Source code for pygeobase.object_base

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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]