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In [1]:



    


import numpy as np
import matplotlib.pyplot as plt

import control as con

%pylab inline
#import plotly.plotly as py

import pandas as pd

from sysident import loadtools



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [2]:



    


def latexify(fig_width=None, fig_height=None, columns=1):
    """Set up matplotlib's RC params for LaTeX plotting.
    Call this before plotting a figure.

    Parameters
    ----------
    fig_width : float, optional, inches
    fig_height : float,  optional, inches
    columns : {1, 2}
    """

    # code adapted from http://www.scipy.org/Cookbook/Matplotlib/LaTeX_Examples

    # Width and max height in inches for IEEE journals taken from
    # computer.org/cms/Computer.org/Journal%20templates/transactions_art_guide.pdf

    assert(columns in [1,2])

    if fig_width is None:
        fig_width = 3.39 if columns==1 else 6.9 # width in inches

    if fig_height is None:
        golden_mean = (sqrt(5)-1.0)/2.0    # Aesthetic ratio
        fig_height = fig_width*golden_mean # height in inches

    MAX_HEIGHT_INCHES = 8.0
    if fig_height > MAX_HEIGHT_INCHES:
        print("WARNING: fig_height too large:" + fig_height + 
              "so will reduce to" + MAX_HEIGHT_INCHES + "inches.")
        fig_height = MAX_HEIGHT_INCHES

    params = {'backend': 'ps',
              'text.latex.preamble': ['\usepackage{gensymb}'],
              'axes.labelsize': 8, # fontsize for x and y labels (was 10)
              'axes.titlesize': 8,
              'text.fontsize': 8, # was 10
              'legend.fontsize': 8, # was 10
              'xtick.labelsize': 8,
              'ytick.labelsize': 8,
              'text.usetex': True,
              'figure.figsize': [fig_width,fig_height],
              'font.family': 'serif'
    }

    matplotlib.rcParams.update(params)


def format_axes(ax):

    for spine in ['top', 'right']:
        ax.spines[spine].set_visible(False)

    for spine in ['left', 'bottom']:
        ax.spines[spine].set_color(SPINE_COLOR)
        ax.spines[spine].set_linewidth(0.5)

    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('left')

    for axis in [ax.xaxis, ax.yaxis]:
        axis.set_tick_params(direction='out', color=SPINE_COLOR)

    return ax



    











In [3]:



    


folder = "/home/lth/catkin_ws/src/pitasc/applications/sysident/systems/"
#fname = folder+"ss1_20180724-082630_poles2_ident_pade1_0:036_control_20180724-092309.npy"
#fname = folder+"ss2_20180724-082631_poles3_ident_pade2_0:036_control_20180724-092409.npy"
#fname = folder+"ss3_position_20180717-104106_poles3_ident_pade1_0:032_control_20180724-092445.npy"
fname = folder+"ss5_20180724-082628_poles3_ident_pade1_0:032_control_20180724-092522.npy"
#fname = folder+"ss6_20180724-082629_poles4_ident_pade2_0:032_control_20180724-092618.npy"



    











In [4]:



    


res, _ = loadtools.loadNPY(fname)
#res.keys()



    











In [5]:



    


plt.plot(*res['plot_model_step'])
plt.plot(*res['plot_input_step'])

#py.iplot(res['plot_model_step'])



    


















    
Out[5]:








[<matplotlib.lines.Line2D at 0x7f796608ce50>]










    
























In [6]:



    


plt.plot(*res['plot_control_step_s'])
plt.plot(*res['plot_control_step_y'])
plt.plot(*res['plot_control_step_delay_y'])
plt.show()

plt.plot(*res['plot_control_step_u'])
plt.plot(*res['plot_control_step_delay_u'])
plt.show()



    


















    

















    
























In [7]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

#fig = plt.figure(figsize=(4, 3))
#fig.subplots_adjust(left=.15, bottom=.16, right=.99, top=.97)

#print res['plot_control_step_s'][0]
ax = plt.subplot(111)    # The big subplot

ax1 = plt.subplot(2, 1, 1)
#plt.plot(res['plot_control_step_y'][0],
#         res['plot_control_step_y'][1],
#         label='step data',
#         color='0.5', ls='solid')

plt.plot(*res['plot_control_step_s'],
         label='desired value',
         color='k', ls='solid')

plt.plot(*res['plot_control_step_y'],
         label='step response (original plant)',
         color='0.75', ls='dashed')

plt.plot(*res['plot_control_step_delay_y'],
         label='step response (delayed plant)',
         color='0.5', ls='dotted')

ax1.set_ylabel('y in m')

plt.xlim(0, 1)
legend()

ax2 = plt.subplot(2, 1, 2)

plt.plot(*res['plot_control_step_u'],
         label='output (original plant)',
         color='0.75', ls='dashed')

plt.plot(*res['plot_control_step_delay_u'],
         label='output (delayed plant)',
         color='0.5', ls='dotted')

plt.xlim(0, 1)
legend()

ax2.set_ylabel('u in m/s')
ax2.set_xlabel('time in s')

#fig.set_size_inches(width, height)
plt.savefig('sim_response.pdf')
#plt.show()



    


















    






/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:1320: UserWarning: findfont: Font family [u'serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))
latexify() plt.rc('text', usetex=False) plt.subplot(2, 1, 1) #plt.plot(res['plot_control_step_y'][0], # res['plot_control_step_y'][1], # label='step data', # color='0.5', ls='solid') plt.plot(*res['plot_control_step_s'], label='desired value', color='k', ls='solid') plt.plot(*res['plot_control_step_y'], label='step response (original plant)', color='0.75', ls='dashed') plt.plot(*res['plot_control_step_delay_y'], label='step response (delayed plant)', color='0.5', ls='dotted') plt.xlim(0, 1.2) legend() plt.subplot(2, 1, 2) plt.plot(*res['plot_control_step_u'], label='output (original plant)', color='0.75', ls='dashed') plt.plot(*res['plot_control_step_delay_u'], label='output (delayed plant)', color='0.5', ls='dotted') plt.xlim(0, 1.2) legend() 




In [ ]:



    






    











In [8]:



    


import pandas as pd
df = pd.read_csv("/home/lth/catkin_ws/src/pitasc/applications/sysident/src/2018-07-27_16-13-30_task_vel_logger.csv")
df['time'] = (df['time'])*1e-9
df['time'] = df['time']-df['time'].values[0]
#print df['time'].values[0]
#data_all[0] = data_all[0]*1e-9 # to seconds
#data_all[0] = data_all[0]-data_all[0][0] # starts at 0

df.set_index('time', inplace=True)
df.drop(columns=['localtime'], inplace=True)

df.head()
#(df.residual - df.task_vel).plot()



    


















    
Out[8]:











  
    

      
      residual
      desired_vel
      task_vel
      control_out
      state
    

    

      time
      
      
      
      
      
    

  
  
    

      0.000000
      -4.163336e-17
      -1.225869e-16
      4.951835e-06
      -1.225869e-16
      "unknown"
    

    

      0.004759
      -2.140294e-07
      -6.301965e-07
      -4.951773e-06
      -6.301965e-07
      "unknown"
    

    

      0.013541
      2.140284e-07
      6.301940e-07
      -9.903561e-06
      6.301936e-07
      "unknown"
    

    

      0.023540
      4.280578e-07
      1.260390e-06
      1.462214e-11
      1.260390e-06
      "unknown"
    

    

      0.028151
      1.284175e-06
      3.781176e-06
      -4.951792e-06
      3.781174e-06
      "unknown"
    

  




















In [9]:



    


df.plot()



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f7963ccfa90>










    
























In [10]:



    


df_linear = df.loc[(df.index > 61.8) & (df.index < 69), ['desired_vel','task_vel','residual']]
df_linear.index = df_linear.index - df_linear.index[0]
df_linear.plot()



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f7963788ed0>










    
























In [11]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

ax1 = plt.subplot(2, 1, 1)
plt.plot(df_linear.index,
         np.zeros(len(df_linear.index)),
         label='desired value',
         color='k', ls='solid')

plt.plot(df_linear.index,
         df_linear.residual,
         label='step response',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend()

ax2 = plt.subplot(2, 1, 2)

plt.plot(df_linear.index,
         df_linear.desired_vel,
         label='desired task velocity',
         color='0.75', ls='dashed')

plt.plot(df_linear.index,
         df_linear.task_vel,
         label='actual task velocity',
         color='0.5', ls='dotted')

#plt.xlim(0, 0.8)
legend()

ax1.set_ylabel('y in m')
ax2.set_ylabel('u in m/s')
ax2.set_xlabel('time in s')

plt.savefig('experiement_lin_control_1.pdf')



    


















    
























In [12]:



    


df_lens_01 = df.loc[(df.index > 74.12) & (df.index < 76.12)]
df_lens_01.index = df_lens_01.index - df_lens_01.index[0]
df_lens_01.plot()

df_linear_cut = df_linear.loc[(df_linear.index < df_lens_01.index[-1])]
df_linear_cut.plot()



    


















    
Out[12]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f796364e390>










    

















    
























In [13]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

ax1 = plt.subplot(2, 1, 1)
plt.plot(df_lens_01.index,
         np.zeros(len(df_lens_01.index)),
         label='desired value',
         color='0.5', ls='dotted')

plt.plot(df_lens_01.index,
         df_lens_01.residual,
         label='step response',
         color='k', ls='solid')

plt.plot(df_linear_cut.index,
         df_linear_cut.residual,
         label='step response (linear)',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend(loc = 'upper right')

ax2 = plt.subplot(2, 1, 2)

plt.plot(df_lens_01.index,
         df_lens_01.desired_vel,
         label='desired task velocity',
         color='0.5', ls='dotted')

plt.plot(df_lens_01.index,
         df_lens_01.task_vel,
         label='actual task velocity',
         color='k', ls='solid')

plt.plot(df_linear_cut.index,
         df_linear_cut.task_vel,
         label='actual task velocity (linear)',
         color='0.75', ls='dashed')

#plt.xlim(0, 0.8)
leg = legend()

# Get the bounding box of the original legend
bb = leg.get_bbox_to_anchor().inverse_transformed(ax.transAxes)

# Change to location of the legend. 
xOffset = -0.3
#bb.x0 += xOffset
bb.x1 += xOffset
leg.set_bbox_to_anchor(bb, transform = ax.transAxes)


ax1.set_ylabel('y in m')
ax2.set_ylabel('u in m/s')
ax2.set_xlabel('time in s')

plt.savefig('experiement_len01_1.pdf')

#plt.subplot(2, 1, 1)
#plt.plot(df_linear.index,
#         df_linear.residual,
#         label='step response',
#         color='0.5', ls='dotted')
#
#plt.xlim(0, 1.2)
#legend()
#
#plt.subplot(2, 1, 2)
#
#plt.plot(df_linear.index,
#         df_linear.desired_vel,
#         label='desired task velocity',
#         color='0.75', ls='dashed')
#
#plt.plot(df_linear.index,
#         df_linear.task_vel,
#         label='actual task velocity',
#         color='0.5', ls='dotted')
#plt.savefig('experiement_all_1.pdf')



    


















    
























In [14]:



    


df_lens_02 = df.loc[(df.index > 80.74) & (df.index < 82)]
df_lens_02.index = df_lens_02.index - df_lens_02.index[0]
df_lens_02.plot()



    


















    
Out[14]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f7963677f90>










    
























In [15]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

plt.subplot(2, 1, 1)
plt.plot(df_lens_02.index,
         np.zeros(len(df_lens_02.index)),
         label='desired value',
         color='k', ls='solid')

plt.plot(df_lens_02.index,
         df_lens_02.residual,
         label='step response',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend()

plt.subplot(2, 1, 2)

plt.plot(df_lens_02.index,
         df_lens_02.desired_vel,
         label='desired task velocity',
         color='0.75', ls='dashed')

plt.plot(df_lens_02.index,
         df_lens_02.task_vel,
         label='actual task velocity',
         color='0.5', ls='dotted')

#plt.xlim(0, 0.8)
legend()

plt.savefig('experiement_len02_1.pdf')



    


















    
























In [ ]:



    






    











In [ ]:



    






    











In [16]:



    


import pandas as pd
df = pd.read_csv("/home/lth/catkin_ws/src/pitasc/applications/sysident/src/2018-07-27_16-08-44_task_vel_logger.csv")
df['time'] = (df['time'])*1e-9
df['time'] = df['time']-df['time'].values[0]
#print df['time'].values[0]
#data_all[0] = data_all[0]*1e-9 # to seconds
#data_all[0] = data_all[0]-data_all[0][0] # starts at 0

df.set_index('time', inplace=True)
df.drop(columns=['localtime'], inplace=True)

df.head()
#(df.residual - df.task_vel).plot()



    


















    
Out[16]:











  
    

      
      residual
      desired_vel
      task_vel
      control_out
      state
    

    

      time
      
      
      
      
      
    

  
  
    

      0.000000
      1.387779e-17
      4.086230e-17
      9.910919e-06
      4.086230e-17
      "unknown"
    

    

      0.007237
      -6.654363e-07
      -1.959337e-06
      -5.291751e-07
      -1.959337e-06
      "unknown"
    

    

      0.015019
      -4.055032e-07
      -1.193980e-06
      -2.920355e-05
      -1.193979e-06
      "unknown"
    

    

      0.023342
      -8.567520e-07
      -2.522652e-06
      -9.381753e-06
      -2.522654e-06
      "unknown"
    

    

      0.031157
      -8.796243e-07
      -2.589999e-06
      -9.910916e-06
      -2.590000e-06
      "unknown"
    

  




















In [17]:



    


df.plot()



    


















    
Out[17]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f7963c5a2d0>










    
























In [18]:



    


df_linear = df.loc[(df.index > 4.7) & (df.index < 11.5), ['desired_vel','task_vel','residual']]
df_linear.index = df_linear.index - df_linear.index[0]
df_linear.plot()



    


















    
Out[18]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f79633ade10>










    
























In [19]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

plt.subplot(2, 1, 1)
plt.plot(df_linear.index,
         np.zeros(len(df_linear.index)),
         label='desired value',
         color='k', ls='solid')

plt.plot(df_linear.index,
         df_linear.residual,
         label='step response',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend()

plt.subplot(2, 1, 2)

plt.plot(df_linear.index,
         df_linear.desired_vel,
         label='desired task velocity',
         color='0.75', ls='dashed')

plt.plot(df_linear.index,
         df_linear.task_vel,
         label='actual task velocity',
         color='0.5', ls='dotted')

#plt.xlim(0, 0.8)
legend()

plt.savefig('experiement_lin_control_2.pdf')



    


















    
























In [20]:



    


df_lens_01 = df.loc[(df.index > 17.03) & (df.index < 19.03)]
df_lens_01.index = df_lens_01.index - df_lens_01.index[0]
df_lens_01.plot()



    


















    
Out[20]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f796327a890>
plt.rc('font', family='serif', serif='Times') #plt.rc('font', family='serif') plt.rc('text', usetex=False) plt.rc('xtick', labelsize=8) plt.rc('ytick', labelsize=8) plt.rc('axes', labelsize=8) # width as measured in inkscape width = 3.487 height = width / 1.618 plt.subplot(2, 1, 1) plt.plot(df_lens_01.index, np.zeros(len(df_lens_01.index)), label='desired value', color='k', ls='solid') plt.plot(df_lens_01.index, df_lens_01.residual, label='step response', color='0.75', ls='dashed') #plt.xlim(0, 1.2) legend() plt.subplot(2, 1, 2) plt.plot(df_lens_01.index, df_lens_01.desired_vel, label='desired task velocity', color='0.75', ls='dashed') plt.plot(df_lens_01.index, df_lens_01.task_vel, label='actual task velocity', color='0.5', ls='dotted') #plt.xlim(0, 0.8) legend() plt.savefig('experiement_len01_2.pdf') 




In [21]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

ax1 = plt.subplot(2, 1, 1)
plt.plot(df_lens_01.index,
         np.zeros(len(df_lens_01.index)),
         label='desired value',
         color='0.5', ls='dotted')

plt.plot(df_lens_01.index,
         df_lens_01.residual,
         label='step response',
         color='k', ls='solid')

plt.plot(df_linear_cut.index,
         df_linear_cut.residual,
         label='step response (linear)',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend(loc = 'upper right')

ax2 = plt.subplot(2, 1, 2)

plt.plot(df_lens_01.index,
         df_lens_01.desired_vel,
         label='desired task velocity',
         color='0.5', ls='dotted')

plt.plot(df_lens_01.index,
         df_lens_01.task_vel,
         label='actual task velocity',
         color='k', ls='solid')

plt.plot(df_linear_cut.index,
         df_linear_cut.task_vel,
         label='actual task velocity (linear)',
         color='0.75', ls='dashed')

#plt.xlim(0, 0.8)
leg = legend()

# Get the bounding box of the original legend
bb = leg.get_bbox_to_anchor().inverse_transformed(ax.transAxes)

# Change to location of the legend. 
xOffset = -0.3
#bb.x0 += xOffset
bb.x1 += xOffset
leg.set_bbox_to_anchor(bb, transform = ax.transAxes)


ax1.set_ylabel('y in m')
ax2.set_ylabel('u in m/s')
ax2.set_xlabel('time in s')

plt.savefig('experiement_len01_2.pdf')

#plt.subplot(2, 1, 1)
#plt.plot(df_linear.index,
#         df_linear.residual,
#         label='step response',
#         color='0.5', ls='dotted')
#
#plt.xlim(0, 1.2)
#legend()
#
#plt.subplot(2, 1, 2)
#
#plt.plot(df_linear.index,
#         df_linear.desired_vel,
#         label='desired task velocity',
#         color='0.75', ls='dashed')
#
#plt.plot(df_linear.index,
#         df_linear.task_vel,
#         label='actual task velocity',
#         color='0.5', ls='dotted')
#plt.savefig('experiement_all_1.pdf')



    


















    
























In [22]:



    


df_lens_02 = df.loc[(df.index > 23.6) & (df.index < 24.9)]
df_lens_02.index = df_lens_02.index - df_lens_02.index[0]
df_lens_02.plot()



    


















    
Out[22]:








<matplotlib.axes._subplots.AxesSubplot at 0x7f7963091810>










    
























In [23]:



    


plt.rc('font', family='serif', serif='Times')
#plt.rc('font', family='serif')
plt.rc('text', usetex=False)
plt.rc('xtick', labelsize=8)
plt.rc('ytick', labelsize=8)
plt.rc('axes', labelsize=8)

# width as measured in inkscape
width = 3.487
height = width / 1.618

plt.subplot(2, 1, 1)
plt.plot(df_lens_02.index,
         np.zeros(len(df_lens_02.index)),
         label='desired value',
         color='k', ls='solid')

plt.plot(df_lens_02.index,
         df_lens_02.residual,
         label='step response',
         color='0.75', ls='dashed')

#plt.xlim(0, 1.2)
legend()

plt.subplot(2, 1, 2)

plt.plot(df_lens_02.index,
         df_lens_02.desired_vel,
         label='desired task velocity',
         color='0.75', ls='dashed')

plt.plot(df_lens_02.index,
         df_lens_02.task_vel,
         label='actual task velocity',
         color='0.5', ls='dotted')

#plt.xlim(0, 0.8)
legend()

plt.savefig('experiement_len02_2.pdf')



    


















    
























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Content source: ipa-lth/jupyter_nb

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