added CSI_doppler_plot_activities.py

Python_code/CSI_doppler_plot_activities.py

@@ -0,0 +1,83 @@
+
+import argparse
+import numpy as np
+import pickle
+from os import listdir
+from plots_utility import plt_fft_doppler_activities, plt_fft_doppler_activities_compact, \
+    plt_fft_doppler_activity_single, plt_fft_doppler_activities_compact_2
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('dir', help='Directory of data')
+    parser.add_argument('sub_dir', help='Sub directory of data')
+    parser.add_argument('feature_length', help='Length along the feature dimension (height)', type=int)
+    parser.add_argument('sliding', help='Number of packet for sliding operations', type=int)
+    parser.add_argument('labels_activities', help='Files names')
+    parser.add_argument('start_plt', help='Start index to plot', type=int)
+    parser.add_argument('end_plt', help='End index to plot', type=int)
+
+    args = parser.parse_args()
+
+    activities = np.asarray(['empty', 'sitting', 'walking', 'running', 'jumping'])
+
+    feature_length = args.feature_length
+    sliding = args.sliding
+    Tc = 6e-3
+    fc = 5e9
+    v_light = 3e8
+
+    exp_dir = args.dir + args.sub_dir + '/'
+
+    labels_activities = args.labels_activities
+    csi_label_dict = []
+    for lab_act in labels_activities.split(','):
+        csi_label_dict.append(lab_act)
+
+    traces_activities = []
+    for ilab in range(len(csi_label_dict)):
+        names = []
+        all_files = listdir(exp_dir)
+        activity = csi_label_dict[ilab]
+
+        start_l = 4
+        end_l = start_l + len(csi_label_dict[ilab])
+        for i in range(len(all_files)):
+            if all_files[i][start_l:end_l] == csi_label_dict[ilab] and all_files[i][-5] != 'p':
+                names.append(all_files[i][:-4])
+
+        names.sort()
+
+        stft_antennas = []
+        for name in names:
+            name_file = exp_dir + name + '.txt'
+
+            with open(name_file, "rb") as fp:  # Pickling
+                stft_sum_1 = pickle.load(fp)
+
+            stft_sum_1_log = 10*np.log10(stft_sum_1)
+
+            stft_sum_1_log = stft_sum_1_log[args.start_plt:min(stft_sum_1_log.shape[0], args.end_plt), :]
+
+            stft_antennas.append(stft_sum_1_log)
+
+        traces_activities.append(stft_antennas)
+
+    name_p = './plots/csi_doppler_activities_' + args.sub_dir + '.pdf'
+    delta_v = round(v_light / (Tc * fc * feature_length), 3)
+    antenna = 0
+    plt_fft_doppler_activities(traces_activities, antenna, activities, sliding, delta_v, name_p)
+
+    name_p = './plots/csi_doppler_activities_' + '_' + args.sub_dir + '_compact.pdf'
+    plt_fft_doppler_activities_compact(traces_activities, antenna, activities, sliding, delta_v, name_p)
+
+    traces_activities_reduced = traces_activities
+    del traces_activities_reduced[2]
+    name_p = './plots/csi_doppler_activities_' + '_' + args.sub_dir + '_compact_2.pdf'
+    plt_fft_doppler_activities_compact_2(traces_activities_reduced, antenna,
+                                         np.asarray(['empty', 'sitting', 'running', 'jumping']),
+                                         sliding, delta_v, name_p)
+
+    name_p = './plots/csi_doppler_single_act.pdf'
+    antenna = 1
+    plt_fft_doppler_activity_single(traces_activities[4], antenna, sliding, delta_v, name_p)

Python_code/plots_utility.py

@@ -604,3 +604,161 @@ def plt_phase(phase_raw, phase_proc, name_plot):
         axi.label_outer()
     plt.savefig(name_plot, bbox_inches='tight')
     plt.close()
+
+
+def plt_fft_doppler_activities(doppler_spectrum_list, antenna, csi_label_dict, sliding_lenght, delta_v, name_plot):
+    fig = plt.figure(constrained_layout=True)
+    fig.set_size_inches(15, 3)
+    widths = [1, 1, 1, 1, 1, 0.5]
+    heights = [1]
+    gs = fig.add_gridspec(ncols=6, nrows=1, width_ratios=widths, height_ratios=heights)
+    step = 20
+    step_x = 5
+    ax = []
+    for a_i in range(5):
+        act = doppler_spectrum_list[a_i][antenna]
+        length_v = mt.floor(act.shape[1] / 2)
+        factor_v = step * (mt.floor(length_v / step))
+        ticks_y = np.arange(length_v - factor_v, length_v + factor_v + 1, step)
+        ticks_x = np.arange(0, act.shape[0] + 1, int(act.shape[0]/step_x))
+
+        ax1 = fig.add_subplot(gs[(0, a_i)])
+        plt1 = ax1.pcolormesh(act.T, cmap='viridis', linewidth=0, rasterized=True)  # , shading='gouraud')
+        plt1.set_edgecolor('face')
+        ax1.set_ylabel(r'$v_p \cos \alpha_p$ [m/s]')
+        ax1.set_xlabel(r'time [s]')
+        ax1.set_yticks(ticks_y + 0.5)
+        ax1.set_yticklabels(np.round((ticks_y - length_v) * delta_v, 2))
+        ax1.set_xticks(ticks_x)
+        ax1.set_xticklabels(np.round(ticks_x * sliding_lenght * 6e-3, 2))
+
+        title_p = csi_label_dict[a_i]
+        ax1.set_title(title_p)
+        ax.append(ax1)
+
+        if a_i == 4:
+            cbar_ax = fig.add_axes([0.92, 0.2, 0.01, 0.7])
+            cbar1 = fig.colorbar(plt1,  cax=cbar_ax, ticks=[-12, -8, -4, 0])
+            cbar1.ax.set_ylabel('normalized power [dB]')
+
+    for axi in ax:
+        axi.label_outer()
+    plt.savefig(name_plot, bbox_inches='tight')
+    plt.close()
+
+
+def plt_fft_doppler_activities_compact(doppler_spectrum_list, antenna, csi_label_dict, sliding_lenght, delta_v, name_plot):
+    fig = plt.figure(constrained_layout=True)
+    fig.set_size_inches(8, 5.5)
+    widths = [1, 1, 1, 1, 1, 1, 0.5]
+    heights = [1, 1]
+    gs = fig.add_gridspec(ncols=7, nrows=2, width_ratios=widths, height_ratios=heights)
+    step = 20
+    step_x = 5
+    ax = []
+    list_plts_pos_row = [0, 0, 1, 1, 1]
+    list_plts_pos_col_start = [1, 3, 0, 2, 4]
+    list_plts_pos_col_end = [3, 5, 2, 4, 6]
+    for a_i in range(5):
+        act = doppler_spectrum_list[a_i][antenna]
+        length_v = mt.floor(act.shape[1] / 2)
+        factor_v = step * (mt.floor(length_v / step))
+        ticks_y = np.arange(length_v - factor_v, length_v + factor_v + 1, step)
+        ticks_x = np.arange(0, act.shape[0] + 1, int(act.shape[0]/step_x))
+
+        ax1 = fig.add_subplot(gs[list_plts_pos_row[a_i], list_plts_pos_col_start[a_i]:list_plts_pos_col_end[a_i]])
+        plt1 = ax1.pcolormesh(act.T, cmap='viridis', linewidth=0, rasterized=True)  # , shading='gouraud')
+        plt1.set_edgecolor('face')
+        ax1.set_xlabel(r'time [s]')
+        ax1.set_yticks(ticks_y + 0.5)
+        if a_i == 0 or a_i == 2:
+            ax1.set_yticklabels(np.round((ticks_y - length_v) * delta_v, 2))
+            ax1.set_ylabel(r'$v_p \cos \alpha_p$ [m/s]')
+        else:
+            ax1.set_yticklabels([])
+            ax1.set_ylabel('')
+        ax1.set_xticks(ticks_x)
+        ax1.set_xticklabels(np.round(ticks_x * sliding_lenght * 6e-3, 2))
+
+        title_p = csi_label_dict[a_i]
+        ax1.set_title(title_p)
+        ax.append(ax1)
+
+        if a_i == 1 or a_i == 4:
+            cbar_ax = fig.add_axes([0.98, 0.2, 0.02, 0.7])
+            cbar1 = fig.colorbar(plt1,  cax=cbar_ax, ticks=[-12, -8, -4, 0])
+            cbar1.ax.set_ylabel('normalized power [dB]')
+
+    plt.savefig(name_plot, bbox_inches='tight')
+    plt.close()
+
+
+def plt_fft_doppler_activities_compact_2(doppler_spectrum_list, antenna, csi_label_dict, sliding_lenght,
+                                         delta_v, name_plot):
+    fig = plt.figure(constrained_layout=True)
+    fig.set_size_inches(6, 5.5)
+    widths = [1, 1, 0.5]
+    heights = [1, 1]
+    gs = fig.add_gridspec(ncols=3, nrows=2, width_ratios=widths, height_ratios=heights)
+    step = 20
+    step_x = 5
+    ax = []
+    list_plts_pos_row = [0, 0, 1, 1]
+    list_plts_pos_col = [0, 1, 0, 1]
+    for a_i in range(4):
+        act = doppler_spectrum_list[a_i][antenna]
+        length_v = mt.floor(act.shape[1] / 2)
+        factor_v = step * (mt.floor(length_v / step))
+        ticks_y = np.arange(length_v - factor_v, length_v + factor_v + 1, step)
+        ticks_x = np.arange(0, act.shape[0] + 1, int(act.shape[0]/step_x))
+
+        ax1 = fig.add_subplot(gs[list_plts_pos_row[a_i], list_plts_pos_col[a_i]])
+        plt1 = ax1.pcolormesh(act.T, cmap='viridis', linewidth=0, rasterized=True)  # , shading='gouraud')
+        plt1.set_edgecolor('face')
+        ax1.set_xlabel(r'time [s]')
+        ax1.set_yticks(ticks_y + 0.5)
+        if a_i == 0 or a_i == 2:
+            ax1.set_yticklabels(np.round((ticks_y - length_v) * delta_v, 2))
+            ax1.set_ylabel(r'$v_p \cos \alpha_p$ [m/s]')
+        else:
+            ax1.set_yticklabels([])
+            ax1.set_ylabel('')
+        ax1.set_xticks(ticks_x)
+        ax1.set_xticklabels(np.round(ticks_x * sliding_lenght * 6e-3, 2))
+
+        title_p = csi_label_dict[a_i]
+        ax1.set_title(title_p)
+        ax.append(ax1)
+
+        if a_i == 1:
+            cbar_ax = fig.add_axes([0.9, 0.2, 0.03, 0.7])
+            cbar1 = fig.colorbar(plt1,  cax=cbar_ax, ticks=[-12, -8, -4, 0])
+            cbar1.ax.set_ylabel('normalized power [dB]')
+
+    plt.savefig(name_plot, bbox_inches='tight')
+    plt.close()
+
+
+def plt_fft_doppler_activity_single(input_a, antenna, sliding_lenght, delta_v, name_plot):
+    fig = plt.figure(constrained_layout=True)
+    fig.set_size_inches(3, 3)
+    step = 20
+    step_x = 5
+    act = input_a[antenna][:340, :]
+    length_v = mt.floor(act.shape[1] / 2)
+    factor_v = step * (mt.floor(length_v / step))
+    ticks_y = np.arange(length_v - factor_v, length_v + factor_v + 1, step)
+    ticks_x = np.arange(0, act.shape[0] + 1, int(act.shape[0]/step_x))
+
+    ax1 = fig.add_subplot()
+    plt1 = ax1.pcolormesh(act.T, cmap='viridis', linewidth=0, rasterized=True)  # , shading='gouraud')
+    plt1.set_edgecolor('face')
+    ax1.set_ylabel(r'$v_p \cos \alpha_p$ [m/s]')
+    ax1.set_xlabel(r'time [s]')
+    ax1.set_yticks(ticks_y + 0.5)
+    ax1.set_yticklabels(np.round((ticks_y - length_v) * delta_v, 2))
+    ax1.set_xticks(ticks_x)
+    ax1.set_xticklabels(np.round(ticks_x * sliding_lenght * 6e-3, 1))
+
+    plt.savefig(name_plot, bbox_inches='tight')
+    plt.close()

README.md

@@ -59,12 +59,17 @@ python CSI_doppler_computation.py <'directory of the reconstructed data'> <'sub-
 ```
 e.g., python CSI_doppler_computation.py ./processed_phase/ S1a,S1b,S1c,S2a,S2b,S3a,S4a,S4b,S5a,S6a,S6b,S7a ./doppler_traces/ 800 800 31 1 -1.2
 
-To plot the Doppler traces use 
+To plot the Doppler traces use (first to plot all the antennas, second single antenna for all the activities) 
 ```bash
 python CSI_doppler_plots_antennas.py <'directory of the reconstructed data'> <'sub-directory of data'> <'length along the feature dimension (height)'> <'sliding length'> <'labels of the activities to be considered'> <'last index to plot'>
 ```
 e.g., python CSI_doppler_plots_antennas.py ./doppler_traces/ S7a 100 1 E,L1,W,R,J1 20000
 
+```bash
+python CSI_doppler_plots_activities.py <'directory of the reconstructed data'> <'sub-directory of data'> <'length along the feature dimension (height)'> <'sliding length'> <'labels of the activities to be considered'> <'first index to plot'> <'last index to plot'>
+```
+e.g., python CSI_doppler_plots_activities.py ./doppler_traces/ S7a 100 1 E,L1,W,R,J1 570 1070
+
 #### Pre-computed Doppler traces
 If you want to skip the above processing steps, you can find the Doppler traces [in this Google Drive folder](https://drive.google.com/drive/folders/1SilO6VD73Lz8sjZ-KQgFnQ2IKRvggqPg?usp=sharing). In the same folder, the sanitized channel measurements for S2a and S7a are uploaded as examples in ```processed_phase```. Exaples of plots of the Doppler traces are also included.