changes

Author: mannypaeza

caiman/train/__init__.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python
 import pkg_resources
 
-from caiman.train.train_cnn_model_keras import cnn_model_keras, save_model_keras, load_model_keras
-from caiman.train.train_cnn_model_pytorch import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch
+from caiman.train.train_cnn_model_helper import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch, cnn_model_keras, save_model_keras, load_model_keras
 
 __version__ = pkg_resources.get_distribution('caiman').version

caiman/train/ground_truth_cnmf_seeded.ipynb

@@ -270,7 +270,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Parameters for the Movie and Name of the Movie "
+    "### Parameters for the Movie"
    ]
   },
   {
@@ -292,7 +292,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analysis "
+    "## Run Analysis"
    ]
   },
   {
@@ -308,16 +308,30 @@
     "d1, d2 = dims\n",
     "images = np.reshape(Yr.T, [T] + list(dims), order='F')\n",
     "Y = np.reshape(Yr, dims + (T,), order='F')\n",
-    "m_images = cm.movie(images)\n",
-    "\n",
-    "# correlation image\n",
+    "m_images = cm.movie(images)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Correlation Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "if m_images.shape[0] < 10000:\n",
     "    Cn = m_images.local_correlations(\n",
     "        swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n",
     "    Cn[np.isnan(Cn)] = 0\n",
     "else:\n",
+    "    #Saved as a tif file\n",
     "    Cn = np.array(cm.load(('/'.join(params_movie['gtname'][0].split('/')[:-2] + [\n",
-    "                  'projections', 'correlation_image_better.tif'])))).squeeze()\n",
+    "                  'projections', 'correlation_image_better.tif'])))).squeeze() \n",
     "\n",
     "plt.imshow(Cn, cmap='gray', vmax=.95)"
    ]
@@ -338,8 +352,15 @@
     "\n",
     "print(radius)\n",
     "print(roi_cons.shape)\n",
-    "pl.imshow(roi_cons.sum(0))\n",
-    "\n",
+    "plt.imshow(roi_cons.sum(0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "if params_movie['kernel'] is not None:  # kernel usually two\n",
     "    kernel = np.ones(\n",
     "        (radius // params_movie['kernel'], radius // params_movie['kernel']), np.uint8)\n",
@@ -349,24 +370,42 @@
     "\n",
     "A_in = np.reshape(roi_cons.transpose(\n",
     "    [2, 1, 0]), (-1, roi_cons.shape[0]), order='C')\n",
-    "pl.figure()\n",
+    "plt.figure()\n",
     "crd = plot_contours(A_in, Cn, thr=.99999)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Parameter Setting"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %% some parameter settings\n",
     "# order of the autoregressive fit to calcium imaging in general one (slow gcamps) or two (fast gcamps fast scanning)\n",
     "p = params_movie['p']\n",
     "# merging threshold, max correlation allowed\n",
-    "merge_thresh = params_movie['merge_thresh']\n",
-    "\n",
-    "# %% Extract spatial and temporal components on patches\n",
-    "# TODO: todocument\n",
+    "merge_thresh = params_movie['merge_thresh']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Extract spatial and temporal components on patches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "if images.shape[0] > 10000:\n",
     "    check_nan = False\n",
     "else:\n",
@@ -383,13 +422,17 @@
     "f = cnm.f\n",
     "snt = cnm.sn\n",
     "print(('Number of components:' + str(A.shape[-1])))\n",
-    "# %%\n",
-    "pl.figure()\n",
-    "# TODO: show screenshot 12`\n",
-    "# TODO : change the way it is used\n",
+    "plt.figure()\n",
     "crd = plot_contours(A, Cn, thr=params_display['thr_plot'])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Threshold Components "
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -400,10 +443,9 @@
     "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, :]), C[:, :], b, f, dims[0], dims[1],\n",
     "                 YrA=YrA[:, :], img=Cn)\n",
     "\n",
-    "#%%\n",
     "c, dview, n_processes = cm.cluster.setup_cluster(\n",
     "    backend='local', n_processes=None, single_thread=False)\n",
-    "#%% thredshold components\n",
+    "\n",
     "min_size_neuro = 3 * 2 * np.pi\n",
     "max_size_neuro = (2 * radius)**2 * np.pi\n",
     "A_thr = cm.source_extraction.cnmf.spatial.threshold_components(A.tocsc()[:, :].toarray(), dims, medw=None, thr_method='max', maxthr=0.2, nrgthr=0.99, extract_cc=True,\n",
@@ -415,37 +457,57 @@
     "                          & (size_neurons < max_size_neuro))[0]\n",
     "A_thr = A_thr[:, idx_size_neuro]\n",
     "print(A_thr.shape)\n",
-    "#%%\n",
+    "\n",
     "crd = plot_contours(scipy.sparse.coo_matrix(\n",
     "    A_thr * 1.), Cn, thr=.99, vmax=0.35)\n",
-    "#%%\n",
+    "\n",
     "roi_cons = np.load(params_movie['gtname'][0])\n",
     "print(roi_cons.shape)\n",
-    "pl.imshow(roi_cons.sum(0))\n",
-    "#%% compare CNMF sedded with ground truth\n",
-    "pl.figure(figsize=(30, 20))\n",
+    "plt.imshow(roi_cons.sum(0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compare CNMF Seeded with ground truth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(30, 20))\n",
     "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(roi_cons, A_thr[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]) * 1., thresh_cost=.7, min_dist=10,\n",
     "                                                                                                     print_assignment=False, plot_results=False, Cn=Cn, labels=['GT', 'Offline'])\n",
-    "pl.rcParams['pdf.fonttype'] = 42\n",
+    "plt.rcParams['pdf.fonttype'] = 42\n",
     "font = {'family': 'Myriad Pro',\n",
     "        'weight': 'regular',\n",
     "        'size': 20}\n",
-    "pl.rc('font', **font)\n",
-    "\n",
-    "#%%\n",
-    "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n",
-    "         tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n",
-    "         A_gt=A, C_gt=C, b_gt=b, f_gt=f, YrA_gt=YrA, d1=d1, d2=d2, idx_components_gt=idx_size_neuro[\n",
-    "             tp_comp],\n",
-    "         idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)\n"
+    "plt.rc('font', **font)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Generating match_masks.npz"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n",
+    "         tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n",
+    "         A_gt=A, C_gt=C, b_gt=b, f_gt=f, YrA_gt=YrA, d1=d1, d2=d2, idx_components_gt=idx_size_neuro[\n",
+    "             tp_comp],\n",
+    "         idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)"
+   ]
   }
  ],
  "metadata": {