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 plot_all_data(path, data, arena, note):
for gt in data:
datasets = {}
color_cycle = itertools.cycle(flymad_plot.EXP_COLORS)
laser_powers_sorted = sorted(data[gt],
cmp=flymad_analysis.cmp_laser,
reverse=True)
for order, laser in enumerate(laser_powers_sorted):
#also sort to make cross-activation first
cross_activation = (re.match('[0-9]+iru$',laser) and gt.endswith('chrim')) or \
(re.match('[0-9]+ru$',laser) and gt.endswith('trp'))
gtdf = data[gt][laser][gt]
laser_desc = flymad_analysis.laser_desc(laser)
datasets[laser] = dict(
xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
label=laser_desc,
order=50 if cross_activation else order,
color=color_cycle.next(),
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
if 1:
if gt == "50660chrim":
cgt = "50660"
gtdf = data[cgt]["350iru"][cgt]
datasets['control'] = dict(
xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
label=flymad_analysis.human_label(cgt), #laser_desc,
order=100,
color=flymad_plot.BLACK,
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
figure_title = "Moonwalker Thorax %s (%s)" % (gt, smoothstr)
fig = plt.figure(figure_title, figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1)
l, axs, figs = flymad_plot.plot_timeseries_with_activation(
ax,
targetbetween=dict(xaxis=gtdf['first']['t'].values,
where=gtdf['first']['laser_state'].values > 0),
sem=True,
downsample=5,
note="%s\n%s\n" % (gt, note),
individual={
k: {
'groupby': ('obj_id', 'trial'),
'xaxis': 't',
'yaxis': 'Vfwd'
}
for k in datasets
},
individual_title=figure_title + ' Individual Traces',
**datasets)
ax.axhline(color='k', linestyle='--', alpha=0.8)
ax.set_ylim(YLIM)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Fwd Velocity (%s/s)' % arena.unit)
ax.set_xlim(XLIM)
flymad_plot.retick_relabel_axis(ax, XTICKS, YTICKS)
fig.savefig(flymad_plot.get_plotpath(path, "moonwalker_%s.png" % gt),
bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path, "moonwalker_%s.svg" % gt),
bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(
path, "moonwalker_%s_individual_%s.png" % (gt, efigname)),
bbox_inches='tight')
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 plot_cross_activation_only(path, data, arena, note):
PLOTS = [
('50660chrim', {
'activation': '350ru',
'cross_activation': '350iru'
}),
('50660trp', {
'activation': '434iru',
'cross_activation': '350ru'
}),
('50660trp', {
'activation': '350iru',
'cross_activation': '350ru'
}),
]
for gt, lasers in PLOTS:
figname = 'vs'.join(lasers.values())
alaser = lasers['activation']
adf = data[gt][alaser][gt]
claser = lasers['cross_activation']
cdf = data[gt][claser][gt]
datasets = {
'activation':
dict(xaxis=adf['mean']['t'].values,
value=adf['mean']['Vfwd'].values,
std=adf['std']['Vfwd'].values,
n=adf['n']['Vfwd'].values,
label='Activation (%s)' % flymad_analysis.laser_desc(alaser),
order=0,
color=flymad_plot.RED,
N=len(adf['df']['obj_id'].unique()),
df=adf['df']),
'cross_activation':
dict(xaxis=cdf['mean']['t'].values,
value=cdf['mean']['Vfwd'].values,
std=cdf['std']['Vfwd'].values,
n=cdf['n']['Vfwd'].values,
label='Cross Activation (%s)' %
flymad_analysis.laser_desc(claser),
order=1,
color=flymad_plot.BLACK,
N=len(cdf['df']['obj_id'].unique()),
df=cdf['df']),
}
figure_title = "Moonwalker Thorax Crosstalk %s (%s)" % (gt, figname)
fig = plt.figure(figure_title, figsize=(10, 8))
ax = fig.add_subplot(1, 1, 1)
l, axs, figs = flymad_plot.plot_timeseries_with_activation(
ax,
targetbetween=dict(xaxis=adf['first']['t'].values,
where=adf['first']['laser_state'].values > 0),
sem=True,
downsample=5,
note="%s\n%s\n" % (gt, note),
individual={
k: {
'groupby': ('obj_id', 'trial'),
'xaxis': 't',
'yaxis': 'Vfwd'
}
for k in datasets
},
individual_title=figure_title + ' Individual Traces',
**datasets)
ax.axhline(color='k', linestyle='--', alpha=0.8)
ax.set_ylim(YLIM)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Fwd Velocity (%s/s)' % arena.unit)
ax.set_xlim(XLIM)
flymad_plot.retick_relabel_axis(ax, XTICKS, YTICKS)
fig.savefig(flymad_plot.get_plotpath(
path, "moonwalker_%s_%s.png" % (gt, figname)),
bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(
path, "moonwalker_%s_%s.svg" % (gt, figname)),
bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(
path, "moonwalker_%s_%s_individual_%s.png" %
(gt, figname, efigname)),
bbox_inches='tight')
def plot_all_data(path, data, arena, note):
for gt in data:
datasets = {}
color_cycle = itertools.cycle(flymad_plot.EXP_COLORS)
laser_powers_sorted = sorted(data[gt], cmp=flymad_analysis.cmp_laser, reverse=True)
for order,laser in enumerate(laser_powers_sorted):
#also sort to make cross-activation first
cross_activation = (re.match('[0-9]+iru$',laser) and gt.endswith('chrim')) or \
(re.match('[0-9]+ru$',laser) and gt.endswith('trp'))
gtdf = data[gt][laser][gt]
laser_desc = flymad_analysis.laser_desc(laser)
datasets[laser] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
label=laser_desc,
order=50 if cross_activation else order,
color=color_cycle.next(),
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
if 1:
if gt == "50660chrim":
cgt = "50660"
gtdf = data[cgt]["350iru"][cgt]
datasets['control'] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
label=flymad_analysis.human_label(cgt),#laser_desc,
order=100,
color=flymad_plot.BLACK,
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
figure_title = "Moonwalker Thorax %s (%s)" % (gt,smoothstr)
fig = plt.figure(figure_title, figsize=(10,8))
ax = fig.add_subplot(1,1,1)
l, axs, figs = flymad_plot.plot_timeseries_with_activation(ax,
targetbetween=dict(xaxis=gtdf['first']['t'].values,
where=gtdf['first']['laser_state'].values>0),
sem=True,
downsample=5,
note="%s\n%s\n" % (gt,note),
individual={k:{'groupby':('obj_id','trial'),'xaxis':'t','yaxis':'Vfwd'} for k in datasets},
individual_title=figure_title + ' Individual Traces',
**datasets
)
ax.axhline(color='k', linestyle='--',alpha=0.8)
ax.set_ylim(YLIM)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Fwd Velocity (%s/s)' % arena.unit)
ax.set_xlim(XLIM)
flymad_plot.retick_relabel_axis(ax, XTICKS, YTICKS)
fig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s.png" % gt), bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s.svg" % gt), bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s_individual_%s.png" % (gt, efigname)), bbox_inches='tight')
def plot_cross_activation_only(path, data, arena, note):
PLOTS = [('50660chrim',{'activation':'350ru','cross_activation':'350iru'}),
('50660trp',{'activation':'434iru','cross_activation':'350ru'}),
('50660trp',{'activation':'350iru','cross_activation':'350ru'}),
]
for gt,lasers in PLOTS:
figname = 'vs'.join(lasers.values())
alaser = lasers['activation']
adf = data[gt][alaser][gt]
claser = lasers['cross_activation']
cdf = data[gt][claser][gt]
datasets = {
'activation':dict(xaxis=adf['mean']['t'].values,
value=adf['mean']['Vfwd'].values,
std=adf['std']['Vfwd'].values,
n=adf['n']['Vfwd'].values,
label='Activation (%s)' % flymad_analysis.laser_desc(alaser),
order=0,
color=flymad_plot.RED,
N=len(adf['df']['obj_id'].unique()),
df=adf['df']),
'cross_activation':dict(
xaxis=cdf['mean']['t'].values,
value=cdf['mean']['Vfwd'].values,
std=cdf['std']['Vfwd'].values,
n=cdf['n']['Vfwd'].values,
label='Cross Activation (%s)' % flymad_analysis.laser_desc(claser),
order=1,
color=flymad_plot.BLACK,
N=len(cdf['df']['obj_id'].unique()),
df=cdf['df']),
}
figure_title = "Moonwalker Thorax Crosstalk %s (%s)" % (gt,figname)
fig = plt.figure(figure_title, figsize=(10,8))
ax = fig.add_subplot(1,1,1)
l,axs, figs = flymad_plot.plot_timeseries_with_activation(ax,
targetbetween=dict(xaxis=adf['first']['t'].values,
where=adf['first']['laser_state'].values>0),
sem=True,
downsample=5,
note="%s\n%s\n" % (gt,note),
individual={k:{'groupby':('obj_id','trial'),'xaxis':'t','yaxis':'Vfwd'} for k in datasets},
individual_title=figure_title + ' Individual Traces',
**datasets
)
ax.axhline(color='k', linestyle='--',alpha=0.8)
ax.set_ylim(YLIM)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Fwd Velocity (%s/s)' % arena.unit)
ax.set_xlim(XLIM)
flymad_plot.retick_relabel_axis(ax, XTICKS, YTICKS)
fig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s_%s.png" % (gt, figname)), bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s_%s.svg" % (gt, figname)), bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(path,"moonwalker_%s_%s_individual_%s.png" % (gt, figname, efigname)), bbox_inches='tight')
