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fix units and enforce all-scenarios flowpaths #80
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@akrherz
akrherz committed Mar 8, 2021
1 parent d6ce19d commit 0922cbe
Showing 1 changed file with 58 additions and 47 deletions.
105 changes: 58 additions & 47 deletions scripts/gridorder2/dumper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@

def get_flowpath_lengths(pgconn):
"""Load some metadata."""
return read_sql(
df = read_sql(
"SELECT scenario, huc_12, fpath, bulk_slope,"
"ST_LENGTH(geom) as len from flowpaths "
"WHERE scenario >= 91 and scenario <= 101 and huc_12 in %s"
Expand All @@ -36,6 +36,15 @@ def get_flowpath_lengths(pgconn):
params=(tuple(HUCS),),
index_col=None,
)
df["useme"] = False
# We only want flowpaths that existed in all scenarios, unsure of the
# most efficient way to do this, so here we are.
gdf = df.groupby(["huc_12", "fpath"]).count()
good = gdf[gdf["bulk_slope"] == 11].index.values.tolist()
for idx, row in df.iterrows():
df.at[idx, "useme"] = (row["huc_12"], row["fpath"]) in good
df = df[df["useme"]]
return df


def compute_yearly(df, bymax=False, individual=False):
Expand Down Expand Up @@ -80,15 +89,16 @@ def main():
"""Do great things."""
pgconn = get_dbconn("idep")
df = get_flowpath_lengths(pgconn)
# df221 = df[df['len'] > 22.1]
# avg_len_full = df.groupby(["scenario", "huc_12"]).mean()
# avg_len_221 = df221.groupby(["scenario", "huc_12"]).mean()
# flowpath_counts_full = df.groupby(["scenario", "huc_12"]).count()
# flowpath_counts_221 = df221.groupby(["scenario", "huc_12"]).count()
df221 = df[df["len"] > 22.1]
avg_len_full = df.groupby(["scenario", "huc_12"]).mean()
avg_len_221 = df221.groupby(["scenario", "huc_12"]).mean()
flowpath_counts_full = df.groupby(["scenario", "huc_12"]).count()
flowpath_counts_221 = df221.groupby(["scenario", "huc_12"]).count()
# For all hillslopes
result_full = compute_yearly(df, bymax=True, individual=True)
opts = dict(bymax=False, individual=False)
result_full = compute_yearly(df, **opts)
# Only those over > 22.1m
# result_221 = compute_yearly(df[df['len'] > 22.1], individual=True)
result_221 = compute_yearly(df[df["len"] > 22.1], **opts)

# Result time
rows = []
Expand All @@ -97,60 +107,61 @@ def main():
fdf = df[(df["huc_12"] == huc_12) & (df["scenario"] == scenario)]
for flowpath in fdf["fpath"].values:
for year in range(2011, 2021):
key = (scenario, huc_12, flowpath, year)
key = (scenario, huc_12, year)
if key not in result_full.index:
print(key)
continue
rows.append(
{
"Flowpath ID": flowpath,
# "Flowpath ID": flowpath,
"HUC12": huc_12,
"Situation": XREF[scenario],
# "Year": year,
"Year": year,
# "Date for daily max rainfall": result_full.at[
# (scenario, huc_12, year), "date"],
"Date for daily max rainfall": result_full.at[
key, "date"
# key, "date"],
# "Date for daily max rainfall": result_full.at[
# key, "date"
# ],
"Number of flowpath": flowpath_counts_full.at[
(scenario, huc_12), "len"
],
"Avg Flowpath Length(m)": avg_len_full.at[
(scenario, huc_12), "len"
],
"Flowpath length(m)": float(
fdf[(fdf["fpath"] == flowpath)]["len"]
),
"Daily Rainfall (mm)": result_full.at[
# "Flowpath length(m)": float(
# fdf[(fdf["fpath"] == flowpath)]["len"]
# ),
"Yearly Rainfall (mm)": result_full.at[
key, "precip"
],
"Daily Runoff (mm)": result_full.at[key, "runoff"],
"Daily Delivery (kg/ha)": result_full.at[
"Yearly Runoff (mm)": result_full.at[
key, "runoff"
],
"Yearly Delivery (kg/m2)": result_full.at[
key, "delivery"
],
"Yearly Detachment (kg/m2)": result_full.at[
key, "av_det"
],
# "Slope Gradient(%)": float(
# fdf[(fdf["fpath"] == flowpath)]["bulk_slope"]
# )
# * 100.0,
"Number of flowpath >22.1": flowpath_counts_221.at[
(scenario, huc_12), "len"
],
"Avg Flowpath Length(m) >22.1": avg_len_221.at[
(scenario, huc_12), "len"
],
"Yearly Runoff (mm) >22.1": result_221.at[
key, "runoff"
],
"Yearly Delivery (kg/m2) >22.1": result_221.at[
key, "delivery"
],
"Daily Detachment (kg/ha)": result_full.at[
"Yearly Detachment (kg/m2) >22.1": result_221.at[
key, "av_det"
],
"Slope Gradient(%)": float(
fdf[(fdf["fpath"] == flowpath)]["bulk_slope"]
)
* 100.0,
# "Number of flowpath": flowpath_counts_full.at[
# (scenario, huc_12), 'len'],
# "Avg Flowpath Length(m)": avg_len_full.at[
# (scenario, huc_12), "len"],
# "Daily Rainfall (mm)": result_full.at[
# (scenario, huc_12, year), "precip"],
# "Daily Runoff (mm)": result_full.at[
# (scenario, huc_12, year), "runoff"],
# "Daily Delivery (kg/ha)": result_full.at[
# (scenario, huc_12, year), "delivery"],
# "Daily Detachment (kg/ha)": result_full.at[
# (scenario, huc_12, year), "av_det"],
# "Number of flowpath >22.1": flowpath_counts_221.at[
# (scenario, huc_12), 'len'],
# "Avg Flowpath Length(m) >22.1": avg_len_221.at[
# (scenario, huc_12), "len"],
# "Daily Runoff (mm) >22.1": result_221.at[
# (scenario, huc_12, year), "runoff"],
# "Daily Delivery (kg/ha) >22.1": result_221.at[
# (scenario, huc_12, year), "delivery"],
# "Daily Detachment (kg/ha) >22.1": result_221.at[
# (scenario, huc_12, year), "av_det"],
}
)
resdf = pd.DataFrame(rows)
Expand Down

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