How to use blimpy - 10 common examples

Returns:

    """
    # Load hit details
    dat = find_event.make_table(dat_filename)
    hit = dat.iloc[hit_id]

    f0 = hit['Freq']

    if bw is None:
        bw_mhz = np.abs(hit['FreqStart'] - hit['FreqEnd'])
    else:
        bw_mhz = bw * 1e-6

    fil = Waterfall(fil_filename, f_start=f0 - bw_mhz / 2, f_stop=f0 + bw_mhz / 2)
    t_duration = (fil.n_ints_in_file - 1) * fil.header['tsamp']

    fil.plot_waterfall()
    plot_event.overlay_drift(f0, f0, f0, hit['DriftRate'], t_duration, offset)

def __split_h5(self, size_limit=SIZE_LIM):
        """
        Creates a plan to select data from single coarse channels.
        :param size_limit:      float,              maximum size in MB that the file is allowed to be
        :return:                list[DATAH5],       the list contains a DATAH5 object for each of the coarse channels in
                                                    the file
        """

        data_list = []

        #Instancing file.
        try:
            fil_file = Waterfall(self.filename)
        except:
            logger.error("Error encountered when trying to open file: %s"%self.filename)
            raise IOError("Error encountered when trying to open file: %s"%self.filename)

        #Finding lowest freq in file.
        f_delt = fil_file.header['foff']
        f0 = fil_file.header['fch1']

        #Looping over the number of coarse channels.
        if self.n_coarse_chan is not None:
            n_coarse_chan = self.n_coarse_chan
        elif fil_file.header.get('n_coarse_chan', None) is not None:
            n_coarse_chan = fil_file.header['n_coarse_chan']
        else:
            n_coarse_chan = int(fil_file.calc_n_coarse_chan())

def __make_h5_file(self,):
        """
        Converts file to h5 format. Saves output in current dir. Sets the filename attribute of the calling DATAHandle
        to the (new) filename.
        :return: void
        """

        fil_file = Waterfall(self.filename)
        bn = os.path.basename(self.filename)
        new_filename = os.path.join(self.out_dir, bn.replace('.fil', '.h5'))
        fil_file.write_to_hdf5(new_filename)
        self.filename = new_filename

source_name_list,
                          offset=0,
                          plot_snr_list=False,
                          **kwargs):

    #load in the data for each individual hit
    for i in range(0, len(candidate_event_dataframe)):
        candidate = candidate_event_dataframe.iloc[i]
        on_source_name = candidate['Source']
        f_mid = candidate['Freq']
        drift_rate = candidate['DriftRate']

        #calculate the length of the total cadence from the fil files' headers
        first_fil = bl.Waterfall(fil_file_list[0], load_data=False)
        tfirst = first_fil.header['tstart']
        last_fil = bl.Waterfall(fil_file_list[-1], load_data=False)
        tlast = last_fil.header['tstart']
        t_elapsed = Time(tlast, format='mjd').unix - Time(tfirst, format='mjd').unix + (last_fil.n_ints_in_file -1) * last_fil.header['tsamp']

        #calculate the width of the plot based on making sure the full drift is visible
        bandwidth = 2.4 * abs(drift_rate)/1e6 * t_elapsed
        bandwidth = np.max((bandwidth, 500./1e6))

        #Get start and stop frequencies based on midpoint and bandwidth
        f_start, f_stop = np.sort((f_mid - (bandwidth/2),  f_mid + (bandwidth/2)))

        #Print useful values
        print('')
        print('*************************************************')
        print('***     The Parameters for This Plot Are:    ****')
        print('Target = ', on_source_name)
        print('Bandwidth = ', round(bandwidth, 5), ' MHz')

:param t_stop:          int         stop integration ID
        :param coarse_chan:     int
        :param n_coarse_chan:  int
        """

        self.filename = filename
        self.closed = False
        self.f_start = f_start
        self.f_stop = f_stop
        self.t_start = t_start
        self.t_stop = t_stop
        self.n_coarse_chan = n_coarse_chan

        #Instancing file.
        try:
            self.fil_file = Waterfall(filename,f_start=self.f_start, f_stop=self.f_stop,t_start=self.t_start, t_stop=self.t_stop,load_data=False)
        except:
            logger.error("Error encountered when trying to open file %s"%filename)
            raise IOError("Error encountered when trying to open file %s"%filename)

        #Getting header
        try:
            if self.n_coarse_chan:
                header = self.__make_data_header(self.fil_file.header,coarse=True)
            else:
                header = self.__make_data_header(self.fil_file.header)
        except:
            logger.debug('The fil_file.header is ' % self.fil_file.header)
            raise IOError("Error accessing header from file: %s." % self.filename)

        self.header = header

f_mid = middle frequency of the event, in MHz
        filter_level = filter level (1,2,or 3) that produced the event
        source_name_list = list of source names in the cadence, in order
        bandwidth = width of the plot, incorporating drift info
        kwargs: keyword args to be passed to matplotlib imshow()
    '''

    #prepare for plotting
    matplotlib.rc('font', **font)

    #set up the sub-plots
    n_plots = len(fil_file_list)
    fig = plt.subplots(n_plots, sharex=True, sharey=True,figsize=(10, 2*n_plots))

    #read in data for the first panel
    fil1 = bl.Waterfall(fil_file_list[0], f_start=f_start, f_stop=f_stop)
    t0 = fil1.header['tstart']
    dummy, plot_data1 = fil1.grab_data()

    #rebin data to plot correctly with fewer points
    dec_fac_x, dec_fac_y = 1, 1
    if plot_data1.shape[0] > MAX_IMSHOW_POINTS[0]:
        dec_fac_x = plot_data1.shape[0] / MAX_IMSHOW_POINTS[0]
    if plot_data1.shape[1] > MAX_IMSHOW_POINTS[1]:
        dec_fac_y =  int(np.ceil(plot_data1.shape[1] /  MAX_IMSHOW_POINTS[1]))
    plot_data1 = rebin(plot_data1, dec_fac_x, dec_fac_y)

    #define more plot parameters
    ### never used: delta_f = 0.000250
    mid_f = np.abs(f_start+f_stop)/2.

    subplots = []

fil_file_list,
                          filter_level,
                          source_name_list,
                          offset=0,
                          plot_snr_list=False,
                          **kwargs):

    #load in the data for each individual hit
    for i in range(0, len(candidate_event_dataframe)):
        candidate = candidate_event_dataframe.iloc[i]
        on_source_name = candidate['Source']
        f_mid = candidate['Freq']
        drift_rate = candidate['DriftRate']

        #calculate the length of the total cadence from the fil files' headers
        first_fil = bl.Waterfall(fil_file_list[0], load_data=False)
        tfirst = first_fil.header['tstart']
        last_fil = bl.Waterfall(fil_file_list[-1], load_data=False)
        tlast = last_fil.header['tstart']
        t_elapsed = Time(tlast, format='mjd').unix - Time(tfirst, format='mjd').unix + (last_fil.n_ints_in_file -1) * last_fil.header['tsamp']

        #calculate the width of the plot based on making sure the full drift is visible
        bandwidth = 2.4 * abs(drift_rate)/1e6 * t_elapsed
        bandwidth = np.max((bandwidth, 500./1e6))

        #Get start and stop frequencies based on midpoint and bandwidth
        f_start, f_stop = np.sort((f_mid - (bandwidth/2),  f_mid + (bandwidth/2)))

        #Print useful values
        print('')
        print('*************************************************')
        print('***     The Parameters for This Plot Are:    ****')

def get_info(self):
        """:return:    dict,    header of the blimpy file"""

        fil_file = Waterfall(self.filename,load_data=False)
        return fil_file.header

#prepare font
    matplotlib.rc('font', **font)

    #Load in the data from fil
    plot_f, plot_data = fil.grab_data(f_start=f_start, f_stop=f_stop)

    #Make sure waterfall plot is under 4k*4k
    dec_fac_x, dec_fac_y = 1, 1

    #rebinning data to plot correctly with fewer points
    if plot_data.shape[0] > MAX_IMSHOW_POINTS[0]:
        dec_fac_x = plot_data.shape[0] / MAX_IMSHOW_POINTS[0]
    if plot_data.shape[1] > MAX_IMSHOW_POINTS[1]:
        dec_fac_y =  int(np.ceil(plot_data.shape[1] /  MAX_IMSHOW_POINTS[1]))
    plot_data = rebin(plot_data, dec_fac_x, dec_fac_y)

    #fix case where frequencies are reversed by fil.grab_data() # Shane Smith PR #82
    if plot_f[-1] < plot_f[0]:
        plot_f = plot_f[::-1]

    #determine extent of the plotting panel for imshow
    extent=(plot_f[0], plot_f[-1], (fil.timestamps[-1]-fil.timestamps[0])*24.*60.*60, 0.0)

    #plot and scale intensity (log vs. linear)
    kwargs['cmap'] = kwargs.get('cmap', 'viridis')
    kwargs['logged'] = True
    if kwargs['logged'] == True:
        plot_data = 10*np.log10(plot_data)
        kwargs.pop('logged')

    #get normalization parameters

#set up the sub-plots
    n_plots = len(fil_file_list)
    fig = plt.subplots(n_plots, sharex=True, sharey=True,figsize=(10, 2*n_plots))

    #read in data for the first panel
    fil1 = bl.Waterfall(fil_file_list[0], f_start=f_start, f_stop=f_stop)
    t0 = fil1.header['tstart']
    dummy, plot_data1 = fil1.grab_data()

    #rebin data to plot correctly with fewer points
    dec_fac_x, dec_fac_y = 1, 1
    if plot_data1.shape[0] > MAX_IMSHOW_POINTS[0]:
        dec_fac_x = plot_data1.shape[0] / MAX_IMSHOW_POINTS[0]
    if plot_data1.shape[1] > MAX_IMSHOW_POINTS[1]:
        dec_fac_y =  int(np.ceil(plot_data1.shape[1] /  MAX_IMSHOW_POINTS[1]))
    plot_data1 = rebin(plot_data1, dec_fac_x, dec_fac_y)

    #define more plot parameters
    ### never used: delta_f = 0.000250
    mid_f = np.abs(f_start+f_stop)/2.

    subplots = []

    #Fill in each subplot for the full plot
    for i,filename in enumerate(fil_file_list):
        #identify panel
        subplot = plt.subplot(n_plots,1,i+1)
        subplots.append(subplot)

        #read in data
        fil = bl.Waterfall(filename, f_start=f_start, f_stop=f_stop)
        #make plot with plot_waterfall