Datasets:
File size: 2,538 Bytes
0a7933d | 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 | #%%
from pathlib import Path
import pandas as pd
import rasterio
from rasterio.warp import transform_bounds
#%%
# IMPORTS
DATA_FOLDER = Path("/home/sabrina/Documents/Tese/05_Dataset/MatchGeo-DEM-v1/data")
REGIONS = {'ATA_MV', 'BRA_SP', 'CHN_WS', 'ESP_EH', 'FIN_LM', 'GER_BN', 'IDN_SV', 'KAZ_AC', 'KSA_WA', 'NAM_HF', 'NZL_KP', 'PHL_TA', 'USA_GC'}
#%%
dict_profile = {
"region": [],
"file_size_mb": [],
"nodata": [],
"crs": [],
"dtype": [],
"resolution":[],
"width": [],
"heigth": [],
"n_tiles": [],
"tile_size": [],
"x_min": [],
"x_max": [],
"y_min": [],
"y_max": [],
"long_min": [],
"long_max": [],
"lat_min": [],
"lat_max": [],
}
#%%
for region in REGIONS:
# Get merged file size
tif_path = Path(DATA_FOLDER, f"{region}/{region}.tif")
file_size = tif_path.stat().st_size / (10**6)
# Get raster profile and bounds
with rasterio.open(tif_path) as src:
profile = src.profile
bounds = src.bounds
try:
lonlat_bounds = transform_bounds(src.crs, "EPSG:4326", *bounds)
except:
lonlat_bounds = transform_bounds("EPSG:25832", "EPSG:4326", *bounds)
# Get number of tiles
tile_path = Path(DATA_FOLDER, f"{region}/tiles")
n_tiles = len(list(tile_path.glob("*.tif")))
# Get tile size
tile0 = list(tile_path.glob("*.tif"))[0]
with rasterio.open(tile0) as src:
tile_size = src.width
# Update dictionary
dict_profile['region'].append(region)
dict_profile['file_size_mb'].append(file_size)
dict_profile['nodata'].append(profile.get('nodata'))
dict_profile['crs'].append(str(profile['crs']))
dict_profile['dtype'].append(profile['dtype'])
dict_profile['resolution'].append(profile['transform'][0])
dict_profile['width'].append(profile['width'])
dict_profile['heigth'].append(profile['height'])
dict_profile['n_tiles'].append(n_tiles)
dict_profile['tile_size'].append(tile_size)
dict_profile['x_min'].append(bounds.left)
dict_profile['x_max'].append(bounds.right)
dict_profile['y_min'].append(bounds.bottom)
dict_profile['y_max'].append(bounds.top)
dict_profile['long_min'].append(lonlat_bounds[0])
dict_profile['long_max'].append(lonlat_bounds[2])
dict_profile['lat_min'].append(lonlat_bounds[1])
dict_profile['lat_max'].append(lonlat_bounds[3])
#%%
# Create DataFrame
df = pd.DataFrame(dict_profile)
print(df)
# %%
df.to_csv(Path(DATA_FOLDER, "metadadata.csv"))
# %%
|