def process_combined_years_and_data(monthly_dates_and_data, month_list, calculation, start_year, end_year, start_month, end_month, model_name):
date_list = [i[0] for i in monthly_dates_and_data]
data = [i[1] for i in monthly_dates_and_data]
#print date_list
# Process for results
results = process_gcm_data(custom_span=len(month_list), sample_method=calculation, date_list=date_list, data=data, start_year=start_year, end_year=end_year, start_month=month_list[0]+1, end_month=month_list[-1]+1)
# Drop nans
results = results.dropna()
#print results, len(results), "results",
#print "Average for all specified years: ", np.mean(results)
#print results
return model_name, np.mean(results)
def process_combined_years_and_data(monthly_dates_and_data, month_list,
calculation, start_year, end_year,
start_month, end_month, model_name):
date_list = [i[0] for i in monthly_dates_and_data]
data = [i[1] for i in monthly_dates_and_data]
#print date_list
# Process for results
results = process_gcm_data(custom_span=len(month_list),
sample_method=calculation,
date_list=date_list,
data=data,
start_year=start_year,
end_year=end_year,
start_month=month_list[0] + 1,
end_month=month_list[-1] + 1)
# Drop nans
results = results.dropna()
#print results, len(results), "results",
#print "Average for all specified years: ", np.mean(results)
#print results
return model_name, np.mean(results)
def process_data(sw_lat, sw_lon, ne_lon, ne_lat, month_list, start_year, end_year, end_month, start_month, variable, data_path, calculation, model_name):
# File handles
pathname = data_path
filehandle=Dataset(pathname,'r',format="NETCDF4")
#print filehandle.variables
try:
lathandle = filehandle.variables['lat']
except:
lathandle = filehandle.variables['Latitude']
try:
lonhandle=filehandle.variables['lon']
except:
lonhandle=filehandle.variables['Longitude']
try:
timehandle=filehandle.variables['time']
except:
try:
timehandle=filehandle.variables['Time']
except:
timehandle=filehandle.variables['day']
# NetCDF variable
datahandle=filehandle.variables[variable]
time_num=len(timehandle)
timeindex=range(time_num) #python starts arrays at 0
time=timehandle[timeindex]
lat_array = lathandle[:]
lon_array = lonhandle[:]
positive_east_longitude = "True"
# Lons from 0-360 converted to -180 to 180
if positive_east_longitude == "True":
lon_array = [x - 360 for x in lon_array[:]]
# Bounding box indices
sw_lat = index_from_numpy_array(np.array(lat_array), sw_lat)
#print SWLat, lat_array[SWLat]
sw_lon = index_from_numpy_array(np.array(lon_array), sw_lon)
#print SWLong, lon_array[SWLong]
ne_lat = index_from_numpy_array(np.array(lat_array), ne_lat)
#print NELat, lat_array[NELat]
ne_lon = index_from_numpy_array(np.array(lon_array), ne_lon)
#print NELong, lon_array[NELong]
closestLat = slice(sw_lat, ne_lat)
closestLon = slice(sw_lon, ne_lon)
# Which months to get data for
#month_list = [11,0,1]
def get_monthly_dates_and_data(month):
""" Function that get dates and data for a single month from a NetCDF File"""
# Month to get data for
request_month = month
# Dimensions
variable_dimensions = datahandle.dimensions
# Dictionary to map dimension with index value
variable_index_dictionary = {}
# Slice out month data for all years
timeindex = slice(request_month,len(timehandle),12)
#print timeindex
for var in variable_dimensions:
if var == "time" or var == "Time" or var == "day":
variable_index_dictionary[var] = timeindex
if var == "lat" or var == "Latitude":
variable_index_dictionary[var] = closestLat
if var == "lon" or var == "Longitude":
variable_index_dictionary[var] = closestLon
# Dictionary to map the order of the dimensions
variable_dimensions_dictionary = {}
variable_dimensions_dictionary[0] = datahandle.dimensions[0]
variable_dimensions_dictionary[1] = datahandle.dimensions[1]
variable_dimensions_dictionary[2] = datahandle.dimensions[2]
#print variable_dimensions_dictionary
# Get the dates using calculation for now leap years
dates = [get_date_no_leap_year(x) for x in timehandle[timeindex]]
#print dates, len(dates)
# Get the data
data = datahandle[variable_index_dictionary[variable_dimensions_dictionary[0]], variable_index_dictionary[variable_dimensions_dictionary[1]], variable_index_dictionary[variable_dimensions_dictionary[2]]]
#print "Temporal resolution from NetCDF= ", len(data[:])
# Average or sum over spatial subset region
data_spatial_analysis = spatial_subset(data, method="mean")
#print "Spatial subset temporal resolution = ", len(data_spatial_analysis)
dataset = []
for i in range(len(dates)):
dataset.append([dates[i], data_spatial_analysis[i]])
return dataset
# Get and sort monthly dates and data
monthly_dates_and_data = []
for x in month_list:
#print x
monthly_dates_and_data += get_monthly_dates_and_data(x)
monthly_dates_and_data.sort()
#print monthly_dates_and_data
date_list = [i[0] for i in monthly_dates_and_data]
data = [i[1] for i in monthly_dates_and_data]
#print date_list
# Process for results
results = process_gcm_data(custom_span=len(month_list), sample_method=calculation, date_list=date_list, data=data, start_year=start_year, end_year=end_year, start_month=month_list[0]+1, end_month=month_list[-1]+1)
# Drop nans
results = results.dropna()
#print results, len(results), "results",
#print "Average for all specified years: ", np.mean(results)
return model_name, np.mean(results)
def process_data(sw_lat, sw_lon, ne_lon, ne_lat, month_list, start_year,
end_year, end_month, start_month, variable, data_path,
calculation, model_name):
# File handles
pathname = data_path
filehandle = Dataset(pathname, 'r', format="NETCDF4")
#print filehandle.variables
try:
lathandle = filehandle.variables['lat']
except:
lathandle = filehandle.variables['Latitude']
try:
lonhandle = filehandle.variables['lon']
except:
lonhandle = filehandle.variables['Longitude']
try:
timehandle = filehandle.variables['time']
except:
try:
timehandle = filehandle.variables['Time']
except:
timehandle = filehandle.variables['day']
# NetCDF variable
datahandle = filehandle.variables[variable]
time_num = len(timehandle)
timeindex = range(time_num) #python starts arrays at 0
time = timehandle[timeindex]
lat_array = lathandle[:]
lon_array = lonhandle[:]
positive_east_longitude = "True"
# Lons from 0-360 converted to -180 to 180
if positive_east_longitude == "True":
lon_array = [x - 360 for x in lon_array[:]]
# Bounding box indices
sw_lat = index_from_numpy_array(np.array(lat_array), sw_lat)
#print SWLat, lat_array[SWLat]
sw_lon = index_from_numpy_array(np.array(lon_array), sw_lon)
#print SWLong, lon_array[SWLong]
ne_lat = index_from_numpy_array(np.array(lat_array), ne_lat)
#print NELat, lat_array[NELat]
ne_lon = index_from_numpy_array(np.array(lon_array), ne_lon)
#print NELong, lon_array[NELong]
closestLat = slice(sw_lat, ne_lat)
closestLon = slice(sw_lon, ne_lon)
# Which months to get data for
#month_list = [11,0,1]
def get_monthly_dates_and_data(month):
""" Function that get dates and data for a single month from a NetCDF File"""
# Month to get data for
request_month = month
# Dimensions
variable_dimensions = datahandle.dimensions
# Dictionary to map dimension with index value
variable_index_dictionary = {}
# Slice out month data for all years
timeindex = slice(request_month, len(timehandle), 12)
#print timeindex
for var in variable_dimensions:
if var == "time" or var == "Time" or var == "day":
variable_index_dictionary[var] = timeindex
if var == "lat" or var == "Latitude":
variable_index_dictionary[var] = closestLat
if var == "lon" or var == "Longitude":
variable_index_dictionary[var] = closestLon
# Dictionary to map the order of the dimensions
variable_dimensions_dictionary = {}
variable_dimensions_dictionary[0] = datahandle.dimensions[0]
variable_dimensions_dictionary[1] = datahandle.dimensions[1]
variable_dimensions_dictionary[2] = datahandle.dimensions[2]
#print variable_dimensions_dictionary
# Get the dates using calculation for now leap years
dates = [get_date_no_leap_year(x) for x in timehandle[timeindex]]
#print dates, len(dates)
# Get the data
data = datahandle[
variable_index_dictionary[variable_dimensions_dictionary[0]],
variable_index_dictionary[variable_dimensions_dictionary[1]],
variable_index_dictionary[variable_dimensions_dictionary[2]]]
#print "Temporal resolution from NetCDF= ", len(data[:])
# Average or sum over spatial subset region
data_spatial_analysis = spatial_subset(data, method="mean")
#print "Spatial subset temporal resolution = ", len(data_spatial_analysis)
dataset = []
for i in range(len(dates)):
dataset.append([dates[i], data_spatial_analysis[i]])
return dataset
# Get and sort monthly dates and data
monthly_dates_and_data = []
for x in month_list:
#print x
monthly_dates_and_data += get_monthly_dates_and_data(x)
monthly_dates_and_data.sort()
#print monthly_dates_and_data
date_list = [i[0] for i in monthly_dates_and_data]
data = [i[1] for i in monthly_dates_and_data]
#print date_list
# Process for results
results = process_gcm_data(custom_span=len(month_list),
sample_method=calculation,
date_list=date_list,
data=data,
start_year=start_year,
end_year=end_year,
start_month=month_list[0] + 1,
end_month=month_list[-1] + 1)
# Drop nans
results = results.dropna()
#print results, len(results), "results",
#print "Average for all specified years: ", np.mean(results)
return model_name, np.mean(results)