예제 #1
0
파일: plot_ndfd.py 프로젝트: akrherz/DEV
def main():
    """Go Main"""
    grbs = pygrib.open('ds.snow.bin')
    # skip 1-off first field
    total = None
    lats = lons = None
    for grb in grbs[1:]:
        if lats is None:
            lats, lons = grb.latlons()
            total = grb['values']
            continue
        total += grb['values']
    # TODO tz-hack here
    analtime = grb.analDate - datetime.timedelta(hours=5)

    mp = MapPlot(
        sector='custom', west=-100, east=-92, north=45, south=41,
        axisbg='tan',
        title=("NWS Forecasted Accumulated Snowfall "
               "thru 7 PM 12 April 2019"),
        subtitle='NDFD Forecast Issued %s' % (
            analtime.strftime("%-I %p %-d %B %Y"), )
    )
    cmap = nwssnow()
    cmap.set_bad('tan')
    mp.pcolormesh(
        lons, lats, total * 39.3701,
        [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36],
        cmap=cmap,
        units='inch')

    mp.drawcounties()
    mp.drawcities()
    mp.postprocess(filename='test.png')
    mp.close()
예제 #2
0
 def test_colorramps(self):
     """make sure our colorramps are happy"""
     c = plot.james()
     self.assertEquals(c.N, 12)
     c = plot.james2()
     self.assertEquals(c.N, 12)
     c = plot.whitebluegreenyellowred()
     self.assertEquals(c.N, 236)
     c = plot.nwssnow()
     self.assertEquals(c.N, 11)
예제 #3
0
파일: test_geoplot.py 프로젝트: yyqyu/pyIEM
def test_colorramps():
    """make sure our colorramps are happy"""
    c = plot.james()
    assert c.N == 12
    c = plot.james2()
    assert c.N == 12
    c = plot.whitebluegreenyellowred()
    assert c.N == 236
    c = plot.nwssnow()
    assert c.N == 11
예제 #4
0
 def test_colorramps(self):
     """make sure our colorramps are happy"""
     c = plot.james()
     self.assertEquals(c.N, 12)
     c = plot.james2()
     self.assertEquals(c.N, 12)
     c = plot.whitebluegreenyellowred()
     self.assertEquals(c.N, 236)
     c = plot.nwssnow()
     self.assertEquals(c.N, 11)
예제 #5
0
def run(basets, endts, view):
    """Generate this plot for the given basets"""

    df = read_sql("""SELECT state,
        max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
        from lsrs WHERE type in ('S') and magnitude >= 0 and
        valid > %s and valid < %s GROUP by state, lon, lat
        """,
                  POSTGIS,
                  params=(basets, endts),
                  index_col=None)
    df['used'] = False
    df['textplot'] = True
    df.sort_values(by='val', ascending=False, inplace=True)

    # Now, we need to add in zeros, lets say we are looking at a .25 degree box
    mybuffer = 0.75
    newrows = []
    for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
        for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
            df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat + mybuffer)) &
                     (df['lon'] >= lon) & (df['lon'] < (lon + mybuffer))]
            if len(df2.index) == 0:
                newrows.append(
                    dict(lon=(lon + mybuffer / 2.),
                         lat=(lat + mybuffer / 2.),
                         val=0,
                         used=True,
                         textplot=False))
                continue
            maxval = df.at[df2.index[0], 'val']
            df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
            df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
    dfnew = pd.DataFrame(newrows)
    df = df.append(dfnew)
    cdf = df[df['used']]
    tdf = df[df['textplot']]

    rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
    cmap = nwssnow()
    m = MapPlot(sector='iowa',
                axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values,
               cdf['lat'].values,
               cdf['val'].values,
               rng,
               cmap=cmap)
    m.drawcounties()
    m.plot_values(tdf['lon'].values,
                  tdf['lat'].values,
                  tdf['val'].values,
                  fmt='%.1f')
    m.drawcities()
    pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()

    # slightly different title to help uniqueness
    m = MapPlot(sector='iowa',
                axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports valid over past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values,
               cdf['lat'].values,
               cdf['val'].values,
               rng,
               cmap=cmap)
    m.drawcounties()
    m.drawcities()
    pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()

    m = MapPlot(sector='midwest',
                axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values,
               cdf['lat'].values,
               cdf['val'].values,
               rng,
               cmap=cmap)
    m.drawcities()
    pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()
예제 #6
0
def run(basets, endts, view):
    """Generate this plot for the given basets"""

    df = read_sql("""SELECT state,
        max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
        from lsrs WHERE type in ('S') and magnitude >= 0 and
        valid > %s and valid < %s GROUP by state, lon, lat
        """, POSTGIS, params=(basets, endts), index_col=None)
    df['used'] = False
    df['textplot'] = True
    df.sort_values(by='val', ascending=False, inplace=True)

    # Now, we need to add in zeros, lets say we are looking at a .25 degree box
    mybuffer = 0.75
    newrows = []
    for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, mybuffer):
        for lon in np.arange(reference.MW_WEST, reference.MW_EAST, mybuffer):
            df2 = df[(df['lat'] >= lat) & (df['lat'] < (lat+mybuffer)) &
                     (df['lon'] >= lon) & (df['lon'] < (lon+mybuffer))]
            if len(df2.index) == 0:
                newrows.append(dict(lon=(lon+mybuffer/2.),
                                    lat=(lat+mybuffer/2.),
                                    val=0, used=True, textplot=False))
                continue
            maxval = df.at[df2.index[0], 'val']
            df.loc[df2[df2['val'] > (maxval * 0.5)].index, 'used'] = True
            df.loc[df2[df2['val'] < (maxval * 0.5)].index, 'textplot'] = False
    dfnew = pd.DataFrame(newrows)
    df = df.append(dfnew)
    cdf = df[df['used']]
    tdf = df[df['textplot']]

    rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
    cmap = nwssnow()
    m = MapPlot(sector='iowa', axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
               cmap=cmap)
    m.drawcounties()
    m.plot_values(tdf['lon'].values, tdf['lat'].values, tdf['val'].values,
                  fmt='%.1f')
    pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()

    # slightly different title to help uniqueness
    m = MapPlot(sector='iowa', axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports valid over past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
               cmap=cmap)
    m.drawcounties()
    pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()

    m = MapPlot(sector='midwest', axisbg='white',
                title="Local Storm Report Snowfall Total Analysis",
                subtitle=("Reports past 12 hours: %s"
                          "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    m.contourf(cdf['lon'].values, cdf['lat'].values, cdf['val'].values, rng,
               cmap=cmap)
    pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
    m.postprocess(view=view, pqstr=pqstr)
    m.close()
예제 #7
0
def run(basets, endts, view):
    """Generate this plot for the given basets"""
    pgconn = get_dbconn('postgis', user='******')

    df = read_sql("""SELECT state,
        max(magnitude) as val, ST_x(geom) as lon, ST_y(geom) as lat
        from lsrs WHERE type in ('S') and magnitude >= 0 and
        valid > %s and valid < %s GROUP by state, lon, lat
        """,
                  pgconn,
                  params=(basets, endts),
                  index_col=None)
    df.sort_values(by='val', ascending=False, inplace=True)
    df['useme'] = False
    # First, we need to filter down the in-bound values to get rid of small
    cellsize = 0.33
    newrows = []
    for lat in np.arange(reference.MW_SOUTH, reference.MW_NORTH, cellsize):
        for lon in np.arange(reference.MW_WEST, reference.MW_EAST, cellsize):
            # Look around this box at 1x
            df2 = df[(df['lat'] >= (lat - cellsize))
                     & (df['lat'] < (lat + cellsize)) &
                     (df['lon'] >= (lon - cellsize)) & (df['lon'] <
                                                        (lon + cellsize))]
            if df2.empty:
                # If nothing was found, check 2x
                df3 = df[(df['lat'] >= (lat - cellsize * 2.))
                         & (df['lat'] < (lat + cellsize * 2.)) &
                         (df['lon'] >=
                          (lon - cellsize * 2.)) & (df['lon'] <
                                                    (lon + cellsize * 2.))]
                if df3.empty:
                    # If nothing found, place a zero here
                    newrows.append({
                        'lon': lon,
                        'lat': lat,
                        'val': 0,
                        'useme': True,
                        'state': 'NA'
                    })
                continue
            maxval = df.at[df2.index[0], 'val']
            df.loc[df2[df2['val'] > (maxval * 0.8)].index, 'useme'] = True

    dfall = pd.concat([df, pd.DataFrame(newrows)], ignore_index=True)
    df2 = dfall[dfall['useme']]
    xi = np.arange(reference.MW_WEST, reference.MW_EAST, cellsize)
    yi = np.arange(reference.MW_SOUTH, reference.MW_NORTH, cellsize)
    xi, yi = np.meshgrid(xi, yi)
    gridder = Rbf(df2['lon'].values,
                  df2['lat'].values,
                  pd.to_numeric(df2['val'].values, errors='ignore'),
                  function='thin_plate')
    vals = gridder(xi, yi)
    vals[np.isnan(vals)] = 0

    rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
    cmap = nwssnow()
    mp = MapPlot(sector='iowa',
                 axisbg='white',
                 title="Local Storm Report Snowfall Total Analysis",
                 subtitle=("Reports past 12 hours: %s"
                           "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    if df['val'].max() > 0:
        mp.contourf(xi, yi, vals, rng, cmap=cmap)
    mp.drawcounties()
    if not df.empty:
        mp.plot_values(df['lon'].values,
                       df['lat'].values,
                       df['val'].values,
                       fmt='%.1f',
                       labelbuffer=2)
    mp.drawcities()
    pqstr = "plot c 000000000000 lsr_snowfall.png bogus png"
    mp.postprocess(view=view, pqstr=pqstr)
    mp.close()

    # slightly different title to help uniqueness
    mp = MapPlot(sector='iowa',
                 axisbg='white',
                 title="Local Storm Report Snowfall Total Analysis",
                 subtitle=("Reports valid over past 12 hours: %s"
                           "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    if df['val'].max() > 0:
        mp.contourf(xi, yi, vals, rng, cmap=cmap)
    mp.drawcounties()
    mp.drawcities()
    pqstr = "plot c 000000000000 lsr_snowfall_nv.png bogus png"
    mp.postprocess(view=view, pqstr=pqstr)
    mp.close()

    mp = MapPlot(sector='midwest',
                 axisbg='white',
                 title="Local Storm Report Snowfall Total Analysis",
                 subtitle=("Reports past 12 hours: %s"
                           "" % (endts.strftime("%d %b %Y %I:%M %p"), )))
    if df['val'].max() > 0:
        mp.contourf(xi, yi, vals, rng, cmap=cmap)
    mp.drawcities()
    pqstr = "plot c 000000000000 mw_lsr_snowfall.png bogus png"
    mp.postprocess(view=view, pqstr=pqstr)
    mp.close()
예제 #8
0
파일: p207.py 프로젝트: stormchas4/iem
def plotter(fdict):
    """ Go """
    ctx = get_autoplot_context(fdict, get_description())
    if ctx["sz"] < 5:
        ctx["sz"] = 5
    if ctx["hours"] > 300:
        ctx["hours"] = 300
    endts = ctx["endts"]
    basets = endts - datetime.timedelta(hours=ctx["hours"])
    # Retrieve available obs
    df = load_data(ctx, basets, endts)
    # figure out our grid bounds
    ctx["bnds2163"] = compute_grid_bounds(ctx)
    # add zeros and QC
    df = add_zeros(df, ctx)
    # do gridding
    df2 = df[df[USEME]]
    lons, lats, vals = do_analysis(df2, ctx)

    rng = [0.01, 1, 2, 3, 4, 6, 8, 12, 18, 24, 30, 36]
    cmap = nwssnow()
    if ctx["t"] == "cwa":
        sector = "cwa"
    else:
        sector = "state" if len(ctx["csector"]) == 2 else ctx["csector"]

    mp = MapPlot(
        sector=sector,
        state=ctx["csector"],
        cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
        axisbg="white",
        title=("NWS Local Storm Report%s Snowfall Total Analysis") %
        (" & COOP" if ctx["coop"] == "yes" else "", ),
        subtitle=("%.0f reports over past %.0f hours till %s, "
                  "grid size: %.0fkm, Rbf: %s"
                  "" % (
                      len(df2.index),
                      ctx["hours"],
                      endts.strftime("%d %b %Y %I:%M %p"),
                      ctx["sz"],
                      ctx["f"],
                  )),
    )
    if df2["val"].max() > 0 and ctx["p"] in ["both", "contour"]:
        mp.contourf(lons,
                    lats,
                    vals,
                    rng,
                    cmap=cmap,
                    clip_on=(ctx["t"] != "cwa"))
        # Allow analysis to bleed outside the CWA per request.
        if ctx["t"] == "cwa":
            mp.draw_mask(sector="conus")
            mp.draw_cwas(linewidth=2)
    mp.drawcounties()
    if not df.empty and ctx["p"] in ["both", "plot"]:
        df2 = df[df["plotme"]]
        mp.plot_values(
            df2["lon"].values,
            df2["lat"].values,
            df2["val"].values,
            fmt="%.1f",
            labelbuffer=2,
        )
    if ctx["z"] == "plot":
        df2 = df[df["state"] == "Z"]
        if not df2.empty:
            mp.plot_values(
                df2["lon"].values,
                df2["lat"].values,
                df2["state"].values,
                fmt="%s",
                color="#FF0000",
                labelbuffer=0,
            )
    mp.drawcities()
    return mp.fig, df.drop(["geo", "plotme"], axis=1)