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How to use test_dyn method in assertpy

Best Python code snippet using assertpy_python

pendulum_test.py

Source:pendulum_test.py

1from __future__ import absolute_import, division, print_function2import base643from cmath import nan4import imageio5import matplotlib6import matplotlib.pyplot as plt7import numpy as np8import PIL.Image9import reverb10import zlib11import os12import cv2 as cv13import glob14import tensorflow as tf15import tensorflow_probability as tfp16tfd = tfp.distributions17from tensorflow import keras18from tensorflow.keras import layers, Model, models19from tensorflow.keras.layers import Dense20from tf_agents.environments import suite_gym21from tf_agents.environments import tf_py_environment22# from cartpole_noise import CartPoleEnvNoise23from cartpole_high import CartPoleEnvNoise24# gym_env = CartPoleEnvNoise(1.0)25gym_env = suite_gym.wrap_env(CartPoleEnvNoise(1.0, 2))26# env = suite_gym.wrap_env(gym_env)27# env_name = 'CartPole-v0'28# env = suite_gym.load(env_name)29eval_env = tf_py_environment.TFPyEnvironment(gym_env)30class CustomLossNLL(tf.losses.Loss):31    @tf.function32    def call(self, y_true, y_pred):33        mean, log_sigma = tf.split(y_pred, 2, axis=-1)34        y_target, temp =tf.split(y_true,2,axis=-1)35        sigma = tf.nn.softplus(log_sigma)36        dist = tfp.distributions.MultivariateNormalDiag(loc=mean, scale_diag=sigma)37        loss = -tf.reduce_mean(dist.log_prob(y_target))38        return loss39def run_models(env, dyn_model, obs_model, saved_policy):40    zero_vec = np.zeros((1,5))41    time_step = env.reset()42    obs=time_step.observation.numpy()43    state = np.concatenate((obs, np.array([0]).reshape(1,1)), axis=1)44    test_state=[]; test_dyn=[]; test_obs=[]45    # video_full = cv.VideoWriter('video_full_3.avi', 0, 30, (600,400))46    # video_sliced = cv.VideoWriter('video_sliced_3.avi', 0, 30, (300,75))47    policy_state = saved_policy.get_initial_state(batch_size=1)48    for j in range(200):49        if not time_step.is_last():50            # use ground truth state51            img_full=env.render(mode='rgb_array').numpy().reshape(400,600,3)52            cut = cv.pyrDown(img_full[167:317,:,:])53            gray = cv.cvtColor(cut, cv.COLOR_BGR2GRAY)54            img = np.abs((gray/255)-1)55            if j == 0:56                prev_img = img57            obs = np.concatenate((img.reshape(1,75,300,1), prev_img.reshape(1,75,300,1)), axis=0)58            obs = obs.reshape(1,2,75,300,1)59            obs_pred = obs_model(obs, training=False)60            state_pred = dyn_model(state, training=False)61            62            policy_step = saved_policy.action(time_step, policy_state)63            policy_state = policy_step.state64            action = policy_step.action.numpy()65            time_step = env.step(action)66            obs=time_step.observation.numpy()67            state = np.concatenate((obs, np.array([action]).reshape(1,1)), axis=1)68            prev_img = img69            test_state.append(state)70            test_dyn.append(state_pred)71            test_obs.append(obs_pred.numpy())72            # video_full.write(img_full)73            # video_sliced.write(gray)74        else:75            break76    # cv.destroyAllWindows()77    # video_full.release()78    # video_sliced.release()79    test_state = np.asarray(test_state).reshape(-1,5)80    test_dyn = np.asarray(test_dyn).reshape(-1,10)81    dynamics_mean, dynamics_log_sigma = tf.split(test_dyn, 2, axis=-1)82    dynamics_sigma = np.sqrt(tf.nn.softplus(dynamics_log_sigma))83    # obs_mean, obs_log_sigma = tf.split(test_obs, 2, axis=-1)84    # obs_mean=obs_mean.numpy().reshape(-1,5)85    # obs_sigma = np.sqrt(tf.nn.softplus(obs_log_sigma)).reshape(-1,5)86    plt.figure(10)87    for i in range(5):88        plt.subplot(5,1,i+1)89        plt.plot(test_state[:,i], color='k')90        plt.plot(dynamics_mean[:,i], color='b')91        plt.fill_between(np.linspace(0,dynamics_mean.shape[0],dynamics_mean.shape[0]), dynamics_mean[:,i]+dynamics_sigma[:,i], dynamics_mean[:,i]-dynamics_sigma[:,i], facecolor='b', alpha=.2)92        plt.plot(np.asarray(test_obs)[:,:,i], color='g')93        # plt.fill_between(np.linspace(0,obs_mean.shape[0],obs_mean.shape[0]), obs_mean[:,i]+obs_sigma[:,i], obs_mean[:,i]-obs_sigma[:,i], facecolor='g', alpha=.2)94    plt.show()95def run_filter(env, dyn_model, obs_model, saved_policy):96    savename = 'Pendulum_specklenoise'97    video_full = cv.VideoWriter(savename+'.avi', 0, 30, (600,400), 0)98    time_step = env.reset()99    state = np.concatenate((time_step.observation.numpy(), np.array([0]).reshape(1,1), np.zeros([1,5])), axis=1)100    test_state=[]; test_dyn=[]; test_obs=[]; test_filter=[]101    cycle_state = state102    policy_state = saved_policy.get_initial_state(batch_size=1)103    for j in range(500):104        if not time_step.is_last():105            img_full=env.render(mode='rgb_array').numpy().reshape(400,600,3)106            # gray = cv.cvtColor(img_full, cv.COLOR_BGR2GRAY).reshape(400,600,1)107            gray = img_full108            # speckle noise109            # gauss = np.random.normal(0,.5,gray.size)110            # gauss = gauss.reshape(gray.shape[0],gray.shape[1], gray.shape[2]).astype('uint8')111            # noise = gray + gray * gauss112            # Salt noise113            # gauss = np.random.normal(0,1,gray.size)114            # gauss = gauss.reshape(gray.shape[0],gray.shape[1], gray.shape[2]).astype('uint8')115            # noise = cv.add(gray,gauss).reshape(400,600,1)116            # Gaussian Blur117            # noise = cv.GaussianBlur(gray.reshape(400,600), (7, 7), 0).reshape(400,600,1)118            # No noise119            noise = gray120            video_full.write(noise)121            # cv.imshow("full_img", noise)122            # cv.waitKey(1)123            cut = cv.pyrDown(noise[167:317,:,:])124            img = np.abs((cut/255)-1)125            # cv.imshow("full_img", cut)126            # cv.waitKey(1)127            if j == 0:128                prev_img = img129            obs = np.concatenate((img.reshape(1,75,300,-1), prev_img.reshape(1,75,300,-1)), axis=0)130            obs = obs.reshape(1,2,75,300,-1)131            obs_pred = obs_model(obs, training=False)132            obs_mean, obs_logvar = np.split(obs_pred, 2, axis=-1)133            obs_var = tf.nn.softplus(obs_logvar).numpy()134            state_pred = dyn_model(cycle_state[-1,0:5].reshape(1,-1), training=False)135            # state_pred = dyn_model(state[0,0:5].reshape(1,-1), training=False)136            dyn_mean, dyn_logvar = np.split(state_pred, 2, axis=-1)137            dyn_var = tf.nn.softplus(dyn_logvar).numpy()138            proc_var = (dyn_var + cycle_state[-1,5:10])*2139            filtered_state = (obs_var/(proc_var+obs_var))*dyn_mean + (proc_var/(proc_var+obs_var))*obs_mean140            filtered_var = proc_var*(proc_var+obs_var)*obs_var141            new_state = np.concatenate((filtered_state, filtered_var), axis=1)142            cycle_state = np.concatenate((cycle_state, new_state), axis=0)143            policy_step = saved_policy.action(time_step, policy_state)144            policy_state = policy_step.state145            action = policy_step.action.numpy()146            print(action)147            time_step = env.step(action)148            state = np.concatenate((time_step.observation.numpy(), np.array([action]).reshape(1,1), np.zeros([1,5])), axis=1)149            prev_img = img150            test_state.append(state)151            test_dyn.append(state_pred)152            test_obs.append(obs_pred)153            test_filter.append(new_state)154        else:155            break156    cv.destroyAllWindows()157    video_full.release()158    test_state = np.asarray(test_state).reshape(-1,10)159    test_dyn = np.asarray(test_dyn).reshape(-1,10)160    test_obs = np.asarray(test_obs).reshape(-1,10)161    test_filter = np.asarray(test_filter).reshape(-1,10)162    plt.figure(1, figsize=(10,10))163    for i in range(5):164        plt.subplot(5,1,i+1)165        plt.plot(test_state[:,i], c='#173f5f')166        # plt.plot(test_dyn[:,i], c='b')167        # plt.fill_between(np.linspace(0,test_dyn.shape[0]-1,test_dyn.shape[0]), test_dyn[:,i]+tf.nn.softplus(test_dyn[:,i+5]), test_dyn[:,i]-tf.nn.softplus(test_dyn[:,i+5]), facecolor='b', alpha=.2)168        plt.plot(test_obs[:,i], c=[.4705, .7921, .6470])169        plt.fill_between(np.linspace(0,test_obs.shape[0]-1,test_obs.shape[0]), test_obs[:,i]+tf.nn.softplus(test_obs[:,i+5]), test_obs[:,i]-tf.nn.softplus(test_obs[:,i+5]), facecolor='g', alpha=.2)170        plt.plot(test_filter[:,i], c='b')171        plt.fill_between(np.linspace(0,test_filter.shape[0]-1,test_filter.shape[0]), test_filter[:,i]+test_filter[:,i+5], test_filter[:,i]-test_filter[:,i+5], facecolor='b', alpha=.2)172    173    plt.subplot(5,1,1)174    plt.ylabel('Cart Position')175    plt.subplot(5,1,2)176    plt.ylabel('Cart Velocity')177    plt.subplot(5,1,3)178    plt.ylabel('Pole Position')179    plt.subplot(5,1,4)180    plt.ylabel('Pole Velocity')181    plt.subplot(5,1,5)182    i=4183    plt.plot(test_dyn[:,i], c='b')184    plt.fill_between(np.linspace(0,test_dyn.shape[0]-1,test_dyn.shape[0]), test_dyn[:,i]+tf.nn.softplus(test_dyn[:,i+5]), test_dyn[:,i]-tf.nn.softplus(test_dyn[:,i+5]), facecolor='b', alpha=.2)185    plt.ylabel('Control Force')186    # plt.ylim([-.25, 1.25])187    plt.xlabel('Time (t)')188    plt.savefig(savename+'.pdf', bbox_inches='tight')189    plt.show()190if __name__=='__main__':191    # control model192    ctrl_model = tf.compat.v2.saved_model.load('/home/geoffrey/Research/data/pendulum_high/models/ctrl0')193    # dyn_model = build_dynamics_model()194    dyn_model = models.load_model('/home/geoffrey/Research/data/pendulum_high/models/dyn_simple0')195    # obs_model = build_timedistributed_observation_model()196    obs_model = models.load_model('/home/geoffrey/Research/data/pendulum_high/models/obs0', custom_objects={'CustomLossNLL': CustomLossNLL()})197    run_models(eval_env, dyn_model, obs_model, ctrl_model)...

sliding_window_max.py

Source:sliding_window_max.py

1"""2You are given an array of integers `nums`, there is a sliding window of size `k`3which is moving from the very left of the array to the very right.4You can only see the `k` numbers in the window. Each time the sliding window moves5right by one position.6Return the max sliding window.7## Example 1:8- Input: `nums = [1,3,-1,-3,5,3,6,7]`, `k = 3`9- Output: `[3, 3, 5, 5, 6, 7]`10- Explanation:11Window position                Max12---------------               -----13[1  3  -1] -3  5  3  6  7       314 1 [3  -1  -3] 5  3  6  7       315 1  3 [-1  -3  5] 3  6  7       516 1  3  -1 [-3  5  3] 6  7       517 1  3  -1  -3 [5  3  6] 7       618 1  3  -1  -3  5 [3  6  7]      719## Example 2:20- Input: `nums = [1]`, `k = 1`21- Output: `[1]`22## Example 3:23- Input: `nums = [1, -1]`, `k = 1`24- Output: `[1, -1]`25## Example 4:26- Input: `nums = [9, 11]`, `k = 2`27- Output: `[11]`28## Example 5:29- Input: `nums = [4, -2]`, `k = 2`30- Output: `[4]`31## Constraints:32- `1 <= nums.length <= 105`33- `-104 <= nums[i] <= 104`34- `1 <= k <= nums.length`35"""36import unittest37from collections import deque38import util39class Solution(object):40    def max_sliding_window_dynamic(self, nums, k):41        """42        - time  : `O(N)`, since all we do is `3` passes along the array of length `N`.43        - space : `O(N)` to keep left and right arrays of length `N`, and output array of length `N - k + 1`44        """45        n = len(nums)46        if n * k == 0:47            return []48        if k == 1:49            return nums50        left, right = ([0 for _ in range(n)] for _ in range(2))51        left[0] = nums[0]52        right[n - 1] = nums[n - 1]53        for i in range(1, n):54            # from left to right55            if i % k == 0:56                # block start57                left[i] = nums[i]58            else:59                left[i] = max(left[i - 1], nums[i])60            # from right to left61            j = n - i - 162            if (j + 1) % k == 0:63                # block end64                right[j] = nums[j]65            else:66                right[j] = max(right[j + 1], nums[j])67        return [68            max(left[i + k - 1], right[i]) for i in range(n - k + 1)69        ]70    def max_sliding_window_deque(self, nums, k):71        """72        - time  : `O(N)`, since each element is processed exactly twice - it's index added and then removed from the deque.73        - space : `O(N)`, since `O(Nâk+1)` is used for an output array and `O(k)` for a deque.74        """75        # base cases76        n = len(nums)77        if n * k == 0:78            return []79        if k == 1:80            return nums81        def clean_deque(i):82            # remove indexes of elements not from sliding window83            if deq and deq[0] == i - k:84                deq.popleft()85            # remove from deq indexes of all elements86            # which are smaller than current element nums[i]87            while deq and nums[i] > nums[deq[-1]]:88                deq.pop()89        # init deque and output90        deq = deque()91        max_idx = 092        for i in range(k):93            clean_deque(i)94            deq.append(i)95            # compute max in nums[:k]96            if nums[i] > nums[max_idx]:97                max_idx = i98        output = [nums[max_idx]]99        # build output100        for i in range(k, n):101            clean_deque(i)102            deq.append(i)103            output.append(nums[deq[0]])104        return output105    def max_sliding_window_bad(self, nums, k):106        """107        - time : O(N ^ 2)108        - space: O(N)109        """110        if len(nums) < k:111            raise ValueError("k cannot be larger than len(nums)")112        return [113            max(nums[i:i+k]) for i in range(len(nums) - k + 1)114        ]115# class Test(unittest.TestCase):116#     def setUp(self) -> None:117#         super().setUp()118#         self.fn = Solution().max_sliding_window_dynamic119    # def test_001(self):120    #     self.assertEqual(121    #         [3, 3, 5, 5, 6, 7],122    #         self.fn([1, 3, -1, -3, 5, 3, 6, 7], 3)123    #     )124if __name__ == "__main__":125    test_deq = util.Tester(Solution().max_sliding_window_deque)126    test_many = util.TestMany(127        [128            Solution().max_sliding_window_bad,129            Solution().max_sliding_window_deque,130            Solution().max_sliding_window_dynamic131        ]132    )133    test_many.test(134        [3, 3, 5, 5, 6, 7],135        ([1, 3, -1, -3, 5, 3, 6, 7], 3)136    )137    # test_dyn = util.Tester(Solution().max_sliding_window_dynamic)138    # print(test_dyn)139    # test_dyn.test(140    #     [3, 3, 5, 5, 6, 7],141    #     ([1, 3, -1, -3, 5, 3, 6, 7], 3)142    # )143    # test_dyn.run()...

play_simulation.py

Source:play_simulation.py

1#! /usr/bin/env python2import numpy as np3import rospy, tf2_ros, tf, geometry_msgs.msg, sensor_msgs.msg4import pdb5import pat3.utils as p3_u6import pat3.algebra as p3_al7import pat3.frames as p3_fr8import pat3.vehicles.fixed_wing.legacy_6dof as p1_fw_dyn9# should be in ros_pat10import pat3.ros_utils as p3_rpu11import pat3.test.fixed_wing.test_01_dynamics as test_dyn12import pat3.test.fixed_wing.test_02_att_ctl  as test_pil13class Agent(p3_rpu.PeriodicNode):14    mode_cst, mode_sim = range(2)15    def __init__(self, mode=mode_sim, name='ros_pat_play_simulation'):16        p3_rpu.PeriodicNode.__init__(self, name)17        self.mode = mode18        pos_ned = [0, 0, -1]19        self.T_w2b = np.eye(4)20        self.T_b2w = np.linalg.inv(self.T_w2b)21        self.T_a2b = np.eye(4)22        self.T_b2a = self.T_a2b#np.linalg.inv(self.T_a2b) # FIXME23        save=False24        savefile_name = '/tmp/pat_traj.npz'25        #savefile_name = '/tmp/pat_glider_circle.npz'26        if mode == self.mode_sim:27            if save:28                #self.time, self.X = test_dyn.sim_step_thr()29                #self.time, self.X = test_dyn.sim_step_ail()30                #self.time, self.X = test_dyn.sim_step_ele()31                self.time, self.X, self.U = test_dyn.sim_step_rud()32                #self.time, self.X, self.U = test_pil.test_step_theta(dm)33                np.savez(savefile_name, time=self.time, X=self.X)34                print('saved {}'.format(savefile_name))35            else:36                print('loading {}'.format(savefile_name))37                _data =  np.load(savefile_name)38                self.time, self.X =[_data[k] for k in ['time', 'X']] 39            self.sim_dt = self.time[1] - self.time[0]40            self.sim_dur = self.time[-1] - self.time[0]41            42        self.tf_pub = p3_rpu.TransformPublisher()43        self.marker_pub = p3_rpu.PoseArrayPublisher(dae='ds_glider_full.dae')44        self.odom_pub = p3_rpu.OdomPublisher()45        self.joint_state_pub = p3_rpu.JointStatePublisher(what=name)46        47    def periodic(self):48        now = rospy.Time.now()49        self.tf_pub.send_w_enu_to_ned_transform(now)50        if self.mode == self.mode_sim:51            t_sim = np.fmod(now.to_sec(), self.sim_dur)52            idx_t = int(t_sim / self.sim_dt)53            X_eul = self.X[idx_t, p3_fr.SixDOFAeroEuler.sv_slice_eul]54            #X_eul[0] = np.sin(t_sim)55            #print t_sim, idx_t, self.X[idx_t, p3_fr.SixDOFAeroEuler.sv_slice_eul]56            p3_u.set_rot(self.T_w2b, p3_al.rmat_of_euler(X_eul))57            #_set_trans(self.T_w2b, self.X[idx_t, p3_fr.SixDOFAeroEuler.sv_slice_pos])58            self.T_b2w = np.linalg.inv(self.T_w2b)59            p3_u.set_trans(self.T_b2w, self.X[idx_t, p3_fr.SixDOFAeroEuler.sv_slice_pos])60            va, alpha, beta = self.X[idx_t, p3_fr.SixDOFAeroEuler.sv_slice_vaero]61            p3_u.set_rot(self.T_a2b, p3_fr.R_aero_to_body(alpha, beta))62            self.T_b2a = self.T_a2b#np.linalg.inv(self.T_a2b) # FIXME63            64        #joint_states = [self.dail, -self.dail, self.dele, self.dele, self.dflap, self.dflap]65        joint_states = [0, 0, 0, 0, 0, 0]66        self.joint_state_pub.publish(joint_states, now)67        68        self.tf_pub.send_w_ned_to_b_transform(now, self.T_b2w)69        self.tf_pub.send_b_to_a_transform(now, self.T_b2a)70        self.tf_pub.send_transform('w_ned', 'ds_glider/base_link', now, self.T_b2w)71        self.marker_pub.publish([self.T_b2w])72        self.odom_pub.publish(self.T_b2w, now)73        74def main():75    Agent().run(25)76if __name__ == "__main__":77    np.set_printoptions(linewidth=500)...

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