Python MapPlot.plot_values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 plot_values [as 别名]
def do_month(ts, routes='m'):
    """
    Generate the plot for a given month, please
    """
    sql = """SELECT station, sum(precip) as total, max(day) as lastday
           from alldata_ia WHERE year = %s and month = %s
           and station != 'IA0000' and substr(station,2,1) != 'C'
           GROUP by station""" % (ts.year, ts.month)

    lats = []
    lons = []
    vals = []
    lastday = None
    ccursor.execute(sql)
    for row in ccursor:
        if row['station'] not in nt.sts:
            continue
        if lastday is None:
            lastday = row['lastday']
        lats.append(nt.sts[row['station']]['lat'])
        lons.append(nt.sts[row['station']]['lon'])
        vals.append(row['total'])

    m = MapPlot(title='%s - %s' % (ts.strftime("%d %B %Y"),
                                   lastday.strftime("%d %B %Y")),
                subtitle="%s Total Precipitation [inch]" % (
                                    ts.strftime("%B %Y"),))
    m.contourf(lons, lats, vals, [0, 0.1, 0.25, 0.5, 0.75, 1, 2, 3, 4, 5, 6,
                                  7])
    m.plot_values(lons, lats, vals, fmt='%.2f')

    pqstr = ("plot %s %s summary/iemre_iowa_total_precip.png "
             "%s/summary/iemre_iowa_total_precip.png png"
             ) % (routes, ts.strftime("%Y%m%d%H%M"), ts.strftime("%Y/%m"))
    m.postprocess(pqstr=pqstr)

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def runYear(year):
    # Grab the data
    sql = """SELECT station, sum(precip) as total, max(day)
           from alldata_ia WHERE year = %s and
           station != 'IA0000' and
           substr(station,3,1) != 'C' and
           precip is not null GROUP by station""" % (year,)

    lats = []
    lons = []
    vals = []
    labels = []
    ccursor.execute( sql )
    for row in ccursor:
        sid = row['station']
        if not nt.sts.has_key(sid):
            continue
        labels.append( sid[2:] )
        lats.append( nt.sts[sid]['lat'] )
        lons.append( nt.sts[sid]['lon'] )
        vals.append( row['total'] )
        maxday = row['max']

    m = MapPlot(title="Total Precipitation [inch] (%s)" % (year,),
                subtitle='1 January - %s' % (maxday.strftime("%d %B"),),
                axisbg='white')
    m.plot_values(lons, lats, vals, labels=labels, fmt='%.2f',
                  labeltextsize=8, labelcolor='tan')
    pqstr = "plot m %s bogus %s/summary/total_precip.png png" % (
                                        now.strftime("%Y%m%d%H%M"), year,)
    m.postprocess(pqstr=pqstr)

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def main():
    """Map some CLI data"""
    pgconn = get_dbconn('iem')

    df = read_sql("""
    WITH data as (
        SELECT station, snow_jul1 - snow_jul1_normal as s
        from cli_data where valid = '2019-02-18' and snow_jul1 > 0
        and snow_jul1_normal > 0)

    select station, st_x(geom) as lon, st_y(geom) as lat, c.s as val from
    data c JOIN stations s on (s.id = c.station)
    WHERE s.network = 'NWSCLI'
    """, pgconn, index_col=None)
    df['color'] = '#ff0000'
    df.loc[df['val'] > 0, 'color'] = '#0000ff'

    mp = MapPlot(sector='midwest', axisbg='white',
                 title=("2018-2019 Snowfall Total Departure "
                        "from Average [inches]"),
                 subtitle='18 Feb 2019 Based on NWS CLI Reporting Sites')
    mp.plot_values(
        df['lon'].values, df['lat'].values,
        df['val'].values, fmt='%.1f', textsize=12, color=df['color'].values,
        labelbuffer=1)
    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 plot_values [as 别名]
def runYear(year):
    # Grab the data
    sql = """SELECT station,
        sum(case when precip >= 0.01 then 1 else 0 end) as days, max(day)
        from alldata_ia WHERE year = %s and substr(station,3,1) != 'C' 
        and station != 'IA0000' GROUP by station""" % (year,)

    lats = []
    lons = []
    vals = []
    labels = []
    ccursor.execute( sql )
    for row in ccursor:
        sid = row['station'].upper()
        if not nt.sts.has_key(sid):
            continue
        labels.append( sid[2:] )
        lats.append( nt.sts[sid]['lat'] )
        lons.append( nt.sts[sid]['lon'] )
        vals.append( row['days'] )
        maxday = row['max']

    #---------- Plot the points
    m = MapPlot(title="Days with Measurable Precipitation (%s)" % (year,),
                subtitle='Map valid January 1 - %s' % (maxday.strftime("%b %d")),
                axisbg='white')
    m.plot_values(lons, lats, vals, fmt='%.0f', labels=labels,
                  labeltextsize=8, labelcolor='tan')
    m.drawcounties()
    pqstr = "plot m %s bogus %s/summary/precip_days.png png" % (
                                        now.strftime("%Y%m%d%H%M"), year,)
    m.postprocess(pqstr=pqstr)

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def doday():
    """
    Create a plot of precipitation stage4 estimates for some day
    """
    sts = mx.DateTime.DateTime(2013,5,25,12)
    ets = mx.DateTime.DateTime(2013,5,31,12)
    interval = mx.DateTime.RelativeDateTime(days=1)
    now = sts
    total = None
    while now < ets:
        fp = "/mesonet/ARCHIVE/data/%s/stage4/ST4.%s.24h.grib" % (
            now.strftime("%Y/%m/%d"), 
            now.strftime("%Y%m%d%H") )
        if os.path.isfile(fp):
            lts = now
            grbs = pygrib.open(fp)

            if total is None:
                g = grbs[1]
                total = g["values"]
                lats, lons = g.latlons()
            else:
                total += grbs[1]["values"]
            grbs.close()
        now += interval
        
    m = MapPlot(sector='iowa', title='NOAA Stage IV & Iowa ASOS Precipitation',
                subtitle='25-30 May 2013')
    m.pcolormesh(lons, lats, total / 25.4, numpy.arange(0,14.1,1), latlon=True,
                 units='inch')
    m.drawcounties()
    m.plot_values(dlons, dlats, dvals, '%.02f')
    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 plot_values [as 别名]
def plot(data, v):
    ''' Actually plot this data '''
    nt = NetworkTable("ISUSM")
    lats = []
    lons = []
    vals = []
    valid = None
    for sid in data.keys():
        if data[sid][v] is None:
            continue
        lats.append(nt.sts[sid]['lat'])
        lons.append(nt.sts[sid]['lon'])
        vals.append(data[sid][v])
        valid = data[sid]['valid']

    if valid is None:
        m = MapPlot(sector='iowa', axisbg='white',
                    title=('ISU Soil Moisture Network :: %s'
                           '') % (CTX[v]['title'], ),
                    figsize=(8.0, 6.4))
        m.plot_values([-95, ], [41.99, ], ['No Data Found'], '%s', textsize=30)
        m.postprocess(web=True)
        return

    m = MapPlot(sector='iowa', axisbg='white',
                title='ISU Soil Moisture Network :: %s' % (CTX[v]['title'],),
                subtitle='valid %s' % (valid.strftime("%-d %B %Y %I:%M %p"),),
                figsize=(8.0, 6.4))
    m.plot_values(lons, lats, vals, '%.1f')
    m.drawcounties()
    m.postprocess(web=True)

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    import matplotlib.cm as cm

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

    sector = fdict.get('sector', 'IA')
    date1 = datetime.datetime.strptime(fdict.get('date1', '2015-01-01'),
                                       '%Y-%m-%d')
    date2 = datetime.datetime.strptime(fdict.get('date2', '2015-02-01'),
                                       '%Y-%m-%d')

    table = "alldata_%s" % (sector, ) if sector != 'midwest' else "alldata"
    cursor.execute("""
    WITH obs as (
        SELECT station, sday, day, precip from """ + table + """ WHERE
        day >= %s and day < %s and precip >= 0 and
        substr(station, 3, 1) != 'C' and substr(station, 3, 4) != '0000'),
    climo as (
        SELECT station, to_char(valid, 'mmdd') as sday, precip from
        climate51),
    combo as (
        SELECT o.station, o.precip - c.precip as d from obs o JOIN climo c ON
        (o.station = c.station and o.sday = c.sday)),
    deltas as (
        SELECT station, sum(d) from combo GROUP by station)

    SELECT d.station, d.sum, ST_x(t.geom), ST_y(t.geom) from deltas d
    JOIN stations t on (d.station = t.id) WHERE t.network ~* 'CLIMATE'
    """, (date1, date2))

    rows = []
    for row in cursor:
        rows.append(dict(station=row[0], delta=row[1], lon=row[2],
                         lat=row[3]))
    df = pd.DataFrame(rows)
    lons = np.array(df['lon'])
    vals = np.array(df['delta'])
    lats = np.array(df['lat'])
    sector2 = "state" if sector != 'midwest' else 'midwest'
    m = MapPlot(sector=sector2, state=sector, axisbg='white',
                title=('%s - %s Precipitation Departure [inch]'
                       ) % (date1.strftime("%d %b %Y"),
                            date2.strftime("%d %b %Y")),
                subtitle='%s vs 1950-2014 Climatology' % (date1.year,))
    rng = int(max([0 - np.min(vals), np.max(vals)]))
    cmap = cm.get_cmap('RdYlBu')
    cmap.set_bad('white')
    m.contourf(lons, lats, vals, np.linspace(0 - rng - 0.5, rng + 0.6, 10,
                                             dtype='i'),
               cmap=cmap, units='inch')
    m.plot_values(lons, lats, vals, fmt='%.2f')
    if sector == 'iowa':
        m.drawcounties()

    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 plot_values [as 别名]
def main():
    """Go!"""
    title = 'NOAA MRMS Q3: RADAR + Guage Corrected Rainfall Estimates + NWS Storm Reports'
    mp = MapPlot(sector='custom',
                 north=42.3, east=-93.0, south=41.65, west=-94.1,
                 axisbg='white',
                 titlefontsize=14,
                 title=title,
                 subtitle='Valid: 14 June 2018')

    shp = shapefile.Reader('cities.shp')
    for record in shp.shapeRecords():
        geo = shape(record.shape)
        mp.ax.add_geometries([geo], ccrs.PlateCarree(), zorder=Z_OVERLAY2,
                             facecolor='None', edgecolor='k', lw=2)

    grbs = pygrib.open('MRMS_GaugeCorr_QPE_24H_00.00_20180614-200000.grib2')
    grb = grbs.message(1)
    pcpn = distance(grb['values'], 'MM').value('IN')
    lats, lons = grb.latlons()
    lons -= 360.
    clevs = [0.01, 0.1, 0.3, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10]
    cmap = nwsprecip()
    cmap.set_over('k')

    mp.pcolormesh(lons, lats, pcpn, clevs, cmap=cmap, latlon=True,
                  units='inch')
    lons, lats, vals, labels = get_data()
    mp.drawcounties()
    mp.plot_values(lons, lats, vals, "%s", labels=labels,
                   labelbuffer=1, labelcolor='white')

    mp.drawcities(labelbuffer=5, minarea=0.2)
    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 plot_values [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    pgconn = psycopg2.connect(database='coop', host='iemdb', user='nobody')

    state = fdict.get('state', 'IA')[:2]
    varname = fdict.get('var', 'total_precip')
    sector = fdict.get('sector', 'state')
    opt = fdict.get('opt', 'both')
    over = fdict.get('over', 'monthly')
    month = int(fdict.get('month', datetime.date.today().month))

    df = read_sql("""
    WITH data as (
        SELECT station, extract(month from valid) as month,
        sum(precip) as total_precip, avg(high) as avg_high,
        avg(low) as avg_low, avg((high+low)/2.) as avg_temp
        from ncdc_climate81 GROUP by station, month)

    SELECT station, ST_X(geom) as lon, ST_Y(geom) as lat, month,
    total_precip, avg_high, avg_low, avg_temp from data d JOIN stations t
    ON (d.station = t.id) WHERE t.network = 'NCDC81' and
    t.state in ('IA', 'ND', 'SD', 'NE', 'KS', 'MO', 'IL', 'WI', 'MN', 'MI',
    'IN', 'OH', 'KY')
    """, pgconn, index_col=['station', 'month'])

    if over == 'monthly':
        title = "%s %s" % (calendar.month_name[month], PDICT3[varname])
        df.reset_index(inplace=True)
        df2 = df[df['month'] == month]
    else:
        title = "Annual %s" % (PDICT3[varname], )
        if varname == 'total_precip':
            df2 = df.sum(axis=0, level='station')
        else:
            df2 = df.mean(axis=0, level='station')
        df2['lat'] = df['lat'].mean(axis=0, level='station')
        df2['lon'] = df['lon'].mean(axis=0, level='station')
    m = MapPlot(sector=sector, state=state, axisbg='white',
                title=('NCEI 1981-2010 Climatology of %s'
                       ) % (title,),
                subtitle=('based on National Centers for '
                          'Environmental Information (NCEI) 1981-2010'
                          ' Climatology'))
    levels = np.linspace(df2[varname].min(), df2[varname].max(), 10)
    levels = [round(x, PRECISION[varname]) for x in levels]
    if opt in ['both', 'contour']:
        m.contourf(df2['lon'].values, df2['lat'].values,
                   df2[varname].values, levels, units=UNITS[varname])
    if sector == 'state':
        m.drawcounties()
    if opt in ['both', 'values']:
        m.plot_values(df2['lon'].values, df2['lat'].values,
                      df2[varname].values,
                      fmt='%%.%if' % (PRECISION[varname],))

    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 plot_values [as 别名]
def test_overlap():
    """ Do some checking of our overlaps logic """
    mp = MapPlot(sector='midwest', continentalcolor='white', nocaption=True)
    lons = np.linspace(-99, -90, 100)
    lats = np.linspace(38, 44, 100)
    vals = lats
    labels = ['%.2f' % (s,) for s in lats]
    mp.plot_values(lons, lats, vals, fmt='%.2f', labels=labels)
    return mp.fig

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def test_drawrandomtext():
    """See if we can handle the fun that is drawing random text"""
    mp = MapPlot(sector='iowa', title='Fun Text, here and there',
                 continentalcolor='white', debug=True, nocaption=True)
    mp.plot_values([-94, -92, -91, -92],
                   [42, 41, 43, 42.4],
                   ['One', 'Two\nTwo', 'Three\nThree\nThree',
                   'Four\nFour\nFour\nFour'], showmarker=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 plot_values [as 别名]
def plot_precip_month(valid):
    """ Go Main Go

    Args:
      valid (datetime): The timestamp we are interested in!
    """
    pgconn = psycopg2.connect(database='iem', host='iemdb', user='nobody')
    cursor = pgconn.cursor()

    d1 = valid.replace(day=1)
    d2 = d1 + datetime.timedelta(days=35)
    d2 = d2.replace(day=1)

    cursor.execute("""SELECT sum(pday), id, st_x(geom), st_y(geom)
    from summary s JOIN stations t on
    (t.iemid = s.iemid) WHERE s.day >= %s and s.day < %s
    and t.network in ('IA_COOP', 'NE_COOP', 'MO_COOP', 'IL_COOP', 'WI_COOP',
    'MN_COOP')
    and pday is not null and pday >= 0 and
    extract(hour from coop_valid) between 5 and 10
    GROUP by id, st_x, st_y""", (d1.date(), d2.date()))
    labels = []
    vals = []
    lats = []
    lons = []
    for row in cursor:
        labels.append(row[1])
        vals.append(pretty(row[0]))
        lats.append(row[3])
        lons.append(row[2])

    m = MapPlot(title='%s NWS COOP Month Precipitation Totals [inch]' % (
                                            valid.strftime("%-d %b %Y"),),
                subtitle='Reports valid between 6 and 9 AM',
                axisbg='white', figsize=(10.24, 7.68))
    m.plot_values(lons, lats, vals, fmt='%s', labels=labels,
                  labelcolor='tan')
    m.drawcounties()

    pqstr = "plot ac %s0000 coopMonthPlot.gif coopMonthPlot.gif gif" % (
                                                    valid.strftime("%Y%m%d"),)

    m.postprocess(pqstr=pqstr)
    m.close()

    pgconn.close()

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def plotter(fdict):
    """ Go """
    import matplotlib
    matplotlib.use('agg')
    from pyiem.plot import MapPlot
    pgconn = psycopg2.connect(dbname='coop', host='iemdb', user='nobody')
    ctx = util.get_autoplot_context(fdict, get_description())
    sector = ctx['sector']
    varname = ctx['var']
    year = ctx['year']
    popt = ctx['popt']
    threshold = ctx['threshold']
    table = "alldata_%s" % (sector,)
    df = read_sql("""
    WITH data as (
        SELECT station, """ + SQLOPT[varname] + """ as doy
        from """ + table + """
        WHERE year = %s GROUP by station
    )
    select station, doy, st_x(geom) as lon, st_y(geom) as lat
    from data d JOIN stations t on (d.station = t.id) WHERE
    t.network = %s and substr(station, 3, 4) != '0000'
    and substr(station, 3, 1) != 'C' and doy not in (0, 400) ORDER by doy
    """, pgconn, params=(threshold, year, '%sCLIMATE' % (sector,)),
                  index_col='station')
    if len(df.index) == 0:
        return "No data found!"

    def f(val):
        ts = datetime.date(year, 1, 1) + datetime.timedelta(days=(val - 1))
        return ts.strftime("%-m/%-d")

    df['pdate'] = df['doy'].apply(f)

    m = MapPlot(sector='state', state=sector, axisbg='white', nocaption=True,
                title="%s %s %s$^\circ$F" % (year, PDICT2[varname], threshold),
                subtitle='based on NWS COOP and IEM Daily Estimates')
    levs = np.linspace(df['doy'].min() - 3, df['doy'].max() + 3, 7, dtype='i')
    levlables = map(f, levs)
    if popt == 'contour':
        m.contourf(df['lon'], df['lat'], df['doy'], levs, clevlabels=levlables)
    m.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
    m.drawcounties()

    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 plot_values [as 别名]
def test_issue98_labelbar():
    """Sometimes our label bar sucks."""
    mp = MapPlot(
        title='Proportional Colorbar with some rotation',
        sector='iowa', nocaption=True)
    cmap = plot.maue()
    cmap.set_under('white')
    cmap.set_over('black')
    clevs = np.arange(0, 1., 0.1)
    clevs[-1] = 3.987654
    norm = mpcolors.BoundaryNorm(clevs, cmap.N)
    mp.plot_values(
        [-94, -92, -91, -92], [42, 41, 43, 42.4],
        ['0.5', '0.25', '1.0', '5.0'], color=cmap(norm([0.5, 0.25, 1.0, 5.0])),
        showmarker=True
    )
    mp.draw_colorbar(clevs, cmap, norm, spacing='proportional')
    return mp.fig

# 需要导入模块: from pyiem.plot import MapPlot [as 别名]
# 或者: from pyiem.plot.MapPlot import plot_values [as 别名]
def main():
    """Go Main!"""
    nt = NetworkTable("IACLIMATE")
    pgconn = get_dbconn('coop')

    df = read_sql("""
    with monthly as (
        select station, year, month, avg((high+low)/2.) from alldata_ia
        WHERE day < '2018-06-01' and high is not null
        GROUP by station, year, month),
    agg as (
        select station, year, month, avg,
        lag(avg) OVER (PARTITION by station ORDER by year ASC, month ASC)
        from monthly),
    agg2 as (
        select station, year, month, avg, lag, avg - lag as val,
        rank() OVER (PARTITION by station ORDER by avg - lag DESC)
        from agg WHERE lag is not null)
    select * from agg2 where rank = 1 ORDER by station
    """, pgconn, index_col='station')
    df['lat'] = 0.
    df['lon'] = 0.
    for station, _ in df.iterrows():
        if station in nt.sts and station != 'IA0000' and station[2] != 'C':
            df.at[station, 'lat'] = nt.sts[station]['lat']
            df.at[station, 'lon'] = nt.sts[station]['lon']

    mp = MapPlot(title="Largest Positive Change in Month to Month Average Temperature",
                 subtitle=('values in red set record for April to May 2018'), sector='state',
                 state='IA',
                 drawstates=True, continentalcolor='white')
    df2 = df[df['year'] == 2018]
    mp.plot_values(df2['lon'].values,
                   df2['lat'].values,
                   df2['val'].values, fmt='%.1f', textsize=12, labelbuffer=5,
                   color='r')
    df2 = df[df['year'] != 2018]
    mp.plot_values(df2['lon'].values,
                   df2['lat'].values,
                   df2['val'].values, fmt='%.1f', textsize=12, labelbuffer=5,
                   color='b')
    mp.drawcounties()
    mp.postprocess(filename='test.png')
    mp.close()