feat: add file to present data

Present_data.py

@@ -13,14 +13,56 @@ def plot_arrays(N, y):
     plt.plot(N, np.abs(y))
     plt.show()
 
+def plot_compare_two_df(df_old, df_new):
+    x = get_xory_from_df('x', df_old)
+    y1 = get_xory_from_df('y', df_old)
+    y2 = get_xory_from_df('y', df_new)
 
-# DATA FUNCTIONS
+    figure, axis = plt.subplots(1, 2)
-
+    axis[0].plot(x, y1)
-def get_data()
+    axis[0].set_title('Original data')
+    axis[1].plot(x, y2)
+    axis[1].set_title('Analyzed data')
+    plt.show()
 
 
+# DATA FUNCTIONS:
 
+# The CSV_handler takes in data_type, but only for visuals. 
+# E.g. handler = CSV_handler('soft')
+
+# Loads in data. Choose data_type: hard, hardPP, soft og softPP as str. Returns None
+def load_data(csv_handler:CSV_handler, data_type):
+    switcher = {
+                'hard': csv_handler.load_hard_original_emg_data(),
+                'hardPP':csv_handler.load_hard_PP_emg_data(),
+                'soft':csv_handler.load_soft_original_emg_data(),
+                'softPP':csv_handler.load_soft_PP_emg_data(),
+                }
+    return switcher.get(data_type)
+
+# Retrieved data. Send in loaded csv_handler and data detailes you want. Returns DataFrame
+def get_data(csv_handler:CSV_handler, subject_nr, which_arm, session, emg_nr):
+    data_frame = csv_handler.get_df_from_data_dict(subject_nr, which_arm, session, emg_nr)
+    return data_frame
+    
+
+# MAIN:
 
 def main():
 
+    csv_handler = CSV_handler('hard')
+    load_data(csv_handler, 'hard')
+    data_frame = get_data(csv_handler, 1, 'left', 1, 1)
+
+    N_trans, cA, cD = wavelet_db4_denoising(data_frame)
+    data_frame_freq = make_df_from_xandy(N_trans, cA, 1)
+
+    cA_filt, cD_filt = soft_threshold_filter(cA, cD, 0.4)
+    data_frame_freq_filt = make_df_from_xandy(N_trans, cA_filt, 1)
+
+    plot_compare_two_df(data_frame_freq, data_frame_freq_filt)
+
     return None
+
+main()