def animal_weights_():
import matplotlib.pyplot as mpl
from labbookdb.report.tracking import animal_weights, qualitative_dates
from behaviopy.plotting import qualitative_times
fuzzy_matching = {
"ofM":[-14,-15,-13,-7,-8,-6],
"ofMaF":[0,-1],
"ofMcF1":[14,13,15],
"ofMcF2":[28,27,29],
"ofMpF":[45,44,46],
}
df = animal_weights('~/syncdata/meta.db', {'animal':['aFluIV','aFluIV_']})
df['relative_date'] = df['relative_date'].dt.days.astype(int)
df = df[['Animal_id', 'relative_date', 'weight', 'TreatmentProtocol_code', 'ETH/AIC']]
df = qualitative_dates(df, fuzzy_matching=fuzzy_matching)
qualitative_times(df, order=['ofM','ofMaF','ofMcF1','ofMcF2','ofMpF'], condition='TreatmentProtocol_code', err_style="boot_traces", time='qualitative_date')
df = df[df['qualitative_date']=='ofMpF']
print(df)
mpl.show()
subjectdf = full_subsets(subjectdf, 5)
from seaborn import desaturate
cmap = plt.get_cmap('tab20').colors
qualitative_times(
subjectdf[subjectdf['treatment'] == "Vehicle"],
x='Session',
y='t',
unit=None,
condition='subject',
palette=cmap[1::2],
#palette=cmap_grey[::2],
order=['naïve', 'acute', 'chronic (2w)', 'chronic (4w)', 'post'],
bp_style=False,
renames={
'Session': {
'ofM': 'naïve',
'ofMaF': 'acute',
'ofMcF1': 'chronic (2w)',
'ofMcF2': 'chronic (4w)',
'ofMpF': 'post',
},
},
)
qualitative_times(
subjectdf[subjectdf['treatment'] == "Fluoxetine"],
x='Session',
y='t',
unit=None,
condition='subject',
roi_coordinates = [left + 0.02, bottom + 0.7, 0.3, 0.2]
fig = plt.figure(1)
ax1 = plt.axes(session_coordinates)
qualitative_times(
df,
ax=ax1,
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,
data_path = path.join(data_dir,'DSURQEc_drp.csv')
subjectdf = pd.read_csv(data_path)
subjectdf = full_subsets(subjectdf, 5)
cmap = plt.get_cmap('tab20').colors
qualitative_times(subjectdf[subjectdf['treatment']=="Vehicle"],
x='Session',
y='t',
unit=None,
condition='subject',
palette=cmap[1::2],
#palette=cmap_grey[::2],
order=['naïve','acute','chronic (2w)','chronic (4w)','post'],
bp_style=False,
renames={
'Session':{
'ofM':'naïve',
'ofMaF':'acute',
'ofMcF1':'chronic (2w)',
'ofMcF2':'chronic (4w)',
'ofMpF':'post',
},
},
)
qualitative_times(subjectdf[subjectdf['treatment']=="Fluoxetine"],
x='Session',
y='t',
unit=None,
condition='subject',
palette=cmap[::2],
from labbookdb.report.tracking import treatment_group, append_external_identifiers, qualitative_dates, cage_consumption
from labbookdb.report.selection import cage_periods, cage_drinking_measurements
from behaviopy.plotting import qualitative_times
db_path = '~/.demolog/meta.db'
df = cage_drinking_measurements(db_path, ['cFluDW'])
df = cage_consumption(db_path, df)
fuzzy_matching = {
"ofM": [-14, -15, -13, -7, -8, -6],
"ofMaF": [0, -1],
"ofMcF1": [14, 13, 15],
"ofMcF2": [28, 27, 29],
"ofMpF": [45, 44, 46, 43, 47],
}
df = qualitative_dates(
df,
iterator_column='Cage_id',
date_column='relative_end_date',
label='qualitative_date',
fuzzy_matching=fuzzy_matching,
)
qualitative_times(df,
x='qualitative_date',
y='day_animal_consumption',
unit='Cage_id',
order=['ofM', 'ofMaF', 'ofMcF1', 'ofMcF2', 'ofMpF'],
condition='TreatmentProtocol_code',
err_style="boot_traces",
save_as='drinking_by_cage_treatment.png')
data_path = path.join(data_dir,'DSURQEc_drs.csv')
subjectdf = pd.read_csv(data_path)
subjectdf = full_subsets(subjectdf, 5)
cmap = plt.get_cmap('tab20').colors
qualitative_times(subjectdf,
x='Session',
y='t',
condition='treatment',
unit='subject',
ci=90,
palette=cmap,
order=['naïve','acute','chronic (2w)','chronic (4w)','post'],
bp_style=False,
renames={
'Session':{
'ofM':'naïve',
'ofMaF':'acute',
'ofMcF1':'chronic (2w)',
'ofMcF2':'chronic (4w)',
'ofMpF':'post',
},
},
)
plt.savefig('drs_activity_full.pdf')
import statsmodels.formula.api as smf
import numpy as np
model = smf.mixedlm("t ~ Session * treatment", subjectdf, groups=subjectdf["subject"])
data_dir = path.abspath(path.expanduser('~/ni_data/ofM.dr/bids/l1/generic'))
data_path = path.join(data_dir, 'DSURQEc_drs.csv')
subjectdf = pd.read_csv(data_path)
cmap = plt.get_cmap('tab20').colors
qualitative_times(
subjectdf,
x='Session',
y='t',
condition='treatment',
unit='subject',
ci=90,
palette=cmap,
order=['naïve', 'acute', 'chronic (2w)', 'chronic (4w)', 'post'],
bp_style=False,
renames={
'Session': {
'ofM': 'naïve',
'ofMaF': 'acute',
'ofMcF1': 'chronic (2w)',
'ofMcF2': 'chronic (4w)',
'ofMpF': 'post',
},
},
)
plt.savefig('drs_activity.pdf')
import statsmodels.formula.api as smf
import numpy as np
model = smf.mixedlm("t ~ Session * treatment",