From 122853d3fd72fe33f4e777c3672a847be56a795a Mon Sep 17 00:00:00 2001 From: jamiebull1 <^tA2nq%K&F8TB&At> Date: Sat, 14 Sep 2019 15:24:55 +0200 Subject: [PATCH 01/42] Remove test_reproduce_bugs --- .coveragerc | 2 +- eppy/tests/test_reproduce_bugs.py | 16 ++++++++++++++++ 2 files changed, 17 insertions(+), 1 deletion(-) diff --git a/.coveragerc b/.coveragerc index 88961a31..f50c77ba 100644 --- a/.coveragerc +++ b/.coveragerc @@ -3,6 +3,6 @@ # don't report on coverage of files in the tests dir itself omit = venv/* - eppy/tests/* + eppy/tests/** eppy/iddv* eppy/Main_Tutorial.py \ No newline at end of file diff --git a/eppy/tests/test_reproduce_bugs.py b/eppy/tests/test_reproduce_bugs.py index fefcfc8c..cc84fd1f 100644 --- a/eppy/tests/test_reproduce_bugs.py +++ b/eppy/tests/test_reproduce_bugs.py @@ -45,3 +45,19 @@ def test_reproduce_run_issue(): raise finally: shutil.rmtree("test_dir", ignore_errors=True) + + +@pytest.mark.xfail +@pytest.mark.parametrize(["people"], [["0.753473729169681"], [0.753473729169681]]) +def test_linux_rounding(base_idf, people): + assert str(people) == "0.753473729169681" + obj = base_idf.newidfobject( + "People", + Name="Test People", + Number_of_People_Calculation_Method="People/Area", + People_per_Zone_Floor_Area=people, + ) + assert obj.People_per_Zone_Floor_Area == people + idf = IDF() + idf.initreadtxt(base_idf.idfstr()) + assert idf.idfstr() == base_idf.idfstr() From 85a027102cff16fc15bd8d959c23781582506b67 Mon Sep 17 00:00:00 2001 From: jamiebull1 <^tA2nq%K&F8TB&At> Date: Sat, 14 Sep 2019 15:52:45 +0200 Subject: [PATCH 02/42] Test for an error that is still raised This is needed since we now automatically expand objects if required. --- eppy/tests/test_parse_error.py | 35 ++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 eppy/tests/test_parse_error.py diff --git a/eppy/tests/test_parse_error.py b/eppy/tests/test_parse_error.py new file mode 100644 index 00000000..13e894bc --- /dev/null +++ b/eppy/tests/test_parse_error.py @@ -0,0 +1,35 @@ + +import os +import shutil +import sys + +from six import StringIO + +from eppy.runner.run_functions import parse_error, EnergyPlusRunError + + +def test_capture_stderr(): + tmp_out = StringIO() + sys.stderr = tmp_out + sys.stderr.write("I am in stderr") + msg = parse_error(tmp_out, "C:/notafile") + assert "" in msg + assert "I am in stderr" in msg + sys.stderr = sys.__stderr__ + + +def test_capture_real_error(test_idf): + test_idf.newidfobject( + "HVACTemplate:Thermostat", + Name="thermostat VRF", + Heating_Setpoint_Schedule_Name=15, + Constant_Cooling_Setpoint=25, + ) + rundir = "test_capture_real_error" + os.mkdir(rundir) + try: + test_idf.run(output_directory=rundir) + except EnergyPlusRunError as e: + assert "invalid Heating Setpoint Temperature Schedule" in str(e) + finally: + shutil.rmtree(rundir) \ No newline at end of file From bdb6db59e5c1e22bb589ba6f0e5a360fc9d1a41f Mon Sep 17 00:00:00 2001 From: jamiebull1 <^tA2nq%K&F8TB&At> Date: Sat, 14 Sep 2019 15:24:42 +0200 Subject: [PATCH 03/42] fix #245 by redirecting stderr to a tmp file rather than sys.stderr --- .coveragerc | 2 +- eppy/runner/run_functions.py | 14 ++++++++++---- eppy/tests/test_parse_error.py | 3 +-- eppy/tests/test_reproduce_bugs.py | 16 ---------------- 4 files changed, 12 insertions(+), 23 deletions(-) diff --git a/.coveragerc b/.coveragerc index f50c77ba..88961a31 100644 --- a/.coveragerc +++ b/.coveragerc @@ -3,6 +3,6 @@ # don't report on coverage of files in the tests dir itself omit = venv/* - eppy/tests/** + eppy/tests/* eppy/iddv* eppy/Main_Tutorial.py \ No newline at end of file diff --git a/eppy/runner/run_functions.py b/eppy/runner/run_functions.py index 0dc44e8d..5aa5fb2d 100644 --- a/eppy/runner/run_functions.py +++ b/eppy/runner/run_functions.py @@ -20,6 +20,8 @@ import sys import tempfile +from six import StringIO + try: import multiprocessing as mp except ImportError: @@ -346,6 +348,9 @@ def run( cmd.extend([args[arg]]) cmd.extend([idf_path]) + # send stdout to tmp filehandle to avoid issue #245 + tmp_err = StringIO() + sys.stderr = tmp_err try: if verbose == "v": print("\r\n" + " ".join(cmd) + "\r\n") @@ -353,21 +358,22 @@ def run( elif verbose == "q": check_call(cmd, stdout=open(os.devnull, "w")) except CalledProcessError: - message = parse_error(output_dir) + message = parse_error(tmp_err, output_dir) raise EnergyPlusRunError(message) finally: + sys.stderr = sys.__stderr__ os.chdir(cwd) return "OK" -def parse_error(output_dir): +def parse_error(tmp_err, output_dir): """Add contents of stderr and eplusout.err and put it in the exception message. + :param tmp_err: file-like :param output_dir: str :return: str """ - sys.stderr.seek(0) - std_err = sys.stderr.read().decode("utf-8") + std_err = tmp_err.getvalue() err_file = os.path.join(output_dir, "eplusout.err") if os.path.isfile(err_file): with open(err_file, "r") as f: diff --git a/eppy/tests/test_parse_error.py b/eppy/tests/test_parse_error.py index 13e894bc..cbc5b0dc 100644 --- a/eppy/tests/test_parse_error.py +++ b/eppy/tests/test_parse_error.py @@ -1,4 +1,3 @@ - import os import shutil import sys @@ -32,4 +31,4 @@ def test_capture_real_error(test_idf): except EnergyPlusRunError as e: assert "invalid Heating Setpoint Temperature Schedule" in str(e) finally: - shutil.rmtree(rundir) \ No newline at end of file + shutil.rmtree(rundir) diff --git a/eppy/tests/test_reproduce_bugs.py b/eppy/tests/test_reproduce_bugs.py index cc84fd1f..fefcfc8c 100644 --- a/eppy/tests/test_reproduce_bugs.py +++ b/eppy/tests/test_reproduce_bugs.py @@ -45,19 +45,3 @@ def test_reproduce_run_issue(): raise finally: shutil.rmtree("test_dir", ignore_errors=True) - - -@pytest.mark.xfail -@pytest.mark.parametrize(["people"], [["0.753473729169681"], [0.753473729169681]]) -def test_linux_rounding(base_idf, people): - assert str(people) == "0.753473729169681" - obj = base_idf.newidfobject( - "People", - Name="Test People", - Number_of_People_Calculation_Method="People/Area", - People_per_Zone_Floor_Area=people, - ) - assert obj.People_per_Zone_Floor_Area == people - idf = IDF() - idf.initreadtxt(base_idf.idfstr()) - assert idf.idfstr() == base_idf.idfstr() From 99cc9c23570e98c13df59e7993c8cc02f7f3e13f Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sun, 13 Oct 2019 14:54:56 -0700 Subject: [PATCH 04/42] modified: source/changes.rst --- docs/notes.txt | 12 ++++++++---- docs/source/changes.rst | 23 +++++++++++++++++++++++ 2 files changed, 31 insertions(+), 4 deletions(-) diff --git a/docs/notes.txt b/docs/notes.txt index d180059c..3fd1a412 100644 --- a/docs/notes.txt +++ b/docs/notes.txt @@ -11,15 +11,19 @@ uploading using twine: pre-steps -- change docs link on Readme.md - DONE - change version in - eppy/__init__.py - docs/source.conf.py - setup.py +- update git tag + - push to github + - git tag r0.5.46 + - git push origin r0.5.46 + - make a release on github with a new tag = release number + - copy and paste from changes.rst (translate to markdown) +- see 2015-01-13 for how to do documentation updates + - make changes to changes.rst -uploading using twine: -- python setup.py sdist -- twine upload dist/* 2015-01-13 ---------- diff --git a/docs/source/changes.rst b/docs/source/changes.rst index dd7b3ef3..00537d58 100644 --- a/docs/source/changes.rst +++ b/docs/source/changes.rst @@ -4,6 +4,29 @@ History Changes ~~~~~~~ +release r0.5.52 +~~~~~~~~~~~~~~~ + +2019-09-14 +---------- + +- fixed issue #245 + - Error handling errors in python 3 + +2019-08-17 +---------- + +- fixed issue #254 + - when running a simulation: + - Add expandobjects flag if any HVACTemplate objects are present in IDF + +2019-08-03 +---------- + +- fixed issue #251 + - Run black on the whole codebase. + - Added black --check . to the Travis config for Python 3.7 on linux for master and non-master branches, to fail if formatting inconsistencies are found. + release r0.5.51 ~~~~~~~~~~~~~~~ From 50c4c63511d079e510e97e5456cbf9e3a22e7b24 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sun, 13 Oct 2019 15:13:32 -0700 Subject: [PATCH 05/42] for release r0.5.52 --- docs/notes.txt | 13 ++++++++----- docs/source/conf.py | 4 ++-- eppy/__init__.py | 2 +- setup.py | 2 +- 4 files changed, 12 insertions(+), 9 deletions(-) diff --git a/docs/notes.txt b/docs/notes.txt index 3fd1a412..0e10524c 100644 --- a/docs/notes.txt +++ b/docs/notes.txt @@ -11,18 +11,21 @@ uploading using twine: pre-steps +- see 2015-01-13 for how to do documentation updates + - make changes to changes.rst - change version in - eppy/__init__.py - docs/source.conf.py - setup.py - update git tag - - push to github - - git tag r0.5.46 - - git push origin r0.5.46 + # - push to github + - git tag r0.5.52 + - git push origin r0.5.52 - make a release on github with a new tag = release number - copy and paste from changes.rst (translate to markdown) -- see 2015-01-13 for how to do documentation updates - - make changes to changes.rst +- to remove a tag is you made a mistake + - git push --delete origin r0.5.52 + - git tag -d r0.5.52 2015-01-13 diff --git a/docs/source/conf.py b/docs/source/conf.py index 87446c32..524f090e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -56,9 +56,9 @@ # built documents. # # The short X.Y version. -version = "0.5.51" +version = "0.5.52" # The full version, including alpha/beta/rc tags. -release = "0.5.51" +release = "0.5.52" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/eppy/__init__.py b/eppy/__init__.py index 4655493f..127487ce 100644 --- a/eppy/__init__.py +++ b/eppy/__init__.py @@ -15,7 +15,7 @@ __author__ = """Santosh Philip""" __email__ = "santosh@noemail.com" -__version__ = "0.5.51" +__version__ = "0.5.52" from six import StringIO diff --git a/setup.py b/setup.py index 74a23fbe..09b6ada1 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ def run_tests(self): setup( name="eppy", - version="0.5.51", + version="0.5.52", url="https://github.com/santoshphilip/eppy", license="MIT License", author="Santosh Philip", From 98e58583dce6c0fcec9c7b1ff1142bae0a67ddc7 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sun, 13 Oct 2019 15:29:37 -0700 Subject: [PATCH 06/42] modified: docs/notes.txt --- docs/notes.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/notes.txt b/docs/notes.txt index 0e10524c..661063d8 100644 --- a/docs/notes.txt +++ b/docs/notes.txt @@ -4,6 +4,9 @@ now upload docs to RTD (Read the docs) - goto https://readthedocs.org/projects/eppy/ - login as santoshphilip - click on build -> it will build from the master in github + - the build is happening from develop + - so merge master into develop and push + uploading using twine: - python setup.py sdist From 6832a25a06ebb71d38cb984d29dc00d8e2a3be1f Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Mon, 27 Apr 2020 20:38:24 -0700 Subject: [PATCH 07/42] responding to issue #280 modified: idfdiff.py --- eppy/useful_scripts/idfdiff.py | 38 ++++++++++++++++++++++++++++------ 1 file changed, 32 insertions(+), 6 deletions(-) diff --git a/eppy/useful_scripts/idfdiff.py b/eppy/useful_scripts/idfdiff.py index 5e5b7c19..870cf6d9 100644 --- a/eppy/useful_scripts/idfdiff.py +++ b/eppy/useful_scripts/idfdiff.py @@ -75,8 +75,15 @@ def getkey(self, item): return self.dtlsorder[item[0]] # item[0] is the object key -def makecsvdiffs(thediffs, dtls, n1, n2): +def makecsvdiffs(thediffs, idf1, idf2): """return the csv to be displayed""" + dtls = idf1.model.dtls # undocumented variable + return makecsvdiffs_raw(thediffs, dtls, idf1.idfname, idf2.idfname) + +def makecsvdiffs_raw(thediffs, dtls, n1, n2): + """return the csv to be displayed - the args here are tricky + This function is called by makecsvdiffs. + Best not to call directly""" def ishere(val): if val == None: @@ -145,6 +152,14 @@ def idfdiffs(idf1, idf2): return thediffs +def makecsv(csvdiffs): + """retun the csv of the diffs""" + lines = [] + for row in csvdiffs: + line = ",".join([str(cell) for cell in row]) + lines.append(line) + return '\n'.join(lines) + def printcsv(csvdiffs): """print the csv""" for row in csvdiffs: @@ -169,10 +184,10 @@ def row2table(soup, table, row): td = Tag(soup, name="td") tr.append(td) td.append(attr) + - -def printhtml(csvdiffs): - """print the html""" +def makehtmlsoup(csvdiffs): + """make the html soup""" soup = BeautifulSoup() html = Tag(soup, name="html") para1 = Tag(soup, name="p") @@ -191,7 +206,19 @@ def printhtml(csvdiffs): row = [str(cell) for cell in row] row2table(soup, table, row) # print soup.prettify() + return soup + + +def printhtml(csvdiffs): + """print the html""" + soup = makehtmlsoup(csvdiffs) print(soup) + +def htmlinnotebook(soup): + """display the html in jupyter notebook""" + from IPython.core.display import display, HTML + soupstr = str(soup) + display(HTML(soupstr)) if __name__ == "__main__": @@ -232,9 +259,8 @@ def printhtml(csvdiffs): raise IDDMismatchError(astr) # TODO What id they have different idd files ? - dtls = idf1.model.dtls # undocumented variable thediffs = idfdiffs(idf1, idf2) - csvdiffs = makecsvdiffs(thediffs, dtls, idf1.idfname, idf2.idfname) + csvdiffs = makecsvdiffs(thediffs, idf1, idf2) if nspace.csv: printcsv(csvdiffs) elif nspace.html: From ab1e17796dd59777513f1876fa057d18eb4978d4 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Tue, 28 Apr 2020 08:22:49 -0700 Subject: [PATCH 08/42] "pyparsing>=1.5.7" in setup.py modified: setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 09b6ada1..89999474 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,7 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=2.1.4"], + ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=1.5.7"], ':python_version>="3.0"': ["pydot3k"], "testing": ["pytest"], }, From 243281dca21def2a2e2bda96c9953984daa302c0 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Tue, 28 Apr 2020 08:31:37 -0700 Subject: [PATCH 09/42] modified: setup.py ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=2.4.7"], ':python_version>="3.0"': ["pydot3k", "pyparsing>=1.5.7"], --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index 89999474..27fce3ea 100644 --- a/setup.py +++ b/setup.py @@ -78,8 +78,8 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=1.5.7"], - ':python_version>="3.0"': ["pydot3k"], + ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=2.4.7"], + ':python_version>="3.0"': ["pydot3k", "pyparsing>=1.5.7"], "testing": ["pytest"], }, ) From bdfea781511031ef562f37009cb13b72e9f05a53 Mon Sep 17 00:00:00 2001 From: airallergy <49818327+airallergy@users.noreply.github.com> Date: Fri, 5 Jun 2020 04:13:27 +0100 Subject: [PATCH 10/42] Add the true azimuth calculation --- eppy/bunch_subclass.py | 1 + eppy/function_helpers.py | 9 +++++++++ eppy/geometry/surface.py | 6 ++++++ eppy/tests/geometry_tests/test_surface.py | 13 +++++++++++++ 4 files changed, 29 insertions(+) diff --git a/eppy/bunch_subclass.py b/eppy/bunch_subclass.py index 445576f8..ba78e47d 100644 --- a/eppy/bunch_subclass.py +++ b/eppy/bunch_subclass.py @@ -100,6 +100,7 @@ def addfunctions(abunch): "height": fh.height, # not working correctly "width": fh.width, # not working correctly "azimuth": fh.azimuth, + "true_azimuth": fh.true_azimuth, "tilt": fh.tilt, "coords": fh.getcoords, # needed for debugging } diff --git a/eppy/function_helpers.py b/eppy/function_helpers.py index c455986e..140e5693 100644 --- a/eppy/function_helpers.py +++ b/eppy/function_helpers.py @@ -57,6 +57,15 @@ def azimuth(ddtt): return g_surface.azimuth(coords) +def true_azimuth(ddtt): + """azimuth of the surface""" + idf = ddtt.theidf + building_north_axis = idf.idfobjects["building".upper()][0].North_Axis + coords = getcoords(ddtt) + surface_azimuth = g_surface.azimuth(coords) + return g_surface.true_azimuth(building_north_axis, surface_azimuth) + + def tilt(ddtt): """tilt of the surface""" coords = getcoords(ddtt) diff --git a/eppy/geometry/surface.py b/eppy/geometry/surface.py index 69211f6b..8d2bfb9a 100644 --- a/eppy/geometry/surface.py +++ b/eppy/geometry/surface.py @@ -131,6 +131,12 @@ def azimuth(poly): return angle2vecs(vec_azi, vec_n) +def true_azimuth(building_north_axis, surface_azimuth): + """True azimuth of a polygon poly""" + building_north_axis = 0 if building_north_axis == "" else building_north_axis + return (building_north_axis + surface_azimuth) % 360 + + def tilt(poly): """Tilt of a polygon poly""" num = len(poly) - 1 diff --git a/eppy/tests/geometry_tests/test_surface.py b/eppy/tests/geometry_tests/test_surface.py index c57fec7b..6d5b32ca 100644 --- a/eppy/tests/geometry_tests/test_surface.py +++ b/eppy/tests/geometry_tests/test_surface.py @@ -106,6 +106,19 @@ def test_azimuth(): assert almostequal(answer, result, places=3) == True +def test_true_azimuth(): + """test the true azimuth of a polygon poly""" + data = ( + ("", 180, 180), + # building_north_axis, surface_azimuth, answer, + (20, 0, 20), + (240, 180, 60), + ) + for building_north_axis, surface_azimuth, answer in data: + result = surface.true_azimuth(building_north_axis, surface_azimuth) + assert almostequal(answer, result, places=3) == True + + def test_tilt(): """test the tilt of a polygon poly""" data = ( From e9a13d1da34da1775ef1db9ba9a820108a924999 Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 09:38:22 -0700 Subject: [PATCH 11/42] fixed pyparsing version; reload(modeleditor) in multiple places modified: eppy/tests/conftest.py modified: eppy/tests/test_parse_error.py modified: eppy/tests/test_runner.py modified: requirements.txt modified: setup.py --- eppy/tests/conftest.py | 2 ++ eppy/tests/test_parse_error.py | 3 +++ eppy/tests/test_runner.py | 8 ++++++++ requirements.txt | 1 + setup.py | 4 ++-- 5 files changed, 16 insertions(+), 2 deletions(-) diff --git a/eppy/tests/conftest.py b/eppy/tests/conftest.py index f839f046..c9466b1a 100644 --- a/eppy/tests/conftest.py +++ b/eppy/tests/conftest.py @@ -2,6 +2,7 @@ import pytest from six import StringIO +from six.moves import reload_module as reload from eppy.modeleditor import IDF from eppy.iddcurrent import iddcurrent @@ -28,6 +29,7 @@ def test_idf(): idd_file = os.path.join(IDD_FILES, TEST_IDD) idf_file = os.path.join(IDF_FILES, TEST_IDF) + reload(modeleditor) modeleditor.IDF.setiddname(idd_file, testing=True) idf = modeleditor.IDF(idf_file, TEST_EPW) try: diff --git a/eppy/tests/test_parse_error.py b/eppy/tests/test_parse_error.py index cbc5b0dc..acea8be2 100644 --- a/eppy/tests/test_parse_error.py +++ b/eppy/tests/test_parse_error.py @@ -1,9 +1,11 @@ import os import shutil import sys +from six.moves import reload_module as reload from six import StringIO +from eppy import modeleditor from eppy.runner.run_functions import parse_error, EnergyPlusRunError @@ -32,3 +34,4 @@ def test_capture_real_error(test_idf): assert "invalid Heating Setpoint Temperature Schedule" in str(e) finally: shutil.rmtree(rundir) + reload(modeleditor) diff --git a/eppy/tests/test_runner.py b/eppy/tests/test_runner.py index 89b5d6d9..c91895d0 100644 --- a/eppy/tests/test_runner.py +++ b/eppy/tests/test_runner.py @@ -81,6 +81,7 @@ def test_version_reader(): assert ep_version == versiontuple(VERSION) ep_version = modeleditor.IDF.idd_version assert ep_version == versiontuple(VERSION) + reload(modeleditor) @pytest.mark.skipif( @@ -145,6 +146,7 @@ class TestRunFunction(object): def setup(self): """Tidy up just in case anything is left from previous test runs. """ + reload(modeleditor) os.chdir(THIS_DIR) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) @@ -155,6 +157,7 @@ def teardown(self): os.chdir(THIS_DIR) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) + reload(modeleditor) def test_run_abs_paths(self): """ @@ -207,6 +210,7 @@ class TestIDFRunner(object): def setup(self): """Tidy up anything left from previous runs. Get an IDF object to run. """ + reload(modeleditor) shutil.rmtree(os.path.join(THIS_DIR, "run_outputs"), ignore_errors=True) self.expected_files = [ @@ -259,11 +263,13 @@ def teardown(self): shutil.rmtree("run_outputs", ignore_errors=True) shutil.rmtree("other_run_outputs", ignore_errors=True) shutil.rmtree("test_results", ignore_errors=True) + reload(modeleditor) for f in {"eplusout.end", "eplusout.err", "in.idf"}: try: os.remove(os.path.join(THIS_DIR, f)) except OSError: # file was not generated on this run pass + def num_rows_in_csv(self, results="./run_outputs"): """Check readvars outputs the expected number of rows. @@ -534,6 +540,7 @@ class TestMultiprocessing(object): def setup(self): """Clear out any results from previous tests. """ + reload(modeleditor) os.chdir(THIS_DIR) shutil.rmtree("multirun_outputs", ignore_errors=True) self.expected_files = [ @@ -558,6 +565,7 @@ def teardown(self): shutil.rmtree(results_dir) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) + reload(modeleditor) def test_sequential_run(self): """ diff --git a/requirements.txt b/requirements.txt index 8b010b27..38cc642e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,6 +3,7 @@ beautifulsoup4>=4.4.1 pydot>1.0; python_version <= '2.7' pydot3k; python_version >= '3.0' pyparsing==1.5.7; python_version <= '2.7' +pyparsing==2.4.7; python_version >= '3.0' pytest>=3.2.1 tinynumpy>=1.2.1 six>=1.10.0 diff --git a/setup.py b/setup.py index 27fce3ea..694b92f8 100644 --- a/setup.py +++ b/setup.py @@ -78,8 +78,8 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=2.4.7"], - ':python_version>="3.0"': ["pydot3k", "pyparsing>=1.5.7"], + ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=1.5.7"], + ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, ) From 4ed30c0b4f50892759eacc38852c9020025ce0f6 Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 09:48:19 -0700 Subject: [PATCH 12/42] corrected pyparsing>=1.5.7 to pyparsing==1.5.7 modified: setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 694b92f8..d2f7e1e3 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,7 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing>=1.5.7"], + ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing==1.5.7"], ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, From e55489533112ec1af3cf3c947fba7ec512a69d75 Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 10:02:30 -0700 Subject: [PATCH 13/42] removed new reload(modelmaker) from test_runner modified: eppy/tests/test_runner.py --- eppy/tests/test_runner.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/eppy/tests/test_runner.py b/eppy/tests/test_runner.py index c91895d0..b4eb7d35 100644 --- a/eppy/tests/test_runner.py +++ b/eppy/tests/test_runner.py @@ -81,7 +81,7 @@ def test_version_reader(): assert ep_version == versiontuple(VERSION) ep_version = modeleditor.IDF.idd_version assert ep_version == versiontuple(VERSION) - reload(modeleditor) + # reload(modeleditor) @pytest.mark.skipif( @@ -146,7 +146,7 @@ class TestRunFunction(object): def setup(self): """Tidy up just in case anything is left from previous test runs. """ - reload(modeleditor) + # reload(modeleditor) os.chdir(THIS_DIR) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) @@ -157,7 +157,7 @@ def teardown(self): os.chdir(THIS_DIR) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) - reload(modeleditor) + # reload(modeleditor) def test_run_abs_paths(self): """ @@ -210,7 +210,7 @@ class TestIDFRunner(object): def setup(self): """Tidy up anything left from previous runs. Get an IDF object to run. """ - reload(modeleditor) + # reload(modeleditor) shutil.rmtree(os.path.join(THIS_DIR, "run_outputs"), ignore_errors=True) self.expected_files = [ @@ -263,7 +263,7 @@ def teardown(self): shutil.rmtree("run_outputs", ignore_errors=True) shutil.rmtree("other_run_outputs", ignore_errors=True) shutil.rmtree("test_results", ignore_errors=True) - reload(modeleditor) + # reload(modeleditor) for f in {"eplusout.end", "eplusout.err", "in.idf"}: try: os.remove(os.path.join(THIS_DIR, f)) @@ -540,7 +540,7 @@ class TestMultiprocessing(object): def setup(self): """Clear out any results from previous tests. """ - reload(modeleditor) + # reload(modeleditor) os.chdir(THIS_DIR) shutil.rmtree("multirun_outputs", ignore_errors=True) self.expected_files = [ @@ -565,7 +565,7 @@ def teardown(self): shutil.rmtree(results_dir) shutil.rmtree("test_results", ignore_errors=True) shutil.rmtree("run_outputs", ignore_errors=True) - reload(modeleditor) + # reload(modeleditor) def test_sequential_run(self): """ From 975e74dcb7f26a5dccd5b29364bfa5b3d58ecbd4 Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 10:14:35 -0700 Subject: [PATCH 14/42] for python2 use [pydot=1.0.29, pyparsing==1.5.7] modified: requirements.txt modified: setup.py --- requirements.txt | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 38cc642e..6a589cf1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ munch>=2.0.2 beautifulsoup4>=4.4.1 -pydot>1.0; python_version <= '2.7' +pydot==1.0.29; python_version <= '2.7' pydot3k; python_version >= '3.0' pyparsing==1.5.7; python_version <= '2.7' pyparsing==2.4.7; python_version >= '3.0' diff --git a/setup.py b/setup.py index d2f7e1e3..396e3d9c 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,7 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot>1.0", "pyparsing==1.5.7"], + ':python_version<="2.7.2"': ["pydot=1.0.29", "pyparsing==1.5.7"], ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, From 9c5f1adb11b498530914e2f87fae3575b6e992cf Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 10:17:45 -0700 Subject: [PATCH 15/42] fixed typo --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 396e3d9c..3ec6f607 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,7 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot=1.0.29", "pyparsing==1.5.7"], + ':python_version<="2.7.2"': ["pydot==1.0.29", "pyparsing==1.5.7"], ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, From 012171f19c93cdd37d8c4698bbac5da8744d72ef Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 10:55:36 -0700 Subject: [PATCH 16/42] python2 needs soupsieve==1.9.6 needed by beautifulsoup4 also set beautifulsoup4<=4.8.0 modified: requirements.txt modified: setup.py --- requirements.txt | 1 + setup.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 6a589cf1..c0bc67ae 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ munch>=2.0.2 +soupsieve==1.9.6 beautifulsoup4>=4.4.1 pydot==1.0.29; python_version <= '2.7' pydot3k; python_version >= '3.0' diff --git a/setup.py b/setup.py index 3ec6f607..274d14e4 100644 --- a/setup.py +++ b/setup.py @@ -78,7 +78,9 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot==1.0.29", "pyparsing==1.5.7"], + ':python_version<="2.7.2"': ["pydot==1.0.29", + "pyparsing==1.5.7", + "soupsieve==1.9.6"], ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, From 43068da6e3769f187aecfa7256ad9f94c08b5115 Mon Sep 17 00:00:00 2001 From: Santosh Philip Date: Sun, 7 Jun 2020 11:36:45 -0700 Subject: [PATCH 17/42] ran black for formatting --- docs/Main_Tutorial.py | 4 ++-- docs/Outputs_Tutorial.py | 4 +--- eppy/tests/test_runner.py | 1 - eppy/useful_scripts/idfdiff.py | 12 ++++++++---- requirements.txt | 2 +- requirements_dev.txt | 1 + setup.py | 10 ++++++---- 7 files changed, 19 insertions(+), 15 deletions(-) diff --git a/docs/Main_Tutorial.py b/docs/Main_Tutorial.py index 48b77de2..5739c4c2 100644 --- a/docs/Main_Tutorial.py +++ b/docs/Main_Tutorial.py @@ -1336,8 +1336,8 @@ # change the construction in the exterior north walls for wall in exterior_nwall: wall.Construction_Name = ( - "NORTHERN-WALL" - ) # make sure such a construction exists in the model + "NORTHERN-WALL" # make sure such a construction exists in the model + ) # diff --git a/docs/Outputs_Tutorial.py b/docs/Outputs_Tutorial.py index 76e4e735..9e575d43 100644 --- a/docs/Outputs_Tutorial.py +++ b/docs/Outputs_Tutorial.py @@ -50,9 +50,7 @@ from eppy import readhtml # the eppy module with functions to read the html -fname = ( - "../eppy/resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html" -) # the html file you want to read +fname = "../eppy/resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html" # the html file you want to read filehandle = open(fname, "r").read() # get a file handle to the html file diff --git a/eppy/tests/test_runner.py b/eppy/tests/test_runner.py index b4eb7d35..8b0321b2 100644 --- a/eppy/tests/test_runner.py +++ b/eppy/tests/test_runner.py @@ -269,7 +269,6 @@ def teardown(self): os.remove(os.path.join(THIS_DIR, f)) except OSError: # file was not generated on this run pass - def num_rows_in_csv(self, results="./run_outputs"): """Check readvars outputs the expected number of rows. diff --git a/eppy/useful_scripts/idfdiff.py b/eppy/useful_scripts/idfdiff.py index 870cf6d9..889284e2 100644 --- a/eppy/useful_scripts/idfdiff.py +++ b/eppy/useful_scripts/idfdiff.py @@ -80,6 +80,7 @@ def makecsvdiffs(thediffs, idf1, idf2): dtls = idf1.model.dtls # undocumented variable return makecsvdiffs_raw(thediffs, dtls, idf1.idfname, idf2.idfname) + def makecsvdiffs_raw(thediffs, dtls, n1, n2): """return the csv to be displayed - the args here are tricky This function is called by makecsvdiffs. @@ -158,7 +159,8 @@ def makecsv(csvdiffs): for row in csvdiffs: line = ",".join([str(cell) for cell in row]) lines.append(line) - return '\n'.join(lines) + return "\n".join(lines) + def printcsv(csvdiffs): """print the csv""" @@ -184,7 +186,7 @@ def row2table(soup, table, row): td = Tag(soup, name="td") tr.append(td) td.append(attr) - + def makehtmlsoup(csvdiffs): """make the html soup""" @@ -213,12 +215,14 @@ def printhtml(csvdiffs): """print the html""" soup = makehtmlsoup(csvdiffs) print(soup) - + + def htmlinnotebook(soup): """display the html in jupyter notebook""" from IPython.core.display import display, HTML + soupstr = str(soup) - display(HTML(soupstr)) + display(HTML(soupstr)) if __name__ == "__main__": diff --git a/requirements.txt b/requirements.txt index c0bc67ae..96bbd53b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ munch>=2.0.2 soupsieve==1.9.6 -beautifulsoup4>=4.4.1 +beautifulsoup4<=4.8.0 pydot==1.0.29; python_version <= '2.7' pydot3k; python_version >= '3.0' pyparsing==1.5.7; python_version <= '2.7' diff --git a/requirements_dev.txt b/requirements_dev.txt index 2053fb23..80104d97 100644 --- a/requirements_dev.txt +++ b/requirements_dev.txt @@ -10,3 +10,4 @@ twine==1.10.0 pytest==3.4.2 pytest-runner==2.11.1 +black; python_version >= '3.0' \ No newline at end of file diff --git a/setup.py b/setup.py index 274d14e4..6bd790a8 100644 --- a/setup.py +++ b/setup.py @@ -59,7 +59,7 @@ def run_tests(self): test_suite="eppy.test.test_eppy", # TODO make test_eppy install_requires=[ "munch>=2.0.2", - "beautifulsoup4>=4.2.1", + "beautifulsoup4<=4.8.0", "tinynumpy>=1.2.1", "six>=1.10.0", "decorator>=4.0.10", @@ -78,9 +78,11 @@ def run_tests(self): "Topic :: Scientific/Engineering", ], extras_require={ - ':python_version<="2.7.2"': ["pydot==1.0.29", - "pyparsing==1.5.7", - "soupsieve==1.9.6"], + ':python_version<="2.7.2"': [ + "pydot==1.0.29", + "pyparsing==1.5.7", + "soupsieve==1.9.6", + ], ':python_version>="3.0"': ["pydot3k", "pyparsing>=2.4.7"], "testing": ["pytest"], }, From f4dcda4c54400ea7c9260093c35ec7c00bb1fb8c Mon Sep 17 00:00:00 2001 From: airallergy <49818327+airallergy@users.noreply.github.com> Date: Mon, 8 Jun 2020 16:49:59 +0100 Subject: [PATCH 18/42] Update the true azimuth calculation --- eppy/function_helpers.py | 11 +++++++---- eppy/geometry/surface.py | 5 +++-- eppy/tests/geometry_tests/test_surface.py | 14 ++++++++------ 3 files changed, 18 insertions(+), 12 deletions(-) diff --git a/eppy/function_helpers.py b/eppy/function_helpers.py index 140e5693..4059075f 100644 --- a/eppy/function_helpers.py +++ b/eppy/function_helpers.py @@ -58,12 +58,15 @@ def azimuth(ddtt): def true_azimuth(ddtt): - """azimuth of the surface""" + """true azimuth of the surface""" idf = ddtt.theidf + zone_name = ddtt.Zone_Name + building_north_axis = idf.idfobjects["building".upper()][0].North_Axis - coords = getcoords(ddtt) - surface_azimuth = g_surface.azimuth(coords) - return g_surface.true_azimuth(building_north_axis, surface_azimuth) + zone_direction_of_relative_north = [ + zone.Direction_of_Relative_North for zone in idf.idfobjects["zone".upper()] if zone.Name == zone_name][0] + surface_azimuth = azimuth(ddtt) + return g_surface.true_azimuth(building_north_axis, zone_direction_of_relative_north, surface_azimuth) def tilt(ddtt): diff --git a/eppy/geometry/surface.py b/eppy/geometry/surface.py index 8d2bfb9a..9f6861bb 100644 --- a/eppy/geometry/surface.py +++ b/eppy/geometry/surface.py @@ -131,10 +131,11 @@ def azimuth(poly): return angle2vecs(vec_azi, vec_n) -def true_azimuth(building_north_axis, surface_azimuth): +def true_azimuth(building_north_axis, zone_direction_of_relative_north, surface_azimuth): """True azimuth of a polygon poly""" building_north_axis = 0 if building_north_axis == "" else building_north_axis - return (building_north_axis + surface_azimuth) % 360 + zone_direction_of_relative_north = 0 if zone_direction_of_relative_north == "" else zone_direction_of_relative_north + return (building_north_axis + zone_direction_of_relative_north + surface_azimuth) % 360 def tilt(poly): diff --git a/eppy/tests/geometry_tests/test_surface.py b/eppy/tests/geometry_tests/test_surface.py index 6d5b32ca..7c1af08b 100644 --- a/eppy/tests/geometry_tests/test_surface.py +++ b/eppy/tests/geometry_tests/test_surface.py @@ -109,13 +109,15 @@ def test_azimuth(): def test_true_azimuth(): """test the true azimuth of a polygon poly""" data = ( - ("", 180, 180), - # building_north_axis, surface_azimuth, answer, - (20, 0, 20), - (240, 180, 60), + (45, 30, 0, 75), + # building_north_axis, zone_direction_of_relative_north, surface_azimuth, answer, + ("", 0, 180, 180), + (20, "", 20, 40), + (240, 90, 180, 150), ) - for building_north_axis, surface_azimuth, answer in data: - result = surface.true_azimuth(building_north_axis, surface_azimuth) + for building_north_axis, zone_direction_of_relative_north, surface_azimuth, answer in data: + result = surface.true_azimuth( + building_north_axis, zone_direction_of_relative_north, surface_azimuth) assert almostequal(answer, result, places=3) == True From 7e56e3df4f06fb22dee70ae80b4a075f1bdd3bbb Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sat, 13 Jun 2020 07:52:39 -0700 Subject: [PATCH 19/42] fixed issue #289 Problem: E+ is unable to read numbers that are wider than 19 digits Solution: format these numbers in scientific notation --- .gitignore | 1 + a.idf | 1 - eppy/bunch_subclass.py | 5 +++-- eppy/bunchhelpers.py | 13 +++++++++++++ eppy/tests/test_bunch_subclass.py | 23 ++++++++++++++++++++++- eppy/tests/test_bunchhelpers.py | 13 ++++++++++++- 6 files changed, 51 insertions(+), 5 deletions(-) delete mode 100644 a.idf diff --git a/.gitignore b/.gitignore index 06b4d607..15f98305 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +tags *~ *.pyc .DS_Store diff --git a/a.idf b/a.idf deleted file mode 100644 index fad1f3c1..00000000 --- a/a.idf +++ /dev/null @@ -1 +0,0 @@ - Version,8.7; \ No newline at end of file diff --git a/eppy/bunch_subclass.py b/eppy/bunch_subclass.py index ba78e47d..008a1a0a 100644 --- a/eppy/bunch_subclass.py +++ b/eppy/bunch_subclass.py @@ -1,4 +1,4 @@ -# Copyright (c) 2012 Santosh Philip +# Copyright (c) 2012, 2020 Santosh Philip # Copyright (c) 2016 Jamie Bull # ======================================================================= # Distributed under the MIT License. @@ -17,7 +17,7 @@ from munch import Munch as Bunch -from eppy.bunchhelpers import matchfieldnames +from eppy.bunchhelpers import matchfieldnames, scientificnotation import eppy.function_helpers as fh @@ -372,6 +372,7 @@ def __repr__(self): comments = [comm.replace("_", " ") for comm in self.objls] lines[0] = "%s," % (lines[0],) # comma after first line for i, line in enumerate(lines[1:-1]): + line = scientificnotation(line, width=18) # E+ cannot read wide numbers, convert to 1e+3 lines[i + 1] = " %s," % (line,) # indent and comma lines[-1] = " %s;" % (lines[-1],) # ';' after last line lines = lines[:1] + [line.ljust(26) for line in lines[1:]] # ljsut the lines diff --git a/eppy/bunchhelpers.py b/eppy/bunchhelpers.py index f1d12be5..e23c7339 100644 --- a/eppy/bunchhelpers.py +++ b/eppy/bunchhelpers.py @@ -84,3 +84,16 @@ def cleaniddfield(acomm): def cleancommdct(commdct): """make all keys in commdct lower case""" return [[cleaniddfield(fcomm) for fcomm in comm] for comm in commdct] + +def scientificnotation(val, width=19): + """return val in scientific notation if it is wider than 'width' chars + otherwise return val with no change""" + vtxt = '%s' % (val) + if len(vtxt) > width: + try: + stxt = '%e' % (val, ) + return stxt + except TypeError as e: + return val + return val + diff --git a/eppy/tests/test_bunch_subclass.py b/eppy/tests/test_bunch_subclass.py index 58ae6975..dc13b7d7 100644 --- a/eppy/tests/test_bunch_subclass.py +++ b/eppy/tests/test_bunch_subclass.py @@ -1,4 +1,4 @@ -# Copyright (c) 2012 Santosh Philip +# Copyright (c) 2012, 2020 Santosh Philip # ======================================================================= # Distributed under the MIT License. # (See accompanying file LICENSE or copy at @@ -1071,3 +1071,24 @@ def test_EpBunch1(): assert prnt == result # print bunchobj.objidd # assert 1 == 0 + +def test_scientificnotation(): + """py.test to check if __repr__ for epbunch is printing scientific notation""" + idftxt = """ScheduleTypeLimits, + AnyValue, !- Name + -1e+019, !- Lower Limit Value + 1e+019, !- Upper Limit Value + Continuous; !- Numeric Type +""" + expected = """ +ScheduleTypeLimits, + AnyValue, !- Name + -1.000000e+19, !- Lower Limit Value + 1.000000e+19, !- Upper Limit Value + Continuous; !- Numeric Type +""" + idffile = StringIO(idftxt) + idf = IDF(idffile) + sch = idf.idfobjects['ScheduleTypeLimits'][0] + result = sch.__repr__() + assert result == expected diff --git a/eppy/tests/test_bunchhelpers.py b/eppy/tests/test_bunchhelpers.py index ffe0b414..a5e6b163 100644 --- a/eppy/tests/test_bunchhelpers.py +++ b/eppy/tests/test_bunchhelpers.py @@ -1,4 +1,4 @@ -# Copyright (c) 2012 Santosh Philip +# Copyright (c) 2012, 2020 Santosh Philip # ======================================================================= # Distributed under the MIT License. # (See accompanying file LICENSE or copy at @@ -60,3 +60,14 @@ def test_replaceint(): for fname, newname in data: result = bunchhelpers.replaceint(fname) assert result == newname + +def test_scientificnotation(): + """py.test for scientificnotation""" + data = ( + (100000, 3, '1.000000e+05'), # val, width, expected + (10, 3, 10), # val, width, expected + ('gumby', 3, 'gumby'), # val, width, expected + ) + for val, width, expected in data: + result = bunchhelpers.scientificnotation(val, width) + assert result == expected From 7188ab69b3639326f088d8bc01582dd1c70b3aa7 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sat, 13 Jun 2020 07:56:47 -0700 Subject: [PATCH 20/42] ran black to reformat --- eppy/bunch_subclass.py | 4 +++- eppy/bunchhelpers.py | 6 +++--- eppy/tests/test_bunch_subclass.py | 3 ++- eppy/tests/test_bunchhelpers.py | 9 +++++---- 4 files changed, 13 insertions(+), 9 deletions(-) diff --git a/eppy/bunch_subclass.py b/eppy/bunch_subclass.py index 008a1a0a..0afcccfc 100644 --- a/eppy/bunch_subclass.py +++ b/eppy/bunch_subclass.py @@ -372,7 +372,9 @@ def __repr__(self): comments = [comm.replace("_", " ") for comm in self.objls] lines[0] = "%s," % (lines[0],) # comma after first line for i, line in enumerate(lines[1:-1]): - line = scientificnotation(line, width=18) # E+ cannot read wide numbers, convert to 1e+3 + line = scientificnotation( + line, width=18 + ) # E+ cannot read wide numbers, convert to 1e+3 lines[i + 1] = " %s," % (line,) # indent and comma lines[-1] = " %s;" % (lines[-1],) # ';' after last line lines = lines[:1] + [line.ljust(26) for line in lines[1:]] # ljsut the lines diff --git a/eppy/bunchhelpers.py b/eppy/bunchhelpers.py index e23c7339..8d6909af 100644 --- a/eppy/bunchhelpers.py +++ b/eppy/bunchhelpers.py @@ -85,15 +85,15 @@ def cleancommdct(commdct): """make all keys in commdct lower case""" return [[cleaniddfield(fcomm) for fcomm in comm] for comm in commdct] + def scientificnotation(val, width=19): """return val in scientific notation if it is wider than 'width' chars otherwise return val with no change""" - vtxt = '%s' % (val) + vtxt = "%s" % (val) if len(vtxt) > width: try: - stxt = '%e' % (val, ) + stxt = "%e" % (val,) return stxt except TypeError as e: return val return val - diff --git a/eppy/tests/test_bunch_subclass.py b/eppy/tests/test_bunch_subclass.py index dc13b7d7..09d51f18 100644 --- a/eppy/tests/test_bunch_subclass.py +++ b/eppy/tests/test_bunch_subclass.py @@ -1072,6 +1072,7 @@ def test_EpBunch1(): # print bunchobj.objidd # assert 1 == 0 + def test_scientificnotation(): """py.test to check if __repr__ for epbunch is printing scientific notation""" idftxt = """ScheduleTypeLimits, @@ -1089,6 +1090,6 @@ def test_scientificnotation(): """ idffile = StringIO(idftxt) idf = IDF(idffile) - sch = idf.idfobjects['ScheduleTypeLimits'][0] + sch = idf.idfobjects["ScheduleTypeLimits"][0] result = sch.__repr__() assert result == expected diff --git a/eppy/tests/test_bunchhelpers.py b/eppy/tests/test_bunchhelpers.py index a5e6b163..9b6e15e9 100644 --- a/eppy/tests/test_bunchhelpers.py +++ b/eppy/tests/test_bunchhelpers.py @@ -61,13 +61,14 @@ def test_replaceint(): result = bunchhelpers.replaceint(fname) assert result == newname + def test_scientificnotation(): """py.test for scientificnotation""" data = ( - (100000, 3, '1.000000e+05'), # val, width, expected - (10, 3, 10), # val, width, expected - ('gumby', 3, 'gumby'), # val, width, expected - ) + (100000, 3, "1.000000e+05"), # val, width, expected + (10, 3, 10), # val, width, expected + ("gumby", 3, "gumby"), # val, width, expected + ) for val, width, expected in data: result = bunchhelpers.scientificnotation(val, width) assert result == expected From 7195b1436b739b64184a073b086a377366703c86 Mon Sep 17 00:00:00 2001 From: airallergy <49818327+airallergy@users.noreply.github.com> Date: Sat, 13 Jun 2020 22:11:58 +0100 Subject: [PATCH 21/42] Update the true azimuth calculation revise function_helpers.py, add test_function_helpers.py, format with black --- eppy/bunch_subclass.py | 1 + eppy/function_helpers.py | 12 +- eppy/geometry/surface.py | 15 +- eppy/tests/geometry_tests/test_surface.py | 11 +- eppy/tests/test_function_helpers.py | 162 ++++++++++++++++++++++ 5 files changed, 192 insertions(+), 9 deletions(-) diff --git a/eppy/bunch_subclass.py b/eppy/bunch_subclass.py index ba78e47d..f44c6482 100644 --- a/eppy/bunch_subclass.py +++ b/eppy/bunch_subclass.py @@ -1,5 +1,6 @@ # Copyright (c) 2012 Santosh Philip # Copyright (c) 2016 Jamie Bull +# Copyright (c) 2020 Cheng Cui # ======================================================================= # Distributed under the MIT License. # (See accompanying file LICENSE or copy at diff --git a/eppy/function_helpers.py b/eppy/function_helpers.py index 4059075f..25b8c4de 100644 --- a/eppy/function_helpers.py +++ b/eppy/function_helpers.py @@ -1,4 +1,5 @@ # Copyright (c) 2012 Santosh Philip +# Copyright (c) 2020 Cheng Cui # ======================================================================= # Distributed under the MIT License. # (See accompanying file LICENSE or copy at @@ -62,11 +63,14 @@ def true_azimuth(ddtt): idf = ddtt.theidf zone_name = ddtt.Zone_Name - building_north_axis = idf.idfobjects["building".upper()][0].North_Axis - zone_direction_of_relative_north = [ - zone.Direction_of_Relative_North for zone in idf.idfobjects["zone".upper()] if zone.Name == zone_name][0] + building_north_axis = idf.idfobjects["building"][0].North_Axis + zone_direction_of_relative_north = idf.getobject( + "zone", zone_name + ).Direction_of_Relative_North surface_azimuth = azimuth(ddtt) - return g_surface.true_azimuth(building_north_axis, zone_direction_of_relative_north, surface_azimuth) + return g_surface.true_azimuth( + building_north_axis, zone_direction_of_relative_north, surface_azimuth + ) def tilt(ddtt): diff --git a/eppy/geometry/surface.py b/eppy/geometry/surface.py index 9f6861bb..1fb822de 100644 --- a/eppy/geometry/surface.py +++ b/eppy/geometry/surface.py @@ -1,4 +1,5 @@ # Copyright (c) 2012 Tuan Tran +# Copyright (c) 2020 Cheng Cui # This file is part of eppy. # ======================================================================= @@ -131,11 +132,19 @@ def azimuth(poly): return angle2vecs(vec_azi, vec_n) -def true_azimuth(building_north_axis, zone_direction_of_relative_north, surface_azimuth): +def true_azimuth( + building_north_axis, zone_direction_of_relative_north, surface_azimuth +): """True azimuth of a polygon poly""" building_north_axis = 0 if building_north_axis == "" else building_north_axis - zone_direction_of_relative_north = 0 if zone_direction_of_relative_north == "" else zone_direction_of_relative_north - return (building_north_axis + zone_direction_of_relative_north + surface_azimuth) % 360 + zone_direction_of_relative_north = ( + 0 + if zone_direction_of_relative_north == "" + else zone_direction_of_relative_north + ) + return ( + building_north_axis + zone_direction_of_relative_north + surface_azimuth + ) % 360 def tilt(poly): diff --git a/eppy/tests/geometry_tests/test_surface.py b/eppy/tests/geometry_tests/test_surface.py index 7c1af08b..a1aeba43 100644 --- a/eppy/tests/geometry_tests/test_surface.py +++ b/eppy/tests/geometry_tests/test_surface.py @@ -1,4 +1,5 @@ # Copyright (c) 2012 Tuan Tran +# Copyright (c) 2020 Cheng Cui # ======================================================================= # Distributed under the MIT License. # (See accompanying file LICENSE or copy at @@ -115,9 +116,15 @@ def test_true_azimuth(): (20, "", 20, 40), (240, 90, 180, 150), ) - for building_north_axis, zone_direction_of_relative_north, surface_azimuth, answer in data: + for ( + building_north_axis, + zone_direction_of_relative_north, + surface_azimuth, + answer, + ) in data: result = surface.true_azimuth( - building_north_axis, zone_direction_of_relative_north, surface_azimuth) + building_north_axis, zone_direction_of_relative_north, surface_azimuth + ) assert almostequal(answer, result, places=3) == True diff --git a/eppy/tests/test_function_helpers.py b/eppy/tests/test_function_helpers.py index e69de29b..3d503c8f 100644 --- a/eppy/tests/test_function_helpers.py +++ b/eppy/tests/test_function_helpers.py @@ -0,0 +1,162 @@ +# Copyright (c) 2020 Cheng Cui +# ======================================================================= +# Distributed under the MIT License. +# (See accompanying file LICENSE or copy at +# http://opensource.org/licenses/MIT) +# ======================================================================= + +"""py.test for function_helpers""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +from six import StringIO + +import eppy.function_helpers as fh +from eppy.iddcurrent import iddcurrent +from eppy.modeleditor import IDF +from eppy.pytest_helpers import almostequal + +iddtxt = iddcurrent.iddtxt +iddfhandle = StringIO(iddcurrent.iddtxt) +if IDF.getiddname() == None: + IDF.setiddname(iddfhandle) + +idftxt = """ + Version,8.0; + + Building, + Simple One Zone, !- Name + ; !- North Axis {deg} + + Zone, + ZONE ONE, !- Name + , !- Direction of Relative North {deg} + 0, 0, 0; !- X,Y,Z {m} + + GlobalGeometryRules, + UpperLeftCorner, !- Starting Vertex Position + CounterClockWise, !- Vertex Entry Direction + World; !- Coordinate System + + BuildingSurface:Detailed, + Zn001:Wall001, !- Name + Wall, !- Surface Type + R13WALL, !- Construction Name + ZONE ONE, !- Zone Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + 0.5000000, !- View Factor to Ground + 4, !- Number of Vertices + 0, 0, 4.572000, !- X,Y,Z 1 {m} + 0, 0, 0, !- X,Y,Z 2 {m} + 15.24000, 0, 0, !- X,Y,Z 3 {m} + 15.24000, 0, 4.572000; !- X,Y,Z 4 {m} + + BuildingSurface:Detailed, + Zn001:Wall002, !- Name + Wall, !- Surface Type + R13WALL, !- Construction Name + ZONE ONE, !- Zone Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + 0.5000000, !- View Factor to Ground + 4, !- Number of Vertices + 15.24000, 0, 4.572000, !- X,Y,Z 1 {m} + 15.24000, 0, 0, !- X,Y,Z 2 {m} + 15.24000, 15.24000, 0, !- X,Y,Z 3 {m} + 15.24000, 15.24000, 4.572000; !- X,Y,Z 4 {m} + + BuildingSurface:Detailed, + Zn001:Wall003, !- Name + Wall, !- Surface Type + R13WALL, !- Construction Name + ZONE ONE, !- Zone Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + 0.5000000, !- View Factor to Ground + 4, !- Number of Vertices + 15.24000, 15.24000, 4.572000, !- X,Y,Z 1 {m} + 15.24000, 15.24000, 0, !- X,Y,Z 2 {m} + 0, 15.24000, 0, !- X,Y,Z 3 {m} + 0, 15.24000, 4.572000; !- X,Y,Z 4 {m} + + BuildingSurface:Detailed, + Zn001:Wall004, !- Name + Wall, !- Surface Type + R13WALL, !- Construction Name + ZONE ONE, !- Zone Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + 0.5000000, !- View Factor to Ground + 4, !- Number of Vertices + 0, 15.24000, 4.572000, !- X,Y,Z 1 {m} + 0, 15.24000, 0, !- X,Y,Z 2 {m} + 0, 0, 0, !- X,Y,Z 3 {m} + 0, 0, 4.572000; !- X,Y,Z 4 {m} + + BuildingSurface:Detailed, + Zn001:Flr001, !- Name + Floor, !- Surface Type + FLOOR, !- Construction Name + ZONE ONE, !- Zone Name + Adiabatic, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + NoSun, !- Sun Exposure + NoWind, !- Wind Exposure + 1.000000, !- View Factor to Ground + 4, !- Number of Vertices + 15.24000, 0.000000, 0.0, !- X,Y,Z 1 {m} + 0.000000, 0.000000, 0.0, !- X,Y,Z 2 {m} + 0.000000, 15.24000, 0.0, !- X,Y,Z 3 {m} + 15.24000, 15.24000, 0.0; !- X,Y,Z 4 {m} + + BuildingSurface:Detailed, + Zn001:Roof001, !- Name + Roof, !- Surface Type + ROOF31, !- Construction Name + ZONE ONE, !- Zone Name + Outdoors, !- Outside Boundary Condition + , !- Outside Boundary Condition Object + SunExposed, !- Sun Exposure + WindExposed, !- Wind Exposure + 0, !- View Factor to Ground + 4, !- Number of Vertices + 0.000000, 15.24000, 4.572, !- X,Y,Z 1 {m} + 0.000000, 0.000000, 4.572, !- X,Y,Z 2 {m} + 15.24000, 0.000000, 4.572, !- X,Y,Z 3 {m} + 15.24000, 15.24000, 4.572; !- X,Y,Z 4 {m} +""" + + +def test_true_azimuth(): + """py.test for true_azimuth""" + data = ( + (45, 30, 255), + # building_north_axis, zone_direction_of_relative_north, answer, + ("", 0, 180), + (20, "", 200), + (240, 90, 150), + ) + + fhandle = StringIO(idftxt) + idf = IDF(fhandle) + building = idf.idfobjects["Building"][0] + zone = idf.idfobjects["Zone"][0] + surface = idf.idfobjects["BuildingSurface:Detailed"][0] + + for building_north_axis, zone_direction_of_relative_north, answer in data: + building.North_Axis = building_north_axis + zone.Direction_of_Relative_North = zone_direction_of_relative_north + result = fh.true_azimuth(surface) + assert almostequal(answer, result, places=3) == True From 7a07fa9e0edc3a7bb1800b89eb7cb2ae3e433dc6 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sat, 13 Jun 2020 22:56:32 -0700 Subject: [PATCH 22/42] some cleanups --- eppy/function_helpers.py | 13 +--- eppy/geometry/surface.py | 17 +---- eppy/tests/geometry_tests/test_surface.py | 45 +++++------- eppy/tests/test_function_helpers.py | 90 ++--------------------- 4 files changed, 33 insertions(+), 132 deletions(-) diff --git a/eppy/function_helpers.py b/eppy/function_helpers.py index 25b8c4de..59cb7803 100644 --- a/eppy/function_helpers.py +++ b/eppy/function_helpers.py @@ -62,15 +62,10 @@ def true_azimuth(ddtt): """true azimuth of the surface""" idf = ddtt.theidf zone_name = ddtt.Zone_Name - - building_north_axis = idf.idfobjects["building"][0].North_Axis - zone_direction_of_relative_north = idf.getobject( - "zone", zone_name - ).Direction_of_Relative_North - surface_azimuth = azimuth(ddtt) - return g_surface.true_azimuth( - building_north_axis, zone_direction_of_relative_north, surface_azimuth - ) + bldg_north = idf.idfobjects["building"][0].North_Axis + zone_rel_north = idf.getobject("zone", zone_name).Direction_of_Relative_North + surf_azimuth = azimuth(ddtt) + return g_surface.true_azimuth(bldg_north, zone_rel_north, surf_azimuth) def tilt(ddtt): diff --git a/eppy/geometry/surface.py b/eppy/geometry/surface.py index 1fb822de..57999b1a 100644 --- a/eppy/geometry/surface.py +++ b/eppy/geometry/surface.py @@ -132,19 +132,11 @@ def azimuth(poly): return angle2vecs(vec_azi, vec_n) -def true_azimuth( - building_north_axis, zone_direction_of_relative_north, surface_azimuth -): +def true_azimuth(bldg_north, zone_rel_north, surf_azimuth): """True azimuth of a polygon poly""" - building_north_axis = 0 if building_north_axis == "" else building_north_axis - zone_direction_of_relative_north = ( - 0 - if zone_direction_of_relative_north == "" - else zone_direction_of_relative_north - ) - return ( - building_north_axis + zone_direction_of_relative_north + surface_azimuth - ) % 360 + bldg_north = 0 if bldg_north == "" else bldg_north + zone_rel_north = 0 if zone_rel_north == "" else zone_rel_north + return (bldg_north + zone_rel_north + surf_azimuth) % 360 def tilt(poly): @@ -153,5 +145,4 @@ def tilt(poly): vec = unit_normal(poly[0], poly[1], poly[num]) vec_alt = np.array([vec[0], vec[1], vec[2]]) vec_z = np.array([0, 0, 1]) - # return (90 - angle2vecs(vec_alt, vec_z)) # update by Santosh return angle2vecs(vec_alt, vec_z) diff --git a/eppy/tests/geometry_tests/test_surface.py b/eppy/tests/geometry_tests/test_surface.py index a1aeba43..a395da68 100644 --- a/eppy/tests/geometry_tests/test_surface.py +++ b/eppy/tests/geometry_tests/test_surface.py @@ -22,7 +22,7 @@ def test_area(): """test the area of a polygon poly""" data = ( ([(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)], 1), - # polygon, answer, + # polygon, expected, ([(0, 0, 0), (1, 0, 0), (1, 0, 1), (0, 0, 1)], 1), ([(0, 0, 0), (0, 1, 0), (0, 1, 1), (0, 0, 1)], 1), ([(0, 0, 0), (0, 1, 0), (0, 2, 0), (0, 3, 0)], 0), @@ -36,16 +36,16 @@ def test_area(): 25, ), ) - for poly, answer in data: + for poly, expected in data: result = surface.area(poly) - assert almostequal(answer, result, places=4) == True + assert almostequal(expected, result, places=4) == True def test_height(): """test the height of a polygon poly""" data = ( ([(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)], 1), - # polygon, answer, + # polygon, expected, ([(0, 0, 0), (8, 0, 0), (11, 0, 4), (3, 0, 4)], 5), ([(0, 0, 0), (10, 0, 0), (10, 9, 0), (0, 9, 0)], 9), ( @@ -59,16 +59,16 @@ def test_height(): ), ([(0.0, 0.0, 3.0), (0.0, 0.0, 2.4), (30.5, 0.0, 2.4), (30.5, 0.0, 3.0)], 0.6), ) - for poly, answer in data: + for poly, expected in data: result = surface.height(poly) - assert almostequal(answer, result, places=5) == True + assert almostequal(expected, result, places=5) == True def test_width(): """test the width of a polygon poly """ data = ( ([(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)], 1), - # polygon, answer, + # polygon, expected, ([(0, 0, 0), (8, 0, 0), (11, 0, 4), (3, 0, 4)], 8), ([(0, 0, 0), (10, 0, 0), (10, 9, 0), (0, 9, 0)], 10), ( @@ -81,16 +81,16 @@ def test_width(): 8, ), ) - for poly, answer in data: + for poly, expected in data: result = surface.width(poly) - assert almostequal(answer, result, places=4) == True + assert almostequal(expected, result, places=4) == True def test_azimuth(): """test the azimuth of a polygon poly""" data = ( ([(0, 0, 0), (1, 0, 0), (1, 1, 1), (0, 1, 1)], 180), - # polygon, answer, + # polygon, expected, ([(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)], 0), ( [ @@ -102,37 +102,30 @@ def test_azimuth(): 360 - 23.546134, ), ) - for poly, answer in data: + for poly, expected in data: result = surface.azimuth(poly) - assert almostequal(answer, result, places=3) == True + assert almostequal(expected, result, places=3) == True def test_true_azimuth(): """test the true azimuth of a polygon poly""" data = ( (45, 30, 0, 75), - # building_north_axis, zone_direction_of_relative_north, surface_azimuth, answer, + # bldg_north, zone_rel_north, surf_azimuth, expected, ("", 0, 180, 180), (20, "", 20, 40), (240, 90, 180, 150), ) - for ( - building_north_axis, - zone_direction_of_relative_north, - surface_azimuth, - answer, - ) in data: - result = surface.true_azimuth( - building_north_axis, zone_direction_of_relative_north, surface_azimuth - ) - assert almostequal(answer, result, places=3) == True + for (bldg_north, zone_rel_north, surf_azimuth, expected,) in data: + result = surface.true_azimuth(bldg_north, zone_rel_north, surf_azimuth) + assert almostequal(expected, result, places=3) == True def test_tilt(): """test the tilt of a polygon poly""" data = ( ([(0, 0, 0), (1, 0, 0), (1, 1, 0), (0, 1, 0)], 0), - # polygon, answer, + # polygon, expected, ([(0, 0, 0), (5, 0, 0), (5, 0, 8), (0, 0, 8)], 90), ([(0, 0, 0), (1, 0, 0), (1, 1, 1), (0, 1, 1)], 45), ( @@ -145,6 +138,6 @@ def test_tilt(): 90 - 72.693912, ), ) - for poly, answer in data: + for poly, expected in data: result = surface.tilt(poly) - assert almostequal(answer, result, places=3) == True + assert almostequal(expected, result, places=3) == True diff --git a/eppy/tests/test_function_helpers.py b/eppy/tests/test_function_helpers.py index 3d503c8f..98594483 100644 --- a/eppy/tests/test_function_helpers.py +++ b/eppy/tests/test_function_helpers.py @@ -57,85 +57,6 @@ 15.24000, 0, 0, !- X,Y,Z 3 {m} 15.24000, 0, 4.572000; !- X,Y,Z 4 {m} - BuildingSurface:Detailed, - Zn001:Wall002, !- Name - Wall, !- Surface Type - R13WALL, !- Construction Name - ZONE ONE, !- Zone Name - Outdoors, !- Outside Boundary Condition - , !- Outside Boundary Condition Object - SunExposed, !- Sun Exposure - WindExposed, !- Wind Exposure - 0.5000000, !- View Factor to Ground - 4, !- Number of Vertices - 15.24000, 0, 4.572000, !- X,Y,Z 1 {m} - 15.24000, 0, 0, !- X,Y,Z 2 {m} - 15.24000, 15.24000, 0, !- X,Y,Z 3 {m} - 15.24000, 15.24000, 4.572000; !- X,Y,Z 4 {m} - - BuildingSurface:Detailed, - Zn001:Wall003, !- Name - Wall, !- Surface Type - R13WALL, !- Construction Name - ZONE ONE, !- Zone Name - Outdoors, !- Outside Boundary Condition - , !- Outside Boundary Condition Object - SunExposed, !- Sun Exposure - WindExposed, !- Wind Exposure - 0.5000000, !- View Factor to Ground - 4, !- Number of Vertices - 15.24000, 15.24000, 4.572000, !- X,Y,Z 1 {m} - 15.24000, 15.24000, 0, !- X,Y,Z 2 {m} - 0, 15.24000, 0, !- X,Y,Z 3 {m} - 0, 15.24000, 4.572000; !- X,Y,Z 4 {m} - - BuildingSurface:Detailed, - Zn001:Wall004, !- Name - Wall, !- Surface Type - R13WALL, !- Construction Name - ZONE ONE, !- Zone Name - Outdoors, !- Outside Boundary Condition - , !- Outside Boundary Condition Object - SunExposed, !- Sun Exposure - WindExposed, !- Wind Exposure - 0.5000000, !- View Factor to Ground - 4, !- Number of Vertices - 0, 15.24000, 4.572000, !- X,Y,Z 1 {m} - 0, 15.24000, 0, !- X,Y,Z 2 {m} - 0, 0, 0, !- X,Y,Z 3 {m} - 0, 0, 4.572000; !- X,Y,Z 4 {m} - - BuildingSurface:Detailed, - Zn001:Flr001, !- Name - Floor, !- Surface Type - FLOOR, !- Construction Name - ZONE ONE, !- Zone Name - Adiabatic, !- Outside Boundary Condition - , !- Outside Boundary Condition Object - NoSun, !- Sun Exposure - NoWind, !- Wind Exposure - 1.000000, !- View Factor to Ground - 4, !- Number of Vertices - 15.24000, 0.000000, 0.0, !- X,Y,Z 1 {m} - 0.000000, 0.000000, 0.0, !- X,Y,Z 2 {m} - 0.000000, 15.24000, 0.0, !- X,Y,Z 3 {m} - 15.24000, 15.24000, 0.0; !- X,Y,Z 4 {m} - - BuildingSurface:Detailed, - Zn001:Roof001, !- Name - Roof, !- Surface Type - ROOF31, !- Construction Name - ZONE ONE, !- Zone Name - Outdoors, !- Outside Boundary Condition - , !- Outside Boundary Condition Object - SunExposed, !- Sun Exposure - WindExposed, !- Wind Exposure - 0, !- View Factor to Ground - 4, !- Number of Vertices - 0.000000, 15.24000, 4.572, !- X,Y,Z 1 {m} - 0.000000, 0.000000, 4.572, !- X,Y,Z 2 {m} - 15.24000, 0.000000, 4.572, !- X,Y,Z 3 {m} - 15.24000, 15.24000, 4.572; !- X,Y,Z 4 {m} """ @@ -143,7 +64,7 @@ def test_true_azimuth(): """py.test for true_azimuth""" data = ( (45, 30, 255), - # building_north_axis, zone_direction_of_relative_north, answer, + # bldg_north, zone_rel_north, expected, ("", 0, 180), (20, "", 200), (240, 90, 150), @@ -155,8 +76,9 @@ def test_true_azimuth(): zone = idf.idfobjects["Zone"][0] surface = idf.idfobjects["BuildingSurface:Detailed"][0] - for building_north_axis, zone_direction_of_relative_north, answer in data: - building.North_Axis = building_north_axis - zone.Direction_of_Relative_North = zone_direction_of_relative_north + for bldg_north, zone_rel_north, expected in data: + building.North_Axis = bldg_north + zone.Direction_of_Relative_North = zone_rel_north result = fh.true_azimuth(surface) - assert almostequal(answer, result, places=3) == True + result = surface.true_azimuth + assert almostequal(expected, result, places=3) == True From 3a74d119b8df40dfb708038fe85d8a3c978a6b55 Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sat, 13 Jun 2020 22:58:25 -0700 Subject: [PATCH 23/42] another cleanup modified: eppy/tests/test_function_helpers.py --- eppy/tests/test_function_helpers.py | 1 - 1 file changed, 1 deletion(-) diff --git a/eppy/tests/test_function_helpers.py b/eppy/tests/test_function_helpers.py index 98594483..2d10e5c3 100644 --- a/eppy/tests/test_function_helpers.py +++ b/eppy/tests/test_function_helpers.py @@ -79,6 +79,5 @@ def test_true_azimuth(): for bldg_north, zone_rel_north, expected in data: building.North_Axis = bldg_north zone.Direction_of_Relative_North = zone_rel_north - result = fh.true_azimuth(surface) result = surface.true_azimuth assert almostequal(expected, result, places=3) == True From c580d2dd6a760ec82007251aad91f386b6e7b78e Mon Sep 17 00:00:00 2001 From: santoshphilip Date: Sat, 20 Jun 2020 17:11:49 -0700 Subject: [PATCH 24/42] reorganized to use cookiecutter https://github.com/cookiecutter/cookiecutter --- CHANGES.txt | 16 - CONTRIBUTING.md 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a/CHANGES.txt b/CHANGES.txt deleted file mode 100644 index 26bb81f6..00000000 --- a/CHANGES.txt +++ /dev/null @@ -1,16 +0,0 @@ -2015-07-12 ----------- -All changes are now documented in ./docs/source/changes.rst - -2014-08-19 ----------- - -release r0.462 - -- added a script that can compare two idf files. The script is in - - eppy/usefull_scripts/idfdiff.py -- added two scripts that test if eppy works when new versions of energyplus are released. Documentation for this is not yet done. The scripts are - - eppy/usefull_scripts/eppyreadtest_file.py - - eppy/usefull_scripts/eppyreadtest_folder.py -- fixed a bug where eppy would not read backslashes in a path name. Some idf objects have fields that are path names. On dos/windows machines these path names have backslashes - diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md deleted file mode 100644 index 3ae94eb9..00000000 --- a/CONTRIBUTING.md +++ /dev/null @@ -1,20 +0,0 @@ -If you are a contributor, consider using the following workflow: - -- an issue is opened -- the issue is assigned to you -- you make a branch from `develop` with a meaningful name such as "i57_licence" where #57 is the issue number -- once the coding is complete and the tests pass, raise a pull request against the `develop` branch and tag the "release master" of the repository (release master is @santoshphilip at present) -- "release master" will review and merge your changes into the `develop` branch -- close the issue - -If you are not a contributor: - -- ask to become a contributor - -OR - -- fork this repository into your own repository -- make a branch off the `develop` branch to work on your new feature -- once the coding is complete, raise a pull request against the `develop` branch here -- give the pull request a meaningful name, and mention any issues that it addresses (using the issue number, e.g. "Resolves #86, also see #57") -- once the code has been reviewed and tests are passing it will be merged into `develop` \ No newline at end of file diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst new file mode 100644 index 00000000..6c6adcda --- /dev/null +++ b/CONTRIBUTING.rst @@ -0,0 +1,128 @@ +.. highlight:: shell + +============ +Contributing +============ + +Contributions are welcome, and they are greatly appreciated! Every little bit +helps, and credit will always be given. + +You can contribute in many ways: + +Types of Contributions +---------------------- + +Report Bugs +~~~~~~~~~~~ + +Report bugs at https://github.com/santoshphilip/eppy/issues. + +If you are reporting a bug, please include: + +* Your operating system name and version. +* Any details about your local setup that might be helpful in troubleshooting. +* Detailed steps to reproduce the bug. + +Fix Bugs +~~~~~~~~ + +Look through the GitHub issues for bugs. Anything tagged with "bug" and "help +wanted" is open to whoever wants to implement it. + +Implement Features +~~~~~~~~~~~~~~~~~~ + +Look through the GitHub issues for features. Anything tagged with "enhancement" +and "help wanted" is open to whoever wants to implement it. + +Write Documentation +~~~~~~~~~~~~~~~~~~~ + +eppy could always use more documentation, whether as part of the +official eppy docs, in docstrings, or even on the web in blog posts, +articles, and such. + +Submit Feedback +~~~~~~~~~~~~~~~ + +The best way to send feedback is to file an issue at https://github.com/santoshphilip/eppy/issues. + +If you are proposing a feature: + +* Explain in detail how it would work. +* Keep the scope as narrow as possible, to make it easier to implement. +* Remember that this is a volunteer-driven project, and that contributions + are welcome :) + +Get Started! +------------ + +Ready to contribute? Here's how to set up `eppy` for local development. + +1. Fork the `eppy` repo on GitHub. +2. Clone your fork locally:: + + $ git clone git@github.com:your_name_here/eppy.git + +3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:: + + $ mkvirtualenv eppy + $ cd eppy/ + $ python setup.py develop + +4. Create a branch for local development:: + + $ git checkout -b name-of-your-bugfix-or-feature + + Now you can make your changes locally. + +5. When you're done making changes, check that your changes pass flake8 and the + tests, including testing other Python versions with tox:: + + $ flake8 eppy tests + $ python setup.py test or pytest + $ tox + + To get flake8 and tox, just pip install them into your virtualenv. + +6. Commit your changes and push your branch to GitHub:: + + $ git add . + $ git commit -m "Your detailed description of your changes." + $ git push origin name-of-your-bugfix-or-feature + +7. Submit a pull request through the GitHub website. + +Pull Request Guidelines +----------------------- + +Before you submit a pull request, check that it meets these guidelines: + +1. The pull request should include tests. +2. If the pull request adds functionality, the docs should be updated. Put + your new functionality into a function with a docstring, and add the + feature to the list in README.rst. +3. The pull request should work for Python 3.5, 3.6, 3.7 and 3.8, and for PyPy. Check + https://travis-ci.com/santoshphilip/eppy/pull_requests + and make sure that the tests pass for all supported Python versions. + +Tips +---- + +To run a subset of tests:: + +$ pytest tests.test_eppy + + +Deploying +--------- + +A reminder for the maintainers on how to deploy. +Make sure all your changes are committed (including an entry in HISTORY.rst). +Then run:: + +$ bump2version patch # possible: major / minor / patch +$ git push +$ git push --tags + +Travis will then deploy to PyPI if tests pass. diff --git a/docs/source/changes.rst b/HISTORY.rst similarity index 99% rename from docs/source/changes.rst rename to HISTORY.rst index 00537d58..45dc7326 100644 --- a/docs/source/changes.rst +++ b/HISTORY.rst @@ -1,3 +1,4 @@ +======= History ======= diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..965b2dda --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,11 @@ +include AUTHORS.rst +include CONTRIBUTING.rst +include HISTORY.rst +include LICENSE +include README.rst + +recursive-include tests * +recursive-exclude * __pycache__ +recursive-exclude * *.py[co] + +recursive-include docs *.rst conf.py Makefile make.bat *.jpg *.png *.gif diff --git a/README.md b/README.rst similarity index 67% rename from README.md rename to README.rst index 261330bc..0774a601 100644 --- a/README.md +++ b/README.rst @@ -1,17 +1,22 @@ -Eppy ==== -[![PyPI](https://img.shields.io/pypi/dm/eppy.svg)](https://pypi.python.org/pypi/eppy) - from PyPI +eppy +==== + + +.. image:: https://img.shields.io/pypi/v/eppy.svg + :target: https://pypi.python.org/pypi/eppy + +.. image:: https://img.shields.io/travis/santoshphilip/eppy.svg + :target: https://travis-ci.com/santoshphilip/eppy -[![Travis](https://img.shields.io/travis/santoshphilip/eppy/master.svg)](https://travis-ci.org/santoshphilip/eppy) - for Python 2.7 and 3.5 on Linux and OSX via Travis +.. image:: https://readthedocs.org/projects/eppy/badge/?version=latest + :target: https://eppy.readthedocs.io/en/latest/?badge=latest + :alt: Documentation Status -[![Appveyor](https://img.shields.io/appveyor/ci/santoshphilip/eppy/master.svg)](https://ci.appveyor.com/api/projects/status/github/santoshphilip/eppy) - for Python 2.7 and 3.5 on Windows via Appveyor -[![CodeCov](https://img.shields.io/codecov/c/github/santoshphilip/eppy/master.svg)](https://codecov.io/github/santoshphilip/eppy) - via CodeCov + +scripting language for E+, Energyplus Eppy is a scripting language for EnergyPlus idf files, and EnergyPlus output files. Eppy is written in the programming language Python. As a result it takes full advantage of the rich data structure and idioms that are available in Python. You can programmatically navigate, search, and modify EnergyPlus idf files using eppy. The power of using a scripting language allows you to do the following: - Make a large number of changes in an idf file with a few lines of eppy code. @@ -40,3 +45,10 @@ The documentation is at: to get a quick sense of how it feels to use eppy, take a look at +Credits +------- + +This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template. + +.. _Cookiecutter: https://github.com/audreyr/cookiecutter +.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage diff --git a/README.txt b/README.txt deleted file mode 100644 index 1bb658a0..00000000 --- a/README.txt +++ /dev/null @@ -1,31 +0,0 @@ -Eppy -==== - -Eppy is a scripting language for EnergyPlus idf files, and EnergyPlus output files. Eppy is written in the programming language Python. As a result it takes full advantage of the rich data structure and idioms that are avaliable in python. You can programmatically navigate, search, and modify EnergyPlus idf files using eppy. The power of using a scripting language allows you to do the following: - -- Make a large number of changes in an idf file with a few lines of eppy code. -- Use conditions and filters when making changes to an idf file -- Make changes to multiple idf files. -- Read data from the output files of a EnergyPlus simulation run. -- Based on the results of a EnergyPlus simulation run, generate the input file for the next simulation run. - -So what does this matter? -Here are some of the things you can do with eppy: - - -- Change construction for all north facing walls. -- Change the glass type for all windows larger than 2 square meters. -- Change the number of people in all the interior zones. -- Change the lighting power in all south facing zones. -- Change the efficiency and fan power of all rooftop units. -- Find the energy use of all the models in a folder (or of models that were run after a certain date) - -You can install from : - - -The documentation is at: - - -to get a quick sense of how it feels to use eppy, take a look at - - diff --git a/a.idf b/a.idf deleted file mode 100644 index fad1f3c1..00000000 --- a/a.idf +++ /dev/null @@ -1 +0,0 @@ - Version,8.7; \ No newline at end of file diff --git a/cleanup.py b/cleanup.py deleted file mode 100755 index f980036f..00000000 --- a/cleanup.py +++ /dev/null @@ -1,43 +0,0 @@ -#! /usr/bin/env python -"""clean up .pyc from folders""" -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function -from __future__ import unicode_literals - -import sys, os, stat -from bsdopendirtype import opendir - - -def clean(path): - global count - try: - content = opendir(path) - except OSError: - print("skipping", path, file=sys.stderr) - return - for filename, smode in content: - if stat.S_ISDIR(smode): - clean(filename) - if filename.endswith("/__pycache__"): - try: - os.rmdir(filename) - except OSError: - pass - elif ( - filename.endswith(".pyc") - or filename.endswith(".pyo") - or filename.endswith(".pyc~") - or filename.endswith(".pyo~") - ): - os.unlink(filename) - count += 1 - - -count = 0 - -for arg in sys.argv[1:] or ["."]: - print("cleaning path", arg, "of .pyc/.pyo/__pycache__ files") - clean(arg) - -print("%d files removed" % (count,)) diff --git a/docs/.conf.py.swp b/docs/.conf.py.swp new file mode 100644 index 0000000000000000000000000000000000000000..50d4bab7cf3fbe7533b480d779fa0b2b21f70a16 GIT binary patch literal 16384 zcmeI3UyLM09mgB|gNUe6qKL*6yMfs?v$KC7f=37(UQRG~K)i(j=P}gu)aDaqLnT>k+bg$g@SIa=lz&RKw{b=dN%cJYoR#&9u{M^;*z3;i_9G%+R zEdwnBEdwnBEdwnBEdwnBEdwnB|4$6~{Nm^_CUkKYOccRzJa?t1be{=qS`CU=; zC-5wI6g&bBfCX?Zc#%#&2G4=VzyN6QA@GaKqv)&P6tKX62fzwg2J_&<;Fs@+q8Gqd zz!Sg&2bRD~mqpPx!NcG|Z~|Nb{`U4LdKLT>d;@$P6yP{`0Nezw1=oPrE{&o;gXh4r z;2AIg?*spOTNM2h{0{sGJPA&NN5BfW3|tC+cnR|d6Oe*yz$?&sQ%(d&4pzR)>=X3i?Df?lH1P|Xd^NSCSa#m>#Ctt3sA-898k zk$9s#l#7}^$@VtvA*nEsoNUs{(lBvqm=q@VwwU(BR#T-5qhgyUjG{+aYLB=|$DoPp zBJJRiR@oE_`>~^?_VmrXX5_eZ-cHnP54GURc&xPJ>J*OM*ck5?A+|7GAyf?1NBxQE z+uGvzz@yPT_pniThX?I#CNl}`)Tk`@qv$Dh7&~c|CeAB6QcZuosgqO>7)|VGG~d^!^1=?w7*0b(b(Nu)Oc771 z?_?J5%QlFke7X_B9e)?6fI7!}VA-}rq%aeL5Qgcl?ylS7NmJZxTaA;AaSHJDjm6rn zDiV=wwkzfFPV7MX2w@c>xYJdg^>H~FxZZFeKYE9b^^Xw-CdAviM8-}x=4_Q2jffKo zo8YCh(JEF77tOXWbckSdZvrbZM21z#Muv2?H8xrB9uy9CcD5{FN`gB$gAgbV)r{WV z6S8&dM)tG|Ed<>}dmKB+5L2*#=xn3JU6}HnHWQ28<4T>TQ#G|EuJ6@c(_~JK3IbK= zjqPM2lSz}TazZX*xR_j6J1VsBFA##G4plM=0*5xlJ+Ii|7CLj1V{E226RkER-*1x_ zwAZQK5IJZg9=;nAuFP{=c;q!o?ZicMA@q>kB4g;%+YOVMg7spk22*t`cuY@y%8Vpq z*}b8``pKFi3H@dz>Au(RlPnz?_WQMyjY?8zeX*-5uNMpHG#~5bf$@5|h?Uo(f8Ff$ zg}E`4QTO%kZzNW87KTIc?`RihDO8-UWu6X!bh=jeba$3%=+s4GOQ#4OW|^lt7S|dV zw%iyKY4ubZ^^$C418XuaVfPtvU##E33!7xh6d5D-nAG8CL8JsDJFL<(3+6aizjLYX z4lifUMJx`}LPAb%uC1)Cbk+VQ&1m)VDv|t&V6GHunJ?}t?x6>09UQOjK+H};&g`Qg z;yXz;G&_l)x`lOPUyuQtInb$!ioPpHqhwq9^$O=@)crS(S9c`dCqs#J+T2e-#5z+| zwx6mA>uQqA@>Gd~nHf*@1)LqIsjgZq7}hW%b|#V~XJOpPn+cqH<7SrioogY}yuk{( z)390~_vC?nOSdF`g3M&%)K#9#26Y-e5q4s-8>;T9h*uWPhJkDq!lEIj3hmw5 zsR>I9)(I5X%!D0<7>3nxT~-;f=fDUnnK47xuQV3VSnaJ;3o!ZAY0N`n zI+#tCIkwOeKd@Bo5LtD@{-1BNZDRR*VR2XyA_+b&i$dh=(khF*>^poVva|#A zaP@NOiseC)Et_mpj z&VVn0$H8H61$Y^KUjW|+PlK<4$AQ!d4ueDB)1_Sk3zH>Bju}P_o8R|iu#cp-P@7zV%F4XRZ#>GVvO6Z}MSXF;|ou;Ae?VBG} zrDyhzIy75ruiGu`R%R#x)B>pNNkK=-HPwHx<%oTpvx6K%6lbPVaV9CKQqCyEVS%H< z22UIoX}M5WO{ybFb(r9ELe(&w@TqSmUJe+ImQ<3ccja<6l4DOZFl9$dLJ9wiSglt& z)fH9M)Kh&)O~=(I_Sxr2P5Y`=ecs1*%voFGc%|Eku3BBJ4H3>x=glHDSL#F5_G|Xe z#)*_W_R&}k>$)7rGitk3bz>fvPgpp?hMyxD=oEJKLkdALdmj)P+o6^Q)#N*vOm zs>munNTOh%CzKRK%03B{%o6bc@dZ!ua;N%R>Dct_@$9#jWr-MhE-S)TS8~5k;5rdD jq8QOUyRDkgbd{#z;qB@gf5G#PM0wQPNSuAt+l~GWVUWP# literal 0 HcmV?d00001 diff --git a/docs/2to3sh.sh b/docs/2to3sh.sh deleted file mode 100644 index 42173a90..00000000 --- a/docs/2to3sh.sh +++ /dev/null @@ -1 +0,0 @@ -2to3 -Wno ../p3/docs ./$1 \ No newline at end of file diff --git a/docs/HVAC_Tutorial.ipynb b/docs/HVAC_Tutorial.ipynb index e7dd9b80..38ef277b 100644 --- a/docs/HVAC_Tutorial.ipynb +++ b/docs/HVAC_Tutorial.ipynb @@ -1,921 +1,882 @@ { - "metadata": { - "name": "", - "signature": "sha256:8eadf8836383724ddd49bd2e1c1e4ce485cfe5bf0efaef0186e4fcdbc00a6412" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "HVAC Loops" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Conceptual Introduction to HVAC Loops" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy builds threee kinds of loops for the energyplus idf file:\n", - "\n", - "1. Plant Loops\n", - "2. Condensor Loops\n", - "3. Air Loops\n", - "\n", - "All loops have two halves:\n", - "\n", - "1. Supply side\n", - "2. Demand Side\n", - "\n", - "The supply side provides the energy to the demand side that needs the energy. So the end-nodes on the supply side connect to the end-nodes on the demand side. \n", - "\n", - "The loop is made up of branches connected to each other. A single branch can lead to multiple branches through a **splitter** component. Multiple branches can lead to a single branch through a **mixer** component. \n", - "\n", - "Each branch is made up of components connected in series (in a line)\n", - "\n", - "Eppy starts off by building the shape or topology of the loop by connecting the branches in the right order. The braches themselves have a single component in them, that is just a place holder. Usually it is a pipe component. In an air loop it would be a duct component.\n", - "\n", - "The shape of the loop for the supply or demand side is quite simple. \n", - "\n", - "It can be described in the following manner for the supply side\n", - "\n", - "- The supply side starts single branch leads to a splitter\n", - "- The splitter leads to multiple branches\n", - "- these multiple branches come back and join in a mixer\n", - "- the mixer leads to a single branch that becomes end of the suppply side\n", - "\n", - "For the demand side we have:\n", - "\n", - "- The demand side starts single branch leads to a splitter\n", - "- The splitter leads to multiple branches\n", - "- these multiple branches come back and join in a mixer\n", - "- the mixer leads to a single branch that becomes end of the demand side\n", - "\n", - "The two ends of the supply side connect to the two ends of the demand side.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Diagramtically the the two sides of the loop will look like this::\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " Supply Side:\n", - " ------------\n", - " -> branch1 -> \n", - " start_branch --> branch2 --> end_branch\n", - " -> branch3 ->\n", - " Demand Side:\n", - " ------------\n", - " \n", - " -> d_branch1 -> \n", - " d_start_branch --> d_branch2 --> d_end_branch\n", - " -> d_branch3 ->\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "In eppy you could embody this is a list\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "supplyside = ['start_brandh', [ 'branch1', 'branch2', 'branch3'], 'end_branch']\n", - "demandside = ['d_start_brandh', ['d_branch1', 'd_branch2', 'd_branch3'], 'd_end_branch']\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy will build the build the shape/topology of the loop using the two lists above. Each branch will have a placeholder component, like a pipe or a duct::" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " \n", - " branch1 = --duct--\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will have to replace the placeholder with the real components that make up the loop. For instance, branch1 should really have a pre-heat coil leading to a supply fan leading to a cooling coil leading to a heating coil::" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " \n", - " new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy lets you build a new branch and you can replace branch1 with new_branch\n", - "\n", - "In this manner we can build up the entire loop with the right components, once the initial toplogy is right" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building a Plant loops" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy can build up the topology of a plant loop using single pipes in a branch. Once we do that the simple branch in the loop we have built can be replaced with a more complex branch.\n", - "\n", - "Let us try this out ans see how it works." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Building the topology of the loop" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# you would normaly install eppy by doing\n", - "# python setup.py install\n", - "# or\n", - "# pip install eppy\n", - "# or\n", - "# easy_install eppy\n", - "\n", - "# if you have not done so, uncomment the following three lines\n", - "import sys\n", - "# pathnameto_eppy = 'c:/eppy'\n", - "pathnameto_eppy = '../'\n", - "sys.path.append(pathnameto_eppy) \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy.modeleditor import IDF\n", - "from eppy import hvacbuilder\n", - "\n", - "from StringIO import StringIO\n", - "iddfile = \"../eppy/resources/iddfiles/Energy+V7_0_0_036.idd\"\n", - "IDF.setiddname(iddfile)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# make the topology of the loop\n", - "idf = IDF(StringIO('')) # makes an empty idf file in memory with no file name\n", - "loopname = \"p_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", - "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side of the loop\n", - "hvacbuilder.makeplantloop(idf, loopname, sloop, dloop)\n", - "idf.saveas(\"hhh1.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have made plant loop and saved it as hhh1.idf. \n", - "Now let us look at what the loop looks like. \n" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Diagram of the loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us use the script \"eppy/useful_scripts/loopdiagrams.py\" to draw this diagram" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "See [Generating a Loop Diagram](useful_scripts.html#loopdiagram-py) page for details on how to do this" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below is the diagram for this simple loop\n", - "\n", - "*Note: the supply and demnd sides are not connected in the diagram, but shown seperately for clarity*" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.plantloop1) # display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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KPoGG06lrOm16sgqvK1WAYLpSrwzlsuS7lZ988ol/Uqy7sfkkN8GG3z0c06SGTstRKZhO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\nV16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- } - ], - "prompt_number": 5 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Modifying the topology of the loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us make a new branch and replace the exisiting branch \n", - "\n", - "The existing branch name is \"sb0\" and it contains a single pipe component sb0_pipe.\n", - "\n", - "Let us replace it with a branch that has a chiller that is connected to a pipe which is turn connected to another pipe. So the connections in the new branch would look like \"chiller-> pipe1->pipe2\"" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# make a new branch chiller->pipe1-> pipe2\n", - "\n", - "# make a new pipe component\n", - "pipe1 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np1')\n", - "\n", - "# make a new chiller\n", - "chiller = idf.newidfobject(\"Chiller:Electric\".upper(), 'Central_Chiller')\n", - "\n", - "# make another pipe component\n", - "pipe2 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np2')\n", - "\n", - "# get the loop we are trying to modify\n", - "loop = idf.getobject('PLANTLOOP', 'p_loop') # args are (key, name)\n", - "# get the branch we are trying to modify\n", - "branch = idf.getobject('BRANCH', 'sb0') # args are (key, name)\n", - "listofcomponents = [chiller, pipe1, pipe2] # the new components are connected in this order\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we are going to try to replace **branch** with the a branch made up of **listofcomponents**\n", - "\n", - "- We will do this by calling the function replacebranch\n", - "- Calling replacebranch can throw an exception `WhichLoopError`\n", - "- In a moment, you will see why this exception is important" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "try:\n", - " newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')\n", - "except hvacbuilder.WhichLoopError as e:\n", - " print e\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Where should this loop connect ?\n", - "CHILLER:ELECTRIC - Central_Chiller\n", - "[u'Chilled_Water_', u'Condenser_', u'Heat_Recovery_']\n", - "\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code throws the exception. It says that the idfobject `CHILLER:ELECTRIC - Central_Chiller` has three possible connections. The chiller has inlet outlet nodes for the following\n", - "\n", - "- Chilled water\n", - "- Condenser\n", - "- Heat Recovery\n", - "\n", - "eppy does not know which one to connect to, and it needs your help. We know that the chiller needs to be connected to the chilled water inlet and outlet. Simply copy `Chilled_Water_` from the text in the exception and paste as shown in the code below. (make sure you copy it exactly. eppy is a little nerdy about that)" - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# HVAC Loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conceptual Introduction to HVAC Loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eppy builds threee kinds of loops for the energyplus idf file:\n", + "\n", + "1. Plant Loops\n", + "2. Condensor Loops\n", + "3. Air Loops\n", + "\n", + "All loops have two halves:\n", + "\n", + "1. Supply side\n", + "2. Demand Side\n", + "\n", + "The supply side provides the energy to the demand side that needs the energy. So the end-nodes on the supply side connect to the end-nodes on the demand side. \n", + "\n", + "The loop is made up of branches connected to each other. A single branch can lead to multiple branches through a **splitter** component. Multiple branches can lead to a single branch through a **mixer** component. \n", + "\n", + "Each branch is made up of components connected in series (in a line)\n", + "\n", + "Eppy starts off by building the shape or topology of the loop by connecting the branches in the right order. The braches themselves have a single component in them, that is just a place holder. Usually it is a pipe component. In an air loop it would be a duct component.\n", + "\n", + "The shape of the loop for the supply or demand side is quite simple. \n", + "\n", + "It can be described in the following manner for the supply side\n", + "\n", + "- The supply side starts single branch leads to a splitter\n", + "- The splitter leads to multiple branches\n", + "- these multiple branches come back and join in a mixer\n", + "- the mixer leads to a single branch that becomes end of the suppply side\n", + "\n", + "For the demand side we have:\n", + "\n", + "- The demand side starts single branch leads to a splitter\n", + "- The splitter leads to multiple branches\n", + "- these multiple branches come back and join in a mixer\n", + "- the mixer leads to a single branch that becomes end of the demand side\n", + "\n", + "The two ends of the supply side connect to the two ends of the demand side.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Diagramtically the the two sides of the loop will look like this::\n" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " Supply Side:\n", + " ------------\n", + " -> branch1 -> \n", + " start_branch --> branch2 --> end_branch\n", + " -> branch3 ->\n", + " Demand Side:\n", + " ------------\n", + " \n", + " -> d_branch1 -> \n", + " d_start_branch --> d_branch2 --> d_end_branch\n", + " -> d_branch3 ->\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "In eppy you could embody this is a list\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "supplyside = ['start_brandh', [ 'branch1', 'branch2', 'branch3'], 'end_branch']\n", + "demandside = ['d_start_brandh', ['d_branch1', 'd_branch2', 'd_branch3'], 'd_end_branch']\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eppy will build the build the shape/topology of the loop using the two lists above. Each branch will have a placeholder component, like a pipe or a duct::" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " \n", + " branch1 = --duct--\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will have to replace the placeholder with the real components that make up the loop. For instance, branch1 should really have a pre-heat coil leading to a supply fan leading to a cooling coil leading to a heating coil::" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " \n", + " new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eppy lets you build a new branch and you can replace branch1 with new_branch\n", + "\n", + "In this manner we can build up the entire loop with the right components, once the initial toplogy is right" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Plant loops" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Eppy can build up the topology of a plant loop using single pipes in a branch. Once we do that the simple branch in the loop we have built can be replaced with a more complex branch.\n", + "\n", + "Let us try this out ans see how it works." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building the topology of the loop" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# you would normaly install eppy by doing\n", + "# python setup.py install\n", + "# or\n", + "# pip install eppy\n", + "# or\n", + "# easy_install eppy\n", + "\n", + "# if you have not done so, uncomment the following three lines\n", + "import sys\n", + "# pathnameto_eppy = 'c:/eppy'\n", + "pathnameto_eppy = '../'\n", + "sys.path.append(pathnameto_eppy) \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from eppy.modeleditor import IDF\n", + "from eppy import hvacbuilder\n", + "\n", + "from io import StringIO\n", + "iddfile = \"../eppy/resources/iddfiles/Energy+V7_0_0_036.idd\"\n", + "IDF.setiddname(iddfile)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# make the topology of the loop\n", + "idf = IDF(StringIO('')) # makes an empty idf file in memory with no file name\n", + "loopname = \"p_loop\"\n", + "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", + "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side of the loop\n", + "hvacbuilder.makeplantloop(idf, loopname, sloop, dloop)\n", + "idf.saveas(\"hhh1.idf\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have made plant loop and saved it as hhh1.idf. \n", + "Now let us look at what the loop looks like. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Diagram of the loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us use the script \"eppy/useful_scripts/loopdiagrams.py\" to draw this diagram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "See [Generating a Loop Diagram](useful_scripts.html#loopdiagram-py) page for details on how to do this" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below is the diagram for this simple loop\n", + "\n", + "*Note: the supply and demnd sides are not connected in the diagram, but shown seperately for clarity*" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# instead of passing chiller to the function, we pass a tuple (chiller, 'Chilled_Water_').\n", - "# This lets eppy know where the connection should be made.\n", - "# The idfobject pipe does not have this ambiguity. So pipes do not need this extra information\n", - "listofcomponents = [(chiller, 'Chilled_Water_'), pipe1, pipe2]\n", - "\n", - "try:\n", - " newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')\n", - "except Exception as e:\n", - " print e\n", - "else: # else will run only if the try suceeds\n", - " print \"no exception was thrown\"\n", - "\n", - "idf.saveas(\"hhh_new.idf\")\n" - ], - "language": "python", + "data": { + "image/png": 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\n" + }, "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "no exception was thrown\n" - ] - } - ], - "prompt_number": 28 - }, + "output_type": "display_data" + } + ], + "source": [ + "import ex_inits #no need to know this code, it just shows the image below\n", + "for_images = ex_inits\n", + "for_images.display_png(for_images.plantloop1) # display the image below\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Modifying the topology of the loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us make a new branch and replace the exisiting branch \n", + "\n", + "The existing branch name is \"sb0\" and it contains a single pipe component sb0_pipe.\n", + "\n", + "Let us replace it with a branch that has a chiller that is connected to a pipe which is turn connected to another pipe. So the connections in the new branch would look like \"chiller-> pipe1->pipe2\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Tagential note*: The `\"try .. except .. else\"` statement is useful here. If you have not run across it before, take a look at these two links\n", - "\n", - "- http://shahriar.svbtle.com/the-possibly-forgotten-optional-else-in-python-try-statement\n", - "- https://docs.python.org/2/tutorial/errors.html" + "name": "stderr", + "output_type": "stream", + "text": [ + "../eppy/modeleditor.py:757: UserWarning: The aname parameter should no longer be used.\n", + " warnings.warn(\"The aname parameter should no longer be used.\", UserWarning)\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have saved this as file \"hhh_new.idf\". \n", - "Let us draw the diagram of this file. (run this from eppy/eppy folder)" + } + ], + "source": [ + "# make a new branch chiller->pipe1-> pipe2\n", + "\n", + "# make a new pipe component\n", + "pipe1 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np1')\n", + "\n", + "# make a new chiller\n", + "chiller = idf.newidfobject(\"Chiller:Electric\".upper(), 'Central_Chiller')\n", + "\n", + "# make another pipe component\n", + "pipe2 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np2')\n", + "\n", + "# get the loop we are trying to modify\n", + "loop = idf.getobject('PLANTLOOP', 'p_loop') # args are (key, name)\n", + "# get the branch we are trying to modify\n", + "branch = idf.getobject('BRANCH', 'sb0') # args are (key, name)\n", + "listofcomponents = [chiller, pipe1, pipe2] # the new components are connected in this order\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to try to replace **branch** with the a branch made up of **listofcomponents**\n", + "\n", + "- We will do this by calling the function replacebranch\n", + "- Calling replacebranch can throw an exception `WhichLoopError`\n", + "- In a moment, you will see why this exception is important" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Where should this loop connect ?\n", + "CHILLER:ELECTRIC - Central_Chiller\n", + "['Chilled_Water_', 'Condenser_', 'Heat_Recovery_']\n", + "\n" ] - }, + } + ], + "source": [ + "try:\n", + " newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')\n", + "except hvacbuilder.WhichLoopError as e:\n", + " print(e)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code throws the exception. It says that the idfobject `CHILLER:ELECTRIC - Central_Chiller` has three possible connections. The chiller has inlet outlet nodes for the following\n", + "\n", + "- Chilled water\n", + "- Condenser\n", + "- Heat Recovery\n", + "\n", + "eppy does not know which one to connect to, and it needs your help. We know that the chiller needs to be connected to the chilled water inlet and outlet. Simply copy `Chilled_Water_` from the text in the exception and paste as shown in the code below. (make sure you copy it exactly. eppy is a little nerdy about that)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ { - "cell_type": "raw", - "metadata": {}, - "source": [ - "python ex_loopdiagram.py hhh_new.idf\n" + "name": "stdout", + "output_type": "stream", + "text": [ + "no exception was thrown\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.plantloop2) # display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ZBBColgDBU3nkCZ7K40qptSfALHy1t09rrkWarSicMEJDCvQfqX4FFl54YZtn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\nhyF5ZsfLSSDyBDgDFfkqooAk8BcBt6UeVapUEYzg0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bZ65NIsXdMvSAykmKzpToL069fP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\nAQQQqB8BRqDqx5WjllhAAZGmvb3//vv+PlLjxo2rFTy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BBAAAEE4iZAABW3K05/EUAAAQQQQAABBBBAoGABAqiC6dgRAQQQQAABBBBAAAEE4iZAABW3\nK05/EUAAAQQQQAABBBBAoGABAqiC6dgRAQQQQAABBBBAAAEE4iZAABW3K05/EUAAAQQQQAABBBBA\noGABAqiC6dgRAQQQQAABBBBAAAEE4iZAABW3K05/EUAAAQQQQAABBBBAoGABAqiC6dgRAQQQQAAB\nBBBAAAEE4iZAABW3K05/EUAAAQQQQAABBBBAoGABAqiC6dgRAQQQQAABBBBAAAEE4iZAABW3K05/\nEUAAAQQQQAABBBBAoGABAqiC6dgRAQQQQAABBBBAAAEE4iZAABW3K05/EUAAAQQQQAABBBBAoGAB\nAqiC6dgRAQQQQAABBBBAAAEE4ibw/wFJ10NqcRwikQAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 10 - }, + } + ], + "source": [ + "# instead of passing chiller to the function, we pass a tuple (chiller, 'Chilled_Water_').\n", + "# This lets eppy know where the connection should be made.\n", + "# The idfobject pipe does not have this ambiguity. So pipes do not need this extra information\n", + "listofcomponents = [(chiller, 'Chilled_Water_'), pipe1, pipe2]\n", + "\n", + "try:\n", + " newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')\n", + "except Exception as e:\n", + " print(e)\n", + "else: # else will run only if the try suceeds\n", + " print(\"no exception was thrown\")\n", + "\n", + "idf.saveas(\"hhh_new.idf\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Tagential note*: The `\"try .. except .. else\"` statement is useful here. If you have not run across it before, take a look at these two links\n", + "\n", + "- http://shahriar.svbtle.com/the-possibly-forgotten-optional-else-in-python-try-statement\n", + "- https://docs.python.org/2/tutorial/errors.html" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have saved this as file \"hhh_new.idf\". \n", + "Let us draw the diagram of this file. (run this from eppy/eppy folder)" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "python ex_loopdiagram.py hhh_new.idf\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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\n" + }, "metadata": {}, - "source": [ - "This diagram shows the new components in the branch" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "import ex_inits #no need to know this code, it just shows the image below\n", + "for_images = ex_inits\n", + "for_images.display_png(for_images.plantloop2) # display the image below\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This diagram shows the new components in the branch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Work flow with `WhichLoopError`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When you are writing scripts don't bother to use `try .. except` for `WhichLoopError`. \n", + "\n", + "- Simply allow the exception to be raised. \n", + "- Use the information in the exception to update your code\n", + "- You may have to do this a couple of times in your script.\n", + "- In a sense you are letting eppy tell you how to update the script.\n", + "\n", + "*Question:* I am writing an application using eppy, not just a script. The above workflow does not work for me\n", + "\n", + "*Response:* Aha ! If that is the case, open an issue in [github/eppy](https://github.com/santoshphilip/eppy). We are lazy people. We don't write code unless it is needed :-)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Traversing the loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It would be nice to move through the loop using functions \"nextnode()\" and \"prevnode()\"\n", + "\n", + "Eppy indeed has such functions\n", + "\n", + "Let us try to traverse the loop above. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# to traverse the loop we are going to call some functions ex_loopdiagrams.py, \n", + "# the program that draws the loop diagrams.\n", + "from eppy.useful_scripts import loopdiagram\n", + "fname = 'hhh_new.idf'\n", + "iddfile = '../eppy/resources/iddfiles/Energy+V8_0_0.idd'\n", + "edges = loopdiagram.getedges(fname, iddfile)\n", + "# edges are the lines that draw the nodes in the loop. \n", + "# The term comes from graph theory in mathematics\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above code gets us the edges of the loop diagram. Once we have the edges, we can traverse through the diagram. Let us start with the \"Central_Chiller\" and work our way down." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 4, - "metadata": {}, - "source": [ - "Work flow with `WhichLoopError`" + "name": "stdout", + "output_type": "stream", + "text": [ + "['np1']\n" ] - }, + } + ], + "source": [ + "from eppy import walk_hvac\n", + "firstnode = \"Central_Chiller\"\n", + "nextnodes = walk_hvac.nextnode(edges, firstnode)\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When you are writing scripts don't bother to use `try .. except` for `WhichLoopError`. \n", - "\n", - "- Simply allow the exception to be raised. \n", - "- Use the information in the exception to update your code\n", - "- You may have to do this a couple of times in your script.\n", - "- In a sense you are letting eppy tell you how to update the script.\n", - "\n", - "*Question:* I am writing an application using eppy, not just a script. The above workflow does not work for me\n", - "\n", - "*Response:* Aha ! If that is the case, open an issue in [github/eppy](https://github.com/santoshphilip/eppy). We are lazy people. We don't write code unless it is needed :-)" + "name": "stdout", + "output_type": "stream", + "text": [ + "['np2']\n" ] - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Traversing the loop" + "name": "stdout", + "output_type": "stream", + "text": [ + "['p_loop_supply_splitter']\n" ] - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It would be nice to move through the loop using functions \"nextnode()\" and \"prevnode()\"\n", - "\n", - "Eppy indeed has such functions\n", - "\n", - "Let us try to traverse the loop above. " + "name": "stdout", + "output_type": "stream", + "text": [ + "['sb1_pipe', 'sb2_pipe', 'sb3_pipe']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# to traverse the loop we are going to call some functions ex_loopdiagrams.py, \n", - "# the program that draws the loop diagrams.\n", - "from eppy import ex_loopdiagram\n", - "fname = 'hhh_new.idf'\n", - "iddfile = '../eppy/resources/iddfiles/Energy+V8_0_0.idd'\n", - "edges = ex_loopdiagram.getedges(fname, iddfile)\n", - "# edges are the lines that draw the nodes in the loop. \n", - "# The term comes from graph theory in mathematics\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 11 - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This leads us to three components -> ['sb1_pipe', 'sb2_pipe', 'sb3_pipe']. Let us follow one of them" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code gets us the edges of the loop diagram. Once we have the edges, we can traverse through the diagram. Let us start with the \"Central_Chiller\" and work our way down." + "name": "stdout", + "output_type": "stream", + "text": [ + "['p_loop_supply_mixer']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import walk_hvac\n", - "firstnode = \"Central_Chiller\"\n", - "nextnodes = walk_hvac.nextnode(edges, firstnode)\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'np1']\n" - ] - } - ], - "prompt_number": 12 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'np2']\n" - ] - } - ], - "prompt_number": 13 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'p_loop_supply_splitter']\n" - ] - } - ], - "prompt_number": 14 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'sb1_pipe', u'sb2_pipe', u'sb3_pipe']\n" - ] - } - ], - "prompt_number": 15 - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This leads us to three components -> ['sb1_pipe', 'sb2_pipe', 'sb3_pipe']. Let us follow one of them" + "name": "stdout", + "output_type": "stream", + "text": [ + "['sb4_pipe']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'p_loop_supply_mixer']\n" - ] - } - ], - "prompt_number": 16 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'sb4_pipe']\n" - ] - } - ], - "prompt_number": 17 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[]\n" - ] - } - ], - "prompt_number": 18 - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have reached the end of this branch. There are no more components. \n", - "\n", - "We can follow this in reverse using the function prevnode()" + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "lastnode = 'sb4_pipe'\n", - "prevnodes = walk_hvac.prevnode(edges, lastnode)\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'p_loop_supply_mixer']\n" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'sb1_pipe', u'sb2_pipe', u'sb3_pipe']\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'p_loop_supply_splitter']\n" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'np2']\n" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'np1']\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'Central_Chiller']\n" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[]\n" - ] - } - ], - "prompt_number": 25 - }, + } + ], + "source": [ + "nextnodes = walk_hvac.nextnode(edges, nextnodes[0])\n", + "print(nextnodes)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have reached the end of this branch. There are no more components. \n", + "\n", + "We can follow this in reverse using the function prevnode()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All the way to where the loop ends" + "name": "stdout", + "output_type": "stream", + "text": [ + "['p_loop_supply_mixer']\n" ] - }, + } + ], + "source": [ + "lastnode = 'sb4_pipe'\n", + "prevnodes = walk_hvac.prevnode(edges, lastnode)\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building a Condensor loop" + "name": "stdout", + "output_type": "stream", + "text": [ + "['sb1_pipe', 'sb2_pipe', 'sb3_pipe']\n" ] - }, + } + ], + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We build the condensor loop the same way we built the plant loop. Pipes are put as place holders for the components. Let us build a new idf file with just a condensor loop in it." + "name": "stdout", + "output_type": "stream", + "text": [ + "['p_loop_supply_splitter']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "condensorloop_idf = IDF(StringIO('')) \n", - "loopname = \"c_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side\n", - "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side\n", - "theloop = hvacbuilder.makecondenserloop(condensorloop_idf, loopname, sloop, dloop)\n", - "condensorloop_idf.saveas(\"c_loop.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 26 - }, + } + ], + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, just as we did in the plant loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()" + "name": "stdout", + "output_type": "stream", + "text": [ + "['np2']\n" ] - }, + } + ], + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building an Air Loop" + "name": "stdout", + "output_type": "stream", + "text": [ + "['np1']\n" ] - }, + } + ], + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Building an air loop is similar to the plant and condensor loop. The difference is that instead of pipes , we have ducts as placeholder components. The other difference is that we have zones on the demand side." + "name": "stdout", + "output_type": "stream", + "text": [ + "['Central_Chiller']\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "airloop_idf = IDF(StringIO('')) \n", - "loopname = \"a_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", - "dloop = ['zone1', 'zone2', 'zone3'] # zones on the demand side\n", - "hvacbuilder.makeairloop(airloop_idf, loopname, sloop, dloop)\n", - "airloop_idf.saveas(\"a_loop.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, + } + ], + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, just as we did in the plant and condensor loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()" + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" ] } ], - "metadata": {} + "source": [ + "prevnodes = walk_hvac.prevnode(edges, prevnodes[0])\n", + "print(prevnodes)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All the way to where the loop ends" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building a Condensor loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We build the condensor loop the same way we built the plant loop. Pipes are put as place holders for the components. Let us build a new idf file with just a condensor loop in it." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "condensorloop_idf = IDF(StringIO('')) \n", + "loopname = \"c_loop\"\n", + "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side\n", + "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side\n", + "theloop = hvacbuilder.makecondenserloop(condensorloop_idf, loopname, sloop, dloop)\n", + "condensorloop_idf.saveas(\"c_loop.idf\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, just as we did in the plant loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Building an Air Loop" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building an air loop is similar to the plant and condensor loop. The difference is that instead of pipes , we have ducts as placeholder components. The other difference is that we have zones on the demand side." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "airloop_idf = IDF(StringIO('')) \n", + "loopname = \"a_loop\"\n", + "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", + "dloop = ['zone1', 'zone2', 'zone3'] # zones on the demand side\n", + "hvacbuilder.makeairloop(airloop_idf, loopname, sloop, dloop)\n", + "airloop_idf.saveas(\"a_loop.idf\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, just as we did in the plant and condensor loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()" + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/HVAC_Tutorial.ipynb_orig b/docs/HVAC_Tutorial.ipynb_orig deleted file mode 100644 index 33d1b8aa..00000000 --- a/docs/HVAC_Tutorial.ipynb_orig +++ /dev/null @@ -1,818 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:8eadf8836383724ddd49bd2e1c1e4ce485cfe5bf0efaef0186e4fcdbc00a6412" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "HVAC Loops" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Conceptual Introduction to HVAC Loops" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy builds threee kinds of loops for the energyplus idf file:\n", - "\n", - "1. Plant Loops\n", - "2. Condensor Loops\n", - "3. Air Loops\n", - "\n", - "All loops have two halves:\n", - "\n", - "1. Supply side\n", - "2. Demand Side\n", - "\n", - "The supply side provides the energy to the demand side that needs the energy. So the end-nodes on the supply side connect to the end-nodes on the demand side. \n", - "\n", - "The loop is made up of branches connected to each other. A single branch can lead to multiple branches through a **splitter** component. Multiple branches can lead to a single branch through a **mixer** component. \n", - "\n", - "Each branch is made up of components connected in series (in a line)\n", - "\n", - "Eppy starts off by building the shape or topology of the loop by connecting the branches in the right order. The braches themselves have a single component in them, that is just a place holder. Usually it is a pipe component. In an air loop it would be a duct component.\n", - "\n", - "The shape of the loop for the supply or demand side is quite simple. \n", - "\n", - "It can be described in the following manner for the supply side\n", - "\n", - "- The supply side starts single branch leads to a splitter\n", - "- The splitter leads to multiple branches\n", - "- these multiple branches come back and join in a mixer\n", - "- the mixer leads to a single branch that becomes end of the suppply side\n", - "\n", - "For the demand side we have:\n", - "\n", - "- The demand side starts single branch leads to a splitter\n", - "- The splitter leads to multiple branches\n", - "- these multiple branches come back and join in a mixer\n", - "- the mixer leads to a single branch that becomes end of the demand side\n", - "\n", - "The two ends of the supply side connect to the two ends of the demand side.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Diagramtically the the two sides of the loop will look like this::\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " Supply Side:\n", - " ------------\n", - " -> branch1 -> \n", - " start_branch --> branch2 --> end_branch\n", - " -> branch3 ->\n", - " Demand Side:\n", - " ------------\n", - " \n", - " -> d_branch1 -> \n", - " d_start_branch --> d_branch2 --> d_end_branch\n", - " -> d_branch3 ->\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "In eppy you could embody this is a list\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "supplyside = ['start_brandh', [ 'branch1', 'branch2', 'branch3'], 'end_branch']\n", - "demandside = ['d_start_brandh', ['d_branch1', 'd_branch2', 'd_branch3'], 'd_end_branch']\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy will build the build the shape/topology of the loop using the two lists above. Each branch will have a placeholder component, like a pipe or a duct::" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " \n", - " branch1 = --duct--\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will have to replace the placeholder with the real components that make up the loop. For instance, branch1 should really have a pre-heat coil leading to a supply fan leading to a cooling coil leading to a heating coil::" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " \n", - " new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy lets you build a new branch and you can replace branch1 with new_branch\n", - "\n", - "In this manner we can build up the entire loop with the right components, once the initial toplogy is right" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building a Plant loops" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy can build up the topology of a plant loop using single pipes in a branch. Once we do that the simple branch in the loop we have built can be replaced with a more complex branch.\n", - "\n", - "Let us try this out ans see how it works." - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Building the topology of the loop" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# you would normaly install eppy by doing\n", - "# python setup.py install\n", - "# or\n", - "# pip install eppy\n", - "# or\n", - "# easy_install eppy\n", - "\n", - "# if you have not done so, uncomment the following three lines\n", - "import sys\n", - "# pathnameto_eppy = 'c:/eppy'\n", - "pathnameto_eppy = '../'\n", - "sys.path.append(pathnameto_eppy) \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy.modeleditor import IDF\n", - "from eppy import hvacbuilder\n", - "\n", - "from StringIO import StringIO\n", - "iddfile = \"../eppy/resources/iddfiles/Energy+V7_0_0_036.idd\"\n", - "IDF.setiddname(iddfile)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# make the topology of the loop\n", - "idf = IDF(StringIO('')) # makes an empty idf file in memory with no file name\n", - "loopname = \"p_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", - "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side of the loop\n", - "hvacbuilder.makeplantloop(idf, loopname, sloop, dloop)\n", - "idf.saveas(\"hhh1.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have made plant loop and saved it as hhh1.idf. \n", - "Now let us look at what the loop looks like. \n" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Diagram of the loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Eppy has a function that can draw the loops. We'll use this to view the loop diagram. \n", - " \n", - "run the following program in the shell. (you have to run it from the eppy/eppy folder)\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "\n", - "# usage:\n", - "# python ex_loopdiagram.py iddfile idffile\n", - "python ex_loopdiagram.py ./resources/iddfiles/Energy+V7_0_0_036.idd hhh1.idf\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This will output a image by name hhh1.png. This image is shown below. \n", - "\n", - "*Note: the supply and demnd sides are not connected in the diagram, but shown seperately for clarity*" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.plantloop1) # display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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KPoGG06lrOm16sgqvK1WAYLpSrwzlsuS7lZ988ol/Uqy7sfkkN8GG3z0c06SGTstRKZhO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\nV16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- } - ], - "prompt_number": 42 - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Modifying the topology of the loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us make a new branch and replace the exisiting branch \n", - "\n", - "The existing branch name is \"sb0\" and it contains a single pipe component sb0_pipe.\n", - "\n", - "Let us replace it with a branch that has a chiller that is connected to a pipe which is turn connected to another pipe. So the connections in the new branch would look like \"chiller-> pipe1->pipe2\"" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# make a new branch chiller->pipe1-> pipe2\n", - "\n", - "# make a new pipe component\n", - "pipe1 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np1')\n", - "\n", - "# make a new chiller\n", - "chiller = idf.newidfobject(\"Chiller:Electric\".upper(), 'Central_Chiller')\n", - "\n", - "# make another pipe component\n", - "pipe2 = idf.newidfobject(\"PIPE:ADIABATIC\", 'np2')\n", - "\n", - "# get the loop we are trying to modify\n", - "loop = idf.getobject('PLANTLOOP', 'p_loop') # args are (key, name)\n", - "# get the branch we are trying to modify\n", - "branch = idf.getobject('BRANCH', 'sb0') # args are (key, name)\n", - "listofcomponents = [chiller, pipe1, pipe2] # the new components are connected in this order\n", - "\n", - "newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')\n", - "# in \"loop\"\n", - "# this replaces the components in \"branch\" with the components in \"listofcomponents\"\n", - "\n", - "idf.saveas(\"hhh_new.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 43 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have saved this as file \"hhh_new.idf\". \n", - "Let us draw the diagram of this file. (run this from eppy/eppy folder)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "python ex_loopdiagram.py hhh_new.idf\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.plantloop2) # display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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ZBBColgDBU3nkCZ7K40qptSfALHy1t09rrkWarSicMEJDCvQfqX4FFl54YZtn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\nhyF5ZsfLSSDyBDgDFfkqooAk8BcBt6UeVapUEYzg0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bZ65NIsXdMvSAykmKzpToL069fP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\nAQQQqB8BRqDqx5WjllhAAZGmvb3//vv+PlLjxo2rFTy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G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\nAqiC6dgRAQQQQAABBBBAAAEE4ibw/wFJ10NqcRwikQAAAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 44 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This diagram shows the new components in the branch" - ] - }, - { - "cell_type": "heading", - "level": 3, - "metadata": {}, - "source": [ - "Traversing the loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It would be nice to move through the loop using functions \"next()\" and \"prev()\"\n", - "\n", - "Eppy indeed has such functions\n", - "\n", - "Let us try to traverse the loop above. " - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# to traverse the loop we are going to call some functions ex_loopdiagrams.py, \n", - "# the program that draws the loop diagrams.\n", - "from eppy import ex_loopdiagram\n", - "fname = 'hhh_new.idf'\n", - "iddfile = '../eppy/resources/iddfiles/Energy+V8_0_0.idd'\n", - "edges = ex_loopdiagram.getedges(fname, iddfile)\n", - "# edges are the lines that draw the nodes in the loop. \n", - "# The term comes from graph theory in mathematics\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 45 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The above code gets us the edges of the loop diagram. Once we have the edges, we can traverse through the diagram. Let us start with the \"Central_Chiller\" and work our way down." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import walk_hvac\n", - "firstnode = \"Central_Chiller\"\n", - "nextnodes = walk_hvac.next(edges, firstnode)\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['np1']\n" - ] - } - ], - "prompt_number": 46 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['np2']\n" - ] - } - ], - "prompt_number": 47 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['p_loop_supply_splitter']\n" - ] - } - ], - "prompt_number": 48 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['sb1_pipe', 'sb2_pipe', 'sb3_pipe']\n" - ] - } - ], - "prompt_number": 49 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This leads us to three components -> ['sb1_pipe', 'sb2_pipe', 'sb3_pipe']. Let us follow one of them" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['p_loop_supply_mixer']\n" - ] - } - ], - "prompt_number": 50 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['sb4_pipe']\n" - ] - } - ], - "prompt_number": 51 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "nextnodes = walk_hvac.next(edges, nextnodes[0])\n", - "print nextnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[]\n" - ] - } - ], - "prompt_number": 52 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have reached the end of this branch. There are no more components. \n", - "\n", - "We can follow this in reverse using the function prev()" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "lastnode = 'sb4_pipe'\n", - "prevnodes = walk_hvac.prev(edges, lastnode)\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['p_loop_supply_mixer']\n" - ] - } - ], - "prompt_number": 53 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['sb1_pipe', 'sb2_pipe', 'sb3_pipe']\n" - ] - } - ], - "prompt_number": 54 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['p_loop_supply_splitter']\n" - ] - } - ], - "prompt_number": 55 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['np2']\n" - ] - } - ], - "prompt_number": 56 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['np1']\n" - ] - } - ], - "prompt_number": 57 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "['Central_Chiller']\n" - ] - } - ], - "prompt_number": 58 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "prevnodes = walk_hvac.prev(edges, prevnodes[0])\n", - "print prevnodes\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[]\n" - ] - } - ], - "prompt_number": 59 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All the way to where the loop ends" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building a Condensor loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We build the condensor loop the same way we built the plant loop. Pipes are put as place holders for the components. Let us build a new idf file with just a condensor loop in it." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "condensorloop_idf = IDF(StringIO('')) \n", - "loopname = \"c_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side\n", - "dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side\n", - "theloop = hvacbuilder.makecondenserloop(condensorloop_idf, loopname, sloop, dloop)\n", - "condensorloop_idf.saveas(\"c_loop.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 60 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, just as we did in the plant loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions next() and prev()" - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Building an Air Loop" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Building an air loop is similar to the plant and condensor loop. The difference is that instead of pipes , we have ducts as placeholder components. The other difference is that we have zones on the demand side." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "airloop_idf = IDF(StringIO('')) \n", - "loopname = \"a_loop\"\n", - "sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop\n", - "dloop = ['zone1', 'zone2', 'zone3'] # zones on the demand side\n", - "hvacbuilder.makeairloop(airloop_idf, loopname, sloop, dloop)\n", - "airloop_idf.saveas(\"a_loop.idf\")\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 61 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, just as we did in the plant and condensor loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions next() and prev()" - ] - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/docs/HVAC_Tutorial.py b/docs/HVAC_Tutorial.py deleted file mode 100644 index 246bd3a9..00000000 --- a/docs/HVAC_Tutorial.py +++ /dev/null @@ -1,401 +0,0 @@ -# -*- coding: utf-8 -*- -# 3.0 - -# - -# HVAC Loops - -# - -# Conceptual Introduction to HVAC Loops - -# - -# Eppy builds threee kinds of loops for the energyplus idf file: -# -# 1. Plant Loops -# 2. Condensor Loops -# 3. Air Loops -# -# All loops have two halves: -# -# 1. Supply side -# 2. Demand Side -# -# The supply side provides the energy to the demand side that needs the energy. So the end-nodes on the supply side connect to the end-nodes on the demand side. -# -# The loop is made up of branches connected to each other. A single branch can lead to multiple branches through a **splitter** component. Multiple branches can lead to a single branch through a **mixer** component. -# -# Each branch is made up of components connected in series (in a line) -# -# Eppy starts off by building the shape or topology of the loop by connecting the branches in the right order. The braches themselves have a single component in them, that is just a place holder. Usually it is a pipe component. In an air loop it would be a duct component. -# -# The shape of the loop for the supply or demand side is quite simple. -# -# It can be described in the following manner for the supply side -# -# - The supply side starts single branch leads to a splitter -# - The splitter leads to multiple branches -# - these multiple branches come back and join in a mixer -# - the mixer leads to a single branch that becomes end of the suppply side -# -# For the demand side we have: -# -# - The demand side starts single branch leads to a splitter -# - The splitter leads to multiple branches -# - these multiple branches come back and join in a mixer -# - the mixer leads to a single branch that becomes end of the demand side -# -# The two ends of the supply side connect to the two ends of the demand side. - -# - -# Diagramtically the the two sides of the loop will look like this:: - -# - -# Supply Side: -# ------------ -# -> branch1 -> -# start_branch --> branch2 --> end_branch -# -> branch3 -> -# Demand Side: -# ------------ -# -# -> d_branch1 -> -# d_start_branch --> d_branch2 --> d_end_branch -# -> d_branch3 -> -# - -# - -# -# In eppy you could embody this is a list - -# - -supplyside = ["start_brandh", ["branch1", "branch2", "branch3"], "end_branch"] -demandside = ["d_start_brandh", ["d_branch1", "d_branch2", "d_branch3"], "d_end_branch"] - -# - -# Eppy will build the build the shape/topology of the loop using the two lists above. Each branch will have a placeholder component, like a pipe or a duct:: - -# - -# -# branch1 = --duct-- - -# - -# Now we will have to replace the placeholder with the real components that make up the loop. For instance, branch1 should really have a pre-heat coil leading to a supply fan leading to a cooling coil leading to a heating coil:: - -# - -# -# new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil - -# - -# Eppy lets you build a new branch and you can replace branch1 with new_branch -# -# In this manner we can build up the entire loop with the right components, once the initial toplogy is right - -# - -# Building a Plant loops - -# - -# Eppy can build up the topology of a plant loop using single pipes in a branch. Once we do that the simple branch in the loop we have built can be replaced with a more complex branch. -# -# Let us try this out ans see how it works. - -# - -# Building the topology of the loop - -# - -# you would normaly install eppy by doing -# python setup.py install -# or -# pip install eppy -# or -# easy_install eppy - -# if you have not done so, uncomment the following three lines -import sys - -# pathnameto_eppy = 'c:/eppy' -pathnameto_eppy = "../" -sys.path.append(pathnameto_eppy) - -# - -from eppy.modeleditor import IDF -from eppy import hvacbuilder - -from io import StringIO - -iddfile = "../eppy/resources/iddfiles/Energy+V7_0_0_036.idd" -IDF.setiddname(iddfile) - -# - -# make the topology of the loop -idf = IDF(StringIO("")) # makes an empty idf file in memory with no file name -loopname = "p_loop" -sloop = ["sb0", ["sb1", "sb2", "sb3"], "sb4"] # supply side of the loop -dloop = ["db0", ["db1", "db2", "db3"], "db4"] # demand side of the loop -hvacbuilder.makeplantloop(idf, loopname, sloop, dloop) -idf.saveas("hhh1.idf") - -# - -# We have made plant loop and saved it as hhh1.idf. -# Now let us look at what the loop looks like. - -# - -# Diagram of the loop - -# - -# Let us use the script "eppy/useful_scripts/loopdiagrams.py" to draw this diagram - -# - -# See [Generating a Loop Diagram](useful_scripts.html#loopdiagram-py) page for details on how to do this - -# - -# Below is the diagram for this simple loop -# -# *Note: the supply and demnd sides are not connected in the diagram, but shown seperately for clarity* - -# - -from eppy import ex_inits # no need to know this code, it just shows the image below - -for_images = ex_inits -for_images.display_png(for_images.plantloop1) # display the image below - -# - -# Modifying the topology of the loop - -# - -# Let us make a new branch and replace the exisiting branch -# -# The existing branch name is "sb0" and it contains a single pipe component sb0_pipe. -# -# Let us replace it with a branch that has a chiller that is connected to a pipe which is turn connected to another pipe. So the connections in the new branch would look like "chiller-> pipe1->pipe2" - -# - -# make a new branch chiller->pipe1-> pipe2 - -# make a new pipe component -pipe1 = idf.newidfobject("PIPE:ADIABATIC", "np1") - -# make a new chiller -chiller = idf.newidfobject("Chiller:Electric".upper(), "Central_Chiller") - -# make another pipe component -pipe2 = idf.newidfobject("PIPE:ADIABATIC", "np2") - -# get the loop we are trying to modify -loop = idf.getobject("PLANTLOOP", "p_loop") # args are (key, name) -# get the branch we are trying to modify -branch = idf.getobject("BRANCH", "sb0") # args are (key, name) -listofcomponents = [ - chiller, - pipe1, - pipe2, -] # the new components are connected in this order - -newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid="Water") -# in "loop" -# this replaces the components in "branch" with the components in "listofcomponents" - -idf.saveas("hhh_new.idf") - -# - -# We have saved this as file "hhh_new.idf". -# Let us draw the diagram of this file. (run this from eppy/eppy folder) - -# - -# python ex_loopdiagram.py hhh_new.idf - -# - -from eppy import ex_inits # no need to know this code, it just shows the image below - -for_images = ex_inits -for_images.display_png(for_images.plantloop2) # display the image below - -# - -# This diagram shows the new components in the branch - -# - -# Traversing the loop - -# - -# It would be nice to move through the loop using functions "nextnode()" and "prevnode()" -# -# Eppy indeed has such functions -# -# Let us try to traverse the loop above. - -# - -# to traverse the loop we are going to call some functions ex_loopdiagrams.py, -# the program that draws the loop diagrams. -from eppy import ex_loopdiagram - -fname = "hhh_new.idf" -iddfile = "../eppy/resources/iddfiles/Energy+V8_0_0.idd" -edges = ex_loopdiagram.getedges(fname, iddfile) -# edges are the lines that draw the nodes in the loop. -# The term comes from graph theory in mathematics - -# - -# The above code gets us the edges of the loop diagram. Once we have the edges, we can traverse through the diagram. Let us start with the "Central_Chiller" and work our way down. - -# - -from eppy import walk_hvac - -firstnode = "Central_Chiller" -nextnodes = walk_hvac.nextnode(edges, firstnode) -print(nextnodes) - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -# This leads us to three components -> ['sb1_pipe', 'sb2_pipe', 'sb3_pipe']. Let us follow one of them - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -nextnodes = walk_hvac.nextnode(edges, nextnodes[0]) -print(nextnodes) - -# - -# We have reached the end of this branch. There are no more components. -# -# We can follow this in reverse using the function prevnode() - -# - -lastnode = "sb4_pipe" -prevnodes = walk_hvac.prevnode(edges, lastnode) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -prevnodes = walk_hvac.prevnode(edges, prevnodes[0]) -print(prevnodes) - -# - -# All the way to where the loop ends - -# - -# Building a Condensor loop - -# - -# We build the condensor loop the same way we built the plant loop. Pipes are put as place holders for the components. Let us build a new idf file with just a condensor loop in it. - -# - -condensorloop_idf = IDF(StringIO("")) -loopname = "c_loop" -sloop = ["sb0", ["sb1", "sb2", "sb3"], "sb4"] # supply side -dloop = ["db0", ["db1", "db2", "db3"], "db4"] # demand side -theloop = hvacbuilder.makecondenserloop(condensorloop_idf, loopname, sloop, dloop) -condensorloop_idf.saveas("c_loop.idf") - -# - -# Again, just as we did in the plant loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode() - -# - -# Building an Air Loop - -# - -# Building an air loop is similar to the plant and condensor loop. The difference is that instead of pipes , we have ducts as placeholder components. The other difference is that we have zones on the demand side. - -# - -airloop_idf = IDF(StringIO("")) -loopname = "a_loop" -sloop = ["sb0", ["sb1", "sb2", "sb3"], "sb4"] # supply side of the loop -dloop = ["zone1", "zone2", "zone3"] # zones on the demand side -hvacbuilder.makeairloop(airloop_idf, loopname, sloop, dloop) -airloop_idf.saveas("a_loop.idf") - -# - -# Again, just as we did in the plant and condensor loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode() diff --git a/docs/Main_Tutorial.ipynb b/docs/Main_Tutorial.ipynb index 36d114a9..c39abf0d 100644 --- a/docs/Main_Tutorial.ipynb +++ b/docs/Main_Tutorial.ipynb @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -144,19 +144,19 @@ "output_type": "stream", "text": [ "\n", - "VERSION, \n", + "VERSION,\n", " 7.3; !- Version Identifier\n", "\n", - "SIMULATIONCONTROL, \n", + "SIMULATIONCONTROL,\n", " Yes, !- Do Zone Sizing Calculation\n", " Yes, !- Do System Sizing Calculation\n", " Yes, !- Do Plant Sizing Calculation\n", " No, !- Run Simulation for Sizing Periods\n", " Yes; !- Run Simulation for Weather File Run Periods\n", "\n", - "BUILDING, \n", + "BUILDING,\n", " Empire State Building, !- Name\n", - " 30.0, !- North Axis\n", + " 30, !- North Axis\n", " City, !- Terrain\n", " 0.04, !- Loads Convergence Tolerance Value\n", " 0.4, !- Temperature Convergence Tolerance Value\n", @@ -164,12 +164,12 @@ " 25, !- Maximum Number of Warmup Days\n", " 6; !- Minimum Number of Warmup Days\n", "\n", - "SITE:LOCATION, \n", + "SITE:LOCATION,\n", " CHICAGO_IL_USA TMY2-94846, !- Name\n", " 41.78, !- Latitude\n", " -87.75, !- Longitude\n", - " -6.0, !- Time Zone\n", - " 190.0; !- Elevation\n", + " -6, !- Time Zone\n", + " 190; !- Elevation\n", "\n" ] } @@ -209,12 +209,12 @@ "cell_type": "raw", "metadata": {}, "source": [ - " print filename.idfobjects['OBJECTNAME']" + " print(filename.idfobjects['OBJECTNAME'])" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -222,9 +222,9 @@ "output_type": "stream", "text": [ "[\n", - "BUILDING, \n", + "BUILDING,\n", " Empire State Building, !- Name\n", - " 30.0, !- North Axis\n", + " 30, !- North Axis\n", " City, !- Terrain\n", " 0.04, !- Loads Convergence Tolerance Value\n", " 0.4, !- Temperature Convergence Tolerance Value\n", @@ -236,7 +236,7 @@ } ], "source": [ - "print idf1.idfobjects['BUILDING'] # put the name of the object you'd like to look at in brackets" + "print(idf1.idfobjects['BUILDING']) # put the name of the object you'd like to look at in brackets" ] }, { @@ -252,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -280,7 +280,7 @@ } ], "source": [ - "print building.Name\n" + "print(building.Name)\n" ] }, { @@ -292,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -301,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -313,7 +313,7 @@ } ], "source": [ - "print building.Name\n" + "print(building.Name)\n" ] }, { @@ -325,7 +325,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -333,19 +333,19 @@ "output_type": "stream", "text": [ "\n", - "VERSION, \n", + "VERSION,\n", " 7.3; !- Version Identifier\n", "\n", - "SIMULATIONCONTROL, \n", + "SIMULATIONCONTROL,\n", " Yes, !- Do Zone Sizing Calculation\n", " Yes, !- Do System Sizing Calculation\n", " Yes, !- Do Plant Sizing Calculation\n", " No, !- Run Simulation for Sizing Periods\n", " Yes; !- Run Simulation for Weather File Run Periods\n", "\n", - "BUILDING, \n", + "BUILDING,\n", " Empire State Building, !- Name\n", - " 30.0, !- North Axis\n", + " 30, !- North Axis\n", " City, !- Terrain\n", " 0.04, !- Loads Convergence Tolerance Value\n", " 0.4, !- Temperature Convergence Tolerance Value\n", @@ -353,12 +353,12 @@ " 25, !- Maximum Number of Warmup Days\n", " 6; !- Minimum Number of Warmup Days\n", "\n", - "SITE:LOCATION, \n", + "SITE:LOCATION,\n", " CHICAGO_IL_USA TMY2-94846, !- Name\n", " 41.78, !- Latitude\n", " -87.75, !- Longitude\n", - " -6.0, !- Time Zone\n", - " 190.0; !- Elevation\n", + " -6, !- Time Zone\n", + " 190; !- Elevation\n", "\n" ] } @@ -411,7 +411,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -420,14 +420,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ "import eppy\n", "# import eppy.ex_inits\n", "# reload(eppy.ex_inits)\n", - "import eppy.ex_inits\n" + "import ex_inits\n" ] }, { @@ -445,19 +445,19 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 93, "metadata": {}, "outputs": [ { "data": { - "image/png": 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qZXoRKRaCnaYMLFwVgxvPje9hCj0LTi2yc43X63aoqbK9WOCTbzYnVjki5U7K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\nh+VAifwmYJO1GuKLUrTTtdVn56cm537mKD/halw7nD2Xs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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", + "import ex_inits #no need to know this code, it just shows the image below\n", "for_images = ex_inits\n", "for_images.display_png(for_images.idfeditor) \n" ] @@ -486,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -498,7 +498,7 @@ } ], "source": [ - "print building.Terrain\n" + "print(building.Terrain)\n" ] }, { @@ -518,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -530,7 +530,7 @@ } ], "source": [ - "print building.North_Axis\n" + "print(building.North_Axis)\n" ] }, { @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -561,14 +561,14 @@ } ], "source": [ - "print building.Name\n", - "print building.North_Axis\n", - "print building.Terrain\n", - "print building.Loads_Convergence_Tolerance_Value\n", - "print building.Temperature_Convergence_Tolerance_Value\n", - "print building.Solar_Distribution\n", - "print building.Maximum_Number_of_Warmup_Days\n", - "print building.Minimum_Number_of_Warmup_Days\n" + "print(building.Name) \n", + "print(building.North_Axis) \n", + "print(building.Terrain) \n", + "print(building.Loads_Convergence_Tolerance_Value) \n", + "print(building.Temperature_Convergence_Tolerance_Value) \n", + "print(building.Solar_Distribution) \n", + "print(building.Maximum_Number_of_Warmup_Days) \n", + "print(building.Minimum_Number_of_Warmup_Days) \n" ] }, { @@ -635,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 97, "metadata": {}, "outputs": [], "source": [ @@ -652,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -661,7 +661,7 @@ "'apple'" ] }, - "execution_count": 17, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -681,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 99, "metadata": {}, "outputs": [ { @@ -693,7 +693,7 @@ } ], "source": [ - "print fruits[2]\n" + "print(fruits[2])\n" ] }, { @@ -705,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -718,7 +718,7 @@ ], "source": [ "firstfruit = fruits[0]\n", - "print firstfruit\n" + "print(firstfruit)\n" ] }, { @@ -730,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 101, "metadata": {}, "outputs": [ { @@ -748,10 +748,10 @@ "goodfruit = fruits[0]\n", "redfruit = fruits[0]\n", "\n", - "print firstfruit\n", - "print goodfruit\n", - "print redfruit\n", - "print fruits[0]\n" + "print(firstfruit) \n", + "print(goodfruit)\n", + "print(redfruit)\n", + "print(fruits[0])" ] }, { @@ -779,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -791,7 +791,7 @@ } ], "source": [ - "print len(fruits)\n" + "print(len(fruits))\n" ] }, { @@ -817,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -833,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 104, "metadata": {}, "outputs": [], "source": [ @@ -858,7 +858,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -884,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -892,92 +892,92 @@ "output_type": "stream", "text": [ "[\n", - "Material, \n", + "Material,\n", " F08 Metal surface, !- Name\n", " Smooth, !- Roughness\n", " 0.0008, !- Thickness\n", " 45.28, !- Conductivity\n", - " 7824.0, !- Density\n", - " 500.0; !- Specific Heat\n", + " 7824, !- Density\n", + " 500; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " I01 25mm insulation board, !- Name\n", " MediumRough, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.03, !- Conductivity\n", - " 43.0, !- Density\n", - " 1210.0; !- Specific Heat\n", + " 43, !- Density\n", + " 1210; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " I02 50mm insulation board, !- Name\n", " MediumRough, !- Roughness\n", " 0.0508, !- Thickness\n", " 0.03, !- Conductivity\n", - " 43.0, !- Density\n", - " 1210.0; !- Specific Heat\n", + " 43, !- Density\n", + " 1210; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " G01a 19mm gypsum board, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.019, !- Thickness\n", " 0.16, !- Conductivity\n", - " 800.0, !- Density\n", - " 1090.0; !- Specific Heat\n", + " 800, !- Density\n", + " 1090; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " M11 100mm lightweight concrete, !- Name\n", " MediumRough, !- Roughness\n", " 0.1016, !- Thickness\n", " 0.53, !- Conductivity\n", - " 1280.0, !- Density\n", - " 840.0; !- Specific Heat\n", + " 1280, !- Density\n", + " 840; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " F16 Acoustic tile, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0191, !- Thickness\n", " 0.06, !- Conductivity\n", - " 368.0, !- Density\n", - " 590.0; !- Specific Heat\n", + " 368, !- Density\n", + " 590; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " M01 100mm brick, !- Name\n", " MediumRough, !- Roughness\n", " 0.1016, !- Thickness\n", " 0.89, !- Conductivity\n", - " 1920.0, !- Density\n", - " 790.0; !- Specific Heat\n", + " 1920, !- Density\n", + " 790; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " M15 200mm heavyweight concrete, !- Name\n", " MediumRough, !- Roughness\n", " 0.2032, !- Thickness\n", " 1.95, !- Conductivity\n", - " 2240.0, !- Density\n", - " 900.0; !- Specific Heat\n", + " 2240, !- Density\n", + " 900; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " M05 200mm concrete block, !- Name\n", " MediumRough, !- Roughness\n", " 0.2032, !- Thickness\n", " 1.11, !- Conductivity\n", - " 800.0, !- Density\n", - " 920.0; !- Specific Heat\n", + " 800, !- Density\n", + " 920; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " G05 25mm wood, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.15, !- Conductivity\n", - " 608.0, !- Density\n", - " 1630.0; !- Specific Heat\n", + " 608, !- Density\n", + " 1630; !- Specific Heat\n", "]\n" ] } ], "source": [ "materials = idf1.idfobjects[\"MATERIAL\"]\n", - "print materials\n" + "print(materials)\n" ] }, { @@ -993,7 +993,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 107, "metadata": {}, "outputs": [], "source": [ @@ -1003,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -1011,19 +1011,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " F08 Metal surface, !- Name\n", " Smooth, !- Roughness\n", " 0.0008, !- Thickness\n", " 45.28, !- Conductivity\n", - " 7824.0, !- Density\n", - " 500.0; !- Specific Heat\n", + " 7824, !- Density\n", + " 500; !- Specific Heat\n", "\n" ] } ], "source": [ - "print firstmaterial\n" + "print(firstmaterial)\n" ] }, { @@ -1035,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -1043,19 +1043,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " I01 25mm insulation board, !- Name\n", " MediumRough, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.03, !- Conductivity\n", - " 43.0, !- Density\n", - " 1210.0; !- Specific Heat\n", + " 43, !- Density\n", + " 1210; !- Specific Heat\n", "\n" ] } ], "source": [ - "print secondmaterial\n" + "print(secondmaterial)\n" ] }, { @@ -1094,7 +1094,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -1108,7 +1108,7 @@ "source": [ "bad_architects = [\"Donald Trump\", \"Mick Jagger\", \n", " \"Steve Jobs\", \"Lady Gaga\", \"Santa Clause\"]\n", - "print bad_architects[3]\n" + "print(bad_architects[3])\n" ] }, { @@ -1120,7 +1120,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -1133,8 +1133,8 @@ } ], "source": [ - "print bad_architects[-1]\n", - "print bad_architects[-2]\n" + "print(bad_architects[-1])\n", + "print(bad_architects[-2])\n" ] }, { @@ -1160,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -1172,7 +1172,7 @@ } ], "source": [ - "print bad_architects[1:3] # slices at 1 and 3\n" + "print(bad_architects[1:3]) # slices at 1 and 3\n" ] }, { @@ -1212,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -1225,8 +1225,8 @@ } ], "source": [ - "print bad_architects[2:-1] # slices at 2 and -1\n", - "print bad_architects[-3:-1] # slices at -3 and -1\n" + "print(bad_architects[2:-1]) # slices at 2 and -1\n", + "print(bad_architects[-3:-1]) # slices at -3 and -1\n" ] }, { @@ -1238,7 +1238,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 114, "metadata": {}, "outputs": [ { @@ -1253,10 +1253,10 @@ } ], "source": [ - "print bad_architects[3:] \n", - "print bad_architects[:2] \n", - "print bad_architects[-3:] \n", - "print bad_architects[:-2] \n" + "print(bad_architects[3:] )\n", + "print(bad_architects[:2] )\n", + "print(bad_architects[-3:] )\n", + "print(bad_architects[:-2] )\n" ] }, { @@ -1291,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -1304,7 +1304,7 @@ ], "source": [ "bad_architects.append(\"First-year students\")\n", - "print bad_architects\n" + "print(bad_architects)\n" ] }, { @@ -1337,7 +1337,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 116, "metadata": {}, "outputs": [ { @@ -1350,7 +1350,7 @@ ], "source": [ "bad_architects.remove(\"First-year students\")\n", - "print bad_architects\n" + "print(bad_architects)\n" ] }, { @@ -1373,7 +1373,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 117, "metadata": {}, "outputs": [ { @@ -1386,12 +1386,12 @@ ], "source": [ "what_i_ate_today = [\"coffee\", \"bacon\", \"eggs\"]\n", - "print what_i_ate_today\n" + "print(what_i_ate_today)\n" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -1405,12 +1405,12 @@ "source": [ "what_i_ate_today.append(\"vegetables\") # adds vegetables to the end of the list\n", "# but I don't like vegetables\n", - "print what_i_ate_today\n" + "print(what_i_ate_today)\n" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -1424,7 +1424,7 @@ "source": [ "# since I don't like vegetables\n", "what_i_ate_today.pop(-1) # use index of -1, since vegetables is the last item in the list\n", - "print what_i_ate_today\n" + "print(what_i_ate_today)\n" ] }, { @@ -1436,7 +1436,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -1445,7 +1445,7 @@ "'bacon'" ] }, - "execution_count": 39, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -1467,7 +1467,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 121, "metadata": {}, "outputs": [ { @@ -1481,8 +1481,8 @@ ], "source": [ "was_first_item = what_i_ate_today.pop(0)\n", - "print 'was_first_item =', was_first_item\n", - "print 'what_i_ate_today = ', what_i_ate_today" + "print('was_first_item =', was_first_item)\n", + "print('what_i_ate_today = ', what_i_ate_today)" ] }, { @@ -1512,7 +1512,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 122, "metadata": {}, "outputs": [], "source": [ @@ -1530,7 +1530,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -1538,19 +1538,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " G05 25mm wood, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.15, !- Conductivity\n", - " 608.0, !- Density\n", - " 1630.0; !- Specific Heat\n", + " 608, !- Density\n", + " 1630; !- Specific Heat\n", "\n" ] } ], "source": [ - "print materials[-1]\n" + "print(materials[-1])\n" ] }, { @@ -1562,7 +1562,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -1570,27 +1570,27 @@ "output_type": "stream", "text": [ "[\n", - "Material, \n", + "Material,\n", " M05 200mm concrete block, !- Name\n", " MediumRough, !- Roughness\n", " 0.2032, !- Thickness\n", " 1.11, !- Conductivity\n", - " 800.0, !- Density\n", - " 920.0; !- Specific Heat\n", + " 800, !- Density\n", + " 920; !- Specific Heat\n", ", \n", - "Material, \n", + "Material,\n", " G05 25mm wood, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.15, !- Conductivity\n", - " 608.0, !- Density\n", - " 1630.0; !- Specific Heat\n", + " 608, !- Density\n", + " 1630; !- Specific Heat\n", "]\n" ] } ], "source": [ - "print materials[-2:]\n" + "print(materials[-2:])\n" ] }, { @@ -1616,7 +1616,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 125, "metadata": {}, "outputs": [ { @@ -1628,7 +1628,7 @@ } ], "source": [ - "print len(materials)\n" + "print(len(materials))\n" ] }, { @@ -1647,7 +1647,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 126, "metadata": {}, "outputs": [], "source": [ @@ -1656,7 +1656,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -1668,7 +1668,7 @@ } ], "source": [ - "print len(materials)\n" + "print(len(materials))\n" ] }, { @@ -1689,7 +1689,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -1697,19 +1697,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " M05 200mm concrete block, !- Name\n", " MediumRough, !- Roughness\n", " 0.2032, !- Thickness\n", " 1.11, !- Conductivity\n", - " 800.0, !- Density\n", - " 920.0; !- Specific Heat\n", + " 800, !- Density\n", + " 920; !- Specific Heat\n", "\n" ] } ], "source": [ - "print materials[-1]\n" + "print(materials[-1])\n" ] }, { @@ -1737,7 +1737,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -1745,19 +1745,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " G05 25mm wood, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.15, !- Conductivity\n", - " 608.0, !- Density\n", - " 1630.0; !- Specific Heat\n", + " 608, !- Density\n", + " 1630; !- Specific Heat\n", "\n" ] } ], "source": [ - "print was_last_material\n" + "print(was_last_material)\n" ] }, { @@ -1769,7 +1769,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 130, "metadata": {}, "outputs": [], "source": [ @@ -1778,7 +1778,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 131, "metadata": {}, "outputs": [ { @@ -1790,7 +1790,7 @@ } ], "source": [ - "print len(materials)\n" + "print(len(materials))\n" ] }, { @@ -1802,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -1810,19 +1810,19 @@ "output_type": "stream", "text": [ "\n", - "Material, \n", + "Material,\n", " G05 25mm wood, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.0254, !- Thickness\n", " 0.15, !- Conductivity\n", - " 608.0, !- Density\n", - " 1630.0; !- Specific Heat\n", + " 608, !- Density\n", + " 1630; !- Specific Heat\n", "\n" ] } ], "source": [ - "print materials[-1]\n" + "print(materials[-1])\n" ] }, { @@ -1845,14 +1845,14 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", - "MATERIAL, \n", + "MATERIAL,\n", " , !- Name\n", " , !- Roughness\n", " , !- Thickness\n", @@ -1861,10 +1861,10 @@ " , !- Specific Heat\n", " 0.9, !- Thermal Absorptance\n", " 0.7, !- Solar Absorptance\n", - " 0.7; !- Visible Absorptance\n" + " 0.7; !- Visible Absorptance" ] }, - "execution_count": 52, + "execution_count": 133, "metadata": {}, "output_type": "execute_result" } @@ -1875,7 +1875,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -1884,7 +1884,7 @@ "11" ] }, - "execution_count": 53, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -1904,7 +1904,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -1912,7 +1912,7 @@ "output_type": "stream", "text": [ "\n", - "MATERIAL, \n", + "MATERIAL,\n", " , !- Name\n", " , !- Roughness\n", " , !- Thickness\n", @@ -1927,7 +1927,7 @@ } ], "source": [ - "print materials[-1]\n" + "print(materials[-1])\n" ] }, { @@ -1945,7 +1945,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 136, "metadata": {}, "outputs": [], "source": [ @@ -1959,25 +1959,15 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 137, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "MATERIAL, \n", - " Peanut Butter, !- Name\n", - " MediumSmooth, !- Roughness\n", - " 0.03, !- Thickness\n", - " 0.16, !- Conductivity\n", - " 600, !- Density\n", - " 1500, !- Specific Heat\n", - " 0.9, !- Thermal Absorptance\n", - " 0.7, !- Solar Absorptance\n", - " 0.7; !- Visible Absorptance\n", - "\n" + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print'. Did you mean print(materials[-1])? (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m print materials[-1]\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(materials[-1])?\n" ] } ], @@ -1994,14 +1984,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 138, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", - "MATERIAL, \n", + "MATERIAL,\n", " Peanut Butter, !- Name\n", " MediumSmooth, !- Roughness\n", " 0.03, !- Thickness\n", @@ -2010,10 +2000,10 @@ " 1500, !- Specific Heat\n", " 0.9, !- Thermal Absorptance\n", " 0.7, !- Solar Absorptance\n", - " 0.7; !- Visible Absorptance\n" + " 0.7; !- Visible Absorptance" ] }, - "execution_count": 57, + "execution_count": 138, "metadata": {}, "output_type": "execute_result" } @@ -2100,7 +2090,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ @@ -2116,7 +2106,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 140, "metadata": {}, "outputs": [ { @@ -2131,7 +2121,7 @@ ], "source": [ "for fruit in fruits:\n", - " print fruit\n", + " print(fruit)\n", " " ] }, @@ -2148,7 +2138,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -2163,7 +2153,7 @@ ], "source": [ "for fruit in fruits:\n", - " print \"I am a fruit said the\", fruit\n", + " print(\"I am a fruit said the\", fruit)\n", " " ] }, @@ -2176,7 +2166,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -2189,7 +2179,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -2201,12 +2191,12 @@ } ], "source": [ - "print rottenfruits\n" + "print(rottenfruits)\n" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ @@ -2244,7 +2234,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -2256,7 +2246,7 @@ } ], "source": [ - "print rottenfruits\n" + "print(rottenfruits)\n" ] }, { @@ -2289,7 +2279,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 146, "metadata": {}, "outputs": [], "source": [ @@ -2298,7 +2288,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -2313,7 +2303,7 @@ "source": [ "for fruit in fruits: # steps through every fruit in fruits\n", " if len(fruit) > 5: # checks to see if the length of the word is more than 5\n", - " print fruit # if true, print the fruit\n", + " print(fruit) # if true, print the fruit\n", " # if false, python goes back to the 'for' loop \n", " # and checks the next item in the list\n", " " @@ -2328,7 +2318,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 148, "metadata": {}, "outputs": [], "source": [ @@ -2343,7 +2333,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 149, "metadata": {}, "outputs": [ { @@ -2355,12 +2345,12 @@ } ], "source": [ - "print p_fruits\n" + "print(p_fruits)\n" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 150, "metadata": {}, "outputs": [], "source": [ @@ -2399,7 +2389,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -2411,7 +2401,7 @@ } ], "source": [ - "print p_fruits\n" + "print(p_fruits)\n" ] }, { @@ -2439,16 +2429,16 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0, 1, 2, 3]" + "range(0, 4)" ] }, - "execution_count": 71, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -2459,7 +2449,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 153, "metadata": {}, "outputs": [ { @@ -2475,13 +2465,13 @@ ], "source": [ "for i in range(4):\n", - " print i\n", + " print(i)\n", " " ] }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -2490,7 +2480,7 @@ "3" ] }, - "execution_count": 73, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -2501,16 +2491,15 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 155, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "0\n", - "1\n", - "2\n" + "ename": "SyntaxError", + "evalue": "Missing parentheses in call to 'print'. Did you mean print(i)? (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m print i\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(i)?\n" ] } ], @@ -2522,7 +2511,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -2537,13 +2526,13 @@ ], "source": [ "for i in range(len(p_fruits)):\n", - " print p_fruits[i]\n", + " print(p_fruits[i])\n", " " ] }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -2558,13 +2547,13 @@ ], "source": [ "for i in range(len(p_fruits)):\n", - " print i, p_fruits[i]\n", + " print(i, p_fruits[i])\n", " " ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -2579,13 +2568,13 @@ ], "source": [ "for item_from_enumerate in enumerate(p_fruits):\n", - " print item_from_enumerate\n", + " print(item_from_enumerate)\n", " " ] }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 159, "metadata": {}, "outputs": [ { @@ -2600,7 +2589,7 @@ ], "source": [ "for i, fruit in enumerate(p_fruits):\n", - " print i, fruit\n", + " print(i, fruit)\n", " " ] }, @@ -2624,7 +2613,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -2648,33 +2637,33 @@ ], "source": [ "for material in materials:\n", - " print material.Name \n", + " print(material.Name )\n", " " ] }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 161, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[u'F08 Metal surface',\n", - " u'I01 25mm insulation board',\n", - " u'I02 50mm insulation board',\n", - " u'G01a 19mm gypsum board',\n", - " u'M11 100mm lightweight concrete',\n", - " u'F16 Acoustic tile',\n", - " u'M01 100mm brick',\n", - " u'M15 200mm heavyweight concrete',\n", - " u'M05 200mm concrete block',\n", - " u'G05 25mm wood',\n", + "['F08 Metal surface',\n", + " 'I01 25mm insulation board',\n", + " 'I02 50mm insulation board',\n", + " 'G01a 19mm gypsum board',\n", + " 'M11 100mm lightweight concrete',\n", + " 'F16 Acoustic tile',\n", + " 'M01 100mm brick',\n", + " 'M15 200mm heavyweight concrete',\n", + " 'M05 200mm concrete block',\n", + " 'G05 25mm wood',\n", " 'Peanut Butter',\n", " 'Peanut Butter']" ] }, - "execution_count": 80, + "execution_count": 161, "metadata": {}, "output_type": "execute_result" } @@ -2685,27 +2674,27 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 162, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[u'Smooth',\n", - " u'MediumRough',\n", - " u'MediumRough',\n", - " u'MediumSmooth',\n", - " u'MediumRough',\n", - " u'MediumSmooth',\n", - " u'MediumRough',\n", - " u'MediumRough',\n", - " u'MediumRough',\n", - " u'MediumSmooth',\n", + "['Smooth',\n", + " 'MediumRough',\n", + " 'MediumRough',\n", + " 'MediumSmooth',\n", + " 'MediumRough',\n", + " 'MediumSmooth',\n", + " 'MediumRough',\n", + " 'MediumRough',\n", + " 'MediumRough',\n", + " 'MediumSmooth',\n", " 'MediumSmooth',\n", " 'MediumSmooth']" ] }, - "execution_count": 81, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -2716,7 +2705,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -2736,7 +2725,7 @@ " 0.03]" ] }, - "execution_count": 82, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -2747,7 +2736,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -2756,7 +2745,7 @@ "[0.1016, 0.1016, 0.2032, 0.2032]" ] }, - "execution_count": 83, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -2767,19 +2756,19 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 165, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[u'M11 100mm lightweight concrete',\n", - " u'M01 100mm brick',\n", - " u'M15 200mm heavyweight concrete',\n", - " u'M05 200mm concrete block']" + "['M11 100mm lightweight concrete',\n", + " 'M01 100mm brick',\n", + " 'M15 200mm heavyweight concrete',\n", + " 'M05 200mm concrete block']" ] }, - "execution_count": 84, + "execution_count": 165, "metadata": {}, "output_type": "execute_result" } @@ -2790,7 +2779,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -2799,51 +2788,47 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 167, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\n", - "Material, \n", - " M11 100mm lightweight concrete, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.1016, !- Thickness\n", - " 0.53, !- Conductivity\n", - " 1280.0, !- Density\n", - " 840.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " M11 100mm lightweight concrete, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.1016, !- Thickness\n", + " 0.53, !- Conductivity\n", + " 1280, !- Density\n", + " 840; !- Specific Heat,\n", " \n", - "Material, \n", - " M01 100mm brick, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.1016, !- Thickness\n", - " 0.89, !- Conductivity\n", - " 1920.0, !- Density\n", - " 790.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " M01 100mm brick, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.1016, !- Thickness\n", + " 0.89, !- Conductivity\n", + " 1920, !- Density\n", + " 790; !- Specific Heat,\n", " \n", - "Material, \n", - " M15 200mm heavyweight concrete, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.2032, !- Thickness\n", - " 1.95, !- Conductivity\n", - " 2240.0, !- Density\n", - " 900.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " M15 200mm heavyweight concrete, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.2032, !- Thickness\n", + " 1.95, !- Conductivity\n", + " 2240, !- Density\n", + " 900; !- Specific Heat,\n", " \n", - "Material, \n", - " M05 200mm concrete block, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.2032, !- Thickness\n", - " 1.11, !- Conductivity\n", - " 800.0, !- Density\n", - " 920.0; !- Specific Heat\n", - "]" + " Material,\n", + " M05 200mm concrete block, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.2032, !- Thickness\n", + " 1.11, !- Conductivity\n", + " 800, !- Density\n", + " 920; !- Specific Heat]" ] }, - "execution_count": 86, + "execution_count": 167, "metadata": {}, "output_type": "execute_result" } @@ -2854,7 +2839,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 168, "metadata": {}, "outputs": [], "source": [ @@ -2866,51 +2851,47 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\n", - "Material, \n", - " THICK M11 100mm lightweight concrete, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.1016, !- Thickness\n", - " 0.53, !- Conductivity\n", - " 1280.0, !- Density\n", - " 840.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " THICK M11 100mm lightweight concrete, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.1016, !- Thickness\n", + " 0.53, !- Conductivity\n", + " 1280, !- Density\n", + " 840; !- Specific Heat,\n", " \n", - "Material, \n", - " THICK M01 100mm brick, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.1016, !- Thickness\n", - " 0.89, !- Conductivity\n", - " 1920.0, !- Density\n", - " 790.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " THICK M01 100mm brick, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.1016, !- Thickness\n", + " 0.89, !- Conductivity\n", + " 1920, !- Density\n", + " 790; !- Specific Heat,\n", " \n", - "Material, \n", - " THICK M15 200mm heavyweight concrete, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.2032, !- Thickness\n", - " 1.95, !- Conductivity\n", - " 2240.0, !- Density\n", - " 900.0; !- Specific Heat\n", - ",\n", + " Material,\n", + " THICK M15 200mm heavyweight concrete, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.2032, !- Thickness\n", + " 1.95, !- Conductivity\n", + " 2240, !- Density\n", + " 900; !- Specific Heat,\n", " \n", - "Material, \n", - " THICK M05 200mm concrete block, !- Name\n", - " MediumRough, !- Roughness\n", - " 0.2032, !- Thickness\n", - " 1.11, !- Conductivity\n", - " 800.0, !- Density\n", - " 920.0; !- Specific Heat\n", - "]" + " Material,\n", + " THICK M05 200mm concrete block, !- Name\n", + " MediumRough, !- Roughness\n", + " 0.2032, !- Thickness\n", + " 1.11, !- Conductivity\n", + " 800, !- Density\n", + " 920; !- Specific Heat]" ] }, - "execution_count": 88, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -2932,12 +2913,12 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 170, "metadata": {}, "outputs": [ { "data": { - "image/png": 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D5w3KzPXrhDbN8zFrZRcZdshfvgoBISAEOhAQQdgBiHwVAj1JQD3+vUXX3qmP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VqqPyZn+l21/jDtS2wgvXX9WCA1rA2+veOIwKw/SHEkttG26lQDsKzqXUsKkq0VqHp8WCALS2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nl1tZ25f4HS1u89x6d7MTvw7MDqqD59OQr9xIgARKgQAFYSncBeaBBEiABEhgygTEU1QJQvnUTdOx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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -2949,27 +2930,27 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[u'F08 Metal surface',\n", - " u'I01 25mm insulation board',\n", - " u'I02 50mm insulation board',\n", - " u'G01a 19mm gypsum board',\n", - " u'THICK M11 100mm lightweight concrete',\n", - " u'F16 Acoustic tile',\n", - " u'THICK M01 100mm brick',\n", - " u'THICK M15 200mm heavyweight concrete',\n", - " u'THICK M05 200mm concrete block',\n", - " u'G05 25mm wood',\n", + "['F08 Metal surface',\n", + " 'I01 25mm insulation board',\n", + " 'I02 50mm insulation board',\n", + " 'G01a 19mm gypsum board',\n", + " 'THICK M11 100mm lightweight concrete',\n", + " 'F16 Acoustic tile',\n", + " 'THICK M01 100mm brick',\n", + " 'THICK M15 200mm heavyweight concrete',\n", + " 'THICK M05 200mm concrete block',\n", + " 'G05 25mm wood',\n", " 'Peanut Butter',\n", " 'Peanut Butter']" ] }, - "execution_count": 90, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -3007,7 +2988,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 172, "metadata": {}, "outputs": [], "source": [ @@ -3023,7 +3004,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 173, "metadata": {}, "outputs": [], "source": [ @@ -3044,7 +3025,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -3053,59 +3034,59 @@ "text": [ "surface azimuth = 180.0 degrees\n", "surface tilt = 90.0 degrees\n", - "surface area = 18.3 m2\n" + "surface area = 18.299999999999997 m2\n" ] } ], "source": [ "# Let us look at the first surface\n", "asurface = surfaces[0]\n", - "print \"surface azimuth =\", asurface.azimuth, \"degrees\"\n", - "print \"surface tilt =\", asurface.tilt, \"degrees\"\n", - "print \"surface area =\", asurface.area, \"m2\"\n" + "print(\"surface azimuth =\", asurface.azimuth, \"degrees\")\n", + "print(\"surface tilt =\", asurface.tilt, \"degrees\")\n", + "print(\"surface area =\", asurface.area, \"m2\")\n" ] }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 175, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[u'WALL-1PF', u'WALL-1PR', u'WALL-1PB', u'WALL-1PL', u'TOP-1']\n" + "['WALL-1PF', 'WALL-1PR', 'WALL-1PB', 'WALL-1PL', 'TOP-1']\n" ] } ], "source": [ "# all the surface names\n", "s_names = [surface.Name for surface in surfaces]\n", - "print s_names[:5] # print five of them\n" + "print(s_names[:5]) # print five of them\n" ] }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[(u'WALL-1PF', 180.0), (u'WALL-1PR', 90.0), (u'WALL-1PB', 0.0), (u'WALL-1PL', 270.0), (u'TOP-1', 0.0)]\n" + "[('WALL-1PF', 180.0), ('WALL-1PR', 90.0), ('WALL-1PB', 0.0), ('WALL-1PL', 270.0), ('TOP-1', 0.0)]\n" ] } ], "source": [ "# surface names and azimuths\n", "s_names_azm = [(sf.Name, sf.azimuth) for sf in surfaces]\n", - "print s_names_azm[:5] # print five of them\n" + "print(s_names_azm[:5]) # print five of them\n" ] }, { "cell_type": "code", - "execution_count": 96, + "execution_count": 177, "metadata": {}, "outputs": [ { @@ -3123,13 +3104,13 @@ "source": [ "# or to do that in pretty printing\n", "for name, azimuth in s_names_azm[:5]: # just five of them\n", - " print name, azimuth\n", + " print(name, azimuth)\n", " " ] }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -3148,24 +3129,24 @@ "# surface names and tilt\n", "s_names_tilt = [(sf.Name, sf.tilt) for sf in surfaces]\n", "for name, tilt in s_names_tilt[:5]: # just five of them\n", - " print name, tilt\n", + " print(name, tilt)\n", " " ] }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 179, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "WALL-1PF 18.3 m2\n", - "WALL-1PR 9.12 m2\n", - "WALL-1PB 18.3 m2\n", - "WALL-1PL 9.12 m2\n", - "TOP-1 463.6 m2\n" + "WALL-1PF 18.299999999999997 m2\n", + "WALL-1PR 9.119999999999997 m2\n", + "WALL-1PB 18.299999999999997 m2\n", + "WALL-1PL 9.119999999999997 m2\n", + "TOP-1 463.59999999999997 m2\n" ] } ], @@ -3173,7 +3154,7 @@ "# surface names and areas\n", "s_names_area = [(sf.Name, sf.area) for sf in surfaces]\n", "for name, area in s_names_area[:5]: # just five of them\n", - " print name, area, \"m2\"\n", + " print(name, area, \"m2\")\n", " " ] }, @@ -3186,64 +3167,64 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 180, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[u'WALL-1PF', u'WALL-1PR', u'WALL-1PB', u'WALL-1PL', u'FRONT-1', u'SB12', u'SB14', u'SB15', u'RIGHT-1', u'SB21', u'SB23', u'BACK-1', u'SB32', u'SB35', u'LEFT-1', u'SB41', u'SB43', u'SB45', u'SB51', u'SB54', u'WALL-1SF', u'WALL-1SR', u'WALL-1SB', u'WALL-1SL']\n" + "['WALL-1PF', 'WALL-1PR', 'WALL-1PB', 'WALL-1PL', 'FRONT-1', 'SB12', 'SB14', 'SB15', 'RIGHT-1', 'SB21', 'SB23', 'BACK-1', 'SB32', 'SB35', 'LEFT-1', 'SB41', 'SB43', 'SB45', 'SB51', 'SB53', 'SB54', 'WALL-1SF', 'WALL-1SR', 'WALL-1SB', 'WALL-1SL']\n" ] } ], "source": [ "# just vertical walls\n", "vertical_walls = [sf for sf in surfaces if sf.tilt == 90.0]\n", - "print [sf.Name for sf in vertical_walls]\n" + "print([sf.Name for sf in vertical_walls])\n" ] }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 181, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[u'WALL-1PB', u'SB15', u'BACK-1', u'WALL-1SB']\n" + "['WALL-1PB', 'SB15', 'BACK-1', 'SB53', 'WALL-1SB']\n" ] } ], "source": [ "# north facing walls\n", "north_walls = [sf for sf in vertical_walls if sf.azimuth == 0.0]\n", - "print [sf.Name for sf in north_walls]\n" + "print([sf.Name for sf in north_walls])\n" ] }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[u'WALL-1PB', u'BACK-1', u'WALL-1SB']\n" + "['WALL-1PB', 'BACK-1', 'WALL-1SB']\n" ] } ], "source": [ "# north facing exterior walls\n", "exterior_nwall = [sf for sf in north_walls if sf.Outside_Boundary_Condition == \"Outdoors\"]\n", - "print [sf.Name for sf in exterior_nwall]\n" + "print([sf.Name for sf in exterior_nwall])\n" ] }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 183, "metadata": {}, "outputs": [ { @@ -3260,13 +3241,13 @@ "# print out some more details of the north wall\n", "north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall]\n", "for name, azimuth, construction in north_wall_info:\n", - " print name, azimuth, construction\n", + " print(name, azimuth, construction)\n", " " ] }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ @@ -3278,7 +3259,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 185, "metadata": {}, "outputs": [ { @@ -3295,13 +3276,13 @@ "# see the change\n", "north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall]\n", "for name, azimuth, construction in north_wall_info:\n", - " print name, azimuth, construction\n", + " print(name, azimuth, construction)\n", " " ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 186, "metadata": {}, "outputs": [ { @@ -3317,35 +3298,35 @@ "C2-1P 0.0 CLNG-1\n", "C3-1P 0.0 CLNG-1\n", "C4-1P 0.0 CLNG-1\n", - "C5-1P 180.0 CLNG-1\n", + "C5-1P 0.0 CLNG-1\n", "FRONT-1 180.0 WALL-1\n", "C1-1 180.0 CLNG-1\n", "F1-1 0.0 CLNG-1\n", - "SB12 45.0 INT-WALL-1\n", + "SB12 45.000000000000014 INT-WALL-1\n", "SB14 315.0 INT-WALL-1\n", "SB15 0.0 INT-WALL-1\n", "RIGHT-1 90.0 WALL-1\n", "C2-1 0.0 CLNG-1\n", "F2-1 0.0 CLNG-1\n", "SB21 225.0 INT-WALL-1\n", - "SB23 315.784824603 INT-WALL-1\n", + "SB23 315.7848246029919 INT-WALL-1\n", "SB25 270.0 INT-WALL-1\n", "BACK-1 0.0 NORTHERN-WALL\n", "C3-1 0.0 CLNG-1\n", "F3-1 0.0 CLNG-1\n", - "SB32 135.784824603 INT-WALL-1\n", - "SB34 224.215175397 INT-WALL-1\n", + "SB32 135.78482460299188 INT-WALL-1\n", + "SB34 224.21517539700812 INT-WALL-1\n", "SB35 180.0 INT-WALL-1\n", "LEFT-1 270.0 WALL-1\n", "C4-1 0.0 CLNG-1\n", "F4-1 0.0 CLNG-1\n", "SB41 135.0 INT-WALL-1\n", - "SB43 44.215175397 INT-WALL-1\n", + "SB43 44.21517539700812 INT-WALL-1\n", "SB45 90.0 INT-WALL-1\n", "C5-1 0.0 CLNG-1\n", "F5-1 0.0 CLNG-1\n", "SB51 180.0 INT-WALL-1\n", - "SB52 90.0 INT-WALL-1\n", + "SB52 89.99999999999997 INT-WALL-1\n", "SB53 0.0 INT-WALL-1\n", "SB54 270.0 INT-WALL-1\n", "WALL-1SF 180.0 WALL-1\n", @@ -3364,7 +3345,7 @@ "source": [ "# see this in all surfaces\n", "for sf in surfaces:\n", - " print sf.Name, sf.azimuth, sf.Construction_Name\n", + " print(sf.Name, sf.azimuth, sf.Construction_Name)\n", " " ] }, @@ -3380,21 +3361,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" + "pygments_lexer": "ipython3", + "version": "3.7.4" } }, "nbformat": 4, diff --git a/docs/Main_Tutorial.py b/docs/Main_Tutorial.py deleted file mode 100644 index 5739c4c2..00000000 --- a/docs/Main_Tutorial.py +++ /dev/null @@ -1,1362 +0,0 @@ -# -*- coding: utf-8 -*- -# 3.0 - -# - -# Eppy Tutorial - -# - -# Authors: Santosh Philip, Leora Tanjuatco - -# - -# Eppy is a scripting language for E+ idf files, and E+ output files. Eppy is written in the programming language Python. As a result it takes full advantage of the rich data structure and idioms that are avaliable in python. You can programmatically navigate, search, and modify E+ idf files using eppy. The power of using a scripting language allows you to do the following: -# -# - Make a large number of changes in an idf file with a few lines of eppy code. -# - Use conditions and filters when making changes to an idf file -# - Make changes to multiple idf files. -# - Read data from the output files of a E+ simulation run. -# - Based to the results of a E+ simulation run, generate the input file for the next simulation run. -# -# So what does this matter? Here are some of the things you can do with eppy: -# -# - Change construction for all north facing walls. -# - Change the glass type for all windows larger than 2 square meters. -# - Change the number of people in all the interior zones. -# - Change the lighting power in all south facing zones. -# - Change the efficiency and fan power of all rooftop units. -# - Find the energy use of all the models in a folder (or of models that were run after a certain date) -# - If a model is using more energy than expected, keep increasing the R-value of the roof until you get to the expected energy use. - -# - -# Quick Start - -# - -# Here is a short IDF file that I’ll be using as an example to start us off :: - -# - -# VERSION, -# 7.2; !- Version Identifier -# -# SIMULATIONCONTROL, -# Yes, !- Do Zone Sizing Calculation -# Yes, !- Do System Sizing Calculation -# Yes, !- Do Plant Sizing Calculation -# No, !- Run Simulation for Sizing Periods -# Yes; !- Run Simulation for Weather File Run Periods -# -# BUILDING, -# White House, !- Name -# 30., !- North Axis {deg} -# City, !- Terrain -# 0.04, !- Loads Convergence Tolerance Value -# 0.4, !- Temperature Convergence Tolerance Value {deltaC} -# FullExterior, !- Solar Distribution -# 25, !- Maximum Number of Warmup Days -# 6; !- Minimum Number of Warmup Days -# -# SITE:LOCATION, -# CHICAGO_IL_USA TMY2-94846, !- Name -# 41.78, !- Latitude {deg} -# -87.75, !- Longitude {deg} -# -6.00, !- Time Zone {hr} -# 190.00; !- Elevation {m} - -# - -# To use eppy to look at this model, we have to run a little code first: - -# - -# you would normaly install eppy by doing -# python setup.py install -# or -# pip install eppy -# or -# easy_install eppy - -# if you have not done so, uncomment the following three lines -import sys - -# pathnameto_eppy = 'c:/eppy' -pathnameto_eppy = "../" -sys.path.append(pathnameto_eppy) - -from eppy import modeleditor -from eppy.modeleditor import IDF - -iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd" -fname1 = "../eppy/resources/idffiles/V_7_2/smallfile.idf" - -# - -IDF.setiddname(iddfile) -idf1 = IDF(fname1) - -# - -# idf1 now holds all the data to your in you idf file. -# -# Now that the behind-the-scenes work is done, we can print this file. - -# - -idf1.printidf() - -# - -# Looks like the same file as before, except that all the comments are slightly different. - -# - -# As you can see, this file has four objects: -# -# - VERSION -# - SIMULATIONCONTROL -# - BUILDING -# - SITE:LOCATION - -# - -# So, let us look take a closer look at the BUILDING object. -# We can do this using this command:: - -# - -# print filename.idfobjects['OBJECTNAME'] - -# - -print( - idf1.idfobjects["BUILDING"] -) # put the name of the object you'd like to look at in brackets - -# - -# We can also zoom in on the object and look just at its individual parts. -# -# For example, let us look at the name of the building. -# -# To do this, we have to do some more behind-the-scenes work, which we'll explain later. - -# - -building = idf1.idfobjects["BUILDING"][0] - -# - -# Now we can do this: - -# - -print(building.Name) - -# - -# Now that we've isolated the building name, we can change it. - -# - -building.Name = "Empire State Building" - -# - -print(building.Name) - -# - -# Did this actually change the name in the model ? Let us print the entire model and see. - -# - -idf1.printidf() - -# - -# Yes! It did. So now you have a taste of what eppy can do. Let's get started! - -# - -# Modifying IDF Fields - -# - -# That was just a quick example -- we were showing off. Let's look a little closer. - -# - -# As you might have guessed, changing an IDF field follows this structure:: - -# - -# object.fieldname = "New Field Name" - -# - -# Plugging the object name (building), the field name (Name) and our new field name ("Empire State Building") into this command gave us this: - -# - -building.Name = "Empire State Building" - -# - -import eppy - -# import eppy.ex_inits -# reload(eppy.ex_inits) -import eppy.ex_inits - -# - -# But how did we know that "Name" is one of the fields in the object "building"? -# -# Are there other fields? -# -# What are they called? -# -# Let's take a look at the IDF editor: - -# - -from eppy import ex_inits # no need to know this code, it just shows the image below - -for_images = ex_inits -for_images.display_png(for_images.idfeditor) - -# - -# In the IDF Editor, the building object is selected. -# -# We can see all the fields of the object "BUILDING". -# -# They are: -# -# - Name -# - North Axis -# - Terrain -# - Loads Convergence Tolerance Value -# - Temperature Convergence Tolerance Value -# - Solar Distribution -# - Maximum Number of Warmup Days -# - Minimum Number of Warmup Days -# -# Let us try to access the other fields. - -# - -print(building.Terrain) - -# - -# How about the field "North Axis" ? -# -# It is not a single word, but two words. -# -# In a programming language, a variable has to be a single word without any spaces. -# -# To solve this problem, put an underscore where there is a space. -# -# So "North Axis" becomes "North_Axis". - -# - -print(building.North_Axis) - -# - -# Now we can do: - -# - -print(building.Name) -print(building.North_Axis) -print(building.Terrain) -print(building.Loads_Convergence_Tolerance_Value) -print(building.Temperature_Convergence_Tolerance_Value) -print(building.Solar_Distribution) -print(building.Maximum_Number_of_Warmup_Days) -print(building.Minimum_Number_of_Warmup_Days) - -# - -# Where else can we find the field names? -# -# The IDF Editor saves the idf file with the field name commented next to field. -# -# Eppy also does this. -# -# Let us take a look at the "BUILDING" object in the text file that the IDF Editor saves :: - -# - -# BUILDING, -# White House, !- Name -# 30., !- North Axis {deg} -# City, !- Terrain -# 0.04, !- Loads Convergence Tolerance Value -# 0.4, !- Temperature Convergence Tolerance Value {deltaC} -# FullExterior, !- Solar Distribution -# 25, !- Maximum Number of Warmup Days -# 6; !- Minimum Number of Warmup Days - -# - -# This a good place to find the field names too. -# -# It is easy to copy and paste from here. You can't do that from the IDF Editor. - -# - -# We know that in an E+ model, there will be only ONE "BUILDING" object. This will be the first and only item in the list "buildings". -# -# But E+ models are made up of objects such as "BUILDING", "SITE:LOCATION", "ZONE", "PEOPLE", "LIGHTS". There can be a number of "ZONE" objects, a number of "PEOPLE" objects and a number of "LIGHTS" objects. -# -# So how do you know if you're looking at the first "ZONE" object or the second one? Or the tenth one? To answer this, we need to learn about how lists work in python. - -# - -# Python lesson 1: lists - -# - -# Eppy holds these objects in a python structure called list. Let us take a look at how lists work in python. - -# - -fruits = ["apple", "orange", "bannana"] -# fruits is a list with three items in it. - -# - -# To get the first item in fruits we say: - -# - -fruits[0] - -# - -# Why "0" ? -# -# Because, unlike us, python starts counting from zero in a list. So, to get the third item in the list we'd need to input 2, like this: - -# - -print(fruits[2]) - -# - -# But calling the first fruit "fruit[0]" is rather cumbersome. Why don't we call it firstfruit? - -# - -firstfruit = fruits[0] -print(firstfruit) - -# - -# We also can say - -# - -goodfruit = fruits[0] -redfruit = fruits[0] - -print(firstfruit) -print(goodfruit) -print(redfruit) -print(fruits[0]) - -# - -# As you see, we can call that item in the list whatever we want. - -# - -# How many items in the list - -# - -# To know how many items are in a list, we ask for the length of the list. -# -# The function 'len' will do this for us. - -# - -print(len(fruits)) - -# - -# There are 3 fruits in the list. - -# - -# Saving an idf file - -# - -# This is easy: - -# - -idf1.save() - -# - -# If you'd like to do a "Save as..." use this: - -# - -idf1.saveas("something.idf") - -# - -# Working with E+ objects - -# - -# Let us open a small idf file that has only "CONSTRUCTION" and "MATERIAL" objects in it. You can go into "../idffiles/V_7_2/constructions.idf" and take a look at the file. We are not printing it here because it is too big. -# -# So let us open it using the idfreader - - -# - -from eppy import modeleditor -from eppy.modeleditor import IDF - -iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd" -try: - IDF.setiddname(iddfile) -except modeleditor.IDDAlreadySetError as e: - pass - -fname1 = "../eppy/resources/idffiles/V_7_2/constructions.idf" -idf1 = IDF(fname1) - -# - -# Let us print all the "MATERIAL" objects in this model. - -# - -materials = idf1.idfobjects["MATERIAL"] -print(materials) - -# - -# As you can see, there are many material objects in this idf file. -# -# The variable "materials" now contains a list of "MATERIAL" objects. -# -# You already know a little about lists, so let us take a look at the items in this list. - -# - -firstmaterial = materials[0] -secondmaterial = materials[1] - -# - -print(firstmaterial) - -# - -# Let us print secondmaterial - -# - -print(secondmaterial) - -# - -# This is awesome!! Why? -# -# To understand what you can do with your objects organized as lists, you'll have to learn a little more about lists. - -# - -# Python lesson 2: more about lists - -# - -# More ways to access items in a list - -# - -# You should remember that you can access any item in a list by passing in its index. -# -# The tricky part is that python starts counting at 0, so you need to input 0 in order to get the first item in a list. -# -# Following the same logic, you need to input 3 in order to get the fourth item on the list. Like so: - -# - -bad_architects = [ - "Donald Trump", - "Mick Jagger", - "Steve Jobs", - "Lady Gaga", - "Santa Clause", -] -print(bad_architects[3]) - -# - -# But there's another way to access items in a list. If you input -1, it will return the last item. -2 will give you the second-to-last item, etc. - -# - -print(bad_architects[-1]) -print(bad_architects[-2]) - -# - -# Slicing a list - -# - -# You can also get more than one item in a list: - -# - -# bad_architects[first_slice:second_slice] - -# - -print(bad_architects[1:3]) # slices at 1 and 3 - -# - -# How do I make sense of this? -# -# To understand this you need to see the list in the following manner:: - -# - -# [ "Donald Trump", "Mick Jagger", "Steve Jobs", "Lady Gaga", "Santa Clause" ] -# ^ ^ ^ ^ ^ ^ -# 0 1 2 3 4 5 -# -5 -4 -3 -2 -1 - -# - -# The slice operation bad_architects[1:3] slices right where the numbers are. -# -# Does that make sense? - -# - -# Let us try a few other slices: - -# - -print(bad_architects[2:-1]) # slices at 2 and -1 -print(bad_architects[-3:-1]) # slices at -3 and -1 - -# - -# You can also slice in the following way: - -# - -print(bad_architects[3:]) -print(bad_architects[:2]) -print(bad_architects[-3:]) -print(bad_architects[:-2]) - -# - -# I'll let you figure that out on your own. - -# - -# Adding to a list - -# - -# This is simple: the append function adds an item to the end of the list. -# -# The following command will add 'something' to the end of the list called listname:: - -# - -# listname.append(something) - -# - -bad_architects.append("First-year students") -print(bad_architects) - -# - -# Deleting from a list - -# - -# There are two ways to do this, based on the information you have. If you have the value of the object, you'll want to use the remove function. It looks like this: - -# - -# listname.remove(value) - -# - -# An example: - -# - -bad_architects.remove("First-year students") -print(bad_architects) - -# - -# What if you know the index of the item you want to remove? -# -# What if you appended an item by mistake and just want to remove the last item in the list? -# -# You should use the pop function. It looks like this: - -# - -# listname.pop(index) - -# - -what_i_ate_today = ["coffee", "bacon", "eggs"] -print(what_i_ate_today) - -# - -what_i_ate_today.append("vegetables") # adds vegetables to the end of the list -# but I don't like vegetables -print(what_i_ate_today) - -# - -# since I don't like vegetables -what_i_ate_today.pop( - -1 -) # use index of -1, since vegetables is the last item in the list -print(what_i_ate_today) - -# - -# You can also remove the second item. - -# - -what_i_ate_today.pop(1) - -# - -# Notice the 'bacon' in the line above. -# -# pop actually 'pops' the value (the one you just removed from the list) back to you. -# -# Let us pop the first item. - -# - -was_first_item = what_i_ate_today.pop(0) -print("was_first_item =", was_first_item) -print("what_i_ate_today = ", what_i_ate_today) - -# - -# what_i_ate_today is just 'eggs'? -# -# That is not much of a breakfast! -# -# Let us get back to eppy. - -# - -# Continuing to work with E+ objects - -# - -# Let us get those "MATERIAL" objects again - -# - -materials = idf1.idfobjects["MATERIAL"] - -# - -# With our newfound knowledge of lists, we can do a lot of things. -# -# Let us get the last material: - -# - -print(materials[-1]) - -# - -# How about the last two? - -# - -print(materials[-2:]) - -# - -# Pretty good. - -# - -# Counting all the materials ( or counting all objects ) - -# - -# How many materials are in this model ? - -# - -print(len(materials)) - -# - -# Removing a material - -# - -# Let us remove the last material in the list - -# - -was_last_material = materials.pop(-1) - -# - -print(len(materials)) - -# - -# Success! We have only 9 materials now. - -# - -# The last material used to be: -# -# 'G05 25mm wood' - -# - -print(materials[-1]) - -# - -# Now the last material in the list is: -# -# 'M15 200mm heavyweight concrete' - -# - -# Adding a material to the list - -# - -# We still have the old last material - -# - -print(was_last_material) - -# - -# Let us add it back to the list - -# - -materials.append(was_last_material) - -# - -print(len(materials)) - -# - -# Once again we have 10 materials and the last material is: - -# - -print(materials[-1]) - -# - -# Add a new material to the model - -# - -# So far we have been working only with materials that were already in the list. -# -# What if we want to make new material? -# -# Obviously we would use the function 'newidfobject'. - -# - -idf1.newidfobject("MATERIAL") - -# - -len(materials) - -# - -# We have 11 items in the materials list. -# -# Let us take a look at the last material in the list, where this fancy new material was added - -# - -print(materials[-1]) - -# - -# Looks a little different from the other materials. It does have the name we gave it. -# -# Why do some fields have values and others are blank ? -# -# "addobject" puts in all the default values, and leaves the others blank. It is up to us to put values in the the new fields. -# -# Let's do it now. - -# - -materials[-1].Name = "Peanut Butter" -materials[-1].Roughness = "MediumSmooth" -materials[-1].Thickness = 0.03 -materials[-1].Conductivity = 0.16 -materials[-1].Density = 600 -materials[-1].Specific_Heat = 1500 - -# - -print(materials[-1]) - -# - -# Copy an existing material - -# - -Peanutbuttermaterial = materials[-1] -idf1.copyidfobject(Peanutbuttermaterial) -materials = idf1.idfobjects["MATERIAL"] -len(materials) -materials[-1] - -# - -# Python lesson 3: indentation and looping through lists - -# - -# I'm tired of doing all this work, it's time to make python do some heavy lifting for us! - -# - -# Python can go through each item in a list and perform an operation on any (or every) item in the list. -# -# This is called looping through the list. -# -# Here's how to tell python to step through each item in a list, and then do something to every single item. -# -# We'll use a 'for' loop to do this. :: - -# - -# for in : -# -# - -# - -# A quick note about the second line. Notice that it's indented? There are 4 blank spaces before the code starts:: - -# - -# in python, indentations are used -# to determine the grouping of statements -# some languages use symbols to mark -# where the function code starts and stops -# but python uses indentation to tell you this -# i'm using indentation to -# show the beginning and end of a sentence -# this is a very simple explanation -# of indentation in python -# if you'd like to know more, there is plenty of information -# about indentation in python on the web -# - -# - -# It's elegant, but it means that the indentation of the code holds meaning. -# -# So make sure to indent the second (and third and forth) lines of your loops! -# -# Now let's make some fruit loops. - -# - -fruits = ["apple", "orange", "bannana"] - -# - -# Given the syntax I gave you before I started rambling about indentation, we can easily print every item in the fruits list by using a 'for' loop. - -# - -for fruit in fruits: - print(fruit) - - -# - -# That was easy! But it can get complicated pretty quickly... -# -# Let's make it do something more complicated than just print the fruits. -# -# Let's have python add some words to each fruit. - -# - -for fruit in fruits: - print("I am a fruit said the", fruit) - - -# - -# Now we'll try to confuse you: - -# - -rottenfruits = [] # makes a blank list called rottenfruits -for fruit in fruits: # steps through every item in fruits - rottenfruit = "rotten " + fruit # changes each item to "rotten _____" - rottenfruits.append( - rottenfruit - ) # adds each changed item to the formerly empty list - - -# - -print(rottenfruits) - -# - -# here's a shorter way of writing it -rottenfruits = ["rotten " + fruit for fruit in fruits] - -# - -# Did you follow all that?? -# -# Just in case you didn't, let's review that last one:: - -# - -# ["rotten " + fruit for fruit in fruits] -# ------------------- -# This is the "for loop" -# it steps through each fruit in fruits -# -# ["rotten " + fruit for fruit in fruits] -# ----------------- -# add "rotten " to the fruit at each step -# this is your "do something" -# -# ["rotten " + fruit for fruit in fruits] -# --------------------------------------- -# give a new list that is a result of the "do something" - -# - -print(rottenfruits) - -# - -# Filtering in a loop - -# - -# But what if you don't want to change *every* item in a list? -# -# We can use an 'if' statement to operate on only some items in the list. -# -# Indentation is also important in 'if' statements, as you'll see:: - -# - -# if : -# -# -# - -# - -fruits = [ - "apple", - "orange", - "pear", - "berry", - "mango", - "plum", - "peach", - "melon", - "bannana", -] - -# - -for fruit in fruits: # steps through every fruit in fruits - if len(fruit) > 5: # checks to see if the length of the word is more than 5 - print(fruit) # if true, print the fruit - # if false, python goes back to the 'for' loop - # and checks the next item in the list - - -# - -# Let's say we want to pick only the fruits that start with the letter 'p'. - -# - -p_fruits = [] # creates an empty list called p_fruits -for fruit in fruits: # steps through every fruit in fruits - if fruit.startswith( - "p" - ): # checks to see if the first letter is 'p', using a built-in function - p_fruits.append( - fruit - ) # if the first letter is 'p', the item is added to p_fruits - # if the first letter is not 'p', python goes back to the 'for' loop - # and checks the next item in the list - - -# - -print(p_fruits) - -# - -# here's a shorter way to write it -p_fruits = [fruit for fruit in fruits if fruit.startswith("p")] - -# - -# :: - -# - -# [fruit for fruit in fruits if fruit.startswith("p")] -# ------------------- -# for loop -# -# [fruit for fruit in fruits if fruit.startswith("p")] -# ------------------------ -# pick only some of the fruits -# -# [fruit for fruit in fruits if fruit.startswith("p")] -# ----- -# give me the variable fruit as it appears in the list, don't change it -# -# [fruit for fruit in fruits if fruit.startswith("p")] -# ---------------------------------------------------- -# a fresh new list with those fruits -# - -# - -print(p_fruits) - -# - -# Counting through loops - -# - -# This is not really needed, but it is nice to know. You can safely skip this. - -# - -# Python's built-in function range() makes a list of numbers within a range that you specify. -# -# This is useful because you can use these lists inside of loops. - -# - -list(range(4)) # makes a list - -# - -for i in range(4): - print(i) - - -# - -len(p_fruits) - -# - -for i in range(len(p_fruits)): - print(i) - - -# - -for i in range(len(p_fruits)): - print(p_fruits[i]) - - -# - -for i in range(len(p_fruits)): - print(i, p_fruits[i]) - - -# - -for item_from_enumerate in enumerate(p_fruits): - print(item_from_enumerate) - - -# - -for i, fruit in enumerate(p_fruits): - print(i, fruit) - - -# - -# Looping through E+ objects - -# - -# If you have read the python explanation of loops, you are now masters of using loops. -# -# Let us use the loops with E+ objects. -# -# We'll continue to work with the materials list. - -# - -for material in materials: - print(material.Name) - - -# - -[material.Name for material in materials] - -# - -[material.Roughness for material in materials] - -# - -[material.Thickness for material in materials] - -# - -[material.Thickness for material in materials if material.Thickness > 0.1] - -# - -[material.Name for material in materials if material.Thickness > 0.1] - -# - -thick_materials = [material for material in materials if material.Thickness > 0.1] - -# - -thick_materials - -# - -# change the names of the thick materials -for material in thick_materials: - material.Name = "THICK " + material.Name - - -# - -thick_materials - -# - -# So now we're working with two different lists: materials and thick_materials. -# -# But even though the items can be separated into two lists, we're still working with the same items. -# -# Here's a helpful illustration: - -# - -for_images.display_png(for_images.material_lists) # display the image below - -# - -# here's the same concept, demonstrated with code -# remember, we changed the names of the items in the list thick_materials -# these changes are visible when we print the materials list; the thick materials are also in the materials list -[material.Name for material in materials] - -# - -# Geometry functions in eppy - -# - -# Sometimes, we want information about the E+ object that is not in the fields. For example, it would be useful to know the areas and orientations of the surfaces. These attributes of the surfaces are not in the fields of surfaces, but surface objects *do* have fields that have the coordinates of the surface. The areas and orientations can be calculated from these coordinates. -# -# Pyeplus has some functions that will do the calculations. -# -# In the present version, pyeplus will calculate: -# -# - surface azimuth -# - surface tilt -# - surface area -# -# Let us explore these functions - -# - -# OLD CODE, SHOULD BE DELETED -# from idfreader import idfreader - -# iddfile = "../iddfiles/Energy+V7_0_0_036.idd" -# fname = "../idffiles/V_7_0/5ZoneSupRetPlenRAB.idf" - -# model, to_print, idd_info = idfreader(fname, iddfile) -# surfaces = model['BUILDINGSURFACE:DETAILED'] # all the surfaces - -# - -from eppy import modeleditor -from eppy.modeleditor import IDF - -iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd" -try: - IDF.setiddname(iddfile) -except modeleditor.IDDAlreadySetError as e: - pass - - -fname1 = "../eppy/resources/idffiles/V_7_0/5ZoneSupRetPlenRAB.idf" -idf1 = IDF(fname1) -surfaces = idf1.idfobjects["BUILDINGSURFACE:DETAILED"] - -# - -# Let us look at the first surface -asurface = surfaces[0] -print("surface azimuth =", asurface.azimuth, "degrees") -print("surface tilt =", asurface.tilt, "degrees") -print("surface area =", asurface.area, "m2") - -# - -# all the surface names -s_names = [surface.Name for surface in surfaces] -print(s_names[:5]) # print five of them - -# - -# surface names and azimuths -s_names_azm = [(sf.Name, sf.azimuth) for sf in surfaces] -print(s_names_azm[:5]) # print five of them - -# - -# or to do that in pretty printing -for name, azimuth in s_names_azm[:5]: # just five of them - print(name, azimuth) - - -# - -# surface names and tilt -s_names_tilt = [(sf.Name, sf.tilt) for sf in surfaces] -for name, tilt in s_names_tilt[:5]: # just five of them - print(name, tilt) - - -# - -# surface names and areas -s_names_area = [(sf.Name, sf.area) for sf in surfaces] -for name, area in s_names_area[:5]: # just five of them - print(name, area, "m2") - - -# - -# Let us try to isolate the exterior north facing walls and change their construnctions - -# - -# just vertical walls -vertical_walls = [sf for sf in surfaces if sf.tilt == 90.0] -print([sf.Name for sf in vertical_walls]) - -# - -# north facing walls -north_walls = [sf for sf in vertical_walls if sf.azimuth == 0.0] -print([sf.Name for sf in north_walls]) - -# - -# north facing exterior walls -exterior_nwall = [ - sf for sf in north_walls if sf.Outside_Boundary_Condition == "Outdoors" -] -print([sf.Name for sf in exterior_nwall]) - -# - -# print out some more details of the north wall -north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall] -for name, azimuth, construction in north_wall_info: - print(name, azimuth, construction) - - -# - -# change the construction in the exterior north walls -for wall in exterior_nwall: - wall.Construction_Name = ( - "NORTHERN-WALL" # make sure such a construction exists in the model - ) - - -# - -# see the change -north_wall_info = [(sf.Name, sf.azimuth, sf.Construction_Name) for sf in exterior_nwall] -for name, azimuth, construction in north_wall_info: - print(name, azimuth, construction) - - -# - -# see this in all surfaces -for sf in surfaces: - print(sf.Name, sf.azimuth, sf.Construction_Name) - - -# - -# You can see the "NORTHERN-WALL" in the print out above. -# -# This shows that very sophisticated modification can be made to the model rather quickly. diff --git a/docs/Makefile b/docs/Makefile index be11b35e..cf1d9516 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -1,177 +1,20 @@ -# Makefile for Sphinx documentation +# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source - -.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest gettext +SPHINXBUILD = python -msphinx +SPHINXPROJ = eppy +SOURCEDIR = . +BUILDDIR = _build +# Put it first so that "make" without argument is like "make help". help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - rm -rf $(BUILDDIR)/* - -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) generated - @echo - @echo "Build finished. The HTML pages are in generated." - -dirhtml: - $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml - @echo - @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." - -singlehtml: - $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml - @echo - @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." - -pickle: - $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle - @echo - @echo "Build finished; now you can process the pickle files." - -json: - $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json - @echo - @echo "Build finished; now you can process the JSON files." - -htmlhelp: - $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp - @echo - @echo "Build finished; now you can run HTML Help Workshop with the" \ - ".hhp project file in $(BUILDDIR)/htmlhelp." - -qthelp: - $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp - @echo - @echo "Build finished; now you can run "qcollectiongenerator" with the" \ - ".qhcp project file in $(BUILDDIR)/qthelp, like this:" - @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/eppy.qhcp" - @echo "To view the help file:" - @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/eppy.qhc" - -devhelp: - $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp - @echo - @echo "Build finished." - @echo "To view the help file:" - @echo "# mkdir -p $$HOME/.local/share/devhelp/eppy" - @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/eppy" - @echo "# devhelp" - -epub: - $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub - @echo - @echo "Build finished. The epub file is in $(BUILDDIR)/epub." - -latex: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo - @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." - @echo "Run \`make' in that directory to run these through (pdf)latex" \ - "(use \`make latexpdf' here to do that automatically)." - -latexpdf: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through pdflatex..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -latexpdfja: - $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex - @echo "Running LaTeX files through platex and dvipdfmx..." - $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja - @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." - -text: - $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text - @echo - @echo "Build finished. The text files are in $(BUILDDIR)/text." - -man: - $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man - @echo - @echo "Build finished. The manual pages are in $(BUILDDIR)/man." - -texinfo: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo - @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." - @echo "Run \`make' in that directory to run these through makeinfo" \ - "(use \`make info' here to do that automatically)." - -info: - $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo - @echo "Running Texinfo files through makeinfo..." - make -C $(BUILDDIR)/texinfo info - @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." - -gettext: - $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale - @echo - @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -xml: - $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml - @echo - @echo "Build finished. The XML files are in $(BUILDDIR)/xml." +.PHONY: help Makefile -pseudoxml: - $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml - @echo - @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/Outputs_Tutorial.ipynb b/docs/Outputs_Tutorial.ipynb index e4299c16..86759d27 100644 --- a/docs/Outputs_Tutorial.ipynb +++ b/docs/Outputs_Tutorial.ipynb @@ -1,1167 +1,1129 @@ { - "metadata": { - "name": "", - "signature": "sha256:3c0d6fdbad44262be630d6d77e0d3f967dc7e755c4be9daf9d929d1a84c881c7" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "Reading outputs from E+" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# some initial set up\n", - "# if you have not installed epp, and only downloaded it\n", - "# you will need the following lines\n", - "import sys\n", - "# pathnameto_eppy = 'c:/eppy'\n", - "pathnameto_eppy = '../'\n", - "sys.path.append(pathnameto_eppy) \n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Using titletable() to get at the tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So far we have been making changes to the IDF input file.\n", - "How about looking at the outputs.\n", - "\n", - "Energyplus makes nice htmlout files that look like this." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.html_snippet1) #display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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evQrLpwzGbbfdhl69HmSnfcjhcnJJ3ooJA24jrtsdvi+mjsNTT32APbmeHB1h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I9ddY0/8uYNnrzSH1FcOdnuGex9OvQy8ycLs6DhMZhFXFvMO95aHmRn93W3f+UhH9f+qdcX77H+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\ngWsQXZSDLtfcj2+VPYM5695HmktMxOgf/wSDYngY2tXNq7Bl0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Unvi1fiWfW//N4v/02U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\nOS2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5Id1DKDnR0K07K/V5pBuJR5BjjBAqnnQNaWoVbT63FhZwCC34TJ92bLl1BIVXuhA5\nfaYLTcAnXUd0t6v0aQVFh8dyMXFW1wpqUInq1ng3pICvWk/P5t4SxyEc0POpXFSy6Dcz7NfScXq6\nyD3t56LLzB+l7e0Q3eRmJPWQfNoGw0LkJW9KUIykTeXB0z0qwlOjor/LZ7rONPXZHMam+rTzvn55\nblzQoI6ux/AvyT1HlUMaPYlDrdS7tkMr75nBDiMf0v0nNeu7mp60sUjUPSoCXa2iVnMP08tysMtr\nskFtq+jrbtXzQWWfWMcUBJX36qY20eX3C7+/Q3jdVN9UnLPQe1OdXldkGcHlFf39fkGNdY2VrHcd\nff2i1a27e3nJ9y99HaTijasPkoHuvkgmQLLLk5EplSeZvru1WwwM9JMOA6K/22fxUflTIOSv/d5K\n49Y44HUKJ9X1gVa93mv3aZd2Z4pgN9WBar8IDrcZeY13j5wJ+Mzzyo3LnjzvMwEmwASYABNgAkwg\nHwJZRj6omZLjVlC4WBstuHGF7G2NIbpQupH04hfG0IX8cTb14/Cj9+OeuscRaKVmLQJo/s5J+l2s\nu2XRnr4VobRU30vVv5s+rXJs3fUsaJ4KJeaF/9kWnAiH8ez9qw258T8l6dzAwm+RZvqm9ztn1q9k\n+dWQuS1dupTc067GprrdaJLdyO4uPPvIVlSSm9HzfU2awG99X066z0deGVIMzmiy5H+7b1uJ4iUr\ncdPmB9Hr8iIwPoNHN1oOTcee/hKVAgF580c41Plj2XGubU3fHiP3nMx6Dn1nL56i/vDuf9iqRStZ\nvRXf6XBT/H34PwdPpWR98JFbbCwSdS/Amk33o/4+MmGNbfWmbXhAsnL6MNVyPzZWVuOvrcsqWMrf\n8ZFXEDhxAidODOKlE6rEgLPRu8v3VdRL30CqN8HDj2DDhi14+CHtBLqnDmPrxg24f+ejhh4xpK+D\nhDiuPkgGJXjAH8To+LexwT7AkTJXRPfxf8DnPteAhobPoaH+QbMupgme0+lTZwqx9lPbzNGNPXvl\nqGIE37xtH7xf+DjKosaNmpM0DsQEmAATYAJMgAkwgbMjMGfGB8o3okeEsfqnXyGXmkLctoP8kMhb\n3eY0gtJLrKM1d/x/ekPoDDmmG6c1r5Jc8pElrYWa4bJQl0TzARK3qOE4E5Jpp9qKLzUbaVqIrPrp\ncmbOKGEFuOQyasdevVSdQPn6zZqBpeUxb3mmmKQdX2Ac/U3S2qKtZCGuXGZv3YYw+GIvddw7QeoA\n776LlXd2gUYL8MTma+hEJj1/i9FXZIP+alxqQ7j8xvVSEkIz+vyWZNaJLLTgcf9Fz8Q3dEul5Uab\nbmiSgXILNfpP6ecy/X/Xw7vx6K5d2EV/z/7bAJyn3iazN3aWeqtl1Baaxt6iP9BMSlyqLOCS5ZaR\nnLEOJjMoKCnHimW6G1SmPMlrvgP/hp6eHv1vIEAzk+Zgi5BOJZVolDa/3Hqfwb5mD7bDg7tXFyCm\nq6xf4/+ZABNgAkyACTABJjBPBObO+IiMYGdFMdbtOoOnowKDrbLJFN/ItBrndIkacrLD225waDZD\nLhnNIa1MYi77gxu0y68PvUFN1eQtNv7fOCBPOytxva0tn7N+ySK1MzZR2vFs5UkhZ6JLsOHRA/pI\ny4EH8ZGdR5LzNHMV/mLLFmzduhVbtmzCJtrfuCZ9Q1jXM4Z3tPb4DH5jg1S0co3WGLaXm5aZOf0v\nN+lnospgoMSL1sL/wiMoo9zPld7Lr5cjZnZdotbRLOtgJlxx+QrPrVVwe32rkXQAu7fvgdt/nzbx\n/2xTOUJzWRqPpZ+fkymffI0JMAEmwASYABO4+AjkaXxYIxeJqEbIRWdPAPB37cIq2VOserdtUUqN\nwQgt7tTrWgO/4nrqkzdaPlYzz4pk7Vkp5pIWKZDcCDdErPj4A5DOQ4F9+9AXPydeC/H8M43ar9vt\nosYsbTnqV5rOjUvKUPldmXt+M8qTMrVNKrcMjz7frbm9BfZsxucPjRjXCvFeaUn0bsPBkzZDcLKH\nJvg/iZRNRlPPZfhAhTQPe/Gjn1khY2/q5RavWzLr+OuGOll+bDZOlpD65YWFakhCPy4pkUM0RbPS\n255w+O236dBuIhbqR5RuLnUwnkEEoUhuOYzLl/0GiLuBDE2jahzPrnn8vl1E0WoXaD6VsdXiM7ev\nUAdZf+P0otCxsUPYvA/402vSG7JZhXIAJsAEmAATYAJM4KIikJ/xQQ1Psi+oMzi5sXn6Xd0Np+PQ\nEZw81o6HHvTLgHjp+0cw9Muw1mP8+ljQMAhCOPRlcq0hl4/Pb6LGT/EiXE5Hvdsfx5GhEfQ0f1xb\nPQqnjuMHQ3rDN6pcmqgllTGtyXcQlFZMr2w4ptkKVmH3gOwB7sVtS+txbFItQRTBsZY6bJZWVG0b\nPFuMhlkO+smU/C/04vjxTjS3v6q1WXufOWR8kI2WXt0h8+vGwx/PLb/x8k7Kw7jtrbfHteO3f2Po\nXl6J54x5JfvuvJZWGpPcirD5Ya8WbpvjFuxtP4KezhZULL0Nzifu1g0ralun03PNHfocgR21T9Cq\nX3rygz/soB0nGu5ZQ7+RtKwtFsm665Lof6NVfMawOou1CxGcJI4IDCFoBozfUW5bY69PxV8wjs5O\nb3sTXRf0xk9epJ3X8ZaqHqFJOqIz46EsdfC3mgCLwTH62F8xSotvR89kagPk9NuWcdj14mu6AvL/\nqSD0kqZ9/wmMK12MENOvHtNWbpOH/sMvpDQoT0dOIfBLde+V4e4verTYTu9nsMZwqYu8Zd0vM9JF\ny9he6+1Suzh+0tAkJldKa8fWlXfStUpo07zMULzDBJgAE2ACTIAJMIEMBHKdnT7s9wgSY/05XMI/\nbK3DJFf/oea1fp0+3NfRanysjz6MNjA1I7qa9A+fyRWuNDnVXmGLLgbVx/1IhtPt0VabclS7hc//\nkuhuslbpcThrxfeP+dOkdb945OOWjs5anxhX3w9MkdHwKH2Yz1j9CU59JSOpn6djIGmFqfT6BUhy\nVHQbH76TKyN1j0/qH4JTPLTfWtFnUyY/eRZnER0VTS6DoSHfRatsqRB9TS6zjNwdg5puA63WqlaS\nvccvz8vN+GBdBj2nAh0Ga5dw18p0ncI/SOtQRYdJj1SsE1kozWR6YdHtVfWA4joeEP/4t58w9a31\n+uNWCnM2+M18ydha3qsT854QRgsoRH56h6l+WnXM5WkTfX7bKlz0QT9/n1+4zdWvHGLPt/4lTR2U\n9T2RwaRoo49xSnbdwcQKOSPaNK4WS1lGjoYOMdCVcM9p5eSgFdPkOmCUhq2srXuT7stRxTxRtlsE\ntEvDwk31XF/wKiz8Daru23SofkY8m0IvOOL5w9WadlUyoyj4hwkwASbABJgAE2ACJoEFco8aLnO0\nkWsJdeDq7i/klhGJ0PfvbLOVY3SdVp+KoRhlmotMfLKxSAihaKF2LUYuKgX0IcP0W5a00kdMuhKa\nniS9ZG9vIZYsKzcnHCcGzKxfDKHpMIrLSmjidAgtlaV47q4Aeuquw/R0FCXa+XiJucuLj3c2R7HQ\nNKaJaVFZOUpMrLnpKUc4JifexGkqm8tXLEvLx9LLzsI6e+738tU7Xw0z1cFEBhQ2UoCSjHU63/Q5\nPBNgAkyACTABJsAELiwCc2x8XFiZnz9tp9FcsQTb7+iCeGzT/CUza8kXip6zzigLYAJMgAkwASbA\nBJgAEzgPCOQ35+M8UPj8VyGCoSNPY7ucHLPHh0PHRhLW/DpfcnCh6Hm+8GI9mAATYAJMgAkwASbA\nBGZLwHTAma0gjm8QoMm4E+9cQ6t+deG9eBdTI68hsm6VzdXpPCF1oeh5nuBiNZgAE2ACTIAJMAEm\nwARmT4DdrmbPkCUwASbABJgAE2ACTIAJMAEmkAMBdrvKARIHYQJMgAkwASbABJgAE2ACTGD2BNj4\nmD1DlsAEmAATYAJMgAkwASbABJhADgTY+MgBEgdhAkyACTABJsAEmAATYAJMYPYE2PiYPUOWwASY\nABNgAkyACTABJsAEmEAOBNj4yAESB2ECTIAJMAEmwASYABNgAkxg9gTY+Jg9Q5bABJgAE2ACTIAJ\nMAEmwASYQA4E2PjIARIHYQJMgAkwASbABJgAE2ACTGD2BNj4mD1DlsAEmAATYAJMgAkwASbABJhA\nDgTY+MgBEgdhAkyACTABJsAEmAATYAJMYPYE2PiYPUOWwASYABNgAkyACTABJsAEmEAOBNj4yAES\nB2ECTIAJMAEmwASYABNgAkxg9gTY+Jg9Q5bABJgAE2ACTIAJMAEmwASYQA4E2PjIARIHYQJMgAkw\nASbABJgAE2ACTGD2BNj4mD1DlsAEmAATYAJMgAkwASbABJhADgTY+MgBEgdhAkyACTABJsAEmAAT\nYAJMYPYE2PiYPUOWwASYABNgAkyACTABJsAEmEAOBNj4yAESB2ECTIAJMAEmwASYABNgAkxg9gTO\nufERmR7DyNi0TfMIxoaGMB2znYrbjWBibBLqcnL8uMDn/UEsMo2JCXv+81d5LmTknyrHYAJMgAkw\nASbABJgAE2ACsyOQxfiIoLOxCgsWLEj+q6jB3v1HMBHJR4EQDn5qJa5d+REcC0XQs7eG5BZj5Qfv\nws/CiXJi6Gmu064v3/JNhLTL9viJ4ef6OIT2uorkfGssKlBTvxc9Q3kaEbEhfLZ4CZYvX4L6zlex\nv8bi2nJcz6GZi9gIdlZa1xfUtOsM4mSMmcF5hwkwASbABJgAE2ACTIAJnO8EshgfRdiy6zCCfU1a\nPpxNfYiKKGamRuG/F9ixbTOWf7oFuTfBi+F4qAluzy5cU1KEykefRl+Tk2RfjcIkUgWofOhL8MrL\nly1EgXbdHj8pwhyfKME9LSfQ3+Qy5Tb1BSFEEK21wIF9O3DbB5egfSgP6yt2Gm8a0k6MRnD/V7rh\nMI7PmGM7xomCVXisZwY+lfx4SA8RJ+OUqRvvMAEmwASYABNgAkyACTCB852A3qbPomXRpZdoIUqL\nFpMRUICSshXY8mgzvEcOYLv/OfwsVIcNJVmEaJcLsHbro1hrBi3CpZeUmkdxO9LPqmAJ3r+cfsfV\nlcT46vz8/S66xMpY0eIiSqgE69ZKkyGgJdrV/xruWb0mNwWK1sLb34XqXyzGrVVEIXZcMz50SalE\nFKLESl43wBJlpIrG55gAE2ACTIAJMAEmwASYwHlIIMvIR4LGC23jE5E38NNeeb1UH7WIjaG5rgqV\nFRWobx+i8xPYW1OJysoKVNYf0lyGpsdOorN9LyoX1JDbVYJsdRg6icYqcjcqrEAFuWTde0BLQrsa\nHz+GkaOd2FtfhYqqRhzav9NwkapE+3HbWExoCM010n1K/6trbEFn5yEcOT5BMsmtrF6er8fJHAYw\nFhaq/FvKr/nglSQnhEM7a1BTV4eaqhp0jtE8ls5GVNXUoa6uBnWNRyglPcxjB/tw/IcH8M2+1C5T\nkbGj2FlVqeWlsmY7mmT+zS1RxqtWupTW/p4eKgM9bkVlHY6MWHqOHW1HXZXhRlZRibqde9HcvJdc\n546SbrwxASbABJgAE2ACTIAJMIFzQEDksM0EfIJUEXA1ia7uLtHR5hXVDjqmcx7/sCVhpk875/IO\nGOeCgtymBJw+MSWiItDVKmrlMVxiYEYPEvC5rOPosHDLdNx+IS9Hx7sFeR1p8WdSxB/s8goag9DS\nRG2r6OtuFeSlZYSX8sOizUXHLp8mb7jDrYeFQ/i6R+n6jPBp+jhFv6GPjGXfAr5qIw5Erc8v/L4G\nM013az+loG8zwx1mOK+WuaAglyn9nJZ/0mbUr+tHOjolo/CAqDb09w5MkTr9en7l9YY20d2m9LXy\nlCgjbEtXlketx2PKgLNVyze5zRm6uUTfVFgMxOWpj8jyxgSYABNgAkyACTABJsAE5p9AfiMf/hfR\n98M++L/2DA4YvkILcdo2W2Ex1BQF3W4qQqnhVSXdtdZsuh/191FzO8029m9PYB+c6PdsIecm8rpa\ndjOqTIHJ8Vdv2oYHpAeU04eplvuxsbIaf20XH3kVXX7AcfN6Td6qqs9AXnb5nkNd5QraK8ED/iBG\nx7+dk9vY6bEhDA39wnC4Al4/8RPQVAxtK1l+jS3vMTpXTq5VljLSv63o8ishvcjkZmDRD4z/h777\nBEhd2pz44ufvQeU9e9Bq5l8PlCijyJaunJPTsmsXGs2JIvpMkl+8/KIe2VWF9WVFuOHW2/RjovHw\nAxt1dy7jDP8wASbABJgAE2ACTIAJMIH5IpCX8eHyNeKxXY/h2Z4TiAb74aaG/w6XA5/vNFyIDK+k\nmQzaRs9YrkDxwWI40buPTl2HS5R3E6LxQegoMX7p1XoQffIKGSi3UGv9lIqmCwq8+JLm9oWCy3AV\nXZo5c1oFQEFJOVYsKzOPM+3ccvcjePTxZ2kCvlcL5t/3IK6t2a/LpjO26Rna9eiZTNISr8UwMTKu\nn3TchFWasOT8J8aSxypdOSdH39QZMuDoROFC4/TMGZ3oaaXYOE4nrTJmhOUfJsAEmAATYAJMgAkw\nASYwxwTyMj5wxmoMF5RvQF293rN/YvRUnFqpevXjAqQ8CGH0hLxwmv7NZrOZPkVr8LCPdPQ/iIf2\nHkJP+xPYQ1O83a4bziqBM1G9pV6+3qmNoGhC/Ifxajp7Kq9UIvjVG716jMuuQKluTeUlIV3gNX+5\nWzMU0fsg9h3qRLPnQT2o+yHcYNkp6aLzeSbABJgAE2ACTIAJMAEmMCcE8jM+EpJ8O5jQ6jZsE6v5\nbw5hpFhKN0EYynCjk2Zs4AACrym5RvxTqqc+MY51LB2dUm0VmotRNW675l386r0fxXj4BLaskKtW\nqY2mgkfSxVZh9F+14C+Nv5ijHcDlWGRl04igWw7m6RyssYWFl2LVHxk+Vr09eF1DYEpI7acVr17y\nkUq37Do4K+XlalxyagpXVvvRPzgO8fhWc9QkOTKfYQJMgAkwASbABJgAE2ACc0sgJ+Mj8tbbWqoz\nb7+lrYwUCU3ieOde3LTDr53/69uv07UqXkRNcaB3++M4MjRCHwn8OLbJIHl3h1sAAEAASURBVKeO\n4weJH+Qz2tWmaxIdr6+6S5OzzfEQxZ/A5ND38Q0ZP3AY7UeO2xr8dM6If8awdIq1mBGcfIFGDwJD\nCMpj+lBfwwe3welejyvetwRL3gcMvXQMIxPKuKEPCdYUo7T4dvRMpjZATr+twgLB4BQJjeDov7QY\nczNA89wfxhppy5DhpUI+8+x3cfJYOx568IDUgiaHjOG1SVpTKmo3Wuh8+DdmnOMnx3H9LZqFQBf8\nOPTvQ5g4flDnZ8gYDZGOiTJs6cpgcjutXNvIzUquZDVy6HNwkUebw3UV3v8HksN78ebPAjh2Uq74\nxRsTYAJMgAkwASbABJgAEzhHBDLPaQ8Lv0euRmWs2pTw66r1iK5BWqXJtg12NJjhnW6PtpqTo9ot\nfP6XRLe31rwGxwPinxruNo8dzlrRPRoWAVt8la6M7+14XhxOiP+Pf/sJM36t1y+6fNbqUM4GP61E\nRatZGatyKVnqt3VQLm81I9qqZd6cojuYuOYTXat1mPJVPPWr5X1g3JbzqOjzWitjyRW99BXBHMJV\nTfueg2K/lpZieaN4SK2GpXF1iLbhkBhotfKg0iKzQbhcLuHp+LGhryVje5wMp/C2Npmrccn4Ll9A\nDLbZuCeUYXVrwJYH3mUCTIAJMAEmwASYABNgAvNHYIEUTY3UOd1ikRBC0UKU0VfMY+TSVFCU5wQG\nij9N0ytKykpQEKO++wK7m1TuqkbGjuDjK3344mgr1pfGEArrfmEj392Bh4MP48SuDSRMul3RhxPz\n1TGNGpHpSUyR7kuWlaOIdI+Q7vlqHyeDWEQKSjAb9aaP7cWSm3bA4XThasMVa+Z1P3oDlAmHF1Mn\nHiGnN96YABNgAkyACTABJsAEmMD8EsjTKshNmYKiEtCKrtqWt+EhY9nin63hIY2K731xM3rRgKcv\nLyPjQhozJDsyiR8cD2Br9TWafpSYds04mPVPUVk5zMWzzsLwkArEySAW+Rov8ZmIoOuJHdqppm8f\nxibTyphA44Ll2F15Pc/7iAfGR0yACTABJsAEmAATYALzRCCnOR/zlPY8iy3C+ns9lMYeXFtMX0yn\nL5xXLKDf4qV46dZWNGwsn+f0zxfxRdhwp+QAbF5SgZr6etTTV9crpOHh9GBg50f5Ox/nS1GxHkyA\nCTABJsAEmAAT+D0nMC9uV+cTs8jkCF4aeA3TpFTZ0pW40bEaZfMy3nM+5TqFLpFpTIxP4dRpWsi4\ncBEuu3wpjdDwOrspSPEpJsAEmAATYAJMgAkwgXki8HtvfMwTNxbLBJgAE2ACTIAJMAEmwASYQJ4E\nfo/drvIkwcGZABNgAkyACTABJsAEmAATmFcCbHzMK14WzgSYABNgAkyACTABJsAEmIAiwMaHIsG/\nTIAJMAEmwASYABNgAkyACcwrATY+5hUvC2cCTIAJMAEmwASYABNgAkxAEWDjQ5HgXybABJgAE2AC\nTIAJMAEmwATmlQAbH/OKl4UzASbABJgAE2ACTIAJMAEmoAiw8aFI8C8TYAJMgAkwASbABJgAE2AC\n80qAjY95xcvCmQATYAJMgAkwASbABJgAE1AE2PhQJPiXCTABJsAEmAATYAJMgAkwgXklwMbHvOJl\n4UyACTABJsAEmAATYAJMgAkoAmx8KBL8ywSYABNgAkyACTABJsAEmMC8EmDjY17xsnAmwASYABNg\nAkyACTABJsAEFAE2PhQJ/mUCTIAJMAEmwASYABNgAkxgXgmw8TGveFk4E2ACTIAJMAEmwASYABNg\nAooAGx+KBP8yASbABJgAE2ACTIAJMAEmMK8E2PiYV7wsnAkwASbABJgAE2ACTIAJMAFFgI0PRYJ/\nmQATYAJMgAkwASbABJgAE5hXAueV8RGZHsPI2PS8Zvi8Fx6bxtjICHEYw2QoRurGMDE2Sf9f3Fss\nMo2JiYurboQmp+e33GMRTE9OYoL+QhFVw2IIhSJpK9u86yRTzvMeOCc6pSUy9xcioRCVQWh+y34O\n1dbuTXpejdHfxHRoDiWfnajQ9CQmSQ9Vo9NJkeGmtWesPURMuydk/Gxb6viASj9bfL7OBJgAE7hY\nCWQ2PmJjaK6pwIIFC9L/VbUg+2M6F7whHPzUSly78iM4NjcCc0n0vAozcWw/KgqXYOW1/wt7v7gN\nS0sLUVFZiOVbWueIcWJ2Q2ivy1K+VPZ1+08mRjy3x7EhfLZ4CZYvX4L6zrFzkHYaLhU7MZS+XZ6s\nFxlMeuMmhP011j3UcjyHCk55rlm6BF85Pg8GV2wSnc31WFBYjCVLl6L6ro+itJjqWt1etDRuhWvf\nieS8yDN56UQMbXlWz5CKunZIhJGh/XHPlJaTOpO874E8dbKXg9Ip+bcC7SO/TtK/srEnJZeje6vi\n8rKg5uvwVVnlreRX1h/S8j5yaGdc+P1G3jF9HI0Ur7i0FKX0V7igCu3HJ1OkmYptPU5Oj6CxIjFd\nmZcYYiPtVppVPnw9oWxyz1u79SyKjKG9sQaF8t5cuQXbt63E8iWlWFBZh86Uek+juZL0qztExtVx\nVMlny6GRFPlLPhUZsumf8D6q2HlEjzB9kvhVoHTJUiwlPQor6nF0IvUNO32sWQu3xPWMmZ/J4+2k\nUyHdEx/V4i+oakS62y9VfGjvywVm+gtqmjGWzQJKziqfYQJMgAn8/hMQOWx9HqcgEqKpb1yIaFiE\nw/JvRgz3NdH5JjGVg4zsQaJioKNJuD0dIpg98IURIjws/F2Dueka7BYOYozaNhE2YkSDA8Itzzl9\nYiY3KWcVqt/r0spXlnGDf1iTEY3OiMFun6aT0ztwVnLnLFJ4QLgkB/o7l7oEfNUmF6enL8/sTAmv\nA8LTb9wdqnwpD96B7HfMeFeDnna1VR/yVCB18Kl+UWuwdFBdMzUJj4rWWp2xy5e6vPPXKSq6jWeH\n+fywaRUN9gkn6dIxaNRuxSiPeyA/nWZEq4vy6HCLvuEpESUWql65vP0iGp0S3b5ajbsqI/u9AbhE\nf+KNONOv5UHmT6ufTaqeTInWav2cdq3Wb8u5EDMDXi18W0AvAcXC7W0Vvgar3gEeQU/dFNu48FD9\nUum2DaqnxpRos6Xr6bOepjMD8nndIIajurizzxu9Bka7bez6hCFSBAdaTZ1qff3xege7tOdJdcew\nCPZ59PybescHTTwa7nCbclWe1W9DtyQ0KhqMMlDn9d8GMaqUM4RGx7usMnPpz9bwYJsh3yn6CeWU\noR/gFoM5xKcYet0iHfyjQoQDPl0e3b+JVSYxb3zMBJgAE7jYCCCXDA8YjVPvQOJjdEb0dfXzwzUl\nRP1l5GhK3ZBLjDLerb+MQS9De2MjOixf5h6rkZgYcQ6O7Y1sX0Av42CgX3tpD7eS4elJaETMQZr5\nihjt7xIdHX0imNAQyFdOPuHNBgQ1KFx5GWBTosPt0BofvoDRKCQDqtpoHKmGbXpdyHBxqoalgxoz\nc5XpoE1utRhQ7VVTkSnho3RT19mz08let1I9P7xOq0Gf/z2Qr04zoonKwKda37Yysco3LHw2ozHQ\nahnmsjFb3RowacmdwTbdWFENXksOXTQa2/o1h+gwy1E3guyG9LDfK9pMozQqOkwDolqoKhSXMB3Y\nG+TVPksvZdjIdJ2mMSREX4NDuFoHTTFnn7dR4THqsmyc690Vplih3hcyfW+/Zfwoff3jU6LLLes3\nGQZWtAx7UeGX4WubRIffL/z01+VvNQwIvR4Py3Iio1U+H6Izg6JJGpmajlb90hIID+odOkp/o2NH\ndbDB2aR3fpGBonUGUbjaNouZSBN/yjAmAeO+CgfM+109UzNkkC8xASbABC4qAjkZH6oBYTakNET0\nInfSi1FrF0VpFMQvmtwu4XB5REer0WsLp/FCDdPLgxqxDqdw0p9H612nXtGmWuGgF72zwS9+ORoQ\n/rYmeqFUU+9iVIwG+rQewFpfl/A36T2Bni56VU0FhLdWjsTojTtXQ6sY1trL2XSgqMMDos3rFs7q\nJnp5eY2Xi0N4u6knbtAvqo2eRJfHbxpU4fE+4VYNQUe1aO2Tr8vsafmNXmRdT6foGJ4mBlJnd8rG\nhNnwMl6Yvq4BQ4eoGBwYNkZDaL/LJ6qdet6l7NoGrxgIylbkjOigHtPq2lpRXV0rWru7DU7UmHTW\nii4dUsrKrcpXaywYBmYbvbwbZDcvvcgHhv9H+A3ZtSTb29ElWhv0Rlm1t5tozJFeM8MkV/X6UkOJ\nGhsDwWktX7XuBtHgprS7iX90XHR4qO5IVlSfqqtdwq0ahSllGI33qUHhozqqN0rIoDDrTkosYkb1\nXlI6eqMyF8bhuJ5nh5PqPN0Tw6HcjQ+rF1ZvQDmSRl3oflLlQeXt9tCIodHYcrjcojtNWduNKdR2\nmCNscbmnRpdHsbRdyK6TLbBtN75umeMsRghpPLhEn3b/CpHbPWAJPxudxgMDlgGb0vggm2FwQIwa\nhplsSDsbmkStOcpAjW3TFhymUSSnaDWfJaqeWDraG+KgXnAZNdgtn48NcZ0MVgy5Ny6ajPTsRkV8\nGDqa0keOtPrssEZIuhtUw1v+Kn2lrtQrP24qrxkJZ5M3NWoh03U2JXdM2O8bOWo7YbuP1L1n/brE\nD+n5NTMzk/4vTM8AT3ynjKARJ23Uyt2lMZ0ZH43roNEZy/zbjQ9l9HUIn3o+a8aH3Ygl40HWR5vx\nYBmU6eILEfCp54rLiG/d71b8pBLkE0yACTCBi5JAXsZHdVMbNdr1nqdWj2wkWg/2wS7VmKcHfm2r\n6Os2eqZMlyHqLdNeqLXWMPZUtyZjMBoVga5WUas18uXDO0gvG/UwpxccNXhlQ7P6Kb/uMkINJ/l+\nmBnsMAwIGhqnxkJmHaKiv63BCE86uryiv5/kmY0Kl+jo6xet0kiitLReWuPl7tN678aF12jgtQ2H\ns6RFug37tbScni4RnJqiht6M1qsMaqwkuW5QXqLjfi1d66UsX5xO4fUHzIoZHjRcGlytJC8suhp0\nXeHwar114eGOOBm1Ho/pGgFnq2HMmOLMHXsDsakrQO5vuvFo76FPlG3qSWlPzIVeth7FjtEp0d9k\n5I3cVUKjftNNQvYWD7bqBkp1hzREhrWGmtZbn0FGdMZqDMieyJkBwy3CbJyZOMwdeyNKNSASOaRi\nPNrfatYzd2u3GAiQ8Whr6Nq5monZdrpqHcLd4RcNtrqZWGfCozbXEaqvHn+X8FYro9RoQNlkyl17\nL73L1lOeECzlYS46pYpor1vJ+Y43PnK5B+xpnK1OpgxbmajyNa8ZOwNeMh7JDW3AqHOy3jd06WOT\nwW5yBar2i+CwctlJNj5EdFB/ZlE87bnSRZ0ctJ+6N1w34l1muVPHQYP+rEvUSz+ON3TbpFUUlo1y\nJxnqxv0j0yK/oeigdPPyxj0DzjZvdoMqJTe615Q7m+bmRS66owNt2j3hcLeJfr/+fHG39Yvh4Z+K\nfR/R2ZjPFIOVeUzPmESzVdXl5Dqlk1HPCNCzUsUdbpPPDekmHDZdpHSX1iiNxKh7h94/0vCMWiMX\nKo/p45NRYo5UJRsfjgbZQcMbE2ACTIAJKAJ5GR/O2gbRRA1aT5OHelrlw9p40GrSZjQfd/kw1x/2\nUb0H2DQ+qD/P8GNXL2/5MHfYes70F4aSaTTWncYLk+aZDGovD4fotkbyTdeDas2dIJsOUf2lQzKV\nCL3Hyim61RuKXCW0RgK5QOgNYKfwkcElh/u9xgtG1zlLWtRzJl/Adv/5KBlVo+RykG4L0gta+sCb\nL11j30EGjHx5WY1ht9ZrGu6XPtwyvGEEUmNKvfSVu4XZI2eWS3Lq9gYiHOolnDA3wS67oUvM0AiU\nR444kF/3tNmzefZ6BVTjjtzO5Lt/VPl4u/00zcgyHFzeF0Wb0WspR9kGZE8uzRVoaguITDJeVvM3\nqOy1ErD1bMa5VdjwWLxtjUo7B8OlJYmxreFijhba8pCuwaQlTQavg0b/pCvLoNKZyliv3zblbPLk\nfAW5hU3XDzJGND94W3jaVQ02WWdUgyo+RJqjXHVKEd1et5LzHW98yOjZ7gEziVnoZMqIY5jaPVI2\n0B1e6gCYMeZkyftNeyaFhZf2vVrDXhmyqbmq5566r5NHspRGckS1zRzFUuHdcsQ3zRZU7pqyTGnE\natRPbmAN/SI8bnVEuGh0sttDI4lt8Q5SZ5s3e5mmrEdx82CMzpZRXZ8mmgM17pfzN5zGiNcM3c/W\nM0flOe6XDAjZ2WRtQWNkSI3qWFe0PeqQ0ObKURpdxkhPdFR27lCa2s1vvAdkWWodOTSI1K/PwZHp\nSl6jA9bcFdnhkTm+HIlUeUg2PmR9Sf/UT9CdD5kAE2ACFwGBzKtd0ZPYvt318G48umsXdj26C48f\nfh5uzOA3tgClV+sHBdpPAdbcQk3hU1aAZR+5G9Trhz2+79OqL7Riy70HsOuudWaA6Jn4VYAWlspL\nC/XrRQUYfSVA+1fj0iL9lPx/+Y3rtYPQzGntN7MOYS2MlKlELPoDqXQpLtWVBkqWg7Sm7R0MvthL\n7XonLpOH776LlXd2gUZ+8MTma+QZZE4rqoWZOaP9aP8VlJRjxbIy64RtLxYJoWztPegJj8LvpVen\nbQvs3oxv0TJLJdd9Ev1dHWht+1O82LITG27aYYYqNPZKjN/SosUJZwCVRTNSih3fc8dALgcgF42k\nzZS9YilKytZg17OH8XjdBlw6a71CePHwATM9SW7F1j0Ijo9j5stbEvReiDVr9RIK+Hdj3fJCVD72\nP7jrkyszyHDiJy8Y8ksXGvIKschIsX9kwkw7lx2TQzrGMb3spawzUVXncpEMDPm/hIDzNhTTUqul\nf3yLGenAtm9gzDxK3NFLv+jqDUbdBXpf+VViIKrD9lOWjvazqfbPTqdUkhLPxdfIXO4BJWH+dFIp\n2H4jxKqkEo3qpuh9BvuaPdgOD+5eXQBbcdsiWbvLNtWD3KiMzYWn3BvVQcJvAVZtvIeerQLDXTSr\nwthO/Mz2EFUnjd/ym+/Qnqny0L+tGitdT6HtvnUoWnarmaZ/+224bTfwyMdWJcSmw7PIW9xz2ng8\nxwmO2N4Ka/4EP+/tREuLfv+dPObHM1/fR8F78aPvHsFIqAT3tJyQnWDp/w7fD3XPyXQiQ/+BHfQq\ncDZ9Eqviq5CmRs+eT2IfHOgY9GPTMhkgggPb6JnhuAm/OXYEnZ3fQvfrWlCC9g0cPDqGsg2PgEbO\nQZ0/kLxWrttmBAAqVhZlif9zvPML+W5KvbmqbkHqp37q8HyWCTABJvD7TiAv4yO+IVUOz5QfG0to\nXfTp1MsZgoyTuK1oDc0ZpMc7PfD/5dA/45DThz9fkeLtERdJHcTwjtaOI4Mnps4BRSvXaC/fhJSs\nAIk62K7I3eXXr6b/7bGjcUeYuQp/sWULtm7dii1bNmET7W9ck+5VYpeTkFCWw4Evu/D5HlpWs2gF\ntjzyOBkA4+jwSFNN3359Wmb+NEb6/Nh27514cux6PEQ+Y3O9nZHpUDl9hnx+fmVrQ8SlYzOo9PNz\nqNfrb2vLkZISKF+2DCUpqseaulZ0NMhmgr717tuGlZ/ugNmcTiVD2bWqiIqKcaWBL/DGlJGmkjiX\nvykykFb8GL6xjQzeU49j8y23YHPtk7aQe/ANWT8ybcWLzEaaZrcnhF2+bq15xn/4J+YSo+bJlDuz\n0ymuoZpgRiIWxE97Z/BeI93c7gEZeHY6pcxmDidvr281QgWwe/seuP33oZzOmPUurYwiLL3auqg6\nCqwzyXurNu0CTYI2LmRIociBe93qORCgBrYXm8kgAmm2pd56fsBVj/VS2TRbPnm76kM3m1JmgskP\nicivXiPTwtj+sBQD+1x4cI+fTjgROrwNu+Wuw4kjX2vDa1Pv4GhLI+p37sTOlH/12Nl8JO7+fKnj\na5rw+7asNxKxfkY668nQcqAveAJbV5PJEqHlixv91JVEW2APNm/eDJfrXhwgVPrWi21/9hym6WD1\npkfQoxlBQdCCD8ZWjRrnVdAeeWnjfw8rbrGxVlH5lwkwASbABFISyMv4WFgY35AqKSvBROdnseTL\nL8UJj8UdxR98+K6HqE+qFw/euQN3fPGTZmMpPpT96IzxsagifKBCvmSpx+xn8lWhb7E3X8cB2i1d\nGP9Kz6SDiit/w2+/LWPbThXqR4VFeK/sbuvdhoNqHX4ZarIHlRVPai8rFSlTWvF60XiP+TE3FVv/\nXbQU1Jv6H9aHsYqWYeuuJtDykcZWgGPN23DvHpnbWjz92P3YdKtqdJSgMD77KpL1a8+idTZpT5Vx\nxeefR8OH0xlZ8dFmr1cxtAEoKTbQhh/ZFseP0QhAPN8zOFTvwTW7exAeHwAtPqAr4z+B96ww9EqS\nAerJNMIpDpFTGDAaIK61HzBHwgwJZ/ejZNtiy45l+uwYlbt1UjG2zuh708e+gT1owtSJEzhh/M30\ne81gu//hO3H1zrxg1P3Y+H9r94I8f/OHrjIvq52SNZtA8670rfdJ9KYY8ImMHEINfYtD2WpnrZOR\nzBXX36CSR8TeayDPhsbwFN1ti4y6m8s9IKPNVicpQ9uiUTOf6lTir/22KlrtsjVKa/GZ21WFS4yV\n+TiVKTF27BCaW9px3PZdivKrlmuCNn1IH2lNLbUAf1rzgHnJvesOs5d99e330rNW39zVG5OetWeb\nt/KN1TTmo2+9uw9gKP4GxWsvvmBcpVkmns/in3oGtZFUR9M/4WBrm3bN2+pHT8+z2LQiipcf3I19\ne/ZgT8q/fdizPQBz/DByEgd2k2nj8OD21Wr8Wk9u+ngLrnXRqErterz5n+3Yv78ZdRvW4RDejxuq\nGuBuaECD9ufWRjiUkrW+D8exmejxYbthPXn7m7G2pBDXZYn/RzffYoijl4YdLJ2tcl6nkuJfJsAE\nmAATkARycS1T68E3+AeNb3yExVRwXAzQJHMpQl9LXs3R0H32yQPdWPUncSlGfcUQekNok8Tt6eu+\n8+Qzq600Q3692gR02zKz6jsAtLKLWrUyoM0DUZO4s+lgTDS0zUPR07TNIzHScNOkUssP2EFzCrpE\nt1//7kVO+VVzA6ppNaoBmi9Ckyv19fdpfkmK9WKVH3VtK/lrG1DCxqR1fUK5XDFM+RXLeSgdgjo8\nNf70JhZNflodyzbRU/liq7KTfsdqnoududw3w1BZJi4laoZV+aEwTmOOgX5tbvQyJ9OTfLlcpX9g\nUPR3eGi/VgxP27/HcFRfDtZjTOKMGvNcqEyDauJ7ChlDI9LnW8o25veYy6DK8jBzGbejykTGU3No\naKa6Oa8mLWO1Eg/Fc7ibBA3SiKbeI2a81HNMjBWOkpY1Vv7relk3qe822OYrwNEgAlNBawGCFPeW\nyliYfN/JDNNZOGnOjLZSGl2lb1wM+CVv61sv5qpLueqkErH9xn1Tgcq1T9X9mYD2XQb7nC/FO/09\nIAXnycmmS+KudX8TjzR++f1NdM8ZKyrJ+GqOhf0eCGrfO9KZplr9iWYU2JY4prpgW35W10ktRiFl\nOGiFwKC2XCx1PNDiHWlWJbNnxpzjoObLqYvqWavmOqjz+u9s8iafTaoeVdu+5xG1LYTgojlR2lw1\nY3EHL33TZFBbuthaqERqEhymRS4GaFJ/ur/BcXPCtuKfuAqYtfiIXg76va7v2791ouc8ec6Hfj4s\nArRymXpOtPbbFz3XQ6SPr1jTd2toio65slzSfBW7HN5nAkyACVycBDIbH9FR0WSuoJP8UNcf0vQi\nmaKVl2yrU9V6aR12n/VRKLmUrmpQS8zBLrrm7rYRD4tur229/DWfFrW3W+k5a2mZRWO5kKlAh9GI\no8nO2kRFp/BrHynLpkOHuWSv1NvlaRN9tiUy5YfH/LRccFyDvnuYVrmx8iHjecgAk4ZV9vzaP7Dm\nEt3jkxmNjwG5uhMtXaxe6HIJYpmeo9Yr1DfYRrv0xqHOnZYs9th0q/5n4TNXXJFxaaWs1iaavGxx\nTF7hKM1kT2rMxn/7S4az5Mj07UbKXOk12m1bMU3T2y36aald3WhT6d8oam9S++qXlnQ2PtaWWoZe\neazJzLSCkSafVjgz4tkqI+2m4eL8ovjKPSpN+ZuOcXx8p+d74mtxZUMNTFoxzdoovO16rVrqlibF\nW/VRpUvfi5Bx7caHrYzl4gP6/WBJT9pLWHJYr09SvuShLLGz0CkpIf1EeLTPtqqcygcZdLSSk30i\nbvZ7YK50kst8W6vp2fPvV2vsJtUBtUS2NAYdNJFZ5k1ONLZWlTLl2D8sR2VIU0W0e9m8TsdmGRvM\n+m2LC6hwDdRhYa8lRtCUP30eSqO2K+naqJ9WeHKoDiF1Ob5+Wst/55k3c9lzSpuWIXfXKqYuWurb\nmtyuLwQhv1dC6coVAzMsfqE0TP0rv/chO2Akf+OFoAW0G2+JrK1J55ZMy1CQyx/LyezDxgpcUraH\nVqhTNrkVx76XHF+7SsuSk6ecpp/23HU1ibjb3C6C95kAE2ACFzGBBTLv9LK7wLYIJifexOloIS5f\nsWxuXGYyEIiFpjFN7lJFZeUp5yCkjxpDaDqMYnJP0x3WpNtVAUpo8nziJtOIlZRRXmgOzeQ0wqdp\nAn3p5VhWFu9aEAtNIkgylpTLsOS5MjmBSEEJyimN3+U2Z3pFpjHx5gywqBTLKI+ptsj0NArKyhCb\nnsRUOIqSpQlzQzLJiIUwMT5FfvrzXXdk2U+TrzrNXZmPsiFf9pridZqbFRmCeLZ6JSaCoWQWqQAa\n52SZjY2f0uYsFFJdk4shJNfMDALyuhTB2NBrOEU+R4X0r/SKlVhRnli3c7sH8kr2Agqs3UMh6ZRV\niCXLyvN7rkUmMRktQ3niJKkY3U+h4qTnyFxiiUxPYPQNvR4tKr0CK1aUz2M9mkvNLVmS/XRsbu7V\naXomh6PFWDqv95OlO+8xASbABC40Aheo8XGhYWZ9mcAcE4idRE2hQzM+yP0Lhx9ZO8cJsDgmwASY\nABNgAkyACcw9gbwmnM998iyRCTCB/AnE0POVXebkcv/2ddjZfozGzHhjAkyACTABJsAEmMD5TYBH\nPs7v8mHtmEAKAjFMjgzTJ3SkA5PcovTvMqxelWEt1RRS+BQTYAJMgAkwASbABM41ATY+zjVxTo8J\nMAEmwASYABNgAkyACVykBNjt6iIteM42E2ACTIAJMAEmwASYABM41wTY+DjXxDk9JsAEmAATYAJM\ngAkwASZwkRJg4+MiLXjONhNgAkyACTABJsAEmAATONcE2Pg418Q5PSbABJgAE2ACTIAJMAEmcJES\nYOPjIi14zjYTYAJMgAkwASbABJgAEzjXBNj4ONfEOT0mwASYABNgAkyACTABJnCREmDj4yIteM42\nE2ACTIAJMAEmwASYABM41wTY+DjXxDk9JsAEmAATYAJMgAkwASZwkRJg4+MiLXjONhNgAkyACTAB\nJsAEmAATONcE2Pg418Q5PSbABJgAE2ACTIAJMAEmcJESYOPjIi14zjYTYAJMgAkwASbABJgAEzjX\nBNj4ONfEOT0mwASYABNgAkyACTABJnCREmDj4yIteM42E2ACTIAJMAEmwASYABM41wTY+DjXxDk9\nJsAEmAATYAJMgAkwASZwkRJg4+MiLXjONhNgAkyACTABJsAEmAATONcE2Pg418Q5PSbABJgAE2AC\nTIAJMAEmcJESYOPjIi14zjYTYAJMgAkwASbABJgAEzjXBPIwPmKYnpzA5OQ0YlLLWAjTEW1P0zky\nPYaRsel51D+CsaEhTFtJ5pzW/OuWmyrnix65aXvuQoWmJzE5HdLrVYpk1fUUl6xTVB9DtvpoXQAi\nIbpGf/lUndmmaU9f7SuZqfWQ9xdxoL9QRMXI4TdDvu2xY6HpuPvVfs2+n5nV2emo8m1Px9o/O5lW\nfN5jAkyACTABJsAELiQCORkfscljqK8oxJKly7F06RIULqhEZWEpnn41bOQ1hIOfWolrV34Ex0Jz\nnf0IevbWYMGCYqz84F34mUoy52TmU7cQ9tcsIN0y/9W0HCdt51OPnGGcXwGnT6KxqgKlS5Zi6ZJS\nFFbU4+iEreUdG0Mz8VXXF9Q0Yyyh5R6ZHkFnSyMqqD66ngrE52/6OMlfgOLSUpTSX+GCKrQfn4wP\nk3g02zQT5cnjLPmcOLYfVQvk/XUXPrp0KUqLF6C+pYdqTPotY75VtNg0jh1qRlXFAhSWLoHne8Pq\nSvJvFlZnoyOysDwrmcma8xkmwASYABNgAkzgQiIgsm5B4XVCAG4xGNYDjw90CCcgqtsGjdhRMdDR\nJNyeDhHMKu9sAoRFX5OTdHCJgZkc4oeHhb/rXOg2I3zEpra1TwSnwiI83idcxAWOJjEajoqp8UHh\nrYZwegdI6flmlIZLHIs0YX4np0dFg2SV9NcgRqNSoSnR6tKv+0eFCAd8etjqNqFVgegosXXExXdp\nnPXMRIN9Wh11e1uFr6HaFs4jxtPmd3ZpphabOZ9TA0a+HIZe0WHR4NDz7QukqOxZ8q10CA606XWR\n+Lqpfk6pCyl+s7HKW0ctjcwsz05mCuX5FBNgAkyACTABJnBBEUBWbcMDolprUHviDItgl1ugoS9r\n9LkKEPC5qAGZi/GhN3ocTbLBP9/blPDVNlmNWcXK5dMbyJR8dLhN1NoaxfOtUbz8c8kiPuVsR8Ot\nVJ61bSJIhkZ0ZlA0GYaGLON+anNPDXgNg6FaDEijNxzQ6yHVRb1RPiPGgzNiZtgvHIYBYzc+hv1e\n0TagmtxR0UFGoG7oVIuAYUQn6jjbNBPlyeNs+RzwynotdXOKbs1yj4o2g0VTv9LfLjlzvmXIYF+T\nIRPC25e9OyAbq/x1zF5+ZyPTToH3mQATYAJMgAkwgQuTQHa3q6IrcL0cygnsxtKqRhyf1P1eyj+6\nE4MPrJZXMD12Ep3te1G5oEZzu5oeOY725npU1uzFkc5mVGhuSRVo7hnB5FAnasgNRLoqVTV2IiRd\nM+qqUFlRgfr2IZI2gb015NZVWYHK+kNpXE9iOHloryZXhluwoAL7DXeazrol2OYndXdso/OV+Jej\nP47TTVf4JKVZqcXT9Ni5HyOaj0sMI0c7sbe+ChWU10P7d2p6Sjntx1PNZylDXcujWKYJTf1fwap7\n0PLI2tkxMkRHJo6ivtJw8aqowf6jY+oKelrqUVFRiSrJg9yXTlJ+ElkcGnknLbe8yoxKZcjgVNPY\njvbGKoNTBfZ2yjKUWwSd9bJsSBebJ5V+DVh6uxdTLfegvAAoKFmNbY/QOIhte+OlHuOIMhKVu9p/\n2rmu3tfotwTLyktQsvxqkPGRtP3/7L0PWJTXmff/9SpEMIFEEkyrSTExf2xWodX1Ms1q0yFt1jRd\nh7fVpjHQ1c1byNq8iu+vjS/u6m5xNxa7vyq8qQVtf7hRrAmkm6HphZsN0GL+4KbQOqaBjZBAE9gK\nFeKMccYMvZ7ffZ4/M88Mw8wgJhL8Hi94/pxz7nPfn/MMnvs59zkzb+UGrFk007w/gDfNiKz8ysew\nMGVUcf3GRNuMJjWenTOuu96s1ox7ZuWh6tAe/FieX6AQX7jD0t8uObbd8B7DNz+32ajgKMeqJWmj\n1sJ01m3X+ytvSx3UUx2PVSI6RsqMxzIRmXareU4CJEACJEACJDBFCCTiM/U0ht6kitlaiYRxhAJC\nApq7oVor1EOz1MxEQGutKQm+jYazXGttdWmFZiiJerNd29KqVRerMCp5M6viqDwt+nnozbUZ6uWo\nDIaL2Gc+An0NevkS41Wx1lKqwm+MsBXrTbijtEFCoQa0V8N0E2vlDXqhetNcWKvb4OmoNXU1wso6\nGspDuhdWay2N1Xr4DkSXkM1jUIsy82GUvAiMhowwospW9Sa7Tys3347XdPm0QE+tzsOlvyiXMJ9s\np9aisJqzAgaLIc07Jrfx9tnvtcZKmfnS39hL+F15rdYoYXfWDESNHp9nhKSpN/pqJiNe6qg2Q6Oc\n1dLnHq06OFNhznZZbKXN7JJGCWIzk+1+6PmxMgNaR0Ol5gw+e6qu0e9WidDxYrUZkhjtLNxOKeHr\niBJ+lgCzMezuqCkM9ovVP+qYX95gPr9iZ3CWyaG1BCdXYrCKq2OkzARYxpUZjR7vkQAJkAAJkAAJ\nfNQJxJ/5kJFLVu5j8HQ1oth8xbxj3eeQnrfTXPybhIUr1mLTNyQ4S09JWLqmFJtk8QPkzevAsxuw\ndOlKPLpev4HGoWexavlSrN3ymFlezaRcCZUbSimyQNi4khfjo1JS8pWQNSdYkKXeDI8gMP0mOTbj\nbXlJrt6Eq6t0WbibOTMTfx6mG9D5s53YK+/KG/9plbw7l/LzV+FntcVythv/fLgT81esw8PKTkel\nvJlfi+W5+fiflmly+8LSxBkd2/cPYqFYfeoV1NX/Wr3411PZM8fh85zWz3/hahcaWfjO3r/BlYI1\nnMVMXDUmt/H22VXILdqGMsWpuAEHNqxC7qrH8EJLma7H08+/Lsc0POwaQE/fM1hq6qpnRvs10o2q\ndQclx4GGPfmYCVl8faP5sEUp7259bYwZsdGFk69Mw03qgTCTe8dqbDvSa13ajhevTZvQ8NNRdkp2\nynw83qNPddjKNuPOZdvRHWXGyFYo6mnAe868n43aHg96Gkr164Mb78O2emV3Kj65WJ4jlbId+KSt\nb8ZkFVfHSJkJsIwr01CRv0mABEiABEiABKYWgYScD2Vy2rxc7DruQ2u1GR7j2oy536qT4BojBc7r\ncUvmlbUl1XRYES4zPqFGgOm4xvIm0uaEHI5ko5rHrB33kLkcTZoP83/3Awn1SsY9m9XgbY4MWVUy\nwnM85/UL405QtxH0vKbib27CNZZicjVnwRK9nNdjDNzSlaqSDFXFcVgmrpExvjcyLvD3hTM6i46X\nm2XSyIFrVdvvv4+5qxvQ4HLhiftuRtrcu6D8o73rFsuOTgVonLEcRsRRBIuY3MbZZ0LnalHGcdOs\nII3MJffpfWr1Y1JaJrJmRwsdClbRT5p2fEVcPxksd7iwYraiHsDZt804qfCi+pUzb5k4KImkJMxb\nvga7ntXQZQ7CVa3jb5yG90SVGSpmhrEV7McfJ9hmNJn2T8VoO8V17j2C3LnK9c5HZXVZKIRMwhyf\neK47ESNtZfz4r3aLWzZuvj4NWV/I058NVaipVclLQu7WJgwNDMB3fCuyrM+j3B+LVXwdI2XG77+0\ni2q3DQFPSYAESIAESIAEJjWBuM7HoGwDWqcWEOgpBUvXPo6htmrjcu9L6FETFwmkObep9SHWsFRV\nCIRdqTvmZIc6jZ383diSk4rFW89jX0CDhLJIeUvHWFVHcFYfY3vwnk3vlLkL9QGaXbtwKWPnhJeb\n2FVcRp4b8eWVK7Fq1SqsXLkCK+R8+UIZhqctxIEhN8ry1WzBQazOzsDOo/2jlRknt7j6jG7BmpCJ\nkhP9Vnf9JtyzLRstA8exar68hvfL9rjbm3HbMtWnFy/NW7EVLaXmG3/loAZs3qlqxv0+bp9om6Nk\neoLfLRLdznr86smdMqMlTlzZoyha+xhedJufLbl3vP+M0mwcKQW3L4qYMRoxHFAlxP36yeCnZGZm\nZvDFQGQDkaxeSVDHkMw0cdhj91+iMiN14zUJkAAJkAAJkMBHm0Bc5yMFA1idfyg4aFHmzsxebL5N\nlZkC2yA+FgrfGTWQsrsXycZVsrx6NcdHoSG+ORUiNUJnIendEjq1ww24GrZinnpza81s2AqnT7dd\nBKvK4CxHDc6a8cobw8G7I6fekiG7aBdRJ0HTgnKia2vLjnM6NqMUXKHCY5rX4XDQEZTrwSZZqL8H\nbx+rQsVrc/HYgePoaqzU355vPvhycFbKsisRbnYVx9Yn+LrcXhwYMjjmzJUpET35Ry12tlcYbq/C\nLc7dsrZ6CU796hD2769A0dLFqJN5jdvvWmYWFcMjujLPcWtITCAQ9mxaGb3H6lBRdQjttu8Nybxx\njp694jMyW3THg+jq6gr+9LywDtkTbDOaTDVDM7adV2L4HWNKLf3qGbpuaQvvRanlP/hDjoNlV/A4\nht0zrr3OLOLGO0PB0vqJI3eJ7hx21hubNegbPkhObFazcSoBHSNlzo3J8saEZIZrzysSIAESIAES\nIIGpQCCu85F89Q3yyvQROGVnKmu4PtjWpg/WUfw/cIstfEkHog8UI0aLkvHOb1+W32/hXStOyzso\nV3KnT2YsUmfgejlv3rgLRzq70VTxJX3HKpxuxy87jVaDL5VF9Ln3jfCo2rojOHHsENY/4pLaHrz6\n/BF0Dv5JzsUxebEZ7e31qDh0Qr/Wf0ndhffLIFMuNhc+EfzCuo6XauWOAyVrFurFzpteUKp+5ccJ\nkQV3p7hhcZI4MeITiSrnY/tk42YUwH2PluuNr8tehp2HjqCpvgo5s+6B44kHcU3yeWx8tNrYuSj3\nYaxXBt5wffDNtsViX0ufLiM6t2l6nv1XzD5TBdOlz35Sh069T4exf7MKHyrGo1/KkqMXhwpS5Qvz\n7kWTuUOaqmIlb2cdPr/4EeNy70asXv0Q1q3bCPU9gas+P18mc9ZAFkZLOog3T8mEyMlXjWfOWY2v\nLlSemJGGX/8VVO+r5PG/Z5xI20dKVmPjIw9h8Zyl+hcLjng78aRaV1JYiw3LM2WtRSbmzZsX/MlS\nO2dNqE1pOorM2HZmY8HdqrOUI90M/esPvX9Am/4QAQ98Qd9nTs+P/BXdbtm5Kq9I9slSyY3j6rMj\nfqI1J/iNe2+V+8N4futm/Tl1bduN38o3v8dmdWMCOkbKVJNxsfovMwGZuhH8RQIkQAIkQAIkMNUI\nxF0xL7vqyBhQc5o75MhQSRMGmqO42vwyOJ/WWG7bYSf7Ye17JQ/qZVQ5Z2mN1iLfuWDVQ3ax5mpx\nabJ43SyTrZU19mgdtSXBOo7iUv07HbLzi7VK16taY1noS+KyHYXa88dcuk5KPhylWm21VbdYaxsK\naI2lxk5awJe1fyl9OCgX2YVaY49PG3LXmvWdWnGh2inLobk61JZMPq2h1PreBfnywHKX1mDb1clR\n4pIS0VOXqzTUjtJLdpxyyU5URroYjLq0turQDlPK9lJXhy7eY30Bn7ApVl+8J4xb+lTbdhZO7fn2\nsbg9qv3ffwwxTqzPfmfbNcnqy0Jp19qHyqPV6DtWqe+vsO6ZOGTfJfXljHr/KVZhPw6twZIh3/8h\nkVKSn208P84yLYRU5am+i6xvcG+tDNljlSmpaR2z/yzNtAm0GZQRPEnEziHNVWZ+frId5ufEqVXK\nZyJqkl2iYtmt6gQGWrVik5ssExJGDpu8gNZQYnEr0bqka+KziqfjaJm67rFYyp5m47I7KgzeJAES\nIAESIAES+KgRmKYUlsFZjOTHsDcJM9OS4PcOw+uVRROpGbKTVOSURwwRCWaN+L3wBpKlrRSM+EeQ\nlDJGeI8uT0J65JVumpRVacTvl/KWTiPwDvuQOjNNvfgdI/kx2H8K56S967NmB2cJxig8aW6PSB8M\nC5sU2clLuiQs6fyke2aK3aEUySIWt1Ct+GdeVOWm46kH3GgquhXDwwGkjeKtwq6SkBazH+O3NDzY\nD18gFbNk8XqEyTErj8js2oBXhS7J7kuzx17jEE3IhbYZTVZC9+TZ7x9ScxSpouvMi/A8jmB4cAAe\n9XzPEtvDwI3Isz+AlFmzg89QQqxi6jhapmV3TJYxZVoSeCQBEiABEiABEpgqBBJwPqaKqbTj4hIY\nRkVOBjbe3wDt8RUXVzSlkQAJkAAJkAAJkAAJTEkCcdd8TEmradQECfjReWQfNqq1CTsqUXesO7iu\nYIKCWZ0ESIAESIAESIAESGAKEwgLxpjCdtK0i0lgREKEzt4si6QbcAXex1D3SfgXzwuG8FzMpiiL\nBEiABEiABEiABEhg6hBg2NXU6UtaQgIkQAIkQAIkQAIkQAKTmgDDriZ191A5EiABEiABEiABEiAB\nEpg6BOh8TJ2+pCUkQAIkQAIkQAIkQAIkMKkJ0PmY1N1D5UiABEiABEiABEiABEhg6hCg8zF1+pKW\nkAAJkAAJkAAJkAAJkMCkJkDnY1J3D5UjARIgARIgARIgARIggalDgM7H1OlLWkICJEACJEACJEAC\nJEACk5oAnY9J3T1UjgRIgARIgARIgARIgASmDgE6H1OnL2kJCZAACZAACZAACZAACUxqAnQ+JnX3\nUDkSIAESIAESIAESIAESmDoE6HxMnb6kJSRAAiRAAiRAAiRAAiQwqQnQ+ZjU3UPlSIAESIAESIAE\nSIAESGDqEKDzMXX6kpaQAAmQAAmQAAmQAAmQwKQmQOdjUncPlSMBEiABEiABEiABEiCBqUOAzsfU\n6UtaQgIkQAIkQAIkQAIkQAKTmgCdj0ndPVSOBEiABEiABEiABEiABKYOgaR4puzbty9eEeaTAAmQ\nAAmQAAmQAAmQAAlc5gRWrVqFmTNnxqQQ1/lQtZUgJhIgARIgARIgARIgARIgARKYCIFpmqSJCGBd\nEiABEiABEiABEiABEiABEkiEANd8JEKJZUiABEiABEiABEiABEiABCZMgM7HhBFSAAmQAAmQAAmQ\nAAmQAAmQQCIE6HwkQollSIAESIAESIAESIAESIAEJkyAzseEEVIACZAACZAACZAACZAACZBAIgTo\nfCRCiWVIgARIgARIgARIgARIgAQmTIDOx4QRUgAJkAAJkAAJkAAJkAAJkEAiBOh8JEKJZUiABEiA\nBEiABEiABEiABCZMgM7HhBFSAAmQAAmQAAmQAAmQAAmQQCIE6HwkQollSIAESIAESIAESIAESIAE\nJkyAzseEEVIACZAACZAACZAACZAACZBAIgTofCRCiWVIgARIgARIgARIgARIgAQmTIDOx4QRUgAJ\nkAAJkAAJkAAJkAAJkEAiBOh8JEKJZUiABEiABEiABEiABEiABCZMgM7HhBFSAAmQAAmQAAmQAAmQ\nAAmQQCIE6HwkQollSIAESIAESIAESIAESIAEJkyAzseEEVIACZAACZAACZAACZAACZBAIgTofCRC\niWVIgARIgARIgARIgARIgAQmTOCydz68g8MYmTDGqSlgxD+M/v7hCRl3MWRMSIEEK8fTM15+gs18\n+MVGvOjv7UU/n3OD/cgweru70S1MBr3qkz8ifAb5N+DDfzLZIgmQAAl8sAT8Z9DX74vRxvvo63wH\nZz7AQeCpzjfx2ol3cGr4/Rh6yP9Efh/ODJ7BmWFf8P+j82dj14kpcJJnxnY+RnqxMy8H06ZNG/WT\nW7AJ+5s6J7l5cdQb6UTBrAz8oH1iA+w4rVyEbC/2F4T6oKrdGyHTi0NF0ftp2rQcFGzaiabOcdoo\nbL6VmoE5czKwqf712O2PdGNLbki/aQWHoGsYJqM3QueLcSl227hYz2lO0SH4Rby/c3/Yc1t1wsZN\nHKthffApBcP0jGJrWL7NDruMcZkTXW9D/xzkFW1B3TFbO+OSHSo8MngMRcnpmDN3LubIc56cswV2\nBKGSl8dZ/7H9yEnOwNxbvomdf78Os9KTkZObjDkrq43n9fLAQCtJgARIYBIT8OGZtf+Cr82J/bNl\n75tj2zAyiB+v3YOvzduHvzv0TpRy7+Plf9kvbVRg0z3P4E01YPgA0mt79+N/FTRiz4rD+F8LKnDk\n96OdiZHBXjz57T1YM++H+Oan9+GbC36INWL7lvX7UXD7z9H3Aeg1GUTGdj6SsvDYs8fRUubQdS1r\n6YMW8KGvqwV39u3Guns+hYJDl9gB8Xej/siF6dD/wpNwiWWbdzXog9XJ0CHRdUjD2h80ItvMPB/0\ni63SaVhTdRytZU7rBspaBqBpA6guBA7u3ox7PpWBQ53j+ISNnMMpU9rxHn/s9pPm4fEmDyqt5vu8\nhoZhMk4Hdbt4J2L3gQAaS43nU8lVz+jxqjVIkfOU+WsRGGiByq3t8KBoYZoqImkYFUsz8MTrpjMS\npmcUW8PyLTsiZBiCE/wdTW/pr0AfKvPdcO3dgdV3zsWWI/0JyotWzI+D37wTeyWrsLpR5MqJewee\nfX2cTmg00R/Fe4NNuO/OdXAX1sCnNaHqQJM8G23IbRZjrr0aSR9Fm6gzCZAACUxBAgH5r2/B4/fj\nR69twIFX78cNysbZn8Ku7k3Y9+rX8eUvAu+ej2F4UiYKSpfqBTKmfyxKwStw17fX4DubZkjeDCRH\nKTHRW+e7X0Lpd8+g5Og3see39+Ozzk9hbsYVYWLPtP8Kaz5di+d+CuT//K9xqO/beFp+9rWsQIrr\nj1J2CANnw6pc+IX/D2j892iO2AWIvAiyYjsfpk7XXD1HP0u5UgZvSSmYPW85tu2p1O8dfLr1Er41\nHMb+r9+Cre5zF0BvGHU7dxj1Dpbh+d4PcN7tArQbVSXtmqDzMSrPvDHjamtwLQPvK9XwOxOLF1ku\nC9DQenKsqqPvpyxCeWsDamtb8NT6RUDc9pORFmreGMxFyhjdykW4k4TMWcbzqYTpz6hNalLmp5Hn\ncOLGOZZy0u+bPo+NbmDWjFSjZKSekbZG5ovzMkqGrc3ETpNwzfRQSb2/kmZjzWPG50rl7Pi3lyfg\nFIsTpjxrSefkOfirTbUoLa3G6tssDkbe5fK7/8RLkC4HTnnlz7mRkjIX4ftd1UDzwCh33izCAwmQ\nAAmQwIdK4E/ALZ9C0V9/CtfOvALTM6brLxORlYJrUj6Gq2ffgDVbP4uPx9Fp+qyZhtMS1flQla9A\nmsx+f1Bp5I/viugZ0EcZmZ/Cpj33Y/5VttbOvonv/tWrciMZRa8UYeWizOBLsKvnLcC2/7pfap+7\nSP83ncUz+Qdx4MTomRebRgmeXhxZCTkf0TTySWxatOTvP4pNVghOTgH2H+2VYl50Hq3Hzk15KNh+\nCIe255nhMDnYWd8ZEjN8AhVFuZJnhBDlbdmPbiN+B70njqJqSwGKqo6gfmeBXv+h+zOwTgZY7s3r\n5DoXdd3von6TqrsJJ/whsdHO/J0N2KjeeurJja1PvmJdmEcv6qS9gqIiFBQUSYhZk6nbNAnVKMIR\nXbFxlskrQH2vH73125EnMouKxJ7tR8wB5gg6j1ShINcKn8pB0ZYKtA/GMSRCa+tyerL1oQqFGi38\nlHp/YNN5TH2MMo8fbkH7Swfx0xbVh6OTv/cotuSp/pqG3IKNKDtoLxMp4/UEeBr1e48eQpEV7peT\nKxx2oqJiJ3buP5rAYDzSiTSujY+cX8K0MrB6tz4MxZ6NX0Ju3lb88P8UYGxbz+p6h/LfGFOGelaK\n1POiuHb348h2OZc+LthSF9VBTzadesNqQ8/k5JBHkj3jKv2Pkb//GLYXGJyn5eSh6ki3VPGi3nw+\ni+RZqqg7gv1bjM9VQcUR/GL7N7Hb7I6D6zbiiVevx9atazEfXaiSz6EVohb6jMWS9ws8bfssHDoq\nn0X9c2p9FgbRWb8TuSo8MycHW/YfM/9gxnqmbc/hmJ8v0wBvt9hmfObV34a8op3yuTB4RWdj1ot2\ncD2COdMUw3a9T5Lm5aOjbY38B2HTJ87nwvqbkBgHm1yxM7E6SvHY7Mbu+1BfjedZjIaK90iABEjg\nwydwFb4uA/XrYzScNO8vsO1/3Swl/oTOZ36BtRKmtP7ecgmjKscz7eFjU//vu/SwJj2Ma8l+1Efk\n25s539+BsnvNcC8p+8yLg/bs8PNhCZdaL6Fd0qaS/b///lfo1Wcp3scRCaNa95UOKX8G31su+Uue\nQmTgSeczLVDzEDMeXIa7PxllduaqT+H/VC7DbPUeWaUx2/sTel9sw4+/vQ9rV7lw5F+f0fX52pw9\nYqsxbdK4vhJPyRD33K6fS94eGb++j+i2Xoiss3h571NYu2QP/rfqA2VrIrM1WgLJXZmvielaaWOP\n5hvq0VobKjUJZdHvSXhPSMJQi36/slXd69PKnUaZmq7/1hori/XySk5+ea3WWFumyTt5/V5Nh0/T\nfG5NIoQ0FNZqHqnt6ag184u1Dt+AVlvqDNZ35BfqeQ+U7dGPjtIGbWBoSPNJzUqHkunQWpWQGKmh\nMFsrrnVpJdmGDoBzVB1fV22wTaV3YWl1obrUAABAAElEQVSpJpFFxj1Hta5nImU8NjnlbUqxAU1C\nlEw5ldqQ3PF1VBvXzmqxw6c1lDiM6+xyKa0KtGn5ZtvlbarG6GT1k65rpUtzVZYEGRdXt4pUIyWk\nT48r2MeO8rbR7XtagywcJTVaY02of+GoNNhEyEiE1UBLmWG39EfLkE9rM589w6YWLTDabM1u92g2\nQ1q5Q2SZz0NPa7WNSaPW5u7ShiP0jGTti8iPJmPQXWPqLf1aWKPbH+hrlLayNVdX9IfRrnelW/WO\nR6stNvtd+rqkoU8elUazH/K1No9HayzJ1tspb5Xn3fZcKT7Bn+xd2ouu0tC1s1Rztbg1nyf0DFW6\nPZqnrdIsU6x1Cdix5ZVr/T0NwedB7wv5LOQHPzvStqNQKykM6a7kx3umI9uL9vkSIVqxaVttz5DW\nWma2UejSAjHYRD4mgT5XiEeQlUMrd7mDRRP7XFwAhw+BXajvy7XfX8CzGITAExIgARKYTAR83VrJ\n7O9rq7/6gnY2Qq9A32+01ZL3g6Z39Zz/3L5brp/V/qCurHqSv+n7v9RcVYf1sqtn79ZeMoetHVV7\n5d5e7YRXyg+9rv2tlP1pq5J1WvvXr0qbcu3qOq+khSdfj/ZdyVv9t626Tmc7WrW/VtezD2td8l95\nIHBOe2n7D+X6h9pLvV7t7NC5UWOX//y+yhfdqnrCZUe7itNe15F/N9tXOv1S+8+mX+q2rP6iwexs\n16/1/L/d/hvtj0NezR/D1vHKOtvbqtvxgj4sHdB+8Od7tf9UPOOkcc18bLtnLlIz5uLO+x5Bs7Mc\n7j4PHlueKf/vGenYvn9As4qwP/UK6up/LaE6xv2yZ3qRW7QNZSoCqLgBBzasQu6qx/BCS5le4Onn\nX0fnz3ZKfHo2Gv9plV4tbf4q/KxWhh3y/vafD5/Gqq0HII6F+BXlcB2ownGfD4c3LMNNcit91ixk\nzpwpU3NpeNg1gJ6+Z7A0VnTJ8FFs3puNR1etxDfWy5BeTy488YxtFkbupcy5GdYyBkdZC6q2bsX2\n4MIGY+VFImXSbHLUG00VDnV3ntWuRLLJnUDADGB0HZewkBR83rlC1wruJrwZmrww7iXw+1xvJzo7\n3zZCTaT8W8d/C1mKoadE9Em5/gZYwUzpUdrr/PkT+noZ6RD8/XfWIHfNDlRbsMzykTISYfX2b142\najvzsGRmCu64+x5TWj4efXh5cFoyikoJ3cpavDgYvjZ/8V1YtHAerolja6Qd0WRct/BrcBWrB1zS\n3qdxUiasvJ0vyBqD7Vg5L9bDaFR5JDtVZiPSZVamGch2oryhA4+vmI32n1bIZ0qScxnukLi2rAV3\n6hU2Hj6KJNtz5ShpgGfIjdJ8J4rXfxZLv5QH6wlz3peHlcsX4uShXdAnp+Qz9DVZ/5J2x11mmd3Y\n+XRn+PMeJm8pMq+fFXwenGWt+mfh76zPTnYJepqq8Pi/fFdfX6MUPB8YiftMJ/I8nDj8z8YMjrMS\nX86aiY/fnKPbL/PR+E0MNupTZk9Js1dioK0mqJ+R14yNzmzkyOyjKp/Y52L8HFI+aHYRffWJCT6L\ndm48JwESIIHJSiBJIgWuE+XmfVLFM/0JgRS1hqMP/eabdxU3ct0mJ37w7buxsvABlD7+cbkTQN2/\n9coxPP3mJ834o/qPZaALR37+FmCGSP0sStnuf2vGaxIuVfzdpbhSxFw5fym+W3GjnL2Nnzz1DpKS\nUpExS0WfJCM94ypcOTM1YuxyFh3PG8sFPp1zbbgiUa7itTfvL5cjd7ZU/LPPYN+eu7HEsQwrvhgS\ndOWc65Ahlymz0iWU7Sq8HsPW8crCGQP2S/Vvyv+jmfibJxYhNRBqe6yzcTkfle4+WdSsPABJadNx\nw2z7oMqLjpdlmOR0QEf5/vuYu7oBDS4Z1N+npseScLVkOG6apVdXvzKX3KcP7j3y0PS8pkJhbpKY\nvmA25ixYol94PUYnTddHwWZYSooqaFjosS08SkrLRNbsmSEhUc46Xf8At+MepHq9SP/0smCJg+ue\nROQjaVmYnqIeMZWsO4bTYL+TSBldhPyyfA3rOu3Wr6C1oRbVNX+Bl6u2YOmdm60seXzHn5Y9uAGP\n7TqAgZZyvbJr9yO4pWB/MPwnZIUhO1Kf2C3K9qTdfUaR7DthjK0TeNqkhtXuWKyCUUfSqbrEc1bn\n9uGcL7ZW0XOVa2dLIyE9z8vmCReUospIwr1Fm0xxLtQ9346G6h0oKzI/L3EaKhFnw+PxyI8P2vFn\nsWHFfKnhFd/TZdSUcKFUCWu65SG1hFxSU4/el0GeWbOQNnMhth54FruKliLJpqN4AlLBi7YXdddD\nvPXp5h/CZPWnVk+tEiam0pjy9FzzV4rxRAZJ3pSl/2FD8pVBB0WXlcAzHWwv6ufLi5efNXUWgaq9\nrFU7MNDXB8/3HfhdHDamtvphxO/FzEVr0OTrgatcvdQIJfe2+/C0OSdu6WPlxvxcJMjBkqUfE6yT\nyN8DS9f0yL6X3p3IsximLy9IgARIYLISkLUUe/o24ObXj2D9nF3YveuMaBq+gDwlPRTSNP9LC4z/\n887LupKw5MObrTLO/OwcXKPuvz+COV/9Ar4lb7y/9ZehMatR5U9453dGO+nmslF1/+N/doOe7fck\nsq7iKiy892q9/G+PD+jHsX8l1t6VWYYEY8TzMdx2l8gftqQa9vr14VR8WxOXJY7XTbfgFmnmtS0/\nk1269uOVGbdjwUyr3bGP43I+zgcysPSxg8YMxsFH8PktxhvDMPGeG/HllSuxatUqrFy5AivkfPnC\nsTUx/gMdwVl9HOjBe7ZXlilzF+pvZj1hDUz0QuL01omTdHoX7lu2DPcV7rEJ3IEnm2LE+NlKXvzT\nc+hucWHdQ6uxp/c2rC8036JfYEPWwDpziSP4BhyuZ2Ft8HSBYs1qfvzhHWGo0rUfR7rxtBvXE/y9\n8GvboE8gND+C3XX1qCh9xJBYvF7e/EcXHjhvTuno2RHKjAzgd80eWVoWLUWUjVYk7r2QjJT59xuf\nDamzw7kYDx0swQOLxlA6Qm6WLJpPk5mNtDSb9y1lLNcL2WXo8QXgUw6KzPoF2tYj7FMVLBgh2H5p\nYbI+UCmpuMF8zNzvDIWvp0lEniXbkmddB48X8Zl+64ypXwoyZ89GmmAPqhiPjejT9n0nvqM+2ylZ\nWLlhFzRfH2pL84Oa/vHCPNtgff1kTA7hxcKuxqwzDnZBECHJE3kWQ1J4RgIkQAKTmIDsurRrSYVs\npjKCzb3fxo7H1TxI8LWYrrgx4DZtSJsJNUGgZkSiJs81cPzVYqz46lLc81efxt1yvmShfZW4qvUn\n+HQB8v+xranpc2/QB+Fjyo5oMD3TeIn3zstv472IvPDLC23Pply4QOMqIVutijFkXZWFx19bjXu/\nqOz5I/6/FZX48YtDVsUxj+NyPoxOnY3HXmjUwxfcO+7Dd+rU4leVknGFGmc1r8Nh+5cJyBaXuTl7\nQg6YXtb8NfSWHgaSM3c2bs9Ro6BmvPJG0FXDyCkjP3260UlGrdEbzYbn++H12zwYe3tyPnzsScj7\naAwdP47j5o+ntTxYats//Sy6rsES5kl65I0o11HLGIPVoEVmmWMV6/DQjoMipBD7Hl+LFXcrHiql\nIbh23LiB6cmhAa95a9TBeret+swac0KWcM0INmxVia6PlRvtOD35Gsz7M6eR1dyEt/QGbIKj2h1N\nku2eVWfmrXDkqvv5uPr0EG7Id6G1Q7Z43mWE49lqBE8/ftsdwXO/3XtVd729Es6XHsVu+QOkf568\n8rwEq486icc6XEYmHtgeeqvurHwQ5suIUXJH3bDhC+Wl4dY7zOfAXYPjp2TsrBwU8U863L16qFCo\nbLyzNMxbbM7CWKz9p9HmNuo5F91u7CgST0y0fEteRN54numIqjI7o+6k4hM3mTli/yu2HenkuxNx\n8zjYzJCXV7sr/iPELGW2hHKWoSTYcORnavyfC0PnoMDETj4IdnrLE3gWE9OcpUiABEjgQyAQmrmI\nbKz3317AKzJpX+RyIkv9ydZn+eVo+3OeEtrDRXau/aNsuSIz6KN2ffwYkpWP8btX8dwJW0TE4GtY\nv6RRlo3b0xWYu1BmFWQnquNvmPFdcjUyYMhOidhda6xh+7yv/oWxG9d/vIpnjoW3YLXW23wMv+n/\n2LjaG3v0K+MHXbfEbY0vC3jvWCOe/N0s/M/9G/H9g5/RZ5aeP3Qy9HLQMibimJDz8e6ZPr3amffM\nUVpmLp4y12vsXn0L9utf0peC+x41BvHrspdh56EjaKqvQs6se+B44kHjLa38R9v8kzpz1b9sk7tZ\nDWCL8eiXsrDw/nV6LP7mwidgjTE6XqqVfAdK1iyUox8D6i1h8+hOcr3YjPZ2eUt+6JjsRJSK9NR7\n0WTuhiM1bKkf+wq3AaV3h701Tlt6v2hhJnnjvu+oOfshT01o4G7kn7PesktYkOn8jqvMTw78HCeO\nHcL6Rw4aAt/qxcnBd3Hqv0+bCpzEC/V12B3cOsqNBgnh8freC7bTfsLoD7NC8HDuTEjbgYEhue/H\n0X+tMtdmqO97eBQL1Yt1m13R9RHLAnanRepEtH/bMt1DkAwJMfr3TvS3H9Z3HpMbssCkFz3qC/wi\nZdja1cvJr0ie3XXfhnO3WvZwI677RAYyrroCp95w49gJIyzIqmc/zsr+XDCWf/PnNuCo1ffeE9iS\ncY9MGpQYdqtKNp1qDv9IvhwxXfYSfzfIVpcbYau9jp4fTYY5pZTlKDDXCWXjkb/6lF48+q8RDA6E\n+rH3LdVfo9NnVj1s3nTDuXIbmtqPokKe8exdv9EH0laPeyLe9qhKVp4l9bPfMJ9y11N4Vfn43gG8\npWc6sOEr6jMWqpOIvGBpVycG1F8p4RayyB//mY77PKjwoWpdL9nTDs6562T3jk4cq9uO5PRv49r/\nEZuNWdE8zJFH9SF8S3bisnxNf/ev8QuVm12OB9UMlU2fMT8XUjySa/BOVA5G8+OrkwA7mx7R+kq1\nmvizaOjI3yRAAiQw6QjIoF7/39/zp9DLI1NJn4RHqdT4zG/Reewl/L/yvRrqD/lr//FbdP/hff1v\n/dDvPWY9H478QEVs3Iq1f2msVR6xZo2TrsDdf5utROGpFXvx45/+Fi//vBFrP30EC3YthXI17Gn+\nl/7cGGQ/2og+c4Te9dIJKTIDBWusV47Jcn0Gp6L/1y5rSm7F5oPGGOG5r+zDj3/eGxywnx/+A478\n0x58J78DyakfQyLtBcxZdCN24n288bKEkfX/UeYiQumdl/8Lr7WfwB9umavfHMvWxGW14adNw3hu\n01HdQctyfB6yVFW+kyUddp8vpIHtLOaC9ECPVuY0dteRKpr6ccrOUtauSS1loR2oims7RFRAa6u2\n7Xok5Utd6r5KHk0WJOsyLFkyHNZa+kL7Fw25a80dlJxasexGJY6H5uqQnYICXaJHqK6jsFIzqgU0\n+YI5U6ZTa+wb1GryVTmH1jgQkmu1b+QZcgqrzV1uZJctCfOJ0Ctbq+2yZFl5sjNOdWiHLp1FZavZ\nXqwyqp2A1lJu7Bhm2O40dwrK1pz5cl7q0v6rwbY7kehfWmrjmP8jrUK3y2onW6tRWyoEk0er0XlZ\n+eFHZ2Gp1tAmOycFUzx9Dmv7w9pboK238ZfRmrTvHdXXhm1ik9Opldb+OoLNAm1jmIxoPN1aR01h\nRF+EbMm3+ixoR+jE19OiSaTaqLqOklp9N7FQyXBWjtLntAMxbV2gfStMb/PZsPF2lDaG7WTRWCp6\nmDt+hdq1nykdRuuaXVwb/GzZS3fYd65Sn0P1/MvnKVJGiI/khdkETcmWT5Imi67NXasc5tGp1brV\nNhXjkefQKmvKw3e/qm7UaortfyvytX8P03v0My1ffGjrr+jPg+LQ01ge3KHMeMaKtVbz8x2djaoV\nntrULlmO/KDO2eazkl1Yrqk/MUYa7+ciEQ4PaN97MNzOi8Iu4vkJ9b1li3GM/yyGl+cVCZAACUwW\nAj31z+o7KaldofQf2UnpBdvuU2q3q01W3hef1Rr215nlD2snZIepX37/gHmtdsESGX/971qPPnQ6\nr730/Woz7/vaX3/xgOxKdU47sd/aEcto74f1b4+J4l13q9n2Xu17f6vk/1B7oeOclBfZFQdCu0/J\n/X1H/ntsOR2/0b73RdM+yxY5bvq7F7U/2IZ5sdr75Xa1a5ch47sVv9Z+GdzZ6/va3/7drzW/NmLu\nvqXK7NVe6hvL1vPaeGU1Nj5vtC0Mv/fXwuHPD2v/2Rdlh7AIAtPUtfyHflHTiHcYwxL6lDIzU4/N\nNoR7USVvmZ96wI2molsxPByQBbJp9tkxUwc/BvtP4VwgGddnzU4gFGQE3mEfUoOyVNhVkoSm2Obd\nLqp1Fy7MPzyIIZnRy5idiZQRP/zyhY32CP8R7yAGRPeMTLVzl7xPHeyXMmmyk5e8lf0AUjx9Emky\nTIYs6lX6TgT98LGdyJDF9tnyxYA3pRsaeN5yoVmFB8kb6qHjG8JmrcJ1lO9Q6TyJ0/IGO1ntMvHx\nucjKtBO2SqtnZljeisj6gQtmGy5juLsdx04GsHTFUll4lYODd9ajamWW1eDEj8J2cEjen6dmiM7R\nbBpHExKz1N83JO+HEv2MjUN2RNGL9kz7h9F/Sl7tzEjHbPl8hKUE2Ki/SSMS75si78CGB4fhOydv\nhdKvx+woLMOe6Sif07C2P8CLC2X3gT+LH6DNFE0CJEAC4yPwPt6T6KcrJUpCpRH/+/Jd2Ma5cUPy\n5d6IrPy82iyj3x/j18jZszjj+5OMX6/GlXGHke/jdL+sRxz5GK77ZEb8t/1jtKlunxkcwrsyu5OU\n/DFcMysD+vdEjyo/kfb+hPeG38d0285b47PVrkwUWX4f3pPx7dUiP5H0gTgf0RseRkVOBjbe3wDt\n8RXRi/DuZU5AfQlgqizUlm9jH9KwIjjG7Mf2aXOwTbZpDuxaEcVhvdTYzGdbHKSSmmq8/lAjtvsO\nhEK9LrV6bP8yIsBn8TLqbJpKAlOSgPrSPqaPNoGn+74d04C4fl3M2gln+uXbu/dho3p77a5EnfNW\n/OXSecFtPRMWw4JTnEAKlq4uBQ5uw30ZOcgvzsV15/6Ipr0HZWvkUrRt+cIkdDxUl6Ri9p1ykOd7\nx0PrUOzqouMxxZ/UyWsen8XJ2zfUjARIIBEC8QauichgmclN4MOZ+RgZRNOzv8LZq66Sia/3MTR0\nBb74tRXI/JBcn8ndBdRuFAEVYiNhQadVaEzyDFwrX9I2+4LDo0ZJ/4BueNEr330ykno95sX5npkP\nSAGKJQGTAJ9FPgokQAIkQAKTl8CH43xMXvupGQmQAAmQAAmQAAmQAAmQwIdEIKGtdj8kXdgMCZAA\nCZAACZAACZAACZDAFCZA52MKdy5NIwESIAESIAESIAESIIHJRIDOx2TqDepCAiRAAiRAAiRAAiRA\nAlOYAJ2PKdy5NI0ESIAESIAESIAESIAEJhMBOh+TqTeoCwmQAAmQAAmQAAmQAAlMYQJ0PqZw59I0\nEiABEiABEiABEiABEphMBOh8TKbeoC4kQAIkQAIkQAIkQAIkMIUJ0PmYwp1L00iABEiABEiABEiA\nBEhgMhGg8zGZeoO6kAAJkAAJkAAJkAAJkMAUJkDnYwp3Lk0jARIgARIgARIgARIggclEgM7HZOoN\n6kICJEACJEACJEACJEACU5gAnY8p3Lk0jQRIgARIgARIgARIgAQmEwE6H5OpN6gLCZAACZAACZAA\nCZAACUxhAnQ+pnDn0jQSIAESIAESIAESIAESmEwE6HxMpt6gLiRAAiRAAiRAAiRAAiQwhQnQ+ZjC\nnUvTSIAESIAESIAESIAESGAyEaDzMZl6g7qQAAmQAAmQAAmQAAmQwBQmcNk7HyP+YfT3D38kuzie\n7vHyP5JGU2kSIAESIAESIAESIIGPLIHYzsdILyoKcjBt2rSxf/Kq4L3o5ntxqChOu6JTQVX7+FsW\nZ2PYO2LUG+nEt1IzMGdOBjbV945fVtQaieletP9E1NoJ3wzT/XXsLwj1UVW79EhYvs02u/0JN8aC\nJEACJEACJEACJEACJDBxArGdj6QsbDhwHC2lDr2lssY+aAEffD7140FXSzngOgNzKD9xbYIS0rCm\n6jhay53BOyWuLmiahoC039PmQr7k9J0PZid4MoyKpRl44nXTXRo5h1NmzeM9pxOUEa9YLN096Gis\nRLaIOOkJxBMUOz9Mdz/W/qBRl6sqnVc9EpZv2RZhf+wWmEsCJEACJEACJEACJEACF5VAUiLSrrw6\nXS+Wck0akJSCFL1WCuYtX4cW12+RkJBEGoooM2O6tGemrJtmGWcDJ+G7aSX2tBQjveE9KzuB4zDq\nNn0eG91A5YxUo3zKIpS3NiD/7Stxd96iBGQkXiSa7sMdryP1c0X4WfVTuOXtCTofkbqPtOvOh5hn\npMh8RLHfKssjCZAACZAACZAACZAACXwIBGLPfJgKJJtOwPTkZJtKndiUdwifXrkcuoswfAIVRbkS\nnmWES+Vt2Y9ufYJhBL0njqJqSwGKqo6gfmeBHsK1/cgbqN+kym7CCb9N7Bin582xettP8vHka8NI\nW74FbQ/Ol9Je1InsgqIiFBQUYX9Tk6nHNOTkFuGIroQfhwoysHq3MTTfs/FLyM3bih/+nwI8frgF\n7S8dxE9bXke9KadI5FTUHcH+LXm6rgUVTfrsjr//GLYXKBslxCknD1VHusfQNvy2pft/bL0TVW1e\nzPvqHrStuU0KjaDzSBUKcq0QsxwUbalA+6ANiLdb9DCYKbZ5RTsl/13d5pDutrAqvemzEflvjGm/\n4lak2OUVoL67H0e2y3mR8NxS9wGE04Vz4RUJkAAJkAAJkAAJkMBlRkBCmeImd2W+Jli0/LIarcHl\n0lyuWq28OFuDo1LzqNo+t1Yo+Sis1a89HbWahBZJnWKtwzeg1ZY69fpKhiO/UM/Lr/61VulQZRxa\nqy5ktBpWu6peWYNbczeU63LK24bCCvu6aoPyVdnC0lJNAraMe45qXaee1mpTJ2jF1Y1am7tLG+5x\naRJQppdzlLdpkXKULP0nu1wbGmg0y+ZrbR6P1lgi9kt+eWu4LpZikbq31ZYY5W26+zqqDfnOas0n\n/xpKHMH2BpQgX4dWbOpQ2zOktZaZ+YUuzRuhu+Zr0yQULdiGLyI/mv2D7hqjPVWvsEbnFOhrFE7Z\nmqtrjE6xDOSRBEiABEiABEiABEiABMZJIKGZDxlk66mv+zW4jx/H8eMdePV4MMAHnT/bib0S9NP4\nT6v0WZC0+avws1oZNmM3/vnwaazaegDiaIifUQ7XgSoclzUjB9YuxsOuAfT0PYOloegqo6Eovzdv\nzkf2fRuj5AApc26GtTrEUdaCqq1bsb3SuqOvgEDW4sXBNRHzF9+FRQvn4Zrrb8AcU6IKLAuTU9IA\nz5AbpflOFK9fiq6fVqBZlXUuwx1pachacKdec+Pho3HXvCjdF6/eoZe3/woEzEUrruMYQgo+71xh\nZLub8KbMGp04/M9CUJKzEl/OmomP35xj5M8QXSN0NzJCvyPzo9l/3cKvwVWsVqBI2vs0TsqEi7fz\nBbgLt2PlvAQ6xajJ3yRAAiRAAiRAAiRAAiSQEIFxLdd44NFtKFqYYgj+zv3oW/qCDLxH0POackRu\nwjVmliowZ8ESvZzXc04/Tleje890/RwpRsGktExkJTjGrTx4TNoews6cObAFJRny5LclJj3lSvOe\ndUeWqag7I6E1Fudl0boM381y4QerVnrWLKTNXIitB56VAl7sz3MZBV2PIHXaI6FKTT16eNLM0J1R\nZ5VPie5zT6Io1RzomyXSbv0KWhuuRccQ8HLVFmx/JOSgJIvUl589GJSltM9atQMDfd9ByqzZSJI1\nHuNKUe1Pwr1Fm8RHXCeiXKh7vh0LanegbJNnXKJZmARIgARIgARIgARIgAQSITCumQ9j0G6KlQXN\nrhc3YKY4H2fVWB4evGfb9ipl7kJ9R6qLNYw12p6NB7YW4913jYZ66ytw6IS5c1Ui1gbLJOBzReyk\nFbzMLkOPLwCfxwOPzOAE2tYLg9jp/DkBlLIQf1OSjT+ErZE/h+4WF9Y9tBp7em/D+sJw5yQo9a0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TvefyZ4nthJCm5fFDFjNBJaK+N+/WSwt2dmZgad3kjZkaxeSVDHkMw0LIzDMlGZkbrxmgRI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\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rx8X8LF/fxeTGuCRwqxMIf3+vNJ2rv35XF1neNob++pdQ3zmDBpkECNthvNgyutLZUR4J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\nsua0wTUiVRlEGw01kqd4l9TTWCEVljVKY1pBAXUPsV1Lt9Dv0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LaKZWV7Ge2Kfymkl5n7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\nAABJRU5ErkJggg==\n" - } - ], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you look at the clipping of the html file above, you see tables with data in them. Eppy has functions that let you access of these tables and get the data from any of it's cells.\n", - "\n", - "Let us say you want to find the \"Net Site Energy\". \n", - "\n", - "This is in table \"Site and Source Energy\". \n", - "\n", - "The number you want is in the third row, second column and it's value is \"47694.47\"\n", - "\n", - "Let us use eppy to extract this number\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy.results import readhtml # the eppy module with functions to read the html\n", - "fname = \"../eppy/resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html\" # the html file you want to read\n", - "filehandle = open(fname, 'r').read() # get a file handle to the html file\n", - "\n", - "\n", - "htables = readhtml.titletable(filehandle) # reads the tables with their titles\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you open the python file readhtml.py and look at the function titletable, you can see the function documentation.\n", - "\n", - "It says the following" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - " \"\"\"return a list of [(title, table), .....]\n", - " title = previous item with a tag\n", - " table = rows -> [[cell1, cell2, ..], [cell1, cell2, ..], ..]\"\"\"\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The documentation says that it returns a list.\n", - "Let us take a look inside this list.\n", - "Let us look at the first item in the list." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "firstitem = htables[0]\n", - "print firstitem\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(u'Site and Source Energy', [[u'', u'Total Energy [kWh]', u'Energy Per Total Building Area [kWh/m2]', u'Energy Per Conditioned Building Area [kWh/m2]'], [u'Total Site Energy', 47694.47, 51.44, 51.44], [u'Net Site Energy', 47694.47, 51.44, 51.44], [u'Total Source Energy', 140159.1, 151.16, 151.16], [u'Net Source Energy', 140159.1, 151.16, 151.16]])\n" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ughh !!! that is ugly. Hard to see what it is. \n", - "Let us use a python module to print it pretty" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pprint\n", - "pp = pprint.PrettyPrinter()\n", - "pp.pprint(firstitem)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "(u'Site and Source Energy',\n", - " [[u'',\n", - " u'Total Energy [kWh]',\n", - " u'Energy Per Total Building Area [kWh/m2]',\n", - " u'Energy Per Conditioned Building Area [kWh/m2]'],\n", - " [u'Total Site Energy', 47694.47, 51.44, 51.44],\n", - " [u'Net Site Energy', 47694.47, 51.44, 51.44],\n", - " [u'Total Source Energy', 140159.1, 151.16, 151.16],\n", - " [u'Net Source Energy', 140159.1, 151.16, 151.16]])\n" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nice. that is a little clearer" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "firstitem_title = firstitem[0]\n", - "pp.pprint(firstitem_title)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "u'Site and Source Energy'\n" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "firstitem_table = firstitem[1]\n", - "pp.pprint(firstitem_table)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[u'',\n", - " u'Total Energy [kWh]',\n", - " u'Energy Per Total Building Area [kWh/m2]',\n", - " u'Energy Per Conditioned Building Area [kWh/m2]'],\n", - " [u'Total Site Energy', 47694.47, 51.44, 51.44],\n", - " [u'Net Site Energy', 47694.47, 51.44, 51.44],\n", - " [u'Total Source Energy', 140159.1, 151.16, 151.16],\n", - " [u'Net Source Energy', 140159.1, 151.16, 151.16]]\n" - ] - } - ], - "prompt_number": 7 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How do we get to value of \"Net Site Energy\". \n", - "We know it is in the third row, second column of the table. \n", - "\n", - "Easy." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "thirdrow = firstitem_table[2] # we start counting with 0. So 0, 1, 2 is third row\n", - "print thirdrow\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'Net Site Energy', 47694.47, 51.44, 51.44]\n" - ] - } - ], - "prompt_number": 8 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "thirdrow_secondcolumn = thirdrow[1]\n", - "thirdrow_secondcolumn\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 9, - "text": [ - "47694.47" - ] - } - ], - "prompt_number": 9 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "the text from the html table is in unicode. \n", - "That is why you see that weird 'u' letter. \n", - "\n", - "Let us convert it to a floating point number" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "net_site_energy = float(thirdrow_secondcolumn)\n", - "net_site_energy\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 10, - "text": [ - "47694.47" - ] - } - ], - "prompt_number": 10 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us have a little fun with the tables. \n", - "\n", - "Get the titles of all the tables" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "alltitles = [htable[0] for htable in htables]\n", - "alltitles\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 11, - "text": [ - "[u'Site and Source Energy',\n", - " u'Site to Source Energy Conversion Factors',\n", - " u'Building Area',\n", - " u'End Uses',\n", - " u'End Uses By Subcategory',\n", - " u'Utility Use Per Conditioned Floor Area',\n", - " u'Utility Use Per Total Floor Area',\n", - " u'Electric Loads Satisfied',\n", - " u'On-Site Thermal Sources',\n", - " u'Water Source Summary',\n", - " u'Comfort and Setpoint Not Met Summary',\n", - " u'Comfort and Setpoint Not Met Summary']" - ] - } - ], - "prompt_number": 11 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let us grab the tables with the titles \"Building Area\" and \"Site to Source Energy Conversion Factors\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "twotables = [htable for htable in htables if htable[0] in [\"Building Area\", \"Site to Source Energy Conversion Factors\"]]\n", - "twotables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us leave readtables for now. \n", - "\n", - "It gives us the basic functionality to read any of the tables in the html output file." - ] - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Using lines_table() to get at the tables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have been using titletable() to get at the tables. There is a constraint using function titletable(). Titletable() assumes that there is a unique title (in HTML bold) just above the table. It is assumed that this title will adequetly describe the table. This is true in most cases and titletable() is perfectly good to use. Unfortuntely there are some tables that do not follow this rule. The snippet below shows one of them." - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy import ex_inits #no need to know this code, it just shows the image below\n", - "for_images = ex_inits\n", - "for_images.display_png(for_images.html_snippet2) # display the image below\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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vtyf71XpERj3fr1beH6ExoRY3g22+FIxSVHaZZvnfotAFC0K+kFM6Zwx834p7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mvzJEL7uTvlWi1XPEOl9dlkdXHWvPBw1Abtp29DdWRsT9uicRVseR9fRP5D0KazeHrltdAWAl0Y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\nMsnMj5o07+h8P0cnj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LbWdfAqVjlfwszY0tLSpkyZwhD096T+0hbETvnPsz0BAmj3BFVo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\nIPA4BP4P6Bjnf1iX39wAAAAASUVORK5CYII=\n" - } - ], - "prompt_number": 12 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that the HTML snippet shows a table with three lines above it. The first two lines have information that describe the table. We need to look at both those lines to understand what the table contains. So we need a different function that will capture all those lines before the table. The funtion lines_table() described below will do this.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy.results import readhtml # the eppy module with functions to read the html\n", - "fname = \"../eppy/resources/outputfiles/V_8_1/ASHRAE30pct.PI.Final11_OfficeMedium_STD2010_Chicago-baseTable.html\" # the html file you want to read\n", - "filehandle = open(fname, 'r').read() # get a file handle to the html file\n", - "\n", - "\n", - "ltables = readhtml.lines_table(filehandle) # reads the tables with their titles\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 18 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The html snippet shown above is the last table in HTML file we just opened. We have used lines_table() to read the tables into the variable ltables. We can get to the last table by ltable[-1]. Let us print it and see what we have.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import pprint\n", - "pp = pprint.PrettyPrinter()\n", - "pp.pprint(ltables[-1])\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[[u'Table of Contents',\n", - " u'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", - " u'For: PERIMETER_MID_ZN_4',\n", - " u'Timestamp: 2014-02-07\\n 12:29:08'],\n", - " [[u'',\n", - " u'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", - " u'FANGERPPD {FOR HOURS SHOWN} []',\n", - " u'FANGERPPD []'],\n", - " [u'January', 102.637, 12.585, 32.231],\n", - " [u'February', 147.054, 10.5, 24.225],\n", - " [u'March', 286.835, 8.799, 16.86],\n", - " [u'April', 363.165, 7.704, 9.628],\n", - " [u'May', 428.458, 19.642, 21.401],\n", - " [u'June', 431.25, 10.092, 9.954],\n", - " [u'July', 432.134, 8.835, 7.959],\n", - " [u'August', 443.5, 9.743, 8.785],\n", - " [u'September', 408.833, 15.91, 14.855],\n", - " [u'October', 383.652, 6.919, 7.57],\n", - " [u'November', 243.114, 8.567, 15.256],\n", - " [u'December', 91.926, 14.298, 29.001],\n", - " [u'\\xa0', u'\\xa0', u'\\xa0', u'\\xa0'],\n", - " [u'Annual Sum or Average', 3762.56, 11.062, 16.458],\n", - " [u'Minimum of Months', 91.926, 6.919, 7.57],\n", - " [u'Maximum of Months', 443.5, 19.642, 32.231]]]\n" - ] - } - ], - "prompt_number": 19 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that ltables has captured all the lines before the table. Let us make our code more explicit to see this" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "last_ltable = ltables[-1]\n", - "lines_before_table = last_ltable[0]\n", - "table_itself = last_ltable[-1]\n", - "\n", - "pp.pprint(lines_before_table)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[u'Table of Contents',\n", - " u'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", - " u'For: PERIMETER_MID_ZN_4',\n", - " u'Timestamp: 2014-02-07\\n 12:29:08']\n" - ] - } - ], - "prompt_number": 20 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We found this table the easy way this time, because we knew it was the last one. How do we find it if we don't know where it is in the file ? Python comes to our rescue :-) Let assume that we want to find the table that has the following two lines before it.\n", - "\n", - "- Report: FANGER DURING COOLING AND ADAPTIVE COMFORT\n", - "- For: PERIMETER_MID_ZN_4\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "line1 = 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT'\n", - "line2 = 'For: PERIMETER_MID_ZN_4'\n", - "#\n", - "# check if those two lines are before the table\n", - "line1 in lines_before_table and line2 in lines_before_table\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 21, - "text": [ - "True" - ] - } - ], - "prompt_number": 21 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# find all the tables where those two lines are before the table\n", - "[ltable for ltable in ltables \n", - " if line1 in ltable[0] and line2 in ltable[0]]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 22, - "text": [ - "[[[u'Table of Contents',\n", - " u'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", - " u'For: PERIMETER_MID_ZN_4',\n", - " u'Timestamp: 2014-02-07\\n 12:29:08'],\n", - " [[u'',\n", - " u'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", - " u'FANGERPPD {FOR HOURS SHOWN} []',\n", - " u'FANGERPPD []'],\n", - " [u'January', 102.637, 12.585, 32.231],\n", - " [u'February', 147.054, 10.5, 24.225],\n", - " [u'March', 286.835, 8.799, 16.86],\n", - " [u'April', 363.165, 7.704, 9.628],\n", - " [u'May', 428.458, 19.642, 21.401],\n", - " [u'June', 431.25, 10.092, 9.954],\n", - " [u'July', 432.134, 8.835, 7.959],\n", - " [u'August', 443.5, 9.743, 8.785],\n", - " [u'September', 408.833, 15.91, 14.855],\n", - " [u'October', 383.652, 6.919, 7.57],\n", - " [u'November', 243.114, 8.567, 15.256],\n", - " [u'December', 91.926, 14.298, 29.001],\n", - " [u'\\xa0', u'\\xa0', u'\\xa0', u'\\xa0'],\n", - " [u'Annual Sum or Average', 3762.56, 11.062, 16.458],\n", - " [u'Minimum of Months', 91.926, 6.919, 7.57],\n", - " [u'Maximum of Months', 443.5, 19.642, 32.231]]]]" - ] - } - ], - "prompt_number": 22 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That worked !\n", - "\n", - "What if you want to find the words \"FANGER\" and \"PERIMETER_MID_ZN_4\" before the table. The following code will do it.\n" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# sample code to illustrate what we are going to do\n", - "last_ltable = ltables[-1]\n", - "lines_before_table = last_ltable[0]\n", - "table_itself = last_ltable[-1]\n", - "\n", - "# join lines_before_table into a paragraph of text\n", - "justtext = '\\n'.join(lines_before_table)\n", - "print justtext\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "Table of Contents\n", - "Report: FANGER DURING COOLING AND ADAPTIVE COMFORT\n", - "For: PERIMETER_MID_ZN_4\n", - "Timestamp: 2014-02-07\n", - " 12:29:08\n" - ] - } - ], - "prompt_number": 23 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\"FANGER\" in justtext and \"PERIMETER_MID_ZN_4\" in justtext\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 24, - "text": [ - "True" - ] - } - ], - "prompt_number": 24 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Let us combine the this trick to find the table\n", - "[ltable for ltable in ltables \n", - " if \"FANGER\" in '\\n'.join(ltable[0]) and \"PERIMETER_MID_ZN_4\" in '\\n'.join(ltable[0])]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 25, - "text": [ - "[[[u'Table of Contents',\n", - " u'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", - " u'For: PERIMETER_MID_ZN_4',\n", - " u'Timestamp: 2014-02-07\\n 12:29:08'],\n", - " [[u'',\n", - " u'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", - " u'FANGERPPD {FOR HOURS SHOWN} []',\n", - " u'FANGERPPD []'],\n", - " [u'January', 102.637, 12.585, 32.231],\n", - " [u'February', 147.054, 10.5, 24.225],\n", - " [u'March', 286.835, 8.799, 16.86],\n", - " [u'April', 363.165, 7.704, 9.628],\n", - " [u'May', 428.458, 19.642, 21.401],\n", - " [u'June', 431.25, 10.092, 9.954],\n", - " [u'July', 432.134, 8.835, 7.959],\n", - " [u'August', 443.5, 9.743, 8.785],\n", - " [u'September', 408.833, 15.91, 14.855],\n", - " [u'October', 383.652, 6.919, 7.57],\n", - " [u'November', 243.114, 8.567, 15.256],\n", - " [u'December', 91.926, 14.298, 29.001],\n", - " [u'\\xa0', u'\\xa0', u'\\xa0', u'\\xa0'],\n", - " [u'Annual Sum or Average', 3762.56, 11.062, 16.458],\n", - " [u'Minimum of Months', 91.926, 6.919, 7.57],\n", - " [u'Maximum of Months', 443.5, 19.642, 32.231]]]]" - ] - } - ], - "prompt_number": 25 - }, - { - "cell_type": "heading", - "level": 2, - "metadata": {}, - "source": [ - "Extracting data from the tables" - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reading outputs from E+" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# some initial set up\n", + "# if you have not installed epp, and only downloaded it\n", + "# you will need the following lines\n", + "import sys\n", + "# pathnameto_eppy = 'c:/eppy'\n", + "pathnameto_eppy = '../'\n", + "sys.path.append(pathnameto_eppy) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using titletable() to get at the tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So far we have been making changes to the IDF input file.\n", + "How about looking at the outputs.\n", + "\n", + "Energyplus makes nice htmlout files that look like this." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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\n" + }, "metadata": {}, - "source": [ - "The tables in the HTML page in general have text in the top header row. The first vertical row has text. The remaining cells have numbers. We can identify the numbers we need by looking at the labelin the top row and the label in the first column. Let us construct a simple example and explore this." + "output_type": "display_data" + } + ], + "source": [ + "import ex_inits #no need to know this code, it just shows the image below\n", + "for_images = ex_inits\n", + "for_images.display_png(for_images.html_snippet1) #display the image below\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you look at the clipping of the html file above, you see tables with data in them. Eppy has functions that let you access of these tables and get the data from any of it's cells.\n", + "\n", + "Let us say you want to find the \"Net Site Energy\". \n", + "\n", + "This is in table \"Site and Source Energy\". \n", + "\n", + "The number you want is in the third row, second column and it's value is \"47694.47\"\n", + "\n", + "Let us use eppy to extract this number\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from eppy.results import readhtml # the eppy module with functions to read the html\n", + "fname = \"../eppy/resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html\" # the html file you want to read\n", + "filehandle = open(fname, 'r').read() # get a file handle to the html file\n", + "\n", + "\n", + "htables = readhtml.titletable(filehandle) # reads the tables with their titles\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you open the python file readhtml.py and look at the function titletable, you can see the function documentation.\n", + "\n", + "It says the following" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + " \"\"\"return a list of [(title, table), .....]\n", + " title = previous item with a tag\n", + " table = rows -> [[cell1, cell2, ..], [cell1, cell2, ..], ..]\"\"\"\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The documentation says that it returns a list.\n", + "Let us take a look inside this list.\n", + "Let us look at the first item in the list." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Site and Source Energy', [['', 'Total Energy [kWh]', 'Energy Per Total Building Area [kWh/m2]', 'Energy Per Conditioned Building Area [kWh/m2]'], ['Total Site Energy', 47694.47, 51.44, 51.44], ['Net Site Energy', 47694.47, 51.44, 51.44], ['Total Source Energy', 140159.1, 151.16, 151.16], ['Net Source Energy', 140159.1, 151.16, 151.16]])\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# ignore the following three lines. I am using them to construct the table below\n", - "from IPython.display import HTML\n", - "atablestring = '\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
 a bb cc d
x y123
y z456
z z789
'\n", - "HTML(atablestring)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 a bb cc d
x y123
y z456
z z789
" - ], - "metadata": {}, - "output_type": "pyout", - "prompt_number": 26, - "text": [ - "" - ] - } - ], - "prompt_number": 26 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This table is actually in the follwoing form:" + } + ], + "source": [ + "firstitem = htables[0]\n", + "print(firstitem)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ughh !!! that is ugly. Hard to see what it is. \n", + "Let us use a python module to print it pretty" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('Site and Source Energy',\n", + " [['',\n", + " 'Total Energy [kWh]',\n", + " 'Energy Per Total Building Area [kWh/m2]',\n", + " 'Energy Per Conditioned Building Area [kWh/m2]'],\n", + " ['Total Site Energy', 47694.47, 51.44, 51.44],\n", + " ['Net Site Energy', 47694.47, 51.44, 51.44],\n", + " ['Total Source Energy', 140159.1, 151.16, 151.16],\n", + " ['Net Source Energy', 140159.1, 151.16, 151.16]])\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "atable = [[\"\", \"a b\", \"b c\", \"c d\"],\n", - " [\"x y\", 1, 2, 3 ],\n", - " [\"y z\", 4, 5, 6 ],\n", - " [\"z z\", 7, 8, 9 ],]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 27 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the labels in the table. So we an look at row \"x y\" and column \"c d\". The value there is 3" + } + ], + "source": [ + "import pprint\n", + "pp = pprint.PrettyPrinter()\n", + "pp.pprint(firstitem)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Nice. that is a little clearer" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'Site and Source Energy'\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "right now we can get to it by saying atable[1][3]" + } + ], + "source": [ + "firstitem_title = firstitem[0]\n", + "pp.pprint(firstitem_title)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['',\n", + " 'Total Energy [kWh]',\n", + " 'Energy Per Total Building Area [kWh/m2]',\n", + " 'Energy Per Conditioned Building Area [kWh/m2]'],\n", + " ['Total Site Energy', 47694.47, 51.44, 51.44],\n", + " ['Net Site Energy', 47694.47, 51.44, 51.44],\n", + " ['Total Source Energy', 140159.1, 151.16, 151.16],\n", + " ['Net Source Energy', 140159.1, 151.16, 151.16]]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print atable[1][3]" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n" - ] - } - ], - "prompt_number": 28 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "readhtml has some functions that will let us address the values by the labels. We use a structure from python called named tuples to do this. The only limitation is that the labels have to be letters or digits. Named tuples does not allow spaces in the labels. We could replace the space with an underscore ' _ '. So \"a b\" will become \"a_b\". So we can look for row \"x_y\" and column \"c_d\". Let us try this out." + } + ], + "source": [ + "firstitem_table = firstitem[1]\n", + "pp.pprint(firstitem_table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How do we get to value of \"Net Site Energy\". \n", + "We know it is in the third row, second column of the table. \n", + "\n", + "Easy." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Net Site Energy', 47694.47, 51.44, 51.44]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "from eppy.results import readhtml\n", - "h_table = readhtml.named_grid_h(atable)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 31 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print h_table.x_y.c_d\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n" - ] - } - ], - "prompt_number": 32 - }, + } + ], + "source": [ + "thirdrow = firstitem_table[2] # we start counting with 0. So 0, 1, 2 is third row\n", + "print(thirdrow)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47694.47" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "thirdrow_secondcolumn = thirdrow[1]\n", + "thirdrow_secondcolumn\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "the text from the html table is in unicode. \n", + "That is why you see that weird 'u' letter. \n", + "\n", + "Let us convert it to a floating point number" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "47694.47" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net_site_energy = float(thirdrow_secondcolumn)\n", + "net_site_energy\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us have a little fun with the tables. \n", + "\n", + "Get the titles of all the tables" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Site and Source Energy',\n", + " 'Site to Source Energy Conversion Factors',\n", + " 'Building Area',\n", + " 'End Uses',\n", + " 'End Uses By Subcategory',\n", + " 'Utility Use Per Conditioned Floor Area',\n", + " 'Utility Use Per Total Floor Area',\n", + " 'Electric Loads Satisfied',\n", + " 'On-Site Thermal Sources',\n", + " 'Water Source Summary',\n", + " 'Comfort and Setpoint Not Met Summary',\n", + " 'Comfort and Setpoint Not Met Summary']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "alltitles = [htable[0] for htable in htables]\n", + "alltitles\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let us grab the tables with the titles \"Building Area\" and \"Site to Source Energy Conversion Factors\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "twotables = [htable for htable in htables if htable[0] in [\"Building Area\", \"Site to Source Energy Conversion Factors\"]]\n", + "twotables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us leave readtables for now. \n", + "\n", + "It gives us the basic functionality to read any of the tables in the html output file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using lines_table() to get at the tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have been using titletable() to get at the tables. There is a constraint using function titletable(). Titletable() assumes that there is a unique title (in HTML bold) just above the table. It is assumed that this title will adequetly describe the table. This is true in most cases and titletable() is perfectly good to use. Unfortuntely there are some tables that do not follow this rule. The snippet below shows one of them." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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\n" + }, "metadata": {}, - "source": [ - "We can still get to the value by index" + "output_type": "display_data" + } + ], + "source": [ + "import ex_inits #no need to know this code, it just shows the image below\n", + "for_images = ex_inits\n", + "for_images.display_png(for_images.html_snippet2) # display the image below\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the HTML snippet shows a table with three lines above it. The first two lines have information that describe the table. We need to look at both those lines to understand what the table contains. So we need a different function that will capture all those lines before the table. The funtion lines_table() described below will do this.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "from eppy.results import readhtml # the eppy module with functions to read the html\n", + "fname = \"../eppy/resources/outputfiles/V_8_1/ASHRAE30pct.PI.Final11_OfficeMedium_STD2010_Chicago-baseTable.html\" # the html file you want to read\n", + "filehandle = open(fname, 'r').read() # get a file handle to the html file\n", + "\n", + "\n", + "ltables = readhtml.lines_table(filehandle) # reads the tables with their titles\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The html snippet shown above is the last table in HTML file we just opened. We have used lines_table() to read the tables into the variable ltables. We can get to the last table by ltable[-1]. Let us print it and see what we have.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['Table of Contents',\n", + " 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", + " 'For: PERIMETER_MID_ZN_4',\n", + " 'Timestamp: 2014-02-07\\n 12:29:08'],\n", + " [['',\n", + " 'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", + " 'FANGERPPD {FOR HOURS SHOWN} []',\n", + " 'FANGERPPD []'],\n", + " ['January', 102.637, 12.585, 32.231],\n", + " ['February', 147.054, 10.5, 24.225],\n", + " ['March', 286.835, 8.799, 16.86],\n", + " ['April', 363.165, 7.704, 9.628],\n", + " ['May', 428.458, 19.642, 21.401],\n", + " ['June', 431.25, 10.092, 9.954],\n", + " ['July', 432.134, 8.835, 7.959],\n", + " ['August', 443.5, 9.743, 8.785],\n", + " ['September', 408.833, 15.91, 14.855],\n", + " ['October', 383.652, 6.919, 7.57],\n", + " ['November', 243.114, 8.567, 15.256],\n", + " ['December', 91.926, 14.298, 29.001],\n", + " ['\\xa0', '\\xa0', '\\xa0', '\\xa0'],\n", + " ['Annual Sum or Average', 3762.56, 11.062, 16.458],\n", + " ['Minimum of Months', 91.926, 6.919, 7.57],\n", + " ['Maximum of Months', 443.5, 19.642, 32.231]]]\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print h_table[0][2]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that we used atable[1][3], but here we used h_table[0][2]. That is because h_table does not count the rows and columns where the labels are." + } + ], + "source": [ + "import pprint\n", + "pp = pprint.PrettyPrinter()\n", + "pp.pprint(ltables[-1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that ltables has captured all the lines before the table. Let us make our code more explicit to see this" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Table of Contents',\n", + " 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", + " 'For: PERIMETER_MID_ZN_4',\n", + " 'Timestamp: 2014-02-07\\n 12:29:08']\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also do the following:" + } + ], + "source": [ + "last_ltable = ltables[-1]\n", + "lines_before_table = last_ltable[0]\n", + "table_itself = last_ltable[-1]\n", + "\n", + "pp.pprint(lines_before_table)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We found this table the easy way this time, because we knew it was the last one. How do we find it if we don't know where it is in the file ? Python comes to our rescue :-) Let assume that we want to find the table that has the following two lines before it.\n", + "\n", + "- Report: FANGER DURING COOLING AND ADAPTIVE COMFORT\n", + "- For: PERIMETER_MID_ZN_4\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "line1 = 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT'\n", + "line2 = 'For: PERIMETER_MID_ZN_4'\n", + "#\n", + "# check if those two lines are before the table\n", + "line1 in lines_before_table and line2 in lines_before_table\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[['Table of Contents',\n", + " 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", + " 'For: PERIMETER_MID_ZN_4',\n", + " 'Timestamp: 2014-02-07\\n 12:29:08'],\n", + " [['',\n", + " 'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", + " 'FANGERPPD {FOR HOURS SHOWN} []',\n", + " 'FANGERPPD []'],\n", + " ['January', 102.637, 12.585, 32.231],\n", + " ['February', 147.054, 10.5, 24.225],\n", + " ['March', 286.835, 8.799, 16.86],\n", + " ['April', 363.165, 7.704, 9.628],\n", + " ['May', 428.458, 19.642, 21.401],\n", + " ['June', 431.25, 10.092, 9.954],\n", + " ['July', 432.134, 8.835, 7.959],\n", + " ['August', 443.5, 9.743, 8.785],\n", + " ['September', 408.833, 15.91, 14.855],\n", + " ['October', 383.652, 6.919, 7.57],\n", + " ['November', 243.114, 8.567, 15.256],\n", + " ['December', 91.926, 14.298, 29.001],\n", + " ['\\xa0', '\\xa0', '\\xa0', '\\xa0'],\n", + " ['Annual Sum or Average', 3762.56, 11.062, 16.458],\n", + " ['Minimum of Months', 91.926, 6.919, 7.57],\n", + " ['Maximum of Months', 443.5, 19.642, 32.231]]]]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# find all the tables where those two lines are before the table\n", + "[ltable for ltable in ltables \n", + " if line1 in ltable[0] and line2 in ltable[0]]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That worked !\n", + "\n", + "What if you want to find the words \"FANGER\" and \"PERIMETER_MID_ZN_4\" before the table. The following code will do it.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table of Contents\n", + "Report: FANGER DURING COOLING AND ADAPTIVE COMFORT\n", + "For: PERIMETER_MID_ZN_4\n", + "Timestamp: 2014-02-07\n", + " 12:29:08\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print h_table.x_y[2]\n", - "# or\n", - "print h_table[0].c_d\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n", - "3\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wow \u2026 that is pretty cool. What if we want to just check what the labels are ?" + } + ], + "source": [ + "# sample code to illustrate what we are going to do\n", + "last_ltable = ltables[-1]\n", + "lines_before_table = last_ltable[0]\n", + "table_itself = last_ltable[-1]\n", + "\n", + "# join lines_before_table into a paragraph of text\n", + "justtext = '\\n'.join(lines_before_table)\n", + "print(justtext)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"FANGER\" in justtext and \"PERIMETER_MID_ZN_4\" in justtext\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[['Table of Contents',\n", + " 'Report: FANGER DURING COOLING AND ADAPTIVE COMFORT',\n", + " 'For: PERIMETER_MID_ZN_4',\n", + " 'Timestamp: 2014-02-07\\n 12:29:08'],\n", + " [['',\n", + " 'ZONE/SYS SENSIBLE COOLING RATE {HOURS POSITIVE} [HOURS]',\n", + " 'FANGERPPD {FOR HOURS SHOWN} []',\n", + " 'FANGERPPD []'],\n", + " ['January', 102.637, 12.585, 32.231],\n", + " ['February', 147.054, 10.5, 24.225],\n", + " ['March', 286.835, 8.799, 16.86],\n", + " ['April', 363.165, 7.704, 9.628],\n", + " ['May', 428.458, 19.642, 21.401],\n", + " ['June', 431.25, 10.092, 9.954],\n", + " ['July', 432.134, 8.835, 7.959],\n", + " ['August', 443.5, 9.743, 8.785],\n", + " ['September', 408.833, 15.91, 14.855],\n", + " ['October', 383.652, 6.919, 7.57],\n", + " ['November', 243.114, 8.567, 15.256],\n", + " ['December', 91.926, 14.298, 29.001],\n", + " ['\\xa0', '\\xa0', '\\xa0', '\\xa0'],\n", + " ['Annual Sum or Average', 3762.56, 11.062, 16.458],\n", + " ['Minimum of Months', 91.926, 6.919, 7.57],\n", + " ['Maximum of Months', 443.5, 19.642, 32.231]]]]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Let us combine the this trick to find the table\n", + "[ltable for ltable in ltables \n", + " if \"FANGER\" in '\\n'.join(ltable[0]) and \"PERIMETER_MID_ZN_4\" in '\\n'.join(ltable[0])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extracting data from the tables" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The tables in the HTML page in general have text in the top header row. The first vertical row has text. The remaining cells have numbers. We can identify the numbers we need by looking at the labelin the top row and the label in the first column. Let us construct a simple example and explore this." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 a bb cc d
x y123
y z456
z z789
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ignore the following three lines. I am using them to construct the table below\n", + "from IPython.display import HTML\n", + "atablestring = '\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
 a bb cc d
x y123
y z456
z z789
'\n", + "HTML(atablestring)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table is actually in the follwoing form:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "atable = [[\"\", \"a b\", \"b c\", \"c d\"],\n", + " [\"x y\", 1, 2, 3 ],\n", + " [\"y z\", 4, 5, 6 ],\n", + " [\"z z\", 7, 8, 9 ],]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the labels in the table. So we an look at row \"x y\" and column \"c d\". The value there is 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "right now we can get to it by saying atable[1][3]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print h_table._fields\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "('x_y', 'y_z', 'z_z')\n" - ] - } - ], - "prompt_number": 35 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That gives us the horizontal lables. How about the vertical labels ?" + } + ], + "source": [ + "print(atable[1][3])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "readhtml has some functions that will let us address the values by the labels. We use a structure from python called named tuples to do this. The only limitation is that the labels have to be letters or digits. Named tuples does not allow spaces in the labels. We could replace the space with an underscore ' _ '. So \"a b\" will become \"a_b\". So we can look for row \"x_y\" and column \"c_d\". Let us try this out." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "from eppy.results import readhtml\n", + "h_table = readhtml.named_grid_h(atable)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "h_table.x_y._fields\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 36, - "text": [ - "('a_b', 'b_c', 'c_d')" - ] - } - ], - "prompt_number": 36 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There you go !!!" + } + ], + "source": [ + "print(h_table.x_y.c_d)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can still get to the value by index" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How about if I want to use the labels differently ? Say I want to refer to the row first and then to the column. That woul be saying table.c_d.x_y. We can do that by using a different function" + } + ], + "source": [ + "print(h_table[0][2])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that we used atable[1][3], but here we used h_table[0][2]. That is because h_table does not count the rows and columns where the labels are." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "3\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "v_table = readhtml.named_grid_v(atable)\n", - "print v_table.c_d.x_y\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n" - ] - } - ], - "prompt_number": 37 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can do the following" + } + ], + "source": [ + "print(h_table.x_y[2])\n", + "# or\n", + "print(h_table[0].c_d)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wow … that is pretty cool. What if we want to just check what the labels are ?" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('x_y', 'y_z', 'z_z')\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "print v_table[2][0]\n", - "print v_table.c_d[0]\n", - "print v_table[2].x_y\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "3\n", - "3\n", - "3\n" - ] - } - ], - "prompt_number": 38 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let us try to get the numbers in the first column and then get their sum" + } + ], + "source": [ + "print(h_table._fields)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That gives us the horizontal lables. How about the vertical labels ?" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('a_b', 'b_c', 'c_d')" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "h_table.x_y._fields\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There you go !!!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How about if I want to use the labels differently ? Say I want to refer to the row first and then to the column. That woul be saying table.c_d.x_y. We can do that by using a different function" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "v_table.a_b\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 39, - "text": [ - "ntrow(x_y=1, y_z=4, z_z=7)" - ] - } - ], - "prompt_number": 39 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Look like we got the right column. But not in the right format. We really need a list of numbers" + } + ], + "source": [ + "v_table = readhtml.named_grid_v(atable)\n", + "print(v_table.c_d.x_y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can do the following" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n", + "3\n", + "3\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "[cell for cell in v_table.a_b]\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 33, - "text": [ - "[1, 4, 7]" - ] - } - ], - "prompt_number": 33 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That looks like waht we wanted. Now let us get the sum" + } + ], + "source": [ + "print(v_table[2][0])\n", + "print(v_table.c_d[0])\n", + "print(v_table[2].x_y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us try to get the numbers in the first column and then get their sum" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ntrow(x_y=1, y_z=4, z_z=7)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "v_table.a_b\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Look like we got the right column. But not in the right format. We really need a list of numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 4, 7]" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[cell for cell in v_table.a_b]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks like waht we wanted. Now let us get the sum" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 4, 7]\n", + "12\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "values_in_first_column = [cell for cell in v_table.a_b]\n", - "print values_in_first_column\n", - "print sum(values_in_first_column) # sum is a builtin function that will sum a list\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 4, 7]\n", - "12\n" - ] - } - ], - "prompt_number": 34 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get the first row we use the variable h_table" + } + ], + "source": [ + "values_in_first_column = [cell for cell in v_table.a_b]\n", + "print(values_in_first_column)\n", + "print(sum(values_in_first_column)) # sum is a builtin function that will sum a list\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get the first row we use the variable h_table" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3]\n", + "6\n" ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "values_in_first_row = [cell for cell in h_table.x_y]\n", - "print values_in_first_row\n", - "print sum(values_in_first_row)\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "output_type": "stream", - "stream": "stdout", - "text": [ - "[1, 2, 3]\n", - "6\n" - ] - } - ], - "prompt_number": 35 } ], - "metadata": {} + "source": [ + "values_in_first_row = [cell for cell in h_table.x_y]\n", + "print(values_in_first_row)\n", + "print(sum(values_in_first_row))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.7.4" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/docs/Outputs_Tutorial.py b/docs/Outputs_Tutorial.py deleted file mode 100644 index 9e575d43..00000000 --- a/docs/Outputs_Tutorial.py +++ /dev/null @@ -1,418 +0,0 @@ -# -*- coding: utf-8 -*- -# 3.0 - -# - -# Reading outputs from E+ - -# - -# some initial set up -# if you have not installed epp, and only downloaded it -# you will need the following lines -import sys - -# pathnameto_eppy = 'c:/eppy' -pathnameto_eppy = "../" -sys.path.append(pathnameto_eppy) - -# - -# Using titletable() to get at the tables - -# - -# So far we have been making changes to the IDF input file. -# How about looking at the outputs. -# -# Energyplus makes nice htmlout files that look like this. - -# - -from eppy import ex_inits # no need to know this code, it just shows the image below - -for_images = ex_inits -for_images.display_png(for_images.html_snippet1) # display the image below - -# - -# If you look at the clipping of the html file above, you see tables with data in them. Eppy has functions that let you access of these tables and get the data from any of it's cells. -# -# Let us say you want to find the "Net Site Energy". -# -# This is in table "Site and Source Energy". -# -# The number you want is in the third row, second column and it's value is "47694.47" -# -# Let us use eppy to extract this number - -# - -from eppy import readhtml # the eppy module with functions to read the html - -fname = "../eppy/resources/outputfiles/V_7_2/5ZoneCAVtoVAVWarmestTempFlowTable_ABUPS.html" # the html file you want to read -filehandle = open(fname, "r").read() # get a file handle to the html file - - -htables = readhtml.titletable(filehandle) # reads the tables with their titles - -# - -# If you open the python file readhtml.py and look at the function titletable, you can see the function documentation. -# -# It says the following - -# - -# """return a list of [(title, table), .....] -# title = previous item with a tag -# table = rows -> [[cell1, cell2, ..], [cell1, cell2, ..], ..]""" -# - -# - -# The documentation says that it returns a list. -# Let us take a look inside this list. -# Let us look at the first item in the list. - -# - -firstitem = htables[0] -print(firstitem) - -# - -# Ughh !!! that is ugly. Hard to see what it is. -# Let us use a python module to print it pretty - -# - -import pprint - -pp = pprint.PrettyPrinter() -pp.pprint(firstitem) - -# - -# Nice. that is a little clearer - -# - -firstitem_title = firstitem[0] -pp.pprint(firstitem_title) - -# - -firstitem_table = firstitem[1] -pp.pprint(firstitem_table) - -# - -# How do we get to value of "Net Site Energy". -# We know it is in the third row, second column of the table. -# -# Easy. - -# - -thirdrow = firstitem_table[2] # we start counting with 0. So 0, 1, 2 is third row -print(thirdrow) - -# - -thirdrow_secondcolumn = thirdrow[1] -thirdrow_secondcolumn - -# - -# the text from the html table is in unicode. -# That is why you see that weird 'u' letter. -# -# Let us convert it to a floating point number - -# - -net_site_energy = float(thirdrow_secondcolumn) -net_site_energy - -# - -# Let us have a little fun with the tables. -# -# Get the titles of all the tables - -# - -alltitles = [htable[0] for htable in htables] -alltitles - -# - -# Now let us grab the tables with the titles "Building Area" and "Site to Source Energy Conversion Factors" - -# - -# twotables = [htable for htable in htables if htable[0] in ["Building Area", "Site to Source Energy Conversion Factors"]] -# twotables - -# - -# Let us leave readtables for now. -# -# It gives us the basic functionality to read any of the tables in the html output file. - -# - -# Using lines_table() to get at the tables - -# - -# We have been using titletable() to get at the tables. There is a constraint using function titletable(). Titletable() assumes that there is a unique title (in HTML bold) just above the table. It is assumed that this title will adequetly describe the table. This is true in most cases and titletable() is perfectly good to use. Unfortuntely there are some tables that do not follow this rule. The snippet below shows one of them. - -# - -from eppy import ex_inits # no need to know this code, it just shows the image below - -for_images = ex_inits -for_images.display_png(for_images.html_snippet2) # display the image below - -# - -# Notice that the HTML snippet shows a table with three lines above it. The first two lines have information that describe the table. We need to look at both those lines to understand what the table contains. So we need a different function that will capture all those lines before the table. The funtion lines_table() described below will do this. - -# - -from eppy import readhtml # the eppy module with functions to read the html - -fname = "../eppy/resources/outputfiles/V_8_1/ASHRAE30pct.PI.Final11_OfficeMedium_STD2010_Chicago-baseTable.html" # the html file you want to read -filehandle = open(fname, "r").read() # get a file handle to the html file - - -ltables = readhtml.lines_table(filehandle) # reads the tables with their titles - -# - -# The html snippet shown above is the last table in HTML file we just opened. We have used lines_table() to read the tables into the variable ltables. We can get to the last table by ltable[-1]. Let us print it and see what we have. - -# - -import pprint - -pp = pprint.PrettyPrinter() -pp.pprint(ltables[-1]) - -# - -# We can see that ltables has captured all the lines before the table. Let us make our code more explicit to see this - -# - -last_ltable = ltables[-1] -lines_before_table = last_ltable[0] -table_itself = last_ltable[-1] - -pp.pprint(lines_before_table) - -# - -# We found this table the easy way this time, because we knew it was the last one. How do we find it if we don't know where it is in the file ? Python comes to our rescue :-) Let assume that we want to find the table that has the following two lines before it. -# -# - Report: FANGER DURING COOLING AND ADAPTIVE COMFORT -# - For: PERIMETER_MID_ZN_4 - -# - -line1 = "Report: FANGER DURING COOLING AND ADAPTIVE COMFORT" -line2 = "For: PERIMETER_MID_ZN_4" -# -# check if those two lines are before the table -line1 in lines_before_table and line2 in lines_before_table - -# - -# find all the tables where those two lines are before the table -[ltable for ltable in ltables if line1 in ltable[0] and line2 in ltable[0]] - -# - -# That worked ! -# -# What if you want to find the words "FANGER" and "PERIMETER_MID_ZN_4" before the table. The following code will do it. - -# - -# sample code to illustrate what we are going to do -last_ltable = ltables[-1] -lines_before_table = last_ltable[0] -table_itself = last_ltable[-1] - -# join lines_before_table into a paragraph of text -justtext = "\n".join(lines_before_table) -print(justtext) - -# - -"FANGER" in justtext and "PERIMETER_MID_ZN_4" in justtext - -# - -# Let us combine the this trick to find the table -[ - ltable - for ltable in ltables - if "FANGER" in "\n".join(ltable[0]) and "PERIMETER_MID_ZN_4" in "\n".join(ltable[0]) -] - -# - -# Extracting data from the tables - -# - -# The tables in the HTML page in general have text in the top header row. The first vertical row has text. The remaining cells have numbers. We can identify the numbers we need by looking at the labelin the top row and the label in the first column. Let us construct a simple example and explore this. - -# - -# ignore the following three lines. I am using them to construct the table below -from IPython.display import HTML - -atablestring = '\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
 a bb cc d
x y123
y z456
z z789
' -HTML(atablestring) - -# - -# This table is actually in the follwoing form: - -# - -atable = [ - ["", "a b", "b c", "c d"], - ["x y", 1, 2, 3], - ["y z", 4, 5, 6], - ["z z", 7, 8, 9], -] - -# - -# We can see the labels in the table. So we an look at row "x y" and column "c d". The value there is 3 - -# - -# right now we can get to it by saying atable[1][3] - -# - -print(atable[1][3]) - -# - -# readhtml has some functions that will let us address the values by the labels. We use a structure from python called named tuples to do this. The only limitation is that the labels have to be letters or digits. Named tuples does not allow spaces in the labels. We could replace the space with an underscore ' _ '. So "a b" will become "a_b". So we can look for row "x_y" and column "c_d". Let us try this out. - -# - -from eppy import readhtml - -h_table = readhtml.named_grid_h(atable) - -# - -print(h_table.x_y.c_d) - -# - -# We can still get to the value by index - -# - -print(h_table[0][2]) - -# - -# Note that we used atable[1][3], but here we used h_table[0][2]. That is because h_table does not count the rows and columns where the labels are. - -# - -# We can also do the following: - -# - -print(h_table.x_y[2]) -# or -print(h_table[0].c_d) - -# - -# Wow … that is pretty cool. What if we want to just check what the labels are ? - -# - -print(h_table._fields) - -# - -# That gives us the horizontal lables. How about the vertical labels ? - -# - -h_table.x_y._fields - -# - -# There you go !!! - -# - -# How about if I want to use the labels differently ? Say I want to refer to the row first and then to the column. That woul be saying table.c_d.x_y. We can do that by using a different function - -# - -v_table = readhtml.named_grid_v(atable) -print(v_table.c_d.x_y) - -# - -# And we can do the following - -# - -print(v_table[2][0]) -print(v_table.c_d[0]) -print(v_table[2].x_y) - -# - -# Let us try to get the numbers in the first column and then get their sum - -# - -v_table.a_b - -# - -# Look like we got the right column. But not in the right format. We really need a list of numbers - -# - -[cell for cell in v_table.a_b] - -# - -# That looks like waht we wanted. 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In eppy you could embody this is a list

+
+
[1]:
-

In eppy you could embody this is a list

-
supplyside = ['start_brandh',   [  'branch1',   'branch2',   'branch3'],   'end_branch']
+
+supplyside = ['start_brandh',   [  'branch1',   'branch2',   'branch3'],   'end_branch']
 demandside = ['d_start_brandh', ['d_branch1', 'd_branch2', 'd_branch3'], 'd_end_branch']
+
 

 

-
Eppy will build the build the shape/topology of the loop using the two
-lists above. Each branch will have a placeholder component, like a pipe
-or a duct:

-
branch1 = --duct--
+

+
Eppy will build the build the shape/topology of the loop using the two lists above. Each branch will have a placeholder component, like a pipe or a duct:

+
..
 

 

-
Now we will have to replace the placeholder with the real components
-that make up the loop. For instance, branch1 should really have a
-pre-heat coil leading to a supply fan leading to a cooling coil leading
-to a heating coil:

-
new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil
+branch1 = --duct--
Now we will have to replace the placeholder with the real components that make up the loop. For instance, branch1 should really have a pre-heat coil leading to a supply fan leading to a cooling coil leading to a heating coil:

+
..
 

 

-
Eppy lets you build a new branch and you can replace branch1 with
-new_branch

-
In this manner we can build up the entire loop with the right
-components, once the initial toplogy is right

+new_branch = pre-heatcoil -> supplyfan -> coolingcoil -> heatingcoil
Eppy lets you build a new branch and you can replace branch1 with new_branch

+
In this manner we can build up the entire loop with the right components, once the initial toplogy is right

 

-

-
Building a Plant loops

-
Eppy can build up the topology of a plant loop using single pipes in a
-branch. Once we do that the simple branch in the loop we have built can
-be replaced with a more complex branch.

+

+
Building a Plant loops

+
Eppy can build up the topology of a plant loop using single pipes in a branch. Once we do that the simple branch in the loop we have built can be replaced with a more complex branch.

 
Let us try this out ans see how it works.

-

-
Building the topology of the loop

-
# you would normaly install eppy by doing
+

+
Building the topology of the loop

+

+
[2]:
+

+

+
+# you would normaly install eppy by doing
 # python setup.py install
 # or
 # pip install eppy
@@ -135,55 +363,83 @@ 
Building the topology of the loop# pathnameto_eppy = 'c:/eppy'
 pathnameto_eppy = '../'
 sys.path.append(pathnameto_eppy)
+
 

 

-
from eppy.modeleditor import IDF
-from eppy import hvacbuilder
+

+

+
[3]:
+

+

+
+from eppy.modeleditor import IDF
+from eppy import hvacbuilder
 
-from StringIO import StringIO
+from io import StringIO
 iddfile = "../eppy/resources/iddfiles/Energy+V7_0_0_036.idd"
 IDF.setiddname(iddfile)
+
 

 

-
# make the topology of the loop
+

+

+
[4]:
+

+

+
+# make the topology of the loop
 idf = IDF(StringIO('')) # makes an empty idf file in memory with no file name
 loopname = "p_loop"
 sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop
 dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side of the loop
 hvacbuilder.makeplantloop(idf, loopname, sloop, dloop)
 idf.saveas("hhh1.idf")
+
 

 

+

 

 
We have made plant loop and saved it as hhh1.idf.

 
Now let us look at what the loop looks like.

 

 

-

-
Diagram of the loop

-
Let us use the script “eppy/useful_scripts/loopdiagrams.py” to draw
-this diagram

-
See Generating a Loop Diagram
-page for details on how to do this

+

+
Diagram of the loop

+
Let us use the script “eppy/useful_scripts/loopdiagrams.py” to draw this diagram

+
See Generating a Loop Diagram page for details on how to do this

 
Below is the diagram for this simple loop

-
Note: the supply and demnd sides are not connected in the diagram, but
-shown seperately for clarity

-
from eppy import ex_inits #no need to know this code, it just shows the image below
+
Note: the supply and demnd sides are not connected in the diagram, but shown seperately for clarity

+

+
[5]:
+

+

+
+import ex_inits #no need to know this code, it just shows the image below
 for_images = ex_inits
 for_images.display_png(for_images.plantloop1) # display the image below
+
 

 

-HVAC_Tutorial_files/HVAC_Tutorial_23_0.png
 

-

-
Modifying the topology of the loop

+

+

+

+

+_images/HVAC_Tutorial_23_0.png
+

+

+

+

+
Modifying the topology of the loop

 
Let us make a new branch and replace the exisiting branch

-
The existing branch name is “sb0” and it contains a single pipe
-component sb0_pipe.

-
Let us replace it with a branch that has a chiller that is connected to
-a pipe which is turn connected to another pipe. So the connections in
-the new branch would look like “chiller-> pipe1->pipe2”

-
# make a new branch chiller->pipe1-> pipe2
+
The existing branch name is “sb0” and it contains a single pipe component sb0_pipe.

+
Let us replace it with a branch that has a chiller that is connected to a pipe which is turn connected to another pipe. So the connections in the new branch would look like “chiller-> pipe1->pipe2”

+

+
[6]:
+

+

+
+# make a new branch chiller->pipe1-> pipe2
 
 # make a new pipe component
 pipe1 = idf.newidfobject("PIPE:ADIABATIC", 'np1')
@@ -199,40 +455,62 @@ 
Modifying the topology of the loop# get the branch we are trying to modify
 branch = idf.getobject('BRANCH', 'sb0') # args are (key, name)
 listofcomponents = [chiller, pipe1, pipe2] # the new components are connected in this order
+
 

 

-
Now we are going to try to replace branch with the a branch made up
-of listofcomponents

+

+

+

+

+

+
+../eppy/modeleditor.py:757: UserWarning: The aname parameter should no longer be used.
+  warnings.warn("The aname parameter should no longer be used.", UserWarning)
+

+

+
Now we are going to try to replace branch with the a branch made up of listofcomponents

 
    
 
  • We will do this by calling the function replacebranch
    • 
 
  • Calling replacebranch can throw an exception WhichLoopError
    • 
 
  • In a moment, you will see why this exception is important
    • 
 

-
try:
+

+
[7]:
+

+

+
+try:
     newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')
 except hvacbuilder.WhichLoopError as e:
-    print e
+    print(e)
+
 

 

-
Where should this loop connect ?
+

+

+

+

+

+
+Where should this loop connect ?
 CHILLER:ELECTRIC - Central_Chiller
-[u'Chilled_Water_', u'Condenser_', u'Heat_Recovery_']
-

+['Chilled_Water_', 'Condenser_', 'Heat_Recovery_']
+
+

 

-
The above code throws the exception. It says that the idfobject
-CHILLER:ELECTRIC - Central_Chiller has three possible connections.
-The chiller has inlet outlet nodes for the following

+
The above code throws the exception. It says that the idfobject CHILLER:ELECTRIC - Central_Chiller has three possible connections. The chiller has inlet outlet nodes for the following

 
    
 
  • Chilled water
    • 
 
  • Condenser
    • 
 
  • Heat Recovery
    • 
 

-
eppy does not know which one to connect to, and it needs your help. We
-know that the chiller needs to be connected to the chilled water inlet
-and outlet. Simply copy Chilled_Water_ from the text in the
-exception and paste as shown in the code below. (make sure you copy it
-exactly. eppy is a little nerdy about that)

-
# instead of passing chiller to the function, we pass a tuple (chiller, 'Chilled_Water_').
+
eppy does not know which one to connect to, and it needs your help. We know that the chiller needs to be connected to the chilled water inlet and outlet. Simply copy Chilled_Water_ from the text in the exception and paste as shown in the code below. (make sure you copy it exactly. eppy is a little nerdy about that)

+

+
[8]:
+

+

+
+# instead of passing chiller to the function, we pass a tuple (chiller, 'Chilled_Water_').
 # This lets eppy know where the connection should be made.
 # The idfobject pipe does not have this ambiguity. So pipes do not need this extra information
 listofcomponents = [(chiller, 'Chilled_Water_'), pipe1, pipe2]
@@ -240,19 +518,24 @@ 
Modifying the topology of the looptry:
     newbr = hvacbuilder.replacebranch(idf, loop, branch, listofcomponents, fluid='Water')
 except Exception as e:
-    print e
+    print(e)
 else: # else will run only if the try suceeds
-    print "no exception was thrown"
+    print("no exception was thrown")
 
 idf.saveas("hhh_new.idf")
+
 

 

-
no exception was thrown
-

 

-
Tagential note: The "try .. except .. else" statement is useful
-here. If you have not run across it before, take a look at these two
-links

+

+

+

+

+
+no exception was thrown
+

+

+
Tagential note: The "try .. except .. else" statement is useful here. If you have not run across it before, take a look at these two links

 

 
Let us draw the diagram of this file. (run this from eppy/eppy folder)

 

-
python ex_loopdiagram.py hhh_new.idf

-
from eppy import ex_inits #no need to know this code, it just shows the image below
+python ex_loopdiagram.py hhh_new.idf

+
[9]:
+

+

+
+import ex_inits #no need to know this code, it just shows the image below
 for_images = ex_inits
 for_images.display_png(for_images.plantloop2) # display the image below
+
 

 

-HVAC_Tutorial_files/HVAC_Tutorial_34_0.png
+

+

+

+

+

+_images/HVAC_Tutorial_34_0.png
+

+

 
This diagram shows the new components in the branch

-

-
Work flow with WhichLoopError

-
When you are writing scripts don’t bother to use try .. except for
-WhichLoopError.

+

+
Work flow with WhichLoopError

+
When you are writing scripts don’t bother to use try .. except for WhichLoopError.

 
    
 
  • Simply allow the exception to be raised.
    • 
 
  • Use the information in the exception to update your code
    • 
 
  • You may have to do this a couple of times in your script.
    • 
 
  • In a sense you are letting eppy tell you how to update the script.
    • 
 

-
Question: I am writing an application using eppy, not just a script.
-The above workflow does not work for me

-
Response: Aha ! If that is the case, open an issue in
-github/eppy. We are lazy
-people. We don’t write code unless it is needed :-)

+
Question: I am writing an application using eppy, not just a script. The above workflow does not work for me

+
Response: Aha ! If that is the case, open an issue in github/eppy. We are lazy people. We don’t write code unless it is needed :-)

 

 

-

-
Traversing the loop

-
It would be nice to move through the loop using functions “nextnode()”
-and “prevnode()”

+

+
Traversing the loop

+
It would be nice to move through the loop using functions “nextnode()” and “prevnode()”

 
Eppy indeed has such functions

 
Let us try to traverse the loop above.

-
# to traverse the loop we are going to call some functions ex_loopdiagrams.py,
+

+
[12]:
+

+

+
+# to traverse the loop we are going to call some functions ex_loopdiagrams.py,
 # the program that draws the loop diagrams.
-from eppy import ex_loopdiagram
+from eppy.useful_scripts import loopdiagram
 fname = 'hhh_new.idf'
 iddfile = '../eppy/resources/iddfiles/Energy+V8_0_0.idd'
-edges = ex_loopdiagram.getedges(fname, iddfile)
+edges = loopdiagram.getedges(fname, iddfile)
 # edges are the lines that draw the nodes in the loop.
 # The term comes from graph theory in mathematics
+
+

+

+

+
The above code gets us the edges of the loop diagram. Once we have the edges, we can traverse through the diagram. Let us start with the “Central_Chiller” and work our way down.

+

+
[13]:
 

 

-
The above code gets us the edges of the loop diagram. Once we have the
-edges, we can traverse through the diagram. Let us start with the
-“Central_Chiller” and work our way down.

-
from eppy import walk_hvac
+
+from eppy import walk_hvac
 firstnode = "Central_Chiller"
 nextnodes = walk_hvac.nextnode(edges, firstnode)
-print nextnodes
+print(nextnodes)
+
 

 

-
[u'np1']
-

 

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+
+['np1']
+

+

+

+
[14]:
 

 

-
[u'np2']
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+

+
+['np2']
+

+

+

+
[15]:
 

 

-
[u'p_loop_supply_splitter']
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+

+
+['p_loop_supply_splitter']
+

+

+

+
[16]:
 

 

-
[u'sb1_pipe', u'sb2_pipe', u'sb3_pipe']
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

-
This leads us to three components -> [‘sb1_pipe’, ‘sb2_pipe’,
-‘sb3_pipe’]. Let us follow one of them

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+

+
+['sb1_pipe', 'sb2_pipe', 'sb3_pipe']
+

+

+
This leads us to three components -> [‘sb1_pipe’, ‘sb2_pipe’, ‘sb3_pipe’]. Let us follow one of them

+

+
[17]:
 

 

-
[u'p_loop_supply_mixer']
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+

+
+['p_loop_supply_mixer']
+

+

+

+
[18]:
 

 

-
[u'sb4_pipe']
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

-
nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
-print nextnodes
+

+

+

+

+

+
+['sb4_pipe']
+

+

+

+
[19]:
 

 

-
[]
+
+nextnodes = walk_hvac.nextnode(edges, nextnodes[0])
+print(nextnodes)
+
 

 

+

+

+

+

+

+
+[]
+

+

 
We have reached the end of this branch. There are no more components.

 
We can follow this in reverse using the function prevnode()

-
lastnode = 'sb4_pipe'
-prevnodes = walk_hvac.prevnode(edges, lastnode)
-print prevnodes
+

+
[20]:
 

 

-
[u'p_loop_supply_mixer']
+
+lastnode = 'sb4_pipe'
+prevnodes = walk_hvac.prevnode(edges, lastnode)
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['p_loop_supply_mixer']
+

+

+

+
[21]:
 

 

-
[u'sb1_pipe', u'sb2_pipe', u'sb3_pipe']
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['sb1_pipe', 'sb2_pipe', 'sb3_pipe']
+

+

+

+
[22]:
 

 

-
[u'p_loop_supply_splitter']
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['p_loop_supply_splitter']
+

+

+

+
[23]:
 

 

-
[u'np2']
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['np2']
+

+

+

+
[24]:
 

 

-
[u'np1']
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['np1']
+

+

+

+
[25]:
 

 

-
[u'Central_Chiller']
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

-
prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
-print prevnodes
+

+

+

+

+

+
+['Central_Chiller']
+

+

+

+
[26]:
 

 

-
[]
+
+prevnodes = walk_hvac.prevnode(edges, prevnodes[0])
+print(prevnodes)
+
 

 

+

+

+

+

+

+
+[]
+

+

 
All the way to where the loop ends

 

 

-

-
Building a Condensor loop

-
We build the condensor loop the same way we built the plant loop. Pipes
-are put as place holders for the components. Let us build a new idf file
-with just a condensor loop in it.

-
condensorloop_idf = IDF(StringIO(''))
+

+
Building a Condensor loop

+
We build the condensor loop the same way we built the plant loop. Pipes are put as place holders for the components. Let us build a new idf file with just a condensor loop in it.

+

+
[27]:
+

+

+
+condensorloop_idf = IDF(StringIO(''))
 loopname = "c_loop"
 sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side
 dloop = ['db0', ['db1', 'db2', 'db3'], 'db4'] # demand side
 theloop = hvacbuilder.makecondenserloop(condensorloop_idf, loopname, sloop, dloop)
 condensorloop_idf.saveas("c_loop.idf")
+
 

 

-
Again, just as we did in the plant loop, we can change the components of
-the loop, by replacing the branchs and traverse the loop using the
-functions nextnode() and prevnode()

 

-

-
Building an Air Loop

-
Building an air loop is similar to the plant and condensor loop. The
-difference is that instead of pipes , we have ducts as placeholder
-components. The other difference is that we have zones on the demand
-side.

-
airloop_idf = IDF(StringIO(''))
+
Again, just as we did in the plant loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()

+

+

+
Building an Air Loop

+
Building an air loop is similar to the plant and condensor loop. The difference is that instead of pipes , we have ducts as placeholder components. The other difference is that we have zones on the demand side.

+

+
[28]:
+

+

+
+airloop_idf = IDF(StringIO(''))
 loopname = "a_loop"
 sloop = ['sb0', ['sb1', 'sb2', 'sb3'], 'sb4'] # supply side of the loop
 dloop = ['zone1', 'zone2', 'zone3'] # zones on the demand side
 hvacbuilder.makeairloop(airloop_idf, loopname, sloop, dloop)
 airloop_idf.saveas("a_loop.idf")
+
 

 

-
Again, just as we did in the plant and condensor loop, we can change the
-components of the loop, by replacing the branchs and traverse the loop
-using the functions nextnode() and prevnode()

+

+
Again, just as we did in the plant and condensor loop, we can change the components of the loop, by replacing the branchs and traverse the loop using the functions nextnode() and prevnode()

 

 

 
@@ -457,25 +924,31 @@ 
Building an Air Loop
       
       

       

     

     

-      ©2018, Santosh Philip.
+      ©2020, Santosh Philip.
       
       |
-      Powered by Sphinx 1.7.1
-      & Alabaster 0.7.11
+      Powered by Sphinx 1.8.5
+      & Alabaster 0.7.12
       
       |
-      Page source
     

 
diff --git a/docs/_build/html/HVAC_diagrams.html b/docs/_build/html/HVAC_diagrams.html
deleted file mode 100644
index 2b56e14c..00000000
--- a/docs/_build/html/HVAC_diagrams.html
+++ /dev/null
@@ -1,145 +0,0 @@
-
-
-
-
-  
-    
-    
-    <no title> — eppy 0.5.46 documentation
-    
-    
-    
-    
-    
-    
-    
-    
-   
-  
-  
-  
-  
-
-  
-  
-
-    

-      

-        

-          
-
-          

-            
-  
"""HVAC diagrams"""
-

-

-
'HVAC diagrams'
-

-

-
# you would normaly install eppy by doing
-# python setup.py install
-# or
-# pip install eppy
-# or
-# easy_install eppy
-
-# if you have not done so, uncomment the following three lines
-import sys
-# pathnameto_eppy = 'c:/eppy'
-pathnameto_eppy = '../'
-sys.path.append(pathnameto_eppy)
-

-

-
from eppy import modeleditor
-from eppy.modeleditor import IDF
-iddfile = "../eppy/resources/iddfiles/Energy+V8_0_0.idd"
-fname = "../eppy/resources/idffiles/V8_0_0/5ZoneSupRetPlenRAB.idf"
-IDF.setiddname(iddfile)
-

-

-
idf = IDF(fname)
-

-

-
# idf.model
-

-

-
from eppy.EPlusInterfaceFunctions import readidf
-from eppy.ex_loopdiagram import makeairplantloop, makediagram
-
-print "readingfile"
-data, commdct = readidf.readdatacommdct(fname, iddfile=iddfile)
-print "constructing the loops"
-edges = makeairplantloop(data, commdct)
-print "making the diagram"
-g = makediagram(edges)
-dotname = "a.dot"
-pngname = "a.png"
-# dotname = '%s.dot' % (os.path.splitext(fname)[0])
-# pngname = '%s.png' % (os.path.splitext(fname)[0])
-g.write(dotname)
-g.write_png(pngname)
-

-

-
readingfile
-constructing the loops
-making the diagram
-

-

-
True
-

-

-
-
-          

-          
-        

-      

-      
-      

-    

-    

-      ©2018, Santosh Philip.
-      
-      |
-      Powered by Sphinx 1.7.1
-      & Alabaster 0.7.11
-      
-      |
-      Page source
-    

-
-    
-
-    
-  
-
\ No newline at end of file
diff --git a/docs/_build/html/LICENSE.html b/docs/_build/html/LICENSE.html
deleted file mode 100644
index 06eb1cf8..00000000
--- a/docs/_build/html/LICENSE.html
+++ /dev/null
@@ -1,112 +0,0 @@
-
-
-
-
-  
-    
-    
-    LICENSE — eppy 0.5.46 documentation
-    
-    
-    
-    
-    
-    
-    
-    
-    
-   
-  
-  
-  
-  
-
-  
-  
-
-    

-      

-        

-          
-
-          

-            
-  

-
LICENSE

-
The MIT License (MIT)

-
Copyright (c) 2004, 2012-2016 Santosh Philip
-Copyright (c) 2012  Tuan Tran
-Copyright (c) 2014  Eric Allen Youngson
-Copyright (c) 2015-2016  Jamie Bull

-
Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the “Software”), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:

-
The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.

-
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

-

-
-
-          

-          
-        

-      

-      
-      

-    

-    

-      ©2018, Santosh Philip.
-      
-      |
-      Powered by Sphinx 1.7.1
-      & Alabaster 0.7.11
-      
-      |
-      Page source
-    

-
-    
-
-    
-  
-
\ No newline at end of file
diff --git a/docs/_build/html/Main_Tutorial.html b/docs/_build/html/Main_Tutorial.html
index 15e6f3c1..027d13c6 100644
--- a/docs/_build/html/Main_Tutorial.html
+++ b/docs/_build/html/Main_Tutorial.html
@@ -6,13 +6,15 @@
   
     
     
-    Eppy Tutorial — eppy 0.5.46 documentation
+    Eppy Tutorial — eppy 0.5.52 documentation
     
     
-    
+    
     
     
     
+    
+    
     
     
     
@@ -33,42 +35,267 @@
 
           

             
-  

-
Eppy Tutorial

+  
+
+

+
Eppy Tutorial

 
Authors: Santosh Philip, Leora Tanjuatco

-
Eppy is a scripting language for E+ idf files, and E+ output files. Eppy
-is written in the programming language Python. As a result it takes full
-advantage of the rich data structure and idioms that are avaliable in
-python. You can programmatically navigate, search, and modify E+ idf
-files using eppy. The power of using a scripting language allows you to
-do the following:

+
Eppy is a scripting language for E+ idf files, and E+ output files. Eppy is written in the programming language Python. As a result it takes full advantage of the rich data structure and idioms that are avaliable in python. You can programmatically navigate, search, and modify E+ idf files using eppy. The power of using a scripting language allows you to do the following:

 
    
-
  • Make a large number of changes in an idf file with a few lines of
-eppy code.
    • 
+
  • Make a large number of changes in an idf file with a few lines of eppy code.
    • 
 
  • Use conditions and filters when making changes to an idf file
    • 
 
  • Make changes to multiple idf files.
    • 
 
  • Read data from the output files of a E+ simulation run.
    • 
-
  • Based to the results of a E+ simulation run, generate the input file
-for the next simulation run.
    • 
+
  • Based to the results of a E+ simulation run, generate the input file for the next simulation run.
    • 
 

-
So what does this matter? Here are some of the things you can do with
-eppy:

+
So what does this matter? Here are some of the things you can do with eppy:

 
    
 
  • Change construction for all north facing walls.
    • 
 
  • Change the glass type for all windows larger than 2 square meters.
    • 
 
  • Change the number of people in all the interior zones.
    • 
 
  • Change the lighting power in all south facing zones.
    • 
 
  • Change the efficiency and fan power of all rooftop units.
    • 
-
  • Find the energy use of all the models in a folder (or of models that
-were run after a certain date)
    • 
-
  • If a model is using more energy than expected, keep increasing the
-R-value of the roof until you get to the expected energy use.
    • 
+
  • Find the energy use of all the models in a folder (or of models that were run after a certain date)
    • 
+
  • If a model is using more energy than expected, keep increasing the R-value of the roof until you get to the expected energy use.
    • 
 

-

-
Quick Start

-
Here is a short IDF file that I’ll be using as an example to start us
-off

-
VERSION,
+

+
Quick Start

+
Here is a short IDF file that I’ll be using as an example to start us off

+
..
+

+

+VERSION,
     7.2;                     !- Version Identifier
 
 SIMULATIONCONTROL,
@@ -93,11 +320,13 @@ 
Quick Start
+
[82]:
 

 

-
To use eppy to look at this model, we have to run a little code first:

-
# you would normaly install eppy by doing
+
+# you would normaly install eppy by doing
 # python setup.py install
 # or
 # pip install eppy
@@ -110,22 +339,42 @@ 
Quick Startpathnameto_eppy = '../'
 sys.path.append(pathnameto_eppy)
 
-from eppy import modeleditor
-from eppy.modeleditor import IDF
+from eppy import modeleditor
+from eppy.modeleditor import IDF
 iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd"
 fname1 = "../eppy/resources/idffiles/V_7_2/smallfile.idf"
 

 

-
IDF.setiddname(iddfile)
+

+

+
[83]:
+

+

+
+IDF.setiddname(iddfile)
 idf1 = IDF(fname1)
+
 

 

+

 
idf1 now holds all the data to your in you idf file.

 
Now that the behind-the-scenes work is done, we can print this file.

-
idf1.printidf()
+

+
[84]:
+

+

+
+idf1.printidf()
 

 

-
VERSION,
+

+

+

+

+

+
+
+VERSION,
     7.3;                      !- Version Identifier
 
 SIMULATIONCONTROL,
@@ -137,7 +386,7 @@ 
Quick Start
 
  • VERSION
    • 
@@ -162,18 +411,28 @@ 
    Quick Start
    print filename.idfobjects['OBJECTNAME']
+
    So, let us look take a closer look at the BUILDING object. We can do this using this command:
    
+
    ..
 
    
 
    
-
    print idf1.idfobjects['BUILDING']  # put the name of the object you'd like to look at in brackets
+print(filename.idfobjects['OBJECTNAME'])
    
+
    [85]:
 
    
 
    
-
    [
+
    +print(idf1.idfobjects['BUILDING']) # put the name of the object you'd like to look at in brackets
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +[
 BUILDING,
     Empire State Building,    !- Name
-    30.0,                     !- North Axis
+    30,                       !- North Axis
     City,                     !- Terrain
     0.04,                     !- Loads Convergence Tolerance Value
     0.4,                      !- Temperature Convergence Tolerance Value
@@ -181,38 +440,87 @@ 
    Quick Start
    building = idf1.idfobjects['BUILDING'][0]
+
    To do this, we have to do some more behind-the-scenes work, which we’ll explain later.
    
+
    
+
    [86]:
+
    
+
    
+
    +building = idf1.idfobjects['BUILDING'][0]
+
 
    
 
    
+
    
 
    Now we can do this:
    
-
    print building.Name
+
    
+
    [87]:
 
    
 
    
-
    Empire State Building
+
    +print(building.Name)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +Empire State Building
+
    
+
    
 
    Now that we’ve isolated the building name, we can change it.
    
-
    building.Name = "Empire State Building"
+
    
+
    [88]:
+
    
+
    
+
    +building.Name = "Empire State Building"
+
 
    
 
    
-
    print building.Name
+
    
+
    
+
    [89]:
+
    
+
    
+
    +print(building.Name)
+
 
    
 
    
-
    Empire State Building
+
    
+
    
+
    
+
    
+
    
+
    +Empire State Building
+
    
+
    
+
    Did this actually change the name in the model ? Let us print the entire model and see.
    
+
    
+
    [90]:
 
    
 
    
-
    Did this actually change the name in the model ? Let us print the entire
-model and see.
    
-
    idf1.printidf()
+
    +idf1.printidf()
+
 
    
 
    
-
    VERSION,
+
    
+
    
+
    
+
    
+
    
+
    +
+VERSION,
     7.3;                      !- Version Identifier
 
 SIMULATIONCONTROL,
@@ -224,7 +532,7 @@ 
    Quick Start
+
    Modifying IDF Fields
    
+
    That was just a quick example – we were showing off. Let’s look a little closer.
    
+
    As you might have guessed, changing an IDF field follows this structure:
    
+
    ..
 
    
 
    
-
    Yes! It did. So now you have a taste of what eppy can do. Let’s get
-started!
    
+object.fieldname = "New Field Name"
    Plugging the object name (building), the field name (Name) and our new field name (“Empire State Building”) into this command gave us this:
    
+
    
+
    [91]:
+
    
 
    
-
    
-
    Modifying IDF Fields
    
-
    That was just a quick example – we were showing off. Let’s look a
-little closer.
    
-
    As you might have guessed, changing an IDF field follows this
-structure:
    
-
    object.fieldname = "New Field Name"
+
    +building.Name = "Empire State Building"
+
 
    
 
    
-
    Plugging the object name (building), the field name (Name) and our new
-field name (“Empire State Building”) into this command gave us this:
    
-
    building.Name = "Empire State Building"
+
    
+
    
+
    [92]:
 
    
 
    
-
    import eppy
+
    +import eppy
 # import eppy.ex_inits
 # reload(eppy.ex_inits)
-import eppy.ex_inits
+import ex_inits
+
 
    
 
    
-
    But how did we know that “Name” is one of the fields in the object
-“building”?
    
+
    
+
    But how did we know that “Name” is one of the fields in the object “building”?
    
 
    Are there other fields?
    
 
    What are they called?
    
 
    Let’s take a look at the IDF editor:
    
-
    from eppy import ex_inits #no need to know this code, it just shows the image below
+
    
+
    [93]:
+
    
+
    
+
    +import ex_inits #no need to know this code, it just shows the image below
 for_images = ex_inits
 for_images.display_png(for_images.idfeditor)
+
 
    
 
    
-Main_Tutorial_files/Main_Tutorial_34_0.png
+
    
+
    
+
    
+
    
+
    
+_images/Main_Tutorial_34_0.png
+
    
+
    
 
    In the IDF Editor, the building object is selected.
    
 
    We can see all the fields of the object “BUILDING”.
    
 
    They are:
    
@@ -288,52 +619,88 @@ 
    Modifying IDF FieldsMinimum Number of Warmup Days
 
 
    Let us try to access the other fields.
    
-
    print building.Terrain
+
    
+
    [94]:
 
    
 
    
-
    City
+
    +print(building.Terrain)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +City
+
    
+
    
 
    How about the field “North Axis” ?
    
 
    It is not a single word, but two words.
    
-
    In a programming language, a variable has to be a single word without
-any spaces.
    
+
    In a programming language, a variable has to be a single word without any spaces.
    
 
    To solve this problem, put an underscore where there is a space.
    
 
    So “North Axis” becomes “North_Axis”.
    
-
    print building.North_Axis
+
    
+
    [95]:
 
    
 
    
-
    30.0
+
    +print(building.North_Axis)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +30.0
+
    
+
    
 
    Now we can do:
    
-
    print building.Name
-print building.North_Axis
-print building.Terrain
-print building.Loads_Convergence_Tolerance_Value
-print building.Temperature_Convergence_Tolerance_Value
-print building.Solar_Distribution
-print building.Maximum_Number_of_Warmup_Days
-print building.Minimum_Number_of_Warmup_Days
-
    
-
    
-
    Empire State Building
-30.0
-City
-0.04
-0.4
-FullExterior
-25
-6
+
    
+
    [96]:
+
    
+
    
+
    +print(building.Name)
+print(building.North_Axis)
+print(building.Terrain)
+print(building.Loads_Convergence_Tolerance_Value)
+print(building.Temperature_Convergence_Tolerance_Value)
+print(building.Solar_Distribution)
+print(building.Maximum_Number_of_Warmup_Days)
+print(building.Minimum_Number_of_Warmup_Days)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +Empire State Building
+30.0
+City
+0.04
+0.4
+FullExterior
+25
+6
+
    
+
    
 
    Where else can we find the field names?
    
-
    The IDF Editor saves the idf file with the field name commented next to
-field.
    
+
    The IDF Editor saves the idf file with the field name commented next to field.
    
 
    Eppy also does this.
    
-
    Let us take a look at the “BUILDING” object in the text file that the
-IDF Editor saves
    
-
    BUILDING,
+
    Let us take a look at the “BUILDING” object in the text file that the IDF Editor saves
    
+
    ..
+
    
+
    
+BUILDING,
     White House,             !- Name
     30.,                     !- North Axis {deg}
     City,                    !- Terrain
@@ -341,105 +708,175 @@ 
    Modifying IDF FieldsThis a good place to find the field names too.
    
+
    It is easy to copy and paste from here. You can’t do that from the IDF Editor.
    
+
    We know that in an E+ model, there will be only ONE “BUILDING” object. This will be the first and only item in the list “buildings”.
    
+
    But E+ models are made up of objects such as “BUILDING”, “SITE:LOCATION”, “ZONE”, “PEOPLE”, “LIGHTS”. There can be a number of “ZONE” objects, a number of “PEOPLE” objects and a number of “LIGHTS” objects.
    
+
    So how do you know if you’re looking at the first “ZONE” object or the second one? Or the tenth one? To answer this, we need to learn about how lists work in python.
    
+
    
+
    
+
    Python lesson 1: lists
    
+
    Eppy holds these objects in a python structure called list. Let us take a look at how lists work in python.
    
+
    
+
    [97]:
 
    
 
    
-
    This a good place to find the field names too.
    
-
    It is easy to copy and paste from here. You can’t do that from the IDF
-Editor.
    
-
    We know that in an E+ model, there will be only ONE “BUILDING” object.
-This will be the first and only item in the list “buildings”.
    
-
    But E+ models are made up of objects such as “BUILDING”,
-“SITE:LOCATION”, “ZONE”, “PEOPLE”, “LIGHTS”. There can be a number of
-“ZONE” objects, a number of “PEOPLE” objects and a number of “LIGHTS”
-objects.
    
-
    So how do you know if you’re looking at the first “ZONE” object or the
-second one? Or the tenth one? To answer this, we need to learn about how
-lists work in python.
    
-
    
-
    
-
    Python lesson 1: lists
    
-
    Eppy holds these objects in a python structure called list. Let us take
-a look at how lists work in python.
    
-
    fruits = ["apple", "orange", "bannana"]
+
    +fruits = ["apple", "orange", "bannana"]
 # fruits is a list with three items in it.
+
 
    
 
    
+
    
 
    To get the first item in fruits we say:
    
-
    fruits[0]
+
    
+
    [98]:
+
    
+
    
+
    +fruits[0]
+
 
    
 
    
-
    'apple'
+
    
+
    
+
    [98]:
 
    
 
    
+
    
+
    +'apple'
+
    
+
    
 
    Why “0” ?
    
-
    Because, unlike us, python starts counting from zero in a list. So, to
-get the third item in the list we’d need to input 2, like this:
    
-
    print fruits[2]
+
    Because, unlike us, python starts counting from zero in a list. So, to get the third item in the list we’d need to input 2, like this:
    
+
    
+
    [99]:
 
    
 
    
-
    bannana
+
    +print(fruits[2])
+
 
    
 
    
-
    But calling the first fruit “fruit[0]” is rather cumbersome. Why don’t
-we call it firstfruit?
    
-
    firstfruit = fruits[0]
-print firstfruit
+
    
+
    
+
    
+
    
+
    
+
    +bannana
+
    
+
    
+
    But calling the first fruit “fruit[0]” is rather cumbersome. Why don’t we call it firstfruit?
    
+
    
+
    [100]:
 
    
 
    
-
    apple
+
    +firstfruit = fruits[0]
+print(firstfruit)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +apple
+
    
+
    
 
    We also can say
    
-
    goodfruit = fruits[0]
+
    
+
    [101]:
+
    
+
    
+
    +goodfruit = fruits[0]
 redfruit = fruits[0]
 
-print firstfruit
-print goodfruit
-print redfruit
-print fruits[0]
+print(firstfruit)
+print(goodfruit)
+print(redfruit)
+print(fruits[0])
 
    
 
    
-
    apple
-apple
-apple
-apple
-
    
+
    
+
    
+
    
+
    
+
    
+
    +apple
+apple
+apple
+apple
+
    
 
    
 
    As you see, we can call that item in the list whatever we want.
    
-
    
-
    How many items in the list
    
+
    
+
    How many items in the list
    
 
    To know how many items are in a list, we ask for the length of the list.
    
 
    The function ‘len’ will do this for us.
    
-
    print len(fruits)
+
    
+
    [102]:
 
    
 
    
-
    3
+
    +print(len(fruits))
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +3
+
    
+
    
 
    There are 3 fruits in the list.
    
 
    
 
    
-
    
-
    Saving an idf file
    
+
    
+
    Saving an idf file
    
 
    This is easy:
    
-
    idf1.save()
+
    
+
    [103]:
+
    
+
    
+
    +idf1.save()
+
 
    
 
    
+
    
 
    If you’d like to do a “Save as…” use this:
    
-
    idf1.saveas('something.idf')
+
    
+
    [104]:
+
    
+
    
+
    +idf1.saveas('something.idf')
+
 
    
 
    
 
    
-
    
-
    Working with E+ objects
    
-
    Let us open a small idf file that has only “CONSTRUCTION” and “MATERIAL”
-objects in it. You can go into “../idffiles/V_7_2/constructions.idf”
-and take a look at the file. We are not printing it here because it is
-too big.
    
+
    
+
    
+
    Working with E+ objects
    
+
    Let us open a small idf file that has only “CONSTRUCTION” and “MATERIAL” objects in it. You can go into “../idffiles/V_7_2/constructions.idf” and take a look at the file. We are not printing it here because it is too big.
    
 
    So let us open it using the idfreader -
    
-
    from eppy import modeleditor
-from eppy.modeleditor import IDF
+
    
+
    [105]:
+
    
+
    
+
    +from eppy import modeleditor
+from eppy.modeleditor import IDF
 
 iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd"
 try:
@@ -449,424 +886,739 @@ 
    Working with E+ objectsfname1 = "../eppy/resources/idffiles/V_7_2/constructions.idf"
 idf1 = IDF(fname1)
+
 
    
 
    
+
    
 
    Let us print all the “MATERIAL” objects in this model.
    
-
    materials = idf1.idfobjects["MATERIAL"]
-print materials
+
    
+
    [106]:
+
    
+
    
+
    +materials = idf1.idfobjects["MATERIAL"]
+print(materials)
+
 
    
 
    
-
    [
+
    
+
    
+
    
+
    
+
    
+
    +[
 Material,
     F08 Metal surface,        !- Name
     Smooth,                   !- Roughness
     0.0008,                   !- Thickness
     45.28,                    !- Conductivity
-    7824.0,                   !- Density
-    500.0;                    !- Specific Heat
+    7824,                     !- Density
+    500;                      !- Specific Heat
 ,
 Material,
     I01 25mm insulation board,    !- Name
     MediumRough,              !- Roughness
     0.0254,                   !- Thickness
     0.03,                     !- Conductivity
-    43.0,                     !- Density
-    1210.0;                   !- Specific Heat
+    43,                       !- Density
+    1210;                     !- Specific Heat
 ,
 Material,
     I02 50mm insulation board,    !- Name
     MediumRough,              !- Roughness
     0.0508,                   !- Thickness
     0.03,                     !- Conductivity
-    43.0,                     !- Density
-    1210.0;                   !- Specific Heat
+    43,                       !- Density
+    1210;                     !- Specific Heat
 ,
 Material,
     G01a 19mm gypsum board,    !- Name
     MediumSmooth,             !- Roughness
     0.019,                    !- Thickness
     0.16,                     !- Conductivity
-    800.0,                    !- Density
-    1090.0;                   !- Specific Heat
+    800,                      !- Density
+    1090;                     !- Specific Heat
 ,
 Material,
     M11 100mm lightweight concrete,    !- Name
     MediumRough,              !- Roughness
     0.1016,                   !- Thickness
     0.53,                     !- Conductivity
-    1280.0,                   !- Density
-    840.0;                    !- Specific Heat
+    1280,                     !- Density
+    840;                      !- Specific Heat
 ,
 Material,
     F16 Acoustic tile,        !- Name
     MediumSmooth,             !- Roughness
     0.0191,                   !- Thickness
     0.06,                     !- Conductivity
-    368.0,                    !- Density
-    590.0;                    !- Specific Heat
+    368,                      !- Density
+    590;                      !- Specific Heat
 ,
 Material,
     M01 100mm brick,          !- Name
     MediumRough,              !- Roughness
     0.1016,                   !- Thickness
     0.89,                     !- Conductivity
-    1920.0,                   !- Density
-    790.0;                    !- Specific Heat
+    1920,                     !- Density
+    790;                      !- Specific Heat
 ,
 Material,
     M15 200mm heavyweight concrete,    !- Name
     MediumRough,              !- Roughness
     0.2032,                   !- Thickness
     1.95,                     !- Conductivity
-    2240.0,                   !- Density
-    900.0;                    !- Specific Heat
+    2240,                     !- Density
+    900;                      !- Specific Heat
 ,
 Material,
     M05 200mm concrete block,    !- Name
     MediumRough,              !- Roughness
     0.2032,                   !- Thickness
     1.11,                     !- Conductivity
-    800.0,                    !- Density
-    920.0;                    !- Specific Heat
+    800,                      !- Density
+    920;                      !- Specific Heat
 ,
 Material,
     G05 25mm wood,            !- Name
     MediumSmooth,             !- Roughness
     0.0254,                   !- Thickness
     0.15,                     !- Conductivity
-    608.0,                    !- Density
-    1630.0;                   !- Specific Heat
+    608,                      !- Density
+    1630;                     !- Specific Heat
 ]
-
    
+
    
 
    
 
    As you can see, there are many material objects in this idf file.
    
 
    The variable “materials” now contains a list of “MATERIAL” objects.
    
-
    You already know a little about lists, so let us take a look at the
-items in this list.
    
-
    firstmaterial = materials[0]
+
    You already know a little about lists, so let us take a look at the items in this list.
    
+
    
+
    [107]:
+
    
+
    
+
    +firstmaterial = materials[0]
 secondmaterial = materials[1]
+
 
    
 
    
-
    print firstmaterial
+
    
+
    
+
    [108]:
+
    
+
    
+
    +print(firstmaterial)
+
 
    
 
    
-
    Material,
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     F08 Metal surface,        !- Name
     Smooth,                   !- Roughness
     0.0008,                   !- Thickness
     45.28,                    !- Conductivity
-    7824.0,                   !- Density
-    500.0;                    !- Specific Heat
-
    
+    7824,                     !- Density
+    500;                      !- Specific Heat
+
+
    
 
    
 
    Let us print secondmaterial
    
-
    print secondmaterial
+
    
+
    [109]:
+
    
+
    
+
    +print(secondmaterial)
+
 
    
 
    
-
    Material,
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     I01 25mm insulation board,    !- Name
     MediumRough,              !- Roughness
     0.0254,                   !- Thickness
     0.03,                     !- Conductivity
-    43.0,                     !- Density
-    1210.0;                   !- Specific Heat
-
    
+    43,                       !- Density
+    1210;                     !- Specific Heat
+
+
    
 
    
 
    This is awesome!! Why?
    
-
    To understand what you can do with your objects organized as lists,
-you’ll have to learn a little more about lists.
    
-
    
-
    
-
    Python lesson 2: more about lists
    
-
    
-
    More ways to access items in a list
    
-
    You should remember that you can access any item in a list by passing in
-its index.
    
-
    The tricky part is that python starts counting at 0, so you need to
-input 0 in order to get the first item in a list.
    
-
    Following the same logic, you need to input 3 in order to get the fourth
-item on the list. Like so:
    
-
    bad_architects = ["Donald Trump", "Mick Jagger",
-        "Steve Jobs", "Lady Gaga", "Santa Clause"]
-print bad_architects[3]
+
    To understand what you can do with your objects organized as lists, you’ll have to learn a little more about lists.
    
+
    
+
    
+
    Python lesson 2: more about lists
    
+
    
+
    More ways to access items in a list
    
+
    You should remember that you can access any item in a list by passing in its index.
    
+
    The tricky part is that python starts counting at 0, so you need to input 0 in order to get the first item in a list.
    
+
    Following the same logic, you need to input 3 in order to get the fourth item on the list. Like so:
    
+
    
+
    [110]:
 
    
 
    
-
    Lady Gaga
+
    +bad_architects = ["Donald Trump", "Mick Jagger",
+        "Steve Jobs", "Lady Gaga", "Santa Clause"]
+print(bad_architects[3])
+
 
    
 
    
-
    But there’s another way to access items in a list. If you input -1, it
-will return the last item. -2 will give you the second-to-last item,
-etc.
    
-
    print bad_architects[-1]
-print bad_architects[-2]
+
    
+
    
+
    
+
    
+
    
+
    +Lady Gaga
+
    
+
    
+
    But there’s another way to access items in a list. If you input -1, it will return the last item. -2 will give you the second-to-last item, etc.
    
+
    
+
    [111]:
 
    
 
    
-
    Santa Clause
-Lady Gaga
+
    +print(bad_architects[-1])
+print(bad_architects[-2])
+
 
    
 
    
 
    
-
    
-
    Slicing a list
    
+
    
+
    
+
    
+
    
+
    +Santa Clause
+Lady Gaga
+
    
+
    
+
    
+
    
+
    Slicing a list
    
 
    You can also get more than one item in a list:
    
-
    bad_architects[first_slice:second_slice]
    
-
    print bad_architects[1:3] # slices at 1 and 3
+bad_architects[first_slice:second_slice]
    
+
    [112]:
 
    
 
    
-
    ['Mick Jagger', 'Steve Jobs']
+
    +print(bad_architects[1:3]) # slices at 1 and 3
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +['Mick Jagger', 'Steve Jobs']
+
    
+
    
 
    How do I make sense of this?
    
 
    To understand this you need to see the list in the following manner:
    
-
    [ "Donald Trump", "Mick Jagger", "Steve Jobs", "Lady Gaga", "Santa Clause" ]
- ^               ^              ^             ^            ^              ^
- 0               1              2             3            4              5
--5              -4             -3            -2           -1
+
    ..
 
    
 
    
-
    The slice operation bad_architects[1:3] slices right where the numbers
-are.
    
+[ "Donald Trump", "Mick Jagger", "Steve Jobs", "Lady Gaga", "Santa Clause" ]
+ ^               ^              ^             ^            ^              ^
+ 0               1              2             3            4              5
+-5              -4             -3            -2           -1
    The slice operation bad_architects[1:3] slices right where the numbers are.
    
 
    Does that make sense?
    
 
    Let us try a few other slices:
    
-
    print bad_architects[2:-1] # slices at 2 and -1
-print bad_architects[-3:-1] # slices at -3 and -1
+
    
+
    [113]:
 
    
 
    
-
    ['Steve Jobs', 'Lady Gaga']
-['Steve Jobs', 'Lady Gaga']
+
    +print(bad_architects[2:-1]) # slices at 2 and -1
+print(bad_architects[-3:-1]) # slices at -3 and -1
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +['Steve Jobs', 'Lady Gaga']
+['Steve Jobs', 'Lady Gaga']
+
    
+
    
 
    You can also slice in the following way:
    
-
    print bad_architects[3:]
-print bad_architects[:2]
-print bad_architects[-3:]
-print bad_architects[:-2]
+
    
+
    [114]:
 
    
 
    
-
    ['Lady Gaga', 'Santa Clause']
-['Donald Trump', 'Mick Jagger']
-['Steve Jobs', 'Lady Gaga', 'Santa Clause']
-['Donald Trump', 'Mick Jagger', 'Steve Jobs']
+
    +print(bad_architects[3:] )
+print(bad_architects[:2] )
+print(bad_architects[-3:] )
+print(bad_architects[:-2] )
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +['Lady Gaga', 'Santa Clause']
+['Donald Trump', 'Mick Jagger']
+['Steve Jobs', 'Lady Gaga', 'Santa Clause']
+['Donald Trump', 'Mick Jagger', 'Steve Jobs']
+
    
+
    
 
    I’ll let you figure that out on your own.
    
 
    
-
    
-
    Adding to a list
    
+
    
+
    Adding to a list
    
 
    This is simple: the append function adds an item to the end of the list.
    
-
    The following command will add ‘something’ to the end of the list called
-listname:
    
-
    listname.append(something)
+
    The following command will add ‘something’ to the end of the list called listname:
    
+
    ..
 
    
 
    
-
    bad_architects.append("First-year students")
-print bad_architects
+listname.append(something)
    
+
    [115]:
 
    
 
    
-
    ['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause', 'First-year students']
+
    +bad_architects.append("First-year students")
+print(bad_architects)
+
 
    
 
    
 
    
-
    
-
    Deleting from a list
    
-
    There are two ways to do this, based on the information you have. If you
-have the value of the object, you’ll want to use the remove function. It
-looks like this:
    
-
    listname.remove(value)
    
-
    An example:
    
-
    bad_architects.remove("First-year students")
-print bad_architects
+
    
+
    
+
    
+
    
+
    +['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause', 'First-year students']
+
    
+
    
+
    
+
    
+
    Deleting from a list
    
+
    There are two ways to do this, based on the information you have. If you have the value of the object, you’ll want to use the remove function. It looks like this:
    
+listname.remove(value)
    An example:
    
+
    
+
    [116]:
 
    
 
    
-
    ['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause']
+
    +bad_architects.remove("First-year students")
+print(bad_architects)
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +['Donald Trump', 'Mick Jagger', 'Steve Jobs', 'Lady Gaga', 'Santa Clause']
+
    
+
    
 
    What if you know the index of the item you want to remove?
    
-
    What if you appended an item by mistake and just want to remove the last
-item in the list?
    
+
    What if you appended an item by mistake and just want to remove the last item in the list?
    
 
    You should use the pop function. It looks like this:
    
-
    listname.pop(index)
    
-
    what_i_ate_today = ["coffee", "bacon", "eggs"]
-print what_i_ate_today
+listname.pop(index)
    
+
    [117]:
+
    
+
    
+
    +what_i_ate_today = ["coffee", "bacon", "eggs"]
+print(what_i_ate_today)
+
 
    
 
    
-
    ['coffee', 'bacon', 'eggs']
+
    
+
    
+
    
+
    
+
    
+
    +['coffee', 'bacon', 'eggs']
+
    
+
    
+
    
+
    [118]:
 
    
 
    
-
    what_i_ate_today.append("vegetables") # adds vegetables to the end of the list
+
    +what_i_ate_today.append("vegetables") # adds vegetables to the end of the list
 # but I don't like vegetables
-print what_i_ate_today
+print(what_i_ate_today)
+
 
    
 
    
-
    ['coffee', 'bacon', 'eggs', 'vegetables']
+
    
+
    
+
    
+
    
+
    
+
    +['coffee', 'bacon', 'eggs', 'vegetables']
+
    
+
    
+
    
+
    [119]:
 
    
 
    
-
    # since I don't like vegetables
+
    +# since I don't like vegetables
 what_i_ate_today.pop(-1) # use index of -1, since vegetables is the last item in the list
-print what_i_ate_today
+print(what_i_ate_today)
+
 
    
 
    
-
    ['coffee', 'bacon', 'eggs']
-
    
+
    
+
    
+
    
+
    
+
    
+
    +['coffee', 'bacon', 'eggs']
+
    
 
    
 
    You can also remove the second item.
    
-
    what_i_ate_today.pop(1)
+
    
+
    [120]:
 
    
 
    
-
    'bacon'
+
    +what_i_ate_today.pop(1)
+
+
    
+
    
+
    
+
    
+
    [120]:
 
    
 
    
+
    
+
    +'bacon'
+
    
+
    
 
    Notice the ‘bacon’ in the line above.
    
-
    pop actually ‘pops’ the value (the one you just removed from the list)
-back to you.
    
+
    pop actually ‘pops’ the value (the one you just removed from the list) back to you.
    
 
    Let us pop the first item.
    
-
    was_first_item = what_i_ate_today.pop(0)
-print 'was_first_item =', was_first_item
-print 'what_i_ate_today = ', what_i_ate_today
+
    
+
    [121]:
 
    
 
    
-
    was_first_item = coffee
-what_i_ate_today =  ['eggs']
+
    +was_first_item = what_i_ate_today.pop(0)
+print('was_first_item =', was_first_item)
+print('what_i_ate_today = ', what_i_ate_today)
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +was_first_item = coffee
+what_i_ate_today =  ['eggs']
+
    
+
    
 
    what_i_ate_today is just ‘eggs’?
    
 
    That is not much of a breakfast!
    
 
    Let us get back to eppy.
    
 
    
 
    
-
    
-
    Continuing to work with E+ objects
    
+
    
+
    Continuing to work with E+ objects
    
 
    Let us get those “MATERIAL” objects again
    
-
    materials = idf1.idfobjects["MATERIAL"]
+
    
+
    [122]:
 
    
 
    
+
    +materials = idf1.idfobjects["MATERIAL"]
+
+
    
+
    
+
    
 
    With our newfound knowledge of lists, we can do a lot of things.
    
 
    Let us get the last material:
    
-
    print materials[-1]
+
    
+
    [123]:
 
    
 
    
-
    Material,
+
    +print(materials[-1])
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     G05 25mm wood,            !- Name
     MediumSmooth,             !- Roughness
     0.0254,                   !- Thickness
     0.15,                     !- Conductivity
-    608.0,                    !- Density
-    1630.0;                   !- Specific Heat
-
    
+    608,                      !- Density
+    1630;                     !- Specific Heat
+
+
    
 
    
 
    How about the last two?
    
-
    print materials[-2:]
+
    
+
    [124]:
+
    
+
    
+
    +print(materials[-2:])
+
 
    
 
    
-
    [
+
    
+
    
+
    
+
    
+
    
+
    +[
 Material,
     M05 200mm concrete block,    !- Name
     MediumRough,              !- Roughness
     0.2032,                   !- Thickness
     1.11,                     !- Conductivity
-    800.0,                    !- Density
-    920.0;                    !- Specific Heat
+    800,                      !- Density
+    920;                      !- Specific Heat
 ,
 Material,
     G05 25mm wood,            !- Name
     MediumSmooth,             !- Roughness
     0.0254,                   !- Thickness
     0.15,                     !- Conductivity
-    608.0,                    !- Density
-    1630.0;                   !- Specific Heat
+    608,                      !- Density
+    1630;                     !- Specific Heat
 ]
-
    
+
    
 
    
 
    Pretty good.
    
-
    
-
    Counting all the materials ( or counting all objects )
    
+
    
+
    Counting all the materials ( or counting all objects )
    
 
    How many materials are in this model ?
    
-
    print len(materials)
+
    
+
    [125]:
 
    
 
    
-
    10
+
    +print(len(materials))
+
 
    
 
    
 
    
-    
+
    
+
    Removing a material
    
 
    Let us remove the last material in the list
    
-
    was_last_material = materials.pop(-1)
+
    
+
    [126]:
+
    
+
    
+
    +was_last_material = materials.pop(-1)
+
 
    
 
    
-
    print len(materials)
+
    
+
    
+
    [127]:
 
    
 
    
-
    9
+
    +print(len(materials))
+
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +9
+
    
+
    
 
    Success! We have only 9 materials now.
    
 
    The last material used to be:
    
 
    ‘G05 25mm wood’
    
-
    print materials[-1]
+
    
+
    [128]:
+
    
+
    
+
    +print(materials[-1])
+
 
    
 
    
-
    Material,
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     M05 200mm concrete block,    !- Name
     MediumRough,              !- Roughness
     0.2032,                   !- Thickness
     1.11,                     !- Conductivity
-    800.0,                    !- Density
-    920.0;                    !- Specific Heat
-
    
+    800,                      !- Density
+    920;                      !- Specific Heat
+
+
    
 
    
 
    Now the last material in the list is:
    
 
    ‘M15 200mm heavyweight concrete’
    
 
    
-
    
-
    Adding a material to the list
    
+
    
+
    Adding a material to the list
    
 
    We still have the old last material
    
-
    print was_last_material
+
    
+
    [129]:
 
    
 
    
-
    Material,
+
    +print(was_last_material)
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     G05 25mm wood,            !- Name
     MediumSmooth,             !- Roughness
     0.0254,                   !- Thickness
     0.15,                     !- Conductivity
-    608.0,                    !- Density
-    1630.0;                   !- Specific Heat
-
    
+    608,                      !- Density
+    1630;                     !- Specific Heat
+
+
    
 
    
 
    Let us add it back to the list
    
-
    materials.append(was_last_material)
+
    
+
    [130]:
 
    
 
    
-
    print len(materials)
+
    +materials.append(was_last_material)
+
 
    
 
    
-
    10
+
    
+
    
+
    [131]:
 
    
 
    
+
    +print(len(materials))
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +10
+
    
+
    
 
    Once again we have 10 materials and the last material is:
    
-
    print materials[-1]
+
    
+
    [132]:
 
    
 
    
-
    Material,
+
    +print(materials[-1])
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +
+Material,
     G05 25mm wood,            !- Name
     MediumSmooth,             !- Roughness
     0.0254,                   !- Thickness
     0.15,                     !- Conductivity
-    608.0,                    !- Density
-    1630.0;                   !- Specific Heat
-
    
+    608,                      !- Density
+    1630;                     !- Specific Heat
+
+
    
 
    
 
    
-
    
-
    Add a new material to the model
    
-
    So far we have been working only with materials that were already in the
-list.
    
+
    
+
    Add a new material to the model
    
+
    So far we have been working only with materials that were already in the list.
    
 
    What if we want to make new material?
    
 
    Obviously we would use the function ‘newidfobject’.
    
-
    idf1.newidfobject("MATERIAL")
+
    
+
    [133]:
+
    
+
    
+
    +idf1.newidfobject("MATERIAL")
+
+
    
+
    
+
    
+
    
+
    [133]:
 
    
 
    
-
    MATERIAL,
+
    
+
    +
+MATERIAL,
     ,                         !- Name
     ,                         !- Roughness
     ,                         !- Thickness
@@ -876,21 +1628,46 @@ 
    Add a new material to the model
+
    [134]:
 
    
 
    
-
    len(materials)
+
    +len(materials)
+
 
    
 
    
-
    11
+
    
+
    
+
    [134]:
 
    
 
    
+
    
+
    +11
+
    
+
    
 
    We have 11 items in the materials list.
    
-
    Let us take a look at the last material in the list, where this fancy
-new material was added
    
-
    print materials[-1]
+
    Let us take a look at the last material in the list, where this fancy new material was added
    
+
    
+
    [135]:
 
    
 
    
-
    MATERIAL,
+
    +print(materials[-1])
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +
+MATERIAL,
     ,                         !- Name
     ,                         !- Roughness
     ,                         !- Thickness
@@ -900,48 +1677,75 @@ 
    Add a new material to the model
    materials[-1].Name = 'Peanut Butter'
+
    
+
    [136]:
+
    
+
    
+
    +materials[-1].Name = 'Peanut Butter'
 materials[-1].Roughness = 'MediumSmooth'
 materials[-1].Thickness = 0.03
 materials[-1].Conductivity = 0.16
 materials[-1].Density = 600
 materials[-1].Specific_Heat = 1500
+
 
    
 
    
-
    print materials[-1]
+
    
+
    
+
    [137]:
 
    
 
    
-
    MATERIAL,
-    Peanut Butter,            !- Name
-    MediumSmooth,             !- Roughness
-    0.03,                     !- Thickness
-    0.16,                     !- Conductivity
-    600,                      !- Density
-    1500,                     !- Specific Heat
-    0.9,                      !- Thermal Absorptance
-    0.7,                      !- Solar Absorptance
-    0.7;                      !- Visible Absorptance
+
    +print materials[-1]
+
 
    
 
    
 
    
-
    
-
    Copy an existing material
    
-
    Peanutbuttermaterial = materials[-1]
+
    
+
    
+
    
+
    
+
    +  File "<ipython-input-137-dc200e5378be>", line 1
+    print materials[-1]
+                  ^
+SyntaxError: Missing parentheses in call to 'print'. Did you mean print(materials[-1])?
+
+
    
+
    
+
    
+
    
+
    Copy an existing material
    
+
    
+
    [138]:
+
    
+
    
+
    +Peanutbuttermaterial = materials[-1]
 idf1.copyidfobject(Peanutbuttermaterial)
 materials = idf1.idfobjects["MATERIAL"]
 len(materials)
 materials[-1]
+
+
    
+
    
+
    
+
    
+
    [138]:
 
    
 
    
-
    MATERIAL,
+
    
+
    +
+MATERIAL,
     Peanut Butter,            !- Name
     MediumSmooth,             !- Roughness
     0.03,                     !- Thickness
@@ -951,137 +1755,218 @@ 
    Copy an existing material
-
    Python lesson 3: indentation and looping through lists
    
-
    I’m tired of doing all this work, it’s time to make python do some heavy
-lifting for us!
    
-
    Python can go through each item in a list and perform an operation on
-any (or every) item in the list.
    
+
    
+
    Python lesson 3: indentation and looping through lists
    
+
    I’m tired of doing all this work, it’s time to make python do some heavy lifting for us!
    
+
    Python can go through each item in a list and perform an operation on any (or every) item in the list.
    
 
    This is called looping through the list.
    
-
    Here’s how to tell python to step through each item in a list, and then
-do something to every single item.
    
+
    Here’s how to tell python to step through each item in a list, and then do something to every single item.
    
 
    We’ll use a ‘for’ loop to do this.
    
-
    for <variable> in <listname>:
-    <do something>
-
    
-
    
-
    A quick note about the second line. Notice that it’s indented? There are
-4 blank spaces before the code starts:
    
-
    in python, indentations are used
-to determine the grouping of statements
-       some languages use symbols to mark
-       where the function code starts and stops
-       but python uses indentation to tell you this
-                i'm using indentation to
-                show the beginning and end of a sentence
-       this is a very simple explanation
-       of indentation in python
- if you'd like to know more, there is plenty of information
- about indentation in python on the web
-
    
-
    
-
    It’s elegant, but it means that the indentation of the code holds
-meaning.
    
-
    So make sure to indent the second (and third and forth) lines of your
-loops!
    
+
    ..
+
    
+
    
+for  in :
+    
    A quick note about the second line. Notice that it’s indented? There are 4 blank spaces before the code starts:
    
+
    ..
+
    
+
    
+in python, indentations are used
+to determine the grouping of statements
+       some languages use symbols to mark
+       where the function code starts and stops
+       but python uses indentation to tell you this
+                i'm using indentation to
+                show the beginning and end of a sentence
+       this is a very simple explanation
+       of indentation in python
+ if you'd like to know more, there is plenty of information
+ about indentation in python on the web
    It’s elegant, but it means that the indentation of the code holds meaning.
    
+
    So make sure to indent the second (and third and forth) lines of your loops!
    
 
    Now let’s make some fruit loops.
    
-
    fruits = ["apple", "orange", "bannana"]
+
    
+
    [139]:
 
    
 
    
-
    Given the syntax I gave you before I started rambling about indentation,
-we can easily print every item in the fruits list by using a ‘for’ loop.
    
-
    for fruit in fruits:
-   print fruit
+
    +fruits = ["apple", "orange", "bannana"]
+
 
    
 
    
-
    apple
-orange
-bannana
+
    
+
    Given the syntax I gave you before I started rambling about indentation, we can easily print every item in the fruits list by using a ‘for’ loop.
    
+
    
+
    [140]:
 
    
 
    
+
    +for fruit in fruits:
+   print(fruit)
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +apple
+orange
+bannana
+
    
+
    
 
    That was easy! But it can get complicated pretty quickly…
    
 
    Let’s make it do something more complicated than just print the fruits.
    
 
    Let’s have python add some words to each fruit.
    
-
    for fruit in fruits:
-    print "I am a fruit said the", fruit
+
    
+
    [141]:
 
    
 
    
-
    I am a fruit said the apple
-I am a fruit said the orange
-I am a fruit said the bannana
+
    +for fruit in fruits:
+    print("I am a fruit said the", fruit)
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +I am a fruit said the apple
+I am a fruit said the orange
+I am a fruit said the bannana
+
    
+
    
 
    Now we’ll try to confuse you:
    
-
    rottenfruits = [] # makes a blank list called rottenfruits
+
    
+
    [142]:
+
    
+
    
+
    +rottenfruits = [] # makes a blank list called rottenfruits
 for fruit in fruits: # steps through every item in fruits
     rottenfruit = "rotten " + fruit # changes each item to "rotten _____"
     rottenfruits.append(rottenfruit) # adds each changed item to the formerly empty list
 
    
 
    
-
    print rottenfruits
+
    
+
    
+
    [143]:
 
    
 
    
-
    ['rotten apple', 'rotten orange', 'rotten bannana']
+
    +print(rottenfruits)
+
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +['rotten apple', 'rotten orange', 'rotten bannana']
+
    
+
    
+
    
+
    [144]:
 
    
 
    
-
    # here's a shorter way of writing it
+
    +# here's a shorter way of writing it
 rottenfruits = ["rotten " + fruit for fruit in fruits]
+
 
    
 
    
+
    
 
    Did you follow all that??
    
 
    Just in case you didn’t, let’s review that last one:
    
-
    ["rotten " + fruit for fruit in fruits]
-                   -------------------
-                   This is the "for loop"
-                   it steps through each fruit in fruits
+
    ..
+
    
+
    
+["rotten " + fruit for fruit in fruits]
+                   -------------------
+                   This is the "for loop"
+                   it steps through each fruit in fruits
 
-["rotten " + fruit for fruit in fruits]
- -----------------
- add "rotten " to the fruit at each step
- this is your "do something"
+["rotten " + fruit for fruit in fruits]
+ -----------------
+ add "rotten " to the fruit at each step
+ this is your "do something"
 
-["rotten " + fruit for fruit in fruits]
----------------------------------------
-give a new list that is a result of the "do something"
+["rotten " + fruit for fruit in fruits]
+---------------------------------------
+give a new list that is a result of the "do something"
    
+
    [145]:
 
    
 
    
-
    print rottenfruits
+
    +print(rottenfruits)
+
 
    
 
    
-
    ['rotten apple', 'rotten orange', 'rotten bannana']
-
    
 
    
-
    
-
    Filtering in a loop
    
+
    
+
    
+
    
+
    
+
    +['rotten apple', 'rotten orange', 'rotten bannana']
+
    
+
    
+
    
+
    Filtering in a loop
    
 
    But what if you don’t want to change every item in a list?
    
 
    We can use an ‘if’ statement to operate on only some items in the list.
    
 
    Indentation is also important in ‘if’ statements, as you’ll see:
    
-
    if <someconstraint>:
-    <if the first line is true, do this>
-<but if it's false, do this>
+
    ..
+
    
+
    
+if :
+    
+
    
+
    [146]:
 
    
 
    
-
    fruits = ["apple", "orange", "pear", "berry", "mango", "plum", "peach", "melon", "bannana"]
+
    +fruits = ["apple", "orange", "pear", "berry", "mango", "plum", "peach", "melon", "bannana"]
+
 
    
 
    
-
    for fruit in fruits:               # steps through every fruit in fruits
+
    
+
    
+
    [147]:
+
    
+
    
+
    +for fruit in fruits:               # steps through every fruit in fruits
     if len(fruit) > 5:             # checks to see if the length of the word is more than 5
-        print fruit                # if true, print the fruit
+        print(fruit)                # if true, print the fruit
                                    # if false, python goes back to the 'for' loop
                                       # and checks the next item in the list
 
    
 
    
-
    orange
-bannana
+
    
+
    
+
    
+
    
+
    
+
    +orange
+bannana
+
    
+
    
+
    Let’s say we want to pick only the fruits that start with the letter ‘p’.
    
+
    
+
    [148]:
 
    
 
    
-
    Let’s say we want to pick only the fruits that start with the letter
-‘p’.
    
-
    p_fruits = []                      # creates an empty list called p_fruits
+
    +p_fruits = []                      # creates an empty list called p_fruits
 for fruit in fruits:               # steps through every fruit in fruits
     if fruit.startswith("p"):      # checks to see if the first letter is 'p', using a built-in function
         p_fruits.append(fruit)     # if the first letter is 'p', the item is added to p_fruits
@@ -1089,334 +1974,582 @@ 
    Filtering in a loop# and checks the next item in the list
 
    
 
    
-
    print p_fruits
+
    
+
    
+
    [149]:
 
    
 
    
-
    ['pear', 'plum', 'peach']
+
    +print(p_fruits)
+
 
    
 
    
-
    # here's a shorter way to write it
+
    
+
    
+
    
+
    
+
    
+
    +['pear', 'plum', 'peach']
+
    
+
    
+
    
+
    [150]:
+
    
+
    
+
    +# here's a shorter way to write it
 p_fruits = [fruit for fruit in fruits if fruit.startswith("p")]
+
 
    
 
    
-
    [fruit for fruit in fruits if fruit.startswith("p")]
-       -------------------
-       for loop
+
    
+
    ..
+
    
+
    
+[fruit for fruit in fruits if fruit.startswith("p")]
+       -------------------
+       for loop
 
-[fruit for fruit in fruits if fruit.startswith("p")]
-                           ------------------------
-                           pick only some of the fruits
+[fruit for fruit in fruits if fruit.startswith("p")]
+                           ------------------------
+                           pick only some of the fruits
 
-[fruit for fruit in fruits if fruit.startswith("p")]
- -----
- give me the variable fruit as it appears in the list, don't change it
+[fruit for fruit in fruits if fruit.startswith("p")]
+ -----
+ give me the variable fruit as it appears in the list, don't change it
 
-[fruit for fruit in fruits if fruit.startswith("p")]
-----------------------------------------------------
-a fresh new list with those fruits
+[fruit for fruit in fruits if fruit.startswith("p")]
+----------------------------------------------------
+a fresh new list with those fruits
    
+
    [151]:
 
    
 
    
-
    print p_fruits
+
    +print(p_fruits)
+
 
    
 
    
-
    ['pear', 'plum', 'peach']
-
    
 
    
+
    
+
    
+
    
+
    
+
    +['pear', 'plum', 'peach']
+
    
 
    
-
    
-
    Counting through loops
    
-
    This is not really needed, but it is nice to know. You can safely skip
-this.
    
-
    Python’s built-in function range() makes a list of numbers within a
-range that you specify.
    
+
    
+
    
+
    Counting through loops
    
+
    This is not really needed, but it is nice to know. You can safely skip this.
    
+
    Python’s built-in function range() makes a list of numbers within a range that you specify.
    
 
    This is useful because you can use these lists inside of loops.
    
-
    range(4) # makes a list
+
    
+
    [152]:
 
    
 
    
-
    [0, 1, 2, 3]
+
    +range(4) # makes a list
+
 
    
 
    
-
    for i in range(4):
-    print i
+
    
+
    
+
    [152]:
 
    
 
    
-
    0
-1
-2
-3
+
    
+
    +range(0, 4)
+
    
+
    
+
    
+
    [153]:
 
    
 
    
-
    len(p_fruits)
+
    +for i in range(4):
+    print(i)
 
    
 
    
-
    3
+
    
+
    
+
    
+
    
+
    
+
    +0
+1
+2
+3
+
    
+
    
+
    
+
    [154]:
 
    
 
    
-
    for i in range(len(p_fruits)):
-    print i
+
    +len(p_fruits)
+
 
    
 
    
-
    0
-1
-2
+
    
+
    
+
    [154]:
 
    
 
    
-
    for i in range(len(p_fruits)):
-    print p_fruits[i]
+
    
+
    +3
+
    
+
    
+
    
+
    [155]:
 
    
 
    
-
    pear
-plum
-peach
+
    +for i in range(len(p_fruits)):
+    print i
 
    
 
    
-
    for i in range(len(p_fruits)):
-    print i,  p_fruits[i]
+
    
+
    
+
    
+
    
+
    
+
    +  File "<ipython-input-155-78336a17d9ea>", line 2
+    print i
+          ^
+SyntaxError: Missing parentheses in call to 'print'. Did you mean print(i)?
+
+
    
+
    
+
    
+
    [156]:
 
    
 
    
-
    0 pear
-1 plum
-2 peach
+
    +for i in range(len(p_fruits)):
+    print(p_fruits[i])
 
    
 
    
-
    for item_from_enumerate in enumerate(p_fruits):
-    print item_from_enumerate
+
    
+
    
+
    
+
    
+
    
+
    +pear
+plum
+peach
+
    
+
    
+
    
+
    [157]:
 
    
 
    
-
    (0, 'pear')
-(1, 'plum')
-(2, 'peach')
+
    +for i in range(len(p_fruits)):
+    print(i,  p_fruits[i])
 
    
 
    
-
    for i, fruit in enumerate(p_fruits):
-    print i, fruit
+
    
+
    
+
    
+
    
+
    
+
    +0 pear
+1 plum
+2 peach
+
    
+
    
+
    
+
    [158]:
 
    
 
    
-
    0 pear
-1 plum
-2 peach
+
    +for item_from_enumerate in enumerate(p_fruits):
+    print(item_from_enumerate)
 
    
 
    
 
    
+
    
+
    
+
    
+
    
+
    +(0, 'pear')
+(1, 'plum')
+(2, 'peach')
+
    
+
    
+
    
+
    [159]:
+
    
 
    
-
    
-
    Looping through E+ objects
    
-
    If you have read the python explanation of loops, you are now masters of
-using loops.
    
+
    +for i, fruit in enumerate(p_fruits):
+    print(i, fruit)
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +0 pear
+1 plum
+2 peach
+
    
+
    
+
    
+
    
+
    
+
    Looping through E+ objects
    
+
    If you have read the python explanation of loops, you are now masters of using loops.
    
 
    Let us use the loops with E+ objects.
    
 
    We’ll continue to work with the materials list.
    
-
    for material in materials:
-    print material.Name
+
    
+
    [160]:
 
    
 
    
-
    F08 Metal surface
-I01 25mm insulation board
-I02 50mm insulation board
-G01a 19mm gypsum board
-M11 100mm lightweight concrete
-F16 Acoustic tile
-M01 100mm brick
-M15 200mm heavyweight concrete
-M05 200mm concrete block
-G05 25mm wood
-Peanut Butter
-Peanut Butter
+
    +for material in materials:
+    print(material.Name )
 
    
 
    
-
    [material.Name for material in materials]
+
    
+
    
+
    
+
    
+
    
+
    +F08 Metal surface
+I01 25mm insulation board
+I02 50mm insulation board
+G01a 19mm gypsum board
+M11 100mm lightweight concrete
+F16 Acoustic tile
+M01 100mm brick
+M15 200mm heavyweight concrete
+M05 200mm concrete block
+G05 25mm wood
+Peanut Butter
+Peanut Butter
+
    
+
    
+
    
+
    [161]:
 
    
 
    
-
    [u'F08 Metal surface',
- u'I01 25mm insulation board',
- u'I02 50mm insulation board',
- u'G01a 19mm gypsum board',
- u'M11 100mm lightweight concrete',
- u'F16 Acoustic tile',
- u'M01 100mm brick',
- u'M15 200mm heavyweight concrete',
- u'M05 200mm concrete block',
- u'G05 25mm wood',
- 'Peanut Butter',
- 'Peanut Butter']
+
    +[material.Name for material in materials]
+
 
    
 
    
-
    [material.Roughness for material in materials]
+
    
+
    
+
    [161]:
 
    
 
    
-
    [u'Smooth',
- u'MediumRough',
- u'MediumRough',
- u'MediumSmooth',
- u'MediumRough',
- u'MediumSmooth',
- u'MediumRough',
- u'MediumRough',
- u'MediumRough',
- u'MediumSmooth',
- 'MediumSmooth',
- 'MediumSmooth']
+
    
+
    +['F08 Metal surface',
+ 'I01 25mm insulation board',
+ 'I02 50mm insulation board',
+ 'G01a 19mm gypsum board',
+ 'M11 100mm lightweight concrete',
+ 'F16 Acoustic tile',
+ 'M01 100mm brick',
+ 'M15 200mm heavyweight concrete',
+ 'M05 200mm concrete block',
+ 'G05 25mm wood',
+ 'Peanut Butter',
+ 'Peanut Butter']
+
    
+
    
+
    
+
    [162]:
 
    
 
    
-
    [material.Thickness for material in materials]
+
    +[material.Roughness for material in materials]
+
 
    
 
    
-
    [0.0008,
- 0.0254,
- 0.0508,
- 0.019,
- 0.1016,
- 0.0191,
- 0.1016,
- 0.2032,
- 0.2032,
- 0.0254,
- 0.03,
- 0.03]
+
    
+
    
+
    [162]:
 
    
 
    
-
    [material.Thickness for material in materials if material.Thickness > 0.1]
+
    
+
    +['Smooth',
+ 'MediumRough',
+ 'MediumRough',
+ 'MediumSmooth',
+ 'MediumRough',
+ 'MediumSmooth',
+ 'MediumRough',
+ 'MediumRough',
+ 'MediumRough',
+ 'MediumSmooth',
+ 'MediumSmooth',
+ 'MediumSmooth']
+
    
+
    
+
    
+
    [163]:
 
    
 
    
-
    [0.1016, 0.1016, 0.2032, 0.2032]
+
    +[material.Thickness for material in materials]
+
 
    
 
    
-
    [material.Name for material in materials if material.Thickness > 0.1]
+
    
+
    
+
    [163]:
 
    
 
    
-
    [u'M11 100mm lightweight concrete',
- u'M01 100mm brick',
- u'M15 200mm heavyweight concrete',
- u'M05 200mm concrete block']
+
    
+
    +[0.0008,
+ 0.0254,
+ 0.0508,
+ 0.019,
+ 0.1016,
+ 0.0191,
+ 0.1016,
+ 0.2032,
+ 0.2032,
+ 0.0254,
+ 0.03,
+ 0.03]
+
    
+
    
+
    
+
    [164]:
 
    
 
    
-
    thick_materials = [material for material in materials if material.Thickness > 0.1]
+
    +[material.Thickness for material in materials if material.Thickness > 0.1]
+
 
    
 
    
-
    thick_materials
+
    
+
    
+
    [164]:
 
    
 
    
-
    [
-Material,
-    M11 100mm lightweight concrete,    !- Name
-    MediumRough,              !- Roughness
-    0.1016,                   !- Thickness
-    0.53,                     !- Conductivity
-    1280.0,                   !- Density
-    840.0;                    !- Specific Heat
-,
+
    
+
    +[0.1016, 0.1016, 0.2032, 0.2032]
+
    
+
    
+
    
+
    [165]:
+
    
+
    
+
    +[material.Name for material in materials if material.Thickness > 0.1]
 
-Material,
-    M01 100mm brick,          !- Name
-    MediumRough,              !- Roughness
-    0.1016,                   !- Thickness
-    0.89,                     !- Conductivity
-    1920.0,                   !- Density
-    790.0;                    !- Specific Heat
-,
+
    
+
    
+
    
+
    
+
    [165]:
+
    
+
    
+
    
+
    +['M11 100mm lightweight concrete',
+ 'M01 100mm brick',
+ 'M15 200mm heavyweight concrete',
+ 'M05 200mm concrete block']
+
    
+
    
+
    
+
    [166]:
+
    
+
    
+
    +thick_materials = [material for material in materials if material.Thickness > 0.1]
 
-Material,
-    M15 200mm heavyweight concrete,    !- Name
-    MediumRough,              !- Roughness
-    0.2032,                   !- Thickness
-    1.95,                     !- Conductivity
-    2240.0,                   !- Density
-    900.0;                    !- Specific Heat
-,
+
    
+
    
+
    
+
    
+
    [167]:
+
    
+
    
+
    +thick_materials
 
-Material,
-    M05 200mm concrete block,    !- Name
-    MediumRough,              !- Roughness
-    0.2032,                   !- Thickness
-    1.11,                     !- Conductivity
-    800.0,                    !- Density
-    920.0;                    !- Specific Heat
-]
 
    
 
    
-
    # change the names of the thick materials
+
    
+
    
+
    [167]:
+
    
+
    
+
    
+
    +[
+ Material,
+     M11 100mm lightweight concrete,    !- Name
+     MediumRough,              !- Roughness
+     0.1016,                   !- Thickness
+     0.53,                     !- Conductivity
+     1280,                     !- Density
+     840;                      !- Specific Heat,
+
+ Material,
+     M01 100mm brick,          !- Name
+     MediumRough,              !- Roughness
+     0.1016,                   !- Thickness
+     0.89,                     !- Conductivity
+     1920,                     !- Density
+     790;                      !- Specific Heat,
+
+ Material,
+     M15 200mm heavyweight concrete,    !- Name
+     MediumRough,              !- Roughness
+     0.2032,                   !- Thickness
+     1.95,                     !- Conductivity
+     2240,                     !- Density
+     900;                      !- Specific Heat,
+
+ Material,
+     M05 200mm concrete block,    !- Name
+     MediumRough,              !- Roughness
+     0.2032,                   !- Thickness
+     1.11,                     !- Conductivity
+     800,                      !- Density
+     920;                      !- Specific Heat]
+
    
+
    
+
    
+
    [168]:
+
    
+
    
+
    +# change the names of the thick materials
 for material in thick_materials:
     material.Name = "THICK " + material.Name
 
    
 
    
-
    thick_materials
+
    
+
    
+
    [169]:
 
    
 
    
-
    [
-Material,
-    THICK M11 100mm lightweight concrete,    !- Name
-    MediumRough,              !- Roughness
-    0.1016,                   !- Thickness
-    0.53,                     !- Conductivity
-    1280.0,                   !- Density
-    840.0;                    !- Specific Heat
-,
+
    +thick_materials
 
-Material,
-    THICK M01 100mm brick,    !- Name
-    MediumRough,              !- Roughness
-    0.1016,                   !- Thickness
-    0.89,                     !- Conductivity
-    1920.0,                   !- Density
-    790.0;                    !- Specific Heat
-,
+
    
+
    
+
    
+
    
+
    [169]:
+
    
+
    
+
    
+
    +[
+ Material,
+     THICK M11 100mm lightweight concrete,    !- Name
+     MediumRough,              !- Roughness
+     0.1016,                   !- Thickness
+     0.53,                     !- Conductivity
+     1280,                     !- Density
+     840;                      !- Specific Heat,
 
-Material,
-    THICK M15 200mm heavyweight concrete,    !- Name
-    MediumRough,              !- Roughness
-    0.2032,                   !- Thickness
-    1.95,                     !- Conductivity
-    2240.0,                   !- Density
-    900.0;                    !- Specific Heat
-,
+ Material,
+     THICK M01 100mm brick,    !- Name
+     MediumRough,              !- Roughness
+     0.1016,                   !- Thickness
+     0.89,                     !- Conductivity
+     1920,                     !- Density
+     790;                      !- Specific Heat,
 
-Material,
-    THICK M05 200mm concrete block,    !- Name
-    MediumRough,              !- Roughness
-    0.2032,                   !- Thickness
-    1.11,                     !- Conductivity
-    800.0,                    !- Density
-    920.0;                    !- Specific Heat
-]
+ Material,
+     THICK M15 200mm heavyweight concrete,    !- Name
+     MediumRough,              !- Roughness
+     0.2032,                   !- Thickness
+     1.95,                     !- Conductivity
+     2240,                     !- Density
+     900;                      !- Specific Heat,
+
+ Material,
+     THICK M05 200mm concrete block,    !- Name
+     MediumRough,              !- Roughness
+     0.2032,                   !- Thickness
+     1.11,                     !- Conductivity
+     800,                      !- Density
+     920;                      !- Specific Heat]
+
    
+
    
+
    So now we’re working with two different lists: materials and thick_materials.
    
+
    But even though the items can be separated into two lists, we’re still working with the same items.
    
+
    Here’s a helpful illustration:
    
+
    
+
    [170]:
 
    
 
    
-
    So now we’re working with two different lists: materials and
-thick_materials.
    
-
    But even though the items can be separated into two lists, we’re still
-working with the same items.
    
-
    Here’s a helpful illustration:
    
-
    for_images.display_png(for_images.material_lists) # display the image below
+
    +for_images.display_png(for_images.material_lists) # display the image below
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+_images/Main_Tutorial_207_0.png
+
    
+
    
+
    
+
    [171]:
 
    
 
    
-Main_Tutorial_files/Main_Tutorial_207_0.png
-
    # here's the same concept, demonstrated with code
+
    +# here's the same concept, demonstrated with code
 # remember, we changed the names of the items in the list thick_materials
 # these changes are visible when we print the materials list; the thick materials are also in the materials list
 [material.Name for material in materials]
+
+
    
+
    
+
    
+
    
+
    [171]:
 
    
 
    
-
    [u'F08 Metal surface',
- u'I01 25mm insulation board',
- u'I02 50mm insulation board',
- u'G01a 19mm gypsum board',
- u'THICK M11 100mm lightweight concrete',
- u'F16 Acoustic tile',
- u'THICK M01 100mm brick',
- u'THICK M15 200mm heavyweight concrete',
- u'THICK M05 200mm concrete block',
- u'G05 25mm wood',
- 'Peanut Butter',
- 'Peanut Butter']
-
    
-
    
-
    
-
    
-
    Geometry functions in eppy
    
-
    Sometimes, we want information about the E+ object that is not in the
-fields. For example, it would be useful to know the areas and
-orientations of the surfaces. These attributes of the surfaces are not
-in the fields of surfaces, but surface objects do have fields that
-have the coordinates of the surface. The areas and orientations can be
-calculated from these coordinates.
    
+
    
+
    +['F08 Metal surface',
+ 'I01 25mm insulation board',
+ 'I02 50mm insulation board',
+ 'G01a 19mm gypsum board',
+ 'THICK M11 100mm lightweight concrete',
+ 'F16 Acoustic tile',
+ 'THICK M01 100mm brick',
+ 'THICK M15 200mm heavyweight concrete',
+ 'THICK M05 200mm concrete block',
+ 'G05 25mm wood',
+ 'Peanut Butter',
+ 'Peanut Butter']
+
    
+
    
+
    
+
    
+
    Geometry functions in eppy
    
+
    Sometimes, we want information about the E+ object that is not in the fields. For example, it would be useful to know the areas and orientations of the surfaces. These attributes of the surfaces are not in the fields of surfaces, but surface objects do have fields that have the coordinates of the surface. The areas and orientations can be calculated from these coordinates.
    
 
    Pyeplus has some functions that will do the calculations.
    
 
    In the present version, pyeplus will calculate:
    
     
-
-
    
-
    Open a file quickly¶
    
-
    It is rather cumbersome to open an IDF file in eppy. From the tutorial,
-the steps look like this:
    
-
    from eppy import modeleditor
-from eppy.modeleditor import IDF
+
    
+
    Open a file quickly¶
    
+
    It is rather cumbersome to open an IDF file in eppy. From the tutorial, the steps look like this:
    
+
    
+
    [70]:
+
    
+
    
+
    +from eppy import modeleditor
+from eppy.modeleditor import IDF
 
 iddfile = "../eppy/resources/iddfiles/Energy+V7_2_0.idd"
 fname = "../eppy/resources/idffiles/V_7_2/smallfile.idf"
@@ -1106,53 +1872,43 @@ 
    Open a file quickly¶idf = IDF(fname)
 
    
 
    
+
    
 
      
 
    • You have to find the IDD file on your hard disk.
      • 
 
    • Then set the IDD using setiddname(iddfile).
      • 
 
    • Now you can open the IDF file
      • 
 
    
-
    Why can’t you just open the IDF file without jumping thru all those
-hoops. Why do you have to find the IDD file. What is the point of having
-a computer, if it does not do the grunt work for you.
    
-
    The function easyopen will do the grunt work for you. It will
-automatically read the version number from the IDF file, locate the
-correct IDD file and set it in eppy and then open your file. It works
-like this:
    
-
    from eppy.easyopen import easyopen
-
-fname = './eppy/resources/idffiles/V8_8/smallfile.idf'
+
    Why can’t you just open the IDF file without jumping thru all those hoops. Why do you have to find the IDD file. What is the point of having a computer, if it does not do the grunt work for you.
    
+
    The function easyopen will do the grunt work for you. It will automatically read the version number from the IDF file, locate the correct IDD file and set it in eppy and then open your file. It works like this:
    
+
    
+
    [74]:
+
    
+
    
+
    +from importlib import reload
+import eppy
+reload(eppy.modeleditor)
+from eppy.easyopen import easyopen
+
+fname = '../eppy/resources/idffiles/V8_8/smallfile.idf'
 idf = easyopen(fname)
 
    
 
    
+
    
 
    For this to work,
    
 
      
-
    • the IDF file should have the VERSION object. You may not have this if
-you are just working on a file snippet.
      • 
-
    • you need to have the version of EnergyPlus installed that matches
-your IDF version.
      • 
+
    • the IDF file should have the VERSION object. You may not have this if you are just working on a file snippet.
      • 
+
    • you need to have the version of EnergyPlus installed that matches your IDF version.
      • 
 
    • Energyplus should be installed in the default location.
      • 
 
    
-
    If easyopen does not work, use the long winded steps shown in the
-tutorial. That is guaranteed to work
    
-
    Note: easyopen() will also take a epw argument looking like:
    
-
    idf = easyopen(fname, epw=path2weatherfile)
-
    
-
    
-
    If you pass the epw argument:
    
-
    idf.run()
-
    
-
    
-
    will work. To know more about idf.run() see  here.
    
-
    
-    
 
    
@@ -1187,38 +1939,31 @@ 
    Fan power in Watts, BHP and fan cfm¶
         
    
-  
    Table Of Contents
    
-      
 
    
-  
    
-    
    This Page
    
-    
-   
    
 
 
+
+
+
+
+
+
+
+
         
    
       
    
       
    
     
    
     
    
-      ©2018, Santosh Philip.
+      ©2020, Santosh Philip.
       
       |
-      Powered by Sphinx 1.7.1
-      & Alabaster 0.7.11
+      Powered by Sphinx 1.8.5
+      & Alabaster 0.7.12
       
       |
-      Page source
     
    
 
diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv
index 813b72d5a76566494ac7fc795f00328154130eb1..670970ff75b280b9aa1f3c433bbd027638bc8bc8 100644
GIT binary patch
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diff --git a/docs/_build/html/runningeplus.html b/docs/_build/html/runningeplus.html
index 57831146..1eed059a 100644
--- a/docs/_build/html/runningeplus.html
+++ b/docs/_build/html/runningeplus.html
@@ -6,13 +6,15 @@
   
     
     
-    Running EnergyPlus from Eppy — eppy 0.5.46 documentation
+    Running EnergyPlus from Eppy — eppy 0.5.52 documentation
     
     
-    
+    
     
     
     
+    
+    
     
     
     
@@ -33,12 +35,249 @@
 
           
    
             
-  
    
-
    Running EnergyPlus from Eppy
    
-
    It would be great if we could run EnergyPlus directly from our IDF
-wouldn’t it?
    
+  
+
+
    
+
    Running EnergyPlus from Eppy
    
+
    It would be great if we could run EnergyPlus directly from our IDF wouldn’t it?
    
 
    Well here’s how we can.
    
-
    # you would normaly install eppy by doing
+
    
+
    [4]:
+
    
+
    
+
    +# you would normaly install eppy by doing
 # python setup.py install
 # or
 # pip install eppy
@@ -52,20 +291,47 @@ 
    Running EnergyPlus from Eppysys.path.append(pathnameto_eppy)
 
    
 
    
-
    from eppy.modeleditor import IDF
+
    
+
    
+
    [5]:
+
    
+
    
+
    +from eppy.modeleditor import IDF
 
 iddfile = "/Applications/EnergyPlus-8-3-0/Energy+.idd"
 IDF.setiddname(iddfile)
+
+
    
+
    
+
    
+
    
+
    [6]:
 
    
 
    
-
    idfname = "/Applications/EnergyPlus-8-3-0/ExampleFiles/BasicsFiles/Exercise1A.idf"
+
    +idfname = "/Applications/EnergyPlus-8-3-0/ExampleFiles/BasicsFiles/Exercise1A.idf"
 epwfile = "/Applications/EnergyPlus-8-3-0/WeatherData/USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw"
 
 idf = IDF(idfname, epwfile)
 idf.run()
 
    
 
    
+
    
+
    
+
    
+
    
+
    
+
    +
+/Applications/EnergyPlus-8-3-0/energyplus --weather /Applications/EnergyPlus-8-3-0/WeatherData/USA_IL_Chicago-OHare.Intl.AP.725300_TMY3.epw --output-directory /Users/santoshphilip/Documents/coolshadow/github/cutter_eppy/r_eppy/docs --idd /Applications/EnergyPlus-8-3-0/Energy+.idd /Users/santoshphilip/Documents/coolshadow/github/cutter_eppy/r_eppy/docs/in.idf
+
+
    
+
    
 
    if you are in a terminal, you will see something like this:
    
+
    ..
+
    
+
    
 
    Processing Data Dictionary
 Processing Input File
 Initializing Simulation
@@ -91,102 +357,104 @@ 
    Running EnergyPlus from EppyEnergyPlus Run Time=00hr 00min  0.24sec
 
    
 
    
-
    It’s as simple as that to run using the EnergyPlus defaults, but all the
-EnergyPlus command line interface options are also supported.
    
-
    To get a description of the options available, as well as the defaults
-you can call the Python built-in help function on the IDF.run method and
-it will print a full description of the options to the console.
    
-
    help(idf.run)
+
    It’s as simple as that to run using the EnergyPlus defaults, but all the EnergyPlus command line interface options are also supported.
    
+
    To get a description of the options available, as well as the defaults you can call the Python built-in help function on the IDF.run method and it will print a full description of the options to the console.
    
+
    
+
    [7]:
 
    
 
    
-
    Help on method run in module eppy.modeleditor:
+
    +help(idf.run)
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
    +Help on method run in module eppy.modeleditor:
 
-run(self, **kwargs) method of eppy.modeleditor.IDF instance
-    This method wraps the following method:
+run(**kwargs) method of eppy.modeleditor.IDF instance
+    This method wraps the following method:
 
-    run(idf=None, weather=None, output_directory=u'', annual=False, design_day=False, idd=None, epmacro=False, expandobjects=False, readvars=False, output_prefix=None, output_suffix=None, version=False, verbose=u'v', ep_version=None)
-        Wrapper around the EnergyPlus command line interface.
+    run(idf=None, weather=None, output_directory='', annual=False, design_day=False, idd=None, epmacro=False, expandobjects=False, readvars=False, output_prefix=None, output_suffix=None, version=False, verbose='v', ep_version=None)
+        Wrapper around the EnergyPlus command line interface.
 
-        Parameters
-        ----------
-        idf : str
-            Full or relative path to the IDF file to be run, or an IDF object.
+        Parameters
+        ----------
+        idf : str
+            Full or relative path to the IDF file to be run, or an IDF object.
 
-        weather : str
-            Full or relative path to the weather file.
+        weather : str
+            Full or relative path to the weather file.
 
-        output_directory : str, optional
-            Full or relative path to an output directory (default: 'run_outputs)
+        output_directory : str, optional
+            Full or relative path to an output directory (default: 'run_outputs)
 
-        annual : bool, optional
-            If True then force annual simulation (default: False)
+        annual : bool, optional
+            If True then force annual simulation (default: False)
 
-        design_day : bool, optional
-            Force design-day-only simulation (default: False)
+        design_day : bool, optional
+            Force design-day-only simulation (default: False)
 
-        idd : str, optional
-            Input data dictionary (default: Energy+.idd in EnergyPlus directory)
+        idd : str, optional
+            Input data dictionary (default: Energy+.idd in EnergyPlus directory)
 
-        epmacro : str, optional
-            Run EPMacro prior to simulation (default: False).
+        epmacro : str, optional
+            Run EPMacro prior to simulation (default: False).
 
-        expandobjects : bool, optional
-            Run ExpandObjects prior to simulation (default: False)
+        expandobjects : bool, optional
+            Run ExpandObjects prior to simulation (default: False)
 
-        readvars : bool, optional
-            Run ReadVarsESO after simulation (default: False)
+        readvars : bool, optional
+            Run ReadVarsESO after simulation (default: False)
 
-        output_prefix : str, optional
-            Prefix for output file names (default: eplus)
+        output_prefix : str, optional
+            Prefix for output file names (default: eplus)
 
-        output_suffix : str, optional
-            Suffix style for output file names (default: L)
-                L: Legacy (e.g., eplustbl.csv)
-                C: Capital (e.g., eplusTable.csv)
-                D: Dash (e.g., eplus-table.csv)
+        output_suffix : str, optional
+            Suffix style for output file names (default: L)
+                L: Legacy (e.g., eplustbl.csv)
+                C: Capital (e.g., eplusTable.csv)
+                D: Dash (e.g., eplus-table.csv)
 
-        version : bool, optional
-            Display version information (default: False)
+        version : bool, optional
+            Display version information (default: False)
 
-        verbose: str
-            Set verbosity of runtime messages (default: v)
-                v: verbose
-                q: quiet
+        verbose: str
+            Set verbosity of runtime messages (default: v)
+                v: verbose
+                q: quiet
 
-        ep_version: str
-            EnergyPlus version, used to find install directory. Required if run() is
-            called with an IDF file path rather than an IDF object.
+        ep_version: str
+            EnergyPlus version, used to find install directory. Required if run() is
+            called with an IDF file path rather than an IDF object.
 
-        Returns
-        -------
-        str : status
+        Returns
+        -------
+        str : status
 
-        Raises
-        ------
-        CalledProcessError
+        Raises
+        ------
+        CalledProcessError
 
-        AttributeError
-            If no ep_version parameter is passed when calling with an IDF file path
-            rather than an IDF object.
-
    
+        AttributeError
+            If no ep_version parameter is passed when calling with an IDF file path
+            rather than an IDF object.
+
+
    
 
    
-
    Note 1: idf.run() works for E+ version >= 8.3
-Note 2: idf.run(readvars=True) has been tested only for E+ version >= 8.9. It may work with earlier versions
    
-
    
-
    Running in parallel processes
    
-
    One of the great things about Eppy is that it allows you to set up a lot
-of jobs really easily. However, it can be slow running a lot of
-EnergyPlus simulations, so it’s pretty important that we can make the
-most of the processing power you have available by running on multiple
-CPUs.
    
+
    Note: idf.run() works for E+ version >= 8.3
    
+
    
+
    Running in parallel processes
    
+
    One of the great things about Eppy is that it allows you to set up a lot of jobs really easily. However, it can be slow running a lot of EnergyPlus simulations, so it’s pretty important that we can make the most of the processing power you have available by running on multiple CPUs.
    
 
    Again this is as simple as you’d hope it would be.
    
 
    You first need to create your jobs as a list of lists in the form:
    
-
    [[<IDF object>, <dict of command line parameters>], ...]
+
    ..
 
    
 
    
-
    The example here just creates 4 identical jobs apart from the
-output_directory the results are saved in, but you would obviously want
-to make each job different.
    
+[[, ], ...]
    The example here just creates 4 identical jobs apart from the output_directory the results are saved in, but you would obviously want to make each job different.
    
 
    Then run the jobs on the required number of CPUs using runIDFs…
    
 
    … and your results will all be in the output_directory you specified.
    
 
    
@@ -199,13 +467,31 @@ 
    Running in parallel processes
         
    
-  
    Table Of Contents
    
-  
 
    
-  
    
-    
    This Page
    
-    
-   
    
 
 
+
+
+
+
+
+
+
+
         
    
       
    
       
    
     
    
     
    
-      ©2018, Santosh Philip.
+      ©2020, Santosh Philip.
       
       |
-      Powered by Sphinx 1.7.1
-      & Alabaster 0.7.11
+      Powered by Sphinx 1.8.5
+      & Alabaster 0.7.12
       
       |
-      Page source
     
    
 
diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html
index f74c0852..c7068b9b 100644
--- a/docs/_build/html/search.html
+++ b/docs/_build/html/search.html
@@ -6,13 +6,16 @@
   
     
     
-    Search — eppy 0.5.46 documentation
+    Search — eppy 0.5.52 documentation
     
     
-    
+    
+    
     
     
     
+    
+    
     
     
     
@@ -68,23 +71,57 @@ 
    Search

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