Merge pull request #377 from SANDAG/OpenPaths_no_virtualEnv

remove EMME virtual environment setup

Author: bhargavasana

add_python_virtualenv.bat

@@ -43,7 +43,6 @@
 import pandas as pd, numpy as np
 #import datetime
 import matplotlib.pyplot as plt
-import seaborn as sns
 import warnings
 import traceback as _traceback
 
@@ -179,9 +178,6 @@ def __call__(self, path= "",
         _m.logbook_write("Generating density variables")
         self.get_density()
 
-        # _m.logbook_write("Creating comparison plots")
-        # self.make_plots()
-        
         _m.logbook_write("Finished running 4Ds")
 
     def get_intersection_count(self):
@@ -297,115 +293,4 @@ def density_function(mgra_in):
         mgra_landuse[self.base_cols+self.new_cols].to_csv(os.path.join(self.path, self.mgradata_file), index = False, float_format='%.4f' )
 
         self.mgra_data = mgra_landuse
-        print( "*** Finished ***")
-
-    #plot comparisons of build and old density values and create heat maps
-    def make_plots(self):
-        if len(self.mgra_data) == 0:
-            self.build = pd.read_csv(os.path.join(self.path, self.mgradata_file))
-        else:
-            self.build = self.mgra_data
-                
-        def plot_continuous(field):
-            #colors
-            rsg_orange = '#f68b1f'
-            rsg_marine = '#006fa1'
-            #rsg_leaf   = '#63af5e'
-            #rsg_grey   = '#48484a'
-            #rsg_mist   = '#dcddde'
-                        
-            max = self.base[field].max() + self.base[field].max()%5
-            div = max/5 if max/5 >= 10 else max/2
-            bins = np.linspace(0,max,div)
-            plt.hist(self.base[field], bins, density = True, alpha = 0.5, label = 'Base', color = rsg_marine)
-            plt.hist(self.build[field], bins, density = True, alpha = 0.5, label = 'Build', color = rsg_orange)
-            mean_base = self.base[field].mean()
-            mean = self.build[field].mean()
-            median_base = self.base[field].median()
-            median = self.build[field].median()
-            plt.axvline(mean_base, color = 'b', linestyle = '-', label = 'Base Mean')
-            plt.axvline(median_base, color = 'b', linestyle = '--', label = 'Base Median')
-            plt.axvline(mean, color = 'r', linestyle = '-', label = 'Build Mean')
-            plt.axvline(median, color = 'r', linestyle = '--',label = 'Build Median')
-            plt.legend(loc = 'upper right')
-            ylims = plt.ylim()[1]
-            plt.text(mean_base + div/4, ylims-ylims/32, "mean: {:0.2f}".format(mean_base), color = 'b')
-            plt.text(mean_base + div/4, ylims - 5*ylims/32, "median: {:0.0f}".format(median_base), color = 'b')
-            plt.text(mean_base + div/4, ylims-2*ylims/32, "mean: {:0.2f}".format(mean), size = 'medium',color = 'r')
-            plt.text(mean_base + div/4, ylims-6*ylims/32, "median: {:0.0f}".format(median), color = 'r')
-            plt.text(self.base[field].min() , ylims/32, "min: {:0.0f}".format(self.base[field].min()), color = 'b')
-            plt.text(self.base[field].max()-div , ylims/32, "max: {:0.0f}".format(self.base[field].max()), color = 'b')
-            plt.text(self.build[field].min() , 2*ylims/32, "min: {:0.0f}".format(self.build[field].min()), color = 'r')
-            plt.text(self.base[field].max()-div , 2*ylims/32, "max: {:0.0f}".format(self.build[field].max()), color = 'r')
-
-            plt.xlabel(field)
-            plt.ylabel("MGRA's")
-            plt.title(field.replace('den','') + ' Density')
-            outfile = _join(self.path, "output", '4Ds_{}_plot.png'.format(field))
-            if os.path.isfile(outfile):
-                os.remove(outfile)
-            plt.savefig(outfile)
-            plt.clf()
-
-        def plot_discrete(field):
-            fig, ax = plt.subplots()
-            df1 = discretedf_base.groupby(field, as_index = False).agg({'mgra':'count','type':'first'})
-            df2 = discretedf_build.groupby(field, as_index = False).agg({'mgra':'count','type':'first'})
-            df = df1.append(df2)
-            ax = sns.barplot(x=field, y = 'mgra', hue = 'type', data = df)
-            ax.set_title(field)
-            outfile = _join(self.path, "output", '4Ds_{}_plot.png'.format(field))
-            if os.path.isfile(outfile):
-                    os.remove(outfile)
-            ax.get_figure().savefig(outfile)
-
-        self.base = pd.read_csv(os.path.join(self.ref_path, self.mgradata_file))
-        self.base['type'] = 'base'
-        self.build['type'] = 'build'
-
-        discretedf_base = self.base[['mgra','type']+self.discrete_fields]
-        discretedf_build = self.build[['mgra','type']+self.discrete_fields]
-        
-        for f in self.continuous_fields:
-            plot_continuous(f)            
-        for f in self.discrete_fields:
-            plot_discrete(f)
-            
-        if self.maps:
-            import geopandas as gpd
-            import folium
-            from branca.colormap import linear
-            compare_int = self.base.merge(self.build, how = 'outer', on = 'mgra', suffixes = ['_base','_build'])
-            compare_int['diff'] = compare_int['TotInt'] - compare_int['totint']
-
-            compare_int = gpd.read_file(self.mgra_shape_file).rename(columns = {'MGRA':'mgra'}).merge(compare_int, how = 'left', on = 'mgra')
-            compare_int = compare_int.to_crs({'init': 'epsg:4326'})
-
-            colormap = linear.OrRd_09.scale(
-                    compare_int.TotInt.min(),
-                    compare_int.TotInt.max())
-            colormapA = linear.RdBu_04.scale(
-                    compare_int['diff'].min(),
-                    compare_int['diff'].min()*-1)
-
-            compare_int['colordiff'] = compare_int['diff'].map(lambda n: colormapA(n))
-            compare_int['colororig'] = compare_int['TotInt'].map(lambda n: colormap(n))
-            compare_int['colornew'] = compare_int['totint'].map(lambda n: colormap(n))
-            
-            def makeheatmap(self,df, colormp,color_field,caption):
-                mapname = folium.Map(location=[32.76, -117.15], zoom_start = 13.459)
-                folium.GeoJson(compare_int,
-                        style_function=lambda feature: {
-                        'fillColor': feature['properties'][color_field],
-                        'color' : rsg_marine,
-                        'weight' : 0,
-                        'fillOpacity' : 0.75,
-                        }).add_to(mapname)
-        
-                colormp.caption = caption
-                colormp.add_to(mapname)
-                return mapname
-                        
-            makeheatmap(compare_int,colormapA,'colordiff','Intersection Diff (base - build)').save('diff_intersections.html')   
-            makeheatmap(compare_int,colormap,'colororig','Intersections').save('base_intersections.html')   
-            makeheatmap(compare_int,colormap,'colororig','Intersections').save('build_intersections.html')   
+        print( "*** Finished ***")

requirements.txt

@@ -57,7 +57,6 @@
 """
 
 TOOLBOX_ORDER = 1
-VIRUTALENV_PATH = "C:\\python_virtualenv\\abm15_4_0"
 
 import inro.modeller as _m
 import inro.emme.database.emmebank as _eb
@@ -208,21 +207,7 @@ def __call__(self, main_directory, scenario_id, scenario_title, emmebank_title,
         gen_utils.log_snapshot("Master run model", str(self), attributes)
 
         modeller = _m.Modeller()
-        # Checking that the virtualenv path is set and the folder is installed
-        if not os.path.exists(VIRUTALENV_PATH):
-            raise Exception("Python virtual environment not installed at expected location %s" % VIRUTALENV_PATH)
-        venv_path = os.environ.get("PYTHON_VIRTUALENV")
-        if not venv_path:
-            raise Exception("Environment variable PYTHON_VIRTUALENV not set, start Emme from 'start_emme_with_virtualenv.bat'")
-        if not venv_path == VIRUTALENV_PATH:
-            raise Exception("PYTHON_VIRTUALENV is not the expected value (%s instead of %s)" % (venv_path, VIRUTALENV_PATH))
-        venv_path_found = False
-        for path in sys.path:
-            if VIRUTALENV_PATH in path:
-                venv_path_found = True
-                break
-        if not venv_path_found:
-            raise Exception("Python virtual environment not found in system path %s" % VIRUTALENV_PATH)
+    
         copy_scenario = modeller.tool("inro.emme.data.scenario.copy_scenario")
         run4Ds = modeller.tool("sandag.import.run4Ds")
         import_network = modeller.tool("sandag.import.import_network")

src/main/emme/python_virtualenv.pth

@@ -24,12 +24,6 @@ rem create toolbox
 python .\\common\\python\\emme\\toolbox\\build_toolbox.py -s .\\common\\python\\emme\\toolbox -p %SCENARIO_FOLDER%\emme_project\Scripts\sandag_toolbox.mtbx
 copy ".\\common\\python\\emme\\solutions.mtbx" "%SCENARIO_FOLDER%\emme_project\Scripts\solutions.mtbx"
 
-rem create a batch script at startup
-(
-echo set python_virtualenv=C:\python_virtualenv\abm15_4_0
-echo start "TITLE" "%E_PATH%\\programs\\EmmeDesktop.exe" ./emme_project.emp
-)>%SCENARIO_FOLDER%\emme_project\start_emme_with_virtualenv.bat
-
 rem mkdir %SCENARIO_FOLDER%\emme_project\Scripts\yaml
 rem copy .\\common\\python\\emme\\yaml\\*.* %SCENARIO_FOLDER%\emme_project\Scripts\yaml