def main():
"""Go Main Go"""
pgconn = get_dbconn('scan')
for station in ['S2004', 'S2196', 'S2002', 'S2072', 'S2068',
'S2031', 'S2001', 'S2047']:
df = read_sql("""
select extract(year from valid + '2 months'::interval) as wy,
tmpf, dwpf from alldata where station = %s and tmpf is not null
and dwpf is not null
""", pgconn, params=(station, ), index_col=None)
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (
meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['saturation_mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['vpd'] = df['saturation_vapor_pressure'] - df['vapor_pressure']
means = df.groupby('wy').mean()
counts = df.groupby('wy').count()
for yr, row in means.iterrows():
print(("%s,%s,%.0f,%.3f"
) % (yr, station, counts.at[yr, 'vpd'], row['vpd']))
def run_calcs(df):
"""Do our maths"""
df['mixingratio'] = meteorology.mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df['vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
df['mixingratio'].values * units('kg/kg')).to(units('kPa'))
df['saturation_mixingratio'] = (meteorology.mixing_ratio(
temperature(df['tmpf'].values, 'F')).value('KG/KG'))
df['saturation_vapor_pressure'] = mcalc.vapor_pressure(
1000. * units.mbar,
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'] = meteorology.dewpoint_from_pq(
pressure(1000, 'MB'), mixingratio(df['mixingratio'].values,
'KG/KG')).value('F')
return df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
nt = NetworkTable(network)
year = int(fdict.get('year', 2015))
df = read_sql("""
SELECT extract(year from valid) as year,
extract(doy from valid) as doy, dwpf from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
""", pgconn, params=(station,), index_col=None)
df['mixing_ratio'] = mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
df2 = df[['doy', 'mixing_ratio']].groupby('doy').describe()
df2 = df2.unstack(level=-1)
dyear = df[df['year'] == year]
df3 = dyear[['doy', 'mixing_ratio']].groupby('doy').describe()
df3 = df3.unstack(level=-1)
df3['diff'] = df3[('mixing_ratio', 'mean')] - df2[('mixing_ratio', 'mean')]
(fig, ax) = plt.subplots(2, 1)
ax[0].fill_between(df2.index.values, df2[('mixing_ratio', 'min')] * 1000.,
df2[('mixing_ratio', 'max')] * 1000., color='gray')
ax[0].plot(df2.index.values, df2[('mixing_ratio', 'mean')] * 1000.,
label="Climatology")
ax[0].plot(df3.index.values, df3[('mixing_ratio', 'mean')] * 1000.,
color='r', label="%s" % (year, ))
ax[0].set_title(("%s [%s]\nDaily Mean Surface Water Vapor Mixing Ratio"
) % (station, nt.sts[station]['name']))
ax[0].set_ylabel("Mixing Ratio ($g/kg$)")
ax[0].set_xlim(0, 366)
# ax[0].set_ylim(0, 26.)
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)
rects = ax[1].bar(df3.index.values, df3['diff'] * 1000.0, edgecolor='b')
for rect in rects:
if rect.get_y() < 0.:
rect.set_facecolor('r')
rect.set_edgecolor('r')
else:
rect.set_facecolor('b')
ax[1].set_ylabel("%.0f Departure ($g/kg$)" % (year, ))
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 """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
nt = NetworkTable(network)
year = ctx['year']
varname = ctx['var']
_ = PDICT[varname]
df = read_sql("""
SELECT extract(year from valid) as year,
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)
df['relh'] = relh(temperature(df['tmpf'].values, 'F'),
temperature(df['dwpf'].values, 'F')).value('%')
df['tmpc'] = temperature(df['tmpf'].values, 'F').value('C')
df['svp'] = 0.611 * np.exp(17.502 * df['tmpc'].values /
(240.97 + df['tmpc'].values))
df['vpd'] = (1. - (df['relh'] / 100.)) * df['svp']
df['mixing_ratio'] = mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
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)
units = '$g/kg$' if varname == 'mixing_ratio' else 'hPa'
ax[1].set_ylabel("%.0f Departure (%s)" % (year, units))
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 """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
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]
cursor.execute("""
SELECT tmpf::int as t, dwpf from alldata where station = %s
and tmpf is not null and dwpf is not null and dwpf <= tmpf
and tmpf >= 0 and tmpf <= 140
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((140,), 'f')
counts = np.zeros((140,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
rows = []
for i in range(140):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
rows.append(dict(tmpf=i, dwpf=d, rh=rh))
df = pd.DataFrame(rows)
tmpf = df['tmpf']
dwpf = df['dwpf']
rh = df['rh']
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.bar(tmpf-0.5, dwpf, 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(tmpf, rh, 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, max(tmpf)+2)
ax.set_xlim(0, max(tmpf)+2)
ax.set_xlabel("Air Temperature $^\circ$F")
return fig, df
def test_mixingratio():
"""Test the mixing ratio calculation"""
r = meteorology.mixing_ratio(datatypes.temperature(70, "F"))
assert abs(r.value("KG/KG") - 0.016) < 0.001
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor()
station = fdict.get('station', 'DSM')
network = fdict.get('network', 'IA_ASOS')
season = fdict.get('season', 'winter')
_ = MDICT[season]
startyear = int(fdict.get('year', 1893))
nt = NetworkTable(network)
today = datetime.datetime.now()
lastyear = today.year
if season == 'all':
months = range(1, 13)
elif season == 'spring':
months = [3, 4, 5]
if today.month > 5:
lastyear += 1
elif season == 'fall':
months = [9, 10, 11]
if today.month > 11:
lastyear += 1
elif season == 'summer':
months = [6, 7, 8]
if today.month > 8:
lastyear += 1
elif season == 'winter':
months = [12, 1, 2]
if today.month > 2:
lastyear += 1
else:
ts = datetime.datetime.strptime("2000-" + season + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
lastyear += 1
cursor.execute(
"""
SELECT valid, dwpf from alldata where station = %s and dwpf > -90 and
dwpf < 100 and extract(month from valid) in %s
""", (station, tuple(months)))
rows = []
for row in cursor:
if (row[0].month not in months or row[0].year < startyear
or row[0].year >= lastyear):
continue
yr = (row[0] + datetime.timedelta(days=31)).year
r = meteorology.mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
rows.append(dict(year=yr, r=r))
df = pd.DataFrame(rows)
group = df.groupby('year')
df = group.aggregate(np.average)
def to_dwpf(val):
return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(val,
'KG/KG')).value('F')
df['r'] = df['r'].apply(to_dwpf)
data = np.array(df['r'])
years = np.array(df.index.astype('i'))
(fig, ax) = plt.subplots(1, 1)
avgv = np.average(data)
colorabove = 'seagreen'
colorbelow = 'lightsalmon'
bars = ax.bar(years - 0.4, data, fc=colorabove, ec=colorabove)
for i, bar in enumerate(bars):
if data[i] < avgv:
bar.set_facecolor(colorbelow)
bar.set_edgecolor(colorbelow)
ax.axhline(avgv, lw=2, color='k', zorder=2, label='Average')
h_slope, intercept, r_value, _, _ = stats.linregress(years, data)
ax.plot(years,
h_slope * np.array(years) + intercept,
'--',
lw=2,
color='k',
label='Trend')
ax.text(0.01,
0.99,
"Avg: %.1f, slope: %.2f F/century, R$^2$=%.2f" %
(avgv, h_slope * 100., r_value**2),
transform=ax.transAxes,
va='top',
bbox=dict(color='white'))
ax.set_xlabel("Year")
ax.set_xlim(min(years) - 1, max(years) + 1)
ax.set_ylim(min(data) - 5, max(data) + max(data) / 10.)
ax.set_ylabel("Average Dew Point [F]")
ax.grid(True)
msg = ("[%s] %s %.0f-%.0f Average Dew Point [%s] ") % (
station, nt.sts[station]['name'], min(years), max(years),
MDICT[season])
tokens = msg.split()
sz = len(tokens) / 2
ax.set_title(" ".join(tokens[:sz]) + "\n" + " ".join(tokens[sz:]))
ax.legend(ncol=1)
return fig, df
def test_mixingratio(self):
"""Test the mixing ratio calculation"""
r = meteorology.mixing_ratio(datatypes.temperature(70, 'F'))
self.assertAlmostEquals(r.value('KG/KG'), 0.016, 3)
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
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]
cursor.execute(
"""
SELECT drct::int as t, dwpf 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
""", (station, tuple(months)))
sums = np.zeros((361, ), 'f')
counts = np.zeros((361, ), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
sums[0] = sums[360]
counts[0] = counts[360]
rows = []
for i in range(361):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')).value('F')
rows.append(dict(drct=i, dwpf=d))
df = pd.DataFrame(rows)
drct = df['drct']
dwpf = df['dwpf']
(fig, ax) = plt.subplots(1, 1)
ax.bar(drct, dwpf, 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(min(dwpf) - 5, max(dwpf) + 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, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
nt = NetworkTable(network)
year = int(fdict.get('year', 2015))
varname = fdict.get('var', 'mixing_ratio')
_ = PDICT[varname]
df = read_sql("""
SELECT extract(year from valid) as year,
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)
df['relh'] = relh(temperature(df['tmpf'].values, 'F'),
temperature(df['dwpf'].values, 'F')).value('%')
df['tmpc'] = temperature(df['tmpf'].values, 'F').value('C')
df['svp'] = 0.611 * np.exp(17.502 * df['tmpc'].values /
(240.97 + df['tmpc'].values))
df['vpd'] = (1. - (df['relh'] / 100.)) * df['svp']
df['mixing_ratio'] = mixing_ratio(
temperature(df['dwpf'].values, 'F')).value('KG/KG')
dailymeans = df[['year', 'doy', varname]].groupby(['year', 'doy']).mean()
dailymeans = dailymeans.reset_index()
df2 = dailymeans[['doy', varname]].groupby('doy').describe()
df2 = df2.unstack(level=-1)
dyear = df[df['year'] == year]
df3 = dyear[['doy', varname]].groupby('doy').describe()
df3 = df3.unstack(level=-1)
df3['diff'] = df3[(varname, 'mean')] - df2[(varname, 'mean')]
(fig, ax) = plt.subplots(2, 1)
multiplier = 1000. if varname == 'mixing_ratio' else 10.
ax[0].fill_between(df2.index.values, df2[(varname, 'min')] * multiplier,
df2[(varname, 'max')] * multiplier, color='gray')
ax[0].plot(df2.index.values, df2[(varname, 'mean')] * multiplier,
label="Climatology")
ax[0].plot(df3.index.values, df3[(varname, 'mean')] * 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.index.values, df3['diff'] * multiplier,
facecolor=cabove, edgecolor=cabove)
for rect in rects:
if rect.get_y() < 0.:
rect.set_facecolor(cbelow)
rect.set_edgecolor(cbelow)
units = '$g/kg$' if varname == 'mixing_ratio' else 'hPa'
ax[1].set_ylabel("%.0f Departure (%s)" % (year, units))
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 """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor()
station = fdict.get('station', 'DSM')
network = fdict.get('network', 'IA_ASOS')
season = fdict.get('season', 'winter')
_ = MDICT[season]
startyear = int(fdict.get('year', 1893))
nt = NetworkTable(network)
today = datetime.datetime.now()
lastyear = today.year
if season == 'all':
months = range(1, 13)
elif season == 'spring':
months = [3, 4, 5]
if today.month > 5:
lastyear += 1
elif season == 'fall':
months = [9, 10, 11]
if today.month > 11:
lastyear += 1
elif season == 'summer':
months = [6, 7, 8]
if today.month > 8:
lastyear += 1
elif season == 'winter':
months = [12, 1, 2]
if today.month > 2:
lastyear += 1
else:
ts = datetime.datetime.strptime("2000-"+season+"-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
lastyear += 1
cursor.execute("""
SELECT valid, dwpf from alldata where station = %s and dwpf > -90 and
dwpf < 100 and extract(month from valid) in %s
""", (station, tuple(months)))
rows = []
for row in cursor:
if (row[0].month not in months or row[0].year < startyear or
row[0].year >= lastyear):
continue
yr = (row[0] + datetime.timedelta(days=31)).year
r = meteorology.mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
rows.append(dict(year=yr, r=r))
df = pd.DataFrame(rows)
group = df.groupby('year')
df = group.aggregate(np.average)
def to_dwpf(val):
return meteorology.dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(val, 'KG/KG')
).value('F')
df['r'] = df['r'].apply(to_dwpf)
data = np.array(df['r'])
years = np.array(df.index.astype('i'))
(fig, ax) = plt.subplots(1, 1)
avgv = np.average(data)
colorabove = 'seagreen'
colorbelow = 'lightsalmon'
bars = ax.bar(years - 0.4, data, fc=colorabove, ec=colorabove)
for i, bar in enumerate(bars):
if data[i] < avgv:
bar.set_facecolor(colorbelow)
bar.set_edgecolor(colorbelow)
ax.axhline(avgv, lw=2, color='k', zorder=2, label='Average')
h_slope, intercept, r_value, _, _ = stats.linregress(years, data)
ax.plot(years, h_slope * np.array(years) + intercept, '--',
lw=2, color='k', label='Trend')
ax.text(0.01, 0.99, "Avg: %.1f, slope: %.2f F/century, R$^2$=%.2f" % (
avgv, h_slope * 100., r_value ** 2),
transform=ax.transAxes, va='top', bbox=dict(color='white'))
ax.set_xlabel("Year")
ax.set_xlim(min(years)-1, max(years)+1)
ax.set_ylim(min(data)-5, max(data) + max(data)/10.)
ax.set_ylabel("Average Dew Point [F]")
ax.grid(True)
msg = ("[%s] %s %.0f-%.0f Average Dew Point [%s] "
) % (station, nt.sts[station]['name'],
min(years), max(years), MDICT[season])
tokens = msg.split()
sz = len(tokens) / 2
ax.set_title(" ".join(tokens[:sz]) + "\n" + " ".join(tokens[sz:]))
ax.legend(ncol=1)
return fig, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
ASOS = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = ASOS.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
month = fdict.get('month', 'all')
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]
cursor.execute("""
SELECT drct::int as t, dwpf 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
""", (station, tuple(months)))
sums = np.zeros((361,), 'f')
counts = np.zeros((361,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
sums[0] = sums[360]
counts[0] = counts[360]
rows = []
for i in range(361):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rows.append(dict(drct=i, dwpf=d))
df = pd.DataFrame(rows)
drct = df['drct']
dwpf = df['dwpf']
(fig, ax) = plt.subplots(1, 1)
ax.bar(drct-5, dwpf, ec='green', fc='green', width=10)
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(min(dwpf)-5, max(dwpf)+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, df
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
month = fdict.get('month', 'all')
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]
cursor.execute("""
SELECT tmpf::int as t, dwpf from alldata where station = %s
and tmpf is not null and dwpf is not null and dwpf <= tmpf
and tmpf >= 0 and tmpf <= 140
and extract(month from valid) in %s
""", (station, tuple(months)))
sums = np.zeros((140,), 'f')
counts = np.zeros((140,), 'f')
for row in cursor:
r = mixing_ratio(temperature(row[1], 'F')).value('KG/KG')
sums[row[0]] += r
counts[row[0]] += 1
rows = []
for i in range(140):
if counts[i] < 3:
continue
r = sums[i] / float(counts[i])
d = dewpoint_from_pq(pressure(1000, 'MB'),
mixingratio(r, 'KG/KG')
).value('F')
rh = relh(temperature(i, 'F'), temperature(d, 'F')).value('%')
rows.append(dict(tmpf=i, dwpf=d, rh=rh))
df = pd.DataFrame(rows)
tmpf = df['tmpf']
dwpf = df['dwpf']
rh = df['rh']
(fig, ax) = plt.subplots(1, 1)
ax.bar(tmpf-0.5, dwpf, 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(tmpf, rh, 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, max(tmpf)+2)
ax.set_xlim(0, max(tmpf)+2)
ax.set_xlabel("Air Temperature $^\circ$F")
return fig, df
def test_mixingratio():
"""Test the mixing ratio calculation"""
r = meteorology.mixing_ratio(datatypes.temperature(70, 'F'))
assert abs(r.value('KG/KG') - 0.016) < 0.001
def plotter(fdict):
""" Go """
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
pgconn = psycopg2.connect(database='asos', host='iemdb', user='******')
station = fdict.get('zstation', 'AMW')
network = fdict.get('network', 'IA_ASOS')
nt = NetworkTable(network)
year = int(fdict.get('year', 2015))
df = read_sql("""
SELECT extract(year from valid) as year,
extract(doy from valid) as doy, dwpf from alldata
where station = %s and dwpf > -50 and dwpf < 90 and
tmpf > -50 and tmpf < 120 and valid > '1950-01-01'
""",
pgconn,
params=(station, ),
index_col=None)
df['mixing_ratio'] = mixing_ratio(temperature(df['dwpf'].values,
'F')).value('KG/KG')
df2 = df[['doy', 'mixing_ratio']].groupby('doy').describe()
df2 = df2.unstack(level=-1)
dyear = df[df['year'] == year]
df3 = dyear[['doy', 'mixing_ratio']].groupby('doy').describe()
df3 = df3.unstack(level=-1)
df3['diff'] = df3[('mixing_ratio', 'mean')] - df2[('mixing_ratio', 'mean')]
(fig, ax) = plt.subplots(2, 1)
ax[0].fill_between(df2.index.values,
df2[('mixing_ratio', 'min')] * 1000.,
df2[('mixing_ratio', 'max')] * 1000.,
color='gray')
ax[0].plot(df2.index.values,
df2[('mixing_ratio', 'mean')] * 1000.,
label="Climatology")
ax[0].plot(df3.index.values,
df3[('mixing_ratio', 'mean')] * 1000.,
color='r',
label="%s" % (year, ))
ax[0].set_title(("%s [%s]\nDaily Mean Surface Water Vapor Mixing Ratio") %
(station, nt.sts[station]['name']))
ax[0].set_ylabel("Mixing Ratio ($g/kg$)")
ax[0].set_xlim(0, 366)
# ax[0].set_ylim(0, 26.)
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)
rects = ax[1].bar(df3.index.values, df3['diff'] * 1000.0, edgecolor='b')
for rect in rects:
if rect.get_y() < 0.:
rect.set_facecolor('r')
rect.set_edgecolor('r')
else:
rect.set_facecolor('b')
ax[1].set_ylabel("%.0f Departure ($g/kg$)" % (year, ))
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 test_mixingratio(self):
"""Test the mixing ratio calculation"""
r = meteorology.mixing_ratio(datatypes.temperature(70, 'F'))
self.assertAlmostEquals(r.value('KG/KG'), 0.016, 3)