Reading files¶

In [1]:

import your

your.__version__

Out[1]:

'0.6.5'

Loading data¶

Download and extract a sample filterbank. Here we use the filterbanks containing FRB180417.

In [2]:

import logging

logging_format = "%(asctime)s - %(funcName)s -%(name)s - %(levelname)s - %(message)s"
logging.basicConfig(level=logging.INFO, format=logging_format)

In [3]:

import os
import tempfile
import pylab as plt
from urllib.request import urlretrieve

In [4]:

temp_dir = tempfile.TemporaryDirectory()
download_path = str(temp_dir.name) + "/FRB180417.fil"
url = "https://zenodo.org/record/3905426/files/FRB180417.fil"
urlretrieve(
    url, download_path,
)
fil_file = download_path

Unified Reader¶

We can now read the file using Your as,

In [5]:

your_object = your.Your(fil_file)

Similarly using your we can read

• list of psrfits files,
• single psrfits file,
• single filterbank file.

Unified Header¶

Regardless of the file source, each your object has a unified header which can be accessed as follows. your_header displays all the attributes. These can be accessed as your_object.your_header.attribute, e.g. to get the sampling interval use your_object.your_header.tsamp.

In [6]:

print(your_object.your_header)

Unified Header:
  "basename": "FRB180417"
  "bw": -336.0
  "center_freq": 1297.0
  "dec_deg": 13.9644755659
  "dtype": "uint8"
  "fch1": 1465.0
  "filelist": [
    "/tmp/tmp07hog7ks/FRB180417.fil"
  ]
  "filename": "/tmp/tmp07hog7ks/FRB180417.fil"
  "foff": -1.0
  "format": "fil"
  "frequency_decimation_factor": 1
  "gb": 75.6193324131258
  "gl": 97.96004046706304
  "native_foff": -1.0
  "native_nchans": 336
  "native_nspectra": 5120.0
  "native_tsamp": 0.00126646875
  "nbits": 8
  "nchans": 336
  "npol": 1
  "nspectra": 5120
  "poln_order": "I"
  "ra_deg": 186.656816998
  "source_name": "src1"
  "time_decimation_factor": 1
  "tsamp": 0.00126646875
  "tstart": 58682.620316710374
  "tstart_utc": "2019-07-18T14:53:15.364"

Reading data¶

You can read the data in the file using the get_data method. The output is a numpy array with shape (nsamp, nchans).

In [7]:

data = your_object.get_data(nstart=0, nsamp=4096)
data.shape

Out[7]:

(4096, 336)

And display the data!

In [8]:

plt.figure(figsize=(8, 6))
plt.imshow(data.T, aspect="auto")
plt.xlabel("Time Samples")
plt.ylabel("Frequency Channels")
plt.colorbar()

Out[8]:

<matplotlib.colorbar.Colorbar at 0x7f772d66e190>

Bandpass¶

You can create the bandpass of the entire file or a few specific spectra by using your_object.bandpass

In [9]:

bandpass = your_object.bandpass()

2021-04-25 14:08:00,041 - bandpass -your.your - WARNING - This will read all the data in the RAM. Might be slow as well.

And plot and save the bandpass using the save_bandpass function from your.utils.plotter.

In [10]:

from your.utils.plotter import save_bandpass

In [11]:

save_bandpass(your_object, bandpass)