Update script.py

OpenCLIM · Sep 17, 2024 · 42a335e · 42a335e
1 parent be7fdac
commit 42a335e
Showing 1 changed file with 113 additions and 102 deletions.
diff --git a/script.py b/script.py
@@ -37,106 +37,117 @@
 vector_output = os.path.join(outputs_path, location + '.gpkg')
 print('Vector Output File Name:', vector_output)
 
+buildings_premade = glob(vector_path + '/*.gpkg', recursive= True)
+print('buildings_premade:', buildings_premade)
+
 boundary = gpd.read_file(boundary_1[0])
-grid = glob(grids_path + "/*_5km.gpkg", recursive = True)
-print('Grid File:',grid)
-grid = gpd.read_file(grid[0])
-
-# Ensure all of the polygons are defined by the same crs
-boundary.set_crs(epsg=27700, inplace=True)
-grid.set_crs(epsg=27700, inplace=True)
-
-# Identify which of the 5km OS grid cells fall within the chosen city boundary
-cells_needed = gpd.overlay(boundary,grid, how='intersection')
-list = cells_needed['tile_name']
-
-# Identify which of the 100km OS grid cells fall within the chosen city boundary 
-# This will determine which folders are needed to retrieve the DTM for the area
-
-check=[]
-check=pd.DataFrame(check)
-check['cell_code']=['AAAAAA' for n in range(len(list))]
-a_length = len(list[0])
-cell='A'
-
-# Look at each 5km cell that falls in the area and examine the first two digits
-for i in range(0,len(list)):
-    cell=list[i]
-    check.cell_code[i] = cell[a_length - 6:a_length - 4]
-
-# Remove any duplicates, reset the index - dataframe for the 100km cells
-grid_100 = check.drop_duplicates()
-grid_100.reset_index(inplace=True, drop=True)
-
-# Create a dataframe for the 5km cells
-grid_5=cells_needed['tile_name']
-grid_5=pd.DataFrame(grid_5)
-
-# Establish which zip files need to be unzipped
-files_to_unzip=[]
-files_to_unzip=pd.DataFrame(files_to_unzip)
-files_to_unzip=['XX' for n in range(len(grid_100))]
-for i in range(0,len(grid_100)):
-    name=grid_100.cell_code[i]
-    name_path = os.path.join(vector_path, name + '.zip')
-    files_to_unzip[i] = name_path
-
-# Unzip the required files
-for i in range (0,len(files_to_unzip)):
-    if os.path.exists(files_to_unzip[i]) :
-        with ZipFile(files_to_unzip[i],'r') as zip:
-            # extract the files into the inputs directory
-            zip.extractall(vector_path)
-
-# Create a list of each grid cell that lies within the boundary (which gpkg are we looking for)
-grid_5['file_name'] = grid_5['tile_name']+'.gpkg'
-archive=[]
-archive=pd.DataFrame(archive)
-archive=['XX' for n in range(len(grid_5))]
-
-# Check if the gpkgs for each cell exist
-for i in range(0,len(grid_5)):
-    name = grid_5.file_name[i]
-    path = glob(vector_path + '/**/' + name, recursive=True)
-    archive[i] = path
-
-# Remove the empty grid cells from the list
-while([] in archive):
-    archive.remove([])
-
-# Create a list of all of the gpkgs to be merged
-to_merge=[]
-to_merge=['XX' for n in range(len(archive))]
-for i in range (0,len(archive)):
-    file_path = os.path.splitext(archive[i][0])
-    filename=file_path[0].split("/")
-    to_merge[i]=filename[4]+'.gpkg'
-
-# Create a geodatabase and merge the data from each gpkg together
-original = []
-original=gpd.GeoDataFrame(original)
-for cell in to_merge:
-    gdf = gpd.read_file('/data/inputs/vectors/%s' %cell)
-    original = pd.concat([gdf, original],ignore_index=True)
-
-# Print to a gpkg file
-original.to_file(os.path.join(vector_output),driver='GPKG',index=False)
-
-print('Running vector clip')
-
-vector = gpd.read_file(vector_output)
-clipped = gpd.clip(vector,boundary)
-
-# Print to a gpkg file
-clipped.to_file(os.path.join(outputs_path, location + '_clip.gpkg'),driver='GPKG',index=False)
-
-# Remove unclipped file
-os.remove(vector_output)
-
-# Move the clipped file into a new folder and remove the _clip
-src=os.path.join(outputs_path, location + '_clip.gpkg')
-dst=os.path.join(buildings_path, location + '.gpkg')
-shutil.copy(src,dst)
-
-# Remove duplicate file
-os.remove(os.path.join(outputs_path, location + '_clip.gpkg'))
+
+if len(buildings_premade)==1:
+    buildings = gpd.read_file(buildings_premade[0])
+    clipped = gpd.clip(buildings,boundary)
+    # Print to a gpkg file
+    clipped.to_file(os.path.join(buildings_path, location + '.gpkg'),driver='GPKG',index=False)
+
+if len(buildings_premade) == 0:
+    grid = glob(grids_path + "/*_5km.gpkg", recursive = True)
+    print('Grid File:',grid)
+    grid = gpd.read_file(grid[0])
+
+    # Ensure all of the polygons are defined by the same crs
+    boundary.set_crs(epsg=27700, inplace=True)
+    grid.set_crs(epsg=27700, inplace=True)
+
+    # Identify which of the 5km OS grid cells fall within the chosen city boundary
+    cells_needed = gpd.overlay(boundary,grid, how='intersection')
+    list = cells_needed['tile_name']
+
+    # Identify which of the 100km OS grid cells fall within the chosen city boundary 
+    # This will determine which folders are needed to retrieve the DTM for the area
+
+    check=[]
+    check=pd.DataFrame(check)
+    check['cell_code']=['AAAAAA' for n in range(len(list))]
+    a_length = len(list[0])
+    cell='A'
+
+    # Look at each 5km cell that falls in the area and examine the first two digits
+    for i in range(0,len(list)):
+        cell=list[i]
+        check.cell_code[i] = cell[a_length - 6:a_length - 4]
+
+    # Remove any duplicates, reset the index - dataframe for the 100km cells
+    grid_100 = check.drop_duplicates()
+    grid_100.reset_index(inplace=True, drop=True)
+
+    # Create a dataframe for the 5km cells
+    grid_5=cells_needed['tile_name']
+    grid_5=pd.DataFrame(grid_5)
+
+    # Establish which zip files need to be unzipped
+    files_to_unzip=[]
+    files_to_unzip=pd.DataFrame(files_to_unzip)
+    files_to_unzip=['XX' for n in range(len(grid_100))]
+    for i in range(0,len(grid_100)):
+        name=grid_100.cell_code[i]
+        name_path = os.path.join(vector_path, name + '.zip')
+        files_to_unzip[i] = name_path
+
+    # Unzip the required files
+    for i in range (0,len(files_to_unzip)):
+        if os.path.exists(files_to_unzip[i]) :
+            with ZipFile(files_to_unzip[i],'r') as zip:
+                # extract the files into the inputs directory
+                zip.extractall(vector_path)
+
+    # Create a list of each grid cell that lies within the boundary (which gpkg are we looking for)
+    grid_5['file_name'] = grid_5['tile_name']+'.gpkg'
+    archive=[]
+    archive=pd.DataFrame(archive)
+    archive=['XX' for n in range(len(grid_5))]
+
+    # Check if the gpkgs for each cell exist
+    for i in range(0,len(grid_5)):
+        name = grid_5.file_name[i]
+        path = glob(vector_path + '/**/' + name, recursive=True)
+        archive[i] = path
+
+    # Remove the empty grid cells from the list
+    while([] in archive):
+        archive.remove([])
+
+    # Create a list of all of the gpkgs to be merged
+    to_merge=[]
+    to_merge=['XX' for n in range(len(archive))]
+    for i in range (0,len(archive)):
+        file_path = os.path.splitext(archive[i][0])
+        filename=file_path[0].split("/")
+        to_merge[i]=filename[4]+'.gpkg'
+
+    # Create a geodatabase and merge the data from each gpkg together
+    original = []
+    original=gpd.GeoDataFrame(original)
+    for cell in to_merge:
+        gdf = gpd.read_file('/data/inputs/vectors/%s' %cell)
+        original = pd.concat([gdf, original],ignore_index=True)
+
+    # Print to a gpkg file
+    original.to_file(os.path.join(vector_output),driver='GPKG',index=False)
+
+    print('Running vector clip')
+
+    vector = gpd.read_file(vector_output)
+    clipped = gpd.clip(vector,boundary)
+
+    # Print to a gpkg file
+    clipped.to_file(os.path.join(outputs_path, location + '_clip.gpkg'),driver='GPKG',index=False)
+
+    # Remove unclipped file
+    os.remove(vector_output)
+
+    # Move the clipped file into a new folder and remove the _clip
+    src=os.path.join(outputs_path, location + '_clip.gpkg')
+    dst=os.path.join(buildings_path, location + '.gpkg')
+    shutil.copy(src,dst)
+
+    # Remove duplicate file
+    os.remove(os.path.join(outputs_path, location + '_clip.gpkg'))