Python MapPlot.fill_cwas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_cwas [as 别名]
def main():
    """Go Main"""
    df = get_database_data()
    print(df)
    vals = {}
    labels = {}
    for wfo, row in df.iterrows():
        if wfo == 'JSJ':
            wfo = 'SJU'
        vals[wfo] = row['percent']
        labels[wfo] = '%.0f%%' % (row['percent'], )
        #if row['count'] == 0:
        #    labels[wfo] = '-'

    bins = np.arange(0, 101, 10)    
    #bins = [1, 25, 50, 75, 100, 125, 150, 200, 300]
    #bins = [-50, -25, -10, -5, 0, 5, 10, 25, 50]
    # bins[0] = 1
    #clevlabels = ['N', 'NE', 'E', 'SE', 'S', 'SW', 'W', 'NW', 'N']
    cmap = plt.get_cmap('PuOr')
    mp = MapPlot(sector='nws', continentalcolor='white', figsize=(12., 9.),
                 title=("2018 Percentage of Time with 1+ Flood Warning Active"),
                 subtitle=('1 January - 30 September 2018, based on IEM archives'))
    mp.fill_cwas(vals, bins=bins, lblformat='%s', labels=labels,
                 cmap=cmap, ilabel=True,  # clevlabels=clevlabels,
                 units='percent')
    
    mp.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_cwas [as 别名]
def main():
    """Go MAin"""
    df = pd.read_csv('flood_emergencies.csv')
    df2 = df[['source', 'eventid', 'phenomena', 'significance', 'year']
             ].drop_duplicates()
    gdf = df2.groupby('source').count()
    vals = {}
    labels = {}
    for wfo, row in gdf.iterrows():
        if wfo == 'TJSJ':
            wfo = 'SJU'
        else:
            wfo = wfo[1:]
        vals[wfo] = int(row['eventid'])
        labels[wfo] = "%s" % (row['eventid'], )

    bins = list(range(0, 31, 3))
    bins[0] = 1.
    cmap = plt.get_cmap('plasma_r')
    cmap.set_over('black')
    cmap.set_under('white')
    mp = MapPlot(sector='nws', continentalcolor='white', figsize=(12., 9.),
                 title=("2003-2018 Flash Flood Emergency Events"),
                 subtitle=('based on unofficial IEM archives, searching '
                           '"FFS", "FLW", "FFS".'))
    mp.fill_cwas(vals, bins=bins, lblformat='%s', labels=labels,
                 cmap=cmap, ilabel=True,  # clevlabels=month_abbr[1:],
                 units='count')
    mp.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_cwas [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    utc = datetime.datetime.utcnow()
    bins = [0, 1, 14, 31, 91, 182, 273, 365, 730, 1460, 2920, 3800]
    pgconn = psycopg2.connect(database='postgis', host='iemdb', user='nobody')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)

    phenomena = fdict.get('phenomena', 'TO')
    significance = fdict.get('significance', 'W')

    cursor.execute("""
     select wfo,  extract(days from ('TODAY'::date - max(issue))) as m
     from warnings where significance = %s and phenomena = %s
     GROUP by wfo ORDER by m ASC
    """, (significance, phenomena))
    data = {}
    rows = []
    for row in cursor:
        wfo = row[0] if row[0] != 'JSJ' else 'SJU'
        rows.append(dict(wfo=wfo, days=row[1]))
        data[wfo] = max([row[1], 0])
    df = pd.DataFrame(rows)

    m = MapPlot(sector='nws', axisbg='white', nocaption=True,
                title='Days since Last %s %s by NWS Office' % (
                        vtec._phenDict.get(phenomena, phenomena),
                        vtec._sigDict.get(significance, significance)),
                subtitle='Valid %s' % (utc.strftime("%d %b %Y %H%M UTC"),))
    m.fill_cwas(data, bins=bins, ilabel=True, units='Days',
                lblformat='%.0f')

    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_cwas [as 别名]
def main():
    """Go Main Go"""
    cursor = POSTGIS.cursor()
    cursor2 = POSTGIS.cursor()

    phenomena = 'WS'

    cursor.execute("""
    SELECT ugc, issue, init_expire, wfo from warnings where phenomena = %s and
    significance = 'A' and issue > '2005-10-01' ORDER by issue ASC
    """, (phenomena, ))
    total = cursor.rowcount
    print('Events is %s' % (total, ))

    hits = {}
    hits2 = {}
    totals = {}
    misses = 0
    for row in tqdm(cursor, total=total):
        wfo = row[3]
        if wfo not in hits:
            hits[wfo] = {}
        if wfo not in totals:
            totals[wfo] = 0
        totals[wfo] += 1
        cursor2.execute("""
        SELECT distinct phenomena, significance from warnings
        where ugc = %s and expire > %s and issue < %s and wfo = %s
        """, (row[0], row[1], row[2], wfo))
        for row2 in cursor2:
            key = "%s.%s" % (row2[0], row2[1])
            if key not in hits[wfo]:
                hits[wfo][key] = 0
            hits[wfo][key] += 1
            if key not in hits2:
                hits2[key] = 0
            hits2[key] += 1
        if cursor2.rowcount == 0:
            misses += 1

    data = {}
    for wfo in hits:
        data[wfo] = hits[wfo].get(
                    '%s.W' % (phenomena,), 0) / float(totals[wfo]) * 100.0

    mp = MapPlot(sector='nws', axisbg='white',
                 title=("Conversion [%] of Winter Storm Watch "
                        "Counties/Parishes into Winter Storm Warnings"),
                 titlefontsize=14,
                 subtitle=('1 Oct 2005 - 29 Mar 2018, Overall %s/%s %.1f%%'
                           ) % (hits2['%s.W' % (phenomena, )], total,
                                hits2['%s.W' % (phenomena, )] / float(total) * 100.))
    mp.fill_cwas(data, ilabel=True, lblformat='%.0f')
    mp.postprocess(filename='test.png')

    print('Misses %s %.1f%%' % (misses, misses / float(total) * 100.0))
    for key in hits2:
        print('%s %s %.1f%%' % (key, hits2[key],
                                hits2[key] / float(total) * 100.0))

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_cwas [as 别名]
def plot():
    """Make a pretty plot"""
    df = pd.read_csv('wfo.csv')
    df.set_index('wfo', inplace=True)
    m = MapPlot(sector='conus',
                title="Percentage of Flash Flood Watches receiving 1+ FFW",
                subtitle='PRELIMINARY PLOT! Please do not share :)')
    cmap = plt.get_cmap('jet')
    df2 = df[df['freq'].notnull()]
    m.fill_cwas(df2['freq'].to_dict(), cmap=cmap, units='%',
                lblformat='%.0f', ilabel=True)
    m.postprocess(filename='test.png')
    m.close()

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_cwas [as 别名]
def make_map():
    """Generate a map plot"""
    df = pd.read_csv('vertex_intersects.csv')
    gdf = df.groupby('wfo').sum()
    allvals = (gdf['allhits'] - gdf['cwahits']) / gdf['verticies'] * 100.
    avgv = ((gdf['allhits'].sum() - gdf['cwahits'].sum()) /
            gdf['verticies'].sum() * 100.)
    mp = MapPlot(sector='nws', continentalcolor='white',
                 title=('Percent of SVR+TOR Warning Vertices within 2km '
                        'of County Border'),
                 subtitle=('1 Oct 2007 through 29 Mar 2018, '
                           'Overall Avg: %.1f%%, * CWA Borders Excluded'
                           ) % (avgv,))
    mp.fill_cwas(allvals.to_dict(), ilabel=True, lblformat='%.0f')
    mp.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_cwas [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    bins = [0, 1, 14, 31, 91, 182, 273, 365, 730, 1460, 2920, 3800]
    pgconn = psycopg2.connect(database='postgis', host='iemdb', user='nobody')
    cursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
    ctx = get_autoplot_context(fdict, get_description())
    phenomena = ctx['phenomena']
    significance = ctx['significance']
    edate = ctx.get('edate')
    if edate is None:
        edate = datetime.datetime.utcnow()
    else:
        edate = datetime.datetime(edate.year, edate.month,
                                  edate.day, 0, 0)
    edate = edate.replace(tzinfo=pytz.timezone("UTC"))

    cursor.execute("""
     select wfo,  extract(days from (%s::date - max(issue))) as m
     from warnings where significance = %s and phenomena = %s
     and issue < %s
     GROUP by wfo ORDER by m ASC
    """, (edate, significance, phenomena, edate))
    if cursor.rowcount == 0:
        return ("No Events Found for %s %s (%s.%s)"
                ) % (vtec._phenDict.get(phenomena, phenomena),
                     vtec._sigDict.get(significance, significance),
                     phenomena, significance)
    data = {}
    rows = []
    for row in cursor:
        wfo = row[0] if row[0] != 'JSJ' else 'SJU'
        rows.append(dict(wfo=wfo, days=row[1]))
        data[wfo] = max([row[1], 0])
    df = pd.DataFrame(rows)
    df.set_index('wfo', inplace=True)

    m = MapPlot(sector='nws', axisbg='white', nocaption=True,
                title='Days since Last %s %s by NWS Office' % (
                        vtec._phenDict.get(phenomena, phenomena),
                        vtec._sigDict.get(significance, significance)),
                subtitle='Valid %s' % (edate.strftime("%d %b %Y %H%M UTC"),))
    m.fill_cwas(data, bins=bins, ilabel=True, units='Days',
                lblformat='%.0f')

    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_cwas [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    pgconn = psycopg2.connect(database='postgis', host='iemdb', user='nobody')
    ctx = get_autoplot_context(fdict, get_description())
    sts = ctx['sdate']
    sts = sts.replace(tzinfo=pytz.utc)
    ets = ctx['edate']
    ets = ets.replace(tzinfo=pytz.utc)
    myfilter = ctx['filter']
    if myfilter == 'NONE':
        tlimiter = ''
    elif myfilter == 'NRS':
        tlimiter = " and typetext not in ('HEAVY RAIN', 'SNOW') "
    else:
        tlimiter = " and typetext = '%s' " % (myfilter,)

    df = read_sql("""
    SELECT wfo, count(*) from lsrs
    WHERE valid >= %s and valid < %s """ + tlimiter + """
    GROUP by wfo ORDER by wfo ASC
    """, pgconn, params=(sts, ets), index_col='wfo')
    data = {}
    for wfo, row in df.iterrows():
        data[wfo] = row['count']
    maxv = df['count'].max()
    bins = np.linspace(0, maxv, 12, dtype='i')
    bins[-1] += 1
    p = MapPlot(sector='nws', axisbg='white',
                title='Local Storm Report Counts by NWS Office',
                subtitle=('Valid %s - %s UTC, type limiter: %s'
                          ) % (sts.strftime("%d %b %Y %H:%M"),
                               ets.strftime("%d %b %Y %H:%M"),
                               MDICT.get(myfilter)))
    p.fill_cwas(data, bins=bins, ilabel=True)

    return p.fig, df

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_cwas [as 别名]
def main():
    """Go Main"""
    vals = {}
    for line in data.split("\n"):
        wfo, valid = line.strip().split("|")
        wfo = wfo.strip()
        year = valid.strip()[:4]
        wfo = wfo[1:]
        if wfo == 'JSJ':
            wfo = 'SJU'
        vals[wfo] = int(year)
    print(vals)
    #bins = [1, 25, 50, 75, 100, 125, 150, 200, 300]
    bins = np.arange(2002, 2019, 2)
    cmap = plt.get_cmap('PuOr')
    mp = MapPlot(sector='nws', continentalcolor='white', figsize=(12., 9.),
                 title=("Year of Last RWS Text Product Issuance"),
                 subtitle=('based on IEM archives'))
    mp.fill_cwas(vals, bins=bins, lblformat='%s',  # , labels=labels,
                 cmap=cmap, ilabel=True,  # clevlabels=clevlabels,
                 units='Year')
    
    mp.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_cwas [as 别名]
from pyiem.plot import MapPlot
import pandas as pd

df = pd.read_csv('vertex.csv', index_col='WFO', na_values=['None'])
df['r'] = (df['CNTY_HITS'] - df['CWA_HITS']) / df['ALL'] * 100.
avgv = (df['CNTY_HITS'] - df['CWA_HITS']).sum() / float(df['ALL'].sum()) * 100.

m = MapPlot(sector='nws', axisbg='white',
            title='Percent of SVR+TOR Warning Vertices within 2km of County Border',
            subtitle='1 Oct 2007 through 23 May 2016, Overall Avg: %.1f%%, * CWA Borders Excluded' % (avgv,))
m.fill_cwas(df['r'], ilabel=True, lblformat='%.0f')
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_cwas [as 别名]
def test_plot():
    """ Exercise the API """
    mp = MapPlot(sector='midwest', nocaption=True)
    mp.fill_cwas({'DMX': 80, 'MKX': 5, 'SJU': 30, 'AJK': 40},
                 units='no units')
    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_cwas [as 别名]
data = {}
labels = {}
uniq = []
for line in text.split("\n"):
    tokens = line.replace(" ", "").split("|")
    wfo = tokens[0][1:]
    if tokens[0][0] == 'P':
        wfo = tokens[0]
    key = "%s" % (tokens[1], )
    if not nt.sts.has_key(wfo):
        continue
    # P
    wfo = tokens[0][1:]
    if not key in uniq:
        uniq.append( key )
    data[ wfo ] = len(uniq) -1
    labels[ wfo ] = key
    if wfo == 'JSJ':
        labels['SJU'] = labels['JSJ']

bins = range(len(uniq)+1)
uniq.append('')

p = MapPlot(sector='nws', axisbg='white',
                 title="2009-2013 Most Frequently issued non-SHEF 3char AWIPS ID",
                 subtitle='RR* products were excluded from this analysis')
p.fill_cwas(data, bins=bins, labels=labels, lblformat='%s', clevlabels=uniq)
p.postprocess(filename='test.png')
#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_cwas [as 别名]
    cursor2.execute("""
    SELECT distinct phenomena, significance from warnings_temp
    where ugc = %s and expire > %s and issue < %s and wfo = %s
    """, (row[0], row[1], row[2], wfo))
    for row2 in cursor2:
        key = "%s.%s" % (row2[0], row2[1])
        if key not in hits[wfo]:
            hits[wfo][key] = 0
        hits[wfo][key] += 1
        if key not in hits2:
            hits2[key] = 0
        hits2[key] += 1
    if cursor2.rowcount == 0:
        misses += 1

data = {}
for wfo in hits.keys():
    data[wfo] = hits[wfo].get(
                '%s.W' % (phenomena,), 0) / float(totals[wfo]) * 100.0

m = MapPlot(sector='nws', axisbg='white',
            title=("Conversion [%] of Severe T'Storm Watch Counties/Parishes into "
                   "SVR Warnings"), titlefontsize=14,
            subtitle='1 Oct 2005 - 19 May 2016')
m.fill_cwas(data, ilabel=True, lblformat='%.0f')
m.postprocess(filename='test.png')

print 'Misses %s %.1f%%' % (misses, misses / float(total) * 100.0)
for key in hits2.keys():
    print '%s %s %.1f%%' % (key, hits2[key], hits2[key] / float(total) * 100.0)

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import fill_cwas [as 别名]
def test_plot2():
    """ Exercise NWS plot API """
    mp = MapPlot(sector='nws', continentalcolor='white', nocaption=True)
    mp.fill_cwas({'DMX': 80, 'MKX': 5, 'SJU': 30, 'AJK': 40, 'HFO': 50},
                 units='NWS Something or Another', ilabel=True)
    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_cwas [as 别名]
def test_plot22():
    """plot cwas that are filled"""
    mp = MapPlot(sector='iowa', continentalcolor='white', nocaption=True)
    mp.fill_cwas({'DMX': 80, 'MKX': 5, 'SJU': 30, 'AJK': 40, 'HFO': 50},
                 units='NWS Something or Another')
    return mp.fig