pytesmo.io package

Subpackages

• pytesmo.io.bufr package
  • Submodules
  • pytesmo.io.bufr.bufr module
  • Module contents
• pytesmo.io.ismn package
  • Submodules
  • pytesmo.io.ismn.interface module
  • pytesmo.io.ismn.metadata_collector module
  • pytesmo.io.ismn.readers module
  • Module contents
• pytesmo.io.sat package
  • Submodules
  • pytesmo.io.sat.ascat module
  • pytesmo.io.sat.ers module
  • pytesmo.io.sat.h_saf module
  • Module contents

Submodules

pytesmo.io.dataset_base module

Created on Mar 19, 2014

@author: Christoph Paulik christoph.paulik@geo.tuwien.ac.at

class pytesmo.io.dataset_base.DatasetImgBase(path, grid=None, img_offsets=array([datetime.timedelta(0)], dtype=object))

Bases: object

Dateset base class that implements basic functions and also abstract methods that have to be implemented by child classes.

Parameters:

path : string

Path to dataset.

grid : pytesmo.grid.grids.BasicGrid of CellGrid instance, optional

Grid on which all the images of the dataset are stored. This is not relevant for datasets that are stored e.g. in orbit geometry

img_offsets : numpy array of timedeltas, optional

Gives the time offsets in a day that the images of one day have. e.g.: a model data with 6 hourly data with images at 0, 6, 12 and 18 o’clock would have img_offsets=np.array([timedelta(hours=0),

timedelta(hours=6), timedelta(hours=12), timedelta(hours=18)])

Default is set to np.array([timedelta(hours=0)]) for daily images with timestamp 0

Methods

daily_images(day, **kwargs)

Yield all images for a day.

Parameters:

day : datetime.date or datetime.datetime

Specific day.

Returns:

data : dict

dictionary of numpy arrays that hold the image data for each variable of the dataset

metadata : dict

dictionary of numpy arrays that hold the metadata

timestamp : datetime.datetime

exact timestamp of the image

lon : numpy.array or None

array of longitudes, if None self.grid will be assumed

lat : numpy.array or None

array of latitudes, if None self.grid will be assumed

time_var : string or None

variable name of observation times in the data dict, if None all observations have the same timestamp

read_img(timestamp, **kwargs)

Return an image if a specific datetime is given.

Parameters:

timestamp : datetime.datetime

Time stamp.

Returns:

data : dict

dictionary of numpy arrays that hold the image data for each variable of the dataset

metadata : dict

dictionary of numpy arrays that hold the metadata

timestamp : datetime.datetime

exact timestamp of the image

lon : numpy.array or None

array of longitudes, if None self.grid will be assumed

lat : numpy.array or None

array of latitudes, if None self.grid will be assumed

time_var : string or None

variable name of observation times in the data dict, if None all observations have the same timestamp

class pytesmo.io.dataset_base.DatasetTSBase(path, grid)

Bases: object

Dateset base class that implements basic functions and also abstract methods that have to be implemented by child classes.

Parameters:

path : string

Path to dataset.

grid : pytesmo.grid.grids.BasicGrid of CellGrid instance

Grid on which the time series data is stored.

Methods

iter_ts(ll_bbox=None)

Yield all time series for a grid or for grid points in a given lon/lat bound box (ll_bbox).

Parameters:

ll_bbox : tuple of floats (latmin, latmax, lonmin, lonmax)

Set to lon/lat bounding box to yield only points in that area.

Returns:

data : pandas.DataFrame

pandas.DateFrame with DateTimeIndex

read_gp(gpi, **kwargs)

Reads time series for a given grid point index(gpi)

Parameters:

gpi : int

grid point index

Returns:

data : pandas.DataFrame

pandas.DateFrame with DateTimeIndex

read_ts(*args, **kwargs)

Takes either 1 or 2 arguments and calls the correct function which is either reading the gpi directly or finding the nearest gpi from given lat,lon coordinates and then reading it

Module contents