def updtChart(site_triplet, siteName):
print('Working on WTEQ POR Chart for ' + siteName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
sitePlotData = []
PORplotData = []
sitePlotNormData = []
validTrip = [site_triplet]
sensor = r"WTEQ"
date_series = [
date(2015, 10, 1) + datetime.timedelta(days=x) for x in range(0, 366)
] #could use any year with a leap day
if validTrip:
normData = []
for triplet in validTrip:
url = '/'.join([
dataUrl, 'normals', 'DAILY', sensor,
triplet.replace(':', '_') + '.json'
])
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
normData.append(jTemp)
sitePlotNormData = np.array(normData[0]['values'], dtype=np.float)
sitePlotNormData = sitePlotNormData.tolist()
beginDateDict = {}
for siteMeta in meta:
beginDateDict.update({
str(siteMeta['stationTriplet']):
dt.strptime(str(siteMeta['beginDate']), "%Y-%m-%d %H:%M:%S")
})
siteBeginDate = min(beginDateDict.values())
sYear = siteBeginDate.year
if siteBeginDate.year > sYear:
if siteBeginDate.month < 10:
sYear = siteBeginDate.year
else:
if siteBeginDate.month == 10 and siteBeginDate.day == 1:
sYear = siteBeginDate.year
else:
sYear = siteBeginDate.year + 1
sDate = date(sYear, 10, 1).strftime("%Y-%m-%d")
eDate = today.date().strftime("%Y-%m-%d")
data = []
for triplet in validTrip:
url = '/'.join(
[dataUrl, 'DAILY', sensor,
triplet.replace(':', '_') + '.json'])
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
data.append(trimToOct1(jTemp))
for dataSite in data:
if dataSite:
padMissingData(dataSite, sDate, eDate)
sitePlotData = np.array(data[0]['values'], dtype=np.float)
PORplotData = list(
[sitePlotData[i:i + 366] for i in range(0, len(sitePlotData), 366)])
allButCurrWY = list(PORplotData)
del allButCurrWY[-1]
statsData = list(map(list, zip(*allButCurrWY)))
if len(statsData[0]) > 1:
statsData[151] = statsData[150]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
minData = [np.nanmin(a) for a in statsData]
maxData = [np.nanmax(a) for a in statsData]
meanData = [np.nanpercentile(a, 50) for a in statsData]
lowestData = [np.nanpercentile(a, 10) for a in statsData]
highestData = [np.nanpercentile(a, 90) for a in statsData]
lowData = [np.nanpercentile(a, 30) for a in statsData]
highData = [np.nanpercentile(a, 70) for a in statsData]
future_date_pad = 14
if len(PORplotData[-1]) > 351:
future_date_pad = 366 - len(PORplotData[-1]) - 1
sliderDates = list(
chain([(date_series[0])] + [
date_series[get_last_non_zero_index(maxData[0:305]) +
future_date_pad]
]))
else:
sliderDates = list(chain([(date_series[0])] + [date_series[-1]]))
if len(PORplotData) > 0:
for index, i in enumerate(PORplotData):
if index == len(PORplotData) - 1:
trace.extend([
go.Scatter(x=date_series,
y=i,
name=str(sYear + index + 1),
visible=True,
connectgaps=True,
line=dict(color='rgb(0,0,0)'))
])
elif np.nansum(i) > 0:
trace.extend([
go.Scatter(x=date_series,
y=i,
name=str(sYear + index + 1),
visible='legendonly',
connectgaps=True)
])
if meanData:
if lowestData:
trace.extend([
go.Scatter(x=date_series,
y=minData,
legendgroup='centiles',
name=r'Min',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,0,1,0.15)',
fill='none',
showlegend=False,
hoverinfo='none')
])
trace.extend([
go.Scatter(x=date_series,
y=lowestData,
legendgroup='centiles',
name=r'10%',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,0,1,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if lowData:
trace.extend([
go.Scatter(x=date_series,
y=lowData,
legendgroup='centiles',
name=r'30%',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,237,0,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if highData:
trace.extend([
go.Scatter(x=date_series,
y=highData,
legendgroup='centiles',
name=r'Stats. Shading',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(115,237,115,0.15)',
fill='tonexty',
showlegend=True,
hoverinfo='none')
])
if highestData:
trace.extend([
go.Scatter(x=date_series,
y=highestData,
legendgroup='centiles',
connectgaps=True,
name=r'90%',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(0,237,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
trace.extend([
go.Scatter(x=date_series,
y=maxData,
legendgroup='centiles',
name=r'Max',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(1,0,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if minData:
trace.extend([
go.Scatter(x=date_series,
y=minData,
name=r'Min',
visible=True,
hoverinfo='none',
connectgaps=True,
line=dict(color='rgba(237,0,0,0.5)'))
])
if len(sitePlotNormData) > 0:
trace.extend([
go.Scatter(x=date_series,
y=sitePlotNormData,
name=r"Normal ('81-'10)",
connectgaps=True,
visible=True,
hoverinfo='none',
line=dict(color='rgba(0,237,0,0.4)'))
])
if meanData:
if len(sitePlotNormData) > 0:
trace.extend([
go.Scatter(x=date_series,
y=meanData,
name=r'Normal (POR)',
visible='legendonly',
hoverinfo='none',
connectgaps=True,
line=dict(color='rgba(0,237,0,0.4)', dash='dash'))
])
else:
trace.extend([
go.Scatter(x=date_series,
y=meanData,
name=r'Normal (POR)',
connectgaps=True,
visible=True,
hoverinfo='none',
line=dict(color='rgba(0,237,0,0.4)'))
])
if maxData:
trace.extend([
go.Scatter(x=date_series,
y=maxData,
name=r'Max',
visible=True,
hoverinfo='none',
connectgaps=True,
line=dict(color='rgba(0,0,237,0.4)'))
])
annoText = str(
r"Statistical shading breaks at 10th, 30th, 50th, 70th, and 90th Percentiles<br>Normal ('81-'10) - Official median calculated from 1981 thru 2010 data <br>Normal (POR) - Unofficial mean calculated from Period of Record data <br>For more information visit: <a href='https://www.wcc.nrcs.usda.gov/normals/30year_normals_data.htm'>30 year normals calcuation description</a>"
)
asterisk = ''
if len(sitePlotNormData) == 0:
sitePlotNormData = meanData
annoText = annoText + '<br>*POR data used to calculate Normals since no published 30-year normals available for this site'
asterisk = '*'
jDay = len(PORplotData[-1]) - 1
if len(sitePlotNormData) == 0:
perNorm = r'N/A'
else:
perNorm = str('{0:g}'.format(
100 * round(PORplotData[-1][jDay] / sitePlotNormData[jDay], 2)))
perPeak = str('{0:g}'.format(
100 * round(PORplotData[-1][jDay] / max(sitePlotNormData), 2)))
if not math.isnan(PORplotData[-1][jDay]):
centile = ordinal(
int(
round(
stats.percentileofscore(statsData[jDay],
PORplotData[-1][jDay]), 0)))
else:
centile = 'N/A'
dayOfPeak = sitePlotNormData.index(max(sitePlotNormData))
if jDay > dayOfPeak:
tense = r'Since'
else:
tense = r'Until'
daysToPeak = str(abs(jDay - dayOfPeak))
annoData = str(r"Current" + asterisk + ":<br>% of Normal - " + perNorm +
r"%<br>" + r"% Normal Peak - " + perPeak + r"%<br>" +
r"Days " + tense + r" Normal Peak - " + daysToPeak + r"<br>"
r"Percentile Rank- " + centile)
layout = go.Layout(images=[
dict(
source=
"https://upload.wikimedia.org/wikipedia/commons/thumb/7/7f/US-NaturalResourcesConservationService-Logo.svg/2000px-US-NaturalResourcesConservationService-Logo.svg.png",
xref="paper",
yref="paper",
x=0,
y=0.9,
xanchor="left",
yanchor="bottom",
sizex=0.4,
sizey=0.1,
opacity=0.5,
layer="above")
],
annotations=[
dict(font=dict(size=10),
text=annoText,
x=0,
y=-0.41,
yref='paper',
xref='paper',
align='left',
showarrow=False),
dict(font=dict(size=10),
text=annoData,
x=0,
y=0.9,
yref='paper',
xref='paper',
align='left',
xanchor="left",
yanchor="top",
showarrow=False)
],
legend=dict(traceorder='reversed',
tracegroupgap=1,
bordercolor='#E2E2E2',
borderwidth=2),
showlegend=True,
title='Snow Water Equivalent at<br>' + siteName,
height=622,
width=700,
autosize=False,
yaxis=dict(title=r'Snow Water Equivalent (in.)',
hoverformat='.1f',
tickformat="0f"),
xaxis=dict(range=sliderDates,
tickformat="%b %e",
rangeselector=dict(buttons=list([
dict(count=9,
label='Jan',
step='month',
stepmode='todate'),
dict(count=6,
label='Apr',
step='month',
stepmode='todate'),
dict(count=3,
label='July',
step='month',
stepmode='todate'),
dict(label='WY', step='all')
])),
rangeslider=dict(thickness=0.1),
type='date'))
return {'data': trace, 'layout': layout}
def updtChart(basinName, basinSites):
basin = basinName
print('Working on PREC Projection Chart for ' + basinName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
plotData = []
basinPlotData = []
PORplotData = []
basinNormData = []
basinPlotNormData = []
validTrip = []
networks = [r'SNTL', r'SCAN', r'SNTLT']
sensor = r"PREC"
dataPath = path.join(this_dir, 'data', 'metaData', sensor, 'metaData.json')
with open(dataPath, 'r') as j:
meta = json.load(j)
meta[:] = [
x for x in meta if str.split(x['stationTriplet'], ":")[2] in networks
and str.split(x['stationTriplet'], ":")[0] in basinSites
]
validTrip = [x['stationTriplet'] for x in meta]
date_series = [
date(2015, 10, 1) + datetime.timedelta(days=x) for x in range(0, 366)
] #could use any year with a leap day
if validTrip:
normData = []
for triplet in validTrip:
dataPath = dataPath = path.join(
this_dir, 'data', 'norms', sensor,
triplet.replace(':', '_') + '.json')
with open(dataPath, 'r') as j:
jTemp = json.load(j)
normData.append(jTemp)
basinNormData = [
np.array(x['values'], dtype=np.float) for x in normData
if x['values']
]
if basinNormData:
basinPlotNormData = list(
np.nanmean(np.array([i for i in basinNormData]), axis=0))
validTrip[:] = [
x for index, x in enumerate(validTrip) if normData[index]['values']
]
beginDateDict = {}
for siteMeta in meta:
beginDateDict.update({
str(siteMeta['stationTriplet']):
dt.strptime(str(siteMeta['beginDate']), "%Y-%m-%d %H:%M:%S")
})
basinBeginDate = min(beginDateDict.values())
sYear = basinBeginDate.year
if basinBeginDate.year > sYear:
if basinBeginDate.month < 10:
sYear = basinBeginDate.year
else:
if basinBeginDate.month == 10 and basinBeginDate.day == 1:
sYear = basinBeginDate.year
else:
sYear = basinBeginDate.year + 1
sDate = date(sYear, 10, 1).strftime("%Y-%m-%d")
eDate = (today.date() - datetime.timedelta(days=1)).strftime("%Y-%m-%d")
data = []
for triplet in validTrip:
dataPath = path.join(this_dir, 'data', sensor,
triplet.replace(':', '_') + '.json')
with open(dataPath, 'r') as j:
jTemp = json.load(j)
data.append(jTemp)
for dataSite in data:
if dataSite:
padMissingData(dataSite, sDate, eDate)
plotData = [np.array(x['values'], dtype=np.float) for x in data]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
basinPlotData = list(
np.nanmean(np.array([i for i in plotData]), axis=0))
PORplotData = list(
[basinPlotData[i:i + 366] for i in range(0, len(basinPlotData), 366)])
allButCurrWY = list(PORplotData)
del allButCurrWY[-1]
statsData = list(map(list, zip(*allButCurrWY)))
if len(statsData[0]) > 1:
statsData[151] = statsData[150]
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
minData = [np.nanmin(a) for a in statsData]
maxData = [np.nanmax(a) for a in statsData]
meanData = [np.nanmean(a) for a in statsData]
lowestData = [np.nanpercentile(a, 10) for a in statsData]
highestData = [np.nanpercentile(a, 90) for a in statsData]
lowData = [np.nanpercentile(a, 30) for a in statsData]
highData = [np.nanpercentile(a, 70) for a in statsData]
sliderDates = list(chain([(date_series[0])] + [date_series[-1]]))
else:
sliderDates = list(chain([(date_series[0])] + [date_series[-1]]))
jDay = len(PORplotData[-1]) - 1
lastValue = PORplotData[-1][-1]
nanList = [np.nan] * jDay
projData = [
createPRECProjTrace(a, jDay, lastValue, nanList) for a in allButCurrWY
]
statsProj = list(map(list, zip(*projData)))
cleanStatsProj = list(statsProj)
if cleanStatsProj:
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
minProj = [np.nanmin(a) for a in cleanStatsProj]
maxProj = [np.nanmax(a) for a in cleanStatsProj]
medianProj = [np.nanpercentile(a, 50) for a in cleanStatsProj]
lowestProj = [np.nanpercentile(a, 10) for a in cleanStatsProj]
highestProj = [np.nanpercentile(a, 90) for a in cleanStatsProj]
lowProj = [np.nanpercentile(a, 30) for a in cleanStatsProj]
highProj = [np.nanpercentile(a, 70) for a in cleanStatsProj]
if len(PORplotData) > 0:
for index, i in enumerate(PORplotData):
if index == len(PORplotData) - 1:
trace.extend([
go.Scatter(x=date_series,
y=i,
name=str(sYear + index + 1),
visible=True,
connectgaps=True,
line=dict(color='rgb(0,0,0)'))
])
elif np.nansum(i) > 0:
trace.extend([
go.Scatter(x=date_series,
y=projData[index],
name=str(sYear + index + 1),
visible='legendonly',
connectgaps=True)
])
if medianProj:
if minProj:
trace.extend([
go.Scatter(x=date_series,
y=minProj,
name=r'Min Proj',
visible=True,
connectgaps=True,
line=dict(color='rgba(237,0,0,0.4)'))
])
if lowestProj:
trace.extend([
go.Scatter(x=date_series,
y=lowestProj,
name=r'10% Proj',
visible=True,
connectgaps=True,
line=dict(color='rgba(237,0,1,0.4)'))
])
if lowProj:
trace.extend([
go.Scatter(x=date_series,
y=lowProj,
name=r'30% Proj',
visible=True,
connectgaps=True,
line=dict(color='rgba(0,237,0,0.4)'))
])
if medianProj:
trace.extend([
go.Scatter(x=date_series,
y=medianProj,
name=r'50% Proj',
connectgaps=True,
visible=True,
line=dict(color='rgba(0,237,0,0.4)'))
])
if highProj:
trace.extend([
go.Scatter(x=date_series,
y=highProj,
name=r'70% Proj',
visible=True,
connectgaps=True,
line=dict(color='rgba(115,237,115,0.4)'))
])
if highestProj:
trace.extend([
go.Scatter(x=date_series,
y=highestProj,
connectgaps=True,
name=r'90% Proj',
visible=True,
line=dict(color='rgba(1,237,237,0.4)'))
])
if maxProj:
trace.extend([
go.Scatter(x=date_series,
y=maxProj,
name=r'Max Proj',
visible=True,
connectgaps=True,
line=dict(color='rgba(0,0,237,0.4)'))
])
if meanData:
if lowestData:
trace.extend([
go.Scatter(x=date_series,
y=minData,
legendgroup='centiles',
name=r'Min',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,0,1,0.15)',
fill='none',
showlegend=False,
hoverinfo='none')
])
trace.extend([
go.Scatter(x=date_series,
y=lowestData,
legendgroup='centiles',
name=r'10%',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,0,1,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if lowData:
trace.extend([
go.Scatter(x=date_series,
y=lowData,
legendgroup='centiles',
name=r'30%',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(237,237,0,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if highData:
trace.extend([
go.Scatter(x=date_series,
y=highData,
legendgroup='centiles',
name=r'Stats. Shading',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(115,237,115,0.15)',
fill='tonexty',
showlegend=True,
hoverinfo='none')
])
if highestData:
trace.extend([
go.Scatter(x=date_series,
y=highestData,
legendgroup='centiles',
connectgaps=True,
name=r'90%',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(0,237,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
trace.extend([
go.Scatter(x=date_series,
y=maxData,
legendgroup='centiles',
name=r'Max',
visible=True,
mode='line',
line=dict(width=0),
connectgaps=True,
fillcolor='rgba(1,0,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none')
])
if basinPlotNormData:
trace.extend([
go.Scatter(x=date_series,
y=basinPlotNormData,
name=r"Normal ('81-'10)",
connectgaps=True,
visible=True,
hoverinfo='none',
line=dict(color='rgba(0,237,0,0.4)'))
])
if meanData:
if basinPlotNormData:
trace.extend([
go.Scatter(x=date_series,
y=meanData,
name=r'Normal (POR)',
visible='legendonly',
hoverinfo='none',
connectgaps=True,
line=dict(color='rgba(0,237,0,0.4)', dash='dash'))
])
else:
trace.extend([
go.Scatter(x=date_series,
y=meanData,
name=r'Normal (POR)',
connectgaps=True,
visible=True,
hoverinfo='none',
line=dict(color='rgba(0,237,0,0.4)'))
])
annoText = str(
r"Statistical shading breaks at 10th, 30th, 50th, 70th, and 90th Percentiles<br>Normal ('81-'10) - Official mean calculated from 1981 thru 2010 data <br>Normal (POR) - Unofficial mean calculated from Period of Record data <br>For more information visit: <a href='https://www.wcc.nrcs.usda.gov/normals/30year_normals_data.htm'>30 year normals calcuation description</a>"
)
asterisk = ''
if not basinPlotNormData:
basinPlotNormData = meanData
annoText = annoText + '<br>*POR data used to calculate Normals since no published 30-year normals available for this basin'
asterisk = '*'
if basinPlotNormData[jDay] == 0:
perNorm = r'N/A'
else:
perNorm = str('{0:g}'.format(
100 * round(PORplotData[-1][jDay] / basinPlotNormData[jDay], 2)))
perPeak = str('{0:g}'.format(
100 * round(PORplotData[-1][jDay] / max(basinPlotNormData), 2)))
if not math.isnan(PORplotData[-1][jDay]):
centile = ordinal(
int(
round(
stats.percentileofscore(statsData[jDay],
PORplotData[-1][jDay]), 0)))
else:
centile = 'N/A'
dayOfPeak = basinPlotNormData.index(max(basinPlotNormData))
if jDay > dayOfPeak:
tense = r'Since'
else:
tense = r'Until'
daysToPeak = str(abs(jDay - dayOfPeak))
annoData = str(r"Current" + asterisk + ":<br>% of Normal - " + perNorm +
r"%<br>" + r"% of Yearly Avg - " + perPeak + r"%<br>" +
r"Days " + tense + r" End of WY - " + daysToPeak + r"<br>"
r"Percentile Rank- " + centile)
layout = go.Layout(images=[
dict(
source=
"https://upload.wikimedia.org/wikipedia/commons/thumb/7/7f/US-NaturalResourcesConservationService-Logo.svg/2000px-US-NaturalResourcesConservationService-Logo.svg.png",
xref="paper",
yref="paper",
x=0,
y=0.9,
xanchor="left",
yanchor="bottom",
sizex=0.4,
sizey=0.1,
opacity=0.5,
layer="above")
],
annotations=[
dict(font=dict(size=10),
text=annoText,
x=0,
y=-0.41,
yref='paper',
xref='paper',
align='left',
showarrow=False),
dict(font=dict(size=10),
text=annoData,
x=0,
y=0.9,
yref='paper',
xref='paper',
align='left',
xanchor="left",
yanchor="top",
showarrow=False)
],
legend=dict(traceorder='reversed',
tracegroupgap=1,
bordercolor='#E2E2E2',
borderwidth=2),
showlegend=True,
title='Precipitation Projections in<br> ' + str(basin),
height=622,
width=700,
autosize=False,
yaxis=dict(title=r'Precipitation (in.)',
hoverformat=".1f",
tickformat="0f"),
xaxis=dict(range=sliderDates,
tickformat="%b %e",
rangeselector=dict(buttons=list([
dict(count=9,
label='Jan',
step='month',
stepmode='todate'),
dict(count=6,
label='Apr',
step='month',
stepmode='todate'),
dict(count=3,
label='July',
step='month',
stepmode='todate'),
dict(label='WY', step='all')
])),
rangeslider=dict(thickness=0.1),
type='date'))
return {'data': trace, 'layout': layout}