{
"cells": [
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"from scipy.signal import find_peaks\n",
"def turn_trace_to_spk(data, min_hight_nspk = -50, min_hight_bspk = -20, prom_nspk = 10, prom_bspk = 30):\n",
"\n",
" ln_data = len(data)\n",
" dict_all = {}\n",
" peaks_nspk_all = []\n",
" peaks_bspk_all = []\n",
" properties_nspk_all = []\n",
" properties_bspk_all = []\n",
"\n",
" for i in range (0, ln_data):\n",
" x = np.squeeze(data[i])\n",
" peaks_nspk, properties_nspk = find_peaks(x, height=(min_hight_nspk, min_hight_bspk),prominence = prom_nspk)\n",
" peaks_bspk, properties_bspk = find_peaks(x, height=(min_hight_bspk),prominence = prom_bspk)\n",
" peaks_nspk_all.append(peaks_nspk)\n",
" peaks_bspk_all.append(peaks_bspk)\n",
" properties_nspk_all.append(properties_nspk)\n",
" properties_bspk_all.append(properties_bspk)\n",
"\n",
" dict_all['peaks_nspk_all'] = peaks_nspk_all\n",
" dict_all['peaks_bspk_all'] = peaks_bspk_all\n",
" dict_all['properties_nspk_all'] = properties_nspk_all\n",
" dict_all['properties_bspk_all'] = properties_bspk_all\n",
"\n",
" return dict_all\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from scipy.ndimage import gaussian_filter as gaus_filter\n",
"def make_spk_rate(data, len_vec, do_Nspk = True, do_Bspk = True, Method = 'reflect',Nspk_time_ms = 5, Bspk_time_ms = 8): \n",
" dict_all = {}\n",
" Bspk_number = np.zeros(len_vec)\n",
" Nspk_number = np.zeros(len_vec)\n",
" dt = 0.025\n",
" nr_iter = len(data['peaks_nspk_all'])\n",
" for i in range (0, nr_iter):\n",
" pk_nspk = data['peaks_nspk_all'][i]\n",
" pk_bspk = data['peaks_bspk_all'][i]\n",
" Nspk_number[pk_nspk] = Nspk_number[pk_nspk]+1\n",
" Bspk_number[pk_bspk] = Bspk_number[pk_bspk]+1\n",
" Bspk_rate = Bspk_number/nr_iter/(dt*0.001) \n",
" Nspk_rate = Nspk_number/nr_iter/(dt*0.001) \n",
"\n",
"# if gaus_positive_half:\n",
"# long_rate_Bspk = np.append([[Bspk_rate]],[[Bspk_rate]])\n",
"# stand_dev_Bspk = int(Bspk_time_ms/dt)\n",
"# x = np.arange(0,int(nr_std*stand_dev_Bspk)+1)\n",
"# gaussian_curve_Bspk = np.exp(-np.square(x/stand_dev_Bspk)/2)\n",
"# len_gauss = len(gaussian_curve_Bspk)\n",
"\n",
"# smoothed_Bspk = np.zeros(len_vec)\n",
"# indx = 0\n",
"# for jj in range(len(smoothed_Bspk)):\n",
"# smoothed_Bspk[jj] = np.sum(long_rate_Bspk[indx:indx+len_gauss]*gaussian_curve_Bspk) \n",
"# indx = indx+1\n",
"# smoothed_Bspk = smoothed_Bspk/np.sum(gaussian_curve_Bspk)\n",
"\n",
"# else:\n",
"# long_rate_Bspk = np.append([[Bspk_rate],[Bspk_rate]],[[Bspk_rate]])\n",
"# stand_dev_Bspk = int(Bspk_time_ms/dt)\n",
"# x = np.arange(int(-nr_std*stand_dev_Bspk),int(nr_std*stand_dev_Bspk)+1)\n",
"# gaussian_curve_Bspk = np.exp(-np.square(x/stand_dev_Bspk)/2)\n",
"# len_gauss = len(gaussian_curve_Bspk)\n",
"\n",
"# smoothed_Bspk = np.zeros(len_vec)\n",
"# indx = len_vec - int(nr_std*stand_dev_Bspk)\n",
"# for jj in range(len(smoothed_Bspk)):\n",
"# smoothed_Bspk[jj] = np.sum(long_rate_Bspk[indx:indx+len_gauss]*gaussian_curve_Bspk) \n",
"# indx = indx+1\n",
"# smoothed_Bspk = smoothed_Bspk/np.sum(gaussian_curve_Bspk)\n",
"\n",
" \n",
" smoothed_data_Nspk = gaus_filter(Nspk_rate, Nspk_time_ms/dt, order=0, mode=Method, truncate=4.0)\n",
" smoothed_data_Bspk = gaus_filter(Bspk_rate, Bspk_time_ms/dt, order=0, mode=Method, truncate=4.0)\n",
" dict_all['Bspk_number'] = Bspk_number\n",
" dict_all['Nspk_number'] = Nspk_number\n",
" dict_all['Bspk_rate_smoothed'] = smoothed_data_Bspk\n",
" dict_all['Nspk_rate_smoothed'] = smoothed_data_Nspk\n",
"\n",
" return dict_all"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def make_shaped_spk_rate(data, shape_data, do_Nspk = True, do_Bspk = True, Method = 'reflect',Nspk_time_ms = 5, Bspk_time_ms = 8): \n",
" dict_all = {}\n",
" dt = 0.025\n",
" nr_iter = shape_data[0]\n",
" len_data = shape_data[0]*shape_data[1]\n",
" mr_iter = shape_data[0]\n",
" Bspk_vec = np.zeros(len_data)\n",
" Nspk_vec = np.zeros(len_data)\n",
"\n",
" peaks_nspk_loc = data['peaks_nspk_all']\n",
" peaks_bspk_loc = data['peaks_bspk_all']\n",
" Nspk_vec[np.asarray(peaks_nspk_loc)] = 1\n",
" Bspk_vec[np.asarray(peaks_bspk_loc)] = 1\n",
" \n",
" Nspk_vec = np.reshape(Nspk_vec,shape_data)\n",
" Bspk_vec = np.reshape(Bspk_vec,shape_data)\n",
"\n",
" Nspk_number = np.sum(Nspk_vec, axis=0)\n",
" Bspk_number = np.sum(Bspk_vec, axis=0)\n",
" Bspk_rate = Bspk_number/nr_iter/(dt*0.001) \n",
" Nspk_rate = Nspk_number/nr_iter/(dt*0.001) \n",
"\n",
" smoothed_data_Nspk = gaus_filter(Nspk_rate, Nspk_time_ms/dt, order=0, mode=Method, truncate=4.0)\n",
" smoothed_data_Bspk = gaus_filter(Bspk_rate, Bspk_time_ms/dt, order=0, mode=Method, truncate=4.0)\n",
" dict_all['Bspk_number'] = Bspk_number\n",
" dict_all['Nspk_number'] = Nspk_number\n",
" dict_all['Bspk_rate_smoothed'] = smoothed_data_Bspk\n",
" dict_all['Nspk_rate_smoothed'] = smoothed_data_Nspk\n",
"\n",
" return dict_all"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mean_traces(data):\n",
" nr_iter = len(data)\n",
" sum_all = np.zeros(len(data[1]))\n",
" for i in range (0, nr_iter):\n",
" sum_all = sum_all + data[i]\n",
" mean_all = sum_all/nr_iter \n",
" return mean_all"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def trace_to_trials(data, eliminate_trials = 1, trial_length = 200, dt = 0.025):\n",
" \n",
" eliminate_ms = eliminate_trials*trial_length\n",
" elim_dt = int(eliminate_ms/dt)\n",
" trial_length_dt = int(trial_length/dt)\n",
" nr_trials = int((len(data) - elim_dt)*dt/trial_length)\n",
"\n",
" all_trials = []\n",
" idx = elim_dt\n",
" for ii in range(nr_trials):\n",
" trial = data[idx:idx+trial_length_dt]\n",
" all_trials.append(trial)\n",
" idx = idx+trial_length_dt\n",
" return all_trials, trial_length_dt;"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"def bin_data(data,eliminate_trials = 0,trial_length = 5):\n",
" vec_cut,leng = trace_to_trials(data, eliminate_trials = eliminate_trials,trial_length = trial_length)\n",
" one_trace = np.ones(np.shape(vec_cut)[1]).T[:,np.newaxis]\n",
" bin_trace = np.matmul(vec_cut,one_trace)\n",
" return bin_trace"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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