Spaces:
Sleeping
Sleeping
File size: 3,687 Bytes
164d1c0 731c0dd 164d1c0 731c0dd 164d1c0 731c0dd 164d1c0 731c0dd 164d1c0 1189f83 164d1c0 1189f83 164d1c0 1189f83 731c0dd 164d1c0 1189f83 731c0dd 164d1c0 e301a24 164d1c0 e301a24 164d1c0 e301a24 164d1c0 0b30b31 164d1c0 b36c6ed d3b1063 e301a24 164d1c0 b36c6ed 164d1c0 | 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 | import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
import matplotlib.gridspec as gridspec
def show_two_plots(gdf, name1, name2, vmin, vmax, colorscheme, title1, title2, label, split=False):
print('split', split)
if gdf.empty:
raise ValueError("GeoDataFrame is empty")
required_cols = [name1, name2]
for col in required_cols:
if col not in gdf.columns:
raise ValueError(f"Column '{col}' not found in gdf")
#Create a shared colormap and normalization
cmap = plt.colormaps[colorscheme]
norm = colors.Normalize(vmin=vmin, vmax=vmax)
# Create subplots
fig = plt.figure(figsize=(12, 6))
gs = gridspec.GridSpec(1, 3, width_ratios=[1, 1, 0.05], wspace=0.3)
axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1]), fig.add_subplot(gs[2]) ]
if split == False:
# First plot
gdf.plot(column=name1, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
else:
# Split data into three GeoDataFrames
# below_100 = gdf[gdf[split] <= 100]
above_capacity = gdf[gdf[split] > (100-gdf['%Construccion'])]
above_100 = gdf[gdf[split] > 100]
above_colors = {"capacity":"chocolate", "100":"firebrick"}
# Plot values ≤ 100 using colormap
gdf.plot(column=name1, cmap=colorscheme, ax=axes[0], vmin=vmin, vmax=vmax, marker='s', markersize=10)
# Plot values > capacity in orange
above_capacity.plot(color=above_colors['capacity'], ax=axes[0], label='> capacidad debido a la construcción', marker='x', markersize=6)
# Plot values > 100 in red
above_100.plot(color=above_colors['100'], ax=axes[0], label='> 100% cobertura arbórea', marker='x', markersize=6)
# Add legend manually for orange points
orange_patch = plt.Line2D([0], [0], marker='o', color='w', label='> capacidad debido a la construcción',
markerfacecolor=above_colors['capacity'], markersize=8)
# Add legend manually for red points
red_patch = plt.Line2D([0], [0], marker='o', color='w', label='> 100% cobertura arbórea',
markerfacecolor=above_colors['100'], markersize=8)
axes[0].legend(handles=[orange_patch, red_patch])
# axes[0].set_title(title1)
axes[0].set_title(title1, pad=-30)
axes[0].set_axis_off()
# Second plot (original)
gdf.plot(column=name2, cmap=cmap, norm=norm, ax=axes[1],marker='s', markersize=10)
axes[1].set_title(title2, pad=-30)
axes[1].set_axis_off()
# Shared colorbar
sm = cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = [] # Dummy array for the colormap
cbar = fig.colorbar(sm, cax=axes[2], orientation='vertical', fraction=0.03, pad=0.02)
cbar.set_label(label)
return fig
def show_one_plot(gdf, name, vmin, vmax, colorscheme, title, label):
# Second set of plots
cmap = plt.colormaps[colorscheme]
norm = colors.Normalize(vmin, vmax)
# Create subplots
fig = plt.figure(figsize=(12, 6))
gs = gridspec.GridSpec(1, 2, width_ratios=[1, 0.025], wspace=0.3)
axes = [fig.add_subplot(gs[0]), fig.add_subplot(gs[1])]
gdf.plot(column=name, cmap=cmap, norm=norm, ax=axes[0], marker='s', markersize=10)
axes[0].set_title(title, pad=-30)
axes[0].set_axis_off()
# Add a colorbar
sm = cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = [] # Dummy data for the colorbar
cbar = fig.colorbar(sm, cax=axes[1], orientation='vertical', fraction=0.03, pad=0.02)
cbar.set_label(label)
return fig
|