Update

.ipynb_checkpoints/CCM-checkpoint.py

@@ -91,6 +91,7 @@ def CCM_trace_save(data, name):
     
     """
     import numpy as np
+    import os
     import h5py
 
     #rename for kEDM processing
@@ -119,14 +120,15 @@ def ccm_stats(file, mode):
     Inputs:
         file (str): filename - should be a '-CCMxmap.h5' file
         mode (str): what data type you want:
+                        'ccm' = matrix of ccm values - rows = effects of a timeseries, columns = causes of a timeseries (ie. cell xy = to what extent does x cause y; cell yx = to what extent does y cause x).  
                         'c_to_sz' = cells that drive the seizure
                         'sz_to_c' = cells that are driven by the seizure
                         'e' = embedding dimension of each cell
                         'rd_cause' = non-linear causing neurons - mean rhodiff of neurons that are caused by neuron of interest
                         'rd_int' =  #non-linear integration - mean rhodiff of neurons that cause neuron of interest
                         
     Returns:
-            (np array): 1d vector of interest (length = n cells) containing ccm stats
+            (np array): np array of interest (length = n cells) containing ccm stats
     
     
     """      
@@ -135,10 +137,15 @@ def ccm_stats(file, mode):
     import numpy as np
     data = h5py.File(file)
 
-    if mode != 'c_to_sz' and mode != 'sz_to_c' and mode != 'e' and mode != 'rd_cause' and mode != 'rd_int':
+    if mode != 'c_to_sz' and mode != 'sz_to_c' and mode != 'e' and mode != 'rd_cause' and mode != 'rd_int' and mode != 'ccm':
         print('Mode name does not match options')
         return()
 
+    if mode == 'ccm':
+        ccm = np.array(data['ccm']) 
+        ccm = ccm[1:,1:] #Remove mean trace
+        return(ccm)
+
     if mode == 'c_to_sz':
         ccm = np.array(data['ccm']) 
         c_to_sz = ccm[1:,0] #cells that drive the seizure
@@ -155,14 +162,14 @@ def ccm_stats(file, mode):
 
     else:
         rd_m = data['rhodiff'][1:,1:] #remove seizure values
-        np.fill_diagonal(rd_m,0) #remove self-ccm values 
+        np.fill_diagonal(rd_m,np.nan) #remove self-ccm values 
 
         if mode == 'rd_cause':
-            rd_cause = np.apply_along_axis(np.mean,1,rd_m) #non-linear causing - mean rhodiff of neurons that are caused by neuron of interest
+            rd_cause = np.apply_along_axis(np.nanmean,1,rd_m) #non-linear causing - mean rhodiff of neurons that are caused by neuron of interest
             return(rd_cause)
 
         if mode == 'rd_int':
-            rd_int = np.apply_along_axis(np.mean,0,rd_m) #non-linear integration - mean rhodiff of neurons that cause neuron of interest
+            rd_int = np.apply_along_axis(np.nanmean,0,rd_m) #non-linear integration - mean rhodiff of neurons that cause neuron of interest
             return(rd_int)
 
 

.ipynb_checkpoints/CCM_process-checkpoint.ipynb

@@ -2,16 +2,16 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'3.7.6 (default, Jan  8 2020, 13:42:34) \\n[Clang 4.0.1 (tags/RELEASE_401/final)]'"
+       "'3.7.4 (default, Aug 13 2019, 15:17:50) \\n[Clang 4.0.1 (tags/RELEASE_401/final)]'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -98,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
        "['datNeuralSPIKFiltAndStim.npy']"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,43 +117,79 @@
     "#CCM_trace_save function renames and saves trace data as: time x cells, and \n",
     "# then saves the trace in the correct orientation in h5 format\n",
     "\n",
-    "tr_l = adfn.return_files(Fdata, 'PTZ-WILDTYPE-CCM/kostas', '*.npy*')\n",
+    "tr_l = adfn.return_files(Fdata, 'PTZ-WILDTYPE-CCM/kostas', '*dat*.npy*')\n",
     "tr_l"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(13670, 6336)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.load(tr_l[0]).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%autoreload"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "('datNeuralSPIKFiltAndStim.npy',\n",
-       " 'KOSTAS_220315-01_2photon_sess-01-6dpf_run-01_trace')"
+       " 'KOSTAS_220628-800_2photon_sess-01-6dpf_run-01_trace')"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#NB - must have datatype after 'run-XX_'\n",
     "data = tr_l[0]\n",
-    "name = 'KOSTAS_220315-01_2photon_sess-01-6dpf_run-01_trace'\n",
+    "name = 'KOSTAS_220628-800_2photon_sess-01-6dpf_run-01_trace'\n",
     "data, name"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Save \n",
     "cfn.CCM_trace_save(data, name)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -172,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,