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('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 """
pgconn = get_dbconn('asos')
ctx = get_autoplot_context(fdict, get_description())
station = ctx['zstation']
network = ctx['network']
syear = ctx['syear']
eyear = ctx['eyear']
groupby = ctx['groupby']
sts = datetime.date(syear, 1, 1)
ets = datetime.date(eyear + 1, 1, 1)
nt = NetworkTable(network)
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=(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=(nt.sts[station]['tzname'], station, sts, ets),
index_col=None)
if df.empty:
raise ValueError("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" % (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, 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