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Tutorials/README.md

@@ -1,4 +1,4 @@
-# Tutorial 
+# Tutorial
 These notebooks are designed to introduce the methodology of GOSTNets to novice python users; in order to properly implement these, users should have a basic knowledge of
 - Python
 - Jupyter notebooks

Tutorials/tutorial_data/rek2.cpg

@@ -1 +1 @@
-UTF-8
+UTF-8

Tutorials/tutorial_data/rek2.prj

@@ -1 +1 @@
-PROJCS["ISN93_Lambert_1993",GEOGCS["GCS_ISN93",DATUM["D_Islands_Network_1993",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",64.25],PARAMETER["standard_parallel_2",65.75],PARAMETER["latitude_of_origin",65],PARAMETER["central_meridian",-19],PARAMETER["false_easting",500000],PARAMETER["false_northing",500000],UNIT["Meter",1]]
+PROJCS["ISN93_Lambert_1993",GEOGCS["GCS_ISN93",DATUM["D_Islands_Network_1993",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["standard_parallel_1",64.25],PARAMETER["standard_parallel_2",65.75],PARAMETER["latitude_of_origin",65],PARAMETER["central_meridian",-19],PARAMETER["false_easting",500000],PARAMETER["false_northing",500000],UNIT["Meter",1]]

Tutorials/tutorial_data/rek_grid.cpg

@@ -1 +1 @@
-UTF-8
+UTF-8

Tutorials/tutorial_data/rek_grid.prj

@@ -1 +1 @@
-PROJCS["ISN93_Lambert_1993",GEOGCS["GCS_ISN93",DATUM["D_Islands_Network_1993",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",500000.0],PARAMETER["central_meridian",-19.0],PARAMETER["standard_parallel_1",64.25],PARAMETER["standard_parallel_2",65.75],PARAMETER["latitude_of_origin",65.0],UNIT["Meter",1.0]]
+PROJCS["ISN93_Lambert_1993",GEOGCS["GCS_ISN93",DATUM["D_Islands_Network_1993",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Lambert_Conformal_Conic"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",500000.0],PARAMETER["central_meridian",-19.0],PARAMETER["standard_parallel_1",64.25],PARAMETER["standard_parallel_2",65.75],PARAMETER["latitude_of_origin",65.0],UNIT["Meter",1.0]]

Tutorials/tutorial_data/rek_grid_pop.cpg

@@ -1 +1 @@
-ISO-8859-1
+ISO-8859-1

Tutorials/tutorial_data/rek_grid_pop.prj

@@ -1 +1 @@
-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]

Tutorials/tutorial_outputs/placeholder.txt

@@ -1 +1 @@
-outputs will go here!
+outputs will go here!

src/GOSTnets/__init__.py

@@ -14,4 +14,4 @@
     __version__ = version("GOSTnets")
 except PackageNotFoundError:
     # package is not installed
-    pass
+    pass

src/GOSTnets/calculate_od_raw.py

@@ -12,7 +12,7 @@
 
 def calculateOD_gdf(G, origins, destinations, fail_value=-1, weight="time", calculate_snap=False, wgs84 = {'init':'epsg:4326'}):
     ''' Calculate Origin destination matrix from GeoDataframes
-    
+
     Args:
         G (networkx graph): describes the road network. Often extracted using OSMNX
         origins (geopandas dataframe): source locations for calculating access
@@ -44,8 +44,8 @@ def calculateOD_gdf(G, origins, destinations, fail_value=-1, weight="time", calc
         for idx, row in originsUTM.iterrows():
             outputMatrix[idx,:] = outputMatrix[idx,:] + row['tTime_sec']
         outputMatrix = outputMatrix
-    return(outputMatrix)    
-    
+    return(outputMatrix)
+
 def calculateOD_csv(G, originCSV, destinationCSV='', oLat="Lat", oLon="Lon", dLat="Lat", dLon="Lon", crs={'init':'epsg:4326'}, fail_value=-1, weight='time', calculate_snap=False):
     """
     Calculate OD matrix from csv files of points
@@ -88,27 +88,27 @@ def calculate_gravity(od, oWeight=[], dWeight=[], decayVals=[0.01,
                                                         0.0000962704,   # Market access halves every 120 mins
                                                         0.0000385082,   # Market access halves every 300 mins
                                                         0.00001]):
-    
+
 
     if len(oWeight) != od.shape[0]:
         oWeight = [1] * od.shape[0]
     if len(dWeight) != od.shape[1]:
         dWeight = [1] * od.shape[1]
     allRes = []
-    
+
     od_df = pd.DataFrame(od)
-    
-    
+
+
     for dist_decay in decayVals:
         decayFunction = lambda x: np.exp(-1 * dist_decay * x)
-        
+
         summedVals = np.sum(decayFunction(od_df) * dWeight, axis=1) * oWeight
-        
+
         allRes.append(summedVals)
-        
+
     res = pd.DataFrame(allRes).transpose()
     res.columns = columns=['d_%s' % d for d in decayVals]
-    
+
     return(res)
 
 
@@ -122,10 +122,10 @@ def calculate_gravity(od, oWeight=[], dWeight=[], decayVals=[0.01,
 #                                                         0.0000385082,   # Market access halves every 300 mins
 #                                                         0.00001]):
 #     ''' Calculate the gravity weight between origins and destinations.
-    
+
 #     Args:
-#         od (ndarray): matrix of travel time    
-#         oWeight/dWeight (array, optional) - array of weights for calculating weight; reverts to 1 for if not defined   
+#         od (ndarray): matrix of travel time
+#         oWeight/dWeight (array, optional) - array of weights for calculating weight; reverts to 1 for if not defined
 #         decayVals (array, optional): decayVals to calculate for gravity. Each value will be returned as a column of results
 #     Returns:
 #         geopandas: columns of decayvals
@@ -148,5 +148,5 @@ def calculate_gravity(od, oWeight=[], dWeight=[], decayVals=[0.01,
 #     res = pd.DataFrame(allRes).transpose()
 #     res.columns = columns=['d_%s' % d for d in decayVals]
 #     return(res)
-    
-    
+
+

src/GOSTnets/conversion_utils.py

@@ -13,12 +13,12 @@
 
 def rasterize_od_results(inD, outFile, field, template = None):
     ''' Convert gridded point data frame to raster of commensurate size and resolution
-    
+
     INPUT
     inD [ geopandas data frame ] - OD matrix as point data frame
     outFile [ string ] - path to save output raster
     field [ string ] - field to rasterize
-    
+
     RETURNS
     None
     '''
@@ -45,7 +45,7 @@ def rasterize_od_results(inD, outFile, field, template = None):
         new_dataset.write_band(1, burned)
 
         new_dataset.close()
-        
+
     else:
         #create grid from input shapefile
         # get xs, ys, and values from origin points
@@ -64,7 +64,7 @@ def rasterize_od_results(inD, outFile, field, template = None):
         xRes = (xx.max() - xx.min()) / len(unique_xs)
         yRes = (yy.max() - yy.min()) / len(unique_ys)
         # get the right transformation for raster file
-        trans = rasterio.transform.from_bounds(xx.min() - (xRes/2), yy.min() - (yRes/2), 
+        trans = rasterio.transform.from_bounds(xx.min() - (xRes/2), yy.min() - (yRes/2),
                                                xx.max() - (xRes/2), yy.max() - (yRes/2),
                                                x_pixels - 1, y_pixels - 1)
         new_dataset = rasterio.open(
@@ -74,10 +74,10 @@ def rasterize_od_results(inD, outFile, field, template = None):
             crs=inD.crs,
             transform=trans
         )
-        
+
         shapes = ((row.geometry,row[field]) for idx, row in inD.iterrows())
         burned = features.rasterize(shapes=shapes, fill=0, out_shape=grid_array.shape, transform=new_dataset.transform)
-        burned = burned.astype(grid_array.dtype)    
+        burned = burned.astype(grid_array.dtype)
         new_dataset.write_band(1, burned)
-        
-        new_dataset.close()
+
+        new_dataset.close()