stable version

build/lib/pyjamalib/DataHandler.py

@@ -1,3 +1,4 @@
+import pyjamalib
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -483,14 +484,14 @@ def toDataframe(data,data_calib,low_pass=.1,freq=75):
  https://github.com/tuliofalmeida/pyjama
  https://github.com/tuliofalmeida/jama 
  """ 
- time,acc,gyr,mag= pjl.DataHandler.get_imu_data(data)
- time_calib,acc_calib,gyr_calib,mag_calib = pjl.DataHandler.get_imu_data(data_calib)
+ time,acc,gyr,mag= DataHandler.get_imu_data(data)
+ time_calib,acc_calib,gyr_calib,mag_calib = DataHandler.get_imu_data(data_calib)
  time = np.arange(0, len(time)/freq, 1/freq)
 
- acc, gyr, mag = pjl.DataHandler.calibration_imu(acc,gyr,mag,mag_calib)
- accf = pjl.DataProcessing.low_pass_filter(acc,low_pass)
- gyrf = pjl.DataProcessing.low_pass_filter(gyr,low_pass)
- magf = pjl.DataProcessing.low_pass_filter(mag,low_pass)
+ acc, gyr, mag = DataHandler.calibration_imu(acc,gyr,mag,mag_calib)
+ accf = pyjamalib.DataProcessing.low_pass_filter(acc,low_pass)
+ gyrf = pyjamalib.DataProcessing.low_pass_filter(gyr,low_pass)
+ magf = pyjamalib.DataProcessing.low_pass_filter(mag,low_pass)
 
  df = pd.DataFrame({'Time':time[:] ,
  'Acc_X':acc[:,0] ,'Acc_Y': acc[:,1] ,'Acc_Z': acc[:,2] ,

pyjamalib.egg-info/PKG-INFO

@@ -1,9 +1,9 @@
 Metadata-Version: 2.1
 Name: pyjamalib
-Version: 0.7.5
+Version: 1.0.0
 Summary: A library for analyze joint angles from IMU data
 Home-page: https://github.com/tuliofalmeida/pyjama
-Author: Túlio 1F. Almeida
+Author: Túlio F. Almeida
 Author-email: tuliofalmeida@hotmail.com
 License: MIT
 Description: 
@@ -70,7 +70,7 @@ Description:
  -----------
 
  - Example of using the library using data extracted using JAMA. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tuliofalmeida/pyjama/blob/main/PyJama_JAMA_exemple.ipynb) 
- - Example of using the library using data extracted using Vicon and Xsens. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tuliofalmeida/pyjama/blob/main/Pyjama_Validation.ipynb) 
+ - Example of using the library using data extracted using Vicon and Xsens. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tuliofalmeida/pyjama/blob/main/Pyjama_total_capture.ipynb) 
 
  # Contributing
  --------------

pyjamalib/DataHandler.py

@@ -1,3 +1,4 @@
+import pyjamalib
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
@@ -483,14 +484,14 @@ def toDataframe(data,data_calib,low_pass=.1,freq=75):
  https://github.com/tuliofalmeida/pyjama
  https://github.com/tuliofalmeida/jama 
  """ 
- time,acc,gyr,mag= pjl.DataHandler.get_imu_data(data)
- time_calib,acc_calib,gyr_calib,mag_calib = pjl.DataHandler.get_imu_data(data_calib)
+ time,acc,gyr,mag= DataHandler.get_imu_data(data)
+ time_calib,acc_calib,gyr_calib,mag_calib = DataHandler.get_imu_data(data_calib)
  time = np.arange(0, len(time)/freq, 1/freq)
 
- acc, gyr, mag = pjl.DataHandler.calibration_imu(acc,gyr,mag,mag_calib)
- accf = pjl.DataProcessing.low_pass_filter(acc,low_pass)
- gyrf = pjl.DataProcessing.low_pass_filter(gyr,low_pass)
- magf = pjl.DataProcessing.low_pass_filter(mag,low_pass)
+ acc, gyr, mag = DataHandler.calibration_imu(acc,gyr,mag,mag_calib)
+ accf = pyjamalib.DataProcessing.low_pass_filter(acc,low_pass)
+ gyrf = pyjamalib.DataProcessing.low_pass_filter(gyr,low_pass)
+ magf = pyjamalib.DataProcessing.low_pass_filter(mag,low_pass)
 
  df = pd.DataFrame({'Time':time[:] ,
  'Acc_X':acc[:,0] ,'Acc_Y': acc[:,1] ,'Acc_Z': acc[:,2] ,

pyjamalib/DataProcessing.py

@@ -2473,8 +2473,8 @@ def varianceEstimation(data):
  return mean,std
 
  def pattern_extraction(jointAngle,time,threshold,cycle=2,df=True,plot=False,bias=0,
- save=False, save_name='Data',fsize=30, title='Pattern Extraction',
- yaxis='Angle (°)',xaxis='Cycle (%)'):
+ flip = False ,save=False, save_name='Data',fsize=30, 
+ title='Pattern Extraction', yaxis='Angle (°)',xaxis='Cycle (%)'):
  """ Find and extract patterns from the data. 
  Able to plot all overlapping pattern data 
  for easy understanding. This function was 
@@ -2506,6 +2506,8 @@ def pattern_extraction(jointAngle,time,threshold,cycle=2,df=True,plot=False,bias
  Determines if the function will return the plot.
  bias: int optional
  Value to compensate the cycle adjust.
+ flip: bool
+ Variable to compensate the cycle representation. 
  save: bool
  If true save a fig as pdf
  save_name: str
@@ -2539,7 +2541,7 @@ def pattern_extraction(jointAngle,time,threshold,cycle=2,df=True,plot=False,bias
  if plot == True:
  plt.figure(figsize=(12,9))
 
- diff = DataProcessing.low_pass_filter((jointAngle[1:] - jointAngle[:-1]))
+ diff = pyjamalib.DataProcessing.low_pass_filter((jointAngle[1:] - jointAngle[:-1]))
  zero_crossings = np.where(np.diff(np.signbit(jointAngle-threshold)))[0]
  Time_Data_Array = []
  mCicloqtd = cycle
@@ -2555,17 +2557,21 @@ def pattern_extraction(jointAngle,time,threshold,cycle=2,df=True,plot=False,bias
  Time_Data_Array.append((tempo[ç],data[ç]));
 
  if plot == True:
- plt.plot(tempo, DataProcessing.low_pass_filter(jointAngle[zero_crossings[0+ciclo]:zero_crossings[mCicloqtd+ciclo]]));
-
+ if flip == True:
+ plt.plot(tempo,pyjamalib.DataProcessing.low_pass_filter(np.flip(data))) 
+ else:
+ plt.plot(tempo,pyjamalib.DataProcessing.low_pass_filter(data))
  if plot == True:
  plt.title(title,fontsize = fsize);
  plt.ylabel(yaxis,fontsize = fsize);
  plt.xlabel(xaxis,fontsize = fsize);
+ plt.yticks(fontsize = fsize)
+ plt.xticks(fontsize = fsize)
  plt.grid();
  if save == True:
  plt.savefig(save_name + '.pdf')
  plt.show();
-
+ 
  return np.asarray(Time_Data_Array),np.asarray(rom)
 
  def patternCI(all_x, all_y,poly_degree = 1,CI = 1.96,df = True,plot=False,
@@ -2664,6 +2670,8 @@ def patternCI(all_x, all_y,poly_degree = 1,CI = 1.96,df = True,plot=False,
  plt.title(title).set_size(fsize);
  plt.xlabel(x_label).set_size(fsize);
  plt.ylabel(y_label).set_size(fsize);
+ plt.yticks(fontsize = fsize)
+ plt.xticks(fontsize = fsize)
  plt.legend([label1, label2, label3], loc='upper right',fontsize = lsize)
  plt.grid();
  if save == True:
@@ -2741,6 +2749,8 @@ def data_comparison(Time_Data_Array,statistics_1,statistics_2, plot=True,
  plt.title(title,fontsize = fsize);
  plt.ylabel(yaxis,fontsize = fsize);
  plt.xlabel(xaxis,fontsize = fsize);
+ plt.yticks(fontsize = fsize);
+ plt.xticks(fontsize = fsize);
  plt.grid();
  if save:
  plt.savefig(save_name + '.pdf') 

setup.py

@@ -7,8 +7,8 @@
 
  name="pyjamalib",
  packages = ['pyjamalib'],
- version="0.7.5",
- author="Túlio 1F. Almeida",
+ version="1.0.0",
+ author="Túlio F. Almeida",
  author_email="tuliofalmeida@hotmail.com",
  description="A library for analyze joint angles from IMU data",
  long_description=long_description,