from thesis import *
import geomappy as mp
M_p = p = mp.Raster("data/precipitation/precipitation.tif")
pet = mp.Raster("data/pet/et0_yr.tif", fill_value=0)[M_p.bounds]
p = M_p[0]
M_p_pet = mp.Raster("data/pet/p_pet.tif",
mode='w',
ref_map="data/precipitation/precipitation.tif",
overwrite=True)
M_p_pet[0] = p / pet
mp.Raster.close()
CORR_WTD_T_15 = "data/correlation_wtd_t/correlation_wtd_t_15.tif"
CORR_P_T_15 = "/Volumes/Elements SE/Thesis/Data/correlation_p_t/correlation_p_t_15.tif"
CORR_T_FAPAR_15 = "data/correlation_t_fapar/correlation_t_fapar_ge3_15.tif"
CORR_P_PET_FAPAR_15 = "data/correlation_p_pet_fapar/correlation_p_pet_fapar_ge3_15.tif"
CORR_WTD_FAPAR_15_DOWNSAMPLED_10 = "data/correlation_wtd_fapar/correlation_wtd_fapar_ge3_15_downsampled_10.tif"
CORR_P_FAPAR_15_DOWNSAMPLED_10 = "data/correlation_p_fapar/correlation_p_fapar_ge3_15_downsampled_10.tif"
CORR_P_WTD_15_DOWNSAMPLED_10 = "data/correlation_p_wtd/correlation_p_wtd_15_downsampled_10.tif"
CORR_P_PET_WTD_15_DOWNSAMPLED_10 = "data/correlation_p_pet_wtd/correlation_p_pet_wtd_15_downsampled_10.tif"
CORR_WTD_T_15_DOWNSAMPLED_10 = "data/correlation_wtd_t/correlation_wtd_t_15_downsampled_10.tif"
CORR_P_T_15_DOWNSAMPLED_10 = "/Volumes/Elements SE/Thesis/Data/correlation_p_t/correlation_p_t_15_downsampled_10.tif"
CORR_T_FAPAR_15_DOWNSAMPLED_10 = "data/correlation_t_fapar/correlation_t_fapar_ge3_15_downsampled_10.tif"
CORR_P_PET_FAPAR_15_DOWNSAMPLED_10 = "data/correlation_p_pet_fapar/correlation_p_pet_fapar_ge3_15_downsampled_10.tif"
### Correlate WTD and fPAR
M_wtd = mp.Raster(WTD, tiles=(10, 10), window_size=15)
M_th = mp.Raster(TH, tiles=(10, 10), window_size=15)
M_fapar = mp.Raster(FAPAR, tiles=(10, 10), window_size=15)
with mp.Raster(CORR_WTD_FAPAR_15, mode='w', ref_map=WTD, tiles=(10, 10), window_size=15) as M_corr_wtd_fapar_15:
for i in M_wtd:
progress_bar((i + 1) / M_wtd.c_tiles)
wtd = M_wtd[i]
th = M_th[i]
fapar = M_fapar[i]
fapar[th < 3] = np.nan
M_corr_wtd_fapar_15[i] = mp.correlate_maps(wtd, fapar, window_size=15, fraction_accepted=0.25)
M_corr_wtd_fapar_15 = mp.Raster(CORR_WTD_FAPAR_15, tiles=(10, 10))
M_corr_wtd_fapar_15.focal_mean(output_file=CORR_WTD_FAPAR_15_DOWNSAMPLED_10, window_size=10, reduce=True,
fraction_accepted=0)
"""
Convert water table depth dataset to be defined negatively
"""
from thesis import *
import geomappy as mp
import numpy as np
WTD_ORIGINAL = "data/wtd/wtd_original.tif"
WTD_CONVERTED = "data/wtd/wtd_2.tif"
with mp.Raster(WTD_ORIGINAL) as src:
with mp.Raster(WTD_CONVERTED, ref_map=WTD_ORIGINAL, mode='w', nodata=np.nan, dtype=np.float32, overwrite=True) \
as dst:
dst[0] = -src[0]
return corr_t, corr_bs, corr_pe
def t_test_threshold_table(offset_fraction=0, window_size=15):
n_max = window_size**2
sign_threshold = np.full(n_max + 1, np.nan)
for n in range(0, n_max + 1):
df = n - 2 - (offset_fraction * n)
x = t.ppf(0.95, df=df)
r = x / np.sqrt(df + x**2)
sign_threshold[n] = x / np.sqrt(df + x**2)
return sign_threshold
M_wtd = mp.Raster(
"/Volumes/Elements SE/Thesis/Data_raw/wtd/wtd_South_America_CF.tif",
epsg=4326)
wtd = -M_wtd[0]
th = mp.Raster("data/tree_height/tree_height_global.tif")[M_wtd.bounds]
fapar = mp.Raster("data/fapar/mean_fpar_reprojected.tif")[M_wtd.bounds]
p = mp.Raster("data/pet/p_pet.tif")[M_wtd.bounds]
fapar[th < 3] = np.nan
# offset_fraction value from previous file (06_global_threshold_table.py)
WTD_corr_t, WTD_corr_bs, WTD_corr_pe = correlation_significance_inference(
wtd,
fapar,
window_size=15,
fraction_accepted=0.25,
t_test_thresholds=t_test_threshold_table(offset_fraction=0.178))
P_corr_t, P_corr_bs, P_corr_pe = correlation_significance_inference(
import geopandas as gpd
import geomappy as mp
import numpy as np
import pandas as pd
from functions import calc_emissions
M_extent_1 = mp.Raster("data/River_network/global_chans_30arcs.tif")
M_water_bodies = mp.Raster("data/reprojected/water_bodies.tif")
M_extent_10 = mp.Raster("data/reprojected/extent_10.tif")
M_extent_20 = mp.Raster("data/reprojected/extent_20.tif")
M_extent_50 = mp.Raster("data/reprojected/extent_50.tif")
M_extent_100 = mp.Raster("data/reprojected/extent_100.tif")
M_extent_200 = mp.Raster("data/reprojected/extent_200.tif")
M_extent_500 = mp.Raster("data/reprojected/extent_500.tif")
M_mpw = mp.Raster("data/reprojected/mpw.tif")
env = {
"M_extent_1": M_extent_1,
"M_extent_10": M_extent_10,
"M_extent_20": M_extent_20,
"M_extent_50": M_extent_50,
"M_extent_100": M_extent_100,
"M_extent_200": M_extent_200,
"M_extent_500": M_extent_500,
"M_water_bodies": M_water_bodies,
"M_mpw": M_mpw
}
continents = ['eu', 'as', 'af', 'au', 'ca', 'na', 'sa']
for continent in continents:
print(continent)
import thesis
import geomappy as mp
from geomappy.utils import progress_bar
import numpy as np
np.warnings.filterwarnings('ignore')
M_corr_wtd = mp.Raster("data/cmap_2d_significant/wtd_fapar_sign.tif")
M_corr_p_pet = mp.Raster("data/cmap_2d_significant/p_pet_fapar_sign.tif")
CLASSES = "data/cmap_2d_significant/classes_serial.tif"
CLASSES_FLOAT = "data/cmap_2d_significant/classes_serial_float.tif"
CLASSES_DOWNSAMPLED_10 = "data/cmap_2d_significant/classes_serial_downsampled_10.tif"
profile = M_corr_p_pet.profile
profile['dtype'] = np.int8
profile['nodata'] = -1
M_classes = mp.Raster(CLASSES, mode='w', profile=profile, overwrite=True)
profile['dtype'] = np.float64
M_classes_float = mp.Raster(CLASSES_FLOAT,
mode='w',
profile=profile,
overwrite=True)
mp.Raster.set_tiles((4, 4))
classes = np.arange(9, dtype=np.int8).reshape((3, 3))
classes_float = np.arange(9, dtype=np.float64).reshape((3, 3))
for i in M_corr_wtd:
progress_bar((i + 1) / M_corr_wtd.c_tiles)
import thesis
import geomappy as mp
from geomappy.rolling import rolling_sum
from geomappy.utils import progress_bar
from scipy.stats import t
import numpy as np
np.warnings.filterwarnings(
'ignore') # comparison with nans that can be ignored
M_fapar = mp.Raster("data/fapar/mean_fpar_reprojected.tif")
M_wtd = mp.Raster("data/wtd/wtd.tif")
M_corr = mp.Raster(
"data/correlation_wtd_fapar/correlation_wtd_fapar_ge3_15.tif")
M_th = mp.Raster("data/tree_height/tree_height_global.tif")
M_sig = mp.Raster("data/cmap_2d_significant/wtd_fapar_sign.tif",
mode="w",
ref_map="data/wtd/wtd.tif")
window_size = 15
fringe = window_size // 2
n_max = window_size**2
sign_threshold = np.full(n_max + 1, np.nan)
for n in range(0, n_max + 1):
# The 0.178 value was obtained from 06_global_threshold_table.py
df = n - 2 - (0.178 * n)
x = t.ppf(0.95, df=df)
r = x / np.sqrt(df + x**2)
sign_threshold[n] = x / np.sqrt(df + x**2)
warnings.filterwarnings("once")
def update_class_map(ind, val):
global classified_map, accessed_map
ind = np.logical_and(~accessed_map, ind)
classified_map[ind] = val
accessed_map[ind] = True
wtd = "data/wtd/wtd_original.tif"
wtd_mean_5 = "data/wtd/wtd_mean_5.tif"
wtd_std_5 = "data/wtd/wtd_std_5.tif"
output_map = "data/landscape_classes/landscape_classes.tif"
M_wtd = mp.Raster(wtd, tiles=(20, 20))
M_wtd.focal_mean(output_file=wtd_mean_5, window_size=5)
M_wtd.focal_std(output_file=wtd_std_5, window_size=5)
M_wtd_mean_5 = mp.Raster(wtd_mean_5, tiles=(20, 20))
M_wtd_std_5 = mp.Raster(wtd_std_5, tiles=(20, 20))
M_output = mp.Raster(output_map,
ref_map=wtd,
tiles=(20, 20),
mode="w",
dtype=np.int8,
nodata=0,
overwrite=True)
for i in M_wtd:
LANDSCAPE_DOWNSAMPLED_10 = "data/landscape_classes/landscape_downsampled_10.tif"
LANDSCAPE_DOWNSAMPLED_10_DISPLAY = "data/landscape_classes/landscape_downsampled_10_display.tif"
P_DOWNSAMPLED_10 = "data/precipitation/precipitation_downsampled_10.tif"
PET_DOWNSAMPLED_10 = "data/pet/pet_downsampled_10.tif"
P_PET_DOWNSAMPLED_10 = "data/pet/p_pet_downsampled_10.tif"
WTD_DOWNSAMPLED_10 = "data/wtd/wtd_downsampled_10.tif"
WTD_STD_DOWNSAMPLED_10 = "data/wtd/wtd_std_5_downsampled_10.tif"
TH_DOWNSAMPLED_10 = "data/tree_height/tree_height_global_downsampled_10.tif"
FAPAR_DOWNSAMPLED_10 = "data/fapar/mean_fpar_downsampled_10.tif"
M_clim = mp.Raster(CLIMATE, tiles=(10, 20))
M_p = mp.Raster(P, tiles=(10, 20))
M_pet = mp.Raster(PET, tiles=(10, 20), fill_value=0)
M_p_pet = mp.Raster(P_PET, tiles=(10, 20))
M_wtd = mp.Raster(WTD, tiles=(10, 20))
M_wtd_std = mp.Raster(WTD_STD, tiles=(10, 20))
M_th = mp.Raster(TH, tiles=(10, 20))
M_fapar = mp.Raster(FAPAR, tiles=(10, 20))
M_landscape = mp.Raster(LANDSCAPE, tiles=(20, 20))
M_clim.focal_majority(output_file=CLIMATE_DOWNSAMPLED_10,
window_size=10,
reduce=True,
fraction_accepted=0,
majority_mode='nan')
M_clim.focal_majority(output_file=CLIMATE_DOWNSAMPLED_10_DISPLAY,
def draw_thesis_location_zoom(ind):
"""
Drawing array of nine maps, displaying the input data and results. Not meant for custom use.
Parameters
----------
ind : .
look at mp.RasterBase.get_pointer()
"""
classes = mp.Raster(
"data/cmap_2d_significant/classes_serial_downsampled_10.tif")
climate = mp.Raster("data/climate/climate_downsampled_10_display.tif")
landscape = mp.Raster(
"data/landscape_classes/landscape_downsampled_10_display.tif")
p_pet = mp.Raster("data/pet/p_pet_downsampled_10.tif")
fapar = mp.Raster("data/fapar/mean_fpar_downsampled_10.tif")
corr_wtd_fapar = mp.Raster(
"data/correlation_wtd_fapar/correlation_wtd_fapar_ge3_15_downsampled_10.tif"
)
corr_p_pet_fapar = mp.Raster(
"data/correlation_p_pet_fapar/correlation_p_pet_fapar_ge3_15_downsampled_10.tif"
)
corr_p_pet_wtd = mp.Raster(
"data/correlation_p_pet_wtd/correlation_p_pet_wtd_15_downsampled_10.tif"
)
wtd = mp.Raster("data/wtd/wtd_downsampled_10.tif")
f, ax = plt.subplots(3, 3, figsize=(20, 20))
plt.tight_layout(h_pad=-15, w_pad=7)
ax = [mp.basemap(ax=cax, coastlines=False) for cax in ax.flatten()]
basemap_kwargs = {
'coastline_linewidth': 0.3,
'xticks': 5,
'yticks': 5,
'resolution': '10m'
}
fontsize = 14
# Classes
cmap = cmap_2d((3, 3), alpha=0.5, diverging_alpha=0.25)
cmap[1, 1, :] = (0.9, 0.9, 0.9)
cmap = cmap.reshape((9, 3))
classes.plot_classified_map(ind,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend=None,
colors=cmap,
force_equal_figsize=True,
ax=ax[0],
fontsize=fontsize)
ax[0].set_title("Ecohydrological classes", fontsize=fontsize)
# P/PET
bins = np.arange(0, 3.1, 0.2)
p_pet.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Blues",
legend='colorbar',
ax=ax[1],
fontsize=fontsize,
legend_kwargs={
'title': "[-]",
'title_font': {
'pad': 10
}
})
ax[1].set_title("P/PET", fontsize=fontsize)
# WTD
bins = [0, 1, 2, 5, 10, 15, 20, 25, 35, 50, 100, 300]
bins = bins[::-1]
bins = [-i for i in bins]
wtd.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Blues",
legend='colorbar',
ax=ax[2],
fontsize=fontsize,
legend_kwargs={
'title': "[m]",
'title_font': {
'pad': 10
}
})
ax[2].set_title("WTD", fontsize=fontsize)
# FAPAR
bins = [0, 10, 20, 30, 40, 50, 60, 70, 100]
fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Greens",
legend='colorbar',
ax=ax[3],
fontsize=fontsize,
legend_kwargs={
'title': "[%]",
'title_font': {
'pad': 10
}
})
ax[3].set_title("fAPAR", fontsize=fontsize)
# Climate
cmap = [(1, 1, 1)]
bins = [0]
labels = ["Water"]
with open("data/climate/koppen_legend.txt") as f:
for line in f:
line = line.strip()
try:
int(line[0])
rgb = [
int(c) / 255 for c in line[line.find('[') + 1:-1].split()
]
cmap.append(rgb)
labels.append(line.split()[1])
bins.append(line[:line.find(':')])
except:
pass
bins = np.array(bins, dtype=np.int)
climate.plot_classified_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
colors=cmap,
labels=labels,
legend='colorbar',
ax=ax[4],
fontsize=fontsize,
suppress_warnings=True,
clip_legend=True)
ax[4].set_title("Climate", fontsize=fontsize)
# Landscape
cmap = [
'#004dac', '#729116', '#b1bc1d', '#e7de23', '#af9a15', '#785707',
'#fff9f2'
]
labels = [
'Wetland and open water', 'Lowland', 'Undulating', 'Hilly',
'Low mountainous', 'Mountainous', 'High mountainous'
]
bins = [1, 2, 3, 4, 5, 6, 7]
landscape.plot_classified_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
colors=cmap,
labels=labels,
legend='colorbar',
ax=ax[5],
fontsize=fontsize,
suppress_warnings=True)
ax[5].set_title("Landscape", fontsize=fontsize)
# Correlation WTD and fAPAR
bins = [-1, -0.5, -0.25, -0.15, -0.05, 0.05, 0.15, 0.25, 0.5, 1]
corr_wtd_fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[6],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[6].set_title("Correlation WTD & fAPAR", fontsize=fontsize)
# Correlation P and fAPAR
corr_p_pet_fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[7],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[7].set_title("Correlation P/PET & fAPAR", fontsize=fontsize)
# Correlation P/PET and fAPAR
corr_p_pet_wtd.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[8],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[8].set_title("Correlation P/PET & WTD", fontsize=fontsize)
mp.Raster.close(verbose=False)
def draw_thesis_map(loc,
loc_small=None,
classified=False,
legend_2d=False,
legend=None,
fontsize=28,
**kwargs):
"""
Plot map for thesis. Not intended for reuse.
"""
fontdict = {'fontsize': fontsize, 'va': 'center', 'ha': 'center'}
M_thesis = mp.Raster(loc)
if isinstance(loc_small, type(None)):
loc_small = loc
M_thesis_small = mp.Raster(loc_small)
inset_coords = (
(-93, 29, -78, 44), # Mississippi
(7, 35, 22, 50), # Italy
(15, -7, 30, 8), # Congo
(142, -35, 157, -20)) # Australia
bounds = [-180, -89, 180, 90]
basemap_kwargs = {'coastline_linewidth': 0.25}
ax = mp.basemap(*bounds, figsize=(40, 40), **basemap_kwargs)
bounds = (-180, -60, 180, 90)
extent = (bounds[0], bounds[2], bounds[1], bounds[3])
if classified:
ax, _legend = M_thesis.plot_classified_map(bounds,
ax=ax,
suppress_warnings=True,
legend=legend,
extent=extent,
transform=ax.projection,
**kwargs)
else:
ax, _legend = M_thesis.plot_map(bounds,
ax=ax,
legend=legend,
extent=extent,
transform=ax.projection,
**kwargs)
inset_locations = [(-55, -88, 44, 44), (-5, -88, 44, 44),
(45, -88, 44, 44), (95, -88, 44, 44)]
inset_locations = [((loc[0] + 180) / 360, (loc[1] + 90) / 180,
loc[2] / 360, loc[3] / 180) for loc in inset_locations]
labels = [chr(65 + i) for i in range(len(inset_locations))]
for i in range(len(inset_locations)):
left, bottom, right, top = inset_coords[i]
ax_inset = plt.gcf().add_subplot(projection=ccrs.PlateCarree(),
label=f"{np.random.rand()}")
basemap_kwargs = {'grid': False, 'coastline_linewidth': 0.5}
if classified:
ax_inset, _ = M_thesis_small.plot_classified_map(
inset_coords[i],
basemap=True,
fontsize=fontsize,
resolution='10m',
ax=ax_inset,
xticks=[],
yticks=[],
basemap_kwargs=basemap_kwargs,
suppress_warnings=True,
legend=None,
**kwargs)
else:
ax_inset, _ = M_thesis_small.plot_map(
inset_coords[i],
basemap=True,
fontsize=fontsize,
resolution='10m',
ax=ax_inset,
xticks=[],
yticks=[],
basemap_kwargs=basemap_kwargs,
legend=None,
**kwargs)
ip = InsetPosition(ax, inset_locations[i])
ax_inset.set_axes_locator(ip)
bounds_to_polygons([inset_coords[i]]).plot_shapes(ax=ax,
facecolor=(1, 1, 1,
0.5))
text_location_x = (left + right) / 2
text_location_y = (top + bottom) / 2
t = ax.text(text_location_x,
text_location_y,
labels[i],
fontdict=fontdict)
t.set_bbox(dict(facecolor='white', alpha=0.8, edgecolor='grey'))
text_location_x = inset_locations[i][0] + inset_locations[i][2] / 2
text_location_y = inset_locations[i][1] + inset_locations[i][3] + 0.015
t = ax.text(text_location_x,
text_location_y,
labels[i],
fontdict=fontdict,
transform=ax.transAxes,
zorder=5)
t.set_bbox(dict(facecolor='white', alpha=0.6, edgecolor='grey'))
if legend_2d:
data = mp.Raster(
"data/cmap_2d_significant/classes_serial_downsampled_10.tif")[0]
data[np.isnan(data)] = -1
bins, vals = np.unique(data, return_counts=True)
bins = bins[1:]
vals = vals[1:]
vals = vals / vals.sum() * 100
axins = inset_axes(ax,
width="100%",
height="100%",
bbox_to_anchor=(-0.01, 0.25, 0.26, 0.26),
bbox_transform=ax.transAxes)
cmap = cmap_2d((3, 3), alpha=0.5, diverging_alpha=0.25)
cmap[1, 1, :] = (0.9, 0.9, 0.9)
axins.imshow(cmap, origin='lower')
axins.set_xticks([0, 1, 2], minor=False)
axins.set_yticks([0, 1, 2], minor=False)
axins.set_xticklabels([u'\u2014', "0", "+"],
minor=False,
fontdict=fontdict)
axins.set_yticklabels([u'\u2014', "0", "+"],
minor=False,
fontsize=fontsize)
axins.set_xlabel("Correlation WTD and fAPAR",
fontdict=fontdict,
labelpad=20)
axins.set_ylabel("Correlation P/PET and fAPAR",
fontdict=fontdict,
labelpad=20)
axins.tick_params(axis='both', which='both', length=0, pad=20)
for i in range(3):
for j in range(3):
t = axins.text(i,
j,
f'{vals[i + 3 * j]:1.0f}',
fontdict=fontdict)
t.set_bbox(dict(facecolor='white', alpha=0.6,
edgecolor='grey'))
ax.tick_params(axis='both',
which='both',
length=0,
labelsize=fontsize,
pad=12)
if isinstance(_legend, ColorbarBase):
_legend.ax.tick_params(labelsize=fontsize, pad=12)
mp.Raster.close(verbose=False)
return ax, _legend
def draw_thesis_location_zoom_full(ind, ticks=1):
"""
Drawing array of nine maps, displaying the input data and results. Not meant for custom use.
Parameters
----------
ind : .
look at mp.RasterBase.get_pointer()
"""
classes = mp.Raster("data/cmap_2d_significant/classes_serial.tif")
climate = mp.Raster("data/climate/climate.tif")
landscape = mp.Raster("data/wtd/wtd_std_5.tif")
p_pet = mp.Raster("data/pet/p_pet.tif")
fapar = mp.Raster("data/fapar/mean_fpar_reprojected.tif")
corr_wtd_fapar = mp.Raster(
"data/correlation_wtd_fapar/correlation_wtd_fapar_ge3_15.tif")
corr_p_pet_fapar = mp.Raster(
"data/correlation_p_pet_fapar/correlation_p_pet_fapar_ge3_15.tif")
corr_p_pet_wtd = mp.Raster(
"data/correlation_p_pet_wtd/correlation_p_pet_wtd_15.tif")
wtd = mp.Raster("data/wtd/wtd.tif")
f, ax = plt.subplots(3, 3, figsize=(20, 20))
plt.tight_layout(h_pad=-15, w_pad=7)
ax = [mp.basemap(ax=cax, coastlines=False) for cax in ax.flatten()]
basemap_kwargs = {
'coastline_linewidth': 0.3,
'xticks': ticks,
'yticks': ticks,
'resolution': '10m'
}
fontsize = 14
# Classes
cmap = cmap_2d((3, 3), alpha=0.5, diverging_alpha=0.25)
cmap[1, 1, :] = (0.9, 0.9, 0.9)
cmap = cmap.reshape((9, 3))
cmap = np.vstack(((1, 1, 1), cmap))
classes.plot_classified_map(ind,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend=None,
colors=cmap,
force_equal_figsize=True,
ax=ax[0],
fontsize=fontsize,
suppress_warnings=True)
ax[0].set_title("Ecohydrological classes", fontsize=fontsize)
# P/PET
bins = np.arange(0, 3.1, 0.2)
p_pet.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Blues",
legend='colorbar',
ax=ax[1],
fontsize=fontsize,
legend_kwargs={
'title': "[-]",
'title_font': {
'pad': 10
}
})
ax[1].set_title("P/PET", fontsize=fontsize)
# WTD
bins = [0, 1, 2, 5, 10, 15, 20, 25, 35, 50, 100, 200, 300, 1000]
bins = bins[::-1]
bins = [-i for i in bins]
wtd.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Blues",
legend='colorbar',
ax=ax[2],
fontsize=fontsize,
legend_kwargs={
'title': "[m]",
'title_font': {
'pad': 10
}
})
ax[2].set_title("WTD", fontsize=fontsize)
# FAPAR
bins = [0, 10, 20, 30, 40, 50, 60, 70, 100]
fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
cmap="Greens",
legend='colorbar',
ax=ax[3],
fontsize=fontsize,
legend_kwargs={
'title': "[%]",
'title_font': {
'pad': 10
}
})
ax[3].set_title("fAPAR", fontsize=fontsize)
# Climate
cmap = [(1, 1, 1)]
bins = [0]
labels = ["Water"]
with open("data/climate/koppen_legend.txt") as f:
for line in f:
line = line.strip()
try:
int(line[0])
rgb = [
int(c) / 255 for c in line[line.find('[') + 1:-1].split()
]
cmap.append(rgb)
labels.append(line.split()[1])
bins.append(line[:line.find(':')])
except:
pass
bins = np.array(bins, dtype=np.int)
climate.plot_classified_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
colors=cmap,
labels=labels,
legend='colorbar',
ax=ax[4],
fontsize=fontsize,
suppress_warnings=True,
clip_legend=True)
ax[4].set_title("Climate", fontsize=fontsize)
# Landscape
dem_cmap = np.vstack((cmap_from_borders(("#0000b8", "#006310"),
n=2,
return_type="list"),
cmap_from_borders(("#006310", "#faea00"),
n=7,
return_type="list"),
cmap_from_borders(("#faea00", "#504100"),
n=8,
return_type="list"),
cmap_from_borders(("#504100", "#ffffff"),
n=2,
return_type="list")))
bins = np.round(np.hstack(((0, ), np.logspace(0, np.log10(400), num=18))),
1)
landscape.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
cmap=ListedColormap(dem_cmap),
ax=ax[5],
fontsize=fontsize)
ax[5].set_title("Landscape", fontsize=fontsize)
# Correlation WTD and fAPAR
bins = [-1, -0.5, -0.25, -0.15, -0.05, 0.05, 0.15, 0.25, 0.5, 1]
corr_wtd_fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[6],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[6].set_title("Correlation WTD & fAPAR", fontsize=fontsize)
# Correlation P and fAPAR
corr_p_pet_fapar.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[7],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[7].set_title("Correlation P/PET & fAPAR", fontsize=fontsize)
# Correlation P/PET and fAPAR
corr_p_pet_wtd.plot_map(ind,
bins=bins,
basemap=True,
basemap_kwargs=basemap_kwargs,
legend='colorbar',
ax=ax[8],
fontsize=fontsize,
cmap="RdYlBu_r")
ax[8].set_title("Correlation P/PET & WTD", fontsize=fontsize)
mp.Raster.close(verbose=False)
import geomappy as mp
from geomappy.focal_statistics.focal_correlation import correlate_maps as correlate_maps_njit
from geomappy.focal_statistics.c_focal_correlation import correlate_maps as correlate_maps_cython
from geomappy.focal_statistics.focal_correlation import correlate_maps_base as correlate_maps_numpy
import matplotlib.pyplot as plt
import numpy as np
map1 = mp.Raster("data/wtd.tif").values
map2 = mp.Raster("data/tree_height.asc").values
print("data loaded")
# %timeit correlate_maps_cython(map1, map2, window_size=5, fraction_accepted=0.25, verbose=True, reduce=False)
# %timeit correlate_maps_cython(map1, map2, window_size=5, fraction_accepted=0.25, verbose=True, reduce=True)
for f in [0, 0.25, 1]:
for reduce in [False, True]:
c1 = correlate_maps_njit(map1,
map2,
window_size=5,
fraction_accepted=f,
verbose=True,
reduce=reduce)
c2 = correlate_maps_cython(map1,
map2,
window_size=5,
fraction_accepted=f,
verbose=True,
reduce=reduce)
if not reduce:
c3 = correlate_maps_numpy(map1,
import thesis
import geomappy as mp
import numpy as np
CLASSES = "data/cmap_2d_significant/classes_serial.tif"
M_classes = mp.Raster(CLASSES)
classes_data = M_classes[0].astype(np.int8) + 1
profile = M_classes.profile
profile['dtype'] = np.int8
profile['nodata'] = 0
profile['compress'] = 'LZW'
mp.Raster("data/export/classes.tif", mode='w',
profile=profile)[0] = classes_data
LANDSCAPE = "data/landscape_classes/landscape_classes.tif"
M_landscape = mp.Raster(LANDSCAPE)
landscape_data = M_landscape[0].astype(np.int8)
profile = M_landscape.profile
profile['dtype'] = np.int8
profile['nodata'] = 0
profile['compress'] = 'LZW'
mp.Raster("data/export/landscape.tif", mode='w',
profile=profile)[0] = landscape_data
mp.Raster.close()
import numpy as np import pandas as pd CORR_WTD_FAPAR = "data/correlation_wtd_fapar/correlation_wtd_fapar_ge3_15_downsampled_10.tif" CORR_P_PET_FAPAR = "data/correlation_p_pet_fapar/correlation_p_pet_fapar_ge3_15_downsampled_10.tif" CORR_P_PET_WTD = "data/correlation_p_pet_wtd/correlation_p_pet_wtd_15_downsampled_10.tif" LANDSCAPE = "data/landscape_classes/landscape_downsampled_10.tif" CLIMATE = "data/climate/climate_downsampled_10.tif" CLASSES = "data/cmap_2d_significant/classes_serial_downsampled_10.tif" P_PET = "data/pet/p_pet_downsampled_10.tif" WTD = "data/wtd/wtd_downsampled_10.tif" WTD_STD = "data/wtd/wtd_std_5_downsampled_10.tif" TH = "data/tree_height/tree_height_global_downsampled_10.tif" FAPAR = "data/fapar/mean_fpar_downsampled_10.tif" M_corr_wtd_fapar = mp.Raster(CORR_WTD_FAPAR) M_corr_p_pet_fapar = mp.Raster(CORR_P_PET_FAPAR) M_corr_p_pet_wtd = mp.Raster(CORR_P_PET_WTD) M_climate = mp.Raster(CLIMATE) M_p_pet = mp.Raster(P_PET) M_wtd = mp.Raster(WTD) M_wtd_std = mp.Raster(WTD_STD) M_landscape = mp.Raster(LANDSCAPE) M_classes = mp.Raster(CLASSES, fill_value=-1) M_fapar = mp.Raster(FAPAR) M_th = mp.Raster(TH) mp.Raster.set_tiles((10, 20)) df = None for i in M_wtd:
return bs, pe
def calc_df_offset(value, n=225):
df_offset = 0
while True:
df = n - 2 - df_offset
t_value = t.ppf(0.95, df=df)
threshold = t_value / np.sqrt(df + t_value ** 2)
if threshold > value:
return df_offset
df_offset = df_offset + 1
M_th = mp.Raster("data/tree_height/tree_height_global.tif")
M_fapar = mp.Raster("data/fapar/mean_fpar_reprojected.tif")
M_p_pet = mp.Raster("data/pet/p_pet.tif")
M_wtd = mp.Raster("data/wtd/wtd.tif")
mp.Raster.set_window_size(15)
mp.Raster.set_tiles((20, 20))
d_p_pet = {}
d_wtd = {}
for i in M_th:
progress_bar((i+1) / M_th.c_tiles)
th = M_th[i]
fapar = M_fapar[i]
fapar[th < 3] = np.nan
p_pet = M_p_pet[i]
