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@edobez
Created January 24, 2019 16:15
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import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from netCDF4 import Dataset
import netCDF4 as nc4
import os
import gdal
TILE_PARAMS = {
'h17v04': (50, 40, -15.557, 0.013, 0.001),
'h17v05': (40, 30, -13.054, 0.011, 0.001)
}
def compute_band_value(refl, coord, tile_params, tile_size=2400):
"""
Compute band value at specified coordinates
:param refl: Handle to object open with gdal.Open
:param coord: 2-tuple with coordinate values
:param tile_params: 5-tuple with tile parameters
:param tile_size: size of tile (default = 2400)
:return: band value
"""
lon_refl, lat_refl = coord
lat1, lat0, lon1, lon0, res = tile_params
# define borders of final file that I want
wgs84_wkt = 'GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.01745329251994328,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]'
# create dimensions final file
x_size = int((lon1 - lon0)/(-1*res))
y_size = int((lat1 - lat0)/(res))
lats = np.linspace(lat1, lat0-res, num=y_size)
lats = np.around(lats[:], decimals=3)
lons = np.linspace(lon1, lon0-res, num=x_size)
lons = np.around(lons[:], decimals=3)
# define geotrasformation
geot = [lon1, res, 0., lat1, 0., -1*res]
# create temporary (that's why 'MEM') file of same size of final file, filled with array of null raster values (-9999.9)
dst = gdal.GetDriverByName('MEM').Create(
'', x_size, y_size, 1, gdal.GDT_Float32,) # last one is the data type
dst.GetRasterBand(1).WriteArray(
np.ones((y_size, x_size), dtype=np.float32) * -9999.9)
dst.SetGeoTransform(geot)
dst.SetProjection(wgs84_wkt)
# create temporary file of signle band equal to var of interest from starting netcdf (the tile)
src = gdal.GetDriverByName('MEM').Create(
'', tile_size, tile_size, 1, gdal.GDT_Float32,)
tile = refl
band = tile.GetRasterBand(1).ReadAsArray()
src.GetRasterBand(1).WriteArray(band)
src.SetGeoTransform(tile.GetGeoTransform())
src.SetProjection(tile.GetProjection())
gdal.ReprojectImage(src, dst, src.GetProjection(
), dst.GetProjection(), gdal.GRA_NearestNeighbour)
idx_dst_x = np.where(lons == lon_refl)[0][0]-1
idx_dst_y = np.where(lats == lat_refl)[0][0]-1
return dst.ReadAsArray()[idx_dst_y, idx_dst_x]
def extract_data_from_file(filepath, df):
"""
Extract band values from file, on the coordinates specified in the input dataframe.
Returns a dataframe of the same type of the input one, but filled with data.
:param filepath: complete path of the file to be processed
:param df: pandas DataFrame containing only rows of specified tile
:return: As input df but filled with data
"""
ds = gdal.Open(filepath)
sub_ds = ds.GetSubDatasets()
# Create a list of names: [refl1, refl2, ..., refl7]
bands = ['refl{}'.format(n+1) for n in range(7)]
# Foreach band
for band_idx, band_name in enumerate(bands):
band_refl = gdal.Open(sub_ds[band_idx+7][0])
# Foreach coordinate
for c_idx in df.index:
tile_params = TILE_PARAMS[df['tile']]
coord = (round(df['Lon_WGS84'], 3), round(df['Lat_WGS84'], 3))
df.at[c_idx, band_name] = compute_band_value(
band_refl, coord, tile_params) # Fill dataframe with data
return df
def find_tile_file(tile_name, folder_path):
"""
Find file relative to tile inside specified folder
:return: Complete path of file containing the string 'tile_name'
"""
for file in os.listdir(folder_path):
if tile_name in file:
return os.path.abspath(file)
coordinate_filepath = '/g/data1a/xc0/user/scortechini/reflectance/alltogether.csv'
coordinate_header = ['ID', 'X', 'Y', 'Lat_WGS84', 'Lon_WGS84', 'tile',
'refl1', 'refl2', 'refl3', 'refl4', 'refl5', 'refl6', 'refl7']
coordinates = pd.read_csv(coordinate_filepath, names=coordinate_header)
data_folder = '/g/data2/u39/public/data/modis/lpdaac-tiles-c6/MCD43A4.006'
# Foreach date
dataframes_over_dates = []
for folder in os.listdir(data_folder):
# Group over tiles and interate
dataframes_over_tiles = []
for tile_name, group in coordinates.groupby('tile'):
tile_filepath = find_tile_file(tile_name, folder)
tile_dataframe = extract_data_from_file(tile_filepath, group)
dataframes_over_tiles.append(tile_dataframe)
# Concatenate dataframe of different tiles together and add to list
concatenated = pd.concat(dataframes_over_tiles).sort_index()
dataframes_over_dates.append(concatenated)
# Now you have a list of DataFrames. Each DF is relative to one folder/date and has the same structure of the original `coordinates` DF.
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