Source code for poets.image.resampling

# Copyright (c) 2014, Vienna University of Technology (TU Wien), Department
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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