Custom Script

A basic approach is a custom script that generates a database according to the Cinema Specifications, CinemaScience Specifications. This approach may be appropriate for both simulation and experimental data, run statistics, or other already extant datasets. Taking in one CDB, performing analysis and outputting an updated CDB is a common workflow.

Any programming language can be used but here we demonstrate a pandas dataframe approach. In this pseudo-script, an input CSV file is read in, manipulated, has a final image FILE column added and then is written out to a CDB. Along the way, the necessary directories are created and the images are moved over from an input directory to the data/image directory.

#! /usr/bin/env python
# Psuedo code to generate a custom Cinema Database

import sys, os
import pandas as pd

###############################################
# writes the data frame to the CDB data.csv file
################################################
def write_data(fname, df):
    sys.stderr.write ("Writing Cinema database data.csv...\n")

    with open(fname, "w") as output:
                df.to_csv(fname, mode='w', sep=',' , index=False)
################################################

# get database parameters from sys.argv
runParams = [180101, 00]   # Default run params
if len(sys.argv) == 3:
    runParams[0] = int( float(sys.argv[1]) )
    runParams[1] = int( float(sys.argv[2]) )
else:
    sys.stderr.write ( ("Warning, no run information, using defaults: {};{}\n").format(runParams[0],runParams[1]) )

# Setup CDB directory structure and default image for file
sys.stderr.write ( 'Run Params: {}_{}\n'.format(runParams[0],runParams[1]))
sys.stderr.write ( 'Creating Cinema Database directory structure ...\n' )
inputDir = 'input/'
dataDir  = 'data/run_{}_{}.cdb'.format(runParams[0],runParams[1] )
imageDir = dataDir+'/image'
defaultFILEimg = 'cinemaNoFILE.png'

# check for dataDir; copy default File img to image directory
os.makedirs(dataDir, exist_ok=True)
os.system('/bin/cp ' + defaultFILEimg +' '+ imageDir+'/.')

# Read in the already-existing dataset
sys.stderr.write ("Reading in datafile file...\n")
inputFile = inputDir + 'run_{}_{}.csv'.format(runParams[0],runParams[1] )
dfRun = pd.read_csv(inputFile, sep=',')

#  manipulate the dfRun dataframe:
sys.stderr.write ("Analysis and Image File Code Block...\n")
dfRun['newVar'] = dfRun['var1']/dfRun['var2']    # perform some analysis
if 'uselessProperty' in dfRun.columns:           # delete a column
    dfRun = dfRun.drop(['uselessProperty'], axis =1)
# ...etc...
dfRun['FILE'] = defaultFILEimg                   # add the final FILE column for images
for index, row in dfRun.iterrows():              # select an image to show for that row
    thisValue = row['newVar']
    thisImage = inputDir + 'run_{}_{}_{}.png'.format(thisValue, runParams[0],runParams[1])
    if os.path.isfile(thisImage):                # check if it exists and if so
         row['FILE'] = imageDir + thisImage      # set the FILE variable to that image
         os.system('/bin/mv ' + inputDir+thisImage +' '+ imageDir+'/.')  # and move it to the image directory

# Write out the dataframe to a Cinema database data.csv
datafilename = dataDir+'/data.csv'
write_data( datafilename, dfRun)

Alternately, one can use a text editor or spreadsheet program to generate the necessary CSV files.