def read_ts(self, location, region=None, variable=None, shapefile=None,
scaled=True):
"""Gets timeseries from netCDF file for a gridpoint.
Parameters
----------
location : int or tuple of floats
Either Grid point index as integer value or Longitude/Latitude
given as tuple.
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects all available variables if None.
shapefile : str, optional
Path to custom shapefile.
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
-------
df : pd.DataFrame
Timeseries for selected variables.
"""
if region is None:
region = self.dest_regions[0]
if type(location) is tuple:
if region == 'global':
grid = RegularGrid(self.dest_sp_res)
else:
grid = ShapeGrid(region, self.dest_sp_res, shapefile)
gp, _ = grid.find_nearest_gpi(location[0], location[1])
else:
gp = location
if variable is None:
variable = self.get_variables()
else:
variable = self.check_variable(variable)
variable = [variable]
source_file = self.src_file[region]
var_dates = self._check_current_date()
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
dates = num2date(time[:], units=time.units, calendar=time.calendar)
position = np.where(nc.variables['gpi'][:] == gp)
lat_pos = position[0][0]
lon_pos = position[1][0]
df = pd.DataFrame(index=pd.DatetimeIndex(dates))
for ncvar in variable:
begin = np.where(dates == var_dates[region][ncvar][0])[0][0]
end = np.where(dates == var_dates[region][ncvar][1])[0][0]
df[ncvar] = np.NAN
for i in range(begin, end + 1):
df[ncvar][i] = nc.variables[ncvar][i, lat_pos, lon_pos]
if 'scaling_factor' in nc.variables[ncvar].ncattrs():
vvar = nc.variables[ncvar]
if vvar.getncattr('scaling_factor') < 0:
df[ncvar] = (df[ncvar] *
float(vvar.getncattr('scaling_factor')))
else:
df[ncvar] = (df[ncvar] /
float(vvar.getncattr('scaling_factor')))
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
df[ncvar] = self._scale_values(df[ncvar])
return df
def bulkread_ts(self,
locations,
region=None,
variable=None,
shapefile=None,
scaled=True,
grid=None):
"""Gets timeseries from netCDF file for a number of gridpoints.
Parameters
----------
location : list of int or list of tuples
Either a list of Grid point indices given as integer value [0,1,2]
or a list of Longitude/Latitude tuples [(0.0, 0.0),(45.0, 45.0)].
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects all available variables if None.
shapefile : str, optional
Path to custom shapefile.
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
grid : poets.grid.grids RegularGrid or ShapeGrid, optional
Grid with point and lon/lat information; defaults to None.
Returns
-------
df_list : list of pd.DataFrames
List with timeseries for selected variables.
gpis : list of int
List of gpi values.
"""
if region is None:
region = self.valid_regions[0]
if type(locations[0]) is tuple:
if grid is None:
if region == 'global':
grid = RegularGrid(self.dest_sp_res)
else:
grid = ShapeGrid(region, self.dest_sp_res, shapefile)
gpis = []
for loc in locations:
gp, _ = grid.find_nearest_gpi(loc[0], loc[1])
gpis.append(gp)
else:
gpis = locations
variable = self._set_variable(variable)
source_file = self.src_file[region]
var_dates = self._check_current_date()
df_list = {}
lat_pos = []
lon_pos = []
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
dates = num2date(time[:], units=time.units, calendar=time.calendar)
for gp in gpis:
position = np.where(nc.variables['gpi'][:] == gp)
lat_pos = np.append(lat_pos, int(position[0][0]))
lon_pos = np.append(lon_pos, int(position[1][0]))
lat_pos = map(int, lat_pos)
lon_pos = map(int, lon_pos)
df = pd.DataFrame(index=pd.DatetimeIndex(dates))
for ncv in variable:
begin = np.where(dates == var_dates[region][ncv][0])[0][0]
end = np.where(dates == var_dates[region][ncv][1])[0][0]
ts = nc.variables[ncv][begin:end + 1, :, :]
ts = ts[:, lat_pos, lon_pos]
ts = np.swapaxes(ts, 1, 0)
for idx, gp in enumerate(gpis):
df['gpi_' + str(gp)] = np.NAN
df['gpi_' + str(gp)][begin:end + 1] = ts[idx, :]
if nc.variables[ncv]._FillValue is not None:
df = df.replace(nc.variables[ncv]._FillValue, np.NAN)
if 'scaling_factor' in nc.variables[ncv].ncattrs():
nv = nc.variables[ncv]
if nv.getncattr('scaling_factor') < 0:
df[ncv] = (df[ncv] *
float(nv.getncattr('scaling_factor')))
else:
df[ncv] = (df[ncv] /
float(nv.getncattr('scaling_factor')))
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
df[ncv] = self._scale_values(df[ncv])
df_list[ncv] = df
return df_list, gpis
def bulkread_ts(self, locations, region=None, variable=None,
shapefile=None, scaled=True, grid=None):
"""Gets timeseries from netCDF file for a number of gridpoints.
Parameters
----------
location : list of int or list of tuples
Either a list of Grid point indices given as integer value [0,1,2]
or a list of Longitude/Latitude tuples [(0.0, 0.0),(45.0, 45.0)].
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects all available variables if None.
shapefile : str, optional
Path to custom shapefile.
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
grid : poets.grid.grids RegularGrid or ShapeGrid, optional
Grid with point and lon/lat information; defaults to None.
Returns
-------
df_list : list of pd.DataFrames
List with timeseries for selected variables.
gpis : list of int
List of gpi values.
"""
if region is None:
region = self.valid_regions[0]
if type(locations[0]) is tuple:
if grid is None:
if region == 'global':
grid = RegularGrid(self.dest_sp_res)
else:
grid = ShapeGrid(region, self.dest_sp_res, shapefile)
gpis = []
for loc in locations:
gp, _ = grid.find_nearest_gpi(loc[0], loc[1])
gpis.append(gp)
else:
gpis = locations
variable = self._set_variable(variable)
source_file = self.src_file[region]
var_dates = self._check_current_date()
df_list = {}
lat_pos = []
lon_pos = []
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
dates = num2date(time[:], units=time.units, calendar=time.calendar)
for gp in gpis:
position = np.where(nc.variables['gpi'][:] == gp)
lat_pos = np.append(lat_pos, int(position[0][0]))
lon_pos = np.append(lon_pos, int(position[1][0]))
lat_pos = map(int, lat_pos)
lon_pos = map(int, lon_pos)
df = pd.DataFrame(index=pd.DatetimeIndex(dates))
for ncv in variable:
begin = np.where(dates == var_dates[region][ncv][0])[0][0]
end = np.where(dates == var_dates[region][ncv][1])[0][0]
ts = nc.variables[ncv][begin:end + 1, :, :]
ts = ts[:, lat_pos, lon_pos]
ts = np.swapaxes(ts, 1, 0)
for idx, gp in enumerate(gpis):
df['gpi_' + str(gp)] = ts[idx, :]
if nc.variables[ncv]._FillValue is not None:
df = df.replace(nc.variables[ncv]._FillValue, np.NAN)
if 'scaling_factor' in nc.variables[ncv].ncattrs():
nv = nc.variables[ncv]
if nv.getncattr('scaling_factor') < 0:
df[ncv] = (df[ncv] *
float(nv.getncattr('scaling_factor')))
else:
df[ncv] = (df[ncv] /
float(nv.getncattr('scaling_factor')))
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
df[ncv] = self._scale_values(df[ncv])
df_list[ncv] = df
return df_list, gpis
def read_ts(self,
location,
region=None,
variable=None,
shapefile=None,
scaled=True):
"""Gets timeseries from netCDF file for one gridpoint.
Parameters
----------
location : int or tuple of floats
Either Grid point index as integer value or Longitude/Latitude
given as tuple.
region : str, optional
Region of interest, set to first defined region if not set.
variable : str, optional
Variable to display, selects all available variables if None.
shapefile : str, optional
Path to custom shapefile.
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
-------
df : pd.DataFrame
Timeseries for selected variables.
"""
if region is None:
region = self.valid_regions[0]
if type(location) is tuple:
if region == 'global':
grid = RegularGrid(self.dest_sp_res)
else:
grid = ShapeGrid(region, self.dest_sp_res, shapefile)
gp, _ = grid.find_nearest_gpi(location[0], location[1])
else:
gp = location
variable = self._set_variable(variable)
source_file = self.src_file[region]
var_dates = self._check_current_date()
with Dataset(source_file, 'r', format='NETCDF4') as nc:
time = nc.variables['time']
dates = num2date(time[:], units=time.units, calendar=time.calendar)
position = np.where(nc.variables['gpi'][:] == gp)
lat_pos = position[0][0]
lon_pos = position[1][0]
df = pd.DataFrame(index=pd.DatetimeIndex(dates))
for ncvar in variable:
begin = np.where(dates == var_dates[region][ncvar][0])[0][0]
end = np.where(dates == var_dates[region][ncvar][1])[0][0]
df[ncvar] = np.NAN
ts = nc.variables[ncvar][begin:end + 1, lat_pos, lon_pos]
df[ncvar][begin:end + 1] = ts
if nc.variables[ncvar]._FillValue is not None:
df = df.replace(nc.variables[ncvar]._FillValue, np.NAN)
if 'scaling_factor' in nc.variables[ncvar].ncattrs():
vvar = nc.variables[ncvar]
if vvar.getncattr('scaling_factor') < 0:
df[ncvar] = (df[ncvar] *
float(vvar.getncattr('scaling_factor')))
else:
df[ncvar] = (df[ncvar] /
float(vvar.getncattr('scaling_factor')))
if scaled:
if self.valid_range is not None:
if self.data_range is not None:
df[ncvar] = self._scale_values(df[ncvar])
return df