Adding all gifs to the doc (#586)

* update docs

* update docs

* update docs

* update docs

docs/modules/control/control_main.rst

@@ -4,4 +4,5 @@ Control
 =================
 
 .. include:: inverted_pendulum_mpc_control/inverted_pendulum_mpc_control.rst
+.. include:: move_to_a_pose_control/move_to_a_pose_control.rst
 

docs/modules/control/move_to_a_pose_control/move_to_a_pose_control.rst

@@ -0,0 +1,11 @@
+Move to a pose control
+----------------------
+
+This is a simulation of moving to a pose control
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
+
+Ref:
+
+-  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
+   p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__

docs/modules/path_tracking/cgmres_nmpc/cgmres_nmpc.rst

@@ -2,16 +2,16 @@
 Nonlinear Model Predictive Control with C-GMRES
 -----------------------------------------------
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_1_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_1_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_2_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_2_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_3_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_3_0.png
    :width: 600px
 
-.. image:: cgmres_nmpc_files/cgmres_nmpc_4_0.png
+.. image:: cgmres_nmpc/cgmres_nmpc_4_0.png
    :width: 600px
 
 .. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/cgmres_nmpc/animation.gif

docs/modules/path_tracking/lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst

@@ -0,0 +1,13 @@
+.. _linearquadratic-regulator-(lqr)-speed-and-steering-control:
+
+Linear–quadratic regulator (LQR) speed and steering control
+-----------------------------------------------------------
+
+Path tracking simulation with LQR speed and steering control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
+
+References:
+~~~~~~~~~~~
+
+-  `Towards fully autonomous driving: Systems and algorithms <http://ieeexplore.ieee.org/document/5940562/>`__

docs/modules/path_tracking/lqr_steering_control/lqr_steering_control.rst

@@ -0,0 +1,14 @@
+.. _linearquadratic-regulator-(lqr)-steering-control:
+
+Linear–quadratic regulator (LQR) steering control
+-------------------------------------------------
+
+Path tracking simulation with LQR steering control and PID speed
+control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
+
+References:
+~~~~~~~~~~~
+-  `ApolloAuto/apollo: An open autonomous driving platform <https://github.com/ApolloAuto/apollo>`_
+

docs/modules/path_tracking/path_tracking_main.rst

@@ -3,94 +3,11 @@
 Path Tracking
 =============
 
-move to a pose control
-----------------------
-
-This is a simulation of moving to a pose control
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/move_to_pose/animation.gif
-
-Ref:
-
--  `P. I. Corke, "Robotics, Vision and Control" \| SpringerLink
-   p102 <https://link.springer.com/book/10.1007/978-3-642-20144-8>`__
-
-Pure pursuit tracking
----------------------
-
-Path tracking simulation with pure pursuit steering control and PID
-speed control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
-
-The red line is a target course, the green cross means the target point
-for pure pursuit control, the blue line is the tracking.
-
-Ref:
-
--  `A Survey of Motion Planning and Control Techniques for Self-driving
-   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
-
-Stanley control
----------------
-
-Path tracking simulation with Stanley steering control and PID speed
-control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
-
-Ref:
-
--  `Stanley: The robot that won the DARPA grand
-   challenge <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`__
-
--  `Automatic Steering Methods for Autonomous Automobile Path
-   Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`__
-
-Rear wheel feedback control
----------------------------
-
-Path tracking simulation with rear wheel feedback steering control and
-PID speed control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
-
-Ref:
-
--  `A Survey of Motion Planning and Control Techniques for Self-driving
-   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__
-
-.. _linearquadratic-regulator-(lqr)-steering-control:
-
-Linear–quadratic regulator (LQR) steering control
--------------------------------------------------
-
-Path tracking simulation with LQR steering control and PID speed
-control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_steer_control/animation.gif
-
-Ref:
-
--  `ApolloAuto/apollo: An open autonomous driving
-   platform <https://github.com/ApolloAuto/apollo>`__
-
-.. _linearquadratic-regulator-(lqr)-speed-and-steering-control:
-
-Linear–quadratic regulator (LQR) speed and steering control
------------------------------------------------------------
-
-Path tracking simulation with LQR speed and steering control.
-
-.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/lqr_speed_steer_control/animation.gif
-
-Ref:
-
--  `Towards fully autonomous driving: Systems and algorithms - IEEE
-   Conference
-   Publication <http://ieeexplore.ieee.org/document/5940562/>`__
-
-.. include:: Model_predictive_speed_and_steering_control.rst
-
-.. include:: cgmres_nmpc.rst
+.. include:: pure_pursuit_tracking/pure_pursuit_tracking.rst
+.. include:: stanley_control/stanley_control.rst
+.. include:: rear_wheel_feedback_control/rear_wheel_feedback_control.rst
+.. include:: lqr_steering_control/lqr_steering_control.rst
+.. include:: lqr_speed_and_steering_control/lqr_speed_and_steering_control.rst
+.. include:: model_predictive_speed_and_steering_control/model_predictive_speed_and_steering_control.rst
+.. include:: cgmres_nmpc/cgmres_nmpc.rst
 

docs/modules/path_tracking/pure_pursuit_tracking/pure_pursuit_tracking.rst

@@ -0,0 +1,16 @@
+Pure pursuit tracking
+---------------------
+
+Path tracking simulation with pure pursuit steering control and PID
+speed control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/pure_pursuit/animation.gif
+
+The red line is a target course, the green cross means the target point
+for pure pursuit control, the blue line is the tracking.
+
+References:
+~~~~~~~~~~~
+
+-  `A Survey of Motion Planning and Control Techniques for Self-driving
+   Urban Vehicles <https://arxiv.org/abs/1604.07446>`_

docs/modules/path_tracking/rear_wheel_feedback_control/rear_wheel_feedback_control.rst

@@ -0,0 +1,12 @@
+Rear wheel feedback control
+---------------------------
+
+Path tracking simulation with rear wheel feedback steering control and
+PID speed control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/rear_wheel_feedback/animation.gif
+
+References:
+~~~~~~~~~~~
+-  `A Survey of Motion Planning and Control Techniques for Self-driving
+   Urban Vehicles <https://arxiv.org/abs/1604.07446>`__

docs/modules/path_tracking/stanley_control/stanley_control.rst

@@ -0,0 +1,16 @@
+Stanley control
+---------------
+
+Path tracking simulation with Stanley steering control and PID speed
+control.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/PathTracking/stanley_controller/animation.gif
+
+References:
+~~~~~~~~~~~
+
+-  `Stanley: The robot that won the DARPA grand
+   challenge <http://robots.stanford.edu/papers/thrun.stanley05.pdf>`_
+
+-  `Automatic Steering Methods for Autonomous Automobile Path
+   Tracking <https://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf>`_

docs/modules/slam/FastSLAM1/FastSLAM1.rst

@@ -2,8 +2,7 @@
 FastSLAM1.0
 -----------
 
-.. image:: FastSLAM1_files/FastSLAM1_1_0.png
-   :width: 600px
+.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
 
 
 
@@ -12,6 +11,9 @@ Simulation
 
 This is a feature based SLAM example using FastSLAM 1.0.
 
+.. image:: FastSLAM1/FastSLAM1_1_0.png
+   :width: 600px
+
 The blue line is ground truth, the black line is dead reckoning, the red
 line is the estimated trajectory with FastSLAM.
 
@@ -20,8 +22,6 @@ The red points are particles of FastSLAM.
 Black points are landmarks, blue crosses are estimated landmark
 positions by FastSLAM.
 
-.. figure:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
-   :alt: gif
 
 Introduction
 ~~~~~~~~~~~~
@@ -527,19 +527,13 @@ indices
 
 
 
-.. image:: FastSLAM1_files/FastSLAM1_12_0.png
-
-
-
-.. image:: FastSLAM1_files/FastSLAM1_12_1.png
+.. image:: FastSLAM1/FastSLAM1_12_0.png
+.. image:: FastSLAM1/FastSLAM1_12_1.png
 
 
 References
 ~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
 
--  `FastSLAM Lecture`_
-
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
+-  `FastSLAM Lecture <http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf>`_

docs/modules/slam/FastSLAM2/FastSLAM2.rst

@@ -0,0 +1,16 @@
+FastSLAM 2.0
+------------
+
+This is a feature based SLAM example using FastSLAM 2.0.
+
+The animation has the same meanings as one of FastSLAM 1.0.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
+
+References
+~~~~~~~~~~
+
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
+
+- `SLAM simulations by Tim Bailey <http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm>`_
+

docs/modules/slam/ekf_slam/ekf_slam.rst

@@ -8,7 +8,7 @@ line is the estimated trajectory with EKF SLAM.
 
 The green crosses are estimated landmarks.
 
-|4|
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif
 
 Simulation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -581,13 +581,11 @@ reckoning and control functions are passed along here as well.
     New LM
     New LM
 
-.. image:: ekf_slam_files/ekf_slam_1_0.png
+.. image:: ekf_slam/ekf_slam_1_0.png
 
 References:
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
--  `PROBABILISTIC ROBOTICS`_
+- `PROBABILISTIC ROBOTICS <http://www.probabilistic-robotics.org/>`_
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
 
-.. |4| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/EKFSLAM/animation.gif

docs/modules/slam/graph_slam/graphSLAM_SE2_example.rst

@@ -44,7 +44,7 @@ The Dataset
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_4_0.png
 
 
 Each edge in this dataset is a constraint that compares the measured
@@ -122,7 +122,7 @@ dataset and plot them.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_8_0.png
 
 
 .. code:: ipython3
@@ -131,7 +131,7 @@ dataset and plot them.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_9_0.png
 
 
 Optimization
@@ -165,8 +165,7 @@ different data sources into a single optimization problem.
             6             215.8405          -0.000000
 
 
-.. figure:: graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
-   :alt: Graph_SLAM_optimization.gif
+.. figure:: graph_slam/graphSLAM_SE2_example_files/Graph_SLAM_optimization.gif
 
 .. code:: ipython3
 
@@ -174,7 +173,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_13_0.png
 
 
 .. code:: ipython3
@@ -196,7 +195,7 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_15_0.png
 
 
 .. code:: ipython3
@@ -205,5 +204,5 @@ different data sources into a single optimization problem.
 
 
 
-.. image:: graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
+.. image:: graph_slam/graphSLAM_SE2_example_files/graphSLAM_SE2_example_16_0.png
 

docs/modules/slam/graph_slam/graphSLAM_doc.rst

@@ -142,7 +142,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_2_0.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_0.png
 
 
 .. parsed-literal::
@@ -157,7 +157,7 @@ created based on the information of the motion and the observation.
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_2_2.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_2_2.png
 
 
 In particular, the tasks are split into 2 parts, graph construction, and
@@ -289,7 +289,7 @@ robot with 3DoF, namely, :math:`[x, y, \theta]^T`
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_4_0.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_4_0.png
 
 
 .. code:: ipython3
@@ -420,7 +420,7 @@ zero since :math:`x_j + d_j cos(\psi_j + \theta_j)` should equal
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_9_1.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_9_1.png
 
 
 Since the constraints equations derived before are non-linear,
@@ -494,12 +494,9 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 
 
 
-.. image:: graphSLAM_doc_files/graphSLAM_doc_11_1.png
-
-
-
-.. image:: graphSLAM_doc_files/graphSLAM_doc_11_2.png
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_1.png
 
+.. image:: graph_slam/graphSLAM_doc_files/graphSLAM_doc_11_2.png
 
 .. code:: ipython3
 
@@ -546,11 +543,11 @@ Similarly, :math:`B = \frac{\partial e_{ij}}{\partial \boldsymbol{x}_j}`
 The references:
 ^^^^^^^^^^^^^^^
 
--  http://robots.stanford.edu/papers/thrun.graphslam.pdf
+-  `The GraphSLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures <http://robots.stanford.edu/papers/thrun.graphslam.pdf>`_
 
--  http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf
+-  `Introduction to Mobile Robotics Graph-Based SLAM <http://ais.informatik.uni-freiburg.de/teaching/ss13/robotics/slides/16-graph-slam.pdf>`_
 
--  http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
+-  `A Tutorial on Graph-Based SLAM <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
 
 N.B. An additional step is required that uses the estimated path to
 update the belief regarding the map.

docs/modules/slam/graph_slam/graphSLAM_formulation.rst

@@ -213,4 +213,6 @@ Using this notation, we obtain the optimal update as
 We apply this update to the poses via :eq:`update` and repeat until convergence.
 
 
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
+.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010)
+.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43.
+

docs/modules/slam/graph_slam/graph_slam.rst

@@ -0,0 +1,24 @@
+Graph based SLAM
+----------------
+
+This is a graph based SLAM example.
+
+The blue line is ground truth.
+
+The black line is dead reckoning.
+
+The red line is the estimated trajectory with Graph based SLAM.
+
+The black stars are landmarks for graph edge generation.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif
+
+.. include:: graph_slam/graphSLAM_doc.rst
+.. include:: graph_slam/graphSLAM_formulation.rst
+.. include:: graph_slam/graphSLAM_SE2_example.rst
+
+References:
+~~~~~~~~~~~
+
+- `A Tutorial on Graph-Based SLAM <http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf>`_
+

docs/modules/slam/iterative_closest_point_matching/iterative_closest_point_matching.rst

@@ -0,0 +1,16 @@
+.. _iterative-closest-point-(icp)-matching:
+
+Iterative Closest Point (ICP) Matching
+--------------------------------------
+
+This is a 2D ICP matching example with singular value decomposition.
+
+It can calculate a rotation matrix and a translation vector between
+points to points.
+
+.. image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
+
+References
+~~~~~~~~~~
+
+- `Introduction to Mobile Robotics: Iterative Closest Point Algorithm <https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf>`_

docs/modules/slam/slam_main.rst

@@ -5,77 +5,9 @@ SLAM
 
 Simultaneous Localization and Mapping(SLAM) examples
 
-.. _iterative-closest-point-(icp)-matching:
+.. include:: iterative_closest_point_matching/iterative_closest_point_matching.rst
+.. include:: ekf_slam/ekf_slam.rst
+.. include:: FastSLAM1/FastSLAM1.rst
+.. include:: FastSLAM2/FastSLAM2.rst
+.. include:: graph_slam/graph_slam.rst
 
-Iterative Closest Point (ICP) Matching
---------------------------------------
-
-This is a 2D ICP matching example with singular value decomposition.
-
-It can calculate a rotation matrix and a translation vector between
-points to points.
-
-|3|
-
-Ref:
-
--  `Introduction to Mobile Robotics: Iterative Closest Point Algorithm`_
-
-
-.. include:: ekf_slam.rst
-
-
-.. include:: FastSLAM1.rst
-
-FastSLAM 2.0
-------------
-
-This is a feature based SLAM example using FastSLAM 2.0.
-
-The animation has the same meanings as one of FastSLAM 1.0.
-
-|6|
-
-References
-~~~~~~~~~~
-
--  `PROBABILISTIC ROBOTICS`_
-
--  `SLAM simulations by Tim Bailey`_
-
-Graph based SLAM
-----------------
-
-This is a graph based SLAM example.
-
-The blue line is ground truth.
-
-The black line is dead reckoning.
-
-The red line is the estimated trajectory with Graph based SLAM.
-
-The black stars are landmarks for graph edge generation.
-
-|7|
-
-.. include:: graphSLAM_doc.rst
-.. include:: graphSLAM_formulation.rst
-.. include:: graphSLAM_SE2_example.rst
-
-Ref:
-
--  `A Tutorial on Graph-Based SLAM`_
-
-.. _`Introduction to Mobile Robotics: Iterative Closest Point Algorithm`: https://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
-.. _PROBABILISTIC ROBOTICS: http://www.probabilistic-robotics.org/
-.. _SLAM simulations by Tim Bailey: http://www-personal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
-.. _A Tutorial on Graph-Based SLAM: http://www2.informatik.uni-freiburg.de/~stachnis/pdf/grisetti10titsmag.pdf
-.. _FastSLAM Lecture: http://ais.informatik.uni-freiburg.de/teaching/ws12/mapping/pdf/slam10-fastslam.pdf
-
-.. [blanco2010tutorial] Blanco, J.-L.A tutorial onSE(3) transformation parameterization and on-manifold optimization.University of Malaga, Tech. Rep 3(2010)
-.. [grisetti2010tutorial] Grisetti, G., Kummerle, R., Stachniss, C., and Burgard, W.A tutorial on graph-based SLAM.IEEE Intelligent Transportation Systems Magazine 2, 4 (2010), 31–43.
-
-.. |3| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/iterative_closest_point/animation.gif
-.. |5| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM1/animation.gif
-.. |6| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/FastSLAM2/animation.gif
-.. |7| image:: https://github.com/AtsushiSakai/PythonRoboticsGifs/raw/master/SLAM/GraphBasedSLAM/animation.gif