autoplot of temp frequency when it is precipitating

https://mesonet.agron.iastate.edu/plotting/auto/?q=146

htdocs/plotting/auto/scripts/__init__.py

@@ -238,6 +238,8 @@
          'label': "Monthly Frequency of Wind Speeds by Air Temperature"},
         {'id': "37",
          'label': "MOS Forecasted Temperature Ranges + Observations"},
+        {'id': "146",
+         'label': "Temperature Frequency by Week During Precipitation"},
         {'id': "16", 'label': "Wind Rose when specified criterion is meet"},
     ]},
     {'label': 'NASS Quickstats (USDA Crop Statistics)', 'options': [

htdocs/plotting/auto/scripts100/p146.py

@@ -0,0 +1,91 @@
+import psycopg2
+from pyiem.network import Table as NetworkTable
+import datetime
+import calendar
+import numpy as np
+from pandas.io.sql import read_sql
+import pandas as pd
+
+
+def get_description():
+    """ Return a dict describing how to call this plotter """
+    d = dict()
+    d['data'] = True
+    d['description'] = """This chart displays the frequency of having
+    measurable precipitation reported by an ASOS site and the air temperature
+    that was reported at the same time.  This chart makes an assumption
+    about the two values being coincident, whereas in actuality they may not
+    have been.
+    """
+    d['arguments'] = [
+        dict(type='zstation', name='zstation', default='AMW',
+             label='Select Station:'),
+    ]
+    return d
+
+
+def plotter(fdict):
+    """ Go """
+    import matplotlib
+    matplotlib.use('agg')
+    import matplotlib.pyplot as plt
+    pgconn = psycopg2.connect(database='asos', host='iemdb', user='nobody')
+
+    station = fdict.get('zstation', 'AMW')
+    network = fdict.get('network', 'IA_ASOS')
+    nt = NetworkTable(network)
+
+    df = read_sql("""
+    WITH obs as (
+        SELECT date_trunc('hour', valid) as t, avg(tmpf) as avgt from alldata
+        WHERE station = %s and p01i >= 0.01 and tmpf is not null
+        GROUP by t
+    )
+
+    SELECT extract(week from t) as week, avgt from obs
+    """, pgconn, params=(station,), index_col=None)
+
+    sts = datetime.datetime(2012, 1, 1)
+    xticks = []
+    for i in range(1, 13):
+        ts = sts.replace(month=i)
+        xticks.append(int(ts.strftime("%j")))
+
+    (fig, ax) = plt.subplots(1, 1)
+
+    bins = np.arange(df['avgt'].min() - 5, df['avgt'].max() + 5, 2)
+    H, xedges, yedges = np.histogram2d(df['week'].values,
+                                       df['avgt'].values, [range(0, 54), bins])
+    rows = []
+    for i, x in enumerate(xedges[:-1]):
+        for j, y in enumerate(yedges[:-1]):
+            rows.append(dict(tmpf=y, week=x, count=H[i, j]))
+    resdf = pd.DataFrame(rows)
+
+    years = datetime.date.today().year - nt.sts[station]['archive_begin'].year
+    H = np.ma.array(H) / float(years)
+    H.mask = np.ma.where(H < 0.1, True, False)
+    res = ax.pcolormesh((xedges - 1) * 7, yedges, H.transpose(),
+                        cmap=plt.get_cmap("jet"))
+    fig.colorbar(res, label='Hours per week per year')
+    ax.set_xticks(xticks)
+    ax.set_xticklabels(calendar.month_abbr[1:])
+    ax.set_xlim(0, 366)
+
+    y = []
+    for i in range(np.shape(H)[0]):
+        y.append(np.ma.sum(H[i, :] * (bins[:-1]+0.5)) / np.ma.sum(H[i, :]))
+
+    ax.plot(xedges[:-1]*7, y, zorder=3, lw=3, color='w')
+    ax.plot(xedges[:-1]*7, y, zorder=3, lw=1, color='k', label='Average')
+    ax.legend(loc=2)
+
+    ax.set_title(("[%s] %s (%s-%s)\n"
+                  "Temperature Frequency During Precipitation by Week"
+                  ) % (station, nt.sts[station]['name'],
+                       nt.sts[station]['archive_begin'].year,
+                       datetime.date.today().year))
+    ax.grid(True)
+    ax.set_ylabel("Temperature [$^\circ$F]")
+
+    return fig, resdf