def test_check_period(self):
dat = datetime(2014, 11, 20)
assert check_period('dekad', dat) == datetime(2014, 11, 20, 0, 0)
assert check_period('week', dat) == datetime(2014, 11, 23, 0, 0)
assert check_period('monthly', dat) == datetime(2014, 11, 30, 0, 0)
def read_img(self, date, region=None, variable=None, scaled=True):
"""Gets images from netCDF file for certain date
Parameters
----------
date : datetime
Date of the image.
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects first available variables if None.
scaled : bool, optional
If true, data will be scaled to a predefined range; if false, data
will be shown as given in rawdata file; defaults to True.
Returns
-------
img : numpy.ndarray
Image of selected date.
lon : numpy.array
Array with longitudes.
lat : numpy.array
Array with latitudes.
metadata : dict
Dictionary containing metadata of the variable.
"""
if region is None:
region = self.dest_regions[0]
if variable is None:
variable = self.get_variables()[0]
else:
# Renames variable name to SOURCE_variable
variable = self.check_variable(variable)
source_file = self.src_file[region]
# get dekad of date:
date = dt.check_period(self.dest_temp_res, date)
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
datenum = date2num(date, units=time.units, calendar=time.calendar)
position = np.where(time[:] == datenum)[0][0]
var = nc.variables[variable]
img = var[position]
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
metadata = {}
for attr in var.ncattrs():
if attr[0] != '_' and attr != 'scale_factor':
metadata[attr] = var.getncattr(attr)
if not metadata:
metadata = None
if 'scaling_factor' in var.ncattrs():
if metadata['scaling_factor'] < 0:
img = img * float(metadata['scaling_factor'])
else:
img = img / float(metadata['scaling_factor'])
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
img = self._scale_values(img)
return img, lon, lat, metadata
def read_img(self, date, region=None, variable=None, scaled=True):
"""Gets images from netCDF file for certain date
Parameters
----------
date : datetime
Date of the image.
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects first available variables if None.
scaled : bool, optional
If true, data will be scaled to a predefined range; if false, data
will be shown as given in rawdata file; defaults to True.
Returns
-------
img : numpy.ndarray
Image of selected date.
lon : numpy.array
Array with longitudes.
lat : numpy.array
Array with latitudes.
metadata : dict
Dictionary containing metadata of the variable.
"""
if region is None:
region = self.valid_regions[0]
if variable is None:
variable = self.get_variables()[0]
else:
# Renames variable name to SOURCE_variable
variable = self.check_variable(variable)
source_file = self.src_file[region]
# get dekad of date:
date = dt.check_period(self.dest_temp_res, date)
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
datenum = date2num(date, units=time.units, calendar=time.calendar)
position = np.where(time[:] == datenum)[0][0]
var = nc.variables[variable]
img = var[position]
lon = nc.variables['lon'][:]
lat = nc.variables['lat'][:]
metadata = {}
for attr in var.ncattrs():
if attr[0] != '_' and attr != 'scale_factor':
metadata[attr] = var.getncattr(attr)
if not metadata:
metadata = None
if 'scaling_factor' in var.ncattrs():
if metadata['scaling_factor'] < 0:
img = img * float(metadata['scaling_factor'])
else:
img = img / float(metadata['scaling_factor'])
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
img = self._scale_values(img)
return img, lon, lat, metadata
