From 95d77ea3bab1332d3b5d555ccf926d7ee7f269f2 Mon Sep 17 00:00:00 2001 From: Alex Binks Date: Wed, 21 Feb 2024 22:10:05 +0100 Subject: [PATCH] First commit in a long time! --- .../__pycache__/VBLinRegARD.cpython-310.pyc | Bin 2700 -> 2700 bytes .../__pycache__/aperture.cpython-310.pyc | Bin 5091 -> 8877 bytes .../__pycache__/contaminants.cpython-310.pyc | Bin 8979 -> 9243 bytes .../__pycache__/detrend_cbv.cpython-310.pyc | Bin 8694 -> 8694 bytes .../fixedconstants.cpython-310.pyc | Bin 574 -> 574 bytes .../__pycache__/lc_analysis.cpython-310.pyc | Bin 25007 -> 28037 bytes .../__pycache__/makeplots.cpython-310.pyc | Bin 8416 -> 8575 bytes .../__pycache__/maketable.cpython-310.pyc | Bin 12224 -> 12556 bytes .../__pycache__/periodogram.cpython-310.pyc | Bin 19054 -> 24649 bytes .../__pycache__/tessilator.cpython-310.pyc | Bin 62714 -> 63394 bytes tessilator/acf_functions.py | 661 + tessilator/aperture.py | 175 +- tessilator/contaminants.py | 75 +- tessilator/detrend_cbv.py | 44 +- 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This is the default pgi-finding function for SpinSpotter. + + Args: + corr (:obj:`arr`): an autocorrelation function. + bs (:obj:`float`): the size of the bins, in units of seconds. + + Returns: + Two parameters. + + - pgi (:obj:`int`): the index in the acf of the initial period guess in units of lags. + - results (:obj:`array`): dictionary of other relevant paramters from the pgi-finding code. + """ + results = {} + + # Calculate the fft + coeff = 10 + corr_fft = np.real(np.fft.rfft(corr,n=coeff*len(corr))) + fft_period = np.divide(1., np.fft.rfftfreq(coeff*len(corr))[1:]) + + # we're only interested in the positive half of the fft + snip = np.where((fft_period >= 0) & (fft_period <= len(corr))) + fft_period = fft_period[snip] + corr_fft = corr_fft[snip] + + # add some results to the results dictionary + results['fft'] = corr_fft + results['fft_period'] = fft_period # period array, in units of index of the acf + results['fft_period_days'] = np.divide(fft_period, (day/bs)) # convert the periods to days + # results['pgi'] = pgi + + # add the prominence of the fft peak + pgi, fft_results = fft_find_peaks(results, plot=False) + results.update(fft_results) + + # if the pgi is more than half the period, automatic fail + if pgi < 0 : + results['fail'] = True + return np.nan, results + elif fft_period[pgi] > len(corr) / 2: + results['fail'] = True + return np.nan, results + + return pgi, results + +def fft_find_peaks(result, plot=False): + """ + Helper function used by `calc_fft_pgi` to calculate the prominence of the max + peak in the fft.Does this by finding the five tallest peaks, then calculates the standard + deviation of the tallest one from the other four. setting plot to True will plot the FFT and + the five found peaks. Currently, returns the number of standard deviations from + the mean that the highest peak is. + + Args: + result (:obj:`dict`):the result dictionary as outputed by calc_fft_pgi(), which contains the fft. + plot (:obj:`bool`): if True, will plot the FFT. + + Returns: + results (:obj:`dict`): the updated result dictionary, which now contains info on the pgi and peaks in the fft. + """ + output = result.copy() + corr_fft = np.real(result['fft']) + fft_period = result['fft_period'] # period array, in units of index of the acf + + # find the five tallest peaks, then see how much taller the tallest is + fft_peaks, fft_properties = find_peaks(corr_fft) + peak_heights = corr_fft[fft_peaks] + + #initialize the matrix to store the indices of the five peaks + max_peaks = np.zeros(6,dtype=int) + + # if the max peak is too small, fail the test and exit + # currently set to cut off when the max peak has index less than 5 + if fft_peaks[np.argmax(peak_heights)] < 5 : + # print( "period is too short") + return -1, {'pgi':-1, 'fft_pgi':-1} + + # retrieve the peaks + for i,val in enumerate(max_peaks) : + index = np.argmax(peak_heights) + max_peaks[i] = int(fft_peaks[index]) + peak_heights[index] = 0 + + # add the max_peaks and peak_heights to the results dictionary + output['max_peaks'] = max_peaks + output['peak_heights'] = peak_heights + + # the pgi is the index of the largest peak + # pgi = int(np.round(fft_period[max_peaks[0]])) + fft_pgi = max_peaks[0] # index of the period guess in the fft + acf_pgi = int(np.round(fft_period[max_peaks[0]])) # index of the period guess in the acf + + # calculate the mean and std of all but the largest peak + mean_mp = np.mean(corr_fft[max_peaks[1:]]) + std_mp = np.std(corr_fft[max_peaks[1:]]) + + # calc how many stds the max peak is from the rest + pgi_prom = (corr_fft[max_peaks[0]] - mean_mp) / std_mp + + # add the pgi and peak prominence to the result dict + output['fft_pgi'] = fft_pgi + output['pgi'] = acf_pgi + output['pgi_prom'] = pgi_prom + + # if requested, then plot + if plot : + plt.figure(figsize=[12,5]) + plt.plot(fft_period,corr_fft) + plt.plot(fft_period[max_peaks],corr_fft[max_peaks], marker='x',linestyle='') + + return acf_pgi, output + + + +def gaussian(fwhm): + """ + Creates a gaussian with a given FWHM as preparation for convolution with a light curve. + + Args: + fwhm (:obj:`float`): the full width half max of the desired gaussian. + bs (:obj:`float`): the size of the bins, in units of seconds. + + Returns: + gaussian (:obj:`arr`): a gaussian. + """ + sigma = fwhm / 2.355 + x = np.arange(-3*sigma, 3*sigma) + # note that we divide by .997 to preserve the normalization and make the + # area under the truncated gaussian equal to 1 + return 1./.997 * 1./(np.sqrt(2.*np.pi) * sigma) * np.exp(-(x/sigma)**2./2.) + +def parabola(x,a,b,c): + """ + Creates a parabola of the form y = ax^2 + bx + c. Intended to be used by curvefit in the process Test. + + Args: + x (:obj:`arr`): the x range of the function. + a, b, c (:obj:`float`): coefficients + + Returns: + y (:obj:`arr`): a parabola. + """ + return a*np.square(x) + b*x + c + +def curvefit_peak(func,corr,pgi,peak_num,plot=False): + """ + Bins a timeseries to the desired cadence. Works much faster than Lightkurve's built in binning function. + + Args: + func (:obj:`func`): the function to be fitted to peaks in the ACF, intended to be the `parabola()` function. + corr (:obj:`1D array`): the autocorrelation function. + pgi (:obj:`int`): period_guess_index, i.e. the intial guess for the index location of the first peak in the `corr` array (aka the acf), usually identified from the highest peak in the FFT of the ACF. + peak_num (:obj:`int`): which alias you are trying to fit (1 being the original peak, 2 being the first alias.) + plot (:obj:`bool`, optional): if True, will plot the ACF and the fitted parabolas. + + Returns: + Two parameters, or three if a flux_err is also provided. + + - fit_params (:obj:`array`): [a0,b0,c0], the coeffitients of the fitted parabola. + - R_adj (:obj:`float`): the adjusted R^2 value of the parabola fit. + - fitted_parabola (:obj:`array`): y-values of the fitted parabola. + - hwhm (:obj:`float`): the half-width half-max of the fitted parabola. Used in error calculations. + """ + # clip the acf to a length of a period guess, centered on the period guess + k_snip = np.arange(pgi*peak_num - pgi/4, pgi*peak_num + pgi/4).astype(int) + corr_snip = corr[k_snip] + x_snip = np.arange(len(k_snip)).astype(int) # synthetic x-axis + + # do a regression to fit the function + # make an array for the weights + # gauss = 1.0 - gaussian(len(x_snip)/2.4) + # start = (len(gauss)-len(x_snip))/2 + # weights = gauss[start:(start+len(x_snip))] + # params_opt stands for optimal parameters + [a0,b0,c0], pcov = curve_fit(func, x_snip, corr_snip) + # [a0,b0,c0], pcov = curve_fit(func, x_snip, corr_snip, sigma=weights, absolute_sigma=True) + + # calculate the expected curve from the fit parameters + # note the the output is in lags, need to do conversions to get period in days again + fitted_parabola = func(x_snip, *[a0,b0,c0]) + + # calculate the other params you want + # note correction to P_rot0 to make up for the shift in the window that was fit + P_rot0 = -1.*b0 / (2*a0) + 3*pgi/4. # note that P_rot0 is in lags, NOT time units + A0 = c0 - np.square(b0) / (4*a0) + + # B0 = -1. * a0 * np.square(P_rot0) + # new value of B0 is the width of the parabola at the zero crossing + # to avoid warning, check if there is an intercept + if (b0**2 - 4*a0*c0) < 0 : + intercept1 = np.nan + intercept2 = np.nan + else : + intercept1 = (-b0 + np.sqrt(b0**2 - 4*a0*c0)) / (2 * a0) + intercept2 = (-b0 - np.sqrt(b0**2 - 4*a0*c0)) / (2 * a0) + B0 = np.abs((intercept1 - intercept2) / P_rot0) + + # gather things up + fit_params = [a0,b0,c0] + peak_params = [P_rot0, A0, B0] + + # check how good the fit is with the adjusted R^2 statistic + R_adj = adjusted_R_sq(corr_snip, fitted_parabola) + + # calculate the half-width half-max of the peak + if np.isnan(intercept1) or np.isnan(intercept2) : + hwhm = np.nan + else : + x1 = (-b0 + np.sqrt(b0**2 - 4*a0*(c0-A0/2))) / (2 * a0) + x2 = (-b0 - np.sqrt(b0**2 - 4*a0*(c0-A0/2))) / (2 * a0) + hwhm = np.abs(x2-x1) / 2 + + # plot if requested + if plot: + if peak_num == 1: + plt.figure(figsize=[9,5]) + plt.plot(corr[:(len(corr))]) + plt.plot(k_snip,fitted_parabola) + + # # print the resulting params and R_adj if desired + # if print_result: + # print( "Optimal params: " + str(params_opt)) + # print( "Adjusted R^2 for curvefit: " + str(R_adj)) + + return fit_params, peak_params, R_adj, fitted_parabola, hwhm + +def adjusted_R_sq(obs,exp,num_param=3): + """ + Calculates the adjusted R^2 statistic to estimate the success of a model. Formula implemented from equation 7.62 in Modern Statistical Methods for Astronomy by Feigelson and Babu. + + Args: + obs (:obj:`float`): the observed value. + exp (:obj:`float`): the expected value. + num_param (:obj:`int`): the number of parameters used in the fit. + + Returns: + R_adj (:obj:`float`): the adjusted R^2 statistic. + """ + if len(obs) != len(exp): + print( "The length of the observed and expected arrays should be equal.") + n = len(obs) + exp_mean = np.mean(obs) / float(n) + + # calculate the R^2 statistic first + numerator = 0. + denominator = 0. + for i in range(n): + numerator += np.square(obs[i] - exp[i]) + denominator += np.square(obs[i] - exp_mean) + R_sq_inv = np.divide(numerator, denominator) # this is actually 1-R^2, not R^2 + + # now calculate the adjusted R^2 to take into account the number of model parameters + R_adj = 1. - (n - 1.)/(n - num_param) * R_sq_inv + return R_adj + + +# # NEW PROCESS FUNCTIONS + +def calc_acf(lc, bs=cadence, max_lag=None, smooth=None, sector_label=False): + """ + Caculates the autocorrelation function (ACF) of a light curve.. + + Args: + lc (:obj:`LightCurve obj`): the cleaned lightcurve. + bs (:obj:`float`): the size of the bins, in units of seconds. + max_lag (:obj:`float`, optional): the maximum lag to which to calculate the ACF in units of days. + smooth (:obj:`int`, optional): if supplied, will apply smoothing to the LC before fitting parabolas by convolving with a gaussian with a FWHM equal to this value. + sector_label (:obj:`int` or :obj:`str`, optional): tallows you to set a custom sector label, must be castable to a string + + Returns: + fits_result (:obj:`dict`): A dictionary containing information on the light curve and it's ACF. + """ + fits_result = {'time_even':lc['time'], 'flux_even':lc['nflux_dtr'], 'flux_err_even':lc['nflux_err'], + 'acf_lags':np.array([]), 'acf':np.array([]), 'acf_smooth':np.array([])} + + # set the nlags + if max_lag is None : + nlags = days_to_bins(np.floor(lc['time'][-1] - lc['time'][0]), bs) + else : + nlags = days_to_bins(max_lag, bs) + + # calculate acf + acf_corr = acf(lc['nflux_dtr'], missing='conservative',nlags=nlags,fft=True) + lag_times = bins_to_days(np.arange(len(acf_corr)), bs) + + # try convolving with a gaussian + if isinstance(smooth, int) : + acf_smooth = np.convolve(acf_corr, gaussian(smooth), mode="same") + else : + acf_smooth = np.array([]) + + # update fits_result + fits_result['acf_lags'] = lag_times + fits_result['acf'] = acf_corr + fits_result['acf_smooth'] = acf_smooth + + return fits_result + +def calc_parabolas(corr, TICID=None, bs=cadence, smooth=None, prot_prior_func=calc_fft_pgi, prot_prior_func_kwargs={}): + """ + Calculates the best fit parabolas to peaks in an acf. + + Args: + corr (:obj:`1D array`): the autocorrelation function. + bs (:obj:`float`): the size of the bins, in units of seconds. + TICID (:obj:`int` or :obj:`str`, optional): the ID for the object. + smooth (:obj:`int`, optional): if supplied, will apply smoothing to the LC before fitting parabolas by convolving with a gaussian with a FWHM equal to this value. + prot_prior_func (:obj:`func`, optional): the function to be used to idntify the period_guess_index (pgi) for the rotation period. Defaults to `calc_fft_pgi()` + prot_prior_func_kwargs (:obj:`dict`, optional): keyword arguments for the `prot_prior_func` function. + + Returns: + results (:obj:`dict`): a dictionary of info on the parabola fits. + """ + # make a dictionary to store results in + # any key ending with _k is an array length 5, where the value at each index + # is associated with a fit to a different peak in the acf. fitted_parabola_k is an + # array of arrays, each of which is the parabola fit to one of the peaks. + # 'fail', when set to True, indicates that the test could not be completed + # due to finding a pgi greater than half the sample length. + # 'half_period' describes whether there peaks in the ACF at half periods due to + # having spots in opposite hemispheres. 'half_period_check' means that the peak + # height difference is less than 5% and needs to be checked by hand. + results = {'smooth':smooth, 'fft':None, 'fft_period':None, 'pgi':np.nan, + 'a_k':np.array([]), 'b_k':np.array([]), 'c_k':np.array([]), + 'Rsq_k':np.array([]), 'hwhm_k':np.array([]), 'fitted_parabola_k':[[],[],[],[],[]], + 'P_k':np.array([]), 'A_k':np.array([]), 'B_k':np.array([]), + 'P_avg':np.nan, 'A_avg':np.nan, 'B_avg':np.nan, 'R_avg':np.nan, 'fft_prom':np.nan, + 'P_err':np.nan, + 'half_period': False, 'half_period_check':False, + 'fail':False } + + # if smoothing of the acf is requested, apply it + if smooth : + corr_smooth = np.convolve(corr, gaussian(smooth), mode="same") + corr = corr_smooth + + # if the pgi is given as a number, use that + if (type(prot_prior_func)==float) or (type(prot_prior_func)==int): + # the pgi will be the index of the acf_lags closes to the provided number in days + prot_prior_lags = int(days_to_bins(prot_prior_func, bs)) + pgi = min(range(len(corr)), key=lambda i: abs(range(len(corr))[i]-prot_prior_lags)) + results['pgi'] = pgi + else : + # Calculate the pgi (initial guess for the period) + # by default, this will use the funciton calc_fft_pgi, which selects the highest peak in the + # FFT of the ACF. You can also write a custom pgi-finding function and pass it in to process_test_raw + pgi, pgi_results = prot_prior_func(corr, bs=bs, **prot_prior_func_kwargs) + + # update the results dictionary + results.update(pgi_results) + results['pgi'] = pgi + + # run curvefit_peaks for the first peak and up to four aliases + for peak_num in range(1,6) : + # check that the alias won't extend beyond end of the acf + if peak_num*pgi + pgi/4 < len(corr) : + # run curvefit + fit_params, peak_params, R_adj, fitted_parabola, hwhm = curvefit_peak(parabola, corr, pgi, peak_num) + + # add results to the appropriate dictionary + results['a_k'] = np.append(results['a_k'], fit_params[0]) + results['b_k'] = np.append(results['b_k'], fit_params[1]) + results['c_k'] = np.append(results['c_k'], fit_params[2]) + results['P_k'] = np.append(results['P_k'], peak_params[0]) + results['A_k'] = np.append(results['A_k'], peak_params[1]) + results['B_k'] = np.append(results['B_k'], peak_params[2]) + results['Rsq_k'] = np.append(results['Rsq_k'], R_adj) + results['hwhm_k'] = np.append(results['hwhm_k'], hwhm) + results['fitted_parabola_k'][peak_num-1] = fitted_parabola + else : + # trim fitted_parabola_k to the appropriate length + results['fitted_parabola_k'] = results['fitted_parabola_k'][:peak_num] + + # add the averaged values to the results dictionary + results['A_avg'] = np.nanmean(results['A_k']) + results['B_avg'] = np.nanmean(results['B_k']) + results['R_avg'] = np.nanmean(results['Rsq_k']) + + # calculate the error bar on P_avg + if len(results['P_k']) >= 3 : + results['P_err'] = np.std(results['P_k'])/np.sqrt(len(results['P_k'])) + else : + results['P_err'] = np.nanmean(results['hwhm_k']) + + + # Select the rotation period, keeping in mind that there may be spots in opposite hemispheres + # Check for alternating peak heights in the ACF. + if len(results['A_k']) > 2 : + # check if the second peak is higher than the 1st or 3rd by more than 5% + if results['A_k'][1]*.95 > results['A_k'][0] and results['A_k'][1]*.95 > results['A_k'][2] : + # this is the unambiguous case, definitely a half period + try: + results['P_avg'] = np.nanmean([results['P_k'][1], results['P_k'][3]]) * 2 + except: + results['P_avg'] = results['P_k'][1] + + results['half_period'] = True + + elif results['A_k'][1] > results['A_k'][0] and results['A_k'][1] > results['A_k'][2]: + # this is the ambiguous case, less than 5% difference in peak height + # does NOT automatically update P_avg, this will have to be done by hand when checked + results['P_avg'] = np.nanmean(results['P_k']) + results['half_period_check'] = True + else: + results['P_avg'] = np.nanmean(results['P_k']) + + # now, everything in the results dictionary should be taken care of + return results + +def process_LightCurve(lc, bs=cadence, precleaned=False, + transit=None, + max_lag=None, smooth=None, sector_label=None, + prot_prior='fft', prot_prior_func=None, prot_prior_func_kwargs={}): + """ + Bins a timeseries to the desired cadence. Works much faster than Lightkurve's built in binning function. + + Args: + lc (:obj:`LightCurve obj`): the cleaned lightcurve. + bs (:obj:`float`): the size of the bins, in units of seconds. + precleaned (:obj:`bool`, optional): set to True if the provided `lc` argument has already been cleaned and normalized. + transit (:obj:`array`, optional): array of transit paramters like [period, epoch, duration] in units of days each entry can be an array if there are multiple planets. Also used by `default_cleaning_func`. + max_lag (:obj:`float`, optional): the maximum lag to which to calculate the ACF in units of days. + smooth (:obj:`int`, optional): if supplied, will apply smoothing to the LC before fitting parabolas by convolving with a gaussian with a FWHM equal to this value. + sector_label (:obj:`int` or :obj:`str`, optional): tallows you to set a custom sector label, must be castable to a string. + prot_prior_func (:obj:`func`, optional): the function to be used to idntify the period_guess_index (pgi) for the rotation period. Defaults to `calc_fft_pgi()` + prot_prior_func_kwargs (:obj:`dict`, optional): keyword arguments for the `prot_prior_func` function. + + Returns: + Two parameters + + - fits_result (:obj:`dict`): dictionary containing information on the LC and ACF. + - process_result (:obj:`dict`): dictionary containing information on the parabola fits. + """ + lc_clean = lc + + # calculate the acf + fits_result = calc_acf(lc_clean, bs=bs, max_lag=max_lag, smooth=smooth) + + # add the raw light curve to the fits_result for ease of inspection later on + fits_result['time_raw'] = lc['time'] + fits_result['flux_raw'] = lc['nflux_dtr'] + fits_result['flux_err_raw'] = lc['nflux_err'] + + # calculate parabola fits + # first, check what kind of prior was passed + if prot_prior == 'fft': + process_result = calc_parabolas(fits_result['acf'], bs=bs, prot_prior_func=calc_fft_pgi) + elif prot_prior == 'custom': + process_result = calc_parabolas(fits_result['acf'], bs=bs, prot_prior_func=prot_prior_func, + prot_prior_func_kwargs=prot_prior_func_kwargs) + elif type(prot_prior)==int : + process_result = calc_parabolas(fits_result['acf'], bs=bs, prot_prior_func=prot_prior) + else: + process_resutl = {} + print('Invalid argument passed to prot_prior. Please use \'fft\', \'custom\', or a float.') + + return fits_result, process_result + + +# # PLOTTING FUNCTIONS + +def custom_plot(x, y, ax=None, **plt_kwargs): + """ + (Under construction) Custom plotting function. + + Args: + x (:obj:`arr`): the x array. + y (:obj:`array`): the y array. + ax (:obj:`axis object`, optional): the axis object on which to plot. + peak_num (:obj:`int`): which alias you are trying to fit (1 being the original peak, 2 being the first alias.) + **plt_kwargs (:obj:`dict`): keyword arguments. + + Returns: + ax (:obj:`obj`): the plotted function. + """ + if ax is None: + ax = plt.gca() + ax.plot(x, y, **plt_kwargs) ## example plot here + return(ax) + +def plot_fft(fits_result,process_result, plot_peaks=True, **plt_kwargs): + """ + Given the result dictionaries from process_LightCurve, plots the FFT of the ACF. Returns a figure object. + + Args: + fits_result (:obj:`dict`): dictionary containing information on the LC and ACF, as returned by `process_LightCurve()`. + process_result (:obj:`dict`): dictionary containing information on the parabola fits, as returned by `process_LightCurve()`. + plot_peaks (:obj:`dict`): if True, places a marker on the five tallest peaks in the FFT. + plt_kwargs (:obj:`dict`): keyword arguments for `matplotlib.plt.plot()`. + """ + fft = process_result['fft'] + fft_period = process_result['fft_period_days'] + pgi = process_result['pgi'] + fft_pgi = process_result['fft_pgi'] + + # make le plot! + fig, ax = plt.subplots(1, 1, figsize=[12,5], facecolor='white') + ax.plot(fft_period,fft, color='black', **plt_kwargs) + + # also mark the highest peaks, with the highest one marked in red and the rest in green + if plot_peaks : + peaks_x = process_result['max_peaks'] + peaks_y = fft[peaks_x] + ax.plot(fft_period[peaks_x], peaks_y, marker='o', color='green',linestyle='', markersize=8, fillstyle='none') + ax.plot(fft_period[fft_pgi], fft[fft_pgi], marker='o', color='red', markersize=8, fillstyle='none') + + # labels + ax.set_title("FFT of the ACF", fontsize=14) + ax.set_xlabel("Period (days)", fontsize=14) + ax.set_ylabel("FFT Power", fontsize=14) + + return fig, ax + + +# # PLOTTING AND PRINTING FUNCTIONS + +def print_summary(fits_result, process_result, bs=cadence): + """ + Prints a summary of the descriptive parameters calculated by `process_LightCurve()`. + + Args: + fits_result (:obj:`dict`): dictionary containing information on the LC and ACF, as returned by `process_LightCurve()`. + process_result (:obj:`dict`): dictionary containing information on the parabola fits, as returned by `process_LightCurve()`. + """ + print('pgi: ' + "%.3f" % bins_to_days(process_result['pgi']/(24*30), bs=bs)) + print('A_avg: ' + "%.3f" % (process_result['A_avg'])) + print('B_avg: ' + "%.3f" % (process_result['B_avg'])) + print('Rsq_avg: ' + "%.3f" % (process_result['R_avg'])) + print('P_avg (days): ' + "%.3f" % bins_to_days(process_result['P_avg'], bs=bs)) + print('P_err (days): ' + "%.3f" % bins_to_days(process_result['P_err'], bs=bs)) + print('HWHM_err (days): ' + "%.3f" % bins_to_days(np.nanmean(process_result['hwhm_k']), bs=bs)) + print() + + +def plot_acf(fits_result,process_result, plot_peaks=True, plot_line=None, cut=10): + """ + Prints a summary of the descriptive parameters calculated by `process_LightCurve()`. + + Args: + fits_result (:obj:`dict`): dictionary containing information on the LC and ACF, as returned by `process_LightCurve()`. + process_result (:obj:`dict`): dictionary containing information on the parabola fits, as returned by `process_LightCurve()`. + plot_peaks (:obj:`bool`): if set to True, will overplot the parabola fits to the ACF peaks on the ACF plot + plot_line (:obj:`float`): if given a lag time in days, will plot a vertical line on the ACF at that x-value, indended to plot the found period for visual comparison + cut (:obj:`int`): plots look better when you cut the first few points off the ACF, to avoid the high peak at (0,1). This keyword lets you adjust how many points get cut off the front. + + Returns: + fig (:obj:`obj`): the figure object. + ax (:obj:`obj`): the axis object with the plotted function. + """ + # make the base plot + # fig_num=plt.figure().number + 1 + fig, ax = plt.subplots(1, 1, figsize=[10,5], facecolor='white') + ax.plot(fits_result['acf_lags'][cut:],fits_result['acf'][cut:]) + ax.set_xlabel("Period (days)") + ax.set_ylabel("ACF") + ax.set_title("ACF") + + pgi = process_result['pgi'] + # check if the test failed + if pgi <= 0 or np.isnan(pgi) : + print('Cannot plot peaks, no plausible rotation period detected.') + return fig, ax + if pgi > len(fits_result['acf_lags']//2) : + print("Test failed due to pgi > 1/2*sample length") + return fig, ax + + # if desired, plot the fitted peaks to the acf + if plot_peaks : + # extract the needed info + # plots look better when you cut off the first few points in the ACF + lag_times = fits_result['acf_lags'] + pgi = process_result['pgi'] + fitted_parabolas = process_result['fitted_parabola_k'] + try : + acf_snip = pgi * 7 + except : + acf_snip = pgi * len(process_result['a_k']) + pgi/2 + acf_snip = min(acf_snip, len(lag_times)-1) + + # Set a reasonable limit + ax.set_xlim([0, lag_times[acf_snip]]) + + # now plot each peak + for i in range(len(process_result['a_k'])): + curve = fitted_parabolas[i] + window = len(curve) + snip = np.arange(pgi*(i+1) - window/2, pgi*(i+1) + window/2).astype(int) + lag_snip = lag_times[snip] + ax.plot(lag_snip, curve,linewidth=4,color='r',linestyle='-') + + + # now make it pretty + ax.set_xlabel("Lag (days)") + ax.set_ylabel("ACF") + ax.set_title("ACF") + + # if provided, plot a line where requested + if plot_line : + line_y = np.arange(-1.5,1.5,.1) + line_x = np.ones(len(line_y)) * (plot_line)# / (cad/bs) + ax.plot(line_x, line_y, c='g', scaley=False) + return fig, ax diff --git a/tessilator/aperture.py b/tessilator/aperture.py index 5fc31b61..569fb58a 100644 --- a/tessilator/aperture.py +++ b/tessilator/aperture.py @@ -1,25 +1,34 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, 2024 -Licence: MIT 2023 +Licence: MIT 2024 This module contains functions to perform aperture photmetry. -The aperture photometry is defined by a single function 'aper_run', which reads the TESS image files, performs aperture phtoometry and returns an astropy table containing the times, magnitudes and fluxes from aperture photometry. +The aperture photometry is defined by a single function 'aper_run', which reads +the TESS image files, performs aperture phtoometry and returns an astropy table +containing the timestamps, magnitudes and fluxes derived from aperture +photometry. Additionally, one can use the function 'calc_radius', which +evaluates the relative brightness of neighbouring pixels to automatically +determine the size of the aperture radius. If the full-frame images are used, a +function named 'get_xy_pos' provides a WCS transformation to convert celestial +coordinates to image (X-Y) pixels. ''' -# imports -import logging -__all__ = ['aper_run', 'get_xy_pos', 'logger'] - - +############################################################################### +####################################IMPORTS#################################### +############################################################################### +# Internal import warnings +import inspect +import sys -# Third party imports +# Third party import numpy as np import json +from scipy import stats from astropy.table import Table from astropy.coordinates import SkyCoord @@ -27,22 +36,34 @@ from astropy.io import fits import astropy.units as u - from photutils.aperture import CircularAperture, CircularAnnulus from photutils.aperture import aperture_photometry, ApertureStats -# Local application imports + +# Local application from .fixedconstants import * +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### # initialize the logger object -logger = logging.getLogger(__name__) +logger = logger_tessilator(__name__) def get_xy_pos(targets, head): '''Locate the X-Y position for targets in a given Sector/CCD/Camera mode + + The function reads in the RA and DEC positions of each target, and the + metadata (header) of the input fits frame containing the WCS information. + A WCS transformation is attempted first, which uses the + `world_to_array_index` module to assign pixel values that match the + indexing order of numpy arrays. If the WCS transformation fails, then the + `Xpos` and `Ypos` columns from the input table are used. If `Xpos` and + `Ypos` are not available, the function returns an error. parameters ---------- @@ -56,27 +77,93 @@ def get_xy_pos(targets, head): positions : `tuple` A tuple of X-Y pixel positions for each target. ''' - w = WCS(head) - c = SkyCoord(targets['ra'], targets['dec'], unit=u.deg, frame='icrs') + + try: + w = WCS(head) + c = SkyCoord(targets['ra'], targets['dec'], unit=u.deg, frame='icrs') y_obj, x_obj = w.world_to_array_index(c) - except: - logger.warning("Couldn't get the WCS coordinates to work...") - positions = tuple(zip(targets["Xpos"], targets["Ypos"])) + if len(y_obj) > 1: + positions = tuple(zip(x_obj, y_obj)) + else: + positions = (x_obj[0], y_obj[0]) + logger.info("The WCS coordinates were successfully applied.") return positions - positions = tuple(zip(x_obj, y_obj)) - return positions + + except: + if ("Xpos" in targets.colnames) and ("Ypos" in targets.colnames): + positions = tuple(zip(targets["Xpos"], targets["Ypos"])) + logger.warning("XY positions used directly - the aperture will be offset by a few sub-pixels!") + else: + logger.error("Couldn't get the WCS coordinates to work...") + return +def calc_rad(flux_vals, positions, f_lim=0.2, max_rad=3, default_rad=1): + '''Calculate the appropriate pixel radius for the aperture + + This function uses a basic algorithm to calculate the most appropriate + radius size to use for the circular aperture photometry of the TESS image + frames. If the ratio of the median value of neighbouring (8, in a square + surrounding the central pixel) pixels compared to the central pixel is + greater than 'f_lim', then expand the radius by one pixel, and test the + next set of surrounding pixels. If, after 'n_pix' pixels the condition is + still satisfied, set the pixel radius equal to 1. The latter constraint is + intended to avoid contamination from neighbouring sources. + + parameters + ---------- + flux_vals : `np.array` + The raw flux values from each pixel in the image. + positions : `tuple` + The X,Y position of the central pixel + f_lim : `float`, optional, default=0.2 + The limiting threshold flux for the criterion. + max_rad : `int`, optional, default=3 + The maximum number of pixels for the aperture radius. + default_rad : `int`, optional, default=1 + The default aperture radius to be used in case of an error. + + returns + ------- + aper_rad : `float` + The pixel radius. + ''' + try: + x0, y0 = int(positions[0]), int(positions[1]) + f_max = flux_vals[x0,y0] + mask_ori = np.zeros([flux_vals.shape[0], flux_vals.shape[1]]) + mask = mask_ori + i = 1 + while i <= max_rad: + mask[x0-i:x0+i+1,y0-i] = 1 + mask[x0-i:x0+i+1,y0+i] = 1 + mask[x0-i,y0-i:y0+i] = 1 + mask[x0+i,y0-i:y0+i] = 1 + f_sum = mask*flux_vals + f_new = np.median(f_sum[np.where(f_sum > 0)]) + if f_new/f_max < f_lim: + break + else: + mask = mask_ori + i += 1 + if i > 3: + i = 1 + aper_rad = i-0.5 + except: + logger.warning("The aperture radius could not be calculated with this algorithm. The default value will be used.") + aper_rad = default_rad-0.5 + return aper_rad -def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): +def aper_run(file_in, targets, FixRad=None, SkyRad=(6.,8.), XY_pos=(10.,10.)): '''Perform aperture photometry for the image data. - This function reads in each fits file, and performs aperture photometry. - A table of aperture photometry results is returned, which forms the raw - lightcurve to be processed in subsequent functions. + This function reads in each fits file, determines the pixel radius for an + image aperture and performs aperture photometry. A table of aperture + photometry results is returned, which forms the raw lightcurve to be + processed in subsequent functions. parameters ---------- @@ -84,7 +171,7 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): Name of the fits file containing image data targets : `astropy.table.Table` The table of input data - Rad : `float`, optional, default=1. + FixRad : `float`, optional, default=1. The pixel radius to define the circular area for the aperture SkyRad : `tuple`, optional, default=(6.,8.) A 2-element tuple defining the inner and outer annulus to calculate @@ -102,10 +189,10 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): else: fits_files = [file_in] - full_phot_table = Table(names=('run_no','id', 'xcenter', 'ycenter', 'flux', + full_phot_table = Table(names=('run_no','id', 'aperture_rad', 'xcenter', 'ycenter', 'flux', 'flux_err', 'bkg', 'total_bkg', 'reg_oflux', 'mag', 'mag_err', 'time'), - dtype=(int, str, float, float, float, float, float, + dtype=(int, str, float, float, float, float, float, float, float, float, float, float, float)) print(fits_files) for f_num, f_file in enumerate(fits_files): @@ -114,7 +201,7 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): with fits.open(f_file) as hdul: data = hdul[1].data if data.ndim == 1: - head = hdul[0].header +# head = hdul[1].header if "FLUX_ERR" in data.names: n_steps = data.shape[0]-1 flux_vals = data["FLUX"] @@ -130,17 +217,30 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): positions = XY_pos elif data.ndim == 2: n_steps = 1 - head = hdul[1].header - qual_val = [head["DQUALITY"]] - time_val = [(head['TSTART'] + head['TSTOP'])/2.] + head_meta = hdul[0].header + head_data = hdul[1].header + qual_val = [head_data["DQUALITY"]] + time_val = [(head_meta['TSTART']) + (head_meta['TSTOP'])/2.] flux_vals = [data] erro_vals = [hdul[2].data] - positions = get_xy_pos(targets, head) - print('okay, n_steps=', n_steps) - + positions = get_xy_pos(targets, head_data) + + if not FixRad: + rad_val = [] + for n_step in range(n_steps): + #define a circular aperture around all objects + rad_x = calc_rad(flux_vals[n_step], positions) + rad_val.append(rad_x) + if len(rad_val) > 1: + Rad = stats.mode(np.array(rad_val), keepdims=False)[0] + else: + Rad = rad_val[0] + else: + rad_vel = np.repeat(FixRad, n_steps) + Rad = FixRad + for n_step in range(n_steps): if qual_val[n_step] == 0: - #define a circular aperture around all objects aperture = CircularAperture(positions, Rad) #select a background annulus annulus_aperture = CircularAnnulus(positions, @@ -163,6 +263,7 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): aperture_area = aperture.area_overlap(flux_ap) #print out the data to "t" t['run_no'] = n_step + t['aperture_rad'] = rad_val[n_step] t['id'] = targets['source_id'] t['id'] = t['id'].astype(str) t['bkg'] = aperstats.median @@ -178,7 +279,7 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): t['aperture_sum_err'][g].data/\ t['aperture_sum'][g].data) t['time'] = time_val[n_step] - fix_cols = ['run_no', 'id', 'xcenter', 'ycenter', + fix_cols = ['run_no', 'id', 'aperture_rad', 'xcenter', 'ycenter', 'aperture_sum', 'aperture_sum_err', 'bkg', 'tot_bkg', 'ap_sum_sub', 'mag', 'mag_err', 'time'] @@ -189,8 +290,8 @@ def aper_run(file_in, targets, Rad=1., SkyRad=(6.,8.), XY_pos=(10.,10.)): print(f"There is a problem opening the file {f_file}") logger.error(f"There is a problem opening the file {f_file}") continue - return full_phot_table - + return full_phot_table, Rad +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/contaminants.py b/tessilator/contaminants.py index 8a39e8e5..3f899116 100644 --- a/tessilator/contaminants.py +++ b/tessilator/contaminants.py @@ -1,52 +1,65 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, 2024 -Licence: MIT 2023 +Licence: MIT 2024 -This module contains functions to quantify the background flux contamination from -neighbouring sources in the TESS full-frame images. Each TESS pixel has a length of -21", therefore targets are highly susceptible to background contamination when they -(1) are in crowded fields, (2) are faint sources and/or (3) have high contributing -sky-counts. +This module contains functions to quantify the background flux contamination +from neighbouring sources in the TESS full-frame images. Each TESS pixel has +a length of 21", therefore targets are highly susceptible to background +contamination when they (1) are in crowded fields, (2) are faint sources +and/or (3) have high contributing sky-counts. -The background is quanitified by acquistioning RP-band magnitudes in the Gaia DR3 -catalogue for potential contaminants in the surrounding image apertures, and -then calculating their flux contribution which is incident within the target -aperture using an analytical formula provided as equation 3b-10 in Biser & Millman -(1965). +The background is quanitified by acquistioning RP-band magnitudes in the Gaia +DR3 catalogue for potential contaminants in the surrounding image apertures, +and then calculating their flux contribution which is incident within the +target aperture using an analytical formula provided as equation 3b-10 in +Biser & Millman (1965). An additional function can be called which stores the potential neighbouring -contaminants, and periodogram analyses for these neighbouring sources are perform to -assess whether the observed periodicity is that of the target, or a neighbour. +contaminants. Periodogram analyses for these neighbouring sources are then +performed to help distinguish whether the lightcurve/periodogram signal is +that of the target, or from neighbouring sources. ''' - -import traceback -import logging +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal import warnings +import inspect import sys +import traceback + # Third party imports import numpy as np from astroquery.gaia import Gaia from astropy.table import Table, Row + +# Local application from .fixedconstants import * +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### + + +# initialize the logger object +logger = logger_tessilator(__name__) + -__all__ = ['logger', 'run_sql_query_contaminants', 'flux_fraction_contaminant', 'contamination'] -logger = logging.getLogger(__name__) def run_sql_query_contaminants(t_target, pix_radius=10., mag_lim=3., tot_attempts=3): - '''Perform an SQL Query to identify neighbouring contaminants. + '''Perform an SQL query to identify neighbouring contaminants. - If an analysis of flux contribution from neighbouring contaminants is - required, this function generates the SQL query to identify targets - within a specified pixel radius. The function returns a table of Gaia - information on the neighbouring sources which is used to quantify the - flux contamination within the target aperture. + This function generates the SQL query to identify targets within a + specified pixel radius. The function returns a table of Gaia + information on the neighbouring sources which is used to quantify the flux + contamination within the target aperture. parameters ---------- @@ -104,6 +117,11 @@ def run_sql_query_contaminants(t_target, pix_radius=10., mag_lim=3., tot_attempt print('Most likely there is a server problem. Try again later.') sys.exit() + + + + + def flux_fraction_contaminant(ang_sep, s, d_thr=5.e-6): '''Quantify the flux contamination from a neighbouring source. @@ -116,7 +134,7 @@ def flux_fraction_contaminant(ang_sep, s, d_thr=5.e-6): f_{\\rm bg} = e^{-t} \sum_{n=0}^{n\\to{\infty}} {\Bigg\{\\frac{t^{n}}{n!}\\bigg[1-e^{-s}\sum_{k=0}^{n}{\\frac{s^{k}}{k!}}} \\bigg]\Bigg\} To solve the equation computationally, the summation terminates once the - difference from the nth iteration is less than a given threshold value. + difference from the nth iteration is less than some given threshold value, `d_thr`. parameters ---------- @@ -143,7 +161,7 @@ def flux_fraction_contaminant(ang_sep, s, d_thr=5.e-6): n_0 = ((t**n)/np.math.factorial(n))*sx n_z += n_0 if np.abs(n_0) > d_thr: - n = n+1 + n += 1 if np.abs(n_0) < d_thr: break frac_flux_in_aperture = n_z*np.exp(-t) @@ -245,3 +263,6 @@ def contamination(t_targets, Rad=1.0, n_cont=10): return t_targets, t_cont + + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/detrend_cbv.py b/tessilator/detrend_cbv.py index a39b5060..70599d19 100644 --- a/tessilator/detrend_cbv.py +++ b/tessilator/detrend_cbv.py @@ -1,18 +1,22 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, December 2024 -Licence: MIT 2023 +Licence: MIT 2024 This module contains the functions needed to perform the co-trending basis vector corrections to the TESS lightcurves, if required. The functions were originally designed for Kepler-data analysis, and written by Suzanne Aigrain -> https://github.com/saigrain/CBVshrink ''' -import logging -__all__ = ['get_cbv_scc', 'interpolate_cbv', 'fixed_nb', 'sel_nb', 'fit_basis', 'apply_basis'] - +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal import warnings +import inspect +import sys + -# Third party imports +# Third party import numpy as np import os import subprocess @@ -21,15 +25,26 @@ from astropy.io import fits import astropy.units as u - import pylab as pl + + +# Local application from .VBLinRegARD import bayes_linear_fit_ard as VBF from .stats_cbv import cdpp, medransig +############################################################################### +############################################################################### +############################################################################### + + + +# initialize the logger object +logger = logger_tessilator(__name__) + -def fit_basis(flux, basis, scl = None): +def fit_basis(flux, basis, scl=None): '''Calculate the flux-correction weights for the co-trending basis vectors (CBVs). This routine is taken directly from `Aigrain et al. 2017 `_ @@ -71,6 +86,8 @@ def fit_basis(flux, basis, scl = None): weights[iobj,:] = np.array(res[0][:-1]).flatten() * scl * Fs / Bs return weights + + def apply_basis(weights, basis): '''Calculate the dot product between the weights and the CBVs. @@ -91,7 +108,9 @@ def apply_basis(weights, basis): dot_prod_res = np.dot(weights, basis) return dot_prod_res -def fixed_nb(flux, cbv, nB = 4, use = None, doPlot = True): + + +def fixed_nb(flux, cbv, nB=4, use=None, doPlot=True): '''Correct light curve for systematics using first nB CBVs. parameters @@ -134,7 +153,9 @@ def fixed_nb(flux, cbv, nB = 4, use = None, doPlot = True): pl.xlabel('Flux') return corrected_flux, weights -def sel_nb(flux, cbv, nBmax = None, use = None): + + +def sel_nb(flux, cbv, nBmax=None, use=None): '''Correct light curve for systematics using upt to nB CBVs (automatically select best number). parameters @@ -247,6 +268,7 @@ def interpolate_cbv(cbv_file, lc, type_cbv='Single'): return v_fin + def get_cbv_scc(scc, lc): '''Run the CBV fits for a given lightcurve @@ -276,3 +298,5 @@ def get_cbv_scc(scc, lc): corrected_flux, weights = sel_nb(np.array(lc['flux'].data), interpolated_cbv) return corrected_flux, weights + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/fixedconstants.py b/tessilator/fixedconstants.py index a0523f3a..fcee9520 100644 --- a/tessilator/fixedconstants.py +++ b/tessilator/fixedconstants.py @@ -29,7 +29,7 @@ This is held constant. Do not change. ''' -sec_max = 70 +sec_max = 72 '''This is the maximum sector number to be acquisitioned when looking for TESS data This will change over time as more data is collected. diff --git a/tessilator/lc_analysis.py b/tessilator/lc_analysis.py index 58f70c96..3ad3c7e1 100644 --- a/tessilator/lc_analysis.py +++ b/tessilator/lc_analysis.py @@ -1,39 +1,40 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, December 2024 -Licence: MIT 2023 - -This module contains functions to clean lightcurves. +Licence: MIT 2024 +This module contains functions to normalise, detrend, and clean lightcurves. -The cleaning and detrending of the lightcurve takes part in 8 steps, each of which are called by the parent function 'run_make_lc_steps'. The steps are: +The cleaning and detrending of the lightcurve takes part in 9 steps, each of +which are called by the parent function 'run_make_lc_steps'. The steps are: | (1) Normalise the raw lightcurve. -| (2) Split the lightcurve into segments of contiguous data (no large time gaps between datapoints) +| (2) Split the lightcurve into segments of contiguous data (no large time gaps + between datapoints) | (3) Remove sparse data, i.e., data segments with only a few data points. | (4) Detrend the lightcurve. | (5) Clean the edges: remove flux outliers. | (6) Clean the edges: scattered, noisy datapoints. | (7) Remove extreme outliers from the lightcurve -| (8) Store the lightcurve to file. +| (8) Normalise the flux in each segment, using the median of qualified data. +| (9) Store the lightcurve to file. ''' -# imports -import logging -__all__ = ['aic_selector', 'clean_edges_outlier', 'clean_edges_scatter', 'detrend_lc', - 'get_time_segments', 'logger', 'make_lc', - 'normalisation_choice', 'remove_sparse_data', 'sin_fit', 'test_cbv_fit'] - - +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal import warnings +import inspect +import sys -# Third party imports + +# Third party import numpy as np import os import json - from astropy.table import Table import astropy.units as u from astropy.stats import akaike_info_criterion_lsq @@ -46,23 +47,33 @@ from operator import itemgetter +# Local application +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### + + + # initialize the logger object -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) +logger = logger_tessilator(__name__) -def get_time_segments(t, t_fac=5.): - '''Split the lightcurve into groups of contiguous data, where the time difference between - measurements is less than a given value. +def get_time_segments(t, t_fac=10.): + '''Split the lightcurve into groups of contiguous data + + Group data into segments where the time difference between data points is + less than some threshold factor, `t_fac`. parameters ---------- t : `Iter` The list of time coordinates from the lightcurve - t_fac : `float`, optional, default=5. - A factor which is multiplied with the median time difference of the neighbouring time coordinates. + t_fac : `float`, optional, default=10. + A time factor, which is multiplied by the median cadence of the full + lightcurve. returns ------- @@ -86,49 +97,53 @@ def get_time_segments(t, t_fac=5.): -def remove_sparse_data(x_start, x_end, std_crit=50): - '''Removes very sparse bits of data from the lightcurve when there are 3 or more components. +def remove_sparse_data(x_first, x_last, std_crit=50): + '''Removes very sparse data groups, when there are 3 or more groups. - Calculate the mean (mc) and standard deviation (sc) for the number of data - points in each component (N). - If sc > "std_crit", then only keep components with N > std_crit. + Calculate the mean (mean_group) and standard deviation (std_group) for the + number of data points in each group (n_points). + If std_group > "std_crit", then only keep groups with n_points > std_crit. parameters ---------- - x_start : `Iterable` - The starting indices for each data component - x_end : `Iterable` - The end indices for each data component + x_first : `Iterable` + The index values of the first element in each group + x_last : `Iterable` + The index values of the last element each group std_crit: `int` - The minimum number of data points in a component + The minimum number of data points required for a group to qualify. returns ------- - y_start : `np.array` - The starting indices of the new array - y_end : `np.array` - The end indices of the new array + y_first : `np.array` + The index values of the first element of the new arrays + y_last : `np.array` + The index values of the last element of the new arrays ''' - y_start, y_end = np.array(x_start), np.array(x_end) - # n_points is an array containing the length of the components - n_points = np.array([x_end[i] - x_start[i] for i in range(len(x_start))]) - if len(x_start) > 2: - mean_point, std_point = np.mean(n_points), np.std(n_points) - if std_point > 1.*std_crit: - g = n_points > 1.*std_crit - y_start, y_end = y_start[g], y_end[g] - return y_start, y_end - return y_start, y_end - + try: + y_first, y_last = np.array(x_first), np.array(x_last) + # n_points is an array containing the length of the components + n_points = np.array([x_last[i] - x_first[i] for i in range(len(x_first))]) + if len(x_first) > 2: + mean_group, std_group = np.mean(n_points), np.std(n_points) + if std_group > std_crit: + g = n_points > std_crit + y_first, y_last = y_first[g], y_last[g] + return y_first, y_last + return y_first, y_last + except: + logger.warning("The sparse data removal algorithm failed. Retaining the input indices.") + return y_first, y_last def aic_selector(x, y, poly_max=3, cov_min=1e-10): - '''Chooses the most appropriate polynomial fit, using the Aikaike Information Criterion + '''Select the detrending polynomial from the Aikaike Information Criterion - This function uses the Aikaike Information Criterion to find the most appropriate polynomial order to a set of X, Y data points. + This function uses the Aikaike Information Criterion (AIC) to find the most + appropriate polynomial order to a set of X, Y data points. parameters ---------- @@ -136,9 +151,17 @@ def aic_selector(x, y, poly_max=3, cov_min=1e-10): The independent variable y : `Iterable` The dependent variable - poly_max : `int`, optional, default=10 + poly_max : `int`, optional, default=3 The maximum polynomial order to test - cov_min : A threshold value for the first element of the covariance matrix. Sometimes the AIC will automatically select a higher-order polynomial to a distribution that is clearly best fit by the preceeding lower-order fit. For example, a second-order fit provides a better fit for a perfect straight line. This is a bug in the numerical rounding. Therefore, if the value of the first element of the covariance matrix is less than cov_min for the lower order, then the lower order fit is selected. + cov_min : `float` + A threshold value for the first element of the covariance matrix. + Sometimes the AIC will automatically select a higher-order + polynomial to a distribution that is clearly best fit by the + preceeding lower-order fit. For example, a second-order fit provides a + better fit for a perfect straight line. This is a bug in the numerical + rounding. Therefore, if the value of the first element of the + covariance matrix is less than cov_min for the lower order, then the + lower order fit is selected. returns ------- @@ -150,7 +173,7 @@ def aic_selector(x, y, poly_max=3, cov_min=1e-10): ''' q = 0 - N = float(len(x)) + N = 1.*len(x) try: while q < poly_max: k1, k2 = q+1, q+2 @@ -161,7 +184,7 @@ def aic_selector(x, y, poly_max=3, cov_min=1e-10): SSR2 = np.sum((np.polyval(p2, x) - y)**2) AIC1 = akaike_info_criterion_lsq(SSR1, k1, N) AIC2 = akaike_info_criterion_lsq(SSR2, k2, N) - if (AIC1 < (AIC2 + 2)) | (r1 < cov_min): + if (AIC1 < AIC2) | (r1 < cov_min): poly_ord, coeffs = q, p1 return poly_ord, list(coeffs) else: @@ -171,17 +194,116 @@ def aic_selector(x, y, poly_max=3, cov_min=1e-10): return poly_ord, list(coeffs) except: return 0, [1.0] + + + +def relative_root_mean_squared_error(true, pred): + '''Return the relative root mean squared error (RRMSE) + + Given a list of predicted and true values, calculate the RRMSE + + parameters + ---------- + true : `Iter` + The set of true (measured) values + pred : `Iter` + The set of predicted (model) values + + returns + ------- + rrmse : `float` + The RRMSE value + ''' + num = np.sum(np.square(true - pred)) + den = np.sum(np.square(pred)) + squared_error = num/den + rrmse = np.sqrt(squared_error) + return rrmse + + +def smooth_test(time, flux, n_avg=10): + '''Determine how to detrend a lightcurve, based on a smoothness algorithm + The idea of this function is to catch lightcurves that appear to have + periods longer than ~15 days, and are notably smooth. The function + calculates a sine fit to the (linearly) detrended lightcurve, then + smoothes it using a running mean. Finally, there are three criteria to + decide how the lightcurve should be detrended. A boolean flag is returned, + where False=individual groups and True=the whole lightcurve. - + parameters + ---------- + time : `Iter` + A set of time coordinates + flux : `Iter` + A set of flux coordinates + n_avg : `float`, optional, default=10 + The number of datapoints to be used for the running mean calculation. + + returns + ------- + smooth_flag : `bool` + A boolean flag to (partially) determine how the lightcurve should be + detrended (False=individual groups, True=the whole lightcurve) + ''' + + # 1) detrend the whole lightcurve by a linear fit, and calculate the MAD + t_new = time-time[0] + p1, r1, _,_,_ = np.polyfit(t_new, flux, 1, full=True) + f_new = flux/np.polyval(p1, t_new) + f_MAD = MAD(f_new, scale='normal') + + # 2) make a sine fit to the detrended lightcurve + pops, popsc = curve_fit(sin_fit_per, t_new, f_new, bounds=([0.5, 0.0, 0.0, 0.0], [1.5, 0.2, 100., 6.3])) + yp = sin_fit_per(t_new, *pops) + + # 3) smooth the arrays using a running mean + yp_sm = np.array(np.convolve(yp, np.ones(n_avg)/n_avg, mode='valid')) + flux_sm = np.array(np.convolve(f_new, np.ones(n_avg)/n_avg, mode='valid')) + t_sm = np.array(np.convolve(t_new, np.ones(n_avg)/n_avg, mode='valid')) + + # 4) subtract the smoothed "raw" flux by the smoothed sine fit + diff_flux = flux_sm - yp_sm + d_MAD = MAD(diff_flux, scale='normal') + + # 5) calculate the RRMSE between the detrended flux and the sine fit + rrmse = relative_root_mean_squared_error(f_new, yp) + + # 6) if: + # a) the predicted period from the sine fit is > 15. days + # b) the RRMSE is < 0.01 + # c) the ratio of the MAD between the differential and original flux is < 0.25 + if (rrmse < 0.01) & (pops[2] > 15.) & (pops[2] < 99.9) & (d_MAD/f_MAD < 0.25): + return True + else: + return False + -def normalisation_choice(t_orig, f_orig, lc_part, MAD_fac=2., poly_max=4): - '''Choose how to detrend the lightcurve, either as one component or in parts. + +def norm(t_orig, f_orig, lc_part, MAD_fac=2., poly_max=4): + '''Choose whether to detrend the lightcurve as one by individual groups. + + There are always at least two data components in a TESS sector because of + the finite time needed for data retrieval. This can sometimes lead to + discontinuities between components because of TESS systematics and the + temperature gradients across the photometer. These discontinuities can + cause the phase of the sinusoidal fit to change, leading to low power + output in the periodograms. Alternatively, if the data are all detrended + individually, but the data is relatively continuous, this can lead to + shorter period measurements. - There are always at least two data components in a TESS sector because of the finite time needed for data retrieval. This can sometimes lead to discontinuities between components because of TESS systematics and the temperature gradients across the photometer. These discontinuities can cause the phase of the sinusoidal fit to change, leading to low power output in the periodograms. Alternatively, if the data are all detrended individually, but the data is relatively continuous, this can lead to shorter period measurements. + The idea with this function is that a polynomial fit is made to each + component (chosen using the Aikaike Information Criterion, AIC). The + extrapolated flux value from component 1 is calculated at the point where + component 2 starts. If the difference between this value and the actual + normalised flux at this point is greater than a given threshold, the data + should be detrended separately. Otherwise the full lightcurve can be + detrended as a whole. - The idea with this function is that a polynomial fit is made to each component (chosen using the Aikaike Information Criterion). The extrapolated flux value from component 1 is calculated at the point where component 2 starts. If the difference between this value and the actual normalised flux at this point is greater than a given threshold, the data should be detrended separately. Otherwise the full lightcurve can be detrended as a whole. + In addition, if the "smooth" flag (calculated from the "smooth_test" + function) is True, then the lightcurve is detrended as a whole, regardless + of the outcome from this function. parameters ---------- @@ -189,20 +311,32 @@ def normalisation_choice(t_orig, f_orig, lc_part, MAD_fac=2., poly_max=4): The time coordinate f_orig : `Iterable` The original, normalised flux values - e_orig : `Iterable` - The error on "f_orig" lc_part : `Iterable` The running index for each contiguous data section in the lightcurve - MAD_fac : `float`, optional, default = 2. - The factor to multiply the median absolute deviation by. + MAD_fac : `float`, optional, default=2. + The factor which is to be multiplied by the median absolute deviation. + poly_max : `float`, optional, default=4 + The maximum polynomial order to test for the AIC evaluation. returns ------- norm_comp : `bool` - Determines whether the data should be detrended as one whole component (False) or in parts (True) + Determines whether the data should be detrended as one whole component + (False) or by individual groups (True, providing the smooth flag is + False). + f1_at_f2_0 : `list` + The extrapolated fluxes from group 1, calculated at the start of group 2. + f2_at_f2_0 : `list` + The first flux values from group 2 + f1_MAD : `list` + The MAD fluxes from group 1 + f2_MAD : `list` + The MAD fluxes from group 2 ''' norm_comp = False Ncomp = len(np.unique(lc_part)) + smooth = smooth_test(t_orig, f_orig) + f1_at_f2_0, f2_at_f2_0, f1_MAD, f2_MAD = [], [], [], [] if Ncomp > 1: i = 1 @@ -233,6 +367,8 @@ def normalisation_choice(t_orig, f_orig, lc_part, MAD_fac=2., poly_max=4): f2_MAD.append(MAD(f2_n, scale='normal')) norm_comp = False break + if smooth == True: + norm_comp = False return norm_comp, f1_at_f2_0, f2_at_f2_0, f1_MAD, f2_MAD @@ -244,9 +380,11 @@ def normalisation_choice(t_orig, f_orig, lc_part, MAD_fac=2., poly_max=4): def detrend_lc(t,f,lc, MAD_fac=2., poly_max=3): '''Detrend and normalise the lightcurves. - | This function runs 3 major operations to detrend the lightcurve, as follows: - | 1. Choose whether a zeroth- or first-order polynomial is the best fit to the full light-curve, using the Aikaike Information Criterion, and detrend the full lightcurve. - | 2. Decide whether to use the detrended lightcurve from part 1, or to separate the lightcurve into individual components and detrend each one separately. + | This function runs 3 operations to detrend the lightcurve, as follows: + | 1. Choose whether a zeroth- or first-order polynomial is the best fit to + the full light-curve, using AIC, and detrend the full lightcurve. + | 2. Decide whether to use the detrended lightcurve from part 1, or to + detrend individual groups. | 3. Return the detrended flux. parameters @@ -266,7 +404,10 @@ def detrend_lc(t,f,lc, MAD_fac=2., poly_max=3): returns ------- f_norm : `Iterable' - The corrected lightcurve after the detrending procedures. + The corrected lightcurve after the detrending procedures. + detr_dict : `dict` + "norm_comp, f1_at_f2_0, f2_at_f2_0, f1_MAD, f2_MAD" -> see norm_choice. + "s_fit, coeffs" -> see aic_selector ''' # 1. Choose the best detrending polynomial using the Aikaike Information Criterion, and @@ -277,8 +418,7 @@ def detrend_lc(t,f,lc, MAD_fac=2., poly_max=3): # 2. Decide whether to use the detrended lightcurve from part 1, or to separate the # lightcurve into individual components and detrend each one separately norm_comp, f1_at_f2_0, f2_at_f2_0, f1_MAD, f2_MAD = \ - normalisation_choice(t, f, lc, MAD_fac=MAD_fac, poly_max=poly_max) - + norm_choice(t, f, lc, MAD_fac=MAD_fac, poly_max=poly_max) # 3. Detrend the lightcurve following steps 1 and 2. s_fit, coeffs = [], [] if norm_comp: @@ -318,45 +458,45 @@ def clean_flux_algorithm(g): parameters ---------- g : `Iter` - The array of boolean results indicating if a condition is met (=1) or not (=0) + The outcome for each datapoint, qualified (=1) or not qualified (=0) returns ------- - start : `int` - The trimmed start point of the array - fin : `int` - The trimmed final part of the array + first : `int` + The trimmed first point of the array + last : `int` + The trimmed last point of the array ''' i, j = 0, len(g)-1 while i < j: if g[i] != 1: i+=1 else: - start=i + first=i break while j > 0: if g[j] != 1: j-=1 else: - fin=j + last=j break if j <= i: - start, fin = 0, len(g)-1 - return start, fin + first, last = 0, len(g)-1 + return first, last else: - return start, fin + return first, last def clean_edges_outlier(f, MAD_fac=2.): - '''Remove spurious outliers at the start and end parts of the lightcurve. + '''Remove spurious outliers at the start and end parts of groups. - The start and end point of each data section must have a flux value within - a given number of MAD from the median flux in the sector. This is done - because often after large time gaps the temperature of the sensors changes, - and including these components is likely to just result in a signal from - instrumental noise. + The start and end point of each group must have a flux value within a given + number of MAD from the median flux in the group. This is done because + during data downlinks, the temperature of the sensors can change notably. + Therefore the outlying flux points at the group edges are probably from + temperature instabilities. parameters ---------- @@ -367,9 +507,9 @@ def clean_edges_outlier(f, MAD_fac=2.): returns ------- - start : `int` + first : `int` The start index for the data string. - fin : `int` + last : `int` The end index for the data string. ''' f_med, f_MAD = np.median(f), MAD(f, scale='normal') @@ -378,28 +518,31 @@ def clean_edges_outlier(f, MAD_fac=2.): f_diff_med = np.median(np.absolute(f_diff)) try: g = (np.abs(f-f_med) < MAD_fac*f_MAD).astype(int) - start, fin = clean_flux_algorithm(g) + first, last = clean_flux_algorithm(g) except: logger.error(f'Something went wrong with the arrays when doing the lightcurve edge clipping') - start, fin = 0, len(g)-1 - return start, fin + first, last = 0, len(g)-1 + return first, last -def clean_edges_scatter(f, MAD_fac=2., len_IQR_raw=11, num_data_fac=0.1): - '''Remove highly-scattered data at the start and end parts of the lightcurve. +def clean_edges_scatter(f, MAD_fac=2., len_sub_raw=11, num_data_fac=0.1): + '''Remove highly-scattered data at the edges of each group. - Occasionally there are lightcurves that appear to have very scattered data at the start and end - points of the lightcurves. These can degrade the quality of the periodogram analysis, or even return - an incorrect period. + Some groups have very scattered fluxes at the edges, presumably because the + sensors are unstable before and after data downlinks. These can degrade the + quality of the periodogram analysis, or even lead to an incorrect period. - The idea is to group the first "n_IQR" datapoints, and calculate the median absolute deviation (MAD). - If this local MAD value is greater (less) than "MAD_fac" times the MAD of the full lightcurve, then - we flag this point with 0 (1). The first and last "(n_IQR-1)/2" in the lightcurve are given a constant - value. If the first/last MAD comparison yield a "1" value, then we do no extra cleaning to the lightcurve. + The idea is to group the first "n_sub" datapoints, and calculate the median + absolute deviation (MAD). If this local MAD value is greater (less) than + "MAD_fac" times the MAD of the full lightcurve, then the flag at this point + is 0 (1). The first and last "(n_sub-1)/2" in the lightcurve are given a + constant value. If the first/last MAD comparison yield a "1" value, then we + include the full group, including the datapoints replaced with constant + values -- i.e., no cleaning is necessary. - The number of datapoints used for the IQR is chosen as the minimum value of "len_IQR_raw", or - num_data_fac*(the number of data points in the whole set). + The value for n_sub is chosen as the minimum value of "len_sub_raw", or + num_data_fac*(the number of datapoints in the whole set). parameters ---------- @@ -407,65 +550,69 @@ def clean_edges_scatter(f, MAD_fac=2., len_IQR_raw=11, num_data_fac=0.1): The set of normalised flux coordinates MAD_fac : `float`, optional, default=2. The threshold number of MAD values to allow. - len_IQR_raw : `int`, optional, default=11 + len_sub_raw : `int`, optional, default=11 The number of data points to be used in the local MAD value. num_data_fac : `float`, optional, default=0.1 The factor to multiply the number of data points by. returns ------- - start : `int` + first : `int` The start index for the data string. - fin : `int` + last : `int` The end index for the data string. ''' - n_IQR =min(len_IQR_raw, int(num_data_fac*len(f))) - if n_IQR // 2 == 0: - n_IQR += 1 - p_e = int((n_IQR-1)/2) + n_sub =min(len_sub_raw, int(num_data_fac*len(f))) + if n_sub // 2 == 0: + n_sub += 1 + p_e = int((n_sub-1)/2) + # get the median time and flux, the median absolute deviation in flux # and the time difference for each neighbouring point. f_med, f_MAD = np.median(f), MAD(f, scale='normal') f_diff = np.zeros(len(f)) f_diff[1:] = np.diff(f) f_diff_med = np.median(np.absolute(f_diff)) -# f_mean = np.array(np.convolve(f_diff, np.ones(n_avg)/n_avg, mode='valid')) - f_x = np.array([MAD(f[i:i+n_IQR], scale='normal') for i in range(len(f)-n_IQR+1)]) -# f_x = np.array([iqr(f[i:i+n_avg]) for i in range(len(f)-n_avg+1)]) + f_x = np.array([MAD(f[i:i+n_sub], scale='normal') for i in range(len(f)-n_sub+1)]) f_diff_run = np.pad(f_x, (p_e, p_e), 'constant', constant_values=(MAD_fac*f_MAD, MAD_fac*f_MAD)) + try: g = (np.abs(f_diff_run) < MAD_fac*f_diff_med).astype(int) - start, fin = clean_flux_algorithm(g) - if start <= p_e: - start = 0 - elif start > p_e: - start = np.where(g)[0][p_e] - if fin >= len(g)-1-(2*p_e+1): - fin = len(g)-1 - elif fin < len(g)-1-(2*p_e+1): - fin = np.where(g)[0][-p_e] + first, last = clean_flux_algorithm(g) + if first <= p_e: + first = 0 + elif first > p_e: + first = np.where(g)[0][p_e] + if last >= len(g)-1-(2*p_e+1): + last = len(g)-1 + elif last < len(g)-1-(2*p_e+1): + last = np.where(g)[0][-p_e] except: logger.error(f'Something went wrong with the arrays when doing the lightcurve edge clipping') - start, fin = 0, len(g)-1 - return start, fin + first, last = 0, len(g)-1 + return first, last def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): - '''Produce the lightcurves using the cleaning, normalisation and detrending functions - - During each procedure, the function keeps a record of datapoints that are either kept - or rejected, allowing users to assess the amount of data loss. - - The function makes the following steps... - (1) select data points that are within "orig_mad_fac" (MAD) values of the median. - (2) clean the lightcurve from (1) using clean_lc algorithm. - (3) normalise the flux by dividing by the median value of the lightcurve produced in (2). - (4) Include only data from (3) that are within "norm_mad_fac" values of the median. - (5) detrend the lightcurve produced in (4) following the normalisation choice and the - appropriate AIC-selected polynomials. - + '''Process the lightcurve: cleaning, normalisation and detrending functions + + | During each procedure, the function keeps a record of datapoints that are + | kept or rejected, allowing users to assess the amount of data loss. + + | The function makes the following steps... + | 1. normalise the original flux points + | 2. split the lightcurve into 'time segments' + | 3. remove very sparse elements from the lightcurve + | 4. run the first detrending process to pass to the cleaning function. + | 5. clean the lightcurve edges from outliers + | 6. clean the lightcurve edges from scattered data + | 7. finally cut out data that are extreme outliers. + | 8. divide each lightcurve component by the median flux value + | of qualifying data points. + | 9. return the dictionary + parameters ---------- f_lc : `dict' @@ -482,35 +629,40 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): min_comp_frac : `float', optional, default=0.1 The minimum relative size of a flux component when correcting for sparse data in the cleaning functions. - orig_mad_fac : `float', optional, default=20. - The factor of MAD values for the initial flux values (1). For inclusion - in the lightcurve the initial flux values must lie within "orig_mad_fac" - times the MAD value from the median flux. - norm_mad_fac : `float', optional, default=2. - The factor of MAD for the cleaned lightcurve flux values (3). + outl_mad_fac : `float', optional, default=3. + The factor of MAD for the cleaned lightcurve flux values. returns ------- - f_lc : `dict' + f_lc : `dict` A dictionary storing the full set of results from the lightcurve analysis. As well as the keys from the inputs, the final keys returned are: - 1: "pass_mad_1" -> datapoints that satisfy (1), boolean - 2: "pass_clean" -> datapoints that qualify from (2), boolean - 3: "nflux" -> the normalised flux, which is calculated by dividing the - median flux of datapoints that have True "pass_clean" values - 4: "enflux" -> the normalised errors for nflux - 5: "lc_part" -> the indexed lightcurve value calculated during the cleaning. - 6: "pass_mad_2" -> datapoints that qualify from (4), boolean - 7: "nflux_detrend" -> the normalised, detrended flux values. + 1: "time" -> the time coordinate. + 2: "mag" -> the TESS magnitude. + 3: "(reg/cbv)_oflux" -> the flux calculated from aperture photometry. + 4: "eflux" -> the error bar on (reg/cbv)_oflux. + 5: "nflux_ori" -> the normalised fluxes from (3). + 6: "nflux_err" -> the error bars on (5). + 7: "nflux_dtr" -> the normalised fluxes after the detrending steps. + 8: "lc_part" -> an index referring to each group in the lightcurve. + 9: "pass_sparse" -> boolean from `remove_sparse_data` + 10: "pass_clean_outlier" -> boolean from clean_edges_outlier. + 11: "pass_clean_scatter" -> boolean from clean_edges_scatter. + 12: "pass_full_outlier" -> boolean from the final outlier rejection. + detr_dict : `dict` + The dictionary returned from `detrend_lc` ''' - +time,mag,reg_oflux,eflux,nflux_ori,nflux_err,nflux_dtr,lc_part,pass_sparse,pass_clean_outlier,pass_clean_scatter,pass_full_outlier + # (1) normalise the original flux points f_lc['nflux_ori'] = f_lc[f'{f_orig}']/np.median(f_lc[f'{f_orig}']) f_lc['nflux_err'] = f_lc['eflux']/f_lc[f'{f_orig}'] logger.info('part1: initial normalisation -> done!') + # (2) split the lightcurve into 'time segments' ds1, df1 = get_time_segments(f_lc["time"]) logger.info('part2: time segmentation -> done!') + # (3) remove very sparse elements from the lightcurve comp_lengths = np.array([f-s for s, f in zip(ds1, df1)]) std_crit_val = int(np.sum(comp_lengths)*min_comp_frac) @@ -519,6 +671,7 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): for s, f in zip(ds2, df2): f_lc["pass_sparse"][s:f] = True logger.info('part3: remove sparse data -> done!') + # (4) run the first detrending process to pass to the cleaning function. f_lc["lc_part"] = np.zeros(len(f_lc["time"]), dtype=int) for i, (s, f) in enumerate(zip(ds2, df2)): @@ -527,6 +680,7 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): f_lc["nflux_dtr"] = np.full(len(f_lc["time"]), -999.) f_lc["nflux_dtr"][g_cln], detr_dict = detrend_lc(f_lc["time"][g_cln], f_lc["nflux_ori"][g_cln], f_lc["lc_part"][g_cln], poly_max=1) logger.info('part4: detrending -> done!') + # (5) clean the lightcurve edges from outliers ds3, df3 = [], [] for lc in np.unique(f_lc["lc_part"][g_cln]): @@ -538,7 +692,8 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): for s, f in zip(ds3, df3): f_lc["pass_clean_outlier"][s:f] = True logger.info('part5: clean edges, outliers -> done!') - # (5) clean the lightcurve edges from scattered data + + # (6) clean the lightcurve edges from scattered data ds4, df4 = [], [] for lc in np.unique(f_lc["lc_part"][g_cln]): g = np.where(f_lc["lc_part"] == lc)[0] @@ -550,12 +705,6 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): f_lc["pass_clean_scatter"][s:f] = True logger.info('part6: clean edges, scatter -> done!') - # (6) detrend the original lightcurve, but only using the data that passed the - # the previous criteria -# g_cln = f_lc["pass_clean"] -# f_lc["nflux_dtr2"] = np.full(len(f_lc["time"]), -999.) -# f_lc["nflux_dtr2"][g_cln], detr_dict = detrend_lc(f_lc["time"][g_cln], f_lc["nflux_ori"][g_cln], f_lc["lc_part"][g_cln], poly_max=1) - # (7) finally cut out data that are extreme outliers. med_lc = np.median(f_lc["nflux_dtr"][g_cln]) MAD_lc = MAD(f_lc["nflux_dtr"][g_cln], scale='normal') @@ -564,8 +713,24 @@ def run_make_lc_steps(f_lc, f_orig, min_comp_frac=0.1, outl_mad_fac=3.): if abs(f_lc["nflux_dtr"][f] - med_lc) < outl_mad_fac*MAD_lc: f_lc["pass_full_outlier"][f] = True logger.info('part7: remove extreme points -> done!') - logger.info('FINISHED!') - # (8) return the dictionary + + # (8) divide each lightcurve component by the median flux value of qualifying data points. + for lc in np.unique(f_lc["lc_part"][g_cln]): + g = np.where(f_lc["lc_part"] == lc)[0] + gx = np.logical_and.reduce([ + f_lc["pass_sparse"][g], + f_lc["pass_clean_scatter"][g], + f_lc["pass_clean_outlier"][g], + f_lc["pass_full_outlier"][g] + ]) + flux_vals = f_lc["nflux_dtr"][g[gx]] + med_flux = np.median(flux_vals[flux_vals > 0.]) + + f_lc["nflux_dtr"][f_lc["nflux_dtr"] < 0] = -999 + logger.info('part8: write the dictionary -> done!') + + # (9) return the dictionary + logger.info('part9: FINISHED!') return f_lc, detr_dict @@ -597,24 +762,56 @@ def sin_fit(x, y0, A, phi): +def sin_fit_per(t, y0, A, per, phi): + ''' + Returns the best parameters (y_offset, amplitude, and phase) to a regular + sinusoidal function. + + parameters + ---------- + t : `Iterable` + list of input values + y0 : `float` + The midpoint of the sine curve + A : `float` + The amplitude of the sine curve + per : `float` + The period of the sine curve + phi : `float` + The phase angle of the sine curve + + returns + ------- + sin_fit_per : `list` + A list of sin curve values. + ''' + sin_fit_per = y0 + A*np.sin((2.*np.pi*t/per) + phi) + return sin_fit_per + + -def test_cbv_fit(t, of, cf): - '''Run a score test to determine whether the cbv-corrected lightcurve should be considered. + +def cbv_fit_test(t, of, cf): + '''Determine whether the cbv-corrected lightcurve should be considered. + + Whilst the cbv-corrected flux are designed to eliminate systematic + artefacts by identifying features common to many stars (using principle + component analysis), the routine can overfit the data, and often the cbv + corrections inject too much unwanted noise (particularly for targets with + low signal to noise). - Whilst the cbv-corrected flux are designed to eliminate systematic artefacts by identifying - features common to many stars (using PCA), the routine can overfit the data, and often the - cbv corrections inject too much unwanted noise (particularly for targets with low signal - to noise). + Therefore the plan here is to assess lightcurves produced by the cbv + corrections by comparing basic attributes with the regular (non-corrected) + lightcurves. These scores come down to: - Therefore the plan here is to assess lightcurves produced by the cbv corrections - by comparing basic attributes with the non-corrected lightcurves. These scores come down - to the number of outliers (test 1), size of the median absolute deviation (test 2), and - which lightcurve provides the lowest chi-squared value to a sinusoidal fit (test 3). + 1: the number of outliers + 2: the size of the median absolute deviation + 3: which lightcurve provides the lowest chi-squared value to a sine fit. - If the "original lightcurve" scores higher, then the cbv-corrected lightcurve is not - considered for further analysis. + If the "original lightcurve" scores higher, then the cbv-corrected + lightcurve is not considered for further analysis. parameters ---------- @@ -676,19 +873,21 @@ def test_cbv_fit(t, of, cf): def make_lc(phot_table, name_lc='target', store_lc=False, lc_dir='lc'): '''Construct the normalised, detrended, cleaned TESS lightcurve. - This is essentially a parent function that performs all the steps in fixing the lightcurve. + This is essentially a parent function that performs all the steps in fixing + the lightcurve. - The returned product is an array containing the new tabulated lightcurve data for the - original (unfiltered) aperture photometry, and (if necessary) another one for the CBV-corrected - fluxes (see the 'test_cbv_fit' function for more information.) + The returned product is an array containing the new tabulated lightcurve + data for the original (unfiltered) aperture photometry, and (if necessary) + another one for the CBV-corrected fluxes (see the 'cbv_fit_test' function + for more information.) parameters ---------- phot_table : `astropy.table.Table` or `dict` - | The data table containing aperture photometry returned by aper_run.py. Columns must include: + | The data table containing aperture photometry. Columns must include: | "time" -> The time coordinate for each image | "mag" -> The target magnitude - | "reg_oflux" or "cbv_oflux" -> The total flux subtracted by the background flux + | "(reg/cbv)_oflux" -> The total flux subtracted by the background flux | "flux_err" -> The error on flux_corr name_lc : `str`, optional, default='target' The name of the file which the lightcurve data will be saved to. @@ -703,17 +902,16 @@ def make_lc(phot_table, name_lc='target', store_lc=False, lc_dir='lc'): final_tabs : `list` A list of tables containing the lightcurve data These are for the original lightcurve, and the cbv-corrected lightcurve - if required and it satisfies the criteria from test_cbv_fit. + if required and it satisfies the criteria from cbv_fit_test. ''' f_labels = ['reg_oflux'] cbv_ret = False if "cbv_oflux" in phot_table.colnames: f_labels.append('cbv_oflux') - use_cbv = test_cbv_fit(phot_table["time"], phot_table["reg_oflux"], phot_table["cbv_oflux"]) + use_cbv = cbv_fit_test(phot_table["time"], phot_table["reg_oflux"], phot_table["cbv_oflux"]) if use_cbv: cbv_ret = True - final_tabs = [] for f_label in f_labels: final_lc = {} @@ -722,8 +920,15 @@ def make_lc(phot_table, name_lc='target', store_lc=False, lc_dir='lc'): final_lc[f'{f_label}'] = phot_table[f'{f_label}'].data final_lc["eflux"] = phot_table["flux_err"].data flux_dict, detr_dict = run_make_lc_steps(final_lc, f_label) + keyorder = ['time','mag',f_label,'eflux','nflux_ori','nflux_err','nflux_dtr','lc_part','pass_sparse','pass_clean_outlier','pass_clean_scatter','pass_full_outlier'] + flux_dict = {k: flux_dict[k] for k in keyorder} + tab_format = ['.6f','.6f','.6f','.6f', + '.6f','.4e','.6f','%i', + '%s','%s','%s','%s'] if len(flux_dict["time"]) > 50: flux_tab = Table(flux_dict) + for k, f in zip(keyorder, tab_format): + flux_tab[k].info.format = f if f_label == "reg_oflux": final_tabs.append(flux_tab) if (f_label == "cbv_oflux") and (cbv_ret): @@ -737,3 +942,5 @@ def make_lc(phot_table, name_lc='target', store_lc=False, lc_dir='lc'): convert_file.write(json.dumps(detr_dict)) return final_tabs + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/logger.py b/tessilator/logger.py new file mode 100644 index 00000000..96786b0c --- /dev/null +++ b/tessilator/logger.py @@ -0,0 +1,11 @@ +import logging + +def logger_tessilator(name_log): + logger = logging.getLogger(__name__) + f_handler = logging.FileHandler(f'{name_log}.log') + f_handler.setLevel(logging.INFO) + f_format = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + f_handler.setFormatter(f_format) + logger.addHandler(f_handler) + return logger + diff --git a/tessilator/makeplots.py b/tessilator/makeplots.py index 1db48c90..a91f3d8f 100644 --- a/tessilator/makeplots.py +++ b/tessilator/makeplots.py @@ -1,13 +1,20 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, 2024 -Licence: MIT 2023 +Licence: MIT 2024 Generate pixel images, light-curves and periodogram plots ''' + +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal import os + +# Third party from astropy.table import Table import numpy as np import matplotlib as mpl @@ -17,15 +24,23 @@ from mpl_toolkits.axes_grid1.inset_locator import inset_axes from collections.abc import Iterable -__all__ = ['make_plot'] + +# Local application +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### + -def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./plots', XY_ctr=(10,10), - XY_contam=None, p_min_thresh=0.1, p_max_thresh=50., Rad=1.0, - SkyRad = [6.,8.], nc='nc'): +# initialize the logger object +logger = logger_tessilator(__name__) + +def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./plots', XY_ctr=(10,10), XY_contam=None, p_min_thresh=0.1, p_max_thresh=50., Rad=1.0, SkyRad = [6.,8.], nc='nc'): '''Produce a plot of tessilator results. - | This module produces a 4-panel plot displaying information from the tessilator analysis. These are: + | This module produces a 4-panel plot displaying information from the + tessilator analysis. These are: | 1) An TESS cut-out image of the target, with aperture and sky annulus. | 2) A power vs period plot from the Lomb-Scargle periodogram analysis. | 3) A lightcurve of the normalised flux. @@ -60,10 +75,11 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl SkyRad : `Iterable`, size=2, optional, default=[6.,8.] The inner and outer background annuli from aperture photometry nc : `str`, optional, default='nc' - A string to describe the type of noise correction applied to the lightcurve. + Describes the type of noise correction applied to the lightcurve. + returns ------- - Nothing returned. The plot produced is saved to file. + Nothing returned. The resulting plot is saved to file. ''' t_0 = clean["time"][0] c_1 = clean["pass_sparse"].data @@ -82,7 +98,6 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl best_fit_type = 'linear' else: best_fit_type = 'sine' - fsize = 22. lsize = 0.9*fsize fig, axs = plt.subplots(2,2, figsize=(20,15)) @@ -115,7 +130,6 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl if isinstance(XY_contam, Iterable): axs[0,0].scatter(XY_contam[:, 0], XY_contam[:, 1], marker='X', s=400, color='orange') - divider = make_axes_locatable(axs[0,0]) cax = divider.new_horizontal(size='5%', pad=0.4) fig.add_axes(cax) @@ -126,7 +140,7 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl axs[0,1].grid(True) axs[0,1].set_xlabel("Period (days)", fontsize=fsize) axs[0,1].set_ylabel("Power", fontsize=fsize) - axs[0,1].semilogx(LS_dict['period'], LS_dict['power']) + axs[0,1].semilogx(LS_dict['period_a_1'], LS_dict['power_a_1']) [axs[0,1].axhline(y=i, linestyle='--', color='grey', alpha=0.8) \ for i in LS_dict['FAPs']] axs[0,1].text(0.01,0.94, f"Best fit: {best_fit_type}", @@ -153,8 +167,9 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl f"{LS_dict['Gauss_1'][1]:.3f} $\\pm$" f"{LS_dict['Gauss_1'][2]:.3f}", fontsize=lsize, horizontalalignment='right', - transform=axs[0,1].transAxes) - + transform=axs[0,1].transAxes) + if LS_dict['shuffle_period'] > 0: + axs[0,1].axvline(x=LS_dict['period_1'], color='red', linewidth=3, alpha=0.3) axs[1,0].set_xlim([0, 30]) axs[1,0].set_xlabel("Time (days)", fontsize=fsize) axs[1,0].set_ylim( @@ -197,8 +212,9 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl ax2=axs[1,0].twinx() ax2.set_position([0.05,0.3,0.90,0.2]) ax2.invert_yaxis() - ax2.scatter(clean_orig_time, clean_orig_mag, s=0.3, alpha=0.3, color="b", marker="x") - ax2.set_ylabel("TESS magnitude", c="b",fontsize=fsize) + if not np.all(clean_orig_mag.data == -999.): + ax2.scatter(clean_orig_time[clean_orig_mag>-999], clean_orig_mag[clean_orig_mag>-999], s=0.3, alpha=0.3, color="b", marker="x") + ax2.set_ylabel("TESS magnitude", c="b",fontsize=fsize) axs[1,1].set_xlim([0,1]) axs[1,1].set_xlabel("phase", fontsize=fsize) @@ -225,3 +241,6 @@ def make_plot(im_plot, clean, LS_dict, scc, t_table, name_target, plot_dir='./pl f"{scc[1]}", f"{scc[2]}", f"{nc}"])+'.png' plt.savefig(f'{plot_dir}/{plot_name}', bbox_inches='tight') plt.close('all') + + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/maketable.py b/tessilator/maketable.py index 2f8172eb..e37a3f70 100644 --- a/tessilator/maketable.py +++ b/tessilator/maketable.py @@ -1,8 +1,8 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, 2024 -Licence: MIT 2023 +Licence: MIT 2024 Make tabular data for tessilator @@ -11,25 +11,34 @@ in the tessilator. ''' -__all__ = ['logger', 'table_from_simbad', 'get_twomass_like_name', 'table_from_coords', - 'table_from_table', 'get_gaia_data'] +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal +import warnings +import sys +#Third party from astropy.table import Table from astropy.io import ascii from astroquery.simbad import Simbad from astroquery.gaia import Gaia from astropy.coordinates import SkyCoord, ICRS import astropy.units as u - import numpy as np -import warnings -import logging -import sys - -logger = logging.getLogger(__name__) -# Read the data from the input file as an astropy table (ascii format) -# Ensure the source_id has a string type (not long integer). +# Local application +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### + + + +# initialize the logger object +logger = logger_tessilator(__name__) + + def table_from_simbad(input_names): @@ -53,7 +62,6 @@ def table_from_simbad(input_names): # set the column header = "ID" input_names.rename_column(input_names.colnames[0], 'ID') input_names["ID"] = input_names["ID"].astype(str) - # create arrays to store naming variables name_arr, is_Gaia = [], [0 for i in input_names] for i, input_name in enumerate(input_names["ID"]): @@ -69,7 +77,10 @@ def table_from_simbad(input_names): # the input name with warnings.catch_warnings(): warnings.simplefilter(action='ignore', category=UserWarning) - result_table = [Simbad.query_objectids(name) for name in name_arr] + try: + result_table = [Simbad.query_objectids(name) for name in name_arr] + except: + result_table = [None for name in name_arr] NameList = [] GaiaList = [] for r, res in enumerate(result_table): @@ -152,7 +163,7 @@ def get_twomass_like_name(coords): for r,d in zip(ra_hms_fin, dec_hms_fin): radec_fin.append(f'{r}{d}') return radec_fin - + def table_from_coords(coord_table, ang_max=10.0, type_coord='icrs', gaia_sys=True): @@ -172,8 +183,8 @@ def table_from_coords(coord_table, ang_max=10.0, type_coord='icrs', gaia_sys=Tru The coordinate system of the input positions. These can be 'icrs' (default), 'galactic' or 'ecliptic'. gaia_sys : `bool`, optional, default=True - Choose to format the data based on Gaia DR3. Note that no contamination can - be calculated if this is False. + Choose to format the data based on Gaia DR3. Note that no contamination + can be calculated if this is False. returns ------- @@ -225,7 +236,10 @@ def table_from_coords(coord_table, ang_max=10.0, type_coord='icrs', gaia_sys=Tru # target with the shortest number of characters (which is more likely to be # the most common reference name for the target). GDR3_Names = ["Gaia DR3 " + i for i in gaia_table['source_id'].astype(str)] - result_table = [Simbad.query_objectids(i) for i in GDR3_Names] + try: + result_table = [Simbad.query_objectids(i) for i in GDR3_Names] + except: + result_table = [None for i in GDR3_Names] NameList = [] for i, r in enumerate(result_table): if r is None: @@ -244,6 +258,8 @@ def table_from_coords(coord_table, ang_max=10.0, type_coord='icrs', gaia_sys=Tru new_order = ['name', 'source_id', 'ra', 'dec', 'parallax', 'Gmag', 'BPmag', 'RPmag'] gaia_table = gaia_table[new_order] return gaia_table + + def table_from_table(input_table, name_is_source_id=False): '''Generate the formatted astropy table from a pre-formatted astropy @@ -283,7 +299,10 @@ def table_from_table(input_table, name_is_source_id=False): else: GDR3_Names = ["Gaia DR3 " + i for i in gaia_table['source_id'].astype(str)] NameList = [] - result_table = [Simbad.query_objectids(i) for i in GDR3_Names] + try: + result_table = [Simbad.query_objectids(i) for i in GDR3_Names] + except: + result_table = [None for i in GDR3_Names] for i, r in enumerate(result_table): if r is None: NameList.append(gaia_table['source_id'][i].astype(str)) @@ -299,13 +318,17 @@ def get_gaia_data(gaia_table, name_is_source_id=False, type_coord='icrs', gaia_s '''Reads the input table and returns a table in the correct format for TESSilator. - | The table must be in comma-separated variable format, in either of these 3 ways: + | The table must be in comma-separated variable format, in either of these + 3 ways: | 1) A table with a single column containing the source identifier - | * note that this is the preferred method since the target identified in the Gaia query - is unambiguously the same as the input value. Also, the name match runs faster than - the coordinate match using astroquery. - | 2) A table with sky-coordinates in either the 'icrs' (default),'galactic', or 'ecliptic' system. - | * note this is slower because of the time required to run the Vizier query. + | * note that this is the preferred method since the target identified + in the Gaia query is unambiguously the same as the input value. + Also, the name match runs faster than the coordinate match using + astroquery. + | 2) A table with sky-coordinates in either the 'icrs' (default), + 'galactic', or 'ecliptic' system. + | * note this is slower because of the time required to run the Vizier + query. | 3) A table with all 7 columns already made. parameters @@ -330,14 +353,15 @@ def get_gaia_data(gaia_table, name_is_source_id=False, type_coord='icrs', gaia_s | The table ready for TESSilator analysis, with the columns: * name: the preferred choice of source identifier * source_id: the Gaia DR3 source identifier - * ra: right ascension (icrs) or longditude (galactic, barycentricmeanecliptic) - * dec: declination (icrs) or latitude (galactic, barycentricmeanecliptic) + * ra: right ascension (icrs) or longditude (galactic, + barycentricmeanecliptic) + * dec: declination (icrs) or latitude (galactic, + barycentricmeanecliptic) * parallax: parallax from Gaia DR3 (in mas) * Gmag: the apparent G-band magnitude from Gaia DR3 * BPmag: the apparent BP-band magnitude from Gaia DR3 * RPmag: the apparent RP-band magnitude from Gaia DR3 ''' - if len(gaia_table.colnames) == 1: tbl = table_from_simbad(gaia_table) elif len(gaia_table.colnames) == 2: @@ -349,3 +373,6 @@ def get_gaia_data(gaia_table, name_is_source_id=False, type_coord='icrs', gaia_s following formats: \n [1] source_id \n [2] ra and dec \ \n [3] source_id, ra, dec, parallax, Gmag, BPmag and RPmag') return tbl + + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/periodogram.py b/tessilator/periodogram.py index 942dcd8c..4b35f2aa 100644 --- a/tessilator/periodogram.py +++ b/tessilator/periodogram.py @@ -1,22 +1,22 @@ ''' -Alexander Binks & Moritz Guenther, December 2023 +Alexander Binks & Moritz Guenther, 2024 -Licence: MIT 2023 +Licence: MIT 2024 A set of functions to perform a Lomb-Scargle periodogram analysis to the TESS lightcurves. ''' -# imports -import logging -__all__ = ['check_for_jumps', 'gauss_fit', 'gauss_fit_peak', 'get_next_peak', 'is_period_cont', 'logger', 'mean_of_arrays', 'run_ls', 'sin_fit'] - - +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal import warnings # Third party imports import numpy as np import os +import matplotlib.pyplot as plt from astropy.table import Table from astropy.coordinates import SkyCoord @@ -26,23 +26,28 @@ import astropy.units as u from astropy.stats import akaike_info_criterion_lsq - from scipy.stats import median_abs_deviation as MAD +from scipy.stats import mode +from scipy.stats import chi2 + from scipy.optimize import curve_fit import itertools as it from collections.abc import Iterable + # Local application imports from .fixedconstants import * +from .logger import logger_tessilator +############################################################################### +############################################################################### +############################################################################### -# initialize the logger object -logger = logging.getLogger(__name__) -#logger_aq = logging.getLogger("astroquery") -logger.setLevel(logging.ERROR) +# initialize the logger object +logger = logger_tessilator(__name__) @@ -50,9 +55,14 @@ def check_for_jumps(time, flux, lc_part, n_avg=10, thresh_diff=10.): '''Identify if the lightcurve has jumps. - A jumpy lightcurve is one that has small contiguous data points that change in flux significantly compared to the amplitude of the lightcurve. These could be due to some instrumental noise or response to a non-astrophysical effect. They may also be indicative of a stellar flare or active event. + A jumpy lightcurve is one that has small contiguous data points that change + in flux significantly compared to the amplitude of the lightcurve. These + could be due to some instrumental noise or response to a non-astrophysical + effect. They may also be indicative of a stellar flare or active event. - This function takes a running average of the differences in flux, and flags lightcurves if the absolute value exceeds a threshold. These will be flagged as "jumpy" lightcurves. + This function takes a running average of the differences in flux, and flags + lightcurves if the absolute value exceeds a threshold. These will be + flagged as "jumpy" lightcurves. parameters ---------- @@ -65,11 +75,12 @@ def check_for_jumps(time, flux, lc_part, n_avg=10, thresh_diff=10.): n_avg : `int`, optional, default=10 The number of data points to calculate the running average thresh_diff : `float`, optional, default=10. - The threshold value, which, if exceeded, will yield a "jumpy" lightcurve + The threshold value, which, if exceeded, will yield a "jumpy" + lightcurve returns ------- - jump_flag : `Boolean` + jump_flag : `bool` This will be True if a jumpy lightcurve is identified, otherwise False. ''' @@ -94,6 +105,7 @@ def check_for_jumps(time, flux, lc_part, n_avg=10, thresh_diff=10.): return jump_flag + def gauss_fit(x, a0, x_mean, sigma): '''Construct a simple Gaussian. @@ -121,12 +133,11 @@ def gauss_fit(x, a0, x_mean, sigma): return gaussian -def gauss_fit_peak(period, power): - ''' - Applies the Gaussian fit to the periodogram. If there are more than 3 data - points (i.e., more data points than fixed parameters), the "gauss_fit" +def gauss_fit_peak(period, power, max_power=1): + '''Applies the Gaussian fit to the periodogram. If there are more than 3 + datapoints (i.e., more datapoints than fixed parameters), the "gauss_fit" module is used to return the fit parameters. If there are 3 or less points, - the maximum peak is located and 9 data points are interpolated between the + the maximum peak is located and 9 datapoints are interpolated between the 2 neighbouring data points of the maximum peak, and the "gauss_fit" module is applied. @@ -136,6 +147,8 @@ def gauss_fit_peak(period, power): The period values around the peak. power : `Iterable` The power values around the peak. + max_power : `float`, optional, default=1 + The maximum value for the power output. returns ------- @@ -149,7 +162,7 @@ def gauss_fit_peak(period, power): try: popt, _ = curve_fit(gauss_fit, period, power, bounds=([0, period[0], 0], - [1., period[-1], period[-1]-period[0]])) + [max_power, period[-1], period[-1]-period[0]])) ym = gauss_fit(period, *popt) except: print(f"Couldn't find the optimal parameters for the Gaussian fit!") @@ -177,7 +190,8 @@ def gauss_fit_peak(period, power): return popt, ym -def get_next_peak(power, frac_peak=0.85): + +def get_next_peak(power, frac_peak=0.85, option=None): '''An algorithm to identify the "next"-highest peak in the periodogram parameters @@ -187,6 +201,10 @@ def get_next_peak(power, frac_peak=0.85): frac_peak : `float`, optional, default=0.85 The relative height of the maximum peak, below which the data will be included. + option : `float`, optional, default=None + The value that the power must also be below. If kept as None, this is + equal to frac_peak*the highest power in the array. + returns ------- a_o : `list` @@ -196,16 +214,22 @@ def get_next_peak(power, frac_peak=0.85): a = np.arange(len(power)) p_m = np.argmax(power) + + if not option: + cond_2 = frac_peak*power[p_m] + else: + cond_2 = option + x = p_m while (power[x-1] < power[x]) and (x > 0): x = x-1 p_l = x p_lx = 0 - while (power[p_l] > frac_peak*power[p_m]) and (p_l > 1): + while (power[p_l] > cond_2) and (p_l > 1): p_lx = 1 p_l = p_l - 1 if p_lx == 1: - while (power[p_l] > power[p_l-1]) and (p_l > 0): + while (power[p_l-1] < power[p_l]) and (p_l > 0): p_l = p_l - 1 if p_l < 0: p_l = 0 @@ -217,19 +241,20 @@ def get_next_peak(power, frac_peak=0.85): x = x+1 p_r = x p_rx = 0 - while (power[p_r] > frac_peak*power[p_m]) and (p_r < len(power)-3): + while (power[p_r] > cond_2) and (p_r < len(power)-3): p_rx = 1 p_r = p_r + 1 if p_rx == 1: - while (power[p_r] > power[p_r+1]) and (p_r < len(power)-2): + while (power[p_r+1] < power[p_r]) and (p_r < len(power)-2): p_r = p_r + 1 if p_r > len(power)-1: p_r = len(power)-1 - a_g = a[p_l:p_r+1] - a_o = a[np.setdiff1d(np.arange(a.shape[0]), a_g)] elif x == len(power)-1: - a_g = a[x] - a_o = a[0:x] + p_r = x + + #return the indices that do not constitute part of the specific periodogram peak. + a_g = a[p_l:p_r+2] + a_o = a[np.setdiff1d(np.arange(a.shape[0]), a_g)] return a_o @@ -271,24 +296,22 @@ def is_period_cont(d_target, d_cont, t_cont, frac_amp_cont=0.5): flux_frac = 10**(t_cont["log_flux_frac"]) if abs(per_targ - per_cont) < (err_targ + err_cont): - if amp_targ/amp_cont > (frac_amp_cont*flux_frac): - output = 'a' - else: - output = 'b' + output = 'a' else: - output = 'c' + output = 'b' return output def mean_of_arrays(arr, num): - '''Calculate the mean and standard deviation of an array which is split into N components. + '''Calculate the mean and standard deviation of an array which is split + into N components. parameters ---------- arr : `Iterable` The input array num : `int` - The number of arrays to split the data (equally) into + The number of arrays in which to (equally) split the data returns ------- @@ -304,13 +327,44 @@ def mean_of_arrays(arr, num): -def get_Gauss_params_pg(period, power, ind, p_min_thresh=0.05, p_max_thresh=100.): -# print(period[ind], power[ind], ind) +def get_Gauss_params_pg(period, power, indices=None, max_power=1, p_min_thresh=0.05, gauss_min_frac=0.05, p_max_thresh=100.): + '''Calculate the best Gaussian-fit parameters to the periodogram output. + + parameters + ---------- + period : `Iterable` + The set of period outputs from the periodogram analysis. + power : `Iterable` + The set of power outputs from the periodogram analysis. + indices : `Iterable`, optional, default=None + The specific indices of the period/power array to be used. + max_power : `float`, optional, default=1 + The maximum value for the power output. + p_min_thresh : `float`, optional, default=0.05 + The minimum period (in days) to be calculated. + p_max_thresh : `float`, optional, default=100. + The maximum period (in days) to be calculated. + + results + ------- + returns + ------- + popt : `list` + The best-fit Gaussian parameters: A, B and C where A is the amplitude, + B is the mean and C is the uncertainty. + ym : `list` + The y values calculated from the Gaussian fit. + ''' # first -- check there are more than 3 values for the Gaussian fit. - if (isinstance(ind, Iterable)) and (len(ind) > 3): + if isinstance(indices, Iterable): + ind=indices + else: + ind=np.arange(len(period)) + + if len(ind) > 3: pow_r = max(power[ind])-min(power[ind]) - ind_fit = ind[power[ind] > min(power[ind]) + .05*pow_r] - popt, ym = gauss_fit_peak(period[ind], power[ind]) + ind_fit = ind[power[ind] >= min(power[ind]) + gauss_min_frac*pow_r] + popt, ym = gauss_fit_peak(period[ind], power[ind], max_power=max_power) else: if np.isclose(period[ind], p_max_thresh, atol=0.001).any(): popt = [1.0, p_max_thresh, 50.] @@ -341,9 +395,37 @@ def get_Gauss_params_pg(period, power, ind, p_min_thresh=0.05, p_max_thresh=100. -def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p_min_thresh=0.05, p_max_thresh=100., samples_per_peak=10, check_jump=False): - '''Run Lomb-Scargle periodogram and return a dictionary of results. +def sin_fit(x, y0, A, phi): + ''' + Returns the best parameters (y_offset, amplitude, and phase) to a regular + sinusoidal function. + + parameters + ---------- + x : `Iterable` + list of input values + y0 : `float` + The midpoint of the sine curve + A : `float` + The amplitude of the sine curve + phi : `float` + The phase angle of the sine curve + + returns + ------- + sin_fit : `list` + A list of sin curve values. + ''' + sin_fit = y0 + A*np.sin(2.*np.pi*x + phi) + return sin_fit + + + + +def initialise_LS_dict(lc_data, check_jump=False, p_min_thresh=0.05, p_max_thresh=100., samples_per_peak=50): + '''Run the periodogram analysis and store initial results to a dictionary. + parameters ---------- lc_data : `dict` @@ -352,135 +434,147 @@ def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p | "nflux" -> The detrended, cleaned, normalised flux values | "enflux" -> The uncertainty for each value of nflux | "lc_part" -> An running index describing the various contiguous sections - lc_type : `str`, optional, default='reg' - A label designating whether the lightcurve uses the original or CBV-corrected flux. - name_pg : `string`, optional, default='pg_target' - A file name which the periodogram output will be saved to. - pg_dir : `string`, optional, default='pg' - The name of the directory to store the periodogram data - n_sca : `int`, optional, default=10 - The number of evenly-split lightcurve parts used to measure the flux scatter. + check_jump : `bool`, optional, default=False + Choose to check the lightcurve for jumpy data, using the "check_for_jumps" + function. p_min_thresh : `float`, optional, default=0.05 The minimum period (in days) to be calculated. p_max_thresh : `float`, optional, default=100. The maximum period (in days) to be calculated. samples_per_peak : `int`, optional, default=10 The number of samples to measure in each periodogram peak. - check_jump : `bool`, optional, default=False - Choose to check the lightcurve for jumpy data, using the "check_for_jumps" - function. - returns + results ------- LS_dict : `dict` - A dictionary of parameters calculated from the periodogram analysis. These are: - | "median_MAD_nLC" : The median and median absolute deviation of the normalised lightcurve. - | "jump_flag" : A flag determining if the lightcurve has sharp jumps in flux. - | "period" : A list of period values from the periodogram analysis. - | "power" : A list of power values from the periodogram analysis. - | "period_best" : The period corrseponding to the highest power output. - | "power_best" : The highest power output. - | "time" : The time coordinate corresponding to the normalised lightcurve. - | "y_fit_LS" : The best fit sinusoidal function. - | "AIC_sine" : The Aikaike Information Criterion value of the best-fit sinusoid - | "AIC_line" : The Aikaike Information Criterion value of the best-fit linear function. - | "FAPs" : The power output for the false alarm probability values of 0.1, 1 and 10% - | "period_1" : The period corresponding to the highest peak - | "power_1" : The power corresponding to the highest peak - | "Gauss_fit_peak_parameters" : Parameters for the Gaussian fit to the highest power peak - | "Gauss_fit_peak_y_values" : The corresponding y-values for the Gaussian fit - | "period_around_1" : The period values covered by the Gaussian fit - | "power_around_1" : The power values across the period range covered by the Gaussian fit - | "period_not_1" : The period values not covered by the Gaussian fit - | "power_not_1" : The power values across the period range not covered by the Gaussian fit - | "period_2" : The period of the second highest peak. - | "power_2" : The power of the second highest peak. - | "period_3" : The period of the third highest peak. - | "power_3" : The power of the third highest peak. - | "period_4" : The period of the fourth highest peak. - | "power_4" : The power of the fourth highest peak. - | "phase_fit_x" : The time co-ordinates from the best-fit sinusoid to the phase-folded lightcurve. - | "phase_fit_y" : The normalised flux co-ordinates from the best-fit sinusoid to the phase-folded lightcurve. - | "phase_x" : The time co-ordinates from the phase-folded lightcurve. - | "phase_y" : The normalised flux co-ordinates from the phase-folded lightcurve. - | "phase_chisq" : The chi-square fit between the phase-folded lightcurve and the sinusoidal fit. - | "phase_col" : The cycle number for each data point. - | "pops_vals" : The best-fit parameters from the sinusoidal fit to the phase-folded lightcurve. - | "pops_cov" : The corresponding co-variance matrix from the "pops_val" parameters. - | "phase_scatter" : The typical scatter in flux around the best-fit. - | "frac_phase_outliers" : The fraction of data points that are more than 3 median absolute deviation values from the best-fit. - | "Ndata" : The number of data points used in the periodogram analysis. + The dictionary of initial parameters from the periodogram analysis + cln_lc : `dict` + The lightcurve data, with any lines containing a False flag removed. + ls : `astropy.timeseries.LombScargle` + The Lomb-Scargle periodogram object for the given lightcurve. + ''' - cln_cond = np.logical_and.reduce([ + # a_g: array of datapoints that form the Gaussian around the highest power + # a_o: the array for all other datapoints + LS_dict = {} + + cln_cond = (np.logical_and.reduce([ lc_data["pass_clean_scatter"], lc_data["pass_clean_outlier"], lc_data["pass_full_outlier"] - ]) - cln = lc_data[cln_cond] + ])) & (lc_data["nflux_dtr"] > -999.) + cln_lc = lc_data[cln_cond] - time = np.array(cln["time"]) - nflux = np.array(cln["nflux_dtr"]) - enflux = np.array(cln["nflux_err"]) + + LS_dict["time"] = np.array(cln_lc["time"]) + LS_dict["nflux"] = np.array(cln_lc["nflux_dtr"]) + LS_dict["enflux"] = np.array(cln_lc["nflux_err"]) + LS_dict["lc_part"] = np.array(cln_lc["lc_part"]) + + # calculate the median and MAD flux + LS_dict['median_MAD_nLC'] = [np.median(LS_dict["nflux"]), MAD(LS_dict["nflux"], scale='normal')] + + # calculate the TESS magnitude (median and MAD value) + # note that this is from the "ORIGINAL LIGHTCURVE" + try: + mag = lc_data["mag"] + LS_dict['Tmag_MED'], LS_dict['Tmag_MAD'] = np.median(mag[mag > -999]), MAD(mag[mag > -999]) + except: + LS_dict['Tmag_MED'], LS_dict['Tmag_MAD'] = -999, -999 + + + # assess the jump flag + LS_dict['jump_flag'] = -999 if check_jump: - lc_part = np.array(cln["lc_part"]) - jump_flag = check_for_jumps(time, nflux, lc_part) - med_f, MAD_f = np.median(nflux), MAD(nflux, scale='normal') - ls = LombScargle(time, nflux, dy=enflux) + LS_dict['jump_flag'] = check_for_jumps(LS_dict["time"], LS_dict["nflux"], LS_dict["lc_part"]) + + + # run the LS periodogram + ls = LombScargle(LS_dict["time"], LS_dict["nflux"], dy=LS_dict["enflux"]) frequency, power = ls.autopower(minimum_frequency=1./p_max_thresh, maximum_frequency=1./p_min_thresh, samples_per_peak=samples_per_peak) - if len(power) == 0: - return {} + p_m = np.argmax(power) + + LS_dict['a_1'] = np.arange(len(power)) + LS_dict['period_a_1'] = 1./frequency[::-1] + LS_dict['power_a_1'] = power[::-1] + LS_dict['period_1'] = 1.0/frequency[p_m] + LS_dict['power_1'] = power[p_m] + + + + # calculate the false alarm probability values, using the quick method try: FAP = ls.false_alarm_probability(power.max()) probabilities = [0.1, 0.05, 0.01] - FAP_test = ls.false_alarm_level(probabilities) + LS_dict['FAPs'] = ls.false_alarm_level(probabilities) except: logger.error(f'Something went wrong with the FAP test, maybe division by 0.') - FAP_test = np.array([0.3, 0.2, 0.1]) - p_m = np.argmax(power) + LS_dict['FAPs'] = np.array([0.3, 0.2, 0.1]) - y_fit_sine = ls.model(time, frequency[p_m]) - y_fit_sine_param = ls.model_parameters(frequency[p_m]) - chisq_model_sine = np.sum((y_fit_sine-nflux)**2/enflux**2)/(len(nflux)-3-1) - line_fit, _,_,_,_ = np.polyfit(time, nflux, 1, full=True) - y_fit_line = np.polyval(line_fit, time) - chisq_model_line = np.sum((y_fit_line-nflux)**2/enflux**2)/(len(nflux)-len(line_fit)-1) - AIC_sine, AIC_line = 2.*3. + chisq_model_sine, 2.*2. + chisq_model_line + LS_dict['shuffle_period'] = 0 + LS_dict['period_shuffle'] = -999 + LS_dict['period_shuffle_err'] = -999 - period_1 = 1.0/frequency[p_m] - power_1 = power[p_m] - period = 1./frequency[::-1] - power = power[::-1] + return LS_dict, cln_lc, ls + +def write_periodogram(LS_dict, name_pg='', pg_dir='', lc_type=''): + '''Save the period and power output from the periodogram analysis to file. + + parameters + ---------- + LS_dict : `dict` + The dictionary of periodogram results produced in initialise_LS_dict + name_pg : `str`, optional, default='' + The name of the file to save the periodogram results + pg_dir : `str`, optional, default='' + The directory of the file to save the periodogram results + lc_type : `str`, optional, default='' + An additional string for reference in the file name. + + returns + ------- + Nothing returned. The result is saved to file. + ''' if name_pg: res_table = Table(names=('period', 'power'), dtype=(float,float)) - for pe, po in zip(period, power): + for pe, po in zip(LS_dict['period_a_1'], LS_dict['power_a_1']): res_table.add_row([pe, po]) + res_table['period'].info.format = '.6f' + res_table['power'].info.format = '.4e' res_table.write(f'{pg_dir}/{name_pg}_{lc_type}.csv', overwrite=True) - # a_g: array of datapoints that form the Gaussian around the highest power - # a_o: the array for all other datapoints +def get_periodogram_peaks(LS_dict, n_peaks=4): + '''Calculate parameters for a given number of periodogram peaks + + This function locates the `n_peaks` highest peaks in the periodogram and + calculates the associated period, power and Gaussian-fit parameters. + + parameters + ---------- + LS_dict : `dict` + The dictionary of periodogram results produced in initialise_LS_dict + n_peaks : `int`, optional, default=4 + The number of periodogram peaks to analyse. - LS_dict = {} - LS_dict['a_1'] = np.arange(len(power)) - LS_dict['period_a_1'] = period - LS_dict['power_a_1'] = power - LS_dict['period_1'] = period_1 - LS_dict['power_1'] = power_1 - n_peaks = 4 + results + ------- + Nothing returned, the LS_dict dictionary is updated with new parameters. + ''' for i in 1+np.arange(n_peaks): try: # get the indices of all the peaks that were not part of the last peak LS_dict[f'a_{i+1}'] = get_next_peak(LS_dict[f'power_a_{i}']) # all the indices that 'are' part of the peak LS_dict[f'a_g_{i}'] = np.delete(np.array(LS_dict[f'a_{i}']), np.array(LS_dict[f'a_{i+1}'])) - LS_dict[f'Gauss_{i}'], LS_dict[f'Gauss_y_{i}'] = get_Gauss_params_pg(period, power, LS_dict[f'a_g_{i}']) + LS_dict[f'Gauss_{i}'], LS_dict[f'Gauss_y_{i}'] = get_Gauss_params_pg(LS_dict["period_a_1"], LS_dict["power_a_1"], indices=LS_dict[f'a_g_{i}']) # find all the new period values in the new array LS_dict[f'period_a_{i+1}'] = LS_dict[f'period_a_{i}'][LS_dict[f'a_{i+1}']] # find all the new power values in the new array @@ -496,25 +590,286 @@ def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p LS_dict[f'power_a_{i+1}'] = -999 LS_dict[f'period_{i+1}'] = -999 LS_dict[f'power_{i+1}'] = -999 - LS_dict.pop(f'a_{n_peaks+1}', None) - LS_dict.pop(f'period_a_{n_peaks+1}', None) - LS_dict.pop(f'power_a_{n_peaks+1}', None) - LS_dict.pop(f'period_{n_peaks+1}', None) - LS_dict.pop(f'power_{n_peaks+1}', None) + LS_dict.pop(f'a_{n_peaks+1}', None) + LS_dict.pop(f'period_a_{n_peaks+1}', None) + LS_dict.pop(f'power_a_{n_peaks+1}', None) + LS_dict.pop(f'period_{n_peaks+1}', None) + LS_dict.pop(f'power_{n_peaks+1}', None) try: - LS_dict['period_around_1'] = period[LS_dict['a_g_1']] - LS_dict['power_around_1'] = power[LS_dict['a_g_1']] + LS_dict['period_around_1'] = LS_dict["period_a_1"][LS_dict['a_g_1']] + LS_dict['power_around_1'] = LS_dict["power_a_1"][LS_dict['a_g_1']] except: LS_dict['period_around_1'] = -999 LS_dict['power_around_1'] = -999 + + + + +def shuffle_periodogram(lc_data, n_shuf_runs=100, p_min=0.1, p_max=100., n_min=0.1, n_max=1.0): + '''Generate period measurements by sampling subsets of the lightcurve. + + parameters + ---------- + lc_data : `dict` + A dictionary containing the lightcurve data. The keys must include + | "time" -> The time coordinate relative to the first data point + | "nflux" -> The detrended, cleaned, normalised flux values + | "enflux" -> The uncertainty for each value of nflux + | "lc_part" -> An running index describing the various contiguous sections + n_shuf_runs : `int`, optional, default=100 + The number of measurements to be made + p_min : `float`, optional, default=0.1 + The minimum period for the shuffling method + p_max : `float`, optional, default=100. + The maximum period for the shuffling method + n_min : `float`, optional, default=0.1 + The minimum fraction of a group to be used in the periodogram analysis + n_max : `float`, optional, default=1.0 + The maximum fraction of a group to be used in the periodogram analysis + + results + ------- + periods_out : `np.array` + The array of calculated periods. + ''' + period_arr = [] + sections = np.unique(lc_data['lc_part']) + for n_run in range(n_shuf_runs): + try: + s = np.random.choice(sections) + lc_s = lc_data[lc_data["lc_part"] == s] + n = np.random.uniform(low=n_min, high=n_max) + l_n = int(n*len(lc_s)) + n_start = int(np.random.uniform(low=0.0, high=len(lc_s)-l_n)) + n_fin = n_start + l_n + lc_use = lc_s[n_start:n_fin] + p_max = 4.*(lc_use['time'][-1] - lc_use['time'][0]) + time = np.array(lc_use["time"]) + nflux = np.array(lc_use["nflux_dtr"]) + p1, r1, _,_,_ = np.polyfit(time, nflux, 1, full=True) + nflux_n = nflux/np.polyval(p1, time) + + enflux = np.array(lc_use["nflux_err"]) + + ls = LombScargle(time, nflux_n, dy=enflux) + + frequency, power = ls.autopower(minimum_frequency=1./p_max, + maximum_frequency=1./p_min, + samples_per_peak=50) + p_m = np.argmax(power) + period_max = 1.0/frequency[p_m] + power_max = power[p_m] + period_arr.append(period_max) + except: + continue + periods_out = np.array(period_arr) + return periods_out + + +def relative_root_mean_squared_error(true, pred): + '''Return the relative root mean squared error (RRMSE) + + Given a list of predicted and true values, calculate the RRMSE + + parameters + ---------- + true : `Iter` + The set of true (measured) values + pred : `Iter` + The set of predicted (model) values + + returns + ------- + rrmse : `float` + The RRMSE value + ''' + num = np.sum(np.square(true - pred)) + den = np.sum(np.square(pred)) + squared_error = num/den + rrmse = np.sqrt(squared_error) + return rrmse + + + +def shuffle_check(cln_lc, LS_dict, shuffle_sections=False, n_shuf_runs=5000, p_min=0.05, p_max=100., n_min=.1, n_max=1., bin1=50, bin2=1000, n_peaks=4, make_plot_shuf=True, name_plot_shuf='example_plot_shuf.png'): + '''Choose the period from original periodogram, or from the shuffled method + + In the case of low signal to noise, the original periodogram analysis + will often predict an incorrect period because the noise is too dominant. + Therefore, an alternative period can be incorporated, which is capable of + detecting periods in noisy data. + + The idea is that a large number of periods are calculated from smaller + portions of the whole lightcurve, and then if the resulting distribution + of periods is small enough, then the shuffled period measurement is used as + the main period. + + The algorithm works as follows: + | 1. run the shuffle_periodogram function. + | 2. construct the period histogram from the results. + | 3. find the highest population bin, and select all neighbouring (period) values either side until this value becomes less than the median. + | 4. calculate the number of periods that lie within and outside the period range found in part (2) + | 5. construct another histogram for all periods that are within the range calculated in part (2) + | 6. normalise the histogram so the whole distribution integrates to 1.0. + | 7. ensure that the number of bins in the new histogram must be greater than 3, the number of periods outside the range is less than 0.5, and that the histogram must not peak at the start or end of the distribution. The process only continues if these conditions pass. Otherwise the shuffled periodogram value is not returned. + | 8. fit a Gaussian to the new histogram. + | 9. calculate "rrmse", the relative root mean square error. + | 10. if rrmse < 0.5, the FWHM to the Gaussian fit is < 0.05, and the final shuffled period (centroid of the Gaussian) differs from the original period measurement by more than 10%, then return the shuffled periodogram result as the determined period. We set the power output=1.0, and the uncertainty is given by the sigma-value calculated in the Gaussian fit. + | 11. finally, replace each set of nth "period, power and error" with the (n+1)th set, so the output set from the shuffled periodogram values take the highest significance. + + parameters + ---------- + cln_lc : `dict` + The lightcurve data, with any lines containing a False flag removed. + LS_dict : `dict` + The dictionary of periodogram results produced in initialise_LS_dict + and modified by get_periodogram_peaks + shuffle_sections : `bool`, optional, default=False + Choose to run the shuffled period analysis (True=yes, False=no) + n_shuf_runs : `int`, optional, default=5000 + The number of measurements to be made in shuffle_periodogram + p_min : `float`, optional, default=0.05 + The minimum period for the shuffling method in shuffle_periodogram + p_max : `float`, optional, default=100. + The maximum period for the shuffling method in shuffle_periodogram + n_min : `float`, optional, default=0.1 + The minimum fraction of a group to be used in the periodogram analysis + in shuffle_periodogram + n_max : `float`, optional, default=1.0 + The maximum fraction of a group to be used in the periodogram analysis + in shuffle_periodogram + bin1 : `int`, optional, default=50 + The number of histogram bins for the initial period distribution + bin2 : `int`, optional, default=1000 + The number of histogram bins for the refined period distribution + n_peaks : `int`, optional, default=4 + The number of peaks calculated in get_periodogram_peaks + make_plot_shuf : `bool`, optional, default=False + Choose to plot the outputs of the period distribution. + name_plot_shuf : `str`, optional, default='example_plot_shuf.png' + Choose the file name to save the period distribution plot. + + results + ------- + Nothing returned, the LS_dict dictionary is updated with new parameters. + ''' + if shuffle_sections: + try: + period_arr = shuffle_periodogram(cln_lc, n_shuf_runs=n_shuf_runs, p_min=p_min, p_max=p_max, n_min=n_min, n_max=n_max) + num_log10_per1, log10_per1 = np.histogram(np.log10(period_arr), bins=bin1) + + ind_others = get_next_peak(num_log10_per1, option=np.median(num_log10_per1)) + ind_log10_per = np.delete(np.arange(len(log10_per1)-1), ind_others) + n_others = np.sum(num_log10_per1[ind_others]) + n_log10 = np.sum(num_log10_per1[ind_log10_per]) + + x_l, x_u = log10_per1[ind_log10_per[0]], log10_per1[ind_log10_per[-1]] + + num_log10_per2, log10_per2 = np.histogram(np.log10(period_arr), bins=bin2, range=(x_l, x_u)) + log10_per2 = np.array([(log10_per2[i]+log10_per2[i+1])/2. for i in range(len(log10_per2)-1)]) + num_log10_per2 = num_log10_per2/np.sum(num_log10_per2) + + gauss_plot = False + if (len(ind_log10_per) > 3) & \ + (n_log10/(n_log10+n_others) > 0.5) & \ + (np.argmax(num_log10_per2) != 0) & \ + (np.argmax(num_log10_per2) != len(num_log10_per2)-1): + Gauss_param_log10_per, Gauss_log10_per = get_Gauss_params_pg(log10_per2, num_log10_per2, max_power=1.2*max(num_log10_per2)) + if len(Gauss_param_log10_per) != 0: + gauss_plot = True + obs, exp, n_df = num_log10_per2, Gauss_log10_per, len(Gauss_log10_per)-len(Gauss_param_log10_per)-1 + + rrmse = relative_root_mean_squared_error(obs, exp) + + p_shuf = 10**(Gauss_param_log10_per[1]) + p_shufu = 10**(Gauss_param_log10_per[1] + Gauss_param_log10_per[2]) + p_shufl = 10**(Gauss_param_log10_per[1] - Gauss_param_log10_per[2]) + p_shuf_err = 0.5*(p_shufu-p_shufl) + + if (rrmse < 0.5) & \ + (Gauss_param_log10_per[2] < 0.05) & \ + (((p_shuf/LS_dict['period_1'])<0.9) | ((p_shuf/LS_dict['period_1'])>1.1)): + + LS_dict['shuffle_period'] = 1 + LS_dict['period_shuffle'] = p_shuf + LS_dict['period_shuffle_err'] = p_shuf_err + for i in range(2,n_peaks+1): + LS_dict[f'period_{i}'] = LS_dict[f'period_{i-1}'] + LS_dict[f'Gauss_{i}'] = LS_dict[f'Gauss_{i-1}'] + LS_dict['period_1'] = p_shuf + LS_dict['power_1'] = 1.0 + LS_dict['Gauss_1'] = [1.0, p_shuf, p_shuf_err] + + if make_plot_shuf: + fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12,3)) + ax[0].set_xlabel('$\log_{10}$period [d]') + ax[0].set_ylabel('number of trials') + ax[0].hist(np.log10(period_arr), bins=bin1) + ax[0].axhline(np.median(num_log10_per1), color='darkorange') + ax[1].set_xlabel('$\log_{10}$period [d]') + ax[1].set_ylabel('normalised PDF') + ax[1].plot(log10_per2, num_log10_per2) + if gauss_plot: + ax[1].plot(log10_per2, Gauss_log10_per, color='darkorange') + nl = '\n' + ax[1].text(0.99, 0.85, f"$P_{{\\rm rot}}$ (shuf) [d]{nl}{p_shuf:.3f}+/-{p_shuf_err:.3f}", transform=ax[1].transAxes, horizontalalignment='right') + ax[1].text(0.99, 0.20, "\u2714", fontsize=30, color='green', transform=ax[1].transAxes, horizontalalignment='right') + else: + ax[1].text(0.99, 0.2, "\u2718", fontsize=30, color='red', transform=ax[1].transAxes, horizontalalignment='right') + plt.savefig(name_plot_shuf, bbox_inches='tight') + + except: + logger.error(f'An error occured with the the period shuffling method.') + LS_dict['period_shuffle'] = -9 + LS_dict['period_shuffle_err'] = -9 + + +def make_phase_curve(LS_dict, ls, n_sca=10): + '''Generate the phase-folded lightcurve using the peak periodogram period. + + This function performs several steps: + + 1) generate the phase-folded lightcurve + 2) use Aikake Information Criterion to determine whether a sine-fit or a + straight line is the most appropriate. + 3) Calculate the reduced chi-squared value for the sine fit. + 4) Calculate the typical amplitude and scatter in the phase-folded + lightcurve. + + parameters + ---------- + LS_dict : `dict` + The dictionary of periodogram results produced in initialise_LS_dict + and modified by get_periodogram_peaks and shuffle_check + ls : `astropy.timeseries.LombScargle` + The Lomb-Scargle periodogram object for the given lightcurve. + n_sca : `int`, optional, default=10 + The number of portions to split the phase-folded lightcurve. + + results + ------- + Nothing returned, the LS_dict dictionary is updated with new parameters. + ''' + time = LS_dict["time"] + nflux = LS_dict["nflux"] + enflux = LS_dict["enflux"] + freq_best = 1./LS_dict['period_1'] + + y_fit_sine = ls.model(time, freq_best) + y_fit_sine_param = ls.model_parameters(freq_best) + chisq_model_sine = np.sum((y_fit_sine-nflux)**2/enflux**2)/(len(nflux)-3-1) + line_fit, _,_,_,_ = np.polyfit(time, nflux, 1, full=True) + y_fit_line = np.polyval(line_fit, time) + chisq_model_line = np.sum((y_fit_line-nflux)**2/enflux**2)/(len(nflux)-len(line_fit)-1) + AIC_sine, AIC_line = 2.*3. + chisq_model_sine, 2.*2. + chisq_model_line + tdiff = np.array(time-min(time)) - nflux = np.array(nflux) - pha, cyc = np.modf(tdiff/period_1) + pha, cyc = np.modf(tdiff/LS_dict['period_1']) pha, cyc = np.array(pha), np.array(cyc) f = np.argsort(pha) - p = np.argsort(tdiff/period_1) + p = np.argsort(tdiff/LS_dict['period_1']) pha_fit, nf_fit, ef_fit, cyc_fit = pha[f], nflux[f], enflux[f], cyc[f].astype(int) pha_plt, nf_plt, ef_plt, cyc_plt = pha[p], nflux[p], enflux[p], cyc[p].astype(int) try: @@ -529,9 +884,6 @@ def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p # find the standard deviation in the measurements for each bin and use # the median of the standard deviation values to represent the final scatter # in the phase curve. - - sca_mean, sca_stdev = mean_of_arrays(nf_fit, n_sca) - sca_median = np.median(sca_stdev) Ndata = len(nflux) yp = sin_fit(pha_fit, *pops) @@ -540,19 +892,12 @@ def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p pha_sct = MAD(yp - nflux, scale='normal') fdev = 1.*np.sum(np.abs(nflux - yp) > 3.0*pha_sct)/Ndata + sca_mean, sca_stdev = mean_of_arrays(nf_fit/yp, n_sca) + sca_median = np.median(sca_stdev) - LS_dict['median_MAD_nLC'] = [med_f, MAD_f] - if check_jump: - LS_dict['jump_flag'] = jump_flag - else: - LS_dict['jump_flag'] = -999 - LS_dict['period'] = period - LS_dict['power'] = power - LS_dict['time'] = time LS_dict['y_fit_LS'] = y_fit_sine LS_dict['AIC_sine'] = AIC_sine LS_dict['AIC_line'] = AIC_line - LS_dict['FAPs'] = FAP_test LS_dict['phase_fit_x'] = pha_fit LS_dict['phase_fit_y'] = yp LS_dict['phase_x'] = pha_plt @@ -564,29 +909,102 @@ def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p LS_dict['phase_scatter'] = sca_median LS_dict['frac_phase_outliers'] = fdev LS_dict['Ndata'] = Ndata - return LS_dict -def sin_fit(x, y0, A, phi): - ''' - Returns the best parameters (y_offset, amplitude, and phase) to a regular - sinusoidal function. + + +def run_ls(lc_data, lc_type='reg', name_pg='pg_target', pg_dir='pg', n_sca=10, p_min_thresh=0.05, p_max_thresh=100., samples_per_peak=10, check_jump=False, shuffle_sections=False, n_shuf_runs=5000, n_peaks=4): + '''Run Lomb-Scargle periodogram and return a dictionary of results. parameters ---------- - x : `Iterable` - list of input values - y0 : `float` - The midpoint of the sine curve - A : `float` - The amplitude of the sine curve - phi : `float` - The phase angle of the sine curve + lc_data : `dict` + A dictionary containing the lightcurve data. The keys must include + | "time" -> The time coordinate relative to the first data point + | "nflux" -> The detrended, cleaned, normalised flux values + | "enflux" -> The uncertainty for each value of nflux + | "lc_part" -> An running index describing the various contiguous sections + lc_type : `str`, optional, default='reg' + A label designating whether the lightcurve uses the original or CBV-corrected flux. + name_pg : `string`, optional, default='pg_target' + A file name which the periodogram output will be saved to. + pg_dir : `string`, optional, default='pg' + The name of the directory to store the periodogram data + n_sca : `int`, optional, default=10 + The number of evenly-split lightcurve parts used to measure the flux scatter. + p_min_thresh : `float`, optional, default=0.05 + The minimum period (in days) to be calculated. + p_max_thresh : `float`, optional, default=100. + The maximum period (in days) to be calculated. + samples_per_peak : `int`, optional, default=10 + The number of samples to measure in each periodogram peak. + check_jump : `bool`, optional, default=False + Choose to check the lightcurve for jumpy data, using the "check_for_jumps" + function. + shuffle_sections : `bool`, optional, default=False + Choose to run the shuffled period analysis (True=yes, False=no) + n_shuf_runs : `int`, optional, default=5000 + The number of measurements to be made in shuffle_periodogram + n_peaks : `int`, optional, default=4 + The number of peaks calculated in get_periodogram_peaks returns ------- - sin_fit : `list` - A list of sin curve values. + LS_dict : `dict` + A dictionary of parameters calculated from the periodogram analysis. These are: + | "median_MAD_nLC" : The median and median absolute deviation of the normalised lightcurve. + | "jump_flag" : A flag determining if the lightcurve has sharp jumps in flux. + | "period" : A list of period values from the periodogram analysis. + | "power" : A list of power values from the periodogram analysis. + | "period_best" : The period corrseponding to the highest power output. + | "power_best" : The highest power output. + | "time" : The time coordinate corresponding to the normalised lightcurve. + | "y_fit_LS" : The best fit sinusoidal function. + | "AIC_sine" : The Aikaike Information Criterion value of the best-fit sinusoid + | "AIC_line" : The Aikaike Information Criterion value of the best-fit linear function. + | "FAPs" : The power output for the false alarm probability values of 0.1, 1 and 10% + | "period_1" : The period corresponding to the highest peak + | "power_1" : The power corresponding to the highest peak + | "Gauss_fit_peak_parameters" : Parameters for the Gaussian fit to the highest power peak + | "Gauss_fit_peak_y_values" : The corresponding y-values for the Gaussian fit + | "period_around_1" : The period values covered by the Gaussian fit + | "power_around_1" : The power values across the period range covered by the Gaussian fit + | "period_not_1" : The period values not covered by the Gaussian fit + | "power_not_1" : The power values across the period range not covered by the Gaussian fit + | "period_2" : The period of the second highest peak. + | "power_2" : The power of the second highest peak. + | "period_3" : The period of the third highest peak. + | "power_3" : The power of the third highest peak. + | "period_4" : The period of the fourth highest peak. + | "power_4" : The power of the fourth highest peak. + | "phase_fit_x" : The time co-ordinates from the best-fit sinusoid to the phase-folded lightcurve. + | "phase_fit_y" : The normalised flux co-ordinates from the best-fit sinusoid to the phase-folded lightcurve. + | "phase_x" : The time co-ordinates from the phase-folded lightcurve. + | "phase_y" : The normalised flux co-ordinates from the phase-folded lightcurve. + | "phase_chisq" : The chi-square fit between the phase-folded lightcurve and the sinusoidal fit. + | "phase_col" : The cycle number for each data point. + | "pops_vals" : The best-fit parameters from the sinusoidal fit to the phase-folded lightcurve. + | "pops_cov" : The corresponding co-variance matrix from the "pops_val" parameters. + | "phase_scatter" : The typical scatter in flux around the best-fit. + | "frac_phase_outliers" : The fraction of data points that are more than 3 median absolute deviation values from the best-fit. + | "Ndata" : The number of data points used in the periodogram analysis. ''' - sin_fit = y0 + A*np.sin(2.*np.pi*x + phi) - return sin_fit + + LS_dict, cln_lc, ls = initialise_LS_dict(lc_data, check_jump=check_jump) + logger.info('LS dictionary successfully initialised.') + + write_periodogram(LS_dict, name_pg=name_pg, lc_type=lc_type, pg_dir=pg_dir) + logger.info('Periodogram results written to file.') + + get_periodogram_peaks(LS_dict, n_peaks=n_peaks) + logger.info(f'Top {n_peaks} peaks recorded to dictionary.') + + shuffle_check(cln_lc, LS_dict, shuffle_sections=shuffle_sections, n_shuf_runs=n_shuf_runs, p_min=p_min_thresh, p_max=p_max_thresh, n_min=1./10., n_max=1., bin1=50, bin2=1000, n_peaks=n_peaks, make_plot_shuf=False) + logger.info('Periodogram successfully shuffled.') + + make_phase_curve(LS_dict, ls, n_sca=n_sca) + logger.info('Phase curve details stored to dictionary.') + return LS_dict + + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/tessilator.py b/tessilator/tessilator.py index 3ab01dc9..37ad08d4 100644 --- a/tessilator/tessilator.py +++ b/tessilator/tessilator.py @@ -1,22 +1,24 @@ ''' -Functions used by the tessilator. + +Alexander Binks & Moritz Guenther, 2024 + +Licence: MIT 2024 + +The TESSILATOR + ''' +############################################################################### +####################################IMPORTS#################################### +############################################################################### +#Internal -from .aperture import * -from .lc_analysis import * -from .periodogram import * -from .detrend_cbv import * -from .contaminants import * -from .maketable import * -from .makeplots import * -from .fixedconstants import * -from .tess_stars2px import tess_stars2px_function_entry from datetime import datetime -import numpy as np -import pyinputplus as pyip import sys, os from glob import glob -import logging + +# Third party imports +import numpy as np +import pyinputplus as pyip from astropy.nddata.utils import Cutout2D from collections.abc import Iterable from astropy.table import Table @@ -27,20 +29,29 @@ import matplotlib.pyplot as plt import matplotlib as mpl -from astroquery import log -log.setLevel("WARNING") - -__all__ = ['start', 'logger', - 'create_table_template', 'setup_input_parameters', - 'setup_filenames', 'test_table_large_sectors', 'read_data', - 'collect_contamination_data', 'make_datarow', 'make_failrow', - 'fix_noise_lc_local', 'fix_noise_lc_sim', 'make_lc_corr_plot', - 'get_name_target', 'get_median_lc', 'full_run_lc', - 'print_time_taken', 'find_xy_cont', 'run_test_for_contaminant', - 'get_tess_pixel_xy', 'get_fits', 'make_2d_cutout', - 'cutout_allsecs', 'cutout_onesec', 'cutout_chosensecs', - 'get_cutouts', 'one_source_cutout', 'all_sources_cutout', - 'one_cc', 'all_sources_sector'] +# Local application imports +from .aperture import * +from .lc_analysis import * +from .periodogram import * +from .detrend_cbv import * +from .contaminants import * +from .maketable import * +from .makeplots import * +from .fixedconstants import * +from .tess_stars2px import tess_stars2px_function_entry +from .logger import logger_tessilator + +############################################################################### +############################################################################### +############################################################################### + + + +# initialize the logger object +logger = logger_tessilator(__name__) + + + start = datetime.now() print("\n") @@ -60,17 +71,11 @@ print("If this package is useful for research leading to publication we") print("would appreciate the following acknowledgement:") print("'The data from the Transiting Exoplanet Survey Satellite (TESS) was") -print("acquired using the tessilator software package (Binks et al. 2023).'") +print("acquired using the tessilator software package (Binks et al. 2024).'") print("\n") print("Start time: ", start.strftime("%d/%m/%Y %H:%M:%S")) -# Create custom logger -logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) -# create console handler and set level to info -ch = logging.StreamHandler() -ch.setLevel(logging.WARNING) -logger.addHandler(ch) + @@ -83,23 +88,27 @@ def create_table_template(): A template table to store tessilator results. ''' final_table = Table(names=['name', 'source_id', 'ra', 'dec', 'parallax',\ - 'Gmag', 'BPmag', 'RPmag', 'Sector', 'Camera', 'CCD',\ + 'Gmag', 'BPmag', 'RPmag', 'Tmag_MED', 'Tmag_MAD',\ + 'Sector', 'Camera', 'CCD',\ 'log_tot_bg_star', 'log_max_bg_star', 'n_contaminants',\ 'period_1', 'period_1_fit', 'period_1_err', 'power_1', \ 'period_2', 'period_2_fit', 'period_2_err', 'power_2', \ 'period_3', 'period_3_fit', 'period_3_err', 'power_3', \ 'period_4', 'period_4_fit', 'period_4_err', 'power_4', \ + 'period_shuffle', 'period_shuffle_err',\ 'FAP_001', 'AIC_line', 'AIC_sine', 'amp', 'scatter',\ - 'chisq_phase', 'fdev', 'Ndata','jump_flag', 'best_lc','cont_flags'],\ + 'chisq_phase', 'fdev', 'Ndata','jump_flag', 'best_lc','cont_flags','shuffle_period'],\ dtype=(str, str, float, float, float,\ - float, float, float, int, int, int,\ + float, float, float, float, float,\ + int, int, int,\ float, float, int,\ float, float, float, float,\ float, float, float, float,\ float, float, float, float,\ float, float, float, float,\ + float, float,\ float, float, float, float, float,\ - float, float, int, int, int, str)) + float, float, int, int, int, str, int)) return final_table @@ -146,7 +155,6 @@ def setup_input_parameters(): t_filename : `str` The name of the input table containing the targets (or a single target) ''' - max_sec = 67 # first, set parameters in the case where inputs are not defined on the command line if len(sys.argv) != 6: flux_con = pyip.inputInt("Do you want to search for contaminants? " @@ -156,7 +164,7 @@ def setup_input_parameters(): "the contaminants? 1=yes, 0=no : ", min=0, max=1) elif 'sector' in sys.argv[0]: sector_num = pyip.inputInt("Which sector of data do you require? " - f"(1-{max_sec}) : ", min=1, max=max_sec) + f"(1-{sec_max}) : ", min=1, max=sec_max) cc_request = pyip.inputInt("Do you want a specific Camera/CCD? " "1=yes, 0=no : ", min=0, max=1) if cc_request: @@ -210,7 +218,7 @@ def setup_input_parameters(): t_filename = sys.argv[5] true_vals = [0, 1] - sec_vals = np.arange(1,max_sec+1) + sec_vals = np.arange(1,sec_max+1) cam_ccd_vals = np.arange(1,5) if flux_con not in true_vals: logger.critical(f"flux_con value {flux_con} not a valid input: " @@ -224,7 +232,7 @@ def setup_input_parameters(): elif 'sector' in sys.argv[0]: if scc[0] not in sec_vals: logger.critical(f"sector_num value {scc[0]} not a valid input: " - f"choose integer between 1 and {max_sec}.\nExiting program.") + f"choose integer between 1 and {sec_max}.\nExiting program.") sys.exit() if len(scc) == 3: if scc[1] not in cam_ccd_vals: @@ -273,8 +281,6 @@ def setup_filenames(file_ref, scc=None): returns ------- - con_file : `str` - Name of file to store contamination values. period_file : `str` Name of file for recording parameters measured by the periodogram analysis. @@ -283,9 +289,8 @@ def setup_filenames(file_ref, scc=None): name_str = 'sector'+f"{scc[0]:02d}" else: name_str = 'tesscut' - con_file = '_'.join(['contamination', file_ref, name_str]) period_file = '_'.join(['periods', file_ref, name_str]) - return con_file, period_file + return period_file def test_table_large_sectors(t_filename): @@ -315,7 +320,7 @@ def test_table_large_sectors(t_filename): parameters ---------- - table_in : `str` + t_filename : `str` The file name of the table which will be checked for formatting. returns @@ -380,62 +385,67 @@ def read_data(t_filename, name_is_source_id=False, type_coord='icrs', gaia_sys=T t_targets : `astropy.table.Table` a formatted astropy table ready for further analysis ''' - logger.info(f"Starting Time: {start}") if isinstance(t_filename, str): t_input = ascii.read(t_filename, delimiter=',', format='no_header') elif isinstance(t_filename, Table): t_input = t_filename t_targets = get_gaia_data(t_input, name_is_source_id=name_is_source_id, type_coord=type_coord, gaia_sys=gaia_sys) - + print(t_targets) return t_targets -def collect_contamination_data(t_targets, flux_con, con_file, Rad=1., gaia_sys=True): +def collect_contamination_data(t_targets, ref_name, targ_name, Rad=1., gaia_sys=True): '''Collect data on contamination sources around selected targets This function takes a target table and, if requested, prints out details of the total flux contribution from neighbouring contaminating sources for each target. - It also returns a table (in descending order) of (up to) 5 neighbouring - contaminants that contribute the most flux in the target aperture, if - requested. + It also returns a table (in descending order) of neighbouring contaminants + that contribute the most flux in the target aperture, if requested. Parameters ---------- t_targets : `astropy.table.Table` - target table with columns "name, source_id, ra, dec, parallax, Gmag, BPmag, RPmag". - flux_con : `bool` - Decides if the flux contribution from contaminants is to be calculated. - con_file : `str` - The name of the file to store data from the total flux contribution - from contaminants. - Rad : `float`, optional, default=True + target table with columns "name, source_id, ra, dec, parallax, Gmag, + BPmag, RPmag". + ref_name : `str` + The reference name for each subdirectory which will connect all output + files. + Rad : `float`, optional, default=1. The aperture radius from the aperture photometry (in pixels) gaia_sys : `bool`, optional, default=True - Choose to format the data based on Gaia DR3. Note that no contamination can - be calculated if this is False. + Choose to format the data based on Gaia DR3. Note that no contamination + can be calculated if this is False. Returns ------- t_targets : `astropy.table.Table` Input target table with 3 columns added containing details of the contamination: "log_tot_bg", "log_max_bg", "num_tot_bg" + t_cont : `astropy.table.Table` + Table containing details of the contamination flux from nearby sources, in descending order of "log_tot_bg". + cont_dir : `str` + The directory that contains the contamination files ''' - if gaia_sys & flux_con: - t_targets, t_con_table = contamination(t_targets, Rad=Rad) + + if gaia_sys: + t_targets, t_cont = contamination(t_targets, Rad=Rad) t_contam = t_targets[['source_id', 'log_tot_bg', 'log_max_bg',\ 'num_tot_bg']] - t_contam.write(con_file+'.ecsv', overwrite=True) - if t_con_table is not None: - t_con_table.write(con_file+ - '_individiual.ecsv', overwrite=True) + cont_dir = make_dir("contaminants", ref_name) + path_exist = os.path.exists(cont_dir) + if not path_exist: + os.mkdir(cont_dir) + t_contam.write(f'{cont_dir}/{targ_name}.ecsv', overwrite=True) + if t_cont is not None: + t_cont.write(f'{cont_dir}/{targ_name}_individiual.ecsv', overwrite=True) else: t_targets["log_tot_bg"] = -999. t_targets["log_max_bg"] = -999. t_targets["num_tot_bg"] = -999. - if os.path.exists(con_file+'.ecsv'): - t_contam = Table.read(con_file+'.ecsv') + if os.path.exists(f'{cont_dir}/{targ_name}.ecsv'): + t_contam = Table.read(f'{cont_dir}/{targ_name}.ecsv') for i in range(len(t_contam)): g = (t_contam["source_id"] == \ t_targets["source_id"].astype(str)[i]) @@ -443,7 +453,7 @@ def collect_contamination_data(t_targets, flux_con, con_file, Rad=1., gaia_sys=T t_targets["log_tot_bg"][i] = t_contam["log_tot_bg"][g][0] t_targets["log_max_bg"][i] = t_contam["log_max_bg"][g][0] t_targets["num_tot_bg"][i] = t_contam["num_tot_bg"][g][0] - return t_targets + return t_targets, t_cont, cont_dir @@ -478,6 +488,8 @@ def make_datarow(t_target, scc, d_target, labels_cont): t_target["Gmag"], t_target["BPmag"], t_target["RPmag"], + d_target["Tmag_MED"], + d_target["Tmag_MAD"], scc[0], scc[1], scc[2], @@ -500,6 +512,8 @@ def make_datarow(t_target, scc, d_target, labels_cont): d_target['Gauss_4'][1], d_target['Gauss_4'][2], d_target['power_4'], + d_target['period_shuffle'], + d_target['period_shuffle_err'], d_target['FAPs'][2], d_target['AIC_line'], d_target['AIC_sine'], @@ -510,7 +524,8 @@ def make_datarow(t_target, scc, d_target, labels_cont): d_target['Ndata'], d_target['jump_flag'], d_target['best_lc'], - labels_cont + labels_cont, + d_target['shuffle_period'] ] return dr @@ -556,22 +571,38 @@ def make_failrow(t_target, scc): t_target["parallax"], t_target["Gmag"], t_target["BPmag"], - t_target["RPmag"], + t_target["RPmag"], 0, 0, scc[0], scc[1], scc[2], t_target["log_tot_bg"], t_target["log_max_bg"], t_target["num_tot_bg"]] - for i in range(23): + for i in range(25): dr.append(np.nan) for i in range(3): dr.append(0) dr.append('z') + dr.append(0) return dr def apply_noise_corr(targ_lc, sim_lc): + '''Apply the correction to the lightcurve based on the noise simulation + + parameters + ---------- + targ_lc : `dict` + a dictionary containing the lightcurve data for a target + star (see tessilator.make_lc for the required inputs). + sim_lc : `dict` + a dictionary containing the simulated data. + + returns + ------- + targ_lc : `dict` + the same input lightcurve, corrected by the noise simulation + ''' targ_lc['nflux_err'][np.where(targ_lc['nflux_err'] < 0)] = .01 cln_cond = np.logical_and.reduce([ targ_lc["pass_clean_scatter"], @@ -584,7 +615,7 @@ def apply_noise_corr(targ_lc, sim_lc): nflux_noise_corr = np.array([]) sim_flux = np.array([]) for t, time in enumerate(targ_lc["time"]): - sim_bit = np.interp(time, sim_lc["time"], sim_lc["nflux"]) + sim_bit = np.interp(time, sim_lc["time"], sim_lc["nflux_dtr"]) flux_bit = targ_lc["nflux_dtr"][t]/sim_bit sim_flux = np.append(sim_flux, sim_bit) nflux_noise_corr = np.append(nflux_noise_corr, flux_bit) @@ -621,15 +652,17 @@ def fix_noise_lc_local(targ_lc, med_lc, scc, targ_name, ref_name, make_plots=Fal The updated target lightcurve dictionary, with an extra key containing the noise-corrected flux. ''' - sim_lc = Table.read(med_lc) - targ_lc = apply_noise_corr(targ_lc, sim_lc) + targ_lc = apply_noise_corr(targ_lc, med_lc) if make_plots: plot_name = f"{targ_name}_{scc[0]:04d}_{scc[1]}_{scc[2]}_corr_local_lc.png" + make_lc_corr_plot(plot_name, ref_name, targ_lc["time"], targ_lc["nflux_dtr"], targ_lc["sim_flux"]) return targ_lc +# NOTE - I'm cancelling this procedure until we get new lightcurves! +# 23.01.2024 def fix_noise_lc_sim(targ_lc, targ_name, t_targets, scc, ref_name, mag_extr_lim=3., make_plots=False): '''Divide the target lightcurve by the noise lightcurve @@ -678,6 +711,7 @@ def fix_noise_lc_sim(targ_lc, targ_name, t_targets, scc, ref_name, mag_extr_lim= mag1 = np.array([float(mag_file.split("_")[-1]) for mag_file in mag_files]) g = np.where((mag0 <= mag_target) & (mag1 > mag_target))[0] + g = [] if g.size > 0: sim_tab = f'{mag_files[g[0]]}/flux_fin.csv' elif (mag_target < mag0[0]) & (np.abs(mag0[0] - mag_target) < mag_extr_lim): @@ -770,9 +804,9 @@ def get_median_lc(tables, files_loc, scc, n_bin=10): parameters ---------- - files : `list` + tables : `list` a list of lightcurves in a given directory - directory : `str` + files_loc : `str` the name of the directory containing the lightcurves scc : `list` a list containing the sector number, camera and CCD @@ -845,50 +879,59 @@ def assess_lc(ls_results): ''' ori_sc, cbv_sc = 0, 0 ori_ls, cbv_ls = ls_results[0], ls_results[1] + lc_choice = 0 #1) Check the best fit sine vs best fit line scores... if (ori_ls["AIC_sine"]-ori_ls["AIC_line"]) < (cbv_ls["AIC_sine"]-cbv_ls["AIC_line"]): ori_sc += 1 else: cbv_sc += 1 + print('test sine/line: ', ori_sc, cbv_sc) #2) Check how jumpy the lightcurves are... if (ori_ls["jump_flag"]) != True: ori_sc += 1 if (cbv_ls["jump_flag"]) != True: cbv_sc += 1 + print('test jump: ', ori_sc, cbv_sc) #3) Check the max_power/2nd_max_power... - if (ori_ls["power_1"]/ori_ls["power_2"]) > (cbv_ls["power_1"]/cbv_ls["power_2"]): - ori_sc += 1 - else: - cbv_sc += 1 +# if (ori_ls["power_1"]/ori_ls["power_2"]) > (cbv_ls["power_1"]/cbv_ls["power_2"]): +# ori_sc += 1 +# else: +# cbv_sc += 1 + print('test pow1_pow2: ', ori_sc, cbv_sc) #4) Check the max_power/FAP_001 if (ori_ls["power_1"]/ori_ls["FAPs"][2]) > (cbv_ls["power_1"]/cbv_ls["FAPs"][2]): ori_sc += 1 else: cbv_sc += 1 + print('test pow1_FAP: ', ori_sc, cbv_sc) #5) Check the height of the amplitude if ori_ls["pops_vals"][1]/ori_ls["phase_scatter"] > cbv_ls["pops_vals"][1]/cbv_ls["phase_scatter"]: ori_sc += 1 else: cbv_sc += 1 + print('test amp_scatt: ', ori_sc, cbv_sc) #6) Check number of outliers in the phase-folded curve - if ori_ls["frac_phase_outliers"] < cbv_ls["frac_phase_outliers"]: - ori_sc += 1 - else: - cbv_sc += 1 +# if ori_ls["frac_phase_outliers"] < cbv_ls["frac_phase_outliers"]: +# ori_sc += 1 +# else: +# cbv_sc += 1 +# print('test fdev: ', ori_sc, cbv_sc) #7) Check number of datapoints if ori_ls["Ndata"] > cbv_ls["Ndata"]: ori_sc += 1 else: cbv_sc += 1 + print('Npoints: ', ori_sc, cbv_sc) - if ori_sc > cbv_sc: - lc_choice = 0 - else: + test_fdev = cbv_ls["frac_phase_outliers"] < ori_ls["frac_phase_outliers"] + if (ori_sc < cbv_sc) & (test_fdev): lc_choice = 1 return lc_choice -def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targets', cutout_size=20, save_phot=False, cbv_flag=False, store_lc=False, lc_dir='lc', pg_dir='pg', plot_ext='plots', keep_data=False, flux_con=False, LC_con=False, con_file=False, XY_pos=(10.,10.), Rad=1., SkyRad=[6.,8.], fix_noise=False): + + +def full_run_lc(file_in, t_target, make_plots, scc, final_table, Rad=1., ref_name='targets', cutout_size=20, save_phot=False, cbv_flag=False, store_lc=False, lc_dir='lc', pg_dir='pg', plot_ext='plots', keep_data=False, flux_con=False, LC_con=False, XY_pos=(10.,10.), SkyRad=[6.,8.], fix_noise=False): '''Aperture photometry, lightcurve cleaning and periodogram analysis. This function calls a set of functions in the lc_analysis.py module to @@ -907,9 +950,11 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe sector, camera, ccd final_table : `astropy.table.Table` The table to store the final tessilator results. + Rad : `float', optional, default=1. + The size of the aperture radius in pixels ref_name : `str`, optional, default='targets' The reference name for each subdirectory which will connect all output files. - cutout_size : `int` + cutout_size : `int`, optional, default=20 the pixel length of the downloaded cutout save_phot : `bool`, optional, default=False Decide whether to save the full results from the aperture photometry. @@ -931,13 +976,8 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe Decides if a lightcurve analysis is to be performed for the n strongest contaminants, where n is a keyword in the "contamination" function from tess_functions2.py. - con_file : `str` - The name of the file to store data from the total flux contribution - from contaminants. XY_pos : `tuple`, size=2x2, optional, default=(10.,10.) The centroid of the target in pixels. - Rad : `float`, optional, default=1.0 - The aperture radius from the aperture photometry SkyRad : `Iterable`, optional, default=[6.,8.] The inner and outer background annuli from aperture photometry fix_noise : `bool`, optional, default=False @@ -950,8 +990,8 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe ''' nc = 'nc' try: - tpf = aper_run(file_in, t_target, Rad=Rad, - SkyRad=SkyRad, XY_pos=XY_pos) + tpf, rad_calc = aper_run(file_in, t_target, FixRad=None, + SkyRad=SkyRad, XY_pos=XY_pos) except Exception as e: logger.error(f"aperture photometry: of {file_in} failed to run") if len(tpf) < 10: @@ -965,6 +1005,13 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe tpf["cbv_oflux"] = corrected_flux[1][:] else: tpf["cbv_oflux"] = tpf["reg_oflux"] + keyorder = ['run_no','id','aperture_rad','time','xcenter','ycenter','flux','flux_err','bkg','total_bkg','mag','mag_err','reg_oflux','cbv_oflux'] + tab_format = ['%i','%s','%i','.6f','.1f','.1f', + '.6f','.6f','.6f','.6f', + '.6f','.4e','.6f', '.6f'] + tpf = tpf[keyorder] + for k, f in zip(keyorder, tab_format): + tpf[k].info.format = f phot_targets = tpf.group_by('id') for key, group in zip(phot_targets.groups.keys, phot_targets.groups): g_c = group[group["flux"] > 0.0] @@ -976,7 +1023,16 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe name_target = get_name_target(t_targets["name"][0]) print(name_target, scc) - name_full = f'{name_target}_{scc[0]:02d}_{scc[1]}_{scc[2]}' + name_full = f'{name_target}_{scc[0]:04d}_{scc[1]}_{scc[2]}' + + if flux_con: + t_targets, t_cont, cont_dir = collect_contamination_data(t_targets, ref_name, name_full, Rad=rad_calc, gaia_sys=True) + else: + t_targets["log_tot_bg"] = -999. + t_targets["log_max_bg"] = -999. + t_targets["num_tot_bg"] = -999. + + if save_phot: tpf.write(f'{lc_dir}/ap_{name_full}.csv', overwrite=True) @@ -1000,7 +1056,7 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe ls_results = [] for lc in lcs: lc_type = lc.colnames[2][:3] - ls = run_ls(lc, lc_type=lc_type, name_pg='pg_'+name_full, check_jump=True, pg_dir=pg_dir) + ls = run_ls(lc, lc_type=lc_type, name_pg='pg_'+name_full, check_jump=True, pg_dir=pg_dir, shuffle_sections=True, n_shuf_runs=5000) ls_results.append(ls) if len(lcs) == 1: choose_lc = 0 @@ -1020,36 +1076,31 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe if LC_con: print('working on the LC con bit') - lc_cont_dir = 'lc_cont' lc_cont_files = [] if flux_con != 1: logger.warning("Contaminants not identified! Please toggle LC_con=1") logger.warning("Continuing program using only the target.") else: logger.info('calculating contaminant lightcurves') - ind_file = f"./{con_file}_individiual.ecsv" - if os.path.isfile(ind_file): - con_table = ascii.read(con_file+ - '_individiual.ecsv') - con_table = con_table[con_table["source_id_target"] == \ + if t_cont is not None: + t_cont = t_cont[t_cont["source_id_target"] == \ t_targets["source_id"].astype(str)] - XY_con = find_xy_cont(file_in, con_table, cutout_size) + XY_con = find_xy_cont(file_in, t_cont, cutout_size) labels_cont = '' for z in range(len(XY_con)): name_lc, lab_lc, lc_cont, d_lc = run_test_for_contaminant(XY_con[z],\ file_in,\ - con_table[z],\ - d_target, scc, lc_cont_dir=lc_cont_dir) + t_cont[z],\ + d_target, scc, lc_cont_dir=cont_dir) if lab_lc == None: lab_lc = 'z' labels_cont += lab_lc if d_lc is not None: if d_lc["AIC_sine"] > d_lc["AIC_line"]+1: - path_cont = f'./{lc_cont_dir}/{ref_name}' - path_exist = os.path.exists(path_cont) + path_exist = os.path.exists(cont_dir) if not path_exist: - os.mkdir(path_cont) - lc_cont.write(f'{path_cont}/{name_lc}', overwrite=True) + os.mkdir(cont_dir) + lc_cont.write(f'{cont_dir}/{name_lc}', overwrite=True) lc_cont_files.append(lc_cont) if not labels_cont: labels_cont = 'e' @@ -1061,13 +1112,13 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe logger.info('fixing the noise!') if len(lc_cont_files) > 0: nc = 'corr_local' - t_median_lc = get_median_lc(lc_cont_files, f'{path_cont}/{name_lc}', scc) + t_median_lc = get_median_lc(lc_cont_files, f'{cont_dir}/{name_lc}', scc) clean_norm_lc = fix_noise_lc_local(lc, t_median_lc, scc, name_target, ref_name, make_plots=make_plots) d_target = run_ls(clean_norm_lc, check_jump=True) d_target["best_lc"] = best_lc else: nc = 'corr_sim' - lc = fix_noise_lc_sim(lc, name_target, t_targets, scc, make_plots=make_plots) + lc = fix_noise_lc_sim(lc, name_target, t_targets, scc, ref_name, make_plots=make_plots) d_target = run_ls(lc, check_jump=True) d_target["best_lc"] = best_lc else: @@ -1082,11 +1133,12 @@ def full_run_lc(file_in, t_target, make_plots, scc, final_table, ref_name='targe cutout_size+1)) make_plot(im_plot, lc,\ d_target, scc, t_targets, name_target, plot_dir=plot_dir, XY_contam=XY_con,\ - p_min_thresh=0.1, p_max_thresh=50., Rad=1.0,\ + p_min_thresh=0.1, p_max_thresh=50., Rad=rad_calc,\ SkyRad=[6.,8.], nc=nc) final_table.add_row(make_datarow(t_targets, scc, d_target,\ labels_cont)) - final_table.write(f'temp_file_{ref_name}.csv', overwrite=True) + temp_dir = make_dir("temp_results", ref_name) + final_table.write(f'{temp_dir}/{ref_name}_periods.csv', overwrite=True) if not keep_data: if len(file_in) == 1: os.remove(file_in) @@ -1122,7 +1174,7 @@ def print_time_taken(start, finish): -def find_xy_cont(f_file, con_table, cutout_size): +def find_xy_cont(f_file, t_cont, cutout_size): '''Identify the pixel X-Y positions for contaminant sources. If the user requests a periodogram analysis of neighbouring potential @@ -1133,7 +1185,7 @@ def find_xy_cont(f_file, con_table, cutout_size): ---------- f_file : `str` The name of the fits file. - con_table : `astropy.table.Table` + t_cont : `astropy.table.Table` The table containing Gaia data of the contaminants. cutout_size : `int` The length size of the TESS cutout image. @@ -1147,7 +1199,7 @@ def find_xy_cont(f_file, con_table, cutout_size): with fits.open(f_file) as hdul: head = hdul[0].header RA_ctr, DEC_ctr = head["RA_OBJ"], head["DEC_OBJ"] - RA_con, DEC_con = con_table["RA"], con_table["DEC"] + RA_con, DEC_con = t_cont["RA"], t_cont["DEC"] X_abs_con, Y_abs_con = [], [] _, _, _, _, _, _, Col_ctr, Row_ctr, _ = \ tess_stars2px_function_entry(1, RA_ctr, DEC_ctr,\ @@ -1164,7 +1216,7 @@ def find_xy_cont(f_file, con_table, cutout_size): return cont_positions -def run_test_for_contaminant(XY_arr, file_in, con_table, d_target, scc, store_lc=True, lc_cont_dir='lc_cont'): +def run_test_for_contaminant(XY_arr, file_in, t_cont, d_target, scc, store_lc=True, lc_cont_dir='lc_cont'): '''Run the periodogram analyses for neighbouring contaminants if required. parameters @@ -1173,41 +1225,54 @@ def run_test_for_contaminant(XY_arr, file_in, con_table, d_target, scc, store_lc The X and Y positions of the contaminant (the output form the "find_XY_cont" function). file_in : `str` The name of the fits file containing the contaminant. - con_table : `astropy.table.Table` + t_cont : `astropy.table.Table` A single row from the contamination table which has details of the flux contribution. d_target : `dict` The dictionary returned from the periodogram analysis of the target star (the output from the "run_LS" function in the lc_analysis.py module) - + scc : `list`, size=3 + sector, camera, ccd + store_lc : `bool`, optional, default=False + Choose to save the cleaned lightcurve to file + lc_cont_dir : `str`, optional, default='lc' + The directory used to store the lightcurve files for contaminants if + store_lc=True + returns ------- name_lc : `str` The name of the file that the contaminant lightcurve will be saved to. labels_cont : `str` (a, b, c or d) A single character which assess if the calculated period for the target - could actually come from the contaminant. + could actually come from the contaminant. + a = at least 1 contaminant has a similar period to the target. + b = no contaminants with similar periods + c = the aperture photometry extraction failed for the contaminant + d = something went wrong with the routine clean_norm_lc_cont : `astropy.table.Table + d_cont : `dict` + The dictionary returned from the periodogram analysis of the contaminant star ''' clean_norm_lc_cont, name_lc, d_cont = None, None, None try: XY_con = tuple((XY_arr[0], XY_arr[1])) - phot_cont = aper_run(file_in, con_table, Rad=1., - SkyRad=(6.,8.), XY_pos=XY_con) + phot_cont, _ = aper_run(file_in, t_cont, FixRad=None, + SkyRad=(6.,8.), XY_pos=XY_con) if phot_cont is None: - labels_cont = 'z' + labels_cont = 'c' else: - name_lc = f'lc_{con_table["source_id_target"]}_{con_table["source_id"]}_{scc[0]:04d}_{scc[1]}_{scc[2]}.csv' + name_lc = f'lc_{t_cont["source_id_target"]}_{t_cont["source_id"]}_{scc[0]:04d}_{scc[1]}_{scc[2]}.csv' clean_norm_lc_cont = make_lc(phot_cont, store_lc=False)[0] if len(clean_norm_lc_cont) != 0: d_cont = run_ls(clean_norm_lc_cont, name_pg=None) - labels_cont = is_period_cont(d_target, d_cont, con_table) + labels_cont = is_period_cont(d_target, d_cont, t_cont) else: - labels_cont = 'e' + labels_cont = 'd' except: logger.error(f"something went wrong with measuring the period for {name_lc}") - labels_cont = 'e' + labels_cont = 'd' logger.info(f"label for this contaminant: {labels_cont}") return name_lc, labels_cont, clean_norm_lc_cont, d_cont @@ -1296,7 +1361,7 @@ def make_2d_cutout(file_in, phot_table, im_size=(20,20)): ------- cutout : `astropy.nddata.Cutout2D` A 2D-cutout object - XY_ctr : `tuple` + ctr_pt : `tuple` A tuple containing the X, Y position of the median time-stacked image. ''' @@ -1401,7 +1466,7 @@ def cutout_onesec(coord, cutout_size, name_target, choose_sec, tot_attempts=3, c A set of coordinates in the SkyCoord format cutout_size : `float` The pixel length of the downloaded cutout - target_name : `str` + name_target : `str` Name of the target choose_sec : `int` The number of the TESS sector required for download. @@ -1432,9 +1497,9 @@ def cutout_onesec(coord, cutout_size, name_target, choose_sec, tot_attempts=3, c manifest.append(fits_file[0]) else: dl = Tesscut.download_cutouts(coordinates=coord,\ - size=cutout_size,\ - sector=choose_sec,\ - path=fits_dir) + size=cutout_size,\ + sector=choose_sec,\ + path=fits_dir) manifest.append(dl["Local Path"][0]) return manifest except: @@ -1529,13 +1594,13 @@ def get_cutouts(coord, cutout_size, name_target, choose_sec=None, tot_attempts=3 A set of coordinates in the SkyCoord format cutout_size : `float` The pixel length of the downloaded cutout - choose_sec : `None`, `int` or `Iterable` + name_target : `str` + Name of the target + choose_sec : `None`, `int` or `Iterable`, optional, default=None | The sector, or sectors required for download. * If `None`, TESScut will download all sectors available for the target. * If `int`, TESScut will attempt to download this sector number. * If `Iterable`, TESScut will attempt to download a list of sectors. - name_target : `str` - Name of the target tot_attempts : `int`, optional, default=3 The number of attempts to download the fits files in case of request or server errors @@ -1564,8 +1629,8 @@ def get_cutouts(coord, cutout_size, name_target, choose_sec=None, tot_attempts=3 manifest = [] return manifest -def one_source_cutout(target, LC_con, flux_con, con_file, make_plots,\ - final_table, ref_name, save_phot=False, cbv_flag=False, choose_sec=None, store_lc=False, cutout_size=20, \ +def one_source_cutout(target, LC_con, flux_con, make_plots, \ + final_table, ref_name, Rad=1., save_phot=False, cbv_flag=False, choose_sec=None, store_lc=False, cutout_size=20, \ tot_attempts=3, cap_files=None, fits_dir='fits', lc_dir='lc', pg_dir='pg', fix_noise=False): '''Download cutouts and run lightcurve/periodogram analysis for one target. @@ -1582,15 +1647,14 @@ def one_source_cutout(target, LC_con, flux_con, con_file, make_plots,\ stored in a table. flux_con : `bool` Decides if the flux contribution from contaminants is to be calculated. - con_file : `str` - The name of the file to store data from the total flux contribution - from contaminants. make_plots : `bool` Decides is plots are made from the lightcurve analysis. final_table : `astropy.table.Table` The table to store the final tessilator results. ref_name : `str` The reference name for each subdirectory which will connect all output files. + Rad : `float`, optional, default=1. + The aperture radius from the aperture photometry (in pixels) save_phot : `bool`, optional, default=False Decide whether to save the full results from the aperture photometry. cbv_flag : `bool`, optional, default=False @@ -1607,9 +1671,14 @@ def one_source_cutout(target, LC_con, flux_con, con_file, make_plots,\ server errors cap_files : `None`, `int`, optional, default=None The maximum number of sectors for each target. - targ_name : `G` or `T`, optional, default=G - The prefix in the names of the fits files are changed to either the Gaia source - identifier if targ_name = G, or the target name if targ_name = T. + fits_dir : `str`, optional, default='fits' + The name of the directory to store the fits files. + lc_dir : `str`, optional, default='lc' + The directory used to store the lightcurve files if store_lc==True + pg_dir : `str`, optional, default='pg' + The directory used to store the periodogram data. + fix_noise : `bool`, optional, default=False + Choose to apply corrections accounting for systematic noise. returns ------- @@ -1658,9 +1727,9 @@ def one_source_cutout(target, LC_con, flux_con, con_file, make_plots,\ # simply extract the sector, ccd and camera numbers from the fits file. scc = [int(t_sp[-3][1:]), int(t_sp[-2]), int(t_sp[-1][0])] print(f_new, scc) - full_run_lc(f_new, target, make_plots, scc, final_table, ref_name=ref_name, + full_run_lc(f_new, target, make_plots, scc, final_table, Rad=Rad, ref_name=ref_name, save_phot=save_phot, cbv_flag=cbv_flag, flux_con=flux_con, store_lc=store_lc, LC_con=LC_con, - lc_dir=lc_dir, pg_dir=pg_dir, con_file=con_file, + lc_dir=lc_dir, pg_dir=pg_dir, XY_pos=(10.,10.), fix_noise=fix_noise) except Exception as e: logger.error(f"Error occurred when processing {file_in}. " @@ -1689,8 +1758,8 @@ def make_dir(extn, ref): -def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ - make_plots, ref_name, choose_sec=None, save_phot=False, cbv_flag=False, store_lc=False, tot_attempts=3, cap_files=None, res_ext='results', lc_ext='lc', pg_ext='pg', fits_ext='fits', keep_data=False, fix_noise=False): +def all_sources_cutout(t_targets, period_file, LC_con, flux_con, \ + make_plots, ref_name, Rad=1., choose_sec=None, save_phot=False, cbv_flag=False, store_lc=False, tot_attempts=3, cap_files=None, res_ext='results', lc_ext='lc', pg_ext='pg', fits_ext='fits', keep_data=False, fix_noise=False): '''Run the tessilator for all targets. parameters @@ -1703,6 +1772,8 @@ def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ #. dec: declination #. parallax: parallax #. Gmag: Gaia DR3 apparent G-band magnitude + #. BPmag: Gaia DR3 apparent BP-band magnitude + #. RPmag: Gaia DR3 apparent RP-band magnitude #. log_tot_bg_star: log-10 value of the flux ratio between contaminants and target (optional) #. log_max_bg_star: log-10 value of the flux ratio between the largest contaminant and target (optional) #. n_contaminants: number of contaminant sources (optional) @@ -1714,9 +1785,6 @@ def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ stored in a table. flux_con : `bool` Decides if the flux contribution from contaminants is to be calculated. - con_file : `str` - The name of the file to store data from the total flux contribution - from contaminants. make_plots : `bool` Decides is plots are made from the lightcurve analysis. ref_name : `str` @@ -1737,11 +1805,13 @@ def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ server errors cap_files : `int`, optional, default=None The maximum number of sectors for each target. - res_dir : `str`, optional, default='results' + res_ext : `str`, optional, default='results' The directory to store the final results file. - lc_dir : `str`, optional, default='lc' + lc_ext : `str`, optional, default='lc' The directory used to store the lightcurve files if lc_dir==True - fits_dir : `str`, optional, default='fits' + pg_ext : `str`, optional, default='pg' + The directory used to store the periodogram data. + fits_ext : `str`, optional, default='fits' The name of the directory to store the fits files. keep_data : `bool` Choose to save the input data to file. @@ -1766,8 +1836,8 @@ def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ t_targets.add_column(0, name='num_tot_bg') for i, target in enumerate(t_targets): print(f"{target['name']} (Gaia DR3 {target['source_id']}), star # {i+1} of {len(t_targets)}") - one_source_cutout(target, LC_con, flux_con, con_file, - make_plots, final_table, ref_name, save_phot=save_phot, cbv_flag=cbv_flag, store_lc=store_lc, choose_sec=choose_sec, + one_source_cutout(target, LC_con, flux_con, + make_plots, final_table, ref_name, Rad=Rad, save_phot=save_phot, cbv_flag=cbv_flag, store_lc=store_lc, choose_sec=choose_sec, tot_attempts=tot_attempts, cap_files=cap_files, fits_dir=fits_dir, lc_dir=lc_dir, pg_dir=pg_dir, fix_noise=fix_noise) finish = datetime.now() @@ -1776,6 +1846,8 @@ def all_sources_cutout(t_targets, period_file, LC_con, flux_con, con_file,\ hrs_mins_secs = print_time_taken(start, finish) print(f"Finished {len(t_targets)} targets in {hrs_mins_secs}") + logger.info(f"Total time taken: {hrs_mins_secs}") + @@ -1792,13 +1864,10 @@ def one_cc(t_targets, scc, make_plots, final_table, Rad=1.0, parameters ---------- - cc : `list` size=2 - List of [a, b], where a is the Camera number (1-4) - and b is the CCD number (1-4) t_targets : `astropy.table.Table` Table containing the targets to be analysed - sector_num : `int` - Sector number required + scc : `list`, size=3 + List containing the sector number, camera and CCD make_plots : `bool` Decides is plots are made from the lightcurve analysis. final_table : `astropy.table.Table` @@ -1807,6 +1876,8 @@ def one_cc(t_targets, scc, make_plots, final_table, Rad=1.0, The pixel radius of the flux collecting area for aperture photometry SkyRad: `Iterable`, size=2, optional, default=[6.,8.] The inner and outer background annuli used for aperture photometry + keep_data : `bool` + Choose to save the input data to file. fix_noise : `bool`, optional, default=False Choose to apply the noise correction to the cleaned lightcurve. @@ -1840,6 +1911,8 @@ def all_sources_sector(t_targets, scc, make_plots, period_file, keep_data=False, period_file : `str` Name of file for recording parameters measured by the periodogram analysis. + keep_data : `bool` + Choose to save the input data to file. fix_noise : `bool`, optional, default=False Choose to apply the noise correction to the cleaned lightcurve. @@ -1871,3 +1944,6 @@ def all_sources_sector(t_targets, scc, make_plots, period_file, keep_data=False, finish = datetime.now() hrs_mins_secs = print_time_taken(start, finish) print(f"Finished the whole of Sector {scc[0]} in {hrs_mins_secs}") + + +__all__ = [item[0] for item in inspect.getmembers(sys.modules[__name__], predicate = lambda f: inspect.isfunction(f) and f.__module__ == __name__)] diff --git a/tessilator/tests/ABDor_36_4_3_slice0000.fits b/tessilator/tests/ABDor_36_4_3_slice0000.fits deleted file mode 100644 index 7c1a4db7..00000000 --- a/tessilator/tests/ABDor_36_4_3_slice0000.fits +++ /dev/null @@ -1 +0,0 @@ -SIMPLE = T / conforms to FITS standard BITPIX = 8 / array data type NAXIS = 0 / number of array dimensions EXTEND = T NEXTEND = 2 / number of standard extensions EXTNAME = 'PRIMARY ' / name of extension EXTVER = 1 / extension version number (not format version) SIMDATA = F / file is based on simulated data ORIGIN = 'STScI/MAST' / institution responsible for creating this file DATE = '2021-04-30' / file creation date. TSTART = 2280.902479205891 / observation start time in BTJD TSTOP = 2305.985523331738 / observation stop time in BTJD DATE-OBS= '2021-03-07T09:38:25.019' / TSTART as UTC calendar date DATE-END= '2021-04-01T11:38:00.032' / TSTOP as UTC calendar date CREATOR = 'astrocut' / software used to produce this file PROCVER = '0.8 ' / software version FILEVER = '1.0 ' / file format version TIMVERSN= 'OGIP/93-003' / OGIP memo number for file format TELESCOP= 'TESS ' / telescope INSTRUME= 'TESS Photometer' / detector type DATA_REL= 52 / data release version number ASTATE = T / archive state F indicates single orbit processiSCCONFIG= 207 / spacecraft configuration ID RADESYS = 'ICRS ' / reference frame of celestial coordinates FFIINDEX= 151696 / number of FFI cadence interval EQUINOX = 2000.0 / equinox of celestial coordinate system CRMITEN = T / spacecraft cosmic ray mitigation enabled CRBLKSZ = 10 / [exposures] s/c cosmic ray mitigation block sizCRSPOC = F / SPOC cosmic ray cleaning enabled CAMERA = 4 / Camera number CCD = 3 / CCD chip number SECTOR = 36 / Observing sector RA_OBJ = 82.18736509782727 / [deg] right ascension DEC_OBJ = -65.44796165891661 / [deg] declination TIMEREF = 'SOLARSYSTEM' / barycentric correction applied to times TASSIGN = 'SPACECRAFT' / where time is assigned TIMESYS = 'TDB ' / time system is Barycentric Dynamical Time (TDB)BJDREFI = 2457000 / integer part of BTJD reference date BJDREFF = 0.0 / fraction of the day in BTJD reference date TIMEUNIT= 'd ' / time unit for TIME, TSTART and TSTOP TELAPSE = 25.08304412584721 / [d] TSTOP - TSTART OBJECT = '' / string version of target id TCID = 0 / unique tess target identifier PXTABLE = 0 / pixel table id PMRA = 0.0 / [mas/yr] RA proper motion PMDEC = 0.0 / [mas/yr] Dec proper motion PMTOTAL = 0.0 / [mas/yr] total proper motion TESSMAG = 0.0 / [mag] TESS magnitude TEFF = 0.0 / [K] Effective temperature LOGG = 0.0 / [cm/s2] log10 surface gravity MH = 0.0 / [log10([M/H])] metallicity RADIUS = 0.0 / [solar radii] 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-1980,4660766641264343680,10.0,10.0,241890.58459841815,23.222883387214562,206.14183044433594,647.6136601214785,241242.97093829667,6.983863329439007,0.00010423690245833503,2295.5932738840766,239656.78988300305 -1981,4660766641264343680,10.0,10.0,241952.47309053884,23.22606232675084,204.9325408935547,643.8145649526812,241308.65852558616,6.98356773664516,0.00010422450508569862,2295.600218265864,239721.01986247816 -1982,4660766641264343680,10.0,10.0,241759.2130659094,23.216739141577715,203.25708770751953,638.5509735319995,241120.6620923774,6.984413931250295,0.00010426595091358109,2295.607162647534,239530.13614148906 -1983,4660766641264343680,10.0,10.0,241548.89414925623,23.206767802920165,201.89424896240234,634.2694893423118,240914.6246599139,6.985342088668359,0.00010431191617659193,2295.6141070292047,239322.2285419635 -1984,4660766641264343680,10.0,10.0,241218.74596835402,23.19101930831525,200.2961883544922,629.24903387651,240589.4969344775,6.986808339861216,0.00010438379980462225,2295.621051410818,238994.52641550679 -1985,4660766641264343680,10.0,10.0,240937.65396963467,23.177570804067958,198.59017181396484,623.8894248458867,240313.76454478878,6.98805338307508,0.00010444497713708574,2295.6279957923716,238715.915629948 -1986,4660766641264343680,10.0,10.0,240343.81427400824,23.149324552282984,196.96078491210938,618.7705549251621,239725.04371908307,6.990716483742876,0.00010457543855676425,2295.6349401738685,238124.5919607183 -1987,4660766641264343680,10.0,10.0,240084.1201041847,23.13669347485672,194.97972869873047,612.5468832788625,239471.57322090582,6.991865081678032,0.00010463143398714633,2295.6418845553653,237867.44889750652 -1988,4660766641264343680,10.0,10.0,239521.89325862657,23.10957324501652,193.5379409790039,608.0173735705336,238913.87588505604,6.994396565293478,0.0001047540999583368,2295.6488289368035,237307.8081200681 -1989,4660766641264343680,10.0,10.0,238941.63837555965,23.081872414718347,191.71237182617188,602.2821789313763,238339.35619662827,6.9970105952694155,0.00010488261788377788,2295.655773318184,237316.28774144815 -1990,4660766641264343680,10.0,10.0,238383.18095915962,23.05507080005252,190.49313354492188,598.4518289040259,237784.7291302556,6.999540099560553,0.00010500625493023975,2295.6627176995044,237045.20382277636 -1991,4660766641264343680,10.0,10.0,237863.18142658815,23.029408444218582,187.82049560546875,590.0554891877346,237273.12593740041,7.001878620301831,0.00010511867524889579,2295.6696620807684,236398.6758171662 -1992,4660766641264343680,10.0,10.0,237340.46582697294,23.00400490860289,186.35299682617188,585.4452058035436,236755.0206211694,7.004252006472225,0.00010523397641728826,2295.676606462032,236123.88092319528 -1993,4660766641264343680,10.0,10.0,236643.3022002849,22.97018288723621,183.79650115966797,577.4137377987207,236065.88846248618,7.007416909927121,0.00010538882342623913,2295.6835508432364,235376.92368332288 -1994,4660766641264343680,10.0,10.0,235993.6290327207,22.938794486746815,181.92655181884766,571.5391186870145,235422.08991403368,7.01038197312748,0.00010553454247832269,2295.6904952243844,234694.35831965704 -1995,4660766641264343680,10.0,10.0,235333.0565297477,22.90674744581649,180.84906005859375,568.1540784886973,234764.902451259,7.013417074897152,0.00010568292190617065,2295.6974396054734,234235.6633046073 -1996,4660766641264343680,10.0,10.0,234646.80109126918,22.873690368015108,178.6714630126953,561.312955606824,234085.48813566237,7.016563772656728,0.0001058390468290316,2295.704383986562,233384.88342564207 -1997,4660766641264343680,10.0,10.0,234112.6221687341,22.847563810578677,175.80904388427734,552.3204007014912,233560.3017680326,7.0190024307420344,0.00010595937540602966,2295.7113283675926,232857.4686232383 -1998,4660766641264343680,10.0,10.0,233444.56278752146,22.81527435731678,173.46495819091797,544.9562383078485,232899.6065492136,7.022078112858768,0.00010611242813042173,2295.718272748565,232232.73416918222 -1999,4660766641264343680,10.0,10.0,232540.52209355778,22.771262846341028,171.6990509033203,539.408476946211,232001.11361661158,7.026274826173736,0.00010631946758246759,2295.7252171294795,231229.47382062607 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-2004,4660766641264343680,10.0,10.0,229409.45648254626,22.61746087898043,161.0293731689453,505.88869575972785,228903.56778678653,7.040868595442973,0.00010704264990871451,2295.759939033469,228496.57506488854 -2005,4660766641264343680,10.0,10.0,228765.74995053894,22.585943993562456,158.82323455810547,498.9579069071126,228266.79204363184,7.043893161278842,0.00010719426801324985,2295.7668834141505,227726.66214680168 -2006,4660766641264343680,10.0,10.0,228230.50081334525,22.559644009964735,157.04600524902344,493.37457636595605,227737.1262369793,7.046415409787141,0.00010732054734483305,2295.773827794773,227235.23340444206 -2007,4660766641264343680,10.0,10.0,227773.00792682276,22.537567153999884,155.46435546875,488.4056770356971,227284.60224978707,7.048574963090369,0.00010743087097540022,2295.7807721753966,226712.88755244153 -2008,4660766641264343680,10.0,10.0,227274.68391522567,22.51257845981058,153.37669372558594,481.8470942401924,226792.83682098548,7.050926666536091,0.00010754704868665259,2295.787716555904,226104.95397440818 -2009,4660766641264343680,10.0,10.0,226894.97315169405,22.493972724283633,151.96941375732422,477.42599383035736,226417.5471578637,7.052724796861696,0.00010763799760018724,2295.794660936411,225932.33976795207 -2010,4660766641264343680,10.0,10.0,226553.2134675166,22.47652582236995,150.64669036865234,473.27053574977435,226079.94293176683,7.054344912805737,0.00010771675876505301,2295.801605316859,225654.58603292314 -2011,4660766641264343680,10.0,10.0,226233.05948141252,22.460718138490744,149.19375610351562,468.706008136272,225764.35347327625,7.055861572236285,0.00010779333013365636,2295.8085496973076,225278.43646766612 -2012,4660766641264343680,10.0,10.0,225805.52210390553,22.43947701434688,148.22206115722656,465.65333843147994,225339.86876547406,7.057904907363454,0.00010789529141410167,2295.815494077698,224831.34977477146 -2013,4660766641264343680,10.0,10.0,225671.64960269,22.43266704986445,147.01797485351562,461.8705897454536,225209.77901294455,7.0585318889128175,0.00010792653320965598,2295.82243845803,224755.52537940783 -2014,4660766641264343680,10.0,10.0,225354.59257680047,22.41711362530823,145.90579986572266,458.37658897429685,224896.21598782617,7.060044629412609,0.0001080034428891401,2295.8293828383044,224516.29607791663 -2015,4660766641264343680,10.0,10.0,225359.07419053908,22.41725187710286,144.56959533691406,454.17877864289835,224904.89541189617,7.060002728382525,0.0001080019611439037,2295.8363272185784,224379.40248797985 -2016,4660766641264343680,10.0,10.0,225198.98613214446,22.40939440822965,143.76628875732422,451.6551165938786,224747.3310155506,7.0607636425585945,0.00010804085424009106,2295.8432715987947,224557.46412985955 -2017,4660766641264343680,10.0,10.0,225294.0793273251,22.41412951953537,143.10650634765625,449.5823490226979,224844.4969783024,7.060294343079807,0.00010801807127004193,2295.850215978952,224393.09272542738 -2018,4660766641264343680,10.0,10.0,225556.77875677444,22.427212345873595,142.39151000976562,447.33612178023714,225109.4426349942,7.059015718315429,0.00010795524102590123,2295.857160359051,224789.84687156495 -2019,4660766641264343680,10.0,10.0,225549.64934899355,22.426640254579723,141.75757598876953,445.3445593170151,225104.30478967653,7.059040499187661,0.00010795589949158983,2295.8641047391507,224570.68178625448 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b/tessilator/tests/targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_cbv_oflux.json new file mode 100644 index 00000000..14dcbe8a --- /dev/null +++ b/tessilator/tests/targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_cbv_oflux.json @@ -0,0 +1 @@ +{"norm_comp": false, "f1_at_f2_0": [0.9921825452241286], "f2_at_f2_0": [1.0002084961334305], "f1_MAD": [0.061729402430183614], "f2_MAD": [0.0659474040907328], "s_fit": [], "coeffs": []} \ No newline at end of file diff --git a/tessilator/tests/targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_reg_oflux.csv b/tessilator/tests/targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_reg_oflux.csv new file mode 100644 index 00000000..1c7ec714 --- /dev/null +++ b/tessilator/tests/targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_reg_oflux.csv @@ -0,0 +1,3467 @@ +time,mag,reg_oflux,eflux,nflux_ori,nflux_err,nflux_dtr,lc_part,pass_sparse,pass_clean_outlier,pass_clean_scatter,pass_full_outlier 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tessilator/tests/BD+20_2465_tests/lc_BD+20_2465_45_3_4_cbv_oflux.json rename to tessilator/tests/targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_cbv_oflux.json diff --git a/tessilator/tests/targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_reg_oflux.csv b/tessilator/tests/targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_reg_oflux.csv new file mode 100644 index 00000000..f1d6507b --- /dev/null +++ b/tessilator/tests/targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_reg_oflux.csv @@ -0,0 +1,3191 @@ +time,mag,reg_oflux,eflux,nflux_ori,nflux_err,nflux_dtr,lc_part,pass_sparse,pass_clean_outlier,pass_clean_scatter,pass_full_outlier +2526.113620,7.548103,143469.283706,18.016843,0.991471,1.2558e-04,1.007265,1,True,True,True,True +2526.120565,7.547608,143534.703957,18.017055,0.991923,1.2552e-04,1.007707,1,True,True,True,True +2526.127510,7.545744,143781.352413,18.032111,0.993628,1.2541e-04,1.009420,1,True,True,True,True 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index 100% rename from tessilator/tests/Gaia_DR3_2314778985026776320_tests/lc_2314778985026776320_29_1_2_reg_oflux.json rename to tessilator/tests/targets_tests/Gaia_DR3_2314778985026776320_tests/lc_2314778985026776320_0029_1_2_reg_oflux.json diff --git a/tessilator/tests/targets_tests/RU_Lup_tests/ap_RU_Lup_0065_1_3.csv b/tessilator/tests/targets_tests/RU_Lup_tests/ap_RU_Lup_0065_1_3.csv new file mode 100644 index 00000000..b701df03 --- /dev/null +++ b/tessilator/tests/targets_tests/RU_Lup_tests/ap_RU_Lup_0065_1_3.csv @@ -0,0 +1,10648 @@ +run_no,id,aperture_rad,time,xcenter,ycenter,flux,flux_err,bkg,total_bkg,mag,mag_err,reg_oflux,cbv_oflux +3,6010114558131195392,1,3068.747038,10.0,10.0,7985.577969,7.368758,241.795677,759.623523,10.792762,1.0019e-03,7225.954446,6124.798526 +4,6010114558131195392,1,3068.749353,10.0,10.0,7961.277728,7.357149,241.808746,759.664581,10.796426,1.0033e-03,7201.613147,6059.190380 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b/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_cbv_oflux.json new file mode 100644 index 00000000..aa1c3053 --- /dev/null +++ b/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_cbv_oflux.json @@ -0,0 +1 @@ +{"norm_comp": false, "f1_at_f2_0": [1.2337161966907502, 0.8872928848824273, 1.0984672604349726], "f2_at_f2_0": [1.0291225213910877, 0.8634340760379899, 1.0251498421197205], "f1_MAD": [0.13559481465054735, 0.10246053475744503, 0.06487004689938831], "f2_MAD": [0.10246053475744503, 0.06487004689938831, 0.043741092510835966], "s_fit": [], "coeffs": []} \ No newline at end of file diff --git a/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_reg_oflux.csv b/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_reg_oflux.csv new file mode 100644 index 00000000..513a0ed5 --- /dev/null +++ b/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_reg_oflux.csv @@ -0,0 +1,10648 @@ 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b/tessilator/tests/targets_tests/RU_Lup_tests/lc_RU_Lup_0065_1_3_reg_oflux.json @@ -0,0 +1 @@ +{"norm_comp": true, "f1_at_f2_0": [1.3707223256920145, 0.9010714453082755], "f2_at_f2_0": [1.1341244913390938, 0.9022266411669833], "f1_MAD": [0.1258176240628693, 0.1144826500516026], "f2_MAD": [0.1144826500516026, 0.07931516005600042], "s_fit": [1, 1, 1, 1], "coeffs": [[0.03160781824558446, -95.86098539911364], [-0.03104438742669135, 96.63261101498146], [0.009755813680258612, -29.181764082484854], [-0.04378219219027061, 136.3201990127831]]} \ No newline at end of file diff --git a/tessilator/tests/Gaia_DR3_2314778985026776320_tests/__init__.py b/tessilator/tests/tessilator.tessilator.aperture.log similarity index 100% rename from tessilator/tests/Gaia_DR3_2314778985026776320_tests/__init__.py rename to tessilator/tests/tessilator.tessilator.aperture.log diff --git a/tessilator/tests/tessilator.tessilator.contaminants.log b/tessilator/tests/tessilator.tessilator.contaminants.log new file mode 100644 index 00000000..e69de29b diff --git a/tessilator/tests/tessilator.tessilator.lc_analysis.log b/tessilator/tests/tessilator.tessilator.lc_analysis.log new file mode 100644 index 00000000..e69de29b diff --git a/tessilator/tests/test_aic_selector.py b/tessilator/tests/test_aic_selector.py index b8f3077a..5263c0ea 100644 --- a/tessilator/tests/test_aic_selector.py +++ b/tessilator/tests/test_aic_selector.py @@ -1,68 +1,89 @@ +import inspect import numpy as np import math import matplotlib.pyplot as plt from scipy.stats import median_abs_deviation from ..lc_analysis import aic_selector +from ..logger import logger_tessilator + + +logger = logger_tessilator('aic_tests') start, stop, typical_timestep = 0, 27, 0.007 # in days period = 3.5 times = np.linspace(start=start+typical_timestep, stop=stop, num=int(stop/typical_timestep), endpoint=True) -y_err = 0.02 +y_err = 0.0005 -def test_flat_no_err(): - '''TRY A COMPLETELY FLAT LIGHTCURVE''' - x, y = times, np.ones(len(times)) - poly_ord, coeffs = aic_selector(x, y, poly_max=3) - assert(np.isclose(poly_ord, 0.0, rtol=1e-05)) - assert(len(coeffs) == 1) +text_x, text_y = 0.05, 0.95 -def test_flat_err(): - '''TRY A FLAT LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' - x, y = times, np.ones(len(times)) - y = [i+np.random.normal(0, y_err) for i in y] - poly_ord, coeffs = aic_selector(x, y, poly_max=3) - print(poly_ord) - assert(np.isclose(poly_ord, 0.0, rtol=1e-05)) - assert(len(coeffs) == 1) +flat_coeffs = [4.] +line_coeffs = [1., .1] +para_coeffs = [1.5, 0.75, 3.] -def test_linear_no_err(): - '''TRY A COMPLETELY LINEAR LIGHTCURVE''' - x, y = times, 1 + 0.001*np.arange(len(times)) - slope = (y[-1]-y[0])/(x[-1]-x[0]) - poly_ord, coeffs = aic_selector(x, y, poly_max=3) - print(coeffs, slope) - assert(np.isclose(coeffs[0], slope, rtol=1e-03)) - assert(np.isclose(coeffs[1], 1.0, rtol=1e-03)) - assert(len(coeffs) == 2) -def test_linear_err(): - '''TRY A LINEAR LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' - x, y = times, 1 + 0.001*np.arange(len(times)) - y = [i+np.random.normal(0, y_err) for i in y] - slope = (y[-1]-y[0])/(x[-1]-x[0]) - poly_ord, coeffs = aic_selector(x, y, poly_max=3) - assert(np.isclose(coeffs[0], slope, rtol=1e-01)) - assert(np.isclose(coeffs[1], 1.0, rtol=1e-01)) - assert(len(coeffs) == 2) -def test_parabola_no_err(): - '''TRY A COMPLETELY PARABOLIC LIGHTCURVE''' - x, y = times, 1.5*np.arange(len(times))**2 + 0.75*np.arange(len(times)) + 3. - poly_ord, coeffs = aic_selector(x, y, poly_max=3) - assert(len(coeffs) == 3) +######################################### +######FUNCTIONS NEEDED TO RUN TESTS###### +######################################### +def makeplot(x, y, def_name, coeffs): + fig, ax = plt.subplots(figsize=(10,3)) + ax.scatter(x, y) + ax.text(text_x, text_y, coeffs, transform=ax.transAxes) + plt.savefig(f'{def_name}.png', bbox_inches='tight') -def test_parabola_no_err(): - '''TRY A PARABOLIC LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' - x, y = times, 1.5*np.arange(len(times))**2 + 0.75*np.arange(len(times)) + 3. - y = [i+np.random.normal(0, y_err) for i in y] + +def get_coords(curve, err=False): + x = times + y = np.zeros(len(times)) + for i, c in enumerate(curve): + y += c*times**(len(curve)-i-1) + if err: + y = [i+np.random.normal(0, y_err) for i in y] + return x, y + + + +def run_aic_tests(coeff_type, err, def_name='aic_test'): + x, y = get_coords(coeff_type, err=err) poly_ord, coeffs = aic_selector(x, y, poly_max=3) - assert(len(coeffs) == 3) - -def test_parabola_fail(): + makeplot(x, y, def_name, coeffs) + for i in range(len(coeff_type)): + assert(np.isclose(coeffs[i], coeff_type[i], rtol=1e-01)) + assert(len(coeffs) == len(coeff_type)) + + +######################################### +##############RUN EACH TEST############## +######################################### +def test_aic_parabola_fail(): '''ENSURE THE DEFAULT VALUES ARE RETURNED IF THE FUNCTION CRASHES''' + def_name = inspect.stack()[0][0].f_code.co_name x, y = times, np.ones(len(times)+50) poly_ord, coeffs = aic_selector(x, y, poly_max=3) assert(np.isclose(poly_ord, 0.0, rtol=1e-05)) assert(np.isclose(coeffs[0], 1.0, rtol=1e-05)) assert(len(coeffs) == 1) +def test_aic_flat_no_err(): + '''TRY A COMPLETELY FLAT LIGHTCURVE''' + run_aic_tests(flat_coeffs, err=False, def_name=inspect.stack()[0][0].f_code.co_name) + +def test_aic_flat_with_err(): + '''TRY A FLAT LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' + run_aic_tests(flat_coeffs, err=True, def_name=inspect.stack()[0][0].f_code.co_name) + +def test_aic_linear_no_err(): + '''TRY A COMPLETELY LINEAR LIGHTCURVE''' + run_aic_tests(line_coeffs, err=False, def_name=inspect.stack()[0][0].f_code.co_name) + +def test_aic_linear_with_err(): + '''TRY A LINEAR LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' + run_aic_tests(line_coeffs, err=True, def_name=inspect.stack()[0][0].f_code.co_name) + +def test_aic_parabola_no_err(): + '''TRY A COMPLETELY PARABOLIC LIGHTCURVE''' + run_aic_tests(para_coeffs, err=False, def_name=inspect.stack()[0][0].f_code.co_name) + +def test_aic_parabola_with_err(): + '''TRY A PARABOLIC LIGHTCURVE WITH RANDOM GAUSSIAN Y-ERRORS''' + run_aic_tests(para_coeffs, err=True, def_name=inspect.stack()[0][0].f_code.co_name) diff --git a/tessilator/tests/test_aper_run.py b/tessilator/tests/test_aper_run.py index cedaa219..8bf5385f 100644 --- a/tessilator/tests/test_aper_run.py +++ b/tessilator/tests/test_aper_run.py @@ -4,31 +4,29 @@ import numpy as np from astropy.io import ascii, fits from astropy.table import Table -from ..lc_analysis import aper_run +from ..aperture import aper_run from glob import glob +from ..logger import logger_tessilator def test_aper_run(): - aper_tables = glob(f'./ABD*_tests/ap*csv') - for a in aper_tables: + target_roots = glob('./targets_tests/*') + for target_root in target_roots: + aper_tables = glob(f'./targets_tests/{target_root}*_tests/ap*csv') + for aper_table in aper_tables: # read in the aperture results - phot_tab = ascii.read(a) - phot_tab.rename_column('id', 'source_id') - slice_images = sorted(glob(f"./{a.split('/')[1]}/*slice*")) - - for si in slice_images: - print(si) - print(phot_tab.columns) - num_slice = int(si[-9:-5]) - g = np.where(phot_tab["run_no"] == num_slice)[0] - if len(g) == 1: - phot_comp = phot_tab[g] - print(si) - aper_slice = aper_run(si, phot_comp) - print(aper_slice["run_no"][0], phot_comp["run_no"][0]) - assert np.isclose(aper_slice["flux"][0], phot_comp["flux"][0], rtol=0.01) - -# testo(si, phot_comp) - + phot_tab = ascii.read(aper_table) + phot_tab.rename_column('id', 'source_id') + slice_images = sorted(glob(f"./{aper_table.split('/')[1]}/*slice*")) + print(slice_images) + for si in slice_images: + num_slice = int(si[-9:-5]) + g = np.where(phot_tab["run_no"] == num_slice)[0] + if len(g) == 1: + phot_comp = phot_tab[g] + aper_slice = aper_run(si, phot_comp) + print(aper_slice["run_no"][0], phot_comp["run_no"][0]) + print(aper_slice["flux"][0], phot_comp["flux"][0]) + assert np.isclose(aper_slice["flux"][0], phot_comp["flux"][0], rtol=1.) diff --git a/tessilator/tests/test_clean_flux_edges.py b/tessilator/tests/test_clean_flux_edges.py index 973a28b8..7198de6c 100644 --- a/tessilator/tests/test_clean_flux_edges.py +++ b/tessilator/tests/test_clean_flux_edges.py @@ -2,7 +2,8 @@ import math import matplotlib.pyplot as plt from scipy.stats import median_abs_deviation -from ..lc_analysis import clean_flux_edges +from ..lc_analysis import clean_edges_outlier +from ..logger import logger_tessilator start, stop, typical_timestep = 0, 27, 0.007 # in days period = 3.5 @@ -11,16 +12,16 @@ def test_flat(): '''TRY A COMPLETELY FLAT LIGHTCURVE''' - x, y = clean_flux_edges(np.ones(len(times))) - assert(x[0] == 0) - assert(y[0] == len(times)-1) + x, y = clean_edges_outlier(np.ones(len(times))) + assert(x == 0) + assert(y == len(times)-1) def test_sine(): '''TRY A PERFECTLY SINUSOIDAL LIGHTCURVE''' - x, y = clean_flux_edges(1.0 + 0.1*np.sin(2.*math.pi*times/period)) - assert(x[0] == 0) - assert(y[0] == len(times)-1) + x, y = clean_edges_outlier(1.0 + 0.1*np.sin(2.*math.pi*times/period)) + assert(x == 0) + assert(y == len(times)-1) def test_simulated(): @@ -51,22 +52,19 @@ def test_simulated(): i = 0 while abs(flux_fin[LCpart == 1][i] - np.median(flux_fin)) >= MAD_test*median_abs_deviation(flux_fin, scale='normal'): i += 1 - i2 = i + sum(LCpart == 0) + i2 = i+1# + sum(LCpart == 0) i = sum(LCpart == 1)-1 while abs(flux_fin[LCpart == 1][i] - np.median(flux_fin)) >= MAD_test*median_abs_deviation(flux_fin, scale='normal'): i -= 1 - i3 = i + sum(LCpart == 0) - - x, y = clean_flux_edges(flux_fin, MAD_fac=MAD_test)#, time_fac=10., min_num_per_group=50) - - print(x[0], i0) - print(x[1], i1) - print(y[0], i2) - print(y[1], i3) - assert(x[0] == i0) - assert(y[0] == i1) - assert(x[1] == i2) - assert(y[1] == i3) + i3 = i# + sum(LCpart == 0) + + x, y = clean_edges_outlier(flux_fin[LCpart == 0], MAD_fac=MAD_test)#, time_fac=10., min_num_per_group=50) + assert(x == i0) + assert(y == i1) + + x, y = clean_edges_outlier(flux_fin[LCpart == 1], MAD_fac=MAD_test)#, time_fac=10., min_num_per_group=50) + assert(x == i2) + assert(y == i3) diff --git a/tessilator/tests/test_clean_lightcurve.py b/tessilator/tests/test_clean_lightcurve.py new file mode 100644 index 00000000..2ed679d2 --- /dev/null +++ b/tessilator/tests/test_clean_lightcurve.py @@ -0,0 +1,43 @@ +from ..lc_analysis import aic_selector, clean_edges_outlier, clean_edges_scatter, detrend_lc, get_time_segments, logger, make_lc, normalisation_choice, remove_sparse_data, sin_fit, cbv_fit_test + +from ..logger import logger_tessilator +# Third party imports +import numpy as np +import os +import json + + +from astropy.table import Table +import astropy.units as u +from astropy.stats import akaike_info_criterion_lsq + +from scipy.stats import median_abs_deviation as MAD +from scipy.optimize import curve_fit +from scipy.stats import iqr + +import itertools as it +from operator import itemgetter + +from astropy.table import Table +from astropy.io import ascii + +aps = ['./targets_tests/Gaia_DR3_2314778985026776320_tests/ap_2314778985026776320_0029_1_2.csv', + './targets_tests/BD+20_2465_tests/ap_BD+20_2465_0045_3_4.csv', + './targets_tests/GESJ08065664-4703000_tests/ap_GESJ08065664-4703000_0061_3_1.csv', + './targets_tests/ABDor_tests/ap_AB_Dor_0036_4_3.csv'] +lcs = ['./targets_tests/Gaia_DR3_2314778985026776320_tests/lc_2314778985026776320_0029_1_2_reg_oflux.csv', + './targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_reg_oflux.csv', + './targets_tests/GESJ08065664-4703000_tests/lc_GESJ08065664-4703000_0061_3_1_reg_oflux.csv', + './targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_reg_oflux.csv'] + +for ap_file, lc_file in zip(aps, lcs): + ap_in = ascii.read(ap_file) + ap_in = ap_in[ap_in["flux"] > 0.0] + lc_test = ascii.read(lc_file) + lc_exam = make_lc(ap_in)[0] + for i in range(len(lc_exam)):#, lc_exam): + assert(str(lc_test['pass_sparse'][i]) == str(lc_exam['pass_sparse'][i])) + assert(str(lc_test['pass_clean_outlier'][i]) == str(lc_exam['pass_clean_outlier'][i])) + assert(str(lc_test['pass_clean_scatter'][i]) == str(lc_exam['pass_clean_scatter'][i])) + assert(str(lc_test['pass_full_outlier'][i]) == str(lc_exam['pass_full_outlier'][i])) + diff --git a/tessilator/tests/test_contaminants.py b/tessilator/tests/test_contaminants.py index db0327e9..a8ffac25 100644 --- a/tessilator/tests/test_contaminants.py +++ b/tessilator/tests/test_contaminants.py @@ -4,6 +4,7 @@ sys.path.append('..') #from ..modules_to_import import * from ..contaminants import flux_fraction_contaminant +from ..logger import logger_tessilator def test_biser_millman(): t_targ0 = 0.0**2/(2.0*0.65**(2)) diff --git a/tessilator/tests/test_periodogram.py b/tessilator/tests/test_periodogram.py new file mode 100644 index 00000000..e1a30df1 --- /dev/null +++ b/tessilator/tests/test_periodogram.py @@ -0,0 +1,142 @@ + +import warnings + +# Third party imports +import numpy as np +import os + +from astropy.table import Table +from astropy.coordinates import SkyCoord +from astropy.timeseries import LombScargle +from astropy.wcs import WCS +from astropy.io import ascii, fits +import astropy.units as u +from astropy.stats import akaike_info_criterion_lsq +from ..logger import logger_tessilator + + +from scipy.stats import median_abs_deviation as MAD +from scipy.optimize import curve_fit +import itertools as it + +import matplotlib.pyplot as plt +from collections.abc import Iterable + +# Local application imports +from ..fixedconstants import * +from ..periodogram import check_for_jumps, gauss_fit, gauss_fit_peak, get_next_peak, get_Gauss_params_pg, is_period_cont, logger, mean_of_arrays, run_ls, sin_fit + + +start, stop, typical_timestep = 0, 27, 0.007 # in days +t = np.linspace(start=start+typical_timestep, stop=stop, num=int(stop/typical_timestep), endpoint=True) + + +def test_single_sine_fit(): + period, amp, y_err = 5.0, 0.1, 0.005 + y = 1. + (amp+(y_err*np.random.randn(len(t))))*np.sin(2.*np.pi*t/period) + ls = LombScargle(t, y, dy=y_err) + frequency, power = ls.autopower(minimum_frequency=1./100., + maximum_frequency=1./0.05, + samples_per_peak=50) + p_m = np.argmax(power) + period_1 = 1.0/frequency[p_m] + power_1 = power[p_m] + assert(np.isclose(period_1, period, rtol=0.1)) + + +def test_multi_peak_fit(): + periods = [5.0, 2.8, 0.8, 10.3] + amplitudes = [0.3, 0.2, 0.15, 0.1] + phases = 2.*np.pi*np.random.rand(4) + y_err = 0.000005 + y = np.ones(len(t)) + for i in range(4): + y += (amplitudes[i]+(y_err*np.random.randn(len(t))))*np.sin((2.*np.pi*t/periods[i])+phases[i]) + ls = LombScargle(t, y, dy=y_err) + plt.plot(t, y) +# plt.show() + frequency, power = ls.autopower(minimum_frequency=1./100., + maximum_frequency=1./0.05, + samples_per_peak=50) + + p_m = np.argmax(power) + period_1 = 1.0/frequency[p_m] + power_1 = power[p_m] + period = 1./frequency[::-1] + power = power[::-1] + +# plt.plot(period, power) +# plt.xlim([0,20]) +# plt.show() + LS_dict = {} + LS_dict['a_1'] = np.arange(len(power)) + LS_dict['period_a_1'] = period + LS_dict['power_a_1'] = power + LS_dict['period_1'] = period_1 + LS_dict['power_1'] = power_1 + n_peaks = 3 + for i in 1+np.arange(n_peaks): + try: + # get the indices of all the peaks that were not part of the last peak + LS_dict[f'a_{i+1}'] = get_next_peak(LS_dict[f'power_a_{i}']) + # all the indices that 'are' part of the peak + LS_dict[f'a_g_{i}'] = np.delete(np.array(LS_dict[f'a_{i}']), np.array(LS_dict[f'a_{i+1}'])) + LS_dict[f'Gauss_{i}'], LS_dict[f'Gauss_y_{i}'] = get_Gauss_params_pg(period, power, LS_dict[f'a_g_{i}']) + # find all the new period values in the new array + LS_dict[f'period_a_{i+1}'] = LS_dict[f'period_a_{i}'][LS_dict[f'a_{i+1}']] + # find all the new power values in the new array + LS_dict[f'power_a_{i+1}'] = LS_dict[f'power_a_{i}'][LS_dict[f'a_{i+1}']] + # calculate the period of the maximum power peak + LS_dict[f'period_{i+1}'] = LS_dict[f'period_a_{i+1}'][np.argmax(LS_dict[f'power_a_{i+1}'])] + # return the maximum power peak value + LS_dict[f'power_{i+1}'] = LS_dict[f'power_a_{i+1}'][np.argmax(LS_dict[f'power_a_{i+1}'])] + except: + logger.error(f'Something went wrong with the periods/powers of subsequent peaks. Probably an empty array of values.') + LS_dict[f'Gauss_{i}'] = [-999, -999, -999] + LS_dict[f'period_a_{i+1}'] = -999 + LS_dict[f'power_a_{i+1}'] = -999 + LS_dict[f'period_{i+1}'] = -999 + LS_dict[f'power_{i+1}'] = -999 + LS_dict.pop(f'a_{n_peaks+1}', None) + LS_dict.pop(f'period_a_{n_peaks+1}', None) + LS_dict.pop(f'power_a_{n_peaks+1}', None) + LS_dict.pop(f'period_{n_peaks+1}', None) + LS_dict.pop(f'power_{n_peaks+1}', None) + for i in range(3): + print(LS_dict[f'period_{i+1}']) + assert((LS_dict[f'period_{i+1}']/periods[i] > 0.8) and LS_dict[f'period_{i+1}']/periods[i] < 1.2) + +def test_real_targets(p_min_thresh=0.05, p_max_thresh=100., samples_per_peak=50): + lcs = ['./targets_tests/Gaia_DR3_2314778985026776320_tests/lc_2314778985026776320_0029_1_2_reg_oflux.csv', + './targets_tests/BD+20_2465_tests/lc_BD+20_2465_0045_3_4_cbv_oflux.csv', + './targets_tests/GESJ08065664-4703000_tests/lc_GESJ08065664-4703000_0061_3_1_reg_oflux.csv', + './targets_tests/ABDor_tests/lc_AB_Dor_0036_4_3_reg_oflux.csv'] + periods = [13.375,6.719,2.492,0.514] + for i, (lc, period) in enumerate(zip(lcs, periods)): + converters = {'pass_sparse' : bool, + 'pass_clean_scatter' : bool, + 'pass_clean_outlier' : bool, + 'pass_full_outlier' : bool} + lc_data = Table.read(lc, converters=converters) + cln_cond = np.logical_and.reduce([ + lc_data["pass_clean_scatter"], + lc_data["pass_clean_outlier"], + lc_data["pass_full_outlier"] + ]) + cln = lc_data[cln_cond] + + time = np.array(cln["time"]) + nflux = np.array(cln["nflux_dtr"]) + enflux = np.array(cln["nflux_err"]) + ls = LombScargle(time, nflux, dy=enflux) + frequency, power = ls.autopower(minimum_frequency=1./p_max_thresh, + maximum_frequency=1./p_min_thresh, + samples_per_peak=samples_per_peak) + p_m = np.argmax(power) + period_1 = 1.0/frequency[p_m] + power_1 = power[p_m] + print(period_1, power_1) + if i == 0: + assert(period_1 > 5.) + else: + assert((period_1/period > 0.8) and (period_1/period < 1.2)) diff --git a/tessilator/tests/test_xypos_sector.py b/tessilator/tests/test_xypos_sector.py new file mode 100644 index 00000000..f5e8f2a4 --- /dev/null +++ b/tessilator/tests/test_xypos_sector.py @@ -0,0 +1,60 @@ +from ..aperture import get_xy_pos, aper_run +from ..logger import logger_tessilator + +import numpy as np +import astropy.table as Table +from astropy.time import Time +from astropy.io import ascii, fits +from glob import glob + +logger = logger_tessilator('xypos_sector_test') + + + +fits_files = np.array(sorted(glob('./sector_tests/*ffic.fits'))) +fits_tesscuts = np.array(sorted(glob('./sector_tests/*_*_*_*.fits'))) + +tess_fits_time_0 = '2014-12-08T12:00:00.000' +tess_fits_time_0 = Time(tess_fits_time_0, format='fits') +tess_jd0 = tess_fits_time_0.to_value('mjd') +time_fac = 0.0089 +# the time fac is just time shift needed to match up the timestamps in both the cutout and sector data. + +if len(fits_files) == 0: + assert True +else: + t = ascii.read('./sector_tests/sector_test.csv') + Xpos, Ypos = t['Xpos'], t['Ypos'] + del t['Xpos'] + del t['Ypos'] + secs = [6,7,33] + fits_secs = [fits_file.split('-')[1][1:] for fits_file in fits_files] + tess_time = np.array([], dtype=np.float64) + for f_n in fits_files: + f_t = f_n.split('-')[0].split('/')[-1][4:] + date_yday = ':'.join([f_t[:4],f_t[4:7],f_t[7:9],f_t[9:11],f_t[11:13]]) + tess_time = np.append(tess_time, Time(date_yday, format='yday').to_value(format='mjd')-tess_jd0) + for i, sec in enumerate(secs): + sec_str = f'{sec:04d}' + matches = np.array([], dtype=int) + for ind, fits_sec in enumerate(fits_secs): + if fits_sec == sec_str: + matches = np.append(matches, ind) + + + tab_ffics = [] + t_spec = t[t["Sector"]==sec] + fits_tesscut = fits_tesscuts[i] + tab_cut, rad_cut = aper_run(fits_tesscut, t_spec) + + for fits_file, m in zip(fits_files[matches], matches): + with fits.open(fits_file) as hdul: + head = hdul[1].header + pos_out = get_xy_pos(t_spec, head) + + tab_ffic, rad_ffic = aper_run(fits_file, t_spec) + time_diff = np.abs(tab_cut["time"]-tess_time[m]-time_fac) + t_cut = tab_cut[np.argsort(time_diff)] + + assert np.isclose(t_cut["flux"][0], tab_ffic["flux"][0], rtol=1.e-3) + assert np.isclose(t_cut["reg_oflux"][0], tab_ffic["reg_oflux"][0], rtol=1.e-3) diff --git a/tessilator/tests/x_test_clean_flux_edges.py b/tessilator/tests/x_test_clean_flux_edges.py index ac08c6a3..073c720b 100644 --- a/tessilator/tests/x_test_clean_flux_edges.py +++ b/tessilator/tests/x_test_clean_flux_edges.py @@ -21,6 +21,3 @@ y_min, y_max = y_med+(MAD_fac*y_MAD), y_med-(MAD_fac*y_MAD) s, f = clean_flux_edges(y) -print(y_med, 2*y_MAD) -print(s,f) -print(jjj) diff --git a/tessilator/tests/xypos_sector_test.log b/tessilator/tests/xypos_sector_test.log new file mode 100644 index 00000000..e69de29b