def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
binsize = ctx["binsize"]
month = ctx["month"]
year = ctx.get("year")
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
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]
ddf = read_sql(
f"SELECT high, low, year, month from {table} WHERE station = %s "
"and year > 1892 and high >= low and month in %s",
pgconn,
params=(station, tuple(months)),
index_col=None,
)
if ddf.empty:
raise NoDataFound("No Data Found.")
bins = np.arange(-40, 121, binsize)
hist, xedges, yedges = np.histogram2d(ddf["low"], ddf["high"], bins)
rows = []
for i, xedge in enumerate(xedges[:-1]):
for j, yedge in enumerate(yedges[:-1]):
rows.append(dict(high=yedge, low=xedge, count=hist[i, j]))
df = pd.DataFrame(rows)
ab = nt.sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
years = float(datetime.datetime.now().year - ab.year)
hist = np.ma.array(hist / years)
hist.mask = np.where(hist < (1.0 / years), True, False)
ar = np.argwhere(hist.max() == hist)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
res = ax.pcolormesh(xedges, yedges, hist.T)
fig.colorbar(res, label="Days per Year")
ax.grid(True)
ax.set_title(("%s [%s]\n"
"Daily High vs Low Temp Histogram (month=%s)") %
(nt.sts[station]["name"], station, month.upper()))
ax.set_ylabel(r"High Temperature $^{\circ}\mathrm{F}$")
ax.set_xlabel(r"Low Temperature $^{\circ}\mathrm{F}$")
xmax = ar[0][0]
ymax = ar[0][1]
ax.text(
0.65,
0.15,
("Largest Frequency: %.1f days\n"
"High: %.0f-%.0f Low: %.0f-%.0f") % (
hist[xmax, ymax],
yedges[ymax],
yedges[ymax + 1],
xedges[xmax],
xedges[xmax + 1],
),
ha="center",
va="center",
transform=ax.transAxes,
bbox=dict(color="white"),
)
ax.axhline(32, linestyle="-", lw=1, color="k")
ax.text(
120,
32,
r"32$^\circ$F",
va="center",
ha="right",
color="white",
bbox=dict(color="k"),
fontsize=8,
)
ax.axvline(32, linestyle="-", lw=1, color="k")
ax.text(
32,
117,
r"32$^\circ$F",
va="top",
ha="center",
color="white",
bbox=dict(facecolor="k", edgecolor="none"),
fontsize=8,
)
if year:
label = str(year)
if month == "winter":
ddf["year"] = (ddf[((ddf["month"] == 1) |
(ddf["month"] == 2))]["year"] - 1)
label = "Dec %s - Feb %s" % (year, year + 1)
ddf2 = ddf[ddf["year"] == year]
ax.scatter(
ddf2["low"],
ddf2["high"],
marker="x",
label=label,
edgecolor="white",
facecolor="red",
)
ax.legend()
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
hours = ctx["hours"]
interval = ctx["interval"]
varname = ctx["var"]
if interval > 10 or interval < 0.1:
raise NoDataFound(
"Invalid interval provided, positive number less than 10"
)
cursor.execute(
"""
WITH one as (
select valid, """
+ varname
+ """ as t from alldata where
station = %s and """
+ varname
+ """ is not null
),
two as (SELECT valid + '%s hours'::interval as v, t from one
)
SELECT extract(week from one.valid), two.t - one.t
from one JOIN two on (one.valid = two.v)
""",
(station, hours),
)
weeks = []
deltas = []
for row in cursor:
weeks.append(row[0])
deltas.append(float(row[1]))
sts = datetime.datetime(2012, 1, 1)
xticks = []
for i in range(1, 13):
ts = sts.replace(month=i)
xticks.append(int(ts.strftime("%j")))
# We want bins centered on zero
bins = compute_bins(interval)
hist, xedges, yedges = np.histogram2d(weeks, deltas, [range(0, 54), bins])
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
years = float(datetime.datetime.now().year - ab.year)
hist = np.ma.array(hist / years / 7.0)
hist.mask = np.where(hist < (1.0 / years), True, False)
(fig, ax) = plt.subplots(1, 1)
res = ax.pcolormesh((xedges - 1) * 7, yedges, hist.transpose())
fig.colorbar(res, label="Hours per Day")
ax.grid(True)
ax.set_title(
("%s [%s] Histogram\n(bin=%s) of %s Hour %s Change")
% (
ctx["_nt"].sts[station]["name"],
station,
interval,
hours,
PDICT[varname],
)
)
ax.set_ylabel("%s Change" % (PDICT[varname],))
ax.set_xticks(xticks)
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_xlim(0, 366)
rng = max([max(deltas), 0 - min(deltas)])
ax.set_ylim(0 - rng * 1.3, rng * 1.3)
return fig
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
if station not in ctx["_nt"].sts: # This is needed.
raise NoDataFound("Unknown station metadata.")
varname = ctx["var"]
hour = int(ctx["hour"])
month = ctx["month"]
level = ctx["level"]
agg = ctx["agg"]
offset = 0
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
offset = 32
elif month == "spring":
months = [3, 4, 5]
elif month == "mjj":
months = [5, 6, 7]
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]
name = ctx["_nt"].sts[station]["name"]
stations = [station]
if station.startswith("_"):
name = ctx["_nt"].sts[station]["name"].split("--")[0]
stations = (
ctx["_nt"].sts[station]["name"].split("--")[1].strip().split(" ")
)
pgconn = get_dbconn("postgis")
if varname in ["tmpc", "dwpc", "height", "smps"]:
leveltitle = " @ %s hPa" % (level,)
dfin = read_sql(
"""
select extract(year from f.valid + '%s days'::interval) as year,
avg("""
+ varname
+ """) as avg_"""
+ varname
+ """,
min("""
+ varname
+ """) as min_"""
+ varname
+ """,
max("""
+ varname
+ """) as max_"""
+ varname
+ """,
count(*)
from raob_profile p JOIN raob_flights f on (p.fid = f.fid)
WHERE f.station in %s and p.pressure = %s and
extract(hour from f.valid at time zone 'UTC') = %s and
extract(month from f.valid) in %s
GROUP by year ORDER by year ASC
""",
pgconn,
params=(offset, tuple(stations), level, hour, tuple(months)),
index_col="year",
)
else:
leveltitle = ""
dfin = read_sql(
"""
select extract(year from f.valid + '%s days'::interval) as year,
count(*),
avg("""
+ varname
+ """) as avg_"""
+ varname
+ """,
min("""
+ varname
+ """) as min_"""
+ varname
+ """,
max("""
+ varname
+ """) as max_"""
+ varname
+ """
from raob_flights f
WHERE f.station in %s and
extract(hour from f.valid at time zone 'UTC') = %s and
extract(month from f.valid) in %s
GROUP by year ORDER by year ASC
""",
pgconn,
params=(offset, tuple(stations), hour, tuple(months)),
index_col="year",
)
# need quorums
df = dfin[dfin["count"] > ((len(months) * 28) * 0.75)]
if df.empty:
raise NoDataFound("No data was found!")
colname = "%s_%s" % (agg, varname)
fig, ax = plt.subplots(1, 1)
avgv = df[colname].mean()
bars = ax.bar(df.index.values, df[colname], align="center")
for i, _bar in enumerate(bars):
val = df.iloc[i][colname]
if val < avgv:
_bar.set_color("blue")
else:
_bar.set_color("red")
ax.set_xlim(df.index.min() - 1, df.index.max() + 1)
rng = df[colname].max() - df[colname].min()
ax.set_ylim(df[colname].min() - rng * 0.1, df[colname].max() + rng * 0.1)
ax.axhline(avgv, color="k")
ax.text(df.index.values[-1] + 2, avgv, "Avg:\n%.1f" % (avgv,))
ax.set_xlabel("Year")
ax.set_ylabel("%s %s%s" % (PDICT4[agg], PDICT3[varname], leveltitle))
plt.gcf().text(
0.5,
0.9,
("%s %s %02i UTC Sounding\n" "%s %s%s over %s")
% (
station,
name,
hour,
PDICT4[agg],
PDICT3[varname],
leveltitle,
MDICT[month],
),
ha="center",
va="bottom",
)
ax.grid(True)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
year = ctx["year"]
month = ctx["month"]
effective_date = ctx["date"]
table = "alldata_%s" % (station[:2], )
oldmonth = datetime.date(year, month, 1)
sts = datetime.date(effective_date.year, effective_date.month, 1)
ets = (sts + datetime.timedelta(days=35)).replace(day=1)
days = int((ets - sts).days)
# beat month
cursor.execute(
"""
SELECT extract(day from day), (high+low)/2. from
""" + table + """ WHERE station = %s and year = %s and month = %s
ORDER by day ASC
""",
(station, year, month),
)
if cursor.rowcount == 0:
raise NoDataFound("No Data Found.")
prevmonth = []
for row in cursor:
prevmonth.append(float(row[1]))
# build history
cursor.execute(
"""
SELECT year, day, (high+low)/2. from
""" + table + """ WHERE station = %s and month = %s and
extract(day from day) <= %s and day < %s
ORDER by day ASC
""",
(station, effective_date.month, days, ets),
)
for i, row in enumerate(cursor):
if i == 0:
baseyear = row[0]
data = np.ma.ones((effective_date.year - row[0] + 1, days)) * -99
data[row[0] - baseyear, row[1].day - 1] = row[2]
# overwrite our current month's data
currentdata = data[-1, :effective_date.day - 1]
for i in range(np.shape(data)[0] - 1):
data[i, :effective_date.day - 1] = currentdata
data.mask = data < -98
avgs = np.ma.zeros(np.shape(data))
days = np.shape(data)[1]
prevavg = []
for i in range(days):
avgs[:, i] = np.sum(data[:, :i + 1], 1) / float(i + 1)
prevavg.append(np.sum(prevmonth[:i + 1]) / float(i + 1))
avgs.mask = data.mask
(fig, ax) = plt.subplots(1, 1)
beats = 0
for yr in range(np.shape(data)[0] - 1):
if avgs[yr, -1] > prevavg[-1]:
beats += 1
ax.plot(np.arange(1, days + 1), avgs[yr, :], zorder=1, color="tan")
lv = avgs[-1, effective_date.day - 1]
if np.ma.is_masked(lv):
lv = avgs[-1, effective_date.day - 2]
ax.plot(
np.arange(1, effective_date.day),
avgs[-1, :effective_date.day - 1],
zorder=3,
lw=2,
color="brown",
label=r"%s %s, %.2f$^\circ$F" %
(calendar.month_abbr[effective_date.month], effective_date.year, lv),
)
# For historical, we can additionally plot the month values
today = datetime.date.today().replace(day=1)
if effective_date < today:
ax.plot(
np.arange(1, days + 1),
avgs[-1, :],
lw=2,
color="brown",
linestyle="-.",
zorder=2,
label=r"%s %s Final, %.2f$^\circ$F" % (
calendar.month_abbr[effective_date.month],
effective_date.year,
avgs[-1, -1],
),
)
ax.plot(
np.arange(1,
len(prevavg) + 1),
prevavg,
lw=2,
color="k",
zorder=3,
label=r"%s %s, %.2f$^\circ$F" %
(calendar.month_abbr[oldmonth.month], oldmonth.year, prevavg[-1]),
)
ax.set_title(("[%s] %s scenarios for %s\n"
"1-%s [%s] + %s-%s [%s-%s] beats %s %s %s/%s (%.1f%%)") % (
station,
ctx["_nt"].sts[station]["name"],
effective_date.strftime("%b %Y"),
effective_date.day,
effective_date.year,
effective_date.day + 1,
days,
baseyear,
effective_date.year - 1,
calendar.month_abbr[oldmonth.month],
oldmonth.year,
beats,
np.shape(data)[0] - 1,
beats / float(np.shape(data)[0] - 1) * 100.0,
))
ax.set_xlim(1, days)
ax.set_ylabel(r"Month to Date Average Temp $^\circ$F")
ax.set_xlabel("Day of Month")
ax.grid(True)
ax.legend(loc="best", fontsize=10)
return fig
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=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 plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
hour = ctx["hour"]
year = ctx["year"]
month = ctx["month"]
df = read_sql(
"""
WITH obs as (
SELECT to_char(valid, 'YYYYmmdd') as yyyymmdd,
SUM(case when (skyc1 = 'OVC' or skyc2 = 'OVC' or skyc3 = 'OVC'
or skyc4 = 'OVC') then 1 else 0 end)
from alldata where station = %s
and valid > '1951-01-01'
and extract(hour from (valid at time zone %s) +
'10 minutes'::interval ) = %s
GROUP by yyyymmdd)
SELECT substr(o.yyyymmdd,1,4)::int as year,
substr(o.yyyymmdd,5,2)::int as month,
sum(case when o.sum >= 1 then 1 else 0 end) as hits, count(*)
from obs o GROUP by year, month ORDER by year ASC, month ASC
""",
pgconn,
params=(station, ctx["_nt"].sts[station]["tzname"], hour),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found.")
df["freq"] = df["hits"] / df["count"] * 100.0
climo = df.groupby("month").sum()
climo["freq"] = climo["hits"] / climo["count"] * 100.0
(fig, ax) = plt.subplots(2, 1)
ax[0].bar(
climo.index.values - 0.2,
climo["freq"].values,
fc="red",
ec="red",
width=0.4,
label="Climatology",
align="center",
)
for i, row in climo.iterrows():
ax[0].text(i - 0.2,
row["freq"] + 1,
"%.0f" % (row["freq"], ),
ha="center")
thisyear = df[df["year"] == year]
if not thisyear.empty:
ax[0].bar(
thisyear["month"].values + 0.2,
thisyear["freq"].values,
fc="blue",
ec="blue",
width=0.4,
label=str(year),
align="center",
)
for i, row in thisyear.iterrows():
ax[0].text(
row["month"] + 0.2,
row["freq"] + 1,
"%.0f" % (row["freq"], ),
ha="center",
)
ax[0].set_ylim(0, 100)
ax[0].set_xlim(0.5, 12.5)
ax[0].legend(ncol=2)
ax[0].set_yticks([0, 10, 25, 50, 75, 90, 100])
ax[0].set_xticks(range(1, 13))
ax[0].grid(True)
ax[0].set_xticklabels(calendar.month_abbr[1:])
ax[0].set_ylabel("Frequency [%]")
ax[0].set_title(
("%.0f-%s [%s] %s\n"
"Frequency of %s Cloud Observation of Overcast") % (
df["year"].min(),
datetime.datetime.now().year,
station,
ctx["_nt"].sts[station]["name"],
datetime.datetime(2000, 1, 1, hour, 0).strftime("%I %p"),
))
# Plot second one now
obs = df[df["month"] == month]
ax[1].bar(obs["year"].values, obs["freq"].values, fc="tan", ec="orange")
ax[1].set_ylim(0, 100)
ax[1].grid(True)
ax[1].set_yticks([0, 10, 25, 50, 75, 90, 100])
ax[1].axhline(obs["freq"].mean())
ax[1].set_ylabel("%s Frequency [%%]" % (calendar.month_abbr[month], ))
ax[1].set_xlim(obs["year"].min() - 2, obs["year"].max() + 2)
return fig, df
def get_df(ctx):
"""Figure out what data we need to fetch here"""
ctx["ugc"] = ctx["_nt"].sts[ctx["station"]]["ugc_zone"]
pgconn = get_dbconn("postgis")
ctx["s1"] = "Y"
ctx["s2"] = "W"
if ctx["var"] == "heat":
ctx["p1"] = "HT"
ctx["p2"] = "EH"
else:
ctx["p1"] = "WC"
ctx["p2"] = "WC"
# Thankfully, all the above are zone based
events = read_sql(
"""
SELECT generate_series(issue, expire, '1 minute'::interval) as valid,
(phenomena ||'.'|| significance) as vtec
from warnings WHERE ugc = %s and (
(phenomena = %s and significance = %s) or
(phenomena = %s and significance = %s)
) ORDER by issue ASC
""",
pgconn,
params=(ctx["ugc"], ctx["p1"], ctx["s1"], ctx["p2"], ctx["s2"]),
index_col="valid",
)
if events.empty:
raise NoDataFound("No Alerts were found for UGC: %s" % (ctx["ugc"],))
pgconn = get_dbconn("asos")
thres = "tmpf > 70" if ctx["var"] == "heat" else "tmpf < 40"
obs = read_sql(
"""
SELECT valid, tmpf::int as tmpf, feel
from alldata where station = %s
and valid > %s and """
+ thres
+ """
and feel is not null ORDER by valid
""",
pgconn,
params=(ctx["station"], str(events.index.values[0])),
index_col="valid",
)
ctx["title"] = (
"%s [%s] (%s to %s)\n" "Frequency of NWS Headline for %s by %s"
) % (
ctx["_nt"].sts[ctx["station"]]["name"],
ctx["station"],
str(events.index.values[0])[:10],
str(obs.index.values[-1])[:10],
ctx["ugc"],
PDICT2[ctx["var"]],
)
if ctx["opt"] == "yes":
if ctx["var"] == "heat":
obs = obs[obs["feel"] > obs["tmpf"]]
else:
obs = obs[obs["feel"] < obs["tmpf"]]
obs["feel"] = obs["feel"].round(0)
res = obs.join(events)
res.fillna("None", inplace=True)
counts = res[["feel", "vtec"]].groupby(["feel", "vtec"]).size()
df = pd.DataFrame(counts)
df.columns = ["count"]
df.reset_index(inplace=True)
ctx["df"] = df.pivot(index="feel", columns="vtec", values="count")
ctx["df"].fillna(0, inplace=True)
ctx["df"]["Total"] = ctx["df"].sum(axis=1)
for vtec in [
"%s.%s" % (ctx["p1"], ctx["s1"]),
"%s.%s" % (ctx["p2"], ctx["s2"]),
"None",
]:
if vtec not in ctx["df"].columns:
ctx["df"][vtec] = 0.0
ctx["df"][vtec + "%"] = ctx["df"][vtec] / ctx["df"]["Total"] * 100.0
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
month = ctx['month']
varname = ctx['var']
days = ctx['days']
table = "alldata_%s" % (station[:2], )
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]
elif month == 'octmar':
months = [10, 11, 12, 1, 2, 3]
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]
sorder = 'ASC' if varname in [
'min_greatest_low',
] else 'DESC'
df = read_sql("""WITH data as (
SELECT month, day, day - '%s days'::interval as start_date,
count(*) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as count,
sum(precip) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as total_precip,
min(high) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as max_least_high,
max(low) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as min_greatest_low
from """ + table + """ WHERE station = %s)
SELECT day as end_date, start_date, """ + varname + """ from data WHERE
month in %s and
extract(month from start_date) in %s and count = %s
ORDER by """ + varname + """ """ + sorder + """ LIMIT 10
""",
pgconn,
params=(days - 1, days - 1, days - 1, days - 1, days - 1,
station, tuple(months), tuple(months), days),
index_col=None)
if df.empty:
raise NoDataFound('Error, no results returned!')
ylabels = []
fmt = '%.2f' if varname in [
'total_precip',
] else '%.0f'
for _, row in df.iterrows():
# no strftime support for old days, so we hack at it
lbl = fmt % (row[varname], )
if days > 1:
sts = row['end_date'] - datetime.timedelta(days=(days - 1))
if sts.month == row['end_date'].month:
lbl += " -- %s %s-%s, %s" % (calendar.month_abbr[sts.month],
sts.day, row['end_date'].day,
sts.year)
else:
lbl += " -- %s %s, %s to\n %s %s, %s" % (
calendar.month_abbr[sts.month], sts.day, sts.year,
calendar.month_abbr[row['end_date'].month],
row['end_date'].day, row['end_date'].year)
else:
lbl += " -- %s %s, %s" % (
calendar.month_abbr[row['end_date'].month],
row['end_date'].day, row['end_date'].year)
ylabels.append(lbl)
ax = plt.axes([0.1, 0.1, 0.5, 0.8])
plt.gcf().set_size_inches(8, 6)
ax.barh(range(10, 0, -1),
df[varname],
ec='green',
fc='green',
height=0.8,
align='center')
ax2 = ax.twinx()
ax2.set_ylim(0.5, 10.5)
ax.set_ylim(0.5, 10.5)
ax2.set_yticks(range(1, 11))
ax.set_yticks(range(1, 11))
ax.set_yticklabels(["#%s" % (x, ) for x in range(1, 11)][::-1])
ax2.set_yticklabels(ylabels[::-1])
ax.grid(True, zorder=11)
ax.set_xlabel(("Precipitation [inch]" if varname in ['total_precip'] else
r'Temperature $^\circ$F'))
ab = ctx['_nt'].sts[station]['archive_begin']
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(("%s [%s] Top 10 Events\n"
"%s [days=%s] (%s) "
"(%s-%s)") %
(ctx['_nt'].sts[station]['name'], station, METRICS[varname],
days, MDICT[month], ab.year, datetime.datetime.now().year),
size=12)
return plt.gcf(), df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
sdate = ctx['sdate']
edate = ctx['edate']
year2 = ctx.get('year2', 0)
year3 = ctx.get('year3', 0)
year4 = ctx.get('year4', 0)
wantedyears = [sdate.year, year2, year3, year4]
yearcolors = ['r', 'g', 'b', 'purple']
gddbase = ctx['base']
gddceil = ctx['ceil']
whichplots = ctx['which']
glabel = "gdd%s%s" % (gddbase, gddceil)
table = "alldata_%s" % (station[:2], )
df = read_sql("""
WITH avgs as (
SELECT sday, avg(gddxx(%s, %s, high, low)) as c""" + glabel + """,
avg(sdd86(high, low)) as csdd86, avg(precip) as cprecip
from """ + table + """
WHERE station = %s GROUP by sday
)
SELECT day, gddxx(%s, %s, high, low) as o""" + glabel + """,
c""" + glabel + """, o.precip as oprecip, cprecip,
sdd86(o.high, o.low) as osdd86, csdd86 from """ + table + """ o
JOIN avgs a on (o.sday = a.sday)
WHERE station = %s and o.sday != '0229' ORDER by day ASC
""",
pgconn,
params=(gddbase, gddceil, station, gddbase, gddceil,
station),
index_col='day')
df["precip_diff"] = df["oprecip"] - df["cprecip"]
df[glabel + "_diff"] = df["o" + glabel] - df["c" + glabel]
xlen = int((edate - sdate).days) + 1 # In case of leap day
ab = ctx['_nt'].sts[station]['archive_begin']
if ab is None:
raise NoDataFound("Unknown station metadata.")
years = (datetime.datetime.now().year - ab.year) + 1
acc = np.zeros((years, xlen))
acc[:] = np.nan
pacc = np.zeros((years, xlen))
pacc[:] = np.nan
sacc = np.zeros((years, xlen))
sacc[:] = np.nan
if whichplots == 'all':
fig = plt.figure(figsize=(9, 12))
ax1 = fig.add_axes([0.1, 0.7, 0.8, 0.2])
ax2 = fig.add_axes([0.1, 0.6, 0.8, 0.1],
sharex=ax1,
facecolor='#EEEEEE')
ax3 = fig.add_axes([0.1, 0.35, 0.8, 0.2], sharex=ax1)
ax4 = fig.add_axes([0.1, 0.1, 0.8, 0.2], sharex=ax1)
title = ("GDD(base=%.0f,ceil=%.0f), Precip, & "
"SDD(base=86)") % (gddbase, gddceil)
elif whichplots == 'gdd':
fig = plt.figure()
ax1 = fig.add_axes([0.14, 0.31, 0.8, 0.57])
ax2 = fig.add_axes([0.14, 0.11, 0.8, 0.2],
sharex=ax1,
facecolor='#EEEEEE')
title = ("GDD(base=%.0f,ceil=%.0f)") % (gddbase, gddceil)
elif whichplots == 'precip':
fig = plt.figure()
ax3 = fig.add_axes([0.1, 0.11, 0.8, 0.75])
ax1 = ax3
title = "Precipitation"
elif whichplots == 'sdd':
fig = plt.figure()
ax4 = fig.add_axes([0.1, 0.1, 0.8, 0.8])
ax1 = ax4
title = "Stress Degree Days (base=86)"
ax1.set_title(("Accumulated %s\n%s %s") %
(title, station, ctx['_nt'].sts[station]['name']),
fontsize=18 if whichplots == 'all' else 14)
ab = ctx['_nt'].sts[station]['archive_begin']
if ab is None:
raise NoDataFound("Unknown station metadata.")
for year in range(ab.year, datetime.datetime.now().year + 1):
sts = sdate.replace(year=year)
ets = sts + datetime.timedelta(days=(xlen - 1))
x = df.loc[sts:ets, 'o' + glabel].cumsum()
if x.empty:
continue
acc[(year - sdate.year), :len(x.index)] = x.values
x = df.loc[sts:ets, 'oprecip'].cumsum()
pacc[(year - sdate.year), :len(x.index)] = x.values
x = df.loc[sts:ets, 'osdd86'].cumsum()
sacc[(year - sdate.year), :len(x.index)] = x.values
if year not in wantedyears:
continue
color = yearcolors[wantedyears.index(year)]
yearlabel = sts.year
if sts.year != ets.year:
yearlabel = "%s-%s" % (sts.year, ets.year)
if whichplots in ['gdd', 'all']:
ax1.plot(range(len(x.index)),
df.loc[sts:ets, "o" + glabel].cumsum().values,
zorder=6,
color=color,
label='%s' % (yearlabel, ),
lw=2)
# Get cumulated precip
p = df.loc[sts:ets, 'oprecip'].cumsum()
if whichplots in ['all', 'precip']:
ax3.plot(range(len(p.index)),
p.values,
color=color,
lw=2,
zorder=6,
label='%s' % (yearlabel, ))
p = df.loc[sts:ets, 'osdd86'].cumsum()
if whichplots in ['all', 'sdd']:
ax4.plot(range(len(p.index)),
p.values,
color=color,
lw=2,
zorder=6,
label='%s' % (yearlabel, ))
# Plot Climatology
if wantedyears.index(year) == 0:
x = df.loc[sts:ets, "c" + glabel].cumsum()
if whichplots in ['all', 'gdd']:
ax1.plot(range(len(x.index)),
x.values,
color='k',
label='Climatology',
lw=2,
zorder=5)
x = df.loc[sts:ets, "cprecip"].cumsum()
if whichplots in ['all', 'precip']:
ax3.plot(range(len(x.index)),
x.values,
color='k',
label='Climatology',
lw=2,
zorder=5)
x = df.loc[sts:ets, "csdd86"].cumsum()
if whichplots in ['all', 'sdd']:
ax4.plot(range(len(x.index)),
x.values,
color='k',
label='Climatology',
lw=2,
zorder=5)
x = df.loc[sts:ets, glabel + "_diff"].cumsum()
if whichplots in ['all', 'gdd']:
ax2.plot(range(len(x.index)),
x.values,
color=color,
linewidth=2,
linestyle='--')
xmin = np.nanmin(acc, 0)
xmax = np.nanmax(acc, 0)
if whichplots in ['all', 'gdd']:
ax1.fill_between(range(len(xmin)), xmin, xmax, color='lightblue')
ax1.grid(True)
ax2.grid(True)
xmin = np.nanmin(pacc, 0)
xmax = np.nanmax(pacc, 0)
if whichplots in ['all', 'precip']:
ax3.fill_between(range(len(xmin)), xmin, xmax, color='lightblue')
ax3.set_ylabel("Precipitation [inch]", fontsize=16)
ax3.grid(True)
xmin = np.nanmin(sacc, 0)
xmax = np.nanmax(sacc, 0)
if whichplots in ['all', 'sdd']:
ax4.fill_between(range(len(xmin)), xmin, xmax, color='lightblue')
ax4.set_ylabel(r"SDD Base 86 $^{\circ}\mathrm{F}$", fontsize=16)
ax4.grid(True)
if whichplots in ['all', 'gdd']:
ax1.set_ylabel((r"GDD Base %.0f Ceil %.0f $^{\circ}\mathrm{F}$") %
(gddbase, gddceil),
fontsize=16)
ax1.text(0.5,
0.9,
"%s/%s - %s/%s" %
(sdate.month, sdate.day, edate.month, edate.day),
transform=ax1.transAxes,
ha='center')
ylim = ax2.get_ylim()
spread = max([abs(ylim[0]), abs(ylim[1])]) * 1.1
ax2.set_ylim(0 - spread, spread)
ax2.text(0.02,
0.1,
" Accumulated Departure ",
transform=ax2.transAxes,
bbox=dict(facecolor='white', ec='#EEEEEE'))
ax2.yaxis.tick_right()
xticks = []
xticklabels = []
wanted = [
1,
] if xlen > 31 else [1, 7, 15, 22, 29]
now = sdate
i = 0
while now <= edate:
if now.day in wanted:
xticks.append(i)
xticklabels.append(now.strftime("%-d\n%b"))
now += datetime.timedelta(days=1)
i += 1
if whichplots in ['all', 'gdd']:
ax2.set_xticks(xticks)
ax2.set_xticklabels(xticklabels)
ax1.legend(loc=2, prop={'size': 12})
# Remove ticks on the top most plot
for label in ax1.get_xticklabels():
label.set_visible(False)
ax1.set_xlim(0, xlen + 1)
if whichplots in ['all', 'precip']:
ax3.set_xticks(xticks)
ax3.set_xticklabels(xticklabels)
ax3.legend(loc=2, prop={'size': 10})
ax3.set_xlim(0, xlen + 1)
if whichplots in ['all', 'sdd']:
ax4.set_xticks(xticks)
ax4.set_xticklabels(xticklabels)
ax4.legend(loc=2, prop={'size': 10})
ax4.set_xlim(0, xlen + 1)
return fig, df
def get_data(ctx):
"""Build out our data."""
pgconn = get_dbconn("iem")
ctx["nt"] = NetworkTable("NWSCLI")
varname = ctx["var"]
today = ctx["sdate"]
yesterday = today - datetime.timedelta(days=1)
d180 = today - datetime.timedelta(days=180)
df = read_sql(
"""
with obs as (
select station, valid,
(case when low > low_normal then 1 else 0 end) as low_hit,
(case when high > high_normal then 1 else 0 end) as high_hit,
(case when precip > 0.009 then 1 else 0 end) as precip_hit
from cli_data
where high is not null
and high_normal is not null and low is not null and
low_normal is not null and precip is not null
and valid > %s and valid <= %s),
totals as (
SELECT station,
max(case when low_hit = 0 then valid else %s end) as last_low_below,
max(case when low_hit = 1 then valid else %s end) as last_low_above,
max(case when high_hit = 0 then valid else %s end) as last_high_below,
max(case when high_hit = 1 then valid else %s end) as last_high_above,
max(case when precip_hit = 0 then valid else %s end) as last_dry,
max(case when precip_hit = 1 then valid else %s end) as last_wet,
count(*) as count from obs GROUP by station)
SELECT station, last_low_below, last_low_above, last_high_below,
last_high_above, last_dry, last_wet
from totals where count > 170
""",
pgconn,
params=(d180, today, d180, d180, d180, d180, d180, d180),
index_col="station",
)
if df.empty:
raise NoDataFound("No Data Found.")
df["lat"] = None
df["lon"] = None
df["val"] = None
df["color"] = ""
df["label"] = ""
df["precip_days"] = (df["last_dry"] - df["last_wet"]).dt.days
df["low_days"] = (df["last_low_above"] - df["last_low_below"]).dt.days
df["high_days"] = (df["last_high_above"] - df["last_high_below"]).dt.days
# reorder the frame so that the largest values come first
df = df.reindex(
df[varname + "_days"].abs().sort_values(ascending=False).index
)
for station, row in df.iterrows():
if station not in ctx["nt"].sts:
continue
df.at[station, "lat"] = ctx["nt"].sts[station]["lat"]
df.at[station, "lon"] = ctx["nt"].sts[station]["lon"]
if varname == "precip":
last_wet = row["last_wet"]
days = 0 if last_wet in [today, yesterday] else row["precip_days"]
else:
days = row[varname + "_days"]
df.at[station, "val"] = days
df.at[station, "color"] = "#FF0000" if days > 0 else "#0000FF"
df.at[station, "label"] = station[1:]
df = df[pd.notnull(df["lon"])]
ctx["df"] = gpd.GeoDataFrame(
df, geometry=gpd.points_from_xy(df["lon"], df["lat"])
)
ctx["subtitle"] = (
"based on NWS CLI Sites, map approximately " "valid for %s"
) % (today.strftime("%-d %b %Y"),)
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
hours = ctx["hours"]
mydir = ctx["dir"]
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]
tzname = ctx["_nt"].sts[station]["tzname"]
# backwards intuitive
sortdir = "ASC" if mydir == "warm" else "DESC"
df = read_sql(
"""
WITH data as (
SELECT valid at time zone %s as valid, tmpf from alldata
where station = %s and tmpf between -100 and 150
and extract(month from valid) in %s),
doffset as (
SELECT valid - '%s hours'::interval as valid, tmpf from data),
agg as (
SELECT d.valid, d.tmpf as tmpf1, o.tmpf as tmpf2
from data d JOIN doffset o on (d.valid = o.valid))
SELECT valid as valid1, valid + '%s hours'::interval as valid2,
tmpf1, tmpf2 from agg
ORDER by (tmpf1 - tmpf2) """
+ sortdir
+ """ LIMIT 50
""",
pgconn,
params=(tzname, station, tuple(months), hours, hours),
index_col=None,
)
df["diff"] = (df["tmpf1"] - df["tmpf2"]).abs()
if df.empty:
raise NoDataFound("No database entries found for station, sorry!")
fig = plt.figure()
ax = plt.axes([0.55, 0.1, 0.4, 0.8])
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
fig.text(
0.5,
0.95,
("[%s] %s Top 10 %s\n" "Over %s Hour Period (%s-%s) [%s]")
% (
station,
ctx["_nt"].sts[station]["name"],
MDICT[mydir],
hours,
ab.year,
datetime.date.today().year,
MDICT2[month],
),
ha="center",
va="center",
)
labels = []
for i in range(10):
row = df.iloc[i]
ax.barh(i + 1, row["diff"], color="b", align="center")
sts = row["valid1"]
ets = row["valid2"]
labels.append(
("%.0f to %.0f -> %.0f\n%s - %s")
% (
row["tmpf1"],
row["tmpf2"],
row["diff"],
sts.strftime("%-d %b %Y %I:%M %p"),
ets.strftime("%-d %b %Y %I:%M %p"),
)
)
ax.set_yticks(range(1, 11))
ax.set_yticklabels(labels)
ax.set_ylim(10.5, 0.5)
ax.grid(True)
return fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
csector = ctx["csector"]
sdate = make_tuesday(ctx["sdate"])
edate = make_tuesday(ctx["edate"])
dlevel = ctx["d"]
griddelta = 0.1
mp = MapPlot(
sector=("state" if len(csector) == 2 else csector),
state=ctx["csector"],
title=('%s at or above "%s" %s - %s') % (
PDICT2[ctx["w"]],
PDICT[dlevel],
sdate.strftime("%b %-d, %Y"),
edate.strftime("%b %-d, %Y"),
),
subtitle=("based on weekly US Drought Monitor Analysis, "
"%.2f$^\circ$ grid analysis") % (griddelta, ),
continentalcolor="white",
titlefontsize=14,
)
# compute the affine
(west, east, south, north) = mp.ax.get_extent(ccrs.PlateCarree())
raster = np.zeros((int(
(north - south) / griddelta), int((east - west) / griddelta)))
lons = np.arange(raster.shape[1]) * griddelta + west
lats = np.arange(0, 0 - raster.shape[0], -1) * griddelta + north
lats = lats[::-1]
affine = Affine(griddelta, 0.0, west, 0.0, 0 - griddelta, north)
# get the geopandas data
pgconn = get_dbconn("postgis")
df = read_postgis(
"""
with d as (
select valid, (ST_Dump(st_simplify(geom, 0.01))).geom from usdm where
valid >= %s and valid <= %s and dm >= %s and
ST_Intersects(geom, ST_GeomFromEWKT('SRID=4326;POLYGON((%s %s, %s %s,
%s %s, %s %s, %s %s))'))
)
select valid, st_collect(geom) as the_geom from d GROUP by valid
""",
pgconn,
params=(
sdate,
edate,
dlevel,
west,
south,
west,
north,
east,
north,
east,
south,
west,
south,
),
geom_col="the_geom",
)
if df.empty:
raise NoDataFound("No Data Found, sorry!")
# loop over the cached stats
czs = CachingZonalStats(affine)
czs.compute_gridnav(df["the_geom"], raster)
for nav in czs.gridnav:
if nav is None:
continue
grid = np.ones((nav.ysz, nav.xsz))
grid[nav.mask] = 0.0
jslice = slice(nav.y0, nav.y0 + nav.ysz)
islice = slice(nav.x0, nav.x0 + nav.xsz)
raster[jslice, islice] += grid
maxval = 10 if np.max(raster) < 11 else np.max(raster)
ramp = np.linspace(1, maxval + 1, 11, dtype="i")
if ctx["w"] == "percent":
ramp = np.arange(0, 101, 10)
ramp[0] = 1.0
ramp[-1] = 100.1
# we add one since we are rectified to tuesdays, so we have an extra
# week in there
raster = raster / ((edate - sdate).days / 7.0 + 1.0) * 100.0
# plot
cmap = stretch_cmap(ctx["cmap"], ramp)
cmap.set_under("white")
cmap.set_bad("white")
mp.pcolormesh(
lons,
lats,
np.flipud(raster),
ramp,
cmap=cmap,
units="count" if ctx["w"] == "weeks" else "Percent",
)
if len(csector) == 2:
mp.drawcounties()
mp.drawcities()
rows = []
for j in range(raster.shape[0]):
for i in range(raster.shape[1]):
rows.append(dict(lon=lons[i], lat=lats[j], value=raster[j, i]))
return mp.fig, pd.DataFrame(rows)
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
today = datetime.datetime.now()
year = ctx['year']
jdaylimit = 367
if year == today.year:
jdaylimit = int(today.strftime("%j"))
table = "alldata_%s" % (station[:2], )
endyear = int(datetime.datetime.now().year) + 1
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
cursor.execute(
"""
select precip, sum(precip) OVER (ORDER by precip ASC) as rsum,
sum(precip) OVER () as tsum,
min(year) OVER () as minyear from """ + table + """ where
station = %s and precip >= 0.01 and extract(doy from day) < %s and
year < extract(year from now()) ORDER by precip ASC
""", (station, jdaylimit))
if cursor.rowcount == 0:
raise NoDataFound("No Data Found.")
total = None
base = None
bins = [
0.01,
]
minyear = None
row = None
for i, row in enumerate(cursor):
if i == 0:
minyear = row['minyear']
total = row['tsum']
onefifth = total / 5.0
base = onefifth
if row['rsum'] > base:
bins.append(row['precip'])
base += onefifth
normal = total / float(endyear - minyear - 1)
# A rounding edge case
if row['precip'] != bins[-1]:
bins.append(row['precip'])
df = pd.DataFrame(
{
'bin': range(1, 6),
'lower': bins[0:-1],
'upper': bins[1:]
},
index=range(1, 6))
yearlybins = np.zeros((endyear - minyear, 5), 'f')
yearlytotals = np.zeros((endyear - minyear, 5), 'f')
cursor.execute(
"""
SELECT year,
sum(case when precip >= %s and precip < %s then 1 else 0 end) as bin0,
sum(case when precip >= %s and precip < %s then 1 else 0 end) as bin1,
sum(case when precip >= %s and precip < %s then 1 else 0 end) as bin2,
sum(case when precip >= %s and precip < %s then 1 else 0 end) as bin3,
sum(case when precip >= %s and precip < %s then 1 else 0 end) as bin4,
sum(case when precip >= %s and precip < %s then precip else 0 end) as tot0,
sum(case when precip >= %s and precip < %s then precip else 0 end) as tot1,
sum(case when precip >= %s and precip < %s then precip else 0 end) as tot2,
sum(case when precip >= %s and precip < %s then precip else 0 end) as tot3,
sum(case when precip >= %s and precip < %s then precip else 0 end) as tot4
from """ + table + """ where extract(doy from day) < %s and
station = %s and precip > 0 and year > 1879 GROUP by year
""", (bins[0], bins[1], bins[1], bins[2], bins[2], bins[3], bins[3],
bins[4], bins[4], bins[5], bins[0], bins[1], bins[1], bins[2],
bins[2], bins[3], bins[3], bins[4], bins[4], bins[5], jdaylimit,
station))
for row in cursor:
for i in range(5):
yearlybins[int(row[0]) - minyear, i] = row['bin%s' % (i, )]
yearlytotals[int(row[0]) - minyear, i] = row['tot%s' % (i, )]
avgs = np.average(yearlybins, 0)
df['avg_days'] = avgs
dlast = yearlybins[year - minyear, :]
df['days_%s' % (year, )] = dlast
df['precip_%s' % (year, )] = yearlytotals[year - minyear, :]
df['normal_%s' % (year, )] = normal / 5.
ybuffer = (max([max(avgs), max(dlast)]) + 2) * 0.05
bars = ax.bar(np.arange(5) - 0.2,
avgs,
width=0.4,
fc='b',
align='center',
label='Average = %.2f"' % (normal, ))
for i, _bar in enumerate(bars):
ax.text(_bar.get_x() + 0.2,
avgs[i] + ybuffer,
"%.1f" % (avgs[i], ),
ha='center',
zorder=2)
delta = yearlytotals[year - minyear, i] / normal * 100.0 - 20.0
ax.text(i,
max(avgs[i], dlast[i]) + 2 * ybuffer,
"%s%.1f%%" % (
"+" if delta > 0 else "",
delta,
),
ha='center',
color='r',
bbox=dict(pad=0, facecolor='white', edgecolor='white'))
bars = ax.bar(np.arange(5) + 0.2,
dlast,
width=0.4,
fc='r',
align='center',
label='%s = %.2f"' %
(year, np.sum(yearlytotals[year - minyear, :])))
for i, _bar in enumerate(bars):
ax.text(_bar.get_x() + 0.2,
dlast[i] + ybuffer,
"%.0f" % (dlast[i], ),
ha='center')
ax.text(0.7,
0.8,
("Red text represents %s bin total\nprecip "
"departure from average") % (year, ),
transform=ax.transAxes,
color='r',
ha='center',
va='top',
bbox=dict(facecolor='white', edgecolor='white'))
ax.legend()
ax.grid(True)
ax.set_ylabel("Days")
ax.text(0.5,
-0.05,
("Precipitation Bins [inch], split into equal 20%%"
" by rain volume (%.2fin)") % (normal / 5.0, ),
transform=ax.transAxes,
va='top',
ha='center')
addl = ""
if jdaylimit < 367:
addl = " thru %s" % (today.strftime("%-d %b"), )
ax.set_title(
("%s [%s] [%s-%s]\nDaily Precipitation Contributions%s") %
(ctx['_nt'].sts[station]['name'], station, minyear, endyear - 2, addl))
ax.set_xticks(np.arange(0, 5))
xlabels = []
for i in range(5):
xlabels.append("%.2f-%.2f" % (bins[i], bins[i + 1]))
ax.set_xticklabels(xlabels)
ax.set_ylim(top=ax.get_ylim()[1] * 1.1)
return fig, df
def plotter(fdict):
""" Go """
pgconn = util.get_dbconn("postgis")
ctx = util.get_autoplot_context(fdict, get_description())
ctx["_nt"].sts["_ALL"] = dict(name="All WFOs")
station = ctx["station"][:4]
syear = ctx["year"]
eyear = ctx["eyear"]
# optional parameter, this could return null
ltype = ctx.get("ltype")
wfo_limiter = " and wfo = '%s' " % (station if len(station) == 3 else
station[1:], )
if station == "_ALL":
wfo_limiter = ""
typetext_limiter = ""
if ltype:
if len(ltype) == 1:
typetext_limiter = " and typetext = '%s'" % (ltype[0], )
else:
typetext_limiter = " and typetext in %s" % (tuple(ltype), )
df = read_sql(
f"""
select extract(year from valid)::int as yr, upper(source) as src,
count(*) from lsrs
where valid > '{syear}-01-01' and
valid < '{eyear + 1}-01-01' {wfo_limiter} {typetext_limiter}
GROUP by yr, src
""",
pgconn,
)
if df.empty:
raise NoDataFound("No data found")
# pivot the table so that we can fill out zeros
df = df.pivot(index="yr", columns="src", values="count")
df = df.fillna(0).reset_index()
df = df.melt(id_vars="yr", value_name="count")
df["rank"] = df.groupby(["yr"])["count"].rank(ascending=False,
method="first")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
# Do syear as left side
for year in range(syear, eyear):
dyear = df[df["yr"] == year].sort_values(by=["rank"], ascending=True)
if not dyear.empty:
break
year += 1
syear = year
i = 1
ylabels = []
leftsrcs = []
usedline = 0
for _, row in dyear.iterrows():
src = row["src"]
leftsrcs.append(src)
ylabels.append("%s (%.0f)" % (src, row["count"]))
d = df[df["src"] == src].sort_values(by=["yr"])
ax.plot(
np.array(d["yr"]),
np.array(d["rank"]),
lw=2,
label=src,
marker=MARKERS[usedline % len(MARKERS)],
)
i += 1
usedline += 1
if i > 20:
break
ax.set_yticks(range(1, len(ylabels) + 1))
ax.set_yticklabels(["%s %s" % (s, i + 1) for i, s in enumerate(ylabels)])
ax.set_ylim(0.5, 20.5)
ax2 = ax.twinx()
# Do last year as right side
dyear = df[df["yr"] == eyear].sort_values(by=["rank"], ascending=True)
i = 0
y2labels = []
for _, row in dyear.iterrows():
i += 1
if i > 20:
break
src = row["src"]
y2labels.append("%s (%.0f)" % (src, row["count"]))
if src not in leftsrcs:
d = df[df["src"] == src].sort_values(by=["yr"])
ax.plot(
np.array(d["yr"]),
np.array(d["rank"]),
lw=2,
label=src,
marker=MARKERS[usedline % len(MARKERS)],
)
usedline += 1
ax2.set_yticks(range(1, len(y2labels) + 1))
ax2.set_yticklabels(["%s %s" % (i + 1, s) for i, s in enumerate(y2labels)])
ax2.set_ylim(0.5, 20.5)
ax.set_position([0.3, 0.13, 0.4, 0.75])
ax2.set_position([0.3, 0.13, 0.4, 0.75])
ax.set_xticks(range(df["yr"].min(), df["yr"].max(), 2))
for tick in ax.get_xticklabels():
tick.set_rotation(90)
ax.grid()
fig.text(0.15, 0.88, "%s" % (syear, ), fontsize=14, ha="center")
fig.text(0.85, 0.88, "%s" % (eyear, ), fontsize=14, ha="center")
fig.text(
0.5,
0.97,
"NWS %s Local Storm Report Sources Ranks" %
(ctx["_nt"].sts[station]["name"], ),
ha="center",
)
if ltype:
label = "For LSR Types: %s" % (repr(ltype), )
if len(label) > 90:
label = "%s..." % (label[:90], )
fig.text(0.5, 0.93, label, ha="center")
return fig, df
def plotter(fdict):
""" Go """
from seaborn import heatmap
ctx = get_autoplot_context(fdict, get_description())
pgconn = get_dbconn("coop")
table = "alldata_%s" % (ctx["station"][:2], )
df = read_sql(
"""
select day, sday, precip, high,
extract(doy from day)::int as doy, year
from """ + table + """ WHERE
station = %s ORDER by day ASC
""",
pgconn,
params=(ctx["station"], ),
index_col="day",
parse_dates="day",
)
if df.empty:
raise NoDataFound("Did not find any data for station!")
if ctx["var"] == "trail_precip_percent":
climo = df[["precip", "sday"]].groupby("sday").mean()
df["precip_avg"] = df.merge(climo,
left_on="sday",
right_index=True,
suffixes=("", "_avg"))["precip_avg"]
df["trail_precip_percent"] = (
df["precip"].rolling(ctx["days"]).sum() /
df["precip_avg"].rolling(ctx["days"]).sum() * 100.0)
levels = [0, 25, 50, 75, 100, 150, 200, 250, 300]
label = "Percent"
elif ctx["var"] == "daily_high_depart":
climo = df[["high", "sday"]].groupby("sday").mean()
df["high_avg"] = df.merge(climo,
left_on="sday",
right_index=True,
suffixes=("", "_avg"))["high_avg"]
df["daily_high_depart"] = df["high"] - df["high_avg"]
levels = list(range(-20, 21, 4))
label = "Temperature [F] Departure"
baseyear = max([df["year"].min(), ctx["syear"]])
endyear = min([df["year"].max(), ctx["eyear"]])
years = endyear - baseyear + 1
cmap = plt.get_cmap(ctx["cmap"])
norm = mpcolors.BoundaryNorm(levels, cmap.N)
data = np.full((years, 366), np.nan)
df2 = df[(df["year"] >= baseyear) & (df["year"] <= endyear)]
for day, row in df2.iterrows():
data[day.year - baseyear, row["doy"] - 1] = row[ctx["var"]]
fig, ax = plt.subplots(1, 1)
heatmap(
data,
cmap=cmap,
norm=norm,
ax=ax,
cbar_kws={
"spacing": "proportional",
"label": label
},
)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:], rotation=0)
yticks = []
yticklabels = []
delta = 5 if (endyear - baseyear) < 30 else 10
for i, year in enumerate(range(baseyear, endyear + 1)):
if year % delta == 0:
yticks.append(i + 0.5)
yticklabels.append(year)
ax.set_yticks(yticks[::-1])
ax.set_yticklabels(yticklabels[::-1], rotation=0)
ax.xaxis.grid(True, color="k")
ax.set_title("[%s] %s (%s-%s)\n%s" % (
ctx["station"],
ctx["_nt"].sts[ctx["station"]]["name"],
ctx["syear"],
ctx["eyear"],
PDICT[ctx["var"]].replace("XX", str(ctx["days"])),
))
return fig, df
def load(dirname, location, sdate):
""" Read a file please """
data = []
idx = []
fn = "%s/%s.met" % (dirname, location)
if not os.path.isfile(fn):
raise NoDataFound("File was not found.")
for line in open(fn):
line = line.strip()
if not line.startswith("19") and not line.startswith("20"):
continue
tokens = line.split()
if float(tokens[5]) > 90:
continue
data.append(tokens)
ts = datetime.date(int(tokens[0]), 1,
1) + datetime.timedelta(days=int(tokens[1]) - 1)
idx.append(ts)
if len(data[0]) < 10:
cols = ["year", "doy", "radn", "maxt", "mint", "rain"]
else:
cols = [
"year",
"doy",
"radn",
"maxt",
"mint",
"rain",
"gdd",
"st4",
"st12",
"st24",
"st50",
"sm12",
"sm24",
"sm50",
]
df = pd.DataFrame(data, index=idx, columns=cols)
for col in cols:
df[col] = pd.to_numeric(df[col], errors="coerce")
if len(data[0]) < 10:
df["gdd"] = gdd(
temperature(df["maxt"].values, "C"),
temperature(df["mint"].values, "C"),
)
bins = []
today = datetime.date.today()
for valid, _ in df.iterrows():
if valid >= today:
bins.append(0)
continue
if sdate == "nov1" and valid.month >= 11:
bins.append(valid.year + 1)
continue
if valid.month < today.month:
bins.append(valid.year)
continue
if valid.month == today.month and valid.day < today.day:
bins.append(valid.year)
continue
bins.append(0)
df["bin"] = bins
df["rain"] = distance(df["rain"].values, "MM").value("IN")
df["avgt"] = temperature((df["maxt"] + df["mint"]) / 2.0, "C").value("F")
return df
def plotter(fdict):
""" Go """
ctx = util.get_autoplot_context(fdict, get_description())
state = ctx["state"]
syear = ctx["syear"]
eyear = ctx["eyear"]
fips = ""
for key in state_fips:
if state_fips[key] == state:
fips = key
payload = "{'area':'%s', 'type':'state', 'statstype':'2'}" % (fips, )
headers = {}
headers["Accept"] = "application/json, text/javascript, */*; q=0.01"
headers["Content-Type"] = "application/json; charset=UTF-8"
req = util.exponential_backoff(requests.post,
SERVICE,
payload,
headers=headers)
if req is None:
raise NoDataFound("Drought Web Service failed to deliver data.")
jdata = req.json()
if "d" not in jdata:
raise NoDataFound("Data Not Found.")
df = pd.DataFrame(jdata["d"])
df["Date"] = pd.to_datetime(df["ReleaseDate"])
df.sort_values("Date", ascending=True, inplace=True)
df["x"] = df["Date"] + datetime.timedelta(hours=(3.5 * 24))
fig = plt.figure(figsize=(7, 9))
ax = fig.add_axes([0.1, 0.1, 0.87, 0.84])
lastrow = None
for year, gdf in df.groupby(df.Date.dt.year):
if year < syear or year > eyear:
continue
xs = []
ys = []
for _, row in gdf.iterrows():
if lastrow is None:
lastrow = row
delta = ((lastrow["D4"] - row["D4"]) * 5.0 +
(lastrow["D3"] - row["D3"]) * 4.0 +
(lastrow["D2"] - row["D2"]) * 3.0 +
(lastrow["D1"] - row["D1"]) * 2.0 +
(lastrow["D0"] - row["D0"]))
xs.append(int(row["Date"].strftime("%j")))
ys.append(year + (0 - delta) / 100.0)
lastrow = row
if len(xs) < 4:
continue
fcube = interp1d(xs, ys, kind="cubic")
xnew = np.arange(xs[0], xs[-1])
yval = np.ones(len(xnew)) * year
ynew = fcube(xnew)
ax.fill_between(
xnew,
yval,
ynew,
where=(ynew < yval),
facecolor="blue",
interpolate=True,
)
ax.fill_between(
xnew,
yval,
ynew,
where=(ynew >= yval),
facecolor="red",
interpolate=True,
)
ax.set_ylim(eyear + 1, syear - 1)
ax.set_xlim(0, 366)
ax.set_xlabel(("curve height of 1 year is 1 effective drought category "
"change over area of %s") % (state_names[state], ))
ax.set_ylabel("Year, thru %s" % (df.Date.max().strftime("%d %b %Y"), ))
ax.set_title(("%.0f-%.0f US Drought Monitor Weekly Change for %s\n"
"curve height represents change in intensity + coverage") %
(syear, eyear, state_names[state]))
ax.grid(True)
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_yticks(
np.arange(ax.get_ylim()[0] - 1, ax.get_ylim()[1], -1, dtype="i"))
fig.text(0.02, 0.03, "Blue areas are improving conditions", color="b")
fig.text(0.4, 0.03, "Red areas are degrading conditions", color="r")
return fig, df[["Date", "NONE", "D0", "D1", "D2", "D3", "D4"]]
def plotter(fdict):
""" Go """
font0 = FontProperties()
font0.set_family("monospace")
font0.set_size(16)
pgconn = get_dbconn("iem")
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 day as date, max_tmpf as max, min_tmpf as min,
max_tmpf::int - min_tmpf::int as difference
from summary s JOIN stations t on (s.iemid = t.iemid)
where t.id = %s and t.network = %s
and extract(month from day) in %s
and max_tmpf is not null and min_tmpf is not null
ORDER by difference DESC, date DESC LIMIT 10
""",
pgconn,
params=(station, ctx["network"], tuple(months)),
parse_dates=("date",),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found,")
df["rank"] = df["difference"].rank(ascending=False, method="min")
fig = plt.figure(figsize=(5.5, 4))
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
fig.text(
0.5,
0.9,
(
"%s [%s] %s-%s\n"
"Top 10 Local Calendar Day [%s] "
"Temperature Differences"
)
% (
ctx["_nt"].sts[station]["name"],
station,
ab.year,
datetime.date.today().year,
month.capitalize(),
),
ha="center",
)
fig.text(
0.1, 0.81, " # Date Diff Low High", fontproperties=font0
)
y = 0.74
for _, row in df.iterrows():
fig.text(
0.1,
y,
("%2.0f %11s %3.0f %3.0f %3.0f")
% (
row["rank"],
row["date"].strftime("%d %b %Y"),
row["difference"],
row["min"],
row["max"],
),
fontproperties=font0,
)
y -= 0.07
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("postgis")
pcursor = pgconn.cursor()
ctx = get_autoplot_context(fdict, get_description())
ctx["_nt"].sts["_ALL"] = dict(name="ALL WFOs")
syear = ctx["syear"]
eyear = ctx["eyear"] + 1
station = ctx["station"][:4]
sts = datetime.date(syear, 1, 1)
ets = datetime.date(eyear, 1, 1)
wfo_limiter = " and wfo = '%s' " % (station if len(station) == 3 else
station[1:], )
if station == "_ALL":
wfo_limiter = ""
pcursor.execute(
f"""
select phenomena, significance, min(issue), count(*) from warnings
where ugc is not null and issue > %s
and issue < %s {wfo_limiter}
GROUP by phenomena, significance ORDER by count DESC
""",
(sts, ets),
)
if pcursor.rowcount == 0:
raise NoDataFound("No data found.")
labels = []
vals = []
cnt = 1
rows = []
for row in pcursor:
label = ("%s. %s (%s.%s)") % (
cnt,
vtec.get_ps_string(row[0], row[1]),
row[0],
row[1],
)
if cnt < 26:
labels.append(label)
vals.append(row[3])
rows.append(
dict(
phenomena=row[0],
significance=row[1],
count=row[3],
wfo=station,
))
cnt += 1
df = pd.DataFrame(rows)
(fig, ax) = plt.subplots(1, 1, figsize=(7, 10))
vals = np.array(vals)
ax.barh(np.arange(len(vals)),
vals / float(vals[0]) * 100.0,
align="center")
for i in range(1, len(vals)):
y = vals[i] / float(vals[0]) * 100.0
ax.text(y + 1, i, "%.1f%%" % (y, ), va="center")
fig.text(
0.5,
0.95,
"%s-%s NWS %s Watch/Warning/Advisory Totals" % (
syear,
eyear - 1 if (eyear - 1 != syear) else "",
ctx["_nt"].sts[station]["name"],
),
ha="center",
)
fig.text(
0.5,
0.05,
"Event+County/Zone Count, Relative to #%s" % (labels[0], ),
ha="center",
fontsize=10,
)
ax.set_ylim(len(vals), -0.5)
ax.grid(True)
ax.set_yticklabels(labels)
ax.set_yticks(np.arange(len(vals)))
ax.set_position([0.5, 0.1, 0.45, 0.83])
ax.set_xticks([0, 10, 25, 50, 75, 90, 100])
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos', user='******')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
threshold = ctx['threshold']
mydir = ctx['dir']
hours = ctx['hours']
varname = ctx['var']
month = ctx['m'] if fdict.get('month') is None else fdict.get('month')
year_limiter = ""
y1, y2 = None, None
if 'yrange' in ctx:
y1, y2 = ctx['yrange'].split("-")
year_limiter = (" and valid >= '%s-01-01' and valid < '%s-01-01' ") % (
int(y1), int(y2))
if month == 'all':
months = range(1, 13)
sts = datetime.datetime(2000, 1, 1)
ets = datetime.datetime(2000, 12, 31)
elif month == 'fall':
months = [9, 10, 11]
sts = datetime.datetime(2000, 9, 1)
ets = datetime.datetime(2000, 11, 30)
elif month == 'spring':
months = [3, 4, 5]
sts = datetime.datetime(2000, 3, 1)
ets = datetime.datetime(2000, 5, 31)
elif month == 'summer':
months = [6, 7, 8]
sts = datetime.datetime(2000, 6, 1)
ets = datetime.datetime(2000, 8, 31)
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]
sts = datetime.datetime(2000, ts.month, 1)
ets = sts + datetime.timedelta(days=35)
ets = ets.replace(day=1)
cursor.execute(
"""
SELECT valid, round(""" + varname + """::numeric,0)
from alldata where station = %s """ + year_limiter + """
and """ + varname + """ is not null and
extract(month from valid) in %s
ORDER by valid ASC
""", (station, tuple(months)))
(fig, ax) = plt.subplots(1, 1, figsize=(9, 6))
interval = datetime.timedelta(hours=hours)
valid = []
tmpf = []
year = 0
lines = []
for row in cursor:
if ((month != 'all' and year != row[0].year) or
(valid and (row[0] - valid[-1]) > datetime.timedelta(hours=3))):
year = row[0].year
lines = plot(ax, interval, valid, tmpf, lines, mydir, month)
valid = []
tmpf = []
if ((mydir == 'above' and row[1] >= threshold)
or (mydir == 'below' and row[1] < threshold)):
valid.append(row[0])
tmpf.append(row[1])
if ((mydir == 'above' and row[1] < threshold)
or (mydir == 'below' and row[1] >= threshold)):
valid.append(row[0])
tmpf.append(row[1])
lines = plot(ax, interval, valid, tmpf, lines, mydir, month)
valid = []
tmpf = []
lines = plot(ax, interval, valid, tmpf, lines, mydir, month)
compute_xlabels(ax)
rows = []
for line in lines:
# Ensure we don't send datetimes to pandas
rows.append(
dict(start=line.period_start.strftime("%Y-%m-%d %H:%M"),
end=line.period_end.strftime("%Y-%m-%d %H:%M"),
hours=line.hours,
days=line.days))
df = pd.DataFrame(rows)
ax.grid(True)
ax.set_ylabel(r"%s $^\circ$F" % (PDICT2.get(varname), ))
ab = ctx['_nt'].sts[station]['archive_begin']
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(("%s-%s [%s] %s\n"
r"%s :: %.0fd%.0fh+ Streaks %s %s$^\circ$F") %
(y1 if y1 is not None else ab.year,
y2 if y2 is not None else datetime.datetime.now().year,
station, ctx['_nt'].sts[station]['name'], MDICT.get(month),
hours / 24, hours % 24, mydir, threshold))
# ax.axhline(32, linestyle='-.', linewidth=2, color='k')
# ax.set_ylim(bottom=43)
ax.set_xlabel(("* Due to timezones and leapday, there is some ambiguity"
" with the plotted dates"))
ax.set_position([0.1, 0.25, 0.85, 0.65])
ax.legend(loc='upper center',
bbox_to_anchor=(0.5, -0.165),
fancybox=True,
shadow=True,
ncol=5,
fontsize=12,
columnspacing=1)
return fig, df
def get_data(ctx, startyear):
"""Get data"""
pgconn = get_dbconn("asos")
today = datetime.datetime.now()
lastyear = today.year
deltadays = 0
if ctx["season"] == "all":
months = range(1, 13)
elif ctx["season"] == "water_year":
deltadays = 92
months = range(1, 13)
elif ctx["season"] == "spring":
months = [3, 4, 5]
if today.month > 5:
lastyear += 1
elif ctx["season"] == "spring2":
months = [4, 5, 6]
if today.month > 6:
lastyear += 1
elif ctx["season"] == "fall":
months = [9, 10, 11]
if today.month > 11:
lastyear += 1
elif ctx["season"] == "summer":
months = [6, 7, 8]
if today.month > 8:
lastyear += 1
elif ctx["season"] == "winter":
deltadays = 33
months = [12, 1, 2]
if today.month > 2:
lastyear += 1
else:
ts = datetime.datetime.strptime("2000-" + ctx["season"] + "-01",
"%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
lastyear += 1
hours = range(24)
if ctx.get("hours"):
try:
tokens = [int(i.strip()) for i in ctx["hours"].split("-")]
hours = range(tokens[0], tokens[1] + 1)
except ValueError:
raise Exception("malformed hour limiter, sorry.")
ctx["hour_limiter"] = "[%s-%s]" % (
utc(2017, 1, 1, tokens[0]).strftime("%-I %p"),
utc(2017, 1, 1, tokens[1]).strftime("%-I %p"),
)
df = read_sql(
"""
WITH obs as (
SELECT valid at time zone %s as valid, tmpf, dwpf, relh,
coalesce(mslp, alti * 33.8639, 1013.25) as slp
from alldata WHERE station = %s and dwpf > -90
and dwpf < 100 and tmpf >= dwpf and
extract(month from valid) in %s and
extract(hour from valid at time zone %s) in %s
and report_type = 2
)
SELECT valid,
extract(year from valid + '%s days'::interval)::int as year,
tmpf, dwpf, slp, relh from obs
""",
pgconn,
params=(
ctx["_nt"].sts[ctx["station"]]["tzname"],
ctx["station"],
tuple(months),
ctx["_nt"].sts[ctx["station"]]["tzname"],
tuple(hours),
deltadays,
),
index_col=None,
)
if df.empty:
raise NoDataFound("No data found.")
df = df[(df["year"] >= startyear) & (df["year"] < lastyear)]
return df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("postgis")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
ugc = ctx["ugc"]
phenomena = ctx["phenomena"]
significance = ctx["significance"]
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
cursor.execute(
"""
SELECT s.wfo, s.tzname, u.name from ugcs u
JOIN stations s on (u.wfo = s.id)
where ugc = %s and end_ts is null and s.network = 'WFO'
""",
(ugc, ),
)
wfo = None
tzname = None
name = ""
if cursor.rowcount == 1:
row = cursor.fetchone()
tzname = row[1]
wfo = row[0]
name = row[2]
cursor.execute(
"""
SELECT count(*), min(issue at time zone %s), max(issue at time zone %s)
from warnings WHERE ugc = %s and phenomena = %s and significance = %s
and wfo = %s
""",
(tzname, tzname, ugc, phenomena, significance, wfo),
)
row = cursor.fetchone()
cnt = row[0]
sts = row[1]
ets = row[2]
if sts is None:
raise NoDataFound("No Results Found, try flipping zone/county")
cursor.execute(
"""
WITH coverage as (
SELECT extract(year from issue) as yr, eventid,
generate_series(issue at time zone %s,
expire at time zone %s, '1 minute'::interval) as s
from warnings where
ugc = %s and phenomena = %s and significance = %s and wfo = %s),
minutes as (SELECT distinct yr, eventid,
(extract(hour from s)::numeric * 60. +
extract(minute from s)::numeric) as m
from coverage)
SELECT minutes.m, count(*) from minutes GROUP by m
""",
(tzname, tzname, ugc, phenomena, significance, wfo),
)
data = np.zeros((1440, ), "f")
for row in cursor:
data[int(row[0])] = row[1]
df = pd.DataFrame(
dict(minute=pd.Series(np.arange(1440)), events=pd.Series(data)))
vals = data / float(cnt) * 100.0
ax.bar(np.arange(1440), vals, ec="b", fc="b")
if np.max(vals) > 50:
ax.set_ylim(0, 100)
ax.set_yticks([0, 10, 25, 50, 75, 90, 100])
ax.grid()
ax.set_xticks(range(0, 1441, 60))
ax.set_xticklabels([
"Mid",
"",
"",
"3 AM",
"",
"",
"6 AM",
"",
"",
"9 AM",
"",
"",
"Noon",
"",
"",
"3 PM",
"",
"",
"6 PM",
"",
"",
"9 PM",
"",
"",
"Mid",
])
ax.set_xlabel("Timezone: %s (Daylight or Standard)" % (tzname, ))
ax.set_ylabel("Frequency [%%] out of %s Events" % (cnt, ))
ax.set_title(("[%s] %s :: %s (%s.%s)\n%s Events - %s to %s") % (
ugc,
name,
vtec.get_ps_string(phenomena, significance),
phenomena,
significance,
cnt,
sts.strftime("%Y-%m-%d %I:%M %p"),
ets.strftime("%Y-%m-%d %I:%M %p"),
))
ax.set_xlim(0, 1441)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
varname = ctx["var"]
bs = ctx["_nt"].sts[station]["archive_begin"]
if bs is None:
raise NoDataFound("No Metadata found.")
res = """\
# IEM Climodat https://mesonet.agron.iastate.edu/climodat/
# Report Generated: %s
# Climate Record: %s -> %s
# Site Information: [%s] %s
# Contact Information: Daryl Herzmann [email protected] 515.294.5978
""" % (
datetime.date.today().strftime("%d %b %Y"),
bs.date(),
datetime.date.today(),
station,
ctx["_nt"].sts[station]["name"],
)
if varname == "maxmin":
res += (
"# DAILY RECORD HIGHS AND LOWS OCCURRING DURING %s-%s FOR "
"STATION NUMBER %s\n"
" JAN FEB MAR APR MAY JUN JUL "
"AUG SEP OCT NOV DEC\n"
" DY MX MN MX MN MX MN MX MN MX MN MX MN MX MN MX "
"MN MX MN MX MN MX MN MX MN\n") % (
bs.year, datetime.date.today().year, station)
elif varname == "means":
res += ("# DAILY MEAN HIGHS AND LOWS FOR STATION NUMBER %s\n"
" JAN FEB MAR APR MAY JUN JUL "
"AUG SEP OCT NOV DEC\n"
" DY MX MN MX MN MX MN MX MN MX MN MX MN MX MN "
"MX MN MX MN MX MN MX MN MX MN\n") % (station, )
elif varname == "range":
res += ("# RECORD LARGEST AND SMALLEST DAILY RANGES (MAX-MIN) "
"FOR STATION NUMBER %s\n"
" JAN FEB MAR APR MAY JUN JUL "
"AUG SEP OCT NOV DEC\n"
" DY MX MN MX MN MX MN MX MN MX MN MX MN MX MN "
"MX MN MX MN MX MN MX MN MX MN\n") % (station, )
else:
res += (f"# DAILY MAXIMUM PRECIPITATION FOR STATION NUMBER {station}\n"
" JAN FEB MAR APR MAY JUN JUL "
"AUG SEP OCT NOV DEC\n")
df = read_sql(
"SELECT * from climate WHERE station = %s",
pgconn,
params=(station, ),
index_col="valid",
)
bad = " ****" if varname == "precip" else " *** ***"
for day in range(1, 32):
res += "%3i" % (day, )
for mo in range(1, 13):
try:
ts = datetime.date(2000, mo, day)
if ts not in df.index:
res += bad
continue
except Exception:
res += bad
continue
row = df.loc[ts]
if row["max_high"] is None or row["min_low"] is None:
res += bad
continue
if varname == "maxmin":
res += "%4i%4i" % (row["max_high"], row["min_low"])
elif varname == "range":
res += "%4i%4i" % (row["max_range"], row["min_range"])
elif varname == "means":
res += "%4i%4i" % (row["high"], row["low"])
else:
res += "%6.2f" % (row["max_precip"], )
res += "\n"
return None, df, res
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
state = ctx["state"][:2]
sector = ctx["sector"]
opt = ctx["opt"]
p1syear = ctx["p1syear"]
p1eyear = ctx["p1eyear"]
p2syear = ctx["p2syear"]
p2eyear = ctx["p2eyear"]
varname = ctx["var"]
table = "alldata"
if sector == "state":
table = "alldata_%s" % (state, )
df = read_sql(
"""
WITH season1 as (
SELECT station, year,
min(case when month > 7 and low < 32 then
extract(doy from day) else 366 end) as first_freeze,
max(case when month < 7 and low < 32 then
extract(doy from day) else 0 end) as last_freeze
from """ + table + """ WHERE
year >= %s and year <= %s GROUP by station, year),
season2 as (
SELECT station, year,
min(case when month > 7 and low < 32 then
extract(doy from day) else 366 end) as first_freeze,
max(case when month < 7 and low < 32 then
extract(doy from day) else 0 end) as last_freeze
from """ + table + """ WHERE
year >= %s and year <= %s GROUP by station, year),
agg as (
SELECT p1.station, avg(p1.first_freeze) as p1_first_fall,
avg(p1.last_freeze) as p1_last_spring,
avg(p2.first_freeze) as p2_first_fall,
avg(p2.last_freeze) as p2_last_spring
from season1 as p1 JOIN season2 as p2 on (p1.station = p2.station)
GROUP by p1.station)
SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat,
d.* from agg d JOIN stations t ON (d.station = t.id)
WHERE t.network ~* 'CLIMATE'
and substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'
""",
pgconn,
params=[p1syear, p1eyear, p2syear, p2eyear],
index_col="station",
)
if df.empty:
raise NoDataFound("No Data Found")
df["p1_season"] = df["p1_first_fall"] - df["p1_last_spring"]
df["p2_season"] = df["p2_first_fall"] - df["p2_last_spring"]
df["season_delta"] = df["p2_season"] - df["p1_season"]
df["spring_delta"] = df["p2_last_spring"] - df["p1_last_spring"]
df["fall_delta"] = df["p2_first_fall"] - df["p1_first_fall"]
# Reindex so that most extreme values are first
df = df.reindex(df[varname +
"_delta"].abs().sort_values(ascending=False).index)
title = PDICT3[varname]
mp = MapPlot(
sector=sector,
state=state,
axisbg="white",
title=("%.0f-%.0f minus %.0f-%.0f %s Difference") %
(p2syear, p2eyear, p1syear, p1eyear, title),
subtitle=("based on IEM Archives"),
titlefontsize=14,
)
# Create 9 levels centered on zero
abval = df[varname + "_delta"].abs().max()
levels = centered_bins(abval)
if opt in ["both", "contour"]:
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname + "_delta"].values,
levels,
cmap=plt.get_cmap(ctx["cmap"]),
units="days",
)
if sector == "state":
mp.drawcounties()
if opt in ["both", "values"]:
mp.plot_values(
df["lon"].values,
df["lat"].values,
df[varname + "_delta"].values,
fmt="%.1f",
labelbuffer=5,
)
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx["v"]
df = get_df(ctx)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(
sector=("state" if ctx["t"] == "state" else "cwa"),
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title="%s for %s on %s" % (PDICT2[ctx["v"]], ctx["title"], ctx["day"]),
nocaption=True,
titlefontsize=16,
)
ramp = None
cmap = get_cmap(ctx["cmap"])
extend = "both"
if varname in ["max_gust", "max_sknt"]:
extend = "max"
ramp = np.arange(0, 40, 4)
ramp = np.append(ramp, np.arange(40, 80, 10))
ramp = np.append(ramp, np.arange(80, 120, 20))
# Data QC, cough
if ctx.get("above"):
df = df[df[varname] < ctx["above"]]
if ctx.get("below"):
df = df[df[varname] > ctx["below"]]
# with QC done, we compute ramps
if ramp is None:
ramp = np.linspace(df[varname].min() - 5,
df[varname].max() + 5,
10,
dtype="i")
if ctx["p"] == "both":
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
ramp,
units=VARUNITS[varname],
cmap=cmap,
spacing="proportional",
extend=extend,
)
if ctx["t"] == "state":
df2 = df[df[ctx["t"]] == ctx[ctx["t"]]]
else:
df2 = df[df["wfo"] == ctx["wfo"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2[varname].values,
"%.1f" if varname in ["max_gust", "max_sknt"] else "%.0f",
labelbuffer=3,
)
mp.drawcounties()
if ctx["t"] == "cwa":
mp.draw_cwas()
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop", user="******")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
thisyear = datetime.datetime.now().year
year = ctx["year"]
base = ctx["base"]
ceiling = ctx["ceiling"]
varname = ctx["var"]
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
ab = nt.sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown Station Metadata.")
syear = max(ab.year, 1893)
glabel = "%s%s%s" % (varname, base, ceiling)
gfunc = "gddxx(%s, %s, high, low)" % (base, ceiling)
title = "base=%s/ceil=%s" % (base, ceiling)
if varname in ["hdd", "cdd"]:
gfunc = "%s(high, low, %s)" % (varname, base)
title = "base=%s" % (base, )
elif varname == "sdd":
gfunc = "case when high > %s then high - %s else 0 end" % (
ceiling,
ceiling,
)
title = "base=%s" % (ceiling, )
df = read_sql(
"""
SELECT year, sday,
""" + gfunc + """ as """ + glabel + """
from """ + table + """ WHERE station = %s and year > 1892
and sday != '0229'
""",
pgconn,
params=(station, ),
)
if df.empty:
raise NoDataFound("No data Found.")
# Do some magic!
df2 = (df[[
"sday", glabel
]].groupby("sday").describe(percentiles=[0.05, 0.25, 0.75, 0.95]))
df2 = df2.unstack(level=-1)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.plot(
np.arange(1, 366),
df2[(glabel, "mean")],
color="r",
zorder=2,
lw=2.0,
label="Average",
)
_data = df[df["year"] == year][[glabel, "sday"]]
_data.sort_values(by="sday", inplace=True)
ax.scatter(
np.arange(1, _data[glabel].shape[0] + 1),
_data[glabel],
color="b",
zorder=2,
label="%s" % (year, ),
)
ax.bar(
np.arange(1, 366),
df2[(glabel, "95%")] - df2[(glabel, "5%")],
bottom=df2[(glabel, "5%")],
ec="tan",
fc="tan",
zorder=1,
label="5-95 Percentile",
)
ax.bar(
np.arange(1, 366),
df2[(glabel, "75%")] - df2[(glabel, "25%")],
bottom=df2[(glabel, "25%")],
ec="lightblue",
fc="lightblue",
zorder=1,
label="25-75 Percentile",
)
ax.set_xlim(0, 367)
if varname == "gdd":
ax.set_ylim(-0.25, 40)
ax.grid(True)
ax.set_title("%s-%s %s [%s]\n%s %s (%s)" % (
syear,
thisyear,
nt.sts[station]["name"],
station,
year,
PDICT[varname],
title,
))
ax.set_ylabel(r"Daily Accumulation $^{\circ}\mathrm{F}$")
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax.legend(ncol=2)
ax.set_xticklabels(calendar.month_abbr[1:])
# collapse the multiindex for columns
df = pd.DataFrame(df2)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
varname = ctx["var"]
month = ctx["month"]
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
yr = "year as yr"
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
yr = "extract(year from o.day - '60 days'::interval) as yr"
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(
"""
WITH avgs as (
SELECT sday, avg(high) as avg_high,
avg(low) as avg_low,
avg((high+low)/2.) as avg_temp from """ + table + """ WHERE
station = %s GROUP by sday)
SELECT """ + yr + """,
sum(case when o.high > a.avg_high then 1 else 0 end) as high_above,
sum(case when o.low > a.avg_low then 1 else 0 end) as low_above,
sum(case when (o.high+o.low)/2. > a.avg_temp then 1 else 0 end)
as avg_above,
count(*) as days from """ + table + """ o, avgs a WHERE o.station = %s
and o.sday = a.sday and month in %s
GROUP by yr ORDER by yr ASC
""",
pgconn,
params=(station, station, tuple(months)),
index_col="yr",
)
if df.empty:
raise NoDataFound("No Data Found.")
df["high_freq"] = df["high_above"] / df["days"].astype("f") * 100.0
df["low_freq"] = df["low_above"] / df["days"].astype("f") * 100.0
df["avg_freq"] = df["avg_above"] / df["days"].astype("f") * 100.0
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
avgv = df[varname + "_freq"].mean()
colorabove = "r"
colorbelow = "b"
data = df[varname + "_freq"].values
bars = ax.bar(df.index.values,
data,
fc=colorabove,
ec=colorabove,
align="center")
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)
txt = ax.text(
bars[10].get_x(),
avgv,
"Avg: %.1f%%" % (avgv, ),
color="yellow",
fontsize=14,
va="center",
)
txt.set_path_effects([PathEffects.withStroke(linewidth=5, foreground="k")])
ax.set_ylim(0, 100)
ax.set_yticks([0, 10, 25, 50, 75, 90, 100])
ax.set_xlabel("Year")
ax.set_xlim(bars[0].get_x() - 1, bars[-1].get_x() + 1)
ax.set_ylabel("Frequency [%]")
ax.grid(True)
msg = ("[%s] %s %s-%s Percentage of Days with %s Above Average (month=%s)"
) % (
station,
nt.sts[station]["name"],
df.index.values.min(),
df.index.values.max(),
PDICT[varname],
month.upper(),
)
tokens = msg.split()
sz = int(len(tokens) / 2)
ax.set_title(" ".join(tokens[:sz]) + "\n" + " ".join(tokens[sz:]))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
month = ctx["month"]
table = "alldata_%s" % (station[:2],)
# beat month
cursor.execute(
"""
with obs as (
SELECT sday, avg(high) as avgh, avg(low) as avgl,
avg((high+low)/2.) as avgt from """
+ table
+ """
WHERE station = %s and month = %s GROUP by sday
), c81 as (
SELECT to_char(valid, 'mmdd') as sday, high, low, (high+low)/2. as avgt
from ncdc_climate81 where station = %s
), c71 as (
SELECT to_char(valid, 'mmdd') as sday, high, low, (high+low)/2. as avgt
from ncdc_climate71 where station = %s
)
SELECT o.sday, o.avgh, c81.high, c71.high,
o.avgl, c81.low, c71.low,
o.avgt, c81.avgt, c71.avgt from
obs o, c81, c71 where o.sday = c81.sday and o.sday = c71.sday
ORDER by o.sday ASC
""",
(station, month, ctx["_nt"].sts[station]["ncdc81"], station),
)
if cursor.rowcount == 0:
raise NoDataFound("No Data Found.")
o_avgh = []
o_avgl = []
o_avgt = []
c81_avgh = []
c81_avgl = []
c81_avgt = []
c71_avgh = []
c71_avgl = []
c71_avgt = []
days = []
for row in cursor:
days.append(int(row[0][2:]))
o_avgh.append(float(row[1]))
o_avgl.append(float(row[4]))
o_avgt.append(float(row[7]))
c81_avgh.append(row[2])
c81_avgl.append(row[5])
c81_avgt.append(row[8])
c71_avgh.append(row[3])
c71_avgl.append(row[6])
c71_avgt.append(row[9])
df = pd.DataFrame(
dict(
day=pd.Series(days),
iem_avgh=pd.Series(o_avgh),
iem_avgl=pd.Series(o_avgl),
iem_avgt=pd.Series(o_avgt),
ncei81_avgh=pd.Series(c81_avgh),
ncei81_avgl=pd.Series(c81_avgl),
ncei81_avgt=pd.Series(c81_avgt),
ncei71_avgh=pd.Series(c71_avgh),
ncei71_avgl=pd.Series(c71_avgl),
ncei71_avgt=pd.Series(c71_avgt),
)
)
days = np.array(days)
(fig, ax) = plt.subplots(3, 1, sharex=True, figsize=(8, 6))
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax[0].set_title(
("%s %s Daily Climate Comparison\n" "Observation Period: %s-%s for %s")
% (
station,
ctx["_nt"].sts[station]["name"],
ab.year,
datetime.datetime.now().year,
calendar.month_name[month],
),
fontsize=12,
)
ax[0].bar(days, o_avgh, width=0.8, fc="tan", align="center")
ax[0].plot(days, c81_avgh, lw=2, zorder=2, color="g")
ax[0].plot(days, c71_avgh, lw=2, zorder=2, color="r")
ax[0].grid(True)
ax[0].set_ylabel(r"High Temp $^\circ$F")
ax[0].set_ylim(bottom=min([min(o_avgh), min(c71_avgh), min(c81_avgh)]) - 2)
ax[1].bar(days, o_avgl, width=0.8, fc="tan", align="center")
ax[1].plot(days, c81_avgl, lw=2, zorder=2, color="g")
ax[1].plot(days, c71_avgl, lw=2, zorder=2, color="r")
ax[1].grid(True)
ax[1].set_ylabel(r"Low Temp $^\circ$F")
ax[1].set_ylim(bottom=min([min(o_avgl), min(c71_avgl), min(c81_avgl)]) - 2)
ax[2].bar(
days,
o_avgt,
width=0.8,
fc="tan",
align="center",
label="IEM Observered Avg",
)
ax[2].plot(
days, c81_avgt, lw=2, zorder=2, color="g", label="NCEI 1981-2010"
)
ax[2].plot(
days, c71_avgt, lw=2, zorder=2, color="r", label="NCEI 1971-2000"
)
ax[2].grid(True)
ax[2].legend(
loc="lower center",
bbox_to_anchor=(0.5, 0.95),
fancybox=True,
shadow=True,
ncol=3,
scatterpoints=1,
fontsize=10,
)
ax[2].set_ylabel(r"Average Temp $^\circ$F")
ax[2].set_ylim(bottom=min([min(o_avgt), min(c71_avgt), min(c81_avgt)]) - 2)
ax[2].set_xlabel("Day of %s" % (calendar.month_name[month],))
ax[2].set_xlim(0.5, len(days) + 0.5)
return fig, df
def plot1(ctx):
"""Do main plotting logic"""
df = read_sql(
"""
SELECT * from sm_hourly WHERE
station = %s and valid BETWEEN %s and %s ORDER by valid ASC
""",
ctx["pgconn"],
params=(ctx["station"], ctx["sts"], ctx["ets"]),
index_col="valid",
)
if df.empty:
raise NoDataFound("No Data Found for This Plot.")
slrkw = df["slrkw_avg_qc"]
d12sm = df["calc_vwc_12_avg_qc"]
d12t = df["t12_c_avg_qc"]
d24t = df["t24_c_avg_qc"]
d50t = df["t50_c_avg_qc"]
d24sm = df["calc_vwc_24_avg_qc"]
d50sm = df["calc_vwc_50_avg_qc"]
rain = df["rain_mm_tot_qc"]
tair = df["tair_c_avg_qc"]
tsoil = df["tsoil_c_avg_qc"]
valid = df.index.values
(fig, ax) = plt.subplots(3, 1, sharex=True, figsize=(8, 8))
ax[0].grid(True)
ax2 = ax[0].twinx()
ax[0].set_zorder(ax2.get_zorder() + 1)
ax[0].patch.set_visible(False)
# arange leads to funky values
ax2.set_yticks([-0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0])
ax2.set_yticklabels([0.6, 0.5, 0.4, 0.3, 0.2, 0.1, 0])
ax2.set_ylim(-0.6, 0)
ax2.set_ylabel("Hourly Precipitation [inch]")
b1 = ax2.bar(valid, 0 - rain / 25.4, width=0.04, fc="b", ec="b", zorder=4)
l1 = None
l2 = None
l3 = None
if not d12sm.isnull().all():
l1, = ax[0].plot(valid,
d12sm * 100.0,
linewidth=2,
color="r",
zorder=5)
if not d24sm.isnull().all():
l2, = ax[0].plot(valid,
d24sm * 100.0,
linewidth=2,
color="purple",
zorder=5)
if not d50sm.isnull().all():
l3, = ax[0].plot(valid,
d50sm * 100.0,
linewidth=2,
color="black",
zorder=5)
ax[0].set_ylabel("Volumetric Soil Water Content [%]", fontsize=10)
days = (ctx["ets"] - ctx["sts"]).days
if days >= 3:
interval = max(int(days / 7), 1)
ax[0].xaxis.set_major_locator(
mdates.DayLocator(interval=interval,
tz=pytz.timezone("America/Chicago")))
ax[0].xaxis.set_major_formatter(
mdates.DateFormatter("%-d %b\n%Y",
tz=pytz.timezone("America/Chicago")))
else:
ax[0].xaxis.set_major_locator(
mdates.AutoDateLocator(maxticks=10,
tz=pytz.timezone("America/Chicago")))
ax[0].xaxis.set_major_formatter(
mdates.DateFormatter("%-I %p\n%d %b",
tz=pytz.timezone("America/Chicago")))
ax[0].set_title(("ISUSM Station: %s Timeseries") %
(ctx["_nt"].sts[ctx["station"]]["name"], ))
box = ax[0].get_position()
ax[0].set_position(
[box.x0, box.y0 + box.height * 0.05, box.width, box.height * 0.95])
box = ax2.get_position()
ax2.set_position(
[box.x0, box.y0 + box.height * 0.05, box.width, box.height * 0.95])
if None not in [l1, l2, l3]:
ax[0].legend(
[l1, l2, l3, b1],
["12 inch", "24 inch", "50 inch", "Hourly Precip"],
bbox_to_anchor=(0.5, -0.15),
ncol=4,
loc="center",
fontsize=12,
)
# ----------------------------------------
if not d12t.isnull().all():
ax[1].plot(
valid,
temperature(d12t, "C").value("F"),
linewidth=2,
color="r",
label="12in",
)
if not d24t.isnull().all():
ax[1].plot(
valid,
temperature(d24t, "C").value("F"),
linewidth=2,
color="purple",
label="24in",
)
if not d50t.isnull().all():
ax[1].plot(
valid,
temperature(d50t, "C").value("F"),
linewidth=2,
color="black",
label="50in",
)
ax[1].grid(True)
ax[1].set_ylabel(r"Temperature $^\circ$F")
box = ax[1].get_position()
ax[1].set_position(
[box.x0, box.y0 + box.height * 0.05, box.width, box.height * 0.95])
# ------------------------------------------------------
ax2 = ax[2].twinx()
l3, = ax2.plot(valid, slrkw, color="g", zorder=1, lw=2)
ax2.set_ylabel("Solar Radiation [W/m^2]", color="g")
l1, = ax[2].plot(
valid,
temperature(tair, "C").value("F"),
linewidth=2,
color="blue",
zorder=2,
)
l2, = ax[2].plot(
valid,
temperature(tsoil, "C").value("F"),
linewidth=2,
color="brown",
zorder=2,
)
ax[2].grid(True)
ax[2].legend(
[l1, l2, l3],
["Air", '4" Soil', "Solar Radiation"],
bbox_to_anchor=(0.5, 1.1),
loc="center",
ncol=3,
)
ax[2].set_ylabel(r"Temperature $^\circ$F")
ax[2].set_zorder(ax2.get_zorder() + 1)
ax[2].patch.set_visible(False)
ax[0].set_xlim(df.index.min(), df.index.max())
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
varname = ctx['varname']
agg = ctx['agg']
table = "alldata_%s" % (station[:2], )
lastday = datetime.date.today()
if varname == 'total_precip' and agg == 'max':
lastday += datetime.timedelta(days=1)
else:
lastday = lastday.replace(day=1)
df = read_sql("""SELECT year, month, avg((high+low)/2.) as avg_temp,
avg(high) as avg_high_temp, avg(low) as avg_low_temp,
sum(precip) as total_precip
from """ + table +
""" where station = %s and day < %s GROUP by year, month
""",
pgconn,
params=(station, lastday))
if df.empty:
raise NoDataFound("No Data Found.")
resdf = pd.DataFrame(
dict(monthname=pd.Series(calendar.month_abbr[1:], index=range(1, 13))),
index=pd.Series(range(1, 13), name='month'))
for _varname in PDICT:
for _agg in [min, max]:
df2 = df[[_varname, 'month', 'year']]
df2 = df2[df[_varname] == df.groupby('month')[_varname].transform(
_agg)].copy()
df2.rename(columns={
'year': '%s_%s_year' % (_agg.__name__, _varname),
_varname: '%s_%s' % (_agg.__name__, _varname)
},
inplace=True)
df2.set_index('month', inplace=True)
resdf = resdf.join(df2)
# The above can end up with duplicates
resdf = resdf.groupby(level=0)
resdf = resdf.last()
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
col = "%s_%s" % (agg, varname)
ax.bar(np.arange(1, 13), resdf[col], fc='pink', align='center')
for month, row in resdf.iterrows():
if np.isnan(row[col]):
continue
ax.text(month,
row[col],
"%.0f\n%.2f" % (row[col + '_year'], row[col]),
ha='center',
va='bottom')
ax.set_xlim(0.5, 12.5)
ax.set_ylim(top=resdf[col].max() * 1.2)
ab = ctx['_nt'].sts[station]['archive_begin']
if ab is None:
raise NoDataFound("Unknown Station Metadata.")
ax.set_title(("[%s] %s\n%s %s [%s-%s]\n") %
(station, ctx['_nt'].sts[station]['name'], PDICT2[agg],
PDICT[varname], ab.year, lastday.year))
ylabel = r"Temperature $^\circ$F"
if varname in ['total_precip']:
ylabel = 'Precipitation [inch]'
ax.set_ylabel(ylabel)
ax.grid(True)
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_xticks(np.arange(1, 13))
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width, box.height * 0.95])
return fig, resdf