def do_stats(path, smoothstr, expmean, ctrlmean, expstd, ctrlstd, expn, ctrln, df2): exp_genotype = 'OK371shib' ctrl_genotype = 'wshib' df2 = add_obj_id(df2) df2['t'] = df2['align'] - 10 df2 = df2[df2['t'] <= 40] expdf = df2[df2['Genotype'] == exp_genotype] ctrldf = df2[df2['Genotype'] == ctrl_genotype] data = { 'OK371>ShiTs': dict(df=expdf), 'controls': dict(df=ctrldf), } names = ['OK371>ShiTs', 'controls'] num_bins = 40 fname_prefix = flymad_plot.get_plotpath( path, 'OK371_pvalues_%d_bins' % (num_bins, )) madplot.view_pairwise_stats_plotly( data, names, fname_prefix, align_colname='align', stat_colname='v', num_bins=num_bins, )
def do_stats(path,data,d1_arena,note): datasets = {} laser_powers = set() for gt in data: laser_powers_sorted = sorted(data[gt], cmp=flymad_analysis.cmp_laser, reverse=True) for order,laser in enumerate(laser_powers_sorted): laser_powers.add( laser ) gtdf = data[gt][laser][gt] fake_gt_name = '%s-%s'%(gt,laser) datasets[fake_gt_name] = gtdf # do the stats stat_info = [ ('trp_basic_control1', ['50660trp-350iru','50660-350iru']), ('trp_basic_control2', ['50660trp-350iru','wtrp-350iru']), ('trp_basic_pooled_controls', ['50660trp-350iru','my_pooled_controls-350iru']), ('chrimson_crosstalk_controls', ['50660chrim-350iru','50660-350iru']), ('chrimson_crosstalk_activation_low_1', ['50660chrim-350iru','50660chrim-028ru']), ('chrimson_crosstalk_activation_low_2', ['50660-350iru','50660chrim-028ru']), ('chrimson_activation_high', ['50660-350ru','50660chrim-028ru']), ('trp_crosstalk_activation_low', ['50660trp-183iru','50660trp-350ru']), ('trp_activation_high', ['50660trp-183iru','50660trp-434iru']), ] gt_names = {'50660':'Gal4-control', 'wtrp':'UAS-control', 'my_pooled_controls':'controls', '50660chrim':'MW>Chrim', '50660trp':'MW>TrpA1', } human_label_dict = {} for laser in laser_powers: laser_human = flymad_analysis.laser_desc(laser) assert laser_human != laser for gt in ['50660','50660chrim','50660trp','wtrp','my_pooled_controls']: gt_human = gt_names[gt] assert gt_human != gt key = '%s-%s'%(gt,laser) value = '%s %s'%(gt_human,laser_human) human_label_dict[key] = value pooldf = pd.concat([datasets['wtrp-350iru']['df'],datasets['50660-350iru']['df']]) datasets['my_pooled_controls-350iru'] = dict(df=pooldf) num_bins = [40] for num_bin in num_bins: for experiment_name, exp_gts in stat_info: fname_prefix = flymad_plot.get_plotpath(path,'moonwalker_stats_%s_%d_bins'%(experiment_name,num_bin)) madplot.view_pairwise_stats_plotly(datasets, exp_gts, fname_prefix, align_colname='t', stat_colname='Vfwd', layout_title='p-values', num_bins=num_bin, human_label_dict=human_label_dict, )
def do_stats(path, smoothstr, expmean, ctrlmean, expstd, ctrlstd, expn, ctrln, df2): exp_genotype = 'OK371shib' ctrl_genotype = 'wshib' df2 = add_obj_id(df2) df2['t'] = df2['align']-10 df2 = df2[ df2['t'] <= 40 ] expdf = df2[df2['Genotype'] == exp_genotype] ctrldf = df2[df2['Genotype']== ctrl_genotype] data={ 'OK371>ShiTs':dict(df=expdf), 'controls':dict(df=ctrldf), } names=['OK371>ShiTs','controls'] num_bins=40 fname_prefix = flymad_plot.get_plotpath(path,'OK371_pvalues_%d_bins'%(num_bins,)) madplot.view_pairwise_stats_plotly( data, names, fname_prefix, align_colname='align', stat_colname='v', num_bins=num_bins, )
arena = madplot.Arena('mm') note = "%s %s\n%r\nmedfilt %s" % (arena.unit, smoothstr, arena, medfilt) cache_fname = os.path.join(os.path.dirname(path), 'speed.madplot-cache') cache_args = ([os.path.basename(b) for b in bags], smoothstr, args.smooth, medfilt, genotypes, args.min_experiment_duration) data = None if args.only_plot: data = madplot.load_bagfile_cache(cache_args, cache_fname) if data is None: data = prepare_data(bags, arena, smoothstr, args.smooth, medfilt, genotypes, args.min_experiment_duration) madplot.save_bagfile_cache(data, cache_args, cache_fname) if args.stats: fname_prefix = flymad_plot.get_plotpath(path, 'csv_speed') madplot.view_pairwise_stats_plotly( data, genotypes, fname_prefix, align_colname='t', stat_colname='v', ) plot_data(path, data, arena, note) if args.show: plt.show()
def do_stats(path, data, d1_arena, note): datasets = {} laser_powers = set() for gt in data: laser_powers_sorted = sorted(data[gt], cmp=flymad_analysis.cmp_laser, reverse=True) for order, laser in enumerate(laser_powers_sorted): laser_powers.add(laser) gtdf = data[gt][laser][gt] fake_gt_name = '%s-%s' % (gt, laser) datasets[fake_gt_name] = gtdf # do the stats stat_info = [ ('trp_basic_control1', ['50660trp-350iru', '50660-350iru']), ('trp_basic_control2', ['50660trp-350iru', 'wtrp-350iru']), ('trp_basic_pooled_controls', ['50660trp-350iru', 'my_pooled_controls-350iru']), ('chrimson_crosstalk_controls', ['50660chrim-350iru', '50660-350iru']), ('chrimson_crosstalk_activation_low_1', ['50660chrim-350iru', '50660chrim-028ru']), ('chrimson_crosstalk_activation_low_2', ['50660-350iru', '50660chrim-028ru']), ('chrimson_activation_high', ['50660-350ru', '50660chrim-028ru']), ('trp_crosstalk_activation_low', ['50660trp-183iru', '50660trp-350ru']), ('trp_activation_high', ['50660trp-183iru', '50660trp-434iru']), ] gt_names = { '50660': 'Gal4-control', 'wtrp': 'UAS-control', 'my_pooled_controls': 'controls', '50660chrim': 'MW>Chrim', '50660trp': 'MW>TrpA1', } human_label_dict = {} for laser in laser_powers: laser_human = flymad_analysis.laser_desc(laser) assert laser_human != laser for gt in [ '50660', '50660chrim', '50660trp', 'wtrp', 'my_pooled_controls' ]: gt_human = gt_names[gt] assert gt_human != gt key = '%s-%s' % (gt, laser) value = '%s %s' % (gt_human, laser_human) human_label_dict[key] = value pooldf = pd.concat( [datasets['wtrp-350iru']['df'], datasets['50660-350iru']['df']]) datasets['my_pooled_controls-350iru'] = dict(df=pooldf) num_bins = [40] for num_bin in num_bins: for experiment_name, exp_gts in stat_info: fname_prefix = flymad_plot.get_plotpath( path, 'moonwalker_stats_%s_%d_bins' % (experiment_name, num_bin)) madplot.view_pairwise_stats_plotly( datasets, exp_gts, fname_prefix, align_colname='t', stat_colname='Vfwd', layout_title='p-values', num_bins=num_bin, human_label_dict=human_label_dict, )
medfilt = args.median_filter smoothstr = '%s' % {True:'smooth',False:'nosmooth'}[args.smooth] arena = madplot.Arena('mm') note = "%s %s\n%r\nmedfilt %s" % (arena.unit, smoothstr, arena, medfilt) cache_fname = os.path.join(os.path.dirname(path),'speed.madplot-cache') cache_args = ([os.path.basename(b) for b in bags], smoothstr, args.smooth, medfilt, genotypes, args.min_experiment_duration) data = None if args.only_plot: data = madplot.load_bagfile_cache(cache_args, cache_fname) if data is None: data = prepare_data(bags, arena, smoothstr, args.smooth, medfilt, genotypes, args.min_experiment_duration) madplot.save_bagfile_cache(data, cache_args, cache_fname) if args.stats: fname_prefix = flymad_plot.get_plotpath(path,'csv_speed') madplot.view_pairwise_stats_plotly(data, genotypes, fname_prefix, align_colname='t', stat_colname='v', ) plot_data(path, data, arena, note) if args.show: plt.show()
calibration_file = os.path.join(args.calibration_dir, CALIBRATION_FILE) arena = madplot.Arena( 'mm', **flymad_analysis.get_arena_conf(calibration_file=calibration_file)) note = "%s %s\n%r\nmedfilt %s" % (arena.unit, smoothstr, arena, medfilt) cache_fname = os.path.join(path,'speed.madplot-cache') cache_args = (path, arena, smoothstr, args.smooth, medfilt, GENOTYPES) data = None if args.only_plot: data = madplot.load_bagfile_cache(cache_args, cache_fname) if data is None: data = prepare_data(path, arena, smoothstr, args.smooth, medfilt, GENOTYPES) madplot.save_bagfile_cache(data, cache_args, cache_fname) fname_prefix = flymad_plot.get_plotpath(path,'csv_speed') madplot.view_pairwise_stats_plotly(data, [EXP_GENOTYPE, CTRL_GENOTYPE, EXP2_GENOTYPE], fname_prefix, align_colname='t', stat_colname='v', ) plot_data(path, data, arena, note) if args.show: plt.show()
calibration_file = os.path.join(args.calibration_dir, CALIBRATION_FILE) arena = madplot.Arena( 'mm', **flymad_analysis.get_arena_conf(calibration_file=calibration_file)) note = "%s %s\n%r\nmedfilt %s" % (arena.unit, smoothstr, arena, medfilt) cache_fname = os.path.join(path, 'speed.madplot-cache') cache_args = (path, arena, smoothstr, args.smooth, medfilt, GENOTYPES) data = None if args.only_plot: data = madplot.load_bagfile_cache(cache_args, cache_fname) if data is None: data = prepare_data(path, arena, smoothstr, args.smooth, medfilt, GENOTYPES) madplot.save_bagfile_cache(data, cache_args, cache_fname) fname_prefix = flymad_plot.get_plotpath(path, 'csv_speed') madplot.view_pairwise_stats_plotly( data, [EXP_GENOTYPE, CTRL_GENOTYPE, EXP2_GENOTYPE], fname_prefix, align_colname='t', stat_colname='v', ) plot_data(path, data, arena, note) if args.show: plt.show()