def run_calcs(df, ctx):
"""Do our maths."""
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[ctx["station"]]["elevation"] * units("m"),
).to(units("millibar"))
# Compute the mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# Compute the saturation mixing ratio
df["saturation_mixingratio"] = mcalc.saturation_mixing_ratio(
df["pressure"].values * units("millibars"),
df["tmpf"].values * units("degF"),
)
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
df["saturation_vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["saturation_mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
df["vpd"] = df["saturation_vapor_pressure"] - df["vapor_pressure"]
# remove any NaN rows
df = df.dropna()
group = df.groupby("year")
df = group.aggregate(np.average)
df["dwpf"] = (mcalc.dewpoint(df["vapor_pressure"].values *
units("kPa")).to(units("degF")).m)
return df
def run_calcs(df, ctx):
"""Do our maths."""
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
ctx['nt'].sts[ctx['station']]['elevation'] * units('m')).to(
units('millibar'))
# Compute the mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values * units('percent'),
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars'))
# Compute the saturation mixing ratio
df['saturation_mixingratio'] = mcalc.saturation_mixing_ratio(
df['pressure'].values * units('millibars'),
df['tmpf'].values * units('degF'))
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
group = df.groupby('year')
df = group.aggregate(np.average)
df['dwpf'] = mcalc.dewpoint(df['vapor_pressure'].values * units('kPa')).to(
units('degF')).m
return df
def test_add_height_to_pressure(array_type):
"""Test the pressure at height above pressure calculation."""
mask = [False, True, False]
pressure_in = array_type([1000., 900., 800.], 'hPa', mask=mask)
height = array_type([877.17421094, 500., 300.], 'meter', mask=mask)
pressure_out = add_height_to_pressure(pressure_in, height)
truth = array_type([900., 846.725, 770.666], 'hPa', mask=mask)
assert_array_almost_equal(pressure_out, truth, 2)
def test_add_height_to_pressure():
"""Test the pressure at height above pressure calculation."""
pressure = add_height_to_pressure(1000 * units.hPa,
877.17421094 * units.meter)
assert_almost_equal(pressure, 900 * units.hPa, 5)
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql(
"""
SELECT drct::int as t, dwpf, tmpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)),
)
if df.empty:
raise NoDataFound("No Data Found.")
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# compute pressure
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
means = df.groupby("t").mean().copy()
# compute dewpoint now
means["dwpf"] = (mcalc.dewpoint(means["vapor_pressure"].values *
units("kPa")).to(units("degF")).m)
(fig, ax) = plt.subplots(1, 1)
ax.bar(
means.index.values,
means["dwpf"].values,
ec="green",
fc="green",
width=10,
align="center",
)
ax.grid(True, zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)") % (
ctx["_nt"].sts[station]["name"],
station,
month.upper(),
max([1973, ab.year]),
datetime.datetime.now().year,
),
size=10,
)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(means["dwpf"].min() - 5, means["dwpf"].max() + 5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(["N", "NE", "E", "SE", "S", "SW", "W", "NW", "N"])
ax.set_xlabel("Wind Direction")
return fig, means["dwpf"]
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
nt = NetworkTable(network)
year = ctx['year']
varname = ctx['var']
df = read_sql("""
SELECT extract(year from valid) as year,
coalesce(mslp, alti * 33.8639, 1013.25) as slp,
extract(doy from valid) as doy, tmpf, dwpf from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
and report_type = 2
""",
pgconn,
params=(station, ),
index_col=None)
# compute RH
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'), df['dwpf'].values * units('degF'))
# saturation vapor pressure
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
nt.sts[station]['elevation'] * units('m')).to(units('millibar'))
# Compute the relative humidity
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'), df['dwpf'].values * units('degF'))
# Compute the mixing ratio
df['mixing_ratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values, df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars'))
# Compute the saturation mixing ratio
df['saturation_mixingratio'] = mcalc.saturation_mixing_ratio(
df['pressure'].values * units('millibars'),
df['tmpf'].values * units('degF'))
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixing_ratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
dailymeans = df[['year', 'doy', varname]].groupby(['year', 'doy']).mean()
dailymeans = dailymeans.reset_index()
df2 = dailymeans[['doy', varname]].groupby('doy').describe()
dyear = df[df['year'] == year]
df3 = dyear[['doy', varname]].groupby('doy').describe()
df3[(varname, 'diff')] = df3[(varname, 'mean')] - df2[(varname, 'mean')]
(fig, ax) = plt.subplots(2, 1, figsize=(8, 6))
multiplier = 1000. if varname == 'mixing_ratio' else 10.
ax[0].fill_between(df2[(varname, 'min')].index.values,
df2[(varname, 'min')].values * multiplier,
df2[(varname, 'max')].values * multiplier,
color='gray')
ax[0].plot(df2[(varname, 'mean')].index.values,
df2[(varname, 'mean')].values * multiplier,
label="Climatology")
ax[0].plot(df3[(varname, 'mean')].index.values,
df3[(varname, 'mean')].values * multiplier,
color='r',
label="%s" % (year, ))
ax[0].set_title(("%s [%s]\nDaily Mean Surface %s") %
(station, nt.sts[station]['name'], PDICT[varname]))
lbl = ("Mixing Ratio ($g/kg$)"
if varname == 'mixing_ratio' else PDICT[varname])
ax[0].set_ylabel(lbl)
ax[0].set_xlim(0, 366)
ax[0].set_ylim(bottom=0)
ax[0].set_xticks(
(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax[0].set_xticklabels(calendar.month_abbr[1:])
ax[0].grid(True)
ax[0].legend(loc=2, fontsize=10)
cabove = 'b' if varname == 'mixing_ratio' else 'r'
cbelow = 'r' if cabove == 'b' else 'b'
rects = ax[1].bar(df3[(varname, 'diff')].index.values,
df3[(varname, 'diff')].values * multiplier,
facecolor=cabove,
edgecolor=cabove)
for rect in rects:
if rect.get_height() < 0.:
rect.set_facecolor(cbelow)
rect.set_edgecolor(cbelow)
plunits = '$g/kg$' if varname == 'mixing_ratio' else 'hPa'
ax[1].set_ylabel("%.0f Departure (%s)" % (year, plunits))
ax[1].set_xlim(0, 366)
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)
return fig, df3
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
year = ctx["year"]
varname = ctx["var"]
df = read_sql(
"""
SELECT extract(year from valid) as year,
coalesce(mslp, alti * 33.8639, 1013.25) as slp,
extract(doy from valid) as doy, tmpf, dwpf, relh from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
and report_type = 2
""",
pgconn,
params=(station, ),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data was found.")
# saturation vapor pressure
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# Compute the mixing ratio
df["mixing_ratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# Compute the saturation mixing ratio
df["saturation_mixingratio"] = mcalc.saturation_mixing_ratio(
df["pressure"].values * units("millibars"),
df["tmpf"].values * units("degF"),
)
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixing_ratio"].values * units("kg/kg"),
).to(units("kPa"))
df["saturation_vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["saturation_mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
df["vpd"] = df["saturation_vapor_pressure"] - df["vapor_pressure"]
dailymeans = df[["year", "doy", varname]].groupby(["year", "doy"]).mean()
dailymeans = dailymeans.reset_index()
df2 = dailymeans[["doy", varname]].groupby("doy").describe()
dyear = df[df["year"] == year]
df3 = dyear[["doy", varname]].groupby("doy").describe()
df3[(varname, "diff")] = df3[(varname, "mean")] - df2[(varname, "mean")]
(fig, ax) = plt.subplots(2, 1, figsize=(8, 6))
multiplier = 1000.0 if varname == "mixing_ratio" else 10.0
ax[0].fill_between(
df2[(varname, "min")].index.values,
df2[(varname, "min")].values * multiplier,
df2[(varname, "max")].values * multiplier,
color="gray",
)
ax[0].plot(
df2[(varname, "mean")].index.values,
df2[(varname, "mean")].values * multiplier,
label="Climatology",
)
ax[0].plot(
df3[(varname, "mean")].index.values,
df3[(varname, "mean")].values * multiplier,
color="r",
label="%s" % (year, ),
)
ax[0].set_title(("%s [%s]\nDaily Mean Surface %s") %
(station, ctx["_nt"].sts[station]["name"], PDICT[varname]))
lbl = ("Mixing Ratio ($g/kg$)"
if varname == "mixing_ratio" else PDICT[varname])
ax[0].set_ylabel(lbl)
ax[0].set_xlim(0, 366)
ax[0].set_ylim(bottom=0)
ax[0].set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax[0].set_xticklabels(calendar.month_abbr[1:])
ax[0].grid(True)
ax[0].legend(loc=2, fontsize=10)
cabove = "b" if varname == "mixing_ratio" else "r"
cbelow = "r" if cabove == "b" else "b"
rects = ax[1].bar(
df3[(varname, "diff")].index.values,
df3[(varname, "diff")].values * multiplier,
facecolor=cabove,
edgecolor=cabove,
)
for rect in rects:
if rect.get_height() < 0.0:
rect.set_facecolor(cbelow)
rect.set_edgecolor(cbelow)
plunits = "$g/kg$" if varname == "mixing_ratio" else "hPa"
ax[1].set_ylabel("%.0f Departure (%s)" % (year, plunits))
ax[1].set_xlim(0, 366)
ax[1].set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax[1].set_xticklabels(calendar.month_abbr[1:])
ax[1].grid(True)
return fig, df3
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
h1 = int(ctx['h1'])
h2 = int(ctx['h2'])
varname = ctx['v']
tzname = ctx['_nt'].sts[station]['tzname']
df = read_sql("""
WITH data as (
SELECT valid at time zone %s + '10 minutes'::interval as localvalid,
date_trunc(
'hour', valid at time zone %s + '10 minutes'::interval) as v,
tmpf, dwpf, sknt, drct, alti, relh, random() as r,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s and report_type = 2
and extract(hour from valid at time zone %s + '10 minutes'::interval)
in (%s, %s)),
agg as (
select *, extract(hour from v) as hour,
rank() OVER (PARTITION by v ORDER by localvalid ASC, r ASC) from data
)
SELECT *, date(
case when hour = %s
then date(v - '1 day'::interval)
else date(v) end) from agg WHERE rank = 1
""",
pgconn,
params=(tzname, tzname, station, tzname, h1, h2,
h2 if h2 < h1 else -1),
index_col=None)
if df.empty:
raise NoDataFound("No data was found.")
if varname == 'q':
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
ctx['_nt'].sts[station]['elevation'] * units('m')).to(
units('millibar'))
# compute mixing ratio
df['q'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values * units('percent'), df['tmpf'].values *
units('degF'), df['pressure'].values * units('millibars')) * 1000.
# pivot
df = df.pivot(index='date', columns='hour', values=varname).reset_index()
df = df.dropna()
df['doy'] = pd.to_numeric(pd.to_datetime(df['date']).dt.strftime("%j"))
df['year'] = pd.to_datetime(df['date']).dt.year
df['week'] = (df['doy'] / 7).astype(int)
df['delta'] = df[h2] - df[h1]
(fig, ax) = plt.subplots(1, 1)
if ctx['opt'] == 'no':
ax.set_xlabel("Plotted lines are smoothed over %.0f days" %
(ctx['smooth'], ))
ax.set_ylabel(
"%s %s Difference" %
(PDICT[varname], "Accumulated Sum" if ctx['opt'] == 'yes' else ''))
if ctx['opt'] == 'no':
# Histogram
H, xedges, yedges = np.histogram2d(df['doy'].values,
df['delta'].values,
bins=(50, 50))
ax.pcolormesh(xedges,
yedges,
H.transpose(),
cmap=plt.get_cmap(ctx['cmap']),
alpha=0.5)
# Plot an average line
gdf = df.groupby('doy').mean().rolling(ctx['smooth'],
min_periods=1,
center=True).mean()
y = gdf['delta'] if ctx['opt'] == 'no' else gdf['delta'].cumsum()
ax.plot(gdf.index.values,
y,
label='Average',
zorder=6,
lw=2,
color='k',
linestyle='-.')
# Plot selected year
for i in range(1, 5):
year = ctx.get("y%s" % (i, ))
if year is None:
continue
df2 = df[df['year'] == year]
if not df2.empty:
gdf = df2.groupby('doy').mean().rolling(ctx['smooth'],
min_periods=1,
center=True).mean()
y = gdf['delta'] if ctx['opt'] == 'no' else gdf['delta'].cumsum()
ax.plot(gdf.index.values, y, label=str(year), lw=2, zorder=10)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_xlim(1, 366)
ax.grid(True)
ax.legend(loc='best', ncol=5)
sts = datetime.datetime(2000, 6, 1, h1)
ets = datetime.datetime(2000, 6, 1, h2)
title = ("%s [%s] %s Difference (%.0f-%.0f)\n"
"%s minus %s (%s) (timezone: %s)") % (
ctx['_nt'].sts[station]['name'], station, PDICT[varname],
df['year'].min(), df['year'].max(), ets.strftime("%-I %p"),
sts.strftime("%-I %p"),
"same day" if h2 > h1 else "previous day", tzname)
fitbox(fig, title, 0.05, 0.95, 0.91, 0.99, ha='center')
return fig, df
def test_add_height_to_pressure():
"""Test the pressure at height above pressure calculation."""
pressure = add_height_to_pressure(1000 * units.hPa, 877.17421094 * units.meter)
assert_almost_equal(pressure, 900 * units.hPa, 5)
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql("""
SELECT drct::int as t, dwpf, tmpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)))
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
nt.sts[station]['elevation'] * units('m')).to(units('millibar'))
# compute mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values * units('percent'),
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars'))
# compute pressure
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
means = df.groupby('t').mean().copy()
# compute dewpoint now
means['dwpf'] = mcalc.dewpoint(means['vapor_pressure'].values *
units('kPa')).to(units('degF')).m
(fig, ax) = plt.subplots(1, 1)
ax.bar(means.index.values,
means['dwpf'].values,
ec='green',
fc='green',
width=10,
align='center')
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Wind Direction (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly obs > 3 knots at given direction)") %
(nt.sts[station]['name'], station, month.upper(),
max([1973, nt.sts[station]['archive_begin'].year
]), datetime.datetime.now().year),
size=10)
ax.set_ylabel("Dew Point [F]")
ax.set_ylim(means['dwpf'].min() - 5, means['dwpf'].max() + 5)
ax.set_xlim(-5, 365)
ax.set_xticks([0, 45, 90, 135, 180, 225, 270, 315, 360])
ax.set_xticklabels(['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N'])
ax.set_xlabel("Wind Direction")
return fig, means['dwpf']
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
h1 = int(ctx["h1"])
h2 = int(ctx["h2"])
varname = ctx["v"]
tzname = ctx["_nt"].sts[station]["tzname"]
df = read_sql(
"""
WITH data as (
SELECT valid at time zone %s + '10 minutes'::interval as localvalid,
date_trunc(
'hour', valid at time zone %s + '10 minutes'::interval) as v,
tmpf, dwpf, sknt, drct, alti, relh, random() as r,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s and report_type = 2
and extract(hour from valid at time zone %s + '10 minutes'::interval)
in (%s, %s)),
agg as (
select *, extract(hour from v) as hour,
rank() OVER (PARTITION by v ORDER by localvalid ASC, r ASC) from data
)
SELECT *, date(
case when hour = %s
then date(v - '1 day'::interval)
else date(v) end) from agg WHERE rank = 1
""",
pgconn,
params=(
tzname,
tzname,
station,
tzname,
h1,
h2,
h2 if h2 < h1 else -1,
),
index_col=None,
)
if df.empty:
raise NoDataFound("No data was found.")
if varname == "q":
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["q"] = (mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
) * 1000.0)
# pivot
df = df.pivot(index="date", columns="hour", values=varname).reset_index()
df = df.dropna()
df["doy"] = pd.to_numeric(pd.to_datetime(df["date"]).dt.strftime("%j"))
df["year"] = pd.to_datetime(df["date"]).dt.year
df["week"] = (df["doy"] / 7).astype(int)
df["delta"] = df[h2] - df[h1]
(fig, ax) = plt.subplots(1, 1)
if ctx["opt"] == "no":
ax.set_xlabel("Plotted lines are smoothed over %.0f days" %
(ctx["smooth"], ))
ax.set_ylabel(
"%s %s Difference" %
(PDICT[varname], "Accumulated Sum" if ctx["opt"] == "yes" else ""))
if ctx["opt"] == "no":
# Histogram
H, xedges, yedges = np.histogram2d(df["doy"].values,
df["delta"].values,
bins=(50, 50))
ax.pcolormesh(
xedges,
yedges,
H.transpose(),
cmap=get_cmap(ctx["cmap"]),
alpha=0.5,
)
# Plot an average line
gdf = (df.groupby("doy").mean().rolling(ctx["smooth"],
min_periods=1,
center=True).mean())
y = gdf["delta"] if ctx["opt"] == "no" else gdf["delta"].cumsum()
ax.plot(
gdf.index.values,
y,
label="Average",
zorder=6,
lw=2,
color="k",
linestyle="-.",
)
# Plot selected year
for i in range(1, 5):
year = ctx.get("y%s" % (i, ))
if year is None:
continue
df2 = df[df["year"] == year]
if not df2.empty:
gdf = (df2.groupby("doy").mean().rolling(ctx["smooth"],
min_periods=1,
center=True).mean())
y = gdf["delta"] if ctx["opt"] == "no" else gdf["delta"].cumsum()
ax.plot(gdf.index.values, y, label=str(year), lw=2, zorder=10)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_xlim(1, 366)
ax.grid(True)
ax.legend(loc="best", ncol=5)
sts = datetime.datetime(2000, 6, 1, h1)
ets = datetime.datetime(2000, 6, 1, h2)
title = ("%s [%s] %s Difference (%.0f-%.0f)\n"
"%s minus %s (%s) (timezone: %s)") % (
ctx["_nt"].sts[station]["name"],
station,
PDICT[varname],
df["year"].min(),
df["year"].max(),
ets.strftime("%-I %p"),
sts.strftime("%-I %p"),
"same day" if h2 > h1 else "previous day",
tzname,
)
fitbox(fig, title, 0.05, 0.95, 0.91, 0.99, ha="center")
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql(
"""
SELECT tmpf::int as tmpf, dwpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""",
pgconn,
params=(station, tuple(months)),
)
if df.empty:
raise NoDataFound("No Data Found.")
# Convert sea level pressure to station pressure
df["pressure"] = mcalc.add_height_to_pressure(
df["slp"].values * units("millibars"),
ctx["_nt"].sts[station]["elevation"] * units("m"),
).to(units("millibar"))
# compute mixing ratio
df["mixingratio"] = mcalc.mixing_ratio_from_relative_humidity(
df["relh"].values * units("percent"),
df["tmpf"].values * units("degF"),
df["pressure"].values * units("millibars"),
)
# compute pressure
df["vapor_pressure"] = mcalc.vapor_pressure(
df["pressure"].values * units("millibars"),
df["mixingratio"].values * units("kg/kg"),
).to(units("kPa"))
means = df.groupby("tmpf").mean().copy()
# compute dewpoint now
means["dwpf"] = (
mcalc.dewpoint(means["vapor_pressure"].values * units("kPa"))
.to(units("degF"))
.m
)
means.reset_index(inplace=True)
# compute RH again
means["relh"] = (
mcalc.relative_humidity_from_dewpoint(
means["tmpf"].values * units("degF"),
means["dwpf"].values * units("degF"),
)
* 100.0
)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(
means["tmpf"].values - 0.5,
means["dwpf"].values - 0.5,
ec="green",
fc="green",
width=1,
)
ax.grid(True, zorder=11)
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
(
"%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
)
% (
ctx["_nt"].sts[station]["name"],
station,
month.upper(),
ab.year,
datetime.datetime.now().year,
),
size=10,
)
ax.plot([0, 140], [0, 140], color="b")
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(means["tmpf"].values, means["relh"].values, color="k")
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, means["tmpf"].max() + 2)
ax.set_xlim(0, means["tmpf"].max() + 2)
ax.set_xlabel(r"Air Temperature $^\circ$F")
return fig, means[["tmpf", "dwpf", "relh"]]
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-"+month+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
df = read_sql("""
SELECT tmpf::int as tmpf, dwpf,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata where station = %s
and drct is not null and dwpf is not null and dwpf <= tmpf
and sknt > 3 and drct::int %% 10 = 0
and extract(month from valid) in %s
and report_type = 2
""", pgconn, params=(station, tuple(months)))
# Convert sea level pressure to station pressure
df['pressure'] = mcalc.add_height_to_pressure(
df['slp'].values * units('millibars'),
nt.sts[station]['elevation'] * units('m')
).to(units('millibar'))
# compute RH
df['relh'] = mcalc.relative_humidity_from_dewpoint(
df['tmpf'].values * units('degF'),
df['dwpf'].values * units('degF')
)
# compute mixing ratio
df['mixingratio'] = mcalc.mixing_ratio_from_relative_humidity(
df['relh'].values,
df['tmpf'].values * units('degF'),
df['pressure'].values * units('millibars')
)
# compute pressure
df['vapor_pressure'] = mcalc.vapor_pressure(
df['pressure'].values * units('millibars'),
df['mixingratio'].values * units('kg/kg')
).to(units('kPa'))
means = df.groupby('tmpf').mean().copy()
# compute dewpoint now
means['dwpf'] = mcalc.dewpoint(
means['vapor_pressure'].values * units('kPa')
).to(units('degF')).m
means.reset_index(inplace=True)
# compute RH again
means['relh'] = mcalc.relative_humidity_from_dewpoint(
means['tmpf'].values * units('degF'),
means['dwpf'].values * units('degF')
) * 100.
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(
means['tmpf'].values - 0.5, means['dwpf'].values - 0.5,
ec='green', fc='green', width=1
)
ax.grid(True, zorder=11)
ax.set_title(("%s [%s]\nAverage Dew Point by Air Temperature (month=%s) "
"(%s-%s)\n"
"(must have 3+ hourly observations at the given temperature)"
) % (nt.sts[station]['name'], station, month.upper(),
nt.sts[station]['archive_begin'].year,
datetime.datetime.now().year), size=10)
ax.plot([0, 140], [0, 140], color='b')
ax.set_ylabel("Dew Point [F]")
y2 = ax.twinx()
y2.plot(means['tmpf'].values, means['relh'].values, color='k')
y2.set_ylabel("Relative Humidity [%] (black line)")
y2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
y2.set_ylim(0, 100)
ax.set_ylim(0, means['tmpf'].max() + 2)
ax.set_xlim(0, means['tmpf'].max() + 2)
ax.set_xlabel(r"Air Temperature $^\circ$F")
return fig, means[['tmpf', 'dwpf', 'relh']]
