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localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test_wids.json

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+{"__kind__": "wids-shard-index-v1", "wids_version": 1, "shardlist": [{"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test/data-000000.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test/data-000001.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test/data-000002.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test/data-000003.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/test/data-000004.tar", "nsamples": 3247, "filesize": 758087680}], "name": "quickdraw-test"}

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/count.txt

@@ -0,0 +1 @@
+138051

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/spectrogram_stat.txt

@@ -0,0 +1,5 @@
+shape: (7, 65, 126)
+mean: -116.69683837890625
+std: 10.933423042297363
+min: -155.63348388671875
+max: -25.49519920349121

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train_wids.json

@@ -0,0 +1 @@
+{"__kind__": "wids-shard-index-v1", "wids_version": 1, "shardlist": [{"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000000.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000001.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000002.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000003.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000004.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000005.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000006.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000007.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000008.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000009.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000010.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000011.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000012.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000013.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000014.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000015.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000016.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000017.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000018.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000019.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000020.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000021.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000022.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000023.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000024.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000025.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000026.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/train/data-000027.tar", "nsamples": 3051, "filesize": 712325120}], "name": "quickdraw-train"}

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/count.txt

@@ -0,0 +1 @@
+23222

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/spectrogram_stat.txt

@@ -0,0 +1,5 @@
+shape: (7, 65, 126)
+mean: -117.12362670898438
+std: 11.01742172241211
+min: -155.63348388671875
+max: -27.76291275024414

localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val_wids.json

@@ -0,0 +1 @@
+{"__kind__": "wids-shard-index-v1", "wids_version": 1, "shardlist": [{"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/data-000000.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/data-000001.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/data-000002.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/data-000003.tar", "nsamples": 5000, "filesize": 1167370240}, {"url": "/home/wadhah.zai/projects/vibro_sense/datasets/localisation/filtered/processed_all_20KHz_nfft_128_split_material_wood_wd/val/data-000004.tar", "nsamples": 3222, "filesize": 752250880}], "name": "quickdraw-val"}

objects/dataset/main.py

@@ -0,0 +1,68 @@
+import csv
+import os
+import pandas as pd
+
+def cut_tittle(file_path):
+    lines = list()
+    Tittle = ['LECROYWS3024z', 'Segments', 'Segment', '#1']
+    with open(os.path.join(root, file), 'r') as readFile:
+        reader = csv.reader(readFile)
+        for row in reader:
+            lines.append(row)
+            for field in row:
+                for members in Tittle:
+                    if field == members:
+                        lines.remove(row)
+
+    with open(os.path.join(root, file), 'w') as writeFile:
+        writer = csv.writer(writeFile)
+        writer.writerows(lines)
+    df = pd.read_csv(os.path.join(root, file))
+    df.to_csv(os.path.join(root, file), index=False)
+
+def annex_data(file_path):
+    index_file = file.split(".")
+    index_file2 = file.split("_")
+    index_Folder = root.split('\\')
+    if index_file2[0] != 'Frq':
+        df_file = pd.read_csv(os.path.join(root, file))
+        df['Time'] = df_file['Time'].copy()
+        df['Ampl' + index_file[0]] = df_file['Ampl'].copy()
+    # saving the dataframe
+    if index_file2[0] != 'Frq':
+        #df.to_csv(root + "/" + "Frq_" + index_Folder[1] + "_" + index_Folder[2] + ".csv", index=False)
+        df.to_csv(root + "/" + "Frq_" + index_Folder[0] + "_" + index_Folder[1] + ".csv", index=False)
+
+def rename_file(file_path, cont):
+    old_name = os.path.join(root, file)
+
+    new_name = root + "/" + str(cont) + ".csv"
+    os.rename(old_name, new_name)
+
+#test = 'Frequency_Vibration'
+#test = 'Pattern_Test_2'
+#test = 'Pattern_Test'
+#test = 'Beam_Test'
+#test = 'Pattern_Test_3'
+#test = 'FingertipTest'
+#test = 'HoldAndSlide_Test'
+test = 'dataset'
+for root, subdirectories, files in os.walk(test):
+    for subdirectory in subdirectories:
+        print(os.path.join(root, subdirectory))
+    df = pd.DataFrame()
+    cont = 0
+    for file in files:
+        a = file.split('.')
+        if a[1] == 'csv':
+            print(os.path.join(root, file))
+            #rename_file(os.path.join(root, file), cont)
+            cut_tittle(os.path.join(root, file))
+            #annex_data(os.path.join(root, file))
+            print(root)
+            print(df.columns.values)
+            cont = cont + 1
+    del df
+    print(cont)
+
+print('end')

objects/dataset/object_49/Kinesthetics/Squeeze/Position_Current/object_0_49_2_HoldAndSqueze.csv

@@ -0,0 +1,7 @@
+Unnamed: 0,Pos_300,Cur_300,Pos_400,Cur_400,Pos_500,Cur_500,Pos_600,Cur_600,Pos_700,Cur_700,CurrentObject
+Thumb_Flextion,-23.43,0.0386231,-22.9,0.0611125,-23.25,0.0943576999999999,-16.4,0.1427588,-15.96,0.1936044,0.1462299899999999
+Index_Flexion,2.15,0.0371564,2.24,0.0611125,6.37,0.0968022,7.16,0.1349363999999999,11.47,0.1945822,0.1646126299999999
+Middle_Flexion,70.99,0.0371564,70.99,0.0611125,70.9,0.0958244,70.99,0.1403143,70.99,0.1896931999999999,0.17272837
+Ring_Litte_Flexion,37.93,0.0356897,38.02,0.0601347,38.11,0.0958244,38.37,0.1373809,57.36,0.1960489,0.20230682
+Wrist_Flexion,-89.98,0.0,-89.98,0.0166225999999999,-89.98,0.0156448,-89.98,0.0156448,-92.09,0.0,0.1763951199999999
+Thumb_Adduction,94.9,2.0230682,89.98,2.019157,89.98,0.0,89.98,0.0,91.91,2.0206237,0.16798604

objects/dataset/object_49/Kinesthetics/Squeeze/Position_Current/object_0_49_3_HoldAndSqueze.csv

@@ -0,0 +1,7 @@
+Unnamed: 0,Pos_300,Cur_300,Pos_400,Cur_400,Pos_500,Cur_500,Pos_600,Cur_600,Pos_700,Cur_700,CurrentObject
+Thumb_Flextion,-13.93,0.0386231,-13.49,0.0601347,-13.67,0.0943576999999999,-13.67,0.1364031,-2.07,0.1984934,0.13087853
+Index_Flexion,13.49,0.0371564,13.58,0.0601347,36.53,0.0943576999999999,39.78,0.1364031,63.08,0.1945822,0.15625244
+Middle_Flexion,56.22,0.0371564,56.57,0.0640459,64.31,0.0943576999999999,64.31,0.1373809,78.29,0.180893,0.1648081899999999
+Ring_Litte_Flexion,26.51,0.0356897,26.33,0.0601347,44.18,0.0968022,44.26,0.1364031,68.97,0.1945822,0.1828486
+Wrist_Flexion,-92.18,0.0386231,-89.98,0.0322674,-89.98,0.0166225999999999,-89.98,0.0205337999999999,-89.98,0.0156448,0.16221702
+Thumb_Adduction,95.78,2.0269794,89.98,2.0333351,89.98,2.0230682,89.98,2.0181792,89.98,2.0103568,0.1510701

objects/dataset/object_49/Kinesthetics/Squeeze/Position_Current/object_0_49_4_HoldAndSqueze.csv

@@ -0,0 +1,7 @@
+Unnamed: 0,Pos_300,Cur_300,Pos_400,Cur_400,Pos_500,Cur_500,Pos_600,Cur_600,Pos_700,Cur_700,CurrentObject
+Thumb_Flextion,-31.87,0.0371564,-31.43,0.0625792,-31.6,0.092891,-22.9,0.1373809,-22.64,0.1945822,0.14378549
+Index_Flexion,5.41,0.0386231,5.58,0.058668,5.67,0.0943576999999999,9.63,0.1388476,9.89,0.1945822,0.16627489
+Middle_Flexion,71.08,0.0371564,71.25,0.0611125,71.08,0.0958244,71.08,0.1388476,71.25,0.1896931999999999,0.1699905299999999
+Ring_Litte_Flexion,36.44,0.0356897,36.26,0.0601347,36.79,0.0958244,37.76,0.1373809,70.02,0.1984934,0.19780894
+Wrist_Flexion,-89.98,0.0229783,-89.98,0.0156448,-89.98,0.0356897,-89.98,0.0166225999999999,-89.98,0.0259117,0.1749284199999999
+Thumb_Adduction,94.46,2.0181792,89.98,2.0103568,89.98,0.0,91.91,2.0103568,91.91,2.0308906,0.16695935