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()