refactored test for timeseries

using spm HRF from nilearn to reduce code and test range of TRs and onset intervals when extracting post cue trs for conditions. The peak should be within similar window, adjusted for index of TR

tests/test_modules.py

@@ -16,6 +16,9 @@
 from nilearn.masking import compute_multi_brain_mask
 from hypothesis import given, strategies as st
 from hypothesis.extra.numpy import arrays
+import warnings
+warnings.filterwarnings("ignore", message="The nilearn.glm module is experimental.", category=FutureWarning)
+from nilearn.glm.first_level import make_first_level_design_matrix
 
 
 def generate_img_pair(r: float, dir: Path, use_mask: bool = False, tol: float = .001,
@@ -365,3 +368,57 @@ def test_runtrlocked_events(tmp_path):
 
     trlocked_events(events_path=events_path, onsets_column=onset_name, trial_name=trial_name,
                     bold_tr=bold_tr, bold_vols=bold_vols, separator=',')
+
+
+def create_conv_mat(eventsdf, tr_dur=None, acq_dur=None):
+    vol_time = acq_dur
+    tr = tr_dur
+    design_mat = make_first_level_design_matrix(
+        frame_times=np.linspace(0, vol_time, int(vol_time/tr)),
+        events=eventsdf, hrf_model='spm',
+        drift_model=None, high_pass=None)
+    return design_mat
+
+
+@pytest.mark.parametrize("TR", [.8, 1.4, 2, 2.6])
+@pytest.mark.parametrize("interval", [10, 15, 20])
+def test_testsimtrpeak(tmp_path, TR, interval):
+    onsets = np.arange(0, 160, interval)
+    dur_opts = [1.5, 2, 2.5]
+    prob_durs = [.50, .25, .25]
+    np.random.seed(11)
+    durations = np.random.choice(
+        dur_opts, size=len(onsets), p=prob_durs
+    )
+
+    events_df = pd.DataFrame({
+        "onset": onsets,
+        "duration": durations,
+        "trial_type": "Phacking101"
+    })
+    last_onset = events_df['onset'].iloc[-1]
+    tr = TR
+    conv_vals = create_conv_mat(eventsdf=events_df, tr_dur=tr, acq_dur=last_onset)
+
+    # create n = 1 compatible timeseries for test
+    convolved_stacked = np.vstack([conv_vals['Phacking101']])
+    convolved_stacked = convolved_stacked.reshape((conv_vals.shape[0] * (conv_vals.shape[1] - 1), 1))
+    timeseries_reshaped = np.reshape(convolved_stacked, (1, len(convolved_stacked), 1))
+
+    events_file_name = tmp_path / "sub-01_run-01_test-events.csv"
+    events_df.to_csv(events_file_name, sep='\t')
+
+    conditions = ['Phacking101']
+    trdelay = int(15 / tr)
+    df = extract_postcue_trs_for_conditions(events_data=[events_file_name], onset='onset', trial_name='trial_type',
+                                            bold_tr=TR, bold_vols=len(timeseries_reshaped[0]),
+                                            time_series=timeseries_reshaped,
+                                            conditions=conditions, tr_delay=trdelay,
+                                            list_trpaths=['sub-01_run-01'])
+    tr_peak = df.groupby('TR')['Mean_Signal'].mean().idxmax()
+    min_tr = np.floor(float(6 / tr))
+    max_tr = np.ceil(float(10 / tr))
+    peak_in_tr = np.arange(min_tr, max_tr, .1)
+    is_in_array = np.any(np.isclose(peak_in_tr, tr_peak))
+    print(f"Checking whether {tr_peak} TR HRF peak is between range min {min_tr} and max {max_tr}")
+    assert is_in_array, f"Peak error: Peak should occurs between 5-8sec, peak {round(tr_peak * tr, 2)}"