"""

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

import os

def delete_out_files(work_directory, file_name=''):

"""

Delete all output files of a certain name from PRMS simulations,

can be useful since files can be large and may not be being used.

For example initial condition output files are often large and not

always used, alternatively animation files may no longer be needed.

Arguments:

work_directory (str): path to directory with simulation outputs

two directories above where the actual prms_ic.out files exist.

file_name (str) = Name of the PRMS output file(s) to be removed,

default='' empty string- nothing will be deleted.

e.g. if you have several simulation directories:

"test/results/intcp:-26.50_slope:0.49",

"test/results/intcp:-11.68_slope:0.54",

"test/results/intcp:-4.70_slope:0.51",

"test/results/intcp:-35.39_slope:0.39",

"test/results/intcp:-20.91_slope:0.41"

each of these contains an '/outputs' folder with a prms_ic.out

file that you would like to delete. In this case, delete all ic

files like so:

>>> work_dir = 'test/results/'

>>> delete_ic_files(work_dir, file_name='prms_ic.out')

Returns:

None

"""

for fd in os.listdir(work_directory):

if os.path.isdir(os.path.join(work_directory,fd)):

try:

os.remove(os.path.join(work_directory, fd, 'outputs', file_name))

except: # file might not exist

continue

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)

def percent_bias(observed, modeled):

"""

Calculates percent bias

Arguments:

observed (numpy.ndarray): historic observational data

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

"""

return 100 * ( sum( modeled - observed ) / sum( observed ) )

def rmse(observed, modeled):

"""

Calculates root mean squared error

Arguments:

observed (numpy.ndarray): historic observational data

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

"""

return np.sqrt( sum((observed - modeled)**2) / len(observed) )