def build(self, variable, ftp):
        """Build initial dataset."""
        # We need to build these three files
        paths = self.get_paths(variable)

        # Retrieve rasters
        self.get_rasters(variable, ftp)

        # Reproject for area calculations
        for file in glob(os.path.join(self.tif_folder, "*tif")):
            self.reproject(file)

        # Convert originals to NetCDF file
        files = glob(os.path.join(self.tif_folder, variable + "*tif"))
        toNetCDF(tfiles=files, ncfiles=None, savepath=paths["original"],
                 index=variable, proj=4326, year1=1895, month1=1,
                 year2=TODAYS_DATE.year - 2, month2=12, wmode="w",
                 percentiles=False)

        # Convert projected files to NetCDF file
        files = glob(os.path.join(self.tif_folder, "proj_*tif"))
        toNetCDFAlbers(tfiles=files, ncfiles=None, savepath=paths["albers"],
                       index=variable, proj=PROJ, year1=1895, month1=1,
                       year2=TODAYS_DATE.year - 2, month2=12, wmode="w",
                       percentiles=False)

        # Create a percentile dataset
        toNetCDFPercentile(paths["original"], paths["percentile"])

        # Clear temp folder
        self._clear_tif()
示例#2
0
                 ncfiles=None,
                 savepath=nc_path,
                 index=index,
                 proj=4326,
                 year1=1980,
                 month1=1,
                 year2=todays_date.year,
                 month2=12,
                 wmode='w',
                 percentiles=False)
        toNetCDFAlbers(tfiles=tfiles_proj,
                       ncfiles=None,
                       savepath=nc_path_proj,
                       index=index,
                       proj=proj,
                       year1=1980,
                       month1=1,
                       year2=todays_date.year,
                       month2=12,
                       wmode='w',
                       percentiles=False)

        # Now lets get the percentile values
        pc_path = os.path.join(data_path,
                               "data/droughtindices/netcdfs/" + "percentiles",
                               index + ".nc")
        toNetCDFPercentile(nc_path, pc_path)

# Close connection with FTP server
ftp.quit()
示例#3
0
                 percentiles=False)

        # We are also including percentiles, so lets build another dataset
        pc_path = os.path.join(data_path,
                               'data/droughtindices/netcdfs/percentiles',
                               index_map[index] + '.nc')
        print("Calculating Percentiles...")
        toNetCDFPercentile(nc_path, pc_path)

        # Now create the alber's netcdf
        toNetCDFAlbers(tfiles_proj,
                       ncfiles,
                       nc_path_proj,
                       index,
                       proj=proj,
                       year1=1895,
                       month1=1,
                       year2=todays_date.year,
                       month2=todays_date.month,
                       wmode='w',
                       percentiles=False)

# We use a pre-made min/max value data frame to set color scales
print("Downloads and transformations complete, establishing index ranges...")
index_paths = [
    os.path.join(data_path, 'data/droughtindices/netcdfs', i + '.nc')
    for i in local_indices
]
maxes = {}
mins = {}
for i in tqdm(range(len(index_paths)), position=0):
示例#4
0
                 ncfiles=None,
                 savepath=original_path,
                 index=variable,
                 proj=4326,
                 year1=1895,
                 month1=1,
                 year2=todays_date.year,
                 month2=12,
                 wmode='w',
                 percentiles=False)
        toNetCDFAlbers(tfiles=tfiles_proj,
                       ncfiles=None,
                       savepath=albers_path,
                       index=variable,
                       proj=proj,
                       year1=1895,
                       month1=1,
                       year2=todays_date.year,
                       month2=12,
                       wmode='w',
                       percentiles=False)
        toNetCDFPercentile(original_path, percentile_path)

        # Empty tif folder
        for t in glob(os.path.join(tif_folder, '*')):
            os.remove(t)

# One last thing, we only have min and max vapor pressure deficit
meanNC(minsrc='data/droughtindices/netcdfs/albers/vpdmin.nc',
       maxsrc='data/droughtindices/netcdfs/albers/vpdmax.nc',
       dst='data/droughtindices/netcdfs/albers/vpdmean.nc')