Python MapPlot.fill_climdiv方法代码示例

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')

    year = int(fdict.get('year', 2014))
    month = int(fdict.get('month', 9))
    varname = fdict.get('var', 'precip')

    lastyear = datetime.date.today().year
    years = lastyear - 1893 + 1

    df = read_sql("""
    with monthly as (
        SELECT year, station,
        sum(precip) as p,
        avg((high+low)/2.) as avgt
        from alldata
        WHERE substr(station,3,1) = 'C' and month = %s
        GROUP by year, station),
    ranks as (
        SELECT station, year,
        rank() OVER (PARTITION by station ORDER by p DESC) as precip_rank,
        rank() OVER (PARTITION by station ORDER by avgt DESC) as avgt_rank
        from monthly)

    SELECT station, precip_rank, avgt_rank from ranks
    where year = %s """, pgconn, params=(month, year), index_col='station')

    m = MapPlot(sector='midwest', axisbg='white',
                title='%s %s %s Ranks by Climate District' % (
                        year, calendar.month_name[month], PDICT[varname]),
                subtitle=('Based on IEM Estimates, '
                          '1 is %s out of %s total years (1893-%s)'
                          ) % ('wettest' if varname == 'precip' else 'hottest',
                               years, lastyear)
                )
    cmap = cm.get_cmap("BrBG_r" if varname == 'precip' else 'BrBG')
    cmap.set_under('white')
    cmap.set_over('black')
    m.fill_climdiv(df[varname+'_rank'], ilabel=True, plotmissing=False,
                   bins=[1, 5, 10, 25, 50, 75, 100, years-10, years-5, years],
                   cmap=cmap)

    return m.fig, df

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)

    year = int(fdict.get('year', 2014))
    month = int(fdict.get('month', 9))

    lastyear = datetime.date.today().year
    years = lastyear - 1893 + 1

    cursor.execute("""
    with monthly as (
        SELECT year, station, sum(precip) as p from alldata
        WHERE substr(station,3,1) = 'C' and month = %s
        GROUP by year, station),
    ranks as (
        SELECT station, year,
        rank() OVER (PARTITION by station ORDER by p DESC) from monthly)

    SELECT station, rank from ranks where year = %s """, (month, year))
    data = {}
    rows = []
    for row in cursor:
        data[row['station']] = row['rank']
        rows.append(dict(station=row['station'], rank=row['rank']))
    df = pd.DataFrame(rows)

    m = MapPlot(sector='midwest', axisbg='white',
                title='%s %s Precipitation Total Ranks by Climate District' % (
                        year, calendar.month_name[month]),
                subtitle=('Based on IEM Estimates, '
                          '1 is wettest out of %s total years (1893-%s)'
                          ) % (years, lastyear)
                )
    cmap = cm.get_cmap("BrBG_r")
    cmap.set_under('white')
    cmap.set_over('black')
    m.fill_climdiv(data,
                   bins=[1, 5, 10, 25, 50, 75, 100, years-10, years-5, years],
                   cmap=cmap)

    return m.fig, df

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
from pyiem.plot import MapPlot
import numpy
import psycopg2

dbconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')
cursor = dbconn.cursor()
clim = {}
cursor.execute("""SELECT station, avg(sum) from 
  (SELECT station, year, sum(precip) from alldata where
  year < 2013 and year > 1950 and substr(station,3,1) = 'C' and month in (4,5) 
  and sday < '0520' GROUP by station, year) as foo
  GROUP by station""")
for row in cursor:
  clim[ row[0] ] = row[1]

data = {}
cursor.execute("""SELECT station, sum(precip) from alldata where
  year = 2013 and substr(station,3,1) = 'C' and month in (4,5) GROUP by station""")
for row in cursor:
  data[ row[0] ] = row[1] - clim[ row[0] ]

m = MapPlot(sector='midwest', title='IEM Estimated Climate Division Precipitation Departure [inch]', subtitle='1 April thru 19 May 2013 vs 1950-2012 Climate')
m.fill_climdiv(data, bins=numpy.arange(5,-5.1,-0.25), lblformat='%.1f')
m.postprocess(filename='test.svg')
import iemplot
iemplot.makefeature('test')

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')

    year = int(fdict.get('year', 2014))
    month = fdict.get('month', 9)
    varname = fdict.get('var', 'precip')
    l = "0 days"
    if month == 'fall':
        months = [9, 10, 11]
        label = "Fall (SON)"
    elif month == 'winter':
        months = [12, 1, 2]
        l = "32 days"
        label = "Winter (DJF)"
    elif month == 'spring':
        months = [3, 4, 5]
        label = "Spring (MAM)"
    elif month == 'summer':
        months = [6, 7, 8]
        label = "Summer (JJA)"
    else:
        months = [int(month), ]
        label = calendar.month_name[int(month)]

    lastyear = datetime.date.today().year
    years = lastyear - 1893 + 1

    df = read_sql("""
    with monthly as (
        SELECT extract(year from day + '"""+l+"""'::interval) as myyear,
        station, sum(precip) as p,
        avg((high+low)/2.) as avgt,
        avg(high) as avghi
        from alldata
        WHERE substr(station,3,1) = 'C' and month in %s
        GROUP by myyear, station),
    ranks as (
        SELECT station, myyear as year,
        avg(p) OVER (PARTITION by station) as avg_precip,
        stddev(p) OVER (PARTITION by station) as std_precip,
        p as precip,
        avg(avghi) OVER (PARTITION by station) as avg_high,
        stddev(avghi) OVER (PARTITION by station) as std_high,
        avghi as high,
        rank() OVER (PARTITION by station ORDER by p DESC) as precip_rank,
        rank() OVER (PARTITION by station ORDER by avgt DESC) as avgt_rank
        from monthly)

    SELECT station, precip_rank, avgt_rank,
    ((high - avg_high) / std_high) - ((precip - avg_precip) / std_precip)
    as arridity from ranks
    where year = %s """, pgconn, params=(tuple(months), year),
                  index_col='station')

    subtitle = ('Based on IEM Estimates, '
                '1 is %s out of %s total years (1893-%s)'
                ) % ('wettest' if varname == 'precip' else 'hottest',
                     years, lastyear)
    if varname == 'arridity':
        subtitle = "Std Average High Temp Departure minus Std Precip Departure"
    m = MapPlot(sector='midwest', axisbg='white',
                title='%s %s %s %sby Climate District' % (
                        year, label, PDICT[varname],
                        'Ranks ' if varname != 'arridity' else ''),
                subtitle=subtitle)
    cmap = cm.get_cmap("BrBG_r" if varname in ['precip', 'arridity']
                       else 'BrBG')
    cmap.set_under('white')
    cmap.set_over('black')
    bins = [1, 5, 10, 25, 50, 75, 100, years-10, years-5, years]
    pvar = varname+'_rank'
    fmt = '%.0f'
    if varname == 'arridity':
        bins = np.arange(-4, 4.1, 1)
        pvar = varname
        fmt = '%.1f'
    m.fill_climdiv(df[pvar], ilabel=True, plotmissing=False, lblformat=fmt,
                   bins=bins,
                   cmap=cmap)

    return m.fig, df

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
import matplotlib.cm as cm

pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')
cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)

cursor.execute("""
  with monthly as (
   SELECT station, extract(year from day + '40 days'::interval) as yr,
   avg((high+low)/2.) from alldata
   where month in (12,1,2) and substr(station,3,1) = 'C' GROUP by station, yr
   ),
   nextup as (
  SELECT station, yr, rank() OVER (PARTITION by station ORDER by avg ASC)
  from monthly
  )

  SELECT station, rank from nextup where yr = 2015
  """)
data = {}
for row in cursor:
    data[row['station']] = row['rank']

m = MapPlot(sector='midwest', axisbg='white',
            title='Dec 2014 - Jan/Feb 2015 Average Temperature Rank (1 is coldest)',
            subtitle='Based on IEM Estimates of Climate District Data (1893-2015)')
cmap = cm.get_cmap("BrBG_r")
cmap.set_under('black')
cmap.set_over('black')
m.fill_climdiv(data, cmap=cmap, bins=[1, 10, 25, 50, 75, 100, 115, 123])
m.postprocess(filename='test.png')

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
def test_plot3():
    """ Exercise climdiv plot API """
    mp = MapPlot(sector='iowa', nocaption=True)
    mp.fill_climdiv({'IAC001': 80, 'AKC003': 5, 'HIC003': 30,
                     'AJK': 40, 'HFO': 50})
    return mp.fig

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_climdiv [as 别名]
def test_climdiv():
    """Run tests agains the fill_climdiv"""
    mp = MapPlot(sector='conus', title="Climate Divisions", nocaption=True)
    data = {'IAC001':  10, 'MNC001': 20, 'NMC001': 30}
    mp.fill_climdiv(data, ilabel=True)
    return mp.fig