def __init__(self, sunCalc_dict, save_location):
self.sunCalc_dict = sunCalc_dict
self.sunCalc_location = SC(location = sunCalc_dict)
def __init__(self, sunCalc_dict, save_location):
self.sunCalc_dict = sunCalc_dict
self.sunCalc_location = SC(location=sunCalc_dict)
class WeatherToCSV(object):
def __init__(self, sunCalc_dict, save_location):
self.sunCalc_dict = sunCalc_dict
self.sunCalc_location = SC(location = sunCalc_dict)
def serverConnect(self, user, password, server):
#general function to connect to a server
self.connection = pymssql.connect(server, user, password)
self.cursor = self.connection.cursor()
def serverDisconnect(self):
self.connection.close()
def convertToUTC(self,timeString,sunCalc_object):
#function that use the timezone in the sunCalc object to convert to UTC time
converted_date = datetime.strptime(timeString, '%m/%d/%y %I:%M')
converted_date = converted_date + timedelta(hours = -sunCalc_object.TimeZone)
return datetime.strftime(converted_date,'%m/%d/%y %H:%M')
def pullMinuteData(self,date_start,date_end,database,DB_location):
#function that pulls weather data from a database on the loaded server
#you specify a location (a column in the database) and a date range to pull from
#data will then be converted to hourly data and set a class attribute in lists
#write the attributes
self.date_start = date_start
self.date_end = date_end
self.database = database
self.DB_location = DB_location
date_start_UTC = self.convertToUTC(date_start,self.sunCalc_location)
date_end_UTC = self.convertToUTC(date_end,self.sunCalc_location)
sqlQuery = """select TimeStamp, GlobalSolar_Avg, DiffuseSolar_Avg, AirTemp_C_Avg, RH_Avg, WindSpd_ms_WVc1, WindSpd_ms_WVc2 from %s
where Location ='%s' and TimeStamp >= '%s' and TimeStamp <= '%s' order by timestamp asc""" % (database, DB_location, date_start_UTC,date_end_UTC)
self.cursor.execute(sqlQuery)
#temp storage
self.timestamp = []
self.GHI = []
self.Diff = []
self.Tamb = []
self.RH = []
self.Wspd = []
self.Wdir = []
#store from curson
for row in self.cursor:
self.timestamp.append(row[0] + timedelta(hours = self.sunCalc_location.TimeZone)) #convert back to local time
self.GHI.append(row[1])
self.Diff.append(row[2])
self.Tamb.append(row[3])
self.RH.append(row[4])
self.Wspd.append(row[5])
self.Wdir.append(row[6])
def constructBetweenDates(self):
#essentially a test function
#makes a weather file between the dates you added to view data
#will not work with SAM unless you give it a years worth of data
#please use constructYear for data sets with less than a years worth of data
#algorthim for converting 5 minute date to hourly averages
#need numpy arrays in column format (need to transpose the list)
#making sure not to modify self.timestamp
timestamp = np.array(self.timestamp)
timestamp = np.transpose(timestamp)
#create a 2d array 6 columns by x rows
min_matrix = np.array([self.GHI,self.Diff,self.Tamb,self.RH,self.Wspd,self.Wdir])
min_matrix = np.transpose(min_matrix)
#create data fram and set my index to my time numpy array
min_matrix = pd.DataFrame(min_matrix,index = timestamp, columns= ['GHI','Diff','Tamb','RH','Wspd','Wdir'])
#average the 5 minute data by taking the mean over an hour (very flexible)
hour_matrix = min_matrix.resample('H', how='mean')
#pull data from pandas DataFrame Object
datetime_list = hour_matrix.index.to_pydatetime()
GHI_list = [float(item) for item in hour_matrix['GHI'].get_values()]
Diff_list = [float(item) for item in hour_matrix['Diff'].get_values()]
Tamb_list = [float(item) for item in hour_matrix['Tamb'].get_values()]
RH_list = [float(item) for item in hour_matrix['RH'].get_values()]
Wspd_list = [float(item) for item in hour_matrix['Wspd'].get_values()]
Wdir_list = [float(item) for item in hour_matrix['Wdir'].get_values()]
#storage for writing to csv file
self.weather = []
pressure = 1013.25 #in mBar (sea-level)
###inputs for csv will be [Year, Month,Day,Hour,GHI,DNI,DHI,Tdry,Twet,RH,PRES,Wspd,Wdir,Albedo]
for i in range(len(datetime_list)):
info = self.sunCalc_location.point_calc(dt1 = ephem.Date(datetime_list[i]))
#from Wet-Bulb Temperature from Relative Humidity and Air Temperature by Roland Stull
#valid for 101.325 kPa (doesnt account for elevation)
wet_temp = Tamb_list[i]*np.arctan(0.151977*(RH_list[i]+8.313659)**(1/2)) + np.arctan(RH_list[i]+Tamb_list[i]) \
- np.arctan(RH_list[i]-1.6767331) + .00391838*(RH_list[i]**(3/2))*np.arctan(.023101*RH_list[i]) - 4.686035
if info[2]<0 or info[3]<-70 or info[3]>70:
self.weather.append([datetime_list[i].year, datetime_list[i].month,datetime_list[i].day, \
datetime_list[i].hour,0,0,0,Tamb_list[i],wet_temp,RH_list[i], pressure, \
Wspd_list[i], Wdir_list[i],self.sunCalc_dict['Albedo']])
else:
Ze = 90-info[2]
DNI = (GHI_list[i]-Diff_list[i])/np.cos(np.radians(Ze))
self.weather.append([datetime_list[i].year, datetime_list[i].month, datetime_list[i].day, \
datetime_list[i].hour, GHI_list[i], DNI, Diff_list[i], Tamb_list[i], wet_temp, \
RH_list[i], pressure, Wspd_list[i], Wdir_list[i], self.sunCalc_dict['Albedo']])
#open csv writer, write the headers
save_date_start = datetime.strptime(self.date_start, '%m/%d/%y %I:%M')
save_date_start = datetime.strftime(save_date_start,'%m%d%y%H')
save_date_end = datetime.strptime(self.date_end, '%m/%d/%y %I:%M')
save_date_end = datetime.strftime(save_date_end,'%m%d%y%H')
file_name = r"%s(%s)_(%s - %s).csv" % (self.sunCalc_dict['Name'],self.DB_location,save_date_start,save_date_end)
print file_name
header1 = ['Source','Location ID', 'City','State','Country','Latitude','Longitude','Time Zone', 'Elevation']
header1data = [self.sunCalc_dict['Source'],self.sunCalc_dict['LocationID'],self.sunCalc_dict['Name'], \
self.sunCalc_dict['State'], self.sunCalc_dict['Country'],self.sunCalc_dict['latitude'], \
self.sunCalc_dict['longitude'], self.sunCalc_dict['TimeZone'], self.sunCalc_dict['Elevation']]
header2 = ['Year', 'Month','Day','Hour','GHI','DNI','DHI','Tdry','Twet','RH','Pres','Wspd','Wdir','Albedo']
with open(file_name,'w') as f:
csv_write = csv.writer(f,lineterminator = '\n',delimiter = ',')
csv_write.writerow(header1)
csv_write.writerow(header1data)
csv_write.writerow(header2)
for row in self.weather:
csv_write.writerow(row)
def constructYear(self):
#smartly constructs a yearly weather file for NREL SAM simulations
#check to see if the data given exceeds a 1 year range (i.e. from 2013-2014)
#this is maintain compliance with SAM
upperBound = datetime.strptime(self.date_start, '%m/%d/%y %I:%M')
lowerBound = datetime.strptime(self.date_end, '%m/%d/%y %I:%M')
if upperBound.year == lowerBound.year:
#algorthim for converting 5 minute date to hourly averages
#need numpy arrays in column format (need to transpose the list)
#making sure not to modify self.timestamp
timestamp = np.array(self.timestamp)
timestamp = np.transpose(timestamp)
#create a 2d array 6 columns by x rows
min_matrix = np.array([self.GHI,self.Diff,self.Tamb,self.RH,self.Wspd,self.Wdir])
min_matrix = np.transpose(min_matrix)
#create data fram and set my index to my time numpy array
min_matrix = pd.DataFrame(min_matrix,index = timestamp, columns= ['GHI','Diff','Tamb','RH','Wspd','Wdir'])
#average the 5 minute data by taking the mean over an hour (very flexible)
hour_matrix = min_matrix.resample('H', how='mean')
#fill in empty data for the year from which you are collecting
#doesn't matter which you choose and lower and upper are equal in this case
year_value = upperBound.year
stringLower = '%s-01-01 00:00:00' % (year_value)
stringUpper = '%s-12-31 23:00:00' % (year_value)
idx = pd.date_range(stringLower,stringUpper, freq = 'H')
idx = pd.DatetimeIndex(idx)
year_matrix = hour_matrix.reindex(index=idx, fill_value=0)
#pull data from pandas DataFrame Object
datetime_list = year_matrix.index.to_pydatetime()
GHI_list = [float(item) for item in year_matrix['GHI'].get_values()]
Diff_list = [float(item) for item in year_matrix['Diff'].get_values()]
Tamb_list = [float(item) for item in year_matrix['Tamb'].get_values()]
RH_list = [float(item) for item in year_matrix['RH'].get_values()]
Wspd_list = [float(item) for item in year_matrix['Wspd'].get_values()]
Wdir_list = [float(item) for item in year_matrix['Wdir'].get_values()]
#storage for writing to csv file
self.weather = []
pressure = 1013.25 #in mBar (sea-level)
###inputs for csv will be [Year, Month,Day,Hour,GHI,DNI,DHI,Tdry,Twet,RH,PRES,Wspd,Wdir,Albedo]
for i in range(len(datetime_list)):
info = self.sunCalc_location.point_calc(dt1 = ephem.Date(datetime_list[i]))
#from Wet-Bulb Temperature from Relative Humidity and Air Temperature by Roland Stull
#valid for 101.325 kPa (doesnt account for elevation)
wet_temp = Tamb_list[i]*np.arctan(0.151977*(RH_list[i]+8.313659)**(1/2)) + np.arctan(RH_list[i]+Tamb_list[i]) \
- np.arctan(RH_list[i]-1.6767331) + .00391838*(RH_list[i]**(3/2))*np.arctan(.023101*RH_list[i]) - 4.686035
if info[2]<0 or info[3]<-70 or info[3]>70:
self.weather.append([datetime_list[i].year, datetime_list[i].month,datetime_list[i].day, \
datetime_list[i].hour,0,0,0,Tamb_list[i],wet_temp,RH_list[i], pressure, \
Wspd_list[i], Wdir_list[i],self.sunCalc_dict['Albedo']])
else:
Ze = 90-info[2]
DNI = (GHI_list[i]-Diff_list[i])/np.cos(np.radians(Ze))
self.weather.append([datetime_list[i].year, datetime_list[i].month, datetime_list[i].day, \
datetime_list[i].hour, GHI_list[i], DNI, Diff_list[i], Tamb_list[i], wet_temp, \
RH_list[i], pressure, Wspd_list[i], Wdir_list[i], self.sunCalc_dict['Albedo']])
file_name = r"%s(%s)_%s.csv" % (self.sunCalc_dict['Name'],self.DB_location,year_value)
print file_name
header1 = ['Source','Location ID', 'City','State','Country','Latitude','Longitude','Time Zone', 'Elevation']
header1data = [self.sunCalc_dict['Source'],self.sunCalc_dict['LocationID'],self.sunCalc_dict['Name'], \
self.sunCalc_dict['State'], self.sunCalc_dict['Country'],self.sunCalc_dict['latitude'], \
self.sunCalc_dict['longitude'], self.sunCalc_dict['TimeZone'], self.sunCalc_dict['Elevation']]
header2 = ['Year', 'Month','Day','Hour','GHI','DNI','DHI','Tdry','Twet','RH','Pres','Wspd','Wdir','Albedo']
with open(file_name,'w') as f:
csv_write = csv.writer(f,lineterminator = '\n',delimiter = ',')
csv_write.writerow(header1)
csv_write.writerow(header1data)
csv_write.writerow(header2)
for row in self.weather:
csv_write.writerow(row)
else:
print "please enter a date range with the same calendar year"
print "This is to insure compliance with NREL SAM weather files"
class WeatherToCSV(object):
def __init__(self, sunCalc_dict, save_location):
self.sunCalc_dict = sunCalc_dict
self.sunCalc_location = SC(location=sunCalc_dict)
def serverConnect(self, user, password, server):
#general function to connect to a server
self.connection = pymssql.connect(server, user, password)
self.cursor = self.connection.cursor()
def serverDisconnect(self):
self.connection.close()
def convertToUTC(self, timeString, sunCalc_object):
#function that use the timezone in the sunCalc object to convert to UTC time
converted_date = datetime.strptime(timeString, '%m/%d/%y %I:%M')
converted_date = converted_date + timedelta(
hours=-sunCalc_object.TimeZone)
return datetime.strftime(converted_date, '%m/%d/%y %H:%M')
def pullMinuteData(self, date_start, date_end, database, DB_location):
#function that pulls weather data from a database on the loaded server
#you specify a location (a column in the database) and a date range to pull from
#data will then be converted to hourly data and set a class attribute in lists
#write the attributes
self.date_start = date_start
self.date_end = date_end
self.database = database
self.DB_location = DB_location
date_start_UTC = self.convertToUTC(date_start, self.sunCalc_location)
date_end_UTC = self.convertToUTC(date_end, self.sunCalc_location)
sqlQuery = """select TimeStamp, GlobalSolar_Avg, DiffuseSolar_Avg, AirTemp_C_Avg, RH_Avg, WindSpd_ms_WVc1, WindSpd_ms_WVc2 from %s
where Location ='%s' and TimeStamp >= '%s' and TimeStamp <= '%s' order by timestamp asc""" % (
database, DB_location, date_start_UTC, date_end_UTC)
self.cursor.execute(sqlQuery)
#temp storage
self.timestamp = []
self.GHI = []
self.Diff = []
self.Tamb = []
self.RH = []
self.Wspd = []
self.Wdir = []
#store from curson
for row in self.cursor:
self.timestamp.append(
row[0] + timedelta(hours=self.sunCalc_location.TimeZone)
) #convert back to local time
self.GHI.append(row[1])
self.Diff.append(row[2])
self.Tamb.append(row[3])
self.RH.append(row[4])
self.Wspd.append(row[5])
self.Wdir.append(row[6])
def constructBetweenDates(self):
#essentially a test function
#makes a weather file between the dates you added to view data
#will not work with SAM unless you give it a years worth of data
#please use constructYear for data sets with less than a years worth of data
#algorthim for converting 5 minute date to hourly averages
#need numpy arrays in column format (need to transpose the list)
#making sure not to modify self.timestamp
timestamp = np.array(self.timestamp)
timestamp = np.transpose(timestamp)
#create a 2d array 6 columns by x rows
min_matrix = np.array(
[self.GHI, self.Diff, self.Tamb, self.RH, self.Wspd, self.Wdir])
min_matrix = np.transpose(min_matrix)
#create data fram and set my index to my time numpy array
min_matrix = pd.DataFrame(
min_matrix,
index=timestamp,
columns=['GHI', 'Diff', 'Tamb', 'RH', 'Wspd', 'Wdir'])
#average the 5 minute data by taking the mean over an hour (very flexible)
hour_matrix = min_matrix.resample('H', how='mean')
#pull data from pandas DataFrame Object
datetime_list = hour_matrix.index.to_pydatetime()
GHI_list = [float(item) for item in hour_matrix['GHI'].get_values()]
Diff_list = [float(item) for item in hour_matrix['Diff'].get_values()]
Tamb_list = [float(item) for item in hour_matrix['Tamb'].get_values()]
RH_list = [float(item) for item in hour_matrix['RH'].get_values()]
Wspd_list = [float(item) for item in hour_matrix['Wspd'].get_values()]
Wdir_list = [float(item) for item in hour_matrix['Wdir'].get_values()]
#storage for writing to csv file
self.weather = []
pressure = 1013.25 #in mBar (sea-level)
###inputs for csv will be [Year, Month,Day,Hour,GHI,DNI,DHI,Tdry,Twet,RH,PRES,Wspd,Wdir,Albedo]
for i in range(len(datetime_list)):
info = self.sunCalc_location.point_calc(
dt1=ephem.Date(datetime_list[i]))
#from Wet-Bulb Temperature from Relative Humidity and Air Temperature by Roland Stull
#valid for 101.325 kPa (doesnt account for elevation)
wet_temp = Tamb_list[i]*np.arctan(0.151977*(RH_list[i]+8.313659)**(1/2)) + np.arctan(RH_list[i]+Tamb_list[i]) \
- np.arctan(RH_list[i]-1.6767331) + .00391838*(RH_list[i]**(3/2))*np.arctan(.023101*RH_list[i]) - 4.686035
if info[2] < 0 or info[3] < -70 or info[3] > 70:
self.weather.append([datetime_list[i].year, datetime_list[i].month,datetime_list[i].day, \
datetime_list[i].hour,0,0,0,Tamb_list[i],wet_temp,RH_list[i], pressure, \
Wspd_list[i], Wdir_list[i],self.sunCalc_dict['Albedo']])
else:
Ze = 90 - info[2]
DNI = (GHI_list[i] - Diff_list[i]) / np.cos(np.radians(Ze))
self.weather.append([datetime_list[i].year, datetime_list[i].month, datetime_list[i].day, \
datetime_list[i].hour, GHI_list[i], DNI, Diff_list[i], Tamb_list[i], wet_temp, \
RH_list[i], pressure, Wspd_list[i], Wdir_list[i], self.sunCalc_dict['Albedo']])
#open csv writer, write the headers
save_date_start = datetime.strptime(self.date_start, '%m/%d/%y %I:%M')
save_date_start = datetime.strftime(save_date_start, '%m%d%y%H')
save_date_end = datetime.strptime(self.date_end, '%m/%d/%y %I:%M')
save_date_end = datetime.strftime(save_date_end, '%m%d%y%H')
file_name = r"%s(%s)_(%s - %s).csv" % (self.sunCalc_dict['Name'],
self.DB_location,
save_date_start, save_date_end)
print file_name
header1 = [
'Source', 'Location ID', 'City', 'State', 'Country', 'Latitude',
'Longitude', 'Time Zone', 'Elevation'
]
header1data = [self.sunCalc_dict['Source'],self.sunCalc_dict['LocationID'],self.sunCalc_dict['Name'], \
self.sunCalc_dict['State'], self.sunCalc_dict['Country'],self.sunCalc_dict['latitude'], \
self.sunCalc_dict['longitude'], self.sunCalc_dict['TimeZone'], self.sunCalc_dict['Elevation']]
header2 = [
'Year', 'Month', 'Day', 'Hour', 'GHI', 'DNI', 'DHI', 'Tdry',
'Twet', 'RH', 'Pres', 'Wspd', 'Wdir', 'Albedo'
]
with open(file_name, 'w') as f:
csv_write = csv.writer(f, lineterminator='\n', delimiter=',')
csv_write.writerow(header1)
csv_write.writerow(header1data)
csv_write.writerow(header2)
for row in self.weather:
csv_write.writerow(row)
def constructYear(self):
#smartly constructs a yearly weather file for NREL SAM simulations
#check to see if the data given exceeds a 1 year range (i.e. from 2013-2014)
#this is maintain compliance with SAM
upperBound = datetime.strptime(self.date_start, '%m/%d/%y %I:%M')
lowerBound = datetime.strptime(self.date_end, '%m/%d/%y %I:%M')
if upperBound.year == lowerBound.year:
#algorthim for converting 5 minute date to hourly averages
#need numpy arrays in column format (need to transpose the list)
#making sure not to modify self.timestamp
timestamp = np.array(self.timestamp)
timestamp = np.transpose(timestamp)
#create a 2d array 6 columns by x rows
min_matrix = np.array([
self.GHI, self.Diff, self.Tamb, self.RH, self.Wspd, self.Wdir
])
min_matrix = np.transpose(min_matrix)
#create data fram and set my index to my time numpy array
min_matrix = pd.DataFrame(
min_matrix,
index=timestamp,
columns=['GHI', 'Diff', 'Tamb', 'RH', 'Wspd', 'Wdir'])
#average the 5 minute data by taking the mean over an hour (very flexible)
hour_matrix = min_matrix.resample('H', how='mean')
#fill in empty data for the year from which you are collecting
#doesn't matter which you choose and lower and upper are equal in this case
year_value = upperBound.year
stringLower = '%s-01-01 00:00:00' % (year_value)
stringUpper = '%s-12-31 23:00:00' % (year_value)
idx = pd.date_range(stringLower, stringUpper, freq='H')
idx = pd.DatetimeIndex(idx)
year_matrix = hour_matrix.reindex(index=idx, fill_value=0)
#pull data from pandas DataFrame Object
datetime_list = year_matrix.index.to_pydatetime()
GHI_list = [
float(item) for item in year_matrix['GHI'].get_values()
]
Diff_list = [
float(item) for item in year_matrix['Diff'].get_values()
]
Tamb_list = [
float(item) for item in year_matrix['Tamb'].get_values()
]
RH_list = [float(item) for item in year_matrix['RH'].get_values()]
Wspd_list = [
float(item) for item in year_matrix['Wspd'].get_values()
]
Wdir_list = [
float(item) for item in year_matrix['Wdir'].get_values()
]
#storage for writing to csv file
self.weather = []
pressure = 1013.25 #in mBar (sea-level)
###inputs for csv will be [Year, Month,Day,Hour,GHI,DNI,DHI,Tdry,Twet,RH,PRES,Wspd,Wdir,Albedo]
for i in range(len(datetime_list)):
info = self.sunCalc_location.point_calc(
dt1=ephem.Date(datetime_list[i]))
#from Wet-Bulb Temperature from Relative Humidity and Air Temperature by Roland Stull
#valid for 101.325 kPa (doesnt account for elevation)
wet_temp = Tamb_list[i]*np.arctan(0.151977*(RH_list[i]+8.313659)**(1/2)) + np.arctan(RH_list[i]+Tamb_list[i]) \
- np.arctan(RH_list[i]-1.6767331) + .00391838*(RH_list[i]**(3/2))*np.arctan(.023101*RH_list[i]) - 4.686035
if info[2] < 0 or info[3] < -70 or info[3] > 70:
self.weather.append([datetime_list[i].year, datetime_list[i].month,datetime_list[i].day, \
datetime_list[i].hour,0,0,0,Tamb_list[i],wet_temp,RH_list[i], pressure, \
Wspd_list[i], Wdir_list[i],self.sunCalc_dict['Albedo']])
else:
Ze = 90 - info[2]
DNI = (GHI_list[i] - Diff_list[i]) / np.cos(np.radians(Ze))
self.weather.append([datetime_list[i].year, datetime_list[i].month, datetime_list[i].day, \
datetime_list[i].hour, GHI_list[i], DNI, Diff_list[i], Tamb_list[i], wet_temp, \
RH_list[i], pressure, Wspd_list[i], Wdir_list[i], self.sunCalc_dict['Albedo']])
file_name = r"%s(%s)_%s.csv" % (self.sunCalc_dict['Name'],
self.DB_location, year_value)
print file_name
header1 = [
'Source', 'Location ID', 'City', 'State', 'Country',
'Latitude', 'Longitude', 'Time Zone', 'Elevation'
]
header1data = [self.sunCalc_dict['Source'],self.sunCalc_dict['LocationID'],self.sunCalc_dict['Name'], \
self.sunCalc_dict['State'], self.sunCalc_dict['Country'],self.sunCalc_dict['latitude'], \
self.sunCalc_dict['longitude'], self.sunCalc_dict['TimeZone'], self.sunCalc_dict['Elevation']]
header2 = [
'Year', 'Month', 'Day', 'Hour', 'GHI', 'DNI', 'DHI', 'Tdry',
'Twet', 'RH', 'Pres', 'Wspd', 'Wdir', 'Albedo'
]
with open(file_name, 'w') as f:
csv_write = csv.writer(f, lineterminator='\n', delimiter=',')
csv_write.writerow(header1)
csv_write.writerow(header1data)
csv_write.writerow(header2)
for row in self.weather:
csv_write.writerow(row)
else:
print "please enter a date range with the same calendar year"
print "This is to insure compliance with NREL SAM weather files"