diff --git a/gym/scripts/3/4/3rd_actuator_pca.png b/gym/scripts/3/4/3rd_actuator_pca.png new file mode 100644 index 0000000..97e1a8b Binary files /dev/null and b/gym/scripts/3/4/3rd_actuator_pca.png differ diff --git a/gym/scripts/3/4/4th_leg_pca.png b/gym/scripts/3/4/4th_leg_pca.png new file mode 100644 index 0000000..f8136ef Binary files /dev/null and b/gym/scripts/3/4/4th_leg_pca.png differ diff --git a/gym/scripts/3/4/Phase Plot.png b/gym/scripts/3/4/Phase Plot.png new file mode 100644 index 0000000..ecf5743 Binary files /dev/null and b/gym/scripts/3/4/Phase Plot.png differ diff --git a/gym/scripts/3/4/Time Plot.png b/gym/scripts/3/4/Time Plot.png new file mode 100644 index 0000000..d81a3af Binary files /dev/null and b/gym/scripts/3/4/Time Plot.png differ diff --git a/gym/scripts/3/4/all_actuators.png b/gym/scripts/3/4/all_actuators.png new file mode 100644 index 0000000..92cd097 Binary files /dev/null and b/gym/scripts/3/4/all_actuators.png differ diff --git a/gym/scripts/3/4/all_actuators_all_legs.png b/gym/scripts/3/4/all_actuators_all_legs.png new file mode 100644 index 0000000..92cd097 Binary files /dev/null and b/gym/scripts/3/4/all_actuators_all_legs.png differ diff --git a/gym/scripts/3/4/plot_base_ang_vel.png b/gym/scripts/3/4/plot_base_ang_vel.png new file mode 100644 index 0000000..84c5e2e Binary files /dev/null and b/gym/scripts/3/4/plot_base_ang_vel.png differ diff --git a/gym/scripts/3/4/plot_base_lin_vel.png b/gym/scripts/3/4/plot_base_lin_vel.png new file mode 100644 index 0000000..c20a3b9 Binary files /dev/null and b/gym/scripts/3/4/plot_base_lin_vel.png differ diff --git a/gym/scripts/3/4/plot_commands.png b/gym/scripts/3/4/plot_commands.png new file mode 100644 index 0000000..b3ea7e5 Binary files /dev/null and b/gym/scripts/3/4/plot_commands.png differ diff --git a/gym/scripts/3/4/plot_dof pos error_last5_0.png b/gym/scripts/3/4/plot_dof pos error_last5_0.png new file mode 100644 index 0000000..bc2aaca Binary files /dev/null and b/gym/scripts/3/4/plot_dof pos error_last5_0.png differ diff --git a/gym/scripts/3/4/plot_dof pos error_last5_1.png b/gym/scripts/3/4/plot_dof pos error_last5_1.png new file mode 100644 index 0000000..aeb0146 Binary files /dev/null and b/gym/scripts/3/4/plot_dof pos error_last5_1.png differ diff --git a/gym/scripts/3/4/plot_dof pos error_last5_2.png b/gym/scripts/3/4/plot_dof pos error_last5_2.png new file mode 100644 index 0000000..e151eff Binary files /dev/null and b/gym/scripts/3/4/plot_dof pos error_last5_2.png differ diff --git a/gym/scripts/3/4/plot_dof pos error_last5_3.png b/gym/scripts/3/4/plot_dof pos error_last5_3.png new file mode 100644 index 0000000..b433e37 Binary files /dev/null and b/gym/scripts/3/4/plot_dof pos error_last5_3.png differ diff --git a/gym/scripts/3/4/plot_grf_last5_0.png b/gym/scripts/3/4/plot_grf_last5_0.png new file mode 100644 index 0000000..9b04989 Binary files /dev/null and b/gym/scripts/3/4/plot_grf_last5_0.png differ diff --git a/gym/scripts/3/4/plot_grf_last5_1.png b/gym/scripts/3/4/plot_grf_last5_1.png new file mode 100644 index 0000000..bee9dae Binary files /dev/null and b/gym/scripts/3/4/plot_grf_last5_1.png differ diff --git a/gym/scripts/3/4/plot_grf_last5_2.png b/gym/scripts/3/4/plot_grf_last5_2.png new file mode 100644 index 0000000..e96dcc6 Binary files /dev/null and b/gym/scripts/3/4/plot_grf_last5_2.png differ diff --git a/gym/scripts/3/4/plot_grf_last5_3.png b/gym/scripts/3/4/plot_grf_last5_3.png new file mode 100644 index 0000000..e9bdf69 Binary files /dev/null and b/gym/scripts/3/4/plot_grf_last5_3.png differ diff --git a/gym/scripts/3/4/plot_q_last5_0.png b/gym/scripts/3/4/plot_q_last5_0.png new file mode 100644 index 0000000..2c6ba10 Binary files /dev/null and b/gym/scripts/3/4/plot_q_last5_0.png differ diff --git a/gym/scripts/3/4/plot_q_last5_1.png b/gym/scripts/3/4/plot_q_last5_1.png new file mode 100644 index 0000000..56d0d1f Binary files /dev/null and b/gym/scripts/3/4/plot_q_last5_1.png differ diff --git a/gym/scripts/3/4/plot_q_last5_2.png b/gym/scripts/3/4/plot_q_last5_2.png new file mode 100644 index 0000000..b715bc2 Binary files /dev/null and b/gym/scripts/3/4/plot_q_last5_2.png differ diff --git a/gym/scripts/3/4/plot_q_last5_3.png b/gym/scripts/3/4/plot_q_last5_3.png new file mode 100644 index 0000000..43992d2 Binary files /dev/null and b/gym/scripts/3/4/plot_q_last5_3.png differ diff --git a/gym/scripts/3/4/plot_qd_last5_0.png b/gym/scripts/3/4/plot_qd_last5_0.png new file mode 100644 index 0000000..a870f7e Binary files /dev/null and b/gym/scripts/3/4/plot_qd_last5_0.png differ diff --git a/gym/scripts/3/4/plot_qd_last5_1.png b/gym/scripts/3/4/plot_qd_last5_1.png new file mode 100644 index 0000000..ac02461 Binary files /dev/null and b/gym/scripts/3/4/plot_qd_last5_1.png differ diff --git a/gym/scripts/3/4/plot_qd_last5_2.png b/gym/scripts/3/4/plot_qd_last5_2.png new file mode 100644 index 0000000..c2eb67e Binary files /dev/null and b/gym/scripts/3/4/plot_qd_last5_2.png differ diff --git a/gym/scripts/3/4/plot_qd_last5_3.png b/gym/scripts/3/4/plot_qd_last5_3.png new file mode 100644 index 0000000..9a20db9 Binary files /dev/null and b/gym/scripts/3/4/plot_qd_last5_3.png differ diff --git a/gym/scripts/3/4/plot_reward.png b/gym/scripts/3/4/plot_reward.png new file mode 100644 index 0000000..93ade43 Binary files /dev/null and b/gym/scripts/3/4/plot_reward.png differ diff --git a/gym/scripts/3/4/plot_tau_last5_0.png b/gym/scripts/3/4/plot_tau_last5_0.png new file mode 100644 index 0000000..5f53309 Binary files /dev/null and b/gym/scripts/3/4/plot_tau_last5_0.png differ diff --git a/gym/scripts/3/4/plot_tau_last5_1.png b/gym/scripts/3/4/plot_tau_last5_1.png new file mode 100644 index 0000000..bb9cfd2 Binary files /dev/null and b/gym/scripts/3/4/plot_tau_last5_1.png differ diff --git a/gym/scripts/3/4/plot_tau_last5_2.png b/gym/scripts/3/4/plot_tau_last5_2.png new file mode 100644 index 0000000..1fd51c7 Binary files /dev/null and b/gym/scripts/3/4/plot_tau_last5_2.png differ diff --git a/gym/scripts/3/4/plot_tau_last5_3.png b/gym/scripts/3/4/plot_tau_last5_3.png new file mode 100644 index 0000000..649cca2 Binary files /dev/null and b/gym/scripts/3/4/plot_tau_last5_3.png differ diff --git a/gym/scripts/Images/0_3.png b/gym/scripts/Images/0_3.png new file mode 100644 index 0000000..03df3e7 Binary files /dev/null and b/gym/scripts/Images/0_3.png differ diff --git a/gym/scripts/Images/0_3q.png b/gym/scripts/Images/0_3q.png new file mode 100644 index 0000000..65f4ab3 Binary files /dev/null and b/gym/scripts/Images/0_3q.png differ diff --git a/gym/scripts/Images/3_6.png b/gym/scripts/Images/3_6.png new file mode 100644 index 0000000..869829b Binary files /dev/null and b/gym/scripts/Images/3_6.png differ diff --git a/gym/scripts/Images/3_6q.png b/gym/scripts/Images/3_6q.png new file mode 100644 index 0000000..4175d69 Binary files /dev/null and b/gym/scripts/Images/3_6q.png differ diff --git a/gym/scripts/Images/6_0q.png b/gym/scripts/Images/6_0q.png new file mode 100644 index 0000000..6400548 Binary files /dev/null and b/gym/scripts/Images/6_0q.png differ diff --git a/gym/scripts/Images/6_9.png b/gym/scripts/Images/6_9.png new file mode 100644 index 0000000..bf85216 Binary files /dev/null and b/gym/scripts/Images/6_9.png differ diff --git a/gym/scripts/Images/6_9q.png b/gym/scripts/Images/6_9q.png new file mode 100644 index 0000000..13f91b8 Binary files /dev/null and b/gym/scripts/Images/6_9q.png differ diff --git a/gym/scripts/Images/9_12.png b/gym/scripts/Images/9_12.png new file mode 100644 index 0000000..13f91b8 Binary files /dev/null and b/gym/scripts/Images/9_12.png differ diff --git a/gym/scripts/Images/9_12q.png b/gym/scripts/Images/9_12q.png new file mode 100644 index 0000000..2daa4e7 Binary files /dev/null and b/gym/scripts/Images/9_12q.png differ diff --git a/gym/scripts/Images/pca plot.png b/gym/scripts/Images/pca plot.png new file mode 100644 index 0000000..0051b82 Binary files /dev/null and b/gym/scripts/Images/pca plot.png differ diff --git a/gym/scripts/Images/pca.png b/gym/scripts/Images/pca.png new file mode 100644 index 0000000..5e2c5d2 Binary files /dev/null and b/gym/scripts/Images/pca.png differ diff --git a/gym/scripts/Images/pca2.png b/gym/scripts/Images/pca2.png new file mode 100644 index 0000000..3aeeab3 Binary files /dev/null and b/gym/scripts/Images/pca2.png differ diff --git a/gym/scripts/Images/pca3.png b/gym/scripts/Images/pca3.png new file mode 100644 index 0000000..992cead Binary files /dev/null and b/gym/scripts/Images/pca3.png differ diff --git a/gym/scripts/Images/pca4.png b/gym/scripts/Images/pca4.png new file mode 100644 index 0000000..19c820a Binary files /dev/null and b/gym/scripts/Images/pca4.png differ diff --git a/gym/scripts/Images/pca_test.png b/gym/scripts/Images/pca_test.png new file mode 100644 index 0000000..44fb3af Binary files /dev/null and b/gym/scripts/Images/pca_test.png differ diff --git a/gym/scripts/Images/pca_test2.png b/gym/scripts/Images/pca_test2.png new file mode 100644 index 0000000..671c526 Binary files /dev/null and b/gym/scripts/Images/pca_test2.png differ diff --git a/gym/scripts/Images/pcaeigen.png b/gym/scripts/Images/pcaeigen.png new file mode 100644 index 0000000..016742d Binary files /dev/null and b/gym/scripts/Images/pcaeigen.png differ diff --git a/gym/scripts/Images/pcavar.png b/gym/scripts/Images/pcavar.png new file mode 100644 index 0000000..ccf83bb Binary files /dev/null and b/gym/scripts/Images/pcavar.png differ diff --git a/gym/scripts/Images/plot_Q0.png b/gym/scripts/Images/plot_Q0.png new file mode 100644 index 0000000..8469fd3 Binary files /dev/null and b/gym/scripts/Images/plot_Q0.png differ diff --git a/gym/scripts/Images/plot_Q1.png b/gym/scripts/Images/plot_Q1.png new file mode 100644 index 0000000..94b5cce Binary files /dev/null and b/gym/scripts/Images/plot_Q1.png differ diff --git a/gym/scripts/Images/plot_Q2.png b/gym/scripts/Images/plot_Q2.png new file mode 100644 index 0000000..34438ec Binary files /dev/null and b/gym/scripts/Images/plot_Q2.png differ diff --git a/gym/scripts/Images/plot_Q3.png b/gym/scripts/Images/plot_Q3.png new file mode 100644 index 0000000..80311e0 Binary files /dev/null and b/gym/scripts/Images/plot_Q3.png differ diff --git a/gym/scripts/Images/plot_Q_last5_0.png b/gym/scripts/Images/plot_Q_last5_0.png new file mode 100644 index 0000000..9445286 Binary files /dev/null and b/gym/scripts/Images/plot_Q_last5_0.png differ diff --git a/gym/scripts/Images/plot_Q_last5_1.png b/gym/scripts/Images/plot_Q_last5_1.png new file mode 100644 index 0000000..1670307 Binary files /dev/null and b/gym/scripts/Images/plot_Q_last5_1.png differ diff --git a/gym/scripts/Images/plot_Q_last5_2.png b/gym/scripts/Images/plot_Q_last5_2.png new file mode 100644 index 0000000..0e85207 Binary files /dev/null and b/gym/scripts/Images/plot_Q_last5_2.png differ diff --git a/gym/scripts/Images/plot_Q_last5_3.png b/gym/scripts/Images/plot_Q_last5_3.png new file mode 100644 index 0000000..3662d15 Binary files /dev/null and b/gym/scripts/Images/plot_Q_last5_3.png differ diff --git a/gym/scripts/Images/skeletonoutput.png b/gym/scripts/Images/skeletonoutput.png new file mode 100644 index 0000000..d81a3af Binary files /dev/null and b/gym/scripts/Images/skeletonoutput.png differ diff --git a/gym/scripts/Images/skeletonoutput1.png b/gym/scripts/Images/skeletonoutput1.png new file mode 100644 index 0000000..34854d0 Binary files /dev/null and b/gym/scripts/Images/skeletonoutput1.png differ diff --git a/gym/scripts/Images/torqu_pca3.png b/gym/scripts/Images/torqu_pca3.png new file mode 100644 index 0000000..48b1ce0 Binary files /dev/null and b/gym/scripts/Images/torqu_pca3.png differ diff --git a/gym/scripts/Images/torque_pca1.png b/gym/scripts/Images/torque_pca1.png new file mode 100644 index 0000000..af5b355 Binary files /dev/null and b/gym/scripts/Images/torque_pca1.png differ diff --git a/gym/scripts/Images/torque_pca2.png b/gym/scripts/Images/torque_pca2.png new file mode 100644 index 0000000..a8d5a0b Binary files /dev/null and b/gym/scripts/Images/torque_pca2.png differ diff --git a/gym/scripts/Images/torque_pca4.png b/gym/scripts/Images/torque_pca4.png new file mode 100644 index 0000000..22a2bbb Binary files /dev/null and b/gym/scripts/Images/torque_pca4.png differ diff --git a/gym/scripts/Phase Plot.png b/gym/scripts/Phase Plot.png new file mode 100644 index 0000000..1270d5e Binary files /dev/null and b/gym/scripts/Phase Plot.png differ diff --git a/gym/scripts/Q/plot_Q0.png b/gym/scripts/Q/plot_Q0.png new file mode 100644 index 0000000..d3c0e5b Binary files /dev/null and b/gym/scripts/Q/plot_Q0.png differ diff --git a/gym/scripts/Q/plot_Q1.png b/gym/scripts/Q/plot_Q1.png new file mode 100644 index 0000000..18e9ee0 Binary files /dev/null and b/gym/scripts/Q/plot_Q1.png differ diff --git a/gym/scripts/Q/plot_Q2.png b/gym/scripts/Q/plot_Q2.png new file mode 100644 index 0000000..acad17d Binary files /dev/null and b/gym/scripts/Q/plot_Q2.png differ diff --git a/gym/scripts/Q/plot_Q3.png b/gym/scripts/Q/plot_Q3.png new file mode 100644 index 0000000..0f6d5f2 Binary files /dev/null and b/gym/scripts/Q/plot_Q3.png differ diff --git a/gym/scripts/Qd/plot_Qd0.png b/gym/scripts/Qd/plot_Qd0.png new file mode 100644 index 0000000..c57d283 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd0.png differ diff --git a/gym/scripts/Qd/plot_Qd0_last5.png b/gym/scripts/Qd/plot_Qd0_last5.png new file mode 100644 index 0000000..2aa6f96 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd0_last5.png differ diff --git a/gym/scripts/Qd/plot_Qd1.png b/gym/scripts/Qd/plot_Qd1.png new file mode 100644 index 0000000..06f9a90 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd1.png differ diff --git a/gym/scripts/Qd/plot_Qd1_last5.png b/gym/scripts/Qd/plot_Qd1_last5.png new file mode 100644 index 0000000..097ae28 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd1_last5.png differ diff --git a/gym/scripts/Qd/plot_Qd2.png b/gym/scripts/Qd/plot_Qd2.png new file mode 100644 index 0000000..b8dc466 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd2.png differ diff --git a/gym/scripts/Qd/plot_Qd2_last5.png b/gym/scripts/Qd/plot_Qd2_last5.png new file mode 100644 index 0000000..7266b14 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd2_last5.png differ diff --git a/gym/scripts/Qd/plot_Qd3.png b/gym/scripts/Qd/plot_Qd3.png new file mode 100644 index 0000000..7334453 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd3.png differ diff --git a/gym/scripts/Qd/plot_Qd3_last5.png b/gym/scripts/Qd/plot_Qd3_last5.png new file mode 100644 index 0000000..276462d Binary files /dev/null and b/gym/scripts/Qd/plot_Qd3_last5.png differ diff --git a/gym/scripts/Qd/plot_Qd_last_0.png b/gym/scripts/Qd/plot_Qd_last_0.png new file mode 100644 index 0000000..661a210 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd_last_0.png differ diff --git a/gym/scripts/Qd/plot_Qd_last_1.png b/gym/scripts/Qd/plot_Qd_last_1.png new file mode 100644 index 0000000..4453f4b Binary files /dev/null and b/gym/scripts/Qd/plot_Qd_last_1.png differ diff --git a/gym/scripts/Qd/plot_Qd_last_2.png b/gym/scripts/Qd/plot_Qd_last_2.png new file mode 100644 index 0000000..8578158 Binary files /dev/null and b/gym/scripts/Qd/plot_Qd_last_2.png differ diff --git a/gym/scripts/Qd/plot_Qd_last_3.png b/gym/scripts/Qd/plot_Qd_last_3.png new file mode 100644 index 0000000..ceb66df Binary files /dev/null and b/gym/scripts/Qd/plot_Qd_last_3.png differ diff --git a/gym/scripts/Tau/plot_Tau0.png b/gym/scripts/Tau/plot_Tau0.png new file mode 100644 index 0000000..0dfdefe Binary files /dev/null and b/gym/scripts/Tau/plot_Tau0.png differ diff --git a/gym/scripts/Tau/plot_Tau1.png b/gym/scripts/Tau/plot_Tau1.png new file mode 100644 index 0000000..f4687ec Binary files /dev/null and b/gym/scripts/Tau/plot_Tau1.png differ diff --git a/gym/scripts/Tau/plot_Tau2.png b/gym/scripts/Tau/plot_Tau2.png new file mode 100644 index 0000000..b95fcf2 Binary files /dev/null and b/gym/scripts/Tau/plot_Tau2.png differ diff --git a/gym/scripts/Tau/plot_Tau3.png b/gym/scripts/Tau/plot_Tau3.png new file mode 100644 index 0000000..052db09 Binary files /dev/null and b/gym/scripts/Tau/plot_Tau3.png differ diff --git a/gym/scripts/Tau/plot_Tau_last50.png b/gym/scripts/Tau/plot_Tau_last50.png new file mode 100644 index 0000000..92af84e Binary files /dev/null and b/gym/scripts/Tau/plot_Tau_last50.png differ diff --git a/gym/scripts/Tau/plot_Tau_last51.png b/gym/scripts/Tau/plot_Tau_last51.png new file mode 100644 index 0000000..1cd3acb Binary files /dev/null and b/gym/scripts/Tau/plot_Tau_last51.png differ diff --git a/gym/scripts/Tau/plot_Tau_last52.png b/gym/scripts/Tau/plot_Tau_last52.png new file mode 100644 index 0000000..611f9b5 Binary files /dev/null and b/gym/scripts/Tau/plot_Tau_last52.png differ diff --git a/gym/scripts/Tau/plot_Tau_last53.png b/gym/scripts/Tau/plot_Tau_last53.png new file mode 100644 index 0000000..94cea78 Binary files /dev/null and b/gym/scripts/Tau/plot_Tau_last53.png differ diff --git a/gym/scripts/Time Plot_leg0.png b/gym/scripts/Time Plot_leg0.png new file mode 100644 index 0000000..0697a3f Binary files /dev/null and b/gym/scripts/Time Plot_leg0.png differ diff --git a/gym/scripts/Time Plot_leg1.png b/gym/scripts/Time Plot_leg1.png new file mode 100644 index 0000000..80b40c1 Binary files /dev/null and b/gym/scripts/Time Plot_leg1.png differ diff --git a/gym/scripts/Time Plot_leg2.png b/gym/scripts/Time Plot_leg2.png new file mode 100644 index 0000000..d392242 Binary files /dev/null and b/gym/scripts/Time Plot_leg2.png differ diff --git a/gym/scripts/Time Plot_leg3.png b/gym/scripts/Time Plot_leg3.png new file mode 100644 index 0000000..2f132cf Binary files /dev/null and b/gym/scripts/Time Plot_leg3.png differ diff --git a/gym/scripts/all_actuators_all_legs.png b/gym/scripts/all_actuators_all_legs.png new file mode 100644 index 0000000..54a77d6 Binary files /dev/null and b/gym/scripts/all_actuators_all_legs.png differ diff --git a/gym/scripts/pca.ipynb b/gym/scripts/pca.ipynb new file mode 100644 index 0000000..42e7e1d --- /dev/null +++ b/gym/scripts/pca.ipynb @@ -0,0 +1,618 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from ast import literal_eval\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.decomposition import PCA\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50050,)\n" + ] + } + ], + "source": [ + "# def shuffle_along_axis(a, axis):\n", + "# idx = np.random.rand(*a.shape).argsort(axis=axis)\n", + "# return np.take_along_axis(a,idx,axis=axis)\n", + "\n", + "xls = pd.ExcelFile(\"/home/aileen/ORCAgym/gym/scripts/mini_cheetah_logs.xlsx\")\n", + "df1 = pd.read_excel(xls, 'q').to_numpy()[:,9:12]\n", + "#df2 = pd.read_excel(xls, 'qd').to_numpy()\n", + "#tau = pd.read_excel(xls, 'tau').to_numpy()\n", + "\n", + "df_feet_contacts = pd.read_excel(xls,'grf').to_numpy()\n", + "\n", + "#df_shuffled = shuffle_along_axis(df1,0)\n", + "# df1 = shuffle_along_axis(df1,0)\n", + "\n", + "df1 = np.random.permutation(df1[:,0])\n", + "df1 = df1[np.random.permutation(np.arange(df1.shape[0])), :]\n", + "\n", + "print(df1.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 354, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0.02897564 -0.78196044 1.54992202]\n", + "[ 0.02897564 -0.78196044 1.54992202]\n" + ] + }, + { + "ename": "ValueError", + "evalue": "s must be a scalar, or float array-like with the same size as x and y", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[354], line 17\u001b[0m\n\u001b[1;32m 15\u001b[0m plt\u001b[38;5;241m.\u001b[39mcla()\n\u001b[1;32m 16\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m---> 17\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf1_norm\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf1_norm\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mred\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 18\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(df_shuffled_norm[:, \u001b[38;5;241m0\u001b[39m], df_shuffled_norm[:, \u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 19\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m~/anaconda3/envs/gpugym/lib/python3.8/site-packages/matplotlib/pyplot.py:2817\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, data, **kwargs)\u001b[0m\n\u001b[1;32m 2810\u001b[0m @_copy_docstring_and_deprecators(Axes.plot)\n\u001b[1;32m 2811\u001b[0m def plot(*args, scalex=True, scaley=True, data=None, **kwargs):\n\u001b[1;32m 2812\u001b[0m return gca().plot(\n\u001b[1;32m 2813\u001b[0m *args, scalex=scalex, scaley=scaley,\n\u001b[1;32m 2814\u001b[0m **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0;32m-> 2817\u001b[0m # Autogenerated by boilerplate.py. Do not edit as changes will be lost.\n\u001b[1;32m 2818\u001b[0m @_copy_docstring_and_deprecators(Axes.plot_date)\n\u001b[1;32m 2819\u001b[0m def plot_date(\n\u001b[1;32m 2820\u001b[0m x, y, fmt='o', tz=None, xdate=True, ydate=False, *,\n\u001b[1;32m 2821\u001b[0m data=None, **kwargs):\n\u001b[1;32m 2822\u001b[0m return gca().plot_date(\n\u001b[1;32m 2823\u001b[0m x, y, fmt=fmt, tz=tz, xdate=xdate, ydate=ydate,\n\u001b[1;32m 2824\u001b[0m **({\"data\": data} if data is not None else {}), **kwargs)\n", + "File \u001b[0;32m~/anaconda3/envs/gpugym/lib/python3.8/site-packages/matplotlib/__init__.py:1414\u001b[0m, in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1374\u001b[0m \u001b[38;5;124;03mA decorator to add a 'data' kwarg to a function.\u001b[39;00m\n\u001b[1;32m 1375\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1408\u001b[0m \u001b[38;5;124;03m func.__wrapped__(value, label=\"key\")\u001b[39;00m\n\u001b[1;32m 1409\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# Return the actual decorator.\u001b[39;00m\n\u001b[1;32m 1412\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m 1413\u001b[0m _preprocess_data,\n\u001b[0;32m-> 1414\u001b[0m replace_names\u001b[38;5;241m=\u001b[39mreplace_names, label_namer\u001b[38;5;241m=\u001b[39mlabel_namer)\n\u001b[1;32m 1416\u001b[0m sig \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39msignature(func)\n\u001b[1;32m 1417\u001b[0m varargs_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/anaconda3/envs/gpugym/lib/python3.8/site-packages/matplotlib/axes/_axes.py:4377\u001b[0m, in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001b[0m\n\u001b[1;32m 4372\u001b[0m mcolors\u001b[38;5;241m.\u001b[39mto_rgba_array(kwcolor)\n\u001b[1;32m 4373\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m 4374\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 4375\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcolor\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m kwarg must be a color or sequence of color \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 4376\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecs. For a sequence of values to be color-mapped, use \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 4377\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m argument instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 4378\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m edgecolors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 4379\u001b[0m edgecolors \u001b[38;5;241m=\u001b[39m kwcolor\n", + "\u001b[0;31mValueError\u001b[0m: s must be a scalar, or float array-like with the same size as x and y" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3ff0eb479a1441ba9f69315fd1ba6d6b", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import sklearn\n", + "\n", + "df1 = pd.read_excel(xls, 'q').to_numpy()[:,9:12]\n", + "df_shuffled = sklearn.utils.shuffle(df1)\n", + "\n", + "df1_mean = np.mean(df1, axis=0)\n", + "df_shuffled_mean = np.mean(df_shuffled, axis=0)\n", + "print(df1_mean)\n", + "print(df_shuffled_mean)\n", + "\n", + "df1_norm = df1 - df1_mean\n", + "df_shuffled_norm = df_shuffled - df_shuffled_mean\n", + "\n", + "\n", + "plt.cla()\n", + "fig = plt.figure()\n", + "plt.scatter(df1_norm[:, 0], df1_norm[:, 1], df1_norm[:, 2], 'red')\n", + "plt.scatter(df_shuffled_norm[:, 0], df_shuffled_norm[:, 1], df_shuffled_norm[:, 2])\n", + "plt.show()\n", + "\n", + "\n", + "pca_1 = PCA(n_components=3)\n", + "pca_shuffled = PCA(n_components=3)\n", + "pc_1 = pca_1.fit_transform(df1_norm)\n", + "pc_shuffled = pca_shuffled.fit_transform(df_shuffled_norm)\n", + "\n", + "print(pca_1.components_)\n", + "print(pca_shuffled.components_)" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([25313, 7912, 36198, ..., 25579, 26843, 28005])" + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.random.permutation(np.arange(df1.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50050, 3)\n", + "(50050, 3)\n", + "-2.1131725518060123e-16 1.0000000000000013\n", + " feature0 feature1 feature2\n", + "50045 -0.802128 -0.013940 0.727944\n", + "50046 -0.010162 -1.055352 0.299491\n", + "50047 -0.499800 -2.982903 1.590129\n", + "50048 0.559808 1.187040 -0.831179\n", + "50049 0.339325 1.340672 1.381058\n", + "[[ 0.08899281 0.69464256 -0.71382911]\n", + " [-0.9581873 0.25538254 0.12906142]\n", + " [-0.27195105 -0.67249645 -0.68832489]]\n", + "8.326672684688674e-17\n", + "1.6653345369377348e-16\n", + "5.551115123125783e-17\n", + "Explained variation per principal component: [0.41468235 0.33969713 0.24562052]\n" + ] + } + ], + "source": [ + "#PCA\n", + "df_shuffled = sklearn.utils.shuffle(df1)\n", + "\n", + "dataset = pd.DataFrame(df_shuffled)\n", + "features = ['1_rf_haa', 'rf_hfe', 'rf_kfe', '2_lf_haa', 'lf_hfe', 'lf_kfe', '3_rh_haa', 'rh_hfe', 'rh_kfe', '4_lh_haa', 'lh_hfe', 'lh_kfe'][9:12]\n", + "dataset.columns = features\n", + "\n", + "features = np.char.mod('%s', features).tolist()\n", + "x = dataset.loc[:, features].values\n", + "print(dataset.loc[:, features].values.shape)\n", + "x = StandardScaler().fit_transform(x) # normalizing the features\n", + "print(x.shape)\n", + "print(np.mean(x[:,1]),np.std(x[:,1]))\n", + "feat_cols = ['feature'+str(i) for i in range(x.shape[1])]\n", + "normalised = pd.DataFrame(x,columns=feat_cols)\n", + "print(normalised.tail())\n", + "\n", + "pca = PCA(n_components=3)\n", + "principalComponents = pca.fit_transform(x)\n", + "\n", + "print(pca.components_)\n", + "print(sum(pca.components_[:,0]*pca.components_[:,1]))\n", + "print(sum(pca.components_[:,1]*pca.components_[:,2]))\n", + "print(sum(pca.components_[:,2]*pca.components_[:,0]))\n", + "\n", + "\n", + "principal_Df = pd.DataFrame(data = principalComponents\n", + " , columns = ['principal component 1', 'principal component 2', 'principal component 3'])\n", + "\n", + "#print(principal_Df.tail())\n", + "\n", + "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))\n", + "\n", + "#print(swing_dataset['label'] )\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0888963045878612e-16\n", + "1.5707963267948966\n", + "1.5707963267948966\n", + "1.5707963267948963\n", + "1.5707963267948966\n" + ] + } + ], + "source": [ + "import math \n", + "a = pca.components_[:,0]\n", + "b = pca.components_[:,1]\n", + "c= pca.components_[:,2]\n", + "print(np.dot(a,b))\n", + "print(math.acos(np.dot(a,b)/(np.linalg.norm(a)*np.linalg.norm(b))))\n", + "print(math.pi/2)\n", + "\n", + "print(math.acos(np.dot(c,b)/(np.linalg.norm(c)*np.linalg.norm(b))))\n", + "\n", + "\n", + "print(math.acos(np.dot(c,a)/(np.linalg.norm(c)*np.linalg.norm(a))))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6db8659694b34851933f691eded318d9", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aileen/anaconda3/envs/gpugym/lib/python3.8/site-packages/matplotlib/collections.py:981: RuntimeWarning:\n", + "\n", + "invalid value encountered in sqrt\n", + "\n", + "/home/aileen/anaconda3/envs/gpugym/lib/python3.8/site-packages/matplotlib/collections.py:981: RuntimeWarning:\n", + "\n", + "invalid value encountered in sqrt\n", + "\n" + ] + } + ], + "source": [ + "plt.cla()\n", + "fig = plt.figure()\n", + "plt.ylabel('Eigenvalues')\n", + "plt.xlabel('# of Features')\n", + "plt.title('PCA Eigenvalues')\n", + "plt.ylim(0,max(pca.explained_variance_))\n", + "plt.style.context('seaborn-whitegrid')\n", + "plt.axhline(y=1,color='r',linestyle='--')\n", + "plt.plot([1,2,3],pca.explained_variance_)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "51bb3396099a4e13a62006245c39afbc", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variance = pca.explained_variance_ratio_\n", + "var = np.cumsum(np.round(variance,decimals = 3)*100)\n", + "\n", + "fig = plt.figure()\n", + "plt.ylabel('% Variance Explained')\n", + "plt.xlabel('# of Features')\n", + "plt.title('PCA Variance Explained')\n", + "plt.ylim(min(var),100.5)\n", + "plt.style.context('seaborn-whitegrid')\n", + "plt.axhline(y=80,color='r',linestyle='--')\n", + "plt.plot([1,2,3],var)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.patches import FancyArrowPatch\n", + "from mpl_toolkits.mplot3d import proj3d\n", + "\n", + "class Arrow3D(FancyArrowPatch):\n", + " def __init__(self, xs, ys, zs, *args, **kwargs):\n", + " super().__init__((0,0), (0,0), *args, **kwargs)\n", + " self._verts3d = xs, ys, zs\n", + "\n", + " def do_3d_projection(self, renderer=None):\n", + " xs3d, ys3d, zs3d = self._verts3d\n", + " xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n", + " self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n", + "\n", + " return np.min(zs)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 384, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0.11071345 -0.97649994 -0.20039428]\n", + " [ 0.86418528 0.26026334 -0.49554669]\n", + " [-0.88805473 0.13152801 -0.50721029]]\n", + "[1.24407189 1.01911175 0.7368763 ]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8dce4f3d71de40e6a9050a311fe3c0cb", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib widget\n", + "fig = plt.figure(figsize=(10,10))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter3D(normalised.loc[:,\"feature0\"],normalised.loc[:,\"feature1\"],normalised.loc[:,\"feature2\"],s=5)\n", + " # #principal_Df.loc[:, 'principal component 1']\n", + " # , principal_Df.loc[:, 'principal component 2']\n", + " # ,principal_Df.loc[:, 'principal component 3'], s = 5)\n", + "eigenvalues = pca.explained_variance_\n", + "coeff = pca.components_.T* (eigenvalues)\n", + "lambda_pca = pca.singular_values_\n", + "print(coeff)\n", + "print(eigenvalues)\n", + "\n", + "for i in range(3):\n", + " arrow_prop_dict = dict(mutation_scale=20, arrowstyle='-|>', color='r', shrinkA=0, shrinkB=0)\n", + "\n", + " a = Arrow3D([0,coeff[i,0]], [0,coeff[i,1]], [0,coeff[i,2]], **arrow_prop_dict)\n", + " ax.add_artist(a)\n", + " #plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, coeff[i,2]*1.15, \"Var\"+str(i+1), color = 'g', ha = 'center', va = 'center')\n", + "\n", + "plt.title(f\"Principal Component Analysis of Dataset\",fontsize=20)\n", + "ax.set_xlabel(features[0],fontsize=20)\n", + "ax.set_ylabel(features[1],fontsize=20)\n", + "ax.set_zlabel(features[2],fontsize=20)\n", + "plt.show()\n", + "#plt.savefig(f'pca plot')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 402, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50050, 3)\n", + "[[ 0.08899281 -0.9581873 -0.27195105]\n", + " [ 0.69464256 0.25538254 -0.67249645]\n", + " [-0.71382911 0.12906142 -0.68832489]]\n" + ] + } + ], + "source": [ + "\n", + "df_phase = pd.read_excel(xls, 'oscillators_phase').to_numpy() #only 1 column\n", + "#df_feet_contacts = pd.read_excel(xls,'feet_contact_forces').to_numpy()\n", + "\n", + "keys = features #for humanoid: [\"right_hip_yaw\", \"right_hip_abad\",\"right_hip_pitch\",\"right_knee\",\"right_ankle\",\"left_hip_yaw\",\"left_hip_abad\",\"left_hip_pitch\",\"left_knee\",\"left_ankle\"]\n", + "n= 50 #16 for humanoid\n", + "m=12 #10 for humanoid\n", + "data = dict.fromkeys(keys, np.empty((0,n)))\n", + "new_q = np.empty((0,n))\n", + "new_q_norm = np.empty((0,n))\n", + "\n", + "R = pca.components_.T\n", + "print(df1_norm.shape)\n", + "print(R)\n", + "\n", + "rotated = np.matmul(R,df1_norm.T).T\n", + "phase_env1 = np.empty((0,n))\n", + "phase_env2 = np.empty((0,n))\n", + "phase_env3 = np.empty((0,n))\n", + "phase_env4 = np.empty((0,n))\n", + "phases = [phase_env1, phase_env2, phase_env3, phase_env4]\n", + "for i in range(0,df_phase.shape[0], n):\n", + " for p in range(len(phases)):\n", + " phases[p] = np.vstack([phases[p],df_phase[i:i+n,p:p+1].T])\n", + " new_q = np.vstack([new_q, rotated[i:i+n,0:1].T])\n", + " new_q_norm = np.vstack([new_q, df1_norm[i:i+n,0:1].T])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 403, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1001, 50)\n", + "(1001, 50)\n", + "1001\n", + "(50050, 3)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "98654fa4516c47fb982a20d2ed0ea019", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f95a29c1fb2e4db2896b21e917200424", + "version_major": 2, + "version_minor": 0 + }, + "image/png": "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", + "text/html": [ + "\n", + "
\n", + "
\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "last = 0\n", + "fig = plt.figure(figsize=(6,6))\n", + "print(new_q.shape)\n", + "print(phases[3].shape)\n", + "print(len(phases[3][:,0]))\n", + "for j in range(0,10):\n", + " for p in range(1,len(phases[3][:,j])):\n", + " if phases[3][p,j] < phases[3][p-1,j]:\n", + " line, = plt.plot(phases[3][last:p,j],new_q[last:p,j],\"-o\", markersize=2, color='r')\n", + " last = p+1\n", + "\n", + "plt.ylabel('Rotated Q (rad)',fontsize=20)\n", + "plt.xlabel('Phase (rad)',fontsize=20)\n", + "fig = plt.figure(figsize=(6,6))\n", + "\n", + "last = 0\n", + "print(df1_norm.shape)\n", + "for j in range(0,10):\n", + " for p in range(1,len(phases[3][:,j])):\n", + " if phases[3][p,j] < phases[3][p-1,j]:\n", + " line, = plt.plot(phases[3][last:p,j],new_q_norm[last:p,j],\"-o\", markersize=2, color='r')\n", + " last = p+1\n", + "plt.ylabel('Q (rad)',fontsize=20)\n", + "plt.xlabel('Phase (rad)',fontsize=20)\n", + "plt.show()\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gym/scripts/pca.py b/gym/scripts/pca.py new file mode 100644 index 0000000..07e78a6 --- /dev/null +++ b/gym/scripts/pca.py @@ -0,0 +1,82 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from ast import literal_eval +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA + + +import os + +from gym.envs import __init__ +from gym import LEGGED_GYM_ROOT_DIR +from gym.utils import get_args, task_registry + +# torch needs to be imported after isaacgym imports in local source +import torch +import pandas as pd +import numpy as np +import pandas as pd + + +def setup(args): + env_cfg, train_cfg = task_registry.create_cfgs(args) + env_cfg.env.num_envs = min(env_cfg.env.num_envs, 16) + if hasattr(env_cfg, "push_robots"): + env_cfg.push_robots.toggle = False + env_cfg.commands.resampling_time = 1 + env_cfg.env.episode_length_s = 9999 + env_cfg.env.num_projectiles = 20 + task_registry.make_gym_and_sim() + env = task_registry.make_env(args.task, env_cfg) + env.cfg.init_state.reset_mode = "reset_to_range" + + return env +args = get_args() +env = setup(args) + +xls = pd.ExcelFile("/home/aileen/ORCAgym/gym/scripts/mini_cheetah_logs.xlsx") +df1 = pd.read_excel(xls, 'q').to_numpy() +df2 = pd.read_excel(xls, 'qd').to_numpy() +df_feet_contacts = pd.read_excel(xls,'grf').to_numpy() + +print(env.dof_names) +#PCA +dataset = pd.DataFrame(df1) +features = env.dof_names +dataset.columns = features + +features = np.char.mod('%s', features).tolist() +x = dataset.loc[:, features].values +print(dataset.loc[:, features].values.shape) +x = StandardScaler().fit_transform(x) # normalizing the features +print(x.shape) +print(np.mean(x),np.std(x)) +feat_cols = ['feature'+str(i) for i in range(x.shape[1])] +normalised = pd.DataFrame(x,columns=feat_cols) +print(normalised.tail()) + +pca = PCA(n_components=3) +principalComponents = pca.fit_transform(x) +principal_Df = pd.DataFrame(data = principalComponents + , columns = ['principal component 1', 'principal component 2', 'principal component 3']) + +print(principal_Df.tail()) + +print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_)) + +#print(swing_dataset['label'] ) + +plt.figure() +plt.figure(figsize=(10,10)) +ax = plt.axes(projection ="3d") + +plt.title(f"Principal Component Analysis of Dataset",fontsize=20) +ax.scatter3D(principal_Df.loc[:, 'principal component 1'] + , principal_Df.loc[:, 'principal component 2'] + ,principal_Df.loc[:, 'principal component 3'], s = 5) +ax.set_xlabel('Principal Component - 1',fontsize=20) +ax.set_ylabel('Principal Component - 2',fontsize=20) +ax.set_zlabel('Principal Component - 3',fontsize=20) +plt.show() +#plt.savefig(f'pca plot') \ No newline at end of file diff --git a/gym/scripts/pcasteve.py b/gym/scripts/pcasteve.py new file mode 100644 index 0000000..c12ac71 --- /dev/null +++ b/gym/scripts/pcasteve.py @@ -0,0 +1,121 @@ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from ast import literal_eval +from sklearn.preprocessing import StandardScaler +from sklearn.decomposition import PCA +from mpl_toolkits.mplot3d import Axes3D +from matplotlib.patches import FancyArrowPatch +from mpl_toolkits.mplot3d import proj3d +from gym.envs import __init__ +from gym import LEGGED_GYM_ROOT_DIR +from gym.utils import get_args, task_registry + + +def setup(args): + env_cfg, train_cfg = task_registry.create_cfgs(args) + env_cfg.env.num_envs = min(env_cfg.env.num_envs, 16) + if hasattr(env_cfg, "push_robots"): + env_cfg.push_robots.toggle = False + env_cfg.commands.resampling_time = 1 + env_cfg.env.episode_length_s = 9999 + env_cfg.env.num_projectiles = 20 + task_registry.make_gym_and_sim() + env = task_registry.make_env(args.task, env_cfg) + env.cfg.init_state.reset_mode = "reset_to_range" + return env + +def shuffle_along_axis(a, axis): + idx = np.random.rand(*a.shape).argsort(axis=axis) + return np.take_along_axis(a,idx,axis=axis) +class Arrow3D(FancyArrowPatch): + def __init__(self, xs, ys, zs, *args, **kwargs): + super().__init__((0,0), (0,0), *args, **kwargs) + self._verts3d = xs, ys, zs + + def do_3d_projection(self, renderer=None): + xs3d, ys3d, zs3d = self._verts3d + xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M) + self.set_positions((xs[0],ys[0]),(xs[1],ys[1])) + + return np.min(zs) + +args = get_args() +env = setup(args) + +#import data +set_num = 4 #from 1-4 for 4 legs +xls = pd.ExcelFile("/home/aileen/ORCAgym/gym/scripts/mini_cheetah_logs.xlsx") +df1 = pd.read_excel(xls, 'q').to_numpy()[:,(set_num-1)*3:set_num*3] +#df2 = pd.read_excel(xls, 'qd').to_numpy() +#tau = pd.read_excel(xls, 'tau').to_numpy() +#df_feet_contacts = pd.read_excel(xls,'grf').to_numpy() + +#randomize +df1 = shuffle_along_axis(df1,0) +# df1 = np.random.permutation(df1[:,0]) +#df1 = df1[np.random.permutation(np.arange(df1.shape[0])), :] +print(df1.shape) + +#PCA +dataset = pd.DataFrame(df1) +features = env.dof_names[(set_num-1)*3:set_num*3] +dataset.columns = features + +features = np.char.mod('%s', features).tolist() +x = dataset.loc[:, features].values +print(dataset.loc[:, features].values.shape) +x = StandardScaler().fit_transform(x) # normalizing the features +print(x.shape) +print(np.mean(x),np.std(x)) +feat_cols = ['feature'+str(i) for i in range(x.shape[1])] +normalised = pd.DataFrame(x,columns=feat_cols) +print(normalised.tail()) + +pca = PCA(n_components=3) +principalComponents = pca.fit_transform(x) + +print(pca.components_) +print(sum(pca.components_[:,0]*pca.components_[:,1])) +print(sum(pca.components_[:,1]*pca.components_[:,2])) +print(sum(pca.components_[:,2]*pca.components_[:,0])) +principal_Df = pd.DataFrame(data = principalComponents + , columns = ['principal component 1', 'principal component 2', 'principal component 3']) +print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_)) + + +#checks on orthogonality +import math +a = pca.components_[:,0] +b = pca.components_[:,1] +c= pca.components_[:,2] +print(np.dot(a,b)) +print(math.acos(np.dot(a,b)/(np.linalg.norm(a)*np.linalg.norm(b)))) +print(math.pi/2) +print(math.acos(np.dot(c,b)/(np.linalg.norm(c)*np.linalg.norm(b)))) +print(math.acos(np.dot(c,a)/(np.linalg.norm(c)*np.linalg.norm(a)))) + +#plotting +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111, projection='3d') +ax.scatter3D(normalised.loc[:,"feature0"],normalised.loc[:,"feature1"],normalised.loc[:,"feature2"],s=5) + # #principal_Df.loc[:, 'principal component 1'] + # , principal_Df.loc[:, 'principal component 2'] + # ,principal_Df.loc[:, 'principal component 3'], s = 5) +coeff = pca.components_.T +print(coeff) +for i in range(3): + arrow_prop_dict = dict(mutation_scale=20, arrowstyle='-|>', color='r', shrinkA=0, shrinkB=0) + + a = Arrow3D([0,coeff[i,0]], [0,coeff[i,1]], [0,coeff[i,2]], **arrow_prop_dict) + ax.add_artist(a) + #plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, coeff[i,2]*1.15, "Var"+str(i+1), color = 'g', ha = 'center', va = 'center') + +plt.title(f"Principal Component Analysis of Dataset",fontsize=20) +ax.set_xlabel(features[0],fontsize=20) +ax.set_ylabel(features[1],fontsize=20) +ax.set_zlabel(features[2],fontsize=20) +plt.show() + + +#plt.savefig(f'pca plot') diff --git a/gym/scripts/play_ORC.py b/gym/scripts/play_ORC.py index 4fd0de2..345e9c5 100644 --- a/gym/scripts/play_ORC.py +++ b/gym/scripts/play_ORC.py @@ -7,6 +7,8 @@ from ORC import adjust_settings # torch needs to be imported after isaacgym imports in local source import torch +import numpy as np +import pandas as pd def setup(args): @@ -14,20 +16,20 @@ def setup(args): env_cfg.env.num_envs = 50 if hasattr(env_cfg, "push_robots"): env_cfg.push_robots.toggle = False - env_cfg.commands.resampling_time = 9999 + # env_cfg.commands.resampling_time = 9999 env_cfg.env.episode_length_s = 9999 env_cfg.init_state.timeout_reset_ratio = 1. env_cfg.domain_rand.randomize_base_mass = False env_cfg.domain_rand.randomize_friction = False env_cfg.terrain.mesh_type = "plane" env_cfg.osc.init_to = 'random' - env_cfg.osc.process_noise = 0. - env_cfg.osc.omega_var = 0. - env_cfg.osc.coupling_var = 0. - env_cfg.commands.ranges.lin_vel_x = [0., 0.] - env_cfg.commands.ranges.lin_vel_y = 0. - env_cfg.commands.ranges.yaw_vel = 0. - env_cfg.commands.var = 0. + # env_cfg.osc.process_noise = 0. + # env_cfg.osc.omega_var = 0. + # env_cfg.osc.coupling_var = 0. + # env_cfg.commands.ranges.lin_vel_x = [0., 0.] + # env_cfg.commands.ranges.lin_vel_y = 0. + # env_cfg.commands.ranges.yaw_vel = 0. + # env_cfg.commands.var = 0. train_cfg.policy.noise.scale = 1.0 @@ -52,27 +54,63 @@ def setup(args): def play(env, runner, train_cfg): + saveLogs = True + log = {'dof_pos_obs': [], + 'dof_vel': [], + 'torques': [], + 'grf': [], + 'oscillators': [], + 'base_lin_vel': [], + 'base_ang_vel': [], + 'commands': [], + 'dof_pos_error': [], + 'reward': [], + 'dof_names': [], + } RECORD_FRAMES = False + print(env.dof_names) + # * set up interface: GamepadInterface(env) or KeyboardInterface(env) COMMANDS_INTERFACE = hasattr(env, "commands") - if COMMANDS_INTERFACE: - # interface = GamepadInterface(env) - interface = KeyboardInterface(env) - img_idx = 0 + # if COMMANDS_INTERFACE: + # # interface = GamepadInterface(env) + # interface = KeyboardInterface(env) + # img_idx = 0 for i in range(10*int(env.max_episode_length)): if RECORD_FRAMES: - if i % 5: + if i % 5: filename = os.path.join(LEGGED_GYM_ROOT_DIR, 'gym', 'scripts', 'gifs', train_cfg.runner.experiment_name, f"{img_idx}.png") + #print(filename) env.gym.write_viewer_image_to_file(env.viewer, filename) img_idx += 1 - if COMMANDS_INTERFACE: - interface.update(env) + #print(env.num_envs) + #print(env.torques.size()) + log['dof_pos_obs'] += (env.dof_pos_obs.tolist()) + log['dof_vel'] += (env.dof_vel.tolist()) + log['torques'] += (env.torques.tolist()) + log['grf'] += env.grf.tolist() + log['oscillators'] += env.oscillators.tolist() + log['base_lin_vel'] += env.base_lin_vel.tolist() + log['base_ang_vel'] += env.base_ang_vel.tolist() + log['commands'] += env.commands.tolist() + log['dof_pos_error']+=(env.default_dof_pos - env.dof_pos).tolist() + + reward_weights = runner.policy_cfg['reward']['weights'] + log['reward'] += runner.get_rewards(reward_weights).tolist() + + print(i) + if i ==1000 and saveLogs: + log['dof_names'] = env.dof_names + np.savez('new_logs', **log) + + # if COMMANDS_INTERFACE: + # interface.update(env) runner.set_actions(runner.get_inference_actions()) env.step() diff --git a/gym/scripts/plot.py b/gym/scripts/plot.py new file mode 100644 index 0000000..f7b8ed7 --- /dev/null +++ b/gym/scripts/plot.py @@ -0,0 +1,251 @@ + +import numpy as np +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.patches import FancyArrowPatch +from mpl_toolkits.mplot3d import proj3d + +def create_plot(phases, variable_name,data, keys): + #plot + handles = [] + last = 0 + color_map = ['r','g','b'] + + for k in range(0,4): + color_map = ['r','g','b'] + for i in range(k*3,k*3+3): + color = color_map.pop() + #breaking up lines that loop over + for j in range(45,50): + for p in range(1,len(phases[k][:,j])): + if phases[k][p,j] < phases[k][p-1,j]: + line, = plt.plot(phases[k][last:p,j],data[keys[i]][last:p,j],"-o", markersize=2, color=color, label=keys[i]) + last = p+1 + handles.append(line) + + plt.ylabel(f'{variable_name} (rad)',fontsize=20) + plt.xlabel('Phase (rad)',fontsize=20) + plt.title(f"{variable_name} vs Phase",fontsize=20) + plt.legend(handles=handles) + plt.savefig(f'plot_{variable_name}_last5_{k}') + handles = [] + plt.cla() + +def create_plot_small(phases, variable_name,data,keys): + #plot + last = 0 + for k in range(0,4): + #breaking up lines that loop over + for j in range(45,50): + for p in range(1,len(phases[k][:,j])): + if phases[k][p,j] < phases[k][p-1,j]: + line, = plt.plot(phases[k][last:p,j],data[keys[k]][last:p,j],"-o", markersize=2, color='k', label=keys[k]) + last = p+1 + plt.ylabel(f'{variable_name}',fontsize=20) + plt.xlabel('Phase (rad)',fontsize=20) + plt.title(f"{variable_name} vs Phase",fontsize=20) + plt.savefig(f'plot_{variable_name}_last5_{k}') + plt.cla() + + +def plot_data(variable_name, full_data, df_phase): + data, smalldata, phases, new_vardata, samples = process_data(variable_name, full_data, df_phase) + keys = full_data['dof_names'] + if new_vardata.shape[0]/samples>4: + create_plot(phases, variable_name,data, keys) + else: + create_plot_small(phases, variable_name,smalldata,['1','2','3','4']) + +def process_data(variable_name, full_data, df_phase): + vardata = full_data[variable_name] + keys = full_data['dof_names'] #for humanoid: ["right_hip_yaw", "right_hip_abad","right_hip_pitch","right_knee","right_ankle","left_hip_yaw","left_hip_abad","left_hip_pitch","left_knee","left_ankle"] + n= 50 #16 for humanoid + m=vardata.shape[1] #10 for humanoid + data = dict.fromkeys(keys, np.empty((0,n))) + smalldata = dict.fromkeys(['1','2','3','4'], np.empty((0,n))) + samples = vardata.shape[0]/n + #format data as dict of actuators, each with samples (m) x envs (n) array + new_vardata = np.empty((0,n)) + + phase_env1 = np.empty((0,n)) + phase_env2 = np.empty((0,n)) + phase_env3 = np.empty((0,n)) + phase_env4 = np.empty((0,n)) + phases = [phase_env1, phase_env2, phase_env3, phase_env4] + + for i in range(0,df_phase.shape[0], n): + for p in range(len(phases)): + phases[p] = np.vstack([phases[p],df_phase[i:i+n,p:p+1].T]) + new_vardata = np.vstack([new_vardata, vardata[i:i+n,:].T]) + + if new_vardata.shape[0]/samples>4: + for i in range(0,new_vardata.shape[0],m): + for k in range(len(keys)): + #CHANGE THIS TO CHANGE PLOT + data[keys[k]]= np.vstack([data[keys[k]], new_vardata[i+k,:]]) + else: + for i in range(0,new_vardata.shape[0],m): + for k in range(4): + #CHANGE THIS TO CHANGE PLOT + smalldata[str(k+1)]= np.vstack([smalldata[str(k+1)], new_vardata[i+k,:]]) + + return data, smalldata, phases, new_vardata, samples + +def phase_plot(full_data, df_phase): + names = full_data['dof_names'] + q, _, phases, _,_ = process_data('dof_pos_obs', full_data, df_phase) + qd,_,_,_,_= process_data('dof_vel', full_data, df_phase) + color_map = ['r','g','b'] + command_data, command_new_vardata, comamnd_m = reorg_data('commands', full_data) + command_indexes = [0] + + for i in range(1,command_data['x'].shape[0]): + if command_data['x'][i-1,0] !=command_data['x'][i,0]: + command_indexes.append(i) + #print(command_indexes) + + #plot + handles = [] + fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize = (10*4,10)) + fig.suptitle(f"Phase Plot",fontsize=20) + color_map = ['r','g','b'] + axes = [ax1, ax2, ax3, ax4] + for i in range(0,4): + plotted_joint = names[i*3+2] + color_map = ['r','g','b'] + for k in range(1,len(command_indexes)): + color = color_map.pop() + #print('here') + for j in range(45,50): + line, = axes[i].plot(q[plotted_joint][command_indexes[k-1]:command_indexes[k],j],qd[plotted_joint][command_indexes[k-1]:command_indexes[k],j],"-o", markersize=2, color=color, label=command_indexes[k]) + handles.append(line) + axes[i].set(xlabel='q', ylabel='qd') + axes[i].legend(handles=handles) + axes[i].title.set_text(plotted_joint) + #plt.show() + handles = [] + fig.savefig(f'Phase Plot') + +def time_plot(full_data, df_phase): + q, _,phases,_,_ = process_data('dof_pos_obs', full_data, df_phase) + keys = full_data['dof_names'] #for humanoid: ["right_hip_yaw", "right_hip_abad","right_hip_pitch","right_knee","right_ankle","left_hip_yaw","left_hip_abad","left_hip_pitch","left_knee","left_ankle"] + color_map = ['r','g','b'] + + handles = [] + #int(len(keys)/4) + for k in range(0,4): + color_map = ['r','g','b'] + for i in range(k*3,k*3+3): + color = color_map.pop() + #breaking up lines that loop over + for j in range(45,50): + line, = plt.plot(q[keys[i]][:,j],"-o", markersize=2, color=color, label=keys[i]) + handles.append(line) + plt.ylabel(f'q',fontsize=20) + plt.xlabel('time',fontsize=20) + plt.title(f"Time Plot",fontsize=20) + plt.legend(handles=handles) + #plt.show() + plt.savefig(f'Time Plot_leg{k}') + handles = [] + plt.cla() + +def check_means(variable_name, full_data, df_phase): + data, phases,_,_,_ = process_data(variable_name, full_data, df_phase) + keys = full_data['dof_names'] + means = [] + std=[] + for k in keys: + d = np.reshape(data[k],(1,data[k].shape[0]*data[k].shape[1])) + means.append(np.mean(d)) + std.append(np.std(d)) + means = np.array(means) + std = np.array(std) + + #comparison + means_comp = np.empty((4,4)) + std_comp=np.empty((4,4)) + for i in range(0,4): + for j in range(0,4): + means_comp[i,j] = np.allclose(means[i*3:i*3+3],means[j*3:j*3+3], atol=1e-1) + std_comp[i,j] = np.allclose(std[i*3:i*3+3],std[j*3:j*3+3], atol=0.6) + print(keys) + print(means_comp) + print(std_comp) + print(means) + +class Arrow3D(FancyArrowPatch): + def __init__(self, xs, ys, zs, *args, **kwargs): + super().__init__((0,0), (0,0), *args, **kwargs) + self._verts3d = xs, ys, zs + + def do_3d_projection(self, renderer=None): + xs3d, ys3d, zs3d = self._verts3d + xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M) + self.set_positions((xs[0],ys[0]),(xs[1],ys[1])) + + return np.min(zs) + +def reorg_data(variable_name, full_data): + vardata = full_data[variable_name] + n= 50 #16 for humanoid + #format data as dict of actuators, each with samples (m) x envs (n) array + new_vardata = np.empty((0,n)) + keys = ['x','y','z'] + data = dict.fromkeys(keys, np.empty((0,n))) + m=vardata.shape[1] #10 for humanoid + #(vardata.shape) + for i in range(0,vardata.shape[0], n): + new_vardata = np.vstack([new_vardata, vardata[i:i+n,:].T]) + + if m >1: + for i in range(0,new_vardata.shape[0],m): + for k in range(3): + data[keys[k]]= np.vstack([data[keys[k]], new_vardata[i+k,:]]) + return data, new_vardata, m + +def reorg_plot_data(variable_name, full_data): + data, new_vardata, m = reorg_data(variable_name, full_data) + if m>1: + fig = plt.figure(figsize=(10,10)) + + ax = fig.add_subplot(111, projection='3d') + vals = np.hstack((data['x'][:,0:1],data['y'][:,0:1],data['z'][:,0:1])) + #print(vals) + ax.scatter3D(data['x'][:,0:1],data['y'][:,0:1],data['z'][:,0:1]) + a = Arrow3D([0,1], [0,0], [0,0], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") + ax.add_artist(a) + a = Arrow3D([0,0], [0,1], [0,0], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") + ax.add_artist(a) + a = Arrow3D([0,0], [0,0], [0,1], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") + ax.add_artist(a) + ax.set_xlabel('$X$') + ax.set_ylabel('$Y$') + ax.set_zlabel('$Z$') + ax.set_aspect('equal') + + plt.savefig(f'plot_{variable_name}') + #plt.show() + else: + fig = plt.figure(figsize=(10,10)) + x = np.arange(new_vardata[:,0:1].shape[0]) + plt.plot(x,new_vardata[:,0]) + plt.savefig(f'plot_{variable_name}') + + + + +data = dict(np.load("new_logs.npz")) +data_names = list(data.keys()) +data['reward'] = np.reshape(data['reward'], (data['reward'].shape[0],1)) +# for d in data_names[0:5]: +# print(d) +# plot_data(d, data, data['oscillators']) +# #check_means(d, data, data['oscillators']) #ONLY WORKS FOR data with all 12 dof +# time_plot(data, data['oscillators']) +phase_plot(data, data['oscillators']) +# for d in data_names[5:-1]: +# print(d) +# reorg_plot_data(d, data) + + diff --git a/gym/scripts/plot_base_ang_vel.png b/gym/scripts/plot_base_ang_vel.png new file mode 100644 index 0000000..1fa4ed8 Binary files /dev/null and b/gym/scripts/plot_base_ang_vel.png differ diff --git a/gym/scripts/plot_base_lin_vel.png b/gym/scripts/plot_base_lin_vel.png new file mode 100644 index 0000000..130f295 Binary files /dev/null and b/gym/scripts/plot_base_lin_vel.png differ diff --git a/gym/scripts/plot_commands.png b/gym/scripts/plot_commands.png new file mode 100644 index 0000000..30fce14 Binary files /dev/null and b/gym/scripts/plot_commands.png differ diff --git a/gym/scripts/plot_dof_pos_error.png b/gym/scripts/plot_dof_pos_error.png new file mode 100644 index 0000000..3a0c1b9 Binary files /dev/null and b/gym/scripts/plot_dof_pos_error.png differ diff --git a/gym/scripts/plot_dof_pos_obs_last5_0.png b/gym/scripts/plot_dof_pos_obs_last5_0.png new file mode 100644 index 0000000..bc421cd Binary files /dev/null and b/gym/scripts/plot_dof_pos_obs_last5_0.png differ diff --git a/gym/scripts/plot_dof_pos_obs_last5_1.png b/gym/scripts/plot_dof_pos_obs_last5_1.png new file mode 100644 index 0000000..3690fdb Binary files /dev/null and b/gym/scripts/plot_dof_pos_obs_last5_1.png differ diff --git a/gym/scripts/plot_dof_pos_obs_last5_2.png b/gym/scripts/plot_dof_pos_obs_last5_2.png new file mode 100644 index 0000000..db54ce1 Binary files /dev/null and b/gym/scripts/plot_dof_pos_obs_last5_2.png differ diff --git a/gym/scripts/plot_dof_pos_obs_last5_3.png b/gym/scripts/plot_dof_pos_obs_last5_3.png new file mode 100644 index 0000000..26dc2be Binary files /dev/null and b/gym/scripts/plot_dof_pos_obs_last5_3.png differ diff --git a/gym/scripts/plot_dof_vel_last5_0.png b/gym/scripts/plot_dof_vel_last5_0.png new file mode 100644 index 0000000..5f35db9 Binary files /dev/null and b/gym/scripts/plot_dof_vel_last5_0.png differ diff --git a/gym/scripts/plot_dof_vel_last5_1.png b/gym/scripts/plot_dof_vel_last5_1.png new file mode 100644 index 0000000..4d1b624 Binary files /dev/null and b/gym/scripts/plot_dof_vel_last5_1.png differ diff --git a/gym/scripts/plot_dof_vel_last5_2.png b/gym/scripts/plot_dof_vel_last5_2.png new file mode 100644 index 0000000..ca6049e Binary files /dev/null and b/gym/scripts/plot_dof_vel_last5_2.png differ diff --git a/gym/scripts/plot_dof_vel_last5_3.png b/gym/scripts/plot_dof_vel_last5_3.png new file mode 100644 index 0000000..a09e1f3 Binary files /dev/null and b/gym/scripts/plot_dof_vel_last5_3.png differ diff --git a/gym/scripts/plot_grf_last5_0.png b/gym/scripts/plot_grf_last5_0.png new file mode 100644 index 0000000..fc8666c Binary files /dev/null and b/gym/scripts/plot_grf_last5_0.png differ diff --git a/gym/scripts/plot_grf_last5_1.png b/gym/scripts/plot_grf_last5_1.png new file mode 100644 index 0000000..f96e1ea Binary files /dev/null and b/gym/scripts/plot_grf_last5_1.png differ diff --git a/gym/scripts/plot_grf_last5_2.png b/gym/scripts/plot_grf_last5_2.png new file mode 100644 index 0000000..9a3b1fa Binary files /dev/null and b/gym/scripts/plot_grf_last5_2.png differ diff --git a/gym/scripts/plot_grf_last5_3.png b/gym/scripts/plot_grf_last5_3.png new file mode 100644 index 0000000..2695b3d Binary files /dev/null and b/gym/scripts/plot_grf_last5_3.png differ diff --git a/gym/scripts/plot_oscillators_last5_0.png b/gym/scripts/plot_oscillators_last5_0.png new file mode 100644 index 0000000..43764ae Binary files /dev/null and b/gym/scripts/plot_oscillators_last5_0.png differ diff --git a/gym/scripts/plot_oscillators_last5_1.png b/gym/scripts/plot_oscillators_last5_1.png new file mode 100644 index 0000000..23738dc Binary files /dev/null and b/gym/scripts/plot_oscillators_last5_1.png differ diff --git a/gym/scripts/plot_oscillators_last5_2.png b/gym/scripts/plot_oscillators_last5_2.png new file mode 100644 index 0000000..14babb3 Binary files /dev/null and b/gym/scripts/plot_oscillators_last5_2.png differ diff --git a/gym/scripts/plot_oscillators_last5_3.png b/gym/scripts/plot_oscillators_last5_3.png new file mode 100644 index 0000000..c2be03d Binary files /dev/null and b/gym/scripts/plot_oscillators_last5_3.png differ diff --git a/gym/scripts/plot_reward.png b/gym/scripts/plot_reward.png new file mode 100644 index 0000000..7319b8b Binary files /dev/null and b/gym/scripts/plot_reward.png differ diff --git a/gym/scripts/plot_torques_last5_0.png b/gym/scripts/plot_torques_last5_0.png new file mode 100644 index 0000000..a3bae32 Binary files /dev/null and b/gym/scripts/plot_torques_last5_0.png differ diff --git a/gym/scripts/plot_torques_last5_1.png b/gym/scripts/plot_torques_last5_1.png new file mode 100644 index 0000000..09c0092 Binary files /dev/null and b/gym/scripts/plot_torques_last5_1.png differ diff --git a/gym/scripts/plot_torques_last5_2.png b/gym/scripts/plot_torques_last5_2.png new file mode 100644 index 0000000..2d66f86 Binary files /dev/null and b/gym/scripts/plot_torques_last5_2.png differ diff --git a/gym/scripts/plot_torques_last5_3.png b/gym/scripts/plot_torques_last5_3.png new file mode 100644 index 0000000..49dcac7 Binary files /dev/null and b/gym/scripts/plot_torques_last5_3.png differ diff --git a/gym/scripts/skeletonoutput.png b/gym/scripts/skeletonoutput.png new file mode 100644 index 0000000..d81a3af Binary files /dev/null and b/gym/scripts/skeletonoutput.png differ diff --git a/gym/scripts/skeletonpca.ipynb b/gym/scripts/skeletonpca.ipynb new file mode 100644 index 0000000..7666ea3 --- /dev/null +++ b/gym/scripts/skeletonpca.ipynb @@ -0,0 +1,110 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#dataset creation\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sklearn\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.patches import FancyArrowPatch\n", + "from mpl_toolkits.mplot3d import proj3d\n", + "\n", + "class Arrow3D(FancyArrowPatch):\n", + " def __init__(self, xs, ys, zs, *args, **kwargs):\n", + " super().__init__((0,0), (0,0), *args, **kwargs)\n", + " self._verts3d = xs, ys, zs\n", + "\n", + " def do_3d_projection(self, renderer=None):\n", + " xs3d, ys3d, zs3d = self._verts3d\n", + " xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M)\n", + " self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n", + "\n", + " return np.min(zs)\n", + " \n", + "data = np.random.normal(0,0.5, size=(3, 8))\n", + "data = np.hstack([data,np.random.normal(5,0.5, size=(3, 8))])\n", + "data = np.hstack([data,np.random.normal(8,0.5, size=(3, 8))])\n", + "fig = plt.figure(figsize=(10,10))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "print(data.shape)\n", + "ax.scatter3D(data[0,:],data[1,:],data[2,:],s=5)\n", + "plt.show()\n", + "\n", + "\n", + "mean = np.mean(data,axis=0)\n", + "normalized = data - mean\n", + "norm_shuffled = sklearn.utils.shuffle(normalized)\n", + "\n", + "#PCA\n", + "A = np.cov(norm_shuffled)\n", + "\n", + "eigvals, eigvecs = np.linalg.eig(A)\n", + "\n", + "order = np.zeros(eigvals.shape)\n", + "\n", + "eigvals_copy = eigvals.copy()\n", + "for i in range(1,1+eigvals.shape[0]):\n", + " print(np.argmax(eigvals_copy))\n", + " order[np.argmax(eigvals_copy)] = i\n", + " eigvals_copy[np.argmax(eigvals_copy)] = 0\n", + "print(order)\n", + "print(eigvecs)\n", + "\n", + "W = np.empty((3, 0))\n", + "for i in range(1,1+eigvals.shape[0]):\n", + " index = np.squeeze(np.argwhere(order == i))\n", + " #print(index.shape)\n", + " W = np.hstack([W,eigvecs[:,index:index+1]])\n", + "\n", + "data = np.matmul(W.T, norm_shuffled)\n", + "print(data.shape)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#plotting\n", + "fig = plt.figure(figsize=(10,10))\n", + "ax = fig.add_subplot(111, projection='3d')\n", + "ax.scatter3D(data[0,:],data[1,:],data[2,:],s=5)\n", + " # #principal_Df.loc[:, 'principal component 1']\n", + " # , principal_Df.loc[:, 'principal component 2']\n", + " # ,principal_Df.loc[:, 'principal component 3'], s = 5)\n", + "\n", + "print(\"plotting\")\n", + "print(eigvecs)\n", + "print(eigvals)\n", + "coeff = eigvecs\n", + "print(coeff)\n", + "for i in range(3):\n", + " arrow_prop_dict = dict(mutation_scale=20, arrowstyle='-|>', color='r', shrinkA=0, shrinkB=0)\n", + " print(coeff[:,i])\n", + " a = Arrow3D([0,coeff[0,i]], [0,coeff[1,i]], [0,coeff[2,i]], **arrow_prop_dict)\n", + " ax.add_artist(a)\n", + " #plt.text(coeff[i,0]* 1.15, coeff[i,1] * 1.15, coeff[i,2]*1.15, \"Var\"+str(i+1), color = 'g', ha = 'center', va = 'center')\n", + "\n", + "plt.title(f\"Principal Component Analysis of Dataset\",fontsize=20)\n", + "plt.show()" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gym/scripts/skeletonpca.py b/gym/scripts/skeletonpca.py new file mode 100644 index 0000000..8508d2f --- /dev/null +++ b/gym/scripts/skeletonpca.py @@ -0,0 +1,161 @@ +#dataset creation +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D +from matplotlib.patches import FancyArrowPatch +from mpl_toolkits.mplot3d import proj3d +import pandas as pd +from sklearn.decomposition import PCA + +class Arrow3D(FancyArrowPatch): + def __init__(self, xs, ys, zs, *args, **kwargs): + super().__init__((0,0), (0,0), *args, **kwargs) + self._verts3d = xs, ys, zs + + def do_3d_projection(self, renderer=None): + xs3d, ys3d, zs3d = self._verts3d + xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, self.axes.M) + self.set_positions((xs[0],ys[0]),(xs[1],ys[1])) + + return np.min(zs) + +def prepare_clustered_data(mean, std, data, clustersize): + s = np.vstack([np.random.normal(mean[0],std[0], size=(1, clustersize)), + np.random.normal(mean[1],std[1], size=(1, clustersize)), + np.random.normal(mean[2],std[2], size=(1, clustersize))]) + data = np.hstack([data,s]) + return data + +def norm_standardize_shuffle(data): + mean = np.reshape(np.mean(data,axis=1),(data.shape[0],1)) + stdev = np.reshape(np.std(data,axis=1),(data.shape[0],1)) + norm_std = (data - mean)#/stdev + return norm_std + +def order_eigenvalues(eigvals): + order = np.zeros(eigvals.shape) + eigvals_copy = eigvals.copy() + for i in range(1,1+eigvals.shape[0]): + order[np.argmax(eigvals_copy)] = i + eigvals_copy[np.argmax(eigvals_copy)] = float('-inf') + return order + +def construct_eigvec_matrix(eigvals,eigvecs, order): + W = np.empty((eigvecs.shape[0], 0)) + for i in range(1,1+eigvals.shape[0]): + index = np.squeeze(np.argwhere(order == i)) + W = np.hstack([W,eigvecs[:,index:index+1]]) + return W +def cov_value(x,y): + + mean_x = sum(x) / float(len(x)) + mean_y = sum(y) / float(len(y)) + + sub_x = [i - mean_x for i in x] + sub_y = [i - mean_y for i in y] + + + sum_value = sum([sub_y[i]*sub_x[i] for i in range(len(x))]) + denom = float(len(x)-1) + + cov = sum_value/denom + return cov +def covariance(arr): + c = [[cov_value(a,b) for a in arr] for b in arr] + return c + + +def my_pca(norm_standardized_shuffled): + print('starting pca') + # #PCA + A = np.cov(norm_standardized_shuffled) + # print("covariance check") + print(A) + # print(np.cov(norm_standardized_shuffled)) + eigvals, eigvecs = np.linalg.eig(A) + print("found eigvecs") + order = order_eigenvalues(eigvals) + var = eigvals/np.sum(eigvals) + print('found all') + W = construct_eigvec_matrix(eigvals,eigvecs, order) + proj_data = np.matmul(W.T, norm_standardized_shuffled) + print("Eigenvectors (columns): \n"+ str(eigvecs)) + print("Eigenvalues: "+ str(eigvals)) + print("Variances: "+ str(var)) + print(proj_data.shape) + return proj_data, eigvecs, eigvals, var + +def sklearnpca(norm_standardized_shuffled): + + pca = PCA(n_components = 3) + proj_data = pca.fit_transform(norm_standardized_shuffled).T + print("sklearn pca var" + str(pca.explained_variance_ratio_)) + print(pca.components_.T) + eigvecs = pca.components_.T + var = pca.explained_variance_ratio_ + print(proj_data.shape) + return proj_data + + + + +full_data = dict(np.load("new_logs.npz")) +data_names = list(full_data.keys()) +#data for indivdual leg +#set_num = 4 #from 1-4 for 4 legs +# data = full_data['dof_pos_obs'][:,(set_num-1)*3:set_num*3].T +# print(data.shape) + +# data for actuator type +# actuator_type = 3 +# full_data = full_data['dof_pos_obs'] +# data = np.empty((full_data.shape[0],0)) +# for d in range(4): +# data = np.hstack((data, full_data[:,actuator_type+d*3-1:actuator_type+d*3])) +# data = data.T +# print(data.shape) + +#all 12 actuators +data = full_data['dof_pos_obs'] +norm_standardized_shuffled = norm_standardize_shuffle(data) + +# #plot for sanity check +# fig = plt.figure(figsize=(10,10)) +# ax = fig.add_subplot(111, projection='3d') +# ax.scatter3D(data[0,:],data[1,:],data[2,:],s=5) +# ax.set_aspect('equal') +# ax.set_xlabel('$X$') +# ax.set_ylabel('$Y$') +# ax.set_zlabel('$Z$') +# plt.show() + +proj_data = sklearnpca(norm_standardized_shuffled) + +#plotting +fig = plt.figure(figsize=(10,10)) +ax = fig.add_subplot(111, projection='3d') +ax.scatter3D(proj_data[0,:],proj_data[1,:],proj_data[2,:],s=5) +# for i in range(3): +# #no arrow heads +# #ax.plot([0, eigvecs[0,i]], [0, eigvecs[1,i]], [0, eigvecs[2,i]], color='blue', alpha=0.8, lw=3) +# #with arrow heads + scaling based on variance + +##UNCOMMENT FOR EIGVEC ARROWS +# a = Arrow3D([0,eigvecs[0,i]*var[i]], [0,eigvecs[1,i]*var[i]], [0,eigvecs[2,i]*var[i]], mutation_scale=5, lw=1, arrowstyle="-|>", color="r") +# ax.add_artist(a) +a = Arrow3D([0,1], [0,0], [0,0], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") +ax.add_artist(a) +a = Arrow3D([0,0], [0,1], [0,0], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") +ax.add_artist(a) +a = Arrow3D([0,0], [0,0], [0,1], mutation_scale=5, lw=1, arrowstyle="-|>", color="b") +ax.add_artist(a) +ax.set_aspect('equal') +ax.set_xlabel('$X$') +ax.set_ylabel('$Y$') +ax.set_zlabel('$Z$') +plt.draw() +plt.title(f"Principal Component Analysis of Dataset",fontsize=20) +#plt.show() +plt.savefig("all_actuators_all_legs.png") + + diff --git a/gym/scripts/ssa.py b/gym/scripts/ssa.py new file mode 100644 index 0000000..840b2d5 --- /dev/null +++ b/gym/scripts/ssa.py @@ -0,0 +1,108 @@ +import numpy as np +import matplotlib.pyplot as plt +from pyts.decomposition import SingularSpectrumAnalysis +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from ast import literal_eval + +import os + +from gym.envs import __init__ +from gym import LEGGED_GYM_ROOT_DIR +from gym.utils import get_args, task_registry + +# torch needs to be imported after isaacgym imports in local source +import torch +import pandas as pd +import numpy as np +import pandas as pd + + +def setup(args): + env_cfg, train_cfg = task_registry.create_cfgs(args) + env_cfg.env.num_envs = min(env_cfg.env.num_envs, 16) + if hasattr(env_cfg, "push_robots"): + env_cfg.push_robots.toggle = False + env_cfg.commands.resampling_time = 1 + env_cfg.env.episode_length_s = 9999 + env_cfg.env.num_projectiles = 20 + task_registry.make_gym_and_sim() + env = task_registry.make_env(args.task, env_cfg) + env.cfg.init_state.reset_mode = "reset_to_range" + + return env +args = get_args() +env = setup(args) + +xls = pd.ExcelFile("/home/aileen/ORCAgym/gym/scripts/mini_cheetah_logs.xlsx") +q = pd.read_excel(xls, 'q').to_numpy() +qd = pd.read_excel(xls, 'qd').to_numpy() +tau = pd.read_excel(xls, 'tau').to_numpy() + +df_phase = pd.read_excel(xls, 'oscillators_phase').to_numpy() #only 1 column +#df_feet_contacts = pd.read_excel(xls,'feet_contact_forces').to_numpy() + +keys = env.dof_names #for humanoid: ["right_hip_yaw", "right_hip_abad","right_hip_pitch","right_knee","right_ankle","left_hip_yaw","left_hip_abad","left_hip_pitch","left_knee","left_ankle"] +n= 50 #16 for humanoid +m=12 #10 for humanoid +data = dict.fromkeys(keys, np.empty((0,n))) + +#format data as dict of actuators, each with samples (m) x envs (n) array +new_q = np.empty((0,n)) +new_qd = np.empty((0,n)) +new_tau = np.empty((0,n)) + +phase_env1 = np.empty((0,n)) +phase_env2 = np.empty((0,n)) +phase_env3 = np.empty((0,n)) +phase_env4 = np.empty((0,n)) +phases = [phase_env1, phase_env2, phase_env3, phase_env4] +for i in range(0,df_phase.shape[0], n): + for p in range(len(phases)): + phases[p] = np.vstack([phases[p],df_phase[i:i+n,p:p+1].T]) + new_q = np.vstack([new_q, q[i:i+n,:].T]) + new_qd = np.vstack([new_qd, qd[i:i+n,:].T]) + new_tau = np.vstack([new_tau, tau[i:i+n,:].T]) + +for i in range(0,new_q.shape[0],m): + for k in range(len(keys)): + #CHANGE THIS TO CHANGE PLOT + data[keys[k]]= np.vstack([data[keys[k]], new_q[i+k,:]]) + +n_samples, n_timestamps = 100, 48 +rng = np.random.RandomState(41) +X = rng.randn(n_samples, n_timestamps) +print(X.shape) + +X = data[keys[0]] +print(X.shape) +# We decompose the time series into three subseries +window_size = 15 +groups = [np.arange(i, i + 5) for i in range(0, 11, 5)] + +# Singular Spectrum Analysis +ssa = SingularSpectrumAnalysis(window_size=15, groups=groups) +X_ssa = ssa.fit_transform(X) + +# Show the results for the first time series and its subseries +plt.figure(figsize=(16, 6)) + +ax1 = plt.subplot(121) +ax1.plot(X[0], 'o-', label='Original') +ax1.legend(loc='best', fontsize=14) + +ax2 = plt.subplot(122) +for i in range(len(groups)): + ax2.plot(X_ssa[0, i], 'o--', label='SSA {0}'.format(i + 1)) +ax2.legend(loc='best', fontsize=14) + +plt.suptitle('Singular Spectrum Analysis', fontsize=20) + +plt.tight_layout() +plt.subplots_adjust(top=0.88) +plt.show() + +# The first subseries consists of the trend of the original time series. +# The second and third subseries consist of noise. +