def download_daily_discharge(USGS_siteCode,
beginDate,
endDate,
outFile,
Q_max_min_mean="mean"):
# Example of calling GetValuesObject from a WaterOneFlow web service
# Then create a time series plot using matplotlib
from suds.client import Client
from pandas import Series
import matplotlib.pyplot as plt
# Create the inputs needed for the web service call
wsdlURL = 'http://hydroportal.cuahsi.org/nwisuv/cuahsi_1_1.asmx?WSDL'
NWIS = Client(wsdlURL).service
variableCode = 'NWISUV:00060'
siteCode = 'NWISUV:' + USGS_siteCode
beginDate = beginDate #'2015-08-01'
endDate = endDate #'2015-11-06'
# Create a new object named "NWIS" for calling the web service methods
# Call the GetValuesObject method to return datavalues
response = NWIS.GetValuesObject(siteCode, variableCode, beginDate, endDate)
# Get the site's name from the response
try:
siteName = response.timeSeries[0].sourceInfo.siteName
except Exception, e:
print 'ERROR: SiteCode: %s does not have the data' % USGS_siteCode
return
def btn_RetrieveDataOnButtonClick(self, event):
from suds.client import Client
# self.m_textCtrl22.Value = 'NWISUV:10105900'
# self.m_textCtrl23.Value = 'NWISUV:00060'
# self.m_textCtrl24.Value = '2016-10-20'
# self.m_textCtrl25.Value = '2016-11-06'
# Check whether user select needed items correctly
validation_check = self.check_validation()
if not validation_check:
return
# Create the inputs needed for the web service call //daily values
wsdlURL = 'http://hydroportal.cuahsi.org/nwisdv/cuahsi_1_1.asmx?WSDL'
# siteCode = self.m_textCtrl22.Value #'NWISUV:10105900'
# variableCode = self.m_textCtrl23.Value #'NWISUV:00060'
siteCode = self.comboBox_Code.Value #'NWISUV:10105900'
variableCode = self.comboBox_VariableCode.Value #'NWISUV:00060'
beginDate = self.m_textCtrl24.Value #'2016-10-20'
endDate = self.m_textCtrl25.Value #'2016-11-06'
#siteCode= NWISUV:10039500 for BEAR RIVER AT BORDER, WY
# variableCode NWISDV:00060/DataType=Mean for Discharge, cubic feet per second
# Create a new object named "NWIS" for calling the web service methods
NWIS = Client(wsdlURL).service
# Call the GetValuesObject method to return datavalues
try:
self.response = NWIS.GetValuesObject(siteCode, variableCode,
beginDate, endDate)
except Exception as e:
message = msg_somethigWrong(
None,
msg=
'\n\n\nError, The provided values do not have data in the server.\nPlease make sure to enter correct values.'
)
message.ShowModal()
return
self.btn_Load.Enabled = True
# Get the site's name from the response
siteName = self.response.timeSeries[0].sourceInfo.siteName
from suds.client import Client
from pandas import Series
import matplotlib.pyplot as plt
# Create the inputs needed for the web service call
wsdlURL = 'http://hydroportal.cuahsi.org/nwisuv/cuahsi_1_1.asmx?WSDL'
siteCode = 'NWISUV:10105900'
variableCode = 'NWISUV:00060'
beginDate = '2016-10-20'
endDate = '2016-11-06'
# Create a new object named "NWIS" for calling the web service methods
NWIS = Client(wsdlURL).service
# Call the GetValuesObject method to return datavalues
response = NWIS.GetValuesObject(siteCode, variableCode, beginDate, endDate)
# Get the site's name from the response
siteName = response.timeSeries[0].sourceInfo.siteName
# Create some blank lists in which to put the values and their dates
a = [] # The values
b = [] # The dates
# Get the values and their dates from the web service response
values = response.timeSeries[0].values[0].value
# Loop through the values and load into the blank lists using append
for v in values:
a.append(float(v.value))
b.append(v._dateTime)
def search_gamut_data(request):
if request.method == 'GET':
get_data = request.GET
siteCodestr = str(get_data['siteCode'])
beginDate = str(get_data['beginDate'])
endDate = str(get_data['endDate'])
# Create the inputs needed for the web service call
if "LR" in siteCodestr:
wsdlURL = 'http://data.iutahepscor.org/loganriverwof/cuahsi_1_1.asmx?WSDL'
elif "PR" in siteCodestr:
wsdlURL = 'http://data.iutahepscor.org/provoriverwof/cuahsi_1_1.asmx?WSDL'
else:
wsdlURL = 'http://data.iutahepscor.org/redbuttecreekwof/cuahsi_1_1.asmx?WSDL'
siteCode = ':' + siteCodestr
variableCode_pH = 'iutah:pH'
variableCode_Temp = 'iutah:WaterTemp_EXO'
variableCode_DO = 'iutah:ODO'
variableCode_N = 'iutah:Nitrate-N'
# Create a new object named "NWIS" for calling the web service methods
NWIS = Client(wsdlURL).service
# Call the GetValuesObject method to return datavalues
response_pH = NWIS.GetValuesObject(siteCode, variableCode_pH,
beginDate, endDate, '')
response_Temp = NWIS.GetValuesObject(siteCode, variableCode_Temp,
beginDate, endDate, '')
response_DO = NWIS.GetValuesObject(siteCode, variableCode_DO,
beginDate, endDate, '')
response_N = NWIS.GetValuesObject(siteCode, variableCode_N, beginDate,
endDate, '')
# Get the site's name from the response
siteName = response_pH.timeSeries[0].sourceInfo.siteName
# Create some blank lists in which to put the values and their dates
a1 = [] # The pH values
b1 = [] # The dates
a2 = [] # The Temperature values
b2 = [] # The dates
a3 = [] # The DO values
b3 = [] # The dates
a4 = [] # The Nitrate-N values
b4 = [] # The dates
# Get the values and their dates from the web service response
pH_values = response_pH.timeSeries[0].values[0].value
Temp_values = response_Temp.timeSeries[0].values[0].value
DO_values = response_DO.timeSeries[0].values[0].value
N_values = response_N.timeSeries[0].values[0].value
# Loop through the values and load into the blank lists using append
for v in pH_values:
if float(v.value) > -100.0:
a1.append(float(v.value))
b1.append(v._dateTime)
for v in Temp_values:
if float(v.value) > -100.0:
a2.append(float(v.value))
b2.append(v._dateTime)
for v in DO_values:
if float(v.value) > -100.0:
a3.append(float(v.value))
b3.append(v._dateTime)
for v in N_values:
if float(v.value) > -100.0:
a4.append(float(v.value))
b4.append(v._dateTime)
pH_ts = Series(a1, index=b1)
dailyAvepH = pH_ts.resample('D', how='mean')
dailyAvepH.to_csv("/tmp/avepH.csv")
with open('/tmp/avepH.csv', 'rb') as f:
reader = csv.reader(f)
your_list = list(reader)
a5 = []
b5 = []
for line in your_list:
b5.append(line[0] + "T00:00:00")
a5.append(float(line[1]))
return JsonResponse({
'success': "123",
'a1': a1,
'b1': b1,
'a2': a2,
'b2': b2,
'a3': a3,
'b3': b3,
'a4': a4,
'b4': b4,
'a5': a5,
'b5': b5,
})
def download_daily_discharge(USGS_siteCode, beginDate, endDate,outFile, Q_max_min_mean= "mean"):
"""
HW7 in hydroinformatics
startDate, endDate: string, format- yyyy-mm-dd
Q_max_min_mean: string, "max", "mean" or "min"
"""
# GetValuesObject from a WaterOneFlow web service
# Then create a time series plot using matplotlib
from suds.client import Client
from pandas import Series
# import matplotlib.pyplot as plt
# Create the inputs needed for the web service call
wsdlURL = 'http://hydroportal.cuahsi.org/nwisuv/cuahsi_1_1.asmx?WSDL'
siteCode = 'NWISUV:%s'%USGS_siteCode
variableCode = 'NWISUV:00060'
# Create a new object named "NWIS" for calling the web service methods
NWIS = Client(wsdlURL).service
# Call the GetValuesObject method to return datavalues
response = NWIS.GetValuesObject(siteCode, variableCode, beginDate, endDate)
# Get the site's name from the response
siteName = response.timeSeries[0].sourceInfo.siteName
# Create some blank lists in which to put the values and their dates
a = [] # The values
b = [] # The dates
# Get the values and their dates from the web service response
values = response.timeSeries[0].values[0].value
# Loop through the values and load into the blank lists using append
for v in values:
a.append(float(v.value))
b.append(v._dateTime)
# Create a Pandas Series object from the lists
# Set the index of the Series object to the dates
ts = Series(a, index=b)
# # resample is like group by clause in SQL
# # summed = ts.resample('1D', how='sum') #ts.resample('1440T', how='sum')
# ts_maxx = ts.resample('1D', how='max')
# ts_minn = ts.resample('1D', how='min')
# ts_mean = ts.resample('1D', how='mean')
# # Use MatPlotLib to create a plot of the time series
# # Create a plot of the streamflow statistics
# # ------------------------------------------
# # Create a figure object and add a subplot
# # figure() creates a big area where we can create multiple or single drawings
# fig = plt.figure()
# ax = fig.add_subplot(1, 1, 1) # arguments for add_subplot - add_subplot(nrows, ncols, plot_number)
#
# # Call the plot() methods on the series object to plot the data
# ts.plot(color='0.9', linestyle='solid', label='15-minute streamflow values')
# ts_maxx.plot(color='red', linestyle='solid', label='Daily streamflow values', marker="o")
# ts_mean.plot(color='green', linestyle='solid', label='Daily streamflow values', marker="o")
# ts_minn.plot(color='blue', linestyle='solid', label='Daily streamflow values', marker="o")
#
# # Set some properties of the subplot to make it look nice
# ax.set_ylabel('Discharge, cubic feet per second')
# ax.set_xlabel('Date')
# ax.grid(True)
# ax.set_title(siteName)
#
# # Add a legend with some customizations
# legend = ax.legend(loc='upper left', shadow=True)
#
# # Create a frame around the legend.
# frame = legend.get_frame()
# frame.set_facecolor('0.95')
# # Set the font size in the legend
# for label in legend.get_texts():
# label.set_fontsize('large')
#
# for label in legend.get_lines():
# label.set_linewidth(1.5) # the legend line width
#
# plt.savefig("HW7.png")
# plt.show()
if Q_max_min_mean.lower() == "max":
ts_maxx = ts.resample('1D').max()
r = ts_maxx
elif Q_max_min_mean.lower() == "min":
ts_minn = ts.resample('1D').min
r = ts_minn
else:
# old syntax was ts_mean = ts.resample('1D' , how='mean')
ts_mean = ts.resample('1D').mean()
r = ts_mean
r.to_csv(outFile)
# change format of the date in the saved file
# replace - with ,
f = file(outFile, "r")
str_to_save = f.read().replace('-',",")
f.close()
#save it again
f = file(outFile, "w")
f.write(str_to_save)
f = np.loadtxt(outFile, delimiter=",")
q = f[:,-1]
q = q * 0.028316847
# to make shape of q (x,1) instead of (x,)
qq = np.zeros((q.shape[0], 1))
qq[:, 0] = q
# take the first 3 columns,
# add two extra colum = 0 0 for min and sec
# add third column = q
# save by seperating by space " "
date_part = f[:,:-1]
empty_col = np.zeros((f.shape[0],1))
date_n_hour = np.append(date_part,empty_col, axis =1 )
date_hour_n_min = np.append(date_n_hour, empty_col, axis=1)
date_hr_min_n_Q = np.append(date_hour_n_min,qq, axis=1)
np.savetxt(outFile,date_hr_min_n_Q, fmt='%i %i %i %i %i %f' , delimiter= "\t")
return
# -*- coding: utf-8 -*-
from suds.client import Client
from pandas import Series
#Create a new object named NWIS for calling the web service methods (https://fedorahosted.org/suds/wiki/Documentation)
NWIS = Client(
"http://river.sdsc.edu/wateroneflow/NWIS/UnitValues.asmx?WSDL").service
#Call the GetValuesObject method (http://river.sdsc.edu/wateroneflow/NWIS/UnitValues.asmx?op=GetValuesObject)
response = NWIS.GetValuesObject("USGS:10109000", "USGS:00060", "2014-10-31",
"2014-11-04")
#(If you get an error message saying ‘Error connecting to USGS’, double check your Internet connection and the input parameters above.)
#create a Pandas Series object from the response
a = []
b = []
values = response.timeSeries.values.value
for v in values:
a.append(float(v.value))
b.append(v._dateTime)
ts = Series(a, index=b)
#print the site’s minimum value and datetime of occurrence to the console
print "Minimum streamflow was %s cfs on %s" % (ts.min(), ts.idxmin())
#(this should produce the following output: Minimum streamflow was 81.0 cfs on 2014-11-03 16:30:00)
