def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
get_data(ctx)
title = ("Consecutive Days with %s Temp " "above(+)/below(-) Average") % (
ctx["var"].capitalize(),
)
if ctx["var"] == "precip":
title = "Days Since Last Measurable Precipitation"
mp = MapPlot(
sector=ctx["sector"],
state=ctx["state"],
cwa=(ctx["wfo"] if len(ctx["wfo"]) == 3 else ctx["wfo"][1:]),
axisbg="tan",
statecolor="#EEEEEE",
title=title,
subtitle=ctx["subtitle"],
)
df2 = ctx["df"][pd.notnull(ctx["df"]["lon"])]
mp.plot_values(
df2["lon"].values,
df2["lat"].values,
df2["val"].values,
color=df2["color"].values,
labels=df2["label"].values,
labeltextsize=(8 if ctx["sector"] != "state" else 12),
textsize=(12 if ctx["sector"] != "state" else 16),
labelbuffer=10,
)
return mp.fig, ctx["df"]
def plotter(fdict):
""" Go """
from pyiem.plot.geoplot import MapPlot
import matplotlib.pyplot as plt
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
nt = NetworkTable(ctx['network'])
year = ctx['year']
dbconn = get_dbconn('coop')
table = "alldata_%s" % (station[:2], )
df = read_sql("""
with yearly as (
select year, sum(precip), station from alldata_ia
where precip is not null and sday < '1012' GROUP by station, year),
ames as (
select year, sum from yearly where station = %s),
combo as (
select y.year, y.station, y.sum as other, a.sum as ames
from yearly y JOIN ames a on (y.year = a.year)
WHERE y.station != %s and y.station != 'IA0807'),
y2017 as (
select * from combo where year = 2017),
agg as (
SELECT station, st_x(geom) as lon, st_y(geom) as lat,
sum(case when other > ames then 1 else 0 end) as hits, count(*)
from combo c JOIN stations t on (c.station = t.id)
WHERE t.network = %s and c.year < 2017
GROUP by station, lon, lat)
SELECT y.ames as ames, y.other as other, a.* from
y2017 y JOIN agg a on (y.station = a.station)
""",
dbconn,
params=(station, station, ctx['network']),
index_col='station')
df = df[df['count'] > 50]
df['h2017'] = 'red'
df.loc[df['other'] > df['ames'], 'h2017'] = 'blue'
df['freq'] = df['hits'] / df['count'] * 100.
df.sort_values('freq', inplace=True)
mp = MapPlot(continentalcolor='white',
title=('Percentage of Years with Higher Precipitation Total '
'than Ames'),
subtitle=('For 1 January thru 12 October Period, sites '
'in blue are higher for 2017'))
mp.plot_values(df['lon'].values,
df['lat'].values,
df['freq'].values,
'%.0f',
labelbuffer=1,
color=df['h2017'].values)
#mp.contourf(df['lon'].values, df['lat'].values, df['freq'].values,
# range(0, 101, 10), cmap=plt.get_cmap('plasma_r'))
mp.drawcounties()
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
sector = ctx['sector']
if len(sector) != 2:
raise ValueError("Sorry, this app does not support multi-state plots.")
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 df.empty:
raise ValueError("No data found!")
def f(val):
ts = datetime.date(year, 1, 1) + datetime.timedelta(days=int(val - 1))
return ts.strftime("%-m/%-d")
df['pdate'] = df['doy'].apply(f)
mp = MapPlot(sector=('state' if len(sector) == 2 else sector),
state=ctx['sector'],
continental_color='white',
nocaption=True,
title=r"%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 = list(map(f, levs))
if popt == 'contour':
mp.contourf(df['lon'],
df['lat'],
df['doy'],
levs,
clevlabels=levlables)
mp.plot_values(df['lon'], df['lat'], df['pdate'], labelbuffer=5)
mp.drawcounties()
return mp.fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
sector = ctx["sector"]
if len(sector) != 2:
raise NoDataFound("Sorry, this app doesn't support multi-state plots.")
varname = ctx["var"]
year = ctx["year"]
popt = ctx["popt"]
threshold = ctx["threshold"]
table = "alldata_%s" % (sector,)
nt = NetworkTable("%sCLIMATE" % (sector,))
syear = ctx.get("syear", 1893)
eyear = ctx.get("eyear", datetime.date.today().year)
df = read_sql(
"""
-- get the domain of data
WITH events as (
SELECT
station, month,
case when month < 7 then year - 1 else year end as winter_year,
year,
extract(doy from day) as doy,
day
from """
+ table
+ """
WHERE """
+ SQLOPT[varname]
+ """ and
month in %s and
substr(station, 3, 4) != '0000'
and substr(station, 3, 1) not in ('C', 'T')
and year >= %s and year <= %s
), agg as (
SELECT station, winter_year, year, doy, day,
case when month < 7 then doy + 366 else doy end as winter_doy,
rank() OVER (
PARTITION by """
+ YRGP[varname]
+ """, station
ORDER by day """
+ ORDER[varname]
+ """)
from events)
select * from agg where rank = 1
""",
pgconn,
params=(threshold, tuple(MONTH_DOMAIN[varname]), syear, eyear),
index_col="station",
)
doy = USEDOY[varname]
def f(val):
"""Make a pretty date."""
base = datetime.date(2000, 1, 1)
date = base + datetime.timedelta(days=int(val))
return date.strftime("%-m/%-d")
if ctx.get("p") is None:
df2 = df[df[YRGP[varname]] == year].copy()
title = r"%s %s %s$^\circ$F" % (year, PDICT2[varname], threshold)
df2["pdate"] = df2["day"].apply(lambda x: x.strftime("%-m/%-d"))
extra = ""
else:
df2 = df[[doy]].groupby("station").quantile(ctx["p"] / 100.0).copy()
title = r"%.0f%s Percentile Date of %s %s$^\circ$F" % (
ctx["p"],
th(str(ctx["p"])),
PDICT2[varname],
threshold,
)
df2["pdate"] = df2[doy].apply(f)
extra = ", period of record: %.0f-%.0f" % (
df["year"].min(),
df["year"].max(),
)
if df2.empty:
raise NoDataFound("No Data was found")
for station in df2.index.values:
if station not in nt.sts:
continue
df2.at[station, "lat"] = nt.sts[station]["lat"]
df2.at[station, "lon"] = nt.sts[station]["lon"]
mp = MapPlot(
sector="state",
state=ctx["sector"],
continental_color="white",
nocaption=True,
title=title,
subtitle="based on NWS COOP and IEM Daily Estimates%s" % (extra,),
)
levs = np.linspace(df2[doy].min() - 1, df2[doy].max() + 1, 7, dtype="i")
if "cint" in ctx:
levs = np.arange(
df2[doy].min() - 1, df2[doy].max() + 1, ctx["cint"], dtype="i"
)
levlables = list(map(f, levs))
if popt == "contour":
mp.contourf(
df2["lon"],
df2["lat"],
df2[doy],
levs,
clevlabels=levlables,
cmap=ctx["cmap"],
)
mp.plot_values(df2["lon"], df2["lat"], df2["pdate"], labelbuffer=5)
mp.drawcounties()
return mp.fig, df[["year", "winter_doy", "doy"]]
def plotter(fdict):
""" Go """
pgconn = get_dbconn('iem')
ctx = get_autoplot_context(fdict, get_description())
varname = ctx['var']
sector = ctx['sector']
state = ctx['state']
wfo = ctx['wfo']
nt = NetworkTable("NWSCLI")
today = ctx['sdate']
yesterday = today - datetime.timedelta(days=1)
d180 = today - datetime.timedelta(days=180)
df = read_sql("""
with obs as (
select station, valid,
(case when low > low_normal then 1 else 0 end) as low_hit,
(case when high > high_normal then 1 else 0 end) as high_hit,
(case when precip >= 0.01 then 1 else 0 end) as precip_hit
from cli_data
where high is not null
and high_normal is not null and low is not null and
low_normal is not null
and valid > %s and valid <= %s),
totals as (
SELECT station,
max(case when low_hit = 0 then valid else %s end) as last_low_below,
max(case when low_hit = 1 then valid else %s end) as last_low_above,
max(case when high_hit = 0 then valid else %s end) as last_high_below,
max(case when high_hit = 1 then valid else %s end) as last_high_above,
max(case when precip_hit = 0 then valid else %s end) as last_dry,
max(case when precip_hit = 1 then valid else %s end) as last_wet,
count(*) as count from obs GROUP by station)
SELECT station, last_low_below, last_low_above, last_high_below,
last_high_above, last_dry, last_wet
from totals where count > 170
""",
pgconn,
params=(d180, today, d180, d180, d180, d180, d180, d180),
index_col='station')
lats = []
lons = []
vals = []
colors = []
labels = []
df['precip_days'] = (df['last_dry'] - df['last_wet']).dt.days
df['low_days'] = (df['last_low_above'] - df['last_low_below']).dt.days
df['high_days'] = (df['last_high_above'] - df['last_high_below']).dt.days
# reorder the frame so that the largest values come first
df = df.reindex(df[varname +
'_days'].abs().sort_values(ascending=False).index)
for station, row in df.iterrows():
if station not in nt.sts:
continue
lats.append(nt.sts[station]['lat'])
lons.append(nt.sts[station]['lon'])
if varname == 'precip':
last_wet = row['last_wet']
days = 0 if last_wet in [today, yesterday] else row['precip_days']
else:
days = row[varname + '_days']
vals.append(days)
colors.append('r' if days > 0 else 'b')
labels.append(station[1:])
title = ('Consecutive Days with %s Temp '
'above(+)/below(-) Average') % (varname.capitalize(), )
if varname == 'precip':
title = 'Days Since Last Measurable Precipitation'
mp = MapPlot(sector=sector,
state=state,
cwa=(wfo if len(wfo) == 3 else wfo[1:]),
axisbg='tan',
statecolor='#EEEEEE',
title=title,
subtitle=('based on NWS CLI Sites, map approximately '
'valid for %s') % (today.strftime("%-d %b %Y"), ))
mp.plot_values(lons,
lats,
vals,
color=colors,
labels=labels,
labeltextsize=(8 if sector != 'state' else 12),
textsize=(12 if sector != 'state' else 16),
labelbuffer=10)
return mp.fig, df
def make_overviewmap(form):
"""Draw a pretty map of just the HUC."""
huc = form.get("huc")
plt.close()
projection = EPSG[5070]
if huc is None:
huclimiter = ""
elif len(huc) >= 8:
huclimiter = " and substr(huc_12, 1, 8) = '%s' " % (huc[:8],)
with get_sqlalchemy_conn("idep") as conn:
df = read_postgis(
f"""
SELECT simple_geom as geom, huc_12,
ST_x(ST_Transform(ST_Centroid(geom), 4326)) as centroid_x,
ST_y(ST_Transform(ST_Centroid(geom), 4326)) as centroid_y,
hu_12_name
from huc12 i WHERE i.scenario = 0 {huclimiter}
""",
conn,
geom_col="geom",
index_col="huc_12",
)
minx, miny, maxx, maxy = df["geom"].total_bounds
buf = float(form.get("zoom", 10.0)) * 1000.0 # 10km
hucname = "" if huc not in df.index else df.at[huc, "hu_12_name"]
subtitle = "The HUC8 is in tan"
if len(huc) == 12:
subtitle = "HUC12 highlighted in red, the HUC8 it resides in is in tan"
m = MapPlot(
axisbg="#EEEEEE",
logo="dep",
sector="custom",
south=miny - buf,
north=maxy + buf,
west=minx - buf,
east=maxx + buf,
projection=projection,
continentalcolor="white",
title="DEP HUC %s:: %s" % (huc, hucname),
subtitle=subtitle,
titlefontsize=20,
subtitlefontsize=18,
caption="Daily Erosion Project",
)
for _huc12, row in df.iterrows():
p = Polygon(
row["geom"].exterior.coords,
fc="red" if _huc12 == huc else "tan",
ec="k",
zorder=Z_OVERLAY2,
lw=0.1,
)
m.ax.add_patch(p)
# If this is our HUC, add some text to prevent cities overlay overlap
if _huc12 == huc:
m.plot_values(
[row["centroid_x"]],
[row["centroid_y"]],
[" . "],
color="None",
outlinecolor="None",
)
if huc is not None:
m.drawcounties()
m.drawcities()
ram = BytesIO()
plt.savefig(ram, format="png", dpi=100)
plt.close()
ram.seek(0)
return ram.read(), True