python - Not saving all matplotlib graphs from script output? -

i have script run in ipython, , takes input .csv file of gene_names , pushes them loop, where

with open('c:\users\work\desktop\book1.csv', 'ru') f: reader = csv.reader(f) pdfpages('poopyheadjoe04.pdf') pdf:     row in reader:         gene_name = row         probe_exclusion_keyword = []         print gene_name 

the gene_name values list(in .csv file) fed line, if inference_method == "approximate_random": (in scripts.py)

with open('c:\users\work\desktop\book1.csv', 'ru') f:     reader = csv.reader(f)     pdfpages('poopyheadjoe04.pdf') pdf:         row in reader:             gene_name = row             probe_exclusion_keyword = []             print gene_name              print "fetching probe ids gene %s" % gene_name             probes_dict = get_probes_from_genes(gene_name)             print "found %s probes: %s" % (len(probes_dict), ", ".join(probes_dict.values()))              if probe_exclusion_keyword:                 probes_dict = {probe_id: probe_name (probe_id, probe_name) in probes_dict.iteritems() if not args.probe_exclusion_keyword in probe_name}                 print "probes after applying exclusion cryterion: %s" % (", ".join(probes_dict.values()))              print "fetching expression values probes %s" % (", ".join(probes_dict.values()))             expression_values, well_ids, donor_names = get_expression_values_from_probe_ids(                 probes_dict.keys())             print "found data %s wells sampled across %s donors" % (len(well_ids), len(set(donor_names)))              print "combining information selected probes"             combined_expression_values = combine_expression_values(             expression_values, method=probes_reduction_method)              print "translating locations of wells mni space"             mni_coordinates = get_mni_coordinates_from_wells(well_ids)              print "checking values of provided nifti file @ locations"             nifti_values = get_values_at_locations(             stat_map, mni_coordinates, mask_file=mask, radius=radius, verbose=true)          # preparing data frame             names = ["nifti values", "%s expression" % gene_name, "donor id"]             data = pd.dataframe(np.array(             [nifti_values, combined_expression_values, donor_names]).t, columns=names)             data = data.convert_objects(convert_numeric=true)             len_before = len(data)             data.dropna(axis=0, inplace=true)             nans = len_before - len(data)              if nans > 0:                 print "%s wells fall outside of mask" % nans              if inference_method == "fixed":                 print "performing fixed effect analysis"                 fixed_effects(data, ["nifti values", "%s expression" % gene_name])              **if inference_method == "approximate_random":**                 print "performing approximate random effect analysis"                 approximate_random_effects(                 data, ["nifti values", "%s expression" % gene_name], "donor id")                 print "poopy"                 pdf.savefig()                 plt.ion() #should add ion() here?              if inference_method == "bayesian_random":                 print "fitting bayesian hierarchical model"                 bayesian_random_effects(                 data, ["nifti values", "%s expression" % gene_name], "donor id", n_samples, n_burnin)          # if __name__ == '__main__':    #what do? start trigger script run?         # main() 

that triggers approximate_random_effects(in analysis.py) plot 2 graphs, violinplot , lmplot:

def approximate_random_effects(data, labels, group):      correlation_per_donor = {}     donor_id in set(data[group]):         correlation_per_donor[donor_id], _, _, _, _ = linregress(list(data[labels[0]][data[group] == donor_id]),                                                        list(data[labels[1]][data[group] == donor_id]))     average_slope = np.array(correlation_per_donor.values()).mean()     t, p_val = ttest_1samp(correlation_per_donor.values(), 0)     print "averaged slope across donors = %g (t=%g, p=%g)"%(average_slope, t, p_val)     sns.violinplot([correlation_per_donor.values()], inner="points", names=["donors"])     plt.ylabel("linear regression slopes between %s , %s"%(labels[0],labels[1]))     plt.axhline(0, color="red")      sns.lmplot(labels[0], labels[1], data, hue=group, col=group, col_wrap=3)     plt.ion()      return average_slope, t, p_val 

i'm trying save both graphs gene_names pdf file, following "saving multiple figures 1 pdf file in matplotlib" , matplotlib.pdfpages approach.

however, in pdf file, getting lmplot gene_names , not violin plot. , interestingly, adding print def approximate_random_effects (of analysis.py) not reflect output. do fix this?

thanks! appreciated!

you need create 2 different axes plots, 1 violin plot, , 1 imshow plot. @ top of approximate_random_effects function add

fig, (ax_left, ax_right) = plt.subplots(1,2, figsize=(6,3)) # change figsize size want, default ugly 2 plots 

or

fig, (ax_top, ax_bottom) = plt.subplots(2, figsize=(3, 6)) # change figsize size want, default ugly 2 plots 

depending on if want top , bottom or side side. rest of answer, assume side side.

now need change plotting calls adding ax=ax_left , ax=ax_right

sns.violinplot([correlation_per_donor.values()], inner="points", names=["donors"], ax=ax_left) 

and

sns.lmplot(labels[0], labels[1], data, hue=group, col=group, col_wrap=3, ax=ax_right)