def main(argv):
"""Get the tillage date."""
huc12 = argv[1]
fpath = argv[2]
wbfn = "/i/59/wb/%s/%s/%s_%s.wb" % (huc12[:8], huc12[8:], huc12, fpath)
wbdf = read_wb(wbfn)
wbdf = wbdf[wbdf["ofe"] == 1]
(fig, ax) = plt.subplots(1, 1)
for threshold in range(15, 50, 5):
df = wbdf[wbdf["sw1"] < threshold].groupby("jday").count()
df = df.reindex(list(range(1, 367))).fillna(0)
vals = (df["sw1"].rolling(7).mean() / 13.0 * 100, )
ax.plot(df.index.values, vals[0], label="%s%%" % (threshold, ))
ax.set_xticks([1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335])
ax.set_xticklabels(calendar.month_abbr[1:])
ax.grid(True)
ax.set_xlabel("* 7 Day Smoother Applied")
ax.set_xlim(90, 153)
ax.set_ylabel("Frequency [%]")
ax.set_title(
"huc12: %s fpath: %s\n2007-2019 Frequency of 0-10 cm Soil Moisture below threshold"
% (huc12, fpath))
ax.legend()
fig.savefig("test.png")
def readfile(huc12, fn):
"""Read one file please"""
try:
df = dep_utils.read_wb(fn)
except Exception as exp:
print("\nABORT: Attempting to read: %s resulted in: %s\n" % (fn, exp))
return None
return df
def readfile(huc12, fn):
"""Read one file please"""
try:
df = dep_utils.read_wb(fn)
except Exception as exp:
print("\nABORT: Attempting to read: %s resulted in: %s\n" % (fn, exp))
return None
return df
def compute_tillage_date(scenario, row):
"""Get the tillage date."""
wbfn = "/i/0/wb/%s/%s/%s_%s.wb" % (
row["huc_12"][:8],
row["huc_12"][8:],
row["huc_12"],
row["fpath"],
)
wbdf = read_wb(wbfn)
wbdf2 = wbdf[((wbdf["ofe"] == 1)
& (wbdf["date"] >= APR15)
& (wbdf["date"] <= MAY30))]
if scenario in THRESHOLDS:
the_threshold = THRESHOLDS[scenario]
else:
the_threshold = get_threshold_bypl(scenario, row)
wbdf3 = wbdf2[wbdf2["sw1"] < the_threshold]
if len(wbdf3.index) > 0:
tillage_date = wbdf3.iloc[0]["date"]
else:
tillage_date = MAY30
return tillage_date
def main(argv):
"""Do things"""
filename = argv[1]
wb = read_wb(filename)
(fig, ax) = plt.subplots(2, 1)
ax[0].set_title("Water Balance for %s" % (filename.split("/")[-1], ))
ax[0].plot(wb['date'], wb['sw'])
ax[0].set_ylabel("Total Soil Water [mm]")
ax[0].grid(True)
for year in [2009, 2012, 2015]:
df2 = wb[wb['year'] == year]
ax[1].plot(df2['jday'], df2['sw'], label=str(year))
ax[1].legend(ncol=10)
ax[1].set_xticks(
(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax[1].set_xticklabels(calendar.month_abbr[1:])
ax[1].grid(True)
ax[1].set_ylabel("Total Soil Water [mm]")
fig.savefig('test.png')
def main(argv):
"""Do things"""
filename = argv[1]
wb = read_wb(filename)
(fig, ax) = plt.subplots(2, 1)
ax[0].set_title("Water Balance for %s" % (filename.split("/")[-1], ))
ax[0].plot(wb['date'], wb['sw'])
ax[0].set_ylabel("Total Soil Water [mm]")
ax[0].grid(True)
for year in [2009, 2012, 2015]:
df2 = wb[wb['year'] == year]
ax[1].plot(df2['jday'], df2['sw'], label=str(year))
ax[1].legend(ncol=10)
ax[1].set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305,
335, 365))
ax[1].set_xticklabels(calendar.month_abbr[1:])
ax[1].grid(True)
ax[1].set_ylabel("Total Soil Water [mm]")
fig.savefig('test.png')
def do_scenario(scenario, plantdate, hucdf):
"""Process this scenario."""
index = pd.MultiIndex.from_product(
[range(2008, 2019), range(1, 74)], names=["year", "period"])
df = pd.DataFrame(index=index).reset_index()
def f(row):
"""Make date."""
return datetime.date(row["year"], 1, 1) + datetime.timedelta(
days=int(row["period"] - 1) * 5 + 2)
df["5day_middle_date"] = df.apply(f, axis=1)
df = df.set_index(["year", "period"])
smdfs = []
flowpaths = 0
for _, row in hucdf.iterrows():
huc12 = row["HUC12"]
for fn in glob.glob("/i/%s/wb/%s/%s/*" %
(scenario, huc12[:8], huc12[8:])):
smdfs.append(read_wb(fn))
flowpaths += 1
smdf = pd.concat(smdfs)
del smdfs
envdfs = []
for _, row in hucdf.iterrows():
huc12 = row["HUC12"]
for fn in glob.glob("/i/%s/env/%s/%s/*" %
(scenario, huc12[:8], huc12[8:])):
envdfs.append(read_env(fn))
envdf = pd.concat(envdfs)
envdf["jday"] = pd.to_numeric(envdf["date"].dt.strftime("%j"),
downcast="integer")
del envdfs
# only one ofe 1
smdf = smdf[smdf["ofe"] == 1]
smdf["period"] = (smdf["jday"] + 5) // 5
envdf["period"] = (envdf["jday"] + 5) // 5
# only consider 2008 thru 2018 data
smdf = smdf[(smdf["year"] > 2007) & (smdf["year"] < 2019)]
envdf = envdf[(envdf["year"] > 2007) & (envdf["year"] < 2019)]
gdf = envdf.groupby(["year", "period"]).mean()
df["5day_precip_mm"] = gdf["precip"]
df["5day_detach_kgm2"] = gdf["av_det"]
gdf = smdf.groupby(["year", "period"]).mean()
df["5day_soilmoist"] = gdf["sw1"]
gdf = envdf.groupby("year").sum() / flowpaths
df = df.join(gdf[["precip", "av_det"]])
df = df.rename(
{
"precip": "annual_precip_mm",
"av_det": "annual_detach_kgm2"
}, axis=1)
gdf = (smdf[smdf["jday"] == int(plantdate.strftime("%j"))].groupby(
"year").mean())
df = df.join(gdf["sw1"])
df = df.rename({"sw1": "plant_soilmoist"}, axis=1)
df["plant_date"] = plantdate.strftime("%m %d")
df["mlra_id"] = hucdf.iloc[0]["MLRA"]
df = df.fillna(0)
LOG.info("done with %s %s", plantdate, hucdf.iloc[0]["MLRA"])
return df
def main(argv):
"""Do things"""
dfs = []
for fn in glob.glob("/i/0/wb/07100004/0704/*"):
df = read_wb(fn)
df["fpath"] = int(fn.split("_")[1][:-3])
dfs.append(df)
df = pd.concat(dfs)
ranges = df.groupby(["fpath", "ofe"]).describe()
year = 2018
for doy in tqdm(range(1, 365)):
date = datetime.date(year, 1, 1) + datetime.timedelta(days=(doy - 1))
wb = df[(df["year"] == year) & (df["jday"] == doy)].copy()
wb = wb.set_index(["fpath", "ofe"])
for f2 in ["sw1", "sw2", "sw"]:
for f1 in ["min", "max"]:
wb["%s_%s" % (f2, f1)] = ranges[f2, f1]
wb["%s_range" % (f2, )] = (wb["%s_max" % (f2, )] - wb["%s_min" %
(f2, )])
wb["%s_percent" % (f2, )] = ((wb[f2] - wb["%s_min" % (f2, )]) /
wb["%s_range" % (f2, )] * 100.0)
sns.set(style="white", palette="muted", color_codes=True)
(fig, ax) = plt.subplots(3, 2, figsize=(7, 7))
sns.despine(left=True)
fig.text(
0.5,
0.98,
"%s :: Water Balance for 071000040704" %
(date.strftime("%d %B %Y"), ),
ha="center",
)
# ---------------------------------------------------
myax = ax[0, 0]
sns.distplot(wb["sw1"], hist=False, color="g", ax=myax, rug=True)
myax.set_xlabel("0-10cm Soil Water [mm]")
myax.axvline(wb["sw1"].mean(), color="r")
myax.set_xlim(0, 60)
myax = ax[0, 1]
sns.distplot(wb["sw1_percent"],
hist=False,
color="g",
ax=myax,
rug=True)
myax.set_xlabel("0-10cm Soil Water of Capacity [%]")
myax.axvline(wb["sw1_percent"].mean(), color="r")
myax.set_xlim(0, 100)
# ---------------------------------------------------------
myax = ax[1, 0]
sns.distplot(wb["sw2"], hist=False, color="g", ax=myax, rug=True)
myax.set_xlabel("10-20cm Soil Water [mm]")
myax.axvline(wb["sw2"].mean(), color="r")
myax.set_xlim(0, 60)
myax = ax[1, 1]
sns.distplot(wb["sw2_percent"],
hist=False,
color="g",
ax=myax,
rug=True)
myax.set_xlabel("10-20cm Soil Water of Capacity [%]")
myax.axvline(wb["sw2_percent"].mean(), color="r")
myax.set_xlim(0, 100)
# -------------------------------------------------------
myax = ax[2, 0]
sns.distplot(wb["sw"], hist=False, color="g", ax=myax, rug=True)
myax.set_xlabel("Total Soil Water [mm]")
myax.axvline(wb["sw"].mean(), color="r")
myax.set_xlim(150, 650)
myax = ax[2, 1]
sns.distplot(wb["sw_percent"],
hist=False,
color="g",
ax=myax,
rug=True)
myax.set_xlabel("Total Soil Water of Capacity [%]")
myax.axvline(wb["sw_percent"].mean(), color="r")
myax.set_xlim(0, 100)
plt.setp(ax, yticks=[])
plt.tight_layout()
fig.savefig("frames/%05i.png" % (doy - 1, ))
plt.close()
def test_wb():
"""read a WB file please"""
df = dep.read_wb(get_path('wb.txt'))
assert abs(df['precip'].max() - 162.04) < 0.01
def test_wb():
"""read a WB file please"""
df = dep.read_wb(get_path("wb.txt"))
assert abs(df["precip"].max() - 162.04) < 0.01
def test_wb(self):
"""read a WB file please"""
df = dep.read_wb(get_path('wb.txt'))
self.assertAlmostEquals(df['precip'].max(), 162.04, 2)
def test_wb():
"""read a WB file please"""
df = dep.read_wb(get_path('wb.txt'))
assert abs(df['precip'].max() - 162.04) < 0.01
