removing extraneous matplotlib backend selections

omf/cymeToGridlab.py

@@ -18,18 +18,9 @@
 from os.path import join as pJoin
 import numpy as np
 from numpy.linalg import inv
-
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
-from matplotlib import pyplot as plt
-
 from omf import feeder
 from omf.solvers import gridlabd
 
-
 m2ft = 1.0 / 0.3048  # Conversion factor for meters to feet
 
 
@@ -3466,6 +3457,7 @@ def _tests(keepFiles=True):
 			# Draw the GLM.
 			myGraph = feeder.treeToNxGraph(cyme_base)
 			feeder.latLonNxGraph(myGraph, neatoLayout=False)
+			from matplotlib import pyplot as plt
 			plt.savefig(outputDir + testFilename + ".png")
 			with open(pJoin(outputDir, "convResults.txt"), "a") as resultsFile:
 				resultsFile.write("DREW GLM FOR " + testFilename + "\n")

omf/feeder.py

@@ -5,10 +5,6 @@
 import networkx as nx
 from omf.solvers.opendss import dssConvert
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 
 # Wireframe for new OMD objects:

omf/loadModelingScada.py

@@ -3,10 +3,6 @@
 from os.path import join as pJoin
 import numpy as np
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 from omf import feeder
 from omf.solvers import gridlabd

omf/models/cvrDynamic.py

@@ -1,16 +1,9 @@
 ''' Calculate CVR impacts using a targetted set of dynamic loadflows. '''
 
-import json, os, shutil, math, calendar, platform
+import json, os, shutil, math, calendar
 from datetime import datetime as dt, timedelta
 from os.path import join as pJoin
-
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
 # OMF feeder
 from omf import feeder
 from omf.solvers import gridlabd

omf/models/cvrStatic.py

@@ -5,10 +5,6 @@
 from os.path import join as pJoin
 
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 
 # OMF imports

omf/models/derInterconnection.py

@@ -1,21 +1,11 @@
 ''' perform analysis pertaining to the addition of a DER interconnection on a feeder. '''
-import glob, json, os, tempfile, shutil, csv, math, warnings, random, copy, base64, platform
+import glob, json, os, tempfile, shutil, csv, math, random, copy, base64
 from os.path import join as pJoin
 import networkx as nx
-
-# Hack: Agg backend doesn't work for interactivity. Switch to something we can use:
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
-# dateutil imports
 from dateutil import parser
 from dateutil.relativedelta import *
-
-# OMF imports 
 from omf import feeder
 from omf.solvers import gridlabd
 from omf.models import __neoMetaModel__

omf/models/evInterconnection.py

@@ -6,10 +6,6 @@
 import numpy as np
 
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 #plt.style.use('seaborn')
 

omf/models/faultAnalysis.py

@@ -1,17 +1,9 @@
 ''' Determine impacts of distribution faults. '''
 
-import json, os, shutil, csv, warnings, base64, platform
+import json, os, shutil, csv, base64
 from os.path import join as pJoin
 
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
-from matplotlib import pyplot as plt
-
 # dateutil imports
-from dateutil import parser
 from dateutil.relativedelta import *
 
 # OMF imports

omf/models/flisr.py

@@ -1,23 +1,10 @@
 ''' Calculate FLISR, fault location isolation and restoration. '''
-import random, re, datetime, json, os, tempfile, shutil, csv, math, platform, base64
+import re, json, os, tempfile, shutil
 from os.path import join as pJoin
 import pandas as pd
-import numpy as np
-import scipy
-from scipy import spatial
-import scipy.stats as st
-from sklearn.preprocessing import LabelEncoder
-import plotly as py
-import plotly.graph_objs as go
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
-from matplotlib import pyplot as plt
 import networkx as nx
 # OMF imports
-from omf import feeder, geo, distNetViz
+from omf import feeder, geo
 import omf
 import omf.feeder
 import omf.geo

omf/models/networkStructure.py

@@ -1,24 +1,13 @@
 ''' Reconstruct distribution network structure from meter data. '''
-import json, os, tempfile, shutil, csv, math, re, base64, platform
+import json, os, tempfile, shutil, csv, math, re, base64
 from os.path import join as pJoin
 import pandas as pd
 import networkx as nx
 from sklearn import svm
 from sklearn import metrics
 from numpy import array
-
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
-# dateutil imports
-from dateutil import parser
 from dateutil.relativedelta import *
-
-# OMF imports
 import omf
 from omf import feeder, geo
 from omf.models import __neoMetaModel__

omf/models/phaseId.py

@@ -1,5 +1,5 @@
 ''' Identify phasing from meter data.'''
-import json, datetime, csv, os, shutil, re, warnings, itertools, base64, platform
+import datetime, csv, os, shutil, re, itertools, base64, platform
 from os.path import join as pJoin
 from base64 import b64decode
 from zipfile import ZipFile
@@ -11,14 +11,7 @@
 from scipy.stats import linregress
 from sklearn import preprocessing
 from sklearn.metrics import confusion_matrix
-
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
 from omf.models import __neoMetaModel__
 from omf.models.__neoMetaModel__ import *
 from omf.solvers import sandia_ami_phase_id

omf/models/resilientDist.py

@@ -1,19 +1,12 @@
 ''' Run micot-GFM, micot-RDT, and GridLAB-D to determine an optimal distribution resiliency investment. '''
 
-import json, os, shutil, subprocess, datetime, re, random, copy, warnings, base64, platform
+import json, os, shutil, subprocess, datetime, random, copy, base64, platform
 import os.path
 from os.path import join as pJoin
 import numpy as np
 import networkx as nx
-
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
-# OMF imports
 import omf
 from omf import feeder, weather, distNetViz
 from omf.models import __neoMetaModel__
@@ -128,7 +121,7 @@ def changeCellSize(self, cellSize):
 		self.hazardObj["cellsize"] = cellSize
 
 	def mapValue(self, value, fromMin, fromMax, toMin=.7, toMax=1):
-		newValue = float(value - fromMind) / float(fromMax-fromMin)
+		newValue = float(value - fromMin) / float(fromMax-fromMin)
 		return toMin + (newValue * (toMax-toMin))
 
 	def mapRanges(self, values, fromMin, fromMax):

omf/models/smartSwitching.py

@@ -8,12 +8,7 @@
 import plotly.graph_objs as go
 from plotly.tools import make_subplots
 import networkx as nx
-
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 
 # dateutil imports

omf/models/solarConsumer.py

@@ -1,16 +1,8 @@
 ''' Calculate solar costs and benefits for consumers. '''
 
-import shutil, datetime, platform
+import shutil, datetime
 from os.path import join as pJoin
-
-import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
-# OMF imports
 from omf import weather
 from omf.solvers import nrelsam2013
 from omf.models import __neoMetaModel__

omf/models/solarEngineering.py

@@ -9,11 +9,6 @@
 from matplotlib import pyplot as plt
 from matplotlib import animation
 from matplotlib.animation import FuncAnimation
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-	# plt.rcParams['animation.ffmpeg_path'] = '/usr/local/bin/ffmpeg'
-else:
-	matplotlib.use('Agg')
 from omf import feeder, weather
 from omf.solvers import gridlabd
 from omf.models import __neoMetaModel__

omf/models/transmission.py

@@ -3,18 +3,12 @@
 Tested on Linux and macOS.
 '''
 
-import json, os, shutil, subprocess, math, platform, base64
+import json, os, shutil, platform, base64
 from os.path import join as pJoin
-
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
 import matplotlib.cm as cm
 import pandapower as ppow
-
 from omf import transmission as network
 from omf.models import __neoMetaModel__
 from omf.models.__neoMetaModel__ import *

omf/models/voltageDrop.py

@@ -1,19 +1,12 @@
 ''' Graph the voltage drop on a feeder. '''
-import json, os, tempfile, shutil, csv, math, warnings, base64, platform
+import json, os, tempfile, shutil, csv, math, base64
 from os.path import join as pJoin
 import networkx as nx
-
 import matplotlib
-if platform.system() == 'Darwin':
-	matplotlib.use('TkAgg')
-else:
-	matplotlib.use('Agg')
 from matplotlib import pyplot as plt
-
 # dateutil imports
 from dateutil import parser
 from dateutil.relativedelta import *
-
 # OMF imports 
 import omf
 from omf import feeder

omf/runOmf.command

@@ -2,5 +2,5 @@
 # Command to start up the OMF web server locally. Works on Unix, macOS.
 script_dir=$(dirname "$0")
 cd "$script_dir"
-python web.py
+python3 web.py
 read -n1 -r -p "Server stopped. Press space to continue..." key