Merge pull request #231 from ejolly/master

Big Design Matrix overhaul to make working with multiple-runs much easier/intuitive. Former-commit-id: 8d5ab1a
cosanlab · May 9, 2018 · d2a3833 · d2a3833
2 parents 3d284fa + 774a3a6
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diff --git a/.gitignore b/.gitignore
@@ -11,7 +11,7 @@ dist/
 .cache/
 htmlcov
 .pytest_cache/*
-
+dev/ 
 # Logs and databases #
 ######################
 *.log

diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_001.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_001.png
diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_002.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_002.png
diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_003.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_003.png
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diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_005.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_005.png
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diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_007.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_007.png
diff --git a/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_008.png b/docs/auto_examples/01_DataOperations/images/sphx_glr_plot_design_matrix_008.png
diff --git a/...o_examples/01_DataOperations/images/thumb/sphx_glr_plot_design_matrix_thumb.png b/...o_examples/01_DataOperations/images/thumb/sphx_glr_plot_design_matrix_thumb.png
diff --git a/docs/auto_examples/01_DataOperations/plot_design_matrix.ipynb b/docs/auto_examples/01_DataOperations/plot_design_matrix.ipynb
diff --git a/docs/auto_examples/01_DataOperations/plot_design_matrix.py b/docs/auto_examples/01_DataOperations/plot_design_matrix.py
diff --git a/docs/auto_examples/01_DataOperations/plot_design_matrix.py.md5 b/docs/auto_examples/01_DataOperations/plot_design_matrix.py.md5
@@ -1 +1 @@
-674c1c7e55385d8ebc2e109812f5c3b8
+d25dc9c6791965dca7df05760d6549bf
diff --git a/docs/auto_examples/01_DataOperations/plot_design_matrix.rst b/docs/auto_examples/01_DataOperations/plot_design_matrix.rst
diff --git a/docs/auto_examples/01_DataOperations/plot_design_matrix_codeobj.pickle b/docs/auto_examples/01_DataOperations/plot_design_matrix_codeobj.pickle
diff --git a/docs/auto_examples/01_DataOperations/plot_mask_codeobj.pickle b/docs/auto_examples/01_DataOperations/plot_mask_codeobj.pickle
diff --git a/docs/auto_examples/01_DataOperations/plot_neurovault_io_codeobj.pickle b/docs/auto_examples/01_DataOperations/plot_neurovault_io_codeobj.pickle
diff --git a/docs/auto_examples/02_Analysis/plot_decomposition_codeobj.pickle b/docs/auto_examples/02_Analysis/plot_decomposition_codeobj.pickle
diff --git a/docs/auto_examples/02_Analysis/plot_multivariate_classification_codeobj.pickle b/docs/auto_examples/02_Analysis/plot_multivariate_classification_codeobj.pickle
diff --git a/docs/auto_examples/02_Analysis/plot_similarity_example_codeobj.pickle b/docs/auto_examples/02_Analysis/plot_similarity_example_codeobj.pickle
diff --git a/docs/auto_examples/02_Analysis/plot_univariate_regression_codeobj.pickle b/docs/auto_examples/02_Analysis/plot_univariate_regression_codeobj.pickle
diff --git a/docs/auto_examples/auto_examples_jupyter.zip b/docs/auto_examples/auto_examples_jupyter.zip
diff --git a/docs/auto_examples/auto_examples_python.zip b/docs/auto_examples/auto_examples_python.zip
diff --git a/docs/auto_examples/index.rst b/docs/auto_examples/index.rst
@@ -235,13 +235,13 @@ Neuroimaging Analysis Examples
 
   .. container:: sphx-glr-download
 
-    :download:`Download all examples in Python source code: auto_examples_python.zip <//Users/lukechang/Github/nltools/docs/auto_examples/auto_examples_python.zip>`
+    :download:`Download all examples in Python source code: auto_examples_python.zip <//Users/Esh/Documents/Python/Cosan/nltools/docs/auto_examples/auto_examples_python.zip>`
 
 
 
   .. container:: sphx-glr-download
 
-    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//Users/lukechang/Github/nltools/docs/auto_examples/auto_examples_jupyter.zip>`
+    :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip <//Users/Esh/Documents/Python/Cosan/nltools/docs/auto_examples/auto_examples_jupyter.zip>`
 
 
 .. only:: html

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diff --git a/docs/backreferences/nltools.utils.examples b/docs/backreferences/nltools.utils.examples
diff --git a/docs/backreferences/nltools.utils.get_anatomical.examples b/docs/backreferences/nltools.utils.get_anatomical.examples
diff --git a/docs/backreferences/nltools.utils.get_resource_path.examples b/docs/backreferences/nltools.utils.get_resource_path.examples
diff --git a/docs/backreferences/nltools.utils.glover_hrf.examples b/docs/backreferences/nltools.utils.glover_hrf.examples
diff --git a/docs/backreferences/nltools.utils.glover_time_derivative.examples b/docs/backreferences/nltools.utils.glover_time_derivative.examples
diff --git a/docs/backreferences/nltools.utils.set_algorithm.examples b/docs/backreferences/nltools.utils.set_algorithm.examples
diff --git a/docs/backreferences/nltools.utils.set_decomposition_algorithm.examples b/docs/backreferences/nltools.utils.set_decomposition_algorithm.examples
diff --git a/docs/backreferences/nltools.utils.spm_dispersion_derivative.examples b/docs/backreferences/nltools.utils.spm_dispersion_derivative.examples
diff --git a/docs/backreferences/nltools.utils.spm_hrf.examples b/docs/backreferences/nltools.utils.spm_hrf.examples
diff --git a/docs/backreferences/nltools.utils.spm_time_derivative.examples b/docs/backreferences/nltools.utils.spm_time_derivative.examples
diff --git a/examples/01_DataOperations/plot_design_matrix.py b/examples/01_DataOperations/plot_design_matrix.py
diff --git a/nltools/data/brain_data.py b/nltools/data/brain_data.py
@@ -327,6 +327,19 @@ def write(self, file_name=None):
 
         self.to_nifti().to_filename(file_name)
 
+    def scale(self, scale_val=100.):
+        """ Scale all values such that theya re on the range 0 - scale_val, via grand-mean scaling. This is NOT global-scaling/intensity normalization. This is useful for ensuring that data is on a common scale (e.g. good for multiple runs, participants, etc) and if the default value of 100 is used, can be interpreted as something akin to (but not exactly) "percent signal change." This is consistent with default behavior in AFNI and SPM. Change this value to 10000 to make consistent with FSL.
+
+        Args:
+            scale_val (int/float): what value to send the grand-mean to; default 100
+
+        """
+
+        out = deepcopy(self)
+        out.data = out.data / out.data.mean() * scale_val
+
+        return out
+
     def plot(self, limit=5, anatomical=None, **kwargs):
         """ Create a quick plot of self.data.  Will plot each image separately
 
@@ -399,18 +412,23 @@ def regress(self,mode='ols',**kwargs):
         b,t,p,df,res = regress(self.X,self.data,mode=mode,**kwargs)
         sigma = np.std(res,axis=0,ddof=self.X.shape[1])
 
-        b_out = deepcopy(self)
-        b_out.data = b
-        t_out = deepcopy(self)
+        # Prevent copy of all data in self multiple times; instead start with an empty instance and copy only needed attributes from self, and use this as a template for other outputs
+        b_out = self.__class__()
+        b_out.mask = deepcopy(self.mask)
+        b_out.nifti_masker = deepcopy(self.nifti_masker)
+
+        # Use this as template for other outputs before setting data
+        t_out = b_out.copy()
         t_out.data = t
-        p_out = deepcopy(self)
+        p_out = b_out.copy()
         p_out.data = p
-        df_out = deepcopy(self)
+        df_out = b_out.copy()
         df_out.data = df
-        sigma_out = deepcopy(self)
+        sigma_out = b_out.copy()
         sigma_out.data = sigma
-        res_out = deepcopy(self)
+        res_out = b_out.copy()
         res_out.data = res
+        b_out.data = b
 
         return {'beta': b_out, 't': t_out, 'p': p_out, 'df': df_out,
                 'sigma': sigma_out, 'residual': res_out}
@@ -497,11 +515,12 @@ def ttest(self, threshold_dict=None):
 
         return out
 
-    def append(self, data):
+    def append(self, data, **kwargs):
         """ Append data to Brain_Data instance
 
         Args:
             data: Brain_Data instance to append
+            kwargs: optional inputs to Design_Matrix append
 
         Returns:
             out: new appended Brain_Data instance
@@ -532,7 +551,7 @@ def append(self, data):
                 out.Y = self.Y.append(data.Y)
             if self.X.size:
                 if isinstance(self.X, pd.DataFrame):
-                    out.X = self.X.append(data.X)
+                    out.X = self.X.append(data.X,**kwargs)
                 else:
                     out.X = np.vstack([self.X, data.X])
         return out
@@ -1155,11 +1174,11 @@ def r_to_z(self):
         out.data = fisher_r_to_z(out.data)
         return out
 
-    def filter(self,sampling_rate=None, high_pass=None,low_pass=None,**kwargs):
+    def filter(self,sampling_freq=None, high_pass=None,low_pass=None,**kwargs):
         ''' Apply 5th order butterworth filter to data. Wraps nilearn functionality. Does not default to detrending and standardizing like nilearn implementation, but this can be overridden using kwargs.
 
         Args:
-            sampling_rate: sampling rate in seconds (i.e. TR)
+            sampling_freq: sampling freq in hertz (i.e. 1 / TR)
             high_pass: high pass cutoff frequency
             low_pass: low pass cutoff frequency
             kwargs: other keyword arguments to nilearn.signal.clean
@@ -1168,18 +1187,18 @@ def filter(self,sampling_rate=None, high_pass=None,low_pass=None,**kwargs):
             Brain_Data: Filtered Brain_Data instance
         '''
 
-        if sampling_rate is None:
+        if sampling_freq is None:
             raise ValueError("Need to provide sampling rate (TR)!")
         if high_pass is None and low_pass is None:
             raise ValueError("high_pass and/or low_pass cutoff must be"
                             "provided!")
-        if sampling_rate is None:
+        if sampling_freq is None:
             raise ValueError("Need to provide TR!")
 
         standardize = kwargs.get('standardize',False)
         detrend = kwargs.get('detrend',False)
         out = self.copy()
-        out.data = clean(out.data,t_r=sampling_rate,detrend=detrend,standardize=standardize,high_pass=high_pass,low_pass=low_pass,**kwargs)
+        out.data = clean(out.data,t_r= 1. / sampling_freq,detrend=detrend,standardize=standardize,high_pass=high_pass,low_pass=low_pass,**kwargs)
         return out
 
     def dtype(self):