def plot_ci_for_ms(path, figname, vals, figsize=(6,4)):
figname = os.path.splitext(figname)[0]
NAMES = {'TH>trpA1 head':'TH>trpA1 (head)',
'TH>trpA1 thorax':'TH>trpA1 (thorax)',
'TH head':'TH (head)',
'TH thorax':'TH (thorax)',
'trpA1 head':'trpA1 (head)',
'trpA1 thorax':'trpA1 (thorax)',
'controls head':'controls (head)',
'controls thorax':'controls (thorax)',
}
COLORS = {'TH>trpA1 head':flymad_plot.RED,
'TH>trpA1 thorax':flymad_plot.ORANGE,
'TH head':flymad_plot.GREEN,
'TH thorax':flymad_plot.BLUE,
'trpA1 head':flymad_plot.GREEN,
'trpA1 thorax':flymad_plot.BLUE,
'controls head':flymad_plot.GREEN,
'controls thorax':flymad_plot.BLUE,
}
ORDER = ['TH>trpA1 head',
'TH>trpA1 thorax',
'TH head',
'TH thorax',
'trpA1 head',
'trpA1 thorax',
'controls head',
'controls thorax',
]
figure_title = "THGAL4 %s cumulative incidence" % figname
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(1,1,1)
for gt in sorted(vals, cmp=lambda a,b: cmp(ORDER.index(a), ORDER.index(b))):
ax.plot(vals[gt]['x'],vals[gt]['y'],
lw=2,clip_on=False,
color=COLORS[gt],label=NAMES[gt])
spine_placer(ax, location='left,bottom' )
ax.legend()
ax.set_xlabel('Time (s)')
ax.set_ylabel('Cumulative incidence (%)')
ax.set_ylim([0,100])
ax.set_xlim([0,20])
flymad_plot.retick_relabel_axis(ax, [0,10,20], [0,100])
fig.savefig(flymad_plot.get_plotpath(path,"thgal4_ci_%s.png" % figname), bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path,"thgal4_ci_%s.svg" % figname), 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)
def plot_ci_for_ms(path, figname, vals, figsize=(6, 4)):
figname = os.path.splitext(figname)[0]
NAMES = {
'TH>trpA1 head': 'TH>trpA1 (head)',
'TH>trpA1 thorax': 'TH>trpA1 (thorax)',
'TH head': 'TH (head)',
'TH thorax': 'TH (thorax)',
'trpA1 head': 'trpA1 (head)',
'trpA1 thorax': 'trpA1 (thorax)',
'controls head': 'controls (head)',
'controls thorax': 'controls (thorax)',
}
COLORS = {
'TH>trpA1 head': flymad_plot.RED,
'TH>trpA1 thorax': flymad_plot.ORANGE,
'TH head': flymad_plot.GREEN,
'TH thorax': flymad_plot.BLUE,
'trpA1 head': flymad_plot.GREEN,
'trpA1 thorax': flymad_plot.BLUE,
'controls head': flymad_plot.GREEN,
'controls thorax': flymad_plot.BLUE,
}
ORDER = [
'TH>trpA1 head',
'TH>trpA1 thorax',
'TH head',
'TH thorax',
'trpA1 head',
'trpA1 thorax',
'controls head',
'controls thorax',
]
figure_title = "THGAL4 %s cumulative incidence" % figname
fig = plt.figure(figsize=figsize)
ax = fig.add_subplot(1, 1, 1)
for gt in sorted(vals,
cmp=lambda a, b: cmp(ORDER.index(a), ORDER.index(b))):
ax.plot(vals[gt]['x'],
vals[gt]['y'],
lw=2,
clip_on=False,
color=COLORS[gt],
label=NAMES[gt])
spine_placer(ax, location='left,bottom')
ax.legend()
ax.set_xlabel('Time (s)')
ax.set_ylabel('Cumulative incidence (%)')
ax.set_ylim([0, 100])
ax.set_xlim([0, 20])
flymad_plot.retick_relabel_axis(ax, [0, 10, 20], [0, 100])
fig.savefig(flymad_plot.get_plotpath(path, "thgal4_ci_%s.png" % figname),
bbox_inches='tight')
fig.savefig(flymad_plot.get_plotpath(path, "thgal4_ci_%s.svg" % figname),
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')
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')
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_data(path, data, arena, note):
LABELS = {'OK371shits-130h':'OK371>ShibireTS (head)',
'OK371shits-nolaser':'Control',
'OK371shits-130t':'OK371>ShibireTS (thorax)',
}
COLORS = {'OK371shits-130h':flymad_plot.RED,
'OK371shits-nolaser':flymad_plot.BLACK,
'OK371shits-130t':flymad_plot.ORANGE,
}
ORDERS = {'OK371shits-130h':2,
'OK371shits-nolaser':4,
'OK371shits-130t':3,
}
fig2 = plt.figure("Speed")
ax = fig2.add_subplot(1,1,1)
datasets = {}
for gt in data:
gtdf = data[gt]
datasets[gt] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['v'].values,
std=gtdf['std']['v'].values,
n=gtdf['n']['v'].values,
label=LABELS[gt],
color=COLORS[gt],
order=ORDERS[gt],
df=gtdf['df'],
N=len(gtdf['df']['obj_id'].unique()))
ctrlfirst = data['OK371shits-nolaser']['first']
result_d = flymad_plot.plot_timeseries_with_activation(ax,
targetbetween=dict(xaxis=ctrlfirst['t'].values,
where=ctrlfirst['laser_state'].values>0),
downsample=5,
sem=True,
note="OK371shits\n%s\n" % note,
individual={k:{'groupby':'obj_id','xaxis':'t','yaxis':'v'} for k in datasets},
individual_title='Speed Individual Traces',
return_dict=True,
**datasets
)
figs = result_d['figs']
ax.set_xlabel('Time (s)')
ax.set_ylabel('Speed (%s/s)' % arena.unit)
ax.set_xlim([-15, 30])
ax.set_ylim([0, 18])
flymad_plot.retick_relabel_axis(ax, [-15, 0, 15, 30], [0, 5, 10, 15])
fig2.savefig(flymad_plot.get_plotpath(path,"speed_plot.png"), bbox_inches='tight')
fig2.savefig(flymad_plot.get_plotpath(path,"speed_plot.svg"), bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(path,"speed_plot_individual_%s.png" % efigname), bbox_inches='tight')
def plot_data(path, data, arena, note):
LABELS = {
'OK371shits-130h': 'OK371>ShibireTS (head)',
'OK371shits-nolaser': 'Control',
'OK371shits-130t': 'OK371>ShibireTS (thorax)',
}
COLORS = {
'OK371shits-130h': flymad_plot.RED,
'OK371shits-nolaser': flymad_plot.BLACK,
'OK371shits-130t': flymad_plot.ORANGE,
}
ORDERS = {
'OK371shits-130h': 2,
'OK371shits-nolaser': 4,
'OK371shits-130t': 3,
}
fig2 = plt.figure("Speed")
ax = fig2.add_subplot(1, 1, 1)
datasets = {}
for gt in data:
gtdf = data[gt]
datasets[gt] = dict(xaxis=gtdf['mean']['t'].values,
value=gtdf['mean']['v'].values,
std=gtdf['std']['v'].values,
n=gtdf['n']['v'].values,
label=LABELS[gt],
color=COLORS[gt],
order=ORDERS[gt],
df=gtdf['df'],
N=len(gtdf['df']['obj_id'].unique()))
ctrlfirst = data['OK371shits-nolaser']['first']
result_d = flymad_plot.plot_timeseries_with_activation(
ax,
targetbetween=dict(xaxis=ctrlfirst['t'].values,
where=ctrlfirst['laser_state'].values > 0),
downsample=5,
sem=True,
note="OK371shits\n%s\n" % note,
individual={
k: {
'groupby': 'obj_id',
'xaxis': 't',
'yaxis': 'v'
}
for k in datasets
},
individual_title='Speed Individual Traces',
return_dict=True,
**datasets)
figs = result_d['figs']
ax.set_xlabel('Time (s)')
ax.set_ylabel('Speed (%s/s)' % arena.unit)
ax.set_xlim([-15, 30])
ax.set_ylim([0, 18])
flymad_plot.retick_relabel_axis(ax, [-15, 0, 15, 30], [0, 5, 10, 15])
fig2.savefig(flymad_plot.get_plotpath(path, "speed_plot.png"),
bbox_inches='tight')
fig2.savefig(flymad_plot.get_plotpath(path, "speed_plot.svg"),
bbox_inches='tight')
for efigname, efig in figs.iteritems():
efig.savefig(flymad_plot.get_plotpath(
path, "speed_plot_individual_%s.png" % efigname),
bbox_inches='tight')
def plot_data(arena, path, smoothstr, data):
COLORS = {"NINE":flymad_plot.RED,
"CSSHITS":flymad_plot.BLUE,
"NINGal4":flymad_plot.GREEN,
'pooled controls':flymad_plot.BLACK,
}
ORDERS = {"NINE":1,"pooled controls":2,"NINGal4":3,"CSSHITS":4}
LABELS = {"NINE":"NINE>ShiTS","pooled controls":"Controls","NINGal4":"Gal4-control","CSSHITS":"ShiTS-control"}
# ---- pool controls ------------
data['pooled controls'] = copy.deepcopy(data['CSSHITS'])
assert np.allclose(data['NINGal4']['save_times'], data['pooled controls']['save_times'])
# copy 'NINGal4' data into pooled controls
for dname in data['NINGal4']:
for row in data['NINGal4'][dname]:
if dname in ['save_times','stimulus_velocity']:
continue
elif dname=='chunk':
warnings.warn('not updating old chunk data for pooled controls')
else:
data['pooled controls'][dname].append(row)
# for row in data['NINGal4']['chunk']:
# for key in row:
# data['pooled controls']['chunk'][key].append(row[key])
# ----- raw timeseries plots ------------
fig_angular_vel = plt.figure()
fig_linear_vel = plt.figure()
fig_summary_angular = plt.figure()
ax_summary_angular = fig_summary_angular.add_subplot(211)
ax_summary_linear = fig_summary_angular.add_subplot(212)
pool_stats = collections.defaultdict(list)
#order = ['NINE', 'CSSHITS', 'NINGal4']
order = ['NINE', 'pooled controls']
n_subplots = len(order)
ax = None
ax_angular_vel = fig_angular_vel.add_subplot(111)
ax_linear_vel = fig_linear_vel.add_subplot(111)
did_stimulus_plot = False
plot_angular_datasets = collections.OrderedDict()
plot_linear_datasets = collections.OrderedDict()
for gti,gt in enumerate(order):
# --- calculate stimulus -----------
stim_times, stim_vel = data[gt]['stimulus_velocity']
if 1:
'''
# 10 revolutions take 25 seconds at standard speed
real_stim_vel = 10*2*np.pi/25.0 # 10 revolutions/second
print 'real_stim_vel',real_stim_vel
print 'gain[0]',real_stim_vel/stim_vel[100]
print 'gain[1]',real_stim_vel/stim_vel[1001]
'''
gain = 0.00083775 # found using above
stim_vel = gain*stim_vel
if not did_stimulus_plot:
ax_angular_vel.plot( stim_times,
stim_vel*R2D,flymad_plot.ORANGE,
lw=2, label='Stimulus', clip_on=False)
did_stimulus_plot = True
transition_idxs = np.nonzero(abs(stim_vel[1:]-stim_vel[:-1]))[0]
#print 'stim_times[transition_idxs]',stim_times[transition_idxs]
n_transitions = len(transition_idxs)
# ---- done calculating stimulus ---
all_angular_vel_timeseries = np.array(data[gt]['timeseries_angular_vel'])
all_linear_vel_timeseries = np.array(data[gt]['timeseries_vel'])
times = data[gt]['save_times']
mean_angular_timeseries = np.mean( all_angular_vel_timeseries, axis=0 )
error_angular_timeseries = np.std( all_angular_vel_timeseries, axis=0 )
mean_linear_timeseries = np.mean( all_linear_vel_timeseries, axis=0 )
error_linear_timeseries = np.std( all_linear_vel_timeseries, axis=0 )
if 1:
bin_data = []
ang_means = []
ang_stds = []
lin_means = []
lin_stds = []
for i in range(n_transitions):
this_data = dict(index=i,
angular_velocity_values=[],
linear_velocity_values=[],
)
start_time = stim_times[transition_idxs[i]]
if (i+1)<n_transitions:
stop_time = stim_times[transition_idxs[i+1]]
else:
# final bin
stop_time = stim_times[-1]
#print 'start_time, stop_time',start_time, stop_time
start_idx = np.argmin(abs(times - start_time))
stop_idx = np.argmin(abs(times - stop_time))
#print 'times[start_idx], times[stop_idx]',times[start_idx], times[stop_idx]
this_data['start_time'] = times[start_idx]
this_data['stop_time'] = times[stop_idx]
assert len( all_angular_vel_timeseries)== len(all_linear_vel_timeseries)
for j in range( len(all_angular_vel_timeseries)):
this_angular_timeseries = all_angular_vel_timeseries[j]
this_linear_timeseries = all_linear_vel_timeseries[j]
good = ~np.isnan(this_angular_timeseries[start_idx:stop_idx])
ang = np.mean(this_angular_timeseries[start_idx:stop_idx][good])
lin = np.mean(this_linear_timeseries[start_idx:stop_idx][good])
this_data['angular_velocity_values'].append( ang )
this_data['linear_velocity_values'].append( lin )
pool_stats['gt'].append(gt)
pool_stats['bin_number'].append(i)
pool_stats['bin_start'].append( this_data['start_time'] )
pool_stats['bin_stop'].append( this_data['stop_time'] )
pool_stats['nth_fly_of_gt'].append(j)
pool_stats['mean_angular_velocity_during_bin'].append( ang )
pool_stats['mean_linear_velocity_during_bin'].append( lin )
this_data[ 'ang_mean' ] = np.mean( this_data['angular_velocity_values'] )
this_data[ 'ang_std' ] = np.std( this_data['angular_velocity_values'] )
this_data[ 'lin_mean' ] = np.mean( this_data['linear_velocity_values'] )
this_data[ 'lin_std' ] = np.std( this_data['linear_velocity_values'] )
bin_data.append( this_data )
ang_means.append( this_data['ang_mean'] )
ang_stds.append( this_data['ang_std'] )
lin_means.append( this_data['lin_mean'] )
lin_stds.append( this_data['lin_std'] )
ang_means = np.array(ang_means)
ang_stds = np.array(ang_stds)
lin_means = np.array(lin_means)
lin_stds = np.array(lin_stds)
ax_summary_angular.axhline(0,color='black')
ax_summary_angular.plot( ang_means*R2D, label=gt, color=COLORS[gt] )
ax_summary_angular.plot( (ang_means+ang_stds)*R2D, color=COLORS[gt] )
ax_summary_angular.plot( (ang_means-ang_stds)*R2D, color=COLORS[gt] )
ax_summary_linear.axhline(0,color='black')
ax_summary_linear.plot( lin_means, label=gt, color=COLORS[gt] )
ax_summary_linear.plot( lin_means+lin_stds, color=COLORS[gt] )
ax_summary_linear.plot( lin_means-lin_stds, color=COLORS[gt] )
this_data_angular_vel = {'xaxis':times,
'value':mean_angular_timeseries*R2D,
'std':error_angular_timeseries*R2D,
'label':gt,
'color':COLORS[gt],
'label':LABELS[gt],
'order':ORDERS[gt],
}
this_data_linear_vel = {'xaxis':times,
'value':mean_linear_timeseries,
'std':error_linear_timeseries,
'label':gt,
'color':COLORS[gt],
'label':LABELS[gt],
'order':ORDERS[gt],
}
plot_angular_datasets[gt]=this_data_angular_vel
plot_linear_datasets[gt]=this_data_linear_vel
tb = {'xaxis':data['pooled controls']['save_times'],
'where':data['pooled controls']['laser_power']>1,
}
flymad_plot.plot_timeseries_with_activation(
ax_angular_vel,
downsample=2,
targetbetween=[tb],
**plot_angular_datasets)
ax_angular_vel.axhline(0,color='black')
ax_angular_vel.set_xlabel('Time (s)')
ax_angular_vel.set_ylabel('Angular velocity (deg/s)')
ax_angular_vel.set_ylim(-200,200)
ax_angular_vel.set_xlim(0,300)
flymad_plot.retick_relabel_axis(ax_angular_vel, [0,150,300], [-200,0,200])
figname = 'optoresponse_ts_angular_vel'
fig_angular_vel.savefig(flymad_plot.get_plotpath(path,"%s.png" % figname), bbox_inches='tight')
fig_angular_vel.savefig(flymad_plot.get_plotpath(path,"%s.svg" % figname), bbox_inches='tight')
flymad_plot.plot_timeseries_with_activation(
ax_linear_vel,
targetbetween=[tb],
downsample=3,
**plot_linear_datasets)
ax_linear_vel.set_ylim([0,40])
ax_linear_vel.set_xlim(0,300)
# ax_linear_vel.spines['bottom'].set_bounds(0,330.0)
# ax_linear_vel.spines['bottom'].set_linewidth(0.3)
# ax_linear_vel.spines['left'].set_linewidth(0.3)
ax_linear_vel.set_xlabel('Time (s)')
ax_linear_vel.set_ylabel('Velocity (%s/s)' % arena.unit)
flymad_plot.retick_relabel_axis(ax_linear_vel, [0,150,300], [0,20,40])
figname = 'optoresponse_ts_linear_vel'
fig_linear_vel.savefig(flymad_plot.get_plotpath(path,"%s.png" % figname), bbox_inches='tight')
fig_linear_vel.savefig(flymad_plot.get_plotpath(path,"%s.svg" % figname), bbox_inches='tight')
if 1:
def p2star(p):
if p < 0.001:
result = '***'
elif p < 0.01:
result = '**'
elif p < 0.05:
result = '*'
else:
result = ''
result += ' %.3g'%p
return result
# calculate p values
pool_stats_df = pd.DataFrame(pool_stats)
df_fname = 'optomotor_stats.df'
pool_stats_df.to_pickle(df_fname)
sig = do_stats( df_fname )
for i in range(len(sig['bin_num'])):
bin_num = sig['bin_num'][i]
p_value = sig['ang'][i]
ax_summary_angular.text( bin_num,
0,
p2star(p_value))
p_value = sig['ang_zero'][i]
ax_summary_angular.text( bin_num,
-100,
p2star(p_value))
ax_summary_angular.text( bin_num,
140,
'%s - %s'%(sig['bin_start'][i],
sig['bin_stop'][i]),
)
p_value = sig['lin'][i]
ax_summary_linear.text( bin_num,
5,
p2star(p_value))
ax_summary_angular.set_ylabel('angular velocity (deg/sec)')
ax_summary_linear.set_ylabel('velocity (mm/sec)')
ax_summary_linear.set_xlabel('bin number')
fig_fname = 'fig_summary.png'
fig_summary_angular.savefig(fig_fname)
print 'saved',fig_fname
fig_fname = 'fig_summary.svg'
fig_summary_angular.savefig(fig_fname)
print 'saved',fig_fname
fig_fname = 'fig_summary.pdf'
fig_summary_angular.savefig(fig_fname)
print 'saved',fig_fname
# -----------------------
# plot individual timeseries
for gti,gt in enumerate(order):
fig_gt = plt.figure('indiv_timeseries_'+gt)
n_rows = len(data[gt]['timeseries_vel'])
n_cols = 2 # linear and angular
save_times = data[gt]['save_times']
ax_lin = None
ax_ang = None
for i in range(n_rows):
angular_vel = data[gt]['timeseries_angular_vel'][i]
linear_vel = data[gt]['timeseries_vel'][i]
ax_lin = fig_gt.add_subplot( n_rows, 2, i*2+1, sharex=ax_lin, sharey=ax_lin )
ax_ang = fig_gt.add_subplot( n_rows, 2, i*2+2, sharex=ax_ang, sharey=ax_ang )
ax_ang.axhline(0,color='black')
ax_lin.plot( save_times, linear_vel )
ax_ang.plot( save_times, angular_vel*R2D )
bag = os.path.basename(data[gt]['bag_fname'][i])
ax_lin.text(50,50,'lin '+bag)
ax_ang.text(50,3*R2D,'ang '+bag)
ax_lin.set_ylim(0,50)
ax_ang.set_ylim(-300,300)
plt.figtext(0.05, 0.05, gt)
fname = 'individual_%s.pdf'%gt
fig_gt.savefig(fname)
print 'saved',fname
# -----------------------
if 1:
return
# - John's category stuff ----------------
#pprint.pprint(data)
figt = plt.figure('dtheta %s' % (smoothstr), figsize=(16,10))
axt = figt.add_subplot(1,1,1)
figv = plt.figure('v %s' % (smoothstr), figsize=(16,10))
axv = figv.add_subplot(1,1,1)
for gt in order:
xdata = []
ytdata = []
yvdata = []
for xlabel in sorted(data[gt]['chunk']):
xloc = int(xlabel[0])
for mean_dtheta,mean_v in data[gt]['chunk'][xlabel]:
xdata.append(xloc)
ytdata.append(mean_dtheta)
yvdata.append(mean_v)
# print gt,xdata,ydata
axt.plot(xdata,ytdata,'o',color=COLORS[gt],markersize=5,label=gt)
axv.plot(xdata,yvdata,'o',color=COLORS[gt],markersize=5,label=gt)
for ax in (axt,axv):
ax.legend()
ax.set_xlim(0,9)
ax.set_xticks([int(s[0]) for s in sorted(data["NINE"]['chunk'])])
ax.set_xticklabels([s[2:] for s in sorted(data["NINE"]['chunk'])])
figpath = os.path.join(path,'dtheta_%s.png' % (smoothstr))
figt.savefig(figpath)
print "wrote", figpath
figpath = os.path.join(path,'v_%s.png' % (smoothstr))
figv.savefig(figpath)
print "wrote", figpath