def plot_all_data(path, data, arena, note, laser='350iru'):
ORDER = ["50660trp","50660","wtrp"]
COLORS = {"50660trp":flymad_plot.RED,"50660":flymad_plot.BLACK,"wtrp":flymad_plot.BLUE}
datasets = {}
figure_title = "Moonwalker"
fig = plt.figure(figure_title, figsize=TS_FIGSIZE)
ax = fig.add_subplot(1,1,1)
for gt in data:
gtdf = data[gt][laser][gt]
datasets[gt] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
order=ORDER.index(gt),
label=flymad_analysis.human_label(gt),
color=COLORS[gt],
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
l, axs, figs = flymad_plot.plot_timeseries_with_activation(ax,
targetbetween=dict(xaxis=data['50660trp'][laser]['50660trp']['first']['t'].values,
where=data['50660trp'][laser]['50660trp']['first']['laser_state'].values>0),
downsample=5,
sem=True,
note="%s\n" % (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('Forward 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_simple.png"), bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path,"moonwalker_simple.svg"), bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(path,"moonwalker_simple_individual_%s.png" % efigname), bbox_inches='tight')
def plot_data(path, data):
ci_html_buf = ''
for exp_name in data:
gts = data[exp_name].keys()
laser = '130ht'
gts_string = 'vs'.join(gts)
figname = laser + '_' + gts_string
fig = plt.figure("Song (%s)" % figname, figsize=(10, 6))
ax = fig.add_subplot(1, 1, 1)
datasets = {}
for gt in gts:
if flymad_analysis.genotype_is_exp(gt):
color = flymad_plot.RED
order = 1
elif flymad_analysis.genotype_is_ctrl(gt):
color = flymad_plot.BLACK
order = 2
elif flymad_analysis.genotype_is_trp_ctrl(gt):
order = 3
color = flymad_plot.BLUE
else:
color = 'cyan'
order = 0
gtdf = data[exp_name][gt]
datasets[gt] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['zx'].values,
std=gtdf['std']['zx'].values,
n=gtdf['n']['zx'].values,
order=order,
df=gtdf,
label=flymad_analysis.human_label(gt),
color=color,
N=len(gtdf['df']['obj_id'].unique()))
pvalue_buf = ''
for gt in datasets:
label = flymad_analysis.human_label(gt)
if '>' not in label:
continue
# OK, this is a Gal4 + UAS - do head vs thorax stats
gtdf = data[exp_name][gt]['df']
ci_data = do_cum_incidence(gtdf, label)
ci_html_buf += ci_data['buf']
ax_cum = ci_data['ax']
spine_placer(ax_cum, location='left,bottom')
ax_cum.set_ylabel('Fraction extending wing (%)')
ax_cum.set_xlabel('Time (s)')
note = '%s\n%s\np-value: %.3g\n%d flies\nn=%d, %d' % (
label, madplot.p2stars(ci_data['p_value']), ci_data['p_value'],
len(gtdf['obj_id'].unique()), ci_data['n_head'],
ci_data['n_thorax'])
ax_cum.text(
0,
1, #top left
note,
fontsize=10,
horizontalalignment='left',
verticalalignment='top',
transform=ax_cum.transAxes,
zorder=0)
ci_data['fig'].savefig(flymad_plot.get_plotpath(
path, "song_cum_inc_%s.png" % figname),
bbox_inches='tight')
ci_data['fig'].savefig(flymad_plot.get_plotpath(
path, "song_cum_inc_%s.svg" % figname),
bbox_inches='tight')
# for i in range(len(head_times)):
# head_values = gtdf[gtdf['t']==head_times[i]]
# thorax_values = gtdf[gtdf['t']==thorax_times[i]]
# test1 = head_values['zx'].values
# test2 = thorax_values['zx'].values
# hval, pval = kruskal(test1, test2)
# pvalue_buf += 'Pulse %d: Head vs thorax WEI p-value: %.3g (n=%d, %d)\n'%(
# i+1, pval, len(test1), len(test2) )
#all experiments used identical activation times
headtargetbetween = dict(
xaxis=data['pIP10']['wtrpmyc']['first']['t'].values,
where=data['pIP10']['wtrpmyc']['first']['ttm'].values > 0)
thoraxtargetbetween = dict(
xaxis=data['pIP10']['wtrpmyc']['first']['t'].values,
where=data['pIP10']['wtrpmyc']['first']['ttm'].values < 0)
flymad_plot.plot_timeseries_with_activation(
ax,
targetbetween=[headtargetbetween, thoraxtargetbetween],
sem=True,
note=pvalue_buf,
**datasets)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Wing extension index')
ax.set_ylim([-0.05, 0.4] if gts_string ==
"40347vswtrpmycvs40347trpmyc" else [-0.05, 1.0])
ax.set_xlim([-10, 180])
flymad_plot.retick_relabel_axis(
ax, [0, 60, 120, 180], [0, 0.2, 0.4]
if gts_string == "40347vswtrpmycvs40347trpmyc" else [0, 0.5, 1])
fig.savefig(flymad_plot.get_plotpath(path, "song_%s.png" % figname),
bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path, "song_%s.svg" % figname),
bbox_inches='tight')
with open(flymad_plot.get_plotpath(path, "song_cum_in.html"),
mode='w') as fd:
fd.write(ci_html_buf)
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_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_all_data(path, data, arena, note, laser='350iru'):
ORDER = ["50660trp", "50660", "wtrp"]
COLORS = {
"50660trp": flymad_plot.RED,
"50660": flymad_plot.BLACK,
"wtrp": flymad_plot.BLUE
}
datasets = {}
figure_title = "Moonwalker"
fig = plt.figure(figure_title, figsize=TS_FIGSIZE)
ax = fig.add_subplot(1, 1, 1)
for gt in data:
gtdf = data[gt][laser][gt]
datasets[gt] = dict(
xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['Vfwd'].values,
std=gtdf['std']['Vfwd'].values,
n=gtdf['n']['Vfwd'].values,
order=ORDER.index(gt),
label=flymad_analysis.human_label(gt),
color=COLORS[gt],
N=len(gtdf['df']['obj_id'].unique()),
df=gtdf['df'],
)
l, axs, figs = flymad_plot.plot_timeseries_with_activation(
ax,
targetbetween=dict(
xaxis=data['50660trp'][laser]['50660trp']['first']['t'].values,
where=data['50660trp'][laser]['50660trp']['first']
['laser_state'].values > 0),
downsample=5,
sem=True,
note="%s\n" % (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('Forward 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_simple.png"),
bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path, "moonwalker_simple.svg"),
bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(
path, "moonwalker_simple_individual_%s.png" % efigname),
bbox_inches='tight')
def plot_data(path, data):
ci_html_buf = ''
for exp_name in data:
gts = data[exp_name].keys()
laser = '130ht'
gts_string = 'vs'.join(gts)
figname = laser + '_' + gts_string
fig = plt.figure("Song (%s)" % figname,figsize=(10,6))
ax = fig.add_subplot(1,1,1)
datasets = {}
for gt in gts:
if flymad_analysis.genotype_is_exp(gt):
color = flymad_plot.RED
order = 1
elif flymad_analysis.genotype_is_ctrl(gt):
color = flymad_plot.BLACK
order = 2
elif flymad_analysis.genotype_is_trp_ctrl(gt):
order = 3
color = flymad_plot.BLUE
else:
color = 'cyan'
order = 0
gtdf = data[exp_name][gt]
datasets[gt] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['zx'].values,
std=gtdf['std']['zx'].values,
n=gtdf['n']['zx'].values,
order=order,
df=gtdf,
label=flymad_analysis.human_label(gt),
color=color,
N=len(gtdf['df']['obj_id'].unique()))
pvalue_buf = ''
for gt in datasets:
label=flymad_analysis.human_label(gt)
if '>' not in label:
continue
# OK, this is a Gal4 + UAS - do head vs thorax stats
gtdf = data[exp_name][gt]['df']
ci_data = do_cum_incidence(gtdf, label)
ci_html_buf += ci_data['buf']
ax_cum = ci_data['ax']
spine_placer(ax_cum, location='left,bottom' )
ax_cum.set_ylabel('Fraction extending wing (%)')
ax_cum.set_xlabel('Time (s)')
note = '%s\n%s\np-value: %.3g\n%d flies\nn=%d, %d'%(label,
madplot.p2stars(ci_data['p_value']),
ci_data['p_value'],
len(gtdf['obj_id'].unique()),
ci_data['n_head'],
ci_data['n_thorax'])
ax_cum.text(0, 1, #top left
note,
fontsize=10,
horizontalalignment='left',
verticalalignment='top',
transform=ax_cum.transAxes,
zorder=0)
ci_data['fig'].savefig(flymad_plot.get_plotpath(path,"song_cum_inc_%s.png" % figname),
bbox_inches='tight')
ci_data['fig'].savefig(flymad_plot.get_plotpath(path,"song_cum_inc_%s.svg" % figname),
bbox_inches='tight')
# for i in range(len(head_times)):
# head_values = gtdf[gtdf['t']==head_times[i]]
# thorax_values = gtdf[gtdf['t']==thorax_times[i]]
# test1 = head_values['zx'].values
# test2 = thorax_values['zx'].values
# hval, pval = kruskal(test1, test2)
# pvalue_buf += 'Pulse %d: Head vs thorax WEI p-value: %.3g (n=%d, %d)\n'%(
# i+1, pval, len(test1), len(test2) )
#all experiments used identical activation times
headtargetbetween = dict(xaxis=data['pIP10']['wtrpmyc']['first']['t'].values,
where=data['pIP10']['wtrpmyc']['first']['ttm'].values > 0)
thoraxtargetbetween = dict(xaxis=data['pIP10']['wtrpmyc']['first']['t'].values,
where=data['pIP10']['wtrpmyc']['first']['ttm'].values < 0)
flymad_plot.plot_timeseries_with_activation(ax,
targetbetween=[headtargetbetween,thoraxtargetbetween],
sem=True,
note=pvalue_buf,
**datasets
)
ax.set_xlabel('Time (s)')
ax.set_ylabel('Wing extension index')
ax.set_ylim([-0.05,0.4] if gts_string == "40347vswtrpmycvs40347trpmyc" else [-0.05,1.0])
ax.set_xlim([-10,180])
flymad_plot.retick_relabel_axis(ax, [0, 60, 120, 180],
[0, 0.2, 0.4] if gts_string == "40347vswtrpmycvs40347trpmyc" else [0, 0.5, 1])
fig.savefig(flymad_plot.get_plotpath(path,"song_%s.png" % figname), bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path,"song_%s.svg" % figname), bbox_inches='tight')
with open(flymad_plot.get_plotpath(path,"song_cum_in.html"),mode='w') as fd:
fd.write(ci_html_buf)
