pairplot.py

import matplotlib.pyplot as plt
from astropy.table import Table
import numpy as np
import seaborn as sns
import pandas as pd
from scipy.stats import kendalltau

sns.set(        )

import fnmatch
import os
import os.path
import sys

def main():
    ann_metadata = pd.read_csv(sys.argv[1])

    if len(sys.argv) == 3:
        deltas = pd.read_csv(sys.argv[2], sep=" ") #, header=0, names=["ID","EXP_ID","RF_DELTA","KNN_DELTA","AVG_DELTA"])
        '''
        print ann_metadata.columns
        print ""
        print deltas.columns
        print ""
        '''

        ann_metadata = pd.merge(ann_metadata, deltas, how='left', on=['EXP_ID'])
        '''
        ann_metadata["AVG_5K_FLUX"] = 0

        path = '.'
        for file in os.listdir(path):
            if fnmatch.fnmatch(file, sys.argv[2]):
                data = Table.read(os.path.join(path, file), format="ascii.csv")
                mask = (data['ivar'] == 0)
                total_flux = data['flux']+data['con_flux']
                avg_5k_flux = np.average(total_flux[1518:1558], weights=data['ivar'][1518:1558])

                file_split = file.split("-")
                plate = file_split[0][12:]
                mjd = file_split[1]
                exp = file_split[2][3:]
                index_val = ann_metadata[(ann_metadata["PLATE"]==int(plate)) &
                            (ann_metadata["MJD"]==int(mjd)) &
                            (ann_metadata["EXP_ID"]==int(exp))].index
                ann_metadata.loc[index_val,"AVG_5K_FLUX"] = avg_5k_flux
            '''
    '''
    del ann_metadata["TAI-END"]
    del ann_metadata["TAI-BEG"]
    del ann_metadata["PLATE"]
    del ann_metadata["MJD"]
    del ann_metadata["EXP_ID"]
    '''

    hue=None
    if len(sys.argv) == 3:
        #ann_metadata = ann_metadata[ann_metadata["AVG_5K_FLUX"]!=0]
        ann_metadata = ann_metadata.dropna()
        ann_metadata["RF_DELTA"] = np.sqrt(ann_metadata["RF_DELTA"])
        #hue="AVG_5K_FLUX"

    #print ann_metadata

    '''
    g = sns.pairplot(ann_metadata, vars={"LUNAR_MAGNITUDE", "LUNAR_ELV",
        "LUNAR_SEP", "SOLAR_ELV", "SOLAR_SEP"}, hue=hue)
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"LUNAR_MAGNITUDE", "LUNAR_ELV",
        "LUNAR_SEP"}, hue=hue)
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"SOLAR_ELV", "SOLAR_SEP", "SS_AREA", "SS_COUNT"})
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"AIRMASS", "GALACTIC_CORE_SEP",
        "GALACTIC_PLANE_SEP", "AVG_5K_FLUX"})
    plt.show()
    plt.close()

    g = sns.jointplot(ann_metadata["ALT"], ann_metadata["AIRMASS"], stat_func=kendalltau)
    plt.show()
    plt.close()

    g = sns.jointplot(ann_metadata["ALT"], ann_metadata["LUNAR_SEP"], stat_func=kendalltau)
    plt.show()
    plt.close()
    '''

    g = sns.pairplot(ann_metadata, vars={"LUNAR_MAGNITUDE", "LUNAR_ELV", "LUNAR_SEP", "RF_DELTA"})
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"SOLAR_ELV", "SOLAR_SEP", "SS_AREA", "SS_COUNT", "RF_DELTA"})
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"AIRMASS", "GALACTIC_CORE_SEP", "GALACTIC_PLANE_SEP", "RF_DELTA"})
    plt.show()
    plt.close()

    g = sns.pairplot(ann_metadata, vars={"RA", "DEC", "ALT", "AZ", "RF_DELTA"})
    plt.show()
    plt.close()


if __name__ == '__main__':
    main()