Gridded_Precipitation/climate.py at main · DANIELV95/Gridded_Precipitation · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 20 13:41:08 2023

@author: HIDRAULICA-Dani
"""

import os
import pandas as pd
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
import rasterio
from rasterio.plot import show
from osgeo import gdal

os.chdir('D:/DANI/2023/TEMA_TORMENTAS/DATOS/CLIMAS/Map_KG-Global/')
os.listdir()

coord = pd.read_csv('D:/DANI/2023/TEMA_TORMENTAS/DATOS/CLIMAS/Map_KG-Global/KG_1986-2010_5m.csv')
Lon = coord['lon'].unique()
Lat = coord['lat'].unique()

# lons = []
# lats = []
# kgs = [[],[]]

# for row, i in coord.iterrows():
#     print(row, i)
#     lon = i[0]
#     lat = i[1]
#     kg = i[2]

#     if (~np.isin(lon,lons)):
#         lons.append(lon)

#     if (~np.isin(lat,lats)):
#         lats.append(lat)

#     kgs.append(kg)

KG_grid = coord.pivot(index='lat', columns='lon', values='KG')
KG_grid.index.name = None
KG_grid.columns.name = None
KG_grid = KG_grid.sort_index(ascending=False)

KG_arr = KG_grid.values
# KG_arr[np.isnan(KG_arr)] = -9999

rows = KG_grid.index.values
cols = KG_grid.columns.values

KG_arr.shape

N = KG_grid.index[0]
# KG_grid.index[-1]
W = KG_grid.columns[0]
len(KG_grid.columns)

KG_grid.columns[1]-KG_grid.columns[2]
KG_grid.index[1]-KG_grid.index[2]


ras_filename = './KG_climate.tif'
new_transform = rasterio.transform.from_origin(west=W, north=N, xsize=0.08333333, ysize=0.08333333)


driver = gdal.GetDriverByName('GTiff')
driver.Register()
outds = driver.Create(ras_filename, xsize=KG_arr.shape[1], ysize=KG_arr.shape[0],
                    bands=1, eType=gdal.GDT_Float64)
outds.SetGeoTransform(new_transform)

driver = None
outds = None
raster = None


new_transform = rasterio.transform.from_origin(west=W, north=N, xsize=KG_arr.shape[1], ysize=KG_arr.shape[0])
new_transform

new_transform = rasterio.transform.from_origin(west=W, north=N, xsize=0.08333333, ysize=0.08333333)


rasterio.transform.xy(new_transform, rows, cols, zs=KG_arr, offset='center') #, **rpc_options)

plt.imshow(KG_arr)


KG = rasterio.open(
    './KG_climate.tif', 'w',
    driver = 'GTiff',
    height = KG_arr.shape[0],
    width = KG_arr.shape[1],
    count = 1,
    nodata = -9999,
    dtype = KG_arr.dtype,
    crs = 4326,
    transform = new_transform,
    compress='lzw'
)

KG.close()

show(KG_arr)

KG_grid.values.dtype

show(KG_grid)

plt.scatter(coord['lon'], coord['lat'], s=1, marker=',')

lat.min()
lat.max()

lon.min()
lon.max()

len(coord)

len(lat)
len(lon)