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utils_metadata.py
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utils_metadata.py
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import utm
import scipy
import random
import numpy as np
import geopandas as gpd
from pyproj import Transformer
from sklearn.neighbors import KDTree
from scipy.spatial import distance_matrix
from oggm import utils
import xgboost as xgb
import catboost as cb
from PIL import Image
import matplotlib.pyplot as plt
def haversine(lon1, lat1, lon2, lat2):
"""
Calculate the great circle distance in kilometers between two points
on the earth (specified in decimal degrees)
"""
# convert decimal degrees to radians
lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
# haversine formula
dlon = lon2 - lon1
dlat = lat2 - lat1
a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
c = 2 * np.arcsin(np.sqrt(a))
r = 6371 # Radius of earth in kilometers. Determines return value units.
return c * r
def lmax_with_covex_hull(geometry, glacier_epsg):
'''
This method calculates lmax using the geometry convex hull.
It should be exactly equivalent to lmax_imputer with KDTree but much faster.
'''
geometry_epsg = geometry.to_crs(epsg=glacier_epsg) # Geodataframe
gl_geom = geometry_epsg.iloc[0].geometry # Polygon
# Compute the convex hull
convex_hull = gl_geom.convex_hull
# Extract coordinates from the convex hull's exterior
coords_hull = np.array(convex_hull.exterior.coords)
# Compute pairwise distances between all points on the convex hull
dist_matrix = distance_matrix(coords_hull, coords_hull)
lmax = np.max(dist_matrix)
return lmax
def lmax_imputer(geometry, glacier_epsg):
'''
geometry: glacier external geometry as pandas geodataframe in 4326 prjection
glacier_epsg: glacier espg
return: lmax in meters
'''
geometry_epsg = geometry.to_crs(epsg=glacier_epsg)
glacier_vertices = np.array(geometry_epsg.iloc[0].geometry.exterior.coords)
tree_lmax = KDTree(glacier_vertices)
dists, _ = tree_lmax.query(glacier_vertices, k=len(glacier_vertices))
lmax = np.max(dists)
return lmax
def from_lat_lon_to_utm_and_epsg(lat, lon):
"""https://github.com/Turbo87/utm"""
# Note lat lon can be also NumPy arrays.
# In this case zone letter and number will be calculate from first entry.
easting, northing, zone_number, zone_letter = utm.from_latlon(lat, lon)
southern_hemisphere_TrueFalse = True if zone_letter < 'N' else False
epsg_code = 32600 + zone_number + southern_hemisphere_TrueFalse * 100
return (easting, northing, zone_number, zone_letter, epsg_code)
def gaussian_filter_with_nans(U, sigma, trunc=4.0):
# Since the reprojection into utm leads to distortions (=nans) we need to take care of this during filtering
# From David in https://stackoverflow.com/questions/18697532/gaussian-filtering-a-image-with-nan-in-python
V = U.copy()
V[np.isnan(U)] = 0
VV = scipy.ndimage.gaussian_filter(V, sigma=[sigma, sigma], mode='nearest', truncate=trunc)
W = np.ones_like(U)
W[np.isnan(U)] = 0
WW = scipy.ndimage.gaussian_filter(W, sigma=[sigma, sigma], mode='nearest', truncate=trunc)
WW[WW == 0] = np.nan
filtered_U = VV / WW
return filtered_U
def get_cmap(name):
from matplotlib.colors import LinearSegmentedColormap
if name == 'white_electric_blue':
# (0.11764706, 0.56470588, 1.0)] dodgerblue
colors = [(1, 1, 1), (0.0, 0.0, .8)] # White to electric blue
cm = LinearSegmentedColormap.from_list(name, colors)
if name == 'black_electric_green':
cm = LinearSegmentedColormap.from_list(name, ['#000000', '#00FF00'])
if name == 'black_electric_blue':
cm = LinearSegmentedColormap.from_list(name, ['#000000', '#0000CC'])
return cm
def calc_geoid_heights(lons=None, lats=None, h_wgs84=None):
'''Calculates orthometric heights'''
transformer = Transformer.from_crs("epsg:4326", "epsg:3855", always_xy=True)
_, _, h_egm2008 = transformer.transform(lons, lats, h_wgs84)
return h_egm2008
def calc_volume_glacier(y1=None, y2=None, area=0, h_egm2008=None):
'''
:param y1: numpy.ndarray. Ice thickness [m]
:param y2: numpy.ndarray. Ice thickness [m]
:param area: float [km2]
:return: volume [km3].
'''
y_xgb = y1
y_cat = y2
N = len(y1)
f = 0.001 * area / N
# Millan or Farinotti
if y2 is None:
volume = np.sum(y1) * f
return volume
# iceboost
else:
y_mean = 0.5 * (y_xgb + y_cat)
y_mean = np.where(y_mean < 0, 0, y_mean)
# volume ice
volume = np.sum(y_mean) * f
# volume ice above sea level
#volume_af = np.sum(np.where(h_egm2008 - y_mean > 0, y_mean, h_egm2008)) * f
# volume ice below sea level
volume_bsl = np.sum(np.where(h_egm2008 - y_mean > 0, 0.0, y_mean - h_egm2008)) * f
err_points = np.std((y_xgb, y_cat), axis=0)
# This error considers the point-wise spread between the models
err_volume_points = np.sqrt(np.sum(err_points**2)) * f
# This error is the semi-difference of the 2 modeled volumes.
err_volume_range = 0.5 * np.abs(np.sum(y_xgb) - np.sum(y_cat)) * f
# Add in quadrature the two errors
err_volume = np.sqrt(err_volume_points**2 + err_volume_range**2)
return volume, err_volume, volume_bsl
def get_random_glacier_rgiid(name=None, rgi=11, area=None, seed=None):
"""Provide a rgi number and seed. This method returns a
random glacier rgiid name.
If not rgi is passed, any rgi region is good.
"""
# setup oggm version
utils.get_rgi_dir(version='62')
utils.get_rgi_intersects_dir(version='62')
if name is not None: return name
if seed is not None:
np.random.seed(seed)
if rgi is not None:
oggm_rgi_shp = utils.get_rgi_region_file(f"{rgi:02d}", version='62')
oggm_rgi_glaciers = gpd.read_file(oggm_rgi_shp, engine='pyogrio')
if area is not None:
oggm_rgi_glaciers = oggm_rgi_glaciers[oggm_rgi_glaciers['Area'] > area]
rgi_ids = oggm_rgi_glaciers['RGIId'].dropna().unique().tolist()
rgiid = np.random.choice(rgi_ids)
return rgiid
def create_train_test(df, rgi=None, frac=0.1, full_shuffle=None, seed=None):
"""
- rgi se voglio creare il test in una particolare regione
- frac: quanto lo voglio grande in percentuale alla grandezza del rgi
"""
if seed is not None:
random.seed(seed)
if rgi is not None and full_shuffle is True:
df_rgi = df[df['RGI'] == rgi]
test = df_rgi.sample(frac=frac, random_state=seed)
train = df.drop(test.index)
return train, test
if full_shuffle is True:
test = df.sample(frac=frac, random_state=seed)
train = df.drop(test.index)
return train, test
# create test based on rgi
if rgi is not None:
df_rgi = df[df['RGI']==rgi]
else:
df_rgi = df
minimum_test_size = round(frac * len(df_rgi))
unique_glaciers = df_rgi['RGIId'].unique()
random.shuffle(unique_glaciers)
selected_glaciers = []
n_total_points = 0
#print(unique_glaciers)
for glacier_name in unique_glaciers:
if n_total_points < minimum_test_size:
selected_glaciers.append(glacier_name)
n_points = df_rgi[df_rgi['RGIId'] == glacier_name].shape[0]
n_total_points += n_points
#print(glacier_name, n_points, n_total_points)
else:
#print('Finished with', n_total_points, 'points, and', len(selected_glaciers), 'glaciers.')
break
test = df_rgi[df_rgi['RGIId'].isin(selected_glaciers)]
train = df.drop(test.index)
#print(test['RGI'].value_counts())
#print(test['RGIId'].value_counts())
#print('Total test size: ', len(test))
#print(train.describe().T)
#input('wait')
return train, test
def load_models(config_file):
model_xgb_filename = config_file.model_input_dir + config_file.model_filename_xgb
iceboost_xgb = xgb.Booster()
iceboost_xgb.load_model(model_xgb_filename)
model_cat_filename = config_file.model_input_dir + config_file.model_filename_cat
iceboost_cat = cb.CatBoostRegressor()
iceboost_cat.load_model(model_cat_filename, format='cbm')
return iceboost_xgb, iceboost_cat
def create_PIL_image(array, png_resolution=None):
"""
Given 2d numpy ndarray returns PIL image for .png
"""
array = np.flipud(array)
array_min = np.nanmin(array)
array_max = np.nanmax(array)
array_normalized = (array - array_min) / (array_max - array_min) * 255
array_normalized = np.nan_to_num(array_normalized, nan=0).astype(np.uint8)
colormap = plt.cm.jet
colored_array = colormap(array_normalized)
colored_array = (colored_array[:, :, :3] * 255).astype(np.uint8)
alpha_channel = np.where(np.isnan(array), 0, 255).astype(np.uint8)
rgba_array = np.dstack((colored_array, alpha_channel))
image = Image.fromarray(rgba_array)
image_resized = image.resize((png_resolution, png_resolution), Image.Resampling.LANCZOS)
return image_resized