[FIX] Fix categorical contrasts (#251)

* Tutorial update,categorical variables not unsigned

* Version 0.3.1

.gitignore

@@ -1,3 +1,6 @@
+*.prof 
+local_scripts/*
+
 docs/_build
 docs/generated
 test_python

brainstat/__init__.py

@@ -1,2 +1,2 @@
 """Neuroimaging statistics toolbox."""
-__version__ = "0.3.0"
+__version__ = "0.3.1"

brainstat/mesh/utils.py

@@ -198,16 +198,12 @@ def mesh_edges(
 
 
 def _mask_edges(edges: np.ndarray, mask: ArrayLike) -> Tuple[np.ndarray, np.ndarray]:
-    # TODO: this section is sloppily written.
     missing_edges = np.where(np.logical_not(mask))
-    remove_edges = np.zeros(edges.shape, dtype=bool)
-    for i in range(edges.shape[0]):
-        for j in range(edges.shape[1]):
-            remove_edges[i, j] = (edges[i, j] == missing_edges).any()
+    remove_edges = np.isin(edges, missing_edges)
     idx = ~np.any(remove_edges, axis=1)
-    edges = edges[idx, :]
-    edges = _make_contiguous(edges)
-    return edges, idx
+    edges_new = edges[idx, :]
+    edges_new = _make_contiguous(edges_new)
+    return edges_new, idx
 
 
 def _make_contiguous(Y: np.ndarray) -> np.ndarray:
@@ -223,7 +219,10 @@ def _make_contiguous(Y: np.ndarray) -> np.ndarray:
     numpy.ndarray
         Array Y converted to contiguous numbers in range(np.unique(Y).size).
     """
-    val = np.unique(Y)
-    for i in range(val.size):
-        Y[Y == val[i]] = i
-    return Y
+    Y_flat = Y.copy().flatten()
+    val = np.unique(Y_flat)
+    new_val = np.arange(val.size)
+
+    D = dict(np.array([val, new_val]).T)
+    Y_new = [D[i] for i in Y_flat]
+    return np.reshape(Y_new, Y.shape)

brainstat/stats/SLM.py

@@ -248,7 +248,7 @@ def _surfstat_to_brainstat_rft(self) -> None:
                         yeo7_index = yeo7[self.P["peak"]["vertid"][i]]
                         if "yeo7" not in self.P["peak"]:
                             self.P["peak"]["yeo7"] = []
-                        self.P["peak"]["yeo7"][i] = np.take(yeo_names, yeo7_index)
+                        self.P["peak"]["yeo7"].append(np.take(yeo_names, yeo7_index))
 
             if "clus" in self.P:
                 for i in range(len(self.P["clus"]["clusid"])):

brainstat/stats/terms.py

@@ -84,7 +84,7 @@ def to_df(
             )
         df.columns = names
 
-    return pd.get_dummies(df)
+    return pd.get_dummies(df, dtype=np.int8)
 
 
 def get_index(df: pd.DataFrame) -> Optional[int]:

docs/python/generated_tutorials/plot_tutorial_01_basics.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "from brainstat.stats.terms import FixedEffect\n\nterm_age = FixedEffect(demographics.AGE_AT_SCAN)\n# Subtract 1 from DX_GROUP so patient == 0 and healthy == 1.\nterm_patient = FixedEffect(demographics.DX_GROUP - 1)\nmodel = term_age + term_patient"
+        "from brainstat.stats.terms import FixedEffect\n\ndemographics.DX_GROUP[demographics.DX_GROUP == 1] = \"Patient\"\ndemographics.DX_GROUP[demographics.DX_GROUP == 2] = \"Control\"\n\nterm_age = FixedEffect(demographics.AGE_AT_SCAN)\nterm_patient = FixedEffect(demographics.DX_GROUP)\nmodel = term_age + term_patient"
       ]
     },
     {
@@ -134,7 +134,7 @@
       },
       "outputs": [],
       "source": [
-        "from brainstat.stats.SLM import SLM\n\ncontrast_age = model.AGE_AT_SCAN\nslm_age = SLM(\n    model, contrast_age, surf=pial_combined, mask=mask, correction=[\"fdr\", \"rft\"]\n)\nslm_age.fit(thickness)"
+        "from brainstat.stats.SLM import SLM\n\ncontrast_age = model.AGE_AT_SCAN\nslm_age = SLM(\n    model, contrast_age, surf=\"civet41k\", mask=mask, correction=[\"fdr\", \"rft\"]\n)\nslm_age.fit(thickness)"
       ]
     },
     {
@@ -206,7 +206,7 @@
       },
       "outputs": [],
       "source": [
-        "# Note the minus in front of contrast_age to test for decreasing thickness with age.\nslm_age_onetailed = SLM(\n    model,\n    -contrast_age,\n    surf=pial_combined,\n    correction=[\"fdr\", \"rft\"],\n    mask=mask,\n    two_tailed=False,\n)\nslm_age_onetailed.fit(thickness)\n\nplot_slm_results(slm_age_onetailed)"
+        "# Note the minus in front of contrast_age to test for decreasing thickness with age.\nslm_age_onetailed = SLM(\n    model,\n    -contrast_age,\n    surf=\"civet41k\",\n    correction=[\"fdr\", \"rft\"],\n    mask=mask,\n    two_tailed=False,\n)\nslm_age_onetailed.fit(thickness)\n\nplot_slm_results(slm_age_onetailed)"
       ]
     },
     {
@@ -224,7 +224,7 @@
       },
       "outputs": [],
       "source": [
-        "contrast_patient = model.DX_GROUP\nslm_patient = SLM(\n    model,\n    contrast_patient,\n    surf=pial_combined,\n    mask=mask,\n    correction=[\"fdr\", \"rft\"],\n)\nslm_patient.fit(thickness)\n\nplot_slm_results(slm_patient)"
+        "contrast_patient = model.DX_GROUP_Patient - model.DX_GROUP_Control\nslm_patient = SLM(\n    model,\n    contrast_patient,\n    surf=\"civet41k\",\n    mask=mask,\n    correction=[\"fdr\", \"rft\"],\n)\nslm_patient.fit(thickness)\n\nplot_slm_results(slm_patient)"
       ]
     },
     {
@@ -242,7 +242,7 @@
       },
       "outputs": [],
       "source": [
-        "from brainstat.stats.terms import MixedEffect\n\nrandom_site = MixedEffect(demographics.SITE_ID, name_ran=\"Site\")\n\nmodel_random = term_age + term_patient + random_site\nslm_random = SLM(\n    model_random,\n    contrast_age,\n    surf=pial_combined,\n    mask=mask,\n    correction=[\"fdr\", \"rft\"],\n)\nslm_random.fit(thickness)"
+        "from brainstat.stats.terms import MixedEffect\n\nrandom_site = MixedEffect(demographics.SITE_ID, name_ran=\"Site\")\n\nmodel_random = term_age + term_patient + random_site\nslm_random = SLM(\n    model_random,\n    contrast_age,\n    surf=\"civet41k\",\n    mask=mask,\n    correction=[\"fdr\", \"rft\"],\n)\nslm_random.fit(thickness)"
       ]
     },
     {

docs/python/generated_tutorials/plot_tutorial_01_basics.py

@@ -53,9 +53,11 @@
 
 from brainstat.stats.terms import FixedEffect
 
+demographics.DX_GROUP[demographics.DX_GROUP == 1] = "Patient"
+demographics.DX_GROUP[demographics.DX_GROUP == 2] = "Control"
+
 term_age = FixedEffect(demographics.AGE_AT_SCAN)
-# Subtract 1 from DX_GROUP so patient == 0 and healthy == 1.
-term_patient = FixedEffect(demographics.DX_GROUP - 1)
+term_patient = FixedEffect(demographics.DX_GROUP)
 model = term_age + term_patient
 
 ###################################################################
@@ -90,7 +92,7 @@
 
 contrast_age = model.AGE_AT_SCAN
 slm_age = SLM(
-    model, contrast_age, surf=pial_combined, mask=mask, correction=["fdr", "rft"]
+    model, contrast_age, surf="civet41k", mask=mask, correction=["fdr", "rft"]
 )
 slm_age.fit(thickness)
 
@@ -189,7 +191,7 @@ def plot_slm_results(slm):
 slm_age_onetailed = SLM(
     model,
     -contrast_age,
-    surf=pial_combined,
+    surf="civet41k",
     correction=["fdr", "rft"],
     mask=mask,
     two_tailed=False,
@@ -202,11 +204,11 @@ def plot_slm_results(slm):
 # Similarly, we could perform an analysis to assess cortical thickness
 # differences across healthy and patient groups whilst correcting for age.
 
-contrast_patient = model.DX_GROUP
+contrast_patient = model.DX_GROUP_Patient - model.DX_GROUP_Control
 slm_patient = SLM(
     model,
     contrast_patient,
-    surf=pial_combined,
+    surf="civet41k",
     mask=mask,
     correction=["fdr", "rft"],
 )
@@ -232,7 +234,7 @@ def plot_slm_results(slm):
 slm_random = SLM(
     model_random,
     contrast_age,
-    surf=pial_combined,
+    surf="civet41k",
     mask=mask,
     correction=["fdr", "rft"],
 )

docs/python/generated_tutorials/plot_tutorial_01_basics.py.md5

@@ -1 +1 @@
-723808218958d768ba424e630d2f8fea
+bbd08ec3b86f5c05a2b26cbb9e58b554

docs/python/generated_tutorials/sg_execution_times.rst

@@ -5,10 +5,10 @@
 
 Computation times
 =================
-**04:11.183** total execution time for **python_generated_tutorials** files:
+**01:57.138** total execution time for **python_generated_tutorials** files:
 
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_python_generated_tutorials_plot_tutorial_01_basics.py` (``plot_tutorial_01_basics.py``)   | 04:11.183 | 0.0 MB |
+| :ref:`sphx_glr_python_generated_tutorials_plot_tutorial_01_basics.py` (``plot_tutorial_01_basics.py``)   | 01:57.138 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+
 | :ref:`sphx_glr_python_generated_tutorials_plot_tutorial_02_context.py` (``plot_tutorial_02_context.py``) | 00:00.000 | 0.0 MB |
 +----------------------------------------------------------------------------------------------------------+-----------+--------+

docs/python/tutorials/plot_tutorial_01_basics.py

@@ -53,9 +53,11 @@
 
 from brainstat.stats.terms import FixedEffect
 
+demographics.DX_GROUP[demographics.DX_GROUP == 1] = "Patient"
+demographics.DX_GROUP[demographics.DX_GROUP == 2] = "Control"
+
 term_age = FixedEffect(demographics.AGE_AT_SCAN)
-# Subtract 1 from DX_GROUP so patient == 0 and healthy == 1.
-term_patient = FixedEffect(demographics.DX_GROUP - 1)
+term_patient = FixedEffect(demographics.DX_GROUP)
 model = term_age + term_patient
 
 ###################################################################
@@ -90,7 +92,7 @@
 
 contrast_age = model.AGE_AT_SCAN
 slm_age = SLM(
-    model, contrast_age, surf=pial_combined, mask=mask, correction=["fdr", "rft"]
+    model, contrast_age, surf="civet41k", mask=mask, correction=["fdr", "rft"]
 )
 slm_age.fit(thickness)
 
@@ -189,7 +191,7 @@ def plot_slm_results(slm):
 slm_age_onetailed = SLM(
     model,
     -contrast_age,
-    surf=pial_combined,
+    surf="civet41k",
     correction=["fdr", "rft"],
     mask=mask,
     two_tailed=False,
@@ -202,11 +204,11 @@ def plot_slm_results(slm):
 # Similarly, we could perform an analysis to assess cortical thickness
 # differences across healthy and patient groups whilst correcting for age.
 
-contrast_patient = model.DX_GROUP
+contrast_patient = model.DX_GROUP_Patient - model.DX_GROUP_Control
 slm_patient = SLM(
     model,
     contrast_patient,
-    surf=pial_combined,
+    surf="civet41k",
     mask=mask,
     correction=["fdr", "rft"],
 )
@@ -232,7 +234,7 @@ def plot_slm_results(slm):
 slm_random = SLM(
     model_random,
     contrast_age,
-    surf=pial_combined,
+    surf="civet41k",
     mask=mask,
     correction=["fdr", "rft"],
 )