def plot_sky(days, vsby, data, station, nt, sts):
"""Sky plot variant."""
fig = plt.figure(figsize=(8, 6))
# vsby plot
ax = plt.axes([0.1, 0.08, 0.8, 0.03])
ax.set_xticks(np.arange(0, days*24+1, 24))
ax.set_xticklabels(np.arange(1, days+1))
ax.set_yticks([])
cmap = cm.get_cmap('gray')
cmap.set_bad('white')
res = ax.imshow(
vsby, aspect='auto', extent=[0, days*24, 0, 1], vmin=0, cmap=cmap,
vmax=10)
cax = plt.axes([0.915, 0.08, 0.035, 0.2])
fig.colorbar(res, cax=cax)
fig.text(0.02, 0.09, "Visibility\n[miles]", va='center')
# clouds
ax = plt.axes([0.1, 0.16, 0.8, 0.7])
ax.set_facecolor('skyblue')
ax.set_xticks(np.arange(0, days*24+1, 24))
ax.set_xticklabels(np.arange(1, days+1))
fig.text(
0.5, 0.935,
('[%s] %s %s Clouds & Visibility\nbased on ASOS METAR Cloud Amount '
'/Level and Visibility Reports'
) % (station, nt.sts[station]['name'], sts.strftime("%b %Y")),
ha='center', fontsize=14)
cmap = cm.get_cmap('gray_r')
cmap.set_bad('white')
cmap.set_under('skyblue')
ax.imshow(np.flipud(data), aspect='auto', extent=[0, days*24, 0, 250],
cmap=cmap, vmin=1)
ax.set_yticks(range(0, 260, 50))
ax.set_yticklabels(range(0, 25, 5))
ax.set_ylabel("Cloud Levels [1000s feet]")
fig.text(0.45, 0.02, "Day of %s (UTC Timezone)" % (sts.strftime("%b %Y"),))
r1 = Rectangle((0, 0), 1, 1, fc='skyblue')
r2 = Rectangle((0, 0), 1, 1, fc='white')
r3 = Rectangle((0, 0), 1, 1, fc='k')
r4 = Rectangle((0, 0), 1, 1, fc='#EEEEEE')
ax.grid(True)
ax.legend(
[r1, r4, r2, r3], ['Clear', 'Some', 'Unknown', 'Obscured by Overcast'],
loc='lower center', fontsize=14,
bbox_to_anchor=(0.5, 0.99), fancybox=True, shadow=True, ncol=4)
return fig
def plot_vsby(days, vsby, station, ctx, sts):
"""Sky plot variant."""
fig = plt.figure(figsize=(8, 6))
# need to convert vsby to 2-d
data = np.ones((100, days * 24)) * -3
for i in range(days * 24):
val = vsby[0, i]
if np.ma.is_masked(val):
continue
val = min([int(val * 10), 100])
data[val:, i] = val / 10.0
data[:val, i] = -1
data = np.ma.array(data, mask=np.where(data < -1, True, False))
# clouds
ax = plt.axes([0.1, 0.1, 0.8, 0.8])
ax.set_facecolor("skyblue")
ax.set_xticks(np.arange(0, days * 24 + 1, 24))
ax.set_xticklabels(np.arange(1, days + 1))
fig.text(
0.5,
0.935,
("[%s] %s %s Visibility\nbased on hourly ASOS METAR Visibility Reports"
) % (station, ctx["_nt"].sts[station]["name"], sts.strftime("%b %Y")),
ha="center",
fontsize=14,
)
cmap = cm.get_cmap("gray")
cmap.set_bad("white")
cmap.set_under("skyblue")
res = ax.imshow(
np.flipud(data),
aspect="auto",
extent=[0, days * 24, 0, 100],
cmap=cmap,
vmin=0,
vmax=10,
)
cax = plt.axes([0.915, 0.08, 0.035, 0.2])
fig.colorbar(res, cax=cax)
ax.set_yticks(range(0, 101, 10))
ax.set_yticklabels(range(0, 11, 1))
ax.set_ylabel("Visibility [miles]")
fig.text(0.45, 0.02,
"Day of %s (UTC Timezone)" % (sts.strftime("%b %Y"), ))
ax.grid(True)
return fig
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
table = "alldata_%s" % (station[:2], )
df = read_sql(
"""
WITH data as (
SELECT sday, day, year,
rank() OVER (PARTITION by sday ORDER by high DESC) as max_high_rank,
rank() OVER (PARTITION by sday ORDER by high ASC) as min_high_rank,
rank() OVER (PARTITION by sday ORDER by low DESC) as max_low_rank,
rank() OVER (PARTITION by sday ORDER by low ASC) as min_low_rank
from """ + table + """ WHERE station = %s and high is not null
and low is not null)
SELECT *,
extract(doy from
('2000-'||substr(sday, 1, 2)||'-'||substr(sday, 3, 2))::date) as doy
from data WHERE max_high_rank = 1 or min_high_rank = 1 or
max_low_rank = 1 or min_low_rank = 1 ORDER by day ASC
""",
pgconn,
params=(station, ),
index_col=None,
)
if df.empty:
raise NoDataFound("No Data Found.")
fig = plt.figure(figsize=(12, 6))
fig.text(
0.5,
0.95,
("[%s] %s Year of Daily Records, ties included") %
(station, ctx["_nt"].sts[station]["name"]),
ha="center",
fontsize=16,
)
ax = plt.axes([0.04, 0.55, 0.35, 0.35])
magic(ax, df, "max_high_rank", "Maximum High (warm)", ctx)
ax = plt.axes([0.04, 0.1, 0.35, 0.35])
magic(ax, df, "min_high_rank", "Minimum High (cold)", ctx)
ax = plt.axes([0.54, 0.55, 0.35, 0.35])
magic(ax, df, "max_low_rank", "Maximum Low (warm)", ctx)
ax = plt.axes([0.54, 0.1, 0.35, 0.35])
magic(ax, df, "min_low_rank", "Minimum Low (cold)", ctx)
return plt.gcf(), df
def make_colorbar(clevs, norm, cmap):
""" Manual Color Bar """
ax = plt.axes([0.02, 0.1, 0.05, 0.8], frameon=False, yticks=[], xticks=[])
under = clevs[0] - (clevs[1] - clevs[0])
over = clevs[-1] + (clevs[-1] - clevs[-2])
blevels = np.concatenate([[under], clevs, [over]])
cb2 = mpcolorbar.ColorbarBase(
ax,
cmap=cmap,
norm=norm,
boundaries=blevels,
extend="both",
ticks=None,
spacing="uniform",
orientation="vertical",
)
for i, lev in enumerate(clevs):
y = float(i) / (len(clevs) - 1)
fmt = "%g"
txt = cb2.ax.text(0.5, y, fmt % (lev,), va="center", ha="center")
txt.set_path_effects(
[PathEffects.withStroke(linewidth=2, foreground="w")]
)
ax.yaxis.set_ticklabels([])
def magic(ax, df, colname, title, ctx):
"""You can do magic"""
df2 = df[df[colname] == 1]
ax.text(0, 1.02, title, transform=ax.transAxes)
ax.set_xlim(0, 367)
ax.grid(True)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365))
ax.set_xticklabels(calendar.month_abbr[1:])
bbox = ax.get_position()
sideax = plt.axes([bbox.x1 + 0.01, bbox.y0, 0.09, 0.35])
ylim = [df['year'].min(), df['year'].max()]
year0 = ylim[0] - (ylim[0] % 10)
year1 = ylim[1] + (10 - ylim[1] % 10)
cmap = plt.get_cmap(ctx['cmap'])
norm = mpcolors.BoundaryNorm(np.arange(year0, year1 + 1, 10), cmap.N)
ax.scatter(df2['doy'], df2['year'], color=cmap(norm(df2['year'].values)))
ax.set_yticks(np.arange(year0, year1, 20))
ax.set_ylim(*ylim)
cnts, edges = np.histogram(df2['year'].values,
np.arange(year0, year1 + 1, 10))
sideax.barh(edges[:-1],
cnts,
height=10,
align='edge',
color=cmap(norm(edges[:-1])))
sideax.set_yticks(np.arange(year0, year1, 20))
sideax.set_yticklabels([])
sideax.set_ylim(*ylim)
sideax.grid(True)
sideax.set_xlabel("Decade Count")
def main(argv):
"""Go Main Go."""
huc12 = argv[1]
fpath = argv[2]
year = int(argv[3])
prop_cycle = plt.rcParams["axes.prop_cycle"]
colors = prop_cycle.by_key()["color"]
data = {}
for scenario in range(59, 70):
df = read_crop("/i/%s/crop/%s/%s/%s_%s.crop" %
(scenario, huc12[:8], huc12[8:], huc12, fpath))
data[scenario] = df[df["ofe"] == 1].set_index("date")
ax1 = plt.axes([0.15, 0.5, 0.85, 0.35])
ax2 = plt.axes([0.15, 0.1, 0.85, 0.35])
baseline = data[59][data[59].index.year == year]
for scenario in range(60, 70):
color = colors[scenario - 60]
date = datetime.date(2000, 4,
15) + datetime.timedelta(days=(scenario - 60) * 5)
scendata = data[scenario][data[scenario]["year"] == year]
delta = scendata["canopy_percent"] - baseline["canopy_percent"]
x = delta.index.to_pydatetime()
ax1.plot(
x,
scendata["canopy_percent"] * 100.0,
label=date.strftime("%b %d"),
color=color,
)
ax2.plot(x, delta.values * 100.0, color=color)
ax1.set_xlim(datetime.date(year, 4, 15), datetime.date(year, 7, 15))
ax2.set_xlim(datetime.date(year, 4, 15), datetime.date(year, 7, 15))
ax1.xaxis.set_major_locator(mdates.DayLocator([1]))
ax1.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
ax2.xaxis.set_major_locator(mdates.DayLocator([1]))
ax2.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
ax1.set_ylabel("Coverage [%]")
ax2.set_ylabel("Absolute Differnece from Apr 10 [%]")
ax2.set_ylim(-101, 0)
ax1.set_title("huc12: %s fpath: %s\n%s Canopy Coverage by Planting Date" %
(huc12, fpath, year))
ax1.grid()
ax2.grid()
ax1.legend(loc=2, ncol=2)
plt.gcf().savefig("test.png")
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
table = "alldata_%s" % (station[:2], )
nt = NetworkTable("%sCLIMATE" % (station[:2], ))
df = read_sql("""
WITH data as (
SELECT sday, day, year,
rank() OVER (PARTITION by sday ORDER by high DESC) as max_high_rank,
rank() OVER (PARTITION by sday ORDER by high ASC) as min_high_rank,
rank() OVER (PARTITION by sday ORDER by low DESC) as max_low_rank,
rank() OVER (PARTITION by sday ORDER by low ASC) as min_low_rank
from """ + table + """ WHERE station = %s and high is not null
and low is not null)
SELECT *,
extract(doy from
('2000-'||substr(sday, 1, 2)||'-'||substr(sday, 3, 2))::date) as doy
from data WHERE max_high_rank = 1 or min_high_rank = 1 or
max_low_rank = 1 or min_low_rank = 1 ORDER by day ASC
""",
pgconn,
params=(station, ),
index_col=None)
fig = plt.figure(figsize=(12, 6))
fig.text(0.5,
0.95, ("[%s] %s Year of Daily Records, ties included") %
(station, nt.sts[station]['name']),
ha='center',
fontsize=16)
ax = plt.axes([0.04, 0.55, 0.35, 0.35])
magic(ax, df, 'max_high_rank', 'Maximum High (warm)', ctx)
ax = plt.axes([0.04, 0.1, 0.35, 0.35])
magic(ax, df, 'min_high_rank', 'Minimum High (cold)', ctx)
ax = plt.axes([0.54, 0.55, 0.35, 0.35])
magic(ax, df, 'max_low_rank', 'Maximum Low (warm)', ctx)
ax = plt.axes([0.54, 0.1, 0.35, 0.35])
magic(ax, df, 'min_low_rank', 'Minimum Low (cold)', ctx)
return plt.gcf(), df
def main(argv):
"""Go Main Go."""
huc12 = argv[1]
year = int(argv[2])
prop_cycle = plt.rcParams["axes.prop_cycle"]
colors = prop_cycle.by_key()["color"]
pgconn = get_dbconn("idep")
df = read_sql(
"""
SELECT scenario, huc_12, avg_delivery, valid from results_by_huc12
WHERE scenario >= 59 and scenario < 70 and
extract(year from valid) = %s and huc_12 = %s ORDER by valid ASC
""",
pgconn,
params=(year, huc12),
)
df["valid"] = pd.to_datetime(df["valid"])
ax = plt.axes([0.2, 0.1, 0.75, 0.75])
baseline = df[df["scenario"] == 59].copy().set_index("valid")
yticklabels = []
col = "avg_delivery"
for scenario in range(60, 70):
color = colors[scenario - 60]
date = datetime.date(2000, 4,
15) + datetime.timedelta(days=(scenario - 60) * 5)
scendata = df[df["scenario"] == scenario].copy().set_index("valid")
delta = scendata[col] - baseline[col]
delta = delta[delta != 0]
total = ((scendata[col].sum() - baseline[col].sum()) /
baseline[col].sum()) * 100.0
yticklabels.append("%s %4.2f%%" % (date.strftime("%b %d"), total))
x = delta.index.to_pydatetime()
# res = ax.scatter(x, delta.values + (scenario - 60))
for idx, val in enumerate(delta):
ax.arrow(
x[idx],
scenario - 60,
0,
val * 10.0,
head_width=4,
head_length=0.1,
fc=color,
ec=color,
)
ax.axhline(scenario - 60, color=color)
ax.set_xlim(datetime.date(year, 1, 1), datetime.date(year + 1, 1, 1))
ax.set_ylim(-0.5, 10)
ax.xaxis.set_major_locator(mdates.DayLocator([1]))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
ax.set_title("huc12: %s \n%s Daily Change in Delivery vs Apr 10 Planting" %
(huc12, year))
ax.grid(axis="x")
ax.set_yticks(range(10))
ax.set_yticklabels(yticklabels)
plt.gcf().savefig("test.png")
def main(argv):
"""Go Main Go."""
huc12 = argv[1]
fpath = argv[2]
year = int(argv[3])
prop_cycle = plt.rcParams["axes.prop_cycle"]
colors = prop_cycle.by_key()["color"]
data = {}
for scenario in range(59, 70):
data[scenario] = read_env(
"/i/%s/env/%s/%s/%s_%s.env" %
(scenario, huc12[:8], huc12[8:], huc12, fpath)).set_index("date")
print(data[scenario]["av_det"].sum())
ax = plt.axes([0.2, 0.1, 0.75, 0.75])
baseline = data[59][data[59].index.year == year]
yticklabels = []
for scenario in range(60, 70):
color = colors[scenario - 60]
date = datetime.date(2000, 4,
15) + datetime.timedelta(days=(scenario - 60) * 5)
scendata = data[scenario][data[scenario].index.year == year]
delta = scendata["sed_del"] - baseline["sed_del"]
delta = delta[delta != 0]
total = ((scendata["sed_del"].sum() - baseline["sed_del"].sum()) /
baseline["sed_del"].sum()) * 100.0
yticklabels.append("%s %4.2f%%" % (date.strftime("%b %d"), total))
x = delta.index.to_pydatetime()
# res = ax.scatter(x, delta.values + (scenario - 60))
for idx, val in enumerate(delta):
ax.arrow(
x[idx],
scenario - 60,
0,
val,
head_width=0.5,
head_length=0.1,
fc=color,
ec=color,
)
ax.axhline(scenario - 60, color=color)
ax.set_xlim(datetime.date(year, 1, 1), datetime.date(year + 1, 1, 1))
ax.set_ylim(-0.5, 10)
ax.xaxis.set_major_locator(mdates.DayLocator([1]))
ax.xaxis.set_major_formatter(mdates.DateFormatter("%b"))
ax.set_title(
"huc12: %s fpath: %s\n%s Daily Change in Delivery vs Apr 10 Planting" %
(huc12, fpath, year))
ax.grid(axis="x")
ax.set_yticks(range(10))
ax.set_yticklabels(yticklabels)
plt.gcf().savefig("test.png")
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
lagmonths = ctx['lag']
months = ctx['months']
month = ctx['month']
highyears = [int(x) for x in ctx['year'].split(",")]
h = ctx['h']
wantmonth = month + lagmonths
yearoffset = 0
if month + lagmonths < 1:
wantmonth = 12 - (month + lagmonths)
yearoffset = 1
wanted = []
deltas = []
for m in range(month, month + months):
if m < 13:
wanted.append(m)
deltas.append(0)
else:
wanted.append(m - 12)
deltas.append(-1)
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
elnino = {}
ccursor.execute("""SELECT monthdate, soi_3m, anom_34 from elnino""")
for row in ccursor:
if row[0].month != wantmonth:
continue
elnino[row[0].year + yearoffset] = dict(soi_3m=row[1], anom_34=row[2])
ccursor.execute(
"""
SELECT year, month, sum(precip), avg((high+low)/2.)
from """ + table + """
where station = %s GROUP by year, month
""", (station, ))
yearly = {}
for row in ccursor:
(_year, _month, _precip, _temp) = row
if _month not in wanted:
continue
effectiveyear = _year + deltas[wanted.index(_month)]
nino = elnino.get(effectiveyear, {}).get('soi_3m', None)
if nino is None:
continue
data = yearly.setdefault(effectiveyear,
dict(precip=0, temp=[], nino=nino))
data['precip'] += _precip
data['temp'].append(float(_temp))
fig = plt.figure(figsize=(10, 6))
ax = plt.axes([0.1, 0.12, 0.5, 0.75])
msg = ("[%s] %s\n%s\n%s SOI (3 month average)") % (
station, nt.sts[station]['name'], title(wanted),
datetime.date(2000, wantmonth, 1).strftime("%B"))
ax.set_title(msg)
cmap = plt.get_cmap("RdYlGn")
zdata = np.arange(-2.0, 2.1, 0.5)
norm = mpcolors.BoundaryNorm(zdata, cmap.N)
rows = []
xs = []
ys = []
for year in yearly:
x = yearly[year]['precip']
y = np.average(yearly[year]['temp'])
xs.append(x)
ys.append(y)
val = yearly[year]['nino']
c = cmap(norm([val])[0])
if h == 'hide' and val > -0.5 and val < 0.5:
ax.scatter(x,
y,
facecolor='#EEEEEE',
edgecolor='#EEEEEE',
s=30,
zorder=2,
marker='s')
else:
ax.scatter(x,
y,
facecolor=c,
edgecolor='k',
s=60,
zorder=3,
marker='o')
if year in highyears:
ax.text(x,
y + 0.2,
"%s" % (year, ),
ha='center',
va='bottom',
zorder=5)
rows.append(dict(year=year, precip=x, tmpf=y, soi3m=val))
ax.axhline(np.average(ys), lw=2, color='k', linestyle='-.', zorder=2)
ax.axvline(np.average(xs), lw=2, color='k', linestyle='-.', zorder=2)
sm = plt.cm.ScalarMappable(norm, cmap)
sm.set_array(zdata)
cb = plt.colorbar(sm, extend='both')
cb.set_label("<-- El Nino :: SOI :: La Nina -->")
ax.grid(True)
ax.set_xlim(left=-0.01)
ax.set_xlabel("Total Precipitation [inch], Avg: %.2f" % (np.average(xs), ))
ax.set_ylabel((r"Average Temperature $^\circ$F, "
"Avg: %.1f") % (np.average(ys), ))
df = pd.DataFrame(rows)
ax2 = plt.axes([0.67, 0.6, 0.28, 0.35])
ax2.scatter(df['soi3m'].values, df['tmpf'].values)
ax2.set_xlabel("<-- El Nino :: SOI :: La Nina -->")
ax2.set_ylabel(r"Avg Temp $^\circ$F")
slp, intercept, r_value, _, _ = stats.linregress(df['soi3m'].values,
df['tmpf'].values)
y1 = -2.0 * slp + intercept
y2 = 2.0 * slp + intercept
ax2.plot([-2, 2], [y1, y2])
ax2.text(0.97,
0.9,
"R$^2$=%.2f" % (r_value**2, ),
ha='right',
transform=ax2.transAxes,
bbox=dict(color='white'))
ax2.grid(True)
ax3 = plt.axes([0.67, 0.1, 0.28, 0.35])
ax3.scatter(df['soi3m'].values, df['precip'].values)
ax3.set_xlabel("<-- El Nino :: SOI :: La Nina -->")
ax3.set_ylabel("Total Precip [inch]")
slp, intercept, r_value, _, _ = stats.linregress(df['soi3m'].values,
df['precip'].values)
y1 = -2.0 * slp + intercept
y2 = 2.0 * slp + intercept
ax3.plot([-2, 2], [y1, y2])
ax3.text(0.97,
0.9,
"R$^2$=%.2f" % (r_value**2, ),
ha='right',
transform=ax3.transAxes,
bbox=dict(color='white'))
ax3.grid(True)
return fig, df
def main(argv):
"""Go Main Go"""
pgconn = get_dbconn('postgis')
pcursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
pcursor2 = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
# Preparation
sortOpt = argv[1]
ts = datetime.datetime.utcnow() - datetime.timedelta(hours=1)
sts = ts.replace(tzinfo=pytz.utc,
hour=0,
minute=0,
second=0,
microsecond=0)
if len(argv) == 5:
sts = sts.replace(year=int(argv[1]),
month=int(argv[2]),
day=int(argv[3]))
sortOpt = argv[4]
ets = sts + datetime.timedelta(hours=24)
opts = {
'W': {
'fnadd': '-wfo',
'sortby': 'wfo ASC, phenomena ASC, eventid ASC'
},
'S': {
'fnadd': '',
'sortby': 'size DESC'
},
'T': {
'fnadd': '-time',
'sortby': 'issue ASC'
}
}
# Defaults
thumbpx = 100
cols = 10
# Find largest polygon either in height or width
sql = """SELECT *, ST_area2d(ST_transform(geom,2163)) as size,
(ST_xmax(ST_transform(geom,2163)) -
ST_xmin(ST_transform(geom,2163))) as width,
(ST_ymax(ST_transform(geom,2163)) -
ST_ymin(ST_transform(geom,2163))) as height
from sbw_%s WHERE status = 'NEW' and issue >= '%s' and issue < '%s' and
phenomena IN ('TO','SV') """ % (sts.year, sts, ets)
pcursor.execute(sql)
maxDimension = 0
mybuffer = 10000
i = 0
torCount = 0
torSize = 0
svrCount = 0
svrSize = 0
for row in pcursor:
w = float(row['width'])
h = float(row['height'])
if w > maxDimension:
maxDimension = w
if h > maxDimension:
maxDimension = h
if row['phenomena'] == "SV":
svrCount += 1
svrSize += float(row['size'])
if row['phenomena'] == "TO":
torCount += 1
torSize += float(row['size'])
i += 1
sql = """
SELECT phenomena, sum( ST_area2d(ST_transform(u.geom,2163)) ) as size
from warnings_%s w JOIN ugcs u on (u.gid = w.gid)
WHERE issue >= '%s' and issue < '%s' and
significance = 'W' and phenomena IN ('TO','SV') GROUP by phenomena
""" % (sts.year, sts, ets)
pcursor.execute(sql)
for row in pcursor:
if row['phenomena'] == "TO":
totalTorCar = 100.0 * (1.0 - (torSize / float(row['size'])))
if row['phenomena'] == "SV":
totalSvrCar = 100.0 * (1.0 - (svrSize / float(row['size'])))
# Make mosaic image
header = 35
mosaic = Image.new('RGB', (thumbpx * cols,
((int(i / cols) + 1) * thumbpx) + header))
draw = ImageDraw.Draw(mosaic)
imagemap = open('imap.txt', 'w')
utcnow = datetime.datetime.utcnow()
imagemap.write("<!-- %s %s -->\n" %
(utcnow.strftime("%Y-%m-%d %H:%M:%S"), sortOpt))
imagemap.write("<map name='mymap'>\n")
# Find my polygons
gdf = read_postgis("""
SELECT *, ST_area2d(ST_transform(geom,2163)) as size,
(ST_xmax(ST_transform(geom,2163)) +
ST_xmin(ST_transform(geom,2163))) /2.0 as xc,
(ST_ymax(ST_transform(geom,2163)) +
ST_ymin(ST_transform(geom,2163))) /2.0 as yc,
ST_transform(geom, 2163) as utmgeom
from sbw_""" + str(sts.year) + """ WHERE
status = 'NEW' and issue >= %s and issue < %s and
phenomena IN ('TO','SV') and eventid is not null
ORDER by """ + opts[sortOpt]['sortby'] + """
""",
pgconn,
params=(sts, ets),
geom_col='utmgeom',
index_col=None)
# Write metadata to image
tmp = Image.open("logo_small.png")
mosaic.paste(tmp, (3, 2))
s = "IEM Summary of NWS Storm Based Warnings issued %s UTC" % (
sts.strftime("%d %b %Y"), )
(w, h) = FONT2.getsize(s)
draw.text((54, 3), s, font=FONT2)
s = "Generated: %s UTC" % (
datetime.datetime.utcnow().strftime("%d %b %Y %H:%M:%S"), )
draw.text((54, 3 + h), s, font=FONT10)
if svrCount > 0:
s = ("%3i SVR: Avg Size %5.0f km^2 CAR: %.0f%%") % (
svrCount, (svrSize / float(svrCount)) / 1000000, totalSvrCar)
draw.text((54 + w + 10, 8), s, font=FONT10, fill="#ffff00")
if torCount > 0:
s = ("%3i TOR: Avg Size %5.0f km^2 CAR: %.0f%%") % (
torCount, (torSize / float(torCount)) / 1000000, totalTorCar)
draw.text((54 + w + 10, 22), s, font=FONT10, fill="#ff0000")
if pcursor.rowcount == 0:
s = "No warnings in database for this date"
draw.text((100, 78), s, font=FONT2, fill="#ffffff")
i = 0
for _, row in gdf.iterrows():
# - Map each polygon
x0 = float(row['xc']) - (maxDimension / 2.0) - mybuffer
x1 = float(row['xc']) + (maxDimension / 2.0) + mybuffer
y0 = float(row['yc']) - (maxDimension / 2.0) - 1.75 * mybuffer
y1 = float(row['yc']) + (maxDimension / 2.0) + 0.25 * mybuffer
fig = plt.figure(figsize=(thumbpx / 100., thumbpx / 100.))
ax = plt.axes([0, 0, 1, 1], facecolor='black')
ax.set_xlim(x0, x1)
ax.set_ylim(y0, y1)
for poly in row['utmgeom']:
xs, ys = poly.exterior.xy
color = 'r' if row['phenomena'] == 'TO' else 'yellow'
ax.plot(xs, ys, color=color, lw=2)
fig.savefig('tmp.png')
plt.close()
my = int(i / cols) * thumbpx + header
mx0 = (i % cols) * thumbpx
# - Add each polygon to mosaic
tmp = Image.open("tmp.png")
mosaic.paste(tmp, (mx0, my))
del tmp
os.remove("tmp.png")
# Compute CAR!
sql = """
select sum(ST_area2d(ST_transform(u.geom,2163))) as csize
from warnings_%s w
JOIN ugcs u on (u.gid = w.gid) WHERE
phenomena = '%s' and significance = '%s' and eventid = %s
and w.wfo = '%s'
""" % (row['issue'].year, row['phenomena'], row['significance'],
row['eventid'], row['wfo'])
pcursor2.execute(sql)
row2 = pcursor2.fetchone()
car = "NA"
carColor = (255, 255, 255)
if row2 and row2['csize'] is not None:
csize = float(row2['csize'])
carF = 100.0 * (1.0 - (row['size'] / csize))
car = "%.0f" % (carF, )
if carF > 75:
carColor = (0, 255, 0)
if carF < 25:
carColor = (255, 0, 0)
# Draw Text!
issue = row['issue']
s = "%s.%s.%s.%s" % (row['wfo'], row['phenomena'], row['eventid'],
issue.strftime("%H%M"))
# (w, h) = font10.getsize(s)
# print s, h
draw.text((mx0 + 2, my + thumbpx - 10), s, font=FONT10)
s = "%.0f sq km %s%%" % (row['size'] / 1000000.0, car)
draw.text((mx0 + 2, my + thumbpx - (20)),
s,
font=FONT10,
fill=carColor)
# Image map
url = ("/vtec/#%s-O-NEW-K%s-%s-%s-%04i") % (
ts.year, row['wfo'], row['phenomena'], row['significance'],
row['eventid'])
altxt = "Click for text/image"
imagemap.write(
("<area href=\"%s\" alt=\"%s\" title=\"%s\" "
"shape=\"rect\" coords=\"%s,%s,%s,%s\">\n") %
(url, altxt, altxt, mx0, my, mx0 + thumbpx, my + thumbpx))
i += 1
for i in range(len(gdf.index)):
my = int(i / cols) * thumbpx + header
mx0 = (i % cols) * thumbpx
if mx0 == 0:
draw.line(
(0, my + thumbpx + 2, (thumbpx * cols), my + thumbpx + 2),
(0, 120, 200))
mosaic.save("test.png")
del mosaic
imagemap.write("</map>")
imagemap.close()
cmd = ("/home/ldm/bin/pqinsert -p "
"'plot a %s0000 blah sbwsum%s.png png' test.png") % (
sts.strftime("%Y%m%d"), opts[sortOpt]['fnadd'])
subprocess.call(cmd, shell=True)
cmd = ("/home/ldm/bin/pqinsert -p "
"'plot a %s0000 blah sbwsum-imap%s.txt txt' imap.txt") % (
sts.strftime("%Y%m%d"), opts[sortOpt]['fnadd'])
subprocess.call(cmd, shell=True)
os.remove("test.png")
os.remove("imap.txt")
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
month1 = ctx["month1"]
month2 = ctx["month2"]
highlight = ctx["highlight"]
varname = ctx["var"]
p1 = ctx.get("p1")
p2 = ctx.get("p2")
days = ctx["days"]
opt = ctx["opt"]
table = "alldata_%s" % (station[:2], )
m1data, y1, y2 = get_data(pgconn, table, station, month1, p1, varname,
days, opt)
m2data, y3, y4 = get_data(pgconn, table, station, month2, p2, varname,
days, opt)
pc1 = np.percentile(m1data, range(0, 101, 1))
pc2 = np.percentile(m2data, range(0, 101, 1))
df = pd.DataFrame({
"%s_%s_%s_%s" % (MDICT[month1], varname, y1, y2):
pd.Series(pc1),
"%s_%s_%s_%s" % (MDICT[month2], varname, y3, y4):
pd.Series(pc2),
"quantile":
pd.Series(range(0, 101, 5)),
})
s_slp, s_int, s_r, _, _ = stats.linregress(pc1, pc2)
fig = plt.gcf()
fig.set_size_inches(10.24, 7.68)
ax = plt.axes([0.1, 0.11, 0.4, 0.76])
ax.scatter(pc1[::5], pc2[::5], s=40, marker="s", color="b", zorder=3)
ax.plot(
pc1,
pc1 * s_slp + s_int,
lw=3,
color="r",
zorder=2,
label=r"Fit R$^2$=%.2f" % (s_r**2, ),
)
ax.axvline(highlight, zorder=1, color="k")
y = highlight * s_slp + s_int
ax.axhline(y, zorder=1, color="k")
ax.text(
pc1[0],
y,
r"%.0f $^\circ$F" % (y, ),
va="center",
bbox=dict(color="white"),
)
ax.text(
highlight,
pc2[0],
r"%.0f $^\circ$F" % (highlight, ),
ha="center",
rotation=90,
bbox=dict(color="white"),
)
t2 = PDICT[varname]
if days > 1:
t2 = "%s %s over %s days" % (ODICT[opt], PDICT[varname], days)
fig.suptitle(("[%s] %s\n%s (%s-%s) vs %s (%s-%s)\n%s") % (
station,
ctx["_nt"].sts[station]["name"],
MDICT[month2],
y1,
y2,
MDICT[month1],
y3,
y4,
t2,
))
ax.set_xlabel(r"%s (%s-%s) %s $^\circ$F" %
(MDICT[month1], y1, y2, PDICT[varname]))
ax.set_ylabel(r"%s (%s-%s) %s $^\circ$F" %
(MDICT[month2], y3, y4, PDICT[varname]))
ax.text(
0.95,
0.05,
"Quantile - Quantile Plot",
transform=ax.transAxes,
ha="right",
)
ax.grid(True)
ax.legend(loc=2)
# Second
ax = plt.axes([0.55, 0.18, 0.27, 0.68])
ax.set_title("Distribution")
v1 = ax.violinplot(m1data, positions=[0], showextrema=True, showmeans=True)
b = v1["bodies"][0]
m = np.mean(b.get_paths()[0].vertices[:, 0])
b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0],
-np.inf, m)
b.set_color("r")
for lbl in ["cmins", "cmeans", "cmaxes"]:
v1[lbl].set_color("r")
v1 = ax.violinplot(m2data, positions=[0], showextrema=True, showmeans=True)
b = v1["bodies"][0]
m = np.mean(b.get_paths()[0].vertices[:, 0])
b.get_paths()[0].vertices[:, 0] = np.clip(b.get_paths()[0].vertices[:, 0],
m, np.inf)
b.set_color("b")
for lbl in ["cmins", "cmeans", "cmaxes"]:
v1[lbl].set_color("b")
pr0 = plt.Rectangle((0, 0), 1, 1, fc="r")
pr1 = plt.Rectangle((0, 0), 1, 1, fc="b")
ax.legend(
(pr0, pr1),
(
r"%s (%s-%s), $\mu$=%.1f" %
(MDICT[month1], y1, y2, np.mean(m1data)),
r"%s (%s-%s), $\mu$=%.1f" %
(MDICT[month2], y3, y4, np.mean(m2data)),
),
ncol=1,
loc=(0.5, -0.15),
)
ax.set_ylabel(r"%s $^\circ$F" % (PDICT[varname], ))
ax.grid()
# Third
monofont = FontProperties(family="monospace")
y = 0.86
x = 0.83
col1 = "%s_%s_%s_%s" % (MDICT[month1], varname, y1, y2)
col2 = "%s_%s_%s_%s" % (MDICT[month2], varname, y3, y4)
fig.text(x, y + 0.04, "Percentile Data Diff")
for percentile in [
100,
99,
98,
97,
96,
95,
92,
90,
75,
50,
25,
10,
8,
5,
4,
3,
2,
1,
]:
row = df.loc[percentile]
fig.text(x, y, "%3i" % (percentile, ), fontproperties=monofont)
fig.text(
x + 0.025,
y,
"%5.1f" % (row[col1], ),
fontproperties=monofont,
color="r",
)
fig.text(
x + 0.07,
y,
"%5.1f" % (row[col2], ),
fontproperties=monofont,
color="b",
)
fig.text(
x + 0.11,
y,
"%5.1f" % (row[col2] - row[col1], ),
fontproperties=monofont,
)
y -= 0.04
return fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
typ = ctx["typ"]
sort = ctx["sort"]
date = ctx["date"]
pgconn = get_dbconn("postgis")
sts = utc(date.year, date.month, date.day)
ets = sts + datetime.timedelta(hours=24)
opts = {
"W": {
"fnadd": "-wfo",
"sortby": "wfo ASC, phenomena ASC, eventid ASC",
},
"S": {
"fnadd": "",
"sortby": "size DESC"
},
"T": {
"fnadd": "-time",
"sortby": "issue ASC"
},
}
phenoms = {"W": ["TO", "SV"], "F": ["FF"], "M": ["MA"]}
# Defaults
thumbpx = 100
cols = 10
mybuffer = 10000
header = 35
# Find largest polygon either in height or width
gdf = read_postgis(
"""
SELECT wfo, phenomena, eventid, issue,
ST_area2d(ST_transform(geom,2163)) as size,
(ST_xmax(ST_transform(geom,2163)) +
ST_xmin(ST_transform(geom,2163))) /2.0 as xc,
(ST_ymax(ST_transform(geom,2163)) +
ST_ymin(ST_transform(geom,2163))) /2.0 as yc,
ST_transform(geom, 2163) as utmgeom,
(ST_xmax(ST_transform(geom,2163)) -
ST_xmin(ST_transform(geom,2163))) as width,
(ST_ymax(ST_transform(geom,2163)) -
ST_ymin(ST_transform(geom,2163))) as height
from sbw_""" + str(sts.year) + """
WHERE status = 'NEW' and issue >= %s and issue < %s and
phenomena IN %s and eventid is not null
ORDER by """ + opts[sort]["sortby"] + """
""",
pgconn,
params=(sts, ets, tuple(phenoms[typ])),
geom_col="utmgeom",
index_col=None,
)
# For size reduction work
df = read_sql(
"""
SELECT w.wfo, phenomena, eventid,
sum(ST_area2d(ST_transform(u.geom,2163))) as county_size
from
warnings_""" + str(sts.year) + """ w JOIN ugcs u on (u.gid = w.gid)
WHERE issue >= %s and issue < %s and
significance = 'W' and phenomena IN %s
GROUP by w.wfo, phenomena, eventid
""",
pgconn,
params=(sts, ets, tuple(phenoms[typ])),
index_col=["wfo", "phenomena", "eventid"],
)
# Join the columns
gdf = gdf.merge(df, on=["wfo", "phenomena", "eventid"])
gdf["ratio"] = (1.0 - (gdf["size"] / gdf["county_size"])) * 100.0
# Make mosaic image
events = len(df.index)
rows = int(events / cols) + 1
if events % cols == 0:
rows -= 1
if rows == 0:
rows = 1
ypixels = (rows * thumbpx) + header
fig = plt.figure(figsize=(thumbpx * cols / 100.0, ypixels / 100.0))
plt.axes([0, 0, 1, 1], facecolor="black")
imagemap = StringIO()
utcnow = utc()
imagemap.write("<!-- %s %s -->\n" %
(utcnow.strftime("%Y-%m-%d %H:%M:%S"), sort))
imagemap.write("<map name='mymap'>\n")
# Write metadata to image
mydir = os.sep.join(
[os.path.dirname(os.path.abspath(__file__)), "../../../images"])
logo = mpimage.imread("%s/logo_reallysmall.png" % (mydir, ))
y0 = fig.get_figheight() * 100.0 - logo.shape[0] - 5
fig.figimage(logo, 5, y0, zorder=3)
i = 0
# amount of NDC y space we have for axes plotting
ytop = 1 - header / float((rows * 100) + header)
dy = ytop / float(rows)
ybottom = ytop
# Sumarize totals
y = ytop
dy2 = (1.0 - ytop) / 2.0
for phenomena, df2 in gdf.groupby("phenomena"):
car = (1.0 - df2["size"].sum() / df2["county_size"].sum()) * 100.0
fitbox(
fig,
("%i %s.W: Avg size %5.0f km^2 CAR: %.0f%%") %
(len(df2.index), phenomena, df2["size"].mean() / 1e6, car),
0.8,
0.99,
y,
y + dy2,
color=COLORS[phenomena],
)
y += dy2
fitbox(
fig,
"NWS %s Storm Based Warnings issued %s UTC" % (
" + ".join([VTEC_PHENOMENA[p] for p in phenoms[typ]]),
sts.strftime("%d %b %Y"),
),
0.05,
0.79,
ytop + dy2,
0.999,
color="white",
)
fitbox(
fig,
"Generated: %s UTC, IEM Autplot #203" %
(utcnow.strftime("%d %b %Y %H:%M:%S"), ),
0.05,
0.79,
ytop,
0.999 - dy2,
color="white",
)
# We want to reserve 14pts at the bottom and buffer the plot by 10km
# so we compute this in the y direction, since it limits us
max_dimension = max([gdf["width"].max(), gdf["height"].max()])
yspacing = max_dimension / 2.0 + mybuffer
xspacing = yspacing * 1.08 # approx
for _, row in gdf.iterrows():
# - Map each polygon
x0 = float(row["xc"]) - xspacing
x1 = float(row["xc"]) + xspacing
y0 = float(row["yc"]) - yspacing - (yspacing * 0.14)
y1 = float(row["yc"]) + yspacing - (yspacing * 0.14)
col = i % 10
if col == 0:
ybottom -= dy
ax = plt.axes(
[col * 0.1, ybottom, 0.1, dy],
facecolor="black",
xticks=[],
yticks=[],
aspect="auto",
)
for x in ax.spines:
ax.spines[x].set_visible(False)
ax.set_xlim(x0, x1)
ax.set_ylim(y0, y1)
for poly in row["utmgeom"]:
xs, ys = poly.exterior.xy
color = COLORS[row["phenomena"]]
ax.plot(xs, ys, color=color, lw=2)
car = "NA"
carColor = "white"
if not pd.isnull(row["ratio"]):
carf = row["ratio"]
car = "%.0f" % (carf, )
if carf > 75:
carColor = "green"
if carf < 25:
carColor = "red"
# Draw Text!
issue = row["issue"]
s = "%s.%s.%s.%s" % (
row["wfo"],
row["phenomena"],
row["eventid"],
issue.strftime("%H%M"),
)
# (w, h) = font10.getsize(s)
# print s, h
ax.text(
0,
0,
s,
transform=ax.transAxes,
color="white",
va="bottom",
fontsize=7,
)
s = "%.0f sq km %s%%" % (row["size"] / 1000000.0, car)
ax.text(
0,
0.1,
s,
transform=ax.transAxes,
color=carColor,
va="bottom",
fontsize=7,
)
# Image map
url = ("/vtec/#%s-O-NEW-K%s-%s-%s-%04i") % (
sts.year,
row["wfo"],
row["phenomena"],
"W",
row["eventid"],
)
altxt = "Click for text/image"
pos = ax.get_position()
mx0 = pos.x0 * 1000.0
my = (1.0 - pos.y1) * ypixels
imagemap.write(
('<area href="%s" alt="%s" title="%s" '
'shape="rect" coords="%.0f,%.0f,%.0f,%.0f">\n') %
(url, altxt, altxt, mx0, my, mx0 + thumbpx, my + thumbpx))
i += 1
faux = plt.axes([0, 0, 1, 1], facecolor="None", zorder=100)
for i in range(1, rows):
faux.axhline(i * dy, lw=1.0, color="blue")
imagemap.write("</map>")
imagemap.seek(0)
if gdf.empty:
fitbox(fig, "No warnings Found!", 0.2, 0.8, 0.2, 0.5, color="white")
df = gdf.drop("utmgeom", axis=1)
return fig, df, imagemap.read()
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
today = datetime.datetime.now()
thisyear = today.year
df = read_sql("""
with data as (
select year, month, extract(doy from day) as doy,
generate_series(32, high) as t from """ + table + """
where station = %s and year < %s),
agger as (
SELECT year, t, min(doy), max(doy) from data GROUP by year, t)
SELECT t as tmpf, avg(min) as min_jday,
avg(max) as max_jday from agger GROUP by t ORDER by t ASC
""",
pgconn,
params=(station, thisyear),
index_col='tmpf')
if df.empty:
raise NoDataFound("No Data Found.")
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
ax2 = plt.axes([0.81, 0.1, 0.15, 0.8])
height = df['min_jday'][:] + 365. - df['max_jday']
ax2.plot(height, df.index.values)
ax2.set_xticks([30, 90, 180, 365])
plt.setp(ax2.get_yticklabels(), visible=False)
ax2.set_ylim(32, df.index.values.max() + 5)
ax2.grid(True)
ax2.text(0.96,
0.02,
"Days",
transform=ax2.transAxes,
bbox=dict(color='white'),
ha='right')
ax.text(0.96,
0.02,
"Period",
transform=ax.transAxes,
bbox=dict(color='white'),
ha='right')
ax.set_ylim(32, df.index.values.max() + 5)
ax.barh(df.index.values - 0.5,
height,
left=df['max_jday'].values,
ec='tan',
fc='tan',
height=1.1)
days = np.array([1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335])
days = np.concatenate([days, days + 365])
ax.set_xticks(days)
months = calendar.month_abbr[1:] + calendar.month_abbr[1:]
ax.set_xticklabels(months)
ax.set_ylabel("High Temperature $^\circ$F")
ax.set_xlim(min(df['max_jday']) - 1, max(df['max_jday'] + height) + 1)
ax.grid(True)
msg = ("[%s] %s Period Between Average Last and "
"First High Temperature of Year") % (station,
nt.sts[station]['name'])
tokens = msg.split()
sz = int(len(tokens) / 2)
ax.set_title(" ".join(tokens[:sz]) + "\n" + " ".join(tokens[sz:]))
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
network = ctx['network']
month = ctx['month']
varname = ctx['var']
days = ctx['days']
nt = NetworkTable(network)
table = "alldata_%s" % (station[:2], )
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
elif month == 'octmar':
months = [10, 11, 12, 1, 2, 3]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
sorder = 'ASC' if varname in [
'min_greatest_low',
] else 'DESC'
df = read_sql("""WITH data as (
SELECT month, day, day - '%s days'::interval as start_date,
count(*) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as count,
sum(precip) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as total_precip,
min(high) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as max_least_high,
max(low) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as min_greatest_low
from """ + table + """ WHERE station = %s)
SELECT day as end_date, start_date, """ + varname + """ from data WHERE
month in %s and
extract(month from start_date) in %s and count = %s
ORDER by """ + varname + """ """ + sorder + """ LIMIT 10
""",
pgconn,
params=(days - 1, days - 1, days - 1, days - 1, days - 1,
station, tuple(months), tuple(months), days),
index_col=None)
if df.empty:
raise ValueError('Error, no results returned!')
ylabels = []
fmt = '%.2f' if varname in [
'total_precip',
] else '%.0f'
for _, row in df.iterrows():
# no strftime support for old days, so we hack at it
lbl = fmt % (row[varname], )
if days > 1:
sts = row['end_date'] - datetime.timedelta(days=(days - 1))
if sts.month == row['end_date'].month:
lbl += " -- %s %s-%s, %s" % (calendar.month_abbr[sts.month],
sts.day, row['end_date'].day,
sts.year)
else:
lbl += " -- %s %s, %s to\n %s %s, %s" % (
calendar.month_abbr[sts.month], sts.day, sts.year,
calendar.month_abbr[row['end_date'].month],
row['end_date'].day, row['end_date'].year)
else:
lbl += " -- %s %s, %s" % (
calendar.month_abbr[row['end_date'].month],
row['end_date'].day, row['end_date'].year)
ylabels.append(lbl)
ax = plt.axes([0.1, 0.1, 0.5, 0.8])
plt.gcf().set_size_inches(8, 6)
ax.barh(range(10, 0, -1),
df[varname],
ec='green',
fc='green',
height=0.8,
align='center')
ax2 = ax.twinx()
ax2.set_ylim(0.5, 10.5)
ax.set_ylim(0.5, 10.5)
ax2.set_yticks(range(1, 11))
ax.set_yticks(range(1, 11))
ax.set_yticklabels(["#%s" % (x, ) for x in range(1, 11)][::-1])
ax2.set_yticklabels(ylabels[::-1])
ax.grid(True, zorder=11)
ax.set_xlabel(("Precipitation [inch]" if varname in ['total_precip'] else
r'Temperature $^\circ$F'))
ax.set_title(("%s [%s] Top 10 Events\n"
"%s [days=%s] (%s) "
"(%s-%s)") %
(nt.sts[station]['name'], station, METRICS[varname], days,
MDICT[month], nt.sts[station]['archive_begin'].year,
datetime.datetime.now().year),
size=12)
return plt.gcf(), df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
month = ctx['month']
varname = ctx['var']
nt = NetworkTable(network)
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
elif month == 'octmar':
months = [10, 11, 12, 1, 2, 3]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
(agg, dbvar) = varname.split("_")
sorder = 'DESC' if agg == 'max' else 'ASC'
df = read_sql("""WITH data as (
SELECT valid at time zone %s as v, p01i from alldata
WHERE station = %s and
extract(month from valid at time zone %s) in %s)
SELECT v as valid, p01i from data
ORDER by """ + dbvar + """ """ + sorder + """ NULLS LAST LIMIT 100
""",
pgconn,
params=(nt.sts[station]['tzname'], station,
nt.sts[station]['tzname'], tuple(months)),
index_col=None)
if df.empty:
raise ValueError('Error, no results returned!')
ylabels = []
fmt = '%.2f' if varname in [
'max_p01i',
] else '%.0f'
hours = []
y = []
lastval = -99
ranks = []
currentrank = 0
rows2keep = []
for idx, row in df.iterrows():
key = row['valid'].strftime("%Y%m%d%H")
if key in hours:
continue
rows2keep.append(idx)
hours.append(key)
y.append(row[dbvar])
lbl = fmt % (row[dbvar], )
lbl += " -- %s" % (row['valid'].strftime("%b %d, %Y %-I:%M %p"), )
ylabels.append(lbl)
if row[dbvar] != lastval:
currentrank += 1
ranks.append(currentrank)
lastval = row[dbvar]
if len(ylabels) == 10:
break
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.1, 0.1, 0.5, 0.8])
ax.barh(range(10, 0, -1),
y,
ec='green',
fc='green',
height=0.8,
align='center')
ax2 = ax.twinx()
ax2.set_ylim(0.5, 10.5)
ax.set_ylim(0.5, 10.5)
ax2.set_yticks(range(1, 11))
ax.set_yticks(range(1, 11))
ax.set_yticklabels(["#%s" % (x, ) for x in ranks][::-1])
ax2.set_yticklabels(ylabels[::-1])
ax.grid(True, zorder=11)
ax.set_xlabel(("Precipitation [inch]"
if varname in ['max_p01i'] else r"Temperature $^\circ$F"))
ax.set_title(
("%s [%s] Top 10 Events\n"
"%s (%s) "
"(%s-%s)") %
(nt.sts[station]['name'], station, METRICS[varname], MDICT[month],
nt.sts[station]['archive_begin'].year, datetime.datetime.now().year),
size=12)
fig.text(0.98,
0.03,
"Timezone: %s" % (nt.sts[station]['tzname'], ),
ha='right')
return fig, df.loc[rows2keep]
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
if station not in ctx["_nt"].sts: # This is needed.
raise NoDataFound("Unknown station metadata.")
varname = ctx["var"]
ts = ctx["date"]
hour = int(ctx["hour"])
ts = utc(ts.year, ts.month, ts.day, hour)
which = ctx["which"]
vlimit = ""
if which == "month":
vlimit = (" and extract(month from f.valid) = %s ") % (ts.month, )
name = ctx["_nt"].sts[station]["name"]
stations = [station]
if station.startswith("_"):
name = ctx["_nt"].sts[station]["name"].split("--")[0]
stations = (
ctx["_nt"].sts[station]["name"].split("--")[1].strip().split(" "))
pgconn = get_dbconn("postgis")
df = read_sql(
"""
with data as (
select f.valid,
p.pressure, count(*) OVER (PARTITION by p.pressure),
min(valid at time zone 'UTC') OVER () as min_valid,
max(valid at time zone 'UTC') OVER () as max_valid,
p.tmpc,
rank() OVER (PARTITION by p.pressure ORDER by p.tmpc ASC) as tmpc_rank,
min(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_min,
max(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_max,
p.dwpc,
rank() OVER (PARTITION by p.pressure ORDER by p.dwpc ASC) as dwpc_rank,
min(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_min,
max(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_max,
p.height as hght,
rank() OVER (
PARTITION by p.pressure ORDER by p.height ASC) as hght_rank,
min(p.height) OVER (PARTITION by p.pressure) as hght_min,
max(p.height) OVER (PARTITION by p.pressure) as hght_max,
p.smps,
rank() OVER (PARTITION by p.pressure ORDER by p.smps ASC) as smps_rank,
min(p.smps) OVER (PARTITION by p.pressure) as smps_min,
max(p.smps) OVER (PARTITION by p.pressure) as smps_max
from raob_flights f JOIN raob_profile p on (f.fid = p.fid)
WHERE f.station in %s
and extract(hour from f.valid at time zone 'UTC') = %s
""" + vlimit + """
and p.pressure in (925, 850, 700, 500, 400, 300, 250, 200,
150, 100, 70, 50, 10))
select * from data where valid = %s ORDER by pressure DESC
""",
pgconn,
params=(tuple(stations), hour, ts),
index_col="pressure",
)
if df.empty:
raise NoDataFound(("Sounding for %s was not found!") %
(ts.strftime("%Y-%m-%d %H:%M"), ))
df = df.drop("valid", axis=1)
for key in PDICT3.keys():
df[key + "_percentile"] = df[key + "_rank"] / df["count"] * 100.0
# manual hackery to get 0 and 100th percentile
df.loc[df[key] == df[key + "_max"], key + "_percentile"] = 100.0
df.loc[df[key] == df[key + "_min"], key + "_percentile"] = 0.0
ax = plt.axes([0.1, 0.12, 0.65, 0.75])
bars = ax.barh(range(len(df.index)),
df[varname + "_percentile"],
align="center")
y2labels = []
fmt = "%.1f" if varname not in ["hght"] else "%.0f"
for i, mybar in enumerate(bars):
ax.text(
mybar.get_width() + 1,
i,
"%.1f" % (mybar.get_width(), ),
va="center",
bbox=dict(color="white"),
)
y2labels.append((fmt + " (" + fmt + " " + fmt + ")") % (
df.iloc[i][varname],
df.iloc[i][varname + "_min"],
df.iloc[i][varname + "_max"],
))
ax.set_yticks(range(len(df.index)))
ax.set_yticklabels(["%.0f" % (a, ) for a in df.index.values])
ax.set_ylim(-0.5, len(df.index) - 0.5)
ax.set_xlabel("Percentile [100 = highest]")
ax.set_ylabel("Mandatory Pressure Level (hPa)")
plt.gcf().text(
0.5,
0.9,
("%s %s %s Sounding\n"
"(%s-%s) Percentile Ranks (%s) for %s at %sz") %
(
station,
name,
ts.strftime("%Y/%m/%d %H UTC"),
pd.Timestamp(df.iloc[0]["min_valid"]).year,
pd.Timestamp(df.iloc[0]["max_valid"]).year,
("All Year" if which == "none" else calendar.month_name[ts.month]),
PDICT3[varname],
hour,
),
ha="center",
va="bottom",
)
ax.grid(True)
ax.set_xticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
ax.set_xlim(0, 110)
ax.text(1.02, 1, "Ob (Min Max)", transform=ax.transAxes)
ax2 = ax.twinx()
ax2.set_ylim(-0.5, len(df.index) - 0.5)
ax2.set_yticks(range(len(df.index)))
ax2.set_yticklabels(y2labels)
return plt.gcf(), df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station'][:4]
phenomena = ctx['phenomena']
significance = ctx['significance']
split = ctx['split']
opt = ctx['opt']
state = ctx['state']
nt = NetworkTable('WFO')
wfolimiter = " wfo = '%s' " % (station, )
if opt == 'state':
wfolimiter = " substr(ugc, 1, 2) = '%s' " % (state, )
if split == 'jan1':
sql = """
SELECT extract(year from issue)::int as year,
min(issue at time zone 'UTC') as min_issue,
max(issue at time zone 'UTC') as max_issue,
count(distinct wfo || eventid)
from warnings where """ + wfolimiter + """
and phenomena = %s and significance = %s
GROUP by year ORDER by year ASC
"""
else:
sql = """
SELECT
extract(year from issue - '6 months'::interval)::int as year,
min(issue at time zone 'UTC') as min_issue,
max(issue at time zone 'UTC') as max_issue,
count(distinct wfo || eventid)
from warnings where """ + wfolimiter + """
and phenomena = %s and significance = %s
GROUP by year ORDER by year ASC
"""
df = read_sql(sql,
pgconn,
params=(phenomena, significance),
index_col=None)
if df.empty:
raise ValueError("No data found for query")
# Since many VTEC events start in 2005, we should not trust any
# data that has its first year in 2005
if df['year'].min() == 2005:
df = df[df['year'] > 2005]
def myfunc(row):
year = row[0]
valid = row[1]
if year == valid.year:
return int(valid.strftime("%j"))
else:
days = (datetime.date(year + 1, 1, 1) -
datetime.date(year, 1, 1)).days
return int(valid.strftime("%j")) + days
df['startdoy'] = df[['year', 'min_issue']].apply(myfunc, axis=1)
df['enddoy'] = df[['year', 'max_issue']].apply(myfunc, axis=1)
df.set_index('year', inplace=True)
# allow for small bars when there is just one event
df.loc[df['enddoy'] == df['startdoy'], 'enddoy'] = df['enddoy'] + 1
ends = df['enddoy'].values
starts = df['startdoy'].values
years = df.index.values
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.1, 0.1, 0.7, 0.8])
ax.barh(years, (ends - starts), left=starts, fc='blue', align='center')
ax.axvline(np.average(starts[:-1]), lw=2, color='red')
ax.axvline(np.average(ends[:-1]), lw=2, color='red')
ax.set_xlabel(("Avg Start Date: %s, End Date: %s") %
((datetime.date(2000, 1, 1) + datetime.timedelta(
days=int(np.average(starts[:-1])))).strftime("%-d %b"),
(datetime.date(2000, 1, 1) + datetime.timedelta(
days=int(np.average(ends[:-1])))).strftime("%-d %b")),
color='red')
title = "[%s] NWS %s" % (station, nt.sts[station]['name'])
if opt == 'state':
title = ("NWS Issued Alerts for State of %s") % (
reference.state_names[state], )
ax.set_title(("%s\nPeriod between First and Last %s") %
(title, vtec.get_ps_string(phenomena, significance)))
ax.grid()
days = [1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335]
days = days + [x + 365 for x in days]
ax.set_xticks(days)
ax.set_xticklabels(calendar.month_abbr[1:] + calendar.month_abbr[1:])
ax.set_xlim(df['startdoy'].min() - 10, df['enddoy'].max() + 10)
ax.set_ylabel("Year")
ax.set_ylim(years[0] - 0.5, years[-1] + 0.5)
xFormatter = FormatStrFormatter('%d')
ax.yaxis.set_major_formatter(xFormatter)
ax = plt.axes([0.82, 0.1, 0.13, 0.8])
ax.barh(years, df['count'], fc='blue', align='center')
ax.set_ylim(years[0] - 0.5, years[-1] + 0.5)
plt.setp(ax.get_yticklabels(), visible=False)
ax.grid(True)
ax.set_xlabel("# Events")
ax.yaxis.set_major_formatter(xFormatter)
xloc = plt.MaxNLocator(3)
ax.xaxis.set_major_locator(xloc)
return fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
varname = ctx['var']
network = 'RAOB'
ts = ctx['date']
hour = int(ctx['hour'])
ts = datetime.datetime(ts.year, ts.month, ts.day, hour)
ts = ts.replace(tzinfo=pytz.utc)
which = ctx['which']
vlimit = ''
if which == 'month':
vlimit = (" and extract(month from f.valid) = %s ") % (ts.month, )
nt = NetworkTable(network)
name = nt.sts[station]['name']
stations = [
station,
]
if station.startswith("_"):
name = nt.sts[station]['name'].split("--")[0]
stations = nt.sts[station]['name'].split("--")[1].strip().split(" ")
pgconn = get_dbconn('postgis')
df = read_sql("""
with data as (
select f.valid, p.pressure, count(*) OVER (PARTITION by p.pressure),
min(valid) OVER () as min_valid, max(valid) OVER () as max_valid,
p.tmpc,
rank() OVER (PARTITION by p.pressure ORDER by p.tmpc ASC) as tmpc_rank,
min(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_min,
max(p.tmpc) OVER (PARTITION by p.pressure) as tmpc_max,
p.dwpc,
rank() OVER (PARTITION by p.pressure ORDER by p.dwpc ASC) as dwpc_rank,
min(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_min,
max(p.dwpc) OVER (PARTITION by p.pressure) as dwpc_max,
p.height as hght,
rank() OVER (
PARTITION by p.pressure ORDER by p.height ASC) as hght_rank,
min(p.height) OVER (PARTITION by p.pressure) as hght_min,
max(p.height) OVER (PARTITION by p.pressure) as hght_max,
p.smps,
rank() OVER (PARTITION by p.pressure ORDER by p.smps ASC) as smps_rank,
min(p.smps) OVER (PARTITION by p.pressure) as smps_min,
max(p.smps) OVER (PARTITION by p.pressure) as smps_max
from raob_flights f JOIN raob_profile p on (f.fid = p.fid)
WHERE f.station in %s
and extract(hour from f.valid at time zone 'UTC') = %s
""" + vlimit + """
and p.pressure in (925, 850, 700, 500, 400, 300, 250, 200,
150, 100, 70, 50, 10))
select * from data where valid = %s ORDER by pressure DESC
""",
pgconn,
params=(tuple(stations), hour, ts),
index_col='pressure')
if df.empty:
raise ValueError(("Sounding for %s was not found!") %
(ts.strftime("%Y-%m-%d %H:%M"), ))
for key in PDICT3.keys():
df[key + '_percentile'] = df[key + '_rank'] / df['count'] * 100.
# manual hackery to get 0 and 100th percentile
df.loc[df[key] == df[key + '_max'], key + '_percentile'] = 100.
df.loc[df[key] == df[key + '_min'], key + '_percentile'] = 0.
ax = plt.axes([0.1, 0.12, 0.65, 0.75])
bars = ax.barh(range(len(df.index)),
df[varname + '_percentile'],
align='center')
y2labels = []
fmt = '%.1f' if varname not in [
'hght',
] else '%.0f'
for i, mybar in enumerate(bars):
ax.text(mybar.get_width() + 1,
i,
'%.1f' % (mybar.get_width(), ),
va='center',
bbox=dict(color='white'))
y2labels.append((fmt + ' (' + fmt + ' ' + fmt + ')') %
(df.iloc[i][varname], df.iloc[i][varname + "_min"],
df.iloc[i][varname + "_max"]))
ax.set_yticks(range(len(df.index)))
ax.set_yticklabels(['%.0f' % (a, ) for a in df.index.values])
ax.set_ylim(-0.5, len(df.index) - 0.5)
ax.set_xlabel("Percentile [100 = highest]")
ax.set_ylabel("Mandatory Pressure Level (hPa)")
plt.gcf().text(
0.5,
0.9, ("%s %s %s Sounding\n"
"(%s-%s) Percentile Ranks (%s) for %s") %
(station, name, ts.strftime("%Y/%m/%d %H UTC"),
df.iloc[0]['min_valid'].year, df.iloc[0]['max_valid'].year,
("All Year" if which == 'none' else calendar.month_name[ts.month]),
PDICT3[varname]),
ha='center',
va='bottom')
ax.grid(True)
ax.set_xticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
ax.set_xlim(0, 110)
ax.text(1.02, 1, 'Ob (Min Max)', transform=ax.transAxes)
ax2 = ax.twinx()
ax2.set_ylim(-0.5, len(df.index) - 0.5)
ax2.set_yticks(range(len(df.index)))
ax2.set_yticklabels(y2labels)
return plt.gcf(), df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
hours = ctx['hours']
mydir = ctx['dir']
month = ctx['month']
if month == 'all':
months = range(1, 13)
elif month == 'fall':
months = [9, 10, 11]
elif month == 'winter':
months = [12, 1, 2]
elif month == 'spring':
months = [3, 4, 5]
elif month == 'summer':
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", '%Y-%b-%d')
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
nt = NetworkTable(network)
tzname = nt.sts[station]['tzname']
# backwards intuitive
sortdir = "ASC" if mydir == 'warm' else 'DESC'
df = read_sql("""
WITH data as (
SELECT valid at time zone %s as valid, tmpf from alldata
where station = %s and tmpf between -100 and 150
and extract(month from valid) in %s),
doffset as (
SELECT valid - '%s hours'::interval as valid, tmpf from data),
agg as (
SELECT d.valid, d.tmpf as tmpf1, o.tmpf as tmpf2
from data d JOIN doffset o on (d.valid = o.valid))
SELECT valid as valid1, valid + '%s hours'::interval as valid2,
tmpf1, tmpf2 from agg
ORDER by (tmpf1 - tmpf2) """ + sortdir + """ LIMIT 50
""",
pgconn,
params=(tzname, station, tuple(months), hours, hours),
index_col=None)
df['diff'] = (df['tmpf1'] - df['tmpf2']).abs()
if df.empty:
raise ValueError("No database entries found for station, sorry!")
fig = plt.figure()
ax = plt.axes([0.55, 0.1, 0.4, 0.8])
fig.text(0.5,
0.95, ('[%s] %s Top 10 %s\n'
'Over %s Hour Period (%s-%s) [%s]') %
(station, nt.sts[station]['name'], MDICT[mydir],
hours, nt.sts[station]['archive_begin'].year,
datetime.date.today().year, MDICT2[month]),
ha='center',
va='center')
labels = []
for i in range(10):
row = df.iloc[i]
ax.barh(i + 1, row['diff'], color='b', align='center')
sts = row['valid1']
ets = row['valid2']
labels.append(("%.0f to %.0f -> %.0f\n%s - %s") %
(row['tmpf1'], row['tmpf2'], row['diff'],
sts.strftime("%-d %b %Y %I:%M %p"),
ets.strftime("%-d %b %Y %I:%M %p")))
ax.set_yticks(range(1, 11))
ax.set_yticklabels(labels)
ax.set_ylim(10.5, 0.5)
ax.grid(True)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
hours = ctx["hours"]
mydir = ctx["dir"]
month = ctx["month"]
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
tzname = ctx["_nt"].sts[station]["tzname"]
# backwards intuitive
sortdir = "ASC" if mydir == "warm" else "DESC"
df = read_sql(
"""
WITH data as (
SELECT valid at time zone %s as valid, tmpf from alldata
where station = %s and tmpf between -100 and 150
and extract(month from valid) in %s),
doffset as (
SELECT valid - '%s hours'::interval as valid, tmpf from data),
agg as (
SELECT d.valid, d.tmpf as tmpf1, o.tmpf as tmpf2
from data d JOIN doffset o on (d.valid = o.valid))
SELECT valid as valid1, valid + '%s hours'::interval as valid2,
tmpf1, tmpf2 from agg
ORDER by (tmpf1 - tmpf2) """
+ sortdir
+ """ LIMIT 50
""",
pgconn,
params=(tzname, station, tuple(months), hours, hours),
index_col=None,
)
df["diff"] = (df["tmpf1"] - df["tmpf2"]).abs()
if df.empty:
raise NoDataFound("No database entries found for station, sorry!")
fig = plt.figure()
ax = plt.axes([0.55, 0.1, 0.4, 0.8])
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
fig.text(
0.5,
0.95,
("[%s] %s Top 10 %s\n" "Over %s Hour Period (%s-%s) [%s]")
% (
station,
ctx["_nt"].sts[station]["name"],
MDICT[mydir],
hours,
ab.year,
datetime.date.today().year,
MDICT2[month],
),
ha="center",
va="center",
)
labels = []
for i in range(10):
row = df.iloc[i]
ax.barh(i + 1, row["diff"], color="b", align="center")
sts = row["valid1"]
ets = row["valid2"]
labels.append(
("%.0f to %.0f -> %.0f\n%s - %s")
% (
row["tmpf1"],
row["tmpf2"],
row["diff"],
sts.strftime("%-d %b %Y %I:%M %p"),
ets.strftime("%-d %b %Y %I:%M %p"),
)
)
ax.set_yticks(range(1, 11))
ax.set_yticklabels(labels)
ax.set_ylim(10.5, 0.5)
ax.grid(True)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
month = ctx["month"]
varname = ctx["var"]
days = ctx["days"]
table = "alldata_%s" % (station[:2], )
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
elif month == "octmar":
months = [10, 11, 12, 1, 2, 3]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01", "%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
sorder = "ASC" if varname in ["min_greatest_low"] else "DESC"
df = read_sql(
"""WITH data as (
SELECT month, day, day - '%s days'::interval as start_date,
count(*) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as count,
sum(precip) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as total_precip,
min(high) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as max_least_high,
max(low) OVER (ORDER by day ASC ROWS BETWEEN %s preceding and
current row) as min_greatest_low
from """ + table + """ WHERE station = %s)
SELECT day as end_date, start_date, """ + varname + """ from data WHERE
month in %s and
extract(month from start_date) in %s and count = %s
ORDER by """ + varname + """ """ + sorder + """ LIMIT 10
""",
pgconn,
params=(
days - 1,
days - 1,
days - 1,
days - 1,
days - 1,
station,
tuple(months),
tuple(months),
days,
),
index_col=None,
)
if df.empty:
raise NoDataFound("Error, no results returned!")
ylabels = []
fmt = "%.2f" if varname in ["total_precip"] else "%.0f"
for _, row in df.iterrows():
# no strftime support for old days, so we hack at it
lbl = fmt % (row[varname], )
if days > 1:
sts = row["end_date"] - datetime.timedelta(days=(days - 1))
if sts.month == row["end_date"].month:
lbl += " -- %s %s-%s, %s" % (
calendar.month_abbr[sts.month],
sts.day,
row["end_date"].day,
sts.year,
)
else:
lbl += " -- %s %s, %s to\n %s %s, %s" % (
calendar.month_abbr[sts.month],
sts.day,
sts.year,
calendar.month_abbr[row["end_date"].month],
row["end_date"].day,
row["end_date"].year,
)
else:
lbl += " -- %s %s, %s" % (
calendar.month_abbr[row["end_date"].month],
row["end_date"].day,
row["end_date"].year,
)
ylabels.append(lbl)
ax = plt.axes([0.1, 0.1, 0.5, 0.8])
plt.gcf().set_size_inches(8, 6)
ax.barh(
range(10, 0, -1),
df[varname],
ec="green",
fc="green",
height=0.8,
align="center",
)
ax2 = ax.twinx()
ax2.set_ylim(0.5, 10.5)
ax.set_ylim(0.5, 10.5)
ax2.set_yticks(range(1, 11))
ax.set_yticks(range(1, 11))
ax.set_yticklabels(["#%s" % (x, ) for x in range(1, 11)][::-1])
ax2.set_yticklabels(ylabels[::-1])
ax.grid(True, zorder=11)
ax.set_xlabel(("Precipitation [inch]" if varname in ["total_precip"] else
r"Temperature $^\circ$F"))
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
ax.set_title(
("%s [%s] Top 10 Events\n"
"%s [days=%s] (%s) "
"(%s-%s)") % (
ctx["_nt"].sts[station]["name"],
station,
METRICS[varname],
days,
MDICT[month],
ab.year,
datetime.datetime.now().year,
),
size=12,
)
return plt.gcf(), df
def __init__(self, sector='iowa', figsize=(10.24, 7.68), **kwargs):
"""Construct a MapPlot
Args:
sector (str): plot domain, set 'custom' to bring your own projection
kwargs:
projection (cartopy.crs,optional): bring your own projection
north (float,optional): Plot top bounds (degN Lat)
south (float,optional): Plot bottom bounds (degN Lat)
east (float,optional): Plot right bounds (degE Lon)
west (float,optional): Plot left bounds (degE Lon)
titlefontsize (int): fontsize to use for the plot title
subtitlefontsize (int): fontsize to use for the plot subtitle
continentalcolor (color): color to use for continental coloring
debug (bool): enable debugging
aspect (str): plot aspect, defaults to equal
"""
self.debug = kwargs.get('debug', False)
self.fig = plt.figure(num=None, figsize=figsize,
dpi=kwargs.get('dpi', 100))
# Storage of axes within this plot
self.state = None
self.cwa = None
self.textmask = None # For our plot_values magic, to prevent overlap
self.sector = sector
self.cax = plt.axes(CAX_BOUNDS, frameon=False,
yticks=[], xticks=[])
self.axes = []
self.ax = None
# hack around sector=iowa
if self.sector == 'iowa':
self.sector = 'state'
self.state = 'IA'
sector_setter(self, MAIN_AX_BOUNDS, **kwargs)
for _a in self.axes:
if _a is None:
continue
# legacy usage of axisbg here
_c = kwargs.get('axisbg',
kwargs.get('continentalcolor',
'#EEEEEE'))
_a.add_feature(cfeature.LAND, facecolor=_c, zorder=Z_CF)
coasts = cfeature.NaturalEarthFeature('physical', 'coastline',
'10m',
edgecolor='black',
facecolor='none')
_a.add_feature(coasts, lw=1.0, zorder=Z_POLITICAL)
_a.add_feature(cfeature.BORDERS, lw=1.0, zorder=Z_POLITICAL)
_a.add_feature(cfeature.OCEAN, facecolor=(0.4471, 0.6235, 0.8117),
zorder=Z_CF)
_a.add_feature(cfeature.LAKES, facecolor=(0.4471, 0.6235, 0.8117),
zorder=Z_CF)
if 'nostates' not in kwargs:
states = load_pickle_geo('us_states.pickle')
_a.add_geometries(
[val[b'geom'] for key, val in states.items()],
crs=ccrs.PlateCarree(), lw=1.0,
edgecolor=kwargs.get('statebordercolor', 'k'),
facecolor='None', zorder=Z_POLITICAL
)
if not kwargs.get('nologo'):
self.iemlogo()
if "title" in kwargs:
self.fig.text(0.09 if not kwargs.get('nologo') else 0.02, 0.94,
kwargs.get("title"),
fontsize=kwargs.get('titlefontsize', 18))
if "subtitle" in kwargs:
self.fig.text(0.09 if not kwargs.get('nologo') else 0.02, 0.91,
kwargs.get("subtitle"),
fontsize=kwargs.get('subtitlefontsize', 12))
if 'nocaption' not in kwargs:
self.fig.text(0.01, 0.03, ("%s :: generated %s"
) % (
kwargs.get('caption', 'Iowa Environmental Mesonet'),
datetime.datetime.now().strftime("%d %B %Y %I:%M %p %Z"),))
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
month = ctx["month"]
varname = ctx["var"]
tzname = ctx["_nt"].sts[station]["tzname"]
if ctx.get("sdate") and ctx.get("edate"):
date_limiter = (
" and (to_char(valid at time zone '%s', 'mmdd') >= '%s'"
" %s to_char(valid at time zone '%s', 'mmdd') <= '%s')") % (
tzname,
ctx["sdate"].strftime("%m%d"),
"or" if ctx["sdate"] > ctx["edate"] else "and",
tzname,
ctx["edate"].strftime("%m%d"),
)
title = "between %s and %s" % (
ctx["sdate"].strftime("%-d %b"),
ctx["edate"].strftime("%-d %b"),
)
if ctx["sdate"] == ctx["edate"]:
date_limiter = (
"and to_char(valid at time zone '%s', 'mmdd') = '%s'") % (
tzname, ctx["sdate"].strftime("%m%d"))
title = "on %s" % (ctx["sdate"].strftime("%-d %b"), )
else:
if month == "all":
months = range(1, 13)
elif month == "fall":
months = [9, 10, 11]
elif month == "winter":
months = [12, 1, 2]
elif month == "spring":
months = [3, 4, 5]
elif month == "summer":
months = [6, 7, 8]
elif month == "octmar":
months = [10, 11, 12, 1, 2, 3]
else:
ts = datetime.datetime.strptime("2000-" + month + "-01",
"%Y-%b-%d")
# make sure it is length two for the trick below in SQL
months = [ts.month, 999]
date_limiter = (
" and extract(month from valid at time zone '%s') in %s") % (
tzname, tuple(months))
title = MDICT[month]
if ctx.get("hour") is not None:
date_limiter += (
f" and extract(hour from valid at time zone '{tzname}' "
f"+ '10 minutes'::interval) = {ctx['hour']}")
dt = datetime.datetime(2000, 1, 1, ctx["hour"])
title += " @" + dt.strftime("%-I %p")
(agg, dbvar) = varname.split("_")
if agg in ["max", "min"]:
titlelabel = "Top"
sorder = "DESC" if agg == "max" else "ASC"
df = read_sql(
f"""
WITH data as (
SELECT valid at time zone %s as v, {dbvar} from alldata
WHERE station = %s {date_limiter})
SELECT v as valid, {dbvar} from data
ORDER by {dbvar} {sorder} NULLS LAST LIMIT 100
""",
pgconn,
params=(ctx["_nt"].sts[station]["tzname"], station),
index_col=None,
)
else:
titlelabel = "Most Recent"
op = ">=" if agg == "above" else "<"
threshold = float(ctx.get("threshold", 100))
df = read_sql(
f"SELECT valid at time zone %s as valid, {dbvar} from alldata "
f"WHERE station = %s {date_limiter} and {dbvar} {op} {threshold} "
"ORDER by valid DESC LIMIT 100",
pgconn,
params=(ctx["_nt"].sts[station]["tzname"], station),
index_col=None,
)
if df.empty:
raise NoDataFound("Error, no results returned!")
ylabels = []
fmt = "%.0f" if dbvar in ["tmpf", "dwpf"] else "%.2f"
hours = []
y = []
lastval = -99
ranks = []
currentrank = 0
rows2keep = []
for idx, row in df.iterrows():
key = row["valid"].strftime("%Y%m%d%H")
if key in hours or pd.isnull(row[dbvar]):
continue
rows2keep.append(idx)
hours.append(key)
y.append(row[dbvar])
lbl = fmt % (row[dbvar], )
lbl += " -- %s" % (row["valid"].strftime("%b %d, %Y %-I:%M %p"), )
ylabels.append(lbl)
if row[dbvar] != lastval or agg in ["above", "below"]:
currentrank += 1
ranks.append(currentrank)
lastval = row[dbvar]
if len(ylabels) == 10:
break
if not y:
raise NoDataFound("No data found.")
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.1, 0.1, 0.5, 0.8])
ax.barh(
range(len(y), 0, -1),
y,
ec="green",
fc="green",
height=0.8,
align="center",
)
ax2 = ax.twinx()
ax2.set_ylim(0.5, 10.5)
ax.set_ylim(0.5, 10.5)
ax2.set_yticks(range(1, len(y) + 1))
ax.set_yticks(range(1, len(y) + 1))
ax.set_yticklabels(["#%s" % (x, ) for x in ranks][::-1])
ax2.set_yticklabels(ylabels[::-1])
ax.grid(True, zorder=11)
ax.set_xlabel("%s %s" % (METRICS[varname], UNITS[dbvar]))
ab = ctx["_nt"].sts[station]["archive_begin"]
if ab is None:
raise NoDataFound("Unknown station metadata.")
fitbox(
fig,
("%s [%s] %s 10 Events\n%s %s (%s) (%s-%s)") % (
ctx["_nt"].sts[station]["name"],
station,
titlelabel,
METRICS[varname],
ctx.get("threshold") if agg in ["above", "below"] else "",
title,
ab.year,
datetime.datetime.now().year,
),
0.01,
0.99,
0.91,
0.99,
ha="center",
)
fig.text(
0.98,
0.03,
"Timezone: %s" % (ctx["_nt"].sts[station]["tzname"], ),
ha="right",
)
return fig, df.loc[rows2keep]
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
syear = ctx["syear"]
eyear = ctx["eyear"]
groupby = ctx["groupby"]
sts = datetime.date(syear, 1, 1)
ets = datetime.date(eyear + 1, 1, 1)
code = ctx["code"]
if code == "PSN":
code = "+SN"
PDICT["+SN"] = PDICT["PSN"]
if groupby == "week":
data = np.ma.zeros((24, 52), "f")
df = read_sql(
"""
WITH data as (
SELECT valid at time zone %s + '10 minutes'::interval as v
from alldata where
station = %s and
array_to_string(wxcodes, '') LIKE '%%"""
+ code
+ """%%'
and valid > %s and valid < %s),
agg as (
SELECT distinct extract(week from v)::int as week,
extract(doy from v)::int as doy,
extract(year from v)::int as year,
extract(hour from v)::int as hour
from data)
SELECT week, year, hour, count(*) from agg
WHERE week < 53
GROUP by week, year, hour
""",
pgconn,
params=(ctx["_nt"].sts[station]["tzname"], station, sts, ets),
index_col=None,
)
else:
data = np.ma.zeros((24, 366), "f")
df = read_sql(
"""
WITH data as (
SELECT valid at time zone %s + '10 minutes'::interval as v
from alldata where
station = %s and
array_to_string(wxcodes, '') LIKE '%%"""
+ code
+ """%%'
and valid > %s and valid < %s),
agg as (
SELECT distinct
extract(doy from v)::int as doy,
extract(year from v)::int as year,
extract(hour from v)::int as hour
from data)
SELECT doy, year, hour, count(*) from agg
GROUP by doy, year, hour
""",
pgconn,
params=(ctx["_nt"].sts[station]["tzname"], station, sts, ets),
index_col=None,
)
if df.empty:
raise NoDataFound("No data was found, sorry!")
minyear = df["year"].min()
maxyear = df["year"].max()
for _, row in df.iterrows():
data[row["hour"], row[groupby] - 1] += 1
data.mask = np.where(data == 0, True, False)
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.11, 0.25, 0.7, 0.65])
cax = plt.axes([0.82, 0.04, 0.02, 0.15])
res = ax.imshow(
data, aspect="auto", rasterized=True, interpolation="nearest"
)
fig.colorbar(res, cax=cax)
xloc = plt.MaxNLocator(4)
cax.yaxis.set_major_locator(xloc)
cax.set_ylabel("Count")
ax.set_ylim(-0.5, 23.5)
ax.set_yticks((0, 4, 8, 12, 16, 20))
ax.set_ylabel("Local Time, %s" % (ctx["_nt"].sts[station]["tzname"],))
ax.set_yticklabels(("Mid", "4 AM", "8 AM", "Noon", "4 PM", "8 PM"))
ax.set_title(
("[%s] %s %s Reports\n[%.0f - %.0f]" " by hour and %s")
% (
station,
ctx["_nt"].sts[station]["name"],
PDICT[code],
minyear,
maxyear,
PDICT2[groupby].replace("group ", ""),
)
)
ax.grid(True)
lax = plt.axes([0.11, 0.1, 0.7, 0.15])
if groupby == "week":
ax.set_xticks(np.arange(0, 55, 7))
lax.bar(np.arange(0, 52), np.ma.sum(data, 0), facecolor="tan")
lax.set_xlim(-0.5, 51.5)
lax.set_xticks(np.arange(0, 55, 7))
lax.set_xticklabels(
(
"Jan 1",
"Feb 19",
"Apr 8",
"May 27",
"Jul 15",
"Sep 2",
"Oct 21",
"Dec 9",
)
)
else:
ax.set_xticks(
[1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365]
)
lax.bar(np.arange(0, 366), np.ma.sum(data, 0), facecolor="tan")
lax.set_xlim(-0.5, 365.5)
lax.set_xticks(
[1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 365]
)
lax.set_xticklabels(calendar.month_abbr[1:])
plt.setp(ax.get_xticklabels(), visible=False)
# Bottom grid
lax.grid(True)
yloc = plt.MaxNLocator(3)
lax.yaxis.set_major_locator(yloc)
lax.yaxis.get_major_ticks()[-1].label1.set_visible(False)
# Right grid
rax = plt.axes([0.81, 0.25, 0.15, 0.65])
rax.barh(np.arange(0, 24) - 0.4, np.ma.sum(data, 1), facecolor="tan")
rax.set_ylim(-0.5, 23.5)
rax.set_yticks([])
xloc = plt.MaxNLocator(3)
rax.xaxis.set_major_locator(xloc)
rax.xaxis.get_major_ticks()[0].label1.set_visible(False)
rax.grid(True)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("asos")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["zstation"]
date = ctx["date"]
opt = ctx["opt"]
varname = ctx["v"]
tzname = ctx["_nt"].sts[station]["tzname"]
# Resolve how to limit the query data
limiter = ""
if opt == "day":
limiter = (f" and to_char(valid at time zone '{tzname}', 'mmdd') = "
f"'{date.strftime('%m%d')}' ")
subtitle = (f"For Date of {date.strftime('%-d %b')}, "
f"{date.strftime('%-d %b %Y')} plotted in bottom panel")
datefmt = "%I %p"
elif opt == "week":
limiter = f" and extract(week from valid) = {date.strftime('%V')} "
subtitle = (
f"For ISO Week of {date.strftime('%V')}, "
f"week of {date.strftime('%-d %b %Y')} plotted in bottom panel")
datefmt = "%-d %b"
elif opt == "month":
limiter = f" and extract(month from valid) = {date.strftime('%m')} "
subtitle = (f"For Month of {date.strftime('%B')}, "
f"{date.strftime('%b %Y')} plotted in bottom panel")
datefmt = "%-d"
else:
subtitle = f"All Year, {date.year} plotted in bottom panel"
datefmt = "%-d %b"
# Load up all the values, since we need pandas to do some heavy lifting
obsdf = read_sql(
f"""
select valid at time zone 'UTC' as utc_valid,
extract(year from valid at time zone %s) as year,
extract(hour from valid at time zone %s +
'10 minutes'::interval)::int as hr, {varname}
from alldata WHERE station = %s and {varname} is not null {limiter}
and report_type = 2 ORDER by valid ASC
""",
pgconn,
params=(tzname, tzname, station),
index_col=None,
)
if obsdf.empty:
raise NoDataFound("No data was found.")
# Assign percentiles
obsdf["quantile"] = obsdf[["hr", varname]].groupby("hr").rank(pct=True)
# Compute actual percentiles
qtile = (obsdf[["hr", varname
]].groupby("hr").quantile(np.arange(0, 1.01,
0.05)).reset_index())
qtile = qtile.rename(columns={"level_1": "quantile"})
(fig, ax) = plt.subplots(2, 1)
cmap = get_cmap(ctx["cmap"])
for hr, gdf in qtile.groupby("hr"):
ax[0].plot(
gdf["quantile"].values * 100.0,
gdf[varname].values,
color=cmap(hr / 23.0),
label=str(hr),
)
ax[0].set_xlim(0, 100)
ax[0].grid(True)
ax[0].set_ylabel(PDICT[varname])
ax[0].set_xlabel("Percentile")
ax[0].set_position([0.13, 0.55, 0.71, 0.34])
cax = plt.axes([0.86, 0.55, 0.03, 0.33],
frameon=False,
yticks=[],
xticks=[])
cb = ColorbarBase(cax, cmap=cmap)
cb.set_ticks(np.arange(0, 1, 4.0 / 24.0))
cb.set_ticklabels(["Mid", "4 AM", "8 AM", "Noon", "4 PM", "8 PM"])
cb.set_label("Local Hour")
thisyear = obsdf[obsdf["year"] == date.year]
if not thisyear.empty:
ax[1].plot(thisyear["utc_valid"].values,
thisyear["quantile"].values * 100.0)
ax[1].grid(True)
ax[1].set_ylabel("Percentile")
ax[1].set_ylim(-1, 101)
ax[1].xaxis.set_major_formatter(
mdates.DateFormatter(datefmt, tz=pytz.timezone(tzname)))
if opt == "day":
ax[1].set_xlabel(f"Timezone: {tzname}")
title = ("%s %s %s Percentiles\n%s") % (
station,
ctx["_nt"].sts[station]["name"],
PDICT[varname],
subtitle,
)
fitbox(fig, title, 0.01, 0.99, 0.91, 0.99, ha="center", va="center")
return fig, qtile
def plotter(fdict):
""" Go """
pgconn = get_dbconn('postgis')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station'][:4]
nt = NetworkTable('WFO')
nt.sts['_ALL'] = {'name': 'All Offices'}
fig = plt.figure(figsize=(8, 14 if station != '_ALL' else 21))
ax = [None, None]
ax[0] = plt.axes([0.1, 0.75, 0.85, 0.2])
ax[1] = plt.axes([0.1, 0.05, 0.85, 0.65])
if station == '_ALL':
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
phenomena is not null and significance is not null and
issue > '2005-01-01'
""",
pgconn,
index_col=None)
else:
df = read_sql("""
SELECT distinct extract(year from issue) as year,
phenomena, significance from warnings WHERE
wfo = %s and phenomena is not null and significance is not null
and issue > '2005-01-01'
""",
pgconn,
params=(station, ),
index_col=None)
df['wfo'] = station
df['year'] = df['year'].astype('i')
gdf = df.groupby('year').count()
ax[0].bar(gdf.index.values,
gdf['wfo'],
width=0.8,
fc='b',
ec='b',
align='center')
for yr, row in gdf.iterrows():
ax[0].text(yr, row['wfo'] + 1, "%s" % (row['wfo'], ), ha='center')
ax[0].set_title(
("[%s] NWS %s\nCount of Distinct VTEC Phenomena/"
"Significance - %i to %i") %
(station, nt.sts[station]['name'], df['year'].min(), df['year'].max()))
ax[0].grid()
ax[0].set_ylabel("Count")
ax[0].set_xlim(gdf.index.values.min() - 0.5, gdf.index.values.max() + 0.5)
pos = {}
i = 1
df.sort_values(['phenomena', 'significance'], inplace=True)
for _, row in df.iterrows():
key = "%s.%s" % (row['phenomena'], row['significance'])
if key not in pos:
pos[key] = i
i += 1
ax[1].text(row['year'],
pos[key],
key,
ha='center',
va='center',
fontsize=10,
bbox=dict(color='white'))
ax[1].set_title("VTEC <Phenomena.Significance> Issued by Year")
ax[1].set_ylim(0, i)
ax[1].grid(True)
ax[1].set_xlim(gdf.index.values.min() - 0.5, gdf.index.values.max() + 0.5)
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
year = ctx["year"]
varname = ctx["var"]
table = "alldata_%s" % (station[:2], )
df = read_sql(
"""
WITH agg as (
SELECT sday, max(coalesce(narr_srad, 0))
from """ + table + """ where
station = %s and year > 1978 GROUP by sday),
obs as (
SELECT sday, day, narr_srad, merra_srad, hrrr_srad
from """ + table + """ WHERE
station = %s and year = %s)
SELECT a.sday, a.max as max_narr, o.day, o.narr_srad, o.merra_srad,
o.hrrr_srad from agg a LEFT JOIN obs o on (a.sday = o.sday)
ORDER by a.sday ASC
""",
pgconn,
params=(station, station, year),
index_col="sday",
)
if df.empty:
raise NoDataFound("No Data Found.")
df["max_narr_smooth"] = (df["max_narr"].rolling(window=7,
min_periods=1,
center=True).mean())
df["best"] = (df["narr_srad"].fillna(df["merra_srad"]).fillna(
df["hrrr_srad"]))
# hack for leap day here
if df["best"].loc["0229"] is None:
df = df.drop("0229")
fig = plt.figure(figsize=(8, 6))
ax = plt.axes([0.1, 0.1, 0.6, 0.8])
ax.fill_between(
range(len(df.index)),
0,
df["max_narr_smooth"],
color="tan",
label="Max",
)
if not np.isnan(df[varname].max()):
ax.bar(
range(len(df.index)),
df[varname],
fc="g",
ec="g",
label="%s" % (year, ),
)
ax.set_xticks((1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335))
ax.set_xticklabels(calendar.month_abbr[1:])
ax.set_xlim(0, 366)
lyear = datetime.date.today().year - 1
ax.set_title(("[%s] %s Daily Solar Radiation\n"
"1979-%s NARR Climatology w/ %s ") %
(station, ctx["_nt"].sts[station]["name"], lyear, year))
ax.legend()
ax.grid(True)
ax.set_ylabel("Shortwave Solar Radiation $MJ$ $d^{-1}$")
# Do the x,y scatter plots
for i, combo in enumerate([
("narr_srad", "merra_srad"),
("narr_srad", "hrrr_srad"),
("hrrr_srad", "merra_srad"),
]):
ax3 = plt.axes([0.78, 0.1 + (0.3 * i), 0.2, 0.2])
xmax = df[combo[0]].max()
xlabel = combo[0].replace("_srad", "").upper()
ylabel = combo[1].replace("_srad", "").upper()
ymax = df[combo[1]].max()
if np.isnan(xmax) or np.isnan(ymax):
ax3.text(
0.5,
0.5,
"%s or %s\nis missing" % (xlabel, ylabel),
ha="center",
va="center",
)
ax3.get_xaxis().set_visible(False)
ax3.get_yaxis().set_visible(False)
continue
c = df[[combo[0], combo[1]]].corr()
ax3.text(
0.5,
1.01,
"Pearson Corr: %.2f" % (c.iat[1, 0], ),
fontsize=10,
ha="center",
transform=ax3.transAxes,
)
ax3.scatter(df[combo[0]],
df[combo[1]],
edgecolor="None",
facecolor="green")
maxv = max([ax3.get_ylim()[1], ax3.get_xlim()[1]])
ax3.set_ylim(0, maxv)
ax3.set_xlim(0, maxv)
ax3.plot([0, maxv], [0, maxv], color="k")
ax3.set_xlabel(
r"%s $\mu$=%.1f" % (xlabel, df[combo[0]].mean()),
labelpad=0,
fontsize=12,
)
ax3.set_ylabel(r"%s $\mu$=%.1f" % (ylabel, df[combo[1]].mean()),
fontsize=12)
return fig, df
def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
df = get_data(ctx)
cmap = cm.get_cmap(ctx["cmap"])
maxval = df["delta"].max()
if maxval > 50:
bins = np.arange(0, 101, 10)
elif maxval > 25:
bins = np.arange(0, 51, 5)
else:
bins = np.arange(0, 21, 2)
bins[0] = 0.01
norm = mpcolors.BoundaryNorm(bins, cmap.N)
(fig, ax) = plt.subplots(1, 1, figsize=(6.4, 6.4))
yearmax = df[["year", "delta"]].groupby("year").max()
for year, df2 in df.groupby("year"):
for _, row in df2.iterrows():
# NOTE: minus 3.5 to center the 7 day bar
ax.bar(
row["doy"] - 3.5,
1,
bottom=year - 0.5,
width=7,
ec="None",
fc=cmap(norm([row["delta"]]))[0],
)
sts = datetime.datetime(2000, 1,
1) + datetime.timedelta(days=int(df["doy"].min()))
ets = datetime.datetime(2000, 1,
1) + datetime.timedelta(days=int(df["doy"].max()))
now = sts
interval = datetime.timedelta(days=1)
jdays = []
labels = []
while now < ets:
if now.day in [1, 8, 15, 22]:
fmt = "%-d\n%b" if now.day == 1 else "%-d"
jdays.append(int(now.strftime("%j")))
labels.append(now.strftime(fmt))
now += interval
ax.set_xticks(jdays)
ax.set_xticklabels(labels)
minyear = df["year"].min()
maxyear = df["year"].max()
ax.set_yticks(range(minyear, maxyear + 1))
ylabels = []
for yr in range(minyear, maxyear + 1):
if yr % 5 == 0:
ylabels.append("%s %.0f" % (yr, yearmax.at[yr, "delta"]))
else:
ylabels.append("%.0f" % (yearmax.at[yr, "delta"], ))
ax.set_yticklabels(ylabels, fontsize=10)
ax.set_ylim(minyear - 0.5, maxyear + 0.5)
ax.set_xlim(min(jdays), max(jdays))
ax.grid(linestyle="-", linewidth="0.5", color="#EEEEEE", alpha=0.7)
ax.set_title(("USDA NASS Weekly %s %s Progress\n"
"%s %% %s over weekly periods\n"
"yearly max labelled on left hand side") % (
ctx["unit_desc"],
PDICT2.get(ctx["commodity_desc"]),
state_names[ctx["state"]],
PDICT.get(ctx["unit_desc"]),
))
ax.set_position([0.13, 0.1, 0.71, 0.78])
cax = plt.axes([0.86, 0.12, 0.03, 0.75],
frameon=False,
yticks=[],
xticks=[])
cb = ColorbarBase(cax, norm=norm, cmap=cmap)
cb.set_label("% Acres")
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn("coop")
ccursor = pgconn.cursor(cursor_factory=psycopg2.extras.DictCursor)
ctx = get_autoplot_context(fdict, get_description())
station = ctx["station"]
year = ctx["year"]
gdd1 = ctx["gdd1"]
gdd2 = ctx["gdd2"]
table = "alldata_%s" % (station[:2], )
nt = network.Table("%sCLIMATE" % (station[:2], ))
ccursor.execute(
"""
SELECT day, gddxx(%s, %s, high, low) as gdd
from """ + table + """ WHERE year = %s and station = %s
ORDER by day ASC
""",
(ctx["gddbase"], ctx["gddceil"], year, station),
)
days = []
gdds = []
for row in ccursor:
gdds.append(float(row["gdd"]))
days.append(row["day"])
yticks = []
yticklabels = []
jan1 = datetime.datetime(year, 1, 1)
for i in range(110, 330):
ts = jan1 + datetime.timedelta(days=i)
if ts.day == 1 or ts.day % 12 == 1:
yticks.append(i)
yticklabels.append(ts.strftime("%-d %b"))
gdds = np.array(gdds)
sts = datetime.datetime(year, 4, 1)
ets = datetime.datetime(year, 6, 10)
now = sts
sz = len(gdds)
days2 = []
starts = []
heights = []
success = []
rows = []
while now < ets:
idx = int(now.strftime("%j")) - 1
running = 0
while idx < sz and running < gdd1:
running += gdds[idx]
idx += 1
idx0 = idx
while idx < sz and running < gdd2:
running += gdds[idx]
idx += 1
success.append(running >= gdd2)
idx1 = idx
days2.append(now)
starts.append(idx0)
heights.append(idx1 - idx0)
rows.append(
dict(
plant_date=now,
start_doy=idx0,
end_doy=idx1,
success=success[-1],
))
now += datetime.timedelta(days=1)
if True not in success:
raise NoDataFound("No data, pick lower GDD values")
df = pd.DataFrame(rows)
heights = np.array(heights)
success = np.array(success)
starts = np.array(starts)
cmap = get_cmap(ctx["cmap"])
bmin = min(heights[success]) - 1
bmax = max(heights[success]) + 1
bins = np.arange(bmin, bmax + 1.1)
norm = mpcolors.BoundaryNorm(bins, cmap.N)
ax = plt.axes([0.125, 0.125, 0.75, 0.75])
bars = ax.bar(days2, heights, bottom=starts, fc="#EEEEEE")
for i, mybar in enumerate(bars):
if success[i]:
mybar.set_facecolor(cmap(norm([heights[i]])[0]))
ax.grid(True)
ax.set_yticks(yticks)
ax.set_yticklabels(yticklabels)
ax.set_ylim(min(starts) - 7, max(starts + heights) + 7)
ax.xaxis.set_major_formatter(mdates.DateFormatter("%-d\n%b"))
ax.set_xlabel("Planting Date")
ax.set_title(("%s [%s] %s GDD [base=%s,ceil=%s]\n"
"Period between GDD %s and %s, gray bars incomplete") % (
nt.sts[station]["name"],
station,
year,
ctx["gddbase"],
ctx["gddceil"],
gdd1,
gdd2,
))
ax2 = plt.axes([0.92, 0.1, 0.07, 0.8], frameon=False, yticks=[], xticks=[])
ax2.set_xlabel("Days")
for i, mybin in enumerate(bins):
ax2.text(0.52, i, "%g" % (mybin, ), ha="left", va="center", color="k")
# txt.set_path_effects([PathEffects.withStroke(linewidth=2,
# foreground="k")])
ax2.barh(
np.arange(len(bins[:-1])),
[0.5] * len(bins[:-1]),
height=1,
color=cmap(norm(bins[:-1])),
ec="None",
)
ax2.set_xlim(0, 1)
return plt.gcf(), df