Data from the paper: Optical Variability of AGNs in the PTF/iPTF Survey (2017ApJ...834..111C)

Data file: https://epyc.astro.washington.edu/~ncaplar/Data.tar.gz

Short description:

When you un-tar the Data file, you will will presented with directories, contaning light-curves from each chip. Each chip directory contains *.csv files with the light curves of the individual objects. The name of each file is InternalIdentificationNumber_RA_DEC.csv. Note also that this is not the full dataset (up to April 2015), as the time period covered here is until Feb 01 2015

Each file has a following structure:

First line: 1. Redshift, 2. Luminosity, 3. Mass, 4. Ra, 5.Dec

All subsequent lines: 1. PTF field, 2. number of the observation*, 3. date**, 4. re-calibrated magnitude***, 5. re-calibrated error***, 6. instrumental magnitude****, 7. error****, 8. zeropoint with which to correct instrumental magnitude****

*counted backwards (i.e., the latest observation is number 1)
**date in the restframe i.e., the observational date already divided by (1+z), where z is redshift. If interested in actual observational date multiply this value by (1+z) to get MJD date of the observation
*** Re-calibration from Caplar+ 2016
**** From the initial PTF catalogue, Ofek+ 2012

Importing the data:

In order to import the light-curves from *.csv files I recommend something like

    single_LC=[]
    with open(<path to the *.csv file>) as csvDataFile:
        csvReader = csv.reader(csvDataFile)
        for row in csvReader:
            if len(row)==5:
                single_LC_physical_parameters=np.array(row).astype(float)
            if len(row)>5:
                single_LC.append(np.array(row).astype(float))

    single_LC=np.array(single_LC)

This will then give you single_LC array containg all the observed data, while the physical parameters are contained in separate single_LC_physical_parameters array. As an example, in order to see the data, with the x-axis showing the observation dates, and y-axis showing observed magnitudes, we could do:

single_LC_original_time=np.copy(single_LC)
single_LC_original_time[:,2]=single_LC_original_time[:,2]*(1+single_LC_physical_parameters[0])

plt.figure(figsize=(20,6))
plt.errorbar(single_LC_original_time[:,2],single_LC_original_time[:,3]+np.median(single_LC_original_time[:,-1]),yerr=single_LC_original_time[:,4],ls='',fmt='o')
plt.ylabel('magnitude',fontsize=18)
plt.xlabel('MJD',fontsize=18)
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)

As discussed in the paper, the calibration done in the paper is relative and in order to study variablity one should only considered the re-calibrated magnitudes (in the 3rd columns of the dataset), as we did not use zeropoints in order to do absolute calibration. On the plot above I have added median value of zeropoints, determined in Ofek et al. 2012, in order to get roughly correct normalization of the mangitudes for the plotting purposes.

Help:

For problems with the data or if something is not clear, send us an email.