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@@ -1,9 +1,12 @@ |
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import pandas as pd
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import datetime
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+import psycopg2
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+from pandas.io.sql import read_sql
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from collections import OrderedDict
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from pyiem.meteorology import gdd
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from pyiem.datatypes import temperature, distance
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from pyiem.util import get_autoplot_context
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+from scipy import stats
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STATIONS = OrderedDict([
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('ames', 'Central (Ames)'),
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@@ -18,6 +21,26 @@ |
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('jan1', 'January 1'),
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('mar15', 'March 15'),
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])
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+COUNTY = {'ames': 169, 'cobs': 169, 'crawfordsville': 183, 'lewis': 155,
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+ 'nashua': 67, 'sutherland': 141}
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+PDICT = {'yes': 'Colorize Labels by Corn Yield Trend',
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+ 'no': 'No Colorize'}
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+
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+
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+def load_yields(location):
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+ """Loads up the county corn yields"""
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+ pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')
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+ df = read_sql("""select year, num_value as yield
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+ from nass_quickstats where
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+ county_ansi = %s and state_alpha = 'IA' and year >= 1980
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+ and commodity_desc = 'CORN' and statisticcat_desc = 'YIELD'
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+ and unit_desc = 'BU / ACRE' ORDER by year ASC
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+ """, pgconn, params=(COUNTY[location],), index_col='year')
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+ slp, intercept, _, _, _ = stats.linregress(df.index.values,
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+ df['yield'].values)
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+ df['model'] = slp * df.index.values + intercept
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+ df['departure'] = 100. * (df['yield'] - df['model']) / df['model']
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+ return df
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def get_description():
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@@ -29,6 +52,8 @@ def get_description(): |
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label='Select Location:', options=STATIONS),
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dict(type='select', name='s', default='jan1',
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label='Select Plot Start Date:', options=SDATES),
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+ dict(type='select', name='opt', default='no',
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+ label='Plot Corn Yield Trends:', options=PDICT),
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]
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return d
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@@ -89,8 +114,11 @@ def plotter(fdict): |
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import matplotlib
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matplotlib.use('agg')
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import matplotlib.pyplot as plt
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+ import matplotlib.patheffects as PathEffects
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ctx = get_autoplot_context(fdict, get_description())
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location = ctx['location']
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+ opt = ctx['opt']
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+ yields = load_yields(location)
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sdate = ctx['s']
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# we need to compute totals using two datasources
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df = load("/mesonet/share/pickup/yieldfx", location, sdate)
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@@ -120,10 +148,26 @@ def plotter(fdict): |
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(fig, ax) = plt.subplots(1, 1)
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for year, row in resdf.iterrows():
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- color = 'r' if year == today.year else 'k'
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- ax.text(row['avgt'], row['rain'], "%s" % (str(year)[-2:],),
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- color=color, ha='center', va='center', zorder=4)
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-
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+ c = 'k'
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+ sz = 10.
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+ if year in yields.index and opt == 'yes':
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+ myyield = yields.at[year, 'departure']
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+ sz = 10. + (abs(int(myyield)) / 50.) * 20.
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+ c = 'g' if myyield > 0 else 'r'
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+ txt = ax.text(row['avgt'], row['rain'], "%s" % (str(year)[-2:],),
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+ color=c, ha='center', va='center', zorder=4,
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+ fontsize=sz)
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+ txt.set_path_effects([PathEffects.withStroke(linewidth=2,
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+ foreground="w")])
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+ if opt == 'yes':
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+ for y, x in enumerate(range(-50, 51, 10)):
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+ if x == 0:
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+ continue
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+ sz = 10. + (abs(x) / 50.) * 20.
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+ c = 'g' if x > 0 else 'r'
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+ p = '+' if x > 0 else ''
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+ ax.text(1.01, float(y / 11.), "%s%s%%" % (p, x),
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+ transform=ax.transAxes, color=c, fontsize=sz)
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xavg = resdf['avgt'].mean()
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ax.axvline(xavg)
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dx = (resdf['avgt'] - xavg).abs().max()
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@@ -134,9 +178,10 @@ def plotter(fdict): |
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dy = (resdf['rain'] - yavg).abs().max()
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ax.set_ylim(max([0, yavg - (dy * 1.1)]), yavg + (dy * 1.1))
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+ sts = datetime.datetime.strptime(sdate, '%b%d')
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ax.set_title(("%s %s to %s [%s-%s]"
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) % (STATIONS[location],
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- '1 November' if sdate == 'nov1' else '15 March',
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+ sts.strftime("%-d %B"),
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today.strftime("%-d %B"), 1980,
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today.year))
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ax.set_xlabel("Average Temperature [$^\circ$F]")
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@@ -150,6 +195,7 @@ def plotter(fdict): |
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ax.text(0.85, 0.05, "Hot & Dry", transform=ax.transAxes,
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fontsize=14, color='b', zorder=3, ha='center', va='center')
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ax.grid(True)
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+ ax.set_position([0.1, 0.1, 0.7, 0.8])
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return fig, resdf
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