NaiveClimateModelling/plotters.txt at master · DylanKirbs/NaiveClimateModelling · GitHub

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KOPPEN_DICT = {
    "": 0,
    "Af": 1,
    "Am": 2,
    "Aw": 3,
    "As": 4,
    "BWh": 5,
    "BWk": 6,
    "BSh": 7,
    "BSk": 8,
    "Csa": 9,
    "Csb": 10,
    "Csc": 11,
    "Cwa": 12,
    "Cwb": 13,
    "Cwc": 14,
    "Cfa": 15,
    "Cfb": 16,
    "Cfc": 17,
    "Dsa": 18,
    "Dsb": 19,
    "Dsc": 20,
    "Dsd": 21,
    "Dwa": 22,
    "Dwb": 23,
    "Dwc": 24,
    "Dwd": 25,
    "Dfa": 26,
    "Dfb": 27,
    "Dfc": 28,
    "Dfd": 29,
    "ET": 30,
    "EF": 31
}

# Make a plot of the predicted biomes from [0:10000] MAP and [-40:30] MAT
# Create a meshgrid
x = np.linspace(-40, 30, 100)
y = np.linspace(0, 10000, 100)
xx, yy = np.meshgrid(x, y)
# Create a dataframe of the meshgrid
mesh = pd.DataFrame({"MAT": xx.flatten(), "AP": yy.flatten()})
# Classify the meshgrid
mesh_classes = mesh.apply(lambda row: classify(
    Observation(row["MAT"], row["AP"]), biomes).idxmax(), axis=1)

# Reshape the classes to match the meshgrid
mesh_classes = mesh_classes.values.reshape(xx.shape)
mesh_classes = np.vectorize(KOPPEN_DICT.get)(mesh_classes)
# Plot the meshgrid
plt.contourf(xx, yy, mesh_classes, cmap="viridis")
plt.xlabel("Mean Annual Temperature (C)")
plt.ylabel("Annual Precipitation (mm)")
plt.title("Predicted Biomes")

# scatter plot of the biomes
plt.scatter(biomes["MAT mean"], biomes["AP mean"], c="black", s=3)
# Add the biome labels
for i, txt in enumerate(biomes.index):
    plt.annotate(txt, (biomes["MAT mean"][i],
                 biomes["AP mean"][i]), fontsize=3)


plt.savefig("./data/predicted_biomes.png", dpi=300)