'plotter.py' updaten

Complete description

Documents/Master/20-21 Semester 1/AcA Lamoth Data-analysis/Scripts/formules.py

@@ -1,135 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jun 16 09:34:35 2021
-
-@author: -
-"""
-import os
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from datetime import timedelta
-from scipy.optimize import curve_fit
-
-# Formulas
-
-def ReadCSV(filename):
-    df = pd.read_csv(filename, names=['Datetime', 'Acc X','Acc Y', 'Acc Z'], infer_datetime_format=True)
-    df['Datetime'] = pd.to_datetime(df['Datetime'])
-    df['Date'] = [d.date() for d in df['Datetime']]
-    df = df.reindex(columns=['Datetime','Date','Time','Acc X','Acc Y', 'Acc Z'])
-    return df
-
-def CreateDays(x, filename, path):
-    
-    savename = filename.replace('.csv','')
-    savepath = path + savename
-    os.makedirs(savepath)
-    os.chdir(savepath)
-    
-    startdate = x['Date'].iloc[0]
-    week = range(1,8)
-    
-    for i in week:
-        weekdayindex = i-1
-        
-        day = startdate + timedelta(days=weekdayindex)
-        daydate = x['Date'] == startdate + timedelta(days=weekdayindex)
-        dataday = x[daydate]
-        totalweek = {day:dataday}
-        
-        savefile = totalweek[day]
-        varname = filename.replace('.csv','-') + str(day) + '.csv'
-        savefile.to_csv(varname)
-        
-        print(varname +' saved')
-    
-    return(totalweek)
-
-
-def SVMEpoch(DF,ResampRate, ResampData):
-    newDF = pd.DataFrame(DF)
-    newDF['X2'] = np.power(newDF['Acc X'], 2)
-    newDF['Y2'] = np.power(newDF['Acc Y'], 2)
-    newDF['Z2'] = np.power(newDF['Acc Z'], 2)
-    newDF['SVM'] = np.sqrt(newDF[['X2', 'Y2', 'Z2']].sum(axis=1))
-    newDF['Datetime'] = pd.to_datetime(newDF['Datetime'])
-   
-    EpochSVM = newDF.resample(ResampRate, on = ResampData).mean()
-    return(newDF, EpochSVM)
-
-def func(x, a, b, c):
-    return a * np.exp(-b*x) + c
-
-def SlopeWeeker(Keylist, Dict):
-    try:    
-        SlopeWeek = pd.DataFrame(columns=['a','b', 'c', 'Name'])
-        SlopeWeek = SlopeWeek.set_index('Name')
-            
-        for key in Keylist:
-            newDF, EpochSVM = SVMEpoch(Dict[key], '60S', 'Datetime')
-           
-            ENMO = EpochSVM['SVM']-1
-            ENMO = ENMO*1000
-           
-            for value in ENMO:
-                if value < 0:
-                    value = 0
-           
-            BinSize = 5 
-           
-            ENMOmax = int(ENMO.max())
-            
-            if ENMOmax % BinSize == 0:
-                ENMOmax = ENMOmax+1 #to make sure that interference with binsize is impossible
-            
-            MaxBin = int(ENMOmax/BinSize)+1
-            ENMO = ENMO.astype(int)
-           
-            Counter = pd.DataFrame(np.zeros((1,MaxBin)))
-           
-            for x in Counter:
-                Count = (x+1)*BinSize
-                Start = Count - BinSize
-                Number = ENMO.between(Start, Count).sum()
-                Counter[x] = Number
-            
-            Counter = Counter.to_numpy()
-            Counter = Counter.astype(float)
-            Counter = Counter.flatten()
-            
-            Xscale = np.arange(0,ENMOmax, BinSize)
-            Xscale = Xscale.astype(float)
-            
-            popt, _ = curve_fit(func, Xscale, Counter, p0=None) # fit curve through points 
-            a, b, c = popt
-            
-            Trendline = func(Xscale, a, b, c)
-           
-            SlopeWeek.loc[key, 'a'] = a
-            SlopeWeek.loc[key, 'b'] = b
-            SlopeWeek.loc[key, 'c'] = c
-            SlopeWeek.loc[key, 'ENMOmax'] = ENMOmax
-           
-            PtName = key.replace('35694_00000', '')
-            PtName = PtName.replace('resampled-','') 
-            PtName = PtName.replace('.csv','')
-            
-            plt.figure()
-            plt.ylim(0,1440)
-            plt.xlim(0,(ENMOmax+10))
-            plt.title('Intensity plot ' + PtName)
-            plt.xlabel('Movement intensity [bins of ' + str(BinSize) + ' mg]')
-            plt.ylabel('Amount of time spend at intensity [min]')
-            plt.grid()
-            plt.scatter(Xscale, y=Counter)
-            plt.plot(Xscale, Trendline, 'r--')
-            plt.show()
-            
-            PtName = (PtName + '.png')
-            plt.savefig(fname=PtName)
-            
-    except:
-        print(PtName + ' could not be used')
-
-    return SlopeWeek

README.md

@@ -0,0 +1,27 @@
+In this folder you can find 4 scripts that can be used to transfrom 24/7 .csv accelerometer data to intensity plots. The methods are based on the Rowland et al. 2018 article 'Beyond Cut Points: Accelerometer Metrics that Capture the Physical Activity Profile. 
+
+The scripts should be run in the following order:
+
+1. DailyCutter.py   -   This script is used to transform single week .csv files (input) into individual day-files (output)
+                        The WeekPath should be set to the folder containing the week-files of the patient 
+                        The ScriptPath should be set to the folder containing the 'formules.py' script. This script holds
+                        all functions used in the analysis.
+
+2. plotter.py       -   This is the second script used in the analysis and is the script that performs the most. 
+                        Before getting started, the os.chdir() should be set to the 'formules.py' pathway
+                        The rootdir should be set to the folder containing 7 individual .csv day-files
+                        The PtName variable should be set accordingly to the name of the files used. This possibly changes 
+                        between different weeks/different patients.
+                        Finally, the CheckWeel.to_csv() should be filled with the used weeknumber, to save the 
+                        parameters of the trendline for each different week.
+                        After changing these variables to your liking, the script should run automatically, using the 
+                        folder with individual days as input and saving the plots for individual days as well as an 
+                        overview of the entire week as output.
+
+3. Totalplotter.py  -   After running the plotter.py script you should have ended up with 6 .csv files with parameters 
+                        of the trendlines. Make sure that these files are saved within the same folder and set the path
+                        of this folder as the rootdir in this script.
+                        Change the names in the plt.title() and plt.savefig() commands accordingly and run the scripts
+                        The output will be a plot of all 6 weeks in one figure.
+
+

formules.py

@@ -4,7 +4,7 @@ Created on Wed Jun 16 09:34:35 2021
 
 @author: -
 """
-import osa
+import os
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -115,7 +115,7 @@ def SlopeWeeker(Keylist, Dict):
             PtName = PtName.replace('resampled-','') 
             PtName = PtName.replace('.csv','')
             
-            plt.figure()
+            plt.figure(dpi=720)
             plt.ylim(0,1440)
             plt.xlim(0,(ENMOmax+10))
             plt.title('Intensity plot ' + PtName)
@@ -124,10 +124,11 @@ def SlopeWeeker(Keylist, Dict):
             plt.grid()
             plt.scatter(Xscale, y=Counter)
             plt.plot(Xscale, Trendline, 'r--')
-            plt.show()
             
             PtName = (PtName + '.png')
             plt.savefig(fname=PtName)
+
+            plt.show()
             
     except:
         print(PtName + ' could not be used')

plotter.py

@@ -54,7 +54,7 @@ for i in Length:
    
     Xscale = np.arange(0,CheckWeek['ENMOmax'].max(), 5)
    
-    plt.figure()
+    plt.figure(dpi=720)
     plt.ylim(0,1440)
     plt.xlim(0,CheckWeek['ENMOmax'].max())
     plt.title('All weekdays and average plotted ' + PtName)
@@ -69,6 +69,7 @@ for i in Length:
         else:
             plt.plot(Xscale, Y, 'k--')
             
-    plt.show()
     plt.savefig(fname=('Weekplot ' + PtName+ '.png'))
+    plt.show()
+