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‎.gitignore‎

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##
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##
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*.pyc
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*~
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## ipython notebook checkpoint folders
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**/.ipynb_checkpoints/
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venv
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*animation*
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test/data/models/lbcd/prms.out
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test/data/models/lbcd/prms_ic.out
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test/data/models/lbcd/statvar.dat
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notebooks/jupyternb-scenario-series-example/
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docs/build
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**/utility_functions.ipynb
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**/scripts/*.data

‎README.md‎

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# PRMS-Python
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A Python module to assist with calibration, data processing and visualization for the Precipitation Runoff Modeling System (PRMS) computer program.
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## Parameter File Tools
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Currently you can do the following, starting from the root directory.
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PRMS-Python provides a Python interface to PRMS data files and for running
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PRMS simulations. This module tries to improve the management of PRMS simulation
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data while also providing useful "pythonic" tools to do scenario-based PRMS
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simulations. By "scenario-based" modeling we mean, for example, parameter
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sensitivity analysis, where each "scenario" is an iterative perturbation of
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one or many parameters. Another example "scenario-based" modeling exercise would
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be climate scenario modeling: what will happen to modeled outputs if the
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input meteorological data were to change?
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```python
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from prms_python import Parameters
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p = Parameters('test/data/parameter')
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# select PRMS parameter by name, raising KeyError if DNE
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snow_adj = p['snow_adj']
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assert snow_adj.shape == (12, 16)
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## Installation
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# assign values to PRMS parameter
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import numpy as np
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z = np.zeros(snow_adj.shape)
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p['snow_adj'] = z # now p['snow_adj'] is 12x16 matrix of zeros
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Currently it's clone-then-pip:
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# write modified parameters to file
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p.write('newparameters')
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```
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git clone https://github.com/northwest-knowledge-network/prms-python
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```
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## Notes
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See the `models` directory for data and the Windows PRMS executable. To run PRMS just run
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then
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```
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prms myrun.control
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pip install -r requirements.txt
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```
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There are more built distributions for Linux and Windows in the `dists` directory.
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A Python module to assist with calibration, data processing and visualization for the Precipitation Runoff Modeling System (PRMS) computer program.
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## Usage
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* load_PRMSstatvar in ipynb in `scripts/statvarfile.ipynb`
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* modifying params in `scripts/change_param.py`
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* input and output paths defined in control file
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Please read the [Online Documentation](https://prms-python.github.io/docs).
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## Unit tests
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I run them using nose but that's not required. From the root repo directory
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```
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nosetests -v
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```

‎news.txt‎

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This file was deleted.

‎notebooks/statvarfile.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},

‎scripts/README.txt‎

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‎scripts/data.py‎

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"""
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Description to come
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"""
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import numpy as np
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import pandas as pd
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import os
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class Data(object):
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"""
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PRMS data object to read, write and modify time
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series input to PRMS that are located in the PRMS data file
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"""
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## data file constant attributes
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date_header = ['year',
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'month',
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'day',
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'hh',
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'mm',
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'sec']
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valid_input_variables = ('gate_ht',
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'humidity',
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'lake_elev',
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'pan_evap',
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'precip',
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'rain_day',
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'runoff',
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'snowdepth',
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'solrad',
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'tmax',
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'tmin',
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'wind_speed')
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def __init__(self, base_file):
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self.base_file = base_file
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self.metadata = self.__load_metadata()
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self.data_frame = self.__load_data()
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def __load_metadata(self):
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"""
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"""
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## valid input time series that can be put into a data file
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#### starting list of names for header in dataframe
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input_data_names = []
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## append to header list the variables present in the file
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with open(self.base_file, 'r') as inf:
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for idx,l in enumerate(inf):
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if idx == 0: ## first line always string identifier of the file- may use later
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data_head = l.rstrip()
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elif l.startswith('/'): ## comment lines
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continue
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if l.startswith(Data.valid_input_variables): ## header lines with name and number of input variables
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h = l.split() ## split line into list, first element name and second number of columns
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if int(h[1]) > 1: ## more than one input time series of a particular variable
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for el in range(int(h[1])):
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tmp = '{var_name} {var_ind}'.format(var_name = h[0], var_ind = el+1)
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input_data_names.append(tmp)
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elif int(h[1]) == 1:
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input_data_names.append(h[0])
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if l.startswith('#'): ## end of header info and begin time series input data
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data_startline = idx+1 ## 0 indexed line of first data entry
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break
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return dict([('data_startline',data_startline), ('data_variables',input_data_names)])
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def __load_data(self):
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missing_value = -999 ## missing data representation
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df = pd.read_csv(self.base_file, header = -1, skiprows = self.metadata['data_startline'],
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delim_whitespace = True, na_values = [missing_value]) ## read file
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df.columns = Data.date_header + self.metadata['data_variables']
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date = pd.Series(pd.to_datetime(df.year * 10000 + df.month * 100 + df.day, format = '%Y%m%d'), index = df.index)
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df.index = pd.to_datetime(date)
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df.drop(Data.date_header, axis = 1, inplace = True) ## unneeded columns
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df.columns.name = 'input variables' ; df.index.name = 'date'
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return df
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def adjust(self, func, vars_to_adjust):
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for v in vars_to_adjust:
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self.data_frame[v] = self.data_frame[v].apply(func)
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def write(self, out_path):
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## reconstruct original datafile format
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self.data_frame['year'] = self.data_frame.index.year
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self.data_frame['month'] = self.data_frame.index.month
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self.data_frame['day'] = self.data_frame.index.day
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self.data_frame['hh'] = self.data_frame['mm'] = self.data_frame['sec'] = 0
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self.data_frame = self.data_frame[Data.date_header + self.metadata['data_variables']]
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with open(out_path,'w') as outf:
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with open(self.base_file) as data:
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for idx, line in enumerate(data):
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if idx == self.metadata['data_startline']:
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self.data_frame.to_csv(outf, sep=' ', header=None, index=False, na_rep=-999)
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break
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outf.write(line) # write line by line the header lines from original
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