atmopy.observation.location

Package atmopy :: Package observation :: Module location

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Module atmopy.observation.location

Classes
`Station`	Stores information about an observation station

Function Summary
sequence of Station	`filter_stations(filter_func, station_list)` Filters a station list in place according to the given filter.
Station sequence, 1D numpy.array sequence	`filter_stations_observations(filter_func, station_list, observations_list)` Filters a station list and corresponding observations list in place according to the given filter which takes a station and an observation array in argument.
1D numpy.array	`get_simulated_at_location(origin, delta, data, point)` Gets a time sequence of data at specified location using bilinear interpolation.
1D numpy.array	`get_simulated_at_location_closest(origins, deltas, data, point)` Gets a time sequence of data at specified location using closest neighbour values.
sequence of 1D numpy.array	`get_simulated_at_locations(origins, deltas, data, point_list)` Gets a list of time sequences of data at specified locations using bilinear interpolation.
sequence of 1D numpy.array	`get_simulated_at_locations_closest(origins, deltas, data, point_list)` Gets a list of time sequences of data at specified locations using closest neighbours.
1D numpy.array	`get_simulated_at_station(origins, deltas, data, station)` Gets a time sequence of data at specified station using bilinear interpolation.
1D numpy.array	`get_simulated_at_station_closest(origins, deltas, data, station)` Gets a time sequence of data at specified station using the closest data, that is, without interpolation.
sequence of 1D numpy.array	`get_simulated_at_stations(origins, deltas, data, stations)` Gets a list of time sequences of data at specified stations using bilinear interpolation.
Station	`get_station(station_list, station_name)` Gets a Station object given its name and a list of stations.
Boolean	`has_valid_latlon(station)` Tests if the given station has valid latitude and longitude (ie not null).
Boolean	`is_rural(station)` Tests if the station is of rural type.
Boolean	`is_urban(station)` Tests if the station is of urban type.
Boolean sequence	`map_stations(bool_func, station_list)` Returns a boolean list containing the return values of the given function applied on every station of the list.
Boolean sequence	`map_stations_observations(map_func, station_list, observations_list)` Returns a sequence containing the return values of the given function applied on every station and observation of the sequences.

Function Details

filter_stations(filter_func, station_list)

Filters a station list in place according to the given filter. To have a filter not acting in place, use the filter python builtin.

Parameters:: filter_func - The function used to filter the station list. This function must take a Station object as argument, and must return a boolean.
(type=Python function); station_list - sequence of stations to filter.
(type=sequence of Station)

Returns:: Sequence of stations filtered in place.
(type=sequence of Station)

filter_stations_observations(filter_func, station_list, observations_list)

Filters a station list and corresponding observations list in place according to the given filter which takes a station and an observation array in argument.

Parameters:: filter_func - The function used to filter the station sequence and observation sequence . This function must take a Station object and the corresponding observations sequence as argument, and must return a boolean.
(type=Python function); station_list - sequence of stations to filter.
(type=sequence of Station); observations_list - sequence of observations to filter.
(type=sequence of 1D numpy.array)

Returns:: Stations sequence and observations sequence, filtered in place.
(type=Station sequence, 1D numpy.array sequence)

get_simulated_at_location(origin, delta, data, point)

Gets a time sequence of data at specified location using bilinear interpolation.

Parameters:: origin - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); delta - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); point - (latitude, longitude) of the point where the time sequence must be computed.
(type=(float, float) tuple)

Returns:: Time sequence of data at given point.
(type=1D numpy.array)

get_simulated_at_location_closest(origins, deltas, data, point)

Gets a time sequence of data at specified location using closest neighbour values.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); point - (latitude, longitude) of the point where the time sequence must be computed.
(type=(float, float) tuple)

Returns:: Time sequence of data at given point.
(type=1D numpy.array)

get_simulated_at_locations(origins, deltas, data, point_list)

Gets a list of time sequences of data at specified locations using bilinear interpolation.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); point_list - Sequence of (latitude, longitude) of the points where the time sequences must be computed.
(type=sequence of (float, float) tuples)

Returns:: Sequence of time sequences of data at given points.
(type=sequence of 1D numpy.array)

get_simulated_at_locations_closest(origins, deltas, data, point_list)

Gets a list of time sequences of data at specified locations using closest neighbours.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); point_list - Sequence of (latitude, longitude) of the points where the time sequences must be computed.
(type=sequence of (float, float) tuples)

Returns:: Sequence of time sequences of data at given points.
(type=sequence of 1D numpy.array)

get_simulated_at_station(origins, deltas, data, station)

Gets a time sequence of data at specified station using bilinear interpolation.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); station - station where the time sequence must be computed.
(type=Station)

Returns:: Time sequence of data at given point.
(type=1D numpy.array)

get_simulated_at_station_closest(origins, deltas, data, station)

Gets a time sequence of data at specified station using the closest data, that is, without interpolation.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); station - station where the time sequence must be computed.
(type=Station)

Returns:: Time sequence of data at given point.
(type=1D numpy.array)

get_simulated_at_stations(origins, deltas, data, stations)

Gets a list of time sequences of data at specified stations using bilinear interpolation.

Parameters:: origins - Grid origin, ie (t_min, y_min, x_min). Only y_min and x_min are used in this function.
(type=(*, float, float) tuple); deltas - Grid deltas, ie (delta_t, delta_y, delta_x). Only delta_x and delta_y are used in this function.
(type=(*, float, float) tuple); data - 3D array of data to interpolate with T, Y, X dimensions.
(type=3D numpy.array); stations - Sequence of Station giving the stations where the time sequences must be computed.
(type=sequence of (float, float) tuples)

Returns:: Sequence of time sequences of data at given points.
(type=sequence of 1D numpy.array)

get_station(station_list, station_name)

Gets a Station object given its name and a list of stations.

Parameters:: station_list - Sequence of stations in which to search the given station name.
(type=sequence of Station); station_name - Name of the station to search for in the list.
(type=string)

Returns:: The Station object corresponding to the station name in the given list of stations.
(type=Station)

has_valid_latlon(station)

Tests if the given station has valid latitude and longitude (ie not null).

Parameters:: station - The station to test.
(type=Station)

Returns:: True if station has not null lagitude or longitude , False otherwise (both null).
(type=Boolean)

is_rural(station)

Tests if the station is of rural type.

Parameters:: station - The station to test as rural or not.
(type=Station)

Returns:: True if station is marked as rural, False otherwise.
(type=Boolean)

is_urban(station)

Tests if the station is of urban type.

Parameters:: station - The station to test as urban or not.
(type=Station)

Returns:: True if station is marked as urban, False otherwise.
(type=Boolean)

map_stations(bool_func, station_list)

Returns a boolean list containing the return values of the given function applied on every station of the list. This just calls the map builtin function.

Parameters:: bool_func - The function mapped to every station of the station_list.
(type=Python function); station_list - sequence of stations to apply bool_func to.
(type=sequence of Station)

Returns:: A boolean sequence, results of bool_func applied to every stations of the list.
(type=Boolean sequence)

map_stations_observations(map_func, station_list, observations_list)

Returns a sequence containing the return values of the given function applied on every station and observation of the sequences.

Parameters:: map_func - The function used to map the station sequence and observation sequence. This function must take a Station object and the corresponding observations array as argument, and must return a boolean.
(type=Python function); station_list - sequence of stations to map.
(type=sequence of Station); observations_list - sequence of observations to filter.
(type=sequence of 1D numpy.array)

Returns:: A boolean sequence, results of map_func applied to every stations and observations of the sequences.
(type=Boolean sequence)

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