figured out set_locs force_include_bo_locs flag and set the new version to 0.2.0

Author: lucywowen

docs/auto_examples/auto_examples_jupyter.zip

@@ -263,7 +263,7 @@ This example shows debugging process for predict.  Delete before pip push.
     noise = 0
 
     # locs
-    locs = se.simulate_locations(n_elecs=100, random_seed=random_seed)
+    locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
 
     # create model locs from 75 locations
     mo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])
@@ -313,7 +313,7 @@ This example shows debugging process for predict.  Delete before pip push.
     noise = 0
 
     # locs
-    locs = se.simulate_locations(n_elecs=100, random_seed=random_seed)
+    locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
 
     # create model locs from 50 locations
     mo_locs = locs.sample(100, random_state=random_seed).sort_values(['x', 'y', 'z'])
@@ -360,7 +360,7 @@ This example shows debugging process for predict.  Delete before pip push.
     noise = 0
 
     # locs
-    locs = se.simulate_locations(n_elecs=100, random_seed=random_seed)
+    locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
 
     # create model locs from 75 locations
     mo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])
@@ -413,7 +413,7 @@ This example shows debugging process for predict.  Delete before pip push.
     noise = 0
 
     # locs
-    locs = se.simulate_locations(n_elecs=100, random_seed=random_seed)
+    locs = se.simulate_locations(n_elecs=100, set_random_seed=random_seed)
 
     # create brain locs from 75 locations
     bo_locs = locs.sample(75, random_state=random_seed).sort_values(['x', 'y', 'z'])

docs/auto_examples/auto_examples_python.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# Code source: Lucy Owen & Andrew Heusser\n# License: MIT\nimport supereeg as se\nimport numpy as np\n\n# some example locations\nlocs = np.array([[-61., -77.,  -3.],\n                 [-41., -77., -23.],\n                 [-21., -97.,  17.],\n                 [-21., -37.,  77.],\n                 [-21.,  63.,  -3.],\n                 [ -1., -37.,  37.],\n                 [ -1.,  23.,  17.],\n                 [ 19., -57., -23.],\n                 [ 19.,  23.,  -3.],\n                 [ 39., -57.,  17.],\n                 [ 39.,   3.,  37.],\n                 [ 59., -17.,  17.]])\n\n\n# number of timeseries samples\nn_samples = 10\n# number of subjects\nn_subs = 6\n# number of electrodes\nn_elecs = 5\n# simulate some brain objects\ndata = [se.simulate_model_bos(n_samples=10, sample_rate=10, locs=locs, sample_locs = n_elecs, random_seed=123, noise=0) for x in range(n_subs)]\n# create a model from the first 5 brain objects and another from 1 brain object\nmo1 = se.Model(data=data[0:5], locs=locs, n_subs=5)\nmo2 = se.Model(data=data[5:6], locs=locs, n_subs=1)\n\n# adding the models\nmo3 = mo1 + mo2\n\n# plot the added model\nmo3.plot_data()\n# adding these models is the same as making a model from all 6 brain objects at once\nmo3_alt = se.Model(data=data[0:6], locs=locs, n_subs=6)\n# plot the alternate model\nmo3_alt.plot_data()\n# show that they're the same\nassert np.allclose(mo3.get_model(), mo3_alt.get_model())\n# show that the number of subjects is also added\nassert(mo3.n_subs == mo1.n_subs + mo2.n_subs)\n\n# you can also subtract models\nmo2_sub = mo3 - mo1\n\n# plot the subtracted model\nmo2_sub.plot_data()\n# plot the original\nmo2.plot_data()\n# show that subratracting mo1 from mo3 will equal mo2\nassert np.allclose(mo2.get_model(), mo2_sub.get_model(), equal_nan=True)\n# show that the number of subjects is also subtracted\nassert(mo2_sub.n_subs == mo2.n_subs)\n# subtraction also updates the meta field, changing stable from True to False\nmo2.info()\nmo2_sub.info()\n# now that the new model is not stable, so you can't add anything to it\ntry:\n    assert mo2_sub + mo3\nexcept AssertionError:\n    assert True == True"
+        "# Code source: Lucy Owen & Andrew Heusser\n# License: MIT\nimport supereeg as se\nimport numpy as np\n\n# some example locations\nlocs = np.array([[-61., -77.,  -3.],\n                 [-41., -77., -23.],\n                 [-21., -97.,  17.],\n                 [-21., -37.,  77.],\n                 [-21.,  63.,  -3.],\n                 [ -1., -37.,  37.],\n                 [ -1.,  23.,  17.],\n                 [ 19., -57., -23.],\n                 [ 19.,  23.,  -3.],\n                 [ 39., -57.,  17.],\n                 [ 39.,   3.,  37.],\n                 [ 59., -17.,  17.]])\n\n\n# number of timeseries samples\nn_samples = 10\n# number of subjects\nn_subs = 6\n# number of electrodes\nn_elecs = 5\n# simulate some brain objects\ndata = [se.simulate_model_bos(n_samples=10, sample_rate=10, locs=locs, sample_locs = n_elecs, set_random_seed=123, noise=0) for x in range(n_subs)]\n# create a model from the first 5 brain objects and another from 1 brain object\nmo1 = se.Model(data=data[0:5], locs=locs, n_subs=5)\nmo2 = se.Model(data=data[5:6], locs=locs, n_subs=1)\n\n# adding the models\nmo3 = mo1 + mo2\n\n# plot the added model\nmo3.plot_data()\n# adding these models is the same as making a model from all 6 brain objects at once\nmo3_alt = se.Model(data=data[0:6], locs=locs, n_subs=6)\n# plot the alternate model\nmo3_alt.plot_data()\n# show that they're the same\nassert np.allclose(mo3.get_model(), mo3_alt.get_model())\n# show that the number of subjects is also added\nassert(mo3.n_subs == mo1.n_subs + mo2.n_subs)\n\n# you can also subtract models\nmo2_sub = mo3 - mo1\n\n# plot the subtracted model\nmo2_sub.plot_data()\n# plot the original\nmo2.plot_data()\n# show that subratracting mo1 from mo3 will equal mo2\nassert np.allclose(mo2.get_model(), mo2_sub.get_model(), equal_nan=True)\n# show that the number of subjects is also subtracted\nassert(mo2_sub.n_subs == mo2.n_subs)\n# subtraction also updates the meta field, changing stable from True to False\nmo2.info()\nmo2_sub.info()\n# now that the new model is not stable, so you can't add anything to it\ntry:\n    assert mo2_sub + mo3\nexcept AssertionError:\n    assert True == True"
       ], 
       "outputs": [], 
       "metadata": {

docs/auto_examples/debug_predict.ipynb

@@ -42,7 +42,7 @@ In this example, we show you how to add and subtract models.
     # number of electrodes
     n_elecs = 5
     # simulate some brain objects
-    data = [se.simulate_model_bos(n_samples=10, sample_rate=10, locs=locs, sample_locs = n_elecs, random_seed=123, noise=0) for x in range(n_subs)]
+    data = [se.simulate_model_bos(n_samples=10, sample_rate=10, locs=locs, sample_locs = n_elecs, set_random_seed=123, noise=0) for x in range(n_subs)]
     # create a model from the first 5 brain objects and another from 1 brain object
     mo1 = se.Model(data=data[0:5], locs=locs, n_subs=5)
     mo2 = se.Model(data=data[5:6], locs=locs, n_subs=1)

docs/auto_examples/debug_predict.rst

@@ -8,7 +8,7 @@
 
 setup(
     name='supereeg',
-    version='0.1.1',
+    version='0.2.0',
     description=DESCRIPTION,
     long_description=' ',
     author='Contextual Dynamics Laboratory',

docs/auto_examples/model_add_subtract.ipynb

@@ -221,7 +221,8 @@ def _load_from_cache(fname, ftype, sample_inds=None, loc_inds=None, field=None):
         else:
             return Brain(**dd.io.load(fullpath))
     elif ftype is 'mo':
-        #return Model(**dd.io.load(fullpath))
+        # if the model was created using supereeg<0.2.0, load using the "old" format
+        # (i.e. supereeg>=0.2.0 computes model in log space)
         date_created = _load_field(fullpath, field='date_created')
         if datetime.strptime(date_created, "%c")< datetime(2018, 7, 27, 14, 40, 48, 359141):
             num = _load_field(fullpath, field='numerator')

docs/auto_examples/model_add_subtract.rst

@@ -256,10 +256,11 @@ def set_locs(self, new_locs, force_include_bo_locs=False):
         new_locs_in_self = _count_overlapping(self.get_locs(), new_locs)
 
         if np.all(new_locs_in_self):
-            self.locs = self.locs.iloc[new_locs_in_self, :]
+            inds = _count_overlapping(new_locs, self.get_locs())
+            self.locs = self.locs.iloc[inds, :]
             self.n_locs = self.locs.shape[0]
-            self.numerator = self.numerator[new_locs_in_self, :][:, new_locs_in_self]
-            self.denominator = self.denominator[new_locs_in_self, :][:, new_locs_in_self]
+            self.numerator = self.numerator[inds, :][:, inds]
+            self.denominator = self.denominator[inds, :][:, inds]
             return
         else:
             rbf_weights = _log_rbf(new_locs, self.get_locs())