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revision
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‎data/.DS_Store‎

14 KB
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‎data/Fig5_6/ttf_area.npy‎

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‎notebooks/Fig1_CCG.ipynb‎

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@@ -36,7 +36,7 @@
3636
"mouse_ID = '388523'\n",
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"\n",
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"# 1. load df\n",
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"basepath = '/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/example_388523/'\n",
39+
"basepath = '~/data/example_388523/'\n",
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"df_tmp = pd.read_csv(basepath+'mouse'+mouse_ID+'_cortex_meta.csv')\n",
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"df_tmp = df_tmp[df_tmp.FR>2]\n",
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"df_tmp = df_tmp.reset_index()\n",

‎notebooks/Fig1_clustering_method.ipynb‎

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@@ -18,8 +18,6 @@
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"plt.style.use('default')\n",
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"%matplotlib inline\n",
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"\n",
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"sys.path.append(\"/Users/xiaoxuanj/work/work_allen/Ephys/code_library/ephys_code\")\n",
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"\n",
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"import functional_clustering as fc\n"
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]
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},
@@ -37,7 +35,7 @@
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"outputs": [],
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"source": [
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"mouse_ID='388523'\n",
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"basepath = '/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/example_388523/'\n",
38+
"basepath = '~/data/example_388523/'\n",
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"X = np.load(basepath+'mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
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"df = pd.read_csv(basepath+'mouse'+mouse_ID+'_meta_cluster_RF.csv', index_col=0)\n",
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"\n",

‎notebooks/Fig2_weight_Fig3_distribution.ipynb‎

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@@ -17,13 +17,7 @@
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"import matplotlib.pyplot as plt\n",
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"plt.style.use('default')\n",
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"%matplotlib inline\n",
20-
"import seaborn as sns\n",
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"\n",
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"sys.path.append(\"/Users/xiaoxuanj/work/work_allen/Ephys/code_library/ephys_code/\")\n",
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"\n",
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"import data_loader as dl\n",
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"\n",
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"import get_layer_dict as gd\n"
20+
"import seaborn as sns\n"
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]
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},
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{
@@ -500,11 +494,11 @@
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"for idx, mouse_ID in enumerate(mouse_IDs):\n",
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" print(mouse_ID)\n",
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" # 1. load df\n",
503-
" X = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/CM/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
497+
" X = np.load('~/data/CM/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
504498
" # spontaneous\n",
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" #X = np.load('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/adjacency_matrix/RF_onscreen/mouse'+mouse_ID+'_adjacency_matrix_RF_spon.npy')\n",
506500
" \n",
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" df = pd.read_csv('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/meta_cluster_RF/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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" df = pd.read_csv('~/data/CM/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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" DF.append(df)\n",
509503
" print(len(df))\n",
510504
" N[idx, 0] = len(df[df.cluster==1])/len(df)\n",
@@ -988,15 +982,6 @@
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"layers.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.save('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/archive/layer_distribution.npy', layers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"layers = np.array(layers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"np.save('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/archive/area_distribution.npy', layers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
@@ -1425,10 +1401,7 @@
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"\n",
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"plt.title('$r_P$ = ' + str(np.around(pow(r_p,1),2)) + '; $P_P$ = ' + str(np.around(p_p,6)) + '\\n' + \\\n",
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" '$r_S$ = ' + str(np.around(pow(r_s,1),2)) + '; $P_S$ = ' + str(np.around(p_s,6)))\n",
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"\n",
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"#plt.savefig('/Users/xiaoxuanj/Documents/Paper_functional_connectivity/materials/driver_proportion_GHS.pdf')\n",
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"\n",
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"#plt.savefig('/Users/xiaoxuanj/Documents/presentations/COSYNE2020/talk/materials/proportion_HS_platform.pdf')"
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"\n"
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]
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},
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{
@@ -1653,7 +1626,7 @@
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"driven=[]\n",
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"\n",
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"for idx, mouse_ID in enumerate(mouse_IDs):\n",
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" df = pd.read_csv('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/meta_cluster_RF/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
1629+
" df = pd.read_csv('~/data/CM/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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" \n",
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" DF = df\n",
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" \n",
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"driven=[]\n",
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"\n",
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"for idx, mouse_ID in enumerate(mouse_IDs):\n",
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" df = pd.read_csv('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/meta_cluster_RF/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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" df = pd.read_csv('~/data/CM/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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" \n",
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" DF = df\n",
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" \n",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"et"
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]
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"source": []
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}
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],
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"metadata": {

‎notebooks/Fig3_div_conv.ipynb‎

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@@ -18,10 +18,7 @@
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"plt.style.use('default')\n",
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"%matplotlib inline\n",
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"import seaborn as sns\n",
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"\n",
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"sys.path.append(\"/Users/xiaoxuanj/work/work_allen/Ephys/code_library/ephys_code/\")\n",
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"\n",
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"import data_loader as dl\n"
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"\n"
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]
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},
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{
@@ -246,16 +243,8 @@
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" for mouse_ID in mouse_IDs:\n",
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" #print(mouse_ID)\n",
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" # 1. load RF on screen matrix and meta\n",
249-
" X = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
250-
" df = pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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"\n",
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" # 2. load full matrix with FR>2 (N is dominanted by weak connections)\n",
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" #X = np.load('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/adjacency_matrix/mouse'+mouse_ID+'_adjacency_matrix.npy')\n",
254-
" #df = pd.read_csv('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/adjacency_matrix/mouse'+mouse_ID+'_adjacency_matrix.csv')\n",
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"\n",
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" #from denoise_matrix import denoise_matrix\n",
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" #X, k, threshold = denoise_matrix(X, threshold=0.95, plot=True)\n",
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"\n",
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" X = np.load('~/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
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" df = pd.read_csv('~/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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"\n",
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" assert len(df)==np.shape(X)[0]\n",
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"\n",
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"for mouse_ID in mouse_IDs:\n",
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" print(mouse_ID)\n",
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" # 1. load RF on screen matrix and meta\n",
849-
" X = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
850-
" df = pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
838+
" X = np.load('~/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
839+
" df = pd.read_csv('~/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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"\n",
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" # 2. load full matrix with FR>2 (N is dominanted by weak connections)\n",
853842
" #X = np.load('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/adjacency_matrix/mouse'+mouse_ID+'_adjacency_matrix.npy')\n",

‎notebooks/Fig5_6_inout_index.ipynb‎

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"import matplotlib.pyplot as plt\n",
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"plt.style.use('default')\n",
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"%matplotlib inline\n",
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"import seaborn as sns\n",
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"\n",
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"sys.path.append(\"/Users/xiaoxuanj/work/work_allen/Ephys/code_library/ephys_code/\")\n",
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"\n",
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"import data_loader as dl\n",
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"\n",
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"import get_layer_dict as gd\n"
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"import seaborn as sns\n"
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]
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},
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{
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" for mouse_ID in mouse_IDs:\n",
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" print(mouse_ID)\n",
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" # 1. load df\n",
947-
" X = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
941+
" X = np.load('~/data/Fig3/mouse'+mouse_ID+'_adjacency_matrix_RF.npy')\n",
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"\n",
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" #from denoise_matrix import denoise_matrix\n",
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" #X, k, threshold = denoise_matrix(X, threshold=0.95, plot=True)\n",
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"\n",
952-
" df = pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
946+
" df = pd.read_csv('~/data/Fig3/mouse'+mouse_ID+'_meta_cluster_RF.csv')\n",
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"\n",
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" # get unique depth for each probe\n",
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" depth={}\n",
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"source": [
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"# save all \n",
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"AMO_all = np.array(AMO_all)\n",
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"np.save('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/inout_index_threshold.npy', AMO_all)\n",
1014-
"np.save('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/thresholds.npy', thresholds)\n"
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"np.save('~/data/Fig5_6/inout_index_threshold.npy', AMO_all)\n",
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"np.save('~/data/Fig5_6/thresholds.npy', thresholds)\n"
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]
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},
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{
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}
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],
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"source": [
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"AMO_all = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/inout_index_threshold.npy')\n",
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"AMO_all = np.load('~/data/Fig5_6/inout_index_threshold.npy')\n",
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"AMO_all.shape"
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]
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},
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"#X = [ -0.357, -0.059, -0.093, 0.152,0.327, 0.441] # platform paper\n",
12081202
"X = [-0.50149, -0.13929, -0.12294, -0.00431, 0.11828, 0.29330] # CC-CT-TC global HS from Harris\n",
12091203
"\n",
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"#for t in ids:\n",
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" #[AMO, probe_reorder] = np.load('/Users/xiaoxuanj/work/work_allen/Ephys/processed_data/inout_index/threshold'+t+'.npy', allow_pickle=True)\n",
1212-
"\n",
12131204
"for idx in range(6):\n",
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" AMO = AMO_all[idx]\n",
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" plt.figure(figsize=(3,3))\n",

‎notebooks/Fig5_TTF_area.ipynb‎

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‎notebooks/Fig7_psth.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"df_new = pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/population_psth_properties_RF_alltrials_removedlow_corrected.csv')\n"
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"df_new = pd.read_csv('~/data/Fig7/population_psth_properties_RF_alltrials_removedlow_corrected.csv')\n"
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{
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" yerr = np.nanstd(tmp3)\n",
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" g.ax_joint.errorbar(x,y, xerr=xerr, yerr=yerr, ecolor='k', fmt='o', mfc='white', capthick=1)\n",
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" \n",
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"g.set_axis_labels('First peak time (cluster 2)', 'First peak time (cluster 3)', fontsize=14)\n",
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"\n",
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"g.set_axis_labels('First peak time (cluster 2)', 'First peak time (cluster 3)', fontsize=14)\n"
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"mouseID='412804'\n",
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"\n",
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"# 1. load spikes\n",
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"#basepath = '/Volumes/local1/work_allen/Ephys/mouse'+mouseID\n",
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"basepath = '/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/example_psth/'\n",
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"basepath = '~/data/example_psth/'\n",
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"\n",
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"# spikes for preferred condition\n",
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"spikes_pref = np.load(basepath+'psth_pref_trial.npy')\n",

‎notebooks/modular_paper_anova_stats.ipynb‎

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"metadata": {},
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"outputs": [],
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"source": [
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"df_psth=pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/population_psth_properties_RF_alltrials_removedlow_corrected.csv', index_col=0)"
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"df_psth=pd.read_csv('~/data/Fig7/population_psth_properties_RF_alltrials_removedlow_corrected.csv', index_col=0)"
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"metadata": {},
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"outputs": [],
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"df_mi = pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/modulation_index_individual.csv', index_col=0)"
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"df_mi = pd.read_csv('~/data/Fig4/modulation_index_individual.csv', index_col=0)"
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{
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"metadata": {},
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"outputs": [],
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"df=pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/div_conv_threshold0.csv', index_col=0)"
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"df=pd.read_csv('~/data/Fig3/div_conv_threshold0.csv', index_col=0)"
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"metadata": {},
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"outputs": [],
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"source": [
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"df=pd.read_csv('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/div_conv_shuffle_nothreshold.csv', index_col=0)"
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"df=pd.read_csv('~/data/Fig3/div_conv_shuffle_nothreshold.csv', index_col=0)"
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"metadata": {},
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"outputs": [],
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"layers = np.load('/Users/xiaoxuanj/Dropbox/2019 information_flow_paper/Neuron submission/second_submission/data/layer_distribution.npy')"
767+
"layers = np.load('~/data/Fig3/layer_distribution.npy')"
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]
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},
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{

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