ipashchenko

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

ipashchenko

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit


def model1(x, k):
    return k * x

def model2(x, k, a):
    return k * x + x * np.sin(a * x)

ipashchenko

import os
import json
import time
import numpy as np
import scipy as sp
import corner
from uv_data import UVData
from spydiff import import_difmap_model
from model import Model
from stats import LnLikelihood

ipashchenko

def create_mask(shape, region):
    """
    Function that creates rectangular or circular or elliptic gaussian mask.
    
    :param shape:
      Shape of the image.
    :param region:
        Tuple (blc[0], blc[1], trc[0], trc[1],) or (center[0], center[1], r,
        None,) or (center[0], center[1], bmaj, e, bpa). Where ``bpa`` [rad].
        

ipashchenko

from cfx import CFX
from utils import find_file
from raw_data import M5


cfx_file = '/home/ilya/code/frb/data/cfx/RADIOASTRON_RAKS12ER_L_20151105T130000_ASC_V1.cfx'
cfx = CFX(cfx_file)
cfx_data = cfx.parse_cfx('raks12er')
fname = 'rk12er_tr_no0001'
params = cfx_data[fname]

ipashchenko

! final_clean_rms -- revised version of old 'final_clean' script
!
! Version: 2011_05_09
! Change Log:
!    2011_05_09 - limit maximum clean iter in natural clean to 500
!
! Script revised -- May 2011 by D. Homan to improve stopping
! criteria and not overclean images.   See 'deep_map_residual'
! macro below for how this works in detail.  The idea is to
! compare the residual map rms in the frame to a target RMS which

ipashchenko

def clean_difmap(fname, outfname, stokes, mapsize_clean, path=None,
                 path_to_script=None, mapsize_restore=None, beam_restore=None,
                 outpath=None, shift=None, show_difmap_output=False):
    """
    Map self-calibrated uv-data in difmap.
    
    :param fname:
        Filename of uv-data to clean.
    :param outfname:
        Filename with CCs.

ipashchenko

import os
import numpy as np
from components import CGComponent, EGComponent
from from_fits import (create_uvdata_from_fits_file,
                       create_image_from_fits_file,
                       create_clean_image_from_fits_file)
from model import Model
from spydiff import clean_difmap
from data_io import get_fits_image_info
from utils import mas_to_rad, degree_to_rad

ipashchenko

from collections import OrderedDict, Callable

class DefaultOrderedDict(OrderedDict):
    # Source: http://stackoverflow.com/a/6190500/562769
    def __init__(self, default_factory=None, *a, **kw):
        if (default_factory is not None and
           not isinstance(default_factory, Callable)):
            raise TypeError('first argument must be callable')
        OrderedDict.__init__(self, *a, **kw)
        self.default_factory = default_factory

ipashchenko

import numpy as np

vint = np.vectorize(int)
vround = np.vectorize(round)


def de_disperse(image, nu_0, d_nu, d_t, dm_values):
    """
    De-disperse dynamical spestra with grid of user specifies values of DM.