def Convert_grb2_to_nc(input_wgrib, output_nc, band):
import watools.General.raster_conversions as RC
# Get environmental variable
WA_env_paths = os.environ["WA_PATHS"].split(';')
GDAL_env_path = WA_env_paths[0]
GDAL_TRANSLATE_PATH = os.path.join(GDAL_env_path, 'gdal_translate.exe')
# Create command
fullCmd = ' '.join(['"%s" -of netcdf -b %d' %(GDAL_TRANSLATE_PATH, band), input_wgrib, output_nc]) # -r {nearest}
RC.Run_command_window(fullCmd)
return()
def Convert_hdf5_to_tiff(inputname_hdf, Filename_tiff_end, Band_number,
scaling_factor, geo_out):
"""
This function converts the hdf5 files into tiff files
Keyword Arguments:
input_adf -- name, name of the adf file
output_tiff -- Name of the output tiff file
Band_number -- bandnumber of the hdf5 that needs to be converted
scaling_factor -- factor multipied by data is the output array
geo -- [minimum lon, pixelsize, rotation, maximum lat, rotation,
pixelsize], (geospatial dataset)
"""
import watools.General.raster_conversions as RC
# Open the hdf file
g = gdal.Open(inputname_hdf, gdal.GA_ReadOnly)
# Define temporary file out and band name in
name_in = g.GetSubDatasets()[Band_number][0]
# Get environmental variable
WA_env_paths = os.environ["WA_PATHS"].split(';')
GDAL_env_path = WA_env_paths[0]
GDAL_TRANSLATE = os.path.join(GDAL_env_path, 'gdal_translate.exe')
# run gdal translate command
FullCmd = '%s -of GTiff %s %s' % (GDAL_TRANSLATE, name_in,
Filename_tiff_end)
RC.Run_command_window(FullCmd)
# Get the data array
dest = gdal.Open(Filename_tiff_end)
Data = dest.GetRasterBand(1).ReadAsArray()
dest = None
# If the band data is not SM change the DN values into PROBA-V values and write into the spectral_reflectance_PROBAV
Data_scaled = Data * scaling_factor
# Save the PROBA-V as a tif file
Save_as_tiff(Filename_tiff_end, Data_scaled, geo_out, "WGS84")
return ()
def Convert_adf_to_tiff(input_adf, output_tiff):
"""
This function converts the adf files into tiff files
Keyword Arguments:
input_adf -- name, name of the adf file
output_tiff -- Name of the output tiff file
"""
import watools.General.raster_conversions as RC
# Get environmental variable
WA_env_paths = os.environ["WA_PATHS"].split(';')
GDAL_env_path = WA_env_paths[0]
GDAL_TRANSLATE_PATH = os.path.join(GDAL_env_path, 'gdal_translate.exe')
# convert data from ESRI GRID to GeoTIFF
fullCmd = ('"%s" -co COMPRESS=DEFLATE -co PREDICTOR=1 -co '
'ZLEVEL=1 -of GTiff %s %s') % (GDAL_TRANSLATE_PATH, input_adf, output_tiff)
RC.Run_command_window(fullCmd)
return(output_tiff)
def create_sheet4(basin,
period,
units,
data,
output,
template=False,
tolerance=0.01):
"""
Create sheet 4 of the Water Accounting Plus framework.
Parameters
----------
basin : str
The name of the basin.
period : str
The period of analysis.
units : list
A list with strings of the units of the data on sheet 4a and 4b
respectively.
data : list
List with two values pointing to csv files that contains the water data. The csv file has to
follow an specific format. A sample csv is available here:
https://github.com/wateraccounting/wa/tree/master/Sheets/csv
output : list
Filehandles pointing to the jpg files to be created.
template : list or boolean, optional
A list with two entries of the svg files of the sheet. False
uses the standard svg files. Default is False.
tolerance : float, optional
Range used when checked if different totals match with eachother.
Examples
--------
>>> from watools.Sheets import *
>>> create_sheet4(basin='Helmand', period='2007-2011',
units = ['km3/yr', 'km3/yr'],
data = [r'C:\Sheets\csv\Sample_sheet4_part12.csv',
r'C:\Sheets\csv\Sample_sheet4_part12.csv'],
output = [r'C:\Sheets\sheet_4_part1.png',
r'C:\Sheets\sheet_4_part2.png'])
"""
# import WA+ modules
import watools.General.raster_conversions as RC
if data[0] is not None:
df1 = pd.read_csv(data[0], sep=';')
if data[1] is not None:
df2 = pd.read_csv(data[1], sep=';')
# Read csv part 1
if data[0] is not None:
p1 = dict()
p1['sp_r01_c01'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].SUPPLY_GROUNDWATER)
])
p1['sp_r02_c01'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].SUPPLY_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].SUPPLY_GROUNDWATER)
])
p1['sp_r03_c01'] = pd.np.sum([
float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].SUPPLY_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Industry")].SUPPLY_GROUNDWATER)
])
p1['sp_r04_c01'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].SUPPLY_GROUNDWATER)
])
p1['sp_r05_c01'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].SUPPLY_GROUNDWATER)
])
p1['sp_r06_c01'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].SUPPLY_GROUNDWATER)
])
p1['sp_r07_c01'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].SUPPLY_GROUNDWATER)
])
p1['sp_r08_c01'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].SUPPLY_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].SUPPLY_GROUNDWATER)
])
p1['dm_r01_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops")].DEMAND)
p1['dm_r02_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies")].DEMAND)
p1['dm_r03_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].DEMAND)
p1['dm_r04_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Aquaculture")].DEMAND)
p1['dm_r05_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Residential")].DEMAND)
p1['dm_r06_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Greenhouses")].DEMAND)
p1['dm_r07_c01'] = float(
df1.loc[(df1.LANDUSE_TYPE == "Power and Energy")].DEMAND)
p1['dm_r08_c01'] = float(df1.loc[(df1.LANDUSE_TYPE == "Other")].DEMAND)
p1['sp_r01_c02'] = pd.np.sum([
float(
df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops")].CONSUMED_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r02_c02'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Managed water bodies")].CONSUMED_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Managed water bodies")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r03_c02'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Industry")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Industry")].CONSUMED_OTHER),
float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r04_c02'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r05_c02'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Residential")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r06_c02'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r07_c02'] = pd.np.sum([
float(
df1.loc[(df1.LANDUSE_TYPE == "Power and Energy")].CONSUMED_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r08_c02'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].NON_RECOVERABLE_SURFACEWATER)
])
p1['sp_r01_c03'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].RECOVERABLE_SURFACEWATER)
])
p1['sp_r02_c03'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].RECOVERABLE_SURFACEWATER)
])
p1['sp_r03_c03'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].RECOVERABLE_SURFACEWATER)
])
p1['sp_r04_c03'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].RECOVERABLE_SURFACEWATER)
])
p1['sp_r05_c03'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].RECOVERABLE_SURFACEWATER)
])
p1['sp_r06_c03'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].RECOVERABLE_SURFACEWATER)
])
p1['sp_r07_c03'] = pd.np.sum([
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].RECOVERABLE_SURFACEWATER)
])
p1['sp_r08_c03'] = pd.np.sum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].RECOVERABLE_SURFACEWATER)
])
p1['wd_r01_c01'] = pd.np.nansum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].SUPPLY_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].SUPPLY_GROUNDWATER),
float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].SUPPLY_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].SUPPLY_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].SUPPLY_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].SUPPLY_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].SUPPLY_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].SUPPLY_GROUNDWATER)
])
p1['wd_r02_c01'] = pd.np.nansum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].SUPPLY_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].SUPPLY_SURFACEWATER),
float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].SUPPLY_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].SUPPLY_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].SUPPLY_SURFACEWATER)
])
p1['wd_r03_c01'] = pd.np.nansum([p1['wd_r01_c01'], p1['wd_r02_c01']])
p1['sp_r01_c04'] = pd.np.nansum([
p1['sp_r01_c02'], p1['sp_r02_c02'], p1['sp_r03_c02'],
p1['sp_r04_c02'], p1['sp_r05_c02'], p1['sp_r06_c02'],
p1['sp_r07_c02'], p1['sp_r08_c02']
])
p1['of_r03_c02'] = pd.np.nansum([
p1['sp_r01_c03'], p1['sp_r02_c03'], p1['sp_r03_c03'],
p1['sp_r04_c03'], p1['sp_r05_c03'], p1['sp_r06_c03'],
p1['sp_r07_c03'], p1['sp_r08_c03']
])
p1['of_r02_c01'] = pd.np.nansum([
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].RECOVERABLE_SURFACEWATER)
])
p1['of_r04_c01'] = pd.np.nansum([
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].RECOVERABLE_GROUNDWATER),
float(
df1.loc[(df1.LANDUSE_TYPE == "Other")].RECOVERABLE_GROUNDWATER)
])
p1['of_r03_c01'] = pd.np.nansum([
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].NON_RECOVERABLE_SURFACEWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].NON_RECOVERABLE_SURFACEWATER)
])
p1['of_r05_c01'] = pd.np.nansum([
float(df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Industry")].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(df1.LANDUSE_TYPE == "Power and Energy"
)].NON_RECOVERABLE_GROUNDWATER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Other")].NON_RECOVERABLE_GROUNDWATER)
])
p1['of_r04_c02'] = pd.np.nansum([p1['of_r05_c01'], p1['of_r03_c01']])
p1['sp_r02_c04'] = pd.np.nansum([p1['of_r02_c01'], p1['of_r04_c01']])
p1['of_r09_c02'] = pd.np.nansum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Managed water bodies")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Industry")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses")].CONSUMED_OTHER),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].CONSUMED_OTHER),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].CONSUMED_OTHER)
])
p1['of_r02_c02'] = pd.np.nansum([
float(df1.loc[(
df1.LANDUSE_TYPE == "Irrigated crops")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Managed water bodies"
)].NON_CONVENTIONAL_ET),
float(
df1.loc[(df1.LANDUSE_TYPE == "Industry")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Aquaculture")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Residential")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Greenhouses")].NON_CONVENTIONAL_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Power and Energy")].NON_CONVENTIONAL_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].NON_CONVENTIONAL_ET)
])
p1['of_r01_c02'] = pd.np.nansum([
float(
df1.loc[(df1.LANDUSE_TYPE == "Irrigated crops")].CONSUMED_ET),
float(df1.loc[(
df1.LANDUSE_TYPE == "Managed water bodies")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Industry")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Aquaculture")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Residential")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Greenhouses")].CONSUMED_ET),
float(
df1.loc[(df1.LANDUSE_TYPE == "Power and Energy")].CONSUMED_ET),
float(df1.loc[(df1.LANDUSE_TYPE == "Other")].CONSUMED_ET)
])
p1['of_r01_c01'] = pd.np.nansum([p1['of_r02_c02'], p1['of_r01_c02']])
# Read csv part 2
if data[1] is not None:
p2 = dict()
p2['sp_r01_c02'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].CONSUMED_OTHER)
])
p2['sp_r02_c02'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].CONSUMED_OTHER)
])
p2['sp_r03_c02'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops")].CONSUMED_ET),
float(
df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops")].CONSUMED_OTHER)
])
p2['sp_r04_c02'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].CONSUMED_OTHER)
])
p2['sp_r05_c02'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Water Bodies")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Water Bodies")].CONSUMED_OTHER)
])
p2['sp_r06_c02'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].CONSUMED_OTHER)
])
p2['sp_r07_c02'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].CONSUMED_OTHER)
])
p2['sp_r08_c02'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].CONSUMED_OTHER)
])
p2['sp_r01_c03'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Forests")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forests")].RECOVERABLE_GROUNDWATER)
])
p2['sp_r02_c03'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].RECOVERABLE_GROUNDWATER)
])
p2['sp_r03_c03'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].RECOVERABLE_GROUNDWATER)
])
p2['sp_r04_c03'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].RECOVERABLE_GROUNDWATER)
])
p2['sp_r05_c03'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].RECOVERABLE_GROUNDWATER)
])
p2['sp_r06_c03'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Wetlands")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Wetlands")].RECOVERABLE_GROUNDWATER)
])
p2['sp_r07_c03'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].RECOVERABLE_GROUNDWATER)
])
p2['sp_r08_c03'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].RECOVERABLE_GROUNDWATER)
])
p2['sp_r01_c01'] = pd.np.sum([
float(
df2.loc[(df2.LANDUSE_TYPE == "Forests")].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].SUPPLY_GROUNDWATER)
])
p2['sp_r02_c01'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].SUPPLY_SURFACEWATER),
float(
df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].SUPPLY_GROUNDWATER)
])
p2['sp_r03_c01'] = pd.np.sum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].SUPPLY_GROUNDWATER)
])
p2['sp_r04_c01'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].SUPPLY_GROUNDWATER)
])
p2['sp_r05_c01'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].SUPPLY_GROUNDWATER)
])
p2['sp_r06_c01'] = pd.np.sum([
float(
df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].SUPPLY_GROUNDWATER)
])
p2['sp_r07_c01'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].SUPPLY_GROUNDWATER)
])
p2['sp_r08_c01'] = pd.np.sum([
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].SUPPLY_GROUNDWATER)
])
p2['dm_r01_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Forests")].DEMAND)
p2['dm_r02_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].DEMAND)
p2['dm_r03_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops")].DEMAND)
p2['dm_r04_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations")].DEMAND)
p2['dm_r05_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies")].DEMAND)
p2['dm_r06_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].DEMAND)
p2['dm_r07_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands")].DEMAND)
p2['dm_r08_c01'] = float(
df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)")].DEMAND)
p2['wd_r01_c01'] = pd.np.nansum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].SUPPLY_GROUNDWATER),
float(
df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].SUPPLY_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].SUPPLY_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].SUPPLY_GROUNDWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].SUPPLY_GROUNDWATER),
float(
df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].SUPPLY_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].SUPPLY_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].SUPPLY_GROUNDWATER)
])
p2['wd_r03_c01'] = pd.np.nansum([
float(
df2.loc[(df2.LANDUSE_TYPE == "Forests")].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].SUPPLY_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].SUPPLY_SURFACEWATER),
float(
df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].SUPPLY_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].SUPPLY_SURFACEWATER)
])
p2['wd_r02_c01'] = pd.np.nansum([p2['wd_r01_c01'], p2['wd_r03_c01']])
p2['sp_r01_c04'] = pd.np.nansum([
p2['sp_r01_c02'], p2['sp_r02_c02'], p2['sp_r03_c02'],
p2['sp_r04_c02'], p2['sp_r05_c02'], p2['sp_r06_c02'],
p2['sp_r07_c02'], p2['sp_r08_c02']
])
p2['of_r03_c02'] = p2['sp_r02_c04'] = pd.np.nansum([
p2['sp_r01_c03'], p2['sp_r02_c03'], p2['sp_r03_c03'],
p2['sp_r04_c03'], p2['sp_r05_c03'], p2['sp_r06_c03'],
p2['sp_r07_c03'], p2['sp_r08_c03']
])
p2['of_r01_c01'] = p2['of_r01_c02'] = pd.np.nansum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Water Bodies")].CONSUMED_ET),
float(df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].CONSUMED_ET),
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].CONSUMED_ET)
])
p2['of_r02_c02'] = pd.np.nansum([
float(df2.loc[(df2.LANDUSE_TYPE == "Forests")].CONSUMED_OTHER),
float(df2.loc[(df2.LANDUSE_TYPE == "Shrubland")].CONSUMED_OTHER),
float(
df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops")].CONSUMED_OTHER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Forest Plantations")].CONSUMED_OTHER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Water Bodies")].CONSUMED_OTHER),
float(df2.loc[(df2.LANDUSE_TYPE == "Wetlands")].CONSUMED_OTHER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Natural Grasslands")].CONSUMED_OTHER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Other (Non-Manmade)")].CONSUMED_OTHER)
])
p2['of_r03_c01'] = pd.np.nansum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Forests")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Rainfed Crops"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Wetlands")].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].RECOVERABLE_SURFACEWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].RECOVERABLE_SURFACEWATER)
])
p2['of_r02_c01'] = pd.np.nansum([
float(df2.loc[(
df2.LANDUSE_TYPE == "Forests")].RECOVERABLE_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Shrubland")].RECOVERABLE_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Rainfed Crops")].RECOVERABLE_GROUNDWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Forest Plantations"
)].RECOVERABLE_GROUNDWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Water Bodies"
)].RECOVERABLE_GROUNDWATER),
float(df2.loc[(
df2.LANDUSE_TYPE == "Wetlands")].RECOVERABLE_GROUNDWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Natural Grasslands"
)].RECOVERABLE_GROUNDWATER),
float(df2.loc[(df2.LANDUSE_TYPE == "Other (Non-Manmade)"
)].RECOVERABLE_GROUNDWATER)
])
# Calculations & modify svgs
if not template:
path = os.path.dirname(os.path.abspath(__file__))
svg_template_path_1 = os.path.join(path, 'svg', 'sheet_4_part1.svg')
svg_template_path_2 = os.path.join(path, 'svg', 'sheet_4_part2.svg')
else:
svg_template_path_1 = os.path.abspath(template[0])
svg_template_path_2 = os.path.abspath(template[1])
if data[0] is not None:
tree1 = ET.parse(svg_template_path_1)
xml_txt_box = tree1.findall('''.//*[@id='basin1']''')[0]
xml_txt_box.getchildren()[0].text = 'Basin: ' + basin
xml_txt_box = tree1.findall('''.//*[@id='period1']''')[0]
xml_txt_box.getchildren()[0].text = 'Period: ' + period
xml_txt_box = tree1.findall('''.//*[@id='units1']''')[0]
xml_txt_box.getchildren()[0].text = 'Part 1: Manmade ({0})'.format(
units[0])
for key in p1.keys():
xml_txt_box = tree1.findall(".//*[@id='{0}']".format(key))[0]
if not pd.isnull(p1[key]):
xml_txt_box.getchildren()[0].text = '%.2f' % p1[key]
else:
xml_txt_box.getchildren()[0].text = '-'
if data[1] is not None:
tree2 = ET.parse(svg_template_path_2)
xml_txt_box = tree2.findall('''.//*[@id='basin2']''')[0]
xml_txt_box.getchildren()[0].text = 'Basin: ' + basin
xml_txt_box = tree2.findall('''.//*[@id='period2']''')[0]
xml_txt_box.getchildren()[0].text = 'Period: ' + period
xml_txt_box = tree2.findall('''.//*[@id='units2']''')[0]
xml_txt_box.getchildren(
)[0].text = 'Part 2: Natural Landuse ({0})'.format(units[1])
for key in p2.keys():
xml_txt_box = tree2.findall(".//*[@id='{0}']".format(key))[0]
if not pd.isnull(p2[key]):
xml_txt_box.getchildren()[0].text = '%.2f' % p2[key]
else:
xml_txt_box.getchildren()[0].text = '-'
ET.register_namespace("", "http://www.w3.org/2000/svg")
# Get the paths based on the environment variable
if os.name == 'posix':
Path_Inkscape = 'inkscape'
else:
WA_env_paths = os.environ["WA_PATHS"].split(';')
Inkscape_env_path = WA_env_paths[1]
Path_Inkscape = os.path.join(Inkscape_env_path, 'inkscape.exe')
if data[0] is not None:
tempout_path = output[0].replace('.pdf', '_temporary.svg')
tree1.write(tempout_path)
fullCmd = (' ').join([
Path_Inkscape, tempout_path, '--export-pdf=' + output[0], '-d 300'
])
RC.Run_command_window(fullCmd)
time.sleep(10)
os.remove(tempout_path)
if data[1] is not None:
tempout_path = output[1].replace('.pdf', '_temporary.svg')
tree2.write(tempout_path)
fullCmd = (' ').join([
Path_Inkscape, tempout_path, '--export-pdf=' + output[1], '-d 300'
])
RC.Run_command_window(fullCmd)
time.sleep(10)
os.remove(tempout_path)
