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"""
Image class definition for NetCDF files.
"""
import numpy as np
import netCDF4
import datetime
import pandas as pd
import pygeogrids.grids as grids
from pynetcf.base import Dataset
from pynetcf.time_series import OrthoMultiTs
[docs]class ArrayStack(OrthoMultiTs):
"""
Class for writing stacks of arrays (1D) into netCDF.
Array stacks are basically orthogonal multidimensional
array representation netCDF files.
"""
def __init__(self, filename, grid=None, times=None, mode="r", name=""):
self.grid = grid
self.filename = filename
self.times = times
self.variables = []
self.time_var = "time"
self.time_units = "days since 1900-01-01"
self.time_chunksize = 1
self.lon_chunksize = 1
if mode in ["a", "r"]:
super(ArrayStack, self).__init__(filename,
name=name,
mode=mode,
read_dates=False)
self._load_grid()
self._load_times()
if mode == "w":
if grid is None:
raise IOError("grid needs to be defined")
super(ArrayStack, self).__init__(filename,
n_loc=len(self.grid.activegpis),
name=name,
mode=mode,
read_dates=False)
self.dataset.variables[self.lon_var][:] = self.grid.activearrlon
self.dataset.variables[self.lat_var][:] = self.grid.activearrlat
self.dataset.variables[self.loc_ids_name][:] = self.grid.activegpis
self.lat_chunksize = len(self.grid.activegpis)
def _load_grid(self):
"""
Load grid.
"""
lons = self.dataset.variables[self.lon_var][:]
lats = self.dataset.variables[self.lat_var][:]
self.grid = grids.BasicGrid(lons, lats)
def _load_times(self):
"""
Load time stamps.
"""
self.times = netCDF4.num2date(self.dataset.variables["time"][:],
self.time_units)
[docs] def write(self, gpi, data):
"""
Write a time series into the imagestack for gpi.
Parameters
----------
self : type
description
gpi : int or numpy.array
grid point indices to write to
data : dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
gpi = np.atleast_1d(gpi)
for i, gp in enumerate(gpi):
for var in data:
super(ArrayStack, self).write(
gp, {var: np.atleast_1d(np.atleast_2d(data[var])[i, :])},
np.array(self.times))
def __setitem__(self, gpi, data):
"""
Write a time series into the imagestack for gpi.
Parameters
----------
self : type
description
gpi : int or numpy.array
grid point indices to write to
data : dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
self.write(gpi, data)
def __getitem__(self, key):
"""
Get data for given gpi/timestamp.
Parameters
----------
key : datetime or int
Timestamp or gpi.
Returns
-------
data : pandas.DataFrame
Data.
"""
if type(key) == datetime.datetime:
index = netCDF4.date2index(key,
self.dataset.variables[self.time_var])
data = {}
for var in self._get_all_ts_variables():
data[var] = self.dataset.variables[var][:, index]
return data
else:
gpi = np.atleast_1d(key)
for i, gp in enumerate(gpi):
data = super(ArrayStack, self).read_all(gp)
return pd.DataFrame(data, index=self.times)
[docs]class ImageStack(Dataset):
"""
Class for writing stacks of 2D images into NetCDF.
"""
def __init__(self, filename, grid=None, times=None, mode="r", name=""):
self.grid = grid
self.filename = filename
self.times = times
self.variables = []
self.time_var = "time"
self.time_units = "days since 1900-01-01"
self.time_chunksize = 1
self.lon_chunksize = 1
self.lat_chunksize = self.grid.lat2d.shape[1]
super(ImageStack, self).__init__(filename, name=name, mode=mode)
if self.mode == "w":
self._init_dimensions()
self._init_time()
self._init_location_variables()
elif self.mode in ["a", "r"]:
self._load_grid()
self._load_variables()
def _init_dimensions(self):
"""
Initialize dimensions.
"""
self.create_dim("lon", self.grid.lon2d.shape[0])
self.create_dim("lat", self.grid.lat2d.shape[1])
self.create_dim("time", len(self.times))
def _load_grid(self):
"""
Load grid.
"""
lons = self.dataset.variables["lon"][:]
lats = self.dataset.variables["lat"][:]
self.grid = grids.gridfromdims(lons, lats)
def _load_variables(self):
"""
Load variables.
"""
for var in self.dataset.variables:
if self.dataset.variables[var].dimensions == ("time", "lat",
"lon"):
self.variables.append(var)
def _load_times(self):
"""
Load time stamps.
"""
self.times = netCDF4.num2date(self.dataset.variables["time"][:],
self.time_units)
def _init_time(self):
"""
Initialize the dimensions and variables that are the basis of
the format.
"""
time_data = netCDF4.date2num(self.times, self.time_units)
self.write_var(self.time_var,
data=time_data,
dim="time",
attr={
"standard_name": "time",
"long_name": "time of measurement",
"units": self.time_units
},
dtype=np.double,
chunksizes=[self.time_chunksize])
def _init_location_variables(self):
"""
Write station information, longitude, latitude and altitude
"""
self.write_var("lon",
data=self.grid.lon2d[:, 0],
dim="lon",
attr={
"standard_name": "longitude",
"long_name": "location longitude",
"units": "degrees_east",
"valid_range": (-180.0, 180.0)
},
dtype=np.float32)
self.write_var("lat",
data=self.grid.lat2d[0, :],
dim="lat",
attr={
"standard_name": "latitude",
"long_name": "location latitude",
"units": "degrees_north",
"valid_range": (-90.0, 90.0)
},
dtype=np.float32)
[docs] def init_variable(self, var):
"""
Initialize variable.
"""
self.write_var(var,
data=None,
dim=("time", "lat", "lon"),
dtype=np.float32,
attr={"_FillValue": -9999.})
[docs] def write(self, gpi, data):
"""
Write a time series into the imagestack for gpi.
Parameters
----------
self : type
description
gpi : int or numpy.array
grid point indices to write to
data : dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
gpi = np.atleast_1d(gpi)
for i, gp in enumerate(gpi):
row, column = self.grid.gpi2rowcol(gp)
for var in data:
if var not in self.variables:
self.variables.append(var)
self.init_variable(var)
self.dataset.variables[var][:, row, column] = np.atleast_2d(
data[var])[i, :]
def __setitem__(self, gpi, data):
"""
Write a time series into the imagestack for gpi.
Parameters
----------
self: type
description
gpi: int or numpy.array
grid point indices to write to
data: dictionary
dictionary of int or numpy.array for each variable
that should be written
shape must be (len(gpi), len(times))
"""
self.write(gpi, data)
def __getitem__(self, key):
"""
Get data for given gpi.
Parameters
----------
key : int
gpi
Returns
-------
data : pandas.DataFrame
Data.
"""
gpi = np.atleast_1d(key)
data = {}
for i, gp in enumerate(gpi):
row, column = self.grid.gpi2rowcol(gp)
for var in self.variables:
data[var] = self.dataset.variables[var][:, row, column]
return pd.DataFrame(data, index=self.times)