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 TAVG POR Chart for ' + basinName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
plotData = []
basinPlotData = []
PORplotData = []
basinPlotNormData = []
validTrip = []
networks = [r'SNTL', r'SCAN', r'SNTLT']
sensor = r"TAVG"
url = '/'.join([dataUrl, 'metadata', sensor, 'metadata.json'])
with request.urlopen(url) as data:
meta = json.loads(data.read().decode())
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")
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)
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]
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 basinPlotNormData:
trace.extend([
go.Scatter(x=date_series,
y=basinPlotNormData,
name=r"Normal ('81-'10)",
visible=True,
hoverinfo='none',
connectgaps=True,
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',
connectgaps=True,
hoverinfo='none',
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 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>"
)
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 Daily Temperature in ' + str(basin),
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(site_triplet, siteName):
print('Working on SMS POR Chart for ' + siteName)
statsData = []
minData = []
maxData = []
meanData = []
lowestData = []
highestData = []
lowData = []
highData = []
sliderDates = []
meanData = []
trace = []
plotData = []
sitePlotData = []
PORplotData = []
validTrip = [site_triplet]
sensor = r"SMS"
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(site_triplet):
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() -
datetime.timedelta(days=1)).strftime("%Y-%m-%d")
dataDict = {}
sensorDepths = [-8, -20] #[-2,-4,-8,-20,-40] #
for sensorDepth in sensorDepths:
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))
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)
plotData = integrateSMS(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())
sitePlotData = np.array(smsPlotData[0], 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.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]) > 334:
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 Saturation in ' + siteName,
height=622,
width=700,
autosize=False,
yaxis=dict(title=r'Percent Saturation (%)',
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"
url = '/'.join([dataUrl,'metadata', sensor, 'metadata.json'])
with request.urlopen(url) as data:
meta = json.loads(data.read().decode())
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:
url = '/'.join([dataUrl,'normals', 'DAILY', sensor,
triplet.replace(':','_') + '.json'])
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
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:
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)
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}
def updtChart(site_meta):
siteName = site_meta['name']
site_triplet = site_meta['stationTriplet']
meta = [site_meta]
siteTriplet = site_triplet.split(':')
state = siteTriplet[1]
sensor = r"SMS"
date_series = []
print('Working on SMS Contour Chart for ' + siteName)
date_series = [date(2015,10,1) + datetime.timedelta(days=x) for
x in range(0, 366)] # could use any year with a leap day
cscale=[[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'],
[0.2222222222222222, 'rgb(244,109,67)'],
[0.3333333333333333, 'rgb(253,174,97)'],
[0.4444444444444444, 'rgb(254,224,144)'],
[0.5555555555555556, 'rgb(224,243,248)'],
[0.6666666666666666, 'rgb(171,217,233)'],
[0.7777777777777778, 'rgb(116,173,209)'],
[0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']]
eDate = today.date().strftime("%Y-%m-%d")
if today.month > 9:
sDateWY = date(today.year, 10, 1).strftime("%Y-%m-%d")
else:
sDateWY = date(today.year-1, 10, 1).strftime("%Y-%m-%d")
depths = {}
for site in meta:
elements = awdb.service.getStationElements(site['stationTriplet'],sDateWY,eDate)
siteDepths = []
for element in elements:
if element.elementCd == sensor and element.ordinal == 1 and element.duration == "DAILY":
siteDepths.append(element.heightDepth)
depths[site['stationTriplet']] = siteDepths
SMSPlotData = []
if meta:
for validSite in meta:
trace = []
barPrec = []
barSWE = []
depthVals = []
plotData = []
SMSPlotData = []
date_series = []
y = []
for depth in depths[validSite['stationTriplet']]:
y.extend([depth.value])
if len(y) > 1:
smsDepth = [float(min(y))+(0.5*float((y[-1]-y[-2]))),0]
else:
smsDepth = [float(min(y))*1.5,0]
for i in y:
triplet = validSite['stationTriplet']
for depth in depths[triplet]:
data = []
if depth.value == i and depth.unitCd == r'in':
sDate = date(dt.strptime(validSite['beginDate'],"%Y-%m-%d %H:%M:%S").year,
10, 1).strftime("%Y-%m-%d")
url = '/'.join([dataUrl,'DAILY', sensor,
triplet.replace(':','_') + '.json'])
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
data.append(trimToOct1(jTemp))
if data[0]['beginDate']:
sDate = date(dt.strptime(data[0]['beginDate'],"%Y-%m-%d %H:%M:%S").year,
10, 1).strftime("%Y-%m-%d")
date_series = [dt.strptime(sDate,"%Y-%m-%d") +
datetime.timedelta(days=x) for
x in range(0, (dt.strptime(eDate,"%Y-%m-%d")-
dt.strptime(sDate,"%Y-%m-%d")).days+1)]
sliderDates= list(chain([(date_series[0])] + [date_series[-1]]))
if len(date_series) > 365:
currDates= list(chain([date_series[-365]] + [date_series[-1]]))
else:
currDates= list(chain([date_series[0]] + [date_series[-1]]))
if hasattr(data[0],r'values'):
sat = getSaturation(i,validSite['stationTriplet'])
siteDepthData = np.array(data[0]['values'], dtype=np.float)
plotData = [100*(c/int(sat)) if
c else np.nan for c in siteDepthData]
plotData[:] = [100 if c and c > 100 else c for c
in plotData]
if i == max(y):
SMSPlotData.extend([plotData])
depthVals.extend([0])
SMSPlotData.extend([plotData])
depthVals.extend([i])
dataPrec = []
url = '/'.join([dataUrl,'DAILY', 'PREC',
triplet.replace(':','_') + '.json'])
try:
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
dataPrec.append(trimToOct1(jTemp))
except:
dataPrec = [{"values": []}]
#might need to deal with mismatched start date between SMS and PREC
dataSWE = []
url = '/'.join([dataUrl,'DAILY', 'WTEQ',
triplet.replace(':','_') + '.json'])
try:
with request.urlopen(url) as d:
jTemp = json.loads(d.read().decode())
dataSWE.append(trimToOct1(jTemp))
except:
dataSWE = [{"values": []}]
maxPrecRng = 5
if dataPrec[0]['values']:
precValues = np.array(dataPrec[0]['values'], dtype=np.float)
precDelta = list(np.diff(precValues))
if precDelta:
maxPrecRng = list(chain([0], [3 * np.nanmax(precDelta)]))
precDelta.extend([0])
barPrec = go.Bar(x=date_series,y=precDelta,yaxis='y2',
showlegend=True,
marker=dict(color='rgba(0,0,0,0.60)'),
name='Daily Precip.')
if dataSWE[0]['values']:
sweValues = np.array(dataSWE[0]['values'], dtype=np.float)
sweDelta = list(np.diff(sweValues))
sweDeltaNeg = [round(-1*c,1) if
c < 0 else np.nan for c in sweDelta]
sweDeltaNeg.extend([0])
barSWE = go.Bar(x=date_series,y=sweDeltaNeg,yaxis='y2',
showlegend=True,
marker=dict(color='rgba(0,0,0,0.35)'),
name='SWE melt')
if data[0]['values']:
trace = go.Heatmap(z=SMSPlotData,x=date_series,y=depthVals,
connectgaps=True,zsmooth='best',
colorbar=dict(title='% Saturation',
titleside='right',x=1.125),
colorscale=cscale,
hoverinfo='none')
annoText = str(validSite['countyName'] + r' County, ' + state +
r'. Elev = ' +
str(int(round(validSite['elevation'],0))) +
r', Lat = ' + str(round(validSite['latitude'],3)) +
r', Long = ' + str(round(validSite['longitude'],3)))
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= 0.75,
layer= "above"
)],
annotations=[dict(
font=dict(size=10),
text=annoText,
x=0,y=-0.31,
yref='paper',xref='paper',
align='left',
showarrow=False
)],
showlegend = True,
legend=dict(orientation="h",x=0.5,y=1.1),
barmode='stack',
title='Soil Moisture at ' + siteName,
height=622, width=700, autosize=False,
yaxis=dict(title=r'Soil Depth (in.)',range=smsDepth,
tickformat="0f", hoverformat='.1f',),
yaxis2=dict(
title=r'Daily Incremental Precip./Snow Melt (in.)',
overlaying='y',
side='right',
anchor='free',
position=1,
range=maxPrecRng,
tickformat="0f",
hoverformat='.1f',),
xaxis=dict(
range=currDates,
rangeselector=dict(
buttons=list([
dict(count=1,
label='1m',
step='month',
stepmode='backward'),
dict(count=6,
label='6m',
step='month',
stepmode='backward'),
dict(count=1,
label='1y',
step='year',
stepmode='backward'),
dict(count=3,
label='3y',
step='year',
stepmode='backward'),
dict(label='POR', step='all')
])
),
rangeslider=dict(thickness=0.1,range=sliderDates),
type='date'
)
)
plots = [trace, barPrec, barSWE]
figPlots = []
for plot in plots:
if plot:
figPlots.extend([plot])
return {'data': figPlots,
'layout': layout}
