# Copyright (c) 2014, Vienna University of Technology (TU Wien), Department
# of Geodesy and Geoinformation (GEO).
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# Author: Thomas Mistelbauer Thomas.Mistelbauer@geo.tuwien.ac.at
# Creation date: 2014-06-13
import os
import numpy as np
import pandas as pd
import pyresample as pr
import poets.grid.grids as gr
import poets.image.netcdf as nc
from poets.shape.shapes import Shape
from poets.grid.grids import ShapeGrid, RegularGrid
from pytesmo.grid import resample
from shapely.geometry import Point
from poets.image.imagefile import bbox_img
imgfiletypes = ['.png', '.PNG', '.tif', '.tiff', '.TIF', '.TIFF', '.jpg',
'.JPG', '.jpeg', '.JPEG', '.gif', '.GIF']
[docs]def resample_to_shape(source_file, region, sp_res, prefix=None,
nan_value=None, dest_nan_value=None, variables=None,
shapefile=None):
"""
Resamples images and clips country boundaries
Parameters
----------
source_file : str
Path to source file.
region : str
Identifier of the region in the shapefile. If the default shapefile is
used, this would be the FIPS country code.
sp_res : int or float
Spatial resolution of the shape-grid.
prefix : str, optional
Prefix for the variable in the NetCDF file, should be name of source
nan_value : int, float, optional
Not a number value of the original data as given by the data provider
dest_nan_value : int or float, optional
NaN value used in the final NetCDF file.
variables : list of str, optional
Variables to resample from original file.
shapefile : str, optional
Path to shape file, uses "world country admin boundary shapefile" by
default.
Returns
-------
data : dict of numpy.arrays
resampled image
lons : numpy.array
longitudes of the points in the resampled image
lats : numpy.array
latitudes of the points in the resampled image
gpis : numpy.array
grid point indices
timestamp : datetime.date
date of the image
"""
if prefix is not None:
prefix += '_'
_, fileExtension = os.path.splitext(source_file)
if region == 'global':
lon_min = -180
lon_max = 180
lat_min = -90
lat_max = 90
grid = gr.RegularGrid(sp_res=sp_res)
else:
shp = Shape(region, shapefile)
lon_min = shp.bbox[0]
lon_max = shp.bbox[2]
lat_min = shp.bbox[1]
lat_max = shp.bbox[3]
grid = gr.ShapeGrid(region, sp_res, shapefile)
if fileExtension in ['.nc', '.nc3', '.nc4']:
data, src_lon, src_lat, timestamp, metadata = \
nc.clip_bbox(source_file, lon_min, lat_min, lon_max, lat_max)
elif fileExtension in imgfiletypes:
data, src_lon, src_lat, timestamp, metadata = bbox_img(source_file,
region)
if nan_value is not None:
for key in data.keys():
data[key] = np.ma.array(data[key], mask=(data[key] == nan_value))
src_lon, src_lat = np.meshgrid(src_lon, src_lat)
lons = grid.arrlon[0:grid.shape[1]]
dest_lon, dest_lat = np.meshgrid(lons, np.unique(grid.arrlat)[::-1])
gpis = grid.get_bbox_grid_points(grid.arrlat.min(), grid.arrlat.max(),
grid.arrlon.min(), grid.arrlon.max())
search_rad = 180000 * sp_res
data = resample.resample_to_grid(data, src_lon, src_lat, dest_lon,
dest_lat, search_rad=search_rad)
mask = np.zeros(shape=grid.shape, dtype=np.bool)
res_data = {}
for key in data.keys():
if variables is not None:
if key not in variables:
del metadata[key]
continue
if region != 'global':
poly = shp.polygon
for i in range(0, grid.shape[0]):
for j in range(0, grid.shape[1]):
p = Point(dest_lon[i][j], dest_lat[i][j])
if not p.within(poly):
mask[i][j] = True
if data[key].mask[i][j]:
mask[i][j] = True
if prefix is None:
var = key
else:
var = prefix + key
if region == 'global':
mask = data[key].mask
if metadata is not None:
metadata[var] = metadata[key]
if var != key:
del metadata[key]
res_data[var] = np.ma.masked_array(data[key], mask=np.copy(mask),
fill_value=dest_nan_value)
dat = np.copy(res_data[var].data)
dat[mask == True] = dest_nan_value
res_data[var] = np.ma.masked_array(dat, mask=np.copy(mask),
fill_value=dest_nan_value)
return res_data, dest_lon, dest_lat, gpis, timestamp, metadata
[docs]def resample_to_gridpoints(source_file, region, sp_res, shapefile=None):
"""Resamples image to predefined gridpoints.
Parameters
----------
source_file : str
Path to source file.
region : str
Latitudes of source image.
sp_res : int or float
Spatial resolution of the shape-grid.
shapefile : str, optional
Path to shape file, uses "world country admin boundary shapefile" by
default.
Returns
-------
dframe : pandas.DataFrame
Resampled data with gridpoints as index.
"""
shp = Shape(region, shapefile)
lon_min = shp.bbox[0]
lon_max = shp.bbox[2]
lat_min = shp.bbox[1]
lat_max = shp.bbox[3]
grid = ShapeGrid(region, sp_res, shapefile)
gridpoints = grid.get_gridpoints()
data, lon, lat, _, _ = nc.clip_bbox(source_file, lon_min, lat_min, lon_max,
lat_max)
lon, lat = np.meshgrid(lon, lat)
src_grid = pr.geometry.GridDefinition(lon, lat)
des_swath = pr.geometry.SwathDefinition(gridpoints['lon'].values,
gridpoints['lat'].values)
dframe = pd.DataFrame(index=gridpoints.index)
dframe['lon'] = gridpoints.lon
dframe['lat'] = gridpoints.lat
# resampling to country gridpoints
for var in data.keys():
dframe[var] = pr.kd_tree.resample_nearest(src_grid, data[var],
des_swath, 20000)
return dframe
[docs]def average_layers(image, dest_nan_value):
"""
Averages image layers, given as ndimensional masked arrays to one image
Parameters
----------
image : numpy.ma.MaskedArray
Input image to average.
Returns
-------
avg_img : numpy.ma.MaskedArray
Averaged image.
"""
img = np.ma.masked_array(image.mean(0), fill_value=dest_nan_value)
mask = img.mask
data = np.copy(img.data)
data[img.mask == True] = dest_nan_value
avg_img = np.ma.masked_array(data, mask=mask,
fill_value=dest_nan_value)
return avg_img