def plotter(fdict):
""" Go """
ctx = get_autoplot_context(fdict, get_description())
df = get_data(ctx)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.scatter(df['snow_doy'], df['snowfall'], facecolor=df['color'],
edgecolor=df['color'], s=100)
for _, row in df.iterrows():
ax.scatter(row['snowfree_doy'], row['snowfall'], marker='x', s=100,
color=row['color'])
ax.plot([row['snow_doy'], row['snowfree_doy']],
[row['snowfall'], row['snowfall']], lw='2', color=row['color'])
ax.set_xticks([1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335,
1 + 366, 32 + 366, 60 + 366, 91 + 366, 121 + 366,
152 + 366, 182 + 366, 213 + 366, 244 + 366, 274 + 366,
305 + 366, 335 + 366])
ax.set_xticklabels(calendar.month_abbr[1:] + calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylim(bottom=0)
ax2 = ax.twinx()
ptile = np.percentile(df['snow_doy'].values, np.arange(100))
ax2.plot(ptile, np.arange(100), lw=2, color='k')
ax2.set_ylabel(("Frequency of %s Date (CDF) [%%] (black line)"
) % ('Start' if ctx['dir'] == 'first' else 'Last', ))
ax.set_ylabel('Snowfall [inch], Avg: %.1f inch' % (df['snowfall'].mean(),))
ax.set_title(
('[%s] %s %s %s Snowfall\n(color is how long snow remained)'
) % (ctx['station'], ctx['nt'].sts[ctx['station']]['name'],
'Last' if ctx['dir'] == 'last' else 'First',
('Trace+' if ctx['threshold'] < 0.1
else "%.2f+ Inch" % (ctx['threshold'],))))
p0 = plt.Rectangle((0, 0), 1, 1, fc="purple")
p1 = plt.Rectangle((0, 0), 1, 1, fc="g")
p2 = plt.Rectangle((0, 0), 1, 1, fc="b")
p3 = plt.Rectangle((0, 0), 1, 1, fc="r")
ax.legend((p0, p1, p2, p3), (
'> 31 days [%s]' % (len(df[df['color'] == 'purple'].index), ),
'10 - 31 [%s]' % (len(df[df['color'] == 'g'].index), ),
'3 - 10 [%s]' % (len(df[df['color'] == 'b'].index), ),
'< 3 days [%s]' % (len(df[df['color'] == 'r'].index), )),
ncol=4, fontsize=11, loc=(0., -0.15))
ax.set_xlim(df['snow_doy'].min() - 5, df['snowfree_doy'].max() + 5)
box = ax.get_position()
ax.set_position([box.x0, box.y0 + box.height * 0.1, box.width,
box.height * 0.9])
ax2.set_position([box.x0, box.y0 + box.height * 0.1, box.width,
box.height * 0.9])
ax2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
ax2.set_ylim(0, 101)
df.set_index('year', inplace=True)
del df['color']
return fig, df
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())
df = get_data(ctx)
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
ax.scatter(
df["snow_doy"],
df["snowfall"],
facecolor=df["color"],
edgecolor=df["color"],
s=100,
)
for _, row in df.iterrows():
ax.scatter(
row["snowfree_doy"],
row["snowfall"],
marker="x",
s=100,
color=row["color"],
)
ax.plot(
[row["snow_doy"], row["snowfree_doy"]],
[row["snowfall"], row["snowfall"]],
lw="2",
color=row["color"],
)
ax.set_xticks([
1,
32,
60,
91,
121,
152,
182,
213,
244,
274,
305,
335,
1 + 366,
32 + 366,
60 + 366,
91 + 366,
121 + 366,
152 + 366,
182 + 366,
213 + 366,
244 + 366,
274 + 366,
305 + 366,
335 + 366,
])
ax.set_xticklabels(calendar.month_abbr[1:] + calendar.month_abbr[1:])
ax.grid(True)
ax.set_ylim(bottom=0)
ax2 = ax.twinx()
ptile = np.percentile(df["snow_doy"].values, np.arange(100))
ax2.plot(ptile, np.arange(100), lw=2, color="k")
ax2.set_ylabel(("Frequency of %s Date (CDF) [%%] (black line)") %
("Start" if ctx["dir"] == "first" else "Last", ))
ax.set_ylabel("Snowfall [inch], Avg: %.1f inch" %
(df["snowfall"].mean(), ))
ax.set_title(
("[%s] %s %s %s Snowfall\n(color is how long snow remained)") % (
ctx["station"],
ctx["_nt"].sts[ctx["station"]]["name"],
"Last" if ctx["dir"] == "last" else "First",
("Trace+" if ctx["threshold"] == "T" else "%.2f+ Inch" %
(float(ctx["threshold"]), )),
))
p0 = plt.Rectangle((0, 0), 1, 1, fc="purple")
p1 = plt.Rectangle((0, 0), 1, 1, fc="g")
p2 = plt.Rectangle((0, 0), 1, 1, fc="b")
p3 = plt.Rectangle((0, 0), 1, 1, fc="r")
ax.legend(
(p0, p1, p2, p3),
(
"> 31 days [%s]" % (len(df[df["color"] == "purple"].index), ),
"10 - 31 [%s]" % (len(df[df["color"] == "g"].index), ),
"3 - 10 [%s]" % (len(df[df["color"] == "b"].index), ),
"< 3 days [%s]" % (len(df[df["color"] == "r"].index), ),
),
ncol=4,
fontsize=11,
loc=(0.0, -0.15),
)
ax.set_xlim(df["snow_doy"].min() - 5, df["snowfree_doy"].max() + 5)
box = ax.get_position()
ax.set_position(
[box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
ax2.set_position(
[box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
ax2.set_yticks([0, 5, 10, 25, 50, 75, 90, 95, 100])
ax2.set_ylim(0, 101)
df.set_index("year", inplace=True)
del df["color"]
return fig, df
def plotter(fdict):
""" Go """
pgconn = get_dbconn('isuag')
ctx = get_autoplot_context(fdict, get_description())
threshold = 50
threshold_c = temperature(threshold, 'F').value('C')
hours1 = ctx['hours1']
hours2 = ctx['hours2']
station = ctx['station']
oldstation = XREF[station]
df = read_sql("""
with obs as (
select valid, c300, lag(c300) OVER (ORDER by valid ASC) from hourly
where station = %s),
agg1 as (
select valid,
case when c300 > %s and lag < %s then 1
when c300 < %s and lag > %s then -1
else 0 end as t from obs),
agg2 as (
SELECT valid, t from agg1 where t != 0),
agg3 as (
select valid, lead(valid) OVER (ORDER by valid ASC),
t from agg2),
agg4 as (
select extract(year from valid) as yr, valid, lead,
rank() OVER (PARTITION by extract(year from valid) ORDER by valid ASC)
from agg3 where t = 1
and (lead - valid) >= '%s hours'::interval),
agg5 as (
select extract(year from valid) as yr, valid, lead
from agg3 where t = -1)
select f.yr, f.valid as fup, f.lead as flead, d.valid as dup,
d.lead as dlead from agg4 f JOIN agg5 d ON (f.yr = d.yr)
where f.rank = 1 and d.valid > f.valid
ORDER by fup ASC
""",
pgconn,
params=(oldstation, threshold, threshold, threshold,
threshold, hours1),
index_col=None)
if df.empty:
raise ValueError("No Data Found")
df2 = read_sql("""
with obs as (
select valid, tsoil_c_avg,
lag(tsoil_c_avg) OVER (ORDER by valid ASC) from sm_hourly
where station = %s),
agg1 as (
select valid,
case when tsoil_c_avg > %s and lag < %s then 1
when tsoil_c_avg < %s and lag > %s then -1
else 0 end as t from obs),
agg2 as (
SELECT valid, t from agg1 where t != 0),
agg3 as (
select valid, lead(valid) OVER (ORDER by valid ASC),
t from agg2),
agg4 as (
select extract(year from valid) as yr, valid, lead,
rank() OVER (PARTITION by extract(year from valid) ORDER by valid ASC)
from agg3 where t = 1
and (lead - valid) >= '%s hours'::interval),
agg5 as (
select extract(year from valid) as yr, valid, lead
from agg3 where t = -1)
select f.yr, f.valid as fup, f.lead as flead, d.valid as dup,
d.lead as dlead from agg4 f JOIN agg5 d ON (f.yr = d.yr)
where f.rank = 1 and d.valid > f.valid
ORDER by fup ASC
""",
pgconn,
params=(station, threshold_c, threshold_c, threshold_c,
threshold_c, hours1),
index_col=None)
if df2.empty:
raise ValueError("No Data Found")
(fig, ax) = plt.subplots(1, 1, figsize=(8, 6))
d2000 = utc(2000, 1, 1, 6)
for d in [df, df2]:
for _, row in d.iterrows():
if row['dlead'] is None:
continue
f0 = (row['fup'].replace(year=2000) - d2000).total_seconds()
f1 = (row['flead'].replace(year=2000) - d2000).total_seconds()
d0 = (row['dup'].replace(year=2000) - d2000).total_seconds()
d1 = (row['dlead'].replace(year=2000) - d2000).total_seconds()
if d1 < d0:
continue
ax.barh(row['fup'].year, (f1 - f0),
left=f0,
facecolor='r',
align='center',
edgecolor='r')
color = 'lightblue' if (d1 - d0) < (hours2 * 3600) else 'b'
ax.barh(row['fup'].year, (d1 - d0),
left=d0,
facecolor=color,
align='center',
edgecolor=color)
xticks = []
xticklabels = []
for i in range(1, 13):
d2 = d2000.replace(month=i)
xticks.append((d2 - d2000).total_seconds())
xticklabels.append(d2.strftime("%-d %b"))
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
ax.set_xlim(xticks[2], xticks[6])
ax.grid(True)
nt = NetworkTable("ISUSM")
nt2 = NetworkTable("ISUAG")
ax.set_title(
("[%s] %s 4 Inch Soil Temps\n[%s] %s used for pre-%s dates") %
(station, nt.sts[station]['name'], oldstation,
nt2.sts[oldstation]['name'], nt.sts[station]['archive_begin'].year))
ax.set_ylim(df['yr'].min() - 1, df2['yr'].max() + 1)
p0 = plt.Rectangle((0, 0), 1, 1, fc="r")
p1 = plt.Rectangle((0, 0), 1, 1, fc="lightblue")
p2 = plt.Rectangle((0, 0), 1, 1, fc="b")
ax.legend((p0, p1, p2),
('First Period Above %s for %s+ Hours' %
(threshold, hours1), 'Below %s for 1+ Hours' %
(threshold, ), 'Below %s for %s+ Hours' % (threshold, hours2)),
ncol=2,
fontsize=11,
loc=(0., -0.2))
box = ax.get_position()
ax.set_position(
[box.x0, box.y0 + box.height * 0.1, box.width, box.height * 0.9])
return fig, df
def plot_values(self, lons, lats, vals, fmt='%s', valmask=None,
color='#000000', textsize=14, labels=None,
labeltextsize=10, labelcolor='#000000',
showmarker=False, labelbuffer=25, outlinecolor='#FFFFFF'):
"""Plot values onto the map
Args:
lons (list): longitude values to use for placing `vals`
lats (list): latitude values to use for placing `vals`
vals (list): actual values to place on the map
fmt (str, optional): Format specification to use for representing the
values. For example, the default is '%s'.
valmask (list, optional): Boolean list to use as masking of the
`vals` while adding to the map.
color (str, list, optional): Color to use while plotting the `vals`.
This can be a list to specify each color to use with each value.
textsize (str, optional): Font size to draw text.
labels (list, optional): Optional list of labels to place below the
plotting of `vals`
labeltextsize (int, optional): Size of the label text
labelcolor (str, optional): Color to use for drawing labels
showmarker (bool, optional): Place a marker on the map for the label
labelbuffer (int): pixel buffer around labels
outlinecolor (color): color to use for text outlines
"""
if valmask is None:
valmask = [True] * len(lons)
if labels is None:
labels = [''] * len(lons)
if isinstance(color, str):
color = [color] * len(lons)
bbox = self.fig.get_window_extent().transformed(
self.fig.dpi_scale_trans.inverted())
axbbox = self.ax.get_window_extent().transformed(
self.fig.dpi_scale_trans.inverted())
axx0 = axbbox.x0 * self.fig.dpi
axx1 = (axbbox.x0 + axbbox.width) * self.fig.dpi
axy0 = axbbox.y0 * self.fig.dpi
axy1 = (axbbox.y0 + axbbox.height) * self.fig.dpi
figwidth = bbox.width * self.fig.dpi
figheight = bbox.height * self.fig.dpi
if self.textmask is None:
self.textmask = np.zeros((int(figwidth), int(figheight)), bool)
thisax = self.ax
# Create a fake label, to test out our scaling
t0 = self.fig.text(0.5, 0.5, "ABCDEFGHIJ", transform=thisax.transAxes,
color='None', size=textsize)
bbox = t0.get_window_extent(self.fig.canvas.get_renderer())
xpixels_per_char = bbox.width / 10.
ypixels = bbox.height
for o, a, v, m, c, label in zip(lons, lats, vals, valmask,
color, labels):
if not m:
continue
ha = 'center'
mystr = fmt % (v,)
max_mystr_len = max([len(s) for s in mystr.split("\n")])
mystr_lines = len(mystr.split("\n"))
# compute the pixel coordinate of this data point
(x, y) = thisax.projection.transform_point(o, a, ccrs.Geodetic())
(imgx, imgy) = thisax.transData.transform([x, y])
imgx0 = int(imgx - (max_mystr_len * xpixels_per_char / 2.0))
if imgx0 < axx0:
ha = 'left'
imgx0 = imgx
imgx1 = imgx0 + max_mystr_len * xpixels_per_char
if imgx1 > axx1:
imgx1 = imgx
imgx0 = imgx1 - max_mystr_len * xpixels_per_char
ha = 'right'
imgy0 = int(imgy)
imgy1 = imgy0 + mystr_lines * ypixels
# Now we buffer
imgx0 = max([0, imgx0 - labelbuffer])
imgx1 = min([figwidth, (imgx0 + 2 * labelbuffer +
max_mystr_len * xpixels_per_char)])
imgy0 = max([0, imgy0 - labelbuffer * 0.75])
imgy1 = min([figheight, (imgy0 +
mystr_lines * ypixels +
2 * labelbuffer * 0.75)])
_cnt = np.sum(np.where(self.textmask[int(imgx0):int(imgx1),
int(imgy0):int(imgy1)],
1, 0))
# If we have more than 15 pixels of overlap, don't plot this!
if _cnt > 15:
if self.debug:
print("culling |%s| due to overlap, %s" % (repr(mystr),
_cnt))
continue
if self.debug:
rec = plt.Rectangle([imgx0,
imgy0],
(imgx1 - imgx0),
(imgy1 - imgy0),
facecolor='None', edgecolor='r')
self.fig.patches.append(rec)
# Useful for debugging this algo
if self.debug:
print(("label: %s imgx: %s/%s-%s imgy: %s/%s-%s "
"x:%s-%s y:%s-%s _cnt:%s"
) % (repr(mystr), imgx, axx0, axx1, imgy, axy0, axy1,
imgx0, imgx1, imgy0, imgy1, _cnt))
self.textmask[int(imgx0):int(imgx1), int(imgy0):int(imgy1)] = True
t0 = thisax.text(o, a, mystr, color=c,
size=textsize, zorder=Z_OVERLAY+2,
va='center' if not showmarker else 'bottom',
ha=ha, transform=ccrs.PlateCarree())
bbox = t0.get_window_extent(self.fig.canvas.get_renderer())
if self.debug:
rec = plt.Rectangle([bbox.x0, bbox.y0],
bbox.width, bbox.height,
facecolor='None', edgecolor='k')
self.fig.patches.append(rec)
if showmarker:
thisax.scatter(o, a, marker='+', zorder=Z_OVERLAY+2,
color='k', transform=ccrs.PlateCarree())
t0.set_clip_on(True)
t0.set_path_effects([PathEffects.Stroke(linewidth=3,
foreground=outlinecolor),
PathEffects.Normal()])
if label and label != '':
thisax.annotate("%s" % (label, ), xy=(x, y), ha='center',
va='top',
xytext=(0, 0 - textsize/2),
color=labelcolor,
textcoords="offset points",
zorder=Z_OVERLAY+1,
clip_on=True, fontsize=labeltextsize)
def _make_plot(station, df, units, nsector, rmax, hours, months, sname, level,
bins, **kwargs):
"""Generate a matplotlib windrose plot
Args:
station (str): station identifier
df (pd.DataFrame): observations
drct (list): list of wind directions
units (str): units of wind speed
nsector (int): number of bins to use for windrose
rmax (float): radius of the plot
hours (list): hour limit for plot
month (list): month limit for plot
sname (str): station name
level (int): RAOB level in hPa of interest
bins (list): values for binning the wind speeds
Returns:
matplotlib.Figure
"""
# Generate figure
fig = plt.figure(figsize=(8, 8), dpi=100, facecolor='w', edgecolor='w')
rect = [0.12, 0.12, 0.76, 0.76]
ax = WindroseAxes(fig, rect, facecolor='w', rmax=rmax)
fig.add_axes(ax)
wu = WINDUNITS[units] if level is None else RAOB_WINDUNITS[units]
if bins:
wu['bins'] = bins
wu['binlbl'] = []
for i, mybin in enumerate(bins[1:-1]):
wu['binlbl'].append("%g-%g" % (mybin, bins[i + 2]))
wu['binlbl'].append("%g+" % (bins[-1], ))
# Filters the missing values
df2 = df[df['drct'] >= 0]
try:
# Unsure why this bombs out sometimes
ax.bar(df2['drct'].values,
df2['speed'].values,
normed=True,
bins=wu['bins'],
opening=0.8,
edgecolor='white',
nsector=nsector)
except Exception as exp:
sys.stderr.write(str(exp))
# Figure out the shortest bar
mindir = ax._info['dir'][np.argmin(np.sum(ax._info['table'], axis=0))]
ax.set_rlabel_position((450 - mindir) % 360 - 15)
# Adjust the limits so to get a empty center
rmin, rmax = ax.get_ylim()
ax.set_rorigin(0 - (rmax - rmin) * 0.2)
# Make labels have % formatters
ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f%%'))
handles = []
for p in ax.patches_list:
color = p.get_facecolor()
handles.append(
plt.Rectangle((0, 0), 0.1, 0.3, facecolor=color,
edgecolor='black'))
legend = fig.legend(handles,
wu['binlbl'],
bbox_to_anchor=(0.01, 0.01, 0.98, 0.09),
loc='center',
ncol=6,
title='Wind Speed [%s]' % (wu['abbr'], ),
mode=None,
columnspacing=0.9,
handletextpad=0.45,
fontsize=14)
plt.setp(legend.get_texts(), fontsize=10)
# Now we put some fancy debugging info on the plot
tlimit = "Time Domain: "
if len(hours) == 24 and len(months) == 12:
tlimit = "All Year"
if len(hours) < 24:
if len(hours) > 4:
tlimit += "%s-%s" % (
datetime.datetime(2000, 1, 1, hours[0]).strftime("%-I %p"),
datetime.datetime(2000, 1, 1, hours[-1]).strftime("%-I %p"))
else:
for h in hours:
tlimit += "%s," % (datetime.datetime(2000, 1, 1,
h).strftime("%-I %p"), )
if len(months) < 12:
for h in months:
tlimit += "%s," % (datetime.datetime(2000, h, 1).strftime("%b"), )
label = """[%s] %s%s
Windrose Plot [%s]
Period of Record: %s - %s""" % (
station, sname if sname is not None else "((%s))" %
(station, ), "" if level is None else " @%s hPa" %
(level, ), tlimit, df['valid'].min().strftime("%d %b %Y"),
df['valid'].max().strftime("%d %b %Y"))
plt.gcf().text(0.14, 0.99, label, va='top', fontsize=14)
plt.gcf().text(
0.5,
0.5,
"Calm\n%.1f%%" %
(len(df[df['sknt'] == 0].index) / float(len(df2.index)) * 100., ),
ha='center',
va='center',
fontsize=14)
plt.gcf().text(
0.96,
0.11, ("Summary\nobs count: %s\nMissing: %s\nAvg Speed: %.1f %s") %
(len(df.index), len(df.index) - len(df2.index), df['speed'].mean(),
wu['abbr']),
ha='right',
fontsize=14)
if not kwargs.get('nogenerated', False):
plt.gcf().text(0.02,
0.1,
"Generated: %s" %
(datetime.datetime.now().strftime("%d %b %Y"), ),
verticalalignment="bottom",
fontsize=14)
# Denote the direction blowing from
plt.gcf().text(0.02,
0.125,
"Direction is where the wind is\nblowing from, not toward.",
va='bottom')
# Make a logo
im = mpimage.imread('%s/%s' % (DATADIR, 'logo.png'))
plt.figimage(im, 10, 735)
return fig
def plotter(fdict):
""" Go """
pgconn = get_dbconn('coop')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['station']
network = ctx['network']
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], )
nt = NetworkTable(network)
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,
"%.0f $^\circ$F" % (y, ),
va='center',
bbox=dict(color='white'))
ax.text(highlight,
pc2[0],
"%.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, nt.sts[station]['name'], MDICT[month2], y1, y2,
MDICT[month1], y3, y4, t2))
ax.set_xlabel("%s (%s-%s) %s $^\circ$F" %
(MDICT[month1], y1, y2, PDICT[varname]))
ax.set_ylabel("%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 l in ['cmins', 'cmeans', 'cmaxes']:
v1[l].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 l in ['cmins', 'cmeans', 'cmaxes']:
v1[l].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),
("%s (%s-%s), $\mu$=%.1f" % (MDICT[month1], y1, y2, np.mean(m1data)),
"%s (%s-%s), $\mu$=%.1f" % (MDICT[month2], y3, y4, np.mean(m2data))),
ncol=1,
loc=(0.5, -0.15))
ax.set_ylabel("%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