# Copyright (c) 2015, Vienna University of Technology, Department of Geodesy
# and Geoinformation. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of the Vienna University of Technology, Department of
# Geodesy and Geoinformation nor the names of its contributors may be
# used to endorse or promote products derived from this software without
# specific prior written permission.
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL VIENNA UNIVERSITY OF TECHNOLOGY,
# DEPARTMENT OF GEODESY AND GEOINFORMATION BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
[docs]class TS(object):
"""
The TS class represents the base object of a time series.
Parameters
----------
lon : float
Longitude of the time series
lat : float
Latitude of the time series
data : pandas.DataFrame
Pandas DataFrame that holds data for each variable of the time
series
metadata : dict
dictionary that holds metadata
"""
def __init__(self, gpi, lon, lat, data, metadata):
"""
Initialization of the time series object.
"""
self.gpi = gpi
self.lon = lon
self.lat = lat
self.data = data
self.metadata = metadata
def __repr__(self):
return "Time series gpi:%d lat:%2.3f lon:%3.3f" % (self.gpi,
self.lat,
self.lon)
[docs] def plot(self, *args, **kwargs):
"""
wrapper for pandas.DataFrame.plot which adds title to plot
and drops NaN values for plotting
Returns
-------
ax : axes
matplotlib axes of the plot
"""
tempdata = self.data.dropna(how='all')
ax = tempdata.plot(*args, figsize=(15, 5), **kwargs)
ax.set_title(self.__repr__())
return ax
[docs]class Image(object):
"""
The Image class represents the base object of an image.
Parameters
----------
lon : numpy.array
array of longitudes
lat : numpy.array
array of latitudes
data : dict
dictionary of numpy arrays that holds the image data for each
variable of the dataset
metadata : dict
dictionary that holds metadata
timestamp : datetime.datetime
exact timestamp of the image
timekey : str, optional
Key of the time variable, if available, stored in data dictionary.
"""
def __init__(self, lon, lat, data, metadata, timestamp, timekey=None):
"""
Initialization of the image object.
"""
self.lon = lon
self.lat = lat
self.data = data
self.metadata = metadata
self.timestamp = timestamp
self.timekey = timekey
def __iter__(self):
l = [self.data, self.metadata,
self.timestamp, self.lon,
self.lat]
if self.timekey is not None:
l.append(self.data[self.timekey])
else:
l.append(None)
for attr in l:
yield attr
@property
def dtype(self):
"""
Fake numpy recarray dtype field based
on the dictionary keys and the dtype of the
numpy array.
"""
dtype_list = []
for key in sorted(list(self.data)):
dtype_list.append((key, self.data[key].dtype.type))
return np.dtype(dtype_list)
def __getitem__(self, key):
return self.data[key]