From 181eb77ec46d49011efd28c59ab6f3680948074e Mon Sep 17 00:00:00 2001 From: Thibault Barnouin Date: Fri, 2 Jun 2023 16:34:44 +0200 Subject: [PATCH] debug query and improve plots for paper --- src/FOC_reduction.py | 34 +++++++++++++++++++++------------ src/comparison_Kishimoto.py | 7 +++++-- src/lib/background.py | 1 + src/lib/fits.py | 4 ++-- src/lib/plots.py | 13 +++++++------ src/lib/query.py | 10 +++++----- src/lib/reduction.py | 13 ++++++++----- src/overplot.py | 38 ++++++++++++++++++------------------- 8 files changed, 69 insertions(+), 51 deletions(-) diff --git a/src/FOC_reduction.py b/src/FOC_reduction.py index 99b44eb..6b81041 100755 --- a/src/FOC_reduction.py +++ b/src/FOC_reduction.py @@ -28,12 +28,12 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): algo="richardson" # Initial crop - display_crop = False + display_crop = True # Background estimation error_sub_type = 'freedman-diaconis' #sqrt, sturges, rice, scott, freedman-diaconis (default) or shape (example (51,51)) - subtract_error = 1.25 - display_error = False + subtract_error = 1.00 + display_error = True # Data binning rebin = True @@ -42,12 +42,14 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): rebin_operation = 'sum' #sum or average # Alignement - align_center = 'image' #If None will align image to image center + align_center = 'center' #If None will not align the images + display_bkg = False + display_align = False display_data = False # Smoothing - smoothing_function = 'gaussian' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine - smoothing_FWHM = None #If None, no smoothing is done + smoothing_function = 'combine' #gaussian_after, weighted_gaussian_after, gaussian, weighted_gaussian or combine + smoothing_FWHM = 0.20 #If None, no smoothing is done smoothing_scale = 'arcsec' #pixel or arcsec # Rotation @@ -56,7 +58,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): # Final crop crop = False #Crop to desired ROI - final_display = True #Whether to display all polarization map outputs + final_display = False #Whether to display all polarization map outputs # Polarization map output SNRp_cut = 3. #P measurments with SNR>3 @@ -77,15 +79,15 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): target = input("Target name:\n>") else: target, products = retrieve_products(target,proposal_id,output_dir=output_dir) - prod = products[0] - for prods in products[1:]: + prod = products.pop() + for prods in products: main(target=target,infiles=["/".join(pr) for pr in prods],output_dir=output_dir) - data_folder = prod[0,0] + data_folder = prod[0][0] try: plots_folder = data_folder.replace("data","plots") except: plots_folder = "." - infiles = prod[:,1] + infiles = [p[1] for p in prod] data_array, headers = proj_fits.get_obs_data(infiles, data_folder=data_folder, compute_flux=True) figname = "_".join([target,"FOC"]) @@ -98,6 +100,8 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): figtype = "full" else: figtype = "_".join(["".join([s[0] for s in smoothing_function.split("_")]),"".join(["{0:.2f}".format(smoothing_FWHM).replace(".",""),smoothing_scale])]) #additionnal informations + if align_center is None: + figtype += "_not_aligned" # Crop data to remove outside blank margins. data_array, error_array, headers = proj_red.crop_array(data_array, headers, step=5, null_val=0., inside=True, display=display_crop, savename=figname, plots_folder=plots_folder) @@ -110,9 +114,15 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): background = None data_array, error_array, headers, background = proj_red.get_error(data_array, headers, error_array, sub_type=error_sub_type, subtract_error=subtract_error, display=display_error, savename="_".join([figname,"errors"]), plots_folder=plots_folder, return_background=True) + if display_bkg: + proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"bkg"]), plots_folder=plots_folder) + # Align and rescale images with oversampling. data_array, error_array, headers, data_mask = proj_red.align_data(data_array, headers, error_array=error_array, background=background, upsample_factor=10, ref_center=align_center, return_shifts=False) + if display_align: + proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"center",str(align_center)]), plots_folder=plots_folder) + # Rebin data to desired pixel size. if rebin: data_array, error_array, headers, Dxy, data_mask = proj_red.rebin_array(data_array, error_array, headers, pxsize=pxsize, scale=px_scale, operation=rebin_operation, data_mask=data_mask) @@ -125,7 +135,7 @@ def main(target=None, proposal_id=None, infiles=None, output_dir="./data"): #Plot array for checking output if display_data and px_scale.lower() not in ['full','integrate']: - proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"center",align_center]), plots_folder=plots_folder) + proj_plots.plot_obs(data_array, headers, vmin=data_array[data_array>0.].min()*headers[0]['photflam'], vmax=data_array[data_array>0.].max()*headers[0]['photflam'], savename="_".join([figname,"rebin"]), plots_folder=plots_folder) background = np.array([np.array(bkg).reshape(1,1) for bkg in background]) background_error = np.array([np.array(np.sqrt((bkg-background[np.array([h['filtnam1']==head['filtnam1'] for h in headers],dtype=bool)].mean())**2/np.sum([h['filtnam1']==head['filtnam1'] for h in headers]))).reshape(1,1) for bkg,head in zip(background,headers)]) diff --git a/src/comparison_Kishimoto.py b/src/comparison_Kishimoto.py index a81982f..dd4b6ae 100755 --- a/src/comparison_Kishimoto.py +++ b/src/comparison_Kishimoto.py @@ -21,6 +21,9 @@ root_dir_plot_S = path_join(root_dir,'FOC_Reduction','plots','NGC1068','5144') filename_S = "NGC1068_FOC_b_10px.fits" plt.rcParams.update({'font.size': 15}) +SNRi_cut = 30. +SNRp_cut = 3. + data_K = {} data_S = {} for d,i in zip(['I','Q','U','P','PA','sI','sQ','sU','sP','sPA'],[0,1,2,5,8,(3,0,0),(3,1,1),(3,2,2),6,9]): @@ -59,7 +62,7 @@ for d in [data_S, data_K]: d['SNRp'][d['sP']>0.] = d['P'][d['sP']>0.]/d['sP'][d['sP']>0.] d['SNRi'] = np.zeros(d['I'].shape) d['SNRi'][d['sI']>0.] = d['I'][d['sI']>0.]/d['sI'][d['sI']>0.] - d['mask'] = np.logical_and(d['SNRi']>30,d['SNRp']>5) + d['mask'] = np.logical_and(d['SNRi']>SNRi_cut,d['SNRp']>SNRp_cut) data_S['mask'], data_K['mask'] = np.logical_and(data_S['mask'],data_K['mask']), np.logical_and(data_S['mask'],data_K['mask']) @@ -121,7 +124,7 @@ im0 = ax.imshow(data_S['I']*convert_flux,norm=LogNorm(data_S['I'][data_S['I']>0] quiv0 = ax.quiver(data_S['X'],data_S['Y'],data_S['xy_U'],data_S['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.2,color='b',alpha=0.75, label="PA through this pipeline") quiv1 = ax.quiver(data_K['X'],data_K['Y'],data_K['xy_U'],data_K['xy_V'],units='xy',angles='uv',scale=0.5,scale_units='xy',pivot='mid',headwidth=0.,headlength=0.,headaxislength=0.,width=0.1,color='r',alpha=0.75, label="PA through Kishimoto's pipeline") -ax.set_title(r"$SNR_P \geq 5 \; & \; SNR_I \geq 30$") +ax.set_title(r"$SNR_P \geq$ "+str(SNRi_cut)+r"$\; & \; SNR_I \geq $"+str(SNRp_cut)) #ax.coords.grid(True, color='white', ls='dotted', alpha=0.5) ax.coords[0].set_axislabel('Right Ascension (J2000)') ax.coords[0].set_axislabel_position('b') diff --git a/src/lib/background.py b/src/lib/background.py index c94be17..68a69b0 100755 --- a/src/lib/background.py +++ b/src/lib/background.py @@ -56,6 +56,7 @@ def display_bkg(data, background, std_bkg, headers, histograms=None, binning=Non formatter = mdates.ConciseDateFormatter(locator) ax.xaxis.set_major_locator(locator) ax.xaxis.set_major_formatter(formatter) + ax.set_ylim(bottom=0.) ax.set_xlabel("Observation date and time") ax.set_ylabel(r"Flux [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") plt.legend() diff --git a/src/lib/fits.py b/src/lib/fits.py index 5b96ba9..2ed49cf 100755 --- a/src/lib/fits.py +++ b/src/lib/fits.py @@ -79,8 +79,8 @@ def get_obs_data(infiles, data_folder="", compute_flux=False): print(np.unique(cdelt[np.logical_not(is_pol60)],axis=0).size) raise ValueError("Not all images have same pixel size") else: - for head in np.array(headers,dtype=object)[is_pol60]: - head['cdelt1'],head['cdelt2'] = np.unique(cdelt[np.logical_not(is_pol60)],axis=0)[0] + for i in np.arange(len(headers))[is_pol60]: + headers[i]['cdelt1'],headers[i]['cdelt2'] = np.unique(cdelt[np.logical_not(is_pol60)],axis=0)[0] if compute_flux: for i in range(len(infiles)): diff --git a/src/lib/plots.py b/src/lib/plots.py index 4ecd430..77fc2c9 100755 --- a/src/lib/plots.py +++ b/src/lib/plots.py @@ -139,6 +139,7 @@ def plot_obs(data_array, headers, shape=None, vmin=None, vmax=None, rectangle=No if vmin is None or vmax is None: vmin, vmax = convert*data[data>0.].min()/10., convert*data[data>0.].max() #im = axe.imshow(convert*data, vmin=vmin, vmax=vmax, origin='lower', cmap='gray') + data[data*convert 0.: - vmin, vmax = 1/5.0*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) else: - vmin, vmax = 1/5.0*np.mean(np.sqrt(stk_cov.data[0,0][stkI.data > 0.])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov.data[0,0][stkI.data > 0.])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) im = ax.imshow(stkI.data*convert_flux, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.) cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") levelsI = np.linspace(vmax*0.01, vmax*0.99, 10) @@ -334,9 +335,9 @@ def polarization_map(Stokes, data_mask=None, rectangle=None, SNRp_cut=3., SNRi_c display='pf' pf_mask = (stkI.data > 0.) * (pol.data > 0.) if mask.sum() > 0.: - vmin, vmax = 1.*np.mean(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov.data[0,0][mask])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) else: - vmin, vmax = 1.*np.mean(np.sqrt(stk_cov.data[0,0][stkI.data > 0.])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) + vmin, vmax = 1./2.*np.median(np.sqrt(stk_cov.data[0,0][stkI.data > 0.])*convert_flux), np.max(stkI.data[stkI.data > 0.]*convert_flux) im = ax.imshow(stkI.data*convert_flux*pol.data, norm=LogNorm(vmin,vmax), aspect='equal', cmap='inferno', alpha=1.) cbar = plt.colorbar(im, cax=cbar_ax, label=r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]") levelsPf = np.linspace(vmax*0.01, vmax*0.99, 10) @@ -1787,12 +1788,12 @@ class pol_map(object): self.display_selection = "total_flux" if self.display_selection.lower() in ['total_flux']: self.data = self.I*self.convert_flux - vmin, vmax = 1/5.0*np.median(self.data[self.data > 0.]), np.max(self.data[self.data > 0.]) + vmin, vmax = 1./2.*np.median(self.data[self.data > 0.]), np.max(self.data[self.data > 0.]) norm = LogNorm(vmin, vmax) label = r"$F_{\lambda}$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]" elif self.display_selection.lower() in ['pol_flux']: self.data = self.I*self.convert_flux*self.P - vmin, vmax = 1/2.0*np.median(self.I[self.I > 0.]*self.convert_flux), np.max(self.I[self.I > 0.]*self.convert_flux) + vmin, vmax = 1./2.*np.median(self.I[self.I > 0.]*self.convert_flux), np.max(self.I[self.I > 0.]*self.convert_flux) norm = LogNorm(vmin, vmax) label = r"$F_{\lambda} \cdot P$ [$ergs \cdot cm^{-2} \cdot s^{-1} \cdot \AA^{-1}$]" elif self.display_selection.lower() in ['pol_deg']: diff --git a/src/lib/query.py b/src/lib/query.py index 8ef2326..4a819a9 100755 --- a/src/lib/query.py +++ b/src/lib/query.py @@ -91,7 +91,7 @@ def get_product_list(target=None, proposal_id=None): used_pol = np.zeros(3) for dataset in obs[obs['Proposal ID'] == pid]: used_pol[polfilt[dataset['Filters'][0]]] += 1 - if np.all(used_pol < 1): + if np.any(used_pol < 1): obs.remove_rows(np.arange(len(obs))[obs['Proposal ID'] == pid]) tab = unique(obs, ['Target name', 'Proposal ID']) @@ -134,8 +134,8 @@ def get_product_list(target=None, proposal_id=None): for prod in products: prod['proposal_id'] = results['Proposal ID'][results['Dataset']==prod['productFilename'][:len(results['Dataset'][0])].upper()][0] - #for prod in products: - # prod['target_name'] = observations['target_name'][observation['obsid']==prod['obsID']] + for prod in products: + prod['target_name'] = observations['target_name'][observations['obsid']==prod['obsID']][0] tab = unique(products, ['target_name', 'proposal_id']) if np.all(tab['target_name']==tab['target_name'][0]): target = tab['target_name'][0] @@ -156,7 +156,7 @@ def retrieve_products(target=None, proposal_id=None, output_dir='./data'): filepaths = [] #obs_dir = path_join(data_dir, obs['prodposal_id']) #if obs['target_name']!=target: - obs_dir = path_join(path_join(output_dir, obs['target_name']), obs['proposal_id']) + obs_dir = path_join(path_join(output_dir, target), obs['proposal_id']) if not path_exists(obs_dir): system("mkdir -p {0:s} {1:s}".format(obs_dir,obs_dir.replace("data","plots"))) for file in products['productFilename'][products['Obs'] == obs['Obs']]: @@ -169,7 +169,7 @@ def retrieve_products(target=None, proposal_id=None, output_dir='./data'): filepaths.append([obs_dir,file]) prodpaths.append(np.array(filepaths,dtype=str)) - return target, np.array(prodpaths) + return target, prodpaths if __name__ == "__main__": diff --git a/src/lib/reduction.py b/src/lib/reduction.py index 83c4096..5e87c12 100755 --- a/src/lib/reduction.py +++ b/src/lib/reduction.py @@ -290,7 +290,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, crop_headers[i]['naxis1'], crop_headers[i]['naxis2'] = crop_array[i].shape if display: - plt.rcParams.update({'font.size': 20}) + plt.rcParams.update({'font.size': 15}) fig, ax = plt.subplots(figsize=(10,10)) convert_flux = headers[0]['photflam'] data = deepcopy(data_array[0]*convert_flux) @@ -326,7 +326,7 @@ def crop_array(data_array, headers, error_array=None, data_mask=None, step=5, if not(savename is None): #fig.suptitle(savename+'_'+filt+'_crop_region') - fig.savefig(plots_folder+savename+'_'+filt+'_crop_region.png', + fig.savefig("/".join([plots_folder,savename+'_'+filt+'_crop_region.png']), bbox_inches='tight') plot_obs(data_array, headers, vmin=convert_flux*data_array[data_array>0.].mean()/5., vmax=convert_flux*data_array[data_array>0.].max(), rectangle=[rectangle,]*len(headers), @@ -730,11 +730,12 @@ def align_data(data_array, headers, error_array=None, background=None, data_array, ref_data, headers = full_array[:-1], full_array[-1], full_headers[:-1] error_array = err_array[:-1] - + do_shift = True if ref_center is None: # Define the center of the reference image to be the center pixel #if None have been specified ref_center = (np.array(ref_data.shape)/2).astype(int) + do_shift = False elif ref_center.lower() in ['max', 'flux', 'maxflux', 'max_flux']: # Define the center of the reference image to be the pixel of max flux. ref_center = np.unravel_index(np.argmax(ref_data),ref_data.shape) @@ -767,8 +768,10 @@ def align_data(data_array, headers, error_array=None, background=None, rescaled_error[i,res_shift[0]:res_shift[0]+shape[1], res_shift[1]:res_shift[1]+shape[2]] = deepcopy(error_array[i]) # Shift images to align - rescaled_image[i] = sc_shift(rescaled_image[i], shift, order=1, cval=0.) - rescaled_error[i] = sc_shift(rescaled_error[i], shift, order=1, cval=background[i]) + if do_shift: + rescaled_image[i] = sc_shift(rescaled_image[i], shift, order=1, cval=0.) + rescaled_error[i] = sc_shift(rescaled_error[i], shift, order=1, cval=background[i]) + curr_mask = sc_shift(res_mask, shift, order=1, cval=False) mask_vertex = clean_ROI(curr_mask) rescaled_mask[i,mask_vertex[2]:mask_vertex[3],mask_vertex[0]:mask_vertex[1]] = True diff --git a/src/overplot.py b/src/overplot.py index 9e64e1c..a61f4f0 100755 --- a/src/overplot.py +++ b/src/overplot.py @@ -6,47 +6,47 @@ from copy import deepcopy from lib.plots import overplot_radio, overplot_pol, align_pol from matplotlib.colors import LogNorm -Stokes_UV = fits.open("../data/IC5063_x3nl030/IC5063_FOC_c_020.fits") -Stokes_18GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063_18GHz.fits") -Stokes_24GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063_24GHz.fits") -Stokes_103GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063_103GHz.fits") -Stokes_229GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063_229GHz.fits") -Stokes_357GHz = fits.open("../data/IC5063_x3nl030/radio/IC5063_357GHz.fits") -#Stokes_S2 = fits.open("../data/IC5063_x3nl030/POLARIZATION_COMPARISON/S2_rot_crop.fits") -Stokes_IR = fits.open("../data/IC5063_x3nl030/IR/u2e65g01t_c0f_rot.fits") +Stokes_UV = fits.open("./data/IC5063/5918/IC5063_FOC_c_020arcsec.fits") +Stokes_18GHz = fits.open("./data/IC5063/radio/IC5063_18GHz.fits") +Stokes_24GHz = fits.open("./data/IC5063/radio/IC5063_24GHz.fits") +Stokes_103GHz = fits.open("./data/IC5063/radio/IC5063_103GHz.fits") +Stokes_229GHz = fits.open("./data/IC5063/radio/IC5063_229GHz.fits") +Stokes_357GHz = fits.open("./data/IC5063/radio/IC5063_357GHz.fits") +#Stokes_S2 = fits.open("./data/IC5063/POLARIZATION_COMPARISON/S2_rot_crop.fits") +Stokes_IR = fits.open("./data/IC5063/IR/u2e65g01t_c0f_rot.fits") levelsMorganti = np.array([1.,2.,3.,8.,16.,32.,64.,128.]) #levels18GHz = np.array([0.6, 1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_18GHz[0].data.max() levels18GHz = levelsMorganti*0.28*1e-3 A = overplot_radio(Stokes_UV, Stokes_18GHz) -A.plot(levels=levels18GHz, SNRp_cut=2.0, SNRi_cut=15.0, savename='../plots/IC5063_x3nl030/18GHz_overplot_forced.png') +A.plot(levels=levels18GHz, SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/18GHz_overplot_forced.png') #levels24GHz = np.array([1.,1.5, 3, 6, 12, 24, 48, 96])/100.*Stokes_24GHz[0].data.max() levels24GHz = levelsMorganti*0.46*1e-3 B = overplot_radio(Stokes_UV, Stokes_24GHz) -B.plot(levels=levels24GHz, SNRp_cut=2.0, SNRi_cut=15.0, savename='../plots/IC5063_x3nl030/24GHz_overplot_forced.png') +B.plot(levels=levels24GHz, SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/24GHz_overplot_forced.png') levels103GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_103GHz[0].data[Stokes_103GHz[0].data > 0.])) C = overplot_radio(Stokes_UV, Stokes_103GHz) -C.plot(levels=levels103GHz, SNRp_cut=2.0, SNRi_cut=15.0, savename='../plots/IC5063_x3nl030/103GHz_overplot_forced.png') +C.plot(levels=levels103GHz, SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/103GHz_overplot_forced.png') levels229GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_229GHz[0].data[Stokes_229GHz[0].data > 0.])) D = overplot_radio(Stokes_UV, Stokes_229GHz) -D.plot(levels=levels229GHz, SNRp_cut=2.0, SNRi_cut=15.0, savename='../plots/IC5063_x3nl030/229GHz_overplot_forced.png') +D.plot(levels=levels229GHz, SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/229GHz_overplot_forced.png') levels357GHz = np.linspace(1,99,11)/100.*np.max(deepcopy(Stokes_357GHz[0].data[Stokes_357GHz[0].data > 0.])) E = overplot_radio(Stokes_UV, Stokes_357GHz) -E.plot(levels=levels357GHz, SNRp_cut=2.0, SNRi_cut=15.0, savename='../plots/IC5063_x3nl030/357GHz_overplot_forced.png') +E.plot(levels=levels357GHz, SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/357GHz_overplot_forced.png') #F = overplot_pol(Stokes_UV, Stokes_S2) -#F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='../plots/IC5063_x3nl030/S2_overplot_forced.png', norm=LogNorm(vmin=5e-20,vmax=5e-18)) +#F.plot(SNRp_cut=3.0, SNRi_cut=80.0, savename='./plots/IC5063/S2_overplot_forced.png', norm=LogNorm(vmin=5e-20,vmax=5e-18)) G = overplot_pol(Stokes_UV, Stokes_IR, cmap='inferno') -G.plot(SNRp_cut=1.0, SNRi_cut=10.0, savename='../plots/IC5063_x3nl030/IR_overplot_forced.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r') +G.plot(SNRp_cut=1.0, SNRi_cut=10.0, savename='./plots/IC5063/IR_overplot_forced.png', norm=LogNorm(vmin=1e-17,vmax=5e-15), cmap='inferno_r') -#data_folder1 = "../data/M87/POS1/" -#plots_folder1 = "../plots/M87/POS1/" +#data_folder1 = "./data/M87/POS1/" +#plots_folder1 = "./plots/M87/POS1/" #basename1 = "M87_020_log" #M87_1_95 = fits.open(data_folder1+"M87_POS1_1995_FOC_combine_FWHM020.fits") #M87_1_96 = fits.open(data_folder1+"M87_POS1_1996_FOC_combine_FWHM020.fits") @@ -58,8 +58,8 @@ G.plot(SNRp_cut=1.0, SNRi_cut=10.0, savename='../plots/IC5063_x3nl030/IR_overplo #H.plot(SNRp_cut=5.0, SNRi_cut=50.0, savename=plots_folder1+'animated_loop/'+basename1, norm=LogNorm()) #command("convert -delay 50 -loop 0 {0:s}animated_loop/{1:s}*.png {0:s}animated_loop/{1:s}.gif".format(plots_folder1, basename1)) -#data_folder3 = "../data/M87/POS3/" -#plots_folder3 = "../plots/M87/POS3/" +#data_folder3 = "./data/M87/POS3/" +#plots_folder3 = "./plots/M87/POS3/" #basename3 = "M87_020_log" #M87_3_95 = fits.open(data_folder3+"M87_POS3_1995_FOC_combine_FWHM020.fits") #M87_3_96 = fits.open(data_folder3+"M87_POS3_1996_FOC_combine_FWHM020.fits")