def main():
"""Go Main"""
pgconn = get_dbconn('postgis')
df = read_postgis("""
select geom, issue from bot_warnings where wfo = 'PUB'
""", pgconn, geom_col='geom', crs={'init': 'epsg:4326', 'no_defs': True})
bounds = df['geom'].total_bounds
# bounds = [-102.90293903, 40.08745967, -97.75622311, 43.35172981]
bbuf = 0.25
mp = MapPlot(sector='custom', west=bounds[0] - bbuf,
south=bounds[1] - bbuf,
east=bounds[2] + bbuf, north=bounds[3] + bbuf,
continentalcolor='white',
title='Bot Issued Tornado Warnings [2008-2018] for PUB',
subtitle='%s warnings plotted' % (len(df.index), ))
crs_new = ccrs.Mercator()
crs = ccrs.PlateCarree()
new_geometries = [crs_new.project_geometry(ii, src_crs=crs)
for ii in df['geom'].values]
mp.draw_cwas()
mp.ax.add_geometries(new_geometries, crs=crs_new,
edgecolor='r', facecolor='None', alpha=1., lw=0.5,
zorder=10)
mp.postprocess(filename='test.png')
def test_cwa():
"""Exercise the cwa plotting routines"""
mp = MapPlot(sector='cwa', cwa='MKX', nocaption=True)
mp.contourf(np.arange(-94, -89), np.arange(40, 45),
np.arange(5), np.arange(5),
clevlabels=['a', 'b', 'c', 'd', 'e'])
mp.draw_cwas()
assert mp.cwa == 'MKX'
return mp.fig
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
if ctx['t'] == 'state':
bnds = reference.state_bounds[ctx['state']]
title = reference.state_names[ctx['state']]
else:
bnds = reference.wfo_bounds[ctx['wfo']]
nt = NetworkTable("WFO")
title = "NWS CWA %s [%s]" % (nt.sts[ctx['wfo']]['name'], ctx['wfo'])
df, valid = get_df(ctx, bnds)
if df.empty:
raise ValueError("No data was found for your query")
mp = MapPlot(sector=('state' if ctx['t'] == 'state' else 'cwa'),
state=ctx['state'],
cwa=(ctx['wfo'] if len(ctx['wfo']) == 3 else ctx['wfo'][1:]),
axisbg='white',
title='%s for %s' % (PDICT2[ctx['v']], title),
subtitle=('Map valid: %s UTC') %
(valid.strftime("%d %b %Y %H:%M"), ),
nocaption=True,
titlefontsize=16)
mp.contourf(df['lon'].values,
df['lat'].values,
df['vsby'].values,
np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9]),
units='miles',
cmap=plt.get_cmap('gray'))
if ctx['t'] == 'state':
df2 = df[df['state'] == ctx['state']]
else:
df2 = df[df['wfo'] == ctx['wfo']]
mp.plot_values(df2['lon'].values, df2['lat'].values, df2['vsby'].values,
'%.1f')
mp.drawcounties()
if ctx['t'] == 'cwa':
mp.draw_cwas()
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx["v"]
if ctx["t"] == "state":
bnds = reference.state_bounds[ctx["state"]]
title = reference.state_names[ctx["state"]]
else:
bnds = reference.wfo_bounds[ctx["wfo"]]
title = "NWS CWA %s [%s]" % (
ctx["_nt"].sts[ctx["wfo"]]["name"],
ctx["wfo"],
)
df, valid = get_df(ctx, bnds)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(
sector=("state" if ctx["t"] == "state" else "cwa"),
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title="%s for %s" % (PDICT2[ctx["v"]], title),
subtitle=("Map valid: %s UTC") % (valid.strftime("%d %b %Y %H:%M"),),
nocaption=True,
titlefontsize=16,
)
if varname == "vsby":
ramp = np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9])
valunit = "miles"
elif varname == "feel":
valunit = "F"
df["feel"] = (
apparent_temperature(
df["tmpf"].values * units("degF"),
df["relh"].values * units("percent"),
df["sknt"].values * units("knots"),
)
.to(units("degF"))
.m
)
# Data QC, cough
if ctx.get("above"):
df = df[df[varname] < ctx["above"]]
if ctx.get("below"):
df = df[df[varname] > ctx["below"]]
# with QC done, we compute ramps
if varname != "vsby":
ramp = np.linspace(
df[varname].min() - 5, df[varname].max() + 5, 10, dtype="i"
)
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
ramp,
units=valunit,
cmap=get_cmap(ctx["cmap"]),
)
if ctx["t"] == "state":
df2 = df[df["state"] == ctx["state"]]
else:
df2 = df[df["wfo"] == ctx["wfo"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2[varname].values,
"%.1f",
labelbuffer=10,
)
mp.drawcounties()
if ctx["t"] == "cwa":
mp.draw_cwas()
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx['v']
if ctx['t'] == 'state':
bnds = reference.state_bounds[ctx['state']]
title = reference.state_names[ctx['state']]
else:
bnds = reference.wfo_bounds[ctx['wfo']]
title = "NWS CWA %s [%s]" % (ctx['_nt'].sts[ctx['wfo']]['name'],
ctx['wfo'])
df, valid = get_df(ctx, bnds)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(sector=('state' if ctx['t'] == 'state' else 'cwa'),
state=ctx['state'],
cwa=(ctx['wfo'] if len(ctx['wfo']) == 3 else ctx['wfo'][1:]),
axisbg='white',
title='%s for %s' % (PDICT2[ctx['v']], title),
subtitle=('Map valid: %s UTC') %
(valid.strftime("%d %b %Y %H:%M"), ),
nocaption=True,
titlefontsize=16)
if varname == 'vsby':
ramp = np.array([0.01, 0.1, 0.25, 0.5, 1, 2, 3, 5, 8, 9.9])
valunit = 'miles'
elif varname == 'feel':
valunit = 'F'
df['feel'] = apparent_temperature(df['tmpf'].values * units('degF'),
df['relh'].values * units('percent'),
df['sknt'].values *
units('knots')).to(units('degF')).m
# Data QC, cough
if ctx.get('above'):
df = df[df[varname] < ctx['above']]
if ctx.get('below'):
df = df[df[varname] > ctx['below']]
# with QC done, we compute ramps
if varname != 'vsby':
ramp = np.linspace(df[varname].min() - 5,
df[varname].max() + 5,
10,
dtype='i')
mp.contourf(df['lon'].values,
df['lat'].values,
df[varname].values,
ramp,
units=valunit,
cmap=plt.get_cmap(ctx['cmap']))
if ctx['t'] == 'state':
df2 = df[df['state'] == ctx['state']]
else:
df2 = df[df['wfo'] == ctx['wfo']]
mp.plot_values(df2['lon'].values,
df2['lat'].values,
df2[varname].values,
'%.1f',
labelbuffer=10)
mp.drawcounties()
if ctx['t'] == 'cwa':
mp.draw_cwas()
return mp.fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
varname = ctx["v"]
df = get_df(ctx)
if df.empty:
raise NoDataFound("No data was found for your query")
mp = MapPlot(
sector=("state" if ctx["t"] == "state" else "cwa"),
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="white",
title="%s for %s on %s" % (PDICT2[ctx["v"]], ctx["title"], ctx["day"]),
nocaption=True,
titlefontsize=16,
)
ramp = None
cmap = get_cmap(ctx["cmap"])
extend = "both"
if varname in ["max_gust", "max_sknt"]:
extend = "max"
ramp = np.arange(0, 40, 4)
ramp = np.append(ramp, np.arange(40, 80, 10))
ramp = np.append(ramp, np.arange(80, 120, 20))
# Data QC, cough
if ctx.get("above"):
df = df[df[varname] < ctx["above"]]
if ctx.get("below"):
df = df[df[varname] > ctx["below"]]
# with QC done, we compute ramps
if ramp is None:
ramp = np.linspace(df[varname].min() - 5,
df[varname].max() + 5,
10,
dtype="i")
if ctx["p"] == "both":
mp.contourf(
df["lon"].values,
df["lat"].values,
df[varname].values,
ramp,
units=VARUNITS[varname],
cmap=cmap,
spacing="proportional",
extend=extend,
)
if ctx["t"] == "state":
df2 = df[df[ctx["t"]] == ctx[ctx["t"]]]
else:
df2 = df[df["wfo"] == ctx["wfo"]]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2[varname].values,
"%.1f" if varname in ["max_gust", "max_sknt"] else "%.0f",
labelbuffer=3,
)
mp.drawcounties()
if ctx["t"] == "cwa":
mp.draw_cwas()
return mp.fig, df
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)
def test_illinois():
"""Produce a plot that doesn't suck"""
mp = MapPlot(sector='state', state='IL', nocaption=True)
mp.draw_cwas()
return mp.fig
