7 Optimization - trigger selection and plotting

Code

%load_ext jupyter_black
%load_ext autoreload
%autoreload 2

Code

import ocha_stratus as stratus
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.ticker import FuncFormatter

from src.constants import *

Code

blob_name = (
    f"{PROJECT_PREFIX}/processed/storm_stats/stats_with_targets.parquet"
)
df_stats = stratus.load_parquet_from_blob(blob_name)

Code

blob_name = f"{PROJECT_PREFIX}/processed/trigger_metrics_ibtracs_imerg.parquet"
df_results = stratus.load_parquet_from_blob(blob_name)

Code

df_results

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
0	q50_total	18.190000	40.0	NaN	6	27069337	9	7	18	31	quantile	50	total
1	q50_total	18.190000	40.0	115.0	6	27069337	9	7	18	31	quantile	50	total
2	q50_total	18.190000	40.0	75.0	6	27069337	9	7	18	31	quantile	50	total
3	q50_total	18.190000	40.0	90.0	6	27069337	9	7	18	31	quantile	50	total
4	q50_total	18.190000	40.0	25.0	6	27069337	9	7	18	35	quantile	50	total
...	...	...	...	...	...	...	...	...	...	...	...	...	...
752233	q99_roll3_mean_abv	287.727478	65.0	100.0	5	23164562	8	6	12	15	mean_abv	99	roll3
752234	q99_roll3_mean_abv	287.727478	65.0	130.0	5	23164562	8	6	12	15	mean_abv	99	roll3
752235	q99_roll3_mean_abv	287.727478	65.0	30.0	5	23210058	8	6	13	18	mean_abv	99	roll3
752236	q99_roll3_mean_abv	287.727478	65.0	70.0	5	23164562	8	6	12	15	mean_abv	99	roll3
752237	q99_roll3_mean_abv	287.727478	65.0	110.0	5	23164562	8	6	12	15	mean_abv	99	roll3

752238 rows × 13 columns

Code

def get_optimal_triggers(optimize_col: str, years_triggered: int):
    df_results_rp = df_results[df_results["n_years"] == years_triggered]
    df_results_top_single_col = df_results_rp[
        df_results_rp[optimize_col] == df_results_rp[optimize_col].max()
    ]
    df_results_top_duplicates = df_results_top_single_col[
        df_results_top_single_col["impact_sum"]
        == df_results_top_single_col["impact_sum"].max()
    ]
    df_results_top = (
        df_results_top_duplicates.sort_values("rain_thresh")
        .drop_duplicates(
            subset=["wind_speed_max", "wind_speed_max_landfall", "rain_col"]
        )
        .sort_values("wind_speed_max")
        .drop_duplicates(
            subset=["rain_thresh", "wind_speed_max_landfall", "rain_col"]
        )
        .sort_values("wind_speed_max_landfall")
        .drop_duplicates(subset=["rain_thresh", "wind_speed_max", "rain_col"])
    )
    return df_results_top

Code

get_optimal_triggers("target_sum", 6)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
689	q50_total	26.412498	115.0	105.0	5	22343481	6	4	6	8	quantile	50	total

Code

get_optimal_triggers("target_with_cerf_sum", 6)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
148901	q50_roll3_mean_abv	79.962158	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	roll3
44987	q50_total_mean_abv	82.303337	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	total
256955	q80_roll3	73.388992	115.0	105.0	5	22289476	7	5	6	8	quantile	80	roll3
81005	q50_roll2_mean_abv	70.131157	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	roll2
225077	q80_roll2	67.569000	115.0	105.0	5	22289476	7	5	6	8	quantile	80	roll2
167945	q80_total	77.296997	115.0	105.0	5	22289476	7	5	6	8	quantile	80	total
116609	q50_roll3	20.857498	115.0	105.0	5	22289476	7	5	6	8	quantile	50	roll3
16433	q50_total	22.917500	115.0	110.0	5	22289476	7	5	6	8	quantile	50	total

Code

get_optimal_triggers("cerf_sum", 6)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
148901	q50_roll3_mean_abv	79.962158	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	roll3
44987	q50_total_mean_abv	82.303337	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	total
256955	q80_roll3	73.388992	115.0	105.0	5	22289476	7	5	6	8	quantile	80	roll3
81005	q50_roll2_mean_abv	70.131157	115.0	105.0	5	22289476	7	5	6	8	mean_abv	50	roll2
225077	q80_roll2	67.569000	115.0	105.0	5	22289476	7	5	6	8	quantile	80	roll2
167945	q80_total	77.296997	115.0	105.0	5	22289476	7	5	6	8	quantile	80	total
116609	q50_roll3	20.857498	115.0	105.0	5	22289476	7	5	6	8	quantile	50	roll3
16433	q50_total	22.917500	115.0	110.0	5	22289476	7	5	6	8	quantile	50	total

Code

get_optimal_triggers("target_sum", 7)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
72319	q50_roll2	18.574999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll2
119101	q50_roll3	21.314999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll3
10633	q50_total	27.847499	115.0	100.0	6	26262081	8	6	7	9	quantile	50	total

Code

get_optimal_triggers("target_with_cerf_sum", 7)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
72319	q50_roll2	18.574999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll2
119101	q50_roll3	21.314999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll3
10633	q50_total	27.847499	115.0	100.0	6	26262081	8	6	7	9	quantile	50	total

Code

get_optimal_triggers("cerf_sum", 7)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
72319	q50_roll2	18.574999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll2
119101	q50_roll3	21.314999	115.0	100.0	6	26262081	8	6	7	9	quantile	50	roll3
10633	q50_total	27.847499	115.0	100.0	6	26262081	8	6	7	9	quantile	50	total

Code

get_optimal_triggers("target_sum", 8)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
68672	q50_roll2	17.484999	145.0	75.0	6	26586051	8	6	8	11	quantile	50	roll2
118892	q50_roll3	21.314999	90.0	75.0	6	26586051	8	6	8	11	quantile	50	roll3
72110	q50_roll2	18.574999	90.0	75.0	6	26586051	8	6	8	11	quantile	50	roll2
10424	q50_total	27.847499	90.0	75.0	6	26586051	8	6	8	11	quantile	50	total
73172	q50_roll2	25.957497	75.0	75.0	6	26586051	8	6	8	12	quantile	50	roll2
111260	q50_roll3	31.377502	75.0	75.0	6	26586051	8	6	8	12	quantile	50	roll3

Code

get_optimal_triggers("target_with_cerf_sum", 8)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
44571	q50_total_mean_abv	91.037247	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	total
270201	q80_roll3	76.439003	115.0	90.0	6	26452081	9	7	8	10	quantile	80	roll3
148899	q50_roll3_mean_abv	79.962158	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll3
161733	q80_total	88.023996	115.0	90.0	6	26452081	9	7	8	10	quantile	80	total
225075	q80_roll2	67.569000	115.0	90.0	6	26452081	9	7	8	10	quantile	80	roll2
81003	q50_roll2_mean_abv	70.131157	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll2
209433	q80_roll2	64.883003	145.0	90.0	6	26452081	9	7	8	10	quantile	80	roll2
129945	q50_roll3_mean_abv	77.877907	145.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll3
79437	q50_roll2_mean_abv	69.980583	145.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll2
167953	q80_total	77.296997	115.0	100.0	6	26452081	9	7	8	10	quantile	80	total
116617	q50_roll3	20.857498	115.0	100.0	6	26452081	9	7	8	10	quantile	50	roll3
256963	q80_roll3	73.388992	115.0	100.0	6	26452081	9	7	8	10	quantile	80	roll3
44995	q50_total_mean_abv	82.303337	115.0	100.0	6	26452081	9	7	8	10	mean_abv	50	total

Code

get_optimal_triggers("cerf_sum", 8)

	rain_col	rain_thresh	wind_speed_max	wind_speed_max_landfall	target_sum	impact_sum	target_with_cerf_sum	cerf_sum	n_years	n_storms	rain_agg	rain_q	rain_window
44571	q50_total_mean_abv	91.037247	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	total
270201	q80_roll3	76.439003	115.0	90.0	6	26452081	9	7	8	10	quantile	80	roll3
148899	q50_roll3_mean_abv	79.962158	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll3
161733	q80_total	88.023996	115.0	90.0	6	26452081	9	7	8	10	quantile	80	total
225075	q80_roll2	67.569000	115.0	90.0	6	26452081	9	7	8	10	quantile	80	roll2
81003	q50_roll2_mean_abv	70.131157	115.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll2
209433	q80_roll2	64.883003	145.0	90.0	6	26452081	9	7	8	10	quantile	80	roll2
129945	q50_roll3_mean_abv	77.877907	145.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll3
79437	q50_roll2_mean_abv	69.980583	145.0	90.0	6	26452081	9	7	8	10	mean_abv	50	roll2
167953	q80_total	77.296997	115.0	100.0	6	26452081	9	7	8	10	quantile	80	total
116617	q50_roll3	20.857498	115.0	100.0	6	26452081	9	7	8	10	quantile	50	roll3
256963	q80_roll3	73.388992	115.0	100.0	6	26452081	9	7	8	10	quantile	80	roll3
44995	q50_total_mean_abv	82.303337	115.0	100.0	6	26452081	9	7	8	10	mean_abv	50	total

Code

def get_triggered_storms(selected_index):
    selected_trigger = df_results.loc[selected_index]
    print(selected_trigger)
    rain_col = selected_trigger["rain_col"]
    rain_thresh = selected_trigger["rain_thresh"]
    wind_speed_max = selected_trigger["wind_speed_max"]
    wind_speed_max_landfall = selected_trigger["wind_speed_max_landfall"]
    df_triggered = df_stats[
        (df_stats[rain_col] >= rain_thresh)
        & (
            (df_stats["wind_speed_max"] >= wind_speed_max)
            | (df_stats["wind_speed_max_landfall"] >= wind_speed_max_landfall)
        )
    ]
    return df_triggered

Code

def plot_trigger_option(selected_index):
    selected_trigger = df_results.loc[selected_index]
    rain_col = selected_trigger["rain_col"]
    rain_thresh = selected_trigger["rain_thresh"]
    wind_speed_max = selected_trigger["wind_speed_max"]
    wind_speed_max_landfall = selected_trigger["wind_speed_max_landfall"]
    df_triggered = get_triggered_storms(selected_index)

    ymax = df_stats[rain_col].max() * 1.1
    xmax = df_stats["wind_speed_max"].max() * 1.1

    fig, ax = plt.subplots(dpi=200, figsize=(7, 7))

    bubble_sizes = df_stats["Total Affected"].fillna(0)
    # Optional: scale for visual clarity
    bubble_sizes_scaled = (
        bubble_sizes / bubble_sizes.max() * 5000
    )  # Adjust 300 as needed

    # Plot bubbles
    ax.scatter(
        df_stats["wind_speed_max"],
        df_stats[rain_col],
        s=bubble_sizes_scaled,
        alpha=0.3,
        color="crimson",
        edgecolor="none",
        zorder=1,
    )

    for _, row in df_stats.iterrows():
        triggered = row["sid"] in df_triggered["sid"].to_list()
        ax.annotate(
            row["name"].capitalize() + "\n" + str(row["season"]),
            (row["wind_speed_max"], row[rain_col]),
            ha="center",
            va="center",
            fontsize=6,
            color="crimson" if row["cerf"] == True else "k",
            zorder=10 if row["cerf"] else 9,
            alpha=0.8,
            fontstyle="italic" if triggered else "normal",
            fontweight="bold" if triggered else "normal",
        )

    trig_color = "gold"
    ax.axvline(
        wind_speed_max,
        color=trig_color,
        linewidth=0.5,
        zorder=0,
    )
    ax.axvline(
        wind_speed_max_landfall,
        color=trig_color,
        linewidth=0.5,
        linestyle="--",
        zorder=0,
    )
    ax.axhline(
        rain_thresh,
        color=trig_color,
        linewidth=0.5,
        zorder=0,
    )
    ax.add_patch(
        mpatches.Rectangle(
            (wind_speed_max, rain_thresh),  # bottom left
            xmax - wind_speed_max,  # width
            ymax - rain_thresh,  # height
            facecolor=trig_color,
            alpha=0.1,
            zorder=0,
        )
    )

    for cat_value, cat_name in CAT_LIMITS:
        ax.annotate(
            cat_name + " -",
            (cat_value, 0),
            fontstyle="italic",
            color="grey",
            rotation=90,
            va="top",
            ha="center",
            fontsize=8,
        )

    ax.set_ylabel(rain_col)
    ax.set_xlabel("\nMax. wind speed while in ZMA (knots)")

    ax.set_xlim(left=0, right=xmax)
    ax.set_ylim(bottom=0, top=ymax)

    ax.spines.top.set_visible(False)
    ax.spines.right.set_visible(False)

Code

# 6 triggered years, target_with_cerf_sum
plot_trigger_option(225077)

rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall        105.0
target_sum                         5
impact_sum                  22289476
target_with_cerf_sum               7
cerf_sum                           5
n_years                            6
n_storms                           8
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225077, dtype: object

Code

# 6 triggered years, cerf_sum
plot_trigger_option(226352)

rain_col                   q80_roll2
rain_thresh                98.341003
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         4
impact_sum                  22612062
target_with_cerf_sum               6
cerf_sum                           4
n_years                            6
n_storms                           7
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 226352, dtype: object

Code

# 7 triggered years
plot_trigger_option(72319)

rain_col                   q50_roll2
rain_thresh                18.574999
wind_speed_max                 115.0
wind_speed_max_landfall        100.0
target_sum                         6
impact_sum                  26262081
target_with_cerf_sum               8
cerf_sum                           6
n_years                            7
n_storms                           9
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 72319, dtype: object

Code

# 8 triggered years, target_with_cerf_sum
plot_trigger_option(225075)

rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall         90.0
target_sum                         6
impact_sum                  26452081
target_with_cerf_sum               9
cerf_sum                           7
n_years                            8
n_storms                          10
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225075, dtype: object

Code

# 8 triggered years, target_sum
plot_trigger_option(73172)

rain_col                   q50_roll2
rain_thresh                25.957497
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         6
impact_sum                  26586051
target_with_cerf_sum               8
cerf_sum                           6
n_years                            8
n_storms                          12
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 73172, dtype: object

Code

df_stats

	sid	valid_time_min	valid_time_max	wind_speed_max	wind_speed_max_landfall	q50_total	q50_total_mean_abv	q50_roll2	q50_roll2_mean_abv	q50_roll3	...	q99_roll3_mean_abv	cerf	Total Affected	Amount in US$	season	name	name_season	cerf_str	target	target_with_cerf
0	2000233N12316	2000-08-24 00:00:00.000040	2000-08-24 12:00:00.000040	40.0	NaN	18.190000	35.262634	18.152500	34.911701	18.189999	...	70.882492	None	0	NaN	2000	DEBBY	Debby 2000	nan	False	False
1	2000260N15308	2000-09-19 12:00:00.000040	2000-09-20 18:00:00.000040	30.0	NaN	26.412498	68.799995	21.250000	62.718685	23.750000	...	184.656494	None	0	NaN	2000	HELENE	Helene 2000	nan	False	False
2	2001303N13276	2001-11-04 06:00:00.000040	2001-11-05 06:00:00.000040	120.0	115.0	94.369999	214.020828	76.807495	195.712280	94.297501	...	404.398499	None	5900012	NaN	2001	MICHELLE	Michelle 2001	nan	True	True
3	2002258N10300	2002-09-19 18:00:00.000040	2002-09-21 18:00:00.000040	110.0	75.0	78.592495	207.540146	40.644997	143.821213	56.139999	...	794.184509	None	42500	NaN	2002	ISIDORE	Isidore 2002	nan	False	False
4	2002265N10315	2002-09-29 00:00:00.000040	2002-10-02 00:00:00.000040	90.0	90.0	54.195000	89.869759	37.119995	67.565186	43.507500	...	183.679001	None	281470	NaN	2002	LILI	Lili 2002	nan	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
57	2023321N15278	2023-11-18 00:00:00.000039	2023-11-18 00:00:00.000039	30.0	NaN	3.947500	38.300976	3.947500	38.281227	3.947500	...	161.707001	False	0	NaN	2023	UNNAMED	Unnamed 2023	False	False	False
58	2024216N20284	2024-08-02 12:00:00.000039	2024-08-04 00:00:00.000039	35.0	30.0	54.164997	103.377747	40.412502	75.217384	49.295002	...	240.353485	False	0	NaN	2024	DEBBY	Debby 2024	False	False	False
59	2024268N17278	2024-09-25 12:00:00.000039	2024-09-25 12:00:00.000039	65.0	NaN	25.417499	116.488297	21.440001	99.156189	25.417500	...	307.214508	False	0	NaN	2024	HELENE	Helene 2024	False	False	False
60	2024293N21294	2024-10-20 06:00:00.000039	2024-10-22 06:00:00.000039	75.0	75.0	12.205000	61.957958	9.135000	50.185165	12.029999	...	533.183960	True	320000	3499569.0	2024	OSCAR	Oscar 2024	True	True	True
61	2024309N13283	2024-11-06 06:00:00.000039	2024-11-07 06:00:00.000039	100.0	100.0	39.124998	102.100563	34.349998	83.028404	37.614998	...	287.727478	True	4000000	5999888.0	2024	RAFAEL	Rafael 2024	True	True	True

62 rows × 44 columns

Code

def color_df(val):
    if val == "Yes":
        return "background-color: crimson"
    elif val == "No":
        return "background-color: dodgerblue"
    elif val == "Trig.":
        return "background-color: darkorange"
    else:
        return ""

Code

plot_triggers = [
    (226352, "4.3-yr RP"),
    # (225077, "4.3-yr RP<br>[B]"),
    (72319, "3.7-yr RP<br>"),
    (225075, "3.3-yr RP"),
    # (73172, "3.3-yr RP<br>[B]"),
]

Code

df_disp = df_stats.copy()
df_disp = df_disp.rename(columns={"cerf_str": "CERF", "name_season": "Storm"})
df_disp = df_disp.set_index("Storm")
df_disp["CERF"] = df_disp["CERF"].replace(
    {"True": "Yes", "False": "No", "nan": "pre-CERF"}
)


for trig_index, trig_name in plot_triggers:
    df_triggered = get_triggered_storms(trig_index)
    df_disp[trig_name] = (
        df_disp["sid"]
        .isin(df_triggered["sid"].to_list())
        .apply(lambda x: "Trig." if x else "No trig.")
    )

trig_cols = [x[1] for x in plot_triggers]

df_disp = df_disp.sort_values(["Total Affected"] + trig_cols, ascending=False)

cols = trig_cols + ["CERF", "Total Affected"]

display(
    df_disp[cols]
    .style.bar(
        subset="Total Affected",
        color="mediumpurple",
        # vmax=500000,
        props="width: 400px;",
    )
    .map(color_df)
    .set_table_styles(
        {
            "Total Affected": [
                {"selector": "th", "props": [("text-align", "left")]},
                {"selector": "td", "props": [("text-align", "left")]},
            ]
        }
    )
    .format({"Total Affected": "{:,}"})
)

rain_col                   q80_roll2
rain_thresh                98.341003
wind_speed_max                  75.0
wind_speed_max_landfall         75.0
target_sum                         4
impact_sum                  22612062
target_with_cerf_sum               6
cerf_sum                           4
n_years                            6
n_storms                           7
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 226352, dtype: object
rain_col                   q50_roll2
rain_thresh                18.574999
wind_speed_max                 115.0
wind_speed_max_landfall        100.0
target_sum                         6
impact_sum                  26262081
target_with_cerf_sum               8
cerf_sum                           6
n_years                            7
n_storms                           9
rain_agg                    quantile
rain_q                            50
rain_window                    roll2
Name: 72319, dtype: object
rain_col                   q80_roll2
rain_thresh                   67.569
wind_speed_max                 115.0
wind_speed_max_landfall         90.0
target_sum                         6
impact_sum                  26452081
target_with_cerf_sum               9
cerf_sum                           7
n_years                            8
n_storms                          10
rain_agg                    quantile
rain_q                            80
rain_window                    roll2
Name: 225075, dtype: object

	4.3-yr RP	3.7-yr RP	3.3-yr RP	CERF	Total Affected
Storm
Irma 2017	Trig.	Trig.	Trig.	Yes	10,000,000
Michelle 2001	Trig.	Trig.	Trig.	pre-CERF	5,900,012
Rafael 2024	Trig.	Trig.	Trig.	Yes	4,000,000
Ian 2022	No trig.	Trig.	Trig.	Yes	3,200,000
Dennis 2005	Trig.	Trig.	Trig.	pre-CERF	2,500,000
Gustav 2008	No trig.	Trig.	Trig.	No	450,019
Oscar 2024	No trig.	No trig.	No trig.	Yes	320,000
Lili 2002	No trig.	No trig.	No trig.	pre-CERF	281,470
Charley 2004	No trig.	No trig.	No trig.	pre-CERF	244,005
Noel 2007	No trig.	No trig.	No trig.	No	192,488
Matthew 2016	No trig.	No trig.	Trig.	Yes	190,000
Sandy 2012	Trig.	Trig.	Trig.	Yes	162,605
Wilma 2005	No trig.	No trig.	No trig.	pre-CERF	100,000
Paloma 2008	Trig.	Trig.	Trig.	No	49,445
Isaac 2012	No trig.	No trig.	No trig.	No	45,496
Isidore 2002	No trig.	No trig.	No trig.	pre-CERF	42,500
Alberto 2018	No trig.	No trig.	No trig.	No	40,000
Alex 2022	No trig.	No trig.	No trig.	No	6,780
Ivan 2004	No trig.	No trig.	No trig.	pre-CERF	3,245
Michael 2018	No trig.	No trig.	No trig.	No	540
Isaias 2020	No trig.	No trig.	No trig.	No	500
Alberto 2006	No trig.	No trig.	No trig.	No	268
Ike 2008	Trig.	Trig.	Trig.	No	0
Debby 2000	No trig.	No trig.	No trig.	pre-CERF	0
Helene 2000	No trig.	No trig.	No trig.	pre-CERF	0
Unnamed 2002	No trig.	No trig.	No trig.	pre-CERF	0
Bonnie 2004	No trig.	No trig.	No trig.	pre-CERF	0
Jeanne 2004	No trig.	No trig.	No trig.	pre-CERF	0
Arlene 2005	No trig.	No trig.	No trig.	pre-CERF	0
Rita 2005	No trig.	No trig.	No trig.	pre-CERF	0
Alpha 2005	No trig.	No trig.	No trig.	pre-CERF	0
Chris 2006	No trig.	No trig.	No trig.	No	0
Ernesto 2006	No trig.	No trig.	No trig.	No	0
Barry 2007	No trig.	No trig.	No trig.	No	0
Olga 2007	No trig.	No trig.	No trig.	No	0
Fay 2008	No trig.	No trig.	No trig.	No	0
Hanna 2008	No trig.	No trig.	No trig.	No	0
Ida 2009	No trig.	No trig.	No trig.	No	0
Bonnie 2010	No trig.	No trig.	No trig.	No	0
Nicole 2010	No trig.	No trig.	No trig.	No	0
Paula 2010	No trig.	No trig.	No trig.	No	0
Tomas 2010	No trig.	No trig.	No trig.	No	0
Don 2011	No trig.	No trig.	No trig.	No	0
Emily 2011	No trig.	No trig.	No trig.	No	0
Rina 2011	No trig.	No trig.	No trig.	No	0
Dorian 2013	No trig.	No trig.	No trig.	No	0
Hermine 2016	No trig.	No trig.	No trig.	No	0
Nate 2017	No trig.	No trig.	No trig.	No	0
Philippe 2017	No trig.	No trig.	No trig.	No	0
Beryl 2018	No trig.	No trig.	No trig.	No	0
Gordon 2018	No trig.	No trig.	No trig.	No	0
Laura 2020	No trig.	No trig.	No trig.	No	0
Marco 2020	No trig.	No trig.	No trig.	No	0
Eta 2020	No trig.	No trig.	No trig.	No	0
Elsa 2021	No trig.	No trig.	No trig.	No	0
Fred 2021	No trig.	No trig.	No trig.	No	0
Ida 2021	No trig.	No trig.	No trig.	No	0
Arlene 2023	No trig.	No trig.	No trig.	No	0
Idalia 2023	No trig.	No trig.	No trig.	No	0
Unnamed 2023	No trig.	No trig.	No trig.	No	0
Debby 2024	No trig.	No trig.	No trig.	No	0
Helene 2024	No trig.	No trig.	No trig.	No	0

7.1 Data for email scatter plot

Just quickly adding this for easy reference - later. Should clean up where this goes.

Code

blob_name = (
      f"{PROJECT_PREFIX}/processed/storm_stats/stats_with_targets.parquet"
)

df_stats_raw = stratus.load_parquet_from_blob(blob_name)