def test_activity_timecourse_with_inlay():
import pandas as pd
import matplotlib.pyplot as plt
import samri.plotting.maps as maps
import seaborn as sns
from os import path
# Style elements
palette = ["#56B4E9", "#E69F00"]
data_dir = path.join(path.dirname(path.realpath(__file__)),
"../../tests/data")
data_path = path.join(data_dir, 'drs_activity.csv')
df = pd.read_csv(data_path)
df = df.rename(columns={'t': 'Mean t-Statistic'})
df['Session'] = df['Session'].map({
'ofM': 'naïve',
'ofMaF': 'acute',
'ofMcF1': 'chronic/2w',
'ofMcF2': 'chronic/4w',
'ofMpF': 'post',
})
# definitions for the axes
left, width = 0.06, 0.9
bottom, height = 0.06, 0.9
session_coordinates = [left, bottom, width, height]
roi_coordinates = [left + 0.02, bottom + 0.7, 0.3, 0.2]
fig = plt.figure(1)
ax1 = plt.axes(session_coordinates)
sns.pointplot(
x='Session',
y='Mean t-Statistic',
units='subject',
data=df,
hue='treatment',
dodge=True,
palette=palette,
order=['naïve', 'acute', 'chronic/2w', 'chronic/4w', 'post'],
ax=ax1,
ci=95,
)
ax2 = plt.axes(roi_coordinates)
maps.atlas_label(
'/usr/share/mouse-brain-atlases/dsurqec_200micron_roi-dr.nii',
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
plt.savefig('_activity_timecourse_with_inlay.png')
def test_activity_timecourse_with_inlay():
import pandas as pd
import matplotlib.pyplot as plt
import samri.plotting.maps as maps
import seaborn as sns
from os import path
# Style elements
palette=["#56B4E9", "#E69F00"]
data_dir = path.join(path.dirname(path.realpath(__file__)),"../../tests/data")
data_path = path.join(data_dir,'drs_activity.csv')
df = pd.read_csv(data_path)
df = df.rename(columns={'t':'Mean t-Statistic'})
df['Session']=df['Session'].map({
'ofM':'naïve',
'ofMaF':'acute',
'ofMcF1':'chronic/2w',
'ofMcF2':'chronic/4w',
'ofMpF':'post',
})
# definitions for the axes
left, width = 0.06, 0.9
bottom, height = 0.06, 0.9
session_coordinates = [left, bottom, width, height]
roi_coordinates = [left+0.02, bottom+0.7, 0.3, 0.2]
fig = plt.figure(1)
ax1 = plt.axes(session_coordinates)
sns.pointplot(
x='Session',
y='Mean t-Statistic',
units='subject',
data=df,
hue='treatment',
dodge=True,
palette=palette,
order=['naïve','acute','chronic/2w','chronic/4w','post'],
ax=ax1,
ci=95,
)
ax2 = plt.axes(roi_coordinates)
maps.atlas_label("/usr/share/mouse-brain-atlases/dsurqec_200micron_roi-dr.nii",
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
plt.savefig('_activity_timecourse_with_inlay.png')
def plot_roi_by_label(label_names,
save_as="",
):
roi = roi_from_atlaslabel("~/ni_data/templates/roi/DSURQEc_200micron_labels.nii",
mapping="~/ni_data/templates/roi/DSURQE_mapping.csv",
label_names=label_names,
save_as=save_as
)
maps.atlas_label(roi)
plt.show()
x='Session',
y='Mean t-Statistic',
condition='treatment',
unit='subject',
ci=90,
palette=["#56B4E9", "#E69F00"],
order=['nave', 'acute', 'chronic (2w)', 'chronic (4w)', 'post'],
bp_style=False,
renames={
'Session': {
'ofM': 'nave',
'ofMaF': 'acute',
'ofMcF1': 'chronic (2w)',
'ofMcF2': 'chronic (4w)',
'ofMpF': 'post',
},
},
)
ax2 = plt.axes(roi_coordinates)
maps.atlas_label(
"~/ni_data/templates/roi/DSURQEc_drp.nii.gz",
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
save_as(drp_activity.py)
def plot_roi_per_session(
df,
x='Session',
y='Mean t-Statistic',
condition='treatment',
unit='subject',
ci=90,
palette=["#56B4E9", "#E69F00"],
dodge=True,
order=[],
feature_map=True,
roi_left=0.02,
roi_bottom=0.74,
roi_width=0.3,
roi_height=0.2,
roi_anat="/usr/share/mouse-brain-atlases/dsurqec_40micron_masked.nii",
roi_threshold=None,
cut_coords=None,
samri_style=True,
renames=[],
save_as='',
ax=None,
fig=None,
):
"""Plot a ROI t-values over the session timecourse
"""
if samri_style:
plt.style.use(u'seaborn-darkgrid')
plt.style.use('ggplot')
try:
df = path.abspath(path.expanduser(df))
except AttributeError:
pass
# definitions for the axes
height = rcParams['figure.subplot.top']
bottom = rcParams['figure.subplot.bottom']
left = rcParams['figure.subplot.left']
width = rcParams['figure.subplot.right']
session_coordinates = [left, bottom, width, height]
roi_coordinates = [
left + roi_left, bottom + roi_bottom, roi_width, roi_height
]
if not fig:
fig = plt.figure(1)
if renames:
for key in renames:
for subkey in renames[key]:
df.loc[df[key] == subkey, key] = renames[key][subkey]
if not ax:
ax1 = plt.axes(session_coordinates)
else:
ax1 = ax
ax = sns.pointplot(
x=x,
y=y,
units=unit,
data=df,
hue=condition,
dodge=dodge,
palette=sns.color_palette(palette),
order=order,
ax=ax1,
ci=ci,
)
ax.set_ylabel(y)
if isinstance(feature_map, str):
ax2 = plt.axes(roi_coordinates)
if roi_threshold and cut_coords:
maps.stat(
feature,
cut_coords=cut_coords,
template=roi_anat,
annotate=False,
scale=0.3,
show_plot=False,
interpolation=None,
threshold=roi_threshold,
draw_colorbar=False,
ax=ax2,
)
else:
maps.atlas_label(
feature_map,
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
elif feature_map:
try:
features = df['feature'].unique()
except KeyError:
pass
else:
if len(features) > 1:
print(
'WARNING: The features list contains more than one feature. We will highlight the first one in the list. This may be incorrect.'
)
feature = features[0]
ax2 = plt.axes(roi_coordinates)
if path.isfile(feature):
if roi_threshold and cut_coords:
maps.stat(
stat_maps=feature,
cut_coords=cut_coords,
template=roi_anat,
annotate=False,
scale=0.3,
show_plot=False,
interpolation=None,
threshold=roi_threshold,
draw_colorbar=False,
ax=ax2,
)
else:
maps.atlas_label(
feature,
scale=0.3,
color="#E69F00",
ax=ax2,
annotate=False,
alpha=0.8,
)
else:
atlas = df['atlas'].unique()[0]
mapping = df['mapping'].unique()[0]
if isinstance(feature, str):
feature = [feature]
maps.atlas_label(
atlas,
scale=0.3,
color="#E69F00",
ax=ax2,
mapping=mapping,
label_names=feature,
alpha=0.8,
annotate=False,
)
if save_as:
plt.savefig(path.abspath(path.expanduser(save_as)),
bbox_inches='tight')
return fig, ax
def plot_my_roi():
maps.atlas_label("~/ni_data/templates/roi/DSURQEc_dr.nii.gz",
)
plt.show()
