Merge pull request #586 from MouseLand/dev

Dev

.dvc/config

@@ -11,4 +11,4 @@
     url = gdrive://0ACw_QYaWTX7mUk9PVA
     gdrive_use_service_account = true
     gdrive_service_account_email = travis4@suite2p-testdata-dvc.iam.gserviceaccount.com
-    gdrive_service_account_p12_file_path = .dvc/creds/suite2p-testdata-dvc-b0d23791539c.p12
+    gdrive_service_account_p12_file_path = creds/suite2p-testdata-dvc-b0d23791539c.p12

data/test_data.dvc

@@ -1,3 +1,3 @@
 outs:
-- md5: 273c132e8b2d31901a45c70188e68535.dir
+- md5: eaa434ed20e421fccab8089842be58b5.dir
   path: test_data

docs/gui.rst

@@ -236,6 +236,21 @@ added to the *.npy files as the first N ROIs (where N is the number that you dre
 .. image:: _static/manual_roi.png
    :width: 600
 
+Merging ROIs
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+You can merge selected ROIs (multi-select with CTRL) by pressing ALT+ENTER, 
+or get suggested merges in the "Merge ROI" menu. The merged ROIs then must 
+be saved before you close the GUI to write the new ROIs to the *.npy files. 
+Each merged ROI is appended to the end of the list of ROIs (in stat), and the
+ROIs that were merged to create it are in the key 'imerge'. Note in the stat file 
+and other files the original ROIs (that create the ROI) are NOT removed so that 
+you retain the original signals and original suite2p output. In the GUI 
+ROI view the merged ROIs are shown.
+
+The merging of fluorescence is done by taking the mean of the selected cells' 
+fluorescences. The list of merges are available in the stat for you to choose 
+alternative strategies for combining signals.
+
 View registered binary
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

docs/outputs.rst

@@ -21,13 +21,13 @@ All can be loaded in python with numpy
 
    import numpy as np
 
-   F = np.load('F.npy')
-   Fneu = np.load('F.npy')
-   spks = np.load('spks.npy')
-   stat = np.load('stat.npy')
-   ops =  np.load('ops.npy')
+   F = np.load('F.npy', allow_pickle=True)
+   Fneu = np.load('F.npy', allow_pickle=True)
+   spks = np.load('spks.npy', allow_pickle=True)
+   stat = np.load('stat.npy', allow_pickle=True)
+   ops =  np.load('ops.npy', allow_pickle=True)
    ops = ops.item()
-   iscell = np.load('iscell.npy')
+   iscell = np.load('iscell.npy', allow_pickle=True)
 
 MATLAB output
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~

jupyter/make_ops.ipynb

@@ -9,9 +9,9 @@
     "import numpy as np\n",
     "import sys\n",
     "sys.path.insert(0, 'C:/Users/carse/github/suite2p/')\n",
-    "from suite2p import run_s2p\n",
+    "from suite2p import default_ops\n",
     "\n",
-    "ops = run_s2p.default_ops()\n",
+    "ops = default_ops()\n",
     "\n",
     "np.save('../suite2p/ops/ops.npy', ops)"
    ]
@@ -24,7 +24,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "ops = run_s2p.default_ops()\n",
+    "ops = default_ops()\n",
     "\n",
     "ops['1Preg'] = True\n",
     "ops['smooth_sigma'] = 6\n",
@@ -43,7 +43,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "ops = run_s2p.default_ops()\n",
+    "ops = default_ops()\n",
     "\n",
     "ops['connected'] = True\n",
     "ops['allow_overlap'] = True\n",
@@ -74,4 +74,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

jupyter/run_pipeline_sbx.ipynb

@@ -62,7 +62,7 @@
     "ops['um_per_pixel_y'] = um_per_pix_y\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db={})"
+    "opsEnd=run_s2p(ops=ops,db={})"
    ]
   }
  ],

jupyter/run_pipeline_tiffs_or_batch.ipynb

@@ -10,10 +10,10 @@
     "import sys\n",
     "# option to import from github folder\n",
     "sys.path.insert(0, 'C:/Users/carse/github/suite2p/')\n",
-    "from suite2p import run_s2p\n",
+    "from suite2p import run_s2p, default_ops\n",
     "\n",
     "# set your options for running\n",
-    "ops = run_s2p.default_ops() # populates ops with the default options"
+    "ops = default_ops() # populates ops with the default options"
    ]
   },
   {
@@ -38,7 +38,7 @@
     "    }\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db=db)"
+    "opsEnd = run_s2p(ops=ops, db=db)"
    ]
   },
   {
@@ -53,7 +53,7 @@
     "db.append({'data_path': ['C:/Users/carse/github/tiffs2']})\n",
     "\n",
     "for dbi in db:\n",
-    "    opsEnd=run_s2p.run_s2p(ops=ops,db=dbi)"
+    "    opsEnd = run_s2p(ops=ops, db=dbi)"
    ]
   },
   {
@@ -77,7 +77,7 @@
     "\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db=db)\n"
+    "opsEnd = run_s2p(ops=ops,db=db)\n"
    ]
   },
   {
@@ -109,7 +109,7 @@
     "\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db=db)\n"
+    "opsEnd=run_s2p(ops=ops, db=db)\n"
    ]
   },
   {
@@ -121,7 +121,7 @@
     "## change the save directory from 'suite2p' to a chosen name\n",
     "# note the fast_disk will always be in 'suite2p', just the save_path will change\n",
     "\n",
-    "ops = run_s2p.default_ops() # populates ops with the default options\n",
+    "ops = default_ops() # populates ops with the default options\n",
     "ops['sparse_mode'] = 1\n",
     "ops['threshold_scaling'] = 3.0\n",
     "db = {\n",
@@ -134,7 +134,7 @@
     "    }\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db=db)"
+    "opsEnd = run_s2p(ops=ops, db=db)"
    ]
   },
   {
@@ -145,7 +145,7 @@
    "source": [
     "# h5py file with multiple data fields (untested)\n",
     "\n",
-    "ops = run_s2p.default_ops() # populates ops with the default options\n",
+    "ops = default_ops() # populates ops with the default options\n",
     "ops['nplanes'] = 12\n",
     "ops['nchannels'] = 2\n",
     "ops['fs'] = 5.0\n",
@@ -158,7 +158,7 @@
     "    }\n",
     "\n",
     "# run one experiment\n",
-    "opsEnd=run_s2p.run_s2p(ops=ops,db=db)"
+    "opsEnd = run_s2p(ops=ops, db=db)"
    ]
   }
  ],
@@ -183,4 +183,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

setup.py

@@ -17,32 +17,30 @@
       'setuptools_scm',
     ],
     use_scm_version=True,
-    install_requires=[],  # see environment.yml for this info.
-    tests_require=[
-      'pytest',
-      'pytest-qt',
-    ],
-    extras_require={
-      "docs": [
-        'sphinx>=3.0',
-        'sphinxcontrib-apidoc',
-        'sphinx_rtd_theme',
-        'sphinx-prompt',
-        'sphinx-autodoc-typehints',
-        'importlib-metadata',
+    install_requires=['importlib-metadata',
         'natsort',
         'rastermap>0.1.0',
         'tifffile',
         'scanimage-tiff-reader>=1.4.1',
         'pyqtgraph',
-        'importlib-metadata',
         'paramiko',
         'numpy>=1.16',
         'numba>=0.43.1',
         'matplotlib',
         'scipy',
         'h5py',
-        'scikit-learn',
+        'scikit-learn',],  # see environment.yml for this info.
+    tests_require=[
+      'pytest',
+      'pytest-qt',
+    ],
+    extras_require={
+      "docs": [
+        'sphinx>=3.0',
+        'sphinxcontrib-apidoc',
+        'sphinx_rtd_theme',
+        'sphinx-prompt',
+        'sphinx-autodoc-typehints',
       ],
       # Note: Available in pypi, but cleaner to install as pyqt from conda.
       "gui": [

suite2p/classification/classifier.py

@@ -99,6 +99,7 @@ def _get_logp(self, stats):
             x = stats[:,n]
             x[x<self.grid[0,n]]   = self.grid[0,n]
             x[x>self.grid[-1,n]]  = self.grid[-1,n]
+            x[np.isnan(x)] = self.grid[0,n]
             ibin = np.digitize(x, self.grid[:,n], right=True) - 1
             logp[:,n] = np.log(self.p[ibin,n] + 1e-6) - np.log(1-self.p[ibin,n] + 1e-6)
         return logp

suite2p/classification/classify.py

@@ -13,4 +13,6 @@ def classify(stat: np.ndarray,
              ):
     """Returns array of classifier output from classification process."""
     keys = list(set(keys).intersection(set(stat[0])))
-    return Classifier(classfile, keys=keys).run(stat)
+    print(keys)
+    iscell = Classifier(classfile, keys=keys).run(stat)
+    return iscell