plotting.py

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

sns.set_style("whitegrid")
sns.set_context("paper")

import plotly.express as px
import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid1 import make_axes_locatable

from urllib.request import urlopen
import json


def get_counties():
    with urlopen(
        "https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json"
    ) as response:
        counties = json.load(response)
    return counties


def get_hotspot_list():
    hspts = [
        "wfday",
        "heatday",
        "smoke_pm_non_zero",
        "smoke_pm_gt_five",
        "hw",
        "_hws",
        "hs",
        "hws",
        "ws",
        "hs5",
        "hws5",
        "ws5",
        "heatday_2d",
        "smoke_pm_non_zero_2d",
        "wfday_2d",
        "_hws_2d",
        "hs_2d",
        "hws_2d",
        "ws_2d",
    ]
    return hspts


def get_core_dict():
    hsdict = {
        "wfday": {
            "title": "wildfire",
            "title_map": "Total days of wildfire burn zones",
        },
        "heatday": {
            "title": "heat",
            "title_map": "Total days of extreme heat",
        },
        "smoke_pm_non_zero": {
            "title": "smoke_pm_non_zero",
            "title_map": r"Total days of wildfire smoke",
        },
        "hws": {
            "title": "heat_wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of extreme heat, wildfire burn zones and wildfire smoke co-occurrence",
        },
        "hs": {
            "title": "heat_smoke_pollution_non_zero",
            "title_map": r"Total days of extreme heat and wildfire smoke co-occurrence",
        },
        "hw": {
            "title": "heat_wildfire",
            "title_map": "Total days of extreme heat and wildfire burn zones co-occurrence",
        },
        "ws": {
            "title": "wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of wildfire burn zones & wildfire smoke co-occurrence",
        },
    }
    return hsdict


def get_hotspopt_dict():
    hsdict = {
        "wfday": {
            "title": "wildfire",
            "title_map": "Total days of wildfire",
        },
        "heatday": {
            "title": "heat",
            "title_map": "Total days of heat",
        },
        "coldday": {
            "title": "cold",
            "title_map": "Total cold days (under 5th percentile over last 5 years)",
        },
        "polluted": {
            "title": "pollution",
            "title_map": "Total days of high pollution $PM_{2.5}$ (over 35 μg/m\u00b3)",
        },
        "smoke_pm_non_zero": {
            "title": "smoke_pm_non_zero",
            "title_map": r"Total days of smoke $PM_{2.5}$",
        },
        "smoke_pm_gt_five": {
            "title": "smoke_pm_gt_five",
            "title_map": r"Total days of wildfire smoke (over >5μg/$m^3$)",
        },
        "hwp": {
            "title": "heat_wildfire_pollution",
            "title_map": "Total days of heat, wildfire and pollution concurrence",
        },
        "hws": {
            "title": "heat_wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of heat, wildfire and smoke $PM_{2.5}$ concurrence",
        },
        "hp": {
            "title": "heat_pollution",
            "title_map": "Total days of heat and pollution concurrence",
        },
        "hs": {
            "title": "heat_smoke_pollution_non_zero",
            "title_map": r"Total days of heat and smoke $PM_{2.5}$ concurrence",
        },
        "hw": {
            "title": "heat_wildfire",
            "title_map": "Total days of heat and wildfire concurrence",
        },
        "ws": {
            "title": "wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of wildfire & smoke $PM_{2.5}$",
        },
        "_hws": {
            "title": "heat_or_wildfire_or_smoke_pollution",
            "title_map": r"Total days of heat or wildfire or smoke $PM_{2.5}$",
        },
        "hs5": {
            "title": "heat_smoke_pollution_over_five",
            "title_map": r"Total days of heat and smoke $PM_{2.5}$ (over >5μg/$m^3$) concurrence",
        },
        "hws5": {
            "title": "heat_wildfire & smoke_pollution_over_five",
            "title_map": r"Total days of heat, wildfire and smoke $PM_{2.5}$ (over >5μg/$m^3$) concurrence",
        },
        "ws5": {
            "title": "wildfire_smoke_pollution_over_five",
            "title_map": r"Total days of wildfire and smoke $PM_{2.5}$ (over >5μg/$m^3$) concurrence",
        },
        "wfday_2d": {
            "title": "wildfire_2D",
            "title_map": "Total days of wildfire (2D)",
        },
        "smoke_pm_non_zero_2d": {
            "title": "smoke_pm_non_zero_2D",
            "title_map": r"Total days of smoke $PM_{2.5}$ (non zero) (2D)",
        },
        "heatday_2d": {
            "title": "heat_2D",
            "title_map": "Total days of heat (2D)",
        },
        "_hws_2d": {
            "title": "heat_or_wildfire_or_smoke_pollution_2D",
            "title_map": r"Total days of heat or wildfire or smoke $PM_{2.5}$ (2D)",
        },
        "ws_2d": {
            "title": "wildfire_smoke_pollution_non_zero_2D",
            "title_map": r"Total days of wildfire & smoke $PM_{2.5}$ (2D)",
        },
        "hs_2d": {
            "title": "heat_smoke_pollution_non_zero_2D",
            "title_map": r"Total days of heat and non-zero smoke $PM_{2.5}$ concurrence (2D)",
        },
        "hws_2d": {
            "title": "heat_wildfire_smoke_pollution_non_zero_2D",
            "title_map": r"Total days of heat, wildfire and non-zero smoke $PM_{2.5}$ concurrence (2D)",
        },
    }
    return hsdict


def get_hotspopt_dict_county():
    hsdict = {
        "wfday": {
            "title": "wildfire",
            "title_map": "Total days of wildfire",
        },
        "heatday": {
            "title": "heat",
            "title_map": "Total days of heat (over 95th percentile over last 5 years)",
        },
        "coldday": {
            "title": "cold",
            "title_map": "Total cold days (under 5th percentile over last 5 years)",
        },
        "polluted": {
            "title": "pollution",
            "title_map": "Total days of high air pollution (over 35 μg/m\u00b3)",
        },
        "smoke_polluted": {
            "title": "smoke_polluted",
            "title_map": r"Total days of smoke $PM_{2.5} pollution$",
        },
        "hwp": {
            "title": "heat_wildfire_pollution",
            "title_map": "Total days of heat, wildfire and pollution concurrence",
        },
        "hws": {
            "title": "heat_wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of heat, wildfire and non-zero smoke $PM_{2.5}$ concurrence",
        },
        "hp": {
            "title": "heat_pollution",
            "title_map": "Total days of heat and pollution concurrence",
        },
        "hs": {
            "title": "heat_smoke_pollution_non_zero",
            "title_map": r"Total days of heat and non-zero smoke $PM_{2.5}$ concurrence",
        },
        "hw": {
            "title": "heat_wildfire",
            "title_map": "Total days of heat and wildfire concurrence",
        },
        "ws": {
            "title": "wildfire_smoke_pollution_non_zero",
            "title_map": r"Total days of wildfire & smoke $PM_{2.5}$",
        },
        "hwps": {
            "title": "heat_wildfire_pollution",
            "title_map": "Total days of heat or wildfire or pollution",
        },
    }
    return hsdict


def statePlot(df, data, title, cmap, zoom, dpi):
    f, ax = plt.subplots(1, 1, figsize=(8, 8), sharex=True, sharey=True, dpi=dpi)
    f.tight_layout(pad=0.8)
    ax.set_axis_off()
    plt.title(title, fontsize="large")
    divider = make_axes_locatable(ax)
    cax = divider.append_axes("right", size="3%", pad=0.2)
    ux = df.to_crs(epsg=3857).plot(
        data,
        ax=ax,
        edgecolor="k",
        cmap=cmap,
        alpha=1,
        linewidth=0.1,
        legend=True,
        cax=cax,
    )
    # ctx.add_basemap(ux,    zoom=zoom,
    # source=ctx.providers.Stamen.TonerLite);
    plt.ylabel("Days of exposure", fontsize=12)
    # Use savefig to save your map
    plt.tight_layout()
    plt.savefig("figures/zip_hotspots_" + data + ".png")
    plt.show()


def trend_plot(
    trends_df, l, hotspot, title, title_map, year_min="2006", year_max="2021"
):
    f, ax = plt.subplots(1, 1, figsize=(4, 3))
    sns.lineplot(data=trends_df, x="time", y=hotspot, ax=ax)

    sns.lineplot(
        data=trends_df,
        x="time",
        y=hotspot,
        ax=ax,
        linestyle="--",
        color="black",
        linewidth=2.5,
    )

    sns.lineplot(
        data=trends_df[trends_df.GEOID.isin(l)],
        x="time",
        y=hotspot,
        hue="GEOID",
        palette="tab10",
        ax=ax,
    )

    plt.xlabel("Year")
    plt.ylabel("Number of days of concurrence")
    plt.title(title_map)
    plt.xlim([pd.to_datetime(year_min), pd.to_datetime(year_max)])
    plt.tight_layout()
    plt.savefig("figures/trends_" + title + ".png")


def draw_map(df, counties, hotspot, title):
    fig = px.choropleth_mapbox(
        df,
        width=400,
        height=400,
        geojson=counties,
        locations="COUNTY_CODE",
        color=hotspot,
        color_continuous_scale="Viridis",
        mapbox_style="white-bg",
        zoom=4,
        title=title,
        center={"lat": 36, "lon": -119},
        labels={hotspot: "Day count"},
    )
    # fig.update_geos(fitbounds="locations", visible=False)
    # fig.update_layout(width=100, title_text="Side By Side Subplots")
    fig.update_layout(margin={"r": 0, "l": 0, "b": 0})
    fig.show()
    fig.write_image("figures/maps_" + hotspot + "_hotspot.png")


if __name__ == "__main__":
    pass