"""

Utilities for working with PRMS data or other functionality that aren't

appropriate to put elsewhere at this time.

"""

import numpy as np

import pandas as pd

def load_statvar(statvar_file):

"""

Read the statvar file and load into a datetime indexed

Pandas dataframe object

Arguments:

statvar_file (str): statvar file path

Returns:

(pandas.DataFrame) Pandas DataFrame of PRMS variables date indexed

from statvar file

"""

# make list of statistical output variables for df header

column_list = ['index',

'year',

'month',

'day',

'hh',

'mm',

'sec']

# append to header list the variables present in the file

with open(statvar_file, 'r') as inf:

for idx, l in enumerate(inf):

# first line is always number of stat variables

if idx == 0:

n_statvars = int(l)

elif idx <= n_statvars and idx != 0:

column_list.append(l.rstrip().replace(' ', '_'))

else:

break

# arguments for read_csv function

missing_value = -999

skiprows = n_statvars+1

df = pd.read_csv(

statvar_file, delim_whitespace=True, skiprows=skiprows,

header=-1, na_values=[missing_value]

)

# apply correct header names using metadata retrieved from file

df.columns = column_list

date = pd.Series(

pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'),

index=df.index

)

# make the df index the datetime for the time series data

df.index = pd.to_datetime(date)

# drop unneeded columns

df.drop(['index', 'year', 'month', 'day', 'hh', 'mm', 'sec'],

axis=1, inplace=True)

# name dataframe axes (index,columns)

df.columns.name = 'statistical_variables'

df.index.name = 'date'

return df

def load_data_file(data_file):

"""

Read the data file and load into a datetime indexed Pandas dataframe object

Arguments:

data_file (string): data file path

Returns:

df (pandas.DataFrame): Pandas dataframe of input time series data from data file with datetime index

"""

# valid input time series that can be put into a data file

valid_input_variables = ('gate_ht',

'humidity',

'lake_elev',

'pan_evap',

'precip',

'rain_day',

'runoff',

'snowdepth',

'solrad',

'tmax',

'tmin',

'wind_speed')

# starting list of names for header in dataframe

column_list = ['year',

'month',

'day',

'hh',

'mm',

'sec']

# append to header list the variables present in the file

with open(data_file, 'r') as inf:

for idx, l in enumerate(inf):

# first line always string identifier of the file- may use later

if idx == 0:

data_head = l.rstrip()

elif l.startswith('/'): # comment lines

continue

# header lines with name and number of input variables

if l.startswith(valid_input_variables):

# split line into list, first element name and

# second number of columns

h = l.split()

# more than one input time series of a particular variable

if int(h[1]) > 1:

for el in range(int(h[1])):

tmp = '{var_name}_{var_ind}'.format(var_name=h[0],

var_ind=el+1)

column_list.append(tmp)

elif int(h[1]) == 1:

column_list.append(h[0])

# end of header info and begin time series input data

if l.startswith('#'):

skip_line = idx+1

break

# read data file into pandas dataframe object with correct header names

missing_value = -999 # missing data representation

df = pd.read_csv(data_file, header=-1, skiprows=skip_line,

delim_whitespace=True, na_values=[missing_value])

# apply correct header names using metadata retrieved from file

df.columns = column_list

# create date column

date = pd.Series(

pd.to_datetime(df.year*10000+df.month*100+df.day, format='%Y%m%d'),

index=df.index

)

df.index = pd.to_datetime(date) # make the df index the datetime

# drop unneeded columns

df.drop(['year', 'month', 'day', 'hh', 'mm', 'sec'], axis=1, inplace=True)

df.columns.name = 'input variables'

df.index.name = 'date' # name dataframe axes (index,columns)

return df

def nash_sutcliffe(observed, modeled):

"""

Calculates the Nash-Sutcliffe Goodness-of-fit

Arguments:

observed (numpy.ndarray): historic observational data

modeled (numpy.ndarray): model output with matching time index

"""

numerator = sum((observed - modeled)**2)

denominator = sum((observed - np.mean(observed))**2)

return 1 - (numerator/denominator)