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" 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diff --git a/README.md b/README.md
old mode 100644
new mode 100755
index 370078750a5a3fddf07bb722a5e7e63e6fc69e2a..81cf4cb94b63282d9698fda41d8fd60b6dc050f2
--- a/README.md
+++ b/README.md
@@ -1,93 +1,19 @@
-# python_kinematics_app_slider_cranck_mechanism
+Kinematics application of the slider cranck mechanism developped in python
+===============================================================================
+The slider cranck mechanism is a classical linkage assembly that transforms a rotational motion into a linear one. It can be found in many systems such as pumps, thermal engines, ...
+The mechanism is made of a frame(0), an eccentric (1), a connecting rod (2) and a slider (3) depicted by the following figure:
-## Getting started
+
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
+This application aims to illustrate the kinematics of the mechanism. It replicates a free software that was available on MECAMEDIA but which is not maintained. It also exists a [MatLab version](https://github.com/mahmoudsamhoud/Cranck-Slider-Mechanism-Computational-Dynamics-).
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
+This application gives the mechanism configuration as well as position, velocity and acceleration plots of the slider given the position and velocity of the eccentric.
+Besides it illustrates some graphical kinematics tools such as trajectories, velocity vectors and equiprojectivity.
-## Add your files
+
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
+The application works with python37 and the required packages are: numpy and matplotlib
-```
-cd existing_repo
-git remote add origin https://gitlab.univ-lorraine.fr/baldit1/python_kinematics_app_slider_cranck_mechanism.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://gitlab.univ-lorraine.fr/baldit1/python_kinematics_app_slider_cranck_mechanism/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thanks to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
-
-## Name
-Choose a self-explaining name for your project.
-
-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
-
-## Badges
-On some READMEs, you may see small images that convey metadata, such as whether or not all the tests are passing for the project. You can use Shields to add some to your README. Many services also have instructions for adding a badge.
-
-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
-
-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
-
-## Support
-Tell people where they can go to for help. It can be any combination of an issue tracker, a chat room, an email address, etc.
-
-## Roadmap
-If you have ideas for releases in the future, it is a good idea to list them in the README.
-
-## Contributing
-State if you are open to contributions and what your requirements are for accepting them.
-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
+Perspectives are to show instantaneous center of rotation as well as their trajectories. The vector representation could also be improved.
\ No newline at end of file
diff --git a/slider-cranck_mechanism_app.py b/slider-cranck_mechanism_app.py
new file mode 100755
index 0000000000000000000000000000000000000000..013df91766b54f18e61403d4d411fa9e093b8d03
--- /dev/null
+++ b/slider-cranck_mechanism_app.py
@@ -0,0 +1,366 @@
+# Copyright University of Lorraine - ENIM
+# Contributor(s) :
+# Adrien Baldit
+# Contact: adrien.baldit@univ-lorraine.fr
+#
+# This script is a computer program whose purpose is to produce
+# kinematics mechanics practicals.
+#
+# This script is governed under French law and abiding by the rules
+# of Creative Commons distribution of free script. You can use, modify and/ or
+# redistribute the script under the terms of honor
+#
+# As a counterpart to the access to the source code and rights to copy,
+# modify and redistribute granted by the honor, users are provided only
+# with a limited warranty and the script's author, the holder of the
+# economic rights, and the successive licensors have only limited
+# liability.
+#
+# In this respect, the user's attention is drawn to the risks associated
+# with loading, using, modifying and/or developing or reproducing the
+# script by the user in light of its specific status of free software,
+# that may mean that it is complicated to manipulate, and that also
+# therefore means that it is reserved for developers and experienced
+# professionals having in-depth computer knowledge. Users are therefore
+# encouraged to load and test the script's suitability as regards their
+# requirements in conditions enabling the security of their systems and/or
+# data to be ensured and, more generally, to use and operate it in the
+# same conditions as regards security.
+#
+# The fact that you are presently reading this means that you have
+# had knowledge of the rules and accepted them.
+
+#!/usr/bin/python
+# -*- coding:utf8 -*-
+
+import numpy as np
+import matplotlib.pyplot as pl
+import matplotlib.animation as animation
+from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons
+from matplotlib.patches import Rectangle, Arrow
+
+################################
+# Matplotlib display parameter #
+################################
+pl.rc('axes', titlesize=22) # fontsize of the axes title
+pl.rc('axes', labelsize=22) # fontsize of the x and y labels
+pl.rc('xtick', labelsize=17) # fontsize of the tick labels
+pl.rc('ytick', labelsize=17) # fontsize of the tick labels
+pl.rc('legend', fontsize=17) # legend fontsize
+pl.rc('lines', linewidth=4) # legend fontsize
+pl.rc('lines', markersize=10) # legend fontsize
+pl.rc('figure', titlesize=18) # fontsize of the figure title
+pl.rc('legend',title_fontsize=17)
+
+##############################
+# Mechanism input parameters #
+##############################
+# Geometrical parameters
+# connecting rod length
+L = 146.0
+# initial value
+L_init = L
+# minimum
+L_min = 100.0
+# maximum
+L_max = 200.0
+# crank radius
+e = 46.0
+# initial value
+e_init = e
+# minimum
+e_min = 30
+# maximum
+e_max = L_min*0.99
+# slider length
+s = 40
+# initial value
+s_init = s
+# minimum
+s_min = 20
+# maximum
+s_max = 100
+
+# Kinematics parameters
+# Angular position
+alpha = 45.*np.pi/180.
+# Initial angular position
+alpha_init = alpha
+# driving angular velocity [tr.min^{-1}]
+dotalpha_trmin=750.
+# driving angular velocity [rad.s^{-1}]
+dotalpha_init=dotalpha_trmin*np.pi/30.
+dotalpha=dotalpha_init
+dotalpha_min = dotalpha_init*0.01
+dotalpha_max = dotalpha_init*2
+
+# Time parameters
+# final time for one turn [s]
+t_final =2*np.pi/dotalpha
+# time sampling [s]
+dt = t_final/50.
+t_min = 0
+t_max = t_final
+t_init = 1/3*t_final
+
+###############################
+# Functions to compute motion #
+###############################
+# positions
+def positions(e,L,s,dotalpha,t) :
+ alpha = dotalpha*t
+ beta = np.arccos(e*np.sin(alpha)/L)
+ dotbeta = -(e*np.cos(alpha))/(L*np.sin(beta))*dotalpha
+ ddotbeta = (e*np.sin(alpha)*dotalpha**2.-L*np.cos(beta)*dotbeta**2.)/(L*np.sin(beta))
+
+ x1 = e*np.cos(alpha) # x-cooridnate of the crank: Point 1
+ y1 = e*np.sin(alpha) # y-cooridnate of the crank: Point 1
+ y2 = 0 # x-coordinate of the rod: Point 2
+ y3 = 0 # x-coordinate of the rod: Point 2
+ # y-coordinate of the rod: Point 2
+ x2 = e*np.cos(alpha) + np.sqrt( L**2 - (e*np.sin(alpha))**2 )
+ x3 = x2+s
+ dotx2 = -e*dotalpha*np.sin(alpha)+L*dotbeta*np.cos(beta)
+ ddotx2 = -e*dotalpha**2*np.cos(alpha)+L*(ddotbeta*np.cos(beta)-dotbeta**2*np.sin(beta))
+ VBx = -e*dotalpha*np.sin(alpha)
+ VBy = e*dotalpha*np.cos(alpha)
+ return x1,y1,x2,y2,x3,y3,alpha,dotx2,ddotx2,VBx,VBy
+
+# Motion
+def motion(e,L,s,dotalpha,tmin,tmax,dt) :
+ list_t = np.arange(tmin,tmax+dt,dt)
+ list_x1 = []
+ list_y1 = []
+ list_x2 = []
+ list_dotx2 = []
+ list_ddotx2 = []
+ list_y2 = []
+ list_x3 = []
+ list_y3 = []
+ list_al = []
+
+ for tt in list_t :
+ x1,y1,x2,y2,x3,y3,alpha,dotx2,ddotx2,VBx,VBy = positions(e=e,L=L,s=s,dotalpha=dotalpha,t=tt)
+ list_x1.append(x1)
+ list_y1.append(y1)
+ list_x2.append(x2)
+ list_dotx2.append(dotx2)
+ list_ddotx2.append(ddotx2)
+ list_y2.append(y2)
+ list_x3.append(x3)
+ list_y3.append(y3)
+ list_al.append(alpha)
+ return list_t,list_x1,list_y1,list_x2,list_dotx2,list_ddotx2,list_y2,list_x3,list_y3,list_al
+
+# update motion
+def update(val):
+ # reload constant values
+ t = stinit.val
+ s = ssinit.val
+ e = seinit.val
+ L = sLinit.val
+ dotalpha = sdotalphainit.val
+
+
+ x1,y1,x2,y2,x3,y3,alpha,dotx2,ddotx2,VBx,VBy = positions(e=e,L=L,s=s,dotalpha=dotalpha,t=t)
+
+ list_t,list_x1,list_y1,list_x2,list_dotx2,list_ddotx2,list_y2,list_x3,list_y3,list_al = motion(e=e,L=L,s=s,dotalpha=dotalpha,tmin=t_min,tmax=t_max,dt=dt)
+
+
+ slope=(y2-y1)/(x2-x1)
+ intercept=y2-slope*x2
+
+ slope2 = -1/slope
+ intercept2 = (y1+Vscale*VBy)-slope2*(x1+VBx*Vscale)
+ intercept3 = -slope2*(x2+dotx2*Vscale)
+
+ xproj1 = (intercept2-intercept)/(slope-slope2)
+ yproj1 = slope*xproj1+intercept
+ xproj2 = (intercept3-intercept)/(slope-slope2)
+ yproj2 = slope*xproj2+intercept
+
+
+ xmin = -2.1*e
+ xmax = 1.1*(e+L+s)
+ ymin = -1.5*e
+ ymax = 1.5*e
+
+ ax.set_xlim(xmin,xmax)
+ ax.set_ylim(ymin,ymax)
+
+
+ ax2.set_xlim(t_min*1e3,t_max*1e3)
+ ax3.set_xlim(t_min*1e3,t_max*1e3)
+ ax4.set_xlim(t_min*1e3,t_max*1e3)
+ ax2.set_ylim(min(list_x2)*0.9e-3,max(list_x2)*1.1e-3)
+ ax3.set_ylim(min(list_dotx2)*1.1e-3,max(list_dotx2)*1.1e-3)
+ ax4.set_ylim(min(list_ddotx2)*1.1e-3,max(list_ddotx2)*1.1e-3)
+
+ line.set_data([0,x1], [0,y1])
+ point_p.set_data([t*1e3], [x2*1e-3])
+ point_v.set_data([t*1e3], [dotx2*1e-3])
+ point_a.set_data([t*1e3], [ddotx2*1e-3])
+ line2.set_data([x1,x2], [y1,y2])
+ line3.set_data([x2,x3], [y2,y3])
+ pos.set_data(np.array(list_t)*1e3, np.array(list_x2)*1e-3)
+ vel.set_data(np.array(list_t)*1e3, np.array(list_dotx2)*1e-3)
+ acc.set_data(np.array(list_t)*1e3, np.array(list_ddotx2)*1e-3)
+
+ tauB.set_data(list_x1,list_y1)
+ tauC.set_data(list_x2,list_y2)
+
+
+ VB2.set_data([x1,x1+Vscale * VBx], [y1,y1+Vscale * VBy])
+ VC2.set_data([x2,x2+Vscale * dotx2], [y2,y2])
+
+ equi1.set_data([xmin,xmax], [slope*xmin+intercept,slope*xmax+intercept])
+ equi2.set_data([xmin,xmax], [slope2*xmin+intercept2,slope2*xmax+intercept2])
+ equi3.set_data([xmin,xmax], [slope2*xmin+intercept3,slope2*xmax+intercept3])
+ equi21.set_data([x1,xproj1], [y1,yproj1])
+ equi31.set_data([x2,xproj2], [y2,yproj2])
+
+ # add on figure
+ fig2.canvas.draw_idle()
+
+# Updates checking
+def func(label):
+ index = labels.index(label)
+ lines[index].set_visible(not lines[index].get_visible())
+ pl.draw()
+
+# reset function
+def reset(event):
+ seinit.reset()
+ sLinit.reset()
+ ssinit.reset()
+ stinit.reset()
+ sdotalphainit.reset()
+
+#########################
+# Computation of motion #
+#########################
+# Position computation for initial condition
+x1,y1,x2,y2,x3,y3,alpha,dotx2,ddotx2,VBx,VBy = positions(e=e,L=L,s=s,dotalpha=dotalpha,t=t_init)
+# Motion computation for initial condition
+list_t,list_x1,list_y1,list_x2,list_dotx2,list_ddotx2,list_y2,list_x3,list_y3,list_al = motion(e=e,L=L,s=s,dotalpha=dotalpha,tmin=t_min,tmax=t_max,dt=dt)
+
+########################
+# Plotting computation #
+########################
+# Parameters
+Vscale = 1e-2
+xmin = -2.1*e
+xmax = 1.1*(e+L+s)
+ymin = -1.75*e
+ymax = 1.75*e
+
+# lines definitions
+slope=(y2-y1)/(x2-x1)
+intercept=y2-slope*x2
+slope2 = -1/slope
+intercept2 = (y1+Vscale*VBy)-slope2*(x1+VBx*Vscale)
+intercept3 = -slope2*(x2+dotx2*Vscale)
+
+# Projections
+xproj1 = (intercept2-intercept)/(slope-slope2)
+yproj1 = slope*xproj1+intercept
+xproj2 = (intercept3-intercept)/(slope-slope2)
+yproj2 = slope*xproj2+intercept
+
+########################
+# Plotting computation #
+########################
+# Main figure
+fig2 = pl.figure(figsize=(16,8))
+fig2.subplots_adjust(left = 0.07, bottom = 0.11, right = 0.96, top = 0.96, wspace = 0.25, hspace = 0.25)
+# System as function space
+ax = fig2.add_subplot(221, aspect='equal', autoscale_on=False)
+ax.set_xlim(xmin,xmax)
+ax.set_ylim(ymin,ymax)
+ax.grid()
+ax.set_xlabel(r"x $[mm]$")
+ax.set_ylabel(r"y $[mm]$")
+line, = ax.plot([0,x1], [0,y1], 'o-', lw=5, color='red')
+line2, = ax.plot([x1,x2], [y1,y2], 'o-', lw=5, color='green')
+line3, = ax.plot([x2,x3], [y2,y3], 'o-', lw=5, color='blue')
+equi1, = ax.plot([xmin,xmax], [slope*xmin+intercept,slope*xmax+intercept], '--', lw=2, color='black',label="projection line", visible=False)
+equi2, = ax.plot([xmin,xmax], [slope2*xmin+intercept2,slope2*xmax+intercept2], '--', lw=2, color='black',label=r"$\vec{V}(B\in2/0)$ projection", visible=False)
+equi3, = ax.plot([xmin,xmax], [slope2*xmin+intercept3,slope2*xmax+intercept3], '--', lw=2, color='black',label=r"$\vec{V}(C\in2/0)$ projection", visible=False)
+equi21, = ax.plot([x1,xproj1], [y1,yproj1], '-', lw=3, color='cyan',label="equiprojection 1", visible=False)
+equi31, = ax.plot([x2,xproj2], [y2,yproj2], '-', lw=3, color='cyan',label="equiprojection 2", visible=False)
+tauB, = ax.plot(list_x1,list_y1, '--', lw=2, color='red',label = r"$\tau(B\in1/0)$", visible=False)
+tauC, = ax.plot(list_x2,list_y2, '.', lw=2, color='green',label = r"$\tau(C\in3/0)$", visible=False)
+VB2, = ax.plot([x1,x1+Vscale * VBx], [y1,y1+Vscale * VBy], '-', lw=2, color='red',marker="4",label=r"$\vec{V}(B\in1/0)$", visible=False)
+VC2, = ax.plot([x2,x2+Vscale * dotx2], [y2,y2], '-', lw=2, color='green',marker="4",label=r"$\vec{V}(C\in3/0)$", visible=False)
+# Position as function of time
+ax2 = fig2.add_subplot(222, xlim=(t_min*1e3,t_max*1e3), ylim=(min(list_x2)*0.9e-3,max(list_x2)*1.1e-3))
+ax2.grid('on')
+ax2.set_xlabel(r"Time $[ms]$")
+ax2.set_ylabel(r"Position $[m]$")
+pos, = ax2.plot(np.array(list_t)*1e3,np.array(list_x2)*1e-3, 'b-x', mfc = 'w')
+point_p, = ax2.plot([t_init*1e3], [x2*1e-3], 'o', color='red')
+# Velocity as function of time
+ax3 = fig2.add_subplot(223, xlim=(t_min*1e3,t_max*1e3), ylim=(min(list_dotx2)*1.1e-3,max(list_dotx2)*1.1e-3))
+ax3.grid('on')
+ax3.set_xlabel(r"Time $[ms]$")
+ax3.set_ylabel(r"Velocity $[m.s^{-1}]$")
+vel, = ax3.plot(np.array(list_t)*1e3,np.array(list_dotx2)*1e-3, 'b-x', mfc = 'w')
+point_v, = ax3.plot([t_init*1e3],[dotx2*1e-3], 'o', color='red')
+# Acceleration as function of time
+ax4 = fig2.add_subplot(224, xlim=(t_min*1e3,t_max*1e3), ylim=(min(list_ddotx2)*1.1e-3,max(list_ddotx2)*1.1e-3))
+ax4.grid('on')
+ax4.set_xlabel(r"Time $[ms]$")
+ax4.set_ylabel(r"Acceleration $[m.s^{-2}]$")
+acc, = ax4.plot(np.array(list_t)*1e3,np.array(list_ddotx2)*1e-3, 'b-x', mfc = 'w')
+point_a, = ax4.plot([t_init*1e3],[ddotx2*1e-3], 'o', color='red')
+
+
+# Control figure
+fig = pl.figure(2, figsize=(4,6))
+# manipulate remote control
+axcolor = 'lightgoldenrodyellow'
+# definition of the slider positons and values
+spreading=0.4
+init_spread = 0.5
+# connecting rod length
+aLinit = pl.axes([0.25, 0.*spreading+init_spread, 0.6, 0.05], facecolor=axcolor)
+sLinit = Slider(aLinit, r'$L\ [mm]$', L_min, L_max, valinit=L_init)
+# crank radius
+aeinit = pl.axes([0.25, 0.25*spreading+init_spread, 0.6, 0.05], facecolor=axcolor)
+seinit = Slider(aeinit, r'$e\ [mm]$', e_min, e_max, valinit=e_init)
+# slider length
+asinit = pl.axes([0.25, 0.5*spreading+init_spread, 0.6, 0.05], facecolor=axcolor)
+ssinit = Slider(asinit, r'$s\ [mm]$', s_min, s_max, valinit=s_init)
+# time
+atinit = pl.axes([0.25, 0.75*spreading+init_spread, 0.6, 0.05], facecolor=axcolor)
+stinit = Slider(atinit, r'$t\ [s]$', t_min, t_max, valinit=t_init)
+# angular position
+adotalphainit = pl.axes([0.25, 1*spreading+init_spread, 0.6, 0.05], facecolor=axcolor)
+sdotalphainit = Slider(adotalphainit, r'$\dot{\alpha}\ [rad/min]$', dotalpha_min, dotalpha_max, valinit=dotalpha_init)
+
+# change slider values
+seinit.on_changed(update)
+sLinit.on_changed(update)
+ssinit.on_changed(update)
+stinit.on_changed(update)
+sdotalphainit.on_changed(update)
+
+# Make checkbuttons with all plotted lines with correct visibility
+rax = pl.axes([0.05, 0.025, 0.6, 0.35])
+lines = [tauB,tauC,VB2,VC2,equi1,equi2,equi3,equi21,equi31]
+labels = [str(line.get_label()) for line in lines]
+visibility = [line.get_visible() for line in lines]
+
+# Check updates on click
+check = CheckButtons(rax, labels, visibility)
+check.on_clicked(func)
+
+# reset
+# button
+resetax = pl.axes([0.8, 0.025, 0.1, 0.04])
+button = Button(resetax, 'Reset', color=axcolor, hovercolor='0.975')
+# function
+button.on_clicked(reset)
+
+# Show figures
+pl.show()