manual_glassbrain.ipynb

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   "execution_count": 1,
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   "source": [
    "from pathlib import Path\n",
    "from os import path\n",
    "import sys\n",
    "\n",
    "from ci_lib.utils import snakemake_tools\n",
    "from ci_lib.features import Features, Means, Raws, Covariances, AutoCovariances, Moup, AutoCorrelations, Feature_Type\n",
    "from ci_lib.plotting import graph_circle_plot, plot_glassbrain_bokeh\n",
    "from ci_lib import DecompData\n",
    "from ci_lib.feature_selection import RFE_pipeline\n",
    "\n",
    "from ci_lib.networks import construct_network, construct_network_from_feat, add_bokeh_attributes\n",
    "\n",
    "import numpy as np\n",
    "import networkx as nx"
   ]
  },
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   "execution_count": 2,
   "metadata": {
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    "pycharm": {
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   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "df hashes do not match\n"
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      "Graph with 64 nodes and 2080 edges\n",
      "Graph with 64 nodes and 2080 edges\n",
      "{'weight': 0.010082147352364764, 'selected': True, 'node_size': 25, 'color': '#450457'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "from ci_lib.networks.construct_network import construct_network_from_feat\n",
    "\n",
    "\n",
    "n_comps = 64\n",
    "edges = list(range(int(n_comps*(n_comps+1)/2))) #range(row*n_comps,(row+1)*n_comps))\n",
    "\n",
    "resl_path = Path(path.abspath('')) /'results'\n",
    "#svd_path = Path(resl_path/\"GN06_2021-03-26_10-53-05/anatomical_ROI~['VISp-R,VISrl-R,SSp-bfd-R,MOs-R,VISp-L,VISrl-L,SSp-bfd-L,MOs-L']/data.h5\")\n",
    "svd_path = Path(resl_path/\"GN06_2021-03-26_10-53-05/anatomical_ROI~[]/data.h5\")\n",
    "\n",
    "parcellation = DecompData.load(svd_path)\n",
    "print(parcellation.spatial_labels)\n",
    "\n",
    "#feat_path= Path(resl_path/\"GN06_2021-03-26_10-53-05/anatomical_ROI~['VISp-R,VISrl-R,SSp-bfd-R,MOs-R,VISp-L,VISrl-L,SSp-bfd-L,MOs-L']/All/Features/tactile/autocovariance_timelags~[1]/features.h5\")\n",
    "feat_path= Path(resl_path/\"GN06_2021-03-26_10-53-05/anatomical_ROI~[]/All/Features/tactile/autocovariance_timelags~[1]/features.h5\")\n",
    "\n",
    "feat = Moup.load(feat_path)\n",
    "print(feat.feature.shape)\n",
    "\n",
    "feat_val = np.mean(feat.feature[0,:,:,:],axis=0).flatten()\n",
    "\n",
    "cutted_labels=parcellation.spatial_labels[:n_comps] if parcellation.spatial_labels is not None else None\n",
    "\n",
    "#max=0.02\n",
    "#feat_val = np.clip(feat_val,a_min=0,a_max=max)\n",
    "#feat_val = feat_val / max\n",
    "feat_val =  feat_val / np.max(feat_val)\n",
    "np.set_printoptions(suppress = True)\n",
    "print(np.asarray(feat_val))\n",
    "\n",
    "#feat_padded = np.zeros((n_comps,n_comps))\n",
    "#feat_padded[list_best_feat]=feat_val\n",
    "\n",
    "\n",
    "print(\"feat_val\")\n",
    "#print(feat_padded)\n",
    "#rfe_graph = construct_network(edges, n_nodes = n_comps, feat_type = Feature_Type.UNDIRECTED, edge_weight=None)# 0.01+ 99*feat_val/100)\n",
    "#nx.set_edge_attributes(rfe_graph, edge_attrs, \"edge_color\")\n",
    "\n",
    "print(\"new f\")\n",
    "rfe_graph_2 = construct_network_from_feat(edges, n_comps, feat_type = Feature_Type.UNDIRECTED, feat_val = feat_val)\n",
    "\n",
    "rfe_graph = add_bokeh_attributes(rfe_graph_2)\n",
    "print(rfe_graph)\n",
    "print(rfe_graph_2)\n",
    "print(rfe_graph_2.nodes[0])\n",
    "\n",
    "\n",
    "plot_glassbrain_bokeh(graph=rfe_graph,components_spatials=parcellation.spatials,components_labels=cutted_labels,save_path=resl_path/\"glassbrain.html\",small_file=True)\n",
    "\n"
   ]
  }
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