def test_radon(
# add_data, add_box,
add_condition_slope,
add_group_slope,
# do_mean_over_trials,
do_make_change):
window = BayesRegression(df=df_radon,
y='radon',
treatment='floor',
condition=['county'])
# window.plot(x='county').facet(row='floor').display()
window.fit(
add_condition_slope=
add_condition_slope, # do_mean_over_trials=do_mean_over_trials,
add_group_slope=add_group_slope,
do_make_change=do_make_change,
n_draws=100,
num_chains=1,
num_warmup=100)
# window.plot().display()
window.plot(
x=':O', # add_data=add_data,
).display()
window.plot()
window.chart_posterior_kde.display()
# window.chart_data_boxplot.display() # Fold change won't work, bc uneven number of entries
assert len(window.charts) > 3 # Should include kde
def test_fit():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical)
def test_plot_slopes_2levelslope():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron_x_mouse',
group='mouse',
add_data=True)
window.fit(model=models.model_hierarchical, add_group_slope=True)
window.plot().display()
def test_estimate_posteriors_data_overlay_slope():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical)
chart = window.plot(x='neuron', independent_axes=False)
chart.display()
window.chart.facet(row='mouse').display()
def test_facet():
# Slopes:
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical)
window.plot(row='neuron', width=40)
window.plot(x='neuron').facet(column='mouse')
def test_plot_slopes():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse',
add_data=True)
window.fit(model=models.model_hierarchical)
window.plot()
window.plot_BEST()
def test_single_condition_nodata():
window = BayesRegression(df=dfl,
y='Log power',
treatment='stim',
group='mouse')
window.fit(model=models.model_hierarchical,
do_make_change='divide',
dist_y='normal')
alt.layer(*plot_posterior(
df=window.data_and_posterior,
y_title='Log power',
)).display()
window.plot(independent_axes=True).display()
def test_estimate_posteriors_slope(add_condition_slope):
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition=['neuron', 'mouse'],
group='mouse',
add_data=True)
window.fit(models.model_hierarchical,
add_condition_slope=add_condition_slope)
chart = window.plot(x='neuron', column='neuron', row='mouse')
chart.display()
chart = window.plot(x='neuron', column='neuron', row='mouse')
chart.display()
def test_slopes(do_make_change, detail):
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse',
detail=detail)
# try:
window.fit(
model=models.model_hierarchical,
do_make_change=do_make_change,
)
# fold_change_index_cols=('stim', 'mouse', 'neuron_x_mouse'))
window.chart.display()
window.window.chart_data_box_detail.display()
def test_gpu():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition=['neuron', 'mouse'],
group='mouse',
detail='i_trial')
window.fit(model=models.model_hierarchical,
do_make_change='subtract',
add_condition_slope=True,
add_group_slope=True,
use_gpu=True)
assert window.data_and_posterior.dropna(
subset=['mu_intercept_per_group center interval'
])['mouse'].unique().size == 4
def test_two_groups():
df = generate_spikes_stim_types(
mouse_response_slope=3,
n_trials=2,
n_neurons=3,
n_mice=4,
dur=2,
)
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition=['stim_strength', 'neuron_x_mouse'],
group='mouse',
group2='neuron')
window.fit(model=models.model_hierarchical, add_group2_slope=True)
def test_data_replacement1():
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron_x_mouse',
group='mouse',
detail='i_trial',
add_data=True)
window.fit(
model=models.model_hierarchical,
do_make_change='subtract',
add_condition_slope=True,
# add_group_slope=True
)
posterior_no_nan = window.data_and_posterior.dropna(
subset=['mu_intercept_per_group center interval'])
assert posterior_no_nan['mouse'].unique().size == 4
def test_estimate_posteriors_data_overlay_indep_axes_slope(
add_data, add_data_plot, add_group_slope):
window = BayesRegression(df=df,
add_data=add_data,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical,
add_group_slope=add_group_slope)
chart = window.plot(independent_axes=True, add_data=add_data_plot)
chart.display()
if add_group_slope and add_data_plot:
chart = window.facet(column='neuron', row='mouse')
else:
chart = window.facet(column='neuron')
chart.display()
def test_chirp_data1(plot_from_data_and_posterior):
dfdata = pd.read_csv(Path('tests') / 'test_data' / 'chirp_power.csv')
window = BayesRegression(df=dfdata,
y='Log power',
treatment='stim_on',
condition=['Stim phase', 'Inversion'],
group='Subject')
window.fit(model=models.model_hierarchical,
num_chains=1,
n_draws=100,
num_warmup=100)
chart = window.plot(
x='Stim phase', # color='Fid',
add_data=plot_from_data_and_posterior,
independent_axes=True)
chart.display()
chart_data = pd.DataFrame.from_records(
list(json.loads(chart.to_json())['datasets'].values())[0])
assert (chart_data['Stim phase'].unique().astype(float) ==
dfdata['Stim phase'].unique()).all()
def test_plot_slopes_intercepts(do_make_change):
window = BayesRegression(df=dfl,
y='Power',
treatment='stim',
group='mouse',
add_data=True)
# Fit:
window.fit(model=models.model_hierarchical,
add_group_intercept=True,
zscore_y=False,
add_group_slope=False,
robust_slopes=False,
do_make_change=do_make_change,
dist_y='gamma',
num_chains=1,
n_draws=100,
num_warmup=100)
window.plot_intercepts(x='mouse').display()
chart_intercepts = window.chart_posterior_intercept
chart_intercepts.display()
def random_tests():
# TODO make a notebook for this
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical, num_chains=1)
window.plot(x='neuron',
color='mouse',
independent_axes=True,
finalize=True)
window.plot(independent_axes=False, x='neuron:O', color='mouse')
window.plot(add_box=False,
independent_axes=True,
x='neuron:O',
color='mouse')
window.plot(independent_axes=False, x='neuron:O', color='mouse')
chart = window.plot(independent_axes=True, x='neuron:O', color='mouse')
chart.display()
chart.resolve_scale(y='independent')
window.facet(column='neuron')
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse')
window.fit(model=models.model_hierarchical, num_chains=1)
window.plot(x='neuron', color='i_trial')
window.plot() # x='Stim phase', color='Fid')#,independent_axes=True)
window.facet(column='neuron', row='mouse')
def test_chirp_data2():
dfdata = pd.read_csv(Path('tests') / 'test_data' / 'chirp_power.csv')
window = BayesRegression(
df=dfdata,
y='Log power',
treatment='stim_on',
# condition=['Condition code'],
group='Subject',
add_data=True)
window.fit(
model=models.model_hierarchical,
fold_change_index_cols=[ # 'Condition code',
'Brain region', 'Stim phase', 'stim_on', 'Fid', 'Subject',
'Inversion'
],
do_mean_over_trials=True,
num_chains=1,
n_draws=100,
num_warmup=100)
window.plot(x='Stim phase',
color='Fid',
add_data=True,
independent_axes=True).display()
def test_chirp_data(force_correct_fold_change_index_cols):
dfdata = pd.read_csv(Path('tests') / 'test_data' / 'chirp_power.csv')
window = BayesRegression(
df=dfdata,
y='Log power',
treatment='stim_on',
condition=['Stim phase', 'Inversion', 'Brain region'],
group='Subject')
if force_correct_fold_change_index_cols:
window.fit(model=models.model_hierarchical,
fold_change_index_cols=[
'Brain region', 'Stim phase', 'stim_on', 'Fid',
'Subject', 'Inversion'
],
num_chains=1,
n_draws=100,
num_warmup=100)
else:
window.fit(model=models.model_hierarchical,
num_chains=1,
n_draws=100,
num_warmup=100)
window.plot(x='Stim phase', color='Fid', independent_axes=True).display()
def test_estimate_posteriors_two_conditions():
df = generate_spikes_stim_types(
mouse_response_slope=3,
n_trials=2,
n_neurons=3,
n_mice=4,
dur=2,
)
regression = BayesRegression(
BayesWindow(df=df,
y='isi',
treatment='stim',
condition=['neuron', 'stim_strength'],
group='mouse',
add_data=True))
regression.fit(model=models.model_hierarchical,
fold_change_index_cols=None,
do_mean_over_trials=False)
for condition_name in regression.window.condition:
assert condition_name in regression.data_and_posterior.columns, f'{condition_name} not in window.condition'
chart = regression.plot(x='neuron', column='neuron', row='mouse')
chart.display()
def test_estimate_posteriors_slope_uneven_n_data_per_condition():
# Trying to reproduce Uneven number of entries in conditions! Try setting do_take_mean=True
df, df_monster, index_cols, firing_rates = generate_fake_spikes(
n_trials=10,
n_neurons=3,
n_mice=4,
dur=2,
)
df = df.drop(df[(df['i_trial'] == 0) &
# (df['neuron_x_mouse'] == '0m0bayes') &
(df['stim'] == 0)].index)
window = BayesRegression(df=df,
y='isi',
treatment='stim',
condition='neuron',
group='mouse',
add_data=True)
window.fit(models.model_hierarchical, do_make_change='divide')
chart = window.plot(x='neuron', column='neuron', row='mouse')
chart.display()
chart = window.plot(x='neuron', column='neuron', row='mouse')
chart.display()
assert window.data_and_posterior.dropna(
subset=['mu_intercept_per_group center interval'
])['mouse'].unique().size == 4
# def test_stim_strength():
# df = []
# for slope in np.linspace(4, 400, 4):
# df1 = generate_fake_lfp(mouse_response_slope=slope)[0]
# df1['stim_strength'] = slope
# df.append(df1)
# df = pd.concat(df)
# BayesRegression(df=df, 'Power', treatment='stim_strength', group='mouse', detail='i_trial').data_box_detail()
# window = BayesRegression(df=df, 'Power', treatment='stim_strength', condition='mouse', detail='i_trial')
# window.fit(add_condition_slope=True, center_intercept=True, dist_y='normal', num_chains=1, n_draws=100, num_warmup=100)
# window.chart
window = BayesRegression(df=df, y='isi', treatment='stim', group='mouse')
window.fit(model=models.model_hierarchical)
def test_extra_plots():
window.plot_intercepts().display()
window.plot_detail_minus_intercepts().display()
# def test_explore_models():
# window.explore_models(parallel=False)
# window.explore_model_kinds(parallel=False)
