def updtChart(basinName, basinSites):
basin = basinName
print('Working on STO POR Chart for ' + basinName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
plotData = []
basinPlotData = []
PORplotData = []
validTrip = []
networks = [r'SNTL', r'SCAN', r'SNTLT']
sensor = r"STO"
depths = awdb.service.getHeightDepths()
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:
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")
dataDict = {}
sensorDepths = [-2, -4, -8, -20, -40]
for sensorDepth in sensorDepths:
for depth in depths:
if depth.value == sensorDepth and depth.unitCd == r'in':
data = []
for triplet in validTrip:
dataPath = path.join(
this_dir, 'data', sensor, str(abs(sensorDepth)),
triplet.replace(':', '_') + '.json')
with open(dataPath, 'r') as j:
jTemp = json.load(j)
data.append(jTemp)
depthData = {}
for dataSite in data:
siteData = []
if hasattr(dataSite, r'values'):
if dataSite['values']:
# sat = getSaturation(
# sensorDepth,
# str(dataSite['stationTriplet']))
# sat = np.nanmax([float(c) for c in dataSite.values if c != None])
padMissingData(dataSite, sDate, eDate)
siteData = np.array(dataSite['values'],
dtype=np.float)
# siteData[:] = [100 if 100*(c/float(sat)) > 100 else
# 100*(c/float(sat)) for c in siteData]
depthData.update({
str(dataSite['stationTriplet']):
list(siteData)
})
dataDict.update({sensorDepth: dict(depthData)})
depthData.clear()
plotData = {}
plotData = calcSMSAvg(dataDict)
# numDays = max(len(l) for l in plotData.values())
for siteID, smsValues in plotData.items():
plotData.update({siteID: fillMissingData(plotData[siteID], 30)})
smsPlotData = list(plotData.values())
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
basinPlotData = list(
np.nanmean(np.array([i for i in smsPlotData if i]), 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]
future_date_pad = 30
if len(PORplotData[-1]) > 335:
future_date_pad = 366 - len(PORplotData[-1]) - 1
sliderDates = list(
chain([(date_series[0])] +
[date_series[len(PORplotData[-1]) + 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),
fillcolor='rgba(237,0,1,0.15)',
fill='none',
showlegend=False,
hoverinfo='none',
connectgaps=True)
])
trace.extend([
go.Scatter(x=date_series,
y=lowestData,
legendgroup='centiles',
name=r'10%',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(237,0,1,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none',
connectgaps=True)
])
if lowData:
trace.extend([
go.Scatter(x=date_series,
y=lowData,
legendgroup='centiles',
name=r'30%',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(237,237,0,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none',
connectgaps=True)
])
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),
fillcolor='rgba(115,237,115,0.15)',
fill='tonexty',
showlegend=True,
hoverinfo='none',
connectgaps=True)
])
if highestData:
trace.extend([
go.Scatter(x=date_series,
y=highestData,
legendgroup='centiles',
name=r'90%',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(0,237,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none',
connectgaps=True)
])
trace.extend([
go.Scatter(x=date_series,
y=maxData,
legendgroup='centiles',
name=r'Max',
visible=True,
mode='line',
line=dict(width=0),
fillcolor='rgba(1,0,237,0.15)',
fill='tonexty',
showlegend=False,
hoverinfo='none',
connectgaps=True)
])
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 meanData:
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 (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>"
)
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)
],
legend=dict(traceorder='reversed',
tracegroupgap=1,
bordercolor='#E2E2E2',
borderwidth=2),
showlegend=True,
title='Average Soil Temperature in ' + str(basin),
height=622,
width=700,
autosize=False,
yaxis=dict(title=r'Avg. Temperature (°F)',
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(site_triplet, siteName):
print('Working on TAVG POR Chart for ' + siteName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
sitePlotData = []
PORplotData = []
sitePlotNormData = []
validTrip = [site_triplet]
sensor = r"TAVG"
date_series = [date(2015,10,1) + datetime.timedelta(days=x)
for x in range(0, 366)] #could use any year with a leap day
sitePlotNormData = []
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[len(PORplotData[-1])+
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='Average Daily Temperature at<br>' + siteName,
height=622, width=700, autosize=False,
yaxis=dict(title=r'Avg. Daily Temperature (°F)',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 WTEQ POR 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"WTEQ"
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().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.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 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)'))
])
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 not basinPlotNormData:
basinPlotNormData = meanData
annoText = annoText + '<br>*POR data used to calculate Normals since no published 30-year normals available for this basin'
asterisk = '*'
jDay = len(PORplotData[-1]) - 1
# maxList = [np.nanmax(x) for x in allButCurrWY]
# trace.extend([go.Box(y=maxList,x0=date_series[jDay],marker=dict(size=0))])
#
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"% 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 in ' + str(basin),
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}
