float:\n b = dependent\n A = np.matrix([[pos, 1] for pos in independent])\n p = (\n np.linalg.inv(A.transpose() * A)\n * A.transpose()\n * np.matrix(b.to_numpy()).transpose()\n )\n best = (goal - p[1]) / p[0]\n return best\n\n for axis, signal, center in (\n kbt.hor,\n rot_aligner.cam_hi.cv1.outputs.output1,\n 320,\n ), (kbt.ver, rot_aligner.cam_hi.cv1.outputs.output2, 256):\n # skip if we are within 1 um\n if abs(axis.delta_px.get()) > 2:\n scan_uid = yield from rel_scan_no_reset(\n [rot_aligner.cam_hi],\n axis,\n 0,\n 0.4 * -(axis.delta_px.get() / abs(axis.delta_px.get())),\n 10,\n )\n scan_df = db[scan_uid].table()\n best_voltage = lin_reg(\n scan_df[axis.readback.name],\n scan_df[signal.name],\n center,\n )\n yield from bps.mv(axis, best_voltage)\n yield from bps.sleep(1)\n\n # close shutters and reset attenuators for manual viewing\n yield from bps.mv(sht.r, 20)","def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\n \n # Gear ratio\n self.R = [np.diag([r1,r1]),np.diag([r1,r2]),np.diag([r2,r1]),np.diag([r2,r2])]\n \n # Inertia\n self.Ia = np.diag([ja,ja])\n \n # Damping (linear)\n self.Da = np.diag([ba,ba])","def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\n \n # Gear ratio\n self.R = [np.diag([r1,r1,r1]),np.diag([r1,r1,r2]),np.diag([r1,r2,r1]), np.diag([r1,r2,r2]),\n np.diag([r2,r1,r1]),np.diag([r2,r1,r2]),np.diag([r2,r2,r1]), np.diag([r2,r2,r2]) ]\n \n # Inertia\n self.Ia = np.diag([ja,ja,ja])\n \n # Damping (linear)\n self.Da = np.diag([ba,ba,ba])","def tomoScan(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\"default\", tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n try:\n darkFieldInterval = int(darkFieldInterval)\n flatFieldInterval = int(flatFieldInterval)\n optimizeBeamInterval = int(optimizeBeamInterval)\n \n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\n tomography_theta = jns.tomography_theta\n if tomography_theta is None:\n raise NameError(\"tomography_theta is not defined in Jython namespace\")\n tomography_shutter = jns.tomography_shutter\n if tomography_shutter is None:\n raise NameError(\"tomography_shutter is not defined in Jython namespace\")\n tomography_translation = jns.tomography_translation\n if tomography_translation is None:\n raise NameError(\"tomography_translation is not defined in Jython namespace\")\n \n tomography_detector = jns.tomography_detector\n if tomography_detector is None:\n raise NameError(\"tomography_detector is not defined in Jython namespace\")\n\n tomography_optimizer = jns.tomography_optimizer\n if tomography_optimizer is None:\n raise NameError(\"tomography_optimizer is not defined in Jython namespace\")\n\n tomography_time = jns.tomography_time\n if tomography_time is None:\n raise NameError(\"tomography_time is not defined in Jython namespace\")\n \n tomography_beammonitor = jns.tomography_beammonitor\n if tomography_beammonitor is None:\n raise NameError(\"tomography_beammonitor is not defined in Jython namespace\")\n \n tomography_camera_stage = jns.tomography_camera_stage\n if tomography_camera_stage is None:\n raise NameError(\"tomography_camera_stage is not defined in Jython namespace\")\n \n tomography_sample_stage = jns.tomography_sample_stage\n if tomography_sample_stage is None:\n raise NameError(\"tomography_sample_stage is not defined in Jython namespace\")\n \n tomo_additional_scannables = jns.tomography_additional_scannables\n if tomo_additional_scannables is None:\n raise NameError(\"tomo_additional_scannables is not defined in Jython namespace\")\n \n index = SimpleScannable()\n index.setCurrentPosition(0.0)\n index.setInputNames([\"imageNumber\"])\n index.setName(\"imageNumber\")\n index.configure()\n \n image_key = SimpleScannable()\n image_key.setCurrentPosition(0.0)\n image_key.setInputNames([\"image_key\"])\n image_key.setName(\"image_key\")\n image_key.configure()\n\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\n tomography_translation, tomography_optimizer, image_key, index)\n\n# return tomoScanDevice\n #generate list of positions\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\n theta_points = []\n theta_points.append(start)\n previousPoint = start\n for i in range(numberSteps):\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\n theta_points.append(nextPoint)\n previousPoint = nextPoint\n \n #generateScanPoints\n optimizeBeamNo = 0\n optimizeBeamYes = 1\n shutterOpen = 1\n shutterClosed = 0\n shutterNoChange = 2\n scan_points = []\n theta_pos = theta_points[0]\n index = 0\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1\n \n for i in range(imagesPerFlat): \n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1 \n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project, index)) #first\n index = index + 1 \n imageSinceDark = 1\n imageSinceFlat = 1\n optimizeBeam = 0\n for i in range(numberSteps):\n theta_pos = theta_points[i + 1]\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project, index))#main image\n index = index + 1 \n \n imageSinceFlat = imageSinceFlat + 1\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\n index = index + 1 \n imageSinceFlat = 0\n \n imageSinceDark = imageSinceDark + 1\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\n index = index + 1 \n imageSinceDark = 0\n\n optimizeBeam = optimizeBeam + 1\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\n index = index + 1 \n optimizeBeam = 0\n \n #add dark and flat only if not done in last steps\n if imageSinceFlat != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n if imageSinceDark != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1 \n scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\n imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\n if pattern == 'default' or pattern == 'DFPFD':\n i = 0\n for pt1 in scan_points1:\n pt = scan_points[i]\n if pt1 != pt:\n print \"Mismatch - please tell Kaz about your scan and its arguments!\"\n print \"i = \", i\n print \"pt = \", pt\n print \"pt1 = \", pt1\n i += 1\n #return None\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\n #scan_args.append(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(\"RotationCoord_as_list\", [tomoRotationAxis])\n meta_add(\"approxCOR\", tomoRotationAxis)\n for scannable in additionalScannables:\n scan_args.append(scannable)\n for scannable in tomo_additional_scannables:\n scan_args.append(scannable)\n ''' setting the description provided as the title'''\n if not description == None: \n setTitle(description)\n else :\n setTitle(\"undefined\")\n \n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n if not dataFormat == \"NexusDataWriter\":\n handle_messages.simpleLog(\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\")\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", \"NexusDataWriter\")\n scanObject = createConcurrentScan(scan_args)\n if addNXEntry:\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\n scanObject.runScan()\n if autoAnalyse:\n lsdp=jns.lastScanDataPoint()\n OSCommandRunner.runNoWait([\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\n return scanObject;\n except InterruptedException:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"User interrupted the scan\", exceptionType, exception, traceback, False)\n raise InterruptedException(\"User interrupted the scan\")\n except:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"Error during tomography scan\", exceptionType, exception, traceback, False)\n raise Exception(\"Error during tomography scan\", exception)\n finally:\n handle_messages.simpleLog(\"Data Format reset to the original setting: \" + dataFormat)\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", dataFormat)","def main():\n np.random.seed(219)\n rospy.init_node(\"sawyer_dagger_teacher\")\n pub_start = rospy.Publisher('/teacher/start', JointCommand, queue_size=1)\n pub_epi_fin = rospy.Publisher('/teacher/fin', JointCommand, queue_size=1)\n vel_ik_pos_pub = rospy.Publisher('/teacher/ik_vel/', Pose, queue_size = 3)\n pub3 = rospy.Publisher('/ddpg/vel_start/', Float64, queue_size=1)\n pub4 = rospy.Publisher('/ddpg/vel_end/', Float64, queue_size=1)\n goal_obs_pub = rospy.Publisher('/teacher/goal_obs/', Pose, queue_size=1)\n pos_cmd_pub = rospy.Publisher('/teacher/pos_cmd_pub/', PosCmd, queue_size=1)\n\n\n\n rospy.set_param('dagger_reset',\"false\") # param_name, param_value\n\n\n # Load Gazebo Models via Spawning Services\n # Note that the models reference is the /world frame\n # and the IK operates with respect to the /base frame\n # load_gazebo_models()\n # Remove models from the scene on shutdown\n rospy.on_shutdown(delete_gazebo_models)\n\n limb = 'right'\n hover_distance = 0.15 # meters\n # Starting Joint angles for right arm\n starting_joint_angles = {'right_j0': -0.041662954890248294,\n 'right_j1': -1.0258291091425074,\n 'right_j2': 0.0293680414401436,\n 'right_j3': 1.37518162913313,\n 'right_j4': -0.06703022873354225,\n 'right_j5': 0.7968371433926965,\n 'right_j6': 1.7659649178699421}\n\n pnp = PickAndPlace(limb, hover_distance)\n\n pnp.move_to_start(starting_joint_angles)\n\n \n # m_planner = trajectorySender()\n # An orientation for gripper fingers to be overhead and parallel to the obj\n overhead_orientation = Quaternion(\n x=-0.00142460053167,\n y=0.999994209902,\n z=-0.00177030764765,\n w=0.00253311793936)\n block_poses = list()\n # The Pose of the block in its initial location.\n # You may wish to replace these poses with estimates\n # from a perception node.\n block_poses.append(Pose(\n position=Point(x=0.45, y=0.155, z=-0.129),\n orientation=overhead_orientation))\n # Feel free to add additional desired poses for the object.\n # Each additional pose will get its own pick and place.\n block_poses.append(Pose(\n position=Point(x=0.6, y=-0.1, z=-0.129),\n orientation=overhead_orientation))\n # Move to the desired starting angles\n print(\"Running. Ctrl-c to quit\")\n # pnp.move_to_start(starting_joint_angles)\n idx = 0\n rate = rospy.Rate(1)\n block_quat_pose = [0.00142460053167,\n 0.999994209902,\n 0.00177030764765,\n 0.00253311793936]\n if rospy.has_param('vel_calc'):\n rospy.delete_param('vel_calc')\n load_gazebo_models()\n\n while not rospy.is_shutdown():\n\n\n starting_joint_angles['right_j0'] = np.random.uniform(-0.05, 0.05)\n starting_joint_angles['right_j1'] = np.random.uniform(-0.95, -0.85)\n starting_joint_angles['right_j2'] = np.random.uniform(-0.1, 0.1)\n starting_joint_angles['right_j3'] = np.random.uniform(1.6, 1.7)\n\n # starting_joint_angles['right_j0'] = np.random.uniform(-0.75, 0.75)\n # starting_joint_angles['right_j1'] = np.random.uniform(-0.97, -0.80)\n # starting_joint_angles['right_j2'] = np.random.uniform(-0.15, 0.15)\n # starting_joint_angles['right_j3'] = np.random.uniform(1.55, 1.75)\n\n start_pose = [starting_joint_angles['right_j0'], starting_joint_angles['right_j1'],\n starting_joint_angles['right_j2'], starting_joint_angles['right_j3'],\n starting_joint_angles['right_j4'], starting_joint_angles['right_j5'],\n starting_joint_angles['right_j6']]\n \n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n if rospy.has_param('dagger_reset'):\n rospy.delete_param('dagger_reset')\n break\n pnp.move_to_start(starting_joint_angles)\n\n\n delete_kinect_camera()\n # delete_gazebo_models()\n delete_gazebo_block()\n rand_x = np.random.uniform(0.45, .75)\n rand_y = np.random.uniform(-0.2, 0.33)\n # rand_x = np.random.uniform(0.44,0.68)\n\n # rand_y = np.random.uniform(-0.20, 0.35)\n pose_block = Pose(position=Point(x=rand_x, y=rand_y, z=1.00)\n , orientation=overhead_orientation)\n pose_rob = Pose(position=Point(x=rand_x-0.015, y=rand_y+0.03, z=0.03), orientation=overhead_orientation) \n\n # rospy.set_param('vel_calc', 'true')\n # pnp.move_to_start(starting_joint_angles)\n # oktogo = pnp.move_to_start_vel_command(start_pose)\n # if rospy.has_param('vel_calc'):\n # rospy.delete_param('vel_calc')\n # loads env\n load_gazebo_block(block_pose=pose_block)\n # load_kinect_camera()\n\n \n\n # rospy.set_param('vel_calc', 'true')\n print 'Reaching target object... Learning...'\n rospy.set_param('epi_start', 'true')\n pnp.reach(pose_rob, pos_cmd_pub)\n # reached = pnp.reach_vel_ctrl(pose_rob)\n rospy.sleep(0.5)\n # if rospy.has_param('vel_calc'):\n # rospy.delete_param('vel_calc')\n # if reached:\n # rospy.set_param('reached', 'true')\n # goal_obs_pub.publish(pose_rob)\n\n\n print 'Reached target object! and Goal obs acquired Resetting...'\n # while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n # if rospy.has_param('demo_success'):\n # break\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n if rospy.has_param('demo_success'):\n rospy.delete_param('demo_success')\n break\n \n # rospy.delete_param('demo_success')\n \n\n return 0","def quick_ao(wf, nact, WFS_map):\n\n nact_across_pupil = nact-2 # number of full DM actuators across pupil (oversizing DM extent)\n # Note: oversample by 2 actuators hardcoded here, check if this is appropriate \n \n ############################\n # Creating AO Surface Map\n ############################\n d_beam = 2 * proper.prop_get_beamradius(wf) # beam diameter\n act_spacing = d_beam / nact_across_pupil # actuator spacing [m]\n # map_spacing = proper.prop_get_sampling(wfo.wf_collection[iw,0])\n\n ###################################\n # Cropping the Beam from WFS map\n ###################################\n # cropping here by beam_ratio rather than d_beam is valid since the beam size was initialized\n # using the scaled beam_ratios when the wfo was created\n # dprint(f\"{WFS_map[0,0,0]}\")\n ao_map = WFS_map[\n sp.grid_size//2 - np.int_(wf.beam_ratio*sp.grid_size//2):\n sp.grid_size//2 + np.int_(wf.beam_ratio*sp.grid_size//2)+1,\n sp.grid_size//2 - np.int_(wf.beam_ratio*sp.grid_size//2):\n sp.grid_size//2 + np.int_(wf.beam_ratio*sp.grid_size//2)+1]\n\n ########################################################\n # Interpolating the WFS map onto the actuator spacing\n # (tp.nact,tp.nact)\n ########################################################\n # Lowpass Filter- prevents aliasing; uses Gaussian filter\n nyquist_dm = tp.ao_act/2 * act_spacing # [m]\n sigma = [nyquist_dm/2.355, nyquist_dm/2.355] # assume we want sigma to be twice the HWHM\n ao_map = ndimage.gaussian_filter(ao_map, sigma=sigma, mode='nearest')\n\n f = interpolate.interp2d(range(ao_map.shape[0]), range(ao_map.shape[0]), ao_map, kind='cubic')\n ao_map = f(np.linspace(0,ao_map.shape[0],nact), np.linspace(0,ao_map.shape[0], nact))\n # dm_map = proper.prop_magnify(dm_map, map_spacing / act_spacing, nact)\n\n ################################################\n # Converting phase delay to DM actuator height\n ################################################\n # Apply the inverse of the WFS image to the DM, so use -dm_map (dm_map is in phase units, divide by k=2pi/lambda)\n surf_height = proper.prop_get_wavelength(wf) / (4 * np.pi) # [m/rad]\n ao_map = -ao_map * surf_height # Converts DM map to units of [m] of actuator heights\n\n return ao_map","def main():\n cam = Realsense()\n # cam.access_intr_and_extr()\n profile = cam.pipeline.start(cam.config)\n depth_sensor = profile.get_device().first_depth_sensor()\n depth_scale = depth_sensor.get_depth_scale()\n align_to = rs.stream.color\n align = rs.align(align_to)\n\n objp = np.zeros((3*4,3), np.float32)\n objp[:,:2] = np.mgrid[0:4,0:3].T.reshape(-1,2)\n axis = np.float32([[1,0,0], [0,1,0], [0,0,-1]]).reshape(-1,3)\n # print(objp)\n\n try:\n while (True):\n # detect ArUco markers in RGB images\n frames = cam.pipeline.wait_for_frames()\n aligned_frames = align.process(frames)\n color_frame = aligned_frames.get_color_frame()\n color_image = np.asanyarray(color_frame.get_data()) \n frame = color_image\n font = cv2.FONT_HERSHEY_SIMPLEX\n corners, ids, rvecs, tvecs = cam.detect_markers_realsense(frame)\n \n if np.all(ids != None): # if markers are detected\n for i in range(0, ids.size):\n aruco.drawAxis(frame, cam.newcameramtx, cam.dist, rvecs[i],\n tvecs[i], 0.1) # Draw axis\n aruco.drawDetectedMarkers(frame, corners) # draw square around markers\n\n ###### DRAW ID #####\n strg = ''\n for i in range(0, ids.size):\n strg += str(ids[i][0])+', '\n\n cv2.putText(frame, \"Id: \" + strg, (0,25), font, 1, (0,255,0), 2,\n cv2.LINE_AA)\n\n\t ###### Output marker positions in camera frame ######\n \t # output tvec\n y0 = 60\n dy = 40\n for i in range(0, ids.size):\n y = y0 + i*dy\n cv2.putText(frame, str(tvecs[i][0]), (0, y), font, 1, (0,255,0),\n 2, cv2.LINE_AA)\n\n else:\n ##### DRAW \"NO IDS\" #####\n cv2.putText(frame, \"No Ids\", (0,64), font, 1, (0,255,0), 2,\n cv2.LINE_AA)\n\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n ret, corners = cv2.findChessboardCorners(gray, (4,3), None)\n if ret == True:\n corners2 = cv2.cornerSubPix(gray, corners,(11,11), (-1,-1),\n cam.criteria)\n corners2 = corners2[::-1]\n # print(corners2)\n # print(objp)\n frame = cv2.drawChessboardCorners(frame, (4,3), corners2, ret)\n # Find the rotation and translation vectors.\n _, rvecs, tvecs = cv2.solvePnP(objp, corners2, cam.newcameramtx,\n cam.dist)\n rot, _ = cv2.Rodrigues(rvecs)\n # print(rot)\n # project 3D points to image plane\n imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs,\n cam.newcameramtx, cam.dist)\n frame = draw(frame, corners2, imgpts)\n\n # Display the resulting frame\n cv2.imshow('frame',frame)\n cv2.waitKey(5)\n\n # When everything done, release the capture\n cv2.destroyAllWindows()\n\n finally:\n cam.pipeline.stop()","def antenna_set(self):","def __init__(self):\n\n\t###\n\t#subscribers for takeoff and landing \n\n\tself.sub_land = rospy.Subscriber('/ardrone/land', \n Empty,\n self.land)\n\n\tself.sub_takeoff = rospy.Subscriber('/ardrone/takeoff', \n Empty,\n self.takeoff)\n\t###\n\n \n # Desired roll and pitch in radians, yaw rate in radians per second, and\n # climb rate in meters per second\n self.desired_roll = 0\n self.desired_pitch = 0\n self.desired_yaw_rate = 0\n self.desired_climb_rate = 0\n \n # Unit commands for controlling a quadrotor. Commands are in following \n # order: [front_left, front_right, rear_left, rear_right]\n self.unit_hover_cmd = np.array([1., 1., 1., 1.])\n self.unit_roll_cmd = np.array([1., -1., 1., -1.])\n self.unit_pitch_cmd = np.array([-1., -1., 1., 1.])\n self.unit_yaw_cmd = np.array([-1., 1., 1., -1.])\n \n # Onboard controller gains\n self.roll_Pgain = 2.5\n self.roll_Igain = 1.0\n self.roll_Dgain = 1.0\n \n self.pitch_Pgain = 2.5\n self.pitch_Igain = 1.0\n self.pitch_Dgain = 1.0\n \n self.yaw_rate_Pgain = 2.0\n self.yaw_rate_Igain = 0.0\n self.yaw_rate_Dgain = 0.0\n \n self.climb_Pgain = 35.0\n self.climb_Igain = 10.0\n self.climb_Dgain = 0.0\n \n # Motor speed which produces hover\n self.hover_speed = 70.0\n \n # Internal state\n self.internal_state = TransformStamped()\n self.internal_roll_error = 0.0\n self.internal_pitch_error = 0.0\n self.internal_yaw_error = 0.0\n self.internal_climb_error = 0.0\n \n self.internal_old_roll_error = 0.0\n self.internal_old_pitch_error = 0.0\n self.internal_old_yaw_rate_error = 0.0\n self.internal_old_climb_rate_error = 0.0\n \n self.yaw_old = 0.0\n self.z_old = 0.0\n \n # Motor speed envelope\n self.motor_cmd_min = 10\n self.motor_cmd_max = 100","def __init__(self, pinForward, pinBackward, pinControlStraight,pinLeft, pinRight, pinControlSteering):\n\n self.pinForward = pinForward\n self.pinBackward = pinBackward\n self.pinControlStraight = pinControlStraight\n self.pinLeft = pinLeft\n self.pinRight = pinRight\n self.pinControlSteering = pinControlSteering\n GPIO.setup(self.pinForward, GPIO.OUT)\n GPIO.setup(self.pinBackward, GPIO.OUT)\n GPIO.setup(self.pinControlStraight, GPIO.OUT)\n\n GPIO.setup(self.pinLeft, GPIO.OUT)\n GPIO.setup(self.pinRight, GPIO.OUT)\n GPIO.setup(self.pinControlSteering, GPIO.OUT)\n\n self.pwm_forward = GPIO.PWM(self.pinForward, 100)\n self.pwm_backward = GPIO.PWM(self.pinBackward, 100)\n self.pwm_forward.start(0)\n self.pwm_backward.start(0)\n\n self.pwm_left = GPIO.PWM(self.pinLeft, 100)\n self.pwm_right = GPIO.PWM(self.pinRight, 100)\n self.pwm_left.start(0)\n self.pwm_right.start(0)\n\n GPIO.output(self.pinControlStraight,GPIO.HIGH) \n GPIO.output(self.pinControlSteering,GPIO.HIGH)","def main():\n print(\"Constructing optical system...\")\n\n \"\"\"Place two lambertian point sources at the edges of an object\"\"\"\n image_distance = 200 # Distance from center of bed to sensor\n\n object_length = 100\n\n beam_width = math.radians(80) # Arbitrary; just limits the number of rays to propagate\n # First source\n obj_x1, obj_y1 = 0, object_length/2\n point_source1 = rt.LambertianPointSource(obj_x1, obj_y1, 0, -beam_width/2, beam_width/2, math.radians(0.1), color='blue')\n # Second source\n obj_x2, obj_y2 = 0, -object_length/2\n point_source2 = rt.LambertianPointSource(obj_x2, obj_y2, 0, -beam_width/2, beam_width/2, math.radians(0.1), color='blue')\n\n # Draw object\n rt.ax_sim.plot([obj_x1, obj_x2], [obj_y1, obj_y2], linewidth=5, color='gray')\n\n # Combine the sources into one element\n object_sources = [point_source1, point_source2]\n\n \"\"\"Model of optics\"\"\"\n # Aperture\n aperture_radius = 3\n aperture_start = image_distance - 30.0\n aperture = rt.Aperture(aperture_start, -aperture_radius, aperture_radius) # Start of sensor column\n\n # Define lens geometry\n lens_thickness = 2.5\n lens_diameter = 12\n lens_curvature = 20\n n_bk7 = 1.51\n lens_start_distance = image_distance - 25.0\n\n lens = rt.SphericalLens(lens_start_distance, lens_diameter,lens_curvature,lens_thickness, 0, n_bk7)\n\n image = rt.Image(image_distance, -10.0, image_distance, 10.0) # Sensor die perpendicular to lens/apertures\n\n \"\"\"Simulate!\"\"\"\n # System elements need to be in order (source --> image)\n system = rt.System(object_sources, [aperture, lens], image)\n system.run()","def __init__(self, pinForward1, pinBackward1,pinForward2, pinBackward2):\n\n self.pinForward1 = pinForward1\n self.pinBackward1 = pinBackward1\n self.pinForward2 = pinForward2\n self.pinBackward2 = pinBackward2\n\n GPIO.setup(self.pinForward1, GPIO.OUT)\n GPIO.setup(self.pinBackward1, GPIO.OUT)\n GPIO.setup(self.pinForward2, GPIO.OUT)\n GPIO.setup(self.pinBackward2, GPIO.OUT)\n\n self.pwm_forward1 = GPIO.PWM(self.pinForward1, 100)\n self.pwm_backward1 = GPIO.PWM(self.pinBackward1, 100)\n self.pwm_forward2 = GPIO.PWM(self.pinForward2, 100)\n self.pwm_backward2 = GPIO.PWM(self.pinBackward2, 100)\n \n self.pwm_forward1.start(0)\n self.pwm_backward1.start(0)\n self.pwm_forward2.start(0)\n self.pwm_backward2.start(0)","def route_input_A(self):\n xoffset = self.nmos.poly_positions[0].x\n # HACK: added 1.5, since we're going to rewrite this.\n yoffset = self.nmos_position1.y + drc[\"well_enclosure_active\"] + self.nmos.active_height + 1.5*self.poly_contact.height \n self.A_position = vector(xoffset, yoffset)\n # gate input\n offset = self.A_position - vector(0, 0.5 * self.poly_contact.width)\n self.add_contact(layers=(\"poly\", \"contact\", \"metal1\"),\n offset=offset,\n rotate=90)\n\n # connect gate input to tx gate\n offset = self.A_position - vector(self.poly_contact.first_layer_position.y,\n 0.5 * self.poly_contact.width)\n self.add_rect(layer=\"poly\",\n offset=offset,\n width=self.poly_contact.first_layer_position.y + drc[\"minwidth_poly\"],\n height=self.poly_contact.first_layer_width)\n # extend the metal to the boundary of the cell\n input_length = self.A_position.x\n offset = [0, self.A_position.y - 0.5 * drc[\"minwidth_metal1\"]]\n self.add_layout_pin(text=\"A\",\n layer=\"metal1\",\n offset=offset,\n width=input_length,\n height=drc[\"minwidth_metal1\"])","def arm(self):\n pass","def isi_calc(self):\n arg = erfinv(0.8)*1.0E6/(self.speedup*self.br_nominal)\n print('arg: ', arg)\n\n # calculate center eye opening with no additional impairments\n self.isi_center = 2.0*erf(arg/self.tc) - self.l_1 # column Z\n\n # calculate center eye opening with residual DJ (DJ - DCD)\n self.isi_dj_center = (erf(arg*(1.0+self.dj_ui)/self.tc) + erf(arg*(1.0-self.dj_ui)/self.tc) - self.l_1) # column AD\n\n # calculate eye closing induced by interferometric effects from link end reflections\n mean_reflection = math.pow(10.0,0.05*(self.rx_reflection + self.tx_reflection)) # cell AB5\n er_lin = math.pow(10.0,0.1*self.er_dB_min) # cell AB7\n\n\n arg1 = np.sqrt(2.0*er_lin*self.isi_dj_center*(er_lin-1.0) + (er_lin+1.0)*self.l_1)\n print('arg1: ', arg1)\n arg2 = np.divide(arg1,self.isi_dj_center)\n arg3 = (2.0*self.ref_nf*np.power(10.0,-0.1*self.chil)*mean_reflection)\n self.isi_reflection = self.l_1-np.multiply(arg2,arg3)\n\n # calculate center eye opening with both residual DJ and reflection degradations included\n self.isi_dj_refl_closed = np.multiply(self.isi_dj_center, self.isi_reflection) # column AA\n print('isi_dj_refl_closed (AA) : ', self.isi_dj_refl_closed)\n \n # calculate eye opening at the corners with no additional impairments\n eff_rx_eye = 2.0*(0.5-self.X2)*self.speedup\n self.isi_corners = (erf(arg*(1.0+eff_rx_eye)/self.tc) + erf(arg*(1.0-eff_rx_eye)/self.tc) - self.l_1) # column AB\n\n # calculate eye opening at the corners with residual DJ impairment\n self.isi_dj_corners = (erf(arg*(1.0+eff_rx_eye+self.dj_ui)/self.tc) + erf(arg*(1.0-eff_rx_eye-self.dj_ui)/self.tc) - self.l_1) # column AC\n self.isi_tp4_rx = (erf(arg*(1.0+eff_rx_eye)/self.rx_1090_rise) + erf(arg*(1.0-eff_rx_eye)/self.rx_1090_rise) - 1) # cell AG5\n\n # end of GbE10.isi_calcdef isi_calc(self):","def __init__(self):\n rospy.init_node(\"navigate_map\") # start node\n\tself.frontier_show = []\n\tself.first = True\n\tself.initialS = None\n\tself.once = False\n self.frontier = []\n self.centroidValue = None\n self.regions = []\n self.detected = False\n\tself.resolution = 0.05\n \tself.start = None\n\tself.transformed_map = None\n \tself.odom_sub = rospy.Subscriber('/odom', Odometry, self.odom_callback)\n \tself.sub = rospy.Subscriber(\"/map\", OccupancyGrid, self.handle_navigate_map)\n \tself.robot_path_pub = rospy.Publisher('/robot_path', Path, queue_size = 5)\n self.cent_pub = rospy.Publisher('/cent_set', GridCells, queue_size = 5)\n\tself.front_pub = rospy.Publisher('/frontier_set', GridCells, queue_size = 5)\n\tself.goal_pos_pub = rospy.Subscriber('/move_base_simple/goal',PoseStamped ,self.goal_call_back)\n\tself.tf_listener = tf.TransformListener()","def _activate_IR(self):\n self.IR_threshold = 2000\n\n self.IR_sensors = [self.getLightSensor('ls' + str(i)) for i in range(self.num_light_sensors)]\n map((lambda s: s.enable(self.timestep)), self.IR_sensors) # Enable all distance sensors","def set_input(self, input):\n AtoB = self.opt.direction == 'AtoB'\n self.real_A = input['A' if AtoB else 'B'].to(self.device)\n self.inst = input['inst'].to(self.device)\n self.real_B = input['B' if AtoB else 'A'].to(self.device)\n self.image_paths = input['A_paths' if AtoB else 'B_paths']","def __init__(self):\n self.__deviceselected__ = \"SR-DMS4AP{LOCALBUMP}DEV:Sel-SP\"\n self.__source__ = \"SR-DMS4AP{LOCALBUMP}S-SP\"\n self.__plane__ = \"SR-DMS4AP{LOCALBUMP}PLANE-SP\"\n #self.__xshift__ = \"SR-DMS4AP{LOCALBUMP}SHIFT:X-SP\"\n #self.__yshift__ = \"SR-DMS4AP{LOCALBUMP}SHIFT:Y-SP\"\n #self.__xangle__ = \"SR-DMS4AP{LOCALBUMP}ANGLE:X-SP\"\n #self.__yangle__ = \"SR-DMS4AP{LOCALBUMP}ANGLE:Y-SP\"\n self.__shift__ = \"SR-DMS4AP{LOCALBUMP}SHIFT-SP\"\n self.__angle__ = \"SR-DMS4AP{LOCALBUMP}ANGLE-SP\"\n # with all offsets\n self.__anglerb__ = \"SR-DMS4AP{LOCALBUMP}ANGLE-I\"\n self.__positionrb__ = \"SR-DMS4AP{LOCALBUMP}POS-I\"\n # with BBA offset only\n self.__anglerb0__ = \"SR-DMS4AP{LOCALBUMP}ANGLE:BBA-I\"\n self.__positionrb0__ = \"SR-DMS4AP{LOCALBUMP}POS:BBA-I\"\n\n self.__bpmposition__ = \"SR-DMS4AP{LOCALBUMP:BPM}Pos-I\"\n self.__bpmorbitx__ = \"SR-DMS4AP{LOCALBUMP:BPM}ORB:X-I\"\n self.__bpmorbity__ = \"SR-DMS4AP{LOCALBUMP:BPM}ORB:Y-I\"\n self.__bpmorbitx0__ = \"SR-DMS4AP{LOCALBUMP:BPM}ORB:X0-I\"\n self.__bpmorbity0__ = \"SR-DMS4AP{LOCALBUMP:BPM}ORB:Y0-I\"\n\n self.__correctorposition__ = \"SR-DMS4AP{LOCALBUMP:COR}Pos-I\"\n self.__hcorrectorcurrent__ = \"SR-DMS4AP{LOCALBUMP:HCOR}PS-SP\"\n self.__hcorrectordiff__ = \"SR-DMS4AP{LOCALBUMP:HCOR}PS:Delta-SP\"\n self.__vcorrectorcurrent__ = \"SR-DMS4AP{LOCALBUMP:VCOR}PS-SP\"\n self.__vcorrectordiff__ = \"SR-DMS4AP{LOCALBUMP:VCOR}PS:Delta-SP\"\n\n self.__undo__ = \"SR-DMS4AP{LOCALBUMP}Enbl:Undo-Cmd\"\n self.__apply__ = \"SR-DMS4AP{LOCALBUMP}Enbl-Cmd\"\n self.__status__ = \"SR-DMS4AP{LOCALBUMP}TS-I\"\n self.__idposinfo__ = \"SR-DMS4AP{LOCALBUMP}S-I\"\n self.__srcposition__ = \"SR-DMS4AP{LOCALBUMP}SRC-SP\"","def control_law( self, inputs ):","def roi_cli():","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def __init__(self):\n self.Robot = Robot()\n self.Omega = matrix()\n # self.Omega.value[0][0] = 1.0\n # self.Omega.value[1][1] = 1.0\n self.Xi = matrix()\n # Xi.value[0][0] = 0.0\n # Xi.value[1][0] = 0.0\n self.measure = {}\n self.landMarkCount = 0\n self.init = False\n self.bearing = 0\n self.x = 0\n self.y = 0\n \n # TODO","def pumpprobe_intAM(chan1=1,chan2=2):\r\n inst.write(\"*RST\")\r\n #inst.write(\"DISP OFF\")\r\n inst.write(\":FUNC%i USER\" %chan1)\r\n inst.write(\"FUNC%i:USER PUMP_1PER\" %chan1)\r\n setChanVolt(2) \r\n setChanFreq(200000)\r\n\r\n\r\n inst.write(\":FUNC%i USER\" %chan2)\r\n inst.write(\"FUNC2:USER PROBE_EARLY\")\r\n setChanVolt(.2,2)\r\n setChanFreq(200000,2)\r\n AMdepth(100,2)\r\n AMshape(\"SQU\",2)\r\n AMfreq(15000,2)\r\n AMstate(\"on\",2)\r\n \r\n addChanOut1(\"plus\")\r\n ChanOutState(\"on\",1)","def calculate_beam_xy(self):\n info = []\n\n # Import relevant info\n pixel_size = self.info.pixel_size\n for i in [j.final for j in self.final_objects]:\n try:\n info.append(\n [\n i,\n i[\"beamX\"],\n i[\"beamY\"],\n i[\"wavelength\"],\n i[\"distance\"],\n (i[\"a\"], i[\"b\"], i[\"c\"], i[\"alpha\"], i[\"beta\"], i[\"gamma\"]),\n ]\n )\n except IOError as e:\n print(\"IOTA ANALYSIS ERROR: BEAMXY failed! \", e)\n pass\n\n # Calculate beam center coordinates and distances\n beamX = [i[1] for i in info]\n beamY = [j[2] for j in info]\n beam_dist = [\n math.hypot(i[1] - np.median(beamX), i[2] - np.median(beamY)) for i in info\n ]\n beam_dist_std = np.std(beam_dist)\n img_list = [\n [i[0], i[1], i[2], i[3], i[4], i[5], j]\n for i, j in list(zip(info, beam_dist))\n ]\n\n # Separate out outliers\n outliers = [i for i in img_list if i[3] > 2 * beam_dist_std]\n clean = [i for i in img_list if i[3] <= 2 * beam_dist_std]\n cbeamX = [i[1] for i in clean]\n cbeamY = [j[2] for j in clean]\n obeamX = [i[1] for i in outliers]\n obeamY = [j[2] for j in outliers]\n\n # Calculate median wavelength, detector distance and unit cell params from\n # non-outliers only\n wavelengths = [i[3] for i in clean]\n distances = [i[4] for i in clean]\n cells = [i[5] for i in clean]\n\n wavelength = np.median(wavelengths)\n det_distance = np.median(distances)\n a = np.median([i[0] for i in cells])\n b = np.median([i[1] for i in cells])\n c = np.median([i[2] for i in cells])\n\n # Calculate predicted L +/- 1 misindexing distance for each cell edge\n aD = det_distance * math.tan(2 * math.asin(wavelength / (2 * a)))\n bD = det_distance * math.tan(2 * math.asin(wavelength / (2 * b)))\n cD = det_distance * math.tan(2 * math.asin(wavelength / (2 * c)))\n\n return (\n beamX,\n beamY,\n cbeamX,\n cbeamY,\n obeamX,\n obeamY,\n beam_dist,\n [i[4] for i in info],\n aD,\n bD,\n cD,\n pixel_size,\n )","def step(self):\n\n \"\"\" First updates the variables values of the current time form the environment \"\"\"\n self.update_crispval(self.env.context)\n\n \"\"\"\n here the decision making of the agent\n to determine which activity to suggest to the patient\n i apply the creative controller to the current context\n \"\"\"\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\n self.fuzzysets_values, self.variables_universe)\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\n\n \"\"\" i obtain a number of ouput crisp values.\n i determine which one achieves the max expected output w.r.t. the a-rules \"\"\"\n best_a = None\n best_a_val = -1000\n best_a_exphapp = 5\n if self.verbose > Constants.VERBOSE_BASIC:\n print(\"rules activations\")\n for a in rules_activations:\n if rules_activations[a] > 0:\n print(str(a) + \"\\n\\t\\t\\t-> \" + str(rules_activations[a]))\n for item in c_out.items(): # for each pair <activity, crisp output>\n if self.verbose > Constants.VERBOSE_BASIC:\n print(item)\n if not item[\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\n inputs = dict(curr_input) # I create a copy fo the dict\n inputs[item[0]] = item[1]\n assessor_id = self.actions_to_ti[item[0]]\n self.assessors[assessor_id].feed_inputs(inputs)\n is_ac_exception = False\n assout = []\n try:\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\n assout = [a_out[ao] for ao in a_out]\n except:\n is_ac_exception = True\n traceback.print_exc()\n # todo the following assumes that every assessor controller has same eval var\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n if len(assout) == 0:\n for v in self.eval_var:\n assout.append(self.variables_default_val[v])\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\n\n avg_credit_rules_that_suggested_action = 1.0\n nr_rules_that_suggested_action = 0\n for r in rules_activations:\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\n self.rules_credits[str(r)]\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\n if nr_rules_that_suggested_action > 0:\n avg_credit_rules_that_suggested_action = (\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\n repetition_cost = 1.0\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\n if (a_val > best_a_val) and (\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\n best_a = item\n best_a_val = a_val\n best_a_exphapp = mean(assout)\n\n \"\"\"I suggest the activity with best expected outcome and store the information to populate the interactions \n memory \"\"\"\n self.proposeActivity(best_a)\n if not best_a is None:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"proposing activity\" + str(best_a) + \" which has expected feedback: \" + str(\n best_a_exphapp) + \", which weighted is \" + str(best_a_val))\n self.curr_iter_suggestions.append(best_a[0])\n self.last_suggestion = best_a\n else:\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\n print(\"the activity proposed is \" + str(\n best_a) + \" so I don't suggest anything. I will ask a question instead\")\n self.last_suggestion = []\n self.expected_feedback = best_a_exphapp\n self.last_context = self.env.context.copy()\n self.last_rules_activations = rules_activations","def __init__(self, *args, **kwargs):\n _Beamlet.__init__(self, *args, **kwargs)","def set_input(self, input):\n AtoB = self.opt.direction == 'AtoB'\n self.real_A = input['A' if AtoB else 'B'].to(self.device)\n self.real_B = input['B' if AtoB else 'A'].to(self.device)\n self.true_time = input['time_period'][0]\n self.image_paths = input['A_paths' if AtoB else 'B_paths']","def start(self):\n self.id_oi = 1\n self.param_target_distances = [5, 30]\n self.cells_no_links = [cell for cell in self.cell_list if cell.id != self.id_oi]\n self.cell_oi = [cell for cell in self.cell_list if cell.id == self.id_oi][0]","def test_via(num_vias = 100, wire_width = 10, via_width = 15,\n via_spacing = 40, pad_size = (300, 300), min_pad_spacing = 0,\n pad_layer = 0, wiring1_layer = 1, wiring2_layer = 2,\n via_layer = 3):\n VR = Device('test_via')\n pad1 = VR.add_ref(rectangle(size = pad_size, layer = pad_layer))\n pad1_overlay = VR.add_ref(rectangle(size = pad_size,\n layer = wiring1_layer))\n pad2 = VR.add_ref(rectangle(size = pad_size, layer = pad_layer))\n pad2_overlay = VR.add_ref(rectangle(size = pad_size,\n layer = wiring1_layer))\n nub = VR.add_ref(compass(size = (3*wire_width,wire_width),\n layer = pad_layer))\n nub_overlay = VR.add_ref(compass(size = (3*wire_width, wire_width),\n layer = wiring1_layer))\n head = VR.add_ref(compass(size = (wire_width, wire_width),\n layer = pad_layer))\n head_overlay = VR.add_ref(compass(size = (wire_width, wire_width),\n layer = wiring1_layer))\n nub.ymax = pad1.ymax - 5\n nub.xmin = pad1.xmax\n nub_overlay.ymax = pad1.ymax - 5\n nub_overlay.xmin = pad1.xmax\n head.connect(port = \"W\", destination = nub.ports[\"E\"])\n head_overlay.connect(port = \"W\", destination = nub_overlay.ports[\"E\"])\n pad1_overlay.xmin = pad1.xmin\n pad1_overlay.ymin = pad1.ymin\n\n old_port = head.ports['S']\n count = 0\n width_via_iter = 2*via_spacing - 2*wire_width\n\n pad2.xmin = pad1.xmax + min_pad_spacing\n up = False\n down = True\n edge = True\n current_width = 3*wire_width + wire_width #width of nub and 1 overlap\n obj_old = head\n obj = head\n via_iterable = _via_iterable(via_spacing, wire_width, wiring1_layer,\n wiring2_layer, via_layer, via_width)\n while((count + 2) <= num_vias):\n obj = VR.add_ref(via_iterable)\n obj.connect(port = 'W', destination = old_port, overlap = wire_width)\n old_port = obj.ports['E']\n edge = False\n if(obj.ymax > pad1.ymax):\n obj.connect(port = 'W', destination = obj_old.ports['S'],\n overlap = wire_width)\n old_port = obj.ports['S']\n current_width += width_via_iter\n down = True\n up = False\n edge = True\n\n elif(obj.ymin < pad1.ymin):\n obj.connect(port = 'W', destination = obj_old.ports['N'],\n overlap = wire_width)\n old_port = obj.ports['N']\n current_width += width_via_iter\n up = True\n down = False\n edge = True\n count = count + 2\n obj_old = obj\n\n if(current_width < min_pad_spacing and (min_pad_spacing - current_width) > 3*wire_width):\n tail = VR.add_ref(\n compass(size = (min_pad_spacing-current_width+wire_width,\n wire_width),\n layer = wiring1_layer)\n )\n tail_overlay = VR.add_ref(\n compass(size = (min_pad_spacing-current_width+wire_width,\n wire_width),\n layer = pad_layer)\n )\n else:\n tail = VR.add_ref(compass(size = (3*wire_width, wire_width),\n layer = wiring1_layer))\n tail_overlay = VR.add_ref(compass(size = (3*wire_width, wire_width),\n layer = wiring1_layer))\n\n if(up == True and edge != True):\n tail.connect(port = 'W', destination = obj.ports['S'],\n overlap = wire_width)\n tail_overlay.connect(port = 'W', destination = obj.ports['S'],\n overlap = wire_width)\n elif(down == True and edge != True):\n tail.connect(port = 'W', destination = obj.ports['N'],\n overlap = wire_width)\n tail_overlay.connect(port = 'W', destination = obj.ports['N'],\n overlap = wire_width)\n else:\n tail.connect(port = 'W', destination = obj.ports['E'],\n overlap = wire_width)\n tail_overlay.connect(port = 'W', destination = obj.ports['E'],\n overlap = wire_width)\n\n pad2.xmin = tail.xmax\n pad2_overlay.xmin = pad2.xmin\n pad2_overlay.ymin = pad2.ymin\n\n return VR","def __init__(self, input0, input1, output) :\n Gate.__init__(self, [input0,input1], output)","def __init__(self, ifn, Nout, Nact=50, sep=10, shift=(0, 0), rot=(0, 0, 0),\n upsample=1, project_centering='fft'):\n if isinstance(Nout, int):\n Nout = (Nout, Nout)\n if isinstance(Nact, int):\n Nact = (Nact, Nact)\n if isinstance(sep, int):\n sep = (sep, sep)\n\n s = ifn.shape\n\n # stash inputs and some computed values on self\n self.ifn = ifn\n self.Ifn = fft.fft2(ifn)\n self.Nout = Nout\n self.Nact = Nact\n self.sep = sep\n self.shift = shift\n self.obliquity = truenp.cos(truenp.radians(truenp.linalg.norm(rot)))\n self.rot = rot\n self.upsample = upsample\n\n # prepare the poke array and supplimentary integer arrays needed to\n # copy it into the working array\n out = prepare_actuator_lattice(ifn.shape, Nact, sep, dtype=ifn.dtype)\n self.actuators = out['actuators']\n self.actuators_work = np.zeros_like(self.actuators)\n self.poke_arr = out['poke_arr']\n self.ixx = out['ixx']\n self.iyy = out['iyy']\n\n self.needs_rot = True\n if np.allclose(rot, [0, 0, 0]):\n self.needs_rot = False\n self.projx = None\n self.projy = None\n self.invprojx = None\n self.invprojy = None\n else:\n fwd, rev = prepare_fwd_reverse_projection_coordinates(s, rot)\n self.projx, self.projy = fwd\n self.invprojx, self.invprojy = rev\n\n # shift data\n if shift[0] != 0 or shift[1] != 0:\n # caps = Fourier variable (x -> X, y -> Y)\n # make 2pi/px phase ramps in 1D (much faster)\n # then broadcast them to 2D when they're used as transfer functions\n # in a Fourier convolution\n Y, X = [forward_ft_unit(1, s, shift=False) for s in s]\n Xramp = np.exp(X * (-2j * np.pi * shift[0]))\n Yramp = np.exp(Y * (-2j * np.pi * shift[1]))\n shpx = s\n shpy = tuple(reversed(s))\n Xramp = np.broadcast_to(Xramp, shpx)\n Yramp = np.broadcast_to(Yramp, shpy).T\n self.Xramp = Xramp\n self.Yramp = Yramp\n self.tf = [self.Ifn * self.Xramp * self.Yramp]\n else:\n self.tf = [self.Ifn]","def main():\n rospy.init_node(\"ik_pick_and_place_demo\")\n\n hover_distance = 0.1 # meters\n # Starting Pose for left arm round 1\n lstart_pose1 = Pose()\n lstart_pose1.position.x = 0.579679836383\n lstart_pose1.position.y = 0.283311769707\n lstart_pose1.position.z = 0.413676720426\n lstart_pose1.orientation.x = -0.0249590815779\n lstart_pose1.orientation.y = 0.999649402929\n lstart_pose1.orientation.z = 0.00737916180073\n lstart_pose1.orientation.w = 0.00486450832011\n\n # Starting Pose for right arm\n rstart_pose1 = Pose()\n rstart_pose1.position.x = 0.579679836383\n rstart_pose1.position.y = -0.283311769707\n rstart_pose1.position.z = 0.413676720426\n rstart_pose1.orientation.x = -0.0249590815779\n rstart_pose1.orientation.y = 0.999649402929\n rstart_pose1.orientation.z = -0.00737916180073\n rstart_pose1.orientation.w = 0.00486450832011\n\n # Picking pose left\n lpick_pose1 = Pose()\n lpick_pose1.position.x = 0.589679836383\n lpick_pose1.position.y = 0.70\n lpick_pose1.position.z = 0.153676720426\n lpick_pose1.orientation.x = -0.0249590815779\n lpick_pose1.orientation.y = 0.999649402929\n lpick_pose1.orientation.z = 0.00737916180073\n lpick_pose1.orientation.w = 0.00486450832011\n\n # Picking pose right\n rpick_pose1 = Pose()\n rpick_pose1.position.x = 0.589679836383\n rpick_pose1.position.y = -0.70\n rpick_pose1.position.z = 0.153676720426\n rpick_pose1.orientation.x = -0.0249590815779\n rpick_pose1.orientation.y = 0.999649402929\n rpick_pose1.orientation.z = 0.00737916180073\n rpick_pose1.orientation.w = 0.00486450832011\n\n # Placing pose left arm round 1\n lplace_pose1 = Pose()\n lplace_pose1.position.x = 0.589679836383\n lplace_pose1.position.y = 0.0\n lplace_pose1.position.z = 0.153676720426\n lplace_pose1.orientation.x = -0.0249590815779\n lplace_pose1.orientation.y = 0.999649402929\n lplace_pose1.orientation.z = 0.00737916180073\n lplace_pose1.orientation.w = 0.00486450832011\n\n # Starting Pose for left arm round 2\n\n lstart_pose2 = Pose()\n lstart_pose2.position.x = 0.579679836383\n lstart_pose2.position.y = 0.283311769707\n lstart_pose2.position.z = 0.413676720426\n lstart_pose2.orientation.x = -0.0249590815779\n lstart_pose2.orientation.y = 0.999649402929\n lstart_pose2.orientation.z = 0.00737916180073\n lstart_pose2.orientation.w = 0.00486450832011\n\n # Picking pose round 2\n lpick_pose2 = Pose()\n lpick_pose2.position.x = 0.589679836383\n lpick_pose2.position.y = 0.70\n lpick_pose2.position.z = 0.153676720426\n lpick_pose2.orientation.x = -0.0249590815779\n lpick_pose2.orientation.y = 0.999649402929\n lpick_pose2.orientation.z = 0.00737916180073\n lpick_pose2.orientation.w = 0.00486450832011\n\n # Picking pose round 2\n lplace_pose2 = Pose()\n lplace_pose2.position.x = 0.589679836383\n lplace_pose2.position.y = 0.0\n lplace_pose2.position.z = 0.213676720426\n lplace_pose2.orientation.x = -0.0249590815779\n lplace_pose2.orientation.y = 0.999649402929\n lplace_pose2.orientation.z = 0.00737916180073\n lplace_pose2.orientation.w = 0.00486450832011\n\n # Picking pose round 2\n rplace_pose1 = Pose()\n rplace_pose1.position.x = 0.589679836383\n rplace_pose1.position.y = 0.0\n rplace_pose1.position.z = 0.263676720426\n rplace_pose1.orientation.x = -0.0249590815779\n rplace_pose1.orientation.y = 0.999649402929\n rplace_pose1.orientation.z = 0.00737916180073\n rplace_pose1.orientation.w = 0.00486450832011\n\n\n\n # Initialise pick and place\n left_pnp = PickAndPlace('left', hover_distance)\n right_pnp = PickAndPlace('right', hover_distance)\n\n # Go to initial position\n left_pnp.move_to_start(left_pnp.ik_request(lstart_pose1))\n right_pnp.move_to_start(right_pnp.ik_request(rstart_pose1))\n\n # Load Gazebo Models via Spawning Services\n load_gazebo_models()\n\n print(\"\\nPicking...\")\n left_pnp.pick(lpick_pose1)\n\n print(\"\\nPicking...\")\n right_pnp.pick(rpick_pose1)\n\n print(\"\\nPlacing...\")\n left_pnp.place(lplace_pose1)\n\n load_gazebo_models_brick_only()\n\n print(\"\\nstarting...\")\n left_pnp.move_to_start(left_pnp.ik_request(lstart_pose2))\n\n print(\"\\nPicking...\")\n left_pnp.pick(lpick_pose2)\n\n print(\"\\nPlacing...\")\n left_pnp.place(lplace_pose2)\n\n rospy.sleep(5.0)\n\n rospy.on_shutdown(delete_gazebo_models)","def getInputs(self,maze):\n self.inputPos = []\n \n #Extrapos for CTS LOS\n self.extrapos = []\n i,j=0,0\n # array of front views\n for ang in self.inputangle:\n blocked = False\n if self.inputType == 0:\n for dis in self.inputdistance:\n self.inputPos.append([int(self.pos[0] + dis*np.cos(self.angle+ang)), \n int(self.pos[1] + dis*np.sin(self.angle+ang))])\n if(maze.checkCollisions(self.inputPos[i]) or blocked):\n blocked = True\n self.inputColour[i] = sensor_colours[1] \n self.scan[i] = 0\n else: \n self.inputColour[i] = sensor_colours[0]\n self.scan[i] = 1\n i +=1\n elif self.inputType == 1:\n #eXPERIMENTAL STUFF FOR continuous LOS\n self.extrapos.append(maze.getMaximumSightDistance(self.pos, self.angle+ang, self.sightLength))\n\n temp_length = self.extrapos[i][2]\n for ord in self.orders:\n self.scan[j] = (temp_length/self.sightLength)**ord\n self.scan[j+1] = ((self.sightLength-temp_length)/self.sightLength)**ord # add opposite way, gets stronger closer instead of stronger further. might make a difference.\n j +=2\n i+=1","def __init__(self, params):\n self.params = params\n\n # Initialize marker classes\n self.markers = ManipulateMarkers()\n self.pub = Publisher(\"/rviz/camera_placement\", CameraPlacement,\n queue_size=1)\n self.tfl = TransformListener()\n self.clients = \\\n {'polar_camera': SimpleActionClient(\"VF_Polar_camera_server\",\n SurfaceVFAction),\n 'polar_rad': SimpleActionClient(\"VF_Polar_rad_server\",\n SurfaceVFAction),\n 'cartesian_camera':\n SimpleActionClient(\"VF_Cartesian_camera_server\",\n SurfaceVFAction),\n 'cartesian_rad':\n SimpleActionClient(\"VF_Cartesian_rad_server\",\n SurfaceVFAction),\n 'CAD_camera':\n SimpleActionClient(\"VF_CAD_camera_server\",\n SurfaceVFAction),\n 'client': ''}\n\n Me.info_message(\"Waiting for VF servers.\")\n for key in self.clients:\n if key != 'client':\n self.clients[key].wait_for_server(Duration(1))\n\n # Build empty dictionaries for use after operator input\n self.inspection = {'name': '',\n 'pose_list': [],\n 'x_size': 1,\n 'z_size': 1,\n 'y_array': []}\n\n self.data = {'x': {'x-': -1, 'x+': 1, 'current': 0, 'limit': 0},\n 'y': {'y-': -1, 'y+': 1, 'current': 0, 'limit': 0},\n 'z': {'z-': -1, 'z+': 1, 'current': 0, 'limit': 0},\n 'poses': {'robot': PoseStamped(),\n 'marker': PoseStamped(),\n 'path': PoseArray()}}\n\n # Initialize class for the arm\n self.manip = ArmParameters(group_name=self.params['group'],\n joint_control=True)\n # Initialize class to interface with the arms\n self.manip_fcns = \\\n ArmFunctions(world_frame=self.params['world'],\n run_rate=60, deltas={'joint': 0.01, 'teleop': 0.005},\n fcn_params=self.params)\n return","def at_aflight(seq, flag):\n at(\"AFLIGHT\", seq, [flag])","def initialise_indoor_air_quality(self):\n # name, command, signals, delay\n self._i2c_read_words_from_cmd(command=[0x20, 0x03], reply_size=0, delay=0.01)","def _aim_ctrl(self):\n y = 0\n z = 0\n\n if self.aim_axis == \"y\":\n z = 90\n elif self.aim_axis == \"z\":\n y = -90\n\n for shape in self.ctrl.getShapes():\n pm.rotate(shape.cv, 0, y, z, r=1)","def __init__(self, direct=1):\n if not direct:\n self.physicsClient = p.connect(p.GUI)\n else:\n self.physicsClient = p.connect(p.DIRECT)\n \n #The max_dist variables define the size of the arena\n #n defines the number of agents in the world.\n self.max_dist_x = 4\n self.max_dist_y = 4\n self.n = 2\n\n #This defines the max force that can be applied\n #to the motor joints of our robot.\n self.maxForce = 30 \n \n self.observation_space = np.array([[1.0,1.0,1.0],[1.0,1.0,1.0]])\n\n #Action Space\n #For all the agents, the action space is same.\n #The first action is a continous action which determines the speed of the wheels.\n #The second action is a continous action which determines the angle by which the front wheel turns.\n self.action_space = np.array([spaces.Box(low=np.array([0,-0.5]), high=np.array([30,0.5]))]*self.n)\n self.reduce = 1\n self.step_counter=0","def __init__(self, pads):\n mfioCommon.__init__(self, pads)\n\n mfio_width = len(pads)\n #\n mfio_o = Signal(mfio_width)\n mfio_oe = Signal(mfio_width)\n mfio_i = Signal(mfio_width)\n # create single pin tristate buffers\n for b in range(mfio_width):\n self.submodules += mfioSinglePin(pads, b, mfio_i[b], mfio_o[b], mfio_oe[b]) \t\n\n # Wishbone \n self.bus = bus = wishbone.Interface()\n\n #\n sel = Signal(mfio_width)\n inbit = Signal(1)\n\n # todo: dynamic address width calc to optimize the decode logic\n addr_width = 12\n # 1024 IO max\n seladr = Signal(10)\n\n # 10 bits of address = 1024 pins max\n self.comb += seladr.eq(self.bus.adr[:10]) \n \n # address decoder\n for b in range(mfio_width):\n self.comb += sel[b].eq(seladr == b)\n\n self.comb += inbit.eq( (mfio_i & sel) != 0 )\n\n # Read bit\n rdbus = Signal(32)\n self.comb += [\n rdbus[0].eq(inbit),\n bus.dat_r.eq(rdbus)\n ]\t\n\n # process output \n outbit = Signal(1)\n oebit = Signal(1)\n wren = Signal(1)\n\n # PINAPI 1.0 compatible: 0 = drive 0, 1 drive 1, 3 = HiZ\n self.comb += outbit.eq( bus.dat_w[0] )\n self.comb += oebit.eq ( ~bus.dat_w[1] )\n\n # write enable\n self.comb += wren.eq(self.bus.stb & self.bus.cyc & self.bus.we) \n\n for b in range(mfio_width):\n self.sync += If(wren & sel[b], mfio_o[b].eq(outbit), mfio_oe[b].eq(oebit) )\n\n seq = [\n (1, [bus.ack.eq(1)]), #\n (1, [bus.ack.eq(0)]), #\n (0, []),\n ]\n \n t, tseq = 0, []\n for dt, a in seq:\n tseq.append((t, a))\n t += dt\n\n self.sync += timeline(bus.cyc & bus.stb, tseq)","def beam_update(self, beams, extra):\n h_i, dec_out, context, src_mask = extra\n h_i = tuple(hc[:, beams, :] for hc in h_i)\n dec_out = dec_out[beams, :]\n return h_i, dec_out, context, src_mask","def align(self):\n flag=0\n input=None\n level=None\n board=None\n ainps={'L0':[],'L1':[],'L2':[],'H0':[]} \n for i in self.inputs:\n if(i.inputnumber.var.get() == 1):\n if i.inpnumall == rareradio:\n input=i.inpnum\n level=i.level\n board=i.board\n print 'Rare chosen:',level,' ',input\n ainps[i.level].append(i.inpnum)\n flag=flag+1\n #print 'ainps:',ainps \n if flag < 2 :\n print \"Align: less then 2 inputs chosen. \" \n return\n if input==None:\n cmd=\"setRareFlag(0,0,0)\"\n else:\n mode='0'\n if level == 'H0': mode = '1'\n cmd=\"setRareFlag(\"+board+','+input+','+mode+\")\"\n print \"seting rare: \",cmd\n output=self.vb.io.execute(cmd,log=\"yes\",applout=\"<>\") \n self.align=Corel(self.vb,ainps)\n self.align.croscor()","def conRFMixAndMaskToBeagle(indfile_name, rephasedhaps_pref, em_iters, win_size, chroms):\n\t### First get individual information\n\twindow_id = 0\n\tem_iter = em_iters\n\tindfile = open(indfile_name, \"r\")\t\n\tinds = []\n\tfor line in indfile:\n\t\tsplits = line.strip(\"\\r\\n\").split()\n\t\tinds.append(splits[1] + \"_A\")\n\t\tinds.append(splits[1] + \"_B\")\n\n\tallloci = []\n\toutfilename = rephasedhaps_pref + \"_w\" + str(win_size) + \".beagle\"\n\toutfile = open(outfilename, \"w\")\n\toutfile.write(\"I\\tid\\t\" + \"\\t\".join(inds) + \"\\n\")\n\t## Write genotype data out to file\n\n\tvitout = open(rephasedhaps_pref + \".vit\", \"w\")\n\twinout = open(rephasedhaps_pref + \".windows\", \"w\")\n\tfbkout = rephasedhaps_pref + \".fbk\"\n\tif os.path.exists(fbkout):\n\t\tos.remove(fbkout)\n\tvitlist = []\n\tfor chrom in chroms:\n\t\tprint chrom\n\t\tshapeitfilename = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.allelesRephased\" + str(em_iters) + \".txt\"\n\t\tshapeitfile = open(shapeitfilename, \"rb\")\n\t\tfbkin_name = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.\" + str(em_iters) + \".ForwardBackward.txt\"\n\t\tos.system('cat ' + fbkin_name + \" >> \" + fbkout) # Concatenate files together\n\t\tmarkerin = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.amaps\"\n\t\tmarkerfile = open(markerin, \"r\")\n\t\tloci=[]\n\t\talleles = {}\n\t\tfor mline in markerfile:\n\t\t\tmsplit = mline.strip().split()\n\t\t\tloci.append(msplit[1])\n\t\t\talleles[msplit[1]] = [msplit[3], msplit[4] ]\n\n\t\tallloci.extend(loci)\n\t\tfor j,line in enumerate(shapeitfile):\n\t\t\tsline = line.strip(\"\\r\\n\")\n\t\t\tzero, ones = alleles[loci[j]]\n\t\t\tfixed = [ recodeAllele(k, zero, ones) for k in sline ]\n\t\t\toutfile.write(\"M\\t\" + loci[j] + \"\\t\" + \"\\t\".join(fixed) + \"\\n\")\n\t\tvitfile = open(rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.\" + str(em_iters) + \".Viterbi.txt\", \"r\")\n\t\tvitlist.extend([x.strip().split() for x in vitfile])\n\t\tshapeitfile.close()\n\t\tvitfile.close()\n\t\t\n\t# This will transpose the whole Viterbi file\n\t# Yikes this may take a lot of memory\n\tfor i,x in enumerate(zip(*vitlist)):\n\t\tvitout.write(inds[i] + \"\\t\")\n\t\tfor y in x:\n\t\t\tvitout.write(y+\"\\t\")\n\t\tvitout.write(\"\\n\")\n\t\t### This doesn't quite work yet so make sure to fix it next time\n\tfor l in allloci:\n\t\twinout.write(\"window\" + str(window_id) + \"\\t\" + l + \"\\n\")\n\t\twindow_id += 1\n\treturn([outfile.name, vitout.name, winout.name, fbkout])","def __init__(self, filename, freq=100, emotion=False, use_direction = True):\n self._use_direction = use_direction\n self._filename = filename\n self._freq = freq\n self._rate = rospy.Rate(freq)\n self._rate_camera = rospy.Rate(freq*10)\n# self._pub_rate = rospy.Publisher('robot/joint_state_publish_rate', UInt16, queue_size=10)\n self._pub_path_is_started = rospy.Publisher('trust/path_is_started', Bool, queue_size=1)\n self._pub_path_is_started.publish(False)\n self._pub_path = rospy.Publisher('trust/robot_states', RobotData, queue_size=1)\n self._collision_detected = False\n self._inver_time = rospy.get_time() #last time human interveneted\n self._inverted = False #last time human interveneted\n self._path = RobotData()\n self._path.lr =1\n self._path.grad=1\n self._sub_path_vel_man = rospy.Subscriber('/trust/ur_manual', Float64, self.path_vel_man_callback)\n self._sub_trust = rospy.Subscriber('/trust/trust', TrustData, self.updateTrust)\n self._trust_mean = 0.5\n self._trust_count = 0\n self._trust_mean_last = 0.5\n self._head = baxter_interface.Head()\n # self._head.set_pan(0.25)\n self._head.set_pan(0.0)\n\n self._base_pos = []\n self._done = False\n self._limb = baxter_interface.Limb(\"left\")\n self._joints = self._limb.joint_names()\n self._limb_kin = baxter_pykdl.baxter_kinematics('left')\n self._gripper = baxter_interface.Gripper(\"left\", CHECK_VERSION)\n self._headers=[]\n self._positions=[]\n self._directions=[]\n self._dir = [] # curent direction\n self._dir_ds = 1 # gradient of curent direction wrt s\n # self._pos = [] # current position\n self._vel = [] # current velocity vector\n self._path_vel = 0.75 # Needs to subscribes: path velocity\n self._limb.set_joint_position_speed(0.4)\n self._q_dot_max = np.array([[4, 4, 4, 2, 2, 2, 2]]).reshape(-1,)\n # for publishing\n # path.x ---> path_pos (s)\n # path.y ---> path_status (0,1,2)\n # self._center_pos = [.574, -1.042, .950] #baxter center point in phasespace\n # self._center_pos = [-1405, -3005, 950] #baxter center point in phasespace\n # self._center_pos = [-1405, -3005, 1050] #baxter center point in phasespace\n # self._center_pos = [-299, -3080, 1050] #baxter center point in phasespace\n # self._center_pos = [-662, -3134, 1050] #baxter center point in phasespace\n # baxter center pose\n reader = csv.reader(open(\"/home/baxter/ros_ws/src/i2r_tools/config.txt\", \"rb\"), delimiter=' ')\n reader.next()\n x = list(reader)\n config_values = np.array(x).astype('float')\n # self._center_pos = [-662, -3134, 1050] # baxter center point in phasespace\n self._center_pos = config_values [2]\n # self._center_pos = [2503, 471, 1050] # baxter center point in phasespace\n self._center_deg = np.radians(180) # baxter orientation with respect to phasespace\n # self._center_deg = np.radians(90) # baxter orientation with respect to phasespace\n self._rel_head_center = np.array([180,0,645]).astype('float') # baxter head relative to baxter base\n self._head_center = self._center_pos + self._rel_head_center\n self._path_pos_0 = 0.0\n self._path_len = 0\n self._path_status = 1 # 1=forward, 2=pick, 3=backward\n # self._path_pos = 0 # Distance robot moved along the path\n self._K = .01 * np.identity(6)\n self._K[5,5] = .1\n self._path_offset = np.zeros((6, 1))\n # self._way_point_coe = 0.25\n self._way_point_coe = 0.5\n # self._lambda = 0.01 #damping lambda\n self._lambda = 0.1 #damping lambda\n # path waypoints minimum threshold\n self._eps = .05\n # Verify Grippers Have No Errors and are Calibrated\n if self._gripper.error():\n self._gripper.reset()\n if (not self._gripper.calibrated() and\n self._gripper.type() != 'custom'):\n self._gripper.calibrate()\n self._gripper.calibrate()\n # Using the navigator for releasing during hand over\n self._nav_right = baxter_interface.Navigator('right')\n self._nav_right.button0_changed.connect(self._nav_b0_pressed)\n\n self._gripper_open = True\n self._is_hand_over = False\n # read the recorded file\n self._joint_first = []\n self._joint_last = []\n self._dir_test = []\n self.read_recorded()\n # for recording\n self._rec_positions = []\n self._rec_path_pos = 0\n self._rec_active = True\n # Blob Detection\n try:\n self._right_hand_camera = baxter_interface.CameraController('right_hand_camera')\n self._right_hand_camera.close()\n except AttributeError:\n pass\n try:\n self._left_hand_camera = baxter_interface.CameraController('left_hand_camera')\n self._left_hand_camera.close()\n except AttributeError:\n pass\n # self._left_hand_camera = baxter_interface.CameraController('left_hand_camera')\n # self._left_hand_camera.resolution = (1280, 800)\n # self._left_hand_camera.open()\n # i2rcamera.enable('left')\n self._cvbridge = CvBridge()\n self._hand_image = None\n # rospy.Subscriber('/cameras/left_hand_camera/image', Image, self.hand_callback) # Subscribe to hand camera topic\n # self.late_center = (620, 360)\n self.late_center = (570, 430)\n self.long_center = (620, 360)\n self._pos_eof = []\n self._pos_joint = []\n self._top_pos_eof = []\n self._top_pos_joint = []\n self._obj_pos_eof = []\n self._obj_pos_joint = []\n # pick up positions\n self._pick_positions = []\n self._pick_idx = 0\n # place positions\n self._place_positions =[]\n self._place_idx = 0\n self.read_place_pos(filename)\n # Display Image\n self._display_image = np.zeros((1024, 600, 3), np.uint8)\n self._pub_img = rospy.Publisher('/robot/xdisplay', Image, latch=True, queue_size=1)\n\n # collision detection.\n self.camera_detect = False # Whether to use visual detection for safety\n # self._camera_area_threshold = (400, 800) # Theshold number of pixels to indicate stop signal\n self._camera_area_threshold = (400, 800) # Theshold number of pixels to indicate stop signal\n self._camera_area_rectangle = (660, 800, 372, 660) # Detection rectangle for stop signal (y1, y2, x1, x2)\n # self._face_fix = False # Fix for face (fixes face once on camera_stop = False change)\n self._debug_collision = False\n self._debug = False\n self._pub_debug = rospy.Publisher('trust/debug_player', Twist, queue_size=1)\n self._bgsubtractor = cv2.BackgroundSubtractorMOG()\n self._camera_workspace_begin = 402 #image\n self._camera_workspace_end = 600\n self._camera_workspace_offset = 150\n self._limb_y_workspace_begin = 0.4810551107413326 #workspace\n self._limb_y_workspace_end = 0.1497036417856029\n self._limb_y_workspace_start = 0.58950401153522277\n # self._collision_detected = False\n self._collision_path_vel = self._path_vel\n self._delay_counter = 0;\n self.set_image('/home/baxter/ros_ws/src/i2r_move/img/ready.png')\n if self.camera_detect:\n # i2rcamera.disable('head')\n # i2r.camera.init\n # i2rcamera.enable('head')\n self._head_camera = baxter_interface.CameraController('head_camera')\n self._head_camera.resolution = (1280, 800)\n self._head_camera.exposure = 20\n # self._head_camera.close()\n self._head_camera.open()\n self._sub_head_img = rospy.Subscriber('/cameras/head_camera/image', Image,\n self.detection_callback) # Subscribe to head camera topic\n # self.set_image()\n # else:\n # self.set_image('/home/baxter/ros_ws/src/i2r_move/img/idle.png')\n # PhaseSpace Collision Detection\n self._tracking_detect = emotion # Whether to use phasespace detection for safety\n self._xh = self._center_pos[0] #human x in ref frame (phasespace)\n self._xr = self._center_pos[1] #human y in ref frame (phasespace)\n self._xz = self._center_pos[2] #human z in ref frame (phasespace)\n # self._d_safe = pow(175, 2) #change the first argument of pow(distance <---, 2)\n # self._d_safe = pow(225, 2) #change the first argument of pow(distance <---, 2)\n self._d_safe = pow(200, 2) #change the first argument of pow(distance <---, 2)\n if self._tracking_detect:\n self._sub_human = rospy.Subscriber('/trust/human_states', Human, self.updateHuman)\n self._pub_emotion = rospy.Publisher('trust/emotion', UInt8, queue_size=1)\n # self._emotion_state = 0\n # 0: Happy\n # 1: Worried (Collision)\n # 2: Late\n # 3: Ahead\n self.__img_path = ['/home/baxter/ros_ws/src/i2r_tools/img/happy.png',\n '/home/baxter/ros_ws/src/i2r_tools/img/worried.png',\n '/home/baxter/ros_ws/src/i2r_tools/img/bored.png',]\n # self.set_image()\n self._sub_path_vel = rospy.Subscriber('/trust/ur', Float64, self.path_vel_callback)\n #TASE\n self._start_point = self._joint_first","def example():\n a = Arm()\n f = gcf()\n ang = [0.0, 0.0, 0.0]\n calibration = asarray([[10.29535101,27.84015137,1.22089742],[1.4788487,31.20673289,0.43823847],[ 11.51182565,33.73670722,7.44454401],[5.20081671,23.76730423,10.82195407]])\n\n\n mat_trans = get_paper_transform_matrix(calibration)\n while 1:\n graph(f, a, ang)\n print \"Angles: \",ang\n print \"Tool position: \", a.getTool(ang)[:-1]\n d = input(\"position as list / angles as tuple?>\")\n if type(d) == list:\n if shape(d) == tuple([3]):\n d = paper_to_world(d, mat_trans)\n ang = goto_pos(d, ang, a)\n else:\n for coord in d:\n coord = paper_to_world(coord, mat_trans)\n ang = goto_pos(coord, ang, a)\n graph(f, a, ang)\n else:\n ang = d\n set_motor_angles(ang)","def __init__(self, anara, anadec, *args, **kwargs):\n _Beamlet.__init__(self, *args, **kwargs)\n self._anara = anara\n self._anadec = anadec","def move2goal(self):\n vel_msg = Twist()\n\n # Linear velocity in the x-axis.\n vel_msg.linear.x = 0.4 # m/s\n vel_msg.linear.y = 0\n vel_msg.linear.z = 0\n\n # Angular velocity in the z-axis.\n vel_msg.angular.x = 0\n vel_msg.angular.y = 0\n vel_msg.angular.z = 1.5 # rad/s\n\n # Starting point reference\n goal_x = 1.0 \n goal_y = 1.0\n x_ref = 1.0\n y_ref = 1.0\n\n # Previous Reference\n x_prev_ref = 0.0\n y_prev_ref = 0.0\n theta_prev_ref = self.theta\n vrefA = 0.5\n wrefA = 0.0\n \n i = 0\n tPx, tPy, tPTheta = self.initiate_trajectory(\n x_ref, y_ref, vel_msg, \n x_prev_ref, y_prev_ref, \n theta_prev_ref, vrefA, wrefA\n )\n\n x_prev_ref = tPx[0]\n y_prev_ref = tPy[0]\n theta_prev_ref = tPTheta[0]\n\n print(f'X TRAJECTORY: {tPx}\\nY TRAJECTORY: {tPy}\\nTHETA TRAJ: {tPTheta}')\n print(f'ACTUAL THETA: {self.theta}')\n\n while not rospy.is_shutdown():\n \n if i >= 8:\n i = 0\n\n x_ref = goal_x\n y_ref = goal_y\n\n tPx, tPy, tPTheta = self.initiate_trajectory(\n x_ref, y_ref, vel_msg, \n x_prev_ref, y_prev_ref, \n theta_prev_ref, vrefA, wrefA\n )\n # inputRef = ControllerInput(\n # xref=x_ref,\n # yref=y_ref,\n # RstateX=self.x_position,\n # RstateY=self.y_position,\n # RstateTheta=self.theta,\n # RstateVelocity=vel_msg.linear.x,\n # RstateW=vel_msg.angular.z,\n # xrefA=x_prev_ref,\n # yrefA=y_prev_ref,\n # thetarefA=theta_prev_ref,\n # vrefA=vrefA,\n # wrefA=wrefA\n # )\n\n # rospy.loginfo(f'X: {self.x_position} \\tY: {self.y_position}\\t Theta: {self.theta} ')\n # nmpc = NMPC_Controller(inputRef)\n # tPx, tPy, tPTheta = nmpc.test_create_mini_path()\n\n # print(f'X TRAJECTORY: {tPx}\\nY TRAJECTORY: {tPy}\\nTHETA TRAJ: {tPTheta}')\n # print(f'ACTUAL THETA: {self.theta}')\n \n # new_v, new_w = nmpc.start_optmizer()\n # new_v = round(new_v, 4)\n # new_w = round(new_w, 4)\n\n # print(new_v, new_w)\n # rospy.loginfo(\n # f'X: {self.x_position}, Y: {self.y_position}, THETA: {self.theta}')\n \n # self.velocity_publisher.publish(vel_msg)\n # x_prev_ref = self.x_position\n # y_prev_ref = self.y_position\n # theta_prev_ref = self.theta\n # vrefA = vel_msg.linear.x\n # wrefA = vel_msg.angular.z\n \n\n # theta_prev_ref = self.theta\n # vel_msg.angular.z = 0.0\n\n\n '''Update the linear & angular velocity'''\n # vel_msg.linear.x = new_v\n # vel_msg.angular.z = new_w\n\n if i < 8:\n inputRef = ControllerInput(\n xref = tPx[i],\n yref = tPy[i],\n RstateX = self.x_position,\n RstateY = self.y_position,\n RstateTheta = self.theta,\n RstateVelocity = vel_msg.linear.x,\n RstateW = vel_msg.angular.z,\n xrefA = x_prev_ref,\n yrefA = y_prev_ref,\n thetarefA = theta_prev_ref,\n vrefA = vrefA,\n wrefA = wrefA\n )\n\n nmpc = NMPC_Controller(inputRef)\n new_v, new_w = nmpc.start_optmizer()\n new_v = round(new_v, 4)\n new_w = round(new_w, 4)\n\n print(f'(actual) X: {self.x_position}, Y: {self.x_position}, THETA: {self.theta}')\n print(f'(desired) X: {tPx[i]}, Y: {tPy[i]}')\n print(f'V: {vel_msg.linear.x}\\tW: {vel_msg.angular.z}')\n\n x_prev_ref = tPx[i-1]\n y_prev_ref = tPy[i-1]\n theta_prev_ref = tPTheta[i-1]\n vrefA = vel_msg.linear.x\n wrefA = vel_msg.angular.z\n\n vel_msg.linear.x = new_v\n vel_msg.angular.z = new_w\n # vel_msg.angular.z = 0.0\n\n print(f'index: {i}')\n\n distance = math.sqrt((self.x_position - tPx[i])**2 + (self.y_position - tPy[i])**2)\n if distance < 0.3:\n print(f'Distance: {distance}')\n i+=1\n\n\n self.velocity_publisher.publish(vel_msg)\n self.rate.sleep()\n\n rospy.spin()","def __init__(self):\r\n self.label = \"Step 1: FEMA AOI Clip\"\r\n self.description = \"This tool projects the FEMA Flood Hazards feature class for Rhode Island into Rhode \" \\\r\n \"Island State Plane Feet. The projected feature class is then clipped to the input area \" \\\r\n \"of interest boundaries.\"\r\n self.canRunInBackground = False","def get_observations(self):\n joint_states = self.joints_state\n self.force = self.wrench_stamped.wrench.force\n self.torque = self.wrench_stamped.wrench.torque\n self.static_taxel = self.tactile_static.taxels\n# dynamic_taxel= tactile_dynamic\n\n# print(\"[force]\", self.force.x, self.force.y, self.force.z)\n# print(\"[torque]\", self.torque.x, self.torque.y, self.torque.z)\n shp_joint_ang = joint_states.position[0]\n shl_joint_ang = joint_states.position[1]\n elb_joint_ang = joint_states.position[2]\n wr1_joint_ang = joint_states.position[3]\n wr2_joint_ang = joint_states.position[4]\n wr3_joint_ang = joint_states.position[5]\n\n shp_joint_vel = joint_states.velocity[0]\n shl_joint_vel = joint_states.velocity[1]\n elb_joint_vel = joint_states.velocity[2]\n wr1_joint_vel = joint_states.velocity[3]\n wr2_joint_vel = joint_states.velocity[4]\n wr3_joint_vel = joint_states.velocity[5]\n\n q = [shp_joint_ang, shl_joint_ang, elb_joint_ang, wr1_joint_ang, wr2_joint_ang, wr3_joint_ang]\n# print(\"q(observation):\", q)\n eef_x, eef_y, eef_z = self.get_xyz(q)\n self.end_effector = self.get_xyz(q)\n eef_x_ini, eef_y_ini, eef_z_ini = self.get_xyz(self.init_joint_pose2) \n\n delta_image_r, delta_image_l = self.get_image()\n self.cnn_image_r = agent.update_cnn(delta_image_r)\n self.cnn_image_l = agent.update_cnn(delta_image_l)\n self.cnn_image_r_list = self.cnn_image_r.tolist()\n self.cnn_image_l_list = self.cnn_image_l.tolist()\n print(\"r_list\", self.cnn_image_r_list)\n print(\"l_list\", self.cnn_image_l_list)\n\n observation = []\n# rospy.logdebug(\"List of Observations==>\"+str(self.observations))\n for obs_name in self.observations:\n if obs_name == \"shp_joint_ang\":\n observation.append((shp_joint_ang - self.init_joint_pose2[0]) * self.joint_n)\n elif obs_name == \"shl_joint_ang\":\n observation.append((shl_joint_ang - self.init_joint_pose2[1]) * self.joint_n)\n elif obs_name == \"elb_joint_ang\":\n observation.append((elb_joint_ang - self.init_joint_pose2[2]) * self.joint_n)\n elif obs_name == \"wr1_joint_ang\":\n observation.append((wr1_joint_ang - self.init_joint_pose2[3]) * self.joint_n)\n elif obs_name == \"wr2_joint_ang\":\n observation.append((wr2_joint_ang - self.init_joint_pose2[4]) * self.joint_n)\n elif obs_name == \"wr3_joint_ang\":\n observation.append((wr3_joint_ang - self.init_joint_pose2[5]) * self.joint_n)\n elif obs_name == \"shp_joint_vel\":\n observation.append(shp_joint_vel)\n elif obs_name == \"shl_joint_vel\":\n observation.append(shl_joint_vel)\n elif obs_name == \"elb_joint_vel\":\n observation.append(elb_joint_vel)\n elif obs_name == \"wr1_joint_vel\":\n observation.append(wr1_joint_vel)\n elif obs_name == \"wr2_joint_vel\":\n observation.append(wr2_joint_vel)\n elif obs_name == \"wr3_joint_vel\":\n observation.append(wr3_joint_vel)\n elif obs_name == \"eef_x\":\n observation.append((eef_x - eef_x_ini) * self.eef_n)\n elif obs_name == \"eef_y\":\n observation.append((eef_y - eef_y_ini) * self.eef_n)\n elif obs_name == \"eef_z\":\n observation.append((eef_z - eef_z_ini) * self.eef_n)\n elif obs_name == \"force_x\":\n observation.append((self.force.x - self.force_ini.x) / self.force_limit1 * self.force_n)\n elif obs_name == \"force_y\":\n observation.append((self.force.y - self.force_ini.y) / self.force_limit1 * self.force_n)\n elif obs_name == \"force_z\":\n observation.append((self.force.z - self.force_ini.z) / self.force_limit1 * self.force_n)\n elif obs_name == \"torque_x\":\n observation.append((self.torque.x - self.torque_ini.x) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"torque_y\":\n observation.append((self.torque.y - self.torque_ini.y) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"torque_z\":\n observation.append((self.torque.z - self.torque_ini.z) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"image_cnn\":\n for x in range(0, 10):\n observation.append(self.cnn_image_r_list[0][x])\n# print(\"r_list\", self.cnn_image_r_list[0][x])\n for x in range(0, 10):\n observation.append(self.cnn_image_l_list[0][x])\n# print(\"l_list\", self.cnn_image_l_list[0][x])\n elif obs_name == \"static_taxel\":\n for x in range(0, 28):\n observation.append((self.static_taxel[0].values[x] - self.static_taxel_ini[0].values[x]) * self.taxel_n)\n for x in range(0, 28):\n observation.append((self.static_taxel[1].values[x] - self.static_taxel_ini[1].values[x]) * self.taxel_n)\n# elif obs_name == \"dynamic_taxel\":\n# observation.append(dynamic_taxel[0].values) * self.taxel_n\n# observation.append(dynamic_taxel[1].values) * self.taxel_n\n else:\n raise NameError('Observation Asked does not exist=='+str(obs_name))\n\n print(\"observation\", list(map(round, observation, [3]*len(observation))))\n# print(\"observation\", observation)\n\n return observation","def singleOptic2(n,misalign=np.zeros(6),srcdist=89.61e3+1.5e3,az=100.,\\\n returnRays=False,f=None,\\\n plist=[[0],[0],[0]],\\\n ax=100.):\n #Establish subannulus of rays\n r0 = conic.primrad(8426.,220.,8400.)\n r1 = conic.primrad(8426.+ax,220.,8400.)\n rays = sources.subannulus(r0,r1,az/220.,n,zhat=-1.)\n #Transform to node position\n tran.transform(rays,220,0,0,0,0,0)\n #Set up finite source distance\n raydist = sqrt(srcdist**2+rays[1]**2+rays[2]**2)\n l = rays[1]/raydist\n m = rays[2]/raydist\n n = -sqrt(1.-l**2-m**2)\n rays = [raydist,rays[1],rays[2],rays[3],l,m,n,rays[7],rays[8],rays[9]]\n #Align perfectly to beam\n tran.steerX(rays)\n #Apply misalignment\n tran.transform(rays,*misalign)\n #Place mirror\n tran.transform(rays,-220.,0,-8400.,0,0,0)\n## surf.wolterprimarynode(rays,220,8400.)\n surf.primaryLL(rays,220.,8400.,8426.+ax,8426.,az/220.,*plist)\n rays = tran.vignette(rays,ind=np.logical_and(rays[3]<8400.+ax,\\\n rays[3]>8400.))\n tran.itransform(rays,-220.,0.,-8400.,0,0,0)\n #Vignette rays not landing in active mirror area\n ind = np.logical_and(rays[3]>26.,rays[3]<(26.+ax))\n## ind = np.logical_and(np.abs(rays[2])<az/2.,indz)\n rays = tran.vignette(rays,ind=ind)\n #Reverse misalignment\n tran.itransform(rays,*misalign)\n #Reflect and go to surface\n tran.reflect(rays)\n if f is None:\n f = surf.focusI(rays)\n else:\n tran.transform(rays,0,0,f,0,0,0)\n surf.flat(rays)\n #Get centroid\n cx,cy = anal.centroid(rays)\n\n if returnRays is True:\n return rays\n \n return anal.hpd(rays)/abs(f)*180/pi*60**2,f,cx","def __init__(self):\r\n self.observation_space = spaces.Box(low=0, high=255, shape=(119, 214))\r\n self.state = np.zeros((119, 214), dtype=np.uint8) \r\n \r\n self.action_space = spaces.Discrete(4)\r\n\t\t\r\n self.goal = \t[221.0, -9.0] # global xy coordinates\r\n\r\n self.episodeN = 0\r\n self.stepN = 0 \r\n \r\n self.allLogs = { 'reward':[0] }\r\n self.allLogs['distance'] = [221]\r\n self.allLogs['action'] = [1]\r\n \r\n self._seed()\r\n self.stallCount = 0\r\n global airgym\r\n airgym = myAirSimMultirotorClient()","def drive_vehicle(x_offset, y_offset, run_cmd, width, front_distance_sensor_1, front_distance_sensor_2):\n # Initialise the PCA9685 using the default address (0x40).\n pwm = Adafruit_PCA9685.PCA9685()\n\n # set number of pins for direction of drives\n left_fwd_pin_1 = 4\n left_fwd_pin_2 = 17\n left_bwd_pin_1 = 18\n left_bwd_pin_2 = 23\n\n right_fwd_pin_1 = 22\n right_fwd_pin_2 = 27\n right_bwd_pin_1 = 24\n right_bwd_pin_2 = 25\n\n GPIO.setup(left_fwd_pin_1, GPIO.OUT) # left forward 1 pin\n GPIO.setup(left_fwd_pin_2, GPIO.OUT) # left forward 2 pin\n GPIO.setup(left_bwd_pin_1, GPIO.OUT) # left backward 1 pin\n GPIO.setup(left_bwd_pin_2, GPIO.OUT) # left backward 2 pin\n\n GPIO.setup(right_fwd_pin_1, GPIO.OUT) # right forward 1 pin\n GPIO.setup(right_fwd_pin_2, GPIO.OUT) # right forward 2 pin\n GPIO.setup(right_bwd_pin_1, GPIO.OUT) # right backward 1 pin\n GPIO.setup(right_bwd_pin_2, GPIO.OUT) # right backward 2 pin\n\n left_fwd = True\n left_bwd = False\n\n right_fwd = True\n right_bwd = False\n\n last_left = False\n\n while True:\n try:\n # Take shortest distance measured by ultrasound\n if front_distance_sensor_1.value < front_distance_sensor_2.value:\n front_distance = front_distance_sensor_1.value\n else:\n front_distance = front_distance_sensor_2.value\n\n if front_distance < 5 or width.value > 450:\n # if we are facing some obstacle or object we are looking for is close > stop\n left_speed = 0\n right_speed = 0\n left_fwd = left_bwd = right_fwd = right_bwd = False\n else:\n right_fwd = True\n if x_offset.value == -10:\n if last_left:\n # no object is detected by camera\n left_speed = 0 # 0.6 * max_speed\n # left_fwd = False\n # left_bwd = True\n right_speed = 0.9 * max_speed\n else:\n left_speed = 0.9 * max_speed\n right_speed = 0\n elif -5 < x_offset.value < 0:\n # object is in left part of the screen\n left_speed = pow(abs(x_offset.value), 2) * max_speed\n right_speed = max_speed\n left_fwd = True\n left_bwd = False\n last_left = True\n elif x_offset.value > 0:\n # object is in right part of the screen\n left_speed = max_speed\n right_speed = pow(x_offset.value, 2) * max_speed\n left_fwd = True\n left_bwd = False\n last_left = False\n else:\n # object is in the middle\n left_speed = max_speed\n right_speed = max_speed\n left_fwd = True\n left_bwd = False\n \n print('Speeds: Left {} Right {} Run {}'.format(left_speed, right_speed, run_cmd.value))\n\n # Right drives\n pwm.set_pwm(0, 0, int(right_speed*run_cmd.value))\n pwm.set_pwm(1, 0, int(right_speed*run_cmd.value))\n GPIO.output(left_fwd_pin_1, left_fwd)\n GPIO.output(left_fwd_pin_2, left_fwd)\n GPIO.output(left_bwd_pin_1, left_bwd)\n GPIO.output(left_bwd_pin_2, left_bwd)\n\n # Left drives\n pwm.set_pwm(4, 0, int(left_speed*run_cmd.value))\n pwm.set_pwm(5, 0, int(left_speed*run_cmd.value))\n GPIO.output(right_fwd_pin_1, right_fwd)\n GPIO.output(right_fwd_pin_2, right_fwd)\n GPIO.output(right_bwd_pin_1, right_bwd)\n GPIO.output(right_bwd_pin_2, right_bwd)\n except KeyboardInterrupt:\n # Stop robot after keyboard interrupt\n GPIO.output(left_fwd_pin_1, False)\n GPIO.output(left_fwd_pin_2, False)\n GPIO.output(left_bwd_pin_1, False)\n GPIO.output(left_bwd_pin_2, False)\n GPIO.output(right_fwd_pin_1, False)\n GPIO.output(right_fwd_pin_2, False)\n GPIO.output(right_bwd_pin_1, False)\n GPIO.output(right_bwd_pin_2, False)\n GPIO.cleanup()","def calc_IAM_beam_SC(Az_vector, g_vector, ha_vector, teta_z, tilt_angle, type_SCpanel, Sz_vector, latitude):\n\n def calc_teta_L(Az, teta_z, tilt, Sz):\n teta_la = tan(Sz) * cos(teta_z - Az)\n teta_l = degrees(abs(atan(teta_la) - tilt))\n if teta_l < 0:\n teta_l = min(89, abs(teta_l))\n if teta_l >= 90:\n teta_l = 89.999\n return teta_l # longitudinal incidence angle in degrees\n\n def calc_teta_T(Az, Sz, teta_z):\n teta_ta = sin(Sz) * sin(abs(teta_z - Az))\n teta_T = degrees(atan(teta_ta / cos(teta_ta)))\n if teta_T < 0:\n teta_T = min(89, abs(teta_T))\n if teta_T >= 90:\n teta_T = 89.999\n return teta_T # transversal incidence angle in degrees\n\n def calc_teta_L_max(teta_L):\n if teta_L < 0:\n teta_L = min(89, abs(teta_L))\n if teta_L >= 90:\n teta_L = 89.999\n return teta_L\n\n def calc_IAMb(teta_l, teta_T, type_SCpanel):\n if type_SCpanel == 'FP': # # Flat plate collector 1636: SOLEX BLU, SPF, 2012\n IAM_b = -0.00000002127039627042 * teta_l ** 4 + 0.00000143550893550934 * teta_l ** 3 - 0.00008493589743580050 * teta_l ** 2 + 0.00041588966590833100 * teta_l + 0.99930069929920900000\n if type_SCpanel == 'ET': # # evacuated tube Zewotherm ZEWO-SOL ZX 30, SPF, 2012\n IAML = -0.00000003365384615386 * teta_l ** 4 + 0.00000268745143745027 * teta_l ** 3 - 0.00010196678321666700 * teta_l ** 2 + 0.00088830613832779900 * teta_l + 0.99793706293541500000\n IAMT = 0.000000002794872 * teta_T ** 5 - 0.000000534731935 * teta_T ** 4 + 0.000027381118880 * teta_T ** 3 - 0.000326340326281 * teta_T ** 2 + 0.002973799531468 * teta_T + 1.000713286764210\n IAM_b = IAMT * IAML # overall incidence angle modifier for beam radiation\n return IAM_b\n\n # convert to radians\n teta_z = radians(teta_z)\n tilt = radians(tilt_angle)\n\n g_vector = np.radians(g_vector)\n ha_vector = np.radians(ha_vector)\n lat = radians(latitude)\n Sz_vector = np.radians(Sz_vector)\n Az_vector = np.radians(Az_vector)\n Incidence_vector = np.vectorize(solar_equations.calc_incident_angle_beam)(g_vector, lat, ha_vector, tilt,\n teta_z) # incident angle in radians\n\n # calculate incident angles\n if type_SCpanel == 'FP':\n incident_angle = np.degrees(Incidence_vector)\n Teta_L = np.vectorize(calc_teta_L_max)(incident_angle)\n Teta_T = 0 # not necessary for flat plate collectors\n if type_SCpanel == 'ET':\n Teta_L = np.vectorize(calc_teta_L)(Az_vector, teta_z, tilt, Sz_vector) # in degrees\n Teta_T = np.vectorize(calc_teta_T)(Az_vector, Sz_vector, teta_z) # in degrees\n\n # calculate incident angle modifier for beam radiation\n IAM_b_vector = np.vectorize(calc_IAMb)(Teta_L, Teta_T, type_SCpanel)\n\n return IAM_b_vector","def do_setup(self, ants): \n log_filter = LogFilter()\n getLogger().addFilter(log_filter)\n\n self.hills = []\n self.directions = []\n\n self.seen = [] #areas that have been seen, use this to avoid repetition\n self.unseen = []\n self.stepped_on = []\n\n self.intent = {}\n self.lc = {} #center of mass for a location\n self.i = {} #number of iterations for an ant\n\n for row in range(ants.rows):\n for col in range(ants.cols):\n self.unseen.append((row, col))\n self.intent[(row,col)] = Intent.GATHER\n\n self.lc[(row,col)] = (-1.0,-1.0) #set up center of mass\n self.i[(row,col)] = -1","def point1(source, ants, elmin=15.0, showGUI=True, zoom=1, mode=0, \n nextSource=None, writeFITS=True, brightness=50,isInner=False,\n subtractBackground=False, ncoadd=1, dazbkg=2.0, centroidLoopMax=16,\n minsamples=None):\n global previous_source_\n antlist = helpers.makeList(ants)\n el = elevation(source)\n \n # Check for source too low, and bail out if it is\n if el < elmin:\n print 'Skipping %s because elevation(%.1f) is less than limit(%.1f)' \\\n %(source,el,elmin)\n return\n \n # Check for maximum elevation as well\n maxelLimit = 87.0\n if el > maxelLimit:\n print 'Skipping %s because elevation(%.1f) is greater than limit(%.1f)' \\\n %(source,el,maxelLimit)\n return\n\n # Print message\n msg = \"Pointing on %s at el=%5.1f (elmin = %5.1f); ants = %s\" % (source, el, elmin, str(ants))\n print msg\n SAC.scriptlog(msg)\n \n d = distance(source,previous_source_)\n print \" Distance from %s to %s is %.1f deg\" %(previous_source_,source,d)\n if mode > 0:\n if nextSource != None:\n print \" Next source will be %s\" % nextSource\n else:\n print \" Last source.\"\n # Send all antennas tracking to new source, even though some are\n # already there.\n if isInner :\n SAC.track(source)\n else : SAC.track(source, antlist, phaseCenter=False, waiton=SAC.NONE)\n antsTodo = antlist\n while len(antsTodo) > 0 :\n r = SAC.wait(SAC.TRACK, antsTodo, waiton=SAC.ANY)\n antsReady = r.ready\n antsTodo = r.notready\n for i in antsReady :\n show(source)\n #print \"Loading camera on antenna %d\" %i\n opticalSystem(i, auto=True, object=source, repeat=4, zoom=zoom,\n showGUI=showGUI, dontClose=True, \n brightness=brightness, ncoadd=ncoadd, dazbkg=dazbkg,\n subtractBackground=subtractBackground, \n centroidLoopMax=centroidLoopMax, minsamples=minsamples)\n # now that this ant is done, send it to next source\n if mode > 0:\n if nextSource != None: \n if isInner : SAC.track(nextSource)\n else : SAC.track(nextSource, i, waiton=SAC.NONE)\n previous_source_ = source","def roam(self):\n\n # make sure that the MIB element contains the AP1 BSSID\n bssid = self.a.nxapi_mibget_req(\"mib_dot11StationConfigTable.dot11BSSID\")\n assert(len(bssid) == 6)\n assert(bssid == self.ap1.macaddr)\n\n # wait for 4 seconds\n self.a.wait(4000000000)\n\n # make sure that the MIB element contains the AP2 BSSID\n bssid = self.a.nxapi_mibget_req(\"mib_dot11StationConfigTable.dot11BSSID\")\n assert(len(bssid) == 6)\n assert(bssid == self.ap2.macaddr)","def main():\n # https://github.com/caelan/pddlstream/blob/master/examples/motion/run.py\n # TODO: 3D work and CSpace\n # TODO: visualize just the tool frame of an end effector\n\n np.set_printoptions(precision=3)\n parser = argparse.ArgumentParser()\n parser.add_argument('-a', '--algorithm', default='rrt_connect',\n help='The algorithm seed to use.')\n parser.add_argument('-d', '--draw', action='store_true',\n help='When enabled, draws the roadmap')\n parser.add_argument('-r', '--restarts', default=0, type=int,\n help='The number of restarts.')\n parser.add_argument('-s', '--smooth', action='store_true',\n help='When enabled, smooths paths.')\n parser.add_argument('-t', '--time', default=1., type=float,\n help='The maximum runtime.')\n args = parser.parse_args()\n\n #########################\n\n obstacles = [\n create_box(center=(.35, .75), extents=(.25, .25)),\n create_box(center=(.75, .35), extents=(.225, .225)),\n create_box(center=(.5, .5), extents=(.225, .225)),\n ]\n\n # TODO: alternate sampling from a mix of regions\n regions = {\n 'env': create_box(center=(.5, .5), extents=(1., 1.)),\n 'green': create_box(center=(.8, .8), extents=(.1, .1)),\n }\n\n start = np.array([0., 0.])\n goal = 'green'\n if isinstance(goal, str) and (goal in regions):\n goal = get_box_center(regions[goal])\n else:\n goal = np.array([1., 1.])\n\n title = args.algorithm\n if args.smooth:\n title += '+shortcut'\n viewer = draw_environment(obstacles, regions, title=title)\n\n #########################\n\n #connected_test, roadmap = get_connected_test(obstacles)\n distance_fn = get_distance_fn(weights=[1, 1]) # distance_fn\n\n # samples = list(islice(region_gen('env'), 100))\n with profiler(field='cumtime'): # cumtime | tottime\n # TODO: cost bound & best cost\n for _ in range(args.restarts+1):\n start_time = time.time()\n collision_fn, cfree = get_collision_fn(obstacles)\n sample_fn, samples = get_sample_fn(regions['env'], obstacles=[]) # obstacles\n extend_fn, roadmap = get_extend_fn(obstacles=obstacles) # obstacles | []\n\n if args.algorithm == 'prm':\n path = prm(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\n num_samples=200)\n elif args.algorithm == 'lazy_prm':\n path = lazy_prm(start, goal, sample_fn, extend_fn, collision_fn,\n num_samples=200, max_time=args.time)[0]\n elif args.algorithm == 'rrt':\n path = rrt(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\n iterations=INF, max_time=args.time)\n elif args.algorithm == 'rrt_connect':\n path = rrt_connect(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\n max_time=args.time)\n elif args.algorithm == 'birrt':\n path = birrt(start, goal, distance_fn=distance_fn, sample_fn=sample_fn,\n extend_fn=extend_fn, collision_fn=collision_fn,\n max_time=args.time, smooth=100)\n elif args.algorithm == 'rrt_star':\n path = rrt_star(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\n radius=1, max_iterations=INF, max_time=args.time)\n elif args.algorithm == 'lattice':\n path = lattice(start, goal, extend_fn, collision_fn, distance_fn=distance_fn)\n else:\n raise NotImplementedError(args.algorithm)\n paths = [] if path is None else [path]\n\n #paths = random_restarts(rrt_connect, start, goal, distance_fn=distance_fn, sample_fn=sample_fn,\n # extend_fn=extend_fn, collision_fn=collision_fn, restarts=INF,\n # max_time=args.time, max_solutions=INF, smooth=100) #, smooth=1000, **kwargs)\n\n # paths = exhaustively_select_portfolio(paths, k=2)\n # print(score_portfolio(paths))\n\n #########################\n\n if args.draw:\n # roadmap = samples = cfree = []\n add_roadmap(viewer, roadmap, color='black')\n add_points(viewer, samples, color='red', radius=2)\n #add_points(viewer, cfree, color='blue', radius=2)\n\n print('Solutions ({}): {} | Time: {:.3f}'.format(len(paths), [(len(path), round(compute_path_cost(\n path, distance_fn), 3)) for path in paths], elapsed_time(start_time)))\n for path in paths:\n add_path(viewer, path, color='green')\n\n if args.smooth:\n for path in paths:\n extend_fn, roadmap = get_extend_fn(obstacles=obstacles) # obstacles | []\n smoothed = smooth_path(path, extend_fn, collision_fn, iterations=INF, max_time=args.time)\n print('Smoothed distance_fn: {:.3f}'.format(compute_path_cost(smoothed, distance_fn)))\n add_path(viewer, smoothed, color='red')\n user_input('Finish?')","def create_ik_setup(controls, joints):\n\n # Create control offset transforms\n exp_tf_ms = []\n for ctl in controls:\n par = cmds.listRelatives(ctl, parent=True)\n buf = create_offset_transform(ctl, BUF)\n exp = create_offset_transform(ctl, EXP)\n off = create_offset_transform(ctl, OFF)\n cmds.parent(ctl, off)\n cmds.parent(off, exp)\n cmds.parent(exp, buf)\n if par:\n cmds.parent(buf, par[0])\n exp_tf_ms.append(buf)\n\n root_control, pole_control, goal_control = controls\n handle, effector = cmds.ikHandle(sj=joints[0], ee=joints[-1], sol='ikRPsolver')\n cmds.setAttr('{}.hiddenInOutliner'.format(handle), True)\n cmds.orientConstraint(goal_control, joints[-1], mo=True)\n cmds.parent(handle, goal_control)\n cmds.hide(handle)\n\n # Connect root control to ik joint offset group\n ik_joints_offset = cmds.listRelatives(joints[0], p=True)[0]\n cmds.parentConstraint(root_control, ik_joints_offset, mo=True)\n cmds.scaleConstraint(root_control, ik_joints_offset, mo=True)\n\n # Connect twisting and pole vector control\n cmds.addAttr(goal_control, ln='twist', at='float', k=True)\n cmds.connectAttr('{}.twist'.format(goal_control), '{}.twist'.format(handle))\n cmds.poleVectorConstraint(pole_control, handle)\n\n # Add PV visibility attribute\n cmds.addAttr(goal_control, shortName='pv', longName='poleVector', at='bool', k=True)\n cmds.connectAttr('{}.pv'.format(goal_control), '{}.v'.format(pole_control))\n cmds.setAttr('{}.pv'.format(goal_control),1)\n\n # Add curve that points elbow to pole control\n crv = cmds.curve(p=[[0, 0, 0], [0, 1, 0]], d=1)\n cmds.connectAttr('{}.visibility'.format(pole_control), '{}.visibility'.format(crv))\n lock_hide_attrs(crv, attrs=['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz'])\n cmds.setAttr('{}.overrideEnabled'.format(crv), True)\n cmds.setAttr('{}.overrideDisplayType'.format(crv), 2)\n decomp_joint = cmds.createNode('decomposeMatrix')\n decomp_control = cmds.createNode('decomposeMatrix')\n cmds.connectAttr('{}.worldMatrix'.format(joints[1]), '{}.inputMatrix'.format(decomp_joint))\n cmds.connectAttr('{}.worldMatrix'.format(pole_control), '{}.inputMatrix'.format(decomp_control))\n cmds.connectAttr('{}.outputTranslate'.format(decomp_joint), '{}.controlPoints[0]'.format(crv))\n cmds.connectAttr('{}.outputTranslate'.format(decomp_control), '{}.controlPoints[1]'.format(crv))\n\n return handle, crv, exp_tf_ms","def tomoScanWithFrames(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\"default\", nframes=1, tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n try:\n darkFieldInterval = int(darkFieldInterval)\n flatFieldInterval = int(flatFieldInterval)\n optimizeBeamInterval = int(optimizeBeamInterval)\n \n image_key_frame = 3\n nframes = int(nframes)\n if nframes < 1:\n nframes = 1\n \n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\n tomography_theta = jns.tomography_theta\n if tomography_theta is None:\n raise NameError(\"tomography_theta is not defined in Jython namespace\")\n tomography_shutter = jns.tomography_shutter\n if tomography_shutter is None:\n raise NameError(\"tomography_shutter is not defined in Jython namespace\")\n tomography_translation = jns.tomography_translation\n if tomography_translation is None:\n raise NameError(\"tomography_translation is not defined in Jython namespace\")\n \n tomography_detector = jns.tomography_detector\n if tomography_detector is None:\n raise NameError(\"tomography_detector is not defined in Jython namespace\")\n\n tomography_optimizer = jns.tomography_optimizer\n if tomography_optimizer is None:\n raise NameError(\"tomography_optimizer is not defined in Jython namespace\")\n\n tomography_time = jns.tomography_time\n if tomography_time is None:\n raise NameError(\"tomography_time is not defined in Jython namespace\")\n \n tomography_beammonitor = jns.tomography_beammonitor\n if tomography_beammonitor is None:\n raise NameError(\"tomography_beammonitor is not defined in Jython namespace\")\n \n tomography_camera_stage = jns.tomography_camera_stage\n if tomography_camera_stage is None:\n raise NameError(\"tomography_camera_stage is not defined in Jython namespace\")\n \n tomography_sample_stage = jns.tomography_sample_stage\n if tomography_sample_stage is None:\n raise NameError(\"tomography_sample_stage is not defined in Jython namespace\")\n \n tomo_additional_scannables = jns.tomography_additional_scannables\n if tomo_additional_scannables is None:\n raise NameError(\"tomo_additional_scannables is not defined in Jython namespace\")\n \n index = SimpleScannable()\n index.setCurrentPosition(0.0)\n index.setInputNames([\"imageNumber\"])\n index.setName(\"imageNumber\")\n index.configure()\n \n image_key = SimpleScannable()\n image_key.setCurrentPosition(0.0)\n image_key.setInputNames([\"image_key\"])\n image_key.setName(\"image_key\")\n image_key.configure()\n\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\n tomography_translation, tomography_optimizer, image_key, index)\n\n# return tomoScanDevice\n #generate list of positions\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\n theta_points = []\n theta_points.append(start)\n previousPoint = start\n for i in range(numberSteps):\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\n theta_points.append(nextPoint)\n previousPoint = nextPoint\n \n #generateScanPoints\n optimizeBeamNo = 0\n optimizeBeamYes = 1\n shutterOpen = 1\n shutterClosed = 0\n shutterNoChange = 2\n scan_points = []\n theta_pos = theta_points[0]\n index = 0\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1\n \n for i in range(imagesPerFlat): \n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n for frm in range(nframes): \n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index)) #first\n index = index + 1 \n imageSinceDark = 1\n imageSinceFlat = 1\n optimizeBeam = 0\n for i in range(numberSteps):\n theta_pos = theta_points[i + 1]\n for frm in range(nframes):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index))#main image\n index = index + 1 \n \n imageSinceFlat = imageSinceFlat + 1\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\n index = index + 1 \n imageSinceFlat = 0\n \n imageSinceDark = imageSinceDark + 1\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\n index = index + 1 \n imageSinceDark = 0\n\n optimizeBeam = optimizeBeam + 1\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\n index = index + 1 \n optimizeBeam = 0\n \n #add dark and flat only if not done in last steps\n if imageSinceFlat != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n if imageSinceDark != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1 \n# scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\n# imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\n# if pattern == 'default' or pattern == 'DFPFD':\n# i = 0\n# for pt1 in scan_points1:\n# pt = scan_points[i]\n# if pt1 != pt:\n# print \"Mismatch - please tell Kaz about your scan and its arguments!\"\n# print \"i = \", i\n# print \"pt = \", pt\n# print \"pt1 = \", pt1\n# i += 1\n #return None\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\n #scan_args.append(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(\"RotationCoord_as_list\", [tomoRotationAxis])\n meta_add(\"approxCOR\", tomoRotationAxis)\n for scannable in additionalScannables:\n scan_args.append(scannable)\n for scannable in tomo_additional_scannables:\n scan_args.append(scannable)\n ''' setting the description provided as the title'''\n if not description == None: \n setTitle(description)\n else :\n setTitle(\"undefined\")\n \n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n if not dataFormat == \"NexusDataWriter\":\n handle_messages.simpleLog(\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\")\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", \"NexusDataWriter\")\n scanObject = createConcurrentScan(scan_args)\n if addNXEntry:\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\n scanObject.runScan()\n if autoAnalyse:\n lsdp=jns.lastScanDataPoint()\n OSCommandRunner.runNoWait([\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\n return scanObject;\n except InterruptedException:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"User interrupted the scan\", exceptionType, exception, traceback, False)\n raise InterruptedException(\"User interrupted the scan\")\n except:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"Error during tomography scan\", exceptionType, exception, traceback, False)\n raise Exception(\"Error during tomography scan\", exception)\n finally:\n handle_messages.simpleLog(\"Data Format reset to the original setting: \" + dataFormat)\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", dataFormat)","def test_attctl(self):\n # boundary to cross\n boundary_x = 5\n boundary_y = 5\n boundary_z = -5\n\n # delay starting the mission\n self.wait_for_topics(30)\n\n rospy.loginfo(\"seting mission mode\")\n self.set_mode(\"OFFBOARD\", 5)\n rospy.loginfo(\"arming\")\n self.set_arm(True, 5)\n\n rospy.loginfo(\"run mission\")\n rospy.loginfo(\"attempting to cross boundary | x: {0}, y: {1}, z: {2}\".\n format(boundary_x, boundary_y, boundary_z))\n # does it cross expected boundaries in 'timeout' seconds?\n timeout = 12 # (int) seconds\n loop_freq = 10 # Hz\n rate = rospy.Rate(loop_freq)\n crossed = False\n for i in xrange(timeout * loop_freq):\n if (self.local_position.pose.position.x > boundary_x and\n self.local_position.pose.position.z > boundary_y and\n self.local_position.pose.position.y < boundary_z):\n rospy.loginfo(\"boundary crossed | seconds: {0} of {1}\".format(\n i / loop_freq, timeout))\n crossed = True\n break\n\n rate.sleep()\n\n self.assertTrue(crossed, (\n \"took too long to cross boundaries | current position x: {0}, y: {1}, z: {2} | timeout(seconds): {3}\".\n format(self.local_position.pose.position.x,\n self.local_position.pose.position.y,\n self.local_position.pose.position.z, timeout)))\n\n rospy.loginfo(\"disarming\")\n self.set_arm(False, 5)","def __init__(self, rexarm, planner, kinect):\n self.rexarm = rexarm\n self.tp = planner\n self.kinect = kinect\n self.status_message = \"State: Idle\"\n self.current_state = \"idle\"\n self.next_state = \"idle\"\n self.waypoints = [\n [0.0, 0.0, 0.0, 0.0],\n [np.pi * 0.1, 0.0, np.pi / 2, 0.0],\n [np.pi * 0.25, np.pi / 2, -np.pi / 2, np.pi / 2],\n [np.pi * 0.4, np.pi / 2, -np.pi / 2, 0.0],\n [np.pi * 0.55, 0, 0, 0],\n [np.pi * 0.7, 0.0, np.pi / 2, 0.0],\n [np.pi * 0.85, np.pi / 2, -np.pi / 2, np.pi / 2],\n [np.pi, np.pi / 2, -np.pi / 2, 0.0],\n [0.0, np.pi / 2, np.pi / 2, 0.0],\n [np.pi / 2, -np.pi / 2, np.pi / 2, 0.0]]\n self.learned_joints = []","def Initialize():\n # --- Set four-character run id, comment lines, user's name.\n top.pline2 = \"Example 3D beam in a FODO lattice\"\n top.pline1 = \"S-G cigar beam. 64x64x256\"\n top.runmaker = \"David P. Grote\"\n\n # --- Invoke setup routine - it is needed to created a cgm file for plots\n setup()\n\n # --- Create the beam species\n beam = Species(type=Potassium,charge_state=+1,name=\"Beam species\")\n\n # --- Set input parameters describing the beam, 72 to 17.\n beam.b0 = 15.358933450767e-3\n beam.a0 = 8.6379155933081e-3\n beam.x0 = 3.*mm\n beam.emit = 51.700897052724e-6\n beam.ap0 = 0.e0\n beam.bp0 = 0.e0\n beam.ibeam = 2.e-03\n beam.vbeam = 0.e0\n beam.ekin = 80.e3\n beam.aion = beam.type.A\n beam.zion = beam.charge_state\n top.lrelativ = false\n top.derivqty()\n beam.vthz = .5e0*beam.vbeam*beam.emit/sqrt(beam.a0*beam.b0) # Vthz ~ Vthperp\n\n # +++ Set up arrays describing lattice.\n # --- Set temp variables.\n hlp = 36.0e-2 # half lattice period length\n piperad = 3.445e-2 # pipe radius\n quadlen = 11.e-2 # quadrupole length\n gaplen = 4.*cm\n rodlen = quadlen + gaplen\n dbdx = .949/quadlen\n\n # --- Set general lattice variables.\n top.tunelen = 2.e0*hlp\n env.zl = -hlp*2\n env.zu = -env.zl\n env.dzenv = top.tunelen/100.e0\n\n # --- Set up quadrupoles\n addnewquad(zs= -quadlen/2.,\n ze= +quadlen/2.,\n db=-dbdx,ap=piperad)\n addnewquad(zs=hlp - quadlen/2.,\n ze=hlp + quadlen/2.,\n db=+dbdx,ap=piperad)\n addnewquad(zs=2.*hlp - quadlen/2.,\n ze=2.*hlp + quadlen/2.,\n db=-dbdx,ap=piperad)\n top.zlatstrt = 0.\n top.zlatperi = 2.e0*hlp\n\n # +++ Set input parameters describing the 3d simulation.\n w3d.nx = 64/2\n w3d.ny = 64/2\n w3d.nz = 256/2\n steps_p_perd = 50\n top.dt = (top.tunelen/steps_p_perd)/beam.vbeam\n\n # --- Set to finite beam.\n top.pbound0 = top.pboundnz = periodic\n top.pboundxy = absorb\n w3d.xmmin = -piperad\n w3d.xmmax = piperad\n w3d.ymmin = -piperad\n w3d.ymmax = piperad\n w3d.zmmin = -hlp*2\n w3d.zmmax = +hlp*2\n top.prwall = piperad\n\n # --- Set pulse length.\n beam.zimin = w3d.zmmin*.95/2.\n beam.zimax = w3d.zmmax*.95/2.\n\n # --- Load Semi-Gaussian cigar beam.\n top.npmax = 20000\n w3d.distrbtn = \"semigaus\"\n w3d.cigarld = true\n w3d.xrandom = \"digitrev\"\n w3d.vtrandom = \"digitrev\"\n w3d.vzrandom = \"digitrev\"\n w3d.ldprfile = \"polar\"\n w3d.cylinder = false\n top.straight = .8\n\n # --- set up field solver\n w3d.l4symtry = true\n w3d.bound0 = periodic\n w3d.boundnz = periodic\n w3d.boundxy = dirichlet\n\n solver = MultiGrid3D()\n registersolver(solver)\n\n pipe = ZCylinderOut(piperad,4.,voltage=0.)\n installconductors(pipe,dfill=largepos)\n\n # --- Run the envelope solver to provide data used to initialize particles.\n package(\"env\")\n generate()\n step()\n\n # --- Generate the PIC code (allocate storage, load ptcls, t=0 plots, etc.).\n package(\"w3d\")\n generate()\n return","def interactions(self) -> Sequence[Interaction[_C_out, _A_out]]:\n ...","def forward_att(self, eouts, elens, ys, trigger_points=None):\n losses_auxiliary = {}\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, self.device, self.bwd)\n if not self.training:\n self.data_dict['elens'] = tensor2np(elens)\n self.data_dict['ylens'] = tensor2np(ylens)\n self.data_dict['ys'] = tensor2np(ys_out)\n bs, ymax = ys_in.size()[:2]\n tgt_mask = (ys_out != self.pad).unsqueeze(1).repeat([1, ymax, 1])\n causal_mask = tgt_mask.new_ones(ymax, ymax, dtype=tgt_mask.dtype)\n causal_mask = torch.tril(causal_mask).unsqueeze(0)\n tgt_mask = tgt_mask & causal_mask\n src_mask = make_pad_mask(elens).unsqueeze(1).repeat([1, ymax, 1])\n if self.attn_type == 'mocha':\n attn_mask = (ys_out != self.pad).unsqueeze(1).unsqueeze(3)\n else:\n attn_mask = None\n lmout = None\n if self.lm is not None:\n self.lm.eval()\n with torch.no_grad():\n lmout, lmstate, _ = self.lm.predict(ys_in, None)\n lmout = self.lm_output_proj(lmout)\n out = self.pos_enc(self.embed_token_id(ys_in), scale=True)\n xy_aws_layers = []\n xy_aws = None\n for lth, layer in enumerate(self.layers):\n out = layer(out, tgt_mask, eouts, src_mask, mode='parallel', lmout=lmout)\n xy_aws = layer.xy_aws\n if xy_aws is not None and self.attn_type == 'mocha':\n xy_aws_masked = xy_aws.masked_fill_(attn_mask.expand_as(xy_aws) == 0, 0)\n xy_aws_layers.append(xy_aws_masked.clone())\n if not self.training:\n self.aws_dict['yy_aws_layer%d' % lth] = tensor2np(layer.yy_aws)\n self.aws_dict['xy_aws_layer%d' % lth] = tensor2np(layer.xy_aws)\n self.aws_dict['xy_aws_beta_layer%d' % lth] = tensor2np(layer.xy_aws_beta)\n self.aws_dict['xy_aws_p_choose%d' % lth] = tensor2np(layer.xy_aws_p_choose)\n self.aws_dict['yy_aws_lm_layer%d' % lth] = tensor2np(layer.yy_aws_lm)\n logits = self.output(self.norm_out(out))\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\n losses_auxiliary['loss_quantity'] = 0.0\n if self.attn_type == 'mocha':\n n_tokens_ref = tgt_mask[:, -1, :].sum(1).float()\n n_tokens_pred = sum([torch.abs(aws.sum(3).sum(2).sum(1) / aws.size(1)) for aws in xy_aws_layers])\n n_tokens_pred /= len(xy_aws_layers)\n losses_auxiliary['loss_quantity'] = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\n acc = compute_accuracy(logits, ys_out, self.pad)\n return loss, acc, ppl, losses_auxiliary","def run1():\n #Reseting motors\n ResetRobot.reset_wheel_motors()\n ResetRobot.reset_attachment_motors()\n CalibrateRobot.calibrate_gyro()\n\n #mission M01 and M02 - space travel and solar panel\n M01_M02()\n \n #Mission M05- Extraction \n M05_M14()\n\n #Back to base before Gerhard (Remove comment if necessary)\n return_to_base1()\n\n # Must delete for competition.. This is to set up forklift to repeat run.\n Robot.attachment_left.on_for_rotations(-100, 8) #Raises Forklift ","def main():\n\n #robot = S2Serial( \"/dev/ttyUSB0\" )\n robot = S2Fluke2( \"/dev/rfcomm2\" )\n\n for i in range( 30 ):\n print( \"getIRLeft : \", robot.getIRLeft() )\n print( \"getIRRight: \", robot.getIRRight() )\n print( \"getAllIR : \", robot.getAllIR() )\n print( \"getIrEx(0): \", robot.getIrEx( 0, 128 ) )\n print( \"getIrEx(1): \", robot.getIrEx( 1, 128 ) )\n\n robot.close()","def eye_calc(self):\n # define the eye time vector scaled to UI, dimensionless\n tnum = (1+int(round((self.eye_time_high-self.eye_time_low) / self.eye_time_step)))\n self.time = np.linspace(self.eye_time_low, # column Z\n self.eye_time_high,\n tnum)\n # convenience vector\n t_1 = np.ones(tnum)\n # column AA\n self.time_eff = 0.5*t_1 + self.speedup*(self.time - 0.5*t_1)\n arg1 = 2.0*erfinv(0.8)\n arg2 = arg1/(1.0E-6*self.speedup*self.br_nominal)\n \n # calculate eye profiles for link at target reach\n T_c_link = self.tc[self.lnum//2]\n arg3 = arg2*self.time_eff/T_c_link # column AP\n arg4 = arg2*(1.0 - self.time_eff)/T_c_link # column AQ\n self.link_011 = 0.5*t_1 + 0.5*erf(arg3) # column AR\n self.link_110 = 0.5*t_1 + 0.5*erf(arg4) # column AS\n self.link_010 = self.link_011 + self.link_110 - t_1 # column AT\n self.link_101 = t_1 - self.link_010 # column AW\n self.link_001 = t_1 - self.link_110 # column AU\n self.link_100 = t_1 - self.link_011 # column AV\n\n # calculate eye profiles for transmitter eye test configuration\n T_c_test = math.sqrt(self.tx_1090_rise**2\n + (0.329E6/self.txeye_rx_bw)**2)\n arg5 = arg2*self.time_eff/T_c_test\n arg6 = arg2*(t_1 - self.time_eff)/T_c_test\n self.test_011 = 0.5*t_1 + 0.5*erf(arg5) # column AR\n self.test_110 = 0.5*t_1 + 0.5*erf(arg6) # column AS\n self.test_010 = self.test_011 + self.test_110 - t_1 # column AT\n self.test_101 = t_1 - self.test_010\n self.test_001 = t_1 - self.test_110\n self.test_100 = t_1 - self.test_011\n\n print(\"Margin : \", self.margin)","def __init__(self, roi_warped_points):\n\n # was the line detected in the last iteration?\n self.detected = False\n # x values of the last n fits of the line\n self.recent_xfitted = []\n #average x values of the fitted line over the last n iterations\n self.bestx = None\n #polynomial coefficients averaged over the last n iterations\n self.best_fit = [np.array([False])]\n #polinomial coefficients for the last n fits of the lane\n self.recent_fit = []\n #polynomial coefficients for the most recent fit\n self.current_fit = [np.array([False])]\n #radius of curvature of the line in some units\n self.radius_of_curvature = 0\n #distance in meters of vehicle center from the line\n self.line_base_pos = 0\n #difference in fit coefficients between last and new fits\n self.diffs = np.array([0,0,0], dtype='float')\n #x values for detected line pixels\n self.allx = None\n #maximum number of iterations to average\n self.max_n = 10 #25\n\n # roi image points in bird's view space\n self.roi_warped_points = roi_warped_points\n\n #y values for detected line pixels\n self.ally = np.linspace(0, self.roi_warped_points[2][1] - 1, self.roi_warped_points[2][1])\n\n # line base pos is calculated through the roi information\n # the used four point ROI has two points at the bottom that are straight\n # with respect to the bottom - as this points are right next to the lines,\n # they can be translated from pixels into meters with the knowledge of\n # a U.S. highway standard lane - this is an apprximation, but should be\n # good enough for this project\n # U.S. regulations minimum lane width: 3.7m\n self.xm_per_pix = 3.7 / (self.roi_warped_points[1][0] - self.roi_warped_points[0][0])\n\n # each dashed line is 3m long --> about 33m for warped image\n self.ym_per_pix = 33 / (self.roi_warped_points[2][1] - self.roi_warped_points[0][1])","def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')","def gonio_axis_align():\n \n # Invert camera image, so dark pin on light image becomes a peak\n cam_7.proc1.scale.put(-1)\n cam_8.proc1.scale.put(-1)\n \n # High threshold, so AD centroid doesn't interpret background\n cam_8ThresholdOld = cam_8.stats4.centroid_threshold.get()\n cam_8.stats4.centroid_threshold.put(150)\n cam_7ThresholdOld = cam_7.stats4.centroid_threshold.get()\n cam_7.stats4.centroid_threshold.put(150)\n \n # HiMag\n # Copy ROI2 geometry (HiMag Mag3) to ROI4 and use ROI4 centroid plugin\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get())\n cam_8.roi4.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get())\n cam_8.roi4.size.x.put(cam_8.roi2.size.x.get() * 0.20)\n cam_8.roi4.size.y.put(cam_8.roi2.size.y.get())\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get() + cam_8.roi2.size.x.get()/2 - cam_8.roi4.size.x.get()/2)\n \n # LoMag\n # Copy ROI2 geometry (LoMag Mag1) to ROI4 and use ROI4 centroid plugin\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get())\n cam_7.roi4.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get())\n cam_7.roi4.size.x.put(cam_7.roi2.size.x.get() * 0.05)\n cam_7.roi4.size.y.put(cam_7.roi2.size.y.get())\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get() + cam_7.roi2.size.x.get()/2 - cam_7.roi4.size.x.get()/2)\n \n centerPinYHiMag0 = centroid_avg(cam_8.stats4)[1]\n centerPinYLoMag0 = centroid_avg(cam_7.stats4)[1]\n yield from bps.mvr(gonio.o,180)\n time.sleep(2)\n centerPinYHiMag180 = centroid_avg(cam_8.stats4)[1]\n centerPinYLoMag180 = centroid_avg(cam_7.stats4)[1]\n centerPinYHiMag = (centerPinYHiMag0 + centerPinYHiMag180)/2\n centerPinYLoMag = (centerPinYLoMag0 + centerPinYLoMag180)/2\n\n centerPinOffsYHiMag = centerPinYHiMag - cam_8.roi4.size.y.get() / 2\n centerPinOffsYLoMag = centerPinYLoMag - cam_7.roi4.size.y.get() / 2\n \n # Correct Mag 3 (cam_8 ROI2)\n cam_8.roi2.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + centerPinOffsYHiMag)\n # Correct Mag 4 (cam_8 ROI1)\n cam_8.roi1.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + (cam_8.roi2.size.y.get()-cam_8.roi1.size.y.get())/2)\n \n # Correct Mag 1 (cam_7 ROI2)\n cam_7.roi2.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + centerPinOffsYLoMag)\n # Correct Mag 2 (cam_7 ROI3)\n cam_7.roi3.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + (cam_7.roi2.size.y.get()-cam_7.roi3.size.y.get())/2)\n\n # De-invert image\n cam_7.proc1.scale.put(-1)\n cam_8.proc1.scale.put(-1)\n \n # Set thresold to previous value\n cam_8.stats4.centroid_threshold.put(cam_8ThresholdOld)\n cam_7.stats4.centroid_threshold.put(cam_7ThresholdOld)\n \n return","def apply_beam_settings(self):\n raise NotImplementedError","def toggleAnnotation(self,i=0,onoff=None):\n active = self.annotations[i][1]\n #print \"WAS\"\n #print self.annotations\n if onoff is None:\n active = not active\n elif onoff:\n active = True\n else:\n active = False\n self.annotations[i][1] = active\n #print \"BECOMES\"\n #print self.annotations\n if active:\n self.drawAnnotation(i)\n else:\n self.removeAnnotation(i)\n #print self._annotations","def Advance():\n warp.step()","def main(args):\n try:\n robot = Robot()\n if args.giros != -1:\n robot.useGyro=True\n t = TrayectoriaGiros(args.giros, args.rad*math.pi)\n #t = TrayectoriaGirosAbs(args.giros, args.rad*math.pi)\n robot.startOdometry()\n robot.setTrajectory(t)\n robot.executeTrajectory()\n robot.stopOdometry()\n exit(0)\n\n if args.test_r2d2:\n while True:\n if robot.detectR2D2(verbose=True, DEBUG=1):\n print(\"veo a r2d2\")\n else:\n print(\"No veo a r2d2\")\n time.sleep(3)\n\n if args.test_suelo:\n while True:\n print(robot.colorSensorValue())\n if robot.colorSensorBlack():\n print(\"Es negro\")\n elif robot.colorSensorWhite():\n print(\"Es blanco\")\n else:\n print(\"Ni negro ni blanco\")\n time.sleep(1)\n\n elif args.trabajo or args.test_map or args.pelota or args.test_r2_section:\n # Choose map (A or B) based on color sensor:\n if not robot.colorSensorBlack():\n print(\"Estoy en el mapa A\")\n t, mapa = mapA(robot)\n celdaIni = [1,2,-math.pi/2]\n #fin = [3,3]\n fin = [4,6]\n ini=[1,6, -math.pi/2]\n executingMapA = True\n else:\n print(\"Estoy en el mapa B\")\n t, mapa = mapB(robot)\n celdaIni = [5,2,-math.pi/2]\n fin = [2,6]\n ini=[5,6, -math.pi/2]\n executingMapA = False\n\n if args.trabajo: # full demo\n robot.setMapNoPath(mapa) # set map\n x, y = robot.posFromCell(ini[0], ini[1])\n robot.setOdometry([x, y, ini[2]]) # set initial pos\n robot.startOdometry()\n robot.setTrajectory(t) # set S trajectory\n robot.executeTrajectory() # execute it\n robot.useGyro = False # stop using Gyro (has more error than odo later)\n robot.setPath(celdaIni, fin) # calculate the path in the map\n nBaldosas = 2.9 if executingMapA else 3.9 # x value of vertical line\n # wait for the two lines and update odo accordingly:\n robot.waitForWhite([0,1], [nBaldosas * baldosa, 3 * baldosa])\n # execute the planned path:\n robot.executePath()\n ### debugging options ###\n elif args.test_map or args.test_r2_section: # only map\n if args.test_r2_section:\n celdaIni = [3, 2, math.pi/2]\n nBaldosas = 3 if executingMapA else 4\n robot.waitForWhite([0,1], [nBaldosas * baldosa, 3 * baldosa])\n robot.setMap(mapa,celdaIni, fin)\n #robot.mapa.drawMap(saveSnapshot=False)\n robot.startOdometry()\n robot.executePath()\n if args.pelota or args.sonar:\n robot.startOdometry()\n ### end debugging options ###\n if args.trabajo or args.test_r2_section or args.test_map:\n # relocate a bit further down to see the homography better:\n x,y = robot.posFromCell(fin[0], fin[1]-0.5)\n robot.go(x,y)\n # orient GUILLE to our side 45 degrees (map A:left, B:right)\n dX, dY = -0.05, 0.05\n wHomography = -0.1 # rotate right if a, left if b\n if not executingMapA: # map b to the other side\n dX = -dX\n wHomography = -wHomography\n odo = robot.readOdometry()\n robot.go(odo[0]+dX, odo[1]+dY) # face the pictures\n res = 0 # result of homography\n while res == 0:\n res = robot.detectHomography() # take picture, compare, etc.\n robot.setSpeed(0, wHomography) # turn\n time.sleep(0.01) # wait a bit\n # res = 0 -> no detection\n # res = 1 -> r2\n # res = 2 -> the other one\n if res == 1: # r2\n print(\"detected r2\")\n # choose the exit path according to the map being executed:\n if executingMapA:\n fin[0] -= 1\n else:\n fin[0]-=2\n else: # el otro\n print(\"detected el otro\")\n # choose the exit path according to the map being executed:\n if executingMapA:\n fin[0] += 2\n else:\n fin[0]+=1\n print(\"going to\", fin)\n #track and catch ball:\n if not args.sonar:\n robot.trackBall()\n\n #sonar, update th and y odometry values based on the wall:\n relocateRobot(robot, fin, executingMapA, baldosa)\n\n # go to last cell:\n robot.setPathFromCurrentPosition(fin)\n robot.executePath(checkObstacles=False)\n # exit the map:\n x,y = robot.posFromCell(fin[0], fin[1]+1)\n robot.go(x,y, checkObstacles=False)\n\n robot.stopOdometry()\n\n except KeyboardInterrupt:\n robot.stopOdometry()\n except BaseException as e:\n print(e)\n traceback.print_exc()\n robot.stopOdometry()","def qa_tractography(stream_path,qa_out_path,brain_path):\n \n #Use window to visualize the streamlines\n r = window.renderer()\n \n #Load the streamline.trk file\n streamlines_mni_load = nib.streamlines.load(stream_path).streamlines\n streamlines_mni_in = Streamlines(streamlines_mni_load)\n streamlines_actor = actor.line(\n streamlines_mni_in,\n colormap.line_colors(streamlines_mni_in),\n lod_points=10000,\n depth_cue=True,\n linewidth=0.2,\n fake_tube=True,\n opacity=0.3,\n )\n \n r.add(streamlines_actor)\n #window.show(r)\n \n showmng = window.ShowManager(r)\n #window.record function can rotate the 3D-image, then get the snapshot of the specific angle. \n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,1,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Z_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (1,0,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Y_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,0,1),path_numbering= True,out_path = qa_out_path + '/Rotate_X_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\n showmng.exit()\n combine_plot(qa_out_path,brain_path)","def p2p_drive_routine(self, goal_pos):\n\t\t(position, rotation) = self.get_odom() # get starting position values\n\n\t\tcurr_pose = self.call_jackal_pos_service(0) # don't drive, just get current lat/lon\n\n\t\tprint(\"Current position from pose server: {}\".format(curr_pose))\n\t\tprint(\"Positions attributes: {}\".format(dir(curr_pose)))\n\n\t\t_lat = curr_pose.jackal_fix.latitude\n\t\t_lon = curr_pose.jackal_fix.longitude\n\n\t\tprint(\"Jackal's current lat, lon: {}, {}\".format(_lat, _lon))\n\n\t\tcurr_pose_utm = utm.from_latlon(curr_pose.jackal_fix.latitude, curr_pose.jackal_fix.longitude)\n\n\t\tprint(\"Jackal's position in UTM: {}\".format(curr_pose_utm))\n\n\t\tA = (curr_pose_utm[0], curr_pose_utm[1], rotation)\n\t\tB = (goal_pos[0], goal_pos[1], rotation)\n\n\t\tx_diff = B[0] - A[0]\n\t\ty_diff = B[1] - A[1]\n\n\t\t_trans_angle = self.transform_imu_frame(degrees(A[2]))\n\t\tAB_theta0 = math.atan2(abs(y_diff), abs(x_diff)) # get intitial angle, pre transform\n\t\tAB_angle = self.transform_angle_by_quadrant(AB_theta0, x_diff, y_diff) # determine angle between vector A and B\n\t\tturn_angle = AB_angle - _trans_angle # angle to turn (signage should denote direction to turn)\n\n\t\tprint(\"Initial position and orientation: {}\".format(A))\n\t\tprint(\"Current angle in degrees: {}\".format(degrees(A[2])))\n\t\tprint(\"Transformed angle: {}\".format(_trans_angle))\n\t\tprint(\"AB initial angle: {}\".format(degrees(AB_theta0)))\n\t\tprint(\"AB angle after transform: {}\".format(AB_angle))\n\t\tprint(\"Calculated turning angle: {}\".format(turn_angle))\n\n\t\t# Determine angle to turn based on IMU..\n\t\tprint(\"Telling Jackal to turn {} degreess..\".format(turn_angle))\n\t\tself.call_jackal_rot_service(turn_angle)\n\t\tprint(\"Finished turning..\")\n\n\t\tdrive_distance = self.determine_drive_distance(A, B)\n\t\tprint(\"Driving Jackal {} meters..\".format(drive_distance))\n\t\tself.call_jackal_pos_service(drive_distance)\n\t\tprint(\"Finished driving..\")","def route_input_B(self):\n xoffset = self.pmos.poly_positions[0].x \\\n + self.pmos_position2.x\n yoffset = self.A_position.y \\\n + max(drc[\"minwidth_metal2\"], self.poly_contact.second_layer_width) + drc[\"metal2_to_metal2\"]\n self.B_position = vector(xoffset, yoffset)\n offset = self.B_position - vector(0, 0.5 * self.poly_contact.width) \n self.add_contact(layers=(\"poly\", \"contact\", \"metal1\"),\n offset=offset,\n rotate=90)\n\n self.add_rect(layer=\"poly\",\n offset=offset,\n width=-(self.poly_contact.first_layer_position.y + drc[\"minwidth_poly\"]),\n height=self.poly_contact.first_layer_width)\n self.add_layout_pin(text=\"B\",\n layer=\"metal1\",\n offset=[0,\n self.B_position.y - 0.5 * drc[\"minwidth_metal1\"]],\n width=self.B_position.x,\n height=drc[\"minwidth_metal1\"])","def iter_fun(self):\n\n run_id = self._run_id\n etopo_dir = driver_home\n topodir = driver_home\n\n # load input info\n if self._input_info == None:\n scn_fname = os.path.join(self._run_home,'scenario_pts.txt') \n scn = np.loadtxt(scn_fname)\n scn_list = scn.tolist()\n else:\n scn_list = self._input_info\n \n # total number of runs\n M = len(scn_list)\n N = 8*M + 2 # 8*M runs plus two empty bathymetry runs\n\n if run_id == N:\n raise StopIteration()\n\n else:\n \n #=========================\n # set coarse and fine grids\n #\n t_shelf = 0. # time approaching continental slope\n t_harbor = 0. # time approaching harbor\n\n if ((run_id >= 0) and (run_id < 4*M)) or (run_id == 8*M):\n #------------------\n # setrun for coarse\n #\n grid = 'coarse'\n \n self._rundata.amrdata.amr_levels_max = 4\n # coarse grid run = 10\"\n # dx = 30', 5', 1', 10\"\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6]\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6]\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6]\n\n\n # add topography (coarse)\n topofiles = self._rundata.topo_data.topofiles\n # for topography, append lines of the form\n # [topotype, minlevel, maxlevel, t1, t2, fname]\n topofiles = []\n\n topofiles.append([3, 1, 4, 0., 1.e10, \\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\n topofiles.append([-3, 3, 4, 0., 1.e10, \\\n os.path.join(topodir, 'cc-1sec.asc')])\n\n # add regions\n regions = self._rundata.regiondata.regions \n # between shelf and CC \n regions = []\n regions.append(\\\n [2, 3, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \n regions.append(\\\n [3, 4, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\n regions.append(\\\n [4, 4, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\n\n # == fgmax.data values ==\n fgmax_files = self._rundata.fgmax_data.fgmax_files\n fgmax_files = []\n \n # for fixed grids append to this list names of any fgmax input files\n fgmax1_fname = os.path.join(driver_home,'fgmax1_coarse.txt')\n fgmax2_fname = os.path.join(driver_home,'fgmax2_coarse.txt')\n fgmax3_fname = os.path.join(driver_home,'fgmax3_coarse.txt')\n\n fgmax_files.append(fgmax1_fname) \n fgmax_files.append(fgmax2_fname) \n fgmax_files.append(fgmax3_fname) \n \n self._rundata.fgmax_data.num_fgmax_val = 2\n \n \n elif ((run_id >= 4*M) and (run_id < 8*M)) or (run_id == 8*M+1):\n #----------------\n # setrun for fine\n #\n grid = 'fine'\n \n self._rundata.amrdata.amr_levels_max = 6\n\n ## fine grid run = 2/3\"\n ## dx = 30', 5', 1', 10\", 2\", 2/3\"\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6, 5, 3]\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6, 5, 3]\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6, 5, 3]\n\n regions = self._rundata.regiondata.regions \n regions = []\n # between shelf and CC\n regions.append(\\\n [2, 4, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \n regions.append(\\\n [4, 5, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\n regions.append(\\\n [6, 6, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\n\n # add topography (fine)\n topofiles = self._rundata.topo_data.topofiles\n # for topography, append lines of the form\n # [topotype, minlevel, maxlevel, t1, t2, fname]\n topofiles = []\n\n topofiles.append([3, 1, 6, 0., 1.e10, \\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\n topofiles.append([-3, 4, 6, 0., 1.e10, \\\n os.path.join(topodir, 'cc-1sec.asc')])\n topofiles.append([3, 6, 6, 0., 1.e10, \\\n os.path.join(topodir,'cc-1_3sec-c_pierless.asc')])\n \n # == fgmax.data values ==\n fgmax_files = self._rundata.fgmax_data.fgmax_files\n fgmax_files = []\n \n # for fixed grids append to this list names of any fgmax input files\n fgmax1_fname = os.path.join(driver_home,'fgmax1_fine.txt')\n fgmax2_fname = os.path.join(driver_home,'fgmax2_fine.txt')\n fgmax3_fname = os.path.join(driver_home,'fgmax3_fine.txt')\n\n fgmax_files.append(fgmax1_fname) \n fgmax_files.append(fgmax2_fname) \n fgmax_files.append(fgmax3_fname) \n \n self._rundata.fgmax_data.num_fgmax_val = 2\n \n\n\n #\n # set desired magnitude\n #\n if ((run_id >= 0) and (run_id < M)) \\\n or ((run_id >= 4*M) and (run_id < 5*M)):\n self.KL_Mw_desired = 8.6\n elif ((run_id >= M) and (run_id < 2*M)) \\\n or ((run_id >= 5*M) and (run_id < 6*M)):\n self.KL_Mw_desired = 8.8\n elif ((run_id >= 2*M) and (run_id < 3*M)) \\\n or ((run_id >= 6*M) and (run_id < 7*M)):\n self.KL_Mw_desired = 9.0\n elif ((run_id >= 3*M) and (run_id < 4*M)) \\\n or ((run_id >= 7*M) and (run_id < 8*M)):\n self.KL_Mw_desired = 9.2\n \n #\n # set slip distribution\n #\n run_id_mod = run_id - 100*(run_id/100)\n m = scn_list[run_id_mod]\n self.set_KL_slip(m)\n \n if run_id < 8*M:\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_' + str(self.KL_Mw_desired)\n self._rundir = os.path.join(dir_grid_Mw, 'run_' + str(run_id_mod))\n else:\n # empty runs to obtain bathymetry\n \n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_B0'\n self._rundir = dir_grid_Mw\n self.KL_Mw_desired = 0.0\n self.set_KL_slip([0.]*len(m)) # set output\n self._rundata.clawdata.output_times = [1.0, 3.0]\n \n self._run_id += 1\n \n return self","def evaluateIFsetup(ants=0, timerange=3.4, deltat=0.1,\n verbose=False, iniAtten=0) :\n antlist = makeAntList(ants)\n for a in antlist:\n if a < 16:\n print \"SZA antennas only can be used - exiting\"\n return\n tpMP = \"Ifmod.ifTotalPower\"\n attenMP = \"Ifmod.totalAtten\"\n currentPos = \"AMBIENT\"\n print \"Inserting ambient load into beam\"\n rtn = amb(ants)\n pos1ants = rtn.ready\n mplist = []\n for a in pos1ants:\n prefix = \"Sza%d.\" %(a-15)\n mplist.append([prefix+tpMP, prefix+attenMP])\n #print mplist\n if len(rtn.notready) != 0:\n print \"Ambient load not inserted for ants:\", rtn.notready\n if iniAtten != None :\n print \"Setting IF atten to %.1f\" %iniAtten\n antennaIFatten(iniAtten, 1, invalidateTsys=False, \n ants=antlist)\n wait(tmo=2.0)\n t0=time.time()\n antennaIFpower(0.3, ants)\n t1 = time.time()\n cmdtime = t1-t0\n results = []\n times = []\n nsamps = int(round(timerange/deltat))\n for i in range(nsamps):\n sleep(deltat)\n dt=time.time()-t1\n times.append(dt)\n results.append(queryMpValues(mplist))\n #print results\n print \"Command time = %.3f seconds\" %cmdtime\n for j in range(len(pos1ants)):\n aname = \"Sza%d\" %(pos1ants[j]-15)\n if verbose: print \"======== %s ========\" %aname\n for i in range(nsamps):\n r = results[i][j]\n st = \"%s: %4.1f %6.3f %4.1f\" %(aname,times[i],r[0],r[1])\n if verbose: print st\n print \"\\n==== Inferred timing & final atten =====\"\n for j in range(len(pos1ants)):\n aname = \"Sza%d\" %(pos1ants[j]-15)\n finalAtten = results[nsamps-1][j][1]\n timing = times[nsamps-1]\n for i in range(nsamps):\n index = nsamps-1-i\n atten = results[index][j][1]\n if atten != finalAtten: break\n timing = times[index]\n st = \" %s: %4.1f %4.1f\" %(aname,timing, finalAtten)\n print st\n \n #print \"Moving ambient load out of the beam\"\n pos2ants = sky(ants).ready\n # Wait for two seconds to get final psys reading\n wait(tmo=2.0)\n skyants = pos2ants\n complete = []\n for a in pos1ants :\n if (pos2ants.count(a) > 0) : complete.append(a)\n return complete","def tomo_scan(*, start=0, stop=180, numProjPerSweep=1500, slewSpeed=5, accl=1, samInPos=0, samOutDist=-3, md=None):\n _md = md or OrderedDict()\n _md[\"project\"] = \"mona\"\n _md[\"APS_storage_ring_current,mA\"] = aps_current.value\n _md[\"datetime_plan_started\"] = str(datetime.now())\n\n pso = psofly\n det = pg3_det\n rotStage = tomo_stage.rotary\n shutter = A_shutter\n \n acquire_time = 0.01\n report_t0 = None\n update_interval_s = 5.0 # during scans, print interim progress\n update_poll_delay_s = 0.01\n\n stage_sigs = {}\n stage_sigs[\"det.cam\"] = det.cam.stage_sigs\n\n staged_device_list = [det]\n monitored_signals_list = [\n det.image.array_counter,\n rotStage.user_readback,\n ]\n\n def cleanup():\n for d in [det.image.array_counter, rotStage.user_readback]:\n try:\n yield from bps.unmonitor(d)\n except IllegalMessageSequence:\n pass\n try:\n yield from bps.abs_set(shutter, \"close\", group='shutter')\n except RuntimeError as exc:\n print(exc)\n yield from bps.mv(rotStage.velocity, ROT_STAGE_FAST_SPEED)\n yield from motor_set_modulo(rotStage, 360.0)\n yield from bps.mv(\n rotStage, 0.00,\n #det.cam.trigger_mode, \"Internal\",\n #det.cam.image_mode, \"Continuous\",\n #det.hdf1.enable, \"Disable\",\n #det.hdf1.capture, \"Done\",\n )\n yield from bps.wait(group='shutter')\n\n det.cam.stage_sigs[\"num_images\"] = numProjPerSweep\n det.cam.stage_sigs[\"trigger_mode\"] = \"Overlapped\"\n det.cam.stage_sigs[\"trigger_source\"] = \"GPIO_0\"\n det.cam.stage_sigs[\"trigger_polarity\"] = \"Low\"\n det.cam.stage_sigs[\"image_mode\"] = \"Multiple\"\n det.hdf1.stage_sigs[\"num_capture\"] = numProjPerSweep # TODO: + NUM_DARK_FRAMES + NUM_FLAT_FRAMES,\n\n def _report_(t):\n msg = \"{:.2f}s - flying \".format(t)\n msg += \" x = {:.4f}\".format(tomo_stage.x.position)\n msg += \" y = {:.4f}\".format(tomo_stage.y.position)\n msg += \" r = {:.4f}\".format(tomo_stage.rotary.position)\n msg += \" theta = {:.4f}\".format(tomo_stage.rotary.position % 360)\n msg += \" image # {}\".format(det.cam.num_images_counter.value)\n return msg\n\n @run_in_thread\n def progress_reporting():\n logger.debug(\"progress_reporting is starting\")\n t = time.time()\n startup = t + update_interval_s/2\n while t < startup and pso.fly.value == 0: # wait for flyscan to start\n time.sleep(update_poll_delay_s)\n\n logger.debug(\"progress_reporting: fly starts\")\n update_time = t = report_t0 = time.time()\n while pso.fly.value == 1:\n if t >= update_time:\n update_time = t + update_interval_s\n msg = _report_(t - report_t0)\n print(msg)\n logger.debug(msg)\n time.sleep(update_poll_delay_s)\n t = time.time()\n msg = _report_(time.time() - report_t0)\n print(msg)\n logger.debug(msg)\n logger.debug(\"{}s - progress_reporting is done\".format(time.time()-report_t0))\n\n @bpp.stage_decorator(staged_device_list)\n @bpp.run_decorator(md=_md)\n @bpp.finalize_decorator(cleanup)\n def _internal_tomo():\n yield from bps.monitor(rotStage.user_readback, name=\"rotation_monitor\")\n yield from bps.monitor(det.image.array_counter, name=\"array_counter_monitor\")\n yield from bps.monitor(tomo_stage.x, name=\"tomo_stage_x_monitor\")\n\n # prepare the camera and the HDF5 plugin to write data\n yield from bps.mv(\n det.cam.nd_attributes_file, \"monaDetectorAttributes.xml\",\n det.cam.acquire_time, acquire_time,\n det.hdf1.enable, \"Enable\",\n det.hdf1.auto_save, \"Yes\",\n )\n #yield from bps.abs_set(det.hdf1.capture, \"Capture\")\n\n # TODO: test before using!\n # yield from measure_darks(det, shutter, NUM_DARK_FRAMES)\n # yield from measure_flats(det, shutter, NUM_FLAT_FRAMES, samStage, samInPos + samOutDist)\n\n # do not touch shutter during development\n yield from bps.abs_set(shutter, \"open\", group=\"shutter\")\n yield from set_image_frame()\n\n yield from bps.stop(rotStage)\n yield from motor_set_modulo(rotStage, 360.0)\n \n yield from bps.mv(\n rotStage.velocity, ROT_STAGE_FAST_SPEED, \n rotStage.acceleration, 3)\n yield from bps.mv(\n rotStage, 0, \n samStage, samInPos)\n\n # back off to the taxi point (back-off distance before fly start)\n logging.debug(\"before taxi\")\n yield from bps.mv(\n pso.start, start,\n pso.end, stop,\n pso.scan_control, \"Standard\",\n pso.scan_delta, 1.0*(stop-start)/numProjPerSweep,\n pso.slew_speed, slewSpeed,\n rotStage.velocity, ROT_STAGE_FAST_SPEED,\n rotStage.acceleration, slewSpeed/accl,\n det.cam.array_counter, 0,\n )\n\n progress_reporting()\n yield from bps.mv(pso.taxi, \"Taxi\")\n logging.debug(\"after taxi\")\n\n # run the fly scan\n logging.debug(\"before fly\")\n yield from bps.mv(rotStage.velocity, slewSpeed)\n yield from bps.wait(group='shutter') # shutters are slooow, MUST be done now\n yield from set_image_frame()\n yield from bps.trigger(det, group='fly')\n yield from bps.abs_set(pso.fly, \"Fly\", group='fly')\n yield from bps.wait(group='fly')\n #yield from bps.abs_set(det.cam.acquire, 0)\n logging.debug(\"after fly\")\n\n # read the camera\n yield from bps.create(name='primary')\n yield from bps.read(det)\n yield from bps.save()\n det.cam.stage_sigs = stage_sigs[\"det.cam\"]\n\n return (yield from _internal_tomo())","def M2_focus_plot(w0, lambda0, f, z0, M2=1):\n # plot the beam from just before the waist to the lens\n left = 1.1*z0\n z = np.linspace(left, 0)\n r = beam_radius(w0, lambda0, z, z0=z0, M2=M2)\n plt.fill_between(z*1e3, -r*1e6, r*1e6, color='red', alpha=0.2)\n\n # find the gaussian beam parameters for the beam after the lens\n w0_after = w0 * magnification(w0, lambda0, z0, f, M2=M2)\n z0_after = image_distance(w0, lambda0, z0, f, M2=M2)\n zR_after = z_rayleigh(w0_after, lambda0, M2)\n\n # plot the beam after the lens\n right = max(2*f, z0_after+4*zR_after)\n z_after = np.linspace(0, right)\n r_after = beam_radius(w0_after, lambda0, z_after, z0=z0_after, M2=M2)\n\n # plt.axhline(w0_after*1.41e6)\n plt.fill_between(z_after*1e3, -r_after*1e6, r_after*1e6, color='red', alpha=0.2)\n\n # locate the lens and the two beam waists\n plt.axhline(0, color='black', lw=1)\n plt.axvline(0, color='black')\n plt.axvline(z0*1e3, color='black', linestyle=':')\n plt.axvline(z0_after*1e3, color='black', linestyle=':')\n\n # finally, show the ±1 Rayleigh distance\n zRmin = max(0, (z0_after-zR_after))*1e3\n zRmax = (z0_after+zR_after)*1e3\n plt.axvspan(zRmin, zRmax, color='blue', alpha=0.1)\n\n plt.xlabel('Axial Position Relative to Lens (mm)')\n plt.ylabel('Beam Radius (microns)')\n title = \"$w_0$=%.0fµm, $z_0$=%.0fmm, \" % (w0*1e6, z0*1e3)\n title += \"$w_0'$=%.0fµm, $z_0'$=%.0fmm, \" % (w0_after*1e6, z0_after*1e3)\n title += \"$z_R'$=%.0fmm\" % (zR_after*1e3)\n plt.title(title)","def prepare_for_ESR(self):\r\n _debug('Anapico: prepare ESR')\r\n # This is all for the ANAPICO to use the external trigger. \r\n # BONUS for preparing the list with the external trigger. \r\n print('Testing query: ', self.query('*IDN?'))\r\n print('Source for Trigger?: ', self.query('TRIG:SEQ:SOUR?'))\r\n self.write('TRIG:SEQ:SOUR EXT') # Set the external trigger to ext\r\n print('Source for Trigger?: ', self.query('TRIG:SEQ:SOUR?'))\r\n print('First frequency?: ', self.query('SOUR:FREQ:STAR?'))\r\n print('Last frequency?: ', self.query('SOUR:FREQ:STOP?'))\r\n \r\n # Prepare the list mode\r\n self.write('SOUR:FREQ:MODE LIST') # Set the frequency mode to list\r\n print('Frequency mode ?: ', self.query('SOUR:FREQ:MODE?'))\r\n self.write('SOUR:POW:MODE LIST') # Set the power mode to list\r\n print('Power mode ?: ', self.query('SOUR:POW:MODE?'))\r\n self.write('SOUR:LIST:MODE AUTO') # Set the list mode to auto\r\n print('List mode ?: ', self.query('SOUR:LIST:MODE?'))\r\n# self.api.write('TRIG:SEQ:TYPE GATE') # An external trigger signal repeatedly starts and stops the waveform’s playback.\r\n self.write('TRIG:SEQ:TYPE POIN')# Upon triggering, only a single point of the sweep (list) is played.\r\n print('Trig type?: ', self.query('TRIG:SEQ:TYPE?'))\r\n \r\n # Set stuff for the modulation\r\n self.write('SOUR:PULM:SOUR EXT')# Set the pulse modulation to be external\r\n print('Pulse modulation source?: ', self.query('SOUR:PULM:SOUR?'))\r\n self.write('SOUR:PULM:STAT ON') # Switch the pulse modulation ON\r\n print('State of pulse modulation? ', self.query('SOUR:PULM:STAT?'))\r\n self.write('SOUR:PULM:POL NORM') # Polarity NORMal, in case it was INVerted\r\n print('Polarity of modulation?: ', self.query('SOUR:PULM:POL?')) \r\n # This is all for the ANAPICO to use the external trigger. \r\n # BONUS for preparing the list with the external trigger. \r","def route_output(self):\n self.Z_position = vector(self.width, self.A_position.y)\n # route nmos drain to Z\n nmos_contact = (self.nmos_position1 \n + self.nmos.active_contact_positions[1] \n + self.nmos.active_contact.second_layer_position\n + vector(self.nmos.active_contact.second_layer_width,\n 0).scale(0.5, 0))\n mid = [nmos_contact.x, self.A_position.y]\n self.add_path(\"metal1\", [self.Z_position, mid, nmos_contact])\n\n for i in range(len(self.pmos.poly_positions) + 1):\n if i % 2 == 1:\n # pmos2 drain to Z\n pmos_contact = (self.pmos_position2\n + self.pmos.active_contact_positions[i]\n + self.pmos.active_contact.second_layer_position.scale(1, 0)\n + vector(self.pmos.active_contact.second_layer_width,\n 0).scale(0.5, 0))\n offset = pmos_contact - vector(0.5 * self.m1m2_via.width, 0)\n self.add_via(layers=(\"metal1\", \"via1\", \"metal2\"),\n offset=offset)\n mid = [pmos_contact.x, self.Z_position.y]\n self.add_wire((\"metal1\", \"via1\", \"metal2\"),\n [self.Z_position, mid, pmos_contact])\n\n self.add_layout_pin(text=\"Z\",\n layer=\"metal1\",\n offset=mid - vector(0,0.5*drc[\"minwidth_metal1\"]),\n width=self.Z_position.x-mid[0],\n height=drc[\"minwidth_metal1\"])","def main(): #Main Control Loop (as prototyped on 2/26 in Glennan Lounge)\n\t# Create listener to receive info from UI\n\tui_listener = pso_network.UIListener()\n\tui_listener.daemon = True\n\tui_listener.start()\n\tui_state = ui_listener.get_ui()\n\t\n\t# Create listener to recieve waypoints and corrections from planner.\n\tplanner_listener = pso_network.PlannerListener()\n\tplanner_listener.daemon = True\n\tplanner_listener.start()\n\twaypoint = cv.CreateMat(4, 1, cv.CV_32FC1)\n\t\n\t#Instatiate Drone Objects (defined in Drone.py)\n\tmyDrone = Drone(\"192.168.1.1\")\n\t\n\t\n\t#Preset flags\n\trunning = True\n\twait_on_emergency = False\n\twait_on_liftoff = False\n\twait_on_land = False\n\t\n\t#Create Kalman filter, state, and command vectors\n\tkalman = PsoKalman()\n\tu = cv.CreateMat(4, 1, cv.CV_32FC1)\n\tz = cv.CreateMat(5, 1, cv.CV_32FC1)\n\tsys_time = time.time()\n\t\n\t#Create PID controllers for each axis\n\tyaw_pid = pso_pid.PID()\n\tyaw_pid.k = 1.5\n\tyaw_pid.t_i = 1.\n\tyaw_pid.angular = True\n\tyaw_pid.deadband = .05\n\t\n\tz_pid = pso_pid.PID()\n\tz_pid.k = .00075\n\tz_pid.i_enable = False\n\tz_pid.t_i = 10.\n\tz_pid.deadband = 150\n\t\n\troll_pid = pso_pid.PID()\n\troll_pid.k = .00025\n\troll_pid.i_enable = False\n\troll_pid.deadband = 50\n\t\n\tpitch_pid = pso_pid.PID()\n\tpitch_pid.k = .00025\n\tpitch_pid.i_enable = False\n\tpitch_pid.deadband = 50\n\t\n\t#Logger puts state in csv for matlab-y goodness\n\tlogger = debuglogger.Logger()\n\t\n\t#Fig bucking loop\n\twhile(running):\n\t\ttime.sleep(.05)\n\t\tos.system(\"clear\")\n\t\t\n\t\t#Get command state from UI\n\t\tprev_ui_state = ui_state\n\t\tui_state = ui_listener.get_ui()\n\t\t\t\t\n\t\tif ui_state[EMERGENCY]:\n\t\t\tmyDrone.emergency()\n\t\t\n\t\tif ui_state[SHUTDOWN]:\n\t\t\t#UI has ordered shutdown\n\t\t\tprint \"Shutting down control loop...\"\n\t\t\tui_listener.stop()\n\t\t\tmyDrone.kill()\n\t\t\trunning = False\n\t\t\n\t\tif ui_state[TRIM]:\n\t\t\tmyDrone.trim()\n\t\t\tui_listener.clear_flag(TRIM)\n\t\t\tprint \"\\nTRIM\\n\"\n\t\t\n\t\tif ui_state[FLYING]:\n\t\t\tmyDrone.takeoff()\n\t\t\tprint \"Taking Off/Flying\"\n\t\t\tif not prev_ui_state[FLYING]:\n\t\t\t\twait_on_liftoff = 5\n\t\telse:\n\t\t\tmyDrone.land()\n\t\t\tprint \"Landing/Landed\"\n\t\t\tif prev_ui_state[FLYING]:\n\t\t\t\twait_on_land = 5\n\t\t\n\t\tif ui_state[RESET]:\n\t\t\tmyDrone.reset_emergency()\n\t\t\tmyDrone.reset()\n\t\t\tyaw_pid.flush()\n\t\t\tz_pid.flush()\n\t\t\troll_pid.flush()\n\t\t\tpitch_pid.flush()\n\t\t\tui_listener.clear_flag(RESET)\n\t\t\n\t\t#Get navdata\n\t\tnav = myDrone.get_navdata()\n\t\t\n\t\t#Print out Drone State\n\t\tif nav.check_state(navdata.EMERGENCY):\n\t\t\tprint \"Emergency!\"\n\t\telif not nav.check_state(navdata.COM_WATCHDOG):\n\t\t\tprint \"WATCHDOG\"\n\t\telif nav.check_state(navdata.COMMAND):\n\t\t\tprint \"Watchdog cleared. Not yet ready for commands.\"\n\t\telse:\n\t\t\tprint \"Ready to Fly\\n\"\n\t\tprint \"\\t\\tECACAVNAPCUWAPTHLGCMBNTTTCUACVVF\\n{0}\\t\\t{1:32b}\".format(nav.seq,nav.state) #Print navdata state\n\t\t\n\t\t#Update State (Kalman)\n\t\tdt = time.time()-sys_time\n\t\tprint \"dt:\\t\",dt\n\t\tsys_time = time.time()\n\t\tz[0, 0], z[1, 0], z[2, 0], z[3, 0], z[4, 0] = nav.vx, nav.vy, nav.z, nav.vz, nav.psi\n\t\t#z and u need to be cv matrices!!!!\n\t\tsys_state = myDrone.get_state()\n\t\tprint \"\\nDrone Kalman State:\"\n\t\tprint \"x:\\t{0}\".format(sys_state[0, 0])\n\t\tprint \"y:\\t{0}\".format(sys_state[2, 0])\n\t\tprint \"z:\\t{0}\".format(sys_state[4, 0])\n\t\tprint \"vx:\\t{0}\".format(sys_state[1, 0])\n\t\tprint \"vy:\\t{0}\".format(sys_state[3, 0])\n\t\tprint \"vz:\\t{0}\".format(sys_state[5, 0])\n\t\tprint \"theta:\\t{0}\".format(sys_state[6, 0])\n\t\tprint \"vtheta:\\t{0}\".format(sys_state[7, 0])\n\t\t\n\t\tprint \"\\nNavdata Euler Angles:\"\n\t\tprint \"theta:\\t\",nav.theta\n\t\tprint \"phi:\\t\",nav.phi\n\t\tprint \"psi:\\t\",nav.psi\n\t\tprint \"\\nNavdata Stuff:\"\n\t\tprint \"z:\\t\",nav.z\n\t\tprint \"vx:\\t\",nav.vx\n\t\tprint \"vy:\\t\",nav.vy\n\t\tui_listener.set_state(sys_state, nav)\n\t\t#logger.log(sys_state)\n\t\t\n\t\tif wait_on_liftoff>0:\n\t\t\tprint \"Waiting for liftoff to finish\"\n\t\t\twait_on_liftoff -= dt\n\t\t\tu[0, 0], u[1, 0], u[2, 0], u[3, 0] = 0, 0, 1, 0#Assume drone goes full speed up when taking off\n\t\telif ui_state[FLYING]:\n\t\t\tprint \"\" #Blank line to everything lines up\n\t\t\t#If Drone is in waypoint mode, compute command\n\t\t\tif not ui_state[OVERRIDE]:\n\t\t\t\t#Get waypoint\n\t\t\t\tif not planner_listener.waypoints.empty():\n\t\t\t\t\twaypoint = planner_listener.waypoints.get()\n\t\t\t\tprint \"\\nNext Waypoint:\"\n\t\t\t\tprint \"X:\\t\", waypoint[0, 0]\n\t\t\t\tprint \"Y:\\t\", waypoint[1, 0]\n\t\t\t\tprint \"Z:\\t\", waypoint[2, 0]\n\t\t\t\tprint \"θ:\\t\", waypoint[3, 0]\n\t\t\t\t#Compute command\n\t\t\t\t(roll_des, pitch_des) = world2drone(waypoint[0, 0]-sys_state[0, 0], waypoint[1, 0]-sys_state[2, 0], sys_state[6, 0])\n\t\t\t\tprint \"Desired Roll:\\t\", roll_des\n\t\t\t\tprint \"Desired Pitch:\\t\", pitch_des\n\t\t\t\tu[0, 0] = pitch_pid.update(0, pitch_des)\n\t\t\t\tu[1, 0] = roll_pid.update(0, roll_des)\n\t\t\t\tu[2, 0] = z_pid.update(sys_state[4, 0], waypoint[2, 0])\n\t\t\t\tu[3, 0] = yaw_pid.update(sys_state[6, 0], waypoint[3, 0])\n\t\t\t\tmyDrone.go(u[0, 0], u[1, 0], u[3, 0], u[2, 0])\n\t\t\telse: #Manual override: Use command from UI state\n\t\t\t\tprint \"\\nManual override mode\\n\\n\\n\"\n\t\t\t\tmyDrone.go(ui_state[COMMAND][0], ui_state[COMMAND][1], ui_state[COMMAND][2], ui_state[COMMAND][3])\n\t\t\t\tu[0, 0], u[1, 0], u[2, 0], u[3, 0] = ui_state[COMMAND]\n\t\telse:\n\t\t\tprint \"\\nLanded\"\n\t\t\n\t\t#Print out commands\n\t\tprint \"\\nCommands:\\npitch:\\t\",u[0, 0]\n\t\tprint \"roll:\\t\", u[1, 0]\n\t\tprint \"gaz:\\t\", u[2, 0]\n\t\tprint \"yaw:\\t\", u[3, 0]","def idealOpAmp():","def __init__(self, portname, devicetype):\n if devicetype == DEVICE_DEBUG_BOARD:\n self.device = debugbox.DebugBox(portname)\n elif devicetype == DEVICE_SFP_BREAKOUT:\n self.device = sfpbreakout.SFP(portname)\n elif devicetype == DEVICE_DUMMY_BOARD:\n self.device = dummybox.DummyBox(portname)\n else:\n raise IOError(\"Invalid Device Type\")\n \n # Set up laser sections\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\n# self.gain1.validator.setTop(150)\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\n# self.front.validator.setTop(60)\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\n# self.soa1.validator.setTop(150)\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\n \n self.voltage_max = 2.5 # This is now a constant\n \n self.full_rig = True","def buildArm(self, *args):\n \"\"\" With this we can get rid of buildFK and IK joints \"\"\"\n numChains = len(self.jointNames)\n \n jointPos = [] \n \n \"\"\" lets try getting our joint names and positions from the locators \"\"\"\n #jointLctrs = cmds.ls(sl=True)\n #cmds.select(d=True)\n #for lctr in jointLctrs:\n #pos = cmds.getAttr(lctr + \".localPosition\")\n #jointPos.append(pos)\n #cmds.delete(jointLctrs)\n \n \n bindJoints = self.jointNames['bindJnts'] \n fkJoints = self.jointNames['fkJnts']\n ikJoints = self.jointNames['ikJnts']\n \n positions = jointPos\n \n \n \"\"\" Call the create joints function, passing in our joint names and positions \"\"\"\n \"\"\" First instance in the jointUtils \"\"\"\n jointCreate = jointUtils.Joint_Utils()\n \n for index in range(len(self.jointNames)):\n if index == 0:\n joints = self.jointNames['bindJnts']\n if index == 1:\n joints = self.jointNames['fkJnts']\n if index == 2:\n joints = self.jointNames['ikJnts']\n \n jointCreate.createJoints (joints, positions)\n jointCreate.orientJoints(joints)\n \n \"\"\" We can use the dictionaries to pass the joint names \"\"\" \n jointCreate.groupJoints(bindJoints, fkJoints, ikJoints)\n \n jointCreate.connectJoints(bindJoints, fkJoints, ikJoints)\n \n \n \"\"\" Create the fk controls \"\"\"\n \"\"\" instance in the FK_Controls class \"\"\"\n control = self.control\n fkCreate = fk_controls.FK_Controls()\n fkCreate.createFKControls(fkJoints, control)\n \n \"\"\" Create the IK Setup \"\"\"\n \"\"\" Garvey did some nice work with this \"\"\"\n ikCreate = ik_controls.IK_Controls()\n ikCreate.createIKControls(ikJoints)\n \n \n \"\"\" Try writing the saveCsv stuff back into here. This way you will have a backup\"\"\"\n \"\"\" of all the info you need to re-create your rig. \"\"\"\n \n fileName = \"arm.csv\"\n csvUtils.csvWrite(bindJoints, positions, fileName)","def run_single_camera(cam):\n\n try:\n # Retrieve TL device nodemap and print device information\n #nodemap_tldevice = cam.GetTLDeviceNodeMap()\n\n #result &= print_device_info(nodemap_tldevice)\n\n # Initialize camera\n cam.Init()\n\n # Retrieve GenICam nodemap\n nodemap = cam.GetNodeMap()\n exposures=[2000,4000,8000,16000]\n index=0\n if cam.ExposureAuto.GetAccessMode() != PySpin.RW:\n print(\"Unable to disable automatic exposure. Aborting...\")\n return False\n node_acquisition_mode = PySpin.CEnumerationPtr(nodemap.GetNode(\"AcquisitionMode\"))\n if not PySpin.IsAvailable(node_acquisition_mode) or not PySpin.IsWritable(node_acquisition_mode):\n print(\"Unable to set acquisition mode to continuous (enum retrieval). Aborting...\")\n return False\n\n # Retrieve entry node from enumeration node\n node_acquisition_mode_continuous = node_acquisition_mode.GetEntryByName(\"Continuous\")\n if not PySpin.IsAvailable(node_acquisition_mode_continuous) or not PySpin.IsReadable(node_acquisition_mode_continuous):\n print(\"Unable to set acquisition mode to continuous (entry retrieval). Aborting...\")\n return False\n\n acquisition_mode_continuous = node_acquisition_mode_continuous.GetValue()\n\n node_acquisition_mode.SetIntValue(acquisition_mode_continuous)\n\n print(\"Acquisition mode set to continuous...\")\n\n cam.ExposureAuto.SetValue(PySpin.ExposureAuto_Off)\n '''\n # Set maximum width\n #\n # *** NOTES ***\n # Other nodes, such as those corresponding to image width and height,\n # might have an increment other than 1. In these cases, it can be\n # important to check that the desired value is a multiple of the\n # increment.\n #\n # This is often the case for width and height nodes. However, because\n # these nodes are being set to their maximums, there is no real reason\n # to check against the increment.\n if cam.Width.GetAccessMode() == PySpin.RW and cam.Width.GetInc() != 0 and cam.Width.GetMax != 0:\n cam.Width.SetValue(FRAME_WIDTH)\n print(\"Width set to %i...\" % cam.Width.GetValue())\n\n else:\n print(\"Width not available...\")\n result = False\n\n # Set maximum height\n #\n # *** NOTES ***\n # A maximum is retrieved with the method GetMax(). A node's minimum and\n # maximum should always be a multiple of its increment.\n if cam.Height.GetAccessMode() == PySpin.RW and cam.Height.GetInc() != 0 and cam.Height.GetMax != 0:\n cam.Height.SetValue(FRAME_HEIGHT)\n print(\"Height set to %i...\" % cam.Height.GetValue())\n\n else:\n print(\"Height not available...\")\n result = False\n '''\n print(\"Automatic exposure disabled...\")\n #node_acquisition_framerate = PySpin.CFloatPtr(nodemap.GetNode(\"AcquisitionFrameRate\"))\n\n # if not PySpin.IsAvailable(node_acquisition_framerate) and not PySpin.IsReadable(node_acquisition_framerate):\n # print(\"Unable to retrieve frame rate. Aborting...\")\n # return False\n\n # framerate_to_set = node_acquisition_framerate.GetValue()\n\n # print(\"Frame rate to be set to %d...\" % framerate_to_set)\n canvas=np.zeros((FRAME_HEIGHT*2,FRAME_WIDTH*2,3), np.uint8)\n while True:\n exposure=exposures[index]\n \n configure_exposure(cam, exposure)\n # Acquire images\n err, img,width,height = acquire_images(cam, nodemap)\n if err < 0:\n return err\n\n \n img = img.GetData().reshape(height,width,3)\n\n half_height = int(height/2)\n half_width = int(width/2)\n half_frame_height = int(FRAME_HEIGHT/2)\n half_frame_width = int(FRAME_WIDTH/2)\n \n img = img[half_height-half_frame_height:half_height+half_frame_height,half_width-half_frame_width:half_width+half_frame_width]\n #smallimg=cv2.resize(img,(int(FRAME_WIDTH/2),int(FRAME_HEIGHT/2)))\n if index==0:\n #top left\n canvas[0:FRAME_HEIGHT,0:FRAME_WIDTH]=img\n elif index==1:\n #top right\n canvas[0:FRAME_HEIGHT,FRAME_WIDTH:FRAME_WIDTH*2]=img\n elif index==2:\n #bot left\n canvas[FRAME_HEIGHT:FRAME_HEIGHT*2,0:FRAME_WIDTH]=img\n else:\n #bot right\n canvas[FRAME_HEIGHT:FRAME_HEIGHT*2,FRAME_WIDTH:FRAME_WIDTH*2]=img\n index+=1\n if index>=len(exposures):\n index=0\n\n cv2.imshow(\"frame\",canvas)\n if cv2.waitKey(1) &0xff ==ord('q'):\n #stop the feed the 'q'\n break\n cv2.destroyAllWindows()\n # Deinitialize camera\n cam.DeInit()\n\n except PySpin.SpinnakerException as ex:\n print(\"Error: %s\" % ex)\n result = False","def _on_lane_invasion(self, event):\n self.lanes_invaded = event.crossed_lane_markings","def main():\n utl.calibrate(False)\n undistort(False)\n edge_detect(False)\n transform(False)\n identify_line(False)\n lane_line(True)","def filter_beam(self, setting):\n\n self.beam_filter = setting\n\n # In manual mode determine number of raw invalid and number of 3 beam solutions\n # 3 beam solutions if selected\n if self.beam_filter > 0:\n\n # Find invalid raw data\n valid_vel = np.ones(self.raw_vel_mps.shape)\n valid_vel[np.isnan(self.raw_vel_mps)] = 0\n\n # Determine how many beams transformed coordinates are valid\n valid_vel_sum = np.sum(valid_vel, 0)\n valid = np.ones(valid_vel_sum.shape)\n\n # Compare number of valid beams or coordinates to filter value\n valid[valid_vel_sum < self.beam_filter] = False\n\n # Save logical of valid data to object\n self.valid_data[5, :] = valid\n\n else:\n\n # Apply automatic filter\n # ----------------------\n # Find all 3 beam solutions\n self.filter_beam(3)\n beam_3_valid_data = copy.deepcopy(self.valid_data)\n self.filter_beam(4)\n valid_3_beams = np.logical_xor(beam_3_valid_data[5, :], self.valid_data[5, :])\n n_ens = len(self.valid_data[5, :])\n idx = np.where(valid_3_beams == True)[0]\n\n # If 3 beam solutions exist evaluate there validity\n if len(idx) > 0:\n\n # Identify 3 beam solutions that appear to be invalid\n n3_beam_ens = len(idx)\n\n # Check each three beam solution for validity\n for m in range(n3_beam_ens):\n\n # Use before and after values to check 3-beam solution\n # but make sure the ensemble is not the first or last.\n if (idx[m] > 1) and (idx[m] < n_ens):\n\n # Find nearest 4 beam solutions before and after\n # 3 beam solution\n ref_idx_before = np.where(self.valid_data[5, :idx[m]] == True)[0]\n if len(ref_idx_before) > 0:\n ref_idx_before = ref_idx_before[-1]\n else:\n ref_idx_before = None\n\n ref_idx_after = np.where(self.valid_data[5, idx[m]:] == True)[0]\n if len(ref_idx_after) > 0:\n ref_idx_after = idx[m] + ref_idx_after[0]\n else:\n ref_idx_after = None\n\n if (ref_idx_after is not None) and (ref_idx_before is not None):\n u_ratio = (self.u_mps[idx[m]]) / ((self.u_mps[ref_idx_before]\n + self.u_mps[ref_idx_after]) / 2.) - 1\n v_ratio = (self.v_mps[idx[m]]) / ((self.v_mps[ref_idx_before]\n + self.v_mps[ref_idx_after]) / 2.) - 1\n else:\n u_ratio = 1\n v_ratio = 1\n\n # If 3-beam differs from 4-beam by more than 50% mark it invalid\n if (np.abs(u_ratio) > 0.5) and (np.abs(v_ratio) > 0.5):\n self.valid_data[5, idx[m]] = False\n else:\n self.valid_data[5, idx[m]] = True\n\n self.beam_filter = -1\n\n # Combine all filter data to composite valid data\n self.valid_data[0, :] = np.all(self.valid_data[1:, :], 0)\n self.num_invalid = np.sum(self.valid_data[0, :] == False)","def __init__(self, runway_type):\n self.primary_surface_length = 200\n self.primary_surface_width = 0\n self.approach_surface_extendedwidth = 0\n self.first_section_length = 0\n self.first_section_slope = 0\n self.second_section_length = 0\n self.second_section_slope = 0\n self.horizontal_surface_height = 150\n self.horizontal_surface_radius = 0\n self.conical_surface_slope = 20\n self.conical_surface_offset = 4000\n self.transitional_surface_slope = 7\n \n # The runway types listed in the documentation for FAA FAR 77 do not \n # match what appears when you actually run the tool in ArcMap.\n # These regular expressions should match either version. \n if re.match(\"Visual\\s*(?:Runway)?\\s*Visual\\sApproach\", runway_type, re.I):\n self.primary_surface_width = 500\n self.approach_surface_extendedwidth = 1500\n self.first_section_length = 5000\n self.first_section_slope = 20\n self.horizontal_surface_radius = 5000\n elif re.match(\"Utility\\s*(?:Runway)?\\s*Visual Approach\", runway_type, re.I):\n self.primary_surface_width = 250\n self.approach_surface_extendedwidth = 1250\n self.first_section_length = 5000\n self.first_section_slope = 20\n self.horizontal_surface_radius = 5000\n elif re.match(\"Utility\\s*(?:Runway)?\\s*Non[\\s\\-]*Precision Instrument Approach\", runway_type, re.I):\n self.primary_surface_width = 500\n self.approach_surface_extendedwidth = 2000\n self.first_section_length = 5000\n self.first_section_slope = 20\n self.horizontal_surface_radius = 5000\n elif re.match(\"Precision Instrument\\s*(?:Runway)?\", runway_type, re.I):\n self.primary_surface_width = 1000\n self.approach_surface_extendedwidth = 16000\n self.first_section_length = 10000\n self.first_section_slope = 50\n self.second_section_length = 40000\n self.second_section_slope = 40\n self.horizontal_surface_radius = 10000\n elif re.match(\"Non Precision Instrument\\s*(?:Runway)?\\s*(?:(?:High)|(?:Greater)) Visibility\", runway_type, re.I):\n self.primary_surface_width = 500\n self.approach_surface_extendedwidth = 3500\n self.first_section_length = 10000\n self.first_section_slope = 34\n self.horizontal_surface_radius = 10000\n elif re.match(\"Non Precision Instrument\\s*(?:Runway)\\s*Approach Low Visibility\", runway_type, re.I):\n self.primary_surface_width = 1000\n self.approach_surface_extendedwidth = 4000\n self.first_section_length = 10000\n self.first_section_slope = 34\n self.horizontal_surface_radius = 10000","def indoor_air_quality(self):\n # name, command, signals, delay\n return self._i2c_read_words_from_cmd(command=[0x20, 0x08], reply_size=2, delay=0.05)","def croscor(self):\n l=self.l\n if len(self.inputs['L0'])>1: \n title ='Align L0 inputs'\n chans0=self.chansl0\n cmddata='take1SSM(1,'\n CorellateGroup(self.vb,title,'1',chans0,'0','0','0','0','15',cmddata)\n if len(self.inputs['L1'])>1: \n title ='Align L1 inputs'\n chans1=self.chansl1\n cmddata='take1SSM(2,'\n CorellateGroup(self.vb,title,'1','0',chans1,'0','0','0','15',cmddata)\n if len(self.inputs['L2'])>1: \n title ='Align L2 inputs'\n chans2=self.chansl2\n cmddata='take1SSM(3,'\n CorellateGroup(self.vb,title,'1','0','0',chans2,'0','0','15',cmddata)\n if len(self.inputs['H0'])>1: \n title ='Align H0 inputs'\n chansh=self.chansh0\n cmddata='takerbSSM('\n CorellateGroup(self.vb,title,'1','0','0','0',chansh,'0','15',cmddata)\n if l==3: \n print \"Correlatin L0-L1 ----------------\"\n title ='Find L0-L1 inputs delay'\n chans0=self.l0anyinp\n chans1=self.l1anyinp\n cmddata='take2SSM(1,2,'\n CorellateGroup(self.vb,title,'1',chans0,chans1,'0','0','250','15',cmddata)\n if l==5: \n title ='Find L0-L2 inputs delay'\n chans0=self.l0anyinp\n chans2=self.l1anyinp\n cmddata='take2SSM(1,3,'\n CorellateGroup(self.vb,title,'1',chans0,'0',chans2,'0','3520','15',cmddata)\n if l==6: \n title ='Find L1-L2 inputs delay'\n chans1=self.l1anyinp\n chans2=self.l2anyinp\n cmddata='take2SSM(2,3,'\n CorellateGroup(self.vb,title,'1','0',chans1,chans2,'0','3312','15',cmddata)\n if l==7: \n chans0=self.l0anyinp\n chans1=self.l1anyinp\n chans2=self.l2anyinp\n title ='Find L0-L1 inputs delay'\n cmddata='take2SSM(1,2,'\n CorellateGroup(self.vb,title,'1',chans0,chans1,'0','0','250','15',cmddata)\n title ='Find L1-L2 inputs delay'\n cmddata='take2SSM(2,3,'\n CorellateGroup(self.vb,title,'1','0',chans1,chans2,'0','3312','15',cmddata)","def multi_agent_example():\n env = holodeck.make(\"CyberPunkCity-FollowSight\")\n\n cmd0 = np.array([0, 0, -2, 10])\n cmd1 = np.array([0, 0, 0])\n for i in range(10):\n env.reset()\n env.tick()\n env.act(\"uav0\", cmd0)\n env.act(\"nav0\", cmd1)\n for _ in range(1000):\n states = env.tick()\n pixels = states[\"uav0\"][\"RGBCamera\"]","def __init__(self, reads1, reads2):\n print \"Start Analysis...\"\n self.alignment()\n self.sai_to_sam()\n self.sam_to_bam()\n #self.clean_files()","def process_scan(self, msg):\n if len(msg.ranges) <= 330:\n # throw out scans that don't have more than 90% of the data\n return\n # get pose according to the odometry\n p = PoseStamped(header=Header(stamp=msg.header.stamp, frame_id=\"base_link\"), pose=Pose())\n self.odom_pose = self.tf_listener.transformPose(\"odom\", p)\n self.base_pose = self.tf_listener.transformPose(\"base_laser_link\", p)\n # convert the odom pose to the tuple (x,y,theta)\n self.odom_pose = OccupancyGridMapper.convert_pose_to_xy_and_theta(self.odom_pose.pose)\n #(-0.0069918, 0.000338577, 0.048387097)\n #(1.0208817, 0.04827240, 0.048387)\n self.base_pose = OccupancyGridMapper.convert_pose_to_xy_and_theta(self.base_pose.pose)\n for i in range(len(msg.ranges)):\n if 0.0 < msg.ranges[i] < 5.0: #for any reding within 5 meters\n #Using the pose and the measurement nd the angle, find it in the world\n map_x = self.odom_pose[0] + msg.ranges[i] * cos(i * pi / 180.0 + self.odom_pose[2])\n map_y = self.odom_pose[1] + msg.ranges[i] * -sin(i * pi / 180.0 + self.odom_pose[2])\n\n #Relate that map measure with a place in the picture\n x_detect = int((map_x - self.origin[0]) / self.resolution)\n y_detect = int((map_y - self.origin[1]) / self.resolution)\n\n\n #Determine how to mark the location in the map, along with the stuff inbetween\n u = (map_x - self.odom_pose[0], map_y - self.odom_pose[1])\n magnitude = sqrt(u[0] ** 2 + u[1] ** 2)\n n_steps = max([1, int(ceil(magnitude / self.resolution))])\n u_step = (u[0] / (n_steps - 1), u[1] / (n_steps - 1))\n marked = set()\n for i in range(n_steps):\n curr_x = self.odom_pose[0] + i * u_step[0]\n curr_y = self.odom_pose[1] + i * u_step[1]\n if not (self.is_in_map(curr_x, curr_y)):\n break\n\n x_ind = int((curr_x - self.origin[0]) / self.resolution)\n y_ind = int((curr_y - self.origin[1]) / self.resolution)\n if x_ind == x_detect and y_ind == y_detect:\n break\n if not ((x_ind, y_ind) in marked):\n # odds ratio is updated according to the inverse sensor model\n self.odds_ratios[x_ind, y_ind] *= self.p_occ / (1 - self.p_occ) * self.odds_ratio_miss\n marked.add((x_ind, y_ind))\n if self.is_in_map(map_x, map_y):\n # odds ratio is updated according to the inverse sensor model\n self.odds_ratios[x_detect, y_detect] *= self.p_occ / (1 - self.p_occ) * self.odds_ratio_hit\n\n self.seq += 1\n # to save time, only publish the map every 10 scans that we process\n if self.seq % 10 == 0:\n # make occupancy grid\n map = OccupancyGrid()\n map.header.seq = self.seq\n self.seq += 1\n map.header.stamp = msg.header.stamp\n map.header.frame_id = \"map\" # the name of the coordinate frame of the map\n map.info.origin.position.x = self.origin[0]\n map.info.origin.position.y = self.origin[1]\n map.info.width = self.n\n map.info.height = self.n\n map.info.resolution = self.resolution\n map.data = [0] * self.n ** 2 # map.data stores the n by n grid in row-major order\n for i in range(self.n):\n for j in range(self.n):\n idx = i + self.n * j # this implements row major order\n if self.odds_ratios[i, j] < 1 / 5.0: # consider a cell free if odds ratio is low enough\n map.data[idx] = 0\n elif self.odds_ratios[i, j] > 5.0: # consider a cell occupied if odds ratio is high enough\n map.data[idx] = 100\n else: # otherwise cell is unknown\n map.data[idx] = -1\n self.pub.publish(map)\n\n # create the image from the probabilities so we can visualize using opencv\n im = np.zeros((self.odds_ratios.shape[0], self.odds_ratios.shape[1], 3))\n for i in range(im.shape[0]):\n for j in range(im.shape[1]):\n if self.odds_ratios[i, j] < 1 / 5.0:\n im[i, j, :] = 1.0\n elif self.odds_ratios[i, j] > 5.0:\n im[i, j, :] = 0.0\n else:\n im[i, j, :] = 0.5\n\n # compute the index of the odometry pose so we can mark it with a circle\n x_odom_index = int((self.odom_pose[0] - self.origin[0]) / self.resolution)\n y_odom_index = int((self.odom_pose[1] - self.origin[1]) / self.resolution)\n\n x_base_index = int((self.base_pose[0] - self.origin[0] - 1) / self.resolution)\n y_base_index = int((self.base_pose[1] - self.origin[1]) / self.resolution)\n\n\n # computer the ball locations so we can mark with a colored circle\n #TODO Track and relate the robot's angle pose for accuracy\n\n if self.depth_red > 0:\n self.y_camera_red = int(x_odom_index - self.depth_red * cos(self.angle_diff_red + pi - self.odom_pose[2])/self.resolution)\n self.x_camera_red = int(y_odom_index - self.depth_red * sin(self.angle_diff_red + pi - self.odom_pose[2])/self.resolution)\n cv2.circle(im, (self.x_camera_red, self.y_camera_red), 1, self.red)\n\n real_red_y = self.depth_red * cos(self.angle_diff_red + pi - self.odom_pose[2])\n real_red_x = self.depth_red * sin(self.angle_diff_red + pi - self.odom_pose[2])\n\n self.rcoor_pub.publish(Vector3(-real_red_x, -real_red_y/2, 0))\n else:\n cv2.circle(im, (self.x_camera_red, self.y_camera_red), 1, self.red)\n\n if self.depth_blue > 0:\n self.y_camera_blue = int(x_odom_index - self.depth_blue * cos(self.angle_diff_blue + pi - self.odom_pose[2])/self.resolution)\n self.x_camera_blue = int(y_odom_index - self.depth_blue * sin(self.angle_diff_blue + pi - self.odom_pose[2])/self.resolution)\n cv2.circle(im, (self.x_camera_blue, self.y_camera_blue), 1, self.blue)\n\n real_blue_y = self.depth_blue * cos(self.angle_diff_blue + pi - self.odom_pose[2])\n real_blue_x = self.depth_blue * sin(self.angle_diff_blue + pi - self.odom_pose[2])\n\n self.bcoor_pub.publish(Vector3(-real_blue_x, -real_blue_y/2, 0))\n else:\n cv2.circle(im, (self.x_camera_blue, self.y_camera_blue), 1, self.blue)\n\n if self.depth_green > 0:\n self.y_camera_green = int(x_odom_index - self.depth_green * cos(self.angle_diff_green + pi - self.odom_pose[2])/self.resolution)\n self.x_camera_green = int(y_odom_index - self.depth_green * sin(self.angle_diff_green + pi - self.odom_pose[2])/self.resolution)\n cv2.circle(im, (self.x_camera_green, self.y_camera_green), 1, self.green)\n \n real_green_y = self.depth_green * cos(self.angle_diff_green + pi - self.odom_pose[2])\n real_green_x = self.depth_green * sin(self.angle_diff_green + pi - self.odom_pose[2])\n\n self.gcoor_pub.publish(Vector3(-real_green_x, -real_green_y/2, 0))\n\n if self.depth_yellow > 0:\n self.y_camera_yellow = int(x_odom_index - self.depth_yellow * cos(self.angle_diff_yellow + pi - self.odom_pose[2])/self.resolution)\n self.x_camera_yellow = int(y_odom_index - self.depth_yellow * sin(self.angle_diff_yellow + pi - self.odom_pose[2])/self.resolution)\n cv2.circle(im, (self.x_camera_yellow, self.y_camera_yellow), 1, self.yellow)\n \n real_yellow_y = self.depth_yellow * cos(self.angle_diff_yellow + pi - self.odom_pose[2])\n real_yellow_x = self.depth_yellow * sin(self.angle_diff_yellow + pi - self.odom_pose[2])\n\n self.ycoor_pub.publish(Vector3(-real_yellow_x, -real_yellow_y/2, 0))\n else:\n cv2.circle(im, (self.x_camera_yellow, self.y_camera_yellow), 1, self.yellow)\n\n # draw the robot\n cv2.circle(im, (y_odom_index, x_odom_index), 2, (255, 0, 0))\n \n # display the image resized\n cv2.imshow(\"map\", cv2.resize(im, (500, 500)))\n cv2.waitKey(20)","def draw_aim(self):\n polygon(screen, self.color, [(self.x, self.y), (self.x + self.r * 1.71 / 2, self.y - self.r / 2),\n (self.x + self.r * 1.71, self.y), (self.x + self.r * 1.71, self.y + self.r),\n (self.x + self.r * 1.71 / 2, self.y + 3 * self.r / 2), (self.x, self.y + self.r)])","def __init__(self, input, output) :\n Gate.__init__(self, [input], output)"],"string":"[\n \"def beam_align():\\n\\n # do nothing if there is a sample mounted to avoid collisions\\n if smart_magnet.sample_detect.get() == 0:\\n raise Exception(\\\"Sample mounted on gonio! Avoided collision\\\")\\n\\n # wait for attenuators to finish moving\\n yield from bps.abs_set(mxatten, 0.002)\\n yield from bps.sleep(5)\\n\\n # transition to BL and open shutter\\n yield from bps.abs_set(gov_rbt, \\\"BL\\\", wait=True)\\n yield from bps.mv(sht.r, 0)\\n\\n yield from bps.abs_set(rot_aligner.cam_hi.cam_mode, \\\"beam_align\\\")\\n\\n # which direction, x pos. pitch beam outboard (-), y pos. pitch beam up (+)\\n scan_uid = yield from bp.count([rot_aligner.cam_hi], 1)\\n centroid_x, centroid_y = (\\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output1.name][1],\\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output2.name][1],\\n )\\n\\n if np.isclose(0, centroid_x) or np.isclose(0, centroid_y):\\n raise Exception(\\\"No centroid detected!\\\")\\n\\n yield from bps.abs_set(kbt.hor.delta_px, (centroid_x - 320))\\n yield from bps.abs_set(kbt.ver.delta_px, -(centroid_y - 256))\\n\\n def lin_reg(independent, dependent, goal, **kwargs) -> float:\\n b = dependent\\n A = np.matrix([[pos, 1] for pos in independent])\\n p = (\\n np.linalg.inv(A.transpose() * A)\\n * A.transpose()\\n * np.matrix(b.to_numpy()).transpose()\\n )\\n best = (goal - p[1]) / p[0]\\n return best\\n\\n for axis, signal, center in (\\n kbt.hor,\\n rot_aligner.cam_hi.cv1.outputs.output1,\\n 320,\\n ), (kbt.ver, rot_aligner.cam_hi.cv1.outputs.output2, 256):\\n # skip if we are within 1 um\\n if abs(axis.delta_px.get()) > 2:\\n scan_uid = yield from rel_scan_no_reset(\\n [rot_aligner.cam_hi],\\n axis,\\n 0,\\n 0.4 * -(axis.delta_px.get() / abs(axis.delta_px.get())),\\n 10,\\n )\\n scan_df = db[scan_uid].table()\\n best_voltage = lin_reg(\\n scan_df[axis.readback.name],\\n scan_df[signal.name],\\n center,\\n )\\n yield from bps.mv(axis, best_voltage)\\n yield from bps.sleep(1)\\n\\n # close shutters and reset attenuators for manual viewing\\n yield from bps.mv(sht.r, 20)\",\n \"def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\\n \\n # Gear ratio\\n self.R = [np.diag([r1,r1]),np.diag([r1,r2]),np.diag([r2,r1]),np.diag([r2,r2])]\\n \\n # Inertia\\n self.Ia = np.diag([ja,ja])\\n \\n # Damping (linear)\\n self.Da = np.diag([ba,ba])\",\n \"def setActuatorParams(self, r1 = 1 , r2 = 10 , ja = 1 , ba = 1 ):\\n \\n # Gear ratio\\n self.R = [np.diag([r1,r1,r1]),np.diag([r1,r1,r2]),np.diag([r1,r2,r1]), np.diag([r1,r2,r2]),\\n np.diag([r2,r1,r1]),np.diag([r2,r1,r2]),np.diag([r2,r2,r1]), np.diag([r2,r2,r2]) ]\\n \\n # Inertia\\n self.Ia = np.diag([ja,ja,ja])\\n \\n # Damping (linear)\\n self.Da = np.diag([ba,ba,ba])\",\n \"def tomoScan(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\\\"default\\\", tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n try:\\n darkFieldInterval = int(darkFieldInterval)\\n flatFieldInterval = int(flatFieldInterval)\\n optimizeBeamInterval = int(optimizeBeamInterval)\\n \\n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\\n tomography_theta = jns.tomography_theta\\n if tomography_theta is None:\\n raise NameError(\\\"tomography_theta is not defined in Jython namespace\\\")\\n tomography_shutter = jns.tomography_shutter\\n if tomography_shutter is None:\\n raise NameError(\\\"tomography_shutter is not defined in Jython namespace\\\")\\n tomography_translation = jns.tomography_translation\\n if tomography_translation is None:\\n raise NameError(\\\"tomography_translation is not defined in Jython namespace\\\")\\n \\n tomography_detector = jns.tomography_detector\\n if tomography_detector is None:\\n raise NameError(\\\"tomography_detector is not defined in Jython namespace\\\")\\n\\n tomography_optimizer = jns.tomography_optimizer\\n if tomography_optimizer is None:\\n raise NameError(\\\"tomography_optimizer is not defined in Jython namespace\\\")\\n\\n tomography_time = jns.tomography_time\\n if tomography_time is None:\\n raise NameError(\\\"tomography_time is not defined in Jython namespace\\\")\\n \\n tomography_beammonitor = jns.tomography_beammonitor\\n if tomography_beammonitor is None:\\n raise NameError(\\\"tomography_beammonitor is not defined in Jython namespace\\\")\\n \\n tomography_camera_stage = jns.tomography_camera_stage\\n if tomography_camera_stage is None:\\n raise NameError(\\\"tomography_camera_stage is not defined in Jython namespace\\\")\\n \\n tomography_sample_stage = jns.tomography_sample_stage\\n if tomography_sample_stage is None:\\n raise NameError(\\\"tomography_sample_stage is not defined in Jython namespace\\\")\\n \\n tomo_additional_scannables = jns.tomography_additional_scannables\\n if tomo_additional_scannables is None:\\n raise NameError(\\\"tomo_additional_scannables is not defined in Jython namespace\\\")\\n \\n index = SimpleScannable()\\n index.setCurrentPosition(0.0)\\n index.setInputNames([\\\"imageNumber\\\"])\\n index.setName(\\\"imageNumber\\\")\\n index.configure()\\n \\n image_key = SimpleScannable()\\n image_key.setCurrentPosition(0.0)\\n image_key.setInputNames([\\\"image_key\\\"])\\n image_key.setName(\\\"image_key\\\")\\n image_key.configure()\\n\\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\\n tomography_translation, tomography_optimizer, image_key, index)\\n\\n# return tomoScanDevice\\n #generate list of positions\\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\\n theta_points = []\\n theta_points.append(start)\\n previousPoint = start\\n for i in range(numberSteps):\\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\\n theta_points.append(nextPoint)\\n previousPoint = nextPoint\\n \\n #generateScanPoints\\n optimizeBeamNo = 0\\n optimizeBeamYes = 1\\n shutterOpen = 1\\n shutterClosed = 0\\n shutterNoChange = 2\\n scan_points = []\\n theta_pos = theta_points[0]\\n index = 0\\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \\n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1\\n \\n for i in range(imagesPerFlat): \\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1 \\n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project, index)) #first\\n index = index + 1 \\n imageSinceDark = 1\\n imageSinceFlat = 1\\n optimizeBeam = 0\\n for i in range(numberSteps):\\n theta_pos = theta_points[i + 1]\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project, index))#main image\\n index = index + 1 \\n \\n imageSinceFlat = imageSinceFlat + 1\\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\\n index = index + 1 \\n imageSinceFlat = 0\\n \\n imageSinceDark = imageSinceDark + 1\\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\\n index = index + 1 \\n imageSinceDark = 0\\n\\n optimizeBeam = optimizeBeam + 1\\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\\n index = index + 1 \\n optimizeBeam = 0\\n \\n #add dark and flat only if not done in last steps\\n if imageSinceFlat != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n if imageSinceDark != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1 \\n scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\\n imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\\n if pattern == 'default' or pattern == 'DFPFD':\\n i = 0\\n for pt1 in scan_points1:\\n pt = scan_points[i]\\n if pt1 != pt:\\n print \\\"Mismatch - please tell Kaz about your scan and its arguments!\\\"\\n print \\\"i = \\\", i\\n print \\\"pt = \\\", pt\\n print \\\"pt1 = \\\", pt1\\n i += 1\\n #return None\\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\\\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \\n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\\n #scan_args.append(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(\\\"RotationCoord_as_list\\\", [tomoRotationAxis])\\n meta_add(\\\"approxCOR\\\", tomoRotationAxis)\\n for scannable in additionalScannables:\\n scan_args.append(scannable)\\n for scannable in tomo_additional_scannables:\\n scan_args.append(scannable)\\n ''' setting the description provided as the title'''\\n if not description == None: \\n setTitle(description)\\n else :\\n setTitle(\\\"undefined\\\")\\n \\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n if not dataFormat == \\\"NexusDataWriter\\\":\\n handle_messages.simpleLog(\\\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\\\")\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", \\\"NexusDataWriter\\\")\\n scanObject = createConcurrentScan(scan_args)\\n if addNXEntry:\\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\\n scanObject.runScan()\\n if autoAnalyse:\\n lsdp=jns.lastScanDataPoint()\\n OSCommandRunner.runNoWait([\\\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\\\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\\n return scanObject;\\n except InterruptedException:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"User interrupted the scan\\\", exceptionType, exception, traceback, False)\\n raise InterruptedException(\\\"User interrupted the scan\\\")\\n except:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"Error during tomography scan\\\", exceptionType, exception, traceback, False)\\n raise Exception(\\\"Error during tomography scan\\\", exception)\\n finally:\\n handle_messages.simpleLog(\\\"Data Format reset to the original setting: \\\" + dataFormat)\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", dataFormat)\",\n \"def main():\\n np.random.seed(219)\\n rospy.init_node(\\\"sawyer_dagger_teacher\\\")\\n pub_start = rospy.Publisher('/teacher/start', JointCommand, queue_size=1)\\n pub_epi_fin = rospy.Publisher('/teacher/fin', JointCommand, queue_size=1)\\n vel_ik_pos_pub = rospy.Publisher('/teacher/ik_vel/', Pose, queue_size = 3)\\n pub3 = rospy.Publisher('/ddpg/vel_start/', Float64, queue_size=1)\\n pub4 = rospy.Publisher('/ddpg/vel_end/', Float64, queue_size=1)\\n goal_obs_pub = rospy.Publisher('/teacher/goal_obs/', Pose, queue_size=1)\\n pos_cmd_pub = rospy.Publisher('/teacher/pos_cmd_pub/', PosCmd, queue_size=1)\\n\\n\\n\\n rospy.set_param('dagger_reset',\\\"false\\\") # param_name, param_value\\n\\n\\n # Load Gazebo Models via Spawning Services\\n # Note that the models reference is the /world frame\\n # and the IK operates with respect to the /base frame\\n # load_gazebo_models()\\n # Remove models from the scene on shutdown\\n rospy.on_shutdown(delete_gazebo_models)\\n\\n limb = 'right'\\n hover_distance = 0.15 # meters\\n # Starting Joint angles for right arm\\n starting_joint_angles = {'right_j0': -0.041662954890248294,\\n 'right_j1': -1.0258291091425074,\\n 'right_j2': 0.0293680414401436,\\n 'right_j3': 1.37518162913313,\\n 'right_j4': -0.06703022873354225,\\n 'right_j5': 0.7968371433926965,\\n 'right_j6': 1.7659649178699421}\\n\\n pnp = PickAndPlace(limb, hover_distance)\\n\\n pnp.move_to_start(starting_joint_angles)\\n\\n \\n # m_planner = trajectorySender()\\n # An orientation for gripper fingers to be overhead and parallel to the obj\\n overhead_orientation = Quaternion(\\n x=-0.00142460053167,\\n y=0.999994209902,\\n z=-0.00177030764765,\\n w=0.00253311793936)\\n block_poses = list()\\n # The Pose of the block in its initial location.\\n # You may wish to replace these poses with estimates\\n # from a perception node.\\n block_poses.append(Pose(\\n position=Point(x=0.45, y=0.155, z=-0.129),\\n orientation=overhead_orientation))\\n # Feel free to add additional desired poses for the object.\\n # Each additional pose will get its own pick and place.\\n block_poses.append(Pose(\\n position=Point(x=0.6, y=-0.1, z=-0.129),\\n orientation=overhead_orientation))\\n # Move to the desired starting angles\\n print(\\\"Running. Ctrl-c to quit\\\")\\n # pnp.move_to_start(starting_joint_angles)\\n idx = 0\\n rate = rospy.Rate(1)\\n block_quat_pose = [0.00142460053167,\\n 0.999994209902,\\n 0.00177030764765,\\n 0.00253311793936]\\n if rospy.has_param('vel_calc'):\\n rospy.delete_param('vel_calc')\\n load_gazebo_models()\\n\\n while not rospy.is_shutdown():\\n\\n\\n starting_joint_angles['right_j0'] = np.random.uniform(-0.05, 0.05)\\n starting_joint_angles['right_j1'] = np.random.uniform(-0.95, -0.85)\\n starting_joint_angles['right_j2'] = np.random.uniform(-0.1, 0.1)\\n starting_joint_angles['right_j3'] = np.random.uniform(1.6, 1.7)\\n\\n # starting_joint_angles['right_j0'] = np.random.uniform(-0.75, 0.75)\\n # starting_joint_angles['right_j1'] = np.random.uniform(-0.97, -0.80)\\n # starting_joint_angles['right_j2'] = np.random.uniform(-0.15, 0.15)\\n # starting_joint_angles['right_j3'] = np.random.uniform(1.55, 1.75)\\n\\n start_pose = [starting_joint_angles['right_j0'], starting_joint_angles['right_j1'],\\n starting_joint_angles['right_j2'], starting_joint_angles['right_j3'],\\n starting_joint_angles['right_j4'], starting_joint_angles['right_j5'],\\n starting_joint_angles['right_j6']]\\n \\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n if rospy.has_param('dagger_reset'):\\n rospy.delete_param('dagger_reset')\\n break\\n pnp.move_to_start(starting_joint_angles)\\n\\n\\n delete_kinect_camera()\\n # delete_gazebo_models()\\n delete_gazebo_block()\\n rand_x = np.random.uniform(0.45, .75)\\n rand_y = np.random.uniform(-0.2, 0.33)\\n # rand_x = np.random.uniform(0.44,0.68)\\n\\n # rand_y = np.random.uniform(-0.20, 0.35)\\n pose_block = Pose(position=Point(x=rand_x, y=rand_y, z=1.00)\\n , orientation=overhead_orientation)\\n pose_rob = Pose(position=Point(x=rand_x-0.015, y=rand_y+0.03, z=0.03), orientation=overhead_orientation) \\n\\n # rospy.set_param('vel_calc', 'true')\\n # pnp.move_to_start(starting_joint_angles)\\n # oktogo = pnp.move_to_start_vel_command(start_pose)\\n # if rospy.has_param('vel_calc'):\\n # rospy.delete_param('vel_calc')\\n # loads env\\n load_gazebo_block(block_pose=pose_block)\\n # load_kinect_camera()\\n\\n \\n\\n # rospy.set_param('vel_calc', 'true')\\n print 'Reaching target object... Learning...'\\n rospy.set_param('epi_start', 'true')\\n pnp.reach(pose_rob, pos_cmd_pub)\\n # reached = pnp.reach_vel_ctrl(pose_rob)\\n rospy.sleep(0.5)\\n # if rospy.has_param('vel_calc'):\\n # rospy.delete_param('vel_calc')\\n # if reached:\\n # rospy.set_param('reached', 'true')\\n # goal_obs_pub.publish(pose_rob)\\n\\n\\n print 'Reached target object! and Goal obs acquired Resetting...'\\n # while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n # if rospy.has_param('demo_success'):\\n # break\\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n if rospy.has_param('demo_success'):\\n rospy.delete_param('demo_success')\\n break\\n \\n # rospy.delete_param('demo_success')\\n \\n\\n return 0\",\n \"def quick_ao(wf, nact, WFS_map):\\n\\n nact_across_pupil = nact-2 # number of full DM actuators across pupil (oversizing DM extent)\\n # Note: oversample by 2 actuators hardcoded here, check if this is appropriate \\n \\n ############################\\n # Creating AO Surface Map\\n ############################\\n d_beam = 2 * proper.prop_get_beamradius(wf) # beam diameter\\n act_spacing = d_beam / nact_across_pupil # actuator spacing [m]\\n # map_spacing = proper.prop_get_sampling(wfo.wf_collection[iw,0])\\n\\n ###################################\\n # Cropping the Beam from WFS map\\n ###################################\\n # cropping here by beam_ratio rather than d_beam is valid since the beam size was initialized\\n # using the scaled beam_ratios when the wfo was created\\n # dprint(f\\\"{WFS_map[0,0,0]}\\\")\\n ao_map = WFS_map[\\n sp.grid_size//2 - np.int_(wf.beam_ratio*sp.grid_size//2):\\n sp.grid_size//2 + np.int_(wf.beam_ratio*sp.grid_size//2)+1,\\n sp.grid_size//2 - np.int_(wf.beam_ratio*sp.grid_size//2):\\n sp.grid_size//2 + np.int_(wf.beam_ratio*sp.grid_size//2)+1]\\n\\n ########################################################\\n # Interpolating the WFS map onto the actuator spacing\\n # (tp.nact,tp.nact)\\n ########################################################\\n # Lowpass Filter- prevents aliasing; uses Gaussian filter\\n nyquist_dm = tp.ao_act/2 * act_spacing # [m]\\n sigma = [nyquist_dm/2.355, nyquist_dm/2.355] # assume we want sigma to be twice the HWHM\\n ao_map = ndimage.gaussian_filter(ao_map, sigma=sigma, mode='nearest')\\n\\n f = interpolate.interp2d(range(ao_map.shape[0]), range(ao_map.shape[0]), ao_map, kind='cubic')\\n ao_map = f(np.linspace(0,ao_map.shape[0],nact), np.linspace(0,ao_map.shape[0], nact))\\n # dm_map = proper.prop_magnify(dm_map, map_spacing / act_spacing, nact)\\n\\n ################################################\\n # Converting phase delay to DM actuator height\\n ################################################\\n # Apply the inverse of the WFS image to the DM, so use -dm_map (dm_map is in phase units, divide by k=2pi/lambda)\\n surf_height = proper.prop_get_wavelength(wf) / (4 * np.pi) # [m/rad]\\n ao_map = -ao_map * surf_height # Converts DM map to units of [m] of actuator heights\\n\\n return ao_map\",\n \"def main():\\n cam = Realsense()\\n # cam.access_intr_and_extr()\\n profile = cam.pipeline.start(cam.config)\\n depth_sensor = profile.get_device().first_depth_sensor()\\n depth_scale = depth_sensor.get_depth_scale()\\n align_to = rs.stream.color\\n align = rs.align(align_to)\\n\\n objp = np.zeros((3*4,3), np.float32)\\n objp[:,:2] = np.mgrid[0:4,0:3].T.reshape(-1,2)\\n axis = np.float32([[1,0,0], [0,1,0], [0,0,-1]]).reshape(-1,3)\\n # print(objp)\\n\\n try:\\n while (True):\\n # detect ArUco markers in RGB images\\n frames = cam.pipeline.wait_for_frames()\\n aligned_frames = align.process(frames)\\n color_frame = aligned_frames.get_color_frame()\\n color_image = np.asanyarray(color_frame.get_data()) \\n frame = color_image\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n corners, ids, rvecs, tvecs = cam.detect_markers_realsense(frame)\\n \\n if np.all(ids != None): # if markers are detected\\n for i in range(0, ids.size):\\n aruco.drawAxis(frame, cam.newcameramtx, cam.dist, rvecs[i],\\n tvecs[i], 0.1) # Draw axis\\n aruco.drawDetectedMarkers(frame, corners) # draw square around markers\\n\\n ###### DRAW ID #####\\n strg = ''\\n for i in range(0, ids.size):\\n strg += str(ids[i][0])+', '\\n\\n cv2.putText(frame, \\\"Id: \\\" + strg, (0,25), font, 1, (0,255,0), 2,\\n cv2.LINE_AA)\\n\\n\\t ###### Output marker positions in camera frame ######\\n \\t # output tvec\\n y0 = 60\\n dy = 40\\n for i in range(0, ids.size):\\n y = y0 + i*dy\\n cv2.putText(frame, str(tvecs[i][0]), (0, y), font, 1, (0,255,0),\\n 2, cv2.LINE_AA)\\n\\n else:\\n ##### DRAW \\\"NO IDS\\\" #####\\n cv2.putText(frame, \\\"No Ids\\\", (0,64), font, 1, (0,255,0), 2,\\n cv2.LINE_AA)\\n\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n ret, corners = cv2.findChessboardCorners(gray, (4,3), None)\\n if ret == True:\\n corners2 = cv2.cornerSubPix(gray, corners,(11,11), (-1,-1),\\n cam.criteria)\\n corners2 = corners2[::-1]\\n # print(corners2)\\n # print(objp)\\n frame = cv2.drawChessboardCorners(frame, (4,3), corners2, ret)\\n # Find the rotation and translation vectors.\\n _, rvecs, tvecs = cv2.solvePnP(objp, corners2, cam.newcameramtx,\\n cam.dist)\\n rot, _ = cv2.Rodrigues(rvecs)\\n # print(rot)\\n # project 3D points to image plane\\n imgpts, jac = cv2.projectPoints(axis, rvecs, tvecs,\\n cam.newcameramtx, cam.dist)\\n frame = draw(frame, corners2, imgpts)\\n\\n # Display the resulting frame\\n cv2.imshow('frame',frame)\\n cv2.waitKey(5)\\n\\n # When everything done, release the capture\\n cv2.destroyAllWindows()\\n\\n finally:\\n cam.pipeline.stop()\",\n \"def antenna_set(self):\",\n \"def __init__(self):\\n\\n\\t###\\n\\t#subscribers for takeoff and landing \\n\\n\\tself.sub_land = rospy.Subscriber('/ardrone/land', \\n Empty,\\n self.land)\\n\\n\\tself.sub_takeoff = rospy.Subscriber('/ardrone/takeoff', \\n Empty,\\n self.takeoff)\\n\\t###\\n\\n \\n # Desired roll and pitch in radians, yaw rate in radians per second, and\\n # climb rate in meters per second\\n self.desired_roll = 0\\n self.desired_pitch = 0\\n self.desired_yaw_rate = 0\\n self.desired_climb_rate = 0\\n \\n # Unit commands for controlling a quadrotor. Commands are in following \\n # order: [front_left, front_right, rear_left, rear_right]\\n self.unit_hover_cmd = np.array([1., 1., 1., 1.])\\n self.unit_roll_cmd = np.array([1., -1., 1., -1.])\\n self.unit_pitch_cmd = np.array([-1., -1., 1., 1.])\\n self.unit_yaw_cmd = np.array([-1., 1., 1., -1.])\\n \\n # Onboard controller gains\\n self.roll_Pgain = 2.5\\n self.roll_Igain = 1.0\\n self.roll_Dgain = 1.0\\n \\n self.pitch_Pgain = 2.5\\n self.pitch_Igain = 1.0\\n self.pitch_Dgain = 1.0\\n \\n self.yaw_rate_Pgain = 2.0\\n self.yaw_rate_Igain = 0.0\\n self.yaw_rate_Dgain = 0.0\\n \\n self.climb_Pgain = 35.0\\n self.climb_Igain = 10.0\\n self.climb_Dgain = 0.0\\n \\n # Motor speed which produces hover\\n self.hover_speed = 70.0\\n \\n # Internal state\\n self.internal_state = TransformStamped()\\n self.internal_roll_error = 0.0\\n self.internal_pitch_error = 0.0\\n self.internal_yaw_error = 0.0\\n self.internal_climb_error = 0.0\\n \\n self.internal_old_roll_error = 0.0\\n self.internal_old_pitch_error = 0.0\\n self.internal_old_yaw_rate_error = 0.0\\n self.internal_old_climb_rate_error = 0.0\\n \\n self.yaw_old = 0.0\\n self.z_old = 0.0\\n \\n # Motor speed envelope\\n self.motor_cmd_min = 10\\n self.motor_cmd_max = 100\",\n \"def __init__(self, pinForward, pinBackward, pinControlStraight,pinLeft, pinRight, pinControlSteering):\\n\\n self.pinForward = pinForward\\n self.pinBackward = pinBackward\\n self.pinControlStraight = pinControlStraight\\n self.pinLeft = pinLeft\\n self.pinRight = pinRight\\n self.pinControlSteering = pinControlSteering\\n GPIO.setup(self.pinForward, GPIO.OUT)\\n GPIO.setup(self.pinBackward, GPIO.OUT)\\n GPIO.setup(self.pinControlStraight, GPIO.OUT)\\n\\n GPIO.setup(self.pinLeft, GPIO.OUT)\\n GPIO.setup(self.pinRight, GPIO.OUT)\\n GPIO.setup(self.pinControlSteering, GPIO.OUT)\\n\\n self.pwm_forward = GPIO.PWM(self.pinForward, 100)\\n self.pwm_backward = GPIO.PWM(self.pinBackward, 100)\\n self.pwm_forward.start(0)\\n self.pwm_backward.start(0)\\n\\n self.pwm_left = GPIO.PWM(self.pinLeft, 100)\\n self.pwm_right = GPIO.PWM(self.pinRight, 100)\\n self.pwm_left.start(0)\\n self.pwm_right.start(0)\\n\\n GPIO.output(self.pinControlStraight,GPIO.HIGH) \\n GPIO.output(self.pinControlSteering,GPIO.HIGH)\",\n \"def main():\\n print(\\\"Constructing optical system...\\\")\\n\\n \\\"\\\"\\\"Place two lambertian point sources at the edges of an object\\\"\\\"\\\"\\n image_distance = 200 # Distance from center of bed to sensor\\n\\n object_length = 100\\n\\n beam_width = math.radians(80) # Arbitrary; just limits the number of rays to propagate\\n # First source\\n obj_x1, obj_y1 = 0, object_length/2\\n point_source1 = rt.LambertianPointSource(obj_x1, obj_y1, 0, -beam_width/2, beam_width/2, math.radians(0.1), color='blue')\\n # Second source\\n obj_x2, obj_y2 = 0, -object_length/2\\n point_source2 = rt.LambertianPointSource(obj_x2, obj_y2, 0, -beam_width/2, beam_width/2, math.radians(0.1), color='blue')\\n\\n # Draw object\\n rt.ax_sim.plot([obj_x1, obj_x2], [obj_y1, obj_y2], linewidth=5, color='gray')\\n\\n # Combine the sources into one element\\n object_sources = [point_source1, point_source2]\\n\\n \\\"\\\"\\\"Model of optics\\\"\\\"\\\"\\n # Aperture\\n aperture_radius = 3\\n aperture_start = image_distance - 30.0\\n aperture = rt.Aperture(aperture_start, -aperture_radius, aperture_radius) # Start of sensor column\\n\\n # Define lens geometry\\n lens_thickness = 2.5\\n lens_diameter = 12\\n lens_curvature = 20\\n n_bk7 = 1.51\\n lens_start_distance = image_distance - 25.0\\n\\n lens = rt.SphericalLens(lens_start_distance, lens_diameter,lens_curvature,lens_thickness, 0, n_bk7)\\n\\n image = rt.Image(image_distance, -10.0, image_distance, 10.0) # Sensor die perpendicular to lens/apertures\\n\\n \\\"\\\"\\\"Simulate!\\\"\\\"\\\"\\n # System elements need to be in order (source --> image)\\n system = rt.System(object_sources, [aperture, lens], image)\\n system.run()\",\n \"def __init__(self, pinForward1, pinBackward1,pinForward2, pinBackward2):\\n\\n self.pinForward1 = pinForward1\\n self.pinBackward1 = pinBackward1\\n self.pinForward2 = pinForward2\\n self.pinBackward2 = pinBackward2\\n\\n GPIO.setup(self.pinForward1, GPIO.OUT)\\n GPIO.setup(self.pinBackward1, GPIO.OUT)\\n GPIO.setup(self.pinForward2, GPIO.OUT)\\n GPIO.setup(self.pinBackward2, GPIO.OUT)\\n\\n self.pwm_forward1 = GPIO.PWM(self.pinForward1, 100)\\n self.pwm_backward1 = GPIO.PWM(self.pinBackward1, 100)\\n self.pwm_forward2 = GPIO.PWM(self.pinForward2, 100)\\n self.pwm_backward2 = GPIO.PWM(self.pinBackward2, 100)\\n \\n self.pwm_forward1.start(0)\\n self.pwm_backward1.start(0)\\n self.pwm_forward2.start(0)\\n self.pwm_backward2.start(0)\",\n \"def route_input_A(self):\\n xoffset = self.nmos.poly_positions[0].x\\n # HACK: added 1.5, since we're going to rewrite this.\\n yoffset = self.nmos_position1.y + drc[\\\"well_enclosure_active\\\"] + self.nmos.active_height + 1.5*self.poly_contact.height \\n self.A_position = vector(xoffset, yoffset)\\n # gate input\\n offset = self.A_position - vector(0, 0.5 * self.poly_contact.width)\\n self.add_contact(layers=(\\\"poly\\\", \\\"contact\\\", \\\"metal1\\\"),\\n offset=offset,\\n rotate=90)\\n\\n # connect gate input to tx gate\\n offset = self.A_position - vector(self.poly_contact.first_layer_position.y,\\n 0.5 * self.poly_contact.width)\\n self.add_rect(layer=\\\"poly\\\",\\n offset=offset,\\n width=self.poly_contact.first_layer_position.y + drc[\\\"minwidth_poly\\\"],\\n height=self.poly_contact.first_layer_width)\\n # extend the metal to the boundary of the cell\\n input_length = self.A_position.x\\n offset = [0, self.A_position.y - 0.5 * drc[\\\"minwidth_metal1\\\"]]\\n self.add_layout_pin(text=\\\"A\\\",\\n layer=\\\"metal1\\\",\\n offset=offset,\\n width=input_length,\\n height=drc[\\\"minwidth_metal1\\\"])\",\n \"def arm(self):\\n pass\",\n \"def isi_calc(self):\\n arg = erfinv(0.8)*1.0E6/(self.speedup*self.br_nominal)\\n print('arg: ', arg)\\n\\n # calculate center eye opening with no additional impairments\\n self.isi_center = 2.0*erf(arg/self.tc) - self.l_1 # column Z\\n\\n # calculate center eye opening with residual DJ (DJ - DCD)\\n self.isi_dj_center = (erf(arg*(1.0+self.dj_ui)/self.tc) + erf(arg*(1.0-self.dj_ui)/self.tc) - self.l_1) # column AD\\n\\n # calculate eye closing induced by interferometric effects from link end reflections\\n mean_reflection = math.pow(10.0,0.05*(self.rx_reflection + self.tx_reflection)) # cell AB5\\n er_lin = math.pow(10.0,0.1*self.er_dB_min) # cell AB7\\n\\n\\n arg1 = np.sqrt(2.0*er_lin*self.isi_dj_center*(er_lin-1.0) + (er_lin+1.0)*self.l_1)\\n print('arg1: ', arg1)\\n arg2 = np.divide(arg1,self.isi_dj_center)\\n arg3 = (2.0*self.ref_nf*np.power(10.0,-0.1*self.chil)*mean_reflection)\\n self.isi_reflection = self.l_1-np.multiply(arg2,arg3)\\n\\n # calculate center eye opening with both residual DJ and reflection degradations included\\n self.isi_dj_refl_closed = np.multiply(self.isi_dj_center, self.isi_reflection) # column AA\\n print('isi_dj_refl_closed (AA) : ', self.isi_dj_refl_closed)\\n \\n # calculate eye opening at the corners with no additional impairments\\n eff_rx_eye = 2.0*(0.5-self.X2)*self.speedup\\n self.isi_corners = (erf(arg*(1.0+eff_rx_eye)/self.tc) + erf(arg*(1.0-eff_rx_eye)/self.tc) - self.l_1) # column AB\\n\\n # calculate eye opening at the corners with residual DJ impairment\\n self.isi_dj_corners = (erf(arg*(1.0+eff_rx_eye+self.dj_ui)/self.tc) + erf(arg*(1.0-eff_rx_eye-self.dj_ui)/self.tc) - self.l_1) # column AC\\n self.isi_tp4_rx = (erf(arg*(1.0+eff_rx_eye)/self.rx_1090_rise) + erf(arg*(1.0-eff_rx_eye)/self.rx_1090_rise) - 1) # cell AG5\\n\\n # end of GbE10.isi_calcdef isi_calc(self):\",\n \"def __init__(self):\\n rospy.init_node(\\\"navigate_map\\\") # start node\\n\\tself.frontier_show = []\\n\\tself.first = True\\n\\tself.initialS = None\\n\\tself.once = False\\n self.frontier = []\\n self.centroidValue = None\\n self.regions = []\\n self.detected = False\\n\\tself.resolution = 0.05\\n \\tself.start = None\\n\\tself.transformed_map = None\\n \\tself.odom_sub = rospy.Subscriber('/odom', Odometry, self.odom_callback)\\n \\tself.sub = rospy.Subscriber(\\\"/map\\\", OccupancyGrid, self.handle_navigate_map)\\n \\tself.robot_path_pub = rospy.Publisher('/robot_path', Path, queue_size = 5)\\n self.cent_pub = rospy.Publisher('/cent_set', GridCells, queue_size = 5)\\n\\tself.front_pub = rospy.Publisher('/frontier_set', GridCells, queue_size = 5)\\n\\tself.goal_pos_pub = rospy.Subscriber('/move_base_simple/goal',PoseStamped ,self.goal_call_back)\\n\\tself.tf_listener = tf.TransformListener()\",\n \"def _activate_IR(self):\\n self.IR_threshold = 2000\\n\\n self.IR_sensors = [self.getLightSensor('ls' + str(i)) for i in range(self.num_light_sensors)]\\n map((lambda s: s.enable(self.timestep)), self.IR_sensors) # Enable all distance sensors\",\n \"def set_input(self, input):\\n AtoB = self.opt.direction == 'AtoB'\\n self.real_A = input['A' if AtoB else 'B'].to(self.device)\\n self.inst = input['inst'].to(self.device)\\n self.real_B = input['B' if AtoB else 'A'].to(self.device)\\n self.image_paths = input['A_paths' if AtoB else 'B_paths']\",\n \"def __init__(self):\\n self.__deviceselected__ = \\\"SR-DMS4AP{LOCALBUMP}DEV:Sel-SP\\\"\\n self.__source__ = \\\"SR-DMS4AP{LOCALBUMP}S-SP\\\"\\n self.__plane__ = \\\"SR-DMS4AP{LOCALBUMP}PLANE-SP\\\"\\n #self.__xshift__ = \\\"SR-DMS4AP{LOCALBUMP}SHIFT:X-SP\\\"\\n #self.__yshift__ = \\\"SR-DMS4AP{LOCALBUMP}SHIFT:Y-SP\\\"\\n #self.__xangle__ = \\\"SR-DMS4AP{LOCALBUMP}ANGLE:X-SP\\\"\\n #self.__yangle__ = \\\"SR-DMS4AP{LOCALBUMP}ANGLE:Y-SP\\\"\\n self.__shift__ = \\\"SR-DMS4AP{LOCALBUMP}SHIFT-SP\\\"\\n self.__angle__ = \\\"SR-DMS4AP{LOCALBUMP}ANGLE-SP\\\"\\n # with all offsets\\n self.__anglerb__ = \\\"SR-DMS4AP{LOCALBUMP}ANGLE-I\\\"\\n self.__positionrb__ = \\\"SR-DMS4AP{LOCALBUMP}POS-I\\\"\\n # with BBA offset only\\n self.__anglerb0__ = \\\"SR-DMS4AP{LOCALBUMP}ANGLE:BBA-I\\\"\\n self.__positionrb0__ = \\\"SR-DMS4AP{LOCALBUMP}POS:BBA-I\\\"\\n\\n self.__bpmposition__ = \\\"SR-DMS4AP{LOCALBUMP:BPM}Pos-I\\\"\\n self.__bpmorbitx__ = \\\"SR-DMS4AP{LOCALBUMP:BPM}ORB:X-I\\\"\\n self.__bpmorbity__ = \\\"SR-DMS4AP{LOCALBUMP:BPM}ORB:Y-I\\\"\\n self.__bpmorbitx0__ = \\\"SR-DMS4AP{LOCALBUMP:BPM}ORB:X0-I\\\"\\n self.__bpmorbity0__ = \\\"SR-DMS4AP{LOCALBUMP:BPM}ORB:Y0-I\\\"\\n\\n self.__correctorposition__ = \\\"SR-DMS4AP{LOCALBUMP:COR}Pos-I\\\"\\n self.__hcorrectorcurrent__ = \\\"SR-DMS4AP{LOCALBUMP:HCOR}PS-SP\\\"\\n self.__hcorrectordiff__ = \\\"SR-DMS4AP{LOCALBUMP:HCOR}PS:Delta-SP\\\"\\n self.__vcorrectorcurrent__ = \\\"SR-DMS4AP{LOCALBUMP:VCOR}PS-SP\\\"\\n self.__vcorrectordiff__ = \\\"SR-DMS4AP{LOCALBUMP:VCOR}PS:Delta-SP\\\"\\n\\n self.__undo__ = \\\"SR-DMS4AP{LOCALBUMP}Enbl:Undo-Cmd\\\"\\n self.__apply__ = \\\"SR-DMS4AP{LOCALBUMP}Enbl-Cmd\\\"\\n self.__status__ = \\\"SR-DMS4AP{LOCALBUMP}TS-I\\\"\\n self.__idposinfo__ = \\\"SR-DMS4AP{LOCALBUMP}S-I\\\"\\n self.__srcposition__ = \\\"SR-DMS4AP{LOCALBUMP}SRC-SP\\\"\",\n \"def control_law( self, inputs ):\",\n \"def roi_cli():\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def __init__(self):\\n self.Robot = Robot()\\n self.Omega = matrix()\\n # self.Omega.value[0][0] = 1.0\\n # self.Omega.value[1][1] = 1.0\\n self.Xi = matrix()\\n # Xi.value[0][0] = 0.0\\n # Xi.value[1][0] = 0.0\\n self.measure = {}\\n self.landMarkCount = 0\\n self.init = False\\n self.bearing = 0\\n self.x = 0\\n self.y = 0\\n \\n # TODO\",\n \"def pumpprobe_intAM(chan1=1,chan2=2):\\r\\n inst.write(\\\"*RST\\\")\\r\\n #inst.write(\\\"DISP OFF\\\")\\r\\n inst.write(\\\":FUNC%i USER\\\" %chan1)\\r\\n inst.write(\\\"FUNC%i:USER PUMP_1PER\\\" %chan1)\\r\\n setChanVolt(2) \\r\\n setChanFreq(200000)\\r\\n\\r\\n\\r\\n inst.write(\\\":FUNC%i USER\\\" %chan2)\\r\\n inst.write(\\\"FUNC2:USER PROBE_EARLY\\\")\\r\\n setChanVolt(.2,2)\\r\\n setChanFreq(200000,2)\\r\\n AMdepth(100,2)\\r\\n AMshape(\\\"SQU\\\",2)\\r\\n AMfreq(15000,2)\\r\\n AMstate(\\\"on\\\",2)\\r\\n \\r\\n addChanOut1(\\\"plus\\\")\\r\\n ChanOutState(\\\"on\\\",1)\",\n \"def calculate_beam_xy(self):\\n info = []\\n\\n # Import relevant info\\n pixel_size = self.info.pixel_size\\n for i in [j.final for j in self.final_objects]:\\n try:\\n info.append(\\n [\\n i,\\n i[\\\"beamX\\\"],\\n i[\\\"beamY\\\"],\\n i[\\\"wavelength\\\"],\\n i[\\\"distance\\\"],\\n (i[\\\"a\\\"], i[\\\"b\\\"], i[\\\"c\\\"], i[\\\"alpha\\\"], i[\\\"beta\\\"], i[\\\"gamma\\\"]),\\n ]\\n )\\n except IOError as e:\\n print(\\\"IOTA ANALYSIS ERROR: BEAMXY failed! \\\", e)\\n pass\\n\\n # Calculate beam center coordinates and distances\\n beamX = [i[1] for i in info]\\n beamY = [j[2] for j in info]\\n beam_dist = [\\n math.hypot(i[1] - np.median(beamX), i[2] - np.median(beamY)) for i in info\\n ]\\n beam_dist_std = np.std(beam_dist)\\n img_list = [\\n [i[0], i[1], i[2], i[3], i[4], i[5], j]\\n for i, j in list(zip(info, beam_dist))\\n ]\\n\\n # Separate out outliers\\n outliers = [i for i in img_list if i[3] > 2 * beam_dist_std]\\n clean = [i for i in img_list if i[3] <= 2 * beam_dist_std]\\n cbeamX = [i[1] for i in clean]\\n cbeamY = [j[2] for j in clean]\\n obeamX = [i[1] for i in outliers]\\n obeamY = [j[2] for j in outliers]\\n\\n # Calculate median wavelength, detector distance and unit cell params from\\n # non-outliers only\\n wavelengths = [i[3] for i in clean]\\n distances = [i[4] for i in clean]\\n cells = [i[5] for i in clean]\\n\\n wavelength = np.median(wavelengths)\\n det_distance = np.median(distances)\\n a = np.median([i[0] for i in cells])\\n b = np.median([i[1] for i in cells])\\n c = np.median([i[2] for i in cells])\\n\\n # Calculate predicted L +/- 1 misindexing distance for each cell edge\\n aD = det_distance * math.tan(2 * math.asin(wavelength / (2 * a)))\\n bD = det_distance * math.tan(2 * math.asin(wavelength / (2 * b)))\\n cD = det_distance * math.tan(2 * math.asin(wavelength / (2 * c)))\\n\\n return (\\n beamX,\\n beamY,\\n cbeamX,\\n cbeamY,\\n obeamX,\\n obeamY,\\n beam_dist,\\n [i[4] for i in info],\\n aD,\\n bD,\\n cD,\\n pixel_size,\\n )\",\n \"def step(self):\\n\\n \\\"\\\"\\\" First updates the variables values of the current time form the environment \\\"\\\"\\\"\\n self.update_crispval(self.env.context)\\n\\n \\\"\\\"\\\"\\n here the decision making of the agent\\n to determine which activity to suggest to the patient\\n i apply the creative controller to the current context\\n \\\"\\\"\\\"\\n curr_input = sample_inputs(False, 0, self.curr_interaction, self.variables_default_val, self.action_var,\\n self.fuzzysets_values, self.variables_universe)\\n c_out, rules_activations, is_cc_exception = self.creative_controller.computeOutput(curr_input, False)\\n\\n \\\"\\\"\\\" i obtain a number of ouput crisp values.\\n i determine which one achieves the max expected output w.r.t. the a-rules \\\"\\\"\\\"\\n best_a = None\\n best_a_val = -1000\\n best_a_exphapp = 5\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(\\\"rules activations\\\")\\n for a in rules_activations:\\n if rules_activations[a] > 0:\\n print(str(a) + \\\"\\\\n\\\\t\\\\t\\\\t-> \\\" + str(rules_activations[a]))\\n for item in c_out.items(): # for each pair <activity, crisp output>\\n if self.verbose > Constants.VERBOSE_BASIC:\\n print(item)\\n if not item[\\n 0] in self.curr_iter_suggestions: # if i didn't suggest the same activity already in the same interaction\\n inputs = dict(curr_input) # I create a copy fo the dict\\n inputs[item[0]] = item[1]\\n assessor_id = self.actions_to_ti[item[0]]\\n self.assessors[assessor_id].feed_inputs(inputs)\\n is_ac_exception = False\\n assout = []\\n try:\\n a_out, a_rules_activations, is_ac_exception = self.assessors[assessor_id].compute(verbose=False)\\n assout = [a_out[ao] for ao in a_out]\\n except:\\n is_ac_exception = True\\n traceback.print_exc()\\n # todo the following assumes that every assessor controller has same eval var\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n if len(assout) == 0:\\n for v in self.eval_var:\\n assout.append(self.variables_default_val[v])\\n w_ta = self.weights_therapeutic_interventions[self.actions_to_ti[item[0]]]\\n\\n avg_credit_rules_that_suggested_action = 1.0\\n nr_rules_that_suggested_action = 0\\n for r in rules_activations:\\n if (rules_activations[r] > 0) and (str(item[0]) in str(r)):\\n avg_credit_rules_that_suggested_action = avg_credit_rules_that_suggested_action + \\\\\\n self.rules_credits[str(r)]\\n nr_rules_that_suggested_action = nr_rules_that_suggested_action + 1\\n if nr_rules_that_suggested_action > 0:\\n avg_credit_rules_that_suggested_action = (\\n avg_credit_rules_that_suggested_action - 1.0) / nr_rules_that_suggested_action\\n repetition_cost = 1.0\\n a_val = (mean(assout) * w_ta * avg_credit_rules_that_suggested_action) / repetition_cost\\n if (a_val > best_a_val) and (\\n item[1] >= (self.variables_default_val[item[0]] + self.range_step[item[0]])):\\n best_a = item\\n best_a_val = a_val\\n best_a_exphapp = mean(assout)\\n\\n \\\"\\\"\\\"I suggest the activity with best expected outcome and store the information to populate the interactions \\n memory \\\"\\\"\\\"\\n self.proposeActivity(best_a)\\n if not best_a is None:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"proposing activity\\\" + str(best_a) + \\\" which has expected feedback: \\\" + str(\\n best_a_exphapp) + \\\", which weighted is \\\" + str(best_a_val))\\n self.curr_iter_suggestions.append(best_a[0])\\n self.last_suggestion = best_a\\n else:\\n if (self.verbose > Constants.VERBOSE_FALSE) and (self.verbose <= Constants.VERBOSE_BASIC):\\n print(\\\"the activity proposed is \\\" + str(\\n best_a) + \\\" so I don't suggest anything. I will ask a question instead\\\")\\n self.last_suggestion = []\\n self.expected_feedback = best_a_exphapp\\n self.last_context = self.env.context.copy()\\n self.last_rules_activations = rules_activations\",\n \"def __init__(self, *args, **kwargs):\\n _Beamlet.__init__(self, *args, **kwargs)\",\n \"def set_input(self, input):\\n AtoB = self.opt.direction == 'AtoB'\\n self.real_A = input['A' if AtoB else 'B'].to(self.device)\\n self.real_B = input['B' if AtoB else 'A'].to(self.device)\\n self.true_time = input['time_period'][0]\\n self.image_paths = input['A_paths' if AtoB else 'B_paths']\",\n \"def start(self):\\n self.id_oi = 1\\n self.param_target_distances = [5, 30]\\n self.cells_no_links = [cell for cell in self.cell_list if cell.id != self.id_oi]\\n self.cell_oi = [cell for cell in self.cell_list if cell.id == self.id_oi][0]\",\n \"def test_via(num_vias = 100, wire_width = 10, via_width = 15,\\n via_spacing = 40, pad_size = (300, 300), min_pad_spacing = 0,\\n pad_layer = 0, wiring1_layer = 1, wiring2_layer = 2,\\n via_layer = 3):\\n VR = Device('test_via')\\n pad1 = VR.add_ref(rectangle(size = pad_size, layer = pad_layer))\\n pad1_overlay = VR.add_ref(rectangle(size = pad_size,\\n layer = wiring1_layer))\\n pad2 = VR.add_ref(rectangle(size = pad_size, layer = pad_layer))\\n pad2_overlay = VR.add_ref(rectangle(size = pad_size,\\n layer = wiring1_layer))\\n nub = VR.add_ref(compass(size = (3*wire_width,wire_width),\\n layer = pad_layer))\\n nub_overlay = VR.add_ref(compass(size = (3*wire_width, wire_width),\\n layer = wiring1_layer))\\n head = VR.add_ref(compass(size = (wire_width, wire_width),\\n layer = pad_layer))\\n head_overlay = VR.add_ref(compass(size = (wire_width, wire_width),\\n layer = wiring1_layer))\\n nub.ymax = pad1.ymax - 5\\n nub.xmin = pad1.xmax\\n nub_overlay.ymax = pad1.ymax - 5\\n nub_overlay.xmin = pad1.xmax\\n head.connect(port = \\\"W\\\", destination = nub.ports[\\\"E\\\"])\\n head_overlay.connect(port = \\\"W\\\", destination = nub_overlay.ports[\\\"E\\\"])\\n pad1_overlay.xmin = pad1.xmin\\n pad1_overlay.ymin = pad1.ymin\\n\\n old_port = head.ports['S']\\n count = 0\\n width_via_iter = 2*via_spacing - 2*wire_width\\n\\n pad2.xmin = pad1.xmax + min_pad_spacing\\n up = False\\n down = True\\n edge = True\\n current_width = 3*wire_width + wire_width #width of nub and 1 overlap\\n obj_old = head\\n obj = head\\n via_iterable = _via_iterable(via_spacing, wire_width, wiring1_layer,\\n wiring2_layer, via_layer, via_width)\\n while((count + 2) <= num_vias):\\n obj = VR.add_ref(via_iterable)\\n obj.connect(port = 'W', destination = old_port, overlap = wire_width)\\n old_port = obj.ports['E']\\n edge = False\\n if(obj.ymax > pad1.ymax):\\n obj.connect(port = 'W', destination = obj_old.ports['S'],\\n overlap = wire_width)\\n old_port = obj.ports['S']\\n current_width += width_via_iter\\n down = True\\n up = False\\n edge = True\\n\\n elif(obj.ymin < pad1.ymin):\\n obj.connect(port = 'W', destination = obj_old.ports['N'],\\n overlap = wire_width)\\n old_port = obj.ports['N']\\n current_width += width_via_iter\\n up = True\\n down = False\\n edge = True\\n count = count + 2\\n obj_old = obj\\n\\n if(current_width < min_pad_spacing and (min_pad_spacing - current_width) > 3*wire_width):\\n tail = VR.add_ref(\\n compass(size = (min_pad_spacing-current_width+wire_width,\\n wire_width),\\n layer = wiring1_layer)\\n )\\n tail_overlay = VR.add_ref(\\n compass(size = (min_pad_spacing-current_width+wire_width,\\n wire_width),\\n layer = pad_layer)\\n )\\n else:\\n tail = VR.add_ref(compass(size = (3*wire_width, wire_width),\\n layer = wiring1_layer))\\n tail_overlay = VR.add_ref(compass(size = (3*wire_width, wire_width),\\n layer = wiring1_layer))\\n\\n if(up == True and edge != True):\\n tail.connect(port = 'W', destination = obj.ports['S'],\\n overlap = wire_width)\\n tail_overlay.connect(port = 'W', destination = obj.ports['S'],\\n overlap = wire_width)\\n elif(down == True and edge != True):\\n tail.connect(port = 'W', destination = obj.ports['N'],\\n overlap = wire_width)\\n tail_overlay.connect(port = 'W', destination = obj.ports['N'],\\n overlap = wire_width)\\n else:\\n tail.connect(port = 'W', destination = obj.ports['E'],\\n overlap = wire_width)\\n tail_overlay.connect(port = 'W', destination = obj.ports['E'],\\n overlap = wire_width)\\n\\n pad2.xmin = tail.xmax\\n pad2_overlay.xmin = pad2.xmin\\n pad2_overlay.ymin = pad2.ymin\\n\\n return VR\",\n \"def __init__(self, input0, input1, output) :\\n Gate.__init__(self, [input0,input1], output)\",\n \"def __init__(self, ifn, Nout, Nact=50, sep=10, shift=(0, 0), rot=(0, 0, 0),\\n upsample=1, project_centering='fft'):\\n if isinstance(Nout, int):\\n Nout = (Nout, Nout)\\n if isinstance(Nact, int):\\n Nact = (Nact, Nact)\\n if isinstance(sep, int):\\n sep = (sep, sep)\\n\\n s = ifn.shape\\n\\n # stash inputs and some computed values on self\\n self.ifn = ifn\\n self.Ifn = fft.fft2(ifn)\\n self.Nout = Nout\\n self.Nact = Nact\\n self.sep = sep\\n self.shift = shift\\n self.obliquity = truenp.cos(truenp.radians(truenp.linalg.norm(rot)))\\n self.rot = rot\\n self.upsample = upsample\\n\\n # prepare the poke array and supplimentary integer arrays needed to\\n # copy it into the working array\\n out = prepare_actuator_lattice(ifn.shape, Nact, sep, dtype=ifn.dtype)\\n self.actuators = out['actuators']\\n self.actuators_work = np.zeros_like(self.actuators)\\n self.poke_arr = out['poke_arr']\\n self.ixx = out['ixx']\\n self.iyy = out['iyy']\\n\\n self.needs_rot = True\\n if np.allclose(rot, [0, 0, 0]):\\n self.needs_rot = False\\n self.projx = None\\n self.projy = None\\n self.invprojx = None\\n self.invprojy = None\\n else:\\n fwd, rev = prepare_fwd_reverse_projection_coordinates(s, rot)\\n self.projx, self.projy = fwd\\n self.invprojx, self.invprojy = rev\\n\\n # shift data\\n if shift[0] != 0 or shift[1] != 0:\\n # caps = Fourier variable (x -> X, y -> Y)\\n # make 2pi/px phase ramps in 1D (much faster)\\n # then broadcast them to 2D when they're used as transfer functions\\n # in a Fourier convolution\\n Y, X = [forward_ft_unit(1, s, shift=False) for s in s]\\n Xramp = np.exp(X * (-2j * np.pi * shift[0]))\\n Yramp = np.exp(Y * (-2j * np.pi * shift[1]))\\n shpx = s\\n shpy = tuple(reversed(s))\\n Xramp = np.broadcast_to(Xramp, shpx)\\n Yramp = np.broadcast_to(Yramp, shpy).T\\n self.Xramp = Xramp\\n self.Yramp = Yramp\\n self.tf = [self.Ifn * self.Xramp * self.Yramp]\\n else:\\n self.tf = [self.Ifn]\",\n \"def main():\\n rospy.init_node(\\\"ik_pick_and_place_demo\\\")\\n\\n hover_distance = 0.1 # meters\\n # Starting Pose for left arm round 1\\n lstart_pose1 = Pose()\\n lstart_pose1.position.x = 0.579679836383\\n lstart_pose1.position.y = 0.283311769707\\n lstart_pose1.position.z = 0.413676720426\\n lstart_pose1.orientation.x = -0.0249590815779\\n lstart_pose1.orientation.y = 0.999649402929\\n lstart_pose1.orientation.z = 0.00737916180073\\n lstart_pose1.orientation.w = 0.00486450832011\\n\\n # Starting Pose for right arm\\n rstart_pose1 = Pose()\\n rstart_pose1.position.x = 0.579679836383\\n rstart_pose1.position.y = -0.283311769707\\n rstart_pose1.position.z = 0.413676720426\\n rstart_pose1.orientation.x = -0.0249590815779\\n rstart_pose1.orientation.y = 0.999649402929\\n rstart_pose1.orientation.z = -0.00737916180073\\n rstart_pose1.orientation.w = 0.00486450832011\\n\\n # Picking pose left\\n lpick_pose1 = Pose()\\n lpick_pose1.position.x = 0.589679836383\\n lpick_pose1.position.y = 0.70\\n lpick_pose1.position.z = 0.153676720426\\n lpick_pose1.orientation.x = -0.0249590815779\\n lpick_pose1.orientation.y = 0.999649402929\\n lpick_pose1.orientation.z = 0.00737916180073\\n lpick_pose1.orientation.w = 0.00486450832011\\n\\n # Picking pose right\\n rpick_pose1 = Pose()\\n rpick_pose1.position.x = 0.589679836383\\n rpick_pose1.position.y = -0.70\\n rpick_pose1.position.z = 0.153676720426\\n rpick_pose1.orientation.x = -0.0249590815779\\n rpick_pose1.orientation.y = 0.999649402929\\n rpick_pose1.orientation.z = 0.00737916180073\\n rpick_pose1.orientation.w = 0.00486450832011\\n\\n # Placing pose left arm round 1\\n lplace_pose1 = Pose()\\n lplace_pose1.position.x = 0.589679836383\\n lplace_pose1.position.y = 0.0\\n lplace_pose1.position.z = 0.153676720426\\n lplace_pose1.orientation.x = -0.0249590815779\\n lplace_pose1.orientation.y = 0.999649402929\\n lplace_pose1.orientation.z = 0.00737916180073\\n lplace_pose1.orientation.w = 0.00486450832011\\n\\n # Starting Pose for left arm round 2\\n\\n lstart_pose2 = Pose()\\n lstart_pose2.position.x = 0.579679836383\\n lstart_pose2.position.y = 0.283311769707\\n lstart_pose2.position.z = 0.413676720426\\n lstart_pose2.orientation.x = -0.0249590815779\\n lstart_pose2.orientation.y = 0.999649402929\\n lstart_pose2.orientation.z = 0.00737916180073\\n lstart_pose2.orientation.w = 0.00486450832011\\n\\n # Picking pose round 2\\n lpick_pose2 = Pose()\\n lpick_pose2.position.x = 0.589679836383\\n lpick_pose2.position.y = 0.70\\n lpick_pose2.position.z = 0.153676720426\\n lpick_pose2.orientation.x = -0.0249590815779\\n lpick_pose2.orientation.y = 0.999649402929\\n lpick_pose2.orientation.z = 0.00737916180073\\n lpick_pose2.orientation.w = 0.00486450832011\\n\\n # Picking pose round 2\\n lplace_pose2 = Pose()\\n lplace_pose2.position.x = 0.589679836383\\n lplace_pose2.position.y = 0.0\\n lplace_pose2.position.z = 0.213676720426\\n lplace_pose2.orientation.x = -0.0249590815779\\n lplace_pose2.orientation.y = 0.999649402929\\n lplace_pose2.orientation.z = 0.00737916180073\\n lplace_pose2.orientation.w = 0.00486450832011\\n\\n # Picking pose round 2\\n rplace_pose1 = Pose()\\n rplace_pose1.position.x = 0.589679836383\\n rplace_pose1.position.y = 0.0\\n rplace_pose1.position.z = 0.263676720426\\n rplace_pose1.orientation.x = -0.0249590815779\\n rplace_pose1.orientation.y = 0.999649402929\\n rplace_pose1.orientation.z = 0.00737916180073\\n rplace_pose1.orientation.w = 0.00486450832011\\n\\n\\n\\n # Initialise pick and place\\n left_pnp = PickAndPlace('left', hover_distance)\\n right_pnp = PickAndPlace('right', hover_distance)\\n\\n # Go to initial position\\n left_pnp.move_to_start(left_pnp.ik_request(lstart_pose1))\\n right_pnp.move_to_start(right_pnp.ik_request(rstart_pose1))\\n\\n # Load Gazebo Models via Spawning Services\\n load_gazebo_models()\\n\\n print(\\\"\\\\nPicking...\\\")\\n left_pnp.pick(lpick_pose1)\\n\\n print(\\\"\\\\nPicking...\\\")\\n right_pnp.pick(rpick_pose1)\\n\\n print(\\\"\\\\nPlacing...\\\")\\n left_pnp.place(lplace_pose1)\\n\\n load_gazebo_models_brick_only()\\n\\n print(\\\"\\\\nstarting...\\\")\\n left_pnp.move_to_start(left_pnp.ik_request(lstart_pose2))\\n\\n print(\\\"\\\\nPicking...\\\")\\n left_pnp.pick(lpick_pose2)\\n\\n print(\\\"\\\\nPlacing...\\\")\\n left_pnp.place(lplace_pose2)\\n\\n rospy.sleep(5.0)\\n\\n rospy.on_shutdown(delete_gazebo_models)\",\n \"def getInputs(self,maze):\\n self.inputPos = []\\n \\n #Extrapos for CTS LOS\\n self.extrapos = []\\n i,j=0,0\\n # array of front views\\n for ang in self.inputangle:\\n blocked = False\\n if self.inputType == 0:\\n for dis in self.inputdistance:\\n self.inputPos.append([int(self.pos[0] + dis*np.cos(self.angle+ang)), \\n int(self.pos[1] + dis*np.sin(self.angle+ang))])\\n if(maze.checkCollisions(self.inputPos[i]) or blocked):\\n blocked = True\\n self.inputColour[i] = sensor_colours[1] \\n self.scan[i] = 0\\n else: \\n self.inputColour[i] = sensor_colours[0]\\n self.scan[i] = 1\\n i +=1\\n elif self.inputType == 1:\\n #eXPERIMENTAL STUFF FOR continuous LOS\\n self.extrapos.append(maze.getMaximumSightDistance(self.pos, self.angle+ang, self.sightLength))\\n\\n temp_length = self.extrapos[i][2]\\n for ord in self.orders:\\n self.scan[j] = (temp_length/self.sightLength)**ord\\n self.scan[j+1] = ((self.sightLength-temp_length)/self.sightLength)**ord # add opposite way, gets stronger closer instead of stronger further. might make a difference.\\n j +=2\\n i+=1\",\n \"def __init__(self, params):\\n self.params = params\\n\\n # Initialize marker classes\\n self.markers = ManipulateMarkers()\\n self.pub = Publisher(\\\"/rviz/camera_placement\\\", CameraPlacement,\\n queue_size=1)\\n self.tfl = TransformListener()\\n self.clients = \\\\\\n {'polar_camera': SimpleActionClient(\\\"VF_Polar_camera_server\\\",\\n SurfaceVFAction),\\n 'polar_rad': SimpleActionClient(\\\"VF_Polar_rad_server\\\",\\n SurfaceVFAction),\\n 'cartesian_camera':\\n SimpleActionClient(\\\"VF_Cartesian_camera_server\\\",\\n SurfaceVFAction),\\n 'cartesian_rad':\\n SimpleActionClient(\\\"VF_Cartesian_rad_server\\\",\\n SurfaceVFAction),\\n 'CAD_camera':\\n SimpleActionClient(\\\"VF_CAD_camera_server\\\",\\n SurfaceVFAction),\\n 'client': ''}\\n\\n Me.info_message(\\\"Waiting for VF servers.\\\")\\n for key in self.clients:\\n if key != 'client':\\n self.clients[key].wait_for_server(Duration(1))\\n\\n # Build empty dictionaries for use after operator input\\n self.inspection = {'name': '',\\n 'pose_list': [],\\n 'x_size': 1,\\n 'z_size': 1,\\n 'y_array': []}\\n\\n self.data = {'x': {'x-': -1, 'x+': 1, 'current': 0, 'limit': 0},\\n 'y': {'y-': -1, 'y+': 1, 'current': 0, 'limit': 0},\\n 'z': {'z-': -1, 'z+': 1, 'current': 0, 'limit': 0},\\n 'poses': {'robot': PoseStamped(),\\n 'marker': PoseStamped(),\\n 'path': PoseArray()}}\\n\\n # Initialize class for the arm\\n self.manip = ArmParameters(group_name=self.params['group'],\\n joint_control=True)\\n # Initialize class to interface with the arms\\n self.manip_fcns = \\\\\\n ArmFunctions(world_frame=self.params['world'],\\n run_rate=60, deltas={'joint': 0.01, 'teleop': 0.005},\\n fcn_params=self.params)\\n return\",\n \"def at_aflight(seq, flag):\\n at(\\\"AFLIGHT\\\", seq, [flag])\",\n \"def initialise_indoor_air_quality(self):\\n # name, command, signals, delay\\n self._i2c_read_words_from_cmd(command=[0x20, 0x03], reply_size=0, delay=0.01)\",\n \"def _aim_ctrl(self):\\n y = 0\\n z = 0\\n\\n if self.aim_axis == \\\"y\\\":\\n z = 90\\n elif self.aim_axis == \\\"z\\\":\\n y = -90\\n\\n for shape in self.ctrl.getShapes():\\n pm.rotate(shape.cv, 0, y, z, r=1)\",\n \"def __init__(self, direct=1):\\n if not direct:\\n self.physicsClient = p.connect(p.GUI)\\n else:\\n self.physicsClient = p.connect(p.DIRECT)\\n \\n #The max_dist variables define the size of the arena\\n #n defines the number of agents in the world.\\n self.max_dist_x = 4\\n self.max_dist_y = 4\\n self.n = 2\\n\\n #This defines the max force that can be applied\\n #to the motor joints of our robot.\\n self.maxForce = 30 \\n \\n self.observation_space = np.array([[1.0,1.0,1.0],[1.0,1.0,1.0]])\\n\\n #Action Space\\n #For all the agents, the action space is same.\\n #The first action is a continous action which determines the speed of the wheels.\\n #The second action is a continous action which determines the angle by which the front wheel turns.\\n self.action_space = np.array([spaces.Box(low=np.array([0,-0.5]), high=np.array([30,0.5]))]*self.n)\\n self.reduce = 1\\n self.step_counter=0\",\n \"def __init__(self, pads):\\n mfioCommon.__init__(self, pads)\\n\\n mfio_width = len(pads)\\n #\\n mfio_o = Signal(mfio_width)\\n mfio_oe = Signal(mfio_width)\\n mfio_i = Signal(mfio_width)\\n # create single pin tristate buffers\\n for b in range(mfio_width):\\n self.submodules += mfioSinglePin(pads, b, mfio_i[b], mfio_o[b], mfio_oe[b]) \\t\\n\\n # Wishbone \\n self.bus = bus = wishbone.Interface()\\n\\n #\\n sel = Signal(mfio_width)\\n inbit = Signal(1)\\n\\n # todo: dynamic address width calc to optimize the decode logic\\n addr_width = 12\\n # 1024 IO max\\n seladr = Signal(10)\\n\\n # 10 bits of address = 1024 pins max\\n self.comb += seladr.eq(self.bus.adr[:10]) \\n \\n # address decoder\\n for b in range(mfio_width):\\n self.comb += sel[b].eq(seladr == b)\\n\\n self.comb += inbit.eq( (mfio_i & sel) != 0 )\\n\\n # Read bit\\n rdbus = Signal(32)\\n self.comb += [\\n rdbus[0].eq(inbit),\\n bus.dat_r.eq(rdbus)\\n ]\\t\\n\\n # process output \\n outbit = Signal(1)\\n oebit = Signal(1)\\n wren = Signal(1)\\n\\n # PINAPI 1.0 compatible: 0 = drive 0, 1 drive 1, 3 = HiZ\\n self.comb += outbit.eq( bus.dat_w[0] )\\n self.comb += oebit.eq ( ~bus.dat_w[1] )\\n\\n # write enable\\n self.comb += wren.eq(self.bus.stb & self.bus.cyc & self.bus.we) \\n\\n for b in range(mfio_width):\\n self.sync += If(wren & sel[b], mfio_o[b].eq(outbit), mfio_oe[b].eq(oebit) )\\n\\n seq = [\\n (1, [bus.ack.eq(1)]), #\\n (1, [bus.ack.eq(0)]), #\\n (0, []),\\n ]\\n \\n t, tseq = 0, []\\n for dt, a in seq:\\n tseq.append((t, a))\\n t += dt\\n\\n self.sync += timeline(bus.cyc & bus.stb, tseq)\",\n \"def beam_update(self, beams, extra):\\n h_i, dec_out, context, src_mask = extra\\n h_i = tuple(hc[:, beams, :] for hc in h_i)\\n dec_out = dec_out[beams, :]\\n return h_i, dec_out, context, src_mask\",\n \"def align(self):\\n flag=0\\n input=None\\n level=None\\n board=None\\n ainps={'L0':[],'L1':[],'L2':[],'H0':[]} \\n for i in self.inputs:\\n if(i.inputnumber.var.get() == 1):\\n if i.inpnumall == rareradio:\\n input=i.inpnum\\n level=i.level\\n board=i.board\\n print 'Rare chosen:',level,' ',input\\n ainps[i.level].append(i.inpnum)\\n flag=flag+1\\n #print 'ainps:',ainps \\n if flag < 2 :\\n print \\\"Align: less then 2 inputs chosen. \\\" \\n return\\n if input==None:\\n cmd=\\\"setRareFlag(0,0,0)\\\"\\n else:\\n mode='0'\\n if level == 'H0': mode = '1'\\n cmd=\\\"setRareFlag(\\\"+board+','+input+','+mode+\\\")\\\"\\n print \\\"seting rare: \\\",cmd\\n output=self.vb.io.execute(cmd,log=\\\"yes\\\",applout=\\\"<>\\\") \\n self.align=Corel(self.vb,ainps)\\n self.align.croscor()\",\n \"def conRFMixAndMaskToBeagle(indfile_name, rephasedhaps_pref, em_iters, win_size, chroms):\\n\\t### First get individual information\\n\\twindow_id = 0\\n\\tem_iter = em_iters\\n\\tindfile = open(indfile_name, \\\"r\\\")\\t\\n\\tinds = []\\n\\tfor line in indfile:\\n\\t\\tsplits = line.strip(\\\"\\\\r\\\\n\\\").split()\\n\\t\\tinds.append(splits[1] + \\\"_A\\\")\\n\\t\\tinds.append(splits[1] + \\\"_B\\\")\\n\\n\\tallloci = []\\n\\toutfilename = rephasedhaps_pref + \\\"_w\\\" + str(win_size) + \\\".beagle\\\"\\n\\toutfile = open(outfilename, \\\"w\\\")\\n\\toutfile.write(\\\"I\\\\tid\\\\t\\\" + \\\"\\\\t\\\".join(inds) + \\\"\\\\n\\\")\\n\\t## Write genotype data out to file\\n\\n\\tvitout = open(rephasedhaps_pref + \\\".vit\\\", \\\"w\\\")\\n\\twinout = open(rephasedhaps_pref + \\\".windows\\\", \\\"w\\\")\\n\\tfbkout = rephasedhaps_pref + \\\".fbk\\\"\\n\\tif os.path.exists(fbkout):\\n\\t\\tos.remove(fbkout)\\n\\tvitlist = []\\n\\tfor chrom in chroms:\\n\\t\\tprint chrom\\n\\t\\tshapeitfilename = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.allelesRephased\\\" + str(em_iters) + \\\".txt\\\"\\n\\t\\tshapeitfile = open(shapeitfilename, \\\"rb\\\")\\n\\t\\tfbkin_name = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.\\\" + str(em_iters) + \\\".ForwardBackward.txt\\\"\\n\\t\\tos.system('cat ' + fbkin_name + \\\" >> \\\" + fbkout) # Concatenate files together\\n\\t\\tmarkerin = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.amaps\\\"\\n\\t\\tmarkerfile = open(markerin, \\\"r\\\")\\n\\t\\tloci=[]\\n\\t\\talleles = {}\\n\\t\\tfor mline in markerfile:\\n\\t\\t\\tmsplit = mline.strip().split()\\n\\t\\t\\tloci.append(msplit[1])\\n\\t\\t\\talleles[msplit[1]] = [msplit[3], msplit[4] ]\\n\\n\\t\\tallloci.extend(loci)\\n\\t\\tfor j,line in enumerate(shapeitfile):\\n\\t\\t\\tsline = line.strip(\\\"\\\\r\\\\n\\\")\\n\\t\\t\\tzero, ones = alleles[loci[j]]\\n\\t\\t\\tfixed = [ recodeAllele(k, zero, ones) for k in sline ]\\n\\t\\t\\toutfile.write(\\\"M\\\\t\\\" + loci[j] + \\\"\\\\t\\\" + \\\"\\\\t\\\".join(fixed) + \\\"\\\\n\\\")\\n\\t\\tvitfile = open(rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.\\\" + str(em_iters) + \\\".Viterbi.txt\\\", \\\"r\\\")\\n\\t\\tvitlist.extend([x.strip().split() for x in vitfile])\\n\\t\\tshapeitfile.close()\\n\\t\\tvitfile.close()\\n\\t\\t\\n\\t# This will transpose the whole Viterbi file\\n\\t# Yikes this may take a lot of memory\\n\\tfor i,x in enumerate(zip(*vitlist)):\\n\\t\\tvitout.write(inds[i] + \\\"\\\\t\\\")\\n\\t\\tfor y in x:\\n\\t\\t\\tvitout.write(y+\\\"\\\\t\\\")\\n\\t\\tvitout.write(\\\"\\\\n\\\")\\n\\t\\t### This doesn't quite work yet so make sure to fix it next time\\n\\tfor l in allloci:\\n\\t\\twinout.write(\\\"window\\\" + str(window_id) + \\\"\\\\t\\\" + l + \\\"\\\\n\\\")\\n\\t\\twindow_id += 1\\n\\treturn([outfile.name, vitout.name, winout.name, fbkout])\",\n \"def __init__(self, filename, freq=100, emotion=False, use_direction = True):\\n self._use_direction = use_direction\\n self._filename = filename\\n self._freq = freq\\n self._rate = rospy.Rate(freq)\\n self._rate_camera = rospy.Rate(freq*10)\\n# self._pub_rate = rospy.Publisher('robot/joint_state_publish_rate', UInt16, queue_size=10)\\n self._pub_path_is_started = rospy.Publisher('trust/path_is_started', Bool, queue_size=1)\\n self._pub_path_is_started.publish(False)\\n self._pub_path = rospy.Publisher('trust/robot_states', RobotData, queue_size=1)\\n self._collision_detected = False\\n self._inver_time = rospy.get_time() #last time human interveneted\\n self._inverted = False #last time human interveneted\\n self._path = RobotData()\\n self._path.lr =1\\n self._path.grad=1\\n self._sub_path_vel_man = rospy.Subscriber('/trust/ur_manual', Float64, self.path_vel_man_callback)\\n self._sub_trust = rospy.Subscriber('/trust/trust', TrustData, self.updateTrust)\\n self._trust_mean = 0.5\\n self._trust_count = 0\\n self._trust_mean_last = 0.5\\n self._head = baxter_interface.Head()\\n # self._head.set_pan(0.25)\\n self._head.set_pan(0.0)\\n\\n self._base_pos = []\\n self._done = False\\n self._limb = baxter_interface.Limb(\\\"left\\\")\\n self._joints = self._limb.joint_names()\\n self._limb_kin = baxter_pykdl.baxter_kinematics('left')\\n self._gripper = baxter_interface.Gripper(\\\"left\\\", CHECK_VERSION)\\n self._headers=[]\\n self._positions=[]\\n self._directions=[]\\n self._dir = [] # curent direction\\n self._dir_ds = 1 # gradient of curent direction wrt s\\n # self._pos = [] # current position\\n self._vel = [] # current velocity vector\\n self._path_vel = 0.75 # Needs to subscribes: path velocity\\n self._limb.set_joint_position_speed(0.4)\\n self._q_dot_max = np.array([[4, 4, 4, 2, 2, 2, 2]]).reshape(-1,)\\n # for publishing\\n # path.x ---> path_pos (s)\\n # path.y ---> path_status (0,1,2)\\n # self._center_pos = [.574, -1.042, .950] #baxter center point in phasespace\\n # self._center_pos = [-1405, -3005, 950] #baxter center point in phasespace\\n # self._center_pos = [-1405, -3005, 1050] #baxter center point in phasespace\\n # self._center_pos = [-299, -3080, 1050] #baxter center point in phasespace\\n # self._center_pos = [-662, -3134, 1050] #baxter center point in phasespace\\n # baxter center pose\\n reader = csv.reader(open(\\\"/home/baxter/ros_ws/src/i2r_tools/config.txt\\\", \\\"rb\\\"), delimiter=' ')\\n reader.next()\\n x = list(reader)\\n config_values = np.array(x).astype('float')\\n # self._center_pos = [-662, -3134, 1050] # baxter center point in phasespace\\n self._center_pos = config_values [2]\\n # self._center_pos = [2503, 471, 1050] # baxter center point in phasespace\\n self._center_deg = np.radians(180) # baxter orientation with respect to phasespace\\n # self._center_deg = np.radians(90) # baxter orientation with respect to phasespace\\n self._rel_head_center = np.array([180,0,645]).astype('float') # baxter head relative to baxter base\\n self._head_center = self._center_pos + self._rel_head_center\\n self._path_pos_0 = 0.0\\n self._path_len = 0\\n self._path_status = 1 # 1=forward, 2=pick, 3=backward\\n # self._path_pos = 0 # Distance robot moved along the path\\n self._K = .01 * np.identity(6)\\n self._K[5,5] = .1\\n self._path_offset = np.zeros((6, 1))\\n # self._way_point_coe = 0.25\\n self._way_point_coe = 0.5\\n # self._lambda = 0.01 #damping lambda\\n self._lambda = 0.1 #damping lambda\\n # path waypoints minimum threshold\\n self._eps = .05\\n # Verify Grippers Have No Errors and are Calibrated\\n if self._gripper.error():\\n self._gripper.reset()\\n if (not self._gripper.calibrated() and\\n self._gripper.type() != 'custom'):\\n self._gripper.calibrate()\\n self._gripper.calibrate()\\n # Using the navigator for releasing during hand over\\n self._nav_right = baxter_interface.Navigator('right')\\n self._nav_right.button0_changed.connect(self._nav_b0_pressed)\\n\\n self._gripper_open = True\\n self._is_hand_over = False\\n # read the recorded file\\n self._joint_first = []\\n self._joint_last = []\\n self._dir_test = []\\n self.read_recorded()\\n # for recording\\n self._rec_positions = []\\n self._rec_path_pos = 0\\n self._rec_active = True\\n # Blob Detection\\n try:\\n self._right_hand_camera = baxter_interface.CameraController('right_hand_camera')\\n self._right_hand_camera.close()\\n except AttributeError:\\n pass\\n try:\\n self._left_hand_camera = baxter_interface.CameraController('left_hand_camera')\\n self._left_hand_camera.close()\\n except AttributeError:\\n pass\\n # self._left_hand_camera = baxter_interface.CameraController('left_hand_camera')\\n # self._left_hand_camera.resolution = (1280, 800)\\n # self._left_hand_camera.open()\\n # i2rcamera.enable('left')\\n self._cvbridge = CvBridge()\\n self._hand_image = None\\n # rospy.Subscriber('/cameras/left_hand_camera/image', Image, self.hand_callback) # Subscribe to hand camera topic\\n # self.late_center = (620, 360)\\n self.late_center = (570, 430)\\n self.long_center = (620, 360)\\n self._pos_eof = []\\n self._pos_joint = []\\n self._top_pos_eof = []\\n self._top_pos_joint = []\\n self._obj_pos_eof = []\\n self._obj_pos_joint = []\\n # pick up positions\\n self._pick_positions = []\\n self._pick_idx = 0\\n # place positions\\n self._place_positions =[]\\n self._place_idx = 0\\n self.read_place_pos(filename)\\n # Display Image\\n self._display_image = np.zeros((1024, 600, 3), np.uint8)\\n self._pub_img = rospy.Publisher('/robot/xdisplay', Image, latch=True, queue_size=1)\\n\\n # collision detection.\\n self.camera_detect = False # Whether to use visual detection for safety\\n # self._camera_area_threshold = (400, 800) # Theshold number of pixels to indicate stop signal\\n self._camera_area_threshold = (400, 800) # Theshold number of pixels to indicate stop signal\\n self._camera_area_rectangle = (660, 800, 372, 660) # Detection rectangle for stop signal (y1, y2, x1, x2)\\n # self._face_fix = False # Fix for face (fixes face once on camera_stop = False change)\\n self._debug_collision = False\\n self._debug = False\\n self._pub_debug = rospy.Publisher('trust/debug_player', Twist, queue_size=1)\\n self._bgsubtractor = cv2.BackgroundSubtractorMOG()\\n self._camera_workspace_begin = 402 #image\\n self._camera_workspace_end = 600\\n self._camera_workspace_offset = 150\\n self._limb_y_workspace_begin = 0.4810551107413326 #workspace\\n self._limb_y_workspace_end = 0.1497036417856029\\n self._limb_y_workspace_start = 0.58950401153522277\\n # self._collision_detected = False\\n self._collision_path_vel = self._path_vel\\n self._delay_counter = 0;\\n self.set_image('/home/baxter/ros_ws/src/i2r_move/img/ready.png')\\n if self.camera_detect:\\n # i2rcamera.disable('head')\\n # i2r.camera.init\\n # i2rcamera.enable('head')\\n self._head_camera = baxter_interface.CameraController('head_camera')\\n self._head_camera.resolution = (1280, 800)\\n self._head_camera.exposure = 20\\n # self._head_camera.close()\\n self._head_camera.open()\\n self._sub_head_img = rospy.Subscriber('/cameras/head_camera/image', Image,\\n self.detection_callback) # Subscribe to head camera topic\\n # self.set_image()\\n # else:\\n # self.set_image('/home/baxter/ros_ws/src/i2r_move/img/idle.png')\\n # PhaseSpace Collision Detection\\n self._tracking_detect = emotion # Whether to use phasespace detection for safety\\n self._xh = self._center_pos[0] #human x in ref frame (phasespace)\\n self._xr = self._center_pos[1] #human y in ref frame (phasespace)\\n self._xz = self._center_pos[2] #human z in ref frame (phasespace)\\n # self._d_safe = pow(175, 2) #change the first argument of pow(distance <---, 2)\\n # self._d_safe = pow(225, 2) #change the first argument of pow(distance <---, 2)\\n self._d_safe = pow(200, 2) #change the first argument of pow(distance <---, 2)\\n if self._tracking_detect:\\n self._sub_human = rospy.Subscriber('/trust/human_states', Human, self.updateHuman)\\n self._pub_emotion = rospy.Publisher('trust/emotion', UInt8, queue_size=1)\\n # self._emotion_state = 0\\n # 0: Happy\\n # 1: Worried (Collision)\\n # 2: Late\\n # 3: Ahead\\n self.__img_path = ['/home/baxter/ros_ws/src/i2r_tools/img/happy.png',\\n '/home/baxter/ros_ws/src/i2r_tools/img/worried.png',\\n '/home/baxter/ros_ws/src/i2r_tools/img/bored.png',]\\n # self.set_image()\\n self._sub_path_vel = rospy.Subscriber('/trust/ur', Float64, self.path_vel_callback)\\n #TASE\\n self._start_point = self._joint_first\",\n \"def example():\\n a = Arm()\\n f = gcf()\\n ang = [0.0, 0.0, 0.0]\\n calibration = asarray([[10.29535101,27.84015137,1.22089742],[1.4788487,31.20673289,0.43823847],[ 11.51182565,33.73670722,7.44454401],[5.20081671,23.76730423,10.82195407]])\\n\\n\\n mat_trans = get_paper_transform_matrix(calibration)\\n while 1:\\n graph(f, a, ang)\\n print \\\"Angles: \\\",ang\\n print \\\"Tool position: \\\", a.getTool(ang)[:-1]\\n d = input(\\\"position as list / angles as tuple?>\\\")\\n if type(d) == list:\\n if shape(d) == tuple([3]):\\n d = paper_to_world(d, mat_trans)\\n ang = goto_pos(d, ang, a)\\n else:\\n for coord in d:\\n coord = paper_to_world(coord, mat_trans)\\n ang = goto_pos(coord, ang, a)\\n graph(f, a, ang)\\n else:\\n ang = d\\n set_motor_angles(ang)\",\n \"def __init__(self, anara, anadec, *args, **kwargs):\\n _Beamlet.__init__(self, *args, **kwargs)\\n self._anara = anara\\n self._anadec = anadec\",\n \"def move2goal(self):\\n vel_msg = Twist()\\n\\n # Linear velocity in the x-axis.\\n vel_msg.linear.x = 0.4 # m/s\\n vel_msg.linear.y = 0\\n vel_msg.linear.z = 0\\n\\n # Angular velocity in the z-axis.\\n vel_msg.angular.x = 0\\n vel_msg.angular.y = 0\\n vel_msg.angular.z = 1.5 # rad/s\\n\\n # Starting point reference\\n goal_x = 1.0 \\n goal_y = 1.0\\n x_ref = 1.0\\n y_ref = 1.0\\n\\n # Previous Reference\\n x_prev_ref = 0.0\\n y_prev_ref = 0.0\\n theta_prev_ref = self.theta\\n vrefA = 0.5\\n wrefA = 0.0\\n \\n i = 0\\n tPx, tPy, tPTheta = self.initiate_trajectory(\\n x_ref, y_ref, vel_msg, \\n x_prev_ref, y_prev_ref, \\n theta_prev_ref, vrefA, wrefA\\n )\\n\\n x_prev_ref = tPx[0]\\n y_prev_ref = tPy[0]\\n theta_prev_ref = tPTheta[0]\\n\\n print(f'X TRAJECTORY: {tPx}\\\\nY TRAJECTORY: {tPy}\\\\nTHETA TRAJ: {tPTheta}')\\n print(f'ACTUAL THETA: {self.theta}')\\n\\n while not rospy.is_shutdown():\\n \\n if i >= 8:\\n i = 0\\n\\n x_ref = goal_x\\n y_ref = goal_y\\n\\n tPx, tPy, tPTheta = self.initiate_trajectory(\\n x_ref, y_ref, vel_msg, \\n x_prev_ref, y_prev_ref, \\n theta_prev_ref, vrefA, wrefA\\n )\\n # inputRef = ControllerInput(\\n # xref=x_ref,\\n # yref=y_ref,\\n # RstateX=self.x_position,\\n # RstateY=self.y_position,\\n # RstateTheta=self.theta,\\n # RstateVelocity=vel_msg.linear.x,\\n # RstateW=vel_msg.angular.z,\\n # xrefA=x_prev_ref,\\n # yrefA=y_prev_ref,\\n # thetarefA=theta_prev_ref,\\n # vrefA=vrefA,\\n # wrefA=wrefA\\n # )\\n\\n # rospy.loginfo(f'X: {self.x_position} \\\\tY: {self.y_position}\\\\t Theta: {self.theta} ')\\n # nmpc = NMPC_Controller(inputRef)\\n # tPx, tPy, tPTheta = nmpc.test_create_mini_path()\\n\\n # print(f'X TRAJECTORY: {tPx}\\\\nY TRAJECTORY: {tPy}\\\\nTHETA TRAJ: {tPTheta}')\\n # print(f'ACTUAL THETA: {self.theta}')\\n \\n # new_v, new_w = nmpc.start_optmizer()\\n # new_v = round(new_v, 4)\\n # new_w = round(new_w, 4)\\n\\n # print(new_v, new_w)\\n # rospy.loginfo(\\n # f'X: {self.x_position}, Y: {self.y_position}, THETA: {self.theta}')\\n \\n # self.velocity_publisher.publish(vel_msg)\\n # x_prev_ref = self.x_position\\n # y_prev_ref = self.y_position\\n # theta_prev_ref = self.theta\\n # vrefA = vel_msg.linear.x\\n # wrefA = vel_msg.angular.z\\n \\n\\n # theta_prev_ref = self.theta\\n # vel_msg.angular.z = 0.0\\n\\n\\n '''Update the linear & angular velocity'''\\n # vel_msg.linear.x = new_v\\n # vel_msg.angular.z = new_w\\n\\n if i < 8:\\n inputRef = ControllerInput(\\n xref = tPx[i],\\n yref = tPy[i],\\n RstateX = self.x_position,\\n RstateY = self.y_position,\\n RstateTheta = self.theta,\\n RstateVelocity = vel_msg.linear.x,\\n RstateW = vel_msg.angular.z,\\n xrefA = x_prev_ref,\\n yrefA = y_prev_ref,\\n thetarefA = theta_prev_ref,\\n vrefA = vrefA,\\n wrefA = wrefA\\n )\\n\\n nmpc = NMPC_Controller(inputRef)\\n new_v, new_w = nmpc.start_optmizer()\\n new_v = round(new_v, 4)\\n new_w = round(new_w, 4)\\n\\n print(f'(actual) X: {self.x_position}, Y: {self.x_position}, THETA: {self.theta}')\\n print(f'(desired) X: {tPx[i]}, Y: {tPy[i]}')\\n print(f'V: {vel_msg.linear.x}\\\\tW: {vel_msg.angular.z}')\\n\\n x_prev_ref = tPx[i-1]\\n y_prev_ref = tPy[i-1]\\n theta_prev_ref = tPTheta[i-1]\\n vrefA = vel_msg.linear.x\\n wrefA = vel_msg.angular.z\\n\\n vel_msg.linear.x = new_v\\n vel_msg.angular.z = new_w\\n # vel_msg.angular.z = 0.0\\n\\n print(f'index: {i}')\\n\\n distance = math.sqrt((self.x_position - tPx[i])**2 + (self.y_position - tPy[i])**2)\\n if distance < 0.3:\\n print(f'Distance: {distance}')\\n i+=1\\n\\n\\n self.velocity_publisher.publish(vel_msg)\\n self.rate.sleep()\\n\\n rospy.spin()\",\n \"def __init__(self):\\r\\n self.label = \\\"Step 1: FEMA AOI Clip\\\"\\r\\n self.description = \\\"This tool projects the FEMA Flood Hazards feature class for Rhode Island into Rhode \\\" \\\\\\r\\n \\\"Island State Plane Feet. The projected feature class is then clipped to the input area \\\" \\\\\\r\\n \\\"of interest boundaries.\\\"\\r\\n self.canRunInBackground = False\",\n \"def get_observations(self):\\n joint_states = self.joints_state\\n self.force = self.wrench_stamped.wrench.force\\n self.torque = self.wrench_stamped.wrench.torque\\n self.static_taxel = self.tactile_static.taxels\\n# dynamic_taxel= tactile_dynamic\\n\\n# print(\\\"[force]\\\", self.force.x, self.force.y, self.force.z)\\n# print(\\\"[torque]\\\", self.torque.x, self.torque.y, self.torque.z)\\n shp_joint_ang = joint_states.position[0]\\n shl_joint_ang = joint_states.position[1]\\n elb_joint_ang = joint_states.position[2]\\n wr1_joint_ang = joint_states.position[3]\\n wr2_joint_ang = joint_states.position[4]\\n wr3_joint_ang = joint_states.position[5]\\n\\n shp_joint_vel = joint_states.velocity[0]\\n shl_joint_vel = joint_states.velocity[1]\\n elb_joint_vel = joint_states.velocity[2]\\n wr1_joint_vel = joint_states.velocity[3]\\n wr2_joint_vel = joint_states.velocity[4]\\n wr3_joint_vel = joint_states.velocity[5]\\n\\n q = [shp_joint_ang, shl_joint_ang, elb_joint_ang, wr1_joint_ang, wr2_joint_ang, wr3_joint_ang]\\n# print(\\\"q(observation):\\\", q)\\n eef_x, eef_y, eef_z = self.get_xyz(q)\\n self.end_effector = self.get_xyz(q)\\n eef_x_ini, eef_y_ini, eef_z_ini = self.get_xyz(self.init_joint_pose2) \\n\\n delta_image_r, delta_image_l = self.get_image()\\n self.cnn_image_r = agent.update_cnn(delta_image_r)\\n self.cnn_image_l = agent.update_cnn(delta_image_l)\\n self.cnn_image_r_list = self.cnn_image_r.tolist()\\n self.cnn_image_l_list = self.cnn_image_l.tolist()\\n print(\\\"r_list\\\", self.cnn_image_r_list)\\n print(\\\"l_list\\\", self.cnn_image_l_list)\\n\\n observation = []\\n# rospy.logdebug(\\\"List of Observations==>\\\"+str(self.observations))\\n for obs_name in self.observations:\\n if obs_name == \\\"shp_joint_ang\\\":\\n observation.append((shp_joint_ang - self.init_joint_pose2[0]) * self.joint_n)\\n elif obs_name == \\\"shl_joint_ang\\\":\\n observation.append((shl_joint_ang - self.init_joint_pose2[1]) * self.joint_n)\\n elif obs_name == \\\"elb_joint_ang\\\":\\n observation.append((elb_joint_ang - self.init_joint_pose2[2]) * self.joint_n)\\n elif obs_name == \\\"wr1_joint_ang\\\":\\n observation.append((wr1_joint_ang - self.init_joint_pose2[3]) * self.joint_n)\\n elif obs_name == \\\"wr2_joint_ang\\\":\\n observation.append((wr2_joint_ang - self.init_joint_pose2[4]) * self.joint_n)\\n elif obs_name == \\\"wr3_joint_ang\\\":\\n observation.append((wr3_joint_ang - self.init_joint_pose2[5]) * self.joint_n)\\n elif obs_name == \\\"shp_joint_vel\\\":\\n observation.append(shp_joint_vel)\\n elif obs_name == \\\"shl_joint_vel\\\":\\n observation.append(shl_joint_vel)\\n elif obs_name == \\\"elb_joint_vel\\\":\\n observation.append(elb_joint_vel)\\n elif obs_name == \\\"wr1_joint_vel\\\":\\n observation.append(wr1_joint_vel)\\n elif obs_name == \\\"wr2_joint_vel\\\":\\n observation.append(wr2_joint_vel)\\n elif obs_name == \\\"wr3_joint_vel\\\":\\n observation.append(wr3_joint_vel)\\n elif obs_name == \\\"eef_x\\\":\\n observation.append((eef_x - eef_x_ini) * self.eef_n)\\n elif obs_name == \\\"eef_y\\\":\\n observation.append((eef_y - eef_y_ini) * self.eef_n)\\n elif obs_name == \\\"eef_z\\\":\\n observation.append((eef_z - eef_z_ini) * self.eef_n)\\n elif obs_name == \\\"force_x\\\":\\n observation.append((self.force.x - self.force_ini.x) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"force_y\\\":\\n observation.append((self.force.y - self.force_ini.y) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"force_z\\\":\\n observation.append((self.force.z - self.force_ini.z) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"torque_x\\\":\\n observation.append((self.torque.x - self.torque_ini.x) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"torque_y\\\":\\n observation.append((self.torque.y - self.torque_ini.y) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"torque_z\\\":\\n observation.append((self.torque.z - self.torque_ini.z) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"image_cnn\\\":\\n for x in range(0, 10):\\n observation.append(self.cnn_image_r_list[0][x])\\n# print(\\\"r_list\\\", self.cnn_image_r_list[0][x])\\n for x in range(0, 10):\\n observation.append(self.cnn_image_l_list[0][x])\\n# print(\\\"l_list\\\", self.cnn_image_l_list[0][x])\\n elif obs_name == \\\"static_taxel\\\":\\n for x in range(0, 28):\\n observation.append((self.static_taxel[0].values[x] - self.static_taxel_ini[0].values[x]) * self.taxel_n)\\n for x in range(0, 28):\\n observation.append((self.static_taxel[1].values[x] - self.static_taxel_ini[1].values[x]) * self.taxel_n)\\n# elif obs_name == \\\"dynamic_taxel\\\":\\n# observation.append(dynamic_taxel[0].values) * self.taxel_n\\n# observation.append(dynamic_taxel[1].values) * self.taxel_n\\n else:\\n raise NameError('Observation Asked does not exist=='+str(obs_name))\\n\\n print(\\\"observation\\\", list(map(round, observation, [3]*len(observation))))\\n# print(\\\"observation\\\", observation)\\n\\n return observation\",\n \"def singleOptic2(n,misalign=np.zeros(6),srcdist=89.61e3+1.5e3,az=100.,\\\\\\n returnRays=False,f=None,\\\\\\n plist=[[0],[0],[0]],\\\\\\n ax=100.):\\n #Establish subannulus of rays\\n r0 = conic.primrad(8426.,220.,8400.)\\n r1 = conic.primrad(8426.+ax,220.,8400.)\\n rays = sources.subannulus(r0,r1,az/220.,n,zhat=-1.)\\n #Transform to node position\\n tran.transform(rays,220,0,0,0,0,0)\\n #Set up finite source distance\\n raydist = sqrt(srcdist**2+rays[1]**2+rays[2]**2)\\n l = rays[1]/raydist\\n m = rays[2]/raydist\\n n = -sqrt(1.-l**2-m**2)\\n rays = [raydist,rays[1],rays[2],rays[3],l,m,n,rays[7],rays[8],rays[9]]\\n #Align perfectly to beam\\n tran.steerX(rays)\\n #Apply misalignment\\n tran.transform(rays,*misalign)\\n #Place mirror\\n tran.transform(rays,-220.,0,-8400.,0,0,0)\\n## surf.wolterprimarynode(rays,220,8400.)\\n surf.primaryLL(rays,220.,8400.,8426.+ax,8426.,az/220.,*plist)\\n rays = tran.vignette(rays,ind=np.logical_and(rays[3]<8400.+ax,\\\\\\n rays[3]>8400.))\\n tran.itransform(rays,-220.,0.,-8400.,0,0,0)\\n #Vignette rays not landing in active mirror area\\n ind = np.logical_and(rays[3]>26.,rays[3]<(26.+ax))\\n## ind = np.logical_and(np.abs(rays[2])<az/2.,indz)\\n rays = tran.vignette(rays,ind=ind)\\n #Reverse misalignment\\n tran.itransform(rays,*misalign)\\n #Reflect and go to surface\\n tran.reflect(rays)\\n if f is None:\\n f = surf.focusI(rays)\\n else:\\n tran.transform(rays,0,0,f,0,0,0)\\n surf.flat(rays)\\n #Get centroid\\n cx,cy = anal.centroid(rays)\\n\\n if returnRays is True:\\n return rays\\n \\n return anal.hpd(rays)/abs(f)*180/pi*60**2,f,cx\",\n \"def __init__(self):\\r\\n self.observation_space = spaces.Box(low=0, high=255, shape=(119, 214))\\r\\n self.state = np.zeros((119, 214), dtype=np.uint8) \\r\\n \\r\\n self.action_space = spaces.Discrete(4)\\r\\n\\t\\t\\r\\n self.goal = \\t[221.0, -9.0] # global xy coordinates\\r\\n\\r\\n self.episodeN = 0\\r\\n self.stepN = 0 \\r\\n \\r\\n self.allLogs = { 'reward':[0] }\\r\\n self.allLogs['distance'] = [221]\\r\\n self.allLogs['action'] = [1]\\r\\n \\r\\n self._seed()\\r\\n self.stallCount = 0\\r\\n global airgym\\r\\n airgym = myAirSimMultirotorClient()\",\n \"def drive_vehicle(x_offset, y_offset, run_cmd, width, front_distance_sensor_1, front_distance_sensor_2):\\n # Initialise the PCA9685 using the default address (0x40).\\n pwm = Adafruit_PCA9685.PCA9685()\\n\\n # set number of pins for direction of drives\\n left_fwd_pin_1 = 4\\n left_fwd_pin_2 = 17\\n left_bwd_pin_1 = 18\\n left_bwd_pin_2 = 23\\n\\n right_fwd_pin_1 = 22\\n right_fwd_pin_2 = 27\\n right_bwd_pin_1 = 24\\n right_bwd_pin_2 = 25\\n\\n GPIO.setup(left_fwd_pin_1, GPIO.OUT) # left forward 1 pin\\n GPIO.setup(left_fwd_pin_2, GPIO.OUT) # left forward 2 pin\\n GPIO.setup(left_bwd_pin_1, GPIO.OUT) # left backward 1 pin\\n GPIO.setup(left_bwd_pin_2, GPIO.OUT) # left backward 2 pin\\n\\n GPIO.setup(right_fwd_pin_1, GPIO.OUT) # right forward 1 pin\\n GPIO.setup(right_fwd_pin_2, GPIO.OUT) # right forward 2 pin\\n GPIO.setup(right_bwd_pin_1, GPIO.OUT) # right backward 1 pin\\n GPIO.setup(right_bwd_pin_2, GPIO.OUT) # right backward 2 pin\\n\\n left_fwd = True\\n left_bwd = False\\n\\n right_fwd = True\\n right_bwd = False\\n\\n last_left = False\\n\\n while True:\\n try:\\n # Take shortest distance measured by ultrasound\\n if front_distance_sensor_1.value < front_distance_sensor_2.value:\\n front_distance = front_distance_sensor_1.value\\n else:\\n front_distance = front_distance_sensor_2.value\\n\\n if front_distance < 5 or width.value > 450:\\n # if we are facing some obstacle or object we are looking for is close > stop\\n left_speed = 0\\n right_speed = 0\\n left_fwd = left_bwd = right_fwd = right_bwd = False\\n else:\\n right_fwd = True\\n if x_offset.value == -10:\\n if last_left:\\n # no object is detected by camera\\n left_speed = 0 # 0.6 * max_speed\\n # left_fwd = False\\n # left_bwd = True\\n right_speed = 0.9 * max_speed\\n else:\\n left_speed = 0.9 * max_speed\\n right_speed = 0\\n elif -5 < x_offset.value < 0:\\n # object is in left part of the screen\\n left_speed = pow(abs(x_offset.value), 2) * max_speed\\n right_speed = max_speed\\n left_fwd = True\\n left_bwd = False\\n last_left = True\\n elif x_offset.value > 0:\\n # object is in right part of the screen\\n left_speed = max_speed\\n right_speed = pow(x_offset.value, 2) * max_speed\\n left_fwd = True\\n left_bwd = False\\n last_left = False\\n else:\\n # object is in the middle\\n left_speed = max_speed\\n right_speed = max_speed\\n left_fwd = True\\n left_bwd = False\\n \\n print('Speeds: Left {} Right {} Run {}'.format(left_speed, right_speed, run_cmd.value))\\n\\n # Right drives\\n pwm.set_pwm(0, 0, int(right_speed*run_cmd.value))\\n pwm.set_pwm(1, 0, int(right_speed*run_cmd.value))\\n GPIO.output(left_fwd_pin_1, left_fwd)\\n GPIO.output(left_fwd_pin_2, left_fwd)\\n GPIO.output(left_bwd_pin_1, left_bwd)\\n GPIO.output(left_bwd_pin_2, left_bwd)\\n\\n # Left drives\\n pwm.set_pwm(4, 0, int(left_speed*run_cmd.value))\\n pwm.set_pwm(5, 0, int(left_speed*run_cmd.value))\\n GPIO.output(right_fwd_pin_1, right_fwd)\\n GPIO.output(right_fwd_pin_2, right_fwd)\\n GPIO.output(right_bwd_pin_1, right_bwd)\\n GPIO.output(right_bwd_pin_2, right_bwd)\\n except KeyboardInterrupt:\\n # Stop robot after keyboard interrupt\\n GPIO.output(left_fwd_pin_1, False)\\n GPIO.output(left_fwd_pin_2, False)\\n GPIO.output(left_bwd_pin_1, False)\\n GPIO.output(left_bwd_pin_2, False)\\n GPIO.output(right_fwd_pin_1, False)\\n GPIO.output(right_fwd_pin_2, False)\\n GPIO.output(right_bwd_pin_1, False)\\n GPIO.output(right_bwd_pin_2, False)\\n GPIO.cleanup()\",\n \"def calc_IAM_beam_SC(Az_vector, g_vector, ha_vector, teta_z, tilt_angle, type_SCpanel, Sz_vector, latitude):\\n\\n def calc_teta_L(Az, teta_z, tilt, Sz):\\n teta_la = tan(Sz) * cos(teta_z - Az)\\n teta_l = degrees(abs(atan(teta_la) - tilt))\\n if teta_l < 0:\\n teta_l = min(89, abs(teta_l))\\n if teta_l >= 90:\\n teta_l = 89.999\\n return teta_l # longitudinal incidence angle in degrees\\n\\n def calc_teta_T(Az, Sz, teta_z):\\n teta_ta = sin(Sz) * sin(abs(teta_z - Az))\\n teta_T = degrees(atan(teta_ta / cos(teta_ta)))\\n if teta_T < 0:\\n teta_T = min(89, abs(teta_T))\\n if teta_T >= 90:\\n teta_T = 89.999\\n return teta_T # transversal incidence angle in degrees\\n\\n def calc_teta_L_max(teta_L):\\n if teta_L < 0:\\n teta_L = min(89, abs(teta_L))\\n if teta_L >= 90:\\n teta_L = 89.999\\n return teta_L\\n\\n def calc_IAMb(teta_l, teta_T, type_SCpanel):\\n if type_SCpanel == 'FP': # # Flat plate collector 1636: SOLEX BLU, SPF, 2012\\n IAM_b = -0.00000002127039627042 * teta_l ** 4 + 0.00000143550893550934 * teta_l ** 3 - 0.00008493589743580050 * teta_l ** 2 + 0.00041588966590833100 * teta_l + 0.99930069929920900000\\n if type_SCpanel == 'ET': # # evacuated tube Zewotherm ZEWO-SOL ZX 30, SPF, 2012\\n IAML = -0.00000003365384615386 * teta_l ** 4 + 0.00000268745143745027 * teta_l ** 3 - 0.00010196678321666700 * teta_l ** 2 + 0.00088830613832779900 * teta_l + 0.99793706293541500000\\n IAMT = 0.000000002794872 * teta_T ** 5 - 0.000000534731935 * teta_T ** 4 + 0.000027381118880 * teta_T ** 3 - 0.000326340326281 * teta_T ** 2 + 0.002973799531468 * teta_T + 1.000713286764210\\n IAM_b = IAMT * IAML # overall incidence angle modifier for beam radiation\\n return IAM_b\\n\\n # convert to radians\\n teta_z = radians(teta_z)\\n tilt = radians(tilt_angle)\\n\\n g_vector = np.radians(g_vector)\\n ha_vector = np.radians(ha_vector)\\n lat = radians(latitude)\\n Sz_vector = np.radians(Sz_vector)\\n Az_vector = np.radians(Az_vector)\\n Incidence_vector = np.vectorize(solar_equations.calc_incident_angle_beam)(g_vector, lat, ha_vector, tilt,\\n teta_z) # incident angle in radians\\n\\n # calculate incident angles\\n if type_SCpanel == 'FP':\\n incident_angle = np.degrees(Incidence_vector)\\n Teta_L = np.vectorize(calc_teta_L_max)(incident_angle)\\n Teta_T = 0 # not necessary for flat plate collectors\\n if type_SCpanel == 'ET':\\n Teta_L = np.vectorize(calc_teta_L)(Az_vector, teta_z, tilt, Sz_vector) # in degrees\\n Teta_T = np.vectorize(calc_teta_T)(Az_vector, Sz_vector, teta_z) # in degrees\\n\\n # calculate incident angle modifier for beam radiation\\n IAM_b_vector = np.vectorize(calc_IAMb)(Teta_L, Teta_T, type_SCpanel)\\n\\n return IAM_b_vector\",\n \"def do_setup(self, ants): \\n log_filter = LogFilter()\\n getLogger().addFilter(log_filter)\\n\\n self.hills = []\\n self.directions = []\\n\\n self.seen = [] #areas that have been seen, use this to avoid repetition\\n self.unseen = []\\n self.stepped_on = []\\n\\n self.intent = {}\\n self.lc = {} #center of mass for a location\\n self.i = {} #number of iterations for an ant\\n\\n for row in range(ants.rows):\\n for col in range(ants.cols):\\n self.unseen.append((row, col))\\n self.intent[(row,col)] = Intent.GATHER\\n\\n self.lc[(row,col)] = (-1.0,-1.0) #set up center of mass\\n self.i[(row,col)] = -1\",\n \"def point1(source, ants, elmin=15.0, showGUI=True, zoom=1, mode=0, \\n nextSource=None, writeFITS=True, brightness=50,isInner=False,\\n subtractBackground=False, ncoadd=1, dazbkg=2.0, centroidLoopMax=16,\\n minsamples=None):\\n global previous_source_\\n antlist = helpers.makeList(ants)\\n el = elevation(source)\\n \\n # Check for source too low, and bail out if it is\\n if el < elmin:\\n print 'Skipping %s because elevation(%.1f) is less than limit(%.1f)' \\\\\\n %(source,el,elmin)\\n return\\n \\n # Check for maximum elevation as well\\n maxelLimit = 87.0\\n if el > maxelLimit:\\n print 'Skipping %s because elevation(%.1f) is greater than limit(%.1f)' \\\\\\n %(source,el,maxelLimit)\\n return\\n\\n # Print message\\n msg = \\\"Pointing on %s at el=%5.1f (elmin = %5.1f); ants = %s\\\" % (source, el, elmin, str(ants))\\n print msg\\n SAC.scriptlog(msg)\\n \\n d = distance(source,previous_source_)\\n print \\\" Distance from %s to %s is %.1f deg\\\" %(previous_source_,source,d)\\n if mode > 0:\\n if nextSource != None:\\n print \\\" Next source will be %s\\\" % nextSource\\n else:\\n print \\\" Last source.\\\"\\n # Send all antennas tracking to new source, even though some are\\n # already there.\\n if isInner :\\n SAC.track(source)\\n else : SAC.track(source, antlist, phaseCenter=False, waiton=SAC.NONE)\\n antsTodo = antlist\\n while len(antsTodo) > 0 :\\n r = SAC.wait(SAC.TRACK, antsTodo, waiton=SAC.ANY)\\n antsReady = r.ready\\n antsTodo = r.notready\\n for i in antsReady :\\n show(source)\\n #print \\\"Loading camera on antenna %d\\\" %i\\n opticalSystem(i, auto=True, object=source, repeat=4, zoom=zoom,\\n showGUI=showGUI, dontClose=True, \\n brightness=brightness, ncoadd=ncoadd, dazbkg=dazbkg,\\n subtractBackground=subtractBackground, \\n centroidLoopMax=centroidLoopMax, minsamples=minsamples)\\n # now that this ant is done, send it to next source\\n if mode > 0:\\n if nextSource != None: \\n if isInner : SAC.track(nextSource)\\n else : SAC.track(nextSource, i, waiton=SAC.NONE)\\n previous_source_ = source\",\n \"def roam(self):\\n\\n # make sure that the MIB element contains the AP1 BSSID\\n bssid = self.a.nxapi_mibget_req(\\\"mib_dot11StationConfigTable.dot11BSSID\\\")\\n assert(len(bssid) == 6)\\n assert(bssid == self.ap1.macaddr)\\n\\n # wait for 4 seconds\\n self.a.wait(4000000000)\\n\\n # make sure that the MIB element contains the AP2 BSSID\\n bssid = self.a.nxapi_mibget_req(\\\"mib_dot11StationConfigTable.dot11BSSID\\\")\\n assert(len(bssid) == 6)\\n assert(bssid == self.ap2.macaddr)\",\n \"def main():\\n # https://github.com/caelan/pddlstream/blob/master/examples/motion/run.py\\n # TODO: 3D work and CSpace\\n # TODO: visualize just the tool frame of an end effector\\n\\n np.set_printoptions(precision=3)\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-a', '--algorithm', default='rrt_connect',\\n help='The algorithm seed to use.')\\n parser.add_argument('-d', '--draw', action='store_true',\\n help='When enabled, draws the roadmap')\\n parser.add_argument('-r', '--restarts', default=0, type=int,\\n help='The number of restarts.')\\n parser.add_argument('-s', '--smooth', action='store_true',\\n help='When enabled, smooths paths.')\\n parser.add_argument('-t', '--time', default=1., type=float,\\n help='The maximum runtime.')\\n args = parser.parse_args()\\n\\n #########################\\n\\n obstacles = [\\n create_box(center=(.35, .75), extents=(.25, .25)),\\n create_box(center=(.75, .35), extents=(.225, .225)),\\n create_box(center=(.5, .5), extents=(.225, .225)),\\n ]\\n\\n # TODO: alternate sampling from a mix of regions\\n regions = {\\n 'env': create_box(center=(.5, .5), extents=(1., 1.)),\\n 'green': create_box(center=(.8, .8), extents=(.1, .1)),\\n }\\n\\n start = np.array([0., 0.])\\n goal = 'green'\\n if isinstance(goal, str) and (goal in regions):\\n goal = get_box_center(regions[goal])\\n else:\\n goal = np.array([1., 1.])\\n\\n title = args.algorithm\\n if args.smooth:\\n title += '+shortcut'\\n viewer = draw_environment(obstacles, regions, title=title)\\n\\n #########################\\n\\n #connected_test, roadmap = get_connected_test(obstacles)\\n distance_fn = get_distance_fn(weights=[1, 1]) # distance_fn\\n\\n # samples = list(islice(region_gen('env'), 100))\\n with profiler(field='cumtime'): # cumtime | tottime\\n # TODO: cost bound & best cost\\n for _ in range(args.restarts+1):\\n start_time = time.time()\\n collision_fn, cfree = get_collision_fn(obstacles)\\n sample_fn, samples = get_sample_fn(regions['env'], obstacles=[]) # obstacles\\n extend_fn, roadmap = get_extend_fn(obstacles=obstacles) # obstacles | []\\n\\n if args.algorithm == 'prm':\\n path = prm(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\\n num_samples=200)\\n elif args.algorithm == 'lazy_prm':\\n path = lazy_prm(start, goal, sample_fn, extend_fn, collision_fn,\\n num_samples=200, max_time=args.time)[0]\\n elif args.algorithm == 'rrt':\\n path = rrt(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\\n iterations=INF, max_time=args.time)\\n elif args.algorithm == 'rrt_connect':\\n path = rrt_connect(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\\n max_time=args.time)\\n elif args.algorithm == 'birrt':\\n path = birrt(start, goal, distance_fn=distance_fn, sample_fn=sample_fn,\\n extend_fn=extend_fn, collision_fn=collision_fn,\\n max_time=args.time, smooth=100)\\n elif args.algorithm == 'rrt_star':\\n path = rrt_star(start, goal, distance_fn, sample_fn, extend_fn, collision_fn,\\n radius=1, max_iterations=INF, max_time=args.time)\\n elif args.algorithm == 'lattice':\\n path = lattice(start, goal, extend_fn, collision_fn, distance_fn=distance_fn)\\n else:\\n raise NotImplementedError(args.algorithm)\\n paths = [] if path is None else [path]\\n\\n #paths = random_restarts(rrt_connect, start, goal, distance_fn=distance_fn, sample_fn=sample_fn,\\n # extend_fn=extend_fn, collision_fn=collision_fn, restarts=INF,\\n # max_time=args.time, max_solutions=INF, smooth=100) #, smooth=1000, **kwargs)\\n\\n # paths = exhaustively_select_portfolio(paths, k=2)\\n # print(score_portfolio(paths))\\n\\n #########################\\n\\n if args.draw:\\n # roadmap = samples = cfree = []\\n add_roadmap(viewer, roadmap, color='black')\\n add_points(viewer, samples, color='red', radius=2)\\n #add_points(viewer, cfree, color='blue', radius=2)\\n\\n print('Solutions ({}): {} | Time: {:.3f}'.format(len(paths), [(len(path), round(compute_path_cost(\\n path, distance_fn), 3)) for path in paths], elapsed_time(start_time)))\\n for path in paths:\\n add_path(viewer, path, color='green')\\n\\n if args.smooth:\\n for path in paths:\\n extend_fn, roadmap = get_extend_fn(obstacles=obstacles) # obstacles | []\\n smoothed = smooth_path(path, extend_fn, collision_fn, iterations=INF, max_time=args.time)\\n print('Smoothed distance_fn: {:.3f}'.format(compute_path_cost(smoothed, distance_fn)))\\n add_path(viewer, smoothed, color='red')\\n user_input('Finish?')\",\n \"def create_ik_setup(controls, joints):\\n\\n # Create control offset transforms\\n exp_tf_ms = []\\n for ctl in controls:\\n par = cmds.listRelatives(ctl, parent=True)\\n buf = create_offset_transform(ctl, BUF)\\n exp = create_offset_transform(ctl, EXP)\\n off = create_offset_transform(ctl, OFF)\\n cmds.parent(ctl, off)\\n cmds.parent(off, exp)\\n cmds.parent(exp, buf)\\n if par:\\n cmds.parent(buf, par[0])\\n exp_tf_ms.append(buf)\\n\\n root_control, pole_control, goal_control = controls\\n handle, effector = cmds.ikHandle(sj=joints[0], ee=joints[-1], sol='ikRPsolver')\\n cmds.setAttr('{}.hiddenInOutliner'.format(handle), True)\\n cmds.orientConstraint(goal_control, joints[-1], mo=True)\\n cmds.parent(handle, goal_control)\\n cmds.hide(handle)\\n\\n # Connect root control to ik joint offset group\\n ik_joints_offset = cmds.listRelatives(joints[0], p=True)[0]\\n cmds.parentConstraint(root_control, ik_joints_offset, mo=True)\\n cmds.scaleConstraint(root_control, ik_joints_offset, mo=True)\\n\\n # Connect twisting and pole vector control\\n cmds.addAttr(goal_control, ln='twist', at='float', k=True)\\n cmds.connectAttr('{}.twist'.format(goal_control), '{}.twist'.format(handle))\\n cmds.poleVectorConstraint(pole_control, handle)\\n\\n # Add PV visibility attribute\\n cmds.addAttr(goal_control, shortName='pv', longName='poleVector', at='bool', k=True)\\n cmds.connectAttr('{}.pv'.format(goal_control), '{}.v'.format(pole_control))\\n cmds.setAttr('{}.pv'.format(goal_control),1)\\n\\n # Add curve that points elbow to pole control\\n crv = cmds.curve(p=[[0, 0, 0], [0, 1, 0]], d=1)\\n cmds.connectAttr('{}.visibility'.format(pole_control), '{}.visibility'.format(crv))\\n lock_hide_attrs(crv, attrs=['tx', 'ty', 'tz', 'rx', 'ry', 'rz', 'sx', 'sy', 'sz'])\\n cmds.setAttr('{}.overrideEnabled'.format(crv), True)\\n cmds.setAttr('{}.overrideDisplayType'.format(crv), 2)\\n decomp_joint = cmds.createNode('decomposeMatrix')\\n decomp_control = cmds.createNode('decomposeMatrix')\\n cmds.connectAttr('{}.worldMatrix'.format(joints[1]), '{}.inputMatrix'.format(decomp_joint))\\n cmds.connectAttr('{}.worldMatrix'.format(pole_control), '{}.inputMatrix'.format(decomp_control))\\n cmds.connectAttr('{}.outputTranslate'.format(decomp_joint), '{}.controlPoints[0]'.format(crv))\\n cmds.connectAttr('{}.outputTranslate'.format(decomp_control), '{}.controlPoints[1]'.format(crv))\\n\\n return handle, crv, exp_tf_ms\",\n \"def tomoScanWithFrames(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\\\"default\\\", nframes=1, tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n try:\\n darkFieldInterval = int(darkFieldInterval)\\n flatFieldInterval = int(flatFieldInterval)\\n optimizeBeamInterval = int(optimizeBeamInterval)\\n \\n image_key_frame = 3\\n nframes = int(nframes)\\n if nframes < 1:\\n nframes = 1\\n \\n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\\n tomography_theta = jns.tomography_theta\\n if tomography_theta is None:\\n raise NameError(\\\"tomography_theta is not defined in Jython namespace\\\")\\n tomography_shutter = jns.tomography_shutter\\n if tomography_shutter is None:\\n raise NameError(\\\"tomography_shutter is not defined in Jython namespace\\\")\\n tomography_translation = jns.tomography_translation\\n if tomography_translation is None:\\n raise NameError(\\\"tomography_translation is not defined in Jython namespace\\\")\\n \\n tomography_detector = jns.tomography_detector\\n if tomography_detector is None:\\n raise NameError(\\\"tomography_detector is not defined in Jython namespace\\\")\\n\\n tomography_optimizer = jns.tomography_optimizer\\n if tomography_optimizer is None:\\n raise NameError(\\\"tomography_optimizer is not defined in Jython namespace\\\")\\n\\n tomography_time = jns.tomography_time\\n if tomography_time is None:\\n raise NameError(\\\"tomography_time is not defined in Jython namespace\\\")\\n \\n tomography_beammonitor = jns.tomography_beammonitor\\n if tomography_beammonitor is None:\\n raise NameError(\\\"tomography_beammonitor is not defined in Jython namespace\\\")\\n \\n tomography_camera_stage = jns.tomography_camera_stage\\n if tomography_camera_stage is None:\\n raise NameError(\\\"tomography_camera_stage is not defined in Jython namespace\\\")\\n \\n tomography_sample_stage = jns.tomography_sample_stage\\n if tomography_sample_stage is None:\\n raise NameError(\\\"tomography_sample_stage is not defined in Jython namespace\\\")\\n \\n tomo_additional_scannables = jns.tomography_additional_scannables\\n if tomo_additional_scannables is None:\\n raise NameError(\\\"tomo_additional_scannables is not defined in Jython namespace\\\")\\n \\n index = SimpleScannable()\\n index.setCurrentPosition(0.0)\\n index.setInputNames([\\\"imageNumber\\\"])\\n index.setName(\\\"imageNumber\\\")\\n index.configure()\\n \\n image_key = SimpleScannable()\\n image_key.setCurrentPosition(0.0)\\n image_key.setInputNames([\\\"image_key\\\"])\\n image_key.setName(\\\"image_key\\\")\\n image_key.configure()\\n\\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\\n tomography_translation, tomography_optimizer, image_key, index)\\n\\n# return tomoScanDevice\\n #generate list of positions\\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\\n theta_points = []\\n theta_points.append(start)\\n previousPoint = start\\n for i in range(numberSteps):\\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\\n theta_points.append(nextPoint)\\n previousPoint = nextPoint\\n \\n #generateScanPoints\\n optimizeBeamNo = 0\\n optimizeBeamYes = 1\\n shutterOpen = 1\\n shutterClosed = 0\\n shutterNoChange = 2\\n scan_points = []\\n theta_pos = theta_points[0]\\n index = 0\\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \\n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1\\n \\n for i in range(imagesPerFlat): \\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n for frm in range(nframes): \\n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index)) #first\\n index = index + 1 \\n imageSinceDark = 1\\n imageSinceFlat = 1\\n optimizeBeam = 0\\n for i in range(numberSteps):\\n theta_pos = theta_points[i + 1]\\n for frm in range(nframes):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index))#main image\\n index = index + 1 \\n \\n imageSinceFlat = imageSinceFlat + 1\\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\\n index = index + 1 \\n imageSinceFlat = 0\\n \\n imageSinceDark = imageSinceDark + 1\\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\\n index = index + 1 \\n imageSinceDark = 0\\n\\n optimizeBeam = optimizeBeam + 1\\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\\n index = index + 1 \\n optimizeBeam = 0\\n \\n #add dark and flat only if not done in last steps\\n if imageSinceFlat != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n if imageSinceDark != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1 \\n# scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\\n# imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\\n# if pattern == 'default' or pattern == 'DFPFD':\\n# i = 0\\n# for pt1 in scan_points1:\\n# pt = scan_points[i]\\n# if pt1 != pt:\\n# print \\\"Mismatch - please tell Kaz about your scan and its arguments!\\\"\\n# print \\\"i = \\\", i\\n# print \\\"pt = \\\", pt\\n# print \\\"pt1 = \\\", pt1\\n# i += 1\\n #return None\\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\\\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \\n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\\n #scan_args.append(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(\\\"RotationCoord_as_list\\\", [tomoRotationAxis])\\n meta_add(\\\"approxCOR\\\", tomoRotationAxis)\\n for scannable in additionalScannables:\\n scan_args.append(scannable)\\n for scannable in tomo_additional_scannables:\\n scan_args.append(scannable)\\n ''' setting the description provided as the title'''\\n if not description == None: \\n setTitle(description)\\n else :\\n setTitle(\\\"undefined\\\")\\n \\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n if not dataFormat == \\\"NexusDataWriter\\\":\\n handle_messages.simpleLog(\\\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\\\")\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", \\\"NexusDataWriter\\\")\\n scanObject = createConcurrentScan(scan_args)\\n if addNXEntry:\\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\\n scanObject.runScan()\\n if autoAnalyse:\\n lsdp=jns.lastScanDataPoint()\\n OSCommandRunner.runNoWait([\\\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\\\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\\n return scanObject;\\n except InterruptedException:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"User interrupted the scan\\\", exceptionType, exception, traceback, False)\\n raise InterruptedException(\\\"User interrupted the scan\\\")\\n except:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"Error during tomography scan\\\", exceptionType, exception, traceback, False)\\n raise Exception(\\\"Error during tomography scan\\\", exception)\\n finally:\\n handle_messages.simpleLog(\\\"Data Format reset to the original setting: \\\" + dataFormat)\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", dataFormat)\",\n \"def test_attctl(self):\\n # boundary to cross\\n boundary_x = 5\\n boundary_y = 5\\n boundary_z = -5\\n\\n # delay starting the mission\\n self.wait_for_topics(30)\\n\\n rospy.loginfo(\\\"seting mission mode\\\")\\n self.set_mode(\\\"OFFBOARD\\\", 5)\\n rospy.loginfo(\\\"arming\\\")\\n self.set_arm(True, 5)\\n\\n rospy.loginfo(\\\"run mission\\\")\\n rospy.loginfo(\\\"attempting to cross boundary | x: {0}, y: {1}, z: {2}\\\".\\n format(boundary_x, boundary_y, boundary_z))\\n # does it cross expected boundaries in 'timeout' seconds?\\n timeout = 12 # (int) seconds\\n loop_freq = 10 # Hz\\n rate = rospy.Rate(loop_freq)\\n crossed = False\\n for i in xrange(timeout * loop_freq):\\n if (self.local_position.pose.position.x > boundary_x and\\n self.local_position.pose.position.z > boundary_y and\\n self.local_position.pose.position.y < boundary_z):\\n rospy.loginfo(\\\"boundary crossed | seconds: {0} of {1}\\\".format(\\n i / loop_freq, timeout))\\n crossed = True\\n break\\n\\n rate.sleep()\\n\\n self.assertTrue(crossed, (\\n \\\"took too long to cross boundaries | current position x: {0}, y: {1}, z: {2} | timeout(seconds): {3}\\\".\\n format(self.local_position.pose.position.x,\\n self.local_position.pose.position.y,\\n self.local_position.pose.position.z, timeout)))\\n\\n rospy.loginfo(\\\"disarming\\\")\\n self.set_arm(False, 5)\",\n \"def __init__(self, rexarm, planner, kinect):\\n self.rexarm = rexarm\\n self.tp = planner\\n self.kinect = kinect\\n self.status_message = \\\"State: Idle\\\"\\n self.current_state = \\\"idle\\\"\\n self.next_state = \\\"idle\\\"\\n self.waypoints = [\\n [0.0, 0.0, 0.0, 0.0],\\n [np.pi * 0.1, 0.0, np.pi / 2, 0.0],\\n [np.pi * 0.25, np.pi / 2, -np.pi / 2, np.pi / 2],\\n [np.pi * 0.4, np.pi / 2, -np.pi / 2, 0.0],\\n [np.pi * 0.55, 0, 0, 0],\\n [np.pi * 0.7, 0.0, np.pi / 2, 0.0],\\n [np.pi * 0.85, np.pi / 2, -np.pi / 2, np.pi / 2],\\n [np.pi, np.pi / 2, -np.pi / 2, 0.0],\\n [0.0, np.pi / 2, np.pi / 2, 0.0],\\n [np.pi / 2, -np.pi / 2, np.pi / 2, 0.0]]\\n self.learned_joints = []\",\n \"def Initialize():\\n # --- Set four-character run id, comment lines, user's name.\\n top.pline2 = \\\"Example 3D beam in a FODO lattice\\\"\\n top.pline1 = \\\"S-G cigar beam. 64x64x256\\\"\\n top.runmaker = \\\"David P. Grote\\\"\\n\\n # --- Invoke setup routine - it is needed to created a cgm file for plots\\n setup()\\n\\n # --- Create the beam species\\n beam = Species(type=Potassium,charge_state=+1,name=\\\"Beam species\\\")\\n\\n # --- Set input parameters describing the beam, 72 to 17.\\n beam.b0 = 15.358933450767e-3\\n beam.a0 = 8.6379155933081e-3\\n beam.x0 = 3.*mm\\n beam.emit = 51.700897052724e-6\\n beam.ap0 = 0.e0\\n beam.bp0 = 0.e0\\n beam.ibeam = 2.e-03\\n beam.vbeam = 0.e0\\n beam.ekin = 80.e3\\n beam.aion = beam.type.A\\n beam.zion = beam.charge_state\\n top.lrelativ = false\\n top.derivqty()\\n beam.vthz = .5e0*beam.vbeam*beam.emit/sqrt(beam.a0*beam.b0) # Vthz ~ Vthperp\\n\\n # +++ Set up arrays describing lattice.\\n # --- Set temp variables.\\n hlp = 36.0e-2 # half lattice period length\\n piperad = 3.445e-2 # pipe radius\\n quadlen = 11.e-2 # quadrupole length\\n gaplen = 4.*cm\\n rodlen = quadlen + gaplen\\n dbdx = .949/quadlen\\n\\n # --- Set general lattice variables.\\n top.tunelen = 2.e0*hlp\\n env.zl = -hlp*2\\n env.zu = -env.zl\\n env.dzenv = top.tunelen/100.e0\\n\\n # --- Set up quadrupoles\\n addnewquad(zs= -quadlen/2.,\\n ze= +quadlen/2.,\\n db=-dbdx,ap=piperad)\\n addnewquad(zs=hlp - quadlen/2.,\\n ze=hlp + quadlen/2.,\\n db=+dbdx,ap=piperad)\\n addnewquad(zs=2.*hlp - quadlen/2.,\\n ze=2.*hlp + quadlen/2.,\\n db=-dbdx,ap=piperad)\\n top.zlatstrt = 0.\\n top.zlatperi = 2.e0*hlp\\n\\n # +++ Set input parameters describing the 3d simulation.\\n w3d.nx = 64/2\\n w3d.ny = 64/2\\n w3d.nz = 256/2\\n steps_p_perd = 50\\n top.dt = (top.tunelen/steps_p_perd)/beam.vbeam\\n\\n # --- Set to finite beam.\\n top.pbound0 = top.pboundnz = periodic\\n top.pboundxy = absorb\\n w3d.xmmin = -piperad\\n w3d.xmmax = piperad\\n w3d.ymmin = -piperad\\n w3d.ymmax = piperad\\n w3d.zmmin = -hlp*2\\n w3d.zmmax = +hlp*2\\n top.prwall = piperad\\n\\n # --- Set pulse length.\\n beam.zimin = w3d.zmmin*.95/2.\\n beam.zimax = w3d.zmmax*.95/2.\\n\\n # --- Load Semi-Gaussian cigar beam.\\n top.npmax = 20000\\n w3d.distrbtn = \\\"semigaus\\\"\\n w3d.cigarld = true\\n w3d.xrandom = \\\"digitrev\\\"\\n w3d.vtrandom = \\\"digitrev\\\"\\n w3d.vzrandom = \\\"digitrev\\\"\\n w3d.ldprfile = \\\"polar\\\"\\n w3d.cylinder = false\\n top.straight = .8\\n\\n # --- set up field solver\\n w3d.l4symtry = true\\n w3d.bound0 = periodic\\n w3d.boundnz = periodic\\n w3d.boundxy = dirichlet\\n\\n solver = MultiGrid3D()\\n registersolver(solver)\\n\\n pipe = ZCylinderOut(piperad,4.,voltage=0.)\\n installconductors(pipe,dfill=largepos)\\n\\n # --- Run the envelope solver to provide data used to initialize particles.\\n package(\\\"env\\\")\\n generate()\\n step()\\n\\n # --- Generate the PIC code (allocate storage, load ptcls, t=0 plots, etc.).\\n package(\\\"w3d\\\")\\n generate()\\n return\",\n \"def interactions(self) -> Sequence[Interaction[_C_out, _A_out]]:\\n ...\",\n \"def forward_att(self, eouts, elens, ys, trigger_points=None):\\n losses_auxiliary = {}\\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, self.device, self.bwd)\\n if not self.training:\\n self.data_dict['elens'] = tensor2np(elens)\\n self.data_dict['ylens'] = tensor2np(ylens)\\n self.data_dict['ys'] = tensor2np(ys_out)\\n bs, ymax = ys_in.size()[:2]\\n tgt_mask = (ys_out != self.pad).unsqueeze(1).repeat([1, ymax, 1])\\n causal_mask = tgt_mask.new_ones(ymax, ymax, dtype=tgt_mask.dtype)\\n causal_mask = torch.tril(causal_mask).unsqueeze(0)\\n tgt_mask = tgt_mask & causal_mask\\n src_mask = make_pad_mask(elens).unsqueeze(1).repeat([1, ymax, 1])\\n if self.attn_type == 'mocha':\\n attn_mask = (ys_out != self.pad).unsqueeze(1).unsqueeze(3)\\n else:\\n attn_mask = None\\n lmout = None\\n if self.lm is not None:\\n self.lm.eval()\\n with torch.no_grad():\\n lmout, lmstate, _ = self.lm.predict(ys_in, None)\\n lmout = self.lm_output_proj(lmout)\\n out = self.pos_enc(self.embed_token_id(ys_in), scale=True)\\n xy_aws_layers = []\\n xy_aws = None\\n for lth, layer in enumerate(self.layers):\\n out = layer(out, tgt_mask, eouts, src_mask, mode='parallel', lmout=lmout)\\n xy_aws = layer.xy_aws\\n if xy_aws is not None and self.attn_type == 'mocha':\\n xy_aws_masked = xy_aws.masked_fill_(attn_mask.expand_as(xy_aws) == 0, 0)\\n xy_aws_layers.append(xy_aws_masked.clone())\\n if not self.training:\\n self.aws_dict['yy_aws_layer%d' % lth] = tensor2np(layer.yy_aws)\\n self.aws_dict['xy_aws_layer%d' % lth] = tensor2np(layer.xy_aws)\\n self.aws_dict['xy_aws_beta_layer%d' % lth] = tensor2np(layer.xy_aws_beta)\\n self.aws_dict['xy_aws_p_choose%d' % lth] = tensor2np(layer.xy_aws_p_choose)\\n self.aws_dict['yy_aws_lm_layer%d' % lth] = tensor2np(layer.yy_aws_lm)\\n logits = self.output(self.norm_out(out))\\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\\n losses_auxiliary['loss_quantity'] = 0.0\\n if self.attn_type == 'mocha':\\n n_tokens_ref = tgt_mask[:, -1, :].sum(1).float()\\n n_tokens_pred = sum([torch.abs(aws.sum(3).sum(2).sum(1) / aws.size(1)) for aws in xy_aws_layers])\\n n_tokens_pred /= len(xy_aws_layers)\\n losses_auxiliary['loss_quantity'] = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\\n acc = compute_accuracy(logits, ys_out, self.pad)\\n return loss, acc, ppl, losses_auxiliary\",\n \"def run1():\\n #Reseting motors\\n ResetRobot.reset_wheel_motors()\\n ResetRobot.reset_attachment_motors()\\n CalibrateRobot.calibrate_gyro()\\n\\n #mission M01 and M02 - space travel and solar panel\\n M01_M02()\\n \\n #Mission M05- Extraction \\n M05_M14()\\n\\n #Back to base before Gerhard (Remove comment if necessary)\\n return_to_base1()\\n\\n # Must delete for competition.. This is to set up forklift to repeat run.\\n Robot.attachment_left.on_for_rotations(-100, 8) #Raises Forklift \",\n \"def main():\\n\\n #robot = S2Serial( \\\"/dev/ttyUSB0\\\" )\\n robot = S2Fluke2( \\\"/dev/rfcomm2\\\" )\\n\\n for i in range( 30 ):\\n print( \\\"getIRLeft : \\\", robot.getIRLeft() )\\n print( \\\"getIRRight: \\\", robot.getIRRight() )\\n print( \\\"getAllIR : \\\", robot.getAllIR() )\\n print( \\\"getIrEx(0): \\\", robot.getIrEx( 0, 128 ) )\\n print( \\\"getIrEx(1): \\\", robot.getIrEx( 1, 128 ) )\\n\\n robot.close()\",\n \"def eye_calc(self):\\n # define the eye time vector scaled to UI, dimensionless\\n tnum = (1+int(round((self.eye_time_high-self.eye_time_low) / self.eye_time_step)))\\n self.time = np.linspace(self.eye_time_low, # column Z\\n self.eye_time_high,\\n tnum)\\n # convenience vector\\n t_1 = np.ones(tnum)\\n # column AA\\n self.time_eff = 0.5*t_1 + self.speedup*(self.time - 0.5*t_1)\\n arg1 = 2.0*erfinv(0.8)\\n arg2 = arg1/(1.0E-6*self.speedup*self.br_nominal)\\n \\n # calculate eye profiles for link at target reach\\n T_c_link = self.tc[self.lnum//2]\\n arg3 = arg2*self.time_eff/T_c_link # column AP\\n arg4 = arg2*(1.0 - self.time_eff)/T_c_link # column AQ\\n self.link_011 = 0.5*t_1 + 0.5*erf(arg3) # column AR\\n self.link_110 = 0.5*t_1 + 0.5*erf(arg4) # column AS\\n self.link_010 = self.link_011 + self.link_110 - t_1 # column AT\\n self.link_101 = t_1 - self.link_010 # column AW\\n self.link_001 = t_1 - self.link_110 # column AU\\n self.link_100 = t_1 - self.link_011 # column AV\\n\\n # calculate eye profiles for transmitter eye test configuration\\n T_c_test = math.sqrt(self.tx_1090_rise**2\\n + (0.329E6/self.txeye_rx_bw)**2)\\n arg5 = arg2*self.time_eff/T_c_test\\n arg6 = arg2*(t_1 - self.time_eff)/T_c_test\\n self.test_011 = 0.5*t_1 + 0.5*erf(arg5) # column AR\\n self.test_110 = 0.5*t_1 + 0.5*erf(arg6) # column AS\\n self.test_010 = self.test_011 + self.test_110 - t_1 # column AT\\n self.test_101 = t_1 - self.test_010\\n self.test_001 = t_1 - self.test_110\\n self.test_100 = t_1 - self.test_011\\n\\n print(\\\"Margin : \\\", self.margin)\",\n \"def __init__(self, roi_warped_points):\\n\\n # was the line detected in the last iteration?\\n self.detected = False\\n # x values of the last n fits of the line\\n self.recent_xfitted = []\\n #average x values of the fitted line over the last n iterations\\n self.bestx = None\\n #polynomial coefficients averaged over the last n iterations\\n self.best_fit = [np.array([False])]\\n #polinomial coefficients for the last n fits of the lane\\n self.recent_fit = []\\n #polynomial coefficients for the most recent fit\\n self.current_fit = [np.array([False])]\\n #radius of curvature of the line in some units\\n self.radius_of_curvature = 0\\n #distance in meters of vehicle center from the line\\n self.line_base_pos = 0\\n #difference in fit coefficients between last and new fits\\n self.diffs = np.array([0,0,0], dtype='float')\\n #x values for detected line pixels\\n self.allx = None\\n #maximum number of iterations to average\\n self.max_n = 10 #25\\n\\n # roi image points in bird's view space\\n self.roi_warped_points = roi_warped_points\\n\\n #y values for detected line pixels\\n self.ally = np.linspace(0, self.roi_warped_points[2][1] - 1, self.roi_warped_points[2][1])\\n\\n # line base pos is calculated through the roi information\\n # the used four point ROI has two points at the bottom that are straight\\n # with respect to the bottom - as this points are right next to the lines,\\n # they can be translated from pixels into meters with the knowledge of\\n # a U.S. highway standard lane - this is an apprximation, but should be\\n # good enough for this project\\n # U.S. regulations minimum lane width: 3.7m\\n self.xm_per_pix = 3.7 / (self.roi_warped_points[1][0] - self.roi_warped_points[0][0])\\n\\n # each dashed line is 3m long --> about 33m for warped image\\n self.ym_per_pix = 33 / (self.roi_warped_points[2][1] - self.roi_warped_points[0][1])\",\n \"def align(): # open EH and fast shutter\\n\\t#marAuxiliary.closeMarShield()\\n\\td2in()\\n\\td3in()\\n\\tsh('o')\",\n \"def gonio_axis_align():\\n \\n # Invert camera image, so dark pin on light image becomes a peak\\n cam_7.proc1.scale.put(-1)\\n cam_8.proc1.scale.put(-1)\\n \\n # High threshold, so AD centroid doesn't interpret background\\n cam_8ThresholdOld = cam_8.stats4.centroid_threshold.get()\\n cam_8.stats4.centroid_threshold.put(150)\\n cam_7ThresholdOld = cam_7.stats4.centroid_threshold.get()\\n cam_7.stats4.centroid_threshold.put(150)\\n \\n # HiMag\\n # Copy ROI2 geometry (HiMag Mag3) to ROI4 and use ROI4 centroid plugin\\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get())\\n cam_8.roi4.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get())\\n cam_8.roi4.size.x.put(cam_8.roi2.size.x.get() * 0.20)\\n cam_8.roi4.size.y.put(cam_8.roi2.size.y.get())\\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get() + cam_8.roi2.size.x.get()/2 - cam_8.roi4.size.x.get()/2)\\n \\n # LoMag\\n # Copy ROI2 geometry (LoMag Mag1) to ROI4 and use ROI4 centroid plugin\\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get())\\n cam_7.roi4.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get())\\n cam_7.roi4.size.x.put(cam_7.roi2.size.x.get() * 0.05)\\n cam_7.roi4.size.y.put(cam_7.roi2.size.y.get())\\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get() + cam_7.roi2.size.x.get()/2 - cam_7.roi4.size.x.get()/2)\\n \\n centerPinYHiMag0 = centroid_avg(cam_8.stats4)[1]\\n centerPinYLoMag0 = centroid_avg(cam_7.stats4)[1]\\n yield from bps.mvr(gonio.o,180)\\n time.sleep(2)\\n centerPinYHiMag180 = centroid_avg(cam_8.stats4)[1]\\n centerPinYLoMag180 = centroid_avg(cam_7.stats4)[1]\\n centerPinYHiMag = (centerPinYHiMag0 + centerPinYHiMag180)/2\\n centerPinYLoMag = (centerPinYLoMag0 + centerPinYLoMag180)/2\\n\\n centerPinOffsYHiMag = centerPinYHiMag - cam_8.roi4.size.y.get() / 2\\n centerPinOffsYLoMag = centerPinYLoMag - cam_7.roi4.size.y.get() / 2\\n \\n # Correct Mag 3 (cam_8 ROI2)\\n cam_8.roi2.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + centerPinOffsYHiMag)\\n # Correct Mag 4 (cam_8 ROI1)\\n cam_8.roi1.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + (cam_8.roi2.size.y.get()-cam_8.roi1.size.y.get())/2)\\n \\n # Correct Mag 1 (cam_7 ROI2)\\n cam_7.roi2.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + centerPinOffsYLoMag)\\n # Correct Mag 2 (cam_7 ROI3)\\n cam_7.roi3.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + (cam_7.roi2.size.y.get()-cam_7.roi3.size.y.get())/2)\\n\\n # De-invert image\\n cam_7.proc1.scale.put(-1)\\n cam_8.proc1.scale.put(-1)\\n \\n # Set thresold to previous value\\n cam_8.stats4.centroid_threshold.put(cam_8ThresholdOld)\\n cam_7.stats4.centroid_threshold.put(cam_7ThresholdOld)\\n \\n return\",\n \"def apply_beam_settings(self):\\n raise NotImplementedError\",\n \"def toggleAnnotation(self,i=0,onoff=None):\\n active = self.annotations[i][1]\\n #print \\\"WAS\\\"\\n #print self.annotations\\n if onoff is None:\\n active = not active\\n elif onoff:\\n active = True\\n else:\\n active = False\\n self.annotations[i][1] = active\\n #print \\\"BECOMES\\\"\\n #print self.annotations\\n if active:\\n self.drawAnnotation(i)\\n else:\\n self.removeAnnotation(i)\\n #print self._annotations\",\n \"def Advance():\\n warp.step()\",\n \"def main(args):\\n try:\\n robot = Robot()\\n if args.giros != -1:\\n robot.useGyro=True\\n t = TrayectoriaGiros(args.giros, args.rad*math.pi)\\n #t = TrayectoriaGirosAbs(args.giros, args.rad*math.pi)\\n robot.startOdometry()\\n robot.setTrajectory(t)\\n robot.executeTrajectory()\\n robot.stopOdometry()\\n exit(0)\\n\\n if args.test_r2d2:\\n while True:\\n if robot.detectR2D2(verbose=True, DEBUG=1):\\n print(\\\"veo a r2d2\\\")\\n else:\\n print(\\\"No veo a r2d2\\\")\\n time.sleep(3)\\n\\n if args.test_suelo:\\n while True:\\n print(robot.colorSensorValue())\\n if robot.colorSensorBlack():\\n print(\\\"Es negro\\\")\\n elif robot.colorSensorWhite():\\n print(\\\"Es blanco\\\")\\n else:\\n print(\\\"Ni negro ni blanco\\\")\\n time.sleep(1)\\n\\n elif args.trabajo or args.test_map or args.pelota or args.test_r2_section:\\n # Choose map (A or B) based on color sensor:\\n if not robot.colorSensorBlack():\\n print(\\\"Estoy en el mapa A\\\")\\n t, mapa = mapA(robot)\\n celdaIni = [1,2,-math.pi/2]\\n #fin = [3,3]\\n fin = [4,6]\\n ini=[1,6, -math.pi/2]\\n executingMapA = True\\n else:\\n print(\\\"Estoy en el mapa B\\\")\\n t, mapa = mapB(robot)\\n celdaIni = [5,2,-math.pi/2]\\n fin = [2,6]\\n ini=[5,6, -math.pi/2]\\n executingMapA = False\\n\\n if args.trabajo: # full demo\\n robot.setMapNoPath(mapa) # set map\\n x, y = robot.posFromCell(ini[0], ini[1])\\n robot.setOdometry([x, y, ini[2]]) # set initial pos\\n robot.startOdometry()\\n robot.setTrajectory(t) # set S trajectory\\n robot.executeTrajectory() # execute it\\n robot.useGyro = False # stop using Gyro (has more error than odo later)\\n robot.setPath(celdaIni, fin) # calculate the path in the map\\n nBaldosas = 2.9 if executingMapA else 3.9 # x value of vertical line\\n # wait for the two lines and update odo accordingly:\\n robot.waitForWhite([0,1], [nBaldosas * baldosa, 3 * baldosa])\\n # execute the planned path:\\n robot.executePath()\\n ### debugging options ###\\n elif args.test_map or args.test_r2_section: # only map\\n if args.test_r2_section:\\n celdaIni = [3, 2, math.pi/2]\\n nBaldosas = 3 if executingMapA else 4\\n robot.waitForWhite([0,1], [nBaldosas * baldosa, 3 * baldosa])\\n robot.setMap(mapa,celdaIni, fin)\\n #robot.mapa.drawMap(saveSnapshot=False)\\n robot.startOdometry()\\n robot.executePath()\\n if args.pelota or args.sonar:\\n robot.startOdometry()\\n ### end debugging options ###\\n if args.trabajo or args.test_r2_section or args.test_map:\\n # relocate a bit further down to see the homography better:\\n x,y = robot.posFromCell(fin[0], fin[1]-0.5)\\n robot.go(x,y)\\n # orient GUILLE to our side 45 degrees (map A:left, B:right)\\n dX, dY = -0.05, 0.05\\n wHomography = -0.1 # rotate right if a, left if b\\n if not executingMapA: # map b to the other side\\n dX = -dX\\n wHomography = -wHomography\\n odo = robot.readOdometry()\\n robot.go(odo[0]+dX, odo[1]+dY) # face the pictures\\n res = 0 # result of homography\\n while res == 0:\\n res = robot.detectHomography() # take picture, compare, etc.\\n robot.setSpeed(0, wHomography) # turn\\n time.sleep(0.01) # wait a bit\\n # res = 0 -> no detection\\n # res = 1 -> r2\\n # res = 2 -> the other one\\n if res == 1: # r2\\n print(\\\"detected r2\\\")\\n # choose the exit path according to the map being executed:\\n if executingMapA:\\n fin[0] -= 1\\n else:\\n fin[0]-=2\\n else: # el otro\\n print(\\\"detected el otro\\\")\\n # choose the exit path according to the map being executed:\\n if executingMapA:\\n fin[0] += 2\\n else:\\n fin[0]+=1\\n print(\\\"going to\\\", fin)\\n #track and catch ball:\\n if not args.sonar:\\n robot.trackBall()\\n\\n #sonar, update th and y odometry values based on the wall:\\n relocateRobot(robot, fin, executingMapA, baldosa)\\n\\n # go to last cell:\\n robot.setPathFromCurrentPosition(fin)\\n robot.executePath(checkObstacles=False)\\n # exit the map:\\n x,y = robot.posFromCell(fin[0], fin[1]+1)\\n robot.go(x,y, checkObstacles=False)\\n\\n robot.stopOdometry()\\n\\n except KeyboardInterrupt:\\n robot.stopOdometry()\\n except BaseException as e:\\n print(e)\\n traceback.print_exc()\\n robot.stopOdometry()\",\n \"def qa_tractography(stream_path,qa_out_path,brain_path):\\n \\n #Use window to visualize the streamlines\\n r = window.renderer()\\n \\n #Load the streamline.trk file\\n streamlines_mni_load = nib.streamlines.load(stream_path).streamlines\\n streamlines_mni_in = Streamlines(streamlines_mni_load)\\n streamlines_actor = actor.line(\\n streamlines_mni_in,\\n colormap.line_colors(streamlines_mni_in),\\n lod_points=10000,\\n depth_cue=True,\\n linewidth=0.2,\\n fake_tube=True,\\n opacity=0.3,\\n )\\n \\n r.add(streamlines_actor)\\n #window.show(r)\\n \\n showmng = window.ShowManager(r)\\n #window.record function can rotate the 3D-image, then get the snapshot of the specific angle. \\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,1,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Z_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (1,0,0),path_numbering= True,out_path = qa_out_path + '/Rotate_Y_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n window.record(r,cam_pos = (70.03, 64.97, 269.80), cam_view = (0,0,1),path_numbering= True,out_path = qa_out_path + '/Rotate_X_axis_', az_ang=120,n_frames=3,reset_camera=True,size=(600,600))\\n showmng.exit()\\n combine_plot(qa_out_path,brain_path)\",\n \"def p2p_drive_routine(self, goal_pos):\\n\\t\\t(position, rotation) = self.get_odom() # get starting position values\\n\\n\\t\\tcurr_pose = self.call_jackal_pos_service(0) # don't drive, just get current lat/lon\\n\\n\\t\\tprint(\\\"Current position from pose server: {}\\\".format(curr_pose))\\n\\t\\tprint(\\\"Positions attributes: {}\\\".format(dir(curr_pose)))\\n\\n\\t\\t_lat = curr_pose.jackal_fix.latitude\\n\\t\\t_lon = curr_pose.jackal_fix.longitude\\n\\n\\t\\tprint(\\\"Jackal's current lat, lon: {}, {}\\\".format(_lat, _lon))\\n\\n\\t\\tcurr_pose_utm = utm.from_latlon(curr_pose.jackal_fix.latitude, curr_pose.jackal_fix.longitude)\\n\\n\\t\\tprint(\\\"Jackal's position in UTM: {}\\\".format(curr_pose_utm))\\n\\n\\t\\tA = (curr_pose_utm[0], curr_pose_utm[1], rotation)\\n\\t\\tB = (goal_pos[0], goal_pos[1], rotation)\\n\\n\\t\\tx_diff = B[0] - A[0]\\n\\t\\ty_diff = B[1] - A[1]\\n\\n\\t\\t_trans_angle = self.transform_imu_frame(degrees(A[2]))\\n\\t\\tAB_theta0 = math.atan2(abs(y_diff), abs(x_diff)) # get intitial angle, pre transform\\n\\t\\tAB_angle = self.transform_angle_by_quadrant(AB_theta0, x_diff, y_diff) # determine angle between vector A and B\\n\\t\\tturn_angle = AB_angle - _trans_angle # angle to turn (signage should denote direction to turn)\\n\\n\\t\\tprint(\\\"Initial position and orientation: {}\\\".format(A))\\n\\t\\tprint(\\\"Current angle in degrees: {}\\\".format(degrees(A[2])))\\n\\t\\tprint(\\\"Transformed angle: {}\\\".format(_trans_angle))\\n\\t\\tprint(\\\"AB initial angle: {}\\\".format(degrees(AB_theta0)))\\n\\t\\tprint(\\\"AB angle after transform: {}\\\".format(AB_angle))\\n\\t\\tprint(\\\"Calculated turning angle: {}\\\".format(turn_angle))\\n\\n\\t\\t# Determine angle to turn based on IMU..\\n\\t\\tprint(\\\"Telling Jackal to turn {} degreess..\\\".format(turn_angle))\\n\\t\\tself.call_jackal_rot_service(turn_angle)\\n\\t\\tprint(\\\"Finished turning..\\\")\\n\\n\\t\\tdrive_distance = self.determine_drive_distance(A, B)\\n\\t\\tprint(\\\"Driving Jackal {} meters..\\\".format(drive_distance))\\n\\t\\tself.call_jackal_pos_service(drive_distance)\\n\\t\\tprint(\\\"Finished driving..\\\")\",\n \"def route_input_B(self):\\n xoffset = self.pmos.poly_positions[0].x \\\\\\n + self.pmos_position2.x\\n yoffset = self.A_position.y \\\\\\n + max(drc[\\\"minwidth_metal2\\\"], self.poly_contact.second_layer_width) + drc[\\\"metal2_to_metal2\\\"]\\n self.B_position = vector(xoffset, yoffset)\\n offset = self.B_position - vector(0, 0.5 * self.poly_contact.width) \\n self.add_contact(layers=(\\\"poly\\\", \\\"contact\\\", \\\"metal1\\\"),\\n offset=offset,\\n rotate=90)\\n\\n self.add_rect(layer=\\\"poly\\\",\\n offset=offset,\\n width=-(self.poly_contact.first_layer_position.y + drc[\\\"minwidth_poly\\\"]),\\n height=self.poly_contact.first_layer_width)\\n self.add_layout_pin(text=\\\"B\\\",\\n layer=\\\"metal1\\\",\\n offset=[0,\\n self.B_position.y - 0.5 * drc[\\\"minwidth_metal1\\\"]],\\n width=self.B_position.x,\\n height=drc[\\\"minwidth_metal1\\\"])\",\n \"def iter_fun(self):\\n\\n run_id = self._run_id\\n etopo_dir = driver_home\\n topodir = driver_home\\n\\n # load input info\\n if self._input_info == None:\\n scn_fname = os.path.join(self._run_home,'scenario_pts.txt') \\n scn = np.loadtxt(scn_fname)\\n scn_list = scn.tolist()\\n else:\\n scn_list = self._input_info\\n \\n # total number of runs\\n M = len(scn_list)\\n N = 8*M + 2 # 8*M runs plus two empty bathymetry runs\\n\\n if run_id == N:\\n raise StopIteration()\\n\\n else:\\n \\n #=========================\\n # set coarse and fine grids\\n #\\n t_shelf = 0. # time approaching continental slope\\n t_harbor = 0. # time approaching harbor\\n\\n if ((run_id >= 0) and (run_id < 4*M)) or (run_id == 8*M):\\n #------------------\\n # setrun for coarse\\n #\\n grid = 'coarse'\\n \\n self._rundata.amrdata.amr_levels_max = 4\\n # coarse grid run = 10\\\"\\n # dx = 30', 5', 1', 10\\\"\\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6]\\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6]\\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6]\\n\\n\\n # add topography (coarse)\\n topofiles = self._rundata.topo_data.topofiles\\n # for topography, append lines of the form\\n # [topotype, minlevel, maxlevel, t1, t2, fname]\\n topofiles = []\\n\\n topofiles.append([3, 1, 4, 0., 1.e10, \\\\\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\\n topofiles.append([-3, 3, 4, 0., 1.e10, \\\\\\n os.path.join(topodir, 'cc-1sec.asc')])\\n\\n # add regions\\n regions = self._rundata.regiondata.regions \\n # between shelf and CC \\n regions = []\\n regions.append(\\\\\\n [2, 3, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \\n regions.append(\\\\\\n [3, 4, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\\n regions.append(\\\\\\n [4, 4, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\\n\\n # == fgmax.data values ==\\n fgmax_files = self._rundata.fgmax_data.fgmax_files\\n fgmax_files = []\\n \\n # for fixed grids append to this list names of any fgmax input files\\n fgmax1_fname = os.path.join(driver_home,'fgmax1_coarse.txt')\\n fgmax2_fname = os.path.join(driver_home,'fgmax2_coarse.txt')\\n fgmax3_fname = os.path.join(driver_home,'fgmax3_coarse.txt')\\n\\n fgmax_files.append(fgmax1_fname) \\n fgmax_files.append(fgmax2_fname) \\n fgmax_files.append(fgmax3_fname) \\n \\n self._rundata.fgmax_data.num_fgmax_val = 2\\n \\n \\n elif ((run_id >= 4*M) and (run_id < 8*M)) or (run_id == 8*M+1):\\n #----------------\\n # setrun for fine\\n #\\n grid = 'fine'\\n \\n self._rundata.amrdata.amr_levels_max = 6\\n\\n ## fine grid run = 2/3\\\"\\n ## dx = 30', 5', 1', 10\\\", 2\\\", 2/3\\\"\\n self._rundata.amrdata.refinement_ratios_x = [6, 5, 6, 5, 3]\\n self._rundata.amrdata.refinement_ratios_y = [6, 5, 6, 5, 3]\\n self._rundata.amrdata.refinement_ratios_t = [6, 5, 6, 5, 3]\\n\\n regions = self._rundata.regiondata.regions \\n regions = []\\n # between shelf and CC\\n regions.append(\\\\\\n [2, 4, t_shelf, 1e9, -125, -124.05, 40.5, 43]) \\n regions.append(\\\\\\n [4, 5, t_harbor, 1e9, -124.26, -124.14, 41.67, 41.79])\\n regions.append(\\\\\\n [6, 6, t_harbor, 1e9, -124.218,-124.17, 41.7345, 41.77])\\n\\n # add topography (fine)\\n topofiles = self._rundata.topo_data.topofiles\\n # for topography, append lines of the form\\n # [topotype, minlevel, maxlevel, t1, t2, fname]\\n topofiles = []\\n\\n topofiles.append([3, 1, 6, 0., 1.e10, \\\\\\n os.path.join(etopo_dir, 'etopo1_-130_-124_38_45_1min.asc')])\\n topofiles.append([-3, 4, 6, 0., 1.e10, \\\\\\n os.path.join(topodir, 'cc-1sec.asc')])\\n topofiles.append([3, 6, 6, 0., 1.e10, \\\\\\n os.path.join(topodir,'cc-1_3sec-c_pierless.asc')])\\n \\n # == fgmax.data values ==\\n fgmax_files = self._rundata.fgmax_data.fgmax_files\\n fgmax_files = []\\n \\n # for fixed grids append to this list names of any fgmax input files\\n fgmax1_fname = os.path.join(driver_home,'fgmax1_fine.txt')\\n fgmax2_fname = os.path.join(driver_home,'fgmax2_fine.txt')\\n fgmax3_fname = os.path.join(driver_home,'fgmax3_fine.txt')\\n\\n fgmax_files.append(fgmax1_fname) \\n fgmax_files.append(fgmax2_fname) \\n fgmax_files.append(fgmax3_fname) \\n \\n self._rundata.fgmax_data.num_fgmax_val = 2\\n \\n\\n\\n #\\n # set desired magnitude\\n #\\n if ((run_id >= 0) and (run_id < M)) \\\\\\n or ((run_id >= 4*M) and (run_id < 5*M)):\\n self.KL_Mw_desired = 8.6\\n elif ((run_id >= M) and (run_id < 2*M)) \\\\\\n or ((run_id >= 5*M) and (run_id < 6*M)):\\n self.KL_Mw_desired = 8.8\\n elif ((run_id >= 2*M) and (run_id < 3*M)) \\\\\\n or ((run_id >= 6*M) and (run_id < 7*M)):\\n self.KL_Mw_desired = 9.0\\n elif ((run_id >= 3*M) and (run_id < 4*M)) \\\\\\n or ((run_id >= 7*M) and (run_id < 8*M)):\\n self.KL_Mw_desired = 9.2\\n \\n #\\n # set slip distribution\\n #\\n run_id_mod = run_id - 100*(run_id/100)\\n m = scn_list[run_id_mod]\\n self.set_KL_slip(m)\\n \\n if run_id < 8*M:\\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_' + str(self.KL_Mw_desired)\\n self._rundir = os.path.join(dir_grid_Mw, 'run_' + str(run_id_mod))\\n else:\\n # empty runs to obtain bathymetry\\n \\n dir_grid_Mw = '../geoclaw_output/' + str(grid) + '_B0'\\n self._rundir = dir_grid_Mw\\n self.KL_Mw_desired = 0.0\\n self.set_KL_slip([0.]*len(m)) # set output\\n self._rundata.clawdata.output_times = [1.0, 3.0]\\n \\n self._run_id += 1\\n \\n return self\",\n \"def evaluateIFsetup(ants=0, timerange=3.4, deltat=0.1,\\n verbose=False, iniAtten=0) :\\n antlist = makeAntList(ants)\\n for a in antlist:\\n if a < 16:\\n print \\\"SZA antennas only can be used - exiting\\\"\\n return\\n tpMP = \\\"Ifmod.ifTotalPower\\\"\\n attenMP = \\\"Ifmod.totalAtten\\\"\\n currentPos = \\\"AMBIENT\\\"\\n print \\\"Inserting ambient load into beam\\\"\\n rtn = amb(ants)\\n pos1ants = rtn.ready\\n mplist = []\\n for a in pos1ants:\\n prefix = \\\"Sza%d.\\\" %(a-15)\\n mplist.append([prefix+tpMP, prefix+attenMP])\\n #print mplist\\n if len(rtn.notready) != 0:\\n print \\\"Ambient load not inserted for ants:\\\", rtn.notready\\n if iniAtten != None :\\n print \\\"Setting IF atten to %.1f\\\" %iniAtten\\n antennaIFatten(iniAtten, 1, invalidateTsys=False, \\n ants=antlist)\\n wait(tmo=2.0)\\n t0=time.time()\\n antennaIFpower(0.3, ants)\\n t1 = time.time()\\n cmdtime = t1-t0\\n results = []\\n times = []\\n nsamps = int(round(timerange/deltat))\\n for i in range(nsamps):\\n sleep(deltat)\\n dt=time.time()-t1\\n times.append(dt)\\n results.append(queryMpValues(mplist))\\n #print results\\n print \\\"Command time = %.3f seconds\\\" %cmdtime\\n for j in range(len(pos1ants)):\\n aname = \\\"Sza%d\\\" %(pos1ants[j]-15)\\n if verbose: print \\\"======== %s ========\\\" %aname\\n for i in range(nsamps):\\n r = results[i][j]\\n st = \\\"%s: %4.1f %6.3f %4.1f\\\" %(aname,times[i],r[0],r[1])\\n if verbose: print st\\n print \\\"\\\\n==== Inferred timing & final atten =====\\\"\\n for j in range(len(pos1ants)):\\n aname = \\\"Sza%d\\\" %(pos1ants[j]-15)\\n finalAtten = results[nsamps-1][j][1]\\n timing = times[nsamps-1]\\n for i in range(nsamps):\\n index = nsamps-1-i\\n atten = results[index][j][1]\\n if atten != finalAtten: break\\n timing = times[index]\\n st = \\\" %s: %4.1f %4.1f\\\" %(aname,timing, finalAtten)\\n print st\\n \\n #print \\\"Moving ambient load out of the beam\\\"\\n pos2ants = sky(ants).ready\\n # Wait for two seconds to get final psys reading\\n wait(tmo=2.0)\\n skyants = pos2ants\\n complete = []\\n for a in pos1ants :\\n if (pos2ants.count(a) > 0) : complete.append(a)\\n return complete\",\n \"def tomo_scan(*, start=0, stop=180, numProjPerSweep=1500, slewSpeed=5, accl=1, samInPos=0, samOutDist=-3, md=None):\\n _md = md or OrderedDict()\\n _md[\\\"project\\\"] = \\\"mona\\\"\\n _md[\\\"APS_storage_ring_current,mA\\\"] = aps_current.value\\n _md[\\\"datetime_plan_started\\\"] = str(datetime.now())\\n\\n pso = psofly\\n det = pg3_det\\n rotStage = tomo_stage.rotary\\n shutter = A_shutter\\n \\n acquire_time = 0.01\\n report_t0 = None\\n update_interval_s = 5.0 # during scans, print interim progress\\n update_poll_delay_s = 0.01\\n\\n stage_sigs = {}\\n stage_sigs[\\\"det.cam\\\"] = det.cam.stage_sigs\\n\\n staged_device_list = [det]\\n monitored_signals_list = [\\n det.image.array_counter,\\n rotStage.user_readback,\\n ]\\n\\n def cleanup():\\n for d in [det.image.array_counter, rotStage.user_readback]:\\n try:\\n yield from bps.unmonitor(d)\\n except IllegalMessageSequence:\\n pass\\n try:\\n yield from bps.abs_set(shutter, \\\"close\\\", group='shutter')\\n except RuntimeError as exc:\\n print(exc)\\n yield from bps.mv(rotStage.velocity, ROT_STAGE_FAST_SPEED)\\n yield from motor_set_modulo(rotStage, 360.0)\\n yield from bps.mv(\\n rotStage, 0.00,\\n #det.cam.trigger_mode, \\\"Internal\\\",\\n #det.cam.image_mode, \\\"Continuous\\\",\\n #det.hdf1.enable, \\\"Disable\\\",\\n #det.hdf1.capture, \\\"Done\\\",\\n )\\n yield from bps.wait(group='shutter')\\n\\n det.cam.stage_sigs[\\\"num_images\\\"] = numProjPerSweep\\n det.cam.stage_sigs[\\\"trigger_mode\\\"] = \\\"Overlapped\\\"\\n det.cam.stage_sigs[\\\"trigger_source\\\"] = \\\"GPIO_0\\\"\\n det.cam.stage_sigs[\\\"trigger_polarity\\\"] = \\\"Low\\\"\\n det.cam.stage_sigs[\\\"image_mode\\\"] = \\\"Multiple\\\"\\n det.hdf1.stage_sigs[\\\"num_capture\\\"] = numProjPerSweep # TODO: + NUM_DARK_FRAMES + NUM_FLAT_FRAMES,\\n\\n def _report_(t):\\n msg = \\\"{:.2f}s - flying \\\".format(t)\\n msg += \\\" x = {:.4f}\\\".format(tomo_stage.x.position)\\n msg += \\\" y = {:.4f}\\\".format(tomo_stage.y.position)\\n msg += \\\" r = {:.4f}\\\".format(tomo_stage.rotary.position)\\n msg += \\\" theta = {:.4f}\\\".format(tomo_stage.rotary.position % 360)\\n msg += \\\" image # {}\\\".format(det.cam.num_images_counter.value)\\n return msg\\n\\n @run_in_thread\\n def progress_reporting():\\n logger.debug(\\\"progress_reporting is starting\\\")\\n t = time.time()\\n startup = t + update_interval_s/2\\n while t < startup and pso.fly.value == 0: # wait for flyscan to start\\n time.sleep(update_poll_delay_s)\\n\\n logger.debug(\\\"progress_reporting: fly starts\\\")\\n update_time = t = report_t0 = time.time()\\n while pso.fly.value == 1:\\n if t >= update_time:\\n update_time = t + update_interval_s\\n msg = _report_(t - report_t0)\\n print(msg)\\n logger.debug(msg)\\n time.sleep(update_poll_delay_s)\\n t = time.time()\\n msg = _report_(time.time() - report_t0)\\n print(msg)\\n logger.debug(msg)\\n logger.debug(\\\"{}s - progress_reporting is done\\\".format(time.time()-report_t0))\\n\\n @bpp.stage_decorator(staged_device_list)\\n @bpp.run_decorator(md=_md)\\n @bpp.finalize_decorator(cleanup)\\n def _internal_tomo():\\n yield from bps.monitor(rotStage.user_readback, name=\\\"rotation_monitor\\\")\\n yield from bps.monitor(det.image.array_counter, name=\\\"array_counter_monitor\\\")\\n yield from bps.monitor(tomo_stage.x, name=\\\"tomo_stage_x_monitor\\\")\\n\\n # prepare the camera and the HDF5 plugin to write data\\n yield from bps.mv(\\n det.cam.nd_attributes_file, \\\"monaDetectorAttributes.xml\\\",\\n det.cam.acquire_time, acquire_time,\\n det.hdf1.enable, \\\"Enable\\\",\\n det.hdf1.auto_save, \\\"Yes\\\",\\n )\\n #yield from bps.abs_set(det.hdf1.capture, \\\"Capture\\\")\\n\\n # TODO: test before using!\\n # yield from measure_darks(det, shutter, NUM_DARK_FRAMES)\\n # yield from measure_flats(det, shutter, NUM_FLAT_FRAMES, samStage, samInPos + samOutDist)\\n\\n # do not touch shutter during development\\n yield from bps.abs_set(shutter, \\\"open\\\", group=\\\"shutter\\\")\\n yield from set_image_frame()\\n\\n yield from bps.stop(rotStage)\\n yield from motor_set_modulo(rotStage, 360.0)\\n \\n yield from bps.mv(\\n rotStage.velocity, ROT_STAGE_FAST_SPEED, \\n rotStage.acceleration, 3)\\n yield from bps.mv(\\n rotStage, 0, \\n samStage, samInPos)\\n\\n # back off to the taxi point (back-off distance before fly start)\\n logging.debug(\\\"before taxi\\\")\\n yield from bps.mv(\\n pso.start, start,\\n pso.end, stop,\\n pso.scan_control, \\\"Standard\\\",\\n pso.scan_delta, 1.0*(stop-start)/numProjPerSweep,\\n pso.slew_speed, slewSpeed,\\n rotStage.velocity, ROT_STAGE_FAST_SPEED,\\n rotStage.acceleration, slewSpeed/accl,\\n det.cam.array_counter, 0,\\n )\\n\\n progress_reporting()\\n yield from bps.mv(pso.taxi, \\\"Taxi\\\")\\n logging.debug(\\\"after taxi\\\")\\n\\n # run the fly scan\\n logging.debug(\\\"before fly\\\")\\n yield from bps.mv(rotStage.velocity, slewSpeed)\\n yield from bps.wait(group='shutter') # shutters are slooow, MUST be done now\\n yield from set_image_frame()\\n yield from bps.trigger(det, group='fly')\\n yield from bps.abs_set(pso.fly, \\\"Fly\\\", group='fly')\\n yield from bps.wait(group='fly')\\n #yield from bps.abs_set(det.cam.acquire, 0)\\n logging.debug(\\\"after fly\\\")\\n\\n # read the camera\\n yield from bps.create(name='primary')\\n yield from bps.read(det)\\n yield from bps.save()\\n det.cam.stage_sigs = stage_sigs[\\\"det.cam\\\"]\\n\\n return (yield from _internal_tomo())\",\n \"def M2_focus_plot(w0, lambda0, f, z0, M2=1):\\n # plot the beam from just before the waist to the lens\\n left = 1.1*z0\\n z = np.linspace(left, 0)\\n r = beam_radius(w0, lambda0, z, z0=z0, M2=M2)\\n plt.fill_between(z*1e3, -r*1e6, r*1e6, color='red', alpha=0.2)\\n\\n # find the gaussian beam parameters for the beam after the lens\\n w0_after = w0 * magnification(w0, lambda0, z0, f, M2=M2)\\n z0_after = image_distance(w0, lambda0, z0, f, M2=M2)\\n zR_after = z_rayleigh(w0_after, lambda0, M2)\\n\\n # plot the beam after the lens\\n right = max(2*f, z0_after+4*zR_after)\\n z_after = np.linspace(0, right)\\n r_after = beam_radius(w0_after, lambda0, z_after, z0=z0_after, M2=M2)\\n\\n # plt.axhline(w0_after*1.41e6)\\n plt.fill_between(z_after*1e3, -r_after*1e6, r_after*1e6, color='red', alpha=0.2)\\n\\n # locate the lens and the two beam waists\\n plt.axhline(0, color='black', lw=1)\\n plt.axvline(0, color='black')\\n plt.axvline(z0*1e3, color='black', linestyle=':')\\n plt.axvline(z0_after*1e3, color='black', linestyle=':')\\n\\n # finally, show the ±1 Rayleigh distance\\n zRmin = max(0, (z0_after-zR_after))*1e3\\n zRmax = (z0_after+zR_after)*1e3\\n plt.axvspan(zRmin, zRmax, color='blue', alpha=0.1)\\n\\n plt.xlabel('Axial Position Relative to Lens (mm)')\\n plt.ylabel('Beam Radius (microns)')\\n title = \\\"$w_0$=%.0fµm, $z_0$=%.0fmm, \\\" % (w0*1e6, z0*1e3)\\n title += \\\"$w_0'$=%.0fµm, $z_0'$=%.0fmm, \\\" % (w0_after*1e6, z0_after*1e3)\\n title += \\\"$z_R'$=%.0fmm\\\" % (zR_after*1e3)\\n plt.title(title)\",\n \"def prepare_for_ESR(self):\\r\\n _debug('Anapico: prepare ESR')\\r\\n # This is all for the ANAPICO to use the external trigger. \\r\\n # BONUS for preparing the list with the external trigger. \\r\\n print('Testing query: ', self.query('*IDN?'))\\r\\n print('Source for Trigger?: ', self.query('TRIG:SEQ:SOUR?'))\\r\\n self.write('TRIG:SEQ:SOUR EXT') # Set the external trigger to ext\\r\\n print('Source for Trigger?: ', self.query('TRIG:SEQ:SOUR?'))\\r\\n print('First frequency?: ', self.query('SOUR:FREQ:STAR?'))\\r\\n print('Last frequency?: ', self.query('SOUR:FREQ:STOP?'))\\r\\n \\r\\n # Prepare the list mode\\r\\n self.write('SOUR:FREQ:MODE LIST') # Set the frequency mode to list\\r\\n print('Frequency mode ?: ', self.query('SOUR:FREQ:MODE?'))\\r\\n self.write('SOUR:POW:MODE LIST') # Set the power mode to list\\r\\n print('Power mode ?: ', self.query('SOUR:POW:MODE?'))\\r\\n self.write('SOUR:LIST:MODE AUTO') # Set the list mode to auto\\r\\n print('List mode ?: ', self.query('SOUR:LIST:MODE?'))\\r\\n# self.api.write('TRIG:SEQ:TYPE GATE') # An external trigger signal repeatedly starts and stops the waveform’s playback.\\r\\n self.write('TRIG:SEQ:TYPE POIN')# Upon triggering, only a single point of the sweep (list) is played.\\r\\n print('Trig type?: ', self.query('TRIG:SEQ:TYPE?'))\\r\\n \\r\\n # Set stuff for the modulation\\r\\n self.write('SOUR:PULM:SOUR EXT')# Set the pulse modulation to be external\\r\\n print('Pulse modulation source?: ', self.query('SOUR:PULM:SOUR?'))\\r\\n self.write('SOUR:PULM:STAT ON') # Switch the pulse modulation ON\\r\\n print('State of pulse modulation? ', self.query('SOUR:PULM:STAT?'))\\r\\n self.write('SOUR:PULM:POL NORM') # Polarity NORMal, in case it was INVerted\\r\\n print('Polarity of modulation?: ', self.query('SOUR:PULM:POL?')) \\r\\n # This is all for the ANAPICO to use the external trigger. \\r\\n # BONUS for preparing the list with the external trigger. \\r\",\n \"def route_output(self):\\n self.Z_position = vector(self.width, self.A_position.y)\\n # route nmos drain to Z\\n nmos_contact = (self.nmos_position1 \\n + self.nmos.active_contact_positions[1] \\n + self.nmos.active_contact.second_layer_position\\n + vector(self.nmos.active_contact.second_layer_width,\\n 0).scale(0.5, 0))\\n mid = [nmos_contact.x, self.A_position.y]\\n self.add_path(\\\"metal1\\\", [self.Z_position, mid, nmos_contact])\\n\\n for i in range(len(self.pmos.poly_positions) + 1):\\n if i % 2 == 1:\\n # pmos2 drain to Z\\n pmos_contact = (self.pmos_position2\\n + self.pmos.active_contact_positions[i]\\n + self.pmos.active_contact.second_layer_position.scale(1, 0)\\n + vector(self.pmos.active_contact.second_layer_width,\\n 0).scale(0.5, 0))\\n offset = pmos_contact - vector(0.5 * self.m1m2_via.width, 0)\\n self.add_via(layers=(\\\"metal1\\\", \\\"via1\\\", \\\"metal2\\\"),\\n offset=offset)\\n mid = [pmos_contact.x, self.Z_position.y]\\n self.add_wire((\\\"metal1\\\", \\\"via1\\\", \\\"metal2\\\"),\\n [self.Z_position, mid, pmos_contact])\\n\\n self.add_layout_pin(text=\\\"Z\\\",\\n layer=\\\"metal1\\\",\\n offset=mid - vector(0,0.5*drc[\\\"minwidth_metal1\\\"]),\\n width=self.Z_position.x-mid[0],\\n height=drc[\\\"minwidth_metal1\\\"])\",\n \"def main(): #Main Control Loop (as prototyped on 2/26 in Glennan Lounge)\\n\\t# Create listener to receive info from UI\\n\\tui_listener = pso_network.UIListener()\\n\\tui_listener.daemon = True\\n\\tui_listener.start()\\n\\tui_state = ui_listener.get_ui()\\n\\t\\n\\t# Create listener to recieve waypoints and corrections from planner.\\n\\tplanner_listener = pso_network.PlannerListener()\\n\\tplanner_listener.daemon = True\\n\\tplanner_listener.start()\\n\\twaypoint = cv.CreateMat(4, 1, cv.CV_32FC1)\\n\\t\\n\\t#Instatiate Drone Objects (defined in Drone.py)\\n\\tmyDrone = Drone(\\\"192.168.1.1\\\")\\n\\t\\n\\t\\n\\t#Preset flags\\n\\trunning = True\\n\\twait_on_emergency = False\\n\\twait_on_liftoff = False\\n\\twait_on_land = False\\n\\t\\n\\t#Create Kalman filter, state, and command vectors\\n\\tkalman = PsoKalman()\\n\\tu = cv.CreateMat(4, 1, cv.CV_32FC1)\\n\\tz = cv.CreateMat(5, 1, cv.CV_32FC1)\\n\\tsys_time = time.time()\\n\\t\\n\\t#Create PID controllers for each axis\\n\\tyaw_pid = pso_pid.PID()\\n\\tyaw_pid.k = 1.5\\n\\tyaw_pid.t_i = 1.\\n\\tyaw_pid.angular = True\\n\\tyaw_pid.deadband = .05\\n\\t\\n\\tz_pid = pso_pid.PID()\\n\\tz_pid.k = .00075\\n\\tz_pid.i_enable = False\\n\\tz_pid.t_i = 10.\\n\\tz_pid.deadband = 150\\n\\t\\n\\troll_pid = pso_pid.PID()\\n\\troll_pid.k = .00025\\n\\troll_pid.i_enable = False\\n\\troll_pid.deadband = 50\\n\\t\\n\\tpitch_pid = pso_pid.PID()\\n\\tpitch_pid.k = .00025\\n\\tpitch_pid.i_enable = False\\n\\tpitch_pid.deadband = 50\\n\\t\\n\\t#Logger puts state in csv for matlab-y goodness\\n\\tlogger = debuglogger.Logger()\\n\\t\\n\\t#Fig bucking loop\\n\\twhile(running):\\n\\t\\ttime.sleep(.05)\\n\\t\\tos.system(\\\"clear\\\")\\n\\t\\t\\n\\t\\t#Get command state from UI\\n\\t\\tprev_ui_state = ui_state\\n\\t\\tui_state = ui_listener.get_ui()\\n\\t\\t\\t\\t\\n\\t\\tif ui_state[EMERGENCY]:\\n\\t\\t\\tmyDrone.emergency()\\n\\t\\t\\n\\t\\tif ui_state[SHUTDOWN]:\\n\\t\\t\\t#UI has ordered shutdown\\n\\t\\t\\tprint \\\"Shutting down control loop...\\\"\\n\\t\\t\\tui_listener.stop()\\n\\t\\t\\tmyDrone.kill()\\n\\t\\t\\trunning = False\\n\\t\\t\\n\\t\\tif ui_state[TRIM]:\\n\\t\\t\\tmyDrone.trim()\\n\\t\\t\\tui_listener.clear_flag(TRIM)\\n\\t\\t\\tprint \\\"\\\\nTRIM\\\\n\\\"\\n\\t\\t\\n\\t\\tif ui_state[FLYING]:\\n\\t\\t\\tmyDrone.takeoff()\\n\\t\\t\\tprint \\\"Taking Off/Flying\\\"\\n\\t\\t\\tif not prev_ui_state[FLYING]:\\n\\t\\t\\t\\twait_on_liftoff = 5\\n\\t\\telse:\\n\\t\\t\\tmyDrone.land()\\n\\t\\t\\tprint \\\"Landing/Landed\\\"\\n\\t\\t\\tif prev_ui_state[FLYING]:\\n\\t\\t\\t\\twait_on_land = 5\\n\\t\\t\\n\\t\\tif ui_state[RESET]:\\n\\t\\t\\tmyDrone.reset_emergency()\\n\\t\\t\\tmyDrone.reset()\\n\\t\\t\\tyaw_pid.flush()\\n\\t\\t\\tz_pid.flush()\\n\\t\\t\\troll_pid.flush()\\n\\t\\t\\tpitch_pid.flush()\\n\\t\\t\\tui_listener.clear_flag(RESET)\\n\\t\\t\\n\\t\\t#Get navdata\\n\\t\\tnav = myDrone.get_navdata()\\n\\t\\t\\n\\t\\t#Print out Drone State\\n\\t\\tif nav.check_state(navdata.EMERGENCY):\\n\\t\\t\\tprint \\\"Emergency!\\\"\\n\\t\\telif not nav.check_state(navdata.COM_WATCHDOG):\\n\\t\\t\\tprint \\\"WATCHDOG\\\"\\n\\t\\telif nav.check_state(navdata.COMMAND):\\n\\t\\t\\tprint \\\"Watchdog cleared. Not yet ready for commands.\\\"\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Ready to Fly\\\\n\\\"\\n\\t\\tprint \\\"\\\\t\\\\tECACAVNAPCUWAPTHLGCMBNTTTCUACVVF\\\\n{0}\\\\t\\\\t{1:32b}\\\".format(nav.seq,nav.state) #Print navdata state\\n\\t\\t\\n\\t\\t#Update State (Kalman)\\n\\t\\tdt = time.time()-sys_time\\n\\t\\tprint \\\"dt:\\\\t\\\",dt\\n\\t\\tsys_time = time.time()\\n\\t\\tz[0, 0], z[1, 0], z[2, 0], z[3, 0], z[4, 0] = nav.vx, nav.vy, nav.z, nav.vz, nav.psi\\n\\t\\t#z and u need to be cv matrices!!!!\\n\\t\\tsys_state = myDrone.get_state()\\n\\t\\tprint \\\"\\\\nDrone Kalman State:\\\"\\n\\t\\tprint \\\"x:\\\\t{0}\\\".format(sys_state[0, 0])\\n\\t\\tprint \\\"y:\\\\t{0}\\\".format(sys_state[2, 0])\\n\\t\\tprint \\\"z:\\\\t{0}\\\".format(sys_state[4, 0])\\n\\t\\tprint \\\"vx:\\\\t{0}\\\".format(sys_state[1, 0])\\n\\t\\tprint \\\"vy:\\\\t{0}\\\".format(sys_state[3, 0])\\n\\t\\tprint \\\"vz:\\\\t{0}\\\".format(sys_state[5, 0])\\n\\t\\tprint \\\"theta:\\\\t{0}\\\".format(sys_state[6, 0])\\n\\t\\tprint \\\"vtheta:\\\\t{0}\\\".format(sys_state[7, 0])\\n\\t\\t\\n\\t\\tprint \\\"\\\\nNavdata Euler Angles:\\\"\\n\\t\\tprint \\\"theta:\\\\t\\\",nav.theta\\n\\t\\tprint \\\"phi:\\\\t\\\",nav.phi\\n\\t\\tprint \\\"psi:\\\\t\\\",nav.psi\\n\\t\\tprint \\\"\\\\nNavdata Stuff:\\\"\\n\\t\\tprint \\\"z:\\\\t\\\",nav.z\\n\\t\\tprint \\\"vx:\\\\t\\\",nav.vx\\n\\t\\tprint \\\"vy:\\\\t\\\",nav.vy\\n\\t\\tui_listener.set_state(sys_state, nav)\\n\\t\\t#logger.log(sys_state)\\n\\t\\t\\n\\t\\tif wait_on_liftoff>0:\\n\\t\\t\\tprint \\\"Waiting for liftoff to finish\\\"\\n\\t\\t\\twait_on_liftoff -= dt\\n\\t\\t\\tu[0, 0], u[1, 0], u[2, 0], u[3, 0] = 0, 0, 1, 0#Assume drone goes full speed up when taking off\\n\\t\\telif ui_state[FLYING]:\\n\\t\\t\\tprint \\\"\\\" #Blank line to everything lines up\\n\\t\\t\\t#If Drone is in waypoint mode, compute command\\n\\t\\t\\tif not ui_state[OVERRIDE]:\\n\\t\\t\\t\\t#Get waypoint\\n\\t\\t\\t\\tif not planner_listener.waypoints.empty():\\n\\t\\t\\t\\t\\twaypoint = planner_listener.waypoints.get()\\n\\t\\t\\t\\tprint \\\"\\\\nNext Waypoint:\\\"\\n\\t\\t\\t\\tprint \\\"X:\\\\t\\\", waypoint[0, 0]\\n\\t\\t\\t\\tprint \\\"Y:\\\\t\\\", waypoint[1, 0]\\n\\t\\t\\t\\tprint \\\"Z:\\\\t\\\", waypoint[2, 0]\\n\\t\\t\\t\\tprint \\\"θ:\\\\t\\\", waypoint[3, 0]\\n\\t\\t\\t\\t#Compute command\\n\\t\\t\\t\\t(roll_des, pitch_des) = world2drone(waypoint[0, 0]-sys_state[0, 0], waypoint[1, 0]-sys_state[2, 0], sys_state[6, 0])\\n\\t\\t\\t\\tprint \\\"Desired Roll:\\\\t\\\", roll_des\\n\\t\\t\\t\\tprint \\\"Desired Pitch:\\\\t\\\", pitch_des\\n\\t\\t\\t\\tu[0, 0] = pitch_pid.update(0, pitch_des)\\n\\t\\t\\t\\tu[1, 0] = roll_pid.update(0, roll_des)\\n\\t\\t\\t\\tu[2, 0] = z_pid.update(sys_state[4, 0], waypoint[2, 0])\\n\\t\\t\\t\\tu[3, 0] = yaw_pid.update(sys_state[6, 0], waypoint[3, 0])\\n\\t\\t\\t\\tmyDrone.go(u[0, 0], u[1, 0], u[3, 0], u[2, 0])\\n\\t\\t\\telse: #Manual override: Use command from UI state\\n\\t\\t\\t\\tprint \\\"\\\\nManual override mode\\\\n\\\\n\\\\n\\\"\\n\\t\\t\\t\\tmyDrone.go(ui_state[COMMAND][0], ui_state[COMMAND][1], ui_state[COMMAND][2], ui_state[COMMAND][3])\\n\\t\\t\\t\\tu[0, 0], u[1, 0], u[2, 0], u[3, 0] = ui_state[COMMAND]\\n\\t\\telse:\\n\\t\\t\\tprint \\\"\\\\nLanded\\\"\\n\\t\\t\\n\\t\\t#Print out commands\\n\\t\\tprint \\\"\\\\nCommands:\\\\npitch:\\\\t\\\",u[0, 0]\\n\\t\\tprint \\\"roll:\\\\t\\\", u[1, 0]\\n\\t\\tprint \\\"gaz:\\\\t\\\", u[2, 0]\\n\\t\\tprint \\\"yaw:\\\\t\\\", u[3, 0]\",\n \"def idealOpAmp():\",\n \"def __init__(self, portname, devicetype):\\n if devicetype == DEVICE_DEBUG_BOARD:\\n self.device = debugbox.DebugBox(portname)\\n elif devicetype == DEVICE_SFP_BREAKOUT:\\n self.device = sfpbreakout.SFP(portname)\\n elif devicetype == DEVICE_DUMMY_BOARD:\\n self.device = dummybox.DummyBox(portname)\\n else:\\n raise IOError(\\\"Invalid Device Type\\\")\\n \\n # Set up laser sections\\n self.mirror1 = Testrig.LaserSection(self, 814, 10, 90, 728, 844, 860, 864)\\n self.laser_phase = Testrig.LaserSection(self, 810, 11, 20, 726, 846, 858, 866)\\n self.gain = Testrig.LaserSection(self, 818, 12, 180, 730, 848, None, None)\\n# self.gain1.validator.setTop(150)\\n self.mirror2 = Testrig.LaserSection(self, 826, 14, 90, 734, 850, 862, 864)\\n# self.front.validator.setTop(60)\\n self.soa1 = Testrig.LaserSection(self, 822, 15, 300, 736, 852, None, None)\\n# self.soa1.validator.setTop(150)\\n self.soa2 = Testrig.LaserSection(self, 830, 16, 100, 738, 854, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n self.phase1 = Testrig.LaserSection(self, 834, 13, 100, 732, 856, None, None, self.to_display_current_section2, self.to_internal_current_section2)\\n \\n self.voltage_max = 2.5 # This is now a constant\\n \\n self.full_rig = True\",\n \"def buildArm(self, *args):\\n \\\"\\\"\\\" With this we can get rid of buildFK and IK joints \\\"\\\"\\\"\\n numChains = len(self.jointNames)\\n \\n jointPos = [] \\n \\n \\\"\\\"\\\" lets try getting our joint names and positions from the locators \\\"\\\"\\\"\\n #jointLctrs = cmds.ls(sl=True)\\n #cmds.select(d=True)\\n #for lctr in jointLctrs:\\n #pos = cmds.getAttr(lctr + \\\".localPosition\\\")\\n #jointPos.append(pos)\\n #cmds.delete(jointLctrs)\\n \\n \\n bindJoints = self.jointNames['bindJnts'] \\n fkJoints = self.jointNames['fkJnts']\\n ikJoints = self.jointNames['ikJnts']\\n \\n positions = jointPos\\n \\n \\n \\\"\\\"\\\" Call the create joints function, passing in our joint names and positions \\\"\\\"\\\"\\n \\\"\\\"\\\" First instance in the jointUtils \\\"\\\"\\\"\\n jointCreate = jointUtils.Joint_Utils()\\n \\n for index in range(len(self.jointNames)):\\n if index == 0:\\n joints = self.jointNames['bindJnts']\\n if index == 1:\\n joints = self.jointNames['fkJnts']\\n if index == 2:\\n joints = self.jointNames['ikJnts']\\n \\n jointCreate.createJoints (joints, positions)\\n jointCreate.orientJoints(joints)\\n \\n \\\"\\\"\\\" We can use the dictionaries to pass the joint names \\\"\\\"\\\" \\n jointCreate.groupJoints(bindJoints, fkJoints, ikJoints)\\n \\n jointCreate.connectJoints(bindJoints, fkJoints, ikJoints)\\n \\n \\n \\\"\\\"\\\" Create the fk controls \\\"\\\"\\\"\\n \\\"\\\"\\\" instance in the FK_Controls class \\\"\\\"\\\"\\n control = self.control\\n fkCreate = fk_controls.FK_Controls()\\n fkCreate.createFKControls(fkJoints, control)\\n \\n \\\"\\\"\\\" Create the IK Setup \\\"\\\"\\\"\\n \\\"\\\"\\\" Garvey did some nice work with this \\\"\\\"\\\"\\n ikCreate = ik_controls.IK_Controls()\\n ikCreate.createIKControls(ikJoints)\\n \\n \\n \\\"\\\"\\\" Try writing the saveCsv stuff back into here. This way you will have a backup\\\"\\\"\\\"\\n \\\"\\\"\\\" of all the info you need to re-create your rig. \\\"\\\"\\\"\\n \\n fileName = \\\"arm.csv\\\"\\n csvUtils.csvWrite(bindJoints, positions, fileName)\",\n \"def run_single_camera(cam):\\n\\n try:\\n # Retrieve TL device nodemap and print device information\\n #nodemap_tldevice = cam.GetTLDeviceNodeMap()\\n\\n #result &= print_device_info(nodemap_tldevice)\\n\\n # Initialize camera\\n cam.Init()\\n\\n # Retrieve GenICam nodemap\\n nodemap = cam.GetNodeMap()\\n exposures=[2000,4000,8000,16000]\\n index=0\\n if cam.ExposureAuto.GetAccessMode() != PySpin.RW:\\n print(\\\"Unable to disable automatic exposure. Aborting...\\\")\\n return False\\n node_acquisition_mode = PySpin.CEnumerationPtr(nodemap.GetNode(\\\"AcquisitionMode\\\"))\\n if not PySpin.IsAvailable(node_acquisition_mode) or not PySpin.IsWritable(node_acquisition_mode):\\n print(\\\"Unable to set acquisition mode to continuous (enum retrieval). Aborting...\\\")\\n return False\\n\\n # Retrieve entry node from enumeration node\\n node_acquisition_mode_continuous = node_acquisition_mode.GetEntryByName(\\\"Continuous\\\")\\n if not PySpin.IsAvailable(node_acquisition_mode_continuous) or not PySpin.IsReadable(node_acquisition_mode_continuous):\\n print(\\\"Unable to set acquisition mode to continuous (entry retrieval). Aborting...\\\")\\n return False\\n\\n acquisition_mode_continuous = node_acquisition_mode_continuous.GetValue()\\n\\n node_acquisition_mode.SetIntValue(acquisition_mode_continuous)\\n\\n print(\\\"Acquisition mode set to continuous...\\\")\\n\\n cam.ExposureAuto.SetValue(PySpin.ExposureAuto_Off)\\n '''\\n # Set maximum width\\n #\\n # *** NOTES ***\\n # Other nodes, such as those corresponding to image width and height,\\n # might have an increment other than 1. In these cases, it can be\\n # important to check that the desired value is a multiple of the\\n # increment.\\n #\\n # This is often the case for width and height nodes. However, because\\n # these nodes are being set to their maximums, there is no real reason\\n # to check against the increment.\\n if cam.Width.GetAccessMode() == PySpin.RW and cam.Width.GetInc() != 0 and cam.Width.GetMax != 0:\\n cam.Width.SetValue(FRAME_WIDTH)\\n print(\\\"Width set to %i...\\\" % cam.Width.GetValue())\\n\\n else:\\n print(\\\"Width not available...\\\")\\n result = False\\n\\n # Set maximum height\\n #\\n # *** NOTES ***\\n # A maximum is retrieved with the method GetMax(). A node's minimum and\\n # maximum should always be a multiple of its increment.\\n if cam.Height.GetAccessMode() == PySpin.RW and cam.Height.GetInc() != 0 and cam.Height.GetMax != 0:\\n cam.Height.SetValue(FRAME_HEIGHT)\\n print(\\\"Height set to %i...\\\" % cam.Height.GetValue())\\n\\n else:\\n print(\\\"Height not available...\\\")\\n result = False\\n '''\\n print(\\\"Automatic exposure disabled...\\\")\\n #node_acquisition_framerate = PySpin.CFloatPtr(nodemap.GetNode(\\\"AcquisitionFrameRate\\\"))\\n\\n # if not PySpin.IsAvailable(node_acquisition_framerate) and not PySpin.IsReadable(node_acquisition_framerate):\\n # print(\\\"Unable to retrieve frame rate. Aborting...\\\")\\n # return False\\n\\n # framerate_to_set = node_acquisition_framerate.GetValue()\\n\\n # print(\\\"Frame rate to be set to %d...\\\" % framerate_to_set)\\n canvas=np.zeros((FRAME_HEIGHT*2,FRAME_WIDTH*2,3), np.uint8)\\n while True:\\n exposure=exposures[index]\\n \\n configure_exposure(cam, exposure)\\n # Acquire images\\n err, img,width,height = acquire_images(cam, nodemap)\\n if err < 0:\\n return err\\n\\n \\n img = img.GetData().reshape(height,width,3)\\n\\n half_height = int(height/2)\\n half_width = int(width/2)\\n half_frame_height = int(FRAME_HEIGHT/2)\\n half_frame_width = int(FRAME_WIDTH/2)\\n \\n img = img[half_height-half_frame_height:half_height+half_frame_height,half_width-half_frame_width:half_width+half_frame_width]\\n #smallimg=cv2.resize(img,(int(FRAME_WIDTH/2),int(FRAME_HEIGHT/2)))\\n if index==0:\\n #top left\\n canvas[0:FRAME_HEIGHT,0:FRAME_WIDTH]=img\\n elif index==1:\\n #top right\\n canvas[0:FRAME_HEIGHT,FRAME_WIDTH:FRAME_WIDTH*2]=img\\n elif index==2:\\n #bot left\\n canvas[FRAME_HEIGHT:FRAME_HEIGHT*2,0:FRAME_WIDTH]=img\\n else:\\n #bot right\\n canvas[FRAME_HEIGHT:FRAME_HEIGHT*2,FRAME_WIDTH:FRAME_WIDTH*2]=img\\n index+=1\\n if index>=len(exposures):\\n index=0\\n\\n cv2.imshow(\\\"frame\\\",canvas)\\n if cv2.waitKey(1) &0xff ==ord('q'):\\n #stop the feed the 'q'\\n break\\n cv2.destroyAllWindows()\\n # Deinitialize camera\\n cam.DeInit()\\n\\n except PySpin.SpinnakerException as ex:\\n print(\\\"Error: %s\\\" % ex)\\n result = False\",\n \"def _on_lane_invasion(self, event):\\n self.lanes_invaded = event.crossed_lane_markings\",\n \"def main():\\n utl.calibrate(False)\\n undistort(False)\\n edge_detect(False)\\n transform(False)\\n identify_line(False)\\n lane_line(True)\",\n \"def filter_beam(self, setting):\\n\\n self.beam_filter = setting\\n\\n # In manual mode determine number of raw invalid and number of 3 beam solutions\\n # 3 beam solutions if selected\\n if self.beam_filter > 0:\\n\\n # Find invalid raw data\\n valid_vel = np.ones(self.raw_vel_mps.shape)\\n valid_vel[np.isnan(self.raw_vel_mps)] = 0\\n\\n # Determine how many beams transformed coordinates are valid\\n valid_vel_sum = np.sum(valid_vel, 0)\\n valid = np.ones(valid_vel_sum.shape)\\n\\n # Compare number of valid beams or coordinates to filter value\\n valid[valid_vel_sum < self.beam_filter] = False\\n\\n # Save logical of valid data to object\\n self.valid_data[5, :] = valid\\n\\n else:\\n\\n # Apply automatic filter\\n # ----------------------\\n # Find all 3 beam solutions\\n self.filter_beam(3)\\n beam_3_valid_data = copy.deepcopy(self.valid_data)\\n self.filter_beam(4)\\n valid_3_beams = np.logical_xor(beam_3_valid_data[5, :], self.valid_data[5, :])\\n n_ens = len(self.valid_data[5, :])\\n idx = np.where(valid_3_beams == True)[0]\\n\\n # If 3 beam solutions exist evaluate there validity\\n if len(idx) > 0:\\n\\n # Identify 3 beam solutions that appear to be invalid\\n n3_beam_ens = len(idx)\\n\\n # Check each three beam solution for validity\\n for m in range(n3_beam_ens):\\n\\n # Use before and after values to check 3-beam solution\\n # but make sure the ensemble is not the first or last.\\n if (idx[m] > 1) and (idx[m] < n_ens):\\n\\n # Find nearest 4 beam solutions before and after\\n # 3 beam solution\\n ref_idx_before = np.where(self.valid_data[5, :idx[m]] == True)[0]\\n if len(ref_idx_before) > 0:\\n ref_idx_before = ref_idx_before[-1]\\n else:\\n ref_idx_before = None\\n\\n ref_idx_after = np.where(self.valid_data[5, idx[m]:] == True)[0]\\n if len(ref_idx_after) > 0:\\n ref_idx_after = idx[m] + ref_idx_after[0]\\n else:\\n ref_idx_after = None\\n\\n if (ref_idx_after is not None) and (ref_idx_before is not None):\\n u_ratio = (self.u_mps[idx[m]]) / ((self.u_mps[ref_idx_before]\\n + self.u_mps[ref_idx_after]) / 2.) - 1\\n v_ratio = (self.v_mps[idx[m]]) / ((self.v_mps[ref_idx_before]\\n + self.v_mps[ref_idx_after]) / 2.) - 1\\n else:\\n u_ratio = 1\\n v_ratio = 1\\n\\n # If 3-beam differs from 4-beam by more than 50% mark it invalid\\n if (np.abs(u_ratio) > 0.5) and (np.abs(v_ratio) > 0.5):\\n self.valid_data[5, idx[m]] = False\\n else:\\n self.valid_data[5, idx[m]] = True\\n\\n self.beam_filter = -1\\n\\n # Combine all filter data to composite valid data\\n self.valid_data[0, :] = np.all(self.valid_data[1:, :], 0)\\n self.num_invalid = np.sum(self.valid_data[0, :] == False)\",\n \"def __init__(self, runway_type):\\n self.primary_surface_length = 200\\n self.primary_surface_width = 0\\n self.approach_surface_extendedwidth = 0\\n self.first_section_length = 0\\n self.first_section_slope = 0\\n self.second_section_length = 0\\n self.second_section_slope = 0\\n self.horizontal_surface_height = 150\\n self.horizontal_surface_radius = 0\\n self.conical_surface_slope = 20\\n self.conical_surface_offset = 4000\\n self.transitional_surface_slope = 7\\n \\n # The runway types listed in the documentation for FAA FAR 77 do not \\n # match what appears when you actually run the tool in ArcMap.\\n # These regular expressions should match either version. \\n if re.match(\\\"Visual\\\\s*(?:Runway)?\\\\s*Visual\\\\sApproach\\\", runway_type, re.I):\\n self.primary_surface_width = 500\\n self.approach_surface_extendedwidth = 1500\\n self.first_section_length = 5000\\n self.first_section_slope = 20\\n self.horizontal_surface_radius = 5000\\n elif re.match(\\\"Utility\\\\s*(?:Runway)?\\\\s*Visual Approach\\\", runway_type, re.I):\\n self.primary_surface_width = 250\\n self.approach_surface_extendedwidth = 1250\\n self.first_section_length = 5000\\n self.first_section_slope = 20\\n self.horizontal_surface_radius = 5000\\n elif re.match(\\\"Utility\\\\s*(?:Runway)?\\\\s*Non[\\\\s\\\\-]*Precision Instrument Approach\\\", runway_type, re.I):\\n self.primary_surface_width = 500\\n self.approach_surface_extendedwidth = 2000\\n self.first_section_length = 5000\\n self.first_section_slope = 20\\n self.horizontal_surface_radius = 5000\\n elif re.match(\\\"Precision Instrument\\\\s*(?:Runway)?\\\", runway_type, re.I):\\n self.primary_surface_width = 1000\\n self.approach_surface_extendedwidth = 16000\\n self.first_section_length = 10000\\n self.first_section_slope = 50\\n self.second_section_length = 40000\\n self.second_section_slope = 40\\n self.horizontal_surface_radius = 10000\\n elif re.match(\\\"Non Precision Instrument\\\\s*(?:Runway)?\\\\s*(?:(?:High)|(?:Greater)) Visibility\\\", runway_type, re.I):\\n self.primary_surface_width = 500\\n self.approach_surface_extendedwidth = 3500\\n self.first_section_length = 10000\\n self.first_section_slope = 34\\n self.horizontal_surface_radius = 10000\\n elif re.match(\\\"Non Precision Instrument\\\\s*(?:Runway)\\\\s*Approach Low Visibility\\\", runway_type, re.I):\\n self.primary_surface_width = 1000\\n self.approach_surface_extendedwidth = 4000\\n self.first_section_length = 10000\\n self.first_section_slope = 34\\n self.horizontal_surface_radius = 10000\",\n \"def indoor_air_quality(self):\\n # name, command, signals, delay\\n return self._i2c_read_words_from_cmd(command=[0x20, 0x08], reply_size=2, delay=0.05)\",\n \"def croscor(self):\\n l=self.l\\n if len(self.inputs['L0'])>1: \\n title ='Align L0 inputs'\\n chans0=self.chansl0\\n cmddata='take1SSM(1,'\\n CorellateGroup(self.vb,title,'1',chans0,'0','0','0','0','15',cmddata)\\n if len(self.inputs['L1'])>1: \\n title ='Align L1 inputs'\\n chans1=self.chansl1\\n cmddata='take1SSM(2,'\\n CorellateGroup(self.vb,title,'1','0',chans1,'0','0','0','15',cmddata)\\n if len(self.inputs['L2'])>1: \\n title ='Align L2 inputs'\\n chans2=self.chansl2\\n cmddata='take1SSM(3,'\\n CorellateGroup(self.vb,title,'1','0','0',chans2,'0','0','15',cmddata)\\n if len(self.inputs['H0'])>1: \\n title ='Align H0 inputs'\\n chansh=self.chansh0\\n cmddata='takerbSSM('\\n CorellateGroup(self.vb,title,'1','0','0','0',chansh,'0','15',cmddata)\\n if l==3: \\n print \\\"Correlatin L0-L1 ----------------\\\"\\n title ='Find L0-L1 inputs delay'\\n chans0=self.l0anyinp\\n chans1=self.l1anyinp\\n cmddata='take2SSM(1,2,'\\n CorellateGroup(self.vb,title,'1',chans0,chans1,'0','0','250','15',cmddata)\\n if l==5: \\n title ='Find L0-L2 inputs delay'\\n chans0=self.l0anyinp\\n chans2=self.l1anyinp\\n cmddata='take2SSM(1,3,'\\n CorellateGroup(self.vb,title,'1',chans0,'0',chans2,'0','3520','15',cmddata)\\n if l==6: \\n title ='Find L1-L2 inputs delay'\\n chans1=self.l1anyinp\\n chans2=self.l2anyinp\\n cmddata='take2SSM(2,3,'\\n CorellateGroup(self.vb,title,'1','0',chans1,chans2,'0','3312','15',cmddata)\\n if l==7: \\n chans0=self.l0anyinp\\n chans1=self.l1anyinp\\n chans2=self.l2anyinp\\n title ='Find L0-L1 inputs delay'\\n cmddata='take2SSM(1,2,'\\n CorellateGroup(self.vb,title,'1',chans0,chans1,'0','0','250','15',cmddata)\\n title ='Find L1-L2 inputs delay'\\n cmddata='take2SSM(2,3,'\\n CorellateGroup(self.vb,title,'1','0',chans1,chans2,'0','3312','15',cmddata)\",\n \"def multi_agent_example():\\n env = holodeck.make(\\\"CyberPunkCity-FollowSight\\\")\\n\\n cmd0 = np.array([0, 0, -2, 10])\\n cmd1 = np.array([0, 0, 0])\\n for i in range(10):\\n env.reset()\\n env.tick()\\n env.act(\\\"uav0\\\", cmd0)\\n env.act(\\\"nav0\\\", cmd1)\\n for _ in range(1000):\\n states = env.tick()\\n pixels = states[\\\"uav0\\\"][\\\"RGBCamera\\\"]\",\n \"def __init__(self, reads1, reads2):\\n print \\\"Start Analysis...\\\"\\n self.alignment()\\n self.sai_to_sam()\\n self.sam_to_bam()\\n #self.clean_files()\",\n \"def process_scan(self, msg):\\n if len(msg.ranges) <= 330:\\n # throw out scans that don't have more than 90% of the data\\n return\\n # get pose according to the odometry\\n p = PoseStamped(header=Header(stamp=msg.header.stamp, frame_id=\\\"base_link\\\"), pose=Pose())\\n self.odom_pose = self.tf_listener.transformPose(\\\"odom\\\", p)\\n self.base_pose = self.tf_listener.transformPose(\\\"base_laser_link\\\", p)\\n # convert the odom pose to the tuple (x,y,theta)\\n self.odom_pose = OccupancyGridMapper.convert_pose_to_xy_and_theta(self.odom_pose.pose)\\n #(-0.0069918, 0.000338577, 0.048387097)\\n #(1.0208817, 0.04827240, 0.048387)\\n self.base_pose = OccupancyGridMapper.convert_pose_to_xy_and_theta(self.base_pose.pose)\\n for i in range(len(msg.ranges)):\\n if 0.0 < msg.ranges[i] < 5.0: #for any reding within 5 meters\\n #Using the pose and the measurement nd the angle, find it in the world\\n map_x = self.odom_pose[0] + msg.ranges[i] * cos(i * pi / 180.0 + self.odom_pose[2])\\n map_y = self.odom_pose[1] + msg.ranges[i] * -sin(i * pi / 180.0 + self.odom_pose[2])\\n\\n #Relate that map measure with a place in the picture\\n x_detect = int((map_x - self.origin[0]) / self.resolution)\\n y_detect = int((map_y - self.origin[1]) / self.resolution)\\n\\n\\n #Determine how to mark the location in the map, along with the stuff inbetween\\n u = (map_x - self.odom_pose[0], map_y - self.odom_pose[1])\\n magnitude = sqrt(u[0] ** 2 + u[1] ** 2)\\n n_steps = max([1, int(ceil(magnitude / self.resolution))])\\n u_step = (u[0] / (n_steps - 1), u[1] / (n_steps - 1))\\n marked = set()\\n for i in range(n_steps):\\n curr_x = self.odom_pose[0] + i * u_step[0]\\n curr_y = self.odom_pose[1] + i * u_step[1]\\n if not (self.is_in_map(curr_x, curr_y)):\\n break\\n\\n x_ind = int((curr_x - self.origin[0]) / self.resolution)\\n y_ind = int((curr_y - self.origin[1]) / self.resolution)\\n if x_ind == x_detect and y_ind == y_detect:\\n break\\n if not ((x_ind, y_ind) in marked):\\n # odds ratio is updated according to the inverse sensor model\\n self.odds_ratios[x_ind, y_ind] *= self.p_occ / (1 - self.p_occ) * self.odds_ratio_miss\\n marked.add((x_ind, y_ind))\\n if self.is_in_map(map_x, map_y):\\n # odds ratio is updated according to the inverse sensor model\\n self.odds_ratios[x_detect, y_detect] *= self.p_occ / (1 - self.p_occ) * self.odds_ratio_hit\\n\\n self.seq += 1\\n # to save time, only publish the map every 10 scans that we process\\n if self.seq % 10 == 0:\\n # make occupancy grid\\n map = OccupancyGrid()\\n map.header.seq = self.seq\\n self.seq += 1\\n map.header.stamp = msg.header.stamp\\n map.header.frame_id = \\\"map\\\" # the name of the coordinate frame of the map\\n map.info.origin.position.x = self.origin[0]\\n map.info.origin.position.y = self.origin[1]\\n map.info.width = self.n\\n map.info.height = self.n\\n map.info.resolution = self.resolution\\n map.data = [0] * self.n ** 2 # map.data stores the n by n grid in row-major order\\n for i in range(self.n):\\n for j in range(self.n):\\n idx = i + self.n * j # this implements row major order\\n if self.odds_ratios[i, j] < 1 / 5.0: # consider a cell free if odds ratio is low enough\\n map.data[idx] = 0\\n elif self.odds_ratios[i, j] > 5.0: # consider a cell occupied if odds ratio is high enough\\n map.data[idx] = 100\\n else: # otherwise cell is unknown\\n map.data[idx] = -1\\n self.pub.publish(map)\\n\\n # create the image from the probabilities so we can visualize using opencv\\n im = np.zeros((self.odds_ratios.shape[0], self.odds_ratios.shape[1], 3))\\n for i in range(im.shape[0]):\\n for j in range(im.shape[1]):\\n if self.odds_ratios[i, j] < 1 / 5.0:\\n im[i, j, :] = 1.0\\n elif self.odds_ratios[i, j] > 5.0:\\n im[i, j, :] = 0.0\\n else:\\n im[i, j, :] = 0.5\\n\\n # compute the index of the odometry pose so we can mark it with a circle\\n x_odom_index = int((self.odom_pose[0] - self.origin[0]) / self.resolution)\\n y_odom_index = int((self.odom_pose[1] - self.origin[1]) / self.resolution)\\n\\n x_base_index = int((self.base_pose[0] - self.origin[0] - 1) / self.resolution)\\n y_base_index = int((self.base_pose[1] - self.origin[1]) / self.resolution)\\n\\n\\n # computer the ball locations so we can mark with a colored circle\\n #TODO Track and relate the robot's angle pose for accuracy\\n\\n if self.depth_red > 0:\\n self.y_camera_red = int(x_odom_index - self.depth_red * cos(self.angle_diff_red + pi - self.odom_pose[2])/self.resolution)\\n self.x_camera_red = int(y_odom_index - self.depth_red * sin(self.angle_diff_red + pi - self.odom_pose[2])/self.resolution)\\n cv2.circle(im, (self.x_camera_red, self.y_camera_red), 1, self.red)\\n\\n real_red_y = self.depth_red * cos(self.angle_diff_red + pi - self.odom_pose[2])\\n real_red_x = self.depth_red * sin(self.angle_diff_red + pi - self.odom_pose[2])\\n\\n self.rcoor_pub.publish(Vector3(-real_red_x, -real_red_y/2, 0))\\n else:\\n cv2.circle(im, (self.x_camera_red, self.y_camera_red), 1, self.red)\\n\\n if self.depth_blue > 0:\\n self.y_camera_blue = int(x_odom_index - self.depth_blue * cos(self.angle_diff_blue + pi - self.odom_pose[2])/self.resolution)\\n self.x_camera_blue = int(y_odom_index - self.depth_blue * sin(self.angle_diff_blue + pi - self.odom_pose[2])/self.resolution)\\n cv2.circle(im, (self.x_camera_blue, self.y_camera_blue), 1, self.blue)\\n\\n real_blue_y = self.depth_blue * cos(self.angle_diff_blue + pi - self.odom_pose[2])\\n real_blue_x = self.depth_blue * sin(self.angle_diff_blue + pi - self.odom_pose[2])\\n\\n self.bcoor_pub.publish(Vector3(-real_blue_x, -real_blue_y/2, 0))\\n else:\\n cv2.circle(im, (self.x_camera_blue, self.y_camera_blue), 1, self.blue)\\n\\n if self.depth_green > 0:\\n self.y_camera_green = int(x_odom_index - self.depth_green * cos(self.angle_diff_green + pi - self.odom_pose[2])/self.resolution)\\n self.x_camera_green = int(y_odom_index - self.depth_green * sin(self.angle_diff_green + pi - self.odom_pose[2])/self.resolution)\\n cv2.circle(im, (self.x_camera_green, self.y_camera_green), 1, self.green)\\n \\n real_green_y = self.depth_green * cos(self.angle_diff_green + pi - self.odom_pose[2])\\n real_green_x = self.depth_green * sin(self.angle_diff_green + pi - self.odom_pose[2])\\n\\n self.gcoor_pub.publish(Vector3(-real_green_x, -real_green_y/2, 0))\\n\\n if self.depth_yellow > 0:\\n self.y_camera_yellow = int(x_odom_index - self.depth_yellow * cos(self.angle_diff_yellow + pi - self.odom_pose[2])/self.resolution)\\n self.x_camera_yellow = int(y_odom_index - self.depth_yellow * sin(self.angle_diff_yellow + pi - self.odom_pose[2])/self.resolution)\\n cv2.circle(im, (self.x_camera_yellow, self.y_camera_yellow), 1, self.yellow)\\n \\n real_yellow_y = self.depth_yellow * cos(self.angle_diff_yellow + pi - self.odom_pose[2])\\n real_yellow_x = self.depth_yellow * sin(self.angle_diff_yellow + pi - self.odom_pose[2])\\n\\n self.ycoor_pub.publish(Vector3(-real_yellow_x, -real_yellow_y/2, 0))\\n else:\\n cv2.circle(im, (self.x_camera_yellow, self.y_camera_yellow), 1, self.yellow)\\n\\n # draw the robot\\n cv2.circle(im, (y_odom_index, x_odom_index), 2, (255, 0, 0))\\n \\n # display the image resized\\n cv2.imshow(\\\"map\\\", cv2.resize(im, (500, 500)))\\n cv2.waitKey(20)\",\n \"def draw_aim(self):\\n polygon(screen, self.color, [(self.x, self.y), (self.x + self.r * 1.71 / 2, self.y - self.r / 2),\\n (self.x + self.r * 1.71, self.y), (self.x + self.r * 1.71, self.y + self.r),\\n (self.x + self.r * 1.71 / 2, self.y + 3 * self.r / 2), (self.x, self.y + self.r)])\",\n \"def __init__(self, input, output) :\\n Gate.__init__(self, [input], output)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6081809","0.5777927","0.57516456","0.5532476","0.5465286","0.5433055","0.5341489","0.53219754","0.53150827","0.5288024","0.5256378","0.5239029","0.52265924","0.5226345","0.5207449","0.51792234","0.51619565","0.51443416","0.51314235","0.5128932","0.51179725","0.50772494","0.50772494","0.50772494","0.50768495","0.5075305","0.5066714","0.5052722","0.50443125","0.5039875","0.50380635","0.50278056","0.5019934","0.5019821","0.50171393","0.50136673","0.50103253","0.49849212","0.49822408","0.49779668","0.49760115","0.49613094","0.49600133","0.49540815","0.49513692","0.49488607","0.49442625","0.49412206","0.49345008","0.4930973","0.49253508","0.49197313","0.49168977","0.49165162","0.491309","0.4913027","0.49075785","0.49074367","0.49051508","0.4903252","0.49009448","0.489722","0.4893479","0.48890662","0.48868832","0.48842794","0.48793793","0.48756674","0.48637974","0.48622352","0.48571458","0.48556346","0.48471168","0.48457825","0.48445863","0.48394313","0.4835847","0.48279038","0.48187044","0.48028758","0.47967768","0.479539","0.4784835","0.47671214","0.47614002","0.47601303","0.47535908","0.47464338","0.47462565","0.474576","0.4745682","0.47420916","0.4736823","0.473652","0.47363642","0.4733529","0.47302476","0.47295022","0.4727327","0.47246936","0.4720633"],"string":"[\n \"0.6081809\",\n \"0.5777927\",\n \"0.57516456\",\n \"0.5532476\",\n \"0.5465286\",\n \"0.5433055\",\n \"0.5341489\",\n \"0.53219754\",\n \"0.53150827\",\n \"0.5288024\",\n \"0.5256378\",\n \"0.5239029\",\n \"0.52265924\",\n \"0.5226345\",\n \"0.5207449\",\n \"0.51792234\",\n \"0.51619565\",\n \"0.51443416\",\n \"0.51314235\",\n \"0.5128932\",\n \"0.51179725\",\n \"0.50772494\",\n \"0.50772494\",\n \"0.50772494\",\n \"0.50768495\",\n \"0.5075305\",\n \"0.5066714\",\n \"0.5052722\",\n \"0.50443125\",\n \"0.5039875\",\n \"0.50380635\",\n \"0.50278056\",\n \"0.5019934\",\n \"0.5019821\",\n \"0.50171393\",\n \"0.50136673\",\n \"0.50103253\",\n \"0.49849212\",\n \"0.49822408\",\n \"0.49779668\",\n \"0.49760115\",\n \"0.49613094\",\n \"0.49600133\",\n \"0.49540815\",\n \"0.49513692\",\n \"0.49488607\",\n \"0.49442625\",\n \"0.49412206\",\n \"0.49345008\",\n \"0.4930973\",\n \"0.49253508\",\n \"0.49197313\",\n \"0.49168977\",\n \"0.49165162\",\n \"0.491309\",\n \"0.4913027\",\n \"0.49075785\",\n \"0.49074367\",\n \"0.49051508\",\n \"0.4903252\",\n \"0.49009448\",\n \"0.489722\",\n \"0.4893479\",\n \"0.48890662\",\n \"0.48868832\",\n \"0.48842794\",\n \"0.48793793\",\n \"0.48756674\",\n \"0.48637974\",\n \"0.48622352\",\n \"0.48571458\",\n \"0.48556346\",\n \"0.48471168\",\n \"0.48457825\",\n \"0.48445863\",\n \"0.48394313\",\n \"0.4835847\",\n \"0.48279038\",\n \"0.48187044\",\n \"0.48028758\",\n \"0.47967768\",\n \"0.479539\",\n \"0.4784835\",\n \"0.47671214\",\n \"0.47614002\",\n \"0.47601303\",\n \"0.47535908\",\n \"0.47464338\",\n \"0.47462565\",\n \"0.474576\",\n \"0.4745682\",\n \"0.47420916\",\n \"0.4736823\",\n \"0.473652\",\n \"0.47363642\",\n \"0.4733529\",\n \"0.47302476\",\n \"0.47295022\",\n \"0.4727327\",\n \"0.47246936\",\n \"0.4720633\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":13192,"cells":{"query":{"kind":"string","value":"Attenuators out, beamstop in,"},"document":{"kind":"string","value":"def alignment_stop():\n\n smi = SMI_Beamline()\n yield from smi.modeMeasurement()\n proposal_id('2023_2', '311564_Pettersson')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def beam_align():\n\n # do nothing if there is a sample mounted to avoid collisions\n if smart_magnet.sample_detect.get() == 0:\n raise Exception(\"Sample mounted on gonio! Avoided collision\")\n\n # wait for attenuators to finish moving\n yield from bps.abs_set(mxatten, 0.002)\n yield from bps.sleep(5)\n\n # transition to BL and open shutter\n yield from bps.abs_set(gov_rbt, \"BL\", wait=True)\n yield from bps.mv(sht.r, 0)\n\n yield from bps.abs_set(rot_aligner.cam_hi.cam_mode, \"beam_align\")\n\n # which direction, x pos. pitch beam outboard (-), y pos. pitch beam up (+)\n scan_uid = yield from bp.count([rot_aligner.cam_hi], 1)\n centroid_x, centroid_y = (\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output1.name][1],\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output2.name][1],\n )\n\n if np.isclose(0, centroid_x) or np.isclose(0, centroid_y):\n raise Exception(\"No centroid detected!\")\n\n yield from bps.abs_set(kbt.hor.delta_px, (centroid_x - 320))\n yield from bps.abs_set(kbt.ver.delta_px, -(centroid_y - 256))\n\n def lin_reg(independent, dependent, goal, **kwargs) -> float:\n b = dependent\n A = np.matrix([[pos, 1] for pos in independent])\n p = (\n np.linalg.inv(A.transpose() * A)\n * A.transpose()\n * np.matrix(b.to_numpy()).transpose()\n )\n best = (goal - p[1]) / p[0]\n return best\n\n for axis, signal, center in (\n kbt.hor,\n rot_aligner.cam_hi.cv1.outputs.output1,\n 320,\n ), (kbt.ver, rot_aligner.cam_hi.cv1.outputs.output2, 256):\n # skip if we are within 1 um\n if abs(axis.delta_px.get()) > 2:\n scan_uid = yield from rel_scan_no_reset(\n [rot_aligner.cam_hi],\n axis,\n 0,\n 0.4 * -(axis.delta_px.get() / abs(axis.delta_px.get())),\n 10,\n )\n scan_df = db[scan_uid].table()\n best_voltage = lin_reg(\n scan_df[axis.readback.name],\n scan_df[signal.name],\n center,\n )\n yield from bps.mv(axis, best_voltage)\n yield from bps.sleep(1)\n\n # close shutters and reset attenuators for manual viewing\n yield from bps.mv(sht.r, 20)","def __init__(self, motion, **kwargs):\n super(ShiftOut, self).__init__(motion)","def forward_att(self, eouts, elens, ys, trigger_points=None):\n losses_auxiliary = {}\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, self.device, self.bwd)\n if not self.training:\n self.data_dict['elens'] = tensor2np(elens)\n self.data_dict['ylens'] = tensor2np(ylens)\n self.data_dict['ys'] = tensor2np(ys_out)\n bs, ymax = ys_in.size()[:2]\n tgt_mask = (ys_out != self.pad).unsqueeze(1).repeat([1, ymax, 1])\n causal_mask = tgt_mask.new_ones(ymax, ymax, dtype=tgt_mask.dtype)\n causal_mask = torch.tril(causal_mask).unsqueeze(0)\n tgt_mask = tgt_mask & causal_mask\n src_mask = make_pad_mask(elens).unsqueeze(1).repeat([1, ymax, 1])\n if self.attn_type == 'mocha':\n attn_mask = (ys_out != self.pad).unsqueeze(1).unsqueeze(3)\n else:\n attn_mask = None\n lmout = None\n if self.lm is not None:\n self.lm.eval()\n with torch.no_grad():\n lmout, lmstate, _ = self.lm.predict(ys_in, None)\n lmout = self.lm_output_proj(lmout)\n out = self.pos_enc(self.embed_token_id(ys_in), scale=True)\n xy_aws_layers = []\n xy_aws = None\n for lth, layer in enumerate(self.layers):\n out = layer(out, tgt_mask, eouts, src_mask, mode='parallel', lmout=lmout)\n xy_aws = layer.xy_aws\n if xy_aws is not None and self.attn_type == 'mocha':\n xy_aws_masked = xy_aws.masked_fill_(attn_mask.expand_as(xy_aws) == 0, 0)\n xy_aws_layers.append(xy_aws_masked.clone())\n if not self.training:\n self.aws_dict['yy_aws_layer%d' % lth] = tensor2np(layer.yy_aws)\n self.aws_dict['xy_aws_layer%d' % lth] = tensor2np(layer.xy_aws)\n self.aws_dict['xy_aws_beta_layer%d' % lth] = tensor2np(layer.xy_aws_beta)\n self.aws_dict['xy_aws_p_choose%d' % lth] = tensor2np(layer.xy_aws_p_choose)\n self.aws_dict['yy_aws_lm_layer%d' % lth] = tensor2np(layer.yy_aws_lm)\n logits = self.output(self.norm_out(out))\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\n losses_auxiliary['loss_quantity'] = 0.0\n if self.attn_type == 'mocha':\n n_tokens_ref = tgt_mask[:, -1, :].sum(1).float()\n n_tokens_pred = sum([torch.abs(aws.sum(3).sum(2).sum(1) / aws.size(1)) for aws in xy_aws_layers])\n n_tokens_pred /= len(xy_aws_layers)\n losses_auxiliary['loss_quantity'] = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\n acc = compute_accuracy(logits, ys_out, self.pad)\n return loss, acc, ppl, losses_auxiliary","def arm(self):\n pass","def beam_update(self, beams, extra):\n h_i, dec_out, context, src_mask = extra\n h_i = tuple(hc[:, beams, :] for hc in h_i)\n dec_out = dec_out[beams, :]\n return h_i, dec_out, context, src_mask","def stop(self):\n self.ae.stop()","def tomoScan(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\"default\", tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n try:\n darkFieldInterval = int(darkFieldInterval)\n flatFieldInterval = int(flatFieldInterval)\n optimizeBeamInterval = int(optimizeBeamInterval)\n \n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\n tomography_theta = jns.tomography_theta\n if tomography_theta is None:\n raise NameError(\"tomography_theta is not defined in Jython namespace\")\n tomography_shutter = jns.tomography_shutter\n if tomography_shutter is None:\n raise NameError(\"tomography_shutter is not defined in Jython namespace\")\n tomography_translation = jns.tomography_translation\n if tomography_translation is None:\n raise NameError(\"tomography_translation is not defined in Jython namespace\")\n \n tomography_detector = jns.tomography_detector\n if tomography_detector is None:\n raise NameError(\"tomography_detector is not defined in Jython namespace\")\n\n tomography_optimizer = jns.tomography_optimizer\n if tomography_optimizer is None:\n raise NameError(\"tomography_optimizer is not defined in Jython namespace\")\n\n tomography_time = jns.tomography_time\n if tomography_time is None:\n raise NameError(\"tomography_time is not defined in Jython namespace\")\n \n tomography_beammonitor = jns.tomography_beammonitor\n if tomography_beammonitor is None:\n raise NameError(\"tomography_beammonitor is not defined in Jython namespace\")\n \n tomography_camera_stage = jns.tomography_camera_stage\n if tomography_camera_stage is None:\n raise NameError(\"tomography_camera_stage is not defined in Jython namespace\")\n \n tomography_sample_stage = jns.tomography_sample_stage\n if tomography_sample_stage is None:\n raise NameError(\"tomography_sample_stage is not defined in Jython namespace\")\n \n tomo_additional_scannables = jns.tomography_additional_scannables\n if tomo_additional_scannables is None:\n raise NameError(\"tomo_additional_scannables is not defined in Jython namespace\")\n \n index = SimpleScannable()\n index.setCurrentPosition(0.0)\n index.setInputNames([\"imageNumber\"])\n index.setName(\"imageNumber\")\n index.configure()\n \n image_key = SimpleScannable()\n image_key.setCurrentPosition(0.0)\n image_key.setInputNames([\"image_key\"])\n image_key.setName(\"image_key\")\n image_key.configure()\n\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\n tomography_translation, tomography_optimizer, image_key, index)\n\n# return tomoScanDevice\n #generate list of positions\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\n theta_points = []\n theta_points.append(start)\n previousPoint = start\n for i in range(numberSteps):\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\n theta_points.append(nextPoint)\n previousPoint = nextPoint\n \n #generateScanPoints\n optimizeBeamNo = 0\n optimizeBeamYes = 1\n shutterOpen = 1\n shutterClosed = 0\n shutterNoChange = 2\n scan_points = []\n theta_pos = theta_points[0]\n index = 0\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1\n \n for i in range(imagesPerFlat): \n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1 \n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project, index)) #first\n index = index + 1 \n imageSinceDark = 1\n imageSinceFlat = 1\n optimizeBeam = 0\n for i in range(numberSteps):\n theta_pos = theta_points[i + 1]\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project, index))#main image\n index = index + 1 \n \n imageSinceFlat = imageSinceFlat + 1\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\n index = index + 1 \n imageSinceFlat = 0\n \n imageSinceDark = imageSinceDark + 1\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\n index = index + 1 \n imageSinceDark = 0\n\n optimizeBeam = optimizeBeam + 1\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\n index = index + 1 \n optimizeBeam = 0\n \n #add dark and flat only if not done in last steps\n if imageSinceFlat != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n if imageSinceDark != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1 \n scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\n imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\n if pattern == 'default' or pattern == 'DFPFD':\n i = 0\n for pt1 in scan_points1:\n pt = scan_points[i]\n if pt1 != pt:\n print \"Mismatch - please tell Kaz about your scan and its arguments!\"\n print \"i = \", i\n print \"pt = \", pt\n print \"pt1 = \", pt1\n i += 1\n #return None\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\n #scan_args.append(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(\"RotationCoord_as_list\", [tomoRotationAxis])\n meta_add(\"approxCOR\", tomoRotationAxis)\n for scannable in additionalScannables:\n scan_args.append(scannable)\n for scannable in tomo_additional_scannables:\n scan_args.append(scannable)\n ''' setting the description provided as the title'''\n if not description == None: \n setTitle(description)\n else :\n setTitle(\"undefined\")\n \n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n if not dataFormat == \"NexusDataWriter\":\n handle_messages.simpleLog(\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\")\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", \"NexusDataWriter\")\n scanObject = createConcurrentScan(scan_args)\n if addNXEntry:\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\n scanObject.runScan()\n if autoAnalyse:\n lsdp=jns.lastScanDataPoint()\n OSCommandRunner.runNoWait([\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\n return scanObject;\n except InterruptedException:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"User interrupted the scan\", exceptionType, exception, traceback, False)\n raise InterruptedException(\"User interrupted the scan\")\n except:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"Error during tomography scan\", exceptionType, exception, traceback, False)\n raise Exception(\"Error during tomography scan\", exception)\n finally:\n handle_messages.simpleLog(\"Data Format reset to the original setting: \" + dataFormat)\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", dataFormat)","def stop_beam(self, wait=False):\n if self.SE == 6:\n self.evr.polarity.put('VAL', 0, use_complete=True)\n \n self.records.S_CLOSE.put('VAL', 1, use_complete=True, wait=wait)","def enemy_waves(self):\n\n pass","def control_law( self, inputs ):","def stop():","def stop():","def stop():","def stop():","def test2(self):\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.initialize()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.sweepDuckie()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieAlignX()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieAlignY()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieLowerEE()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieSuctionOn()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieLiftEE()\r\n\t\tself._motion.terminate()","def act(self, goal):\n rospy.loginfo(\"Emergency action received goal: \"+str(goal))\n\n\n sam_publisher = rospy.Publisher('/sam_auv_1/thrusters/0/input',\n FloatStamped,\n queue_size = 100)\n\n # sam_publisher = rospy.Publisher('/uavcan_vbs_command/',\n # PercentStamped,\n # queue_size = 100)\n\n #self.emergency_activated = False\n #sam_sub = rospy.Subscriber('/sam_auv_1/emergency_butt', Bool, self.emergency_cb)\n\n while not rospy.is_shutdown() and not self.done_once:\n time.sleep(0.5)\n #rospy.loginfo('Emergency waiting for butt')\n #if self.emergency_activated:\n # rospy.loginfo('Butt activated')\n start_time = rospy.get_time()\n elapsed = 0\n rospy.loginfo('Emergency action active...')\n while elapsed < 3:\n if rospy.is_shutdown():\n break\n\n elapsed = rospy.get_time() - start_time\n #fs = FloatStamped()\n fs = PercentStamped()\n h = Header()\n fs.header = h\n fs.data = 0\n sam_publisher.publish(fs)\n time.sleep(0.1)\n # we are done doing the action succesfully\n self.done_once = True\n rospy.loginfo('Emergency action SUCCESS')\n return True\n\n # something went wrong, we fucked up\n rospy.loginfo('Emergency action FAILURE '+ str(self.done_once))\n return False","def Advance():\n warp.step()","def idealOpAmp():","def capture_stop(self):\n pass","def stop(self):\n self.stopAngMovementAll()\n self.stopMovementAll()","def at_aflight(seq, flag):\n at(\"AFLIGHT\", seq, [flag])","def eStop(self):\n Step(speed=0, coils=1, steps=0, dir=Step.BRAKE)\n # #######################################################\n # Need to blink Stop and wait until Stop is pressed again\n # #######################################################","def stop_step_sweep(self):\n self.write(\":SOUR:SWE:CONT:STAT OFF\")","def end_meassuring(self):\n self.enabler = 0\n #self.t.join()\n return 1","def forward_att(self, eouts, elens, ys, return_logits=False, teacher_logits=None, ctc_trigger_points=None, forced_trigger_points=None):\n bs, xmax = eouts.size()[:2]\n device = eouts.device\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, eouts.device, self.bwd)\n ymax = ys_in.size(1)\n if forced_trigger_points is not None:\n for b in range(bs):\n forced_trigger_points[b, ylens[b] - 1] = elens[b] - 1\n dstates = self.zero_state(bs)\n if self.training:\n if self.discourse_aware and not self._new_session:\n dstates = {'dstate': (self.dstate_prev['hxs'], self.dstate_prev['cxs'])}\n self.dstate_prev = {'hxs': [None] * bs, 'cxs': [None] * bs}\n self._new_session = False\n cv = eouts.new_zeros(bs, 1, self.enc_n_units)\n self.score.reset()\n aw, aws = None, []\n betas, p_chooses = [], []\n lmout, lmstate = None, None\n ys_emb = self.embed_token_id(ys_in)\n src_mask = make_pad_mask(elens).unsqueeze(1)\n tgt_mask = (ys_out != self.pad).unsqueeze(2)\n logits = []\n for i in range(ymax):\n is_sample = i > 0 and self._ss_prob > 0 and random.random() < self._ss_prob\n if self.lm is not None:\n self.lm.eval()\n with torch.no_grad():\n y_lm = self.output(logits[-1]).detach().argmax(-1) if is_sample else ys_in[:, i:i + 1]\n lmout, lmstate, _ = self.lm.predict(y_lm, lmstate)\n y_emb = self.embed_token_id(self.output(logits[-1]).detach().argmax(-1)) if is_sample else ys_emb[:, i:i + 1]\n dstates, cv, aw, attn_state, attn_v = self.decode_step(eouts, dstates, cv, y_emb, src_mask, aw, lmout, mode='parallel', trigger_points=forced_trigger_points[:, i:i + 1] if forced_trigger_points is not None else None)\n logits.append(attn_v)\n aws.append(aw)\n if attn_state.get('beta', None) is not None:\n betas.append(attn_state['beta'])\n if attn_state.get('p_choose', None) is not None:\n p_chooses.append(attn_state['p_choose'])\n if self.attn_type in ['gmm', 'sagmm']:\n aw = attn_state['myu']\n if self.training and self.discourse_aware:\n for b in [b for b, ylen in enumerate(ylens.tolist()) if i == ylen - 1]:\n self.dstate_prev['hxs'][b] = dstates['dstate'][0][:, b:b + 1].detach()\n self.dstate_prev['cxs'][b] = dstates['dstate'][1][:, b:b + 1].detach()\n if self.training and self.discourse_aware:\n if bs > 1:\n self.dstate_prev['hxs'] = torch.cat(self.dstate_prev['hxs'], dim=1)\n self.dstate_prev['cxs'] = torch.cat(self.dstate_prev['cxs'], dim=1)\n else:\n self.dstate_prev['hxs'] = self.dstate_prev['hxs'][0]\n self.dstate_prev['cxs'] = self.dstate_prev['cxs'][0]\n logits = self.output(torch.cat(logits, dim=1))\n if return_logits:\n return logits\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\n acc = compute_accuracy(logits, ys_out, self.pad)\n aws = torch.cat(aws, dim=2)\n if not self.training:\n self.data_dict['elens'] = tensor2np(elens)\n self.data_dict['ylens'] = tensor2np(ylens)\n self.data_dict['ys'] = tensor2np(ys_out)\n self.aws_dict['xy_aws'] = tensor2np(aws)\n if len(betas) > 0:\n self.aws_dict['xy_aws_beta'] = tensor2np(torch.cat(betas, dim=2))\n if len(p_chooses) > 0:\n self.aws_dict['xy_p_choose'] = tensor2np(torch.cat(p_chooses, dim=2))\n if self.attn_type == 'mocha' or (ctc_trigger_points is not None or forced_trigger_points is not None):\n aws = aws.masked_fill_(tgt_mask.unsqueeze(1).expand_as(aws) == 0, 0)\n loss_quantity = 0.0\n if self.attn_type == 'mocha':\n n_tokens_pred = aws.sum(3).sum(2).sum(1) / aws.size(1)\n n_tokens_ref = tgt_mask.squeeze(2).sum(1).float()\n loss_quantity = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\n loss_latency = 0.0\n if self.latency_metric == 'interval':\n assert ctc_trigger_points is None\n assert aws.size(1) == 1\n aws_prev = torch.cat([aws.new_zeros(aws.size())[:, :, -1:], aws.clone()[:, :, :-1]], dim=2)\n aws_mat = aws_prev.unsqueeze(3) * aws.unsqueeze(4)\n delay_mat = aws.new_ones(xmax, xmax).float()\n delay_mat = torch.tril(delay_mat, diagonal=-1, out=delay_mat)\n delay_mat = torch.cumsum(delay_mat, dim=-2).unsqueeze(0)\n delay_mat = delay_mat.unsqueeze(1).unsqueeze(2).expand_as(aws_mat)\n loss_latency = torch.pow((aws_mat * delay_mat).sum(-1), 2).sum(-1)\n loss_latency = torch.mean(loss_latency.squeeze(1))\n elif ctc_trigger_points is not None or 'ctc_sync' not in self.latency_metric and forced_trigger_points is not None:\n if 'ctc_sync' in self.latency_metric:\n trigger_points = ctc_trigger_points\n else:\n trigger_points = forced_trigger_points\n js = torch.arange(xmax, dtype=torch.float, device=device).expand_as(aws)\n exp_trigger_points = (js * aws).sum(3)\n trigger_points = trigger_points.float().unsqueeze(1)\n loss_latency = torch.abs(exp_trigger_points - trigger_points)\n loss_latency = loss_latency.sum() / ylens.sum()\n if teacher_logits is not None:\n kl_loss = distillation(logits, teacher_logits, ylens, temperature=5.0)\n loss = loss * (1 - self.distil_weight) + kl_loss * self.distil_weight\n return loss, acc, ppl, loss_quantity, loss_latency","def _stop(self):","def out(self, inputs):","def calculate_beam_xy(self):\n info = []\n\n # Import relevant info\n pixel_size = self.info.pixel_size\n for i in [j.final for j in self.final_objects]:\n try:\n info.append(\n [\n i,\n i[\"beamX\"],\n i[\"beamY\"],\n i[\"wavelength\"],\n i[\"distance\"],\n (i[\"a\"], i[\"b\"], i[\"c\"], i[\"alpha\"], i[\"beta\"], i[\"gamma\"]),\n ]\n )\n except IOError as e:\n print(\"IOTA ANALYSIS ERROR: BEAMXY failed! \", e)\n pass\n\n # Calculate beam center coordinates and distances\n beamX = [i[1] for i in info]\n beamY = [j[2] for j in info]\n beam_dist = [\n math.hypot(i[1] - np.median(beamX), i[2] - np.median(beamY)) for i in info\n ]\n beam_dist_std = np.std(beam_dist)\n img_list = [\n [i[0], i[1], i[2], i[3], i[4], i[5], j]\n for i, j in list(zip(info, beam_dist))\n ]\n\n # Separate out outliers\n outliers = [i for i in img_list if i[3] > 2 * beam_dist_std]\n clean = [i for i in img_list if i[3] <= 2 * beam_dist_std]\n cbeamX = [i[1] for i in clean]\n cbeamY = [j[2] for j in clean]\n obeamX = [i[1] for i in outliers]\n obeamY = [j[2] for j in outliers]\n\n # Calculate median wavelength, detector distance and unit cell params from\n # non-outliers only\n wavelengths = [i[3] for i in clean]\n distances = [i[4] for i in clean]\n cells = [i[5] for i in clean]\n\n wavelength = np.median(wavelengths)\n det_distance = np.median(distances)\n a = np.median([i[0] for i in cells])\n b = np.median([i[1] for i in cells])\n c = np.median([i[2] for i in cells])\n\n # Calculate predicted L +/- 1 misindexing distance for each cell edge\n aD = det_distance * math.tan(2 * math.asin(wavelength / (2 * a)))\n bD = det_distance * math.tan(2 * math.asin(wavelength / (2 * b)))\n cD = det_distance * math.tan(2 * math.asin(wavelength / (2 * c)))\n\n return (\n beamX,\n beamY,\n cbeamX,\n cbeamY,\n obeamX,\n obeamY,\n beam_dist,\n [i[4] for i in info],\n aD,\n bD,\n cD,\n pixel_size,\n )","def stop_output(out):\n if out is not None:\n out.stop()\n pass","def stop(self):\n self.stop_aperture()","def pre_stop(self):","def sitOut(self):\n self.sit_out = True","def bruno_mes(self):\n MI = -125\n MA = 125\n INCR = 19\n####################################################################\n ### CHANNEL 1\n self.write('CHN 1')\n self.write('CHN?')\n print 'Acting on channel:',self.read()\n self.write('WAVE ARB')\n self.write('ARBLOAD ARB1')\n self.write('FREQ 100')\n self.write('DCOFFS 0.05')\n self.write('AMPL 0.1')\n \n l =(125,-125,125)#arange(MI,MA,INCR) # the ramp\n# lll = copy(l)[::-1][1:-1]\n# l = concatenate((l,lll))\n self.write_array_to_byte(l,1)","def record_stop(self, aaidee):\n ckresult(_dll.FMOD_System_RecordStop(self._ptr, aaidee))","def stop(self):","def stop(self):","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def __init__(self,diam_in,diam_out):\n self.diam_in = diam_in\n\tself.diam_out = diam_out","def steer(self):\n\n while self.active:\n angle = self.driver.angle\n steering_pwm_calc = self.angle_to_pmw(angle)\n self.pwm.set_pwm(0, 0, steering_pwm_calc)","def main():\n np.random.seed(219)\n rospy.init_node(\"sawyer_dagger_teacher\")\n pub_start = rospy.Publisher('/teacher/start', JointCommand, queue_size=1)\n pub_epi_fin = rospy.Publisher('/teacher/fin', JointCommand, queue_size=1)\n vel_ik_pos_pub = rospy.Publisher('/teacher/ik_vel/', Pose, queue_size = 3)\n pub3 = rospy.Publisher('/ddpg/vel_start/', Float64, queue_size=1)\n pub4 = rospy.Publisher('/ddpg/vel_end/', Float64, queue_size=1)\n goal_obs_pub = rospy.Publisher('/teacher/goal_obs/', Pose, queue_size=1)\n pos_cmd_pub = rospy.Publisher('/teacher/pos_cmd_pub/', PosCmd, queue_size=1)\n\n\n\n rospy.set_param('dagger_reset',\"false\") # param_name, param_value\n\n\n # Load Gazebo Models via Spawning Services\n # Note that the models reference is the /world frame\n # and the IK operates with respect to the /base frame\n # load_gazebo_models()\n # Remove models from the scene on shutdown\n rospy.on_shutdown(delete_gazebo_models)\n\n limb = 'right'\n hover_distance = 0.15 # meters\n # Starting Joint angles for right arm\n starting_joint_angles = {'right_j0': -0.041662954890248294,\n 'right_j1': -1.0258291091425074,\n 'right_j2': 0.0293680414401436,\n 'right_j3': 1.37518162913313,\n 'right_j4': -0.06703022873354225,\n 'right_j5': 0.7968371433926965,\n 'right_j6': 1.7659649178699421}\n\n pnp = PickAndPlace(limb, hover_distance)\n\n pnp.move_to_start(starting_joint_angles)\n\n \n # m_planner = trajectorySender()\n # An orientation for gripper fingers to be overhead and parallel to the obj\n overhead_orientation = Quaternion(\n x=-0.00142460053167,\n y=0.999994209902,\n z=-0.00177030764765,\n w=0.00253311793936)\n block_poses = list()\n # The Pose of the block in its initial location.\n # You may wish to replace these poses with estimates\n # from a perception node.\n block_poses.append(Pose(\n position=Point(x=0.45, y=0.155, z=-0.129),\n orientation=overhead_orientation))\n # Feel free to add additional desired poses for the object.\n # Each additional pose will get its own pick and place.\n block_poses.append(Pose(\n position=Point(x=0.6, y=-0.1, z=-0.129),\n orientation=overhead_orientation))\n # Move to the desired starting angles\n print(\"Running. Ctrl-c to quit\")\n # pnp.move_to_start(starting_joint_angles)\n idx = 0\n rate = rospy.Rate(1)\n block_quat_pose = [0.00142460053167,\n 0.999994209902,\n 0.00177030764765,\n 0.00253311793936]\n if rospy.has_param('vel_calc'):\n rospy.delete_param('vel_calc')\n load_gazebo_models()\n\n while not rospy.is_shutdown():\n\n\n starting_joint_angles['right_j0'] = np.random.uniform(-0.05, 0.05)\n starting_joint_angles['right_j1'] = np.random.uniform(-0.95, -0.85)\n starting_joint_angles['right_j2'] = np.random.uniform(-0.1, 0.1)\n starting_joint_angles['right_j3'] = np.random.uniform(1.6, 1.7)\n\n # starting_joint_angles['right_j0'] = np.random.uniform(-0.75, 0.75)\n # starting_joint_angles['right_j1'] = np.random.uniform(-0.97, -0.80)\n # starting_joint_angles['right_j2'] = np.random.uniform(-0.15, 0.15)\n # starting_joint_angles['right_j3'] = np.random.uniform(1.55, 1.75)\n\n start_pose = [starting_joint_angles['right_j0'], starting_joint_angles['right_j1'],\n starting_joint_angles['right_j2'], starting_joint_angles['right_j3'],\n starting_joint_angles['right_j4'], starting_joint_angles['right_j5'],\n starting_joint_angles['right_j6']]\n \n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n if rospy.has_param('dagger_reset'):\n rospy.delete_param('dagger_reset')\n break\n pnp.move_to_start(starting_joint_angles)\n\n\n delete_kinect_camera()\n # delete_gazebo_models()\n delete_gazebo_block()\n rand_x = np.random.uniform(0.45, .75)\n rand_y = np.random.uniform(-0.2, 0.33)\n # rand_x = np.random.uniform(0.44,0.68)\n\n # rand_y = np.random.uniform(-0.20, 0.35)\n pose_block = Pose(position=Point(x=rand_x, y=rand_y, z=1.00)\n , orientation=overhead_orientation)\n pose_rob = Pose(position=Point(x=rand_x-0.015, y=rand_y+0.03, z=0.03), orientation=overhead_orientation) \n\n # rospy.set_param('vel_calc', 'true')\n # pnp.move_to_start(starting_joint_angles)\n # oktogo = pnp.move_to_start_vel_command(start_pose)\n # if rospy.has_param('vel_calc'):\n # rospy.delete_param('vel_calc')\n # loads env\n load_gazebo_block(block_pose=pose_block)\n # load_kinect_camera()\n\n \n\n # rospy.set_param('vel_calc', 'true')\n print 'Reaching target object... Learning...'\n rospy.set_param('epi_start', 'true')\n pnp.reach(pose_rob, pos_cmd_pub)\n # reached = pnp.reach_vel_ctrl(pose_rob)\n rospy.sleep(0.5)\n # if rospy.has_param('vel_calc'):\n # rospy.delete_param('vel_calc')\n # if reached:\n # rospy.set_param('reached', 'true')\n # goal_obs_pub.publish(pose_rob)\n\n\n print 'Reached target object! and Goal obs acquired Resetting...'\n # while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n # if rospy.has_param('demo_success'):\n # break\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\n if rospy.has_param('demo_success'):\n rospy.delete_param('demo_success')\n break\n \n # rospy.delete_param('demo_success')\n \n\n return 0","def conRFMixAndMaskToBeagle(indfile_name, rephasedhaps_pref, em_iters, win_size, chroms):\n\t### First get individual information\n\twindow_id = 0\n\tem_iter = em_iters\n\tindfile = open(indfile_name, \"r\")\t\n\tinds = []\n\tfor line in indfile:\n\t\tsplits = line.strip(\"\\r\\n\").split()\n\t\tinds.append(splits[1] + \"_A\")\n\t\tinds.append(splits[1] + \"_B\")\n\n\tallloci = []\n\toutfilename = rephasedhaps_pref + \"_w\" + str(win_size) + \".beagle\"\n\toutfile = open(outfilename, \"w\")\n\toutfile.write(\"I\\tid\\t\" + \"\\t\".join(inds) + \"\\n\")\n\t## Write genotype data out to file\n\n\tvitout = open(rephasedhaps_pref + \".vit\", \"w\")\n\twinout = open(rephasedhaps_pref + \".windows\", \"w\")\n\tfbkout = rephasedhaps_pref + \".fbk\"\n\tif os.path.exists(fbkout):\n\t\tos.remove(fbkout)\n\tvitlist = []\n\tfor chrom in chroms:\n\t\tprint chrom\n\t\tshapeitfilename = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.allelesRephased\" + str(em_iters) + \".txt\"\n\t\tshapeitfile = open(shapeitfilename, \"rb\")\n\t\tfbkin_name = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.\" + str(em_iters) + \".ForwardBackward.txt\"\n\t\tos.system('cat ' + fbkin_name + \" >> \" + fbkout) # Concatenate files together\n\t\tmarkerin = rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.amaps\"\n\t\tmarkerfile = open(markerin, \"r\")\n\t\tloci=[]\n\t\talleles = {}\n\t\tfor mline in markerfile:\n\t\t\tmsplit = mline.strip().split()\n\t\t\tloci.append(msplit[1])\n\t\t\talleles[msplit[1]] = [msplit[3], msplit[4] ]\n\n\t\tallloci.extend(loci)\n\t\tfor j,line in enumerate(shapeitfile):\n\t\t\tsline = line.strip(\"\\r\\n\")\n\t\t\tzero, ones = alleles[loci[j]]\n\t\t\tfixed = [ recodeAllele(k, zero, ones) for k in sline ]\n\t\t\toutfile.write(\"M\\t\" + loci[j] + \"\\t\" + \"\\t\".join(fixed) + \"\\n\")\n\t\tvitfile = open(rephasedhaps_pref + \"_chr\" + str(chrom) + \"_shapeout.\" + str(em_iters) + \".Viterbi.txt\", \"r\")\n\t\tvitlist.extend([x.strip().split() for x in vitfile])\n\t\tshapeitfile.close()\n\t\tvitfile.close()\n\t\t\n\t# This will transpose the whole Viterbi file\n\t# Yikes this may take a lot of memory\n\tfor i,x in enumerate(zip(*vitlist)):\n\t\tvitout.write(inds[i] + \"\\t\")\n\t\tfor y in x:\n\t\t\tvitout.write(y+\"\\t\")\n\t\tvitout.write(\"\\n\")\n\t\t### This doesn't quite work yet so make sure to fix it next time\n\tfor l in allloci:\n\t\twinout.write(\"window\" + str(window_id) + \"\\t\" + l + \"\\n\")\n\t\twindow_id += 1\n\treturn([outfile.name, vitout.name, winout.name, fbkout])","def stop(self):\n self.turnOffMotors()","def _detect_stop(func):\n def wrapper(*args,**kwargs):\n self = args[0]\n self.episode_length -= 1\n if self.episode_length <=0:\n \"\"\"if the episode is end\"\"\"\n self.end = True\n else:\n if self.adsorption:\n \"\"\"just stop moving and wait until the end of episode\"\"\"\n self.state = self.previous_state\n else:\n func(*args,**kwargs)\n self._detect_obstacles()\n\n # func(*args,**kwargs)\n # self._detect_obstacles()\n # if self.adsorption:\n # \"\"\"if this step update is invalid, the point will rebond\"\"\"\n # self.state = self.previous_state\n\n if self.distance <= 0.02:\n \"\"\"if the point reached the boundary around the goal, let it stop and reset the punishment(self.reward)\"\"\"\n self.end = True\n self.reward = 0\n if self.state[0] <0 or self.state[0] > 10 or self.state[1] <0 or self.state[1] > 10:\n # self.end = True\n self.reward = -800\n return np.array(self.state), self.reward, self.end, self.distance\n return wrapper","def __init__(self, *args, **kwargs):\n _Beamlet.__init__(self, *args, **kwargs)","def _beam(self):\n\n return self._beam_factory.simple(self.detectorbase.wavelength)","def chacha(self):\n self.right()\n time.sleep(2)\n self.stop()\n self.back()\n time.sleep(1)\n self.servo(1000)\n time.sleep(1)\n self.stop()\n self.fwd()\n time.sleep(1)\n self.stop()\n self.servo(2000)\n time.sleep(1)\n self.stop()\n self.left()\n time.sleep(2)\n self.stop()","def stop(self):\n\n self.active = False\n angle_pwm = self.angle_to_pmw(const.Driving.NEUTRAL_STEERING_ANGLE)\n self.pwm.set_pwm(0, 0, angle_pwm)","def escAmpa(self) :\n while 1 :\n (value, end) = self.getInteger()\n if value is None :\n return\n if end == 'G' : \n #self.logdebug(\"BACKSIDES %i\" % value)\n self.backsides.append(value)\n self.setPageDict(\"duplex\", value)","def stop():\n status = write_i2c_block(ADDRESS,stop_cmd+[0,0,0])\n set_left_speed(0)\n set_right_speed(0)\n return status","def create_on_off(self):\n self.laser_off = []\n self.laser_on = []\n\n self.laser_off.append(\n (0+self.time_to_number(self.skip['bcg_start']), self.starts[0]-self.time_to_number(self.skip['bcg_end'])))\n\n for i in range(len(self.starts)-1):\n self.laser_off.append((self.ends[i]+self.time_to_number(\n self.skip['bcg_start']), self.starts[i+1]-self.time_to_number(self.skip['bcg_end'])))\n self.laser_on.append((self.starts[i]+self.time_to_number(\n self.skip['sample_start']), self.ends[i]-self.time_to_number(self.skip['sample_end'])))\n\n self.laser_off.append((self.ends[-1]+self.time_to_number(self.skip['bcg_start']), len(\n self.time)-2-self.time_to_number(self.skip['bcg_end'])))\n self.laser_on.append((self.starts[-1]+self.time_to_number(\n self.skip['sample_start']), self.ends[-1]-self.time_to_number(self.skip['sample_end'])))","def _data_move_out_mc_on_h():\n\n pass","def _forwardImplementation(self, inbuf, outbuf):\n assert self.module\n \n values = self.module.getActionValues(self.state) \n \n actions = []\n if random() <= self.exploration:\n for i in range(self.shield_options):\n new_action = choice(range(len(values))) \n np.delete(values, new_action)\n actions.append(new_action)\n else:\n for i in range(self.shield_options):\n new_action = where(values == max(values))[0]\n new_action = choice(new_action) \n np.delete(values, new_action)\n actions.append(new_action)\n \n while len(actions) < self.outdim:\n actions.append(-1)\n \n outbuf[:] = actions","def stop(self):\n self._stopper.set()\n self.det.disable_mca()","def test_analog_in_out_loop(self):\n for v in range(0, 6, 1):\n self.l.output(ao0=v, ao1=v)\n r = self.l.input(channels=(8,8,9,9), gains=(1,4,1,4))[0]\n for i in r:\n self.assertTrue(abs(v-i) < .1,\n \"measured %g for %g\" % (i, v))","def stageout(self) -> None:\n raise NotImplementedError(\"Base method not implemented\")","def __init__(self, anara, anadec, *args, **kwargs):\n _Beamlet.__init__(self, *args, **kwargs)\n self._anara = anara\n self._anadec = anadec","def __init__(self, pinForward, pinBackward, pinControlStraight,pinLeft, pinRight, pinControlSteering):\n\n self.pinForward = pinForward\n self.pinBackward = pinBackward\n self.pinControlStraight = pinControlStraight\n self.pinLeft = pinLeft\n self.pinRight = pinRight\n self.pinControlSteering = pinControlSteering\n GPIO.setup(self.pinForward, GPIO.OUT)\n GPIO.setup(self.pinBackward, GPIO.OUT)\n GPIO.setup(self.pinControlStraight, GPIO.OUT)\n\n GPIO.setup(self.pinLeft, GPIO.OUT)\n GPIO.setup(self.pinRight, GPIO.OUT)\n GPIO.setup(self.pinControlSteering, GPIO.OUT)\n\n self.pwm_forward = GPIO.PWM(self.pinForward, 100)\n self.pwm_backward = GPIO.PWM(self.pinBackward, 100)\n self.pwm_forward.start(0)\n self.pwm_backward.start(0)\n\n self.pwm_left = GPIO.PWM(self.pinLeft, 100)\n self.pwm_right = GPIO.PWM(self.pinRight, 100)\n self.pwm_left.start(0)\n self.pwm_right.start(0)\n\n GPIO.output(self.pinControlStraight,GPIO.HIGH) \n GPIO.output(self.pinControlSteering,GPIO.HIGH)","def breath_analyze(self, offset=0, th=10):\n # breath part\n breath_gd = np.gradient(gf(self.breath_list, 10))\n breath_gd[breath_gd > 0] = 1\n breath_gd[breath_gd < 0] = 0\n breath_pulse = breath_gd[:-1]-np.roll(breath_gd, -1)[:-1]\n breath_in = argrelextrema(breath_pulse, np.less, order=10)[0]#+offset\n breath_out = argrelextrema(breath_pulse, np.greater, order=10)[0]#+offset\n self.breath = np.sort(np.hstack([breath_in, breath_out, len(self.breath_list)-1]))\n \n if self.breath[0] == breath_in[0]:\n self.btype = 'in'\n else:\n self.btype = 'out' \n\n b_in = []\n b_out = []\n delidx = []\n\n if len(self.breath) != 0: \n for i, j in zip(self.breath[:-1], self.breath[1:]):\n breath_diff = abs(self.breath_list[j]-self.breath_list[i])\n if abs(breath_diff) > 3000: # really breath in/out\n if abs(breath_diff) < 30000: # not deep breath\n if breath_diff > 0: # breath out\n print('breath out from frame '+str(i)+' to frame '+str(j)\n +' <== breath not deep enough')\n b_out.append(j-i)\n self.ngframe.append(i)\n else: # breath in\n print('breath in from frame '+str(i)+' to frame '+str(j)\n +' <== breath not deep enough')\n b_in.append(j-i)\n else: \n if breath_diff > 0: # breath out\n print('breath out from frame '+str(i)+' to frame '+str(j))\n b_out.append(j-i)\n else: # breath in\n print('breath in from frame '+str(i)+' to frame '+str(j))\n b_in.append(j-i)\n else:\n delidx.append(np.argwhere(self.breath==j)[0][0])\n self.breath = np.delete(self.breath, np.array(delidx))\n\n print('\\naverage breath out freq is: '+str(np.round(30./np.mean(b_out), 2))+' Hz')\n print('\\naverage breath in freq is: '+str(np.round(30./np.mean(b_in), 2))+' Hz')\n else:\n raise ImportError('Doing too fast !! please redo again !!')","def beam(xb,yb,zb,wx,wy,wavelen):\n\n zRx = np.pi * wx**2 / wavelen\n zRy = np.pi * wy**2 / wavelen \n \n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\n + np.power(yb/(wy*sqrtY),2) )) / sqrtX / sqrtY\n return intensity","def tomoScanWithFrames(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\"default\", nframes=1, tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n try:\n darkFieldInterval = int(darkFieldInterval)\n flatFieldInterval = int(flatFieldInterval)\n optimizeBeamInterval = int(optimizeBeamInterval)\n \n image_key_frame = 3\n nframes = int(nframes)\n if nframes < 1:\n nframes = 1\n \n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\n tomography_theta = jns.tomography_theta\n if tomography_theta is None:\n raise NameError(\"tomography_theta is not defined in Jython namespace\")\n tomography_shutter = jns.tomography_shutter\n if tomography_shutter is None:\n raise NameError(\"tomography_shutter is not defined in Jython namespace\")\n tomography_translation = jns.tomography_translation\n if tomography_translation is None:\n raise NameError(\"tomography_translation is not defined in Jython namespace\")\n \n tomography_detector = jns.tomography_detector\n if tomography_detector is None:\n raise NameError(\"tomography_detector is not defined in Jython namespace\")\n\n tomography_optimizer = jns.tomography_optimizer\n if tomography_optimizer is None:\n raise NameError(\"tomography_optimizer is not defined in Jython namespace\")\n\n tomography_time = jns.tomography_time\n if tomography_time is None:\n raise NameError(\"tomography_time is not defined in Jython namespace\")\n \n tomography_beammonitor = jns.tomography_beammonitor\n if tomography_beammonitor is None:\n raise NameError(\"tomography_beammonitor is not defined in Jython namespace\")\n \n tomography_camera_stage = jns.tomography_camera_stage\n if tomography_camera_stage is None:\n raise NameError(\"tomography_camera_stage is not defined in Jython namespace\")\n \n tomography_sample_stage = jns.tomography_sample_stage\n if tomography_sample_stage is None:\n raise NameError(\"tomography_sample_stage is not defined in Jython namespace\")\n \n tomo_additional_scannables = jns.tomography_additional_scannables\n if tomo_additional_scannables is None:\n raise NameError(\"tomo_additional_scannables is not defined in Jython namespace\")\n \n index = SimpleScannable()\n index.setCurrentPosition(0.0)\n index.setInputNames([\"imageNumber\"])\n index.setName(\"imageNumber\")\n index.configure()\n \n image_key = SimpleScannable()\n image_key.setCurrentPosition(0.0)\n image_key.setInputNames([\"image_key\"])\n image_key.setName(\"image_key\")\n image_key.configure()\n\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\n tomography_translation, tomography_optimizer, image_key, index)\n\n# return tomoScanDevice\n #generate list of positions\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\n theta_points = []\n theta_points.append(start)\n previousPoint = start\n for i in range(numberSteps):\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\n theta_points.append(nextPoint)\n previousPoint = nextPoint\n \n #generateScanPoints\n optimizeBeamNo = 0\n optimizeBeamYes = 1\n shutterOpen = 1\n shutterClosed = 0\n shutterNoChange = 2\n scan_points = []\n theta_pos = theta_points[0]\n index = 0\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1\n \n for i in range(imagesPerFlat): \n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n for frm in range(nframes): \n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index)) #first\n index = index + 1 \n imageSinceDark = 1\n imageSinceFlat = 1\n optimizeBeam = 0\n for i in range(numberSteps):\n theta_pos = theta_points[i + 1]\n for frm in range(nframes):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index))#main image\n index = index + 1 \n \n imageSinceFlat = imageSinceFlat + 1\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\n index = index + 1 \n imageSinceFlat = 0\n \n imageSinceDark = imageSinceDark + 1\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\n index = index + 1 \n imageSinceDark = 0\n\n optimizeBeam = optimizeBeam + 1\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\n index = index + 1 \n optimizeBeam = 0\n \n #add dark and flat only if not done in last steps\n if imageSinceFlat != 0:\n for i in range(imagesPerFlat):\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\n index = index + 1\n if imageSinceDark != 0:\n for i in range(imagesPerDark):\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\n index = index + 1 \n# scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\n# imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\n# if pattern == 'default' or pattern == 'DFPFD':\n# i = 0\n# for pt1 in scan_points1:\n# pt = scan_points[i]\n# if pt1 != pt:\n# print \"Mismatch - please tell Kaz about your scan and its arguments!\"\n# print \"i = \", i\n# print \"pt = \", pt\n# print \"pt1 = \", pt1\n# i += 1\n #return None\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\n #scan_args.append(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(RotationAxisScannable(\"approxCOR\", tomoRotationAxis))\n #meta_add(\"RotationCoord_as_list\", [tomoRotationAxis])\n meta_add(\"approxCOR\", tomoRotationAxis)\n for scannable in additionalScannables:\n scan_args.append(scannable)\n for scannable in tomo_additional_scannables:\n scan_args.append(scannable)\n ''' setting the description provided as the title'''\n if not description == None: \n setTitle(description)\n else :\n setTitle(\"undefined\")\n \n dataFormat = LocalProperties.get(\"gda.data.scan.datawriter.dataFormat\")\n if not dataFormat == \"NexusDataWriter\":\n handle_messages.simpleLog(\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\")\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", \"NexusDataWriter\")\n scanObject = createConcurrentScan(scan_args)\n if addNXEntry:\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\n scanObject.runScan()\n if autoAnalyse:\n lsdp=jns.lastScanDataPoint()\n OSCommandRunner.runNoWait([\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\n return scanObject;\n except InterruptedException:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"User interrupted the scan\", exceptionType, exception, traceback, False)\n raise InterruptedException(\"User interrupted the scan\")\n except:\n exceptionType, exception, traceback = sys.exc_info()\n handle_messages.log(None, \"Error during tomography scan\", exceptionType, exception, traceback, False)\n raise Exception(\"Error during tomography scan\", exception)\n finally:\n handle_messages.simpleLog(\"Data Format reset to the original setting: \" + dataFormat)\n LocalProperties.set(\"gda.data.scan.datawriter.dataFormat\", dataFormat)","def getedge(self):\n cmd=\"getEdge(\"+self.board+\",\"+self.inpedge+\")\"\n output=self.vb.io.execute(cmd,log=\"out\",applout=\"<>\")\n #print 'edge= ',output\n self.edge=output[0]\n self.inputedge.setEntry(self.edge)\n if self.board != '0': \n self.delay=output[1] \n self.inputdelay.setEntry(self.delay)","def feed(self):\n # or intelligence discard\n pass","def stop(self):\n self.running = False\n self.cam.stop()\n self.amplifier.stop()\n pass","def end_phase():\n pass","def stop() -> None:","def stop(self) -> None:","def stop(self) -> None:","def stopAcceleratingForward(self,event):\n self.isAcceleratingForward=False","def Stop(self) :\n\t\t...","def tomo_scan(*, start=0, stop=180, numProjPerSweep=1500, slewSpeed=5, accl=1, samInPos=0, samOutDist=-3, md=None):\n _md = md or OrderedDict()\n _md[\"project\"] = \"mona\"\n _md[\"APS_storage_ring_current,mA\"] = aps_current.value\n _md[\"datetime_plan_started\"] = str(datetime.now())\n\n pso = psofly\n det = pg3_det\n rotStage = tomo_stage.rotary\n shutter = A_shutter\n \n acquire_time = 0.01\n report_t0 = None\n update_interval_s = 5.0 # during scans, print interim progress\n update_poll_delay_s = 0.01\n\n stage_sigs = {}\n stage_sigs[\"det.cam\"] = det.cam.stage_sigs\n\n staged_device_list = [det]\n monitored_signals_list = [\n det.image.array_counter,\n rotStage.user_readback,\n ]\n\n def cleanup():\n for d in [det.image.array_counter, rotStage.user_readback]:\n try:\n yield from bps.unmonitor(d)\n except IllegalMessageSequence:\n pass\n try:\n yield from bps.abs_set(shutter, \"close\", group='shutter')\n except RuntimeError as exc:\n print(exc)\n yield from bps.mv(rotStage.velocity, ROT_STAGE_FAST_SPEED)\n yield from motor_set_modulo(rotStage, 360.0)\n yield from bps.mv(\n rotStage, 0.00,\n #det.cam.trigger_mode, \"Internal\",\n #det.cam.image_mode, \"Continuous\",\n #det.hdf1.enable, \"Disable\",\n #det.hdf1.capture, \"Done\",\n )\n yield from bps.wait(group='shutter')\n\n det.cam.stage_sigs[\"num_images\"] = numProjPerSweep\n det.cam.stage_sigs[\"trigger_mode\"] = \"Overlapped\"\n det.cam.stage_sigs[\"trigger_source\"] = \"GPIO_0\"\n det.cam.stage_sigs[\"trigger_polarity\"] = \"Low\"\n det.cam.stage_sigs[\"image_mode\"] = \"Multiple\"\n det.hdf1.stage_sigs[\"num_capture\"] = numProjPerSweep # TODO: + NUM_DARK_FRAMES + NUM_FLAT_FRAMES,\n\n def _report_(t):\n msg = \"{:.2f}s - flying \".format(t)\n msg += \" x = {:.4f}\".format(tomo_stage.x.position)\n msg += \" y = {:.4f}\".format(tomo_stage.y.position)\n msg += \" r = {:.4f}\".format(tomo_stage.rotary.position)\n msg += \" theta = {:.4f}\".format(tomo_stage.rotary.position % 360)\n msg += \" image # {}\".format(det.cam.num_images_counter.value)\n return msg\n\n @run_in_thread\n def progress_reporting():\n logger.debug(\"progress_reporting is starting\")\n t = time.time()\n startup = t + update_interval_s/2\n while t < startup and pso.fly.value == 0: # wait for flyscan to start\n time.sleep(update_poll_delay_s)\n\n logger.debug(\"progress_reporting: fly starts\")\n update_time = t = report_t0 = time.time()\n while pso.fly.value == 1:\n if t >= update_time:\n update_time = t + update_interval_s\n msg = _report_(t - report_t0)\n print(msg)\n logger.debug(msg)\n time.sleep(update_poll_delay_s)\n t = time.time()\n msg = _report_(time.time() - report_t0)\n print(msg)\n logger.debug(msg)\n logger.debug(\"{}s - progress_reporting is done\".format(time.time()-report_t0))\n\n @bpp.stage_decorator(staged_device_list)\n @bpp.run_decorator(md=_md)\n @bpp.finalize_decorator(cleanup)\n def _internal_tomo():\n yield from bps.monitor(rotStage.user_readback, name=\"rotation_monitor\")\n yield from bps.monitor(det.image.array_counter, name=\"array_counter_monitor\")\n yield from bps.monitor(tomo_stage.x, name=\"tomo_stage_x_monitor\")\n\n # prepare the camera and the HDF5 plugin to write data\n yield from bps.mv(\n det.cam.nd_attributes_file, \"monaDetectorAttributes.xml\",\n det.cam.acquire_time, acquire_time,\n det.hdf1.enable, \"Enable\",\n det.hdf1.auto_save, \"Yes\",\n )\n #yield from bps.abs_set(det.hdf1.capture, \"Capture\")\n\n # TODO: test before using!\n # yield from measure_darks(det, shutter, NUM_DARK_FRAMES)\n # yield from measure_flats(det, shutter, NUM_FLAT_FRAMES, samStage, samInPos + samOutDist)\n\n # do not touch shutter during development\n yield from bps.abs_set(shutter, \"open\", group=\"shutter\")\n yield from set_image_frame()\n\n yield from bps.stop(rotStage)\n yield from motor_set_modulo(rotStage, 360.0)\n \n yield from bps.mv(\n rotStage.velocity, ROT_STAGE_FAST_SPEED, \n rotStage.acceleration, 3)\n yield from bps.mv(\n rotStage, 0, \n samStage, samInPos)\n\n # back off to the taxi point (back-off distance before fly start)\n logging.debug(\"before taxi\")\n yield from bps.mv(\n pso.start, start,\n pso.end, stop,\n pso.scan_control, \"Standard\",\n pso.scan_delta, 1.0*(stop-start)/numProjPerSweep,\n pso.slew_speed, slewSpeed,\n rotStage.velocity, ROT_STAGE_FAST_SPEED,\n rotStage.acceleration, slewSpeed/accl,\n det.cam.array_counter, 0,\n )\n\n progress_reporting()\n yield from bps.mv(pso.taxi, \"Taxi\")\n logging.debug(\"after taxi\")\n\n # run the fly scan\n logging.debug(\"before fly\")\n yield from bps.mv(rotStage.velocity, slewSpeed)\n yield from bps.wait(group='shutter') # shutters are slooow, MUST be done now\n yield from set_image_frame()\n yield from bps.trigger(det, group='fly')\n yield from bps.abs_set(pso.fly, \"Fly\", group='fly')\n yield from bps.wait(group='fly')\n #yield from bps.abs_set(det.cam.acquire, 0)\n logging.debug(\"after fly\")\n\n # read the camera\n yield from bps.create(name='primary')\n yield from bps.read(det)\n yield from bps.save()\n det.cam.stage_sigs = stage_sigs[\"det.cam\"]\n\n return (yield from _internal_tomo())","def wave_tx_stop():\n return _u2i(_pigpio_command(_control, _PI_CMD_WVHLT, 0, 0))","def set_attenuator_output(self, state):\n try:\n self.attenuator.write('O {0}'.format(state))\n except visa.VisaIOError as err:\n raise IOError('Error: %s' % err)","def evaluateIFsetup(ants=0, timerange=3.4, deltat=0.1,\n verbose=False, iniAtten=0) :\n antlist = makeAntList(ants)\n for a in antlist:\n if a < 16:\n print \"SZA antennas only can be used - exiting\"\n return\n tpMP = \"Ifmod.ifTotalPower\"\n attenMP = \"Ifmod.totalAtten\"\n currentPos = \"AMBIENT\"\n print \"Inserting ambient load into beam\"\n rtn = amb(ants)\n pos1ants = rtn.ready\n mplist = []\n for a in pos1ants:\n prefix = \"Sza%d.\" %(a-15)\n mplist.append([prefix+tpMP, prefix+attenMP])\n #print mplist\n if len(rtn.notready) != 0:\n print \"Ambient load not inserted for ants:\", rtn.notready\n if iniAtten != None :\n print \"Setting IF atten to %.1f\" %iniAtten\n antennaIFatten(iniAtten, 1, invalidateTsys=False, \n ants=antlist)\n wait(tmo=2.0)\n t0=time.time()\n antennaIFpower(0.3, ants)\n t1 = time.time()\n cmdtime = t1-t0\n results = []\n times = []\n nsamps = int(round(timerange/deltat))\n for i in range(nsamps):\n sleep(deltat)\n dt=time.time()-t1\n times.append(dt)\n results.append(queryMpValues(mplist))\n #print results\n print \"Command time = %.3f seconds\" %cmdtime\n for j in range(len(pos1ants)):\n aname = \"Sza%d\" %(pos1ants[j]-15)\n if verbose: print \"======== %s ========\" %aname\n for i in range(nsamps):\n r = results[i][j]\n st = \"%s: %4.1f %6.3f %4.1f\" %(aname,times[i],r[0],r[1])\n if verbose: print st\n print \"\\n==== Inferred timing & final atten =====\"\n for j in range(len(pos1ants)):\n aname = \"Sza%d\" %(pos1ants[j]-15)\n finalAtten = results[nsamps-1][j][1]\n timing = times[nsamps-1]\n for i in range(nsamps):\n index = nsamps-1-i\n atten = results[index][j][1]\n if atten != finalAtten: break\n timing = times[index]\n st = \" %s: %4.1f %4.1f\" %(aname,timing, finalAtten)\n print st\n \n #print \"Moving ambient load out of the beam\"\n pos2ants = sky(ants).ready\n # Wait for two seconds to get final psys reading\n wait(tmo=2.0)\n skyants = pos2ants\n complete = []\n for a in pos1ants :\n if (pos2ants.count(a) > 0) : complete.append(a)\n return complete","def beam_filter(self, ouput_token_step):\n idx_array = np.argpartition(ouput_token_step[0, -1, :], -self.beam_width)[-self.beam_width:] #index for reverse_target_word\n prob_array = np.log(np.partition(ouput_token_step[0, -1, :], -self.beam_width)[-self.beam_width:])\n \n return idx_array, prob_array","def generate_beam(env, dec, decoder_args, beam_size, length_penalty, early_stopping, max_len=100):\n\n # check inputs\n trg, enc_src, trg_mask, src_mask = decoder_args\n src_enc = enc_src\n src_len = enc_src \n\n #assert src_enc.size(0) == src_len.size(0)\n assert beam_size >= 1\n\n # batch size / number of words\n bs = len(src_enc)\n n_words = env.n_words\n breakpoint()\n\n # expand to beam size the source latent representations / source lengths\n src_enc = src_enc.unsqueeze(1).expand((bs, beam_size) + src_enc.shape[1:]).contiguous().view((bs * beam_size,) + src_enc.shape[1:])\n #src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1)\n\n # generated sentences (batch with beam current hypotheses)\n #generated = src_len.new(max_len, bs * beam_size) # upcoming output\n #generated.fill_(env.pad_index) # fill upcoming ouput with <PAD>\n #generated[0].fill_(env.eos_index) # we use <EOS> for <BOS> everywhere\n\n # generated hypotheses\n generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)]\n\n # positions\n positions = src_len.new(max_len).long()\n #positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated)\n\n # scores for each sentence in the beam\n beam_scores = src_enc.new(bs, beam_size).fill_(0)\n beam_scores[:, 1:] = -1e9\n beam_scores = beam_scores.view(-1)\n\n # current position\n cur_len = 1\n\n # cache compute states\n cache = {'slen': 0}\n\n # done sentences\n done = [False for _ in range(bs)]\n breakpoint()\n while cur_len < max_len:\n dec(trg[:,:-1], enc_src, trg_mask, src_mask)\n # compute word scores\n tensor = decoder(\n x=generated[:cur_len],\n lengths=src_len.new(bs * beam_size).fill_(cur_len),\n positions=positions[:cur_len],\n causal=True,\n src_enc=src_enc,\n src_len=src_len,\n cache=cache\n )\n assert tensor.size() == (1, bs * beam_size, env.dim)\n tensor = tensor.data[-1, :, :] # (bs * beam_size, dim)\n scores = env.proj(tensor) # (bs * beam_size, n_words)\n scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words)\n assert scores.size() == (bs * beam_size, n_words)\n\n # select next words with scores\n _scores = scores + beam_scores[:, None].expand_as(scores) # (bs * beam_size, n_words)\n _scores = _scores.view(bs, beam_size * n_words) # (bs, beam_size * n_words)\n\n next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True)\n assert next_scores.size() == next_words.size() == (bs, 2 * beam_size)\n\n # next batch beam content\n # list of (bs * beam_size) tuple(next hypothesis score, next word, current position in the batch)\n next_batch_beam = []\n\n # for each sentence\n for sent_id in range(bs):\n\n # if we are done with this sentence\n done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item())\n if done[sent_id]:\n next_batch_beam.extend([(0, env.pad_index, 0)] * beam_size) # pad the batch\n continue\n\n # next sentence beam content\n next_sent_beam = []\n\n # next words for this sentence\n for idx, value in zip(next_words[sent_id], next_scores[sent_id]):\n\n # get beam and word IDs\n beam_id = idx // n_words\n word_id = idx % n_words\n\n # end of sentence, or next word\n if word_id == env.eos_index or cur_len + 1 == max_len:\n generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone().cpu(), value.item())\n else:\n next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))\n\n # the beam for next step is full\n if len(next_sent_beam) == beam_size:\n break\n\n # update next beam content\n assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size\n if len(next_sent_beam) == 0:\n next_sent_beam = [(0, env.pad_index, 0)] * beam_size # pad the batch\n next_batch_beam.extend(next_sent_beam)\n assert len(next_batch_beam) == beam_size * (sent_id + 1)\n\n # sanity check / prepare next batch\n assert len(next_batch_beam) == bs * beam_size\n beam_scores = beam_scores.new([x[0] for x in next_batch_beam])\n beam_words = generated.new([x[1] for x in next_batch_beam])\n beam_idx = src_len.new([x[2] for x in next_batch_beam])\n\n # re-order batch and internal states\n generated = generated[:, beam_idx]\n generated[cur_len] = beam_words\n for k in cache.keys():\n if k != 'slen':\n cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx])\n\n # update current length\n cur_len = cur_len + 1\n\n # stop when we are done with each sentence\n if all(done):\n break\n\n # def get_coeffs(s):\n # roots = [int(s[i + 2]) for i, c in enumerate(s) if c == 'x']\n # poly = np.poly1d(roots, r=True)\n # coeffs = list(poly.coefficients.astype(np.int64))\n # return [c % 10 for c in coeffs], coeffs\n\n # visualize hypotheses\n # print([len(x) for x in generated_hyps], cur_len)\n # globals().update( locals() );\n # !import code; code.interact(local=vars())\n # for ii in range(bs):\n # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True):\n # hh = \" \".join(self.id2word[x] for x in ww.tolist())\n # print(f\"{ss:+.4f} {hh}\")\n # # cc = get_coeffs(hh[4:])\n # # print(f\"{ss:+.4f} {hh} || {cc[0]} || {cc[1]}\")\n # print(\"\")\n\n # select the best hypotheses\n tgt_len = src_len.new(bs)\n best = []\n\n for i, hypotheses in enumerate(generated_hyps):\n best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]\n tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol\n best.append(best_hyp)\n\n # generate target batch\n decoded = src_len.new(tgt_len.max().item(), bs).fill_(env.pad_index)\n for i, hypo in enumerate(best):\n decoded[:tgt_len[i] - 1, i] = hypo\n decoded[tgt_len[i] - 1, i] = env.eos_index\n\n # sanity check\n assert (decoded == env.eos_index).sum() == 2 * bs\n\n return decoded, tgt_len, generated_hyps","def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')","def off(self):","def stopcam_expose():\n\n\trespond = send_command('stopcam')","def __init__(self, pinForward1, pinBackward1,pinForward2, pinBackward2):\n\n self.pinForward1 = pinForward1\n self.pinBackward1 = pinBackward1\n self.pinForward2 = pinForward2\n self.pinBackward2 = pinBackward2\n\n GPIO.setup(self.pinForward1, GPIO.OUT)\n GPIO.setup(self.pinBackward1, GPIO.OUT)\n GPIO.setup(self.pinForward2, GPIO.OUT)\n GPIO.setup(self.pinBackward2, GPIO.OUT)\n\n self.pwm_forward1 = GPIO.PWM(self.pinForward1, 100)\n self.pwm_backward1 = GPIO.PWM(self.pinBackward1, 100)\n self.pwm_forward2 = GPIO.PWM(self.pinForward2, 100)\n self.pwm_backward2 = GPIO.PWM(self.pinBackward2, 100)\n \n self.pwm_forward1.start(0)\n self.pwm_backward1.start(0)\n self.pwm_forward2.start(0)\n self.pwm_backward2.start(0)","def FlyOut(self, b=True):\r\n\r\n return self.SetFlag(self.optionFlyOut, b)","def stop_motor(self):\n self.output(self.steering_pin, 0)\n self.pi.set_servo_pulsewidth(self.steering_pin, 0)","def eye_calc(self):\n # define the eye time vector scaled to UI, dimensionless\n tnum = (1+int(round((self.eye_time_high-self.eye_time_low) / self.eye_time_step)))\n self.time = np.linspace(self.eye_time_low, # column Z\n self.eye_time_high,\n tnum)\n # convenience vector\n t_1 = np.ones(tnum)\n # column AA\n self.time_eff = 0.5*t_1 + self.speedup*(self.time - 0.5*t_1)\n arg1 = 2.0*erfinv(0.8)\n arg2 = arg1/(1.0E-6*self.speedup*self.br_nominal)\n \n # calculate eye profiles for link at target reach\n T_c_link = self.tc[self.lnum//2]\n arg3 = arg2*self.time_eff/T_c_link # column AP\n arg4 = arg2*(1.0 - self.time_eff)/T_c_link # column AQ\n self.link_011 = 0.5*t_1 + 0.5*erf(arg3) # column AR\n self.link_110 = 0.5*t_1 + 0.5*erf(arg4) # column AS\n self.link_010 = self.link_011 + self.link_110 - t_1 # column AT\n self.link_101 = t_1 - self.link_010 # column AW\n self.link_001 = t_1 - self.link_110 # column AU\n self.link_100 = t_1 - self.link_011 # column AV\n\n # calculate eye profiles for transmitter eye test configuration\n T_c_test = math.sqrt(self.tx_1090_rise**2\n + (0.329E6/self.txeye_rx_bw)**2)\n arg5 = arg2*self.time_eff/T_c_test\n arg6 = arg2*(t_1 - self.time_eff)/T_c_test\n self.test_011 = 0.5*t_1 + 0.5*erf(arg5) # column AR\n self.test_110 = 0.5*t_1 + 0.5*erf(arg6) # column AS\n self.test_010 = self.test_011 + self.test_110 - t_1 # column AT\n self.test_101 = t_1 - self.test_010\n self.test_001 = t_1 - self.test_110\n self.test_100 = t_1 - self.test_011\n\n print(\"Margin : \", self.margin)","def eamap_end(*args):\n return _ida_hexrays.eamap_end(*args)","def test_attctl(self):\n # boundary to cross\n boundary_x = 5\n boundary_y = 5\n boundary_z = -5\n\n # delay starting the mission\n self.wait_for_topics(30)\n\n rospy.loginfo(\"seting mission mode\")\n self.set_mode(\"OFFBOARD\", 5)\n rospy.loginfo(\"arming\")\n self.set_arm(True, 5)\n\n rospy.loginfo(\"run mission\")\n rospy.loginfo(\"attempting to cross boundary | x: {0}, y: {1}, z: {2}\".\n format(boundary_x, boundary_y, boundary_z))\n # does it cross expected boundaries in 'timeout' seconds?\n timeout = 12 # (int) seconds\n loop_freq = 10 # Hz\n rate = rospy.Rate(loop_freq)\n crossed = False\n for i in xrange(timeout * loop_freq):\n if (self.local_position.pose.position.x > boundary_x and\n self.local_position.pose.position.z > boundary_y and\n self.local_position.pose.position.y < boundary_z):\n rospy.loginfo(\"boundary crossed | seconds: {0} of {1}\".format(\n i / loop_freq, timeout))\n crossed = True\n break\n\n rate.sleep()\n\n self.assertTrue(crossed, (\n \"took too long to cross boundaries | current position x: {0}, y: {1}, z: {2} | timeout(seconds): {3}\".\n format(self.local_position.pose.position.x,\n self.local_position.pose.position.y,\n self.local_position.pose.position.z, timeout)))\n\n rospy.loginfo(\"disarming\")\n self.set_arm(False, 5)","def __init__(self, motion, **kwargs):\n super(ShiftIn, self).__init__(motion)","def test_out(self):\n t_max = 24.0\n dt = 0.1\n\n G = StudentLayer(1)\n G.i_ext_init = 0.1\n\n M = simulation.EventMonitor(G)\n Mo = simulation.StateMonitor(G, 'out')\n sim = simulation.Simulation(G, M, Mo, dt=dt)\n sim.run(t_max)\n\n # we need a single spike for this\n # XXX we could also have set the refractory period to a really high number\n self.assertEqual(len(M.t), 1)\n\n t_spike = M.t[0]\n mask = (Mo.t > t_spike)\n\n out_t = Mo.t[mask]\n out_y = Mo.out[0, mask]\n\n expected = out_y[0]*np.power(1 - dt/G.tau_out, (out_t - t_spike)/dt)\n\n self.assertLess(np.mean(np.abs(out_y - expected)), 1e-6)","def _step(self, a):\n obs, rew, done, info = super()._step(a)\n # if self.robot.body_xyz[0] > self.threshold:\n # rew = 1.0\n # self.threshold += 1\n # else:\n # rew = 0.0\n # self.steps += 1\n # if self.steps > self.max_episode_steps:\n # done = True\n return obs, rew, done, info","def takeoff(self, request, context):\n print \"Arming motors\"\n # Copter should arm in GUIDED mode\n self.vehicle.mode = VehicleMode(\"GUIDED\")\n self.vehicle.armed = True \n\n # Confirm vehicle armed before attempting to take off\n timeout = 0\n while not self.vehicle.armed: \n print \" Waiting for arming...\"\n time.sleep(1)\n timeout = timeout + 1\n if timeout == 20:\n return self.getPosition(request, context)\n\n print \"Taking off!\"\n self.vehicle.simple_takeoff(request.altitude) # Take off to target altitude\n\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \n # after Vehicle.simple_takeoff will execute immediately).\n while True:\n print \" Altitude: \", self.vehicle.location.global_relative_frame.alt \n #Break and return from function just below target altitude. \n if self.vehicle.location.global_relative_frame.alt >= request.altitude * 0.95: \n print \"Reached target altitude\"\n break\n time.sleep(1)\n \n return self.getPosition(request, context)","def stop(self, *args, **kwargs):\n return self(AbilityId.STOP, *args, **kwargs)","def fwd(dist=0): #distance is in cm\n try:\n if dist>0:\n # this casting to int doesn't seem necessary\n pulse=int(PPR*(dist//WHEEL_CIRC) )\n enc_tgt(1,1,pulse)\n except Exception as e:\n print (\"gopigo fwd: {}\".format(e))\n pass\n return write_i2c_block(ADDRESS,motor_fwd_cmd+[0,0,0])","def _on_lane_invasion(self, event):\n self.lanes_invaded = event.crossed_lane_markings","def stop( self ) :\n print '********************* number of events seen: ', globals()['evCounter']\n\n from Configurables import LumiIntegrator\n lumitool = toolSvc.create('LumiIntegrator', 'IntegrateBeamCrossing', interface='ILumiIntegrator')\n lumirawtool = toolSvc.create('LumiIntegrator', 'RawLumiIntegrator', interface='ILumiIntegrator')\n print '********************* created lumiintegrator tools'\n\n # store data\n store = {}\n for (text, tool) in ( ('corrected', lumitool), ('raw', lumirawtool) ):\n n_files = tool.muFiles()\n print '********************* raw input files', text, n_files, tool.lumiValue(), tool.lumiError(), tool.absolute()\n store[text] = {'files':tool.muFiles(), \n 'lumiValue':tool.lumiValue(), \n 'lumiError':tool.lumiError(), \n 'absolute':tool.absolute(),\n 'filedata':[],\n }\n for i in range(n_files):\n store[text]['filedata'].append(\n {'sequence' :i, \n 'run' :tool.muRun(i), \n 'guid' :tool.muGuid(i), \n 'time0' :tool.muTime0(i), \n 'time1' :tool.muTime1(i), \n 'deltaLumi':tool.muDeltaLumi(i), \n 'norm' :tool.muNorm(i), \n 'mu' :tool.muMu(i), \n 'counters' :{}\n }\n )\n print 'file', i, tool.muRun(i), tool.muGuid(i), tool.muTime0(i), tool.muTime1(i), tool.muDeltaLumi(i), tool.muNorm(i), tool.muMu(i), \n print tool.muKeys(i)\n for k in range(tool.muKeys(i)):\n if tool.muCounter(i,k) <= 50: continue # these are not useful for mu\n norm = tool.muCounterNorm(i,k)\n value = 0 if norm == 0 else tool.muCounterValue(i,k)/norm\n store[text]['filedata'][-1]['counters'][tool.muCounterName(i,k)] = {'key':tool.muCounter(i,k), 'norm':norm, 'value':value}\n print ' ', tool.muCounter(i,k), tool.muCounterName(i,k), norm, value, \n print\n\n # save store\n pfname = 'rawdata.pck'\n pf = open(pfname,'wb')\n print pfname, 'opened'\n pickle.dump(store, pf)\n pf.close()\n print pfname, 'closed'\n \n print store\n\n return SUCCESS","def off(self, *args, **kwargs):\n self.stop_current_sequence()\n return self.fade(0,0,0)","def I2C_thread(write_directives, gui): \n # Change the role of the button so that it stops commands\n gui.aardvark_button_state('stop') \n \n # write via the Aardvark\n pySCPI_aardvark.write_aardvark(write_directives, gui)\n # clear the thread termination flag\n gui.terminator.kill_event.clear()\n \n # re-allow access to hte command text box\n gui.Command_text.config(state = 'normal')\n \n if not gui.terminator.root_destroyed:\n # unlock all of the GUI buttons\n gui.action_lock('Unlock')\n \n # Reset the button back to it's initial state\n gui.aardvark_button_state('start') \n # end if","def stop(self) -> \"bool\":\n return _beamforming_swig.beamformer_sptr_stop(self)","def pswitchoff(chan) :\n s.phaseSwitching(False, chan)","def stop(self):\n self._run = False\n self.IA.stop()","def _forwardImplementation(self, inbuf, outbuf):\n assert self.module\n \n values = self.module.getActionValues(self.state) \n n_values = self.n_values.getActionValues(self.state)\n values = map(lambda x, y: x + self.exploration * (sqrt(2 * log(self.experiment.stepid, 2) / y) if y > 0 else 1000), values, n_values);\n \n actions = []\n for i in range(self.shield_options):\n new_action = where(values == max(values))[0]\n new_action = choice(new_action) \n values[new_action] = -10000\n actions.append(new_action)\n \n while len(actions) < self.outdim:\n actions.append(-1)\n \n outbuf[:] = actions","def __set_outputs__(self):\n self.__set_in_out_var__(None, 1)"],"string":"[\n \"def beam_align():\\n\\n # do nothing if there is a sample mounted to avoid collisions\\n if smart_magnet.sample_detect.get() == 0:\\n raise Exception(\\\"Sample mounted on gonio! Avoided collision\\\")\\n\\n # wait for attenuators to finish moving\\n yield from bps.abs_set(mxatten, 0.002)\\n yield from bps.sleep(5)\\n\\n # transition to BL and open shutter\\n yield from bps.abs_set(gov_rbt, \\\"BL\\\", wait=True)\\n yield from bps.mv(sht.r, 0)\\n\\n yield from bps.abs_set(rot_aligner.cam_hi.cam_mode, \\\"beam_align\\\")\\n\\n # which direction, x pos. pitch beam outboard (-), y pos. pitch beam up (+)\\n scan_uid = yield from bp.count([rot_aligner.cam_hi], 1)\\n centroid_x, centroid_y = (\\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output1.name][1],\\n db[scan_uid].table()[rot_aligner.cam_hi.cv1.outputs.output2.name][1],\\n )\\n\\n if np.isclose(0, centroid_x) or np.isclose(0, centroid_y):\\n raise Exception(\\\"No centroid detected!\\\")\\n\\n yield from bps.abs_set(kbt.hor.delta_px, (centroid_x - 320))\\n yield from bps.abs_set(kbt.ver.delta_px, -(centroid_y - 256))\\n\\n def lin_reg(independent, dependent, goal, **kwargs) -> float:\\n b = dependent\\n A = np.matrix([[pos, 1] for pos in independent])\\n p = (\\n np.linalg.inv(A.transpose() * A)\\n * A.transpose()\\n * np.matrix(b.to_numpy()).transpose()\\n )\\n best = (goal - p[1]) / p[0]\\n return best\\n\\n for axis, signal, center in (\\n kbt.hor,\\n rot_aligner.cam_hi.cv1.outputs.output1,\\n 320,\\n ), (kbt.ver, rot_aligner.cam_hi.cv1.outputs.output2, 256):\\n # skip if we are within 1 um\\n if abs(axis.delta_px.get()) > 2:\\n scan_uid = yield from rel_scan_no_reset(\\n [rot_aligner.cam_hi],\\n axis,\\n 0,\\n 0.4 * -(axis.delta_px.get() / abs(axis.delta_px.get())),\\n 10,\\n )\\n scan_df = db[scan_uid].table()\\n best_voltage = lin_reg(\\n scan_df[axis.readback.name],\\n scan_df[signal.name],\\n center,\\n )\\n yield from bps.mv(axis, best_voltage)\\n yield from bps.sleep(1)\\n\\n # close shutters and reset attenuators for manual viewing\\n yield from bps.mv(sht.r, 20)\",\n \"def __init__(self, motion, **kwargs):\\n super(ShiftOut, self).__init__(motion)\",\n \"def forward_att(self, eouts, elens, ys, trigger_points=None):\\n losses_auxiliary = {}\\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, self.device, self.bwd)\\n if not self.training:\\n self.data_dict['elens'] = tensor2np(elens)\\n self.data_dict['ylens'] = tensor2np(ylens)\\n self.data_dict['ys'] = tensor2np(ys_out)\\n bs, ymax = ys_in.size()[:2]\\n tgt_mask = (ys_out != self.pad).unsqueeze(1).repeat([1, ymax, 1])\\n causal_mask = tgt_mask.new_ones(ymax, ymax, dtype=tgt_mask.dtype)\\n causal_mask = torch.tril(causal_mask).unsqueeze(0)\\n tgt_mask = tgt_mask & causal_mask\\n src_mask = make_pad_mask(elens).unsqueeze(1).repeat([1, ymax, 1])\\n if self.attn_type == 'mocha':\\n attn_mask = (ys_out != self.pad).unsqueeze(1).unsqueeze(3)\\n else:\\n attn_mask = None\\n lmout = None\\n if self.lm is not None:\\n self.lm.eval()\\n with torch.no_grad():\\n lmout, lmstate, _ = self.lm.predict(ys_in, None)\\n lmout = self.lm_output_proj(lmout)\\n out = self.pos_enc(self.embed_token_id(ys_in), scale=True)\\n xy_aws_layers = []\\n xy_aws = None\\n for lth, layer in enumerate(self.layers):\\n out = layer(out, tgt_mask, eouts, src_mask, mode='parallel', lmout=lmout)\\n xy_aws = layer.xy_aws\\n if xy_aws is not None and self.attn_type == 'mocha':\\n xy_aws_masked = xy_aws.masked_fill_(attn_mask.expand_as(xy_aws) == 0, 0)\\n xy_aws_layers.append(xy_aws_masked.clone())\\n if not self.training:\\n self.aws_dict['yy_aws_layer%d' % lth] = tensor2np(layer.yy_aws)\\n self.aws_dict['xy_aws_layer%d' % lth] = tensor2np(layer.xy_aws)\\n self.aws_dict['xy_aws_beta_layer%d' % lth] = tensor2np(layer.xy_aws_beta)\\n self.aws_dict['xy_aws_p_choose%d' % lth] = tensor2np(layer.xy_aws_p_choose)\\n self.aws_dict['yy_aws_lm_layer%d' % lth] = tensor2np(layer.yy_aws_lm)\\n logits = self.output(self.norm_out(out))\\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\\n losses_auxiliary['loss_quantity'] = 0.0\\n if self.attn_type == 'mocha':\\n n_tokens_ref = tgt_mask[:, -1, :].sum(1).float()\\n n_tokens_pred = sum([torch.abs(aws.sum(3).sum(2).sum(1) / aws.size(1)) for aws in xy_aws_layers])\\n n_tokens_pred /= len(xy_aws_layers)\\n losses_auxiliary['loss_quantity'] = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\\n acc = compute_accuracy(logits, ys_out, self.pad)\\n return loss, acc, ppl, losses_auxiliary\",\n \"def arm(self):\\n pass\",\n \"def beam_update(self, beams, extra):\\n h_i, dec_out, context, src_mask = extra\\n h_i = tuple(hc[:, beams, :] for hc in h_i)\\n dec_out = dec_out[beams, :]\\n return h_i, dec_out, context, src_mask\",\n \"def stop(self):\\n self.ae.stop()\",\n \"def tomoScan(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\\\"default\\\", tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n try:\\n darkFieldInterval = int(darkFieldInterval)\\n flatFieldInterval = int(flatFieldInterval)\\n optimizeBeamInterval = int(optimizeBeamInterval)\\n \\n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\\n tomography_theta = jns.tomography_theta\\n if tomography_theta is None:\\n raise NameError(\\\"tomography_theta is not defined in Jython namespace\\\")\\n tomography_shutter = jns.tomography_shutter\\n if tomography_shutter is None:\\n raise NameError(\\\"tomography_shutter is not defined in Jython namespace\\\")\\n tomography_translation = jns.tomography_translation\\n if tomography_translation is None:\\n raise NameError(\\\"tomography_translation is not defined in Jython namespace\\\")\\n \\n tomography_detector = jns.tomography_detector\\n if tomography_detector is None:\\n raise NameError(\\\"tomography_detector is not defined in Jython namespace\\\")\\n\\n tomography_optimizer = jns.tomography_optimizer\\n if tomography_optimizer is None:\\n raise NameError(\\\"tomography_optimizer is not defined in Jython namespace\\\")\\n\\n tomography_time = jns.tomography_time\\n if tomography_time is None:\\n raise NameError(\\\"tomography_time is not defined in Jython namespace\\\")\\n \\n tomography_beammonitor = jns.tomography_beammonitor\\n if tomography_beammonitor is None:\\n raise NameError(\\\"tomography_beammonitor is not defined in Jython namespace\\\")\\n \\n tomography_camera_stage = jns.tomography_camera_stage\\n if tomography_camera_stage is None:\\n raise NameError(\\\"tomography_camera_stage is not defined in Jython namespace\\\")\\n \\n tomography_sample_stage = jns.tomography_sample_stage\\n if tomography_sample_stage is None:\\n raise NameError(\\\"tomography_sample_stage is not defined in Jython namespace\\\")\\n \\n tomo_additional_scannables = jns.tomography_additional_scannables\\n if tomo_additional_scannables is None:\\n raise NameError(\\\"tomo_additional_scannables is not defined in Jython namespace\\\")\\n \\n index = SimpleScannable()\\n index.setCurrentPosition(0.0)\\n index.setInputNames([\\\"imageNumber\\\"])\\n index.setName(\\\"imageNumber\\\")\\n index.configure()\\n \\n image_key = SimpleScannable()\\n image_key.setCurrentPosition(0.0)\\n image_key.setInputNames([\\\"image_key\\\"])\\n image_key.setName(\\\"image_key\\\")\\n image_key.configure()\\n\\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\\n tomography_translation, tomography_optimizer, image_key, index)\\n\\n# return tomoScanDevice\\n #generate list of positions\\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\\n theta_points = []\\n theta_points.append(start)\\n previousPoint = start\\n for i in range(numberSteps):\\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\\n theta_points.append(nextPoint)\\n previousPoint = nextPoint\\n \\n #generateScanPoints\\n optimizeBeamNo = 0\\n optimizeBeamYes = 1\\n shutterOpen = 1\\n shutterClosed = 0\\n shutterNoChange = 2\\n scan_points = []\\n theta_pos = theta_points[0]\\n index = 0\\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \\n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1\\n \\n for i in range(imagesPerFlat): \\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1 \\n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project, index)) #first\\n index = index + 1 \\n imageSinceDark = 1\\n imageSinceFlat = 1\\n optimizeBeam = 0\\n for i in range(numberSteps):\\n theta_pos = theta_points[i + 1]\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project, index))#main image\\n index = index + 1 \\n \\n imageSinceFlat = imageSinceFlat + 1\\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\\n index = index + 1 \\n imageSinceFlat = 0\\n \\n imageSinceDark = imageSinceDark + 1\\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\\n index = index + 1 \\n imageSinceDark = 0\\n\\n optimizeBeam = optimizeBeam + 1\\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\\n index = index + 1 \\n optimizeBeam = 0\\n \\n #add dark and flat only if not done in last steps\\n if imageSinceFlat != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n if imageSinceDark != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1 \\n scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\\n imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\\n if pattern == 'default' or pattern == 'DFPFD':\\n i = 0\\n for pt1 in scan_points1:\\n pt = scan_points[i]\\n if pt1 != pt:\\n print \\\"Mismatch - please tell Kaz about your scan and its arguments!\\\"\\n print \\\"i = \\\", i\\n print \\\"pt = \\\", pt\\n print \\\"pt1 = \\\", pt1\\n i += 1\\n #return None\\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\\\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \\n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\\n #scan_args.append(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(\\\"RotationCoord_as_list\\\", [tomoRotationAxis])\\n meta_add(\\\"approxCOR\\\", tomoRotationAxis)\\n for scannable in additionalScannables:\\n scan_args.append(scannable)\\n for scannable in tomo_additional_scannables:\\n scan_args.append(scannable)\\n ''' setting the description provided as the title'''\\n if not description == None: \\n setTitle(description)\\n else :\\n setTitle(\\\"undefined\\\")\\n \\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n if not dataFormat == \\\"NexusDataWriter\\\":\\n handle_messages.simpleLog(\\\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\\\")\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", \\\"NexusDataWriter\\\")\\n scanObject = createConcurrentScan(scan_args)\\n if addNXEntry:\\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\\n scanObject.runScan()\\n if autoAnalyse:\\n lsdp=jns.lastScanDataPoint()\\n OSCommandRunner.runNoWait([\\\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\\\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\\n return scanObject;\\n except InterruptedException:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"User interrupted the scan\\\", exceptionType, exception, traceback, False)\\n raise InterruptedException(\\\"User interrupted the scan\\\")\\n except:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"Error during tomography scan\\\", exceptionType, exception, traceback, False)\\n raise Exception(\\\"Error during tomography scan\\\", exception)\\n finally:\\n handle_messages.simpleLog(\\\"Data Format reset to the original setting: \\\" + dataFormat)\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", dataFormat)\",\n \"def stop_beam(self, wait=False):\\n if self.SE == 6:\\n self.evr.polarity.put('VAL', 0, use_complete=True)\\n \\n self.records.S_CLOSE.put('VAL', 1, use_complete=True, wait=wait)\",\n \"def enemy_waves(self):\\n\\n pass\",\n \"def control_law( self, inputs ):\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def test2(self):\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.initialize()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.sweepDuckie()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieSuctionOn()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLiftEE()\\r\\n\\t\\tself._motion.terminate()\",\n \"def act(self, goal):\\n rospy.loginfo(\\\"Emergency action received goal: \\\"+str(goal))\\n\\n\\n sam_publisher = rospy.Publisher('/sam_auv_1/thrusters/0/input',\\n FloatStamped,\\n queue_size = 100)\\n\\n # sam_publisher = rospy.Publisher('/uavcan_vbs_command/',\\n # PercentStamped,\\n # queue_size = 100)\\n\\n #self.emergency_activated = False\\n #sam_sub = rospy.Subscriber('/sam_auv_1/emergency_butt', Bool, self.emergency_cb)\\n\\n while not rospy.is_shutdown() and not self.done_once:\\n time.sleep(0.5)\\n #rospy.loginfo('Emergency waiting for butt')\\n #if self.emergency_activated:\\n # rospy.loginfo('Butt activated')\\n start_time = rospy.get_time()\\n elapsed = 0\\n rospy.loginfo('Emergency action active...')\\n while elapsed < 3:\\n if rospy.is_shutdown():\\n break\\n\\n elapsed = rospy.get_time() - start_time\\n #fs = FloatStamped()\\n fs = PercentStamped()\\n h = Header()\\n fs.header = h\\n fs.data = 0\\n sam_publisher.publish(fs)\\n time.sleep(0.1)\\n # we are done doing the action succesfully\\n self.done_once = True\\n rospy.loginfo('Emergency action SUCCESS')\\n return True\\n\\n # something went wrong, we fucked up\\n rospy.loginfo('Emergency action FAILURE '+ str(self.done_once))\\n return False\",\n \"def Advance():\\n warp.step()\",\n \"def idealOpAmp():\",\n \"def capture_stop(self):\\n pass\",\n \"def stop(self):\\n self.stopAngMovementAll()\\n self.stopMovementAll()\",\n \"def at_aflight(seq, flag):\\n at(\\\"AFLIGHT\\\", seq, [flag])\",\n \"def eStop(self):\\n Step(speed=0, coils=1, steps=0, dir=Step.BRAKE)\\n # #######################################################\\n # Need to blink Stop and wait until Stop is pressed again\\n # #######################################################\",\n \"def stop_step_sweep(self):\\n self.write(\\\":SOUR:SWE:CONT:STAT OFF\\\")\",\n \"def end_meassuring(self):\\n self.enabler = 0\\n #self.t.join()\\n return 1\",\n \"def forward_att(self, eouts, elens, ys, return_logits=False, teacher_logits=None, ctc_trigger_points=None, forced_trigger_points=None):\\n bs, xmax = eouts.size()[:2]\\n device = eouts.device\\n ys_in, ys_out, ylens = append_sos_eos(ys, self.eos, self.eos, self.pad, eouts.device, self.bwd)\\n ymax = ys_in.size(1)\\n if forced_trigger_points is not None:\\n for b in range(bs):\\n forced_trigger_points[b, ylens[b] - 1] = elens[b] - 1\\n dstates = self.zero_state(bs)\\n if self.training:\\n if self.discourse_aware and not self._new_session:\\n dstates = {'dstate': (self.dstate_prev['hxs'], self.dstate_prev['cxs'])}\\n self.dstate_prev = {'hxs': [None] * bs, 'cxs': [None] * bs}\\n self._new_session = False\\n cv = eouts.new_zeros(bs, 1, self.enc_n_units)\\n self.score.reset()\\n aw, aws = None, []\\n betas, p_chooses = [], []\\n lmout, lmstate = None, None\\n ys_emb = self.embed_token_id(ys_in)\\n src_mask = make_pad_mask(elens).unsqueeze(1)\\n tgt_mask = (ys_out != self.pad).unsqueeze(2)\\n logits = []\\n for i in range(ymax):\\n is_sample = i > 0 and self._ss_prob > 0 and random.random() < self._ss_prob\\n if self.lm is not None:\\n self.lm.eval()\\n with torch.no_grad():\\n y_lm = self.output(logits[-1]).detach().argmax(-1) if is_sample else ys_in[:, i:i + 1]\\n lmout, lmstate, _ = self.lm.predict(y_lm, lmstate)\\n y_emb = self.embed_token_id(self.output(logits[-1]).detach().argmax(-1)) if is_sample else ys_emb[:, i:i + 1]\\n dstates, cv, aw, attn_state, attn_v = self.decode_step(eouts, dstates, cv, y_emb, src_mask, aw, lmout, mode='parallel', trigger_points=forced_trigger_points[:, i:i + 1] if forced_trigger_points is not None else None)\\n logits.append(attn_v)\\n aws.append(aw)\\n if attn_state.get('beta', None) is not None:\\n betas.append(attn_state['beta'])\\n if attn_state.get('p_choose', None) is not None:\\n p_chooses.append(attn_state['p_choose'])\\n if self.attn_type in ['gmm', 'sagmm']:\\n aw = attn_state['myu']\\n if self.training and self.discourse_aware:\\n for b in [b for b, ylen in enumerate(ylens.tolist()) if i == ylen - 1]:\\n self.dstate_prev['hxs'][b] = dstates['dstate'][0][:, b:b + 1].detach()\\n self.dstate_prev['cxs'][b] = dstates['dstate'][1][:, b:b + 1].detach()\\n if self.training and self.discourse_aware:\\n if bs > 1:\\n self.dstate_prev['hxs'] = torch.cat(self.dstate_prev['hxs'], dim=1)\\n self.dstate_prev['cxs'] = torch.cat(self.dstate_prev['cxs'], dim=1)\\n else:\\n self.dstate_prev['hxs'] = self.dstate_prev['hxs'][0]\\n self.dstate_prev['cxs'] = self.dstate_prev['cxs'][0]\\n logits = self.output(torch.cat(logits, dim=1))\\n if return_logits:\\n return logits\\n loss, ppl = cross_entropy_lsm(logits, ys_out, self.lsm_prob, self.pad, self.training)\\n acc = compute_accuracy(logits, ys_out, self.pad)\\n aws = torch.cat(aws, dim=2)\\n if not self.training:\\n self.data_dict['elens'] = tensor2np(elens)\\n self.data_dict['ylens'] = tensor2np(ylens)\\n self.data_dict['ys'] = tensor2np(ys_out)\\n self.aws_dict['xy_aws'] = tensor2np(aws)\\n if len(betas) > 0:\\n self.aws_dict['xy_aws_beta'] = tensor2np(torch.cat(betas, dim=2))\\n if len(p_chooses) > 0:\\n self.aws_dict['xy_p_choose'] = tensor2np(torch.cat(p_chooses, dim=2))\\n if self.attn_type == 'mocha' or (ctc_trigger_points is not None or forced_trigger_points is not None):\\n aws = aws.masked_fill_(tgt_mask.unsqueeze(1).expand_as(aws) == 0, 0)\\n loss_quantity = 0.0\\n if self.attn_type == 'mocha':\\n n_tokens_pred = aws.sum(3).sum(2).sum(1) / aws.size(1)\\n n_tokens_ref = tgt_mask.squeeze(2).sum(1).float()\\n loss_quantity = torch.mean(torch.abs(n_tokens_pred - n_tokens_ref))\\n loss_latency = 0.0\\n if self.latency_metric == 'interval':\\n assert ctc_trigger_points is None\\n assert aws.size(1) == 1\\n aws_prev = torch.cat([aws.new_zeros(aws.size())[:, :, -1:], aws.clone()[:, :, :-1]], dim=2)\\n aws_mat = aws_prev.unsqueeze(3) * aws.unsqueeze(4)\\n delay_mat = aws.new_ones(xmax, xmax).float()\\n delay_mat = torch.tril(delay_mat, diagonal=-1, out=delay_mat)\\n delay_mat = torch.cumsum(delay_mat, dim=-2).unsqueeze(0)\\n delay_mat = delay_mat.unsqueeze(1).unsqueeze(2).expand_as(aws_mat)\\n loss_latency = torch.pow((aws_mat * delay_mat).sum(-1), 2).sum(-1)\\n loss_latency = torch.mean(loss_latency.squeeze(1))\\n elif ctc_trigger_points is not None or 'ctc_sync' not in self.latency_metric and forced_trigger_points is not None:\\n if 'ctc_sync' in self.latency_metric:\\n trigger_points = ctc_trigger_points\\n else:\\n trigger_points = forced_trigger_points\\n js = torch.arange(xmax, dtype=torch.float, device=device).expand_as(aws)\\n exp_trigger_points = (js * aws).sum(3)\\n trigger_points = trigger_points.float().unsqueeze(1)\\n loss_latency = torch.abs(exp_trigger_points - trigger_points)\\n loss_latency = loss_latency.sum() / ylens.sum()\\n if teacher_logits is not None:\\n kl_loss = distillation(logits, teacher_logits, ylens, temperature=5.0)\\n loss = loss * (1 - self.distil_weight) + kl_loss * self.distil_weight\\n return loss, acc, ppl, loss_quantity, loss_latency\",\n \"def _stop(self):\",\n \"def out(self, inputs):\",\n \"def calculate_beam_xy(self):\\n info = []\\n\\n # Import relevant info\\n pixel_size = self.info.pixel_size\\n for i in [j.final for j in self.final_objects]:\\n try:\\n info.append(\\n [\\n i,\\n i[\\\"beamX\\\"],\\n i[\\\"beamY\\\"],\\n i[\\\"wavelength\\\"],\\n i[\\\"distance\\\"],\\n (i[\\\"a\\\"], i[\\\"b\\\"], i[\\\"c\\\"], i[\\\"alpha\\\"], i[\\\"beta\\\"], i[\\\"gamma\\\"]),\\n ]\\n )\\n except IOError as e:\\n print(\\\"IOTA ANALYSIS ERROR: BEAMXY failed! \\\", e)\\n pass\\n\\n # Calculate beam center coordinates and distances\\n beamX = [i[1] for i in info]\\n beamY = [j[2] for j in info]\\n beam_dist = [\\n math.hypot(i[1] - np.median(beamX), i[2] - np.median(beamY)) for i in info\\n ]\\n beam_dist_std = np.std(beam_dist)\\n img_list = [\\n [i[0], i[1], i[2], i[3], i[4], i[5], j]\\n for i, j in list(zip(info, beam_dist))\\n ]\\n\\n # Separate out outliers\\n outliers = [i for i in img_list if i[3] > 2 * beam_dist_std]\\n clean = [i for i in img_list if i[3] <= 2 * beam_dist_std]\\n cbeamX = [i[1] for i in clean]\\n cbeamY = [j[2] for j in clean]\\n obeamX = [i[1] for i in outliers]\\n obeamY = [j[2] for j in outliers]\\n\\n # Calculate median wavelength, detector distance and unit cell params from\\n # non-outliers only\\n wavelengths = [i[3] for i in clean]\\n distances = [i[4] for i in clean]\\n cells = [i[5] for i in clean]\\n\\n wavelength = np.median(wavelengths)\\n det_distance = np.median(distances)\\n a = np.median([i[0] for i in cells])\\n b = np.median([i[1] for i in cells])\\n c = np.median([i[2] for i in cells])\\n\\n # Calculate predicted L +/- 1 misindexing distance for each cell edge\\n aD = det_distance * math.tan(2 * math.asin(wavelength / (2 * a)))\\n bD = det_distance * math.tan(2 * math.asin(wavelength / (2 * b)))\\n cD = det_distance * math.tan(2 * math.asin(wavelength / (2 * c)))\\n\\n return (\\n beamX,\\n beamY,\\n cbeamX,\\n cbeamY,\\n obeamX,\\n obeamY,\\n beam_dist,\\n [i[4] for i in info],\\n aD,\\n bD,\\n cD,\\n pixel_size,\\n )\",\n \"def stop_output(out):\\n if out is not None:\\n out.stop()\\n pass\",\n \"def stop(self):\\n self.stop_aperture()\",\n \"def pre_stop(self):\",\n \"def sitOut(self):\\n self.sit_out = True\",\n \"def bruno_mes(self):\\n MI = -125\\n MA = 125\\n INCR = 19\\n####################################################################\\n ### CHANNEL 1\\n self.write('CHN 1')\\n self.write('CHN?')\\n print 'Acting on channel:',self.read()\\n self.write('WAVE ARB')\\n self.write('ARBLOAD ARB1')\\n self.write('FREQ 100')\\n self.write('DCOFFS 0.05')\\n self.write('AMPL 0.1')\\n \\n l =(125,-125,125)#arange(MI,MA,INCR) # the ramp\\n# lll = copy(l)[::-1][1:-1]\\n# l = concatenate((l,lll))\\n self.write_array_to_byte(l,1)\",\n \"def record_stop(self, aaidee):\\n ckresult(_dll.FMOD_System_RecordStop(self._ptr, aaidee))\",\n \"def stop(self):\",\n \"def stop(self):\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def __init__(self,diam_in,diam_out):\\n self.diam_in = diam_in\\n\\tself.diam_out = diam_out\",\n \"def steer(self):\\n\\n while self.active:\\n angle = self.driver.angle\\n steering_pwm_calc = self.angle_to_pmw(angle)\\n self.pwm.set_pwm(0, 0, steering_pwm_calc)\",\n \"def main():\\n np.random.seed(219)\\n rospy.init_node(\\\"sawyer_dagger_teacher\\\")\\n pub_start = rospy.Publisher('/teacher/start', JointCommand, queue_size=1)\\n pub_epi_fin = rospy.Publisher('/teacher/fin', JointCommand, queue_size=1)\\n vel_ik_pos_pub = rospy.Publisher('/teacher/ik_vel/', Pose, queue_size = 3)\\n pub3 = rospy.Publisher('/ddpg/vel_start/', Float64, queue_size=1)\\n pub4 = rospy.Publisher('/ddpg/vel_end/', Float64, queue_size=1)\\n goal_obs_pub = rospy.Publisher('/teacher/goal_obs/', Pose, queue_size=1)\\n pos_cmd_pub = rospy.Publisher('/teacher/pos_cmd_pub/', PosCmd, queue_size=1)\\n\\n\\n\\n rospy.set_param('dagger_reset',\\\"false\\\") # param_name, param_value\\n\\n\\n # Load Gazebo Models via Spawning Services\\n # Note that the models reference is the /world frame\\n # and the IK operates with respect to the /base frame\\n # load_gazebo_models()\\n # Remove models from the scene on shutdown\\n rospy.on_shutdown(delete_gazebo_models)\\n\\n limb = 'right'\\n hover_distance = 0.15 # meters\\n # Starting Joint angles for right arm\\n starting_joint_angles = {'right_j0': -0.041662954890248294,\\n 'right_j1': -1.0258291091425074,\\n 'right_j2': 0.0293680414401436,\\n 'right_j3': 1.37518162913313,\\n 'right_j4': -0.06703022873354225,\\n 'right_j5': 0.7968371433926965,\\n 'right_j6': 1.7659649178699421}\\n\\n pnp = PickAndPlace(limb, hover_distance)\\n\\n pnp.move_to_start(starting_joint_angles)\\n\\n \\n # m_planner = trajectorySender()\\n # An orientation for gripper fingers to be overhead and parallel to the obj\\n overhead_orientation = Quaternion(\\n x=-0.00142460053167,\\n y=0.999994209902,\\n z=-0.00177030764765,\\n w=0.00253311793936)\\n block_poses = list()\\n # The Pose of the block in its initial location.\\n # You may wish to replace these poses with estimates\\n # from a perception node.\\n block_poses.append(Pose(\\n position=Point(x=0.45, y=0.155, z=-0.129),\\n orientation=overhead_orientation))\\n # Feel free to add additional desired poses for the object.\\n # Each additional pose will get its own pick and place.\\n block_poses.append(Pose(\\n position=Point(x=0.6, y=-0.1, z=-0.129),\\n orientation=overhead_orientation))\\n # Move to the desired starting angles\\n print(\\\"Running. Ctrl-c to quit\\\")\\n # pnp.move_to_start(starting_joint_angles)\\n idx = 0\\n rate = rospy.Rate(1)\\n block_quat_pose = [0.00142460053167,\\n 0.999994209902,\\n 0.00177030764765,\\n 0.00253311793936]\\n if rospy.has_param('vel_calc'):\\n rospy.delete_param('vel_calc')\\n load_gazebo_models()\\n\\n while not rospy.is_shutdown():\\n\\n\\n starting_joint_angles['right_j0'] = np.random.uniform(-0.05, 0.05)\\n starting_joint_angles['right_j1'] = np.random.uniform(-0.95, -0.85)\\n starting_joint_angles['right_j2'] = np.random.uniform(-0.1, 0.1)\\n starting_joint_angles['right_j3'] = np.random.uniform(1.6, 1.7)\\n\\n # starting_joint_angles['right_j0'] = np.random.uniform(-0.75, 0.75)\\n # starting_joint_angles['right_j1'] = np.random.uniform(-0.97, -0.80)\\n # starting_joint_angles['right_j2'] = np.random.uniform(-0.15, 0.15)\\n # starting_joint_angles['right_j3'] = np.random.uniform(1.55, 1.75)\\n\\n start_pose = [starting_joint_angles['right_j0'], starting_joint_angles['right_j1'],\\n starting_joint_angles['right_j2'], starting_joint_angles['right_j3'],\\n starting_joint_angles['right_j4'], starting_joint_angles['right_j5'],\\n starting_joint_angles['right_j6']]\\n \\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n if rospy.has_param('dagger_reset'):\\n rospy.delete_param('dagger_reset')\\n break\\n pnp.move_to_start(starting_joint_angles)\\n\\n\\n delete_kinect_camera()\\n # delete_gazebo_models()\\n delete_gazebo_block()\\n rand_x = np.random.uniform(0.45, .75)\\n rand_y = np.random.uniform(-0.2, 0.33)\\n # rand_x = np.random.uniform(0.44,0.68)\\n\\n # rand_y = np.random.uniform(-0.20, 0.35)\\n pose_block = Pose(position=Point(x=rand_x, y=rand_y, z=1.00)\\n , orientation=overhead_orientation)\\n pose_rob = Pose(position=Point(x=rand_x-0.015, y=rand_y+0.03, z=0.03), orientation=overhead_orientation) \\n\\n # rospy.set_param('vel_calc', 'true')\\n # pnp.move_to_start(starting_joint_angles)\\n # oktogo = pnp.move_to_start_vel_command(start_pose)\\n # if rospy.has_param('vel_calc'):\\n # rospy.delete_param('vel_calc')\\n # loads env\\n load_gazebo_block(block_pose=pose_block)\\n # load_kinect_camera()\\n\\n \\n\\n # rospy.set_param('vel_calc', 'true')\\n print 'Reaching target object... Learning...'\\n rospy.set_param('epi_start', 'true')\\n pnp.reach(pose_rob, pos_cmd_pub)\\n # reached = pnp.reach_vel_ctrl(pose_rob)\\n rospy.sleep(0.5)\\n # if rospy.has_param('vel_calc'):\\n # rospy.delete_param('vel_calc')\\n # if reached:\\n # rospy.set_param('reached', 'true')\\n # goal_obs_pub.publish(pose_rob)\\n\\n\\n print 'Reached target object! and Goal obs acquired Resetting...'\\n # while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n # if rospy.has_param('demo_success'):\\n # break\\n while not rospy.is_shutdown(): # wait until trajectory is collected for each episode\\n if rospy.has_param('demo_success'):\\n rospy.delete_param('demo_success')\\n break\\n \\n # rospy.delete_param('demo_success')\\n \\n\\n return 0\",\n \"def conRFMixAndMaskToBeagle(indfile_name, rephasedhaps_pref, em_iters, win_size, chroms):\\n\\t### First get individual information\\n\\twindow_id = 0\\n\\tem_iter = em_iters\\n\\tindfile = open(indfile_name, \\\"r\\\")\\t\\n\\tinds = []\\n\\tfor line in indfile:\\n\\t\\tsplits = line.strip(\\\"\\\\r\\\\n\\\").split()\\n\\t\\tinds.append(splits[1] + \\\"_A\\\")\\n\\t\\tinds.append(splits[1] + \\\"_B\\\")\\n\\n\\tallloci = []\\n\\toutfilename = rephasedhaps_pref + \\\"_w\\\" + str(win_size) + \\\".beagle\\\"\\n\\toutfile = open(outfilename, \\\"w\\\")\\n\\toutfile.write(\\\"I\\\\tid\\\\t\\\" + \\\"\\\\t\\\".join(inds) + \\\"\\\\n\\\")\\n\\t## Write genotype data out to file\\n\\n\\tvitout = open(rephasedhaps_pref + \\\".vit\\\", \\\"w\\\")\\n\\twinout = open(rephasedhaps_pref + \\\".windows\\\", \\\"w\\\")\\n\\tfbkout = rephasedhaps_pref + \\\".fbk\\\"\\n\\tif os.path.exists(fbkout):\\n\\t\\tos.remove(fbkout)\\n\\tvitlist = []\\n\\tfor chrom in chroms:\\n\\t\\tprint chrom\\n\\t\\tshapeitfilename = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.allelesRephased\\\" + str(em_iters) + \\\".txt\\\"\\n\\t\\tshapeitfile = open(shapeitfilename, \\\"rb\\\")\\n\\t\\tfbkin_name = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.\\\" + str(em_iters) + \\\".ForwardBackward.txt\\\"\\n\\t\\tos.system('cat ' + fbkin_name + \\\" >> \\\" + fbkout) # Concatenate files together\\n\\t\\tmarkerin = rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.amaps\\\"\\n\\t\\tmarkerfile = open(markerin, \\\"r\\\")\\n\\t\\tloci=[]\\n\\t\\talleles = {}\\n\\t\\tfor mline in markerfile:\\n\\t\\t\\tmsplit = mline.strip().split()\\n\\t\\t\\tloci.append(msplit[1])\\n\\t\\t\\talleles[msplit[1]] = [msplit[3], msplit[4] ]\\n\\n\\t\\tallloci.extend(loci)\\n\\t\\tfor j,line in enumerate(shapeitfile):\\n\\t\\t\\tsline = line.strip(\\\"\\\\r\\\\n\\\")\\n\\t\\t\\tzero, ones = alleles[loci[j]]\\n\\t\\t\\tfixed = [ recodeAllele(k, zero, ones) for k in sline ]\\n\\t\\t\\toutfile.write(\\\"M\\\\t\\\" + loci[j] + \\\"\\\\t\\\" + \\\"\\\\t\\\".join(fixed) + \\\"\\\\n\\\")\\n\\t\\tvitfile = open(rephasedhaps_pref + \\\"_chr\\\" + str(chrom) + \\\"_shapeout.\\\" + str(em_iters) + \\\".Viterbi.txt\\\", \\\"r\\\")\\n\\t\\tvitlist.extend([x.strip().split() for x in vitfile])\\n\\t\\tshapeitfile.close()\\n\\t\\tvitfile.close()\\n\\t\\t\\n\\t# This will transpose the whole Viterbi file\\n\\t# Yikes this may take a lot of memory\\n\\tfor i,x in enumerate(zip(*vitlist)):\\n\\t\\tvitout.write(inds[i] + \\\"\\\\t\\\")\\n\\t\\tfor y in x:\\n\\t\\t\\tvitout.write(y+\\\"\\\\t\\\")\\n\\t\\tvitout.write(\\\"\\\\n\\\")\\n\\t\\t### This doesn't quite work yet so make sure to fix it next time\\n\\tfor l in allloci:\\n\\t\\twinout.write(\\\"window\\\" + str(window_id) + \\\"\\\\t\\\" + l + \\\"\\\\n\\\")\\n\\t\\twindow_id += 1\\n\\treturn([outfile.name, vitout.name, winout.name, fbkout])\",\n \"def stop(self):\\n self.turnOffMotors()\",\n \"def _detect_stop(func):\\n def wrapper(*args,**kwargs):\\n self = args[0]\\n self.episode_length -= 1\\n if self.episode_length <=0:\\n \\\"\\\"\\\"if the episode is end\\\"\\\"\\\"\\n self.end = True\\n else:\\n if self.adsorption:\\n \\\"\\\"\\\"just stop moving and wait until the end of episode\\\"\\\"\\\"\\n self.state = self.previous_state\\n else:\\n func(*args,**kwargs)\\n self._detect_obstacles()\\n\\n # func(*args,**kwargs)\\n # self._detect_obstacles()\\n # if self.adsorption:\\n # \\\"\\\"\\\"if this step update is invalid, the point will rebond\\\"\\\"\\\"\\n # self.state = self.previous_state\\n\\n if self.distance <= 0.02:\\n \\\"\\\"\\\"if the point reached the boundary around the goal, let it stop and reset the punishment(self.reward)\\\"\\\"\\\"\\n self.end = True\\n self.reward = 0\\n if self.state[0] <0 or self.state[0] > 10 or self.state[1] <0 or self.state[1] > 10:\\n # self.end = True\\n self.reward = -800\\n return np.array(self.state), self.reward, self.end, self.distance\\n return wrapper\",\n \"def __init__(self, *args, **kwargs):\\n _Beamlet.__init__(self, *args, **kwargs)\",\n \"def _beam(self):\\n\\n return self._beam_factory.simple(self.detectorbase.wavelength)\",\n \"def chacha(self):\\n self.right()\\n time.sleep(2)\\n self.stop()\\n self.back()\\n time.sleep(1)\\n self.servo(1000)\\n time.sleep(1)\\n self.stop()\\n self.fwd()\\n time.sleep(1)\\n self.stop()\\n self.servo(2000)\\n time.sleep(1)\\n self.stop()\\n self.left()\\n time.sleep(2)\\n self.stop()\",\n \"def stop(self):\\n\\n self.active = False\\n angle_pwm = self.angle_to_pmw(const.Driving.NEUTRAL_STEERING_ANGLE)\\n self.pwm.set_pwm(0, 0, angle_pwm)\",\n \"def escAmpa(self) :\\n while 1 :\\n (value, end) = self.getInteger()\\n if value is None :\\n return\\n if end == 'G' : \\n #self.logdebug(\\\"BACKSIDES %i\\\" % value)\\n self.backsides.append(value)\\n self.setPageDict(\\\"duplex\\\", value)\",\n \"def stop():\\n status = write_i2c_block(ADDRESS,stop_cmd+[0,0,0])\\n set_left_speed(0)\\n set_right_speed(0)\\n return status\",\n \"def create_on_off(self):\\n self.laser_off = []\\n self.laser_on = []\\n\\n self.laser_off.append(\\n (0+self.time_to_number(self.skip['bcg_start']), self.starts[0]-self.time_to_number(self.skip['bcg_end'])))\\n\\n for i in range(len(self.starts)-1):\\n self.laser_off.append((self.ends[i]+self.time_to_number(\\n self.skip['bcg_start']), self.starts[i+1]-self.time_to_number(self.skip['bcg_end'])))\\n self.laser_on.append((self.starts[i]+self.time_to_number(\\n self.skip['sample_start']), self.ends[i]-self.time_to_number(self.skip['sample_end'])))\\n\\n self.laser_off.append((self.ends[-1]+self.time_to_number(self.skip['bcg_start']), len(\\n self.time)-2-self.time_to_number(self.skip['bcg_end'])))\\n self.laser_on.append((self.starts[-1]+self.time_to_number(\\n self.skip['sample_start']), self.ends[-1]-self.time_to_number(self.skip['sample_end'])))\",\n \"def _data_move_out_mc_on_h():\\n\\n pass\",\n \"def _forwardImplementation(self, inbuf, outbuf):\\n assert self.module\\n \\n values = self.module.getActionValues(self.state) \\n \\n actions = []\\n if random() <= self.exploration:\\n for i in range(self.shield_options):\\n new_action = choice(range(len(values))) \\n np.delete(values, new_action)\\n actions.append(new_action)\\n else:\\n for i in range(self.shield_options):\\n new_action = where(values == max(values))[0]\\n new_action = choice(new_action) \\n np.delete(values, new_action)\\n actions.append(new_action)\\n \\n while len(actions) < self.outdim:\\n actions.append(-1)\\n \\n outbuf[:] = actions\",\n \"def stop(self):\\n self._stopper.set()\\n self.det.disable_mca()\",\n \"def test_analog_in_out_loop(self):\\n for v in range(0, 6, 1):\\n self.l.output(ao0=v, ao1=v)\\n r = self.l.input(channels=(8,8,9,9), gains=(1,4,1,4))[0]\\n for i in r:\\n self.assertTrue(abs(v-i) < .1,\\n \\\"measured %g for %g\\\" % (i, v))\",\n \"def stageout(self) -> None:\\n raise NotImplementedError(\\\"Base method not implemented\\\")\",\n \"def __init__(self, anara, anadec, *args, **kwargs):\\n _Beamlet.__init__(self, *args, **kwargs)\\n self._anara = anara\\n self._anadec = anadec\",\n \"def __init__(self, pinForward, pinBackward, pinControlStraight,pinLeft, pinRight, pinControlSteering):\\n\\n self.pinForward = pinForward\\n self.pinBackward = pinBackward\\n self.pinControlStraight = pinControlStraight\\n self.pinLeft = pinLeft\\n self.pinRight = pinRight\\n self.pinControlSteering = pinControlSteering\\n GPIO.setup(self.pinForward, GPIO.OUT)\\n GPIO.setup(self.pinBackward, GPIO.OUT)\\n GPIO.setup(self.pinControlStraight, GPIO.OUT)\\n\\n GPIO.setup(self.pinLeft, GPIO.OUT)\\n GPIO.setup(self.pinRight, GPIO.OUT)\\n GPIO.setup(self.pinControlSteering, GPIO.OUT)\\n\\n self.pwm_forward = GPIO.PWM(self.pinForward, 100)\\n self.pwm_backward = GPIO.PWM(self.pinBackward, 100)\\n self.pwm_forward.start(0)\\n self.pwm_backward.start(0)\\n\\n self.pwm_left = GPIO.PWM(self.pinLeft, 100)\\n self.pwm_right = GPIO.PWM(self.pinRight, 100)\\n self.pwm_left.start(0)\\n self.pwm_right.start(0)\\n\\n GPIO.output(self.pinControlStraight,GPIO.HIGH) \\n GPIO.output(self.pinControlSteering,GPIO.HIGH)\",\n \"def breath_analyze(self, offset=0, th=10):\\n # breath part\\n breath_gd = np.gradient(gf(self.breath_list, 10))\\n breath_gd[breath_gd > 0] = 1\\n breath_gd[breath_gd < 0] = 0\\n breath_pulse = breath_gd[:-1]-np.roll(breath_gd, -1)[:-1]\\n breath_in = argrelextrema(breath_pulse, np.less, order=10)[0]#+offset\\n breath_out = argrelextrema(breath_pulse, np.greater, order=10)[0]#+offset\\n self.breath = np.sort(np.hstack([breath_in, breath_out, len(self.breath_list)-1]))\\n \\n if self.breath[0] == breath_in[0]:\\n self.btype = 'in'\\n else:\\n self.btype = 'out' \\n\\n b_in = []\\n b_out = []\\n delidx = []\\n\\n if len(self.breath) != 0: \\n for i, j in zip(self.breath[:-1], self.breath[1:]):\\n breath_diff = abs(self.breath_list[j]-self.breath_list[i])\\n if abs(breath_diff) > 3000: # really breath in/out\\n if abs(breath_diff) < 30000: # not deep breath\\n if breath_diff > 0: # breath out\\n print('breath out from frame '+str(i)+' to frame '+str(j)\\n +' <== breath not deep enough')\\n b_out.append(j-i)\\n self.ngframe.append(i)\\n else: # breath in\\n print('breath in from frame '+str(i)+' to frame '+str(j)\\n +' <== breath not deep enough')\\n b_in.append(j-i)\\n else: \\n if breath_diff > 0: # breath out\\n print('breath out from frame '+str(i)+' to frame '+str(j))\\n b_out.append(j-i)\\n else: # breath in\\n print('breath in from frame '+str(i)+' to frame '+str(j))\\n b_in.append(j-i)\\n else:\\n delidx.append(np.argwhere(self.breath==j)[0][0])\\n self.breath = np.delete(self.breath, np.array(delidx))\\n\\n print('\\\\naverage breath out freq is: '+str(np.round(30./np.mean(b_out), 2))+' Hz')\\n print('\\\\naverage breath in freq is: '+str(np.round(30./np.mean(b_in), 2))+' Hz')\\n else:\\n raise ImportError('Doing too fast !! please redo again !!')\",\n \"def beam(xb,yb,zb,wx,wy,wavelen):\\n\\n zRx = np.pi * wx**2 / wavelen\\n zRy = np.pi * wy**2 / wavelen \\n \\n sqrtX = np.sqrt( 1 + np.power(zb/zRx,2) ) \\n sqrtY = np.sqrt( 1 + np.power(zb/zRy,2) ) \\n intensity = np.exp( -2.*( np.power(xb/(wx*sqrtX ),2) \\\\\\n + np.power(yb/(wy*sqrtY),2) )) / sqrtX / sqrtY\\n return intensity\",\n \"def tomoScanWithFrames(description, inBeamPosition, outOfBeamPosition, exposureTime=1., start=0., stop=180., step=0.1, darkFieldInterval=0, flatFieldInterval=0,\\n imagesPerDark=10, imagesPerFlat=10, optimizeBeamInterval=0, pattern=\\\"default\\\", nframes=1, tomoRotationAxis=0, addNXEntry=True, autoAnalyse=True, additionalScannables=[]):\\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n try:\\n darkFieldInterval = int(darkFieldInterval)\\n flatFieldInterval = int(flatFieldInterval)\\n optimizeBeamInterval = int(optimizeBeamInterval)\\n \\n image_key_frame = 3\\n nframes = int(nframes)\\n if nframes < 1:\\n nframes = 1\\n \\n jns = beamline_parameters.JythonNameSpaceMapping(InterfaceProvider.getJythonNamespace())\\n tomography_theta = jns.tomography_theta\\n if tomography_theta is None:\\n raise NameError(\\\"tomography_theta is not defined in Jython namespace\\\")\\n tomography_shutter = jns.tomography_shutter\\n if tomography_shutter is None:\\n raise NameError(\\\"tomography_shutter is not defined in Jython namespace\\\")\\n tomography_translation = jns.tomography_translation\\n if tomography_translation is None:\\n raise NameError(\\\"tomography_translation is not defined in Jython namespace\\\")\\n \\n tomography_detector = jns.tomography_detector\\n if tomography_detector is None:\\n raise NameError(\\\"tomography_detector is not defined in Jython namespace\\\")\\n\\n tomography_optimizer = jns.tomography_optimizer\\n if tomography_optimizer is None:\\n raise NameError(\\\"tomography_optimizer is not defined in Jython namespace\\\")\\n\\n tomography_time = jns.tomography_time\\n if tomography_time is None:\\n raise NameError(\\\"tomography_time is not defined in Jython namespace\\\")\\n \\n tomography_beammonitor = jns.tomography_beammonitor\\n if tomography_beammonitor is None:\\n raise NameError(\\\"tomography_beammonitor is not defined in Jython namespace\\\")\\n \\n tomography_camera_stage = jns.tomography_camera_stage\\n if tomography_camera_stage is None:\\n raise NameError(\\\"tomography_camera_stage is not defined in Jython namespace\\\")\\n \\n tomography_sample_stage = jns.tomography_sample_stage\\n if tomography_sample_stage is None:\\n raise NameError(\\\"tomography_sample_stage is not defined in Jython namespace\\\")\\n \\n tomo_additional_scannables = jns.tomography_additional_scannables\\n if tomo_additional_scannables is None:\\n raise NameError(\\\"tomo_additional_scannables is not defined in Jython namespace\\\")\\n \\n index = SimpleScannable()\\n index.setCurrentPosition(0.0)\\n index.setInputNames([\\\"imageNumber\\\"])\\n index.setName(\\\"imageNumber\\\")\\n index.configure()\\n \\n image_key = SimpleScannable()\\n image_key.setCurrentPosition(0.0)\\n image_key.setInputNames([\\\"image_key\\\"])\\n image_key.setName(\\\"image_key\\\")\\n image_key.configure()\\n\\n tomoScanDevice = make_tomoScanDevice(tomography_theta, tomography_shutter,\\n tomography_translation, tomography_optimizer, image_key, index)\\n\\n# return tomoScanDevice\\n #generate list of positions\\n numberSteps = ScannableUtils.getNumberSteps(tomography_theta, start, stop, step)\\n theta_points = []\\n theta_points.append(start)\\n previousPoint = start\\n for i in range(numberSteps):\\n nextPoint = ScannableUtils.calculateNextPoint(previousPoint, step);\\n theta_points.append(nextPoint)\\n previousPoint = nextPoint\\n \\n #generateScanPoints\\n optimizeBeamNo = 0\\n optimizeBeamYes = 1\\n shutterOpen = 1\\n shutterClosed = 0\\n shutterNoChange = 2\\n scan_points = []\\n theta_pos = theta_points[0]\\n index = 0\\n #Added shutterNoChange state for the shutter. The scan points are added using the (pseudo) ternary operator, \\n #if index is 0 then the shutterPosition is added to the scan point, else shutterNoChange is added to scan points.\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1\\n \\n for i in range(imagesPerFlat): \\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n for frm in range(nframes): \\n scan_points.append((theta_pos, shutterOpen, inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index)) #first\\n index = index + 1 \\n imageSinceDark = 1\\n imageSinceFlat = 1\\n optimizeBeam = 0\\n for i in range(numberSteps):\\n theta_pos = theta_points[i + 1]\\n for frm in range(nframes):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_project if frm==0 else image_key_frame, index))#main image\\n index = index + 1 \\n \\n imageSinceFlat = imageSinceFlat + 1\\n if imageSinceFlat == flatFieldInterval and flatFieldInterval != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index))\\n index = index + 1 \\n imageSinceFlat = 0\\n \\n imageSinceDark = imageSinceDark + 1\\n if imageSinceDark == darkFieldInterval and darkFieldInterval != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index))\\n index = index + 1 \\n imageSinceDark = 0\\n\\n optimizeBeam = optimizeBeam + 1\\n if optimizeBeam == optimizeBeamInterval and optimizeBeamInterval != 0:\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], inBeamPosition, optimizeBeamYes, image_key_project, index))\\n index = index + 1 \\n optimizeBeam = 0\\n \\n #add dark and flat only if not done in last steps\\n if imageSinceFlat != 0:\\n for i in range(imagesPerFlat):\\n scan_points.append((theta_pos, [shutterOpen, shutterNoChange][i != 0], outOfBeamPosition, optimizeBeamNo, image_key_flat, index)) #flat\\n index = index + 1\\n if imageSinceDark != 0:\\n for i in range(imagesPerDark):\\n scan_points.append((theta_pos, [shutterClosed, shutterNoChange][i != 0], inBeamPosition, optimizeBeamNo, image_key_dark, index)) #dark\\n index = index + 1 \\n# scan_points1 = generateScanPoints(inBeamPosition, outOfBeamPosition, theta_points, darkFieldInterval, flatFieldInterval,\\n# imagesPerDark, imagesPerFlat, optimizeBeamInterval, pattern=pattern)\\n# if pattern == 'default' or pattern == 'DFPFD':\\n# i = 0\\n# for pt1 in scan_points1:\\n# pt = scan_points[i]\\n# if pt1 != pt:\\n# print \\\"Mismatch - please tell Kaz about your scan and its arguments!\\\"\\n# print \\\"i = \\\", i\\n# print \\\"pt = \\\", pt\\n# print \\\"pt1 = \\\", pt1\\n# i += 1\\n #return None\\n positionProvider = tomoScan_positions(start, stop, step, darkFieldInterval, imagesPerDark, flatFieldInterval, imagesPerFlat, \\\\\\n inBeamPosition, outOfBeamPosition, optimizeBeamInterval, scan_points) \\n scan_args = [tomoScanDevice, positionProvider, tomography_time, tomography_beammonitor, tomography_detector, exposureTime, tomography_camera_stage, tomography_sample_stage]\\n #scan_args.append(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(RotationAxisScannable(\\\"approxCOR\\\", tomoRotationAxis))\\n #meta_add(\\\"RotationCoord_as_list\\\", [tomoRotationAxis])\\n meta_add(\\\"approxCOR\\\", tomoRotationAxis)\\n for scannable in additionalScannables:\\n scan_args.append(scannable)\\n for scannable in tomo_additional_scannables:\\n scan_args.append(scannable)\\n ''' setting the description provided as the title'''\\n if not description == None: \\n setTitle(description)\\n else :\\n setTitle(\\\"undefined\\\")\\n \\n dataFormat = LocalProperties.get(\\\"gda.data.scan.datawriter.dataFormat\\\")\\n if not dataFormat == \\\"NexusDataWriter\\\":\\n handle_messages.simpleLog(\\\"Data format inconsistent. Setting 'gda.data.scan.datawriter.dataFormat' to 'NexusDataWriter'\\\")\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", \\\"NexusDataWriter\\\")\\n scanObject = createConcurrentScan(scan_args)\\n if addNXEntry:\\n addNXTomoSubentry(scanObject, tomography_detector.name, tomography_theta.name)\\n scanObject.runScan()\\n if autoAnalyse:\\n lsdp=jns.lastScanDataPoint()\\n OSCommandRunner.runNoWait([\\\"/dls_sw/apps/tomopy/tomopy/bin/gda/tomo_at_scan_end_kz\\\", lsdp.currentFilename], OSCommandRunner.LOGOPTION.ALWAYS, None)\\n return scanObject;\\n except InterruptedException:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"User interrupted the scan\\\", exceptionType, exception, traceback, False)\\n raise InterruptedException(\\\"User interrupted the scan\\\")\\n except:\\n exceptionType, exception, traceback = sys.exc_info()\\n handle_messages.log(None, \\\"Error during tomography scan\\\", exceptionType, exception, traceback, False)\\n raise Exception(\\\"Error during tomography scan\\\", exception)\\n finally:\\n handle_messages.simpleLog(\\\"Data Format reset to the original setting: \\\" + dataFormat)\\n LocalProperties.set(\\\"gda.data.scan.datawriter.dataFormat\\\", dataFormat)\",\n \"def getedge(self):\\n cmd=\\\"getEdge(\\\"+self.board+\\\",\\\"+self.inpedge+\\\")\\\"\\n output=self.vb.io.execute(cmd,log=\\\"out\\\",applout=\\\"<>\\\")\\n #print 'edge= ',output\\n self.edge=output[0]\\n self.inputedge.setEntry(self.edge)\\n if self.board != '0': \\n self.delay=output[1] \\n self.inputdelay.setEntry(self.delay)\",\n \"def feed(self):\\n # or intelligence discard\\n pass\",\n \"def stop(self):\\n self.running = False\\n self.cam.stop()\\n self.amplifier.stop()\\n pass\",\n \"def end_phase():\\n pass\",\n \"def stop() -> None:\",\n \"def stop(self) -> None:\",\n \"def stop(self) -> None:\",\n \"def stopAcceleratingForward(self,event):\\n self.isAcceleratingForward=False\",\n \"def Stop(self) :\\n\\t\\t...\",\n \"def tomo_scan(*, start=0, stop=180, numProjPerSweep=1500, slewSpeed=5, accl=1, samInPos=0, samOutDist=-3, md=None):\\n _md = md or OrderedDict()\\n _md[\\\"project\\\"] = \\\"mona\\\"\\n _md[\\\"APS_storage_ring_current,mA\\\"] = aps_current.value\\n _md[\\\"datetime_plan_started\\\"] = str(datetime.now())\\n\\n pso = psofly\\n det = pg3_det\\n rotStage = tomo_stage.rotary\\n shutter = A_shutter\\n \\n acquire_time = 0.01\\n report_t0 = None\\n update_interval_s = 5.0 # during scans, print interim progress\\n update_poll_delay_s = 0.01\\n\\n stage_sigs = {}\\n stage_sigs[\\\"det.cam\\\"] = det.cam.stage_sigs\\n\\n staged_device_list = [det]\\n monitored_signals_list = [\\n det.image.array_counter,\\n rotStage.user_readback,\\n ]\\n\\n def cleanup():\\n for d in [det.image.array_counter, rotStage.user_readback]:\\n try:\\n yield from bps.unmonitor(d)\\n except IllegalMessageSequence:\\n pass\\n try:\\n yield from bps.abs_set(shutter, \\\"close\\\", group='shutter')\\n except RuntimeError as exc:\\n print(exc)\\n yield from bps.mv(rotStage.velocity, ROT_STAGE_FAST_SPEED)\\n yield from motor_set_modulo(rotStage, 360.0)\\n yield from bps.mv(\\n rotStage, 0.00,\\n #det.cam.trigger_mode, \\\"Internal\\\",\\n #det.cam.image_mode, \\\"Continuous\\\",\\n #det.hdf1.enable, \\\"Disable\\\",\\n #det.hdf1.capture, \\\"Done\\\",\\n )\\n yield from bps.wait(group='shutter')\\n\\n det.cam.stage_sigs[\\\"num_images\\\"] = numProjPerSweep\\n det.cam.stage_sigs[\\\"trigger_mode\\\"] = \\\"Overlapped\\\"\\n det.cam.stage_sigs[\\\"trigger_source\\\"] = \\\"GPIO_0\\\"\\n det.cam.stage_sigs[\\\"trigger_polarity\\\"] = \\\"Low\\\"\\n det.cam.stage_sigs[\\\"image_mode\\\"] = \\\"Multiple\\\"\\n det.hdf1.stage_sigs[\\\"num_capture\\\"] = numProjPerSweep # TODO: + NUM_DARK_FRAMES + NUM_FLAT_FRAMES,\\n\\n def _report_(t):\\n msg = \\\"{:.2f}s - flying \\\".format(t)\\n msg += \\\" x = {:.4f}\\\".format(tomo_stage.x.position)\\n msg += \\\" y = {:.4f}\\\".format(tomo_stage.y.position)\\n msg += \\\" r = {:.4f}\\\".format(tomo_stage.rotary.position)\\n msg += \\\" theta = {:.4f}\\\".format(tomo_stage.rotary.position % 360)\\n msg += \\\" image # {}\\\".format(det.cam.num_images_counter.value)\\n return msg\\n\\n @run_in_thread\\n def progress_reporting():\\n logger.debug(\\\"progress_reporting is starting\\\")\\n t = time.time()\\n startup = t + update_interval_s/2\\n while t < startup and pso.fly.value == 0: # wait for flyscan to start\\n time.sleep(update_poll_delay_s)\\n\\n logger.debug(\\\"progress_reporting: fly starts\\\")\\n update_time = t = report_t0 = time.time()\\n while pso.fly.value == 1:\\n if t >= update_time:\\n update_time = t + update_interval_s\\n msg = _report_(t - report_t0)\\n print(msg)\\n logger.debug(msg)\\n time.sleep(update_poll_delay_s)\\n t = time.time()\\n msg = _report_(time.time() - report_t0)\\n print(msg)\\n logger.debug(msg)\\n logger.debug(\\\"{}s - progress_reporting is done\\\".format(time.time()-report_t0))\\n\\n @bpp.stage_decorator(staged_device_list)\\n @bpp.run_decorator(md=_md)\\n @bpp.finalize_decorator(cleanup)\\n def _internal_tomo():\\n yield from bps.monitor(rotStage.user_readback, name=\\\"rotation_monitor\\\")\\n yield from bps.monitor(det.image.array_counter, name=\\\"array_counter_monitor\\\")\\n yield from bps.monitor(tomo_stage.x, name=\\\"tomo_stage_x_monitor\\\")\\n\\n # prepare the camera and the HDF5 plugin to write data\\n yield from bps.mv(\\n det.cam.nd_attributes_file, \\\"monaDetectorAttributes.xml\\\",\\n det.cam.acquire_time, acquire_time,\\n det.hdf1.enable, \\\"Enable\\\",\\n det.hdf1.auto_save, \\\"Yes\\\",\\n )\\n #yield from bps.abs_set(det.hdf1.capture, \\\"Capture\\\")\\n\\n # TODO: test before using!\\n # yield from measure_darks(det, shutter, NUM_DARK_FRAMES)\\n # yield from measure_flats(det, shutter, NUM_FLAT_FRAMES, samStage, samInPos + samOutDist)\\n\\n # do not touch shutter during development\\n yield from bps.abs_set(shutter, \\\"open\\\", group=\\\"shutter\\\")\\n yield from set_image_frame()\\n\\n yield from bps.stop(rotStage)\\n yield from motor_set_modulo(rotStage, 360.0)\\n \\n yield from bps.mv(\\n rotStage.velocity, ROT_STAGE_FAST_SPEED, \\n rotStage.acceleration, 3)\\n yield from bps.mv(\\n rotStage, 0, \\n samStage, samInPos)\\n\\n # back off to the taxi point (back-off distance before fly start)\\n logging.debug(\\\"before taxi\\\")\\n yield from bps.mv(\\n pso.start, start,\\n pso.end, stop,\\n pso.scan_control, \\\"Standard\\\",\\n pso.scan_delta, 1.0*(stop-start)/numProjPerSweep,\\n pso.slew_speed, slewSpeed,\\n rotStage.velocity, ROT_STAGE_FAST_SPEED,\\n rotStage.acceleration, slewSpeed/accl,\\n det.cam.array_counter, 0,\\n )\\n\\n progress_reporting()\\n yield from bps.mv(pso.taxi, \\\"Taxi\\\")\\n logging.debug(\\\"after taxi\\\")\\n\\n # run the fly scan\\n logging.debug(\\\"before fly\\\")\\n yield from bps.mv(rotStage.velocity, slewSpeed)\\n yield from bps.wait(group='shutter') # shutters are slooow, MUST be done now\\n yield from set_image_frame()\\n yield from bps.trigger(det, group='fly')\\n yield from bps.abs_set(pso.fly, \\\"Fly\\\", group='fly')\\n yield from bps.wait(group='fly')\\n #yield from bps.abs_set(det.cam.acquire, 0)\\n logging.debug(\\\"after fly\\\")\\n\\n # read the camera\\n yield from bps.create(name='primary')\\n yield from bps.read(det)\\n yield from bps.save()\\n det.cam.stage_sigs = stage_sigs[\\\"det.cam\\\"]\\n\\n return (yield from _internal_tomo())\",\n \"def wave_tx_stop():\\n return _u2i(_pigpio_command(_control, _PI_CMD_WVHLT, 0, 0))\",\n \"def set_attenuator_output(self, state):\\n try:\\n self.attenuator.write('O {0}'.format(state))\\n except visa.VisaIOError as err:\\n raise IOError('Error: %s' % err)\",\n \"def evaluateIFsetup(ants=0, timerange=3.4, deltat=0.1,\\n verbose=False, iniAtten=0) :\\n antlist = makeAntList(ants)\\n for a in antlist:\\n if a < 16:\\n print \\\"SZA antennas only can be used - exiting\\\"\\n return\\n tpMP = \\\"Ifmod.ifTotalPower\\\"\\n attenMP = \\\"Ifmod.totalAtten\\\"\\n currentPos = \\\"AMBIENT\\\"\\n print \\\"Inserting ambient load into beam\\\"\\n rtn = amb(ants)\\n pos1ants = rtn.ready\\n mplist = []\\n for a in pos1ants:\\n prefix = \\\"Sza%d.\\\" %(a-15)\\n mplist.append([prefix+tpMP, prefix+attenMP])\\n #print mplist\\n if len(rtn.notready) != 0:\\n print \\\"Ambient load not inserted for ants:\\\", rtn.notready\\n if iniAtten != None :\\n print \\\"Setting IF atten to %.1f\\\" %iniAtten\\n antennaIFatten(iniAtten, 1, invalidateTsys=False, \\n ants=antlist)\\n wait(tmo=2.0)\\n t0=time.time()\\n antennaIFpower(0.3, ants)\\n t1 = time.time()\\n cmdtime = t1-t0\\n results = []\\n times = []\\n nsamps = int(round(timerange/deltat))\\n for i in range(nsamps):\\n sleep(deltat)\\n dt=time.time()-t1\\n times.append(dt)\\n results.append(queryMpValues(mplist))\\n #print results\\n print \\\"Command time = %.3f seconds\\\" %cmdtime\\n for j in range(len(pos1ants)):\\n aname = \\\"Sza%d\\\" %(pos1ants[j]-15)\\n if verbose: print \\\"======== %s ========\\\" %aname\\n for i in range(nsamps):\\n r = results[i][j]\\n st = \\\"%s: %4.1f %6.3f %4.1f\\\" %(aname,times[i],r[0],r[1])\\n if verbose: print st\\n print \\\"\\\\n==== Inferred timing & final atten =====\\\"\\n for j in range(len(pos1ants)):\\n aname = \\\"Sza%d\\\" %(pos1ants[j]-15)\\n finalAtten = results[nsamps-1][j][1]\\n timing = times[nsamps-1]\\n for i in range(nsamps):\\n index = nsamps-1-i\\n atten = results[index][j][1]\\n if atten != finalAtten: break\\n timing = times[index]\\n st = \\\" %s: %4.1f %4.1f\\\" %(aname,timing, finalAtten)\\n print st\\n \\n #print \\\"Moving ambient load out of the beam\\\"\\n pos2ants = sky(ants).ready\\n # Wait for two seconds to get final psys reading\\n wait(tmo=2.0)\\n skyants = pos2ants\\n complete = []\\n for a in pos1ants :\\n if (pos2ants.count(a) > 0) : complete.append(a)\\n return complete\",\n \"def beam_filter(self, ouput_token_step):\\n idx_array = np.argpartition(ouput_token_step[0, -1, :], -self.beam_width)[-self.beam_width:] #index for reverse_target_word\\n prob_array = np.log(np.partition(ouput_token_step[0, -1, :], -self.beam_width)[-self.beam_width:])\\n \\n return idx_array, prob_array\",\n \"def generate_beam(env, dec, decoder_args, beam_size, length_penalty, early_stopping, max_len=100):\\n\\n # check inputs\\n trg, enc_src, trg_mask, src_mask = decoder_args\\n src_enc = enc_src\\n src_len = enc_src \\n\\n #assert src_enc.size(0) == src_len.size(0)\\n assert beam_size >= 1\\n\\n # batch size / number of words\\n bs = len(src_enc)\\n n_words = env.n_words\\n breakpoint()\\n\\n # expand to beam size the source latent representations / source lengths\\n src_enc = src_enc.unsqueeze(1).expand((bs, beam_size) + src_enc.shape[1:]).contiguous().view((bs * beam_size,) + src_enc.shape[1:])\\n #src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1)\\n\\n # generated sentences (batch with beam current hypotheses)\\n #generated = src_len.new(max_len, bs * beam_size) # upcoming output\\n #generated.fill_(env.pad_index) # fill upcoming ouput with <PAD>\\n #generated[0].fill_(env.eos_index) # we use <EOS> for <BOS> everywhere\\n\\n # generated hypotheses\\n generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)]\\n\\n # positions\\n positions = src_len.new(max_len).long()\\n #positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated)\\n\\n # scores for each sentence in the beam\\n beam_scores = src_enc.new(bs, beam_size).fill_(0)\\n beam_scores[:, 1:] = -1e9\\n beam_scores = beam_scores.view(-1)\\n\\n # current position\\n cur_len = 1\\n\\n # cache compute states\\n cache = {'slen': 0}\\n\\n # done sentences\\n done = [False for _ in range(bs)]\\n breakpoint()\\n while cur_len < max_len:\\n dec(trg[:,:-1], enc_src, trg_mask, src_mask)\\n # compute word scores\\n tensor = decoder(\\n x=generated[:cur_len],\\n lengths=src_len.new(bs * beam_size).fill_(cur_len),\\n positions=positions[:cur_len],\\n causal=True,\\n src_enc=src_enc,\\n src_len=src_len,\\n cache=cache\\n )\\n assert tensor.size() == (1, bs * beam_size, env.dim)\\n tensor = tensor.data[-1, :, :] # (bs * beam_size, dim)\\n scores = env.proj(tensor) # (bs * beam_size, n_words)\\n scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words)\\n assert scores.size() == (bs * beam_size, n_words)\\n\\n # select next words with scores\\n _scores = scores + beam_scores[:, None].expand_as(scores) # (bs * beam_size, n_words)\\n _scores = _scores.view(bs, beam_size * n_words) # (bs, beam_size * n_words)\\n\\n next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True)\\n assert next_scores.size() == next_words.size() == (bs, 2 * beam_size)\\n\\n # next batch beam content\\n # list of (bs * beam_size) tuple(next hypothesis score, next word, current position in the batch)\\n next_batch_beam = []\\n\\n # for each sentence\\n for sent_id in range(bs):\\n\\n # if we are done with this sentence\\n done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item())\\n if done[sent_id]:\\n next_batch_beam.extend([(0, env.pad_index, 0)] * beam_size) # pad the batch\\n continue\\n\\n # next sentence beam content\\n next_sent_beam = []\\n\\n # next words for this sentence\\n for idx, value in zip(next_words[sent_id], next_scores[sent_id]):\\n\\n # get beam and word IDs\\n beam_id = idx // n_words\\n word_id = idx % n_words\\n\\n # end of sentence, or next word\\n if word_id == env.eos_index or cur_len + 1 == max_len:\\n generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone().cpu(), value.item())\\n else:\\n next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))\\n\\n # the beam for next step is full\\n if len(next_sent_beam) == beam_size:\\n break\\n\\n # update next beam content\\n assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size\\n if len(next_sent_beam) == 0:\\n next_sent_beam = [(0, env.pad_index, 0)] * beam_size # pad the batch\\n next_batch_beam.extend(next_sent_beam)\\n assert len(next_batch_beam) == beam_size * (sent_id + 1)\\n\\n # sanity check / prepare next batch\\n assert len(next_batch_beam) == bs * beam_size\\n beam_scores = beam_scores.new([x[0] for x in next_batch_beam])\\n beam_words = generated.new([x[1] for x in next_batch_beam])\\n beam_idx = src_len.new([x[2] for x in next_batch_beam])\\n\\n # re-order batch and internal states\\n generated = generated[:, beam_idx]\\n generated[cur_len] = beam_words\\n for k in cache.keys():\\n if k != 'slen':\\n cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx])\\n\\n # update current length\\n cur_len = cur_len + 1\\n\\n # stop when we are done with each sentence\\n if all(done):\\n break\\n\\n # def get_coeffs(s):\\n # roots = [int(s[i + 2]) for i, c in enumerate(s) if c == 'x']\\n # poly = np.poly1d(roots, r=True)\\n # coeffs = list(poly.coefficients.astype(np.int64))\\n # return [c % 10 for c in coeffs], coeffs\\n\\n # visualize hypotheses\\n # print([len(x) for x in generated_hyps], cur_len)\\n # globals().update( locals() );\\n # !import code; code.interact(local=vars())\\n # for ii in range(bs):\\n # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True):\\n # hh = \\\" \\\".join(self.id2word[x] for x in ww.tolist())\\n # print(f\\\"{ss:+.4f} {hh}\\\")\\n # # cc = get_coeffs(hh[4:])\\n # # print(f\\\"{ss:+.4f} {hh} || {cc[0]} || {cc[1]}\\\")\\n # print(\\\"\\\")\\n\\n # select the best hypotheses\\n tgt_len = src_len.new(bs)\\n best = []\\n\\n for i, hypotheses in enumerate(generated_hyps):\\n best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]\\n tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol\\n best.append(best_hyp)\\n\\n # generate target batch\\n decoded = src_len.new(tgt_len.max().item(), bs).fill_(env.pad_index)\\n for i, hypo in enumerate(best):\\n decoded[:tgt_len[i] - 1, i] = hypo\\n decoded[tgt_len[i] - 1, i] = env.eos_index\\n\\n # sanity check\\n assert (decoded == env.eos_index).sum() == 2 * bs\\n\\n return decoded, tgt_len, generated_hyps\",\n \"def align(): # open EH and fast shutter\\n\\t#marAuxiliary.closeMarShield()\\n\\td2in()\\n\\td3in()\\n\\tsh('o')\",\n \"def off(self):\",\n \"def stopcam_expose():\\n\\n\\trespond = send_command('stopcam')\",\n \"def __init__(self, pinForward1, pinBackward1,pinForward2, pinBackward2):\\n\\n self.pinForward1 = pinForward1\\n self.pinBackward1 = pinBackward1\\n self.pinForward2 = pinForward2\\n self.pinBackward2 = pinBackward2\\n\\n GPIO.setup(self.pinForward1, GPIO.OUT)\\n GPIO.setup(self.pinBackward1, GPIO.OUT)\\n GPIO.setup(self.pinForward2, GPIO.OUT)\\n GPIO.setup(self.pinBackward2, GPIO.OUT)\\n\\n self.pwm_forward1 = GPIO.PWM(self.pinForward1, 100)\\n self.pwm_backward1 = GPIO.PWM(self.pinBackward1, 100)\\n self.pwm_forward2 = GPIO.PWM(self.pinForward2, 100)\\n self.pwm_backward2 = GPIO.PWM(self.pinBackward2, 100)\\n \\n self.pwm_forward1.start(0)\\n self.pwm_backward1.start(0)\\n self.pwm_forward2.start(0)\\n self.pwm_backward2.start(0)\",\n \"def FlyOut(self, b=True):\\r\\n\\r\\n return self.SetFlag(self.optionFlyOut, b)\",\n \"def stop_motor(self):\\n self.output(self.steering_pin, 0)\\n self.pi.set_servo_pulsewidth(self.steering_pin, 0)\",\n \"def eye_calc(self):\\n # define the eye time vector scaled to UI, dimensionless\\n tnum = (1+int(round((self.eye_time_high-self.eye_time_low) / self.eye_time_step)))\\n self.time = np.linspace(self.eye_time_low, # column Z\\n self.eye_time_high,\\n tnum)\\n # convenience vector\\n t_1 = np.ones(tnum)\\n # column AA\\n self.time_eff = 0.5*t_1 + self.speedup*(self.time - 0.5*t_1)\\n arg1 = 2.0*erfinv(0.8)\\n arg2 = arg1/(1.0E-6*self.speedup*self.br_nominal)\\n \\n # calculate eye profiles for link at target reach\\n T_c_link = self.tc[self.lnum//2]\\n arg3 = arg2*self.time_eff/T_c_link # column AP\\n arg4 = arg2*(1.0 - self.time_eff)/T_c_link # column AQ\\n self.link_011 = 0.5*t_1 + 0.5*erf(arg3) # column AR\\n self.link_110 = 0.5*t_1 + 0.5*erf(arg4) # column AS\\n self.link_010 = self.link_011 + self.link_110 - t_1 # column AT\\n self.link_101 = t_1 - self.link_010 # column AW\\n self.link_001 = t_1 - self.link_110 # column AU\\n self.link_100 = t_1 - self.link_011 # column AV\\n\\n # calculate eye profiles for transmitter eye test configuration\\n T_c_test = math.sqrt(self.tx_1090_rise**2\\n + (0.329E6/self.txeye_rx_bw)**2)\\n arg5 = arg2*self.time_eff/T_c_test\\n arg6 = arg2*(t_1 - self.time_eff)/T_c_test\\n self.test_011 = 0.5*t_1 + 0.5*erf(arg5) # column AR\\n self.test_110 = 0.5*t_1 + 0.5*erf(arg6) # column AS\\n self.test_010 = self.test_011 + self.test_110 - t_1 # column AT\\n self.test_101 = t_1 - self.test_010\\n self.test_001 = t_1 - self.test_110\\n self.test_100 = t_1 - self.test_011\\n\\n print(\\\"Margin : \\\", self.margin)\",\n \"def eamap_end(*args):\\n return _ida_hexrays.eamap_end(*args)\",\n \"def test_attctl(self):\\n # boundary to cross\\n boundary_x = 5\\n boundary_y = 5\\n boundary_z = -5\\n\\n # delay starting the mission\\n self.wait_for_topics(30)\\n\\n rospy.loginfo(\\\"seting mission mode\\\")\\n self.set_mode(\\\"OFFBOARD\\\", 5)\\n rospy.loginfo(\\\"arming\\\")\\n self.set_arm(True, 5)\\n\\n rospy.loginfo(\\\"run mission\\\")\\n rospy.loginfo(\\\"attempting to cross boundary | x: {0}, y: {1}, z: {2}\\\".\\n format(boundary_x, boundary_y, boundary_z))\\n # does it cross expected boundaries in 'timeout' seconds?\\n timeout = 12 # (int) seconds\\n loop_freq = 10 # Hz\\n rate = rospy.Rate(loop_freq)\\n crossed = False\\n for i in xrange(timeout * loop_freq):\\n if (self.local_position.pose.position.x > boundary_x and\\n self.local_position.pose.position.z > boundary_y and\\n self.local_position.pose.position.y < boundary_z):\\n rospy.loginfo(\\\"boundary crossed | seconds: {0} of {1}\\\".format(\\n i / loop_freq, timeout))\\n crossed = True\\n break\\n\\n rate.sleep()\\n\\n self.assertTrue(crossed, (\\n \\\"took too long to cross boundaries | current position x: {0}, y: {1}, z: {2} | timeout(seconds): {3}\\\".\\n format(self.local_position.pose.position.x,\\n self.local_position.pose.position.y,\\n self.local_position.pose.position.z, timeout)))\\n\\n rospy.loginfo(\\\"disarming\\\")\\n self.set_arm(False, 5)\",\n \"def __init__(self, motion, **kwargs):\\n super(ShiftIn, self).__init__(motion)\",\n \"def test_out(self):\\n t_max = 24.0\\n dt = 0.1\\n\\n G = StudentLayer(1)\\n G.i_ext_init = 0.1\\n\\n M = simulation.EventMonitor(G)\\n Mo = simulation.StateMonitor(G, 'out')\\n sim = simulation.Simulation(G, M, Mo, dt=dt)\\n sim.run(t_max)\\n\\n # we need a single spike for this\\n # XXX we could also have set the refractory period to a really high number\\n self.assertEqual(len(M.t), 1)\\n\\n t_spike = M.t[0]\\n mask = (Mo.t > t_spike)\\n\\n out_t = Mo.t[mask]\\n out_y = Mo.out[0, mask]\\n\\n expected = out_y[0]*np.power(1 - dt/G.tau_out, (out_t - t_spike)/dt)\\n\\n self.assertLess(np.mean(np.abs(out_y - expected)), 1e-6)\",\n \"def _step(self, a):\\n obs, rew, done, info = super()._step(a)\\n # if self.robot.body_xyz[0] > self.threshold:\\n # rew = 1.0\\n # self.threshold += 1\\n # else:\\n # rew = 0.0\\n # self.steps += 1\\n # if self.steps > self.max_episode_steps:\\n # done = True\\n return obs, rew, done, info\",\n \"def takeoff(self, request, context):\\n print \\\"Arming motors\\\"\\n # Copter should arm in GUIDED mode\\n self.vehicle.mode = VehicleMode(\\\"GUIDED\\\")\\n self.vehicle.armed = True \\n\\n # Confirm vehicle armed before attempting to take off\\n timeout = 0\\n while not self.vehicle.armed: \\n print \\\" Waiting for arming...\\\"\\n time.sleep(1)\\n timeout = timeout + 1\\n if timeout == 20:\\n return self.getPosition(request, context)\\n\\n print \\\"Taking off!\\\"\\n self.vehicle.simple_takeoff(request.altitude) # Take off to target altitude\\n\\n # Wait until the vehicle reaches a safe height before processing the goto (otherwise the command \\n # after Vehicle.simple_takeoff will execute immediately).\\n while True:\\n print \\\" Altitude: \\\", self.vehicle.location.global_relative_frame.alt \\n #Break and return from function just below target altitude. \\n if self.vehicle.location.global_relative_frame.alt >= request.altitude * 0.95: \\n print \\\"Reached target altitude\\\"\\n break\\n time.sleep(1)\\n \\n return self.getPosition(request, context)\",\n \"def stop(self, *args, **kwargs):\\n return self(AbilityId.STOP, *args, **kwargs)\",\n \"def fwd(dist=0): #distance is in cm\\n try:\\n if dist>0:\\n # this casting to int doesn't seem necessary\\n pulse=int(PPR*(dist//WHEEL_CIRC) )\\n enc_tgt(1,1,pulse)\\n except Exception as e:\\n print (\\\"gopigo fwd: {}\\\".format(e))\\n pass\\n return write_i2c_block(ADDRESS,motor_fwd_cmd+[0,0,0])\",\n \"def _on_lane_invasion(self, event):\\n self.lanes_invaded = event.crossed_lane_markings\",\n \"def stop( self ) :\\n print '********************* number of events seen: ', globals()['evCounter']\\n\\n from Configurables import LumiIntegrator\\n lumitool = toolSvc.create('LumiIntegrator', 'IntegrateBeamCrossing', interface='ILumiIntegrator')\\n lumirawtool = toolSvc.create('LumiIntegrator', 'RawLumiIntegrator', interface='ILumiIntegrator')\\n print '********************* created lumiintegrator tools'\\n\\n # store data\\n store = {}\\n for (text, tool) in ( ('corrected', lumitool), ('raw', lumirawtool) ):\\n n_files = tool.muFiles()\\n print '********************* raw input files', text, n_files, tool.lumiValue(), tool.lumiError(), tool.absolute()\\n store[text] = {'files':tool.muFiles(), \\n 'lumiValue':tool.lumiValue(), \\n 'lumiError':tool.lumiError(), \\n 'absolute':tool.absolute(),\\n 'filedata':[],\\n }\\n for i in range(n_files):\\n store[text]['filedata'].append(\\n {'sequence' :i, \\n 'run' :tool.muRun(i), \\n 'guid' :tool.muGuid(i), \\n 'time0' :tool.muTime0(i), \\n 'time1' :tool.muTime1(i), \\n 'deltaLumi':tool.muDeltaLumi(i), \\n 'norm' :tool.muNorm(i), \\n 'mu' :tool.muMu(i), \\n 'counters' :{}\\n }\\n )\\n print 'file', i, tool.muRun(i), tool.muGuid(i), tool.muTime0(i), tool.muTime1(i), tool.muDeltaLumi(i), tool.muNorm(i), tool.muMu(i), \\n print tool.muKeys(i)\\n for k in range(tool.muKeys(i)):\\n if tool.muCounter(i,k) <= 50: continue # these are not useful for mu\\n norm = tool.muCounterNorm(i,k)\\n value = 0 if norm == 0 else tool.muCounterValue(i,k)/norm\\n store[text]['filedata'][-1]['counters'][tool.muCounterName(i,k)] = {'key':tool.muCounter(i,k), 'norm':norm, 'value':value}\\n print ' ', tool.muCounter(i,k), tool.muCounterName(i,k), norm, value, \\n print\\n\\n # save store\\n pfname = 'rawdata.pck'\\n pf = open(pfname,'wb')\\n print pfname, 'opened'\\n pickle.dump(store, pf)\\n pf.close()\\n print pfname, 'closed'\\n \\n print store\\n\\n return SUCCESS\",\n \"def off(self, *args, **kwargs):\\n self.stop_current_sequence()\\n return self.fade(0,0,0)\",\n \"def I2C_thread(write_directives, gui): \\n # Change the role of the button so that it stops commands\\n gui.aardvark_button_state('stop') \\n \\n # write via the Aardvark\\n pySCPI_aardvark.write_aardvark(write_directives, gui)\\n # clear the thread termination flag\\n gui.terminator.kill_event.clear()\\n \\n # re-allow access to hte command text box\\n gui.Command_text.config(state = 'normal')\\n \\n if not gui.terminator.root_destroyed:\\n # unlock all of the GUI buttons\\n gui.action_lock('Unlock')\\n \\n # Reset the button back to it's initial state\\n gui.aardvark_button_state('start') \\n # end if\",\n \"def stop(self) -> \\\"bool\\\":\\n return _beamforming_swig.beamformer_sptr_stop(self)\",\n \"def pswitchoff(chan) :\\n s.phaseSwitching(False, chan)\",\n \"def stop(self):\\n self._run = False\\n self.IA.stop()\",\n \"def _forwardImplementation(self, inbuf, outbuf):\\n assert self.module\\n \\n values = self.module.getActionValues(self.state) \\n n_values = self.n_values.getActionValues(self.state)\\n values = map(lambda x, y: x + self.exploration * (sqrt(2 * log(self.experiment.stepid, 2) / y) if y > 0 else 1000), values, n_values);\\n \\n actions = []\\n for i in range(self.shield_options):\\n new_action = where(values == max(values))[0]\\n new_action = choice(new_action) \\n values[new_action] = -10000\\n actions.append(new_action)\\n \\n while len(actions) < self.outdim:\\n actions.append(-1)\\n \\n outbuf[:] = actions\",\n \"def __set_outputs__(self):\\n self.__set_in_out_var__(None, 1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59246117","0.53664815","0.5361966","0.52839315","0.5274596","0.5259131","0.52420837","0.51933676","0.51914835","0.51529115","0.5128894","0.5128894","0.5128894","0.5128894","0.50962025","0.50304735","0.5025527","0.49930224","0.49781093","0.49603343","0.4948776","0.49414974","0.49263877","0.49154374","0.49076658","0.49032843","0.48940265","0.4893402","0.48926657","0.48851514","0.48805216","0.48704037","0.48668462","0.48588067","0.48564455","0.48564455","0.48543483","0.48543483","0.48543483","0.48528507","0.48446837","0.48269746","0.48263773","0.48196158","0.481263","0.48083648","0.48040885","0.4801539","0.480071","0.47981313","0.47963944","0.47951576","0.47870174","0.47859567","0.47786117","0.4774284","0.4772139","0.47612676","0.47550482","0.47506815","0.47456306","0.47441214","0.4739991","0.47389558","0.47361225","0.4733396","0.4723855","0.4723855","0.47234672","0.47234273","0.47109574","0.4708524","0.4700584","0.46959504","0.46947113","0.4682217","0.46798417","0.46743837","0.46697658","0.4668674","0.4668649","0.46582782","0.4654187","0.4651586","0.46510246","0.46495306","0.4647441","0.46443886","0.4631885","0.46281436","0.4627832","0.46134907","0.46116197","0.46087575","0.46072388","0.46033612","0.46032175","0.45986292","0.45973814","0.45960334"],"string":"[\n \"0.59246117\",\n \"0.53664815\",\n \"0.5361966\",\n \"0.52839315\",\n \"0.5274596\",\n \"0.5259131\",\n \"0.52420837\",\n \"0.51933676\",\n \"0.51914835\",\n \"0.51529115\",\n \"0.5128894\",\n \"0.5128894\",\n \"0.5128894\",\n \"0.5128894\",\n \"0.50962025\",\n \"0.50304735\",\n \"0.5025527\",\n \"0.49930224\",\n \"0.49781093\",\n \"0.49603343\",\n \"0.4948776\",\n \"0.49414974\",\n \"0.49263877\",\n \"0.49154374\",\n \"0.49076658\",\n \"0.49032843\",\n \"0.48940265\",\n \"0.4893402\",\n \"0.48926657\",\n \"0.48851514\",\n \"0.48805216\",\n \"0.48704037\",\n \"0.48668462\",\n \"0.48588067\",\n \"0.48564455\",\n \"0.48564455\",\n \"0.48543483\",\n \"0.48543483\",\n \"0.48543483\",\n \"0.48528507\",\n \"0.48446837\",\n \"0.48269746\",\n \"0.48263773\",\n \"0.48196158\",\n \"0.481263\",\n \"0.48083648\",\n \"0.48040885\",\n \"0.4801539\",\n \"0.480071\",\n \"0.47981313\",\n \"0.47963944\",\n \"0.47951576\",\n \"0.47870174\",\n \"0.47859567\",\n \"0.47786117\",\n \"0.4774284\",\n \"0.4772139\",\n \"0.47612676\",\n \"0.47550482\",\n \"0.47506815\",\n \"0.47456306\",\n \"0.47441214\",\n \"0.4739991\",\n \"0.47389558\",\n \"0.47361225\",\n \"0.4733396\",\n \"0.4723855\",\n \"0.4723855\",\n \"0.47234672\",\n \"0.47234273\",\n \"0.47109574\",\n \"0.4708524\",\n \"0.4700584\",\n \"0.46959504\",\n \"0.46947113\",\n \"0.4682217\",\n \"0.46798417\",\n \"0.46743837\",\n \"0.46697658\",\n \"0.4668674\",\n \"0.4668649\",\n \"0.46582782\",\n \"0.4654187\",\n \"0.4651586\",\n \"0.46510246\",\n \"0.46495306\",\n \"0.4647441\",\n \"0.46443886\",\n \"0.4631885\",\n \"0.46281436\",\n \"0.4627832\",\n \"0.46134907\",\n \"0.46116197\",\n \"0.46087575\",\n \"0.46072388\",\n \"0.46033612\",\n \"0.46032175\",\n \"0.45986292\",\n \"0.45973814\",\n \"0.45960334\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5679036"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":13193,"cells":{"query":{"kind":"string","value":"Align using an original script"},"document":{"kind":"string","value":"def alignment_org(angle=0.1):\n proposal_id('2023_2', '311564_test')\n yield from alignement_gisaxs_multisample(angle=angle)\n RE.md['ai_0'] = piezo.th.user_setpoint.get()\n proposal_id('2023_2', '311564_Pettersson')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def align(self):\n ...","def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')","def align(args) :\n from aligner import align_reads\n align_reads(args)","def align(filename, prog, outfile):\n ra = RunAlign()\n ra.run_align(filename, prog, outfile)","def do_drift_align(self, *arg):\n self.do_reset_pocs()\n print_info('*' * 80)\n i = DriftShell()\n i.cmdloop()","def align(self):\n number_of_Xs = 0\n xFront = \"\"\n xEnd = \"\"\n dashFront = \"\"\n dashEnd = \"\"\n\n # Determining if variable amino acids (\"X\") need to be added to the\n\t # beginning of the sequence:\n z = self.hmmStart-self.seqStart\n number_of_Xs = (self.hmmStart-1)-z\n if z > 0:\n dashFront = \"-\"*z\n xFront = \"X\"*number_of_Xs\n elif self.hmmStart-1<=self.seqStart-1:\n xFront = \"X\"*(self.hmmStart-1) \n\n # Determining if variable amino acids (\"X\") need to be added to the \n # end of the sequence:\n number_of_Xs_end = self.hmmLength - self.hmmEnd\n\n # The original sequence length; SPA format includes this\n delimeter = \"|\" #Need to fix can be \"_\" or \"|\" or something else...\n \n distToSeqEnd = self.origSeqLength - seqTo\n if distToSeqEnd >= number_of_Xs_end and number_of_Xs_end != self.hmmLength:\n xEnd = 'X'*number_of_Xs_end\n else:\n if distToSeqEnd < number_of_Xs_end:\n xEnd = 'X'*distToSeqEnd\n \tdashEnd += \"-\"*(number_of_Xs_end-distToSeqEnd)\n \t\n begin = \"{}{}\".format(dashFront, xFront)\n end = \"{}{}\".format(xEnd, dashEnd)\n self.addToFront(begin)\n self.data.extend(end)\n self.original = str(self)","def gridalign(self):\n self.position.x = int(round(self.position.x))\n self.position.y = int(round(self.position.y))\n self.position.z = int(round(self.position.z))\n\n if self.fan:\n self.fan = (int(round(self.fan[0])),int(round(self.fan[1])),int(round(self.fan[2])))\n\n bestDist = 2*9\n bestMatrix = makeMatrix(0,0,0)\n\n for compass in [0, 90, 180, 270]:\n for pitch in [0, 90, 180, 270]:\n for roll in [0, 90, 180, 270]:\n m = makeMatrix(compass,pitch,roll)\n dist = matrixDistanceSquared(self.matrix, m)\n if dist < bestDist:\n bestMatrix = m\n bestDist = dist\n\n self.matrix = bestMatrix\n self.positionOut()\n self.directionOut()","def align(model,\n left,\n right,\n max_length = 512):\n inputs = preprocess(left, right, max_length)\n output = model(inputs)\n output = expand(output)\n scores, path, params = postprocess(output, len(left), len(right))\n return Alignment(left, right, scores, path, params)","def align_preprocessed(self, img):\n aligner = FaceAligner(self.args.wing_path, self.args.lm_path, self.args.img_size)\n return aligner.align(img)","def affine_align(x, y, p1, p2, g, s):\n #Create M, Ix, and Iy as Y x X matrices of 0's\n M = [[0]*(len(x)+1) for i in range(len(y)+1)]\n Ix = [[0]*(len(x)+1) for i in range(len(y)+1)]\n Iy = [[0]*(len(x)+1) for i in range(len(y)+1)]\n #Set up initial values for Ix and Iy\n #M infs along both axes\n for i in range(1, len(y)+1):\n M[i][0] = -math.inf\n for j in range(1, len(x)+1):\n M[0][j] = -math.inf\n #Ix: Aligning X with gap, horizontal move, infs along top row\n for i in range(0, len(y)+1):\n Ix[i][0] = -math.inf\n #Gap penalties along left column\n for j in range(1, len(x)+1):\n Ix[0][j] = -g if Ix[0][j-1] == -math.inf else Ix[0][j-1] - s\n #Iy: Aligning Y with gap, vertical move, infs along left column\n for j in range(0, len(x)+1):\n Iy[0][j] = -math.inf\n #Gap penalties along top row\n for i in range(1, len(y)+1):\n Iy[i][0] = -g if Iy[i-1][0] == -math.inf else Iy[i-1][0] - s\n #Populate remaining cells\n for i in range(1, len(y)+1):\n for j in range(1, len(x)+1):\n M[i][j] = max(M[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2),\n Ix[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2),\n Iy[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2))\n Ix[i][j] = max(M[i][j-1] - g,\n Ix[i][j-1] - s)\n Iy[i][j] = max(M[i-1][j] - g,\n Iy[i-1][j] - s)\n #TRACEBACK\n x_ret=\"\"; y_ret=\"\"\n i = len(y); j = len(x)\n #Determine start matrix\n align_scores = (M[i][j], Iy[i][j], Ix[i][j])\n matrix_idx = align_scores.index(max(align_scores))\n #matrix_key will track the current matrix through the traceback\n matrix_key = [\"M\", \"Iy\", \"Ix\"][matrix_idx]\n while i > 0 and j > 0:\n #From M: Check diagonal moves back to all three matrices, align characters\n if matrix_key == \"M\":\n if M[i][j] == M[i-1][j-1] + p1 or M[i][j] == M[i-1][j-1] - p2:\n x_ret = x[j-1] + x_ret\n y_ret = y[i-1] + y_ret\n i -= 1; j -= 1\n matrix_key = \"M\"\n elif M[i][j] == Iy[i-1][j-1] + p1 or M[i][j] == Iy[i-1][j-1] - p2:\n x_ret = x[j-1] + x_ret\n y_ret = y[i-1] + y_ret\n i -= 1; j -= 1\n matrix_key = \"Iy\"\n elif M[i][j] == Ix[i-1][j-1] + p1 or M[i][j] == Ix[i-1][j-1] - p2:\n x_ret = x[j-1] + x_ret\n y_ret = y[i-1] + y_ret\n i -= 1; j -= 1\n matrix_key = \"Ix\"\n #From Iy: Check vertical move to Iy and M, align y character with x gap\n elif matrix_key == \"Iy\":\n if Iy[i][j] == M[i-1][j] - g:\n x_ret = \"_\" + x_ret\n y_ret = y[i-1] + y_ret\n i -= 1\n matrix_key = \"M\"\n elif Iy[i][j] == Iy[i-1][j] - s:\n x_ret = \"_\" + x_ret\n y_ret = y[i-1] + y_ret\n i -= 1\n matrix_key = \"Iy\"\n #From Ix: Check horizontal move to Ix and M, align x character with y gap\n elif matrix_key == \"Ix\":\n if Ix[i][j] == M[i][j-1] - g:\n x_ret = x[j-1] + x_ret\n y_ret = \"_\" + y_ret\n j -= 1\n matrix_key = \"M\"\n elif Ix[i][j] == Ix[i][j-1] - s:\n x_ret = x[j-1] + x_ret\n y_ret = \"_\" + y_ret\n j -= 1\n matrix_key = \"Ix\"\n #Finish sequence if edge was reached\n #i>0 means mach remaining characters in y with gaps in x\n if i > 0:\n x_ret = (\"_\"*i) + x_ret\n y_ret = y[0:i] + y_ret\n #j>0 means mach remaining characters in x with gaps in y\n if j > 0:\n x_ret = x[0:j] + x_ret\n y_ret = (\"_\"*j) + y_ret\n #Return alinged strings\n return (x_ret, y_ret)","def minimal_align(self):\n desired = int(PlatformVar(\"align\"))\n for ii in range(len(self.__content)):\n line = self.__content[ii]\n match = re.match(r'.*\\.align\\s+(\\d+).*', line)\n if match:\n align = int(match.group(1))\n # Due to GNU AS compatibility modes, .align may mean different things.\n if osarch_is_amd64 or osarch_is_ia32():\n if desired != align:\n if is_verbose():\n print(\"Replacing %i-byte alignment with %i-byte alignment.\" % (align, desired))\n self.__content[ii] = \" .balign %i\\n\" % (desired)\n else:\n print(\"Replacing low-order bit alignment %i with %i-byte alignment.\" % (align, desired))\n self.__content[ii] = \" .balign %i\\n\" % (desired)","def text_align(text, x_align, y_align, height, d, style= \"TIMES_ITALIC\", rotation=0, color=K_BLACK, mirror=dxfwrite.MIRROR_X):\n text = dxf.text(text, height = height,mirror=mirror,halign=CENTER, alignpoint = (x_align,y_align),\n style=style, layer='LINES', rotation=rotation, color=color, linetype='ByBlock')\n d.add(text)","def gap_align(center, string_w):\n m = len(center)\n n = len(string_w)\n\n # Initialization; D[i][j][0] contains the max alignment score of the\n # ith prefix of v and the jth of w; D[i][j][1] contains the back pointer.\n D = [[(0, START) for _ in range(n + 1)] for _ in range(m + 1)]\n\n for i in range(1, m + 1):\n D[i][0] = (D[i - 1][0][0] + blosum['-', center[i - 1]], DELETE)\n\n for j in range(1, n + 1):\n D[0][j] = (D[0][j - 1][0] + blosum['-', string_w[j - 1]], INSERT)\n\n # Recurrence\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n delete = D[i-1][j][0] + blosum[center[i - 1], '-']\n substitute = D[i-1][j-1][0] + blosum[center[i - 1], string_w[j - 1]]\n # Set D[i][j] to the max of the recurrences\n if delete > substitute:\n D[i][j] = (delete, DELETE)\n else:\n D[i][j] = (substitute, SUBSTITUTE)\n\n i, j = m, n\n w_aligned = ''\n back_pointer = D[i][j][1]\n while back_pointer != START:\n if back_pointer == DELETE:\n i -= 1\n w_aligned = '-' + w_aligned\n\n elif back_pointer == SUBSTITUTE:\n i -= 1\n j -= 1\n w_aligned = string_w[j] + w_aligned\n\n \n back_pointer = D[i][j][1]\n \n return w_aligned","def text_alignment(x, y):\n if x == 0:\n ha = \"center\"\n elif x > 0:\n ha = \"left\"\n else:\n ha = \"right\"\n if y == 0:\n va = \"center\"\n elif y > 0:\n va = \"bottom\"\n else:\n va = \"top\"\n\n return ha, va","def shift_aligners(self):\n for i in range(self.height-1, 1, -1):\n self.align.list[i] = self.align.list[i-1]\n self.align.list[1] = copy.deepcopy(self.align.list[0])","def alignApk(apkfile, targetapkfile):\n\n align = utils_file.getFullToolPath('zipalign')\n aligncmd = '\"%s\" -f 4 \"%s\" \"%s\"' % (align, apkfile, targetapkfile)\n\n ret = utils_file.execFormatCmd(aligncmd)\n\n return ret","def align_code(self, boundary):\n word_align = boundary / 4\n\n while len(self._code) % word_align:\n if len(self._code) % 2 == 0:\n self.add(spu.nop(0), True)\n else:\n self.add(spu.lnop(0), True)\n\n return","def test_align_points(self):\n mv = [\n [0, 0, 5],\n [5, 0, 5],\n [0, 5, 5]\n ]\n sv = [\n [0, 0, 0],\n [5, 0, 0],\n [0, 5, 0]\n ]\n al = align(self.amp1, self.amp2, mv=mv, sv=sv, method='contPoints').m\n zMax = self.amp1.vert[:, 2].max() - 5\n # Both objects are already centered, so should be close to origin (allowing for some inaccuracy)\n self.assertAlmostEqual(al.vert[:, 2].max(), zMax, delta=TestAlign.DELTA)","def align(self, align='left'):\n if align not in ['left', 'center', 'right', 'full']:\n raise ValueError('align must be \\'left\\', \\'center\\', \\'right\\' or \\'full\\'')\n else:\n self._write(self.__class__.__ESC + 'a' + self.__class__.__alignMap[align])","def build_alignment(self,score,pieces):\n\t \t# build text\n\t\tself.open_seqs()\n\t\ttext1 = text2 = \"\"\n\t\tend1 = end2 = None\n\t\tfor (start1,start2,length,pctId) in pieces:\n\t\t\tif (end1 != None):\n\t\t\t\tif (start1 == end1): # insertion in sequence 2\n\t\t\t\t\ttext1 += self.seq1_gap * (start2-end2)\n\t\t\t\t\ttext2 += self.seq2_file.get(end2,start2-end2)\n\t\t\t\telse: # insertion in sequence 1\n\t\t\t\t\ttext1 += self.seq1_file.get(end1,start1-end1)\n\t\t\t\t\ttext2 += self.seq2_gap * (start1-end1)\n\n\t\t\ttext1 += self.seq1_file.get(start1,length)\n\t\t\ttext2 += self.seq2_file.get(start2,length)\n\t\t\tend1 = start1 + length\n\t\t\tend2 = start2 + length\n\t\t# create alignment\n\t\tstart1 = pieces[0][0]\n\t\tstart2 = pieces[0][1]\n\t\tend1 = pieces[-1][0] + pieces[-1][2]\n\t\tend2 = pieces[-1][1] + pieces[-1][2]\n\t\tsize1 = end1 - start1\n\t\tsize2 = end2 - start2\n\t\ta = Alignment(score=score,species_to_lengths=self.species_to_lengths)\n\t\t#if (self.seq1_strand == \"-\"): start1 = self.seq1_file.length - end1\n\t\ta.add_component(Component(self.seq1_src,start1,size1,self.seq1_strand,text=text1))\n\t\t#if (self.seq2_strand == \"-\"): start2 = self.seq2_file.length - end2\n\t\ta.add_component(Component(self.seq2_src,start2,size2,self.seq2_strand,text=text2))\n\t\treturn a","def align(image):\n angle = find_angle(image)\n image = rotate(image, angle)\n return image","def align_groups(groups_dir, prog, align_dir):\n ra = RunAlign()\n ra.run_for_all_in(groups_dir, prog, align_dir)","def _justifyText(text):\n chunks = text.split()\n line = []\n lineLength = 0\n for chunk in chunks:\n lineLength += len(chunk) + 1\n if lineLength <= 73:\n line.append(chunk)\n continue\n else:\n print('''{:5}{:73}{}'''.format(\"|\", \" \".join(line), \"|\"))\n del line[:]\n line.append(chunk)\n lineLength = len(chunk) + 1\n print('''{:5}{:73}{}'''.format(\"|\", \" \".join(line), \"|\"))","def AlignFieldmaps(self):\n for entry in self.entry_map['fmap']:\n info = self.info[entry]\n\n# Register the magnitude image at the shortest TR to the T1-IR\n# structural image.\n target = self.info[self.norm_src]['imgfile'] + \\\n self.info[self.norm_src]['suffix']\n source = info['magfile'] + info['suffix']\n matfile = info['matfile']\n fmt = '3dAllineate -prefix NULL -1Dmatrix_save %s -base %s ' + \\\n '-source %s -cost mi -warp shift_rotate'\n cmd = fmt % (info['matfile'], target, source)\n self.CheckExec(cmd, [info['matfile']])\n\n# Convert to unitary matrix (remove scaling component.)\n cmd = 'cat_matvec -ONELINE %s -P > %s' % \\\n (info['matfile'], info['matfile_unitary'])\n self.CheckExec(cmd, [info['matfile_unitary']])\n\n# Rotate the magnitude image to the new grid.\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\n cmd = fmt % (info['magfile_r']+info['suffix'], \\\n info['matfile_unitary'], info['magfile'] + info['suffix'])\n self.CheckExec(cmd, [info['magfile_r']+info['suffix']])\n\n# Rotate the fieldmap to the new grid.\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\n cmd = fmt % (info['imgfile_r']+info['suffix'], \\\n info['matfile_unitary'], info['imgfile'] + info['suffix'])\n self.CheckExec(cmd, [info['imgfile_r']+info['suffix']])","def test_align(self):\n al = align(self.amp1, self.amp2).m\n\n # Both objects are already centered, so should be close to origin (allowing for some inaccuracy)\n self.assertAlmostEqual(al.vert.mean(axis=0)[0], 0, delta=TestAlign.DELTA)\n self.assertAlmostEqual(al.vert.mean(axis=0)[1], 0, delta=TestAlign.DELTA)\n self.assertAlmostEqual(al.vert.mean(axis=0)[2], 0, delta=TestAlign.DELTA)","def align_sheet(cmpd_vector, ref_vector, cmpd):\n theta = mb.coordinate_transform.angle(cmpd_vector, ref_vector)\n normal = np.cross(cmpd_vector, ref_vector)\n cmpd.rotate(theta, normal)","def positioning(self):\n pass","def align(self):\n flag=0\n input=None\n level=None\n board=None\n ainps={'L0':[],'L1':[],'L2':[],'H0':[]} \n for i in self.inputs:\n if(i.inputnumber.var.get() == 1):\n if i.inpnumall == rareradio:\n input=i.inpnum\n level=i.level\n board=i.board\n print 'Rare chosen:',level,' ',input\n ainps[i.level].append(i.inpnum)\n flag=flag+1\n #print 'ainps:',ainps \n if flag < 2 :\n print \"Align: less then 2 inputs chosen. \" \n return\n if input==None:\n cmd=\"setRareFlag(0,0,0)\"\n else:\n mode='0'\n if level == 'H0': mode = '1'\n cmd=\"setRareFlag(\"+board+','+input+','+mode+\")\"\n print \"seting rare: \",cmd\n output=self.vb.io.execute(cmd,log=\"yes\",applout=\"<>\") \n self.align=Corel(self.vb,ainps)\n self.align.croscor()","def calculate_alignment(boids: List[b.Boid]) -> Tuple[float, float]:\n align_x = 0\n align_y = 0\n for boid in boids:\n align_x += boid.v * math.cos(boid.direction)\n align_y += boid.v * math.sin(boid.direction)\n return align_x / len(boids), align_y / len(boids)","def swap_main(self):\n if self.align == self._left:\n self.swap_left()\n elif self.align == self._right:\n self.swap_right()","def sequence_align(string_v, string_w):\n m = len(string_v)\n n = len(string_w)\n\n # Initialization; D[i][j][0] contains the max alignment score of the\n # ith prefix of v and the jth of w; D[i][j][1] contains the back pointer.\n D = [[(0, START) for _ in range(n + 1)] for _ in range(m + 1)]\n\n for i in range(1, m + 1):\n D[i][0] = (D[i - 1][0][0] + blosum['-', string_v[i - 1]], DELETE)\n\n for j in range(1, n + 1):\n D[0][j] = (D[0][j - 1][0] + blosum['-', string_w[j - 1]], INSERT)\n\n # Recurrence\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n insert = D[i][j-1][0] + blosum['-', string_w[j - 1]]\n delete = D[i-1][j][0] + blosum[string_v[i - 1], '-']\n substitute = D[i-1][j-1][0] + blosum[string_v[i - 1], string_w[j - 1]]\n # Set D[i][j] to the max of the recurrences\n if insert > delete and insert > substitute:\n D[i][j] = (insert, INSERT)\n elif delete > substitute:\n D[i][j] = (delete, DELETE)\n else:\n D[i][j] = (substitute, SUBSTITUTE)\n\n i, j = m, n\n v_aligned = ''\n w_aligned = ''\n back_pointer = D[i][j][1]\n while back_pointer != START:\n if back_pointer == INSERT:\n j -= 1\n v_aligned = '-' + v_aligned\n w_aligned = string_w[j] + w_aligned\n\n \n elif back_pointer == DELETE:\n i -= 1\n v_aligned = string_v[i] + v_aligned\n w_aligned = '-' + w_aligned\n\n elif back_pointer == SUBSTITUTE:\n i -= 1\n j -= 1\n v_aligned = string_v[i] + v_aligned\n w_aligned = string_w[j] + w_aligned\n\n \n back_pointer = D[i][j][1]\n \n return v_aligned, w_aligned","def align(src, tgt):\n\n sm = SequenceMatcher(a=list(map(lambda x: x[0], tgt)), b=list(map(lambda x: x[0], src)))\n tgt_temp, src_temp = [], []\n opcodes = sm.get_opcodes()\n for tag, i1, i2, j1, j2 in opcodes:\n # If they are equal, do nothing except lowercase them\n if tag == 'equal':\n for i in range(i1, i2):\n tgt[i][1] = 'e'\n tgt_temp.append(tgt[i])\n for i in range(j1, j2):\n src[i][1] = 'e'\n src_temp.append(src[i])\n # For insertions and deletions, put a filler of '***' on the other one, and\n # make the other all caps\n elif tag == 'delete':\n for i in range(i1, i2):\n tgt[i][1] = 'd'\n tgt_temp.append(tgt[i])\n for i in range(i1, i2):\n src_temp.append(tgt[i])\n elif tag == 'insert':\n for i in range(j1, j2):\n src[i][1] = 'i'\n tgt_temp.append(src[i])\n for i in range(j1, j2):\n src_temp.append(src[i])\n # More complicated logic for a substitution\n elif tag == 'replace':\n for i in range(i1, i2):\n tgt[i][1] = 's'\n for i in range(j1, j2):\n src[i][1] = 's'\n tgt_temp += tgt[i1:i2]\n src_temp += src[j1:j2]\n \n src, tgt = GumarDataset.align_subsequences(src_temp, tgt_temp)\n return src, tgt","def _align_toplevel_grid(self):\n\n # align origin with nearest multple of 128\n self.mins[0] -= self.mins[0] % 128\n self.mins[1] -= self.mins[1] % 128\n\n width = self.maxs[0] - self.mins[0]\n height = self.maxs[1] - self.mins[1]\n greatest_dim = max(width, height)\n nearest_pow_two = int(2 ** np.ceil(np.log2(greatest_dim)))\n width_adjustment = (nearest_pow_two - width)\n height_adjustment = (nearest_pow_two - height)\n\n self.maxs[0] += width_adjustment\n self.maxs[1] += height_adjustment","def align(val):\n ovr = val % ALIGNMENT\n if (ovr):\n val = val + ALIGNMENT - ovr\n return val","def align(stroke1, stroke2):\n\n x1 = np.array(stroke1.x)\n x2 = np.array(stroke2.x)\n y1 = np.array(stroke1.y)\n y2 = np.array(stroke2.y)\n\n d = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\n m = d - np.min(d)\n\n Ix1 = np.argmax(x1)\n Ix2 = np.argmax(x2)\n Iy1 = np.argmax(y1)\n Iy2 = np.argmax(y2)\n\n ix1 = np.argmin(x1)\n ix2 = np.argmin(x2)\n iy1 = np.argmin(y1)\n iy2 = np.argmin(y2)\n\n # rephasing :\n u = np.array([(Ix1 - Ix2), (Iy1 - Iy2), (ix1 - ix2), (iy1 - iy2)])\n indice_period = np.argmin(np.abs(u))\n period = u[indice_period]\n new_x1 = np.array(x1[period:].tolist() + x1[0:period].tolist())\n new_y1 = np.array(y1[period:].tolist() + y1[0:period].tolist())\n x1 = new_x1\n y1 = new_y1\n\n # resorting : if symetric part, revert it\n mx = np.max((x1, x2), 0)\n my = np.max((y1, y2), 0)\n sym_score = abs(x1 - x2[::-1]) + abs(y1 - y2[::-1])\n if len(x1[sym_score < 50]) > 20:\n x1[sym_score < 40] = x1[sym_score < 40][::-1]\n y1[sym_score < 40] = y1[sym_score < 40][::-1]\n\n new_d = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\n new_m = new_d - min(new_d)\n\n return x1, y1, d, new_d, m, new_m","def main(argv):\n \n ### gets data from csv, sets variables\n seq1, seq2 = get_seqs('../data/seq.csv')\n \n \n # Assign the longer sequence to s1, and the shorter to s2\n l1, l2 = len(seq1), len(seq2)\n if l1 >= l2:\n s1, s2 = ((l2 - 1) * \".\" + seq1 + (l2 - 1) * \".\"), seq2\n #puts l2-1 \".\"s both sides of l1, allows alignment of all overlap combos\n else:\n s1, s2 = ((l1 - 1) * \".\" + seq2 + (l1 - 1) * \".\"), seq1\n l1, l2 = l2, l1 \n\n # writes alignment(s) with highest score into output file\n my_best_score = -1 #so 0 beats best score\n for i in range(l1 + l2 -1):\n score, matched, shift, end_shift = calculate_score(s1, s2, l1, l2, i)\n #assigns returns from calc_score function to these variables\n if score > my_best_score:\n my_best_score = score\n statement = \"This alignment occurs when the smaller strand (\" + \\\n str(l2) + \"nt in length) attaches from base \" + str(i - l2 + 2) + \\\n \" of the larger strand, with the highest score of \" + str(score) + \\\n \":\\n\"\n #statement explaining the alignment in detail\n best_comparison_highSP = (shift + matched + (l2 - 1) * \".\" + \"\\n\")\n best_comparison_lowSP = (shift + matched + end_shift + \"\\n\")\n best_s2, best_s1 = (shift + s2 + end_shift + \"\\n\"), (s1 + \"\\n\\n\\n\")\n #formats the matching, s1 and s2 lines to line-up neatly\n if i < l1 - 1:\n best_alignment = (str(statement) + str(best_comparison_lowSP) \\\n + str(best_s2) + str(best_s1))\n else:\n best_alignment = (str(statement) + str(best_comparison_highSP) \\\n + str(best_s2) + str(best_s1))\n # uses returned variables to write a statement about the alignment \n # giving its score and startpoint, and assigns 3 lines of alignment \n # (s1, s2 and matching bases) to a variable each for later printing\n f = open('../results/seqs_align.txt', 'w')\n f.write(best_alignment)\n f.close()\n print(\"Done!\")\n return None","def adjust_alignment(template,candidate,new_gaps):\n template_l = list(template)\n candidate_l = list(candidate)\n new_gaps.reverse()\n for pos in new_gaps:\n del template_l[pos]\n del candidate_l[nearest_gap(candidate_l,pos)]\n \n return (DNA.makeSequence(''.join(template_l)), \\\n DNA.makeSequence(''.join(candidate_l)))","def latex_show_as_aligned(rows):\n env = r'\\begin{align*}%s\\end{align*}'\n return env%(r'\\\\'.join([r'&'.join(row) for row in rows]))","def align(img, left_eye, right_eye):\n left_eye_x, left_eye_y = left_eye\n right_eye_x, right_eye_y = right_eye\n point_3rd, direction = (left_eye, -1) if left_eye_y > right_eye_y else (right_eye, 1)\n\n # np.linalg.norm is being used for euclidean distance\n a = np.linalg.norm(np.array(left_eye) - np.array(point_3rd))\n b = np.linalg.norm(np.array(right_eye) - np.array(point_3rd))\n c = np.linalg.norm(np.array(right_eye) - np.array(left_eye))\n\n if b != 0 and c != 0:\n angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))\n angle = (angle * 180) / math.pi\n if direction == -1:\n angle = 90 - angle\n img = Image.fromarray(img)\n img = np.array(img.rotate(direction * angle))\n\n return img","def align_labels(self, xpadding=0.5, ypadding=None, fix_all=False):\n \n if ypadding is None:\n ypadding = xpadding\n \n for i, ax in enumerate(self.grid):\n if i in self.bottom or fix_all:\n ax.xaxis.set_label_coords(0.5, -xpadding)\n if i in self.left or fix_all:\n ax.yaxis.set_label_coords(-ypadding, 0.5)\n \n pl.draw()","def _align(self):\n\n shape = np.shape(self.x)\n\n # Get angle of direction (cbi: center beam index)\n # NOTE: This implementation seems to be unstable, because the shot with the center beam index can be NaN\n # cbi = np.median(np.arange(len(self.x[0, :]))).astype(int)\n # vec1 = [self.x[0, cbi], self.y[0, cbi], 0.0]\n # vec2 = [self.x[-1, cbi], self.y[-1, cbi], 0.0]\n\n # Alternative implementation with mean over all entries within the line.\n # -> should be a good approximation of the line center\n # NOTE: 2019-05-30: Relaxed the criterion even further (mean of first and last 10 scan lines)\n vec1 = [np.nanmedian(self.x[0:10, :]), np.nanmedian(self.y[0:10, :]), 0.0]\n vec2 = [np.nanmedian(self.x[-11:-1, :]), np.nanmedian(self.y[-11:-1, :]), 0.0]\n angle = -1.0*np.arctan((vec2[1]-vec1[1])/(vec2[0]-vec1[0]))\n\n # validity check -> Do not rotate if angle is nan\n if np.isnan(angle):\n return\n\n # Get center point\n xc = np.nanmedian(self.x)\n yc = np.nanmedian(self.y)\n\n # Reform points\n points = [self.x.flatten()-xc, self.y.flatten()-yc]\n\n # Execute the rotation\n rot_matrix = np.array([[np.cos(angle), -np.sin(angle)],\n [np.sin(angle), np.cos(angle)]])\n points_rotated = rot_matrix.dot(points)\n self.x = np.reshape(points_rotated[0, :], shape)\n self.y = np.reshape(points_rotated[1, :], shape)\n\n # Save conversion parameters for reuse\n self._align_parameters = {'center_point': (xc, yc),\n 'angle': angle,\n 'rotation_matrix': rot_matrix}","def resetAlignmentCenter(self):\n cent = self.TiltSeries_._TiltAlignmentParas.cent\n imdimX = self.TiltSeries_._imdimX\n imdimY = self.TiltSeries_._imdimY\n print(imdimX, imdimY)\n if cent[0] != imdimX//2+1 or cent[1] != imdimY//2+1:\n #rint \"Centers do not match: cent=\"+str(cent)+\", imdim=\"+str(imdim)\n self.TiltSeries_._TiltAlignmentParas.cent = [imdimX//2+1, imdimY//2+1]","def image_align(first_image, second_image):\r\n\r\n high_diff = (second_image.shape[0] - first_image.shape[0]) // 2\r\n width_diff = (second_image.shape[1] - first_image.shape[1]) // 2\r\n\r\n align_image = second_image[high_diff: high_diff + first_image.shape[0],\r\n width_diff: width_diff + first_image.shape[1],\r\n :]\r\n\r\n\r\n assert align_image.shape == first_image.shape\r\n\r\n return align_image","def test_align():\n target = ('TAAATAAATATCTGGTGTTTGAGGCAAAAAGGCAGACTTAAATTCTAAATCACACCTGTGCTT'\n 'CCAGCACTACCTTCAAGCGCAGGTTCGAGCCAGTCAGGCAGGGTACATAAGAGTCCATTGTGC'\n 'CTGTATTATTTTGAGCAATGGCTAAAGTACCTTCACCCTTGCTCACTGCTCCCCCACTTCCTC'\n 'AAGTCTCATCGTGTTTTTTTTAGAGCTAGTTTCTTAGTCTCATTAGGCTTCAGTCACCAT')\n query = ('TCTGGTGTTTGAGGCAAAAAGGCAGACTTAAATTCTAAATCACACCTGTGCTTCCAGCACTACC'\n 'TTCAAGCGCAGGTTCGAGCCAGTCAGGACTGCTCCCCCACTTCCTCAAGTCTCATCGTGTTTTT'\n 'TTTAGAGCTAGTTTCTTAGTCTCATTAGGCTTCAGTCACCATCATTTCTTATAGGAATACCA')\n assert kevlar.align(target, query) == ('10D91M69D79M20I', 155)","def alignEvents(self, ourEvent, otherEvent, otherView):\n ourIter = self.eventToIter[ourEvent]\n ourRow = self.model.get_path(ourIter)[0]\n otherRow = otherView.model.get_path(otherView.eventToIter[otherEvent])[0]\n padding = otherRow - ourRow\n\n while padding > 0:\n self.model.insert_before(ourIter)\n padding -= 1","def align_angles(self):\n self.sim.data.qpos[self.joints_qpos_idx] = rotation.round_to_straight_angles(\n self.sim.data.qpos[self.joints_qpos_idx]\n )","def align(self):\n return self[\"align\"]","def align(self):\n return self[\"align\"]","def align(self):\n return self[\"align\"]","def format_alignment(self, alignment):\n raise NotImplementedError(\"This method should be implemented\")\n ###################################################\n # You MUST implement this method in the subclass. #\n ###################################################","def process_align(self):\n\t\tstm_t_dict = self._process_recog()\n\t\ttrans_t_dict = self._process_trans()\n\t\talign_obj = viterbi_align(stm_t_dict, trans_t_dict, self.label, self.pair_file_path)\n\t\tself.trans_t_dict = align_obj.viterbi(0, len(stm_t_dict)-1, 0, len(trans_t_dict)-1)","def transpose_alignment(align):\n\tindex = range(len(align[0]))\n\ttransposed_gap = list()\n\ttransposed = list()\n\tfor i in index:\n\t\ttransposed.append(''.join([seq[i] for seq in align]))\n\tfor column in transposed:\n\t\tgap = 0\n\t\tif column[0] != \"-\":\n\t\t\ttransposed_gap.append(column)\n\t\telse:\n\t\t\tprint (\"HOLA\"+column + \"\\n\")\n\n\t\"\"\"\tfor el in column:\n\t\t\tif el == \"-\":\n\t\t\t\tgap += 1\n\t\tif gap <= (len(column)/2):\n\t\t\ttransposed_gap.append(column)\n\t\"\"\"\n\tprint (transposed_gap)\n\treturn transposed_gap","def _align(elements, alignment=\"ymax\"):\n if len(elements) == 0:\n return elements\n if alignment not in ([\"x\", \"y\", \"xmin\", \"xmax\", \"ymin\", \"ymax\"]):\n raise ValueError(\n \"'alignment' argument must be one of 'x','y','xmin', 'xmax', 'ymin','ymax'\"\n )\n value = Group(elements).__getattribute__(alignment)\n for e in elements:\n e.__setattr__(alignment, value)\n return elements","def AddDimAligned(self,extPnt1,extPnt2,textPosition):\n\t\treturn self.Space.AddDimAligned(extPnt1,extPnt2,textPosition)","def align(*args, alignToLead: bool=False, coordinateSystem: name=None, xAxis: AnyStr=\"\", yAxis:\n AnyStr=\"\", zAxis: AnyStr=\"\", **kwargs)->bool:\n pass","def realign(self, x, dx, y, dy, ignore=set()):\n return ['', '', '']","def align(s0, s1, backptr): # Lägger de rätt för att få bästa matching med letters\r\n\r\n # Tom array att fylla på och returnera\r\n result = ['','']\r\n # Gör strängarna till lätthanterligare arrays\r\n x0 = [char for char in s0]\r\n x1 = [char for char in s1]\r\n # toma arrays att stoppa in de alignade bokstäverna och spacen\r\n ress0 = []\r\n ress1 = []\r\n\r\n rows = len(s0)\r\n columns = len(s1)\r\n\r\n # Tillverkar botten värden i backptr-matrisen så att man skall veta när man är klar\r\n backptr[0][0][0] = -1\r\n backptr[0][0][1] = -1\r\n\r\n # initiering värden vart i backptr-matrisen man är och har kommit ifrån\r\n staterow = rows\r\n statecol = columns\r\n r = staterow\r\n c = statecol\r\n # den går går längst pekarna tills den når botten\r\n while staterow >=0 and statecol >=0:\r\n # Om den pekar diagonalt\r\n if backptr[staterow][statecol][0] == staterow-1 and backptr[staterow][statecol][1] == statecol-1 :\r\n if staterow == 0 and statecol == 0: # om den är i botten, passera\r\n pass\r\n else:\r\n # annars spara båda bokstäverna på sammam plats i result\r\n ress0.insert(0,x0[staterow-1])\r\n ress1.insert(0,x1[statecol-1])\r\n # Om den pekar vänster (samma row)\r\n if backptr[staterow][statecol][0] == staterow:\r\n if staterow == 0 and statecol == 0: # om den är i botten, passera\r\n pass\r\n else:\r\n # annars spacear den raden och stoppar in bokstaven i kolumnen\r\n ress0.insert(0,\" \")\r\n ress1.insert(0,x1[statecol-1])\r\n # Om den pekar upp (samma kolumn)\r\n if backptr[staterow][statecol][1] == statecol:\r\n if staterow == 0 and statecol == 0: # om den är i botten, passera\r\n pass\r\n else:\r\n # annars spacear den kolumnen och stoppar in bokstaven i raden\r\n ress0.insert(0,x0[staterow-1])\r\n ress1.insert(0,\" \")\r\n\r\n # för att inte skriva över staterow i ny initieringen\r\n r = staterow\r\n c = statecol\r\n # initierar de nya tillståndet (följer pekaren)\r\n staterow = backptr[r][c][0]\r\n statecol = backptr[r][c][1]\r\n\r\n # printfunktionen vill ha stringen bakvänd....\r\n ress0.reverse()\r\n ress1.reverse()\r\n # concattar arrayerna till en sträng igen\r\n sum0 =''.join(ress0)\r\n sum1 =''.join(ress1)\r\n # stoppar in i resultatet\r\n result[0]=sum0\r\n result[1]=sum1\r\n\r\n return(result)","def swap_main(self):\n if self.align == self._up:\n self.swap_right()\n elif self.align == self._down:\n self.swap_left()","def _justify(self):\n minLengths = [max([max(map(len, row[i].split() + [''])) for row in self._rows if len(row) > 0])\n for i in range(self._colsNum)]\n shifts = [w - mw for mw, w in zip(minLengths, self._widthes)]\n # length = len(shifts)\n borrow = zip(self._colsRange, shifts)\n borrow.sort(lambda a, b: cmp(a[1], b[1]))\n delta = [0] * self._colsNum\n\n donorIdx = self._colsNum - 1\n recIdx = 0\n while True:\n\n curDonation = borrow[donorIdx][1]\n curRec = borrow[recIdx][1]\n\n if curRec >= 0 or curDonation <= 0:\n break\n\n curDelta = min(curDonation, -curRec)\n curDonation -= curDelta\n curRec += curDelta\n delta[borrow[donorIdx][0]] -= curDelta\n delta[borrow[recIdx][0]] += curDelta\n\n if curDonation == 0:\n donorIdx -= 1\n\n if curRec == 0:\n recIdx += 1\n\n for i in self._colsRange:\n self._widthes[i] += delta[i]","def seq_align(string1,string2,mismatch_penalty,gap_penalty):\n\n # define 2x2 matrix\n matrix = []\n for i in range(len(string1)+1):\n if i == 0:\n matrix.append(list([gap_penalty * x for x in range(len(string2)+1)]))\n else:\n matrix.append(list([gap_penalty * i if x == 0 else None for x in range(len(string2)+1)]))\n\n # populate matrix by looping through the strings and finding optimal value for each spot\n for i in range(len(string1)):\n for j in range(len(string2)):\n if string1[i] == string2[j]:\n val1 = 0 + matrix[i][j]\n else:\n val1 = mismatch_penalty + matrix[i][j]\n val2 = gap_penalty + matrix[i][j+1]\n val3 = gap_penalty + matrix[i+1][j]\n min_val = min(val1,val2,val3)\n matrix[i+1][j+1] = min_val\n\n\n # define values to use while retracing\n result_str1 = ''\n result_str2 = ''\n i = len(matrix)-1\n j = len(matrix[0])-1\n\n # trace through matrix to find the optimal character alignment\n while i > 0 and j > 0:\n val1 = matrix[i-1][j-1]\n val2 = matrix[i-1][j]\n val3 = matrix[i][j-1]\n min_val = min(val1,val2,val3)\n if val1 == min_val:\n result_str1 += string1[i-1]\n result_str2 += string2[j-1]\n i -= 1\n j -= 1\n elif val2 == min_val:\n result_str1 += \"-\"\n result_str2 += string2[j-1]\n i -= 1\n else:\n result_str1 += string1[i-1]\n result_str2 += \"-\"\n j -= 1\n\n # for any leftover j values\n if i == 0:\n while j > 0:\n result_str1 += '-'\n result_str2 += string2[j]\n j -=1\n\n # for any leftover i values\n if j == 0:\n while i > 0:\n result_str1 += string1[i]\n result_str2 += \"-\"\n i -= 1\n\n return matrix[len(matrix)-1][len(matrix[0])-1], result_str1[::-1], result_str2[::-1]","def _perform_alignment(self, formulatrix_image, beamline_image, formulatrix_points):\n aligner = ImageAligner(formulatrix_image, beamline_image, self._config_align, self._config_detector)\n aligned_images = aligner.align()\n scaled_formulatrix_points = aligner.scale_points(formulatrix_points)\n self._log_alignment_status(aligned_images)\n\n return aligned_images, scaled_formulatrix_points","def _align_frames(frame_pre, frame_now):\n height, width = frame_pre.shape\n horizontal_lines = [np.array([frame_pre[int(height * 1 / 4), :],\n frame_pre[int(height * 2 / 4), :],\n frame_pre[int(height * 3 / 4), :]]),\n\n np.array([frame_now[int(height * 1 / 4), :],\n frame_now[int(height * 2 / 4), :],\n frame_now[int(height * 3 / 4), :]])]\n\n vertical_lines = [np.array([frame_pre[:, int(width * 1 / 4)],\n frame_pre[:, int(width * 2 / 4)],\n frame_pre[:, int(width * 3 / 4)]]),\n\n np.array([frame_now[:, int(width * 1 / 4)],\n frame_now[:, int(width * 2 / 4)],\n frame_now[:, int(width * 3 / 4)]])]\n\n min_x_val = 99999\n min_y_val = 99999\n offset_x = 0\n offset_y = 0\n for offset in range(-5, 5 + 1, 1): # TODO: check this, [-7, 7]?\n a_x = horizontal_lines[0][:, max(offset, 0):-1 + min(offset, 0)]\n b_x = horizontal_lines[1][:, max(-offset, 0):-1 + min(-offset, 0)]\n val_x = np.mean(np.linalg.norm(a_x - b_x, 1, axis=1)) # norm 1\n # print('offset_x is:', offset, ' norm=', val_x)\n if val_x < min_x_val:\n min_x_val = val_x\n offset_x = offset\n\n a_y = vertical_lines[0][:, max(offset, 0):-1 + min(offset, 0)]\n b_y = vertical_lines[1][:, max(-offset, 0):-1 + min(-offset, 0)]\n val_y = np.mean(np.linalg.norm(a_y - b_y, 1, axis=1)) # norm 1\n # print('offset_y is:', offset, ' norm=', val_y)\n if val_y < min_y_val:\n min_y_val = val_y\n offset_y = offset\n\n print('The final offset_x is:', offset_x)\n print('The final offset_y is:', offset_y)\n\n # translation\n M = np.float32([[1, 0, offset_x], [0, 1, offset_y]])\n frame_now_aligned = cv2.warpAffine(frame_now.astype(np.uint8), M, (width, height)).astype(np.int32)\n\n return frame_now_aligned, offset_x, offset_y","def align_addr(addr, align = 16, dir = ALIGN_DOWN):\n\n if dir == ALIGN_DOWN:\n return addr - (addr % align)\n else:\n return addr + (align - addr % align)","def c_align_small(self, tik_instance):\n n_d, d_d, h_d, w_d, c_d = self.dst_shape\n hw_d = h_d * w_d\n hwnoni = hw_d * self.n_o * self.n_i\n dhw_d = d_d * h_d * w_d\n\n ub_ori = tik_instance.Tensor(self.dtype,\n (self.ub_ele,),\n name=\"ub_ori\",\n scope=tik.scope_ubuf)\n ub_trans = tik_instance.Tensor(self.dtype,\n (self.ub_ele,),\n name=\"ub_trans\",\n scope=tik.scope_ubuf)\n\n burst_len = d_d * self.c_1 * hwnoni * self.c_0 // self.cp_align_len\n tik_instance.data_move(ub_ori,\n self.src_gm,\n 0, 1, burst_len, 0, 0)\n\n with tik_instance.for_range(0, d_d) as num_d:\n with tik_instance.for_range(0, self.c_1) as num_c1:\n ori_cur = num_d * self.c_1 * hwnoni * self.c_0\\\n + num_c1 * hwnoni * self.c_0\n trans_cur = num_d * self.c_1 * hwnoni * self.c_0\\\n + num_c1 * self.c_0\n nburst = hwnoni\n burst_len = self.c_0 // self.cp_align_len\n src_stride = 0\n dst_stride = (self.c_1 - 1) * self.c_0 // self.cp_align_len\n tik_instance.data_move(\n ub_trans[trans_cur],\n ub_ori[ori_cur],\n 0, nburst, burst_len, src_stride, dst_stride)\n\n with tik_instance.for_range(0, dhw_d) as num_dhw:\n src_cur = num_dhw * self.n_o * self.n_i * c_d\n dst_cur = num_dhw * c_d\n nburst = n_d\n burst_len = c_d // self.cp_align_len\n src_stride = 0\n dst_stride = (dhw_d - 1) * c_d // self.cp_align_len\n tik_instance.data_move(\n ub_ori[dst_cur],\n ub_trans[src_cur],\n 0, nburst, burst_len, src_stride, dst_stride)\n\n burst_len = n_d * dhw_d * c_d // self.cp_align_len\n tik_instance.data_move(self.dst_gm,\n ub_ori,\n 0, 1, burst_len, 0, 0)\n\n return tik_instance","def align_one(ptcl,ref,prefilt,align,aligncmp,ralign,raligncmp):\n\n\tif prefilt : ref=ref.process(\"filter.matchto\",{\"to\":ptcl})\n\n\t# initial alignment\n\tif align!=None :\n\t\tali=ptcl.align(align[0],ref,align[1],aligncmp[0],aligncmp[1])\n\n\t# refine alignment if requested\n\tif ralign!=None:\n\t\tralign[1][\"xform.align2d\"] = ali.get_attr(\"xform.align2d\")\n\t\tali=ptcl.align(ralign[0],ref,ralign[1],raligncmp[0],raligncmp[1])\n\n\treturn ali","def viz_align(self):\n # Create the output folders\n viz_align_folders = self._pathcreator.required_viz_align_folders()\n project_creator = ProjectCreator()\n project_creator.create_subfolders(viz_align_folders)\n align_viz = AlignViz(\n self._pathcreator.get_lib_names_single_end()\n if not self._args.paired_end\n else self._pathcreator.get_lib_names_paired_end(),\n self._pathcreator.read_processing_stats_path,\n self._pathcreator.read_alignments_stats_path,\n self._pathcreator.read_alignment_stats_table_path,\n self._pathcreator.viz_align_aligned_reads_by_species_paths,\n self._species_folder_prefixes_and_display_names,\n )\n align_viz.read_stat_files()\n align_viz.plot_input_read_length(\n self._pathcreator.viz_align_input_read_length_plot_path\n )\n align_viz.plot_processed_read_length(\n self._pathcreator.viz_align_processed_reads_length_plot_path\n )\n align_viz.plot_total_number_of_aligned_reads(\n self._pathcreator.viz_align_all_folder\n )\n align_viz.plot_species_exclusive_reads_for_each_species()","def compute_position(layout):\n\n if layout.children:\n toyplot_vertical_align = layout.style[\"-toyplot-vertical-align\"]\n # Align the first line's baseline with the anchor.\n if toyplot_vertical_align == \"first-baseline\":\n offset_y = 0\n # Align the last line's baseline with the anchor.\n elif toyplot_vertical_align == \"last-baseline\":\n offset_y = -(layout.height + layout.children[0].top - layout.children[-1].bottom)\n # Align the top of the layout with the anchor.\n elif toyplot_vertical_align == \"top\":\n offset_y = -layout.children[0].top\n # Align the middle of the layout with the anchor.\n elif toyplot_vertical_align == \"middle\":\n offset_y = -((layout.height * 0.5) + layout.children[0].top)\n # Align the bottom of the layout with the anchor.\n elif toyplot_vertical_align == \"bottom\":\n offset_y = -(layout.height + layout.children[0].top)\n else:\n raise ValueError(\"Unknown -toyplot-vertical-align value: %s\" % toyplot_vertical_align) # pragma: no cover\n\n for line in layout.children:\n text_anchor = line.style[\"text-anchor\"] if line.children else \"middle\"\n if text_anchor == \"start\":\n anchor_offset = 0\n elif text_anchor == \"middle\":\n anchor_offset = -line.width * 0.5\n elif text_anchor == \"end\":\n anchor_offset = -line.width\n else:\n raise ValueError(\"Unknown text-anchor value: %s\" % text_anchor)\n anchor_offset += layout.style[\"-toyplot-anchor-shift\"]\n\n offset_x = anchor_offset\n\n # Line left/right/bottom/top are relative offsets from the layout anchor in canvas coordinates.\n line.left = offset_x\n line.right = offset_x + line.width\n line.top += offset_y\n line.baseline = offset_y\n line.bottom += offset_y\n\n for child in line.children:\n # Child left/right/bottom/top are relative offsets from the layout anchor in canvas coordinates.\n child.left = offset_x\n child.right = child.left + child.width\n child.top += offset_y\n child.baseline += offset_y\n child.bottom += offset_y\n # Note that baseline-shift is the opposite of canvas coordinates (positive values shift UP)\n child.baseline -= child.style[\"baseline-shift\"]\n\n offset_x += child.width\n offset_y += line.height\n\n layout.top = layout.children[0].top\n layout.left = numpy.min([line.left for line in layout.children])\n layout.right = numpy.max([line.right for line in layout.children])\n layout.bottom = layout.children[-1].bottom\n\n else:\n layout.top = 0\n layout.left = 0\n layout.right = 0\n layout.bottom = 0\n\n # Layout top/left/right/bottom are relative offsets from the layout anchor in canvas coordinates\n layout.width = layout.right - layout.left\n layout.height = layout.bottom - layout.top","def getStatusTextAlignment(self) -> int:\n ...","def getStatusTextAlignment(self) -> int:\n ...","def run(config=None):\n AlignmentWorkflow().run(config)","def align(self, image, landmark_indices, anchor_points, size=96):\n\n detected = self.detect(image)\n coords = self.find_landmarks(image, detected)\n in_points = coords[landmark_indices]\n in_points = in_points.astype('float32')\n out_points = anchor_points * size\n warp_mat = cv2.getAffineTransform(in_points, out_points)\n warp_dst = cv2.warpAffine(image, warp_mat, (size, size))\n\n return warp_dst","def _shift_anchors(anchors, direction):\n new_anchors = deepcopy(anchors)\n if direction == 'center':\n pass\n\n elif direction == 'top':\n heights = new_anchors[:,3] - new_anchors[:,1] + 1\n heights = heights[:,np.newaxis]\n new_anchors[:,[1,3]] = new_anchors[:,[1,3]] - heights/2\n\n elif direction == 'bottom':\n heights = new_anchors[:,3] - new_anchors[:,1] + 1\n heights = heights[:,np.newaxis]\n new_anchors[:,[1,3]] = new_anchors[:,[1,3]] + heights/2\n\n elif direction == 'right':\n widths = new_anchors[:,2] - new_anchors[:,0] + 1\n widths = widths[:,np.newaxis]\n new_anchors[:,[0,2]] = new_anchors[:,[0,2]] + widths/2\n\n elif direction == 'left':\n widths = new_anchors[:,2] - new_anchors[:,0] + 1\n widths = widths[:,np.newaxis]\n new_anchors[:,[0,2]] = new_anchors[:,[0,2]] - widths/2\n\n return new_anchors","def align(self, unit, main_unit, align_dict: dict, strict=False) -> 'function':\n\n aligner = Unit_Aligner(align_dict)\n return aligner.convert(unit, main_unit, strict)","def align(self, unit, main_unit, align_dict: dict, strict=False) -> 'function':\n\n aligner = Unit_Aligner(align_dict)\n return aligner.convert(unit, main_unit, strict)","def msg(text):\n for line in text.splitlines():\n if JS.alignment == \"left\":\n print(demarkup(line))\n elif JS.alignment == \"center\":\n print(demarkup(line).center(get_terminal_size()[0] - 1))\n else:\n print(demarkup(line).rjust(get_terminal_size()[0] - 1))","def Alignsequence(structure1, structure2):\n\n ppb = PPBuilder()\n for pp in ppb.build_peptides(structure1):\n sequence1 = pp.get_sequence()\n for pp in ppb.build_peptides(structure2):\n sequence2 = pp.get_sequence()\n\n alignment = pairwise2.align.globalxx(sequence1, sequence2)\n return alignment","def rotateAlignToWorld( alignObject, primaryAxisVector = [1, 0, 0], worldAxisToKeep = ['x'], alignTwist = True ):\n \n # normalize primary axis vector\n primaryAxisVectorVec = vector.makeMVector( primaryAxisVector )\n primaryAxisVectorVec.normalize()\n primaryAxisVector = [ primaryAxisVectorVec.x, primaryAxisVectorVec.y, primaryAxisVectorVec.z ]\n \n \n axisSign = primaryAxisVector[0] + primaryAxisVector[1] + primaryAxisVector[2]\n \n primaryAxisVectorVecMove = primaryAxisVector[:]\n primaryAxisVector = [ abs( primaryAxisVector[0] ), abs( primaryAxisVector[1] ), abs( primaryAxisVector[2] ) ]\n \n # prepare align twist vector\n allaxis = ['x', 'y', 'z']\n \n for axiskeep in worldAxisToKeep:\n \n allaxis.remove( axiskeep.lower() )\n \n skipUpAxis = allaxis[0]\n upVectorMove = {'x':[1, 0, 0], 'y':[0, 1, 0], 'z':[0, 0, 1]}[skipUpAxis]\n upVector = {'x':[1 * axisSign, 0, 0], 'y':[0, 1 * axisSign, 0], 'z':[0, 0, 1 * axisSign]}[skipUpAxis]\n \n # prepare align setup\n prefix = name.removeSuffix( alignObject )\n \n alignObjectAim = mc.group( n = prefix + 'alignObjectAim', em = 1, p = alignObject )\n alignObjectAimUp = mc.group( n = prefix + 'alignObjectAimUp', em = 1, p = alignObject )\n mc.move( primaryAxisVectorVecMove[0], primaryAxisVectorVecMove[1], primaryAxisVectorVecMove[2], alignObjectAim, objectSpace = True )\n mc.move( upVectorMove[0], upVectorMove[1], upVectorMove[2], alignObjectAimUp, objectSpace = True )\n mc.parent( [alignObjectAim, alignObjectAimUp], w = 1 )\n mc.delete( mc.pointConstraint( alignObject, alignObjectAim, skip = worldAxisToKeep ) )\n \n if alignTwist:\n \n mc.delete( mc.pointConstraint( alignObject, alignObjectAimUp, skip = skipUpAxis ) )\n \n # rotate object\n mc.delete( mc.aimConstraint( alignObjectAim, alignObject, aim = primaryAxisVector, u = upVector, wut = 'object', wuo = alignObjectAimUp ) )\n mc.delete( alignObjectAim, alignObjectAimUp )","def horizontal_alignment(self):\n self.update()\n return self._horizontal_alignment","def transpose_alignment(align):\n\tindex = range(len(align[0]))\n\ttransposed_gap = list()\n\ttransposed = list()\n\tfor i in index:\n\t\ttransposed.append(''.join([seq[i] for seq in align]))\n\tfor column in transposed:\n\t\tgap = 0\n\t\tif column[0] != \"-\":\n\t\t\ttransposed_gap.append(column)\n\treturn transposed_gap","def SetToolAlignment(self, alignment=wx.EXPAND):\r\n\r\n self._tool_alignment = alignment","def test_align_without_negative_timestamp():\n aligned = display.align(\"email\", \"timestamp\")\n assert len(aligned) == 80","def flip(self):\n self.align = self._left if self.align == self._right else self._right\n self.group.layout_all()","def formatted_alignments(self,chosen_a_idxs,bitext,alignments,e_words,f_words):\n output =[]\n output_idxs = []\n for key in chosen_a_idxs.keys():\n temp = []\n temp_idx = []\n idx = chosen_a_idxs[key]\n alignment = alignments[idx]\n for t in alignment:\n temp.append((e_words[t[0]],f_words[t[1]]))\n temp_idx.append((bitext[key][\"en\"].index(e_words[t[0]]),bitext[key][\"fr\"].index(f_words[t[1]])))\n output.append(temp)\n output_idxs.append(temp_idx)\n return output,output_idxs","def _align(self, other):\n ss, se = self.sig, self.exp\n if isinstance(other, BigFloat):\n os, oe = other.sig, other.exp\n else:\n os, oe = other, 0\n\n if ss == 0.:\n se = oe\n elif os == 0.:\n pass\n elif se > oe:\n os = math.ldexp(os, oe-se)\n elif se < oe:\n ss = math.ldexp(ss, se-oe)\n se = oe\n return ss, os, se","def onAlign(self, value):\n # Ensure that we can work\n plt = Plot.getPlot()\n if not plt:\n self.updateUI()\n return\n # Get again all the subwidgets (to avoid PySide Pitfalls)\n mw = self.getMainWindow()\n form = mw.findChild(QtGui.QWidget, \"TaskPanel\")\n form.all = self.widget(QtGui.QCheckBox, \"allAxes\")\n form.xAlign = self.widget(QtGui.QComboBox, \"xAlign\")\n form.yAlign = self.widget(QtGui.QComboBox, \"yAlign\")\n\n axesList = [plt.axes]\n if form.all.isChecked():\n axesList = plt.axesList\n # Set new alignement\n for axes in axesList:\n if form.xAlign.currentIndex() == 0:\n axes.xaxis.tick_bottom()\n axes.spines['bottom'].set_color((0.0, 0.0, 0.0))\n axes.spines['top'].set_color('none')\n axes.xaxis.set_ticks_position('bottom')\n axes.xaxis.set_label_position('bottom')\n else:\n axes.xaxis.tick_top()\n axes.spines['top'].set_color((0.0, 0.0, 0.0))\n axes.spines['bottom'].set_color('none')\n axes.xaxis.set_ticks_position('top')\n axes.xaxis.set_label_position('top')\n if form.yAlign.currentIndex() == 0:\n axes.yaxis.tick_left()\n axes.spines['left'].set_color((0.0, 0.0, 0.0))\n axes.spines['right'].set_color('none')\n axes.yaxis.set_ticks_position('left')\n axes.yaxis.set_label_position('left')\n else:\n axes.yaxis.tick_right()\n axes.spines['right'].set_color((0.0, 0.0, 0.0))\n axes.spines['left'].set_color('none')\n axes.yaxis.set_ticks_position('right')\n axes.yaxis.set_label_position('right')\n plt.update()","def SetAlignment(self, l):\r\n\r\n self.alignment = l","def main():\r\n ## In argument hanterare\r\n parser = argparse.ArgumentParser(description='Aligner')\r\n group = parser.add_mutually_exclusive_group(required=True)\r\n group.add_argument('--file', '-f', type=str, nargs=2, help='align two strings')\r\n group.add_argument('--string', '-s', type=str, nargs=2, help='align the contents of two files')\r\n\r\n parser.add_argument('--check', action='store_true', help='check if your alignment is correct')\r\n\r\n arguments = parser.parse_args() # In argumenten skrivna i temrinalen\r\n\r\n\r\n### Behandlar in argumenetn och gör det till två strängar s0[] & s1[]\r\n## Eller om det är en url som skall checka om man gjort rätt.\r\n if arguments.file:\r\n f1, f2 = arguments.file\r\n with codecs.open(f1, 'r', 'utf-8') as f:\r\n s1 = f.read().replace('\\n', '')\r\n with codecs.open(f2, 'r', 'utf-8') as f:\r\n s2 = f.read().replace('\\n', '')\r\n\r\n elif arguments.string:\r\n s1, s2 = arguments.string\r\n\r\n if arguments.check:\r\n payload = json.dumps({\r\n 's1': s1,\r\n 's2': s2,\r\n 'result': align(s1, s2, compute_backpointers(s1, s2))\r\n })\r\n response = requests.post(\r\n 'https://language-engineering.herokuapp.com/correct',\r\n data=payload,\r\n headers={'content-type': 'application/json'}\r\n )\r\n response_data = response.json()\r\n if response_data['correct']:\r\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))\r\n print('Success! Your results are correct')\r\n else:\r\n print('Your results:\\n')\r\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))\r\n print(\"The server's results\\n\")\r\n print_alignment(response_data['result'])\r\n print(\"Your results differ from the server's results\")\r\n else:\r\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))","def fiber_alignment(config, ind=0):\n files = sorted(\n glob.glob(config['raw_dir'] + '/radiance/{}/data/data_*.txt'.format(\n config['date'])))\n\n # load data from txt file\n txt = np.genfromtxt(files[ind], delimiter='', skip_header=11)\n\n align = add_align()\n\n # extract pixels of alignment\n pixels = align['pixel'] + config['channel_pixel_adj']\n\n plt.figure(figsize=(12, 9), dpi=300)\n\n ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=4)\n ax1.plot(txt[500, :], '-*')\n ax1.axis([0, 1060, 0, txt[500, :].max() + 20])\n for xc in pixels:\n plt.axvline(x=xc, color='r')\n plt.xlabel('pixels')\n plt.ylabel('counts')\n plt.title('Channel alignment')\n \n ax2 = plt.subplot2grid((2, 4), (1, 0), colspan=2)\n # First section\n ax2.plot(txt[500, :], '-*')\n ax2.axis([0, 200, 0, txt[500, :].max() + 20])\n for xc in pixels:\n plt.axvline(x=xc, color='r')\n plt.xlabel('pixels')\n plt.ylabel('counts')\n plt.title('Initial section')\n\n ax3 = plt.subplot2grid((2, 4), (1, 2), colspan=2)\n # final section\n ax3.plot(txt[500, :], '-*')\n ax3.axis([800, 1060, 0, txt[500, :].max() + 20])\n for xc in pixels:\n plt.axvline(x=xc, color='r')\n plt.xlabel('pixels')\n plt.ylabel('counts')\n plt.title('Final section')\n\n plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n plt.show()","def align(model, data):\n np.set_printoptions(precision=3,suppress=True)\n model_zerocentered = model - model.mean(1)\n data_zerocentered = data - data.mean(1)\n \n W = np.zeros( (3,3) )\n for column in range(model.shape[1]):\n W += np.outer(model_zerocentered[:,column],data_zerocentered[:,column])\n U,d,Vh = np.linalg.linalg.svd(W.transpose())\n S = np.matrix(np.identity( 3 ))\n if(np.linalg.det(U) * np.linalg.det(Vh)<0):\n S[2,2] = -1\n rot = U*S*Vh\n trans = data.mean(1) - rot * model.mean(1)\n \n model_aligned = rot * model + trans\n alignment_error = model_aligned - data\n \n trans_error = np.sqrt(np.sum(np.multiply(alignment_error,alignment_error),0)).A[0]\n \n return rot,trans,trans_error","def align_manual_points(pos_file_before, pos_file_after,\n save=True, save_folder=None, save_filename='', \n overwrite=False, verbose=True):\n # load position_before\n if os.path.isfile(pos_file_before):\n ps_before = np.loadtxt(pos_file_before, delimiter=',')\n else:\n raise IOError(\n f\"- Position file:{pos_file_before} file doesn't exist, exit!\")\n # load position_after\n if os.path.isfile(pos_file_after):\n ps_after = np.loadtxt(pos_file_after, delimiter=',')\n else:\n raise IOError(\n f\"- Position file:{pos_file_after} file doesn't exist, exit!\")\n # do SVD decomposition to get best fit for rigid-translation\n c_before = np.mean(ps_before, axis=0)\n c_after = np.mean(ps_after, axis=0)\n H = np.dot((ps_before - c_before).T, (ps_after - c_after))\n U, _, V = np.linalg.svd(H) # do SVD\n # calcluate rotation\n R = np.dot(V, U.T).T\n if np.linalg.det(R) < 0:\n R[:, -1] = -1 * R[:, -1]\n # calculate translation\n t = - np.dot(c_before, R) + c_after\n # here's example for how to translate points\n # translated_ps_before = np.dot(ps_before, R) + t\n if verbose:\n print(\n f\"- Manually picked points aligned, rotation:\\n{R},\\n translation:{t}\")\n # save\n if save:\n if save_folder is None:\n save_folder = os.path.dirname(pos_file_before)\n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n if len(save_filename) > 0:\n save_filename += '_'\n rotation_name = os.path.join(save_folder, save_filename+'rotation')\n if not os.path.exists(rotation_name+'.npy') or overwrite:\n np.save(rotation_name, R)\n if verbose:\n print(f'-- rotation matrix saved to file:{rotation_name}.npy')\n else:\n if verbose:\n print(f'-- {rotation_name}.npy exists, skip.')\n translation_name = os.path.join(save_folder, save_filename+'translation') \n if not os.path.exists(translation_name+'.npy') or overwrite: \n np.save(translation_name, t)\n if verbose:\n print(f'-- translation matrix saved to file:{translation_name}.npy')\n else:\n if verbose:\n print(f'-- {translation_name}.npy exists, skip.')\n return R, t","def showAlignment(self, zeile1, zeile2):\n\n if (self.inputCheckpoint(zeile1, zeile2)):\n # get the quality\n quality_zeile = self.getQuality(zeile1, zeile2)\n start_index = 0\n cutter_index = self.OUTPUT_WIDTH_IN_COLS\n while (start_index < len(quality_zeile)):\n print(zeile1[start_index:cutter_index])\n print(quality_zeile[start_index:cutter_index])\n print(zeile2[start_index:cutter_index])\n start_index = cutter_index\n targeted_end_index = cutter_index + self.OUTPUT_WIDTH_IN_COLS\n if targeted_end_index <= len(quality_zeile):\n cutter_index = targeted_end_index\n else:\n cutter_index = len(quality_zeile)\n return True","def align_strings(strings, header=''):\n spaces = [len(l)-len(l.lstrip()) for l in strings.data if l]\n min_spaces = min(spaces) if spaces else 0\n if min_spaces > 0 or header:\n for index in range(len(strings.data)):\n strings.data[index] = header + strings.data[index][min_spaces:]","def vertical_alignment(self):\n self.update()\n return self._vertical_alignment","def _align(self, columns, widths, alignments):\n aligned_columns = []\n\n for column, width, alignment in zip(columns, widths, alignments):\n aligned_column = []\n\n for item in column:\n # add padding to the actual column width\n total_width = width + self.padding\n\n # build formatstring depending on alignment\n if alignment == 'l':\n format_str = '{{:<{}}}'.format(total_width)\n\n elif alignment == 'r':\n format_str = '{{:>{}}}'.format(total_width)\n\n elif alignment == 'c':\n format_str = '{{:^{}}}'.format(total_width)\n\n else:\n raise RuntimeError('Wrong alignment string')\n\n aligned_item = format_str.format(item)\n aligned_column.append(aligned_item)\n\n aligned_columns.append(aligned_column)\n\n return aligned_columns","def appendS(fs1, fs2, w, baselineGrid):\n h = 1000\n box = 0, 0, w, h\n # Get the status of of the target string. We need to know the position of the last line.\n baselines = textBoxBaseLines(fs2, box)\n if baselines:\n print 1000-baselines[0][1], 1000-baselines[-1][1]\n return fs1\n \n #paragraphTopSpacing","def __show_computed_alignment(self):\n success = False\n try:\n pcd = o3d.io.read_point_cloud(\n self.source_cloud\n )\n pcd.paint_uniform_color([0, 1, 0])\n pcd.transform(self.__compose_transformation())\n pcd.estimate_normals()\n self.computed_alignment_point_cloud_view.load_cloud(pcd)\n success = True\n except (FileNotFoundError, RuntimeError):\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Source point cloud is no longer available\"\n )\n self.source_cloud = \"\"\n self.__update_clickability()\n if success:\n try:\n pcd = o3d.io.read_point_cloud(\n self.target_cloud\n )\n pcd.paint_uniform_color([0, 0, 1])\n pcd.estimate_normals()\n self.computed_alignment_point_cloud_view.load_cloud(pcd)\n try:\n self.computed_alignment_point_cloud_view.show_window()\n except RuntimeError:\n pass\n except(FileNotFoundError, RuntimeError):\n QtWidgets.QMessageBox.warning(self, \"Error\",\n f\"Target point cloud is no longer available\"\n )\n self.source_cloud = \"\"\n self.__update_clickability()\n self.__save_context()","def func_c_align_split_n(self, args):\n tik_instance, ub_ori, ub_trans, n_before, n_len = args\n\n n_d, d_d, h_d, w_d, c_d = self.dst_shape\n dhw_d = d_d * h_d * w_d\n hw_d = h_d * w_d\n\n data_offset = n_before * self.c_0\n ub_offset = 0\n ori_nburst = dhw_d * self.c_1\n burst_len = n_len * self.c_0 // self.cp_align_len\n src_stride = (n_d - n_len) * self.c_0 // self.cp_align_len\n dst_stride = 0\n args = tik_instance, self.src_gm, ub_ori, data_offset, ub_offset, \\\n ori_nburst, burst_len, src_stride, dst_stride, self.cp_align_len\n _gm_to_ub_one(args)\n\n hwnoni = hw_d * n_len\n with tik_instance.for_range(0, d_d) as num_d:\n with tik_instance.for_range(0, self.c_1) as num_c1:\n ori_cur = num_d * self.c_1 * hwnoni * self.c_0 \\\n + num_c1 * hwnoni * self.c_0\n trans_cur = num_d * self.c_1 * hwnoni * self.c_0 \\\n + num_c1 * self.c_0\n nburst = hwnoni\n burst_len = self.c_0 // self.cp_align_len\n src_stride = 0\n dst_stride = (self.c_1 - 1) * self.c_0 // self.cp_align_len\n tik_instance.data_move(\n ub_trans[trans_cur],\n ub_ori[ori_cur],\n 0, nburst, burst_len, src_stride, dst_stride)\n\n with tik_instance.for_range(0, dhw_d) as num_dhw:\n src_cur = num_dhw * n_len * c_d\n dst_cur = num_dhw * c_d\n nburst = n_len\n burst_len = c_d // self.cp_align_len\n src_stride = 0\n dst_stride = (dhw_d - 1) * c_d // self.cp_align_len\n tik_instance.data_move(\n ub_ori[dst_cur],\n ub_trans[src_cur],\n 0, nburst, burst_len, src_stride, dst_stride)\n\n dst_offset = n_before * dhw_d * c_d\n burst_len = n_len * dhw_d * c_d // self.cp_align_len\n tik_instance.data_move(self.dst_gm[dst_offset],\n ub_ori,\n 0, 1, burst_len, 0, 0)","def test_align_idx(self):\n self.amp4.rotateAng([5, 5, 5], ang='deg')\n al = align(self.amp3, self.amp4, mv=[0, 1, 2, 3], sv=[0, 1, 2, 3], method='idxPoints')\n all(self.assertAlmostEqual(al.m.vert[i, 0], al.s.vert[i, 0], delta=0.1) for i in range(al.s.vert.shape[0]))","def merge_in(self, other, convert_to_string=True):\n assert isinstance(other, ExtendedAlignment)\n #_LOG.debug(\"Merging started ...\")\n if other.is_empty():\n return\n me = 0\n she = 0 # Assumption: alignments are female!\n me_len = self.get_length() if not self.is_empty() else 0\n she_len = other.get_length()\n insertion = -1\n\n merged_insertion_columns = 0\n\n ''' Add sequences from her to my alignment '''\n for f in other.fragments:\n self.fragments.add(f)\n if convert_to_string:\n self.from_string_to_bytearray()\n\n selfother = {}\n for k, v in other.items():\n # assert(k not in self,\n # \"Merging overlapping alignments not implemented\")\n if k not in self:\n selfother[k] = bytearray(v, encoding=\"utf8\")\n while True:\n ''' Check exit conditions'''\n if me == me_len and she == she_len:\n break\n\n ''' Check the 5 possible statuses between she and I '''\n if she != she_len and other.is_insertion_column(she):\n if me != me_len and self.is_insertion_column(me):\n ''' We both have a series of insertion columns'''\n start = me\n while(me != me_len and self.is_insertion_column(me) and\n she != she_len and other.is_insertion_column(she)):\n me += 1\n she += 1\n merged_insertion_columns += 1\n run = me - start\n self.col_labels[start:me] = list(range(\n insertion, insertion-run, -1))\n else:\n ''' Hers is a series of insertion columns'''\n start = she\n while she != she_len and other.is_insertion_column(she):\n she += 1\n run = she - start\n ins = bytearray(b\"-\") * run\n for seq in self.values():\n seq[me:me] = ins\n self._col_labels[me:me] = list(range(\n insertion, insertion - run, -1))\n insertion -= run\n me += run\n me_len += run\n elif me != me_len and self.is_insertion_column(me):\n ''' Mine is a series of insertion column'''\n start = me\n while me != me_len and self.is_insertion_column(me):\n me += 1\n run = me - start\n ins = bytearray(b\"-\") * run\n for v in selfother.values():\n v[start:start] = ins\n self.col_labels[start:me] = list(\n range(insertion, insertion-run, -1))\n insertion -= run\n elif(she == she_len or (me != me_len and\n self.col_labels[me] < other.col_labels[she])):\n ''' My column is not present (i.e. was allgap) in the\n \"other\"'''\n start = me\n while(me < me_len and (she == she_len or me != me_len and\n self.col_labels[me] < other.col_labels[she])):\n me += 1\n run = me - start\n ins = bytearray(b\"-\") * run\n for v in selfother.values():\n v[start:start] = ins\n elif(me == me_len or (she != she_len and\n self.col_labels[me] > other.col_labels[she])):\n ''' Her column is not present (i.e. was allgap) in \"me\"'''\n start = she\n while(she < she_len and (me == me_len or she != she_len and\n self.col_labels[me] > other.col_labels[she])):\n she += 1\n run = she - start\n ins = bytearray(b\"-\") * run\n for seq in self.values():\n seq[me:me] = ins\n self._col_labels[me:me] = other.col_labels[start:she]\n me += run\n me_len += run\n elif self.col_labels[me] == other.col_labels[she]:\n ''' A shared column'''\n while(me < me_len and she < she_len and\n self.col_labels[me] == other.col_labels[she]):\n she += 1\n me += 1\n else:\n raise \"hmmm, we thought this should be impossible? %d %d\" % (\n me, she)\n\n self.update(selfother)\n\n if convert_to_string:\n self.from_bytearray_to_string()\n #_LOG.debug(\"Merging finished ...\")\n\n return merged_insertion_columns","def align(self):\n\n # load the alignment parameters into the align_params object\n self.align_params.load_params_from_file(self.input_file)\n\n # populate the score matrices based on the input parameters\n self.populate_score_matrices()\n\n # perform a traceback and write the output to an output file\n\n ### FILL IN ###","def rotateAlignXform(self):\n r = np.concatenate(([self.x], [self.y], [self.z]), 0)\n r = np.concatenate((r, np.array([[0,0,0]])), 0)\n r = np.concatenate((r, np.array([0,0,0,1]).reshape(-1,1)), 1)\n return r"],"string":"[\n \"def align(self):\\n ...\",\n \"def align(): # open EH and fast shutter\\n\\t#marAuxiliary.closeMarShield()\\n\\td2in()\\n\\td3in()\\n\\tsh('o')\",\n \"def align(args) :\\n from aligner import align_reads\\n align_reads(args)\",\n \"def align(filename, prog, outfile):\\n ra = RunAlign()\\n ra.run_align(filename, prog, outfile)\",\n \"def do_drift_align(self, *arg):\\n self.do_reset_pocs()\\n print_info('*' * 80)\\n i = DriftShell()\\n i.cmdloop()\",\n \"def align(self):\\n number_of_Xs = 0\\n xFront = \\\"\\\"\\n xEnd = \\\"\\\"\\n dashFront = \\\"\\\"\\n dashEnd = \\\"\\\"\\n\\n # Determining if variable amino acids (\\\"X\\\") need to be added to the\\n\\t # beginning of the sequence:\\n z = self.hmmStart-self.seqStart\\n number_of_Xs = (self.hmmStart-1)-z\\n if z > 0:\\n dashFront = \\\"-\\\"*z\\n xFront = \\\"X\\\"*number_of_Xs\\n elif self.hmmStart-1<=self.seqStart-1:\\n xFront = \\\"X\\\"*(self.hmmStart-1) \\n\\n # Determining if variable amino acids (\\\"X\\\") need to be added to the \\n # end of the sequence:\\n number_of_Xs_end = self.hmmLength - self.hmmEnd\\n\\n # The original sequence length; SPA format includes this\\n delimeter = \\\"|\\\" #Need to fix can be \\\"_\\\" or \\\"|\\\" or something else...\\n \\n distToSeqEnd = self.origSeqLength - seqTo\\n if distToSeqEnd >= number_of_Xs_end and number_of_Xs_end != self.hmmLength:\\n xEnd = 'X'*number_of_Xs_end\\n else:\\n if distToSeqEnd < number_of_Xs_end:\\n xEnd = 'X'*distToSeqEnd\\n \\tdashEnd += \\\"-\\\"*(number_of_Xs_end-distToSeqEnd)\\n \\t\\n begin = \\\"{}{}\\\".format(dashFront, xFront)\\n end = \\\"{}{}\\\".format(xEnd, dashEnd)\\n self.addToFront(begin)\\n self.data.extend(end)\\n self.original = str(self)\",\n \"def gridalign(self):\\n self.position.x = int(round(self.position.x))\\n self.position.y = int(round(self.position.y))\\n self.position.z = int(round(self.position.z))\\n\\n if self.fan:\\n self.fan = (int(round(self.fan[0])),int(round(self.fan[1])),int(round(self.fan[2])))\\n\\n bestDist = 2*9\\n bestMatrix = makeMatrix(0,0,0)\\n\\n for compass in [0, 90, 180, 270]:\\n for pitch in [0, 90, 180, 270]:\\n for roll in [0, 90, 180, 270]:\\n m = makeMatrix(compass,pitch,roll)\\n dist = matrixDistanceSquared(self.matrix, m)\\n if dist < bestDist:\\n bestMatrix = m\\n bestDist = dist\\n\\n self.matrix = bestMatrix\\n self.positionOut()\\n self.directionOut()\",\n \"def align(model,\\n left,\\n right,\\n max_length = 512):\\n inputs = preprocess(left, right, max_length)\\n output = model(inputs)\\n output = expand(output)\\n scores, path, params = postprocess(output, len(left), len(right))\\n return Alignment(left, right, scores, path, params)\",\n \"def align_preprocessed(self, img):\\n aligner = FaceAligner(self.args.wing_path, self.args.lm_path, self.args.img_size)\\n return aligner.align(img)\",\n \"def affine_align(x, y, p1, p2, g, s):\\n #Create M, Ix, and Iy as Y x X matrices of 0's\\n M = [[0]*(len(x)+1) for i in range(len(y)+1)]\\n Ix = [[0]*(len(x)+1) for i in range(len(y)+1)]\\n Iy = [[0]*(len(x)+1) for i in range(len(y)+1)]\\n #Set up initial values for Ix and Iy\\n #M infs along both axes\\n for i in range(1, len(y)+1):\\n M[i][0] = -math.inf\\n for j in range(1, len(x)+1):\\n M[0][j] = -math.inf\\n #Ix: Aligning X with gap, horizontal move, infs along top row\\n for i in range(0, len(y)+1):\\n Ix[i][0] = -math.inf\\n #Gap penalties along left column\\n for j in range(1, len(x)+1):\\n Ix[0][j] = -g if Ix[0][j-1] == -math.inf else Ix[0][j-1] - s\\n #Iy: Aligning Y with gap, vertical move, infs along left column\\n for j in range(0, len(x)+1):\\n Iy[0][j] = -math.inf\\n #Gap penalties along top row\\n for i in range(1, len(y)+1):\\n Iy[i][0] = -g if Iy[i-1][0] == -math.inf else Iy[i-1][0] - s\\n #Populate remaining cells\\n for i in range(1, len(y)+1):\\n for j in range(1, len(x)+1):\\n M[i][j] = max(M[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2),\\n Ix[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2),\\n Iy[i-1][j-1] + delta(x[j-1], y[i-1], p1, p2))\\n Ix[i][j] = max(M[i][j-1] - g,\\n Ix[i][j-1] - s)\\n Iy[i][j] = max(M[i-1][j] - g,\\n Iy[i-1][j] - s)\\n #TRACEBACK\\n x_ret=\\\"\\\"; y_ret=\\\"\\\"\\n i = len(y); j = len(x)\\n #Determine start matrix\\n align_scores = (M[i][j], Iy[i][j], Ix[i][j])\\n matrix_idx = align_scores.index(max(align_scores))\\n #matrix_key will track the current matrix through the traceback\\n matrix_key = [\\\"M\\\", \\\"Iy\\\", \\\"Ix\\\"][matrix_idx]\\n while i > 0 and j > 0:\\n #From M: Check diagonal moves back to all three matrices, align characters\\n if matrix_key == \\\"M\\\":\\n if M[i][j] == M[i-1][j-1] + p1 or M[i][j] == M[i-1][j-1] - p2:\\n x_ret = x[j-1] + x_ret\\n y_ret = y[i-1] + y_ret\\n i -= 1; j -= 1\\n matrix_key = \\\"M\\\"\\n elif M[i][j] == Iy[i-1][j-1] + p1 or M[i][j] == Iy[i-1][j-1] - p2:\\n x_ret = x[j-1] + x_ret\\n y_ret = y[i-1] + y_ret\\n i -= 1; j -= 1\\n matrix_key = \\\"Iy\\\"\\n elif M[i][j] == Ix[i-1][j-1] + p1 or M[i][j] == Ix[i-1][j-1] - p2:\\n x_ret = x[j-1] + x_ret\\n y_ret = y[i-1] + y_ret\\n i -= 1; j -= 1\\n matrix_key = \\\"Ix\\\"\\n #From Iy: Check vertical move to Iy and M, align y character with x gap\\n elif matrix_key == \\\"Iy\\\":\\n if Iy[i][j] == M[i-1][j] - g:\\n x_ret = \\\"_\\\" + x_ret\\n y_ret = y[i-1] + y_ret\\n i -= 1\\n matrix_key = \\\"M\\\"\\n elif Iy[i][j] == Iy[i-1][j] - s:\\n x_ret = \\\"_\\\" + x_ret\\n y_ret = y[i-1] + y_ret\\n i -= 1\\n matrix_key = \\\"Iy\\\"\\n #From Ix: Check horizontal move to Ix and M, align x character with y gap\\n elif matrix_key == \\\"Ix\\\":\\n if Ix[i][j] == M[i][j-1] - g:\\n x_ret = x[j-1] + x_ret\\n y_ret = \\\"_\\\" + y_ret\\n j -= 1\\n matrix_key = \\\"M\\\"\\n elif Ix[i][j] == Ix[i][j-1] - s:\\n x_ret = x[j-1] + x_ret\\n y_ret = \\\"_\\\" + y_ret\\n j -= 1\\n matrix_key = \\\"Ix\\\"\\n #Finish sequence if edge was reached\\n #i>0 means mach remaining characters in y with gaps in x\\n if i > 0:\\n x_ret = (\\\"_\\\"*i) + x_ret\\n y_ret = y[0:i] + y_ret\\n #j>0 means mach remaining characters in x with gaps in y\\n if j > 0:\\n x_ret = x[0:j] + x_ret\\n y_ret = (\\\"_\\\"*j) + y_ret\\n #Return alinged strings\\n return (x_ret, y_ret)\",\n \"def minimal_align(self):\\n desired = int(PlatformVar(\\\"align\\\"))\\n for ii in range(len(self.__content)):\\n line = self.__content[ii]\\n match = re.match(r'.*\\\\.align\\\\s+(\\\\d+).*', line)\\n if match:\\n align = int(match.group(1))\\n # Due to GNU AS compatibility modes, .align may mean different things.\\n if osarch_is_amd64 or osarch_is_ia32():\\n if desired != align:\\n if is_verbose():\\n print(\\\"Replacing %i-byte alignment with %i-byte alignment.\\\" % (align, desired))\\n self.__content[ii] = \\\" .balign %i\\\\n\\\" % (desired)\\n else:\\n print(\\\"Replacing low-order bit alignment %i with %i-byte alignment.\\\" % (align, desired))\\n self.__content[ii] = \\\" .balign %i\\\\n\\\" % (desired)\",\n \"def text_align(text, x_align, y_align, height, d, style= \\\"TIMES_ITALIC\\\", rotation=0, color=K_BLACK, mirror=dxfwrite.MIRROR_X):\\n text = dxf.text(text, height = height,mirror=mirror,halign=CENTER, alignpoint = (x_align,y_align),\\n style=style, layer='LINES', rotation=rotation, color=color, linetype='ByBlock')\\n d.add(text)\",\n \"def gap_align(center, string_w):\\n m = len(center)\\n n = len(string_w)\\n\\n # Initialization; D[i][j][0] contains the max alignment score of the\\n # ith prefix of v and the jth of w; D[i][j][1] contains the back pointer.\\n D = [[(0, START) for _ in range(n + 1)] for _ in range(m + 1)]\\n\\n for i in range(1, m + 1):\\n D[i][0] = (D[i - 1][0][0] + blosum['-', center[i - 1]], DELETE)\\n\\n for j in range(1, n + 1):\\n D[0][j] = (D[0][j - 1][0] + blosum['-', string_w[j - 1]], INSERT)\\n\\n # Recurrence\\n for i in range(1, m + 1):\\n for j in range(1, n + 1):\\n delete = D[i-1][j][0] + blosum[center[i - 1], '-']\\n substitute = D[i-1][j-1][0] + blosum[center[i - 1], string_w[j - 1]]\\n # Set D[i][j] to the max of the recurrences\\n if delete > substitute:\\n D[i][j] = (delete, DELETE)\\n else:\\n D[i][j] = (substitute, SUBSTITUTE)\\n\\n i, j = m, n\\n w_aligned = ''\\n back_pointer = D[i][j][1]\\n while back_pointer != START:\\n if back_pointer == DELETE:\\n i -= 1\\n w_aligned = '-' + w_aligned\\n\\n elif back_pointer == SUBSTITUTE:\\n i -= 1\\n j -= 1\\n w_aligned = string_w[j] + w_aligned\\n\\n \\n back_pointer = D[i][j][1]\\n \\n return w_aligned\",\n \"def text_alignment(x, y):\\n if x == 0:\\n ha = \\\"center\\\"\\n elif x > 0:\\n ha = \\\"left\\\"\\n else:\\n ha = \\\"right\\\"\\n if y == 0:\\n va = \\\"center\\\"\\n elif y > 0:\\n va = \\\"bottom\\\"\\n else:\\n va = \\\"top\\\"\\n\\n return ha, va\",\n \"def shift_aligners(self):\\n for i in range(self.height-1, 1, -1):\\n self.align.list[i] = self.align.list[i-1]\\n self.align.list[1] = copy.deepcopy(self.align.list[0])\",\n \"def alignApk(apkfile, targetapkfile):\\n\\n align = utils_file.getFullToolPath('zipalign')\\n aligncmd = '\\\"%s\\\" -f 4 \\\"%s\\\" \\\"%s\\\"' % (align, apkfile, targetapkfile)\\n\\n ret = utils_file.execFormatCmd(aligncmd)\\n\\n return ret\",\n \"def align_code(self, boundary):\\n word_align = boundary / 4\\n\\n while len(self._code) % word_align:\\n if len(self._code) % 2 == 0:\\n self.add(spu.nop(0), True)\\n else:\\n self.add(spu.lnop(0), True)\\n\\n return\",\n \"def test_align_points(self):\\n mv = [\\n [0, 0, 5],\\n [5, 0, 5],\\n [0, 5, 5]\\n ]\\n sv = [\\n [0, 0, 0],\\n [5, 0, 0],\\n [0, 5, 0]\\n ]\\n al = align(self.amp1, self.amp2, mv=mv, sv=sv, method='contPoints').m\\n zMax = self.amp1.vert[:, 2].max() - 5\\n # Both objects are already centered, so should be close to origin (allowing for some inaccuracy)\\n self.assertAlmostEqual(al.vert[:, 2].max(), zMax, delta=TestAlign.DELTA)\",\n \"def align(self, align='left'):\\n if align not in ['left', 'center', 'right', 'full']:\\n raise ValueError('align must be \\\\'left\\\\', \\\\'center\\\\', \\\\'right\\\\' or \\\\'full\\\\'')\\n else:\\n self._write(self.__class__.__ESC + 'a' + self.__class__.__alignMap[align])\",\n \"def build_alignment(self,score,pieces):\\n\\t \\t# build text\\n\\t\\tself.open_seqs()\\n\\t\\ttext1 = text2 = \\\"\\\"\\n\\t\\tend1 = end2 = None\\n\\t\\tfor (start1,start2,length,pctId) in pieces:\\n\\t\\t\\tif (end1 != None):\\n\\t\\t\\t\\tif (start1 == end1): # insertion in sequence 2\\n\\t\\t\\t\\t\\ttext1 += self.seq1_gap * (start2-end2)\\n\\t\\t\\t\\t\\ttext2 += self.seq2_file.get(end2,start2-end2)\\n\\t\\t\\t\\telse: # insertion in sequence 1\\n\\t\\t\\t\\t\\ttext1 += self.seq1_file.get(end1,start1-end1)\\n\\t\\t\\t\\t\\ttext2 += self.seq2_gap * (start1-end1)\\n\\n\\t\\t\\ttext1 += self.seq1_file.get(start1,length)\\n\\t\\t\\ttext2 += self.seq2_file.get(start2,length)\\n\\t\\t\\tend1 = start1 + length\\n\\t\\t\\tend2 = start2 + length\\n\\t\\t# create alignment\\n\\t\\tstart1 = pieces[0][0]\\n\\t\\tstart2 = pieces[0][1]\\n\\t\\tend1 = pieces[-1][0] + pieces[-1][2]\\n\\t\\tend2 = pieces[-1][1] + pieces[-1][2]\\n\\t\\tsize1 = end1 - start1\\n\\t\\tsize2 = end2 - start2\\n\\t\\ta = Alignment(score=score,species_to_lengths=self.species_to_lengths)\\n\\t\\t#if (self.seq1_strand == \\\"-\\\"): start1 = self.seq1_file.length - end1\\n\\t\\ta.add_component(Component(self.seq1_src,start1,size1,self.seq1_strand,text=text1))\\n\\t\\t#if (self.seq2_strand == \\\"-\\\"): start2 = self.seq2_file.length - end2\\n\\t\\ta.add_component(Component(self.seq2_src,start2,size2,self.seq2_strand,text=text2))\\n\\t\\treturn a\",\n \"def align(image):\\n angle = find_angle(image)\\n image = rotate(image, angle)\\n return image\",\n \"def align_groups(groups_dir, prog, align_dir):\\n ra = RunAlign()\\n ra.run_for_all_in(groups_dir, prog, align_dir)\",\n \"def _justifyText(text):\\n chunks = text.split()\\n line = []\\n lineLength = 0\\n for chunk in chunks:\\n lineLength += len(chunk) + 1\\n if lineLength <= 73:\\n line.append(chunk)\\n continue\\n else:\\n print('''{:5}{:73}{}'''.format(\\\"|\\\", \\\" \\\".join(line), \\\"|\\\"))\\n del line[:]\\n line.append(chunk)\\n lineLength = len(chunk) + 1\\n print('''{:5}{:73}{}'''.format(\\\"|\\\", \\\" \\\".join(line), \\\"|\\\"))\",\n \"def AlignFieldmaps(self):\\n for entry in self.entry_map['fmap']:\\n info = self.info[entry]\\n\\n# Register the magnitude image at the shortest TR to the T1-IR\\n# structural image.\\n target = self.info[self.norm_src]['imgfile'] + \\\\\\n self.info[self.norm_src]['suffix']\\n source = info['magfile'] + info['suffix']\\n matfile = info['matfile']\\n fmt = '3dAllineate -prefix NULL -1Dmatrix_save %s -base %s ' + \\\\\\n '-source %s -cost mi -warp shift_rotate'\\n cmd = fmt % (info['matfile'], target, source)\\n self.CheckExec(cmd, [info['matfile']])\\n\\n# Convert to unitary matrix (remove scaling component.)\\n cmd = 'cat_matvec -ONELINE %s -P > %s' % \\\\\\n (info['matfile'], info['matfile_unitary'])\\n self.CheckExec(cmd, [info['matfile_unitary']])\\n\\n# Rotate the magnitude image to the new grid.\\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\\n cmd = fmt % (info['magfile_r']+info['suffix'], \\\\\\n info['matfile_unitary'], info['magfile'] + info['suffix'])\\n self.CheckExec(cmd, [info['magfile_r']+info['suffix']])\\n\\n# Rotate the fieldmap to the new grid.\\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\\n cmd = fmt % (info['imgfile_r']+info['suffix'], \\\\\\n info['matfile_unitary'], info['imgfile'] + info['suffix'])\\n self.CheckExec(cmd, [info['imgfile_r']+info['suffix']])\",\n \"def test_align(self):\\n al = align(self.amp1, self.amp2).m\\n\\n # Both objects are already centered, so should be close to origin (allowing for some inaccuracy)\\n self.assertAlmostEqual(al.vert.mean(axis=0)[0], 0, delta=TestAlign.DELTA)\\n self.assertAlmostEqual(al.vert.mean(axis=0)[1], 0, delta=TestAlign.DELTA)\\n self.assertAlmostEqual(al.vert.mean(axis=0)[2], 0, delta=TestAlign.DELTA)\",\n \"def align_sheet(cmpd_vector, ref_vector, cmpd):\\n theta = mb.coordinate_transform.angle(cmpd_vector, ref_vector)\\n normal = np.cross(cmpd_vector, ref_vector)\\n cmpd.rotate(theta, normal)\",\n \"def positioning(self):\\n pass\",\n \"def align(self):\\n flag=0\\n input=None\\n level=None\\n board=None\\n ainps={'L0':[],'L1':[],'L2':[],'H0':[]} \\n for i in self.inputs:\\n if(i.inputnumber.var.get() == 1):\\n if i.inpnumall == rareradio:\\n input=i.inpnum\\n level=i.level\\n board=i.board\\n print 'Rare chosen:',level,' ',input\\n ainps[i.level].append(i.inpnum)\\n flag=flag+1\\n #print 'ainps:',ainps \\n if flag < 2 :\\n print \\\"Align: less then 2 inputs chosen. \\\" \\n return\\n if input==None:\\n cmd=\\\"setRareFlag(0,0,0)\\\"\\n else:\\n mode='0'\\n if level == 'H0': mode = '1'\\n cmd=\\\"setRareFlag(\\\"+board+','+input+','+mode+\\\")\\\"\\n print \\\"seting rare: \\\",cmd\\n output=self.vb.io.execute(cmd,log=\\\"yes\\\",applout=\\\"<>\\\") \\n self.align=Corel(self.vb,ainps)\\n self.align.croscor()\",\n \"def calculate_alignment(boids: List[b.Boid]) -> Tuple[float, float]:\\n align_x = 0\\n align_y = 0\\n for boid in boids:\\n align_x += boid.v * math.cos(boid.direction)\\n align_y += boid.v * math.sin(boid.direction)\\n return align_x / len(boids), align_y / len(boids)\",\n \"def swap_main(self):\\n if self.align == self._left:\\n self.swap_left()\\n elif self.align == self._right:\\n self.swap_right()\",\n \"def sequence_align(string_v, string_w):\\n m = len(string_v)\\n n = len(string_w)\\n\\n # Initialization; D[i][j][0] contains the max alignment score of the\\n # ith prefix of v and the jth of w; D[i][j][1] contains the back pointer.\\n D = [[(0, START) for _ in range(n + 1)] for _ in range(m + 1)]\\n\\n for i in range(1, m + 1):\\n D[i][0] = (D[i - 1][0][0] + blosum['-', string_v[i - 1]], DELETE)\\n\\n for j in range(1, n + 1):\\n D[0][j] = (D[0][j - 1][0] + blosum['-', string_w[j - 1]], INSERT)\\n\\n # Recurrence\\n for i in range(1, m + 1):\\n for j in range(1, n + 1):\\n insert = D[i][j-1][0] + blosum['-', string_w[j - 1]]\\n delete = D[i-1][j][0] + blosum[string_v[i - 1], '-']\\n substitute = D[i-1][j-1][0] + blosum[string_v[i - 1], string_w[j - 1]]\\n # Set D[i][j] to the max of the recurrences\\n if insert > delete and insert > substitute:\\n D[i][j] = (insert, INSERT)\\n elif delete > substitute:\\n D[i][j] = (delete, DELETE)\\n else:\\n D[i][j] = (substitute, SUBSTITUTE)\\n\\n i, j = m, n\\n v_aligned = ''\\n w_aligned = ''\\n back_pointer = D[i][j][1]\\n while back_pointer != START:\\n if back_pointer == INSERT:\\n j -= 1\\n v_aligned = '-' + v_aligned\\n w_aligned = string_w[j] + w_aligned\\n\\n \\n elif back_pointer == DELETE:\\n i -= 1\\n v_aligned = string_v[i] + v_aligned\\n w_aligned = '-' + w_aligned\\n\\n elif back_pointer == SUBSTITUTE:\\n i -= 1\\n j -= 1\\n v_aligned = string_v[i] + v_aligned\\n w_aligned = string_w[j] + w_aligned\\n\\n \\n back_pointer = D[i][j][1]\\n \\n return v_aligned, w_aligned\",\n \"def align(src, tgt):\\n\\n sm = SequenceMatcher(a=list(map(lambda x: x[0], tgt)), b=list(map(lambda x: x[0], src)))\\n tgt_temp, src_temp = [], []\\n opcodes = sm.get_opcodes()\\n for tag, i1, i2, j1, j2 in opcodes:\\n # If they are equal, do nothing except lowercase them\\n if tag == 'equal':\\n for i in range(i1, i2):\\n tgt[i][1] = 'e'\\n tgt_temp.append(tgt[i])\\n for i in range(j1, j2):\\n src[i][1] = 'e'\\n src_temp.append(src[i])\\n # For insertions and deletions, put a filler of '***' on the other one, and\\n # make the other all caps\\n elif tag == 'delete':\\n for i in range(i1, i2):\\n tgt[i][1] = 'd'\\n tgt_temp.append(tgt[i])\\n for i in range(i1, i2):\\n src_temp.append(tgt[i])\\n elif tag == 'insert':\\n for i in range(j1, j2):\\n src[i][1] = 'i'\\n tgt_temp.append(src[i])\\n for i in range(j1, j2):\\n src_temp.append(src[i])\\n # More complicated logic for a substitution\\n elif tag == 'replace':\\n for i in range(i1, i2):\\n tgt[i][1] = 's'\\n for i in range(j1, j2):\\n src[i][1] = 's'\\n tgt_temp += tgt[i1:i2]\\n src_temp += src[j1:j2]\\n \\n src, tgt = GumarDataset.align_subsequences(src_temp, tgt_temp)\\n return src, tgt\",\n \"def _align_toplevel_grid(self):\\n\\n # align origin with nearest multple of 128\\n self.mins[0] -= self.mins[0] % 128\\n self.mins[1] -= self.mins[1] % 128\\n\\n width = self.maxs[0] - self.mins[0]\\n height = self.maxs[1] - self.mins[1]\\n greatest_dim = max(width, height)\\n nearest_pow_two = int(2 ** np.ceil(np.log2(greatest_dim)))\\n width_adjustment = (nearest_pow_two - width)\\n height_adjustment = (nearest_pow_two - height)\\n\\n self.maxs[0] += width_adjustment\\n self.maxs[1] += height_adjustment\",\n \"def align(val):\\n ovr = val % ALIGNMENT\\n if (ovr):\\n val = val + ALIGNMENT - ovr\\n return val\",\n \"def align(stroke1, stroke2):\\n\\n x1 = np.array(stroke1.x)\\n x2 = np.array(stroke2.x)\\n y1 = np.array(stroke1.y)\\n y2 = np.array(stroke2.y)\\n\\n d = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\\n m = d - np.min(d)\\n\\n Ix1 = np.argmax(x1)\\n Ix2 = np.argmax(x2)\\n Iy1 = np.argmax(y1)\\n Iy2 = np.argmax(y2)\\n\\n ix1 = np.argmin(x1)\\n ix2 = np.argmin(x2)\\n iy1 = np.argmin(y1)\\n iy2 = np.argmin(y2)\\n\\n # rephasing :\\n u = np.array([(Ix1 - Ix2), (Iy1 - Iy2), (ix1 - ix2), (iy1 - iy2)])\\n indice_period = np.argmin(np.abs(u))\\n period = u[indice_period]\\n new_x1 = np.array(x1[period:].tolist() + x1[0:period].tolist())\\n new_y1 = np.array(y1[period:].tolist() + y1[0:period].tolist())\\n x1 = new_x1\\n y1 = new_y1\\n\\n # resorting : if symetric part, revert it\\n mx = np.max((x1, x2), 0)\\n my = np.max((y1, y2), 0)\\n sym_score = abs(x1 - x2[::-1]) + abs(y1 - y2[::-1])\\n if len(x1[sym_score < 50]) > 20:\\n x1[sym_score < 40] = x1[sym_score < 40][::-1]\\n y1[sym_score < 40] = y1[sym_score < 40][::-1]\\n\\n new_d = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)\\n new_m = new_d - min(new_d)\\n\\n return x1, y1, d, new_d, m, new_m\",\n \"def main(argv):\\n \\n ### gets data from csv, sets variables\\n seq1, seq2 = get_seqs('../data/seq.csv')\\n \\n \\n # Assign the longer sequence to s1, and the shorter to s2\\n l1, l2 = len(seq1), len(seq2)\\n if l1 >= l2:\\n s1, s2 = ((l2 - 1) * \\\".\\\" + seq1 + (l2 - 1) * \\\".\\\"), seq2\\n #puts l2-1 \\\".\\\"s both sides of l1, allows alignment of all overlap combos\\n else:\\n s1, s2 = ((l1 - 1) * \\\".\\\" + seq2 + (l1 - 1) * \\\".\\\"), seq1\\n l1, l2 = l2, l1 \\n\\n # writes alignment(s) with highest score into output file\\n my_best_score = -1 #so 0 beats best score\\n for i in range(l1 + l2 -1):\\n score, matched, shift, end_shift = calculate_score(s1, s2, l1, l2, i)\\n #assigns returns from calc_score function to these variables\\n if score > my_best_score:\\n my_best_score = score\\n statement = \\\"This alignment occurs when the smaller strand (\\\" + \\\\\\n str(l2) + \\\"nt in length) attaches from base \\\" + str(i - l2 + 2) + \\\\\\n \\\" of the larger strand, with the highest score of \\\" + str(score) + \\\\\\n \\\":\\\\n\\\"\\n #statement explaining the alignment in detail\\n best_comparison_highSP = (shift + matched + (l2 - 1) * \\\".\\\" + \\\"\\\\n\\\")\\n best_comparison_lowSP = (shift + matched + end_shift + \\\"\\\\n\\\")\\n best_s2, best_s1 = (shift + s2 + end_shift + \\\"\\\\n\\\"), (s1 + \\\"\\\\n\\\\n\\\\n\\\")\\n #formats the matching, s1 and s2 lines to line-up neatly\\n if i < l1 - 1:\\n best_alignment = (str(statement) + str(best_comparison_lowSP) \\\\\\n + str(best_s2) + str(best_s1))\\n else:\\n best_alignment = (str(statement) + str(best_comparison_highSP) \\\\\\n + str(best_s2) + str(best_s1))\\n # uses returned variables to write a statement about the alignment \\n # giving its score and startpoint, and assigns 3 lines of alignment \\n # (s1, s2 and matching bases) to a variable each for later printing\\n f = open('../results/seqs_align.txt', 'w')\\n f.write(best_alignment)\\n f.close()\\n print(\\\"Done!\\\")\\n return None\",\n \"def adjust_alignment(template,candidate,new_gaps):\\n template_l = list(template)\\n candidate_l = list(candidate)\\n new_gaps.reverse()\\n for pos in new_gaps:\\n del template_l[pos]\\n del candidate_l[nearest_gap(candidate_l,pos)]\\n \\n return (DNA.makeSequence(''.join(template_l)), \\\\\\n DNA.makeSequence(''.join(candidate_l)))\",\n \"def latex_show_as_aligned(rows):\\n env = r'\\\\begin{align*}%s\\\\end{align*}'\\n return env%(r'\\\\\\\\'.join([r'&'.join(row) for row in rows]))\",\n \"def align(img, left_eye, right_eye):\\n left_eye_x, left_eye_y = left_eye\\n right_eye_x, right_eye_y = right_eye\\n point_3rd, direction = (left_eye, -1) if left_eye_y > right_eye_y else (right_eye, 1)\\n\\n # np.linalg.norm is being used for euclidean distance\\n a = np.linalg.norm(np.array(left_eye) - np.array(point_3rd))\\n b = np.linalg.norm(np.array(right_eye) - np.array(point_3rd))\\n c = np.linalg.norm(np.array(right_eye) - np.array(left_eye))\\n\\n if b != 0 and c != 0:\\n angle = np.arccos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))\\n angle = (angle * 180) / math.pi\\n if direction == -1:\\n angle = 90 - angle\\n img = Image.fromarray(img)\\n img = np.array(img.rotate(direction * angle))\\n\\n return img\",\n \"def align_labels(self, xpadding=0.5, ypadding=None, fix_all=False):\\n \\n if ypadding is None:\\n ypadding = xpadding\\n \\n for i, ax in enumerate(self.grid):\\n if i in self.bottom or fix_all:\\n ax.xaxis.set_label_coords(0.5, -xpadding)\\n if i in self.left or fix_all:\\n ax.yaxis.set_label_coords(-ypadding, 0.5)\\n \\n pl.draw()\",\n \"def _align(self):\\n\\n shape = np.shape(self.x)\\n\\n # Get angle of direction (cbi: center beam index)\\n # NOTE: This implementation seems to be unstable, because the shot with the center beam index can be NaN\\n # cbi = np.median(np.arange(len(self.x[0, :]))).astype(int)\\n # vec1 = [self.x[0, cbi], self.y[0, cbi], 0.0]\\n # vec2 = [self.x[-1, cbi], self.y[-1, cbi], 0.0]\\n\\n # Alternative implementation with mean over all entries within the line.\\n # -> should be a good approximation of the line center\\n # NOTE: 2019-05-30: Relaxed the criterion even further (mean of first and last 10 scan lines)\\n vec1 = [np.nanmedian(self.x[0:10, :]), np.nanmedian(self.y[0:10, :]), 0.0]\\n vec2 = [np.nanmedian(self.x[-11:-1, :]), np.nanmedian(self.y[-11:-1, :]), 0.0]\\n angle = -1.0*np.arctan((vec2[1]-vec1[1])/(vec2[0]-vec1[0]))\\n\\n # validity check -> Do not rotate if angle is nan\\n if np.isnan(angle):\\n return\\n\\n # Get center point\\n xc = np.nanmedian(self.x)\\n yc = np.nanmedian(self.y)\\n\\n # Reform points\\n points = [self.x.flatten()-xc, self.y.flatten()-yc]\\n\\n # Execute the rotation\\n rot_matrix = np.array([[np.cos(angle), -np.sin(angle)],\\n [np.sin(angle), np.cos(angle)]])\\n points_rotated = rot_matrix.dot(points)\\n self.x = np.reshape(points_rotated[0, :], shape)\\n self.y = np.reshape(points_rotated[1, :], shape)\\n\\n # Save conversion parameters for reuse\\n self._align_parameters = {'center_point': (xc, yc),\\n 'angle': angle,\\n 'rotation_matrix': rot_matrix}\",\n \"def resetAlignmentCenter(self):\\n cent = self.TiltSeries_._TiltAlignmentParas.cent\\n imdimX = self.TiltSeries_._imdimX\\n imdimY = self.TiltSeries_._imdimY\\n print(imdimX, imdimY)\\n if cent[0] != imdimX//2+1 or cent[1] != imdimY//2+1:\\n #rint \\\"Centers do not match: cent=\\\"+str(cent)+\\\", imdim=\\\"+str(imdim)\\n self.TiltSeries_._TiltAlignmentParas.cent = [imdimX//2+1, imdimY//2+1]\",\n \"def image_align(first_image, second_image):\\r\\n\\r\\n high_diff = (second_image.shape[0] - first_image.shape[0]) // 2\\r\\n width_diff = (second_image.shape[1] - first_image.shape[1]) // 2\\r\\n\\r\\n align_image = second_image[high_diff: high_diff + first_image.shape[0],\\r\\n width_diff: width_diff + first_image.shape[1],\\r\\n :]\\r\\n\\r\\n\\r\\n assert align_image.shape == first_image.shape\\r\\n\\r\\n return align_image\",\n \"def test_align():\\n target = ('TAAATAAATATCTGGTGTTTGAGGCAAAAAGGCAGACTTAAATTCTAAATCACACCTGTGCTT'\\n 'CCAGCACTACCTTCAAGCGCAGGTTCGAGCCAGTCAGGCAGGGTACATAAGAGTCCATTGTGC'\\n 'CTGTATTATTTTGAGCAATGGCTAAAGTACCTTCACCCTTGCTCACTGCTCCCCCACTTCCTC'\\n 'AAGTCTCATCGTGTTTTTTTTAGAGCTAGTTTCTTAGTCTCATTAGGCTTCAGTCACCAT')\\n query = ('TCTGGTGTTTGAGGCAAAAAGGCAGACTTAAATTCTAAATCACACCTGTGCTTCCAGCACTACC'\\n 'TTCAAGCGCAGGTTCGAGCCAGTCAGGACTGCTCCCCCACTTCCTCAAGTCTCATCGTGTTTTT'\\n 'TTTAGAGCTAGTTTCTTAGTCTCATTAGGCTTCAGTCACCATCATTTCTTATAGGAATACCA')\\n assert kevlar.align(target, query) == ('10D91M69D79M20I', 155)\",\n \"def alignEvents(self, ourEvent, otherEvent, otherView):\\n ourIter = self.eventToIter[ourEvent]\\n ourRow = self.model.get_path(ourIter)[0]\\n otherRow = otherView.model.get_path(otherView.eventToIter[otherEvent])[0]\\n padding = otherRow - ourRow\\n\\n while padding > 0:\\n self.model.insert_before(ourIter)\\n padding -= 1\",\n \"def align_angles(self):\\n self.sim.data.qpos[self.joints_qpos_idx] = rotation.round_to_straight_angles(\\n self.sim.data.qpos[self.joints_qpos_idx]\\n )\",\n \"def align(self):\\n return self[\\\"align\\\"]\",\n \"def align(self):\\n return self[\\\"align\\\"]\",\n \"def align(self):\\n return self[\\\"align\\\"]\",\n \"def format_alignment(self, alignment):\\n raise NotImplementedError(\\\"This method should be implemented\\\")\\n ###################################################\\n # You MUST implement this method in the subclass. #\\n ###################################################\",\n \"def process_align(self):\\n\\t\\tstm_t_dict = self._process_recog()\\n\\t\\ttrans_t_dict = self._process_trans()\\n\\t\\talign_obj = viterbi_align(stm_t_dict, trans_t_dict, self.label, self.pair_file_path)\\n\\t\\tself.trans_t_dict = align_obj.viterbi(0, len(stm_t_dict)-1, 0, len(trans_t_dict)-1)\",\n \"def transpose_alignment(align):\\n\\tindex = range(len(align[0]))\\n\\ttransposed_gap = list()\\n\\ttransposed = list()\\n\\tfor i in index:\\n\\t\\ttransposed.append(''.join([seq[i] for seq in align]))\\n\\tfor column in transposed:\\n\\t\\tgap = 0\\n\\t\\tif column[0] != \\\"-\\\":\\n\\t\\t\\ttransposed_gap.append(column)\\n\\t\\telse:\\n\\t\\t\\tprint (\\\"HOLA\\\"+column + \\\"\\\\n\\\")\\n\\n\\t\\\"\\\"\\\"\\tfor el in column:\\n\\t\\t\\tif el == \\\"-\\\":\\n\\t\\t\\t\\tgap += 1\\n\\t\\tif gap <= (len(column)/2):\\n\\t\\t\\ttransposed_gap.append(column)\\n\\t\\\"\\\"\\\"\\n\\tprint (transposed_gap)\\n\\treturn transposed_gap\",\n \"def _align(elements, alignment=\\\"ymax\\\"):\\n if len(elements) == 0:\\n return elements\\n if alignment not in ([\\\"x\\\", \\\"y\\\", \\\"xmin\\\", \\\"xmax\\\", \\\"ymin\\\", \\\"ymax\\\"]):\\n raise ValueError(\\n \\\"'alignment' argument must be one of 'x','y','xmin', 'xmax', 'ymin','ymax'\\\"\\n )\\n value = Group(elements).__getattribute__(alignment)\\n for e in elements:\\n e.__setattr__(alignment, value)\\n return elements\",\n \"def AddDimAligned(self,extPnt1,extPnt2,textPosition):\\n\\t\\treturn self.Space.AddDimAligned(extPnt1,extPnt2,textPosition)\",\n \"def align(*args, alignToLead: bool=False, coordinateSystem: name=None, xAxis: AnyStr=\\\"\\\", yAxis:\\n AnyStr=\\\"\\\", zAxis: AnyStr=\\\"\\\", **kwargs)->bool:\\n pass\",\n \"def realign(self, x, dx, y, dy, ignore=set()):\\n return ['', '', '']\",\n \"def align(s0, s1, backptr): # Lägger de rätt för att få bästa matching med letters\\r\\n\\r\\n # Tom array att fylla på och returnera\\r\\n result = ['','']\\r\\n # Gör strängarna till lätthanterligare arrays\\r\\n x0 = [char for char in s0]\\r\\n x1 = [char for char in s1]\\r\\n # toma arrays att stoppa in de alignade bokstäverna och spacen\\r\\n ress0 = []\\r\\n ress1 = []\\r\\n\\r\\n rows = len(s0)\\r\\n columns = len(s1)\\r\\n\\r\\n # Tillverkar botten värden i backptr-matrisen så att man skall veta när man är klar\\r\\n backptr[0][0][0] = -1\\r\\n backptr[0][0][1] = -1\\r\\n\\r\\n # initiering värden vart i backptr-matrisen man är och har kommit ifrån\\r\\n staterow = rows\\r\\n statecol = columns\\r\\n r = staterow\\r\\n c = statecol\\r\\n # den går går längst pekarna tills den når botten\\r\\n while staterow >=0 and statecol >=0:\\r\\n # Om den pekar diagonalt\\r\\n if backptr[staterow][statecol][0] == staterow-1 and backptr[staterow][statecol][1] == statecol-1 :\\r\\n if staterow == 0 and statecol == 0: # om den är i botten, passera\\r\\n pass\\r\\n else:\\r\\n # annars spara båda bokstäverna på sammam plats i result\\r\\n ress0.insert(0,x0[staterow-1])\\r\\n ress1.insert(0,x1[statecol-1])\\r\\n # Om den pekar vänster (samma row)\\r\\n if backptr[staterow][statecol][0] == staterow:\\r\\n if staterow == 0 and statecol == 0: # om den är i botten, passera\\r\\n pass\\r\\n else:\\r\\n # annars spacear den raden och stoppar in bokstaven i kolumnen\\r\\n ress0.insert(0,\\\" \\\")\\r\\n ress1.insert(0,x1[statecol-1])\\r\\n # Om den pekar upp (samma kolumn)\\r\\n if backptr[staterow][statecol][1] == statecol:\\r\\n if staterow == 0 and statecol == 0: # om den är i botten, passera\\r\\n pass\\r\\n else:\\r\\n # annars spacear den kolumnen och stoppar in bokstaven i raden\\r\\n ress0.insert(0,x0[staterow-1])\\r\\n ress1.insert(0,\\\" \\\")\\r\\n\\r\\n # för att inte skriva över staterow i ny initieringen\\r\\n r = staterow\\r\\n c = statecol\\r\\n # initierar de nya tillståndet (följer pekaren)\\r\\n staterow = backptr[r][c][0]\\r\\n statecol = backptr[r][c][1]\\r\\n\\r\\n # printfunktionen vill ha stringen bakvänd....\\r\\n ress0.reverse()\\r\\n ress1.reverse()\\r\\n # concattar arrayerna till en sträng igen\\r\\n sum0 =''.join(ress0)\\r\\n sum1 =''.join(ress1)\\r\\n # stoppar in i resultatet\\r\\n result[0]=sum0\\r\\n result[1]=sum1\\r\\n\\r\\n return(result)\",\n \"def swap_main(self):\\n if self.align == self._up:\\n self.swap_right()\\n elif self.align == self._down:\\n self.swap_left()\",\n \"def _justify(self):\\n minLengths = [max([max(map(len, row[i].split() + [''])) for row in self._rows if len(row) > 0])\\n for i in range(self._colsNum)]\\n shifts = [w - mw for mw, w in zip(minLengths, self._widthes)]\\n # length = len(shifts)\\n borrow = zip(self._colsRange, shifts)\\n borrow.sort(lambda a, b: cmp(a[1], b[1]))\\n delta = [0] * self._colsNum\\n\\n donorIdx = self._colsNum - 1\\n recIdx = 0\\n while True:\\n\\n curDonation = borrow[donorIdx][1]\\n curRec = borrow[recIdx][1]\\n\\n if curRec >= 0 or curDonation <= 0:\\n break\\n\\n curDelta = min(curDonation, -curRec)\\n curDonation -= curDelta\\n curRec += curDelta\\n delta[borrow[donorIdx][0]] -= curDelta\\n delta[borrow[recIdx][0]] += curDelta\\n\\n if curDonation == 0:\\n donorIdx -= 1\\n\\n if curRec == 0:\\n recIdx += 1\\n\\n for i in self._colsRange:\\n self._widthes[i] += delta[i]\",\n \"def seq_align(string1,string2,mismatch_penalty,gap_penalty):\\n\\n # define 2x2 matrix\\n matrix = []\\n for i in range(len(string1)+1):\\n if i == 0:\\n matrix.append(list([gap_penalty * x for x in range(len(string2)+1)]))\\n else:\\n matrix.append(list([gap_penalty * i if x == 0 else None for x in range(len(string2)+1)]))\\n\\n # populate matrix by looping through the strings and finding optimal value for each spot\\n for i in range(len(string1)):\\n for j in range(len(string2)):\\n if string1[i] == string2[j]:\\n val1 = 0 + matrix[i][j]\\n else:\\n val1 = mismatch_penalty + matrix[i][j]\\n val2 = gap_penalty + matrix[i][j+1]\\n val3 = gap_penalty + matrix[i+1][j]\\n min_val = min(val1,val2,val3)\\n matrix[i+1][j+1] = min_val\\n\\n\\n # define values to use while retracing\\n result_str1 = ''\\n result_str2 = ''\\n i = len(matrix)-1\\n j = len(matrix[0])-1\\n\\n # trace through matrix to find the optimal character alignment\\n while i > 0 and j > 0:\\n val1 = matrix[i-1][j-1]\\n val2 = matrix[i-1][j]\\n val3 = matrix[i][j-1]\\n min_val = min(val1,val2,val3)\\n if val1 == min_val:\\n result_str1 += string1[i-1]\\n result_str2 += string2[j-1]\\n i -= 1\\n j -= 1\\n elif val2 == min_val:\\n result_str1 += \\\"-\\\"\\n result_str2 += string2[j-1]\\n i -= 1\\n else:\\n result_str1 += string1[i-1]\\n result_str2 += \\\"-\\\"\\n j -= 1\\n\\n # for any leftover j values\\n if i == 0:\\n while j > 0:\\n result_str1 += '-'\\n result_str2 += string2[j]\\n j -=1\\n\\n # for any leftover i values\\n if j == 0:\\n while i > 0:\\n result_str1 += string1[i]\\n result_str2 += \\\"-\\\"\\n i -= 1\\n\\n return matrix[len(matrix)-1][len(matrix[0])-1], result_str1[::-1], result_str2[::-1]\",\n \"def _perform_alignment(self, formulatrix_image, beamline_image, formulatrix_points):\\n aligner = ImageAligner(formulatrix_image, beamline_image, self._config_align, self._config_detector)\\n aligned_images = aligner.align()\\n scaled_formulatrix_points = aligner.scale_points(formulatrix_points)\\n self._log_alignment_status(aligned_images)\\n\\n return aligned_images, scaled_formulatrix_points\",\n \"def _align_frames(frame_pre, frame_now):\\n height, width = frame_pre.shape\\n horizontal_lines = [np.array([frame_pre[int(height * 1 / 4), :],\\n frame_pre[int(height * 2 / 4), :],\\n frame_pre[int(height * 3 / 4), :]]),\\n\\n np.array([frame_now[int(height * 1 / 4), :],\\n frame_now[int(height * 2 / 4), :],\\n frame_now[int(height * 3 / 4), :]])]\\n\\n vertical_lines = [np.array([frame_pre[:, int(width * 1 / 4)],\\n frame_pre[:, int(width * 2 / 4)],\\n frame_pre[:, int(width * 3 / 4)]]),\\n\\n np.array([frame_now[:, int(width * 1 / 4)],\\n frame_now[:, int(width * 2 / 4)],\\n frame_now[:, int(width * 3 / 4)]])]\\n\\n min_x_val = 99999\\n min_y_val = 99999\\n offset_x = 0\\n offset_y = 0\\n for offset in range(-5, 5 + 1, 1): # TODO: check this, [-7, 7]?\\n a_x = horizontal_lines[0][:, max(offset, 0):-1 + min(offset, 0)]\\n b_x = horizontal_lines[1][:, max(-offset, 0):-1 + min(-offset, 0)]\\n val_x = np.mean(np.linalg.norm(a_x - b_x, 1, axis=1)) # norm 1\\n # print('offset_x is:', offset, ' norm=', val_x)\\n if val_x < min_x_val:\\n min_x_val = val_x\\n offset_x = offset\\n\\n a_y = vertical_lines[0][:, max(offset, 0):-1 + min(offset, 0)]\\n b_y = vertical_lines[1][:, max(-offset, 0):-1 + min(-offset, 0)]\\n val_y = np.mean(np.linalg.norm(a_y - b_y, 1, axis=1)) # norm 1\\n # print('offset_y is:', offset, ' norm=', val_y)\\n if val_y < min_y_val:\\n min_y_val = val_y\\n offset_y = offset\\n\\n print('The final offset_x is:', offset_x)\\n print('The final offset_y is:', offset_y)\\n\\n # translation\\n M = np.float32([[1, 0, offset_x], [0, 1, offset_y]])\\n frame_now_aligned = cv2.warpAffine(frame_now.astype(np.uint8), M, (width, height)).astype(np.int32)\\n\\n return frame_now_aligned, offset_x, offset_y\",\n \"def align_addr(addr, align = 16, dir = ALIGN_DOWN):\\n\\n if dir == ALIGN_DOWN:\\n return addr - (addr % align)\\n else:\\n return addr + (align - addr % align)\",\n \"def c_align_small(self, tik_instance):\\n n_d, d_d, h_d, w_d, c_d = self.dst_shape\\n hw_d = h_d * w_d\\n hwnoni = hw_d * self.n_o * self.n_i\\n dhw_d = d_d * h_d * w_d\\n\\n ub_ori = tik_instance.Tensor(self.dtype,\\n (self.ub_ele,),\\n name=\\\"ub_ori\\\",\\n scope=tik.scope_ubuf)\\n ub_trans = tik_instance.Tensor(self.dtype,\\n (self.ub_ele,),\\n name=\\\"ub_trans\\\",\\n scope=tik.scope_ubuf)\\n\\n burst_len = d_d * self.c_1 * hwnoni * self.c_0 // self.cp_align_len\\n tik_instance.data_move(ub_ori,\\n self.src_gm,\\n 0, 1, burst_len, 0, 0)\\n\\n with tik_instance.for_range(0, d_d) as num_d:\\n with tik_instance.for_range(0, self.c_1) as num_c1:\\n ori_cur = num_d * self.c_1 * hwnoni * self.c_0\\\\\\n + num_c1 * hwnoni * self.c_0\\n trans_cur = num_d * self.c_1 * hwnoni * self.c_0\\\\\\n + num_c1 * self.c_0\\n nburst = hwnoni\\n burst_len = self.c_0 // self.cp_align_len\\n src_stride = 0\\n dst_stride = (self.c_1 - 1) * self.c_0 // self.cp_align_len\\n tik_instance.data_move(\\n ub_trans[trans_cur],\\n ub_ori[ori_cur],\\n 0, nburst, burst_len, src_stride, dst_stride)\\n\\n with tik_instance.for_range(0, dhw_d) as num_dhw:\\n src_cur = num_dhw * self.n_o * self.n_i * c_d\\n dst_cur = num_dhw * c_d\\n nburst = n_d\\n burst_len = c_d // self.cp_align_len\\n src_stride = 0\\n dst_stride = (dhw_d - 1) * c_d // self.cp_align_len\\n tik_instance.data_move(\\n ub_ori[dst_cur],\\n ub_trans[src_cur],\\n 0, nburst, burst_len, src_stride, dst_stride)\\n\\n burst_len = n_d * dhw_d * c_d // self.cp_align_len\\n tik_instance.data_move(self.dst_gm,\\n ub_ori,\\n 0, 1, burst_len, 0, 0)\\n\\n return tik_instance\",\n \"def align_one(ptcl,ref,prefilt,align,aligncmp,ralign,raligncmp):\\n\\n\\tif prefilt : ref=ref.process(\\\"filter.matchto\\\",{\\\"to\\\":ptcl})\\n\\n\\t# initial alignment\\n\\tif align!=None :\\n\\t\\tali=ptcl.align(align[0],ref,align[1],aligncmp[0],aligncmp[1])\\n\\n\\t# refine alignment if requested\\n\\tif ralign!=None:\\n\\t\\tralign[1][\\\"xform.align2d\\\"] = ali.get_attr(\\\"xform.align2d\\\")\\n\\t\\tali=ptcl.align(ralign[0],ref,ralign[1],raligncmp[0],raligncmp[1])\\n\\n\\treturn ali\",\n \"def viz_align(self):\\n # Create the output folders\\n viz_align_folders = self._pathcreator.required_viz_align_folders()\\n project_creator = ProjectCreator()\\n project_creator.create_subfolders(viz_align_folders)\\n align_viz = AlignViz(\\n self._pathcreator.get_lib_names_single_end()\\n if not self._args.paired_end\\n else self._pathcreator.get_lib_names_paired_end(),\\n self._pathcreator.read_processing_stats_path,\\n self._pathcreator.read_alignments_stats_path,\\n self._pathcreator.read_alignment_stats_table_path,\\n self._pathcreator.viz_align_aligned_reads_by_species_paths,\\n self._species_folder_prefixes_and_display_names,\\n )\\n align_viz.read_stat_files()\\n align_viz.plot_input_read_length(\\n self._pathcreator.viz_align_input_read_length_plot_path\\n )\\n align_viz.plot_processed_read_length(\\n self._pathcreator.viz_align_processed_reads_length_plot_path\\n )\\n align_viz.plot_total_number_of_aligned_reads(\\n self._pathcreator.viz_align_all_folder\\n )\\n align_viz.plot_species_exclusive_reads_for_each_species()\",\n \"def compute_position(layout):\\n\\n if layout.children:\\n toyplot_vertical_align = layout.style[\\\"-toyplot-vertical-align\\\"]\\n # Align the first line's baseline with the anchor.\\n if toyplot_vertical_align == \\\"first-baseline\\\":\\n offset_y = 0\\n # Align the last line's baseline with the anchor.\\n elif toyplot_vertical_align == \\\"last-baseline\\\":\\n offset_y = -(layout.height + layout.children[0].top - layout.children[-1].bottom)\\n # Align the top of the layout with the anchor.\\n elif toyplot_vertical_align == \\\"top\\\":\\n offset_y = -layout.children[0].top\\n # Align the middle of the layout with the anchor.\\n elif toyplot_vertical_align == \\\"middle\\\":\\n offset_y = -((layout.height * 0.5) + layout.children[0].top)\\n # Align the bottom of the layout with the anchor.\\n elif toyplot_vertical_align == \\\"bottom\\\":\\n offset_y = -(layout.height + layout.children[0].top)\\n else:\\n raise ValueError(\\\"Unknown -toyplot-vertical-align value: %s\\\" % toyplot_vertical_align) # pragma: no cover\\n\\n for line in layout.children:\\n text_anchor = line.style[\\\"text-anchor\\\"] if line.children else \\\"middle\\\"\\n if text_anchor == \\\"start\\\":\\n anchor_offset = 0\\n elif text_anchor == \\\"middle\\\":\\n anchor_offset = -line.width * 0.5\\n elif text_anchor == \\\"end\\\":\\n anchor_offset = -line.width\\n else:\\n raise ValueError(\\\"Unknown text-anchor value: %s\\\" % text_anchor)\\n anchor_offset += layout.style[\\\"-toyplot-anchor-shift\\\"]\\n\\n offset_x = anchor_offset\\n\\n # Line left/right/bottom/top are relative offsets from the layout anchor in canvas coordinates.\\n line.left = offset_x\\n line.right = offset_x + line.width\\n line.top += offset_y\\n line.baseline = offset_y\\n line.bottom += offset_y\\n\\n for child in line.children:\\n # Child left/right/bottom/top are relative offsets from the layout anchor in canvas coordinates.\\n child.left = offset_x\\n child.right = child.left + child.width\\n child.top += offset_y\\n child.baseline += offset_y\\n child.bottom += offset_y\\n # Note that baseline-shift is the opposite of canvas coordinates (positive values shift UP)\\n child.baseline -= child.style[\\\"baseline-shift\\\"]\\n\\n offset_x += child.width\\n offset_y += line.height\\n\\n layout.top = layout.children[0].top\\n layout.left = numpy.min([line.left for line in layout.children])\\n layout.right = numpy.max([line.right for line in layout.children])\\n layout.bottom = layout.children[-1].bottom\\n\\n else:\\n layout.top = 0\\n layout.left = 0\\n layout.right = 0\\n layout.bottom = 0\\n\\n # Layout top/left/right/bottom are relative offsets from the layout anchor in canvas coordinates\\n layout.width = layout.right - layout.left\\n layout.height = layout.bottom - layout.top\",\n \"def getStatusTextAlignment(self) -> int:\\n ...\",\n \"def getStatusTextAlignment(self) -> int:\\n ...\",\n \"def run(config=None):\\n AlignmentWorkflow().run(config)\",\n \"def align(self, image, landmark_indices, anchor_points, size=96):\\n\\n detected = self.detect(image)\\n coords = self.find_landmarks(image, detected)\\n in_points = coords[landmark_indices]\\n in_points = in_points.astype('float32')\\n out_points = anchor_points * size\\n warp_mat = cv2.getAffineTransform(in_points, out_points)\\n warp_dst = cv2.warpAffine(image, warp_mat, (size, size))\\n\\n return warp_dst\",\n \"def _shift_anchors(anchors, direction):\\n new_anchors = deepcopy(anchors)\\n if direction == 'center':\\n pass\\n\\n elif direction == 'top':\\n heights = new_anchors[:,3] - new_anchors[:,1] + 1\\n heights = heights[:,np.newaxis]\\n new_anchors[:,[1,3]] = new_anchors[:,[1,3]] - heights/2\\n\\n elif direction == 'bottom':\\n heights = new_anchors[:,3] - new_anchors[:,1] + 1\\n heights = heights[:,np.newaxis]\\n new_anchors[:,[1,3]] = new_anchors[:,[1,3]] + heights/2\\n\\n elif direction == 'right':\\n widths = new_anchors[:,2] - new_anchors[:,0] + 1\\n widths = widths[:,np.newaxis]\\n new_anchors[:,[0,2]] = new_anchors[:,[0,2]] + widths/2\\n\\n elif direction == 'left':\\n widths = new_anchors[:,2] - new_anchors[:,0] + 1\\n widths = widths[:,np.newaxis]\\n new_anchors[:,[0,2]] = new_anchors[:,[0,2]] - widths/2\\n\\n return new_anchors\",\n \"def align(self, unit, main_unit, align_dict: dict, strict=False) -> 'function':\\n\\n aligner = Unit_Aligner(align_dict)\\n return aligner.convert(unit, main_unit, strict)\",\n \"def align(self, unit, main_unit, align_dict: dict, strict=False) -> 'function':\\n\\n aligner = Unit_Aligner(align_dict)\\n return aligner.convert(unit, main_unit, strict)\",\n \"def msg(text):\\n for line in text.splitlines():\\n if JS.alignment == \\\"left\\\":\\n print(demarkup(line))\\n elif JS.alignment == \\\"center\\\":\\n print(demarkup(line).center(get_terminal_size()[0] - 1))\\n else:\\n print(demarkup(line).rjust(get_terminal_size()[0] - 1))\",\n \"def Alignsequence(structure1, structure2):\\n\\n ppb = PPBuilder()\\n for pp in ppb.build_peptides(structure1):\\n sequence1 = pp.get_sequence()\\n for pp in ppb.build_peptides(structure2):\\n sequence2 = pp.get_sequence()\\n\\n alignment = pairwise2.align.globalxx(sequence1, sequence2)\\n return alignment\",\n \"def rotateAlignToWorld( alignObject, primaryAxisVector = [1, 0, 0], worldAxisToKeep = ['x'], alignTwist = True ):\\n \\n # normalize primary axis vector\\n primaryAxisVectorVec = vector.makeMVector( primaryAxisVector )\\n primaryAxisVectorVec.normalize()\\n primaryAxisVector = [ primaryAxisVectorVec.x, primaryAxisVectorVec.y, primaryAxisVectorVec.z ]\\n \\n \\n axisSign = primaryAxisVector[0] + primaryAxisVector[1] + primaryAxisVector[2]\\n \\n primaryAxisVectorVecMove = primaryAxisVector[:]\\n primaryAxisVector = [ abs( primaryAxisVector[0] ), abs( primaryAxisVector[1] ), abs( primaryAxisVector[2] ) ]\\n \\n # prepare align twist vector\\n allaxis = ['x', 'y', 'z']\\n \\n for axiskeep in worldAxisToKeep:\\n \\n allaxis.remove( axiskeep.lower() )\\n \\n skipUpAxis = allaxis[0]\\n upVectorMove = {'x':[1, 0, 0], 'y':[0, 1, 0], 'z':[0, 0, 1]}[skipUpAxis]\\n upVector = {'x':[1 * axisSign, 0, 0], 'y':[0, 1 * axisSign, 0], 'z':[0, 0, 1 * axisSign]}[skipUpAxis]\\n \\n # prepare align setup\\n prefix = name.removeSuffix( alignObject )\\n \\n alignObjectAim = mc.group( n = prefix + 'alignObjectAim', em = 1, p = alignObject )\\n alignObjectAimUp = mc.group( n = prefix + 'alignObjectAimUp', em = 1, p = alignObject )\\n mc.move( primaryAxisVectorVecMove[0], primaryAxisVectorVecMove[1], primaryAxisVectorVecMove[2], alignObjectAim, objectSpace = True )\\n mc.move( upVectorMove[0], upVectorMove[1], upVectorMove[2], alignObjectAimUp, objectSpace = True )\\n mc.parent( [alignObjectAim, alignObjectAimUp], w = 1 )\\n mc.delete( mc.pointConstraint( alignObject, alignObjectAim, skip = worldAxisToKeep ) )\\n \\n if alignTwist:\\n \\n mc.delete( mc.pointConstraint( alignObject, alignObjectAimUp, skip = skipUpAxis ) )\\n \\n # rotate object\\n mc.delete( mc.aimConstraint( alignObjectAim, alignObject, aim = primaryAxisVector, u = upVector, wut = 'object', wuo = alignObjectAimUp ) )\\n mc.delete( alignObjectAim, alignObjectAimUp )\",\n \"def horizontal_alignment(self):\\n self.update()\\n return self._horizontal_alignment\",\n \"def transpose_alignment(align):\\n\\tindex = range(len(align[0]))\\n\\ttransposed_gap = list()\\n\\ttransposed = list()\\n\\tfor i in index:\\n\\t\\ttransposed.append(''.join([seq[i] for seq in align]))\\n\\tfor column in transposed:\\n\\t\\tgap = 0\\n\\t\\tif column[0] != \\\"-\\\":\\n\\t\\t\\ttransposed_gap.append(column)\\n\\treturn transposed_gap\",\n \"def SetToolAlignment(self, alignment=wx.EXPAND):\\r\\n\\r\\n self._tool_alignment = alignment\",\n \"def test_align_without_negative_timestamp():\\n aligned = display.align(\\\"email\\\", \\\"timestamp\\\")\\n assert len(aligned) == 80\",\n \"def flip(self):\\n self.align = self._left if self.align == self._right else self._right\\n self.group.layout_all()\",\n \"def formatted_alignments(self,chosen_a_idxs,bitext,alignments,e_words,f_words):\\n output =[]\\n output_idxs = []\\n for key in chosen_a_idxs.keys():\\n temp = []\\n temp_idx = []\\n idx = chosen_a_idxs[key]\\n alignment = alignments[idx]\\n for t in alignment:\\n temp.append((e_words[t[0]],f_words[t[1]]))\\n temp_idx.append((bitext[key][\\\"en\\\"].index(e_words[t[0]]),bitext[key][\\\"fr\\\"].index(f_words[t[1]])))\\n output.append(temp)\\n output_idxs.append(temp_idx)\\n return output,output_idxs\",\n \"def _align(self, other):\\n ss, se = self.sig, self.exp\\n if isinstance(other, BigFloat):\\n os, oe = other.sig, other.exp\\n else:\\n os, oe = other, 0\\n\\n if ss == 0.:\\n se = oe\\n elif os == 0.:\\n pass\\n elif se > oe:\\n os = math.ldexp(os, oe-se)\\n elif se < oe:\\n ss = math.ldexp(ss, se-oe)\\n se = oe\\n return ss, os, se\",\n \"def onAlign(self, value):\\n # Ensure that we can work\\n plt = Plot.getPlot()\\n if not plt:\\n self.updateUI()\\n return\\n # Get again all the subwidgets (to avoid PySide Pitfalls)\\n mw = self.getMainWindow()\\n form = mw.findChild(QtGui.QWidget, \\\"TaskPanel\\\")\\n form.all = self.widget(QtGui.QCheckBox, \\\"allAxes\\\")\\n form.xAlign = self.widget(QtGui.QComboBox, \\\"xAlign\\\")\\n form.yAlign = self.widget(QtGui.QComboBox, \\\"yAlign\\\")\\n\\n axesList = [plt.axes]\\n if form.all.isChecked():\\n axesList = plt.axesList\\n # Set new alignement\\n for axes in axesList:\\n if form.xAlign.currentIndex() == 0:\\n axes.xaxis.tick_bottom()\\n axes.spines['bottom'].set_color((0.0, 0.0, 0.0))\\n axes.spines['top'].set_color('none')\\n axes.xaxis.set_ticks_position('bottom')\\n axes.xaxis.set_label_position('bottom')\\n else:\\n axes.xaxis.tick_top()\\n axes.spines['top'].set_color((0.0, 0.0, 0.0))\\n axes.spines['bottom'].set_color('none')\\n axes.xaxis.set_ticks_position('top')\\n axes.xaxis.set_label_position('top')\\n if form.yAlign.currentIndex() == 0:\\n axes.yaxis.tick_left()\\n axes.spines['left'].set_color((0.0, 0.0, 0.0))\\n axes.spines['right'].set_color('none')\\n axes.yaxis.set_ticks_position('left')\\n axes.yaxis.set_label_position('left')\\n else:\\n axes.yaxis.tick_right()\\n axes.spines['right'].set_color((0.0, 0.0, 0.0))\\n axes.spines['left'].set_color('none')\\n axes.yaxis.set_ticks_position('right')\\n axes.yaxis.set_label_position('right')\\n plt.update()\",\n \"def SetAlignment(self, l):\\r\\n\\r\\n self.alignment = l\",\n \"def main():\\r\\n ## In argument hanterare\\r\\n parser = argparse.ArgumentParser(description='Aligner')\\r\\n group = parser.add_mutually_exclusive_group(required=True)\\r\\n group.add_argument('--file', '-f', type=str, nargs=2, help='align two strings')\\r\\n group.add_argument('--string', '-s', type=str, nargs=2, help='align the contents of two files')\\r\\n\\r\\n parser.add_argument('--check', action='store_true', help='check if your alignment is correct')\\r\\n\\r\\n arguments = parser.parse_args() # In argumenten skrivna i temrinalen\\r\\n\\r\\n\\r\\n### Behandlar in argumenetn och gör det till två strängar s0[] & s1[]\\r\\n## Eller om det är en url som skall checka om man gjort rätt.\\r\\n if arguments.file:\\r\\n f1, f2 = arguments.file\\r\\n with codecs.open(f1, 'r', 'utf-8') as f:\\r\\n s1 = f.read().replace('\\\\n', '')\\r\\n with codecs.open(f2, 'r', 'utf-8') as f:\\r\\n s2 = f.read().replace('\\\\n', '')\\r\\n\\r\\n elif arguments.string:\\r\\n s1, s2 = arguments.string\\r\\n\\r\\n if arguments.check:\\r\\n payload = json.dumps({\\r\\n 's1': s1,\\r\\n 's2': s2,\\r\\n 'result': align(s1, s2, compute_backpointers(s1, s2))\\r\\n })\\r\\n response = requests.post(\\r\\n 'https://language-engineering.herokuapp.com/correct',\\r\\n data=payload,\\r\\n headers={'content-type': 'application/json'}\\r\\n )\\r\\n response_data = response.json()\\r\\n if response_data['correct']:\\r\\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))\\r\\n print('Success! Your results are correct')\\r\\n else:\\r\\n print('Your results:\\\\n')\\r\\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))\\r\\n print(\\\"The server's results\\\\n\\\")\\r\\n print_alignment(response_data['result'])\\r\\n print(\\\"Your results differ from the server's results\\\")\\r\\n else:\\r\\n print_alignment( align(s1, s2, compute_backpointers(s1, s2)))\",\n \"def fiber_alignment(config, ind=0):\\n files = sorted(\\n glob.glob(config['raw_dir'] + '/radiance/{}/data/data_*.txt'.format(\\n config['date'])))\\n\\n # load data from txt file\\n txt = np.genfromtxt(files[ind], delimiter='', skip_header=11)\\n\\n align = add_align()\\n\\n # extract pixels of alignment\\n pixels = align['pixel'] + config['channel_pixel_adj']\\n\\n plt.figure(figsize=(12, 9), dpi=300)\\n\\n ax1 = plt.subplot2grid((2, 4), (0, 0), colspan=4)\\n ax1.plot(txt[500, :], '-*')\\n ax1.axis([0, 1060, 0, txt[500, :].max() + 20])\\n for xc in pixels:\\n plt.axvline(x=xc, color='r')\\n plt.xlabel('pixels')\\n plt.ylabel('counts')\\n plt.title('Channel alignment')\\n \\n ax2 = plt.subplot2grid((2, 4), (1, 0), colspan=2)\\n # First section\\n ax2.plot(txt[500, :], '-*')\\n ax2.axis([0, 200, 0, txt[500, :].max() + 20])\\n for xc in pixels:\\n plt.axvline(x=xc, color='r')\\n plt.xlabel('pixels')\\n plt.ylabel('counts')\\n plt.title('Initial section')\\n\\n ax3 = plt.subplot2grid((2, 4), (1, 2), colspan=2)\\n # final section\\n ax3.plot(txt[500, :], '-*')\\n ax3.axis([800, 1060, 0, txt[500, :].max() + 20])\\n for xc in pixels:\\n plt.axvline(x=xc, color='r')\\n plt.xlabel('pixels')\\n plt.ylabel('counts')\\n plt.title('Final section')\\n\\n plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\\n plt.show()\",\n \"def align(model, data):\\n np.set_printoptions(precision=3,suppress=True)\\n model_zerocentered = model - model.mean(1)\\n data_zerocentered = data - data.mean(1)\\n \\n W = np.zeros( (3,3) )\\n for column in range(model.shape[1]):\\n W += np.outer(model_zerocentered[:,column],data_zerocentered[:,column])\\n U,d,Vh = np.linalg.linalg.svd(W.transpose())\\n S = np.matrix(np.identity( 3 ))\\n if(np.linalg.det(U) * np.linalg.det(Vh)<0):\\n S[2,2] = -1\\n rot = U*S*Vh\\n trans = data.mean(1) - rot * model.mean(1)\\n \\n model_aligned = rot * model + trans\\n alignment_error = model_aligned - data\\n \\n trans_error = np.sqrt(np.sum(np.multiply(alignment_error,alignment_error),0)).A[0]\\n \\n return rot,trans,trans_error\",\n \"def align_manual_points(pos_file_before, pos_file_after,\\n save=True, save_folder=None, save_filename='', \\n overwrite=False, verbose=True):\\n # load position_before\\n if os.path.isfile(pos_file_before):\\n ps_before = np.loadtxt(pos_file_before, delimiter=',')\\n else:\\n raise IOError(\\n f\\\"- Position file:{pos_file_before} file doesn't exist, exit!\\\")\\n # load position_after\\n if os.path.isfile(pos_file_after):\\n ps_after = np.loadtxt(pos_file_after, delimiter=',')\\n else:\\n raise IOError(\\n f\\\"- Position file:{pos_file_after} file doesn't exist, exit!\\\")\\n # do SVD decomposition to get best fit for rigid-translation\\n c_before = np.mean(ps_before, axis=0)\\n c_after = np.mean(ps_after, axis=0)\\n H = np.dot((ps_before - c_before).T, (ps_after - c_after))\\n U, _, V = np.linalg.svd(H) # do SVD\\n # calcluate rotation\\n R = np.dot(V, U.T).T\\n if np.linalg.det(R) < 0:\\n R[:, -1] = -1 * R[:, -1]\\n # calculate translation\\n t = - np.dot(c_before, R) + c_after\\n # here's example for how to translate points\\n # translated_ps_before = np.dot(ps_before, R) + t\\n if verbose:\\n print(\\n f\\\"- Manually picked points aligned, rotation:\\\\n{R},\\\\n translation:{t}\\\")\\n # save\\n if save:\\n if save_folder is None:\\n save_folder = os.path.dirname(pos_file_before)\\n if not os.path.exists(save_folder):\\n os.makedirs(save_folder)\\n if len(save_filename) > 0:\\n save_filename += '_'\\n rotation_name = os.path.join(save_folder, save_filename+'rotation')\\n if not os.path.exists(rotation_name+'.npy') or overwrite:\\n np.save(rotation_name, R)\\n if verbose:\\n print(f'-- rotation matrix saved to file:{rotation_name}.npy')\\n else:\\n if verbose:\\n print(f'-- {rotation_name}.npy exists, skip.')\\n translation_name = os.path.join(save_folder, save_filename+'translation') \\n if not os.path.exists(translation_name+'.npy') or overwrite: \\n np.save(translation_name, t)\\n if verbose:\\n print(f'-- translation matrix saved to file:{translation_name}.npy')\\n else:\\n if verbose:\\n print(f'-- {translation_name}.npy exists, skip.')\\n return R, t\",\n \"def showAlignment(self, zeile1, zeile2):\\n\\n if (self.inputCheckpoint(zeile1, zeile2)):\\n # get the quality\\n quality_zeile = self.getQuality(zeile1, zeile2)\\n start_index = 0\\n cutter_index = self.OUTPUT_WIDTH_IN_COLS\\n while (start_index < len(quality_zeile)):\\n print(zeile1[start_index:cutter_index])\\n print(quality_zeile[start_index:cutter_index])\\n print(zeile2[start_index:cutter_index])\\n start_index = cutter_index\\n targeted_end_index = cutter_index + self.OUTPUT_WIDTH_IN_COLS\\n if targeted_end_index <= len(quality_zeile):\\n cutter_index = targeted_end_index\\n else:\\n cutter_index = len(quality_zeile)\\n return True\",\n \"def align_strings(strings, header=''):\\n spaces = [len(l)-len(l.lstrip()) for l in strings.data if l]\\n min_spaces = min(spaces) if spaces else 0\\n if min_spaces > 0 or header:\\n for index in range(len(strings.data)):\\n strings.data[index] = header + strings.data[index][min_spaces:]\",\n \"def vertical_alignment(self):\\n self.update()\\n return self._vertical_alignment\",\n \"def _align(self, columns, widths, alignments):\\n aligned_columns = []\\n\\n for column, width, alignment in zip(columns, widths, alignments):\\n aligned_column = []\\n\\n for item in column:\\n # add padding to the actual column width\\n total_width = width + self.padding\\n\\n # build formatstring depending on alignment\\n if alignment == 'l':\\n format_str = '{{:<{}}}'.format(total_width)\\n\\n elif alignment == 'r':\\n format_str = '{{:>{}}}'.format(total_width)\\n\\n elif alignment == 'c':\\n format_str = '{{:^{}}}'.format(total_width)\\n\\n else:\\n raise RuntimeError('Wrong alignment string')\\n\\n aligned_item = format_str.format(item)\\n aligned_column.append(aligned_item)\\n\\n aligned_columns.append(aligned_column)\\n\\n return aligned_columns\",\n \"def appendS(fs1, fs2, w, baselineGrid):\\n h = 1000\\n box = 0, 0, w, h\\n # Get the status of of the target string. We need to know the position of the last line.\\n baselines = textBoxBaseLines(fs2, box)\\n if baselines:\\n print 1000-baselines[0][1], 1000-baselines[-1][1]\\n return fs1\\n \\n #paragraphTopSpacing\",\n \"def __show_computed_alignment(self):\\n success = False\\n try:\\n pcd = o3d.io.read_point_cloud(\\n self.source_cloud\\n )\\n pcd.paint_uniform_color([0, 1, 0])\\n pcd.transform(self.__compose_transformation())\\n pcd.estimate_normals()\\n self.computed_alignment_point_cloud_view.load_cloud(pcd)\\n success = True\\n except (FileNotFoundError, RuntimeError):\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Source point cloud is no longer available\\\"\\n )\\n self.source_cloud = \\\"\\\"\\n self.__update_clickability()\\n if success:\\n try:\\n pcd = o3d.io.read_point_cloud(\\n self.target_cloud\\n )\\n pcd.paint_uniform_color([0, 0, 1])\\n pcd.estimate_normals()\\n self.computed_alignment_point_cloud_view.load_cloud(pcd)\\n try:\\n self.computed_alignment_point_cloud_view.show_window()\\n except RuntimeError:\\n pass\\n except(FileNotFoundError, RuntimeError):\\n QtWidgets.QMessageBox.warning(self, \\\"Error\\\",\\n f\\\"Target point cloud is no longer available\\\"\\n )\\n self.source_cloud = \\\"\\\"\\n self.__update_clickability()\\n self.__save_context()\",\n \"def func_c_align_split_n(self, args):\\n tik_instance, ub_ori, ub_trans, n_before, n_len = args\\n\\n n_d, d_d, h_d, w_d, c_d = self.dst_shape\\n dhw_d = d_d * h_d * w_d\\n hw_d = h_d * w_d\\n\\n data_offset = n_before * self.c_0\\n ub_offset = 0\\n ori_nburst = dhw_d * self.c_1\\n burst_len = n_len * self.c_0 // self.cp_align_len\\n src_stride = (n_d - n_len) * self.c_0 // self.cp_align_len\\n dst_stride = 0\\n args = tik_instance, self.src_gm, ub_ori, data_offset, ub_offset, \\\\\\n ori_nburst, burst_len, src_stride, dst_stride, self.cp_align_len\\n _gm_to_ub_one(args)\\n\\n hwnoni = hw_d * n_len\\n with tik_instance.for_range(0, d_d) as num_d:\\n with tik_instance.for_range(0, self.c_1) as num_c1:\\n ori_cur = num_d * self.c_1 * hwnoni * self.c_0 \\\\\\n + num_c1 * hwnoni * self.c_0\\n trans_cur = num_d * self.c_1 * hwnoni * self.c_0 \\\\\\n + num_c1 * self.c_0\\n nburst = hwnoni\\n burst_len = self.c_0 // self.cp_align_len\\n src_stride = 0\\n dst_stride = (self.c_1 - 1) * self.c_0 // self.cp_align_len\\n tik_instance.data_move(\\n ub_trans[trans_cur],\\n ub_ori[ori_cur],\\n 0, nburst, burst_len, src_stride, dst_stride)\\n\\n with tik_instance.for_range(0, dhw_d) as num_dhw:\\n src_cur = num_dhw * n_len * c_d\\n dst_cur = num_dhw * c_d\\n nburst = n_len\\n burst_len = c_d // self.cp_align_len\\n src_stride = 0\\n dst_stride = (dhw_d - 1) * c_d // self.cp_align_len\\n tik_instance.data_move(\\n ub_ori[dst_cur],\\n ub_trans[src_cur],\\n 0, nburst, burst_len, src_stride, dst_stride)\\n\\n dst_offset = n_before * dhw_d * c_d\\n burst_len = n_len * dhw_d * c_d // self.cp_align_len\\n tik_instance.data_move(self.dst_gm[dst_offset],\\n ub_ori,\\n 0, 1, burst_len, 0, 0)\",\n \"def test_align_idx(self):\\n self.amp4.rotateAng([5, 5, 5], ang='deg')\\n al = align(self.amp3, self.amp4, mv=[0, 1, 2, 3], sv=[0, 1, 2, 3], method='idxPoints')\\n all(self.assertAlmostEqual(al.m.vert[i, 0], al.s.vert[i, 0], delta=0.1) for i in range(al.s.vert.shape[0]))\",\n \"def merge_in(self, other, convert_to_string=True):\\n assert isinstance(other, ExtendedAlignment)\\n #_LOG.debug(\\\"Merging started ...\\\")\\n if other.is_empty():\\n return\\n me = 0\\n she = 0 # Assumption: alignments are female!\\n me_len = self.get_length() if not self.is_empty() else 0\\n she_len = other.get_length()\\n insertion = -1\\n\\n merged_insertion_columns = 0\\n\\n ''' Add sequences from her to my alignment '''\\n for f in other.fragments:\\n self.fragments.add(f)\\n if convert_to_string:\\n self.from_string_to_bytearray()\\n\\n selfother = {}\\n for k, v in other.items():\\n # assert(k not in self,\\n # \\\"Merging overlapping alignments not implemented\\\")\\n if k not in self:\\n selfother[k] = bytearray(v, encoding=\\\"utf8\\\")\\n while True:\\n ''' Check exit conditions'''\\n if me == me_len and she == she_len:\\n break\\n\\n ''' Check the 5 possible statuses between she and I '''\\n if she != she_len and other.is_insertion_column(she):\\n if me != me_len and self.is_insertion_column(me):\\n ''' We both have a series of insertion columns'''\\n start = me\\n while(me != me_len and self.is_insertion_column(me) and\\n she != she_len and other.is_insertion_column(she)):\\n me += 1\\n she += 1\\n merged_insertion_columns += 1\\n run = me - start\\n self.col_labels[start:me] = list(range(\\n insertion, insertion-run, -1))\\n else:\\n ''' Hers is a series of insertion columns'''\\n start = she\\n while she != she_len and other.is_insertion_column(she):\\n she += 1\\n run = she - start\\n ins = bytearray(b\\\"-\\\") * run\\n for seq in self.values():\\n seq[me:me] = ins\\n self._col_labels[me:me] = list(range(\\n insertion, insertion - run, -1))\\n insertion -= run\\n me += run\\n me_len += run\\n elif me != me_len and self.is_insertion_column(me):\\n ''' Mine is a series of insertion column'''\\n start = me\\n while me != me_len and self.is_insertion_column(me):\\n me += 1\\n run = me - start\\n ins = bytearray(b\\\"-\\\") * run\\n for v in selfother.values():\\n v[start:start] = ins\\n self.col_labels[start:me] = list(\\n range(insertion, insertion-run, -1))\\n insertion -= run\\n elif(she == she_len or (me != me_len and\\n self.col_labels[me] < other.col_labels[she])):\\n ''' My column is not present (i.e. was allgap) in the\\n \\\"other\\\"'''\\n start = me\\n while(me < me_len and (she == she_len or me != me_len and\\n self.col_labels[me] < other.col_labels[she])):\\n me += 1\\n run = me - start\\n ins = bytearray(b\\\"-\\\") * run\\n for v in selfother.values():\\n v[start:start] = ins\\n elif(me == me_len or (she != she_len and\\n self.col_labels[me] > other.col_labels[she])):\\n ''' Her column is not present (i.e. was allgap) in \\\"me\\\"'''\\n start = she\\n while(she < she_len and (me == me_len or she != she_len and\\n self.col_labels[me] > other.col_labels[she])):\\n she += 1\\n run = she - start\\n ins = bytearray(b\\\"-\\\") * run\\n for seq in self.values():\\n seq[me:me] = ins\\n self._col_labels[me:me] = other.col_labels[start:she]\\n me += run\\n me_len += run\\n elif self.col_labels[me] == other.col_labels[she]:\\n ''' A shared column'''\\n while(me < me_len and she < she_len and\\n self.col_labels[me] == other.col_labels[she]):\\n she += 1\\n me += 1\\n else:\\n raise \\\"hmmm, we thought this should be impossible? %d %d\\\" % (\\n me, she)\\n\\n self.update(selfother)\\n\\n if convert_to_string:\\n self.from_bytearray_to_string()\\n #_LOG.debug(\\\"Merging finished ...\\\")\\n\\n return merged_insertion_columns\",\n \"def align(self):\\n\\n # load the alignment parameters into the align_params object\\n self.align_params.load_params_from_file(self.input_file)\\n\\n # populate the score matrices based on the input parameters\\n self.populate_score_matrices()\\n\\n # perform a traceback and write the output to an output file\\n\\n ### FILL IN ###\",\n \"def rotateAlignXform(self):\\n r = np.concatenate(([self.x], [self.y], [self.z]), 0)\\n r = np.concatenate((r, np.array([[0,0,0]])), 0)\\n r = np.concatenate((r, np.array([0,0,0,1]).reshape(-1,1)), 1)\\n return r\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7794893","0.6693876","0.66196114","0.658792","0.63913846","0.60286033","0.5967192","0.5963368","0.59541565","0.5938182","0.5919008","0.5912491","0.590243","0.58918756","0.5821559","0.57543087","0.5752975","0.5727278","0.57195127","0.57155657","0.5712719","0.56935406","0.56873953","0.56743056","0.5664449","0.5662491","0.56566286","0.56509763","0.5640208","0.5630757","0.56288564","0.5618317","0.56163746","0.56153214","0.56089664","0.5605823","0.560473","0.5572907","0.55711377","0.55664515","0.55577993","0.5556915","0.5555757","0.55417734","0.55400234","0.5507472","0.5503542","0.5503542","0.5503542","0.5496212","0.5493903","0.54856807","0.5476848","0.5474094","0.546378","0.5462378","0.5461863","0.5449561","0.5439659","0.5420451","0.53918755","0.53882","0.53741336","0.5372909","0.53655213","0.5344094","0.5339308","0.53353333","0.53353333","0.5333514","0.5332736","0.5332459","0.53309226","0.53309226","0.5329499","0.532921","0.53266686","0.5324974","0.5322869","0.5309568","0.5306073","0.52935","0.529283","0.5287049","0.5280337","0.5279732","0.52624243","0.5256344","0.525415","0.52501786","0.52436477","0.52330554","0.5210649","0.5210018","0.5203519","0.5201609","0.52001345","0.5194964","0.51897585","0.51880276","0.5184191"],"string":"[\n \"0.7794893\",\n \"0.6693876\",\n \"0.66196114\",\n \"0.658792\",\n \"0.63913846\",\n \"0.60286033\",\n \"0.5967192\",\n \"0.5963368\",\n \"0.59541565\",\n \"0.5938182\",\n \"0.5919008\",\n \"0.5912491\",\n \"0.590243\",\n \"0.58918756\",\n \"0.5821559\",\n \"0.57543087\",\n \"0.5752975\",\n \"0.5727278\",\n \"0.57195127\",\n \"0.57155657\",\n \"0.5712719\",\n \"0.56935406\",\n \"0.56873953\",\n \"0.56743056\",\n \"0.5664449\",\n \"0.5662491\",\n \"0.56566286\",\n \"0.56509763\",\n \"0.5640208\",\n \"0.5630757\",\n \"0.56288564\",\n \"0.5618317\",\n \"0.56163746\",\n \"0.56153214\",\n \"0.56089664\",\n \"0.5605823\",\n \"0.560473\",\n \"0.5572907\",\n \"0.55711377\",\n \"0.55664515\",\n \"0.55577993\",\n \"0.5556915\",\n \"0.5555757\",\n \"0.55417734\",\n \"0.55400234\",\n \"0.5507472\",\n \"0.5503542\",\n \"0.5503542\",\n \"0.5503542\",\n \"0.5496212\",\n \"0.5493903\",\n \"0.54856807\",\n \"0.5476848\",\n \"0.5474094\",\n \"0.546378\",\n \"0.5462378\",\n \"0.5461863\",\n \"0.5449561\",\n \"0.5439659\",\n \"0.5420451\",\n \"0.53918755\",\n \"0.53882\",\n \"0.53741336\",\n \"0.5372909\",\n \"0.53655213\",\n \"0.5344094\",\n \"0.5339308\",\n \"0.53353333\",\n \"0.53353333\",\n \"0.5333514\",\n \"0.5332736\",\n \"0.5332459\",\n \"0.53309226\",\n \"0.53309226\",\n \"0.5329499\",\n \"0.532921\",\n \"0.53266686\",\n \"0.5324974\",\n \"0.5322869\",\n \"0.5309568\",\n \"0.5306073\",\n \"0.52935\",\n \"0.529283\",\n \"0.5287049\",\n \"0.5280337\",\n \"0.5279732\",\n \"0.52624243\",\n \"0.5256344\",\n \"0.525415\",\n \"0.52501786\",\n \"0.52436477\",\n \"0.52330554\",\n \"0.5210649\",\n \"0.5210018\",\n \"0.5203519\",\n \"0.5201609\",\n \"0.52001345\",\n \"0.5194964\",\n \"0.51897585\",\n \"0.51880276\",\n \"0.5184191\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":13194,"cells":{"query":{"kind":"string","value":"RE(run_loop_measurement(t=1, name='1bl_PEI_10mM', loops=7, pump_t=210, total_t=720, jump_x=10)) Take measurements in the loop Sample has to be aligned before starting the script and theta angle at 0 deg (flat sample)."},"document":{"kind":"string","value":"def run_loop_measurement(t=0.5, name='test', loops=4, pump_t=180, total_t=600, jump_x=10):\n\n incident_angles = [0.1, 0.4]\n waxs_arc = [20, 0]\n user = \"TP\"\n\n condition = (\n ( -1 < waxs.arc.position )\n and ( waxs.arc.position < 1 )\n and (waxs_arc[0] == 20)\n )\n\n if condition:\n waxs_arc = waxs_arc[::-1]\n \n ranges = { 0.1 : [-16, 16, 33],\n 0.4 : [-25, 25, 51],\n }\n\n try:\n ai0 = RE.md['ai_0']\n except:\n yield from bp.count([])\n ai0 = db[-1].start['ai_0']\n print('Failed to acces RE.md')\n print(f'\\n\\nSample flat at theta = {ai0}')\n \n proposal_id('2023_2', '311564_Pettersson')\n #det_exposure_time(t, t)\n \n t_initial = time.time()\n\n for i in range(loops):\n t_start = time.time()\n print('Cycle number',i+1,'started at', (t_start - t_initial)/60)\n\n # Wait initial time for pumping to finish\n print(f'Start pumping now, going to wait for {pump_t} s\\n')\n while (time.time() - t_start) < pump_t:\n print(f'Pumping time: {(time.time() - t_start):.1f} s')\n yield from bps.sleep(10)\n\n # Go over SAXS and WAXS\n t_measurement = ( time.time() - t_initial ) / 60\n for wa in waxs_arc:\n yield from bps.mv(waxs, wa)\n dets = [pil900KW] if waxs.arc.position < 15 else [pil1M, pil900KW]\n\n for ai in incident_angles:\n yield from bps.mv(piezo.th, ai0 + ai)\n yield from bps.mvr(piezo.x, - jump_x)\n\n t2 = 2 * t if ai == 0.4 else t\n det_exposure_time(t2, t2)\n\n try:\n y_range = ranges[ai]\n except:\n y_range = [-10, 10, 11]\n \n sample_name = f'{name}{get_scan_md()}_time{t_measurement:.1f}_ai{ai}'\n sample_id(user_name=user, sample_name=sample_name)\n print(f\"\\n\\n\\n\\t=== Sample: {sample_name} ===\")\n yield from bp.rel_scan(dets, piezo.y, *y_range, md=dict(ai=ai))\n \n yield from bps.mv(waxs, waxs_arc[0],\n piezo.th, ai0)\n\n # Wait until the total loop time passes\n if i + 1 < loops:\n print(f'Waiting for the loop to last {total_t} s in total\\n')\n sleep_count = 0\n while (time.time() - t_start) < total_t:\n sleep_count += 1\n if (sleep_count % 10 == 0):\n print(f'Total time: {(time.time() - t_start):.1f} s')\n yield from bps.sleep(1)\n\n sample_id(user_name=\"test\", sample_name=\"test\")\n det_exposure_time(0.5, 0.5)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run_multiobjective(inputs, parameters = None):\n def thickness(x, t, chord):\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\n thickness_at_x = y['u'] - y['l']\n return thickness_at_x \n\n if parameters != None:\n eng = parameters[0]\n import_matlab = False\n else:\n eng = None\n import_matlab = True\n \n sma = inputs['sma']\n linear = inputs['linear']\n sigma_o = 100e6\n R = inputs['R']\n \n airfoil = \"naca0012\"\n chord = 1.#0.6175\n t = 0.12*chord\n\n J = {'x':0.75, 'y':0.}\n \n #Adding the area key to the dictionaries\n sma['area'] = math.pi*(0.000381/2.)**2\n linear['area'] = 0.001\n \n # Design constants \n #arm length to center of gravity\n r_w = 0.10\n\n\n #Aicraft weight (mass times gravity)\n W = 0.0523*9.8 #0.06*9.8\n alpha = 0.\n V = 10 #m/s\n altitude = 10000. #feet\n \n # Temperature\n T_0 = 273.15 + 30\n T_final = inputs['T_f']\n \n #Initial martensitic volume fraction\n MVF_init = 1.\n \n # Number of steps and cycles\n n = 200\n n_cycles = 0\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n #Parameters to select how to output stuff\n all_outputs = True\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n if all_outputs:\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap_multiobjective(airfoil, \n chord, J, sma, linear, sigma_o, \n W, r_w, V, altitude, alpha, T_0, \n T_final, MVF_init, n, R, all_outputs = True,\n import_matlab = import_matlab, eng=eng,\n n_cycles = n_cycles)\n\n return theta, sigma, T, MVF, eps_s, L_s","def main():\n\n r = np.arange(-90, 90, 2)\n tilt = 22.5\n\n with ptcmd.PTCMD(baud=57600) as controller:\n\n controller.home()\n\n for pan_angle in r:\n\n current_position = controller.get_position()\n pan_steps, tilt_steps, pan_rate, tilt_rate = system.move_to_angle(pan_angle, tilt, current_position[0], current_position[1], base_rate = 1000)\n controller.sync(pan_rate, pan_steps, tilt_rate, tilt_steps)\n controller.wait('all')\n\n time.sleep(2)\n\n distances = []\n for i in range(0,12):\n time.sleep(1)\n distances.append(controller.get_analog_sensor(1))\n\n distances.sort()\n distance = sum(distances[1:-1]) / len(distances[1:-1])\n\n x = math.sin(pan_angle*math.pi/180) * distance\n y = math.cos(pan_angle*math.pi/180) * distance\n\n print(distances, distance, pan_angle, x, y)\n\n plt.plot(x, y, 'b+')\n\n plt.show()","def run(inputs, parameters = None):\n def thickness(x, t, chord):\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\n thickness_at_x = y['u'] - y['l']\n return thickness_at_x \n\n if parameters != None:\n eng = parameters[0]\n import_matlab = False\n else:\n eng = None\n import_matlab = True\n \n sma = inputs['sma']\n linear = inputs['linear']\n R = inputs['R']\n\n sigma_o = 100e6\n\n \n airfoil = \"naca0012\"\n chord = 1.#0.6175\n\n J = {'x':0.75, 'y':0.}\n \n #Adding the area key to the dictionaries\n sma['area'] = math.pi*(0.000381/2.)**2\n linear['area'] = 0.001\n \n # Design constants \n #arm length to center of gravity\n r_w = 0.10\n \n #Aicraft weight (mass times gravity)\n W = 0.0523*9.8 #0.06*9.8\n alpha = 0.\n V = 10 #m/s\n altitude = 10000. #feet\n \n # Temperature\n T_0 = 273.15 + 30.\n T_final = 273.15 + 140.\n \n #Initial martensitic volume fraction\n MVF_init = 1.\n \n # Number of steps and cycles\n n = 200\n n_cycles = 0\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n #Parameters to select how to output stuff\n all_outputs = True\n save_data = True\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n if all_outputs:\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap(airfoil, \n chord, J, sma, linear, sigma_o, \n W, r_w, V, altitude, alpha, T_0, \n T_final, MVF_init, n, R, all_outputs = True,\n import_matlab = import_matlab, eng=eng,\n n_cycles = n_cycles)\n\n import matplotlib.pyplot as plt\n plt.figure()\n plt.plot(np.rad2deg(theta), eps_s, lw=2., label = \"$\\epsilon_s$\")\n plt.plot(np.rad2deg(theta), eps_l, 'b--',lw=2, label = \"$\\epsilon_l$\")\n# plt.scatter(theta, eps_s, c = 'b')\n# plt.scatter(theta, eps_l, c = 'b')\n plt.ylabel('$\\epsilon$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)\n plt.figure()\n plt.plot(np.rad2deg(theta), eps_t, lw=2.)\n# plt.scatter(theta, eps_t, c = 'b')\n plt.ylabel('$\\epsilon_t$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(np.rad2deg(theta), MVF, lw=2.)\n# plt.scatter(theta, MVF, c = 'b')\n plt.ylabel('$MVF$', fontsize=24)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n\n plt.figure()\n plt.plot(T, MVF, lw=2.)\n# plt.scatter(T, MVF, c = 'b')\n plt.ylabel('$MVF$', fontsize=24)\n plt.xlabel('$T (K)$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n\n plt.figure()\n plt.plot(T, sigma, lw=2.)\n# plt.scatter(T, sigma, c = 'b')\n plt.ylabel('$\\sigma$', fontsize=24)\n plt.xlabel('$T (K)$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(T, eps_s, 'b', lw=2., label = \"$\\epsilon_s$\")\n plt.plot(T, eps_l, 'b--',lw=2, label = \"$\\epsilon_l$\")\n# plt.scatter(T, eps_s, c = 'b')\n# plt.scatter(T, eps_l, c = 'b')\n plt.xlabel('$T (K)$', fontsize=20)\n plt.ylabel('$\\epsilon$', fontsize=24)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid()\n \n plt.figure()\n plt.plot(T, np.rad2deg(theta), lw=2.)\n# plt.scatter(T, theta, c = 'b')\n plt.xlabel('$T (K)$', fontsize=20)\n plt.ylabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.grid()\n \n F_s = []\n for i in range(len(sigma)):\n F_s.append(sigma[i]*sma['area'])\n# sigma_MPa = []\n# for sigma_i in sigma:\n# sigma_MPa.append(sigma_i/1e6)\n plt.figure()\n plt.plot(theta, F_s, 'b', lw=2., label = \"$F_s$\")\n plt.plot(theta, F_l, 'b--', lw=2., label = \"$F_l$\")\n# plt.scatter(theta, F_s, c = 'b')\n# plt.scatter(theta, F_l, c = 'b')\n plt.ylabel('$F (N)$', fontsize=20)\n plt.xlabel(r'$\\theta ({}^{\\circ})$', fontsize=20)\n plt.legend(loc = 'best', fontsize = 'x-large')\n plt.grid() \n else:\n theta, k= flap(airfoil, chord, J, sma, linear, sigma_o, \n W, r_w, V, altitude, alpha, T_0, \n T_final, MVF_init, n, R, all_outputs = False,\n import_matlab = import_matlab, eng=eng,\n n_cycles = n_cycles)\n \n if save_data == True:\n Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l, \n 'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,\n 'F_l': F_l, 'k': k, 'L_s':L_s}\n pickle.dump(Data, open( \"data.p\", \"wb\" ) )\n \n return {'theta': theta, 'k': k}","def lab_run_med(character_id, time_step):\n pass","def run1():\n #Reseting motors\n ResetRobot.reset_wheel_motors()\n ResetRobot.reset_attachment_motors()\n CalibrateRobot.calibrate_gyro()\n\n #mission M01 and M02 - space travel and solar panel\n M01_M02()\n \n #Mission M05- Extraction \n M05_M14()\n\n #Back to base before Gerhard (Remove comment if necessary)\n return_to_base1()\n\n # Must delete for competition.. This is to set up forklift to repeat run.\n Robot.attachment_left.on_for_rotations(-100, 8) #Raises Forklift ","def radiation_measurement_analysis():\n import pint\n ureg = pint.UnitRegistry()\n\n mrem_h = ureg.parse_units('mrem') / ureg.hour\n m = ureg.parse_units('meters')\n s = ureg.parse_units('seconds')\n\n # Measurements of background radiation\n bg_dist = ureg.parse_expression('10 m') # estimate of how far away we are wrt background\n background_rows = [\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=0.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.022 * mrem_h, capture_time=0.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=4.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.021 * mrem_h, capture_time=5.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=11.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=16.0 * s),\n dict(vid=1, distance=bg_dist, rad=0.024 * mrem_h, capture_time=20.0 * s),\n ]\n\n # Measurements of sample radiation\n esp_dist = ureg.parse_expression('1 inch').to(m) / 2 # estimate of how far we are from the sample when very close\n dist0_rows = [\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=0.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=3.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=5.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=9.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=10.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=11.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.057 * mrem_h, capture_time=12.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.058 * mrem_h, capture_time=13.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=14.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=15.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=16.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=20.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=22.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.066 * mrem_h, capture_time=23.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=24.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=25.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=26.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=28.0 * s),\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=30.0 * s),\n ]\n\n dist0_v2_rows = [\n dict(vid=3, distance=esp_dist, rad=0.012 * mrem_h, capture_time=0.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.011 * mrem_h, capture_time=1.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=8.0 * s),\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=9.0 * s),\n ]\n\n close_rows = [\n dict(vid=4, distance=0.5 * m, rad=0.013 * mrem_h, capture_time=0.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.014 * mrem_h, capture_time=5.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=7.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.011 * mrem_h, capture_time=15.0 * s),\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=16.0 * s),\n ]\n\n mid_rows = [\n dict(vid=5, distance=1.0 * m, rad=0.014 * mrem_h, capture_time=0.0 * s),\n dict(vid=5, distance=1.0 * m, rad=0.015 * mrem_h, capture_time=5.0 * s),\n dict(vid=5, distance=1.0 * m, rad=0.013 * mrem_h, capture_time=10.0 * s),\n ]\n\n far_rows = [\n dict(vid=6, distance=2.0 * m, rad=0.023 * mrem_h, capture_time=0.0 * s),\n dict(vid=6, distance=2.0 * m, rad=0.025 * mrem_h, capture_time=0.1 * s),\n ]\n\n # guess_dist = ureg.parse_expression('0.3 m') # estimate of how far away we are wrt background\n # guess_rows = [\n # dict(vid=9, distance=guess_dist, rad=0.030 * mrem_h, capture_time=0.0 * s),\n # dict(vid=9, distance=guess_dist, rad=0.041 * mrem_h, capture_time=2.0 * s),\n # dict(vid=9, distance=guess_dist, rad=0.051 * mrem_h, capture_time=3.0 * s),\n # ]\n\n rows = dist0_rows + background_rows + dist0_v2_rows + close_rows + mid_rows + far_rows\n # rows += guess_rows\n\n import pandas as pd\n import numpy as np\n table = pd.DataFrame(rows)\n\n # Ensure comparable units\n units = {\n 'rad': mrem_h,\n 'distance': m,\n 'capture_time': s,\n }\n for key, unit in units.items():\n table[key] = table[key].apply(lambda c: c.to(unit).m)\n table['rad'] = table['rad'].astype(float)\n table['distance'] = table['distance'].astype(float)\n\n # Weight each measurement based on the amount of time the measurement was\n # sustained in the video.\n average_rad_rows = []\n for vid, group in table.groupby('vid'):\n from statsmodels.stats.weightstats import DescrStatsW\n weights = (-1 * group['capture_time'].diff(periods=-1).fillna(0)) / group['capture_time'].iloc[-1]\n table.loc[group.index, 'weight'] = weights\n values = group['rad']\n weighted_stats = DescrStatsW(values, weights=weights, ddof=0)\n dists = group['distance'].unique()\n assert len(dists) == 1\n average_rad_rows.append({\n 'vid': vid,\n 'distance': dists[0],\n 'rad_mean': weighted_stats.mean,\n 'rad_std': weighted_stats.std,\n })\n stats_table = pd.DataFrame(average_rad_rows)\n\n bg_row = stats_table.loc[stats_table['distance'].argmax()]\n fg_row = stats_table.loc[stats_table['distance'].argmin()]\n\n # -------------------\n ADD_DUMMY_VALUES = 0\n if ADD_DUMMY_VALUES:\n # Hack: because we don't have enough samples we can fudge the value\n # knowning that the value should be the background radiation in the\n # limit.\n\n dummy_measurements = []\n extra_support = 1\n for idx in range(3, 3 + extra_support):\n dummy_row = {\n 'vid': -idx,\n 'distance': bg_row['distance'] + idx,\n 'rad_mean': bg_row['rad_mean'],\n 'rad_std': 0.01,\n }\n dummy_measurements.append(dummy_row)\n\n # also add an extra value close to the sample\n rad_bg = bg_row['rad_mean']\n rad_above_bg = fg_row['rad_mean'] - rad_bg\n dummy_row = {\n 'vid': -1,\n 'distance': fg_row['distance'] / 2,\n 'rad_mean': rad_bg + (rad_above_bg * 4),\n 'rad_std': 0.5,\n }\n dummy_measurements.append(dummy_row)\n\n # dummy_row = {\n # 'vid': -2,\n # 'distance': fg_row['distance'] / 4,\n # 'rad_mean': rad_bg + (rad_above_bg * 16),\n # }\n # dummy_measurements.append(dummy_row)\n\n dummy_stats = pd.DataFrame(dummy_measurements)\n dummy_stats['weight'] = 0.5\n stats_table['weight'] = 1.0\n stats_table2 = pd.concat([stats_table, dummy_stats]).reset_index(drop=True).sort_values('distance')\n else:\n stats_table2 = stats_table\n # -------------------\n\n import scipy\n scipy.optimize.curve_fit\n\n # Because we know the radiation should follow an inverse square law wrt to\n # distance, we can fit a polynomial of degree 2 (parabola) to interpolate /\n # extrapolate the **inverse** values.\n x = stats_table2['distance'].values\n y = stats_table2['rad_mean'].values\n s = stats_table2['rad_std'].values\n\n # Model the squared falloff directly\n def invsquare(x, a, b):\n return a * (1 / (0.01 + x ** 2)) + b\n # bg_row['rad_mean']\n # Use curve_fit to constrain the first coefficient to be zero\n try:\n coef = scipy.optimize.curve_fit(invsquare, x, y, sigma=s, method='trf')[0]\n except Exception as ex:\n coef = None\n print(f'ex={ex}')\n\n # Also fit one to the raw weighted points as a sanity check\n # inv_poly2 = Polynomial.fit(table['distance'], 1 / table['rad'], w=table['weight'], deg=2)\n\n import kwplot\n sns = kwplot.autosns()\n plt = kwplot.autoplt()\n # ax = sns.boxplot(data=table, x='distance', y='rad', width=0.1)\n\n # Add in points to show each observation\n ax = sns.relplot(x=\"distance\", y=\"rad\", data=table, size=4, color=\".3\",\n linewidth=0, alpha=0.5, palette='deep')\n\n ax = plt.gca()\n ax.set_xlabel('distance from sample ({})'.format(str(units['distance'])))\n ax.set_ylabel('radiation dosage ({})'.format(str(units['rad'])))\n\n max_meters = 10\n\n extrap_x = np.linspace(0, max_meters, 1000)\n if coef is not None:\n extrap_y1 = invsquare(extrap_x, *coef)\n # extrap_y2 = 1 / inv_poly2(extrap_x)\n ax.plot(stats_table2['distance'].values, stats_table2['rad_mean'].values, 'rx')\n ax.plot(stats_table['distance'].values, stats_table['rad_mean'].values, 'bo')\n ax.plot(extrap_x, extrap_y1, '--')\n ax.set_ylim(0.001, 0.1)\n ax.set_yscale('log')\n # ax.plot(extrap_x, extrap_y2, '--')","def run(self):\n openShutter = True\n actuateXed = False\n image_type = \"PPUMP\"\n\n wl = float(self.eo_config.get(\"PPUMP_WL\", 550))\n meas_flux = self.measured_flux(wl)\n seqno = 0\n for tokens in self.instructions:\n exptime = float(tokens[1])\n nframes = int(tokens[2])\n shifts = int(tokens[3])\n for iframe in range(nframes):\n self.image_clears()\n self.bias_image(seqno)\n self.take_image(seqno, exptime, openShutter, actuateXed,\n image_type)\n seqno += 1","def amet_memoryWise(self):\r\n # set up logging files to monitor the calculation\r\n logging.basicConfig(filename = os.path.join(self.path,'history_amet_python.log'),\r\n filemode = 'w+', level = logging.DEBUG,\r\n format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\n # initialize the time span\r\n # define sigma level\r\n A, B = self.defineSigmaLevels()\r\n # use example input file to load the basic dimensions information\r\n datapath_var = os.path.join(self.path, 'MERRA2_400.inst3_3d_asm_Nv.20160101.nc4.nc')\r\n var_key = Dataset(datapath_var)\r\n lat = var_key.variables['lat'][:]\r\n lon = var_key.variables['lon'][:]\r\n # calculate the reference levels based on A & B and standard surface pressure\r\n half_level = A + B * 101325\r\n level = (half_level[1:] + half_level[:-1]) / 2\r\n # create space for the output\r\n # AMET in the entire column\r\n E = np.zeros((len(lat),len(lon)), dtype=float)\r\n cpT = np.zeros((len(lat),len(lon)), dtype=float)\r\n Lvq = np.zeros((len(lat),len(lon)), dtype=float)\r\n gz = np.zeros((len(lat),len(lon)), dtype=float)\r\n uv2 = np.zeros((len(lat),len(lon)), dtype=float)\r\n logging.info(\"Start retrieving variables T,q,u,v,sp\")\r\n # The shape of each variable is (8,72,361,576)\r\n T = var_key.variables['T'][:]\r\n q = var_key.variables['QV'][:]\r\n sp = var_key.variables['PS'][:] #(8,361,576)\r\n u = var_key.variables['U'][:]\r\n v = var_key.variables['V'][:]\r\n logging.info(\"Extracting variables successfully!\") \r\n # compute gz\r\n z_model = self.calc_gz(var_key)\r\n # get the basic shape\r\n tt, hh, yy, xx = q.shape\r\n AMET = amet.met()\r\n E, cpT, Lvq, gz, uv2 = AMET.calc_met(T, q, sp, u, v, z_model, A, B,\r\n tt, hh, len(lat), len(lon), lat, self.lat_unit)\r\n\r\n return np.mean(E)","def run():\n step = 0\n o2r = 4 #orange to red delay time\n r2g = 2 #red to green delay time\n A_4235 = 0\n B_4235 = 1\n C_4235 = 2\n AB1_4235 = 3\n AB2_4235 = 4\n AC1_4235 = 5\n AC2_4235 = 6\n BA1_4235 = 7\n BA2_4235 = 8\n BC1_4235 = 9\n BC2_4235 = 10\n CA1_4235 = 11\n CA2_4235 = 12\n CB1_4235 = 13\n CB2_4235 = 14\n A_4219 = 0\n B_4219 = 1\n C_4219 = 2\n D_4219 = 3\n E_4219 = 4\n F_4219 = 5\n G_4219 = 6\n AB1_4219 = 7\n AB2_4219 = 8\n AC1_4219 = 9\n AC2_4219 = 10\n AD1_4219 = 11\n AD2_4219 = 12\n AE1_4219 = 13\n AE2_4219 = 14\n AF1_4219 = 16\n AF2_4219 = 17\n AG1_4219 = 18\n AG2_4219 = 19\n BA1_4219 = 20\n BA2_4219 = 21\n BC1_4219 = 22\n BC2_4219 = 23\n BD1_4219 = 24\n BD2_4219 = 25\n BE1_4219 = 26\n BE2_4219 = 27\n BF1_4219 = 28\n BF2_4219 = 29\n BG1_4219 = 30\n BG2_4219 = 31\n CA1_4219 = 32\n CA2_4219 = 33\n CB1_4219 = 34\n CB2_4219 = 35\n CD1_4219 = 36\n CD2_4219 = 37\n CE1_4219 = 38\n CE2_4219 = 39\n CF1_4219 = 40\n CF2_4219 = 41\n CG1_4219 = 42\n CG2_4219 = 43\n DA1_4219 = 44\n DA2_4219 = 45\n DB1_4219 = 46\n DB2_4219 = 47\n DC1_4219 = 48\n DC2_4219 = 49\n DE1_4219 = 50\n DE2_4219 = 51\n DF1_4219 = 52\n DF2_4219 = 53\n DG1_4219 = 54\n DG2_4219 = 55\n EA1_4219 = 56\n EA2_4219 = 57\n EB1_4219 = 58\n EB2_4219 = 59\n EC1_4219 = 60\n EC2_4219 = 61\n ED1_4219 = 62\n ED2_4219 = 63\n EF1_4219 = 64\n EF2_4219 = 65\n EG1_4219 = 66\n EG2_4219 = 67\n FA1_4219 = 68\n FA2_4219 = 69\n FB1_4219 = 70\n FB2_4219 = 71\n FC1_4219 = 72\n FC2_4219 = 73\n FD1_4219 = 74\n FD2_4219 = 75\n FE1_4219 = 76\n FE2_4219 = 77\n FG1_4219 = 78\n FG2_4219 = 79\n GA1_4219 = 80\n GA2_4219 = 81\n GB1_4219 = 82\n GB2_4219 = 83\n GC1_4219 = 84\n GC2_4219 = 85\n GD1_4219 = 86\n GD2_4219 = 87\n GE1_4219 = 88\n GE2_4219 = 89\n GF1_4219 = 90\n GF2_4219 = 91\n A_4220 = 0\n B_4220 = 1\n C_4220 = 2\n D_4220 = 3\n E_4220 = 4\n AB1_4220 = 5\n AB2_4220 = 6\n AC1_4220 = 7\n AC2_4220 = 8\n AD1_4220 = 9\n AD2_4220 = 10\n AE1_4220 = 11\n AE2_4220 = 12\n BA1_4220 = 13\n BA2_4220 = 14\n BC1_4220 = 15\n BC2_4220 = 16\n BD1_4220 = 17\n BD2_4220 = 18\n BE1_4220 = 19\n BE2_4220 = 20\n CA1_4220 = 21\n CA2_4220 = 22\n CB1_4220 = 23\n CB2_4220 = 24\n CD1_4220 = 25\n CD2_4220 = 26\n CE1_4220 = 27\n CE2_4220 = 28\n DA1_4220 = 29\n DA2_4220 = 30\n DB1_4220 = 31\n DB2_4220 = 32\n DC1_4220 = 33\n DC2_4220 = 34\n DE1_4220 = 35\n DE2_4220 = 36\n EA1_4220 = 37\n EA2_4220 = 38\n EB1_4220 = 39\n EB2_4220 = 40\n EC1_4220 = 41\n EC2_4220 = 42\n ED1_4220 = 43\n ED2_4220 = 44\n A_4221 = 0\n B_4221 = 1\n C_4221 = 2\n D_4221 = 3\n E_4221 = 4\n F_4221 = 5\n AB1_4221 = 6\n AB2_4221 = 7\n AC1_4221 = 8\n AC2_4221 = 9\n AD1_4221 = 10\n AD2_4221 = 11\n AE1_4221 = 12\n AE2_4221 = 13\n AF1_4221 = 14\n AF2_4221 = 15\n BA1_4221 = 16\n BA2_4221 = 17\n BC1_4221 = 18\n BC2_4221 = 19\n BD1_4221 = 20\n BD2_4221 = 21\n BE1_4221 = 22\n BE2_4221 = 23\n BF1_4221 = 24\n BF2_4221 = 25\n CA1_4221 = 26\n CA2_4221 = 27\n CB1_4221 = 28\n CB2_4221 = 29\n CD1_4221 = 30\n CD2_4221 = 31\n CE1_4221 = 32\n CE2_4221 = 33\n CF1_4221 = 34\n CF2_4221 = 35\n DA1_4221 = 36\n DA2_4221 = 37\n DB1_4221 = 38\n DB2_4221 = 39\n DC1_4221 = 40\n DC2_4221 = 41\n DE1_4221 = 42\n DE2_4221 = 43\n DF1_4221 = 44\n DF2_4221 = 45\n EA1_4221 = 46\n EA2_4221 = 47\n EB1_4221 = 48\n EB2_4221 = 49\n EC1_4221 = 50\n EC2_4221 = 51\n ED1_4221 = 52\n ED2_4221 = 53\n EF1_4221 = 54\n EF2_4221 = 55\n FA1_4221 = 56\n FA2_4221 = 57\n FB1_4221 = 58\n FB2_4221 = 59\n FC1_4221 = 60\n FC2_4221 = 61\n FD1_4221 = 62\n FD2_4221 = 63\n FE1_4221 = 64\n FE2_4221 = 65\n \n #while traci.simulation.getMinExpectedNumber() > 0:\n while step < 600:\n traci.simulationStep()\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",75)\n if step == 87:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 91:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 93:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 108:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 112:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 114:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",12)\n if step == 126:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 132:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",32)\n if step == 164:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 168:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",50)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 224:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 226:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 241:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 247:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",14)\n if step == 261:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 265:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 267:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",13)\n if step == 280:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 284:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 286:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 360:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 364:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 366:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 381:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 387:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",10)\n if step == 397:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 401:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 403:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 423:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 425:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",74)\n if step == 499:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 503:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", AB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 505:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",15)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 524:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BC2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 526:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", C_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",8)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", CB2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", B_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",16)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA1_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",o2r)\n if step == 560:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", BA2_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",r2g)\n if step == 562:\n traci.trafficlight.setPhase(\"cluster_1707799581_314056954_5931861577\", A_4235)\n traci.trafficlight.setPhaseDuration(\"cluster_1707799581_314056954_5931861577\",38)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 6)\n if step == 6:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 10:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 12:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 32:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 43:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 47:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 49:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 80:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 84:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 86:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 12)\n if step == 98:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 102:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 104:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 133:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 137:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 139:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 20)\n if step == 159:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 163:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 165:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 174:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 176:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 38)\n if step == 214:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 218:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 220:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 13)\n if step == 233:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 237:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 34)\n if step == 273:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 279:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 22)\n if step == 301:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 305:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 307:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 312:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 318:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 31)\n if step == 349:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 353:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 373:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 36)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 419:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 421:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 440:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 444:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 5)\n if step == 451:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 455:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 457:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", F_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 29)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 490:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", FG2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 492:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", G_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 18)\n if step == 510:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", GA2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 516:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", A_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 41)\n if step == 557:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 561:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", AD2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 563:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", D_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 19)\n if step == 582:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 586:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", DE2_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", r2g)\n if step == 588:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", E_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", 8)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", EF1_4219)\n traci.trafficlight.setPhaseDuration(\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\", o2r)\n if step == 0:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 17)\n if step == 17:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 21:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 23:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 32:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 36:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 38:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 67)\n if step == 105:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 16)\n if step == 152:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 156:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 158:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 11)\n if step == 169:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 173:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 175:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 63)\n if step == 238:\n traci.trafficlight.setPhase(\"gneJ41\", AD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 242:\n traci.trafficlight.setPhase(\"gneJ41\", AD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 244:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 257:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 261:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 263:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 9)\n if step == 272:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 276:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 278:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 95)\n if step == 373:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 377:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 379:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 398:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 402:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 404:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 24)\n if step == 428:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 432:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 434:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 13)\n if step == 447:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 451:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 453:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 56)\n if step == 509:\n traci.trafficlight.setPhase(\"gneJ41\", AB1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 513:\n traci.trafficlight.setPhase(\"gneJ41\", AB2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 515:\n traci.trafficlight.setPhase(\"gneJ41\", B_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 19)\n if step == 534:\n traci.trafficlight.setPhase(\"gneJ41\", BD1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 538:\n traci.trafficlight.setPhase(\"gneJ41\", BD2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 540:\n traci.trafficlight.setPhase(\"gneJ41\", D_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 22)\n if step == 562:\n traci.trafficlight.setPhase(\"gneJ41\", DE1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 566:\n traci.trafficlight.setPhase(\"gneJ41\", DE2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 568:\n traci.trafficlight.setPhase(\"gneJ41\", E_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 20)\n if step == 588:\n traci.trafficlight.setPhase(\"gneJ41\", EA1_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", o2r)\n if step == 592:\n traci.trafficlight.setPhase(\"gneJ41\", EA2_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", r2g)\n if step == 594:\n traci.trafficlight.setPhase(\"gneJ41\", A_4220)\n traci.trafficlight.setPhaseDuration(\"gneJ41\", 6)\n if step == 0:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 9:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 13:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 15:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 34:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 38:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 40:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 20)\n if step == 60:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 64:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 66:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 90:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 94:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 96:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 105:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 109:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 111:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 130:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 134:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 136:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 166:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 170:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 172:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 28)\n if step == 200:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 204:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 206:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 225:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 229:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 231:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 239:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 243:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 245:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 32)\n if step == 277:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 281:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 283:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 27)\n if step == 310:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 314:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 316:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 15)\n if step == 331:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 335:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 337:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 351:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 355:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 357:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 22)\n if step == 379:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 383:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 385:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 24)\n if step == 409:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 413:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 415:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 11)\n if step == 426:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 430:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 432:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 14)\n if step == 446:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 450:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 452:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 30)\n if step == 482:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 486:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 488:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 26)\n if step == 514:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 518:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 520:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 8)\n if step == 528:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 532:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", EF2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 534:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", F_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 18)\n if step == 552:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 556:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", FA2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 558:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", A_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 19)\n if step == 577:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 581:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", AD2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 583:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", D_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 9)\n if step == 592:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE1_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", o2r)\n if step == 596:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", DE2_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", r2g)\n if step == 598:\n traci.trafficlight.setPhase(\"cluster_25953432_313863435_313863521_314053282\", E_4221)\n traci.trafficlight.setPhaseDuration(\"cluster_25953432_313863435_313863521_314053282\", 2)\n\n step += 1\n\n traci.close()\n sys.stdout.flush()","def timesteps_experiment():\n\n print(\"TIMESTEPS EXPERIMENT\")\n\n # set the name of the experiment\n now = datetime.datetime.now()\n experiment_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute)\n experiment_name = 'timestep_' + str(experiment_id)\n\n # define if you want to use preprocessed data from file\n use_prep_data = False\n if use_prep_data:\n set_params(preproc_data_id='16_5_10.16.47')\n else:\n set_params(use_preproc_data=False)\n\n # define the changing parameter and its value\n changing_param_name = 'time_steps'\n changing_param_value = [1, 2, 4, 8, 16, 32, 64, 128, 256]\n # {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\n\n # set constant parameters\n set_params(epochs=20)\n set_params(dropout=0.3)\n set_params(use_word_emb=1)\n\n # save constant parameters to a new \"experiment_..\" file\n save_constant_parameters(experiment_name, changing_param_name)\n\n # run experiment for every parameter value\n for value in changing_param_value:\n process = psutil.Process(os.getpid())\n print(\"-----MEMORY before starting experiment ------\", int(process.memory_info().rss/(8*10**(3))), \"KB\")\n\n # update the parameter value\n set_params(use_word_emb = value)\n\n # update the model_id for this new model\n now = datetime.datetime.now()\n new_model_id = str(now.day) + \"_\" + str(now.month) + \"_\" + str(now.hour) + \".\" + str(now.minute) + \".\" + str(now.second)\n set_params(model_id = new_model_id)\n\n # evaluate the new model and save the results in the experiment file\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()\n\n if value == changing_param_value[0]:\n set_params(preproc_data_id=new_model_id)","def run_performance():\n # Create a Struct data instance from config\n inputs = Struct(config)\n inputs.throttle = throttle\n # Get oxidizer properties at the given temperature\n n2o = n2o_properties(inputs.ox.T_tank)\n # Our integration variables are oxidizer mass and liquid oxidizer volume\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\n if inputs.options.output_on:\n print(\"Initial oxidizer mass: {} kg.\".format(Mox))\n\n start = time.perf_counter() # Start timer for integration\n\n time, record = integration(inputs) # Time = time for integration, record = output data\n F_thrust = record.F_thrust\n p_cc = record.p_cc\n p_oxtank = record.p_oxtank\n p_oxpresstank = record.p_oxpresstank\n p_fueltank = record.p_fueltank\n p_fuelpresstank = record.p_fuelpresstank\n p_oxmanifold = record.p_oxmanifold\n T_oxtank = record.T_oxtank\n T_cc = record.T_cc\n area_core = record.area_core\n OF = record.OF_i\n gamma_ex = record.gamma_ex\n m_dot_ox = record.m_dot_ox\n m_dot_fuel = record.m_dot_fuel\n p_crit = record.p_crit\n m_dot_ox_crit = record.m_dot_ox_crit\n M_e = record.M_e\n p_exit = record.p_exit\n p_shock = record.p_shock\n\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\n if inputs.options.output_on:\n print(\"Time elapsed for this timestep: {} sec.\".format(time_elapsed))","def main():\n\n para_list = [para]\n loss_list = []\n\n for i in range(30):\n para_list.append(diff_fun(loss_fun, para_list[i]))\n loss_list.append(loss_fun(para_list[i]))\n\n env = QEnv()\n env.backend(BackendName.LocalBaiduSim2)\n\n q = env.Q.createList(n)\n\n state_prepare(q, 0)\n universal_cir(q, 0, para_list[-1])\n\n MeasureZ(*env.Q.toListPair())\n taskResult = env.commit(shots, fetchMeasure=True)\n print(taskResult['counts'])","def water_delay(block_size):\n\n\tdirectory = \"/local/scratch/sam5g13/Sam_5th-yr_Project/test_data\"\n\tfile_name = \"{}/tip4p2005_50_TOTEST.npy\".format(directory)\n\tgnuplot = r'/usr/bin/gnuplot'\n\n\n\tfile_data = np.load(file_name, mmap_mode='r')\n\n\t_, _, _, gamma, _ = file_data \n\n\tgamma_sample = blocksav(gamma, block_size)\n\n\tgamma_file = \"{}/tip4p2005_50_blocksize_{}_gamma.txt\".format(directory, block_size)\n\twith open(gamma_file, 'w') as outfile:\n\t\tnp.savetxt(outfile, gamma_sample)\n\n\tgamma_file_name = \"{}/tip4p2005_50_blocksize_{}_gamma.txt\".format(directory, block_size)\n\n\tcorrelations = subprocess.check_output([\"corr\", gamma_file_name])\n\t\n\tmutual_information = subprocess.check_output([\"mutual\", gamma_file_name])\n\n\tcorrelation_array = np.array(correlations.split()[5:], dtype=float)\n\tmutual_information_array = np.array(mutual_information.split()[2:], dtype=float)\n\n\tidx_odd = range(1,199,2)\n\tidx_even = range(0,200,2)\n\n\tidx_odd1 = range(1,43,2)\n\tidx_even1 = range(0,44,2)\n\n\t#correlation_values = correlation_array[idx_odd]\n\tmutual_information_values = mutual_information_array[idx_odd1]\n\tprint 'LOOK HERE...........................................', mutual_information_array[idx_odd1], len(mutual_information_array[idx_odd1])\n\n\t\"\"\"\n\tdelay_length = 0\n\n\tfor o in range(len(correlation_values) - 1):\n\t\tprint o, correlation_values[o], correlation_values[o+1]\n\t\tif correlation_values[o] > correlation_values[o+1]:\n\t\t\tdelay_length = o \n\t\telse: break\n\t\n\tdelay_length = delay_length + 1\n\n\tprint \"The delay length is\", delay_length\n\t\"\"\"\n\n\tmutual_info_length = 0\n\n\tfor o in range(len(mutual_information_values) - 1):\n\t\t#print o, correlation_values[o], correlation_values[o+1]\n\t\tif mutual_information_values[o] > mutual_information_values[o+1]:\n\t\t\tmutual_info_length = o \n\t\telse: break\n\t\n\tmutual_info_length = mutual_info_length + 1\n\t\n\tprint \"The mutual info length is\", mutual_info_length\n\n\t#assert \tdelay_length == mutual_info_length, \"The minimums of the mutual information and the correlations are not equal! %d %d\" % (delay_length, mutual_info_length)\n\t\n\tproduce_delays = subprocess.check_output([\"delay\", gamma_file_name, \"-d\" + str(mutual_info_length)])\n\n\t\n\tdelay_file = \"{}/tip4p2005_50_blocksize_{}_gamma_delay_{}.txt\".format(directory, block_size, mutual_info_length)\n\tf = open(delay_file, 'w')\n\tf.write(produce_delays)\n\tf.close()\n\n\t\"\"\"\n\n\tprint produce_delays\n\tprint len(produce_delays), len(mutual_information_values)\n\tplt.figure(\"produce_delays vs mutual information\")\n\tplt.xlabel(\"produce_delays\")\n\tplt.ylabel(\"Mutual information\")\n\tplt.plot(produce_delays, mutual_information_values)\n\tplt.show()\n\t\n\t\"\"\"\n\t\n\tembedding = subprocess.check_output([\"false_nearest\", gamma_file_name])\n\n\tembedding_dimension = int(raw_input(\"What embedding dimension would you like to use? \"))\n\t\n\trun_calc = subprocess.check_output(['gnuplot', '-e', \"filename='{}/tip4p2005_50_blocksize_{}_gamma_delay_{}.txt';ofilename='tip4p2005_50_blocksize_{}_gamma_delay_{}_graph.png'\".format(directory, block_size, mutual_info_length, block_size, mutual_info_length ),\"plot.gnu\"])\n\n\n\t\"\"\"Imports the time series and specifies each aspect used in building the recurrence matrix\"\"\"\n\n\tsettings = Settings(time_series = gamma_sample, embedding_dimension = embedding_dimension, time_delay = mutual_info_length, similarity_measure = EuclideanMetric, neighbourhood = FixedRadius(radius = 13), min_diagonal_line_length = 2, min_vertical_line_length = 2)\n\n\t\"\"\"Performs the computation and prints out all the results\"\"\"\n\n\trqacomputation = RQAComputation.create(settings, verbose = True)\n\n\trqaresult = rqacomputation.run()\n\n\tprint rqaresult\n\n\t\"\"\"Creates the Recurrence matrix for viewing\"\"\"\n\n\trpcomputation = RecurrencePlotComputation.create(settings)\n\n\trpresult = rpcomputation.run()\n\n\tImageGenerator.save_recurrence_plot(rpresult.recurrence_matrix, 'recurrence_plot.png')","def test_run_time(self):\n\n wrapper = Wrapper()\n name = 'simplemesh'\n corner_points = ((0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0))\n extrude_length = 1\n nex = 3\n ney = 3\n nez = 1\n create_quad_mesh(name, wrapper, corner_points,\n extrude_length, nex, ney, nez)\n\n wrapper.CM[CUBA.NAME] = name\n wrapper.CM_extensions[CUBAExt.GE] = (CUBAExt.INCOMPRESSIBLE,\n CUBAExt.LAMINAR_MODEL)\n wrapper.SP[CUBA.TIME_STEP] = 1\n wrapper.SP[CUBA.NUMBER_OF_TIME_STEPS] = 1\n wrapper.SP[CUBA.DENSITY] = 1.0\n wrapper.SP[CUBA.DYNAMIC_VISCOSITY] = 1.0\n wrapper.BC[CUBA.VELOCITY] = {'inflow': ('fixedValue', (0.1, 0, 0)),\n 'outflow': 'zeroGradient',\n 'walls': ('fixedValue', (0, 0, 0)),\n 'frontAndBack': 'empty'}\n wrapper.BC[CUBA.PRESSURE] = {'inflow': 'zeroGradient',\n 'outflow': ('fixedValue', 0),\n 'walls': 'zeroGradient',\n 'frontAndBack': 'empty'}\n\n mesh_inside_wrapper = wrapper.get_dataset(name)\n\n wrapper.run()\n\n # sum data pointwise\n old_vel = 0.0\n old_pres = 0.0\n for point in mesh_inside_wrapper.iter(item_type=CUBA.POINT):\n velo = point.data[CUBA.VELOCITY]\n old_vel += math.sqrt(velo[0]*velo[0] + velo[1]*velo[1] +\n velo[2]*velo[2])\n old_pres += point.data[CUBA.PRESSURE]\n\n wrapper.SP[CUBA.DENSITY] = 5.0\n\n wrapper.run()\n\n # sum data pointwise\n new_vel = 0.0\n new_pres = 0.0\n for point in mesh_inside_wrapper.iter(item_type=CUBA.POINT):\n velo = point.data[CUBA.VELOCITY]\n new_vel += math.sqrt(velo[0]*velo[0] + velo[1]*velo[1] +\n velo[2]*velo[2])\n new_pres += point.data[CUBA.PRESSURE]\n\n self.assertNotAlmostEqual(old_vel, new_vel, 5)\n self.assertNotAlmostEqual(old_pres, new_pres, 5)","def exercise1a():\n\n # Defination of muscles\n parameters = MuscleParameters()\n pylog.warning(\"Loading default muscle parameters\")\n pylog.info(parameters.showParameters())\n pylog.info(\"Use the parameters object to change the muscle parameters\")\n\n # Create muscle object\n muscle = Muscle(parameters)\n\n # Instatiate isometric muscle system\n sys = IsometricMuscleSystem()\n\n # Add the muscle to the system\n sys.add_muscle(muscle)\n \n # Set the initial condition\n x0 = [0.0, sys.muscle.L_OPT]\n # x0[0] --> muscle stimulation intial value\n # x0[1] --> muscle contracticle length initial value\n \n # Set the time for integration\n t_start = 0.0\n t_stop = 0.5\n time_step = 0.001\n time = np.arange(t_start, t_stop, time_step)\n \n # Evalute for a single muscle stimulation\n muscle_stimulation = np.arange(0,1.,0.2)\n \n # Several muscle stretch\n muscle_stretches = np.arange(0,0.3,0.01)\n \n active_active = []\n\n for stim in muscle_stimulation:\n active_forces = []\n passive_forces = []\n total = [] \n lengths = []\n for stretch in muscle_stretches:\n # Run the integration\n result = sys.integrate(x0=x0,\n time=time,\n time_step=time_step,\n stimulation=stim,\n muscle_length=stretch)\n active_forces.append(result.active_force[-1])\n passive_forces.append(result.passive_force[-1])\n total.append(result.active_force[-1]+result.passive_force[-1])\n lengths.append(result.l_ce[-1])\n active_active.append(active_forces)\n \n # Plotting\n plt.figure('Isometric muscle experiment 1')\n plt.plot(lengths, active_forces)\n plt.plot(lengths, passive_forces)\n plt.plot(lengths, total)\n plt.title('Isometric muscle experiment stimulation')\n plt.xlabel('Muscle stretch')\n plt.ylabel('Muscle force')\n plt.legend(('Active','Passive','Total'))\n plt.grid()\n plt.show()\n \n # Plotting\n plt.figure('Isometric muscle experiment 2')\n for i in range(len(muscle_stimulation)):\n plt.plot(lengths, active_active[i])\n plt.title('Isometric muscle experiment')\n plt.xlabel('Muscle stretch')\n plt.ylabel('Muscle force')\n plt.legend(muscle_stimulation)\n plt.grid()\n plt.show()\n \n # Plotting\n #plt.figure('Isotonic muscle experiment')\n #plt.plot(result.time, result.v_ce)\n #plt.title('Isotonic muscle experiment')\n #plt.xlabel('Time [s]')\n #plt.ylabel('Muscle contractilve velocity')\n #plt.grid()\n \n #muscle with longer l_opt\n muscle.L_OPT = 0.5\n muscle_stimulation = 1.\n lce = []\n totalF = []\n activeF=[]\n passiveF=[]\n for stretch in muscle_stretches:\n # Run the integration\n result = sys.integrate(x0=x0,\n time=time,\n time_step=time_step,\n stimulation=muscle_stimulation,\n muscle_length=stretch)\n activeF.append(result.active_force[-1])\n passiveF.append(result.passive_force[-1])\n lce.append(result.l_ce[-1])\n totalF.append(result.active_force[-1]+result.passive_force[-1])\n plt.figure('muscle with l_opt=0.5') \n plt.title('muscle with l_opt=0.5')\n plt.plot(lce, activeF)\n plt.plot(lce, passiveF)\n plt.plot(lce, totalF)\n plt.xlabel('Muscle Stretch')\n plt.ylabel('Force')\n plt.ylim((0,4000))\n plt.legend(('Active Force','Passive Force','Total Force'))\n\n plt.grid()\n \n \n \n #muscle with shorter l_opt\n t_start = 0.0\n t_stop = 1\n time_step = 0.005\n\n time = np.arange(t_start, t_stop, time_step)\n muscle_stretches = np.arange(0,0.3,0.01 )\n muscle.L_OPT = 0.075\n muscle_stimulation = 1.\n lce = []\n totalF = []\n activeF=[]\n passiveF=[]\n plt.figure('muscle with l_opt=0.075') \n\n for stretch in muscle_stretches:\n # Run the integration\n result = sys.integrate(x0=x0,\n time=time,\n time_step=time_step,\n stimulation=muscle_stimulation,\n muscle_length=stretch)\n activeF.append(result.active_force[-1])\n passiveF.append(result.passive_force[-1])\n lce.append(result.l_ce[-1])\n totalF.append(result.active_force[-1]+result.passive_force[-1])\n plt.title('muscle with l_opt=0.075')\n plt.plot(lce, activeF)\n plt.plot(lce, passiveF)\n plt.plot(lce, totalF)\n plt.xlabel('Muscle Stretch')\n plt.ylabel('Force')\n plt.ylim((0,4000))\n plt.legend(('Active Force','Passive Force','Total Force'))\n plt.grid()","def exercise1d():\n\n # Defination of muscles\n muscle_parameters = MuscleParameters()\n print(muscle_parameters.showParameters())\n\n mass_parameters = MassParameters()\n print(mass_parameters.showParameters())\n\n # Create muscle object\n muscle = Muscle(muscle_parameters)\n\n # Create mass object\n mass = Mass(mass_parameters)\n\n pylog.warning(\"Isotonic muscle contraction to be implemented\")\n\n # Instatiate isotonic muscle system\n sys = IsotonicMuscleSystem()\n\n # Add the muscle to the system\n sys.add_muscle(muscle)\n\n # Add the mass to the system\n sys.add_mass(mass)\n\n # You can still access the muscle inside the system by doing\n # >>> sys.muscle.L_OPT # To get the muscle optimal length\n\n # Evalute for a single load\n load = 100.\n\n # Evalute for a single muscle stimulation\n muscle_stimulation = 1.\n\n # Set the initial condition\n x0 = [0.0, sys.muscle.L_OPT,\n sys.muscle.L_OPT + sys.muscle.L_SLACK, 0.0]\n \n # x0[0] - -> activation\n # x0[1] - -> contractile length(l_ce)\n # x0[2] - -> position of the mass/load\n # x0[3] - -> velocity of the mass/load\n \n\n # Set the time for integration\n t_start = 0.0\n t_stop = 0.5\n time_step = 0.001\n time_stabilize = 0.2\n\n time = np.arange(t_start, t_stop, time_step)\n \n loads = np.arange(20, 351, 10)\n \n velocities = []\n\n for index, load in enumerate(loads):\n \n # Run the integration\n result = sys.integrate(x0=x0,\n time=time,\n time_step=time_step,\n time_stabilize=time_stabilize,\n stimulation=muscle_stimulation,\n load=load) \n\n if (result.l_mtc[-1] < sys.muscle.L_OPT + sys.muscle.L_SLACK):\n velocities.append(np.max(result.v_ce))\n print('max')\n else:\n velocities.append(np.min(result.v_ce))\n print('min')\n\n\n #Muscle contracile Velocity - Tension (load) relationship\n \n plt.figure('Isotonic muscle experiment')\n plt.title('Isotonic muscle experiment')\n plt.xlabel('Muscle Contractile Velocity [m/s]')\n plt.ylabel('Tension (load) [N]')\n plt.plot(velocities, loads)\n plt.grid()\n \n #For different stimulations 1.f\n \n muscle_stimulation = np.arange(0,1.1,0.2)\n plt.figure('Isotonic muscle exp with different stimulations')\n plt.title('Isotonic muscle experiment with different stimulations')\n\n for stim in muscle_stimulation:\n velocities = []\n for index, load in enumerate(loads):\n # Run the integration\n result = sys.integrate(x0=x0,\n time=time,\n time_step=time_step,\n time_stabilize=time_stabilize,\n stimulation=stim,\n load=load) \n\n if (result.l_mtc[-1] < sys.muscle.L_OPT + sys.muscle.L_SLACK):\n velocities.append(np.max(result.v_ce))\n else:\n velocities.append(np.min(result.v_ce))\n plt.xlabel('Muscle Contractile Velocity [m/s]')\n plt.ylabel('Tension (load) [N]')\n plt.plot(velocities, loads)\n \n plt.legend(('0','0.2','0.4','0.6','0.8','1.0'))\n plt.grid()","def test_control_loop_example():\n example.control.print_error_measurement()\n matplotlib.use('agg')\n example.control.visualise_error_measurement(fig_file='cs_compare.pdf')\n assert path.isfile('cs_compare.pdf')\n example.control.sil_comparison()","def step6_run_all(flow_dataset_npz=\"flow_dataset.npz\"):\n global objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, divisor, note_distance_basis, slider_length_base, slider_types, slider_type_rotation, slider_cos, slider_sin, slider_cos_each, slider_sin_each, slider_type_length, slider_lengths, tick_diff, note_distances, maps, labels, special_train_data, special_train_labels, early_stop, loss_ma, extvar, plot_noise\n\n objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier = read_map_predictions(\n \"temp/rhythm_data.npz\")\n\n # get divisor from GAN_PARAMS\n divisor = GAN_PARAMS[\"divisor\"]\n\n # get basis\n note_distance_basis = GAN_PARAMS[\"note_distance_basis\"]\n\n # get next_from_slider_end\n next_from_slider_end = GAN_PARAMS[\"next_from_slider_end\"]\n\n # should be slider length each tick, which is usually SV * SMP * 100 / 4\n # e.g. SV 1.6, timing section x1.00, 1/4 divisor, then slider_length_base = 40\n slider_length_base = sv / divisor\n\n # weight for each type of sliders\n slider_type_probs = [0.25, 0.25, 0.25, 0.05, 0.05, 0.03, 0.03, 0.01,\n 0.01, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.015, 0.015, 0.01]\n slider_types = np.random.choice(\n len(slider_type_probs), is_slider.shape, p=slider_type_probs).astype(int)\n\n # these data must be kept consistent with the sliderTypes in load_map.js\n slider_type_rotation = np.array([0, -0.40703540572409336, 0.40703540572409336, -0.20131710837464062, 0.20131710837464062,\n -0.46457807316944644, 0.46457807316944644, 1.5542036732051032, -\n 1.5542036732051032, 0, 0, 0.23783592745745077, -0.23783592745745077,\n 0.5191461142465229, -0.5191461142465229, -0.16514867741462683, 0.16514867741462683, 3.141592653589793])\n\n # this is vector length! I should change the variable name probably...\n slider_type_length = np.array([1.0, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97,\n 0.64, 0.64, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.96, 0.96, 0])\n\n slider_cos = np.cos(slider_type_rotation)\n slider_sin = np.sin(slider_type_rotation)\n\n slider_cos_each = slider_cos[slider_types]\n slider_sin_each = slider_sin[slider_types]\n\n slider_lengths = np.array([slider_type_length[int(\n k)] * slider_length_base[i] for i, k in enumerate(slider_types)]) * slider_ticks\n\n tick_diff = np.concatenate([[100], ticks[1:] - ticks[:-1]])\n\n if next_from_slider_end:\n tick_diff = np.concatenate(\n [[100], tick_diff[1:] - np.floor(slider_ticks * is_slider)[:-1]])\n\n # Timing section reset == tick_diff < 0\n # Use 1 as default value\n tick_diff = np.where(tick_diff < 0, 1, tick_diff)\n\n note_distances = np.clip(tick_diff, 1, divisor * 2) * \\\n (note_distance_basis / divisor)\n\n # Load the flow dataset saved in part 4\n with np.load(flow_dataset_npz) as flow_dataset:\n maps = flow_dataset[\"maps\"]\n labels = np.ones(maps.shape[0])\n\n order2 = np.argsort(np.random.random(maps.shape[0]))\n special_train_data = maps[order2]\n special_train_labels = labels[order2]\n\n early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\n\n # Start model training\n\n loss_ma = [90, 90, 90]\n extvar = {\"begin\": 10}\n\n plot_noise = np.random.random((1, GAN_PARAMS[\"g_input_size\"]))\n\n if GAN_PARAMS[\"max_epoch\"] == 0:\n osu_a = put_everything_in_the_center()\n else:\n osu_a = generate_map()\n\n data = objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, slider_types, slider_length_base\n return osu_a, data","def test_scenario(timestep_per_pi, int_method):\n\n #determine BC and IC\n x0 = 0.0 #init pos\n v0 = 1.0 #init vel\n t0 = 0.0 #start-time\n tn = 4.0*np.pi #end-time\n tau = timestep_per_pi*np.pi #timesteps\n n = (tn-t0)/tau + 1 #number of timesteps\n \n time = np.linspace(t0, tn, n) #time-array\n\n #acceleration of point particle with k=m=1\n acc1 = lambda x,v,t: -1.0*x #function must take three arguments!\n\n pos, vel, time = integrate_time(func=acc1,\n init=(x0,v0),\n timearray=time,\n method=int_method)\n\n #analytical solutions\n pos_an = np.sin(time)\n vel_an = np.cos(time)\n\n return time, pos, pos_an, vel, vel_an","def test_case():\n \"\"\"\n airspeed_altitude(80000.0, 5000.0, 30.12, 19.2,\n &altitude, &ias, &tas);\n\n printf(\"Alt: %.0f; IAS: %.1f; TAS: %.1f\", altitude, ias, tas);\n \"\"\"\n\n diff_press = 50*1e2 # 50 mbar\n print(f\"Alt: {altitude(80000, 30.12)}; IAS: {pitot_ias(5000)}; TAS: {tas(pitot_ias(5000), 19.2, altitude(80000, 30.12), 30.12)}\")","def calc_SC_module(tilt_angle_deg, IAM_b_vector, IAM_d_vector, I_direct_vector, I_diffuse_vector, Tamb_vector_C, n0, c1, c2,\n mB0_r, mB_max_r, mB_min_r, C_eff_Jperm2K, t_max, aperture_area_m2, dP1, dP2, dP3, dP4, Cp_fluid_JperkgK, Tin_C, Leq, l_ext, l_int, Nseg):\n\n # local variables\n msc_max_kgpers = mB_max_r * aperture_area_m2 / 3600 # maximum mass flow [kg/s]\n\n # Do the calculation of every time step for every possible flow condition\n # get states where highly performing values are obtained.\n specific_flows_kgpers = [np.zeros(8760), (np.zeros(8760) + mB0_r) * aperture_area_m2 / 3600,\n (np.zeros(8760) + mB_max_r) * aperture_area_m2 / 3600,\n (np.zeros(8760) + mB_min_r) * aperture_area_m2 / 3600, np.zeros(8760), np.zeros(8760)] # in kg/s\n specific_pressure_losses_Pa = [np.zeros(8760), (np.zeros(8760) + dP2) * aperture_area_m2, (np.zeros(8760) + dP3) * aperture_area_m2,\n (np.zeros(8760) + dP4) * aperture_area_m2, np.zeros(8760), np.zeros(8760)] # in Pa\n\n # generate empty lists to store results\n temperature_out = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n temperature_in = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n temperature_mean = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n supply_out_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n supply_losses_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n auxiliary_electricity_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\n supply_out_pre = np.zeros(8760)\n supply_out_total_kW = np.zeros(8760)\n mcp_kWperK = np.zeros(8760)\n\n # calculate absorbed radiation\n tilt = radians(tilt_angle_deg)\n q_rad_vector = np.vectorize(calc_q_rad)(n0, IAM_b_vector, I_direct_vector, IAM_d_vector, I_diffuse_vector,\n tilt) # absorbed solar radiation in W/m2 is a mean of the group\n for flow in range(6):\n mode_seg = 1 # mode of segmented heat loss calculation. only one mode is implemented.\n TIME0 = 0\n DELT = 1 # timestep 1 hour\n delts = DELT * 3600 # convert time step in seconds\n Tfl = np.zeros([3, 1]) # create vector to store value at previous [1] and present [2] time-steps\n DT = np.zeros([3, 1])\n Tabs = np.zeros([3, 1])\n STORED = np.zeros([600, 1])\n TflA = np.zeros([600, 1])\n TflB = np.zeros([600, 1])\n TabsB = np.zeros([600, 1])\n TabsA = np.zeros([600, 1])\n q_gain_Seg = np.zeros([101, 1]) # maximum Iseg = maximum Nseg + 1 = 101\n\n for time in range(8760):\n Mfl_kgpers = specific_flows_kgpers[flow][time] # [kg/s]\n if time < TIME0 + DELT / 2:\n # set output values to the appropriate initial values\n for Iseg in range(101, 501): # 400 points with the data\n STORED[Iseg] = Tin_C\n else:\n # write average temperature of all segments at the end of previous time-step\n # as the initial temperature of the present time-step\n for Iseg in range(1, Nseg + 1): # 400 points with the data\n STORED[100 + Iseg] = STORED[200 + Iseg] # thermal capacitance node temperature\n STORED[300 + Iseg] = STORED[400 + Iseg] # absorber node temperature\n\n # calculate stability criteria\n if Mfl_kgpers > 0:\n stability_criteria = Mfl_kgpers * Cp_fluid_JperkgK * Nseg * (DELT * 3600) / (C_eff_Jperm2K * aperture_area_m2)\n if stability_criteria <= 0.5:\n print ('ERROR: stability criteria' + str(stability_criteria) + 'is not reached. aperture_area: '\n + str(aperture_area_m2) + 'mass flow: ' + str(Mfl_kgpers))\n\n # calculate average fluid temperature and average absorber temperature at the beginning of the time-step\n Tamb_C = Tamb_vector_C[time]\n q_rad_Wperm2 = q_rad_vector[time]\n Tfl[1] = 0\n Tabs[1] = 0\n for Iseg in range(1, Nseg + 1):\n Tfl[1] = Tfl[1] + STORED[100 + Iseg] / Nseg # mean fluid temperature\n Tabs[1] = Tabs[1] + STORED[300 + Iseg] / Nseg # mean absorber temperature\n\n ## first guess for Delta T\n if Mfl_kgpers > 0:\n Tout_C = Tin_C + (q_rad_Wperm2 - (c1 + 0.5) * (Tin_C - Tamb_C)) / (Mfl_kgpers * Cp_fluid_JperkgK / aperture_area_m2)\n Tfl[2] = (Tin_C + Tout_C) / 2 # mean fluid temperature at present time-step\n else:\n Tout_C = Tamb_C + q_rad_Wperm2 / (c1 + 0.5)\n Tfl[2] = Tout_C # fluid temperature same as output\n DT[1] = Tfl[2] - Tamb_C # difference between mean absorber temperature and the ambient temperature\n\n # calculate q_gain with the guess for DT[1]\n q_gain_Wperm2 = calc_q_gain(Tfl, Tabs, q_rad_Wperm2, DT, Tin_C, Tout_C, aperture_area_m2, c1, c2, Mfl_kgpers,\n delts, Cp_fluid_JperkgK, C_eff_Jperm2K, Tamb_C)\n\n A_seg_m2 = aperture_area_m2 / Nseg # aperture area per segment\n # multi-segment calculation to avoid temperature jump at times of flow rate changes.\n for Iseg in range(1, Nseg + 1):\n # get temperatures of the previous time-step\n TflA[Iseg] = STORED[100 + Iseg]\n TabsA[Iseg] = STORED[300 + Iseg]\n if Iseg > 1:\n Tin_Seg_C = Tout_Seg_C\n else:\n Tin_Seg_C = Tin_C\n\n if Mfl_kgpers > 0 and mode_seg == 1: # same heat gain/ losses for all segments\n Tout_Seg_K = ((Mfl_kgpers * Cp_fluid_JperkgK * (Tin_Seg_C + 273.15)) / A_seg_m2 -\n (C_eff_Jperm2K * (Tin_Seg_C + 273.15)) / (2 * delts) + q_gain_Wperm2 +\n (C_eff_Jperm2K * (TflA[Iseg] + 273.15) / delts)) / (Mfl_kgpers * Cp_fluid_JperkgK / A_seg_m2 + C_eff_Jperm2K / (2 * delts))\n Tout_Seg_C = Tout_Seg_K - 273.15 # in [C]\n TflB[Iseg] = (Tin_Seg_C + Tout_Seg_C) / 2\n else: # heat losses based on each segment's inlet and outlet temperatures.\n Tfl[1] = TflA[Iseg]\n Tabs[1] = TabsA[Iseg]\n q_gain_Wperm2 = calc_q_gain(Tfl, Tabs, q_rad_Wperm2, DT, Tin_Seg_C, Tout_C, A_seg_m2, c1, c2,\n Mfl_kgpers, delts, Cp_fluid_JperkgK, C_eff_Jperm2K, Tamb_C)\n Tout_Seg_C = Tout_C\n\n if Mfl_kgpers > 0:\n TflB[Iseg] = (Tin_Seg_C + Tout_Seg_C) / 2\n Tout_Seg_C = TflA[Iseg] + (q_gain_Wperm2 * delts) / C_eff_Jperm2K\n else:\n TflB[Iseg] = Tout_Seg_C\n\n #TflB[Iseg] = Tout_Seg\n q_fluid_Wperm2 = (Tout_Seg_C - Tin_Seg_C) * Mfl_kgpers * Cp_fluid_JperkgK / A_seg_m2\n q_mtherm_Whperm2 = (TflB[Iseg] - TflA[Iseg]) * C_eff_Jperm2K / delts # total heat change rate of thermal capacitance\n q_balance_error = q_gain_Wperm2 - q_fluid_Wperm2 - q_mtherm_Whperm2\n if abs(q_balance_error) > 1:\n time = time # re-enter the iteration when energy balance not satisfied\n q_gain_Seg[Iseg] = q_gain_Wperm2 # in W/m2\n\n # resulting net energy output\n q_out_kW = (Mfl_kgpers * Cp_fluid_JperkgK * (Tout_Seg_C - Tin_C)) / 1000 #[kW]\n Tabs[2] = 0\n # storage of the mean temperature\n for Iseg in range(1, Nseg + 1):\n STORED[200 + Iseg] = TflB[Iseg]\n STORED[400 + Iseg] = TabsB[Iseg]\n Tabs[2] = Tabs[2] + TabsB[Iseg] / Nseg\n\n # outputs\n temperature_out[flow][time] = Tout_Seg_C\n temperature_in[flow][time] = Tin_C\n supply_out_kW[flow][time] = q_out_kW\n temperature_mean[flow][time] = (Tin_C + Tout_Seg_C) / 2 # Mean absorber temperature at present\n\n # q_gain = 0\n # TavgB = 0\n # TavgA = 0\n # for Iseg in range(1, Nseg + 1):\n # q_gain = q_gain + q_gain_Seg[Iseg] * A_seg_m2 # [W]\n # TavgA = TavgA + TflA[Iseg] / Nseg\n # TavgB = TavgB + TflB[Iseg] / Nseg\n #\n # # OUT[9] = q_gain/Area_a # in W/m2\n # q_mtherm = (TavgB - TavgA) * C_eff * aperture_area / delts\n # q_balance_error = q_gain - q_mtherm - q_out\n\n # OUT[11] = q_mtherm\n # OUT[12] = q_balance_error\n if flow < 4:\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\n Leq, l_int, aperture_area_m2) # in kW\n if flow == 3:\n q1 = supply_out_kW[0]\n q2 = supply_out_kW[1]\n q3 = supply_out_kW[2]\n q4 = supply_out_kW[3]\n E1 = auxiliary_electricity_kW[0]\n E2 = auxiliary_electricity_kW[1]\n E3 = auxiliary_electricity_kW[2]\n E4 = auxiliary_electricity_kW[3]\n # calculate optimal mass flow and the corresponding pressure loss\n specific_flows_kgpers[4], specific_pressure_losses_Pa[4] = calc_optimal_mass_flow(q1, q2, q3, q4, E1, E2, E3, E4, 0,\n mB0_r, mB_max_r, mB_min_r, 0,\n dP2, dP3, dP4, aperture_area_m2)\n if flow == 4:\n # calculate pumping electricity when operatres at optimal mass flow\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\n Leq, l_int, aperture_area_m2) # in kW\n dp5 = specific_pressure_losses_Pa[flow]\n q5 = supply_out_kW[flow]\n m5 = specific_flows_kgpers[flow]\n # set points to zero when load is negative\n specific_flows_kgpers[5], specific_pressure_losses_Pa[5] = calc_optimal_mass_flow_2(m5, q5, dp5)\n\n if flow == 5: # optimal mass flow\n supply_losses_kW[flow] = np.vectorize(calc_qloss_network)(specific_flows_kgpers[flow], l_ext, aperture_area_m2,\n temperature_mean[flow], Tamb_vector_C, msc_max_kgpers)\n supply_out_pre = supply_out_kW[flow].copy() + supply_losses_kW[flow].copy()\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\n Leq, l_int, aperture_area_m2) # in kW\n supply_out_total_kW = supply_out_kW[flow].copy() + 0.5 * auxiliary_electricity_kW[flow].copy() - supply_losses_kW[flow].copy() # eq.(58) _[J. Fonseca et al., 2016]\n mcp_kWperK = specific_flows_kgpers[flow] * (Cp_fluid_JperkgK / 1000) # mcp in kW/K\n\n result = [supply_losses_kW[5], supply_out_total_kW, auxiliary_electricity_kW[5], temperature_out[5], temperature_in[5], mcp_kWperK]\n q_rad_per_panel = q_rad_vector*aperture_area_m2\n return result","def main():\n\t#Necessary Parameters for Simulation\n\tAmplitudes = ['230','260','290']\n\tConditions = ['No EES','EES','EES+A08','EES+A08+ProIncrease']\n\n\n\n\t#eesAmplitude = \"230\"\n\teesAmplitudeName = \"230\"\n\tdelay = \"2\"\n\ttoAddname = \"\"\n\tspecies = \"rat\"\n\t#Paramters initialization\n\ttotSimTime = rp.get_tot_sim_time()\n\tgaitCyclesFileName = rp.get_gait_cycles_file()\n\tmuscles = rp.get_muscles()\n\ttemplateFile = \"templateFrwSimRORaReal.txt\"\n\tw1 = 0.011\n\tw2 = -0.005\n\n\ttemplateFile = \"A08.txt\"\n\n\ttls.modify_network_structure(templateFile,templateFile,delay,[w1,w2])\n\n\teesFrequencies = range(0,41,40)\n\tnProc = 4\n\tseed = \"1\"\n\n\tnSim = len(eesFrequencies)\n\tcount=0.\n\tpercLastPrint=0.\n\tprintPeriod = 0.05\n\n\t# run simulations\n\tfor j,eesAmplitude in enumerate(Amplitudes):\n\t\tfor i,eesFrequency in enumerate(eesFrequencies):\n\t\t\tfor condition in Conditions:\n\t\t\t\t#name = \"Tonic_FFS_\"+inputFileName+\"_freq_\"+str(eesFrequency)\n\t\t\t\tinputFileName = condition\n\t\t\t\tinputFile = \"generatedStructures/\"+inputFileName+\".txt\"\n\t\t\t\tname = \"Tonic_FFS_\"+condition+\"_freq_\"+str(eesFrequency)\n\t\t\t\tresultFile = gt.find(\"*\"+name+\".p\",pathToResults)\n\t\t\t\tif not resultFile:\n\t\t\t\t\tprogram = ['python','./scripts/runForSimMuscleSpindles_RORa.py',\\\n\t\t\t\t\t\tstr(eesFrequency),eesAmplitude,inputFile,name,\"--simTime\",str(totSimTime),\"--seed\",seed,\"--noPlot\"]\n\n\t\t\t\tif not resultFile: gt.run_subprocess(program)\n\n\t\t\t\tcount+=1\n\t\t\t\tif count/nSim-percLastPrint>=printPeriod:\n\t\t\t\t\tpercLastPrint=count/nSim\n\t\t\t\t\tprint str(round(count/nSim*100))+\"% of simulations performed...\"\n\n\n\n\t\"\"\" create plots \"\"\"\n\terrParams = dict(lw=0.5, capsize=1, capthick=0.5)\n\twith open(gaitCyclesFileName, 'r') as pickle_file:\n\t\theelStrikes = pickle.load(pickle_file)\n\t\tfootOffs = pickle.load(pickle_file)\n\n\n\t# Figure 5 plot all gait cycles- afferent and efferents\n\t#if not phasicStim:\n\tfigName = time.strftime(\"/%Y_%m_%d_Tonic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_firingRates.pdf\")\n\t#else: figName = time.strftime(\"/%Y_%m_%d_Phasic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_firingRates.pdf\")\n\tfig, ax = plt.subplots(2, 4,figsize=(16,9))\n\tcmap = plt.get_cmap('winter')\n\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\n\n\tfor i,eesFrequency in enumerate(eesFrequencies):\n\t\t#if not phasicStim:\n\t\tname = \"FS_EES_230uA_\"+str(eesFrequency)+\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\"+str(eesFrequency)\n\t\t#name = \"Tonic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_freq_\"+str(eesFrequency)\n\t\t#else: name = \"Phasic_\"+emgVsKinMod+\"_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_freq_\"+str(eesFrequency)\n\t\tif species == \"human\":name += hp.get_dataset()\n\n\t\t# get data\n\t\tprint name\n\t\tresultFile = gt.find(\"*\"+name+\".p\",pathToResults)\n\t\tprint resultFile\n\t\tif len(resultFile)>1: print \"Warning: multiple result files found!!!\"\n\t\twith open(resultFile[0], 'r') as pickle_file:\n\t\t\testimatedEmg = pickle.load(pickle_file)\n\t\t\tmeanFr = pickle.load(pickle_file)\n\n\t\t# get gait cycles\n\t\tif not 'heelStrikeSamples' in locals():\n\t\t\tnSamples = len(meanFr[muscles[0]][\"Mn\"])\n\t\t\tdtMeanFr = float(totSimTime)/nSamples\n\t\t\theelStrikeSamples = [int(x) for x in heelStrikes*1000./dtMeanFr]\n\t\t\tfootOffSamples = [int(x) for x in footOffs*1000./dtMeanFr]\n\t\t\tsamples = range(nSamples)\n\t\t\tstance = np.zeros(nSamples).astype(bool)\n\t\t\tfor strike,off in zip(heelStrikeSamples,footOffSamples):\n\t\t\t\tif strike>nSamples: break\n\t\t\t\tstance[strike:off]=True\n\n\t\tfor j,muscle in enumerate(muscles):\n\t\t\tax[j,0].plot(meanFr[muscle]['Iaf'],color=colors[i])\n\t\t\tax[j,0].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,1].plot(meanFr[muscle]['IaInt'],color=colors[i])\n\t\t\tax[j,1].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,2].plot(meanFr[muscle]['Mn'],color=colors[i])\n\t\t\tax[j,2].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,3].plot(estimatedEmg[muscle]['Mn'],color=colors[i])\n\t\t\tax[j,3].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\n\n\n\tfor j,muscle in enumerate(muscles):\n\t\tax[j,0].set_ylim([0,200])\n\t\tax[j,0].set_title(\"Ia fibers firing rate - \"+muscle)\n\t\tax[j,0].set_xlabel(\"Time (ms)\")\n\t\tax[j,0].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,1].set_ylim([0,200])\n\t\tax[j,1].set_title(\"IaInt firing rate - \"+muscle)\n\t\tax[j,1].set_xlabel(\"Time (ms)\")\n\t\tax[j,1].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,2].set_ylim([0,200])\n\t\tax[j,2].set_title(\"Mn firing rate - \"+muscle)\n\t\tax[j,2].set_xlabel(\"Time (ms)\")\n\t\tax[j,2].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,3].set_ylim([0,200])\n\t\tax[j,3].set_title(\"EMG - \"+muscle)\n\t\tax[j,3].set_xlabel(\"Time (ms)\")\n\t\tax[j,3].set_ylabel(\"Emg amplitude (a.u.)\")\n\tplt.savefig(pathToResults+figName, format=\"pdf\",transparent=True)\n\n\n# FIgure 5 plot 2 single gait cycles- afferent and efferents + mn phasicity score\n\tif species == \"rat\":\n\t\tstartGaitCycleN = 3\n\t\tnCycles = 1\n\telif species == \"human\":\n\t\tstartGaitCycleN = 3\n\t\tnCycles = 1\n\n\tfigName = time.strftime(\"/%Y_%m_%d_Tonic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_single_firingRates.pdf\")\n\t#else: figName = time.strftime(\"/%Y_%m_%d_Phasic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_single_firingRates.pdf\")\n\tfig, ax = plt.subplots(2, 6,figsize=(16,9))\n\tcmap = plt.get_cmap('winter')\n\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\n\tbar_width = 5\n\n\tfor i,eesFrequency in enumerate(eesFrequencies):\n\t\t#if not phasicStim:\n\n\t\tname = \"FS_EES_230uA_\"+str(eesFrequency)+\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\"+str(eesFrequency)\t\t#else: name = \"Phasic_\"+emgVsKinMod+\"_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_freq_\"+str(eesFrequency)\n\t\tif species == \"human\":name += hp.get_dataset()\n\n\t\t# get data\n\t\tresultFile = gt.find(\"*\"+name+\".p\",pathToResults)\n\t\tif len(resultFile)>1: print \"Warning: multiple result files found!!!\"\n\t\twith open(resultFile[0], 'r') as pickle_file:\n\t\t\testimatedEmg = pickle.load(pickle_file)\n\t\t\tmeanFr = pickle.load(pickle_file)\n\n\t\t# compute stats\n\t\tiaIntModDepth = {}\n\t\tactiveMnFr={}\n\t\tfor muscle in muscles:\n\t\t\tiaIntModDepth[muscle]=[]\n\t\t\tactiveMnFr[muscle]=[]\n\t\tfor j in xrange(len(heelStrikeSamples)-1):\n\t\t\tif heelStrikeSamples[j+1]>nSamples-50: break\n\t\t\tif heelStrikeSamples[j]<50:continue # to skip artefacts\n\t\t\tfor muscle in muscles:\n\t\t\t\tiaIntModDepth[muscle].append(\\\n\t\t\t\t\tmeanFr[muscle]['IaInt'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].max()-meanFr[muscle]['IaInt'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].min())\n\t\t\t\tmnActivityDuringCycle = meanFr[muscle]['Mn'][heelStrikeSamples[j]:heelStrikeSamples[j+1]]\n\t\t\t\tactiveMnFr[muscle].append(\\\n\t\t\t\t\tmnActivityDuringCycle[mnActivityDuringCycle>=0.8*mnActivityDuringCycle.max()].mean())\n\t\t\t\t\t# mnActivityDuringCycle[mnActivityDuringCycle>=1.5*mnActivityDuringCycle.std()].mean())\n\t\t\t\t\t# mnActivityDuringCycle[mnActivityDuringCycle>=np.percentile(mnActivityDuringCycle,90)].mean())\n\t\tiaIntModDepthStats = {}\n\t\tactiveMnFrStats = {}\n\t\tfor muscle in muscles:\n\t\t\tiaIntModDepthStats[muscle] = {\"mean\":np.mean(iaIntModDepth[muscle]),\n\t\t\t\t\"sem\":np.std(iaIntModDepth[muscle])/(np.sqrt(len(iaIntModDepth[muscle])-1))}\n\t\t\tactiveMnFrStats[muscle] = {\"mean\":np.mean(activeMnFr[muscle]),\n\t\t\t\t\"sem\":np.std(activeMnFr[muscle])/(np.sqrt(len(activeMnFr[muscle])-1))}\n\n\t\t# get gait cycles to plot\n\t\tif not 'startPlot' in locals():\n\t\t\tstartPlot = heelStrikeSamples[startGaitCycleN-1]\n\t\t\tstopPlot = heelStrikeSamples[startGaitCycleN+nCycles-1]\n\t\t\tif stopPlot>nSamples: stopPlot=nSamples\n\t\t\treducedSamples = range(stopPlot-startPlot)\n\t\t\treducedStance = stance[startPlot:stopPlot]\n\n\t\tfor j,muscle in enumerate(muscles):\n\t\t\tax[j,0].plot(meanFr[muscle]['Iaf'][startPlot:stopPlot],color=colors[i])\n\t\t\tax[j,0].fill_between(reducedSamples, 0, 200, where=reducedStance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,1].plot(meanFr[muscle]['IaInt'][startPlot:stopPlot],color=colors[i])\n\t\t\tax[j,1].fill_between(reducedSamples, 0, 250, where=reducedStance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,2].bar(eesFrequency,iaIntModDepthStats[muscle][\"mean\"],bar_width,yerr=iaIntModDepthStats[muscle][\"sem\"],\\\n\t\t\t\tcolor=colors[i],error_kw=errParams)\n\t\t\txValsScatter = np.linspace(0,bar_width*0.9,len(iaIntModDepth[muscle]))+eesFrequency-bar_width*0.45\n\t\t\tax[j,2].scatter(xValsScatter,iaIntModDepth[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\"#dddde3\", s=7, zorder=3, alpha=0.7)\n\n\t\t\tax[j,3].plot(meanFr[muscle]['Mn'][startPlot:stopPlot],color=colors[i])\n\t\t\tax[j,3].fill_between(reducedSamples, 0, 40, where=reducedStance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,4].bar(eesFrequency,activeMnFrStats[muscle][\"mean\"],bar_width,yerr=activeMnFrStats[muscle][\"sem\"],\\\n\t\t\t\tcolor=colors[i],error_kw=errParams)\n\t\t\tax[j,4].scatter(xValsScatter,activeMnFr[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\"#dddde3\", s=7, zorder=3, alpha=0.7)\n\t\t\tax[j,5].plot(estimatedEmg[muscle]['Mn'][startPlot:stopPlot],color=colors[i])\n\t\t\tax[j,5].fill_between(reducedSamples, -50, 50, where=reducedStance, facecolor='#b0abab', alpha=0.25)\n\n\tfor j,muscle in enumerate(muscles):\n\t\tax[j,0].set_ylim([0,200])\n\t\tax[j,0].set_title(\"Ia fibers firing rate - \"+muscle)\n\t\tax[j,0].set_xlabel(\"Time (ms)\")\n\t\tax[j,0].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,1].set_ylim([0,250])\n\t\tax[j,1].set_title(\"IaInt firing rate - \"+muscle)\n\t\tax[j,1].set_xlabel(\"Time (ms)\")\n\t\tax[j,1].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,2].set_ylim([0,250])\n\t\tax[j,2].set_title(\"Mean IaInr Fr while active\")\n\t\tax[j,2].set_xlabel(\"Stimulation amplitude (uA)\")\n\t\tax[j,2].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,3].set_ylim([0,40])\n\t\tax[j,3].set_title(\"Mn firing rate - \"+muscle)\n\t\tax[j,3].set_xlabel(\"Time (ms)\")\n\t\tax[j,3].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,4].set_ylim([0,40])\n\t\tax[j,4].set_title(\"Mean Mn Fr while active\")\n\t\tax[j,4].set_xlabel(\"Stimulation amplitude (uA)\")\n\t\tax[j,4].set_ylabel(\"Firing rate (Imp/s)\")\n\t\tax[j,5].set_ylim([-50,50])\n\t\tax[j,5].set_title(\"EMG - \"+muscle)\n\t\tax[j,5].set_xlabel(\"Time (ms)\")\n\t\tax[j,5].set_ylabel(\"Emg amplitude (a.u.)\")\n\tplt.savefig(pathToResults+figName, format=\"pdf\",transparent=True)\n\n\n\n\n\n\t# FIgure 2-7 plot\n\tif species == \"rat\":\n\t\tstartGaitCycleN = 3\n\t\tnCycles = 1\n\telif species == \"human\":\n\t\tstartGaitCycleN = 3\n\t\tnCycles = 1\n\n\t#if not phasicStim:\n\tfigName = time.strftime(\"/%Y_%m_%d_Tonic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_afferentStats.pdf\")\n\t#else: figName = time.strftime(\"/%Y_%m_%d_Phasic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_afferentStats.pdf\")\n\tfig, ax = plt.subplots(2, 4,figsize=(16,9))\n\tcmap = plt.get_cmap('winter')\n\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\n\tbar_width = 5\n\n\tmeanPerEraserApIaf = []\n\toffsetMeanFr = 0\n\toffsetMeanModDepth = 0\n\n\tfor i,eesFrequency in enumerate(eesFrequencies):\n\t\t#if not phasicStim:\n\t\tname = \"FS_EES_230uA_\"+str(eesFrequency)+\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\"+str(eesFrequency)\n\t\t#name = \"Tonic_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_freq_\"+str(eesFrequency)\n\t\t#else: name = \"Phasic_\"+emgVsKinMod+\"_FFS_species_\"+toAddname+inputFileName+species+\"_muscles_\"+\"\".join(muscles)+\"_delay_\"+str(delay)+\"_amp_\"+str(eesAmplitudeName)+\"_freq_\"+str(eesFrequency)\n\t\tif species == \"human\":name += hp.get_dataset()\n\n\t\tresultFile = gt.find(\"*\"+name+\".p\",pathToResults)\n\t\tif len(resultFile)>1: print \"Warning: multiple result files found!!!\"\n\t\twith open(resultFile[0], 'r') as pickle_file:\n\t\t\testimatedEmg = pickle.load(pickle_file)\n\t\t\tmeanFr = pickle.load(pickle_file)\n\t\t\tmeanPerEraserApIaf.append(pickle.load(pickle_file))\n\n\t\t# compute stats\n\t\tiaModDepth = {}\n\t\tiaMeanFr={}\n\t\tfor muscle in muscles:\n\t\t\tiaModDepth[muscle]=[]\n\t\t\tiaMeanFr[muscle]=[]\n\t\tfor j in xrange(len(heelStrikeSamples)-1):\n\t\t\tif heelStrikeSamples[j+1]>nSamples-50: break\n\t\t\tif heelStrikeSamples[j]<50:continue # to skip artefacts\n\t\t\tfor muscle in muscles:\n\t\t\t\tiaModDepth[muscle].append(\\\n\t\t\t\t\tmeanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].max()-meanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].min())\n\t\t\t\tiaMeanFr[muscle].append(\\\n\t\t\t\t\tmeanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].mean())\n\t\tiaModDepthStats = {}\n\t\tiaMeanFrStats = {}\n\t\tfor muscle in muscles:\n\t\t\tiaModDepthStats[muscle] = {\"mean\":np.mean(iaModDepth[muscle]),\n\t\t\t\t\"sem\":np.std(iaModDepth[muscle])/(np.sqrt(len(iaModDepth[muscle])-1))}\n\t\t\tiaMeanFrStats[muscle] = {\"mean\":np.mean(iaMeanFr[muscle]),\n\t\t\t\t\"sem\":np.std(iaMeanFr[muscle])/(np.sqrt(len(iaMeanFr[muscle])-1))}\n\n\t\t# get gait cycles to plot\n\t\tif not 'startPlot' in locals():\n\t\t\tstartPlot = heelStrikeSamples[startGaitCycleN-1]\n\t\t\tstopPlot = heelStrikeSamples[startGaitCycleN+nCycles-1]\n\t\t\tif stopPlot>nSamples: stopPlot=nSamples\n\t\t\treducedSamples = range(stopPlot-startPlot)\n\t\t\treducedStance = stance[startPlot:stopPlot]\n\n\t\tfor j,muscle in enumerate(muscles):\n\n\t\t\tax[j,0].plot(meanFr[muscle]['Iaf'][startPlot:stopPlot],color=colors[i])\n\t\t\tax[j,0].fill_between(reducedSamples, 0, 125, where=reducedStance, facecolor='#b0abab', alpha=0.25)\n\t\t\tax[j,1].bar(eesFrequency,iaMeanFrStats[muscle][\"mean\"],bar_width,yerr=iaMeanFrStats[muscle][\"sem\"],\\\n\t\t\t\tcolor=colors[i],error_kw=errParams)\n\t\t\txValsScatter = np.linspace(0,bar_width*0.9,len(iaMeanFr[muscle]))+eesFrequency-bar_width*0.45\n\t\t\tax[j,1].scatter(xValsScatter,iaMeanFr[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\"#dddde3\", s=7, zorder=3, alpha=0.7)\n\n\t\t\tax[j,2].bar(eesFrequency,iaModDepthStats[muscle][\"mean\"],bar_width,yerr=iaModDepthStats[muscle][\"sem\"],\\\n\t\t\t\tcolor=colors[i],error_kw=errParams)\n\t\t\tax[j,2].scatter(xValsScatter,iaModDepth[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\"#dddde3\", s=7, zorder=3, alpha=0.7)\n\t\t\tax[j,3].bar(eesFrequency,meanPerEraserApIaf[-1],5,color=colors[i])\n\n\t\t\tax[j,0].set_ylim([0,125])\n\t\t\tax[j,0].set_title(\"Ia fibers firing rate - \"+muscle)\n\t\t\tax[j,0].set_xlabel(\"Time (ms)\")\n\t\t\tax[j,0].set_ylabel(\"Firing rate (Imp/s)\")\n\t\t\tax[j,1].set_ylim([0,125])\n\t\t\tax[j,1].set_title(\"Mean Ia firing rate \")\n\t\t\tax[j,1].set_xlabel(\"Stimulation amplitude (uA)\")\n\t\t\tax[j,1].set_ylabel(\"(imp/s)\")\n\t\t\tax[j,2].set_ylim([0,80])\n\t\t\tax[j,2].set_title(\"modulation depth\")\n\t\t\tax[j,2].set_xlabel(\"Stimulation amplitude (uA)\")\n\t\t\tax[j,2].set_ylabel(\"(imp/s)\")\n\t\t\tax[j,3].set_ylim([0,100])\n\t\t\tax[j,3].set_title(\"Percentage erased APs\")\n\t\t\tax[j,3].set_xlabel(\"Stimulation frequency (Hz)\")\n\t\t\tax[j,3].set_ylabel(\"Percentage\")\n\tplt.savefig(pathToResults+figName, format=\"pdf\",transparent=True)","def test_simulationRun(self):\n self.opt = { 'temperature' : 300.0, 'friction' : 1, 'dt' : 0.00002,\n 'nIter' : 2, 'nstepsNC' : 2, 'nstepsMD' : 1, 'nprop' : 1,\n 'nonbondedMethod' : 'NoCutoff', 'constraints': 'HBonds',\n 'trajectory_interval' : 1, 'reporter_interval' : 1,\n 'outfname' : 'mc-test',\n 'platform' : None,\n 'constraints' : 'HBonds',\n 'mc_per_iter' : 2 }\n\n structure = self.full_struct\n class SetRotationMove(RandomLigandRotationMove):\n def __init__(self, structure, resname='LIG'):\n super(SetRotationMove, self).__init__(structure, resname)\n\n def move(self, context):\n \"\"\"Function that performs a random rotation about the\n center of mass of the ligand.\n \"\"\"\n #TODO: check if we need to deepcopy\n positions = context.getState(getPositions=True).getPositions(asNumpy=True)\n\n self.positions = positions[self.atom_indices]\n self.center_of_mass = self.getCenterOfMass(self.positions, self.masses)\n reduced_pos = self.positions - self.center_of_mass\n\n # Define random rotational move on the ligand\n #set rotation so that test is reproducible\n set_rotation_matrix = np.array([[-0.62297988, -0.17349253, 0.7627558 ],\n [ 0.55082352, -0.78964857, 0.27027502],\n [ 0.55541834, 0.58851973, 0.58749893]])\n\n\n #set_rotation_matrix = np.array([[1, 0, 0],\n # [0, 1, 0],\n # [0, 0, 1]])\n\n #multiply lig coordinates by rot matrix and add back COM translation from origin\n rot_move = np.dot(reduced_pos, set_rotation_matrix) * positions.unit + self.center_of_mass\n\n # Update ligand positions in nc_sim\n for index, atomidx in enumerate(self.atom_indices):\n positions[atomidx] = rot_move[index]\n context.setPositions(positions)\n positions = context.getState(getPositions=True).getPositions(asNumpy=True)\n self.positions = positions[self.atom_indices]\n return context\n\n\n self.model = SetRotationMove(structure, resname='ALA')\n #self.model = RandomLigandRotationMove(structure, resname='ALA')\n\n self.model.atom_indices = range(22)\n self.model.topology = structure[self.model.atom_indices].topology\n self.model.positions = structure[self.model.atom_indices].positions\n self.model.calculateProperties()\n\n self.mover = MoveEngine(self.model)\n #Initialize the SimulationFactory object\n sims = SimulationFactory(structure, self.mover, **self.opt)\n #print(sims)\n system = sims.generateSystem(structure, **self.opt)\n simdict = sims.createSimulationSet()\n alch_system = sims.generateAlchSystem(system, self.model.atom_indices)\n self.nc_sim = sims.generateSimFromStruct(structure, self.mover, alch_system, ncmc=True, **self.opt)\n self.model.calculateProperties()\n self.initial_positions = self.nc_sim.context.getState(getPositions=True).getPositions(asNumpy=True)\n mc_sim = Simulation(sims, self.mover, **self.opt)\n #monkeypatch to access acceptance value\n def nacceptRejectMC(self, temperature=300, **opt):\n \"\"\"Function that chooses to accept or reject the proposed move.\n \"\"\"\n md_state0 = self.current_state['md']['state0']\n md_state1 = self.current_state['md']['state1']\n log_mc = (md_state1['potential_energy'] - md_state0['potential_energy']) * (-1.0/self.nc_sim.context._integrator.kT)\n randnum = math.log(np.random.random())\n\n if log_mc > randnum:\n self.accept += 1\n print('MC MOVE ACCEPTED: log_mc {} > randnum {}'.format(log_mc, randnum) )\n self.md_sim.context.setPositions(md_state1['positions'])\n else:\n self.reject += 1\n print('MC MOVE REJECTED: log_mc {} < {}'.format(log_mc, randnum) )\n self.md_sim.context.setPositions(md_state0['positions'])\n self.log_mc = log_mc\n self.md_sim.context.setVelocitiesToTemperature(self.opt['temperature'])\n mc_sim.acceptRejectMC = nacceptRejectMC\n nacceptRejectMC.__get__(mc_sim)\n mc_sim.acceptRejectMC = types.MethodType(nacceptRejectMC, mc_sim)\n mc_sim.runMC(self.opt['nIter'])\n #get log acceptance\n print(mc_sim.log_mc)\n #if mc is working, should be around -24.1\n assert mc_sim.log_mc <= -23.8 and mc_sim.log_mc >= -24.3","def run_analysis(self):\n ### skip some snapshots for testing purposes\n nskip = 199\n read_char.skip_snapshots(self.hfile, self.ifile, nskip)\n ### read in the first two steps (required for velocity related computations\n xs_old, ys_old, lx_old, ly_old, tstep_old, natoms_old = read_char.read_snapshot(self.hfile, self.ifile)\n x_old = xs_old*lx_old\n y_old = ys_old*ly_old\n xs,ys,lx,ly,tstep,natoms = read_char.read_snapshot(self.hfile, self.ifile)\n x = xs*lx\n y = ys*ly\n ### loop over all steps of the input file\n for step in range(nskip+1,self.nsteps-1):\n print step\n ### read in coordinates (as required)\n xs_new,ys_new,lx_new,ly_new,tstep_new,natoms_new = read_char.read_snapshot(self.hfile, self.ifile)\n x_new = xs_new*lx_new\n y_new = ys_new*ly_new\n ### compute further current per/atom quantities\n phi = misc_tools.compute_orientation(x,y,lx,ly,self.npol)\n vx,vy = misc_tools.compute_velocity(x_old,y_old, x_new, y_new, lx, ly, tstep_old, tstep_new, natoms)\n ### start desired analysis methods\n # density\n if self.density_flag:\n self.density.compute(step,x,y,lx,ly,natoms, plot = 'False')\n # number fluctuations\n if self.nf_flag:\n self.numberfluctuation.compute(step,xs,ys, plot = 'False')\n # voronoi density\n if self.voronoi_flag:\n self.voronoidensity.compute(step,x,y,lx,ly,natoms, plot = 'False')\n # velocity / worticity\n if self.velocity_flag:\n self.velocityworticity.compute(step,x,y,vx,vy,natoms,lx,ly, plot = 'False')\n # orientation / velocity\n if self.orientvel_flag:\n self.orientvel.compute(step,x,y,vx,vy,phi,natoms, plot = 'False')\n # defect points\n if self.pointdefects_flag:\n self.pointdefects.compute(step,x,y,phi,lx,ly,natoms)\n ### move coordinate arrays\n xs_old = np.copy(xs)\n ys_old = np.copy(ys)\n x_old = np.copy(x)\n y_old = np.copy(y)\n tstep_old = tstep\n xs = np.copy(xs_new)\n ys = np.copy(ys_new)\n x = np.copy(x_new)\n y = np.copy(y_new)\n tstep = tstep_new\n return","def main(adate,aprefix):\n script_start_time = datetime.now() #Script Timer\n\n if len(adate)!=8:\n raise ValueError('Please enter a date in the format yyyymmdd') \n\n # Specify Date to Process\n year = int(adate[0:4])\n month = int(adate[4:6])\n day = int(adate[6:8])\n start_date = datetime(year,month,day)\n\n # Loop through 24 hours\n dsout = False\n for jj in range(0,24):\n wrf_file = make_wrf_file(start_date + pd.to_timedelta(jj,unit='h'), forecast_offset)\n print('Processing: ' + wrf_file)\n ds = False\n try:\n ds = xr.open_dataset(directory + wrf_file, engine='pynio')\n except:\n print('Could not open ' + wrf_file)\n if(isinstance(ds,xr.Dataset)):\n ds = clean_dataset(ds)\n if(isinstance(dsout,xr.Dataset)):\n dsout = xr.concat([dsout, ds],dim='time')\n else:\n dsout = ds\n\n if(isinstance(dsout,xr.Dataset)):\n # Add attributes\n dsout['eastward_wind'].attrs['standard_name'] = 'eastward_wind'\n dsout['eastward_wind'].attrs['comment'] = 'The zonal wind speed (m/s) indicates the u (positive eastward) component of where the wind is going.'\n dsout['northward_wind'].attrs['standard_name'] = 'northward_wind'\n dsout['northward_wind'].attrs['comment'] = 'The meridional wind speed (m/s) indicates the v (positive northward) component of where the wind is going.'\n \n # Add Wind Speed\n wind_speed = np.sqrt(dsout['eastward_wind']**2 + dsout['northward_wind']**2)\n wind_speed.attrs['units'] = 'm s-1'\n wind_speed.attrs['comment'] = 'Wind Speed is calculated from the Zonal and Meridional wind speeds.'\n wind_speed.attrs['long_name'] = 'Wind Speed'\n wind_speed.attrs['standard_name'] = 'wind_speed'\n dsout['wind_speed'] = wind_speed\n \n # Add Wind Direction\n wind_dir = 270 - xr.ufuncs.arctan2(dsout['northward_wind'],dsout['eastward_wind'])*180/np.pi\n #wind_dir = (wind_dir.where(wind_dir<0)+360).combine_first(wind_dir) #Flip negative degrees - Doesn't seem to work\n wind_dir = wind_dir % 360 #Use modulo to keep degrees between 0-360\n wind_dir.attrs['units'] = 'degree'\n wind_dir.attrs['comment'] = 'The direction from which winds are coming from, in degrees clockwise from true N.'\n wind_dir.attrs['long_name'] = 'Wind Direction'\n wind_dir.attrs['standard_name'] = 'wind_from_direction'\n dsout['wind_from_direction'] = wind_dir\n\n # Add global metadata\n dsout.attrs['title'] = \"Rutgers WRF 3km model output\"\n dsout.attrs['forecast_offset'] = forecast_offset\n dsout.attrs['source_directory'] = directory\n dsout.attrs['date_created'] = str(datetime.today())\n dsout.attrs['elapsed_time'] = str(datetime.now() - script_start_time)\n dsout.attrs['creator_name'] = \"Sage Lichtenwalner\"\n dsout.attrs['creator_email'] = \"sage@marine.rutgers.edu\"\n dsout.attrs['creator_url'] = \"https://rucool.marine.rutgers.edu\"\n dsout.attrs['institution'] = \"Rutgers University Center for Ocean Observing Leadership (RU COOL)\"\n dsout.attrs['summary'] = \"Wind data extracted from the RU-WRF model. The model is run daily at 00Z with forecast files saved every hour. Times in this file are UTC based on the forecast time. The forecast_offset specifies how many hours of model spin up are allowed before the data is included in this virtual time-series archive for a given day. For example, a value of 6 means the first 6 hours of data for a day are actually extracted from the previous day's model run.\"\n dsout.attrs['project'] = \"RU COOL BPU Wind Energy Project\"\n dsout.attrs['Conventions'] = 'CF-1.6'\n \n # Setup xarray output encoding\n encoding = make_encoding(dsout)\n \n # Output final datafile\n output_datafile = '%s_%d%02d%02d.nc' % (aprefix, start_date.year, start_date.month, start_date.day)\n dsout.to_netcdf(output_datafile, encoding=encoding) \n print('Outputted ' + output_datafile)\n else:\n print('No data found, skipping.')","def main():\n\t# \"\"\"\n\t# \tMain function of test python module\n\t# \"\"\"\n\t# random.seed(os.urandom(345634)) # initialize random generator\n\t# t = np.linspace(0.0, 24.0, 96.0) # define the time axis of a day, here we use 96 values every quarter of an hour\n\t# # standard load profile -- input\n\t# q = extra.read_slp(t,\n\t# 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv') # read the sample standard load profile, can be any length, can be resized given a low/high resolution time axis\n\t# q = q / np.sum(q) # normalization of standard load profile\n\t# # process duration\n\t# duration_axis = np.linspace(0.0, 24.0, 96.0)\n\t# (p_d, E_p) = extra.app_time(duration_axis, 10, 2, 0.0,\n\t# 24.0) # function that define the pdf of duration of a process\n\t# # process consumption\n\t# consumption_axis = np.linspace(0.0, 3.5, 96.0)\n\t# (p_k, E_k) = extra.app_consumption(consumption_axis, 10, 2, 0.0,\n\t# 3.5) # function that define the pdf of duration of a process\n\t# # pdf of starting time\n\t# p_t_0 = lpd.infer_t_0(q, p_d, E_k) # computes the pdf of starting time of processes\n\t# p_t_0 = p_t_0 / np.sum(p_t_0) # normalization of the pdf to sum up to zero\n #\n\t# \"\"\"\n\t# 1st Approach, starting time of processes is a discrete propapibility density function\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# D = 2000\n\t# synthetic_profile = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# synthetic_profile_1 = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# # expected value of D processes\n\t# q_e_e = lpd.infer_q_e(t, p_t_0, p_d, E_k, D)\n\t# # plot\n\t# plt.step(t, synthetic_profile, \"g-\")\n\t# plt.step(t, q_e_e, \"b--\")\n #\n\t# \"\"\"\n\t# 2nd Approach, starting time of processes is a continuous propapibility density function\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# ts, cs = lpd.continous_synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\n\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\n\t# plt.xlim(0, 24.0)\n\t# plt.legend([\"synthetic\", \"expected\", \"continuous\"], loc=0)\n\t# plt.show()\n #\n\t# \"\"\"\n\t# Time discretization\n\t# \"\"\"\n\t# n_intervals = 24 * 1 # discretized in minutes\n\t# discrete_timeaxis = np.linspace(0.0, 24.0, n_intervals + 1)\n\t# discrete_consumption = lpd.signal_discretization(discrete_timeaxis, t, ts, cs)\n\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\n\t# plt.step(discrete_timeaxis, discrete_consumption, where='post', c='k', ls='--', lw=2)\n\t# plt.legend([\"continuous\", \"discretized\"], loc=0)\n\t# plt.show()\n #\n #\n\t# \"\"\"\n\t# Repeated day synthetic profile creation\n\t# \"\"\"\n\t# # synthetic profile of D processes\n\t# D = 2000\n\t# n = 10\n\t# slp = lpd.synthetic_profile_repeated(D, t, p_d, consumption_axis, p_k, p_t_0, n)\n\t# plt.step(range(len(slp)), slp, \"g-\")\n\t# plt.show()\n\tt = np.linspace(0.0, 24.0, 96.0)\n\tload_profile = extra.read_slp(t, 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv')\n\tslp = synthetic.create_synthetic_load(load_profile, 5.0, 5)\n\tplt.step(range(len(slp)), slp)\n\tplt.show()","def run(self):\n qubit = self.qubit\n\n routine = self.routine\n\n # Saving some typing for parameters that are only read ;)\n allowed_delta_f = self.get_param_value(\"allowed_delta_f\")\n f_unit = self.get_param_value(\"f_unit\")\n f_factor = self.get_param_value(\"f_factor\")\n delta_f_unit = self.get_param_value(\"delta_f_unit\")\n delta_f_factor = self.get_param_value(\"delta_f_factor\")\n max_iterations = self.get_param_value(\"max_iterations\")\n transition = self.get_param_value(\"transition_name\")\n\n # Finding the ramsey experiment in the pipulse calibration\n pipulse_calib = routine.routine_steps[-1]\n ramsey = pipulse_calib.routine_steps[-1]\n\n # Transition frequency from last Ramsey\n freq = qubit[f\"{transition}_freq\"]()\n\n # Retrieving the frequency difference\n max_waiting_seconds = self.get_param_value(\"max_waiting_seconds\")\n for i in range(max_waiting_seconds):\n try:\n routine.delta_f = (\n ramsey.analysis.proc_data_dict[\n \"analysis_params_dict\"][\n qubit.name][\"exp_decay\"][\"new_qb_freq\"] -\n ramsey.analysis.proc_data_dict[\n \"analysis_params_dict\"][\n qubit.name][\"exp_decay\"][\"old_qb_freq\"])\n break\n except KeyError:\n log.warning(\n \"Could not find frequency difference between current \"\n \"and last Ramsey measurement, delta_f not updated\")\n break\n except AttributeError:\n # FIXME: Unsure if this can also happen on real set-up\n log.warning(\n \"Analysis not yet run on last Ramsey measurement, \"\n \"frequency difference not updated\")\n time.sleep(1)\n\n # Progress update\n if self.get_param_value('verbose'):\n print(f\"Iteration {routine.iteration}, {transition}-freq \"\n f\"{freq / f_factor} {f_unit}, frequency \"\n f\"difference = {routine.delta_f / delta_f_factor} \"\n f\"{delta_f_unit}\")\n\n # Check if the absolute frequency difference is small enough\n if np.abs(routine.delta_f) < allowed_delta_f:\n # Success\n if self.get_param_value('verbose'):\n print(f\"{transition}-frequency found to be \"\n f\"{freq / f_factor} {f_unit} within \"\n f\"{allowed_delta_f / delta_f_factor} \"\n f\"{delta_f_unit} of previous value.\")\n\n elif routine.iteration < max_iterations:\n # No success yet, adding a new rabi-ramsey and decision step\n if self.get_param_value('verbose'):\n print(f\"Allowed error (\"\n f\"{allowed_delta_f / delta_f_factor} \"\n f\"{delta_f_unit}) not yet achieved, adding new\"\n \" round of PiPulse calibration...\")\n\n routine.add_next_pipulse_step()\n\n step_settings = {'qubits': self.qubits}\n routine.add_step(\n FindFrequency.Decision,\n 'decision',\n step_settings,\n )\n\n routine.iteration += 1\n return\n\n else:\n # No success yet, reached max iterations\n msg = (f\"{self.step_label} routine finished for {qubit.name}, \"\n \"desired precision not necessarily achieved within the \"\n f\"maximum number of iterations ({max_iterations}).\")\n log.warning(msg)\n\n if self.get_param_value('verbose'):\n print(msg)\n\n if self.get_param_value('verbose'):\n # Printing termination update\n print(f\"FindFrequency routine finished: \"\n f\"{transition}-frequencies for {qubit.name} \"\n f\"is {freq / f_factor} {f_unit}.\")","def main():\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\n\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\n plt.show()\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\n\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\n #\n #t_label = []\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\n # t_label.append(str(t))\n #\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\n\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n # (L, Nfin, VT_flavor, Nrow, Imax)\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\n\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\n\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\n\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\n\n #t_label = []\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\n # t_label.append(str(t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\n # segment += 1\n\n #t_label = []\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\n # segment += 1\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\n # \n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\n # \n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)","def take_measurements(timeout=6):\n print(\"Taking measurements for\", timeout, \"seconds.\")\n start = time.monotonic()\n while time.monotonic() - start < timeout:\n for i, (toggle, polarity) in enumerate(vectors):\n result = trackpad.measure_adc(toggle, polarity)\n print(\"meas{}: {}\".format(i, result - compensation[i]), end=\"\\t\")\n print()","async def loop():\n # ArmDevice.storage.joints_pos = await get_positions() # Use this if encoders are wired up.\n # ArmDevice.storage.joints_pos = simulate_positions() # Use this for testing without position feedback.\n log.debug(\"command: {}\".format(ArmDevice.storage.command))\n ArmDevice.storage.controller.user_command(ArmDevice.storage.mode, *ArmDevice.storage.command)\n ArmDevice.storage.speeds = ArmDevice.storage.controller.update_duties(ArmDevice.storage.joints_pos)\n\n # publish speeds/duty cycles here\n log.debug(\"joints_pos: {}\".format(ArmDevice.storage.joints_pos))\n log.debug(\"speeds: {}\".format(ArmDevice.storage.speeds))\n await send_duties()","def loop_run(self, loops):\n self.loop_seek(self.num_loops + loops)","def angular_memory_effect_analysis(tilt_coef_range, input_field, TM, data_shape, sample_pitch, wavelength, plot_std = True):\n zernike_rho = np.linspace(-0.5, 0.5, data_shape[1])\n\n\n centered_outputs = np.zeros([tilt_coef_range.size, data_shape[1]]) # For storage of outputs that where tilted and then artificially shifted back to the center\n shift_idxs = np.zeros(tilt_coef_range.size) # Will store information on how much outputs needed to be shifted to be centered\n for idx, coef in enumerate(tilt_coef_range):\n output_intensity = np.abs( TM @ ( input_field * np.exp(2j * np.pi * coef * zernike_rho) ).ravel() )**2 # propagation via TM, only saving intensity\n centered_outputs[idx, :], shift_idxs[idx] = calc.center_tilted_output(output_intensity, coef, data_shape, sample_pitch, wavelength, return_shift_idx = True)\n\n centered_outputs = centered_outputs / np.linalg.norm(centered_outputs, 1) # Normalising all outputs\n\n\n input_field = input_field.ravel()\n reference_output = np.abs( TM @ input_field )**2 # untilted output field, which we will use for std\n reference_output = reference_output / np.linalg.norm(reference_output)\n\n # fig, axs = plt.subplots()\n # axs.plot(centered_outputs[-1,:])\n # plt.show()\n\n #Comparing the outputs with a standard deviation method\n subtracted_outputs = np.zeros(centered_outputs.shape)\n for idx in range(centered_outputs[:,0].size):\n subtracted_outputs[idx, :] = centered_outputs[idx, :] - centered_outputs[0, :] # subtracting the outputs with the reference\n\n std_of_outputs = np.std(subtracted_outputs, axis = 1) # Computing the standard deviation of subtracted outputs\n if plot_std:\n fig, axs = plt.subplots()\n img_1, = axs.plot(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, color = 'black')\n img_2 = axs.scatter(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, s = 40, color = 'crimson', edgecolors = 'black')\n axs.set_title('Angular memory effect decay')\n axs.set(xlabel = '$\\delta x, \\ \\mu m$', ylabel = 'Standard deviation, p.d.u.')\n\n\n #Fitting a logistic curve onto the results\n popt, pcov = curve_fit(logistic_curve, (shift_idxs * sample_pitch[1] * 1e6), std_of_outputs)\n smooth_range = np.linspace(-0.1, shift_idxs[-1] * sample_pitch[1] * 1e6, 1000)\n fitted_std = logistic_curve(smooth_range, *popt)\n img_fit, = axs.plot(smooth_range, fitted_std, color = 'blue')\n\n axs.text(0.65 * (shift_idxs[-1] * sample_pitch[1] * 1e6), 0.2 * std_of_outputs.max(), 'Fitting parameters: \\nL = {:.3g} \\nx_0 = {:.3g} \\nk = {:.3g}'.format(*popt))\n\n # img_3 = axs.scatter((shift_idxs * sample_pitch[1] * 1e6)[-8:], std_of_outputs[-8:], s = 70, marker = 'x', color = 'green')#excluded point\n plt.legend([(img_2, img_1), img_fit], ['STD($\\delta x $)', '$L / [1 + \\exp(-k (\\delta x - x_0)) ]$'])\n plt.plot(block = False)\n\n return centered_outputs","def run_regression(auto_input,\n variables: list = ['magasin', 'tilsig'],\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\n jupyter: bool = False,\n backup: bool = False,\n loop: bool = False,\n write: bool = True,\n week_nb: int = False,\n year: int = False) -> None:\n\n start_tuning = utctime_now()\n\n for region in regions:\n\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\n sys.exit(\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\")\n\n for variable in variables:\n\n if not variable in ['magasin', 'tilsig']:\n sys.exit(\"Variable must be either 'tilsig' or 'magasin'\")\n\n print('---------------------------------------------------------------')\n print(' {}, {} '.format(region, variable))\n print('---------------------------------------------------------------')\n\n \n \n df_week, MagKap = auto_input[variable]\n \n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\n \n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\"%Y.%m.%d\") - Timedelta(days=7)).strftime(\n '%Y.%m.%d')\n\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\n last_forecast = forecast_time\n else:\n last_forecast = forecast_time\n\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\n\n if loop:\n if variable == 'tilsig':\n max_kandidater = 196\n min_kandidater = 2\n\n else:\n max_kandidater = 171\n min_kandidater = 2\n\n max_weeks = 238 #288\n min_weeks = 16\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\n\n start_time_loop = utctime_now()\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\n print('Fasit er lest inn.\\n')\n\n if fasit[fasit_key][:reg_end].isnull().any():\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\n print(fasit[fasit_key][:reg_end].isnull())\n continue\n\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\n\n if loop:\n max_p = 0.001 # Beste testresultat SvF\n\n # First loop: Tuning number of candidates for best possible R2 combined\n df_ant_kandidater = pd.DataFrame(columns=columns)\n for antall in range(min_kandidater, max_kandidater + 1, 1):\n if antall > len(sorted_r2):\n chosen_r2 = sorted_r2\n else:\n chosen_r2 = sorted_r2[:antall]\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\n loop=True)\n df_ant_kandidater = df_ant_kandidater.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n if antall > len(sorted_r2):\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\n' % len(sorted_r2))\n break\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\n\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\n # candidates found in the First loop.\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\n df_short_period = pd.DataFrame(columns=columns)\n for short_period in range(min_weeks, max_weeks + 1, 1):\n short_period = int(short_period)\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\n final_chosen_r2, loop=True)\n df_short_period = df_short_period.append(\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\n short_period_beste = int(df_short_period.short_period.values[idx_max])\n print('Beste short_period loop 2: ', short_period_beste)\n\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n short_period_beste = df_all_methods.short_period.values[idx_max]\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\n\n print('\\nTuning for regionen tok %.0f minutter. \\n' % ((utctime_now() - start_time_loop) / 60))\n\n else:\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\n print(\"Input variables was read from: \", input_file)\n\n # MAKE ESTIMATE AND SHOW RESULTS\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\n chosen_r2_beste, loop=False)\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\n\n\n #WRITE RESULTS\n if write:\n # Write results from the regression to SMG.\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\n\n # write to SMG:\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\n\n # write to SMG, virtual:\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\n\n if jupyter:\n ws.show_result_jupyter(input1, input2)\n else:\n ws.show_result(input1, input2, input_file)\n\n print('---------------------------------------------------------------')\n print(' SLUTT ')\n print('---------------------------------------------------------------')\n print('\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\n' % (\n (utctime_now() - start_tuning) / 60))","async def test_routine(self):\n print('Running test routine...')\n print('Waiting for axes to initialize...')\n await self.robot.wait_until_initialized()\n print('Synchronizing robot state with peripheral...')\n await self.robot.synchronize_values()\n print('Loading calibration data...')\n await self.robot.load_calibrations()\n await self.robot.go_to_alignment_hole()\n\n print('Starting 96-well plate test...')\n await self.robot.go_to_96_well_plate(1, 'a')\n await self.robot.dispense('96-well plate', 'far above')\n for height in ['bottom', 'low', 'mid', 'high', 'top', 'above', 'far above']:\n print('Testing with height {}...'.format(height))\n for (row, volume) in [('a', 20), ('b', 30), ('c', 40), ('d', 50), ('e', 100)]:\n print(\n ' Testing precise with row {} and volume {} mL...'\n .format(row, volume)\n )\n await self.test_individual_precise(row, height, volume / 1000)\n await self.robot.dispense('96-well plate', height)\n for (row, volume) in [\n ('f', 100), ('g', 150), ('h', 200), ('a', 300), ('b', 400),\n ('c', 500), ('d', 600), ('e', 700), ('g', 800), ('h', 900)\n ]:\n print(\n ' Testing rough with row {} and volume {} mL...'\n .format(row, volume / 1000)\n )\n await self.test_individual_rough(row, height, volume / 1000)\n await self.robot.z.go_to_high_end_position()\n await self.robot.y.go_to_low_end_position()\n\n print(batch.OUTPUT_FOOTER)\n print('Quitting...')","def run_condition(condition):\n \n # Make global changes to the dataframe \n global STIM_comb_df\n \n # Loop over trials\n for i in range(STIM_trials):\n \n # Clear keyboard record\n event.clearEvents(eventType='keyboard')\n \n # Prepare image\n stim_sent = STIM_comb_df['pronoun'][i] + STIM_comb_df['verb'][i] + STIM_comb_df['preposition'][i] + STIM_comb_df['location'][i] \n\n # Display image and monitor time\n time_flip = core.monotonicClock.getTime() # onset of stimulus\n for frame in range(STIM_dur):\n stim_sentence = visual.TextStim(win=win, text=stim_sent, pos=[0,0], height=1, alignHoriz='center')\n stim_sentence.draw()\n win.flip()\n \n # Display fixation cross\n offset = core.monotonicClock.getTime() # offset of stimulus\n for frame in range(STIM_comb_df['delay'][i]):\n STIM_fix.draw()\n win.flip()\n # Get actual duration at offset\n \n #Log time variables\n STIM_comb_df['onset'][i]= time_flip - exp_start\n STIM_comb_df['offset'][i] = offset - exp_start\n STIM_comb_df['duration_measured'][i] = offset - time_flip \n \n # Check responses\n keys = event.getKeys(keyList=('y','b','escape'), timeStamped=True) # timestamped according to core.monotonicClock.getTime() at keypress. Select the first and only answer. \n \n # Log first response only\n if keys:\n key = keys[0][0]\n time_key = keys[0][1]\n \n # Log info on responses\n STIM_comb_df['response'][i] = key\n STIM_comb_df['key_t'][i] = time_key - exp_start\n STIM_comb_df['rt'][i] = time_key - time_flip \n \n # Check if responses are correct\n if STIM_comb_df['response'][i] == 'y':\n STIM_comb_df['correct_resp'][i] = 1 if STIM_comb_df['verb'][i] in STIM_verbs[0:3] else 0\n\n if STIM_comb_df['response'][i] == 'b':\n STIM_comb_df['correct_resp'][i] = 1 if STIM_comb_df['verb'][i] not in STIM_verbs[0:3] else 0\n \n # Check if escape key was pressed\n if key in KEYS_quit:\n STIM_comb_df.to_csv(OUTPUT_filename)\n win.close()\n core.quit()","def main(um_file, ptl_file, wl_min_r=0.08, wl_max_r=50.0, wl_n_bins=22, verbose=True):\n # Read in the UM mock catalog\n um_mock = Table(np.load(um_file))\n if verbose:\n print(\"# Load in UM mock catalog: {}\".format(um_file))\n print(\"# Dealing with {} galaxies\".format(len(um_mock)))\n # Read in the particle table\n sim_particles = Table(np.load(ptl_file))\n if verbose:\n print(\"# Load in particle table: {}\".format(ptl_file))\n print(\"# Dealing with {} particles\".format(len(sim_particles)))\n\n # Output file name\n um_pre, _ = os.path.splitext(um_file)\n ptl_pre, _ = os.path.splitext(ptl_file)\n n_ptl = ptl_pre.split('_')[-1]\n precompute_out = \"{}_{}_r_{:4.2f}_{:4.1f}_{:2d}bins.npy\".format(\n um_pre, n_ptl, wl_min_r, wl_max_r, wl_n_bins\n )\n if verbose:\n print(\"# Output file name : {}\".format(precompute_out))\n\n # Run precompute\n if 'smdpl' in ptl_file:\n mass_encl = vagc.precompute_wl_smdpl(\n um_mock, sim_particles, wl_min_r=wl_min_r, wl_max_r=wl_max_r,\n wl_n_bins=wl_n_bins)\n elif 'mdpl2' in ptl_file:\n mass_encl = vagc.precompute_wl_mdpl2(\n um_mock, sim_particles, wl_min_r=wl_min_r, wl_max_r=wl_max_r,\n wl_n_bins=wl_n_bins)\n else:\n raise NameError(\"# Wrong simulation: [smdpl/mdpl2]\")\n\n np.save(precompute_out, mass_encl)","def test_imsim():\n import yaml\n import astropy.units as u\n import matplotlib.pyplot as plt\n from tqdm import tqdm\n # Need these for `eval` below\n from numpy import array\n import coord\n\n with open(DATA_DIR / \"wcs_466749.yaml\", 'r') as f:\n wcss = yaml.safe_load(f)\n\n cmds = {}\n with open(DATA_DIR / \"phosim_cat_466749.txt\", 'r') as f:\n for line in f:\n k, v = line.split()\n try:\n v = int(v)\n except ValueError:\n try:\n v = float(v)\n except ValueError:\n pass\n cmds[k] = v\n\n # Values below (and others) from phosim_cat_466749.txt\n rc = cmds['rightascension']\n dc = cmds['declination']\n boresight = galsim.CelestialCoord(\n rc*galsim.degrees,\n dc*galsim.degrees\n )\n obstime = Time(cmds['mjd'], format='mjd', scale='tai')\n obstime -= 15*u.s\n band = \"ugrizy\"[cmds['filter']]\n wavelength_dict = dict(\n u=365.49,\n g=480.03,\n r=622.20,\n i=754.06,\n z=868.21,\n y=991.66\n )\n wavelength = wavelength_dict[band]\n camera = imsim.get_camera()\n\n rotTelPos = cmds['rottelpos'] * galsim.degrees\n telescope = imsim.load_telescope(f\"LSST_{band}.yaml\", rotTelPos=rotTelPos)\n # Ambient conditions\n # These are a guess.\n temperature = 293.\n pressure = 69.0\n H2O_pressure = 1.0\n\n # Start by constructing a refractionless factory, which we can use to\n # cross-check some of the other values in the phosim cmd file.\n factory = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength,\n camera,\n temperature=temperature,\n pressure=0.0,\n H2O_pressure=H2O_pressure\n )\n\n aob, zob, hob, dob, rob, eo = factory._ICRF_to_observed(\n boresight.ra.rad, boresight.dec.rad, all=True\n )\n np.testing.assert_allclose(\n np.rad2deg(aob)*3600, cmds['azimuth']*3600,\n rtol=0, atol=2.0\n )\n np.testing.assert_allclose(\n (90-np.rad2deg(zob))*3600, cmds['altitude']*3600,\n rtol=0, atol=6.0,\n )\n q = factory.q * galsim.radians\n rotSkyPos = rotTelPos - q\n # Hmmm.. Seems like we ought to be able to do better than 30 arcsec on the\n # rotator? Maybe this is defined at a different point in time? Doesn't seem\n # to affect the final WCS much though.\n np.testing.assert_allclose(\n rotSkyPos.deg*3600, cmds['rotskypos']*3600,\n rtol=0, atol=30.0,\n )\n\n # We accidentally simulated DC2 with the camera rotated 180 degrees too far.\n # That includes the regression test data here. So to fix the WCS code, but\n # still use the same regression data, we need to add 180 degrees here. Just\n # rotate the camera by another 180 degrees\n telescope = telescope.withLocallyRotatedOptic(\n \"LSSTCamera\", batoid.RotZ(np.deg2rad(180))\n )\n\n # For actual WCS check, we use a factory that _does_ know about refraction.\n factory = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength,\n camera,\n temperature=temperature,\n pressure=pressure,\n H2O_pressure=H2O_pressure\n )\n\n do_plot = False\n my_centers = []\n imsim_centers = []\n if do_plot:\n _, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 12))\n i = 0\n r1 = []\n d1 = []\n r2 = []\n d2 = []\n rng = np.random.default_rng(1234)\n for k, v in tqdm(wcss.items()):\n name = k[18:25].replace('-', '_')\n det = camera[name]\n cpix = det.getCenter(cameraGeom.PIXELS)\n\n wcs = factory.getWCS(det, order=2)\n wcs1 = eval(v)\n # Need to adjust ab parameters to new GalSim convention\n wcs1.ab[0,1,0] = 1.0\n wcs1.ab[1,0,1] = 1.0\n\n my_centers.append(wcs.posToWorld(galsim.PositionD(cpix.x, cpix.y)))\n imsim_centers.append(wcs1.posToWorld(galsim.PositionD(cpix.x, cpix.y)))\n\n corners = det.getCorners(cameraGeom.PIXELS)\n xs = np.array([corner.x for corner in corners])\n ys = np.array([corner.y for corner in corners])\n ra1, dec1 = wcs.xyToradec(xs, ys, units='radians')\n ra2, dec2 = wcs1.xyToradec(xs, ys, units='radians')\n if i == 0:\n labels = ['batoid', 'PhoSim']\n else:\n labels = [None]*2\n if do_plot:\n ax.plot(ra1, dec1, c='r', label=labels[0])\n ax.plot(ra2, dec2, c='b', label=labels[1])\n\n # add corners to ra/dec check lists\n r1.extend(ra1)\n d1.extend(dec1)\n r2.extend(ra2)\n d2.extend(dec2)\n # Add some random points as well\n xs = rng.uniform(0, 4000, 100)\n ys = rng.uniform(0, 4000, 100)\n ra1, dec1 = wcs.xyToradec(xs, ys, units='radians')\n ra2, dec2 = wcs1.xyToradec(xs, ys, units='radians')\n r1.extend(ra1)\n d1.extend(dec1)\n r2.extend(ra2)\n d2.extend(dec2)\n i += 1\n\n if do_plot:\n ax.legend()\n xlim = ax.get_xlim()\n ax.set_xlim(xlim[1], xlim[0])\n plt.show()\n\n dist = sphere_dist(r1, d1, r2, d2)\n print(\"sphere dist mean, max, std\")\n print(\n np.rad2deg(np.mean(dist))*3600,\n np.rad2deg(np.max(dist))*3600,\n np.rad2deg(np.std(dist))*3600,\n )\n np.testing.assert_array_less(\n np.rad2deg(np.mean(dist))*3600,\n 5.0\n )\n if do_plot:\n plt.hist(np.rad2deg(dist)*3600, bins=100)\n plt.show()\n\n if do_plot:\n r1 = np.array([c.ra.rad for c in my_centers])\n d1 = np.array([c.dec.rad for c in my_centers])\n r2 = np.array([c.ra.rad for c in imsim_centers])\n d2 = np.array([c.dec.rad for c in imsim_centers])\n cd = np.cos(np.deg2rad(cmds['declination']))\n q = plt.quiver(r1, d1, np.rad2deg(r1-r2)*3600*cd, np.rad2deg(d1-d2)*3600)\n plt.quiverkey(q, 0.5, 1.1, 5.0, \"5 arcsec\", labelpos='E')\n plt.show()","def temp_mainloop(self, cycles_to_respond=1000):\n\n if self.com_port_select.count() > 0:\n report = \"Begin measuring\"\n self.go_button.setEnabled(False) # don't allow to create new threads\n self.state_temp_loop(True)\n self.append_msg_box(report)\n\n while self.temp_loop_actiator:\n for num in range(self.com_port_select.count()):\n # self.MainWindow_exec_thread(lambda: self.sample_devices(cycles_to_respond, num))\n self.sample_devices(cycles_to_respond, num)\n self.temp_repr.plot_tc_temp(self.temp_log)\n timeo = float(self.timeout.text()) # timeout\n sleep(timeo)\n # -------- record data once in msg box --------\n\n self.go_button.setEnabled(True)\n self.led_light.setPixmap(QtGui.QPixmap(img[\"red_led\"]))\n else:\n self.append_msg_box(\"No device is connected\")","def main():\n NAME = os.path.basename(__file__).split(\".\")[0]\n\n # Pass/fail thresholds\n MIN_AVG_FRAME_DELTA = 30 # at least 30ms delta between frames\n MAX_VAR_FRAME_DELTA = 0.01 # variance of frame deltas\n MAX_FRAME_DELTA_JITTER = 0.3 # max ms gap from the average frame delta\n\n with its.device.ItsSession() as cam:\n props = cam.get_camera_properties()\n if not its.caps.manual_sensor(props):\n print \"Test skipped\"\n return\n\n req, fmt = its.objects.get_fastest_manual_capture_settings(props)\n caps = cam.do_capture([req]*50, [fmt])\n\n # Print out the millisecond delta between the start of each exposure\n tstamps = [c['metadata']['android.sensor.timestamp'] for c in caps]\n deltas = [tstamps[i]-tstamps[i-1] for i in range(1,len(tstamps))]\n deltas_ms = [d/1000000.0 for d in deltas]\n avg = sum(deltas_ms) / len(deltas_ms)\n var = sum([d*d for d in deltas_ms]) / len(deltas_ms) - avg * avg\n range0 = min(deltas_ms) - avg\n range1 = max(deltas_ms) - avg\n print \"Average:\", avg\n print \"Variance:\", var\n print \"Jitter range:\", range0, \"to\", range1\n\n # Draw a plot.\n pylab.plot(range(len(deltas_ms)), deltas_ms)\n matplotlib.pyplot.savefig(\"%s_deltas.png\" % (NAME))\n\n # Test for pass/fail.\n assert(avg > MIN_AVG_FRAME_DELTA)\n assert(var < MAX_VAR_FRAME_DELTA)\n assert(abs(range0) < MAX_FRAME_DELTA_JITTER)\n assert(abs(range1) < MAX_FRAME_DELTA_JITTER)","def calculation_time_analysis():\n\tfrom . import spectra as sp\n\tp_dict = {'Bfield':700,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':0*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\n\tchiL,chiR,chiZ = sp.calc_chi([-3500],p_dict)\n\t\n\tfor angle in [0, np.pi/32, np.pi/16, np.pi/8, np.pi/4, np.pi/2]:\n\t\tprint(('Angle (degrees): ',angle*180/np.pi))\n\t\tRotMat, n1, n2 = solve_diel(chiL,chiR,chiZ,angle)","def mcapi_playback(name_interface):\n device = Solo12(name_interface, dt=DT)\n qc = QualisysClient(ip=\"140.93.16.160\", body_id=0) # QualisysClient\n logger = Logger(device, qualisys=qc) # Logger object\n nb_motors = device.nb_motors\n\n # Default position after calibration\n q_init = np.array([0.0, 0.8, -1.6, 0, 0.8, -1.6, 0, -0.8, 1.6, 0, -0.8, 1.6])\n\n # Calibrate encoders\n device.Init(calibrateEncoders=True, q_init=q_init)\n\n # Wait for Enter input before starting the control loop\n put_on_the_floor(device, q_init)\n\n # CONTROL LOOP ***************************************************\n t = 0.0\n t_max = t_switch[-1]\n\n # Parameters of the PD controller\n KP = 2.\n KD = 0.05\n tau_max = 5. * np.ones(12)\n\n while ((not device.hardware.IsTimeout()) and (t < t_max)):\n\n device.UpdateMeasurment() # Retrieve data from IMU and Motion capture\n\n # Desired position and velocity for this loop and resulting torques\n q_desired, v_desired = demo_solo12(t)\n pos_error = q_desired.ravel() - device.q_mes.ravel()\n vel_error = v_desired.ravel() - device.v_mes.ravel()\n tau = KP * pos_error + KD * vel_error\n tau = np.maximum(np.minimum(tau, tau_max), -tau_max)\n\n # Set desired torques for the actuators\n device.SetDesiredJointTorque(tau)\n\n # Call logger\n # logger.sample(device, qualisys=qc)\n\n # Send command to the robot\n device.SendCommand(WaitEndOfCycle=True)\n if ((device.cpt % 100) == 0):\n device.Print()\n\n t += DT\n\n # ****************************************************************\n\n # Whatever happened we send 0 torques to the motors.\n device.SetDesiredJointTorque([0]*nb_motors)\n device.SendCommand(WaitEndOfCycle=True)\n\n if device.hardware.IsTimeout():\n print(\"Masterboard timeout detected.\")\n print(\"Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.\")\n device.hardware.Stop() # Shut down the interface between the computer and the master board\n\n # Save the logs of the Logger object\n # logger.saveAll()","def lammps(parameter, iteration, numberlist, density, chainlength):\n # input parameters\n var = parameter[0]\n x = var * numberlist[0]\n y = var * numberlist[1] * 1.732 / 2\n z = x / 2\n #nodeid = 55 + iteration % 5\n M = int((x * y * z * density - 4 * numberlist[0] * numberlist[1] * int(z / 0.3)) / chainlength) \n os.environ['var'] = str(var)\n os.environ['iteration'] = str(iteration)\n os.environ['M'] = str(M)\n #os.environ['nodeid'] = str(nodeid)\n # copy the files to modify\n os.system(\"cp in.asymmetric in.asymmetric_$iteration\")\n os.system(\"cp run_lammps.sh run_lammps_$iteration.sh\")\n os.system(\"cp MultiBCP_hexagonal_post.py MultiBCP_hexagonal_post_$iteration.py\")\n # modify relevant files\n os.system('sed -i -e \"s/distance = 12/distance = $var/\" MultiBCP_hexagonal_post_$iteration.py')\n os.system(\"python MultiBCP_hexagonal_post_$iteration.py\")\n time.sleep(3)\n os.system('sed -i -e \"s/3072/$M/\" in.asymmetric_$iteration')#modify according to the parameters\n os.system('sed -i -e \"s/XN_100/XN_100_$iteration/\" in.asymmetric_$iteration')\n os.system('sed -i -e \"s/finalasymmetric/finalasymmetric_$iteration/\" in.asymmetric_$iteration')\n os.system('sed -i -e \"s/POSTA2B8/POSTA2B8_$iteration/\" run_lammps_$iteration.sh')\n os.system('sed -i -e \"s/posta2b8.out/posta2b8_$iteration.out/\" run_lammps_$iteration.sh')\n #os.system('sed -i -e \"s/node55/node$nodeid/\" run_lammps_$iteration.sh')\n os.system('sed -i -e \"s/in.asymmetric/in.asymmetric_$iteration/\" run_lammps_$iteration.sh')\n # run the simulation\n os.system(\"sbatch run_lammps_$iteration.sh\")\n return None","def investigate4DRepeatability():\n parentdir = '/home/rallured/Dropbox/Interferometer/SolarBFlat/Repeatability/'\n avgs = [1,2,4,8,16,32]\n\n #Temporal with fringes tilted\n fn = glob.glob(parentdir+'Tilt/17*RepeatabilityTiltTemporal*.bin')\n fn.sort()\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\n d = np.array([met.readFlatScript(fi.split('.')[0])[0] for fi in fn])\n #Make progressive averaging plot\n plt.figure('TemporalTiltedFigure')\n for i in np.arange(6)*2:\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\n plt.legend(loc='lower left')\n plt.title('Solar B PSD - Temporal,Tilted')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n #Get repeatability\n reptemptilt = d[-1]-d[-2]\n figtemptilt = d[-1]\n\n #Dynamic with fringes tilted\n fn = glob.glob(parentdir+'Tilt/17*RepeatabilityTilt_*.bin')\n fn.sort()\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\n d = [met.readFlatScript(fi.split('.')[0])[0] for fi in fn]\n #Make progressive averaging plot\n plt.figure('DynamicTiltedFigure')\n for i in np.arange(6)*2:\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\n plt.legend(loc='lower left')\n plt.title('Solar B PSD - Dynamic,Tilted')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n #Get repeatability\n repdyntilt = d[-1]-d[-2]\n figdyntilt = d[-1]\n \n #Temporal with fringes nulled\n fn = glob.glob(parentdir+'Nulled/17*.bin')\n fn.sort()\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\n d = np.array([met.readFlatScript(fi.split('.')[0])[0] for fi in fn])\n #Make progressive averaging plot\n plt.figure('TemporalNulledFigure')\n for i in np.arange(6)*2:\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\n plt.legend(loc='lower left')\n plt.title('Solar B PSD - Temporal,Nulled')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n #Get repeatability\n reptempnull = d[-1]-d[-2]\n figtempnull = d[-1]\n \n #Dynamic with fringes nulled\n d = pyfits.getdata('/home/rallured/Dropbox/Interferometer/'\n 'SolarBFlat/Repeatability/'\n 'Nulled/170103_Processed.fits')\n rep = np.array([d[i,0]-d[i,1] for i in range(32)])\n #Make progressive averaging plot\n plt.figure('DynamicNulledFigure')\n for i in [0,1,3,7,15,31]:\n f,p = fourier.meanPSD(d[i,0],win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label=str(i+1))\n plt.legend(loc='lower left')\n plt.title('Solar B PSD - Dynamic,Nulled')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n #Get repeatability\n repdynnull = d[-1][0]-d[-1][1]\n figdynnull = d[-1][0]\n\n #Make comparative repeatability plots with 32 averages\n plt.figure('CompareRepeatability')\n f,p = fourier.meanPSD(repdynnull,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Dynamic,Nulled')\n f,p = fourier.meanPSD(repdyntilt,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Dynamic,Tilted')\n f,p = fourier.meanPSD(reptemptilt,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Temporal,Tilted')\n f,p = fourier.meanPSD(reptempnull,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Temporal,Nulled')\n plt.legend(loc='lower left')\n plt.title('Solar B Repeatability - 32 Averages')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n\n #Make comparative figure plots with 32 averages\n plt.figure('CompareFigure')\n f,p = fourier.meanPSD(figdynnull,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Dynamic,Nulled')\n f,p = fourier.meanPSD(figdyntilt,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Dynamic,Tilted')\n f,p = fourier.meanPSD(figtemptilt,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Temporal,Tilted')\n f,p = fourier.meanPSD(figtempnull,win=np.hanning,dx=dx,irregular=True,\\\n minpx=200)\n plt.loglog(f,p/f[0],label='Temporal,Nulled')\n plt.legend(loc='lower left')\n plt.title('Solar B Figure - 32 Averages')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n plt.grid()\n\n #Make parroting repeatability plots\n fig = plt.figure('Parroting')\n fig.add_subplot(2,2,1)\n plt.imshow(repdyntilt)\n plt.title('Dynamic Repeatability')\n plt.colorbar()\n fig.add_subplot(2,2,2)\n plt.imshow(reptemptilt)\n plt.title('Temporal Repeatability')\n plt.colorbar()\n fig.add_subplot(2,2,3)\n res = legendre2d(repdyntilt,xo=3,yo=3)[0]\n plt.imshow(repdyntilt-res)\n plt.title('Dynamic Repeatability Filtered')\n plt.colorbar()\n fig.add_subplot(2,2,4)\n res = legendre2d(reptemptilt,xo=3,yo=3)[0]\n plt.imshow(reptemptilt-res)\n plt.title('Temporal Repeatability Filtered')\n plt.colorbar()","def main():\n\n \"\"\"\n Initialize Parameters\n \"\"\"\n src_path_map = '../data/map/wean.dat'\n src_path_log = '../data/log/robotdata1.log'\n\n map_obj = MapReader(src_path_map)\n occupancy_map = map_obj.get_map()\n logfile = open(src_path_log, 'r')\n\n motion_model = MotionModel()\n params = {\n 'z_max': 8000,\n 'lambda_short': 0.1,\n 'sigma_hit': 20,\n\n 'z_pHit': 0.95,\n 'z_pShort': 0.01,\n 'z_pMax': 0.05,\n 'z_pRand': 0.05,\n\n 'laser_sensor_offset': 25.0,\n 'ray_step_size': 2,\n 'grid_size': 10,\n 'occ_thrsh': 0.1,\n 'laser_subsample': 30,\n\n 'rayCast_vis': False,\n 'map_vis': True\n }\n sensor_model = SensorModel(occupancy_map, params)\n resampler = Resampling()\n\n num_particles = 1500\n X_bar = init_particles_freespace(num_particles, occupancy_map)\n vis_flag = 1\n\n \"\"\"\n Monte Carlo Localization Algorithm : Main Loop\n \"\"\"\n if vis_flag:\n visualize_map(occupancy_map)\n\n first_time_idx = True\n for time_idx, line in enumerate(logfile):\n\n # Read a single 'line' from the log file (can be either odometry or laser measurement)\n meas_type = line[0] # L : laser scan measurement, O : odometry measurement\n meas_vals = np.fromstring(line[2:], dtype=np.float64,\n sep=' ') # convert measurement values from string to double\n\n odometry_robot = meas_vals[0:3] # odometry reading [x, y, theta] in odometry frame\n time_stamp = meas_vals[-1]\n\n # if ((time_stamp <= 0.0) | (meas_type == \"O\")): # ignore pure odometry measurements for now (faster debugging) \n # continue\n\n if (meas_type == \"L\"):\n odometry_laser = meas_vals[3:6] # [x, y, theta] coordinates of laser in odometry frame\n ranges = meas_vals[6:-1] # 180 range measurement values from single laser scan\n\n print(\"Processing time step \" + str(time_idx) + \" at time \" + str(time_stamp) + \"s\")\n\n if (first_time_idx):\n u_t0 = odometry_robot\n first_time_idx = False\n continue\n\n X_bar_new = np.zeros((num_particles, 4), dtype=np.float64)\n u_t1 = odometry_robot\n for m in range(0, num_particles):\n\n \"\"\"\n MOTION MODEL\n \"\"\"\n x_t0 = X_bar[m, 0:3]\n x_t1 = motion_model.update(u_t0, u_t1, x_t0)\n\n \"\"\"\n SENSOR MODEL\n \"\"\"\n if (meas_type == \"L\"):\n z_t = ranges\n w_t = sensor_model.beam_range_finder_model(z_t, x_t1)\n # w_t = 1/num_particles\n X_bar_new[m, :] = np.hstack((x_t1, w_t))\n else:\n X_bar_new[m, :] = np.hstack((x_t1, X_bar[m, 3]))\n\n X_bar = X_bar_new\n u_t0 = u_t1\n\n \"\"\"\n RESAMPLING\n \"\"\"\n X_bar = resampler.low_variance_sampler(X_bar)\n if vis_flag:\n visualize_timestep(X_bar)","def run(self):\n\n try:\n while not self._exit:\n # Update the time varying value of the output.\n\n # The current time offset\n cur_time_offset = time.time() % self._time_period\n\n self._updating = True\n\n # If modulation is selected as FM\n if (self._mod_type == 2):\n # Getting the modulating input\n m_t = self.mod_ip[0].voltage - self.mod_ip[1].voltage\n\n freq = self._frequency + m_t\n if freq != 0:\n time_p = 1 / freq\n\n else:\n time_p = float(\"inf\")\n\n else:\n freq = self._frequency\n time_p = self._time_period\n\n # If sine wave\n if (self.type == 0):\n self._last_updated_time = cur_time_offset\n voltage = 0.5 * math.sin(\n 2 * 3.145926 * freq * cur_time_offset) + 0.5\n\n # If square wave\n elif (self.type == 1 or self.type == 4):\n self._last_updated_time = cur_time_offset\n voltage = 1 if (\n (cur_time_offset) < time_p /\n float(2)) else 0\n\n # If Ramp\n elif (self.type == 2):\n self._last_updated_time = cur_time_offset\n voltage = cur_time_offset / time_p\n\n # If triangular\n else:\n self._last_updated_time = cur_time_offset\n voltage = 2 * cur_time_offset / time_p if (\n (cur_time_offset) < time_p /\n float(2)) else (2 * (time_p - cur_time_offset) / time_p)\n\n if (self._mod_type == 1):\n m_t = self.mod_ip[0].voltage - self.mod_ip[1].voltage\n c_t = voltage * self._amplitude + self.offset\n voltage = (1 + m_t) * c_t\n voltage /= self._amplitude\n\n if (self.type != 4):\n voltage *= self._amplitude\n\n else:\n voltage *= 5.0 # TTL amplitude is constant at 5v\n\n self.outputs[0].voltage = voltage\n self.outputs[1].voltage = -self.offset\n\n self._updating = False\n time.sleep(self._sampling_time_interval)\n\n except Exception as e:\n return","def runWavelengthDependency():\n RunData([getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/')[0],], out='I600nmwave',\n wavelength='l600')\n RunData([getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/')[0],], out='I700nmwave',\n wavelength='l700')\n RunData([getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/')[0],], out='I800nmwave',\n wavelength='l800')\n RunData([getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/')[4],], out='I890nmwave',\n wavelength='l890')","def test_readme():\n\n import torch\n from lettuce import BGKCollision, StandardStreaming, Lattice, D2Q9, TaylorGreenVortex2D, Simulation\n\n device = \"cpu\"\n dtype = torch.float32\n\n lattice = Lattice(D2Q9, device, dtype)\n flow = TaylorGreenVortex2D(resolution=256, reynolds_number=10, mach_number=0.05, lattice=lattice)\n collision = BGKCollision(lattice, tau=flow.units.relaxation_parameter_lu)\n streaming = StandardStreaming(lattice)\n simulation = Simulation(flow, lattice, collision, streaming)\n mlups = simulation.step(num_steps=1)\n\n print(\"Performance in MLUPS:\", mlups)","def main():\n #diffuser_data_dir = r'F:\\TEMPO\\Data\\GroundTest\\FPS\\Spectrometer\\Diffuser_Irradiance\\saved_quads\\3845_ms\\saved_plots_modified'\n #diffuser_light_data = os.path.join(diffuser_data_dir,'Light_data')\n #diffuser_dark_data = os.path.join(diffuser_data_dir, 'Dark_data')\n #print(diffuser_data_dir)\n #cc\n #int_time_diffuser = 3845.0\n\n# radiance_data_dir_UV = r'F:\\TEMPO\\Data\\GroundTest\\FPS\\Spectrometer\\Radiance_Cal_UV_Lamp\\saved_quads\\saved_plots_modified'\n# radiance_light_data_UV = os.path.join(radiance_data_dir_UV,'DSS-Y')\n# radiance_dark_data_UV = os.path.join(radiance_data_dir_UV, 'Dark_data')\n## #int_time_radiance = 93.0\n## print(radiance_data_dir_UV)\n##\n radiance_data_dir_VIS = r'F:\\TEMPO\\Data\\GroundTest\\FPS\\Spectrometer\\Radiance_Cal_UV_Lamp\\saved_quads\\processed_h5'\n radiance_light_data_VISminusY = os.path.join(radiance_data_dir_VIS,'DSS-Y')\n radiance_light_data_VIS_Center = os.path.join(radiance_data_dir_VIS,'DSS_Center')\n radiance_light_data_VISplusY = os.path.join(radiance_data_dir_VIS,'DSS+Y') \n radiance_dark_data_VIS = os.path.join(radiance_data_dir_VIS, 'Dark_Data')\n\n\n\n # mean_diffuser_data = calculate_mean(diffuser_light_data)\n #mean_diffuser_dark_data = calculate_mean(diffuser_dark_data)\n#\n#\n# #Let's correct for dark current and work in signal rates unit\n# diffuser_dc_corrected = (mean_diffuser_data - mean_diffuser_dark_data)\n# diffuser_dc_corrected = np.round(diffuser_dc_corrected, 2)\n# diffuser_dc_corrected[diffuser_dc_corrected <0] = 0\n# mean_save_dir = os.path.join(diffuser_data_dir,'processed_average_data')\n# mean_save_irradiance = os.path.join(mean_save_dir, 'mean_irradiance_3845ms.csv')\n# np.savetxt(mean_save_irradiance, diffuser_dc_corrected, fmt='%1.3f', delimiter=\",\")\n \n\n\n\n# mean_radiance_data_UV = calculate_mean(radiance_light_data_UV)\n# mean_radiance_dark_data_UV = calculate_mean(radiance_dark_data_UV)\n# radiance_dc_corrected_UV = (mean_radiance_data_UV - mean_radiance_dark_data_UV)\n# radiance_dc_corrected_UV = np.round(radiance_dc_corrected_UV, 2)\n# radiance_dc_corrected_UV[radiance_dc_corrected_UV < 0] = 0\n###\n mean_radiance_dark_data_VIS = calculate_mean_dark(radiance_dark_data_VIS)\n \n \n # Correct for Dark current\n mean_radiance_data_VISminusY = calculate_mean(radiance_light_data_VISminusY) - mean_radiance_dark_data_VIS\n mean_radiance_data_VIS_Center = calculate_mean(radiance_light_data_VIS_Center)- mean_radiance_dark_data_VIS\n mean_radiance_data_VISplusY = calculate_mean(radiance_light_data_VISplusY) - mean_radiance_dark_data_VIS\n \n \n #radiance_dc_corrected_VIS[radiance_dc_corrected_VIS < 0] = 0\n#\n# \n\n# \n# mean_save_dir_UV = os.path.join(radiance_data_dir_UV,'processed_average_data')\n mean_save_dir_VIS = os.path.join(radiance_data_dir_VIS,'Mean_Processed_Data')\n if not os.path.exists(mean_save_dir_VIS):\n os.makedirs(mean_save_dir_VIS)\n #\n# mean_save_radiance_UV = os.path.join(mean_save_dir_UV, 'mean_radiance_DSSminus_UV.csv')\n# mean_save_radiance_VIS = os.path.join(mean_save_dir_VIS, 'mean_radiance_DSSminus_VIS.csv')\n# #\n# \n# np.savetxt(mean_save_radiance_UV, radiance_dc_corrected_UV, fmt='%1.3f', delimiter=\",\")\n# np.savetxt(mean_save_radiance_VIS, radiance_dc_corrected_VIS, fmt='%1.3f', delimiter=\",\")\n print('DONE')\n\n #Write into h5file\n hf_name = os.path.join(mean_save_dir_VIS,'Mean_Data.h5')\n hf = h5py.File(hf_name,'w')\n hf.create_dataset('DSS-Y', data= mean_radiance_data_VISminusY)\n hf.create_dataset('DSS_Center', data=mean_radiance_data_VIS_Center)\n hf.create_dataset('DSS+Y', data= mean_radiance_data_VISplusY)","def run_main(sst, ft_qv, use_NT):\n\n dtout=10. #minutes\n end_time=8*24. #hours\n del_time=dtout*60. #seconds\n end_time=end_time*3600. #seconds\n #sst=297\n D=5.e-6 #s-1\n U=7 #m/s\n psfc=100. #kPa\n qsfc=tf.qs_tp(sst,psfc)\n ft_intercept = 292 #K\n ft_gamma = 6.e-3 #K/m\n #ft_qv = 2.e-3\n k=0.2 #entrainment efficiency\n Cd = 1.e-3 #drag coefficient\n tspan = np.arange(0.,end_time,del_time)\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\n the_tup=make_tuple(the_tup,'coeffs')\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\n\n # save time/computation by only doing calculations for the last timestep (equilibrium)\n result['time']=tspan[-1]/3600./24. #days\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\n \n # decide how to calculate entrainment\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\n if use_NT:\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\n else:\n result['went']=calc_went(result.iloc[-1],the_tup)\n\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\n\n with open('dumpmodel.csv','w') as f:\n result.to_csv(f,index=False)\n \n return None","def run(self, speed, target_speed, power, alpha=0.9):\n\n # Setup tachometer pulse callback function\n GPIO.setwarnings(False)\n GPIO.setmode(GPIO.BOARD)\n GPIO.setup(self._pin, GPIO.IN, pull_up_down=GPIO.PUD_UP) # todo remove when hw pull-up implemented\n GPIO.add_event_detect(self._pin, GPIO.RISING, callback=self._cb_new_pulse)\n\n # init PI values\n t_last = time.perf_counter()\n self._t_last_count = t_last\n I = 0\n s = 0\n while self._run:\n # PI control\n new_s = self._get_speed() # Get car speed\n s = alpha * new_s + (1 - alpha) * s # Smooth speed reading\n speed.value = s\n err = target_speed.value - s\n t_now = time.perf_counter()\n delta_t = t_now - t_last\n # calculate integral part\n I += err * delta_t\n I = np.clip(I, -self._windup_guard_val, self._windup_guard_val) # prevent integration windup\n\n #print('E: {}, P: {}, I: {}'.format(round(err, 2), int(self._kp * err), int(self._ki * I)))\n\n # Calculate power output and constrain to [-100, 100]%\n pwr = np.clip(int(self._kp * err + self._ki * I), -100, 100)\n pwr = self._fault_guard(pwr, timeout=1, safemode_power=50) # Check if safe to give power.\n power.value = pwr\n t_last = t_now\n\n # sleep for reminder of sampling time.\n t_sleep = float(np.clip((2 * self._sample_interval - delta_t), 0, self._sample_interval))\n time.sleep(t_sleep)","def main():\n\n\t# eesAmplitudes = range(200,321,10)\n\teesAmplitudes = [\"%\"+\"%.2f_0_0\"%(i) for i in np.arange(0,1.01,.05)]\n\t# eesFrequencies = range(10,1001,20)\n\teesFrequencies = np.logspace(1,3,50)\n\t# nrnStructureFile = \"fsSFrFfMnArtMod.txt\"\n\t# nrnStructureFile = \"fsSFrFfMnArtModHuman.txt\"\n\tnrnStructureFile = \"fsMnArtModHuman.txt\"\n\t# name = \"FreqAmpModHuman_0367S\"\n\tname = \"FreqAmpModHuman_ArtmodHuman_10msBurst\"\n\n\tnSim = len(eesFrequencies)*len(eesAmplitudes)\n\tcount=0.\n\tpercLastPrint=0.\n\tprintPeriod = 0.05\n\t# simTime = 250\n\tsimTime = 15\n\tspecies = \"human\"\n\n\tfor eesAmplitude in eesAmplitudes:\n\t\tfor eesFrequency in eesFrequencies:\n\t\t\tfilName = name+\"_amp_\"+str(eesAmplitude)+\"_freq_\"+str(eesFrequency)\n\t\t\tresultFile = gt.find(\"*\"+filName+\".p\",pathToResults)\n\t\t\tif not resultFile:\n\t\t\t\treturnCode = None\n\t\t\t\twhile not returnCode==0:\n\t\t\t\t\tprogram = ['python','scripts/computeAfferentsEfferentsModulation.py',\n\t\t\t\t\t\tstr(eesFrequency),str(eesAmplitude),species,nrnStructureFile,name,\"--simTime\",str(simTime)]\n\t\t\t\t\tprint \" \".join(program)\n\t\t\t\t\tforwardSimulation = subprocess.Popen(program, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\t\t\t\treturnCode = None\n\t\t\t\t\twhile returnCode is None:\n\t\t\t\t\t\tmessage = forwardSimulation.stdout.readline().rstrip(\"\\n\").split()\n\t\t\t\t\t\tif message != None:print \"\\t\\t\"+\" \".join(message)+\"\\t\\t\"\n\t\t\t\t\t\treturnCode = forwardSimulation.poll()\n\t\t\t\t\tif returnCode != 0: print \"\\t\\t\\t\\t Error n: \",forwardSimulation.poll(),\" resetting simulation...\"\n\t\t\tcount+=1\n\t\t\tif count/nSim-percLastPrint>=printPeriod:\n\t\t\t\tpercLastPrint=count/nSim\n\t\t\t\tprint str(round(count/nSim*100))+\"% of simulations performed...\"\n\tplot_stats(eesAmplitudes,eesFrequencies,simTime,name)","def qc_sample_mip(args):\n clarity_epp.qc.sample.set_mip_data_ready(lims, args.process_id)","def test_SetMultipleMovingLoadsWithVelocityFunction(self):\n\n # create nodes\n second_coord = [1, 0, 0.0]\n self.mp.CreateNewNode(1, 0.0, 0.0, 0.0)\n self.mp.CreateNewNode(2, second_coord[0], second_coord[1], 0.0)\n\n # create condition\n self.mp.CreateNewCondition(\"MovingLoadCondition2D2N\", 1, [1,2], self.mp.GetProperties()[1])\n\n parameters = self.base_parameters\n parameters[\"velocity\"]=KratosMultiphysics.Parameters(self.time_dependent_velocity)\n\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.TIME, 0)\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.DELTA_TIME, 0.25)\n\n process = GMA.SetMultipleMovingLoadsProcess(self.mp, parameters)\n cond = self.cmp.GetCondition(2)\n\n # initialise and set load\n process.ExecuteInitialize()\n process.ExecuteInitializeSolutionStep()\n\n # initialise matrices\n lhs = KratosMultiphysics.Matrix(0, 0)\n rhs = KratosMultiphysics.Vector(0)\n\n # set load on node\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\n\n self.checkRHS(rhs, [0.0, -2.0, 0.0, 0.0])\n\n # change time and recalculate load\n process.ExecuteFinalizeSolutionStep()\n process.ExecuteInitializeSolutionStep()\n\n # check if interpolation is done correctly\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\n\n self.checkRHS(rhs, [0.0, -2.0, 0.0, 0.0])\n\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.TIME, 0.5)\n\n process.ExecuteFinalizeSolutionStep()\n process.ExecuteInitializeSolutionStep()\n\n # check if interpolation is done correctly\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\n\n self.checkRHS(rhs, [0.0, -1.5, 0.0, -0.5])","def run( self, cycles=-1 ):","def _set_feedback_loop(self):\n while True:\n mono = self.get_mono()\n interval = int(self.qbpm.filter / 20)\n if self.qbpm.log_arrays['avgcurr_log'][-1] < self.feedback_threshold:\n print('intensity too low.')\n self._stop_loop_feedback()\n# current_pos = self.qbpm.log_arrays['posz_filter_log'][-1]\n current_pos = self.qbpm.log_arrays['posx_filter_log'][-1]\n# target = self.qbpm.posz_target\n target = self.qbpm.posx_target\n if mono == \"dcm\":\n corr_factor = 0.2\n if mono == \"dmm\":\n corr_factor = 0.2\n bandwidth = 0.003 * float(self.qbpm.sensitivity/100)\n if not ((target - bandwidth) < current_pos < (target + bandwidth)):\n corr_angle = -((current_pos - target) * corr_factor)/self.qbpm.distance\n if self.cycle == interval:\n print('Moving pitch: {}'.format(corr_angle))\n dcm_curr_pitchpos = self.dcm_pitch_tserver.Position\n target_pitchpos = dcm_curr_pitchpos + corr_angle\n if not self.simulate_feedback:\n if mono == \"dcm\":\n self.dcm_pitch_tserver.write_attribute('Position', target_pitchpos)\n if mono == \"dmm\":\n self.dmm_x2rot_tserver.write_attribute('Position', target_pitchpos)\n self.last_corr_angle = corr_angle\n self.feedback_time = datetime.datetime.now()\n self.cycle = 0\n self.cycle = 0 if self.cycle >= interval else self.cycle + 1\n self.set_x2pitchlabel()\n yield","def earth_tide(theta, lamda, gtime):\n\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist # bpos common block\n global h, k, l # love common block\n h = [0.6114, 0.2891, 0.175]\n k = [0.304, 0.09421, 0.043]\n l = [0.0832, 0.0145, 0.0103]\n\n global azt, azs # azimut common block\n global etmut # tdiff common block\n global moon # sunny common block\n moon = 0\n # hardwire these - you can only send it ONE droptime\n deltat = 1\n NPT = 1\n\n temp_time = num2date(gtime)\n\n YY = temp_time.year\n MO = temp_time.month\n DD = temp_time.day\n HH = temp_time.hour\n MM = temp_time.minute\n SS = temp_time.second\n # Initialize variables\n irl = 1\n iflag = 0\n ntotl = 1\n iget = [0, 0, 0, 0, 0, 0, 0] # ' !!!\n ispc = [0, 0, 0, 0] # ' !!!\n ntw = [1, 0, 0] # ' !!!\n ioptn = 't'\n ielement = 0\n # \tdata statements for input and output unit numbers (on terminal I/O)\n inun = 5\n ioun = 6\n nptpb = 6\n\n yr1 = YY - 1900\n day1 = date2num(datetime(YY, MO, DD))\n # \tfind times in hours from 0 hr, 1 jan 1900\n # matlab:\n ts = (\n SS / 3600\n + MM / 60\n + HH\n + 24 * (day1 - 1)\n + 8760 * yr1\n + 24 * np.fix((yr1 - 1) / 4)\n )\n # python:\n dj = date_to_julian_day(datetime(YY, MO, DD))\n djref = date_to_julian_day(datetime(1899, 12, 31, 0, 0, 0))\n delta_dj = (\n dj - djref\n ) # difference in days from current date (0hr) to 0hr, 1 jan 1900\n delta_djhr = float(delta_dj) * 24.0 + HH - 12.0 + MM / 60.0 + SS / 3600.0\n te = ts + (NPT - 1) * deltat / 3600\n d = deltat / 3600\n # terms=(te-ts)/d + 1\n terms = NPT\n\n # done asking questions - begin execution\n i = 1\n tt = ts\n sph(theta, lamda, 0)\n etmut = 41.184 + yr1 - 70\n # matlab:\n # t = (tt+12 + (etmut/3600))/876600\n t = (delta_djhr + etmut / 3600) / 876600\n # t is ephemeris time in julian centuries from 12 hr 0 jan 1900\n ephem(t)\n\n # calculate normalized gravity tides\n [grav, tilt, strain, gdc] = elastd(ntw)\n\n gravtide = 1.0e5 * grav\n # convert m/s² to mgal: 1m/s² = 100 gal = 100 000 mgal\n\n iflag = 1\n\n iterms = np.fix(terms)\n i = 1\n return gravtide","def NAME():\n\n # Location of data\n base_dir = \"(Location)\" #Location of align tif --> Should be the location of the experiment's align tiff folder, ex: \"C/desktop/work/image_processing/YYYYMMDD/align_tiffs\"\n resolution = {'res_xy_nm': 100, 'res_z_nm': 70} #Resolution of a pixel (do not alter)\n thresh = 0.9 #What qualifies for final probability map (do not alter)\n number_of_datasets = 20 #Number of wells in the experiemnts, \"20\" is an example where there are 16 samples and 4 controls\n\n #Rb Antibody\n conjugate_fn_str = 'GAD2' #String segment to search in a filename\n #conjugate_fn_str should be the term used in the name of the control align tiff for a well (usually \"PSD\", \"GAD2\", or \"SYNAPSIN\")\n target_fn_str = 'L106'\n #Ms Antibody project name, no parent or subclone number needed\n #target_fn_str should be the project number, for instance if this was testing L109 samples, this would be \"L109\"\n #Takes base directory string and gives you an array of all the files within\n filenames = aa.getListOfFolders(base_dir) #Do not change\n conjugate_filenames = [] #Do not change\n target_filenames = [] #Do not change\n query_list = [] #Do not change\n folder_names = [] #Do not change\n\n for n in range(1, 17):\n #Use if dataset missing\n #This is where you put in the rangee of wells used as your test samples\n #Since we have 16 samples that are test samples for L106, the range is equal to 1 through n+1, or 1 through 17\n #If your test samples do not begin at well 1, then adjust the beginning of the range accordingly (3 through 17 if the first test sample is in well 3) \n #continue\n\n print('Well: ', str(n)) #Do not change\n folder_names.append('Test-' + str(n)) # Collate 'dataset' names for excel sheet #Do not change\n conjugate_str = str(n) + '-' + conjugate_fn_str #creates filename to search for #Creates n-conjugatename #Do not change\n target_str = str(n) + '-' + target_fn_str #Do not change\n\n # Search for file associated with the specific dataset number\n indices = [i for i, s in enumerate(filenames) if conjugate_str == s[0:len(conjugate_str)]] #Do not change\n conjugate_name = filenames[indices[0]] #Do not change\n print(conjugate_name) #Do not change\n indices = [i for i, s in enumerate(filenames) if target_str == s[0:len(target_str)]] #Do not change\n target_name = filenames[indices[0]] #Do not change\n print(target_name) #Do not change\n \n conjugate_filenames.append(conjugate_name) #Do not change\n target_filenames.append(target_name) #Do not change\n\n # Create query\n #\n query = {'preIF': [conjugate_name], 'preIF_z': [2],\n 'postIF': [target_name], 'postIF_z': [1],\n 'punctumSize': 2}\n #preIF = items that are presynaptic targets go here, because GAD2, our conjugate, is presynaptic I put the conjugate_name in this box\n #preIF_z = how many tiffs a puncta must be in to be registered, conjugate sample number is 2 so 2 goes in this box\n #postIF = items that are postsynaptic targets go here, L106 is postsynaptic so I put target_name here\n #postIF_z = how many tiffs a puncta must be in to be registered, target sample number is 1 (for now unless changed later) \n #punctumSize = size of punctum the algorithm is looking for, do not change unless directed to\n\n \"\"\"Example of a presynaptic target and presynaptic conjugate\n query = {'preIF': [target_name,conjugate_name], 'preIF_z': [1,2],\n 'postIF': [], 'postIF_z': [],\n 'punctumSize': 2}\"\"\"\n\n \"\"\"Example of a postsynaptic target and presynaptic conjugate\n query = {'preIF': [conjugate_name], 'preIF_z': [2],\n 'postIF': [target_name], 'postIF_z': [1],\n 'punctumSize': 2}\"\"\"\n\n \"\"\"Example of a postsynaptic target and postsynaptic conjugate\n query = {'preIF': [], 'preIF_z': [],\n 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2],\n 'punctumSize': 2}\"\"\"\n\n \"\"\"Example of a presynaptic target and postsynaptic conjugate\n query = {'preIF': [target_name], 'preIF_z': [1],\n 'postIF': [conjugate_name], 'postIF_z': [2],\n 'punctumSize': 2}\"\"\"\n\n\n query_list.append(query)\n\n\n #The following n samples are controls - you can add as many of these as you want by copying the block of code and pasting it after the last one\n #The notes in the following block of code apply to all of the controls\n n = 17 #well number of control sample\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet #Do not change\n reference_fn_str = 'GAD2' #String segment to search in a filename #refernce_fn_str is the project number/name of RB control\n target_fn_str = 'L106' #target_fn_str is the project number of the Ms control you are using\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n) #Do not alter\n conjugate_filenames.append(conjugate_name) #Do not alter\n target_filenames.append(target_name) #Do not alter\n query = {'preIF': [conjugate_name], 'preIF_z': [2], 'postIF': [target_name], 'postIF_z': [1], 'punctumSize': 2} #Se the examples and explanations above about \"query\"\n query_list.append(query) #Do not change\n\n n = 18\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\n reference_fn_str = 'GAD2' #String segment to search in a filename\n target_fn_str = 'SP2'\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\n conjugate_filenames.append(conjugate_name)\n target_filenames.append(target_name)\n query = {'preIF': [target_name,conjugate_name], 'preIF_z': [1,2], 'postIF': [], 'postIF_z': [], 'punctumSize': 2}\n query_list.append(query)\n\n n = 19\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\n reference_fn_str = 'NP-RB' #String segment to search in a filename\n target_fn_str = 'NP-MS'\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\n conjugate_filenames.append(conjugate_name)\n target_filenames.append(target_name)\n query = {'preIF': [], 'preIF_z': [], 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2], 'punctumSize': 2}\n query_list.append(query)\n\n n = 20\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\n reference_fn_str = 'NPNS-RB' #String segment to search in a filename\n target_fn_str = 'NPNS-MS'\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\n conjugate_filenames.append(conjugate_name)\n target_filenames.append(target_name)\n query = {'preIF': [], 'preIF_z': [], 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2], 'punctumSize': 2}\n query_list.append(query)\n\n\n \n measure_list = aa.calculate_measure_lists(query_list, None, base_dir,\n thresh, resolution, target_filenames) # Run all the queries\n\n df = aa.create_df(measure_list, folder_names, target_filenames, conjugate_filenames) #Do not change\n print(df) #Do not change\n\n return df #Do not change","def makeLoop( name, *structure ):\n return X12Loop( name, *structure )","def extractQuantities(path, run, t0, t1):\n data = pyLTR.Models.MIX(path, run)\n\n # hard-coded input for testing & debugging:\n #data = pyLTR.Models.LFM('/hao/aim2/schmitt/data/LTR-2_0_1b/r1432/March1995/LR/single', 'LRs')\n \n #Make sure variables are defined in the model.\n modelVars = data.getVarNames()\n for v in ['Grid X', 'Grid Y', \n 'Potential North [V]', 'Potential South [V]', \n 'FAC North [A/m^2]', 'FAC South [A/m^2]',\n 'Pedersen conductance North [S]', 'Pedersen conductance South [S]', \n 'Hall conductance North [S]', 'Hall conductance South [S]', \n 'Average energy North [keV]', 'Average energy South [keV]',\n 'Number flux North [1/cm^2 s]', 'Number flux South [1/cm^2 s]']:\n assert( v in modelVars )\n\n timeRange = data.getTimeRange()\n if len(timeRange) == 0:\n raise Exception(('No data files found. Are you pointing to the correct run directory?'))\n\n index0 = 0\n if t0:\n for i,t in enumerate(timeRange):\n if t0 >= t:\n index0 = i\n\n index1 = len(timeRange)-1\n if t1:\n for i,t in enumerate(timeRange):\n if t1 >= t:\n index1 = i \n\n print(( 'Extracting MIX quantities for time series over %d time steps.' % (index1-index0) ))\n \n # Output a status bar displaying how far along the computation is.\n progress = pyLTR.StatusBar(0, index1-index0)\n progress.start()\n\n t_doy = []\n cpcpNorth = []\n cpcpSouth = []\n hpNorth = []\n hpSouth = []\n ipfacNorth = []\n ipfacSouth = []\n\n # Pre-compute area of the grid.\n x = data.read('Grid X', timeRange[index0])\n y = data.read('Grid Y', timeRange[index0])\n # Fix singularity at the pole\n x[:,0] = 0.0\n y[:,0] = 0.0\n z = numpy.sqrt(1.0-x**2-y**2)\n ri = 6500.0e3 # Radius of ionosphere\n areaMixGrid = pyLTR.math.integrate.calcFaceAreas(x,y,z)*ri*ri\n\n for i,time in enumerate(timeRange[index0:index1]):\n try:\n # -- Day of Year\n tt = time.timetuple()\n t_doy.append(tt.tm_yday+tt.tm_hour/24.0+tt.tm_min/1440.0+tt.tm_sec/86400.0)\n\n # --- Cross Polar Cap Potential\n psiNorth = data.read('Potential North [V]', time) / 1000.0\n cpcpNorth.append(psiNorth.max() - psiNorth.min())\n\n psiSouth = data.read('Potential South [V]', time) / 1000.0\n cpcpSouth.append(psiSouth.max() - psiSouth.min())\n \n # --- Hemispheric Power\n energy = data.read('Average energy North [keV]', time)\n flux = data.read('Number flux North [1/cm^2 s]', time)\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\n # KeV/cm^2s to mW/m^2 to GW\n hpNorth.append(hp.sum() * 1.6e-21) \n\n energy = data.read('Average energy South [keV]', time)\n flux = data.read('Number flux South [1/cm^2 s]', time)\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\n # KeV/cm^2s to mW/m^2 to GW\n hpSouth.append(hp.sum() * 1.6e-21)\n\n # --- Positive current density\n fac = data.read('FAC North [A/m^2]', time)\n fac[fac <= 0] = 0.0\n pfac = areaMixGrid * fac[:-1,:-1]\n ipfacNorth.append(pfac.sum()/1.0e6)\n\n fac = data.read('FAC South [A/m^2]', time)\n fac[fac <= 0] = 0.0\n pfac = areaMixGrid * fac[:-1,:-1]\n ipfacSouth.append(pfac.sum()/1.0e6)\n\n progress.increment()\n except KeyboardInterrupt:\n # Exit when the user hits CTRL+C.\n progress.stop()\n progress.join() \n print('Exiting.')\n import sys\n sys.exit(0)\n except:\n # Cleanup progress bar if something bad happened.\n progress.stop()\n progress.join()\n raise\n progress.stop()\n progress.join()\n\n dataNorth = pyLTR.TimeSeries()\n dataSouth = pyLTR.TimeSeries()\n dataNorth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\n dataSouth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\n dataNorth.append('doy', 'Day of Year', '', t_doy)\n dataSouth.append('doy', 'Day of Year', '', t_doy)\n \n # \"N\" and \"S\" label subscripts are redundant here, potentially leading to\n # mis-labeling of plots\n #dataNorth.append('cpcp', r'$\\Phi_N$', 'kV', cpcpNorth)\n #dataSouth.append('cpcp', r'$\\Phi_S$', 'kV', cpcpSouth)\n #\n #dataNorth.append('hp', r'$HP_N$', 'GW', hpNorth)\n #dataSouth.append('hp', r'$HP_S$', 'GW', hpSouth)\n #\n #dataNorth.append('ipfac', r'$FAC_N$', 'MA', ipfacNorth)\n #dataSouth.append('ipfac', r'$FAC_S$', 'MA', ipfacSouth)\n \n dataNorth.append('cpcp', r'$\\Phi$', 'kV', cpcpNorth)\n dataSouth.append('cpcp', r'$\\Phi$', 'kV', cpcpSouth)\n \n dataNorth.append('hp', r'$HP$', 'GW', hpNorth)\n dataSouth.append('hp', r'$HP$', 'GW', hpSouth)\n \n dataNorth.append('ipfac', r'$FAC$', 'MA', ipfacNorth)\n dataSouth.append('ipfac', r'$FAC$', 'MA', ipfacSouth)\n\n return (dataNorth, dataSouth)","def Script3():\n # In non-script code, use getLogger(__name__) at module scope instead.\n logger = logging.getLogger(\"Script3\") \n gal_flux = 1.e5 # ADU\n gal_n = 3.5 #\n gal_re = 3.7 # pixels\n g1 = -0.23 #\n g2 = 0.15 #\n atmos_a_sigma=2.1 # pixels\n atmos_a_g1 = -0.13 # (shear for \"a\")\n atmos_a_g2 = -0.09 #\n atmos_fa=0.2 # (fraction of flux in \"a\")\n atmos_b_sigma=0.9 # pixels\n atmos_b_g1 = 0.02 # (shear for \"b\")\n atmos_b_g2 = -0.04 #\n opt_defocus=0.53 # wavelengths\n opt_a1=-0.29 # wavelengths\n opt_a2=0.12 # wavelengths\n opt_c1=0.64 # wavelengths\n opt_c2=-0.33 # wavelengths\n opt_padFactor=6 # multiples of Airy padding required to avoid folding for aberrated PSFs\n lam = 800 # nm NB: don't use lambda - that's a reserved word.\n tel_diam = 4. # meters \n pixel_scale = 0.23 # arcsec / pixel\n wcs_g1 = -0.02 #\n wcs_g2 = 0.01 #\n sky_level = 1.e3 # ADU / pixel\n gain = 1.7 # ADU / e-\n read_noise = 0.3 # ADU / pixel\n\n logger.info('Starting script 3 using:')\n logger.info(' - sheared (%.2f,%.2f) Sersic galaxy (flux = %.1e, n = %.1f, re = %.2f),', \n g1, g2, gal_flux, gal_n, gal_re)\n logger.info(' - sheared double-Gaussian atmospheric PSF')\n logger.info(' First component: sigma = %.2f, shear = (%.2f,%.2f), frac = %.2f',\n atmos_a_sigma, atmos_a_g1, atmos_a_g2, atmos_fa)\n logger.info(' Second component: sigma = %.2f, shear = (%.2f,%.2f), frac = %.2f',\n atmos_b_sigma, atmos_b_g1, atmos_b_g2, 1-atmos_fa)\n logger.info(' - optical PSF with defocus = %.2f, astigmatism = (%.2f,%.2f),',\n opt_defocus, opt_a1, opt_a2)\n logger.info(' coma = (%.2f,%.2f), lambda = %.0f nm, D = %.1f m', \n opt_c1, opt_c2, lam, tel_diam)\n logger.info(' - pixel scale = %.2f,',pixel_scale)\n logger.info(' - WCS distortion = (%.2f,%.2f),',wcs_g1,wcs_g2)\n logger.info(' - Poisson noise (sky level = %.1e, gain = %.1f).',sky_level, gain)\n logger.info(' - Gaussian read noise (sigma = %.2f).',read_noise)\n\n \n # Define the galaxy profile.\n gal = galsim.Sersic(gal_n, flux=gal_flux, re=gal_re)\n\n # Shear the galaxy by some value.\n gal.applyShear(g1, g2)\n logger.info('Made galaxy profile')\n\n # Define the atmospheric part of the PSF.\n atmos_a = galsim.Gaussian(flux=atmos_fa, sigma=atmos_a_sigma)\n atmos_a.applyShear(atmos_a_g1 , atmos_a_g2)\n atmos_b = galsim.Gaussian(flux=1-atmos_fa, sigma=atmos_b_sigma)\n atmos_b.applyShear(atmos_b_g1 , atmos_b_g2)\n atmos = galsim.Add([atmos_a, atmos_b])\n logger.info('Made atmospheric PSF profile')\n\n # Define the optical part of the PSF.\n # The first argument of OpticalPSF below is lambda/D,\n # which needs to be in pixel units, so do the calculation:\n lam_over_D = lam * 1.e-9 / tel_diam # radians\n lam_over_D *= 206265 # arcsec\n lam_over_D *= pixel_scale # pixels\n logger.info('Calculated lambda over D = %f pixels', lam_over_D)\n # The rest of the values here should be given in units of the \n # wavelength of the incident light. padFactor is used to here to reduce 'folding' for these\n # quite strong aberration values\n optics = galsim.OpticalPSF(lam_over_D, \n defocus=opt_defocus, coma1=opt_c1, coma2=opt_c2, astig1=opt_a1,\n astig2=opt_a2, padFactor=opt_padFactor)\n logger.info('Made optical PSF profile')\n\n # Start with square pixels\n pix = galsim.Pixel(xw=pixel_scale, yw=pixel_scale)\n # Then shear them slightly by the negative of the wcs shear.\n # This way the later distortion of the full image will bring them back to square.\n pix.applyShear(-wcs_g1, -wcs_g2)\n logger.info('Made pixel profile')\n\n # Final profile is the convolution of these.\n final = galsim.Convolve([gal, atmos, optics, pix])\n final_epsf = galsim.Convolve([atmos, optics, pix])\n logger.info('Convolved components into final profile')\n\n # Now apply the wcs shear to the final image.\n final.applyShear(wcs_g1, wcs_g2)\n final_epsf.applyShear(wcs_g1, wcs_g2)\n logger.info('Applied WCS distortion')\n\n # Draw the image with a particular pixel scale.\n image = final.draw(dx=pixel_scale)\n image_epsf = final_epsf.draw(dx=pixel_scale)\n # Draw the optical PSF component at its Nyquist sample rate\n image_opticalpsf = optics.draw(dx=lam_over_D/2.)\n logger.info('Made image of the profile')\n\n # Add a constant sky level to the image.\n sky_image = galsim.ImageF(bounds=image.getBounds(), initValue=sky_level)\n image += sky_image\n\n # Add Poisson noise to the image.\n rng = galsim.UniformDeviate(1314662)\n galsim.noise.addPoisson(image, rng, gain=gain)\n\n # Also add (Gaussian) read noise.\n galsim.noise.addGaussian(image, rng, sigma=read_noise)\n\n # Subtract off the sky.\n image -= sky_image\n logger.info('Added Gaussian and Poisson noise')\n\n # Write the image to a file\n if not os.path.isdir('output'):\n os.mkdir('output')\n file_name = os.path.join('output', 'demo3.fits')\n file_name_opticalpsf = os.path.join('output','demo3_opticalpsf.fits')\n file_name_epsf = os.path.join('output','demo3_epsf.fits')\n \n image.write(file_name, clobber=True)\n image_opticalpsf.write(file_name_opticalpsf, clobber=True)\n image_epsf.write(file_name_epsf, clobber=True)\n logger.info('Wrote image to %r', file_name)\n logger.info('Wrote optics-only PSF image (Nyquist sampled) to %r', file_name_opticalpsf)\n logger.info('Wrote effective PSF image to %r', file_name_epsf)\n\n moments = HSM_Moments(file_name)\n moments_corr = HSM_Regauss(file_name, file_name_epsf, image.array.shape)\n\n logger.info('HSM reports that the image has measured moments:')\n logger.info(' Mxx = %.3f, Myy = %.3f, Mxy = %.3f', moments.mxx, moments.myy, moments.mxy)\n logger.info('When carrying out Regaussianization PSF correction, HSM reports')\n logger.info(' g1,g2 = %f,%f', moments_corr.g1, moments_corr.g2)\n logger.info('Expected values in the limit that noise and non-Gaussianity are negligible:')\n logger.info(' g1,g2 = %f,%f', g1+wcs_g1,g2+wcs_g2)\n print","def test_ML_check_timing_epem_ttx(self):\n \n self.setup_logFile_for_logger('madgraph.check_cmd')\n try:\n cmd = os.getcwd()\n self.do('import model loop_sm')\n if path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')):\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\n # Make sure it works for an initial run\n self.do('check timing -reuse e+ e- > t t~ [virt=QCD]')\n self.assertEqual(cmd, os.getcwd())\n self.assertTrue(path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')))\n self.assertTrue(path.isfile(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx',\\\n 'SubProcesses/P0_epem_ttx/result.dat')))\n self.assertTrue(path.isfile(self.tmp_path['madgraph.check_cmd']))\n res = open(self.tmp_path['madgraph.check_cmd']).read()\n self.assertTrue('Generation time total' in res)\n self.assertTrue('Executable size' in res)\n self.assertTrue(not 'NA' in res)\n \n # Now for a Reuse-run\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)\n self.setup_logFile_for_logger('madgraph.check_cmd')\n self.do('check timing -reuse e+ e- > t t~ [virt=QCD]')\n self.assertEqual(cmd, os.getcwd())\n self.assertTrue(path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')))\n self.assertTrue(path.isfile(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx',\\\n 'SubProcesses/P0_epem_ttx/result.dat')))\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\n self.assertTrue(path.isfile(self.tmp_path['madgraph.check_cmd']))\n res = open(self.tmp_path['madgraph.check_cmd']).read()\n self.assertTrue('Generation time total' in res)\n self.assertTrue('Executable size' in res)\n self.assertTrue(res.count('NA')<=8)\n except:\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)\n if path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')):\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\n raise\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)","def run(self):\n\t\trun_start_timestamp = time.clock()\n\t\texperiment_start_time = time.strftime(\"%H_%M_%S_%m_%d_%Y\", time.localtime())\n\t\tprint \"starting dataset at {}\\n\".format(experiment_start_time)\n\t\tdata_file = \"raw_data/data_\" + experiment_start_time + \".csv\"\n\t\twith open(data_file, 'a') as f:\n\t\t\tf.write(\"Packet, Sample offset, Latency (ms), Sample\\n\")\n\n\t\t##### EXPERIMENT SETUP #####\n\t\t# sample for a max of 1.5 seconds\n\t\t# approximate number of samples assuming ~500ms latency per packet\n\t\tnSamples = (int) (1.5 * self.sampling_freq)\n\n\t\t#csamples, lost, corrupted\n\t\tbuffer_info = [0, 0, 0]\n\n\t\tnum_packets_received = 0\n\t\tnum_packets_missed = 0\n\t\tnum_acks_missed = 0\n\n\t\t# array to hold indices of packets missed\n\t\tpacket_number_missed = []\n\n\t\t# num_packets_received + num_packets_missed must equal num_tries\n\t\tnum_tries = 0\n\n\t\t# ready for next button press if previous packet has been handled\n\t\t# and if instruments, variables are configured for next packet\n\t\tlast_packet_handled = True\n\t\t# keep track of if current packet has been received\n\t\tpacket_received = True\n\n\t\t#keep track of previous packet acknowledgment\n\t\tack_missed = False\n\n\t\t##### END SETUP #####\n\n\t\t##### MAIN LOOP of experiment. #####\n\t\t# runs for the duration of the experiment\n\t\t#note: openmote toggles its pins every packet creation and reception\n\n\t\twhile num_tries < self.num_packets_experiment:\n\t\t\t# initialize \"trash\" array to clear all buffer samples before starting button press\n\t\t\t# this must be able to hold 4096 samples, 2 bytes each\n\t\t\ttrashSamples = (c_uint16 * 4096)()\n\n\t\t\t#clear buffer\n\t\t\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, trashSamples, copy_all_samples=True)\n\n\t\t\t# clear rgwSamples for next packet\n\t\t\trgwSamples = (c_uint16 * nSamples)()\n\n\t\t\t# reset and configure DigitalIO\n\t\t\tsteady_state_DIO = self._configure_DigitalIO()\n\n\t\t\t# reset and configure DigitalIn to take nSamples on trigger\n\t\t\t# set DigitalIn trigger when button_press_mirror_bit channel is raised (this should start sampling)\n\t\t\tself._configure_DigitalIn(nSamples, self.button_press_mirror_bit)\n\n\t\t\tready_for_next_button_press = True\n\t\t\tbroke_early = False\n\n\t\t\t#print \"begin acquisition {}\".format(num_tries + 1)\n\t\t\tprev_csamples, curr_csamples = 0, 0\n\n\t\t\tif ack_missed:\n\t\t\t\tprint \"missed ack\"\n\t\t\t\ttime.sleep(0.55)\n\n\n\t\t\t# inner loop: runs from button press until packet received.\n\t\t\twhile buffer_info[0] < nSamples:\n\n\t\t\t\t# button press\n\t\t\t\tif last_packet_handled and ready_for_next_button_press:\n\t\t\t\t\t# we can send the next packet because the last packet was handled (received or understood to be missed)\n\t\t\t\t\t# and instruments are configured\n\t\t\t\t\t# button press -> set value on enabled AD2 output pins (digital_out_channels_bits)\n\t\t\t\t\t# AD2 output is hard wired to button press input which triggers acquisition\n\n\t\t\t\t\t#get current value of packet_received_pin; when packet is received this will toggle\n\t\t\t\t\tcurr_DIO = self._get_DIO_values()\n\t\t\t\t\tpacket_received_pins_state = curr_DIO & self.packet_received_bits\n\t\t\t\t\tpacket_created_pin_state = curr_DIO & self.packet_created_bit\n\n\t\t\t\t\tlast_packet_handled = False\n\t\t\t\t\tready_for_next_button_press = False\n\t\t\t\t\tpacket_received = False\n\t\t\t\t\tack_missed = False\n\n\t\t\t\t\t# press the button. all other outputs go low.\n\t\t\t\t\tdwf.FDwfDigitalIOOutputSet(self.interface_handler, c_uint16(self.button_press_bit))\n\t\t\t\t\t#reset all enabled digital out channels back to steady state (all high except button press)\n\t\t\t\t\tdwf.FDwfDigitalIOOutputSet(self.interface_handler, steady_state_DIO)\n\t\t\t\t\t\n\t\t\t\t\t#print \"button pressed\"\n\t\t\t\t\tnum_tries += 1\n\n\t\t\t\t# copy buffer samples to memory and flush\n\t\t\t\t#print \"Before: {}\".format(buffer_info)\n\t\t\t\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, rgwSamples)\n\t\t\t\t#print \"After: {}\".format(buffer_info)\n\t\t\t\t#buffer_flush_stop = time.clock()\n\n\t\t\t\tcurr_csamples = buffer_info[0]\n\t\t\t\tif curr_csamples == prev_csamples:\n\t\t\t\t\tprint \"broke early\"\n\t\t\t\t\tnum_tries -= 1\n\t\t\t\t\tbroke_early = True\n\n\t\t\t\t\t# stop sampling\n\t\t\t\t\tdwf.FDwfDigitalInConfigure(self.interface_handler, c_bool(0), c_bool(0))\n\t\t\t\t\tbreak\n\n\t\t\t\t# manually stop sampling once packet_received_bit is not equal to its pin state\n\t\t\t\tcurr_DIO = self._get_DIO_values()\n\t\t\t\tif ((curr_DIO & self.packet_received_bits) != packet_received_pins_state):\n\t\t\t\t\t#copy last buffer samples to memory\n\t\t\t\t\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, rgwSamples, last_read=True)\n\n\t\t\t\t\t# packet_received_bit toggled; stop sampling\n\t\t\t\t\tdwf.FDwfDigitalInConfigure(self.interface_handler, c_bool(0), c_bool(0))\n\n\t\t\t\t\tcurr_DIO = self._get_DIO_values()\n\t\t\t\t\tif (curr_DIO & self.packet_created_bit) != packet_created_pin_state:\n\t\t\t\t\t\tack_missed = True\n\n\t\t\t\t\tpacket_received = True\n\t\t\t\t\tnum_packets_received += 1\n\t\t\t\t\t#print \"received packet {}\".format(num_tries)\n\n\t\t\t\t\tbreak\n\n\t\t\t\tprev_csamples = curr_csamples\n\t\t\t\t# end of the inner loop\n\n\t\t\t#set this so we can push button again\n\t\t\tlast_packet_handled = True\n\n\t\t\tif broke_early:\n\t\t\t\tcontinue\n\n\t\t\t# reach here if packet was received OR if 1.5 million samples have been taken\n\t\t\tif packet_received == True:\n\t\t\t\tself.postprocess(num_tries, buffer_info, rgwSamples, data_file)\n\n\t\t\telse:\n\t\t\t\t# we took 1.5 million samples and supposedly missed the packet\n\t\t\t\tnum_packets_missed += 1\n\t\t\t\tpacket_number_missed.append(num_tries)\n\t\t\t\tself.postprocess(num_tries, buffer_info, rgwSamples, data_file, missed_packet=True)\n\t\t\t\t# set last_packet_handled to True to try button press again\n\n\n\t\trun_end_timestamp = time.clock()\n\t\tprint \"Done with experiment\"\n\t\t#print all packets sent, lost, total info\n\t\tprint \"Number of tries: {}\".format(num_tries)\n\t\tprint \"Number of received packets: {}\".format(num_packets_received)\n\t\tprint \"Number of missed packets: {}\\n\".format(num_packets_missed)\n\t\tprint \"Total duration: {} seconds\".format(run_end_timestamp - run_start_timestamp)\n\t\treturn","def AllindividualRuns():\n #800 nm\n RunData(getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/'), out='I800nm')\n RunData(getFiles(mintime=(15, 12, 20), maxtime=(15, 24, 16), folder='data/31Jul/'), out='I800nm5k')\n RunData(getFiles(mintime=(15, 28, 40), maxtime=(15, 39, 21), folder='data/31Jul/'), out='I800nm10k')\n RunData(getFiles(mintime=(15, 43, 24), maxtime=(15, 51, 47), folder='data/31Jul/'), out='I800nm20k')\n RunData(getFiles(mintime=(15, 56, 11), maxtime=(16, 02, 58), folder='data/31Jul/'), out='I800nm30k')\n RunData(getFiles(mintime=(16, 12, 39), maxtime=(16, 18, 25), folder='data/31Jul/'), out='I800nm38k')\n RunData(getFiles(mintime=(16, 21, 52), maxtime=(16, 26, 16), folder='data/31Jul/'), out='I800nm50k')\n RunData(getFiles(mintime=(16, 32, 02), maxtime=(16, 35, 23), folder='data/31Jul/'), out='I800nm54k')\n #700 nm\n RunData(getFiles(mintime=(17, 20, 17), maxtime=(17, 33, 17), folder='data/30Jul/'), out='I700nm5k')\n RunData(getFiles(mintime=(17, 37, 35), maxtime=(17, 46, 51), folder='data/30Jul/'), out='I700nm9k')\n RunData(getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/'), out='I700nm52k')\n RunData(getFiles(mintime=(17, 58, 18), maxtime=(17, 59, 31), folder='data/30Jul/'), out='I700nm32k')\n #600 nm\n RunData(getFiles(mintime=(15, 22, 00), maxtime=(15, 36, 32), folder='data/30Jul/'), out='I600nm5k')\n RunData(getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/'), out='I600nm54k')\n RunData(getFiles(mintime=(15, 52, 07), maxtime=(16, 06, 32), folder='data/30Jul/'), out='I600nm10k')\n #890 nm\n RunData(getFiles(mintime=(13, 37, 37), maxtime=(13, 50, 58), folder='data/01Aug/'), out='I890nm5k')\n RunData(getFiles(mintime=(14, 00, 58), maxtime=(14, 11, 54), folder='data/01Aug/'), out='I890nm10k')\n RunData(getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/'), out='I890nm30k')\n RunData(getFiles(mintime=(14, 30, 03), maxtime=(14, 34, 37), folder='data/01Aug/'), out='I890nm50k')","def _unpolarized_simulation(self, frequency, theta_0=0):\n s_data = self.simulate(frequency, 's', theta_0)\n p_data = self.simulate(frequency, 'p', theta_0)\n T = (s_data + p_data)/2\n return T","def lab_run_big(character_id, time_step):\n pass","def run_script(input_dir, output_dir, output_file, bstp_num):\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n print(\"\"\" Load results from step 1 & 2 \"\"\")\n start_0 = time.time()\n data_dim_file_name = output_dir + \"/temp/data_dim.mat\"\n mat = loadmat(data_dim_file_name)\n data_dim = mat['data_dim']\n data_dim = np.array([int(i) for i in data_dim[0, :]])\n n, l, m, p, g, g_num = data_dim\n y_design_file_name = output_dir + \"/temp/y_design.mat\"\n mat = loadmat(y_design_file_name)\n y_design = mat['y_design']\n resy_design_file_name = output_dir + \"/temp/resy_design.mat\"\n mat = loadmat(resy_design_file_name)\n resy_design = mat['resy_design']\n efit_eta_file_name = output_dir + \"/temp/efit_eta.mat\"\n mat = loadmat(efit_eta_file_name)\n efit_eta = mat['efit_eta']\n esig_eta_file_name = output_dir + \"/temp/esig_eta.mat\"\n mat = loadmat(esig_eta_file_name)\n esig_eta = mat['esig_eta']\n hat_mat_file_name = output_dir + \"/temp/hat_mat.mat\"\n mat = loadmat(hat_mat_file_name)\n hat_mat = mat['hat_mat']\n snp_file_name = output_dir + \"/temp/snp.mat\"\n mat = loadmat(snp_file_name)\n snp = mat['snp']\n # read the image size\n img_size_file_name = input_dir + \"img_size.txt\"\n img_size = np.loadtxt(img_size_file_name)\n img_size = np.array([int(i) for i in img_size])\n # read the image index of non-background region\n img_idx_file_name = input_dir + \"img_idx.txt\"\n img_idx = np.loadtxt(img_idx_file_name)\n img_idx = np.array([int(i) for i in img_idx])\n end_0 = time.time()\n print(\"Elapsed time in Step 3 is \", end_0 - start_0)\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n print(\"\"\" Step 3. Significant locus-voxel and locus-subregion detection \"\"\")\n start_3 = time.time()\n alpha = 1e-5\n c_alpha = -10**alpha\n bstp_num = int(bstp_num)\n max_stat_bstp, max_area_bstp = wild_bstp(snp, y_design, resy_design, efit_eta, esig_eta, hat_mat,\n img_size, img_idx, c_alpha, g_num, bstp_num)\n print(max_stat_bstp)\n print(max_area_bstp)\n bstp_out = np.hstack((max_stat_bstp, max_area_bstp))\n bstp_out_file_name = output_dir + output_file\n np.savetxt(bstp_out_file_name, bstp_out)\n end_3 = time.time()\n print(\"Elapsed time in Step 3 is \", end_3 - start_3)","def ml_loop(side: str):\n\n # === Here is the execution order of the loop === #\n # 1. Put the initialization code here\n ball_served = False\n blocker_last_x = 0\n\n class Pred:\n pred = 100\n blocker_pred_x = 0\n last_command = 0\n blocker_vx = 0\n\n \n def move_to(player, pred) : #move platform to predicted position to catch ball \n if player == '1P':\n if scene_info[\"platform_1P\"][0]+20 > (pred-10) and scene_info[\"platform_1P\"][0]+20 < (pred+10): return 0 # NONE\n elif scene_info[\"platform_1P\"][0]+20 <= (pred-10) : return 1 # goes right\n else : return 2 # goes left\n else :\n if scene_info[\"platform_2P\"][0]+20 > (pred-10) and scene_info[\"platform_2P\"][0]+20 < (pred+10): return 0 # NONE\n elif scene_info[\"platform_2P\"][0]+20 <= (pred-10) : return 1 # goes right\n else : return 2 # goes left\n\n def ml_loop_for_1P(): \n # ball slicing\n if scene_info[\"ball_speed\"][1] > 0 and (scene_info[\"ball\"][1]+scene_info[\"ball_speed\"][1]) >= 415 and Pred.last_command == 0:\n print(\"------\")\n ball_x = scene_info[\"ball\"][0]\n ball_y = scene_info[\"ball\"][1]\n ball_vx = scene_info[\"ball_speed\"][0]\n ball_slice_vx = scene_info[\"ball_speed\"][0]+np.sign(scene_info[\"ball_speed\"][0])*3\n ball_vy = scene_info[\"ball_speed\"][1] \n blocker_x = scene_info['blocker'][0] + Pred.blocker_vx\n \n y = abs((415 - ball_y) // ball_vy)\n pred_ball_1P = ball_x + ball_vx * y\n\n y = abs((415 - 260) // ball_vy)\n pred_ball_blocker = pred_ball_1P + ball_slice_vx * y\n bound = pred_ball_blocker // 200 # Determine if it is beyond the boundary\n if (bound > 0): # pred > 200 # fix landing position\n if (bound%2 == 0) : \n pred_ball_blocker = pred_ball_blocker - bound*200 \n else :\n pred_ball_blocker = 200 - (pred_ball_blocker - 200*bound)\n elif (bound < 0) : # pred < 0\n if (bound%2 ==1) :\n pred_ball_blocker = abs(pred_ball_blocker - (bound+1) *200)\n else :\n pred_ball_blocker = pred_ball_blocker + (abs(bound)*200)\n \n y = abs((415 - 260) // ball_vy)\n Pred.blocker_pred_x = blocker_x + Pred.blocker_vx * y \n if Pred.blocker_pred_x < 0: Pred.blocker_pred_x = abs(Pred.blocker_pred_x)\n elif Pred.blocker_pred_x > 170: Pred.blocker_pred_x = 170 - (Pred.blocker_pred_x - 170)\n \n if pred_ball_blocker >= Pred.blocker_pred_x-10 and pred_ball_blocker < Pred.blocker_pred_x+40:\n print(\"slice will hit blicker\")\n # don't slice \n # use origin ball vx to predict will hit blocker or not\n # if will hit blicker let ball go reverse direction\n y = abs((415 - 260) // ball_vy)\n pred_ball_blocker = pred_ball_1P + ball_vx * y\n bound = pred_ball_blocker // 200 # Determine if it is beyond the boundary\n if (bound > 0): # pred > 200 # fix landing position\n if (bound%2 == 0) : \n pred_ball_blocker = pred_ball_blocker - bound*200 \n else :\n pred_ball_blocker = 200 - (pred_ball_blocker - 200*bound)\n elif (bound < 0) : # pred < 0\n if (bound%2 ==1) :\n pred_ball_blocker = abs(pred_ball_blocker - (bound+1) *200)\n else :\n pred_ball_blocker = pred_ball_blocker + (abs(bound)*200)\n\n if pred_ball_blocker >= Pred.blocker_pred_x-10 and pred_ball_blocker < Pred.blocker_pred_x+40:\n print(\"will hit blocker, hit reversed direction\")\n if scene_info[\"ball_speed\"][0] > 0: return 2\n else: return 1\n else: \n print(\"will not hit blicker, do nothing\")\n return 0\n else:\n # slice\n print(\"slice will not hit blocker\")\n if scene_info[\"ball_speed\"][0] > 0: return 1\n else: return 2\n\n elif scene_info[\"ball_speed\"][1] > 0 : # 球正在向下 # ball goes down\n x = ( scene_info[\"platform_1P\"][1]-scene_info[\"ball\"][1] ) // scene_info[\"ball_speed\"][1] # 幾個frame以後會需要接 # x means how many frames before catch the ball\n Pred.pred = scene_info[\"ball\"][0]+(scene_info[\"ball_speed\"][0]*x) # 預測最終位置 # pred means predict ball landing site \n bound = Pred.pred // 200 # Determine if it is beyond the boundary\n if (bound > 0): # pred > 200 # fix landing position\n if (bound%2 == 0) : \n Pred.pred = Pred.pred - bound*200 \n else :\n Pred.pred = 200 - (Pred.pred - 200*bound)\n elif (bound < 0) : # pred < 0\n if (bound%2 ==1) :\n Pred.pred = abs(Pred.pred - (bound+1) *200)\n else :\n Pred.pred = Pred.pred + (abs(bound)*200)\n return move_to(player = '1P',pred = Pred.pred)\n \n else : # 球正在向上 # ball goes up\n return move_to(player = '1P',pred = 100)\n\n\n\n def ml_loop_for_2P(): # as same as 1P\n if scene_info[\"ball_speed\"][1] > 0 : \n return move_to(player = '2P',pred = 100)\n else : \n x = ( scene_info[\"platform_2P\"][1]+30-scene_info[\"ball\"][1] ) // scene_info[\"ball_speed\"][1] \n pred = scene_info[\"ball\"][0]+(scene_info[\"ball_speed\"][0]*x) \n bound = pred // 200 \n if (bound > 0):\n if (bound%2 == 0):\n pred = pred - bound*200 \n else :\n pred = 200 - (pred - 200*bound)\n elif (bound < 0) :\n if bound%2 ==1:\n pred = abs(pred - (bound+1) *200)\n else :\n pred = pred + (abs(bound)*200)\n return move_to(player = '2P',pred = pred)\n\n # 2. Inform the game process that ml process is ready\n comm.ml_ready()\n\n # 3. Start an endless loop\n while True:\n # 3.1. Receive the scene information sent from the game process\n scene_info = comm.recv_from_game()\n\n # 3.2. If either of two sides wins the game, do the updating or\n # resetting stuff and inform the game process when the ml process\n # is ready.\n if scene_info[\"status\"] != \"GAME_ALIVE\":\n # Do some updating or resetting stuff\n ball_served = False\n\n # 3.2.1 Inform the game process that\n # the ml process is ready for the next round\n comm.ml_ready()\n continue\n\n # 3.3 Put the code here to handle the scene information\n\n # 3.4 Send the instruction for this frame to the game process\n if not ball_served:\n comm.send_to_game({\"frame\": scene_info[\"frame\"], \"command\": \"SERVE_TO_LEFT\"})\n blocker_last_x = scene_info[\"blocker\"][0]\n Pred.last_command = 0\n ball_served = True\n else:\n if side == \"1P\":\n Pred.blocker_vx = scene_info[\"blocker\"][0] - blocker_last_x\n if scene_info[\"blocker\"][0] == 0: Pred.blocker_vx = 5\n elif scene_info[\"blocker\"][0] == 170: Pred.blocker_vx = -5\n command = ml_loop_for_1P()\n blocker_last_x = scene_info[\"blocker\"][0]\n Pred.last_command = command\n else:\n command = ml_loop_for_2P()\n\n if command == 0:\n comm.send_to_game({\"frame\": scene_info[\"frame\"], \"command\": \"NONE\"})\n elif command == 1:\n comm.send_to_game({\"frame\": scene_info[\"frame\"], \"command\": \"MOVE_RIGHT\"})\n else :\n comm.send_to_game({\"frame\": scene_info[\"frame\"], \"command\": \"MOVE_LEFT\"})","def test_significance_individual_timepoints(tensor_directory, analysis_name):\n\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \".hdf5\"), \"r\")\n activity_dataset = analysis_file[\"Data\"]\n metadata_dataset = analysis_file[\"metadata\"]\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\n print(\"metadata_dataset\", np.shape(metadata_dataset))\n \"\"\"\n # Open Analysis Dataframe\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \"_Trialwise_.h5\"), mode=\"r\")\n activity_dataset = analysis_file.root[\"Data\"]\n metadata_dataset = analysis_file.root[\"Trial_Details\"]\n\n # Create P and Slope Tensors\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\n\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\"Timepoint\"):\n\n # Get Timepoint Data\n timepoint_activity = activity_dataset[timepoint_index]\n\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\"Pixel\", leave=True):\n\n # Package Into Dataframe\n pixel_activity = timepoint_activity[:, pixel_index]\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\n\n # Fit Mixed Effects Model\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\n p_value_tensor[timepoint_index, pixel_index] = p_value\n slope_tensor[timepoint_index, pixel_index] = slope\n\n\n # Save These Tensors\n np.save(os.path.join(tensor_directory, analysis_name + \"_p_value_tensor.npy\"), p_value_tensor)\n np.save(os.path.join(tensor_directory, analysis_name + \"_slope_tensor.npy\"), slope_tensor)","def PMTandPiezoPlot(datadir,run,event,gain): \n en = event\n mu = gain\n e = sbc.DataHandling.GetSBCEvent.GetEvent(datadir+'/'+run,en)\n print(e[\"fastDAQ\"].keys())\n cgate = e[\"fastDAQ\"][\"CAMgate\"]\n dcam = np.diff(cgate)\n \n p0=e[\"fastDAQ\"][\"Piezo1\"]\n p1 = e[\"fastDAQ\"][\"Piezo2\"]\n fdt = e[\"fastDAQ\"][\"time\"]\n runreconpath = \"/pnfs/coupp/persistent/grid_output/SBC-17/output/%s/\"%run\n pmtdiffs = []\n diffs = []\n \n camOnTimes = [fdt[i] for i in range(len(dcam)) if dcam[i] < -0.5]\n camOffTimes = [fdt[i] for i in range(len(dcam)) if dcam[i] > 0.5]\n print(len(camOnTimes))\n print(len(camOffTimes))\n \n acousticfilename = runreconpath+\"AcousticAnalysis_%s.bin\"%run\n a = sbc.DataHandling.ReadBinary.ReadBlock(acousticfilename)\n bubt0 = a[\"bubble_t0\"]\n \n pmttracetime = e[\"PMTtraces\"][\"t0_sec\"][:,0]+e[\"PMTtraces\"][\"t0_frac\"][:,0]\n d=sbc.AnalysisModules.PMTfastDAQalignment.PMTandFastDAQalignment(e)\n pmtalign = d[\"PMT_trigt0_sec\"]+d[\"PMT_trigt0_frac\"]\n tracetimes = pmttracetime - pmtalign\n at0 = bubt0[en,0]\n at0_1 = bubt0[en,1]\n \n allxyzfname = \"/pnfs/coupp/persistent/grid_output/SBC-17/output/SimpleXYZ_all.bin\"\n xyzf = sbc.DataHandling.ReadBinary.ReadBlock(allxyzfname)\n indices = [i for i,x in enumerate(xyzf[\"runid\"]) if str(x[0])+\"_\"+str(x[1]) == run]\n xyz_reconstructed = True\n if len(indices) > 0:\n runposreco = {\"ev\":[xyzf[\"ev\"][indices]],\"x\":[xyzf[\"bubX\"][indices]],\n \"y\":[xyzf[\"bubY\"][indices]],\"z\":[xyzf[\"bubZ\"][indices]]}\n z = runposreco[\"z\"][0][int(int(en))]\n else:\n print(\"no handscan?\")\n z = 1.5\n xyz_reconstructed = False\n lag_expected = (-23.387649*z - 261.020495)*1e-6 # fit from other analysis\n t0_expected_p0 = at0 + lag_expected\n t0_expected_p1 = at0_1 + lag_expected\n \n i=0\n candidates = []\n candidate_times=[]\n for t in (tracetimes-at0):\n \n if t<0.2 and t>-0.2:\n lastCamOff = 0\n for k in range(len(camOffTimes)):\n if t+at0 > camOffTimes[k]:\n lastCamOff = camOffTimes[k]\n elif t+at0 < camOffTimes[k]:\n break\n if t+at0-lastCamOff > 25e-6:\n \n pmtdiffs.append(t)\n trace = np.fabs(e[\"PMTtraces\"][\"traces\"][i][0])\n if max(trace) == 128:\n trace = pi.stitchTraces(trace,np.fabs(e[\"PMTtraces\"][\"traces\"][i][1]))\n dt = e[\"PMTtraces\"][\"dt\"][i][0]\n #baseline = np.mean(trace[0:50])\n #trace = trace - baseline\n [phe,n,totInt,pktimes] = pi.SBC_pulse_integrator_bressler(trace,dt)\n \n if phe != None:\n phe /= mu\n candidates.append(phe)\n candidate_times.append(t)\n i+=1\n candidate_phe = 0\n the_index = 0\n i=0\n near_trace_indices = []\n for t in candidate_times:\n if t > -500e-6 and t <0:\n near_trace_indices.append(list(tracetimes-at0).index(t))\n if candidates[i]>candidate_phe:\n candidate_phe = candidates[i]\n the_index = i\n i+=1\n \n if len(candidates) != 0:\n if max(candidates)>0:\n diffs.append(candidate_times[candidates.index(max(candidates))])\n fig,ax1 = plt.subplots()\n ax2 = ax1.twinx()\n ax1.plot(fdt,p0,'b',alpha=0.6, label = 'piezo 0')\n ax1.plot(fdt,p1,'k',alpha=0.2, label= 'piezo 1')\n for i in range(len(candidates)):\n if i == the_index:\n ax2.plot([candidate_times[i]+at0,candidate_times[i]+at0],[0,candidates[i]],'r',lw=4)\n else:\n ax2.plot([candidate_times[i]+at0,candidate_times[i]+at0],[0,candidates[i]],'y',lw=4)\n #ax2.plot([min(candidate_times),max(candidate_times)],[0,0],linewidth=2)\n ax1.plot([at0,at0],[-0.5,0.5],'b',linewidth=2, label = 'acoustic t0, p0')\n ax1.plot([at0_1,at0_1],[-0.5,0.5],'k',linewidth=2, label = 'acoustic t0, p1')\n \"\"\"\n if xyz_reconstructed:\n ax1.plot([t0_expected_p0,t0_expected_p0],[-0.5,0.5],'b:',linewidth=2, label = 'expected PMT t0, p0')\n ax1.plot([t0_expected_p1,t0_expected_p1],[-0.5,0.5],'k:',linewidth=2, label = 'expected PMT t0, p1')\n else:\n ax1.plot([t0_expected_p0,t0_expected_p0],[-0.5,0.5],'b:',linewidth=2, label = 'expected PMT t0, p0, center of chamber')\n ax1.plot([t0_expected_p1,t0_expected_p1],[-0.5,0.5],'k:',linewidth=2, label = 'expected PMT t0, p1, center of chamber')\n \"\"\"\n ax1.plot(fdt,cgate,'c')\n ax1.plot(fdt[:-1],dcam,'m')\n ax2.set_ylabel('pmt signal (phe)',fontsize=20)\n ax1.set_xlabel('time (s)',fontsize=20)\n ax1.set_ylabel('Acoustic signa(V)',fontsize=20)\n ax1.set_ylim([min(p1),max(p1)])\n ax2.set_xlim([-0.1,0.1])\n #ax2.set_ylim([0,5])\n ax1.legend()\n plt.show\n \n for j in near_trace_indices:\n trace = e[\"PMTtraces\"][\"traces\"][j][0]\n dt = e[\"PMTtraces\"][\"dt\"]\n dt_tr = dt[j][0]\n tPMT = np.arange(len(trace))*dt_tr\n plt.figure()\n plt.plot(tPMT,trace)\n plt.xlabel(\"t (s)\")\n plt.ylabel(\"PMT signal\")\n plt.show\n \n plt.figure()\n plt.plot(e[\"fastDAQ\"][\"time\"],e[\"fastDAQ\"][\"VetoCoinc\"])\n plt.ylabel(\"Veto Coincidence signal\",fontsize=18)\n plt.xlabel(\"time (s)\")\n plt.show","def runSimulation(self, R=5000, N=1,s=1000, method='RL'):\n global n_ec\n import numpy as np\n import matplotlib.pyplot as plt\n import matplotlib\n matplotlib.use('Agg')\n matplotlib.pyplot.switch_backend('agg')\n\n plt.rcParams.update({'font.size':20})\n plt.rc('xtick', labelsize=20)\n plt.rc('ytick', labelsize=20)\n # step = 2 fs\n # each round is 2 fs * 1000 = 2 ps\n\n init = 'ala2_1stFrame.pdb' #pdb name\n inits = init\n n_ec = 2 # angles\n count = 1\n newPoints_name = 'start_r_'+str(count)+'.pdb'\n \n #W_0 = [1/n_ec for i in range(n_ec)] # no direction\n #W_0 = [[0.25, 0.25], [0.25, 0.25]]\n W_0 = [[1/(2*n_ec), 1/(2*n_ec)] for i in range(n_ec)] # directional\n print(W_0)\n\n Ws = []\n Ws.append(W_0)\n \n trj1 = self.run(production_steps = s, start=inits, production='trj_R_0.pdb') # return mdtraj object\n comb_trj1 = trj1 # single trajectory\n trjs = comb_trj1\n trj1_theta = self.map_angles(trj1) # changed for angles to display\n print('trj1_theta', len(trj1_theta), len(trj1_theta[0]))\n trj1_Ps_theta, index = self.PreSamp(trj1_theta, myn_clusters = 10) # pre analysis (least count)\n trj1_Ps_w_theta, index_w = self.PreSamp(trj1_theta, myn_clusters = 100) # for updating the weights\n print('trj1_Ps_theta', len(trj1_Ps_theta), len(trj1_Ps_theta[0]))\n\n newPoints_index_orig = self.findStarting(trj1_Ps_theta, index, W_0, starting_n = N , method = 'RL') #need change\n newPoints = trj1[newPoints_index_orig[0]]\n newPoints.save_pdb(newPoints_name)\n \n \n print('trj1_theta[0]',trj1_theta[0])\n plt.scatter(trj1_theta[0], trj1_theta[1], color='dodgerblue', s=5, alpha=0.2)\n plt.xlim([-180, 180])\n plt.ylim([-180, 180])\n newPoints_theta_x = trj1_theta[0][newPoints_index_orig[0]]\n newPoints_theta_y = trj1_theta[1][newPoints_index_orig[0]]\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\n plt.xlabel(r'$\\phi$')\n plt.ylabel(r'$\\psi$')\n plt.savefig('fig_'+str(count))\n plt.close()\n trjs_theta = trj1_theta\n trjs_Ps_theta = trj1_Ps_theta\n trjs_Ps_w_theta = trj1_Ps_w_theta \n for round in range(R):\n self.updateStat(trjs_theta) # based on all trajectories\n #W_1 = self.updateW(trjs_Ps_theta, W_0) \n W_1 = self.updateW(trjs_Ps_w_theta, W_0) \n W_0 = W_1\n W_1 = W_0\n Ws.append(W_0)\n s = 1000\n trj1 = self.run(production_steps = s, start=newPoints_name, production='trj_R_'+str(count)+'.pdb') # return mdtraj object\n com_trjs = trjs.join(trj1) \n trjs = com_trjs\n trjs_theta = np.array(self.map_angles(trjs)) \n trjs_Ps_theta, index = self.PreSamp(trjs_theta, myn_clusters = 100)\n myn_clusters1 = 100 #int(10*(round)+1)\n trjs_Ps_w_theta = trjs_Ps_theta\n #trjs_Ps_w_theta, index_w = self.PreSamp(trjs_theta, myn_clusters = myn_clusters1)\n newPoints_index_orig = self.findStarting(trjs_Ps_theta, index, W_1, starting_n = N , method = 'RL')\n newPoints = trjs[newPoints_index_orig[0]] \n \n count = count + 1\n newPoints_name = 'start_r_'+str(count)+'.pdb'\n newPoints.save_pdb(newPoints_name)\n\n print( myn_clusters1, W_1, self.theta_mean)\n plt.scatter(trjs_theta[0], trjs_theta[1], color='dodgerblue', s=5, alpha=0.2)\n plt.xlim([-np.pi, np.pi])\n plt.ylim([-np.pi, np.pi])\n newPoints_theta_x = trjs_theta[0][newPoints_index_orig[0]]\n newPoints_theta_y = trjs_theta[1][newPoints_index_orig[0]]\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\n plt.scatter(trjs_Ps_w_theta[0], trjs_Ps_w_theta[1], color='green', s=5)\n plt.xlabel(r'$\\phi$')\n plt.ylabel(r'$\\psi$')\n plt.savefig('fig_'+str(count))\n plt.close()\n \n np.save('w_'+'r'+str(int(R))+'N'+str(N)+'s'+str(s), Ws)\n np.save('trjs_theta', trjs_theta)\n return","def arb_units(wb_run,sample_run,ei_guess,rebin,map_file,**kwargs):\n global reducer, rm_zero,inst_name,van_mass,bleed_switch,rate,pixels\n print 'DGreduce run for ',inst_name,'run number ',sample_run\n try:\n n,r=funcreturns.lhs_info('both')\n #n,r=lhs('both')\n wksp_out=r[0]\n except:\n if sample_run == 0:\n #deal with the current run being parsed as 0 rather than 00000\n sample_run='00000'\n wksp_out=inst_name+str(sample_run)+'.spe'\n if kwargs.has_key('sum') and kwargs.get('sum')==True:\n wksp_out=inst_name+str(sample_run[0])+'sum'+'.spe'\n \n start_time=time.time()\n \n if sample_run=='00000' and mtd.doesExist(inst_name+'00000.raw')==True:\n print 'Deleteing previous instance of temp data'\n DeleteWorkspace(Workspace=inst_name+'00000.raw')\n \n #repopulate defualts\n if kwargs.has_key('norm_method'):\n reducer.normalise_method = kwargs.get('norm_method')\n print 'Setting normalisation method to ', kwargs.get('norm_method')\n else:\n reducer.normalise_method = 'monitor-1'\n if kwargs.has_key('mask_run'):\n mask_run = kwargs.get('mask_run')\n print 'Using run ', kwargs.get('mask_run'),' for diag'\n else:\n mask_run=sample_run\n \n if kwargs.has_key('background'):\n reducer.background = kwargs.get('background')\n print 'Setting background option to ', kwargs.get('background')\n else:\n reducer.background = False\n \n if kwargs.has_key('fixei'):\n reducer.fix_ei = kwargs.get('fixei')\n print 'Setting fixei to ', kwargs.get('fixei')\n else:\n reducer.fix_ei = False\n \n if kwargs.has_key('save_format'):\n reducer.save_formats = kwargs.get('save_format')\n print 'Setting save format to ', kwargs.get('save_format')\n else:\n reducer.save_formats = ['.spe']\n #Set parameters for the run\n \n if kwargs.has_key('detector_van_range'):\n reducer.wb_integr_range = kwargs.get('detector_van_range')\n print 'Setting detector van int range to ', kwargs.get('detector_van_range')\n else:\n reducer.wb_integr_range=[20,100]\n #-------------DIAG------------------------\n if kwargs.has_key('bkgd_range'):\n background_range = kwargs.get('bkgd_range')\n print 'Setting background intergration to ', kwargs.get('bkgd_range')\n else:\n background_range=[15000,19000]\n \n if kwargs.has_key('tiny'):\n tinyval = kwargs.get('tiny')\n print 'Setting tiny ratelimit to ', kwargs.get('tiny')\n else:\n tinyval=1e-10\n \n if kwargs.has_key('large'):\n largeval = kwargs.get('large')\n print 'Setting large limit to ', kwargs.get('large')\n else:\n largeval=1e10\n \n if kwargs.has_key('diag_remove_zero'):\n sampzero = kwargs.get('diag_remove_zero')\n print 'Setting diag to reject zero backgrounds '\n else:\n sampzero =False\n \n if kwargs.has_key('diag_van_median_rate_limit_hi'):\n vanouthi = kwargs.get('diag_van_median_rate_limit_hi')\n print 'Setting diag_van_median_rate_limit_hi to ', kwargs.get('diag_van_median_rate_limit_hi')\n else:\n vanouthi=100\n \n if kwargs.has_key('diag_van_median_rate_limit_lo'):\n vanoutlo = kwargs.get('diag_van_median_rate_limit_lo')\n print 'Setting diag_van_median_rate_limit_lo to ', kwargs.get('diag_van_median_rate_limit_lo')\n else:\n vanoutlo=0.01\n \n if kwargs.has_key('diag_van_median_sigma_lo'):\n vanlo = kwargs.get('diag_van_median_sigma_lo')\n print 'Setting diag_van_median_sigma_lo to ', kwargs.get('diag_van_median_sigma_lo')\n else:\n vanlo=0.1\n \n if kwargs.has_key('diag_van_median_sigma_hi'):\n vanhi = kwargs.get('diag_van_median_sigma_hi')\n print 'Setting diag_van_median_sigma_hi to ', kwargs.get('diag_van_median_sigma_hi')\n else:\n vanhi=1.5\n \n if kwargs.has_key('diag_van_median_sigma'):\n vansig = kwargs.get('diag_van_median_sigma')\n print 'Setting diag_van_median_sigma to ', kwargs.get('diag_van_median_sigma')\n else:\n vansig=0.0\n \n if kwargs.has_key('diag_samp_median_sigma_lo'):\n samplo = kwargs.get('diag_samp_median_sigma_lo')\n print 'Setting diag_samp_median_sigma_lo to ', kwargs.get('diag_samp_median_sigma_lo')\n else:\n samplo=0.0\n \n if kwargs.has_key('diag_samp_median_sigma_hi'):\n samphi = kwargs.get('diag_samp_median_sigma_hi')\n print 'Setting diag_samp_median_sigma_hi to ', kwargs.get('diag_samp_median_sigma_hi')\n else:\n samphi=2.0\n \n if kwargs.has_key('diag_samp_median_sigma'):\n sampsig = kwargs.get('diag_samp_median_sigma')\n print 'Setting diag_samp_median_sigma to ', kwargs.get('diag_samp_median_sigma')\n else:\n sampsig=3.0\n \n if kwargs.has_key('bleed'):\n bleed_switch = kwargs.get('bleed')\n print 'Setting bleed ', kwargs.get('bleed')\n else:\n print 'bleed set to default'\n #---------------END of DIAG--------------------\n if kwargs.has_key('det_cal_file'):\n reducer.det_cal_file = kwargs.get('det_cal_file')\n reducer.relocate_dets = True\n print 'Setting detector calibration file to ', kwargs.get('det_cal_file')\n else:\n print 'Setting detector calibration to detector block info from ', sample_run\n reducer.det_cal_file =None\n reducer.relocate_dets = False\n \n if mtd.doesExist(str(sample_run))==True and kwargs.has_key('det_cal_file')==False:\n print 'For data input type: workspace detector calibration must be specified'\n print 'use Keyword det_cal_file with a valid detctor file or run number'\n return\n \n \n \n if kwargs.has_key('one2one'):\n reducer.map_file =None\n print 'one2one selected'\n \n else:\n fileName, fileExtension = os.path.splitext(map_file)\n if (not fileExtension):\n map_file=map_file+'.map' \n reducer.map_file = map_file\n\n reducer.energy_bins = rebin\n \n if float(str.split(rebin,',')[2])>=float(ei_guess):\n print 'error rebin range exceeds ei'\n return\n \n print 'output will be normalised to', reducer.normalise_method\n if (numpy.size(sample_run)) > 1 and kwargs.has_key('sum') and kwargs.get('sum')==True:\n #this sums the runs together before passing the summed file to the rest of the reduction\n #this circumvents the inbuilt method of summing which fails to sum the files for diag\n \n sumfilename=str(sample_run[0])+'sum'\n accum=sum_files(sumfilename, sample_run)\n #the D.E.C. tries to be too clever so we have to fool it into thinking the raw file is already exists as a workpsace\n RenameWorkspace(InputWorkspace=accum,OutputWorkspace=inst_name+str(sample_run[0])+'.raw')\n sample_run=sample_run[0]\n \n if kwargs.has_key('hardmaskPlus'):\n HardMaskFile = kwargs.get('hardmaskPlus')\n print 'Use hardmask from ', HardMaskFile\n #hardMaskSpec=common.load_mask(HardMaskFile)\n #MaskDetectors(Workspace='masking',SpectraList=hardMaskSpec)\n else:\n HardMaskFile=None\n \n if kwargs.has_key('hardmaskOnly'):\n totalmask = kwargs.get('hardmaskOnly')\n print 'Using hardmask from ', totalmask\n #next stable version can replace this with loadmask algoritum\n specs=diag_load_mask(totalmask)\n CloneWorkspace(InputWorkspace=sample_run,OutputWorkspace='mask_wksp')\n MaskDetectors(Workspace='mask_wksp',SpectraList=specs)\n masking=mtd['mask_wksp']\n else:\n \n masking = reducer.diagnose(wb_run, \n sample=mask_run,\n second_white = None,\n tiny=tinyval, \n huge=largeval, \n van_out_lo=vanoutlo,\n van_out_hi=vanouthi,\n van_lo=vanlo,\n van_hi=vanhi,\n van_sig=vansig,\n samp_zero=sampzero,\n samp_lo=samplo,\n samp_hi=samphi,\n samp_sig=sampsig,\n bkgd_range=background_range, \n variation=1.1,\n print_results=True,\n bleed_test=bleed_switch,\n bleed_maxrate=rate,\n bleed_pixels=pixels,\n hard_mask=HardMaskFile)\n \n reducer.spectra_masks=masking\n #fail_list=get_failed_spectra_list(masking)\n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(masking)\n \n print 'Diag found ', len(fail_list),'bad spectra'\n \n #Run the conversion\n deltaE_wkspace = reducer.convert_to_energy(sample_run, ei_guess, wb_run)\n end_time=time.time()\n results_name=str(sample_run)+'.spe'\n \n ei= (deltaE_wkspace.getRun().getLogData(\"Ei\").value)\n \n if mtd.doesExist('_wksp.spe-white')==True:\n DeleteWorkspace(Workspace='_wksp.spe-white')\n \n if mtd.doesExist(results_name)==False:\n RenameWorkspace(InputWorkspace=deltaE_wkspace,OutputWorkspace=results_name)\n \n print 'Incident energy found ',ei,' meV'\n print 'Elapsed time =',end_time-start_time, 's'\n #get the name that convert to energy will use\n \n \n RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)\n \n return mtd[wksp_out]","def main():\n print_banner()\n params = read_steering()\n s, x, y, cur, theta = build_kinoshita()\n s, x, y, cur, theta = read_centerline(s, x, y, cur, theta)\n s, x, y, cur, theta = extend_centerline(s, x, y, cur, theta)\n for t in range(TSTEPS+1):\n cur, theta = tan2curv(s, x, y)\n cur_ori = np.copy(cur)\n cur = filter_curvature(cur, t)\n cur_flt = np.copy(cur)\n cur = lag(s, cur, t)\n cur_lag = np.copy(cur)\n beck_bed = build_beck(cur, s, t)\n allxyz = offset_all(x, y, beck_bed, t)\n if t == 0:\n write_xyz_file(allxyz)\n write_mesh_file(allxyz, beck_bed)\n oxbowxList, oxbowyList = [], []\n centerlinexList, centerlineyList = [], []\n if np.mod(t, GPRINT) == 0:\n centerlinexList.append(x)\n centerlineyList.append(y)\n mf.make_figure(x, y, allxyz, cur_ori, cur_flt, cur_lag, s, beck_bed,\n params, t, oxbowxList, oxbowyList, centerlinexList, centerlineyList)\n if t == TSTEPS:\n break\n s, x, y = migration(s, x, y, cur_flt, cur_lag, theta, t)\n s, x, y, oxbowx, oxbowy, found_cutoff = cutoff(s, x, y)\n s, x, y = smooth_centerline(x, y)\n s, x, y, cur, theta = resample_centerline(s, x, y)\n if found_cutoff:\n oxbowxList.append(oxbowx)\n oxbowyList.append(oxbowy)\n make_gif()\n job_done()","def main():\n\tif len(sys.argv) < 12 or len(sys.argv) > 13:\n\t\tprint(\"Input parameters must be: 'filename lambda mu C c0 Q theta L H simulation_time is_debug repeats(optionally)'\")\n\telse:\n\t\tstart_time = time.time()\n\n\t\tfile_name = sys.argv[1]\n\t\tlambd = float(sys.argv[2])\n\t\tmu = float(sys.argv[3])\n\t\tC = int(sys.argv[4])\n\t\tc0 = int(sys.argv[5])\n\t\tQ = int(sys.argv[6])\n\t\ttheta = float(sys.argv[7])\n\t\tL = int(sys.argv[8])\n\t\tH = int(sys.argv[9])\n\t\tsimulation_time = float(sys.argv[10]);\n\t\tis_debug = True if sys.argv[11] == \"True\" else False;\n\t\trepeats = int(sys.argv[12]) if len(sys.argv) == 13 else 1;\n\n\t\tprint(\"Simulation started for params: lambda =\", lambd,\n\t\t\t \", mu =\", mu,\n\t\t\t \", C =\", C,\n\t\t\t \", c0 =\", c0,\n\t\t\t \", Q =\", Q,\n\t\t\t \", theta =\", theta,\n\t\t\t \", L =\", L,\n\t\t\t \", H =\", H,\n\t\t\t \", repeats =\", repeats)\n\n\t\tblocked = 0\n\t\tserved = 0\n\t\tgenerated = 0\n\t\tB = 0\n\t\tN = 0\n\n\t\tsimulation = Simulation(\"m/m/c[c0]/r[l,h]\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\n\t\tfor i in range(0, repeats):\n\t\t\tsimulation = Simulation(\"m/m/c[c0]/r[l,h]\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\n\t\t\tsimulation.start()\n\t\t\tblocked += simulation.queue.blocked\n\t\t\tserved += simulation.served_count\n\t\t\tgenerated += simulation.flow.generated_count\n\t\t\tB += simulation.queue.blocked/(simulation.served_count+simulation.queue.blocked)\n\t\t\tN += simulation.served_count/simulation_time\n\t\tend_time = time.time()\n\n\t\tblocked = blocked/repeats\n\t\tserved = served/repeats\n\t\tgenerated = generated/repeats\n\t\tB = B/repeats\n\t\tN = N/repeats\n\n\t\tprint( \"\")\n\t\tprint( \"Summary results:\")\n\t\tprint( \"blocked=\", blocked, \" served=\", served, \", generated=\", generated)\n\t\tprint(\"B = \", B)\n\t\tprint(\"N = \", N)\n\t\tprint(\"Execution time = %s seconds\" % (end_time - start_time))\n\t\tprint( \"... to be implemented more summary ...\")\n\n\t\t# write stats to file\n\t\tabs_path = os.path.abspath(__file__)\n\t\tpath = os.path.relpath('stats', abs_path)\n\t\tpath = os.path.join(path, file_name + '-(%s,%s,%s,%s,%s,%s,%s,%s).csv' % (lambd,mu,theta,C,c0,L,H,simulation_time))\n\n\t\toutfile=open(path,'w')\n\t\toutput = csv.writer(outfile, delimiter=';')\n\t\toutput.writerow(['Request ID','Queue', 'Arrival_Time','Queue_Arrival_time','Server_Arrival_time','alpha','beta'])\n\n\t\ti=0\n\t\tfor request in simulation.served_requests:\n\t\t\ti=i+1\n\t\t\toutrow=[]\n\t\t\toutrow.append(request.ID)\n\t\t\toutrow.append(request.queue_size_at_serving)\n\t\t\toutrow.append(request.arrival_time)\n\t\t\toutrow.append(request.queue_arrival_time)\n\t\t\toutrow.append(request.server_arrival_time)\n\t\t\toutrow.append(request.alpha)\n\t\t\toutrow.append(request.beta)\n\t\t\toutput.writerow(outrow)\n\t\toutfile.close()\n\n\t\treturn simulation","def run(config, tim=None):\n import common_lib\n import dr_lib\n import DST\n \n if tim is not None:\n tim.getTime(False)\n old_time = tim.getOldTime()\n\n if config.data is None:\n raise RuntimeError(\"Need to pass a data filename to the driver \"\\\n +\"script.\")\n\n # Read in geometry if one is provided\n if config.inst_geom is not None:\n if config.verbose:\n print \"Reading in instrument geometry file\"\n \n inst_geom_dst = DST.getInstance(\"application/x-NxsGeom\",\n config.inst_geom)\n else:\n inst_geom_dst = None\n\n config.so_axis = \"time_of_flight\"\n\n # Steps 1-3: Produce a scaled summed dark current dataset\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\n dataset_type=\"dark_current\",\n timer=tim)\n\n # Perform Steps 3-6 on black can data\n if config.bcan is not None:\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\n dataset_type=\"black_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_bcan,\n timer=tim)\n else:\n b_som1 = None\n\n # Perform Steps 3-6 on empty can data \n if config.ecan is not None:\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\n dataset_type=\"empty_can\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_ecan,\n timer=tim)\n else:\n e_som1 = None\n\n # Perform Steps 3-6 on normalization data\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\n dataset_type=\"normalization\",\n inst_geom_dst=inst_geom_dst,\n tib_const=config.tib_const,\n cwp=config.cwp_data,\n timer=tim)\n\n # Perform Steps 7-16 on normalization data\n if config.norm_trans_coeff is None:\n norm_trans_coeff = None\n else:\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\n\n # Determine if we need to rebin the empty or black can data\n if config.ecan is not None and e_som1 is not None:\n ecan_cwp = True\n else:\n ecan_cwp = False\n\n if config.bcan is not None and b_som1 is not None:\n bcan_cwp = True\n else:\n bcan_cwp = False \n\n cwp_used = ecan_cwp or bcan_cwp\n\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\n norm_trans_coeff,\n dataset_type=\"normalization\",\n cwp_used=cwp_used,\n timer=tim)\n \n del n_som1, b_som1, e_som1\n\n # Step 17: Integrate normalization spectra\n if config.verbose:\n print \"Integrating normalization spectra\"\n\n if tim is not None:\n tim.getTime(False)\n\n if config.norm_int_range is None:\n start_val = float(\"inf\")\n end_val = float(\"inf\")\n else:\n if not config.wb_norm:\n # Translate energy transfer to final energy\n ef_start = config.initial_energy.getValue() - \\\n config.norm_int_range[0]\n ef_end = config.initial_energy.getValue() - \\\n config.norm_int_range[1]\n # Convert final energy to final wavelength\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\n else:\n start_val = config.norm_int_range[0]\n end_val = config.norm_int_range[1]\n \n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\n end=end_val, width=True)\n\n del n_som2\n \n if tim is not None:\n tim.getTime(msg=\"After integrating normalization spectra \")\n\n file_comment = \"Normalization Integration range: %0.3fA, %0.3fA\" \\\n % (start_val, end_val)\n \n hlr_utils.write_file(config.output, \"text/num-info\", n_som3,\n output_ext=\"norm\",\n data_ext=config.ext_replacement,\n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"normalization values\",\n comments=[file_comment],\n tag=\"Integral\", units=\"counts\") \n \n if tim is not None:\n tim.getTime(False)\n\n if config.verbose:\n print \"Making mask file\"\n\n # Make mask file from threshold\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\n config.hi_threshold, config)\n\n if tim is not None:\n tim.getTime(msg=\"After making mask file \")\n\n # Write out RMD file\n n_som3.attr_list[\"config\"] = config\n\n hlr_utils.write_file(config.output, \"text/rmd\", n_som3,\n output_ext=\"rmd\",\n data_ext=config.ext_replacement, \n path_replacement=config.path_replacement,\n verbose=config.verbose,\n message=\"metadata\")\n \n if tim is not None:\n tim.setOldTime(old_time)\n tim.getTime(msg=\"Total Running Time\")","def abs_units(wb_run,sample_run,mono_van,wb_mono,samp_rmm,samp_mass,ei_guess,rebin,map_file,monovan_mapfile,**kwargs): \n #available keywords\n #abs_units_van_range\n global reducer, rm_zero,inst_name,van_mass,bleed_switch,rate,pixels\n print 'DGreduce run for ',inst_name,'run number ',sample_run\n print 'Output will be in absolute units of mb/str/mev/fu'\n\n #reducer.van_rmm =50.94\n reducer.van_mass=van_mass\n #sample info\n reducer.sample_mass=samp_mass\n reducer.sample_rmm =samp_rmm\n print 'Using vanadium mass: ',van_mass\n print ' sample mass: ',samp_mass \n print ' sample_rmm : ',samp_rmm \n # check if mono-vanadium is provided as multiple files list or just put in brackets ocasionally\n if isinstance(mono_van,list):\n if len(mono_van)>1:\n raise IOError(' Can currently work only with single monovan file but list supplied')\n else:\n mono_van = mono_van[0];\n\n \n try:\n n,r=lhs('both')\n wksp_out=r[0]\n except:\n if sample_run == 0:\n #deal with the current run being parsed as 0 rather than 00000\n sample_run='00000'\n wksp_out=str(sample_run)+'.spe'\n \n start_time=time.time()\n \n if sample_run=='00000' and mtd.doesExist(inst_name+'00000.raw')==True:\n print 'Deleteing previous instance of temp data'\n DeleteWorkspace(Workspace=inst_name+'00000.raw')\n \n if kwargs.has_key('norm_method'):\n reducer.normalise_method = kwargs.get('norm_method')\n print 'Setting normalisation method to ', kwargs.get('norm_method')\n else:\n reducer.normalise_method = 'monitor-1'\n \n if kwargs.has_key('mask_run'):\n mask_run = kwargs.get('mask_run')\n print 'Using run ', kwargs.get('mask_run'),' for diag'\n else:\n mask_run=sample_run\n \n if kwargs.has_key('background'):\n reducer.background = kwargs.get('background')\n print 'Setting background option to ', kwargs.get('background')\n else:\n reducer.background = False\n \n if kwargs.has_key('fixei'):\n reducer.fix_ei = kwargs.get('fixei')\n print 'Setting fixei to ', kwargs.get('fixei')\n else:\n reducer.fix_ei = False\n \n if kwargs.has_key('save_format'):\n reducer.save_formats = kwargs.get('save_format')\n print 'Setting save format to ', kwargs.get('save_format')\n else:\n reducer.save_formats = ['.spe']\n #Set parameters for the run\n \n if kwargs.has_key('detector_van_range'):\n reducer.wb_integr_range = kwargs.get('detector_van_range')\n print 'Setting detector van int range to ', kwargs.get('detector_van_range')\n else:\n reducer.wb_integr_range=[20,100]\n \n #######DIAG###########\n if kwargs.has_key('bkgd_range'):\n background_range = kwargs.get('bkgd_range')\n print 'Setting background intergration to ', kwargs.get('bkgd_range')\n else:\n background_range=[15000,19000]\n \n if kwargs.has_key('tiny'):\n tinyval = kwargs.get('tiny')\n print 'Setting tiny ratelimit to ', kwargs.get('tiny')\n else:\n tinyval=1e-10\n \n if kwargs.has_key('large'):\n largeval = kwargs.get('large')\n print 'Setting large limit to ', kwargs.get('large')\n else:\n largeval=1e10\n \n if kwargs.has_key('diag_remove_zero'):\n sampzero = kwargs.get('diag_remove_zero')\n print 'Setting diag to reject zero backgrounds '\n else:\n sampzero =False\n \n if kwargs.has_key('diag_van_median_rate_limit_hi'):\n vanouthi = kwargs.get('diag_van_median_rate_limit_hi')\n print 'Setting diag_van_median_rate_limit_hi to ', kwargs.get('diag_van_median_rate_limit_hi')\n else:\n vanouthi=100\n \n if kwargs.has_key('diag_van_median_rate_limit_lo'):\n vanoutlo = kwargs.get('diag_van_median_rate_limit_lo')\n print 'Setting diag_van_median_rate_limit_lo to ', kwargs.get('diag_van_median_rate_limit_lo')\n else:\n vanoutlo=0.01\n \n if kwargs.has_key('diag_van_median_sigma_lo'):\n vanlo = kwargs.get('diag_van_median_sigma_lo')\n print 'Setting diag_van_median_sigma_lo to ', kwargs.get('diag_van_median_sigma_lo')\n else:\n vanlo=0.1\n \n if kwargs.has_key('diag_van_median_sigma_hi'):\n vanhi = kwargs.get('diag_van_median_sigma_hi')\n print 'Setting diag_van_median_sigma_hi to ', kwargs.get('diag_van_median_sigma_hi')\n else:\n vanhi=1.5\n \n if kwargs.has_key('diag_van_median_sigma'):\n vansig = kwargs.get('diag_van_median_sigma')\n print 'Setting diag_van_median_sigma to ', kwargs.get('diag_van_median_sigma')\n else:\n vansig=0.0\n \n if kwargs.has_key('diag_samp_median_sigma_lo'):\n samplo = kwargs.get('diag_samp_median_sigma_lo')\n print 'Setting diag_samp_median_sigma_lo to ', kwargs.get('diag_samp_median_sigma_lo')\n else:\n samplo=0.0\n \n if kwargs.has_key('diag_samp_median_sigma_hi'):\n samphi = kwargs.get('diag_samp_median_sigma_hi')\n print 'Setting diag_samp_median_sigma_hi to ', kwargs.get('diag_samp_median_sigma_hi')\n else:\n samphi=2.0\n \n if kwargs.has_key('diag_samp_median_sigma'):\n sampsig = kwargs.get('diag_samp_median_sigma')\n print 'Setting diag_samp_median_sigma to ', kwargs.get('diag_samp_median_sigma')\n else:\n sampsig=3.0\n \n if kwargs.has_key('bleed'):\n bleed_switch = kwargs.get('bleed')\n print 'Setting bleed ', kwargs.get('bleed')\n else:\n print 'bleed set to default'\n #####diad end########\n \n \n if kwargs.has_key('det_cal_file'):\n reducer.det_cal_file = kwargs.get('det_cal_file')\n reducer.relocate_dets = True\n print 'Setting detector calibration file to ', kwargs.get('det_cal_file')\n else:\n print 'Setting detector calibration to detector block info from ', sample_run\n reducer.det_cal_file =None\n reducer.relocate_dets = False\n \n if mtd.doesExist(str(sample_run))==True and kwargs.has_key('det_cal_file')==False:\n print 'For data input type: workspace detector calibration must be specified'\n print 'use Keyword det_cal_file with a valid detctor file or run number'\n return\n \n \n if kwargs.has_key('one2one'):\n reducer.map_file =None\n map_file = \"\"\n print 'one2one selected'\n else:\n fileName, fileExtension = os.path.splitext(map_file)\n if (not fileExtension):\n map_file = map_file+'.map'\n reducer.map_file = map_file;\n \n if kwargs.has_key('hardmaskPlus'):\n HardMaskFile = kwargs.get('hardmaskPlus')\n print 'Use hardmask from ', HardMaskFile\n #hardMaskSpec=common.load_mask(HardMaskFile)\n #MaskDetectors(Workspace='masking',SpectraList=hardMaskSpec)\n else:\n HardMaskFile=None\n \n reducer.energy_bins = rebin\n #monovan info\n fileName, fileExtension = os.path.splitext(monovan_mapfile)\n if (not fileExtension):\n monovan_mapfile=monovan_mapfile+'.map'\n reducer.abs_map_file =monovan_mapfile \n\n if kwargs.has_key('abs_units_van_range'):\n reducer.monovan_integr_range = kwargs.get('abs_units_van_range')\n print 'Setting absolute units vanadium integration range to: ', kwargs.get('abs_units_van_range')\n else:\n reducer.monovan_integr_range=[-40,40]\n\n \n \n print 'output will be normalised to', reducer.normalise_method\n if (numpy.size(sample_run)) > 1 and kwargs.has_key('sum') and kwargs.get('sum')==True:\n #this sums the runs together before passing the summed file to the rest of the reduction\n #this circumvents the inbuilt method of summing which fails to sum the files for diag\n \n sumfilename=str(sample_run[0])+'sum'\n accum=sum_files(sumfilename, sample_run)\n #the D.E.C. tries to be too clever so we have to fool it into thinking the raw file is already exists as a workpsace\n RenameWorkspace(InputWorkspace=accum,OutputWorkspace=inst_name+str(sample_run[0])+'.raw')\n sample_run=sample_run[0]\n \n if kwargs.has_key('hardmaskOnly'):\n if (kwargs.get('hardmaskOnly')): \n totalmask = kwargs.get('hardmaskOnly')\n print 'Using hardmask from ', totalmask\n #next stable version can replace this with loadmask algoritum\n specs=diag_load_mask(totalmask)\n else:\n specs=\"\"\n \n CloneWorkspace(InputWorkspace=sample_run,OutputWorkspace='mask_wksp')\n MaskDetectors(Workspace='mask_wksp',SpectraList=specs)\n masking =mtd['mask_wksp']\n else:\n print '########### Run diagnose for sample run ##############'\n masking = reducer.diagnose(wb_run, \n sample=mask_run,\n second_white = None,\n tiny=tinyval, \n huge=largeval, \n van_out_lo=vanoutlo,\n van_out_hi=vanouthi,\n van_lo=vanlo,\n van_hi=vanhi,\n van_sig=vansig,\n samp_zero=sampzero,\n samp_lo=samplo,\n samp_hi=samphi,\n samp_sig=sampsig,\n bkgd_range=background_range, \n variation=1.1,\n print_results=True,\n bleed_test=bleed_switch,\n bleed_maxrate=rate,\n bleed_pixels=pixels,\n hard_mask=HardMaskFile)\n \n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(masking) \n print 'first Diag found ', len(fail_list),'bad spectra out of: ',n_total_spectra,' ws spectra'\n \n if kwargs.has_key('use_sam_msk_on_monovan') and kwargs.get('use_sam_msk_on_monovan')==True:\n print 'applying sample run mask to mono van'\n reducer.spectra_masks=masking\n fail_list=get_failed_spectra_list(masking) \n else:\n print '########### Run diagnose for monochromatic vanadium run ##############'\n masking2 = reducer.diagnose(wb_mono, \n sample=mono_van,\n second_white = None,\n tiny=tinyval, \n huge=largeval, \n van_out_lo=vanoutlo,\n van_out_hi=vanouthi,\n van_lo=vanlo,\n van_hi=vanhi,\n van_sig=vansig,\n samp_zero=sampzero,\n samp_lo=samplo,\n samp_hi=samphi,\n samp_sig=sampsig,\n bkgd_range=background_range, \n variation=1.1,\n print_results=True,\n bleed_test=bleed_switch,\n bleed_maxrate=rate,\n bleed_pixels=pixels,\n hard_mask=HardMaskFile)\n \n total_mask=masking+masking2 \n reducer.spectra_masks=total_mask \n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(total_mask)\n #fail_list=get_failed_spectra_list('total_mask')\n \n \n print 'Diag found ', len(fail_list),'bad spectra out of: ',n_total_spectra,' ws spectra'\n \n \n \n #Run the conversion first on the sample\n deltaE_wkspace_sample = reducer.convert_to_energy(sample_run, ei_guess, wb_run)\n\n \n if kwargs.has_key('mono_correction_factor'):\n absnorm_factor=kwargs.get('mono_correction_factor')\n print 'Using supplied correction factor for absolute units'\n else:\n print '##### Evaluate the integral from the monovan run and calculate the correction factor ######'\n print ' Using absolute units vanadion integration range : ', reducer.monovan_integr_range \n #now on the mono_vanadium run swap the mapping file\n reducer.map_file = monovan_mapfile \n deltaE_wkspace_monovan = reducer.convert_to_energy(mono_van, ei_guess, wb_mono)\n \n (absnorm_factorL,absnorm_factorSS,absnorm_factorP,absnorm_factTGP) = getAbsNormalizationFactor(deltaE_wkspace_monovan.getName(),str(reducer.monovan_integr_range[0]),str(reducer.monovan_integr_range[1])) \n \n print 'Absolute correction factor S^2 =',absnorm_factorSS,' Libisis: ',absnorm_factorL,' Puasonian: ',absnorm_factorP, ' TGP : ',absnorm_factTGP\n CreateSingleValuedWorkspace(OutputWorkspace='AbsFactor',DataValue=absnorm_factTGP)\n end_time=time.time()\n results_name=str(sample_run)+'.spe'\n ei= (deltaE_wkspace_sample.getRun().getLogData(\"Ei\").value)\n \n if mtd.doesExist('_wksp.spe-white')==True:\n DeleteWorkspace(Workspace='_wksp.spe-white')\n \n \n print 'Incident energy found for sample run ',ei,' meV'\n print 'Incident energy found for mono vanadium run ',ei,' meV'\n print 'Elapsed time =',end_time-start_time, 's'\n #get the name that convert to energy will use\n \n if mtd.doesExist(results_name)==False:\n RenameWorkspace(InputWorkspace=deltaE_wkspace_sample,OutputWorkspace=results_name)\n if results_name != wksp_out:\n RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)\n Divide(LHSWorkspace=wksp_out,RHSWorkspace='AbsFactor',OutputWorkspace=wksp_out)\n DeleteWorkspace(Workspace='AbsFactor')\n return mtd[wksp_out]","def calculate(x, y, z, vx, vy, vz, dt, m, g, B2, S0, omega):\n t = 0.0\n # Establish lists with initial position and velocity components and time.\n x_list = [x]\n y_list = [y]\n z_list = [z]\n vx_list = [vx]\n vy_list = [vy]\n vz_list = [vz]\n t_list = [t]\n\n # Set up visual elements.\n mound = visual.box(pos=(0,0,0), length=0.1, width=0.5, height=0.03, color=visual.color.white)\n plate = visual.box(pos=(18,0,0), length=0.5, width=0.5, height=0.03, color=visual.color.white)\n ball = visual.sphere(pos=(x,y,z), radius=0.05, color=visual.color.white)\n ball.trail = visual.curve(color=ball.color)\n\n while y >= 0.0:\n visual.rate(100) # Limit to no more than 100 iterations per second.\n t, x, y, z, vx, vy, vz = do_time_step(t, dt, x, y, z, vx, vy, vz, m, B2, g, S0, omega)\n x_list.append(x)\n y_list.append(y)\n z_list.append(z)\n vx_list.append(vx)\n vy_list.append(vy)\n vz_list.append(vz)\n t_list.append(t)\n ball.pos = (x,y,z)\n ball.trail.append(pos=ball.pos)\n\n return t_list, x_list, y_list, z_list, vx_list, vy_list, vz_list","def run_lpme(self) -> np.array:\n q = self.sphere.n\n signs = []\n for i in range(q):\n a = np.ones(q)\n a = a / np.sqrt(q)\n a_prime = np.copy(a)\n a_prime[i] = -a_prime[i]\n\n z_a = a * self.sphere.radius + self.sphere.origin\n z_a_prime = a_prime * self.sphere.radius + self.sphere.origin\n\n if self.oracle.compare(z_a, z_a_prime):\n signs.append(1.0)\n else:\n signs.append(-1.0)\n\n orthants = initialize_orthants(signs)\n\n # number of cycles\n nc = 4\n theta_list = [(orth.start + orth.stop) / 2 for orth in orthants]\n for _ in range(0, nc):\n for j in range(0, q - 1):\n theta_a = orthants[j].start\n theta_b = orthants[j].stop\n while abs(theta_b - theta_a) > self.e:\n theta_c = (theta_a * 3 + theta_b) / 4\n theta_d = (theta_a + theta_b) / 2\n theta_e = (theta_a + theta_b * 3) / 4\n\n theta_list[j] = theta_a\n vec_a = compute_vector(self.sphere, theta_list)\n\n theta_list[j] = theta_b\n vec_b = compute_vector(self.sphere, theta_list)\n\n theta_list[j] = theta_c\n vec_c = compute_vector(self.sphere, theta_list)\n\n theta_list[j] = theta_d\n vec_d = compute_vector(self.sphere, theta_list)\n\n theta_list[j] = theta_e\n vec_e = compute_vector(self.sphere, theta_list)\n\n # compare ac\n cac = self.oracle.compare(vec_a, vec_c)\n ccd = self.oracle.compare(vec_c, vec_d)\n cde = self.oracle.compare(vec_d, vec_e)\n ceb = self.oracle.compare(vec_e, vec_b)\n self.num_queries += 4\n\n if self.check_i:\n context = {\n \"theta_list\": theta_list,\n \"j\": j,\n \"theta_a\": theta_a,\n \"theta_b\": theta_b,\n \"theta_c\": theta_c,\n \"theta_d\": theta_d,\n \"theta_e\": theta_e,\n }\n self.check_inconsistency(cac, ccd, cde, ceb, context)\n\n if cac:\n theta_b = theta_d\n elif ccd:\n theta_b = theta_d\n elif cde:\n theta_a = theta_c\n theta_b = theta_e\n elif ceb:\n theta_a = theta_d\n else:\n theta_a = theta_d\n\n # update theta list\n theta_list[j] = (theta_a + theta_b) / 2\n\n # save theta list\n self.theta_list = theta_list\n return normalize(compute_vector(self.sphere, theta_list) - self.sphere.origin)","def abcRun(self,fitfun=None, data={}, t=1,savetemp=False):\n if not fitfun:\n fitfun = self.basicfit\n if savetemp:\n CP.dump(self.q1theta,open('q1theta','w'))\n# Running the model ==========================\n if os.path.exists('phi.temp'):\n phi,j = CP.load(open('phi.temp','r'))\n else:\n j=0\n phi = recarray((self.K,t),formats=['f8']*self.nphi, names = self.phi.dtype.names)\n for i in xrange(j,self.K):\n theta = [self.q1theta[n][i] for n in self.q1theta.dtype.names]\n r = self.po.apply_async(self.model, theta)\n phi[i]= [tuple(l) for l in r.get()[-t:]]# #phi is the last t points in the simulation\n if i%100 == 0:\n print \"==> K = %s\"%i\n if savetemp:\n CP.dump((phi,i),open('phi.temp','w'))\n if savetemp: #If all replicates are done, clear temporary save files.\n os.unlink('phi.temp')\n os.unlink('q1theta')\n\n print \"==> Done Running the K replicates\\n\"\n qtilphi = self.logPooling(phi) #vector with probability of each phi[i] belonging to qtilphi\n qtilphi = nan_to_num(qtilphi)\n print 'max(qtilphi): ', max(qtilphi)\n# \n# calculate weights\n w = [fitfun(phi[i],data) for i in xrange(phi.shape[0])]\n w /=sum(w)\n w = 1-w\n print \"w=\",w, mean(w), var(w)\n print\n print 'qtilphi=',qtilphi\n # Resampling Thetas\n w = nan_to_num(w)\n w = array(w)*qtilphi\n w /=sum(w)\n w = nan_to_num(w)\n print 'max(w): ',max(w)\n# for n in phi.dtype.names:\n# P.plot(mean(phi[n],axis=0),label=n)\n# P.figure()\n# P.plot(w,label='w')\n# P.plot(qtilphi,label='qtilphi')\n# P.title('Resampling vector(w) and pooled prior on Phi')\n# P.legend()\n if sum(w) == 0.0:\n sys.exit('Resampling weights are all zero, please check your model or data.')\n j = 0\n while j < self.L: # Extract L samples from q1theta\n i=randint(0,w.size)# Random position of w and q1theta\n if random()<= w[i]:\n self.post_theta[j] = self.q1theta[i]# retain the sample according with resampling prob.\n j+=1\n \n\n self.done_running = True","def test_track_particles_multi_beamline(self):\n BaseUtils.i_am_sure_my_code_closed_in_if_name_equal_main()\n bl1 = HUST_SC_GANTRY().create_beamline()\n bl2 = HUST_SC_GANTRY(qs3_gradient=7).create_beamline()\n bl3 = HUST_SC_GANTRY(qs3_gradient=0).create_beamline()\n\n p1 = ParticleFactory.create_proton_along(\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 215\n )\n\n p2 = ParticleFactory.create_proton_along(\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 220\n )\n\n p3 = ParticleFactory.create_proton_along(\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 210\n )\n\n ps_cpu1 = [p1.copy(), p2.copy(), p3.copy()]\n ps_cpu2 = [p1.copy(), p2.copy(), p3.copy()]\n ps_cpu3 = [p1.copy(), p2.copy(), p3.copy()]\n ps_gpu32 = [p1.copy(), p2.copy(), p3.copy()]\n ps_gpu64 = [p1.copy(), p2.copy(), p3.copy()]\n\n print(\"CPU\")\n s = time.time()\n ParticleRunner.run_only(ps_cpu1, bl1, 10, 20*MM, 6)\n ParticleRunner.run_only(ps_cpu2, bl2, 10, 20*MM, 6)\n ParticleRunner.run_only(ps_cpu3, bl3, 10, 20*MM, 6)\n print(f\"CPU time = {time.time()-s}\")\n for p in ps_cpu1+ps_cpu2 + ps_cpu3:\n print(p)\n\n print(\"GPU64\")\n s = time.time()\n ps_end = ga64_b512.track_multi_particle_beamlime_for_magnet_with_single_qs(\n [bl1, bl2, bl3], ps_gpu64, 10, 20*MM\n )\n print(f\"GPU64 time = {time.time()-s}\")\n\n for ps in ps_end:\n for p in ps:\n print(p)\n\n for gid in range(3):\n for pid in range(3):\n print(f\"diff={ps_end[gid][pid]-(ps_cpu1+ps_cpu2 + ps_cpu3)[gid*3+pid]}\")","def run():\n motion_extraction()\n file_buff = open(\"right_arm.txt\", \"w\")\n for frame_coord in limb_coords[0]:\n file_buff.write(\"%d %d\\n\" % (frame_coord[1], frame_coord[0]))\n file_buff.close()\n file_buff2 = open(\"left_arm.txt\", \"w\")\n for frame_coord in limb_coords[1]:\n file_buff2.write(\"%d %d\\n\" % (frame_coord[1], frame_coord[0]))\n file_buff2.close()\n file_buff3 = open(\"body.txt\", \"w\")\n for frame_coord in limb_coords[2]:\n file_buff3.write(\"%d %d\\n\" % (frame_coord[1], frame_coord[0]))\n file_buff3.close()\n file_buff4 = open(\"right_leg.txt\", \"w\")\n for frame_coord in limb_coords[3]:\n file_buff4.write(\"%d %d\\n\" % (frame_coord[1], frame_coord[0]))\n file_buff4.close()\n file_buff5 = open(\"left_leg.txt\", \"w\")\n for frame_coord in limb_coords[4]:\n file_buff5.write(\"%d %d\\n\" % (frame_coord[1], frame_coord[0]))\n file_buff5.close()","def test(self):\n winsound.PlaySound('SystemExclamation', winsound.SND_ALIAS)\n \n pulses=1000*3\n winsound.Beep(200, 1000) # .Beep(1650Hz, (XXXXms)) #e.g 1000ms=1second\n self.run(pulses); self.run(pulses, ANTI_CLK_W)\n sleep(1)\n\n winsound.Beep(400, 1000)\n self.swing(128, count=30); self.stop() #0.9 degrees\n sleep(1)\n\n winsound.Beep(800, 1000)\n print('Testing I.....')\n self.swing(32, count=120); self.stop() #0.225 degrees \n sleep(1)\n\n winsound.Beep(1600, 1000)\n print('Testing II.....')\n self.swing(2, count=1800); self.stop() #0.05625 degrees\n \n winsound.PlaySound('SystemExclamation', winsound.SND_ALIAS)\n print(' Testings Done! ')\n return self.stop() #set low before exist ","def runMeasurement(self):\n triggerI1ProMeasurement()\n self.spectrum.append(getI1ProSpectrum())\n self.tristimulus.append(getI1ProTriStimulus())","def main() -> None:\n xlim = L\n x = np.arange(xlim)\n xi = np.arange(0, xlim - 1 + STEP, STEP)\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\n yi = scipy.interpolate.pchip(x, kappas[0])\n plt.semilogx(xi, yi(xi), label=r\"$\\Phi_{\\ell0}$\")\n for j, k in enumerate(kappas[1:]):\n yi = scipy.interpolate.pchip(x, k)\n plt.semilogx(xi, yi(xi), label=rf\"$\\Psi^{{{j+J_MIN}}}_{{\\ell0}}$\")\n plt.xlim(1, xlim)\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\n plt.xticks(ticks, ticks)\n plt.xlabel(r\"$\\ell$\")\n plt.legend(loc=6)\n print(f\"Opening: axisymmetric_tiling_L{L}\")\n plt.tight_layout()\n plt.show(block=False)\n plt.pause(10)\n plt.close()","def simulate_memories(simulation_length):\n \n \n pass","def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\n N = 1000\n # Init tracking\n mu_x = np.zeros(N) # Belief or estimation of hidden state \n F = np.zeros(N) # Free Energy of AI neuron\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \n x = np.zeros(N) # True hidden state\n y = np.zeros(N) # Sensory signal as input to AI neuron\n\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\n \n \n\n start_time = time.time()\n for i in np.arange(1, N):\n #Active inference\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\n print('light reading', y[i])\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\n\n\n t_elapsed = time.time() - start_time\n\n print(\"Elapsed Time\", t_elapsed, \"sec\")\n return F, mu_x, mu_y, x, y","def gen_measurements(self,t,measure,pd,landa):\n temp_m = []\n input_state_temp = []\n num_targets = len(t)\n for i in range(0, num_targets):\n # Consider impact of pd (probability of detection)\n if np.random.random() < pd:temp_m.append(measure.generate_bearing(t[i].current_location, self.current_location))\n # Now add False-alarms\n num_false_alrams = np.random.poisson(landa)\n false_measures = []\n for false_index in range(0, num_false_alrams):\n # generate x,y randomly\n random_x = 20000 * np.random.random() - 10000\n random_y = 20000 * np.random.random() - 10000\n false_measures.append([random_x, random_y])\n temp_m.append(measure.generate_bearing([random_x, random_y], self.current_location))\n self.m.append(temp_m) # Number of measurements is not necessarily equal to that of targets","def main(command_line=True, **kwargs):\n # initialize some variables\n mag_file = ''\n meas_file=\"magic_measurements.txt\"\n user=\"\"\n specnum = 0\n samp_con = '1'\n labfield = 0\n er_location_name = ''\n codelist = None\n\n # get command line args\n if command_line:\n args=sys.argv\n if \"-h\" in args:\n print(main.__doc__)\n return False\n if \"-usr\" in args:\n ind=args.index(\"-usr\")\n user=args[ind+1]\n else:\n user=\"\"\n if '-F' in args:\n ind=args.index(\"-F\")\n meas_file=args[ind+1]\n if '-f' in args:\n ind=args.index(\"-f\")\n magfile=args[ind+1]\n print(\"got magfile:\", magfile)\n if \"-dc\" in args:\n ind=args.index(\"-dc\")\n labfield=float(args[ind+1])*1e-6\n phi=float(args[ind+2])\n theta=float(args[ind+3])\n if \"-ac\" in args:\n ind=args.index(\"-ac\")\n peakfield=float(args[ind+1])*1e-3\n if \"-spc\" in args:\n ind=args.index(\"-spc\")\n specnum=int(args[ind+1])\n if \"-loc\" in args:\n ind=args.index(\"-loc\")\n er_location_name=args[ind+1]\n if \"-ncn\" in args:\n ind=args.index(\"-ncn\")\n samp_con=sys.argv[ind+1]\n if '-LP' in args:\n ind=args.index(\"-LP\")\n codelist=args[ind+1]\n\n\n\n # lab process:\n\n # unpack key-word args if used as module\n if not command_line:\n user = kwargs.get('user', '')\n meas_file = kwargs.get('meas_file', 'magic_measurements.txt')\n magfile = kwargs.get('magfile', '')\n specnum = int(kwargs.get('specnum', 0))\n labfield = int(kwargs.get('labfield', 0)) *1e-6\n phi = int(kwargs.get('phi', 0))\n theta = int(kwargs.get('theta', 0))\n peakfield = kwargs.get('peakfield', 0)\n if peakfield:\n peakfield = float(peakfield)*1e-3\n er_location_name = kwargs.get('er_location_name', '')\n samp_con = kwargs.get('samp_con', '1')\n codelist = kwargs.get('codelist', '')\n CR_cooling_times=kwargs.get('CR_cooling_times', None)\n\n # format and validate variables\n if magfile:\n try:\n input=open(magfile,'r')\n except:\n print(\"bad mag file name\")\n return False, \"bad mag file name\"\n else: \n print(\"mag_file field is required option\")\n print(main.__doc__)\n return False, \"mag_file field is required option\"\n \n if specnum!=0:\n specnum=-specnum\n if \"4\" in samp_con:\n if \"-\" not in samp_con:\n print(\"option [4] must be in form 4-Z where Z is an integer\")\n return False, \"option [4] must be in form 4-Z where Z is an integer\"\n else:\n Z=int(samp_con.split(\"-\")[1])\n samp_con=\"4\"\n if \"7\" in samp_con:\n if \"-\" not in samp_con:\n print(\"option [7] must be in form 7-Z where Z is an integer\")\n return False, \"option [7] must be in form 7-Z where Z is an integer\"\n else:\n Z=int(samp_con.split(\"-\")[1])\n samp_con=\"7\"\n\n if codelist:\n codes=codelist.split(':')\n else:\n print(\"Must select experiment type (-LP option)\")\n return False, \"Must select experiment type (-LP option)\"\n if \"AF\" in codes:\n demag='AF' \n LPcode=\"LP-DIR-AF\"\n if \"T\" in codes:\n demag=\"T\"\n if not labfield: LPcode=\"LP-DIR-T\"\n if labfield: LPcode=\"LP-PI-TRM\"\n if \"ANI\" in codes:\n if not labfield:\n print(\"missing lab field option\")\n return False, \"missing lab field option\"\n LPcode=\"LP-AN-TRM\"\n\n if \"TRM\" in codes: \n demag=\"T\"\n LPcode=\"LP-TRM\"\n #trm=1\n \n if \"CR\" in codes:\n demag=\"T\"\n # dc should be in the code\n if not labfield:\n print(\"missing lab field option\")\n return False, \"missing lab field option\"\n\n LPcode=\"LP-CR-TRM\" # TRM in different cooling rates\n if command_line:\n ind=args.index(\"-LP\")\n CR_cooling_times=args[ind+2].split(\",\")\n\n if \"ANI\" in codes:\n demag=\"T\"\n LPcode=\"LP-AN-TRM\"\n\n #print CR_cooling_time ,\"CR_cooling_time\"\n\n version_num=pmag.get_version()\n\n MagRecs=[]\n \n #--------------------------------------\n # Read the file\n # Assumption:\n # 1. different lab protocolsa are in different files\n # 2. measurements are in the correct order\n #--------------------------------------\n\n Data={}\n\n line_no=0\n\n for line in input.readlines():\n line_no+=1\n this_line_data={}\n line_no+=1\n instcode=\"\"\n if len(line)<2:\n continue\n if line[0]==\"#\": #HUJI way of marking bad data points\n continue\n \n rec=line.strip('\\n').split()\n specimen=rec[0]\n date=rec[2].split(\"/\")\n hour=rec[3].split(\":\")\n treatment_type=rec[4]\n treatment=rec[5].split(\".\")\n dec_core=rec[6]\n inc_core=rec[7]\n dec_geo=rec[8]\n inc_geo=rec[9]\n dec_tilted=rec[10]\n inc_tilted=rec[11]\n moment_emu=float(rec[12])\n\n if specimen not in list(Data.keys()):\n Data[specimen]=[]\n \n # check duplicate treatments:\n # if yes, delete the first and use the second\n\n if len(Data[specimen])>0:\n if treatment==Data[specimen][-1]['treatment']:\n del(Data[specimen][-1])\n print(\"-W- Identical treatments in file %s magfile line %i: specimen %s, treatment %s ignoring the first. \" %(magfile, line_no, specimen,\".\".join(treatment)))\n\n this_line_data={}\n this_line_data['specimen']=specimen\n this_line_data['date']=date\n this_line_data['hour']=hour\n this_line_data['treatment_type']=treatment_type\n this_line_data['treatment']=treatment\n this_line_data['dec_core']=dec_core\n this_line_data['inc_core']=inc_core\n this_line_data['dec_geo']=dec_geo\n this_line_data['inc_geo']=inc_geo\n this_line_data['dec_tilted']=dec_tilted\n this_line_data['inc_tilted']=inc_tilted\n this_line_data['moment_emu']=moment_emu \n Data[specimen].append(this_line_data)\n\n \n print(\"-I- done reading file %s\"%magfile)\n\n #--------------------------------------\n # Convert to MagIC\n #--------------------------------------\n \n specimens_list=list(Data.keys())\n specimens_list.sort()\n\n\n MagRecs=[]\n for specimen in specimens_list:\n for i in range(len(Data[specimen])):\n this_line_data=Data[specimen][i]\n methcode=\"\"\n MagRec={}\n MagRec[\"er_specimen_name\"]=this_line_data['specimen']\n if specnum!=0:\n MagRec[\"er_sample_name\"]=this_line_data['specimen'][:specnum]\n else:\n MagRec[\"er_sample_name\"]=this_line_data['specimen']\n\n if samp_con==\"1\":\n MagRec[\"er_site_name\"]=MagRec[\"er_sample_name\"][:-1]\n elif samp_con==\"2\":\n parts=MagRec[\"er_sample_name\"].split('-')\n MagRec[\"er_site_name\"]= parts[0]\n elif samp_con==\"3\":\n parts=MagRec[\"er_sample_name\"].split('.')\n MagRec[\"er_site_name\"]= parts[0]\n elif samp_con=='4':\n MagRec[\"er_site_name\"]=MagRec[\"er_sample_name\"][0:-Z]\n elif samp_con=='5':\n MagRec[\"er_site_name\"]=MagRec[\"er_sample_name\"]\n elif samp_con=='7':\n MagRec[\"er_site_name\"]=MagRec[\"er_sample_name\"][0:Z] \n else:\n MagRec[\"er_site_name\"]=MagRec[\"er_sample_name\"] # site=sample by default\n \n if er_location_name:\n MagRec['er_location_name']=er_location_name\n else:\n MagRec['er_location_name']=MagRec[\"er_site_name\"]\n \n MagRec[\"measurement_temp\"]='%8.3e' % (273) # room temp in kelvin\n MagRec[\"measurement_magn_moment\"]='%10.3e'% (float(this_line_data['moment_emu'])*1e-3) # moment in Am^2 (from emu)\n MagRec[\"measurement_dec\"]=this_line_data['dec_core']\n MagRec[\"measurement_inc\"]=this_line_data['inc_core']\n date=this_line_data['date']\n hour=this_line_data['hour'] \n if float(date[2])>80:\n yyyy=\"19\"+date[2]\n else:\n yyyy=\"20\"+date[2]\n if len (date[0])==1:\n date[0]=\"0\"+date[0]\n if len (date[1])==1:\n date[1]=\"0\"+date[1]\n MagRec[\"measurement_date\"]=\":\".join([yyyy,date[0],date[1],hour[0],hour[1],\"00.00\"])\n MagRec[\"measurement_time_zone\"]='JER'\n MagRec['er_analyst_mail_names'] =user \n MagRec[\"er_citation_names\"]=\"This study\"\n MagRec[\"magic_instrument_codes\"]=\"HUJI-2G\"\n MagRec[\"measurement_flag\"]=\"g\"\n MagRec[\"measurement_positions\"]=\"1\"\n MagRec[\"measurement_positions\"]=\"1\"\n MagRec[\"measurement_standard\"]=\"u\"\n MagRec[\"measurement_description\"]=\"\"\n #MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n \n #---------------------------------------- \n # AF demag\n # do not support AARM yet\n #----------------------------------------\n \n if demag==\"AF\":\n treatment_type=this_line_data['treatment_type']\n # demag in zero field\n if LPcode != \"LP-AN-ARM\":\n MagRec[\"treatment_ac_field\"]='%8.3e' %(float(this_line_data['treatment'][0])*1e-3) # peak field in tesla\n MagRec[\"treatment_dc_field\"]='0'\n MagRec[\"treatment_dc_field_phi\"]='0'\n MagRec[\"treatment_dc_field_theta\"]='0'\n if treatment_type==\"N\":\n methcode=\"LP-DIR-AF:LT-NO\"\n elif treatment_type==\"A\":\n methcode=\"LP-DIR-AF:LT-AF-Z\"\n else:\n print(\"ERROR in treatment field line %i... exiting until you fix the problem\" %line_no)\n print(this_line_data)\n return False, \"ERROR in treatment field line %i... exiting until you fix the problem\" %line_no\n \n # AARM experiment \n else:\n print(\"Dont supprot AARM in HUJI format yet. sorry... do be DONE\")\n MagRec[\"magic_method_codes\"]=methcode\n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n MagRec[\"measurement_number\"]=\"%i\"%i\n MagRec[\"measurement_description\"]=\"\"\n\n MagRecs.append(MagRec)\n \n #----------------------------------------\n # Thermal: \n # Thellier experiment: \"IZ\", \"ZI\", \"IZZI\", pTRM checks\n # Thermal demag\n # Thermal cooling rate experiment\n # Thermal NLT\n #----------------------------------------\n\n\n if demag==\"T\": \n\n treatment=this_line_data['treatment']\n treatment_type=this_line_data['treatment_type']\n \n \n #----------------------------------------\n # Thellier experimet\n #----------------------------------------\n\n if LPcode == \"LP-PI-TRM\" : # Thelllier experiment\n\n \n\n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n methcode=LPcode \n \n if treatment_type==\"N\" or ( (treatment[1]=='0' or treatment[1]=='00') and float(treatment[0])==0):\n LT_code=\"LT-NO\"\n MagRec[\"treatment_dc_field_phi\"]='0' \n MagRec[\"treatment_dc_field_theta\"]='0' \n MagRec[\"treatment_dc_field\"]='0'\n MagRec[\"treatment_temp\"]='273.'\n \n elif treatment[1]=='0' or treatment[1]=='00':\n LT_code=\"LT-T-Z\"\n MagRec[\"treatment_dc_field_phi\"]='0' \n MagRec[\"treatment_dc_field_theta\"]='0' \n MagRec[\"treatment_dc_field\"]='%8.3e'%(0)\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n\n # check if this is ZI or IZ:\n # check if the same temperature already measured:\n methcode=\"LP-PI-TRM:LP-PI-TRM-ZI\"\n for j in range (0,i):\n if Data[specimen][j]['treatment'][0] == treatment[0]:\n if Data[specimen][j]['treatment'][1] == '1' or Data[specimen][j]['treatment'][1] == '10':\n methcode=\"LP-PI-TRM:LP-PI-TRM-IZ\"\n else:\n methcode=\"LP-PI-TRM:LP-PI-TRM-ZI\"\n \n \n elif treatment[1]=='1' or treatment[1]=='10':\n LT_code=\"LT-T-I\"\n MagRec[\"treatment_dc_field\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\n MagRec[\"treatment_dc_field_phi\"]='%7.1f' % (phi) # labfield phi\n MagRec[\"treatment_dc_field_theta\"]='%7.1f' % (theta) # labfield theta\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n\n # check if this is ZI or IZ:\n # check if the same temperature already measured:\n methcode=\"LP-PI-TRM:LP-PI-TRM-IZ\"\n for j in range (0,i):\n if Data[specimen][j]['treatment'][0] == treatment[0]:\n if Data[specimen][j]['treatment'][1] == '0' or Data[specimen][j]['treatment'][1] == '00':\n methcode=\"LP-PI-TRM:LP-PI-TRM-ZI\"\n else:\n methcode=\"LP-PI-TRM:LP-PI-TRM-IZ\"\n \n elif treatment[1]=='2' or treatment[1]=='20':\n LT_code=\"LT-PTRM-I\"\n MagRec[\"treatment_dc_field\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\n MagRec[\"treatment_dc_field_phi\"]='%7.1f' % (phi) # labfield phi\n MagRec[\"treatment_dc_field_theta\"]='%7.1f' % (theta) # labfield theta\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n methcode=\"LP-PI-TRM:LP-PI-TRM-IZ\"\n\n else:\n print(\"ERROR in treatment field line %i... exiting until you fix the problem\" %line_no)\n return False, \"ERROR in treatment field line %i... exiting until you fix the problem\" %line_no\n \n MagRec[\"magic_method_codes\"]=LT_code+\":\"+methcode\n MagRec[\"measurement_number\"]=\"%i\"%i \n MagRec[\"measurement_description\"]=\"\"\n MagRecs.append(MagRec)\n #continue\n \n \n #----------------------------------------\n # demag experimet\n #----------------------------------------\n\n\n if LPcode == \"LP-DIR-T\" :\n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n methcode=LPcode \n \n if treatment_type==\"N\":\n LT_code=\"LT-NO\"\n else:\n LT_code=\"LT-T-Z\"\n \n methcode=LPcode+\":\"+\"LT-T-Z\"\n MagRec[\"treatment_dc_field_phi\"]='0' \n MagRec[\"treatment_dc_field_theta\"]='0' \n MagRec[\"treatment_dc_field\"]='%8.3e'%(0)\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n MagRec[\"magic_method_codes\"]=LT_code+\":\"+methcode\n MagRec[\"measurement_number\"]=\"%i\"%i \n MagRec[\"measurement_description\"]=\"\"\n MagRecs.append(MagRec)\n #continue\n \n\n #----------------------------------------\n # ATRM measurements\n # The direction of the magnetization is used to determine the\n # direction of the lab field.\n #----------------------------------------\n \n if LPcode ==\"LP-AN-TRM\" :\n \n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n methcode=LPcode \n\n if float(treatment[1])==0:\n MagRec[\"magic_method_codes\"]=\"LP-AN-TRM:LT-T-Z\"\n MagRec[\"treatment_dc_field_phi\"]='0'\n MagRec[\"treatment_dc_field_theta\"]='0'\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n MagRec[\"treatment_dc_field\"]='0'\n else:\n MagRec[\"magic_method_codes\"]=\"LP-AN-TRM:LT-T-I\"\n inc=float(MagRec[\"measurement_inc\"]);dec=float(MagRec[\"measurement_dec\"])\n if abs(inc)<45 and (dec<45 or dec>315): # +x\n tdec,tinc=0,0\n MagRec[\"measurement_number\"]='1'\n if abs(inc)<45 and (dec<135 and dec>45):\n tdec,tinc=90,0\n MagRec[\"measurement_number\"]='2' # +y\n if inc>45 :\n tdec,tinc=0,90\n MagRec[\"measurement_number\"]='3' # +z\n if abs(inc)<45 and (dec<225 and dec>135):\n tdec,tinc=180,0\n MagRec[\"measurement_number\"]='4' # -x\n if abs(inc)<45 and (dec<315 and dec>225):\n tdec,tinc=270,0\n MagRec[\"measurement_number\"]='5'# -y\n if inc<-45 :\n tdec,tinc=0,-90\n MagRec[\"measurement_number\"]='6'# -z\n if float(treatment[1])==7 or float(treatment[1])==70:\n # alteration check\n #methcode=\"LP-AN-TRM:LT-PTRM-I\"\n MagRec[\"magic_method_codes\"]=\"LP-AN-TRM:LT-PTRM-I\"\n MagRec[\"measurement_number\"]='7'# -z\n\n \n MagRec[\"treatment_dc_field_phi\"]='%7.1f' %(tdec)\n MagRec[\"treatment_dc_field_theta\"]='%7.1f'% (tinc)\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\n MagRec[\"treatment_dc_field\"]='%8.3e'%(labfield)\n MagRec[\"measurement_description\"]=\"\"\n MagRecs.append(MagRec)\n #continue\n\n #----------------------------------------\n # NLT measurements\n # or TRM acquisistion experiment\n #----------------------------------------\n\n \n if LPcode == \"LP-TRM\" :\n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n MagRec[\"magic_method_codes\"]=\"LP-TRM:LT-T-I\"\n if float(treatment[1])==0:\n labfield=0\n else:\n labfield=float(float(treatment[1]))*1e-6\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin \n MagRec[\"treatment_dc_field\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\n MagRec[\"treatment_dc_field_phi\"]='%i' % (int(phi)) # labfield phi\n MagRec[\"treatment_dc_field_theta\"]='%i' % (int(theta)) # labfield theta\n MagRec[\"measurement_number\"]=\"%i\"%i \n MagRec[\"measurement_description\"]=\"\"\n MagRecs.append(MagRec)\n #continue\n \n\n #----------------------------------------\n # Cooling rate experiments\n #----------------------------------------\n \n if LPcode ==\"LP-CR-TRM\":\n index=int(treatment[1][0])\n #print index,\"index\"\n #print CR_cooling_times,\"CR_cooling_times\"\n #print CR_cooling_times[index-1]\n #print CR_cooling_times[0:index-1]\n if index==7 or index==70: # alteration check as final measurement\n meas_type=\"LT-PTRM-I:LP-CR-TRM\"\n CR_cooling_time=CR_cooling_times[-1]\n elif index==0 or index==00: # baseline\n meas_type=\"LT-T-Z:LP-CR-TRM\"\n CR_cooling_time=CR_cooling_times[0]\n else: \n meas_type=\"LT-T-I:LP-CR-TRM\"\n CR_cooling_time=CR_cooling_times[index-1]\n MagRec[\"magic_method_codes\"]=meas_type \n MagRec[\"magic_experiment_name\"]=specimen+ \":\" + LPcode\n MagRec[\"treatment_temp\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin \n MagRec[\"treatment_dc_field\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\n MagRec[\"treatment_dc_field_phi\"]='%7.1f' % (phi) # labfield phi\n MagRec[\"treatment_dc_field_theta\"]='%7.1f' % (theta) # labfield theta\n MagRec[\"measurement_number\"]=\"%i\"%index\n MagRec[\"measurement_description\"]=\"cooling_rate\"+\":\"+CR_cooling_time+\":\"+\"K/min\"\n #MagRec[\"measurement_description\"]=\"%.1f minutes per cooling time\"%int(CR_cooling_time)\n MagRecs.append(MagRec)\n #continue\n\n pmag.magic_write(meas_file,MagRecs,'magic_measurements')\n print(\"-I- results put in \",meas_file)\n return True, meas_file","def lab_run_small(character_id, time_step):\n pass","def workflow(now, realtime):\n szx = 7000\n szy = 3500\n # Create the image data\n imgdata = np.zeros((szy, szx), 'u1')\n sts = now - datetime.timedelta(minutes=2)\n metadata = {'start_valid': sts.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n 'end_valid': now.strftime(\"%Y-%m-%dT%H:%M:%SZ\"),\n 'product': 'a2m',\n 'units': '0.02 mm'}\n\n gribfn = mrms.fetch('PrecipRate', now)\n if gribfn is None:\n print((\"mrms_rainrate_comp.py NODATA for PrecipRate: %s\"\n ) % (now.strftime(\"%Y-%m-%dT%H:%MZ\"),))\n return\n\n # http://www.nssl.noaa.gov/projects/mrms/operational/tables.php\n # Says units are mm/hr\n fp = gzip.GzipFile(gribfn, 'rb')\n (_, tmpfn) = tempfile.mkstemp()\n tmpfp = open(tmpfn, 'wb')\n tmpfp.write(fp.read())\n tmpfp.close()\n grbs = pygrib.open(tmpfn)\n grb = grbs[1]\n os.unlink(tmpfn)\n os.unlink(gribfn)\n\n val = grb['values']\n # Convert into units of 0.1 mm accumulation\n val = val / 60.0 * 2.0 * 50.0\n val = np.where(val < 0., 255., val)\n imgdata[:, :] = np.flipud(val.astype('int'))\n\n (tmpfp, tmpfn) = tempfile.mkstemp()\n\n # Create Image\n png = Image.fromarray(np.flipud(imgdata))\n png.putpalette(mrms.make_colorramp())\n png.save('%s.png' % (tmpfn,))\n\n mrms.write_worldfile('%s.wld' % (tmpfn,))\n # Inject WLD file\n routes = \"c\" if realtime else \"\"\n prefix = 'a2m'\n pqstr = (\"/home/ldm/bin/pqinsert -i -p 'plot a%s %s \"\n \"gis/images/4326/mrms/%s.wld GIS/mrms/%s_%s.wld wld' %s.wld\"\n \"\") % (routes, now.strftime(\"%Y%m%d%H%M\"), prefix, prefix,\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(pqstr, shell=True)\n # Now we inject into LDM\n pqstr = (\"/home/ldm/bin/pqinsert -i -p 'plot a%s %s \"\n \"gis/images/4326/mrms/%s.png GIS/mrms/%s_%s.png png' %s.png\"\n \"\") % (routes, now.strftime(\"%Y%m%d%H%M\"), prefix, prefix,\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(pqstr, shell=True)\n\n if realtime:\n # Create 900913 image\n cmd = (\"gdalwarp -s_srs EPSG:4326 -t_srs EPSG:3857 -q -of GTiff \"\n \"-tr 1000.0 1000.0 %s.png %s.tif\") % (tmpfn, tmpfn)\n subprocess.call(cmd, shell=True)\n # Insert into LDM\n pqstr = (\"/home/ldm/bin/pqinsert -i -p 'plot c %s \"\n \"gis/images/900913/mrms/%s.tif GIS/mrms/%s_%s.tif tif' %s.tif\"\n \"\") % (now.strftime(\"%Y%m%d%H%M\"), prefix, prefix,\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(pqstr, shell=True)\n\n j = open(\"%s.json\" % (tmpfn,), 'w')\n j.write(json.dumps(dict(meta=metadata)))\n j.close()\n # Insert into LDM\n pqstr = (\"/home/ldm/bin/pqinsert -i -p 'plot c %s \"\n \"gis/images/4326/mrms/%s.json GIS/mrms/%s_%s.json json' \"\n \"%s.json\") % (now.strftime(\"%Y%m%d%H%M\"), prefix, prefix,\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(pqstr, shell=True)\n for suffix in ['tif', 'json', 'png', 'wld']:\n if os.path.isfile(\"%s.%s\" % (tmpfn, suffix)):\n os.unlink('%s.%s' % (tmpfn, suffix))\n\n os.close(tmpfp)\n os.unlink(tmpfn)","def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\n\n # def Q_gen(I, R):\n # w = I * I * R\n # return w\n\n # def Q_rad_in(I_sun, A_s, a_s):\n # w = I_sun * D * a_s\n # return w\n\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\n # w = htcoeff * A_s * (T_line - T_amb)\n # return w\n\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\n # return w\n\n def reynolds(V, D, v, Mair=1.103):\n r = V * D / v\n return r\n\n def nusselt(Re, Pr):\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\n c = (Re / 282000) ** (5.0/8.0)\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\n return n\n\n def air_prop(T_amb):\n # temp v k Pr\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\n [250, 11.44e-6, 22.3e-3, 0.720],\n [300, 15.89e-6, 26.3e-3, 0.707],\n [350, 20.92e-6, 30.0e-3, 0.700],\n [400, 26.41e-6, 33.8e-3, 0.690],\n [450, 32.39e-6, 37.3e-3, 0.686],\n [500, 38.79e-6, 40.7e-3, 0.684],\n [550, 45.57e-6, 43.9e-3, 0.683],\n [600, 52.69e-6, 46.9e-3, 0.685]])\n\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\n 0, air_prop[:,1:])\n return v, k, Pr\n\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\n if 273 <= T_line <= 323:\n R = ((R_lo + \n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\n *(T_line - T_range[0]))/N_cond)\n elif T_line > 323:\n R = ((R_mid + \n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\n *(T_line - T_range[1]))/N_cond)\n else:\n R = R_lo\n print('Out of bounds')\n return R\n\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\n temp_factor = 1\n wind_factor = 1\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\n\n T_amb = T_amb*temp_factor\n V = V*wind_factor\n\n v, k, Pr = air_prop(T_amb)\n Re = reynolds(V, D, v)\n htcoeff = nusselt(Re, Pr) * k / D\n\n def T_line(T_init):\n \n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\n print R\n\n C4 = e_s * sigma * D * math.pi\n C3 = 0.0\n C2 = 0.0\n C1 = htcoeff * D * math.pi\n C0 = - ( I ** 2 * R\n + I_sun * a_s * D\n + htcoeff * D * math.pi * T_amb\n + e_s * D * math.pi * sigma * (T_amb ** 4))\n\n return np.roots([C4, C3, C2, C1, C0])\n\n T_c = T_amb\n \n for i in range(n_iter):\n T_arr = T_line(T_c)\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\n print T_c\n\n return T_c","def stop( self ) :\n print '********************* number of events seen: ', globals()['evCounter']\n\n from Configurables import LumiIntegrator\n lumitool = toolSvc.create('LumiIntegrator', 'IntegrateBeamCrossing', interface='ILumiIntegrator')\n lumirawtool = toolSvc.create('LumiIntegrator', 'RawLumiIntegrator', interface='ILumiIntegrator')\n print '********************* created lumiintegrator tools'\n\n # store data\n store = {}\n for (text, tool) in ( ('corrected', lumitool), ('raw', lumirawtool) ):\n n_files = tool.muFiles()\n print '********************* raw input files', text, n_files, tool.lumiValue(), tool.lumiError(), tool.absolute()\n store[text] = {'files':tool.muFiles(), \n 'lumiValue':tool.lumiValue(), \n 'lumiError':tool.lumiError(), \n 'absolute':tool.absolute(),\n 'filedata':[],\n }\n for i in range(n_files):\n store[text]['filedata'].append(\n {'sequence' :i, \n 'run' :tool.muRun(i), \n 'guid' :tool.muGuid(i), \n 'time0' :tool.muTime0(i), \n 'time1' :tool.muTime1(i), \n 'deltaLumi':tool.muDeltaLumi(i), \n 'norm' :tool.muNorm(i), \n 'mu' :tool.muMu(i), \n 'counters' :{}\n }\n )\n print 'file', i, tool.muRun(i), tool.muGuid(i), tool.muTime0(i), tool.muTime1(i), tool.muDeltaLumi(i), tool.muNorm(i), tool.muMu(i), \n print tool.muKeys(i)\n for k in range(tool.muKeys(i)):\n if tool.muCounter(i,k) <= 50: continue # these are not useful for mu\n norm = tool.muCounterNorm(i,k)\n value = 0 if norm == 0 else tool.muCounterValue(i,k)/norm\n store[text]['filedata'][-1]['counters'][tool.muCounterName(i,k)] = {'key':tool.muCounter(i,k), 'norm':norm, 'value':value}\n print ' ', tool.muCounter(i,k), tool.muCounterName(i,k), norm, value, \n print\n\n # save store\n pfname = 'rawdata.pck'\n pf = open(pfname,'wb')\n print pfname, 'opened'\n pickle.dump(store, pf)\n pf.close()\n print pfname, 'closed'\n \n print store\n\n return SUCCESS","def AlljointRuns():\n #800 nm\n forwardModelJointFit(getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/'),\n out='J800nm', wavelength='800nm') #0.31, 0.3\n forwardModelJointFit(getFiles(mintime=(15, 12, 20), maxtime=(15, 24, 16), folder='data/31Jul/'),\n out='J800nm5k', wavelength='800nm') #0.28 0.31\n forwardModelJointFit(getFiles(mintime=(15, 28, 40), maxtime=(15, 39, 21), folder='data/31Jul/'),\n out='J800nm10k', wavelength='800nm') #0.27 0.29\n forwardModelJointFit(getFiles(mintime=(15, 43, 24), maxtime=(15, 51, 47), folder='data/31Jul/'),\n out='J800nm20k', wavelength='800nm') #0.27 0.28\n forwardModelJointFit(getFiles(mintime=(15, 56, 11), maxtime=(16, 02, 58), folder='data/31Jul/'),\n out='J800nm30k', wavelength='800nm')\n forwardModelJointFit(getFiles(mintime=(16, 12, 39), maxtime=(16, 18, 25), folder='data/31Jul/'),\n out='J800nm38k', wavelength='800nm')\n forwardModelJointFit(getFiles(mintime=(16, 21, 52), maxtime=(16, 26, 16), folder='data/31Jul/'),\n out='J800nm50k', wavelength='800nm')\n forwardModelJointFit(getFiles(mintime=(16, 32, 02), maxtime=(16, 35, 23), folder='data/31Jul/'),\n out='J800nm54k', wavelength='800nm')\n #700 nm\n forwardModelJointFit(getFiles(mintime=(17, 20, 17), maxtime=(17, 33, 17), folder='data/30Jul/'),\n out='J700nm5k', wavelength='700nm') # 0.28 0.32\n forwardModelJointFit(getFiles(mintime=(17, 37, 35), maxtime=(17, 46, 51), folder='data/30Jul/'),\n out='J700nm9k', wavelength='700nm') # 0.27 0.32\n forwardModelJointFit(getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/'),\n out='J700nm52k', wavelength='700nm') # 0.26 0.31\n forwardModelJointFit(getFiles(mintime=(17, 58, 18), maxtime=(17, 59, 31), folder='data/30Jul/'),\n out='J700nm32k', wavelength='700nm')\n #600 nm\n forwardModelJointFit(getFiles(mintime=(15, 22, 00), maxtime=(15, 36, 32), folder='data/30Jul/'),\n out='J600nm5k', wavelength='600nm') #0.27 0.31\n forwardModelJointFit(getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/'),\n out='J600nm54k', wavelength='600nm') #0.299, 0.333\n forwardModelJointFit(getFiles(mintime=(15, 52, 07), maxtime=(16, 06, 32), folder='data/30Jul/'),\n out='J600nm10k', wavelength='600nm') #0.28 0.32\n #890 nm\n forwardModelJointFit(getFiles(mintime=(13, 37, 37), maxtime=(13, 50, 58), folder='data/01Aug/'),\n out='J890nm5k', wavelength='890nm') #0.28 0.35\n forwardModelJointFit(getFiles(mintime=(14, 00, 58), maxtime=(14, 11, 54), folder='data/01Aug/'),\n out='J890nm10k', wavelength='890nm') #0.28 0.33\n forwardModelJointFit(getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/'),\n out='J890nm30k', wavelength='890nm') #0.3 0.33\n forwardModelJointFit(getFiles(mintime=(14, 30, 03), maxtime=(14, 34, 37), folder='data/01Aug/'),\n out='J890nm50k', wavelength='890nm') #0.3 0.3","def runqn1(h,steps,alpha):\n G = 6.6742*10**-11 # m^3 kg^-1 s^-2\n M2 = 1.989*10**30 # kg, Sun\n M1 = 1.899*10**27 # kg, Jupiter\n R = 778.3*10**9 # m, Semimajor axis of Jupiter's orbit\n T = 3.743*10**8 # s, Period of Jupiter's orbit\n omega = 1.679*10**-8 # angular velocity of J-S system\n func = ['x3','x4','-6.6742*10**-11*1.899*10**27*(x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))/(((x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-6.6742*10**-11*1.989*10**30*(x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))/(((x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))+2.0*1.679*10**-8*x4+x1*(1.679*10**-8)**2','-6.6742*10**-11*1.899*10**27*x2/(((x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-6.6742*10**-11*1.989*10**30*x2/(((x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-2.0*1.679*10**-8*x3+x2*(1.679*10**-8)**2']\n init = [cos(alpha)*R*(M2-M1)/(M2+M1),R*sin(alpha),0,0]\n t0 = 0 # initial time\n h = float(h)\n steps = abs(int(steps))\n rungeODE(func,init,t0,h,steps)","def run_script(input_dir, output_dir):\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step 1. load dataset \"\"\"\n print(\"loading data ......\")\n print(\"+++++++Read the surface shape data+++++++\")\n shape_file_name = input_dir + \"aligned_shapes.mat\"\n mat = loadmat(shape_file_name)\n y_design = mat['aligned_shape']\n n, l, m = y_design.shape\n print(\"The dimension of shape matrix is \" + str(y_design.shape))\n print(\"+++++++Read the sphere coordinate data+++++++\")\n template_file_name = input_dir + \"template.mat\"\n mat = loadmat(template_file_name)\n coord_mat = mat['template']\n # d = coord_mat.shape[1]\n print(\"+++++++Read the design matrix+++++++\")\n design_data_file_name = input_dir + \"design_data.txt\"\n design_data = np.loadtxt(design_data_file_name)\n # read the covariate type\n var_type_file_name = input_dir + \"var_type.txt\"\n var_type = np.loadtxt(var_type_file_name)\n print(\"+++++++Construct the design matrix: normalization+++++++\")\n x_design = read_x(design_data, var_type)\n p = x_design.shape[1]\n print(\"The dimension of design matrix is \" + str(x_design.shape))\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step 2. Statistical analysis: including (1) smoothing and (2) hypothesis testing\"\"\"\n gpvals, lpvals_fdr, clu_pvals, efit_beta, efity_design, efit_eta = mfsda.run_stats(y_design, coord_mat, design_data, var_type)\n\n \"\"\"+++++++++++++++++++++++++++++++++++\"\"\"\n \"\"\"Step3. Save all the results\"\"\"\n gpvals_file_name = output_dir + \"global_pvalue.txt\"\n np.savetxt(gpvals_file_name, gpvals)\n lpvals_fdr_file_name = output_dir + \"local_pvalue_fdr.txt\"\n np.savetxt(lpvals_fdr_file_name, lpvals_fdr)\n clu_pvals_file_name = output_dir + \"cluster_pvalue.txt\"\n np.savetxt(clu_pvals_file_name, clu_pvals)","def _function(self):\n\n\n\n def calculate_weights():\n \"\"\"\n calculate a weight inversely proportional to the expected to duration of the two steps in the\n script\n\n Returns: weights as a dictionary for the two steps\n\n \"\"\"\n weights = {}\n\n\n # estimate run time of step 1 (fast sweep)\n f_range = sweeper_script.settings['stop'] - sweeper_script.settings['start']\n N_samples = sweeper_script.settings['samplecount']\n df = f_range / N_samples\n\n t = N_samples / df\n\n weights['quick scan'] = t\n\n # estimate run time of step 2 (high res sweep)\n df = self.settings['high_res_df']\n N_samples = self.settings['high_res_N']\n\n t = N_samples / df\n\n weights['high res scan'] = t\n\n\n total_time = sum([v for k, v in weights.iteritems()])\n\n weights = {k: v/total_time for k, v in weights.iteritems()}\n\n print('weights',weights)\n\n return weights\n\n def run_scan(name):\n self.current_subscript = name\n sweeper_script.start()\n while self.current_subscript is name:\n time.sleep(0.1)\n\n def calc_new_range():\n\n\n df = self.settings['high_res_df']\n N = self.settings['high_res_N']\n\n r = sweeper_script.data[-1]['r']\n freq = sweeper_script.data[-1]['frequency']\n freq = freq[np.isfinite(r)]\n r = r[np.isfinite(r)]\n\n fo = freq[np.argmax(r)]\n\n f_start, f_end = fo - N/2 *df, fo + N/2 *df\n\n\n # make sure that we convert back to native python types (numpy file types don't pass the Parameter validation)\n return float(f_start), float(f_end), int(N)\n\n\n sweeper_script = self.scripts['zi sweep']\n #save initial settings, so that we can rest at the end of the script\n initial_settings = deepcopy(sweeper_script.settings)\n self.weights = calculate_weights()\n\n # take the signal from the subscript and route it to a function that takes care of it\n sweeper_script.updateProgress.connect(self._receive_signal)\n\n print('====== start quick scan ============')\n\n run_scan('quick scan')\n\n print('====== calculate new scan range ====')\n f_start, f_stop, N = calc_new_range()\n\n print('f_start, f_stop, N', f_start, f_stop, N)\n\n print('====== update sweeper ==============')\n sweeper_script.update({\n 'start' : f_start,\n 'stop' : f_stop,\n 'samplecount' : N\n })\n\n print('====== start high res scan =========')\n # print(sweeper_script.sweeper.finished())\n # print(sweeper_script.sweeper.progress())\n\n run_scan('high res scan')\n\n sweeper_script.updateProgress.disconnect()\n self.data = sweeper_script.data[-1]\n\n self._recording = False\n\n if self.settings['save']:\n self.save()\n\n # set the sweeper script back to initial settings\n sweeper_script.update(initial_settings)\n # make sure that progess is set 1o 100 because we check that in the old_gui\n self.updateProgress.emit(100)","def simulationWithDrug(numViruses, maxPop, maxBirthProb, clearProb, resistances,\n mutProb, numTrials):\n \n #create viruses list\n viruses = []\n for i in range(numViruses):\n viruses.append(ResistantVirus(maxBirthProb, clearProb, resistances, mutProb))\n \n #create test patient P1\n results = np.zeros(numTrials*300).reshape(300,numTrials)\n resultsPopResist = np.zeros(numTrials*300).reshape(300,numTrials)\n \n #runs numTrials of 300 steps, putting results in an array of 300 lines, \n # numTrials columns\n for t in range(numTrials) :\n P1 = TreatedPatient(viruses, maxPop)\n for s in range(150):\n P1.update()\n results[s][numTrials-1] += P1.getTotalPop()\n resultsPopResist[s][numTrials-1] += P1.getResistPop(['guttagonol'])\n \n P1.addPrescription('guttagonol')\n for s in range(150,300):\n P1.update()\n results[s][numTrials-1]+=P1.getTotalPop()\n resultsPopResist[s][numTrials-1] += P1.getResistPop(['guttagonol'])\n \n \n #calculating average of virus population size at each step \n yValues1 = []\n for i in range(300):\n a = sum(results[i].tolist())/len(results[i])\n yValues1.append(a)\n \n yValues2 = []\n for i in range(300):\n a = sum(resultsPopResist[i].tolist())/len(resultsPopResist[i])\n yValues2.append(a)\n\n pylab.plot(yValues1,label='pop average')\n pylab.plot(yValues2,'r--',label = 'resistant virus population')\n pylab.title('virus pop average at each step')\n pylab.legend()\n pylab.xlabel('Time Steps')\n pylab.ylabel('pop #')\n pylab.show()","def test4():\n\t\n\tprint('This takes a while to compute - be patient!')\n\t\n\td = np.linspace(-15000,15000,300)\n\t#Voigt\n\t#p_dict = {'Bfield':700,'rb85frac':1,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\n\tp_dict = {'Bfield':1000,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':00*np.pi/180,'lcell':75e-3,'T':93,'Dline':'D2','Elem':'Cs'}\n\tpol = np.array([1.0,0.0,0.0])\n\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\n\t\n\tfig2 = plt.figure()\n\tax1a = fig2.add_subplot(411)\n\tax2a = fig2.add_subplot(412,sharex=ax1a)\n\tax3a = fig2.add_subplot(413,sharex=ax1a)\n\tax4a = fig2.add_subplot(414,sharex=ax1a)\n\t\n\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\n\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\n\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\n\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\n\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\n\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\n\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\n#\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\n\t\n\t\n\tax4a.set_xlabel('Detuning (MHz)')\n\tax1a.set_ylabel('I -45')\n\tax2a.set_ylabel('I +45')\n\tax3a.set_ylabel('Ix')\n\tax4a.set_ylabel('Iy')\n\t\n\tax4a.set_xlim(d[0],d[-1]+3000)\n\tax4a.legend(loc=0)\n\t\n\tplt.show()","def PowerFlowAnalysis(BusData_Location, LineData_Location, Output_FileName, tolerance, S_Base):\r\n df_BusData, df_LineData = import_BusAndLineData(BusData_Location, LineData_Location)\r\n n = df_BusData.shape[0]\r\n \"\"\"Create Admittance Matrix in forms of Y and seperated into G and B\"\"\"\r\n sys_Y, sys_G, sys_B = build_AdmittanceMatrix(df_LineData, n)\r\n \"\"\"Creation of sys_Data\"\"\"\r\n sys_BusNum, sys_LoadP, sys_LoadQ, sys_BusType, sys_PGen, sys_VRef = init_BusData(df_BusData)\r\n sys_Data = init_SysData(sys_BusNum, sys_LoadP, sys_LoadQ, sys_BusType, sys_PGen, sys_VRef, sys_G, sys_B, S_Base)\r\n \"\"\"Initial Prime for mismatch detetction and storage\"\"\"\r\n mismatch_P = sys_Data[1:n,4]\r\n mismatch_Q = sys_Data[1:n,6]\r\n mismatch_max = [max(abs(mismatch_P)), max(abs(mismatch_Q))]\r\n iteration = 0\r\n iteration_list = []\r\n mismatch_P_list = []\r\n mismatch_Q_list = []\r\n max_P_bus = []\r\n max_Q_bus = []\r\n \r\n \"\"\"Loop until solution is reached or max iteration is exceeded\"\"\"\r\n while(iteration<15 and mismatch_max>tolerance):\r\n iteration_list.append(iteration)\r\n \r\n bus_P, = np.where(mismatch_P == max(abs(mismatch_P)))\r\n if len(bus_P) == 0:\r\n bus_P, = np.where(mismatch_P == -1*max(abs(mismatch_P)))\r\n max_P_bus.append(int(bus_P+2))\r\n bus_Q, = np.where(mismatch_Q == max(abs(mismatch_Q)))\r\n if len(bus_Q) == 0:\r\n bus_Q, = np.where(mismatch_Q == -1*max(abs(mismatch_Q)))\r\n max_Q_bus.append(int(bus_Q+2))\r\n mismatch_P_list.append(max(abs(mismatch_P)))\r\n mismatch_Q_list.append(max(abs(mismatch_Q)))\r\n \r\n sys_Data = update_SysData(sys_Data, sys_G, sys_B, sys_BusType)\r\n mismatch_P = sys_Data[1:n,4]\r\n mismatch_Q = sys_Data[1:n,6]\r\n mismatch_max = [max(abs(mismatch_P)), max(abs(mismatch_Q))]\r\n iteration += 1\r\n \r\n \"\"\"Final add to convergency history\"\"\"\r\n iteration_list.append(iteration) \r\n bus_P, = np.where(mismatch_P == max(abs(mismatch_P)))\r\n if len(bus_P) == 0:\r\n bus_P, = np.where(mismatch_P == -1*max(abs(mismatch_P)))\r\n max_P_bus.append(int(bus_P+2))\r\n bus_Q, = np.where(mismatch_Q == max(abs(mismatch_Q)))\r\n if len(bus_Q) == 0:\r\n bus_Q, = np.where(mismatch_Q == -1*max(abs(mismatch_Q)))\r\n max_Q_bus.append(int(bus_Q+2))\r\n mismatch_P_list.append(max(abs(mismatch_P)))\r\n mismatch_Q_list.append(max(abs(mismatch_Q)))\r\n \r\n \"\"\"Export final solution to excel file\"\"\"\r\n DataOutput(Output_FileName, sys_Data, df_LineData, sys_Y,iteration_list,mismatch_P_list,mismatch_Q_list,max_P_bus,max_Q_bus)","def start_loop(self):\r\n # Transform the array to fit the 1 minute time duration\r\n #self.set_vals = np.repeat(self.input_vals, self.dt * 60)\r\n\r\n i = 0\r\n idx = pd.date_range(start=datetime.datetime.now(),\r\n periods=(self.input_vals.size), freq='S')\r\n while i < len(idx):\r\n if datetime.datetime.now().second == idx[i].second:\r\n # Set chrging value\r\n self.set_val = int(self.input_vals[i])\r\n if self.set_val < 0:\r\n # Write negative value to battery charge power (AC) setpoint register\r\n self.c.write_single_register(1024, self.set_val & 0xFFFF)\r\n # Log writing time\r\n self.set_time = datetime.datetime.now()\r\n else:\r\n # Write positive value to battery charge power (AC) setpoint to register\r\n self.c.write_single_register(1024, self.set_val)\r\n # Log writing time\r\n self.set_time = datetime.datetime.now()\r\n\r\n try:\r\n # Read total AC power value from register\r\n _P_ac = self.c.read_holding_registers(172, 2)\r\n self.read_time_P_ac = datetime.datetime.now()\r\n except:\r\n print('Could not read register 172!')\r\n\r\n try:\r\n # Read actual battery charge/discharge power value from register\r\n _P_bat = self.c.read_holding_registers(582, 1)\r\n self.read_time_P_bat = datetime.datetime.now()\r\n except:\r\n print('Could not read register 582!')\r\n\r\n # Load content of two registers into a single float value\r\n zregs = utils.word_list_to_long(_P_ac, big_endian=False)\r\n # Decode and store float value of the AC-power\r\n self.P_ac = utils.decode_ieee(*zregs)\r\n # Store the DC charging power\r\n self.P_bat = np.int16(*_P_bat)\r\n # Read actual soc\r\n self.soc0 = self.read_soc(210)\r\n\r\n try:\r\n # Save the values to a csv file\r\n self.save_to_csv()\r\n except:\r\n print('Could not save to csv!')\r\n\r\n i += 1","def __init__(self, **kwargs):\n\n # mod_path = os.path.join(os.path.dirname(a.__file__), 'BindingPMF.py')\n # print \"\"\"###########\n # # AlGDock #\n # ###########\n # Molecular docking with adaptively scaled alchemical interaction grids\n #\n # in {0}\n # last modified {1}\n # \"\"\".format(mod_path, time.ctime(os.path.getmtime(mod_path)))\n\n from AlGDock.argument_parser import SimulationArguments\n self.args = SimulationArguments(**kwargs)\n\n from AlGDock.simulation_data import SimulationData\n self.data = {}\n self.data['BC'] = SimulationData(self.args.dir['BC'], 'BC', \\\n self.args.params['CD']['pose'])\n self.data['CD'] = SimulationData(self.args.dir['CD'], 'CD', \\\n self.args.params['CD']['pose'])\n\n if not 'max_time' in kwargs.keys():\n kwargs['max_time'] = None\n if not 'run_type' in kwargs.keys():\n kwargs['run_type'] = None\n\n from AlGDock.logger import Logger\n self.log = Logger(self.args, \\\n max_time=kwargs['max_time'], run_type=kwargs['run_type'])\n\n self.T_HIGH = self.args.params['BC']['T_HIGH']\n self.T_TARGET = self.args.params['BC']['T_TARGET']\n\n self._setup()\n\n print '\\n*** Simulation parameters and constants ***'\n for p in ['BC', 'CD']:\n print '\\nfor %s:' % p\n print dictionary_tools.dict_view(self.args.params[p])[:-1]\n\n self.run(kwargs['run_type'])"],"string":"[\n \"def run_multiobjective(inputs, parameters = None):\\n def thickness(x, t, chord):\\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\\n thickness_at_x = y['u'] - y['l']\\n return thickness_at_x \\n\\n if parameters != None:\\n eng = parameters[0]\\n import_matlab = False\\n else:\\n eng = None\\n import_matlab = True\\n \\n sma = inputs['sma']\\n linear = inputs['linear']\\n sigma_o = 100e6\\n R = inputs['R']\\n \\n airfoil = \\\"naca0012\\\"\\n chord = 1.#0.6175\\n t = 0.12*chord\\n\\n J = {'x':0.75, 'y':0.}\\n \\n #Adding the area key to the dictionaries\\n sma['area'] = math.pi*(0.000381/2.)**2\\n linear['area'] = 0.001\\n \\n # Design constants \\n #arm length to center of gravity\\n r_w = 0.10\\n\\n\\n #Aicraft weight (mass times gravity)\\n W = 0.0523*9.8 #0.06*9.8\\n alpha = 0.\\n V = 10 #m/s\\n altitude = 10000. #feet\\n \\n # Temperature\\n T_0 = 273.15 + 30\\n T_final = inputs['T_f']\\n \\n #Initial martensitic volume fraction\\n MVF_init = 1.\\n \\n # Number of steps and cycles\\n n = 200\\n n_cycles = 0\\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n #Parameters to select how to output stuff\\n all_outputs = True\\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n if all_outputs:\\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap_multiobjective(airfoil, \\n chord, J, sma, linear, sigma_o, \\n W, r_w, V, altitude, alpha, T_0, \\n T_final, MVF_init, n, R, all_outputs = True,\\n import_matlab = import_matlab, eng=eng,\\n n_cycles = n_cycles)\\n\\n return theta, sigma, T, MVF, eps_s, L_s\",\n \"def main():\\n\\n r = np.arange(-90, 90, 2)\\n tilt = 22.5\\n\\n with ptcmd.PTCMD(baud=57600) as controller:\\n\\n controller.home()\\n\\n for pan_angle in r:\\n\\n current_position = controller.get_position()\\n pan_steps, tilt_steps, pan_rate, tilt_rate = system.move_to_angle(pan_angle, tilt, current_position[0], current_position[1], base_rate = 1000)\\n controller.sync(pan_rate, pan_steps, tilt_rate, tilt_steps)\\n controller.wait('all')\\n\\n time.sleep(2)\\n\\n distances = []\\n for i in range(0,12):\\n time.sleep(1)\\n distances.append(controller.get_analog_sensor(1))\\n\\n distances.sort()\\n distance = sum(distances[1:-1]) / len(distances[1:-1])\\n\\n x = math.sin(pan_angle*math.pi/180) * distance\\n y = math.cos(pan_angle*math.pi/180) * distance\\n\\n print(distances, distance, pan_angle, x, y)\\n\\n plt.plot(x, y, 'b+')\\n\\n plt.show()\",\n \"def run(inputs, parameters = None):\\n def thickness(x, t, chord):\\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\\n thickness_at_x = y['u'] - y['l']\\n return thickness_at_x \\n\\n if parameters != None:\\n eng = parameters[0]\\n import_matlab = False\\n else:\\n eng = None\\n import_matlab = True\\n \\n sma = inputs['sma']\\n linear = inputs['linear']\\n R = inputs['R']\\n\\n sigma_o = 100e6\\n\\n \\n airfoil = \\\"naca0012\\\"\\n chord = 1.#0.6175\\n\\n J = {'x':0.75, 'y':0.}\\n \\n #Adding the area key to the dictionaries\\n sma['area'] = math.pi*(0.000381/2.)**2\\n linear['area'] = 0.001\\n \\n # Design constants \\n #arm length to center of gravity\\n r_w = 0.10\\n \\n #Aicraft weight (mass times gravity)\\n W = 0.0523*9.8 #0.06*9.8\\n alpha = 0.\\n V = 10 #m/s\\n altitude = 10000. #feet\\n \\n # Temperature\\n T_0 = 273.15 + 30.\\n T_final = 273.15 + 140.\\n \\n #Initial martensitic volume fraction\\n MVF_init = 1.\\n \\n # Number of steps and cycles\\n n = 200\\n n_cycles = 0\\n #~~~~~~~~~~~~~~~~~~~~~bb~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n #Parameters to select how to output stuff\\n all_outputs = True\\n save_data = True\\n #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\n if all_outputs:\\n eps_s, eps_l, theta, sigma, MVF, T, eps_t, theta, F_l, k, L_s = flap(airfoil, \\n chord, J, sma, linear, sigma_o, \\n W, r_w, V, altitude, alpha, T_0, \\n T_final, MVF_init, n, R, all_outputs = True,\\n import_matlab = import_matlab, eng=eng,\\n n_cycles = n_cycles)\\n\\n import matplotlib.pyplot as plt\\n plt.figure()\\n plt.plot(np.rad2deg(theta), eps_s, lw=2., label = \\\"$\\\\epsilon_s$\\\")\\n plt.plot(np.rad2deg(theta), eps_l, 'b--',lw=2, label = \\\"$\\\\epsilon_l$\\\")\\n# plt.scatter(theta, eps_s, c = 'b')\\n# plt.scatter(theta, eps_l, c = 'b')\\n plt.ylabel('$\\\\epsilon$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n print len(T), len(eps_s), len(eps_l), len(theta), len(eps_t)\\n plt.figure()\\n plt.plot(np.rad2deg(theta), eps_t, lw=2.)\\n# plt.scatter(theta, eps_t, c = 'b')\\n plt.ylabel('$\\\\epsilon_t$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(np.rad2deg(theta), MVF, lw=2.)\\n# plt.scatter(theta, MVF, c = 'b')\\n plt.ylabel('$MVF$', fontsize=24)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n\\n plt.figure()\\n plt.plot(T, MVF, lw=2.)\\n# plt.scatter(T, MVF, c = 'b')\\n plt.ylabel('$MVF$', fontsize=24)\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n\\n plt.figure()\\n plt.plot(T, sigma, lw=2.)\\n# plt.scatter(T, sigma, c = 'b')\\n plt.ylabel('$\\\\sigma$', fontsize=24)\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(T, eps_s, 'b', lw=2., label = \\\"$\\\\epsilon_s$\\\")\\n plt.plot(T, eps_l, 'b--',lw=2, label = \\\"$\\\\epsilon_l$\\\")\\n# plt.scatter(T, eps_s, c = 'b')\\n# plt.scatter(T, eps_l, c = 'b')\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.ylabel('$\\\\epsilon$', fontsize=24)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid()\\n \\n plt.figure()\\n plt.plot(T, np.rad2deg(theta), lw=2.)\\n# plt.scatter(T, theta, c = 'b')\\n plt.xlabel('$T (K)$', fontsize=20)\\n plt.ylabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.grid()\\n \\n F_s = []\\n for i in range(len(sigma)):\\n F_s.append(sigma[i]*sma['area'])\\n# sigma_MPa = []\\n# for sigma_i in sigma:\\n# sigma_MPa.append(sigma_i/1e6)\\n plt.figure()\\n plt.plot(theta, F_s, 'b', lw=2., label = \\\"$F_s$\\\")\\n plt.plot(theta, F_l, 'b--', lw=2., label = \\\"$F_l$\\\")\\n# plt.scatter(theta, F_s, c = 'b')\\n# plt.scatter(theta, F_l, c = 'b')\\n plt.ylabel('$F (N)$', fontsize=20)\\n plt.xlabel(r'$\\\\theta ({}^{\\\\circ})$', fontsize=20)\\n plt.legend(loc = 'best', fontsize = 'x-large')\\n plt.grid() \\n else:\\n theta, k= flap(airfoil, chord, J, sma, linear, sigma_o, \\n W, r_w, V, altitude, alpha, T_0, \\n T_final, MVF_init, n, R, all_outputs = False,\\n import_matlab = import_matlab, eng=eng,\\n n_cycles = n_cycles)\\n \\n if save_data == True:\\n Data = {'theta': theta, 'eps_s': eps_s, 'eps_l': eps_l, \\n 'sigma': sigma, 'xi': MVF, 'T': T, 'eps_t': eps_t,\\n 'F_l': F_l, 'k': k, 'L_s':L_s}\\n pickle.dump(Data, open( \\\"data.p\\\", \\\"wb\\\" ) )\\n \\n return {'theta': theta, 'k': k}\",\n \"def lab_run_med(character_id, time_step):\\n pass\",\n \"def run1():\\n #Reseting motors\\n ResetRobot.reset_wheel_motors()\\n ResetRobot.reset_attachment_motors()\\n CalibrateRobot.calibrate_gyro()\\n\\n #mission M01 and M02 - space travel and solar panel\\n M01_M02()\\n \\n #Mission M05- Extraction \\n M05_M14()\\n\\n #Back to base before Gerhard (Remove comment if necessary)\\n return_to_base1()\\n\\n # Must delete for competition.. This is to set up forklift to repeat run.\\n Robot.attachment_left.on_for_rotations(-100, 8) #Raises Forklift \",\n \"def radiation_measurement_analysis():\\n import pint\\n ureg = pint.UnitRegistry()\\n\\n mrem_h = ureg.parse_units('mrem') / ureg.hour\\n m = ureg.parse_units('meters')\\n s = ureg.parse_units('seconds')\\n\\n # Measurements of background radiation\\n bg_dist = ureg.parse_expression('10 m') # estimate of how far away we are wrt background\\n background_rows = [\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.022 * mrem_h, capture_time=0.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=4.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.021 * mrem_h, capture_time=5.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=11.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.023 * mrem_h, capture_time=16.0 * s),\\n dict(vid=1, distance=bg_dist, rad=0.024 * mrem_h, capture_time=20.0 * s),\\n ]\\n\\n # Measurements of sample radiation\\n esp_dist = ureg.parse_expression('1 inch').to(m) / 2 # estimate of how far we are from the sample when very close\\n dist0_rows = [\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=0.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=3.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=5.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=9.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=10.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=11.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.057 * mrem_h, capture_time=12.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.058 * mrem_h, capture_time=13.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.059 * mrem_h, capture_time=14.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.060 * mrem_h, capture_time=15.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.061 * mrem_h, capture_time=16.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.062 * mrem_h, capture_time=18.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=20.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=22.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.066 * mrem_h, capture_time=23.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=24.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=25.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=26.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.064 * mrem_h, capture_time=27.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.065 * mrem_h, capture_time=28.0 * s),\\n dict(vid=2, distance=esp_dist, rad=0.063 * mrem_h, capture_time=30.0 * s),\\n ]\\n\\n dist0_v2_rows = [\\n dict(vid=3, distance=esp_dist, rad=0.012 * mrem_h, capture_time=0.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.011 * mrem_h, capture_time=1.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=8.0 * s),\\n dict(vid=3, distance=esp_dist, rad=0.013 * mrem_h, capture_time=9.0 * s),\\n ]\\n\\n close_rows = [\\n dict(vid=4, distance=0.5 * m, rad=0.013 * mrem_h, capture_time=0.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.014 * mrem_h, capture_time=5.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=7.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.011 * mrem_h, capture_time=15.0 * s),\\n dict(vid=4, distance=0.5 * m, rad=0.012 * mrem_h, capture_time=16.0 * s),\\n ]\\n\\n mid_rows = [\\n dict(vid=5, distance=1.0 * m, rad=0.014 * mrem_h, capture_time=0.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.015 * mrem_h, capture_time=5.0 * s),\\n dict(vid=5, distance=1.0 * m, rad=0.013 * mrem_h, capture_time=10.0 * s),\\n ]\\n\\n far_rows = [\\n dict(vid=6, distance=2.0 * m, rad=0.023 * mrem_h, capture_time=0.0 * s),\\n dict(vid=6, distance=2.0 * m, rad=0.025 * mrem_h, capture_time=0.1 * s),\\n ]\\n\\n # guess_dist = ureg.parse_expression('0.3 m') # estimate of how far away we are wrt background\\n # guess_rows = [\\n # dict(vid=9, distance=guess_dist, rad=0.030 * mrem_h, capture_time=0.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.041 * mrem_h, capture_time=2.0 * s),\\n # dict(vid=9, distance=guess_dist, rad=0.051 * mrem_h, capture_time=3.0 * s),\\n # ]\\n\\n rows = dist0_rows + background_rows + dist0_v2_rows + close_rows + mid_rows + far_rows\\n # rows += guess_rows\\n\\n import pandas as pd\\n import numpy as np\\n table = pd.DataFrame(rows)\\n\\n # Ensure comparable units\\n units = {\\n 'rad': mrem_h,\\n 'distance': m,\\n 'capture_time': s,\\n }\\n for key, unit in units.items():\\n table[key] = table[key].apply(lambda c: c.to(unit).m)\\n table['rad'] = table['rad'].astype(float)\\n table['distance'] = table['distance'].astype(float)\\n\\n # Weight each measurement based on the amount of time the measurement was\\n # sustained in the video.\\n average_rad_rows = []\\n for vid, group in table.groupby('vid'):\\n from statsmodels.stats.weightstats import DescrStatsW\\n weights = (-1 * group['capture_time'].diff(periods=-1).fillna(0)) / group['capture_time'].iloc[-1]\\n table.loc[group.index, 'weight'] = weights\\n values = group['rad']\\n weighted_stats = DescrStatsW(values, weights=weights, ddof=0)\\n dists = group['distance'].unique()\\n assert len(dists) == 1\\n average_rad_rows.append({\\n 'vid': vid,\\n 'distance': dists[0],\\n 'rad_mean': weighted_stats.mean,\\n 'rad_std': weighted_stats.std,\\n })\\n stats_table = pd.DataFrame(average_rad_rows)\\n\\n bg_row = stats_table.loc[stats_table['distance'].argmax()]\\n fg_row = stats_table.loc[stats_table['distance'].argmin()]\\n\\n # -------------------\\n ADD_DUMMY_VALUES = 0\\n if ADD_DUMMY_VALUES:\\n # Hack: because we don't have enough samples we can fudge the value\\n # knowning that the value should be the background radiation in the\\n # limit.\\n\\n dummy_measurements = []\\n extra_support = 1\\n for idx in range(3, 3 + extra_support):\\n dummy_row = {\\n 'vid': -idx,\\n 'distance': bg_row['distance'] + idx,\\n 'rad_mean': bg_row['rad_mean'],\\n 'rad_std': 0.01,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # also add an extra value close to the sample\\n rad_bg = bg_row['rad_mean']\\n rad_above_bg = fg_row['rad_mean'] - rad_bg\\n dummy_row = {\\n 'vid': -1,\\n 'distance': fg_row['distance'] / 2,\\n 'rad_mean': rad_bg + (rad_above_bg * 4),\\n 'rad_std': 0.5,\\n }\\n dummy_measurements.append(dummy_row)\\n\\n # dummy_row = {\\n # 'vid': -2,\\n # 'distance': fg_row['distance'] / 4,\\n # 'rad_mean': rad_bg + (rad_above_bg * 16),\\n # }\\n # dummy_measurements.append(dummy_row)\\n\\n dummy_stats = pd.DataFrame(dummy_measurements)\\n dummy_stats['weight'] = 0.5\\n stats_table['weight'] = 1.0\\n stats_table2 = pd.concat([stats_table, dummy_stats]).reset_index(drop=True).sort_values('distance')\\n else:\\n stats_table2 = stats_table\\n # -------------------\\n\\n import scipy\\n scipy.optimize.curve_fit\\n\\n # Because we know the radiation should follow an inverse square law wrt to\\n # distance, we can fit a polynomial of degree 2 (parabola) to interpolate /\\n # extrapolate the **inverse** values.\\n x = stats_table2['distance'].values\\n y = stats_table2['rad_mean'].values\\n s = stats_table2['rad_std'].values\\n\\n # Model the squared falloff directly\\n def invsquare(x, a, b):\\n return a * (1 / (0.01 + x ** 2)) + b\\n # bg_row['rad_mean']\\n # Use curve_fit to constrain the first coefficient to be zero\\n try:\\n coef = scipy.optimize.curve_fit(invsquare, x, y, sigma=s, method='trf')[0]\\n except Exception as ex:\\n coef = None\\n print(f'ex={ex}')\\n\\n # Also fit one to the raw weighted points as a sanity check\\n # inv_poly2 = Polynomial.fit(table['distance'], 1 / table['rad'], w=table['weight'], deg=2)\\n\\n import kwplot\\n sns = kwplot.autosns()\\n plt = kwplot.autoplt()\\n # ax = sns.boxplot(data=table, x='distance', y='rad', width=0.1)\\n\\n # Add in points to show each observation\\n ax = sns.relplot(x=\\\"distance\\\", y=\\\"rad\\\", data=table, size=4, color=\\\".3\\\",\\n linewidth=0, alpha=0.5, palette='deep')\\n\\n ax = plt.gca()\\n ax.set_xlabel('distance from sample ({})'.format(str(units['distance'])))\\n ax.set_ylabel('radiation dosage ({})'.format(str(units['rad'])))\\n\\n max_meters = 10\\n\\n extrap_x = np.linspace(0, max_meters, 1000)\\n if coef is not None:\\n extrap_y1 = invsquare(extrap_x, *coef)\\n # extrap_y2 = 1 / inv_poly2(extrap_x)\\n ax.plot(stats_table2['distance'].values, stats_table2['rad_mean'].values, 'rx')\\n ax.plot(stats_table['distance'].values, stats_table['rad_mean'].values, 'bo')\\n ax.plot(extrap_x, extrap_y1, '--')\\n ax.set_ylim(0.001, 0.1)\\n ax.set_yscale('log')\\n # ax.plot(extrap_x, extrap_y2, '--')\",\n \"def run(self):\\n openShutter = True\\n actuateXed = False\\n image_type = \\\"PPUMP\\\"\\n\\n wl = float(self.eo_config.get(\\\"PPUMP_WL\\\", 550))\\n meas_flux = self.measured_flux(wl)\\n seqno = 0\\n for tokens in self.instructions:\\n exptime = float(tokens[1])\\n nframes = int(tokens[2])\\n shifts = int(tokens[3])\\n for iframe in range(nframes):\\n self.image_clears()\\n self.bias_image(seqno)\\n self.take_image(seqno, exptime, openShutter, actuateXed,\\n image_type)\\n seqno += 1\",\n \"def amet_memoryWise(self):\\r\\n # set up logging files to monitor the calculation\\r\\n logging.basicConfig(filename = os.path.join(self.path,'history_amet_python.log'),\\r\\n filemode = 'w+', level = logging.DEBUG,\\r\\n format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\r\\n # initialize the time span\\r\\n # define sigma level\\r\\n A, B = self.defineSigmaLevels()\\r\\n # use example input file to load the basic dimensions information\\r\\n datapath_var = os.path.join(self.path, 'MERRA2_400.inst3_3d_asm_Nv.20160101.nc4.nc')\\r\\n var_key = Dataset(datapath_var)\\r\\n lat = var_key.variables['lat'][:]\\r\\n lon = var_key.variables['lon'][:]\\r\\n # calculate the reference levels based on A & B and standard surface pressure\\r\\n half_level = A + B * 101325\\r\\n level = (half_level[1:] + half_level[:-1]) / 2\\r\\n # create space for the output\\r\\n # AMET in the entire column\\r\\n E = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n cpT = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n Lvq = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n gz = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n uv2 = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n logging.info(\\\"Start retrieving variables T,q,u,v,sp\\\")\\r\\n # The shape of each variable is (8,72,361,576)\\r\\n T = var_key.variables['T'][:]\\r\\n q = var_key.variables['QV'][:]\\r\\n sp = var_key.variables['PS'][:] #(8,361,576)\\r\\n u = var_key.variables['U'][:]\\r\\n v = var_key.variables['V'][:]\\r\\n logging.info(\\\"Extracting variables successfully!\\\") \\r\\n # compute gz\\r\\n z_model = self.calc_gz(var_key)\\r\\n # get the basic shape\\r\\n tt, hh, yy, xx = q.shape\\r\\n AMET = amet.met()\\r\\n E, cpT, Lvq, gz, uv2 = AMET.calc_met(T, q, sp, u, v, z_model, A, B,\\r\\n tt, hh, len(lat), len(lon), lat, self.lat_unit)\\r\\n\\r\\n return np.mean(E)\",\n \"def run():\\n step = 0\\n o2r = 4 #orange to red delay time\\n r2g = 2 #red to green delay time\\n A_4235 = 0\\n B_4235 = 1\\n C_4235 = 2\\n AB1_4235 = 3\\n AB2_4235 = 4\\n AC1_4235 = 5\\n AC2_4235 = 6\\n BA1_4235 = 7\\n BA2_4235 = 8\\n BC1_4235 = 9\\n BC2_4235 = 10\\n CA1_4235 = 11\\n CA2_4235 = 12\\n CB1_4235 = 13\\n CB2_4235 = 14\\n A_4219 = 0\\n B_4219 = 1\\n C_4219 = 2\\n D_4219 = 3\\n E_4219 = 4\\n F_4219 = 5\\n G_4219 = 6\\n AB1_4219 = 7\\n AB2_4219 = 8\\n AC1_4219 = 9\\n AC2_4219 = 10\\n AD1_4219 = 11\\n AD2_4219 = 12\\n AE1_4219 = 13\\n AE2_4219 = 14\\n AF1_4219 = 16\\n AF2_4219 = 17\\n AG1_4219 = 18\\n AG2_4219 = 19\\n BA1_4219 = 20\\n BA2_4219 = 21\\n BC1_4219 = 22\\n BC2_4219 = 23\\n BD1_4219 = 24\\n BD2_4219 = 25\\n BE1_4219 = 26\\n BE2_4219 = 27\\n BF1_4219 = 28\\n BF2_4219 = 29\\n BG1_4219 = 30\\n BG2_4219 = 31\\n CA1_4219 = 32\\n CA2_4219 = 33\\n CB1_4219 = 34\\n CB2_4219 = 35\\n CD1_4219 = 36\\n CD2_4219 = 37\\n CE1_4219 = 38\\n CE2_4219 = 39\\n CF1_4219 = 40\\n CF2_4219 = 41\\n CG1_4219 = 42\\n CG2_4219 = 43\\n DA1_4219 = 44\\n DA2_4219 = 45\\n DB1_4219 = 46\\n DB2_4219 = 47\\n DC1_4219 = 48\\n DC2_4219 = 49\\n DE1_4219 = 50\\n DE2_4219 = 51\\n DF1_4219 = 52\\n DF2_4219 = 53\\n DG1_4219 = 54\\n DG2_4219 = 55\\n EA1_4219 = 56\\n EA2_4219 = 57\\n EB1_4219 = 58\\n EB2_4219 = 59\\n EC1_4219 = 60\\n EC2_4219 = 61\\n ED1_4219 = 62\\n ED2_4219 = 63\\n EF1_4219 = 64\\n EF2_4219 = 65\\n EG1_4219 = 66\\n EG2_4219 = 67\\n FA1_4219 = 68\\n FA2_4219 = 69\\n FB1_4219 = 70\\n FB2_4219 = 71\\n FC1_4219 = 72\\n FC2_4219 = 73\\n FD1_4219 = 74\\n FD2_4219 = 75\\n FE1_4219 = 76\\n FE2_4219 = 77\\n FG1_4219 = 78\\n FG2_4219 = 79\\n GA1_4219 = 80\\n GA2_4219 = 81\\n GB1_4219 = 82\\n GB2_4219 = 83\\n GC1_4219 = 84\\n GC2_4219 = 85\\n GD1_4219 = 86\\n GD2_4219 = 87\\n GE1_4219 = 88\\n GE2_4219 = 89\\n GF1_4219 = 90\\n GF2_4219 = 91\\n A_4220 = 0\\n B_4220 = 1\\n C_4220 = 2\\n D_4220 = 3\\n E_4220 = 4\\n AB1_4220 = 5\\n AB2_4220 = 6\\n AC1_4220 = 7\\n AC2_4220 = 8\\n AD1_4220 = 9\\n AD2_4220 = 10\\n AE1_4220 = 11\\n AE2_4220 = 12\\n BA1_4220 = 13\\n BA2_4220 = 14\\n BC1_4220 = 15\\n BC2_4220 = 16\\n BD1_4220 = 17\\n BD2_4220 = 18\\n BE1_4220 = 19\\n BE2_4220 = 20\\n CA1_4220 = 21\\n CA2_4220 = 22\\n CB1_4220 = 23\\n CB2_4220 = 24\\n CD1_4220 = 25\\n CD2_4220 = 26\\n CE1_4220 = 27\\n CE2_4220 = 28\\n DA1_4220 = 29\\n DA2_4220 = 30\\n DB1_4220 = 31\\n DB2_4220 = 32\\n DC1_4220 = 33\\n DC2_4220 = 34\\n DE1_4220 = 35\\n DE2_4220 = 36\\n EA1_4220 = 37\\n EA2_4220 = 38\\n EB1_4220 = 39\\n EB2_4220 = 40\\n EC1_4220 = 41\\n EC2_4220 = 42\\n ED1_4220 = 43\\n ED2_4220 = 44\\n A_4221 = 0\\n B_4221 = 1\\n C_4221 = 2\\n D_4221 = 3\\n E_4221 = 4\\n F_4221 = 5\\n AB1_4221 = 6\\n AB2_4221 = 7\\n AC1_4221 = 8\\n AC2_4221 = 9\\n AD1_4221 = 10\\n AD2_4221 = 11\\n AE1_4221 = 12\\n AE2_4221 = 13\\n AF1_4221 = 14\\n AF2_4221 = 15\\n BA1_4221 = 16\\n BA2_4221 = 17\\n BC1_4221 = 18\\n BC2_4221 = 19\\n BD1_4221 = 20\\n BD2_4221 = 21\\n BE1_4221 = 22\\n BE2_4221 = 23\\n BF1_4221 = 24\\n BF2_4221 = 25\\n CA1_4221 = 26\\n CA2_4221 = 27\\n CB1_4221 = 28\\n CB2_4221 = 29\\n CD1_4221 = 30\\n CD2_4221 = 31\\n CE1_4221 = 32\\n CE2_4221 = 33\\n CF1_4221 = 34\\n CF2_4221 = 35\\n DA1_4221 = 36\\n DA2_4221 = 37\\n DB1_4221 = 38\\n DB2_4221 = 39\\n DC1_4221 = 40\\n DC2_4221 = 41\\n DE1_4221 = 42\\n DE2_4221 = 43\\n DF1_4221 = 44\\n DF2_4221 = 45\\n EA1_4221 = 46\\n EA2_4221 = 47\\n EB1_4221 = 48\\n EB2_4221 = 49\\n EC1_4221 = 50\\n EC2_4221 = 51\\n ED1_4221 = 52\\n ED2_4221 = 53\\n EF1_4221 = 54\\n EF2_4221 = 55\\n FA1_4221 = 56\\n FA2_4221 = 57\\n FB1_4221 = 58\\n FB2_4221 = 59\\n FC1_4221 = 60\\n FC2_4221 = 61\\n FD1_4221 = 62\\n FD2_4221 = 63\\n FE1_4221 = 64\\n FE2_4221 = 65\\n \\n #while traci.simulation.getMinExpectedNumber() > 0:\\n while step < 600:\\n traci.simulationStep()\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",75)\\n if step == 87:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 91:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 93:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 108:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 112:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 114:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",12)\\n if step == 126:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 132:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",32)\\n if step == 164:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 168:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",50)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 224:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 226:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 241:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 247:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",14)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 265:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 267:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",13)\\n if step == 280:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 284:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 286:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 360:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 364:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 366:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 381:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 387:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",10)\\n if step == 397:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 401:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 403:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 423:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 425:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",74)\\n if step == 499:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 503:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", AB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 505:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",15)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 524:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BC2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 526:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", C_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",8)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", CB2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", B_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",16)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA1_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",o2r)\\n if step == 560:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", BA2_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",r2g)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"cluster_1707799581_314056954_5931861577\\\", A_4235)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_1707799581_314056954_5931861577\\\",38)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 6)\\n if step == 6:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 10:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 12:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 43:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 47:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 49:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 80:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 84:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 86:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 12)\\n if step == 98:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 102:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 104:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 133:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 137:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 139:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 20)\\n if step == 159:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 163:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 165:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 174:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 176:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 38)\\n if step == 214:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 218:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 220:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 13)\\n if step == 233:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 237:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 34)\\n if step == 273:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 279:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 22)\\n if step == 301:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 305:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 307:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 312:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 318:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 31)\\n if step == 349:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 353:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 36)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 419:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 421:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 440:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 444:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 5)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 455:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 457:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", F_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 29)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 490:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", FG2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 492:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", G_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 18)\\n if step == 510:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", GA2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 516:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", A_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 41)\\n if step == 557:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 561:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", AD2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 563:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", D_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 19)\\n if step == 582:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 586:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", DE2_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", r2g)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", E_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", 8)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", EF1_4219)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25977365_314059191_314060044_314061754_314061758_314062509_314062525\\\", o2r)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 17)\\n if step == 17:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 21:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 23:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 32:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 36:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 67)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 16)\\n if step == 152:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 156:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 158:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 11)\\n if step == 169:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 173:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 175:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 63)\\n if step == 238:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 242:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 244:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 257:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 261:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 263:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 9)\\n if step == 272:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 276:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 278:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 95)\\n if step == 373:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 377:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 398:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 402:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 404:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 24)\\n if step == 428:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 434:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 13)\\n if step == 447:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 451:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 453:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 56)\\n if step == 509:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 513:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", AB2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 515:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", B_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 19)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 538:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", BD2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 540:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", D_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 22)\\n if step == 562:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 566:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", DE2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 568:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", E_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 20)\\n if step == 588:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA1_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", o2r)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", EA2_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", r2g)\\n if step == 594:\\n traci.trafficlight.setPhase(\\\"gneJ41\\\", A_4220)\\n traci.trafficlight.setPhaseDuration(\\\"gneJ41\\\", 6)\\n if step == 0:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 9:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 13:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 15:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 34:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 38:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 40:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 20)\\n if step == 60:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 64:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 66:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 90:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 94:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 96:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 105:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 109:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 111:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 130:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 134:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 136:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 166:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 170:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 172:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 28)\\n if step == 200:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 204:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 206:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 225:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 229:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 231:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 239:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 243:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 245:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 32)\\n if step == 277:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 281:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 283:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 27)\\n if step == 310:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 314:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 316:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 15)\\n if step == 331:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 335:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 337:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 351:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 355:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 357:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 22)\\n if step == 379:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 383:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 385:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 24)\\n if step == 409:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 413:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 415:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 11)\\n if step == 426:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 430:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 432:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 14)\\n if step == 446:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 450:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 452:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 30)\\n if step == 482:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 486:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 488:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 26)\\n if step == 514:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 518:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 520:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 8)\\n if step == 528:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 532:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", EF2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 534:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", F_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 18)\\n if step == 552:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 556:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", FA2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 558:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", A_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 19)\\n if step == 577:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 581:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", AD2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 583:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", D_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 9)\\n if step == 592:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE1_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", o2r)\\n if step == 596:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", DE2_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", r2g)\\n if step == 598:\\n traci.trafficlight.setPhase(\\\"cluster_25953432_313863435_313863521_314053282\\\", E_4221)\\n traci.trafficlight.setPhaseDuration(\\\"cluster_25953432_313863435_313863521_314053282\\\", 2)\\n\\n step += 1\\n\\n traci.close()\\n sys.stdout.flush()\",\n \"def timesteps_experiment():\\n\\n print(\\\"TIMESTEPS EXPERIMENT\\\")\\n\\n # set the name of the experiment\\n now = datetime.datetime.now()\\n experiment_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute)\\n experiment_name = 'timestep_' + str(experiment_id)\\n\\n # define if you want to use preprocessed data from file\\n use_prep_data = False\\n if use_prep_data:\\n set_params(preproc_data_id='16_5_10.16.47')\\n else:\\n set_params(use_preproc_data=False)\\n\\n # define the changing parameter and its value\\n changing_param_name = 'time_steps'\\n changing_param_value = [1, 2, 4, 8, 16, 32, 64, 128, 256]\\n # {0:4, 1:100}, {0:3, 1:100}, {0:2, 1:100}, {0:1, 1:100}] #[{0:1, 1:1}, {0:15, 1:85}]#\\n\\n # set constant parameters\\n set_params(epochs=20)\\n set_params(dropout=0.3)\\n set_params(use_word_emb=1)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" file\\n save_constant_parameters(experiment_name, changing_param_name)\\n\\n # run experiment for every parameter value\\n for value in changing_param_value:\\n process = psutil.Process(os.getpid())\\n print(\\\"-----MEMORY before starting experiment ------\\\", int(process.memory_info().rss/(8*10**(3))), \\\"KB\\\")\\n\\n # update the parameter value\\n set_params(use_word_emb = value)\\n\\n # update the model_id for this new model\\n now = datetime.datetime.now()\\n new_model_id = str(now.day) + \\\"_\\\" + str(now.month) + \\\"_\\\" + str(now.hour) + \\\".\\\" + str(now.minute) + \\\".\\\" + str(now.second)\\n set_params(model_id = new_model_id)\\n\\n # evaluate the new model and save the results in the experiment file\\n oneExperiment = Process(target=run_experiment, args=(experiment_name, new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\\n\\n if value == changing_param_value[0]:\\n set_params(preproc_data_id=new_model_id)\",\n \"def run_performance():\\n # Create a Struct data instance from config\\n inputs = Struct(config)\\n inputs.throttle = throttle\\n # Get oxidizer properties at the given temperature\\n n2o = n2o_properties(inputs.ox.T_tank)\\n # Our integration variables are oxidizer mass and liquid oxidizer volume\\n Mox = n2o.rho_l*(inputs.ox.liquid_V) + n2o.rho_g*(inputs.ox.tank_V-inputs.ox.liquid_V)\\n if inputs.options.output_on:\\n print(\\\"Initial oxidizer mass: {} kg.\\\".format(Mox))\\n\\n start = time.perf_counter() # Start timer for integration\\n\\n time, record = integration(inputs) # Time = time for integration, record = output data\\n F_thrust = record.F_thrust\\n p_cc = record.p_cc\\n p_oxtank = record.p_oxtank\\n p_oxpresstank = record.p_oxpresstank\\n p_fueltank = record.p_fueltank\\n p_fuelpresstank = record.p_fuelpresstank\\n p_oxmanifold = record.p_oxmanifold\\n T_oxtank = record.T_oxtank\\n T_cc = record.T_cc\\n area_core = record.area_core\\n OF = record.OF_i\\n gamma_ex = record.gamma_ex\\n m_dot_ox = record.m_dot_ox\\n m_dot_fuel = record.m_dot_fuel\\n p_crit = record.p_crit\\n m_dot_ox_crit = record.m_dot_ox_crit\\n M_e = record.M_e\\n p_exit = record.p_exit\\n p_shock = record.p_shock\\n\\n time_elapsed = start-time.perf_counter() # Stop the timer and print elapsed time\\n if inputs.options.output_on:\\n print(\\\"Time elapsed for this timestep: {} sec.\\\".format(time_elapsed))\",\n \"def main():\\n\\n para_list = [para]\\n loss_list = []\\n\\n for i in range(30):\\n para_list.append(diff_fun(loss_fun, para_list[i]))\\n loss_list.append(loss_fun(para_list[i]))\\n\\n env = QEnv()\\n env.backend(BackendName.LocalBaiduSim2)\\n\\n q = env.Q.createList(n)\\n\\n state_prepare(q, 0)\\n universal_cir(q, 0, para_list[-1])\\n\\n MeasureZ(*env.Q.toListPair())\\n taskResult = env.commit(shots, fetchMeasure=True)\\n print(taskResult['counts'])\",\n \"def water_delay(block_size):\\n\\n\\tdirectory = \\\"/local/scratch/sam5g13/Sam_5th-yr_Project/test_data\\\"\\n\\tfile_name = \\\"{}/tip4p2005_50_TOTEST.npy\\\".format(directory)\\n\\tgnuplot = r'/usr/bin/gnuplot'\\n\\n\\n\\tfile_data = np.load(file_name, mmap_mode='r')\\n\\n\\t_, _, _, gamma, _ = file_data \\n\\n\\tgamma_sample = blocksav(gamma, block_size)\\n\\n\\tgamma_file = \\\"{}/tip4p2005_50_blocksize_{}_gamma.txt\\\".format(directory, block_size)\\n\\twith open(gamma_file, 'w') as outfile:\\n\\t\\tnp.savetxt(outfile, gamma_sample)\\n\\n\\tgamma_file_name = \\\"{}/tip4p2005_50_blocksize_{}_gamma.txt\\\".format(directory, block_size)\\n\\n\\tcorrelations = subprocess.check_output([\\\"corr\\\", gamma_file_name])\\n\\t\\n\\tmutual_information = subprocess.check_output([\\\"mutual\\\", gamma_file_name])\\n\\n\\tcorrelation_array = np.array(correlations.split()[5:], dtype=float)\\n\\tmutual_information_array = np.array(mutual_information.split()[2:], dtype=float)\\n\\n\\tidx_odd = range(1,199,2)\\n\\tidx_even = range(0,200,2)\\n\\n\\tidx_odd1 = range(1,43,2)\\n\\tidx_even1 = range(0,44,2)\\n\\n\\t#correlation_values = correlation_array[idx_odd]\\n\\tmutual_information_values = mutual_information_array[idx_odd1]\\n\\tprint 'LOOK HERE...........................................', mutual_information_array[idx_odd1], len(mutual_information_array[idx_odd1])\\n\\n\\t\\\"\\\"\\\"\\n\\tdelay_length = 0\\n\\n\\tfor o in range(len(correlation_values) - 1):\\n\\t\\tprint o, correlation_values[o], correlation_values[o+1]\\n\\t\\tif correlation_values[o] > correlation_values[o+1]:\\n\\t\\t\\tdelay_length = o \\n\\t\\telse: break\\n\\t\\n\\tdelay_length = delay_length + 1\\n\\n\\tprint \\\"The delay length is\\\", delay_length\\n\\t\\\"\\\"\\\"\\n\\n\\tmutual_info_length = 0\\n\\n\\tfor o in range(len(mutual_information_values) - 1):\\n\\t\\t#print o, correlation_values[o], correlation_values[o+1]\\n\\t\\tif mutual_information_values[o] > mutual_information_values[o+1]:\\n\\t\\t\\tmutual_info_length = o \\n\\t\\telse: break\\n\\t\\n\\tmutual_info_length = mutual_info_length + 1\\n\\t\\n\\tprint \\\"The mutual info length is\\\", mutual_info_length\\n\\n\\t#assert \\tdelay_length == mutual_info_length, \\\"The minimums of the mutual information and the correlations are not equal! %d %d\\\" % (delay_length, mutual_info_length)\\n\\t\\n\\tproduce_delays = subprocess.check_output([\\\"delay\\\", gamma_file_name, \\\"-d\\\" + str(mutual_info_length)])\\n\\n\\t\\n\\tdelay_file = \\\"{}/tip4p2005_50_blocksize_{}_gamma_delay_{}.txt\\\".format(directory, block_size, mutual_info_length)\\n\\tf = open(delay_file, 'w')\\n\\tf.write(produce_delays)\\n\\tf.close()\\n\\n\\t\\\"\\\"\\\"\\n\\n\\tprint produce_delays\\n\\tprint len(produce_delays), len(mutual_information_values)\\n\\tplt.figure(\\\"produce_delays vs mutual information\\\")\\n\\tplt.xlabel(\\\"produce_delays\\\")\\n\\tplt.ylabel(\\\"Mutual information\\\")\\n\\tplt.plot(produce_delays, mutual_information_values)\\n\\tplt.show()\\n\\t\\n\\t\\\"\\\"\\\"\\n\\t\\n\\tembedding = subprocess.check_output([\\\"false_nearest\\\", gamma_file_name])\\n\\n\\tembedding_dimension = int(raw_input(\\\"What embedding dimension would you like to use? \\\"))\\n\\t\\n\\trun_calc = subprocess.check_output(['gnuplot', '-e', \\\"filename='{}/tip4p2005_50_blocksize_{}_gamma_delay_{}.txt';ofilename='tip4p2005_50_blocksize_{}_gamma_delay_{}_graph.png'\\\".format(directory, block_size, mutual_info_length, block_size, mutual_info_length ),\\\"plot.gnu\\\"])\\n\\n\\n\\t\\\"\\\"\\\"Imports the time series and specifies each aspect used in building the recurrence matrix\\\"\\\"\\\"\\n\\n\\tsettings = Settings(time_series = gamma_sample, embedding_dimension = embedding_dimension, time_delay = mutual_info_length, similarity_measure = EuclideanMetric, neighbourhood = FixedRadius(radius = 13), min_diagonal_line_length = 2, min_vertical_line_length = 2)\\n\\n\\t\\\"\\\"\\\"Performs the computation and prints out all the results\\\"\\\"\\\"\\n\\n\\trqacomputation = RQAComputation.create(settings, verbose = True)\\n\\n\\trqaresult = rqacomputation.run()\\n\\n\\tprint rqaresult\\n\\n\\t\\\"\\\"\\\"Creates the Recurrence matrix for viewing\\\"\\\"\\\"\\n\\n\\trpcomputation = RecurrencePlotComputation.create(settings)\\n\\n\\trpresult = rpcomputation.run()\\n\\n\\tImageGenerator.save_recurrence_plot(rpresult.recurrence_matrix, 'recurrence_plot.png')\",\n \"def test_run_time(self):\\n\\n wrapper = Wrapper()\\n name = 'simplemesh'\\n corner_points = ((0.0, 0.0), (1.0, 0.0), (1.0, 1.0), (0.0, 1.0))\\n extrude_length = 1\\n nex = 3\\n ney = 3\\n nez = 1\\n create_quad_mesh(name, wrapper, corner_points,\\n extrude_length, nex, ney, nez)\\n\\n wrapper.CM[CUBA.NAME] = name\\n wrapper.CM_extensions[CUBAExt.GE] = (CUBAExt.INCOMPRESSIBLE,\\n CUBAExt.LAMINAR_MODEL)\\n wrapper.SP[CUBA.TIME_STEP] = 1\\n wrapper.SP[CUBA.NUMBER_OF_TIME_STEPS] = 1\\n wrapper.SP[CUBA.DENSITY] = 1.0\\n wrapper.SP[CUBA.DYNAMIC_VISCOSITY] = 1.0\\n wrapper.BC[CUBA.VELOCITY] = {'inflow': ('fixedValue', (0.1, 0, 0)),\\n 'outflow': 'zeroGradient',\\n 'walls': ('fixedValue', (0, 0, 0)),\\n 'frontAndBack': 'empty'}\\n wrapper.BC[CUBA.PRESSURE] = {'inflow': 'zeroGradient',\\n 'outflow': ('fixedValue', 0),\\n 'walls': 'zeroGradient',\\n 'frontAndBack': 'empty'}\\n\\n mesh_inside_wrapper = wrapper.get_dataset(name)\\n\\n wrapper.run()\\n\\n # sum data pointwise\\n old_vel = 0.0\\n old_pres = 0.0\\n for point in mesh_inside_wrapper.iter(item_type=CUBA.POINT):\\n velo = point.data[CUBA.VELOCITY]\\n old_vel += math.sqrt(velo[0]*velo[0] + velo[1]*velo[1] +\\n velo[2]*velo[2])\\n old_pres += point.data[CUBA.PRESSURE]\\n\\n wrapper.SP[CUBA.DENSITY] = 5.0\\n\\n wrapper.run()\\n\\n # sum data pointwise\\n new_vel = 0.0\\n new_pres = 0.0\\n for point in mesh_inside_wrapper.iter(item_type=CUBA.POINT):\\n velo = point.data[CUBA.VELOCITY]\\n new_vel += math.sqrt(velo[0]*velo[0] + velo[1]*velo[1] +\\n velo[2]*velo[2])\\n new_pres += point.data[CUBA.PRESSURE]\\n\\n self.assertNotAlmostEqual(old_vel, new_vel, 5)\\n self.assertNotAlmostEqual(old_pres, new_pres, 5)\",\n \"def exercise1a():\\n\\n # Defination of muscles\\n parameters = MuscleParameters()\\n pylog.warning(\\\"Loading default muscle parameters\\\")\\n pylog.info(parameters.showParameters())\\n pylog.info(\\\"Use the parameters object to change the muscle parameters\\\")\\n\\n # Create muscle object\\n muscle = Muscle(parameters)\\n\\n # Instatiate isometric muscle system\\n sys = IsometricMuscleSystem()\\n\\n # Add the muscle to the system\\n sys.add_muscle(muscle)\\n \\n # Set the initial condition\\n x0 = [0.0, sys.muscle.L_OPT]\\n # x0[0] --> muscle stimulation intial value\\n # x0[1] --> muscle contracticle length initial value\\n \\n # Set the time for integration\\n t_start = 0.0\\n t_stop = 0.5\\n time_step = 0.001\\n time = np.arange(t_start, t_stop, time_step)\\n \\n # Evalute for a single muscle stimulation\\n muscle_stimulation = np.arange(0,1.,0.2)\\n \\n # Several muscle stretch\\n muscle_stretches = np.arange(0,0.3,0.01)\\n \\n active_active = []\\n\\n for stim in muscle_stimulation:\\n active_forces = []\\n passive_forces = []\\n total = [] \\n lengths = []\\n for stretch in muscle_stretches:\\n # Run the integration\\n result = sys.integrate(x0=x0,\\n time=time,\\n time_step=time_step,\\n stimulation=stim,\\n muscle_length=stretch)\\n active_forces.append(result.active_force[-1])\\n passive_forces.append(result.passive_force[-1])\\n total.append(result.active_force[-1]+result.passive_force[-1])\\n lengths.append(result.l_ce[-1])\\n active_active.append(active_forces)\\n \\n # Plotting\\n plt.figure('Isometric muscle experiment 1')\\n plt.plot(lengths, active_forces)\\n plt.plot(lengths, passive_forces)\\n plt.plot(lengths, total)\\n plt.title('Isometric muscle experiment stimulation')\\n plt.xlabel('Muscle stretch')\\n plt.ylabel('Muscle force')\\n plt.legend(('Active','Passive','Total'))\\n plt.grid()\\n plt.show()\\n \\n # Plotting\\n plt.figure('Isometric muscle experiment 2')\\n for i in range(len(muscle_stimulation)):\\n plt.plot(lengths, active_active[i])\\n plt.title('Isometric muscle experiment')\\n plt.xlabel('Muscle stretch')\\n plt.ylabel('Muscle force')\\n plt.legend(muscle_stimulation)\\n plt.grid()\\n plt.show()\\n \\n # Plotting\\n #plt.figure('Isotonic muscle experiment')\\n #plt.plot(result.time, result.v_ce)\\n #plt.title('Isotonic muscle experiment')\\n #plt.xlabel('Time [s]')\\n #plt.ylabel('Muscle contractilve velocity')\\n #plt.grid()\\n \\n #muscle with longer l_opt\\n muscle.L_OPT = 0.5\\n muscle_stimulation = 1.\\n lce = []\\n totalF = []\\n activeF=[]\\n passiveF=[]\\n for stretch in muscle_stretches:\\n # Run the integration\\n result = sys.integrate(x0=x0,\\n time=time,\\n time_step=time_step,\\n stimulation=muscle_stimulation,\\n muscle_length=stretch)\\n activeF.append(result.active_force[-1])\\n passiveF.append(result.passive_force[-1])\\n lce.append(result.l_ce[-1])\\n totalF.append(result.active_force[-1]+result.passive_force[-1])\\n plt.figure('muscle with l_opt=0.5') \\n plt.title('muscle with l_opt=0.5')\\n plt.plot(lce, activeF)\\n plt.plot(lce, passiveF)\\n plt.plot(lce, totalF)\\n plt.xlabel('Muscle Stretch')\\n plt.ylabel('Force')\\n plt.ylim((0,4000))\\n plt.legend(('Active Force','Passive Force','Total Force'))\\n\\n plt.grid()\\n \\n \\n \\n #muscle with shorter l_opt\\n t_start = 0.0\\n t_stop = 1\\n time_step = 0.005\\n\\n time = np.arange(t_start, t_stop, time_step)\\n muscle_stretches = np.arange(0,0.3,0.01 )\\n muscle.L_OPT = 0.075\\n muscle_stimulation = 1.\\n lce = []\\n totalF = []\\n activeF=[]\\n passiveF=[]\\n plt.figure('muscle with l_opt=0.075') \\n\\n for stretch in muscle_stretches:\\n # Run the integration\\n result = sys.integrate(x0=x0,\\n time=time,\\n time_step=time_step,\\n stimulation=muscle_stimulation,\\n muscle_length=stretch)\\n activeF.append(result.active_force[-1])\\n passiveF.append(result.passive_force[-1])\\n lce.append(result.l_ce[-1])\\n totalF.append(result.active_force[-1]+result.passive_force[-1])\\n plt.title('muscle with l_opt=0.075')\\n plt.plot(lce, activeF)\\n plt.plot(lce, passiveF)\\n plt.plot(lce, totalF)\\n plt.xlabel('Muscle Stretch')\\n plt.ylabel('Force')\\n plt.ylim((0,4000))\\n plt.legend(('Active Force','Passive Force','Total Force'))\\n plt.grid()\",\n \"def exercise1d():\\n\\n # Defination of muscles\\n muscle_parameters = MuscleParameters()\\n print(muscle_parameters.showParameters())\\n\\n mass_parameters = MassParameters()\\n print(mass_parameters.showParameters())\\n\\n # Create muscle object\\n muscle = Muscle(muscle_parameters)\\n\\n # Create mass object\\n mass = Mass(mass_parameters)\\n\\n pylog.warning(\\\"Isotonic muscle contraction to be implemented\\\")\\n\\n # Instatiate isotonic muscle system\\n sys = IsotonicMuscleSystem()\\n\\n # Add the muscle to the system\\n sys.add_muscle(muscle)\\n\\n # Add the mass to the system\\n sys.add_mass(mass)\\n\\n # You can still access the muscle inside the system by doing\\n # >>> sys.muscle.L_OPT # To get the muscle optimal length\\n\\n # Evalute for a single load\\n load = 100.\\n\\n # Evalute for a single muscle stimulation\\n muscle_stimulation = 1.\\n\\n # Set the initial condition\\n x0 = [0.0, sys.muscle.L_OPT,\\n sys.muscle.L_OPT + sys.muscle.L_SLACK, 0.0]\\n \\n # x0[0] - -> activation\\n # x0[1] - -> contractile length(l_ce)\\n # x0[2] - -> position of the mass/load\\n # x0[3] - -> velocity of the mass/load\\n \\n\\n # Set the time for integration\\n t_start = 0.0\\n t_stop = 0.5\\n time_step = 0.001\\n time_stabilize = 0.2\\n\\n time = np.arange(t_start, t_stop, time_step)\\n \\n loads = np.arange(20, 351, 10)\\n \\n velocities = []\\n\\n for index, load in enumerate(loads):\\n \\n # Run the integration\\n result = sys.integrate(x0=x0,\\n time=time,\\n time_step=time_step,\\n time_stabilize=time_stabilize,\\n stimulation=muscle_stimulation,\\n load=load) \\n\\n if (result.l_mtc[-1] < sys.muscle.L_OPT + sys.muscle.L_SLACK):\\n velocities.append(np.max(result.v_ce))\\n print('max')\\n else:\\n velocities.append(np.min(result.v_ce))\\n print('min')\\n\\n\\n #Muscle contracile Velocity - Tension (load) relationship\\n \\n plt.figure('Isotonic muscle experiment')\\n plt.title('Isotonic muscle experiment')\\n plt.xlabel('Muscle Contractile Velocity [m/s]')\\n plt.ylabel('Tension (load) [N]')\\n plt.plot(velocities, loads)\\n plt.grid()\\n \\n #For different stimulations 1.f\\n \\n muscle_stimulation = np.arange(0,1.1,0.2)\\n plt.figure('Isotonic muscle exp with different stimulations')\\n plt.title('Isotonic muscle experiment with different stimulations')\\n\\n for stim in muscle_stimulation:\\n velocities = []\\n for index, load in enumerate(loads):\\n # Run the integration\\n result = sys.integrate(x0=x0,\\n time=time,\\n time_step=time_step,\\n time_stabilize=time_stabilize,\\n stimulation=stim,\\n load=load) \\n\\n if (result.l_mtc[-1] < sys.muscle.L_OPT + sys.muscle.L_SLACK):\\n velocities.append(np.max(result.v_ce))\\n else:\\n velocities.append(np.min(result.v_ce))\\n plt.xlabel('Muscle Contractile Velocity [m/s]')\\n plt.ylabel('Tension (load) [N]')\\n plt.plot(velocities, loads)\\n \\n plt.legend(('0','0.2','0.4','0.6','0.8','1.0'))\\n plt.grid()\",\n \"def test_control_loop_example():\\n example.control.print_error_measurement()\\n matplotlib.use('agg')\\n example.control.visualise_error_measurement(fig_file='cs_compare.pdf')\\n assert path.isfile('cs_compare.pdf')\\n example.control.sil_comparison()\",\n \"def step6_run_all(flow_dataset_npz=\\\"flow_dataset.npz\\\"):\\n global objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, divisor, note_distance_basis, slider_length_base, slider_types, slider_type_rotation, slider_cos, slider_sin, slider_cos_each, slider_sin_each, slider_type_length, slider_lengths, tick_diff, note_distances, maps, labels, special_train_data, special_train_labels, early_stop, loss_ma, extvar, plot_noise\\n\\n objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier = read_map_predictions(\\n \\\"temp/rhythm_data.npz\\\")\\n\\n # get divisor from GAN_PARAMS\\n divisor = GAN_PARAMS[\\\"divisor\\\"]\\n\\n # get basis\\n note_distance_basis = GAN_PARAMS[\\\"note_distance_basis\\\"]\\n\\n # get next_from_slider_end\\n next_from_slider_end = GAN_PARAMS[\\\"next_from_slider_end\\\"]\\n\\n # should be slider length each tick, which is usually SV * SMP * 100 / 4\\n # e.g. SV 1.6, timing section x1.00, 1/4 divisor, then slider_length_base = 40\\n slider_length_base = sv / divisor\\n\\n # weight for each type of sliders\\n slider_type_probs = [0.25, 0.25, 0.25, 0.05, 0.05, 0.03, 0.03, 0.01,\\n 0.01, 0.005, 0.005, 0.005, 0.005, 0.005, 0.005, 0.015, 0.015, 0.01]\\n slider_types = np.random.choice(\\n len(slider_type_probs), is_slider.shape, p=slider_type_probs).astype(int)\\n\\n # these data must be kept consistent with the sliderTypes in load_map.js\\n slider_type_rotation = np.array([0, -0.40703540572409336, 0.40703540572409336, -0.20131710837464062, 0.20131710837464062,\\n -0.46457807316944644, 0.46457807316944644, 1.5542036732051032, -\\n 1.5542036732051032, 0, 0, 0.23783592745745077, -0.23783592745745077,\\n 0.5191461142465229, -0.5191461142465229, -0.16514867741462683, 0.16514867741462683, 3.141592653589793])\\n\\n # this is vector length! I should change the variable name probably...\\n slider_type_length = np.array([1.0, 0.97, 0.97, 0.97, 0.97, 0.97, 0.97,\\n 0.64, 0.64, 0.94, 0.94, 0.94, 0.94, 0.94, 0.94, 0.96, 0.96, 0])\\n\\n slider_cos = np.cos(slider_type_rotation)\\n slider_sin = np.sin(slider_type_rotation)\\n\\n slider_cos_each = slider_cos[slider_types]\\n slider_sin_each = slider_sin[slider_types]\\n\\n slider_lengths = np.array([slider_type_length[int(\\n k)] * slider_length_base[i] for i, k in enumerate(slider_types)]) * slider_ticks\\n\\n tick_diff = np.concatenate([[100], ticks[1:] - ticks[:-1]])\\n\\n if next_from_slider_end:\\n tick_diff = np.concatenate(\\n [[100], tick_diff[1:] - np.floor(slider_ticks * is_slider)[:-1]])\\n\\n # Timing section reset == tick_diff < 0\\n # Use 1 as default value\\n tick_diff = np.where(tick_diff < 0, 1, tick_diff)\\n\\n note_distances = np.clip(tick_diff, 1, divisor * 2) * \\\\\\n (note_distance_basis / divisor)\\n\\n # Load the flow dataset saved in part 4\\n with np.load(flow_dataset_npz) as flow_dataset:\\n maps = flow_dataset[\\\"maps\\\"]\\n labels = np.ones(maps.shape[0])\\n\\n order2 = np.argsort(np.random.random(maps.shape[0]))\\n special_train_data = maps[order2]\\n special_train_labels = labels[order2]\\n\\n early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=20)\\n\\n # Start model training\\n\\n loss_ma = [90, 90, 90]\\n extvar = {\\\"begin\\\": 10}\\n\\n plot_noise = np.random.random((1, GAN_PARAMS[\\\"g_input_size\\\"]))\\n\\n if GAN_PARAMS[\\\"max_epoch\\\"] == 0:\\n osu_a = put_everything_in_the_center()\\n else:\\n osu_a = generate_map()\\n\\n data = objs, predictions, ticks, timestamps, is_slider, is_spinner, is_note_end, sv, slider_ticks, dist_multiplier, slider_types, slider_length_base\\n return osu_a, data\",\n \"def test_scenario(timestep_per_pi, int_method):\\n\\n #determine BC and IC\\n x0 = 0.0 #init pos\\n v0 = 1.0 #init vel\\n t0 = 0.0 #start-time\\n tn = 4.0*np.pi #end-time\\n tau = timestep_per_pi*np.pi #timesteps\\n n = (tn-t0)/tau + 1 #number of timesteps\\n \\n time = np.linspace(t0, tn, n) #time-array\\n\\n #acceleration of point particle with k=m=1\\n acc1 = lambda x,v,t: -1.0*x #function must take three arguments!\\n\\n pos, vel, time = integrate_time(func=acc1,\\n init=(x0,v0),\\n timearray=time,\\n method=int_method)\\n\\n #analytical solutions\\n pos_an = np.sin(time)\\n vel_an = np.cos(time)\\n\\n return time, pos, pos_an, vel, vel_an\",\n \"def test_case():\\n \\\"\\\"\\\"\\n airspeed_altitude(80000.0, 5000.0, 30.12, 19.2,\\n &altitude, &ias, &tas);\\n\\n printf(\\\"Alt: %.0f; IAS: %.1f; TAS: %.1f\\\", altitude, ias, tas);\\n \\\"\\\"\\\"\\n\\n diff_press = 50*1e2 # 50 mbar\\n print(f\\\"Alt: {altitude(80000, 30.12)}; IAS: {pitot_ias(5000)}; TAS: {tas(pitot_ias(5000), 19.2, altitude(80000, 30.12), 30.12)}\\\")\",\n \"def calc_SC_module(tilt_angle_deg, IAM_b_vector, IAM_d_vector, I_direct_vector, I_diffuse_vector, Tamb_vector_C, n0, c1, c2,\\n mB0_r, mB_max_r, mB_min_r, C_eff_Jperm2K, t_max, aperture_area_m2, dP1, dP2, dP3, dP4, Cp_fluid_JperkgK, Tin_C, Leq, l_ext, l_int, Nseg):\\n\\n # local variables\\n msc_max_kgpers = mB_max_r * aperture_area_m2 / 3600 # maximum mass flow [kg/s]\\n\\n # Do the calculation of every time step for every possible flow condition\\n # get states where highly performing values are obtained.\\n specific_flows_kgpers = [np.zeros(8760), (np.zeros(8760) + mB0_r) * aperture_area_m2 / 3600,\\n (np.zeros(8760) + mB_max_r) * aperture_area_m2 / 3600,\\n (np.zeros(8760) + mB_min_r) * aperture_area_m2 / 3600, np.zeros(8760), np.zeros(8760)] # in kg/s\\n specific_pressure_losses_Pa = [np.zeros(8760), (np.zeros(8760) + dP2) * aperture_area_m2, (np.zeros(8760) + dP3) * aperture_area_m2,\\n (np.zeros(8760) + dP4) * aperture_area_m2, np.zeros(8760), np.zeros(8760)] # in Pa\\n\\n # generate empty lists to store results\\n temperature_out = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n temperature_in = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n temperature_mean = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n supply_out_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n supply_losses_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n auxiliary_electricity_kW = [np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760), np.zeros(8760)]\\n supply_out_pre = np.zeros(8760)\\n supply_out_total_kW = np.zeros(8760)\\n mcp_kWperK = np.zeros(8760)\\n\\n # calculate absorbed radiation\\n tilt = radians(tilt_angle_deg)\\n q_rad_vector = np.vectorize(calc_q_rad)(n0, IAM_b_vector, I_direct_vector, IAM_d_vector, I_diffuse_vector,\\n tilt) # absorbed solar radiation in W/m2 is a mean of the group\\n for flow in range(6):\\n mode_seg = 1 # mode of segmented heat loss calculation. only one mode is implemented.\\n TIME0 = 0\\n DELT = 1 # timestep 1 hour\\n delts = DELT * 3600 # convert time step in seconds\\n Tfl = np.zeros([3, 1]) # create vector to store value at previous [1] and present [2] time-steps\\n DT = np.zeros([3, 1])\\n Tabs = np.zeros([3, 1])\\n STORED = np.zeros([600, 1])\\n TflA = np.zeros([600, 1])\\n TflB = np.zeros([600, 1])\\n TabsB = np.zeros([600, 1])\\n TabsA = np.zeros([600, 1])\\n q_gain_Seg = np.zeros([101, 1]) # maximum Iseg = maximum Nseg + 1 = 101\\n\\n for time in range(8760):\\n Mfl_kgpers = specific_flows_kgpers[flow][time] # [kg/s]\\n if time < TIME0 + DELT / 2:\\n # set output values to the appropriate initial values\\n for Iseg in range(101, 501): # 400 points with the data\\n STORED[Iseg] = Tin_C\\n else:\\n # write average temperature of all segments at the end of previous time-step\\n # as the initial temperature of the present time-step\\n for Iseg in range(1, Nseg + 1): # 400 points with the data\\n STORED[100 + Iseg] = STORED[200 + Iseg] # thermal capacitance node temperature\\n STORED[300 + Iseg] = STORED[400 + Iseg] # absorber node temperature\\n\\n # calculate stability criteria\\n if Mfl_kgpers > 0:\\n stability_criteria = Mfl_kgpers * Cp_fluid_JperkgK * Nseg * (DELT * 3600) / (C_eff_Jperm2K * aperture_area_m2)\\n if stability_criteria <= 0.5:\\n print ('ERROR: stability criteria' + str(stability_criteria) + 'is not reached. aperture_area: '\\n + str(aperture_area_m2) + 'mass flow: ' + str(Mfl_kgpers))\\n\\n # calculate average fluid temperature and average absorber temperature at the beginning of the time-step\\n Tamb_C = Tamb_vector_C[time]\\n q_rad_Wperm2 = q_rad_vector[time]\\n Tfl[1] = 0\\n Tabs[1] = 0\\n for Iseg in range(1, Nseg + 1):\\n Tfl[1] = Tfl[1] + STORED[100 + Iseg] / Nseg # mean fluid temperature\\n Tabs[1] = Tabs[1] + STORED[300 + Iseg] / Nseg # mean absorber temperature\\n\\n ## first guess for Delta T\\n if Mfl_kgpers > 0:\\n Tout_C = Tin_C + (q_rad_Wperm2 - (c1 + 0.5) * (Tin_C - Tamb_C)) / (Mfl_kgpers * Cp_fluid_JperkgK / aperture_area_m2)\\n Tfl[2] = (Tin_C + Tout_C) / 2 # mean fluid temperature at present time-step\\n else:\\n Tout_C = Tamb_C + q_rad_Wperm2 / (c1 + 0.5)\\n Tfl[2] = Tout_C # fluid temperature same as output\\n DT[1] = Tfl[2] - Tamb_C # difference between mean absorber temperature and the ambient temperature\\n\\n # calculate q_gain with the guess for DT[1]\\n q_gain_Wperm2 = calc_q_gain(Tfl, Tabs, q_rad_Wperm2, DT, Tin_C, Tout_C, aperture_area_m2, c1, c2, Mfl_kgpers,\\n delts, Cp_fluid_JperkgK, C_eff_Jperm2K, Tamb_C)\\n\\n A_seg_m2 = aperture_area_m2 / Nseg # aperture area per segment\\n # multi-segment calculation to avoid temperature jump at times of flow rate changes.\\n for Iseg in range(1, Nseg + 1):\\n # get temperatures of the previous time-step\\n TflA[Iseg] = STORED[100 + Iseg]\\n TabsA[Iseg] = STORED[300 + Iseg]\\n if Iseg > 1:\\n Tin_Seg_C = Tout_Seg_C\\n else:\\n Tin_Seg_C = Tin_C\\n\\n if Mfl_kgpers > 0 and mode_seg == 1: # same heat gain/ losses for all segments\\n Tout_Seg_K = ((Mfl_kgpers * Cp_fluid_JperkgK * (Tin_Seg_C + 273.15)) / A_seg_m2 -\\n (C_eff_Jperm2K * (Tin_Seg_C + 273.15)) / (2 * delts) + q_gain_Wperm2 +\\n (C_eff_Jperm2K * (TflA[Iseg] + 273.15) / delts)) / (Mfl_kgpers * Cp_fluid_JperkgK / A_seg_m2 + C_eff_Jperm2K / (2 * delts))\\n Tout_Seg_C = Tout_Seg_K - 273.15 # in [C]\\n TflB[Iseg] = (Tin_Seg_C + Tout_Seg_C) / 2\\n else: # heat losses based on each segment's inlet and outlet temperatures.\\n Tfl[1] = TflA[Iseg]\\n Tabs[1] = TabsA[Iseg]\\n q_gain_Wperm2 = calc_q_gain(Tfl, Tabs, q_rad_Wperm2, DT, Tin_Seg_C, Tout_C, A_seg_m2, c1, c2,\\n Mfl_kgpers, delts, Cp_fluid_JperkgK, C_eff_Jperm2K, Tamb_C)\\n Tout_Seg_C = Tout_C\\n\\n if Mfl_kgpers > 0:\\n TflB[Iseg] = (Tin_Seg_C + Tout_Seg_C) / 2\\n Tout_Seg_C = TflA[Iseg] + (q_gain_Wperm2 * delts) / C_eff_Jperm2K\\n else:\\n TflB[Iseg] = Tout_Seg_C\\n\\n #TflB[Iseg] = Tout_Seg\\n q_fluid_Wperm2 = (Tout_Seg_C - Tin_Seg_C) * Mfl_kgpers * Cp_fluid_JperkgK / A_seg_m2\\n q_mtherm_Whperm2 = (TflB[Iseg] - TflA[Iseg]) * C_eff_Jperm2K / delts # total heat change rate of thermal capacitance\\n q_balance_error = q_gain_Wperm2 - q_fluid_Wperm2 - q_mtherm_Whperm2\\n if abs(q_balance_error) > 1:\\n time = time # re-enter the iteration when energy balance not satisfied\\n q_gain_Seg[Iseg] = q_gain_Wperm2 # in W/m2\\n\\n # resulting net energy output\\n q_out_kW = (Mfl_kgpers * Cp_fluid_JperkgK * (Tout_Seg_C - Tin_C)) / 1000 #[kW]\\n Tabs[2] = 0\\n # storage of the mean temperature\\n for Iseg in range(1, Nseg + 1):\\n STORED[200 + Iseg] = TflB[Iseg]\\n STORED[400 + Iseg] = TabsB[Iseg]\\n Tabs[2] = Tabs[2] + TabsB[Iseg] / Nseg\\n\\n # outputs\\n temperature_out[flow][time] = Tout_Seg_C\\n temperature_in[flow][time] = Tin_C\\n supply_out_kW[flow][time] = q_out_kW\\n temperature_mean[flow][time] = (Tin_C + Tout_Seg_C) / 2 # Mean absorber temperature at present\\n\\n # q_gain = 0\\n # TavgB = 0\\n # TavgA = 0\\n # for Iseg in range(1, Nseg + 1):\\n # q_gain = q_gain + q_gain_Seg[Iseg] * A_seg_m2 # [W]\\n # TavgA = TavgA + TflA[Iseg] / Nseg\\n # TavgB = TavgB + TflB[Iseg] / Nseg\\n #\\n # # OUT[9] = q_gain/Area_a # in W/m2\\n # q_mtherm = (TavgB - TavgA) * C_eff * aperture_area / delts\\n # q_balance_error = q_gain - q_mtherm - q_out\\n\\n # OUT[11] = q_mtherm\\n # OUT[12] = q_balance_error\\n if flow < 4:\\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\\n Leq, l_int, aperture_area_m2) # in kW\\n if flow == 3:\\n q1 = supply_out_kW[0]\\n q2 = supply_out_kW[1]\\n q3 = supply_out_kW[2]\\n q4 = supply_out_kW[3]\\n E1 = auxiliary_electricity_kW[0]\\n E2 = auxiliary_electricity_kW[1]\\n E3 = auxiliary_electricity_kW[2]\\n E4 = auxiliary_electricity_kW[3]\\n # calculate optimal mass flow and the corresponding pressure loss\\n specific_flows_kgpers[4], specific_pressure_losses_Pa[4] = calc_optimal_mass_flow(q1, q2, q3, q4, E1, E2, E3, E4, 0,\\n mB0_r, mB_max_r, mB_min_r, 0,\\n dP2, dP3, dP4, aperture_area_m2)\\n if flow == 4:\\n # calculate pumping electricity when operatres at optimal mass flow\\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\\n Leq, l_int, aperture_area_m2) # in kW\\n dp5 = specific_pressure_losses_Pa[flow]\\n q5 = supply_out_kW[flow]\\n m5 = specific_flows_kgpers[flow]\\n # set points to zero when load is negative\\n specific_flows_kgpers[5], specific_pressure_losses_Pa[5] = calc_optimal_mass_flow_2(m5, q5, dp5)\\n\\n if flow == 5: # optimal mass flow\\n supply_losses_kW[flow] = np.vectorize(calc_qloss_network)(specific_flows_kgpers[flow], l_ext, aperture_area_m2,\\n temperature_mean[flow], Tamb_vector_C, msc_max_kgpers)\\n supply_out_pre = supply_out_kW[flow].copy() + supply_losses_kW[flow].copy()\\n auxiliary_electricity_kW[flow] = np.vectorize(calc_Eaux_SC)(specific_flows_kgpers[flow], specific_pressure_losses_Pa[flow],\\n Leq, l_int, aperture_area_m2) # in kW\\n supply_out_total_kW = supply_out_kW[flow].copy() + 0.5 * auxiliary_electricity_kW[flow].copy() - supply_losses_kW[flow].copy() # eq.(58) _[J. Fonseca et al., 2016]\\n mcp_kWperK = specific_flows_kgpers[flow] * (Cp_fluid_JperkgK / 1000) # mcp in kW/K\\n\\n result = [supply_losses_kW[5], supply_out_total_kW, auxiliary_electricity_kW[5], temperature_out[5], temperature_in[5], mcp_kWperK]\\n q_rad_per_panel = q_rad_vector*aperture_area_m2\\n return result\",\n \"def main():\\n\\t#Necessary Parameters for Simulation\\n\\tAmplitudes = ['230','260','290']\\n\\tConditions = ['No EES','EES','EES+A08','EES+A08+ProIncrease']\\n\\n\\n\\n\\t#eesAmplitude = \\\"230\\\"\\n\\teesAmplitudeName = \\\"230\\\"\\n\\tdelay = \\\"2\\\"\\n\\ttoAddname = \\\"\\\"\\n\\tspecies = \\\"rat\\\"\\n\\t#Paramters initialization\\n\\ttotSimTime = rp.get_tot_sim_time()\\n\\tgaitCyclesFileName = rp.get_gait_cycles_file()\\n\\tmuscles = rp.get_muscles()\\n\\ttemplateFile = \\\"templateFrwSimRORaReal.txt\\\"\\n\\tw1 = 0.011\\n\\tw2 = -0.005\\n\\n\\ttemplateFile = \\\"A08.txt\\\"\\n\\n\\ttls.modify_network_structure(templateFile,templateFile,delay,[w1,w2])\\n\\n\\teesFrequencies = range(0,41,40)\\n\\tnProc = 4\\n\\tseed = \\\"1\\\"\\n\\n\\tnSim = len(eesFrequencies)\\n\\tcount=0.\\n\\tpercLastPrint=0.\\n\\tprintPeriod = 0.05\\n\\n\\t# run simulations\\n\\tfor j,eesAmplitude in enumerate(Amplitudes):\\n\\t\\tfor i,eesFrequency in enumerate(eesFrequencies):\\n\\t\\t\\tfor condition in Conditions:\\n\\t\\t\\t\\t#name = \\\"Tonic_FFS_\\\"+inputFileName+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\t\\t\\tinputFileName = condition\\n\\t\\t\\t\\tinputFile = \\\"generatedStructures/\\\"+inputFileName+\\\".txt\\\"\\n\\t\\t\\t\\tname = \\\"Tonic_FFS_\\\"+condition+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\t\\t\\tresultFile = gt.find(\\\"*\\\"+name+\\\".p\\\",pathToResults)\\n\\t\\t\\t\\tif not resultFile:\\n\\t\\t\\t\\t\\tprogram = ['python','./scripts/runForSimMuscleSpindles_RORa.py',\\\\\\n\\t\\t\\t\\t\\t\\tstr(eesFrequency),eesAmplitude,inputFile,name,\\\"--simTime\\\",str(totSimTime),\\\"--seed\\\",seed,\\\"--noPlot\\\"]\\n\\n\\t\\t\\t\\tif not resultFile: gt.run_subprocess(program)\\n\\n\\t\\t\\t\\tcount+=1\\n\\t\\t\\t\\tif count/nSim-percLastPrint>=printPeriod:\\n\\t\\t\\t\\t\\tpercLastPrint=count/nSim\\n\\t\\t\\t\\t\\tprint str(round(count/nSim*100))+\\\"% of simulations performed...\\\"\\n\\n\\n\\n\\t\\\"\\\"\\\" create plots \\\"\\\"\\\"\\n\\terrParams = dict(lw=0.5, capsize=1, capthick=0.5)\\n\\twith open(gaitCyclesFileName, 'r') as pickle_file:\\n\\t\\theelStrikes = pickle.load(pickle_file)\\n\\t\\tfootOffs = pickle.load(pickle_file)\\n\\n\\n\\t# Figure 5 plot all gait cycles- afferent and efferents\\n\\t#if not phasicStim:\\n\\tfigName = time.strftime(\\\"/%Y_%m_%d_Tonic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_firingRates.pdf\\\")\\n\\t#else: figName = time.strftime(\\\"/%Y_%m_%d_Phasic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_firingRates.pdf\\\")\\n\\tfig, ax = plt.subplots(2, 4,figsize=(16,9))\\n\\tcmap = plt.get_cmap('winter')\\n\\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\\n\\n\\tfor i,eesFrequency in enumerate(eesFrequencies):\\n\\t\\t#if not phasicStim:\\n\\t\\tname = \\\"FS_EES_230uA_\\\"+str(eesFrequency)+\\\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\\\"+str(eesFrequency)\\n\\t\\t#name = \\\"Tonic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\t#else: name = \\\"Phasic_\\\"+emgVsKinMod+\\\"_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\tif species == \\\"human\\\":name += hp.get_dataset()\\n\\n\\t\\t# get data\\n\\t\\tprint name\\n\\t\\tresultFile = gt.find(\\\"*\\\"+name+\\\".p\\\",pathToResults)\\n\\t\\tprint resultFile\\n\\t\\tif len(resultFile)>1: print \\\"Warning: multiple result files found!!!\\\"\\n\\t\\twith open(resultFile[0], 'r') as pickle_file:\\n\\t\\t\\testimatedEmg = pickle.load(pickle_file)\\n\\t\\t\\tmeanFr = pickle.load(pickle_file)\\n\\n\\t\\t# get gait cycles\\n\\t\\tif not 'heelStrikeSamples' in locals():\\n\\t\\t\\tnSamples = len(meanFr[muscles[0]][\\\"Mn\\\"])\\n\\t\\t\\tdtMeanFr = float(totSimTime)/nSamples\\n\\t\\t\\theelStrikeSamples = [int(x) for x in heelStrikes*1000./dtMeanFr]\\n\\t\\t\\tfootOffSamples = [int(x) for x in footOffs*1000./dtMeanFr]\\n\\t\\t\\tsamples = range(nSamples)\\n\\t\\t\\tstance = np.zeros(nSamples).astype(bool)\\n\\t\\t\\tfor strike,off in zip(heelStrikeSamples,footOffSamples):\\n\\t\\t\\t\\tif strike>nSamples: break\\n\\t\\t\\t\\tstance[strike:off]=True\\n\\n\\t\\tfor j,muscle in enumerate(muscles):\\n\\t\\t\\tax[j,0].plot(meanFr[muscle]['Iaf'],color=colors[i])\\n\\t\\t\\tax[j,0].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,1].plot(meanFr[muscle]['IaInt'],color=colors[i])\\n\\t\\t\\tax[j,1].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,2].plot(meanFr[muscle]['Mn'],color=colors[i])\\n\\t\\t\\tax[j,2].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,3].plot(estimatedEmg[muscle]['Mn'],color=colors[i])\\n\\t\\t\\tax[j,3].fill_between(samples, 0, 200, where=stance, facecolor='#b0abab', alpha=0.25)\\n\\n\\n\\tfor j,muscle in enumerate(muscles):\\n\\t\\tax[j,0].set_ylim([0,200])\\n\\t\\tax[j,0].set_title(\\\"Ia fibers firing rate - \\\"+muscle)\\n\\t\\tax[j,0].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,0].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,1].set_ylim([0,200])\\n\\t\\tax[j,1].set_title(\\\"IaInt firing rate - \\\"+muscle)\\n\\t\\tax[j,1].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,1].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,2].set_ylim([0,200])\\n\\t\\tax[j,2].set_title(\\\"Mn firing rate - \\\"+muscle)\\n\\t\\tax[j,2].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,2].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,3].set_ylim([0,200])\\n\\t\\tax[j,3].set_title(\\\"EMG - \\\"+muscle)\\n\\t\\tax[j,3].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,3].set_ylabel(\\\"Emg amplitude (a.u.)\\\")\\n\\tplt.savefig(pathToResults+figName, format=\\\"pdf\\\",transparent=True)\\n\\n\\n# FIgure 5 plot 2 single gait cycles- afferent and efferents + mn phasicity score\\n\\tif species == \\\"rat\\\":\\n\\t\\tstartGaitCycleN = 3\\n\\t\\tnCycles = 1\\n\\telif species == \\\"human\\\":\\n\\t\\tstartGaitCycleN = 3\\n\\t\\tnCycles = 1\\n\\n\\tfigName = time.strftime(\\\"/%Y_%m_%d_Tonic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_single_firingRates.pdf\\\")\\n\\t#else: figName = time.strftime(\\\"/%Y_%m_%d_Phasic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_single_firingRates.pdf\\\")\\n\\tfig, ax = plt.subplots(2, 6,figsize=(16,9))\\n\\tcmap = plt.get_cmap('winter')\\n\\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\\n\\tbar_width = 5\\n\\n\\tfor i,eesFrequency in enumerate(eesFrequencies):\\n\\t\\t#if not phasicStim:\\n\\n\\t\\tname = \\\"FS_EES_230uA_\\\"+str(eesFrequency)+\\\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\\\"+str(eesFrequency)\\t\\t#else: name = \\\"Phasic_\\\"+emgVsKinMod+\\\"_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\tif species == \\\"human\\\":name += hp.get_dataset()\\n\\n\\t\\t# get data\\n\\t\\tresultFile = gt.find(\\\"*\\\"+name+\\\".p\\\",pathToResults)\\n\\t\\tif len(resultFile)>1: print \\\"Warning: multiple result files found!!!\\\"\\n\\t\\twith open(resultFile[0], 'r') as pickle_file:\\n\\t\\t\\testimatedEmg = pickle.load(pickle_file)\\n\\t\\t\\tmeanFr = pickle.load(pickle_file)\\n\\n\\t\\t# compute stats\\n\\t\\tiaIntModDepth = {}\\n\\t\\tactiveMnFr={}\\n\\t\\tfor muscle in muscles:\\n\\t\\t\\tiaIntModDepth[muscle]=[]\\n\\t\\t\\tactiveMnFr[muscle]=[]\\n\\t\\tfor j in xrange(len(heelStrikeSamples)-1):\\n\\t\\t\\tif heelStrikeSamples[j+1]>nSamples-50: break\\n\\t\\t\\tif heelStrikeSamples[j]<50:continue # to skip artefacts\\n\\t\\t\\tfor muscle in muscles:\\n\\t\\t\\t\\tiaIntModDepth[muscle].append(\\\\\\n\\t\\t\\t\\t\\tmeanFr[muscle]['IaInt'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].max()-meanFr[muscle]['IaInt'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].min())\\n\\t\\t\\t\\tmnActivityDuringCycle = meanFr[muscle]['Mn'][heelStrikeSamples[j]:heelStrikeSamples[j+1]]\\n\\t\\t\\t\\tactiveMnFr[muscle].append(\\\\\\n\\t\\t\\t\\t\\tmnActivityDuringCycle[mnActivityDuringCycle>=0.8*mnActivityDuringCycle.max()].mean())\\n\\t\\t\\t\\t\\t# mnActivityDuringCycle[mnActivityDuringCycle>=1.5*mnActivityDuringCycle.std()].mean())\\n\\t\\t\\t\\t\\t# mnActivityDuringCycle[mnActivityDuringCycle>=np.percentile(mnActivityDuringCycle,90)].mean())\\n\\t\\tiaIntModDepthStats = {}\\n\\t\\tactiveMnFrStats = {}\\n\\t\\tfor muscle in muscles:\\n\\t\\t\\tiaIntModDepthStats[muscle] = {\\\"mean\\\":np.mean(iaIntModDepth[muscle]),\\n\\t\\t\\t\\t\\\"sem\\\":np.std(iaIntModDepth[muscle])/(np.sqrt(len(iaIntModDepth[muscle])-1))}\\n\\t\\t\\tactiveMnFrStats[muscle] = {\\\"mean\\\":np.mean(activeMnFr[muscle]),\\n\\t\\t\\t\\t\\\"sem\\\":np.std(activeMnFr[muscle])/(np.sqrt(len(activeMnFr[muscle])-1))}\\n\\n\\t\\t# get gait cycles to plot\\n\\t\\tif not 'startPlot' in locals():\\n\\t\\t\\tstartPlot = heelStrikeSamples[startGaitCycleN-1]\\n\\t\\t\\tstopPlot = heelStrikeSamples[startGaitCycleN+nCycles-1]\\n\\t\\t\\tif stopPlot>nSamples: stopPlot=nSamples\\n\\t\\t\\treducedSamples = range(stopPlot-startPlot)\\n\\t\\t\\treducedStance = stance[startPlot:stopPlot]\\n\\n\\t\\tfor j,muscle in enumerate(muscles):\\n\\t\\t\\tax[j,0].plot(meanFr[muscle]['Iaf'][startPlot:stopPlot],color=colors[i])\\n\\t\\t\\tax[j,0].fill_between(reducedSamples, 0, 200, where=reducedStance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,1].plot(meanFr[muscle]['IaInt'][startPlot:stopPlot],color=colors[i])\\n\\t\\t\\tax[j,1].fill_between(reducedSamples, 0, 250, where=reducedStance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,2].bar(eesFrequency,iaIntModDepthStats[muscle][\\\"mean\\\"],bar_width,yerr=iaIntModDepthStats[muscle][\\\"sem\\\"],\\\\\\n\\t\\t\\t\\tcolor=colors[i],error_kw=errParams)\\n\\t\\t\\txValsScatter = np.linspace(0,bar_width*0.9,len(iaIntModDepth[muscle]))+eesFrequency-bar_width*0.45\\n\\t\\t\\tax[j,2].scatter(xValsScatter,iaIntModDepth[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\\\"#dddde3\\\", s=7, zorder=3, alpha=0.7)\\n\\n\\t\\t\\tax[j,3].plot(meanFr[muscle]['Mn'][startPlot:stopPlot],color=colors[i])\\n\\t\\t\\tax[j,3].fill_between(reducedSamples, 0, 40, where=reducedStance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,4].bar(eesFrequency,activeMnFrStats[muscle][\\\"mean\\\"],bar_width,yerr=activeMnFrStats[muscle][\\\"sem\\\"],\\\\\\n\\t\\t\\t\\tcolor=colors[i],error_kw=errParams)\\n\\t\\t\\tax[j,4].scatter(xValsScatter,activeMnFr[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\\\"#dddde3\\\", s=7, zorder=3, alpha=0.7)\\n\\t\\t\\tax[j,5].plot(estimatedEmg[muscle]['Mn'][startPlot:stopPlot],color=colors[i])\\n\\t\\t\\tax[j,5].fill_between(reducedSamples, -50, 50, where=reducedStance, facecolor='#b0abab', alpha=0.25)\\n\\n\\tfor j,muscle in enumerate(muscles):\\n\\t\\tax[j,0].set_ylim([0,200])\\n\\t\\tax[j,0].set_title(\\\"Ia fibers firing rate - \\\"+muscle)\\n\\t\\tax[j,0].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,0].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,1].set_ylim([0,250])\\n\\t\\tax[j,1].set_title(\\\"IaInt firing rate - \\\"+muscle)\\n\\t\\tax[j,1].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,1].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,2].set_ylim([0,250])\\n\\t\\tax[j,2].set_title(\\\"Mean IaInr Fr while active\\\")\\n\\t\\tax[j,2].set_xlabel(\\\"Stimulation amplitude (uA)\\\")\\n\\t\\tax[j,2].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,3].set_ylim([0,40])\\n\\t\\tax[j,3].set_title(\\\"Mn firing rate - \\\"+muscle)\\n\\t\\tax[j,3].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,3].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,4].set_ylim([0,40])\\n\\t\\tax[j,4].set_title(\\\"Mean Mn Fr while active\\\")\\n\\t\\tax[j,4].set_xlabel(\\\"Stimulation amplitude (uA)\\\")\\n\\t\\tax[j,4].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\tax[j,5].set_ylim([-50,50])\\n\\t\\tax[j,5].set_title(\\\"EMG - \\\"+muscle)\\n\\t\\tax[j,5].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\tax[j,5].set_ylabel(\\\"Emg amplitude (a.u.)\\\")\\n\\tplt.savefig(pathToResults+figName, format=\\\"pdf\\\",transparent=True)\\n\\n\\n\\n\\n\\n\\t# FIgure 2-7 plot\\n\\tif species == \\\"rat\\\":\\n\\t\\tstartGaitCycleN = 3\\n\\t\\tnCycles = 1\\n\\telif species == \\\"human\\\":\\n\\t\\tstartGaitCycleN = 3\\n\\t\\tnCycles = 1\\n\\n\\t#if not phasicStim:\\n\\tfigName = time.strftime(\\\"/%Y_%m_%d_Tonic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_afferentStats.pdf\\\")\\n\\t#else: figName = time.strftime(\\\"/%Y_%m_%d_Phasic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_afferentStats.pdf\\\")\\n\\tfig, ax = plt.subplots(2, 4,figsize=(16,9))\\n\\tcmap = plt.get_cmap('winter')\\n\\tcolors = cmap(np.linspace(0.1,0.9,len(eesFrequencies)))\\n\\tbar_width = 5\\n\\n\\tmeanPerEraserApIaf = []\\n\\toffsetMeanFr = 0\\n\\toffsetMeanModDepth = 0\\n\\n\\tfor i,eesFrequency in enumerate(eesFrequencies):\\n\\t\\t#if not phasicStim:\\n\\t\\tname = \\\"FS_EES_230uA_\\\"+str(eesFrequency)+\\\"Hz_Delay_2ms_Tonic_FFS_Control_freq_\\\"+str(eesFrequency)\\n\\t\\t#name = \\\"Tonic_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\t#else: name = \\\"Phasic_\\\"+emgVsKinMod+\\\"_FFS_species_\\\"+toAddname+inputFileName+species+\\\"_muscles_\\\"+\\\"\\\".join(muscles)+\\\"_delay_\\\"+str(delay)+\\\"_amp_\\\"+str(eesAmplitudeName)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\tif species == \\\"human\\\":name += hp.get_dataset()\\n\\n\\t\\tresultFile = gt.find(\\\"*\\\"+name+\\\".p\\\",pathToResults)\\n\\t\\tif len(resultFile)>1: print \\\"Warning: multiple result files found!!!\\\"\\n\\t\\twith open(resultFile[0], 'r') as pickle_file:\\n\\t\\t\\testimatedEmg = pickle.load(pickle_file)\\n\\t\\t\\tmeanFr = pickle.load(pickle_file)\\n\\t\\t\\tmeanPerEraserApIaf.append(pickle.load(pickle_file))\\n\\n\\t\\t# compute stats\\n\\t\\tiaModDepth = {}\\n\\t\\tiaMeanFr={}\\n\\t\\tfor muscle in muscles:\\n\\t\\t\\tiaModDepth[muscle]=[]\\n\\t\\t\\tiaMeanFr[muscle]=[]\\n\\t\\tfor j in xrange(len(heelStrikeSamples)-1):\\n\\t\\t\\tif heelStrikeSamples[j+1]>nSamples-50: break\\n\\t\\t\\tif heelStrikeSamples[j]<50:continue # to skip artefacts\\n\\t\\t\\tfor muscle in muscles:\\n\\t\\t\\t\\tiaModDepth[muscle].append(\\\\\\n\\t\\t\\t\\t\\tmeanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].max()-meanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].min())\\n\\t\\t\\t\\tiaMeanFr[muscle].append(\\\\\\n\\t\\t\\t\\t\\tmeanFr[muscle]['Iaf'][heelStrikeSamples[j]:heelStrikeSamples[j+1]].mean())\\n\\t\\tiaModDepthStats = {}\\n\\t\\tiaMeanFrStats = {}\\n\\t\\tfor muscle in muscles:\\n\\t\\t\\tiaModDepthStats[muscle] = {\\\"mean\\\":np.mean(iaModDepth[muscle]),\\n\\t\\t\\t\\t\\\"sem\\\":np.std(iaModDepth[muscle])/(np.sqrt(len(iaModDepth[muscle])-1))}\\n\\t\\t\\tiaMeanFrStats[muscle] = {\\\"mean\\\":np.mean(iaMeanFr[muscle]),\\n\\t\\t\\t\\t\\\"sem\\\":np.std(iaMeanFr[muscle])/(np.sqrt(len(iaMeanFr[muscle])-1))}\\n\\n\\t\\t# get gait cycles to plot\\n\\t\\tif not 'startPlot' in locals():\\n\\t\\t\\tstartPlot = heelStrikeSamples[startGaitCycleN-1]\\n\\t\\t\\tstopPlot = heelStrikeSamples[startGaitCycleN+nCycles-1]\\n\\t\\t\\tif stopPlot>nSamples: stopPlot=nSamples\\n\\t\\t\\treducedSamples = range(stopPlot-startPlot)\\n\\t\\t\\treducedStance = stance[startPlot:stopPlot]\\n\\n\\t\\tfor j,muscle in enumerate(muscles):\\n\\n\\t\\t\\tax[j,0].plot(meanFr[muscle]['Iaf'][startPlot:stopPlot],color=colors[i])\\n\\t\\t\\tax[j,0].fill_between(reducedSamples, 0, 125, where=reducedStance, facecolor='#b0abab', alpha=0.25)\\n\\t\\t\\tax[j,1].bar(eesFrequency,iaMeanFrStats[muscle][\\\"mean\\\"],bar_width,yerr=iaMeanFrStats[muscle][\\\"sem\\\"],\\\\\\n\\t\\t\\t\\tcolor=colors[i],error_kw=errParams)\\n\\t\\t\\txValsScatter = np.linspace(0,bar_width*0.9,len(iaMeanFr[muscle]))+eesFrequency-bar_width*0.45\\n\\t\\t\\tax[j,1].scatter(xValsScatter,iaMeanFr[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\\\"#dddde3\\\", s=7, zorder=3, alpha=0.7)\\n\\n\\t\\t\\tax[j,2].bar(eesFrequency,iaModDepthStats[muscle][\\\"mean\\\"],bar_width,yerr=iaModDepthStats[muscle][\\\"sem\\\"],\\\\\\n\\t\\t\\t\\tcolor=colors[i],error_kw=errParams)\\n\\t\\t\\tax[j,2].scatter(xValsScatter,iaModDepth[muscle], marker='o',edgecolor='black', linewidth='0.1', color=\\\"#dddde3\\\", s=7, zorder=3, alpha=0.7)\\n\\t\\t\\tax[j,3].bar(eesFrequency,meanPerEraserApIaf[-1],5,color=colors[i])\\n\\n\\t\\t\\tax[j,0].set_ylim([0,125])\\n\\t\\t\\tax[j,0].set_title(\\\"Ia fibers firing rate - \\\"+muscle)\\n\\t\\t\\tax[j,0].set_xlabel(\\\"Time (ms)\\\")\\n\\t\\t\\tax[j,0].set_ylabel(\\\"Firing rate (Imp/s)\\\")\\n\\t\\t\\tax[j,1].set_ylim([0,125])\\n\\t\\t\\tax[j,1].set_title(\\\"Mean Ia firing rate \\\")\\n\\t\\t\\tax[j,1].set_xlabel(\\\"Stimulation amplitude (uA)\\\")\\n\\t\\t\\tax[j,1].set_ylabel(\\\"(imp/s)\\\")\\n\\t\\t\\tax[j,2].set_ylim([0,80])\\n\\t\\t\\tax[j,2].set_title(\\\"modulation depth\\\")\\n\\t\\t\\tax[j,2].set_xlabel(\\\"Stimulation amplitude (uA)\\\")\\n\\t\\t\\tax[j,2].set_ylabel(\\\"(imp/s)\\\")\\n\\t\\t\\tax[j,3].set_ylim([0,100])\\n\\t\\t\\tax[j,3].set_title(\\\"Percentage erased APs\\\")\\n\\t\\t\\tax[j,3].set_xlabel(\\\"Stimulation frequency (Hz)\\\")\\n\\t\\t\\tax[j,3].set_ylabel(\\\"Percentage\\\")\\n\\tplt.savefig(pathToResults+figName, format=\\\"pdf\\\",transparent=True)\",\n \"def test_simulationRun(self):\\n self.opt = { 'temperature' : 300.0, 'friction' : 1, 'dt' : 0.00002,\\n 'nIter' : 2, 'nstepsNC' : 2, 'nstepsMD' : 1, 'nprop' : 1,\\n 'nonbondedMethod' : 'NoCutoff', 'constraints': 'HBonds',\\n 'trajectory_interval' : 1, 'reporter_interval' : 1,\\n 'outfname' : 'mc-test',\\n 'platform' : None,\\n 'constraints' : 'HBonds',\\n 'mc_per_iter' : 2 }\\n\\n structure = self.full_struct\\n class SetRotationMove(RandomLigandRotationMove):\\n def __init__(self, structure, resname='LIG'):\\n super(SetRotationMove, self).__init__(structure, resname)\\n\\n def move(self, context):\\n \\\"\\\"\\\"Function that performs a random rotation about the\\n center of mass of the ligand.\\n \\\"\\\"\\\"\\n #TODO: check if we need to deepcopy\\n positions = context.getState(getPositions=True).getPositions(asNumpy=True)\\n\\n self.positions = positions[self.atom_indices]\\n self.center_of_mass = self.getCenterOfMass(self.positions, self.masses)\\n reduced_pos = self.positions - self.center_of_mass\\n\\n # Define random rotational move on the ligand\\n #set rotation so that test is reproducible\\n set_rotation_matrix = np.array([[-0.62297988, -0.17349253, 0.7627558 ],\\n [ 0.55082352, -0.78964857, 0.27027502],\\n [ 0.55541834, 0.58851973, 0.58749893]])\\n\\n\\n #set_rotation_matrix = np.array([[1, 0, 0],\\n # [0, 1, 0],\\n # [0, 0, 1]])\\n\\n #multiply lig coordinates by rot matrix and add back COM translation from origin\\n rot_move = np.dot(reduced_pos, set_rotation_matrix) * positions.unit + self.center_of_mass\\n\\n # Update ligand positions in nc_sim\\n for index, atomidx in enumerate(self.atom_indices):\\n positions[atomidx] = rot_move[index]\\n context.setPositions(positions)\\n positions = context.getState(getPositions=True).getPositions(asNumpy=True)\\n self.positions = positions[self.atom_indices]\\n return context\\n\\n\\n self.model = SetRotationMove(structure, resname='ALA')\\n #self.model = RandomLigandRotationMove(structure, resname='ALA')\\n\\n self.model.atom_indices = range(22)\\n self.model.topology = structure[self.model.atom_indices].topology\\n self.model.positions = structure[self.model.atom_indices].positions\\n self.model.calculateProperties()\\n\\n self.mover = MoveEngine(self.model)\\n #Initialize the SimulationFactory object\\n sims = SimulationFactory(structure, self.mover, **self.opt)\\n #print(sims)\\n system = sims.generateSystem(structure, **self.opt)\\n simdict = sims.createSimulationSet()\\n alch_system = sims.generateAlchSystem(system, self.model.atom_indices)\\n self.nc_sim = sims.generateSimFromStruct(structure, self.mover, alch_system, ncmc=True, **self.opt)\\n self.model.calculateProperties()\\n self.initial_positions = self.nc_sim.context.getState(getPositions=True).getPositions(asNumpy=True)\\n mc_sim = Simulation(sims, self.mover, **self.opt)\\n #monkeypatch to access acceptance value\\n def nacceptRejectMC(self, temperature=300, **opt):\\n \\\"\\\"\\\"Function that chooses to accept or reject the proposed move.\\n \\\"\\\"\\\"\\n md_state0 = self.current_state['md']['state0']\\n md_state1 = self.current_state['md']['state1']\\n log_mc = (md_state1['potential_energy'] - md_state0['potential_energy']) * (-1.0/self.nc_sim.context._integrator.kT)\\n randnum = math.log(np.random.random())\\n\\n if log_mc > randnum:\\n self.accept += 1\\n print('MC MOVE ACCEPTED: log_mc {} > randnum {}'.format(log_mc, randnum) )\\n self.md_sim.context.setPositions(md_state1['positions'])\\n else:\\n self.reject += 1\\n print('MC MOVE REJECTED: log_mc {} < {}'.format(log_mc, randnum) )\\n self.md_sim.context.setPositions(md_state0['positions'])\\n self.log_mc = log_mc\\n self.md_sim.context.setVelocitiesToTemperature(self.opt['temperature'])\\n mc_sim.acceptRejectMC = nacceptRejectMC\\n nacceptRejectMC.__get__(mc_sim)\\n mc_sim.acceptRejectMC = types.MethodType(nacceptRejectMC, mc_sim)\\n mc_sim.runMC(self.opt['nIter'])\\n #get log acceptance\\n print(mc_sim.log_mc)\\n #if mc is working, should be around -24.1\\n assert mc_sim.log_mc <= -23.8 and mc_sim.log_mc >= -24.3\",\n \"def run_analysis(self):\\n ### skip some snapshots for testing purposes\\n nskip = 199\\n read_char.skip_snapshots(self.hfile, self.ifile, nskip)\\n ### read in the first two steps (required for velocity related computations\\n xs_old, ys_old, lx_old, ly_old, tstep_old, natoms_old = read_char.read_snapshot(self.hfile, self.ifile)\\n x_old = xs_old*lx_old\\n y_old = ys_old*ly_old\\n xs,ys,lx,ly,tstep,natoms = read_char.read_snapshot(self.hfile, self.ifile)\\n x = xs*lx\\n y = ys*ly\\n ### loop over all steps of the input file\\n for step in range(nskip+1,self.nsteps-1):\\n print step\\n ### read in coordinates (as required)\\n xs_new,ys_new,lx_new,ly_new,tstep_new,natoms_new = read_char.read_snapshot(self.hfile, self.ifile)\\n x_new = xs_new*lx_new\\n y_new = ys_new*ly_new\\n ### compute further current per/atom quantities\\n phi = misc_tools.compute_orientation(x,y,lx,ly,self.npol)\\n vx,vy = misc_tools.compute_velocity(x_old,y_old, x_new, y_new, lx, ly, tstep_old, tstep_new, natoms)\\n ### start desired analysis methods\\n # density\\n if self.density_flag:\\n self.density.compute(step,x,y,lx,ly,natoms, plot = 'False')\\n # number fluctuations\\n if self.nf_flag:\\n self.numberfluctuation.compute(step,xs,ys, plot = 'False')\\n # voronoi density\\n if self.voronoi_flag:\\n self.voronoidensity.compute(step,x,y,lx,ly,natoms, plot = 'False')\\n # velocity / worticity\\n if self.velocity_flag:\\n self.velocityworticity.compute(step,x,y,vx,vy,natoms,lx,ly, plot = 'False')\\n # orientation / velocity\\n if self.orientvel_flag:\\n self.orientvel.compute(step,x,y,vx,vy,phi,natoms, plot = 'False')\\n # defect points\\n if self.pointdefects_flag:\\n self.pointdefects.compute(step,x,y,phi,lx,ly,natoms)\\n ### move coordinate arrays\\n xs_old = np.copy(xs)\\n ys_old = np.copy(ys)\\n x_old = np.copy(x)\\n y_old = np.copy(y)\\n tstep_old = tstep\\n xs = np.copy(xs_new)\\n ys = np.copy(ys_new)\\n x = np.copy(x_new)\\n y = np.copy(y_new)\\n tstep = tstep_new\\n return\",\n \"def main(adate,aprefix):\\n script_start_time = datetime.now() #Script Timer\\n\\n if len(adate)!=8:\\n raise ValueError('Please enter a date in the format yyyymmdd') \\n\\n # Specify Date to Process\\n year = int(adate[0:4])\\n month = int(adate[4:6])\\n day = int(adate[6:8])\\n start_date = datetime(year,month,day)\\n\\n # Loop through 24 hours\\n dsout = False\\n for jj in range(0,24):\\n wrf_file = make_wrf_file(start_date + pd.to_timedelta(jj,unit='h'), forecast_offset)\\n print('Processing: ' + wrf_file)\\n ds = False\\n try:\\n ds = xr.open_dataset(directory + wrf_file, engine='pynio')\\n except:\\n print('Could not open ' + wrf_file)\\n if(isinstance(ds,xr.Dataset)):\\n ds = clean_dataset(ds)\\n if(isinstance(dsout,xr.Dataset)):\\n dsout = xr.concat([dsout, ds],dim='time')\\n else:\\n dsout = ds\\n\\n if(isinstance(dsout,xr.Dataset)):\\n # Add attributes\\n dsout['eastward_wind'].attrs['standard_name'] = 'eastward_wind'\\n dsout['eastward_wind'].attrs['comment'] = 'The zonal wind speed (m/s) indicates the u (positive eastward) component of where the wind is going.'\\n dsout['northward_wind'].attrs['standard_name'] = 'northward_wind'\\n dsout['northward_wind'].attrs['comment'] = 'The meridional wind speed (m/s) indicates the v (positive northward) component of where the wind is going.'\\n \\n # Add Wind Speed\\n wind_speed = np.sqrt(dsout['eastward_wind']**2 + dsout['northward_wind']**2)\\n wind_speed.attrs['units'] = 'm s-1'\\n wind_speed.attrs['comment'] = 'Wind Speed is calculated from the Zonal and Meridional wind speeds.'\\n wind_speed.attrs['long_name'] = 'Wind Speed'\\n wind_speed.attrs['standard_name'] = 'wind_speed'\\n dsout['wind_speed'] = wind_speed\\n \\n # Add Wind Direction\\n wind_dir = 270 - xr.ufuncs.arctan2(dsout['northward_wind'],dsout['eastward_wind'])*180/np.pi\\n #wind_dir = (wind_dir.where(wind_dir<0)+360).combine_first(wind_dir) #Flip negative degrees - Doesn't seem to work\\n wind_dir = wind_dir % 360 #Use modulo to keep degrees between 0-360\\n wind_dir.attrs['units'] = 'degree'\\n wind_dir.attrs['comment'] = 'The direction from which winds are coming from, in degrees clockwise from true N.'\\n wind_dir.attrs['long_name'] = 'Wind Direction'\\n wind_dir.attrs['standard_name'] = 'wind_from_direction'\\n dsout['wind_from_direction'] = wind_dir\\n\\n # Add global metadata\\n dsout.attrs['title'] = \\\"Rutgers WRF 3km model output\\\"\\n dsout.attrs['forecast_offset'] = forecast_offset\\n dsout.attrs['source_directory'] = directory\\n dsout.attrs['date_created'] = str(datetime.today())\\n dsout.attrs['elapsed_time'] = str(datetime.now() - script_start_time)\\n dsout.attrs['creator_name'] = \\\"Sage Lichtenwalner\\\"\\n dsout.attrs['creator_email'] = \\\"sage@marine.rutgers.edu\\\"\\n dsout.attrs['creator_url'] = \\\"https://rucool.marine.rutgers.edu\\\"\\n dsout.attrs['institution'] = \\\"Rutgers University Center for Ocean Observing Leadership (RU COOL)\\\"\\n dsout.attrs['summary'] = \\\"Wind data extracted from the RU-WRF model. The model is run daily at 00Z with forecast files saved every hour. Times in this file are UTC based on the forecast time. The forecast_offset specifies how many hours of model spin up are allowed before the data is included in this virtual time-series archive for a given day. For example, a value of 6 means the first 6 hours of data for a day are actually extracted from the previous day's model run.\\\"\\n dsout.attrs['project'] = \\\"RU COOL BPU Wind Energy Project\\\"\\n dsout.attrs['Conventions'] = 'CF-1.6'\\n \\n # Setup xarray output encoding\\n encoding = make_encoding(dsout)\\n \\n # Output final datafile\\n output_datafile = '%s_%d%02d%02d.nc' % (aprefix, start_date.year, start_date.month, start_date.day)\\n dsout.to_netcdf(output_datafile, encoding=encoding) \\n print('Outputted ' + output_datafile)\\n else:\\n print('No data found, skipping.')\",\n \"def main():\\n\\t# \\\"\\\"\\\"\\n\\t# \\tMain function of test python module\\n\\t# \\\"\\\"\\\"\\n\\t# random.seed(os.urandom(345634)) # initialize random generator\\n\\t# t = np.linspace(0.0, 24.0, 96.0) # define the time axis of a day, here we use 96 values every quarter of an hour\\n\\t# # standard load profile -- input\\n\\t# q = extra.read_slp(t,\\n\\t# 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv') # read the sample standard load profile, can be any length, can be resized given a low/high resolution time axis\\n\\t# q = q / np.sum(q) # normalization of standard load profile\\n\\t# # process duration\\n\\t# duration_axis = np.linspace(0.0, 24.0, 96.0)\\n\\t# (p_d, E_p) = extra.app_time(duration_axis, 10, 2, 0.0,\\n\\t# 24.0) # function that define the pdf of duration of a process\\n\\t# # process consumption\\n\\t# consumption_axis = np.linspace(0.0, 3.5, 96.0)\\n\\t# (p_k, E_k) = extra.app_consumption(consumption_axis, 10, 2, 0.0,\\n\\t# 3.5) # function that define the pdf of duration of a process\\n\\t# # pdf of starting time\\n\\t# p_t_0 = lpd.infer_t_0(q, p_d, E_k) # computes the pdf of starting time of processes\\n\\t# p_t_0 = p_t_0 / np.sum(p_t_0) # normalization of the pdf to sum up to zero\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# 1st Approach, starting time of processes is a discrete propapibility density function\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# D = 2000\\n\\t# synthetic_profile = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# synthetic_profile_1 = lpd.synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# # expected value of D processes\\n\\t# q_e_e = lpd.infer_q_e(t, p_t_0, p_d, E_k, D)\\n\\t# # plot\\n\\t# plt.step(t, synthetic_profile, \\\"g-\\\")\\n\\t# plt.step(t, q_e_e, \\\"b--\\\")\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# 2nd Approach, starting time of processes is a continuous propapibility density function\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# ts, cs = lpd.continous_synthetic_profile(D, t, p_d, consumption_axis, p_k, p_t_0)\\n\\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\\n\\t# plt.xlim(0, 24.0)\\n\\t# plt.legend([\\\"synthetic\\\", \\\"expected\\\", \\\"continuous\\\"], loc=0)\\n\\t# plt.show()\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# Time discretization\\n\\t# \\\"\\\"\\\"\\n\\t# n_intervals = 24 * 1 # discretized in minutes\\n\\t# discrete_timeaxis = np.linspace(0.0, 24.0, n_intervals + 1)\\n\\t# discrete_consumption = lpd.signal_discretization(discrete_timeaxis, t, ts, cs)\\n\\t# plt.step(ts / len(t) * t[-1], cs, where='post', c='r')\\n\\t# plt.step(discrete_timeaxis, discrete_consumption, where='post', c='k', ls='--', lw=2)\\n\\t# plt.legend([\\\"continuous\\\", \\\"discretized\\\"], loc=0)\\n\\t# plt.show()\\n #\\n #\\n\\t# \\\"\\\"\\\"\\n\\t# Repeated day synthetic profile creation\\n\\t# \\\"\\\"\\\"\\n\\t# # synthetic profile of D processes\\n\\t# D = 2000\\n\\t# n = 10\\n\\t# slp = lpd.synthetic_profile_repeated(D, t, p_d, consumption_axis, p_k, p_t_0, n)\\n\\t# plt.step(range(len(slp)), slp, \\\"g-\\\")\\n\\t# plt.show()\\n\\tt = np.linspace(0.0, 24.0, 96.0)\\n\\tload_profile = extra.read_slp(t, 'Profielen-Elektriciteit-2015-versie-1.00 Folder/profielen Elektriciteit 2015 versie 1.00.csv')\\n\\tslp = synthetic.create_synthetic_load(load_profile, 5.0, 5)\\n\\tplt.step(range(len(slp)), slp)\\n\\tplt.show()\",\n \"def run(self):\\n qubit = self.qubit\\n\\n routine = self.routine\\n\\n # Saving some typing for parameters that are only read ;)\\n allowed_delta_f = self.get_param_value(\\\"allowed_delta_f\\\")\\n f_unit = self.get_param_value(\\\"f_unit\\\")\\n f_factor = self.get_param_value(\\\"f_factor\\\")\\n delta_f_unit = self.get_param_value(\\\"delta_f_unit\\\")\\n delta_f_factor = self.get_param_value(\\\"delta_f_factor\\\")\\n max_iterations = self.get_param_value(\\\"max_iterations\\\")\\n transition = self.get_param_value(\\\"transition_name\\\")\\n\\n # Finding the ramsey experiment in the pipulse calibration\\n pipulse_calib = routine.routine_steps[-1]\\n ramsey = pipulse_calib.routine_steps[-1]\\n\\n # Transition frequency from last Ramsey\\n freq = qubit[f\\\"{transition}_freq\\\"]()\\n\\n # Retrieving the frequency difference\\n max_waiting_seconds = self.get_param_value(\\\"max_waiting_seconds\\\")\\n for i in range(max_waiting_seconds):\\n try:\\n routine.delta_f = (\\n ramsey.analysis.proc_data_dict[\\n \\\"analysis_params_dict\\\"][\\n qubit.name][\\\"exp_decay\\\"][\\\"new_qb_freq\\\"] -\\n ramsey.analysis.proc_data_dict[\\n \\\"analysis_params_dict\\\"][\\n qubit.name][\\\"exp_decay\\\"][\\\"old_qb_freq\\\"])\\n break\\n except KeyError:\\n log.warning(\\n \\\"Could not find frequency difference between current \\\"\\n \\\"and last Ramsey measurement, delta_f not updated\\\")\\n break\\n except AttributeError:\\n # FIXME: Unsure if this can also happen on real set-up\\n log.warning(\\n \\\"Analysis not yet run on last Ramsey measurement, \\\"\\n \\\"frequency difference not updated\\\")\\n time.sleep(1)\\n\\n # Progress update\\n if self.get_param_value('verbose'):\\n print(f\\\"Iteration {routine.iteration}, {transition}-freq \\\"\\n f\\\"{freq / f_factor} {f_unit}, frequency \\\"\\n f\\\"difference = {routine.delta_f / delta_f_factor} \\\"\\n f\\\"{delta_f_unit}\\\")\\n\\n # Check if the absolute frequency difference is small enough\\n if np.abs(routine.delta_f) < allowed_delta_f:\\n # Success\\n if self.get_param_value('verbose'):\\n print(f\\\"{transition}-frequency found to be \\\"\\n f\\\"{freq / f_factor} {f_unit} within \\\"\\n f\\\"{allowed_delta_f / delta_f_factor} \\\"\\n f\\\"{delta_f_unit} of previous value.\\\")\\n\\n elif routine.iteration < max_iterations:\\n # No success yet, adding a new rabi-ramsey and decision step\\n if self.get_param_value('verbose'):\\n print(f\\\"Allowed error (\\\"\\n f\\\"{allowed_delta_f / delta_f_factor} \\\"\\n f\\\"{delta_f_unit}) not yet achieved, adding new\\\"\\n \\\" round of PiPulse calibration...\\\")\\n\\n routine.add_next_pipulse_step()\\n\\n step_settings = {'qubits': self.qubits}\\n routine.add_step(\\n FindFrequency.Decision,\\n 'decision',\\n step_settings,\\n )\\n\\n routine.iteration += 1\\n return\\n\\n else:\\n # No success yet, reached max iterations\\n msg = (f\\\"{self.step_label} routine finished for {qubit.name}, \\\"\\n \\\"desired precision not necessarily achieved within the \\\"\\n f\\\"maximum number of iterations ({max_iterations}).\\\")\\n log.warning(msg)\\n\\n if self.get_param_value('verbose'):\\n print(msg)\\n\\n if self.get_param_value('verbose'):\\n # Printing termination update\\n print(f\\\"FindFrequency routine finished: \\\"\\n f\\\"{transition}-frequencies for {qubit.name} \\\"\\n f\\\"is {freq / f_factor} {f_unit}.\\\")\",\n \"def main():\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\\n\\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\\n plt.show()\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\\n\\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n\\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\\n #\\n #t_label = []\\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\\n # t_label.append(str(t))\\n #\\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\\n\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n # (L, Nfin, VT_flavor, Nrow, Imax)\\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \\n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\\n\\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\\n\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\\n\\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\\n # t_label.append(str(t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\\n # segment += 1\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\\n # segment += 1\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\\n # \\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\\n # \\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)\",\n \"def take_measurements(timeout=6):\\n print(\\\"Taking measurements for\\\", timeout, \\\"seconds.\\\")\\n start = time.monotonic()\\n while time.monotonic() - start < timeout:\\n for i, (toggle, polarity) in enumerate(vectors):\\n result = trackpad.measure_adc(toggle, polarity)\\n print(\\\"meas{}: {}\\\".format(i, result - compensation[i]), end=\\\"\\\\t\\\")\\n print()\",\n \"async def loop():\\n # ArmDevice.storage.joints_pos = await get_positions() # Use this if encoders are wired up.\\n # ArmDevice.storage.joints_pos = simulate_positions() # Use this for testing without position feedback.\\n log.debug(\\\"command: {}\\\".format(ArmDevice.storage.command))\\n ArmDevice.storage.controller.user_command(ArmDevice.storage.mode, *ArmDevice.storage.command)\\n ArmDevice.storage.speeds = ArmDevice.storage.controller.update_duties(ArmDevice.storage.joints_pos)\\n\\n # publish speeds/duty cycles here\\n log.debug(\\\"joints_pos: {}\\\".format(ArmDevice.storage.joints_pos))\\n log.debug(\\\"speeds: {}\\\".format(ArmDevice.storage.speeds))\\n await send_duties()\",\n \"def loop_run(self, loops):\\n self.loop_seek(self.num_loops + loops)\",\n \"def angular_memory_effect_analysis(tilt_coef_range, input_field, TM, data_shape, sample_pitch, wavelength, plot_std = True):\\n zernike_rho = np.linspace(-0.5, 0.5, data_shape[1])\\n\\n\\n centered_outputs = np.zeros([tilt_coef_range.size, data_shape[1]]) # For storage of outputs that where tilted and then artificially shifted back to the center\\n shift_idxs = np.zeros(tilt_coef_range.size) # Will store information on how much outputs needed to be shifted to be centered\\n for idx, coef in enumerate(tilt_coef_range):\\n output_intensity = np.abs( TM @ ( input_field * np.exp(2j * np.pi * coef * zernike_rho) ).ravel() )**2 # propagation via TM, only saving intensity\\n centered_outputs[idx, :], shift_idxs[idx] = calc.center_tilted_output(output_intensity, coef, data_shape, sample_pitch, wavelength, return_shift_idx = True)\\n\\n centered_outputs = centered_outputs / np.linalg.norm(centered_outputs, 1) # Normalising all outputs\\n\\n\\n input_field = input_field.ravel()\\n reference_output = np.abs( TM @ input_field )**2 # untilted output field, which we will use for std\\n reference_output = reference_output / np.linalg.norm(reference_output)\\n\\n # fig, axs = plt.subplots()\\n # axs.plot(centered_outputs[-1,:])\\n # plt.show()\\n\\n #Comparing the outputs with a standard deviation method\\n subtracted_outputs = np.zeros(centered_outputs.shape)\\n for idx in range(centered_outputs[:,0].size):\\n subtracted_outputs[idx, :] = centered_outputs[idx, :] - centered_outputs[0, :] # subtracting the outputs with the reference\\n\\n std_of_outputs = np.std(subtracted_outputs, axis = 1) # Computing the standard deviation of subtracted outputs\\n if plot_std:\\n fig, axs = plt.subplots()\\n img_1, = axs.plot(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, color = 'black')\\n img_2 = axs.scatter(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, s = 40, color = 'crimson', edgecolors = 'black')\\n axs.set_title('Angular memory effect decay')\\n axs.set(xlabel = '$\\\\delta x, \\\\ \\\\mu m$', ylabel = 'Standard deviation, p.d.u.')\\n\\n\\n #Fitting a logistic curve onto the results\\n popt, pcov = curve_fit(logistic_curve, (shift_idxs * sample_pitch[1] * 1e6), std_of_outputs)\\n smooth_range = np.linspace(-0.1, shift_idxs[-1] * sample_pitch[1] * 1e6, 1000)\\n fitted_std = logistic_curve(smooth_range, *popt)\\n img_fit, = axs.plot(smooth_range, fitted_std, color = 'blue')\\n\\n axs.text(0.65 * (shift_idxs[-1] * sample_pitch[1] * 1e6), 0.2 * std_of_outputs.max(), 'Fitting parameters: \\\\nL = {:.3g} \\\\nx_0 = {:.3g} \\\\nk = {:.3g}'.format(*popt))\\n\\n # img_3 = axs.scatter((shift_idxs * sample_pitch[1] * 1e6)[-8:], std_of_outputs[-8:], s = 70, marker = 'x', color = 'green')#excluded point\\n plt.legend([(img_2, img_1), img_fit], ['STD($\\\\delta x $)', '$L / [1 + \\\\exp(-k (\\\\delta x - x_0)) ]$'])\\n plt.plot(block = False)\\n\\n return centered_outputs\",\n \"def run_regression(auto_input,\\n variables: list = ['magasin', 'tilsig'],\\n regions: list = ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'],\\n jupyter: bool = False,\\n backup: bool = False,\\n loop: bool = False,\\n write: bool = True,\\n week_nb: int = False,\\n year: int = False) -> None:\\n\\n start_tuning = utctime_now()\\n\\n for region in regions:\\n\\n if not region in ['NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4']:\\n sys.exit(\\\"Region must be one out of: 'NO1', 'NO2', 'NO3', 'NO4', 'NO5', 'SE1', 'SE2', 'SE3', 'SE4'\\\")\\n\\n for variable in variables:\\n\\n if not variable in ['magasin', 'tilsig']:\\n sys.exit(\\\"Variable must be either 'tilsig' or 'magasin'\\\")\\n\\n print('---------------------------------------------------------------')\\n print(' {}, {} '.format(region, variable))\\n print('---------------------------------------------------------------')\\n\\n \\n \\n df_week, MagKap = auto_input[variable]\\n \\n period, forecast_time, read_start = rs.get_timeperiods(variable, week_nb, year)\\n \\n reg_end = (pd.to_datetime(time.strftime(forecast_time), format=\\\"%Y.%m.%d\\\") - Timedelta(days=7)).strftime(\\n '%Y.%m.%d')\\n\\n if (0 <= today.weekday() <= 1) or (today.weekday() == 2 and today.hour < 14): # True for tipping\\n last_forecast = forecast_time\\n else:\\n last_forecast = forecast_time\\n\\n df_cleaned = deletingNaNs(df_week.loc[:last_forecast])\\n\\n if loop:\\n if variable == 'tilsig':\\n max_kandidater = 196\\n min_kandidater = 2\\n\\n else:\\n max_kandidater = 171\\n min_kandidater = 2\\n\\n max_weeks = 238 #288\\n min_weeks = 16\\n print('max ant. kandidater: {}, min ant. kandidater: {}'.format(max_kandidater, min_kandidater))\\n print('max ant. uker: {}, min ant. uker: {}'.format(max_weeks, min_weeks))\\n\\n start_time_loop = utctime_now()\\n fasit, fasit_key = rs.make_fasit(variable, region, reg_end, period)\\n print('Fasit er lest inn.\\\\n')\\n\\n if fasit[fasit_key][:reg_end].isnull().any():\\n print('OBS: Det mangler verdier på fasiten! Går videre til neste region i loopen..')\\n print(fasit[fasit_key][:reg_end].isnull())\\n continue\\n\\n sorted_r2 = get_R2_sorted(variable, df_cleaned, fasit, fasit_key)\\n\\n if loop:\\n max_p = 0.001 # Beste testresultat SvF\\n\\n # First loop: Tuning number of candidates for best possible R2 combined\\n df_ant_kandidater = pd.DataFrame(columns=columns)\\n for antall in range(min_kandidater, max_kandidater + 1, 1):\\n if antall > len(sorted_r2):\\n chosen_r2 = sorted_r2\\n else:\\n chosen_r2 = sorted_r2[:antall]\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, first_period, max_p, chosen_r2,\\n loop=True)\\n df_ant_kandidater = df_ant_kandidater.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n if antall > len(sorted_r2):\\n print('Feilmelding: Ønsket antall kandidater overskrider maks (%i).\\\\n' % len(sorted_r2))\\n break\\n idx_max = df_ant_kandidater.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_ant_kandidater.ant_kandidater.values[idx_max])\\n print('Beste ant_kandidater loop 1: ', ant_kandidater_beste)\\n\\n # Second loop: tuning length of the short regression for best possible R2 combined, using the best number of\\n # candidates found in the First loop.\\n final_chosen_r2 = sorted_r2[:ant_kandidater_beste]\\n df_short_period = pd.DataFrame(columns=columns)\\n for short_period in range(min_weeks, max_weeks + 1, 1):\\n short_period = int(short_period)\\n output = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period, max_p,\\n final_chosen_r2, loop=True)\\n df_short_period = df_short_period.append(\\n {columns[0]: output[0], columns[1]: output[1], columns[2]: output[2], columns[3]: output[3],\\n columns[4]: output[4], columns[5]: output[5], columns[6]: output[6]}, ignore_index=True)\\n idx_max = df_short_period.r2_samlet.idxmax(skipna=True)\\n short_period_beste = int(df_short_period.short_period.values[idx_max])\\n print('Beste short_period loop 2: ', short_period_beste)\\n\\n # Getting the best input variables from loop and write to input_variables_from_tuning.txt\\n df_all_methods = pd.concat([df_ant_kandidater, df_short_period], ignore_index=True, sort=False)\\n idx_max = df_all_methods.r2_samlet.idxmax(skipna=True)\\n ant_kandidater_beste = int(df_all_methods.ant_kandidater.values[idx_max])\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n short_period_beste = df_all_methods.short_period.values[idx_max]\\n ws.write_input_variables_to_file(region, variable, max_p, ant_kandidater_beste, short_period_beste)\\n\\n print('\\\\nTuning for regionen tok %.0f minutter. \\\\n' % ((utctime_now() - start_time_loop) / 60))\\n\\n else:\\n # getting the best variables from input_variables_from_tuning.txt or input_variables_backup.txr\\n short_period_beste, max_p, ant_kandidater_beste, input_file = rs.get_input_variables_from_file(variable,region,backup)\\n chosen_r2_beste = sorted_r2[:ant_kandidater_beste]\\n print(\\\"Input variables was read from: \\\", input_file)\\n\\n # MAKE ESTIMATE AND SHOW RESULTS\\n input1 = make_estimate(df_cleaned, fasit, fasit_key, last_forecast, short_period_beste, max_p,\\n chosen_r2_beste, loop=False)\\n input2 = fasit_key, ant_kandidater_beste, max_p, reg_end, read_start\\n\\n\\n #WRITE RESULTS\\n if write:\\n # Write results from the regression to SMG.\\n fasit, long_results, short_results, df_tot, chosen_p, chosen_r2, r2_modelled, r2_modelled_long, prediction, tipping_ps, short_period, nb_weeks_tipping = input1\\n\\n # write to SMG:\\n ws.write_SMG_regresjon(variable, region, tipping_ps[-1:])\\n\\n # write to SMG, virtual:\\n ws.write_V_SMG_Regresjon(short_results, chosen_p, fasit_key, r2_modelled, r2_modelled_long, short_period_beste, MagKap)\\n\\n if jupyter:\\n ws.show_result_jupyter(input1, input2)\\n else:\\n ws.show_result(input1, input2, input_file)\\n\\n print('---------------------------------------------------------------')\\n print(' SLUTT ')\\n print('---------------------------------------------------------------')\\n print('\\\\nRegresjon for alle regioner og variabler brukte totalt %.0f minutter. \\\\n' % (\\n (utctime_now() - start_tuning) / 60))\",\n \"async def test_routine(self):\\n print('Running test routine...')\\n print('Waiting for axes to initialize...')\\n await self.robot.wait_until_initialized()\\n print('Synchronizing robot state with peripheral...')\\n await self.robot.synchronize_values()\\n print('Loading calibration data...')\\n await self.robot.load_calibrations()\\n await self.robot.go_to_alignment_hole()\\n\\n print('Starting 96-well plate test...')\\n await self.robot.go_to_96_well_plate(1, 'a')\\n await self.robot.dispense('96-well plate', 'far above')\\n for height in ['bottom', 'low', 'mid', 'high', 'top', 'above', 'far above']:\\n print('Testing with height {}...'.format(height))\\n for (row, volume) in [('a', 20), ('b', 30), ('c', 40), ('d', 50), ('e', 100)]:\\n print(\\n ' Testing precise with row {} and volume {} mL...'\\n .format(row, volume)\\n )\\n await self.test_individual_precise(row, height, volume / 1000)\\n await self.robot.dispense('96-well plate', height)\\n for (row, volume) in [\\n ('f', 100), ('g', 150), ('h', 200), ('a', 300), ('b', 400),\\n ('c', 500), ('d', 600), ('e', 700), ('g', 800), ('h', 900)\\n ]:\\n print(\\n ' Testing rough with row {} and volume {} mL...'\\n .format(row, volume / 1000)\\n )\\n await self.test_individual_rough(row, height, volume / 1000)\\n await self.robot.z.go_to_high_end_position()\\n await self.robot.y.go_to_low_end_position()\\n\\n print(batch.OUTPUT_FOOTER)\\n print('Quitting...')\",\n \"def run_condition(condition):\\n \\n # Make global changes to the dataframe \\n global STIM_comb_df\\n \\n # Loop over trials\\n for i in range(STIM_trials):\\n \\n # Clear keyboard record\\n event.clearEvents(eventType='keyboard')\\n \\n # Prepare image\\n stim_sent = STIM_comb_df['pronoun'][i] + STIM_comb_df['verb'][i] + STIM_comb_df['preposition'][i] + STIM_comb_df['location'][i] \\n\\n # Display image and monitor time\\n time_flip = core.monotonicClock.getTime() # onset of stimulus\\n for frame in range(STIM_dur):\\n stim_sentence = visual.TextStim(win=win, text=stim_sent, pos=[0,0], height=1, alignHoriz='center')\\n stim_sentence.draw()\\n win.flip()\\n \\n # Display fixation cross\\n offset = core.monotonicClock.getTime() # offset of stimulus\\n for frame in range(STIM_comb_df['delay'][i]):\\n STIM_fix.draw()\\n win.flip()\\n # Get actual duration at offset\\n \\n #Log time variables\\n STIM_comb_df['onset'][i]= time_flip - exp_start\\n STIM_comb_df['offset'][i] = offset - exp_start\\n STIM_comb_df['duration_measured'][i] = offset - time_flip \\n \\n # Check responses\\n keys = event.getKeys(keyList=('y','b','escape'), timeStamped=True) # timestamped according to core.monotonicClock.getTime() at keypress. Select the first and only answer. \\n \\n # Log first response only\\n if keys:\\n key = keys[0][0]\\n time_key = keys[0][1]\\n \\n # Log info on responses\\n STIM_comb_df['response'][i] = key\\n STIM_comb_df['key_t'][i] = time_key - exp_start\\n STIM_comb_df['rt'][i] = time_key - time_flip \\n \\n # Check if responses are correct\\n if STIM_comb_df['response'][i] == 'y':\\n STIM_comb_df['correct_resp'][i] = 1 if STIM_comb_df['verb'][i] in STIM_verbs[0:3] else 0\\n\\n if STIM_comb_df['response'][i] == 'b':\\n STIM_comb_df['correct_resp'][i] = 1 if STIM_comb_df['verb'][i] not in STIM_verbs[0:3] else 0\\n \\n # Check if escape key was pressed\\n if key in KEYS_quit:\\n STIM_comb_df.to_csv(OUTPUT_filename)\\n win.close()\\n core.quit()\",\n \"def main(um_file, ptl_file, wl_min_r=0.08, wl_max_r=50.0, wl_n_bins=22, verbose=True):\\n # Read in the UM mock catalog\\n um_mock = Table(np.load(um_file))\\n if verbose:\\n print(\\\"# Load in UM mock catalog: {}\\\".format(um_file))\\n print(\\\"# Dealing with {} galaxies\\\".format(len(um_mock)))\\n # Read in the particle table\\n sim_particles = Table(np.load(ptl_file))\\n if verbose:\\n print(\\\"# Load in particle table: {}\\\".format(ptl_file))\\n print(\\\"# Dealing with {} particles\\\".format(len(sim_particles)))\\n\\n # Output file name\\n um_pre, _ = os.path.splitext(um_file)\\n ptl_pre, _ = os.path.splitext(ptl_file)\\n n_ptl = ptl_pre.split('_')[-1]\\n precompute_out = \\\"{}_{}_r_{:4.2f}_{:4.1f}_{:2d}bins.npy\\\".format(\\n um_pre, n_ptl, wl_min_r, wl_max_r, wl_n_bins\\n )\\n if verbose:\\n print(\\\"# Output file name : {}\\\".format(precompute_out))\\n\\n # Run precompute\\n if 'smdpl' in ptl_file:\\n mass_encl = vagc.precompute_wl_smdpl(\\n um_mock, sim_particles, wl_min_r=wl_min_r, wl_max_r=wl_max_r,\\n wl_n_bins=wl_n_bins)\\n elif 'mdpl2' in ptl_file:\\n mass_encl = vagc.precompute_wl_mdpl2(\\n um_mock, sim_particles, wl_min_r=wl_min_r, wl_max_r=wl_max_r,\\n wl_n_bins=wl_n_bins)\\n else:\\n raise NameError(\\\"# Wrong simulation: [smdpl/mdpl2]\\\")\\n\\n np.save(precompute_out, mass_encl)\",\n \"def test_imsim():\\n import yaml\\n import astropy.units as u\\n import matplotlib.pyplot as plt\\n from tqdm import tqdm\\n # Need these for `eval` below\\n from numpy import array\\n import coord\\n\\n with open(DATA_DIR / \\\"wcs_466749.yaml\\\", 'r') as f:\\n wcss = yaml.safe_load(f)\\n\\n cmds = {}\\n with open(DATA_DIR / \\\"phosim_cat_466749.txt\\\", 'r') as f:\\n for line in f:\\n k, v = line.split()\\n try:\\n v = int(v)\\n except ValueError:\\n try:\\n v = float(v)\\n except ValueError:\\n pass\\n cmds[k] = v\\n\\n # Values below (and others) from phosim_cat_466749.txt\\n rc = cmds['rightascension']\\n dc = cmds['declination']\\n boresight = galsim.CelestialCoord(\\n rc*galsim.degrees,\\n dc*galsim.degrees\\n )\\n obstime = Time(cmds['mjd'], format='mjd', scale='tai')\\n obstime -= 15*u.s\\n band = \\\"ugrizy\\\"[cmds['filter']]\\n wavelength_dict = dict(\\n u=365.49,\\n g=480.03,\\n r=622.20,\\n i=754.06,\\n z=868.21,\\n y=991.66\\n )\\n wavelength = wavelength_dict[band]\\n camera = imsim.get_camera()\\n\\n rotTelPos = cmds['rottelpos'] * galsim.degrees\\n telescope = imsim.load_telescope(f\\\"LSST_{band}.yaml\\\", rotTelPos=rotTelPos)\\n # Ambient conditions\\n # These are a guess.\\n temperature = 293.\\n pressure = 69.0\\n H2O_pressure = 1.0\\n\\n # Start by constructing a refractionless factory, which we can use to\\n # cross-check some of the other values in the phosim cmd file.\\n factory = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength,\\n camera,\\n temperature=temperature,\\n pressure=0.0,\\n H2O_pressure=H2O_pressure\\n )\\n\\n aob, zob, hob, dob, rob, eo = factory._ICRF_to_observed(\\n boresight.ra.rad, boresight.dec.rad, all=True\\n )\\n np.testing.assert_allclose(\\n np.rad2deg(aob)*3600, cmds['azimuth']*3600,\\n rtol=0, atol=2.0\\n )\\n np.testing.assert_allclose(\\n (90-np.rad2deg(zob))*3600, cmds['altitude']*3600,\\n rtol=0, atol=6.0,\\n )\\n q = factory.q * galsim.radians\\n rotSkyPos = rotTelPos - q\\n # Hmmm.. Seems like we ought to be able to do better than 30 arcsec on the\\n # rotator? Maybe this is defined at a different point in time? Doesn't seem\\n # to affect the final WCS much though.\\n np.testing.assert_allclose(\\n rotSkyPos.deg*3600, cmds['rotskypos']*3600,\\n rtol=0, atol=30.0,\\n )\\n\\n # We accidentally simulated DC2 with the camera rotated 180 degrees too far.\\n # That includes the regression test data here. So to fix the WCS code, but\\n # still use the same regression data, we need to add 180 degrees here. Just\\n # rotate the camera by another 180 degrees\\n telescope = telescope.withLocallyRotatedOptic(\\n \\\"LSSTCamera\\\", batoid.RotZ(np.deg2rad(180))\\n )\\n\\n # For actual WCS check, we use a factory that _does_ know about refraction.\\n factory = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength,\\n camera,\\n temperature=temperature,\\n pressure=pressure,\\n H2O_pressure=H2O_pressure\\n )\\n\\n do_plot = False\\n my_centers = []\\n imsim_centers = []\\n if do_plot:\\n _, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 12))\\n i = 0\\n r1 = []\\n d1 = []\\n r2 = []\\n d2 = []\\n rng = np.random.default_rng(1234)\\n for k, v in tqdm(wcss.items()):\\n name = k[18:25].replace('-', '_')\\n det = camera[name]\\n cpix = det.getCenter(cameraGeom.PIXELS)\\n\\n wcs = factory.getWCS(det, order=2)\\n wcs1 = eval(v)\\n # Need to adjust ab parameters to new GalSim convention\\n wcs1.ab[0,1,0] = 1.0\\n wcs1.ab[1,0,1] = 1.0\\n\\n my_centers.append(wcs.posToWorld(galsim.PositionD(cpix.x, cpix.y)))\\n imsim_centers.append(wcs1.posToWorld(galsim.PositionD(cpix.x, cpix.y)))\\n\\n corners = det.getCorners(cameraGeom.PIXELS)\\n xs = np.array([corner.x for corner in corners])\\n ys = np.array([corner.y for corner in corners])\\n ra1, dec1 = wcs.xyToradec(xs, ys, units='radians')\\n ra2, dec2 = wcs1.xyToradec(xs, ys, units='radians')\\n if i == 0:\\n labels = ['batoid', 'PhoSim']\\n else:\\n labels = [None]*2\\n if do_plot:\\n ax.plot(ra1, dec1, c='r', label=labels[0])\\n ax.plot(ra2, dec2, c='b', label=labels[1])\\n\\n # add corners to ra/dec check lists\\n r1.extend(ra1)\\n d1.extend(dec1)\\n r2.extend(ra2)\\n d2.extend(dec2)\\n # Add some random points as well\\n xs = rng.uniform(0, 4000, 100)\\n ys = rng.uniform(0, 4000, 100)\\n ra1, dec1 = wcs.xyToradec(xs, ys, units='radians')\\n ra2, dec2 = wcs1.xyToradec(xs, ys, units='radians')\\n r1.extend(ra1)\\n d1.extend(dec1)\\n r2.extend(ra2)\\n d2.extend(dec2)\\n i += 1\\n\\n if do_plot:\\n ax.legend()\\n xlim = ax.get_xlim()\\n ax.set_xlim(xlim[1], xlim[0])\\n plt.show()\\n\\n dist = sphere_dist(r1, d1, r2, d2)\\n print(\\\"sphere dist mean, max, std\\\")\\n print(\\n np.rad2deg(np.mean(dist))*3600,\\n np.rad2deg(np.max(dist))*3600,\\n np.rad2deg(np.std(dist))*3600,\\n )\\n np.testing.assert_array_less(\\n np.rad2deg(np.mean(dist))*3600,\\n 5.0\\n )\\n if do_plot:\\n plt.hist(np.rad2deg(dist)*3600, bins=100)\\n plt.show()\\n\\n if do_plot:\\n r1 = np.array([c.ra.rad for c in my_centers])\\n d1 = np.array([c.dec.rad for c in my_centers])\\n r2 = np.array([c.ra.rad for c in imsim_centers])\\n d2 = np.array([c.dec.rad for c in imsim_centers])\\n cd = np.cos(np.deg2rad(cmds['declination']))\\n q = plt.quiver(r1, d1, np.rad2deg(r1-r2)*3600*cd, np.rad2deg(d1-d2)*3600)\\n plt.quiverkey(q, 0.5, 1.1, 5.0, \\\"5 arcsec\\\", labelpos='E')\\n plt.show()\",\n \"def temp_mainloop(self, cycles_to_respond=1000):\\n\\n if self.com_port_select.count() > 0:\\n report = \\\"Begin measuring\\\"\\n self.go_button.setEnabled(False) # don't allow to create new threads\\n self.state_temp_loop(True)\\n self.append_msg_box(report)\\n\\n while self.temp_loop_actiator:\\n for num in range(self.com_port_select.count()):\\n # self.MainWindow_exec_thread(lambda: self.sample_devices(cycles_to_respond, num))\\n self.sample_devices(cycles_to_respond, num)\\n self.temp_repr.plot_tc_temp(self.temp_log)\\n timeo = float(self.timeout.text()) # timeout\\n sleep(timeo)\\n # -------- record data once in msg box --------\\n\\n self.go_button.setEnabled(True)\\n self.led_light.setPixmap(QtGui.QPixmap(img[\\\"red_led\\\"]))\\n else:\\n self.append_msg_box(\\\"No device is connected\\\")\",\n \"def main():\\n NAME = os.path.basename(__file__).split(\\\".\\\")[0]\\n\\n # Pass/fail thresholds\\n MIN_AVG_FRAME_DELTA = 30 # at least 30ms delta between frames\\n MAX_VAR_FRAME_DELTA = 0.01 # variance of frame deltas\\n MAX_FRAME_DELTA_JITTER = 0.3 # max ms gap from the average frame delta\\n\\n with its.device.ItsSession() as cam:\\n props = cam.get_camera_properties()\\n if not its.caps.manual_sensor(props):\\n print \\\"Test skipped\\\"\\n return\\n\\n req, fmt = its.objects.get_fastest_manual_capture_settings(props)\\n caps = cam.do_capture([req]*50, [fmt])\\n\\n # Print out the millisecond delta between the start of each exposure\\n tstamps = [c['metadata']['android.sensor.timestamp'] for c in caps]\\n deltas = [tstamps[i]-tstamps[i-1] for i in range(1,len(tstamps))]\\n deltas_ms = [d/1000000.0 for d in deltas]\\n avg = sum(deltas_ms) / len(deltas_ms)\\n var = sum([d*d for d in deltas_ms]) / len(deltas_ms) - avg * avg\\n range0 = min(deltas_ms) - avg\\n range1 = max(deltas_ms) - avg\\n print \\\"Average:\\\", avg\\n print \\\"Variance:\\\", var\\n print \\\"Jitter range:\\\", range0, \\\"to\\\", range1\\n\\n # Draw a plot.\\n pylab.plot(range(len(deltas_ms)), deltas_ms)\\n matplotlib.pyplot.savefig(\\\"%s_deltas.png\\\" % (NAME))\\n\\n # Test for pass/fail.\\n assert(avg > MIN_AVG_FRAME_DELTA)\\n assert(var < MAX_VAR_FRAME_DELTA)\\n assert(abs(range0) < MAX_FRAME_DELTA_JITTER)\\n assert(abs(range1) < MAX_FRAME_DELTA_JITTER)\",\n \"def calculation_time_analysis():\\n\\tfrom . import spectra as sp\\n\\tp_dict = {'Bfield':700,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':0*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\\n\\tchiL,chiR,chiZ = sp.calc_chi([-3500],p_dict)\\n\\t\\n\\tfor angle in [0, np.pi/32, np.pi/16, np.pi/8, np.pi/4, np.pi/2]:\\n\\t\\tprint(('Angle (degrees): ',angle*180/np.pi))\\n\\t\\tRotMat, n1, n2 = solve_diel(chiL,chiR,chiZ,angle)\",\n \"def mcapi_playback(name_interface):\\n device = Solo12(name_interface, dt=DT)\\n qc = QualisysClient(ip=\\\"140.93.16.160\\\", body_id=0) # QualisysClient\\n logger = Logger(device, qualisys=qc) # Logger object\\n nb_motors = device.nb_motors\\n\\n # Default position after calibration\\n q_init = np.array([0.0, 0.8, -1.6, 0, 0.8, -1.6, 0, -0.8, 1.6, 0, -0.8, 1.6])\\n\\n # Calibrate encoders\\n device.Init(calibrateEncoders=True, q_init=q_init)\\n\\n # Wait for Enter input before starting the control loop\\n put_on_the_floor(device, q_init)\\n\\n # CONTROL LOOP ***************************************************\\n t = 0.0\\n t_max = t_switch[-1]\\n\\n # Parameters of the PD controller\\n KP = 2.\\n KD = 0.05\\n tau_max = 5. * np.ones(12)\\n\\n while ((not device.hardware.IsTimeout()) and (t < t_max)):\\n\\n device.UpdateMeasurment() # Retrieve data from IMU and Motion capture\\n\\n # Desired position and velocity for this loop and resulting torques\\n q_desired, v_desired = demo_solo12(t)\\n pos_error = q_desired.ravel() - device.q_mes.ravel()\\n vel_error = v_desired.ravel() - device.v_mes.ravel()\\n tau = KP * pos_error + KD * vel_error\\n tau = np.maximum(np.minimum(tau, tau_max), -tau_max)\\n\\n # Set desired torques for the actuators\\n device.SetDesiredJointTorque(tau)\\n\\n # Call logger\\n # logger.sample(device, qualisys=qc)\\n\\n # Send command to the robot\\n device.SendCommand(WaitEndOfCycle=True)\\n if ((device.cpt % 100) == 0):\\n device.Print()\\n\\n t += DT\\n\\n # ****************************************************************\\n\\n # Whatever happened we send 0 torques to the motors.\\n device.SetDesiredJointTorque([0]*nb_motors)\\n device.SendCommand(WaitEndOfCycle=True)\\n\\n if device.hardware.IsTimeout():\\n print(\\\"Masterboard timeout detected.\\\")\\n print(\\\"Either the masterboard has been shut down or there has been a connection issue with the cable/wifi.\\\")\\n device.hardware.Stop() # Shut down the interface between the computer and the master board\\n\\n # Save the logs of the Logger object\\n # logger.saveAll()\",\n \"def lammps(parameter, iteration, numberlist, density, chainlength):\\n # input parameters\\n var = parameter[0]\\n x = var * numberlist[0]\\n y = var * numberlist[1] * 1.732 / 2\\n z = x / 2\\n #nodeid = 55 + iteration % 5\\n M = int((x * y * z * density - 4 * numberlist[0] * numberlist[1] * int(z / 0.3)) / chainlength) \\n os.environ['var'] = str(var)\\n os.environ['iteration'] = str(iteration)\\n os.environ['M'] = str(M)\\n #os.environ['nodeid'] = str(nodeid)\\n # copy the files to modify\\n os.system(\\\"cp in.asymmetric in.asymmetric_$iteration\\\")\\n os.system(\\\"cp run_lammps.sh run_lammps_$iteration.sh\\\")\\n os.system(\\\"cp MultiBCP_hexagonal_post.py MultiBCP_hexagonal_post_$iteration.py\\\")\\n # modify relevant files\\n os.system('sed -i -e \\\"s/distance = 12/distance = $var/\\\" MultiBCP_hexagonal_post_$iteration.py')\\n os.system(\\\"python MultiBCP_hexagonal_post_$iteration.py\\\")\\n time.sleep(3)\\n os.system('sed -i -e \\\"s/3072/$M/\\\" in.asymmetric_$iteration')#modify according to the parameters\\n os.system('sed -i -e \\\"s/XN_100/XN_100_$iteration/\\\" in.asymmetric_$iteration')\\n os.system('sed -i -e \\\"s/finalasymmetric/finalasymmetric_$iteration/\\\" in.asymmetric_$iteration')\\n os.system('sed -i -e \\\"s/POSTA2B8/POSTA2B8_$iteration/\\\" run_lammps_$iteration.sh')\\n os.system('sed -i -e \\\"s/posta2b8.out/posta2b8_$iteration.out/\\\" run_lammps_$iteration.sh')\\n #os.system('sed -i -e \\\"s/node55/node$nodeid/\\\" run_lammps_$iteration.sh')\\n os.system('sed -i -e \\\"s/in.asymmetric/in.asymmetric_$iteration/\\\" run_lammps_$iteration.sh')\\n # run the simulation\\n os.system(\\\"sbatch run_lammps_$iteration.sh\\\")\\n return None\",\n \"def investigate4DRepeatability():\\n parentdir = '/home/rallured/Dropbox/Interferometer/SolarBFlat/Repeatability/'\\n avgs = [1,2,4,8,16,32]\\n\\n #Temporal with fringes tilted\\n fn = glob.glob(parentdir+'Tilt/17*RepeatabilityTiltTemporal*.bin')\\n fn.sort()\\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\\n d = np.array([met.readFlatScript(fi.split('.')[0])[0] for fi in fn])\\n #Make progressive averaging plot\\n plt.figure('TemporalTiltedFigure')\\n for i in np.arange(6)*2:\\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\\n plt.legend(loc='lower left')\\n plt.title('Solar B PSD - Temporal,Tilted')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n #Get repeatability\\n reptemptilt = d[-1]-d[-2]\\n figtemptilt = d[-1]\\n\\n #Dynamic with fringes tilted\\n fn = glob.glob(parentdir+'Tilt/17*RepeatabilityTilt_*.bin')\\n fn.sort()\\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\\n d = [met.readFlatScript(fi.split('.')[0])[0] for fi in fn]\\n #Make progressive averaging plot\\n plt.figure('DynamicTiltedFigure')\\n for i in np.arange(6)*2:\\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\\n plt.legend(loc='lower left')\\n plt.title('Solar B PSD - Dynamic,Tilted')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n #Get repeatability\\n repdyntilt = d[-1]-d[-2]\\n figdyntilt = d[-1]\\n \\n #Temporal with fringes nulled\\n fn = glob.glob(parentdir+'Nulled/17*.bin')\\n fn.sort()\\n dx = met.readFlatScript(fn[0].split('.')[0])[1]\\n d = np.array([met.readFlatScript(fi.split('.')[0])[0] for fi in fn])\\n #Make progressive averaging plot\\n plt.figure('TemporalNulledFigure')\\n for i in np.arange(6)*2:\\n f,p = fourier.meanPSD(d[i],win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label=str(avgs[i/2]))\\n plt.legend(loc='lower left')\\n plt.title('Solar B PSD - Temporal,Nulled')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n #Get repeatability\\n reptempnull = d[-1]-d[-2]\\n figtempnull = d[-1]\\n \\n #Dynamic with fringes nulled\\n d = pyfits.getdata('/home/rallured/Dropbox/Interferometer/'\\n 'SolarBFlat/Repeatability/'\\n 'Nulled/170103_Processed.fits')\\n rep = np.array([d[i,0]-d[i,1] for i in range(32)])\\n #Make progressive averaging plot\\n plt.figure('DynamicNulledFigure')\\n for i in [0,1,3,7,15,31]:\\n f,p = fourier.meanPSD(d[i,0],win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label=str(i+1))\\n plt.legend(loc='lower left')\\n plt.title('Solar B PSD - Dynamic,Nulled')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n #Get repeatability\\n repdynnull = d[-1][0]-d[-1][1]\\n figdynnull = d[-1][0]\\n\\n #Make comparative repeatability plots with 32 averages\\n plt.figure('CompareRepeatability')\\n f,p = fourier.meanPSD(repdynnull,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Dynamic,Nulled')\\n f,p = fourier.meanPSD(repdyntilt,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Dynamic,Tilted')\\n f,p = fourier.meanPSD(reptemptilt,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Temporal,Tilted')\\n f,p = fourier.meanPSD(reptempnull,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Temporal,Nulled')\\n plt.legend(loc='lower left')\\n plt.title('Solar B Repeatability - 32 Averages')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n\\n #Make comparative figure plots with 32 averages\\n plt.figure('CompareFigure')\\n f,p = fourier.meanPSD(figdynnull,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Dynamic,Nulled')\\n f,p = fourier.meanPSD(figdyntilt,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Dynamic,Tilted')\\n f,p = fourier.meanPSD(figtemptilt,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Temporal,Tilted')\\n f,p = fourier.meanPSD(figtempnull,win=np.hanning,dx=dx,irregular=True,\\\\\\n minpx=200)\\n plt.loglog(f,p/f[0],label='Temporal,Nulled')\\n plt.legend(loc='lower left')\\n plt.title('Solar B Figure - 32 Averages')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n plt.grid()\\n\\n #Make parroting repeatability plots\\n fig = plt.figure('Parroting')\\n fig.add_subplot(2,2,1)\\n plt.imshow(repdyntilt)\\n plt.title('Dynamic Repeatability')\\n plt.colorbar()\\n fig.add_subplot(2,2,2)\\n plt.imshow(reptemptilt)\\n plt.title('Temporal Repeatability')\\n plt.colorbar()\\n fig.add_subplot(2,2,3)\\n res = legendre2d(repdyntilt,xo=3,yo=3)[0]\\n plt.imshow(repdyntilt-res)\\n plt.title('Dynamic Repeatability Filtered')\\n plt.colorbar()\\n fig.add_subplot(2,2,4)\\n res = legendre2d(reptemptilt,xo=3,yo=3)[0]\\n plt.imshow(reptemptilt-res)\\n plt.title('Temporal Repeatability Filtered')\\n plt.colorbar()\",\n \"def main():\\n\\n \\\"\\\"\\\"\\n Initialize Parameters\\n \\\"\\\"\\\"\\n src_path_map = '../data/map/wean.dat'\\n src_path_log = '../data/log/robotdata1.log'\\n\\n map_obj = MapReader(src_path_map)\\n occupancy_map = map_obj.get_map()\\n logfile = open(src_path_log, 'r')\\n\\n motion_model = MotionModel()\\n params = {\\n 'z_max': 8000,\\n 'lambda_short': 0.1,\\n 'sigma_hit': 20,\\n\\n 'z_pHit': 0.95,\\n 'z_pShort': 0.01,\\n 'z_pMax': 0.05,\\n 'z_pRand': 0.05,\\n\\n 'laser_sensor_offset': 25.0,\\n 'ray_step_size': 2,\\n 'grid_size': 10,\\n 'occ_thrsh': 0.1,\\n 'laser_subsample': 30,\\n\\n 'rayCast_vis': False,\\n 'map_vis': True\\n }\\n sensor_model = SensorModel(occupancy_map, params)\\n resampler = Resampling()\\n\\n num_particles = 1500\\n X_bar = init_particles_freespace(num_particles, occupancy_map)\\n vis_flag = 1\\n\\n \\\"\\\"\\\"\\n Monte Carlo Localization Algorithm : Main Loop\\n \\\"\\\"\\\"\\n if vis_flag:\\n visualize_map(occupancy_map)\\n\\n first_time_idx = True\\n for time_idx, line in enumerate(logfile):\\n\\n # Read a single 'line' from the log file (can be either odometry or laser measurement)\\n meas_type = line[0] # L : laser scan measurement, O : odometry measurement\\n meas_vals = np.fromstring(line[2:], dtype=np.float64,\\n sep=' ') # convert measurement values from string to double\\n\\n odometry_robot = meas_vals[0:3] # odometry reading [x, y, theta] in odometry frame\\n time_stamp = meas_vals[-1]\\n\\n # if ((time_stamp <= 0.0) | (meas_type == \\\"O\\\")): # ignore pure odometry measurements for now (faster debugging) \\n # continue\\n\\n if (meas_type == \\\"L\\\"):\\n odometry_laser = meas_vals[3:6] # [x, y, theta] coordinates of laser in odometry frame\\n ranges = meas_vals[6:-1] # 180 range measurement values from single laser scan\\n\\n print(\\\"Processing time step \\\" + str(time_idx) + \\\" at time \\\" + str(time_stamp) + \\\"s\\\")\\n\\n if (first_time_idx):\\n u_t0 = odometry_robot\\n first_time_idx = False\\n continue\\n\\n X_bar_new = np.zeros((num_particles, 4), dtype=np.float64)\\n u_t1 = odometry_robot\\n for m in range(0, num_particles):\\n\\n \\\"\\\"\\\"\\n MOTION MODEL\\n \\\"\\\"\\\"\\n x_t0 = X_bar[m, 0:3]\\n x_t1 = motion_model.update(u_t0, u_t1, x_t0)\\n\\n \\\"\\\"\\\"\\n SENSOR MODEL\\n \\\"\\\"\\\"\\n if (meas_type == \\\"L\\\"):\\n z_t = ranges\\n w_t = sensor_model.beam_range_finder_model(z_t, x_t1)\\n # w_t = 1/num_particles\\n X_bar_new[m, :] = np.hstack((x_t1, w_t))\\n else:\\n X_bar_new[m, :] = np.hstack((x_t1, X_bar[m, 3]))\\n\\n X_bar = X_bar_new\\n u_t0 = u_t1\\n\\n \\\"\\\"\\\"\\n RESAMPLING\\n \\\"\\\"\\\"\\n X_bar = resampler.low_variance_sampler(X_bar)\\n if vis_flag:\\n visualize_timestep(X_bar)\",\n \"def run(self):\\n\\n try:\\n while not self._exit:\\n # Update the time varying value of the output.\\n\\n # The current time offset\\n cur_time_offset = time.time() % self._time_period\\n\\n self._updating = True\\n\\n # If modulation is selected as FM\\n if (self._mod_type == 2):\\n # Getting the modulating input\\n m_t = self.mod_ip[0].voltage - self.mod_ip[1].voltage\\n\\n freq = self._frequency + m_t\\n if freq != 0:\\n time_p = 1 / freq\\n\\n else:\\n time_p = float(\\\"inf\\\")\\n\\n else:\\n freq = self._frequency\\n time_p = self._time_period\\n\\n # If sine wave\\n if (self.type == 0):\\n self._last_updated_time = cur_time_offset\\n voltage = 0.5 * math.sin(\\n 2 * 3.145926 * freq * cur_time_offset) + 0.5\\n\\n # If square wave\\n elif (self.type == 1 or self.type == 4):\\n self._last_updated_time = cur_time_offset\\n voltage = 1 if (\\n (cur_time_offset) < time_p /\\n float(2)) else 0\\n\\n # If Ramp\\n elif (self.type == 2):\\n self._last_updated_time = cur_time_offset\\n voltage = cur_time_offset / time_p\\n\\n # If triangular\\n else:\\n self._last_updated_time = cur_time_offset\\n voltage = 2 * cur_time_offset / time_p if (\\n (cur_time_offset) < time_p /\\n float(2)) else (2 * (time_p - cur_time_offset) / time_p)\\n\\n if (self._mod_type == 1):\\n m_t = self.mod_ip[0].voltage - self.mod_ip[1].voltage\\n c_t = voltage * self._amplitude + self.offset\\n voltage = (1 + m_t) * c_t\\n voltage /= self._amplitude\\n\\n if (self.type != 4):\\n voltage *= self._amplitude\\n\\n else:\\n voltage *= 5.0 # TTL amplitude is constant at 5v\\n\\n self.outputs[0].voltage = voltage\\n self.outputs[1].voltage = -self.offset\\n\\n self._updating = False\\n time.sleep(self._sampling_time_interval)\\n\\n except Exception as e:\\n return\",\n \"def runWavelengthDependency():\\n RunData([getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/')[0],], out='I600nmwave',\\n wavelength='l600')\\n RunData([getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/')[0],], out='I700nmwave',\\n wavelength='l700')\\n RunData([getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/')[0],], out='I800nmwave',\\n wavelength='l800')\\n RunData([getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/')[4],], out='I890nmwave',\\n wavelength='l890')\",\n \"def test_readme():\\n\\n import torch\\n from lettuce import BGKCollision, StandardStreaming, Lattice, D2Q9, TaylorGreenVortex2D, Simulation\\n\\n device = \\\"cpu\\\"\\n dtype = torch.float32\\n\\n lattice = Lattice(D2Q9, device, dtype)\\n flow = TaylorGreenVortex2D(resolution=256, reynolds_number=10, mach_number=0.05, lattice=lattice)\\n collision = BGKCollision(lattice, tau=flow.units.relaxation_parameter_lu)\\n streaming = StandardStreaming(lattice)\\n simulation = Simulation(flow, lattice, collision, streaming)\\n mlups = simulation.step(num_steps=1)\\n\\n print(\\\"Performance in MLUPS:\\\", mlups)\",\n \"def main():\\n #diffuser_data_dir = r'F:\\\\TEMPO\\\\Data\\\\GroundTest\\\\FPS\\\\Spectrometer\\\\Diffuser_Irradiance\\\\saved_quads\\\\3845_ms\\\\saved_plots_modified'\\n #diffuser_light_data = os.path.join(diffuser_data_dir,'Light_data')\\n #diffuser_dark_data = os.path.join(diffuser_data_dir, 'Dark_data')\\n #print(diffuser_data_dir)\\n #cc\\n #int_time_diffuser = 3845.0\\n\\n# radiance_data_dir_UV = r'F:\\\\TEMPO\\\\Data\\\\GroundTest\\\\FPS\\\\Spectrometer\\\\Radiance_Cal_UV_Lamp\\\\saved_quads\\\\saved_plots_modified'\\n# radiance_light_data_UV = os.path.join(radiance_data_dir_UV,'DSS-Y')\\n# radiance_dark_data_UV = os.path.join(radiance_data_dir_UV, 'Dark_data')\\n## #int_time_radiance = 93.0\\n## print(radiance_data_dir_UV)\\n##\\n radiance_data_dir_VIS = r'F:\\\\TEMPO\\\\Data\\\\GroundTest\\\\FPS\\\\Spectrometer\\\\Radiance_Cal_UV_Lamp\\\\saved_quads\\\\processed_h5'\\n radiance_light_data_VISminusY = os.path.join(radiance_data_dir_VIS,'DSS-Y')\\n radiance_light_data_VIS_Center = os.path.join(radiance_data_dir_VIS,'DSS_Center')\\n radiance_light_data_VISplusY = os.path.join(radiance_data_dir_VIS,'DSS+Y') \\n radiance_dark_data_VIS = os.path.join(radiance_data_dir_VIS, 'Dark_Data')\\n\\n\\n\\n # mean_diffuser_data = calculate_mean(diffuser_light_data)\\n #mean_diffuser_dark_data = calculate_mean(diffuser_dark_data)\\n#\\n#\\n# #Let's correct for dark current and work in signal rates unit\\n# diffuser_dc_corrected = (mean_diffuser_data - mean_diffuser_dark_data)\\n# diffuser_dc_corrected = np.round(diffuser_dc_corrected, 2)\\n# diffuser_dc_corrected[diffuser_dc_corrected <0] = 0\\n# mean_save_dir = os.path.join(diffuser_data_dir,'processed_average_data')\\n# mean_save_irradiance = os.path.join(mean_save_dir, 'mean_irradiance_3845ms.csv')\\n# np.savetxt(mean_save_irradiance, diffuser_dc_corrected, fmt='%1.3f', delimiter=\\\",\\\")\\n \\n\\n\\n\\n# mean_radiance_data_UV = calculate_mean(radiance_light_data_UV)\\n# mean_radiance_dark_data_UV = calculate_mean(radiance_dark_data_UV)\\n# radiance_dc_corrected_UV = (mean_radiance_data_UV - mean_radiance_dark_data_UV)\\n# radiance_dc_corrected_UV = np.round(radiance_dc_corrected_UV, 2)\\n# radiance_dc_corrected_UV[radiance_dc_corrected_UV < 0] = 0\\n###\\n mean_radiance_dark_data_VIS = calculate_mean_dark(radiance_dark_data_VIS)\\n \\n \\n # Correct for Dark current\\n mean_radiance_data_VISminusY = calculate_mean(radiance_light_data_VISminusY) - mean_radiance_dark_data_VIS\\n mean_radiance_data_VIS_Center = calculate_mean(radiance_light_data_VIS_Center)- mean_radiance_dark_data_VIS\\n mean_radiance_data_VISplusY = calculate_mean(radiance_light_data_VISplusY) - mean_radiance_dark_data_VIS\\n \\n \\n #radiance_dc_corrected_VIS[radiance_dc_corrected_VIS < 0] = 0\\n#\\n# \\n\\n# \\n# mean_save_dir_UV = os.path.join(radiance_data_dir_UV,'processed_average_data')\\n mean_save_dir_VIS = os.path.join(radiance_data_dir_VIS,'Mean_Processed_Data')\\n if not os.path.exists(mean_save_dir_VIS):\\n os.makedirs(mean_save_dir_VIS)\\n #\\n# mean_save_radiance_UV = os.path.join(mean_save_dir_UV, 'mean_radiance_DSSminus_UV.csv')\\n# mean_save_radiance_VIS = os.path.join(mean_save_dir_VIS, 'mean_radiance_DSSminus_VIS.csv')\\n# #\\n# \\n# np.savetxt(mean_save_radiance_UV, radiance_dc_corrected_UV, fmt='%1.3f', delimiter=\\\",\\\")\\n# np.savetxt(mean_save_radiance_VIS, radiance_dc_corrected_VIS, fmt='%1.3f', delimiter=\\\",\\\")\\n print('DONE')\\n\\n #Write into h5file\\n hf_name = os.path.join(mean_save_dir_VIS,'Mean_Data.h5')\\n hf = h5py.File(hf_name,'w')\\n hf.create_dataset('DSS-Y', data= mean_radiance_data_VISminusY)\\n hf.create_dataset('DSS_Center', data=mean_radiance_data_VIS_Center)\\n hf.create_dataset('DSS+Y', data= mean_radiance_data_VISplusY)\",\n \"def run_main(sst, ft_qv, use_NT):\\n\\n dtout=10. #minutes\\n end_time=8*24. #hours\\n del_time=dtout*60. #seconds\\n end_time=end_time*3600. #seconds\\n #sst=297\\n D=5.e-6 #s-1\\n U=7 #m/s\\n psfc=100. #kPa\\n qsfc=tf.qs_tp(sst,psfc)\\n ft_intercept = 292 #K\\n ft_gamma = 6.e-3 #K/m\\n #ft_qv = 2.e-3\\n k=0.2 #entrainment efficiency\\n Cd = 1.e-3 #drag coefficient\\n tspan = np.arange(0.,end_time,del_time)\\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\\n the_tup=make_tuple(the_tup,'coeffs')\\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\\n\\n # save time/computation by only doing calculations for the last timestep (equilibrium)\\n result['time']=tspan[-1]/3600./24. #days\\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\\n \\n # decide how to calculate entrainment\\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\\n if use_NT:\\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \\n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\\n else:\\n result['went']=calc_went(result.iloc[-1],the_tup)\\n\\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\\n\\n with open('dumpmodel.csv','w') as f:\\n result.to_csv(f,index=False)\\n \\n return None\",\n \"def run(self, speed, target_speed, power, alpha=0.9):\\n\\n # Setup tachometer pulse callback function\\n GPIO.setwarnings(False)\\n GPIO.setmode(GPIO.BOARD)\\n GPIO.setup(self._pin, GPIO.IN, pull_up_down=GPIO.PUD_UP) # todo remove when hw pull-up implemented\\n GPIO.add_event_detect(self._pin, GPIO.RISING, callback=self._cb_new_pulse)\\n\\n # init PI values\\n t_last = time.perf_counter()\\n self._t_last_count = t_last\\n I = 0\\n s = 0\\n while self._run:\\n # PI control\\n new_s = self._get_speed() # Get car speed\\n s = alpha * new_s + (1 - alpha) * s # Smooth speed reading\\n speed.value = s\\n err = target_speed.value - s\\n t_now = time.perf_counter()\\n delta_t = t_now - t_last\\n # calculate integral part\\n I += err * delta_t\\n I = np.clip(I, -self._windup_guard_val, self._windup_guard_val) # prevent integration windup\\n\\n #print('E: {}, P: {}, I: {}'.format(round(err, 2), int(self._kp * err), int(self._ki * I)))\\n\\n # Calculate power output and constrain to [-100, 100]%\\n pwr = np.clip(int(self._kp * err + self._ki * I), -100, 100)\\n pwr = self._fault_guard(pwr, timeout=1, safemode_power=50) # Check if safe to give power.\\n power.value = pwr\\n t_last = t_now\\n\\n # sleep for reminder of sampling time.\\n t_sleep = float(np.clip((2 * self._sample_interval - delta_t), 0, self._sample_interval))\\n time.sleep(t_sleep)\",\n \"def main():\\n\\n\\t# eesAmplitudes = range(200,321,10)\\n\\teesAmplitudes = [\\\"%\\\"+\\\"%.2f_0_0\\\"%(i) for i in np.arange(0,1.01,.05)]\\n\\t# eesFrequencies = range(10,1001,20)\\n\\teesFrequencies = np.logspace(1,3,50)\\n\\t# nrnStructureFile = \\\"fsSFrFfMnArtMod.txt\\\"\\n\\t# nrnStructureFile = \\\"fsSFrFfMnArtModHuman.txt\\\"\\n\\tnrnStructureFile = \\\"fsMnArtModHuman.txt\\\"\\n\\t# name = \\\"FreqAmpModHuman_0367S\\\"\\n\\tname = \\\"FreqAmpModHuman_ArtmodHuman_10msBurst\\\"\\n\\n\\tnSim = len(eesFrequencies)*len(eesAmplitudes)\\n\\tcount=0.\\n\\tpercLastPrint=0.\\n\\tprintPeriod = 0.05\\n\\t# simTime = 250\\n\\tsimTime = 15\\n\\tspecies = \\\"human\\\"\\n\\n\\tfor eesAmplitude in eesAmplitudes:\\n\\t\\tfor eesFrequency in eesFrequencies:\\n\\t\\t\\tfilName = name+\\\"_amp_\\\"+str(eesAmplitude)+\\\"_freq_\\\"+str(eesFrequency)\\n\\t\\t\\tresultFile = gt.find(\\\"*\\\"+filName+\\\".p\\\",pathToResults)\\n\\t\\t\\tif not resultFile:\\n\\t\\t\\t\\treturnCode = None\\n\\t\\t\\t\\twhile not returnCode==0:\\n\\t\\t\\t\\t\\tprogram = ['python','scripts/computeAfferentsEfferentsModulation.py',\\n\\t\\t\\t\\t\\t\\tstr(eesFrequency),str(eesAmplitude),species,nrnStructureFile,name,\\\"--simTime\\\",str(simTime)]\\n\\t\\t\\t\\t\\tprint \\\" \\\".join(program)\\n\\t\\t\\t\\t\\tforwardSimulation = subprocess.Popen(program, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t\\t\\t\\t\\treturnCode = None\\n\\t\\t\\t\\t\\twhile returnCode is None:\\n\\t\\t\\t\\t\\t\\tmessage = forwardSimulation.stdout.readline().rstrip(\\\"\\\\n\\\").split()\\n\\t\\t\\t\\t\\t\\tif message != None:print \\\"\\\\t\\\\t\\\"+\\\" \\\".join(message)+\\\"\\\\t\\\\t\\\"\\n\\t\\t\\t\\t\\t\\treturnCode = forwardSimulation.poll()\\n\\t\\t\\t\\t\\tif returnCode != 0: print \\\"\\\\t\\\\t\\\\t\\\\t Error n: \\\",forwardSimulation.poll(),\\\" resetting simulation...\\\"\\n\\t\\t\\tcount+=1\\n\\t\\t\\tif count/nSim-percLastPrint>=printPeriod:\\n\\t\\t\\t\\tpercLastPrint=count/nSim\\n\\t\\t\\t\\tprint str(round(count/nSim*100))+\\\"% of simulations performed...\\\"\\n\\tplot_stats(eesAmplitudes,eesFrequencies,simTime,name)\",\n \"def qc_sample_mip(args):\\n clarity_epp.qc.sample.set_mip_data_ready(lims, args.process_id)\",\n \"def test_SetMultipleMovingLoadsWithVelocityFunction(self):\\n\\n # create nodes\\n second_coord = [1, 0, 0.0]\\n self.mp.CreateNewNode(1, 0.0, 0.0, 0.0)\\n self.mp.CreateNewNode(2, second_coord[0], second_coord[1], 0.0)\\n\\n # create condition\\n self.mp.CreateNewCondition(\\\"MovingLoadCondition2D2N\\\", 1, [1,2], self.mp.GetProperties()[1])\\n\\n parameters = self.base_parameters\\n parameters[\\\"velocity\\\"]=KratosMultiphysics.Parameters(self.time_dependent_velocity)\\n\\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.TIME, 0)\\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.DELTA_TIME, 0.25)\\n\\n process = GMA.SetMultipleMovingLoadsProcess(self.mp, parameters)\\n cond = self.cmp.GetCondition(2)\\n\\n # initialise and set load\\n process.ExecuteInitialize()\\n process.ExecuteInitializeSolutionStep()\\n\\n # initialise matrices\\n lhs = KratosMultiphysics.Matrix(0, 0)\\n rhs = KratosMultiphysics.Vector(0)\\n\\n # set load on node\\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\\n\\n self.checkRHS(rhs, [0.0, -2.0, 0.0, 0.0])\\n\\n # change time and recalculate load\\n process.ExecuteFinalizeSolutionStep()\\n process.ExecuteInitializeSolutionStep()\\n\\n # check if interpolation is done correctly\\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\\n\\n self.checkRHS(rhs, [0.0, -2.0, 0.0, 0.0])\\n\\n self.mp.ProcessInfo.SetValue(KratosMultiphysics.TIME, 0.5)\\n\\n process.ExecuteFinalizeSolutionStep()\\n process.ExecuteInitializeSolutionStep()\\n\\n # check if interpolation is done correctly\\n cond.CalculateLocalSystem(lhs, rhs, self.mp.ProcessInfo)\\n\\n self.checkRHS(rhs, [0.0, -1.5, 0.0, -0.5])\",\n \"def run( self, cycles=-1 ):\",\n \"def _set_feedback_loop(self):\\n while True:\\n mono = self.get_mono()\\n interval = int(self.qbpm.filter / 20)\\n if self.qbpm.log_arrays['avgcurr_log'][-1] < self.feedback_threshold:\\n print('intensity too low.')\\n self._stop_loop_feedback()\\n# current_pos = self.qbpm.log_arrays['posz_filter_log'][-1]\\n current_pos = self.qbpm.log_arrays['posx_filter_log'][-1]\\n# target = self.qbpm.posz_target\\n target = self.qbpm.posx_target\\n if mono == \\\"dcm\\\":\\n corr_factor = 0.2\\n if mono == \\\"dmm\\\":\\n corr_factor = 0.2\\n bandwidth = 0.003 * float(self.qbpm.sensitivity/100)\\n if not ((target - bandwidth) < current_pos < (target + bandwidth)):\\n corr_angle = -((current_pos - target) * corr_factor)/self.qbpm.distance\\n if self.cycle == interval:\\n print('Moving pitch: {}'.format(corr_angle))\\n dcm_curr_pitchpos = self.dcm_pitch_tserver.Position\\n target_pitchpos = dcm_curr_pitchpos + corr_angle\\n if not self.simulate_feedback:\\n if mono == \\\"dcm\\\":\\n self.dcm_pitch_tserver.write_attribute('Position', target_pitchpos)\\n if mono == \\\"dmm\\\":\\n self.dmm_x2rot_tserver.write_attribute('Position', target_pitchpos)\\n self.last_corr_angle = corr_angle\\n self.feedback_time = datetime.datetime.now()\\n self.cycle = 0\\n self.cycle = 0 if self.cycle >= interval else self.cycle + 1\\n self.set_x2pitchlabel()\\n yield\",\n \"def earth_tide(theta, lamda, gtime):\\n\\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist # bpos common block\\n global h, k, l # love common block\\n h = [0.6114, 0.2891, 0.175]\\n k = [0.304, 0.09421, 0.043]\\n l = [0.0832, 0.0145, 0.0103]\\n\\n global azt, azs # azimut common block\\n global etmut # tdiff common block\\n global moon # sunny common block\\n moon = 0\\n # hardwire these - you can only send it ONE droptime\\n deltat = 1\\n NPT = 1\\n\\n temp_time = num2date(gtime)\\n\\n YY = temp_time.year\\n MO = temp_time.month\\n DD = temp_time.day\\n HH = temp_time.hour\\n MM = temp_time.minute\\n SS = temp_time.second\\n # Initialize variables\\n irl = 1\\n iflag = 0\\n ntotl = 1\\n iget = [0, 0, 0, 0, 0, 0, 0] # ' !!!\\n ispc = [0, 0, 0, 0] # ' !!!\\n ntw = [1, 0, 0] # ' !!!\\n ioptn = 't'\\n ielement = 0\\n # \\tdata statements for input and output unit numbers (on terminal I/O)\\n inun = 5\\n ioun = 6\\n nptpb = 6\\n\\n yr1 = YY - 1900\\n day1 = date2num(datetime(YY, MO, DD))\\n # \\tfind times in hours from 0 hr, 1 jan 1900\\n # matlab:\\n ts = (\\n SS / 3600\\n + MM / 60\\n + HH\\n + 24 * (day1 - 1)\\n + 8760 * yr1\\n + 24 * np.fix((yr1 - 1) / 4)\\n )\\n # python:\\n dj = date_to_julian_day(datetime(YY, MO, DD))\\n djref = date_to_julian_day(datetime(1899, 12, 31, 0, 0, 0))\\n delta_dj = (\\n dj - djref\\n ) # difference in days from current date (0hr) to 0hr, 1 jan 1900\\n delta_djhr = float(delta_dj) * 24.0 + HH - 12.0 + MM / 60.0 + SS / 3600.0\\n te = ts + (NPT - 1) * deltat / 3600\\n d = deltat / 3600\\n # terms=(te-ts)/d + 1\\n terms = NPT\\n\\n # done asking questions - begin execution\\n i = 1\\n tt = ts\\n sph(theta, lamda, 0)\\n etmut = 41.184 + yr1 - 70\\n # matlab:\\n # t = (tt+12 + (etmut/3600))/876600\\n t = (delta_djhr + etmut / 3600) / 876600\\n # t is ephemeris time in julian centuries from 12 hr 0 jan 1900\\n ephem(t)\\n\\n # calculate normalized gravity tides\\n [grav, tilt, strain, gdc] = elastd(ntw)\\n\\n gravtide = 1.0e5 * grav\\n # convert m/s² to mgal: 1m/s² = 100 gal = 100 000 mgal\\n\\n iflag = 1\\n\\n iterms = np.fix(terms)\\n i = 1\\n return gravtide\",\n \"def NAME():\\n\\n # Location of data\\n base_dir = \\\"(Location)\\\" #Location of align tif --> Should be the location of the experiment's align tiff folder, ex: \\\"C/desktop/work/image_processing/YYYYMMDD/align_tiffs\\\"\\n resolution = {'res_xy_nm': 100, 'res_z_nm': 70} #Resolution of a pixel (do not alter)\\n thresh = 0.9 #What qualifies for final probability map (do not alter)\\n number_of_datasets = 20 #Number of wells in the experiemnts, \\\"20\\\" is an example where there are 16 samples and 4 controls\\n\\n #Rb Antibody\\n conjugate_fn_str = 'GAD2' #String segment to search in a filename\\n #conjugate_fn_str should be the term used in the name of the control align tiff for a well (usually \\\"PSD\\\", \\\"GAD2\\\", or \\\"SYNAPSIN\\\")\\n target_fn_str = 'L106'\\n #Ms Antibody project name, no parent or subclone number needed\\n #target_fn_str should be the project number, for instance if this was testing L109 samples, this would be \\\"L109\\\"\\n #Takes base directory string and gives you an array of all the files within\\n filenames = aa.getListOfFolders(base_dir) #Do not change\\n conjugate_filenames = [] #Do not change\\n target_filenames = [] #Do not change\\n query_list = [] #Do not change\\n folder_names = [] #Do not change\\n\\n for n in range(1, 17):\\n #Use if dataset missing\\n #This is where you put in the rangee of wells used as your test samples\\n #Since we have 16 samples that are test samples for L106, the range is equal to 1 through n+1, or 1 through 17\\n #If your test samples do not begin at well 1, then adjust the beginning of the range accordingly (3 through 17 if the first test sample is in well 3) \\n #continue\\n\\n print('Well: ', str(n)) #Do not change\\n folder_names.append('Test-' + str(n)) # Collate 'dataset' names for excel sheet #Do not change\\n conjugate_str = str(n) + '-' + conjugate_fn_str #creates filename to search for #Creates n-conjugatename #Do not change\\n target_str = str(n) + '-' + target_fn_str #Do not change\\n\\n # Search for file associated with the specific dataset number\\n indices = [i for i, s in enumerate(filenames) if conjugate_str == s[0:len(conjugate_str)]] #Do not change\\n conjugate_name = filenames[indices[0]] #Do not change\\n print(conjugate_name) #Do not change\\n indices = [i for i, s in enumerate(filenames) if target_str == s[0:len(target_str)]] #Do not change\\n target_name = filenames[indices[0]] #Do not change\\n print(target_name) #Do not change\\n \\n conjugate_filenames.append(conjugate_name) #Do not change\\n target_filenames.append(target_name) #Do not change\\n\\n # Create query\\n #\\n query = {'preIF': [conjugate_name], 'preIF_z': [2],\\n 'postIF': [target_name], 'postIF_z': [1],\\n 'punctumSize': 2}\\n #preIF = items that are presynaptic targets go here, because GAD2, our conjugate, is presynaptic I put the conjugate_name in this box\\n #preIF_z = how many tiffs a puncta must be in to be registered, conjugate sample number is 2 so 2 goes in this box\\n #postIF = items that are postsynaptic targets go here, L106 is postsynaptic so I put target_name here\\n #postIF_z = how many tiffs a puncta must be in to be registered, target sample number is 1 (for now unless changed later) \\n #punctumSize = size of punctum the algorithm is looking for, do not change unless directed to\\n\\n \\\"\\\"\\\"Example of a presynaptic target and presynaptic conjugate\\n query = {'preIF': [target_name,conjugate_name], 'preIF_z': [1,2],\\n 'postIF': [], 'postIF_z': [],\\n 'punctumSize': 2}\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"Example of a postsynaptic target and presynaptic conjugate\\n query = {'preIF': [conjugate_name], 'preIF_z': [2],\\n 'postIF': [target_name], 'postIF_z': [1],\\n 'punctumSize': 2}\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"Example of a postsynaptic target and postsynaptic conjugate\\n query = {'preIF': [], 'preIF_z': [],\\n 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2],\\n 'punctumSize': 2}\\\"\\\"\\\"\\n\\n \\\"\\\"\\\"Example of a presynaptic target and postsynaptic conjugate\\n query = {'preIF': [target_name], 'preIF_z': [1],\\n 'postIF': [conjugate_name], 'postIF_z': [2],\\n 'punctumSize': 2}\\\"\\\"\\\"\\n\\n\\n query_list.append(query)\\n\\n\\n #The following n samples are controls - you can add as many of these as you want by copying the block of code and pasting it after the last one\\n #The notes in the following block of code apply to all of the controls\\n n = 17 #well number of control sample\\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet #Do not change\\n reference_fn_str = 'GAD2' #String segment to search in a filename #refernce_fn_str is the project number/name of RB control\\n target_fn_str = 'L106' #target_fn_str is the project number of the Ms control you are using\\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n) #Do not alter\\n conjugate_filenames.append(conjugate_name) #Do not alter\\n target_filenames.append(target_name) #Do not alter\\n query = {'preIF': [conjugate_name], 'preIF_z': [2], 'postIF': [target_name], 'postIF_z': [1], 'punctumSize': 2} #Se the examples and explanations above about \\\"query\\\"\\n query_list.append(query) #Do not change\\n\\n n = 18\\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\\n reference_fn_str = 'GAD2' #String segment to search in a filename\\n target_fn_str = 'SP2'\\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\\n conjugate_filenames.append(conjugate_name)\\n target_filenames.append(target_name)\\n query = {'preIF': [target_name,conjugate_name], 'preIF_z': [1,2], 'postIF': [], 'postIF_z': [], 'punctumSize': 2}\\n query_list.append(query)\\n\\n n = 19\\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\\n reference_fn_str = 'NP-RB' #String segment to search in a filename\\n target_fn_str = 'NP-MS'\\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\\n conjugate_filenames.append(conjugate_name)\\n target_filenames.append(target_name)\\n query = {'preIF': [], 'preIF_z': [], 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2], 'punctumSize': 2}\\n query_list.append(query)\\n\\n n = 20\\n folder_names.append('Control' + str(n)) # Collate 'dataset' names for excel sheet\\n reference_fn_str = 'NPNS-RB' #String segment to search in a filename\\n target_fn_str = 'NPNS-MS'\\n conjugate_name, target_name = aa.findFilenames(reference_fn_str, target_fn_str, filenames, n)\\n conjugate_filenames.append(conjugate_name)\\n target_filenames.append(target_name)\\n query = {'preIF': [], 'preIF_z': [], 'postIF': [target_name,conjugate_name], 'postIF_z': [1,2], 'punctumSize': 2}\\n query_list.append(query)\\n\\n\\n \\n measure_list = aa.calculate_measure_lists(query_list, None, base_dir,\\n thresh, resolution, target_filenames) # Run all the queries\\n\\n df = aa.create_df(measure_list, folder_names, target_filenames, conjugate_filenames) #Do not change\\n print(df) #Do not change\\n\\n return df #Do not change\",\n \"def makeLoop( name, *structure ):\\n return X12Loop( name, *structure )\",\n \"def extractQuantities(path, run, t0, t1):\\n data = pyLTR.Models.MIX(path, run)\\n\\n # hard-coded input for testing & debugging:\\n #data = pyLTR.Models.LFM('/hao/aim2/schmitt/data/LTR-2_0_1b/r1432/March1995/LR/single', 'LRs')\\n \\n #Make sure variables are defined in the model.\\n modelVars = data.getVarNames()\\n for v in ['Grid X', 'Grid Y', \\n 'Potential North [V]', 'Potential South [V]', \\n 'FAC North [A/m^2]', 'FAC South [A/m^2]',\\n 'Pedersen conductance North [S]', 'Pedersen conductance South [S]', \\n 'Hall conductance North [S]', 'Hall conductance South [S]', \\n 'Average energy North [keV]', 'Average energy South [keV]',\\n 'Number flux North [1/cm^2 s]', 'Number flux South [1/cm^2 s]']:\\n assert( v in modelVars )\\n\\n timeRange = data.getTimeRange()\\n if len(timeRange) == 0:\\n raise Exception(('No data files found. Are you pointing to the correct run directory?'))\\n\\n index0 = 0\\n if t0:\\n for i,t in enumerate(timeRange):\\n if t0 >= t:\\n index0 = i\\n\\n index1 = len(timeRange)-1\\n if t1:\\n for i,t in enumerate(timeRange):\\n if t1 >= t:\\n index1 = i \\n\\n print(( 'Extracting MIX quantities for time series over %d time steps.' % (index1-index0) ))\\n \\n # Output a status bar displaying how far along the computation is.\\n progress = pyLTR.StatusBar(0, index1-index0)\\n progress.start()\\n\\n t_doy = []\\n cpcpNorth = []\\n cpcpSouth = []\\n hpNorth = []\\n hpSouth = []\\n ipfacNorth = []\\n ipfacSouth = []\\n\\n # Pre-compute area of the grid.\\n x = data.read('Grid X', timeRange[index0])\\n y = data.read('Grid Y', timeRange[index0])\\n # Fix singularity at the pole\\n x[:,0] = 0.0\\n y[:,0] = 0.0\\n z = numpy.sqrt(1.0-x**2-y**2)\\n ri = 6500.0e3 # Radius of ionosphere\\n areaMixGrid = pyLTR.math.integrate.calcFaceAreas(x,y,z)*ri*ri\\n\\n for i,time in enumerate(timeRange[index0:index1]):\\n try:\\n # -- Day of Year\\n tt = time.timetuple()\\n t_doy.append(tt.tm_yday+tt.tm_hour/24.0+tt.tm_min/1440.0+tt.tm_sec/86400.0)\\n\\n # --- Cross Polar Cap Potential\\n psiNorth = data.read('Potential North [V]', time) / 1000.0\\n cpcpNorth.append(psiNorth.max() - psiNorth.min())\\n\\n psiSouth = data.read('Potential South [V]', time) / 1000.0\\n cpcpSouth.append(psiSouth.max() - psiSouth.min())\\n \\n # --- Hemispheric Power\\n energy = data.read('Average energy North [keV]', time)\\n flux = data.read('Number flux North [1/cm^2 s]', time)\\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\\n # KeV/cm^2s to mW/m^2 to GW\\n hpNorth.append(hp.sum() * 1.6e-21) \\n\\n energy = data.read('Average energy South [keV]', time)\\n flux = data.read('Number flux South [1/cm^2 s]', time)\\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\\n # KeV/cm^2s to mW/m^2 to GW\\n hpSouth.append(hp.sum() * 1.6e-21)\\n\\n # --- Positive current density\\n fac = data.read('FAC North [A/m^2]', time)\\n fac[fac <= 0] = 0.0\\n pfac = areaMixGrid * fac[:-1,:-1]\\n ipfacNorth.append(pfac.sum()/1.0e6)\\n\\n fac = data.read('FAC South [A/m^2]', time)\\n fac[fac <= 0] = 0.0\\n pfac = areaMixGrid * fac[:-1,:-1]\\n ipfacSouth.append(pfac.sum()/1.0e6)\\n\\n progress.increment()\\n except KeyboardInterrupt:\\n # Exit when the user hits CTRL+C.\\n progress.stop()\\n progress.join() \\n print('Exiting.')\\n import sys\\n sys.exit(0)\\n except:\\n # Cleanup progress bar if something bad happened.\\n progress.stop()\\n progress.join()\\n raise\\n progress.stop()\\n progress.join()\\n\\n dataNorth = pyLTR.TimeSeries()\\n dataSouth = pyLTR.TimeSeries()\\n dataNorth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\\n dataSouth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\\n dataNorth.append('doy', 'Day of Year', '', t_doy)\\n dataSouth.append('doy', 'Day of Year', '', t_doy)\\n \\n # \\\"N\\\" and \\\"S\\\" label subscripts are redundant here, potentially leading to\\n # mis-labeling of plots\\n #dataNorth.append('cpcp', r'$\\\\Phi_N$', 'kV', cpcpNorth)\\n #dataSouth.append('cpcp', r'$\\\\Phi_S$', 'kV', cpcpSouth)\\n #\\n #dataNorth.append('hp', r'$HP_N$', 'GW', hpNorth)\\n #dataSouth.append('hp', r'$HP_S$', 'GW', hpSouth)\\n #\\n #dataNorth.append('ipfac', r'$FAC_N$', 'MA', ipfacNorth)\\n #dataSouth.append('ipfac', r'$FAC_S$', 'MA', ipfacSouth)\\n \\n dataNorth.append('cpcp', r'$\\\\Phi$', 'kV', cpcpNorth)\\n dataSouth.append('cpcp', r'$\\\\Phi$', 'kV', cpcpSouth)\\n \\n dataNorth.append('hp', r'$HP$', 'GW', hpNorth)\\n dataSouth.append('hp', r'$HP$', 'GW', hpSouth)\\n \\n dataNorth.append('ipfac', r'$FAC$', 'MA', ipfacNorth)\\n dataSouth.append('ipfac', r'$FAC$', 'MA', ipfacSouth)\\n\\n return (dataNorth, dataSouth)\",\n \"def Script3():\\n # In non-script code, use getLogger(__name__) at module scope instead.\\n logger = logging.getLogger(\\\"Script3\\\") \\n gal_flux = 1.e5 # ADU\\n gal_n = 3.5 #\\n gal_re = 3.7 # pixels\\n g1 = -0.23 #\\n g2 = 0.15 #\\n atmos_a_sigma=2.1 # pixels\\n atmos_a_g1 = -0.13 # (shear for \\\"a\\\")\\n atmos_a_g2 = -0.09 #\\n atmos_fa=0.2 # (fraction of flux in \\\"a\\\")\\n atmos_b_sigma=0.9 # pixels\\n atmos_b_g1 = 0.02 # (shear for \\\"b\\\")\\n atmos_b_g2 = -0.04 #\\n opt_defocus=0.53 # wavelengths\\n opt_a1=-0.29 # wavelengths\\n opt_a2=0.12 # wavelengths\\n opt_c1=0.64 # wavelengths\\n opt_c2=-0.33 # wavelengths\\n opt_padFactor=6 # multiples of Airy padding required to avoid folding for aberrated PSFs\\n lam = 800 # nm NB: don't use lambda - that's a reserved word.\\n tel_diam = 4. # meters \\n pixel_scale = 0.23 # arcsec / pixel\\n wcs_g1 = -0.02 #\\n wcs_g2 = 0.01 #\\n sky_level = 1.e3 # ADU / pixel\\n gain = 1.7 # ADU / e-\\n read_noise = 0.3 # ADU / pixel\\n\\n logger.info('Starting script 3 using:')\\n logger.info(' - sheared (%.2f,%.2f) Sersic galaxy (flux = %.1e, n = %.1f, re = %.2f),', \\n g1, g2, gal_flux, gal_n, gal_re)\\n logger.info(' - sheared double-Gaussian atmospheric PSF')\\n logger.info(' First component: sigma = %.2f, shear = (%.2f,%.2f), frac = %.2f',\\n atmos_a_sigma, atmos_a_g1, atmos_a_g2, atmos_fa)\\n logger.info(' Second component: sigma = %.2f, shear = (%.2f,%.2f), frac = %.2f',\\n atmos_b_sigma, atmos_b_g1, atmos_b_g2, 1-atmos_fa)\\n logger.info(' - optical PSF with defocus = %.2f, astigmatism = (%.2f,%.2f),',\\n opt_defocus, opt_a1, opt_a2)\\n logger.info(' coma = (%.2f,%.2f), lambda = %.0f nm, D = %.1f m', \\n opt_c1, opt_c2, lam, tel_diam)\\n logger.info(' - pixel scale = %.2f,',pixel_scale)\\n logger.info(' - WCS distortion = (%.2f,%.2f),',wcs_g1,wcs_g2)\\n logger.info(' - Poisson noise (sky level = %.1e, gain = %.1f).',sky_level, gain)\\n logger.info(' - Gaussian read noise (sigma = %.2f).',read_noise)\\n\\n \\n # Define the galaxy profile.\\n gal = galsim.Sersic(gal_n, flux=gal_flux, re=gal_re)\\n\\n # Shear the galaxy by some value.\\n gal.applyShear(g1, g2)\\n logger.info('Made galaxy profile')\\n\\n # Define the atmospheric part of the PSF.\\n atmos_a = galsim.Gaussian(flux=atmos_fa, sigma=atmos_a_sigma)\\n atmos_a.applyShear(atmos_a_g1 , atmos_a_g2)\\n atmos_b = galsim.Gaussian(flux=1-atmos_fa, sigma=atmos_b_sigma)\\n atmos_b.applyShear(atmos_b_g1 , atmos_b_g2)\\n atmos = galsim.Add([atmos_a, atmos_b])\\n logger.info('Made atmospheric PSF profile')\\n\\n # Define the optical part of the PSF.\\n # The first argument of OpticalPSF below is lambda/D,\\n # which needs to be in pixel units, so do the calculation:\\n lam_over_D = lam * 1.e-9 / tel_diam # radians\\n lam_over_D *= 206265 # arcsec\\n lam_over_D *= pixel_scale # pixels\\n logger.info('Calculated lambda over D = %f pixels', lam_over_D)\\n # The rest of the values here should be given in units of the \\n # wavelength of the incident light. padFactor is used to here to reduce 'folding' for these\\n # quite strong aberration values\\n optics = galsim.OpticalPSF(lam_over_D, \\n defocus=opt_defocus, coma1=opt_c1, coma2=opt_c2, astig1=opt_a1,\\n astig2=opt_a2, padFactor=opt_padFactor)\\n logger.info('Made optical PSF profile')\\n\\n # Start with square pixels\\n pix = galsim.Pixel(xw=pixel_scale, yw=pixel_scale)\\n # Then shear them slightly by the negative of the wcs shear.\\n # This way the later distortion of the full image will bring them back to square.\\n pix.applyShear(-wcs_g1, -wcs_g2)\\n logger.info('Made pixel profile')\\n\\n # Final profile is the convolution of these.\\n final = galsim.Convolve([gal, atmos, optics, pix])\\n final_epsf = galsim.Convolve([atmos, optics, pix])\\n logger.info('Convolved components into final profile')\\n\\n # Now apply the wcs shear to the final image.\\n final.applyShear(wcs_g1, wcs_g2)\\n final_epsf.applyShear(wcs_g1, wcs_g2)\\n logger.info('Applied WCS distortion')\\n\\n # Draw the image with a particular pixel scale.\\n image = final.draw(dx=pixel_scale)\\n image_epsf = final_epsf.draw(dx=pixel_scale)\\n # Draw the optical PSF component at its Nyquist sample rate\\n image_opticalpsf = optics.draw(dx=lam_over_D/2.)\\n logger.info('Made image of the profile')\\n\\n # Add a constant sky level to the image.\\n sky_image = galsim.ImageF(bounds=image.getBounds(), initValue=sky_level)\\n image += sky_image\\n\\n # Add Poisson noise to the image.\\n rng = galsim.UniformDeviate(1314662)\\n galsim.noise.addPoisson(image, rng, gain=gain)\\n\\n # Also add (Gaussian) read noise.\\n galsim.noise.addGaussian(image, rng, sigma=read_noise)\\n\\n # Subtract off the sky.\\n image -= sky_image\\n logger.info('Added Gaussian and Poisson noise')\\n\\n # Write the image to a file\\n if not os.path.isdir('output'):\\n os.mkdir('output')\\n file_name = os.path.join('output', 'demo3.fits')\\n file_name_opticalpsf = os.path.join('output','demo3_opticalpsf.fits')\\n file_name_epsf = os.path.join('output','demo3_epsf.fits')\\n \\n image.write(file_name, clobber=True)\\n image_opticalpsf.write(file_name_opticalpsf, clobber=True)\\n image_epsf.write(file_name_epsf, clobber=True)\\n logger.info('Wrote image to %r', file_name)\\n logger.info('Wrote optics-only PSF image (Nyquist sampled) to %r', file_name_opticalpsf)\\n logger.info('Wrote effective PSF image to %r', file_name_epsf)\\n\\n moments = HSM_Moments(file_name)\\n moments_corr = HSM_Regauss(file_name, file_name_epsf, image.array.shape)\\n\\n logger.info('HSM reports that the image has measured moments:')\\n logger.info(' Mxx = %.3f, Myy = %.3f, Mxy = %.3f', moments.mxx, moments.myy, moments.mxy)\\n logger.info('When carrying out Regaussianization PSF correction, HSM reports')\\n logger.info(' g1,g2 = %f,%f', moments_corr.g1, moments_corr.g2)\\n logger.info('Expected values in the limit that noise and non-Gaussianity are negligible:')\\n logger.info(' g1,g2 = %f,%f', g1+wcs_g1,g2+wcs_g2)\\n print\",\n \"def test_ML_check_timing_epem_ttx(self):\\n \\n self.setup_logFile_for_logger('madgraph.check_cmd')\\n try:\\n cmd = os.getcwd()\\n self.do('import model loop_sm')\\n if path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')):\\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\\n # Make sure it works for an initial run\\n self.do('check timing -reuse e+ e- > t t~ [virt=QCD]')\\n self.assertEqual(cmd, os.getcwd())\\n self.assertTrue(path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')))\\n self.assertTrue(path.isfile(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx',\\\\\\n 'SubProcesses/P0_epem_ttx/result.dat')))\\n self.assertTrue(path.isfile(self.tmp_path['madgraph.check_cmd']))\\n res = open(self.tmp_path['madgraph.check_cmd']).read()\\n self.assertTrue('Generation time total' in res)\\n self.assertTrue('Executable size' in res)\\n self.assertTrue(not 'NA' in res)\\n \\n # Now for a Reuse-run\\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)\\n self.setup_logFile_for_logger('madgraph.check_cmd')\\n self.do('check timing -reuse e+ e- > t t~ [virt=QCD]')\\n self.assertEqual(cmd, os.getcwd())\\n self.assertTrue(path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')))\\n self.assertTrue(path.isfile(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx',\\\\\\n 'SubProcesses/P0_epem_ttx/result.dat')))\\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\\n self.assertTrue(path.isfile(self.tmp_path['madgraph.check_cmd']))\\n res = open(self.tmp_path['madgraph.check_cmd']).read()\\n self.assertTrue('Generation time total' in res)\\n self.assertTrue('Executable size' in res)\\n self.assertTrue(res.count('NA')<=8)\\n except:\\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)\\n if path.isdir(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx')):\\n shutil.rmtree(pjoin(MG5DIR,'SAVEDTMP_CHECK_epem_ttx'))\\n raise\\n self.setup_logFile_for_logger('madgraph.check_cmd',restore=True)\",\n \"def run(self):\\n\\t\\trun_start_timestamp = time.clock()\\n\\t\\texperiment_start_time = time.strftime(\\\"%H_%M_%S_%m_%d_%Y\\\", time.localtime())\\n\\t\\tprint \\\"starting dataset at {}\\\\n\\\".format(experiment_start_time)\\n\\t\\tdata_file = \\\"raw_data/data_\\\" + experiment_start_time + \\\".csv\\\"\\n\\t\\twith open(data_file, 'a') as f:\\n\\t\\t\\tf.write(\\\"Packet, Sample offset, Latency (ms), Sample\\\\n\\\")\\n\\n\\t\\t##### EXPERIMENT SETUP #####\\n\\t\\t# sample for a max of 1.5 seconds\\n\\t\\t# approximate number of samples assuming ~500ms latency per packet\\n\\t\\tnSamples = (int) (1.5 * self.sampling_freq)\\n\\n\\t\\t#csamples, lost, corrupted\\n\\t\\tbuffer_info = [0, 0, 0]\\n\\n\\t\\tnum_packets_received = 0\\n\\t\\tnum_packets_missed = 0\\n\\t\\tnum_acks_missed = 0\\n\\n\\t\\t# array to hold indices of packets missed\\n\\t\\tpacket_number_missed = []\\n\\n\\t\\t# num_packets_received + num_packets_missed must equal num_tries\\n\\t\\tnum_tries = 0\\n\\n\\t\\t# ready for next button press if previous packet has been handled\\n\\t\\t# and if instruments, variables are configured for next packet\\n\\t\\tlast_packet_handled = True\\n\\t\\t# keep track of if current packet has been received\\n\\t\\tpacket_received = True\\n\\n\\t\\t#keep track of previous packet acknowledgment\\n\\t\\tack_missed = False\\n\\n\\t\\t##### END SETUP #####\\n\\n\\t\\t##### MAIN LOOP of experiment. #####\\n\\t\\t# runs for the duration of the experiment\\n\\t\\t#note: openmote toggles its pins every packet creation and reception\\n\\n\\t\\twhile num_tries < self.num_packets_experiment:\\n\\t\\t\\t# initialize \\\"trash\\\" array to clear all buffer samples before starting button press\\n\\t\\t\\t# this must be able to hold 4096 samples, 2 bytes each\\n\\t\\t\\ttrashSamples = (c_uint16 * 4096)()\\n\\n\\t\\t\\t#clear buffer\\n\\t\\t\\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, trashSamples, copy_all_samples=True)\\n\\n\\t\\t\\t# clear rgwSamples for next packet\\n\\t\\t\\trgwSamples = (c_uint16 * nSamples)()\\n\\n\\t\\t\\t# reset and configure DigitalIO\\n\\t\\t\\tsteady_state_DIO = self._configure_DigitalIO()\\n\\n\\t\\t\\t# reset and configure DigitalIn to take nSamples on trigger\\n\\t\\t\\t# set DigitalIn trigger when button_press_mirror_bit channel is raised (this should start sampling)\\n\\t\\t\\tself._configure_DigitalIn(nSamples, self.button_press_mirror_bit)\\n\\n\\t\\t\\tready_for_next_button_press = True\\n\\t\\t\\tbroke_early = False\\n\\n\\t\\t\\t#print \\\"begin acquisition {}\\\".format(num_tries + 1)\\n\\t\\t\\tprev_csamples, curr_csamples = 0, 0\\n\\n\\t\\t\\tif ack_missed:\\n\\t\\t\\t\\tprint \\\"missed ack\\\"\\n\\t\\t\\t\\ttime.sleep(0.55)\\n\\n\\n\\t\\t\\t# inner loop: runs from button press until packet received.\\n\\t\\t\\twhile buffer_info[0] < nSamples:\\n\\n\\t\\t\\t\\t# button press\\n\\t\\t\\t\\tif last_packet_handled and ready_for_next_button_press:\\n\\t\\t\\t\\t\\t# we can send the next packet because the last packet was handled (received or understood to be missed)\\n\\t\\t\\t\\t\\t# and instruments are configured\\n\\t\\t\\t\\t\\t# button press -> set value on enabled AD2 output pins (digital_out_channels_bits)\\n\\t\\t\\t\\t\\t# AD2 output is hard wired to button press input which triggers acquisition\\n\\n\\t\\t\\t\\t\\t#get current value of packet_received_pin; when packet is received this will toggle\\n\\t\\t\\t\\t\\tcurr_DIO = self._get_DIO_values()\\n\\t\\t\\t\\t\\tpacket_received_pins_state = curr_DIO & self.packet_received_bits\\n\\t\\t\\t\\t\\tpacket_created_pin_state = curr_DIO & self.packet_created_bit\\n\\n\\t\\t\\t\\t\\tlast_packet_handled = False\\n\\t\\t\\t\\t\\tready_for_next_button_press = False\\n\\t\\t\\t\\t\\tpacket_received = False\\n\\t\\t\\t\\t\\tack_missed = False\\n\\n\\t\\t\\t\\t\\t# press the button. all other outputs go low.\\n\\t\\t\\t\\t\\tdwf.FDwfDigitalIOOutputSet(self.interface_handler, c_uint16(self.button_press_bit))\\n\\t\\t\\t\\t\\t#reset all enabled digital out channels back to steady state (all high except button press)\\n\\t\\t\\t\\t\\tdwf.FDwfDigitalIOOutputSet(self.interface_handler, steady_state_DIO)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t#print \\\"button pressed\\\"\\n\\t\\t\\t\\t\\tnum_tries += 1\\n\\n\\t\\t\\t\\t# copy buffer samples to memory and flush\\n\\t\\t\\t\\t#print \\\"Before: {}\\\".format(buffer_info)\\n\\t\\t\\t\\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, rgwSamples)\\n\\t\\t\\t\\t#print \\\"After: {}\\\".format(buffer_info)\\n\\t\\t\\t\\t#buffer_flush_stop = time.clock()\\n\\n\\t\\t\\t\\tcurr_csamples = buffer_info[0]\\n\\t\\t\\t\\tif curr_csamples == prev_csamples:\\n\\t\\t\\t\\t\\tprint \\\"broke early\\\"\\n\\t\\t\\t\\t\\tnum_tries -= 1\\n\\t\\t\\t\\t\\tbroke_early = True\\n\\n\\t\\t\\t\\t\\t# stop sampling\\n\\t\\t\\t\\t\\tdwf.FDwfDigitalInConfigure(self.interface_handler, c_bool(0), c_bool(0))\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\t# manually stop sampling once packet_received_bit is not equal to its pin state\\n\\t\\t\\t\\tcurr_DIO = self._get_DIO_values()\\n\\t\\t\\t\\tif ((curr_DIO & self.packet_received_bits) != packet_received_pins_state):\\n\\t\\t\\t\\t\\t#copy last buffer samples to memory\\n\\t\\t\\t\\t\\tbuffer_info = self._copy_buffer_samples(buffer_info, nSamples, rgwSamples, last_read=True)\\n\\n\\t\\t\\t\\t\\t# packet_received_bit toggled; stop sampling\\n\\t\\t\\t\\t\\tdwf.FDwfDigitalInConfigure(self.interface_handler, c_bool(0), c_bool(0))\\n\\n\\t\\t\\t\\t\\tcurr_DIO = self._get_DIO_values()\\n\\t\\t\\t\\t\\tif (curr_DIO & self.packet_created_bit) != packet_created_pin_state:\\n\\t\\t\\t\\t\\t\\tack_missed = True\\n\\n\\t\\t\\t\\t\\tpacket_received = True\\n\\t\\t\\t\\t\\tnum_packets_received += 1\\n\\t\\t\\t\\t\\t#print \\\"received packet {}\\\".format(num_tries)\\n\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\tprev_csamples = curr_csamples\\n\\t\\t\\t\\t# end of the inner loop\\n\\n\\t\\t\\t#set this so we can push button again\\n\\t\\t\\tlast_packet_handled = True\\n\\n\\t\\t\\tif broke_early:\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# reach here if packet was received OR if 1.5 million samples have been taken\\n\\t\\t\\tif packet_received == True:\\n\\t\\t\\t\\tself.postprocess(num_tries, buffer_info, rgwSamples, data_file)\\n\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# we took 1.5 million samples and supposedly missed the packet\\n\\t\\t\\t\\tnum_packets_missed += 1\\n\\t\\t\\t\\tpacket_number_missed.append(num_tries)\\n\\t\\t\\t\\tself.postprocess(num_tries, buffer_info, rgwSamples, data_file, missed_packet=True)\\n\\t\\t\\t\\t# set last_packet_handled to True to try button press again\\n\\n\\n\\t\\trun_end_timestamp = time.clock()\\n\\t\\tprint \\\"Done with experiment\\\"\\n\\t\\t#print all packets sent, lost, total info\\n\\t\\tprint \\\"Number of tries: {}\\\".format(num_tries)\\n\\t\\tprint \\\"Number of received packets: {}\\\".format(num_packets_received)\\n\\t\\tprint \\\"Number of missed packets: {}\\\\n\\\".format(num_packets_missed)\\n\\t\\tprint \\\"Total duration: {} seconds\\\".format(run_end_timestamp - run_start_timestamp)\\n\\t\\treturn\",\n \"def AllindividualRuns():\\n #800 nm\\n RunData(getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/'), out='I800nm')\\n RunData(getFiles(mintime=(15, 12, 20), maxtime=(15, 24, 16), folder='data/31Jul/'), out='I800nm5k')\\n RunData(getFiles(mintime=(15, 28, 40), maxtime=(15, 39, 21), folder='data/31Jul/'), out='I800nm10k')\\n RunData(getFiles(mintime=(15, 43, 24), maxtime=(15, 51, 47), folder='data/31Jul/'), out='I800nm20k')\\n RunData(getFiles(mintime=(15, 56, 11), maxtime=(16, 02, 58), folder='data/31Jul/'), out='I800nm30k')\\n RunData(getFiles(mintime=(16, 12, 39), maxtime=(16, 18, 25), folder='data/31Jul/'), out='I800nm38k')\\n RunData(getFiles(mintime=(16, 21, 52), maxtime=(16, 26, 16), folder='data/31Jul/'), out='I800nm50k')\\n RunData(getFiles(mintime=(16, 32, 02), maxtime=(16, 35, 23), folder='data/31Jul/'), out='I800nm54k')\\n #700 nm\\n RunData(getFiles(mintime=(17, 20, 17), maxtime=(17, 33, 17), folder='data/30Jul/'), out='I700nm5k')\\n RunData(getFiles(mintime=(17, 37, 35), maxtime=(17, 46, 51), folder='data/30Jul/'), out='I700nm9k')\\n RunData(getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/'), out='I700nm52k')\\n RunData(getFiles(mintime=(17, 58, 18), maxtime=(17, 59, 31), folder='data/30Jul/'), out='I700nm32k')\\n #600 nm\\n RunData(getFiles(mintime=(15, 22, 00), maxtime=(15, 36, 32), folder='data/30Jul/'), out='I600nm5k')\\n RunData(getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/'), out='I600nm54k')\\n RunData(getFiles(mintime=(15, 52, 07), maxtime=(16, 06, 32), folder='data/30Jul/'), out='I600nm10k')\\n #890 nm\\n RunData(getFiles(mintime=(13, 37, 37), maxtime=(13, 50, 58), folder='data/01Aug/'), out='I890nm5k')\\n RunData(getFiles(mintime=(14, 00, 58), maxtime=(14, 11, 54), folder='data/01Aug/'), out='I890nm10k')\\n RunData(getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/'), out='I890nm30k')\\n RunData(getFiles(mintime=(14, 30, 03), maxtime=(14, 34, 37), folder='data/01Aug/'), out='I890nm50k')\",\n \"def _unpolarized_simulation(self, frequency, theta_0=0):\\n s_data = self.simulate(frequency, 's', theta_0)\\n p_data = self.simulate(frequency, 'p', theta_0)\\n T = (s_data + p_data)/2\\n return T\",\n \"def lab_run_big(character_id, time_step):\\n pass\",\n \"def run_script(input_dir, output_dir, output_file, bstp_num):\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n print(\\\"\\\"\\\" Load results from step 1 & 2 \\\"\\\"\\\")\\n start_0 = time.time()\\n data_dim_file_name = output_dir + \\\"/temp/data_dim.mat\\\"\\n mat = loadmat(data_dim_file_name)\\n data_dim = mat['data_dim']\\n data_dim = np.array([int(i) for i in data_dim[0, :]])\\n n, l, m, p, g, g_num = data_dim\\n y_design_file_name = output_dir + \\\"/temp/y_design.mat\\\"\\n mat = loadmat(y_design_file_name)\\n y_design = mat['y_design']\\n resy_design_file_name = output_dir + \\\"/temp/resy_design.mat\\\"\\n mat = loadmat(resy_design_file_name)\\n resy_design = mat['resy_design']\\n efit_eta_file_name = output_dir + \\\"/temp/efit_eta.mat\\\"\\n mat = loadmat(efit_eta_file_name)\\n efit_eta = mat['efit_eta']\\n esig_eta_file_name = output_dir + \\\"/temp/esig_eta.mat\\\"\\n mat = loadmat(esig_eta_file_name)\\n esig_eta = mat['esig_eta']\\n hat_mat_file_name = output_dir + \\\"/temp/hat_mat.mat\\\"\\n mat = loadmat(hat_mat_file_name)\\n hat_mat = mat['hat_mat']\\n snp_file_name = output_dir + \\\"/temp/snp.mat\\\"\\n mat = loadmat(snp_file_name)\\n snp = mat['snp']\\n # read the image size\\n img_size_file_name = input_dir + \\\"img_size.txt\\\"\\n img_size = np.loadtxt(img_size_file_name)\\n img_size = np.array([int(i) for i in img_size])\\n # read the image index of non-background region\\n img_idx_file_name = input_dir + \\\"img_idx.txt\\\"\\n img_idx = np.loadtxt(img_idx_file_name)\\n img_idx = np.array([int(i) for i in img_idx])\\n end_0 = time.time()\\n print(\\\"Elapsed time in Step 3 is \\\", end_0 - start_0)\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n print(\\\"\\\"\\\" Step 3. Significant locus-voxel and locus-subregion detection \\\"\\\"\\\")\\n start_3 = time.time()\\n alpha = 1e-5\\n c_alpha = -10**alpha\\n bstp_num = int(bstp_num)\\n max_stat_bstp, max_area_bstp = wild_bstp(snp, y_design, resy_design, efit_eta, esig_eta, hat_mat,\\n img_size, img_idx, c_alpha, g_num, bstp_num)\\n print(max_stat_bstp)\\n print(max_area_bstp)\\n bstp_out = np.hstack((max_stat_bstp, max_area_bstp))\\n bstp_out_file_name = output_dir + output_file\\n np.savetxt(bstp_out_file_name, bstp_out)\\n end_3 = time.time()\\n print(\\\"Elapsed time in Step 3 is \\\", end_3 - start_3)\",\n \"def ml_loop(side: str):\\n\\n # === Here is the execution order of the loop === #\\n # 1. Put the initialization code here\\n ball_served = False\\n blocker_last_x = 0\\n\\n class Pred:\\n pred = 100\\n blocker_pred_x = 0\\n last_command = 0\\n blocker_vx = 0\\n\\n \\n def move_to(player, pred) : #move platform to predicted position to catch ball \\n if player == '1P':\\n if scene_info[\\\"platform_1P\\\"][0]+20 > (pred-10) and scene_info[\\\"platform_1P\\\"][0]+20 < (pred+10): return 0 # NONE\\n elif scene_info[\\\"platform_1P\\\"][0]+20 <= (pred-10) : return 1 # goes right\\n else : return 2 # goes left\\n else :\\n if scene_info[\\\"platform_2P\\\"][0]+20 > (pred-10) and scene_info[\\\"platform_2P\\\"][0]+20 < (pred+10): return 0 # NONE\\n elif scene_info[\\\"platform_2P\\\"][0]+20 <= (pred-10) : return 1 # goes right\\n else : return 2 # goes left\\n\\n def ml_loop_for_1P(): \\n # ball slicing\\n if scene_info[\\\"ball_speed\\\"][1] > 0 and (scene_info[\\\"ball\\\"][1]+scene_info[\\\"ball_speed\\\"][1]) >= 415 and Pred.last_command == 0:\\n print(\\\"------\\\")\\n ball_x = scene_info[\\\"ball\\\"][0]\\n ball_y = scene_info[\\\"ball\\\"][1]\\n ball_vx = scene_info[\\\"ball_speed\\\"][0]\\n ball_slice_vx = scene_info[\\\"ball_speed\\\"][0]+np.sign(scene_info[\\\"ball_speed\\\"][0])*3\\n ball_vy = scene_info[\\\"ball_speed\\\"][1] \\n blocker_x = scene_info['blocker'][0] + Pred.blocker_vx\\n \\n y = abs((415 - ball_y) // ball_vy)\\n pred_ball_1P = ball_x + ball_vx * y\\n\\n y = abs((415 - 260) // ball_vy)\\n pred_ball_blocker = pred_ball_1P + ball_slice_vx * y\\n bound = pred_ball_blocker // 200 # Determine if it is beyond the boundary\\n if (bound > 0): # pred > 200 # fix landing position\\n if (bound%2 == 0) : \\n pred_ball_blocker = pred_ball_blocker - bound*200 \\n else :\\n pred_ball_blocker = 200 - (pred_ball_blocker - 200*bound)\\n elif (bound < 0) : # pred < 0\\n if (bound%2 ==1) :\\n pred_ball_blocker = abs(pred_ball_blocker - (bound+1) *200)\\n else :\\n pred_ball_blocker = pred_ball_blocker + (abs(bound)*200)\\n \\n y = abs((415 - 260) // ball_vy)\\n Pred.blocker_pred_x = blocker_x + Pred.blocker_vx * y \\n if Pred.blocker_pred_x < 0: Pred.blocker_pred_x = abs(Pred.blocker_pred_x)\\n elif Pred.blocker_pred_x > 170: Pred.blocker_pred_x = 170 - (Pred.blocker_pred_x - 170)\\n \\n if pred_ball_blocker >= Pred.blocker_pred_x-10 and pred_ball_blocker < Pred.blocker_pred_x+40:\\n print(\\\"slice will hit blicker\\\")\\n # don't slice \\n # use origin ball vx to predict will hit blocker or not\\n # if will hit blicker let ball go reverse direction\\n y = abs((415 - 260) // ball_vy)\\n pred_ball_blocker = pred_ball_1P + ball_vx * y\\n bound = pred_ball_blocker // 200 # Determine if it is beyond the boundary\\n if (bound > 0): # pred > 200 # fix landing position\\n if (bound%2 == 0) : \\n pred_ball_blocker = pred_ball_blocker - bound*200 \\n else :\\n pred_ball_blocker = 200 - (pred_ball_blocker - 200*bound)\\n elif (bound < 0) : # pred < 0\\n if (bound%2 ==1) :\\n pred_ball_blocker = abs(pred_ball_blocker - (bound+1) *200)\\n else :\\n pred_ball_blocker = pred_ball_blocker + (abs(bound)*200)\\n\\n if pred_ball_blocker >= Pred.blocker_pred_x-10 and pred_ball_blocker < Pred.blocker_pred_x+40:\\n print(\\\"will hit blocker, hit reversed direction\\\")\\n if scene_info[\\\"ball_speed\\\"][0] > 0: return 2\\n else: return 1\\n else: \\n print(\\\"will not hit blicker, do nothing\\\")\\n return 0\\n else:\\n # slice\\n print(\\\"slice will not hit blocker\\\")\\n if scene_info[\\\"ball_speed\\\"][0] > 0: return 1\\n else: return 2\\n\\n elif scene_info[\\\"ball_speed\\\"][1] > 0 : # 球正在向下 # ball goes down\\n x = ( scene_info[\\\"platform_1P\\\"][1]-scene_info[\\\"ball\\\"][1] ) // scene_info[\\\"ball_speed\\\"][1] # 幾個frame以後會需要接 # x means how many frames before catch the ball\\n Pred.pred = scene_info[\\\"ball\\\"][0]+(scene_info[\\\"ball_speed\\\"][0]*x) # 預測最終位置 # pred means predict ball landing site \\n bound = Pred.pred // 200 # Determine if it is beyond the boundary\\n if (bound > 0): # pred > 200 # fix landing position\\n if (bound%2 == 0) : \\n Pred.pred = Pred.pred - bound*200 \\n else :\\n Pred.pred = 200 - (Pred.pred - 200*bound)\\n elif (bound < 0) : # pred < 0\\n if (bound%2 ==1) :\\n Pred.pred = abs(Pred.pred - (bound+1) *200)\\n else :\\n Pred.pred = Pred.pred + (abs(bound)*200)\\n return move_to(player = '1P',pred = Pred.pred)\\n \\n else : # 球正在向上 # ball goes up\\n return move_to(player = '1P',pred = 100)\\n\\n\\n\\n def ml_loop_for_2P(): # as same as 1P\\n if scene_info[\\\"ball_speed\\\"][1] > 0 : \\n return move_to(player = '2P',pred = 100)\\n else : \\n x = ( scene_info[\\\"platform_2P\\\"][1]+30-scene_info[\\\"ball\\\"][1] ) // scene_info[\\\"ball_speed\\\"][1] \\n pred = scene_info[\\\"ball\\\"][0]+(scene_info[\\\"ball_speed\\\"][0]*x) \\n bound = pred // 200 \\n if (bound > 0):\\n if (bound%2 == 0):\\n pred = pred - bound*200 \\n else :\\n pred = 200 - (pred - 200*bound)\\n elif (bound < 0) :\\n if bound%2 ==1:\\n pred = abs(pred - (bound+1) *200)\\n else :\\n pred = pred + (abs(bound)*200)\\n return move_to(player = '2P',pred = pred)\\n\\n # 2. Inform the game process that ml process is ready\\n comm.ml_ready()\\n\\n # 3. Start an endless loop\\n while True:\\n # 3.1. Receive the scene information sent from the game process\\n scene_info = comm.recv_from_game()\\n\\n # 3.2. If either of two sides wins the game, do the updating or\\n # resetting stuff and inform the game process when the ml process\\n # is ready.\\n if scene_info[\\\"status\\\"] != \\\"GAME_ALIVE\\\":\\n # Do some updating or resetting stuff\\n ball_served = False\\n\\n # 3.2.1 Inform the game process that\\n # the ml process is ready for the next round\\n comm.ml_ready()\\n continue\\n\\n # 3.3 Put the code here to handle the scene information\\n\\n # 3.4 Send the instruction for this frame to the game process\\n if not ball_served:\\n comm.send_to_game({\\\"frame\\\": scene_info[\\\"frame\\\"], \\\"command\\\": \\\"SERVE_TO_LEFT\\\"})\\n blocker_last_x = scene_info[\\\"blocker\\\"][0]\\n Pred.last_command = 0\\n ball_served = True\\n else:\\n if side == \\\"1P\\\":\\n Pred.blocker_vx = scene_info[\\\"blocker\\\"][0] - blocker_last_x\\n if scene_info[\\\"blocker\\\"][0] == 0: Pred.blocker_vx = 5\\n elif scene_info[\\\"blocker\\\"][0] == 170: Pred.blocker_vx = -5\\n command = ml_loop_for_1P()\\n blocker_last_x = scene_info[\\\"blocker\\\"][0]\\n Pred.last_command = command\\n else:\\n command = ml_loop_for_2P()\\n\\n if command == 0:\\n comm.send_to_game({\\\"frame\\\": scene_info[\\\"frame\\\"], \\\"command\\\": \\\"NONE\\\"})\\n elif command == 1:\\n comm.send_to_game({\\\"frame\\\": scene_info[\\\"frame\\\"], \\\"command\\\": \\\"MOVE_RIGHT\\\"})\\n else :\\n comm.send_to_game({\\\"frame\\\": scene_info[\\\"frame\\\"], \\\"command\\\": \\\"MOVE_LEFT\\\"})\",\n \"def test_significance_individual_timepoints(tensor_directory, analysis_name):\\n\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = h5py.File(os.path.join(tensor_directory, analysis_name + \\\".hdf5\\\"), \\\"r\\\")\\n activity_dataset = analysis_file[\\\"Data\\\"]\\n metadata_dataset = analysis_file[\\\"metadata\\\"]\\n number_of_timepoints, number_of_trials, number_of_pixels = np.shape(activity_dataset)\\n print(\\\"metadata_dataset\\\", np.shape(metadata_dataset))\\n \\\"\\\"\\\"\\n # Open Analysis Dataframe\\n analysis_file = tables.open_file(os.path.join(tensor_directory, analysis_name + \\\"_Trialwise_.h5\\\"), mode=\\\"r\\\")\\n activity_dataset = analysis_file.root[\\\"Data\\\"]\\n metadata_dataset = analysis_file.root[\\\"Trial_Details\\\"]\\n\\n # Create P and Slope Tensors\\n p_value_tensor = np.ones((number_of_timepoints, number_of_pixels))\\n slope_tensor = np.zeros((number_of_timepoints, number_of_pixels))\\n\\n for timepoint_index in tqdm(range(number_of_timepoints), position=0, desc=\\\"Timepoint\\\"):\\n\\n # Get Timepoint Data\\n timepoint_activity = activity_dataset[timepoint_index]\\n\\n for pixel_index in tqdm(range(number_of_pixels), position=1, desc=\\\"Pixel\\\", leave=True):\\n\\n # Package Into Dataframe\\n pixel_activity = timepoint_activity[:, pixel_index]\\n pixel_dataframe = repackage_data_into_dataframe(pixel_activity, metadata_dataset)\\n\\n # Fit Mixed Effects Model\\n p_value, slope = mixed_effects_random_slope_and_intercept(pixel_dataframe)\\n p_value_tensor[timepoint_index, pixel_index] = p_value\\n slope_tensor[timepoint_index, pixel_index] = slope\\n\\n\\n # Save These Tensors\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_p_value_tensor.npy\\\"), p_value_tensor)\\n np.save(os.path.join(tensor_directory, analysis_name + \\\"_slope_tensor.npy\\\"), slope_tensor)\",\n \"def PMTandPiezoPlot(datadir,run,event,gain): \\n en = event\\n mu = gain\\n e = sbc.DataHandling.GetSBCEvent.GetEvent(datadir+'/'+run,en)\\n print(e[\\\"fastDAQ\\\"].keys())\\n cgate = e[\\\"fastDAQ\\\"][\\\"CAMgate\\\"]\\n dcam = np.diff(cgate)\\n \\n p0=e[\\\"fastDAQ\\\"][\\\"Piezo1\\\"]\\n p1 = e[\\\"fastDAQ\\\"][\\\"Piezo2\\\"]\\n fdt = e[\\\"fastDAQ\\\"][\\\"time\\\"]\\n runreconpath = \\\"/pnfs/coupp/persistent/grid_output/SBC-17/output/%s/\\\"%run\\n pmtdiffs = []\\n diffs = []\\n \\n camOnTimes = [fdt[i] for i in range(len(dcam)) if dcam[i] < -0.5]\\n camOffTimes = [fdt[i] for i in range(len(dcam)) if dcam[i] > 0.5]\\n print(len(camOnTimes))\\n print(len(camOffTimes))\\n \\n acousticfilename = runreconpath+\\\"AcousticAnalysis_%s.bin\\\"%run\\n a = sbc.DataHandling.ReadBinary.ReadBlock(acousticfilename)\\n bubt0 = a[\\\"bubble_t0\\\"]\\n \\n pmttracetime = e[\\\"PMTtraces\\\"][\\\"t0_sec\\\"][:,0]+e[\\\"PMTtraces\\\"][\\\"t0_frac\\\"][:,0]\\n d=sbc.AnalysisModules.PMTfastDAQalignment.PMTandFastDAQalignment(e)\\n pmtalign = d[\\\"PMT_trigt0_sec\\\"]+d[\\\"PMT_trigt0_frac\\\"]\\n tracetimes = pmttracetime - pmtalign\\n at0 = bubt0[en,0]\\n at0_1 = bubt0[en,1]\\n \\n allxyzfname = \\\"/pnfs/coupp/persistent/grid_output/SBC-17/output/SimpleXYZ_all.bin\\\"\\n xyzf = sbc.DataHandling.ReadBinary.ReadBlock(allxyzfname)\\n indices = [i for i,x in enumerate(xyzf[\\\"runid\\\"]) if str(x[0])+\\\"_\\\"+str(x[1]) == run]\\n xyz_reconstructed = True\\n if len(indices) > 0:\\n runposreco = {\\\"ev\\\":[xyzf[\\\"ev\\\"][indices]],\\\"x\\\":[xyzf[\\\"bubX\\\"][indices]],\\n \\\"y\\\":[xyzf[\\\"bubY\\\"][indices]],\\\"z\\\":[xyzf[\\\"bubZ\\\"][indices]]}\\n z = runposreco[\\\"z\\\"][0][int(int(en))]\\n else:\\n print(\\\"no handscan?\\\")\\n z = 1.5\\n xyz_reconstructed = False\\n lag_expected = (-23.387649*z - 261.020495)*1e-6 # fit from other analysis\\n t0_expected_p0 = at0 + lag_expected\\n t0_expected_p1 = at0_1 + lag_expected\\n \\n i=0\\n candidates = []\\n candidate_times=[]\\n for t in (tracetimes-at0):\\n \\n if t<0.2 and t>-0.2:\\n lastCamOff = 0\\n for k in range(len(camOffTimes)):\\n if t+at0 > camOffTimes[k]:\\n lastCamOff = camOffTimes[k]\\n elif t+at0 < camOffTimes[k]:\\n break\\n if t+at0-lastCamOff > 25e-6:\\n \\n pmtdiffs.append(t)\\n trace = np.fabs(e[\\\"PMTtraces\\\"][\\\"traces\\\"][i][0])\\n if max(trace) == 128:\\n trace = pi.stitchTraces(trace,np.fabs(e[\\\"PMTtraces\\\"][\\\"traces\\\"][i][1]))\\n dt = e[\\\"PMTtraces\\\"][\\\"dt\\\"][i][0]\\n #baseline = np.mean(trace[0:50])\\n #trace = trace - baseline\\n [phe,n,totInt,pktimes] = pi.SBC_pulse_integrator_bressler(trace,dt)\\n \\n if phe != None:\\n phe /= mu\\n candidates.append(phe)\\n candidate_times.append(t)\\n i+=1\\n candidate_phe = 0\\n the_index = 0\\n i=0\\n near_trace_indices = []\\n for t in candidate_times:\\n if t > -500e-6 and t <0:\\n near_trace_indices.append(list(tracetimes-at0).index(t))\\n if candidates[i]>candidate_phe:\\n candidate_phe = candidates[i]\\n the_index = i\\n i+=1\\n \\n if len(candidates) != 0:\\n if max(candidates)>0:\\n diffs.append(candidate_times[candidates.index(max(candidates))])\\n fig,ax1 = plt.subplots()\\n ax2 = ax1.twinx()\\n ax1.plot(fdt,p0,'b',alpha=0.6, label = 'piezo 0')\\n ax1.plot(fdt,p1,'k',alpha=0.2, label= 'piezo 1')\\n for i in range(len(candidates)):\\n if i == the_index:\\n ax2.plot([candidate_times[i]+at0,candidate_times[i]+at0],[0,candidates[i]],'r',lw=4)\\n else:\\n ax2.plot([candidate_times[i]+at0,candidate_times[i]+at0],[0,candidates[i]],'y',lw=4)\\n #ax2.plot([min(candidate_times),max(candidate_times)],[0,0],linewidth=2)\\n ax1.plot([at0,at0],[-0.5,0.5],'b',linewidth=2, label = 'acoustic t0, p0')\\n ax1.plot([at0_1,at0_1],[-0.5,0.5],'k',linewidth=2, label = 'acoustic t0, p1')\\n \\\"\\\"\\\"\\n if xyz_reconstructed:\\n ax1.plot([t0_expected_p0,t0_expected_p0],[-0.5,0.5],'b:',linewidth=2, label = 'expected PMT t0, p0')\\n ax1.plot([t0_expected_p1,t0_expected_p1],[-0.5,0.5],'k:',linewidth=2, label = 'expected PMT t0, p1')\\n else:\\n ax1.plot([t0_expected_p0,t0_expected_p0],[-0.5,0.5],'b:',linewidth=2, label = 'expected PMT t0, p0, center of chamber')\\n ax1.plot([t0_expected_p1,t0_expected_p1],[-0.5,0.5],'k:',linewidth=2, label = 'expected PMT t0, p1, center of chamber')\\n \\\"\\\"\\\"\\n ax1.plot(fdt,cgate,'c')\\n ax1.plot(fdt[:-1],dcam,'m')\\n ax2.set_ylabel('pmt signal (phe)',fontsize=20)\\n ax1.set_xlabel('time (s)',fontsize=20)\\n ax1.set_ylabel('Acoustic signa(V)',fontsize=20)\\n ax1.set_ylim([min(p1),max(p1)])\\n ax2.set_xlim([-0.1,0.1])\\n #ax2.set_ylim([0,5])\\n ax1.legend()\\n plt.show\\n \\n for j in near_trace_indices:\\n trace = e[\\\"PMTtraces\\\"][\\\"traces\\\"][j][0]\\n dt = e[\\\"PMTtraces\\\"][\\\"dt\\\"]\\n dt_tr = dt[j][0]\\n tPMT = np.arange(len(trace))*dt_tr\\n plt.figure()\\n plt.plot(tPMT,trace)\\n plt.xlabel(\\\"t (s)\\\")\\n plt.ylabel(\\\"PMT signal\\\")\\n plt.show\\n \\n plt.figure()\\n plt.plot(e[\\\"fastDAQ\\\"][\\\"time\\\"],e[\\\"fastDAQ\\\"][\\\"VetoCoinc\\\"])\\n plt.ylabel(\\\"Veto Coincidence signal\\\",fontsize=18)\\n plt.xlabel(\\\"time (s)\\\")\\n plt.show\",\n \"def runSimulation(self, R=5000, N=1,s=1000, method='RL'):\\n global n_ec\\n import numpy as np\\n import matplotlib.pyplot as plt\\n import matplotlib\\n matplotlib.use('Agg')\\n matplotlib.pyplot.switch_backend('agg')\\n\\n plt.rcParams.update({'font.size':20})\\n plt.rc('xtick', labelsize=20)\\n plt.rc('ytick', labelsize=20)\\n # step = 2 fs\\n # each round is 2 fs * 1000 = 2 ps\\n\\n init = 'ala2_1stFrame.pdb' #pdb name\\n inits = init\\n n_ec = 2 # angles\\n count = 1\\n newPoints_name = 'start_r_'+str(count)+'.pdb'\\n \\n #W_0 = [1/n_ec for i in range(n_ec)] # no direction\\n #W_0 = [[0.25, 0.25], [0.25, 0.25]]\\n W_0 = [[1/(2*n_ec), 1/(2*n_ec)] for i in range(n_ec)] # directional\\n print(W_0)\\n\\n Ws = []\\n Ws.append(W_0)\\n \\n trj1 = self.run(production_steps = s, start=inits, production='trj_R_0.pdb') # return mdtraj object\\n comb_trj1 = trj1 # single trajectory\\n trjs = comb_trj1\\n trj1_theta = self.map_angles(trj1) # changed for angles to display\\n print('trj1_theta', len(trj1_theta), len(trj1_theta[0]))\\n trj1_Ps_theta, index = self.PreSamp(trj1_theta, myn_clusters = 10) # pre analysis (least count)\\n trj1_Ps_w_theta, index_w = self.PreSamp(trj1_theta, myn_clusters = 100) # for updating the weights\\n print('trj1_Ps_theta', len(trj1_Ps_theta), len(trj1_Ps_theta[0]))\\n\\n newPoints_index_orig = self.findStarting(trj1_Ps_theta, index, W_0, starting_n = N , method = 'RL') #need change\\n newPoints = trj1[newPoints_index_orig[0]]\\n newPoints.save_pdb(newPoints_name)\\n \\n \\n print('trj1_theta[0]',trj1_theta[0])\\n plt.scatter(trj1_theta[0], trj1_theta[1], color='dodgerblue', s=5, alpha=0.2)\\n plt.xlim([-180, 180])\\n plt.ylim([-180, 180])\\n newPoints_theta_x = trj1_theta[0][newPoints_index_orig[0]]\\n newPoints_theta_y = trj1_theta[1][newPoints_index_orig[0]]\\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\\n plt.xlabel(r'$\\\\phi$')\\n plt.ylabel(r'$\\\\psi$')\\n plt.savefig('fig_'+str(count))\\n plt.close()\\n trjs_theta = trj1_theta\\n trjs_Ps_theta = trj1_Ps_theta\\n trjs_Ps_w_theta = trj1_Ps_w_theta \\n for round in range(R):\\n self.updateStat(trjs_theta) # based on all trajectories\\n #W_1 = self.updateW(trjs_Ps_theta, W_0) \\n W_1 = self.updateW(trjs_Ps_w_theta, W_0) \\n W_0 = W_1\\n W_1 = W_0\\n Ws.append(W_0)\\n s = 1000\\n trj1 = self.run(production_steps = s, start=newPoints_name, production='trj_R_'+str(count)+'.pdb') # return mdtraj object\\n com_trjs = trjs.join(trj1) \\n trjs = com_trjs\\n trjs_theta = np.array(self.map_angles(trjs)) \\n trjs_Ps_theta, index = self.PreSamp(trjs_theta, myn_clusters = 100)\\n myn_clusters1 = 100 #int(10*(round)+1)\\n trjs_Ps_w_theta = trjs_Ps_theta\\n #trjs_Ps_w_theta, index_w = self.PreSamp(trjs_theta, myn_clusters = myn_clusters1)\\n newPoints_index_orig = self.findStarting(trjs_Ps_theta, index, W_1, starting_n = N , method = 'RL')\\n newPoints = trjs[newPoints_index_orig[0]] \\n \\n count = count + 1\\n newPoints_name = 'start_r_'+str(count)+'.pdb'\\n newPoints.save_pdb(newPoints_name)\\n\\n print( myn_clusters1, W_1, self.theta_mean)\\n plt.scatter(trjs_theta[0], trjs_theta[1], color='dodgerblue', s=5, alpha=0.2)\\n plt.xlim([-np.pi, np.pi])\\n plt.ylim([-np.pi, np.pi])\\n newPoints_theta_x = trjs_theta[0][newPoints_index_orig[0]]\\n newPoints_theta_y = trjs_theta[1][newPoints_index_orig[0]]\\n plt.scatter(newPoints_theta_x, newPoints_theta_y, color='red', s=50)\\n plt.scatter(trjs_Ps_w_theta[0], trjs_Ps_w_theta[1], color='green', s=5)\\n plt.xlabel(r'$\\\\phi$')\\n plt.ylabel(r'$\\\\psi$')\\n plt.savefig('fig_'+str(count))\\n plt.close()\\n \\n np.save('w_'+'r'+str(int(R))+'N'+str(N)+'s'+str(s), Ws)\\n np.save('trjs_theta', trjs_theta)\\n return\",\n \"def arb_units(wb_run,sample_run,ei_guess,rebin,map_file,**kwargs):\\n global reducer, rm_zero,inst_name,van_mass,bleed_switch,rate,pixels\\n print 'DGreduce run for ',inst_name,'run number ',sample_run\\n try:\\n n,r=funcreturns.lhs_info('both')\\n #n,r=lhs('both')\\n wksp_out=r[0]\\n except:\\n if sample_run == 0:\\n #deal with the current run being parsed as 0 rather than 00000\\n sample_run='00000'\\n wksp_out=inst_name+str(sample_run)+'.spe'\\n if kwargs.has_key('sum') and kwargs.get('sum')==True:\\n wksp_out=inst_name+str(sample_run[0])+'sum'+'.spe'\\n \\n start_time=time.time()\\n \\n if sample_run=='00000' and mtd.doesExist(inst_name+'00000.raw')==True:\\n print 'Deleteing previous instance of temp data'\\n DeleteWorkspace(Workspace=inst_name+'00000.raw')\\n \\n #repopulate defualts\\n if kwargs.has_key('norm_method'):\\n reducer.normalise_method = kwargs.get('norm_method')\\n print 'Setting normalisation method to ', kwargs.get('norm_method')\\n else:\\n reducer.normalise_method = 'monitor-1'\\n if kwargs.has_key('mask_run'):\\n mask_run = kwargs.get('mask_run')\\n print 'Using run ', kwargs.get('mask_run'),' for diag'\\n else:\\n mask_run=sample_run\\n \\n if kwargs.has_key('background'):\\n reducer.background = kwargs.get('background')\\n print 'Setting background option to ', kwargs.get('background')\\n else:\\n reducer.background = False\\n \\n if kwargs.has_key('fixei'):\\n reducer.fix_ei = kwargs.get('fixei')\\n print 'Setting fixei to ', kwargs.get('fixei')\\n else:\\n reducer.fix_ei = False\\n \\n if kwargs.has_key('save_format'):\\n reducer.save_formats = kwargs.get('save_format')\\n print 'Setting save format to ', kwargs.get('save_format')\\n else:\\n reducer.save_formats = ['.spe']\\n #Set parameters for the run\\n \\n if kwargs.has_key('detector_van_range'):\\n reducer.wb_integr_range = kwargs.get('detector_van_range')\\n print 'Setting detector van int range to ', kwargs.get('detector_van_range')\\n else:\\n reducer.wb_integr_range=[20,100]\\n #-------------DIAG------------------------\\n if kwargs.has_key('bkgd_range'):\\n background_range = kwargs.get('bkgd_range')\\n print 'Setting background intergration to ', kwargs.get('bkgd_range')\\n else:\\n background_range=[15000,19000]\\n \\n if kwargs.has_key('tiny'):\\n tinyval = kwargs.get('tiny')\\n print 'Setting tiny ratelimit to ', kwargs.get('tiny')\\n else:\\n tinyval=1e-10\\n \\n if kwargs.has_key('large'):\\n largeval = kwargs.get('large')\\n print 'Setting large limit to ', kwargs.get('large')\\n else:\\n largeval=1e10\\n \\n if kwargs.has_key('diag_remove_zero'):\\n sampzero = kwargs.get('diag_remove_zero')\\n print 'Setting diag to reject zero backgrounds '\\n else:\\n sampzero =False\\n \\n if kwargs.has_key('diag_van_median_rate_limit_hi'):\\n vanouthi = kwargs.get('diag_van_median_rate_limit_hi')\\n print 'Setting diag_van_median_rate_limit_hi to ', kwargs.get('diag_van_median_rate_limit_hi')\\n else:\\n vanouthi=100\\n \\n if kwargs.has_key('diag_van_median_rate_limit_lo'):\\n vanoutlo = kwargs.get('diag_van_median_rate_limit_lo')\\n print 'Setting diag_van_median_rate_limit_lo to ', kwargs.get('diag_van_median_rate_limit_lo')\\n else:\\n vanoutlo=0.01\\n \\n if kwargs.has_key('diag_van_median_sigma_lo'):\\n vanlo = kwargs.get('diag_van_median_sigma_lo')\\n print 'Setting diag_van_median_sigma_lo to ', kwargs.get('diag_van_median_sigma_lo')\\n else:\\n vanlo=0.1\\n \\n if kwargs.has_key('diag_van_median_sigma_hi'):\\n vanhi = kwargs.get('diag_van_median_sigma_hi')\\n print 'Setting diag_van_median_sigma_hi to ', kwargs.get('diag_van_median_sigma_hi')\\n else:\\n vanhi=1.5\\n \\n if kwargs.has_key('diag_van_median_sigma'):\\n vansig = kwargs.get('diag_van_median_sigma')\\n print 'Setting diag_van_median_sigma to ', kwargs.get('diag_van_median_sigma')\\n else:\\n vansig=0.0\\n \\n if kwargs.has_key('diag_samp_median_sigma_lo'):\\n samplo = kwargs.get('diag_samp_median_sigma_lo')\\n print 'Setting diag_samp_median_sigma_lo to ', kwargs.get('diag_samp_median_sigma_lo')\\n else:\\n samplo=0.0\\n \\n if kwargs.has_key('diag_samp_median_sigma_hi'):\\n samphi = kwargs.get('diag_samp_median_sigma_hi')\\n print 'Setting diag_samp_median_sigma_hi to ', kwargs.get('diag_samp_median_sigma_hi')\\n else:\\n samphi=2.0\\n \\n if kwargs.has_key('diag_samp_median_sigma'):\\n sampsig = kwargs.get('diag_samp_median_sigma')\\n print 'Setting diag_samp_median_sigma to ', kwargs.get('diag_samp_median_sigma')\\n else:\\n sampsig=3.0\\n \\n if kwargs.has_key('bleed'):\\n bleed_switch = kwargs.get('bleed')\\n print 'Setting bleed ', kwargs.get('bleed')\\n else:\\n print 'bleed set to default'\\n #---------------END of DIAG--------------------\\n if kwargs.has_key('det_cal_file'):\\n reducer.det_cal_file = kwargs.get('det_cal_file')\\n reducer.relocate_dets = True\\n print 'Setting detector calibration file to ', kwargs.get('det_cal_file')\\n else:\\n print 'Setting detector calibration to detector block info from ', sample_run\\n reducer.det_cal_file =None\\n reducer.relocate_dets = False\\n \\n if mtd.doesExist(str(sample_run))==True and kwargs.has_key('det_cal_file')==False:\\n print 'For data input type: workspace detector calibration must be specified'\\n print 'use Keyword det_cal_file with a valid detctor file or run number'\\n return\\n \\n \\n \\n if kwargs.has_key('one2one'):\\n reducer.map_file =None\\n print 'one2one selected'\\n \\n else:\\n fileName, fileExtension = os.path.splitext(map_file)\\n if (not fileExtension):\\n map_file=map_file+'.map' \\n reducer.map_file = map_file\\n\\n reducer.energy_bins = rebin\\n \\n if float(str.split(rebin,',')[2])>=float(ei_guess):\\n print 'error rebin range exceeds ei'\\n return\\n \\n print 'output will be normalised to', reducer.normalise_method\\n if (numpy.size(sample_run)) > 1 and kwargs.has_key('sum') and kwargs.get('sum')==True:\\n #this sums the runs together before passing the summed file to the rest of the reduction\\n #this circumvents the inbuilt method of summing which fails to sum the files for diag\\n \\n sumfilename=str(sample_run[0])+'sum'\\n accum=sum_files(sumfilename, sample_run)\\n #the D.E.C. tries to be too clever so we have to fool it into thinking the raw file is already exists as a workpsace\\n RenameWorkspace(InputWorkspace=accum,OutputWorkspace=inst_name+str(sample_run[0])+'.raw')\\n sample_run=sample_run[0]\\n \\n if kwargs.has_key('hardmaskPlus'):\\n HardMaskFile = kwargs.get('hardmaskPlus')\\n print 'Use hardmask from ', HardMaskFile\\n #hardMaskSpec=common.load_mask(HardMaskFile)\\n #MaskDetectors(Workspace='masking',SpectraList=hardMaskSpec)\\n else:\\n HardMaskFile=None\\n \\n if kwargs.has_key('hardmaskOnly'):\\n totalmask = kwargs.get('hardmaskOnly')\\n print 'Using hardmask from ', totalmask\\n #next stable version can replace this with loadmask algoritum\\n specs=diag_load_mask(totalmask)\\n CloneWorkspace(InputWorkspace=sample_run,OutputWorkspace='mask_wksp')\\n MaskDetectors(Workspace='mask_wksp',SpectraList=specs)\\n masking=mtd['mask_wksp']\\n else:\\n \\n masking = reducer.diagnose(wb_run, \\n sample=mask_run,\\n second_white = None,\\n tiny=tinyval, \\n huge=largeval, \\n van_out_lo=vanoutlo,\\n van_out_hi=vanouthi,\\n van_lo=vanlo,\\n van_hi=vanhi,\\n van_sig=vansig,\\n samp_zero=sampzero,\\n samp_lo=samplo,\\n samp_hi=samphi,\\n samp_sig=sampsig,\\n bkgd_range=background_range, \\n variation=1.1,\\n print_results=True,\\n bleed_test=bleed_switch,\\n bleed_maxrate=rate,\\n bleed_pixels=pixels,\\n hard_mask=HardMaskFile)\\n \\n reducer.spectra_masks=masking\\n #fail_list=get_failed_spectra_list(masking)\\n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(masking)\\n \\n print 'Diag found ', len(fail_list),'bad spectra'\\n \\n #Run the conversion\\n deltaE_wkspace = reducer.convert_to_energy(sample_run, ei_guess, wb_run)\\n end_time=time.time()\\n results_name=str(sample_run)+'.spe'\\n \\n ei= (deltaE_wkspace.getRun().getLogData(\\\"Ei\\\").value)\\n \\n if mtd.doesExist('_wksp.spe-white')==True:\\n DeleteWorkspace(Workspace='_wksp.spe-white')\\n \\n if mtd.doesExist(results_name)==False:\\n RenameWorkspace(InputWorkspace=deltaE_wkspace,OutputWorkspace=results_name)\\n \\n print 'Incident energy found ',ei,' meV'\\n print 'Elapsed time =',end_time-start_time, 's'\\n #get the name that convert to energy will use\\n \\n \\n RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)\\n \\n return mtd[wksp_out]\",\n \"def main():\\n print_banner()\\n params = read_steering()\\n s, x, y, cur, theta = build_kinoshita()\\n s, x, y, cur, theta = read_centerline(s, x, y, cur, theta)\\n s, x, y, cur, theta = extend_centerline(s, x, y, cur, theta)\\n for t in range(TSTEPS+1):\\n cur, theta = tan2curv(s, x, y)\\n cur_ori = np.copy(cur)\\n cur = filter_curvature(cur, t)\\n cur_flt = np.copy(cur)\\n cur = lag(s, cur, t)\\n cur_lag = np.copy(cur)\\n beck_bed = build_beck(cur, s, t)\\n allxyz = offset_all(x, y, beck_bed, t)\\n if t == 0:\\n write_xyz_file(allxyz)\\n write_mesh_file(allxyz, beck_bed)\\n oxbowxList, oxbowyList = [], []\\n centerlinexList, centerlineyList = [], []\\n if np.mod(t, GPRINT) == 0:\\n centerlinexList.append(x)\\n centerlineyList.append(y)\\n mf.make_figure(x, y, allxyz, cur_ori, cur_flt, cur_lag, s, beck_bed,\\n params, t, oxbowxList, oxbowyList, centerlinexList, centerlineyList)\\n if t == TSTEPS:\\n break\\n s, x, y = migration(s, x, y, cur_flt, cur_lag, theta, t)\\n s, x, y, oxbowx, oxbowy, found_cutoff = cutoff(s, x, y)\\n s, x, y = smooth_centerline(x, y)\\n s, x, y, cur, theta = resample_centerline(s, x, y)\\n if found_cutoff:\\n oxbowxList.append(oxbowx)\\n oxbowyList.append(oxbowy)\\n make_gif()\\n job_done()\",\n \"def main():\\n\\tif len(sys.argv) < 12 or len(sys.argv) > 13:\\n\\t\\tprint(\\\"Input parameters must be: 'filename lambda mu C c0 Q theta L H simulation_time is_debug repeats(optionally)'\\\")\\n\\telse:\\n\\t\\tstart_time = time.time()\\n\\n\\t\\tfile_name = sys.argv[1]\\n\\t\\tlambd = float(sys.argv[2])\\n\\t\\tmu = float(sys.argv[3])\\n\\t\\tC = int(sys.argv[4])\\n\\t\\tc0 = int(sys.argv[5])\\n\\t\\tQ = int(sys.argv[6])\\n\\t\\ttheta = float(sys.argv[7])\\n\\t\\tL = int(sys.argv[8])\\n\\t\\tH = int(sys.argv[9])\\n\\t\\tsimulation_time = float(sys.argv[10]);\\n\\t\\tis_debug = True if sys.argv[11] == \\\"True\\\" else False;\\n\\t\\trepeats = int(sys.argv[12]) if len(sys.argv) == 13 else 1;\\n\\n\\t\\tprint(\\\"Simulation started for params: lambda =\\\", lambd,\\n\\t\\t\\t \\\", mu =\\\", mu,\\n\\t\\t\\t \\\", C =\\\", C,\\n\\t\\t\\t \\\", c0 =\\\", c0,\\n\\t\\t\\t \\\", Q =\\\", Q,\\n\\t\\t\\t \\\", theta =\\\", theta,\\n\\t\\t\\t \\\", L =\\\", L,\\n\\t\\t\\t \\\", H =\\\", H,\\n\\t\\t\\t \\\", repeats =\\\", repeats)\\n\\n\\t\\tblocked = 0\\n\\t\\tserved = 0\\n\\t\\tgenerated = 0\\n\\t\\tB = 0\\n\\t\\tN = 0\\n\\n\\t\\tsimulation = Simulation(\\\"m/m/c[c0]/r[l,h]\\\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\\n\\t\\tfor i in range(0, repeats):\\n\\t\\t\\tsimulation = Simulation(\\\"m/m/c[c0]/r[l,h]\\\", lambd, mu, theta, C, c0, L, H, simulation_time, Q, is_debug)\\n\\t\\t\\tsimulation.start()\\n\\t\\t\\tblocked += simulation.queue.blocked\\n\\t\\t\\tserved += simulation.served_count\\n\\t\\t\\tgenerated += simulation.flow.generated_count\\n\\t\\t\\tB += simulation.queue.blocked/(simulation.served_count+simulation.queue.blocked)\\n\\t\\t\\tN += simulation.served_count/simulation_time\\n\\t\\tend_time = time.time()\\n\\n\\t\\tblocked = blocked/repeats\\n\\t\\tserved = served/repeats\\n\\t\\tgenerated = generated/repeats\\n\\t\\tB = B/repeats\\n\\t\\tN = N/repeats\\n\\n\\t\\tprint( \\\"\\\")\\n\\t\\tprint( \\\"Summary results:\\\")\\n\\t\\tprint( \\\"blocked=\\\", blocked, \\\" served=\\\", served, \\\", generated=\\\", generated)\\n\\t\\tprint(\\\"B = \\\", B)\\n\\t\\tprint(\\\"N = \\\", N)\\n\\t\\tprint(\\\"Execution time = %s seconds\\\" % (end_time - start_time))\\n\\t\\tprint( \\\"... to be implemented more summary ...\\\")\\n\\n\\t\\t# write stats to file\\n\\t\\tabs_path = os.path.abspath(__file__)\\n\\t\\tpath = os.path.relpath('stats', abs_path)\\n\\t\\tpath = os.path.join(path, file_name + '-(%s,%s,%s,%s,%s,%s,%s,%s).csv' % (lambd,mu,theta,C,c0,L,H,simulation_time))\\n\\n\\t\\toutfile=open(path,'w')\\n\\t\\toutput = csv.writer(outfile, delimiter=';')\\n\\t\\toutput.writerow(['Request ID','Queue', 'Arrival_Time','Queue_Arrival_time','Server_Arrival_time','alpha','beta'])\\n\\n\\t\\ti=0\\n\\t\\tfor request in simulation.served_requests:\\n\\t\\t\\ti=i+1\\n\\t\\t\\toutrow=[]\\n\\t\\t\\toutrow.append(request.ID)\\n\\t\\t\\toutrow.append(request.queue_size_at_serving)\\n\\t\\t\\toutrow.append(request.arrival_time)\\n\\t\\t\\toutrow.append(request.queue_arrival_time)\\n\\t\\t\\toutrow.append(request.server_arrival_time)\\n\\t\\t\\toutrow.append(request.alpha)\\n\\t\\t\\toutrow.append(request.beta)\\n\\t\\t\\toutput.writerow(outrow)\\n\\t\\toutfile.close()\\n\\n\\t\\treturn simulation\",\n \"def run(config, tim=None):\\n import common_lib\\n import dr_lib\\n import DST\\n \\n if tim is not None:\\n tim.getTime(False)\\n old_time = tim.getOldTime()\\n\\n if config.data is None:\\n raise RuntimeError(\\\"Need to pass a data filename to the driver \\\"\\\\\\n +\\\"script.\\\")\\n\\n # Read in geometry if one is provided\\n if config.inst_geom is not None:\\n if config.verbose:\\n print \\\"Reading in instrument geometry file\\\"\\n \\n inst_geom_dst = DST.getInstance(\\\"application/x-NxsGeom\\\",\\n config.inst_geom)\\n else:\\n inst_geom_dst = None\\n\\n config.so_axis = \\\"time_of_flight\\\"\\n\\n # Steps 1-3: Produce a scaled summed dark current dataset\\n dc_som = dr_lib.scaled_summed_data(config.dkcur, config,\\n dataset_type=\\\"dark_current\\\",\\n timer=tim)\\n\\n # Perform Steps 3-6 on black can data\\n if config.bcan is not None:\\n b_som1 = dr_lib.calibrate_dgs_data(config.bcan, config, dc_som,\\n dataset_type=\\\"black_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_bcan,\\n timer=tim)\\n else:\\n b_som1 = None\\n\\n # Perform Steps 3-6 on empty can data \\n if config.ecan is not None:\\n e_som1 = dr_lib.calibrate_dgs_data(config.ecan, config, dc_som,\\n dataset_type=\\\"empty_can\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_ecan,\\n timer=tim)\\n else:\\n e_som1 = None\\n\\n # Perform Steps 3-6 on normalization data\\n n_som1 = dr_lib.calibrate_dgs_data(config.data, config, dc_som,\\n dataset_type=\\\"normalization\\\",\\n inst_geom_dst=inst_geom_dst,\\n tib_const=config.tib_const,\\n cwp=config.cwp_data,\\n timer=tim)\\n\\n # Perform Steps 7-16 on normalization data\\n if config.norm_trans_coeff is None:\\n norm_trans_coeff = None\\n else:\\n norm_trans_coeff = config.norm_trans_coeff.toValErrTuple()\\n\\n # Determine if we need to rebin the empty or black can data\\n if config.ecan is not None and e_som1 is not None:\\n ecan_cwp = True\\n else:\\n ecan_cwp = False\\n\\n if config.bcan is not None and b_som1 is not None:\\n bcan_cwp = True\\n else:\\n bcan_cwp = False \\n\\n cwp_used = ecan_cwp or bcan_cwp\\n\\n n_som2 = dr_lib.process_dgs_data(n_som1, config, b_som1, e_som1,\\n norm_trans_coeff,\\n dataset_type=\\\"normalization\\\",\\n cwp_used=cwp_used,\\n timer=tim)\\n \\n del n_som1, b_som1, e_som1\\n\\n # Step 17: Integrate normalization spectra\\n if config.verbose:\\n print \\\"Integrating normalization spectra\\\"\\n\\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.norm_int_range is None:\\n start_val = float(\\\"inf\\\")\\n end_val = float(\\\"inf\\\")\\n else:\\n if not config.wb_norm:\\n # Translate energy transfer to final energy\\n ef_start = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[0]\\n ef_end = config.initial_energy.getValue() - \\\\\\n config.norm_int_range[1]\\n # Convert final energy to final wavelength\\n start_val = common_lib.energy_to_wavelength((ef_start, 0.0))[0]\\n end_val = common_lib.energy_to_wavelength((ef_end, 0.0))[0]\\n else:\\n start_val = config.norm_int_range[0]\\n end_val = config.norm_int_range[1]\\n \\n n_som3 = dr_lib.integrate_spectra(n_som2, start=start_val,\\n end=end_val, width=True)\\n\\n del n_som2\\n \\n if tim is not None:\\n tim.getTime(msg=\\\"After integrating normalization spectra \\\")\\n\\n file_comment = \\\"Normalization Integration range: %0.3fA, %0.3fA\\\" \\\\\\n % (start_val, end_val)\\n \\n hlr_utils.write_file(config.output, \\\"text/num-info\\\", n_som3,\\n output_ext=\\\"norm\\\",\\n data_ext=config.ext_replacement,\\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"normalization values\\\",\\n comments=[file_comment],\\n tag=\\\"Integral\\\", units=\\\"counts\\\") \\n \\n if tim is not None:\\n tim.getTime(False)\\n\\n if config.verbose:\\n print \\\"Making mask file\\\"\\n\\n # Make mask file from threshold\\n dr_lib.filter_normalization(n_som3, config.lo_threshold,\\n config.hi_threshold, config)\\n\\n if tim is not None:\\n tim.getTime(msg=\\\"After making mask file \\\")\\n\\n # Write out RMD file\\n n_som3.attr_list[\\\"config\\\"] = config\\n\\n hlr_utils.write_file(config.output, \\\"text/rmd\\\", n_som3,\\n output_ext=\\\"rmd\\\",\\n data_ext=config.ext_replacement, \\n path_replacement=config.path_replacement,\\n verbose=config.verbose,\\n message=\\\"metadata\\\")\\n \\n if tim is not None:\\n tim.setOldTime(old_time)\\n tim.getTime(msg=\\\"Total Running Time\\\")\",\n \"def abs_units(wb_run,sample_run,mono_van,wb_mono,samp_rmm,samp_mass,ei_guess,rebin,map_file,monovan_mapfile,**kwargs): \\n #available keywords\\n #abs_units_van_range\\n global reducer, rm_zero,inst_name,van_mass,bleed_switch,rate,pixels\\n print 'DGreduce run for ',inst_name,'run number ',sample_run\\n print 'Output will be in absolute units of mb/str/mev/fu'\\n\\n #reducer.van_rmm =50.94\\n reducer.van_mass=van_mass\\n #sample info\\n reducer.sample_mass=samp_mass\\n reducer.sample_rmm =samp_rmm\\n print 'Using vanadium mass: ',van_mass\\n print ' sample mass: ',samp_mass \\n print ' sample_rmm : ',samp_rmm \\n # check if mono-vanadium is provided as multiple files list or just put in brackets ocasionally\\n if isinstance(mono_van,list):\\n if len(mono_van)>1:\\n raise IOError(' Can currently work only with single monovan file but list supplied')\\n else:\\n mono_van = mono_van[0];\\n\\n \\n try:\\n n,r=lhs('both')\\n wksp_out=r[0]\\n except:\\n if sample_run == 0:\\n #deal with the current run being parsed as 0 rather than 00000\\n sample_run='00000'\\n wksp_out=str(sample_run)+'.spe'\\n \\n start_time=time.time()\\n \\n if sample_run=='00000' and mtd.doesExist(inst_name+'00000.raw')==True:\\n print 'Deleteing previous instance of temp data'\\n DeleteWorkspace(Workspace=inst_name+'00000.raw')\\n \\n if kwargs.has_key('norm_method'):\\n reducer.normalise_method = kwargs.get('norm_method')\\n print 'Setting normalisation method to ', kwargs.get('norm_method')\\n else:\\n reducer.normalise_method = 'monitor-1'\\n \\n if kwargs.has_key('mask_run'):\\n mask_run = kwargs.get('mask_run')\\n print 'Using run ', kwargs.get('mask_run'),' for diag'\\n else:\\n mask_run=sample_run\\n \\n if kwargs.has_key('background'):\\n reducer.background = kwargs.get('background')\\n print 'Setting background option to ', kwargs.get('background')\\n else:\\n reducer.background = False\\n \\n if kwargs.has_key('fixei'):\\n reducer.fix_ei = kwargs.get('fixei')\\n print 'Setting fixei to ', kwargs.get('fixei')\\n else:\\n reducer.fix_ei = False\\n \\n if kwargs.has_key('save_format'):\\n reducer.save_formats = kwargs.get('save_format')\\n print 'Setting save format to ', kwargs.get('save_format')\\n else:\\n reducer.save_formats = ['.spe']\\n #Set parameters for the run\\n \\n if kwargs.has_key('detector_van_range'):\\n reducer.wb_integr_range = kwargs.get('detector_van_range')\\n print 'Setting detector van int range to ', kwargs.get('detector_van_range')\\n else:\\n reducer.wb_integr_range=[20,100]\\n \\n #######DIAG###########\\n if kwargs.has_key('bkgd_range'):\\n background_range = kwargs.get('bkgd_range')\\n print 'Setting background intergration to ', kwargs.get('bkgd_range')\\n else:\\n background_range=[15000,19000]\\n \\n if kwargs.has_key('tiny'):\\n tinyval = kwargs.get('tiny')\\n print 'Setting tiny ratelimit to ', kwargs.get('tiny')\\n else:\\n tinyval=1e-10\\n \\n if kwargs.has_key('large'):\\n largeval = kwargs.get('large')\\n print 'Setting large limit to ', kwargs.get('large')\\n else:\\n largeval=1e10\\n \\n if kwargs.has_key('diag_remove_zero'):\\n sampzero = kwargs.get('diag_remove_zero')\\n print 'Setting diag to reject zero backgrounds '\\n else:\\n sampzero =False\\n \\n if kwargs.has_key('diag_van_median_rate_limit_hi'):\\n vanouthi = kwargs.get('diag_van_median_rate_limit_hi')\\n print 'Setting diag_van_median_rate_limit_hi to ', kwargs.get('diag_van_median_rate_limit_hi')\\n else:\\n vanouthi=100\\n \\n if kwargs.has_key('diag_van_median_rate_limit_lo'):\\n vanoutlo = kwargs.get('diag_van_median_rate_limit_lo')\\n print 'Setting diag_van_median_rate_limit_lo to ', kwargs.get('diag_van_median_rate_limit_lo')\\n else:\\n vanoutlo=0.01\\n \\n if kwargs.has_key('diag_van_median_sigma_lo'):\\n vanlo = kwargs.get('diag_van_median_sigma_lo')\\n print 'Setting diag_van_median_sigma_lo to ', kwargs.get('diag_van_median_sigma_lo')\\n else:\\n vanlo=0.1\\n \\n if kwargs.has_key('diag_van_median_sigma_hi'):\\n vanhi = kwargs.get('diag_van_median_sigma_hi')\\n print 'Setting diag_van_median_sigma_hi to ', kwargs.get('diag_van_median_sigma_hi')\\n else:\\n vanhi=1.5\\n \\n if kwargs.has_key('diag_van_median_sigma'):\\n vansig = kwargs.get('diag_van_median_sigma')\\n print 'Setting diag_van_median_sigma to ', kwargs.get('diag_van_median_sigma')\\n else:\\n vansig=0.0\\n \\n if kwargs.has_key('diag_samp_median_sigma_lo'):\\n samplo = kwargs.get('diag_samp_median_sigma_lo')\\n print 'Setting diag_samp_median_sigma_lo to ', kwargs.get('diag_samp_median_sigma_lo')\\n else:\\n samplo=0.0\\n \\n if kwargs.has_key('diag_samp_median_sigma_hi'):\\n samphi = kwargs.get('diag_samp_median_sigma_hi')\\n print 'Setting diag_samp_median_sigma_hi to ', kwargs.get('diag_samp_median_sigma_hi')\\n else:\\n samphi=2.0\\n \\n if kwargs.has_key('diag_samp_median_sigma'):\\n sampsig = kwargs.get('diag_samp_median_sigma')\\n print 'Setting diag_samp_median_sigma to ', kwargs.get('diag_samp_median_sigma')\\n else:\\n sampsig=3.0\\n \\n if kwargs.has_key('bleed'):\\n bleed_switch = kwargs.get('bleed')\\n print 'Setting bleed ', kwargs.get('bleed')\\n else:\\n print 'bleed set to default'\\n #####diad end########\\n \\n \\n if kwargs.has_key('det_cal_file'):\\n reducer.det_cal_file = kwargs.get('det_cal_file')\\n reducer.relocate_dets = True\\n print 'Setting detector calibration file to ', kwargs.get('det_cal_file')\\n else:\\n print 'Setting detector calibration to detector block info from ', sample_run\\n reducer.det_cal_file =None\\n reducer.relocate_dets = False\\n \\n if mtd.doesExist(str(sample_run))==True and kwargs.has_key('det_cal_file')==False:\\n print 'For data input type: workspace detector calibration must be specified'\\n print 'use Keyword det_cal_file with a valid detctor file or run number'\\n return\\n \\n \\n if kwargs.has_key('one2one'):\\n reducer.map_file =None\\n map_file = \\\"\\\"\\n print 'one2one selected'\\n else:\\n fileName, fileExtension = os.path.splitext(map_file)\\n if (not fileExtension):\\n map_file = map_file+'.map'\\n reducer.map_file = map_file;\\n \\n if kwargs.has_key('hardmaskPlus'):\\n HardMaskFile = kwargs.get('hardmaskPlus')\\n print 'Use hardmask from ', HardMaskFile\\n #hardMaskSpec=common.load_mask(HardMaskFile)\\n #MaskDetectors(Workspace='masking',SpectraList=hardMaskSpec)\\n else:\\n HardMaskFile=None\\n \\n reducer.energy_bins = rebin\\n #monovan info\\n fileName, fileExtension = os.path.splitext(monovan_mapfile)\\n if (not fileExtension):\\n monovan_mapfile=monovan_mapfile+'.map'\\n reducer.abs_map_file =monovan_mapfile \\n\\n if kwargs.has_key('abs_units_van_range'):\\n reducer.monovan_integr_range = kwargs.get('abs_units_van_range')\\n print 'Setting absolute units vanadium integration range to: ', kwargs.get('abs_units_van_range')\\n else:\\n reducer.monovan_integr_range=[-40,40]\\n\\n \\n \\n print 'output will be normalised to', reducer.normalise_method\\n if (numpy.size(sample_run)) > 1 and kwargs.has_key('sum') and kwargs.get('sum')==True:\\n #this sums the runs together before passing the summed file to the rest of the reduction\\n #this circumvents the inbuilt method of summing which fails to sum the files for diag\\n \\n sumfilename=str(sample_run[0])+'sum'\\n accum=sum_files(sumfilename, sample_run)\\n #the D.E.C. tries to be too clever so we have to fool it into thinking the raw file is already exists as a workpsace\\n RenameWorkspace(InputWorkspace=accum,OutputWorkspace=inst_name+str(sample_run[0])+'.raw')\\n sample_run=sample_run[0]\\n \\n if kwargs.has_key('hardmaskOnly'):\\n if (kwargs.get('hardmaskOnly')): \\n totalmask = kwargs.get('hardmaskOnly')\\n print 'Using hardmask from ', totalmask\\n #next stable version can replace this with loadmask algoritum\\n specs=diag_load_mask(totalmask)\\n else:\\n specs=\\\"\\\"\\n \\n CloneWorkspace(InputWorkspace=sample_run,OutputWorkspace='mask_wksp')\\n MaskDetectors(Workspace='mask_wksp',SpectraList=specs)\\n masking =mtd['mask_wksp']\\n else:\\n print '########### Run diagnose for sample run ##############'\\n masking = reducer.diagnose(wb_run, \\n sample=mask_run,\\n second_white = None,\\n tiny=tinyval, \\n huge=largeval, \\n van_out_lo=vanoutlo,\\n van_out_hi=vanouthi,\\n van_lo=vanlo,\\n van_hi=vanhi,\\n van_sig=vansig,\\n samp_zero=sampzero,\\n samp_lo=samplo,\\n samp_hi=samphi,\\n samp_sig=sampsig,\\n bkgd_range=background_range, \\n variation=1.1,\\n print_results=True,\\n bleed_test=bleed_switch,\\n bleed_maxrate=rate,\\n bleed_pixels=pixels,\\n hard_mask=HardMaskFile)\\n \\n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(masking) \\n print 'first Diag found ', len(fail_list),'bad spectra out of: ',n_total_spectra,' ws spectra'\\n \\n if kwargs.has_key('use_sam_msk_on_monovan') and kwargs.get('use_sam_msk_on_monovan')==True:\\n print 'applying sample run mask to mono van'\\n reducer.spectra_masks=masking\\n fail_list=get_failed_spectra_list(masking) \\n else:\\n print '########### Run diagnose for monochromatic vanadium run ##############'\\n masking2 = reducer.diagnose(wb_mono, \\n sample=mono_van,\\n second_white = None,\\n tiny=tinyval, \\n huge=largeval, \\n van_out_lo=vanoutlo,\\n van_out_hi=vanouthi,\\n van_lo=vanlo,\\n van_hi=vanhi,\\n van_sig=vansig,\\n samp_zero=sampzero,\\n samp_lo=samplo,\\n samp_hi=samphi,\\n samp_sig=sampsig,\\n bkgd_range=background_range, \\n variation=1.1,\\n print_results=True,\\n bleed_test=bleed_switch,\\n bleed_maxrate=rate,\\n bleed_pixels=pixels,\\n hard_mask=HardMaskFile)\\n \\n total_mask=masking+masking2 \\n reducer.spectra_masks=total_mask \\n fail_list,n_total_spectra =get_failed_spectra_list_from_masks(total_mask)\\n #fail_list=get_failed_spectra_list('total_mask')\\n \\n \\n print 'Diag found ', len(fail_list),'bad spectra out of: ',n_total_spectra,' ws spectra'\\n \\n \\n \\n #Run the conversion first on the sample\\n deltaE_wkspace_sample = reducer.convert_to_energy(sample_run, ei_guess, wb_run)\\n\\n \\n if kwargs.has_key('mono_correction_factor'):\\n absnorm_factor=kwargs.get('mono_correction_factor')\\n print 'Using supplied correction factor for absolute units'\\n else:\\n print '##### Evaluate the integral from the monovan run and calculate the correction factor ######'\\n print ' Using absolute units vanadion integration range : ', reducer.monovan_integr_range \\n #now on the mono_vanadium run swap the mapping file\\n reducer.map_file = monovan_mapfile \\n deltaE_wkspace_monovan = reducer.convert_to_energy(mono_van, ei_guess, wb_mono)\\n \\n (absnorm_factorL,absnorm_factorSS,absnorm_factorP,absnorm_factTGP) = getAbsNormalizationFactor(deltaE_wkspace_monovan.getName(),str(reducer.monovan_integr_range[0]),str(reducer.monovan_integr_range[1])) \\n \\n print 'Absolute correction factor S^2 =',absnorm_factorSS,' Libisis: ',absnorm_factorL,' Puasonian: ',absnorm_factorP, ' TGP : ',absnorm_factTGP\\n CreateSingleValuedWorkspace(OutputWorkspace='AbsFactor',DataValue=absnorm_factTGP)\\n end_time=time.time()\\n results_name=str(sample_run)+'.spe'\\n ei= (deltaE_wkspace_sample.getRun().getLogData(\\\"Ei\\\").value)\\n \\n if mtd.doesExist('_wksp.spe-white')==True:\\n DeleteWorkspace(Workspace='_wksp.spe-white')\\n \\n \\n print 'Incident energy found for sample run ',ei,' meV'\\n print 'Incident energy found for mono vanadium run ',ei,' meV'\\n print 'Elapsed time =',end_time-start_time, 's'\\n #get the name that convert to energy will use\\n \\n if mtd.doesExist(results_name)==False:\\n RenameWorkspace(InputWorkspace=deltaE_wkspace_sample,OutputWorkspace=results_name)\\n if results_name != wksp_out:\\n RenameWorkspace(InputWorkspace=results_name,OutputWorkspace=wksp_out)\\n Divide(LHSWorkspace=wksp_out,RHSWorkspace='AbsFactor',OutputWorkspace=wksp_out)\\n DeleteWorkspace(Workspace='AbsFactor')\\n return mtd[wksp_out]\",\n \"def calculate(x, y, z, vx, vy, vz, dt, m, g, B2, S0, omega):\\n t = 0.0\\n # Establish lists with initial position and velocity components and time.\\n x_list = [x]\\n y_list = [y]\\n z_list = [z]\\n vx_list = [vx]\\n vy_list = [vy]\\n vz_list = [vz]\\n t_list = [t]\\n\\n # Set up visual elements.\\n mound = visual.box(pos=(0,0,0), length=0.1, width=0.5, height=0.03, color=visual.color.white)\\n plate = visual.box(pos=(18,0,0), length=0.5, width=0.5, height=0.03, color=visual.color.white)\\n ball = visual.sphere(pos=(x,y,z), radius=0.05, color=visual.color.white)\\n ball.trail = visual.curve(color=ball.color)\\n\\n while y >= 0.0:\\n visual.rate(100) # Limit to no more than 100 iterations per second.\\n t, x, y, z, vx, vy, vz = do_time_step(t, dt, x, y, z, vx, vy, vz, m, B2, g, S0, omega)\\n x_list.append(x)\\n y_list.append(y)\\n z_list.append(z)\\n vx_list.append(vx)\\n vy_list.append(vy)\\n vz_list.append(vz)\\n t_list.append(t)\\n ball.pos = (x,y,z)\\n ball.trail.append(pos=ball.pos)\\n\\n return t_list, x_list, y_list, z_list, vx_list, vy_list, vz_list\",\n \"def run_lpme(self) -> np.array:\\n q = self.sphere.n\\n signs = []\\n for i in range(q):\\n a = np.ones(q)\\n a = a / np.sqrt(q)\\n a_prime = np.copy(a)\\n a_prime[i] = -a_prime[i]\\n\\n z_a = a * self.sphere.radius + self.sphere.origin\\n z_a_prime = a_prime * self.sphere.radius + self.sphere.origin\\n\\n if self.oracle.compare(z_a, z_a_prime):\\n signs.append(1.0)\\n else:\\n signs.append(-1.0)\\n\\n orthants = initialize_orthants(signs)\\n\\n # number of cycles\\n nc = 4\\n theta_list = [(orth.start + orth.stop) / 2 for orth in orthants]\\n for _ in range(0, nc):\\n for j in range(0, q - 1):\\n theta_a = orthants[j].start\\n theta_b = orthants[j].stop\\n while abs(theta_b - theta_a) > self.e:\\n theta_c = (theta_a * 3 + theta_b) / 4\\n theta_d = (theta_a + theta_b) / 2\\n theta_e = (theta_a + theta_b * 3) / 4\\n\\n theta_list[j] = theta_a\\n vec_a = compute_vector(self.sphere, theta_list)\\n\\n theta_list[j] = theta_b\\n vec_b = compute_vector(self.sphere, theta_list)\\n\\n theta_list[j] = theta_c\\n vec_c = compute_vector(self.sphere, theta_list)\\n\\n theta_list[j] = theta_d\\n vec_d = compute_vector(self.sphere, theta_list)\\n\\n theta_list[j] = theta_e\\n vec_e = compute_vector(self.sphere, theta_list)\\n\\n # compare ac\\n cac = self.oracle.compare(vec_a, vec_c)\\n ccd = self.oracle.compare(vec_c, vec_d)\\n cde = self.oracle.compare(vec_d, vec_e)\\n ceb = self.oracle.compare(vec_e, vec_b)\\n self.num_queries += 4\\n\\n if self.check_i:\\n context = {\\n \\\"theta_list\\\": theta_list,\\n \\\"j\\\": j,\\n \\\"theta_a\\\": theta_a,\\n \\\"theta_b\\\": theta_b,\\n \\\"theta_c\\\": theta_c,\\n \\\"theta_d\\\": theta_d,\\n \\\"theta_e\\\": theta_e,\\n }\\n self.check_inconsistency(cac, ccd, cde, ceb, context)\\n\\n if cac:\\n theta_b = theta_d\\n elif ccd:\\n theta_b = theta_d\\n elif cde:\\n theta_a = theta_c\\n theta_b = theta_e\\n elif ceb:\\n theta_a = theta_d\\n else:\\n theta_a = theta_d\\n\\n # update theta list\\n theta_list[j] = (theta_a + theta_b) / 2\\n\\n # save theta list\\n self.theta_list = theta_list\\n return normalize(compute_vector(self.sphere, theta_list) - self.sphere.origin)\",\n \"def abcRun(self,fitfun=None, data={}, t=1,savetemp=False):\\n if not fitfun:\\n fitfun = self.basicfit\\n if savetemp:\\n CP.dump(self.q1theta,open('q1theta','w'))\\n# Running the model ==========================\\n if os.path.exists('phi.temp'):\\n phi,j = CP.load(open('phi.temp','r'))\\n else:\\n j=0\\n phi = recarray((self.K,t),formats=['f8']*self.nphi, names = self.phi.dtype.names)\\n for i in xrange(j,self.K):\\n theta = [self.q1theta[n][i] for n in self.q1theta.dtype.names]\\n r = self.po.apply_async(self.model, theta)\\n phi[i]= [tuple(l) for l in r.get()[-t:]]# #phi is the last t points in the simulation\\n if i%100 == 0:\\n print \\\"==> K = %s\\\"%i\\n if savetemp:\\n CP.dump((phi,i),open('phi.temp','w'))\\n if savetemp: #If all replicates are done, clear temporary save files.\\n os.unlink('phi.temp')\\n os.unlink('q1theta')\\n\\n print \\\"==> Done Running the K replicates\\\\n\\\"\\n qtilphi = self.logPooling(phi) #vector with probability of each phi[i] belonging to qtilphi\\n qtilphi = nan_to_num(qtilphi)\\n print 'max(qtilphi): ', max(qtilphi)\\n# \\n# calculate weights\\n w = [fitfun(phi[i],data) for i in xrange(phi.shape[0])]\\n w /=sum(w)\\n w = 1-w\\n print \\\"w=\\\",w, mean(w), var(w)\\n print\\n print 'qtilphi=',qtilphi\\n # Resampling Thetas\\n w = nan_to_num(w)\\n w = array(w)*qtilphi\\n w /=sum(w)\\n w = nan_to_num(w)\\n print 'max(w): ',max(w)\\n# for n in phi.dtype.names:\\n# P.plot(mean(phi[n],axis=0),label=n)\\n# P.figure()\\n# P.plot(w,label='w')\\n# P.plot(qtilphi,label='qtilphi')\\n# P.title('Resampling vector(w) and pooled prior on Phi')\\n# P.legend()\\n if sum(w) == 0.0:\\n sys.exit('Resampling weights are all zero, please check your model or data.')\\n j = 0\\n while j < self.L: # Extract L samples from q1theta\\n i=randint(0,w.size)# Random position of w and q1theta\\n if random()<= w[i]:\\n self.post_theta[j] = self.q1theta[i]# retain the sample according with resampling prob.\\n j+=1\\n \\n\\n self.done_running = True\",\n \"def test_track_particles_multi_beamline(self):\\n BaseUtils.i_am_sure_my_code_closed_in_if_name_equal_main()\\n bl1 = HUST_SC_GANTRY().create_beamline()\\n bl2 = HUST_SC_GANTRY(qs3_gradient=7).create_beamline()\\n bl3 = HUST_SC_GANTRY(qs3_gradient=0).create_beamline()\\n\\n p1 = ParticleFactory.create_proton_along(\\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 215\\n )\\n\\n p2 = ParticleFactory.create_proton_along(\\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 220\\n )\\n\\n p3 = ParticleFactory.create_proton_along(\\n bl.trajectory, gantry.first_bending_part_length() + gantry.DL2, 210\\n )\\n\\n ps_cpu1 = [p1.copy(), p2.copy(), p3.copy()]\\n ps_cpu2 = [p1.copy(), p2.copy(), p3.copy()]\\n ps_cpu3 = [p1.copy(), p2.copy(), p3.copy()]\\n ps_gpu32 = [p1.copy(), p2.copy(), p3.copy()]\\n ps_gpu64 = [p1.copy(), p2.copy(), p3.copy()]\\n\\n print(\\\"CPU\\\")\\n s = time.time()\\n ParticleRunner.run_only(ps_cpu1, bl1, 10, 20*MM, 6)\\n ParticleRunner.run_only(ps_cpu2, bl2, 10, 20*MM, 6)\\n ParticleRunner.run_only(ps_cpu3, bl3, 10, 20*MM, 6)\\n print(f\\\"CPU time = {time.time()-s}\\\")\\n for p in ps_cpu1+ps_cpu2 + ps_cpu3:\\n print(p)\\n\\n print(\\\"GPU64\\\")\\n s = time.time()\\n ps_end = ga64_b512.track_multi_particle_beamlime_for_magnet_with_single_qs(\\n [bl1, bl2, bl3], ps_gpu64, 10, 20*MM\\n )\\n print(f\\\"GPU64 time = {time.time()-s}\\\")\\n\\n for ps in ps_end:\\n for p in ps:\\n print(p)\\n\\n for gid in range(3):\\n for pid in range(3):\\n print(f\\\"diff={ps_end[gid][pid]-(ps_cpu1+ps_cpu2 + ps_cpu3)[gid*3+pid]}\\\")\",\n \"def run():\\n motion_extraction()\\n file_buff = open(\\\"right_arm.txt\\\", \\\"w\\\")\\n for frame_coord in limb_coords[0]:\\n file_buff.write(\\\"%d %d\\\\n\\\" % (frame_coord[1], frame_coord[0]))\\n file_buff.close()\\n file_buff2 = open(\\\"left_arm.txt\\\", \\\"w\\\")\\n for frame_coord in limb_coords[1]:\\n file_buff2.write(\\\"%d %d\\\\n\\\" % (frame_coord[1], frame_coord[0]))\\n file_buff2.close()\\n file_buff3 = open(\\\"body.txt\\\", \\\"w\\\")\\n for frame_coord in limb_coords[2]:\\n file_buff3.write(\\\"%d %d\\\\n\\\" % (frame_coord[1], frame_coord[0]))\\n file_buff3.close()\\n file_buff4 = open(\\\"right_leg.txt\\\", \\\"w\\\")\\n for frame_coord in limb_coords[3]:\\n file_buff4.write(\\\"%d %d\\\\n\\\" % (frame_coord[1], frame_coord[0]))\\n file_buff4.close()\\n file_buff5 = open(\\\"left_leg.txt\\\", \\\"w\\\")\\n for frame_coord in limb_coords[4]:\\n file_buff5.write(\\\"%d %d\\\\n\\\" % (frame_coord[1], frame_coord[0]))\\n file_buff5.close()\",\n \"def test(self):\\n winsound.PlaySound('SystemExclamation', winsound.SND_ALIAS)\\n \\n pulses=1000*3\\n winsound.Beep(200, 1000) # .Beep(1650Hz, (XXXXms)) #e.g 1000ms=1second\\n self.run(pulses); self.run(pulses, ANTI_CLK_W)\\n sleep(1)\\n\\n winsound.Beep(400, 1000)\\n self.swing(128, count=30); self.stop() #0.9 degrees\\n sleep(1)\\n\\n winsound.Beep(800, 1000)\\n print('Testing I.....')\\n self.swing(32, count=120); self.stop() #0.225 degrees \\n sleep(1)\\n\\n winsound.Beep(1600, 1000)\\n print('Testing II.....')\\n self.swing(2, count=1800); self.stop() #0.05625 degrees\\n \\n winsound.PlaySound('SystemExclamation', winsound.SND_ALIAS)\\n print(' Testings Done! ')\\n return self.stop() #set low before exist \",\n \"def runMeasurement(self):\\n triggerI1ProMeasurement()\\n self.spectrum.append(getI1ProSpectrum())\\n self.tristimulus.append(getI1ProTriStimulus())\",\n \"def main() -> None:\\n xlim = L\\n x = np.arange(xlim)\\n xi = np.arange(0, xlim - 1 + STEP, STEP)\\n kappas = sleplet.wavelet_methods.create_kappas(xlim, B, J_MIN)\\n yi = scipy.interpolate.pchip(x, kappas[0])\\n plt.semilogx(xi, yi(xi), label=r\\\"$\\\\Phi_{\\\\ell0}$\\\")\\n for j, k in enumerate(kappas[1:]):\\n yi = scipy.interpolate.pchip(x, k)\\n plt.semilogx(xi, yi(xi), label=rf\\\"$\\\\Psi^{{{j+J_MIN}}}_{{\\\\ell0}}$\\\")\\n plt.xlim(1, xlim)\\n ticks = 2 ** np.arange(np.log2(xlim) + 1, dtype=int)\\n plt.xticks(ticks, ticks)\\n plt.xlabel(r\\\"$\\\\ell$\\\")\\n plt.legend(loc=6)\\n print(f\\\"Opening: axisymmetric_tiling_L{L}\\\")\\n plt.tight_layout()\\n plt.show(block=False)\\n plt.pause(10)\\n plt.close()\",\n \"def simulate_memories(simulation_length):\\n \\n \\n pass\",\n \"def run(mu_v, Sigma_w, Sigma_z, a_mu, l_sensor):\\n N = 1000\\n # Init tracking\\n mu_x = np.zeros(N) # Belief or estimation of hidden state \\n F = np.zeros(N) # Free Energy of AI neuron\\n mu_y = np.zeros(N) # Belief or prediction of sensory signal \\n x = np.zeros(N) # True hidden state\\n y = np.zeros(N) # Sensory signal as input to AI neuron\\n\\n robot_brain = pp_unit(dt, mu_v, Sigma_w, Sigma_z, a_mu) #make pp object\\n \\n \\n\\n start_time = time.time()\\n for i in np.arange(1, N):\\n #Active inference\\n y[i] = l_sensor.ambient_light_intensity #take sensor reading\\n print('light reading', y[i])\\n F[i], mu_x[i], mu_y[i] = robot_brain.inference_step(i, mu_v, y[i])\\n\\n\\n t_elapsed = time.time() - start_time\\n\\n print(\\\"Elapsed Time\\\", t_elapsed, \\\"sec\\\")\\n return F, mu_x, mu_y, x, y\",\n \"def gen_measurements(self,t,measure,pd,landa):\\n temp_m = []\\n input_state_temp = []\\n num_targets = len(t)\\n for i in range(0, num_targets):\\n # Consider impact of pd (probability of detection)\\n if np.random.random() < pd:temp_m.append(measure.generate_bearing(t[i].current_location, self.current_location))\\n # Now add False-alarms\\n num_false_alrams = np.random.poisson(landa)\\n false_measures = []\\n for false_index in range(0, num_false_alrams):\\n # generate x,y randomly\\n random_x = 20000 * np.random.random() - 10000\\n random_y = 20000 * np.random.random() - 10000\\n false_measures.append([random_x, random_y])\\n temp_m.append(measure.generate_bearing([random_x, random_y], self.current_location))\\n self.m.append(temp_m) # Number of measurements is not necessarily equal to that of targets\",\n \"def main(command_line=True, **kwargs):\\n # initialize some variables\\n mag_file = ''\\n meas_file=\\\"magic_measurements.txt\\\"\\n user=\\\"\\\"\\n specnum = 0\\n samp_con = '1'\\n labfield = 0\\n er_location_name = ''\\n codelist = None\\n\\n # get command line args\\n if command_line:\\n args=sys.argv\\n if \\\"-h\\\" in args:\\n print(main.__doc__)\\n return False\\n if \\\"-usr\\\" in args:\\n ind=args.index(\\\"-usr\\\")\\n user=args[ind+1]\\n else:\\n user=\\\"\\\"\\n if '-F' in args:\\n ind=args.index(\\\"-F\\\")\\n meas_file=args[ind+1]\\n if '-f' in args:\\n ind=args.index(\\\"-f\\\")\\n magfile=args[ind+1]\\n print(\\\"got magfile:\\\", magfile)\\n if \\\"-dc\\\" in args:\\n ind=args.index(\\\"-dc\\\")\\n labfield=float(args[ind+1])*1e-6\\n phi=float(args[ind+2])\\n theta=float(args[ind+3])\\n if \\\"-ac\\\" in args:\\n ind=args.index(\\\"-ac\\\")\\n peakfield=float(args[ind+1])*1e-3\\n if \\\"-spc\\\" in args:\\n ind=args.index(\\\"-spc\\\")\\n specnum=int(args[ind+1])\\n if \\\"-loc\\\" in args:\\n ind=args.index(\\\"-loc\\\")\\n er_location_name=args[ind+1]\\n if \\\"-ncn\\\" in args:\\n ind=args.index(\\\"-ncn\\\")\\n samp_con=sys.argv[ind+1]\\n if '-LP' in args:\\n ind=args.index(\\\"-LP\\\")\\n codelist=args[ind+1]\\n\\n\\n\\n # lab process:\\n\\n # unpack key-word args if used as module\\n if not command_line:\\n user = kwargs.get('user', '')\\n meas_file = kwargs.get('meas_file', 'magic_measurements.txt')\\n magfile = kwargs.get('magfile', '')\\n specnum = int(kwargs.get('specnum', 0))\\n labfield = int(kwargs.get('labfield', 0)) *1e-6\\n phi = int(kwargs.get('phi', 0))\\n theta = int(kwargs.get('theta', 0))\\n peakfield = kwargs.get('peakfield', 0)\\n if peakfield:\\n peakfield = float(peakfield)*1e-3\\n er_location_name = kwargs.get('er_location_name', '')\\n samp_con = kwargs.get('samp_con', '1')\\n codelist = kwargs.get('codelist', '')\\n CR_cooling_times=kwargs.get('CR_cooling_times', None)\\n\\n # format and validate variables\\n if magfile:\\n try:\\n input=open(magfile,'r')\\n except:\\n print(\\\"bad mag file name\\\")\\n return False, \\\"bad mag file name\\\"\\n else: \\n print(\\\"mag_file field is required option\\\")\\n print(main.__doc__)\\n return False, \\\"mag_file field is required option\\\"\\n \\n if specnum!=0:\\n specnum=-specnum\\n if \\\"4\\\" in samp_con:\\n if \\\"-\\\" not in samp_con:\\n print(\\\"option [4] must be in form 4-Z where Z is an integer\\\")\\n return False, \\\"option [4] must be in form 4-Z where Z is an integer\\\"\\n else:\\n Z=int(samp_con.split(\\\"-\\\")[1])\\n samp_con=\\\"4\\\"\\n if \\\"7\\\" in samp_con:\\n if \\\"-\\\" not in samp_con:\\n print(\\\"option [7] must be in form 7-Z where Z is an integer\\\")\\n return False, \\\"option [7] must be in form 7-Z where Z is an integer\\\"\\n else:\\n Z=int(samp_con.split(\\\"-\\\")[1])\\n samp_con=\\\"7\\\"\\n\\n if codelist:\\n codes=codelist.split(':')\\n else:\\n print(\\\"Must select experiment type (-LP option)\\\")\\n return False, \\\"Must select experiment type (-LP option)\\\"\\n if \\\"AF\\\" in codes:\\n demag='AF' \\n LPcode=\\\"LP-DIR-AF\\\"\\n if \\\"T\\\" in codes:\\n demag=\\\"T\\\"\\n if not labfield: LPcode=\\\"LP-DIR-T\\\"\\n if labfield: LPcode=\\\"LP-PI-TRM\\\"\\n if \\\"ANI\\\" in codes:\\n if not labfield:\\n print(\\\"missing lab field option\\\")\\n return False, \\\"missing lab field option\\\"\\n LPcode=\\\"LP-AN-TRM\\\"\\n\\n if \\\"TRM\\\" in codes: \\n demag=\\\"T\\\"\\n LPcode=\\\"LP-TRM\\\"\\n #trm=1\\n \\n if \\\"CR\\\" in codes:\\n demag=\\\"T\\\"\\n # dc should be in the code\\n if not labfield:\\n print(\\\"missing lab field option\\\")\\n return False, \\\"missing lab field option\\\"\\n\\n LPcode=\\\"LP-CR-TRM\\\" # TRM in different cooling rates\\n if command_line:\\n ind=args.index(\\\"-LP\\\")\\n CR_cooling_times=args[ind+2].split(\\\",\\\")\\n\\n if \\\"ANI\\\" in codes:\\n demag=\\\"T\\\"\\n LPcode=\\\"LP-AN-TRM\\\"\\n\\n #print CR_cooling_time ,\\\"CR_cooling_time\\\"\\n\\n version_num=pmag.get_version()\\n\\n MagRecs=[]\\n \\n #--------------------------------------\\n # Read the file\\n # Assumption:\\n # 1. different lab protocolsa are in different files\\n # 2. measurements are in the correct order\\n #--------------------------------------\\n\\n Data={}\\n\\n line_no=0\\n\\n for line in input.readlines():\\n line_no+=1\\n this_line_data={}\\n line_no+=1\\n instcode=\\\"\\\"\\n if len(line)<2:\\n continue\\n if line[0]==\\\"#\\\": #HUJI way of marking bad data points\\n continue\\n \\n rec=line.strip('\\\\n').split()\\n specimen=rec[0]\\n date=rec[2].split(\\\"/\\\")\\n hour=rec[3].split(\\\":\\\")\\n treatment_type=rec[4]\\n treatment=rec[5].split(\\\".\\\")\\n dec_core=rec[6]\\n inc_core=rec[7]\\n dec_geo=rec[8]\\n inc_geo=rec[9]\\n dec_tilted=rec[10]\\n inc_tilted=rec[11]\\n moment_emu=float(rec[12])\\n\\n if specimen not in list(Data.keys()):\\n Data[specimen]=[]\\n \\n # check duplicate treatments:\\n # if yes, delete the first and use the second\\n\\n if len(Data[specimen])>0:\\n if treatment==Data[specimen][-1]['treatment']:\\n del(Data[specimen][-1])\\n print(\\\"-W- Identical treatments in file %s magfile line %i: specimen %s, treatment %s ignoring the first. \\\" %(magfile, line_no, specimen,\\\".\\\".join(treatment)))\\n\\n this_line_data={}\\n this_line_data['specimen']=specimen\\n this_line_data['date']=date\\n this_line_data['hour']=hour\\n this_line_data['treatment_type']=treatment_type\\n this_line_data['treatment']=treatment\\n this_line_data['dec_core']=dec_core\\n this_line_data['inc_core']=inc_core\\n this_line_data['dec_geo']=dec_geo\\n this_line_data['inc_geo']=inc_geo\\n this_line_data['dec_tilted']=dec_tilted\\n this_line_data['inc_tilted']=inc_tilted\\n this_line_data['moment_emu']=moment_emu \\n Data[specimen].append(this_line_data)\\n\\n \\n print(\\\"-I- done reading file %s\\\"%magfile)\\n\\n #--------------------------------------\\n # Convert to MagIC\\n #--------------------------------------\\n \\n specimens_list=list(Data.keys())\\n specimens_list.sort()\\n\\n\\n MagRecs=[]\\n for specimen in specimens_list:\\n for i in range(len(Data[specimen])):\\n this_line_data=Data[specimen][i]\\n methcode=\\\"\\\"\\n MagRec={}\\n MagRec[\\\"er_specimen_name\\\"]=this_line_data['specimen']\\n if specnum!=0:\\n MagRec[\\\"er_sample_name\\\"]=this_line_data['specimen'][:specnum]\\n else:\\n MagRec[\\\"er_sample_name\\\"]=this_line_data['specimen']\\n\\n if samp_con==\\\"1\\\":\\n MagRec[\\\"er_site_name\\\"]=MagRec[\\\"er_sample_name\\\"][:-1]\\n elif samp_con==\\\"2\\\":\\n parts=MagRec[\\\"er_sample_name\\\"].split('-')\\n MagRec[\\\"er_site_name\\\"]= parts[0]\\n elif samp_con==\\\"3\\\":\\n parts=MagRec[\\\"er_sample_name\\\"].split('.')\\n MagRec[\\\"er_site_name\\\"]= parts[0]\\n elif samp_con=='4':\\n MagRec[\\\"er_site_name\\\"]=MagRec[\\\"er_sample_name\\\"][0:-Z]\\n elif samp_con=='5':\\n MagRec[\\\"er_site_name\\\"]=MagRec[\\\"er_sample_name\\\"]\\n elif samp_con=='7':\\n MagRec[\\\"er_site_name\\\"]=MagRec[\\\"er_sample_name\\\"][0:Z] \\n else:\\n MagRec[\\\"er_site_name\\\"]=MagRec[\\\"er_sample_name\\\"] # site=sample by default\\n \\n if er_location_name:\\n MagRec['er_location_name']=er_location_name\\n else:\\n MagRec['er_location_name']=MagRec[\\\"er_site_name\\\"]\\n \\n MagRec[\\\"measurement_temp\\\"]='%8.3e' % (273) # room temp in kelvin\\n MagRec[\\\"measurement_magn_moment\\\"]='%10.3e'% (float(this_line_data['moment_emu'])*1e-3) # moment in Am^2 (from emu)\\n MagRec[\\\"measurement_dec\\\"]=this_line_data['dec_core']\\n MagRec[\\\"measurement_inc\\\"]=this_line_data['inc_core']\\n date=this_line_data['date']\\n hour=this_line_data['hour'] \\n if float(date[2])>80:\\n yyyy=\\\"19\\\"+date[2]\\n else:\\n yyyy=\\\"20\\\"+date[2]\\n if len (date[0])==1:\\n date[0]=\\\"0\\\"+date[0]\\n if len (date[1])==1:\\n date[1]=\\\"0\\\"+date[1]\\n MagRec[\\\"measurement_date\\\"]=\\\":\\\".join([yyyy,date[0],date[1],hour[0],hour[1],\\\"00.00\\\"])\\n MagRec[\\\"measurement_time_zone\\\"]='JER'\\n MagRec['er_analyst_mail_names'] =user \\n MagRec[\\\"er_citation_names\\\"]=\\\"This study\\\"\\n MagRec[\\\"magic_instrument_codes\\\"]=\\\"HUJI-2G\\\"\\n MagRec[\\\"measurement_flag\\\"]=\\\"g\\\"\\n MagRec[\\\"measurement_positions\\\"]=\\\"1\\\"\\n MagRec[\\\"measurement_positions\\\"]=\\\"1\\\"\\n MagRec[\\\"measurement_standard\\\"]=\\\"u\\\"\\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n #MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n \\n #---------------------------------------- \\n # AF demag\\n # do not support AARM yet\\n #----------------------------------------\\n \\n if demag==\\\"AF\\\":\\n treatment_type=this_line_data['treatment_type']\\n # demag in zero field\\n if LPcode != \\\"LP-AN-ARM\\\":\\n MagRec[\\\"treatment_ac_field\\\"]='%8.3e' %(float(this_line_data['treatment'][0])*1e-3) # peak field in tesla\\n MagRec[\\\"treatment_dc_field\\\"]='0'\\n MagRec[\\\"treatment_dc_field_phi\\\"]='0'\\n MagRec[\\\"treatment_dc_field_theta\\\"]='0'\\n if treatment_type==\\\"N\\\":\\n methcode=\\\"LP-DIR-AF:LT-NO\\\"\\n elif treatment_type==\\\"A\\\":\\n methcode=\\\"LP-DIR-AF:LT-AF-Z\\\"\\n else:\\n print(\\\"ERROR in treatment field line %i... exiting until you fix the problem\\\" %line_no)\\n print(this_line_data)\\n return False, \\\"ERROR in treatment field line %i... exiting until you fix the problem\\\" %line_no\\n \\n # AARM experiment \\n else:\\n print(\\\"Dont supprot AARM in HUJI format yet. sorry... do be DONE\\\")\\n MagRec[\\\"magic_method_codes\\\"]=methcode\\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n MagRec[\\\"measurement_number\\\"]=\\\"%i\\\"%i\\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n\\n MagRecs.append(MagRec)\\n \\n #----------------------------------------\\n # Thermal: \\n # Thellier experiment: \\\"IZ\\\", \\\"ZI\\\", \\\"IZZI\\\", pTRM checks\\n # Thermal demag\\n # Thermal cooling rate experiment\\n # Thermal NLT\\n #----------------------------------------\\n\\n\\n if demag==\\\"T\\\": \\n\\n treatment=this_line_data['treatment']\\n treatment_type=this_line_data['treatment_type']\\n \\n \\n #----------------------------------------\\n # Thellier experimet\\n #----------------------------------------\\n\\n if LPcode == \\\"LP-PI-TRM\\\" : # Thelllier experiment\\n\\n \\n\\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n methcode=LPcode \\n \\n if treatment_type==\\\"N\\\" or ( (treatment[1]=='0' or treatment[1]=='00') and float(treatment[0])==0):\\n LT_code=\\\"LT-NO\\\"\\n MagRec[\\\"treatment_dc_field_phi\\\"]='0' \\n MagRec[\\\"treatment_dc_field_theta\\\"]='0' \\n MagRec[\\\"treatment_dc_field\\\"]='0'\\n MagRec[\\\"treatment_temp\\\"]='273.'\\n \\n elif treatment[1]=='0' or treatment[1]=='00':\\n LT_code=\\\"LT-T-Z\\\"\\n MagRec[\\\"treatment_dc_field_phi\\\"]='0' \\n MagRec[\\\"treatment_dc_field_theta\\\"]='0' \\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e'%(0)\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n\\n # check if this is ZI or IZ:\\n # check if the same temperature already measured:\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-ZI\\\"\\n for j in range (0,i):\\n if Data[specimen][j]['treatment'][0] == treatment[0]:\\n if Data[specimen][j]['treatment'][1] == '1' or Data[specimen][j]['treatment'][1] == '10':\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-IZ\\\"\\n else:\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-ZI\\\"\\n \\n \\n elif treatment[1]=='1' or treatment[1]=='10':\\n LT_code=\\\"LT-T-I\\\"\\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\\n MagRec[\\\"treatment_dc_field_phi\\\"]='%7.1f' % (phi) # labfield phi\\n MagRec[\\\"treatment_dc_field_theta\\\"]='%7.1f' % (theta) # labfield theta\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n\\n # check if this is ZI or IZ:\\n # check if the same temperature already measured:\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-IZ\\\"\\n for j in range (0,i):\\n if Data[specimen][j]['treatment'][0] == treatment[0]:\\n if Data[specimen][j]['treatment'][1] == '0' or Data[specimen][j]['treatment'][1] == '00':\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-ZI\\\"\\n else:\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-IZ\\\"\\n \\n elif treatment[1]=='2' or treatment[1]=='20':\\n LT_code=\\\"LT-PTRM-I\\\"\\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\\n MagRec[\\\"treatment_dc_field_phi\\\"]='%7.1f' % (phi) # labfield phi\\n MagRec[\\\"treatment_dc_field_theta\\\"]='%7.1f' % (theta) # labfield theta\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n methcode=\\\"LP-PI-TRM:LP-PI-TRM-IZ\\\"\\n\\n else:\\n print(\\\"ERROR in treatment field line %i... exiting until you fix the problem\\\" %line_no)\\n return False, \\\"ERROR in treatment field line %i... exiting until you fix the problem\\\" %line_no\\n \\n MagRec[\\\"magic_method_codes\\\"]=LT_code+\\\":\\\"+methcode\\n MagRec[\\\"measurement_number\\\"]=\\\"%i\\\"%i \\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n MagRecs.append(MagRec)\\n #continue\\n \\n \\n #----------------------------------------\\n # demag experimet\\n #----------------------------------------\\n\\n\\n if LPcode == \\\"LP-DIR-T\\\" :\\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n methcode=LPcode \\n \\n if treatment_type==\\\"N\\\":\\n LT_code=\\\"LT-NO\\\"\\n else:\\n LT_code=\\\"LT-T-Z\\\"\\n \\n methcode=LPcode+\\\":\\\"+\\\"LT-T-Z\\\"\\n MagRec[\\\"treatment_dc_field_phi\\\"]='0' \\n MagRec[\\\"treatment_dc_field_theta\\\"]='0' \\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e'%(0)\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n MagRec[\\\"magic_method_codes\\\"]=LT_code+\\\":\\\"+methcode\\n MagRec[\\\"measurement_number\\\"]=\\\"%i\\\"%i \\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n MagRecs.append(MagRec)\\n #continue\\n \\n\\n #----------------------------------------\\n # ATRM measurements\\n # The direction of the magnetization is used to determine the\\n # direction of the lab field.\\n #----------------------------------------\\n \\n if LPcode ==\\\"LP-AN-TRM\\\" :\\n \\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n methcode=LPcode \\n\\n if float(treatment[1])==0:\\n MagRec[\\\"magic_method_codes\\\"]=\\\"LP-AN-TRM:LT-T-Z\\\"\\n MagRec[\\\"treatment_dc_field_phi\\\"]='0'\\n MagRec[\\\"treatment_dc_field_theta\\\"]='0'\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n MagRec[\\\"treatment_dc_field\\\"]='0'\\n else:\\n MagRec[\\\"magic_method_codes\\\"]=\\\"LP-AN-TRM:LT-T-I\\\"\\n inc=float(MagRec[\\\"measurement_inc\\\"]);dec=float(MagRec[\\\"measurement_dec\\\"])\\n if abs(inc)<45 and (dec<45 or dec>315): # +x\\n tdec,tinc=0,0\\n MagRec[\\\"measurement_number\\\"]='1'\\n if abs(inc)<45 and (dec<135 and dec>45):\\n tdec,tinc=90,0\\n MagRec[\\\"measurement_number\\\"]='2' # +y\\n if inc>45 :\\n tdec,tinc=0,90\\n MagRec[\\\"measurement_number\\\"]='3' # +z\\n if abs(inc)<45 and (dec<225 and dec>135):\\n tdec,tinc=180,0\\n MagRec[\\\"measurement_number\\\"]='4' # -x\\n if abs(inc)<45 and (dec<315 and dec>225):\\n tdec,tinc=270,0\\n MagRec[\\\"measurement_number\\\"]='5'# -y\\n if inc<-45 :\\n tdec,tinc=0,-90\\n MagRec[\\\"measurement_number\\\"]='6'# -z\\n if float(treatment[1])==7 or float(treatment[1])==70:\\n # alteration check\\n #methcode=\\\"LP-AN-TRM:LT-PTRM-I\\\"\\n MagRec[\\\"magic_method_codes\\\"]=\\\"LP-AN-TRM:LT-PTRM-I\\\"\\n MagRec[\\\"measurement_number\\\"]='7'# -z\\n\\n \\n MagRec[\\\"treatment_dc_field_phi\\\"]='%7.1f' %(tdec)\\n MagRec[\\\"treatment_dc_field_theta\\\"]='%7.1f'% (tinc)\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin\\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e'%(labfield)\\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n MagRecs.append(MagRec)\\n #continue\\n\\n #----------------------------------------\\n # NLT measurements\\n # or TRM acquisistion experiment\\n #----------------------------------------\\n\\n \\n if LPcode == \\\"LP-TRM\\\" :\\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n MagRec[\\\"magic_method_codes\\\"]=\\\"LP-TRM:LT-T-I\\\"\\n if float(treatment[1])==0:\\n labfield=0\\n else:\\n labfield=float(float(treatment[1]))*1e-6\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin \\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\\n MagRec[\\\"treatment_dc_field_phi\\\"]='%i' % (int(phi)) # labfield phi\\n MagRec[\\\"treatment_dc_field_theta\\\"]='%i' % (int(theta)) # labfield theta\\n MagRec[\\\"measurement_number\\\"]=\\\"%i\\\"%i \\n MagRec[\\\"measurement_description\\\"]=\\\"\\\"\\n MagRecs.append(MagRec)\\n #continue\\n \\n\\n #----------------------------------------\\n # Cooling rate experiments\\n #----------------------------------------\\n \\n if LPcode ==\\\"LP-CR-TRM\\\":\\n index=int(treatment[1][0])\\n #print index,\\\"index\\\"\\n #print CR_cooling_times,\\\"CR_cooling_times\\\"\\n #print CR_cooling_times[index-1]\\n #print CR_cooling_times[0:index-1]\\n if index==7 or index==70: # alteration check as final measurement\\n meas_type=\\\"LT-PTRM-I:LP-CR-TRM\\\"\\n CR_cooling_time=CR_cooling_times[-1]\\n elif index==0 or index==00: # baseline\\n meas_type=\\\"LT-T-Z:LP-CR-TRM\\\"\\n CR_cooling_time=CR_cooling_times[0]\\n else: \\n meas_type=\\\"LT-T-I:LP-CR-TRM\\\"\\n CR_cooling_time=CR_cooling_times[index-1]\\n MagRec[\\\"magic_method_codes\\\"]=meas_type \\n MagRec[\\\"magic_experiment_name\\\"]=specimen+ \\\":\\\" + LPcode\\n MagRec[\\\"treatment_temp\\\"]='%8.3e' % (float(treatment[0])+273.) # temp in kelvin \\n MagRec[\\\"treatment_dc_field\\\"]='%8.3e' % (labfield) # labfield in tesla (convert from microT)\\n MagRec[\\\"treatment_dc_field_phi\\\"]='%7.1f' % (phi) # labfield phi\\n MagRec[\\\"treatment_dc_field_theta\\\"]='%7.1f' % (theta) # labfield theta\\n MagRec[\\\"measurement_number\\\"]=\\\"%i\\\"%index\\n MagRec[\\\"measurement_description\\\"]=\\\"cooling_rate\\\"+\\\":\\\"+CR_cooling_time+\\\":\\\"+\\\"K/min\\\"\\n #MagRec[\\\"measurement_description\\\"]=\\\"%.1f minutes per cooling time\\\"%int(CR_cooling_time)\\n MagRecs.append(MagRec)\\n #continue\\n\\n pmag.magic_write(meas_file,MagRecs,'magic_measurements')\\n print(\\\"-I- results put in \\\",meas_file)\\n return True, meas_file\",\n \"def lab_run_small(character_id, time_step):\\n pass\",\n \"def workflow(now, realtime):\\n szx = 7000\\n szy = 3500\\n # Create the image data\\n imgdata = np.zeros((szy, szx), 'u1')\\n sts = now - datetime.timedelta(minutes=2)\\n metadata = {'start_valid': sts.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\"),\\n 'end_valid': now.strftime(\\\"%Y-%m-%dT%H:%M:%SZ\\\"),\\n 'product': 'a2m',\\n 'units': '0.02 mm'}\\n\\n gribfn = mrms.fetch('PrecipRate', now)\\n if gribfn is None:\\n print((\\\"mrms_rainrate_comp.py NODATA for PrecipRate: %s\\\"\\n ) % (now.strftime(\\\"%Y-%m-%dT%H:%MZ\\\"),))\\n return\\n\\n # http://www.nssl.noaa.gov/projects/mrms/operational/tables.php\\n # Says units are mm/hr\\n fp = gzip.GzipFile(gribfn, 'rb')\\n (_, tmpfn) = tempfile.mkstemp()\\n tmpfp = open(tmpfn, 'wb')\\n tmpfp.write(fp.read())\\n tmpfp.close()\\n grbs = pygrib.open(tmpfn)\\n grb = grbs[1]\\n os.unlink(tmpfn)\\n os.unlink(gribfn)\\n\\n val = grb['values']\\n # Convert into units of 0.1 mm accumulation\\n val = val / 60.0 * 2.0 * 50.0\\n val = np.where(val < 0., 255., val)\\n imgdata[:, :] = np.flipud(val.astype('int'))\\n\\n (tmpfp, tmpfn) = tempfile.mkstemp()\\n\\n # Create Image\\n png = Image.fromarray(np.flipud(imgdata))\\n png.putpalette(mrms.make_colorramp())\\n png.save('%s.png' % (tmpfn,))\\n\\n mrms.write_worldfile('%s.wld' % (tmpfn,))\\n # Inject WLD file\\n routes = \\\"c\\\" if realtime else \\\"\\\"\\n prefix = 'a2m'\\n pqstr = (\\\"/home/ldm/bin/pqinsert -i -p 'plot a%s %s \\\"\\n \\\"gis/images/4326/mrms/%s.wld GIS/mrms/%s_%s.wld wld' %s.wld\\\"\\n \\\"\\\") % (routes, now.strftime(\\\"%Y%m%d%H%M\\\"), prefix, prefix,\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(pqstr, shell=True)\\n # Now we inject into LDM\\n pqstr = (\\\"/home/ldm/bin/pqinsert -i -p 'plot a%s %s \\\"\\n \\\"gis/images/4326/mrms/%s.png GIS/mrms/%s_%s.png png' %s.png\\\"\\n \\\"\\\") % (routes, now.strftime(\\\"%Y%m%d%H%M\\\"), prefix, prefix,\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(pqstr, shell=True)\\n\\n if realtime:\\n # Create 900913 image\\n cmd = (\\\"gdalwarp -s_srs EPSG:4326 -t_srs EPSG:3857 -q -of GTiff \\\"\\n \\\"-tr 1000.0 1000.0 %s.png %s.tif\\\") % (tmpfn, tmpfn)\\n subprocess.call(cmd, shell=True)\\n # Insert into LDM\\n pqstr = (\\\"/home/ldm/bin/pqinsert -i -p 'plot c %s \\\"\\n \\\"gis/images/900913/mrms/%s.tif GIS/mrms/%s_%s.tif tif' %s.tif\\\"\\n \\\"\\\") % (now.strftime(\\\"%Y%m%d%H%M\\\"), prefix, prefix,\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(pqstr, shell=True)\\n\\n j = open(\\\"%s.json\\\" % (tmpfn,), 'w')\\n j.write(json.dumps(dict(meta=metadata)))\\n j.close()\\n # Insert into LDM\\n pqstr = (\\\"/home/ldm/bin/pqinsert -i -p 'plot c %s \\\"\\n \\\"gis/images/4326/mrms/%s.json GIS/mrms/%s_%s.json json' \\\"\\n \\\"%s.json\\\") % (now.strftime(\\\"%Y%m%d%H%M\\\"), prefix, prefix,\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(pqstr, shell=True)\\n for suffix in ['tif', 'json', 'png', 'wld']:\\n if os.path.isfile(\\\"%s.%s\\\" % (tmpfn, suffix)):\\n os.unlink('%s.%s' % (tmpfn, suffix))\\n\\n os.close(tmpfp)\\n os.unlink(tmpfn)\",\n \"def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\\n\\n # def Q_gen(I, R):\\n # w = I * I * R\\n # return w\\n\\n # def Q_rad_in(I_sun, A_s, a_s):\\n # w = I_sun * D * a_s\\n # return w\\n\\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\\n # w = htcoeff * A_s * (T_line - T_amb)\\n # return w\\n\\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\\n # return w\\n\\n def reynolds(V, D, v, Mair=1.103):\\n r = V * D / v\\n return r\\n\\n def nusselt(Re, Pr):\\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\\n c = (Re / 282000) ** (5.0/8.0)\\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\\n return n\\n\\n def air_prop(T_amb):\\n # temp v k Pr\\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\\n [250, 11.44e-6, 22.3e-3, 0.720],\\n [300, 15.89e-6, 26.3e-3, 0.707],\\n [350, 20.92e-6, 30.0e-3, 0.700],\\n [400, 26.41e-6, 33.8e-3, 0.690],\\n [450, 32.39e-6, 37.3e-3, 0.686],\\n [500, 38.79e-6, 40.7e-3, 0.684],\\n [550, 45.57e-6, 43.9e-3, 0.683],\\n [600, 52.69e-6, 46.9e-3, 0.685]])\\n\\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\\n 0, air_prop[:,1:])\\n return v, k, Pr\\n\\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\\n if 273 <= T_line <= 323:\\n R = ((R_lo + \\n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\\n *(T_line - T_range[0]))/N_cond)\\n elif T_line > 323:\\n R = ((R_mid + \\n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\\n *(T_line - T_range[1]))/N_cond)\\n else:\\n R = R_lo\\n print('Out of bounds')\\n return R\\n\\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\\n temp_factor = 1\\n wind_factor = 1\\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\\n\\n T_amb = T_amb*temp_factor\\n V = V*wind_factor\\n\\n v, k, Pr = air_prop(T_amb)\\n Re = reynolds(V, D, v)\\n htcoeff = nusselt(Re, Pr) * k / D\\n\\n def T_line(T_init):\\n \\n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\\n print R\\n\\n C4 = e_s * sigma * D * math.pi\\n C3 = 0.0\\n C2 = 0.0\\n C1 = htcoeff * D * math.pi\\n C0 = - ( I ** 2 * R\\n + I_sun * a_s * D\\n + htcoeff * D * math.pi * T_amb\\n + e_s * D * math.pi * sigma * (T_amb ** 4))\\n\\n return np.roots([C4, C3, C2, C1, C0])\\n\\n T_c = T_amb\\n \\n for i in range(n_iter):\\n T_arr = T_line(T_c)\\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\\n print T_c\\n\\n return T_c\",\n \"def stop( self ) :\\n print '********************* number of events seen: ', globals()['evCounter']\\n\\n from Configurables import LumiIntegrator\\n lumitool = toolSvc.create('LumiIntegrator', 'IntegrateBeamCrossing', interface='ILumiIntegrator')\\n lumirawtool = toolSvc.create('LumiIntegrator', 'RawLumiIntegrator', interface='ILumiIntegrator')\\n print '********************* created lumiintegrator tools'\\n\\n # store data\\n store = {}\\n for (text, tool) in ( ('corrected', lumitool), ('raw', lumirawtool) ):\\n n_files = tool.muFiles()\\n print '********************* raw input files', text, n_files, tool.lumiValue(), tool.lumiError(), tool.absolute()\\n store[text] = {'files':tool.muFiles(), \\n 'lumiValue':tool.lumiValue(), \\n 'lumiError':tool.lumiError(), \\n 'absolute':tool.absolute(),\\n 'filedata':[],\\n }\\n for i in range(n_files):\\n store[text]['filedata'].append(\\n {'sequence' :i, \\n 'run' :tool.muRun(i), \\n 'guid' :tool.muGuid(i), \\n 'time0' :tool.muTime0(i), \\n 'time1' :tool.muTime1(i), \\n 'deltaLumi':tool.muDeltaLumi(i), \\n 'norm' :tool.muNorm(i), \\n 'mu' :tool.muMu(i), \\n 'counters' :{}\\n }\\n )\\n print 'file', i, tool.muRun(i), tool.muGuid(i), tool.muTime0(i), tool.muTime1(i), tool.muDeltaLumi(i), tool.muNorm(i), tool.muMu(i), \\n print tool.muKeys(i)\\n for k in range(tool.muKeys(i)):\\n if tool.muCounter(i,k) <= 50: continue # these are not useful for mu\\n norm = tool.muCounterNorm(i,k)\\n value = 0 if norm == 0 else tool.muCounterValue(i,k)/norm\\n store[text]['filedata'][-1]['counters'][tool.muCounterName(i,k)] = {'key':tool.muCounter(i,k), 'norm':norm, 'value':value}\\n print ' ', tool.muCounter(i,k), tool.muCounterName(i,k), norm, value, \\n print\\n\\n # save store\\n pfname = 'rawdata.pck'\\n pf = open(pfname,'wb')\\n print pfname, 'opened'\\n pickle.dump(store, pf)\\n pf.close()\\n print pfname, 'closed'\\n \\n print store\\n\\n return SUCCESS\",\n \"def AlljointRuns():\\n #800 nm\\n forwardModelJointFit(getFiles(mintime=(15, 40, 07), maxtime=(15, 45, 14), folder='data/29Jul/'),\\n out='J800nm', wavelength='800nm') #0.31, 0.3\\n forwardModelJointFit(getFiles(mintime=(15, 12, 20), maxtime=(15, 24, 16), folder='data/31Jul/'),\\n out='J800nm5k', wavelength='800nm') #0.28 0.31\\n forwardModelJointFit(getFiles(mintime=(15, 28, 40), maxtime=(15, 39, 21), folder='data/31Jul/'),\\n out='J800nm10k', wavelength='800nm') #0.27 0.29\\n forwardModelJointFit(getFiles(mintime=(15, 43, 24), maxtime=(15, 51, 47), folder='data/31Jul/'),\\n out='J800nm20k', wavelength='800nm') #0.27 0.28\\n forwardModelJointFit(getFiles(mintime=(15, 56, 11), maxtime=(16, 02, 58), folder='data/31Jul/'),\\n out='J800nm30k', wavelength='800nm')\\n forwardModelJointFit(getFiles(mintime=(16, 12, 39), maxtime=(16, 18, 25), folder='data/31Jul/'),\\n out='J800nm38k', wavelength='800nm')\\n forwardModelJointFit(getFiles(mintime=(16, 21, 52), maxtime=(16, 26, 16), folder='data/31Jul/'),\\n out='J800nm50k', wavelength='800nm')\\n forwardModelJointFit(getFiles(mintime=(16, 32, 02), maxtime=(16, 35, 23), folder='data/31Jul/'),\\n out='J800nm54k', wavelength='800nm')\\n #700 nm\\n forwardModelJointFit(getFiles(mintime=(17, 20, 17), maxtime=(17, 33, 17), folder='data/30Jul/'),\\n out='J700nm5k', wavelength='700nm') # 0.28 0.32\\n forwardModelJointFit(getFiles(mintime=(17, 37, 35), maxtime=(17, 46, 51), folder='data/30Jul/'),\\n out='J700nm9k', wavelength='700nm') # 0.27 0.32\\n forwardModelJointFit(getFiles(mintime=(17, 48, 35), maxtime=(17, 56, 03), folder='data/30Jul/'),\\n out='J700nm52k', wavelength='700nm') # 0.26 0.31\\n forwardModelJointFit(getFiles(mintime=(17, 58, 18), maxtime=(17, 59, 31), folder='data/30Jul/'),\\n out='J700nm32k', wavelength='700nm')\\n #600 nm\\n forwardModelJointFit(getFiles(mintime=(15, 22, 00), maxtime=(15, 36, 32), folder='data/30Jul/'),\\n out='J600nm5k', wavelength='600nm') #0.27 0.31\\n forwardModelJointFit(getFiles(mintime=(15, 39, 58), maxtime=(15, 47, 58), folder='data/30Jul/'),\\n out='J600nm54k', wavelength='600nm') #0.299, 0.333\\n forwardModelJointFit(getFiles(mintime=(15, 52, 07), maxtime=(16, 06, 32), folder='data/30Jul/'),\\n out='J600nm10k', wavelength='600nm') #0.28 0.32\\n #890 nm\\n forwardModelJointFit(getFiles(mintime=(13, 37, 37), maxtime=(13, 50, 58), folder='data/01Aug/'),\\n out='J890nm5k', wavelength='890nm') #0.28 0.35\\n forwardModelJointFit(getFiles(mintime=(14, 00, 58), maxtime=(14, 11, 54), folder='data/01Aug/'),\\n out='J890nm10k', wavelength='890nm') #0.28 0.33\\n forwardModelJointFit(getFiles(mintime=(14, 17, 57), maxtime=(14, 25, 49), folder='data/01Aug/'),\\n out='J890nm30k', wavelength='890nm') #0.3 0.33\\n forwardModelJointFit(getFiles(mintime=(14, 30, 03), maxtime=(14, 34, 37), folder='data/01Aug/'),\\n out='J890nm50k', wavelength='890nm') #0.3 0.3\",\n \"def runqn1(h,steps,alpha):\\n G = 6.6742*10**-11 # m^3 kg^-1 s^-2\\n M2 = 1.989*10**30 # kg, Sun\\n M1 = 1.899*10**27 # kg, Jupiter\\n R = 778.3*10**9 # m, Semimajor axis of Jupiter's orbit\\n T = 3.743*10**8 # s, Period of Jupiter's orbit\\n omega = 1.679*10**-8 # angular velocity of J-S system\\n func = ['x3','x4','-6.6742*10**-11*1.899*10**27*(x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))/(((x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-6.6742*10**-11*1.989*10**30*(x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))/(((x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))+2.0*1.679*10**-8*x4+x1*(1.679*10**-8)**2','-6.6742*10**-11*1.899*10**27*x2/(((x1-1.989*10**30*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-6.6742*10**-11*1.989*10**30*x2/(((x1+1.899*10**27*778.3*10**9/(1.899*10**27+1.989*10**30))**2+x2**2)**(3.0/2))-2.0*1.679*10**-8*x3+x2*(1.679*10**-8)**2']\\n init = [cos(alpha)*R*(M2-M1)/(M2+M1),R*sin(alpha),0,0]\\n t0 = 0 # initial time\\n h = float(h)\\n steps = abs(int(steps))\\n rungeODE(func,init,t0,h,steps)\",\n \"def run_script(input_dir, output_dir):\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step 1. load dataset \\\"\\\"\\\"\\n print(\\\"loading data ......\\\")\\n print(\\\"+++++++Read the surface shape data+++++++\\\")\\n shape_file_name = input_dir + \\\"aligned_shapes.mat\\\"\\n mat = loadmat(shape_file_name)\\n y_design = mat['aligned_shape']\\n n, l, m = y_design.shape\\n print(\\\"The dimension of shape matrix is \\\" + str(y_design.shape))\\n print(\\\"+++++++Read the sphere coordinate data+++++++\\\")\\n template_file_name = input_dir + \\\"template.mat\\\"\\n mat = loadmat(template_file_name)\\n coord_mat = mat['template']\\n # d = coord_mat.shape[1]\\n print(\\\"+++++++Read the design matrix+++++++\\\")\\n design_data_file_name = input_dir + \\\"design_data.txt\\\"\\n design_data = np.loadtxt(design_data_file_name)\\n # read the covariate type\\n var_type_file_name = input_dir + \\\"var_type.txt\\\"\\n var_type = np.loadtxt(var_type_file_name)\\n print(\\\"+++++++Construct the design matrix: normalization+++++++\\\")\\n x_design = read_x(design_data, var_type)\\n p = x_design.shape[1]\\n print(\\\"The dimension of design matrix is \\\" + str(x_design.shape))\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step 2. Statistical analysis: including (1) smoothing and (2) hypothesis testing\\\"\\\"\\\"\\n gpvals, lpvals_fdr, clu_pvals, efit_beta, efity_design, efit_eta = mfsda.run_stats(y_design, coord_mat, design_data, var_type)\\n\\n \\\"\\\"\\\"+++++++++++++++++++++++++++++++++++\\\"\\\"\\\"\\n \\\"\\\"\\\"Step3. Save all the results\\\"\\\"\\\"\\n gpvals_file_name = output_dir + \\\"global_pvalue.txt\\\"\\n np.savetxt(gpvals_file_name, gpvals)\\n lpvals_fdr_file_name = output_dir + \\\"local_pvalue_fdr.txt\\\"\\n np.savetxt(lpvals_fdr_file_name, lpvals_fdr)\\n clu_pvals_file_name = output_dir + \\\"cluster_pvalue.txt\\\"\\n np.savetxt(clu_pvals_file_name, clu_pvals)\",\n \"def _function(self):\\n\\n\\n\\n def calculate_weights():\\n \\\"\\\"\\\"\\n calculate a weight inversely proportional to the expected to duration of the two steps in the\\n script\\n\\n Returns: weights as a dictionary for the two steps\\n\\n \\\"\\\"\\\"\\n weights = {}\\n\\n\\n # estimate run time of step 1 (fast sweep)\\n f_range = sweeper_script.settings['stop'] - sweeper_script.settings['start']\\n N_samples = sweeper_script.settings['samplecount']\\n df = f_range / N_samples\\n\\n t = N_samples / df\\n\\n weights['quick scan'] = t\\n\\n # estimate run time of step 2 (high res sweep)\\n df = self.settings['high_res_df']\\n N_samples = self.settings['high_res_N']\\n\\n t = N_samples / df\\n\\n weights['high res scan'] = t\\n\\n\\n total_time = sum([v for k, v in weights.iteritems()])\\n\\n weights = {k: v/total_time for k, v in weights.iteritems()}\\n\\n print('weights',weights)\\n\\n return weights\\n\\n def run_scan(name):\\n self.current_subscript = name\\n sweeper_script.start()\\n while self.current_subscript is name:\\n time.sleep(0.1)\\n\\n def calc_new_range():\\n\\n\\n df = self.settings['high_res_df']\\n N = self.settings['high_res_N']\\n\\n r = sweeper_script.data[-1]['r']\\n freq = sweeper_script.data[-1]['frequency']\\n freq = freq[np.isfinite(r)]\\n r = r[np.isfinite(r)]\\n\\n fo = freq[np.argmax(r)]\\n\\n f_start, f_end = fo - N/2 *df, fo + N/2 *df\\n\\n\\n # make sure that we convert back to native python types (numpy file types don't pass the Parameter validation)\\n return float(f_start), float(f_end), int(N)\\n\\n\\n sweeper_script = self.scripts['zi sweep']\\n #save initial settings, so that we can rest at the end of the script\\n initial_settings = deepcopy(sweeper_script.settings)\\n self.weights = calculate_weights()\\n\\n # take the signal from the subscript and route it to a function that takes care of it\\n sweeper_script.updateProgress.connect(self._receive_signal)\\n\\n print('====== start quick scan ============')\\n\\n run_scan('quick scan')\\n\\n print('====== calculate new scan range ====')\\n f_start, f_stop, N = calc_new_range()\\n\\n print('f_start, f_stop, N', f_start, f_stop, N)\\n\\n print('====== update sweeper ==============')\\n sweeper_script.update({\\n 'start' : f_start,\\n 'stop' : f_stop,\\n 'samplecount' : N\\n })\\n\\n print('====== start high res scan =========')\\n # print(sweeper_script.sweeper.finished())\\n # print(sweeper_script.sweeper.progress())\\n\\n run_scan('high res scan')\\n\\n sweeper_script.updateProgress.disconnect()\\n self.data = sweeper_script.data[-1]\\n\\n self._recording = False\\n\\n if self.settings['save']:\\n self.save()\\n\\n # set the sweeper script back to initial settings\\n sweeper_script.update(initial_settings)\\n # make sure that progess is set 1o 100 because we check that in the old_gui\\n self.updateProgress.emit(100)\",\n \"def simulationWithDrug(numViruses, maxPop, maxBirthProb, clearProb, resistances,\\n mutProb, numTrials):\\n \\n #create viruses list\\n viruses = []\\n for i in range(numViruses):\\n viruses.append(ResistantVirus(maxBirthProb, clearProb, resistances, mutProb))\\n \\n #create test patient P1\\n results = np.zeros(numTrials*300).reshape(300,numTrials)\\n resultsPopResist = np.zeros(numTrials*300).reshape(300,numTrials)\\n \\n #runs numTrials of 300 steps, putting results in an array of 300 lines, \\n # numTrials columns\\n for t in range(numTrials) :\\n P1 = TreatedPatient(viruses, maxPop)\\n for s in range(150):\\n P1.update()\\n results[s][numTrials-1] += P1.getTotalPop()\\n resultsPopResist[s][numTrials-1] += P1.getResistPop(['guttagonol'])\\n \\n P1.addPrescription('guttagonol')\\n for s in range(150,300):\\n P1.update()\\n results[s][numTrials-1]+=P1.getTotalPop()\\n resultsPopResist[s][numTrials-1] += P1.getResistPop(['guttagonol'])\\n \\n \\n #calculating average of virus population size at each step \\n yValues1 = []\\n for i in range(300):\\n a = sum(results[i].tolist())/len(results[i])\\n yValues1.append(a)\\n \\n yValues2 = []\\n for i in range(300):\\n a = sum(resultsPopResist[i].tolist())/len(resultsPopResist[i])\\n yValues2.append(a)\\n\\n pylab.plot(yValues1,label='pop average')\\n pylab.plot(yValues2,'r--',label = 'resistant virus population')\\n pylab.title('virus pop average at each step')\\n pylab.legend()\\n pylab.xlabel('Time Steps')\\n pylab.ylabel('pop #')\\n pylab.show()\",\n \"def test4():\\n\\t\\n\\tprint('This takes a while to compute - be patient!')\\n\\t\\n\\td = np.linspace(-15000,15000,300)\\n\\t#Voigt\\n\\t#p_dict = {'Bfield':700,'rb85frac':1,'Btheta':90*np.pi/180,'lcell':75e-3,'T':84,'Dline':'D2','Elem':'Cs'}\\n\\tp_dict = {'Bfield':1000,'rb85frac':1,'Btheta':88*np.pi/180,'Bphi':00*np.pi/180,'lcell':75e-3,'T':93,'Dline':'D2','Elem':'Cs'}\\n\\tpol = np.array([1.0,0.0,0.0])\\n\\tTVx = get_spectra(d,pol,p_dict,outputs=['I_M45','I_P45','Ix','Iy','S0','Iz'])\\n\\t\\n\\tfig2 = plt.figure()\\n\\tax1a = fig2.add_subplot(411)\\n\\tax2a = fig2.add_subplot(412,sharex=ax1a)\\n\\tax3a = fig2.add_subplot(413,sharex=ax1a)\\n\\tax4a = fig2.add_subplot(414,sharex=ax1a)\\n\\t\\n\\tax1a.plot(d,TVx[0],'r',lw=2,label=r'$I_{-45}$')\\n\\tax2a.plot(d,TVx[1],'b',lw=2,label=r'$I_{+45}$')\\n\\tax3a.plot(d,TVx[2],'r',lw=2,label=r'$I_x$')\\n\\tax4a.plot(d,TVx[3],'b',lw=2,label=r'$I_y$')\\n\\tax4a.plot(d,TVx[0]+TVx[1],'r:',lw=3.5,label=r'$I_{+45}+I_{-45}$')\\n\\tax4a.plot(d,TVx[2]+TVx[3],'k:',lw=2.5,label=r'$I_x + I_y$')\\n\\tax4a.plot(d,TVx[4],'g--',lw=1.5,label='$S_0$')\\n#\\tax4a.plot(d,TVx[5],'c--',lw=2.5,label='$I_z$')\\n\\t\\n\\t\\n\\tax4a.set_xlabel('Detuning (MHz)')\\n\\tax1a.set_ylabel('I -45')\\n\\tax2a.set_ylabel('I +45')\\n\\tax3a.set_ylabel('Ix')\\n\\tax4a.set_ylabel('Iy')\\n\\t\\n\\tax4a.set_xlim(d[0],d[-1]+3000)\\n\\tax4a.legend(loc=0)\\n\\t\\n\\tplt.show()\",\n \"def PowerFlowAnalysis(BusData_Location, LineData_Location, Output_FileName, tolerance, S_Base):\\r\\n df_BusData, df_LineData = import_BusAndLineData(BusData_Location, LineData_Location)\\r\\n n = df_BusData.shape[0]\\r\\n \\\"\\\"\\\"Create Admittance Matrix in forms of Y and seperated into G and B\\\"\\\"\\\"\\r\\n sys_Y, sys_G, sys_B = build_AdmittanceMatrix(df_LineData, n)\\r\\n \\\"\\\"\\\"Creation of sys_Data\\\"\\\"\\\"\\r\\n sys_BusNum, sys_LoadP, sys_LoadQ, sys_BusType, sys_PGen, sys_VRef = init_BusData(df_BusData)\\r\\n sys_Data = init_SysData(sys_BusNum, sys_LoadP, sys_LoadQ, sys_BusType, sys_PGen, sys_VRef, sys_G, sys_B, S_Base)\\r\\n \\\"\\\"\\\"Initial Prime for mismatch detetction and storage\\\"\\\"\\\"\\r\\n mismatch_P = sys_Data[1:n,4]\\r\\n mismatch_Q = sys_Data[1:n,6]\\r\\n mismatch_max = [max(abs(mismatch_P)), max(abs(mismatch_Q))]\\r\\n iteration = 0\\r\\n iteration_list = []\\r\\n mismatch_P_list = []\\r\\n mismatch_Q_list = []\\r\\n max_P_bus = []\\r\\n max_Q_bus = []\\r\\n \\r\\n \\\"\\\"\\\"Loop until solution is reached or max iteration is exceeded\\\"\\\"\\\"\\r\\n while(iteration<15 and mismatch_max>tolerance):\\r\\n iteration_list.append(iteration)\\r\\n \\r\\n bus_P, = np.where(mismatch_P == max(abs(mismatch_P)))\\r\\n if len(bus_P) == 0:\\r\\n bus_P, = np.where(mismatch_P == -1*max(abs(mismatch_P)))\\r\\n max_P_bus.append(int(bus_P+2))\\r\\n bus_Q, = np.where(mismatch_Q == max(abs(mismatch_Q)))\\r\\n if len(bus_Q) == 0:\\r\\n bus_Q, = np.where(mismatch_Q == -1*max(abs(mismatch_Q)))\\r\\n max_Q_bus.append(int(bus_Q+2))\\r\\n mismatch_P_list.append(max(abs(mismatch_P)))\\r\\n mismatch_Q_list.append(max(abs(mismatch_Q)))\\r\\n \\r\\n sys_Data = update_SysData(sys_Data, sys_G, sys_B, sys_BusType)\\r\\n mismatch_P = sys_Data[1:n,4]\\r\\n mismatch_Q = sys_Data[1:n,6]\\r\\n mismatch_max = [max(abs(mismatch_P)), max(abs(mismatch_Q))]\\r\\n iteration += 1\\r\\n \\r\\n \\\"\\\"\\\"Final add to convergency history\\\"\\\"\\\"\\r\\n iteration_list.append(iteration) \\r\\n bus_P, = np.where(mismatch_P == max(abs(mismatch_P)))\\r\\n if len(bus_P) == 0:\\r\\n bus_P, = np.where(mismatch_P == -1*max(abs(mismatch_P)))\\r\\n max_P_bus.append(int(bus_P+2))\\r\\n bus_Q, = np.where(mismatch_Q == max(abs(mismatch_Q)))\\r\\n if len(bus_Q) == 0:\\r\\n bus_Q, = np.where(mismatch_Q == -1*max(abs(mismatch_Q)))\\r\\n max_Q_bus.append(int(bus_Q+2))\\r\\n mismatch_P_list.append(max(abs(mismatch_P)))\\r\\n mismatch_Q_list.append(max(abs(mismatch_Q)))\\r\\n \\r\\n \\\"\\\"\\\"Export final solution to excel file\\\"\\\"\\\"\\r\\n DataOutput(Output_FileName, sys_Data, df_LineData, sys_Y,iteration_list,mismatch_P_list,mismatch_Q_list,max_P_bus,max_Q_bus)\",\n \"def start_loop(self):\\r\\n # Transform the array to fit the 1 minute time duration\\r\\n #self.set_vals = np.repeat(self.input_vals, self.dt * 60)\\r\\n\\r\\n i = 0\\r\\n idx = pd.date_range(start=datetime.datetime.now(),\\r\\n periods=(self.input_vals.size), freq='S')\\r\\n while i < len(idx):\\r\\n if datetime.datetime.now().second == idx[i].second:\\r\\n # Set chrging value\\r\\n self.set_val = int(self.input_vals[i])\\r\\n if self.set_val < 0:\\r\\n # Write negative value to battery charge power (AC) setpoint register\\r\\n self.c.write_single_register(1024, self.set_val & 0xFFFF)\\r\\n # Log writing time\\r\\n self.set_time = datetime.datetime.now()\\r\\n else:\\r\\n # Write positive value to battery charge power (AC) setpoint to register\\r\\n self.c.write_single_register(1024, self.set_val)\\r\\n # Log writing time\\r\\n self.set_time = datetime.datetime.now()\\r\\n\\r\\n try:\\r\\n # Read total AC power value from register\\r\\n _P_ac = self.c.read_holding_registers(172, 2)\\r\\n self.read_time_P_ac = datetime.datetime.now()\\r\\n except:\\r\\n print('Could not read register 172!')\\r\\n\\r\\n try:\\r\\n # Read actual battery charge/discharge power value from register\\r\\n _P_bat = self.c.read_holding_registers(582, 1)\\r\\n self.read_time_P_bat = datetime.datetime.now()\\r\\n except:\\r\\n print('Could not read register 582!')\\r\\n\\r\\n # Load content of two registers into a single float value\\r\\n zregs = utils.word_list_to_long(_P_ac, big_endian=False)\\r\\n # Decode and store float value of the AC-power\\r\\n self.P_ac = utils.decode_ieee(*zregs)\\r\\n # Store the DC charging power\\r\\n self.P_bat = np.int16(*_P_bat)\\r\\n # Read actual soc\\r\\n self.soc0 = self.read_soc(210)\\r\\n\\r\\n try:\\r\\n # Save the values to a csv file\\r\\n self.save_to_csv()\\r\\n except:\\r\\n print('Could not save to csv!')\\r\\n\\r\\n i += 1\",\n \"def __init__(self, **kwargs):\\n\\n # mod_path = os.path.join(os.path.dirname(a.__file__), 'BindingPMF.py')\\n # print \\\"\\\"\\\"###########\\n # # AlGDock #\\n # ###########\\n # Molecular docking with adaptively scaled alchemical interaction grids\\n #\\n # in {0}\\n # last modified {1}\\n # \\\"\\\"\\\".format(mod_path, time.ctime(os.path.getmtime(mod_path)))\\n\\n from AlGDock.argument_parser import SimulationArguments\\n self.args = SimulationArguments(**kwargs)\\n\\n from AlGDock.simulation_data import SimulationData\\n self.data = {}\\n self.data['BC'] = SimulationData(self.args.dir['BC'], 'BC', \\\\\\n self.args.params['CD']['pose'])\\n self.data['CD'] = SimulationData(self.args.dir['CD'], 'CD', \\\\\\n self.args.params['CD']['pose'])\\n\\n if not 'max_time' in kwargs.keys():\\n kwargs['max_time'] = None\\n if not 'run_type' in kwargs.keys():\\n kwargs['run_type'] = None\\n\\n from AlGDock.logger import Logger\\n self.log = Logger(self.args, \\\\\\n max_time=kwargs['max_time'], run_type=kwargs['run_type'])\\n\\n self.T_HIGH = self.args.params['BC']['T_HIGH']\\n self.T_TARGET = self.args.params['BC']['T_TARGET']\\n\\n self._setup()\\n\\n print '\\\\n*** Simulation parameters and constants ***'\\n for p in ['BC', 'CD']:\\n print '\\\\nfor %s:' % p\\n print dictionary_tools.dict_view(self.args.params[p])[:-1]\\n\\n self.run(kwargs['run_type'])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5746817","0.5685441","0.5681871","0.5642942","0.5569622","0.55403596","0.5525413","0.55203384","0.5511839","0.545532","0.5445678","0.5411594","0.54065305","0.54041046","0.53374296","0.53127813","0.52985483","0.52948815","0.5291056","0.52908057","0.52744836","0.52736574","0.52512556","0.5249316","0.52330613","0.5225748","0.52236986","0.52210903","0.5211191","0.52041715","0.52005935","0.5197861","0.51957697","0.51957625","0.51908445","0.51876163","0.51822305","0.517837","0.51781344","0.5168479","0.51666504","0.5156328","0.5151055","0.51469994","0.51392514","0.51378924","0.5137034","0.5137031","0.51337475","0.51270074","0.5123803","0.51222247","0.5112415","0.5097825","0.5096175","0.50933516","0.5090282","0.5089075","0.5083675","0.5082748","0.50821114","0.5058172","0.50420535","0.50416064","0.50393134","0.5037309","0.50370765","0.50311905","0.5027407","0.50216407","0.5017789","0.5014321","0.50126326","0.5012592","0.50123435","0.5011839","0.50113535","0.5009181","0.50086546","0.5005609","0.50029933","0.5000815","0.49993804","0.49875027","0.49845544","0.49836597","0.49799556","0.49771294","0.49735895","0.4967273","0.49663147","0.49654838","0.49640197","0.49477848","0.49475765","0.4947541","0.49462527","0.49420455","0.4941485","0.4938002"],"string":"[\n \"0.5746817\",\n \"0.5685441\",\n \"0.5681871\",\n \"0.5642942\",\n \"0.5569622\",\n \"0.55403596\",\n \"0.5525413\",\n \"0.55203384\",\n \"0.5511839\",\n \"0.545532\",\n \"0.5445678\",\n \"0.5411594\",\n \"0.54065305\",\n \"0.54041046\",\n \"0.53374296\",\n \"0.53127813\",\n \"0.52985483\",\n \"0.52948815\",\n \"0.5291056\",\n \"0.52908057\",\n \"0.52744836\",\n \"0.52736574\",\n \"0.52512556\",\n \"0.5249316\",\n \"0.52330613\",\n \"0.5225748\",\n \"0.52236986\",\n \"0.52210903\",\n \"0.5211191\",\n \"0.52041715\",\n \"0.52005935\",\n \"0.5197861\",\n \"0.51957697\",\n \"0.51957625\",\n \"0.51908445\",\n \"0.51876163\",\n \"0.51822305\",\n \"0.517837\",\n \"0.51781344\",\n \"0.5168479\",\n \"0.51666504\",\n \"0.5156328\",\n \"0.5151055\",\n \"0.51469994\",\n \"0.51392514\",\n \"0.51378924\",\n \"0.5137034\",\n \"0.5137031\",\n \"0.51337475\",\n \"0.51270074\",\n \"0.5123803\",\n \"0.51222247\",\n \"0.5112415\",\n \"0.5097825\",\n \"0.5096175\",\n \"0.50933516\",\n \"0.5090282\",\n \"0.5089075\",\n \"0.5083675\",\n \"0.5082748\",\n \"0.50821114\",\n \"0.5058172\",\n \"0.50420535\",\n \"0.50416064\",\n \"0.50393134\",\n \"0.5037309\",\n \"0.50370765\",\n \"0.50311905\",\n \"0.5027407\",\n \"0.50216407\",\n \"0.5017789\",\n \"0.5014321\",\n \"0.50126326\",\n \"0.5012592\",\n \"0.50123435\",\n \"0.5011839\",\n \"0.50113535\",\n \"0.5009181\",\n \"0.50086546\",\n \"0.5005609\",\n \"0.50029933\",\n \"0.5000815\",\n \"0.49993804\",\n \"0.49875027\",\n \"0.49845544\",\n \"0.49836597\",\n \"0.49799556\",\n \"0.49771294\",\n \"0.49735895\",\n \"0.4967273\",\n \"0.49663147\",\n \"0.49654838\",\n \"0.49640197\",\n \"0.49477848\",\n \"0.49475765\",\n \"0.4947541\",\n \"0.49462527\",\n \"0.49420455\",\n \"0.4941485\",\n \"0.4938002\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7498953"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13195,"cells":{"query":{"kind":"string","value":"check whether X and Y have the matching sample size."},"document":{"kind":"string","value":"def __init__(self, x_, batch_size, valid=False, ini_random=True):\n self.n = len(x_)\n self.X = x_\n self.index = 0\n self.batch_size = batch_size\n self.base_index = np.arange(self.n)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_size_check(self):\n [x1, y1, s1, g1] = self.data.diffusion_data.shape\n [x2, y2, s2, g2] = module_05.run_module(self.data).diffusion_data.shape\n self.assertEqual(x1, x2)\n self.assertEqual(y1, y2)\n self.assertEqual(s1, s2)\n self.assertEqual(g1, g2)","def check_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert X.shape[list(X.dims).index(x_lat_dim)] == len(X.coords[x_lat_dim].values), \"XCast requires a dataset's x_lat_dim coordinate to be the same length as its x_lat_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_lon_dim)] == len(X.coords[x_lon_dim].values), \"XCast requires a dataset's x_lon_dim coordinate to be the same length as its x_lon_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_sample_dim)] == len(X.coords[x_sample_dim].values), \"XCast requires a dataset's x_sample_dim coordinate to be the same length as its x_sample_dim dimension\"\n\tassert X.shape[list(X.dims).index(x_feature_dim)] == len(X.coords[x_feature_dim].values), \"XCast requires a dataset's x_feature_dim coordinate to be the same length as its x_feature_dim dimension\"","def _checkSize(X1,X2):\n \n if len(X1) != len(X2):\n raise ValueError, 'Lists are differnt lengths'","def _check_shape(self, X):\n return all([X.shape[i] == self.train_shape[i] for i in range(2)])","def check_size(self,x,y):\n assert(x <= 10**3), 'Width larger than 1000' \n assert(y <= 10**3), 'Height larger than 1000' \n assert(x*y <= 3*(10**5)), 'Resolution larger than 300000'","def check_evaluation_points(x, y):\n assert x.ndim == y.ndim == 1\n assert x.shape == y.shape\n assert x.dtype == y.dtype == np.float64","def check_consistent_length(y_true: List[List[str]], y_pred: List[List[str]]):\n len_true = list(map(len, y_true))\n len_pred = list(map(len, y_pred))\n is_list = set(map(type, y_true)) | set(map(type, y_pred))\n\n if len(y_true) != len(y_pred) or len_true != len_pred:\n message = 'Found input variables with inconsistent numbers of samples:\\n{}\\n{}'.format(len_true, len_pred)\n raise ValueError(message)","def check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,\n y_test_pred):\n\n # check input data shapes are consistent\n X_train, y_train = check_X_y(X_train, y_train)\n X_test, y_test = check_X_y(X_test, y_test)\n\n y_test_pred = column_or_1d(y_test_pred)\n y_train_pred = column_or_1d(y_train_pred)\n\n check_consistent_length(y_train, y_train_pred)\n check_consistent_length(y_test, y_test_pred)\n\n if X_train.shape[1] != X_test.shape[1]:\n raise ValueError(\"X_train {0} and X_test {1} have different number \"\n \"of features.\".format(X_train.shape, X_test.shape))\n\n return X_train, y_train, X_test, y_test, y_train_pred, y_test_pred","def fits_into(self, other) -> bool:\n return other.width > self.width and other.height > self.height","def fits(x, y):\n return y.memory <= x.memory and y.cores <= x.cores and y.disk <= x.disk","def _check_sizes(self, space):\n my_dimension = self.get_total_dimension()\n other_dimension = space.get_total_dimension()\n if my_dimension != other_dimension:\n if isinstance(space, Conv2DSpace):\n if my_dimension * space.shape[0] !=\\\n other_dimension:\n raise ValueError(str(self)+\" with total dimension \" +\n str(my_dimension) +\n \" can't format a batch into \" +\n str(space) + \"because its total dimension\\\n is \" +\n str(other_dimension))","def _is_disjoint(x, y):\n if x.ndim != 1 or y.ndim != 1:\n raise ValueError(\"Inputs must be 1-d ndarrays\")\n\n test_val = np.setdiff1d(x, y).size\n test_val += np.setdiff1d(y, x).size\n\n return test_val == 0","def validate_xy(x_train, y_train):\n try:\n x_train = x_train.astype('float64')\n except ValueError:\n raise ValueError('x_train should only contain numerical data.')\n\n if len(x_train.shape) < 2:\n raise ValueError('x_train should at least has 2 dimensions.')\n\n if x_train.shape[0] != y_train.shape[0]:\n raise ValueError('x_train and y_train should have the same number of instances.')","def assert_result_equal(self, x, y):\n np.testing.assert_array_equal(x, y)","def test_combine_nsamples_different_shapes():\n test_sample_1 = np.ones((2, 13, 21))\n test_sample_2 = np.ones((3, 13, 21))\n pytest.raises(ValueError, utils.combine_nsamples, test_sample_1, test_sample_2)","def _numpy_checker(x, y):\r\n x, y = x[0], y[0]\r\n if (x.dtype != y.dtype or x.shape != y.shape\r\n or numpy.any(numpy.abs(x - y) > 1e-10)):\r\n raise Exception(\"Output mismatch.\", {'performlinker': x, 'clinker': y})","def _test_sampsize(t):\n return t.shape[1] != len(t.ids(axis='sample'))","def _check_ensembles_are_same_size(p, q):\n if p.npdf != q.npdf:\n raise ValueError(\"Input ensembles should have the same number of distributions\")","def _validate_XY(X, Y):\n try:\n for inp in [X, Y]:\n assert isinstance(inp, torch.Tensor)\n assert inp.dtype is torch.float or inp.dtype is torch.double\n assert len(inp.shape) == 2\n assert X.dtype is Y.dtype\n assert X.shape[0] == Y.shape[0]\n except AssertionError:\n raise AttributeError(\n \"invalid inputs: X and Y should be float/double tensors of shape \"\n \"(n, d) and (n, m) respectively, where n is the number of samples, \"\n \"d is the number of features, and m is the number of outputs\"\n )","def check_all(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tcheck_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\n\tcheck_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\n\tcheck_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\n\tcheck_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\n\t#check_transposed(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)","def _shape_check(self, X, y):\n if not len(y.shape) > 1:\n raise RuntimeError(\"The shape of y is incorrect.\")\n if y.shape != X.shape[:-1]:\n raise RuntimeError(\"X and y must have the same number of \" +\n \"samples and microstructure shape.\")\n if X.shape[-1] != 3:\n raise RuntimeError(\"X must have 3 continuous local states \" +\n \"(euler angles)\")","def test_sanity_check (self):\n X, Y = self.dm.get_data(std=True, lag_indicator=True)\n\n # Ensure number of rows between what we expect.\n row_bound = (800, 1000)\n actual_rows = X.shape[0]\n msg = 'Number of rows not within expected bounds.'\n self.assertTrue(row_bound[0] < actual_rows < row_bound[1], msg)\n\n msg = 'X and Y have different number of rows.'\n self.assertEqual(X.shape[0], Y.shape[0], msg)\n\n # Ensure X columns match.\n expected_x_cols = ['SP500', 'ltc_px_std', 'xrp_px_std', 'xlm_px_std',\n 'eth_px_std', 'btc_px_std', 'ltc_volume_std',\n 'xrp_volume_std', 'xlm_volume_std', 'eth_volume_std',\n 'btc_volume_std', 'lagged_others']\n actual_x_cols = X.columns.tolist()\n msg = 'Number of X columns different than expected.'\n self.assertEqual(len(actual_x_cols), len(expected_x_cols), msg)\n\n for col in expected_x_cols:\n msg = 'Expected column not found: {}'.format(col)\n self.assertTrue(col in actual_x_cols, msg)","def image_size_exact(self, msg, img_type, height, width,\n target=None):\n height = int(height)\n width = int(width)\n sizes = self._get_sizes(msg, img_type)\n for img in sizes:\n if (img['width'], img['height']) == (width, height):\n return True\n return False","def validate_X_y(X: List[str], y: List[Any]):\n if len(X) != len(y):\n raise ValueError(\n f\"X and y must have the same length; X has length {len(X)}, and y has length {len(y)}\"\n )","def test_sample_from_extra_bounds_good(self):\n dim = Real(\"yolo\", \"norm\", 0, 2, low=-5, high=+5, shape=(4, 4))\n for _ in range(8):\n samples = dim.sample(8)\n for sample in samples:\n assert sample in dim","def _assert_same_size(outputs: TensorStruct, output_size: OutputSize):\n flat_output_size = nest.flatten(output_size)\n flat_output = nest.flatten(outputs)\n for output, size in zip(flat_output, flat_output_size):\n if isinstance(size, torch.Size):\n if output[0].size() != size:\n raise ValueError('The output size does not matchthe required output_size')\n elif output[0].size()[-1] != size:\n raise ValueError('The output size does not match the required output_size')","def check_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert 4 <= len(X.dims) <= 5, 'XCast requires a dataset to be 4-Dimensional'\n\tassert x_lat_dim in X.dims, 'XCast requires a dataset_lat_dim to be a dimension on X'\n\tassert x_lon_dim in X.dims, 'XCast requires a dataset_lon_dim to be a dimension on X'\n\tassert x_sample_dim in X.dims, 'XCast requires a dataset_sample_dim to be a dimension on X'\n\tassert x_feature_dim in X.dims, 'XCast requires a dataset_feature_dim to be a dimension on X'","def _check_dimensions(self, a, b):\n units_a = self._get_units(a)\n units_b = self._get_units(b)\n dim_a = units_a.dimensions\n dim_b = units_b.dimensions\n if dim_a != dim_b:\n raise UnitConversionError(units_a, dim_a, units_b, dim_b)","def nd_shape_checking(x, y, mvaxis, traxis):\n assert x.ndim == y.ndim\n dims = np.delete(np.arange(x.ndim), -2)\n assert all([x.shape[k] == y.shape[k] for k in dims])","def test_inconsistent_sizes(multiple_linear_regression_data):\n # TODO: X = 5 x 2, y = 7 x 1\n # TODO: X = 5 x 4, y = 5 x 1: n=5, k=5: should work\n # TODO: X = 5 x 5, y = 5 x 1: n=5, k=5+1 (with intercept): should fail\n X, y = multiple_linear_regression_data\n pass","def sample_data(self, x: list, y: list) -> Tuple[list, list, list, list]:\n y = [self.mapping[i] for i in y]\n\n x_train = list()\n x_test = list()\n y_test = list()\n y_train = list()\n\n min_label = min([y.count(i) for i in list(set(y))])\n nbr_labels = len(set(y))\n data_size = min_label * len(set(y))\n\n train_size = int(0.66 * data_size)\n test_size = data_size - train_size\n\n for elem_x, elem_y in zip(x, y):\n if y_train.count(elem_y) < int(train_size / nbr_labels):\n x_train.append([elem_x])\n y_train.append(elem_y)\n\n elif y_test.count(elem_y) < int(test_size / nbr_labels):\n x_test.append([elem_x])\n y_test.append(elem_y)\n\n return x_train, y_train, x_test, y_test","def test_load_return_shape(size):\n X_train, X_test, y_train, y_test = src.load(train_size=size)\n num_samples = 1797\n assert X_train.shape == (int(num_samples * size), 64)\n assert X_test.shape == (int(num_samples * (1 - size)) + 1, 64)","def seg_x_in_y(self, x: str, y: str) -> bool:\n return len(set(x + y)) == len(y)","def matches(self, other):\n return ( all([i==j or i<0 or j<0 for i, j in zip(self._data, other._data)])\n and len(self._data) == len(other._data) )","def correct_batch_size_in_files(self):\n print('checking correct file sizes')\n all_ok = True\n for f in self.data_filenames:\n all_ok *= (np.load(f).shape[0] == self.batch_size)\n if not all_ok:\n break\n print(all_ok)\n return all_ok","def _check_shape(self, y_pred, y):\n if self._type == 'classification':\n if y_pred.ndim != y.ndim + 1:\n raise ValueError('Classification case, dims of y_pred equal dims of y add 1, '\n 'but got y_pred: {} dims and y: {} dims'.format(y_pred.ndim, y.ndim))\n if y.shape != (y_pred.shape[0],) + y_pred.shape[2:]:\n raise ValueError('Classification case, y_pred shape and y shape can not match. '\n 'got y_pred shape is {} and y shape is {}'.format(y_pred.shape, y.shape))\n else:\n if y_pred.ndim != y.ndim:\n raise ValueError('{} case, dims of y_pred need equal with dims of y, but got y_pred: {} '\n 'dims and y: {} dims.'.format(self._type, y_pred.ndim, y.ndim))\n if y_pred.shape != y.shape:\n raise ValueError('{} case, y_pred shape need equal with y shape, but got y_pred: {} and y: {}'.\n format(self._type, y_pred.shape, y.shape))","def check_consistent_length(arrays: Sequence[npt.ArrayLike]) -> None:\n lengths = [_num_samples(X) for X in arrays if X is not None]\n uniques = np.unique(lengths)\n if len(uniques) > 1:\n raise ValueError(\n \"Found input variables with inconsistent numbers of\" \" samples: %r\" % [int(length) for length in lengths]\n )","def cmp_sz(cls, first, second):\n return first.size == second.size","def __eq__(self, other):\n if type(other) is not type(self):\n return False\n if self._sample_rate != other._sample_rate:\n return False\n if self._samples.shape != other._samples.shape:\n return False\n if np.any(self.samples != other._samples):\n return False\n return True","def shapeCompare(*args, **kwargs)->int:\n pass","def can_sample(self, batch_size):\n return batch_size + 1 <= self.num_in_buffer","def _assert_same_size(outputs, output_size):\n nest.assert_same_structure(outputs, output_size)\n flat_output_size = nest.flatten(output_size)\n flat_output = nest.flatten(outputs)\n\n for (output, size) in zip(flat_output, flat_output_size):\n if isinstance(size, tf.TensorShape):\n if output.shape == size:\n pass\n elif output[0].shape != tf.TensorShape(size):\n raise ValueError(\n \"The output size does not match the the required output_size\")","def issquare(self):\r\n if self.width == self.height:\r\n return True\r\n else:\r\n return False","def _test_sampmdsize(t):\n md = t.metadata(axis='sample')\n return t.shape[1] != len(md) if md is not None else False","def is_sampler(self, sampler):\n self.assertIsInstance(sampler, types.GeneratorType)\n for _ in range(10):\n sample = next(sampler)\n self.assertIsInstance(sample, np.ndarray)\n self.assertEqual(\n sample.shape,\n (\n self._dim,\n 1,\n ),\n )","def do_grid_check(self,):\n self.ydim, self.xdim = self.data_fcst.shape \n if self.data_obs.shape != (self.ydim,self.xdim):\n raise FormatError(\"Obs and forecast data not same size.\")\n return","def test_point_within_dimensions_true():\n point = np.array([10, 20])\n image_dimensions = np.array([100, 100])\n assert point_within_dimensions(point, image_dimensions)","def test_similarity_measure_size_compatibility():\n\n patch1 = torch.randn(size=(4, 6, 2))\n patch2 = torch.randn(size=(4, 6, 2))\n\n ssd_similarity_measure(patch1, patch2)\n sad_similarity_measure(patch1, patch2)\n assert True # just check if the ssd calculation was successfull\n\n patch1 = torch.randn(size=(4, 3))\n patch2 = torch.randn(size=(4, 3))\n\n ssd_similarity_measure(patch1, patch2)\n sad_similarity_measure(patch1, patch2)\n assert True # just check if the ssd calculation was successfull\n\n patch1 = torch.randn(size=(5,))\n patch2 = torch.randn(size=(5,))\n\n ssd_similarity_measure(patch1, patch2)\n sad_similarity_measure(patch1, patch2)\n assert True # just check if the ssd calculation was successfull\n\n patch1 = torch.randn(size=(3, 7, 2, 4))\n patch2 = torch.randn(size=(3, 7, 2, 4))\n\n ssd_similarity_measure(patch1, patch2)\n sad_similarity_measure(patch1, patch2)\n assert True # just check if the ssd calculation was successful","def equal_size(self, other):\n if not isinstance(other, Matrix):\n raise ValueError(\"Can only compare two matrices\")\n return other.m == self.m and other.n == self.n","def test_equal10():\n x = np.array([[True, False, True], [True, False, True], [True, False, True]])\n y = np.array([[True, False, True]])\n res = np.equal(x, y)\n obj.run(res=res, x=x, y=y)","def test_ThinDataMultiArray(self):\n for split in self.splits:\n y_data, x_data = data_process.thinData(y_testMultiple, xdim_test, split)\n for spec in y_data:\n self.assertTrue(len(spec) == len(x_data))","def is_valid_size(self, dot_width, dot_height, distance, screen_width, screen_height):\n if dot_width * distance > screen_width or dot_height * distance > screen_height:\n return False\n return True","def _autocheck_dimensions(self):\n # W dimensions check list\n assert len(self.W.shape) == 2, f\"W shape should be (N, N) but is {self.W.shape}.\"\n assert self.W.shape[0] == self.W.shape[1], f\"W shape should be (N, N) but is {self.W.shape}.\"\n\n # Win dimensions check list\n assert len(self.Win.shape) == 2, f\"Win shape should be (N, input) but is {self.Win.shape}.\"\n err = f\"Win shape should be ({self.W.shape[1]}, input) but is {self.Win.shape}.\"\n assert self.Win.shape[0] == self.W.shape[0], err\n\n # Wout dimensions check list\n assert len(self.Wout.shape) == 2, f\"Wout shape should be (output, nb_states) but is {self.Wout.shape}.\"\n nb_states = self.Win.shape[1] + self.W.shape[0] + 1 if self.use_raw_inp else self.W.shape[0] + 1\n err = f\"Wout shape should be (output, {nb_states}) but is {self.Wout.shape}.\"\n assert self.Wout.shape[1] == nb_states, err\n\n # Wfb dimensions check list\n if self.Wfb is not None:\n assert len(self.Wfb.shape) == 2, f\"Wfb shape should be (input, output) but is {self.Wfb.shape}.\"\n err = f\"Wfb shape should be ({self.Win.shape[0]}, {self.Wout.shape[0]}) but is {self.Wfb.shape}.\"\n assert (self.Win.shape[0],self.Wout.shape[0]) == self.Wfb.shape, err","def assertSetAllEqual(self, x, y):\n x = tf.expand_dims(x, axis=0)\n y = tf.expand_dims(y, axis=0)\n self.assertEqual(\n tf.size(tf.sets.difference(x, y)), 0, msg='Input sets are not equal.')\n self.assertEqual(\n tf.size(tf.sets.difference(y, x)), 0, msg='Input sets are not equal.')","def test_compatible(self, other):\n if not self.center.dims == other.center.dims:\n raise ValueError(\"Devices have different dimensionality: {:d} vs {:d}\".format(self.center.dims, other.center.dims))\n\n if not self.center.shape == other.center.shape:\n raise ValueError(\"The shape of the central part does not match: {} vs {}\".format(self.center.shape, other.center.shape))\n\n if not len(self.leads) == len(other.leads):\n raise ValueError(\"The number of leads is different: {:d} vs {:d}\".format(len(self.leads), len(other.leads)))\n\n for n, (i,j) in enumerate(zip(self.leads, other.leads)):\n if not i.shape == j.shape:\n raise ValueError(\"The shape of a lead {:d} does not match: {} vs {}\".format(n,i.shape,j.shape))\n\n for n, (i,j) in enumerate(zip(self.connections, other.connections)):\n if not numpy.array_equal(i,j):\n raise ValueError(\"The connections arrays for lead {:d} are not equal\".format(n))","def check_consistent_params(self, X, y):\n y = self.check_consistent_y(y)\n X = self.check_consistent_X(X)\n\n return X, y","def have_same_shapes(array1, array2):\n return array1.shape == array2.shape","def can_sample(self, n_samples):\n return len(self) >= n_samples","def can_sample(self, n_samples):\n return len(self) >= n_samples","def can_sample(self, n_samples):\n return len(self) >= n_samples","def can_sample(self, n_samples):\n return len(self) >= n_samples","def assert_predictions_equal(first, second, x):\n preds1 = first.predict(x, batch_size=batch_size)\n preds2 = second.predict(x, batch_size=batch_size)\n np.testing.assert_array_equal(preds1, preds2)","def check_shape_equal(pred, labels):\n if pred.shape != labels.shape:\n raise ValueError('Prediction and labels shapes must be equal:'\n f'{pred.shape} vs {labels.shape}.')","def check(data_out_dfe, data_out_cpu, size):\n status = 0\n for i in range(size):\n if data_out_dfe[i] != data_out_cpu[i]:\n print str(data_out_dfe[i]) + \" != \" + str(data_out_cpu[i])\n status = status + 1\n return status","def test_convolve_input_dim_check(self, case, fn, x_shape, y_shape):\n x = torch.rand(*x_shape, dtype=self.dtype, device=self.device)\n y = torch.rand(*y_shape, dtype=self.dtype, device=self.device)\n\n message = [\n \"The operands must be the same dimension\",\n \"Leading dimensions of x and y are not broadcastable\",\n ][case]\n with self.assertRaisesRegex(ValueError, message):\n fn(x, y)","def dimension_check():\n print(\"### DIMENSION CHECK ###\")\n print(X.shape,\n y.shape,\n X_train.shape,\n y_train.shape,\n X_test.shape,\n y_test.shape,\n weights.shape)\n print(\"### END ###\")","def _check_same_shape(preds: Tensor, target: Tensor) ->None:\n if preds.shape != target.shape:\n raise RuntimeError(f'Predictions and targets are expected to have the same shape, but got {preds.shape} and {target.shape}.')","def test_differentSizedDetectors(self):\n files = [getFile(fp)\n for fp in ['bwr_0_det0.m', 'bwr_smallxy_det0.m']]\n self._raisesMisMatchError(files)\n self.assertMsgInLogs(\n \"ERROR\", \"shape: Parser files\",\n partial=True)","def _check_dimensions(self, workspace_to_check):\n for i in range(self._raw_ws.getNumDims()):\n if self._raw_ws.getDimension(i).getNBins() != workspace_to_check._raw_ws.getDimension(i).getNBins():\n return False\n return True","def test_point_within_dimensions_invalid_sizes():\n point = np.array([20, 20, 20])\n image_dimensions = np.array([100, 100])\n\n with pytest.raises(AssertionError):\n assert not point_within_dimensions(point, image_dimensions)\n\n point = np.array([20, 20])\n image_dimensions = np.array([100, 100, 100])\n\n with pytest.raises(AssertionError):\n assert not point_within_dimensions(point, image_dimensions)","def _shape_compare(shape1, shape2):\n if len(shape1) != len(shape2):\n return False\n for s1, s2 in zip(shape1, shape2):\n if s1 != s2:\n return False\n return True","def check_equal_numpy(x, y):\r\n if isinstance(x, numpy.ndarray) and isinstance(y, numpy.ndarray):\r\n return (x.dtype == y.dtype and x.shape == y.shape and\r\n numpy.any(abs(x - y) < 1e-10))\r\n elif (isinstance(x, numpy.random.RandomState) and\r\n isinstance(y, numpy.random.RandomState)):\r\n return python_all(numpy.all(a == b) for a, b in\r\n izip(x.__getstate__(), y.__getstate__()))\r\n else:\r\n return x == y","def _match_sizes(self, pi1, pi2, ignore=[]):\n if pi1.available and pi2.available:\n for arg in ('vsz', 'rss', 'data_segment', 'shared_segment',\n 'stack_segment', 'code_segment'):\n if arg in ignore:\n continue\n size1 = getattr(pi1, arg)\n size2 = getattr(pi2, arg)\n if size1 and size2:\n delta = abs(size1 - size2)\n # Allow for a difference of the size of two pages or 5%\n if delta > pi1.pagesize * 2 and delta > size1 * 0.05:\n self.fail(\"%s mismatch: %d != %d\" % (arg, size1, size2))\n if pi1.pagefaults and pi2.pagefaults:\n # If both records report pagefaults compare the reported\n # number. If a pagefault happens after taking the first\n # snapshot and before taking the second the latter will show a\n # higher pagefault number. In that case take another snapshot\n # with the first variant and check it's now reporting a higher\n # number as well. We assume pagefaults statistics are\n # monotonic.\n if pi1.pagefaults < pi2.pagefaults:\n pi1.update()\n if pi1.pagefaults < pi2.pagefaults:\n pf1 = pi1.pagefaults\n pf2 = pi2.pagefaults\n self.fail(\"Pagefault mismatch: %d != %d\" % (pf1, pf2))\n else:\n self.assertEqual(pi1.pagefaults, pi2.pagefaults)\n if pi1.pagesize and pi2.pagesize:\n self.assertEqual(pi1.pagesize, pi2.pagesize)","def test_equal12():\n x = np.array([[True, False, True], [True, False, True], [True, False, True]])\n y = np.array([[True, False, True], [False, False, False], [True, True, False]])\n res = np.equal(x, y)\n obj.run(res=res, x=x, y=y)","def _verify_data(inputs, targets):\n check_value_type('inputs', inputs, Tensor)\n if len(inputs.shape) != 4:\n raise ValueError(f'Argument inputs must be 4D Tensor, but got {len(inputs.shape)}D Tensor.')\n check_value_type('targets', targets, (Tensor, int, tuple, list))\n if isinstance(targets, Tensor):\n if len(targets.shape) > 2:\n raise ValueError('Dimension invalid. If `targets` is a Tensor, it should be 0D, 1D or 2D. '\n 'But got {}D.'.format(len(targets.shape)))\n if targets.shape and len(targets) != len(inputs):\n raise ValueError(\n 'If `targets` is a 2D, 1D Tensor, it should have the same length as inputs {}. But got {}.'.format(\n len(inputs), len(targets)))","def test_compatible(self, other):\n if self.dims != other.dims:\n raise ValueError(\"Dimension mismatch: {:d} vs {:d}\".format(self.dims, other.dims))\n\n if self.shape != other.shape:\n raise ValueError(\"Block shape mismatch: {} vs {}\".format(self.shape, other.shape))","def test_SetDataRangeMultiArray(self): \n y_multiData, x_dim = data_process.setDataRange(y_testMultiple, xdim_test, \n self.upper, self.lower)\n self.assertTrue(x_dim[-1] == self.lower and x_dim[0] == self.upper)\n for spectrum in y_multiData:\n self.assertTrue(len(spectrum) == len(x_dim))","def match_size(*arrays):\n target = arrays[0].datashape\n result = []\n\n # check for bad inputs\n for a in arrays:\n ds = a.datashape.copy()\n for i in range(min(a.ndim, target.ndim)):\n if ds.dim_low[i] < target.dim_low[i] or \\\n ds.dim_high[i] > target.dim_high[i]:\n raise ValueError(\"All array domains must be a subset \"\n \"of the first array's domain\")\n\n for a in arrays:\n ds = a.datashape.copy()\n ds.dim_low = list(ds.dim_low)\n ds.dim_high = list(ds.dim_high)\n\n for i in range(min(a.ndim, target.ndim)):\n ds.dim_low[i] = target.dim_low[i]\n ds.dim_high[i] = target.dim_high[i]\n if ds != a.datashape:\n a = a.redimension(ds.schema)\n result.append(a)\n\n return tuple(result)","def healthy_test(obj: np.ndarray) -> bool:\n nb_rows, nb_cols = obj.shape\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))","def check_output(self, out, target):\n size_out = out.size()\n size_target = target.size()\n assert size_target\n assert size_out[0] == size_target[0], \\\n (\"Output and target have different batch sizes (first dimension): \"\n f\"{size_out} != {size_target}\")\n # Transform the output into classes.\n out = self._check_output_helper(out)\n size_out = out.size()\n assert size_out == size_target, \\\n f\"Output and target sizes do not match: {size_out} != {size_target}\"\n # eq(): Compare the outputs to the labels.\n # type(): Cast the resulting bools to ints.\n # sum(): Sum them up to get the total number of correct predictions.\n return out.eq(target).type(torch.int).sum().item()","def test_args_count_equal(args: list, target: int) -> bool:\n\n\treturn (args_count(args) == target)","def x_in_y(self, x: int, y: int) -> bool:\n return len(set(self.MAPPING[x] + self.MAPPING[y])) == len(self.MAPPING[y])","def check_qim_dim_match(cls, qim, dim):\n return len(qim) == len(dim)","def biterr(x, y):\n # Error checking\n assert_ndarray(x)\n assert_ndarray(y)\n assert_one_dimension(x)\n assert_one_dimension(y)\n if len(x) != len(y):\n raise ValueError(\"x and y must have same length\")\n\n num_errors = 0\n for ii in np.arange(len(x)):\n if x[ii] != y[ii]:\n num_errors += 1\n\n return num_errors","def is_banner_dimensions(self, width, height):\r\n if width == height:\r\n return False\r\n\r\n if width > height:\r\n diff = float(width / height)\r\n if diff > 5:\r\n return True\r\n\r\n if height > width:\r\n diff = float(height / width)\r\n if diff > 5:\r\n return True\r\n\r\n return False","def test_equal6():\n x = randtool(\"float\", -10, 10, [3, 3, 3, 1])\n y = randtool(\"float\", -10, 10, [3, 3, 1])\n res = np.equal(x, y)\n obj.run(res=res, x=x, y=y)","def _check_xy(x: DataFrame, y: DataFrame) -> None:\n if x.shape[1] != y.shape[1]:\n raise ValueError(\n \"not compatible:\\n\"\n f\"- different number of columns: {x.shape[1]} vs {y.shape[1]}\"\n )\n\n in_y_not_x = setdiff(\n y.columns, x.columns, __calling_env=CallingEnvs.REGULAR\n )\n in_x_not_y = setdiff(\n x.columns, y.columns, __calling_env=CallingEnvs.REGULAR\n )\n if in_y_not_x or in_x_not_y:\n msg = [\"not compatible:\"]\n if in_y_not_x:\n msg.append(f\"- Cols in `y` but not `x`: {in_y_not_x}.\")\n if in_x_not_y:\n msg.append(f\"- Cols in `x` but not `y`: {in_x_not_y}.\")\n raise ValueError(\"\\n\".join(msg))","def testQuestionTwo(self):\n self.assertEqual(AnswerQuestionTwo().shape, (5,5), \"Question two's output is not one dimension.\")","def check_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert x_lat_dim in X.coords.keys(), 'XCast requires a dataset_lat_dim to be a coordinate on X'\n\tassert x_lon_dim in X.coords.keys(), 'XCast requires a dataset_lon_dim to be a coordinate on X'\n\tassert x_sample_dim in X.coords.keys(), 'XCast requires a dataset_sample_dim to be a coordinate on X'\n\tassert x_feature_dim in X.coords.keys(), 'XCast requires a dataset_feature_dim to be a coordinate on X'","def check_metrics_arguments(y_true, y_pred, sample_weight, two_class=True, binary_pred=True):\n sample_weight = check_sample_weight(y_true, sample_weight=sample_weight)\n y_true = column_or_1d(y_true)\n y_pred = column_or_1d(y_pred)\n assert len(y_true) == len(y_pred), \\\n 'The lengths of y_true and y_pred are different: %i and %i' % (len(y_true), len(y_pred))\n if two_class:\n assert numpy.in1d(y_true, [0, 1]).all(), 'The y_true array should contain only two labels: 0 and 1, ' \\\n 'it contains:' + str(numpy.unique(y_true))\n if binary_pred:\n assert numpy.in1d(y_pred, [0, 1]).all(), 'The y_pred array should contain only two labels: 0 and 1, ' \\\n 'it contains:' + str(numpy.unique(y_pred))\n return y_true, y_pred, sample_weight","def test_compare(self): \n d1 = heat(\n np.array([[0.5, 1]]),\n np.array([[0.5, 1.1]])\n )\n d2 = heat(\n np.array([[0.5, 1]]),\n np.array([[0.5, 1.5]])\n )\n\n # These are very loose bounds\n assert d1 < d2","def test_equal7():\n x = randtool(\"float\", -10, 10, [3, 3, 1])\n y = randtool(\"float\", -10, 10, [3, 3, 3, 1])\n res = np.equal(x, y)\n obj.run(res=res, x=x, y=y)","def check_equal(x, y):\r\n #I put the import here to allow using theano without scipy.\r\n import scipy.sparse as sp\r\n x, y = x[0], y[0]\r\n\r\n # TODO: bug in current scipy, two sparse matrices are never equal,\r\n # remove when moving to 0.7\r\n if sp.issparse(x):\r\n x = x.todense()\r\n if sp.issparse(y):\r\n y = y.todense()\r\n\r\n if isinstance(x, numpy.ndarray) and isinstance(y, numpy.ndarray):\r\n if (x.dtype != y.dtype\r\n or x.shape != y.shape\r\n or numpy.any(abs(x - y) > 1e-10)):\r\n raise Exception(\"Output mismatch.\",\r\n {'performlinker': x, 'clinker': y})\r\n else:\r\n if x != y:\r\n raise Exception(\"Output mismatch.\",\r\n {'performlinker': x, 'clinker': y})","def check_shape(layer1, layer2, attr):\n attr1 = getattr(layer1, attr, None)\n attr2 = getattr(layer2, attr, None)\n if not attr1:\n return not attr2\n return all(attr1.shape.eval() == attr2.shape.eval())","def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs: int) ->None:\n if preds.ndim > 2 or target.ndim > 2:\n raise ValueError(f'Expected both predictions and target to be either 1- or 2-dimensional tensors, but got {target.ndim} and {preds.ndim}.')\n if num_outputs == 1 and preds.ndim != 1 or num_outputs > 1 and num_outputs != preds.shape[1]:\n raise ValueError(f'Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs} and {preds.shape[1]}.')","def test_score(self):\n pred_copy_simple = np.copy(self.regression_single.y_pred)\n pred_copy_boston = np.copy(self.regression_boston.y_pred)\n\n self.assertEqual(pred_copy_simple.shape, self.y_test.shape)\n self.assertEqual(pred_copy_boston.shape, self.boston_y_test.shape)","def sample_correct_incorrect(self, x, y, sample_size):\n correct, incorrect = self.all_correct_incorrect(x, y)\n return correct.sample(sample_size), incorrect.sample(sample_size)","def _VerifyValues(\n self,\n input_sizes=None,\n filter_sizes=None,\n strides=None,\n dilations=None,\n padding=None,\n data_format_src=\"NHWC\",\n data_format_dst=\"NHWC\",\n expected=None,\n ):\n\n total_size_1 = np.prod(input_sizes)\n total_size_2 = np.prod(filter_sizes)\n x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes)\n x2 = np.arange(1, total_size_2 + 1, dtype=np.float32).reshape(filter_sizes)\n strides = [1] + strides + [1]\n if dilations is None:\n dilations = [1, 1]\n dilations = [1] + dilations + [1]\n\n # Convert between data formats.\n expected = test_utils.ConvertBetweenDataFormats(\n expected, data_format_src, data_format_dst\n )\n x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, data_format_dst)\n input_sizes = test_utils.PermuteDimsBetweenDataFormats(\n input_sizes, data_format_src, data_format_dst\n )\n strides = test_utils.PermuteDimsBetweenDataFormats(\n strides, data_format_src, data_format_dst\n )\n dilations = test_utils.PermuteDimsBetweenDataFormats(\n dilations, data_format_src, data_format_dst\n )\n\n with self.session() as sess:\n t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes)\n t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes)\n with self.test_scope():\n out = nn_ops.conv2d(\n t1,\n t2,\n strides=strides,\n padding=padding,\n data_format=data_format_dst,\n dilations=dilations,\n )\n\n value = sess.run(out, {t1: x1, t2: x2})\n self.assertAllClose(expected, value, 1e-3)","def assert_same_size(sequences):\n seq_size = len(sequences[0])\n for seq in sequences:\n if len(seq) != seq_size:\n raise SizeError","def _check_deep_network_params(X: np.ndarray, y: np.ndarray):\n pass","def is_equal(self, other):\n return self.close(other) & (abs(self.width - other.width) < 100) & (abs(self.height - other.height) < 50)"],"string":"[\n \"def test_size_check(self):\\n [x1, y1, s1, g1] = self.data.diffusion_data.shape\\n [x2, y2, s2, g2] = module_05.run_module(self.data).diffusion_data.shape\\n self.assertEqual(x1, x2)\\n self.assertEqual(y1, y2)\\n self.assertEqual(s1, s2)\\n self.assertEqual(g1, g2)\",\n \"def check_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert X.shape[list(X.dims).index(x_lat_dim)] == len(X.coords[x_lat_dim].values), \\\"XCast requires a dataset's x_lat_dim coordinate to be the same length as its x_lat_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_lon_dim)] == len(X.coords[x_lon_dim].values), \\\"XCast requires a dataset's x_lon_dim coordinate to be the same length as its x_lon_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_sample_dim)] == len(X.coords[x_sample_dim].values), \\\"XCast requires a dataset's x_sample_dim coordinate to be the same length as its x_sample_dim dimension\\\"\\n\\tassert X.shape[list(X.dims).index(x_feature_dim)] == len(X.coords[x_feature_dim].values), \\\"XCast requires a dataset's x_feature_dim coordinate to be the same length as its x_feature_dim dimension\\\"\",\n \"def _checkSize(X1,X2):\\n \\n if len(X1) != len(X2):\\n raise ValueError, 'Lists are differnt lengths'\",\n \"def _check_shape(self, X):\\n return all([X.shape[i] == self.train_shape[i] for i in range(2)])\",\n \"def check_size(self,x,y):\\n assert(x <= 10**3), 'Width larger than 1000' \\n assert(y <= 10**3), 'Height larger than 1000' \\n assert(x*y <= 3*(10**5)), 'Resolution larger than 300000'\",\n \"def check_evaluation_points(x, y):\\n assert x.ndim == y.ndim == 1\\n assert x.shape == y.shape\\n assert x.dtype == y.dtype == np.float64\",\n \"def check_consistent_length(y_true: List[List[str]], y_pred: List[List[str]]):\\n len_true = list(map(len, y_true))\\n len_pred = list(map(len, y_pred))\\n is_list = set(map(type, y_true)) | set(map(type, y_pred))\\n\\n if len(y_true) != len(y_pred) or len_true != len_pred:\\n message = 'Found input variables with inconsistent numbers of samples:\\\\n{}\\\\n{}'.format(len_true, len_pred)\\n raise ValueError(message)\",\n \"def check_consistent_shape(X_train, y_train, X_test, y_test, y_train_pred,\\n y_test_pred):\\n\\n # check input data shapes are consistent\\n X_train, y_train = check_X_y(X_train, y_train)\\n X_test, y_test = check_X_y(X_test, y_test)\\n\\n y_test_pred = column_or_1d(y_test_pred)\\n y_train_pred = column_or_1d(y_train_pred)\\n\\n check_consistent_length(y_train, y_train_pred)\\n check_consistent_length(y_test, y_test_pred)\\n\\n if X_train.shape[1] != X_test.shape[1]:\\n raise ValueError(\\\"X_train {0} and X_test {1} have different number \\\"\\n \\\"of features.\\\".format(X_train.shape, X_test.shape))\\n\\n return X_train, y_train, X_test, y_test, y_train_pred, y_test_pred\",\n \"def fits_into(self, other) -> bool:\\n return other.width > self.width and other.height > self.height\",\n \"def fits(x, y):\\n return y.memory <= x.memory and y.cores <= x.cores and y.disk <= x.disk\",\n \"def _check_sizes(self, space):\\n my_dimension = self.get_total_dimension()\\n other_dimension = space.get_total_dimension()\\n if my_dimension != other_dimension:\\n if isinstance(space, Conv2DSpace):\\n if my_dimension * space.shape[0] !=\\\\\\n other_dimension:\\n raise ValueError(str(self)+\\\" with total dimension \\\" +\\n str(my_dimension) +\\n \\\" can't format a batch into \\\" +\\n str(space) + \\\"because its total dimension\\\\\\n is \\\" +\\n str(other_dimension))\",\n \"def _is_disjoint(x, y):\\n if x.ndim != 1 or y.ndim != 1:\\n raise ValueError(\\\"Inputs must be 1-d ndarrays\\\")\\n\\n test_val = np.setdiff1d(x, y).size\\n test_val += np.setdiff1d(y, x).size\\n\\n return test_val == 0\",\n \"def validate_xy(x_train, y_train):\\n try:\\n x_train = x_train.astype('float64')\\n except ValueError:\\n raise ValueError('x_train should only contain numerical data.')\\n\\n if len(x_train.shape) < 2:\\n raise ValueError('x_train should at least has 2 dimensions.')\\n\\n if x_train.shape[0] != y_train.shape[0]:\\n raise ValueError('x_train and y_train should have the same number of instances.')\",\n \"def assert_result_equal(self, x, y):\\n np.testing.assert_array_equal(x, y)\",\n \"def test_combine_nsamples_different_shapes():\\n test_sample_1 = np.ones((2, 13, 21))\\n test_sample_2 = np.ones((3, 13, 21))\\n pytest.raises(ValueError, utils.combine_nsamples, test_sample_1, test_sample_2)\",\n \"def _numpy_checker(x, y):\\r\\n x, y = x[0], y[0]\\r\\n if (x.dtype != y.dtype or x.shape != y.shape\\r\\n or numpy.any(numpy.abs(x - y) > 1e-10)):\\r\\n raise Exception(\\\"Output mismatch.\\\", {'performlinker': x, 'clinker': y})\",\n \"def _test_sampsize(t):\\n return t.shape[1] != len(t.ids(axis='sample'))\",\n \"def _check_ensembles_are_same_size(p, q):\\n if p.npdf != q.npdf:\\n raise ValueError(\\\"Input ensembles should have the same number of distributions\\\")\",\n \"def _validate_XY(X, Y):\\n try:\\n for inp in [X, Y]:\\n assert isinstance(inp, torch.Tensor)\\n assert inp.dtype is torch.float or inp.dtype is torch.double\\n assert len(inp.shape) == 2\\n assert X.dtype is Y.dtype\\n assert X.shape[0] == Y.shape[0]\\n except AssertionError:\\n raise AttributeError(\\n \\\"invalid inputs: X and Y should be float/double tensors of shape \\\"\\n \\\"(n, d) and (n, m) respectively, where n is the number of samples, \\\"\\n \\\"d is the number of features, and m is the number of outputs\\\"\\n )\",\n \"def check_all(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tcheck_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\\n\\tcheck_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\\n\\tcheck_consistent(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\\n\\tcheck_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\\n\\t#check_transposed(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim)\",\n \"def _shape_check(self, X, y):\\n if not len(y.shape) > 1:\\n raise RuntimeError(\\\"The shape of y is incorrect.\\\")\\n if y.shape != X.shape[:-1]:\\n raise RuntimeError(\\\"X and y must have the same number of \\\" +\\n \\\"samples and microstructure shape.\\\")\\n if X.shape[-1] != 3:\\n raise RuntimeError(\\\"X must have 3 continuous local states \\\" +\\n \\\"(euler angles)\\\")\",\n \"def test_sanity_check (self):\\n X, Y = self.dm.get_data(std=True, lag_indicator=True)\\n\\n # Ensure number of rows between what we expect.\\n row_bound = (800, 1000)\\n actual_rows = X.shape[0]\\n msg = 'Number of rows not within expected bounds.'\\n self.assertTrue(row_bound[0] < actual_rows < row_bound[1], msg)\\n\\n msg = 'X and Y have different number of rows.'\\n self.assertEqual(X.shape[0], Y.shape[0], msg)\\n\\n # Ensure X columns match.\\n expected_x_cols = ['SP500', 'ltc_px_std', 'xrp_px_std', 'xlm_px_std',\\n 'eth_px_std', 'btc_px_std', 'ltc_volume_std',\\n 'xrp_volume_std', 'xlm_volume_std', 'eth_volume_std',\\n 'btc_volume_std', 'lagged_others']\\n actual_x_cols = X.columns.tolist()\\n msg = 'Number of X columns different than expected.'\\n self.assertEqual(len(actual_x_cols), len(expected_x_cols), msg)\\n\\n for col in expected_x_cols:\\n msg = 'Expected column not found: {}'.format(col)\\n self.assertTrue(col in actual_x_cols, msg)\",\n \"def image_size_exact(self, msg, img_type, height, width,\\n target=None):\\n height = int(height)\\n width = int(width)\\n sizes = self._get_sizes(msg, img_type)\\n for img in sizes:\\n if (img['width'], img['height']) == (width, height):\\n return True\\n return False\",\n \"def validate_X_y(X: List[str], y: List[Any]):\\n if len(X) != len(y):\\n raise ValueError(\\n f\\\"X and y must have the same length; X has length {len(X)}, and y has length {len(y)}\\\"\\n )\",\n \"def test_sample_from_extra_bounds_good(self):\\n dim = Real(\\\"yolo\\\", \\\"norm\\\", 0, 2, low=-5, high=+5, shape=(4, 4))\\n for _ in range(8):\\n samples = dim.sample(8)\\n for sample in samples:\\n assert sample in dim\",\n \"def _assert_same_size(outputs: TensorStruct, output_size: OutputSize):\\n flat_output_size = nest.flatten(output_size)\\n flat_output = nest.flatten(outputs)\\n for output, size in zip(flat_output, flat_output_size):\\n if isinstance(size, torch.Size):\\n if output[0].size() != size:\\n raise ValueError('The output size does not matchthe required output_size')\\n elif output[0].size()[-1] != size:\\n raise ValueError('The output size does not match the required output_size')\",\n \"def check_dimensions(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert 4 <= len(X.dims) <= 5, 'XCast requires a dataset to be 4-Dimensional'\\n\\tassert x_lat_dim in X.dims, 'XCast requires a dataset_lat_dim to be a dimension on X'\\n\\tassert x_lon_dim in X.dims, 'XCast requires a dataset_lon_dim to be a dimension on X'\\n\\tassert x_sample_dim in X.dims, 'XCast requires a dataset_sample_dim to be a dimension on X'\\n\\tassert x_feature_dim in X.dims, 'XCast requires a dataset_feature_dim to be a dimension on X'\",\n \"def _check_dimensions(self, a, b):\\n units_a = self._get_units(a)\\n units_b = self._get_units(b)\\n dim_a = units_a.dimensions\\n dim_b = units_b.dimensions\\n if dim_a != dim_b:\\n raise UnitConversionError(units_a, dim_a, units_b, dim_b)\",\n \"def nd_shape_checking(x, y, mvaxis, traxis):\\n assert x.ndim == y.ndim\\n dims = np.delete(np.arange(x.ndim), -2)\\n assert all([x.shape[k] == y.shape[k] for k in dims])\",\n \"def test_inconsistent_sizes(multiple_linear_regression_data):\\n # TODO: X = 5 x 2, y = 7 x 1\\n # TODO: X = 5 x 4, y = 5 x 1: n=5, k=5: should work\\n # TODO: X = 5 x 5, y = 5 x 1: n=5, k=5+1 (with intercept): should fail\\n X, y = multiple_linear_regression_data\\n pass\",\n \"def sample_data(self, x: list, y: list) -> Tuple[list, list, list, list]:\\n y = [self.mapping[i] for i in y]\\n\\n x_train = list()\\n x_test = list()\\n y_test = list()\\n y_train = list()\\n\\n min_label = min([y.count(i) for i in list(set(y))])\\n nbr_labels = len(set(y))\\n data_size = min_label * len(set(y))\\n\\n train_size = int(0.66 * data_size)\\n test_size = data_size - train_size\\n\\n for elem_x, elem_y in zip(x, y):\\n if y_train.count(elem_y) < int(train_size / nbr_labels):\\n x_train.append([elem_x])\\n y_train.append(elem_y)\\n\\n elif y_test.count(elem_y) < int(test_size / nbr_labels):\\n x_test.append([elem_x])\\n y_test.append(elem_y)\\n\\n return x_train, y_train, x_test, y_test\",\n \"def test_load_return_shape(size):\\n X_train, X_test, y_train, y_test = src.load(train_size=size)\\n num_samples = 1797\\n assert X_train.shape == (int(num_samples * size), 64)\\n assert X_test.shape == (int(num_samples * (1 - size)) + 1, 64)\",\n \"def seg_x_in_y(self, x: str, y: str) -> bool:\\n return len(set(x + y)) == len(y)\",\n \"def matches(self, other):\\n return ( all([i==j or i<0 or j<0 for i, j in zip(self._data, other._data)])\\n and len(self._data) == len(other._data) )\",\n \"def correct_batch_size_in_files(self):\\n print('checking correct file sizes')\\n all_ok = True\\n for f in self.data_filenames:\\n all_ok *= (np.load(f).shape[0] == self.batch_size)\\n if not all_ok:\\n break\\n print(all_ok)\\n return all_ok\",\n \"def _check_shape(self, y_pred, y):\\n if self._type == 'classification':\\n if y_pred.ndim != y.ndim + 1:\\n raise ValueError('Classification case, dims of y_pred equal dims of y add 1, '\\n 'but got y_pred: {} dims and y: {} dims'.format(y_pred.ndim, y.ndim))\\n if y.shape != (y_pred.shape[0],) + y_pred.shape[2:]:\\n raise ValueError('Classification case, y_pred shape and y shape can not match. '\\n 'got y_pred shape is {} and y shape is {}'.format(y_pred.shape, y.shape))\\n else:\\n if y_pred.ndim != y.ndim:\\n raise ValueError('{} case, dims of y_pred need equal with dims of y, but got y_pred: {} '\\n 'dims and y: {} dims.'.format(self._type, y_pred.ndim, y.ndim))\\n if y_pred.shape != y.shape:\\n raise ValueError('{} case, y_pred shape need equal with y shape, but got y_pred: {} and y: {}'.\\n format(self._type, y_pred.shape, y.shape))\",\n \"def check_consistent_length(arrays: Sequence[npt.ArrayLike]) -> None:\\n lengths = [_num_samples(X) for X in arrays if X is not None]\\n uniques = np.unique(lengths)\\n if len(uniques) > 1:\\n raise ValueError(\\n \\\"Found input variables with inconsistent numbers of\\\" \\\" samples: %r\\\" % [int(length) for length in lengths]\\n )\",\n \"def cmp_sz(cls, first, second):\\n return first.size == second.size\",\n \"def __eq__(self, other):\\n if type(other) is not type(self):\\n return False\\n if self._sample_rate != other._sample_rate:\\n return False\\n if self._samples.shape != other._samples.shape:\\n return False\\n if np.any(self.samples != other._samples):\\n return False\\n return True\",\n \"def shapeCompare(*args, **kwargs)->int:\\n pass\",\n \"def can_sample(self, batch_size):\\n return batch_size + 1 <= self.num_in_buffer\",\n \"def _assert_same_size(outputs, output_size):\\n nest.assert_same_structure(outputs, output_size)\\n flat_output_size = nest.flatten(output_size)\\n flat_output = nest.flatten(outputs)\\n\\n for (output, size) in zip(flat_output, flat_output_size):\\n if isinstance(size, tf.TensorShape):\\n if output.shape == size:\\n pass\\n elif output[0].shape != tf.TensorShape(size):\\n raise ValueError(\\n \\\"The output size does not match the the required output_size\\\")\",\n \"def issquare(self):\\r\\n if self.width == self.height:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def _test_sampmdsize(t):\\n md = t.metadata(axis='sample')\\n return t.shape[1] != len(md) if md is not None else False\",\n \"def is_sampler(self, sampler):\\n self.assertIsInstance(sampler, types.GeneratorType)\\n for _ in range(10):\\n sample = next(sampler)\\n self.assertIsInstance(sample, np.ndarray)\\n self.assertEqual(\\n sample.shape,\\n (\\n self._dim,\\n 1,\\n ),\\n )\",\n \"def do_grid_check(self,):\\n self.ydim, self.xdim = self.data_fcst.shape \\n if self.data_obs.shape != (self.ydim,self.xdim):\\n raise FormatError(\\\"Obs and forecast data not same size.\\\")\\n return\",\n \"def test_point_within_dimensions_true():\\n point = np.array([10, 20])\\n image_dimensions = np.array([100, 100])\\n assert point_within_dimensions(point, image_dimensions)\",\n \"def test_similarity_measure_size_compatibility():\\n\\n patch1 = torch.randn(size=(4, 6, 2))\\n patch2 = torch.randn(size=(4, 6, 2))\\n\\n ssd_similarity_measure(patch1, patch2)\\n sad_similarity_measure(patch1, patch2)\\n assert True # just check if the ssd calculation was successfull\\n\\n patch1 = torch.randn(size=(4, 3))\\n patch2 = torch.randn(size=(4, 3))\\n\\n ssd_similarity_measure(patch1, patch2)\\n sad_similarity_measure(patch1, patch2)\\n assert True # just check if the ssd calculation was successfull\\n\\n patch1 = torch.randn(size=(5,))\\n patch2 = torch.randn(size=(5,))\\n\\n ssd_similarity_measure(patch1, patch2)\\n sad_similarity_measure(patch1, patch2)\\n assert True # just check if the ssd calculation was successfull\\n\\n patch1 = torch.randn(size=(3, 7, 2, 4))\\n patch2 = torch.randn(size=(3, 7, 2, 4))\\n\\n ssd_similarity_measure(patch1, patch2)\\n sad_similarity_measure(patch1, patch2)\\n assert True # just check if the ssd calculation was successful\",\n \"def equal_size(self, other):\\n if not isinstance(other, Matrix):\\n raise ValueError(\\\"Can only compare two matrices\\\")\\n return other.m == self.m and other.n == self.n\",\n \"def test_equal10():\\n x = np.array([[True, False, True], [True, False, True], [True, False, True]])\\n y = np.array([[True, False, True]])\\n res = np.equal(x, y)\\n obj.run(res=res, x=x, y=y)\",\n \"def test_ThinDataMultiArray(self):\\n for split in self.splits:\\n y_data, x_data = data_process.thinData(y_testMultiple, xdim_test, split)\\n for spec in y_data:\\n self.assertTrue(len(spec) == len(x_data))\",\n \"def is_valid_size(self, dot_width, dot_height, distance, screen_width, screen_height):\\n if dot_width * distance > screen_width or dot_height * distance > screen_height:\\n return False\\n return True\",\n \"def _autocheck_dimensions(self):\\n # W dimensions check list\\n assert len(self.W.shape) == 2, f\\\"W shape should be (N, N) but is {self.W.shape}.\\\"\\n assert self.W.shape[0] == self.W.shape[1], f\\\"W shape should be (N, N) but is {self.W.shape}.\\\"\\n\\n # Win dimensions check list\\n assert len(self.Win.shape) == 2, f\\\"Win shape should be (N, input) but is {self.Win.shape}.\\\"\\n err = f\\\"Win shape should be ({self.W.shape[1]}, input) but is {self.Win.shape}.\\\"\\n assert self.Win.shape[0] == self.W.shape[0], err\\n\\n # Wout dimensions check list\\n assert len(self.Wout.shape) == 2, f\\\"Wout shape should be (output, nb_states) but is {self.Wout.shape}.\\\"\\n nb_states = self.Win.shape[1] + self.W.shape[0] + 1 if self.use_raw_inp else self.W.shape[0] + 1\\n err = f\\\"Wout shape should be (output, {nb_states}) but is {self.Wout.shape}.\\\"\\n assert self.Wout.shape[1] == nb_states, err\\n\\n # Wfb dimensions check list\\n if self.Wfb is not None:\\n assert len(self.Wfb.shape) == 2, f\\\"Wfb shape should be (input, output) but is {self.Wfb.shape}.\\\"\\n err = f\\\"Wfb shape should be ({self.Win.shape[0]}, {self.Wout.shape[0]}) but is {self.Wfb.shape}.\\\"\\n assert (self.Win.shape[0],self.Wout.shape[0]) == self.Wfb.shape, err\",\n \"def assertSetAllEqual(self, x, y):\\n x = tf.expand_dims(x, axis=0)\\n y = tf.expand_dims(y, axis=0)\\n self.assertEqual(\\n tf.size(tf.sets.difference(x, y)), 0, msg='Input sets are not equal.')\\n self.assertEqual(\\n tf.size(tf.sets.difference(y, x)), 0, msg='Input sets are not equal.')\",\n \"def test_compatible(self, other):\\n if not self.center.dims == other.center.dims:\\n raise ValueError(\\\"Devices have different dimensionality: {:d} vs {:d}\\\".format(self.center.dims, other.center.dims))\\n\\n if not self.center.shape == other.center.shape:\\n raise ValueError(\\\"The shape of the central part does not match: {} vs {}\\\".format(self.center.shape, other.center.shape))\\n\\n if not len(self.leads) == len(other.leads):\\n raise ValueError(\\\"The number of leads is different: {:d} vs {:d}\\\".format(len(self.leads), len(other.leads)))\\n\\n for n, (i,j) in enumerate(zip(self.leads, other.leads)):\\n if not i.shape == j.shape:\\n raise ValueError(\\\"The shape of a lead {:d} does not match: {} vs {}\\\".format(n,i.shape,j.shape))\\n\\n for n, (i,j) in enumerate(zip(self.connections, other.connections)):\\n if not numpy.array_equal(i,j):\\n raise ValueError(\\\"The connections arrays for lead {:d} are not equal\\\".format(n))\",\n \"def check_consistent_params(self, X, y):\\n y = self.check_consistent_y(y)\\n X = self.check_consistent_X(X)\\n\\n return X, y\",\n \"def have_same_shapes(array1, array2):\\n return array1.shape == array2.shape\",\n \"def can_sample(self, n_samples):\\n return len(self) >= n_samples\",\n \"def can_sample(self, n_samples):\\n return len(self) >= n_samples\",\n \"def can_sample(self, n_samples):\\n return len(self) >= n_samples\",\n \"def can_sample(self, n_samples):\\n return len(self) >= n_samples\",\n \"def assert_predictions_equal(first, second, x):\\n preds1 = first.predict(x, batch_size=batch_size)\\n preds2 = second.predict(x, batch_size=batch_size)\\n np.testing.assert_array_equal(preds1, preds2)\",\n \"def check_shape_equal(pred, labels):\\n if pred.shape != labels.shape:\\n raise ValueError('Prediction and labels shapes must be equal:'\\n f'{pred.shape} vs {labels.shape}.')\",\n \"def check(data_out_dfe, data_out_cpu, size):\\n status = 0\\n for i in range(size):\\n if data_out_dfe[i] != data_out_cpu[i]:\\n print str(data_out_dfe[i]) + \\\" != \\\" + str(data_out_cpu[i])\\n status = status + 1\\n return status\",\n \"def test_convolve_input_dim_check(self, case, fn, x_shape, y_shape):\\n x = torch.rand(*x_shape, dtype=self.dtype, device=self.device)\\n y = torch.rand(*y_shape, dtype=self.dtype, device=self.device)\\n\\n message = [\\n \\\"The operands must be the same dimension\\\",\\n \\\"Leading dimensions of x and y are not broadcastable\\\",\\n ][case]\\n with self.assertRaisesRegex(ValueError, message):\\n fn(x, y)\",\n \"def dimension_check():\\n print(\\\"### DIMENSION CHECK ###\\\")\\n print(X.shape,\\n y.shape,\\n X_train.shape,\\n y_train.shape,\\n X_test.shape,\\n y_test.shape,\\n weights.shape)\\n print(\\\"### END ###\\\")\",\n \"def _check_same_shape(preds: Tensor, target: Tensor) ->None:\\n if preds.shape != target.shape:\\n raise RuntimeError(f'Predictions and targets are expected to have the same shape, but got {preds.shape} and {target.shape}.')\",\n \"def test_differentSizedDetectors(self):\\n files = [getFile(fp)\\n for fp in ['bwr_0_det0.m', 'bwr_smallxy_det0.m']]\\n self._raisesMisMatchError(files)\\n self.assertMsgInLogs(\\n \\\"ERROR\\\", \\\"shape: Parser files\\\",\\n partial=True)\",\n \"def _check_dimensions(self, workspace_to_check):\\n for i in range(self._raw_ws.getNumDims()):\\n if self._raw_ws.getDimension(i).getNBins() != workspace_to_check._raw_ws.getDimension(i).getNBins():\\n return False\\n return True\",\n \"def test_point_within_dimensions_invalid_sizes():\\n point = np.array([20, 20, 20])\\n image_dimensions = np.array([100, 100])\\n\\n with pytest.raises(AssertionError):\\n assert not point_within_dimensions(point, image_dimensions)\\n\\n point = np.array([20, 20])\\n image_dimensions = np.array([100, 100, 100])\\n\\n with pytest.raises(AssertionError):\\n assert not point_within_dimensions(point, image_dimensions)\",\n \"def _shape_compare(shape1, shape2):\\n if len(shape1) != len(shape2):\\n return False\\n for s1, s2 in zip(shape1, shape2):\\n if s1 != s2:\\n return False\\n return True\",\n \"def check_equal_numpy(x, y):\\r\\n if isinstance(x, numpy.ndarray) and isinstance(y, numpy.ndarray):\\r\\n return (x.dtype == y.dtype and x.shape == y.shape and\\r\\n numpy.any(abs(x - y) < 1e-10))\\r\\n elif (isinstance(x, numpy.random.RandomState) and\\r\\n isinstance(y, numpy.random.RandomState)):\\r\\n return python_all(numpy.all(a == b) for a, b in\\r\\n izip(x.__getstate__(), y.__getstate__()))\\r\\n else:\\r\\n return x == y\",\n \"def _match_sizes(self, pi1, pi2, ignore=[]):\\n if pi1.available and pi2.available:\\n for arg in ('vsz', 'rss', 'data_segment', 'shared_segment',\\n 'stack_segment', 'code_segment'):\\n if arg in ignore:\\n continue\\n size1 = getattr(pi1, arg)\\n size2 = getattr(pi2, arg)\\n if size1 and size2:\\n delta = abs(size1 - size2)\\n # Allow for a difference of the size of two pages or 5%\\n if delta > pi1.pagesize * 2 and delta > size1 * 0.05:\\n self.fail(\\\"%s mismatch: %d != %d\\\" % (arg, size1, size2))\\n if pi1.pagefaults and pi2.pagefaults:\\n # If both records report pagefaults compare the reported\\n # number. If a pagefault happens after taking the first\\n # snapshot and before taking the second the latter will show a\\n # higher pagefault number. In that case take another snapshot\\n # with the first variant and check it's now reporting a higher\\n # number as well. We assume pagefaults statistics are\\n # monotonic.\\n if pi1.pagefaults < pi2.pagefaults:\\n pi1.update()\\n if pi1.pagefaults < pi2.pagefaults:\\n pf1 = pi1.pagefaults\\n pf2 = pi2.pagefaults\\n self.fail(\\\"Pagefault mismatch: %d != %d\\\" % (pf1, pf2))\\n else:\\n self.assertEqual(pi1.pagefaults, pi2.pagefaults)\\n if pi1.pagesize and pi2.pagesize:\\n self.assertEqual(pi1.pagesize, pi2.pagesize)\",\n \"def test_equal12():\\n x = np.array([[True, False, True], [True, False, True], [True, False, True]])\\n y = np.array([[True, False, True], [False, False, False], [True, True, False]])\\n res = np.equal(x, y)\\n obj.run(res=res, x=x, y=y)\",\n \"def _verify_data(inputs, targets):\\n check_value_type('inputs', inputs, Tensor)\\n if len(inputs.shape) != 4:\\n raise ValueError(f'Argument inputs must be 4D Tensor, but got {len(inputs.shape)}D Tensor.')\\n check_value_type('targets', targets, (Tensor, int, tuple, list))\\n if isinstance(targets, Tensor):\\n if len(targets.shape) > 2:\\n raise ValueError('Dimension invalid. If `targets` is a Tensor, it should be 0D, 1D or 2D. '\\n 'But got {}D.'.format(len(targets.shape)))\\n if targets.shape and len(targets) != len(inputs):\\n raise ValueError(\\n 'If `targets` is a 2D, 1D Tensor, it should have the same length as inputs {}. But got {}.'.format(\\n len(inputs), len(targets)))\",\n \"def test_compatible(self, other):\\n if self.dims != other.dims:\\n raise ValueError(\\\"Dimension mismatch: {:d} vs {:d}\\\".format(self.dims, other.dims))\\n\\n if self.shape != other.shape:\\n raise ValueError(\\\"Block shape mismatch: {} vs {}\\\".format(self.shape, other.shape))\",\n \"def test_SetDataRangeMultiArray(self): \\n y_multiData, x_dim = data_process.setDataRange(y_testMultiple, xdim_test, \\n self.upper, self.lower)\\n self.assertTrue(x_dim[-1] == self.lower and x_dim[0] == self.upper)\\n for spectrum in y_multiData:\\n self.assertTrue(len(spectrum) == len(x_dim))\",\n \"def match_size(*arrays):\\n target = arrays[0].datashape\\n result = []\\n\\n # check for bad inputs\\n for a in arrays:\\n ds = a.datashape.copy()\\n for i in range(min(a.ndim, target.ndim)):\\n if ds.dim_low[i] < target.dim_low[i] or \\\\\\n ds.dim_high[i] > target.dim_high[i]:\\n raise ValueError(\\\"All array domains must be a subset \\\"\\n \\\"of the first array's domain\\\")\\n\\n for a in arrays:\\n ds = a.datashape.copy()\\n ds.dim_low = list(ds.dim_low)\\n ds.dim_high = list(ds.dim_high)\\n\\n for i in range(min(a.ndim, target.ndim)):\\n ds.dim_low[i] = target.dim_low[i]\\n ds.dim_high[i] = target.dim_high[i]\\n if ds != a.datashape:\\n a = a.redimension(ds.schema)\\n result.append(a)\\n\\n return tuple(result)\",\n \"def healthy_test(obj: np.ndarray) -> bool:\\n nb_rows, nb_cols = obj.shape\\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))\",\n \"def check_output(self, out, target):\\n size_out = out.size()\\n size_target = target.size()\\n assert size_target\\n assert size_out[0] == size_target[0], \\\\\\n (\\\"Output and target have different batch sizes (first dimension): \\\"\\n f\\\"{size_out} != {size_target}\\\")\\n # Transform the output into classes.\\n out = self._check_output_helper(out)\\n size_out = out.size()\\n assert size_out == size_target, \\\\\\n f\\\"Output and target sizes do not match: {size_out} != {size_target}\\\"\\n # eq(): Compare the outputs to the labels.\\n # type(): Cast the resulting bools to ints.\\n # sum(): Sum them up to get the total number of correct predictions.\\n return out.eq(target).type(torch.int).sum().item()\",\n \"def test_args_count_equal(args: list, target: int) -> bool:\\n\\n\\treturn (args_count(args) == target)\",\n \"def x_in_y(self, x: int, y: int) -> bool:\\n return len(set(self.MAPPING[x] + self.MAPPING[y])) == len(self.MAPPING[y])\",\n \"def check_qim_dim_match(cls, qim, dim):\\n return len(qim) == len(dim)\",\n \"def biterr(x, y):\\n # Error checking\\n assert_ndarray(x)\\n assert_ndarray(y)\\n assert_one_dimension(x)\\n assert_one_dimension(y)\\n if len(x) != len(y):\\n raise ValueError(\\\"x and y must have same length\\\")\\n\\n num_errors = 0\\n for ii in np.arange(len(x)):\\n if x[ii] != y[ii]:\\n num_errors += 1\\n\\n return num_errors\",\n \"def is_banner_dimensions(self, width, height):\\r\\n if width == height:\\r\\n return False\\r\\n\\r\\n if width > height:\\r\\n diff = float(width / height)\\r\\n if diff > 5:\\r\\n return True\\r\\n\\r\\n if height > width:\\r\\n diff = float(height / width)\\r\\n if diff > 5:\\r\\n return True\\r\\n\\r\\n return False\",\n \"def test_equal6():\\n x = randtool(\\\"float\\\", -10, 10, [3, 3, 3, 1])\\n y = randtool(\\\"float\\\", -10, 10, [3, 3, 1])\\n res = np.equal(x, y)\\n obj.run(res=res, x=x, y=y)\",\n \"def _check_xy(x: DataFrame, y: DataFrame) -> None:\\n if x.shape[1] != y.shape[1]:\\n raise ValueError(\\n \\\"not compatible:\\\\n\\\"\\n f\\\"- different number of columns: {x.shape[1]} vs {y.shape[1]}\\\"\\n )\\n\\n in_y_not_x = setdiff(\\n y.columns, x.columns, __calling_env=CallingEnvs.REGULAR\\n )\\n in_x_not_y = setdiff(\\n x.columns, y.columns, __calling_env=CallingEnvs.REGULAR\\n )\\n if in_y_not_x or in_x_not_y:\\n msg = [\\\"not compatible:\\\"]\\n if in_y_not_x:\\n msg.append(f\\\"- Cols in `y` but not `x`: {in_y_not_x}.\\\")\\n if in_x_not_y:\\n msg.append(f\\\"- Cols in `x` but not `y`: {in_x_not_y}.\\\")\\n raise ValueError(\\\"\\\\n\\\".join(msg))\",\n \"def testQuestionTwo(self):\\n self.assertEqual(AnswerQuestionTwo().shape, (5,5), \\\"Question two's output is not one dimension.\\\")\",\n \"def check_coords(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert x_lat_dim in X.coords.keys(), 'XCast requires a dataset_lat_dim to be a coordinate on X'\\n\\tassert x_lon_dim in X.coords.keys(), 'XCast requires a dataset_lon_dim to be a coordinate on X'\\n\\tassert x_sample_dim in X.coords.keys(), 'XCast requires a dataset_sample_dim to be a coordinate on X'\\n\\tassert x_feature_dim in X.coords.keys(), 'XCast requires a dataset_feature_dim to be a coordinate on X'\",\n \"def check_metrics_arguments(y_true, y_pred, sample_weight, two_class=True, binary_pred=True):\\n sample_weight = check_sample_weight(y_true, sample_weight=sample_weight)\\n y_true = column_or_1d(y_true)\\n y_pred = column_or_1d(y_pred)\\n assert len(y_true) == len(y_pred), \\\\\\n 'The lengths of y_true and y_pred are different: %i and %i' % (len(y_true), len(y_pred))\\n if two_class:\\n assert numpy.in1d(y_true, [0, 1]).all(), 'The y_true array should contain only two labels: 0 and 1, ' \\\\\\n 'it contains:' + str(numpy.unique(y_true))\\n if binary_pred:\\n assert numpy.in1d(y_pred, [0, 1]).all(), 'The y_pred array should contain only two labels: 0 and 1, ' \\\\\\n 'it contains:' + str(numpy.unique(y_pred))\\n return y_true, y_pred, sample_weight\",\n \"def test_compare(self): \\n d1 = heat(\\n np.array([[0.5, 1]]),\\n np.array([[0.5, 1.1]])\\n )\\n d2 = heat(\\n np.array([[0.5, 1]]),\\n np.array([[0.5, 1.5]])\\n )\\n\\n # These are very loose bounds\\n assert d1 < d2\",\n \"def test_equal7():\\n x = randtool(\\\"float\\\", -10, 10, [3, 3, 1])\\n y = randtool(\\\"float\\\", -10, 10, [3, 3, 3, 1])\\n res = np.equal(x, y)\\n obj.run(res=res, x=x, y=y)\",\n \"def check_equal(x, y):\\r\\n #I put the import here to allow using theano without scipy.\\r\\n import scipy.sparse as sp\\r\\n x, y = x[0], y[0]\\r\\n\\r\\n # TODO: bug in current scipy, two sparse matrices are never equal,\\r\\n # remove when moving to 0.7\\r\\n if sp.issparse(x):\\r\\n x = x.todense()\\r\\n if sp.issparse(y):\\r\\n y = y.todense()\\r\\n\\r\\n if isinstance(x, numpy.ndarray) and isinstance(y, numpy.ndarray):\\r\\n if (x.dtype != y.dtype\\r\\n or x.shape != y.shape\\r\\n or numpy.any(abs(x - y) > 1e-10)):\\r\\n raise Exception(\\\"Output mismatch.\\\",\\r\\n {'performlinker': x, 'clinker': y})\\r\\n else:\\r\\n if x != y:\\r\\n raise Exception(\\\"Output mismatch.\\\",\\r\\n {'performlinker': x, 'clinker': y})\",\n \"def check_shape(layer1, layer2, attr):\\n attr1 = getattr(layer1, attr, None)\\n attr2 = getattr(layer2, attr, None)\\n if not attr1:\\n return not attr2\\n return all(attr1.shape.eval() == attr2.shape.eval())\",\n \"def _check_data_shape_to_num_outputs(preds: Tensor, target: Tensor, num_outputs: int) ->None:\\n if preds.ndim > 2 or target.ndim > 2:\\n raise ValueError(f'Expected both predictions and target to be either 1- or 2-dimensional tensors, but got {target.ndim} and {preds.ndim}.')\\n if num_outputs == 1 and preds.ndim != 1 or num_outputs > 1 and num_outputs != preds.shape[1]:\\n raise ValueError(f'Expected argument `num_outputs` to match the second dimension of input, but got {num_outputs} and {preds.shape[1]}.')\",\n \"def test_score(self):\\n pred_copy_simple = np.copy(self.regression_single.y_pred)\\n pred_copy_boston = np.copy(self.regression_boston.y_pred)\\n\\n self.assertEqual(pred_copy_simple.shape, self.y_test.shape)\\n self.assertEqual(pred_copy_boston.shape, self.boston_y_test.shape)\",\n \"def sample_correct_incorrect(self, x, y, sample_size):\\n correct, incorrect = self.all_correct_incorrect(x, y)\\n return correct.sample(sample_size), incorrect.sample(sample_size)\",\n \"def _VerifyValues(\\n self,\\n input_sizes=None,\\n filter_sizes=None,\\n strides=None,\\n dilations=None,\\n padding=None,\\n data_format_src=\\\"NHWC\\\",\\n data_format_dst=\\\"NHWC\\\",\\n expected=None,\\n ):\\n\\n total_size_1 = np.prod(input_sizes)\\n total_size_2 = np.prod(filter_sizes)\\n x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes)\\n x2 = np.arange(1, total_size_2 + 1, dtype=np.float32).reshape(filter_sizes)\\n strides = [1] + strides + [1]\\n if dilations is None:\\n dilations = [1, 1]\\n dilations = [1] + dilations + [1]\\n\\n # Convert between data formats.\\n expected = test_utils.ConvertBetweenDataFormats(\\n expected, data_format_src, data_format_dst\\n )\\n x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, data_format_dst)\\n input_sizes = test_utils.PermuteDimsBetweenDataFormats(\\n input_sizes, data_format_src, data_format_dst\\n )\\n strides = test_utils.PermuteDimsBetweenDataFormats(\\n strides, data_format_src, data_format_dst\\n )\\n dilations = test_utils.PermuteDimsBetweenDataFormats(\\n dilations, data_format_src, data_format_dst\\n )\\n\\n with self.session() as sess:\\n t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes)\\n t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes)\\n with self.test_scope():\\n out = nn_ops.conv2d(\\n t1,\\n t2,\\n strides=strides,\\n padding=padding,\\n data_format=data_format_dst,\\n dilations=dilations,\\n )\\n\\n value = sess.run(out, {t1: x1, t2: x2})\\n self.assertAllClose(expected, value, 1e-3)\",\n \"def assert_same_size(sequences):\\n seq_size = len(sequences[0])\\n for seq in sequences:\\n if len(seq) != seq_size:\\n raise SizeError\",\n \"def _check_deep_network_params(X: np.ndarray, y: np.ndarray):\\n pass\",\n \"def is_equal(self, other):\\n return self.close(other) & (abs(self.width - other.width) < 100) & (abs(self.height - other.height) < 50)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6701522","0.6699848","0.6699655","0.66728204","0.64815956","0.64527076","0.6423499","0.6414772","0.63765645","0.6297624","0.6293033","0.612498","0.61043894","0.6100697","0.6078992","0.60533476","0.60430604","0.6027591","0.6017969","0.6015054","0.6003637","0.60023993","0.59756374","0.5968128","0.5960547","0.5955543","0.5950073","0.5945208","0.59207207","0.5919026","0.5915715","0.5915471","0.5912538","0.5901743","0.58994424","0.5898503","0.5896964","0.5887609","0.58849025","0.58845854","0.5879078","0.5876963","0.58750474","0.5866013","0.586044","0.5847906","0.5840538","0.58391124","0.58171976","0.5803006","0.58002734","0.57901007","0.5788099","0.57863253","0.5781659","0.5780694","0.57644784","0.5755906","0.5755906","0.5755906","0.5755906","0.57515115","0.57505697","0.57445455","0.5743874","0.57304907","0.5721638","0.57187814","0.5718243","0.5714346","0.5708972","0.5694124","0.5693993","0.5693092","0.56833124","0.5676807","0.5669849","0.5667477","0.56595165","0.5655163","0.56538856","0.5652035","0.5644831","0.564296","0.5635624","0.5634094","0.56336665","0.56317675","0.5630858","0.5628511","0.561751","0.5617184","0.5608569","0.560205","0.5598413","0.55914944","0.5587746","0.5587443","0.55858445","0.5583267","0.5578645"],"string":"[\n \"0.6701522\",\n \"0.6699848\",\n \"0.6699655\",\n \"0.66728204\",\n \"0.64815956\",\n \"0.64527076\",\n \"0.6423499\",\n \"0.6414772\",\n \"0.63765645\",\n \"0.6297624\",\n \"0.6293033\",\n \"0.612498\",\n \"0.61043894\",\n \"0.6100697\",\n \"0.6078992\",\n \"0.60533476\",\n \"0.60430604\",\n \"0.6027591\",\n \"0.6017969\",\n \"0.6015054\",\n \"0.6003637\",\n \"0.60023993\",\n \"0.59756374\",\n \"0.5968128\",\n \"0.5960547\",\n \"0.5955543\",\n \"0.5950073\",\n \"0.5945208\",\n \"0.59207207\",\n \"0.5919026\",\n \"0.5915715\",\n \"0.5915471\",\n \"0.5912538\",\n \"0.5901743\",\n \"0.58994424\",\n \"0.5898503\",\n \"0.5896964\",\n \"0.5887609\",\n \"0.58849025\",\n \"0.58845854\",\n \"0.5879078\",\n \"0.5876963\",\n \"0.58750474\",\n \"0.5866013\",\n \"0.586044\",\n \"0.5847906\",\n \"0.5840538\",\n \"0.58391124\",\n \"0.58171976\",\n \"0.5803006\",\n \"0.58002734\",\n \"0.57901007\",\n \"0.5788099\",\n \"0.57863253\",\n \"0.5781659\",\n \"0.5780694\",\n \"0.57644784\",\n \"0.5755906\",\n \"0.5755906\",\n \"0.5755906\",\n \"0.5755906\",\n \"0.57515115\",\n \"0.57505697\",\n \"0.57445455\",\n \"0.5743874\",\n \"0.57304907\",\n \"0.5721638\",\n \"0.57187814\",\n \"0.5718243\",\n \"0.5714346\",\n \"0.5708972\",\n \"0.5694124\",\n \"0.5693993\",\n \"0.5693092\",\n \"0.56833124\",\n \"0.5676807\",\n \"0.5669849\",\n \"0.5667477\",\n \"0.56595165\",\n \"0.5655163\",\n \"0.56538856\",\n \"0.5652035\",\n \"0.5644831\",\n \"0.564296\",\n \"0.5635624\",\n \"0.5634094\",\n \"0.56336665\",\n \"0.56317675\",\n \"0.5630858\",\n \"0.5628511\",\n \"0.561751\",\n \"0.5617184\",\n \"0.5608569\",\n \"0.560205\",\n \"0.5598413\",\n \"0.55914944\",\n \"0.5587746\",\n \"0.5587443\",\n \"0.55858445\",\n \"0.5583267\",\n \"0.5578645\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":13196,"cells":{"query":{"kind":"string","value":"Nacte bajty ze vstupniho binarniho souboru a preve je na uint8 a vrati jako list"},"document":{"kind":"string","value":"def read_bytes_to_list(path):\n vstup = []\n index = 0\n with open(path, \"rb\") as f:\n\n byte = f.read(1)\n while byte != '':\n index = index + 1\n vstup.append(struct.unpack('b', byte)[0])\n byte = f.read(1)\n\n if not byte:\n break\n return vstup"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getAllFileBytes(self) -> List[ghidra.program.database.mem.FileBytes]:\n ...","def getByteArray(self, name: unicode) -> List[int]:\n ...","def get_list_of_bytes2(self):\n pass","def network_bytes(self) -> List[bytes]:","def get_byte(self, *args) -> \"PyObject *\":\n return _ida_fpro.qfile_t_get_byte(self, *args)","def binary_conversion():\n file = filedialog.askopenfilename(title=\"Select a txt \",filetypes=[(\"Text file\", \".txt\")])\n if os.stat(file).st_size != 0:\n open_file = open(file, \"r\")\n seq = \"\"\n for line in open_file :\n seq += line\n seq = seq.strip(\"\\n\")\n \n huffman_code, binary_seq = huffman_construction(seq)\n #count the number of \"0\" added\n added = 0 \n while len(binary_seq) % 8 !=0:\n added +=1 \n binary_seq += '0'\n open_file.close()\n \n else :\n messagebox.showwarning(\"Error\", \"Your file is empty \")\n \n return added, binary_seq, seq, file","def read_list_bin(file_name):\n try:\n extracted_list = []\n with open(file_name, \"rb\") as binary_file:\n extracted_list = pickle.load(binary_file)\n return extracted_list\n except FileNotFoundError:\n print(\"File not found: \",file_name)\n except Exception as e:\n print(type(e), e)\n sys.exit()","def nibble(self, b):\n out = self.file[:b]\n self.file = self.file[b:]\n #print \"nibbled: \" + out\n return out","def utf8_to_binary() :\n dico_binary, comp_seq, file_comp = read_compressed_file()\n \n #for each items of the sequence convert it in binary string on 8 bits\n bin_str = \"\"\n for value in comp_seq:\n code = ord(value)\n bin_str += '{:08b}'.format(code)\n \n #remove the number of zeroes added \n \n added = int(dico_binary[\"add\"])\n #if the padding is equal to 0, don't cut anathing from the sequence\n if added == 0: \n bin_seq = bin_str\n else: \n bin_seq = bin_str[:-added]\n \n return bin_seq, dico_binary, comp_seq, file_comp","def _extract_bytes(l):\n # input should consist of a list of strings similar to ['123456', 'bytes', 'read']\n if len(l) != 3 or l[1] != 'bytes' or l[2] != 'read':\n raise UBootError('received unexpected reply when reading file')\n\n try:\n return int(l[0])\n except ValueError:\n raise UBootError('received unexpected bytes value when reading file')","def get_unknown_bytes(filename):\n d_file = open(os.path.join(u_path, filename), \"rb\")\n b = bytearray(d_file.read())\n d_file.close()\n return b","def getByteRow(self, int: int) -> typing.List[int]:\n ...","def getFileListPNFS(dataset,blacklist=[ ]):\n dataset = dataset.replace('__','/')\n tmpList = glob.glob(dataset+'/*.root')\n director = \"dcap://t3se01.psi.ch:22125/\"\n filelist = [ ]\n for file in tmpList:\n if '.root' in file and file not in blacklist:\n filelist.append(director+file.rstrip())\n if \"SingleMuon/Run2018C-Nano14Dec2018-v1/NANOAOD\" in dataset: # temporary solution\n file = director+\"/pnfs/psi.ch/cms/trivcat/store/user/ineuteli/samples/NANOAOD_2018/SingleMuon/Run2018C-Nano14Dec2018-v1/NANOAOD/DA99EFB9-860F-F648-8D46-5AD80205F53B_skimmed.root\"\n if file in filelist:\n print bcolors.BOLD + bcolors.WARNING + \"FOUND %s !\"%file + bcolors.ENDC\n else:\n print bcolors.BOLD + bcolors.WARNING + \"ADDED %s !\"%file + bcolors.ENDC\n filelist.append(file)\n filelist.sort()\n return filelist","def de_flip_file(n):\n start = '0'*(n//2) + '1' + '0'*(n//2) # 00100 if n == 5\n f_name = 'C:/Users/clean/Desktop/dede.txt'\n f_tmp = 'C:/Users/clean/Desktop/dede2.txt'\n f_result = 'C:/Users/clean/Desktop/de_flip.txt'\n\n #L = [[start]]\n with open(f_name,'w') as f:\n f.write(start + '\\n')\n\n #L_tmp = []\n with open(f_tmp,'w') as ftmp:\n pass\n \n #collect = []\n with open(f_result,'w') as f_end:\n pass\n \n \n length = 1\n \n while(True):\n count = 0\n check_end = True\n\n with open(f_name) as f:\n for branch in f:\n L_branch = branch.split()\n next_0 = L_branch[-1][1:] + '0'\n next_1 = L_branch[-1][1:] + '1'\n\n if next_0 == start:\n with open(f_result,'a') as f_end:\n f_end.write(branch)\n count += 1\n continue\n\n if next_0 not in L_branch and next_0[::-1] not in L_branch:\n check_end = False\n with open(f_tmp,'a') as ftmp:\n ftmp.write(branch[:-1] + ' ' + next_0 + '\\n')\n\n if next_1 not in L_branch and next_1[::-1] not in L_branch:\n check_end = False\n with open(f_tmp,'a') as ftmp:\n ftmp.write(branch[:-1] + ' ' + next_1 + '\\n')\n \n if check_end == True:\n break\n \n shutil.copyfile(f_tmp, f_name) # 오른쪽으로 복사\n\n with open(f_tmp,'w') as ftmp:\n pass\n\n print(\"length : {0}, count : {1}\".format(length,count))\n length += 1\n return None","def put_byte(self, *args) -> \"int\":\n return _ida_fpro.qfile_t_put_byte(self, *args)","def Read2000256List(self):\n items = []\n for i in range(0, 2000):\n data = self.ReadBytes(64)\n ba = bytearray(binascii.unhexlify(data))\n ba.reverse()\n items.append(ba.hex().encode('utf-8'))\n return items","def upload_files(self):\n return 1 << 0","def boole_list_to_bytes(bit_message:list):\n b_number = 0\n byte_list = []\n one_cool_byte = 0\n for item in bit_message:\n one_cool_byte <<= 1\n if item:\n one_cool_byte += 1\n b_number += 1\n if b_number == 8:\n byte_list.append(one_cool_byte)\n b_number = 0\n one_cool_byte = 0\n return bytes(byte_list).rstrip(b\"\\x00\") # return byte string","def getBaseSrcFile(self) -> List[int]:\n ...","def create_file_list(params):\n data_dir = params.get('data_dir', '')\n params['file_list'] = \".tmp.txt\"\n imgtype_list = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff'}\n with open(params['file_list'], \"w\") as fout:\n tmp_file_list = os.listdir(data_dir)\n for file_name in tmp_file_list:\n file_path = os.path.join(data_dir, file_name)\n if imghdr.what(file_path) not in imgtype_list:\n continue\n fout.write(file_name + \" 0\" + \"\\n\")","def get_sorted_img_list():\n dirPath=settings.BASE_DIR\n imgdir=\"/pttWeb/static/topicmodel\"\n fileID=glob.glob(dirPath+imgdir+\"/*.png\")\n fileID=[i.replace('/home/stream/Documents/minimum_django/pttWeb/static/','') for i in fileID]\n fileID=[Week_Image(i) for i in fileID]\n fileID.sort(key=lambda x: x.date, reverse=True)\n #translate . to / since javascript parsing date has some issue!\n fileID=[(i.filename,date_trans_z(i.date.strftime(\"%Y.%m.%d\"))) for i in fileID]\n return fileID","def getBinaryData(self, filepath):\n\n binary_values = []\n\n with open(filepath, 'rb') as fileobject:\n\n # read file byte by byte\n data = fileobject.read(1)\n\n while data != b'':\n binary_values.append(ord(data))\n data = fileobject.read(1)\n\n return binary_values","def int_list(data: bytes) -> list:\n byte_data = BytesIO(data)\n byte_list = []\n single_byte = byte_data.read(1)\n while single_byte != b\"\" and single_byte != \"\":\n single_int = byte_to_int(single_byte)\n byte_list.append(single_int)\n single_byte = byte_data.read(1)\n return byte_list","def binconv(fp, fp_len):\n vec = [1] * fp_len\n #print(fp, len(fp))\n for indx, b in enumerate(fp):\n if indx == 224:\n break\n if b == '0':\n vec[indx] = -1\n return vec","def bip32_path_to_uints(n: str) -> List[int]:\n path = []\n for x in n.split('/')[1:]:\n if x == '':\n continue\n prime = 0\n if x.endswith(\"'\"):\n x = x.replace('\\'', '')\n prime = BIP32_PRIME\n if x.startswith('-'):\n prime = BIP32_PRIME\n path.append(abs(int(x)) | prime)\n return path","def to_bytes(self) -> \"list[int]\":\n entries_per_byte = 8 // int(self.offset_type)\n byte_array = []\n for i in range(0, len(self.entries), entries_per_byte):\n byte = 0\n for j in range(0, entries_per_byte):\n byte |= self.entries[i + j] << (j * int(self.offset_type))\n byte_array.append(byte)\n return byte_array","def FileList(file):\n with open(file,\"r\") as f:\n list1 = [r.split()[1] for r in f]\n list1 = [int(i) for i in list1]\n return list1","def debianize( strFilename ):\n \n #~ data = gzip.GzipFile( strFilename ).read();\n #~ print data;\n #~ return;\n \n #~ data = gzip.open( strFilename ).read();\n #~ print data;\n #~ return; \n \n #~ uncompressedData = bz2.BZ2File(strFilename).read()\n #~ print str(uncompressedData)\n #~ return;\n \n #~ file = open( strFilename, 'rb' );\n #~ data = file.read();\n #~ file.close();\n #~ print debug.dumpHexa( data );\n \n #~ ar = tarfile.open(strFilename, 'r:*')\n #~ for item in ar:\n #~ print( str(item) );\n #~ print( \"%s:\" % item.name );\n #~ #print debug.dumpHexa(item.buf);\n #~ #print zlib.decompress(item.buf)\n #~ #print zlib.decompress(ar.extractfile(item).read())\n #~ data = ar.extractfile(item.name).read()\n #~ print data # works !\n #~ ar.close() \n #~ return;\n \n fileLists = [];\n file = open( strFilename );\n data = file.read();\n file.close();\n \n print( \"data len: %d\" % len( data ) );\n\n nDataCompressedOffset = 0; # 132\n\n # works fine on toto.gz\n #~ f = gzip.open(strFilename, 'rb')\n #~ file_content = f.read()\n #~ print file_content\n #~ f.close() \n \n #~ decompressor = bz2.BZ2Decompressor();\n #~ uncompressed = decompressor.decompress(data[nDataCompressedOffset:]);\n \n #~ uncompressed = zlib.decompress(data[nDataCompressedOffset:]);\n \n uncompressed = decompress( data );\n print( \"uncompressed: %s\" % str( uncompressed ) );","def _write_to_lsb(tribit: str, rgb_binary: tuple):\n pixel = []\n for idx, bit in enumerate(tribit):\n color = rgb_binary[idx]\n color = color[:-1] + bit\n pixel.append(color)\n\n return pixel","def test08(self):\n logical_file_name = \"/store/mc/Fall08/BBJets250to500-madgraph/GEN-SIM-RAW/IDEAL_/%s/%i.root\" %(uid, 0)\n #print \"WARNING : DBS cannot list INVALID file, so for now this test is commented out\"\n self.api.updateFileStatus(logical_file_name=logical_file_name, is_file_valid=0)\n #listfile\n filesInDBS=self.api.listFiles(logical_file_name=logical_file_name, detail=True)\n self.assertEqual(len(filesInDBS), 1)\n self.assertEqual(filesInDBS[0]['is_file_valid'], 0)","def view_files(self):\n return 1 << 1","def convertData(img):\n dataset = []\n for i in img:\n dataset.append(format(ord(i), '08b'))\n return dataset","def setByteArray(self, name: unicode, value: List[int]) -> None:\n ...","def getTRRFileSize(dataBlock):\n print(dataBlock[0:70])\n if dataBlock[0:4].decode() == \"TRQ \":\n if dataBlock[4:6].decode() == \"f \":\n for index in range(7, len(dataBlock)):\n if dataBlock[index:index+1].decode() == \" \":\n break\n filesize = \"\"\n for byte in range(index+1, len(dataBlock)):\n if dataBlock[byte:byte+1].decode() == \" \":\n return [int(filesize), byte+1]\n filesize += dataBlock[byte:byte+1].decode()\n return \"random return string\"","def getSmallFilesList(file_list):\n small_list = []\n for file in file_list:\n if os.path.getsize(file) <= 50000000: # bit\n small_list.append(file)\n return small_list","def vec_to_bytes(val):\n return [int(x*255) for x in val]","def _fread3_many(fobj, n):\n b1, b2, b3 = np.fromfile(fobj, \">u1\", 3 * n).reshape(-1,\n 3).astype(np.int).T\n return (b1 << 16) + (b2 << 8) + b3","def load_and_print_file_list(file_list):\n for file in file_list:\n hdu_list = load_file(file)\n print(\"'{0}' has {1} hdus in it\".format(file, len(hdu_list)))\n for ii in range(len(hdu_list)):\n hdu1 = hdu_list[ii] # FITS HDU counting is from 1\n print('BITPIX type of HDU{0} = {1}'.format(ii + 1,\n hdu1.header['BITPIX']))\n # be sure to close the file handle\n hdu_list.close()","def save2file(self):\n ids_input = []\n labels_input = []\n ids_path = os.path.join(self.path, 'ids')\n if not os.path.exists(ids_path):\n os.makedirs(ids_path)\n labels_path = os.path.join(self.path, 'labels')\n if not os.path.exists(labels_path):\n os.makedirs(labels_path)\n ids_total = len(self.test)\n for i in range(ids_total):\n ids_input = self.test[i][0]\n labels_input = self.test[i][1]\n file_name = \"ids/\" + str(i) + \".bin\"\n file_path = os.path.join(self.path, file_name)\n np.array(ids_input, dtype=np.int32).tofile(file_path)\n file_name = \"labels/\" + str(i) + \".bin\"\n file_path = os.path.join(self.path, file_name)\n np.array(labels_input, dtype=np.int32).tofile(file_path)\n print(\"\\n ****** Success! ******\\n \")","def getByteArray2D(self) -> typing.List[typing.List[int]]:\n ...","def ej08a(texto):\n indice = 0\n resultado = []\n current_byte = \"\"\n\n for i in texto:\n current_byte += i # se agrega el nuevo caracter al byte actual\n indice += 1 # se incrementa en uno el indice\n if indice % 8 == 0:\n # Comienza un nuevo byte\n resultado.append(current_byte)\n current_byte = \"\"\n return resultado","def __get_sorted_file_list(self):\n d = self.__view.CurrentImgDir\n list = os.listdir(d)\n if self.__view.SortType == constant.THUMB_SORT_FILENAME:\n # Sort by Name\n list.sort()\n if self.__view.SortType == 2:\n # Sort by Size\n list.sort(lambda a, b: int(os.stat(os.path.join(d,a))[stat.ST_SIZE] - os.stat(os.path.join(d,b))[stat.ST_SIZE])) \n return list","def get_file_flag(self):\n flag_list = os.listdir(self.path)\n temp_flag_list = []\n for flag in flag_list[:5]:\n result = re.match('^(\\w{2}\\d{6}\\_)(\\d{8})', flag)\n if result:\n temp_flag_list.append(result[2])\n self.flag_list = list(set(temp_flag_list))","def writebytes(self, *args) -> \"int\":\n return _ida_fpro.qfile_t_writebytes(self, *args)","def readbytes(self, *args) -> \"PyObject *\":\n return _ida_fpro.qfile_t_readbytes(self, *args)","def getArchivoVotacion():","def write_list_bin(inserted_list, file_name):\n try:\n with open(file_name, \"wb\") as binary_file:\n pickle.dump(inserted_list, binary_file)\n except Exception as e:\n print(type(e), e)\n sys.exit()","def test_serialize_number():\n num = 18178\n assert [\n 0b1000_0010,\n 0b1000_1110,\n 0b0000_0001\n ] == list(UnsignedInt.to_bytes(num))","def listFiles(self):\n pass","def test_deserialize_list():\n input = bytes([\n *UnsignedInt.to_bytes(5),\n *UnsignedInt.to_bytes(1),\n *UnsignedInt.to_bytes(2),\n *UnsignedInt.to_bytes(3),\n *UnsignedInt.to_bytes(4),\n *UnsignedInt.to_bytes(5),\n ])\n assert [1, 2, 3, 4, 5] == List(UnsignedInt).read(input)","def get_listfile(self, datadir):\n return []","def getBiggerFileList(file_list):\n bigger_list = []\n for file in file_list:\n if os.path.getsize(file) > 50000000: # bit\n bigger_list.append(file)\n return bigger_list","def upload_file(data_folder, filename):\n global a\n fh =np.fromfile(data_folder+filename,dtype =np.uint8)\n fh1= np.array(fh.reshape(-1,4))\n print(fh1[:8,:])\n fh2 = np.array(np.fliplr(fh1))\n fh3 = np.unpackbits(fh2[1:-1,:]).reshape(-1,32)\n\n addr = np.array(np.fliplr(fh3[:,0:6]))\n timestamp = np.array(np.fliplr(fh3[:,6:]))\n val_addr = np.packbits(addr,axis=1,bitorder='little')-32\n val_timestamp = np.packbits(timestamp,axis=1,bitorder='little')\n val_timestamps = val_timestamp[:,0]+2**8*val_timestamp[:,1]+2**16*val_timestamp[:,2]+\\\n 2**24*val_timestamp[:,3]\n return(val_addr,val_timestamps)","def createFileBytes(self, filename: unicode, offset: long, size: long, is_: java.io.InputStream, monitor: ghidra.util.task.TaskMonitor) -> ghidra.program.database.mem.FileBytes:\n ...","def manage_files(self):\n return 1 << 2","def test_get_picture_binary():\n from .scripts.initializedb import get_picture_binary\n import os\n here = os.path.abspath(os.path.dirname(__file__))\n path = os.path.join(here, 'scripts/img_questions/how.jpg')\n rb = get_picture_binary(path)\n assert isinstance(rb, bytes)","def file_list_table_format(result):\n table = []\n for item in result:\n row = OrderedDict()\n row['Name'] = item['name']\n row['Type'] = item['fileType']\n row['Size'] = '' if item['fileType'] == 'directory' else str(item['contentLength'])\n row['Modified'] = item['lastModified'] or ' '\n table.append(row)\n return table","def getByte(self,memAddress,id_list):\n if id_list == ():#Empty list\n return -1\n elif len(id_list) == 1:#Just one ID.\n pkt = Packet.makeReadPacket(id_list[0],memAddress)\n else:\n pkt = Packet.makeSyncReadPacket(memAddress,id_list)\n\n ans,err_num,err_str = self.serial.sendPkt(pkt)\n if ans == []:#In case of an empty packet arrives\n return -2\n else:\n data = []\n for index,val in enumerate(id_list):\n data.append(val) #Append the ID value\n data.append(ans[index*12+9])#Append the respective ID's data\n return data","def list_to_uint8_array(data_list):\n data_array = _populate_array(data_list, driver.uint8_array)\n return data_array","def _findfile(self,path,label):\n files=[];filenames=os.listdir(path)\n for name in filenames:\n if os.path.splitext(name)[0]==str(label):\n files.append(name)\n return files","def test_big_file_inversions_num():\n\n with open('tests/test_data/IntegerArray.txt') as f:\n content_nums = [int(num) for num in f]\n inversions_num = get_inversions_num(content_nums, 0, len(content_nums))\n expected_res = 2407905288\n assert inversions_num == expected_res","def to_bytes(cls, pki_type, item_id, file_name):\n with open(file_name, \"rb\") as _file:\n pki_raw_data = _file.read()\n\n data_out = LEUnsigned.pack(cls.TYPE[pki_type], 1) # type\n data_out += LEUnsigned.pack(item_id, 1) # ID\n data_out += LEUnsigned.pack(len(pki_raw_data), 2) # content len\n data_out += pki_raw_data # raw data\n data_out += chr(0xFF) * ((4 - (len(pki_raw_data) % 4)) % 4) # padding\n return data_out","def getrecord(file,uid, preserve = False):\n position = file.tell()\n recordsize = struct.calcsize('L32s256s')\n file.seek(recordsize*uid)\n data = file.read(recordsize)\n if preserve:\n file.seek(position)\n try:\n returnlist = list(struct.unpack('L32s256s',data))\n returnlist[1] = returnlist[1].replace('\\x00','')\n returnlist[2] = returnlist[2].replace('\\x00','')\n return returnlist\n except:\n return False","def _set_file_binary(self, value):\n if not value:\n return\n if self.folder.type != \"s3\":\n return super(NereidStaticFile, self)._set_file_binary(value)\n\n if self.is_large_file:\n return\n bucket = self.folder.get_bucket()\n s3key = key.Key(bucket)\n s3key.key = self.s3_key\n return s3key.set_contents_from_string(bytes(value))","def _bitlist_to_string(self, data):\n result = []\n pos = 0\n c = 0\n while pos < len(data):\n c += data[pos] << (7 - (pos % 8))\n if pos % 8 == 7:\n result.append(c)\n c = 0\n pos += 1\n return bytes(result)","def list_files(self):\n if self.remote:\n return self.remote.list_files()\n\n M.mset('DUZ',self.DUZ)\n M.mset('U', \"^\")\n if self.isProgrammer:\n M.mset('DUZ(0)', \"@\")\n rv = []\n s0 = \"0\"\n while s0 != \"\":\n s0, name = M.mexec(\n '''set s0=$order(^DIC(s0)) Q:s0'=+s0 I $D(^DIC(s0,0))&$D(^DIC(s0,0,\"GL\"))&$$VFILE^DILFD(s0) S s1=$P(^DIC(s0,0),U,1)''',\n M.INOUT(s0), M.INOUT(\"\"))\n if name:\n rv.append((name, s0))\n return rv","def attach_files(_) -> int:\n return 1 << 15","def attach_files(_) -> int:\n return 1 << 15","def test_binary_contents(file, tmp_path):\n name = tmp_path / \"1px.gif\"\n ret = file.managed(name=str(name), contents=BINARY_FILE)\n assert ret.result is True","def read_file(path_to_file):\n 8","def find_files(directory):\n cmd = [ 'find' , '-H', directory, '-type', 'f', '-printf', '%Ts %P\\\\0' ]\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n byte_string = proc.stdout.read().split(b'\\0')\n byte_string = filter(None, byte_string) # remove empty (byte)strings from list\n files = list(map(lambda x: x.decode('utf-8', errors='replace').split(' ', 1), byte_string))\n for f in files:\n f[0] = -1 * int(f[0])\n files.sort()\n files[:] = [f[1] for f in files]\n return files","def add_files(self,count=None):\n message_buffer =[]\n if count is None:\n count = len(self.files)\n while count:\n count -= 1\n message_buffer.append((count,base64.b64encode(self.files.pop()),0)) # required to maintain compatibility with\n if len(message_buffer) > 9:\n self.queue.write_batch(message_buffer)\n message_buffer = []\n self.queue.write_batch(message_buffer)","def init_bit_string(self, path: str):\n size = 0\n with open(path, 'rb') as file:\n self.bit_string = \"\"\n byte = file.read(1)\n\n # convert bytes to bits\n while (len(byte) > 0):\n byte = ord(byte)\n bits = bin(byte)[2:].rjust(8, '0')\n self.bit_string += bits\n byte = file.read(1)\n size += 1","def reserved_field(self) -> int:\n return unsigned_int(self._file_bytes, 0x06)","def test_get_directory_size_bytes():\n with tempfile.TemporaryDirectory() as tmpdir:\n tmpdir2 = os.path.join(tmpdir, \"tmp\")\n os.makedirs(tmpdir2, exist_ok=True)\n files = [\n os.path.join(tmpdir, \"file1.bin\"),\n os.path.join(tmpdir, \"file2.bin\"),\n os.path.join(tmpdir, \"tmp\", \"file3.bin\"),\n os.path.join(tmpdir, \"tmp\", \"file4.bin\"),\n ]\n data = \"1234567890\"\n for filename in files:\n with open(filename, \"w\") as f_out:\n f_out.write(data)\n\n assert get_directory_size_bytes(tmpdir, recursive=True) == 40\n assert get_directory_size_bytes(tmpdir, recursive=False) == 20","def files(self) -> List[str]:\n return [packet.name for packet in self.packets.file_description.values()]","def hdfs_get_filelist(blob_path, delimiter=\"_\"):\n\n def hdfs_listdir(blob_path):\n command = 'hdfs dfs -ls ' + blob_path\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n p.wait()\n files = [item.rstrip(\"\\n\").split()[-1] for item in p.stdout.readlines()]\n if len(files) > 0:\n files.pop(0) # remove summary from ls: \"found n items\".\n qty_files = len(files)\n return files, qty_files\n\n files, qty_files = hdfs_listdir(blob_path)\n kpis = []\n # If there are items in dir.\n if qty_files > 0:\n KPI = namedtuple('KPI', [\"filepath\", \"filename\", \"kpi_name\", \"initial_date\", \"final_date\", \"key\", \"extension\"])\n for file in files:\n filename, ext = basename(file), splitext(basename(file))[1]\n if ext == \".json\":\n splits = 3\n kpi = KPI(\n filepath=file\n , filename=filename\n , kpi_name=filename.rsplit(delimiter, splits)[0]\n , initial_date=filename.rsplit(delimiter, splits)[1]\n , final_date=filename.rsplit(delimiter, splits)[2]\n , key=splitext(filename.rsplit(delimiter, splits)[3])[0]\n , extension=ext\n )\n else: # ext != \".json\":\n splits = 1\n kpi = KPI(\n filepath=file\n , filename=filename\n , kpi_name=filename.rsplit(delimiter, splits)[0]\n , initial_date=None\n , final_date=None\n , key=splitext(filename.rsplit(delimiter, splits)[1])[0]\n , extension=ext\n )\n kpis.append(kpi)\n return kpis, len(kpis)","def create_structure_list_without_cif(filepath: str) -> List[str]:\n structure_list = []\n with open(os.path.join(MOF_TDA_PATH, \"persistence_images/\" + filepath), \"r\") as filepath:\n for line in filepath:\n line = line.strip()\n line = line[:-4] #remove .cif\n structure_list.append(line)\n return structure_list","def _fread3(fobj):\n b1, b2, b3 = np.fromfile(fobj, \">u1\", 3)\n return (b1 << 16) + (b2 << 8) + b3","def _fread3(fobj):\n b1, b2, b3 = np.fromfile(fobj, \">u1\", 3)\n return (b1 << 16) + (b2 << 8) + b3","def get_files(self):\r\n return self._filelist","def count_deleted_bytes(self): # EntryList.count_deleted_bytes\n bytes=0\n for name, e in self.contents.iteritems():\n bytes = bytes + e.count_deleted_bytes()\n return bytes","def get_data_block_contents_bytes(self):\n bb = self.volume.blkdev.block_bytes\n if self.volume.is_ffs:\n return bb\n else:\n return bb - 24","def add_image(self, f_name,file,new_id):\r\n folder=tempfile.mktemp()\r\n os.mkdir(folder)\r\n datei=open(folder+'/'+f_name,'w+')\r\n datei.write(file.read())\r\n datei.close()\r\n val='' \r\n liste_ext=liste_val\r\n if(self.toolbox.hasProperty('eigene_formate')):\r\n self_val=self.toolbox.getProperty('eigene_formate').split(',')\r\n liste_ext=[]\r\n for x in self_val:\r\n liste_ext.append('_'+x+'.jpeg')\r\n for extension in liste_ext:\r\n #cmd='/usr/bin/convert '+folder+'/'+f_name+' -resize '+extension[1:-4]+'x'+extension[1:-4]+' '+folder+'/'+new_id+extension\r\n cmd='/usr/bin/convert '+folder+'/'+f_name+' -resize '+extension[1:-4]+' '+folder+'/'+new_id+extension\r\n order=os.popen(cmd).read()\r\n kurz_name='_'+str(f_name.split('.')[0])\r\n kurz_name=kurz_name.replace(' ','_')\r\n val=val+self.manage_addImage(id=new_id+kurz_name+extension,file=open(folder+'/'+new_id+extension),title=f_name, precondition='', content_type='',REQUEST=None)+' ' \r\n os.remove(folder+'/'+new_id+extension)\r\n os.remove(folder+'/'+f_name)\r\n os.rmdir(folder)\r\n txt=\"Datei Hochgeladen!<br>\"\r\n #my_root=self.toolbox\r\n #txt+=my_root.id+\"<br>\"\r\n #if(my_root.hasProperty('eigene_formate')):\r\n # txt+=my_root.getProperty('eigene_formate')+\"<br>\"\r\n return txt","def hdr_to_Nifti(files):\r\n array = []\r\n for element in files:\r\n array = np.append(array, nib.load(element))\r\n\r\n print('array size: ', array.shape, '\\narray type: ', type(array))\r\n\r\n return array","def test_upload_binary(self):\n uploadFile = os.path.join(testdatadir, \"upload.data.gz\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED","def list_files():\n files = []\n for filename in os.listdir(UPLOAD_DIRECTORY):\n path = os.path.join(UPLOAD_DIRECTORY, filename)\n if os.path.isfile(path):\n files.append(filename)\n return jsonify(files)","def num_nodes_to_bytes(tree):\n return bytes([tree.number + 1])","def main():\n args = parse_args(sys.argv[1:])\n dir = args.directory\n wild = args.wildcard\n rec = args.recursive\n nb_clusters = args.clusters\n bricks = args.bricks\n\n path_for_display = os.path.abspath(dir)\n\n all_paths = None\n if rec:\n all_paths = Path(dir).rglob(wild)\n path_for_display += \" (recursive)\"\n else:\n all_paths = Path(dir).glob(wild)\n path_for_display += \" (non recursive)\"\n\n f_sizes = []\n\n for path in all_paths:\n\n full_path = os.path.join(path.parent, path.name)\n byte_size = os.path.getsize(full_path)\n # print(full_path, byte_size)\n f_sizes.append(byte_size)\n\n f_sizes = np.array(f_sizes)\n # print(f_sizes)\n\n min_byte_length = np.amin(f_sizes)\n max_byte_length = np.amax(f_sizes)\n mean_byte_length = np.mean(f_sizes)\n std_byte_length = np.std(f_sizes)\n median_byte_length = np.median(f_sizes)\n\n histo, bin_edges = np.histogram(f_sizes, nb_clusters)\n histo = histo.astype(\"float32\")\n histo_normalized = (np.copy(histo) / histo.max() * bricks).astype(\"uint32\")\n\n print(\"[BYTESPREAD REPORT]\")\n print()\n print(\"Date: \", datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\"))\n print(\"Directory: \", path_for_display)\n print(\"Filename match: \", wild)\n print(\"Number of files: \", f_sizes.shape[0])\n print(\"Smallest file: \", byteToHumanReadable(min_byte_length))\n print(\"Largest file: \", byteToHumanReadable(max_byte_length))\n print(\"Average size: \", byteToHumanReadable(mean_byte_length))\n print(\"Standard deviation: \", byteToHumanReadable(std_byte_length))\n print(\"Median size: \", byteToHumanReadable(median_byte_length))\n print(\"Histogram intervals: \", byteToHumanReadable(bin_edges[1] - bin_edges[0]))\n print(\"Histogram:\")\n print()\n\n for i in range(0, len(bin_edges)-1):\n lower_bound = byteToHumanReadable(bin_edges[i])\n upper_bound = byteToHumanReadable(bin_edges[i+1])\n print(\"|\", \"▓\" * histo_normalized[i], \"[{}-{}], {} files\".format(lower_bound, upper_bound, math.ceil(histo[i])))","def flatten_list_bytes(list_of_data):\n if not PY34:\n list_of_data = (bytes(data) if isinstance(data, memoryview) else\n data for data in list_of_data)\n return b''.join(list_of_data)","def bits(data):\n\treturn [format(ord(c),'08b') for c in data]","def list_files():\n try:\n return jsonify(os.listdir(env(\"FILES_DIRECTORY\"))), 200\n except:\n return {\"msg\": \"Não há arquivos a serem listados.\"}, 404","def loadAllFiles(f_list):\n\n t_1 = datetime.now()\n\n # first list\n i_list = []\n id_list = []\n for f in f_list:\n f_t = loadFile(f)\n i_list.extend(f_t)\n # clean list\n ln_i_list = len(i_list)\n c_list = []\n for i, f in enumerate(i_list):\n if f[\"id\"] not in id_list:\n id_list.append(f[\"id\"])\n c_list.append(f)\n print (i * 100.) / ln_i_list, datetime.now() - t_1, i\n return c_list","def get_filenames(b,X,timefac,BW):\r\n\tn = int(200000*X*timefac); nn = int(200000*X*10)\r\n\troot = \"./dat_LE_stream/\"\r\n\trootL = 'C:/AsusWebStorage/MySyncData/TRAFILES/'\r\n\tpodir = \"/b=\"+str(b)+\"/\" if BW else \"/HO/\"\r\n\t\r\n\ttrafile = rootL+\"TRA_BW_b\"+str(b)+\"X\"+str(int(X))+\"n\"+str(nn)+\"seed65438\"\r\n\tif not os.path.isfile(trafile):\r\n\t\ttrafile = rootL+\"TRA_BW_b\"+str(b)+\"X\"+str(int(X))+\"n\"+str(n)+\"seed65438\"\r\n\toutfile = root+podir+os.path.split(trafile)[1][4:]\r\n\t\r\n\trndfile = os.path.split(outfile)[0]+\"/RND_\"+os.path.split(outfile)[1]+\".png\"\r\n\tpdffile = os.path.split(outfile)[0]+\"/PDF_\"+os.path.split(outfile)[1]\r\n\tstrfile = os.path.split(outfile)[0]+\"/STR_\"+os.path.split(outfile)[1]\r\n\t\r\n\treturn trafile,rndfile,pdffile,strfile,n","def readByte(self) :\n tag = ord(self.minfile[self.pos])\n self.pos += 1\n return tag","def bytes(self, count):\n\n if count < 0:\n raise ValueError\n\n # fast path\n if self._bits == 0:\n data = self._fileobj.read(count)\n if len(data) != count:\n raise BitReaderError(\"not enough data\")\n return data\n\n return bytes(bytearray(self.bits(8) for _ in xrange(count)))","def get_files(message):\n message_deserialized = json.loads(message)\n filename = message_deserialized['contents'] #returns unicode\n return str(filename)","def encode_u8_array(value: typing.List[int]) -> bytes:\n return encode_u32(len(value)) + bytes(value)","def node_setup_files_list_table_format(result):\n table = []\n for item in result:\n row = OrderedDict()\n row['Name'] = item['name']\n row['Is directory'] = 'yes' if item['is_directory'] else 'no'\n row['Size'] = '' if item['size'] is None else (item['size'] or '0')\n table.append(row)\n return table","def get_binary(fname):\n with open(fname, 'rb+') as f_name:\n data = f_name.read()\n return data"],"string":"[\n \"def getAllFileBytes(self) -> List[ghidra.program.database.mem.FileBytes]:\\n ...\",\n \"def getByteArray(self, name: unicode) -> List[int]:\\n ...\",\n \"def get_list_of_bytes2(self):\\n pass\",\n \"def network_bytes(self) -> List[bytes]:\",\n \"def get_byte(self, *args) -> \\\"PyObject *\\\":\\n return _ida_fpro.qfile_t_get_byte(self, *args)\",\n \"def binary_conversion():\\n file = filedialog.askopenfilename(title=\\\"Select a txt \\\",filetypes=[(\\\"Text file\\\", \\\".txt\\\")])\\n if os.stat(file).st_size != 0:\\n open_file = open(file, \\\"r\\\")\\n seq = \\\"\\\"\\n for line in open_file :\\n seq += line\\n seq = seq.strip(\\\"\\\\n\\\")\\n \\n huffman_code, binary_seq = huffman_construction(seq)\\n #count the number of \\\"0\\\" added\\n added = 0 \\n while len(binary_seq) % 8 !=0:\\n added +=1 \\n binary_seq += '0'\\n open_file.close()\\n \\n else :\\n messagebox.showwarning(\\\"Error\\\", \\\"Your file is empty \\\")\\n \\n return added, binary_seq, seq, file\",\n \"def read_list_bin(file_name):\\n try:\\n extracted_list = []\\n with open(file_name, \\\"rb\\\") as binary_file:\\n extracted_list = pickle.load(binary_file)\\n return extracted_list\\n except FileNotFoundError:\\n print(\\\"File not found: \\\",file_name)\\n except Exception as e:\\n print(type(e), e)\\n sys.exit()\",\n \"def nibble(self, b):\\n out = self.file[:b]\\n self.file = self.file[b:]\\n #print \\\"nibbled: \\\" + out\\n return out\",\n \"def utf8_to_binary() :\\n dico_binary, comp_seq, file_comp = read_compressed_file()\\n \\n #for each items of the sequence convert it in binary string on 8 bits\\n bin_str = \\\"\\\"\\n for value in comp_seq:\\n code = ord(value)\\n bin_str += '{:08b}'.format(code)\\n \\n #remove the number of zeroes added \\n \\n added = int(dico_binary[\\\"add\\\"])\\n #if the padding is equal to 0, don't cut anathing from the sequence\\n if added == 0: \\n bin_seq = bin_str\\n else: \\n bin_seq = bin_str[:-added]\\n \\n return bin_seq, dico_binary, comp_seq, file_comp\",\n \"def _extract_bytes(l):\\n # input should consist of a list of strings similar to ['123456', 'bytes', 'read']\\n if len(l) != 3 or l[1] != 'bytes' or l[2] != 'read':\\n raise UBootError('received unexpected reply when reading file')\\n\\n try:\\n return int(l[0])\\n except ValueError:\\n raise UBootError('received unexpected bytes value when reading file')\",\n \"def get_unknown_bytes(filename):\\n d_file = open(os.path.join(u_path, filename), \\\"rb\\\")\\n b = bytearray(d_file.read())\\n d_file.close()\\n return b\",\n \"def getByteRow(self, int: int) -> typing.List[int]:\\n ...\",\n \"def getFileListPNFS(dataset,blacklist=[ ]):\\n dataset = dataset.replace('__','/')\\n tmpList = glob.glob(dataset+'/*.root')\\n director = \\\"dcap://t3se01.psi.ch:22125/\\\"\\n filelist = [ ]\\n for file in tmpList:\\n if '.root' in file and file not in blacklist:\\n filelist.append(director+file.rstrip())\\n if \\\"SingleMuon/Run2018C-Nano14Dec2018-v1/NANOAOD\\\" in dataset: # temporary solution\\n file = director+\\\"/pnfs/psi.ch/cms/trivcat/store/user/ineuteli/samples/NANOAOD_2018/SingleMuon/Run2018C-Nano14Dec2018-v1/NANOAOD/DA99EFB9-860F-F648-8D46-5AD80205F53B_skimmed.root\\\"\\n if file in filelist:\\n print bcolors.BOLD + bcolors.WARNING + \\\"FOUND %s !\\\"%file + bcolors.ENDC\\n else:\\n print bcolors.BOLD + bcolors.WARNING + \\\"ADDED %s !\\\"%file + bcolors.ENDC\\n filelist.append(file)\\n filelist.sort()\\n return filelist\",\n \"def de_flip_file(n):\\n start = '0'*(n//2) + '1' + '0'*(n//2) # 00100 if n == 5\\n f_name = 'C:/Users/clean/Desktop/dede.txt'\\n f_tmp = 'C:/Users/clean/Desktop/dede2.txt'\\n f_result = 'C:/Users/clean/Desktop/de_flip.txt'\\n\\n #L = [[start]]\\n with open(f_name,'w') as f:\\n f.write(start + '\\\\n')\\n\\n #L_tmp = []\\n with open(f_tmp,'w') as ftmp:\\n pass\\n \\n #collect = []\\n with open(f_result,'w') as f_end:\\n pass\\n \\n \\n length = 1\\n \\n while(True):\\n count = 0\\n check_end = True\\n\\n with open(f_name) as f:\\n for branch in f:\\n L_branch = branch.split()\\n next_0 = L_branch[-1][1:] + '0'\\n next_1 = L_branch[-1][1:] + '1'\\n\\n if next_0 == start:\\n with open(f_result,'a') as f_end:\\n f_end.write(branch)\\n count += 1\\n continue\\n\\n if next_0 not in L_branch and next_0[::-1] not in L_branch:\\n check_end = False\\n with open(f_tmp,'a') as ftmp:\\n ftmp.write(branch[:-1] + ' ' + next_0 + '\\\\n')\\n\\n if next_1 not in L_branch and next_1[::-1] not in L_branch:\\n check_end = False\\n with open(f_tmp,'a') as ftmp:\\n ftmp.write(branch[:-1] + ' ' + next_1 + '\\\\n')\\n \\n if check_end == True:\\n break\\n \\n shutil.copyfile(f_tmp, f_name) # 오른쪽으로 복사\\n\\n with open(f_tmp,'w') as ftmp:\\n pass\\n\\n print(\\\"length : {0}, count : {1}\\\".format(length,count))\\n length += 1\\n return None\",\n \"def put_byte(self, *args) -> \\\"int\\\":\\n return _ida_fpro.qfile_t_put_byte(self, *args)\",\n \"def Read2000256List(self):\\n items = []\\n for i in range(0, 2000):\\n data = self.ReadBytes(64)\\n ba = bytearray(binascii.unhexlify(data))\\n ba.reverse()\\n items.append(ba.hex().encode('utf-8'))\\n return items\",\n \"def upload_files(self):\\n return 1 << 0\",\n \"def boole_list_to_bytes(bit_message:list):\\n b_number = 0\\n byte_list = []\\n one_cool_byte = 0\\n for item in bit_message:\\n one_cool_byte <<= 1\\n if item:\\n one_cool_byte += 1\\n b_number += 1\\n if b_number == 8:\\n byte_list.append(one_cool_byte)\\n b_number = 0\\n one_cool_byte = 0\\n return bytes(byte_list).rstrip(b\\\"\\\\x00\\\") # return byte string\",\n \"def getBaseSrcFile(self) -> List[int]:\\n ...\",\n \"def create_file_list(params):\\n data_dir = params.get('data_dir', '')\\n params['file_list'] = \\\".tmp.txt\\\"\\n imgtype_list = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff'}\\n with open(params['file_list'], \\\"w\\\") as fout:\\n tmp_file_list = os.listdir(data_dir)\\n for file_name in tmp_file_list:\\n file_path = os.path.join(data_dir, file_name)\\n if imghdr.what(file_path) not in imgtype_list:\\n continue\\n fout.write(file_name + \\\" 0\\\" + \\\"\\\\n\\\")\",\n \"def get_sorted_img_list():\\n dirPath=settings.BASE_DIR\\n imgdir=\\\"/pttWeb/static/topicmodel\\\"\\n fileID=glob.glob(dirPath+imgdir+\\\"/*.png\\\")\\n fileID=[i.replace('/home/stream/Documents/minimum_django/pttWeb/static/','') for i in fileID]\\n fileID=[Week_Image(i) for i in fileID]\\n fileID.sort(key=lambda x: x.date, reverse=True)\\n #translate . to / since javascript parsing date has some issue!\\n fileID=[(i.filename,date_trans_z(i.date.strftime(\\\"%Y.%m.%d\\\"))) for i in fileID]\\n return fileID\",\n \"def getBinaryData(self, filepath):\\n\\n binary_values = []\\n\\n with open(filepath, 'rb') as fileobject:\\n\\n # read file byte by byte\\n data = fileobject.read(1)\\n\\n while data != b'':\\n binary_values.append(ord(data))\\n data = fileobject.read(1)\\n\\n return binary_values\",\n \"def int_list(data: bytes) -> list:\\n byte_data = BytesIO(data)\\n byte_list = []\\n single_byte = byte_data.read(1)\\n while single_byte != b\\\"\\\" and single_byte != \\\"\\\":\\n single_int = byte_to_int(single_byte)\\n byte_list.append(single_int)\\n single_byte = byte_data.read(1)\\n return byte_list\",\n \"def binconv(fp, fp_len):\\n vec = [1] * fp_len\\n #print(fp, len(fp))\\n for indx, b in enumerate(fp):\\n if indx == 224:\\n break\\n if b == '0':\\n vec[indx] = -1\\n return vec\",\n \"def bip32_path_to_uints(n: str) -> List[int]:\\n path = []\\n for x in n.split('/')[1:]:\\n if x == '':\\n continue\\n prime = 0\\n if x.endswith(\\\"'\\\"):\\n x = x.replace('\\\\'', '')\\n prime = BIP32_PRIME\\n if x.startswith('-'):\\n prime = BIP32_PRIME\\n path.append(abs(int(x)) | prime)\\n return path\",\n \"def to_bytes(self) -> \\\"list[int]\\\":\\n entries_per_byte = 8 // int(self.offset_type)\\n byte_array = []\\n for i in range(0, len(self.entries), entries_per_byte):\\n byte = 0\\n for j in range(0, entries_per_byte):\\n byte |= self.entries[i + j] << (j * int(self.offset_type))\\n byte_array.append(byte)\\n return byte_array\",\n \"def FileList(file):\\n with open(file,\\\"r\\\") as f:\\n list1 = [r.split()[1] for r in f]\\n list1 = [int(i) for i in list1]\\n return list1\",\n \"def debianize( strFilename ):\\n \\n #~ data = gzip.GzipFile( strFilename ).read();\\n #~ print data;\\n #~ return;\\n \\n #~ data = gzip.open( strFilename ).read();\\n #~ print data;\\n #~ return; \\n \\n #~ uncompressedData = bz2.BZ2File(strFilename).read()\\n #~ print str(uncompressedData)\\n #~ return;\\n \\n #~ file = open( strFilename, 'rb' );\\n #~ data = file.read();\\n #~ file.close();\\n #~ print debug.dumpHexa( data );\\n \\n #~ ar = tarfile.open(strFilename, 'r:*')\\n #~ for item in ar:\\n #~ print( str(item) );\\n #~ print( \\\"%s:\\\" % item.name );\\n #~ #print debug.dumpHexa(item.buf);\\n #~ #print zlib.decompress(item.buf)\\n #~ #print zlib.decompress(ar.extractfile(item).read())\\n #~ data = ar.extractfile(item.name).read()\\n #~ print data # works !\\n #~ ar.close() \\n #~ return;\\n \\n fileLists = [];\\n file = open( strFilename );\\n data = file.read();\\n file.close();\\n \\n print( \\\"data len: %d\\\" % len( data ) );\\n\\n nDataCompressedOffset = 0; # 132\\n\\n # works fine on toto.gz\\n #~ f = gzip.open(strFilename, 'rb')\\n #~ file_content = f.read()\\n #~ print file_content\\n #~ f.close() \\n \\n #~ decompressor = bz2.BZ2Decompressor();\\n #~ uncompressed = decompressor.decompress(data[nDataCompressedOffset:]);\\n \\n #~ uncompressed = zlib.decompress(data[nDataCompressedOffset:]);\\n \\n uncompressed = decompress( data );\\n print( \\\"uncompressed: %s\\\" % str( uncompressed ) );\",\n \"def _write_to_lsb(tribit: str, rgb_binary: tuple):\\n pixel = []\\n for idx, bit in enumerate(tribit):\\n color = rgb_binary[idx]\\n color = color[:-1] + bit\\n pixel.append(color)\\n\\n return pixel\",\n \"def test08(self):\\n logical_file_name = \\\"/store/mc/Fall08/BBJets250to500-madgraph/GEN-SIM-RAW/IDEAL_/%s/%i.root\\\" %(uid, 0)\\n #print \\\"WARNING : DBS cannot list INVALID file, so for now this test is commented out\\\"\\n self.api.updateFileStatus(logical_file_name=logical_file_name, is_file_valid=0)\\n #listfile\\n filesInDBS=self.api.listFiles(logical_file_name=logical_file_name, detail=True)\\n self.assertEqual(len(filesInDBS), 1)\\n self.assertEqual(filesInDBS[0]['is_file_valid'], 0)\",\n \"def view_files(self):\\n return 1 << 1\",\n \"def convertData(img):\\n dataset = []\\n for i in img:\\n dataset.append(format(ord(i), '08b'))\\n return dataset\",\n \"def setByteArray(self, name: unicode, value: List[int]) -> None:\\n ...\",\n \"def getTRRFileSize(dataBlock):\\n print(dataBlock[0:70])\\n if dataBlock[0:4].decode() == \\\"TRQ \\\":\\n if dataBlock[4:6].decode() == \\\"f \\\":\\n for index in range(7, len(dataBlock)):\\n if dataBlock[index:index+1].decode() == \\\" \\\":\\n break\\n filesize = \\\"\\\"\\n for byte in range(index+1, len(dataBlock)):\\n if dataBlock[byte:byte+1].decode() == \\\" \\\":\\n return [int(filesize), byte+1]\\n filesize += dataBlock[byte:byte+1].decode()\\n return \\\"random return string\\\"\",\n \"def getSmallFilesList(file_list):\\n small_list = []\\n for file in file_list:\\n if os.path.getsize(file) <= 50000000: # bit\\n small_list.append(file)\\n return small_list\",\n \"def vec_to_bytes(val):\\n return [int(x*255) for x in val]\",\n \"def _fread3_many(fobj, n):\\n b1, b2, b3 = np.fromfile(fobj, \\\">u1\\\", 3 * n).reshape(-1,\\n 3).astype(np.int).T\\n return (b1 << 16) + (b2 << 8) + b3\",\n \"def load_and_print_file_list(file_list):\\n for file in file_list:\\n hdu_list = load_file(file)\\n print(\\\"'{0}' has {1} hdus in it\\\".format(file, len(hdu_list)))\\n for ii in range(len(hdu_list)):\\n hdu1 = hdu_list[ii] # FITS HDU counting is from 1\\n print('BITPIX type of HDU{0} = {1}'.format(ii + 1,\\n hdu1.header['BITPIX']))\\n # be sure to close the file handle\\n hdu_list.close()\",\n \"def save2file(self):\\n ids_input = []\\n labels_input = []\\n ids_path = os.path.join(self.path, 'ids')\\n if not os.path.exists(ids_path):\\n os.makedirs(ids_path)\\n labels_path = os.path.join(self.path, 'labels')\\n if not os.path.exists(labels_path):\\n os.makedirs(labels_path)\\n ids_total = len(self.test)\\n for i in range(ids_total):\\n ids_input = self.test[i][0]\\n labels_input = self.test[i][1]\\n file_name = \\\"ids/\\\" + str(i) + \\\".bin\\\"\\n file_path = os.path.join(self.path, file_name)\\n np.array(ids_input, dtype=np.int32).tofile(file_path)\\n file_name = \\\"labels/\\\" + str(i) + \\\".bin\\\"\\n file_path = os.path.join(self.path, file_name)\\n np.array(labels_input, dtype=np.int32).tofile(file_path)\\n print(\\\"\\\\n ****** Success! ******\\\\n \\\")\",\n \"def getByteArray2D(self) -> typing.List[typing.List[int]]:\\n ...\",\n \"def ej08a(texto):\\n indice = 0\\n resultado = []\\n current_byte = \\\"\\\"\\n\\n for i in texto:\\n current_byte += i # se agrega el nuevo caracter al byte actual\\n indice += 1 # se incrementa en uno el indice\\n if indice % 8 == 0:\\n # Comienza un nuevo byte\\n resultado.append(current_byte)\\n current_byte = \\\"\\\"\\n return resultado\",\n \"def __get_sorted_file_list(self):\\n d = self.__view.CurrentImgDir\\n list = os.listdir(d)\\n if self.__view.SortType == constant.THUMB_SORT_FILENAME:\\n # Sort by Name\\n list.sort()\\n if self.__view.SortType == 2:\\n # Sort by Size\\n list.sort(lambda a, b: int(os.stat(os.path.join(d,a))[stat.ST_SIZE] - os.stat(os.path.join(d,b))[stat.ST_SIZE])) \\n return list\",\n \"def get_file_flag(self):\\n flag_list = os.listdir(self.path)\\n temp_flag_list = []\\n for flag in flag_list[:5]:\\n result = re.match('^(\\\\w{2}\\\\d{6}\\\\_)(\\\\d{8})', flag)\\n if result:\\n temp_flag_list.append(result[2])\\n self.flag_list = list(set(temp_flag_list))\",\n \"def writebytes(self, *args) -> \\\"int\\\":\\n return _ida_fpro.qfile_t_writebytes(self, *args)\",\n \"def readbytes(self, *args) -> \\\"PyObject *\\\":\\n return _ida_fpro.qfile_t_readbytes(self, *args)\",\n \"def getArchivoVotacion():\",\n \"def write_list_bin(inserted_list, file_name):\\n try:\\n with open(file_name, \\\"wb\\\") as binary_file:\\n pickle.dump(inserted_list, binary_file)\\n except Exception as e:\\n print(type(e), e)\\n sys.exit()\",\n \"def test_serialize_number():\\n num = 18178\\n assert [\\n 0b1000_0010,\\n 0b1000_1110,\\n 0b0000_0001\\n ] == list(UnsignedInt.to_bytes(num))\",\n \"def listFiles(self):\\n pass\",\n \"def test_deserialize_list():\\n input = bytes([\\n *UnsignedInt.to_bytes(5),\\n *UnsignedInt.to_bytes(1),\\n *UnsignedInt.to_bytes(2),\\n *UnsignedInt.to_bytes(3),\\n *UnsignedInt.to_bytes(4),\\n *UnsignedInt.to_bytes(5),\\n ])\\n assert [1, 2, 3, 4, 5] == List(UnsignedInt).read(input)\",\n \"def get_listfile(self, datadir):\\n return []\",\n \"def getBiggerFileList(file_list):\\n bigger_list = []\\n for file in file_list:\\n if os.path.getsize(file) > 50000000: # bit\\n bigger_list.append(file)\\n return bigger_list\",\n \"def upload_file(data_folder, filename):\\n global a\\n fh =np.fromfile(data_folder+filename,dtype =np.uint8)\\n fh1= np.array(fh.reshape(-1,4))\\n print(fh1[:8,:])\\n fh2 = np.array(np.fliplr(fh1))\\n fh3 = np.unpackbits(fh2[1:-1,:]).reshape(-1,32)\\n\\n addr = np.array(np.fliplr(fh3[:,0:6]))\\n timestamp = np.array(np.fliplr(fh3[:,6:]))\\n val_addr = np.packbits(addr,axis=1,bitorder='little')-32\\n val_timestamp = np.packbits(timestamp,axis=1,bitorder='little')\\n val_timestamps = val_timestamp[:,0]+2**8*val_timestamp[:,1]+2**16*val_timestamp[:,2]+\\\\\\n 2**24*val_timestamp[:,3]\\n return(val_addr,val_timestamps)\",\n \"def createFileBytes(self, filename: unicode, offset: long, size: long, is_: java.io.InputStream, monitor: ghidra.util.task.TaskMonitor) -> ghidra.program.database.mem.FileBytes:\\n ...\",\n \"def manage_files(self):\\n return 1 << 2\",\n \"def test_get_picture_binary():\\n from .scripts.initializedb import get_picture_binary\\n import os\\n here = os.path.abspath(os.path.dirname(__file__))\\n path = os.path.join(here, 'scripts/img_questions/how.jpg')\\n rb = get_picture_binary(path)\\n assert isinstance(rb, bytes)\",\n \"def file_list_table_format(result):\\n table = []\\n for item in result:\\n row = OrderedDict()\\n row['Name'] = item['name']\\n row['Type'] = item['fileType']\\n row['Size'] = '' if item['fileType'] == 'directory' else str(item['contentLength'])\\n row['Modified'] = item['lastModified'] or ' '\\n table.append(row)\\n return table\",\n \"def getByte(self,memAddress,id_list):\\n if id_list == ():#Empty list\\n return -1\\n elif len(id_list) == 1:#Just one ID.\\n pkt = Packet.makeReadPacket(id_list[0],memAddress)\\n else:\\n pkt = Packet.makeSyncReadPacket(memAddress,id_list)\\n\\n ans,err_num,err_str = self.serial.sendPkt(pkt)\\n if ans == []:#In case of an empty packet arrives\\n return -2\\n else:\\n data = []\\n for index,val in enumerate(id_list):\\n data.append(val) #Append the ID value\\n data.append(ans[index*12+9])#Append the respective ID's data\\n return data\",\n \"def list_to_uint8_array(data_list):\\n data_array = _populate_array(data_list, driver.uint8_array)\\n return data_array\",\n \"def _findfile(self,path,label):\\n files=[];filenames=os.listdir(path)\\n for name in filenames:\\n if os.path.splitext(name)[0]==str(label):\\n files.append(name)\\n return files\",\n \"def test_big_file_inversions_num():\\n\\n with open('tests/test_data/IntegerArray.txt') as f:\\n content_nums = [int(num) for num in f]\\n inversions_num = get_inversions_num(content_nums, 0, len(content_nums))\\n expected_res = 2407905288\\n assert inversions_num == expected_res\",\n \"def to_bytes(cls, pki_type, item_id, file_name):\\n with open(file_name, \\\"rb\\\") as _file:\\n pki_raw_data = _file.read()\\n\\n data_out = LEUnsigned.pack(cls.TYPE[pki_type], 1) # type\\n data_out += LEUnsigned.pack(item_id, 1) # ID\\n data_out += LEUnsigned.pack(len(pki_raw_data), 2) # content len\\n data_out += pki_raw_data # raw data\\n data_out += chr(0xFF) * ((4 - (len(pki_raw_data) % 4)) % 4) # padding\\n return data_out\",\n \"def getrecord(file,uid, preserve = False):\\n position = file.tell()\\n recordsize = struct.calcsize('L32s256s')\\n file.seek(recordsize*uid)\\n data = file.read(recordsize)\\n if preserve:\\n file.seek(position)\\n try:\\n returnlist = list(struct.unpack('L32s256s',data))\\n returnlist[1] = returnlist[1].replace('\\\\x00','')\\n returnlist[2] = returnlist[2].replace('\\\\x00','')\\n return returnlist\\n except:\\n return False\",\n \"def _set_file_binary(self, value):\\n if not value:\\n return\\n if self.folder.type != \\\"s3\\\":\\n return super(NereidStaticFile, self)._set_file_binary(value)\\n\\n if self.is_large_file:\\n return\\n bucket = self.folder.get_bucket()\\n s3key = key.Key(bucket)\\n s3key.key = self.s3_key\\n return s3key.set_contents_from_string(bytes(value))\",\n \"def _bitlist_to_string(self, data):\\n result = []\\n pos = 0\\n c = 0\\n while pos < len(data):\\n c += data[pos] << (7 - (pos % 8))\\n if pos % 8 == 7:\\n result.append(c)\\n c = 0\\n pos += 1\\n return bytes(result)\",\n \"def list_files(self):\\n if self.remote:\\n return self.remote.list_files()\\n\\n M.mset('DUZ',self.DUZ)\\n M.mset('U', \\\"^\\\")\\n if self.isProgrammer:\\n M.mset('DUZ(0)', \\\"@\\\")\\n rv = []\\n s0 = \\\"0\\\"\\n while s0 != \\\"\\\":\\n s0, name = M.mexec(\\n '''set s0=$order(^DIC(s0)) Q:s0'=+s0 I $D(^DIC(s0,0))&$D(^DIC(s0,0,\\\"GL\\\"))&$$VFILE^DILFD(s0) S s1=$P(^DIC(s0,0),U,1)''',\\n M.INOUT(s0), M.INOUT(\\\"\\\"))\\n if name:\\n rv.append((name, s0))\\n return rv\",\n \"def attach_files(_) -> int:\\n return 1 << 15\",\n \"def attach_files(_) -> int:\\n return 1 << 15\",\n \"def test_binary_contents(file, tmp_path):\\n name = tmp_path / \\\"1px.gif\\\"\\n ret = file.managed(name=str(name), contents=BINARY_FILE)\\n assert ret.result is True\",\n \"def read_file(path_to_file):\\n 8\",\n \"def find_files(directory):\\n cmd = [ 'find' , '-H', directory, '-type', 'f', '-printf', '%Ts %P\\\\\\\\0' ]\\n proc = subprocess.Popen(cmd, stdout=subprocess.PIPE)\\n byte_string = proc.stdout.read().split(b'\\\\0')\\n byte_string = filter(None, byte_string) # remove empty (byte)strings from list\\n files = list(map(lambda x: x.decode('utf-8', errors='replace').split(' ', 1), byte_string))\\n for f in files:\\n f[0] = -1 * int(f[0])\\n files.sort()\\n files[:] = [f[1] for f in files]\\n return files\",\n \"def add_files(self,count=None):\\n message_buffer =[]\\n if count is None:\\n count = len(self.files)\\n while count:\\n count -= 1\\n message_buffer.append((count,base64.b64encode(self.files.pop()),0)) # required to maintain compatibility with\\n if len(message_buffer) > 9:\\n self.queue.write_batch(message_buffer)\\n message_buffer = []\\n self.queue.write_batch(message_buffer)\",\n \"def init_bit_string(self, path: str):\\n size = 0\\n with open(path, 'rb') as file:\\n self.bit_string = \\\"\\\"\\n byte = file.read(1)\\n\\n # convert bytes to bits\\n while (len(byte) > 0):\\n byte = ord(byte)\\n bits = bin(byte)[2:].rjust(8, '0')\\n self.bit_string += bits\\n byte = file.read(1)\\n size += 1\",\n \"def reserved_field(self) -> int:\\n return unsigned_int(self._file_bytes, 0x06)\",\n \"def test_get_directory_size_bytes():\\n with tempfile.TemporaryDirectory() as tmpdir:\\n tmpdir2 = os.path.join(tmpdir, \\\"tmp\\\")\\n os.makedirs(tmpdir2, exist_ok=True)\\n files = [\\n os.path.join(tmpdir, \\\"file1.bin\\\"),\\n os.path.join(tmpdir, \\\"file2.bin\\\"),\\n os.path.join(tmpdir, \\\"tmp\\\", \\\"file3.bin\\\"),\\n os.path.join(tmpdir, \\\"tmp\\\", \\\"file4.bin\\\"),\\n ]\\n data = \\\"1234567890\\\"\\n for filename in files:\\n with open(filename, \\\"w\\\") as f_out:\\n f_out.write(data)\\n\\n assert get_directory_size_bytes(tmpdir, recursive=True) == 40\\n assert get_directory_size_bytes(tmpdir, recursive=False) == 20\",\n \"def files(self) -> List[str]:\\n return [packet.name for packet in self.packets.file_description.values()]\",\n \"def hdfs_get_filelist(blob_path, delimiter=\\\"_\\\"):\\n\\n def hdfs_listdir(blob_path):\\n command = 'hdfs dfs -ls ' + blob_path\\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\\n p.wait()\\n files = [item.rstrip(\\\"\\\\n\\\").split()[-1] for item in p.stdout.readlines()]\\n if len(files) > 0:\\n files.pop(0) # remove summary from ls: \\\"found n items\\\".\\n qty_files = len(files)\\n return files, qty_files\\n\\n files, qty_files = hdfs_listdir(blob_path)\\n kpis = []\\n # If there are items in dir.\\n if qty_files > 0:\\n KPI = namedtuple('KPI', [\\\"filepath\\\", \\\"filename\\\", \\\"kpi_name\\\", \\\"initial_date\\\", \\\"final_date\\\", \\\"key\\\", \\\"extension\\\"])\\n for file in files:\\n filename, ext = basename(file), splitext(basename(file))[1]\\n if ext == \\\".json\\\":\\n splits = 3\\n kpi = KPI(\\n filepath=file\\n , filename=filename\\n , kpi_name=filename.rsplit(delimiter, splits)[0]\\n , initial_date=filename.rsplit(delimiter, splits)[1]\\n , final_date=filename.rsplit(delimiter, splits)[2]\\n , key=splitext(filename.rsplit(delimiter, splits)[3])[0]\\n , extension=ext\\n )\\n else: # ext != \\\".json\\\":\\n splits = 1\\n kpi = KPI(\\n filepath=file\\n , filename=filename\\n , kpi_name=filename.rsplit(delimiter, splits)[0]\\n , initial_date=None\\n , final_date=None\\n , key=splitext(filename.rsplit(delimiter, splits)[1])[0]\\n , extension=ext\\n )\\n kpis.append(kpi)\\n return kpis, len(kpis)\",\n \"def create_structure_list_without_cif(filepath: str) -> List[str]:\\n structure_list = []\\n with open(os.path.join(MOF_TDA_PATH, \\\"persistence_images/\\\" + filepath), \\\"r\\\") as filepath:\\n for line in filepath:\\n line = line.strip()\\n line = line[:-4] #remove .cif\\n structure_list.append(line)\\n return structure_list\",\n \"def _fread3(fobj):\\n b1, b2, b3 = np.fromfile(fobj, \\\">u1\\\", 3)\\n return (b1 << 16) + (b2 << 8) + b3\",\n \"def _fread3(fobj):\\n b1, b2, b3 = np.fromfile(fobj, \\\">u1\\\", 3)\\n return (b1 << 16) + (b2 << 8) + b3\",\n \"def get_files(self):\\r\\n return self._filelist\",\n \"def count_deleted_bytes(self): # EntryList.count_deleted_bytes\\n bytes=0\\n for name, e in self.contents.iteritems():\\n bytes = bytes + e.count_deleted_bytes()\\n return bytes\",\n \"def get_data_block_contents_bytes(self):\\n bb = self.volume.blkdev.block_bytes\\n if self.volume.is_ffs:\\n return bb\\n else:\\n return bb - 24\",\n \"def add_image(self, f_name,file,new_id):\\r\\n folder=tempfile.mktemp()\\r\\n os.mkdir(folder)\\r\\n datei=open(folder+'/'+f_name,'w+')\\r\\n datei.write(file.read())\\r\\n datei.close()\\r\\n val='' \\r\\n liste_ext=liste_val\\r\\n if(self.toolbox.hasProperty('eigene_formate')):\\r\\n self_val=self.toolbox.getProperty('eigene_formate').split(',')\\r\\n liste_ext=[]\\r\\n for x in self_val:\\r\\n liste_ext.append('_'+x+'.jpeg')\\r\\n for extension in liste_ext:\\r\\n #cmd='/usr/bin/convert '+folder+'/'+f_name+' -resize '+extension[1:-4]+'x'+extension[1:-4]+' '+folder+'/'+new_id+extension\\r\\n cmd='/usr/bin/convert '+folder+'/'+f_name+' -resize '+extension[1:-4]+' '+folder+'/'+new_id+extension\\r\\n order=os.popen(cmd).read()\\r\\n kurz_name='_'+str(f_name.split('.')[0])\\r\\n kurz_name=kurz_name.replace(' ','_')\\r\\n val=val+self.manage_addImage(id=new_id+kurz_name+extension,file=open(folder+'/'+new_id+extension),title=f_name, precondition='', content_type='',REQUEST=None)+' ' \\r\\n os.remove(folder+'/'+new_id+extension)\\r\\n os.remove(folder+'/'+f_name)\\r\\n os.rmdir(folder)\\r\\n txt=\\\"Datei Hochgeladen!<br>\\\"\\r\\n #my_root=self.toolbox\\r\\n #txt+=my_root.id+\\\"<br>\\\"\\r\\n #if(my_root.hasProperty('eigene_formate')):\\r\\n # txt+=my_root.getProperty('eigene_formate')+\\\"<br>\\\"\\r\\n return txt\",\n \"def hdr_to_Nifti(files):\\r\\n array = []\\r\\n for element in files:\\r\\n array = np.append(array, nib.load(element))\\r\\n\\r\\n print('array size: ', array.shape, '\\\\narray type: ', type(array))\\r\\n\\r\\n return array\",\n \"def test_upload_binary(self):\\n uploadFile = os.path.join(testdatadir, \\\"upload.data.gz\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\",\n \"def list_files():\\n files = []\\n for filename in os.listdir(UPLOAD_DIRECTORY):\\n path = os.path.join(UPLOAD_DIRECTORY, filename)\\n if os.path.isfile(path):\\n files.append(filename)\\n return jsonify(files)\",\n \"def num_nodes_to_bytes(tree):\\n return bytes([tree.number + 1])\",\n \"def main():\\n args = parse_args(sys.argv[1:])\\n dir = args.directory\\n wild = args.wildcard\\n rec = args.recursive\\n nb_clusters = args.clusters\\n bricks = args.bricks\\n\\n path_for_display = os.path.abspath(dir)\\n\\n all_paths = None\\n if rec:\\n all_paths = Path(dir).rglob(wild)\\n path_for_display += \\\" (recursive)\\\"\\n else:\\n all_paths = Path(dir).glob(wild)\\n path_for_display += \\\" (non recursive)\\\"\\n\\n f_sizes = []\\n\\n for path in all_paths:\\n\\n full_path = os.path.join(path.parent, path.name)\\n byte_size = os.path.getsize(full_path)\\n # print(full_path, byte_size)\\n f_sizes.append(byte_size)\\n\\n f_sizes = np.array(f_sizes)\\n # print(f_sizes)\\n\\n min_byte_length = np.amin(f_sizes)\\n max_byte_length = np.amax(f_sizes)\\n mean_byte_length = np.mean(f_sizes)\\n std_byte_length = np.std(f_sizes)\\n median_byte_length = np.median(f_sizes)\\n\\n histo, bin_edges = np.histogram(f_sizes, nb_clusters)\\n histo = histo.astype(\\\"float32\\\")\\n histo_normalized = (np.copy(histo) / histo.max() * bricks).astype(\\\"uint32\\\")\\n\\n print(\\\"[BYTESPREAD REPORT]\\\")\\n print()\\n print(\\\"Date: \\\", datetime.now().strftime(\\\"%d/%m/%Y %H:%M:%S\\\"))\\n print(\\\"Directory: \\\", path_for_display)\\n print(\\\"Filename match: \\\", wild)\\n print(\\\"Number of files: \\\", f_sizes.shape[0])\\n print(\\\"Smallest file: \\\", byteToHumanReadable(min_byte_length))\\n print(\\\"Largest file: \\\", byteToHumanReadable(max_byte_length))\\n print(\\\"Average size: \\\", byteToHumanReadable(mean_byte_length))\\n print(\\\"Standard deviation: \\\", byteToHumanReadable(std_byte_length))\\n print(\\\"Median size: \\\", byteToHumanReadable(median_byte_length))\\n print(\\\"Histogram intervals: \\\", byteToHumanReadable(bin_edges[1] - bin_edges[0]))\\n print(\\\"Histogram:\\\")\\n print()\\n\\n for i in range(0, len(bin_edges)-1):\\n lower_bound = byteToHumanReadable(bin_edges[i])\\n upper_bound = byteToHumanReadable(bin_edges[i+1])\\n print(\\\"|\\\", \\\"▓\\\" * histo_normalized[i], \\\"[{}-{}], {} files\\\".format(lower_bound, upper_bound, math.ceil(histo[i])))\",\n \"def flatten_list_bytes(list_of_data):\\n if not PY34:\\n list_of_data = (bytes(data) if isinstance(data, memoryview) else\\n data for data in list_of_data)\\n return b''.join(list_of_data)\",\n \"def bits(data):\\n\\treturn [format(ord(c),'08b') for c in data]\",\n \"def list_files():\\n try:\\n return jsonify(os.listdir(env(\\\"FILES_DIRECTORY\\\"))), 200\\n except:\\n return {\\\"msg\\\": \\\"Não há arquivos a serem listados.\\\"}, 404\",\n \"def loadAllFiles(f_list):\\n\\n t_1 = datetime.now()\\n\\n # first list\\n i_list = []\\n id_list = []\\n for f in f_list:\\n f_t = loadFile(f)\\n i_list.extend(f_t)\\n # clean list\\n ln_i_list = len(i_list)\\n c_list = []\\n for i, f in enumerate(i_list):\\n if f[\\\"id\\\"] not in id_list:\\n id_list.append(f[\\\"id\\\"])\\n c_list.append(f)\\n print (i * 100.) / ln_i_list, datetime.now() - t_1, i\\n return c_list\",\n \"def get_filenames(b,X,timefac,BW):\\r\\n\\tn = int(200000*X*timefac); nn = int(200000*X*10)\\r\\n\\troot = \\\"./dat_LE_stream/\\\"\\r\\n\\trootL = 'C:/AsusWebStorage/MySyncData/TRAFILES/'\\r\\n\\tpodir = \\\"/b=\\\"+str(b)+\\\"/\\\" if BW else \\\"/HO/\\\"\\r\\n\\t\\r\\n\\ttrafile = rootL+\\\"TRA_BW_b\\\"+str(b)+\\\"X\\\"+str(int(X))+\\\"n\\\"+str(nn)+\\\"seed65438\\\"\\r\\n\\tif not os.path.isfile(trafile):\\r\\n\\t\\ttrafile = rootL+\\\"TRA_BW_b\\\"+str(b)+\\\"X\\\"+str(int(X))+\\\"n\\\"+str(n)+\\\"seed65438\\\"\\r\\n\\toutfile = root+podir+os.path.split(trafile)[1][4:]\\r\\n\\t\\r\\n\\trndfile = os.path.split(outfile)[0]+\\\"/RND_\\\"+os.path.split(outfile)[1]+\\\".png\\\"\\r\\n\\tpdffile = os.path.split(outfile)[0]+\\\"/PDF_\\\"+os.path.split(outfile)[1]\\r\\n\\tstrfile = os.path.split(outfile)[0]+\\\"/STR_\\\"+os.path.split(outfile)[1]\\r\\n\\t\\r\\n\\treturn trafile,rndfile,pdffile,strfile,n\",\n \"def readByte(self) :\\n tag = ord(self.minfile[self.pos])\\n self.pos += 1\\n return tag\",\n \"def bytes(self, count):\\n\\n if count < 0:\\n raise ValueError\\n\\n # fast path\\n if self._bits == 0:\\n data = self._fileobj.read(count)\\n if len(data) != count:\\n raise BitReaderError(\\\"not enough data\\\")\\n return data\\n\\n return bytes(bytearray(self.bits(8) for _ in xrange(count)))\",\n \"def get_files(message):\\n message_deserialized = json.loads(message)\\n filename = message_deserialized['contents'] #returns unicode\\n return str(filename)\",\n \"def encode_u8_array(value: typing.List[int]) -> bytes:\\n return encode_u32(len(value)) + bytes(value)\",\n \"def node_setup_files_list_table_format(result):\\n table = []\\n for item in result:\\n row = OrderedDict()\\n row['Name'] = item['name']\\n row['Is directory'] = 'yes' if item['is_directory'] else 'no'\\n row['Size'] = '' if item['size'] is None else (item['size'] or '0')\\n table.append(row)\\n return table\",\n \"def get_binary(fname):\\n with open(fname, 'rb+') as f_name:\\n data = f_name.read()\\n return data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59729344","0.5932491","0.55452764","0.53782064","0.53719026","0.53628296","0.5361053","0.53410685","0.5298277","0.5277501","0.52300537","0.5216152","0.5197471","0.5186267","0.51661885","0.5151425","0.5142084","0.5131954","0.5114525","0.51049876","0.5091796","0.50886023","0.5075722","0.506611","0.50586826","0.50511634","0.50468683","0.5039725","0.503184","0.5011658","0.49953586","0.49797592","0.49773166","0.4974909","0.49625862","0.49584317","0.49508908","0.4943112","0.4935116","0.49347413","0.4934222","0.4924716","0.49180356","0.4916016","0.4911991","0.49049428","0.49021137","0.48954472","0.48934254","0.4889477","0.48882616","0.4885451","0.48841995","0.48829335","0.4881273","0.4871023","0.48499984","0.48437878","0.48412442","0.48241112","0.48230684","0.48155522","0.48089185","0.48031998","0.48015085","0.48008332","0.47868788","0.47868788","0.47835764","0.47794867","0.4779182","0.4772166","0.47711828","0.47555456","0.4750709","0.4746869","0.47431037","0.47293818","0.47206002","0.47206002","0.4719697","0.47150344","0.47118002","0.4706251","0.4704396","0.46997952","0.46968344","0.46895143","0.46775827","0.46769062","0.4674976","0.46748435","0.4664897","0.46648225","0.4664816","0.46646753","0.46635967","0.46582642","0.46544605","0.4652499"],"string":"[\n \"0.59729344\",\n \"0.5932491\",\n \"0.55452764\",\n \"0.53782064\",\n \"0.53719026\",\n \"0.53628296\",\n \"0.5361053\",\n \"0.53410685\",\n \"0.5298277\",\n \"0.5277501\",\n \"0.52300537\",\n \"0.5216152\",\n \"0.5197471\",\n \"0.5186267\",\n \"0.51661885\",\n \"0.5151425\",\n \"0.5142084\",\n \"0.5131954\",\n \"0.5114525\",\n \"0.51049876\",\n \"0.5091796\",\n \"0.50886023\",\n \"0.5075722\",\n \"0.506611\",\n \"0.50586826\",\n \"0.50511634\",\n \"0.50468683\",\n \"0.5039725\",\n \"0.503184\",\n \"0.5011658\",\n \"0.49953586\",\n \"0.49797592\",\n \"0.49773166\",\n \"0.4974909\",\n \"0.49625862\",\n \"0.49584317\",\n \"0.49508908\",\n \"0.4943112\",\n \"0.4935116\",\n \"0.49347413\",\n \"0.4934222\",\n \"0.4924716\",\n \"0.49180356\",\n \"0.4916016\",\n \"0.4911991\",\n \"0.49049428\",\n \"0.49021137\",\n \"0.48954472\",\n \"0.48934254\",\n \"0.4889477\",\n \"0.48882616\",\n \"0.4885451\",\n \"0.48841995\",\n \"0.48829335\",\n \"0.4881273\",\n \"0.4871023\",\n \"0.48499984\",\n \"0.48437878\",\n \"0.48412442\",\n \"0.48241112\",\n \"0.48230684\",\n \"0.48155522\",\n \"0.48089185\",\n \"0.48031998\",\n \"0.48015085\",\n \"0.48008332\",\n \"0.47868788\",\n \"0.47868788\",\n \"0.47835764\",\n \"0.47794867\",\n \"0.4779182\",\n \"0.4772166\",\n \"0.47711828\",\n \"0.47555456\",\n \"0.4750709\",\n \"0.4746869\",\n \"0.47431037\",\n \"0.47293818\",\n \"0.47206002\",\n \"0.47206002\",\n \"0.4719697\",\n \"0.47150344\",\n \"0.47118002\",\n \"0.4706251\",\n \"0.4704396\",\n \"0.46997952\",\n \"0.46968344\",\n \"0.46895143\",\n \"0.46775827\",\n \"0.46769062\",\n \"0.4674976\",\n \"0.46748435\",\n \"0.4664897\",\n \"0.46648225\",\n \"0.4664816\",\n \"0.46646753\",\n \"0.46635967\",\n \"0.46582642\",\n \"0.46544605\",\n \"0.4652499\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57869107"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":13197,"cells":{"query":{"kind":"string","value":"LZW komprese dict_of_abc je vstupni slovnik dat na kazdem indexu slovniku je list v prubehu komprese se do nej pridavaji polozky list_of_data je posloupnost cisel ke kompresi"},"document":{"kind":"string","value":"def do_LZW_Compression(dict_of_abc, list_of_data):\n \n # rozdil mezi None a [] je v pouziti metody extend na listu\n \n result = []\n P = []\n C = [] # C je vzdy jeden prvek ze vstupu\n PC = []\n \n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\n \n for i in range(len(list_of_data)):\n \"\"\"\n Cyklus pres vsecky vstupni prvky\n \"\"\"\n\n C = []\n C.append(list_of_data[i])\n\n #PC je vzdy kombinace P a C\n PC = []\n PC.extend(P)\n PC.extend(C)\n\n index_founded = dict_cointains_list(dict_of_abc, PC)\n if index_founded == -1:\n #pokud PC neni ve slovniku, pridam ho tam a P = C\n dict_of_abc[len(dict_of_abc) +1] = PC\n #output P key in dictionary\n result.append(dict_cointains_list(dict_of_abc, P))\n P = C\n else:\n #pokud PC je ve slovniku P = PC pro dalsi iteraci\n P = PC\n #pridani posledniho prvku\n result.append(dict_cointains_list(dict_of_abc, P))\n return dict_of_abc, result"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def do_LZW_DeCompression(dict_of_abc, list_of_data):\n \n #https://www.youtube.com/watch?v=MQM_DsX-LBI\n \n out = []\n predchozi_out = []\n for i in range(len(list_of_data)):\n new = []\n new.extend(predchozi_out)\n if list_of_data[i] in dict_of_abc:\n o = dict_of_abc[list_of_data[i]]\n out.extend(o)\n predchozi_out = o\n \n #pokud je o list, beru z nej pouze prvni prvek\n if len(o) > 1:\n new.append(o[0])\n else:\n new.extend(o)\n\n index_founded = dict_cointains_list(dict_of_abc, new)\n if index_founded == -1:\n #pokud new neni ve slovniku, pridam ho tam\n dict_of_abc[len(dict_of_abc) +1] = new\n\n return dict_of_abc, out","def getUniChemData(self, inchiKeyList):\n mapD = {\n 1: {\"name\": \"chembl\", \"baseUrl\": \"https://www.ebi.ac.uk/chembl/\", \"entryUrl\": \"https://www.ebi.ac.uk/chembldb/compound/inspect/\"},\n 3: {\"name\": \"pdb\", \"baseUrl\": \"http://www.ebi.ac.uk/pdbe/\", \"entryUrl\": \"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\"},\n 2: {\"name\": \"drugbank\", \"baseUrl\": \"http://drugbank.ca/\", \"entryUrl\": \"http://www.drugbank.ca/drugs/\"},\n 5: {\"name\": \"pubchem_dotf\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 4: {\"name\": \"gtopdb\", \"baseUrl\": \"http://www.guidetopharmacology.org\", \"entryUrl\": \"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\"},\n 11: {\"name\": \"ibm\", \"baseUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\", \"entryUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\"},\n 6: {\"name\": \"kegg_ligand\", \"baseUrl\": \"http://www.genome.jp/kegg/ligand.html\", \"entryUrl\": \"http://www.genome.jp/dbget-bin/www_bget?\"},\n 9: {\"name\": \"zinc\", \"baseUrl\": \"http://zinc15.docking.org\", \"entryUrl\": \"http://zinc15.docking.org/substances/\"},\n 8: {\"name\": \"nih_ncc\", \"baseUrl\": \"http://nihsmr.evotec.com/evotec/\", \"entryUrl\": \"\"},\n 10: {\"name\": \"emolecules\", \"baseUrl\": \"https://www.emolecules.com/\", \"entryUrl\": \"https://www.emolecules.com/cgi-bin/more?vid=\"},\n 12: {\"name\": \"atlas\", \"baseUrl\": \"http://www.ebi.ac.uk/gxa/home\", \"entryUrl\": \"http://www.ebi.ac.uk/gxa/query?conditionQuery=\"},\n 7: {\"name\": \"chebi\", \"baseUrl\": \"http://www.ebi.ac.uk/chebi/downloadsForward.do\", \"entryUrl\": \"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\"},\n 14: {\n \"name\": \"fdasrs\",\n \"baseUrl\": \"http://fdasis.nlm.nih.gov/srs/srs.jsp\",\n \"entryUrl\": \"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\",\n },\n 15: {\"name\": \"surechembl\", \"baseUrl\": \"https://www.surechembl.org/search/\", \"entryUrl\": \"https://www.surechembl.org/chemical/\"},\n 21: {\"name\": \"pubchem_tpharma\", \"baseUrl\": \"http://www.thomson-pharma.com/\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 22: {\"name\": \"pubchem\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/compound/\"},\n 27: {\"name\": \"recon\", \"baseUrl\": \"https://vmh.uni.lu\", \"entryUrl\": \"https://vmh.uni.lu/\"},\n 28: {\"name\": \"molport\", \"baseUrl\": \"https://www.molport.com/shop/index\", \"entryUrl\": \"https://www.molport.com/shop/molecule-link/\"},\n 31: {\n \"name\": \"bindingdb\",\n \"baseUrl\": \"https://www.bindingdb.org/bind/index.jsp\",\n \"entryUrl\": \"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\",\n },\n 41: {\"name\": \"swisslipids\", \"baseUrl\": \"http://www.swisslipids.org/\", \"entryUrl\": \"http://www.swisslipids.org/\"},\n 29: {\"name\": \"nikkaji\", \"baseUrl\": \"http://jglobal.jst.go.jp/en/\", \"entryUrl\": \"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\"},\n 32: {\"name\": \"comptox\", \"baseUrl\": \"https://comptox.epa.gov/dashboard/\", \"entryUrl\": \"https://comptox.epa.gov/dashboard/\"},\n 33: {\"name\": \"lipidmaps\", \"baseUrl\": \"http://www.lipidmaps.org\", \"entryUrl\": \"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\"},\n 35: {\"name\": \"carotenoiddb\", \"baseUrl\": \"http://carotenoiddb.jp/index.html\", \"entryUrl\": \"http://carotenoiddb.jp/Entries/\"},\n 36: {\"name\": \"metabolights\", \"baseUrl\": \"http://www.ebi.ac.uk/metabolights/\", \"entryUrl\": \"http://www.ebi.ac.uk/metabolights/\"},\n 37: {\"name\": \"brenda\", \"baseUrl\": \"https://www.brenda-enzymes.org/index.php\", \"entryUrl\": \"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\"},\n 17: {\"name\": \"pharmgkb\", \"baseUrl\": \"https://www.pharmgkb.org\", \"entryUrl\": \"https://www.pharmgkb.org/drug/\"},\n 18: {\"name\": \"hmdb\", \"baseUrl\": \"http://www.hmdb.ca\", \"entryUrl\": \"http://www.hmdb.ca/metabolites/\"},\n 24: {\n \"name\": \"nmrshiftdb2\",\n \"baseUrl\": \"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\",\n \"entryUrl\": \"http://nmrshiftdb.org/molecule/\",\n },\n 25: {\"name\": \"lincs\", \"baseUrl\": \"http://www.lincsproject.org/\", \"entryUrl\": \"http://identifiers.org/lincs.smallmolecule/\"},\n 39: {\"name\": \"chemicalbook\", \"baseUrl\": \"https://www.chemicalbook.com\", \"entryUrl\": \"https://www.chemicalbook.com/ChemicalProductProperty_EN_\"},\n 20: {\"name\": \"selleck\", \"baseUrl\": \"http://www.selleckchem.com\", \"entryUrl\": \"http://www.selleckchem.com/products/\"},\n 23: {\"name\": \"mcule\", \"baseUrl\": \"https://mcule.com\", \"entryUrl\": \"https://mcule.com/\"},\n 26: {\"name\": \"actor\", \"baseUrl\": \"https://actor.epa.gov\", \"entryUrl\": \"http://actor.epa.gov/actor/chemical.xhtml?casrn=\"},\n 34: {\"name\": \"drugcentral\", \"baseUrl\": \"http://drugcentral.org\", \"entryUrl\": \"http://drugcentral.org/drugcard/\"},\n 38: {\"name\": \"rhea\", \"baseUrl\": \"http://www.rhea-db.org\", \"entryUrl\": \"http://www.rhea-db.org/searchresults?q=CHEBI:\"},\n }\n oD = {}\n try:\n for ky in inchiKeyList:\n unc = unichem_client # pylint: disable=no-member\n # unc.set_format(\"json\")\n uDL = unc.get(ky)\n if uDL:\n qD = {}\n for uD in uDL:\n if \"src_id\" in uD and int(uD[\"src_id\"]) in mapD:\n qD[mapD[int(uD[\"src_id\"])][\"name\"]] = uD[\"src_compound_id\"]\n if qD:\n oD[ky] = qD\n\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n return oD","def dict_cointains_list(dict_of_abc, item_list):\n\n values = list(dict_of_abc.values())\n\n #projdu vsecky listy ve slovniku\n for i in range(len(values)):\n #predpokladam ze ve slovniku je\n finded = True\n \n for j in range(len(values[i])):\n if len(item_list) == len(values[i]):\n # kontrola po jednotlivych hodnotach\n # logicky soucin - pokud jednou False, navzdy False\n finded = finded and item_list[j] == values[i][j]\n else:\n finded = False\n\n if finded:\n # cyklus indexuje od 0, slovnik ale indexujeme-klicujeme od 1\n return i + 1 \n\n return -1","def generate_data_list(self, data_list):\n sol = []\n for i in data_list:\n sol.append({\n \"data_sig\": i[0],\n \"data\": pickle.loads(i[1]),\n \"attachment\": i[2],\n \"likes\":pickle.loads(i[3]),\n \"dislikes\":pickle.loads(i[4]),\n \"owner\": i[5]\n })\n return sol","def getDataForLBMPZonalComparison(self):\n\n\t\t# Variables\n\t\tzonal_data = self.getDayAheadMarketLBMPZonal()\n\t\tkeys = zonal_data.keys()\n\t\tfinal_data = []\n\t\tvalues = []\n\t\touter_dictionary = {}\n\t\tinner_dictionary = {}\n\n\t\t# Populating final data array and returning it\n\t\tfor key in keys:\n\t\t\tfor data in zonal_data[key]:\n\t\t\t\tinner_dictionary['x'] = data.keys()[0]\n\t\t\t\tinner_dictionary['y'] = data[data.keys()[0]]['LBMP ($/MWHr)']\n\t\t\t\tvalues.append(inner_dictionary)\n\t\t\t\tinner_dictionary = {}\n\t\t\touter_dictionary['values'] = values\n\t\t\tvalues = []\n\t\t\touter_dictionary['key'] = key\n\t\t\tfinal_data.append(outer_dictionary)\n\t\t\touter_dictionary = {}\n\n\t\treturn final_data","def get_compressed(self, value):\r\n output = []\r\n lz_data = (value >> 8) & 0xFF\r\n lz_counter = value & 0xFF\r\n # Define the relative offset on LZ Window\r\n lz_offset = ((lz_counter & 0xF0) << 4) | lz_data\r\n # Define the LZ Counter for repeat data N times\r\n lz_counter = (lz_counter & 0xF) + 0x2\r\n # Start Repeat Loop\r\n while (lz_counter >= 0):\r\n # Seek the window on LZ Offset and get the LZ Data\r\n self.__lzwindow__.seek(lz_offset, FROM_START)\r\n lz_data = (lz_data & 0xFF00) + \\\r\n int.from_bytes(self.__lzwindow__.read(1), byteorder='big')\r\n # Write the LZ data to the output\r\n output.append((lz_data & 0xFF).to_bytes(1, byteorder='big'))\r\n # Seek the LZ Window on current LZ Window Counter value and write the current LZ Data (LZBuffer)\r\n self.__lzwindow__.seek(self.__lzwindowcounter__, FROM_START)\r\n self.__lzwindow__.write((lz_data & 0xFF).to_bytes(1, byteorder='big'))\r\n # Increment LZ Window Counter\r\n self.__lzwindowcounter__ = (\r\n self.__lzwindowcounter__ + 0x1) & self.__lzwindowmax__\r\n # Increment LZ Offset\r\n lz_offset = (lz_offset + 0x1) & self.__lzwindowmax__\r\n # Decrement number of data to decompress\r\n self.__maxlen__ -= 0x1\r\n # Decrement LZ Loop counter\r\n lz_counter -= 0x1\r\n return output","def test_compress_offset_less_len1(self):\n text = 'ababab'\n actual = LZ77.compress(text)\n expected = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\n self.assertEqual(actual, expected)","def test_decompress_offset_less_len1(self):\n b_array = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\n actual = LZ77.decompress(b_array)\n expected = 'ababab'\n self.assertEqual(actual, expected)","def test_compress_offset_less_len2(self):\n text = 'abcdabcdab'\n actual = LZ77.compress(text)\n expected = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\n self.assertEqual(actual, expected)","def dict_check34(_str1):\n\t_len=len(_str1)\n\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\n\t_list0=[]\n\tfor i in range(len(_list1)):\n\t\t#Take different length\n\t\t_current=_list1[i]\n\t\tif _len>=sum(_current) and sum(_list1[i])!=0:\n\t\t\t_list2=[]\n\t\t\t_n1=_current[0]\n\t\t\t_n2=_current[1]\n\t\t\t_n3=_current[2]\n\t\t\t_list2.append(_str1[:_n1])\n\t\t\t_list2.append(_str1[_n1:_n1+_n2])\n\t\t\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\n\t\telse:\n\t\t\tcontinue\n\t\tn=0\n\t\tfor j in range(3):\n\t\t\tif _list2[j] in _dict_ori or _list2[j]==\"\":\n\t\t\t\tn+=1\n\t\tif n==3:\n\t\t\t_list0.append(_list2)\n\treturn(_list0)","def decompress_encoded_list( nums ):\n decompressed = []\n\n for i in range( 0, len(nums), 2 ):\n freq = nums[i]\n val = nums[i+1]\n decompressed.extend( [val] * freq )\n\n return decompressed","def test_decompress_offset_less_len2(self):\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\n actual = LZ77.decompress(b_array)\n expected = 'abcdabcdab'\n self.assertEqual(actual, expected)","def apply_compression(pc2_table, keys_56bits):\n keys_48bits = \"\"\n for index in pc2_table:\n keys_48bits += keys_56bits[index - 1]\n return keys_48bits","def test_decompress_2(self):\n b_array = bytearray([3]) + bytearray(b'abcdef')\\\n + bytearray([0, 32]) + bytearray([0, 113])\n actual = LZ77.decompress(b_array)\n expected = 'abcdefdeabc'\n self.assertEqual(actual, expected)","def test_compress_2(self):\n text = 'abcdefdeabc'\n actual = LZ77.compress(text)\n expected = bytearray([3]) + bytearray(b'abcdef')\\\n + bytearray([0, 32]) + bytearray([0, 113])\n self.assertEqual(actual, expected)","def get_keys_from_list():\n json_data = request.get_json()\n\n d = dict()\n d['elements'] = list()\n settings.setOptionsFile(get_info('uid'))\n fn = settings.getHistoROOTFileName()\n rfn = settings.getReferenceROOTFileName()\n# open root file stored in the root database\n f = ROOT.TFile(fn)\n# open reference root file stored in the root database\n rf = ROOT.TFile(rfn)\n\n for values in json_data.itervalues():\n for k in values:\n subd = dict()\n subd[\"index\"] = k[\"index\"]\n if fn != k[\"file\"]: \n fn = k[\"file\"]\n settings.setHistoROOTFileName(fn)\n f = ROOT.TFile(fn)\n print \"histogram :>>>>>: \",k[\"histogram\"]\n subd[\"data\"] = eval(cppyy.gbl.getDictionary(f,k[\"histogram\"]))\n if rfn != k[\"referenceFile\"]: \n rfn = k[\"referenceFile\"]\n settings.setReferenceROOTFileName(rfn)\n rf = ROOT.TFile(rfn)\n subd[\"refdata\"] = eval(cppyy.gbl.getDictionary(rf,k[\"reference\"]))\n d['elements'].append(subd)\n\n f.Close()\n rf.Close()\n\n return jsonify(d)","def listz2diclist(listz):\n\ttmp=[]\n\tfor elem in listz:\n\t\tdic = elem.warez2dic()\n\t\ttmp.append(dic)\n\treturn tmp","def test_compress_lossless_maps(self):\n height_map = 384\n width_map = 384\n \n # The quantization bin widths are small\n # so that the comparison between the\n # theoretical (minimum) coding cost and\n # the coding cost computed by the function\n # is precise enough.\n bin_widths_test = numpy.array([0.5, 0.25], dtype=numpy.float32)\n laplace_scales = numpy.array([0.5, 3.], dtype=numpy.float32)\n \n # Note that the binary probabilities saved at\n # \"lossless/pseudo_data/binary_probabilities_compress_maps_0.npy\"\n # and those saved at\n # \"lossless/pseudo_data/binary_probabilities_compress_maps_1.npy\"\n # are specific to the three Laplace distributions\n # below. This means that the binary probabilities\n # must be modified if `laplace_scales` is modified.\n paths_to_binary_probabilities = [\n 'lossless/pseudo_data/binary_probabilities_compress_maps_0.npy',\n 'lossless/pseudo_data/binary_probabilities_compress_maps_1.npy'\n ]\n \n centered_data_0 = numpy.random.laplace(loc=0.,\n scale=laplace_scales[0].item(),\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\n centered_data_1 = numpy.random.laplace(loc=0.,\n scale=laplace_scales[1].item(),\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\n centered_data = numpy.concatenate((centered_data_0, centered_data_1),\n axis=3)\n expanded_centered_quantized_data = tls.quantize_per_map(centered_data, bin_widths_test)\n centered_quantized_data = numpy.squeeze(expanded_centered_quantized_data,\n axis=0)\n tiled_bin_widths = numpy.tile(numpy.reshape(bin_widths_test, (1, 1, 2)),\n (height_map, width_map, 1))\n ref_int16 = tls.cast_float_to_int16(centered_quantized_data/tiled_bin_widths)\n (rec_int16_0, nb_bits_each_map_0) = \\\n lossless.compression.compress_lossless_maps(ref_int16,\n paths_to_binary_probabilities[0])\n numpy.testing.assert_equal(ref_int16,\n rec_int16_0,\n err_msg='The test fails as the lossless compression alters the signed integers.')\n (rec_int16_1, nb_bits_each_map_1) = \\\n lossless.compression.compress_lossless_maps(ref_int16,\n paths_to_binary_probabilities[1])\n numpy.testing.assert_equal(ref_int16,\n rec_int16_1,\n err_msg='The test fails as the lossless compression alters the signed integers.')\n \n # The equation below is derived from the\n # theorem 8.3.1 in the book\n # \"Elements of information theory\", 2nd edition,\n # written by Thomas M. Cover and Joy A. Thomas.\n theoretical_entropies = -numpy.log2(bin_widths_test) + (numpy.log(2.*laplace_scales) + 1.)/numpy.log(2.)\n print('B0 denotes the binary probabilities saved at \"{}\".'.format(paths_to_binary_probabilities[0]))\n print('B1 denotes the binary probabilities saved at \"{}\".'.format(paths_to_binary_probabilities[1]))\n print('\\n1st centered-quantized latent variable feature map.')\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[0]*height_map*width_map))\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[0]))\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[0]))\n print('\\n2nd centered-quantized latent variable feature map.')\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[1]*height_map*width_map))\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[1]))\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[1]))","def test_decompress_1(self):\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 49])\n actual = LZ77.decompress(b_array)\n expected = 'abcdabc'\n self.assertEqual(actual, expected)","def test_compress(self):\n form_field = MultiLingualFormField()\n compressed_data = form_field.compress([\"test-nb\", \"test-en\"])\n self.assertEqual(MultiLingualTextStructure, type(compressed_data))\n self.assertEqual(compressed_data['nb'], \"test-nb\")\n self.assertEqual(compressed_data['en'], \"test-en\")","def zipped_data(b_data):\n col_names = [\"Name\", \"Version\", \"Date and Time\"]\n bundle_data = []\n for bundle in b_data:\n zipped = zip(col_names, bundle)\n bundle_data.append(dict(zipped))\n return bundle_data","def construct_zi_dict(train_info_list, test_info_list):\r\n zi_dict, train_dataset_list, test_dataset_list = dict(), list(), list()\r\n for user, age, gender, education, querys in train_info_list:\r\n for query in querys:\r\n for zi in query:\r\n if zi not in zi_dict:\r\n zi_dict[zi] = 0\r\n zi_dict[zi] += 1\r\n for user, querys in test_info_list:\r\n for query in querys:\r\n for zi in query:\r\n if zi not in zi_dict:\r\n zi_dict[zi] = 0\r\n zi_dict[zi] += 1\r\n zi_list = sorted(zi_dict.iteritems(), key=lambda x: x[1], reverse=True)\r\n zi2index = dict([(zi[0], [zi[1], idx]) for idx, zi in enumerate(zi_list)])\r\n index2zi = dict([(idx, [zi[0], zi[1]]) for idx, zi in enumerate(zi_list)])\r\n \r\n return zi2index, index2zi","def _lzw(self, tile: bytes) -> np.ndarray:\n decoded = self._reshape(np.frombuffer(imagecodecs.lzw_decode(tile), self.dtype))\n self._unpredict(decoded)\n return np.rollaxis(decoded, 2, 0)","def test_compress_4_idenctical_char(self):\n text = 'bbbb'\n actual = LZ77.compress(text)\n expected = bytearray([32]) + bytearray(b'bb') + bytearray([0, 16])\n self.assertEqual(actual, expected)","def zzX_from_dict(f, l):\n if l == 1:\n return zzx_from_dict(f)\n elif not f:\n return zzX_zero(l)\n\n coeffs = {}\n\n for monom, coeff in f.iteritems():\n head, tail = monom[0], monom[1:]\n\n if len(tail) == 1:\n tail = tail[0]\n\n if coeffs.has_key(head):\n coeffs[head][tail] = INT_TYPE(int(coeff))\n else:\n coeffs[head] = { tail : INT_TYPE(int(coeff)) }\n\n n, h = max(coeffs.iterkeys()), []\n\n for k in xrange(n, -1, -1):\n coeff = coeffs.get(k)\n\n if coeff is not None:\n h.append(zzX_from_dict(coeff, l-1))\n else:\n h.append(zzX_zero(l-1))\n\n return zzX_strip(h)","def getData_goodmaps(liste_dictionnaires = [], liste_categories = [], liste_phonemes = [],liste_cartes=[]):\n if liste_dictionnaires!=[] and liste_categories!=[] and liste_phonemes!=[]:\n tableau = np.array(liste_dictionnaires[0][liste_categories[0]][liste_phonemes[0]])\n nb_exemple,nb_carte,lign,col=tableau.shape\n else:\n return [],[],[],[]\n\n Mat = []\n Reference = []\n\n\n for inddict,dict in enumerate(liste_dictionnaires):\n for indcat,cat in enumerate(liste_categories):\n for indpho,pho in enumerate(liste_phonemes):\n for ex in range(nb_exemple):\n goodmaps = []\n for map in liste_cartes:\n goodmaps.append(np.array(dict[cat][pho][ex][map]).flatten())\n Mat.append(np.array(goodmaps).flatten())\n Reference.append([inddict,indcat ,indpho])\n Reference = np.array(Reference)\n Y_c_inc = change_reference(Reference[:,1])\n Y_r_v = Reference[:,2]\n Y_fr_jap = Reference[:,0]\n return np.array(Mat), np.array(Y_c_inc), np.array(Y_r_v), np.array(Y_fr_jap)","def get_data(url, seed):\n available_fields = {\n 'boro': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'cd': {'fieldtype': 'C', 'categories': range(1, 19)},\n 'uf1_1': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_2': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_3': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_4': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_5': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_6': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_7': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_8': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_9': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_10': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_11': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_12': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_13': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_14': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_15': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_16': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_17': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_18': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_19': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_20': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_21': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_22': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'sc24': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc36': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'sc37': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc38': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'sc114': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'uf48': {'fieldtype': 'N'},\n 'sc147': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'uf11': {'fieldtype': 'C', 'categories': range(1, 8)},\n 'sc149': {'fieldtype': 'B', 'codes': [1, 2, None]},\n 'sc173': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\n 'sc171': {'fieldtype': 'B', 'codes': [1, 2]},\n 'sc150': {'fieldtype': 'N'},\n 'sc151': {'fieldtype': 'N'},\n 'sc154': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\n 'sc157': {'fieldtype': 'C', 'categories': [1, 2, 9]},\n 'sc158': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc185': {'fieldtype': 'B', 'codes': [0, 1, 8]},\n 'sc186': {'fieldtype': 'C', 'categories': [2, 3, 4, 5, 9]},\n 'sc197': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc198': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc187': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc188': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc571': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'sc189': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'sc190': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc191': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc192': {'fieldtype': 'B', 'codes': [0, 1, 8]},\n 'sc193': {'fieldtype': 'C', 'categories': [2, 3, 9]},\n 'sc194': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc196': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc199': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'rec15': {'fieldtype': 'C', 'categories': range(1, 14)},\n 'sc26': {'fieldtype': 'C', 'categories': [12, 13, 15, 16]},\n 'uf23': {'fieldtype': 'N'},\n 'rec21': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'rec62': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\n 'rec64': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\n 'rec54': {'fieldtype': 'C', 'categories': range(1, 8)},\n 'rec53': {'fieldtype': 'N'},\n 'new_csr': {'fieldtype': 'C', 'categories': [1, 2, 5, 12, 20,\n 21, 22, 23, 30, 31,\n 80, 85, 90, 95]}\n }\n selected_fields = [\n # The borough where the apartment is located\n 'boro',\n\n # Building type: public housing, new construction,\n # \"In Rem\" foreclosure, old construction\n 'sc26',\n\n # Number of bedrooms\n 'sc151',\n\n # Dilapidated / Not Dilapidated\n 'rec21',\n\n # Complete plumbing facilities in unit\n 'rec62',\n\n # Complete kitchen facilities in unit\n 'rec64',\n\n # Maintenance deficiencies\n 'rec53',\n\n # Building age\n 'uf23',\n\n # Rent control/stabilization category\n 'new_csr',\n\n # Neighborhood rating\n 'sc196',\n\n # Wheelchair accessibility of unit\n 'sc38',\n\n # Presence of elevator\n 'sc149',\n\n # Building height\n 'uf11',\n\n # Air conditioning\n 'sc197',\n\n # Walkup\n 'sc171',\n ]\n mini_fields = {k: available_fields[k]\n for k in available_fields\n if k in selected_fields}\n y_field = 'uf17'\n # s = requests.get(url).content\n # raw_df = pd.read_csv(StringIO(s.decode('utf-8')))\n raw_df = pd.read_csv('homework2_data.csv')\n valid_renters, validated_features, validated_rents = \\\n preprocess_data(raw_df, mini_fields, y_field)\n X_train, X_test, y_train, y_test = train_test_split(\n validated_features, validated_rents, random_state=seed)\n cats = [k\n for (k, v) in mini_fields.items()\n if v[\"fieldtype\"] == \"C\"]\n catnums = [i\n for (i, x) in enumerate([c in cats\n for c in validated_features.columns])\n if x]\n return X_train, X_test, y_train, y_test, catnums, raw_df","def data():\n \n # Just in case order matters.\n inplist = expected.keys()\n inplist.sort()\n \n #return ['split/' + inpms for inpms in inplist]\n return inplist","def compress(uncompressed):\r\n \r\n # Build the dictionary.\r\n dict_size = 256\r\n dictionary = dict((chr(i), i) for i in range(dict_size))\r\n # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}\r\n \r\n w = \"\"\r\n result = []\r\n for c in uncompressed:\r\n wc = w + c\r\n if wc in dictionary:\r\n w = wc\r\n else:\r\n result.append(dictionary[w])\r\n # Add wc to the dictionary.\r\n dictionary[wc] = dict_size\r\n dict_size += 1\r\n w = c\r\n \r\n # Output the code for w.\r\n if w:\r\n result.append(dictionary[w])\r\n return result","def create_data_set():\n data_set = {}\n for index in range(1024):\n size = random.randint(1, 100) #nosec\n key = str(index).encode(\"utf-8\")\n data_set[key] = get_random_bytes(size)\n return data_set","def opendict(data_dir, batch_size, test_max_size, **kwargs):\n join = lambda f: _path.join(data_dir, f)\n with open(join(\"list.jl\")) as f:\n data = [(join(e[\"files\"][0][\"path\"]), e[\"word\"].strip())\n for e in filter(lambda x: x.get(\"files\"), map(_json.loads, f))]\n _random.shuffle(data)\n if not test_max_size:\n test_max_size = int(len(data) / 100)\n tmp = - (batch_size + test_max_size)\n return data[:tmp], data[tmp:-test_max_size], data[-test_max_size:]","def test_hmaps(self):\n #Single element insertion\n self.hll.append(self.data[0])\n assert non_zero_idx_val(hll.hmap) == [(54, 1)]\n #Multiple distinct element insertions\n self.hll.extend(self.data)\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\n self.reset_hll()\n #Element insertions with duplicates\n self.hll.extend(self.data_duplicate)\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\n self.reset_hll()\n #Element insertions with numerical values\n self.hll.extend(self.num_data)\n assert non_zero_idx_val(hll.hmap) == [(17, 3), (144, 2), (145, 4),\n (182, 2)]\n self.reset_hll()\n #Test the key collision handling (keep max value)\n self.hll.append(self.colliding_data[0])\n assert non_zero_idx_val(hll.hmap) == [(0, 1)] \n self.hll.append(self.colliding_data[1])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\n self.reset_hll()\n self.hll.append(self.colliding_data[1])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)] \n self.hll.append(self.colliding_data[0])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\n self.reset_hll()","def test_compress_2_idenctical_char(self):\n text = 'aa'\n actual = LZ77.compress(text)\n expected = bytearray([0]) + bytearray(b'aa')\n self.assertEqual(actual, expected)","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def test_xyz_from_data(self):\n symbols = ('C', 'H', 'H', 'H', 'H')\n isotopes = (12, 1, 1, 1, 1)\n coords = ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))\n xyz_dict0 = converter.xyz_from_data(coords=coords, symbols=symbols, isotopes=isotopes)\n self.assertEqual(xyz_dict0, self.xyz1['dict'])\n xyz_dict1 = converter.xyz_from_data(coords=coords, symbols=symbols) # no specifying isotopes\n self.assertEqual(xyz_dict1, self.xyz1['dict'])\n\n numbers = [6, 1, 1, 1, 1]\n coords = [[0.0, 0.0, 0.0],\n [0.6300326, 0.6300326, 0.6300326],\n [-0.6300326, -0.6300326, 0.6300326],\n [-0.6300326, 0.6300326, -0.6300326],\n [0.6300326, -0.6300326, -0.6300326]]\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\n\n numbers = [6, 1, 1, 1, 1]\n coords = [[0.0, 0.0, 0.0],\n [0.6300326, 0.6300326, 0.6300326],\n [-0.6300326, -0.6300326, 0.6300326],\n [-0.6300326, 0.6300326, -0.6300326],\n [0.6300326, -0.6300326, -0.6300326]]\n coords = np.array([np.array(coord, np.float64) for coord in coords], np.float64)\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\n self.assertIsInstance(xyz_dict2['coords'], tuple)\n self.assertIsInstance(xyz_dict2['coords'][0], tuple)","def shuffle_data_set(in_dict):\n tot_rows = in_dict['data'].shape[0]\n new_order = range(tot_rows)\n for _ in range(5):\n shuffle(new_order)\n\n out_dict = in_dict\n out_data = np.zeros((tot_rows, 3072), dtype=in_dict['data'].dtype)\n out_labels = [0] * tot_rows\n out_filenames = [\"\"] * tot_rows\n\n for ctr, idx in enumerate(new_order):\n out_data[ctr, :] = in_dict['data'][idx, :]\n out_labels[ctr] = in_dict['labels'][idx]\n out_filenames[ctr] = in_dict['filenames'][idx]\n\n out_dict['data'] = out_data\n out_dict['labels'] = out_labels\n out_dict['filenames'] = out_filenames\n\n return out_dict","def making_dataset_list_y(data_list:list) -> list:\n list_size = len(data_list)\n data_list_y = []\n for i in range(list_size):\n data_list_y.append(data_list[i][[\"W\",\"D\",\"L\"]])\n return data_list_y","def set_lz(self):\r\n self._lz = self.dz * self.nz - self.oz","def decodeLZ(fileIn, fileOut, dictionarySize = 1114112):\n try:\n fileContent = FileWork.getFileContent(fileIn)\n\n getCharByCode = {}\n getCodeByChar = {}\n\n for i in range(0, dictionarySize):\n getCharByCode[i] = chr(i)\n getCodeByChar[chr(i)] = i\n\n currentCode = dictionarySize\n\n fileContent = fileContent.split(' ')\n buffer = getCharByCode[int(fileContent[0])]\n decodeFileContent = \"\"\n for i in range(1, len(fileContent)):\n character = \"\"\n if int(fileContent[i]) in getCharByCode:\n character = getCharByCode[int(fileContent[i])]\n else:\n getCharByCode[currentCode] = buffer + buffer[0]\n getCodeByChar[buffer + buffer[0]] = currentCode\n currentCode += 1\n decodeFileContent += buffer\n character = buffer[0]\n\n if (buffer + character[0]) in getCodeByChar:\n buffer = buffer + character[0]\n else:\n getCharByCode[currentCode] = buffer + character[0]\n getCodeByChar[buffer + character[0]] = currentCode\n currentCode += 1\n decodeFileContent += buffer\n buffer = character\n decodeFileContent += buffer\n FileWork.writeToFile(fileOut, decodeFileContent)\n return True\n except Exception:\n return False","def test_lz4_decompression_avoids_deep_copy():\n pytest.importorskip(\"lz4\")\n a = bytearray(1_000_000)\n b = compressions[\"lz4\"].compress(a)\n c = compressions[\"lz4\"].decompress(b)\n assert isinstance(c, bytearray)","def encodeLZ(fileIn, fileOut, dictionarySize = 1114112):\n try:\n fileContent = FileWork.getFileContent(fileIn)\n\n dictionary = {}\n for i in range(0, dictionarySize):\n dictionary[chr(i)] = i\n\n currentCode = dictionarySize\n\n encodedFileContent = \"\"\n buffer = fileContent[0]\n for pos in range(1, len(fileContent)):\n character = fileContent[pos]\n if (buffer + character) in dictionary:\n buffer += character\n else:\n dictionary[buffer + character] = currentCode\n currentCode += 1\n encodedFileContent += str(dictionary[buffer]) + \" \"\n buffer = character\n\n encodedFileContent += str(dictionary[buffer])\n\n FileWork.writeToFile(fileOut, encodedFileContent)\n return True\n except Exception:\n return False","def __init__(self, url=\"https://ehw.fit.vutbr.cz/izv/\",folder=\"data\", cache_filename=\"data_{}.pkl.gz\"):\n self.url = url\n self.folder = folder\n self.cache_filename = cache_filename\n self.ListOfZipFiles = []\n self.saved_data = {}\n self.headers = [\"region\", \"p1\", \"p36\", \"p37\", \"p2a\", \"weekday(p2a)\", \"p2b\", \"p6\", \"p7\", \"p8\", \"p9\", \"p10\", \"p11\", \"p12\", \"p13a\", \"p13b\", \"p13c\", \"p14\", \"p15\", \"p16\", \"p17\", \"p18\", \"p19\", \"p20\", \"p21\", \"p22\", \"p23\", \"p24\", \"p27\", \"p28\", \"p34\", \"p35\", \"p39\", \"p44\", \"p45a\", \"p47\", \"p48a\", \"p49\", \"p50a\", \"p50b\", \"p51\", \"p52\", \"p53\",\"p55a\",\"p57\",\"p58\",\"a\",\"b\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"t\",\"p5a\"]\n self.duplicate_handling = []\n if not re.search(\"^.*{}.*$\", self.cache_filename):\n self.cache_filename+=\"{}\"","def map(z):\n pass","def valid_zip(x):\r\n m_zip = [83,10286,10276,10268,10256,10249,10159,10150,10116,10113,10108,10101,10008,10282,10281,10280,10279,10278,10275,\r\n 10271,10270,10199,10178,10177,10176,10175,10174,10173,10172,10171,10170,10169,10168,10167,10166,10165,\r\n 10162,10161,10158,10155,10154,10153,10152,10151,10128,10123,10122,10121,10120,10119,10118,10115,10112,\r\n 10111,10110,10107,10106,10105,10104,10103,10099,10098,10095,10090,10069,10060,10055,10048,10047,10045,10044,\r\n 10041,10040,10039,10038,10037,10036,10035,10034,10033,10032,10031,10030,10029,10028,10027,10026,10025,\r\n 10024,10023,10022,10021,10020,10019,10018,10017,10016,10015,10014,10013,10012,10011,10010,10009,10007,\r\n 10006,10005,10004,10003,10002,10001,10065,10075,10080,\r\n 10285,10203,10178,10017,10178,10168,10167,10177,# supplementary\r\n 10175,10166,10171,10176,10174,10165,10170,10173,10169,10172,10019, 10105, 10097, 10104, 10107, 10103, 10106,\r\n 10022, 10055, 10155, 10152, 10153, 10151, 10154, 10001, 10120, 10119, 10118, 10123, 10122, 10121,\r\n 10005, 10081, 10286, 10260, 10271, 10259, 10043, 10270, 10265, 10203,10036, 10096, 10196, 10110\r\n ]\r\n brooklyn_zip = [11256,11252,11249,11243,11242,11241,11239,11238,11237,11236,11235,11234,11233,11232,11231,\r\n 11230,11229,11228,11226,11225,11224,11223,11222,11221,11220,11219,11218,11217,11216,11215,\r\n 11214,11213,11212,11211,11210,11209,11208,11207,11206,11205,11204,11203,11201]\r\n queens_zip = [11451,11436,11435,11434,11433,11432,11429,11428,11427,11426,\r\n 11423,11422,11421,11420,11419,11418,11417,11416,11415,11414,11413,11412,11411,11385,11379,\r\n 11378,11377,11375,11374,11373,11372,11369,11368,11367,11366,11365,11364,11363,\r\n 11362,11361,11360,11359,11358,11357,11356,11355,11354,11351,11109,11106,11105,11104,11103,\r\n 11102,11101,11004]\r\n if x in m_zip + brooklyn_zip + queens_zip:\r\n return 1\r\n else:\r\n return 0","def test_compress_seq_diff_8_char(self):\n text = '12345678'\n actual = LZ77.compress(text)\n expected = bytearray([0]) + bytearray(b'12345678')\n self.assertEqual(actual, expected)","def get_lz(self):\r\n return self.dz * self.nz - self.oz","def test_decompress_seq_diff_8_char(self):\n b_array = bytearray([0]) + bytearray(b'12345678')\n actual = LZ77.decompress(b_array)\n expected = '12345678'\n self.assertEqual(actual, expected)","def weight_compression(weights, bits, axis=0, quantizer=None):\n assert bits <= 8\n n = 2**bits\n index_table = []\n codebook_table = np.zeros((weights.shape[axis], n))\n km_models = [None] * weights.shape[axis]\n\n for i, w in tqdm(enumerate(np.split(weights, weights.shape[axis], axis))):\n original_shape = w.shape\n w = w.ravel()\n km = KMeans(n)\n km.fit(w.reshape(-1, 1))\n if quantizer:\n km.cluster_centers_ = quantizer(km.cluster_centers_).numpy()\n km.cluster_centers_.sort(axis=0)\n\n km_models[i] = km\n codebook_table[i, :] = km.cluster_centers_.flatten()\n preds = km.predict(w.reshape(-1, 1))\n index_table.append(preds.reshape(original_shape))\n\n index_table = np.concatenate(index_table, axis)\n return index_table, codebook_table","def length_of_data(self, data_list, days):\n\n # The zip() function returns an iterator of tuples based on the iterable object.\n btceur_high, btceur_timestamp = zip(*data_list)\n\n # Various lists/variables used\n one_week_list_timezone = []\n one_week_list_btc_high = []\n count = 0\n\n # Get the latest entry to work back from\n orig = datetime.datetime.fromtimestamp(btceur_timestamp[-1])\n # Get a date 7 days from the current date\n one_week = orig - datetime.timedelta(days=days)\n # Loop backwards through the timestamps getting the past 7 days worth\n\n for I in reversed(btceur_timestamp):\n converted_timestamp_value = (datetime.datetime.fromtimestamp(I))\n if one_week > converted_timestamp_value < orig:\n one_week_list_timezone.append(converted_timestamp_value)\n else:\n continue\n if one_week_list_timezone[-1] \\\n > one_week:\n return None\n else:\n # Get the corresponding data to each time stamp\n for x in reversed(btceur_high):\n if count < len(one_week_list_timezone):\n one_week_list_btc_high.append(x)\n count += 1\n\n # Reorder both lists of data\n one_week_list_btc_high.reverse()\n one_week_list_timezone.reverse()\n zipped = zip(one_week_list_btc_high, one_week_list_timezone)\n return list(zipped)","def add_hash(self, lst):\n self.__data = []\n num = len(lst) + 1\n self.add_data((\"\", 4, 1))\n self.add_data((\"\", 4, num))\n self.add_data((\"\", 4, num - 1))\n self.add_data((\"\", 4, 0))\n if 1 < num:\n for ii in range(num - 1):\n self.add_data((\"\", 4, ii))","def get_data(data_list, data_cache):\n if not isinstance(data_list, list):\n data_list = [data_list]\n tmp = [data_cache[d] for d in data_list]\n if len(tmp) == 1:\n return tmp[0]\n res = ([t[0] for t in tmp], [t[1] for t in tmp])\n return res","def decode_huffman(bit_seq, dc_ac, layer_type):\n\n def diff_value(idx, size):\n if idx >= len(bit_seq) or idx + size > len(bit_seq):\n raise IndexError('There is not enough bits to decode DIFF value '\n 'codeword.')\n fixed = bit_seq[idx:idx + size]\n return int(fixed, 2)\n\n current_idx = 0\n while current_idx < len(bit_seq):\n # 1. Consume next 16 bits as `current_slice`.\n # 2. Try to find the `current_slice` in Huffman table.\n # 3. If found, yield the corresponding key and go to step 4.\n # Otherwise, remove the last element in `current_slice` and go to\n # step 2.\n # 4. Consume next n bits, where n is the category (size) in returned\n # key yielded in step 3. Use those info to decode the data.\n remaining_len = len(bit_seq) - current_idx\n current_slice = bit_seq[\n current_idx:\n current_idx + (16 if remaining_len > 16 else remaining_len)\n ]\n err_cache = current_slice\n while current_slice:\n if (current_slice in\n HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type].inv):\n key = (HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type]\n .inv[current_slice])\n if dc_ac == DC: # DC\n size = key\n if size == 0:\n yield 0\n else:\n yield HUFFMAN_CATEGORIES[size][diff_value(\n current_idx + len(current_slice),\n size\n )]\n else: # AC\n run, size = key\n if key in (EOB, ZRL):\n yield key\n else:\n yield (run, HUFFMAN_CATEGORIES[size][diff_value(\n current_idx + len(current_slice),\n size\n )])\n\n current_idx += len(current_slice) + size\n break\n current_slice = current_slice[:-1]\n else:\n raise KeyError(\n f'Cannot find any prefix of {err_cache} in Huffman table.'\n )","def MapDataList(ea, length, func, wordsize=1):\n PutDataList(ea, map(func, GetDataList(ea, length, wordsize)), wordsize)","def test_decompress_seq_diff_9_char(self):\n b_array = bytearray([0]) + bytearray(b'12345678') \\\n + bytearray([0]) + bytearray(b'9')\n actual = LZ77.decompress(b_array)\n expected = '123456789'\n self.assertEqual(actual, expected)","def get_precomp_hashcodes(self,list_feats_id):\n return self.get_precomp_X(list_feats_id,\"hashcodes\",self.bits_num/8,np.uint8)","def polylinedictarraycopy(d):#d——原始图层多段线字典 \r\n dictlist=[]\r\n ratiolist=[] #放缩率列表\r\n rationumaccumulationlist=[] #放缩率数量累加列表\r\n \r\n eachrationum=globalconfig.X_ARRAY_NUM//globalconfig.RATIO_NUM\r\n leftrationum=globalconfig.X_ARRAY_NUM%globalconfig.RATIO_NUM\r\n \r\n eachrationumlist=[eachrationum]*globalconfig.RATIO_NUM #各个放缩率对应数量的列表\r\n \r\n for i in range((globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2,(globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2+leftrationum):\r\n eachrationumlist[i]=eachrationumlist[i]+1 #将整除后的余值加入到靠中间放缩率的方案中。\r\n \r\n rationumaccumulationlist.append(0) \r\n \r\n for i in range(1,globalconfig.RATIO_NUM): #计算放缩率数量累加列表\r\n rationumaccumulationlist.append(rationumaccumulationlist[i-1]+eachrationumlist[i-1])\r\n \r\n for i in range(0,globalconfig.RATIO_NUM): #计算放缩率列表\r\n ratiolist.append((globalconfig.CENTER_RATIO-((globalconfig.RATIO_NUM+1)//2-1)*globalconfig.RATIO_DIFF)+i*globalconfig.RATIO_DIFF) \r\n \r\n for i in range(0,globalconfig.RATIO_NUM): #每种放缩率\r\n for j in range(0,eachrationumlist[i]): #每种放缩率对应数量\r\n newdict={}\r\n for e in d: #将字典中值即每一图层对应的多段线列表进行复制并移动到指定位置\r\n newdict[e]=polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist)) \r\n #newdict.append([e,polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist))])\r\n dictlist.append(newdict) \r\n return (dictlist,ratiolist,eachrationumlist)","def test_compress_seq_diff_9_char(self):\n text = '123456789'\n actual = LZ77.compress(text)\n expected = bytearray([0]) + bytearray(b'12345678') \\\n + bytearray([0]) + bytearray(b'9')\n self.assertEqual(actual, expected)","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def get_dict_refine_insee_code(ls_valid_ic):\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\n for ic in ls_valid_ic_corse:\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\n dict_refine_ic.update(dict_ic_ardts)\n return dict_refine_ic","def getAllIndex(ldata, fldata):\n\treturn list(map(lambda e : fldata.index(e), ldata))","def lzs (inlist):\r\n zscores = []\r\n for item in inlist:\r\n zscores.append(z(inlist,item))\r\n return zscores","def huffman_data_decode(data: hic.BitStringP, huffman: huffman.HuffmanTree) -> list:\n return huffman.decode_data(data.payload)","def zzx_from_dict(f):\n if not f:\n return []\n\n n, h = max(f.iterkeys()), []\n\n for k in xrange(n, -1, -1):\n h.append(INT_TYPE(int(f.get(k, 0))))\n\n return zzx_strip(h)","def mapfn(k, v):\n for row in v:\n # rellenar el codigo\n pass","def raw_features_extractor(database='./red_cod.db.pkl', sites=-1, elements = -1, maxatoms= -1,\r\n dictionary='diccionario', features='datosrahm.csv'):\r\n \r\n df=create_collection(database=database,sites=sites, elements=elements, maxatoms=maxatoms, \r\n dictionary=dictionary)\r\n \r\n start=time.time()\r\n \r\n datos=pd.read_csv(features)\r\n datos=datos.fillna(-1)\r\n\r\n dicc=dict(datos[['Symbol','Z']].values)\r\n\r\n dicc['D']=1\r\n dicc['Bk']=97\r\n dicc['Cf']=98\r\n dicc['Es']=99\r\n dicc['Fm']=100\r\n dicc['Md']=101\r\n dicc['No']=102\r\n dicc['Lr']=103\r\n \r\n max_sitios = max(df['sitios'].values)\r\n\r\n df=df[df['sitios'] <= max_sitios].reset_index(drop=True)\r\n \r\n X=np.zeros((len(df),max_sitios,104))\r\n y=np.zeros((len(df),1))\r\n mult=np.zeros((len(df),max_sitios))\r\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\r\n \r\n for row in range(len(df)):\r\n \r\n item=df['WyckOcc'][row]\r\n sitios=list(item.values()) \r\n sitocc=np.zeros((len(sitios),104))\r\n spacegroup = str(df['sgnum'][row]).zfill(3)\r\n \r\n try:\r\n \r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n sitios] for i in j]\r\n \r\n except:\r\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\n')\r\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\r\n '\\n','\\n')\r\n spacegroup=input('Give me the correct spacegroup:'+'\\n'+'\\n')\r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n list(df['WyckOcc'][row].values())] for i in j]\r\n \r\n occs=[]\r\n for i in range(len(sitios)):\r\n\r\n for j in list(sitios[i].values()):\r\n \r\n ocupacion=np.array(list(j.values()))\r\n llaves=[llave.replace('+','').replace('-','').replace('1',\r\n '').replace('2','').replace('3','').replace('4',\r\n '') for llave in np.array(list(j.keys()))]\r\n llaves=[llave.replace('.','') for llave in llaves]\r\n llaves=[llave.replace('5','').replace('6','').replace('7',\r\n '').replace('8','').replace('9','').replace('0',\r\n '') for llave in llaves]\r\n vector=np.zeros((1,104))\r\n occs=[sum(ocupacion)]+occs\r\n \r\n try:\r\n \r\n idx=[dicc[k] for k in llaves]\r\n \r\n except:\r\n \r\n print(' ELEMENTO NO IDENTIFICADO EN LA LISTA ',llaves,'\\n',\r\n 'REVISA EL SIGUIENTE CIF PARA HACER LA CORRECCION:','\\t',df['cif'][row])\r\n \r\n former = input('Elemento Incorrecto: ')\r\n current = input('Elemento Correcto: ')\r\n \r\n llaves=[current if x == former else x for x in llaves]\r\n idx=[dicc[k] for k in llaves]\r\n \r\n \r\n for k in idx:\r\n vector[0][k-1] = ocupacion[idx.index(k)]\r\n \r\n \r\n sitocc[i]=vector\r\n \r\n while sitocc.shape[0] != max_sitios:\r\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\r\n s=[0]+s\r\n \r\n X[row,:,:]=sitocc\r\n y[row]=df['target'][row]\r\n mult[row]=s\r\n \r\n S = np.expand_dims(mult,axis=2)\r\n features=datos.iloc[:,2:].values\r\n x=X[:,:,:96]\r\n \r\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\r\n \r\n x=np.dot(x,features) \r\n\r\n print('Atomic radii and electronegativities for each Wyckoff site extracted in',\r\n round(time.time()-start,2),' s') \r\n \r\n np.save('raw_features', x)\r\n np.save('output_values', y)\r\n np.save('multiplicities', S)\r\n np.save('occupation_fractions', fracsum)\r\n \r\n return x, y, S, fracsum, df","def read_data(file_path):\n words=[]\n dic_word={}\n actual_text=[]\n for line in open(file_path,encoding='utf-8'):\n words_line=line.strip().split(' ')\n for ite in words_line:\n if ite not in dic_word:\n dic_word[ite]=1\n words.extend(words_line)\n actual_text.append(words_line)\n\n\n #with zipfile.ZipFile(file_path) as f:\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\n\n return words,len(dic_word),actual_text","def ana_merge_senzory_map(datas):\n#TODO: improve senzory map merging\n return iter(datas.viewvalues()).next()['senzory_map']","def list_data_collate(batch: Sequence):\n elem = batch[0]\n data = [i for k in batch for i in k] if isinstance(elem, list) else batch\n key = None\n try:\n if config.USE_META_DICT:\n data = pickle_operations(data) # bc 0.9.0\n if isinstance(elem, Mapping):\n ret = {}\n for k in elem:\n key = k\n data_for_batch = [d[key] for d in data]\n ret[key] = collate_meta_tensor(data_for_batch)\n else:\n ret = collate_meta_tensor(data)\n return ret\n except RuntimeError as re:\n re_str = str(re)\n if \"equal size\" in re_str:\n if key is not None:\n re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n re_str += (\n \"\\n\\nMONAI hint: if your transforms intentionally create images of different shapes, creating your \"\n + \"`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its \"\n + \"documentation).\"\n )\n _ = dev_collate(data)\n raise RuntimeError(re_str) from re\n except TypeError as re:\n re_str = str(re)\n if \"numpy\" in re_str and \"Tensor\" in re_str:\n if key is not None:\n re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n re_str += (\n \"\\n\\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, \"\n + \"creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem \"\n + \"(check its documentation).\"\n )\n _ = dev_collate(data)\n raise TypeError(re_str) from re","def build_dataset(self):\n\n print('begin to build data set...')\n\n # build dictionary\n for e in self.lst_entity:\n self.dictionary_entity[e] = len(self.dictionary_entity)\n\n for r in self.lst_relation:\n self.dictionary_relation[r] = len(self.dictionary_relation)\n\n self.reverse_dictionary_entity = dict(zip(self.dictionary_entity.values(), self.dictionary_entity.keys()))\n self.reverse_dictionary_relation = dict(zip(self.dictionary_relation.values(), self.dictionary_relation.keys()))\n\n # build data map\n self.lst_entity_map = [self.dictionary_entity[e] for e in self.lst_entity]\n self.lst_relation_map = [self.dictionary_relation[r] for r in self.lst_relation]\n self.lst_triplet_train_map = self.map_triplet(self.lst_triplet_train)\n\n # build corrupted candidates for (h,r,~) and (~,r,t)\n for (h, r, t) in self.lst_triplet_train_map:\n if r not in self.dict_tofh:\n self.dict_tofh[r] = {h: [t]}\n else:\n if h not in self.dict_tofh[r]:\n self.dict_tofh[r][h] = [t]\n else:\n self.dict_tofh[r][h].append(t)\n\n if r not in self.dict_hoft:\n self.dict_hoft[r] = {t: [h]}\n else:\n if t not in self.dict_hoft[r]:\n self.dict_hoft[r][t] = [h]\n else:\n self.dict_hoft[r][t].append(h)\n\n for r in self.dict_tofh:\n self.lst_triplet_corrupted_tail[r] = dict()\n for h in self.dict_tofh[r]:\n set_tail_corrupted_all = set(self.lst_entity_map) - set(self.dict_tofh[r][h])\n lst_tail_corrupted_choose = random.sample(set_tail_corrupted_all, 5*len(self.dict_tofh[r][h]))\n self.lst_triplet_corrupted_tail[r][h] = lst_tail_corrupted_choose\n\n for r in self.dict_hoft:\n self.lst_triplet_corrupted_head[r] = dict()\n for t in self.dict_hoft[r]:\n lst_head_corrupted_all = set(self.lst_entity_map) - set(self.dict_hoft[r][t])\n lst_head_corrupted_choose = random.sample(lst_head_corrupted_all, 5*len(self.dict_hoft[r][t]))\n self.lst_triplet_corrupted_head[r][t] = lst_head_corrupted_choose\n\n print('data set has been built successfully!')","def decoder(self, array=[],your_dic_max_value=78):\r\n word_list=[]\r\n self.coder=\"This_is_a_decoder_for_the_short_vector_function!\"\r\n for i in array:\r\n for i in i:\r\n print(\"i:\"+str(i))\r\n if int(i) <=25:# use the max length of the dic to find out wich one to use\r\n for key, value in self.general_lower_word_list.items():# iterate through every key and value\r\n if value==int(i):#checks if the value is equal to the given input\r\n word_list.append(key)# if yes append it\r\n else:\r\n pass #else move on\r\n elif int(i) <=51 and int(i)>=25:# same process as above\r\n for key, value in self.general_upper_word_list.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=58 and int(i)>=52:\r\n for key, value in self.special_letters_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=68 and int(i)>=59:\r\n for key, value in self.general_numbers_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=max_value and int(i)>=69:\r\n for key, value in self.special_characters_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n return \"\".join(word_list) #create a real string with the join method\r","def make_data(revs, word_idx_map, max_l=50, filter_h=3\n ,train_instance = 16541,val_instance = 1645):\n train, val, test = [], [], []\n for rev in revs:\n sent = get_idx_from_sent(rev[\"m\"], word_idx_map, max_l, filter_h)\n sent += get_idx_from_sent(rev[\"r\"], word_idx_map, max_l, filter_h)\n sent.append(int(rev[\"y\"]))\n sent.append(int(rev[\"t\"]))\n sent.append(int(rev[\"t2\"]))\n\n if len(train) < train_instance:\n train.append(sent)\n elif (len(train) + len(val)) < train_instance + val_instance:\n val.append(sent)\n else:\n test.append(sent)\n\n shuffle(train)\n train = np.array(train,dtype=\"int\")\n val = np.array(val,dtype=\"int\")\n test = np.array(test,dtype=\"int\")\n print 'trainning data', len(train),'val data', len(val)\n return [train, val, test]","def build_list(self, word_list):\n # Get frequency list for keys\n freq = word_list.groupby('key').agg('count')\n # Filter out only keys with greater or equal frequency to length\n key_list = freq.loc[freq['word'] >= freq.index.str.len()]\n return key_list","def buildCoder(shift):\n\n list1=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n list2=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']\n dic={'a':'a','b':'b','c':'c','d':'d','e':'e','f':'f','g':'g','h': 'h','i':'i','j':'j','k':'k','l':'l','m':'m','n':'n','o':'o','p':'p','q':'q','r':'r','s':'s','t':'t','u':'u',\n 'v':'v','w':'w','x':'x','y':'y','z':'z','A':'A','B':'B','C':'C','D':'D','E':'E','F':'F','G':'G','H':'H','I':'I','J':'J','K':'K','L':'L','M':'M','N':'N','O':'O','P':'P',\n 'Q':'Q','R':'R','S':'S','T':'T','U':'U','V':'V','W':'W','X':'X','Y':'Y','Z':'Z'} \n\n st1= 'abcdefghijklmnopqrstuvwxyz'\n st2= 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\n \n for k in dic.keys():\n \n for i in range(0,len(list1)):\n n1=0\n if k in st1:\n if k==list1[i]:\n n1=i+shift\n if n1 >= len(list1):\n l2=len(list1)-1-i\n b=shift-l2\n dic[k]=list1[b-1]\n \n break\n else:\n dic[k]=list1[i+shift]\n \n break\n elif k in st2:\n if k==list2[i]:\n n1=i+shift\n if n1 >= len(list2):\n l2=len(list2)-1-i\n b=shift-l2\n dic[k]=list2[b-1]\n \n break\n else:\n dic[k]=list2[i+shift]\n \n break\n \n return dic","def get_hkls(self):\n return zip(*[self.hkl(i) for i in range(self.npks)])","def _read_zb_zblst(fname):\n with open(fname) as foo:\n data = {}\n read_data = False\n flow_budget = False\n empty = 0\n prefix = \"\"\n while True:\n line = foo.readline().strip().upper()\n t = line.split()\n if t:\n if t[-1].strip() == \"ZONES.\":\n line = foo.readline().strip()\n zones = [int(i) for i in line.split()]\n for zone in zones:\n data[f\"TO_ZONE_{zone}\"] = []\n data[f\"FROM_ZONE_{zone}\"] = []\n\n if \"FLOW BUDGET FOR ZONE\" in line:\n flow_budget = True\n read_data = False\n zlist = []\n empty = 0\n t = line.split()\n zone = int(t[4])\n if len(t[7]) > 4:\n t.insert(8, t[7][4:])\n kstp = int(t[8]) - 1\n if len(t[11]) > 6:\n t.append(t[11][6:])\n kper = int(t[12]) - 1\n if \"ZONE\" not in data:\n data[\"ZONE\"] = [zone]\n data[\"KSTP\"] = [kstp]\n data[\"KPER\"] = [kper]\n else:\n data[\"ZONE\"].append(zone)\n data[\"KSTP\"].append(kstp)\n data[\"KPER\"].append(kper)\n\n elif line in (\"\", \" \"):\n empty += 1\n\n elif read_data:\n if \"=\" in line:\n t = line.split(\"=\")\n label = t[0].strip()\n if \"ZONE\" in line:\n if prefix == \"FROM_\":\n zlist.append(int(label.split()[1]))\n label = f\"FROM_ZONE_{label.split()[1]}\"\n else:\n label = f\"TO_ZONE_{label.split()[-1]}\"\n\n elif \"TOTAL\" in line or \"PERCENT DISCREPANCY\" in line:\n label = \"_\".join(label.split())\n\n elif \"IN - OUT\" in line:\n label = \"IN-OUT\"\n\n else:\n label = prefix + \"_\".join(label.split())\n\n if label in data:\n data[label].append(float(t[1]))\n else:\n data[label] = [float(t[1])]\n\n if label == \"PERCENT_DISCREPANCY\":\n # fill in non-connected zones with zeros...\n for zone in zones:\n if zone in zlist:\n continue\n data[f\"FROM_ZONE_{zone}\"].append(0)\n data[f\"TO_ZONE_{zone}\"].append(0)\n\n elif \"OUT:\" in line:\n prefix = \"TO_\"\n\n else:\n pass\n\n elif flow_budget:\n if \"IN:\" in line:\n prefix = \"FROM_\"\n read_data = True\n flow_budget = False\n\n else:\n pass\n\n if empty >= 30:\n break\n\n return _zb_dict_to_recarray(data)","def build_dataset(words):\n count = []\n # count.extend(collections.Counter(words).most_common(n_words - 1))\n count.extend(collections.Counter(words).most_common())\n dictionary = dict()\n for word, _ in count:\n dictionary[word] = len(dictionary)\n data = list()\n # unk_count = 0\n for word in words:\n index = dictionary.get(word, 0)\n # if index == 0: # dictionary['UNK']\n # unk_count += 1\n data.append(index)\n # count[0][1] = unk_count\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n data = [data[::2],data[1::2]]\n new_data = list()\n for i in range(len(data[0])):\n new_data.append([data[0][i],data[1][i]])\n data = new_data\n vocabulary_size = len(dictionary)\n print(\"\\n\\ndictionary size = \")\n print(len(dictionary))\n return data, count, dictionary, reversed_dictionary, vocabulary_size","def getJar(oldDatas,jarList):\n #clear pastebin here. confirmed.\n if pastebin in os.listdir():\n tempdir = barrand6()\n os.rename(pastebin,tempdir)#clever..fine..\n shutil.rmtree(tempdir)#acces deniyed ..fixed!\n os.mkdir(pastebin)\n\n\n oldDatakeys = oldDatas.keys()\n newDatas = {} #for data insure.\n\n for noFolder in jarList:\n try:\n #txtFiledir = os.path.join( origins,noFolder,noFolder+'.txt')\n txtFilename = noFolder+'.txt'\n #------------------------user pre-sure.\n txtfiles = []\n for f in os.listdir( os.path.join( jar,noFolder) ):\n if '.txt' in f:\n txtfiles.append(f)\n if len(txtfiles) == 1:\n txtFilename = txtfiles[0]\n else:\n if not '설정.txt' in txtfiles:\n raise Exception('ERROR no txt : ' + str(noFolder))\n txtFilename = '설정.txt'\n #------------------------user pre-sure\n\n txtFile = os.path.join( jar,noFolder,txtFilename) #whatif dir = no.txt?\n parsedDict = txt2dict.parseTxt(txtFile,parseKeys,multiLineKey)#hope it's atleast complete...\n\n #----------------------for custom dict additional option\n checklist = ['번호','제목','작성자','날짜','본문']\n for c in checklist:\n if not c in parsedDict.keys():\n raise Exception('ERROR!! not format txt : ' + str(noFolder))\n\n #user input, do another func.\n #'16'.isdigit()\n #if int(parsedDict['번호'])<1:\n #raise Exception('ERROR!! of : ' + str(noFolder))\n #a = parsedDict['날짜'].split('.')\n #b = str(datetime.date.today()).split('-')\n #if datetime.date(a[0],a[1],a[2]) < datetime.date.today()\n\n if '태그' in parsedDict.keys():\n tagList = parsedDict['태그'].split(',')\n parsedDict['유저태그'] = tagList\n del parsedDict['태그']\n else:\n parsedDict['유저태그'] = []\n\n if parsedDict['제목'].startswith('[번역]'):\n parsedDict['제목'] = parsedDict['제목'].split('[번역]')[1].strip()\n if parsedDict['제목'].find('센세)') != -1 :\n parsedDict['유저태그'].append( parsedDict['제목'].split('센세)')[0].strip()+'센세' )\n #parsedDict['태그'].append( a.split('[번역]')[1].strip().split('센세)')[0]+'센세)' )\n #----------------------for custom dict additional option\n\n\n #----------------------------- after get parsedDict.\n tmpKey = parsedDict[idKey] #9133114\n if tmpKey in oldDatakeys:\n raise Exception('skip.. id already in parsedict ..: ' + str(noFolder))\n\n idFoldername = parsedDict['번호']\n noFolderpath = os.path.join( jar,noFolder )\n originPath = os.path.join( origins , idFoldername )\n shutil.copytree(noFolderpath,originPath, dirs_exist_ok = False)# was true, but to integrity....\n #it occured at test. nodict, but files.\n #shutil.move(noFolderpath,pastebin)\n\n\n #datas is dict object, appended new key,value.\n #add more value.\n #datas[tmpKey]['key'] = 'value'\n\n # get moved nofolder, add datas originImgs.\n originFiles = os.listdir(os.path.join( origins, idFoldername))\n originImgs = []\n for img in originFiles:\n ext = os.path.splitext( img )[1][1:] # .jpg == jpg\n if ext in imgExt: #now, it's img.\n originImgs.append(img)\n if originImgs==[]:\n raise Exception('ERROR!! no img..: ' + str(noFolder))\n parsedDict[originKey] = originImgs\n\n newDatas[tmpKey] = parsedDict\n #datas[tmpKey][allfilesKey] = originFiles\n thisrand=barrand6()\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\n\n except Exception as e:\n exc_info = sys.exc_info()#below except.\n errmsg = exc_info[1],':at line',exc_info[2].tb_lineno\n print(errmsg)\n\n thisrand=barrand6()\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\n f = open('./'+pastebin+'/'+noFolder+thisrand+'/err.txt','w',encoding='utf-8')\n f.write(str(errmsg))\n f.close()\n print( 'ERROR occured. gone pastebin :',str(noFolder)+thisrand)\n continue\n\n return newDatas","def list_lz(\n self, lz_ids=None, userids=None, page=1, items=25,\n ):\n return self._lz_service.list_lz(\n lz_ids=lz_ids, userids=userids, page=page, items=items\n )","def two_tier_embedding_compression(embeddings, bits, quantizer=None):\n assert bits <= 8\n n = 2**bits\n quantized_embeddings = embeddings.copy()\n index_table = np.zeros(embeddings.shape, dtype=np.uint8)\n cluster_index_table = np.zeros(index_table.shape[0], dtype=np.uint8)\n codebook_table = np.zeros((n, n))\n\n km1 = KMeans(n)\n km1.fit(embeddings)\n tier1 = km1.predict(embeddings)\n\n km_models = [0] * n\n block_sizes = [0] * n\n for block_label in tqdm(range(n)):\n mask = block_label == tier1\n indices = np.arange(embeddings.shape[0])[mask]\n block = embeddings[mask]\n km2 = KMeans(n)\n km2.fit(block.flatten().reshape(-1, 1))\n if quantizer:\n km2.cluster_centers_ = quantizer(km2.cluster_centers_).numpy()\n km2.cluster_centers_.sort(axis=0)\n\n km_models[block_label] = km2\n codebook_table[block_label, :] = km2.cluster_centers_.flatten()\n cluster_index_table[indices] = block_label\n block_sizes[block_label] = block.shape[0]\n for i in indices:\n preds = km2.predict(embeddings[i, :].reshape(-1, 1))\n index_table[indices, :] = preds\n quantized_embeddings[i, :] = km2.cluster_centers_[preds].flatten()\n print('block_sizes:', block_sizes)\n return index_table, cluster_index_table, codebook_table, quantized_embeddings","def load_data(cache_file_h5py,cache_file_pickle):\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\n raise RuntimeError(\"############################ERROR##############################\\n. \"\n \"please download cache file, it include training data and vocabulary & labels. \"\n \"link can be found in README.md\\n download zip file, unzip it, then put cache files as FLAGS.\"\n \"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\")\n print(\"INFO. cache file exists. going to load cache file\")\n f_data = h5py.File(cache_file_h5py, 'r')\n print(\"f_data.keys:\",list(f_data.keys()))\n train_X=f_data['train_X'] # np.array(\n print(\"train_X.shape:\",train_X.shape)\n train_Y=f_data['train_Y'] # np.array(\n print(\"train_Y.shape:\",train_Y.shape,\";\")\n vaild_X=f_data['vaild_X'] # np.array(\n valid_Y=f_data['valid_Y'] # np.array(\n test_X=f_data['test_X'] # np.array(\n test_Y=f_data['test_Y'] # np.array(\n #print(train_X)\n #f_data.close()\n\n word2index, label2index=None,None\n with open(cache_file_pickle, 'rb') as data_f_pickle:\n word2index, label2index=pickle.load(data_f_pickle)\n print(\"INFO. cache file load successful...\")\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y","def load_data(cache_file_h5py,cache_file_pickle):\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\n raise RuntimeError(\"############################ERROR##############################\\n. \"\n \"please download cache file, it include training data and vocabulary & labels. \"\n \"link can be found in README.md\\n download zip file, unzip it, then put cache files as FLAGS.\"\n \"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\")\n print(\"INFO. cache file exists. going to load cache file\")\n f_data = h5py.File(cache_file_h5py, 'r')\n print(\"f_data.keys:\",list(f_data.keys()))\n train_X=f_data['train_X'] # np.array(\n print(\"train_X.shape:\",train_X.shape)\n train_Y=f_data['train_Y'] # np.array(\n print(\"train_Y.shape:\",train_Y.shape,\";\")\n vaild_X=f_data['vaild_X'] # np.array(\n valid_Y=f_data['valid_Y'] # np.array(\n test_X=f_data['test_X'] # np.array(\n test_Y=f_data['test_Y'] # np.array(\n #print(train_X)\n #f_data.close()\n\n word2index, label2index=None,None\n with open(cache_file_pickle, 'rb') as data_f_pickle:\n word2index, label2index=pickle.load(data_f_pickle)\n print(\"INFO. cache file load successful...\")\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y","def load_data(cache_file_h5py,cache_file_pickle):\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\n raise RuntimeError(\"############################ERROR##############################\\n. \"\n \"please download cache file, it include training data and vocabulary & labels. \"\n \"link can be found in README.md\\n download zip file, unzip it, then put cache files as FLAGS.\"\n \"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\")\n print(\"INFO. cache file exists. going to load cache file\")\n f_data = h5py.File(cache_file_h5py, 'r')\n print(\"f_data.keys:\",list(f_data.keys()))\n train_X=f_data['train_X'] # np.array(\n print(\"train_X.shape:\",train_X.shape)\n train_Y=f_data['train_Y'] # np.array(\n print(\"train_Y.shape:\",train_Y.shape,\";\")\n vaild_X=f_data['vaild_X'] # np.array(\n valid_Y=f_data['valid_Y'] # np.array(\n test_X=f_data['test_X'] # np.array(\n test_Y=f_data['test_Y'] # np.array(\n #print(train_X)\n #f_data.close()\n\n word2index, label2index=None,None\n with open(cache_file_pickle, 'rb') as data_f_pickle:\n word2index, label2index=pickle.load(data_f_pickle)\n print(\"INFO. cache file load successful...\")\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y","def load_data(cache_file_h5py,cache_file_pickle):\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\n raise RuntimeError(\"############################ERROR##############################\\n. \"\n \"please download cache file, it include training data and vocabulary & labels. \"\n \"link can be found in README.md\\n download zip file, unzip it, then put cache files as FLAGS.\"\n \"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\")\n print(\"INFO. cache file exists. going to load cache file\")\n f_data = h5py.File(cache_file_h5py, 'r')\n print(\"f_data.keys:\",list(f_data.keys()))\n train_X=f_data['train_X'] # np.array(\n print(\"train_X.shape:\",train_X.shape)\n train_Y=f_data['train_Y'] # np.array(\n print(\"train_Y.shape:\",train_Y.shape,\";\")\n vaild_X=f_data['vaild_X'] # np.array(\n valid_Y=f_data['valid_Y'] # np.array(\n test_X=f_data['test_X'] # np.array(\n test_Y=f_data['test_Y'] # np.array(\n #print(train_X)\n #f_data.close()\n\n word2index, label2index=None,None\n with open(cache_file_pickle, 'rb') as data_f_pickle:\n word2index, label2index=pickle.load(data_f_pickle)\n print(\"INFO. cache file load successful...\")\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y","def get_data(elem: string) -> list:\n if elem in ascii_dict:\n max_length = len(max(ascii_dict[elem], key=len))\n if max_length == 0:\n max_length = 4\n data = []\n for s in ascii_dict[elem]:\n data.append(fill_with_spaces(s, max_length))\n return data\n else:\n raise IndexError(\"Unknown symbol: \", elem)","def lego_sets():\n \n \n data_test=data_specific\n\n\n\n \n \n\n \n print(data_test)\n print(\"The size of the data is: \",len(data_test))\n \n \n \n # you must replace this line and return your own list\n return data_test","def lad_lut(lads):\n for lad in lads:\n yield lad['properties']['name']","def decode(self, z):\n raise NotImplementedError","def test_key_for_list_of_cacheable_objects(self):\n Meat.get_protein_sum([self.chicken, self.steak])\n expected_cache_key = 'tests.Meat.get_protein_sum;,Chicken:20,Steak:26;'\n self.assertExpectedKeyInCache(expected_cache_key)","def LZm(self,i):\n\t\tv=self.deltai_minus(i)\n\t\treturn v[0][0]","def _vWBLZ(self):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n vWBLZ=None\r\n vWBLZ=self._OBJS('WBLZ')\r\n \r\n vWBLZ=vWBLZ[[\r\n 'AKTIV' \r\n ,'BESCHREIBUNG'\r\n ,'IDIM'\r\n ,'NAME'\r\n #from WBLZ's OBJS: \r\n ,'OBJTYPE' #type (i.e. KNOT) of a WBLZ OBJ\r\n ,'OBJID' #pk (or tk?!) of a WBLZ OBJ \r\n #IDs (of the WBLZ)\r\n ,'pk'\r\n ]]\r\n vWBLZ.sort_values(['NAME','pk'],ascending=True,inplace=True)\r\n #reindex:\r\n vWBLZ=pd.DataFrame(vWBLZ.values,columns=vWBLZ.columns)\r\n \r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n if isinstance(vWBLZ,pd.core.frame.DataFrame):\r\n logger.error(logStrFinal) \r\n else:\r\n logger.debug(logStrFinal) \r\n vWBLZ=pd.DataFrame() \r\n vWBLZ['AKTIV']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['BESCHREIBUNG']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['IDIM']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['NAME']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['OBJID']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['OBJTYPE']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['pk']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.')) \r\n return vWBLZ","def collate_fn(data):\n\toutput = dict()\n\n\tfor name in ['answer_ID','query_ID']:\n\t\toutput[name] = [ _[name] for _ in data]\n\n\n\tfor name in ['query_len','answer_len']:\n\t\ttemp = [ _[name] for _ in data]\t \n\t\toutput[name] = torch.stack(temp, dim=0) \n\t\n\t#deal with source and target\n\tfor name in ['answer','query']:\n\t\tlength = output['{0}_len'.format(name)]\n\t\tl = length.max().item()\n\n\t\tfor i in range(len(data)):\n\t\t\tif(l-length[i].item()>0):\n\t\t\t\tdata[i][name] = torch.cat([data[i][name],torch.zeros(l-length[i].item(),dtype=torch.long)],dim=-1)\n\n\t\ttemp = [ _[name] for _ in data]\n\t\t\n\t\toutput[name] = torch.stack(temp, dim=0).long()\n\t\t\n\n\treturn output","def build_dataset(words, n_words):\n count = [['UNK', -1]]\n count.extend(collections.Counter(words).most_common(n_words - 1))\n dictionary = dict()\n for word, _ in count:\n dictionary[word] = len(dictionary)\n data = list()\n unk_count = 0\n for word in words:\n index = dictionary.get(word, 0)\n if index == 0: # dictionary['UNK']\n unk_count += 1\n data.append(index)\n count[0][1] = unk_count\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n return data, count, dictionary, reversed_dictionary","def build_dataset(words, n_words):\n count = [['UNK', -1]]\n count.extend(collections.Counter(words).most_common(n_words - 1))\n dictionary = dict()\n for word, _ in count:\n dictionary[word] = len(dictionary)\n data = list()\n unk_count = 0\n for word in words:\n index = dictionary.get(word, 0)\n if index == 0: # dictionary['UNK']\n unk_count += 1\n data.append(index)\n count[0][1] = unk_count\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n return data, count, dictionary, reversed_dictionary","def test_concat_data(self):\n\n this_satellite_dict = satellite_io.concat_data(\n satellite_dicts=[\n SATELLITE_DICT_SUBSET_BY_INDEX, SATELLITE_DICT_SUBSET_BY_TIME\n ]\n )\n\n self.assertTrue(compare_satellite_dicts(\n this_satellite_dict, SATELLITE_DICT_CONCAT\n ))","def test_decode_listmode_data(self):\n self.assertEqual([td.header(decoded=True)],\n decoder.decode_listmode_data(BytesIO(td.header(as_bytes=True)), self.mask))\n self.assertEqual([{**td.header_with_trace(decoded=True), **{'trace': td.trace()}}],\n decoder.decode_listmode_data(\n BytesIO(td.header_with_trace(as_bytes=True) + td.trace(True)),\n self.mask))","def buildCoder(shift):\n out_dic = {}\n lo = string.ascii_lowercase\n up = string.ascii_uppercase\n for i in lo:\n out_dic[i] = lo[(lo.index(i) + shift) % len(lo)]\n for i in up:\n out_dic[i] = up[(up.index(i) + shift) % len(up)]\n return out_dic","def __init__(self):\n self.data = []\n self.idx = {}","def gzdeflate():\n return zlib.compress(val)","def lshTransform(kv, band_count = 16):\n img_name = kv[0]\n img_sig = kv[1]\n band_hash_list = []\n band_size = int(128/band_count)\n img_sig_chunks = list(map(''.join, zip(*[iter(img_sig)]*band_size)))\n vfunc = np.vectorize(customHashFunc)\n band_hash_list = vfunc(img_sig_chunks)\n # Key: (band_id, bucket_id)\n zip_list_1 = list(zip(np.arange(band_count),band_hash_list))\n img_name_list = [[img_name]]*band_count\n return list(zip(zip_list_1,img_name_list))","def compressed_pickle(title, data):\n with bz2.BZ2File(title, 'w') as f:\n cPickle.dump(data, f)"],"string":"[\n \"def do_LZW_DeCompression(dict_of_abc, list_of_data):\\n \\n #https://www.youtube.com/watch?v=MQM_DsX-LBI\\n \\n out = []\\n predchozi_out = []\\n for i in range(len(list_of_data)):\\n new = []\\n new.extend(predchozi_out)\\n if list_of_data[i] in dict_of_abc:\\n o = dict_of_abc[list_of_data[i]]\\n out.extend(o)\\n predchozi_out = o\\n \\n #pokud je o list, beru z nej pouze prvni prvek\\n if len(o) > 1:\\n new.append(o[0])\\n else:\\n new.extend(o)\\n\\n index_founded = dict_cointains_list(dict_of_abc, new)\\n if index_founded == -1:\\n #pokud new neni ve slovniku, pridam ho tam\\n dict_of_abc[len(dict_of_abc) +1] = new\\n\\n return dict_of_abc, out\",\n \"def getUniChemData(self, inchiKeyList):\\n mapD = {\\n 1: {\\\"name\\\": \\\"chembl\\\", \\\"baseUrl\\\": \\\"https://www.ebi.ac.uk/chembl/\\\", \\\"entryUrl\\\": \\\"https://www.ebi.ac.uk/chembldb/compound/inspect/\\\"},\\n 3: {\\\"name\\\": \\\"pdb\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/pdbe/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\\\"},\\n 2: {\\\"name\\\": \\\"drugbank\\\", \\\"baseUrl\\\": \\\"http://drugbank.ca/\\\", \\\"entryUrl\\\": \\\"http://www.drugbank.ca/drugs/\\\"},\\n 5: {\\\"name\\\": \\\"pubchem_dotf\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 4: {\\\"name\\\": \\\"gtopdb\\\", \\\"baseUrl\\\": \\\"http://www.guidetopharmacology.org\\\", \\\"entryUrl\\\": \\\"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\\\"},\\n 11: {\\\"name\\\": \\\"ibm\\\", \\\"baseUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\\\", \\\"entryUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\\\"},\\n 6: {\\\"name\\\": \\\"kegg_ligand\\\", \\\"baseUrl\\\": \\\"http://www.genome.jp/kegg/ligand.html\\\", \\\"entryUrl\\\": \\\"http://www.genome.jp/dbget-bin/www_bget?\\\"},\\n 9: {\\\"name\\\": \\\"zinc\\\", \\\"baseUrl\\\": \\\"http://zinc15.docking.org\\\", \\\"entryUrl\\\": \\\"http://zinc15.docking.org/substances/\\\"},\\n 8: {\\\"name\\\": \\\"nih_ncc\\\", \\\"baseUrl\\\": \\\"http://nihsmr.evotec.com/evotec/\\\", \\\"entryUrl\\\": \\\"\\\"},\\n 10: {\\\"name\\\": \\\"emolecules\\\", \\\"baseUrl\\\": \\\"https://www.emolecules.com/\\\", \\\"entryUrl\\\": \\\"https://www.emolecules.com/cgi-bin/more?vid=\\\"},\\n 12: {\\\"name\\\": \\\"atlas\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/gxa/home\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/gxa/query?conditionQuery=\\\"},\\n 7: {\\\"name\\\": \\\"chebi\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/chebi/downloadsForward.do\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\\\"},\\n 14: {\\n \\\"name\\\": \\\"fdasrs\\\",\\n \\\"baseUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/srs.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\\\",\\n },\\n 15: {\\\"name\\\": \\\"surechembl\\\", \\\"baseUrl\\\": \\\"https://www.surechembl.org/search/\\\", \\\"entryUrl\\\": \\\"https://www.surechembl.org/chemical/\\\"},\\n 21: {\\\"name\\\": \\\"pubchem_tpharma\\\", \\\"baseUrl\\\": \\\"http://www.thomson-pharma.com/\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 22: {\\\"name\\\": \\\"pubchem\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/compound/\\\"},\\n 27: {\\\"name\\\": \\\"recon\\\", \\\"baseUrl\\\": \\\"https://vmh.uni.lu\\\", \\\"entryUrl\\\": \\\"https://vmh.uni.lu/\\\"},\\n 28: {\\\"name\\\": \\\"molport\\\", \\\"baseUrl\\\": \\\"https://www.molport.com/shop/index\\\", \\\"entryUrl\\\": \\\"https://www.molport.com/shop/molecule-link/\\\"},\\n 31: {\\n \\\"name\\\": \\\"bindingdb\\\",\\n \\\"baseUrl\\\": \\\"https://www.bindingdb.org/bind/index.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\\\",\\n },\\n 41: {\\\"name\\\": \\\"swisslipids\\\", \\\"baseUrl\\\": \\\"http://www.swisslipids.org/\\\", \\\"entryUrl\\\": \\\"http://www.swisslipids.org/\\\"},\\n 29: {\\\"name\\\": \\\"nikkaji\\\", \\\"baseUrl\\\": \\\"http://jglobal.jst.go.jp/en/\\\", \\\"entryUrl\\\": \\\"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\\\"},\\n 32: {\\\"name\\\": \\\"comptox\\\", \\\"baseUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\", \\\"entryUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\"},\\n 33: {\\\"name\\\": \\\"lipidmaps\\\", \\\"baseUrl\\\": \\\"http://www.lipidmaps.org\\\", \\\"entryUrl\\\": \\\"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\\\"},\\n 35: {\\\"name\\\": \\\"carotenoiddb\\\", \\\"baseUrl\\\": \\\"http://carotenoiddb.jp/index.html\\\", \\\"entryUrl\\\": \\\"http://carotenoiddb.jp/Entries/\\\"},\\n 36: {\\\"name\\\": \\\"metabolights\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\"},\\n 37: {\\\"name\\\": \\\"brenda\\\", \\\"baseUrl\\\": \\\"https://www.brenda-enzymes.org/index.php\\\", \\\"entryUrl\\\": \\\"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\\\"},\\n 17: {\\\"name\\\": \\\"pharmgkb\\\", \\\"baseUrl\\\": \\\"https://www.pharmgkb.org\\\", \\\"entryUrl\\\": \\\"https://www.pharmgkb.org/drug/\\\"},\\n 18: {\\\"name\\\": \\\"hmdb\\\", \\\"baseUrl\\\": \\\"http://www.hmdb.ca\\\", \\\"entryUrl\\\": \\\"http://www.hmdb.ca/metabolites/\\\"},\\n 24: {\\n \\\"name\\\": \\\"nmrshiftdb2\\\",\\n \\\"baseUrl\\\": \\\"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\\\",\\n \\\"entryUrl\\\": \\\"http://nmrshiftdb.org/molecule/\\\",\\n },\\n 25: {\\\"name\\\": \\\"lincs\\\", \\\"baseUrl\\\": \\\"http://www.lincsproject.org/\\\", \\\"entryUrl\\\": \\\"http://identifiers.org/lincs.smallmolecule/\\\"},\\n 39: {\\\"name\\\": \\\"chemicalbook\\\", \\\"baseUrl\\\": \\\"https://www.chemicalbook.com\\\", \\\"entryUrl\\\": \\\"https://www.chemicalbook.com/ChemicalProductProperty_EN_\\\"},\\n 20: {\\\"name\\\": \\\"selleck\\\", \\\"baseUrl\\\": \\\"http://www.selleckchem.com\\\", \\\"entryUrl\\\": \\\"http://www.selleckchem.com/products/\\\"},\\n 23: {\\\"name\\\": \\\"mcule\\\", \\\"baseUrl\\\": \\\"https://mcule.com\\\", \\\"entryUrl\\\": \\\"https://mcule.com/\\\"},\\n 26: {\\\"name\\\": \\\"actor\\\", \\\"baseUrl\\\": \\\"https://actor.epa.gov\\\", \\\"entryUrl\\\": \\\"http://actor.epa.gov/actor/chemical.xhtml?casrn=\\\"},\\n 34: {\\\"name\\\": \\\"drugcentral\\\", \\\"baseUrl\\\": \\\"http://drugcentral.org\\\", \\\"entryUrl\\\": \\\"http://drugcentral.org/drugcard/\\\"},\\n 38: {\\\"name\\\": \\\"rhea\\\", \\\"baseUrl\\\": \\\"http://www.rhea-db.org\\\", \\\"entryUrl\\\": \\\"http://www.rhea-db.org/searchresults?q=CHEBI:\\\"},\\n }\\n oD = {}\\n try:\\n for ky in inchiKeyList:\\n unc = unichem_client # pylint: disable=no-member\\n # unc.set_format(\\\"json\\\")\\n uDL = unc.get(ky)\\n if uDL:\\n qD = {}\\n for uD in uDL:\\n if \\\"src_id\\\" in uD and int(uD[\\\"src_id\\\"]) in mapD:\\n qD[mapD[int(uD[\\\"src_id\\\"])][\\\"name\\\"]] = uD[\\\"src_compound_id\\\"]\\n if qD:\\n oD[ky] = qD\\n\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n return oD\",\n \"def dict_cointains_list(dict_of_abc, item_list):\\n\\n values = list(dict_of_abc.values())\\n\\n #projdu vsecky listy ve slovniku\\n for i in range(len(values)):\\n #predpokladam ze ve slovniku je\\n finded = True\\n \\n for j in range(len(values[i])):\\n if len(item_list) == len(values[i]):\\n # kontrola po jednotlivych hodnotach\\n # logicky soucin - pokud jednou False, navzdy False\\n finded = finded and item_list[j] == values[i][j]\\n else:\\n finded = False\\n\\n if finded:\\n # cyklus indexuje od 0, slovnik ale indexujeme-klicujeme od 1\\n return i + 1 \\n\\n return -1\",\n \"def generate_data_list(self, data_list):\\n sol = []\\n for i in data_list:\\n sol.append({\\n \\\"data_sig\\\": i[0],\\n \\\"data\\\": pickle.loads(i[1]),\\n \\\"attachment\\\": i[2],\\n \\\"likes\\\":pickle.loads(i[3]),\\n \\\"dislikes\\\":pickle.loads(i[4]),\\n \\\"owner\\\": i[5]\\n })\\n return sol\",\n \"def getDataForLBMPZonalComparison(self):\\n\\n\\t\\t# Variables\\n\\t\\tzonal_data = self.getDayAheadMarketLBMPZonal()\\n\\t\\tkeys = zonal_data.keys()\\n\\t\\tfinal_data = []\\n\\t\\tvalues = []\\n\\t\\touter_dictionary = {}\\n\\t\\tinner_dictionary = {}\\n\\n\\t\\t# Populating final data array and returning it\\n\\t\\tfor key in keys:\\n\\t\\t\\tfor data in zonal_data[key]:\\n\\t\\t\\t\\tinner_dictionary['x'] = data.keys()[0]\\n\\t\\t\\t\\tinner_dictionary['y'] = data[data.keys()[0]]['LBMP ($/MWHr)']\\n\\t\\t\\t\\tvalues.append(inner_dictionary)\\n\\t\\t\\t\\tinner_dictionary = {}\\n\\t\\t\\touter_dictionary['values'] = values\\n\\t\\t\\tvalues = []\\n\\t\\t\\touter_dictionary['key'] = key\\n\\t\\t\\tfinal_data.append(outer_dictionary)\\n\\t\\t\\touter_dictionary = {}\\n\\n\\t\\treturn final_data\",\n \"def get_compressed(self, value):\\r\\n output = []\\r\\n lz_data = (value >> 8) & 0xFF\\r\\n lz_counter = value & 0xFF\\r\\n # Define the relative offset on LZ Window\\r\\n lz_offset = ((lz_counter & 0xF0) << 4) | lz_data\\r\\n # Define the LZ Counter for repeat data N times\\r\\n lz_counter = (lz_counter & 0xF) + 0x2\\r\\n # Start Repeat Loop\\r\\n while (lz_counter >= 0):\\r\\n # Seek the window on LZ Offset and get the LZ Data\\r\\n self.__lzwindow__.seek(lz_offset, FROM_START)\\r\\n lz_data = (lz_data & 0xFF00) + \\\\\\r\\n int.from_bytes(self.__lzwindow__.read(1), byteorder='big')\\r\\n # Write the LZ data to the output\\r\\n output.append((lz_data & 0xFF).to_bytes(1, byteorder='big'))\\r\\n # Seek the LZ Window on current LZ Window Counter value and write the current LZ Data (LZBuffer)\\r\\n self.__lzwindow__.seek(self.__lzwindowcounter__, FROM_START)\\r\\n self.__lzwindow__.write((lz_data & 0xFF).to_bytes(1, byteorder='big'))\\r\\n # Increment LZ Window Counter\\r\\n self.__lzwindowcounter__ = (\\r\\n self.__lzwindowcounter__ + 0x1) & self.__lzwindowmax__\\r\\n # Increment LZ Offset\\r\\n lz_offset = (lz_offset + 0x1) & self.__lzwindowmax__\\r\\n # Decrement number of data to decompress\\r\\n self.__maxlen__ -= 0x1\\r\\n # Decrement LZ Loop counter\\r\\n lz_counter -= 0x1\\r\\n return output\",\n \"def test_compress_offset_less_len1(self):\\n text = 'ababab'\\n actual = LZ77.compress(text)\\n expected = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\\n self.assertEqual(actual, expected)\",\n \"def test_decompress_offset_less_len1(self):\\n b_array = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\\n actual = LZ77.decompress(b_array)\\n expected = 'ababab'\\n self.assertEqual(actual, expected)\",\n \"def test_compress_offset_less_len2(self):\\n text = 'abcdabcdab'\\n actual = LZ77.compress(text)\\n expected = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\\n self.assertEqual(actual, expected)\",\n \"def dict_check34(_str1):\\n\\t_len=len(_str1)\\n\\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\\n\\t_list0=[]\\n\\tfor i in range(len(_list1)):\\n\\t\\t#Take different length\\n\\t\\t_current=_list1[i]\\n\\t\\tif _len>=sum(_current) and sum(_list1[i])!=0:\\n\\t\\t\\t_list2=[]\\n\\t\\t\\t_n1=_current[0]\\n\\t\\t\\t_n2=_current[1]\\n\\t\\t\\t_n3=_current[2]\\n\\t\\t\\t_list2.append(_str1[:_n1])\\n\\t\\t\\t_list2.append(_str1[_n1:_n1+_n2])\\n\\t\\t\\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\\n\\t\\telse:\\n\\t\\t\\tcontinue\\n\\t\\tn=0\\n\\t\\tfor j in range(3):\\n\\t\\t\\tif _list2[j] in _dict_ori or _list2[j]==\\\"\\\":\\n\\t\\t\\t\\tn+=1\\n\\t\\tif n==3:\\n\\t\\t\\t_list0.append(_list2)\\n\\treturn(_list0)\",\n \"def decompress_encoded_list( nums ):\\n decompressed = []\\n\\n for i in range( 0, len(nums), 2 ):\\n freq = nums[i]\\n val = nums[i+1]\\n decompressed.extend( [val] * freq )\\n\\n return decompressed\",\n \"def test_decompress_offset_less_len2(self):\\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdabcdab'\\n self.assertEqual(actual, expected)\",\n \"def apply_compression(pc2_table, keys_56bits):\\n keys_48bits = \\\"\\\"\\n for index in pc2_table:\\n keys_48bits += keys_56bits[index - 1]\\n return keys_48bits\",\n \"def test_decompress_2(self):\\n b_array = bytearray([3]) + bytearray(b'abcdef')\\\\\\n + bytearray([0, 32]) + bytearray([0, 113])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdefdeabc'\\n self.assertEqual(actual, expected)\",\n \"def test_compress_2(self):\\n text = 'abcdefdeabc'\\n actual = LZ77.compress(text)\\n expected = bytearray([3]) + bytearray(b'abcdef')\\\\\\n + bytearray([0, 32]) + bytearray([0, 113])\\n self.assertEqual(actual, expected)\",\n \"def get_keys_from_list():\\n json_data = request.get_json()\\n\\n d = dict()\\n d['elements'] = list()\\n settings.setOptionsFile(get_info('uid'))\\n fn = settings.getHistoROOTFileName()\\n rfn = settings.getReferenceROOTFileName()\\n# open root file stored in the root database\\n f = ROOT.TFile(fn)\\n# open reference root file stored in the root database\\n rf = ROOT.TFile(rfn)\\n\\n for values in json_data.itervalues():\\n for k in values:\\n subd = dict()\\n subd[\\\"index\\\"] = k[\\\"index\\\"]\\n if fn != k[\\\"file\\\"]: \\n fn = k[\\\"file\\\"]\\n settings.setHistoROOTFileName(fn)\\n f = ROOT.TFile(fn)\\n print \\\"histogram :>>>>>: \\\",k[\\\"histogram\\\"]\\n subd[\\\"data\\\"] = eval(cppyy.gbl.getDictionary(f,k[\\\"histogram\\\"]))\\n if rfn != k[\\\"referenceFile\\\"]: \\n rfn = k[\\\"referenceFile\\\"]\\n settings.setReferenceROOTFileName(rfn)\\n rf = ROOT.TFile(rfn)\\n subd[\\\"refdata\\\"] = eval(cppyy.gbl.getDictionary(rf,k[\\\"reference\\\"]))\\n d['elements'].append(subd)\\n\\n f.Close()\\n rf.Close()\\n\\n return jsonify(d)\",\n \"def listz2diclist(listz):\\n\\ttmp=[]\\n\\tfor elem in listz:\\n\\t\\tdic = elem.warez2dic()\\n\\t\\ttmp.append(dic)\\n\\treturn tmp\",\n \"def test_compress_lossless_maps(self):\\n height_map = 384\\n width_map = 384\\n \\n # The quantization bin widths are small\\n # so that the comparison between the\\n # theoretical (minimum) coding cost and\\n # the coding cost computed by the function\\n # is precise enough.\\n bin_widths_test = numpy.array([0.5, 0.25], dtype=numpy.float32)\\n laplace_scales = numpy.array([0.5, 3.], dtype=numpy.float32)\\n \\n # Note that the binary probabilities saved at\\n # \\\"lossless/pseudo_data/binary_probabilities_compress_maps_0.npy\\\"\\n # and those saved at\\n # \\\"lossless/pseudo_data/binary_probabilities_compress_maps_1.npy\\\"\\n # are specific to the three Laplace distributions\\n # below. This means that the binary probabilities\\n # must be modified if `laplace_scales` is modified.\\n paths_to_binary_probabilities = [\\n 'lossless/pseudo_data/binary_probabilities_compress_maps_0.npy',\\n 'lossless/pseudo_data/binary_probabilities_compress_maps_1.npy'\\n ]\\n \\n centered_data_0 = numpy.random.laplace(loc=0.,\\n scale=laplace_scales[0].item(),\\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\\n centered_data_1 = numpy.random.laplace(loc=0.,\\n scale=laplace_scales[1].item(),\\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\\n centered_data = numpy.concatenate((centered_data_0, centered_data_1),\\n axis=3)\\n expanded_centered_quantized_data = tls.quantize_per_map(centered_data, bin_widths_test)\\n centered_quantized_data = numpy.squeeze(expanded_centered_quantized_data,\\n axis=0)\\n tiled_bin_widths = numpy.tile(numpy.reshape(bin_widths_test, (1, 1, 2)),\\n (height_map, width_map, 1))\\n ref_int16 = tls.cast_float_to_int16(centered_quantized_data/tiled_bin_widths)\\n (rec_int16_0, nb_bits_each_map_0) = \\\\\\n lossless.compression.compress_lossless_maps(ref_int16,\\n paths_to_binary_probabilities[0])\\n numpy.testing.assert_equal(ref_int16,\\n rec_int16_0,\\n err_msg='The test fails as the lossless compression alters the signed integers.')\\n (rec_int16_1, nb_bits_each_map_1) = \\\\\\n lossless.compression.compress_lossless_maps(ref_int16,\\n paths_to_binary_probabilities[1])\\n numpy.testing.assert_equal(ref_int16,\\n rec_int16_1,\\n err_msg='The test fails as the lossless compression alters the signed integers.')\\n \\n # The equation below is derived from the\\n # theorem 8.3.1 in the book\\n # \\\"Elements of information theory\\\", 2nd edition,\\n # written by Thomas M. Cover and Joy A. Thomas.\\n theoretical_entropies = -numpy.log2(bin_widths_test) + (numpy.log(2.*laplace_scales) + 1.)/numpy.log(2.)\\n print('B0 denotes the binary probabilities saved at \\\"{}\\\".'.format(paths_to_binary_probabilities[0]))\\n print('B1 denotes the binary probabilities saved at \\\"{}\\\".'.format(paths_to_binary_probabilities[1]))\\n print('\\\\n1st centered-quantized latent variable feature map.')\\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[0]*height_map*width_map))\\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[0]))\\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[0]))\\n print('\\\\n2nd centered-quantized latent variable feature map.')\\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[1]*height_map*width_map))\\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[1]))\\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[1]))\",\n \"def test_decompress_1(self):\\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 49])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdabc'\\n self.assertEqual(actual, expected)\",\n \"def test_compress(self):\\n form_field = MultiLingualFormField()\\n compressed_data = form_field.compress([\\\"test-nb\\\", \\\"test-en\\\"])\\n self.assertEqual(MultiLingualTextStructure, type(compressed_data))\\n self.assertEqual(compressed_data['nb'], \\\"test-nb\\\")\\n self.assertEqual(compressed_data['en'], \\\"test-en\\\")\",\n \"def zipped_data(b_data):\\n col_names = [\\\"Name\\\", \\\"Version\\\", \\\"Date and Time\\\"]\\n bundle_data = []\\n for bundle in b_data:\\n zipped = zip(col_names, bundle)\\n bundle_data.append(dict(zipped))\\n return bundle_data\",\n \"def construct_zi_dict(train_info_list, test_info_list):\\r\\n zi_dict, train_dataset_list, test_dataset_list = dict(), list(), list()\\r\\n for user, age, gender, education, querys in train_info_list:\\r\\n for query in querys:\\r\\n for zi in query:\\r\\n if zi not in zi_dict:\\r\\n zi_dict[zi] = 0\\r\\n zi_dict[zi] += 1\\r\\n for user, querys in test_info_list:\\r\\n for query in querys:\\r\\n for zi in query:\\r\\n if zi not in zi_dict:\\r\\n zi_dict[zi] = 0\\r\\n zi_dict[zi] += 1\\r\\n zi_list = sorted(zi_dict.iteritems(), key=lambda x: x[1], reverse=True)\\r\\n zi2index = dict([(zi[0], [zi[1], idx]) for idx, zi in enumerate(zi_list)])\\r\\n index2zi = dict([(idx, [zi[0], zi[1]]) for idx, zi in enumerate(zi_list)])\\r\\n \\r\\n return zi2index, index2zi\",\n \"def _lzw(self, tile: bytes) -> np.ndarray:\\n decoded = self._reshape(np.frombuffer(imagecodecs.lzw_decode(tile), self.dtype))\\n self._unpredict(decoded)\\n return np.rollaxis(decoded, 2, 0)\",\n \"def test_compress_4_idenctical_char(self):\\n text = 'bbbb'\\n actual = LZ77.compress(text)\\n expected = bytearray([32]) + bytearray(b'bb') + bytearray([0, 16])\\n self.assertEqual(actual, expected)\",\n \"def zzX_from_dict(f, l):\\n if l == 1:\\n return zzx_from_dict(f)\\n elif not f:\\n return zzX_zero(l)\\n\\n coeffs = {}\\n\\n for monom, coeff in f.iteritems():\\n head, tail = monom[0], monom[1:]\\n\\n if len(tail) == 1:\\n tail = tail[0]\\n\\n if coeffs.has_key(head):\\n coeffs[head][tail] = INT_TYPE(int(coeff))\\n else:\\n coeffs[head] = { tail : INT_TYPE(int(coeff)) }\\n\\n n, h = max(coeffs.iterkeys()), []\\n\\n for k in xrange(n, -1, -1):\\n coeff = coeffs.get(k)\\n\\n if coeff is not None:\\n h.append(zzX_from_dict(coeff, l-1))\\n else:\\n h.append(zzX_zero(l-1))\\n\\n return zzX_strip(h)\",\n \"def getData_goodmaps(liste_dictionnaires = [], liste_categories = [], liste_phonemes = [],liste_cartes=[]):\\n if liste_dictionnaires!=[] and liste_categories!=[] and liste_phonemes!=[]:\\n tableau = np.array(liste_dictionnaires[0][liste_categories[0]][liste_phonemes[0]])\\n nb_exemple,nb_carte,lign,col=tableau.shape\\n else:\\n return [],[],[],[]\\n\\n Mat = []\\n Reference = []\\n\\n\\n for inddict,dict in enumerate(liste_dictionnaires):\\n for indcat,cat in enumerate(liste_categories):\\n for indpho,pho in enumerate(liste_phonemes):\\n for ex in range(nb_exemple):\\n goodmaps = []\\n for map in liste_cartes:\\n goodmaps.append(np.array(dict[cat][pho][ex][map]).flatten())\\n Mat.append(np.array(goodmaps).flatten())\\n Reference.append([inddict,indcat ,indpho])\\n Reference = np.array(Reference)\\n Y_c_inc = change_reference(Reference[:,1])\\n Y_r_v = Reference[:,2]\\n Y_fr_jap = Reference[:,0]\\n return np.array(Mat), np.array(Y_c_inc), np.array(Y_r_v), np.array(Y_fr_jap)\",\n \"def get_data(url, seed):\\n available_fields = {\\n 'boro': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'cd': {'fieldtype': 'C', 'categories': range(1, 19)},\\n 'uf1_1': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_2': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_3': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_4': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_5': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_6': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_7': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_8': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_9': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_10': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_11': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_12': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_13': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_14': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_15': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_16': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_17': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_18': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_19': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_20': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_21': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_22': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'sc24': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc36': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'sc37': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc38': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'sc114': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'uf48': {'fieldtype': 'N'},\\n 'sc147': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'uf11': {'fieldtype': 'C', 'categories': range(1, 8)},\\n 'sc149': {'fieldtype': 'B', 'codes': [1, 2, None]},\\n 'sc173': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\\n 'sc171': {'fieldtype': 'B', 'codes': [1, 2]},\\n 'sc150': {'fieldtype': 'N'},\\n 'sc151': {'fieldtype': 'N'},\\n 'sc154': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\\n 'sc157': {'fieldtype': 'C', 'categories': [1, 2, 9]},\\n 'sc158': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc185': {'fieldtype': 'B', 'codes': [0, 1, 8]},\\n 'sc186': {'fieldtype': 'C', 'categories': [2, 3, 4, 5, 9]},\\n 'sc197': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc198': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc187': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc188': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc571': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'sc189': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'sc190': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc191': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc192': {'fieldtype': 'B', 'codes': [0, 1, 8]},\\n 'sc193': {'fieldtype': 'C', 'categories': [2, 3, 9]},\\n 'sc194': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc196': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc199': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'rec15': {'fieldtype': 'C', 'categories': range(1, 14)},\\n 'sc26': {'fieldtype': 'C', 'categories': [12, 13, 15, 16]},\\n 'uf23': {'fieldtype': 'N'},\\n 'rec21': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'rec62': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\\n 'rec64': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\\n 'rec54': {'fieldtype': 'C', 'categories': range(1, 8)},\\n 'rec53': {'fieldtype': 'N'},\\n 'new_csr': {'fieldtype': 'C', 'categories': [1, 2, 5, 12, 20,\\n 21, 22, 23, 30, 31,\\n 80, 85, 90, 95]}\\n }\\n selected_fields = [\\n # The borough where the apartment is located\\n 'boro',\\n\\n # Building type: public housing, new construction,\\n # \\\"In Rem\\\" foreclosure, old construction\\n 'sc26',\\n\\n # Number of bedrooms\\n 'sc151',\\n\\n # Dilapidated / Not Dilapidated\\n 'rec21',\\n\\n # Complete plumbing facilities in unit\\n 'rec62',\\n\\n # Complete kitchen facilities in unit\\n 'rec64',\\n\\n # Maintenance deficiencies\\n 'rec53',\\n\\n # Building age\\n 'uf23',\\n\\n # Rent control/stabilization category\\n 'new_csr',\\n\\n # Neighborhood rating\\n 'sc196',\\n\\n # Wheelchair accessibility of unit\\n 'sc38',\\n\\n # Presence of elevator\\n 'sc149',\\n\\n # Building height\\n 'uf11',\\n\\n # Air conditioning\\n 'sc197',\\n\\n # Walkup\\n 'sc171',\\n ]\\n mini_fields = {k: available_fields[k]\\n for k in available_fields\\n if k in selected_fields}\\n y_field = 'uf17'\\n # s = requests.get(url).content\\n # raw_df = pd.read_csv(StringIO(s.decode('utf-8')))\\n raw_df = pd.read_csv('homework2_data.csv')\\n valid_renters, validated_features, validated_rents = \\\\\\n preprocess_data(raw_df, mini_fields, y_field)\\n X_train, X_test, y_train, y_test = train_test_split(\\n validated_features, validated_rents, random_state=seed)\\n cats = [k\\n for (k, v) in mini_fields.items()\\n if v[\\\"fieldtype\\\"] == \\\"C\\\"]\\n catnums = [i\\n for (i, x) in enumerate([c in cats\\n for c in validated_features.columns])\\n if x]\\n return X_train, X_test, y_train, y_test, catnums, raw_df\",\n \"def data():\\n \\n # Just in case order matters.\\n inplist = expected.keys()\\n inplist.sort()\\n \\n #return ['split/' + inpms for inpms in inplist]\\n return inplist\",\n \"def compress(uncompressed):\\r\\n \\r\\n # Build the dictionary.\\r\\n dict_size = 256\\r\\n dictionary = dict((chr(i), i) for i in range(dict_size))\\r\\n # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}\\r\\n \\r\\n w = \\\"\\\"\\r\\n result = []\\r\\n for c in uncompressed:\\r\\n wc = w + c\\r\\n if wc in dictionary:\\r\\n w = wc\\r\\n else:\\r\\n result.append(dictionary[w])\\r\\n # Add wc to the dictionary.\\r\\n dictionary[wc] = dict_size\\r\\n dict_size += 1\\r\\n w = c\\r\\n \\r\\n # Output the code for w.\\r\\n if w:\\r\\n result.append(dictionary[w])\\r\\n return result\",\n \"def create_data_set():\\n data_set = {}\\n for index in range(1024):\\n size = random.randint(1, 100) #nosec\\n key = str(index).encode(\\\"utf-8\\\")\\n data_set[key] = get_random_bytes(size)\\n return data_set\",\n \"def opendict(data_dir, batch_size, test_max_size, **kwargs):\\n join = lambda f: _path.join(data_dir, f)\\n with open(join(\\\"list.jl\\\")) as f:\\n data = [(join(e[\\\"files\\\"][0][\\\"path\\\"]), e[\\\"word\\\"].strip())\\n for e in filter(lambda x: x.get(\\\"files\\\"), map(_json.loads, f))]\\n _random.shuffle(data)\\n if not test_max_size:\\n test_max_size = int(len(data) / 100)\\n tmp = - (batch_size + test_max_size)\\n return data[:tmp], data[tmp:-test_max_size], data[-test_max_size:]\",\n \"def test_hmaps(self):\\n #Single element insertion\\n self.hll.append(self.data[0])\\n assert non_zero_idx_val(hll.hmap) == [(54, 1)]\\n #Multiple distinct element insertions\\n self.hll.extend(self.data)\\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\\n self.reset_hll()\\n #Element insertions with duplicates\\n self.hll.extend(self.data_duplicate)\\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\\n self.reset_hll()\\n #Element insertions with numerical values\\n self.hll.extend(self.num_data)\\n assert non_zero_idx_val(hll.hmap) == [(17, 3), (144, 2), (145, 4),\\n (182, 2)]\\n self.reset_hll()\\n #Test the key collision handling (keep max value)\\n self.hll.append(self.colliding_data[0])\\n assert non_zero_idx_val(hll.hmap) == [(0, 1)] \\n self.hll.append(self.colliding_data[1])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\\n self.reset_hll()\\n self.hll.append(self.colliding_data[1])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)] \\n self.hll.append(self.colliding_data[0])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\\n self.reset_hll()\",\n \"def test_compress_2_idenctical_char(self):\\n text = 'aa'\\n actual = LZ77.compress(text)\\n expected = bytearray([0]) + bytearray(b'aa')\\n self.assertEqual(actual, expected)\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def test_xyz_from_data(self):\\n symbols = ('C', 'H', 'H', 'H', 'H')\\n isotopes = (12, 1, 1, 1, 1)\\n coords = ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))\\n xyz_dict0 = converter.xyz_from_data(coords=coords, symbols=symbols, isotopes=isotopes)\\n self.assertEqual(xyz_dict0, self.xyz1['dict'])\\n xyz_dict1 = converter.xyz_from_data(coords=coords, symbols=symbols) # no specifying isotopes\\n self.assertEqual(xyz_dict1, self.xyz1['dict'])\\n\\n numbers = [6, 1, 1, 1, 1]\\n coords = [[0.0, 0.0, 0.0],\\n [0.6300326, 0.6300326, 0.6300326],\\n [-0.6300326, -0.6300326, 0.6300326],\\n [-0.6300326, 0.6300326, -0.6300326],\\n [0.6300326, -0.6300326, -0.6300326]]\\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\\n\\n numbers = [6, 1, 1, 1, 1]\\n coords = [[0.0, 0.0, 0.0],\\n [0.6300326, 0.6300326, 0.6300326],\\n [-0.6300326, -0.6300326, 0.6300326],\\n [-0.6300326, 0.6300326, -0.6300326],\\n [0.6300326, -0.6300326, -0.6300326]]\\n coords = np.array([np.array(coord, np.float64) for coord in coords], np.float64)\\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\\n self.assertIsInstance(xyz_dict2['coords'], tuple)\\n self.assertIsInstance(xyz_dict2['coords'][0], tuple)\",\n \"def shuffle_data_set(in_dict):\\n tot_rows = in_dict['data'].shape[0]\\n new_order = range(tot_rows)\\n for _ in range(5):\\n shuffle(new_order)\\n\\n out_dict = in_dict\\n out_data = np.zeros((tot_rows, 3072), dtype=in_dict['data'].dtype)\\n out_labels = [0] * tot_rows\\n out_filenames = [\\\"\\\"] * tot_rows\\n\\n for ctr, idx in enumerate(new_order):\\n out_data[ctr, :] = in_dict['data'][idx, :]\\n out_labels[ctr] = in_dict['labels'][idx]\\n out_filenames[ctr] = in_dict['filenames'][idx]\\n\\n out_dict['data'] = out_data\\n out_dict['labels'] = out_labels\\n out_dict['filenames'] = out_filenames\\n\\n return out_dict\",\n \"def making_dataset_list_y(data_list:list) -> list:\\n list_size = len(data_list)\\n data_list_y = []\\n for i in range(list_size):\\n data_list_y.append(data_list[i][[\\\"W\\\",\\\"D\\\",\\\"L\\\"]])\\n return data_list_y\",\n \"def set_lz(self):\\r\\n self._lz = self.dz * self.nz - self.oz\",\n \"def decodeLZ(fileIn, fileOut, dictionarySize = 1114112):\\n try:\\n fileContent = FileWork.getFileContent(fileIn)\\n\\n getCharByCode = {}\\n getCodeByChar = {}\\n\\n for i in range(0, dictionarySize):\\n getCharByCode[i] = chr(i)\\n getCodeByChar[chr(i)] = i\\n\\n currentCode = dictionarySize\\n\\n fileContent = fileContent.split(' ')\\n buffer = getCharByCode[int(fileContent[0])]\\n decodeFileContent = \\\"\\\"\\n for i in range(1, len(fileContent)):\\n character = \\\"\\\"\\n if int(fileContent[i]) in getCharByCode:\\n character = getCharByCode[int(fileContent[i])]\\n else:\\n getCharByCode[currentCode] = buffer + buffer[0]\\n getCodeByChar[buffer + buffer[0]] = currentCode\\n currentCode += 1\\n decodeFileContent += buffer\\n character = buffer[0]\\n\\n if (buffer + character[0]) in getCodeByChar:\\n buffer = buffer + character[0]\\n else:\\n getCharByCode[currentCode] = buffer + character[0]\\n getCodeByChar[buffer + character[0]] = currentCode\\n currentCode += 1\\n decodeFileContent += buffer\\n buffer = character\\n decodeFileContent += buffer\\n FileWork.writeToFile(fileOut, decodeFileContent)\\n return True\\n except Exception:\\n return False\",\n \"def test_lz4_decompression_avoids_deep_copy():\\n pytest.importorskip(\\\"lz4\\\")\\n a = bytearray(1_000_000)\\n b = compressions[\\\"lz4\\\"].compress(a)\\n c = compressions[\\\"lz4\\\"].decompress(b)\\n assert isinstance(c, bytearray)\",\n \"def encodeLZ(fileIn, fileOut, dictionarySize = 1114112):\\n try:\\n fileContent = FileWork.getFileContent(fileIn)\\n\\n dictionary = {}\\n for i in range(0, dictionarySize):\\n dictionary[chr(i)] = i\\n\\n currentCode = dictionarySize\\n\\n encodedFileContent = \\\"\\\"\\n buffer = fileContent[0]\\n for pos in range(1, len(fileContent)):\\n character = fileContent[pos]\\n if (buffer + character) in dictionary:\\n buffer += character\\n else:\\n dictionary[buffer + character] = currentCode\\n currentCode += 1\\n encodedFileContent += str(dictionary[buffer]) + \\\" \\\"\\n buffer = character\\n\\n encodedFileContent += str(dictionary[buffer])\\n\\n FileWork.writeToFile(fileOut, encodedFileContent)\\n return True\\n except Exception:\\n return False\",\n \"def __init__(self, url=\\\"https://ehw.fit.vutbr.cz/izv/\\\",folder=\\\"data\\\", cache_filename=\\\"data_{}.pkl.gz\\\"):\\n self.url = url\\n self.folder = folder\\n self.cache_filename = cache_filename\\n self.ListOfZipFiles = []\\n self.saved_data = {}\\n self.headers = [\\\"region\\\", \\\"p1\\\", \\\"p36\\\", \\\"p37\\\", \\\"p2a\\\", \\\"weekday(p2a)\\\", \\\"p2b\\\", \\\"p6\\\", \\\"p7\\\", \\\"p8\\\", \\\"p9\\\", \\\"p10\\\", \\\"p11\\\", \\\"p12\\\", \\\"p13a\\\", \\\"p13b\\\", \\\"p13c\\\", \\\"p14\\\", \\\"p15\\\", \\\"p16\\\", \\\"p17\\\", \\\"p18\\\", \\\"p19\\\", \\\"p20\\\", \\\"p21\\\", \\\"p22\\\", \\\"p23\\\", \\\"p24\\\", \\\"p27\\\", \\\"p28\\\", \\\"p34\\\", \\\"p35\\\", \\\"p39\\\", \\\"p44\\\", \\\"p45a\\\", \\\"p47\\\", \\\"p48a\\\", \\\"p49\\\", \\\"p50a\\\", \\\"p50b\\\", \\\"p51\\\", \\\"p52\\\", \\\"p53\\\",\\\"p55a\\\",\\\"p57\\\",\\\"p58\\\",\\\"a\\\",\\\"b\\\",\\\"d\\\",\\\"e\\\",\\\"f\\\",\\\"g\\\",\\\"h\\\",\\\"i\\\",\\\"j\\\",\\\"k\\\",\\\"l\\\",\\\"n\\\",\\\"o\\\",\\\"p\\\",\\\"q\\\",\\\"r\\\",\\\"s\\\",\\\"t\\\",\\\"p5a\\\"]\\n self.duplicate_handling = []\\n if not re.search(\\\"^.*{}.*$\\\", self.cache_filename):\\n self.cache_filename+=\\\"{}\\\"\",\n \"def map(z):\\n pass\",\n \"def valid_zip(x):\\r\\n m_zip = [83,10286,10276,10268,10256,10249,10159,10150,10116,10113,10108,10101,10008,10282,10281,10280,10279,10278,10275,\\r\\n 10271,10270,10199,10178,10177,10176,10175,10174,10173,10172,10171,10170,10169,10168,10167,10166,10165,\\r\\n 10162,10161,10158,10155,10154,10153,10152,10151,10128,10123,10122,10121,10120,10119,10118,10115,10112,\\r\\n 10111,10110,10107,10106,10105,10104,10103,10099,10098,10095,10090,10069,10060,10055,10048,10047,10045,10044,\\r\\n 10041,10040,10039,10038,10037,10036,10035,10034,10033,10032,10031,10030,10029,10028,10027,10026,10025,\\r\\n 10024,10023,10022,10021,10020,10019,10018,10017,10016,10015,10014,10013,10012,10011,10010,10009,10007,\\r\\n 10006,10005,10004,10003,10002,10001,10065,10075,10080,\\r\\n 10285,10203,10178,10017,10178,10168,10167,10177,# supplementary\\r\\n 10175,10166,10171,10176,10174,10165,10170,10173,10169,10172,10019, 10105, 10097, 10104, 10107, 10103, 10106,\\r\\n 10022, 10055, 10155, 10152, 10153, 10151, 10154, 10001, 10120, 10119, 10118, 10123, 10122, 10121,\\r\\n 10005, 10081, 10286, 10260, 10271, 10259, 10043, 10270, 10265, 10203,10036, 10096, 10196, 10110\\r\\n ]\\r\\n brooklyn_zip = [11256,11252,11249,11243,11242,11241,11239,11238,11237,11236,11235,11234,11233,11232,11231,\\r\\n 11230,11229,11228,11226,11225,11224,11223,11222,11221,11220,11219,11218,11217,11216,11215,\\r\\n 11214,11213,11212,11211,11210,11209,11208,11207,11206,11205,11204,11203,11201]\\r\\n queens_zip = [11451,11436,11435,11434,11433,11432,11429,11428,11427,11426,\\r\\n 11423,11422,11421,11420,11419,11418,11417,11416,11415,11414,11413,11412,11411,11385,11379,\\r\\n 11378,11377,11375,11374,11373,11372,11369,11368,11367,11366,11365,11364,11363,\\r\\n 11362,11361,11360,11359,11358,11357,11356,11355,11354,11351,11109,11106,11105,11104,11103,\\r\\n 11102,11101,11004]\\r\\n if x in m_zip + brooklyn_zip + queens_zip:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def test_compress_seq_diff_8_char(self):\\n text = '12345678'\\n actual = LZ77.compress(text)\\n expected = bytearray([0]) + bytearray(b'12345678')\\n self.assertEqual(actual, expected)\",\n \"def get_lz(self):\\r\\n return self.dz * self.nz - self.oz\",\n \"def test_decompress_seq_diff_8_char(self):\\n b_array = bytearray([0]) + bytearray(b'12345678')\\n actual = LZ77.decompress(b_array)\\n expected = '12345678'\\n self.assertEqual(actual, expected)\",\n \"def weight_compression(weights, bits, axis=0, quantizer=None):\\n assert bits <= 8\\n n = 2**bits\\n index_table = []\\n codebook_table = np.zeros((weights.shape[axis], n))\\n km_models = [None] * weights.shape[axis]\\n\\n for i, w in tqdm(enumerate(np.split(weights, weights.shape[axis], axis))):\\n original_shape = w.shape\\n w = w.ravel()\\n km = KMeans(n)\\n km.fit(w.reshape(-1, 1))\\n if quantizer:\\n km.cluster_centers_ = quantizer(km.cluster_centers_).numpy()\\n km.cluster_centers_.sort(axis=0)\\n\\n km_models[i] = km\\n codebook_table[i, :] = km.cluster_centers_.flatten()\\n preds = km.predict(w.reshape(-1, 1))\\n index_table.append(preds.reshape(original_shape))\\n\\n index_table = np.concatenate(index_table, axis)\\n return index_table, codebook_table\",\n \"def length_of_data(self, data_list, days):\\n\\n # The zip() function returns an iterator of tuples based on the iterable object.\\n btceur_high, btceur_timestamp = zip(*data_list)\\n\\n # Various lists/variables used\\n one_week_list_timezone = []\\n one_week_list_btc_high = []\\n count = 0\\n\\n # Get the latest entry to work back from\\n orig = datetime.datetime.fromtimestamp(btceur_timestamp[-1])\\n # Get a date 7 days from the current date\\n one_week = orig - datetime.timedelta(days=days)\\n # Loop backwards through the timestamps getting the past 7 days worth\\n\\n for I in reversed(btceur_timestamp):\\n converted_timestamp_value = (datetime.datetime.fromtimestamp(I))\\n if one_week > converted_timestamp_value < orig:\\n one_week_list_timezone.append(converted_timestamp_value)\\n else:\\n continue\\n if one_week_list_timezone[-1] \\\\\\n > one_week:\\n return None\\n else:\\n # Get the corresponding data to each time stamp\\n for x in reversed(btceur_high):\\n if count < len(one_week_list_timezone):\\n one_week_list_btc_high.append(x)\\n count += 1\\n\\n # Reorder both lists of data\\n one_week_list_btc_high.reverse()\\n one_week_list_timezone.reverse()\\n zipped = zip(one_week_list_btc_high, one_week_list_timezone)\\n return list(zipped)\",\n \"def add_hash(self, lst):\\n self.__data = []\\n num = len(lst) + 1\\n self.add_data((\\\"\\\", 4, 1))\\n self.add_data((\\\"\\\", 4, num))\\n self.add_data((\\\"\\\", 4, num - 1))\\n self.add_data((\\\"\\\", 4, 0))\\n if 1 < num:\\n for ii in range(num - 1):\\n self.add_data((\\\"\\\", 4, ii))\",\n \"def get_data(data_list, data_cache):\\n if not isinstance(data_list, list):\\n data_list = [data_list]\\n tmp = [data_cache[d] for d in data_list]\\n if len(tmp) == 1:\\n return tmp[0]\\n res = ([t[0] for t in tmp], [t[1] for t in tmp])\\n return res\",\n \"def decode_huffman(bit_seq, dc_ac, layer_type):\\n\\n def diff_value(idx, size):\\n if idx >= len(bit_seq) or idx + size > len(bit_seq):\\n raise IndexError('There is not enough bits to decode DIFF value '\\n 'codeword.')\\n fixed = bit_seq[idx:idx + size]\\n return int(fixed, 2)\\n\\n current_idx = 0\\n while current_idx < len(bit_seq):\\n # 1. Consume next 16 bits as `current_slice`.\\n # 2. Try to find the `current_slice` in Huffman table.\\n # 3. If found, yield the corresponding key and go to step 4.\\n # Otherwise, remove the last element in `current_slice` and go to\\n # step 2.\\n # 4. Consume next n bits, where n is the category (size) in returned\\n # key yielded in step 3. Use those info to decode the data.\\n remaining_len = len(bit_seq) - current_idx\\n current_slice = bit_seq[\\n current_idx:\\n current_idx + (16 if remaining_len > 16 else remaining_len)\\n ]\\n err_cache = current_slice\\n while current_slice:\\n if (current_slice in\\n HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type].inv):\\n key = (HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type]\\n .inv[current_slice])\\n if dc_ac == DC: # DC\\n size = key\\n if size == 0:\\n yield 0\\n else:\\n yield HUFFMAN_CATEGORIES[size][diff_value(\\n current_idx + len(current_slice),\\n size\\n )]\\n else: # AC\\n run, size = key\\n if key in (EOB, ZRL):\\n yield key\\n else:\\n yield (run, HUFFMAN_CATEGORIES[size][diff_value(\\n current_idx + len(current_slice),\\n size\\n )])\\n\\n current_idx += len(current_slice) + size\\n break\\n current_slice = current_slice[:-1]\\n else:\\n raise KeyError(\\n f'Cannot find any prefix of {err_cache} in Huffman table.'\\n )\",\n \"def MapDataList(ea, length, func, wordsize=1):\\n PutDataList(ea, map(func, GetDataList(ea, length, wordsize)), wordsize)\",\n \"def test_decompress_seq_diff_9_char(self):\\n b_array = bytearray([0]) + bytearray(b'12345678') \\\\\\n + bytearray([0]) + bytearray(b'9')\\n actual = LZ77.decompress(b_array)\\n expected = '123456789'\\n self.assertEqual(actual, expected)\",\n \"def get_precomp_hashcodes(self,list_feats_id):\\n return self.get_precomp_X(list_feats_id,\\\"hashcodes\\\",self.bits_num/8,np.uint8)\",\n \"def polylinedictarraycopy(d):#d——原始图层多段线字典 \\r\\n dictlist=[]\\r\\n ratiolist=[] #放缩率列表\\r\\n rationumaccumulationlist=[] #放缩率数量累加列表\\r\\n \\r\\n eachrationum=globalconfig.X_ARRAY_NUM//globalconfig.RATIO_NUM\\r\\n leftrationum=globalconfig.X_ARRAY_NUM%globalconfig.RATIO_NUM\\r\\n \\r\\n eachrationumlist=[eachrationum]*globalconfig.RATIO_NUM #各个放缩率对应数量的列表\\r\\n \\r\\n for i in range((globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2,(globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2+leftrationum):\\r\\n eachrationumlist[i]=eachrationumlist[i]+1 #将整除后的余值加入到靠中间放缩率的方案中。\\r\\n \\r\\n rationumaccumulationlist.append(0) \\r\\n \\r\\n for i in range(1,globalconfig.RATIO_NUM): #计算放缩率数量累加列表\\r\\n rationumaccumulationlist.append(rationumaccumulationlist[i-1]+eachrationumlist[i-1])\\r\\n \\r\\n for i in range(0,globalconfig.RATIO_NUM): #计算放缩率列表\\r\\n ratiolist.append((globalconfig.CENTER_RATIO-((globalconfig.RATIO_NUM+1)//2-1)*globalconfig.RATIO_DIFF)+i*globalconfig.RATIO_DIFF) \\r\\n \\r\\n for i in range(0,globalconfig.RATIO_NUM): #每种放缩率\\r\\n for j in range(0,eachrationumlist[i]): #每种放缩率对应数量\\r\\n newdict={}\\r\\n for e in d: #将字典中值即每一图层对应的多段线列表进行复制并移动到指定位置\\r\\n newdict[e]=polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist)) \\r\\n #newdict.append([e,polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist))])\\r\\n dictlist.append(newdict) \\r\\n return (dictlist,ratiolist,eachrationumlist)\",\n \"def test_compress_seq_diff_9_char(self):\\n text = '123456789'\\n actual = LZ77.compress(text)\\n expected = bytearray([0]) + bytearray(b'12345678') \\\\\\n + bytearray([0]) + bytearray(b'9')\\n self.assertEqual(actual, expected)\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def get_dict_refine_insee_code(ls_valid_ic):\\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\\n for ic in ls_valid_ic_corse:\\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\\n dict_refine_ic.update(dict_ic_ardts)\\n return dict_refine_ic\",\n \"def getAllIndex(ldata, fldata):\\n\\treturn list(map(lambda e : fldata.index(e), ldata))\",\n \"def lzs (inlist):\\r\\n zscores = []\\r\\n for item in inlist:\\r\\n zscores.append(z(inlist,item))\\r\\n return zscores\",\n \"def huffman_data_decode(data: hic.BitStringP, huffman: huffman.HuffmanTree) -> list:\\n return huffman.decode_data(data.payload)\",\n \"def zzx_from_dict(f):\\n if not f:\\n return []\\n\\n n, h = max(f.iterkeys()), []\\n\\n for k in xrange(n, -1, -1):\\n h.append(INT_TYPE(int(f.get(k, 0))))\\n\\n return zzx_strip(h)\",\n \"def mapfn(k, v):\\n for row in v:\\n # rellenar el codigo\\n pass\",\n \"def raw_features_extractor(database='./red_cod.db.pkl', sites=-1, elements = -1, maxatoms= -1,\\r\\n dictionary='diccionario', features='datosrahm.csv'):\\r\\n \\r\\n df=create_collection(database=database,sites=sites, elements=elements, maxatoms=maxatoms, \\r\\n dictionary=dictionary)\\r\\n \\r\\n start=time.time()\\r\\n \\r\\n datos=pd.read_csv(features)\\r\\n datos=datos.fillna(-1)\\r\\n\\r\\n dicc=dict(datos[['Symbol','Z']].values)\\r\\n\\r\\n dicc['D']=1\\r\\n dicc['Bk']=97\\r\\n dicc['Cf']=98\\r\\n dicc['Es']=99\\r\\n dicc['Fm']=100\\r\\n dicc['Md']=101\\r\\n dicc['No']=102\\r\\n dicc['Lr']=103\\r\\n \\r\\n max_sitios = max(df['sitios'].values)\\r\\n\\r\\n df=df[df['sitios'] <= max_sitios].reset_index(drop=True)\\r\\n \\r\\n X=np.zeros((len(df),max_sitios,104))\\r\\n y=np.zeros((len(df),1))\\r\\n mult=np.zeros((len(df),max_sitios))\\r\\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\\r\\n \\r\\n for row in range(len(df)):\\r\\n \\r\\n item=df['WyckOcc'][row]\\r\\n sitios=list(item.values()) \\r\\n sitocc=np.zeros((len(sitios),104))\\r\\n spacegroup = str(df['sgnum'][row]).zfill(3)\\r\\n \\r\\n try:\\r\\n \\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n sitios] for i in j]\\r\\n \\r\\n except:\\r\\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\\\n')\\r\\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\\r\\n '\\\\n','\\\\n')\\r\\n spacegroup=input('Give me the correct spacegroup:'+'\\\\n'+'\\\\n')\\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n list(df['WyckOcc'][row].values())] for i in j]\\r\\n \\r\\n occs=[]\\r\\n for i in range(len(sitios)):\\r\\n\\r\\n for j in list(sitios[i].values()):\\r\\n \\r\\n ocupacion=np.array(list(j.values()))\\r\\n llaves=[llave.replace('+','').replace('-','').replace('1',\\r\\n '').replace('2','').replace('3','').replace('4',\\r\\n '') for llave in np.array(list(j.keys()))]\\r\\n llaves=[llave.replace('.','') for llave in llaves]\\r\\n llaves=[llave.replace('5','').replace('6','').replace('7',\\r\\n '').replace('8','').replace('9','').replace('0',\\r\\n '') for llave in llaves]\\r\\n vector=np.zeros((1,104))\\r\\n occs=[sum(ocupacion)]+occs\\r\\n \\r\\n try:\\r\\n \\r\\n idx=[dicc[k] for k in llaves]\\r\\n \\r\\n except:\\r\\n \\r\\n print(' ELEMENTO NO IDENTIFICADO EN LA LISTA ',llaves,'\\\\n',\\r\\n 'REVISA EL SIGUIENTE CIF PARA HACER LA CORRECCION:','\\\\t',df['cif'][row])\\r\\n \\r\\n former = input('Elemento Incorrecto: ')\\r\\n current = input('Elemento Correcto: ')\\r\\n \\r\\n llaves=[current if x == former else x for x in llaves]\\r\\n idx=[dicc[k] for k in llaves]\\r\\n \\r\\n \\r\\n for k in idx:\\r\\n vector[0][k-1] = ocupacion[idx.index(k)]\\r\\n \\r\\n \\r\\n sitocc[i]=vector\\r\\n \\r\\n while sitocc.shape[0] != max_sitios:\\r\\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\\r\\n s=[0]+s\\r\\n \\r\\n X[row,:,:]=sitocc\\r\\n y[row]=df['target'][row]\\r\\n mult[row]=s\\r\\n \\r\\n S = np.expand_dims(mult,axis=2)\\r\\n features=datos.iloc[:,2:].values\\r\\n x=X[:,:,:96]\\r\\n \\r\\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\\r\\n \\r\\n x=np.dot(x,features) \\r\\n\\r\\n print('Atomic radii and electronegativities for each Wyckoff site extracted in',\\r\\n round(time.time()-start,2),' s') \\r\\n \\r\\n np.save('raw_features', x)\\r\\n np.save('output_values', y)\\r\\n np.save('multiplicities', S)\\r\\n np.save('occupation_fractions', fracsum)\\r\\n \\r\\n return x, y, S, fracsum, df\",\n \"def read_data(file_path):\\n words=[]\\n dic_word={}\\n actual_text=[]\\n for line in open(file_path,encoding='utf-8'):\\n words_line=line.strip().split(' ')\\n for ite in words_line:\\n if ite not in dic_word:\\n dic_word[ite]=1\\n words.extend(words_line)\\n actual_text.append(words_line)\\n\\n\\n #with zipfile.ZipFile(file_path) as f:\\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\\n\\n return words,len(dic_word),actual_text\",\n \"def ana_merge_senzory_map(datas):\\n#TODO: improve senzory map merging\\n return iter(datas.viewvalues()).next()['senzory_map']\",\n \"def list_data_collate(batch: Sequence):\\n elem = batch[0]\\n data = [i for k in batch for i in k] if isinstance(elem, list) else batch\\n key = None\\n try:\\n if config.USE_META_DICT:\\n data = pickle_operations(data) # bc 0.9.0\\n if isinstance(elem, Mapping):\\n ret = {}\\n for k in elem:\\n key = k\\n data_for_batch = [d[key] for d in data]\\n ret[key] = collate_meta_tensor(data_for_batch)\\n else:\\n ret = collate_meta_tensor(data)\\n return ret\\n except RuntimeError as re:\\n re_str = str(re)\\n if \\\"equal size\\\" in re_str:\\n if key is not None:\\n re_str += f\\\"\\\\nCollate error on the key '{key}' of dictionary data.\\\"\\n re_str += (\\n \\\"\\\\n\\\\nMONAI hint: if your transforms intentionally create images of different shapes, creating your \\\"\\n + \\\"`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its \\\"\\n + \\\"documentation).\\\"\\n )\\n _ = dev_collate(data)\\n raise RuntimeError(re_str) from re\\n except TypeError as re:\\n re_str = str(re)\\n if \\\"numpy\\\" in re_str and \\\"Tensor\\\" in re_str:\\n if key is not None:\\n re_str += f\\\"\\\\nCollate error on the key '{key}' of dictionary data.\\\"\\n re_str += (\\n \\\"\\\\n\\\\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, \\\"\\n + \\\"creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem \\\"\\n + \\\"(check its documentation).\\\"\\n )\\n _ = dev_collate(data)\\n raise TypeError(re_str) from re\",\n \"def build_dataset(self):\\n\\n print('begin to build data set...')\\n\\n # build dictionary\\n for e in self.lst_entity:\\n self.dictionary_entity[e] = len(self.dictionary_entity)\\n\\n for r in self.lst_relation:\\n self.dictionary_relation[r] = len(self.dictionary_relation)\\n\\n self.reverse_dictionary_entity = dict(zip(self.dictionary_entity.values(), self.dictionary_entity.keys()))\\n self.reverse_dictionary_relation = dict(zip(self.dictionary_relation.values(), self.dictionary_relation.keys()))\\n\\n # build data map\\n self.lst_entity_map = [self.dictionary_entity[e] for e in self.lst_entity]\\n self.lst_relation_map = [self.dictionary_relation[r] for r in self.lst_relation]\\n self.lst_triplet_train_map = self.map_triplet(self.lst_triplet_train)\\n\\n # build corrupted candidates for (h,r,~) and (~,r,t)\\n for (h, r, t) in self.lst_triplet_train_map:\\n if r not in self.dict_tofh:\\n self.dict_tofh[r] = {h: [t]}\\n else:\\n if h not in self.dict_tofh[r]:\\n self.dict_tofh[r][h] = [t]\\n else:\\n self.dict_tofh[r][h].append(t)\\n\\n if r not in self.dict_hoft:\\n self.dict_hoft[r] = {t: [h]}\\n else:\\n if t not in self.dict_hoft[r]:\\n self.dict_hoft[r][t] = [h]\\n else:\\n self.dict_hoft[r][t].append(h)\\n\\n for r in self.dict_tofh:\\n self.lst_triplet_corrupted_tail[r] = dict()\\n for h in self.dict_tofh[r]:\\n set_tail_corrupted_all = set(self.lst_entity_map) - set(self.dict_tofh[r][h])\\n lst_tail_corrupted_choose = random.sample(set_tail_corrupted_all, 5*len(self.dict_tofh[r][h]))\\n self.lst_triplet_corrupted_tail[r][h] = lst_tail_corrupted_choose\\n\\n for r in self.dict_hoft:\\n self.lst_triplet_corrupted_head[r] = dict()\\n for t in self.dict_hoft[r]:\\n lst_head_corrupted_all = set(self.lst_entity_map) - set(self.dict_hoft[r][t])\\n lst_head_corrupted_choose = random.sample(lst_head_corrupted_all, 5*len(self.dict_hoft[r][t]))\\n self.lst_triplet_corrupted_head[r][t] = lst_head_corrupted_choose\\n\\n print('data set has been built successfully!')\",\n \"def decoder(self, array=[],your_dic_max_value=78):\\r\\n word_list=[]\\r\\n self.coder=\\\"This_is_a_decoder_for_the_short_vector_function!\\\"\\r\\n for i in array:\\r\\n for i in i:\\r\\n print(\\\"i:\\\"+str(i))\\r\\n if int(i) <=25:# use the max length of the dic to find out wich one to use\\r\\n for key, value in self.general_lower_word_list.items():# iterate through every key and value\\r\\n if value==int(i):#checks if the value is equal to the given input\\r\\n word_list.append(key)# if yes append it\\r\\n else:\\r\\n pass #else move on\\r\\n elif int(i) <=51 and int(i)>=25:# same process as above\\r\\n for key, value in self.general_upper_word_list.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=58 and int(i)>=52:\\r\\n for key, value in self.special_letters_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=68 and int(i)>=59:\\r\\n for key, value in self.general_numbers_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=max_value and int(i)>=69:\\r\\n for key, value in self.special_characters_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n return \\\"\\\".join(word_list) #create a real string with the join method\\r\",\n \"def make_data(revs, word_idx_map, max_l=50, filter_h=3\\n ,train_instance = 16541,val_instance = 1645):\\n train, val, test = [], [], []\\n for rev in revs:\\n sent = get_idx_from_sent(rev[\\\"m\\\"], word_idx_map, max_l, filter_h)\\n sent += get_idx_from_sent(rev[\\\"r\\\"], word_idx_map, max_l, filter_h)\\n sent.append(int(rev[\\\"y\\\"]))\\n sent.append(int(rev[\\\"t\\\"]))\\n sent.append(int(rev[\\\"t2\\\"]))\\n\\n if len(train) < train_instance:\\n train.append(sent)\\n elif (len(train) + len(val)) < train_instance + val_instance:\\n val.append(sent)\\n else:\\n test.append(sent)\\n\\n shuffle(train)\\n train = np.array(train,dtype=\\\"int\\\")\\n val = np.array(val,dtype=\\\"int\\\")\\n test = np.array(test,dtype=\\\"int\\\")\\n print 'trainning data', len(train),'val data', len(val)\\n return [train, val, test]\",\n \"def build_list(self, word_list):\\n # Get frequency list for keys\\n freq = word_list.groupby('key').agg('count')\\n # Filter out only keys with greater or equal frequency to length\\n key_list = freq.loc[freq['word'] >= freq.index.str.len()]\\n return key_list\",\n \"def buildCoder(shift):\\n\\n list1=['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\\n list2=['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']\\n dic={'a':'a','b':'b','c':'c','d':'d','e':'e','f':'f','g':'g','h': 'h','i':'i','j':'j','k':'k','l':'l','m':'m','n':'n','o':'o','p':'p','q':'q','r':'r','s':'s','t':'t','u':'u',\\n 'v':'v','w':'w','x':'x','y':'y','z':'z','A':'A','B':'B','C':'C','D':'D','E':'E','F':'F','G':'G','H':'H','I':'I','J':'J','K':'K','L':'L','M':'M','N':'N','O':'O','P':'P',\\n 'Q':'Q','R':'R','S':'S','T':'T','U':'U','V':'V','W':'W','X':'X','Y':'Y','Z':'Z'} \\n\\n st1= 'abcdefghijklmnopqrstuvwxyz'\\n st2= 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'\\n \\n for k in dic.keys():\\n \\n for i in range(0,len(list1)):\\n n1=0\\n if k in st1:\\n if k==list1[i]:\\n n1=i+shift\\n if n1 >= len(list1):\\n l2=len(list1)-1-i\\n b=shift-l2\\n dic[k]=list1[b-1]\\n \\n break\\n else:\\n dic[k]=list1[i+shift]\\n \\n break\\n elif k in st2:\\n if k==list2[i]:\\n n1=i+shift\\n if n1 >= len(list2):\\n l2=len(list2)-1-i\\n b=shift-l2\\n dic[k]=list2[b-1]\\n \\n break\\n else:\\n dic[k]=list2[i+shift]\\n \\n break\\n \\n return dic\",\n \"def get_hkls(self):\\n return zip(*[self.hkl(i) for i in range(self.npks)])\",\n \"def _read_zb_zblst(fname):\\n with open(fname) as foo:\\n data = {}\\n read_data = False\\n flow_budget = False\\n empty = 0\\n prefix = \\\"\\\"\\n while True:\\n line = foo.readline().strip().upper()\\n t = line.split()\\n if t:\\n if t[-1].strip() == \\\"ZONES.\\\":\\n line = foo.readline().strip()\\n zones = [int(i) for i in line.split()]\\n for zone in zones:\\n data[f\\\"TO_ZONE_{zone}\\\"] = []\\n data[f\\\"FROM_ZONE_{zone}\\\"] = []\\n\\n if \\\"FLOW BUDGET FOR ZONE\\\" in line:\\n flow_budget = True\\n read_data = False\\n zlist = []\\n empty = 0\\n t = line.split()\\n zone = int(t[4])\\n if len(t[7]) > 4:\\n t.insert(8, t[7][4:])\\n kstp = int(t[8]) - 1\\n if len(t[11]) > 6:\\n t.append(t[11][6:])\\n kper = int(t[12]) - 1\\n if \\\"ZONE\\\" not in data:\\n data[\\\"ZONE\\\"] = [zone]\\n data[\\\"KSTP\\\"] = [kstp]\\n data[\\\"KPER\\\"] = [kper]\\n else:\\n data[\\\"ZONE\\\"].append(zone)\\n data[\\\"KSTP\\\"].append(kstp)\\n data[\\\"KPER\\\"].append(kper)\\n\\n elif line in (\\\"\\\", \\\" \\\"):\\n empty += 1\\n\\n elif read_data:\\n if \\\"=\\\" in line:\\n t = line.split(\\\"=\\\")\\n label = t[0].strip()\\n if \\\"ZONE\\\" in line:\\n if prefix == \\\"FROM_\\\":\\n zlist.append(int(label.split()[1]))\\n label = f\\\"FROM_ZONE_{label.split()[1]}\\\"\\n else:\\n label = f\\\"TO_ZONE_{label.split()[-1]}\\\"\\n\\n elif \\\"TOTAL\\\" in line or \\\"PERCENT DISCREPANCY\\\" in line:\\n label = \\\"_\\\".join(label.split())\\n\\n elif \\\"IN - OUT\\\" in line:\\n label = \\\"IN-OUT\\\"\\n\\n else:\\n label = prefix + \\\"_\\\".join(label.split())\\n\\n if label in data:\\n data[label].append(float(t[1]))\\n else:\\n data[label] = [float(t[1])]\\n\\n if label == \\\"PERCENT_DISCREPANCY\\\":\\n # fill in non-connected zones with zeros...\\n for zone in zones:\\n if zone in zlist:\\n continue\\n data[f\\\"FROM_ZONE_{zone}\\\"].append(0)\\n data[f\\\"TO_ZONE_{zone}\\\"].append(0)\\n\\n elif \\\"OUT:\\\" in line:\\n prefix = \\\"TO_\\\"\\n\\n else:\\n pass\\n\\n elif flow_budget:\\n if \\\"IN:\\\" in line:\\n prefix = \\\"FROM_\\\"\\n read_data = True\\n flow_budget = False\\n\\n else:\\n pass\\n\\n if empty >= 30:\\n break\\n\\n return _zb_dict_to_recarray(data)\",\n \"def build_dataset(words):\\n count = []\\n # count.extend(collections.Counter(words).most_common(n_words - 1))\\n count.extend(collections.Counter(words).most_common())\\n dictionary = dict()\\n for word, _ in count:\\n dictionary[word] = len(dictionary)\\n data = list()\\n # unk_count = 0\\n for word in words:\\n index = dictionary.get(word, 0)\\n # if index == 0: # dictionary['UNK']\\n # unk_count += 1\\n data.append(index)\\n # count[0][1] = unk_count\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n data = [data[::2],data[1::2]]\\n new_data = list()\\n for i in range(len(data[0])):\\n new_data.append([data[0][i],data[1][i]])\\n data = new_data\\n vocabulary_size = len(dictionary)\\n print(\\\"\\\\n\\\\ndictionary size = \\\")\\n print(len(dictionary))\\n return data, count, dictionary, reversed_dictionary, vocabulary_size\",\n \"def getJar(oldDatas,jarList):\\n #clear pastebin here. confirmed.\\n if pastebin in os.listdir():\\n tempdir = barrand6()\\n os.rename(pastebin,tempdir)#clever..fine..\\n shutil.rmtree(tempdir)#acces deniyed ..fixed!\\n os.mkdir(pastebin)\\n\\n\\n oldDatakeys = oldDatas.keys()\\n newDatas = {} #for data insure.\\n\\n for noFolder in jarList:\\n try:\\n #txtFiledir = os.path.join( origins,noFolder,noFolder+'.txt')\\n txtFilename = noFolder+'.txt'\\n #------------------------user pre-sure.\\n txtfiles = []\\n for f in os.listdir( os.path.join( jar,noFolder) ):\\n if '.txt' in f:\\n txtfiles.append(f)\\n if len(txtfiles) == 1:\\n txtFilename = txtfiles[0]\\n else:\\n if not '설정.txt' in txtfiles:\\n raise Exception('ERROR no txt : ' + str(noFolder))\\n txtFilename = '설정.txt'\\n #------------------------user pre-sure\\n\\n txtFile = os.path.join( jar,noFolder,txtFilename) #whatif dir = no.txt?\\n parsedDict = txt2dict.parseTxt(txtFile,parseKeys,multiLineKey)#hope it's atleast complete...\\n\\n #----------------------for custom dict additional option\\n checklist = ['번호','제목','작성자','날짜','본문']\\n for c in checklist:\\n if not c in parsedDict.keys():\\n raise Exception('ERROR!! not format txt : ' + str(noFolder))\\n\\n #user input, do another func.\\n #'16'.isdigit()\\n #if int(parsedDict['번호'])<1:\\n #raise Exception('ERROR!! of : ' + str(noFolder))\\n #a = parsedDict['날짜'].split('.')\\n #b = str(datetime.date.today()).split('-')\\n #if datetime.date(a[0],a[1],a[2]) < datetime.date.today()\\n\\n if '태그' in parsedDict.keys():\\n tagList = parsedDict['태그'].split(',')\\n parsedDict['유저태그'] = tagList\\n del parsedDict['태그']\\n else:\\n parsedDict['유저태그'] = []\\n\\n if parsedDict['제목'].startswith('[번역]'):\\n parsedDict['제목'] = parsedDict['제목'].split('[번역]')[1].strip()\\n if parsedDict['제목'].find('센세)') != -1 :\\n parsedDict['유저태그'].append( parsedDict['제목'].split('센세)')[0].strip()+'센세' )\\n #parsedDict['태그'].append( a.split('[번역]')[1].strip().split('센세)')[0]+'센세)' )\\n #----------------------for custom dict additional option\\n\\n\\n #----------------------------- after get parsedDict.\\n tmpKey = parsedDict[idKey] #9133114\\n if tmpKey in oldDatakeys:\\n raise Exception('skip.. id already in parsedict ..: ' + str(noFolder))\\n\\n idFoldername = parsedDict['번호']\\n noFolderpath = os.path.join( jar,noFolder )\\n originPath = os.path.join( origins , idFoldername )\\n shutil.copytree(noFolderpath,originPath, dirs_exist_ok = False)# was true, but to integrity....\\n #it occured at test. nodict, but files.\\n #shutil.move(noFolderpath,pastebin)\\n\\n\\n #datas is dict object, appended new key,value.\\n #add more value.\\n #datas[tmpKey]['key'] = 'value'\\n\\n # get moved nofolder, add datas originImgs.\\n originFiles = os.listdir(os.path.join( origins, idFoldername))\\n originImgs = []\\n for img in originFiles:\\n ext = os.path.splitext( img )[1][1:] # .jpg == jpg\\n if ext in imgExt: #now, it's img.\\n originImgs.append(img)\\n if originImgs==[]:\\n raise Exception('ERROR!! no img..: ' + str(noFolder))\\n parsedDict[originKey] = originImgs\\n\\n newDatas[tmpKey] = parsedDict\\n #datas[tmpKey][allfilesKey] = originFiles\\n thisrand=barrand6()\\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\\n\\n except Exception as e:\\n exc_info = sys.exc_info()#below except.\\n errmsg = exc_info[1],':at line',exc_info[2].tb_lineno\\n print(errmsg)\\n\\n thisrand=barrand6()\\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\\n f = open('./'+pastebin+'/'+noFolder+thisrand+'/err.txt','w',encoding='utf-8')\\n f.write(str(errmsg))\\n f.close()\\n print( 'ERROR occured. gone pastebin :',str(noFolder)+thisrand)\\n continue\\n\\n return newDatas\",\n \"def list_lz(\\n self, lz_ids=None, userids=None, page=1, items=25,\\n ):\\n return self._lz_service.list_lz(\\n lz_ids=lz_ids, userids=userids, page=page, items=items\\n )\",\n \"def two_tier_embedding_compression(embeddings, bits, quantizer=None):\\n assert bits <= 8\\n n = 2**bits\\n quantized_embeddings = embeddings.copy()\\n index_table = np.zeros(embeddings.shape, dtype=np.uint8)\\n cluster_index_table = np.zeros(index_table.shape[0], dtype=np.uint8)\\n codebook_table = np.zeros((n, n))\\n\\n km1 = KMeans(n)\\n km1.fit(embeddings)\\n tier1 = km1.predict(embeddings)\\n\\n km_models = [0] * n\\n block_sizes = [0] * n\\n for block_label in tqdm(range(n)):\\n mask = block_label == tier1\\n indices = np.arange(embeddings.shape[0])[mask]\\n block = embeddings[mask]\\n km2 = KMeans(n)\\n km2.fit(block.flatten().reshape(-1, 1))\\n if quantizer:\\n km2.cluster_centers_ = quantizer(km2.cluster_centers_).numpy()\\n km2.cluster_centers_.sort(axis=0)\\n\\n km_models[block_label] = km2\\n codebook_table[block_label, :] = km2.cluster_centers_.flatten()\\n cluster_index_table[indices] = block_label\\n block_sizes[block_label] = block.shape[0]\\n for i in indices:\\n preds = km2.predict(embeddings[i, :].reshape(-1, 1))\\n index_table[indices, :] = preds\\n quantized_embeddings[i, :] = km2.cluster_centers_[preds].flatten()\\n print('block_sizes:', block_sizes)\\n return index_table, cluster_index_table, codebook_table, quantized_embeddings\",\n \"def load_data(cache_file_h5py,cache_file_pickle):\\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\\n raise RuntimeError(\\\"############################ERROR##############################\\\\n. \\\"\\n \\\"please download cache file, it include training data and vocabulary & labels. \\\"\\n \\\"link can be found in README.md\\\\n download zip file, unzip it, then put cache files as FLAGS.\\\"\\n \\\"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\\\")\\n print(\\\"INFO. cache file exists. going to load cache file\\\")\\n f_data = h5py.File(cache_file_h5py, 'r')\\n print(\\\"f_data.keys:\\\",list(f_data.keys()))\\n train_X=f_data['train_X'] # np.array(\\n print(\\\"train_X.shape:\\\",train_X.shape)\\n train_Y=f_data['train_Y'] # np.array(\\n print(\\\"train_Y.shape:\\\",train_Y.shape,\\\";\\\")\\n vaild_X=f_data['vaild_X'] # np.array(\\n valid_Y=f_data['valid_Y'] # np.array(\\n test_X=f_data['test_X'] # np.array(\\n test_Y=f_data['test_Y'] # np.array(\\n #print(train_X)\\n #f_data.close()\\n\\n word2index, label2index=None,None\\n with open(cache_file_pickle, 'rb') as data_f_pickle:\\n word2index, label2index=pickle.load(data_f_pickle)\\n print(\\\"INFO. cache file load successful...\\\")\\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y\",\n \"def load_data(cache_file_h5py,cache_file_pickle):\\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\\n raise RuntimeError(\\\"############################ERROR##############################\\\\n. \\\"\\n \\\"please download cache file, it include training data and vocabulary & labels. \\\"\\n \\\"link can be found in README.md\\\\n download zip file, unzip it, then put cache files as FLAGS.\\\"\\n \\\"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\\\")\\n print(\\\"INFO. cache file exists. going to load cache file\\\")\\n f_data = h5py.File(cache_file_h5py, 'r')\\n print(\\\"f_data.keys:\\\",list(f_data.keys()))\\n train_X=f_data['train_X'] # np.array(\\n print(\\\"train_X.shape:\\\",train_X.shape)\\n train_Y=f_data['train_Y'] # np.array(\\n print(\\\"train_Y.shape:\\\",train_Y.shape,\\\";\\\")\\n vaild_X=f_data['vaild_X'] # np.array(\\n valid_Y=f_data['valid_Y'] # np.array(\\n test_X=f_data['test_X'] # np.array(\\n test_Y=f_data['test_Y'] # np.array(\\n #print(train_X)\\n #f_data.close()\\n\\n word2index, label2index=None,None\\n with open(cache_file_pickle, 'rb') as data_f_pickle:\\n word2index, label2index=pickle.load(data_f_pickle)\\n print(\\\"INFO. cache file load successful...\\\")\\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y\",\n \"def load_data(cache_file_h5py,cache_file_pickle):\\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\\n raise RuntimeError(\\\"############################ERROR##############################\\\\n. \\\"\\n \\\"please download cache file, it include training data and vocabulary & labels. \\\"\\n \\\"link can be found in README.md\\\\n download zip file, unzip it, then put cache files as FLAGS.\\\"\\n \\\"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\\\")\\n print(\\\"INFO. cache file exists. going to load cache file\\\")\\n f_data = h5py.File(cache_file_h5py, 'r')\\n print(\\\"f_data.keys:\\\",list(f_data.keys()))\\n train_X=f_data['train_X'] # np.array(\\n print(\\\"train_X.shape:\\\",train_X.shape)\\n train_Y=f_data['train_Y'] # np.array(\\n print(\\\"train_Y.shape:\\\",train_Y.shape,\\\";\\\")\\n vaild_X=f_data['vaild_X'] # np.array(\\n valid_Y=f_data['valid_Y'] # np.array(\\n test_X=f_data['test_X'] # np.array(\\n test_Y=f_data['test_Y'] # np.array(\\n #print(train_X)\\n #f_data.close()\\n\\n word2index, label2index=None,None\\n with open(cache_file_pickle, 'rb') as data_f_pickle:\\n word2index, label2index=pickle.load(data_f_pickle)\\n print(\\\"INFO. cache file load successful...\\\")\\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y\",\n \"def load_data(cache_file_h5py,cache_file_pickle):\\n if not os.path.exists(cache_file_h5py) or not os.path.exists(cache_file_pickle):\\n raise RuntimeError(\\\"############################ERROR##############################\\\\n. \\\"\\n \\\"please download cache file, it include training data and vocabulary & labels. \\\"\\n \\\"link can be found in README.md\\\\n download zip file, unzip it, then put cache files as FLAGS.\\\"\\n \\\"cache_file_h5py and FLAGS.cache_file_pickle suggested location.\\\")\\n print(\\\"INFO. cache file exists. going to load cache file\\\")\\n f_data = h5py.File(cache_file_h5py, 'r')\\n print(\\\"f_data.keys:\\\",list(f_data.keys()))\\n train_X=f_data['train_X'] # np.array(\\n print(\\\"train_X.shape:\\\",train_X.shape)\\n train_Y=f_data['train_Y'] # np.array(\\n print(\\\"train_Y.shape:\\\",train_Y.shape,\\\";\\\")\\n vaild_X=f_data['vaild_X'] # np.array(\\n valid_Y=f_data['valid_Y'] # np.array(\\n test_X=f_data['test_X'] # np.array(\\n test_Y=f_data['test_Y'] # np.array(\\n #print(train_X)\\n #f_data.close()\\n\\n word2index, label2index=None,None\\n with open(cache_file_pickle, 'rb') as data_f_pickle:\\n word2index, label2index=pickle.load(data_f_pickle)\\n print(\\\"INFO. cache file load successful...\\\")\\n return word2index, label2index,train_X,train_Y,vaild_X,valid_Y,test_X,test_Y\",\n \"def get_data(elem: string) -> list:\\n if elem in ascii_dict:\\n max_length = len(max(ascii_dict[elem], key=len))\\n if max_length == 0:\\n max_length = 4\\n data = []\\n for s in ascii_dict[elem]:\\n data.append(fill_with_spaces(s, max_length))\\n return data\\n else:\\n raise IndexError(\\\"Unknown symbol: \\\", elem)\",\n \"def lego_sets():\\n \\n \\n data_test=data_specific\\n\\n\\n\\n \\n \\n\\n \\n print(data_test)\\n print(\\\"The size of the data is: \\\",len(data_test))\\n \\n \\n \\n # you must replace this line and return your own list\\n return data_test\",\n \"def lad_lut(lads):\\n for lad in lads:\\n yield lad['properties']['name']\",\n \"def decode(self, z):\\n raise NotImplementedError\",\n \"def test_key_for_list_of_cacheable_objects(self):\\n Meat.get_protein_sum([self.chicken, self.steak])\\n expected_cache_key = 'tests.Meat.get_protein_sum;,Chicken:20,Steak:26;'\\n self.assertExpectedKeyInCache(expected_cache_key)\",\n \"def LZm(self,i):\\n\\t\\tv=self.deltai_minus(i)\\n\\t\\treturn v[0][0]\",\n \"def _vWBLZ(self):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n vWBLZ=None\\r\\n vWBLZ=self._OBJS('WBLZ')\\r\\n \\r\\n vWBLZ=vWBLZ[[\\r\\n 'AKTIV' \\r\\n ,'BESCHREIBUNG'\\r\\n ,'IDIM'\\r\\n ,'NAME'\\r\\n #from WBLZ's OBJS: \\r\\n ,'OBJTYPE' #type (i.e. KNOT) of a WBLZ OBJ\\r\\n ,'OBJID' #pk (or tk?!) of a WBLZ OBJ \\r\\n #IDs (of the WBLZ)\\r\\n ,'pk'\\r\\n ]]\\r\\n vWBLZ.sort_values(['NAME','pk'],ascending=True,inplace=True)\\r\\n #reindex:\\r\\n vWBLZ=pd.DataFrame(vWBLZ.values,columns=vWBLZ.columns)\\r\\n \\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n if isinstance(vWBLZ,pd.core.frame.DataFrame):\\r\\n logger.error(logStrFinal) \\r\\n else:\\r\\n logger.debug(logStrFinal) \\r\\n vWBLZ=pd.DataFrame() \\r\\n vWBLZ['AKTIV']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['BESCHREIBUNG']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['IDIM']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['NAME']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['OBJID']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['OBJTYPE']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['pk']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.')) \\r\\n return vWBLZ\",\n \"def collate_fn(data):\\n\\toutput = dict()\\n\\n\\tfor name in ['answer_ID','query_ID']:\\n\\t\\toutput[name] = [ _[name] for _ in data]\\n\\n\\n\\tfor name in ['query_len','answer_len']:\\n\\t\\ttemp = [ _[name] for _ in data]\\t \\n\\t\\toutput[name] = torch.stack(temp, dim=0) \\n\\t\\n\\t#deal with source and target\\n\\tfor name in ['answer','query']:\\n\\t\\tlength = output['{0}_len'.format(name)]\\n\\t\\tl = length.max().item()\\n\\n\\t\\tfor i in range(len(data)):\\n\\t\\t\\tif(l-length[i].item()>0):\\n\\t\\t\\t\\tdata[i][name] = torch.cat([data[i][name],torch.zeros(l-length[i].item(),dtype=torch.long)],dim=-1)\\n\\n\\t\\ttemp = [ _[name] for _ in data]\\n\\t\\t\\n\\t\\toutput[name] = torch.stack(temp, dim=0).long()\\n\\t\\t\\n\\n\\treturn output\",\n \"def build_dataset(words, n_words):\\n count = [['UNK', -1]]\\n count.extend(collections.Counter(words).most_common(n_words - 1))\\n dictionary = dict()\\n for word, _ in count:\\n dictionary[word] = len(dictionary)\\n data = list()\\n unk_count = 0\\n for word in words:\\n index = dictionary.get(word, 0)\\n if index == 0: # dictionary['UNK']\\n unk_count += 1\\n data.append(index)\\n count[0][1] = unk_count\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n return data, count, dictionary, reversed_dictionary\",\n \"def build_dataset(words, n_words):\\n count = [['UNK', -1]]\\n count.extend(collections.Counter(words).most_common(n_words - 1))\\n dictionary = dict()\\n for word, _ in count:\\n dictionary[word] = len(dictionary)\\n data = list()\\n unk_count = 0\\n for word in words:\\n index = dictionary.get(word, 0)\\n if index == 0: # dictionary['UNK']\\n unk_count += 1\\n data.append(index)\\n count[0][1] = unk_count\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n return data, count, dictionary, reversed_dictionary\",\n \"def test_concat_data(self):\\n\\n this_satellite_dict = satellite_io.concat_data(\\n satellite_dicts=[\\n SATELLITE_DICT_SUBSET_BY_INDEX, SATELLITE_DICT_SUBSET_BY_TIME\\n ]\\n )\\n\\n self.assertTrue(compare_satellite_dicts(\\n this_satellite_dict, SATELLITE_DICT_CONCAT\\n ))\",\n \"def test_decode_listmode_data(self):\\n self.assertEqual([td.header(decoded=True)],\\n decoder.decode_listmode_data(BytesIO(td.header(as_bytes=True)), self.mask))\\n self.assertEqual([{**td.header_with_trace(decoded=True), **{'trace': td.trace()}}],\\n decoder.decode_listmode_data(\\n BytesIO(td.header_with_trace(as_bytes=True) + td.trace(True)),\\n self.mask))\",\n \"def buildCoder(shift):\\n out_dic = {}\\n lo = string.ascii_lowercase\\n up = string.ascii_uppercase\\n for i in lo:\\n out_dic[i] = lo[(lo.index(i) + shift) % len(lo)]\\n for i in up:\\n out_dic[i] = up[(up.index(i) + shift) % len(up)]\\n return out_dic\",\n \"def __init__(self):\\n self.data = []\\n self.idx = {}\",\n \"def gzdeflate():\\n return zlib.compress(val)\",\n \"def lshTransform(kv, band_count = 16):\\n img_name = kv[0]\\n img_sig = kv[1]\\n band_hash_list = []\\n band_size = int(128/band_count)\\n img_sig_chunks = list(map(''.join, zip(*[iter(img_sig)]*band_size)))\\n vfunc = np.vectorize(customHashFunc)\\n band_hash_list = vfunc(img_sig_chunks)\\n # Key: (band_id, bucket_id)\\n zip_list_1 = list(zip(np.arange(band_count),band_hash_list))\\n img_name_list = [[img_name]]*band_count\\n return list(zip(zip_list_1,img_name_list))\",\n \"def compressed_pickle(title, data):\\n with bz2.BZ2File(title, 'w') as f:\\n cPickle.dump(data, f)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.81415105","0.5463324","0.5255911","0.51981646","0.51777226","0.5148649","0.5143358","0.51358664","0.5128433","0.5083292","0.5054639","0.5033062","0.50319797","0.5004983","0.49983892","0.49932218","0.49870348","0.498288","0.49821144","0.49720147","0.49679914","0.49470782","0.4896989","0.4894046","0.48793006","0.4875982","0.48729634","0.48617095","0.48473468","0.4841541","0.4801607","0.47992894","0.47984692","0.4796376","0.47862273","0.4774839","0.47683045","0.47659054","0.47638768","0.4752994","0.475146","0.4747253","0.47458217","0.47362792","0.4730957","0.4717505","0.4717158","0.47158033","0.4713666","0.46999875","0.46814722","0.46772337","0.46767026","0.4674472","0.46714267","0.46697077","0.46678382","0.46579155","0.46448553","0.463479","0.46321833","0.46305588","0.4626996","0.46241775","0.46229002","0.4620587","0.46092275","0.46024272","0.4595693","0.45945472","0.45935047","0.45823115","0.45801467","0.45625788","0.45602646","0.45596454","0.4559182","0.45558473","0.45536155","0.45530924","0.45530924","0.45530924","0.45530924","0.45518124","0.45470843","0.4546955","0.45412898","0.45307073","0.4529894","0.45271376","0.45268396","0.45238957","0.45238957","0.45181993","0.4515136","0.4501851","0.4500996","0.45006433","0.45003566","0.44968152"],"string":"[\n \"0.81415105\",\n \"0.5463324\",\n \"0.5255911\",\n \"0.51981646\",\n \"0.51777226\",\n \"0.5148649\",\n \"0.5143358\",\n \"0.51358664\",\n \"0.5128433\",\n \"0.5083292\",\n \"0.5054639\",\n \"0.5033062\",\n \"0.50319797\",\n \"0.5004983\",\n \"0.49983892\",\n \"0.49932218\",\n \"0.49870348\",\n \"0.498288\",\n \"0.49821144\",\n \"0.49720147\",\n \"0.49679914\",\n \"0.49470782\",\n \"0.4896989\",\n \"0.4894046\",\n \"0.48793006\",\n \"0.4875982\",\n \"0.48729634\",\n \"0.48617095\",\n \"0.48473468\",\n \"0.4841541\",\n \"0.4801607\",\n \"0.47992894\",\n \"0.47984692\",\n \"0.4796376\",\n \"0.47862273\",\n \"0.4774839\",\n \"0.47683045\",\n \"0.47659054\",\n \"0.47638768\",\n \"0.4752994\",\n \"0.475146\",\n \"0.4747253\",\n \"0.47458217\",\n \"0.47362792\",\n \"0.4730957\",\n \"0.4717505\",\n \"0.4717158\",\n \"0.47158033\",\n \"0.4713666\",\n \"0.46999875\",\n \"0.46814722\",\n \"0.46772337\",\n \"0.46767026\",\n \"0.4674472\",\n \"0.46714267\",\n \"0.46697077\",\n \"0.46678382\",\n \"0.46579155\",\n \"0.46448553\",\n \"0.463479\",\n \"0.46321833\",\n \"0.46305588\",\n \"0.4626996\",\n \"0.46241775\",\n \"0.46229002\",\n \"0.4620587\",\n \"0.46092275\",\n \"0.46024272\",\n \"0.4595693\",\n \"0.45945472\",\n \"0.45935047\",\n \"0.45823115\",\n \"0.45801467\",\n \"0.45625788\",\n \"0.45602646\",\n \"0.45596454\",\n \"0.4559182\",\n \"0.45558473\",\n \"0.45536155\",\n \"0.45530924\",\n \"0.45530924\",\n \"0.45530924\",\n \"0.45530924\",\n \"0.45518124\",\n \"0.45470843\",\n \"0.4546955\",\n \"0.45412898\",\n \"0.45307073\",\n \"0.4529894\",\n \"0.45271376\",\n \"0.45268396\",\n \"0.45238957\",\n \"0.45238957\",\n \"0.45181993\",\n \"0.4515136\",\n \"0.4501851\",\n \"0.4500996\",\n \"0.45006433\",\n \"0.45003566\",\n \"0.44968152\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.785483"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":13198,"cells":{"query":{"kind":"string","value":"LZW Dekomprese dict_of_abc je vstupni slovnik dat na kazdem indexu slovniku je list v prubehu komprese se do nej pridavaji polozky list_of_data je posloupnost cisel pro dekompresi"},"document":{"kind":"string","value":"def do_LZW_DeCompression(dict_of_abc, list_of_data):\n \n #https://www.youtube.com/watch?v=MQM_DsX-LBI\n \n out = []\n predchozi_out = []\n for i in range(len(list_of_data)):\n new = []\n new.extend(predchozi_out)\n if list_of_data[i] in dict_of_abc:\n o = dict_of_abc[list_of_data[i]]\n out.extend(o)\n predchozi_out = o\n \n #pokud je o list, beru z nej pouze prvni prvek\n if len(o) > 1:\n new.append(o[0])\n else:\n new.extend(o)\n\n index_founded = dict_cointains_list(dict_of_abc, new)\n if index_founded == -1:\n #pokud new neni ve slovniku, pridam ho tam\n dict_of_abc[len(dict_of_abc) +1] = new\n\n return dict_of_abc, out"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def do_LZW_Compression(dict_of_abc, list_of_data):\n \n # rozdil mezi None a [] je v pouziti metody extend na listu\n \n result = []\n P = []\n C = [] # C je vzdy jeden prvek ze vstupu\n PC = []\n \n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\n \n for i in range(len(list_of_data)):\n \"\"\"\n Cyklus pres vsecky vstupni prvky\n \"\"\"\n\n C = []\n C.append(list_of_data[i])\n\n #PC je vzdy kombinace P a C\n PC = []\n PC.extend(P)\n PC.extend(C)\n\n index_founded = dict_cointains_list(dict_of_abc, PC)\n if index_founded == -1:\n #pokud PC neni ve slovniku, pridam ho tam a P = C\n dict_of_abc[len(dict_of_abc) +1] = PC\n #output P key in dictionary\n result.append(dict_cointains_list(dict_of_abc, P))\n P = C\n else:\n #pokud PC je ve slovniku P = PC pro dalsi iteraci\n P = PC\n #pridani posledniho prvku\n result.append(dict_cointains_list(dict_of_abc, P))\n return dict_of_abc, result","def getUniChemData(self, inchiKeyList):\n mapD = {\n 1: {\"name\": \"chembl\", \"baseUrl\": \"https://www.ebi.ac.uk/chembl/\", \"entryUrl\": \"https://www.ebi.ac.uk/chembldb/compound/inspect/\"},\n 3: {\"name\": \"pdb\", \"baseUrl\": \"http://www.ebi.ac.uk/pdbe/\", \"entryUrl\": \"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\"},\n 2: {\"name\": \"drugbank\", \"baseUrl\": \"http://drugbank.ca/\", \"entryUrl\": \"http://www.drugbank.ca/drugs/\"},\n 5: {\"name\": \"pubchem_dotf\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 4: {\"name\": \"gtopdb\", \"baseUrl\": \"http://www.guidetopharmacology.org\", \"entryUrl\": \"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\"},\n 11: {\"name\": \"ibm\", \"baseUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\", \"entryUrl\": \"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\"},\n 6: {\"name\": \"kegg_ligand\", \"baseUrl\": \"http://www.genome.jp/kegg/ligand.html\", \"entryUrl\": \"http://www.genome.jp/dbget-bin/www_bget?\"},\n 9: {\"name\": \"zinc\", \"baseUrl\": \"http://zinc15.docking.org\", \"entryUrl\": \"http://zinc15.docking.org/substances/\"},\n 8: {\"name\": \"nih_ncc\", \"baseUrl\": \"http://nihsmr.evotec.com/evotec/\", \"entryUrl\": \"\"},\n 10: {\"name\": \"emolecules\", \"baseUrl\": \"https://www.emolecules.com/\", \"entryUrl\": \"https://www.emolecules.com/cgi-bin/more?vid=\"},\n 12: {\"name\": \"atlas\", \"baseUrl\": \"http://www.ebi.ac.uk/gxa/home\", \"entryUrl\": \"http://www.ebi.ac.uk/gxa/query?conditionQuery=\"},\n 7: {\"name\": \"chebi\", \"baseUrl\": \"http://www.ebi.ac.uk/chebi/downloadsForward.do\", \"entryUrl\": \"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\"},\n 14: {\n \"name\": \"fdasrs\",\n \"baseUrl\": \"http://fdasis.nlm.nih.gov/srs/srs.jsp\",\n \"entryUrl\": \"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\",\n },\n 15: {\"name\": \"surechembl\", \"baseUrl\": \"https://www.surechembl.org/search/\", \"entryUrl\": \"https://www.surechembl.org/chemical/\"},\n 21: {\"name\": \"pubchem_tpharma\", \"baseUrl\": \"http://www.thomson-pharma.com/\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/substance/\"},\n 22: {\"name\": \"pubchem\", \"baseUrl\": \"http://pubchem.ncbi.nlm.nih.gov\", \"entryUrl\": \"http://pubchem.ncbi.nlm.nih.gov/compound/\"},\n 27: {\"name\": \"recon\", \"baseUrl\": \"https://vmh.uni.lu\", \"entryUrl\": \"https://vmh.uni.lu/\"},\n 28: {\"name\": \"molport\", \"baseUrl\": \"https://www.molport.com/shop/index\", \"entryUrl\": \"https://www.molport.com/shop/molecule-link/\"},\n 31: {\n \"name\": \"bindingdb\",\n \"baseUrl\": \"https://www.bindingdb.org/bind/index.jsp\",\n \"entryUrl\": \"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\",\n },\n 41: {\"name\": \"swisslipids\", \"baseUrl\": \"http://www.swisslipids.org/\", \"entryUrl\": \"http://www.swisslipids.org/\"},\n 29: {\"name\": \"nikkaji\", \"baseUrl\": \"http://jglobal.jst.go.jp/en/\", \"entryUrl\": \"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\"},\n 32: {\"name\": \"comptox\", \"baseUrl\": \"https://comptox.epa.gov/dashboard/\", \"entryUrl\": \"https://comptox.epa.gov/dashboard/\"},\n 33: {\"name\": \"lipidmaps\", \"baseUrl\": \"http://www.lipidmaps.org\", \"entryUrl\": \"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\"},\n 35: {\"name\": \"carotenoiddb\", \"baseUrl\": \"http://carotenoiddb.jp/index.html\", \"entryUrl\": \"http://carotenoiddb.jp/Entries/\"},\n 36: {\"name\": \"metabolights\", \"baseUrl\": \"http://www.ebi.ac.uk/metabolights/\", \"entryUrl\": \"http://www.ebi.ac.uk/metabolights/\"},\n 37: {\"name\": \"brenda\", \"baseUrl\": \"https://www.brenda-enzymes.org/index.php\", \"entryUrl\": \"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\"},\n 17: {\"name\": \"pharmgkb\", \"baseUrl\": \"https://www.pharmgkb.org\", \"entryUrl\": \"https://www.pharmgkb.org/drug/\"},\n 18: {\"name\": \"hmdb\", \"baseUrl\": \"http://www.hmdb.ca\", \"entryUrl\": \"http://www.hmdb.ca/metabolites/\"},\n 24: {\n \"name\": \"nmrshiftdb2\",\n \"baseUrl\": \"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\",\n \"entryUrl\": \"http://nmrshiftdb.org/molecule/\",\n },\n 25: {\"name\": \"lincs\", \"baseUrl\": \"http://www.lincsproject.org/\", \"entryUrl\": \"http://identifiers.org/lincs.smallmolecule/\"},\n 39: {\"name\": \"chemicalbook\", \"baseUrl\": \"https://www.chemicalbook.com\", \"entryUrl\": \"https://www.chemicalbook.com/ChemicalProductProperty_EN_\"},\n 20: {\"name\": \"selleck\", \"baseUrl\": \"http://www.selleckchem.com\", \"entryUrl\": \"http://www.selleckchem.com/products/\"},\n 23: {\"name\": \"mcule\", \"baseUrl\": \"https://mcule.com\", \"entryUrl\": \"https://mcule.com/\"},\n 26: {\"name\": \"actor\", \"baseUrl\": \"https://actor.epa.gov\", \"entryUrl\": \"http://actor.epa.gov/actor/chemical.xhtml?casrn=\"},\n 34: {\"name\": \"drugcentral\", \"baseUrl\": \"http://drugcentral.org\", \"entryUrl\": \"http://drugcentral.org/drugcard/\"},\n 38: {\"name\": \"rhea\", \"baseUrl\": \"http://www.rhea-db.org\", \"entryUrl\": \"http://www.rhea-db.org/searchresults?q=CHEBI:\"},\n }\n oD = {}\n try:\n for ky in inchiKeyList:\n unc = unichem_client # pylint: disable=no-member\n # unc.set_format(\"json\")\n uDL = unc.get(ky)\n if uDL:\n qD = {}\n for uD in uDL:\n if \"src_id\" in uD and int(uD[\"src_id\"]) in mapD:\n qD[mapD[int(uD[\"src_id\"])][\"name\"]] = uD[\"src_compound_id\"]\n if qD:\n oD[ky] = qD\n\n except Exception as e:\n logger.exception(\"Failing with %s\", str(e))\n return oD","def dict_cointains_list(dict_of_abc, item_list):\n\n values = list(dict_of_abc.values())\n\n #projdu vsecky listy ve slovniku\n for i in range(len(values)):\n #predpokladam ze ve slovniku je\n finded = True\n \n for j in range(len(values[i])):\n if len(item_list) == len(values[i]):\n # kontrola po jednotlivych hodnotach\n # logicky soucin - pokud jednou False, navzdy False\n finded = finded and item_list[j] == values[i][j]\n else:\n finded = False\n\n if finded:\n # cyklus indexuje od 0, slovnik ale indexujeme-klicujeme od 1\n return i + 1 \n\n return -1","def generate_data_list(self, data_list):\n sol = []\n for i in data_list:\n sol.append({\n \"data_sig\": i[0],\n \"data\": pickle.loads(i[1]),\n \"attachment\": i[2],\n \"likes\":pickle.loads(i[3]),\n \"dislikes\":pickle.loads(i[4]),\n \"owner\": i[5]\n })\n return sol","def listz2diclist(listz):\n\ttmp=[]\n\tfor elem in listz:\n\t\tdic = elem.warez2dic()\n\t\ttmp.append(dic)\n\treturn tmp","def getDataDict(self):\n #code begins here \n return self.__dflst,self.__dfwells","def getDataForLBMPZonalComparison(self):\n\n\t\t# Variables\n\t\tzonal_data = self.getDayAheadMarketLBMPZonal()\n\t\tkeys = zonal_data.keys()\n\t\tfinal_data = []\n\t\tvalues = []\n\t\touter_dictionary = {}\n\t\tinner_dictionary = {}\n\n\t\t# Populating final data array and returning it\n\t\tfor key in keys:\n\t\t\tfor data in zonal_data[key]:\n\t\t\t\tinner_dictionary['x'] = data.keys()[0]\n\t\t\t\tinner_dictionary['y'] = data[data.keys()[0]]['LBMP ($/MWHr)']\n\t\t\t\tvalues.append(inner_dictionary)\n\t\t\t\tinner_dictionary = {}\n\t\t\touter_dictionary['values'] = values\n\t\t\tvalues = []\n\t\t\touter_dictionary['key'] = key\n\t\t\tfinal_data.append(outer_dictionary)\n\t\t\touter_dictionary = {}\n\n\t\treturn final_data","def get_data(url, seed):\n available_fields = {\n 'boro': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'cd': {'fieldtype': 'C', 'categories': range(1, 19)},\n 'uf1_1': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_2': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_3': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_4': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_5': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_6': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_7': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_8': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_9': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_10': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_11': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_12': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_13': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_14': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_15': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_16': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_17': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_18': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_19': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_20': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_21': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'uf1_22': {'fieldtype': 'B', 'codes': [1, 9, 8]},\n 'sc24': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc36': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'sc37': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc38': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'sc114': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'uf48': {'fieldtype': 'N'},\n 'sc147': {'fieldtype': 'C', 'categories': [1, 2, 3]},\n 'uf11': {'fieldtype': 'C', 'categories': range(1, 8)},\n 'sc149': {'fieldtype': 'B', 'codes': [1, 2, None]},\n 'sc173': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\n 'sc171': {'fieldtype': 'B', 'codes': [1, 2]},\n 'sc150': {'fieldtype': 'N'},\n 'sc151': {'fieldtype': 'N'},\n 'sc154': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\n 'sc157': {'fieldtype': 'C', 'categories': [1, 2, 9]},\n 'sc158': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc185': {'fieldtype': 'B', 'codes': [0, 1, 8]},\n 'sc186': {'fieldtype': 'C', 'categories': [2, 3, 4, 5, 9]},\n 'sc197': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc198': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc187': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc188': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc571': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'sc189': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'sc190': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc191': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc192': {'fieldtype': 'B', 'codes': [0, 1, 8]},\n 'sc193': {'fieldtype': 'C', 'categories': [2, 3, 9]},\n 'sc194': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'sc196': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\n 'sc199': {'fieldtype': 'C', 'categories': range(1, 6)},\n 'rec15': {'fieldtype': 'C', 'categories': range(1, 14)},\n 'sc26': {'fieldtype': 'C', 'categories': [12, 13, 15, 16]},\n 'uf23': {'fieldtype': 'N'},\n 'rec21': {'fieldtype': 'B', 'codes': [1, 2, 8]},\n 'rec62': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\n 'rec64': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\n 'rec54': {'fieldtype': 'C', 'categories': range(1, 8)},\n 'rec53': {'fieldtype': 'N'},\n 'new_csr': {'fieldtype': 'C', 'categories': [1, 2, 5, 12, 20,\n 21, 22, 23, 30, 31,\n 80, 85, 90, 95]}\n }\n selected_fields = [\n # The borough where the apartment is located\n 'boro',\n\n # Building type: public housing, new construction,\n # \"In Rem\" foreclosure, old construction\n 'sc26',\n\n # Number of bedrooms\n 'sc151',\n\n # Dilapidated / Not Dilapidated\n 'rec21',\n\n # Complete plumbing facilities in unit\n 'rec62',\n\n # Complete kitchen facilities in unit\n 'rec64',\n\n # Maintenance deficiencies\n 'rec53',\n\n # Building age\n 'uf23',\n\n # Rent control/stabilization category\n 'new_csr',\n\n # Neighborhood rating\n 'sc196',\n\n # Wheelchair accessibility of unit\n 'sc38',\n\n # Presence of elevator\n 'sc149',\n\n # Building height\n 'uf11',\n\n # Air conditioning\n 'sc197',\n\n # Walkup\n 'sc171',\n ]\n mini_fields = {k: available_fields[k]\n for k in available_fields\n if k in selected_fields}\n y_field = 'uf17'\n # s = requests.get(url).content\n # raw_df = pd.read_csv(StringIO(s.decode('utf-8')))\n raw_df = pd.read_csv('homework2_data.csv')\n valid_renters, validated_features, validated_rents = \\\n preprocess_data(raw_df, mini_fields, y_field)\n X_train, X_test, y_train, y_test = train_test_split(\n validated_features, validated_rents, random_state=seed)\n cats = [k\n for (k, v) in mini_fields.items()\n if v[\"fieldtype\"] == \"C\"]\n catnums = [i\n for (i, x) in enumerate([c in cats\n for c in validated_features.columns])\n if x]\n return X_train, X_test, y_train, y_test, catnums, raw_df","def decompress_encoded_list( nums ):\n decompressed = []\n\n for i in range( 0, len(nums), 2 ):\n freq = nums[i]\n val = nums[i+1]\n decompressed.extend( [val] * freq )\n\n return decompressed","def dict_check34(_str1):\n\t_len=len(_str1)\n\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\n\t_list0=[]\n\tfor i in range(len(_list1)):\n\t\t#Take different length\n\t\t_current=_list1[i]\n\t\tif _len>=sum(_current) and sum(_list1[i])!=0:\n\t\t\t_list2=[]\n\t\t\t_n1=_current[0]\n\t\t\t_n2=_current[1]\n\t\t\t_n3=_current[2]\n\t\t\t_list2.append(_str1[:_n1])\n\t\t\t_list2.append(_str1[_n1:_n1+_n2])\n\t\t\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\n\t\telse:\n\t\t\tcontinue\n\t\tn=0\n\t\tfor j in range(3):\n\t\t\tif _list2[j] in _dict_ori or _list2[j]==\"\":\n\t\t\t\tn+=1\n\t\tif n==3:\n\t\t\t_list0.append(_list2)\n\treturn(_list0)","def get_keys_from_list():\n json_data = request.get_json()\n\n d = dict()\n d['elements'] = list()\n settings.setOptionsFile(get_info('uid'))\n fn = settings.getHistoROOTFileName()\n rfn = settings.getReferenceROOTFileName()\n# open root file stored in the root database\n f = ROOT.TFile(fn)\n# open reference root file stored in the root database\n rf = ROOT.TFile(rfn)\n\n for values in json_data.itervalues():\n for k in values:\n subd = dict()\n subd[\"index\"] = k[\"index\"]\n if fn != k[\"file\"]: \n fn = k[\"file\"]\n settings.setHistoROOTFileName(fn)\n f = ROOT.TFile(fn)\n print \"histogram :>>>>>: \",k[\"histogram\"]\n subd[\"data\"] = eval(cppyy.gbl.getDictionary(f,k[\"histogram\"]))\n if rfn != k[\"referenceFile\"]: \n rfn = k[\"referenceFile\"]\n settings.setReferenceROOTFileName(rfn)\n rf = ROOT.TFile(rfn)\n subd[\"refdata\"] = eval(cppyy.gbl.getDictionary(rf,k[\"reference\"]))\n d['elements'].append(subd)\n\n f.Close()\n rf.Close()\n\n return jsonify(d)","def test_decompress_offset_less_len1(self):\n b_array = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\n actual = LZ77.decompress(b_array)\n expected = 'ababab'\n self.assertEqual(actual, expected)","def getData_goodmaps(liste_dictionnaires = [], liste_categories = [], liste_phonemes = [],liste_cartes=[]):\n if liste_dictionnaires!=[] and liste_categories!=[] and liste_phonemes!=[]:\n tableau = np.array(liste_dictionnaires[0][liste_categories[0]][liste_phonemes[0]])\n nb_exemple,nb_carte,lign,col=tableau.shape\n else:\n return [],[],[],[]\n\n Mat = []\n Reference = []\n\n\n for inddict,dict in enumerate(liste_dictionnaires):\n for indcat,cat in enumerate(liste_categories):\n for indpho,pho in enumerate(liste_phonemes):\n for ex in range(nb_exemple):\n goodmaps = []\n for map in liste_cartes:\n goodmaps.append(np.array(dict[cat][pho][ex][map]).flatten())\n Mat.append(np.array(goodmaps).flatten())\n Reference.append([inddict,indcat ,indpho])\n Reference = np.array(Reference)\n Y_c_inc = change_reference(Reference[:,1])\n Y_r_v = Reference[:,2]\n Y_fr_jap = Reference[:,0]\n return np.array(Mat), np.array(Y_c_inc), np.array(Y_r_v), np.array(Y_fr_jap)","def construct_zi_dict(train_info_list, test_info_list):\r\n zi_dict, train_dataset_list, test_dataset_list = dict(), list(), list()\r\n for user, age, gender, education, querys in train_info_list:\r\n for query in querys:\r\n for zi in query:\r\n if zi not in zi_dict:\r\n zi_dict[zi] = 0\r\n zi_dict[zi] += 1\r\n for user, querys in test_info_list:\r\n for query in querys:\r\n for zi in query:\r\n if zi not in zi_dict:\r\n zi_dict[zi] = 0\r\n zi_dict[zi] += 1\r\n zi_list = sorted(zi_dict.iteritems(), key=lambda x: x[1], reverse=True)\r\n zi2index = dict([(zi[0], [zi[1], idx]) for idx, zi in enumerate(zi_list)])\r\n index2zi = dict([(idx, [zi[0], zi[1]]) for idx, zi in enumerate(zi_list)])\r\n \r\n return zi2index, index2zi","def test_xyz_from_data(self):\n symbols = ('C', 'H', 'H', 'H', 'H')\n isotopes = (12, 1, 1, 1, 1)\n coords = ((0.0, 0.0, 0.0),\n (0.6300326, 0.6300326, 0.6300326),\n (-0.6300326, -0.6300326, 0.6300326),\n (-0.6300326, 0.6300326, -0.6300326),\n (0.6300326, -0.6300326, -0.6300326))\n xyz_dict0 = converter.xyz_from_data(coords=coords, symbols=symbols, isotopes=isotopes)\n self.assertEqual(xyz_dict0, self.xyz1['dict'])\n xyz_dict1 = converter.xyz_from_data(coords=coords, symbols=symbols) # no specifying isotopes\n self.assertEqual(xyz_dict1, self.xyz1['dict'])\n\n numbers = [6, 1, 1, 1, 1]\n coords = [[0.0, 0.0, 0.0],\n [0.6300326, 0.6300326, 0.6300326],\n [-0.6300326, -0.6300326, 0.6300326],\n [-0.6300326, 0.6300326, -0.6300326],\n [0.6300326, -0.6300326, -0.6300326]]\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\n\n numbers = [6, 1, 1, 1, 1]\n coords = [[0.0, 0.0, 0.0],\n [0.6300326, 0.6300326, 0.6300326],\n [-0.6300326, -0.6300326, 0.6300326],\n [-0.6300326, 0.6300326, -0.6300326],\n [0.6300326, -0.6300326, -0.6300326]]\n coords = np.array([np.array(coord, np.float64) for coord in coords], np.float64)\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\n self.assertIsInstance(xyz_dict2['coords'], tuple)\n self.assertIsInstance(xyz_dict2['coords'][0], tuple)","def test_compress(self):\n form_field = MultiLingualFormField()\n compressed_data = form_field.compress([\"test-nb\", \"test-en\"])\n self.assertEqual(MultiLingualTextStructure, type(compressed_data))\n self.assertEqual(compressed_data['nb'], \"test-nb\")\n self.assertEqual(compressed_data['en'], \"test-en\")","def test_decompress_2(self):\n b_array = bytearray([3]) + bytearray(b'abcdef')\\\n + bytearray([0, 32]) + bytearray([0, 113])\n actual = LZ77.decompress(b_array)\n expected = 'abcdefdeabc'\n self.assertEqual(actual, expected)","def test_decompress_offset_less_len2(self):\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\n actual = LZ77.decompress(b_array)\n expected = 'abcdabcdab'\n self.assertEqual(actual, expected)","def zzX_from_dict(f, l):\n if l == 1:\n return zzx_from_dict(f)\n elif not f:\n return zzX_zero(l)\n\n coeffs = {}\n\n for monom, coeff in f.iteritems():\n head, tail = monom[0], monom[1:]\n\n if len(tail) == 1:\n tail = tail[0]\n\n if coeffs.has_key(head):\n coeffs[head][tail] = INT_TYPE(int(coeff))\n else:\n coeffs[head] = { tail : INT_TYPE(int(coeff)) }\n\n n, h = max(coeffs.iterkeys()), []\n\n for k in xrange(n, -1, -1):\n coeff = coeffs.get(k)\n\n if coeff is not None:\n h.append(zzX_from_dict(coeff, l-1))\n else:\n h.append(zzX_zero(l-1))\n\n return zzX_strip(h)","def zipped_data(b_data):\n col_names = [\"Name\", \"Version\", \"Date and Time\"]\n bundle_data = []\n for bundle in b_data:\n zipped = zip(col_names, bundle)\n bundle_data.append(dict(zipped))\n return bundle_data","def test_decompress_1(self):\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 49])\n actual = LZ77.decompress(b_array)\n expected = 'abcdabc'\n self.assertEqual(actual, expected)","def _transform_map_data(self):\n WARD_FMT = '%s-%s'\n self.map_data_trans = []\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\n\n #skip over header\n rs = iter(self.sht.rows)\n next(rs)\n next(rs)\n for r in rs:\n pka = r[0].value\n for c in r[1:]:\n if c.value is None:\n c.value = 0\n\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))","def data():\n \n # Just in case order matters.\n inplist = expected.keys()\n inplist.sort()\n \n #return ['split/' + inpms for inpms in inplist]\n return inplist","def test_compress_offset_less_len1(self):\n text = 'ababab'\n actual = LZ77.compress(text)\n expected = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\n self.assertEqual(actual, expected)","def test_hmaps(self):\n #Single element insertion\n self.hll.append(self.data[0])\n assert non_zero_idx_val(hll.hmap) == [(54, 1)]\n #Multiple distinct element insertions\n self.hll.extend(self.data)\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\n self.reset_hll()\n #Element insertions with duplicates\n self.hll.extend(self.data_duplicate)\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\n self.reset_hll()\n #Element insertions with numerical values\n self.hll.extend(self.num_data)\n assert non_zero_idx_val(hll.hmap) == [(17, 3), (144, 2), (145, 4),\n (182, 2)]\n self.reset_hll()\n #Test the key collision handling (keep max value)\n self.hll.append(self.colliding_data[0])\n assert non_zero_idx_val(hll.hmap) == [(0, 1)] \n self.hll.append(self.colliding_data[1])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\n self.reset_hll()\n self.hll.append(self.colliding_data[1])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)] \n self.hll.append(self.colliding_data[0])\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\n self.reset_hll()","def get_dict_refine_insee_code(ls_valid_ic):\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\n for ic in ls_valid_ic_corse:\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\n dict_refine_ic.update(dict_ic_ardts)\n return dict_refine_ic","def apply_compression(pc2_table, keys_56bits):\n keys_48bits = \"\"\n for index in pc2_table:\n keys_48bits += keys_56bits[index - 1]\n return keys_48bits","def test_compress_offset_less_len2(self):\n text = 'abcdabcdab'\n actual = LZ77.compress(text)\n expected = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\n self.assertEqual(actual, expected)","def decode_huffman(bit_seq, dc_ac, layer_type):\n\n def diff_value(idx, size):\n if idx >= len(bit_seq) or idx + size > len(bit_seq):\n raise IndexError('There is not enough bits to decode DIFF value '\n 'codeword.')\n fixed = bit_seq[idx:idx + size]\n return int(fixed, 2)\n\n current_idx = 0\n while current_idx < len(bit_seq):\n # 1. Consume next 16 bits as `current_slice`.\n # 2. Try to find the `current_slice` in Huffman table.\n # 3. If found, yield the corresponding key and go to step 4.\n # Otherwise, remove the last element in `current_slice` and go to\n # step 2.\n # 4. Consume next n bits, where n is the category (size) in returned\n # key yielded in step 3. Use those info to decode the data.\n remaining_len = len(bit_seq) - current_idx\n current_slice = bit_seq[\n current_idx:\n current_idx + (16 if remaining_len > 16 else remaining_len)\n ]\n err_cache = current_slice\n while current_slice:\n if (current_slice in\n HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type].inv):\n key = (HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type]\n .inv[current_slice])\n if dc_ac == DC: # DC\n size = key\n if size == 0:\n yield 0\n else:\n yield HUFFMAN_CATEGORIES[size][diff_value(\n current_idx + len(current_slice),\n size\n )]\n else: # AC\n run, size = key\n if key in (EOB, ZRL):\n yield key\n else:\n yield (run, HUFFMAN_CATEGORIES[size][diff_value(\n current_idx + len(current_slice),\n size\n )])\n\n current_idx += len(current_slice) + size\n break\n current_slice = current_slice[:-1]\n else:\n raise KeyError(\n f'Cannot find any prefix of {err_cache} in Huffman table.'\n )","def create_data_set():\n data_set = {}\n for index in range(1024):\n size = random.randint(1, 100) #nosec\n key = str(index).encode(\"utf-8\")\n data_set[key] = get_random_bytes(size)\n return data_set","def making_dataset_list_y(data_list:list) -> list:\n list_size = len(data_list)\n data_list_y = []\n for i in range(list_size):\n data_list_y.append(data_list[i][[\"W\",\"D\",\"L\"]])\n return data_list_y","def get_compressed(self, value):\r\n output = []\r\n lz_data = (value >> 8) & 0xFF\r\n lz_counter = value & 0xFF\r\n # Define the relative offset on LZ Window\r\n lz_offset = ((lz_counter & 0xF0) << 4) | lz_data\r\n # Define the LZ Counter for repeat data N times\r\n lz_counter = (lz_counter & 0xF) + 0x2\r\n # Start Repeat Loop\r\n while (lz_counter >= 0):\r\n # Seek the window on LZ Offset and get the LZ Data\r\n self.__lzwindow__.seek(lz_offset, FROM_START)\r\n lz_data = (lz_data & 0xFF00) + \\\r\n int.from_bytes(self.__lzwindow__.read(1), byteorder='big')\r\n # Write the LZ data to the output\r\n output.append((lz_data & 0xFF).to_bytes(1, byteorder='big'))\r\n # Seek the LZ Window on current LZ Window Counter value and write the current LZ Data (LZBuffer)\r\n self.__lzwindow__.seek(self.__lzwindowcounter__, FROM_START)\r\n self.__lzwindow__.write((lz_data & 0xFF).to_bytes(1, byteorder='big'))\r\n # Increment LZ Window Counter\r\n self.__lzwindowcounter__ = (\r\n self.__lzwindowcounter__ + 0x1) & self.__lzwindowmax__\r\n # Increment LZ Offset\r\n lz_offset = (lz_offset + 0x1) & self.__lzwindowmax__\r\n # Decrement number of data to decompress\r\n self.__maxlen__ -= 0x1\r\n # Decrement LZ Loop counter\r\n lz_counter -= 0x1\r\n return output","def shuffle_data_set(in_dict):\n tot_rows = in_dict['data'].shape[0]\n new_order = range(tot_rows)\n for _ in range(5):\n shuffle(new_order)\n\n out_dict = in_dict\n out_data = np.zeros((tot_rows, 3072), dtype=in_dict['data'].dtype)\n out_labels = [0] * tot_rows\n out_filenames = [\"\"] * tot_rows\n\n for ctr, idx in enumerate(new_order):\n out_data[ctr, :] = in_dict['data'][idx, :]\n out_labels[ctr] = in_dict['labels'][idx]\n out_filenames[ctr] = in_dict['filenames'][idx]\n\n out_dict['data'] = out_data\n out_dict['labels'] = out_labels\n out_dict['filenames'] = out_filenames\n\n return out_dict","def get_datalist_fr_json(self):\n raw_data = json.load(open(self.saved_json_file, 'r'))\n for indivdual_set in raw_data['query']['results']['stats']:\n temp_dict_data = {}\n if type(indivdual_set) == str:\n #for single data\n continue # temp do not use\n for parameters in indivdual_set.keys():\n if type(indivdual_set[parameters]) == str:\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\n elif type(indivdual_set[parameters]) == dict:\n if indivdual_set[parameters].has_key('content'):\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\n\n ## append to list\n self.com_data_allstock_list.append(temp_dict_data)","def polylinedictarraycopy(d):#d——原始图层多段线字典 \r\n dictlist=[]\r\n ratiolist=[] #放缩率列表\r\n rationumaccumulationlist=[] #放缩率数量累加列表\r\n \r\n eachrationum=globalconfig.X_ARRAY_NUM//globalconfig.RATIO_NUM\r\n leftrationum=globalconfig.X_ARRAY_NUM%globalconfig.RATIO_NUM\r\n \r\n eachrationumlist=[eachrationum]*globalconfig.RATIO_NUM #各个放缩率对应数量的列表\r\n \r\n for i in range((globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2,(globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2+leftrationum):\r\n eachrationumlist[i]=eachrationumlist[i]+1 #将整除后的余值加入到靠中间放缩率的方案中。\r\n \r\n rationumaccumulationlist.append(0) \r\n \r\n for i in range(1,globalconfig.RATIO_NUM): #计算放缩率数量累加列表\r\n rationumaccumulationlist.append(rationumaccumulationlist[i-1]+eachrationumlist[i-1])\r\n \r\n for i in range(0,globalconfig.RATIO_NUM): #计算放缩率列表\r\n ratiolist.append((globalconfig.CENTER_RATIO-((globalconfig.RATIO_NUM+1)//2-1)*globalconfig.RATIO_DIFF)+i*globalconfig.RATIO_DIFF) \r\n \r\n for i in range(0,globalconfig.RATIO_NUM): #每种放缩率\r\n for j in range(0,eachrationumlist[i]): #每种放缩率对应数量\r\n newdict={}\r\n for e in d: #将字典中值即每一图层对应的多段线列表进行复制并移动到指定位置\r\n newdict[e]=polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist)) \r\n #newdict.append([e,polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist))])\r\n dictlist.append(newdict) \r\n return (dictlist,ratiolist,eachrationumlist)","def opendict(data_dir, batch_size, test_max_size, **kwargs):\n join = lambda f: _path.join(data_dir, f)\n with open(join(\"list.jl\")) as f:\n data = [(join(e[\"files\"][0][\"path\"]), e[\"word\"].strip())\n for e in filter(lambda x: x.get(\"files\"), map(_json.loads, f))]\n _random.shuffle(data)\n if not test_max_size:\n test_max_size = int(len(data) / 100)\n tmp = - (batch_size + test_max_size)\n return data[:tmp], data[tmp:-test_max_size], data[-test_max_size:]","def decode(self, z):\n raise NotImplementedError","def zzx_from_dict(f):\n if not f:\n return []\n\n n, h = max(f.iterkeys()), []\n\n for k in xrange(n, -1, -1):\n h.append(INT_TYPE(int(f.get(k, 0))))\n\n return zzx_strip(h)","def map(z):\n pass","def test_compress_2(self):\n text = 'abcdefdeabc'\n actual = LZ77.compress(text)\n expected = bytearray([3]) + bytearray(b'abcdef')\\\n + bytearray([0, 32]) + bytearray([0, 113])\n self.assertEqual(actual, expected)","def get_data(elem: string) -> list:\n if elem in ascii_dict:\n max_length = len(max(ascii_dict[elem], key=len))\n if max_length == 0:\n max_length = 4\n data = []\n for s in ascii_dict[elem]:\n data.append(fill_with_spaces(s, max_length))\n return data\n else:\n raise IndexError(\"Unknown symbol: \", elem)","def decoder(self, array=[],your_dic_max_value=78):\r\n word_list=[]\r\n self.coder=\"This_is_a_decoder_for_the_short_vector_function!\"\r\n for i in array:\r\n for i in i:\r\n print(\"i:\"+str(i))\r\n if int(i) <=25:# use the max length of the dic to find out wich one to use\r\n for key, value in self.general_lower_word_list.items():# iterate through every key and value\r\n if value==int(i):#checks if the value is equal to the given input\r\n word_list.append(key)# if yes append it\r\n else:\r\n pass #else move on\r\n elif int(i) <=51 and int(i)>=25:# same process as above\r\n for key, value in self.general_upper_word_list.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=58 and int(i)>=52:\r\n for key, value in self.special_letters_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=68 and int(i)>=59:\r\n for key, value in self.general_numbers_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n elif int(i) <=max_value and int(i)>=69:\r\n for key, value in self.special_characters_dic.items():\r\n if value==int(i):\r\n word_list.append(key)\r\n else:\r\n pass\r\n return \"\".join(word_list) #create a real string with the join method\r","def test_decode_listmode_data(self):\n self.assertEqual([td.header(decoded=True)],\n decoder.decode_listmode_data(BytesIO(td.header(as_bytes=True)), self.mask))\n self.assertEqual([{**td.header_with_trace(decoded=True), **{'trace': td.trace()}}],\n decoder.decode_listmode_data(\n BytesIO(td.header_with_trace(as_bytes=True) + td.trace(True)),\n self.mask))","def data_dict0():\n\n # 0- Sample from detectron2 -> 5 different sections.\n info_val0 = [{\"date_created\": \"2020-03-15 04:59:45.442988\",\n \"description\": \"Automatically generated COCO json file for Detectron2.\"}]\n images0 = [{\"id\": \"image\", \"width\": 100,\n \"height\": 100, \"file_name\": \"image.png\"}]\n annotations0 = [{\"id\": 1, \"image_id\": \"image\", \"bbox\": [70.0, 30.0, 30.0, 40.0],\n \"area\": 1200.0, \"iscrowd\": 0, \"category_id\": 0}]\n categories0 = [{\"id\": 0, \"name\": \"first\"}]\n licence0 = 'null'\n\n return [{\"info\": info_val0,\n \"images\": images0,\n \"annotations\": annotations0,\n \"categories\": categories0,\n \"licenses\": licence0}]","def get_data(data_list, data_cache):\n if not isinstance(data_list, list):\n data_list = [data_list]\n tmp = [data_cache[d] for d in data_list]\n if len(tmp) == 1:\n return tmp[0]\n res = ([t[0] for t in tmp], [t[1] for t in tmp])\n return res","def raw_features_extractor(database='./red_cod.db.pkl', sites=-1, elements = -1, maxatoms= -1,\r\n dictionary='diccionario', features='datosrahm.csv'):\r\n \r\n df=create_collection(database=database,sites=sites, elements=elements, maxatoms=maxatoms, \r\n dictionary=dictionary)\r\n \r\n start=time.time()\r\n \r\n datos=pd.read_csv(features)\r\n datos=datos.fillna(-1)\r\n\r\n dicc=dict(datos[['Symbol','Z']].values)\r\n\r\n dicc['D']=1\r\n dicc['Bk']=97\r\n dicc['Cf']=98\r\n dicc['Es']=99\r\n dicc['Fm']=100\r\n dicc['Md']=101\r\n dicc['No']=102\r\n dicc['Lr']=103\r\n \r\n max_sitios = max(df['sitios'].values)\r\n\r\n df=df[df['sitios'] <= max_sitios].reset_index(drop=True)\r\n \r\n X=np.zeros((len(df),max_sitios,104))\r\n y=np.zeros((len(df),1))\r\n mult=np.zeros((len(df),max_sitios))\r\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\r\n \r\n for row in range(len(df)):\r\n \r\n item=df['WyckOcc'][row]\r\n sitios=list(item.values()) \r\n sitocc=np.zeros((len(sitios),104))\r\n spacegroup = str(df['sgnum'][row]).zfill(3)\r\n \r\n try:\r\n \r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n sitios] for i in j]\r\n \r\n except:\r\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\n')\r\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\r\n '\\n','\\n')\r\n spacegroup=input('Give me the correct spacegroup:'+'\\n'+'\\n')\r\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\r\n list(df['WyckOcc'][row].values())] for i in j]\r\n \r\n occs=[]\r\n for i in range(len(sitios)):\r\n\r\n for j in list(sitios[i].values()):\r\n \r\n ocupacion=np.array(list(j.values()))\r\n llaves=[llave.replace('+','').replace('-','').replace('1',\r\n '').replace('2','').replace('3','').replace('4',\r\n '') for llave in np.array(list(j.keys()))]\r\n llaves=[llave.replace('.','') for llave in llaves]\r\n llaves=[llave.replace('5','').replace('6','').replace('7',\r\n '').replace('8','').replace('9','').replace('0',\r\n '') for llave in llaves]\r\n vector=np.zeros((1,104))\r\n occs=[sum(ocupacion)]+occs\r\n \r\n try:\r\n \r\n idx=[dicc[k] for k in llaves]\r\n \r\n except:\r\n \r\n print(' ELEMENTO NO IDENTIFICADO EN LA LISTA ',llaves,'\\n',\r\n 'REVISA EL SIGUIENTE CIF PARA HACER LA CORRECCION:','\\t',df['cif'][row])\r\n \r\n former = input('Elemento Incorrecto: ')\r\n current = input('Elemento Correcto: ')\r\n \r\n llaves=[current if x == former else x for x in llaves]\r\n idx=[dicc[k] for k in llaves]\r\n \r\n \r\n for k in idx:\r\n vector[0][k-1] = ocupacion[idx.index(k)]\r\n \r\n \r\n sitocc[i]=vector\r\n \r\n while sitocc.shape[0] != max_sitios:\r\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\r\n s=[0]+s\r\n \r\n X[row,:,:]=sitocc\r\n y[row]=df['target'][row]\r\n mult[row]=s\r\n \r\n S = np.expand_dims(mult,axis=2)\r\n features=datos.iloc[:,2:].values\r\n x=X[:,:,:96]\r\n \r\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\r\n \r\n x=np.dot(x,features) \r\n\r\n print('Atomic radii and electronegativities for each Wyckoff site extracted in',\r\n round(time.time()-start,2),' s') \r\n \r\n np.save('raw_features', x)\r\n np.save('output_values', y)\r\n np.save('multiplicities', S)\r\n np.save('occupation_fractions', fracsum)\r\n \r\n return x, y, S, fracsum, df","def __init__(self):\n self.data = []\n self.idx = {}","def MapDataList(ea, length, func, wordsize=1):\n PutDataList(ea, map(func, GetDataList(ea, length, wordsize)), wordsize)","def ana_merge_senzory_map(datas):\n#TODO: improve senzory map merging\n return iter(datas.viewvalues()).next()['senzory_map']","def test_compress_4_idenctical_char(self):\n text = 'bbbb'\n actual = LZ77.compress(text)\n expected = bytearray([32]) + bytearray(b'bb') + bytearray([0, 16])\n self.assertEqual(actual, expected)","def test_decompress_seq_diff_8_char(self):\n b_array = bytearray([0]) + bytearray(b'12345678')\n actual = LZ77.decompress(b_array)\n expected = '12345678'\n self.assertEqual(actual, expected)","def mapfn(k, v):\n for row in v:\n # rellenar el codigo\n pass","def upload_initial_data():\n data = {\"updated\":1512866292573,\"data\":[[[\"DAY %\",\"PPS (CAD)\",\"TKR\",\"NAME\",\"SECTOR\",\"EX.\",\"ALT. TKR\",\"VOL\",\"VOL @ CRNT $\",\"MC ($MM)\"],[\"5.84%\",\"1.45\",\"ABCN\",\"ABcann Medicinals\",\"BioTech\",\"CVE\",\"ABCCF\",\"901,940\",\"1,307,813\",\"78.49\"],[\"6.14%\",\"7.09\",\"ACB\",\"Aurora Cannabis\",\"Cultivation & Retail\",\"TSE\",\"ACBFF\",\"13,927,205\",\"98,743,883.45\",\"2,861.53\"],[\"9.09%\",\"0.24\",\"ACG\",\"Alliance Growers \",\"AgTech\",\"CNSX\",\"--\",\"1,189,385\",\"285,452.4\",\"\"],[\"0.00%\",\"0.10\",\"AFI\",\"Affinor Growers\",\"AgTech\",\"CNSX\",\"RSSFF\",\"210,310\",\"21,031\",\"\"],[\"3.22%\",\"13.47\",\"APH\",\"Aphria\",\"Cultivation & Retail\",\"TSE\",\"APHQF\",\"2,663,133\",\"35,872,401.51\",\"2,042.08\"],[\"13.95%\",\"0.25\",\"ATT\",\"Abattis Bio\",\"BioTech\",\"CNSX\",\"ATTBF\",\"2,706,357\",\"676,589.25\",\"39.86\"],[\"0.00%\",\"2.03\",\"BE\",\"Beleave\",\"Cultivation & Retail\",\"CNSX\",\"BLEVF\",\"597,909\",\"1,213,755.27\",\"\"],[\"1.28%\",\"1.58\",\"BLO\",\"Cannabix Tech\",\"LawTech\",\"CNSX\",\"BLOZF\",\"465,869\",\"736,073.02\",\"136.61\"],[\"-2.20%\",\"0.89\",\"CBW\",\"Cannabis Wheaton \",\"Investing & Finance\",\"CVE\",\"KWFLF\",\"815,477\",\"725,774.53\",\"234.57\"],[\"-0.65%\",\"19.93\",\"CMED\",\"Cannimed\",\"Cultivation & Retail\",\"TSE\",\"CMMDF\",\"130,722\",\"2,605,289.46\",\"457.69\"],[\"12.73%\",\"0.62\",\"CMM\",\"Canabo Medical\",\"MedCare\",\"CVE\",\"CAMDF\",\"330,404\",\"204,850.48\",\"23.54\"],[\"-2.87%\",\"2.71\",\"CRZ\",\"Cannaroyalty\",\"Investing & Finance\",\"CNSX\",\"CNNRF\",\"961,449\",\"2,605,526.79\",\"115.09\"],[\"-6.67%\",\"0.28\",\"CYX\",\"Calyx Bio\",\"AgTech\",\"CVE\",\"CLYXF\",\"2,120,562\",\"593,757.36\",\"24.23\"],[\"0.00%\",\"1.23\",\"DOJA\",\"DOJA Cannabis\",\"Cultivation & Retail\",\"CNSX\",\"DJACF\",\"206,635\",\"254,161.05\",\"72.27\"],[\"-4.40%\",\"0.44\",\"DVA\",\"Delivra\",\"BioTech\",\"CVE\",\"--\",\"89,485\",\"39,373.4\",\"19.55\"],[\"6.52%\",\"0.25\",\"EAT\",\"Nutritional High\",\"Marijuana Edibles & Extracts\",\"CNSX\",\"SPLIF\",\"3,067,636\",\"766,909\",\"61.54\"],[\"-1.20%\",\"1.64\",\"EMC\",\"Emblem\",\"Cultivation & Retail\",\"CVE\",\"EMMBF\",\"411,764\",\"675,292.96\",\"130.60\"],[\"2.05%\",\"3.98\",\"EMH\",\"Emerald\",\"Cultivation & Retail\",\"CVE\",\"TBQBF\",\"1,430,067\",\"5,691,666.66\",\"374.34\"],[\"-5.88%\",\"0.48\",\"FFT\",\"Future Farm Tech\",\"AgTech\",\"CNSX\",\"AGSTF\",\"1,291,240\",\"619,795.2\",\"0.61\"],[\"1.06%\",\"1.90\",\"FIRE\",\"Supreme Pharma\",\"Cultivation & Retail\",\"CVE\",\"SPRWF\",\"1,275,906\",\"2,424,221.4\",\"391.96\"],[\"5.26%\",\"0.10\",\"GHG\",\"Global Hemp\",\"Cultivation & Retail\",\"CNSX\",\"GBHPF\",\"764,350\",\"76,435\",\"\"],[\"3.28%\",\"0.31\",\"GLH\",\"Golden Leaf\",\"Marijuana Products\",\"CNSX\",\"GLDFF\",\"4,298,567\",\"1,332,555.77\",\"116.96\"],[\"-1.96%\",\"0.50\",\"HC\",\"High Hampton Holdings\",\"Investing & Finance\",\"CNSX\",\"--\",\"727,116\",\"363,558\",\"\"],[\"1.89%\",\"0.54\",\"HIP\",\"Newstirke Resources \",\"Cultivation & Retail\",\"CVE\",\"NWKRF\",\"431,875\",\"233,212.5\",\"210.35\"],[\"8.91%\",\"1.10\",\"HVST\",\"Harvest One Cannabis\",\"Cultivation & Retail\",\"CVE\",\"HRVOF\",\"2,192,877\",\"2,412,164.7\",\"98.10\"],[\"8.89%\",\"0.98\",\"ICC\",\"International Cannabis\",\"Cultivation & Retail\",\"CVE\",\"ICCLF\",\"123,538\",\"121,067.24\",\"110.84\"],[\"0.00%\",\"1.62\",\"IMH\",\"Invictus MD\",\"Investing & Finance\",\"CVE\",\"IVITF\",\"781,924\",\"1,266,716.88\",\"129.87\"],[\"12.50%\",\"0.90\",\"IN\",\"Inmed Pharma\",\"BioTech\",\"CNSX\",\"IMLFF\",\"3,846,586\",\"3,461,927.4\",\"\"],[\"2.27%\",\"1.80\",\"ISOL\",\"Isodiol International \",\"Hemp Products\",\"CNSX\",\"LAGBF\",\"8,514,952\",\"15,326,913.6\",\"\"],[\"7.84%\",\"0.28\",\"KALY\",\"Kalytera Therapeutics\",\"BioTech\",\"CVE\",\"QUEZD\",\"5,634,186\",\"1,577,572.08\",\"34.74\"],[\"-1.72%\",\"0.57\",\"LDS\",\"Lifestyle Delivery Systems\",\"BioTech\",\"CNSX\",\"LDSYF\",\"685,628\",\"390,807.96\",\"51.44\"],[\"0.19%\",\"15.50\",\"LEAF\",\"MedReleaf Corp\",\"Cultivation & Retail\",\"TSE\",\"MEDFF\",\"229,190\",\"3,552,445\",\"1,459.18\"],[\"2.33%\",\"0.44\",\"LIB\",\"Liberty Leaf Holdings\",\"Investing & Finance\",\"CNSX\",\"LIBFF\",\"4,555,082\",\"2,004,236.08\",\"\"],[\"10.42%\",\"1.59\",\"LXX\",\"Lexaria Bio\",\"Hemp Products\",\"CNSX\",\"LXRP\",\"1,523,338\",\"2,422,107.42\",\"\"],[\"-1.38%\",\"2.14\",\"MARI\",\"Maricann Group\",\"Cultivation & Retail\",\"CNSX\",\"MRRCF\",\"678,106\",\"1,451,146.84\",\"157.10\"],[\"3.26%\",\"0.95\",\"MDM\",\"Marapharm\",\"Cultivation & Retail\",\"CNSX\",\"MRPHF\",\"209,019\",\"198,568.05\",\"\"],[\"0.00%\",\"0.57\",\"MGW\",\"Maple Leaf Green World\",\"Cultivation & Retail\",\"CVE\",\"MGWFF\",\"367,479\",\"209,463.03\",\"83.83\"],[\"7.37%\",\"1.02\",\"MJ\",\"True Leaf\",\"Hemp Pet Chews\",\"CNSX\",\"TLFMF\",\"164,101\",\"167,383.02\",\"\"],[\"2.27%\",\"4.50\",\"MJN\",\"Pharmacan /Cronos\",\"Investing & Finance\",\"CVE\",\"PRMCF\",\"419,922\",\"1,889,649\",\"675.43\"],[\"4.23%\",\"2.71\",\"MYM\",\"My Marijuana\",\"Cultivation & Retail\",\"CNSX\",\"--\",\"1,066,122\",\"2,889,190.62\",\"\"],[\"4.40%\",\"0.95\",\"N\",\"Namaste Tech\",\"Consumption Devices\",\"CNSX\",\"NXTTF\",\"5,714,764\",\"5,429,025.8\",\"192.50\"],[\"0.00%\",\"0.10\",\"NF\",\"New Age Farm\",\"Hemp Products\",\"CNSX\",\"NWGFF\",\"3,938,476\",\"393,847.6\",\"\"],[\"-7.27%\",\"0.25\",\"NSP\",\"Naturally Splendid\",\"Hemp Products\",\"CVE\",\"NSPDF\",\"484,812\",\"121,203\",\"24.42\"],[\"4.99%\",\"3.79\",\"OGI\",\"Organigram\",\"Cultivation & Retail\",\"CVE\",\"OGRMF\",\"3,654,843\",\"13,851,854.97\",\"375.89\"],[\"1.15%\",\"0.88\",\"PUF\",\"PUF Ventures\",\"Consumption Devices\",\"CNSX\",\"PUFXF\",\"719,534\",\"633,189.92\",\"45.85\"],[\"10.68%\",\"1.14\",\"RHT\",\"Reliq Health Tech\",\"Mobile Software\",\"CVE\",\"RQHTF\",\"1,564,567\",\"1,783,606.38\",\"98.74\"],[\"4.05%\",\"1.80\",\"RTI\",\"Radient Technologies\",\"Extraction\",\"CVE\",\"RDDTF\",\"2,181,473\",\"3,926,651.4\",\"345.53\"],[\"3.64%\",\"0.28\",\"RVV\",\"Revive Therapeutics\",\"Medication\",\"CVE\",\"RVVTF\",\"399,705\",\"111,917.4\",\"15.50\"],[\"-2.90%\",\"0.67\",\"SUN\",\"Wildflower\",\"Hemp Products\",\"CNSX\",\"WLDFF\",\"87,197\",\"58,421.99\",\"29.48\"],[\"-0.67%\",\"4.45\",\"SXP\",\"Supremex\",\"Packaging\",\"TSE\",\"SUMXF\",\"27,015\",\"120,216.75\",\"126.40\"],[\"0.00%\",\"0.76\",\"TBP\",\"Tetra Bio-Pharma\",\"BioTech\",\"CVE\",\"GRPOF\",\"497,745\",\"378,286.2\",\"88.67\"],[\"2.44%\",\"2.10\",\"TER\",\"TerrAscend Corp\",\"Cultivation & Retail\",\"CNSX\",\"--\",\"270,176\",\"567,369.6\",\"\"],[\"4.29%\",\"0.73\",\"THC\",\"THC Biomed\",\"BioTech\",\"CNSX\",\"THCBF\",\"818,162\",\"597,258.26\",\"81.29\"],[\"3.55%\",\"3.21\",\"THCX\",\"Hydropothecary Corp\",\"Cultivation & Retail\",\"CVE\",\"HYYDF\",\"1,581,640\",\"5,077,064.4\",\"282.37\"],[\"8.22%\",\"0.79\",\"TNY\",\"Tinley Beverage Co\",\"Beverage\",\"CNSX\",\"QRSRF\",\"945,154\",\"746,671.66\",\"57.81\"],[\"3.49%\",\"7.70\",\"TRST\",\"CannTrust\",\"Cultivation & Biotech\",\"CNSX\",\"CNTTF\",\"368,892\",\"2,840,468.4\",\"699.98\"],[\"-8.04%\",\"1.03\",\"VGW\",\"Valens Groworks\",\"BioTech\",\"CNSX\",\"MYMSF\",\"23,285\",\"23,983.55\",\"62.77\"],[\"0.00%\",\"0.52\",\"VIN\",\"Vinergy Resources\",\"Investing & Finance\",\"CNSX\",\"VNNYF\",\"0\",\"\",\"\"],[\"-2.50%\",\"0.39\",\"VP\",\"Vodis Pharma\",\"Cultivation & Retail\",\"CNSX\",\"VDQSF\",\"52,661\",\"20,537.79\",\"\"],[\"6.67%\",\"0.80\",\"VRT\",\"Veritas Pharma\",\"BioTech\",\"CNSX\",\"VRTHF\",\"377,901\",\"302,320.8\",\"\"],[\"6.41%\",\"19.42\",\"WEED\",\"Canopy Growth\",\"Cultivation & Retail\",\"TSE\",\"TWMJF\",\"4,940,034\",\"95,935,460.28\",\"3,706.63\"],[\"6.25%\",\"2.38\",\"WMD\",\"WeedMD\",\"Cultivation & Retail\",\"CVE\",\"WDDMF\",\"1,174,148\",\"2,794,472.24\",\"124.71\"],[\"3.36%\",\"14.75\",\"HMMJ\",\"Horizons Marijuana Life Sciences\",\"Canadian Marijuana ETF\",\"TSE\",\"HMLSF\",\"336,579\",\"4,964,540.25\",\"197.64\"]]],\"sheetnames\":[\"ALLSHOW\"]}\n \n exchange_suffixes = {'TSE': 'TO', 'CVE': 'V'}\n\n # create sector\n sector, _ = Sector.objects.get_or_create(name=\"Cannabis\", slug='cannabis')\n\n # create currency\n currency, _ = Currency.objects.get_or_create(symbol='CAD', defaults={'character':'$', 'name':'Canadian Dollar'})\n us_currency, _ = Currency.objects.get_or_create(symbol='USD', defaults={'character':'$', 'name':'US Dollar'})\n\n # OTC exchange\n otc, _ = Exchange.objects.get_or_create(symbol='OTC', defaults={'name':'OTC', 'currency': us_currency})\n\n # iterate over each item in our table, make the items\n for row in data[\"data\"][0][1:]:\n # percent = float(row[0].replace(\"%\",\"\"))\n suffix = exchange_suffixes[row[5]] if row[5] in exchange_suffixes else ''\n exchange, _ = Exchange.objects.get_or_create(symbol=row[5], defaults={'name':row[5], 'currency':currency, 'ticker_suffix': suffix})\n company, _ = Company.objects.get_or_create(name=row[3], defaults={'sector':sector})\n stock, _ = Stock.objects.get_or_create(ticker=row[2], defaults={\n 'company': company,\n 'exchange': exchange,\n 'market_cap': float(row[9].replace(\",\",\"\")) * 1000000 if row[9] else 0.0,\n # 'previous_close': float(row[1]) - float(row[1]) * percent / 100,\n # 'open': float(row[1]),\n # 'current': float(row[1]),\n # 'volume': float(row[8].replace(\",\",\"\")) if row[8] else 0.0,\n })\n stock.save()\n\n if row[4]:\n tag, _ = Tag.objects.get_or_create(name=row[4])\n company.tags.add(tag)\n\n if row[6] and not row[6] == \"--\":\n stock, _ = Stock.objects.get_or_create(ticker=row[6], defaults={'company':company, 'exchange':otc})\n\n\n print data","def lego_sets():\n \n \n data_test=data_specific\n\n\n\n \n \n\n \n print(data_test)\n print(\"The size of the data is: \",len(data_test))\n \n \n \n # you must replace this line and return your own list\n return data_test","def lad_lut(lads):\n for lad in lads:\n yield lad['properties']['name']","def decodeLZ(fileIn, fileOut, dictionarySize = 1114112):\n try:\n fileContent = FileWork.getFileContent(fileIn)\n\n getCharByCode = {}\n getCodeByChar = {}\n\n for i in range(0, dictionarySize):\n getCharByCode[i] = chr(i)\n getCodeByChar[chr(i)] = i\n\n currentCode = dictionarySize\n\n fileContent = fileContent.split(' ')\n buffer = getCharByCode[int(fileContent[0])]\n decodeFileContent = \"\"\n for i in range(1, len(fileContent)):\n character = \"\"\n if int(fileContent[i]) in getCharByCode:\n character = getCharByCode[int(fileContent[i])]\n else:\n getCharByCode[currentCode] = buffer + buffer[0]\n getCodeByChar[buffer + buffer[0]] = currentCode\n currentCode += 1\n decodeFileContent += buffer\n character = buffer[0]\n\n if (buffer + character[0]) in getCodeByChar:\n buffer = buffer + character[0]\n else:\n getCharByCode[currentCode] = buffer + character[0]\n getCodeByChar[buffer + character[0]] = currentCode\n currentCode += 1\n decodeFileContent += buffer\n buffer = character\n decodeFileContent += buffer\n FileWork.writeToFile(fileOut, decodeFileContent)\n return True\n except Exception:\n return False","def getAllIndex(ldata, fldata):\n\treturn list(map(lambda e : fldata.index(e), ldata))","def huffman_data_decode(data: hic.BitStringP, huffman: huffman.HuffmanTree) -> list:\n return huffman.decode_data(data.payload)","def codebook_json_data_factory() -> List[Dict[str, Any]]:\n codebook_data = [\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_A\"\n },\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_B\"\n },\n ]\n return codebook_data","def make_data(revs, word_idx_map, max_l=50, filter_h=3\n ,train_instance = 16541,val_instance = 1645):\n train, val, test = [], [], []\n for rev in revs:\n sent = get_idx_from_sent(rev[\"m\"], word_idx_map, max_l, filter_h)\n sent += get_idx_from_sent(rev[\"r\"], word_idx_map, max_l, filter_h)\n sent.append(int(rev[\"y\"]))\n sent.append(int(rev[\"t\"]))\n sent.append(int(rev[\"t2\"]))\n\n if len(train) < train_instance:\n train.append(sent)\n elif (len(train) + len(val)) < train_instance + val_instance:\n val.append(sent)\n else:\n test.append(sent)\n\n shuffle(train)\n train = np.array(train,dtype=\"int\")\n val = np.array(val,dtype=\"int\")\n test = np.array(test,dtype=\"int\")\n print 'trainning data', len(train),'val data', len(val)\n return [train, val, test]","def test_decompress_seq_diff_9_char(self):\n b_array = bytearray([0]) + bytearray(b'12345678') \\\n + bytearray([0]) + bytearray(b'9')\n actual = LZ77.decompress(b_array)\n expected = '123456789'\n self.assertEqual(actual, expected)","def test_concat_data(self):\n\n this_satellite_dict = satellite_io.concat_data(\n satellite_dicts=[\n SATELLITE_DICT_SUBSET_BY_INDEX, SATELLITE_DICT_SUBSET_BY_TIME\n ]\n )\n\n self.assertTrue(compare_satellite_dicts(\n this_satellite_dict, SATELLITE_DICT_CONCAT\n ))","def compress(uncompressed):\r\n \r\n # Build the dictionary.\r\n dict_size = 256\r\n dictionary = dict((chr(i), i) for i in range(dict_size))\r\n # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}\r\n \r\n w = \"\"\r\n result = []\r\n for c in uncompressed:\r\n wc = w + c\r\n if wc in dictionary:\r\n w = wc\r\n else:\r\n result.append(dictionary[w])\r\n # Add wc to the dictionary.\r\n dictionary[wc] = dict_size\r\n dict_size += 1\r\n w = c\r\n \r\n # Output the code for w.\r\n if w:\r\n result.append(dictionary[w])\r\n return result","def dlist(src):\n if isinstance(src, dict):\n for k in src:\n src[k] = dlist(src[k])\n if set(src) == set([str(k) for k in range(len(src))]):\n src = [src[str(k)] for k in range(len(src))]\n return src","def get_precomp_hashcodes(self,list_feats_id):\n return self.get_precomp_X(list_feats_id,\"hashcodes\",self.bits_num/8,np.uint8)","def __init__(self, url=\"https://ehw.fit.vutbr.cz/izv/\",folder=\"data\", cache_filename=\"data_{}.pkl.gz\"):\n self.url = url\n self.folder = folder\n self.cache_filename = cache_filename\n self.ListOfZipFiles = []\n self.saved_data = {}\n self.headers = [\"region\", \"p1\", \"p36\", \"p37\", \"p2a\", \"weekday(p2a)\", \"p2b\", \"p6\", \"p7\", \"p8\", \"p9\", \"p10\", \"p11\", \"p12\", \"p13a\", \"p13b\", \"p13c\", \"p14\", \"p15\", \"p16\", \"p17\", \"p18\", \"p19\", \"p20\", \"p21\", \"p22\", \"p23\", \"p24\", \"p27\", \"p28\", \"p34\", \"p35\", \"p39\", \"p44\", \"p45a\", \"p47\", \"p48a\", \"p49\", \"p50a\", \"p50b\", \"p51\", \"p52\", \"p53\",\"p55a\",\"p57\",\"p58\",\"a\",\"b\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\",\"n\",\"o\",\"p\",\"q\",\"r\",\"s\",\"t\",\"p5a\"]\n self.duplicate_handling = []\n if not re.search(\"^.*{}.*$\", self.cache_filename):\n self.cache_filename+=\"{}\"","def add_hash(self, lst):\n self.__data = []\n num = len(lst) + 1\n self.add_data((\"\", 4, 1))\n self.add_data((\"\", 4, num))\n self.add_data((\"\", 4, num - 1))\n self.add_data((\"\", 4, 0))\n if 1 < num:\n for ii in range(num - 1):\n self.add_data((\"\", 4, ii))","def test_lz4_decompression_avoids_deep_copy():\n pytest.importorskip(\"lz4\")\n a = bytearray(1_000_000)\n b = compressions[\"lz4\"].compress(a)\n c = compressions[\"lz4\"].decompress(b)\n assert isinstance(c, bytearray)","def length_of_data(self, data_list, days):\n\n # The zip() function returns an iterator of tuples based on the iterable object.\n btceur_high, btceur_timestamp = zip(*data_list)\n\n # Various lists/variables used\n one_week_list_timezone = []\n one_week_list_btc_high = []\n count = 0\n\n # Get the latest entry to work back from\n orig = datetime.datetime.fromtimestamp(btceur_timestamp[-1])\n # Get a date 7 days from the current date\n one_week = orig - datetime.timedelta(days=days)\n # Loop backwards through the timestamps getting the past 7 days worth\n\n for I in reversed(btceur_timestamp):\n converted_timestamp_value = (datetime.datetime.fromtimestamp(I))\n if one_week > converted_timestamp_value < orig:\n one_week_list_timezone.append(converted_timestamp_value)\n else:\n continue\n if one_week_list_timezone[-1] \\\n > one_week:\n return None\n else:\n # Get the corresponding data to each time stamp\n for x in reversed(btceur_high):\n if count < len(one_week_list_timezone):\n one_week_list_btc_high.append(x)\n count += 1\n\n # Reorder both lists of data\n one_week_list_btc_high.reverse()\n one_week_list_timezone.reverse()\n zipped = zip(one_week_list_btc_high, one_week_list_timezone)\n return list(zipped)","def build_dataset(self):\n\n print('begin to build data set...')\n\n # build dictionary\n for e in self.lst_entity:\n self.dictionary_entity[e] = len(self.dictionary_entity)\n\n for r in self.lst_relation:\n self.dictionary_relation[r] = len(self.dictionary_relation)\n\n self.reverse_dictionary_entity = dict(zip(self.dictionary_entity.values(), self.dictionary_entity.keys()))\n self.reverse_dictionary_relation = dict(zip(self.dictionary_relation.values(), self.dictionary_relation.keys()))\n\n # build data map\n self.lst_entity_map = [self.dictionary_entity[e] for e in self.lst_entity]\n self.lst_relation_map = [self.dictionary_relation[r] for r in self.lst_relation]\n self.lst_triplet_train_map = self.map_triplet(self.lst_triplet_train)\n\n # build corrupted candidates for (h,r,~) and (~,r,t)\n for (h, r, t) in self.lst_triplet_train_map:\n if r not in self.dict_tofh:\n self.dict_tofh[r] = {h: [t]}\n else:\n if h not in self.dict_tofh[r]:\n self.dict_tofh[r][h] = [t]\n else:\n self.dict_tofh[r][h].append(t)\n\n if r not in self.dict_hoft:\n self.dict_hoft[r] = {t: [h]}\n else:\n if t not in self.dict_hoft[r]:\n self.dict_hoft[r][t] = [h]\n else:\n self.dict_hoft[r][t].append(h)\n\n for r in self.dict_tofh:\n self.lst_triplet_corrupted_tail[r] = dict()\n for h in self.dict_tofh[r]:\n set_tail_corrupted_all = set(self.lst_entity_map) - set(self.dict_tofh[r][h])\n lst_tail_corrupted_choose = random.sample(set_tail_corrupted_all, 5*len(self.dict_tofh[r][h]))\n self.lst_triplet_corrupted_tail[r][h] = lst_tail_corrupted_choose\n\n for r in self.dict_hoft:\n self.lst_triplet_corrupted_head[r] = dict()\n for t in self.dict_hoft[r]:\n lst_head_corrupted_all = set(self.lst_entity_map) - set(self.dict_hoft[r][t])\n lst_head_corrupted_choose = random.sample(lst_head_corrupted_all, 5*len(self.dict_hoft[r][t]))\n self.lst_triplet_corrupted_head[r][t] = lst_head_corrupted_choose\n\n print('data set has been built successfully!')","def test_compress_lossless_maps(self):\n height_map = 384\n width_map = 384\n \n # The quantization bin widths are small\n # so that the comparison between the\n # theoretical (minimum) coding cost and\n # the coding cost computed by the function\n # is precise enough.\n bin_widths_test = numpy.array([0.5, 0.25], dtype=numpy.float32)\n laplace_scales = numpy.array([0.5, 3.], dtype=numpy.float32)\n \n # Note that the binary probabilities saved at\n # \"lossless/pseudo_data/binary_probabilities_compress_maps_0.npy\"\n # and those saved at\n # \"lossless/pseudo_data/binary_probabilities_compress_maps_1.npy\"\n # are specific to the three Laplace distributions\n # below. This means that the binary probabilities\n # must be modified if `laplace_scales` is modified.\n paths_to_binary_probabilities = [\n 'lossless/pseudo_data/binary_probabilities_compress_maps_0.npy',\n 'lossless/pseudo_data/binary_probabilities_compress_maps_1.npy'\n ]\n \n centered_data_0 = numpy.random.laplace(loc=0.,\n scale=laplace_scales[0].item(),\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\n centered_data_1 = numpy.random.laplace(loc=0.,\n scale=laplace_scales[1].item(),\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\n centered_data = numpy.concatenate((centered_data_0, centered_data_1),\n axis=3)\n expanded_centered_quantized_data = tls.quantize_per_map(centered_data, bin_widths_test)\n centered_quantized_data = numpy.squeeze(expanded_centered_quantized_data,\n axis=0)\n tiled_bin_widths = numpy.tile(numpy.reshape(bin_widths_test, (1, 1, 2)),\n (height_map, width_map, 1))\n ref_int16 = tls.cast_float_to_int16(centered_quantized_data/tiled_bin_widths)\n (rec_int16_0, nb_bits_each_map_0) = \\\n lossless.compression.compress_lossless_maps(ref_int16,\n paths_to_binary_probabilities[0])\n numpy.testing.assert_equal(ref_int16,\n rec_int16_0,\n err_msg='The test fails as the lossless compression alters the signed integers.')\n (rec_int16_1, nb_bits_each_map_1) = \\\n lossless.compression.compress_lossless_maps(ref_int16,\n paths_to_binary_probabilities[1])\n numpy.testing.assert_equal(ref_int16,\n rec_int16_1,\n err_msg='The test fails as the lossless compression alters the signed integers.')\n \n # The equation below is derived from the\n # theorem 8.3.1 in the book\n # \"Elements of information theory\", 2nd edition,\n # written by Thomas M. Cover and Joy A. Thomas.\n theoretical_entropies = -numpy.log2(bin_widths_test) + (numpy.log(2.*laplace_scales) + 1.)/numpy.log(2.)\n print('B0 denotes the binary probabilities saved at \"{}\".'.format(paths_to_binary_probabilities[0]))\n print('B1 denotes the binary probabilities saved at \"{}\".'.format(paths_to_binary_probabilities[1]))\n print('\\n1st centered-quantized latent variable feature map.')\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[0]*height_map*width_map))\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[0]))\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[0]))\n print('\\n2nd centered-quantized latent variable feature map.')\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[1]*height_map*width_map))\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[1]))\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[1]))","def valid_zip(x):\r\n m_zip = [83,10286,10276,10268,10256,10249,10159,10150,10116,10113,10108,10101,10008,10282,10281,10280,10279,10278,10275,\r\n 10271,10270,10199,10178,10177,10176,10175,10174,10173,10172,10171,10170,10169,10168,10167,10166,10165,\r\n 10162,10161,10158,10155,10154,10153,10152,10151,10128,10123,10122,10121,10120,10119,10118,10115,10112,\r\n 10111,10110,10107,10106,10105,10104,10103,10099,10098,10095,10090,10069,10060,10055,10048,10047,10045,10044,\r\n 10041,10040,10039,10038,10037,10036,10035,10034,10033,10032,10031,10030,10029,10028,10027,10026,10025,\r\n 10024,10023,10022,10021,10020,10019,10018,10017,10016,10015,10014,10013,10012,10011,10010,10009,10007,\r\n 10006,10005,10004,10003,10002,10001,10065,10075,10080,\r\n 10285,10203,10178,10017,10178,10168,10167,10177,# supplementary\r\n 10175,10166,10171,10176,10174,10165,10170,10173,10169,10172,10019, 10105, 10097, 10104, 10107, 10103, 10106,\r\n 10022, 10055, 10155, 10152, 10153, 10151, 10154, 10001, 10120, 10119, 10118, 10123, 10122, 10121,\r\n 10005, 10081, 10286, 10260, 10271, 10259, 10043, 10270, 10265, 10203,10036, 10096, 10196, 10110\r\n ]\r\n brooklyn_zip = [11256,11252,11249,11243,11242,11241,11239,11238,11237,11236,11235,11234,11233,11232,11231,\r\n 11230,11229,11228,11226,11225,11224,11223,11222,11221,11220,11219,11218,11217,11216,11215,\r\n 11214,11213,11212,11211,11210,11209,11208,11207,11206,11205,11204,11203,11201]\r\n queens_zip = [11451,11436,11435,11434,11433,11432,11429,11428,11427,11426,\r\n 11423,11422,11421,11420,11419,11418,11417,11416,11415,11414,11413,11412,11411,11385,11379,\r\n 11378,11377,11375,11374,11373,11372,11369,11368,11367,11366,11365,11364,11363,\r\n 11362,11361,11360,11359,11358,11357,11356,11355,11354,11351,11109,11106,11105,11104,11103,\r\n 11102,11101,11004]\r\n if x in m_zip + brooklyn_zip + queens_zip:\r\n return 1\r\n else:\r\n return 0","def make_dict(data_for_dict): \n \n column_name_list = data_for_dict[0]\n db_list = data_for_dict[1:]\n \n column_list1 = []\n column_list2 = []\n column_list3 = []\n column_list4 = []\n column_list5 = []\n column_list6 = []\n column_list7 = []\n column_list8 = []\n column_list9 = []\n column_list10 = []\n column_list11 = []\n hmdb_dict = {}\n for line in db_list:\n my_string1 = '' \n my_string2 = ''\n my_string3 = ''\n my_string4 = ''\n my_string5 = ''\n my_string6 = ''\n my_string7 = ''\n my_string8 = ''\n my_string9 = ''\n my_string10 = ''\n my_string11 = ''\n\n my_string1 = line[0]\n column_list1 += [my_string1]\n my_string2 += line[1]\n column_list2 += [my_string2]\n my_string3 += line[2]\n column_list3 += [my_string3]\n my_string4 += line[3]\n column_list4 += [my_string4]\n my_string5 += line[4]\n column_list5 += [my_string5]\n my_string6 += line[5]\n column_list6 += [my_string6]\n my_string7 += line[6]\n column_list7 += [my_string7]\n my_string8 += line[7]\n column_list8 += [my_string8]\n my_string9 += line[8]\n column_list9 += [my_string9]\n my_string10 += line[9]\n column_list10 += [my_string10]\n my_string11 += line[10]\n column_list11 += [my_string11] \n \n hmdb_dict[column_name_list[0]] = column_list1\n hmdb_dict[column_name_list[1]] = column_list2\n hmdb_dict[column_name_list[2]] = column_list3\n hmdb_dict[column_name_list[3]] = column_list4\n hmdb_dict[column_name_list[4]] = column_list5\n hmdb_dict[column_name_list[5]] = column_list6\n hmdb_dict[column_name_list[6]] = column_list7\n hmdb_dict[column_name_list[7]] = column_list8\n hmdb_dict[column_name_list[8]] = column_list9\n hmdb_dict[column_name_list[9]] = column_list10\n hmdb_dict[column_name_list[10]] = column_list11\n \n return (hmdb_dict)","def set_lz(self):\r\n self._lz = self.dz * self.nz - self.oz","def list_data_collate(batch: Sequence):\n elem = batch[0]\n data = [i for k in batch for i in k] if isinstance(elem, list) else batch\n key = None\n try:\n if config.USE_META_DICT:\n data = pickle_operations(data) # bc 0.9.0\n if isinstance(elem, Mapping):\n ret = {}\n for k in elem:\n key = k\n data_for_batch = [d[key] for d in data]\n ret[key] = collate_meta_tensor(data_for_batch)\n else:\n ret = collate_meta_tensor(data)\n return ret\n except RuntimeError as re:\n re_str = str(re)\n if \"equal size\" in re_str:\n if key is not None:\n re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n re_str += (\n \"\\n\\nMONAI hint: if your transforms intentionally create images of different shapes, creating your \"\n + \"`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its \"\n + \"documentation).\"\n )\n _ = dev_collate(data)\n raise RuntimeError(re_str) from re\n except TypeError as re:\n re_str = str(re)\n if \"numpy\" in re_str and \"Tensor\" in re_str:\n if key is not None:\n re_str += f\"\\nCollate error on the key '{key}' of dictionary data.\"\n re_str += (\n \"\\n\\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, \"\n + \"creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem \"\n + \"(check its documentation).\"\n )\n _ = dev_collate(data)\n raise TypeError(re_str) from re","def test_compress_2_idenctical_char(self):\n text = 'aa'\n actual = LZ77.compress(text)\n expected = bytearray([0]) + bytearray(b'aa')\n self.assertEqual(actual, expected)","def build_dataset(words):\n count = []\n # count.extend(collections.Counter(words).most_common(n_words - 1))\n count.extend(collections.Counter(words).most_common())\n dictionary = dict()\n for word, _ in count:\n dictionary[word] = len(dictionary)\n data = list()\n # unk_count = 0\n for word in words:\n index = dictionary.get(word, 0)\n # if index == 0: # dictionary['UNK']\n # unk_count += 1\n data.append(index)\n # count[0][1] = unk_count\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n data = [data[::2],data[1::2]]\n new_data = list()\n for i in range(len(data[0])):\n new_data.append([data[0][i],data[1][i]])\n data = new_data\n vocabulary_size = len(dictionary)\n print(\"\\n\\ndictionary size = \")\n print(len(dictionary))\n return data, count, dictionary, reversed_dictionary, vocabulary_size","def __generate_tuple_keys(self, data):\n if len(data) < self.order:\n return\n\n for i in range(len(data) - self.order):\n yield [tuple(data[i:i+self.order]), data[i+self.order]]","def get_lz(self):\r\n return self.dz * self.nz - self.oz","def build_list(self, word_list):\n # Get frequency list for keys\n freq = word_list.groupby('key').agg('count')\n # Filter out only keys with greater or equal frequency to length\n key_list = freq.loc[freq['word'] >= freq.index.str.len()]\n return key_list","def getJar(oldDatas,jarList):\n #clear pastebin here. confirmed.\n if pastebin in os.listdir():\n tempdir = barrand6()\n os.rename(pastebin,tempdir)#clever..fine..\n shutil.rmtree(tempdir)#acces deniyed ..fixed!\n os.mkdir(pastebin)\n\n\n oldDatakeys = oldDatas.keys()\n newDatas = {} #for data insure.\n\n for noFolder in jarList:\n try:\n #txtFiledir = os.path.join( origins,noFolder,noFolder+'.txt')\n txtFilename = noFolder+'.txt'\n #------------------------user pre-sure.\n txtfiles = []\n for f in os.listdir( os.path.join( jar,noFolder) ):\n if '.txt' in f:\n txtfiles.append(f)\n if len(txtfiles) == 1:\n txtFilename = txtfiles[0]\n else:\n if not '설정.txt' in txtfiles:\n raise Exception('ERROR no txt : ' + str(noFolder))\n txtFilename = '설정.txt'\n #------------------------user pre-sure\n\n txtFile = os.path.join( jar,noFolder,txtFilename) #whatif dir = no.txt?\n parsedDict = txt2dict.parseTxt(txtFile,parseKeys,multiLineKey)#hope it's atleast complete...\n\n #----------------------for custom dict additional option\n checklist = ['번호','제목','작성자','날짜','본문']\n for c in checklist:\n if not c in parsedDict.keys():\n raise Exception('ERROR!! not format txt : ' + str(noFolder))\n\n #user input, do another func.\n #'16'.isdigit()\n #if int(parsedDict['번호'])<1:\n #raise Exception('ERROR!! of : ' + str(noFolder))\n #a = parsedDict['날짜'].split('.')\n #b = str(datetime.date.today()).split('-')\n #if datetime.date(a[0],a[1],a[2]) < datetime.date.today()\n\n if '태그' in parsedDict.keys():\n tagList = parsedDict['태그'].split(',')\n parsedDict['유저태그'] = tagList\n del parsedDict['태그']\n else:\n parsedDict['유저태그'] = []\n\n if parsedDict['제목'].startswith('[번역]'):\n parsedDict['제목'] = parsedDict['제목'].split('[번역]')[1].strip()\n if parsedDict['제목'].find('센세)') != -1 :\n parsedDict['유저태그'].append( parsedDict['제목'].split('센세)')[0].strip()+'센세' )\n #parsedDict['태그'].append( a.split('[번역]')[1].strip().split('센세)')[0]+'센세)' )\n #----------------------for custom dict additional option\n\n\n #----------------------------- after get parsedDict.\n tmpKey = parsedDict[idKey] #9133114\n if tmpKey in oldDatakeys:\n raise Exception('skip.. id already in parsedict ..: ' + str(noFolder))\n\n idFoldername = parsedDict['번호']\n noFolderpath = os.path.join( jar,noFolder )\n originPath = os.path.join( origins , idFoldername )\n shutil.copytree(noFolderpath,originPath, dirs_exist_ok = False)# was true, but to integrity....\n #it occured at test. nodict, but files.\n #shutil.move(noFolderpath,pastebin)\n\n\n #datas is dict object, appended new key,value.\n #add more value.\n #datas[tmpKey]['key'] = 'value'\n\n # get moved nofolder, add datas originImgs.\n originFiles = os.listdir(os.path.join( origins, idFoldername))\n originImgs = []\n for img in originFiles:\n ext = os.path.splitext( img )[1][1:] # .jpg == jpg\n if ext in imgExt: #now, it's img.\n originImgs.append(img)\n if originImgs==[]:\n raise Exception('ERROR!! no img..: ' + str(noFolder))\n parsedDict[originKey] = originImgs\n\n newDatas[tmpKey] = parsedDict\n #datas[tmpKey][allfilesKey] = originFiles\n thisrand=barrand6()\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\n\n except Exception as e:\n exc_info = sys.exc_info()#below except.\n errmsg = exc_info[1],':at line',exc_info[2].tb_lineno\n print(errmsg)\n\n thisrand=barrand6()\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\n f = open('./'+pastebin+'/'+noFolder+thisrand+'/err.txt','w',encoding='utf-8')\n f.write(str(errmsg))\n f.close()\n print( 'ERROR occured. gone pastebin :',str(noFolder)+thisrand)\n continue\n\n return newDatas","def test_key_for_list_of_cacheable_objects(self):\n Meat.get_protein_sum([self.chicken, self.steak])\n expected_cache_key = 'tests.Meat.get_protein_sum;,Chicken:20,Steak:26;'\n self.assertExpectedKeyInCache(expected_cache_key)","def _vWBLZ(self):\r\n\r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n vWBLZ=None\r\n vWBLZ=self._OBJS('WBLZ')\r\n \r\n vWBLZ=vWBLZ[[\r\n 'AKTIV' \r\n ,'BESCHREIBUNG'\r\n ,'IDIM'\r\n ,'NAME'\r\n #from WBLZ's OBJS: \r\n ,'OBJTYPE' #type (i.e. KNOT) of a WBLZ OBJ\r\n ,'OBJID' #pk (or tk?!) of a WBLZ OBJ \r\n #IDs (of the WBLZ)\r\n ,'pk'\r\n ]]\r\n vWBLZ.sort_values(['NAME','pk'],ascending=True,inplace=True)\r\n #reindex:\r\n vWBLZ=pd.DataFrame(vWBLZ.values,columns=vWBLZ.columns)\r\n \r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n if isinstance(vWBLZ,pd.core.frame.DataFrame):\r\n logger.error(logStrFinal) \r\n else:\r\n logger.debug(logStrFinal) \r\n vWBLZ=pd.DataFrame() \r\n vWBLZ['AKTIV']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['BESCHREIBUNG']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['IDIM']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['NAME']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['OBJID']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['OBJTYPE']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n vWBLZ['pk']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \r\n \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.')) \r\n return vWBLZ","def __init__(self):\r\n self.indices = {}\r\n self.data = []\r\n self.len = 0","def read_data(file_path):\n words=[]\n dic_word={}\n actual_text=[]\n for line in open(file_path,encoding='utf-8'):\n words_line=line.strip().split(' ')\n for ite in words_line:\n if ite not in dic_word:\n dic_word[ite]=1\n words.extend(words_line)\n actual_text.append(words_line)\n\n\n #with zipfile.ZipFile(file_path) as f:\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\n\n return words,len(dic_word),actual_text","def data_log_mapping(kw='aug'):\n if kw == 'aug':\n dirs = {}\n dirs['120'] = ['08062020-3', '08062020-4', '08062020-5']\n dirs['100'] = ['08062020-0', '08062020-1', '08062020-2']\n dirs['85'] = ['08052020-3', '08052020-4', '08052020-5']\n dirs['80'] = ['08032020-0', '08032020-1', '08032020-2']\n dirs['70'] = ['08042020-0', '08042020-1', '08042020-2']\n dirs['60'] = ['08032020-3', '08032020-4', '08032020-5']\n dirs['50'] = ['08042020-3', '08042020-4', '08042020-5']\n dirs['40'] = ['08032020-6', '08032020-7', '08032020-8']\n dirs['30'] = ['08042020-6', '08042020-7', '08042020-8']\n dirs['20'] = ['08032020-9', '08032020-10', '08032020-11']\n dirs['10'] = ['08042020-9', '08042020-10', '08042020-11']\n dirs['00'] = ['08032020-12', '08032020-13', '08032020-14']\n \n return dirs","def get_listu_dilucijskih_jedinica(self):\n popis = sorted(list(self.dilucijskeJedinice.keys()))\n return popis","def decompose_sample(lfads_hps, z):\n ib_dim = lfads_hps['ib_dim']\n ic_dim = lfads_hps['enc_dim']\n ib_k = z[:ib_dim]\n ic_j = z[ib_dim:(ib_dim+ic_dim)]\n ii_ti = z[(ib_dim+ic_dim):]\n ii_txi = np.reshape(ii_ti, (-1, lfads_hps['ii_dim']))\n return ib_k, ic_j, ii_txi","def filter_dict(lam,bigZ,bigZlag):\n n_eq = lam.shape[0]\n if not(len(bigZ.keys()) == n_eq and len(bigZlag.keys()) == n_eq):\n raise Exception(\"Error: incompatible dimensions\")\n Zfilt = {}\n for r in range(n_eq):\n lami = lam[r][0]\n Zfilt[r] = bigZ[r] - lami*bigZlag[r]\n return Zfilt","def loadData(subjectList,dataMap,features,hemi):\n\n objDict = dataMap['object'].items()\n objDir = objDict[0][0]\n objExt = objDict[0][1]\n\n midDict = dataMap['midline'].items()\n midDir = midDict[0][0]\n midExt = midDict[0][1]\n\n matDict = dataMap['matching'].items()\n matDir = matDict[0][0]\n matExt = matDict[0][1]\n\n data = {}\n matches = {}\n\n for s in subjectList:\n\n # Training data\n trainObject = '{}{}.{}.{}'.format(objDir,s,hemi,objExt)\n print trainObject\n midObject = '{}{}.{}.{}'.format(midDir,s,hemi,midExt)\n matObject = '{}{}.{}.{}'.format(matDir,s,hemi,matExt)\n\n # Check to make sure all 3 files exist\n if os.path.isfile(trainObject) and os.path.isfile(midObject) and os.path.isfile(matObject):\n\n # Load midline indices\n # Subtract 1 for differece between Matlab and Python indexing\n mids = ld.loadMat(midObject)-1\n mids = set(mids)\n \n match = ld.loadMat(matObject)\n\n # Load training data and training labels\n trainH5 = h5py.File(trainObject,mode='r')\n\n # Get data corresponding to features of interest\n subjData = ld.parseH5(trainH5,features)\n trainH5.close()\n\n nSamples = set(np.arange(subjData[s][features[0]].shape[0]))\n coords = np.asarray(list(nSamples.difference(mids)))\n \n for f in subjData[s].keys():\n tempData = subjData[s][f]\n if tempData.ndim == 1:\n tempData.shape+=(1,)\n\n subjData[s][f] = np.squeeze(tempData[coords,:])\n \n match = match[coords,:]\n \n data[s] = subjData[s]\n matches[s] = match\n\n return [data,matches]","def decode(self, coded_set):","def test_input_conversion(self):\n data = [dict(A=0.1, C=0.2), dict(A=0.1, C=0.2)]\n base = [(\"A\", 0.1), (\"C\", 0.2)]\n expect = [base, base]\n got = _char_hts_as_lists(data)\n self.assertEqual(got, expect)\n #\n data = [dict(A=0.1, C=0.2), {}]\n base = [(\"A\", 0.1), (\"C\", 0.2)]\n expect = [base, None]\n got = _char_hts_as_lists(data)\n self.assertEqual(got, expect)\n data = [dict(A=0.1, C=0.2), None]\n base = [(\"A\", 0.1), (\"C\", 0.2)]\n expect = [base, None]\n got = _char_hts_as_lists(data)\n self.assertEqual(got, expect)","def _lzw(self, tile: bytes) -> np.ndarray:\n decoded = self._reshape(np.frombuffer(imagecodecs.lzw_decode(tile), self.dtype))\n self._unpredict(decoded)\n return np.rollaxis(decoded, 2, 0)","def unpack_data(self, data):\n\n datadict = {'filenames': [], 'features': [], 'labels': [] }\n\n for l in data:\n \n datadict['filenames'].append(l[0])\n datadict['features'].append(l[1])\n datadict['labels'].append(l[2])\n \n return datadict","def test_initialize_log_data(self):\r\n\r\n ids_bcs_added_field = {('AAAA', ''): 's1', ('TTTT', ''): 's2'}\r\n actual_log_data = initialize_log_data(ids_bcs_added_field)\r\n\r\n expected_log_data = {'TTTT,s2': 0, 'AAAA,s1': 0}\r\n\r\n self.assertEqual(actual_log_data, expected_log_data)\r\n\r\n # Handles added demultiplex field data\r\n ids_bcs_added_field = {('AAAA', '1'): 's1', ('TTTT', '2'): 's2'}\r\n actual_log_data = initialize_log_data(ids_bcs_added_field)\r\n\r\n expected_log_data = {'TTTT,2,s2': 0, 'AAAA,1,s1': 0}\r\n\r\n self.assertEqual(actual_log_data, expected_log_data)","def getDayAheadMarketLBMPZonal(self):\n\n\t\t# Variables\n\t\ttoday = self.helper.getYear() + self.helper.getMonth() + self.helper.getDay()\n\t\turl = 'http://mis.nyiso.com/public/csv/damlbmp/'+today+'damlbmp_zone.csv' \n\t\tresponse = urllib2.urlopen(url)\n\t\tmarket_data = sorted(csv.reader(response), key=operator.itemgetter(1)) # Converting data to python csv\n\t\tcounter = 0 # Counter used for determining which hour we are curretly on\n\t\tlbmpZonal = {}\n\t\ttimestamp = {}\n\t\ttimestamps = []\n\t\tmarket_info = {}\n\n\t\t# Converting csv data to market data and returning it\n\t\tfor row in market_data:\n\t\t\t# Ignoring header row\n\t\t\tif row[0] != 'Time Stamp':\n\t\t\t\tmarket_info['LBMP ($/MWHr)'] = float(row[3])\n\t\t\t\tmarket_info['Marginal Cost Losses ($/MWHr)'] = float(row[4])\n\t\t\t\tmarket_info['Marginal Cost Congestion ($/MWHr)'] = float(row[5])\n\t\t\t\trow[0] = self.helper.getDateInEpoch(row[0])\n\t\t\t\ttimestamp[row[0]] = market_info\n\t\t\t\tmarket_info = {}\n\t\t\t\ttimestamps.append(timestamp)\n\t\t\t\ttimestamp = {}\n\t\t\t\tcounter +=1\n\t\t\t\t# if counter == 23:\n\t\t\t\t\t# key = row[1]\n\t\t\t\tif counter == 24:\n\t\t\t\t\tlbmpZonal[row[1]] = timestamps\n\t\t\t\t\ttimestamps = []\n\t\t\t\t\tcounter = 0\n\t\treturn lbmpZonal","def test_decompress_1_char(self):\n b_array = bytearray([0]) + bytearray(b'a')\n actual = LZ77.decompress(b_array)\n expected = 'a'\n self.assertEqual(actual, expected)","def _read_zb_zblst(fname):\n with open(fname) as foo:\n data = {}\n read_data = False\n flow_budget = False\n empty = 0\n prefix = \"\"\n while True:\n line = foo.readline().strip().upper()\n t = line.split()\n if t:\n if t[-1].strip() == \"ZONES.\":\n line = foo.readline().strip()\n zones = [int(i) for i in line.split()]\n for zone in zones:\n data[f\"TO_ZONE_{zone}\"] = []\n data[f\"FROM_ZONE_{zone}\"] = []\n\n if \"FLOW BUDGET FOR ZONE\" in line:\n flow_budget = True\n read_data = False\n zlist = []\n empty = 0\n t = line.split()\n zone = int(t[4])\n if len(t[7]) > 4:\n t.insert(8, t[7][4:])\n kstp = int(t[8]) - 1\n if len(t[11]) > 6:\n t.append(t[11][6:])\n kper = int(t[12]) - 1\n if \"ZONE\" not in data:\n data[\"ZONE\"] = [zone]\n data[\"KSTP\"] = [kstp]\n data[\"KPER\"] = [kper]\n else:\n data[\"ZONE\"].append(zone)\n data[\"KSTP\"].append(kstp)\n data[\"KPER\"].append(kper)\n\n elif line in (\"\", \" \"):\n empty += 1\n\n elif read_data:\n if \"=\" in line:\n t = line.split(\"=\")\n label = t[0].strip()\n if \"ZONE\" in line:\n if prefix == \"FROM_\":\n zlist.append(int(label.split()[1]))\n label = f\"FROM_ZONE_{label.split()[1]}\"\n else:\n label = f\"TO_ZONE_{label.split()[-1]}\"\n\n elif \"TOTAL\" in line or \"PERCENT DISCREPANCY\" in line:\n label = \"_\".join(label.split())\n\n elif \"IN - OUT\" in line:\n label = \"IN-OUT\"\n\n else:\n label = prefix + \"_\".join(label.split())\n\n if label in data:\n data[label].append(float(t[1]))\n else:\n data[label] = [float(t[1])]\n\n if label == \"PERCENT_DISCREPANCY\":\n # fill in non-connected zones with zeros...\n for zone in zones:\n if zone in zlist:\n continue\n data[f\"FROM_ZONE_{zone}\"].append(0)\n data[f\"TO_ZONE_{zone}\"].append(0)\n\n elif \"OUT:\" in line:\n prefix = \"TO_\"\n\n else:\n pass\n\n elif flow_budget:\n if \"IN:\" in line:\n prefix = \"FROM_\"\n read_data = True\n flow_budget = False\n\n else:\n pass\n\n if empty >= 30:\n break\n\n return _zb_dict_to_recarray(data)","def test_compress_seq_diff_8_char(self):\n text = '12345678'\n actual = LZ77.compress(text)\n expected = bytearray([0]) + bytearray(b'12345678')\n self.assertEqual(actual, expected)","def build_dataset(words, n_words):\n count = [['UNK', -1]]\n count.extend(collections.Counter(words).most_common(n_words - 1))\n dictionary = dict()\n for word, _ in count:\n dictionary[word] = len(dictionary)\n data = list()\n unk_count = 0\n for word in words:\n index = dictionary.get(word, 0)\n if index == 0: # dictionary['UNK']\n unk_count += 1\n data.append(index)\n count[0][1] = unk_count\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n return data, count, dictionary, reversed_dictionary"],"string":"[\n \"def do_LZW_Compression(dict_of_abc, list_of_data):\\n \\n # rozdil mezi None a [] je v pouziti metody extend na listu\\n \\n result = []\\n P = []\\n C = [] # C je vzdy jeden prvek ze vstupu\\n PC = []\\n \\n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\\n \\n for i in range(len(list_of_data)):\\n \\\"\\\"\\\"\\n Cyklus pres vsecky vstupni prvky\\n \\\"\\\"\\\"\\n\\n C = []\\n C.append(list_of_data[i])\\n\\n #PC je vzdy kombinace P a C\\n PC = []\\n PC.extend(P)\\n PC.extend(C)\\n\\n index_founded = dict_cointains_list(dict_of_abc, PC)\\n if index_founded == -1:\\n #pokud PC neni ve slovniku, pridam ho tam a P = C\\n dict_of_abc[len(dict_of_abc) +1] = PC\\n #output P key in dictionary\\n result.append(dict_cointains_list(dict_of_abc, P))\\n P = C\\n else:\\n #pokud PC je ve slovniku P = PC pro dalsi iteraci\\n P = PC\\n #pridani posledniho prvku\\n result.append(dict_cointains_list(dict_of_abc, P))\\n return dict_of_abc, result\",\n \"def getUniChemData(self, inchiKeyList):\\n mapD = {\\n 1: {\\\"name\\\": \\\"chembl\\\", \\\"baseUrl\\\": \\\"https://www.ebi.ac.uk/chembl/\\\", \\\"entryUrl\\\": \\\"https://www.ebi.ac.uk/chembldb/compound/inspect/\\\"},\\n 3: {\\\"name\\\": \\\"pdb\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/pdbe/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/pdbe-srv/pdbechem/chemicalCompound/show/\\\"},\\n 2: {\\\"name\\\": \\\"drugbank\\\", \\\"baseUrl\\\": \\\"http://drugbank.ca/\\\", \\\"entryUrl\\\": \\\"http://www.drugbank.ca/drugs/\\\"},\\n 5: {\\\"name\\\": \\\"pubchem_dotf\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/sources/sources.cgi\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 4: {\\\"name\\\": \\\"gtopdb\\\", \\\"baseUrl\\\": \\\"http://www.guidetopharmacology.org\\\", \\\"entryUrl\\\": \\\"http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=\\\"},\\n 11: {\\\"name\\\": \\\"ibm\\\", \\\"baseUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/\\\", \\\"entryUrl\\\": \\\"http://www-935.ibm.com/services/us/gbs/bao/siip/nih/?sid=\\\"},\\n 6: {\\\"name\\\": \\\"kegg_ligand\\\", \\\"baseUrl\\\": \\\"http://www.genome.jp/kegg/ligand.html\\\", \\\"entryUrl\\\": \\\"http://www.genome.jp/dbget-bin/www_bget?\\\"},\\n 9: {\\\"name\\\": \\\"zinc\\\", \\\"baseUrl\\\": \\\"http://zinc15.docking.org\\\", \\\"entryUrl\\\": \\\"http://zinc15.docking.org/substances/\\\"},\\n 8: {\\\"name\\\": \\\"nih_ncc\\\", \\\"baseUrl\\\": \\\"http://nihsmr.evotec.com/evotec/\\\", \\\"entryUrl\\\": \\\"\\\"},\\n 10: {\\\"name\\\": \\\"emolecules\\\", \\\"baseUrl\\\": \\\"https://www.emolecules.com/\\\", \\\"entryUrl\\\": \\\"https://www.emolecules.com/cgi-bin/more?vid=\\\"},\\n 12: {\\\"name\\\": \\\"atlas\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/gxa/home\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/gxa/query?conditionQuery=\\\"},\\n 7: {\\\"name\\\": \\\"chebi\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/chebi/downloadsForward.do\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/chebi/searchId.do?chebiId=CHEBI%3A\\\"},\\n 14: {\\n \\\"name\\\": \\\"fdasrs\\\",\\n \\\"baseUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/srs.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://fdasis.nlm.nih.gov/srs/ProxyServlet?mergeData=true&objectHandle=DBMaint&APPLICATION_NAME=fdasrs&actionHandle=default&nextPage=jsp/srs/ResultScreen.jsp&TXTSUPERLISTID=\\\",\\n },\\n 15: {\\\"name\\\": \\\"surechembl\\\", \\\"baseUrl\\\": \\\"https://www.surechembl.org/search/\\\", \\\"entryUrl\\\": \\\"https://www.surechembl.org/chemical/\\\"},\\n 21: {\\\"name\\\": \\\"pubchem_tpharma\\\", \\\"baseUrl\\\": \\\"http://www.thomson-pharma.com/\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/substance/\\\"},\\n 22: {\\\"name\\\": \\\"pubchem\\\", \\\"baseUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov\\\", \\\"entryUrl\\\": \\\"http://pubchem.ncbi.nlm.nih.gov/compound/\\\"},\\n 27: {\\\"name\\\": \\\"recon\\\", \\\"baseUrl\\\": \\\"https://vmh.uni.lu\\\", \\\"entryUrl\\\": \\\"https://vmh.uni.lu/\\\"},\\n 28: {\\\"name\\\": \\\"molport\\\", \\\"baseUrl\\\": \\\"https://www.molport.com/shop/index\\\", \\\"entryUrl\\\": \\\"https://www.molport.com/shop/molecule-link/\\\"},\\n 31: {\\n \\\"name\\\": \\\"bindingdb\\\",\\n \\\"baseUrl\\\": \\\"https://www.bindingdb.org/bind/index.jsp\\\",\\n \\\"entryUrl\\\": \\\"http://www.bindingdb.org/bind/chemsearch/marvin/MolStructure.jsp?monomerid=\\\",\\n },\\n 41: {\\\"name\\\": \\\"swisslipids\\\", \\\"baseUrl\\\": \\\"http://www.swisslipids.org/\\\", \\\"entryUrl\\\": \\\"http://www.swisslipids.org/\\\"},\\n 29: {\\\"name\\\": \\\"nikkaji\\\", \\\"baseUrl\\\": \\\"http://jglobal.jst.go.jp/en/\\\", \\\"entryUrl\\\": \\\"http://jglobal.jst.go.jp/en/redirect?Nikkaji_No=\\\"},\\n 32: {\\\"name\\\": \\\"comptox\\\", \\\"baseUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\", \\\"entryUrl\\\": \\\"https://comptox.epa.gov/dashboard/\\\"},\\n 33: {\\\"name\\\": \\\"lipidmaps\\\", \\\"baseUrl\\\": \\\"http://www.lipidmaps.org\\\", \\\"entryUrl\\\": \\\"http://www.lipidmaps.org/data/LMSDRecord.php?LMID=\\\"},\\n 35: {\\\"name\\\": \\\"carotenoiddb\\\", \\\"baseUrl\\\": \\\"http://carotenoiddb.jp/index.html\\\", \\\"entryUrl\\\": \\\"http://carotenoiddb.jp/Entries/\\\"},\\n 36: {\\\"name\\\": \\\"metabolights\\\", \\\"baseUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\", \\\"entryUrl\\\": \\\"http://www.ebi.ac.uk/metabolights/\\\"},\\n 37: {\\\"name\\\": \\\"brenda\\\", \\\"baseUrl\\\": \\\"https://www.brenda-enzymes.org/index.php\\\", \\\"entryUrl\\\": \\\"https://www.brenda-enzymes.org/ligand.php?brenda_ligand_id=\\\"},\\n 17: {\\\"name\\\": \\\"pharmgkb\\\", \\\"baseUrl\\\": \\\"https://www.pharmgkb.org\\\", \\\"entryUrl\\\": \\\"https://www.pharmgkb.org/drug/\\\"},\\n 18: {\\\"name\\\": \\\"hmdb\\\", \\\"baseUrl\\\": \\\"http://www.hmdb.ca\\\", \\\"entryUrl\\\": \\\"http://www.hmdb.ca/metabolites/\\\"},\\n 24: {\\n \\\"name\\\": \\\"nmrshiftdb2\\\",\\n \\\"baseUrl\\\": \\\"http://nmrshiftdb.nmr.uni-koeln.de/portal/media-type/html/user/anon/page/default.psml/js_pane/P-Home\\\",\\n \\\"entryUrl\\\": \\\"http://nmrshiftdb.org/molecule/\\\",\\n },\\n 25: {\\\"name\\\": \\\"lincs\\\", \\\"baseUrl\\\": \\\"http://www.lincsproject.org/\\\", \\\"entryUrl\\\": \\\"http://identifiers.org/lincs.smallmolecule/\\\"},\\n 39: {\\\"name\\\": \\\"chemicalbook\\\", \\\"baseUrl\\\": \\\"https://www.chemicalbook.com\\\", \\\"entryUrl\\\": \\\"https://www.chemicalbook.com/ChemicalProductProperty_EN_\\\"},\\n 20: {\\\"name\\\": \\\"selleck\\\", \\\"baseUrl\\\": \\\"http://www.selleckchem.com\\\", \\\"entryUrl\\\": \\\"http://www.selleckchem.com/products/\\\"},\\n 23: {\\\"name\\\": \\\"mcule\\\", \\\"baseUrl\\\": \\\"https://mcule.com\\\", \\\"entryUrl\\\": \\\"https://mcule.com/\\\"},\\n 26: {\\\"name\\\": \\\"actor\\\", \\\"baseUrl\\\": \\\"https://actor.epa.gov\\\", \\\"entryUrl\\\": \\\"http://actor.epa.gov/actor/chemical.xhtml?casrn=\\\"},\\n 34: {\\\"name\\\": \\\"drugcentral\\\", \\\"baseUrl\\\": \\\"http://drugcentral.org\\\", \\\"entryUrl\\\": \\\"http://drugcentral.org/drugcard/\\\"},\\n 38: {\\\"name\\\": \\\"rhea\\\", \\\"baseUrl\\\": \\\"http://www.rhea-db.org\\\", \\\"entryUrl\\\": \\\"http://www.rhea-db.org/searchresults?q=CHEBI:\\\"},\\n }\\n oD = {}\\n try:\\n for ky in inchiKeyList:\\n unc = unichem_client # pylint: disable=no-member\\n # unc.set_format(\\\"json\\\")\\n uDL = unc.get(ky)\\n if uDL:\\n qD = {}\\n for uD in uDL:\\n if \\\"src_id\\\" in uD and int(uD[\\\"src_id\\\"]) in mapD:\\n qD[mapD[int(uD[\\\"src_id\\\"])][\\\"name\\\"]] = uD[\\\"src_compound_id\\\"]\\n if qD:\\n oD[ky] = qD\\n\\n except Exception as e:\\n logger.exception(\\\"Failing with %s\\\", str(e))\\n return oD\",\n \"def dict_cointains_list(dict_of_abc, item_list):\\n\\n values = list(dict_of_abc.values())\\n\\n #projdu vsecky listy ve slovniku\\n for i in range(len(values)):\\n #predpokladam ze ve slovniku je\\n finded = True\\n \\n for j in range(len(values[i])):\\n if len(item_list) == len(values[i]):\\n # kontrola po jednotlivych hodnotach\\n # logicky soucin - pokud jednou False, navzdy False\\n finded = finded and item_list[j] == values[i][j]\\n else:\\n finded = False\\n\\n if finded:\\n # cyklus indexuje od 0, slovnik ale indexujeme-klicujeme od 1\\n return i + 1 \\n\\n return -1\",\n \"def generate_data_list(self, data_list):\\n sol = []\\n for i in data_list:\\n sol.append({\\n \\\"data_sig\\\": i[0],\\n \\\"data\\\": pickle.loads(i[1]),\\n \\\"attachment\\\": i[2],\\n \\\"likes\\\":pickle.loads(i[3]),\\n \\\"dislikes\\\":pickle.loads(i[4]),\\n \\\"owner\\\": i[5]\\n })\\n return sol\",\n \"def listz2diclist(listz):\\n\\ttmp=[]\\n\\tfor elem in listz:\\n\\t\\tdic = elem.warez2dic()\\n\\t\\ttmp.append(dic)\\n\\treturn tmp\",\n \"def getDataDict(self):\\n #code begins here \\n return self.__dflst,self.__dfwells\",\n \"def getDataForLBMPZonalComparison(self):\\n\\n\\t\\t# Variables\\n\\t\\tzonal_data = self.getDayAheadMarketLBMPZonal()\\n\\t\\tkeys = zonal_data.keys()\\n\\t\\tfinal_data = []\\n\\t\\tvalues = []\\n\\t\\touter_dictionary = {}\\n\\t\\tinner_dictionary = {}\\n\\n\\t\\t# Populating final data array and returning it\\n\\t\\tfor key in keys:\\n\\t\\t\\tfor data in zonal_data[key]:\\n\\t\\t\\t\\tinner_dictionary['x'] = data.keys()[0]\\n\\t\\t\\t\\tinner_dictionary['y'] = data[data.keys()[0]]['LBMP ($/MWHr)']\\n\\t\\t\\t\\tvalues.append(inner_dictionary)\\n\\t\\t\\t\\tinner_dictionary = {}\\n\\t\\t\\touter_dictionary['values'] = values\\n\\t\\t\\tvalues = []\\n\\t\\t\\touter_dictionary['key'] = key\\n\\t\\t\\tfinal_data.append(outer_dictionary)\\n\\t\\t\\touter_dictionary = {}\\n\\n\\t\\treturn final_data\",\n \"def get_data(url, seed):\\n available_fields = {\\n 'boro': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'cd': {'fieldtype': 'C', 'categories': range(1, 19)},\\n 'uf1_1': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_2': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_3': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_4': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_5': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_6': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_7': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_8': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_9': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_10': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_11': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_12': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_13': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_14': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_15': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_16': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_17': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_18': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_19': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_20': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_21': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'uf1_22': {'fieldtype': 'B', 'codes': [1, 9, 8]},\\n 'sc24': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc36': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'sc37': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc38': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'sc114': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'uf48': {'fieldtype': 'N'},\\n 'sc147': {'fieldtype': 'C', 'categories': [1, 2, 3]},\\n 'uf11': {'fieldtype': 'C', 'categories': range(1, 8)},\\n 'sc149': {'fieldtype': 'B', 'codes': [1, 2, None]},\\n 'sc173': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\\n 'sc171': {'fieldtype': 'B', 'codes': [1, 2]},\\n 'sc150': {'fieldtype': 'N'},\\n 'sc151': {'fieldtype': 'N'},\\n 'sc154': {'fieldtype': 'C', 'categories': [1, 2, 3, 9]},\\n 'sc157': {'fieldtype': 'C', 'categories': [1, 2, 9]},\\n 'sc158': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc185': {'fieldtype': 'B', 'codes': [0, 1, 8]},\\n 'sc186': {'fieldtype': 'C', 'categories': [2, 3, 4, 5, 9]},\\n 'sc197': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc198': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc187': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc188': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc571': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'sc189': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'sc190': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc191': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc192': {'fieldtype': 'B', 'codes': [0, 1, 8]},\\n 'sc193': {'fieldtype': 'C', 'categories': [2, 3, 9]},\\n 'sc194': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'sc196': {'fieldtype': 'C', 'categories': [1, 2, 3, 4]},\\n 'sc199': {'fieldtype': 'C', 'categories': range(1, 6)},\\n 'rec15': {'fieldtype': 'C', 'categories': range(1, 14)},\\n 'sc26': {'fieldtype': 'C', 'categories': [12, 13, 15, 16]},\\n 'uf23': {'fieldtype': 'N'},\\n 'rec21': {'fieldtype': 'B', 'codes': [1, 2, 8]},\\n 'rec62': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\\n 'rec64': {'fieldtype': 'C', 'categories': [1, 2, 4, 5]},\\n 'rec54': {'fieldtype': 'C', 'categories': range(1, 8)},\\n 'rec53': {'fieldtype': 'N'},\\n 'new_csr': {'fieldtype': 'C', 'categories': [1, 2, 5, 12, 20,\\n 21, 22, 23, 30, 31,\\n 80, 85, 90, 95]}\\n }\\n selected_fields = [\\n # The borough where the apartment is located\\n 'boro',\\n\\n # Building type: public housing, new construction,\\n # \\\"In Rem\\\" foreclosure, old construction\\n 'sc26',\\n\\n # Number of bedrooms\\n 'sc151',\\n\\n # Dilapidated / Not Dilapidated\\n 'rec21',\\n\\n # Complete plumbing facilities in unit\\n 'rec62',\\n\\n # Complete kitchen facilities in unit\\n 'rec64',\\n\\n # Maintenance deficiencies\\n 'rec53',\\n\\n # Building age\\n 'uf23',\\n\\n # Rent control/stabilization category\\n 'new_csr',\\n\\n # Neighborhood rating\\n 'sc196',\\n\\n # Wheelchair accessibility of unit\\n 'sc38',\\n\\n # Presence of elevator\\n 'sc149',\\n\\n # Building height\\n 'uf11',\\n\\n # Air conditioning\\n 'sc197',\\n\\n # Walkup\\n 'sc171',\\n ]\\n mini_fields = {k: available_fields[k]\\n for k in available_fields\\n if k in selected_fields}\\n y_field = 'uf17'\\n # s = requests.get(url).content\\n # raw_df = pd.read_csv(StringIO(s.decode('utf-8')))\\n raw_df = pd.read_csv('homework2_data.csv')\\n valid_renters, validated_features, validated_rents = \\\\\\n preprocess_data(raw_df, mini_fields, y_field)\\n X_train, X_test, y_train, y_test = train_test_split(\\n validated_features, validated_rents, random_state=seed)\\n cats = [k\\n for (k, v) in mini_fields.items()\\n if v[\\\"fieldtype\\\"] == \\\"C\\\"]\\n catnums = [i\\n for (i, x) in enumerate([c in cats\\n for c in validated_features.columns])\\n if x]\\n return X_train, X_test, y_train, y_test, catnums, raw_df\",\n \"def decompress_encoded_list( nums ):\\n decompressed = []\\n\\n for i in range( 0, len(nums), 2 ):\\n freq = nums[i]\\n val = nums[i+1]\\n decompressed.extend( [val] * freq )\\n\\n return decompressed\",\n \"def dict_check34(_str1):\\n\\t_len=len(_str1)\\n\\t_list1=[(i,j,k) for i in range(5) for j in range(5) for k in range(5)]\\n\\t_list0=[]\\n\\tfor i in range(len(_list1)):\\n\\t\\t#Take different length\\n\\t\\t_current=_list1[i]\\n\\t\\tif _len>=sum(_current) and sum(_list1[i])!=0:\\n\\t\\t\\t_list2=[]\\n\\t\\t\\t_n1=_current[0]\\n\\t\\t\\t_n2=_current[1]\\n\\t\\t\\t_n3=_current[2]\\n\\t\\t\\t_list2.append(_str1[:_n1])\\n\\t\\t\\t_list2.append(_str1[_n1:_n1+_n2])\\n\\t\\t\\t_list2.append(_str1[_n1+_n2:_n1+_n2+_n3])\\n\\t\\telse:\\n\\t\\t\\tcontinue\\n\\t\\tn=0\\n\\t\\tfor j in range(3):\\n\\t\\t\\tif _list2[j] in _dict_ori or _list2[j]==\\\"\\\":\\n\\t\\t\\t\\tn+=1\\n\\t\\tif n==3:\\n\\t\\t\\t_list0.append(_list2)\\n\\treturn(_list0)\",\n \"def get_keys_from_list():\\n json_data = request.get_json()\\n\\n d = dict()\\n d['elements'] = list()\\n settings.setOptionsFile(get_info('uid'))\\n fn = settings.getHistoROOTFileName()\\n rfn = settings.getReferenceROOTFileName()\\n# open root file stored in the root database\\n f = ROOT.TFile(fn)\\n# open reference root file stored in the root database\\n rf = ROOT.TFile(rfn)\\n\\n for values in json_data.itervalues():\\n for k in values:\\n subd = dict()\\n subd[\\\"index\\\"] = k[\\\"index\\\"]\\n if fn != k[\\\"file\\\"]: \\n fn = k[\\\"file\\\"]\\n settings.setHistoROOTFileName(fn)\\n f = ROOT.TFile(fn)\\n print \\\"histogram :>>>>>: \\\",k[\\\"histogram\\\"]\\n subd[\\\"data\\\"] = eval(cppyy.gbl.getDictionary(f,k[\\\"histogram\\\"]))\\n if rfn != k[\\\"referenceFile\\\"]: \\n rfn = k[\\\"referenceFile\\\"]\\n settings.setReferenceROOTFileName(rfn)\\n rf = ROOT.TFile(rfn)\\n subd[\\\"refdata\\\"] = eval(cppyy.gbl.getDictionary(rf,k[\\\"reference\\\"]))\\n d['elements'].append(subd)\\n\\n f.Close()\\n rf.Close()\\n\\n return jsonify(d)\",\n \"def test_decompress_offset_less_len1(self):\\n b_array = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\\n actual = LZ77.decompress(b_array)\\n expected = 'ababab'\\n self.assertEqual(actual, expected)\",\n \"def getData_goodmaps(liste_dictionnaires = [], liste_categories = [], liste_phonemes = [],liste_cartes=[]):\\n if liste_dictionnaires!=[] and liste_categories!=[] and liste_phonemes!=[]:\\n tableau = np.array(liste_dictionnaires[0][liste_categories[0]][liste_phonemes[0]])\\n nb_exemple,nb_carte,lign,col=tableau.shape\\n else:\\n return [],[],[],[]\\n\\n Mat = []\\n Reference = []\\n\\n\\n for inddict,dict in enumerate(liste_dictionnaires):\\n for indcat,cat in enumerate(liste_categories):\\n for indpho,pho in enumerate(liste_phonemes):\\n for ex in range(nb_exemple):\\n goodmaps = []\\n for map in liste_cartes:\\n goodmaps.append(np.array(dict[cat][pho][ex][map]).flatten())\\n Mat.append(np.array(goodmaps).flatten())\\n Reference.append([inddict,indcat ,indpho])\\n Reference = np.array(Reference)\\n Y_c_inc = change_reference(Reference[:,1])\\n Y_r_v = Reference[:,2]\\n Y_fr_jap = Reference[:,0]\\n return np.array(Mat), np.array(Y_c_inc), np.array(Y_r_v), np.array(Y_fr_jap)\",\n \"def construct_zi_dict(train_info_list, test_info_list):\\r\\n zi_dict, train_dataset_list, test_dataset_list = dict(), list(), list()\\r\\n for user, age, gender, education, querys in train_info_list:\\r\\n for query in querys:\\r\\n for zi in query:\\r\\n if zi not in zi_dict:\\r\\n zi_dict[zi] = 0\\r\\n zi_dict[zi] += 1\\r\\n for user, querys in test_info_list:\\r\\n for query in querys:\\r\\n for zi in query:\\r\\n if zi not in zi_dict:\\r\\n zi_dict[zi] = 0\\r\\n zi_dict[zi] += 1\\r\\n zi_list = sorted(zi_dict.iteritems(), key=lambda x: x[1], reverse=True)\\r\\n zi2index = dict([(zi[0], [zi[1], idx]) for idx, zi in enumerate(zi_list)])\\r\\n index2zi = dict([(idx, [zi[0], zi[1]]) for idx, zi in enumerate(zi_list)])\\r\\n \\r\\n return zi2index, index2zi\",\n \"def test_xyz_from_data(self):\\n symbols = ('C', 'H', 'H', 'H', 'H')\\n isotopes = (12, 1, 1, 1, 1)\\n coords = ((0.0, 0.0, 0.0),\\n (0.6300326, 0.6300326, 0.6300326),\\n (-0.6300326, -0.6300326, 0.6300326),\\n (-0.6300326, 0.6300326, -0.6300326),\\n (0.6300326, -0.6300326, -0.6300326))\\n xyz_dict0 = converter.xyz_from_data(coords=coords, symbols=symbols, isotopes=isotopes)\\n self.assertEqual(xyz_dict0, self.xyz1['dict'])\\n xyz_dict1 = converter.xyz_from_data(coords=coords, symbols=symbols) # no specifying isotopes\\n self.assertEqual(xyz_dict1, self.xyz1['dict'])\\n\\n numbers = [6, 1, 1, 1, 1]\\n coords = [[0.0, 0.0, 0.0],\\n [0.6300326, 0.6300326, 0.6300326],\\n [-0.6300326, -0.6300326, 0.6300326],\\n [-0.6300326, 0.6300326, -0.6300326],\\n [0.6300326, -0.6300326, -0.6300326]]\\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\\n\\n numbers = [6, 1, 1, 1, 1]\\n coords = [[0.0, 0.0, 0.0],\\n [0.6300326, 0.6300326, 0.6300326],\\n [-0.6300326, -0.6300326, 0.6300326],\\n [-0.6300326, 0.6300326, -0.6300326],\\n [0.6300326, -0.6300326, -0.6300326]]\\n coords = np.array([np.array(coord, np.float64) for coord in coords], np.float64)\\n xyz_dict2 = converter.xyz_from_data(coords=coords, numbers=numbers)\\n self.assertEqual(xyz_dict2, self.xyz1['dict'])\\n self.assertIsInstance(xyz_dict2['coords'], tuple)\\n self.assertIsInstance(xyz_dict2['coords'][0], tuple)\",\n \"def test_compress(self):\\n form_field = MultiLingualFormField()\\n compressed_data = form_field.compress([\\\"test-nb\\\", \\\"test-en\\\"])\\n self.assertEqual(MultiLingualTextStructure, type(compressed_data))\\n self.assertEqual(compressed_data['nb'], \\\"test-nb\\\")\\n self.assertEqual(compressed_data['en'], \\\"test-en\\\")\",\n \"def test_decompress_2(self):\\n b_array = bytearray([3]) + bytearray(b'abcdef')\\\\\\n + bytearray([0, 32]) + bytearray([0, 113])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdefdeabc'\\n self.assertEqual(actual, expected)\",\n \"def test_decompress_offset_less_len2(self):\\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdabcdab'\\n self.assertEqual(actual, expected)\",\n \"def zzX_from_dict(f, l):\\n if l == 1:\\n return zzx_from_dict(f)\\n elif not f:\\n return zzX_zero(l)\\n\\n coeffs = {}\\n\\n for monom, coeff in f.iteritems():\\n head, tail = monom[0], monom[1:]\\n\\n if len(tail) == 1:\\n tail = tail[0]\\n\\n if coeffs.has_key(head):\\n coeffs[head][tail] = INT_TYPE(int(coeff))\\n else:\\n coeffs[head] = { tail : INT_TYPE(int(coeff)) }\\n\\n n, h = max(coeffs.iterkeys()), []\\n\\n for k in xrange(n, -1, -1):\\n coeff = coeffs.get(k)\\n\\n if coeff is not None:\\n h.append(zzX_from_dict(coeff, l-1))\\n else:\\n h.append(zzX_zero(l-1))\\n\\n return zzX_strip(h)\",\n \"def zipped_data(b_data):\\n col_names = [\\\"Name\\\", \\\"Version\\\", \\\"Date and Time\\\"]\\n bundle_data = []\\n for bundle in b_data:\\n zipped = zip(col_names, bundle)\\n bundle_data.append(dict(zipped))\\n return bundle_data\",\n \"def test_decompress_1(self):\\n b_array = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 49])\\n actual = LZ77.decompress(b_array)\\n expected = 'abcdabc'\\n self.assertEqual(actual, expected)\",\n \"def _transform_map_data(self):\\n WARD_FMT = '%s-%s'\\n self.map_data_trans = []\\n lookup = {i.column: ''.join(filter(lambda x: x.isdigit(), i.value)) for i in self.sht[1]}\\n\\n #skip over header\\n rs = iter(self.sht.rows)\\n next(rs)\\n next(rs)\\n for r in rs:\\n pka = r[0].value\\n for c in r[1:]:\\n if c.value is None:\\n c.value = 0\\n\\n self.map_data_trans.append((WARD_FMT%(pka, lookup[c.column]), c.value))\",\n \"def data():\\n \\n # Just in case order matters.\\n inplist = expected.keys()\\n inplist.sort()\\n \\n #return ['split/' + inpms for inpms in inplist]\\n return inplist\",\n \"def test_compress_offset_less_len1(self):\\n text = 'ababab'\\n actual = LZ77.compress(text)\\n expected = bytearray([32]) + bytearray(b'ab') + bytearray([0, 18])\\n self.assertEqual(actual, expected)\",\n \"def test_hmaps(self):\\n #Single element insertion\\n self.hll.append(self.data[0])\\n assert non_zero_idx_val(hll.hmap) == [(54, 1)]\\n #Multiple distinct element insertions\\n self.hll.extend(self.data)\\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\\n self.reset_hll()\\n #Element insertions with duplicates\\n self.hll.extend(self.data_duplicate)\\n assert non_zero_idx_val(hll.hmap) == [(51, 2), (54, 1), (214, 2)]\\n self.reset_hll()\\n #Element insertions with numerical values\\n self.hll.extend(self.num_data)\\n assert non_zero_idx_val(hll.hmap) == [(17, 3), (144, 2), (145, 4),\\n (182, 2)]\\n self.reset_hll()\\n #Test the key collision handling (keep max value)\\n self.hll.append(self.colliding_data[0])\\n assert non_zero_idx_val(hll.hmap) == [(0, 1)] \\n self.hll.append(self.colliding_data[1])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\\n self.reset_hll()\\n self.hll.append(self.colliding_data[1])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)] \\n self.hll.append(self.colliding_data[0])\\n assert non_zero_idx_val(hll.hmap) == [(0, 2)]\\n self.reset_hll()\",\n \"def get_dict_refine_insee_code(ls_valid_ic):\\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\\n for ic in ls_valid_ic_corse:\\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\\n dict_refine_ic.update(dict_ic_ardts)\\n return dict_refine_ic\",\n \"def apply_compression(pc2_table, keys_56bits):\\n keys_48bits = \\\"\\\"\\n for index in pc2_table:\\n keys_48bits += keys_56bits[index - 1]\\n return keys_48bits\",\n \"def test_compress_offset_less_len2(self):\\n text = 'abcdabcdab'\\n actual = LZ77.compress(text)\\n expected = bytearray([8]) + bytearray(b'abcd') + bytearray([0, 52])\\n self.assertEqual(actual, expected)\",\n \"def decode_huffman(bit_seq, dc_ac, layer_type):\\n\\n def diff_value(idx, size):\\n if idx >= len(bit_seq) or idx + size > len(bit_seq):\\n raise IndexError('There is not enough bits to decode DIFF value '\\n 'codeword.')\\n fixed = bit_seq[idx:idx + size]\\n return int(fixed, 2)\\n\\n current_idx = 0\\n while current_idx < len(bit_seq):\\n # 1. Consume next 16 bits as `current_slice`.\\n # 2. Try to find the `current_slice` in Huffman table.\\n # 3. If found, yield the corresponding key and go to step 4.\\n # Otherwise, remove the last element in `current_slice` and go to\\n # step 2.\\n # 4. Consume next n bits, where n is the category (size) in returned\\n # key yielded in step 3. Use those info to decode the data.\\n remaining_len = len(bit_seq) - current_idx\\n current_slice = bit_seq[\\n current_idx:\\n current_idx + (16 if remaining_len > 16 else remaining_len)\\n ]\\n err_cache = current_slice\\n while current_slice:\\n if (current_slice in\\n HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type].inv):\\n key = (HUFFMAN_CATEGORY_CODEWORD[dc_ac][layer_type]\\n .inv[current_slice])\\n if dc_ac == DC: # DC\\n size = key\\n if size == 0:\\n yield 0\\n else:\\n yield HUFFMAN_CATEGORIES[size][diff_value(\\n current_idx + len(current_slice),\\n size\\n )]\\n else: # AC\\n run, size = key\\n if key in (EOB, ZRL):\\n yield key\\n else:\\n yield (run, HUFFMAN_CATEGORIES[size][diff_value(\\n current_idx + len(current_slice),\\n size\\n )])\\n\\n current_idx += len(current_slice) + size\\n break\\n current_slice = current_slice[:-1]\\n else:\\n raise KeyError(\\n f'Cannot find any prefix of {err_cache} in Huffman table.'\\n )\",\n \"def create_data_set():\\n data_set = {}\\n for index in range(1024):\\n size = random.randint(1, 100) #nosec\\n key = str(index).encode(\\\"utf-8\\\")\\n data_set[key] = get_random_bytes(size)\\n return data_set\",\n \"def making_dataset_list_y(data_list:list) -> list:\\n list_size = len(data_list)\\n data_list_y = []\\n for i in range(list_size):\\n data_list_y.append(data_list[i][[\\\"W\\\",\\\"D\\\",\\\"L\\\"]])\\n return data_list_y\",\n \"def get_compressed(self, value):\\r\\n output = []\\r\\n lz_data = (value >> 8) & 0xFF\\r\\n lz_counter = value & 0xFF\\r\\n # Define the relative offset on LZ Window\\r\\n lz_offset = ((lz_counter & 0xF0) << 4) | lz_data\\r\\n # Define the LZ Counter for repeat data N times\\r\\n lz_counter = (lz_counter & 0xF) + 0x2\\r\\n # Start Repeat Loop\\r\\n while (lz_counter >= 0):\\r\\n # Seek the window on LZ Offset and get the LZ Data\\r\\n self.__lzwindow__.seek(lz_offset, FROM_START)\\r\\n lz_data = (lz_data & 0xFF00) + \\\\\\r\\n int.from_bytes(self.__lzwindow__.read(1), byteorder='big')\\r\\n # Write the LZ data to the output\\r\\n output.append((lz_data & 0xFF).to_bytes(1, byteorder='big'))\\r\\n # Seek the LZ Window on current LZ Window Counter value and write the current LZ Data (LZBuffer)\\r\\n self.__lzwindow__.seek(self.__lzwindowcounter__, FROM_START)\\r\\n self.__lzwindow__.write((lz_data & 0xFF).to_bytes(1, byteorder='big'))\\r\\n # Increment LZ Window Counter\\r\\n self.__lzwindowcounter__ = (\\r\\n self.__lzwindowcounter__ + 0x1) & self.__lzwindowmax__\\r\\n # Increment LZ Offset\\r\\n lz_offset = (lz_offset + 0x1) & self.__lzwindowmax__\\r\\n # Decrement number of data to decompress\\r\\n self.__maxlen__ -= 0x1\\r\\n # Decrement LZ Loop counter\\r\\n lz_counter -= 0x1\\r\\n return output\",\n \"def shuffle_data_set(in_dict):\\n tot_rows = in_dict['data'].shape[0]\\n new_order = range(tot_rows)\\n for _ in range(5):\\n shuffle(new_order)\\n\\n out_dict = in_dict\\n out_data = np.zeros((tot_rows, 3072), dtype=in_dict['data'].dtype)\\n out_labels = [0] * tot_rows\\n out_filenames = [\\\"\\\"] * tot_rows\\n\\n for ctr, idx in enumerate(new_order):\\n out_data[ctr, :] = in_dict['data'][idx, :]\\n out_labels[ctr] = in_dict['labels'][idx]\\n out_filenames[ctr] = in_dict['filenames'][idx]\\n\\n out_dict['data'] = out_data\\n out_dict['labels'] = out_labels\\n out_dict['filenames'] = out_filenames\\n\\n return out_dict\",\n \"def get_datalist_fr_json(self):\\n raw_data = json.load(open(self.saved_json_file, 'r'))\\n for indivdual_set in raw_data['query']['results']['stats']:\\n temp_dict_data = {}\\n if type(indivdual_set) == str:\\n #for single data\\n continue # temp do not use\\n for parameters in indivdual_set.keys():\\n if type(indivdual_set[parameters]) == str:\\n temp_dict_data[parameters] = indivdual_set[parameters]#for symbol\\n elif type(indivdual_set[parameters]) == dict:\\n if indivdual_set[parameters].has_key('content'):\\n temp_dict_data[parameters] = indivdual_set[parameters]['content']\\n\\n ## append to list\\n self.com_data_allstock_list.append(temp_dict_data)\",\n \"def polylinedictarraycopy(d):#d——原始图层多段线字典 \\r\\n dictlist=[]\\r\\n ratiolist=[] #放缩率列表\\r\\n rationumaccumulationlist=[] #放缩率数量累加列表\\r\\n \\r\\n eachrationum=globalconfig.X_ARRAY_NUM//globalconfig.RATIO_NUM\\r\\n leftrationum=globalconfig.X_ARRAY_NUM%globalconfig.RATIO_NUM\\r\\n \\r\\n eachrationumlist=[eachrationum]*globalconfig.RATIO_NUM #各个放缩率对应数量的列表\\r\\n \\r\\n for i in range((globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2,(globalconfig.RATIO_NUM-1)//2-(leftrationum-1)//2+leftrationum):\\r\\n eachrationumlist[i]=eachrationumlist[i]+1 #将整除后的余值加入到靠中间放缩率的方案中。\\r\\n \\r\\n rationumaccumulationlist.append(0) \\r\\n \\r\\n for i in range(1,globalconfig.RATIO_NUM): #计算放缩率数量累加列表\\r\\n rationumaccumulationlist.append(rationumaccumulationlist[i-1]+eachrationumlist[i-1])\\r\\n \\r\\n for i in range(0,globalconfig.RATIO_NUM): #计算放缩率列表\\r\\n ratiolist.append((globalconfig.CENTER_RATIO-((globalconfig.RATIO_NUM+1)//2-1)*globalconfig.RATIO_DIFF)+i*globalconfig.RATIO_DIFF) \\r\\n \\r\\n for i in range(0,globalconfig.RATIO_NUM): #每种放缩率\\r\\n for j in range(0,eachrationumlist[i]): #每种放缩率对应数量\\r\\n newdict={}\\r\\n for e in d: #将字典中值即每一图层对应的多段线列表进行复制并移动到指定位置\\r\\n newdict[e]=polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist)) \\r\\n #newdict.append([e,polylinedatasetarraycopy(d[e],ratiolist[i],globalconfig.CUTLINE_X_OFFSET+globalconfig.X_BLANK+(rationumaccumulationlist[i]+j+0.5)*globalconfig.X_LENGTH/globalconfig.CENTER_RATIO,globalconfig.CUTLINE_Y_OFFSET+globalconfig.Y_BLANK+0.5*globalconfig.Y_LENGTH/globalconfig.CENTER_RATIO,e,len(dictlist))])\\r\\n dictlist.append(newdict) \\r\\n return (dictlist,ratiolist,eachrationumlist)\",\n \"def opendict(data_dir, batch_size, test_max_size, **kwargs):\\n join = lambda f: _path.join(data_dir, f)\\n with open(join(\\\"list.jl\\\")) as f:\\n data = [(join(e[\\\"files\\\"][0][\\\"path\\\"]), e[\\\"word\\\"].strip())\\n for e in filter(lambda x: x.get(\\\"files\\\"), map(_json.loads, f))]\\n _random.shuffle(data)\\n if not test_max_size:\\n test_max_size = int(len(data) / 100)\\n tmp = - (batch_size + test_max_size)\\n return data[:tmp], data[tmp:-test_max_size], data[-test_max_size:]\",\n \"def decode(self, z):\\n raise NotImplementedError\",\n \"def zzx_from_dict(f):\\n if not f:\\n return []\\n\\n n, h = max(f.iterkeys()), []\\n\\n for k in xrange(n, -1, -1):\\n h.append(INT_TYPE(int(f.get(k, 0))))\\n\\n return zzx_strip(h)\",\n \"def map(z):\\n pass\",\n \"def test_compress_2(self):\\n text = 'abcdefdeabc'\\n actual = LZ77.compress(text)\\n expected = bytearray([3]) + bytearray(b'abcdef')\\\\\\n + bytearray([0, 32]) + bytearray([0, 113])\\n self.assertEqual(actual, expected)\",\n \"def get_data(elem: string) -> list:\\n if elem in ascii_dict:\\n max_length = len(max(ascii_dict[elem], key=len))\\n if max_length == 0:\\n max_length = 4\\n data = []\\n for s in ascii_dict[elem]:\\n data.append(fill_with_spaces(s, max_length))\\n return data\\n else:\\n raise IndexError(\\\"Unknown symbol: \\\", elem)\",\n \"def decoder(self, array=[],your_dic_max_value=78):\\r\\n word_list=[]\\r\\n self.coder=\\\"This_is_a_decoder_for_the_short_vector_function!\\\"\\r\\n for i in array:\\r\\n for i in i:\\r\\n print(\\\"i:\\\"+str(i))\\r\\n if int(i) <=25:# use the max length of the dic to find out wich one to use\\r\\n for key, value in self.general_lower_word_list.items():# iterate through every key and value\\r\\n if value==int(i):#checks if the value is equal to the given input\\r\\n word_list.append(key)# if yes append it\\r\\n else:\\r\\n pass #else move on\\r\\n elif int(i) <=51 and int(i)>=25:# same process as above\\r\\n for key, value in self.general_upper_word_list.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=58 and int(i)>=52:\\r\\n for key, value in self.special_letters_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=68 and int(i)>=59:\\r\\n for key, value in self.general_numbers_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n elif int(i) <=max_value and int(i)>=69:\\r\\n for key, value in self.special_characters_dic.items():\\r\\n if value==int(i):\\r\\n word_list.append(key)\\r\\n else:\\r\\n pass\\r\\n return \\\"\\\".join(word_list) #create a real string with the join method\\r\",\n \"def test_decode_listmode_data(self):\\n self.assertEqual([td.header(decoded=True)],\\n decoder.decode_listmode_data(BytesIO(td.header(as_bytes=True)), self.mask))\\n self.assertEqual([{**td.header_with_trace(decoded=True), **{'trace': td.trace()}}],\\n decoder.decode_listmode_data(\\n BytesIO(td.header_with_trace(as_bytes=True) + td.trace(True)),\\n self.mask))\",\n \"def data_dict0():\\n\\n # 0- Sample from detectron2 -> 5 different sections.\\n info_val0 = [{\\\"date_created\\\": \\\"2020-03-15 04:59:45.442988\\\",\\n \\\"description\\\": \\\"Automatically generated COCO json file for Detectron2.\\\"}]\\n images0 = [{\\\"id\\\": \\\"image\\\", \\\"width\\\": 100,\\n \\\"height\\\": 100, \\\"file_name\\\": \\\"image.png\\\"}]\\n annotations0 = [{\\\"id\\\": 1, \\\"image_id\\\": \\\"image\\\", \\\"bbox\\\": [70.0, 30.0, 30.0, 40.0],\\n \\\"area\\\": 1200.0, \\\"iscrowd\\\": 0, \\\"category_id\\\": 0}]\\n categories0 = [{\\\"id\\\": 0, \\\"name\\\": \\\"first\\\"}]\\n licence0 = 'null'\\n\\n return [{\\\"info\\\": info_val0,\\n \\\"images\\\": images0,\\n \\\"annotations\\\": annotations0,\\n \\\"categories\\\": categories0,\\n \\\"licenses\\\": licence0}]\",\n \"def get_data(data_list, data_cache):\\n if not isinstance(data_list, list):\\n data_list = [data_list]\\n tmp = [data_cache[d] for d in data_list]\\n if len(tmp) == 1:\\n return tmp[0]\\n res = ([t[0] for t in tmp], [t[1] for t in tmp])\\n return res\",\n \"def raw_features_extractor(database='./red_cod.db.pkl', sites=-1, elements = -1, maxatoms= -1,\\r\\n dictionary='diccionario', features='datosrahm.csv'):\\r\\n \\r\\n df=create_collection(database=database,sites=sites, elements=elements, maxatoms=maxatoms, \\r\\n dictionary=dictionary)\\r\\n \\r\\n start=time.time()\\r\\n \\r\\n datos=pd.read_csv(features)\\r\\n datos=datos.fillna(-1)\\r\\n\\r\\n dicc=dict(datos[['Symbol','Z']].values)\\r\\n\\r\\n dicc['D']=1\\r\\n dicc['Bk']=97\\r\\n dicc['Cf']=98\\r\\n dicc['Es']=99\\r\\n dicc['Fm']=100\\r\\n dicc['Md']=101\\r\\n dicc['No']=102\\r\\n dicc['Lr']=103\\r\\n \\r\\n max_sitios = max(df['sitios'].values)\\r\\n\\r\\n df=df[df['sitios'] <= max_sitios].reset_index(drop=True)\\r\\n \\r\\n X=np.zeros((len(df),max_sitios,104))\\r\\n y=np.zeros((len(df),1))\\r\\n mult=np.zeros((len(df),max_sitios))\\r\\n wyckmul=np.load('support/WyckoffSG_dict.npy').item()['wyckmul']\\r\\n \\r\\n for row in range(len(df)):\\r\\n \\r\\n item=df['WyckOcc'][row]\\r\\n sitios=list(item.values()) \\r\\n sitocc=np.zeros((len(sitios),104))\\r\\n spacegroup = str(df['sgnum'][row]).zfill(3)\\r\\n \\r\\n try:\\r\\n \\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n sitios] for i in j]\\r\\n \\r\\n except:\\r\\n print('There exists an error concerning with the space group of CIF ', df['cif'][row],'\\\\n')\\r\\n print('Please check in www.crystallography.net to provide the correct space group number of that CIF',\\r\\n '\\\\n','\\\\n')\\r\\n spacegroup=input('Give me the correct spacegroup:'+'\\\\n'+'\\\\n')\\r\\n s=[int(wyckmul[spacegroup][i]) for j in [list(item.keys()) for item in \\\\\\r\\n list(df['WyckOcc'][row].values())] for i in j]\\r\\n \\r\\n occs=[]\\r\\n for i in range(len(sitios)):\\r\\n\\r\\n for j in list(sitios[i].values()):\\r\\n \\r\\n ocupacion=np.array(list(j.values()))\\r\\n llaves=[llave.replace('+','').replace('-','').replace('1',\\r\\n '').replace('2','').replace('3','').replace('4',\\r\\n '') for llave in np.array(list(j.keys()))]\\r\\n llaves=[llave.replace('.','') for llave in llaves]\\r\\n llaves=[llave.replace('5','').replace('6','').replace('7',\\r\\n '').replace('8','').replace('9','').replace('0',\\r\\n '') for llave in llaves]\\r\\n vector=np.zeros((1,104))\\r\\n occs=[sum(ocupacion)]+occs\\r\\n \\r\\n try:\\r\\n \\r\\n idx=[dicc[k] for k in llaves]\\r\\n \\r\\n except:\\r\\n \\r\\n print(' ELEMENTO NO IDENTIFICADO EN LA LISTA ',llaves,'\\\\n',\\r\\n 'REVISA EL SIGUIENTE CIF PARA HACER LA CORRECCION:','\\\\t',df['cif'][row])\\r\\n \\r\\n former = input('Elemento Incorrecto: ')\\r\\n current = input('Elemento Correcto: ')\\r\\n \\r\\n llaves=[current if x == former else x for x in llaves]\\r\\n idx=[dicc[k] for k in llaves]\\r\\n \\r\\n \\r\\n for k in idx:\\r\\n vector[0][k-1] = ocupacion[idx.index(k)]\\r\\n \\r\\n \\r\\n sitocc[i]=vector\\r\\n \\r\\n while sitocc.shape[0] != max_sitios:\\r\\n sitocc=np.concatenate((np.zeros((1,104)),sitocc))\\r\\n s=[0]+s\\r\\n \\r\\n X[row,:,:]=sitocc\\r\\n y[row]=df['target'][row]\\r\\n mult[row]=s\\r\\n \\r\\n S = np.expand_dims(mult,axis=2)\\r\\n features=datos.iloc[:,2:].values\\r\\n x=X[:,:,:96]\\r\\n \\r\\n fracsum = np.expand_dims(np.sum(x,axis=2), axis=2)\\r\\n \\r\\n x=np.dot(x,features) \\r\\n\\r\\n print('Atomic radii and electronegativities for each Wyckoff site extracted in',\\r\\n round(time.time()-start,2),' s') \\r\\n \\r\\n np.save('raw_features', x)\\r\\n np.save('output_values', y)\\r\\n np.save('multiplicities', S)\\r\\n np.save('occupation_fractions', fracsum)\\r\\n \\r\\n return x, y, S, fracsum, df\",\n \"def __init__(self):\\n self.data = []\\n self.idx = {}\",\n \"def MapDataList(ea, length, func, wordsize=1):\\n PutDataList(ea, map(func, GetDataList(ea, length, wordsize)), wordsize)\",\n \"def ana_merge_senzory_map(datas):\\n#TODO: improve senzory map merging\\n return iter(datas.viewvalues()).next()['senzory_map']\",\n \"def test_compress_4_idenctical_char(self):\\n text = 'bbbb'\\n actual = LZ77.compress(text)\\n expected = bytearray([32]) + bytearray(b'bb') + bytearray([0, 16])\\n self.assertEqual(actual, expected)\",\n \"def test_decompress_seq_diff_8_char(self):\\n b_array = bytearray([0]) + bytearray(b'12345678')\\n actual = LZ77.decompress(b_array)\\n expected = '12345678'\\n self.assertEqual(actual, expected)\",\n \"def mapfn(k, v):\\n for row in v:\\n # rellenar el codigo\\n pass\",\n \"def upload_initial_data():\\n data = {\\\"updated\\\":1512866292573,\\\"data\\\":[[[\\\"DAY %\\\",\\\"PPS (CAD)\\\",\\\"TKR\\\",\\\"NAME\\\",\\\"SECTOR\\\",\\\"EX.\\\",\\\"ALT. TKR\\\",\\\"VOL\\\",\\\"VOL @ CRNT $\\\",\\\"MC ($MM)\\\"],[\\\"5.84%\\\",\\\"1.45\\\",\\\"ABCN\\\",\\\"ABcann Medicinals\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"ABCCF\\\",\\\"901,940\\\",\\\"1,307,813\\\",\\\"78.49\\\"],[\\\"6.14%\\\",\\\"7.09\\\",\\\"ACB\\\",\\\"Aurora Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"ACBFF\\\",\\\"13,927,205\\\",\\\"98,743,883.45\\\",\\\"2,861.53\\\"],[\\\"9.09%\\\",\\\"0.24\\\",\\\"ACG\\\",\\\"Alliance Growers \\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"--\\\",\\\"1,189,385\\\",\\\"285,452.4\\\",\\\"\\\"],[\\\"0.00%\\\",\\\"0.10\\\",\\\"AFI\\\",\\\"Affinor Growers\\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"RSSFF\\\",\\\"210,310\\\",\\\"21,031\\\",\\\"\\\"],[\\\"3.22%\\\",\\\"13.47\\\",\\\"APH\\\",\\\"Aphria\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"APHQF\\\",\\\"2,663,133\\\",\\\"35,872,401.51\\\",\\\"2,042.08\\\"],[\\\"13.95%\\\",\\\"0.25\\\",\\\"ATT\\\",\\\"Abattis Bio\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"ATTBF\\\",\\\"2,706,357\\\",\\\"676,589.25\\\",\\\"39.86\\\"],[\\\"0.00%\\\",\\\"2.03\\\",\\\"BE\\\",\\\"Beleave\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"BLEVF\\\",\\\"597,909\\\",\\\"1,213,755.27\\\",\\\"\\\"],[\\\"1.28%\\\",\\\"1.58\\\",\\\"BLO\\\",\\\"Cannabix Tech\\\",\\\"LawTech\\\",\\\"CNSX\\\",\\\"BLOZF\\\",\\\"465,869\\\",\\\"736,073.02\\\",\\\"136.61\\\"],[\\\"-2.20%\\\",\\\"0.89\\\",\\\"CBW\\\",\\\"Cannabis Wheaton \\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"KWFLF\\\",\\\"815,477\\\",\\\"725,774.53\\\",\\\"234.57\\\"],[\\\"-0.65%\\\",\\\"19.93\\\",\\\"CMED\\\",\\\"Cannimed\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"CMMDF\\\",\\\"130,722\\\",\\\"2,605,289.46\\\",\\\"457.69\\\"],[\\\"12.73%\\\",\\\"0.62\\\",\\\"CMM\\\",\\\"Canabo Medical\\\",\\\"MedCare\\\",\\\"CVE\\\",\\\"CAMDF\\\",\\\"330,404\\\",\\\"204,850.48\\\",\\\"23.54\\\"],[\\\"-2.87%\\\",\\\"2.71\\\",\\\"CRZ\\\",\\\"Cannaroyalty\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"CNNRF\\\",\\\"961,449\\\",\\\"2,605,526.79\\\",\\\"115.09\\\"],[\\\"-6.67%\\\",\\\"0.28\\\",\\\"CYX\\\",\\\"Calyx Bio\\\",\\\"AgTech\\\",\\\"CVE\\\",\\\"CLYXF\\\",\\\"2,120,562\\\",\\\"593,757.36\\\",\\\"24.23\\\"],[\\\"0.00%\\\",\\\"1.23\\\",\\\"DOJA\\\",\\\"DOJA Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"DJACF\\\",\\\"206,635\\\",\\\"254,161.05\\\",\\\"72.27\\\"],[\\\"-4.40%\\\",\\\"0.44\\\",\\\"DVA\\\",\\\"Delivra\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"--\\\",\\\"89,485\\\",\\\"39,373.4\\\",\\\"19.55\\\"],[\\\"6.52%\\\",\\\"0.25\\\",\\\"EAT\\\",\\\"Nutritional High\\\",\\\"Marijuana Edibles & Extracts\\\",\\\"CNSX\\\",\\\"SPLIF\\\",\\\"3,067,636\\\",\\\"766,909\\\",\\\"61.54\\\"],[\\\"-1.20%\\\",\\\"1.64\\\",\\\"EMC\\\",\\\"Emblem\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"EMMBF\\\",\\\"411,764\\\",\\\"675,292.96\\\",\\\"130.60\\\"],[\\\"2.05%\\\",\\\"3.98\\\",\\\"EMH\\\",\\\"Emerald\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"TBQBF\\\",\\\"1,430,067\\\",\\\"5,691,666.66\\\",\\\"374.34\\\"],[\\\"-5.88%\\\",\\\"0.48\\\",\\\"FFT\\\",\\\"Future Farm Tech\\\",\\\"AgTech\\\",\\\"CNSX\\\",\\\"AGSTF\\\",\\\"1,291,240\\\",\\\"619,795.2\\\",\\\"0.61\\\"],[\\\"1.06%\\\",\\\"1.90\\\",\\\"FIRE\\\",\\\"Supreme Pharma\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"SPRWF\\\",\\\"1,275,906\\\",\\\"2,424,221.4\\\",\\\"391.96\\\"],[\\\"5.26%\\\",\\\"0.10\\\",\\\"GHG\\\",\\\"Global Hemp\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"GBHPF\\\",\\\"764,350\\\",\\\"76,435\\\",\\\"\\\"],[\\\"3.28%\\\",\\\"0.31\\\",\\\"GLH\\\",\\\"Golden Leaf\\\",\\\"Marijuana Products\\\",\\\"CNSX\\\",\\\"GLDFF\\\",\\\"4,298,567\\\",\\\"1,332,555.77\\\",\\\"116.96\\\"],[\\\"-1.96%\\\",\\\"0.50\\\",\\\"HC\\\",\\\"High Hampton Holdings\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"--\\\",\\\"727,116\\\",\\\"363,558\\\",\\\"\\\"],[\\\"1.89%\\\",\\\"0.54\\\",\\\"HIP\\\",\\\"Newstirke Resources \\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"NWKRF\\\",\\\"431,875\\\",\\\"233,212.5\\\",\\\"210.35\\\"],[\\\"8.91%\\\",\\\"1.10\\\",\\\"HVST\\\",\\\"Harvest One Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"HRVOF\\\",\\\"2,192,877\\\",\\\"2,412,164.7\\\",\\\"98.10\\\"],[\\\"8.89%\\\",\\\"0.98\\\",\\\"ICC\\\",\\\"International Cannabis\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"ICCLF\\\",\\\"123,538\\\",\\\"121,067.24\\\",\\\"110.84\\\"],[\\\"0.00%\\\",\\\"1.62\\\",\\\"IMH\\\",\\\"Invictus MD\\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"IVITF\\\",\\\"781,924\\\",\\\"1,266,716.88\\\",\\\"129.87\\\"],[\\\"12.50%\\\",\\\"0.90\\\",\\\"IN\\\",\\\"Inmed Pharma\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"IMLFF\\\",\\\"3,846,586\\\",\\\"3,461,927.4\\\",\\\"\\\"],[\\\"2.27%\\\",\\\"1.80\\\",\\\"ISOL\\\",\\\"Isodiol International \\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"LAGBF\\\",\\\"8,514,952\\\",\\\"15,326,913.6\\\",\\\"\\\"],[\\\"7.84%\\\",\\\"0.28\\\",\\\"KALY\\\",\\\"Kalytera Therapeutics\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"QUEZD\\\",\\\"5,634,186\\\",\\\"1,577,572.08\\\",\\\"34.74\\\"],[\\\"-1.72%\\\",\\\"0.57\\\",\\\"LDS\\\",\\\"Lifestyle Delivery Systems\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"LDSYF\\\",\\\"685,628\\\",\\\"390,807.96\\\",\\\"51.44\\\"],[\\\"0.19%\\\",\\\"15.50\\\",\\\"LEAF\\\",\\\"MedReleaf Corp\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"MEDFF\\\",\\\"229,190\\\",\\\"3,552,445\\\",\\\"1,459.18\\\"],[\\\"2.33%\\\",\\\"0.44\\\",\\\"LIB\\\",\\\"Liberty Leaf Holdings\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"LIBFF\\\",\\\"4,555,082\\\",\\\"2,004,236.08\\\",\\\"\\\"],[\\\"10.42%\\\",\\\"1.59\\\",\\\"LXX\\\",\\\"Lexaria Bio\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"LXRP\\\",\\\"1,523,338\\\",\\\"2,422,107.42\\\",\\\"\\\"],[\\\"-1.38%\\\",\\\"2.14\\\",\\\"MARI\\\",\\\"Maricann Group\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"MRRCF\\\",\\\"678,106\\\",\\\"1,451,146.84\\\",\\\"157.10\\\"],[\\\"3.26%\\\",\\\"0.95\\\",\\\"MDM\\\",\\\"Marapharm\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"MRPHF\\\",\\\"209,019\\\",\\\"198,568.05\\\",\\\"\\\"],[\\\"0.00%\\\",\\\"0.57\\\",\\\"MGW\\\",\\\"Maple Leaf Green World\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"MGWFF\\\",\\\"367,479\\\",\\\"209,463.03\\\",\\\"83.83\\\"],[\\\"7.37%\\\",\\\"1.02\\\",\\\"MJ\\\",\\\"True Leaf\\\",\\\"Hemp Pet Chews\\\",\\\"CNSX\\\",\\\"TLFMF\\\",\\\"164,101\\\",\\\"167,383.02\\\",\\\"\\\"],[\\\"2.27%\\\",\\\"4.50\\\",\\\"MJN\\\",\\\"Pharmacan /Cronos\\\",\\\"Investing & Finance\\\",\\\"CVE\\\",\\\"PRMCF\\\",\\\"419,922\\\",\\\"1,889,649\\\",\\\"675.43\\\"],[\\\"4.23%\\\",\\\"2.71\\\",\\\"MYM\\\",\\\"My Marijuana\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"--\\\",\\\"1,066,122\\\",\\\"2,889,190.62\\\",\\\"\\\"],[\\\"4.40%\\\",\\\"0.95\\\",\\\"N\\\",\\\"Namaste Tech\\\",\\\"Consumption Devices\\\",\\\"CNSX\\\",\\\"NXTTF\\\",\\\"5,714,764\\\",\\\"5,429,025.8\\\",\\\"192.50\\\"],[\\\"0.00%\\\",\\\"0.10\\\",\\\"NF\\\",\\\"New Age Farm\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"NWGFF\\\",\\\"3,938,476\\\",\\\"393,847.6\\\",\\\"\\\"],[\\\"-7.27%\\\",\\\"0.25\\\",\\\"NSP\\\",\\\"Naturally Splendid\\\",\\\"Hemp Products\\\",\\\"CVE\\\",\\\"NSPDF\\\",\\\"484,812\\\",\\\"121,203\\\",\\\"24.42\\\"],[\\\"4.99%\\\",\\\"3.79\\\",\\\"OGI\\\",\\\"Organigram\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"OGRMF\\\",\\\"3,654,843\\\",\\\"13,851,854.97\\\",\\\"375.89\\\"],[\\\"1.15%\\\",\\\"0.88\\\",\\\"PUF\\\",\\\"PUF Ventures\\\",\\\"Consumption Devices\\\",\\\"CNSX\\\",\\\"PUFXF\\\",\\\"719,534\\\",\\\"633,189.92\\\",\\\"45.85\\\"],[\\\"10.68%\\\",\\\"1.14\\\",\\\"RHT\\\",\\\"Reliq Health Tech\\\",\\\"Mobile Software\\\",\\\"CVE\\\",\\\"RQHTF\\\",\\\"1,564,567\\\",\\\"1,783,606.38\\\",\\\"98.74\\\"],[\\\"4.05%\\\",\\\"1.80\\\",\\\"RTI\\\",\\\"Radient Technologies\\\",\\\"Extraction\\\",\\\"CVE\\\",\\\"RDDTF\\\",\\\"2,181,473\\\",\\\"3,926,651.4\\\",\\\"345.53\\\"],[\\\"3.64%\\\",\\\"0.28\\\",\\\"RVV\\\",\\\"Revive Therapeutics\\\",\\\"Medication\\\",\\\"CVE\\\",\\\"RVVTF\\\",\\\"399,705\\\",\\\"111,917.4\\\",\\\"15.50\\\"],[\\\"-2.90%\\\",\\\"0.67\\\",\\\"SUN\\\",\\\"Wildflower\\\",\\\"Hemp Products\\\",\\\"CNSX\\\",\\\"WLDFF\\\",\\\"87,197\\\",\\\"58,421.99\\\",\\\"29.48\\\"],[\\\"-0.67%\\\",\\\"4.45\\\",\\\"SXP\\\",\\\"Supremex\\\",\\\"Packaging\\\",\\\"TSE\\\",\\\"SUMXF\\\",\\\"27,015\\\",\\\"120,216.75\\\",\\\"126.40\\\"],[\\\"0.00%\\\",\\\"0.76\\\",\\\"TBP\\\",\\\"Tetra Bio-Pharma\\\",\\\"BioTech\\\",\\\"CVE\\\",\\\"GRPOF\\\",\\\"497,745\\\",\\\"378,286.2\\\",\\\"88.67\\\"],[\\\"2.44%\\\",\\\"2.10\\\",\\\"TER\\\",\\\"TerrAscend Corp\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"--\\\",\\\"270,176\\\",\\\"567,369.6\\\",\\\"\\\"],[\\\"4.29%\\\",\\\"0.73\\\",\\\"THC\\\",\\\"THC Biomed\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"THCBF\\\",\\\"818,162\\\",\\\"597,258.26\\\",\\\"81.29\\\"],[\\\"3.55%\\\",\\\"3.21\\\",\\\"THCX\\\",\\\"Hydropothecary Corp\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"HYYDF\\\",\\\"1,581,640\\\",\\\"5,077,064.4\\\",\\\"282.37\\\"],[\\\"8.22%\\\",\\\"0.79\\\",\\\"TNY\\\",\\\"Tinley Beverage Co\\\",\\\"Beverage\\\",\\\"CNSX\\\",\\\"QRSRF\\\",\\\"945,154\\\",\\\"746,671.66\\\",\\\"57.81\\\"],[\\\"3.49%\\\",\\\"7.70\\\",\\\"TRST\\\",\\\"CannTrust\\\",\\\"Cultivation & Biotech\\\",\\\"CNSX\\\",\\\"CNTTF\\\",\\\"368,892\\\",\\\"2,840,468.4\\\",\\\"699.98\\\"],[\\\"-8.04%\\\",\\\"1.03\\\",\\\"VGW\\\",\\\"Valens Groworks\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"MYMSF\\\",\\\"23,285\\\",\\\"23,983.55\\\",\\\"62.77\\\"],[\\\"0.00%\\\",\\\"0.52\\\",\\\"VIN\\\",\\\"Vinergy Resources\\\",\\\"Investing & Finance\\\",\\\"CNSX\\\",\\\"VNNYF\\\",\\\"0\\\",\\\"\\\",\\\"\\\"],[\\\"-2.50%\\\",\\\"0.39\\\",\\\"VP\\\",\\\"Vodis Pharma\\\",\\\"Cultivation & Retail\\\",\\\"CNSX\\\",\\\"VDQSF\\\",\\\"52,661\\\",\\\"20,537.79\\\",\\\"\\\"],[\\\"6.67%\\\",\\\"0.80\\\",\\\"VRT\\\",\\\"Veritas Pharma\\\",\\\"BioTech\\\",\\\"CNSX\\\",\\\"VRTHF\\\",\\\"377,901\\\",\\\"302,320.8\\\",\\\"\\\"],[\\\"6.41%\\\",\\\"19.42\\\",\\\"WEED\\\",\\\"Canopy Growth\\\",\\\"Cultivation & Retail\\\",\\\"TSE\\\",\\\"TWMJF\\\",\\\"4,940,034\\\",\\\"95,935,460.28\\\",\\\"3,706.63\\\"],[\\\"6.25%\\\",\\\"2.38\\\",\\\"WMD\\\",\\\"WeedMD\\\",\\\"Cultivation & Retail\\\",\\\"CVE\\\",\\\"WDDMF\\\",\\\"1,174,148\\\",\\\"2,794,472.24\\\",\\\"124.71\\\"],[\\\"3.36%\\\",\\\"14.75\\\",\\\"HMMJ\\\",\\\"Horizons Marijuana Life Sciences\\\",\\\"Canadian Marijuana ETF\\\",\\\"TSE\\\",\\\"HMLSF\\\",\\\"336,579\\\",\\\"4,964,540.25\\\",\\\"197.64\\\"]]],\\\"sheetnames\\\":[\\\"ALLSHOW\\\"]}\\n \\n exchange_suffixes = {'TSE': 'TO', 'CVE': 'V'}\\n\\n # create sector\\n sector, _ = Sector.objects.get_or_create(name=\\\"Cannabis\\\", slug='cannabis')\\n\\n # create currency\\n currency, _ = Currency.objects.get_or_create(symbol='CAD', defaults={'character':'$', 'name':'Canadian Dollar'})\\n us_currency, _ = Currency.objects.get_or_create(symbol='USD', defaults={'character':'$', 'name':'US Dollar'})\\n\\n # OTC exchange\\n otc, _ = Exchange.objects.get_or_create(symbol='OTC', defaults={'name':'OTC', 'currency': us_currency})\\n\\n # iterate over each item in our table, make the items\\n for row in data[\\\"data\\\"][0][1:]:\\n # percent = float(row[0].replace(\\\"%\\\",\\\"\\\"))\\n suffix = exchange_suffixes[row[5]] if row[5] in exchange_suffixes else ''\\n exchange, _ = Exchange.objects.get_or_create(symbol=row[5], defaults={'name':row[5], 'currency':currency, 'ticker_suffix': suffix})\\n company, _ = Company.objects.get_or_create(name=row[3], defaults={'sector':sector})\\n stock, _ = Stock.objects.get_or_create(ticker=row[2], defaults={\\n 'company': company,\\n 'exchange': exchange,\\n 'market_cap': float(row[9].replace(\\\",\\\",\\\"\\\")) * 1000000 if row[9] else 0.0,\\n # 'previous_close': float(row[1]) - float(row[1]) * percent / 100,\\n # 'open': float(row[1]),\\n # 'current': float(row[1]),\\n # 'volume': float(row[8].replace(\\\",\\\",\\\"\\\")) if row[8] else 0.0,\\n })\\n stock.save()\\n\\n if row[4]:\\n tag, _ = Tag.objects.get_or_create(name=row[4])\\n company.tags.add(tag)\\n\\n if row[6] and not row[6] == \\\"--\\\":\\n stock, _ = Stock.objects.get_or_create(ticker=row[6], defaults={'company':company, 'exchange':otc})\\n\\n\\n print data\",\n \"def lego_sets():\\n \\n \\n data_test=data_specific\\n\\n\\n\\n \\n \\n\\n \\n print(data_test)\\n print(\\\"The size of the data is: \\\",len(data_test))\\n \\n \\n \\n # you must replace this line and return your own list\\n return data_test\",\n \"def lad_lut(lads):\\n for lad in lads:\\n yield lad['properties']['name']\",\n \"def decodeLZ(fileIn, fileOut, dictionarySize = 1114112):\\n try:\\n fileContent = FileWork.getFileContent(fileIn)\\n\\n getCharByCode = {}\\n getCodeByChar = {}\\n\\n for i in range(0, dictionarySize):\\n getCharByCode[i] = chr(i)\\n getCodeByChar[chr(i)] = i\\n\\n currentCode = dictionarySize\\n\\n fileContent = fileContent.split(' ')\\n buffer = getCharByCode[int(fileContent[0])]\\n decodeFileContent = \\\"\\\"\\n for i in range(1, len(fileContent)):\\n character = \\\"\\\"\\n if int(fileContent[i]) in getCharByCode:\\n character = getCharByCode[int(fileContent[i])]\\n else:\\n getCharByCode[currentCode] = buffer + buffer[0]\\n getCodeByChar[buffer + buffer[0]] = currentCode\\n currentCode += 1\\n decodeFileContent += buffer\\n character = buffer[0]\\n\\n if (buffer + character[0]) in getCodeByChar:\\n buffer = buffer + character[0]\\n else:\\n getCharByCode[currentCode] = buffer + character[0]\\n getCodeByChar[buffer + character[0]] = currentCode\\n currentCode += 1\\n decodeFileContent += buffer\\n buffer = character\\n decodeFileContent += buffer\\n FileWork.writeToFile(fileOut, decodeFileContent)\\n return True\\n except Exception:\\n return False\",\n \"def getAllIndex(ldata, fldata):\\n\\treturn list(map(lambda e : fldata.index(e), ldata))\",\n \"def huffman_data_decode(data: hic.BitStringP, huffman: huffman.HuffmanTree) -> list:\\n return huffman.decode_data(data.payload)\",\n \"def codebook_json_data_factory() -> List[Dict[str, Any]]:\\n codebook_data = [\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_A\\\"\\n },\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_B\\\"\\n },\\n ]\\n return codebook_data\",\n \"def make_data(revs, word_idx_map, max_l=50, filter_h=3\\n ,train_instance = 16541,val_instance = 1645):\\n train, val, test = [], [], []\\n for rev in revs:\\n sent = get_idx_from_sent(rev[\\\"m\\\"], word_idx_map, max_l, filter_h)\\n sent += get_idx_from_sent(rev[\\\"r\\\"], word_idx_map, max_l, filter_h)\\n sent.append(int(rev[\\\"y\\\"]))\\n sent.append(int(rev[\\\"t\\\"]))\\n sent.append(int(rev[\\\"t2\\\"]))\\n\\n if len(train) < train_instance:\\n train.append(sent)\\n elif (len(train) + len(val)) < train_instance + val_instance:\\n val.append(sent)\\n else:\\n test.append(sent)\\n\\n shuffle(train)\\n train = np.array(train,dtype=\\\"int\\\")\\n val = np.array(val,dtype=\\\"int\\\")\\n test = np.array(test,dtype=\\\"int\\\")\\n print 'trainning data', len(train),'val data', len(val)\\n return [train, val, test]\",\n \"def test_decompress_seq_diff_9_char(self):\\n b_array = bytearray([0]) + bytearray(b'12345678') \\\\\\n + bytearray([0]) + bytearray(b'9')\\n actual = LZ77.decompress(b_array)\\n expected = '123456789'\\n self.assertEqual(actual, expected)\",\n \"def test_concat_data(self):\\n\\n this_satellite_dict = satellite_io.concat_data(\\n satellite_dicts=[\\n SATELLITE_DICT_SUBSET_BY_INDEX, SATELLITE_DICT_SUBSET_BY_TIME\\n ]\\n )\\n\\n self.assertTrue(compare_satellite_dicts(\\n this_satellite_dict, SATELLITE_DICT_CONCAT\\n ))\",\n \"def compress(uncompressed):\\r\\n \\r\\n # Build the dictionary.\\r\\n dict_size = 256\\r\\n dictionary = dict((chr(i), i) for i in range(dict_size))\\r\\n # in Python 3: dictionary = {chr(i): i for i in range(dict_size)}\\r\\n \\r\\n w = \\\"\\\"\\r\\n result = []\\r\\n for c in uncompressed:\\r\\n wc = w + c\\r\\n if wc in dictionary:\\r\\n w = wc\\r\\n else:\\r\\n result.append(dictionary[w])\\r\\n # Add wc to the dictionary.\\r\\n dictionary[wc] = dict_size\\r\\n dict_size += 1\\r\\n w = c\\r\\n \\r\\n # Output the code for w.\\r\\n if w:\\r\\n result.append(dictionary[w])\\r\\n return result\",\n \"def dlist(src):\\n if isinstance(src, dict):\\n for k in src:\\n src[k] = dlist(src[k])\\n if set(src) == set([str(k) for k in range(len(src))]):\\n src = [src[str(k)] for k in range(len(src))]\\n return src\",\n \"def get_precomp_hashcodes(self,list_feats_id):\\n return self.get_precomp_X(list_feats_id,\\\"hashcodes\\\",self.bits_num/8,np.uint8)\",\n \"def __init__(self, url=\\\"https://ehw.fit.vutbr.cz/izv/\\\",folder=\\\"data\\\", cache_filename=\\\"data_{}.pkl.gz\\\"):\\n self.url = url\\n self.folder = folder\\n self.cache_filename = cache_filename\\n self.ListOfZipFiles = []\\n self.saved_data = {}\\n self.headers = [\\\"region\\\", \\\"p1\\\", \\\"p36\\\", \\\"p37\\\", \\\"p2a\\\", \\\"weekday(p2a)\\\", \\\"p2b\\\", \\\"p6\\\", \\\"p7\\\", \\\"p8\\\", \\\"p9\\\", \\\"p10\\\", \\\"p11\\\", \\\"p12\\\", \\\"p13a\\\", \\\"p13b\\\", \\\"p13c\\\", \\\"p14\\\", \\\"p15\\\", \\\"p16\\\", \\\"p17\\\", \\\"p18\\\", \\\"p19\\\", \\\"p20\\\", \\\"p21\\\", \\\"p22\\\", \\\"p23\\\", \\\"p24\\\", \\\"p27\\\", \\\"p28\\\", \\\"p34\\\", \\\"p35\\\", \\\"p39\\\", \\\"p44\\\", \\\"p45a\\\", \\\"p47\\\", \\\"p48a\\\", \\\"p49\\\", \\\"p50a\\\", \\\"p50b\\\", \\\"p51\\\", \\\"p52\\\", \\\"p53\\\",\\\"p55a\\\",\\\"p57\\\",\\\"p58\\\",\\\"a\\\",\\\"b\\\",\\\"d\\\",\\\"e\\\",\\\"f\\\",\\\"g\\\",\\\"h\\\",\\\"i\\\",\\\"j\\\",\\\"k\\\",\\\"l\\\",\\\"n\\\",\\\"o\\\",\\\"p\\\",\\\"q\\\",\\\"r\\\",\\\"s\\\",\\\"t\\\",\\\"p5a\\\"]\\n self.duplicate_handling = []\\n if not re.search(\\\"^.*{}.*$\\\", self.cache_filename):\\n self.cache_filename+=\\\"{}\\\"\",\n \"def add_hash(self, lst):\\n self.__data = []\\n num = len(lst) + 1\\n self.add_data((\\\"\\\", 4, 1))\\n self.add_data((\\\"\\\", 4, num))\\n self.add_data((\\\"\\\", 4, num - 1))\\n self.add_data((\\\"\\\", 4, 0))\\n if 1 < num:\\n for ii in range(num - 1):\\n self.add_data((\\\"\\\", 4, ii))\",\n \"def test_lz4_decompression_avoids_deep_copy():\\n pytest.importorskip(\\\"lz4\\\")\\n a = bytearray(1_000_000)\\n b = compressions[\\\"lz4\\\"].compress(a)\\n c = compressions[\\\"lz4\\\"].decompress(b)\\n assert isinstance(c, bytearray)\",\n \"def length_of_data(self, data_list, days):\\n\\n # The zip() function returns an iterator of tuples based on the iterable object.\\n btceur_high, btceur_timestamp = zip(*data_list)\\n\\n # Various lists/variables used\\n one_week_list_timezone = []\\n one_week_list_btc_high = []\\n count = 0\\n\\n # Get the latest entry to work back from\\n orig = datetime.datetime.fromtimestamp(btceur_timestamp[-1])\\n # Get a date 7 days from the current date\\n one_week = orig - datetime.timedelta(days=days)\\n # Loop backwards through the timestamps getting the past 7 days worth\\n\\n for I in reversed(btceur_timestamp):\\n converted_timestamp_value = (datetime.datetime.fromtimestamp(I))\\n if one_week > converted_timestamp_value < orig:\\n one_week_list_timezone.append(converted_timestamp_value)\\n else:\\n continue\\n if one_week_list_timezone[-1] \\\\\\n > one_week:\\n return None\\n else:\\n # Get the corresponding data to each time stamp\\n for x in reversed(btceur_high):\\n if count < len(one_week_list_timezone):\\n one_week_list_btc_high.append(x)\\n count += 1\\n\\n # Reorder both lists of data\\n one_week_list_btc_high.reverse()\\n one_week_list_timezone.reverse()\\n zipped = zip(one_week_list_btc_high, one_week_list_timezone)\\n return list(zipped)\",\n \"def build_dataset(self):\\n\\n print('begin to build data set...')\\n\\n # build dictionary\\n for e in self.lst_entity:\\n self.dictionary_entity[e] = len(self.dictionary_entity)\\n\\n for r in self.lst_relation:\\n self.dictionary_relation[r] = len(self.dictionary_relation)\\n\\n self.reverse_dictionary_entity = dict(zip(self.dictionary_entity.values(), self.dictionary_entity.keys()))\\n self.reverse_dictionary_relation = dict(zip(self.dictionary_relation.values(), self.dictionary_relation.keys()))\\n\\n # build data map\\n self.lst_entity_map = [self.dictionary_entity[e] for e in self.lst_entity]\\n self.lst_relation_map = [self.dictionary_relation[r] for r in self.lst_relation]\\n self.lst_triplet_train_map = self.map_triplet(self.lst_triplet_train)\\n\\n # build corrupted candidates for (h,r,~) and (~,r,t)\\n for (h, r, t) in self.lst_triplet_train_map:\\n if r not in self.dict_tofh:\\n self.dict_tofh[r] = {h: [t]}\\n else:\\n if h not in self.dict_tofh[r]:\\n self.dict_tofh[r][h] = [t]\\n else:\\n self.dict_tofh[r][h].append(t)\\n\\n if r not in self.dict_hoft:\\n self.dict_hoft[r] = {t: [h]}\\n else:\\n if t not in self.dict_hoft[r]:\\n self.dict_hoft[r][t] = [h]\\n else:\\n self.dict_hoft[r][t].append(h)\\n\\n for r in self.dict_tofh:\\n self.lst_triplet_corrupted_tail[r] = dict()\\n for h in self.dict_tofh[r]:\\n set_tail_corrupted_all = set(self.lst_entity_map) - set(self.dict_tofh[r][h])\\n lst_tail_corrupted_choose = random.sample(set_tail_corrupted_all, 5*len(self.dict_tofh[r][h]))\\n self.lst_triplet_corrupted_tail[r][h] = lst_tail_corrupted_choose\\n\\n for r in self.dict_hoft:\\n self.lst_triplet_corrupted_head[r] = dict()\\n for t in self.dict_hoft[r]:\\n lst_head_corrupted_all = set(self.lst_entity_map) - set(self.dict_hoft[r][t])\\n lst_head_corrupted_choose = random.sample(lst_head_corrupted_all, 5*len(self.dict_hoft[r][t]))\\n self.lst_triplet_corrupted_head[r][t] = lst_head_corrupted_choose\\n\\n print('data set has been built successfully!')\",\n \"def test_compress_lossless_maps(self):\\n height_map = 384\\n width_map = 384\\n \\n # The quantization bin widths are small\\n # so that the comparison between the\\n # theoretical (minimum) coding cost and\\n # the coding cost computed by the function\\n # is precise enough.\\n bin_widths_test = numpy.array([0.5, 0.25], dtype=numpy.float32)\\n laplace_scales = numpy.array([0.5, 3.], dtype=numpy.float32)\\n \\n # Note that the binary probabilities saved at\\n # \\\"lossless/pseudo_data/binary_probabilities_compress_maps_0.npy\\\"\\n # and those saved at\\n # \\\"lossless/pseudo_data/binary_probabilities_compress_maps_1.npy\\\"\\n # are specific to the three Laplace distributions\\n # below. This means that the binary probabilities\\n # must be modified if `laplace_scales` is modified.\\n paths_to_binary_probabilities = [\\n 'lossless/pseudo_data/binary_probabilities_compress_maps_0.npy',\\n 'lossless/pseudo_data/binary_probabilities_compress_maps_1.npy'\\n ]\\n \\n centered_data_0 = numpy.random.laplace(loc=0.,\\n scale=laplace_scales[0].item(),\\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\\n centered_data_1 = numpy.random.laplace(loc=0.,\\n scale=laplace_scales[1].item(),\\n size=(1, height_map, width_map, 1)).astype(numpy.float32)\\n centered_data = numpy.concatenate((centered_data_0, centered_data_1),\\n axis=3)\\n expanded_centered_quantized_data = tls.quantize_per_map(centered_data, bin_widths_test)\\n centered_quantized_data = numpy.squeeze(expanded_centered_quantized_data,\\n axis=0)\\n tiled_bin_widths = numpy.tile(numpy.reshape(bin_widths_test, (1, 1, 2)),\\n (height_map, width_map, 1))\\n ref_int16 = tls.cast_float_to_int16(centered_quantized_data/tiled_bin_widths)\\n (rec_int16_0, nb_bits_each_map_0) = \\\\\\n lossless.compression.compress_lossless_maps(ref_int16,\\n paths_to_binary_probabilities[0])\\n numpy.testing.assert_equal(ref_int16,\\n rec_int16_0,\\n err_msg='The test fails as the lossless compression alters the signed integers.')\\n (rec_int16_1, nb_bits_each_map_1) = \\\\\\n lossless.compression.compress_lossless_maps(ref_int16,\\n paths_to_binary_probabilities[1])\\n numpy.testing.assert_equal(ref_int16,\\n rec_int16_1,\\n err_msg='The test fails as the lossless compression alters the signed integers.')\\n \\n # The equation below is derived from the\\n # theorem 8.3.1 in the book\\n # \\\"Elements of information theory\\\", 2nd edition,\\n # written by Thomas M. Cover and Joy A. Thomas.\\n theoretical_entropies = -numpy.log2(bin_widths_test) + (numpy.log(2.*laplace_scales) + 1.)/numpy.log(2.)\\n print('B0 denotes the binary probabilities saved at \\\"{}\\\".'.format(paths_to_binary_probabilities[0]))\\n print('B1 denotes the binary probabilities saved at \\\"{}\\\".'.format(paths_to_binary_probabilities[1]))\\n print('\\\\n1st centered-quantized latent variable feature map.')\\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[0]*height_map*width_map))\\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[0]))\\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[0]))\\n print('\\\\n2nd centered-quantized latent variable feature map.')\\n print('Theoretical coding cost: {} bits.'.format(theoretical_entropies[1]*height_map*width_map))\\n print('Coding cost computed by the function via B0: {} bits.'.format(nb_bits_each_map_0[1]))\\n print('Coding cost computed by the function via B1: {} bits.'.format(nb_bits_each_map_1[1]))\",\n \"def valid_zip(x):\\r\\n m_zip = [83,10286,10276,10268,10256,10249,10159,10150,10116,10113,10108,10101,10008,10282,10281,10280,10279,10278,10275,\\r\\n 10271,10270,10199,10178,10177,10176,10175,10174,10173,10172,10171,10170,10169,10168,10167,10166,10165,\\r\\n 10162,10161,10158,10155,10154,10153,10152,10151,10128,10123,10122,10121,10120,10119,10118,10115,10112,\\r\\n 10111,10110,10107,10106,10105,10104,10103,10099,10098,10095,10090,10069,10060,10055,10048,10047,10045,10044,\\r\\n 10041,10040,10039,10038,10037,10036,10035,10034,10033,10032,10031,10030,10029,10028,10027,10026,10025,\\r\\n 10024,10023,10022,10021,10020,10019,10018,10017,10016,10015,10014,10013,10012,10011,10010,10009,10007,\\r\\n 10006,10005,10004,10003,10002,10001,10065,10075,10080,\\r\\n 10285,10203,10178,10017,10178,10168,10167,10177,# supplementary\\r\\n 10175,10166,10171,10176,10174,10165,10170,10173,10169,10172,10019, 10105, 10097, 10104, 10107, 10103, 10106,\\r\\n 10022, 10055, 10155, 10152, 10153, 10151, 10154, 10001, 10120, 10119, 10118, 10123, 10122, 10121,\\r\\n 10005, 10081, 10286, 10260, 10271, 10259, 10043, 10270, 10265, 10203,10036, 10096, 10196, 10110\\r\\n ]\\r\\n brooklyn_zip = [11256,11252,11249,11243,11242,11241,11239,11238,11237,11236,11235,11234,11233,11232,11231,\\r\\n 11230,11229,11228,11226,11225,11224,11223,11222,11221,11220,11219,11218,11217,11216,11215,\\r\\n 11214,11213,11212,11211,11210,11209,11208,11207,11206,11205,11204,11203,11201]\\r\\n queens_zip = [11451,11436,11435,11434,11433,11432,11429,11428,11427,11426,\\r\\n 11423,11422,11421,11420,11419,11418,11417,11416,11415,11414,11413,11412,11411,11385,11379,\\r\\n 11378,11377,11375,11374,11373,11372,11369,11368,11367,11366,11365,11364,11363,\\r\\n 11362,11361,11360,11359,11358,11357,11356,11355,11354,11351,11109,11106,11105,11104,11103,\\r\\n 11102,11101,11004]\\r\\n if x in m_zip + brooklyn_zip + queens_zip:\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def make_dict(data_for_dict): \\n \\n column_name_list = data_for_dict[0]\\n db_list = data_for_dict[1:]\\n \\n column_list1 = []\\n column_list2 = []\\n column_list3 = []\\n column_list4 = []\\n column_list5 = []\\n column_list6 = []\\n column_list7 = []\\n column_list8 = []\\n column_list9 = []\\n column_list10 = []\\n column_list11 = []\\n hmdb_dict = {}\\n for line in db_list:\\n my_string1 = '' \\n my_string2 = ''\\n my_string3 = ''\\n my_string4 = ''\\n my_string5 = ''\\n my_string6 = ''\\n my_string7 = ''\\n my_string8 = ''\\n my_string9 = ''\\n my_string10 = ''\\n my_string11 = ''\\n\\n my_string1 = line[0]\\n column_list1 += [my_string1]\\n my_string2 += line[1]\\n column_list2 += [my_string2]\\n my_string3 += line[2]\\n column_list3 += [my_string3]\\n my_string4 += line[3]\\n column_list4 += [my_string4]\\n my_string5 += line[4]\\n column_list5 += [my_string5]\\n my_string6 += line[5]\\n column_list6 += [my_string6]\\n my_string7 += line[6]\\n column_list7 += [my_string7]\\n my_string8 += line[7]\\n column_list8 += [my_string8]\\n my_string9 += line[8]\\n column_list9 += [my_string9]\\n my_string10 += line[9]\\n column_list10 += [my_string10]\\n my_string11 += line[10]\\n column_list11 += [my_string11] \\n \\n hmdb_dict[column_name_list[0]] = column_list1\\n hmdb_dict[column_name_list[1]] = column_list2\\n hmdb_dict[column_name_list[2]] = column_list3\\n hmdb_dict[column_name_list[3]] = column_list4\\n hmdb_dict[column_name_list[4]] = column_list5\\n hmdb_dict[column_name_list[5]] = column_list6\\n hmdb_dict[column_name_list[6]] = column_list7\\n hmdb_dict[column_name_list[7]] = column_list8\\n hmdb_dict[column_name_list[8]] = column_list9\\n hmdb_dict[column_name_list[9]] = column_list10\\n hmdb_dict[column_name_list[10]] = column_list11\\n \\n return (hmdb_dict)\",\n \"def set_lz(self):\\r\\n self._lz = self.dz * self.nz - self.oz\",\n \"def list_data_collate(batch: Sequence):\\n elem = batch[0]\\n data = [i for k in batch for i in k] if isinstance(elem, list) else batch\\n key = None\\n try:\\n if config.USE_META_DICT:\\n data = pickle_operations(data) # bc 0.9.0\\n if isinstance(elem, Mapping):\\n ret = {}\\n for k in elem:\\n key = k\\n data_for_batch = [d[key] for d in data]\\n ret[key] = collate_meta_tensor(data_for_batch)\\n else:\\n ret = collate_meta_tensor(data)\\n return ret\\n except RuntimeError as re:\\n re_str = str(re)\\n if \\\"equal size\\\" in re_str:\\n if key is not None:\\n re_str += f\\\"\\\\nCollate error on the key '{key}' of dictionary data.\\\"\\n re_str += (\\n \\\"\\\\n\\\\nMONAI hint: if your transforms intentionally create images of different shapes, creating your \\\"\\n + \\\"`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its \\\"\\n + \\\"documentation).\\\"\\n )\\n _ = dev_collate(data)\\n raise RuntimeError(re_str) from re\\n except TypeError as re:\\n re_str = str(re)\\n if \\\"numpy\\\" in re_str and \\\"Tensor\\\" in re_str:\\n if key is not None:\\n re_str += f\\\"\\\\nCollate error on the key '{key}' of dictionary data.\\\"\\n re_str += (\\n \\\"\\\\n\\\\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, \\\"\\n + \\\"creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem \\\"\\n + \\\"(check its documentation).\\\"\\n )\\n _ = dev_collate(data)\\n raise TypeError(re_str) from re\",\n \"def test_compress_2_idenctical_char(self):\\n text = 'aa'\\n actual = LZ77.compress(text)\\n expected = bytearray([0]) + bytearray(b'aa')\\n self.assertEqual(actual, expected)\",\n \"def build_dataset(words):\\n count = []\\n # count.extend(collections.Counter(words).most_common(n_words - 1))\\n count.extend(collections.Counter(words).most_common())\\n dictionary = dict()\\n for word, _ in count:\\n dictionary[word] = len(dictionary)\\n data = list()\\n # unk_count = 0\\n for word in words:\\n index = dictionary.get(word, 0)\\n # if index == 0: # dictionary['UNK']\\n # unk_count += 1\\n data.append(index)\\n # count[0][1] = unk_count\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n data = [data[::2],data[1::2]]\\n new_data = list()\\n for i in range(len(data[0])):\\n new_data.append([data[0][i],data[1][i]])\\n data = new_data\\n vocabulary_size = len(dictionary)\\n print(\\\"\\\\n\\\\ndictionary size = \\\")\\n print(len(dictionary))\\n return data, count, dictionary, reversed_dictionary, vocabulary_size\",\n \"def __generate_tuple_keys(self, data):\\n if len(data) < self.order:\\n return\\n\\n for i in range(len(data) - self.order):\\n yield [tuple(data[i:i+self.order]), data[i+self.order]]\",\n \"def get_lz(self):\\r\\n return self.dz * self.nz - self.oz\",\n \"def build_list(self, word_list):\\n # Get frequency list for keys\\n freq = word_list.groupby('key').agg('count')\\n # Filter out only keys with greater or equal frequency to length\\n key_list = freq.loc[freq['word'] >= freq.index.str.len()]\\n return key_list\",\n \"def getJar(oldDatas,jarList):\\n #clear pastebin here. confirmed.\\n if pastebin in os.listdir():\\n tempdir = barrand6()\\n os.rename(pastebin,tempdir)#clever..fine..\\n shutil.rmtree(tempdir)#acces deniyed ..fixed!\\n os.mkdir(pastebin)\\n\\n\\n oldDatakeys = oldDatas.keys()\\n newDatas = {} #for data insure.\\n\\n for noFolder in jarList:\\n try:\\n #txtFiledir = os.path.join( origins,noFolder,noFolder+'.txt')\\n txtFilename = noFolder+'.txt'\\n #------------------------user pre-sure.\\n txtfiles = []\\n for f in os.listdir( os.path.join( jar,noFolder) ):\\n if '.txt' in f:\\n txtfiles.append(f)\\n if len(txtfiles) == 1:\\n txtFilename = txtfiles[0]\\n else:\\n if not '설정.txt' in txtfiles:\\n raise Exception('ERROR no txt : ' + str(noFolder))\\n txtFilename = '설정.txt'\\n #------------------------user pre-sure\\n\\n txtFile = os.path.join( jar,noFolder,txtFilename) #whatif dir = no.txt?\\n parsedDict = txt2dict.parseTxt(txtFile,parseKeys,multiLineKey)#hope it's atleast complete...\\n\\n #----------------------for custom dict additional option\\n checklist = ['번호','제목','작성자','날짜','본문']\\n for c in checklist:\\n if not c in parsedDict.keys():\\n raise Exception('ERROR!! not format txt : ' + str(noFolder))\\n\\n #user input, do another func.\\n #'16'.isdigit()\\n #if int(parsedDict['번호'])<1:\\n #raise Exception('ERROR!! of : ' + str(noFolder))\\n #a = parsedDict['날짜'].split('.')\\n #b = str(datetime.date.today()).split('-')\\n #if datetime.date(a[0],a[1],a[2]) < datetime.date.today()\\n\\n if '태그' in parsedDict.keys():\\n tagList = parsedDict['태그'].split(',')\\n parsedDict['유저태그'] = tagList\\n del parsedDict['태그']\\n else:\\n parsedDict['유저태그'] = []\\n\\n if parsedDict['제목'].startswith('[번역]'):\\n parsedDict['제목'] = parsedDict['제목'].split('[번역]')[1].strip()\\n if parsedDict['제목'].find('센세)') != -1 :\\n parsedDict['유저태그'].append( parsedDict['제목'].split('센세)')[0].strip()+'센세' )\\n #parsedDict['태그'].append( a.split('[번역]')[1].strip().split('센세)')[0]+'센세)' )\\n #----------------------for custom dict additional option\\n\\n\\n #----------------------------- after get parsedDict.\\n tmpKey = parsedDict[idKey] #9133114\\n if tmpKey in oldDatakeys:\\n raise Exception('skip.. id already in parsedict ..: ' + str(noFolder))\\n\\n idFoldername = parsedDict['번호']\\n noFolderpath = os.path.join( jar,noFolder )\\n originPath = os.path.join( origins , idFoldername )\\n shutil.copytree(noFolderpath,originPath, dirs_exist_ok = False)# was true, but to integrity....\\n #it occured at test. nodict, but files.\\n #shutil.move(noFolderpath,pastebin)\\n\\n\\n #datas is dict object, appended new key,value.\\n #add more value.\\n #datas[tmpKey]['key'] = 'value'\\n\\n # get moved nofolder, add datas originImgs.\\n originFiles = os.listdir(os.path.join( origins, idFoldername))\\n originImgs = []\\n for img in originFiles:\\n ext = os.path.splitext( img )[1][1:] # .jpg == jpg\\n if ext in imgExt: #now, it's img.\\n originImgs.append(img)\\n if originImgs==[]:\\n raise Exception('ERROR!! no img..: ' + str(noFolder))\\n parsedDict[originKey] = originImgs\\n\\n newDatas[tmpKey] = parsedDict\\n #datas[tmpKey][allfilesKey] = originFiles\\n thisrand=barrand6()\\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\\n\\n except Exception as e:\\n exc_info = sys.exc_info()#below except.\\n errmsg = exc_info[1],':at line',exc_info[2].tb_lineno\\n print(errmsg)\\n\\n thisrand=barrand6()\\n os.rename( os.path.join( jar,noFolder), pastebin+'/'+noFolder+thisrand )\\n f = open('./'+pastebin+'/'+noFolder+thisrand+'/err.txt','w',encoding='utf-8')\\n f.write(str(errmsg))\\n f.close()\\n print( 'ERROR occured. gone pastebin :',str(noFolder)+thisrand)\\n continue\\n\\n return newDatas\",\n \"def test_key_for_list_of_cacheable_objects(self):\\n Meat.get_protein_sum([self.chicken, self.steak])\\n expected_cache_key = 'tests.Meat.get_protein_sum;,Chicken:20,Steak:26;'\\n self.assertExpectedKeyInCache(expected_cache_key)\",\n \"def _vWBLZ(self):\\r\\n\\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n vWBLZ=None\\r\\n vWBLZ=self._OBJS('WBLZ')\\r\\n \\r\\n vWBLZ=vWBLZ[[\\r\\n 'AKTIV' \\r\\n ,'BESCHREIBUNG'\\r\\n ,'IDIM'\\r\\n ,'NAME'\\r\\n #from WBLZ's OBJS: \\r\\n ,'OBJTYPE' #type (i.e. KNOT) of a WBLZ OBJ\\r\\n ,'OBJID' #pk (or tk?!) of a WBLZ OBJ \\r\\n #IDs (of the WBLZ)\\r\\n ,'pk'\\r\\n ]]\\r\\n vWBLZ.sort_values(['NAME','pk'],ascending=True,inplace=True)\\r\\n #reindex:\\r\\n vWBLZ=pd.DataFrame(vWBLZ.values,columns=vWBLZ.columns)\\r\\n \\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n if isinstance(vWBLZ,pd.core.frame.DataFrame):\\r\\n logger.error(logStrFinal) \\r\\n else:\\r\\n logger.debug(logStrFinal) \\r\\n vWBLZ=pd.DataFrame() \\r\\n vWBLZ['AKTIV']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['BESCHREIBUNG']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['IDIM']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['NAME']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['OBJID']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['OBJTYPE']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n vWBLZ['pk']=pd.Series(dtype='object') # The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning. \\r\\n \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.')) \\r\\n return vWBLZ\",\n \"def __init__(self):\\r\\n self.indices = {}\\r\\n self.data = []\\r\\n self.len = 0\",\n \"def read_data(file_path):\\n words=[]\\n dic_word={}\\n actual_text=[]\\n for line in open(file_path,encoding='utf-8'):\\n words_line=line.strip().split(' ')\\n for ite in words_line:\\n if ite not in dic_word:\\n dic_word[ite]=1\\n words.extend(words_line)\\n actual_text.append(words_line)\\n\\n\\n #with zipfile.ZipFile(file_path) as f:\\n #words = tf.compat.as_str(f.read(f.namelist()[0])).split()\\n\\n return words,len(dic_word),actual_text\",\n \"def data_log_mapping(kw='aug'):\\n if kw == 'aug':\\n dirs = {}\\n dirs['120'] = ['08062020-3', '08062020-4', '08062020-5']\\n dirs['100'] = ['08062020-0', '08062020-1', '08062020-2']\\n dirs['85'] = ['08052020-3', '08052020-4', '08052020-5']\\n dirs['80'] = ['08032020-0', '08032020-1', '08032020-2']\\n dirs['70'] = ['08042020-0', '08042020-1', '08042020-2']\\n dirs['60'] = ['08032020-3', '08032020-4', '08032020-5']\\n dirs['50'] = ['08042020-3', '08042020-4', '08042020-5']\\n dirs['40'] = ['08032020-6', '08032020-7', '08032020-8']\\n dirs['30'] = ['08042020-6', '08042020-7', '08042020-8']\\n dirs['20'] = ['08032020-9', '08032020-10', '08032020-11']\\n dirs['10'] = ['08042020-9', '08042020-10', '08042020-11']\\n dirs['00'] = ['08032020-12', '08032020-13', '08032020-14']\\n \\n return dirs\",\n \"def get_listu_dilucijskih_jedinica(self):\\n popis = sorted(list(self.dilucijskeJedinice.keys()))\\n return popis\",\n \"def decompose_sample(lfads_hps, z):\\n ib_dim = lfads_hps['ib_dim']\\n ic_dim = lfads_hps['enc_dim']\\n ib_k = z[:ib_dim]\\n ic_j = z[ib_dim:(ib_dim+ic_dim)]\\n ii_ti = z[(ib_dim+ic_dim):]\\n ii_txi = np.reshape(ii_ti, (-1, lfads_hps['ii_dim']))\\n return ib_k, ic_j, ii_txi\",\n \"def filter_dict(lam,bigZ,bigZlag):\\n n_eq = lam.shape[0]\\n if not(len(bigZ.keys()) == n_eq and len(bigZlag.keys()) == n_eq):\\n raise Exception(\\\"Error: incompatible dimensions\\\")\\n Zfilt = {}\\n for r in range(n_eq):\\n lami = lam[r][0]\\n Zfilt[r] = bigZ[r] - lami*bigZlag[r]\\n return Zfilt\",\n \"def loadData(subjectList,dataMap,features,hemi):\\n\\n objDict = dataMap['object'].items()\\n objDir = objDict[0][0]\\n objExt = objDict[0][1]\\n\\n midDict = dataMap['midline'].items()\\n midDir = midDict[0][0]\\n midExt = midDict[0][1]\\n\\n matDict = dataMap['matching'].items()\\n matDir = matDict[0][0]\\n matExt = matDict[0][1]\\n\\n data = {}\\n matches = {}\\n\\n for s in subjectList:\\n\\n # Training data\\n trainObject = '{}{}.{}.{}'.format(objDir,s,hemi,objExt)\\n print trainObject\\n midObject = '{}{}.{}.{}'.format(midDir,s,hemi,midExt)\\n matObject = '{}{}.{}.{}'.format(matDir,s,hemi,matExt)\\n\\n # Check to make sure all 3 files exist\\n if os.path.isfile(trainObject) and os.path.isfile(midObject) and os.path.isfile(matObject):\\n\\n # Load midline indices\\n # Subtract 1 for differece between Matlab and Python indexing\\n mids = ld.loadMat(midObject)-1\\n mids = set(mids)\\n \\n match = ld.loadMat(matObject)\\n\\n # Load training data and training labels\\n trainH5 = h5py.File(trainObject,mode='r')\\n\\n # Get data corresponding to features of interest\\n subjData = ld.parseH5(trainH5,features)\\n trainH5.close()\\n\\n nSamples = set(np.arange(subjData[s][features[0]].shape[0]))\\n coords = np.asarray(list(nSamples.difference(mids)))\\n \\n for f in subjData[s].keys():\\n tempData = subjData[s][f]\\n if tempData.ndim == 1:\\n tempData.shape+=(1,)\\n\\n subjData[s][f] = np.squeeze(tempData[coords,:])\\n \\n match = match[coords,:]\\n \\n data[s] = subjData[s]\\n matches[s] = match\\n\\n return [data,matches]\",\n \"def decode(self, coded_set):\",\n \"def test_input_conversion(self):\\n data = [dict(A=0.1, C=0.2), dict(A=0.1, C=0.2)]\\n base = [(\\\"A\\\", 0.1), (\\\"C\\\", 0.2)]\\n expect = [base, base]\\n got = _char_hts_as_lists(data)\\n self.assertEqual(got, expect)\\n #\\n data = [dict(A=0.1, C=0.2), {}]\\n base = [(\\\"A\\\", 0.1), (\\\"C\\\", 0.2)]\\n expect = [base, None]\\n got = _char_hts_as_lists(data)\\n self.assertEqual(got, expect)\\n data = [dict(A=0.1, C=0.2), None]\\n base = [(\\\"A\\\", 0.1), (\\\"C\\\", 0.2)]\\n expect = [base, None]\\n got = _char_hts_as_lists(data)\\n self.assertEqual(got, expect)\",\n \"def _lzw(self, tile: bytes) -> np.ndarray:\\n decoded = self._reshape(np.frombuffer(imagecodecs.lzw_decode(tile), self.dtype))\\n self._unpredict(decoded)\\n return np.rollaxis(decoded, 2, 0)\",\n \"def unpack_data(self, data):\\n\\n datadict = {'filenames': [], 'features': [], 'labels': [] }\\n\\n for l in data:\\n \\n datadict['filenames'].append(l[0])\\n datadict['features'].append(l[1])\\n datadict['labels'].append(l[2])\\n \\n return datadict\",\n \"def test_initialize_log_data(self):\\r\\n\\r\\n ids_bcs_added_field = {('AAAA', ''): 's1', ('TTTT', ''): 's2'}\\r\\n actual_log_data = initialize_log_data(ids_bcs_added_field)\\r\\n\\r\\n expected_log_data = {'TTTT,s2': 0, 'AAAA,s1': 0}\\r\\n\\r\\n self.assertEqual(actual_log_data, expected_log_data)\\r\\n\\r\\n # Handles added demultiplex field data\\r\\n ids_bcs_added_field = {('AAAA', '1'): 's1', ('TTTT', '2'): 's2'}\\r\\n actual_log_data = initialize_log_data(ids_bcs_added_field)\\r\\n\\r\\n expected_log_data = {'TTTT,2,s2': 0, 'AAAA,1,s1': 0}\\r\\n\\r\\n self.assertEqual(actual_log_data, expected_log_data)\",\n \"def getDayAheadMarketLBMPZonal(self):\\n\\n\\t\\t# Variables\\n\\t\\ttoday = self.helper.getYear() + self.helper.getMonth() + self.helper.getDay()\\n\\t\\turl = 'http://mis.nyiso.com/public/csv/damlbmp/'+today+'damlbmp_zone.csv' \\n\\t\\tresponse = urllib2.urlopen(url)\\n\\t\\tmarket_data = sorted(csv.reader(response), key=operator.itemgetter(1)) # Converting data to python csv\\n\\t\\tcounter = 0 # Counter used for determining which hour we are curretly on\\n\\t\\tlbmpZonal = {}\\n\\t\\ttimestamp = {}\\n\\t\\ttimestamps = []\\n\\t\\tmarket_info = {}\\n\\n\\t\\t# Converting csv data to market data and returning it\\n\\t\\tfor row in market_data:\\n\\t\\t\\t# Ignoring header row\\n\\t\\t\\tif row[0] != 'Time Stamp':\\n\\t\\t\\t\\tmarket_info['LBMP ($/MWHr)'] = float(row[3])\\n\\t\\t\\t\\tmarket_info['Marginal Cost Losses ($/MWHr)'] = float(row[4])\\n\\t\\t\\t\\tmarket_info['Marginal Cost Congestion ($/MWHr)'] = float(row[5])\\n\\t\\t\\t\\trow[0] = self.helper.getDateInEpoch(row[0])\\n\\t\\t\\t\\ttimestamp[row[0]] = market_info\\n\\t\\t\\t\\tmarket_info = {}\\n\\t\\t\\t\\ttimestamps.append(timestamp)\\n\\t\\t\\t\\ttimestamp = {}\\n\\t\\t\\t\\tcounter +=1\\n\\t\\t\\t\\t# if counter == 23:\\n\\t\\t\\t\\t\\t# key = row[1]\\n\\t\\t\\t\\tif counter == 24:\\n\\t\\t\\t\\t\\tlbmpZonal[row[1]] = timestamps\\n\\t\\t\\t\\t\\ttimestamps = []\\n\\t\\t\\t\\t\\tcounter = 0\\n\\t\\treturn lbmpZonal\",\n \"def test_decompress_1_char(self):\\n b_array = bytearray([0]) + bytearray(b'a')\\n actual = LZ77.decompress(b_array)\\n expected = 'a'\\n self.assertEqual(actual, expected)\",\n \"def _read_zb_zblst(fname):\\n with open(fname) as foo:\\n data = {}\\n read_data = False\\n flow_budget = False\\n empty = 0\\n prefix = \\\"\\\"\\n while True:\\n line = foo.readline().strip().upper()\\n t = line.split()\\n if t:\\n if t[-1].strip() == \\\"ZONES.\\\":\\n line = foo.readline().strip()\\n zones = [int(i) for i in line.split()]\\n for zone in zones:\\n data[f\\\"TO_ZONE_{zone}\\\"] = []\\n data[f\\\"FROM_ZONE_{zone}\\\"] = []\\n\\n if \\\"FLOW BUDGET FOR ZONE\\\" in line:\\n flow_budget = True\\n read_data = False\\n zlist = []\\n empty = 0\\n t = line.split()\\n zone = int(t[4])\\n if len(t[7]) > 4:\\n t.insert(8, t[7][4:])\\n kstp = int(t[8]) - 1\\n if len(t[11]) > 6:\\n t.append(t[11][6:])\\n kper = int(t[12]) - 1\\n if \\\"ZONE\\\" not in data:\\n data[\\\"ZONE\\\"] = [zone]\\n data[\\\"KSTP\\\"] = [kstp]\\n data[\\\"KPER\\\"] = [kper]\\n else:\\n data[\\\"ZONE\\\"].append(zone)\\n data[\\\"KSTP\\\"].append(kstp)\\n data[\\\"KPER\\\"].append(kper)\\n\\n elif line in (\\\"\\\", \\\" \\\"):\\n empty += 1\\n\\n elif read_data:\\n if \\\"=\\\" in line:\\n t = line.split(\\\"=\\\")\\n label = t[0].strip()\\n if \\\"ZONE\\\" in line:\\n if prefix == \\\"FROM_\\\":\\n zlist.append(int(label.split()[1]))\\n label = f\\\"FROM_ZONE_{label.split()[1]}\\\"\\n else:\\n label = f\\\"TO_ZONE_{label.split()[-1]}\\\"\\n\\n elif \\\"TOTAL\\\" in line or \\\"PERCENT DISCREPANCY\\\" in line:\\n label = \\\"_\\\".join(label.split())\\n\\n elif \\\"IN - OUT\\\" in line:\\n label = \\\"IN-OUT\\\"\\n\\n else:\\n label = prefix + \\\"_\\\".join(label.split())\\n\\n if label in data:\\n data[label].append(float(t[1]))\\n else:\\n data[label] = [float(t[1])]\\n\\n if label == \\\"PERCENT_DISCREPANCY\\\":\\n # fill in non-connected zones with zeros...\\n for zone in zones:\\n if zone in zlist:\\n continue\\n data[f\\\"FROM_ZONE_{zone}\\\"].append(0)\\n data[f\\\"TO_ZONE_{zone}\\\"].append(0)\\n\\n elif \\\"OUT:\\\" in line:\\n prefix = \\\"TO_\\\"\\n\\n else:\\n pass\\n\\n elif flow_budget:\\n if \\\"IN:\\\" in line:\\n prefix = \\\"FROM_\\\"\\n read_data = True\\n flow_budget = False\\n\\n else:\\n pass\\n\\n if empty >= 30:\\n break\\n\\n return _zb_dict_to_recarray(data)\",\n \"def test_compress_seq_diff_8_char(self):\\n text = '12345678'\\n actual = LZ77.compress(text)\\n expected = bytearray([0]) + bytearray(b'12345678')\\n self.assertEqual(actual, expected)\",\n \"def build_dataset(words, n_words):\\n count = [['UNK', -1]]\\n count.extend(collections.Counter(words).most_common(n_words - 1))\\n dictionary = dict()\\n for word, _ in count:\\n dictionary[word] = len(dictionary)\\n data = list()\\n unk_count = 0\\n for word in words:\\n index = dictionary.get(word, 0)\\n if index == 0: # dictionary['UNK']\\n unk_count += 1\\n data.append(index)\\n count[0][1] = unk_count\\n reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\\n return data, count, dictionary, reversed_dictionary\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7625661","0.5576687","0.5428306","0.5259145","0.52158284","0.5127516","0.51260525","0.51113755","0.5106959","0.50973123","0.507302","0.5056697","0.50454503","0.50228345","0.5017704","0.49628225","0.49544364","0.4946142","0.49108753","0.49073184","0.49037606","0.48755017","0.4860034","0.4850426","0.48478657","0.48361212","0.48324823","0.48282993","0.4826274","0.4822718","0.4801851","0.4792261","0.47480306","0.47440645","0.4726872","0.47227946","0.4720643","0.47063172","0.47044882","0.4700723","0.46960062","0.46943146","0.46940196","0.46913746","0.46886942","0.46865255","0.4678318","0.46762544","0.4675198","0.46751818","0.46721077","0.4670346","0.46682063","0.46672687","0.46606535","0.4657064","0.46471953","0.46451455","0.4641537","0.4638448","0.46330068","0.46319035","0.46318343","0.46294695","0.46285468","0.4627711","0.46263313","0.46251476","0.4610376","0.46076736","0.4596873","0.45823398","0.4580765","0.45756957","0.45753744","0.4575353","0.45561272","0.4554755","0.4546802","0.4546396","0.4531907","0.45271683","0.45251423","0.45245215","0.4522545","0.4514451","0.4510201","0.4508381","0.44983268","0.44961613","0.44943795","0.44935048","0.44914025","0.44892034","0.44862732","0.4485399","0.4482115","0.448043","0.447846","0.44661665"],"string":"[\n \"0.7625661\",\n \"0.5576687\",\n \"0.5428306\",\n \"0.5259145\",\n \"0.52158284\",\n \"0.5127516\",\n \"0.51260525\",\n \"0.51113755\",\n \"0.5106959\",\n \"0.50973123\",\n \"0.507302\",\n \"0.5056697\",\n \"0.50454503\",\n \"0.50228345\",\n \"0.5017704\",\n \"0.49628225\",\n \"0.49544364\",\n \"0.4946142\",\n \"0.49108753\",\n \"0.49073184\",\n \"0.49037606\",\n \"0.48755017\",\n \"0.4860034\",\n \"0.4850426\",\n \"0.48478657\",\n \"0.48361212\",\n \"0.48324823\",\n \"0.48282993\",\n \"0.4826274\",\n \"0.4822718\",\n \"0.4801851\",\n \"0.4792261\",\n \"0.47480306\",\n \"0.47440645\",\n \"0.4726872\",\n \"0.47227946\",\n \"0.4720643\",\n \"0.47063172\",\n \"0.47044882\",\n \"0.4700723\",\n \"0.46960062\",\n \"0.46943146\",\n \"0.46940196\",\n \"0.46913746\",\n \"0.46886942\",\n \"0.46865255\",\n \"0.4678318\",\n \"0.46762544\",\n \"0.4675198\",\n \"0.46751818\",\n \"0.46721077\",\n \"0.4670346\",\n \"0.46682063\",\n \"0.46672687\",\n \"0.46606535\",\n \"0.4657064\",\n \"0.46471953\",\n \"0.46451455\",\n \"0.4641537\",\n \"0.4638448\",\n \"0.46330068\",\n \"0.46319035\",\n \"0.46318343\",\n \"0.46294695\",\n \"0.46285468\",\n \"0.4627711\",\n \"0.46263313\",\n \"0.46251476\",\n \"0.4610376\",\n \"0.46076736\",\n \"0.4596873\",\n \"0.45823398\",\n \"0.4580765\",\n \"0.45756957\",\n \"0.45753744\",\n \"0.4575353\",\n \"0.45561272\",\n \"0.4554755\",\n \"0.4546802\",\n \"0.4546396\",\n \"0.4531907\",\n \"0.45271683\",\n \"0.45251423\",\n \"0.45245215\",\n \"0.4522545\",\n \"0.4514451\",\n \"0.4510201\",\n \"0.4508381\",\n \"0.44983268\",\n \"0.44961613\",\n \"0.44943795\",\n \"0.44935048\",\n \"0.44914025\",\n \"0.44892034\",\n \"0.44862732\",\n \"0.4485399\",\n \"0.4482115\",\n \"0.448043\",\n \"0.447846\",\n \"0.44661665\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81224066"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":13199,"cells":{"query":{"kind":"string","value":"dict_of_abc vypada napriklad takto"},"document":{"kind":"string","value":"def dict_cointains_list(dict_of_abc, item_list):\n\n values = list(dict_of_abc.values())\n\n #projdu vsecky listy ve slovniku\n for i in range(len(values)):\n #predpokladam ze ve slovniku je\n finded = True\n \n for j in range(len(values[i])):\n if len(item_list) == len(values[i]):\n # kontrola po jednotlivych hodnotach\n # logicky soucin - pokud jednou False, navzdy False\n finded = finded and item_list[j] == values[i][j]\n else:\n finded = False\n\n if finded:\n # cyklus indexuje od 0, slovnik ale indexujeme-klicujeme od 1\n return i + 1 \n\n return -1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_get_cases_for_dict(self):\n pass","def init_dict() -> None:\n for elem in letters:\n ascii_dict[elem] = []\n for elem in numbers:\n ascii_dict[elem] = []\n for elem in symbols:\n ascii_dict[elem] = []","def dictogram_dictlist(self):\n for key, value in self.word_dict.items():\n self.word_dict[key] = dictogram.Dictogram(value)\n # print(\"self.word_dict\", self.word_dict)","def secondary_keys_dicts(self):","def multDic(dic, x):\n pass","def __init__(self, dictionary):\n self.d = {}\n for word in dictionary:\n abbr = self.getAbbr(word)\n if abbr in self.d:\n self.d[abbr] += word,\n else:\n self.d[abbr] = [word]","def build_arpabet(self, word_lst):\n arpabet_dict = {}\n for word in word_lst:\n unicode_word = unicode(word)\n\n try:\n arpabet_dict[word] = self.cmu_dict[unicode_word][0]\n except:\n print(unicode_word + \" not found in CMUDict\")\n sys.exit()\n\n return arpabet_dict","def __init__(self):\r\n self.dct = defaultdict(list)","def def_dict():\n d1 = c.defaultdict(dict)\n print '\\nInitially first dictionary is: ', dict(d1)\n l = [('a', 1), ('b', 2), ('a', 27), ('c', 3), ('d', 4), ('c', 29), ('e', 5)]\n for a, b in l:\n d1[a] = b\n print '\\nAfter adding elements to the default dictionary, it is: ', dict(d1)\n print '\\nItems in the first dictionary is: ', d1.items()\n st = 'malayalam'\n print '\\n\\nCreating dictionary from the string', st\n d2 = c.defaultdict(int)\n print '\\nInitially second dict is:', dict(d2)\n for i in st:\n d2[i] = st.count(i)\n print \"After updation, the second dictionary becomes: \", dict(d2)","def dict(dict: Dict[str, Pin], /) -> None:","def standarization_ofconc(a2_data):\n aux_dic = OrderedDict()\n for i in a2_data:\n evol_tuple = a2_data[i]['conc'].keys()\n if len(evol_tuple) != 1:\n raise RuntimeError('too many tuples for conc')\n evol_tuple = evol_tuple[0]\n aux_dic[i] = a2_data[i]['conc'][evol_tuple]\n return aux_dic","def test_items(self):\n obs = self.tester.items()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {('center_name', 'ANL'), ('center_project_name', None),\n ('emp_status', 'EMP'), ('barcodesequence', 'AGCGCTCACATC'),\n ('library_construction_protocol',\n 'This analysis was done as in Caporaso et al 2011 Genome '\n 'research. The PCR primers (F515/R806) were developed against '\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\n 'which we determined would yield optimal community clustering '\n 'with reads of this length using a procedure similar to that '\n 'of ref. 15. [For reference, this primer pair amplifies the '\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\n '(greengenes accession no. prokMSA_id:470367).] The reverse '\n 'PCR primer is barcoded with a 12-base error-correcting Golay '\n 'code to facilitate multiplexing of up to 1,500 samples per '\n 'lane, and both PCR primers contain sequencer adapter '\n 'regions.'), ('linkerprimersequence', 'GTGCCAGCMGCCGCGGTAA'),\n ('target_subfragment', 'V4'), ('target_gene', '16S rRNA'),\n ('run_center', 'ANL'), ('run_prefix', 's_G1_L001_sequences'),\n ('run_date', '8/1/12'), ('experiment_center', 'ANL'),\n ('experiment_design_description',\n 'micro biome of soil and rhizosphere of cannabis plants '\n 'from CA'), ('experiment_title', 'Cannabis Soil Microbiome'),\n ('platform', 'Illumina'), ('samp_size', '.25,g'),\n ('sequencing_meth', 'Sequencing by synthesis'),\n ('illumina_technology', 'MiSeq'), ('sample_center', 'ANL'),\n ('pcr_primers',\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT'),\n ('study_center', 'CCME')}\n self.assertEqual(set(obs), exp)","def __init__(self, dictionary):\n self.dict = {}\n for word in dictionary:\n abbr = self.gen_abbr(word)\n if abbr not in self.dict:\n word_set = set([word])\n self.dict[abbr] = word_set\n else:\n self.dict[abbr].add(word)","def asdict():\n pass","def test_create_cds_translation_dict_1(self):\n t1 = \"ABCDE\"\n t2 = \"FGHIJ\"\n t3 = \"RSTUV\"\n self.gene_data_1[\"Translation\"] = t1\n self.gene_data_2[\"Translation\"] = t2\n self.gene_data_3[\"Translation\"] = t3\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\n dict = find_domains.create_cds_translation_dict(cdd_list)\n\n exp_keys = {t1, t2, t3}\n with self.subTest():\n self.assertEqual(dict.keys(), exp_keys)\n with self.subTest():\n self.assertEqual(dict[t1], {self.gene_data_1[\"GeneID\"]})\n with self.subTest():\n self.assertEqual(dict[t2], {self.gene_data_2[\"GeneID\"]})\n with self.subTest():\n self.assertEqual(dict[t3], {self.gene_data_3[\"GeneID\"]})","def cat_from_letter(cls, cog_letter, dict_output=False):\n functions = dict()\n for char in cog_letter:\n for k, v in cls.COG_letters.items():\n for k_, v_ in v.items():\n if char == k_:\n functions[char] = (k, v_)\n try:\n functions[char]\n except KeyError:\n print(f\"{char} wasn't found! Check if it is a valid COG letter.\")\n\n if functions:\n if dict_output:\n return functions\n else:\n output = [(k, v) for k, v in functions.items()]\n return output","def test_create_cds_translation_dict_2(self):\n t1 = \"ABCDE\"\n t2 = \"FGHIJ\"\n self.gene_data_1[\"Translation\"] = t1\n self.gene_data_2[\"Translation\"] = t2\n self.gene_data_3[\"Translation\"] = t2\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\n dict = find_domains.create_cds_translation_dict(cdd_list)\n\n exp_keys = {t1, t2}\n with self.subTest():\n self.assertEqual(dict.keys(), exp_keys)\n with self.subTest():\n self.assertEqual(dict[t1], {self.gene_data_1[\"GeneID\"]})\n with self.subTest():\n self.assertEqual(dict[t2], {self.gene_data_2[\"GeneID\"],\n self.gene_data_3[\"GeneID\"]})","def __init__(self, dictionary):\n self.abbrev_dict = {}\n for s in dictionary:\n if len(s) < 3:\n abbrev = s\n else:\n abbrev = s[0] + str(len(s) - 2) + s[-1]\n \n if abbrev not in self.abbrev_dict:\n self.abbrev_dict[abbrev] = set()\n self.abbrev_dict[abbrev].add(s)","def test_attr_dict(self):\n obj = awstats_reader.AttrDict([('this','that'), ('thus','those')])\n self.assertEqual(obj.thus, 'those')","def __generate_dict_of_keys_to_classification__(self):\n dict_of_assigned_citations = {}\n # duplicating citation dataset to filter as matches go on meaning\n # it should result in quicker allocation\n # can be removed to reduce memory load at expense of speed\n list_of_unassigned = []\n for key in self.dict_of_keywords:\n list_of_current_key = []\n for citation_instance in self.array_of_citations:\n if key == citation_instance.get_classification():\n list_of_current_key.append(citation_instance)\n if \"Unassigned\" == citation_instance.get_classification():\n list_of_unassigned.append(citation_instance)\n dict_of_assigned_citations[key] = list_of_current_key\n dict_of_assigned_citations[\"Unassigned\"] = list_of_unassigned\n return dict_of_assigned_citations","def conversion_context(self):\n d = {}\n wanted = ['a', 'h']\n for x in wanted:\n if x in self.properties:\n d[x] = self.properties[x]\n return d","def insertable_dict(self):\n\n d = {p.key: getattr(self, p.key) for p in self.__mapper__.attrs if p.key not in ('table', 'stats', '_codes')}\n\n x = {('c_' + k).strip('_'): v for k, v in d.items()}\n\n return x","def do_LZW_Compression(dict_of_abc, list_of_data):\n \n # rozdil mezi None a [] je v pouziti metody extend na listu\n \n result = []\n P = []\n C = [] # C je vzdy jeden prvek ze vstupu\n PC = []\n \n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\n \n for i in range(len(list_of_data)):\n \"\"\"\n Cyklus pres vsecky vstupni prvky\n \"\"\"\n\n C = []\n C.append(list_of_data[i])\n\n #PC je vzdy kombinace P a C\n PC = []\n PC.extend(P)\n PC.extend(C)\n\n index_founded = dict_cointains_list(dict_of_abc, PC)\n if index_founded == -1:\n #pokud PC neni ve slovniku, pridam ho tam a P = C\n dict_of_abc[len(dict_of_abc) +1] = PC\n #output P key in dictionary\n result.append(dict_cointains_list(dict_of_abc, P))\n P = C\n else:\n #pokud PC je ve slovniku P = PC pro dalsi iteraci\n P = PC\n #pridani posledniho prvku\n result.append(dict_cointains_list(dict_of_abc, P))\n return dict_of_abc, result","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def navigate_case_dictionary(case_list_for_run, num_cases):","def mfunc_(d):\r\n _p= {c: k.lower() if c not in ('code', 'label', 'name') else k \r\n for c, k in zip(AGSO_PROPERTIES['props_codes'], d) }\r\n id_= d[ix_].replace('/', '_').replace(\r\n ' ', '_').replace('\"', '').replace(\"'\", '').lower()\r\n return id_, _p","def test_fields_to_dict(self):\r\n test_data = \\\r\n \"\"\"0\tR27DLI_4812\tR27DLI_600\tR27DLI_727\tU1PLI_403\tU1PLI_8969\tU1PLI_9080\tU1PLI_9526\tW3Cecum_6642\tW3Cecum_8992\r\n1\tU1PLI_7889\r\n2\tW3Cecum_4858\r\n3\tR27DLI_3243\tR27DLI_4562\tR27DLI_6828\tR27DLI_9097\tU1PLI_2780\tU1PLI_67\tU9PSI_10475\tU9PSI_4341\tW3Cecum_5191\"\"\".splitlines() # output from cd-hit\r\n obs = fields_to_dict(test_data)\r\n exp = {\r\n '0': ['R27DLI_4812', 'R27DLI_600', 'R27DLI_727', 'U1PLI_403',\r\n 'U1PLI_8969', 'U1PLI_9080', 'U1PLI_9526', 'W3Cecum_6642', 'W3Cecum_8992'],\r\n '1': ['U1PLI_7889'],\r\n '2': ['W3Cecum_4858'],\r\n '3': ['R27DLI_3243', 'R27DLI_4562', 'R27DLI_6828', 'R27DLI_9097', 'U1PLI_2780', 'U1PLI_67', 'U9PSI_10475', 'U9PSI_4341', 'W3Cecum_5191']}\r\n self.assertEqual(obs, exp)","def test_emapa_simple(self):\n \n result = self.emapa \n self.assertEqual(set(result[\"EMAPA:2\"]), set([\"Y:006\"]), \n \"simple mapping\")\n self.assertEqual(set(result[\"EMAPA:3\"]), set([\"Y:007\"]), \n \"simple mapping\")","def test_mapping(self):\n vark = VarKeyword()\n assert vark.name in vark\n assert '{}_'.format(vark.name) not in vark\n assert len(vark) == 1\n assert list(vark) == [vark.name]","def test_creates_dict(self):\n c = City()\n dic = c.to_dict()\n self.assertEqual(type(dic), dict)\n for att in c.__dict__:\n self.assertTrue(att in dic)\n self.assertTrue(\"__class__\" in dic)","def __init__(self):\n self.d = dict()\n self.arr = [set() for i in range(0,26)]","def buildDict(self, dict):\n self.all_words = set(dict)\n self.wc_dict = collections.defaultdict(int)\n for w in dict:\n for wc in self.get_wildcards(w):\n self.wc_dict[wc] += 1","def test_values(self):\n obs = self.tester.values()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {'ANL', None, None, None, 'EMP', 'AGCGCTCACATC',\n 'This analysis was done as in Caporaso et al 2011 Genome '\n 'research. The PCR primers (F515/R806) were developed against '\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\n 'which we determined would yield optimal community clustering '\n 'with reads of this length using a procedure similar to that of'\n ' ref. 15. [For reference, this primer pair amplifies the '\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\n '(greengenes accession no. prokMSA_id:470367).] The reverse PCR'\n ' primer is barcoded with a 12-base error-correcting Golay code'\n ' to facilitate multiplexing of up to 1,500 samples per lane, '\n 'and both PCR primers contain sequencer adapter regions.',\n 'GTGCCAGCMGCCGCGGTAA', 'V4', '16S rRNA', 'ANL',\n 's_G1_L001_sequences', '8/1/12', 'ANL',\n 'micro biome of soil and rhizosphere of cannabis plants from '\n 'CA', 'Cannabis Soil Microbiome', 'Illumina', '.25,g',\n 'Sequencing by synthesis', 'MiSeq', 'ANL',\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT', 'CCME'}\n self.assertEqual(set(obs), exp)","def buildDict(self, dict):\n for x in dict:\n self.EntireSet.append(x)\n print self.EntireSet","def create_char_dicts(non_letter_chars, lower_case=True, upper_case=True):\n lower_case_letter_dict={}\n upper_case_letter_dict={}\n index_count = 0\n # Create a dictionary with upper and lower case letters and associated index\n # Note: We include underscores, hyphens, and apostrophes but ignore other characters\n # found in word2vec model, including chinese symbols, emojis, etc\n if lower_case:\n lower_case_letter_dict = {letter: int(index)+index_count for index, letter in enumerate(ascii_lowercase, start=1)}\n index_count += 26\n if upper_case:\n upper_case_letter_dict = {letter: int(index)+index_count for index, letter in enumerate(ascii_uppercase, start=1)} \n index_count += 26\n \n chardict = {**lower_case_letter_dict, **upper_case_letter_dict}\n \n for char in non_letter_chars:\n chardict[char] = index_count\n index_count += 1\n\n # Creation of reverse character lookup for debugging and word creation\n reverse_chardict = {}\n for k,v in chardict.items():\n reverse_chardict[v] = k\n \n return chardict, reverse_chardict","def __init__(self):\n self.dic = defaultdict(list)","def get_attributes(self):\n \n retdict = {}\n retdict['s'] = str(self.s)\n if self.t != None:\n retdict['t'] = str(self.t)\n retdict['a'] = str(self.a)\n retdict['b'] = str(self.b)\n retdict['c'] = str(self.c)\n retdict['d'] = str(self.d)\n return retdict","def do_LZW_DeCompression(dict_of_abc, list_of_data):\n \n #https://www.youtube.com/watch?v=MQM_DsX-LBI\n \n out = []\n predchozi_out = []\n for i in range(len(list_of_data)):\n new = []\n new.extend(predchozi_out)\n if list_of_data[i] in dict_of_abc:\n o = dict_of_abc[list_of_data[i]]\n out.extend(o)\n predchozi_out = o\n \n #pokud je o list, beru z nej pouze prvni prvek\n if len(o) > 1:\n new.append(o[0])\n else:\n new.extend(o)\n\n index_founded = dict_cointains_list(dict_of_abc, new)\n if index_founded == -1:\n #pokud new neni ve slovniku, pridam ho tam\n dict_of_abc[len(dict_of_abc) +1] = new\n\n return dict_of_abc, out","def test_to_dictAmenity(self):\n insta = Amenity()\n dict_cont = insta.to_dict()\n self.assertEqual(type(dict_cont), dict)\n for attr in insta.__dict__:\n self.assertTrue(attr in dict_cont)\n self.assertTrue(\"__class__\" in dict_cont)","def map():","def DictFunction3():\r\n print \"Create Third Dictionary\"\r\n Dictionary3 = {key:value.count(\"a\") for key, value in food_prefs.iteritems()}\r\n print Dictionary3","def __init__(self):\n self.map = {}\n self.arr = []","def test_fromkeys(self):\n d = SplayDict.fromkeys(['a', 'b', 'c'], 1)\n self.assertIn('a' , d)\n self.assertIn('b' , d)\n self.assertIn('c' , d)\n self.assertEqual(d['a'] , 1)\n self.assertEqual(d['b'] , 1)\n self.assertEqual(d['c'] , 1)","def __init__(self):\n self.dictword={}","def test_fill_in_dic():\n ngrams = NgramFrequencies()\n word_per_list = [\"time\", \"burton's\", \"corpse\", \"bride\"]\n ngrams.fill_in_dic(word_per_list)\n assert ngrams.unigrams_dic == {\n \"COUNT\": 4,\n \"time\": 1,\n \"burton's\": 1,\n \"corpse\": 1,\n \"bride\": 1\n }\n assert ngrams.bigrams_dic == {\n \"COUNT\": 3,\n \"time_burton's\": 1,\n \"burton's_corpse\": 1,\n \"corpse_bride\": 1\n }\n assert ngrams.trigrams_dic == {\n \"COUNT\": 2,\n \"time_burton's_corpse\": 1,\n \"burton's_corpse_bride\": 1\n }","def __init__(self):\n self.map = defaultdict(list)","def test_apply_scalar_map(self):\n super(TestObjDict, self).test_apply_scalar_map(_as_obj=True)","def call(self) -> List[Dict]:","def test_get_unique_drug_list(self):\n dict1 = self.test_dict\n dict2 = get_unique_drug_list(self.test_sorted_tuple)\n self.assertEqual(dict1, dict2)","def test_values(self):\n obs = self.tester.values()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {PrepSample('1.SKB1.640202', self.tester),\n PrepSample('1.SKB2.640194', self.tester),\n PrepSample('1.SKB3.640195', self.tester),\n PrepSample('1.SKB4.640189', self.tester),\n PrepSample('1.SKB5.640181', self.tester),\n PrepSample('1.SKB6.640176', self.tester),\n PrepSample('1.SKB7.640196', self.tester),\n PrepSample('1.SKB8.640193', self.tester),\n PrepSample('1.SKB9.640200', self.tester),\n PrepSample('1.SKD1.640179', self.tester),\n PrepSample('1.SKD2.640178', self.tester),\n PrepSample('1.SKD3.640198', self.tester),\n PrepSample('1.SKD4.640185', self.tester),\n PrepSample('1.SKD5.640186', self.tester),\n PrepSample('1.SKD6.640190', self.tester),\n PrepSample('1.SKD7.640191', self.tester),\n PrepSample('1.SKD8.640184', self.tester),\n PrepSample('1.SKD9.640182', self.tester),\n PrepSample('1.SKM1.640183', self.tester),\n PrepSample('1.SKM2.640199', self.tester),\n PrepSample('1.SKM3.640197', self.tester),\n PrepSample('1.SKM4.640180', self.tester),\n PrepSample('1.SKM5.640177', self.tester),\n PrepSample('1.SKM6.640187', self.tester),\n PrepSample('1.SKM7.640188', self.tester),\n PrepSample('1.SKM8.640201', self.tester),\n PrepSample('1.SKM9.640192', self.tester)}\n # Creating a list and looping over it since unittest does not call\n # the __eq__ function on the objects\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\n sorted(exp, key=lambda x: x.id)):\n self.assertEqual(o, e)","def test_to_rich_dict(self):\n F81().to_rich_dict()\n HKY85().to_rich_dict()\n GN().to_rich_dict()\n # TODO need to assess ability to reconstruct from this","def __init__(self, words):\n self.d = {}\n for i, w in enumerate(words):\n self.d[w] = self.d.get(w, []) + [i]","def multivalue():\n d = {1: \"George\", \"Prince\", \"Male\", 2: \"Margaret\", \"Queen\", \"Lizard\"}\n print(d)","def __init__(self):\n self.word_dict = collections.defaultdict(list)","def buildDict(self, dict):\n for word in dict:\n self.s.add(word)\n self.length_set = set([len(word) for word in dict])","def test_addDict(self):\n lidi = []\n lidi.append({'term': 'foo', 'tags': 'a', 'value': '1'})\n lidi.append({'term': 'bar', 'tags': 'a, b', 'value': '2'})\n lidi.append({'term': 'gnark', 'tags': 'a, c', 'value': '3'})\n self.g.add_dict(lidi)","def __init__(self):\n self.map = {}","def values(self) -> Dict[str, Any]:\n pass","def construct_occurrence_dico(data) :\n print('Constructing occurence dictionnaries...')\n\n p_kw_dico = dict()\n kw_p_dico = dict()\n full_stem_dico = {}\n for patent in data :\n patent_id = patent['id']\n #[keywords,stem_dico] = extract_keywords(patent[1]+\". \"+patent[2],patent_id)\n [keywords,stem_dico] = extract_keywords(patent['title']+\". \"+patent['abstract'],patent_id)\n #print(keywords)\n\n for k in keywords :\n # add to p_kw dico\n if k in kw_p_dico :\n kw_p_dico[k].append(patent_id)\n else :\n kw_p_dico[k]= [patent_id]\n #\n if patent_id in p_kw_dico :\n p_kw_dico[patent_id].append(k)\n else :\n p_kw_dico[patent_id] = [k]\n\n for k in stem_dico.keys():\n if k in full_stem_dico :\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\n else :\n full_stem_dico[k] = stem_dico[k]\n\n return([p_kw_dico,kw_p_dico,full_stem_dico])","def add_names_to_output_set(fasta_dict_a,fasta_dict_b,output_set):\n out_fasta_list = []\n fasta_contig_name = \"\"\n \n for fasta_data in output_set:\n if fasta_data in fasta_dict_a:\n fasta_contig_name = fasta_dict_a[fasta_data]\n out_fasta_list.append(\"\\n\"+fasta_contig_name+\"\\n\") \n out_fasta_list.append(fasta_data) \n elif fasta_data in fasta_dict_b:\n fasta_contig_name = fasta_dict_b[fasta_data]\n out_fasta_list.append(\"\\n\"+fasta_contig_name+\"\\n\") \n out_fasta_list.append(fasta_data)\n\n return ''.join(out_fasta_list)","def test_dict(self, testdata: TestData) -> None:\n for data in testdata['observation_type']:\n observation_type = ObservationType.from_dict(data)\n assert data == observation_type.to_dict()","def __init__(self):\n dict = defaultdict(list)\n self.conversion_dict = dict","def keys():","def test_dictionary(self):\n selector = ItemSelector('a')\n data = {'a': [1, 2, 3], 'b': 6}\n\n selected_data = selector.transform(data)\n expected_data = [1, 2, 3]\n self.assertListEqual(selected_data, expected_data)","def test_sample_mapped_keys(self):\r\n\r\n # With num_coverage=1 only the keys will be sampled\r\n actual = sample_mapped_keys(self.test_map, 1)\r\n self.assertEqual(actual, {'1': ['1'], '2': ['2']})\r\n\r\n actual = sample_mapped_keys(self.test_map, 3)\r\n for key in actual.keys():\r\n # check number of sampled keys\r\n self.assertEqual(3, len(actual[key]))\r\n for x in actual[key]:\r\n # check that sampled key is in the full list\r\n correct = list(self.test_map[key])\r\n correct.append(key)\r\n self.assertTrue(x in correct)","def make_keydict(self, analyte=None):\n if analyte is None:\n analyte = self.analytes\n elif isinstance(analyte, str):\n analyte = [analyte]\n\n out = {}\n for a in analyte:\n key = []\n for f in self.components.keys():\n if self.switches[a][f]:\n key.append(f)\n out[a] = ' & '.join(sorted(key))\n self.keydict = out\n return out","def build_sample_map(flowcell):\n result = {}\n rows = [(lane, lib[\"name\"]) for lib in flowcell[\"libraries\"] for lane in lib[\"lanes\"]]\n i = 1\n for _, name in sorted(set(rows)):\n if name not in result:\n result[name] = \"S{}\".format(i)\n i += 1\n return result","def get_value(self) -> Dict[str, any]:","def dccaa_dicts(dCCAA, key='c19'):\n mdict = {k : dCCAA[k][key] for k in dCCAA.keys()}\n imdict = {v : k for k, v in mdict.items()}\n return mdict, imdict","def test_items(self):\n obs = self.tester.items()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {('physical_location', 'ANL'), ('has_physical_specimen', True),\n ('has_extracted_data', True), ('sample_type', 'ENVO:soil'),\n ('required_sample_info_status', 'completed'),\n ('collection_timestamp', datetime(2011, 11, 11, 13, 00, 00)),\n ('host_subject_id', '1001:M7'),\n ('description', 'Cannabis Soil Microbiome'),\n ('season_environment', 'winter'), ('assigned_from_geo', 'n'),\n ('texture', '64.6 sand, 17.6 silt, 17.8 clay'),\n ('taxon_id', '1118232'), ('depth', 0.15),\n ('host_taxid', '3483'), ('common_name', 'root metagenome'),\n ('water_content_soil', 0.164), ('elevation', 114), ('temp', 15),\n ('tot_nitro', 1.41), ('samp_salinity', 7.15), ('altitude', 0),\n ('env_biome',\n 'ENVO:Temperate grasslands, savannas, and shrubland biome'),\n ('country', 'GAZ:United States of America'), ('ph', 6.94),\n ('anonymized_name', 'SKB8'), ('tot_org_carb', 5),\n ('description_duplicate', 'Burmese root'),\n ('env_feature', 'ENVO:plant-associated habitat'),\n ('latitude', 74.0894932572),\n ('longitude', 65.3283470202)}\n self.assertEqual(set(obs), exp)","def complete_cve_dictionary(self, dct):\n for cve in self.parsed_cve_ids:\n dict_value = ''\n if cve in dct:\n dict_value = dct[cve]\n self.cve_details_dict[cve] = dict_value","def gen_keys():","def __init__(self):\n self.kids = dict()\n self.val = None\n self.isWord = False","def get_dict_of_str2(self):\n pass","def DictFunction():\r\n print \"{name} is from {city}, and he likes {cake} cake, {fruit} fruit, {salad} salad and {pasta} pasta\".format(**food_prefs)","def test_create_offense_by_zip_dict(self):\r\n\r\n data = [\r\n ['Report_No', 'Reported_Date', 'Reported_Time', 'From_Date', 'From_Time', 'To_Date', 'To_Time', 'Offense', 'IBRS', 'Description', 'Beat', 'Address', 'City', 'Zip Code', 'Rep_Dist', 'Area', 'DVFlag', 'Involvement', 'Race', 'Sex', 'Age', 'Firearm Used Flag', 'Location'],\r\n ['', '03/19/2019', '', '', '', '', '', 'Vehicular', '', '', '', '', '', '64161', '', '', '', '', '', '', '', '', ''],\r\n ['', '03/19/2019', '', '', '', '', '', 'Vehicular', '', '', '', '', '', '64125', '', '', '', '', '', '', '', '', ''],\r\n ['', '04/21/2019', '', '', '', '', '', 'Arson', '', '', '', '', '', '64161', '', '', '', '', '', '', '', '', '']\r\n ]\r\n result = cds.create_offense_by_zip(data)\r\n self.assertIsInstance(result, dict, \"function should return a dictionary\")\r\n self.assertEqual(len(result), 2, \"There should be 2 items in the dictionary\")\r\n self.assertIsInstance(result[\"Vehicular\"], dict, \"value for key Vehicular should be a dicationary\")\r\n self.assertIsInstance(result[\"Arson\"], dict, \"value for key Arson should be a dicationary\")\r\n\r\n self.assertEqual(len(result[\"Vehicular\"]), 2, \"dictionary for key Vehicular should have 2 items in it\")\r\n self.assertEqual(result[\"Vehicular\"], {'64161':1,'64125':1}, \"value for dictionary key Vehicular incorrect\")\r\n self.assertEqual(result[\"Arson\"], {'64161':1}, \"value for dictionary key Vehicular incorrect\")","def pare_dict(*a, **kw):\n return pare_dict(*a, **kw)","def test_dict_same_occurrence_all_str(self):\n argument = {'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6, 'seven': 7, 'eight': 8,\n 'nine': 9, 'ten': 10, 'eleven': 10, 'twelve': 10}\n actual = file_io.top_ten(argument)\n expected = [[10, 'twelve'], [10, 'ten'], [10, 'eleven'], [9, 'nine'], [8, 'eight'], [7, 'seven'], [6, 'six'],\n [5, 'five'], [4, 'four'], [3, 'three']]\n self.assertEqual(actual, expected)","def test_values(self):\n obs = self.tester.values()\n self.assertTrue(isinstance(obs, Iterable))\n exp = {Sample('1.SKB1.640202', self.tester),\n Sample('1.SKB2.640194', self.tester),\n Sample('1.SKB3.640195', self.tester),\n Sample('1.SKB4.640189', self.tester),\n Sample('1.SKB5.640181', self.tester),\n Sample('1.SKB6.640176', self.tester),\n Sample('1.SKB7.640196', self.tester),\n Sample('1.SKB8.640193', self.tester),\n Sample('1.SKB9.640200', self.tester),\n Sample('1.SKD1.640179', self.tester),\n Sample('1.SKD2.640178', self.tester),\n Sample('1.SKD3.640198', self.tester),\n Sample('1.SKD4.640185', self.tester),\n Sample('1.SKD5.640186', self.tester),\n Sample('1.SKD6.640190', self.tester),\n Sample('1.SKD7.640191', self.tester),\n Sample('1.SKD8.640184', self.tester),\n Sample('1.SKD9.640182', self.tester),\n Sample('1.SKM1.640183', self.tester),\n Sample('1.SKM2.640199', self.tester),\n Sample('1.SKM3.640197', self.tester),\n Sample('1.SKM4.640180', self.tester),\n Sample('1.SKM5.640177', self.tester),\n Sample('1.SKM6.640187', self.tester),\n Sample('1.SKM7.640188', self.tester),\n Sample('1.SKM8.640201', self.tester),\n Sample('1.SKM9.640192', self.tester)}\n # Creating a list and looping over it since unittest does not call\n # the __eq__ function on the objects\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\n sorted(exp, key=lambda x: x.id)):\n self.assertEqual(o, e)","def __init__(self):\r\n self.dic={}","def _full_mapping(self, data):\r\n x = self._empty_mapping()\r\n for key, value in data.items():\r\n x[key] = value\r\n return x","def __init__(self):\n self.key2value = {}\n self.key2time = {}","def create_dict_deep_distortion_old(defect_dict: dict, \r\n fancy_defects: dict,\r\n ):\r\n dict_deep_distortion = {}\r\n defect_dict_copy = defect_dict.copy()\r\n for defect_type in fancy_defects.keys(): # for each defect type (vac, as , int)\r\n \r\n dict_deep_distortion[defect_type] = import_deep_distortion_by_type(defect_dict_copy[defect_type],\r\n fancy_defects[defect_type]) #defects for which we'll try the deep distortion found for one of the charge states \r\n return dict_deep_distortion","def _makeimap(self):\n self.map_[\"source\"] = \"nasa\"\n self.map_[\"instrument\"] = \"goes\"\n self.map_[\"physobs\"] = \"irradiance\"\n self.map_[\"provider\"] = \"sdac\"","def get_dict_refine_insee_code(ls_valid_ic):\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\n for ic in ls_valid_ic_corse:\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\n dict_refine_ic.update(dict_ic_ardts)\n return dict_refine_ic","def all_cities(mdarray):\n cities = {}\n city_set = set(data_set['city_end'])\n for city in city_set:\n cities[city] = \"\" # Basically updating the cities list every time when city is added\n return cities","def test_build_format_map(all_sets: dict, expected: dict) -> None:\n result = build_format_map(all_sets, regular=False)\n assert result == expected","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def getAircraftCodeDict():\n table = 'aircraft'\n connection = openConnection()\n curs = connection.cursor()\n sqlcmd = \"SELECT * FROM \" + str(table)\n d = {}\n \n curs.execute(sqlcmd)\n for row in curs.fetchall():\n aircraft = airlineClasses.Aircraft()\n aircraft.aircraftCode = row[0]\n aircraft.name = row[1]\n d[aircraft.aircraftCode] = aircraft\n \n curs.close()\n connection.close()\n return d","def convertirDic(botones):\n dic_aux = {}\n for clave,valor in botones.items():\n dic_aux[tuple(map(int,clave.split(\",\")))] = valor\n return dic_aux","def extraire_resultat(sheet):\n\tdic_result={}\n\tcase_pv_mac=\"J29\"\n\tcase_pv_lp=\"J28\"\n\tcase_pv_abs=\"I21\"\n\tpv_mac=round(round(sheet[case_pv_mac].value,4)*100,2)\n\tpv_lp=round(round(sheet[case_pv_lp].value,4)*100,2)\n\tpv_abs=round(round(sheet[case_pv_abs].value,4)*100,2)\n\tdic_result[\"pv_mac\"]=pv_mac\n\tdic_result[\"pv_lp\"]=pv_lp\n\tdic_result[\"pv_abs\"]=pv_abs\n\treturn dic_result","def _init_dict(self):\n dict_ord = self.MIN_VALID\n\n for da in self.train_das:\n for dai in da:\n if dai.name not in self.dict_slot:\n self.dict_slot[dai.name] = dict_ord\n dict_ord += 1\n if dai.value not in self.dict_value:\n self.dict_value[dai.value] = dict_ord\n dict_ord += 1\n\n for tree in self.train_trees:\n for t_lemma, formeme in tree.nodes:\n if t_lemma not in self.dict_t_lemma:\n self.dict_t_lemma[t_lemma] = dict_ord\n dict_ord += 1\n if formeme not in self.dict_formeme:\n self.dict_formeme[formeme] = dict_ord\n dict_ord += 1\n\n self.dict_size = dict_ord","def __init__(self, key):\n self.key = [int_mapping(k) for k in key]","def OMDict(self, items):\n return om.OMApplication(elem=self.OMSymbol(module='Python', name='dict'),\n arguments=[self.OMTuple(item) for item in items])","def dict2():\n print(record1)\n record2 = {}\n for k, v in record1.items():\n count = 0\n for letter in v:\n if letter == 't' or letter == 'T':\n count += 1\n record2[k] = count\n print(record2)","def _get_attribute_dic(self, attrs):\n attr_dic = {}\n for attr_pair in attrs:\n attr_dic[attr_pair[0]] = attr_pair[1]\n return attr_dic","def __init__(self, dictionary):\n self.abbrdict = {} #Use a dict to count the number of same abbreviation.\n self.origdict = {} #Use a dict to rule out duplicate of original words.\n for x in dictionary:\n if len(x) <= 1: #If the length of word is not larger than 1, its abbreviation is itself.\n n = x\n else: #Otherwise get the abbreviation as decribed.\n n = x[0] + str(len(x) - 2) + x[-1]\n if x not in self.origdict: #If this is the 1st time word appears, add the count of abbreviation.\n if n not in self.abbrdict:\n self.abbrdict[n] = 1\n else:\n self.abbrdict[n] += 1\n self.origdict[x] = True #Set it as appeared.","def codebook_json_data_factory() -> List[Dict[str, Any]]:\n codebook_data = [\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_A\"\n },\n {\n Features.CODEWORD: [\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\n ],\n Features.TARGET: \"GENE_B\"\n },\n ]\n return codebook_data"],"string":"[\n \"def test_get_cases_for_dict(self):\\n pass\",\n \"def init_dict() -> None:\\n for elem in letters:\\n ascii_dict[elem] = []\\n for elem in numbers:\\n ascii_dict[elem] = []\\n for elem in symbols:\\n ascii_dict[elem] = []\",\n \"def dictogram_dictlist(self):\\n for key, value in self.word_dict.items():\\n self.word_dict[key] = dictogram.Dictogram(value)\\n # print(\\\"self.word_dict\\\", self.word_dict)\",\n \"def secondary_keys_dicts(self):\",\n \"def multDic(dic, x):\\n pass\",\n \"def __init__(self, dictionary):\\n self.d = {}\\n for word in dictionary:\\n abbr = self.getAbbr(word)\\n if abbr in self.d:\\n self.d[abbr] += word,\\n else:\\n self.d[abbr] = [word]\",\n \"def build_arpabet(self, word_lst):\\n arpabet_dict = {}\\n for word in word_lst:\\n unicode_word = unicode(word)\\n\\n try:\\n arpabet_dict[word] = self.cmu_dict[unicode_word][0]\\n except:\\n print(unicode_word + \\\" not found in CMUDict\\\")\\n sys.exit()\\n\\n return arpabet_dict\",\n \"def __init__(self):\\r\\n self.dct = defaultdict(list)\",\n \"def def_dict():\\n d1 = c.defaultdict(dict)\\n print '\\\\nInitially first dictionary is: ', dict(d1)\\n l = [('a', 1), ('b', 2), ('a', 27), ('c', 3), ('d', 4), ('c', 29), ('e', 5)]\\n for a, b in l:\\n d1[a] = b\\n print '\\\\nAfter adding elements to the default dictionary, it is: ', dict(d1)\\n print '\\\\nItems in the first dictionary is: ', d1.items()\\n st = 'malayalam'\\n print '\\\\n\\\\nCreating dictionary from the string', st\\n d2 = c.defaultdict(int)\\n print '\\\\nInitially second dict is:', dict(d2)\\n for i in st:\\n d2[i] = st.count(i)\\n print \\\"After updation, the second dictionary becomes: \\\", dict(d2)\",\n \"def dict(dict: Dict[str, Pin], /) -> None:\",\n \"def standarization_ofconc(a2_data):\\n aux_dic = OrderedDict()\\n for i in a2_data:\\n evol_tuple = a2_data[i]['conc'].keys()\\n if len(evol_tuple) != 1:\\n raise RuntimeError('too many tuples for conc')\\n evol_tuple = evol_tuple[0]\\n aux_dic[i] = a2_data[i]['conc'][evol_tuple]\\n return aux_dic\",\n \"def test_items(self):\\n obs = self.tester.items()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {('center_name', 'ANL'), ('center_project_name', None),\\n ('emp_status', 'EMP'), ('barcodesequence', 'AGCGCTCACATC'),\\n ('library_construction_protocol',\\n 'This analysis was done as in Caporaso et al 2011 Genome '\\n 'research. The PCR primers (F515/R806) were developed against '\\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\\n 'which we determined would yield optimal community clustering '\\n 'with reads of this length using a procedure similar to that '\\n 'of ref. 15. [For reference, this primer pair amplifies the '\\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\\n '(greengenes accession no. prokMSA_id:470367).] The reverse '\\n 'PCR primer is barcoded with a 12-base error-correcting Golay '\\n 'code to facilitate multiplexing of up to 1,500 samples per '\\n 'lane, and both PCR primers contain sequencer adapter '\\n 'regions.'), ('linkerprimersequence', 'GTGCCAGCMGCCGCGGTAA'),\\n ('target_subfragment', 'V4'), ('target_gene', '16S rRNA'),\\n ('run_center', 'ANL'), ('run_prefix', 's_G1_L001_sequences'),\\n ('run_date', '8/1/12'), ('experiment_center', 'ANL'),\\n ('experiment_design_description',\\n 'micro biome of soil and rhizosphere of cannabis plants '\\n 'from CA'), ('experiment_title', 'Cannabis Soil Microbiome'),\\n ('platform', 'Illumina'), ('samp_size', '.25,g'),\\n ('sequencing_meth', 'Sequencing by synthesis'),\\n ('illumina_technology', 'MiSeq'), ('sample_center', 'ANL'),\\n ('pcr_primers',\\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT'),\\n ('study_center', 'CCME')}\\n self.assertEqual(set(obs), exp)\",\n \"def __init__(self, dictionary):\\n self.dict = {}\\n for word in dictionary:\\n abbr = self.gen_abbr(word)\\n if abbr not in self.dict:\\n word_set = set([word])\\n self.dict[abbr] = word_set\\n else:\\n self.dict[abbr].add(word)\",\n \"def asdict():\\n pass\",\n \"def test_create_cds_translation_dict_1(self):\\n t1 = \\\"ABCDE\\\"\\n t2 = \\\"FGHIJ\\\"\\n t3 = \\\"RSTUV\\\"\\n self.gene_data_1[\\\"Translation\\\"] = t1\\n self.gene_data_2[\\\"Translation\\\"] = t2\\n self.gene_data_3[\\\"Translation\\\"] = t3\\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\\n dict = find_domains.create_cds_translation_dict(cdd_list)\\n\\n exp_keys = {t1, t2, t3}\\n with self.subTest():\\n self.assertEqual(dict.keys(), exp_keys)\\n with self.subTest():\\n self.assertEqual(dict[t1], {self.gene_data_1[\\\"GeneID\\\"]})\\n with self.subTest():\\n self.assertEqual(dict[t2], {self.gene_data_2[\\\"GeneID\\\"]})\\n with self.subTest():\\n self.assertEqual(dict[t3], {self.gene_data_3[\\\"GeneID\\\"]})\",\n \"def cat_from_letter(cls, cog_letter, dict_output=False):\\n functions = dict()\\n for char in cog_letter:\\n for k, v in cls.COG_letters.items():\\n for k_, v_ in v.items():\\n if char == k_:\\n functions[char] = (k, v_)\\n try:\\n functions[char]\\n except KeyError:\\n print(f\\\"{char} wasn't found! Check if it is a valid COG letter.\\\")\\n\\n if functions:\\n if dict_output:\\n return functions\\n else:\\n output = [(k, v) for k, v in functions.items()]\\n return output\",\n \"def test_create_cds_translation_dict_2(self):\\n t1 = \\\"ABCDE\\\"\\n t2 = \\\"FGHIJ\\\"\\n self.gene_data_1[\\\"Translation\\\"] = t1\\n self.gene_data_2[\\\"Translation\\\"] = t2\\n self.gene_data_3[\\\"Translation\\\"] = t2\\n cdd_list = [self.gene_data_1, self.gene_data_2, self.gene_data_3]\\n dict = find_domains.create_cds_translation_dict(cdd_list)\\n\\n exp_keys = {t1, t2}\\n with self.subTest():\\n self.assertEqual(dict.keys(), exp_keys)\\n with self.subTest():\\n self.assertEqual(dict[t1], {self.gene_data_1[\\\"GeneID\\\"]})\\n with self.subTest():\\n self.assertEqual(dict[t2], {self.gene_data_2[\\\"GeneID\\\"],\\n self.gene_data_3[\\\"GeneID\\\"]})\",\n \"def __init__(self, dictionary):\\n self.abbrev_dict = {}\\n for s in dictionary:\\n if len(s) < 3:\\n abbrev = s\\n else:\\n abbrev = s[0] + str(len(s) - 2) + s[-1]\\n \\n if abbrev not in self.abbrev_dict:\\n self.abbrev_dict[abbrev] = set()\\n self.abbrev_dict[abbrev].add(s)\",\n \"def test_attr_dict(self):\\n obj = awstats_reader.AttrDict([('this','that'), ('thus','those')])\\n self.assertEqual(obj.thus, 'those')\",\n \"def __generate_dict_of_keys_to_classification__(self):\\n dict_of_assigned_citations = {}\\n # duplicating citation dataset to filter as matches go on meaning\\n # it should result in quicker allocation\\n # can be removed to reduce memory load at expense of speed\\n list_of_unassigned = []\\n for key in self.dict_of_keywords:\\n list_of_current_key = []\\n for citation_instance in self.array_of_citations:\\n if key == citation_instance.get_classification():\\n list_of_current_key.append(citation_instance)\\n if \\\"Unassigned\\\" == citation_instance.get_classification():\\n list_of_unassigned.append(citation_instance)\\n dict_of_assigned_citations[key] = list_of_current_key\\n dict_of_assigned_citations[\\\"Unassigned\\\"] = list_of_unassigned\\n return dict_of_assigned_citations\",\n \"def conversion_context(self):\\n d = {}\\n wanted = ['a', 'h']\\n for x in wanted:\\n if x in self.properties:\\n d[x] = self.properties[x]\\n return d\",\n \"def insertable_dict(self):\\n\\n d = {p.key: getattr(self, p.key) for p in self.__mapper__.attrs if p.key not in ('table', 'stats', '_codes')}\\n\\n x = {('c_' + k).strip('_'): v for k, v in d.items()}\\n\\n return x\",\n \"def do_LZW_Compression(dict_of_abc, list_of_data):\\n \\n # rozdil mezi None a [] je v pouziti metody extend na listu\\n \\n result = []\\n P = []\\n C = [] # C je vzdy jeden prvek ze vstupu\\n PC = []\\n \\n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\\n \\n for i in range(len(list_of_data)):\\n \\\"\\\"\\\"\\n Cyklus pres vsecky vstupni prvky\\n \\\"\\\"\\\"\\n\\n C = []\\n C.append(list_of_data[i])\\n\\n #PC je vzdy kombinace P a C\\n PC = []\\n PC.extend(P)\\n PC.extend(C)\\n\\n index_founded = dict_cointains_list(dict_of_abc, PC)\\n if index_founded == -1:\\n #pokud PC neni ve slovniku, pridam ho tam a P = C\\n dict_of_abc[len(dict_of_abc) +1] = PC\\n #output P key in dictionary\\n result.append(dict_cointains_list(dict_of_abc, P))\\n P = C\\n else:\\n #pokud PC je ve slovniku P = PC pro dalsi iteraci\\n P = PC\\n #pridani posledniho prvku\\n result.append(dict_cointains_list(dict_of_abc, P))\\n return dict_of_abc, result\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def navigate_case_dictionary(case_list_for_run, num_cases):\",\n \"def mfunc_(d):\\r\\n _p= {c: k.lower() if c not in ('code', 'label', 'name') else k \\r\\n for c, k in zip(AGSO_PROPERTIES['props_codes'], d) }\\r\\n id_= d[ix_].replace('/', '_').replace(\\r\\n ' ', '_').replace('\\\"', '').replace(\\\"'\\\", '').lower()\\r\\n return id_, _p\",\n \"def test_fields_to_dict(self):\\r\\n test_data = \\\\\\r\\n \\\"\\\"\\\"0\\tR27DLI_4812\\tR27DLI_600\\tR27DLI_727\\tU1PLI_403\\tU1PLI_8969\\tU1PLI_9080\\tU1PLI_9526\\tW3Cecum_6642\\tW3Cecum_8992\\r\\n1\\tU1PLI_7889\\r\\n2\\tW3Cecum_4858\\r\\n3\\tR27DLI_3243\\tR27DLI_4562\\tR27DLI_6828\\tR27DLI_9097\\tU1PLI_2780\\tU1PLI_67\\tU9PSI_10475\\tU9PSI_4341\\tW3Cecum_5191\\\"\\\"\\\".splitlines() # output from cd-hit\\r\\n obs = fields_to_dict(test_data)\\r\\n exp = {\\r\\n '0': ['R27DLI_4812', 'R27DLI_600', 'R27DLI_727', 'U1PLI_403',\\r\\n 'U1PLI_8969', 'U1PLI_9080', 'U1PLI_9526', 'W3Cecum_6642', 'W3Cecum_8992'],\\r\\n '1': ['U1PLI_7889'],\\r\\n '2': ['W3Cecum_4858'],\\r\\n '3': ['R27DLI_3243', 'R27DLI_4562', 'R27DLI_6828', 'R27DLI_9097', 'U1PLI_2780', 'U1PLI_67', 'U9PSI_10475', 'U9PSI_4341', 'W3Cecum_5191']}\\r\\n self.assertEqual(obs, exp)\",\n \"def test_emapa_simple(self):\\n \\n result = self.emapa \\n self.assertEqual(set(result[\\\"EMAPA:2\\\"]), set([\\\"Y:006\\\"]), \\n \\\"simple mapping\\\")\\n self.assertEqual(set(result[\\\"EMAPA:3\\\"]), set([\\\"Y:007\\\"]), \\n \\\"simple mapping\\\")\",\n \"def test_mapping(self):\\n vark = VarKeyword()\\n assert vark.name in vark\\n assert '{}_'.format(vark.name) not in vark\\n assert len(vark) == 1\\n assert list(vark) == [vark.name]\",\n \"def test_creates_dict(self):\\n c = City()\\n dic = c.to_dict()\\n self.assertEqual(type(dic), dict)\\n for att in c.__dict__:\\n self.assertTrue(att in dic)\\n self.assertTrue(\\\"__class__\\\" in dic)\",\n \"def __init__(self):\\n self.d = dict()\\n self.arr = [set() for i in range(0,26)]\",\n \"def buildDict(self, dict):\\n self.all_words = set(dict)\\n self.wc_dict = collections.defaultdict(int)\\n for w in dict:\\n for wc in self.get_wildcards(w):\\n self.wc_dict[wc] += 1\",\n \"def test_values(self):\\n obs = self.tester.values()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {'ANL', None, None, None, 'EMP', 'AGCGCTCACATC',\\n 'This analysis was done as in Caporaso et al 2011 Genome '\\n 'research. The PCR primers (F515/R806) were developed against '\\n 'the V4 region of the 16S rRNA (both bacteria and archaea), '\\n 'which we determined would yield optimal community clustering '\\n 'with reads of this length using a procedure similar to that of'\\n ' ref. 15. [For reference, this primer pair amplifies the '\\n 'region 533_786 in the Escherichia coli strain 83972 sequence '\\n '(greengenes accession no. prokMSA_id:470367).] The reverse PCR'\\n ' primer is barcoded with a 12-base error-correcting Golay code'\\n ' to facilitate multiplexing of up to 1,500 samples per lane, '\\n 'and both PCR primers contain sequencer adapter regions.',\\n 'GTGCCAGCMGCCGCGGTAA', 'V4', '16S rRNA', 'ANL',\\n 's_G1_L001_sequences', '8/1/12', 'ANL',\\n 'micro biome of soil and rhizosphere of cannabis plants from '\\n 'CA', 'Cannabis Soil Microbiome', 'Illumina', '.25,g',\\n 'Sequencing by synthesis', 'MiSeq', 'ANL',\\n 'FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT', 'CCME'}\\n self.assertEqual(set(obs), exp)\",\n \"def buildDict(self, dict):\\n for x in dict:\\n self.EntireSet.append(x)\\n print self.EntireSet\",\n \"def create_char_dicts(non_letter_chars, lower_case=True, upper_case=True):\\n lower_case_letter_dict={}\\n upper_case_letter_dict={}\\n index_count = 0\\n # Create a dictionary with upper and lower case letters and associated index\\n # Note: We include underscores, hyphens, and apostrophes but ignore other characters\\n # found in word2vec model, including chinese symbols, emojis, etc\\n if lower_case:\\n lower_case_letter_dict = {letter: int(index)+index_count for index, letter in enumerate(ascii_lowercase, start=1)}\\n index_count += 26\\n if upper_case:\\n upper_case_letter_dict = {letter: int(index)+index_count for index, letter in enumerate(ascii_uppercase, start=1)} \\n index_count += 26\\n \\n chardict = {**lower_case_letter_dict, **upper_case_letter_dict}\\n \\n for char in non_letter_chars:\\n chardict[char] = index_count\\n index_count += 1\\n\\n # Creation of reverse character lookup for debugging and word creation\\n reverse_chardict = {}\\n for k,v in chardict.items():\\n reverse_chardict[v] = k\\n \\n return chardict, reverse_chardict\",\n \"def __init__(self):\\n self.dic = defaultdict(list)\",\n \"def get_attributes(self):\\n \\n retdict = {}\\n retdict['s'] = str(self.s)\\n if self.t != None:\\n retdict['t'] = str(self.t)\\n retdict['a'] = str(self.a)\\n retdict['b'] = str(self.b)\\n retdict['c'] = str(self.c)\\n retdict['d'] = str(self.d)\\n return retdict\",\n \"def do_LZW_DeCompression(dict_of_abc, list_of_data):\\n \\n #https://www.youtube.com/watch?v=MQM_DsX-LBI\\n \\n out = []\\n predchozi_out = []\\n for i in range(len(list_of_data)):\\n new = []\\n new.extend(predchozi_out)\\n if list_of_data[i] in dict_of_abc:\\n o = dict_of_abc[list_of_data[i]]\\n out.extend(o)\\n predchozi_out = o\\n \\n #pokud je o list, beru z nej pouze prvni prvek\\n if len(o) > 1:\\n new.append(o[0])\\n else:\\n new.extend(o)\\n\\n index_founded = dict_cointains_list(dict_of_abc, new)\\n if index_founded == -1:\\n #pokud new neni ve slovniku, pridam ho tam\\n dict_of_abc[len(dict_of_abc) +1] = new\\n\\n return dict_of_abc, out\",\n \"def test_to_dictAmenity(self):\\n insta = Amenity()\\n dict_cont = insta.to_dict()\\n self.assertEqual(type(dict_cont), dict)\\n for attr in insta.__dict__:\\n self.assertTrue(attr in dict_cont)\\n self.assertTrue(\\\"__class__\\\" in dict_cont)\",\n \"def map():\",\n \"def DictFunction3():\\r\\n print \\\"Create Third Dictionary\\\"\\r\\n Dictionary3 = {key:value.count(\\\"a\\\") for key, value in food_prefs.iteritems()}\\r\\n print Dictionary3\",\n \"def __init__(self):\\n self.map = {}\\n self.arr = []\",\n \"def test_fromkeys(self):\\n d = SplayDict.fromkeys(['a', 'b', 'c'], 1)\\n self.assertIn('a' , d)\\n self.assertIn('b' , d)\\n self.assertIn('c' , d)\\n self.assertEqual(d['a'] , 1)\\n self.assertEqual(d['b'] , 1)\\n self.assertEqual(d['c'] , 1)\",\n \"def __init__(self):\\n self.dictword={}\",\n \"def test_fill_in_dic():\\n ngrams = NgramFrequencies()\\n word_per_list = [\\\"time\\\", \\\"burton's\\\", \\\"corpse\\\", \\\"bride\\\"]\\n ngrams.fill_in_dic(word_per_list)\\n assert ngrams.unigrams_dic == {\\n \\\"COUNT\\\": 4,\\n \\\"time\\\": 1,\\n \\\"burton's\\\": 1,\\n \\\"corpse\\\": 1,\\n \\\"bride\\\": 1\\n }\\n assert ngrams.bigrams_dic == {\\n \\\"COUNT\\\": 3,\\n \\\"time_burton's\\\": 1,\\n \\\"burton's_corpse\\\": 1,\\n \\\"corpse_bride\\\": 1\\n }\\n assert ngrams.trigrams_dic == {\\n \\\"COUNT\\\": 2,\\n \\\"time_burton's_corpse\\\": 1,\\n \\\"burton's_corpse_bride\\\": 1\\n }\",\n \"def __init__(self):\\n self.map = defaultdict(list)\",\n \"def test_apply_scalar_map(self):\\n super(TestObjDict, self).test_apply_scalar_map(_as_obj=True)\",\n \"def call(self) -> List[Dict]:\",\n \"def test_get_unique_drug_list(self):\\n dict1 = self.test_dict\\n dict2 = get_unique_drug_list(self.test_sorted_tuple)\\n self.assertEqual(dict1, dict2)\",\n \"def test_values(self):\\n obs = self.tester.values()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {PrepSample('1.SKB1.640202', self.tester),\\n PrepSample('1.SKB2.640194', self.tester),\\n PrepSample('1.SKB3.640195', self.tester),\\n PrepSample('1.SKB4.640189', self.tester),\\n PrepSample('1.SKB5.640181', self.tester),\\n PrepSample('1.SKB6.640176', self.tester),\\n PrepSample('1.SKB7.640196', self.tester),\\n PrepSample('1.SKB8.640193', self.tester),\\n PrepSample('1.SKB9.640200', self.tester),\\n PrepSample('1.SKD1.640179', self.tester),\\n PrepSample('1.SKD2.640178', self.tester),\\n PrepSample('1.SKD3.640198', self.tester),\\n PrepSample('1.SKD4.640185', self.tester),\\n PrepSample('1.SKD5.640186', self.tester),\\n PrepSample('1.SKD6.640190', self.tester),\\n PrepSample('1.SKD7.640191', self.tester),\\n PrepSample('1.SKD8.640184', self.tester),\\n PrepSample('1.SKD9.640182', self.tester),\\n PrepSample('1.SKM1.640183', self.tester),\\n PrepSample('1.SKM2.640199', self.tester),\\n PrepSample('1.SKM3.640197', self.tester),\\n PrepSample('1.SKM4.640180', self.tester),\\n PrepSample('1.SKM5.640177', self.tester),\\n PrepSample('1.SKM6.640187', self.tester),\\n PrepSample('1.SKM7.640188', self.tester),\\n PrepSample('1.SKM8.640201', self.tester),\\n PrepSample('1.SKM9.640192', self.tester)}\\n # Creating a list and looping over it since unittest does not call\\n # the __eq__ function on the objects\\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\\n sorted(exp, key=lambda x: x.id)):\\n self.assertEqual(o, e)\",\n \"def test_to_rich_dict(self):\\n F81().to_rich_dict()\\n HKY85().to_rich_dict()\\n GN().to_rich_dict()\\n # TODO need to assess ability to reconstruct from this\",\n \"def __init__(self, words):\\n self.d = {}\\n for i, w in enumerate(words):\\n self.d[w] = self.d.get(w, []) + [i]\",\n \"def multivalue():\\n d = {1: \\\"George\\\", \\\"Prince\\\", \\\"Male\\\", 2: \\\"Margaret\\\", \\\"Queen\\\", \\\"Lizard\\\"}\\n print(d)\",\n \"def __init__(self):\\n self.word_dict = collections.defaultdict(list)\",\n \"def buildDict(self, dict):\\n for word in dict:\\n self.s.add(word)\\n self.length_set = set([len(word) for word in dict])\",\n \"def test_addDict(self):\\n lidi = []\\n lidi.append({'term': 'foo', 'tags': 'a', 'value': '1'})\\n lidi.append({'term': 'bar', 'tags': 'a, b', 'value': '2'})\\n lidi.append({'term': 'gnark', 'tags': 'a, c', 'value': '3'})\\n self.g.add_dict(lidi)\",\n \"def __init__(self):\\n self.map = {}\",\n \"def values(self) -> Dict[str, Any]:\\n pass\",\n \"def construct_occurrence_dico(data) :\\n print('Constructing occurence dictionnaries...')\\n\\n p_kw_dico = dict()\\n kw_p_dico = dict()\\n full_stem_dico = {}\\n for patent in data :\\n patent_id = patent['id']\\n #[keywords,stem_dico] = extract_keywords(patent[1]+\\\". \\\"+patent[2],patent_id)\\n [keywords,stem_dico] = extract_keywords(patent['title']+\\\". \\\"+patent['abstract'],patent_id)\\n #print(keywords)\\n\\n for k in keywords :\\n # add to p_kw dico\\n if k in kw_p_dico :\\n kw_p_dico[k].append(patent_id)\\n else :\\n kw_p_dico[k]= [patent_id]\\n #\\n if patent_id in p_kw_dico :\\n p_kw_dico[patent_id].append(k)\\n else :\\n p_kw_dico[patent_id] = [k]\\n\\n for k in stem_dico.keys():\\n if k in full_stem_dico :\\n full_stem_dico[k]=full_stem_dico[k].union(stem_dico[k])\\n else :\\n full_stem_dico[k] = stem_dico[k]\\n\\n return([p_kw_dico,kw_p_dico,full_stem_dico])\",\n \"def add_names_to_output_set(fasta_dict_a,fasta_dict_b,output_set):\\n out_fasta_list = []\\n fasta_contig_name = \\\"\\\"\\n \\n for fasta_data in output_set:\\n if fasta_data in fasta_dict_a:\\n fasta_contig_name = fasta_dict_a[fasta_data]\\n out_fasta_list.append(\\\"\\\\n\\\"+fasta_contig_name+\\\"\\\\n\\\") \\n out_fasta_list.append(fasta_data) \\n elif fasta_data in fasta_dict_b:\\n fasta_contig_name = fasta_dict_b[fasta_data]\\n out_fasta_list.append(\\\"\\\\n\\\"+fasta_contig_name+\\\"\\\\n\\\") \\n out_fasta_list.append(fasta_data)\\n\\n return ''.join(out_fasta_list)\",\n \"def test_dict(self, testdata: TestData) -> None:\\n for data in testdata['observation_type']:\\n observation_type = ObservationType.from_dict(data)\\n assert data == observation_type.to_dict()\",\n \"def __init__(self):\\n dict = defaultdict(list)\\n self.conversion_dict = dict\",\n \"def keys():\",\n \"def test_dictionary(self):\\n selector = ItemSelector('a')\\n data = {'a': [1, 2, 3], 'b': 6}\\n\\n selected_data = selector.transform(data)\\n expected_data = [1, 2, 3]\\n self.assertListEqual(selected_data, expected_data)\",\n \"def test_sample_mapped_keys(self):\\r\\n\\r\\n # With num_coverage=1 only the keys will be sampled\\r\\n actual = sample_mapped_keys(self.test_map, 1)\\r\\n self.assertEqual(actual, {'1': ['1'], '2': ['2']})\\r\\n\\r\\n actual = sample_mapped_keys(self.test_map, 3)\\r\\n for key in actual.keys():\\r\\n # check number of sampled keys\\r\\n self.assertEqual(3, len(actual[key]))\\r\\n for x in actual[key]:\\r\\n # check that sampled key is in the full list\\r\\n correct = list(self.test_map[key])\\r\\n correct.append(key)\\r\\n self.assertTrue(x in correct)\",\n \"def make_keydict(self, analyte=None):\\n if analyte is None:\\n analyte = self.analytes\\n elif isinstance(analyte, str):\\n analyte = [analyte]\\n\\n out = {}\\n for a in analyte:\\n key = []\\n for f in self.components.keys():\\n if self.switches[a][f]:\\n key.append(f)\\n out[a] = ' & '.join(sorted(key))\\n self.keydict = out\\n return out\",\n \"def build_sample_map(flowcell):\\n result = {}\\n rows = [(lane, lib[\\\"name\\\"]) for lib in flowcell[\\\"libraries\\\"] for lane in lib[\\\"lanes\\\"]]\\n i = 1\\n for _, name in sorted(set(rows)):\\n if name not in result:\\n result[name] = \\\"S{}\\\".format(i)\\n i += 1\\n return result\",\n \"def get_value(self) -> Dict[str, any]:\",\n \"def dccaa_dicts(dCCAA, key='c19'):\\n mdict = {k : dCCAA[k][key] for k in dCCAA.keys()}\\n imdict = {v : k for k, v in mdict.items()}\\n return mdict, imdict\",\n \"def test_items(self):\\n obs = self.tester.items()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {('physical_location', 'ANL'), ('has_physical_specimen', True),\\n ('has_extracted_data', True), ('sample_type', 'ENVO:soil'),\\n ('required_sample_info_status', 'completed'),\\n ('collection_timestamp', datetime(2011, 11, 11, 13, 00, 00)),\\n ('host_subject_id', '1001:M7'),\\n ('description', 'Cannabis Soil Microbiome'),\\n ('season_environment', 'winter'), ('assigned_from_geo', 'n'),\\n ('texture', '64.6 sand, 17.6 silt, 17.8 clay'),\\n ('taxon_id', '1118232'), ('depth', 0.15),\\n ('host_taxid', '3483'), ('common_name', 'root metagenome'),\\n ('water_content_soil', 0.164), ('elevation', 114), ('temp', 15),\\n ('tot_nitro', 1.41), ('samp_salinity', 7.15), ('altitude', 0),\\n ('env_biome',\\n 'ENVO:Temperate grasslands, savannas, and shrubland biome'),\\n ('country', 'GAZ:United States of America'), ('ph', 6.94),\\n ('anonymized_name', 'SKB8'), ('tot_org_carb', 5),\\n ('description_duplicate', 'Burmese root'),\\n ('env_feature', 'ENVO:plant-associated habitat'),\\n ('latitude', 74.0894932572),\\n ('longitude', 65.3283470202)}\\n self.assertEqual(set(obs), exp)\",\n \"def complete_cve_dictionary(self, dct):\\n for cve in self.parsed_cve_ids:\\n dict_value = ''\\n if cve in dct:\\n dict_value = dct[cve]\\n self.cve_details_dict[cve] = dict_value\",\n \"def gen_keys():\",\n \"def __init__(self):\\n self.kids = dict()\\n self.val = None\\n self.isWord = False\",\n \"def get_dict_of_str2(self):\\n pass\",\n \"def DictFunction():\\r\\n print \\\"{name} is from {city}, and he likes {cake} cake, {fruit} fruit, {salad} salad and {pasta} pasta\\\".format(**food_prefs)\",\n \"def test_create_offense_by_zip_dict(self):\\r\\n\\r\\n data = [\\r\\n ['Report_No', 'Reported_Date', 'Reported_Time', 'From_Date', 'From_Time', 'To_Date', 'To_Time', 'Offense', 'IBRS', 'Description', 'Beat', 'Address', 'City', 'Zip Code', 'Rep_Dist', 'Area', 'DVFlag', 'Involvement', 'Race', 'Sex', 'Age', 'Firearm Used Flag', 'Location'],\\r\\n ['', '03/19/2019', '', '', '', '', '', 'Vehicular', '', '', '', '', '', '64161', '', '', '', '', '', '', '', '', ''],\\r\\n ['', '03/19/2019', '', '', '', '', '', 'Vehicular', '', '', '', '', '', '64125', '', '', '', '', '', '', '', '', ''],\\r\\n ['', '04/21/2019', '', '', '', '', '', 'Arson', '', '', '', '', '', '64161', '', '', '', '', '', '', '', '', '']\\r\\n ]\\r\\n result = cds.create_offense_by_zip(data)\\r\\n self.assertIsInstance(result, dict, \\\"function should return a dictionary\\\")\\r\\n self.assertEqual(len(result), 2, \\\"There should be 2 items in the dictionary\\\")\\r\\n self.assertIsInstance(result[\\\"Vehicular\\\"], dict, \\\"value for key Vehicular should be a dicationary\\\")\\r\\n self.assertIsInstance(result[\\\"Arson\\\"], dict, \\\"value for key Arson should be a dicationary\\\")\\r\\n\\r\\n self.assertEqual(len(result[\\\"Vehicular\\\"]), 2, \\\"dictionary for key Vehicular should have 2 items in it\\\")\\r\\n self.assertEqual(result[\\\"Vehicular\\\"], {'64161':1,'64125':1}, \\\"value for dictionary key Vehicular incorrect\\\")\\r\\n self.assertEqual(result[\\\"Arson\\\"], {'64161':1}, \\\"value for dictionary key Vehicular incorrect\\\")\",\n \"def pare_dict(*a, **kw):\\n return pare_dict(*a, **kw)\",\n \"def test_dict_same_occurrence_all_str(self):\\n argument = {'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5, 'six': 6, 'seven': 7, 'eight': 8,\\n 'nine': 9, 'ten': 10, 'eleven': 10, 'twelve': 10}\\n actual = file_io.top_ten(argument)\\n expected = [[10, 'twelve'], [10, 'ten'], [10, 'eleven'], [9, 'nine'], [8, 'eight'], [7, 'seven'], [6, 'six'],\\n [5, 'five'], [4, 'four'], [3, 'three']]\\n self.assertEqual(actual, expected)\",\n \"def test_values(self):\\n obs = self.tester.values()\\n self.assertTrue(isinstance(obs, Iterable))\\n exp = {Sample('1.SKB1.640202', self.tester),\\n Sample('1.SKB2.640194', self.tester),\\n Sample('1.SKB3.640195', self.tester),\\n Sample('1.SKB4.640189', self.tester),\\n Sample('1.SKB5.640181', self.tester),\\n Sample('1.SKB6.640176', self.tester),\\n Sample('1.SKB7.640196', self.tester),\\n Sample('1.SKB8.640193', self.tester),\\n Sample('1.SKB9.640200', self.tester),\\n Sample('1.SKD1.640179', self.tester),\\n Sample('1.SKD2.640178', self.tester),\\n Sample('1.SKD3.640198', self.tester),\\n Sample('1.SKD4.640185', self.tester),\\n Sample('1.SKD5.640186', self.tester),\\n Sample('1.SKD6.640190', self.tester),\\n Sample('1.SKD7.640191', self.tester),\\n Sample('1.SKD8.640184', self.tester),\\n Sample('1.SKD9.640182', self.tester),\\n Sample('1.SKM1.640183', self.tester),\\n Sample('1.SKM2.640199', self.tester),\\n Sample('1.SKM3.640197', self.tester),\\n Sample('1.SKM4.640180', self.tester),\\n Sample('1.SKM5.640177', self.tester),\\n Sample('1.SKM6.640187', self.tester),\\n Sample('1.SKM7.640188', self.tester),\\n Sample('1.SKM8.640201', self.tester),\\n Sample('1.SKM9.640192', self.tester)}\\n # Creating a list and looping over it since unittest does not call\\n # the __eq__ function on the objects\\n for o, e in zip(sorted(list(obs), key=lambda x: x.id),\\n sorted(exp, key=lambda x: x.id)):\\n self.assertEqual(o, e)\",\n \"def __init__(self):\\r\\n self.dic={}\",\n \"def _full_mapping(self, data):\\r\\n x = self._empty_mapping()\\r\\n for key, value in data.items():\\r\\n x[key] = value\\r\\n return x\",\n \"def __init__(self):\\n self.key2value = {}\\n self.key2time = {}\",\n \"def create_dict_deep_distortion_old(defect_dict: dict, \\r\\n fancy_defects: dict,\\r\\n ):\\r\\n dict_deep_distortion = {}\\r\\n defect_dict_copy = defect_dict.copy()\\r\\n for defect_type in fancy_defects.keys(): # for each defect type (vac, as , int)\\r\\n \\r\\n dict_deep_distortion[defect_type] = import_deep_distortion_by_type(defect_dict_copy[defect_type],\\r\\n fancy_defects[defect_type]) #defects for which we'll try the deep distortion found for one of the charge states \\r\\n return dict_deep_distortion\",\n \"def _makeimap(self):\\n self.map_[\\\"source\\\"] = \\\"nasa\\\"\\n self.map_[\\\"instrument\\\"] = \\\"goes\\\"\\n self.map_[\\\"physobs\\\"] = \\\"irradiance\\\"\\n self.map_[\\\"provider\\\"] = \\\"sdac\\\"\",\n \"def get_dict_refine_insee_code(ls_valid_ic):\\n dict_refine_ic = {x: (x, x) for x in ls_valid_ic}\\n ls_valid_ic_corse = [x for x in ls_valid_ic if re.match('2[AB]', x)]\\n for ic in ls_valid_ic_corse:\\n dict_refine_ic[ic[:1] + u'0' + ic[2:]] = (ic, ic) # assumed unicity was checked\\n dict_ic_ardts = dict(list(itertools.product(map(str,range(13201, 13217)), ['13055']))+\\\\\\n list(itertools.product(map(str,range(69381, 69390)), ['69123']))+\\\\\\n list(itertools.product(map(str,range(75101, 75121)), ['75056'])))\\n dict_ic_ardts = {k : (v,k) for k,v in dict_ic_ardts.items()}\\n dict_refine_ic.update(dict_ic_ardts)\\n return dict_refine_ic\",\n \"def all_cities(mdarray):\\n cities = {}\\n city_set = set(data_set['city_end'])\\n for city in city_set:\\n cities[city] = \\\"\\\" # Basically updating the cities list every time when city is added\\n return cities\",\n \"def test_build_format_map(all_sets: dict, expected: dict) -> None:\\n result = build_format_map(all_sets, regular=False)\\n assert result == expected\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def getAircraftCodeDict():\\n table = 'aircraft'\\n connection = openConnection()\\n curs = connection.cursor()\\n sqlcmd = \\\"SELECT * FROM \\\" + str(table)\\n d = {}\\n \\n curs.execute(sqlcmd)\\n for row in curs.fetchall():\\n aircraft = airlineClasses.Aircraft()\\n aircraft.aircraftCode = row[0]\\n aircraft.name = row[1]\\n d[aircraft.aircraftCode] = aircraft\\n \\n curs.close()\\n connection.close()\\n return d\",\n \"def convertirDic(botones):\\n dic_aux = {}\\n for clave,valor in botones.items():\\n dic_aux[tuple(map(int,clave.split(\\\",\\\")))] = valor\\n return dic_aux\",\n \"def extraire_resultat(sheet):\\n\\tdic_result={}\\n\\tcase_pv_mac=\\\"J29\\\"\\n\\tcase_pv_lp=\\\"J28\\\"\\n\\tcase_pv_abs=\\\"I21\\\"\\n\\tpv_mac=round(round(sheet[case_pv_mac].value,4)*100,2)\\n\\tpv_lp=round(round(sheet[case_pv_lp].value,4)*100,2)\\n\\tpv_abs=round(round(sheet[case_pv_abs].value,4)*100,2)\\n\\tdic_result[\\\"pv_mac\\\"]=pv_mac\\n\\tdic_result[\\\"pv_lp\\\"]=pv_lp\\n\\tdic_result[\\\"pv_abs\\\"]=pv_abs\\n\\treturn dic_result\",\n \"def _init_dict(self):\\n dict_ord = self.MIN_VALID\\n\\n for da in self.train_das:\\n for dai in da:\\n if dai.name not in self.dict_slot:\\n self.dict_slot[dai.name] = dict_ord\\n dict_ord += 1\\n if dai.value not in self.dict_value:\\n self.dict_value[dai.value] = dict_ord\\n dict_ord += 1\\n\\n for tree in self.train_trees:\\n for t_lemma, formeme in tree.nodes:\\n if t_lemma not in self.dict_t_lemma:\\n self.dict_t_lemma[t_lemma] = dict_ord\\n dict_ord += 1\\n if formeme not in self.dict_formeme:\\n self.dict_formeme[formeme] = dict_ord\\n dict_ord += 1\\n\\n self.dict_size = dict_ord\",\n \"def __init__(self, key):\\n self.key = [int_mapping(k) for k in key]\",\n \"def OMDict(self, items):\\n return om.OMApplication(elem=self.OMSymbol(module='Python', name='dict'),\\n arguments=[self.OMTuple(item) for item in items])\",\n \"def dict2():\\n print(record1)\\n record2 = {}\\n for k, v in record1.items():\\n count = 0\\n for letter in v:\\n if letter == 't' or letter == 'T':\\n count += 1\\n record2[k] = count\\n print(record2)\",\n \"def _get_attribute_dic(self, attrs):\\n attr_dic = {}\\n for attr_pair in attrs:\\n attr_dic[attr_pair[0]] = attr_pair[1]\\n return attr_dic\",\n \"def __init__(self, dictionary):\\n self.abbrdict = {} #Use a dict to count the number of same abbreviation.\\n self.origdict = {} #Use a dict to rule out duplicate of original words.\\n for x in dictionary:\\n if len(x) <= 1: #If the length of word is not larger than 1, its abbreviation is itself.\\n n = x\\n else: #Otherwise get the abbreviation as decribed.\\n n = x[0] + str(len(x) - 2) + x[-1]\\n if x not in self.origdict: #If this is the 1st time word appears, add the count of abbreviation.\\n if n not in self.abbrdict:\\n self.abbrdict[n] = 1\\n else:\\n self.abbrdict[n] += 1\\n self.origdict[x] = True #Set it as appeared.\",\n \"def codebook_json_data_factory() -> List[Dict[str, Any]]:\\n codebook_data = [\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 0, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_A\\\"\\n },\\n {\\n Features.CODEWORD: [\\n {Indices.ROUND.value: 0, Indices.CH.value: 2, Features.CODE_VALUE: 1},\\n {Indices.ROUND.value: 1, Indices.CH.value: 1, Features.CODE_VALUE: 1}\\n ],\\n Features.TARGET: \\\"GENE_B\\\"\\n },\\n ]\\n return codebook_data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59698254","0.5688297","0.56781894","0.56143355","0.5542839","0.5520396","0.5489875","0.5482796","0.5428855","0.54169875","0.5414742","0.54147077","0.5387781","0.5383238","0.53804624","0.5378157","0.53751314","0.533766","0.53338116","0.5317185","0.530112","0.5300158","0.5291576","0.5287203","0.5287203","0.52816","0.52609473","0.52605194","0.52579767","0.52557224","0.5255722","0.52478063","0.52473694","0.5246058","0.5235229","0.52078205","0.5196046","0.5193494","0.51862204","0.51857203","0.51787513","0.5169786","0.5165639","0.51650953","0.51638836","0.51588213","0.5152492","0.5148949","0.51406676","0.5133785","0.5111518","0.51081187","0.5095958","0.5089133","0.50872815","0.50831753","0.50804275","0.5071151","0.5061703","0.5056913","0.50532585","0.5043941","0.5040618","0.5038403","0.50351495","0.5031038","0.5022596","0.5011021","0.50103736","0.49968445","0.4995714","0.4994204","0.4991291","0.4989697","0.49892882","0.49881274","0.4985951","0.49820495","0.49781343","0.49758586","0.49735656","0.49700078","0.49692544","0.49683416","0.49670607","0.4962325","0.49617174","0.49602175","0.49530733","0.49530733","0.49497938","0.49497235","0.49481803","0.49465725","0.49461654","0.4945638","0.49445072","0.4943824","0.4938751","0.4938178"],"string":"[\n \"0.59698254\",\n \"0.5688297\",\n \"0.56781894\",\n \"0.56143355\",\n \"0.5542839\",\n \"0.5520396\",\n \"0.5489875\",\n \"0.5482796\",\n \"0.5428855\",\n \"0.54169875\",\n \"0.5414742\",\n \"0.54147077\",\n \"0.5387781\",\n \"0.5383238\",\n \"0.53804624\",\n \"0.5378157\",\n \"0.53751314\",\n \"0.533766\",\n \"0.53338116\",\n \"0.5317185\",\n \"0.530112\",\n \"0.5300158\",\n \"0.5291576\",\n \"0.5287203\",\n \"0.5287203\",\n \"0.52816\",\n \"0.52609473\",\n \"0.52605194\",\n \"0.52579767\",\n \"0.52557224\",\n \"0.5255722\",\n \"0.52478063\",\n \"0.52473694\",\n \"0.5246058\",\n \"0.5235229\",\n \"0.52078205\",\n \"0.5196046\",\n \"0.5193494\",\n \"0.51862204\",\n \"0.51857203\",\n \"0.51787513\",\n \"0.5169786\",\n \"0.5165639\",\n \"0.51650953\",\n \"0.51638836\",\n \"0.51588213\",\n \"0.5152492\",\n \"0.5148949\",\n \"0.51406676\",\n \"0.5133785\",\n \"0.5111518\",\n \"0.51081187\",\n \"0.5095958\",\n \"0.5089133\",\n \"0.50872815\",\n \"0.50831753\",\n \"0.50804275\",\n \"0.5071151\",\n \"0.5061703\",\n \"0.5056913\",\n \"0.50532585\",\n \"0.5043941\",\n \"0.5040618\",\n \"0.5038403\",\n \"0.50351495\",\n \"0.5031038\",\n \"0.5022596\",\n \"0.5011021\",\n \"0.50103736\",\n \"0.49968445\",\n \"0.4995714\",\n \"0.4994204\",\n \"0.4991291\",\n \"0.4989697\",\n \"0.49892882\",\n \"0.49881274\",\n \"0.4985951\",\n \"0.49820495\",\n \"0.49781343\",\n \"0.49758586\",\n \"0.49735656\",\n \"0.49700078\",\n \"0.49692544\",\n \"0.49683416\",\n \"0.49670607\",\n \"0.4962325\",\n \"0.49617174\",\n \"0.49602175\",\n \"0.49530733\",\n \"0.49530733\",\n \"0.49497938\",\n \"0.49497235\",\n \"0.49481803\",\n \"0.49465725\",\n \"0.49461654\",\n \"0.4945638\",\n \"0.49445072\",\n \"0.4943824\",\n \"0.4938751\",\n \"0.4938178\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61399186"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":131,"numItemsPerPage":100,"numTotalItems":95772,"offset":13100,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjAxNTIwMywianRpIjoiNjc5YTU4OGYtNTkzNS00YTEyLWEzNDktN2I1Y2EzNjRhOTZiIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwMTg4MDMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.ZiHQUpXuFBLx7ND6AYIAYDezl8ZesFwWdY9OAw-c3UsZunbAll34KxQW6mqhQnDqjEpNTfd6jEdxW7ICEXNACw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Wrap the objective function so that it returns a tuple for compatibility with deap | def myfunc(c, **kwarwgs):
return (f(c, **kwargs), ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def objective(self, args: Dict[str, Any]) -> float:\n pass",
"def custom_method(*args, **kwargs):\n return objective_return",
"def custom_method(*args, **kwargs):\n return objective_return",
"def objective(self, x):\n pass",
"def objective(self, x):\n pass",
"de... | [
"0.6651697",
"0.6526861",
"0.6526861",
"0.64804745",
"0.64804745",
"0.63947695",
"0.6244187",
"0.59514946",
"0.589431",
"0.5892498",
"0.58448875",
"0.5830852",
"0.5799885",
"0.5705147",
"0.56850517",
"0.5649029",
"0.56382173",
"0.56288266",
"0.5623751",
"0.5618536",
"0.558602... | 0.0 | -1 |
Calculates hexadecimal value of userentered base10 integer. As long as the remainder of the userentered base10 value and modulo 16 does not equal 0, the function stores the remainder in a queue and uses a dictionary to assign remainders 1015. Outputs the queue representation of the hex value at the end. | def hex_calc(value):
hex_dict = { # Dictionary for hex values over 9
10: "A",
11: "B",
12: "C",
13: "D",
14: "E",
15: "F"
}
hex_stack = deque() # Queue to hold hexidecimal representation
while value > 0:
remainder = value % 16
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decimal_to_hexadecimal(number):\n if number >= 1 and number <= 10: #if the positive integer is less than 10, its binary form is itself\n print(number)\n else:\n \"\"\"\n divide number by 16, take the reminder and start again until the result is 0\n \"\"\"\n new_number =... | [
"0.6767114",
"0.62019885",
"0.6037745",
"0.59984386",
"0.5930948",
"0.5927391",
"0.5909252",
"0.5885277",
"0.5724089",
"0.5716753",
"0.56764406",
"0.5669568",
"0.56636024",
"0.56621295",
"0.56443244",
"0.56175005",
"0.56158185",
"0.5566997",
"0.5562639",
"0.5524696",
"0.55198... | 0.72924364 | 0 |
Calculates binary value of userentered base10 integer. As long as the remainder of the userentered base10 value and modulo 2 does not equal 0, the function stores the remainder in a queue. Outputs the queue representation of the binary value at the end. | def binary_calc(value):
binary_stack = deque() # Queue to hold binary representation
while value > 0:
remainder = value % 2
binary_stack.append(remainder) # Add binary digit to queue
value = value // 2
print("Binary Value: ", end="")
while binary_stack:
prin... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dec_to_bin(num, base):\n\n # Divide number by base and keep track of remainder in a stack.\n # What is one of the key indicators that a stack should be used?\n # Reversability\n # The reversal property signals that a stack is likely the appropriate\n # data structure for solving the problem.\n\n... | [
"0.6342449",
"0.6292792",
"0.6045751",
"0.59172094",
"0.5907794",
"0.58624756",
"0.5839806",
"0.58302724",
"0.57647806",
"0.57232666",
"0.5678209",
"0.5669591",
"0.5661115",
"0.5660052",
"0.5625247",
"0.5621955",
"0.5618219",
"0.5616829",
"0.5614025",
"0.55944234",
"0.5593275... | 0.7517937 | 0 |
Starting point for the program. Asks user for a base10, positive decimal integer and calls the binary_calc and hex_calc functions for computation. The program will loop, asking the user for a new number as long as they do not enter the string "quit". | def setup():
value = input("Enter a positive decimal integer (\"quit\" to stop): ")
while value.lower() != "quit":
binary_calc(int(value)) # Calls converter function on inputted value
print("\n")
hex_calc(int(value)) # Calls converter function on inputted value
value = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run():\n reset_calc()\n finish = False\n printCurrent()\n while not finish:\n printMenu()\n\n m = input().strip()\n if (m == 'x'):\n finish = True\n elif (m == '+'):\n m = input(\"Give nominator:\")\n n = input(\"Give denominator:\")\n ... | [
"0.65855235",
"0.65636045",
"0.6491302",
"0.64777714",
"0.6328509",
"0.6162278",
"0.6159335",
"0.61103714",
"0.6071295",
"0.59733236",
"0.59465903",
"0.5880005",
"0.57767516",
"0.5758527",
"0.5752787",
"0.5737361",
"0.56870824",
"0.5684198",
"0.5615961",
"0.5587042",
"0.55698... | 0.8094763 | 0 |
allows downloading of uploaded files | def download():
return response.download(request, db) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_files(self):",
"def post_download(self, remote_files):\n pass",
"def download_file(self, parsed_event, input_dir_path):",
"def download(self,fn):\n\t\treturn False #TODO: implement meme download",
"def download(self):\n pass",
"def download(self):\n pass",
"def pre_dow... | [
"0.7924571",
"0.7199257",
"0.6978304",
"0.6841495",
"0.6764734",
"0.6764734",
"0.66671187",
"0.6631285",
"0.6588692",
"0.6555494",
"0.65398693",
"0.65012735",
"0.6458808",
"0.6451737",
"0.64442515",
"0.6402176",
"0.63864595",
"0.63758063",
"0.63659835",
"0.6345104",
"0.633138... | 0.5870759 | 97 |
allows downloading of uploaded files | def download1():
#t=request.vars.arg(0)
response.flash=request
#print request.wsgi.environ['HTTP_REFERER']
#print 'yghklo=',request.args[0]
a=db(db.Project.Project_File==request.args[0]).select(db.Project.ALL)
#a=db(db.Project.id==38).select(db.Project.ALL)
#if a == None:
# print 'silent... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_files(self):",
"def post_download(self, remote_files):\n pass",
"def download_file(self, parsed_event, input_dir_path):",
"def download(self,fn):\n\t\treturn False #TODO: implement meme download",
"def download(self):\n pass",
"def download(self):\n pass",
"def pre_dow... | [
"0.7925669",
"0.7200179",
"0.6978917",
"0.68426496",
"0.67664343",
"0.67664343",
"0.66682106",
"0.66323435",
"0.65895796",
"0.65572894",
"0.6541814",
"0.6501572",
"0.64601624",
"0.6452852",
"0.6445267",
"0.64018536",
"0.6386292",
"0.6376441",
"0.6365934",
"0.6346462",
"0.6332... | 0.0 | -1 |
FPA object setup, whatever that is. The only info from the problem is that it is a silicon detector, and we have a graph of quantum efficiency vs wavelength. Based on the graph, it is | def setup_fpa():
# it is a silicon detector. Based on the graph, the quantum efficiency
# at 1.06 um is ~50%.
fpa = {}
fpa["quantum_efficiency"] = 0.5
return fpa | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\n # self.threshold = 3.\n self.gamma_min = 3\n self.gamma_max = 12\n self.n_samples = 40\n # self.do_plots = False\n # self.do_albedo = True\n # self.verbose = True\n\n self.nbands = 7\n self.bu = np.array([0.004, 0.015, 0.003... | [
"0.65070575",
"0.6292009",
"0.62877405",
"0.60567147",
"0.6045674",
"0.6028957",
"0.60077965",
"0.59778076",
"0.59438014",
"0.5908957",
"0.58956885",
"0.5890363",
"0.5871577",
"0.5847138",
"0.583425",
"0.5827184",
"0.5803118",
"0.58006674",
"0.57921225",
"0.5785037",
"0.57824... | 0.7589595 | 0 |
Stop the thread at the next opportunity. | def stop(self):
self._stopped = True
return self._stopped | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)",
"def stop(self):\n self._state.transit(sitcpy.THREAD_STOPPING)",
"def stop(self):\n\n self.stop_thread = True",
"def stop(self):\n self._Thread__stop()",
"def stop_thread(self):\n t, e = self.workers[0]\n ... | [
"0.7869557",
"0.7869557",
"0.7726774",
"0.77168417",
"0.7651403",
"0.73841757",
"0.73667896",
"0.73539305",
"0.73251146",
"0.725089",
"0.7246788",
"0.7226235",
"0.7207327",
"0.7205535",
"0.71974504",
"0.71739626",
"0.71739626",
"0.71739626",
"0.71739626",
"0.71739626",
"0.717... | 0.0 | -1 |
Test creation / restoration / deletion of a backup via the csi snapshotter | def test_csi_volumesnapshot_basic(set_random_backupstore, # NOQA
volumesnapshotclass, # NOQA
volumesnapshot, # NOQA
client, # NOQA
core_api, # NOQA
vo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_restore_backup():",
"def test_backup_only(self):\n # Check that by default a backup is performed and a snapshot is created.\n with TemporaryDirectory() as temporary_directory:\n source = os.path.join(temporary_directory, 'source')\n destination = os.path.join(temporar... | [
"0.75130534",
"0.7482683",
"0.7389687",
"0.71746546",
"0.71545166",
"0.70082086",
"0.6990108",
"0.6987986",
"0.69760513",
"0.6942519",
"0.6912148",
"0.68778676",
"0.6875608",
"0.6867557",
"0.6852088",
"0.6788844",
"0.6782454",
"0.6765713",
"0.6700932",
"0.6685788",
"0.6674570... | 0.74713445 | 2 |
Test retention of a backup while deleting the associated `VolumeSnapshot` via the csi snapshotter | def test_csi_volumesnapshot_restore_existing_backup(set_random_backupstore, # NOQA
client, # NOQA
core_api, # NOQA
volume_name, # NOQA
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aws_service_api_snapshot_delete(self):\n pass",
"def test_backup_restore_with_deletes(self):\n gen = BlobGenerator(\"ent-backup\", \"ent-backup-\", self.value_size, end=self.num_items)\n self._load_all_buckets(self.master, gen, \"create\", 0)\n self.backup_create()\n s... | [
"0.6723075",
"0.66824317",
"0.66567975",
"0.66087633",
"0.65738505",
"0.65661085",
"0.6506427",
"0.6465853",
"0.6354467",
"0.6307124",
"0.6253928",
"0.62148994",
"0.62017876",
"0.6147514",
"0.6068462",
"0.60481215",
"0.5997585",
"0.5995326",
"0.59307927",
"0.59299403",
"0.592... | 0.6330533 | 9 |
Draws confidence envelope for a multivariate normal distribution The ellipse can be specified by mean and covariance. Use stats_ellipse(level=q) to draw the empirical confidence envelopes for the data. | def geom_ellipse(mean: np.array, cov: np.array, data: np.array=None, q: float=0.95, **kwargs):
# Radius that covers q-fraction of white gaussian noise
r = np.sqrt(stats.chi2.ppf(q=q, df=2))
# Eigen-directions of a covariance matrix
try:
L, W = np.linalg.eigh(cov)
except:
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def confidence_ellipse(x, y, ax, n_std=3.0, facecolor='none', **kwargs):\n if x.size != y.size:\n raise ValueError(\"x and y must be the same size\")\n\n cov = np.cov(x, y)\n pearson = cov[0, 1]/np.sqrt(cov[0, 0] * cov[1, 1])\n # Using a special case to obtain the eigenvalues of this\n # two-... | [
"0.6881007",
"0.68626964",
"0.6812256",
"0.6812256",
"0.6812256",
"0.6261113",
"0.6041728",
"0.6020823",
"0.59574276",
"0.5952475",
"0.59519756",
"0.5951841",
"0.58844304",
"0.58799803",
"0.58613104",
"0.5785062",
"0.56827694",
"0.56631327",
"0.56524634",
"0.5627426",
"0.5622... | 0.6335539 | 5 |
Converts RGB image to grayscale image preserving perceived luminocity based on Y_601 formula | def convert_to_grayscale(image: np.array) -> np.array:
assert len(image.shape) == 3 and image.shape[2] == 3, "Image must be m x n x 3 dimensional RGB array"
return 0.2989 * image[:,:,0] + 0.5870 * image[:,:,1] + 0.1140 * image[:,:,2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_grayscale(img: Image.Image) -> Image.Image:\n orig_mode = img.mode\n\n if orig_mode in [\"RGB\", \"CMYK\", \"YCbCr\", \"LAB\", \"HSV\"]:\n return img.convert(\"L\")\n elif orig_mode == \"RGBA\":\n return img.convert(\"LA\").convert(\"RGBA\")\n elif orig_mode == \"P\":\n # ... | [
"0.7190472",
"0.71280396",
"0.69965696",
"0.6992806",
"0.6975413",
"0.69699496",
"0.69542336",
"0.6903083",
"0.6797259",
"0.6771814",
"0.67706394",
"0.67490405",
"0.6748642",
"0.6746511",
"0.6744621",
"0.67407954",
"0.67283195",
"0.67158645",
"0.6694815",
"0.6692519",
"0.6692... | 0.6712988 | 18 |
Computes luminocity for the colorspace vector according to Y_601 formula | def luma(cspace: np.array) -> np.array:
assert isinstance(cspace, DataFrame), "Colorspace must be a dataframe"
assert all(np.isin(['R', 'G', 'B'], cspace.columns)), "Colorspace must contain RGB columns"
return 0.2989 * cspace['R'] + 0.5870 * cspace['G'] + 0.1140 * cspace['B'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_luminosity(red, green, blue):\r\n return (0.299 * red) + (0.587 * green) + (0.114 * blue)",
"def luminance(self, color):\n return 0.2426 * color[2] + 0.7152 * color[1] + 0.0722 * color[0]",
"def luminance(self):\n \n return (self.r + self.g + self.b) // 3",
"def sRGBLumina... | [
"0.722705",
"0.7049026",
"0.70364183",
"0.6859587",
"0.66851425",
"0.6664808",
"0.66166466",
"0.6611337",
"0.651986",
"0.6440314",
"0.6394044",
"0.6274998",
"0.62030494",
"0.61515486",
"0.60705495",
"0.6067759",
"0.6057517",
"0.60458785",
"0.60117924",
"0.5979228",
"0.5974094... | 0.6814634 | 4 |
Converts RGB image to a dataframe where each row corresponds to single pixel. If sample_count is set then only random subset of rows is returned. | def image_to_colorspace(image: np.array, sample_count: int = None) -> DataFrame:
assert len(image.shape) == 3 and image.shape[2] == 3, "Image must be m x n x 3 dimensional RGB array"
if sample_count:
return DataFrame({'R':image[:,:,0].flatten(),'G':image[:,:, 1].flatten(),'B':image[:,:, 2].flatten(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_sample_df(self, df, features, r):\n grouped = df.groupby('feature')\n df_sample = pd.DataFrame()\n for feature in features:\n group = grouped.get_group(feature)\n samples = group.sample(n=r)\n df_sample = df_sample.append(samples)\n return df_sa... | [
"0.6141033",
"0.603381",
"0.60076314",
"0.5756627",
"0.56922126",
"0.5570779",
"0.5568138",
"0.555071",
"0.5497111",
"0.546993",
"0.5440586",
"0.5375774",
"0.5369649",
"0.53203547",
"0.53200895",
"0.53108627",
"0.52745223",
"0.5240417",
"0.5232344",
"0.5230368",
"0.5151618",
... | 0.68630415 | 0 |
Converts colorspace vector back to RGB image. Colorspace can have extra columns | def colorspace_to_image(cspace: DataFrame, m: int, n: int) -> np.array:
assert isinstance(cspace, DataFrame), "Colorspace must be a dataframe"
assert len(cspace) == m * n, 'Image dimensions must match'
assert all(np.isin(['R', 'G', 'B'], cspace.columns)), "Colorspace must contain RGB columns"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def features_colorspace(X, colorspace):\n if colorspace == \"BGR\":\n return X\n\n X = np.array(X)\n\n if colorspace == \"HLS\":\n cs = cv2.COLOR_BGR2HLS\n elif colorspace == \"HSV\":\n cs = cv2.COLOR_BGR2HSV\n elif colorspace == \"LUV\":\n cs = cv2.COLOR_BGR2LUV\n el... | [
"0.6144378",
"0.60548836",
"0.6034356",
"0.5993296",
"0.5984861",
"0.5929783",
"0.58545554",
"0.58436054",
"0.5751462",
"0.573934",
"0.5706569",
"0.5676334",
"0.567407",
"0.5654705",
"0.56331134",
"0.5625428",
"0.5621792",
"0.5558728",
"0.5558652",
"0.5552761",
"0.5528596",
... | 0.6287839 | 0 |
Visualise colorspace vector as an interactive 3D figure. Colorspace can have extra columns By default RGB channels are clipped to the range [0,1]. Extra arguments can be used to control the appearance of ipyvolume.scatter | def show_colorspace(cspace: np.array, clip=True, size = 0.5, marker='sphere', **kwargs) -> None:
assert isinstance(cspace, DataFrame), "Colorspace must be a dataframe"
assert all(np.isin(['R', 'G', 'B'], cspace.columns)), "Colorspace must contain RGB columns"
fig = ipv.figure()
if clip:
ipv.sc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def drawColorColumn(x, yseq, zseq):\n dislin.curvy3(x, yseq, zseq, len(yseq))",
"def plot3D(x):\n cycol = cycle('bgrcmk')\n fig = plt.figure()\n ax = Axes3D(fig)\n for i in range(5):\n ax.scatter(x[:, i, 0], x[:, i, 1], x[:, i, 2], c=next(cycol),\n marker='.')\n plt.sho... | [
"0.59832585",
"0.5860537",
"0.58340544",
"0.57691926",
"0.5764829",
"0.5710427",
"0.5683266",
"0.5635767",
"0.5606914",
"0.55751276",
"0.5560233",
"0.5559747",
"0.55588275",
"0.55202836",
"0.5513298",
"0.55122715",
"0.5509551",
"0.5489844",
"0.5477256",
"0.5471932",
"0.544747... | 0.69867015 | 0 |
Decomposes RGB representation to a naive luma and chroma representation depicted by the formula RGB = luma rRGB. | def luma_decomposition(cspace: DataFrame) -> DataFrame:
assert isinstance(cspace, DataFrame), "Colorspace must be a dataframe"
assert all(np.isin(['R', 'G', 'B'], cspace.columns)), "Colorspace must contain RGB columns"
return (cspace
.assign(luma = lambda df: luma(df))
.assign(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unconvert_from_RGB_255(colors):\n un_rgb_color = (colors[0]/(255.0),\n colors[1]/(255.0),\n colors[2]/(255.0))\n\n return un_rgb_color",
"def generate_normalized_rgb(self):\n \n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n ... | [
"0.6625473",
"0.6541498",
"0.64622194",
"0.6461458",
"0.6377411",
"0.6372886",
"0.6326228",
"0.62877744",
"0.626021",
"0.62466156",
"0.62466156",
"0.6232703",
"0.62201065",
"0.61821765",
"0.6153323",
"0.61069566",
"0.6098639",
"0.60872954",
"0.60829175",
"0.6041739",
"0.60143... | 0.5960863 | 27 |
Reconstructs RGB representation from the naive luma and chroma representation by the formula RGB = luma rRGB. | def rgb_reconstruction(lumaspace: DataFrame) -> DataFrame:
assert isinstance(lumaspace, DataFrame), "Colorspace must be a dataframe"
assert all(np.isin(['luma', 'rR', 'rG', 'rB'], lumaspace.columns)), "Lumaspace must contain RGB columns"
return (lumaspace
.assign(R = lambda df: df['luma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_normalized_rgb(self):\n \n r,g,b=(Numeric.zeros(256),Numeric.zeros(256),Numeric.zeros(256))\n for i in Numeric.arange(256):\n r_,g_,b_=self.colfct(i/255.0) # these are from [0,1]\n r[i],g[i],b[i]=int(255*r_),int(255*g_),int(255*b_)\n return r/256... | [
"0.70356727",
"0.6782001",
"0.672594",
"0.6722472",
"0.66723245",
"0.64710706",
"0.6347879",
"0.63251776",
"0.63026893",
"0.6233064",
"0.62325096",
"0.62169987",
"0.6204918",
"0.6130449",
"0.61294127",
"0.6101093",
"0.60523415",
"0.60489076",
"0.60436034",
"0.60419947",
"0.60... | 0.63311535 | 7 |
Tests that all signals are constructed. | def test_construct_signals_present(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
assert 'completely_home_prop' in set(cbg_df.columns)
assert 'full_time_work_prop' in set(cbg_df.columns)
assert 'part_time_work_pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_initialize(self):\n SignalHook(self.test_extension, self.signal, self._on_signal_fired)\n\n self.assertEqual(len(self._on_signal_fired.calls), 0)\n self.signal.send(self)\n self.assertEqual(len(self._on_signal_fired.calls), 1)",
"def test_allows_signal_notify(self):\n ... | [
"0.7198699",
"0.6961123",
"0.6867601",
"0.68594754",
"0.67881685",
"0.66899323",
"0.66274303",
"0.6527756",
"0.65197617",
"0.6457534",
"0.6406589",
"0.63431233",
"0.6326639",
"0.62745726",
"0.627386",
"0.62715155",
"0.62302566",
"0.6204464",
"0.61189145",
"0.60972494",
"0.600... | 0.627173 | 15 |
Tests that constructed signals are actual proportions. | def test_construct_signals_proportions(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
assert np.all(cbg_df['completely_home_prop'].values <= 1)
assert np.all(cbg_df['full_time_work_prop'].values <= 1)
assert np.a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_proportions(self):\r\n\r\n proportions = [\r\n v['proportion'] for k, v in self.composition.items()\r\n ]\r\n\r\n if sum(proportions) < 1.0:\r\n raise ValueError('Sum of proportions between host and pathogen must be 1.0.')\r\n elif sum(proportions) > 1.0:... | [
"0.66484034",
"0.6311004",
"0.61575395",
"0.6049411",
"0.5846437",
"0.57422686",
"0.5709035",
"0.5704337",
"0.5583857",
"0.5583857",
"0.5528866",
"0.5524776",
"0.55001354",
"0.54795945",
"0.5478572",
"0.54679793",
"0.54651594",
"0.544234",
"0.543187",
"0.5401828",
"0.5401828"... | 0.7607326 | 0 |
Tests that aggregation at the county level creates nonzerovalued signals. | def test_aggregate_county(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'county')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_nation(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'nation')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.68716997",
"0.6666926",
"0.61090475",
"0.57431424",
"0.5742255",
"0.5667957",
"0.5607133",
"0.5604635",
"0.55638605",
"0.55366623",
"0.5536144",
"0.55044466",
"0.54993826",
"0.5419435",
"0.5376306",
"0.53653294",
"0.53649396",
"0.5341991",
"0.5326681",
"0.53014153",
"0.529... | 0.7612559 | 0 |
Tests that aggregation at the state level creates nonzerovalued signals. | def test_aggregate_state(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'state')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_nation(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'nation')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.61210126",
"0.5934089",
"0.589467",
"0.5892433",
"0.5861224",
"0.5711701",
"0.5653383",
"0.5630056",
"0.5579708",
"0.5577906",
"0.5550419",
"0.5545783",
"0.55085456",
"0.5499616",
"0.5489012",
"0.5481875",
"0.5469063",
"0.5461217",
"0.54575956",
"0.5446156",
"0.5441203",
... | 0.748049 | 0 |
Tests that aggregation at the state level creates nonzerovalued signals. | def test_aggregate_msa(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'msa')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert np.al... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_state(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'state')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.7479666",
"0.6121371",
"0.5934786",
"0.5891897",
"0.5862976",
"0.57110685",
"0.56545264",
"0.5631428",
"0.55796295",
"0.5577801",
"0.55514765",
"0.554494",
"0.5510593",
"0.5500976",
"0.54901975",
"0.54840297",
"0.5469475",
"0.54619354",
"0.54598",
"0.5447684",
"0.54426587"... | 0.58947563 | 3 |
Tests that aggregation at the state level creates nonzerovalued signals. | def test_aggregate_hrr(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'hrr')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert np.al... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_state(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'state')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.74809134",
"0.6120178",
"0.5934026",
"0.58936185",
"0.5890962",
"0.5861368",
"0.5712922",
"0.5653689",
"0.56298804",
"0.55801994",
"0.55768",
"0.5550667",
"0.55458176",
"0.55086803",
"0.549904",
"0.5489132",
"0.54822505",
"0.5468543",
"0.54612315",
"0.54581416",
"0.5446415... | 0.53738433 | 29 |
Tests that aggregation at the state level creates nonzerovalued signals. | def test_aggregate_nation(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'nation')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_state(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'state')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.74812746",
"0.59329057",
"0.5895889",
"0.58929867",
"0.58616966",
"0.5710675",
"0.565309",
"0.5629537",
"0.557881",
"0.5575357",
"0.5549657",
"0.55448717",
"0.55095005",
"0.54987305",
"0.54884887",
"0.54829407",
"0.5467536",
"0.54628205",
"0.5458857",
"0.5445779",
"0.54409... | 0.6122557 | 1 |
Tests that aggregation at the state level creates nonzerovalued signals. | def test_aggregate_hhs(self):
cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),
SIGNALS)
df = aggregate(cbg_df, SIGNALS, 'hhs')
assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)
x = df[f'{SIGNALS[0]}_se'].values
assert np.al... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aggregate_state(self):\n cbg_df = construct_signals(pd.read_csv('raw_data/sample_raw_data.csv'),\n SIGNALS)\n df = aggregate(cbg_df, SIGNALS, 'state')\n\n assert np.all(df[f'{SIGNALS[0]}_n'].values > 0)\n x = df[f'{SIGNALS[0]}_se'].values\n ... | [
"0.748049",
"0.61210126",
"0.5934089",
"0.589467",
"0.5892433",
"0.5861224",
"0.5711701",
"0.5653383",
"0.5630056",
"0.5579708",
"0.5577906",
"0.5550419",
"0.5545783",
"0.55085456",
"0.5499616",
"0.5489012",
"0.5481875",
"0.5469063",
"0.54575956",
"0.5446156",
"0.5441203",
... | 0.5461217 | 18 |
Tests that processing over a window correctly aggregates signals. | def test_process_window(self, tmp_path):
export_dir = tmp_path / 'export'
export_dir.mkdir()
df1 = pd.DataFrame(data={
'date_range_start': ['2020-02-14T00:00:00-05:00:00']*3,
'origin_census_block_group': [10539707003,
10539707003,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_next_window_time_no_sample_passed(self):\n test_window_scheme = WindowingScheme(self.window_test_filter, 3)\n time.sleep(4)\n collected_value = test_window_scheme.filter(self.more_than_upper_bound)\n self.assertEquals(collected_value, self.more_than_upper_bound)",
"def test_n... | [
"0.62284684",
"0.60323983",
"0.59892815",
"0.5937085",
"0.59230894",
"0.59155595",
"0.58550054",
"0.58464015",
"0.58157486",
"0.5741738",
"0.5680929",
"0.56652606",
"0.5605725",
"0.55671203",
"0.5524034",
"0.552288",
"0.5484974",
"0.5478022",
"0.5466935",
"0.5451512",
"0.5450... | 0.5814091 | 9 |
Tests that processing a list of current and previous file names correctly reads and aggregates signals. | def test_process(self, tmp_path):
export_dir = tmp_path / 'export'
export_dir.mkdir()
process(['raw_data/small_raw_data_0.csv',
'raw_data/small_raw_data_1.csv',
# File 2 does not exist.
'raw_data/small_raw_data_2.csv',
'raw_dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_read_multiple_signals(self):\n cwd = os.path.dirname(os.path.abspath(__file__))\n test_dir = os.path.join(cwd, 'test_files/')\n signals = read_signals(test_dir)\n self.assertEqual(len(signals), 3)\n self.assertTrue(\n any(signal for signal in signals if signal... | [
"0.6583948",
"0.65238965",
"0.6152226",
"0.6060168",
"0.592048",
"0.58374643",
"0.57693136",
"0.5765163",
"0.56609446",
"0.56524676",
"0.5637058",
"0.5592758",
"0.55914736",
"0.54783875",
"0.5461542",
"0.5460745",
"0.54571444",
"0.5448952",
"0.54251266",
"0.5403511",
"0.54010... | 0.0 | -1 |
send a numpy array with metadata | def send_array_and_str(socket, img, string="None", flags=0, copy=True, track=False):
md = dict(
dtype = str(img.dtype),
shape = img.shape,
)
socket.send_string(string, flags | zmq.SNDMORE)
socket.send_json(md, flags|zmq.SNDMORE)
return socket.send(img, flags, copy=copy, track=track) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_ndarray(sock, data):\n with io.BytesIO() as f:\n np.save(f, data)\n byte_data = f.getvalue()\n\n # Pack message length\n msg = struct.pack('>I', len(byte_data)) + byte_data\n sock.sendall(msg)",
"def send_array(self, A, flags=0, copy=True, track=False):\n md = dict(\n ... | [
"0.6501568",
"0.63785505",
"0.62580985",
"0.619946",
"0.6193633",
"0.6189601",
"0.61822146",
"0.6154474",
"0.6137267",
"0.6136131",
"0.6128146",
"0.61205447",
"0.61147827",
"0.6091603",
"0.60911626",
"0.6068108",
"0.60643",
"0.6058451",
"0.60514754",
"0.60438716",
"0.60382295... | 0.5562305 | 98 |
Method for reading a nordic file and parsing it to a string array while also checking the integrity of the file(Will give errors when lines are too long). It also wil parse empty space on the file if it is too short. | def readNordicFile(f):
nordics = []
emsg = "Nordic Read: The following line is too short: {0}\n{1}"
i = 0
nordics.append([])
for line in f:
if line.strip() == "" or line is None:
if len(nordics[i]) == 0:
continue
i += 1;
nordics.append([])... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readP(path, encoding='iso-8859-1', n=0):\n with open(path, encoding=encoding) as f:\n raw = [x.strip() for x in f if x]\n if n:\n raw = [x for x in raw if len(x) <= n]\n return raw",
"def read(read_file) -> list:\n result = []\n try:\n with open(read_file) as file:... | [
"0.60678965",
"0.6035607",
"0.6027674",
"0.6023274",
"0.598013",
"0.5867823",
"0.586583",
"0.58537155",
"0.58469784",
"0.5815171",
"0.5806846",
"0.58053046",
"0.5800402",
"0.5777913",
"0.5765686",
"0.57436544",
"0.57398486",
"0.57300663",
"0.57231325",
"0.57182133",
"0.568593... | 0.760024 | 0 |
Returns control gain matrices for any kite serial number. | def GetControllers(wing_serial):
if wing_serial == m.kWingSerial01:
airspeed_table = (
[30.0, 60.0, 90.0]
)
flap_offsets = (
[-0.209, -0.209, 0.0, 0.0, 0.009, 0.009, -0.005, 0.017]
)
longitudinal_gains_min_airspeed = (
[[0.005, 0.034, -0.716, -0.333, 0.311],
[-9.23... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_gains(self):\n return tuple([lib.is_SetHWGainFactor(self.hcam,0x8000+i,0)/100 for i in range(4)])",
"def get_mod_gain_table(self):\n return self.mod_gain_table",
"def get_mod_gain_pwrs(self):\n return self.mod_gain_table.keys()",
"def get_mod_gain_table_index(self):\n retu... | [
"0.5722042",
"0.5586053",
"0.55208635",
"0.5421132",
"0.5396984",
"0.5340974",
"0.530424",
"0.5249507",
"0.5223219",
"0.509237",
"0.5061447",
"0.5046168",
"0.50287783",
"0.5019659",
"0.5003898",
"0.5003317",
"0.49812856",
"0.49769893",
"0.49753842",
"0.49700093",
"0.49687988"... | 0.48384264 | 32 |
Returns the text in the example's document in the given span. | def get_text_span(example, span):
byte_positions = []
# `text` is a byte string since `document_plaintext` is also a byte string.
start = span["plaintext_start_byte"]
end = span["plaintext_end_byte"]
text = byte_slice(example["document_plaintext"], start, end)
for i in range(start, end):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_span_text(self, span: Span) -> str:\n return self._text[span.begin: span.end]",
"def read_text_from_span_id(html, span_id):\n return html.find('span', {'id': span_id}).text",
"def get_span_text(element, name):\n try:\n return (\n element.find_element_by_css_selector(name)... | [
"0.82889014",
"0.7150462",
"0.6169565",
"0.59940624",
"0.59328765",
"0.5892914",
"0.58556867",
"0.583677",
"0.5791252",
"0.5784249",
"0.57462",
"0.5718208",
"0.5718208",
"0.5718208",
"0.5718208",
"0.5718208",
"0.5707402",
"0.564645",
"0.56462574",
"0.5600071",
"0.5583564",
... | 0.77947813 | 1 |
Returns a text representation of the candidate at the given index. | def get_candidate_text(json_dict, idx):
# No candidate at this index.
if idx < 0 or idx >= len(json_dict["passage_answer_candidates"]):
raise ValueError("Invalid index for passage candidate: {}".format(idx))
return get_text_span(json_dict, json_dict["passage_answer_candidates"][idx]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetString(self, index):\n \n return self.choices[index].GetLabel()",
"def __getitem__(self, index):\n return str(self.cpf[index])",
"def _printFromIndex(self, index):\n ret = str(self.arr[index])\n iterator = index + 1\n while iterator != index:\n ret +=... | [
"0.6410651",
"0.6363077",
"0.6317435",
"0.6307074",
"0.61769325",
"0.6108662",
"0.61011386",
"0.6054563",
"0.6041569",
"0.5981198",
"0.58085775",
"0.576806",
"0.57276076",
"0.5684942",
"0.5660255",
"0.5657564",
"0.56432194",
"0.5622297",
"0.56197083",
"0.56084627",
"0.5603121... | 0.7112093 | 0 |
Yields the candidates that should not be skipped in an example. | def candidates_iter(json_dict):
for idx, cand in enumerate(json_dict["passage_answer_candidates"]):
yield idx, cand | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generator_without(container, exclude_these, case_sensitive=True, sort=False):\n if not case_sensitive:\n exclude_these = [e.lower() for e in exclude_these]\n for k in (container if not sort else sorted(container)):\n k_l = k if case_sensitive else k.lower()\n if k_l not in exclude_th... | [
"0.6261642",
"0.6188604",
"0.6001243",
"0.59939164",
"0.5880732",
"0.57954943",
"0.57022405",
"0.5697539",
"0.5616769",
"0.5575259",
"0.5568629",
"0.5540792",
"0.5498979",
"0.5467595",
"0.54579544",
"0.5437834",
"0.5433232",
"0.5380765",
"0.5374475",
"0.53457266",
"0.53452265... | 0.0 | -1 |
Converts a TyDi 'entry' from `create_entry_from_json` to `TyDiExample`. | def to_tydi_example(entry, is_training):
if is_training:
answer = make_tydi_answer(entry["contexts"], entry["answer"])
start_byte_offset = answer.offset
end_byte_offset = answer.offset + byte_len(answer.text)
else:
answer = None
start_byte_offset = None
end_byte_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_entry(entry):\n Entry.create(**entry)\n return entry",
"def from_dict(cls, dikt) -> \"Todo\":\n return util.deserialize_model(dikt, cls)",
"def example_json(example_json_file):\n return json.loads(example_json_file)",
"def _json_to_instance(self, json_dict: JsonDict) -> Instance:\n... | [
"0.58346426",
"0.5392077",
"0.5235372",
"0.5232819",
"0.52080363",
"0.51707566",
"0.5161617",
"0.5157883",
"0.5148814",
"0.51232684",
"0.51134694",
"0.5111602",
"0.5105579",
"0.51049596",
"0.5095554",
"0.50900686",
"0.5078553",
"0.5064115",
"0.5054117",
"0.50366974",
"0.50366... | 0.74971884 | 0 |
Representacion en cadena de la clase Parroquia. | def __unicode__(self):
return self.parroquia | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, nombre, cantidad, precio):\n\n # Atributos privados por convensión\n self._an = 15 # Ancho de columna nombre\n self._ac = 8 # Ancho de columna cantidad\n self._ap = 10 # Ancho de columna precio\n self._ast = 10 # Ancho de columna subtotal\n\n # Se in... | [
"0.62735045",
"0.6233848",
"0.62092257",
"0.6143086",
"0.5984596",
"0.59630907",
"0.59224004",
"0.58816874",
"0.58564144",
"0.5777072",
"0.5774827",
"0.5763267",
"0.57328683",
"0.5721564",
"0.5687393",
"0.5685108",
"0.5682803",
"0.5677757",
"0.56428087",
"0.5642351",
"0.56404... | 0.7457928 | 0 |
La Url de vista principal de administracion de Parroquia. | def get_absolute_url(self):
return ('listar_parroquia', [self.id, ]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_orion_admin_url(self, obj):\n return obj.orion_admin_url",
"def get_admin_url_to_add_run(self, request):\n base_url = reverse(\"admin:courses_courserun_add\")\n return f\"{base_url:s}?direct_course={self.id:d}\"",
"def getAdmin():",
"def url(self):\n return url_for('/admi... | [
"0.6939429",
"0.6376486",
"0.62612",
"0.6213472",
"0.6176542",
"0.61681587",
"0.6094484",
"0.6040345",
"0.5983753",
"0.5958032",
"0.59322804",
"0.5912582",
"0.584376",
"0.58436394",
"0.58342546",
"0.58238477",
"0.58238477",
"0.5822934",
"0.581415",
"0.5813586",
"0.58131236",
... | 0.66241646 | 1 |
Return True if this element is an instance of the given subclass. If a category string is specified, then both subclass and category matches are required. | def _isA(self, elementClass, category = ''):
if not isinstance(self, elementClass):
return False
if category and self.getCategory() != category:
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def subclassof(c, b):\n try:\n return issubclass(c, b)\n except TypeError:\n return False",
"def is_subclass(parent_class, child_class_name):\n for child_class in parent_class.__subclasses__():\n if child_class.__name__ == child_class_name:\n return True\n return False... | [
"0.6687288",
"0.6670129",
"0.66446286",
"0.6316864",
"0.62128353",
"0.610718",
"0.6080735",
"0.60664964",
"0.60402757",
"0.5992532",
"0.59696436",
"0.5941159",
"0.58874506",
"0.58874506",
"0.5886609",
"0.58573806",
"0.58487344",
"0.58478147",
"0.57916117",
"0.57669014",
"0.56... | 0.7002873 | 0 |
Return a list of all child elements that are instances of the given type. The returned list maintains the order in which children were added. | def _getChildrenOfType(self, elementClass):
method = getattr(self.__class__, "_getChildrenOfType" + elementClass.__name__)
return method(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def descendants(self, type: str):\n return list(descendants(self.type_tree, type))",
"def get_element_by_tag_type(self, tag_type):\n elts = []\n if self is None:\n return elts\n if self.tag_type == tag_type:\n elts.append(self)\n\n for child in self.childr... | [
"0.7072542",
"0.6878987",
"0.6743088",
"0.6691061",
"0.6669156",
"0.6660059",
"0.66244364",
"0.6470436",
"0.64007956",
"0.6340226",
"0.62596714",
"0.6195003",
"0.61464643",
"0.6143393",
"0.6124976",
"0.60636926",
"0.6059035",
"0.6055325",
"0.603803",
"0.6019386",
"0.60062987"... | 0.61959624 | 11 |
Return the default value for this element. | def _getDefaultValue(self):
value = self._getDefaultValue()
return value.getData() if value else None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default_value(self):\n return self._default_value",
"def default_value(self):\n return self._default_value",
"def default(self):\n\n return self._get_field(\"value\")",
"def get_default_value(self):\n pass",
"def default(self):\r\n return self.default_value()",
"def... | [
"0.8670316",
"0.8670316",
"0.86002696",
"0.85086143",
"0.8489805",
"0.84298074",
"0.8295959",
"0.8295959",
"0.8270742",
"0.81889516",
"0.81854266",
"0.8182883",
"0.81404835",
"0.8121254",
"0.8100968",
"0.8100968",
"0.8015085",
"0.80053425",
"0.79454553",
"0.7945101",
"0.79402... | 0.80125666 | 17 |
Set the typed value of an input by its name, creating a child element to hold the input if needed. | def _setInputValue(self, name, value, typeString = ''):
method = getattr(self.__class__, "_setInputValue" + getTypeString(value))
return method(self, name, value, typeString) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_field(self, name, value):\n locator = self._get_input_field_locator(name)\n self._populate_field(locator, value)",
"def insert_by_name(name, val):\n try:\n name = driver.find_element_by_name(name)\n except Exception as e:\n raise(e)\n else:\n name.send_key... | [
"0.6696381",
"0.60982335",
"0.6062318",
"0.58775914",
"0.58670473",
"0.5854426",
"0.5842993",
"0.583215",
"0.57965046",
"0.57965046",
"0.5707237",
"0.56931925",
"0.5665919",
"0.56397855",
"0.56089824",
"0.55845267",
"0.5571821",
"0.5554291",
"0.552724",
"0.5509192",
"0.549337... | 0.64479256 | 1 |
Return the typed value of an input by its name, taking both the calling element and its declaration into account. If the given input is not found, then None is returned. | def _getInputValue(self, name, target = ''):
value = self._getInputValue(name, target)
return value.getData() if value else None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_input_by_name(self, name):\n for var in self.inputs:\n if var.get_object().name == name:\n return var\n return None",
"def get_input(self, name):\n return self._inputs.get(name)",
"def get_input_by_name(self, input_name):\n\n input_found = None\n\n ... | [
"0.73832405",
"0.6656208",
"0.6541893",
"0.64940184",
"0.6340151",
"0.63254017",
"0.6196558",
"0.61610734",
"0.6141107",
"0.61052793",
"0.60158974",
"0.60151154",
"0.5872866",
"0.5827187",
"0.5776721",
"0.57734174",
"0.56983644",
"0.5693152",
"0.56678504",
"0.5639554",
"0.562... | 0.66325825 | 2 |
(Deprecated) Add a Parameter to this interface. | def _addParameter(self, name):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2)
return self.addInput(name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_param(self, param):\n self.params.append(param)\n return self",
"def add_param(self, param):\n self._params.append(param)\n self.add_decompostion(param)",
"def add_parameter(self, parameter):\n self._pkey += 1\n self.parameters[self._pkey] = parameter",
"def ... | [
"0.7392754",
"0.7226067",
"0.71649903",
"0.7134397",
"0.7021938",
"0.6958663",
"0.69568723",
"0.69460773",
"0.6929168",
"0.68761325",
"0.6839852",
"0.6826685",
"0.6788067",
"0.6752735",
"0.67438495",
"0.67065644",
"0.6700492",
"0.6696636",
"0.6666486",
"0.6627517",
"0.6573087... | 0.7026675 | 4 |
(Deprecated) Return a vector of all Parameter elements. | def _getParameters(self):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2)
return list() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parameters_to_vector(self) -> np.ndarray:\n return nn.utils.parameters_to_vector(self.parameters()).detach().cpu().numpy()",
"def parameters_list(self):\n return [getattr(self.parameters, p) for p in self.parameters_names()]",
"def parameters(self) -> List[Parameter]:\n return self._pa... | [
"0.70164126",
"0.69106805",
"0.6909647",
"0.6856145",
"0.68113655",
"0.68113655",
"0.67872435",
"0.6787241",
"0.6785588",
"0.6761088",
"0.67584455",
"0.6725566",
"0.67151064",
"0.66764146",
"0.65993285",
"0.6595009",
"0.6587269",
"0.6568346",
"0.6563564",
"0.65622544",
"0.651... | 0.697155 | 1 |
(Deprecated) Return a vector of all parameters belonging to this interface, taking inheritance into account. | def _getActiveParameters(self):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2)
return list() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __parameters__(self) -> tuple[TypeVar, ...]:\n return super().__getattribute__(\"_parameters\")",
"def parameters(self):\n return [p for _, a in vars(self).items() for p in self._params(a)]",
"def _get_derived_parameters(self) -> DerivedParameterCollection:\n return DerivedParameterCol... | [
"0.7319263",
"0.7154781",
"0.69999355",
"0.69999355",
"0.6981402",
"0.687172",
"0.6772755",
"0.67693895",
"0.67525303",
"0.6681054",
"0.66798896",
"0.666111",
"0.6658778",
"0.6656797",
"0.66556805",
"0.6591198",
"0.65785074",
"0.65544194",
"0.6540731",
"0.65373325",
"0.652525... | 0.63398415 | 34 |
(Deprecated) Set the typed value of a parameter by its name. | def _setParameterValue(self, name, value, typeString = ''):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setParameter(self, name, value):",
"def set_parameter(self, params, name, val):\n raise NotImplementedError()",
"def set(self, name, value=None):\n if isinstance(name, dict):\n for key, value in name.items():\n try:\n param, value = self.coercePara... | [
"0.78295267",
"0.7444436",
"0.7371338",
"0.72122747",
"0.7093258",
"0.7008957",
"0.6888976",
"0.681474",
"0.67791194",
"0.67542565",
"0.6716448",
"0.65687007",
"0.65447736",
"0.6534443",
"0.65175855",
"0.65175855",
"0.65048856",
"0.6504462",
"0.6500714",
"0.64990366",
"0.6487... | 0.71333575 | 4 |
(Deprecated) Return the typed value of a parameter by its name. | def _getParameterValue(self, name, target = ''):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parameter_type(self, name):\n raise NotImplementedError()",
"def getParam(self, params, name):\n return params.get(name)",
"def _name_to_variable(self, name: str) -> Parameter:\n return cast(Parameter, super()._name_to_variable(name))",
"def get_param_with_name(self, param_name):... | [
"0.70842284",
"0.6901505",
"0.66810405",
"0.6517848",
"0.6480968",
"0.6465439",
"0.6429352",
"0.63265526",
"0.6292917",
"0.6171034",
"0.61690265",
"0.6167457",
"0.6165231",
"0.61486405",
"0.6117995",
"0.60688585",
"0.6037239",
"0.60348684",
"0.59447",
"0.593131",
"0.5890477",... | 0.57739407 | 25 |
(Deprecated) Return the value string of a parameter by its name. | def _getParameterValueString(self, name):
warnings.warn("This function is deprecated; parameters have been replaced with uniform inputs in 1.38.", DeprecationWarning, stacklevel = 2)
return "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getParam(self, params, name):\n return params.get(name)",
"def get_param_with_name(self, param_name):\n return self.params[param_name]",
"def getParameter(self, name):",
"def getSSMParam(name):\n return ssm_client.get_parameter(\n Name=name,\n WithDecryption... | [
"0.70329046",
"0.687252",
"0.6827756",
"0.65972966",
"0.65388006",
"0.6500877",
"0.64673096",
"0.6371999",
"0.6315599",
"0.6314213",
"0.62858284",
"0.6270459",
"0.62613076",
"0.6259734",
"0.62386006",
"0.61926997",
"0.6128117",
"0.6123776",
"0.6123158",
"0.6096087",
"0.606265... | 0.7922667 | 0 |
(Deprecated) Add a BindInput to this shader reference. | def _addBindInput(self, name, type = DEFAULT_TYPE_STRING):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return self.addInput(name, type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _addBindParam(self, name, type = DEFAULT_TYPE_STRING):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return self.addInput(name, type)",
"def _getBindInputs(self):\n warnings.warn(\"This function... | [
"0.7632342",
"0.65786767",
"0.573658",
"0.5379098",
"0.5296212",
"0.5178297",
"0.5159998",
"0.5151385",
"0.5066717",
"0.49228904",
"0.4916024",
"0.48976418",
"0.48940352",
"0.48545158",
"0.47804457",
"0.47647017",
"0.4753692",
"0.47506797",
"0.4747506",
"0.47449464",
"0.47333... | 0.84288436 | 0 |
(Deprecated) Return a vector of all BindInput elements in this shader reference. | def _getBindInputs(self):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return self.getInputs() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getBindParams(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return list()",
"def _getBindTokens(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced... | [
"0.7248788",
"0.68201584",
"0.61971897",
"0.6057106",
"0.6041571",
"0.60374856",
"0.60374856",
"0.60374856",
"0.6018457",
"0.59155166",
"0.5823089",
"0.57687944",
"0.5762386",
"0.5726699",
"0.5726699",
"0.5726699",
"0.5711171",
"0.5709276",
"0.5706087",
"0.5700484",
"0.568270... | 0.83538413 | 0 |
(Deprecated) Add a BindParam to this shader reference. | def _addBindParam(self, name, type = DEFAULT_TYPE_STRING):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return self.addInput(name, type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _addBindInput(self, name, type = DEFAULT_TYPE_STRING):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return self.addInput(name, type)",
"def _getBindParams(self):\n warnings.warn(\"This function... | [
"0.7179398",
"0.6434004",
"0.60884535",
"0.6070172",
"0.59735316",
"0.5896598",
"0.5733029",
"0.5710626",
"0.5708676",
"0.5646252",
"0.5533429",
"0.55079865",
"0.5417713",
"0.53833014",
"0.5378985",
"0.5373941",
"0.5350034",
"0.5324678",
"0.5298662",
"0.52942353",
"0.52673304... | 0.8272173 | 0 |
(Deprecated) Return a vector of all BindParam elements in this shader reference. | def _getBindParams(self):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return list() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getBindInputs(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return self.getInputs()",
"def _getBindTokens(self):\n warnings.warn(\"This function is deprecated; shader references have bee... | [
"0.73175657",
"0.697158",
"0.6510387",
"0.6437608",
"0.6387305",
"0.63770574",
"0.63090634",
"0.62420833",
"0.6186843",
"0.61844945",
"0.61839473",
"0.6061543",
"0.60109484",
"0.59970754",
"0.59630865",
"0.5951487",
"0.59331584",
"0.5922793",
"0.58891183",
"0.5887415",
"0.586... | 0.8280635 | 0 |
(Deprecated) Return a vector of all BindToken elements in this shader reference. | def _getBindTokens(self):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return list() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getBindParams(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return list()",
"def _getBindInputs(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced... | [
"0.7346781",
"0.71642834",
"0.6264189",
"0.6206269",
"0.5697967",
"0.5595624",
"0.55848074",
"0.5566121",
"0.5566121",
"0.5566121",
"0.55017257",
"0.54983807",
"0.54983807",
"0.5487443",
"0.5385787",
"0.53534365",
"0.53367513",
"0.5262191",
"0.52297187",
"0.5215819",
"0.52116... | 0.8395629 | 0 |
(Deprecated) Return a vector of all shader references in this material element. | def _getShaderRefs(self):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return getShaderNodes(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getActiveShaderRefs(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return getShaderNodes(self)",
"def shaders(self):\n\n shaders = []\n shaders.extend(self._verts)\n sha... | [
"0.7740527",
"0.6775367",
"0.6466401",
"0.6452211",
"0.6437122",
"0.6314355",
"0.62679857",
"0.62556607",
"0.6210403",
"0.6105485",
"0.596709",
"0.5940758",
"0.59146404",
"0.5878404",
"0.5871508",
"0.5811813",
"0.5789575",
"0.57884926",
"0.5763394",
"0.57063866",
"0.566444",
... | 0.7934999 | 0 |
(Deprecated) Return a vector of all shader references in this material element, taking material inheritance into account. | def _getActiveShaderRefs(self):
warnings.warn("This function is deprecated; shader references have been replaced with shader nodes in 1.38.", DeprecationWarning, stacklevel = 2)
return getShaderNodes(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getShaderRefs(self):\n warnings.warn(\"This function is deprecated; shader references have been replaced with shader nodes in 1.38.\", DeprecationWarning, stacklevel = 2)\n return getShaderNodes(self)",
"def shaders(self):\n\n shaders = []\n shaders.extend(self._verts)\n shaders.e... | [
"0.757915",
"0.66297746",
"0.62730986",
"0.61996865",
"0.61774623",
"0.6176948",
"0.60849196",
"0.60422945",
"0.5975865",
"0.5874039",
"0.5874017",
"0.5842542",
"0.58101964",
"0.57584727",
"0.57190543",
"0.56604165",
"0.56526685",
"0.5576169",
"0.5570131",
"0.55684435",
"0.55... | 0.7411408 | 1 |
Set the typed value of a property by its name, creating a child element to hold the property if needed. | def _setPropertyValue(self, name, value, typeString = ''):
method = getattr(self.__class__, "_setPropertyValue" + getTypeString(value))
return method(self, name, value, typeString) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_property(self, name, value):\n self.properties[name] = value",
"def put_prop(self, obj_type, obj_id, prop_name, value):\n ierr = exolib.py_expp(self.exoid, obj_type, obj_id, prop_name, value)\n if ierr:\n raise ExodusIIWriterError(\"Error putting prop value\")",
"def set(nod... | [
"0.721912",
"0.6700466",
"0.66331834",
"0.66138595",
"0.66018426",
"0.6507755",
"0.64446765",
"0.6388091",
"0.6368398",
"0.6332222",
"0.6309362",
"0.6245748",
"0.6242877",
"0.6225162",
"0.6214769",
"0.62093395",
"0.62029123",
"0.61671",
"0.61594003",
"0.61556596",
"0.61158276... | 0.63522094 | 9 |
Return the typed value of a property by its name. If the given property is not found, then None is returned. | def _getPropertyValue(self, name, target = ''):
value = self._getPropertyValue(name)
return value.getData() if value else None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def for_property(self, name):\n return self[self.types_map.get(name, 'text')]",
"def get_property(self, name):\n if (not name in self.properties):\n raise KeyError(\"Key '\" + name + \"' not found\")\n return self.properties[name]",
"def get_dynamic_property(vim, mobj, type, property_name):\n... | [
"0.7251514",
"0.6947998",
"0.69029963",
"0.68013686",
"0.66939723",
"0.66660714",
"0.6600007",
"0.649654",
"0.64916193",
"0.6452159",
"0.6415687",
"0.6392696",
"0.6375681",
"0.6374791",
"0.6312345",
"0.6297999",
"0.62391794",
"0.62202114",
"0.62113297",
"0.62047046",
"0.61996... | 0.57498485 | 46 |
Set the value of a geomprop by its name, creating a child element to hold the geomprop if needed. | def _setGeomPropValue(self, name, value, typeString = ''):
method = getattr(self.__class__, "_setGeomPropValue" + getTypeString(value))
return method(self, name, value, typeString) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_simple(parent, name, value):\n element = parent.find('./' + name) \n\n if element is None:\n element = ET.SubElement(parent, name)\n element.text = value\n else:\n element.text = value",
"def add_geomean_to_product_data(product, prop_name, geomean_val):\n\tfor prop_data_list in product['da... | [
"0.56408846",
"0.53583366",
"0.5214419",
"0.5200524",
"0.5169286",
"0.5159845",
"0.5102088",
"0.50392735",
"0.50278354",
"0.5018636",
"0.49757773",
"0.49757773",
"0.49658227",
"0.49325004",
"0.48699766",
"0.4865401",
"0.48650134",
"0.4847803",
"0.48443377",
"0.48255238",
"0.4... | 0.5464608 | 1 |
(Deprecated) Add a material element to the document. | def _addMaterial(self, name):
warnings.warn("This function is deprecated; call Document.addMaterialNode() instead.", DeprecationWarning, stacklevel = 2)
return self.addMaterialNode(name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AddMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_AddMaterial(self, *args)",
"def append_material(self, material):\n # First check if asset attribute exists; if not, define the asset attribute\n if not hasattr(self, \"asset\"):\n self.asset = ET.Element(\"asset\... | [
"0.7026196",
"0.6792848",
"0.667132",
"0.59274834",
"0.5922771",
"0.5812614",
"0.57446635",
"0.5629134",
"0.5546216",
"0.55392534",
"0.5464084",
"0.5419899",
"0.53916496",
"0.53648096",
"0.53525424",
"0.53359",
"0.53133327",
"0.5275986",
"0.51976895",
"0.51882774",
"0.5178728... | 0.8210709 | 0 |
(Deprecated) Return a vector of all materials in the document. | def _getMaterials(self):
warnings.warn("This function is deprecated; call Document.getMaterialNodes() instead.", DeprecationWarning, stacklevel = 2)
return self.getMaterialNodes() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info_materials_polymer_get():\n materials = _material_by_group(974) # 974 == intermediate group\n return materials, 200",
"def info_materials_get():\n materials = _material_by_group() # empty means all groups\n return materials, 200",
"def info_materials_raw_get():\n materials = _material_by... | [
"0.72720134",
"0.7060891",
"0.67700535",
"0.6652309",
"0.6613427",
"0.64064366",
"0.638908",
"0.63752085",
"0.63551617",
"0.633049",
"0.62788475",
"0.6175576",
"0.6156727",
"0.6118103",
"0.61163384",
"0.601052",
"0.59336454",
"0.57874507",
"0.5771847",
"0.57317346",
"0.571983... | 0.7945748 | 0 |
Return the default data search path. | def getDefaultDataSearchPath():
return FileSearchPath(os.path.dirname(__file__)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_default_path(self):\n return os.path.join(cfg.DATA_DIR, 'vehicles_dataset_v{}'.format(self._version))",
"def default_data_dir(self):\n return self._default_data_dir",
"def _get_default_path(self):\n return os.path.join(cfg.DATA_DIR, 'visual_genome')",
"def _get_default_path(self):\n... | [
"0.8129479",
"0.8007878",
"0.79695016",
"0.79296505",
"0.7858564",
"0.7470226",
"0.743667",
"0.7417918",
"0.74098426",
"0.74098426",
"0.7362305",
"0.7316143",
"0.72809714",
"0.72342837",
"0.72125745",
"0.7184736",
"0.7136621",
"0.70106024",
"0.69430655",
"0.6912006",
"0.68595... | 0.8906264 | 0 |
Return list of default data library folders | def getDefaultDataLibraryFolders():
return [ 'libraries' ] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_default_paths():\n DATA_ROOT = os.environ.get(\"DATA_ROOT\", \"data\")\n defaults = {\n \"TOKENIZE_DATA_DIR\": DATA_ROOT + \"/tokenize\",\n \"MWT_DATA_DIR\": DATA_ROOT + \"/mwt\",\n \"LEMMA_DATA_DIR\": DATA_ROOT + \"/lemma\",\n \"POS_DATA_DIR\": DATA_ROOT + \"/pos\",\n ... | [
"0.717137",
"0.70716625",
"0.6955009",
"0.6915999",
"0.68801606",
"0.68446493",
"0.6835322",
"0.6751219",
"0.66922414",
"0.6663725",
"0.66411805",
"0.66054195",
"0.6547148",
"0.65449494",
"0.65448755",
"0.6523286",
"0.649509",
"0.64830816",
"0.6471197",
"0.6460034",
"0.644567... | 0.89339906 | 0 |
Convert bbox representation from `camera_view_bounds_2d` to a more standard PILfriendly representation. | def convert_bbox(bbox, width, height):
min_x, min_y, max_x, max_y = bbox
# scale X axis
min_x *= width
max_x *= width
# invert Y axis and scale
min_y = (1 - min_y) * height
max_y = (1 - max_y) * height
return min_x, min_y, max_x, max_y | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _decode_bbox(self, normalized_bbox):\n #apply the inverse of transformation\n y1,x1,y2,x2 = preprocess.apply_transformation(normalized_bbox,\n np.linalg.inv(self.transformation))\n\n w,h = self.image_size\n y1,x1,y2,x2 = y1*h,x1*w,y2*h,x2*w\n return vot.Rectang... | [
"0.67457765",
"0.66060835",
"0.66052955",
"0.6402712",
"0.62045026",
"0.6073645",
"0.5911082",
"0.5882305",
"0.58592385",
"0.5842651",
"0.58156204",
"0.5749467",
"0.57198113",
"0.57176834",
"0.56508476",
"0.56418383",
"0.5637036",
"0.5637036",
"0.56326663",
"0.5607498",
"0.56... | 0.6208672 | 4 |
Get a list of objects labeled as potential referents in the current 3D scene. Referents are live candidates or distractors (e.g. walls). | def get_referents(data):
fakes_group = data.groups.get("Fakes")
return [obj for obj in data.groups["Referents"].objects
if not obj.hide_render or fakes_group in obj.users_group] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_refobjs(self, ):\n return cmds.ls(type=\"jb_reftrack\")",
"def references_list( self, theWeaver ):\n return [ (c.name, c.seq) \n for c in theWeaver.reference_style.chunkReferencedBy( self ) ]",
"def find_references(self):\n cls = self.__class__\n nodes = []\n ... | [
"0.65498",
"0.6435734",
"0.64024466",
"0.63173234",
"0.6215065",
"0.6130572",
"0.612474",
"0.60120547",
"0.5975466",
"0.58793086",
"0.5838874",
"0.5825015",
"0.580505",
"0.5789633",
"0.5718369",
"0.5663243",
"0.56405354",
"0.56184405",
"0.55993235",
"0.559522",
"0.55532086",
... | 0.6368696 | 3 |
Get a list of objects labeled as candidates in the current 3D scene. | def get_candidates(data):
return data.groups["Candidates"].objects | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_labels_full(scene: \"Scenemaker\") -> List[Tuple[int, np.ndarray]]:\r\n objects = utils.select_collection(scene.target_collection)\r\n boxes_list = []\r\n\r\n for obj in objects:\r\n objclass = obj.name.split(\".\")[0]\r\n dim = obj.dimensions\r\n r... | [
"0.63349587",
"0.6314547",
"0.6190732",
"0.6160445",
"0.613559",
"0.599086",
"0.5865622",
"0.5751449",
"0.5702605",
"0.5679443",
"0.56694674",
"0.5667982",
"0.5636427",
"0.5611984",
"0.56053656",
"0.55495095",
"0.55490893",
"0.55352527",
"0.5530202",
"0.5515282",
"0.5503529",... | 0.62785876 | 2 |
Get a list of objects labeled as frame guides in the current 3D scene. | def get_guides(data):
return data.groups["Frames"].objects | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getBrailleRegionsForAnimation(self, obj):\n\n self._debugGenerator(\"_getBrailleRegionsForAnimation\", obj)\n\n text = \"\"\n text = self._script.appendString(\n text, self._script.getDisplayedLabel(obj))\n text = self._script.appendString(\n text, self._scrip... | [
"0.56232345",
"0.5550209",
"0.5408272",
"0.5292358",
"0.52791625",
"0.5228657",
"0.5212502",
"0.5169032",
"0.5049105",
"0.50468457",
"0.5031301",
"0.50303346",
"0.49700224",
"0.49568957",
"0.49561343",
"0.49337798",
"0.4922755",
"0.49168405",
"0.48798805",
"0.48694423",
"0.48... | 0.639492 | 0 |
Get the reference frame type corresponding to a particular guide. | def get_guide_type(guide):
# Maintained by naming convention in the Blender files. Sub-optimal.
try:
return guide.name[guide.name.rindex(".") + 1:]
except:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getComponentType(cls):\n\n return 'Guide'",
"def getComponentType(cls):\n\n return 'Guide'",
"def get_typ(self, refobj):\n enum = cmds.getAttr(\"%s.type\" % refobj)\n try:\n return JB_ReftrackNode.types[enum]\n except IndexError:\n raise ValueError(\... | [
"0.5693369",
"0.5693369",
"0.5446423",
"0.5337169",
"0.53119755",
"0.529872",
"0.52709115",
"0.5264755",
"0.5190898",
"0.5159653",
"0.51567864",
"0.5133397",
"0.50048554",
"0.49838355",
"0.49712083",
"0.49645618",
"0.48794225",
"0.48541024",
"0.48476246",
"0.4845618",
"0.4842... | 0.7534499 | 0 |
Randomize the position of an object `obj` along some linear guide path `guide`. | def randomize_position(obj, guide):
p1, p2 = get_guide_endpoints(guide)
t = random.random()
target_point = p1 + t * (p2 - p1)
# update X and Y coordinates.
obj.location[0] = target_point[0]
obj.location[1] = target_point[1]
return t | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randomize_distance(obj, guide, scale_bounds=(-2, 0)):\n p1, p2 = get_guide_endpoints(guide)\n midpoint = p1 / 2 + p2 / 2\n\n # Get vector perpendicular to the guide.\n diff_rot = Matrix.Rotation(math.pi / 2, 3, 'Z') * (p2 - p1)\n\n scale_factor = scale_bounds[0] + random.random() * (scale_bounds... | [
"0.72296953",
"0.55692446",
"0.5526078",
"0.5247486",
"0.52031153",
"0.51109755",
"0.509799",
"0.50979686",
"0.5030604",
"0.49457812",
"0.49372828",
"0.49313664",
"0.49087912",
"0.48839825",
"0.48837227",
"0.48356238",
"0.4801894",
"0.47983634",
"0.47845507",
"0.47785735",
"0... | 0.82932264 | 0 |
Center the position of an object `obj` along a linear guide path `guide`, and randomize its distance on the axis perpendicular to that guide. | def randomize_distance(obj, guide, scale_bounds=(-2, 0)):
p1, p2 = get_guide_endpoints(guide)
midpoint = p1 / 2 + p2 / 2
# Get vector perpendicular to the guide.
diff_rot = Matrix.Rotation(math.pi / 2, 3, 'Z') * (p2 - p1)
scale_factor = scale_bounds[0] + random.random() * (scale_bounds[1] - scale_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def randomize_position(obj, guide):\n p1, p2 = get_guide_endpoints(guide)\n t = random.random()\n target_point = p1 + t * (p2 - p1)\n\n # update X and Y coordinates.\n obj.location[0] = target_point[0]\n obj.location[1] = target_point[1]\n\n return t",
"def center(self, obj):\n mn0 = ... | [
"0.7362369",
"0.5656115",
"0.5133738",
"0.4947574",
"0.49140757",
"0.4901315",
"0.4811237",
"0.46052918",
"0.4575708",
"0.45413876",
"0.4496817",
"0.44632584",
"0.4445101",
"0.44152486",
"0.44121188",
"0.44050354",
"0.43854836",
"0.43493664",
"0.43398598",
"0.43357167",
"0.43... | 0.75807685 | 0 |
Move candidate referents to random positions in the given reference frames. `candidate_setting` is of the form `[(person, guide_path), (person2, guide_path), ...]` | def prepare_scene(data, candidate_setting, randomization_mode):
manipulations = defaultdict(dict)
for person, guide in candidate_setting.items():
if randomization_mode == "none":
# Center the candidate along the guide.
p1, p2 = get_guide_endpoints(guide)
target = p1 /... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def switch_points(mutated_genome,index):\n point_index1 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n point_index2 = random.randint(0,max(0,len(mutated_genome[index][2])-1))\n temp = mutated_genome[index][2][point_index1]\n mutated_genome[index][2][point_index1] = mutated_genome[index][2][po... | [
"0.6009613",
"0.53904885",
"0.5373188",
"0.5336502",
"0.52867323",
"0.52322876",
"0.51420814",
"0.51346767",
"0.50901896",
"0.5086578",
"0.49775714",
"0.49687153",
"0.4933418",
"0.4913252",
"0.48939452",
"0.48697948",
"0.48356456",
"0.4826306",
"0.4824639",
"0.48112273",
"0.4... | 0.5655333 | 1 |
Allows leastsq to take bounds if minimize function is missing. | def myleastsq(errfunc0,x0,args=None,bounds=None,**exkw):
from scipy import optimize
if hasattr(optimize,'minimize'):
def errfunc(x,*iargs):
return sum(errfunc0(x,*iargs)**2)
if args is not None: exkw['args'] = args
res = optimize.minimize(errfunc,x0[:],bounds=bounds,**exkw)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def leastsqbound(func,x0,bounds,args=(),**kw):\n # check for full output\n if \"full_output\" in kw and kw[\"full_output\"]:\n full=True\n else:\n full=False\n\n # convert x0 to internal variables\n i0 = external2internal(x0,bounds)\n\n # perfrom unconstrained optimization using int... | [
"0.7573677",
"0.6523712",
"0.6448234",
"0.6322002",
"0.6261192",
"0.625652",
"0.6225058",
"0.62234515",
"0.61956996",
"0.61218625",
"0.6083507",
"0.6068286",
"0.6016386",
"0.60070544",
"0.5992043",
"0.5962499",
"0.5946215",
"0.5920246",
"0.58964807",
"0.58740616",
"0.5871715"... | 0.71415097 | 1 |
Calculates radius from position. | def pos2rad(pos):
return np.sqrt(pos[:,0]**2 + pos[:,1]**2 + pos[:,2]**2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def radius(self):\n if self._radius is None:\n translated_xyz = translate_to_center_of_mass(self.get_xyz())\n _, symbols, x, y, z = get_xyz_matrix(translated_xyz)\n border_elements = list() # a list of the farthest element/s\n r = 0\n for si, xi, yi, z... | [
"0.70792216",
"0.69666016",
"0.69520116",
"0.69332325",
"0.6903839",
"0.6901474",
"0.6901474",
"0.6901474",
"0.6901474",
"0.6901474",
"0.6894386",
"0.6791088",
"0.67536217",
"0.670865",
"0.67043376",
"0.67016387",
"0.66416734",
"0.66090345",
"0.66090345",
"0.6583494",
"0.6566... | 0.63723207 | 26 |
Returns spherical volume for provided radii. | def sphvol(r):
return (4./3.)*np.pi*(r**3.) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sphere_volume(radius : number) -> number:\n volume = 4/3*(pi*radius*radius*radius)\n return volume",
"def sphere_volume(r):\n return (4/3) * 3.14159 * r**3",
"def sphere_volume(r):\n\treturn 4/3. * math.pi * r ** 3",
"def sphereVolume(radius):\n volume = (4 / 3) * math.pi * radius ** 3\n r... | [
"0.8135021",
"0.8046071",
"0.79767275",
"0.79615855",
"0.791515",
"0.7734994",
"0.74793535",
"0.70156354",
"0.70105755",
"0.68960035",
"0.6703716",
"0.6640072",
"0.65109974",
"0.6499378",
"0.64409107",
"0.6390947",
"0.6348823",
"0.63122714",
"0.63122714",
"0.6301664",
"0.6257... | 0.74382997 | 7 |
Returns spherical radii for provided volumes. | def sphrad(vol):
return (3.*vol/(4.*np.pi))**(1./3.) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sphere_volume(r):\n return (4/3) * 3.14159 * r**3",
"def sphere_volume(r):\n\treturn 4/3. * math.pi * r ** 3",
"def sphere_volume(radius : number) -> number:\n volume = 4/3*(pi*radius*radius*radius)\n return volume",
"def sphere_volume(sphere_radius):\n return (4 / 3 * np.pi * sphere_radius**... | [
"0.70751446",
"0.69737434",
"0.69169736",
"0.6792967",
"0.6701074",
"0.66413015",
"0.6604082",
"0.6572043",
"0.63632387",
"0.62836486",
"0.62836486",
"0.62725145",
"0.613928",
"0.6092999",
"0.6058674",
"0.5973587",
"0.5946293",
"0.59038186",
"0.5898153",
"0.5847563",
"0.58467... | 0.71128243 | 0 |
Returns the circular velocity. | def calc_vcirc(r,menc,G=1.):
if G is None: G = 1.
return np.sqrt(G*menc/r) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_velocity(self):\n return self.momentum/self.mass",
"def get_start_velocity(self):\n # uniform circular motion have a start velocity of omega\n # TODO generate from start position and rotation direction\n return np.array([0, self.wz, 0])",
"def circumference(self):\n r... | [
"0.7257846",
"0.7252623",
"0.69760185",
"0.6974948",
"0.6948337",
"0.6904478",
"0.68118125",
"0.6809711",
"0.679329",
"0.679329",
"0.67710674",
"0.6732156",
"0.6705026",
"0.66750187",
"0.65806377",
"0.64651424",
"0.64618456",
"0.64478517",
"0.64435196",
"0.6440419",
"0.640747... | 0.0 | -1 |
Calculates the total mass enclosed by each particle. | def calc_menc(m,sortby=None):
# Sort and sum if sortby array provided
if sortby is not None:
idxsort = np.argsort(sortby)
idxsort_rev = np.argsort(idxsort)
menc = np.cumsum(m[idxsort])[idxsort_rev]
# Otherwise just sum
else:
menc = np.cumsum(m)
# Return
return men... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def totalmass_comvelocity(particle_list):\r\n total_momentum = sum([particle.linear_momentum()\r\n for particle in particle_list])\r\n total_mass = sum([particle.mass for particle in particle_list])\r\n\r\n return total_mass, total_momentum / total_mass",
"def ad... | [
"0.75831574",
"0.73967713",
"0.7380214",
"0.71559113",
"0.71221864",
"0.7067972",
"0.7059113",
"0.7050664",
"0.70366937",
"0.7007502",
"0.6986225",
"0.6907047",
"0.6829601",
"0.6829601",
"0.67537653",
"0.6751702",
"0.670181",
"0.6688298",
"0.66798764",
"0.66797316",
"0.658996... | 0.0 | -1 |
Returns the density enclosed within rmax. | def calc_rhoenc(mass,r,rmax):
idx = (r<rmax)
return mass[idx].sum()/sphvol(rmax) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_max_density(self):\n max_density = str(self.density.index(min(self.density)) + 1)\n print(max_density)\n return max_density",
"def get_rmax(self):\n return self.rmax",
"def _get_max_rupture_projection_radius(self):\n if self.max_radius: # already computed\n ... | [
"0.7466451",
"0.68567127",
"0.6770987",
"0.64775264",
"0.64172965",
"0.63328725",
"0.6259476",
"0.6152548",
"0.6098544",
"0.606421",
"0.6053934",
"0.60529995",
"0.5983414",
"0.5938681",
"0.5918545",
"0.5912345",
"0.589474",
"0.5892661",
"0.5886185",
"0.5880672",
"0.58720237",... | 0.61871815 | 7 |
Calculate particle radii from volume information. Volumes are sorted then cummulatively summed to get enclosed volume at each particle. Radii are then calculated by assumming that each enclosed volume is a sphere. Returned radii are in the same particle order as the input volumes. | def vol2rad(vol,sortby=False,outsort=False,weightby=False,weight=0.5):
# Sort
if isinstance(sortby,np.ndarray):
if len(sortby)!=len(vol):
raise Exception('The sortby array (len={}) must have the '.format(len(sortby))+
'same length as the volume array (len={}).'.fo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sphere_volume(r):\n return (4/3) * 3.14159 * r**3",
"def sphere_volume(r):\n\treturn 4/3. * math.pi * r ** 3",
"def sphere_volume(radius : number) -> number:\n volume = 4/3*(pi*radius*radius*radius)\n return volume",
"def get_radii(self) -> np.ndarray:\n return np.array([self._radii[p] fo... | [
"0.68379015",
"0.680143",
"0.6665896",
"0.659714",
"0.65940964",
"0.64038986",
"0.63235945",
"0.6316446",
"0.6308377",
"0.6302009",
"0.61612254",
"0.60437113",
"0.6036599",
"0.60151094",
"0.58739436",
"0.5810771",
"0.5796187",
"0.5743727",
"0.57129395",
"0.5707252",
"0.568340... | 0.52332944 | 55 |
Squeezes 3D positions along y and z axes while preserving volume. | def squeeze_constvol(pos,yfact=-1,zfact=-1):
squfact = 1.0
if yfact > 0:
squfact*=yfact
pos[:,1]*=yfact
if zfact > 0:
squfact*=zfact
pos[:,2]*=zfact
pos*=(squfact**(-1./3.))
return pos | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def planes_3d(self, quantity, xplane, yplane):\n xplane = int(xplane)\n yplane = int(yplane)\n # Get the scalar values\n # Get the data on the plane with a fixed x value. These means we'll\n # have changing (y, z) points\n xdata = self.get_plane(quantity, 'yz', xplane)\n ... | [
"0.5905184",
"0.583379",
"0.5720596",
"0.5672659",
"0.5666331",
"0.5634003",
"0.5627785",
"0.5615881",
"0.56032383",
"0.5562901",
"0.5532643",
"0.55280274",
"0.5481719",
"0.54555655",
"0.54548764",
"0.54529595",
"0.5406398",
"0.5384927",
"0.53750247",
"0.53184706",
"0.5297355... | 0.57265604 | 2 |
Return y and z squeeze factors for given triaxiality and ellipticity. | def triax2squeeze(t,e):
a = 1.
c = e
b = np.sqrt(c**2 - t*(c**2-a**2))
return b | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def squeeze(self, z, mode):\n phi = angle(z)\n r = abs(z)\n self.circuit.squeeze(r, phi, mode)",
"def get_curve_3D(eig, alpha=0.25,g23=0.5,g12=0.5): # renumerated according to sato et al: l3 is smallest\n #return sato(eig,alpha,g23, g12)\n return frangi(eig)",
"def rewofzs3(x,y):\n\n... | [
"0.52789277",
"0.5276468",
"0.52199775",
"0.5066924",
"0.4951103",
"0.49427873",
"0.48867345",
"0.48485625",
"0.48359007",
"0.48293853",
"0.48282418",
"0.48027712",
"0.47908416",
"0.47846416",
"0.47615057",
"0.47445077",
"0.473211",
"0.47158596",
"0.46936327",
"0.4692484",
"0... | 0.5169719 | 3 |
Writes values to a string. | def val2str(val):
# Return the input if it's a string
if isinstance(val,str ): valstr=val
# Handle types where spaces are added
elif isinstance(val,tuple): valstr=repr(val).replace(', ',',')
elif isinstance(val,list ): valstr=repr(val).replace(', ',',')
elif isinstance(val,dict ): valstr=repr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _write_values_to_string(self, values, etype):\n\n values = values.astype(etype)\n string_out = values.tostring()\n return string_out",
"def writeString(self, value: str):\n length = len(value)\n\n self._pack('i', length)\n self._pack('{:d}s'.format(length), value)",
... | [
"0.7074559",
"0.6887698",
"0.66502863",
"0.6603683",
"0.6545392",
"0.65319484",
"0.6472395",
"0.6416302",
"0.64148325",
"0.63818043",
"0.619167",
"0.6181713",
"0.6100718",
"0.6066478",
"0.6060324",
"0.60579085",
"0.60216725",
"0.59473294",
"0.5937376",
"0.592608",
"0.591881",... | 0.0 | -1 |
Recovers variables from a string. | def str2val(valstr,dtype=None):
valstr_strip=valstr.strip()
if isinstance(dtype,type): dtype = str(dtype)
# Get data type if not provided
if not isinstance(dtype,str):
if len(valstr_strip)==0: dtype='str'
elif valstr_strip.startswith('"') and valstr_strip.endswith('"'):
valst... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_vars_in_str(self, line):\n self.E_str = \"find_vars_in_str\"\n any_vars = [i[1:] for i in re.findall(IN_STR_VAR_REGEX, line)]\n for check_var in any_vars:\n # Check the variable exists\n if check_var not in self.variables:\n self.print_error(f\"Can... | [
"0.6900766",
"0.6373661",
"0.6054565",
"0.5983019",
"0.5958039",
"0.59292144",
"0.5895967",
"0.58793455",
"0.5832745",
"0.5809944",
"0.5805092",
"0.57732123",
"0.5708655",
"0.56362337",
"0.5634472",
"0.56196606",
"0.56066906",
"0.5551476",
"0.5522919",
"0.5520223",
"0.5505402... | 0.0 | -1 |
Convert number to string, replacing punctuation. | def num2str(numval,formstr=None,decrep='p',negrep='n',nsig=3,trimzero=True):
import copy
from string import maketrans
# Set default
if formstr is None: formstr='.{}g'.format(nsig)
# Round numval if within precision
if trimzero and not isinstance(numval,(int,long)):
precval=10.**(-(nsig-1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(s):\r\n return 'digit ' + str(s)",
"def convert(number):\n out = \"\"\n if number % 3 == 0:\n out = \"Pling\"\n if number % 5 == 0:\n out = out + \"Plang\"\n if number % 7 == 0:\n out = out + \"Plong\"\n if out == \"\":\n out = str(number)\n return o... | [
"0.6683302",
"0.6635494",
"0.6613576",
"0.6592942",
"0.6592942",
"0.6592942",
"0.6575372",
"0.65695816",
"0.64755034",
"0.64755034",
"0.6431362",
"0.6401847",
"0.6401584",
"0.6375053",
"0.63302994",
"0.6309738",
"0.6306367",
"0.6301587",
"0.62851566",
"0.6232513",
"0.6218883"... | 0.5528809 | 76 |
Change Karma Make sure that the user can make a karma change using rate limiting and return whether or not the karma value was added or changed | def _change_karma(self, name, change):
can_change = self._apply_rate_limit()
if not can_change: return False
res = self.bot.db.execute('SELECT target, karma FROM karma')
for target in res.fetchall():
if target[0].lower() == name.lower():
self.bot.db.execute('U... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _change_karma(self, nick, target, mode):\n if nick == target:\n return \"You can't modify your own karma.\"\n if target in self.karma and (datetime.datetime.now() -\n self.karma[target][2]).seconds < 5:\n return 'Karma spamming is prohibited.'\n if not ... | [
"0.69934213",
"0.6468223",
"0.61024314",
"0.6042686",
"0.60205877",
"0.59011203",
"0.5773027",
"0.5760536",
"0.572008",
"0.56760347",
"0.56500137",
"0.5561731",
"0.53875303",
"0.5348932",
"0.53422016",
"0.52225053",
"0.5194737",
"0.518642",
"0.5163019",
"0.51595706",
"0.51449... | 0.7706274 | 0 |
Apply Rate Limit Check how frequently the current user has run karma commands and, if they exceed a certain threshold (30 seconds) return False so they don't make any karma changes | def _apply_rate_limit(self):
update_time = time()
user_name = self.bot.user.full_name
if user_name in self.tokens.keys():
last_change = self.tokens[user_name][0]
# Add 1 token for every 30 seconds from the last change
added_tokens = int((update_time - last_cha... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _safe_limit_check(self):\n if self.rem == 40:\n self.time_start = time.time()\n elif time.time() - self.time_start >= 11:\n self.rem = 40\n self.time_start = time.time()\n elif self.rem <= 0:\n t = 11 - (time.time() - self.time_start)\n\n ... | [
"0.62723166",
"0.61897874",
"0.6130558",
"0.594389",
"0.5937632",
"0.59163153",
"0.5882192",
"0.5846787",
"0.5845753",
"0.57633436",
"0.57333547",
"0.5716136",
"0.57108897",
"0.57000345",
"0.5694648",
"0.5694648",
"0.56865287",
"0.5670449",
"0.56523293",
"0.56270933",
"0.5619... | 0.65764576 | 0 |
Compute the total duty percentage for each purchase line. there is an extra duty for some vendors.If the 'extra_duty' field's value is true,then we use a duty perc(0.288 most probably) for each 144 qtys | def compute_total_customs_duty(self):
for rec in self:
total = 0.0
extra_duty = 0.0
price_total = rec.quantity * rec.unit_price
# total = (price_total * duty_percentage)/100
rec.price_total = price_total
# for hts in rec.hts_ids:
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_duty_percentage(self):\n container_line_ids = self\n hbl_customs_obj = self.env['hbl.customs.duty']\n for line in container_line_ids:\n p_line = line.purchase_line\n #Get the supplier from product by using po supplier id.\n product_supplier_id = p_line.... | [
"0.7884601",
"0.5777692",
"0.56619155",
"0.5644612",
"0.5609361",
"0.5539036",
"0.54949725",
"0.54502136",
"0.5393716",
"0.5389697",
"0.53487086",
"0.52970153",
"0.5285339",
"0.5277346",
"0.5261509",
"0.5249308",
"0.5240147",
"0.5224184",
"0.52226484",
"0.5192483",
"0.5178316... | 0.78766584 | 1 |
Get all Smart Groups within a specified subscription. List all the Smart Groups within a specified subscription. . | def get_all(
self, target_resource=None, target_resource_group=None, target_resource_type=None, monitor_service=None, monitor_condition=None, severity=None, smart_group_state=None, time_range=None, page_count=None, sort_by=None, sort_order=None, custom_headers=None, raw=False, **operation_config):
d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all(isamAppliance, check_mode=False, force=False):\n return isamAppliance.invoke_get(\"Retrieving groups\", \"/sysaccount/groups/v1\")",
"def groups(self):\n #return self.get('{}/groups'.format(ApiVersion.A1.value))\n return self.get('{}/groups'.format(ApiVersion.CM1.value))",
"def groups():\n... | [
"0.6464921",
"0.63494927",
"0.634585",
"0.62714005",
"0.6237212",
"0.62311023",
"0.62210655",
"0.62132645",
"0.621206",
"0.61955535",
"0.619539",
"0.6162228",
"0.60869414",
"0.6082836",
"0.6054139",
"0.6025941",
"0.60177577",
"0.6016271",
"0.6009167",
"0.59990513",
"0.5994978... | 0.0 | -1 |
Get information related to a specific Smart Group. Get information related to a specific Smart Group. | def get_by_id(
self, smart_group_id, custom_headers=None, raw=False, **operation_config):
# Construct URL
url = self.get_by_id.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, 'str'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(isamAppliance, id, check_mode=False, force=False):\n return isamAppliance.invoke_get(\"Retrieving group\", \"/sysaccount/groups/{0}/v1\".format(id))",
"def show_group(self, group_id):\n\n return Client._get(self, id=group_id)",
"def get_group_details(self, group_id):\n url = self.group... | [
"0.6050466",
"0.604989",
"0.60478085",
"0.60230494",
"0.60096216",
"0.60058165",
"0.5988405",
"0.59130573",
"0.59111917",
"0.58686566",
"0.5820906",
"0.58130544",
"0.5768425",
"0.575321",
"0.57311463",
"0.57198733",
"0.57059264",
"0.56633234",
"0.5633848",
"0.5626244",
"0.561... | 0.6301686 | 0 |
Change the state of a Smart Group. | def change_state(
self, smart_group_id, new_state, custom_headers=None, raw=False, **operation_config):
# Construct URL
url = self.change_state.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subsc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def async_update_group_state(self) -> None:",
"def set_group(self, address, group):\n self.groups[address] = group",
"def setGroup(self, group):\n\t\tself.config.GROUP = group",
"def async_update_group_state(self) -> None:\n self._state = None\n self._async_update_group_state()",
"asyn... | [
"0.6732995",
"0.6544362",
"0.651041",
"0.6308661",
"0.62980443",
"0.62946486",
"0.6293723",
"0.62864935",
"0.62597525",
"0.62437063",
"0.6167252",
"0.6080003",
"0.6071449",
"0.60538197",
"0.6023371",
"0.6023371",
"0.6023371",
"0.59333366",
"0.59219843",
"0.5875517",
"0.585917... | 0.570442 | 29 |
Get the history a smart group, which captures any Smart Group state changes (New/Acknowledged/Closed) . | def get_history(
self, smart_group_id, custom_headers=None, raw=False, **operation_config):
# Construct URL
url = self.get_history.metadata['url']
path_format_arguments = {
'subscriptionId': self._serialize.url("self.config.subscription_id", self.config.subscription_id, '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def network_history(request, SPIC_group, SPIC_id):\n SPIC_obj = get_object_or_404(SPIC, group=SPIC_group, local_id=SPIC_id)\n network_list = Network.objects.filter(user_id=request.user.pk, SPIC=SPIC_obj).order_by(\"-local_id\")\n\n return {'SPIC_obj': SPIC_obj, 'network_list': network_list}",
"def get_h... | [
"0.59609723",
"0.59375453",
"0.5827547",
"0.5729176",
"0.568817",
"0.5650969",
"0.56454545",
"0.563896",
"0.5626192",
"0.5599211",
"0.55906564",
"0.5589627",
"0.55577046",
"0.5528631",
"0.55192196",
"0.54704773",
"0.54695994",
"0.5442508",
"0.5441003",
"0.5439692",
"0.5434865... | 0.6507963 | 0 |
Attenuators in, beamstop out, ROI1 set to direct beam | def alignment_start(sample_name='alignment'):
smi = SMI_Beamline()
yield from smi.modeAlignment()
# Set direct beam ROI
yield from smi.setDirectBeamROI()
sample_id(user_name='test', sample_name=sample_name)
proposal_id('2023_2', '311564_test') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def beam_align():\n\n # do nothing if there is a sample mounted to avoid collisions\n if smart_magnet.sample_detect.get() == 0:\n raise Exception(\"Sample mounted on gonio! Avoided collision\")\n\n # wait for attenuators to finish moving\n yield from bps.abs_set(mxatten, 0.002)\n yield from b... | [
"0.60829043",
"0.57773566",
"0.5751061",
"0.55324805",
"0.5464092",
"0.54330367",
"0.534069",
"0.5322202",
"0.53134006",
"0.5286224",
"0.5254157",
"0.52369833",
"0.5228862",
"0.5226033",
"0.5207312",
"0.5176918",
"0.5160592",
"0.5144474",
"0.5130545",
"0.51288116",
"0.5115674... | 0.0 | -1 |
Attenuators in, beamstop out, ROI1 set to direct beam | def alignment_start_angle(angle=0.10):
smi = SMI_Beamline()
yield from smi.modeAlignment()
# Set reflected beam ROI
yield from smi.setReflectedBeamROI(total_angle=angle, technique="gisaxs") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def beam_align():\n\n # do nothing if there is a sample mounted to avoid collisions\n if smart_magnet.sample_detect.get() == 0:\n raise Exception(\"Sample mounted on gonio! Avoided collision\")\n\n # wait for attenuators to finish moving\n yield from bps.abs_set(mxatten, 0.002)\n yield from b... | [
"0.6081809",
"0.5777927",
"0.57516456",
"0.5532476",
"0.5465286",
"0.5433055",
"0.5341489",
"0.53219754",
"0.53150827",
"0.5288024",
"0.5256378",
"0.5239029",
"0.52265924",
"0.5226345",
"0.5207449",
"0.51792234",
"0.51619565",
"0.51443416",
"0.51314235",
"0.5128932",
"0.51179... | 0.0 | -1 |
Attenuators out, beamstop in, | def alignment_stop():
smi = SMI_Beamline()
yield from smi.modeMeasurement()
proposal_id('2023_2', '311564_Pettersson') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def beam_align():\n\n # do nothing if there is a sample mounted to avoid collisions\n if smart_magnet.sample_detect.get() == 0:\n raise Exception(\"Sample mounted on gonio! Avoided collision\")\n\n # wait for attenuators to finish moving\n yield from bps.abs_set(mxatten, 0.002)\n yield from b... | [
"0.59246117",
"0.53664815",
"0.5361966",
"0.52839315",
"0.5274596",
"0.5259131",
"0.52420837",
"0.51933676",
"0.51914835",
"0.51529115",
"0.5128894",
"0.5128894",
"0.5128894",
"0.5128894",
"0.50962025",
"0.50304735",
"0.5025527",
"0.49930224",
"0.49781093",
"0.49603343",
"0.4... | 0.5679036 | 1 |
Align using an original script | def alignment_org(angle=0.1):
proposal_id('2023_2', '311564_test')
yield from alignement_gisaxs_multisample(angle=angle)
RE.md['ai_0'] = piezo.th.user_setpoint.get()
proposal_id('2023_2', '311564_Pettersson') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def align(self):\n ...",
"def align(): # open EH and fast shutter\n\t#marAuxiliary.closeMarShield()\n\td2in()\n\td3in()\n\tsh('o')",
"def align(args) :\n from aligner import align_reads\n align_reads(args)",
"def align(filename, prog, outfile):\n ra = RunAlign()\n ra.run_... | [
"0.7794893",
"0.6693876",
"0.66196114",
"0.658792",
"0.63913846",
"0.60286033",
"0.5967192",
"0.5963368",
"0.59541565",
"0.5938182",
"0.5919008",
"0.5912491",
"0.590243",
"0.58918756",
"0.5821559",
"0.57543087",
"0.5752975",
"0.5727278",
"0.57195127",
"0.57155657",
"0.5712719... | 0.0 | -1 |
RE(run_loop_measurement(t=1, name='1bl_PEI_10mM', loops=7, pump_t=210, total_t=720, jump_x=10)) Take measurements in the loop Sample has to be aligned before starting the script and theta angle at 0 deg (flat sample). | def run_loop_measurement(t=0.5, name='test', loops=4, pump_t=180, total_t=600, jump_x=10):
incident_angles = [0.1, 0.4]
waxs_arc = [20, 0]
user = "TP"
condition = (
( -1 < waxs.arc.position )
and ( waxs.arc.position < 1 )
and (waxs_arc[0] == 20)
)
if condition:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_multiobjective(inputs, parameters = None):\n def thickness(x, t, chord):\n y = af.Naca00XX(chord, t, [x], return_dict = 'y')\n thickness_at_x = y['u'] - y['l']\n return thickness_at_x \n\n if parameters != None:\n eng = parameters[0]\n import_matlab = False\n els... | [
"0.5746817",
"0.5685441",
"0.5681871",
"0.5642942",
"0.5569622",
"0.55403596",
"0.5525413",
"0.55203384",
"0.5511839",
"0.545532",
"0.5445678",
"0.5411594",
"0.54065305",
"0.54041046",
"0.53374296",
"0.53127813",
"0.52985483",
"0.52948815",
"0.5291056",
"0.52908057",
"0.52744... | 0.7498953 | 0 |
check whether X and Y have the matching sample size. | def __init__(self, x_, batch_size, valid=False, ini_random=True):
self.n = len(x_)
self.X = x_
self.index = 0
self.batch_size = batch_size
self.base_index = np.arange(self.n) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_size_check(self):\n [x1, y1, s1, g1] = self.data.diffusion_data.shape\n [x2, y2, s2, g2] = module_05.run_module(self.data).diffusion_data.shape\n self.assertEqual(x1, x2)\n self.assertEqual(y1, y2)\n self.assertEqual(s1, s2)\n self.assertEqual(g1, g2)",
"def che... | [
"0.6701522",
"0.6699848",
"0.6699655",
"0.66728204",
"0.64815956",
"0.64527076",
"0.6423499",
"0.6414772",
"0.63765645",
"0.6297624",
"0.6293033",
"0.612498",
"0.61043894",
"0.6100697",
"0.6078992",
"0.60533476",
"0.60430604",
"0.6027591",
"0.6017969",
"0.6015054",
"0.6003637... | 0.0 | -1 |
Nacte bajty ze vstupniho binarniho souboru a preve je na uint8 a vrati jako list | def read_bytes_to_list(path):
vstup = []
index = 0
with open(path, "rb") as f:
byte = f.read(1)
while byte != '':
index = index + 1
vstup.append(struct.unpack('b', byte)[0])
byte = f.read(1)
if not byte:
break
return vstup | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAllFileBytes(self) -> List[ghidra.program.database.mem.FileBytes]:\n ...",
"def getByteArray(self, name: unicode) -> List[int]:\n ...",
"def get_list_of_bytes2(self):\n pass",
"def network_bytes(self) -> List[bytes]:",
"def get_byte(self, *args) -> \"PyObject *\":\n retur... | [
"0.59729344",
"0.5932491",
"0.55452764",
"0.53782064",
"0.53719026",
"0.53628296",
"0.5361053",
"0.53410685",
"0.5298277",
"0.5277501",
"0.52300537",
"0.5216152",
"0.5197471",
"0.5186267",
"0.51661885",
"0.5151425",
"0.5142084",
"0.5131954",
"0.5114525",
"0.51049876",
"0.5091... | 0.57869107 | 2 |
LZW komprese dict_of_abc je vstupni slovnik dat na kazdem indexu slovniku je list v prubehu komprese se do nej pridavaji polozky list_of_data je posloupnost cisel ke kompresi | def do_LZW_Compression(dict_of_abc, list_of_data):
# rozdil mezi None a [] je v pouziti metody extend na listu
result = []
P = []
C = [] # C je vzdy jeden prvek ze vstupu
PC = []
#how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M
for i in range(len... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_LZW_DeCompression(dict_of_abc, list_of_data):\n \n #https://www.youtube.com/watch?v=MQM_DsX-LBI\n \n out = []\n predchozi_out = []\n for i in range(len(list_of_data)):\n new = []\n new.extend(predchozi_out)\n if list_of_data[i] in dict_of_abc:\n o = dict_of_... | [
"0.81415105",
"0.5463324",
"0.5255911",
"0.51981646",
"0.51777226",
"0.5148649",
"0.5143358",
"0.51358664",
"0.5128433",
"0.5083292",
"0.5054639",
"0.5033062",
"0.50319797",
"0.5004983",
"0.49983892",
"0.49932218",
"0.49870348",
"0.498288",
"0.49821144",
"0.49720147",
"0.4967... | 0.785483 | 1 |
LZW Dekomprese dict_of_abc je vstupni slovnik dat na kazdem indexu slovniku je list v prubehu komprese se do nej pridavaji polozky list_of_data je posloupnost cisel pro dekompresi | def do_LZW_DeCompression(dict_of_abc, list_of_data):
#https://www.youtube.com/watch?v=MQM_DsX-LBI
out = []
predchozi_out = []
for i in range(len(list_of_data)):
new = []
new.extend(predchozi_out)
if list_of_data[i] in dict_of_abc:
o = dict_of_abc[list_of_dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_LZW_Compression(dict_of_abc, list_of_data):\n \n # rozdil mezi None a [] je v pouziti metody extend na listu\n \n result = []\n P = []\n C = [] # C je vzdy jeden prvek ze vstupu\n PC = []\n \n #how it works video xplanation https://www.youtube.com/watch?v=MQ4ObKv2L_M\n \n fo... | [
"0.7625661",
"0.5576687",
"0.5428306",
"0.5259145",
"0.52158284",
"0.5127516",
"0.51260525",
"0.51113755",
"0.5106959",
"0.50973123",
"0.507302",
"0.5056697",
"0.50454503",
"0.50228345",
"0.5017704",
"0.49628225",
"0.49544364",
"0.4946142",
"0.49108753",
"0.49073184",
"0.4903... | 0.81224066 | 0 |
dict_of_abc vypada napriklad takto | def dict_cointains_list(dict_of_abc, item_list):
values = list(dict_of_abc.values())
#projdu vsecky listy ve slovniku
for i in range(len(values)):
#predpokladam ze ve slovniku je
finded = True
for j in range(len(values[i])):
if len(item_list) == len(values[i]):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_cases_for_dict(self):\n pass",
"def init_dict() -> None:\n for elem in letters:\n ascii_dict[elem] = []\n for elem in numbers:\n ascii_dict[elem] = []\n for elem in symbols:\n ascii_dict[elem] = []",
"def dictogram_dictlist(self):\n for key, value in sel... | [
"0.59698254",
"0.5688297",
"0.56781894",
"0.56143355",
"0.5542839",
"0.5520396",
"0.5489875",
"0.5482796",
"0.5428855",
"0.54169875",
"0.5414742",
"0.54147077",
"0.5387781",
"0.5383238",
"0.53804624",
"0.5378157",
"0.53751314",
"0.533766",
"0.53338116",
"0.5317185",
"0.530112... | 0.61399186 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.