def hist_t(hdf, model, name=None):
"""
Plot histograms of the t values in <hdf> for each condition in
<model>.
If <name> is not None the plot is saved as <name>.pdf.
"""
meta = get_model_meta(hdf, model)
hist_list = []
for dm_col in meta['dm']:
# Make an instance RHist for the list.
hist = RHist(name=dm_col, decimals=1)
hist_list.append(hist)
# read_hdf_inc returns a generator so....
tdata = read_hdf_inc(hdf, '/' + model + '/t')
for ts in tdata:
# get the tvals for each instance of model
# and add them to the hist_list,
[hist_list[ii].add(ts[ii]) for ii in range(len(ts) - 1)]
## The last t in ts is the constant, which we
## do not want to plot.
# Create a fig, loop over the hist_list
# plotting each on fig.axes = 0.
fig = plt.figure()
fig.add_subplot(111)
colors = itertools.cycle(
['DarkGray', 'DarkBlue', 'DarkGreen', 'MediumSeaGreen'])
## Using html colors...
[h.plot(fig=fig, color=colors.next(), norm=True) for h in hist_list]
# Prettify the plot
ax = fig.axes[0]
ax.set_xlabel('t-values')
ax.set_ylabel('P(t)')
# Add vetical lines representing significance tresholds
ax.axvline(x=1.7822, label='p < 0.05', color='red', linewidth=4)
ax.axvline(x=2.6810, label='p < 0.01', color='red', linewidth=3)
ax.axvline(x=3.0545, label='p < 0.005', color='red', linewidth=2)
ax.axvline(x=4.3178, label='p < 0.0005', color='red', linewidth=1)
## tval lines assume N=12 subjects
plt.xlim(-10, 15)
plt.legend()
plt.title('{0} -- BOLD: {1}'.format(model, meta['bold']))
if name != None:
plt.savefig(name, format="pdf")
def hist_t(hdf, model, name=None):
"""
Plot histograms of the t values in <hdf> for each condition in
<model>.
If <name> is not None the plot is saved as <name>.pdf.
"""
meta = get_model_meta(hdf, model)
hist_list = []
for dm_col in meta["dm"]:
# Make an instance RHist for the list.
hist = RHist(name=dm_col, decimals=1)
hist_list.append(hist)
# read_hdf_inc returns a generator so....
tdata = read_hdf_inc(hdf, "/" + model + "/t")
for ts in tdata:
# get the tvals for each instance of model
# and add them to the hist_list,
[hist_list[ii].add(ts[ii]) for ii in range(len(ts) - 1)]
## The last t in ts is the constant, which we
## do not want to plot.
# Create a fig, loop over the hist_list
# plotting each on fig.axes = 0.
fig = plt.figure()
fig.add_subplot(111)
colors = itertools.cycle(["DarkGray", "DarkBlue", "DarkGreen", "MediumSeaGreen"])
## Using html colors...
[h.plot(fig=fig, color=colors.next(), norm=True) for h in hist_list]
# Prettify the plot
ax = fig.axes[0]
ax.set_xlabel("t-values")
ax.set_ylabel("P(t)")
# Add vetical lines representing significance tresholds
ax.axvline(x=1.7822, label="p < 0.05", color="red", linewidth=4)
ax.axvline(x=2.6810, label="p < 0.01", color="red", linewidth=3)
ax.axvline(x=3.0545, label="p < 0.005", color="red", linewidth=2)
ax.axvline(x=4.3178, label="p < 0.0005", color="red", linewidth=1)
## tval lines assume N=12 subjects
plt.xlim(-10, 15)
plt.legend()
plt.title("{0} -- BOLD: {1}".format(model, meta["bold"]))
if name != None:
plt.savefig(name, format="pdf")
def create_hist(hdf, model, stat):
"""
Create histograms of stat values in <hdf> for each condition in
<model>.
"""
meta = get_model_meta(hdf, model)
hist_list = []
for dm_col in meta['dm']:
# Make an instance RHist for the list.
hist = RHist(name=dm_col, decimals=1)
hist_list.append(hist)
# read_hdf_inc returns a generator so....
data = read_hdf_inc(hdf,'/'+ model + '/' + stat)
for row in data:
# get the row values for each instance of model
# and add them to the hist_list,
[hist_list[ii].add(row[ii]) for ii in range(len(row)-1)]
## The last t in ts is the constant, which we
## do not want to plot.
return hist_list
