvvPGGb94777yDIUOGOM+UYcOGYd68ebjhhht8y7CHdV/2C5hn7o8//ug8y2tCOBJOqHcn4R6gALSVoyiKoiiKotQJEydOxIUXXoghQ4Zg2LBhePzxx7F+/XrHl33SpEnYuHEjnn76aQDAhAkT8OCDD2LixIkYP348Fi1ahKeeespxiwGA6667DscccwzuuecejBkzBq+//jreffddfPTRRwnvd/fu3bjttttw9tlno2PHjli7di1uvvlm5Ofnx/jBNyaSsuHOwWyO/aPIvFZJFWKXnR7aky68ssyOlbXfuFMyjBJFm7WWu8zAyNYtWgIAFkQ7AXATF/EtVyan8Krfsis7yC5RxslxOSZpkImb5Nu7V8mU9o2yDDLxg1RT5Jt/UOIY7zFQdaBqSBWRKuGRe1YAAKI7jFK5e6v5vnSHUWXK7evDWEXaz2W0cdXFHJYnPTPusUurMyUxvAo3VTt5z0glV8a2BilwQYm5vMtIO08ZAy1VV5luXcZ+x4udZuKyoPon6wz3tWiRyfD4/fff+/YpmTisi9l+WSmAPbDKy4E9O81zqAjOAFG3kPZ5TUkFjcc4SH5prukdkPaJPC+sU4D7LKLyLhNJScVt5MiRKAHwww8/AADmz58PwH0m8Fl2Sl6h2QCfp0KxC4nzBc/1lT1dQUnZgmwyveuQ6ix6mzJScZc9vLxmrAfsofH2aMltSCvS6mx8ec1Y9/ic4PfSstKLvJb8bQCAfBGC5awT9SvvTnni+a4LqGDyd91R1m2rU075PQdgxyR289ynVY19YT3lmBWeAz4T5fUi8rdcPv9kT4VXNedvHuttUE9KbcaS1RXjxo3Dtm3bcPvtt2PTpk0YMGAA5s6di27dugEwvZteb/UePXpg7ty5uOGGG/DQQw+hU6dO+Pvf/+5LJnXUUUfhxRdfxC233ILJkyejV69emD17NoYOHZrwfiORCL7++ms8/fTTKCwsRMeOHXHsscdi9uzZTk9GTQiFQ874iuqWqw1J2XBXFEVRFEVRkoOrrroKV111VdzvZs6cGTNv5MiRWLJkSZXbPOecc3DOOefs834zMzMdY49kIqka7nyTZDpw4gxSrfDHxpXZyUn2FrqDpzhinW/1MvlIpq32prc0ySlG5BqlK2wr8J8XmTdpKh0y5THgvvnKeGynvPZbNlUTvrVTNRmV+jMAYOQI81Z498INpkz2GzfXG1j4lbPNgcd0BwD87QtTXioCfGtkmfhGz/lUTeK94QPu2zzL6D0WeQ4O/PFjAECvIlOG3ZvNtHiTPd1szumeArOt8mI7LtMuW1a+KUtumatwOip8likvlUd5XIk6hDR3GNvudUaR8eKyd0WqQVTu+JnLyQQh8RQgqZwTuU+pzHNbPXv29H1P9Znb9SYlqy6JmHTF+eCDDwDAGZQk1WKqaDcMNsmPojtMCvjKYlP3+WxhTG5U2C5SBeSzBgBS80wdGlhp6vLXbQ4D4CqnPC5vveQ5YQp61k2mW5dOO1ye41zolPD666/79vGvSqPcswdtnOmwiDUBgH/+Rb3N/mZ9t8cpp0zmxX0EKZDx5jXnuizHTvG+5j3I3xueZz6Lq4qJDnq2y33KnjXeZ17V3Fsm3nfebXLKusQBgocffrhn0LPfASYsEoOxvlSmiWaKbZbideaggh6UTMyZb08dhd2OcWdZZn23xyTiKfoeBx54oO9YvL26PCf8zSb8be7cubMpuzinUiUnQWNEvL2asleLy/Das47x3mjO9aehqC9XGTXmVRRFURRFUZQkIKkUdypmX+QZL/JBJbYyxrhJ+02e9oLle+xU7ttcNXzPNjv+fbcdU2u/+aRkmlOR2cokeslobd5iGW+d0cZ8PqyledMO77BHctsxqrlpZrq+89BAb2O+PbdbvcCUV9iwdXJUqajve75pUzEbk7bRLMes7x6u62ureWFbdaO6UWbidUOWrTCsWeX7fmvv43zbYdkdp5tNnznfOWMKSkwBsmx7xz3bjPpZTKV9ozmXhWvN/KINpudjR6Ht01tpx0pGjMrQuoPpSYmkuWoEz396vllHOvFIP/3mHBebCFIR9SJj2oN6YaSLjHSECXJQ8O5DbkvOl57E/fv3931m/Dnh9feqTEGuCjJmn9tkDLhUxejoMr6P3Tu109zfZdsLAbjPF/bscWxNZZlfcacKyPE0AJDd0SyTYdfDQ9KWAwC+atEPQKzjlLf8PO9U2HjOaHXGcstzy/n0L3755ZcBuDaSjJ1/c4dxCTm/u3lmWDwcocBz/kUHmON7YpnryCHdMmRctfd6yfENzbkus9eDY6io7FJdZXp2urh4e3xJUI8TzzMVc9m7xTrJ+du3m55S9viyDOzZiafs8n6V7kifffYZPgPQq1cvHJVijjFsK8cpWbZfvIhpZ1y6jHWPr7jbjliZtsKeZW+bMe7sjbJ/r8P22CmOm9u2baNzXoKcqwC3jvCc8PzzXHXqZMbGyd5JtgW4D67Hz1V5kHNdnn/2OvMe4LmW7m5K/aGKu6IoiqIoiqIoDkmluEtP529bGieG/hX2AAaRLS1aYt7gS3a4mRt3/WT739px1pXlduxZqh2XnmfeoDPb2G/C7Y0KldWOyjtj4M0bMpMFUTXovM1j9i9i+YijA/INXiQ+ka4O13c3s60y49JSUVp97BrjBKWPrSwTFffWn70Ut6wsY0WFq6oysRWVxb07zJs+lfai9UZ12L7KTH/abN781+4xx7V1rx1zaQtqbdNNGfrZn7PbuUpBbg/72tnnhIq7zFAp46yV+FAJ9cZrSvVTOn8Q6f0vY9rjef17t+9dJsjRQipTAwcOBOAqj1988QUAN0Zf+oV7j4v3CtcN6gmgX7vMcUBF8X8OMnVeKu172LNkj93YvcmOO7afN+UlfsU9s5VR91p0cscXOD1+VATtRHA8hzLbKxDbYyCvw08//eTbL+Nu5TmnWkinhueeew6A33M7EongHz9a2LNnDy7qZcfbUj13ejTKuTAA4PK+7vrTv3Ofvd7jIt7sjrJ3obH4UDcEMi5dxi9LhxGqr977nz0y0luc91SQMxOfqVRsuT6Xl97x3uvEMScsB9c56KCDALh18quvvsLwg1sCAEIVpp6l5YoYfFtFl2NFnPLGUdxlrDt/l6mwh+jbns7PZrnZG0w9Oeigls55WLduHQD3XHtzKcieXi5D9Vvmf5G9EjIuPcidxhvjzn2wzvD68J6Q9UaT19U/oVA4MVeZkPq4K4qiKIqiKEqDEYpEEJa2uQHL1YakarhfeumlAEx2LMB9a/0o3B0AcFSaHTMnRqFXlLpvnqU7jBKwdaeZFlX434Sz7fjr/J/Ntkq2mTfhnB1mfk57oy4zBj69lVE66H5Cv1jAowKI0fIS6Vvr+NkKX1srYH4ixCrw4bjzY8pm76vSo3rQtYcxvSVbCgEARRuM2rLjB/N5vX0Ov7PHE8hzTX7eaxSCtulm2rnYVc1lnDCRsYH8zHtEiU+8jJXV+ZxLpxeqaVIRpZInY+C9yg/VLLlNlosK05AhQ3zbovc4lSmp3MaLlWfm4cMPP7zK46GbDPctXSQq7bEc0aJCAG5MO5X2netMr1zRBjN/9xajSJbYXUr2EA7k2s+eSk+sLmNw03LtLLQt/XGpUoEDYj3eZZZFrkNHj0MOOQSA2zshY5153UaMGAEAPgs2Krm++4U9MmX2OAn2GLLMaa5rzqV23PvMHyp8ZYyXGTJoTMV9990HwCRTaS4w8y6R54bKLq8dry2nQLCriIxhD4L7kL10/BzPaYy9VJxyH7x/GftdVFSEhTB19Ip+tiptbyONTi92L2tlmb8XNaYXGfDlSDAHaSvrzPmS6v/86Nc7cPDBZqxcbm4u+vePHTsin4deBxmZN0I6VfHcyYyxcpt05AlSx+VzDnCVdXl9SLx7QWlaJFXDXVEURVEURVEaG/U1ODUpG+5r164F4MZ8OX7LtitJisySFnHfcqPl5u202Fa9tpfZb9nC4aTU/tz+J/OWTZUsai/vTG0lOi03y963J5sps7YxS5u4WPwsR8tLRZ37pvpsxcSYBmedkzgeuWmpvs/V3Uhexb282Cgt9Mkv3WGmewrMdLfdO/GzHcsepLRLeA3iEvZnEGQcr1SBlaqR8dFeqBrJjKgyllUq9FSVeG2oCMnYWu93nHKfVHYHDRoEwI2FZRbTINcg6Wjjheu8//77ANweOq7DTH1B27xpRA+znW0mBn5voZ2dku5JP9tuDpvM/J0/m/u/wH427Lbv+zQ7S17U7s1Ly3HjuvcW2nHERWaaaXvCH7TD+Lp/2cK46Xg9nIP8t+VxUxFds2YNANeZJyhT5gEHHAAA+Pzzz2O+q6ysdJV1W2m3Sk2ZqYjy2RNOdXsNGLN/8QEtAQAPfVnsK3O8jNNx991MmDx5MgDg9NNPBxDr/03kuJR4ymzQOrL+ylwJ/J51kEoz63lQ9m3Ardd8FtB9RSrP3MaAAQOA6JqYstc1AwYMcJ45MvOwzATOY/IepxwPxM88V1yXDk8yc7D0ZOf5SsRJSWYllt75sjeA99Qdd9xR7baV5CApG+6KoiiKoiQ/H0d6YPny5bi8rz1Y1rZopDAVFom/nLAYX6hM2P+dHSrzRcYBWLDA2C+bl4dS9OvXrw6OQlFUca8SGYPG6WvbTbzz6Tl0fLGVwGxXQUzLNhU6dadfoZVqL9WyjLB5aGQUmX2mZJhTlmr7vns9xwG/es4HT6X9Nu32ANgXTRigxMS2O/Hl5b7vqX5747+lOi9VfMIbRvYCsGxBcfhexd3JDOlkiOQ+zTmM0mUkTlbEqshNsTNUZruKJMcO8EFMr2mp5lalvCouVSk6VN68WVW960hvbsZQSvVIKu7x3EGoBlGRYxw647K//PJLAMEZVWWMNN0svLHB0vGB9w6VaMYHO7HsIv7/rv8YX/eJ3ewYX3tMh6O82z1LpfazhM+MErvuyWcKP3vdZsrs8RzsvaJTU7iFUd6jWVHfMQCxPRsyZp1wPs+xVLRlZlteV+85pIIYjUZhcX+24s5eApkpls8WAEiz56WIfQWVOd538ZZpqgTlTGAvGM8N655Uyb3nSl7voJ4LqQJL9xlZv1kWPie8PUDs1WLsNteVmbulQ0qXLl0AbI9bvtrw8ccfi324+2QZWH6WVXqtM0uq1yuf54yx/FKV57mTz9sgxb26OgzE9q5w33LckBz70pzdmZoqSdlwVxRFURSl6fB2cWusXr0agNtgHTlyJABPOKwIOQGCk3xRaVeU+iIUTtAOspZiRFI23Lt27QrAjR9jheUbcbjCKGvprcwbc2YbN1NhRiuzTGvbx52x7BEhDlMtrk41DlLJvd+FbQHZUdADthXkGiOVdkfh8iju/C5oXee46I1L5Z1x+PZyMa4zcbp0eNNRpY/YHviM3c3JMdvML/crkHSPIdxy1yyzXkd7vaz8LGeZlGw7s53tVNEqx6iH3bp1M9uwy/Lzzz/HlFOJpaqYWKliS79zGQMvFVvpdiLzLnjXobf4sGHDAAALFy4EAPz4448A3JhYqr9Uxak0btiwAYD7A864VKpkgKsWs9xU2KhUSVje35jHC6J2ToY9m21valtp31tkFGe6VVniGcKYdj47+JnjZyJp7rlnfaVTE5X3tBaFAIBB6d8AAJbkDnDWoZonveup4lFh5zmW6l6Qgs3teb8vLCzENUM6AMhwfOyje/y9A+yJYA+g19WLKnyOPfbn2sO7AwD+/pkZN+BVa4PuzeYU4y7VUiLHkfAceX3wJUFx8PGcpbyfuR5Vc05lz4xU9OMh4+elQ410NmL9Zoz48uUmkzDdaJiBlY4qAGJi1Tn+iftgPeA+uM8gdyweJ+tNx44dfVMvsjeyV69evu9lT6FcT76QSOU9Xg8prznvCR4Xn1/0kJfPY6XpkJQNd0VRFEVRFEVpLGiMexUwWxzfRvlmzDfvT/b2BQAc0boQAJDVrqWzbouOdpYxeoXbrjEZtipGnYOf86gmMzY+kxna7JHcqYx1t+d71CbHuUWo1+GAiyZ1JekQE+M+4/neyRZLVb4sSKWy41DtS0+lPhygtDuuM97YSVF+qxXVfcs37Wx/T6Wxbbo/9pfzO9njBlr1NJnpcjq2dLbNDLWhTKMutMgy11h6hfOeUKpG+g575xGp9lHhCXIzkctXFaPM63T00UcDcHMyMF6W6tgvfvELAG62wu3bt/u+Z5x6QUGBrwxer2WWm5lRWX4qc9wW55/XxXZ/2bEFgKskl+4w01m5BwIAzij92OzTrnMR+1mQY4/RoMJOMuz56bnpvingPkdYl6Ole+19m+MIpRqF8bCoiflf2nqg07vEc0D69jXPPekBHuTWwuvCuGRmiuT5AuzY9gp/bHt5caldRvv8bCvyzffGuFOFZ4bpSBt7mTiqrezhkfObA3/5y18AuD1QMjsue42CYqS9fuDSZSSo50Kq4XI93mcyI2+87J5ch6ovt8m6xjoZFHct/czZA7dx40bf995nCu9XnpMglyWJ9G3nOabaL8fyeLcrs9IS9gzIGHfuK6i3S/aKxMtpIOsxj5fHx/LL4+U9pTQdkrLhriiKoiiKoiiNhVA4lJjiHq4+zKwqkrrhLp0pGPfmvL3mtAQAZLZr5ayT29Uoe3RCCduKerad1ZA+7zyxjNvObGVnI2tlx+Ll2cqHyJjq9XEPSzU+terTHQrbsYeR+MqIzKAaVwW3OxLoXU/1O3iffkXdjVtnmf29Bt79O8vQkYZx87bTTprt5pO1wyg27YvpjmPHBNuxvtntjJresltLs3wH11c3zb62n2cYtTPbVjLYu0IlVkmMsWPHAgAef/xxZ55UqGTcqVR2glwoqPjI7bF+Am52zrlz5wJwY9KpFm/evNm3b6p9jOeU3tNUzxn7yjIAsZkDWe4tW4yiTicaR6nLMvusKLJj2nfYMe22+n3uNuNvvsfuzWIvFZ8RKZn+Hgkq8Sl2Lx0dqbyKO9dl/WOPGdXrUKTQLFdulL1DbbU/nJ0LZAKhDKNkP/dDGRYvXgzAjcXt2bMnADdG2Rv/D7h1h4P4li1bBsAdLwDY9cxW3DmV8fjM47C3yI5b9gwY4jOB8fAZ4h7x3itSZUw0w2dTRGbe5Pgt3t98/pF4+RmobrMuSYcmwvNM9ViOcZFx6fyeU6rr3m0HKcycz54D9rTJbfGZwTpKEvGr52f21vFcch88Tp5bqf7zHPN4OY3n8CLHl8hcF1L9lj0lRC7P5573eRavt9R7fDKTrbceK/VDfQ1ObT4+W4qiKIqiKIqSxCSl4i7VBb7lM7aTSsPqliOwe/duHFLqvnnSkYXqNVUwqkUVJeIN2VbLGOOenmt7V9tKu8yYSuXdzLPdN6RnelCMu3CVCafafu22CifVca8KThWM82SWVbkOy8QyRjL9PQbVZX31ljOFXs32OoxnzWhl1JRsWz1k3L2znn1uGceebSvtOZ3dGN1wi5YA3BhJqijqVVs7vMoP64t0U5DuMTznMlOfjPekesT6SJUdAN544w0Abqw71WGuy31SeaMqRvWcdnFUk1lWxqV6FS5uIyjGNzMzExP6uyo0XWSosJftMs+NCuf+tXvjbEWZ+QY4Zb3mfU23JVdx9z8PgODeOMa6lzmf7XOfYepUapZR2cK2Q875bbMB7DFjQaz1eLOwhRP7u3LlSgCuEw/PC68TlTl53QBznayo8bFmhlSZx4HPTvrahzyKO3NocFlmX+V18l6TINeS5uQqQ5jVt3fv3gBi1W6eI5l7wavQchnGXdNNSTqaENY9WY/lGBfuk+NJvEo0t8H6Kh2d5POa22LvD+89Osfx3mRvkIw7B2JdVJghmM8Onkvuo127dr4ycJvyOHlcPLfeeHaeO7mOzIDKMvG8BI03IXI8gfd3jdvmdeSzj4q7bBfxuJX6IxSOIBSOJLRcbVDFXVEURVEURVGSgKRU3PmWzjdQvqXys9dhpEWLFgjvdmOmszq48XiAqywzXlMq8lSq3Rh2e2or7qm2z7ijpnlcZTjPjR+3PXcD3racubYq5ShczEjIfZfaKpbHx50KXVRkVZVONNI1xlUBGafrVwVlnH482FPALKdp9jlMb2niMGXmV0J1n+tltDHuFpFW7Zxlfu4zGqmpqeAVlTGA0ntcSQxvnCTVoCBlUzoecF0qPN4YV8BVtI466igAwMsvvxzzHbMY0iGFLjIyppXKGlUv7pPx2pwvY4GB4Jje/Px8jB/Q0mxvx1ZnPjOBulOOh7HrlD02g7107IXjmI7UbH9vFe9rWceqGrwUk4eh3N/rVlFiO2DYvQCp2fY0y85imWM+n5ZTDhRtx5PflzlqJ68TlVLO5zgBnkOvS0hVXt2Ok5XdEyF7JLzLOIT9arp3+9LNhDRHxV1RlCQkHAls38UsVwuSsuGuKIqiKE2VNWvWAAC6d+8OwH2ZYmgFQ2f4MhaUTAhwX2b5EiyT/sgkQTITKffN0Az5YuVNhsRt8KVQ7oPb4As3YagbX+hZBr6gH3DAAQBc60fvyxwH3zLsjutw33wxpbUkxQOWgQNhg5If8dx6Q2U4OFgmdZLXSb6MynMtk2HxWkmrVyB24CuvpxxMzHLyHlKaHs2i4f7zgcc5lbRHpXFlyLLfeFKyzM3PuFapXBNHebdVc6nAy5hxwKOwp9A5IuL77G7b//Zl2Yp7JMOO+bQ/O37KtsNEpaeMjHGXse1eVd63TzrAyPj7ACeckHckuyhvUE9BddlbeX5CGUbpC2ebh+GaLsMBBGe4VJTaYpXusafu+JeKPcyI6q9flq20M4eDG+Nu97plifEudi9crOIe+7iVCjvrqxNHLnrQCD9HI/bYFqdXz45ftzMNj+9verGeXr038FxUB+t+TG4H4SLlTt2GnRPvz97HdMYmuz0jiqIoTYJw2PwlslwtSMqGO7t6+bZL1YFv894GH9+AdfBi8kC1htfOO+CKL2AyPMObeEepHm+ojFRuZEIPhqDxushBW1SNOLCMITL/+Mc/fMt7l6EqxG1yn6y7/J77piLXrVs3ALFWgVyf4R6AO7BNDtI79NBDgYIlVZydpsfGjRudEEKqmjz3crCxl7oKU4n3XA5SPJtjyvY777wTgBtmxiRZQRan8SwzpU2rHNgqB//KhGkyoZEMW+NyvI+825TJtPi8Dhq8KUPg5HHxuUG13Pv8lwmSpAIttylVbvm8k2WPd5yy10H2ZgQlv5LnWh6/LEO8BGVBbRn2DFC15z2kND2SsuGuKIqiKIqiKI2FUCTij06oYrnakJQNd9o6MXaNb9/x7MOoyvONeGnuAADAwPByAEB6GgeZmikHfclBYaQ6W0UnPAZwQ2TsfYRS7O7iiD9kxgmVCeo+oUJgh6JAhMwAQAqTpASE1cQMEhO4g1ZZluAyueUNGGSbEXe2sy3nPNjd5h9aZmAi4wa72teK19Ub78f/peKuiZhqxm9+8xvn/1mzZgGIVdyITFMuBwYztnTQoEEAgH//+98AXIWbA1ABd/ApkwLR3pMqXpCqR9WVyiMV+HXr1gFw7eO8A9OXLDGqOu+V7t274+jIJmD3CkT3mmNgaBzghqk4Cc6cAdz+QaUM+2BoDAdh0wY1LddOFmWHgDn1P9UfIgfArdt2PWU9tsrs5xBTxwc8j+Tzx92X+XzPR2vRtm1btG/f3jnnVFJlMqt46vrevXsRyrO3me5/VtLGNbPYnEMnVCbVvX8y7QHnnD65wm896d0ny0W1kupjc1TcCe9z/tZJi1Y59Z5Pnkf+BhKp2MrES+z1ksq8TIrGfXmVaD4rpLrPdeSzRS7HfWzdagaNyyRJLFs8lZ+x9vzMHjfe99LOUp4PllH2PLIM3p5faUXNcgcp7XyeSatdeS24Harm3usZdM3ltnjPKE2XpGy4K4qiKIqiKEqjQV1lguGbNN/KqbLFSxPMZWXCl0/XGpXoyFZmpDmVqnCmWb6yxJ7aClfQIE8O4nQs3jwXxFGWOU31K/DL8g4CEJwQg2/bUgkI24O/5s+f7yx77LHH+tYZBpNsxRLpyi2+4VOZp4ov1bbKKsYEyJvOSQzlV+Jlz8KiSHcAwLfffgsA6N27MwAgPd2cH14bHi9VdK8qwXPAeVxG0zvvOzK2WSptMk6V575zZ3P9mPCE9yOTxlAV88blMglQ+/btAcSmJ5dqGffFBFzeuu0tG2NgvfcKLSa///57Z13LYs9UrLosFfVUDhzlIHHH5tGoYlTYqTxHcozyHs426nIow3z/cWUn5/jYG8FzKRU6eTzj+5tzbFXYPWfi+GUd+/uSrfY53IlIJIKcnBxHpZSxy4TzWe+8CnckEsGrP6ciFAphTAs7zrqNvxeA540Dar2DcLM7GqU4pbU5nuIt5rrK6+g9dpZHKp/NkY0bNwIADjzwQACxbiUysZEXXnfWPxkjTVVXJrPjlL1brJNB8fWMIQfce4vl4j3F+GvZ2yPhvqk8U01mQiI5Nsa7bR4Pe/rk8UhYBq7PKe9N73gZwP+clGOqZIy7XI69AVIll70b3I60u/UuI8emyHrDe0ZpAMLhBBvutRucqgmYFEVRFEVRFCUJSErFneoV34wLCgoAuDHS8RKI8G2arhRU/BZuMm/jhWWFAIBTWmwzKzL+3LaNC4X9KhPjYGl1GArb0zhhrI6dmq20v/azWShtx1oAsQltWFYeJ49Lvs3HU6K4zGe2ur2rxLzpM7ZYvq2f1MIct1T0+NlRJj0KPL+LSdsrlHYe70eWUQ232rHOVGoZDynVV3r1xnNMoOIi0zzzXCk1h/HuL774IoBYpwPZk9WzZ08AQI8ePQAA7733HgDXa1kqpry+gKsGccptchneG1Sc+D0/s25QyerQoYNvn/wecHvZeK/v3r0bodb+sRuRDE98brZfUUzxfAe4yclSHPtH28Y0p6WZ2kr7Xz/Z4JzDQw45BMBupw6zvGvXrgUQqwrKZ8B9n5rz5I1TphJI9ZLPP6moUrXnuuzFoIrH+id7VrzKKpcJh8N4tzIblmVhtLG9Ro4dR88eB/ZOei1xqbQ/uNKvZhJvzLOMC+Y5uPfee9FcmTJlCgC3N0uOR5A9Nd7fPnmf8Lpv27bNty0i468J7w/OD3KjAWJj1Xn/SL95mcyN5edznc9z3rMcw8IeVh4D4KrWXIbr8JnB374gb3WWieeDPQ2y18Cr3MsYd3luiPTbDzrnHMPA88Zr513e6XEX9UTeE7xnlPonFA474yCrW642qOKuKIqiKIqiKElAUiruHCXOt1wqCYxx8yoAchT65s2bAbhexhyBzbfV2RvMNsZ1zPbND9HBxRaMmDDFEvFzoYgbH0rFmererO9MOfmGLH2wZa8AY/X49s04Px53vLT1VKSp7FHp43iAVatW+c7Hd3bZeJ4u7e13IGCCGidWHh6nGh5nqlBo+JnXwRbreXy8flQXGL/MzHZU5bi8N85PegpL329l3znvvPMAALNnzwbgXgfeK4yzpSL1wQcfAHAzD/JaSDXKq1RRWef1Moo0sH79et+UdYDKGq+39DvmvURlz6voSkW5uLgYLxYDK1aswJSRPWLKlt7SXs9W1Nm7Fo741XknYVimmd773832M6cQqampiEQiTh1nvZP1kYrijh07AMRmXAzydwZie0A45fNAZriM50PtPS9y+Xjxx97z9E5FJ5SXl+P0tub4I638vXPOcw/AQ0sLfMdB4pVJ+oZr7g0X9lDxd8vbswTExkgDbn3ksrwXZSw3r7eM6ZY9MfJ3h5+9qrDMCeCNfwdcRV2uy7rK+fydltthfY+HdHCR6r10vJE9iqyb3JfsDfMeZ9C5INWNV+M5ZZl4bfh8lNfOu64c+8Fta2x7IyCU4ODUUO0Gp6ririiKoiiKoihJQFIq7tL1gkoBFVzviG+pTnEdxr3xDfeHH37wff4obE7N8JQVZj3G4jG2nZ7PVN7t+VTnACDMuHD7DaxHj26+MvENmaq/VMyo0lF9YOweVarbbrvN2denn37qW4ZTboNOLjKenioDY4s/tMw5HBFZY47L3r7XM54p4XnMkTTbi5fLcnyA7apBdcEb6+z9zGvBMvP6SY9fwFVPpB8wl9n7/tMAgPTjfgtl3xg3blzc+e+++y4A4MsvvwTgqubS0YXXgmqS14efcedUmqk08f7jPSBjYVknWFdYB6TS7u154f3D5wLrW9u2bfHwst0YMmQIjgh/5yxv0QPdHsPBu45jOdiTREX57g/XOOtKtViO12Bv2eTJk+GFmTHPOeccVIU3zpvHxXMkezhkVlmp4ksvcJntMV4WTuLNrfBisXu+/fsqca5HkJMN8c5nuXgPaC+ay1dffQXArScyEymn0rMdcHuiWT+j0ShGpmwG0lxXsVA4ApQBn0S6OdeOywfdJ9yn9/eW15PbYOw271XWW5ZJ+ptzn1yPOSDoDBVvvJeMj+c++PsiHW24T26DPYg8Hiru7O1jmbzHyTaHbIsQ2VPGz1wuFArhmMq1QLn927rHfr7sXoZv2w6OiXkHYnsKuG3Wa94jSgNST3aQqrgriqIoiqIoShKQlIo7kXGv8m0diI3n4zJU/OiMITMyMsYsVMF4bVuNYvwlMxnayjtPZKU3QymVOnsatvzx5IxXpLJEJeDXv/617zipHBx66KGB52Lo0KGB33m3edddd8Utg/ShDTH9qfCzB1y/Zje7rO3NS2XGVtoJ90Uljeea86mq8NpQ+YiXJU+qul7HEECV9rrkhBNOAADcd999AGKdI2RMqFR2Aff68b6jek9knC3vAapNvBe4nIyV9caaUpXkGAqq+9x3WloalqYNiFHmg9xN2Ku1ffuPvrLEO/ZbbrkFiVCd0k5uvPFG5/+//OUvANy6yvPP8vCcET5XqEzKuOKqYttlPK30/JY5AIJir2U8PpGe8d55d999d0x5mivscXnmmWcAuOOfpG+69/6X59wXVx3QqVJRUeHUGznGhfcJ6568F7i+9zvWdz7zZe8Q98F7UWaKZY9xIll0qcbLXjhuU8bRs/eWv30sI8ssM8p6j5Pb4rmQPcDct3ST8Xnh70FcysvLnfW915PXQfZIsTevObsvNRbqy1UmqRvuiqIoiqIkxpFlJuSkYqdtp0jTATu88aisYqAUWNp6YAOUrnlwRKkJz6ssNuFCVplp3PMaDAx/A5iv8NMBx9V7+ZTGT1I23Pm2SwWBcbPxXGWkiiPfoqkQMcuifOsOlfqdYQjju6nAV8aJcec6i8LGXzYcEGvK8l933XUJHX9tmDRpEgBXuZH+szx3i0KmzENTzDn1viHy2GU2Wcdhx57+0H4wAKClfdxUUXiOpX9uUNY8r6ons/pJNUWpe3i9pBsJVSLGtEtHCSA23pqe8OwB4zr8TMVNxqlKhSte1mQqzxwjwn3TBSfI+YF1n2qZk4142DDf8vSUBoBly5b51qlLfv/73wMApk6dCsA9ZzJDquwxkOdQuu5wPa+bizcm17sMp3yeynj7oNhfuV0vskdAieXrr78G4PbCynPlPa/yWqSkpAAJDhuIRqMxvVy85nz2speTnwG3HvIek72sfLbL325+Zk4CLsfj4Weq6vGQGVS5Tf5GcCwO98njkj2HMqMsj8l7nFyW84K81WVse1paGlCDR0RVvWDcNu8JpRFQTzHuSdlwVxRFURSlZkRtpb1se6H5bIc9RuykWWl2w3NI6grg+xWq+NYB1l47meNuM5iWZg+RNDvk1GOnqiQZ4XCCDfdmGCrDN2mqazJDozcOTjqU8E1Zjszm2zfj3rj85uyRAIC2u14DAIRT7cplK+uhqK1A2Z/5PeD6PBPpksK3aRmTWh84MfxCUYuJf7WzQYb37HLWDaeZB0+oPEAZE97w0i1HxjfK2HaqF1Tcvcot59FBQMZvKnWPVHJZ33i9ZZZTbyy4VOR4L1B553gH+jVLdV/GsvMz7wOvKrZihXGEkll2qbAF+YTz/qMyJ8fSEO++mDWWGS7rgz/84Q8AgEceeQRAsNNOkI+7zMRIvE4vvNZBz1xOeR2lOivHH8neRm9PGbd96623Vn/wzRTGMT/9tHHPYrZQ1jWvCwnPuaxr+4rMrMtr7e3l4r6kexnhOpzP+4dKOhV39ma1a9cOgHvfsCcuHiwX971lyxbf9zIGnmWR9UKOo+IxeesF1+E+g35/5NgXb5x8Inh7VHiNWT72JGpse/MjKRvuiqIoiqJUT/8dbihFua32lu0yIyMdxd0WYaj6hrPqPuSruUILTifMVghgKRXlMesoyUEoEnGsw6tbrjYkZcOdMWtUvOgDzjdirzOFVJKpDkovWrk8v3diOm31PDXbVuvoZW4r7CnZ5s2bCjUA/DejNwAgTSgeLEtDxnTKMsjseDwvn1Z0BwAckec+yNMd71+710Fmlkzxb0Oq+kR6EFNJYBmokHgVEcZM8przOBiXqNQfVJtY76hs8zO/l04xgKse8VpTiZe+z7y+svdGQucYxpoDwLp163zryDEURGY/9Dk/IFZNk44RgPucOPjgg+OWry658sorAQC33347APd8M5afUzkWQfZ4cep1huGxyyy40s1Eqva8bqynnHJ7XO/666/fhyNWPvvsMwDu2CzZkwXsn98Vb32TvxWyF8X7v7wfCOfL31c53otZtPlM6d3b/JZW1TvN8qxevRqAe397XaSqKkNQWeOp5dyXPCdEti/2VXGPl5OG2+A98NvfqpNacyMpG+6KoiiKoiRAAvG0TmJBmi6o6ltnOAndwv7w2lCkdnHPSsPz1wWrYoSfeNTWxCApG+7Lly8HAAwZMgSAqxBR1fGeOL6h821b+qPK+DbpmkDVYkvfEwEAbb/8p9mHvbzF7HK20v59r+MdVT9TvIXLeFx+DspUWZdwn2+++SaAWKcJOf0maxAAc956h/8LAEjPcOPeAXdQzdaDf2nWFZkcCRUDnntmA+R54z65nnfMAq+xdLHgPXHmmWcmdgKUfUZm6AzyMmYdo4+4d132psh6JmPYpV8/12csPJU5Zij1xtvKOFu6SsgeHn6WD1wZI857TWZh9p6LRB7adUVQbPi0adMAuGomexykah7PCz9oHIBEqvXsAeN14jnjvulupewbDzzwAADgT3/6EwBgxIgRANweSSBYSa4J8Xq4pMocb0wZr7OT0TpODD4Qq1yzd4j3D3MvMN8DXaZYlwE3Lp4x36ynHCfDbfK+ZhmkmwwVbemUxGPyngu2K4Ji27ksx8z5srUGh+nH4L2e3NfChQsBuPeA0vxIyoa7oiiKoijVs6DMDPDcu3cvjrcFprQWtrOJUHtTbHeZUIra69YVoXR7EG2OLV7YZg9OwsaM2NBCRfGSlA33m2++GQDwwgsvAHCVJKloA7Fxq/KNX/oTSzVBvlFvOeR0AED7lfN883/sYdxnMj3bkt7SUsHzxq81FCwDzyHLKBV4r5MAj1WqoYQ6pDyHcvwAlRFuW47Qj3c9pYct3Qd4Tyj1B+9vmRVQKu3eWFsqVfLe5/WU2yBUnugU8cknnwCoehyF9FTu378/APf+4n3IHgPpuSx7A/i9jJEF3PrSGOq0RMaRT5kyBUBs5khOvUotz4Wsw0SORWCP2LZtxnaQWV6VuoEZepnNuFevXs53vF9Z53Jzc4HtqBHhcDjmeSwzqHqfz7yHWF+5LBX0oFwC0iWKyjo/835iDxuzhXqPk/emzLrKbcvxWywLy8rPDGPg841udd76LsftyN9NHjfPPafRaBTYhoThcQNu7H6iWZmVpktSNtwVRVEURakZkTzTqGWAqJO104bhjlSFlf3Pxl7HoqKiAt1+NCEvTvZau7H/QeUvHNGjluYjShMlqRvujGul16v0Bwfct2YiszvK2Lp4DhhAbLxgQf+TfeunxRlZLzMwSmXAW86GQsbrSocJng+pjADuOanOH1iOpKfCQU9emR1POv14z5OMr+Y9oNQ9jJXm9eB1lE4j/NGRbjPedXiteX9Jxc0bN+udT/Vr9OjRAID//ve/vn3Gi8nltqnESfVY3r+yXkrlnnjHbvB46HjVmPm///u/hJf929/+BiC2Tl599dX7tUyKoiQvDz/8MKZOnYpNmzbhoIMOwrRp05xxF/FYsGABJk6ciG+//RadOnXCjTfeiAkTJviWeeWVVzB58mSsXr0avXr1wp///OeYMWxV7be8vBy33HIL5s6dix9++AF5eXk44YQTcPfdd6NTp07ONkaNGoUFCxb4tjtu3Di8+OKLtT0tdUZSN9wVRVEUpbkzceJEAMCDDz7ozKOFIm2TMzMzsQItTdhGF/O57XfvA3C9xel4sqnnMebFtLIy0AqUFqxeKIjxBZqhjMRrNQrEhp1JK+COHTv69skXY+9LNMNzWB4OSuU2pCjAbUhBiWIVw70YPsrB294wW+5LhtNRFJDb5vF5E1AV9B3tnC8KDyUlJWjp2e53333nbIPXuLExe/ZsXH/99Xj44YcxfPhwPPbYYzjllFOwbNkyJ0GYlzVr1uDUU0/F+PHj8eyzz+Ljjz/GVVddhbZt2+Lss88GACxatAjjxo3DHXfcgTPPPBNz5szB2LFj8dFHH2Ho0KEJ7XfPnj1YsmQJJk+ejEMPPRQ7duzA9ddfjzPOOAOff/65r0zjx4937HSB2JDqxob6DymKoiiKoig15r777sNll12Gyy+/HP369cO0adPQpUsXJ6Oz5NFHH0XXrl0xbdo09OvXD5dffjkuvfRS33iYadOmYfTo0Zg0aRL69u2LSZMm4fjjj3d6fRPZb15eHubNm4exY8eiT58+OPLII/HAAw9g8eLFWL9+va9MWVlZ6NChg/PH8T+NlaRW3PkG+t577wFw33q94TF8w2f3t0wbzDdkrkNrQr7Fy250duFz0IhM2Qy4b9fS9pHz+fnCCy+s6SHvd1iGt99+G0Bsanlpn+kNe5AJdxiKwGWlUsOQIQ4s4rnkchzYJ1O3e9ULGa7QWFWIpogceMV7gwNG2f3I68lQKK+lINUwXkc5UEwm4eI9IpO+8B458sgjAQAff/yxr0yAe99QtQtSx2RojEyUJo8/XjgO53kHkzUFbrjhhoYuglIDvCFM77//vu87DpD0WpZu7X1czLN+7969wO7dTh3jlHVUJtHy/vbxOy7LUDhpn8h6zWc+nwM5OTm+MsqQOobFDhgwwNnnN998AyA2DE9as3JfPE5pFR1U77kd73HyWcDjlKF9nM+p/E0Lso+VibQae0haWVkZFi9ejJtuusk3/8QTT3RsKyWLFi3CiSee6Jt30kkn4amnnkJ5eTlSU1OxaNGimGfPSSed5DTc92W/gOlNCYVCMb1Fzz33HJ599lm0b98ep5xyCqZMmeK73o2NpG64K4qiKIqiKPVPQUEBotGoM/aItG/f3vHVl2zevDnu8hUVFSgoKEDHjh0Dl+E292W/paWluOmmm/DrX//aCR8DgAsuuAA9evRAhw4d8M0332DSpEn48ssvMW/evLjbaQw0iYb7t99+C8BNN+5N+EKkYidj8ajGURXm27dM0MQ3aKqJ3K43ExZVA5mimPvguo0JlokVgWXmueRxeu3upGLO46aCIdUXniM5AJHXhEqJXM8Lv+M1P/744/fhaJV9QaYn5/XkAGGqRzKRDwd+e7/jtZb3QDw12wvVMipXLBMTsjDhj3fZvn37xj0OWaagZCpyUDnxDtjkcTA+VlEamg0bNgAADjjgAABufZUKszRs4DOfy7ORw3ucyjYVay/cFusMlU1uQxo38DkgrSa5nLRuZaPMOwic5eS+ZD2W1oxUs2WSKJl8USr03t8j/i8H4nPftL/kccmYd2k/zWPgcrx2yYJ8NlqWVaVpRbzl5fxEtpnofsvLy3HeeeehsrISDz/8sO+78ePHO/8PGDAABx54IIYMGYIlS5Zg0KBBgcfQkGiMu6IoiqIoilIj8vPzEYlEYlTuLVu2xKjhpEOHDnGXT0lJ8XnwV7XNmuy3vLwcY8eOxZo1azBv3jyf2h6PQYMGITU1FatWrapyuYakSSju1157LQBg+vTpAIBu3bo538l4XL5F8w1P2h3KkeUy5k7CN2/vW57cB9+6qVScd955NT7GuoZlevXVVwG450XGn3vjgXnsQeeGaoRMGS3jmmWcIM95vBj3devWAXCvuVJ/XHXVVQDcVNvy+rLXhrHuMiYecK9pUOw6kfHk0q1BjlHxWjMSxihSjZeql1TteW9zuSC7SOLtjWNylMYek6o0H5YsWQLAHbcle8yCxhLJMR9SiWa9j2fBSuWY26SqLV065PgvqWBT/edvAY+B2y8oKHC2xfrNZbjtrVu3+vYt3WGqsx9mmTiWy3te5PNKuszwmcFtB51rmQSKx81r99vf/haNmbS0NAwePBjz5s3zWTXOmzcPY8aMibvOsGHD8MYbb/jmvfPOOxgyZIhzfoYNG4Z58+b54tzfeecdHHXUUTXaLxvtq1atwvz5850Xg6r49ttvUV5e7oyNaow0iYa7oiiKoiiKUr9MnDgRF154IYYMGYJhw4bh8ccfx/r16x1f9kmTJmHjxo14+umnAQATJkzAgw8+iIkTJ2L8+PFYtGgRnnrqKbzwwgvONq+77jocc8wxuOeeezBmzBi8/vrrePfdd/HRRx8lvN+Kigqcc845WLJkCd58801Eo1FHoW/dujXS0tKwevVqPPfcczj11FORn5+PZcuW4Xe/+x0OO+wwDB8+vL5OYY1pUg33Sy+9FICbNARwvVj5BixH1ksfWb7xcsq3bHa/UNnjlNuVo8q9cBsbN27cxyOrP1jGHj16AAh21fF+J88J1QQqsFRRgmIKqUZQTWHloprq9QJWl4vGA6+n7HXi9ZR1C3DvBa+fsXcZ3kOsM5wvlXfp1CSXB9w6K50sgpR36ahEZB2Ip+5///33MfMUpSGhAwenhx12GABXQWY9oALP+iyf4zImXjqMeX8TZFy8HN/E311Zb6W6LXvE+SyhCuodJ8Z53DbLx2VkfeazR46nYRllTzDj1b09y9JvXirqPH6Wm/N5vHK8APf19ddfA4DP9rCxM27cOGzbtg233347Nm3ahAEDBmDu3LlO5MOmTZt89os9evTA3LlzccMNN+Chhx5Cp06d8Pe//93xcAeAo446Ci+++CJuueUWTJ48Gb169cLs2bMdD/dE9rthwwb885//BAAMHDjQV+b58+dj1KhRSEtLw3vvvYf7778fu3fvRpcuXfDLX/4SU6ZMqXa8VUPSpBruiqIoiqIoSv1x1VVXOaGUkpkzZ8bMGzlypBMOFMQ555yDc845Z5/3271790DDAdKlS5eYrKnJQJNsuHtV2bvvvhuAq77xrZlvyFQX+EZMRVB6j3M+1+dULgfEulBIJ43GjBzlL0fLx1uW50KeQzlSnp/Z68HlpaJJ1YUOIdKrVWlYrrnmGgBurDtVJCpc3bt3982PFyMuY9VlnCnvP67L5ahg8b7kWBSpqgGumwb3JWN4pXLO76UThOxR4v3uHbykse1KY+X6668HACcUoUuXLr7vqfbKTKNUHFkHWfcYz83vvW4rVMhZd7w5Vbzb4u8vfwtk/ZaOZax7jHn3/pZynuytkz7tXIfzuS+p9kvHOcZFe58X0sNeqvhclsfF4+E++IyRuU14rRSlKtRVRlEURVEURVGSgCapuHuhWjtr1iwA7tu2dDiRqoL0FeWbMdeTMXxeBUC6U/AN/vLLL9+PR1Y3sIxUZ6hW8Lx4j5PzeC543NILX7oSVBcLzc+qtDduqLyTP/3pTwBclxneK14HBukdzXoms5pKH2fpxkB1n2MyWA+9cYkc38L6J50eKisrcUjRt27Zwmbdb/IO8u2bcD2qZo3ZLkxRJJ999hmAYAcU1hP5Gyifz1SZ+VvqjXEPykoc1NslFWs+OzjltmVsvLcXT46DYdw41X8q8jLPCJ9LMjeEjFeXqr93G9yn7EGUn3lugxR4Xpvzzz8filIdqrgriqIoiqIoShLQ5BV3ctFFFwEA3n77bQCxGdr41i3VYama802ZSgHVZm9GUcJ58TKANnZYZp4XGUfonUfVgSqo9LgN8smVqirn81opycUtt9wCALj33nsBwMk651XBg/zXpQIvx5Bs2bIFgOvfTFWNaph0wPAiM6Xyc3FxMVDpuVdT/LkcWG45NuWTTz4BYCzLFCVZuO+++wAAd955JwBgxIgRvu95v8u8I3K8E5V2OcYJcOsvxzlxXZlHhb2yeXl5ANx6y99T1kE51kX2BnjncRkeB5VzblM+azg+RnrPS+Wdx+tV+bl/niN5vNxXkIMNj++LL74A4F4bRUkEVdwVRVEURVEUJQloNoo7+e677wAA/fv3BxCcLU7Ol162VN6rUgC47sUXX7x/D6IeYJlffvllAPGPk6q89LyXvtkyQyXhcpzy2px00kn78UiU+ubGG28EANx1110AgF/84hfOd23btgXg9tYQKlRUv3744QcArqLF+icVdSpdvNe4fSB2zIR0esjIyMBXGYdg6dKlAID27U0v2oHt/D1JzMD4+eefA1DnByW5ufnmmwEATz31FADgoIPMmA6qxawfVMdl7DvnU8n2ppDn7ya9zzmVmVKp1kunGplvRa4n49K98+S2ZYw6y8a4ciruPD7pMCcdr7y/X/L4+FvIfcheOtmrzN86XgtFqQmquCuKoiiKoihKEtDsFHfphEG3GTnSXsan08uVI9Wl4u6FKmF1yQOSAR7Dm2++CcCvlAZlouTxb9u2DYAb58d1uXxhYSEAN6b9+OOP3+/lVxqOSZMmxcy7/fbbAbj3BKdEZiRkDKx0vpBjT6i2MYsiEBurTqRLRteuXQG4WQtXrFgBwFXa2Aug6pjSlLjssssAAM8//zwAN/8C6yDrlByjxbrH+uFVl6Ubm4wTJzI2XD4P5Lgo1kHpWuOdx3LJvCJcVuZk4Xz+tpOgPCzeGHeZYV2eI/ns4G/l2rVrAbjnXlH2BVXcFUVRFEVRFCUJaHaKu6SmDiZTp04F4CqCUgkEmmYM7GmnnQYAmDZtmjOPSguVC8YO/uEPf6jfwilJw6233ur7TAWe9xLrlYwzperFmFKpdDE+tUOHDs625ZgL6csuM7pyX5o/QGlO/PrXvwYAPPLIIwCA3r17A4jNocA6Kt1bqJ5757FnWmbJltmIWZ85joS9slw/yDHG624WlOGV9Zn7YC8657M3jzH6cmwat8dnjNctjfuUmdj5XKLSznUZ037llVdCUWqLKu6KoiiKoiiKkgQ0e8W9pjR3Nbkp9iYoDQ8VOeklLVUwGc9KqAZ6XWekmwTXDcq0qEq70pyhGjx58mQArvNaz549AcQ6wbD+eJVo1lMZZy7rNceU8XuOd+JUKtpyXJRXcee8du3a+Y6HqrdcR45X43zpKsNjka46gKuwcx2Wj+WmK9ayZcsAAHfccQcUZX+hiruiKIqiKIqiJAGquCuK0mBI5wbpVEQFi/OljzPXa9++PQC/KiYdn6Syxn0wvlZRFFcdnjhxIgAgPz8fQGw2UNZFr6OKzOnRqlUr37oy7wLnU4GX8eXSvYUZlL09a5zH8TEy+zlj2aUfO8dkcVuMx+czhZnAuW+ve450w2K5GU//2WefAdCMqErdoIq7oiiKoiiKoiQBqrgritJgMJ727rvvBuAqclS3qOYxXlV6NXNKVdCrskv/drpkcBkZV6soiqIojR1tuCuKoiiK4oNhHldddRUAYOTIkQCAbt26+ZZj2Avghs/IRIYcCMowlM2bNwNwX7xpycgQGYae8KX6559/BgD85je/CSzviy++CMANm2P4jQzHYzgLxYBOnTr59snB6hQNON87IJ7zyLp16wAACxYsAAA8/PDDgeVUlNrS6EJlfvjhB5x11llo2bIlcnJyMHr0aCxZsqShi6UojZKmUl9uuukm3HTTTSgpKUFJSQkKCwtRWFiIoqIiFBUVobi4GMXFxSgvL0d5eTk6deqETp06YciQIRgyZAgyMzN9MaiA+WEuLS3F3r17sXfvXliW5fNyX79+PdavX+/sW1EURVEaO41Kcd+6dStGjBiBVq1aYfr06cjIyMBdd92FUaNG4bPPPkOfPn0auoiK0mjQ+qIoSl0j1WOGtx1yyCEAXLUcADp27AjAHfApE6nxM5fjfL5QUw0vKCgA4A4MrUppJ+eddx4A4NlnnwUAbN++HYAbRsdycsqBs7KM7B3gMbBHYdOmTc6+2APw1VdfAXAH9I4bN67acipKbWlUDfepU6di69atWLhwodMdd/TRR6NXr1649dZbMXv27AYuoaI0HrS+KIqiKErzokYN9w8//BDHHHMMnn/+eZx//vm+755++mlcdNFF+O9//4vDDz98nwozZ84cHHfccb4YutzcXJx11ll45plnUFFR4Uu8oCiNmdLSUgwbNgy7du3C4sWLHVuyzZs3Y+DAgejbty/ee++9mEGUidIU68ukSZN8n//0pz8BiLV2pGInE7R4j5fzpLUkbeC8CpqiKIkhkwndfvvtzv8nnXQSALceShtXmfxMxp9zOdbRiy++uMblozo/c+ZMAK6yzn2xbHym8Pkgy8heAKr+n376qbOPW2+9FQBw7rnn1rh8ilJbahTjPmLECBx22GF46KGHYr578MEHcfjhh+Pwww+HZVmoqKhI6I+UlJRg9erVTvebl0MOOQQlJSVONjJFSQYyMjLwj3/8A1u2bMGll14KwPxIXHDBBbAsCy+88AIikYjWF0VRFEVREqLGcty1116LSy65BEuXLsXAgQMBmGQDn332GWbNmgUAmDVrFi655JKEtse32h07dsCyLLRu3TpmGc7btm1bTYurKA3KgQceiCeffBLjxo3D/fffj+3bt+ODDz7AW2+95ajGWl+CueWWW3yf77zzTgBATk4OAPd8UE3zOlzwRYfKGpW25cuXAwD+8Ic/1FWxFaXZQPUZACZMmAAAGDBgAACgd+/eANy4csaTE9ZfxrRTbKCTTW2gWs8Y/Z49ewJwY975zCAyidJ3330HAPjmm28AAI8++mity6Qo+4MaN9zPP/98/PGPf8RDDz2EJ554AgDwwAMPoG3bts7AjNNPP93JHFZTZGVK9DtFaayMHTsWH3zwAf7whz8gGo3i5ptvxujRo53vtb4oiqIoipIINW64p6en44orrsBf//pXTJ06FeXl5fjHP/6BiRMnOmmAW7du7cTzJkqrVq0QCoXiqoQcHR5PXVSUZODSSy/FI488grS0NFx77bW+77S+JE5Q2nW+pPCz93/GwdNrmWFLiqLsX4JU6SuvvBKAWxdlfb3//vvrrExSvb/uuusAxMay09f9kUceAQCceOKJdVYmRakN++TjfuWVV6K8vBzTp0/HE088gYqKCqeLDDBd/6mpqQn9kczMTBxwwAH4+uuvY/b39ddfIzMz0+nqUpRkori4GBdeeCF69+6NzMxMXH755b7vtb4oiqIoipII+2Q50bFjR5x77rl4+OGHUVZWhtNPPx1du3Z1vt/Xrv8zzzwT06ZNw48//oguXboAMKPLX331VZxxxhlJ55ChKICJ+1y/fj3++9//YsWKFTjnnHPwt7/9DTfccAMArS81gUo7X2IYx87PXoceqnp0qFi5ciUA4JRTTqmfwiqKAsBVsRsDdanuK0p9sM+/7Ndddx2GDh0KAJgxY4bvuzZt2qBNmzY13ubvf/97PPPMM/jlL3+J22+/Henp6bj77rtRWlqK2267bV+LqigNxpNPPolnn30WM2bMwEEHHYSDDjoIV199Nf74xz9i+PDhOOKII7S+KIqiKIqSECHLmwO8hvTo0QOZmZlYtmzZfivQ6tWr8fvf/x7vv/8+KioqMGzYMNx7770YNGjQftuHotQHX3/9NYYOHYqxY8c6nsIAsHfvXgwfPhzbtm3DF198gZYtW+7zPpprfeEzZ+/evb753l4GqvGrV68GAJxzzjn1VDpFURSluVBUVIS8vDzcdNNNzjiOqigtLcXdd9+NnTt3+jIPJ8o+K+5fffUV1q5dG9fTvTb06tULc+bM2a/bVJSG4OCDD8aePXti5qenp+Pzzz/fL/vQ+qIoiqIozYcaN9xXr16NdevW4eabb0bHjh33KbOZoihKbejfvz8A4MYbb/TN93Yg0rHivvvuq7+CKYqiKEodUmNXmTvuuAOjR4/G7t278dJLLyErK6suyqUoiqIoiqIoiocaK+4zZ870xesqiqI0FPfee29DF0FRFEVR6o198nFXFEVRFEVRFKV+0Ya7oiiKoiiKoiQB2nBXFEVRFEVRlCRAG+6KoiiKoiiKkgRow11RFEVRFEVRkgBtuCuKoiiKoihKEqANd0VRFEVRFEVJArThriiKoiiNkPnz52P06NFo164dcnJycMghh+Dvf/+7kxVYUZTmhzbcFUVRFKWR8e677+KEE05ARUUFnnjiCbz22msYNWoUrrvuOkycOLGhi6coSgNR48ypiqIoiqLULTNnzkRqairefPNNZGdnAwBOOOEErFy5EjNnzsT999/fwCVUFKUhUMVdURRFUaohFAoF/q1du3a/7y81NRVpaWnIzMz0zW/ZsiUyMjL2+/4URUkOVHFXFEVRlGpYtGiR73NJSQkuvPBCRKNRtG7dGpZlJRx7npJS/U/vhAkT8MILL+Daa6/FzTffjKysLLzxxhuYM2cO7rrrrn06BkVRkh9tuCuKoihKNRx55JHO/9FoFGeffTZ27tyJBQsWIDc3FzNnzsQll1yS0LYsy6p2maFDh+L999/Hueeei4ceeggAEIlEcNddd+F3v/vdvh2EoihJjzbcFUVRFKUGXH311fjXv/6FN954A4MGDQIAnH766fjss89qtJ14Kj3V+MWLF+PMM8/E0KFD8dhjjyE7Oxvvv/8+brnlFpSWlmLy5Mn752AURcHDDz+MqVOnYtOmTTjooIMwbdo0jBgxoqGLFRdtuCuKoihKgvzpT3/Co48+iqeeegonn3yyM79169bIy8ur0bZmzZoVo9JTjf9//+//oX379pgzZw4ikQgA4Nhjj0U4HMZtt92GCy64AD179qzl0SiKMnv2bFx//fV4+OGHMXz4cDz22GM45ZRTsGzZMnTt2rWhixeDDk5VFEVRlASYOXMmJk+ejNtuuw2XXnqp77tZs2YhNTU1oT9Cld77R5YuXYrBgwc7jXZy+OGHo7KyEsuXL6/bg1WUZsJ9992Hyy67DJdffjn69euHadOmoUuXLnjkkUcaumhxUcVdURRFUarhrbfewvjx43HppZdiypQpMd/vS6hMmzZt0KZNm7jfderUCZ9//jmi0aiv8c5Bsr/4xS9qtC9FUWIpKyvD4sWLcdNNN/nmn3jiiVi4cGEDlapqtOGuKIqiKFWwZs0anHvuuejZsycuueQSfPLJJ77vDzvssCob4fvCDTfcgGuvvRann346rrjiCmRlZeG9997DX//6V5xwwgk49NBD99u+FKW5UlBQgGg0ivbt2/vmt2/fHps3b67Rtvbu3btflwtCG+6KoiiKUgXr1q3D7t278d1338UdsLZmzRp07959v+7zmmuuQefOnfG3v/0Nl19+OUpKStC9e3dMmTIFN9xww37dl6I0d0KhkO+zZVkx84JIS0tDhw4d8Le//S3h/XXo0AFpaWk1KiPRhruiKIqiVMGoUaMSsnDc35x11lk466yz6n2/itJcyM/PRyQSiVHXt2zZEqPCB5GRkYE1a9agrKws4f2mpaXtcyI1bbgriqIoiqIozY60tDQMHjwY8+bNw5lnnunMnzdvHsaMGZPwdjIyMuoto7E23BVFURRFUZRmycSJE3HhhRdiyJAhGDZsGB5//HGsX78eEyZMaOiixUUb7oqiKIqiKEqzZNy4cdi2bRtuv/12bNq0CQMGDMDcuXPRrVu3hi5aXEJWQwTuKYqiKIqiKIpSIzQBk6IoiqIoiqIkAdpwVxRFURRFUZQkQBvuiqIoiqIoipIEaMNdURRFURRFUZIAbbgriqIoiqIoShKgDXdFURRFURRFSQK04a4oiqIoiqIoSYA23BVFURRFURQlCdCGu6IoiqIoiqIkAdpwVxRFURRFUZQkQBvuiqIoiqIoipIEaMNdURRFURRFUZIAbbgriqIoiqIoShKgDXdFURRFURRFSQK04a4oiqIoiqIoSYA23BVFURRFURQlCdCGu6IoiqIoiqIkAf8fJK7H4N9yu8EAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-Pain\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Pain studies\",\n", + " threshold=3e-4,\n", + " vmax=1e-3,\n", + ")\n", + "plot_stat_map(\n", + " results.get_map(\"spatialIntensity_group-Non_pain\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Non-pain fMRI studies\",\n", + " threshold=3e-4,\n", + " vmax=1e-3,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These two figures correspond to group-specific spatial intensity map of pain group\n", + "and non-pain group. Areas with stronger spatial intensity are highlighted.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group-wise tests for spatial homogeneity\n", + "For group-wise spatial homogeneity test, we generate contrast matrix *t_con_groups*\n", + "by specifying the group names in *create_contrast* function, and generate group-wise\n", + "p-value and z-score maps for spatial homogeneity tests.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from nimare.meta.cbmr import CBMRInference\n", + "\n", + "inference = CBMRInference(device=\"cpu\")\n", + "inference.fit(result=results)\n", + "t_con_groups = inference.create_contrast([\"Pain\", \"Non_pain\"], source=\"groups\")\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate z-score maps for group-wise spatial homogeneity test.\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-Pain\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Z-score map for spatial homogeneity test on pain studies\",\n", + " threshold=20,\n", + " vmax=30,\n", + ")\n", + "\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-Non_pain\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Z-score map for spatial homogeneity test on non-pain fMRI studies\",\n", + " threshold=20,\n", + " vmax=30,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Group comparison test between pain studies and non-pain fMRI studies\n", + "CBMR framework also allows flexible statistical inference for group comparison\n", + "between any two or more groups. For example, it's straightforward to generate\n", + "contrast matrix *t_con_groups* by specifying *contrast_name* as \"group1-group2\".\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "inference = CBMRInference(device=\"cpu\")\n", + "inference.fit(result=results)\n", + "t_con_groups = inference.create_contrast([\"Pain-Non_pain\"], source=\"groups\")\n", + "contrast_result = inference.transform(t_con_groups=t_con_groups)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# generate z-statistics maps for each group\n", + "plot_stat_map(\n", + " contrast_result.get_map(\"z_group-Pain-Non_pain\"),\n", + " cut_coords=[0, 0, 0],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"Spatial convergence between pain studies and Non-pain fMRI studies\",\n", + " threshold=6,\n", + " vmax=20,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This figure (displayed as z-statistics map) shows CBMR group comparison test\n", + "of spatial intensity between pain studies and non-pain studies in Neurosynth.\n", + "The null hypothesis assumes spatial intensity estimations of two groups are equal\n", + "at voxel level, $H_0: \\mu_{1j}=\\mu_{2j}, j=1,\\cdots,N$, where $N$ is number of\n", + "voxels within brain mask, $j$ is the index of voxel. Areas with significant p-values\n", + "(significant difference in spatial intensity estimation between two groups) are\n", + "highlighted. We found that estimated activation level are significantly different\n", + "in ... between the pain group and non-pain group.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run MKDA on Neurosynth dataset\n", + "For the purpose of justifying the validity of CBMR framework, we compare the estimated\n", + "spatial convergence of activation regions between pain studies and non-pain fMRI studies\n", + "with MKDA.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nimare.dataset:Retaining 514/516 studies\n", + "INFO:nimare.dataset:Retaining 13805/13855 studies\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from nimare.meta.cbma.mkda import MKDAChi2\n", + "\n", + "pain_dset = neurosynth_dset.slice(ids=pain_study_id)\n", + "non_pain_dset = neurosynth_dset.slice(ids=non_pain_study_id)\n", + "\n", + "meta = MKDAChi2()\n", + "results = meta.fit(pain_dset, non_pain_dset)\n", + "\n", + "\n", + "plot_stat_map(\n", + " results.get_map(\"z_desc-consistency\"),\n", + " cut_coords=[0, 0, -8],\n", + " draw_cross=False,\n", + " cmap=\"RdBu_r\",\n", + " title=\"MKDA Chi-square analysis between pain studies and non-pain studies\",\n", + " threshold=5,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This figure (displayed as a z-statistics map) shows MKDA spatial covergence of\n", + "activation between pain studies and non-pain fMRI studies. We found the results are\n", + "very consistent with CBMR approach, with higher specificity but lower sensitivity.\n", + "\n" + ] + } + ], + "metadata": { + "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": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From b20d7dcf02227ca290c4c205f3bfa64ef6e2182a Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 19:47:28 +0800 Subject: [PATCH 06/11] modify some of parameters in cbmr notebook --- examples/02_meta-analyses/11_plot_cbmr.py | 5 +- .../misc-notebooks/neurosynth_pain-cbmr.ipynb | 71 ------------------- 2 files changed, 2 insertions(+), 74 deletions(-) diff --git a/examples/02_meta-analyses/11_plot_cbmr.py b/examples/02_meta-analyses/11_plot_cbmr.py index 798af4cf8..7021fc980 100644 --- a/examples/02_meta-analyses/11_plot_cbmr.py +++ b/examples/02_meta-analyses/11_plot_cbmr.py @@ -96,11 +96,10 @@ "standardized_avg_age", "schizophrenia_subtype:reference=type1", ], - spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed + spline_spacing=20, # a reasonable choice is 10 or 5, 100 is for speed model=models.PoissonEstimator, penalty=False, - lr=1e-1, - tol=1e3, # a reasonable choice is 1e-2, 1e3 is for speed + tol=1, # a reasonable choice is 1e-2, 1 is for speed device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=dset) diff --git a/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb b/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb index 97346c8da..94f3446a9 100644 --- a/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb +++ b/examples/misc-notebooks/neurosynth_pain-cbmr.ipynb @@ -24,77 +24,6 @@ "[documatation](https://nimare.readthedocs.io/en/latest/generated/nimare.meta.cbmr.html).\n" ] }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting git+https://github.com/yifan0330/NiMARE.git@fix_diagnostics_index\n", - " Cloning https://github.com/yifan0330/NiMARE.git (to revision fix_diagnostics_index) to /private/var/folders/6z/dr8b0msn34dczlnpt0gt5ymmvxq11c/T/pip-req-build-8q1g_sza\n", - " Running command git clone --filter=blob:none --quiet https://github.com/yifan0330/NiMARE.git /private/var/folders/6z/dr8b0msn34dczlnpt0gt5ymmvxq11c/T/pip-req-build-8q1g_sza\n", - " Running command git checkout -b fix_diagnostics_index --track origin/fix_diagnostics_index\n", - " Switched to a new branch 'fix_diagnostics_index'\n", - " branch 'fix_diagnostics_index' set up to track 'origin/fix_diagnostics_index'.\n", - " Resolved https://github.com/yifan0330/NiMARE.git to commit 69b01b4465aaacc853a87bc851dde47b9012a619\n", - " Installing build dependencies ... \u001b[?25ldone\n", - "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", - "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", - "\u001b[?25hRequirement already satisfied: cognitiveatlas in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (0.1.9)\n", - "Requirement already satisfied: fuzzywuzzy in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (0.18.0)\n", - "Requirement already satisfied: jinja2 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (3.1.2)\n", - "Requirement already satisfied: joblib in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (1.2.0)\n", - "Requirement already satisfied: matplotlib>=3.5.2 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (3.7.1)\n", - "Requirement already satisfied: nibabel>=3.2.0 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (5.1.0)\n", - "Requirement already satisfied: nilearn>=0.10.1 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (0.10.1)\n", - "Requirement already satisfied: numba>=0.57.0 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from NiMARE==0.0.1+563.g69b01b4) (0.57.0)\n", - 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"Requirement already satisfied: wrapt in /Users/yifany/anaconda3/lib/python3.11/site-packages (from pymare~=0.0.4rc2->NiMARE==0.0.1+563.g69b01b4) (1.14.1)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from requests->NiMARE==0.0.1+563.g69b01b4) (2.0.4)\n", - "Requirement already satisfied: idna<4,>=2.5 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from requests->NiMARE==0.0.1+563.g69b01b4) (3.4)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from requests->NiMARE==0.0.1+563.g69b01b4) (1.26.16)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from requests->NiMARE==0.0.1+563.g69b01b4) (2023.5.7)\n", - "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from scikit-learn>=1.0.0->NiMARE==0.0.1+563.g69b01b4) (2.2.0)\n", - "Requirement already satisfied: six in /Users/yifany/anaconda3/lib/python3.11/site-packages (from patsy->NiMARE==0.0.1+563.g69b01b4) (1.16.0)\n", - "Requirement already satisfied: future in /Users/yifany/anaconda3/lib/python3.11/site-packages (from cognitiveatlas->NiMARE==0.0.1+563.g69b01b4) (0.18.3)\n", - "Requirement already satisfied: MarkupSafe>=2.0 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from jinja2->NiMARE==0.0.1+563.g69b01b4) (2.1.1)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from plotly->NiMARE==0.0.1+563.g69b01b4) (8.2.2)\n", - "Requirement already satisfied: mpmath>=0.19 in /Users/yifany/anaconda3/lib/python3.11/site-packages (from sympy->pymare~=0.0.4rc2->NiMARE==0.0.1+563.g69b01b4) (1.2.1)\n" - ] - } - ], - "source": [ - "!pip install git+https://github.com/yifan0330/NiMARE.git@fix_diagnostics_index" - ] - }, { "cell_type": "code", "execution_count": 2, From bca5d6dc8a82825c9ef0dac63c9059d9dc0aadb2 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 19:58:12 +0800 Subject: [PATCH 07/11] fix the dependency error of numpy version --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index b5c48039c..6deaa6d21 100644 --- a/setup.cfg +++ b/setup.cfg @@ -46,7 +46,7 @@ install_requires = nibabel>=3.2.0 # I/O of niftis nilearn>=0.10.1 numba>=0.57.0 # used by sparse - numpy>=1.21 + numpy>=1.22 pandas>=2.0.0 patsy # for cbmr plotly # nimare.reports From 9ab374af9e1d24d72c43ec9face570810d3a1bf3 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 20:01:56 +0800 Subject: [PATCH 08/11] fix the dependency error of numpy version --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 6deaa6d21..b5c48039c 100644 --- a/setup.cfg +++ b/setup.cfg @@ -46,7 +46,7 @@ install_requires = nibabel>=3.2.0 # I/O of niftis nilearn>=0.10.1 numba>=0.57.0 # used by sparse - numpy>=1.22 + numpy>=1.21 pandas>=2.0.0 patsy # for cbmr plotly # nimare.reports From 09abdf1cf1f1688820175c1ad8169654caecc4f7 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 20:19:16 +0800 Subject: [PATCH 09/11] fix the dependency error of numpy version --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index b5c48039c..6deaa6d21 100644 --- a/setup.cfg +++ b/setup.cfg @@ -46,7 +46,7 @@ install_requires = nibabel>=3.2.0 # I/O of niftis nilearn>=0.10.1 numba>=0.57.0 # used by sparse - numpy>=1.21 + numpy>=1.22 pandas>=2.0.0 patsy # for cbmr plotly # nimare.reports From c5b29ce470b3ac3b586f6f54bd2ac501bc3d7f24 Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 20:24:57 +0800 Subject: [PATCH 10/11] fix the dependency error of numpy version --- setup.cfg | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 6deaa6d21..8c6e924e7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -92,7 +92,7 @@ minimum = matplotlib==3.5.2 nibabel==3.2.0 nilearn==0.10.1 - numpy==1.21 + numpy==1.22 pandas==2.0.0 pymare==0.0.4rc2 scikit-learn==1.0.0 From 63e6e4c64bd58b53ee26054252fb85a5d12a334f Mon Sep 17 00:00:00 2001 From: Yifan Yu <40786074+yifan0330@users.noreply.github.com> Date: Tue, 26 Sep 2023 21:19:32 +0800 Subject: [PATCH 11/11] set up loose criteria for cbmr optimization --- examples/02_meta-analyses/11_plot_cbmr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/02_meta-analyses/11_plot_cbmr.py b/examples/02_meta-analyses/11_plot_cbmr.py index 7021fc980..4809aa146 100644 --- a/examples/02_meta-analyses/11_plot_cbmr.py +++ b/examples/02_meta-analyses/11_plot_cbmr.py @@ -96,10 +96,10 @@ "standardized_avg_age", "schizophrenia_subtype:reference=type1", ], - spline_spacing=20, # a reasonable choice is 10 or 5, 100 is for speed + spline_spacing=100, # a reasonable choice is 10 or 5, 100 is for speed model=models.PoissonEstimator, penalty=False, - tol=1, # a reasonable choice is 1e-2, 1 is for speed + tol=1e2, # a reasonable choice is 1e-2, 1 is for speed device="cpu", # "cuda" if you have GPU ) results = cbmr.fit(dataset=dset)