pulumi.Output[str]:\n return pulumi.get(self, \"direction\")","def move_to_joint_pos_delta(self, cmd):\n curr_q = self.joint_angles()\n joint_names = self.joint_names()\n\n joint_command = dict([(joint, curr_q[joint] + cmd[i])\n for i, joint in enumerate(joint_names)])\n\n self.move_to_joint_positions(joint_command)","def getDirection(self):\n return self.ray.direction","def calculate_direction(east, north):\n if not Ensemble.is_bad_velocity(east) and not Ensemble.is_bad_velocity(north):\n bin_dir = (math.atan2(east, north)) * (180.0 / math.pi)\n\n # The range is -180 to 180\n # This moves it to 0 to 360\n if bin_dir < 0.0:\n bin_dir = 360.0 + bin_dir\n\n return bin_dir\n else:\n return Ensemble.BadVelocity","def set_j(cmd, limb, joints, index, delta):\n joint = joints[index]\n cmd[joint] = delta + limb.joint_angle(joint)","def direction(self) -> str:\n return pulumi.get(self, \"direction\")","def get_direction(self):\n return self.direction","def direction(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"direction\")","def _calc_relative_move_direction(self, char, direction):\n if char in (\"Left\", \"Right\"):\n di = -1 if self.video.hflip else 1\n else:\n di = -1 if self.video.vflip else 1\n return direction * di","def calculate_head_direction_from_leds(positions, return_as_deg=False):\n X_led1, Y_led1, X_led2, Y_led2 = positions[:, 0], positions[:, 1], positions[:, 2], positions[:, 3]\n # Calculate head direction\n head_direction = np.arctan2(X_led1 - X_led2, Y_led1 - Y_led2)\n # Put in right perspective in relation to the environment\n offset = +np.pi/2\n head_direction = (head_direction + offset + np.pi) % (2*np.pi) - np.pi\n head_direction *= -1\n if return_as_deg:\n head_direction = head_direction * (180 / np.pi)\n\n return head_direction","def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):\n return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)","def direction(self):\n g = self._grad_f(self._x, *self._args)\n H = self._hess_f(self._x, *self._args)\n self._calls[1:3] += 1\n gr = g\n Hr = g\n damping = 0\n while True:\n try:\n L, _ = cho_factor(Hr, lower=0)\n dx = -cho_solve((L, 0), gr)\n break\n except:\n #print('Ooops... singular Hessian')\n damping = 10 * max(TINY, damping)\n gr = g + damping * self._x\n Hr = H + damping * np.eye(len(self._x))\n return np.nan_to_num(dx)","def update_player_direction(self,direction):\n pass","def update_direction(self):\n direction = self.get_direction()\n\n if direction[1] == NORTH[1]:\n self.current_animation = N_ANIM \n elif direction[1] == SOUTH[1]:\n self.current_animation = S_ANIM \n elif direction[0] == EAST[0]:\n self.current_animation = E_ANIM \n else:\n self.current_animation = W_ANIM","def get_direction(self):\n\n return -1 if self.curr_player == self.PLAYER1 else 1","def find_upwinding_direction(self):\n self.upwinded_face_cell = []\n for cell_index in range(self.mesh.get_number_of_cells()):\n for [face_index, orientation] in zip(self.mesh.get_cell(cell_index), \n self.mesh.get_cell_normal_orientation(cell_index)):\n if orientation*self.current_velocity[face_index]>0:\n self.upwinded_face_cell.append([face_index, cell_index])\n\n ## Set fractional flow for dirichlet cells set by points\n ## based on up-winded flow direction. If the flow is out \n ## of the cell, this is done automatically in previous \n ## loops. However, for flow into the cell, we must find \n ## the saturation from the cell it points to. \n for face_index in self.mesh.get_dirichlet_pointer_faces():\n (cell_index, orientation) = self.mesh.get_dirichlet_pointer(face_index)\n if self.current_velocity[face_index]*orientation<0.:\n self.upwinded_face_cell.append([face_index, cell_index])","def direction(a:tuple, b:tuple, c:tuple)->int:\n return ((b[1] - a[1]) * (c[0] - b[0])) - ((b[0] - a[0]) * (c[1] - b[1]))","def getRobotDirection(self):\n return self.direction","def getRobotDirection(self):\n return self.direction","def getRobotDirection(self):\n return self.direction\n #raise NotImplementedError","def getRobotDirection(self):\n return self.direction\n #raise NotImplementedError","def get_origin_direction(self):\n return self.origin_coordinates[2]","def get_joint_positions(self, joint_angles ): \n\n\n # current angles\n res_joint_angles = joint_angles.copy() \n\n # detect limits\n maskminus= res_joint_angles > self.joint_lims[:,0]\n maskplus = res_joint_angles < self.joint_lims[:,1]\n \n res_joint_angles = res_joint_angles*(maskplus*maskminus) \n res_joint_angles += self.joint_lims[:,0]*(np.logical_not(maskminus) )\n res_joint_angles += self.joint_lims[:,1]*(np.logical_not(maskplus) )\n \n # mirror\n if self.mirror :\n res_joint_angles = -res_joint_angles\n res_joint_angles[0] += np.pi \n \n # calculate x coords of arm edges.\n # the x-axis position of each edge is the \n # sum of its projection on the x-axis\n # and all the projections of the \n # previous edges \n x = np.array([ \n sum([ \n self.segment_lengths[k] *\n np.cos( (res_joint_angles[:(k+1)]).sum() ) \n for k in range(j+1) \n ])\n for j in range(self.number_of_joint) \n ])\n \n # trabslate to the x origin \n x = np.hstack([self.origin[0], x+self.origin[0]])\n\n # calculate y coords of arm edges.\n # the y-axis position of each edge is the \n # sum of its projection on the x-axis\n # and all the projections of the \n # previous edges \n y = np.array([ \n sum([ \n self.segment_lengths[k] *\n np.sin( (res_joint_angles[:(k+1)]).sum() ) \n for k in range(j+1) \n ])\n for j in range(self.number_of_joint) \n ])\n \n # translate to the y origin \n y = np.hstack([self.origin[1], y+self.origin[1]])\n\n pos = np.array([x, y]).T\n \n return (pos, res_joint_angles)","def head_direction(self, target, useAvoidance=False, verbose=False, turnSpeed=1, door = False):\n\n endVector = target\n\n speedLeft, speedRight = self.target_to_left_right_speeds(endVector)\n\n self.logger.debug(\"endVector: %s\" % repr(endVector))\n self.logger.debug(\"Wheel speeds: %d, %d\" % (speedLeft, speedRight))","def getDirectionalMovement(self, currentLocation, direction):\n # get the maximum diagonal vs single line movements based on the robot type\n if self.isCrusader:\n # Crusader can move a maximum of 3 blocks in a single line and 2 blocks in a diagonal line\n singleLineMovementSpeed = 3\n diagonalMovementSpeed = 2\n else:\n # all other units can move a maximum of 2 blocks in a single line and 1 block in a diagonal line\n singleLineMovementSpeed = 2\n diagonalMovementSpeed = 1\n\n # set the x and y directional movements to the current direction's x and y. These will be used to store the maximum movement speed of the robot in a given direction\n xDirectionalMovement = direction[0]\n yDirectionalMovement = direction[1]\n\n # if neither x or y are 0, then the unit is traveling diagonally so we multiply the values by the diagonal movement speed\n if xDirectionalMovement != 0 and yDirectionalMovement != 0:\n xDirectionalMovement *= diagonalMovementSpeed\n yDirectionalMovement *= diagonalMovementSpeed\n else:\n # else we multiply the movement values by the single line movement speed\n xDirectionalMovement *= singleLineMovementSpeed\n yDirectionalMovement *= singleLineMovementSpeed\n\n # store the values of how far the current location is from the target location\n xOffset = self.targetLocation[0] - currentLocation[0]\n yOffset = self.targetLocation[1] - currentLocation[1]\n\n # if the distance from \n if self.isCrusader:\n if xDirectionalMovement != 0:\n if (xOffset > 0 and xOffset < 3) or (xOffset < 0 and xOffset > -3):\n xDirectionalMovement = xOffset\n\n if yDirectionalMovement != 0:\n if (yOffset > 0 and yOffset < 3) or (yOffset < 0 and yOffset > -3):\n yDirectionalMovement = yOffset\n else:\n if xDirectionalMovement != 0 and (xOffset == 1 or xOffset == -1):\n xDirectionalMovement = xOffset\n\n if yDirectionalMovement != 0 and (yOffset == 1 or yOffset == -1):\n yDirectionalMovement = yOffset\n\n return (xDirectionalMovement, yDirectionalMovement)","def update(self):\n self.updateCount = self.updateCount + 1\n if self.updateCount > self.updateCountMax:\n\n # update previous positions\n for i in range(self.length - 1, 0, -1):\n self.x[i] = self.x[i - 1]\n self.y[i] = self.y[i - 1]\n\n # update position of player : party lead\n if self.direction == 0:\n self.x[0] = self.x[0] + self.step\n if self.direction == 1:\n self.x[0] = self.x[0] - self.step\n if self.direction == 2:\n self.y[0] = self.y[0] - self.step\n if self.direction == 3:\n self.y[0] = self.y[0] + self.step\n\n self.updateCount = 0","def joint_angles_callback(self, msg):\n\t\t\t# read the current joint angles from the robot\n\t\t\tpos_curr = np.array([msg.joint1,msg.joint2,msg.joint3,msg.joint4,msg.joint5,msg.joint6,msg.joint7]).reshape((7,1))\n\n\t\t\t# convert to radians\n\t\t\tpos_curr = pos_curr*(math.pi/180.0)\n\n\t\t\t# update torque from PID based on current position\n\t\t\tself.torque = self.Impedence_control(pos_curr)","def direction(self):\n return self.cfg.direction","def motors__Direction(self, speed_l, speed_r):\n\n if speed_l >= 0:\n self.motorDirection(self.motor1DirectionPin, self.motorForward)\n else:\n self.motorDirection(self.motor1DirectionPin, self.motorReverse)\n\n if speed_r >= 0:\n self.motorDirection(self.motor2DirectionPin, self.motorForward)\n else :\n self.motorDirection(self.motor2DirectionPin, self.motorReverse)","def direction(self):\n _direction = self._custom.get(\"direction\")\n if _direction is not None:\n return _direction\n\n _direction = self._infer_direction()\n self._custom[\"direction\"] = _direction\n\n return _direction","def direction(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"direction\")","def getDirection(self):\n if 'N' in str(self.trip_update.trip.trip_id):\n direction = 'northbound'\n if 'S' in str(self.trip_update.trip.trip_id):\n direction = 'southbound'\n return direction","def _get_direction(self, action, direction):\n left = [2,3,1,0]\n right = [3,2,0,1]\n if direction == 0:\n new_direction = action\n elif direction == -1:\n new_direction = left[action]\n elif direction == 1:\n new_direction = right[action]\n else:\n raise Exception(\"getDir received an unspecified case\")\n return new_direction","def direction(self):\n return None if not bool(self.relation) else (self.s_end <= self.o_start)","def _vector_direction(self,\n desired_pos : np.array,\n actual_pos : np.array) -> np.array:\n return desired_pos - actual_pos","def motorsDirection(self, direction):\n\n print (direction)\n if direction == 'r' or direction == 'R':\n self.motorDirection(self.motor1DirectionPin, self.motorReverse)\n self.motorDirection(self.motor2DirectionPin, self.motorReverse)\n print (\"Direction reverse\")\n else:\n self.motorDirection(self.motor1DirectionPin, self.motorForward)\n self.motorDirection(self.motor2DirectionPin, self.motorForward)\n print (\"Direction forward\")","def forward_kinematics(joint_angle_state,num_dof,link_length):\n\t#initialize the xpos and ypos\n\txpos = 0\n\typos = 0\n\tfor i in range(1,num_dof+1):\n\t\txpos += (link_length*np.cos(np.sum(joint_angle_state[0,:i])))\n\t\typos += (link_length*np.sin(np.sum(joint_angle_state[0,:i])))\n\treturn (int(xpos),int(ypos))","def get_direction(curr_pos, next_pos):\n if curr_pos == next_pos:\n return 'CLEAN'\n\n v_dist = next_pos[0] - curr_pos[0]\n h_dist = next_pos[1] - curr_pos[1]\n\n if h_dist != 0:\n if h_dist < 0:\n return 'LEFT'\n else:\n return 'RIGHT'\n else:\n if v_dist < 0:\n return 'UP'\n else:\n return 'DOWN'","def direction(self):\n return self._direction.copy()","def _route_to_goal(self, position, orientation):\n _, (_x,_y) = self._calc_torus_distance(position, self.goal)\n move = None\n\n if orientation == 'up':\n if self.goal[1] > position[1] and _y > 0:\n move = 'move'\n elif self.goal[1] < position[1] and _y < 1:\n move = 'move'\n elif self.goal[0] > position[0]:\n if _x > 0:\n move = 'left'\n else:\n move = 'right'\n else:\n if _x > 0:\n move = 'right'\n else:\n move = 'left'\n\n if orientation == 'down':\n if self.goal[1] < position[1] and _y > 0:\n move = 'move'\n elif self.goal[1] > position[1] and _y < 1:\n move = 'move'\n elif self.goal[0] > position[0]:\n if _x > 0:\n move = 'right'\n else:\n move = 'left'\n else:\n if _x > 0:\n move = 'left'\n else:\n move = 'right'\n\n if orientation == 'right':\n if self.goal[0] < position[0] and _x > 0:\n move = 'move'\n elif self.goal[0] > position[0] and _x < 1:\n move = 'move'\n elif self.goal[1] > position[1]:\n if _y > 0:\n move = 'left'\n else:\n move = 'right'\n else:\n if _y > 0:\n move = 'right'\n else:\n move = 'left'\n\n if orientation == 'left':\n if self.goal[0] > position[0] and _x > 0:\n move = 'move'\n elif self.goal[0] < position[0] and _x < 1:\n move = 'move'\n elif self.goal[1] > position[1]:\n if _y > 0:\n move = 'right'\n else:\n move = 'left'\n else:\n if _y > 0:\n move = 'left'\n else:\n move = 'right'\n\n return move","def __get_new_direction(self):\n return fabs(self.random_generator.normal(self.__direction_mean,\n self.__direction_deviation))","def change_direction(self, direction):\r\n for event in pygame.event.get():\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_ESCAPE:\r\n pygame.quit()\r\n sys.exit()\r\n elif event.key == pygame.K_UP:\r\n if self.direction == [0, 1]:\r\n self.direction == [0, 1]\r\n return self.direction\r\n else:\r\n self.direction = [dx, dy] = [0, -1]\r\n return self.direction\r\n elif event.key == pygame.K_DOWN:\r\n if self.direction == [0, -1]:\r\n self.direction == [0, -1]\r\n return self.direction\r\n else:\r\n self.direction = [dx, dy] = [0, 1]\r\n return self.direction\r\n elif event.key == pygame.K_LEFT:\r\n if self.direction == [1, 0]:\r\n self.direction == [1, 0]\r\n return self.direction\r\n else:\r\n self.direction = [dx, dy] = [-1, 0]\r\n return self.direction\r\n elif event.key == pygame.K_RIGHT:\r\n if self.direction == [-1, 0]:\r\n self.direction == [-1, 0]\r\n return self.direction\r\n else:\r\n self.direction = [dx, dy] = [1, 0]\r\n return self.direction","def get_direction(self):\n is_direction_correct = False\n while not is_direction_correct:\n direction = random.randint(0, 2)\n if direction == 0:\n self.turtle.left(90)\n elif direction == 1:\n self.turtle.right(90)\n else:\n self.turtle.right(0)\n is_direction_correct = self.check_boundary()","def update_direction(self, update_data: dict):\n if self.on_update_direction:\n self.on_update_direction(self, update_data)","def right(self):\r\n z = len(direction_tuple)\r\n if self.d in direction_tuple:\r\n index = direction_tuple.index(self.d)\r\n if index == (z-1):\r\n self.d = direction_tuple[0]\r\n else:\r\n self.d = direction_tuple[index + 1]\r\n else:\r\n print(\"NO VALID ROBOT POSITION\")","def get_desired_joint_position(self):\n return self._position_joint_desired","def get_next_moving_direction(self, nearest_zombie_pos=None):\n if nearest_zombie_pos is None:\n nearest_zombie_pos=(uniform(-1.0, 1.0), uniform(-1.0, 1.0))\n\n vector = (self.pos[0]-nearest_zombie_pos[0], \\\n self.pos[1]-nearest_zombie_pos[1])\n magnitude = math.sqrt(vector[0]**2 + vector[1]**2)\n direction_vector = (vector[0]/magnitude, vector[1]/magnitude)\n return direction_vector","def translate_direction(self):\n xpart = math.sin(self.direction)\n ypart = math.cos(self.direction)\n if ypart > 0:\n print(\"oben \", end='')\n else:\n print(\"unten \", end='')\n if xpart > 0:\n print(\"rechts\")\n else:\n print(\"links\")","def set_direction(self, dir):\n if dir == 0:\n self.direction = [0, -1]\n elif dir == 1:\n self.direction = [1, 0]\n elif dir == 2:\n self.direction = [0, 1]\n elif dir == 3:\n self.direction = [-1, 0]","def direction(point0, point1):\n d = [0, 0, 0]\n vector = [point1[0] - point0[0], point1[1] - point0[1]]\n d[1] = math.atan2(vector[1], vector[0])\n while d[1] <= -np.pi / 2:\n d[1] += np.pi\n return d","def motor_angles(self):\n return np.asarray(self._robot_state.position)","def getDirection(self):\n return self.listener.direction","def dirToIncrement(direction):\n # assume upperleft corner is (0,0)\n if direction == 'up':\n return (0,-1)\n elif direction == 'down':\n return (0,1)\n elif direction == 'left':\n return (-1,0)\n elif direction == 'right':\n return (1,0)\n vertical, horizontal = direction.split()\n v1, h1 = dirToIncrement(vertical)\n v2, h2 = dirToIncrement(horizontal)\n return (v1+v2, h1+h2)","def coordnav(self,dx,dy,dth): \n self.cg_ant = np.array(self.cg)\n self.coord[0] = self.coord[0] - self.cg[0]\n self.coord[1] = self.coord[1] - self.cg[1] \n self.Rz = rotation.matrix([0,0,1],dth)\n self.coord = np.dot(self.Rz,self.coord)\n \n self.coord[0] = self.coord[0] + self.cg[0] + dx\n self.coord[1] = self.coord[1] + self.cg[1] + dy \n \n self.px = self.coord[:,self.px_index]\n self.Bx = self.px-self.cg \n self.basex = self.Bx/math.sqrt(np.dot(self.Bx,self.Bx))","def move(self, twist: Optional[Twist] = None):\n if twist is None:\n left = right = 0\n self.navigation_goal = None\n else:\n linear = np.clip(twist.linear.x, -1, 1)\n angular = np.clip(twist.angular.x, -1, 1)\n left, right = (linear - angular) / 2, (linear + angular) / 2\n # # always give a robot the full velocity at least on one side\n # if (greater := max(abs(left), abs(right))) > 0:\n # left, right = left / greater, right / greater\n\n self.locomotion_lock.acquire()\n self.v_left = SPEEDUP * left\n self.v_right = SPEEDUP * right\n self.locomotion_lock.release()","def compute_angle(self, direction):\n scaled_cosine = self.w1.dot(direction) # ||direction|| cos(theta)\n scaled_sine = self.w2.dot(direction) # ||direction|| sin(theta)\n return np.arctan2(scaled_sine, scaled_cosine)","def move_in_direction(self, direction):\n if direction == NORTH:\n self.__position[y] += 1\n elif direction == NORTHEAST:\n self.__position[x] += 1\n self.__position[y] += 1\n elif direction == EAST:\n self.__position[x] += 1\n elif direction == SOUTHEAST:\n self.__position[x] += 1\n self.__position[y] -= 1\n elif direction == SOUTH:\n self.__position[y] -= 1\n elif direction == SOUTHWEST:\n self.__position[x] -= 1\n self.__position[y] -= 1\n elif direction == WEST:\n self.__position[x] -= 1\n elif direction == NORTHWEST:\n self.__position[x] -= 1\n self.__position[y] += 1","def turn_to_endpoint(previous_direction, w_real, d, d_traveled):\n theta = math.atan((D_ENDPOINT-d_traveled)/w_real)\n phi = np.pi/2 - theta\n\n if previous_direction==0:\n # drone yaws to the right to the direction of the endpoint\n yaw(phi)\n else:\n # drone yaws to the left to the direction of the endpoint\n yaw(-phi)\n phi = -phi\n\n return phi","def compute_direction(self, feats):\n if feats.name == \"ARNC\":\n if feats[\"z-score\"] < -1.5:\n return Directions.long_dir\n elif feats[\"z-score\"] > 1.5:\n return Directions.short_dir\n elif feats.name == \"UNG\":\n if feats[\"z-score\"] < -1.5:\n return Directions.short_dir\n elif feats[\"z-score\"] > 1.5:\n return Directions.long_dir","def get_relative_direction(self, source, destination, orientation):\r\n direction = {}\r\n direction['North'] = {}\r\n direction['North']['North'] = 'Straight'\r\n direction['North']['East'] = 'Right'\r\n direction['North']['West'] = 'Left'\r\n direction['East'] = {}\r\n direction['East']['East'] = 'Straight'\r\n direction['East']['North'] = 'Left'\r\n direction['East']['South'] = 'Right'\r\n direction['South'] = {}\r\n direction['South']['South'] = 'Straight'\r\n direction['South']['East'] = 'Left'\r\n direction['South']['West'] = 'Right'\r\n direction['West'] = {}\r\n direction['West']['West'] = 'Straight'\r\n direction['West']['South'] = 'Left'\r\n direction['West']['North'] = 'Right'\r\n if type(orientation) != str or orientation not in ['North', 'East', 'South', 'West']:\r\n return None\r\n final_orientation = self.get_orientation_from_to(source, destination)\r\n if orientation == final_orientation:\r\n return 'Straight'\r\n else:\r\n print orientation\r\n print final_orientation\r\n return direction[orientation][final_orientation]","def direction(self,four_dir=False):\n a = self.angle()\n if a is None:\n return None\n\n if four_dir:\n if a >= -45 and a <= 45:\n return \"UP\"\n elif a >= 45 and a <= 135:\n return \"RIGHT\"\n elif a >= 135 or a <= -135:\n return \"DOWN\"\n elif a >= -135 and a <= -45:\n return \"LEFT\"\n else:\n raise RuntimeError(\"Couldn't figure out %f\" % a)\n else:\n if a >= -22.5 and a <= 22.5:\n return \"UP\"\n elif a >= 22.5 and a <= 67.5:\n return \"UP-RIGHT\"\n elif a >= 67.5 and a <= 112.5:\n return \"RIGHT\"\n elif a >= 112.5 and a <= 157.5:\n return \"DOWN-RIGHT\"\n elif a >= 157.5 or a <= -157.5:\n return \"DOWN\"\n elif a >= -157.5 and a <= -112.5:\n return \"DOWN-LEFT\"\n elif a >= -112.5 and a <= -67.5:\n return \"LEFT\"\n elif a >= -67.5 and a <= -22.5:\n return \"UP-LEFT\"\n else:\n raise RuntimeError(\"Couldn't figure out %f\" % a)","def update_position(position, direction):\n\n\tif direction == 'left':\n\t\treturn [position[0], position[1] - 1]\n\tif direction == 'right':\n\t\treturn [position[0], position[1] + 1]\n\tif direction == 'down':\n\t\treturn [position[0] + 1, position[1]]\n\tif direction == 'up':\n\t\treturn [position[0] - 1, position[1]]\n\treturn [-1, -1]","async def direction(self, value) -> str:\n if value is None:\n return \"N\"\n\n direction_array = [\n \"N\",\n \"NNE\",\n \"NE\",\n \"ENE\",\n \"E\",\n \"ESE\",\n \"SE\",\n \"SSE\",\n \"S\",\n \"SSW\",\n \"SW\",\n \"WSW\",\n \"W\",\n \"WNW\",\n \"NW\",\n \"NNW\",\n \"N\",\n ]\n direction_str = direction_array[int((value + 11.25) / 22.5)]\n return self._translations[\"wind_dir\"][direction_str]","def getMovement(self):\n # store the robot's current location and set the directional movement to 0,0 so that the robot won't move by default\n currentLocation = (self.me['x'], self.me['y'])\n directionalMovement = (0,0)\n\n # ensure that target location is not none and not equal to the current location\n if self.targetLocation and not currentLocation == self.targetLocation:\n\n # store the direction, directional movement, and the new map location we will trying to move the robot to this round\n direction = self.getDirection(currentLocation, self.targetLocation)\n directionalMovement = self.getDirectionalMovement(currentLocation, direction)\n newLocation = self.getNewLocation(currentLocation, directionalMovement)\n\n # store the current direction for use later\n initialDirection = direction\n\n # by default, the robot is ready to move in the event that the new map location is already passable\n readyToMove = True\n\n # while the new map location is not passable\n while not self.isPassable(newLocation):\n # if unit is a crusader moving diagonally at their fastest pace, set their directional movement to (1,1)\n if self.isCrusader and directionalMovement[0] == 2 and directionalMovement[1] == 2:\n directionalMovement[0] = 1\n directionalMovement[1] = 1\n # or if the unit is traveling faster than 1 block East\n elif directionalMovement[0] > 1:\n # lower the unit's movement East by 1 block\n directionalMovement[0] -= 1\n # or if the unit is traveling faster than 1 block West\n elif directionalMovement[0] < -1:\n # lower the unit's movement West by 1 block\n directionalMovement[0] += 1\n # or if the unit is traveling faster than 1 block South\n elif directionalMovement[1] > 1:\n # lower the unit's movement South by 1 block\n directionalMovement[1] -= 1\n # or if the unit is traveling faster than 1 block North\n elif directionalMovement[1] < -1:\n # lower the unit's movement North by 1 block\n directionalMovement[1] += 1\n # else the unit is already moving the shortest distance they can in the current direction\n else:\n # rotate the robots direction clockwise and proceed\n direction = self.getRotatedDirection(direction, 1)\n\n # if we ened up facing the same direction we started in\n if direction == initialDirection:\n # let the code know we're not ready to move\n readyToMove = False\n # break out of the while loop\n break\n\n # overwrite the directional movement with a new one based on the direction we just got\n directionalMovement = self.getDirectionalMovement(currentLocation, direction)\n\n # overwrite the new location with the location we get from the directional movement we just got\n newLocation = self.getNewLocation(currentLocation, directionalMovement)\n\n # if the robot ended up not being ready to move\n if not readyToMove:\n # change the directional movement back to (0,0) so that it doesn't move\n directionalMovement = (0,0)\n else :\n self.targetLocation = self.getRandomPassableLocation()\n # return the directional movement\n return directionalMovement","def align(self) -> np.ndarray:\n vel = self.state[:, :, Boids.Attr.VEL]\n vel_norm = np.linalg.norm(vel, axis=0)\n orientation = vel / (vel_norm + EPSILON)\n mut_influence = self._perceive(self.p_range)\n desired_orientation = np.dot(orientation, mut_influence)\n desired_orientation = np.multiply(desired_orientation, \n vel_norm + EPSILON)\n return desired_orientation - orientation","def direction(self) -> int:\n return self._direction","def _step_direction(self, rho, phi, direction_reading, *args, **kwargs):\r\n condition = kwargs['obj'] is not None\\\r\n and rho <= self.range\\\r\n and phi <= self.aperture #<= 3*np.pi/self.n_sectors\r\n if direction_reading is None:\r\n direction_reading = 0.\r\n # import pdb; pdb.set_trace()\r\n if condition and direction_reading == 0.0:\r\n my_pos = self.get_position(self.sensors_idx[args[0]]) + np.r_[0, 0, 0.1] #+ np.r_[0, 0, 0.017]\r\n tar_post = kwargs['obj'].position + np.r_[0, 0, 0.07] # my_pos[2]]\r\n ray_res = p.rayTest(my_pos, tar_post, physicsClientId=self.sensor_owner.physics_client)[0][0]\r\n # signal_strength = self.propagation(rho, phi)\r\n if ray_res == kwargs['obj'].id:\r\n direction_reading = 1.\r\n return direction_reading","def get_dir_of_obst(cur_pos: Node, cur_heading: float, obst_to_follow: ObstacleSegment) -> Dir:\n # find closest obstacle boundary point\n closest_dist = math.inf\n closest_bp = obst_to_follow.boundary_points[0]\n for bp in obst_to_follow.get_boundary_points():\n d = cur_pos.dist_2d(bp)\n if d <= closest_dist:\n closest_bp = bp\n closest_dist = d\n\n a = cur_pos.as_node_2d()\n b = cur_pos + Node.from_array(rotate(np.array([0, 0]), np.array([1, 0]), cur_heading))\n p = closest_bp.as_node_2d()\n\n # make a origin\n b = b - a\n p = p - a\n c_p = np.cross(b.as_ndarray_2d(), p.as_ndarray_2d())\n if np.all(c_p > 0):\n return Dir.LEFT\n else:\n return Dir.RIGHT","def calculate_heading_direction_from_position(positions, smoothing=False, return_as_deg=False):\n X, Y = positions[:, 0], positions[:, 1]\n # Smooth diffs instead of speeds directly\n Xdiff = np.diff(X)\n Ydiff = np.diff(Y)\n if smoothing:\n Xdiff = smooth_signal(Xdiff, smoothing)\n Ydiff = smooth_signal(Ydiff, smoothing)\n # Calculate heading direction\n heading_direction = np.arctan2(Ydiff, Xdiff)\n heading_direction = np.append(heading_direction, heading_direction[-1])\n if return_as_deg:\n heading_direction = heading_direction * (180 / np.pi)\n\n return heading_direction","def getBotRightVelocity(self):\n\t\tif len(self.prevPositions) < 2:\n\t\t\tself.velocity = (0,0,0)\n\t\telse:\n\t\t\ttime = self.position[2] - self.prevPositions[len(self.prevPositions)-1][2]\n\t\t\txdist = self.position[1][0] - self.prevPositions[len(self.prevPositions)-1][1][0]\n\t\t\tydist = self.position[1][1] - self.prevPositions[len(self.prevPositions)-1][1][1]\n\t\t\tself.velocity = (xdist,ydist,time.total_seconds())\n\t\treturn self.velocity","def current_direction(self):\n return self._attributes.get(\"current_direction\")","def get_goal_direction(self, cur_goal):\n\t\trho_robot = math.atan2(cur_goal.y - self.cur_pos.y, cur_goal.x - self.cur_pos.x)\n\n\t\tyaw_err = rho_robot - self.rotation\n\t\tif yaw_err < 0:\n\t\t\tself.cur_action = 'tr'\n\t\telse:\n\t\t\tself.cur_action = 'tl'\n\t\tself.next_action_time = rospy.Time.now() + rospy.Duration(abs(yaw_err) / self.angular_speed)","def get_angle_rad_between_joints(joint_a: Joint2D, joint_b: Joint2D) -> float:\n return math.atan2(joint_a.y - joint_b.y, joint_a.x - joint_b.x)","def comp_angle_magnet(self):\n Rbo = self.get_Rbo()\n W0 = self.comp_W0m()\n Harc = self.comp_H_arc()\n if self.is_outwards():\n return float(2 * arctan(W0 / (2 * (Rbo + self.H1 - Harc))))\n else:\n return float(2 * arctan(W0 / (2 * (Rbo - self.H1 - Harc))))\n\n # if self.W0_is_rad:\n # return self.W0\n # else: # Convert W0 from m to rad\n # Rbo = self.get_Rbo()\n # return float(2 * arcsin(self.W0 / (2 * Rbo)))","def get_direction(pi_values):\n if pi_values == (0, 0, 0, 0):\n return 'do not move'\n pi_right, pi_up, pi_left, pi_down = pi_values\n pi_sum = sum((pi_right, pi_up, pi_left, pi_down))\n\n p_right = pi_right / pi_sum\n p_up = pi_up / pi_sum\n p_left = pi_left / pi_sum\n p_down = pi_down / pi_sum\n\n return np.random.choice(\n ('right', 'up', 'left', 'down'),\n p=[p_right, p_up, p_left, p_down])","def GetCGStepDir(self):\n if self.n_iter <= 1:\n self.hvec = self.mol.g_total\n gamma = 0.0\n else:\n v1 = self.traj.grad[-1] - self.traj.grad[-2]\n v1 = v1.reshape((1, const.NUMDIM * self.mol.n_atoms))\n v2 = self.traj.grad[-1].reshape((const.NUMDIM, self.mol.n_atoms, 1))\n gamma = numpy.linalg.norm(numpy.dot(v1, v2))\n gamma *= 1.0 / numpy.linalg.norm(self.traj.grad[-1])**2\n self.hvec = self.mol.g_total + gamma * self.hvec\n self.step_dir = self.hvec"],"string":"[\n \"def junction_direction(start_junction: Cell, end_junction: Cell) -> Direction:\\n dx = end_junction.column - start_junction.column\\n dy = end_junction.row - start_junction.row\\n if dy == 0:\\n return Direction.E if dx > 0 else Direction.W\\n return Direction.S if dy > 0 else Direction.N\",\n \"def direction(self):\\n len = self.length()\\n if len == 0.0:\\n uvec = pos.Pos(np.transpose(np.array([0, 0, 0])))\\n else:\\n uvec = pos.Pos(np.transpose(np.array([(self.end.x - self.start.x) / len,\\n (self.end.y - self.start.y) / len,\\n (self.end.z - self.start.z) / len])))\\n return uvec\",\n \"def direction(self):\\r\\n return 180 - atan2(self.x, self.y)*180/pi\",\n \"def move(self, dt):\\n lims = self.settings['agent']['jointLimits']\\n # print '[move] curr joint Angle:'\\n # print self.jointAngle\\n # print '[move] curr speed:'\\n # print self.speed\\n\\n J = self.jointAngle + dt * np.array(self.speed)\\n self.jointAngle[0] = min(max(J[0], lims[0][0]), lims[0][1])\\n self.jointAngle[1] = min(max(J[1], lims[1][0]), lims[1][1])\\n self.forward_kinematics()\",\n \"def direction(self):\\n g = self._grad_f(self._x, *self._args)\\n self._calls[1] += 1\\n if self._prev_dx is None:\\n dx = -g\\n else:\\n b = max(0, np.dot(g, g - self._prev_g) / np.sum(self._prev_g ** 2))\\n dx = -g + b * self._prev_dx\\n if np.dot(dx, g) > 0:\\n dx = -g\\n self._prev_g = g\\n self._prev_dx = dx\\n return np.nan_to_num(dx)\",\n \"def direction(self):\\n return atan2d(self.y, self.x)\",\n \"def get_normalized_direction(self, direction):\\n return round(self.normal_joystick_slope * direction + self.normal_joystick_intercept, 2)\",\n \"def direction(self):\\n norm=math.sqrt(self.x**2 + self.y**2 + self.z**2)\\n return Vector3(self.x/norm, self.y/norm, self.z/norm)\",\n \"def direction(self) -> np.ndarray:\\n return self._direction\",\n \"def move(self):\\n if (self._dir is Direction.UP) and (self._y_pos is 0):\\n self._dir = Direction.DOWN\\n elif (self._dir is Direction.DOWN) and (self._y_pos+self._len is self._bs):\\n self._dir = Direction.UP\\n elif (self._dir is Direction.LEFT) and (self._x_pos is 0):\\n self._dir = Direction.RIGHT\\n elif (self._dir is Direction.RIGHT) and (self._x_pos+self._len is self._bs):\\n self._dir = Direction.LEFT\\n self.change_pos(self._dir)\\n return self._dir\",\n \"def update_position_direction(self, l):\\n\\n x = self.x + self.mu * l\\n mu = self.mu\\n\\n return x, mu\",\n \"def get_direction(self):\\n return self.actual_coordinates[2]\",\n \"def direction_angle(self):\\n return math.atan2(self.velocity, self.velocity)\",\n \"def _joint_angle_control(self):\\n\\n error = self.target_pos - self.robot_arm_pos\\n return self._pd_control(error) + self.torque\",\n \"def direction(self):\\n if self._is_hit:\\n return Direction.NOT_MOVING\\n return self._dir\",\n \"def find_direction(self):\\n\\t\\tif self.direction == OUTPUT.MOTOR_UP:\\n\\t\\t\\tfor floor in xrange(self.currentFloor+1, config.NUM_FLOORS):\\n\\t\\t\\t if self.orderQueue.has_order_in_floor(floor):\\n\\t\\t\\t\\t\\treturn OUTPUT.MOTOR_UP\\n\\t\\t\\treturn OUTPUT.MOTOR_DOWN\\n\\t\\telse:\\n\\t\\t\\tfor floor in xrange(self.currentFloor-1, -1, -1):\\n\\t\\t\\t\\tif self.orderQueue.has_order_in_floor(floor):\\n\\t\\t\\t\\t\\treturn OUTPUT.MOTOR_DOWN\\n\\t\\t\\treturn OUTPUT.MOTOR_UP\\n\\t\\treturn OUTPUT.MOTOR_UP\",\n \"def comet_joint_orient(pynodes, aim_axis = None, up_axis = None, up_dir = None, do_auto = False):\\n if aim_axis is None:\\n aim_axis = [1, 0, 0]\\n if up_axis is None:\\n up_axis = [0, 0, 1]\\n if up_dir is None:\\n up_dir = [1, 0, 0]\\n # convert to Vector\\n aim_axis = pm.dt.Vector(aim_axis)\\n up_axis = pm.dt.Vector(up_axis)\\n up_dir = pm.dt.Vector(up_dir)\\n # Filter supplied pynodes, if equal to 0 then return false\\n if len(pynodes) == 0:\\n return False\\n\\n # make sure only joint get passed through here\\n pynodes = pm.ls(pynodes, type = 'joint')\\n\\n # init variable prevUp for later use\\n prev_up = pm.dt.Vector()\\n\\n for i, o in enumerate(pynodes):\\n parent_point = None\\n # first we need to unparent everything and then store that,\\n children = o.getChildren()\\n for x in children:\\n x.setParent(None)\\n\\n # find parent for later in case we need it\\n parent = o.getParent()\\n\\n # Now if we have a child joint... aim to that\\n aim_tgt = None\\n for child in children:\\n if child.nodeType() == 'joint':\\n aim_tgt = child\\n break\\n\\n if aim_tgt:\\n # init variable upVec using upDir variable\\n up_vec = pm.dt.Vector(up_dir)\\n\\n # first off... if doAuto is on, we need to guess the cross axis dir\\n if do_auto:\\n # now since the first joint we want to match the second orientation\\n # we kind of hack the things passed in if it is the first joint\\n # ie: if the joint doesnt have a parent... or if the parent it has\\n # has the 'same' position as itself... then we use the 'next' joints\\n # as the up cross calculations\\n jnt_point = o.getRotatePivot(space = 'world')\\n if parent:\\n parent_point.setValue(parent.getRotatePivot(space = 'world'))\\n else:\\n parent_point = jnt_point.copy()\\n aim_tgt_point = aim_tgt.getRotatePivot(space = 'world')\\n\\n # how close to we consider 'same'?\\n tol = 0.0001\\n\\n point_cond = jnt_point - parent_point\\n pos_cond = [abs(x) for x in point_cond.tolist()]\\n if not parent or pos_cond[0] <= tol and pos_cond[1] <= tol and pos_cond[2] <= tol:\\n # get aimChild\\n aim_child = None\\n aim_children = aim_tgt.getChildren(type = 'joint')\\n if aim_children:\\n aim_child = aim_children[0]\\n\\n # get aimChild vector\\n if aim_child:\\n aim_child_point = aim_child.getRotatePivot(space = 'world')\\n else:\\n aim_child_point = pm.dt.Vector()\\n\\n # find the up vector using child vector of aim target\\n up_vec = (jnt_point - aim_tgt_point).cross(aim_child_point - aim_tgt_point)\\n else:\\n # find the up vector using the parent vector\\n up_vec = (parent_point - jnt_point).cross(aim_tgt_point - jnt_point)\\n\\n # reorient the current joint\\n a_cons = pm.aimConstraint(\\n aim_tgt, o, aimVector = aim_axis, upVector = up_axis, worldUpVector = up_vec.tolist(),\\n worldUpType = 'vector', weight = 1\\n )\\n pm.delete(a_cons)\\n\\n # now compare the up we used to the prev one\\n current_up = up_vec.normal()\\n # dot product for angle between... store for later\\n dot = current_up.dot(prev_up)\\n prev_up = up_vec\\n\\n if i > 0 >= dot:\\n # adjust the rotation axis 180 if it looks like we have flopped the wrong way!\\n # FIXME: some shit need to fix here\\n # pm.xform( o, relative = True, objectSpace = True, rotateAxis = True )\\n o.rotateX.set(o.rotateX.get() + (aim_axis.x * 180))\\n o.rotateY.set(o.rotateY.get() + (aim_axis.y * 180))\\n o.rotateZ.set(o.rotateZ.get() + (aim_axis.z * 180))\\n\\n prev_up *= -1\\n elif parent:\\n # otherwise if there is no target, just dup orientation of parent...\\n transformation.align(o, parent, mode = 'rotate')\\n\\n # and now finish clearing out joint axis ...\\n pm.joint(o, e = True, zeroScaleOrient = True)\\n transformation.freeze_transform(o)\\n\\n # now that we are done ... reparent\\n if len(children) > 0:\\n for x in children:\\n x.setParent(o)\\n\\n return True\",\n \"def update_position_direction(self, l):\\n\\n x = np.sqrt(self.x**2 + l**2 + 2 * l * self.x * self.mu)\\n mu = (l + self.x * self.mu) / x\\n\\n return x, mu\",\n \"def get_direction(self):\\r\\n return self.__direction\",\n \"def dmove_joint(self, delta_pos: [list, np.ndarray]) -> [bool, np.ndarray]:\\n if not isinstance(delta_pos, np.ndarray):\\n delta_pos = np.array(delta_pos)\\n abs_pos = np.array(self.get_current_joint_position()) # or self._position_joint_desired ?\\n abs_pos += delta_pos\\n return self.move_joint(abs_pos)\",\n \"def read_direction(self):\\n global motor_direction\\n with self._lock:\\n return motor_direction\",\n \"def update_direction(self, move : np.ndarray, direction: np.ndarray):\\r\\n pos = move.copy()\\r\\n \\r\\n\\r\\n pos += direction\\r\\n while(self.in_board(pos)):\\r\\n if self.board[pos[0],pos[1]] == self.turn:\\r\\n pos -= direction\\r\\n while((pos != move).any()):\\r\\n self.board[pos[0], pos[1]] = self.turn\\r\\n self.count += 1\\r\\n pos -= direction\\r\\n break\\r\\n\\r\\n elif self.board[pos[0],pos[1]] == 0:\\r\\n\\r\\n break\\r\\n else:\\r\\n pos += direction\",\n \"def calc_relative_direction(ship_dir, ww_dir):\\n if ship_dir not in (\\\"N\\\", \\\"E\\\", \\\"S\\\", \\\"W\\\"):\\n raise Exception(\\\"Direction not accepted.\\\")\\n ww_360 = ww_dir\\n ww_360[ww_360 < 0] = 360 + ww_dir[0]\\n if ship_dir in (\\\"N\\\"):\\n dir_4 = np.full((len(ww_dir), 1), 2)\\n dir_4[(ww_dir < 45) | (ww_dir > 315)] = 1\\n dir_4[(ww_dir > 135) & (ww_dir < 225)] = 3\\n if ship_dir in (\\\"E\\\"):\\n dir_4 = np.full((len(ww_dir), 1), 2)\\n dir_4[(ww_dir > 45) & (ww_dir < 135)] = 1\\n dir_4[(ww_dir > 225) & (ww_dir < 315)] = 3\\n if ship_dir in (\\\"W\\\"):\\n dir_4 = np.full((len(ww_dir), 1), 2)\\n dir_4[(ww_dir > 45) & (ww_dir < 135)] = 3\\n dir_4[(ww_dir > 225) & (ww_dir < 315)] = 1\\n if ship_dir in (\\\"S\\\"):\\n dir_4 = np.full((len(ww_dir), 1), 2)\\n dir_4[(ww_dir < 45) | (ww_dir > 315)] = 3\\n dir_4[(ww_dir > 135) & (ww_dir < 225)] = 1\\n return dir_4\",\n \"def get_direction_matrix(self) -> int:\",\n \"def __calc_target_angle(self, delta_angle, direction):\\n if self.is_reverse:\\n direction = not direction\\n\\n if direction:\\n if self.current_angle - delta_angle < 0 or self.current_angle - delta_angle > pi:\\n return self.current_angle\\n return self.current_angle - delta_angle # this mines (-) for cw.\\n else:\\n if self.current_angle + delta_angle < 0 or self.current_angle + delta_angle > pi:\\n return self.current_angle\\n return self.current_angle + delta_angle\",\n \"def direction(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"direction\\\")\",\n \"def move_to_joint_pos_delta(self, cmd):\\n curr_q = self.joint_angles()\\n joint_names = self.joint_names()\\n\\n joint_command = dict([(joint, curr_q[joint] + cmd[i])\\n for i, joint in enumerate(joint_names)])\\n\\n self.move_to_joint_positions(joint_command)\",\n \"def getDirection(self):\\n return self.ray.direction\",\n \"def calculate_direction(east, north):\\n if not Ensemble.is_bad_velocity(east) and not Ensemble.is_bad_velocity(north):\\n bin_dir = (math.atan2(east, north)) * (180.0 / math.pi)\\n\\n # The range is -180 to 180\\n # This moves it to 0 to 360\\n if bin_dir < 0.0:\\n bin_dir = 360.0 + bin_dir\\n\\n return bin_dir\\n else:\\n return Ensemble.BadVelocity\",\n \"def set_j(cmd, limb, joints, index, delta):\\n joint = joints[index]\\n cmd[joint] = delta + limb.joint_angle(joint)\",\n \"def direction(self) -> str:\\n return pulumi.get(self, \\\"direction\\\")\",\n \"def get_direction(self):\\n return self.direction\",\n \"def direction(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"direction\\\")\",\n \"def _calc_relative_move_direction(self, char, direction):\\n if char in (\\\"Left\\\", \\\"Right\\\"):\\n di = -1 if self.video.hflip else 1\\n else:\\n di = -1 if self.video.vflip else 1\\n return direction * di\",\n \"def calculate_head_direction_from_leds(positions, return_as_deg=False):\\n X_led1, Y_led1, X_led2, Y_led2 = positions[:, 0], positions[:, 1], positions[:, 2], positions[:, 3]\\n # Calculate head direction\\n head_direction = np.arctan2(X_led1 - X_led2, Y_led1 - Y_led2)\\n # Put in right perspective in relation to the environment\\n offset = +np.pi/2\\n head_direction = (head_direction + offset + np.pi) % (2*np.pi) - np.pi\\n head_direction *= -1\\n if return_as_deg:\\n head_direction = head_direction * (180 / np.pi)\\n\\n return head_direction\",\n \"def construct_direction(self, embs_source: torch.Tensor, embs_target: torch.Tensor):\\n return (embs_target.mean(0) - embs_source.mean(0)).unsqueeze(0)\",\n \"def direction(self):\\n g = self._grad_f(self._x, *self._args)\\n H = self._hess_f(self._x, *self._args)\\n self._calls[1:3] += 1\\n gr = g\\n Hr = g\\n damping = 0\\n while True:\\n try:\\n L, _ = cho_factor(Hr, lower=0)\\n dx = -cho_solve((L, 0), gr)\\n break\\n except:\\n #print('Ooops... singular Hessian')\\n damping = 10 * max(TINY, damping)\\n gr = g + damping * self._x\\n Hr = H + damping * np.eye(len(self._x))\\n return np.nan_to_num(dx)\",\n \"def update_player_direction(self,direction):\\n pass\",\n \"def update_direction(self):\\n direction = self.get_direction()\\n\\n if direction[1] == NORTH[1]:\\n self.current_animation = N_ANIM \\n elif direction[1] == SOUTH[1]:\\n self.current_animation = S_ANIM \\n elif direction[0] == EAST[0]:\\n self.current_animation = E_ANIM \\n else:\\n self.current_animation = W_ANIM\",\n \"def get_direction(self):\\n\\n return -1 if self.curr_player == self.PLAYER1 else 1\",\n \"def find_upwinding_direction(self):\\n self.upwinded_face_cell = []\\n for cell_index in range(self.mesh.get_number_of_cells()):\\n for [face_index, orientation] in zip(self.mesh.get_cell(cell_index), \\n self.mesh.get_cell_normal_orientation(cell_index)):\\n if orientation*self.current_velocity[face_index]>0:\\n self.upwinded_face_cell.append([face_index, cell_index])\\n\\n ## Set fractional flow for dirichlet cells set by points\\n ## based on up-winded flow direction. If the flow is out \\n ## of the cell, this is done automatically in previous \\n ## loops. However, for flow into the cell, we must find \\n ## the saturation from the cell it points to. \\n for face_index in self.mesh.get_dirichlet_pointer_faces():\\n (cell_index, orientation) = self.mesh.get_dirichlet_pointer(face_index)\\n if self.current_velocity[face_index]*orientation<0.:\\n self.upwinded_face_cell.append([face_index, cell_index])\",\n \"def direction(a:tuple, b:tuple, c:tuple)->int:\\n return ((b[1] - a[1]) * (c[0] - b[0])) - ((b[0] - a[0]) * (c[1] - b[1]))\",\n \"def getRobotDirection(self):\\n return self.direction\",\n \"def getRobotDirection(self):\\n return self.direction\",\n \"def getRobotDirection(self):\\n return self.direction\\n #raise NotImplementedError\",\n \"def getRobotDirection(self):\\n return self.direction\\n #raise NotImplementedError\",\n \"def get_origin_direction(self):\\n return self.origin_coordinates[2]\",\n \"def get_joint_positions(self, joint_angles ): \\n\\n\\n # current angles\\n res_joint_angles = joint_angles.copy() \\n\\n # detect limits\\n maskminus= res_joint_angles > self.joint_lims[:,0]\\n maskplus = res_joint_angles < self.joint_lims[:,1]\\n \\n res_joint_angles = res_joint_angles*(maskplus*maskminus) \\n res_joint_angles += self.joint_lims[:,0]*(np.logical_not(maskminus) )\\n res_joint_angles += self.joint_lims[:,1]*(np.logical_not(maskplus) )\\n \\n # mirror\\n if self.mirror :\\n res_joint_angles = -res_joint_angles\\n res_joint_angles[0] += np.pi \\n \\n # calculate x coords of arm edges.\\n # the x-axis position of each edge is the \\n # sum of its projection on the x-axis\\n # and all the projections of the \\n # previous edges \\n x = np.array([ \\n sum([ \\n self.segment_lengths[k] *\\n np.cos( (res_joint_angles[:(k+1)]).sum() ) \\n for k in range(j+1) \\n ])\\n for j in range(self.number_of_joint) \\n ])\\n \\n # trabslate to the x origin \\n x = np.hstack([self.origin[0], x+self.origin[0]])\\n\\n # calculate y coords of arm edges.\\n # the y-axis position of each edge is the \\n # sum of its projection on the x-axis\\n # and all the projections of the \\n # previous edges \\n y = np.array([ \\n sum([ \\n self.segment_lengths[k] *\\n np.sin( (res_joint_angles[:(k+1)]).sum() ) \\n for k in range(j+1) \\n ])\\n for j in range(self.number_of_joint) \\n ])\\n \\n # translate to the y origin \\n y = np.hstack([self.origin[1], y+self.origin[1]])\\n\\n pos = np.array([x, y]).T\\n \\n return (pos, res_joint_angles)\",\n \"def head_direction(self, target, useAvoidance=False, verbose=False, turnSpeed=1, door = False):\\n\\n endVector = target\\n\\n speedLeft, speedRight = self.target_to_left_right_speeds(endVector)\\n\\n self.logger.debug(\\\"endVector: %s\\\" % repr(endVector))\\n self.logger.debug(\\\"Wheel speeds: %d, %d\\\" % (speedLeft, speedRight))\",\n \"def getDirectionalMovement(self, currentLocation, direction):\\n # get the maximum diagonal vs single line movements based on the robot type\\n if self.isCrusader:\\n # Crusader can move a maximum of 3 blocks in a single line and 2 blocks in a diagonal line\\n singleLineMovementSpeed = 3\\n diagonalMovementSpeed = 2\\n else:\\n # all other units can move a maximum of 2 blocks in a single line and 1 block in a diagonal line\\n singleLineMovementSpeed = 2\\n diagonalMovementSpeed = 1\\n\\n # set the x and y directional movements to the current direction's x and y. These will be used to store the maximum movement speed of the robot in a given direction\\n xDirectionalMovement = direction[0]\\n yDirectionalMovement = direction[1]\\n\\n # if neither x or y are 0, then the unit is traveling diagonally so we multiply the values by the diagonal movement speed\\n if xDirectionalMovement != 0 and yDirectionalMovement != 0:\\n xDirectionalMovement *= diagonalMovementSpeed\\n yDirectionalMovement *= diagonalMovementSpeed\\n else:\\n # else we multiply the movement values by the single line movement speed\\n xDirectionalMovement *= singleLineMovementSpeed\\n yDirectionalMovement *= singleLineMovementSpeed\\n\\n # store the values of how far the current location is from the target location\\n xOffset = self.targetLocation[0] - currentLocation[0]\\n yOffset = self.targetLocation[1] - currentLocation[1]\\n\\n # if the distance from \\n if self.isCrusader:\\n if xDirectionalMovement != 0:\\n if (xOffset > 0 and xOffset < 3) or (xOffset < 0 and xOffset > -3):\\n xDirectionalMovement = xOffset\\n\\n if yDirectionalMovement != 0:\\n if (yOffset > 0 and yOffset < 3) or (yOffset < 0 and yOffset > -3):\\n yDirectionalMovement = yOffset\\n else:\\n if xDirectionalMovement != 0 and (xOffset == 1 or xOffset == -1):\\n xDirectionalMovement = xOffset\\n\\n if yDirectionalMovement != 0 and (yOffset == 1 or yOffset == -1):\\n yDirectionalMovement = yOffset\\n\\n return (xDirectionalMovement, yDirectionalMovement)\",\n \"def update(self):\\n self.updateCount = self.updateCount + 1\\n if self.updateCount > self.updateCountMax:\\n\\n # update previous positions\\n for i in range(self.length - 1, 0, -1):\\n self.x[i] = self.x[i - 1]\\n self.y[i] = self.y[i - 1]\\n\\n # update position of player : party lead\\n if self.direction == 0:\\n self.x[0] = self.x[0] + self.step\\n if self.direction == 1:\\n self.x[0] = self.x[0] - self.step\\n if self.direction == 2:\\n self.y[0] = self.y[0] - self.step\\n if self.direction == 3:\\n self.y[0] = self.y[0] + self.step\\n\\n self.updateCount = 0\",\n \"def joint_angles_callback(self, msg):\\n\\t\\t\\t# read the current joint angles from the robot\\n\\t\\t\\tpos_curr = np.array([msg.joint1,msg.joint2,msg.joint3,msg.joint4,msg.joint5,msg.joint6,msg.joint7]).reshape((7,1))\\n\\n\\t\\t\\t# convert to radians\\n\\t\\t\\tpos_curr = pos_curr*(math.pi/180.0)\\n\\n\\t\\t\\t# update torque from PID based on current position\\n\\t\\t\\tself.torque = self.Impedence_control(pos_curr)\",\n \"def direction(self):\\n return self.cfg.direction\",\n \"def motors__Direction(self, speed_l, speed_r):\\n\\n if speed_l >= 0:\\n self.motorDirection(self.motor1DirectionPin, self.motorForward)\\n else:\\n self.motorDirection(self.motor1DirectionPin, self.motorReverse)\\n\\n if speed_r >= 0:\\n self.motorDirection(self.motor2DirectionPin, self.motorForward)\\n else :\\n self.motorDirection(self.motor2DirectionPin, self.motorReverse)\",\n \"def direction(self):\\n _direction = self._custom.get(\\\"direction\\\")\\n if _direction is not None:\\n return _direction\\n\\n _direction = self._infer_direction()\\n self._custom[\\\"direction\\\"] = _direction\\n\\n return _direction\",\n \"def direction(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"direction\\\")\",\n \"def getDirection(self):\\n if 'N' in str(self.trip_update.trip.trip_id):\\n direction = 'northbound'\\n if 'S' in str(self.trip_update.trip.trip_id):\\n direction = 'southbound'\\n return direction\",\n \"def _get_direction(self, action, direction):\\n left = [2,3,1,0]\\n right = [3,2,0,1]\\n if direction == 0:\\n new_direction = action\\n elif direction == -1:\\n new_direction = left[action]\\n elif direction == 1:\\n new_direction = right[action]\\n else:\\n raise Exception(\\\"getDir received an unspecified case\\\")\\n return new_direction\",\n \"def direction(self):\\n return None if not bool(self.relation) else (self.s_end <= self.o_start)\",\n \"def _vector_direction(self,\\n desired_pos : np.array,\\n actual_pos : np.array) -> np.array:\\n return desired_pos - actual_pos\",\n \"def motorsDirection(self, direction):\\n\\n print (direction)\\n if direction == 'r' or direction == 'R':\\n self.motorDirection(self.motor1DirectionPin, self.motorReverse)\\n self.motorDirection(self.motor2DirectionPin, self.motorReverse)\\n print (\\\"Direction reverse\\\")\\n else:\\n self.motorDirection(self.motor1DirectionPin, self.motorForward)\\n self.motorDirection(self.motor2DirectionPin, self.motorForward)\\n print (\\\"Direction forward\\\")\",\n \"def forward_kinematics(joint_angle_state,num_dof,link_length):\\n\\t#initialize the xpos and ypos\\n\\txpos = 0\\n\\typos = 0\\n\\tfor i in range(1,num_dof+1):\\n\\t\\txpos += (link_length*np.cos(np.sum(joint_angle_state[0,:i])))\\n\\t\\typos += (link_length*np.sin(np.sum(joint_angle_state[0,:i])))\\n\\treturn (int(xpos),int(ypos))\",\n \"def get_direction(curr_pos, next_pos):\\n if curr_pos == next_pos:\\n return 'CLEAN'\\n\\n v_dist = next_pos[0] - curr_pos[0]\\n h_dist = next_pos[1] - curr_pos[1]\\n\\n if h_dist != 0:\\n if h_dist < 0:\\n return 'LEFT'\\n else:\\n return 'RIGHT'\\n else:\\n if v_dist < 0:\\n return 'UP'\\n else:\\n return 'DOWN'\",\n \"def direction(self):\\n return self._direction.copy()\",\n \"def _route_to_goal(self, position, orientation):\\n _, (_x,_y) = self._calc_torus_distance(position, self.goal)\\n move = None\\n\\n if orientation == 'up':\\n if self.goal[1] > position[1] and _y > 0:\\n move = 'move'\\n elif self.goal[1] < position[1] and _y < 1:\\n move = 'move'\\n elif self.goal[0] > position[0]:\\n if _x > 0:\\n move = 'left'\\n else:\\n move = 'right'\\n else:\\n if _x > 0:\\n move = 'right'\\n else:\\n move = 'left'\\n\\n if orientation == 'down':\\n if self.goal[1] < position[1] and _y > 0:\\n move = 'move'\\n elif self.goal[1] > position[1] and _y < 1:\\n move = 'move'\\n elif self.goal[0] > position[0]:\\n if _x > 0:\\n move = 'right'\\n else:\\n move = 'left'\\n else:\\n if _x > 0:\\n move = 'left'\\n else:\\n move = 'right'\\n\\n if orientation == 'right':\\n if self.goal[0] < position[0] and _x > 0:\\n move = 'move'\\n elif self.goal[0] > position[0] and _x < 1:\\n move = 'move'\\n elif self.goal[1] > position[1]:\\n if _y > 0:\\n move = 'left'\\n else:\\n move = 'right'\\n else:\\n if _y > 0:\\n move = 'right'\\n else:\\n move = 'left'\\n\\n if orientation == 'left':\\n if self.goal[0] > position[0] and _x > 0:\\n move = 'move'\\n elif self.goal[0] < position[0] and _x < 1:\\n move = 'move'\\n elif self.goal[1] > position[1]:\\n if _y > 0:\\n move = 'right'\\n else:\\n move = 'left'\\n else:\\n if _y > 0:\\n move = 'left'\\n else:\\n move = 'right'\\n\\n return move\",\n \"def __get_new_direction(self):\\n return fabs(self.random_generator.normal(self.__direction_mean,\\n self.__direction_deviation))\",\n \"def change_direction(self, direction):\\r\\n for event in pygame.event.get():\\r\\n if event.type == pygame.KEYDOWN:\\r\\n if event.key == pygame.K_ESCAPE:\\r\\n pygame.quit()\\r\\n sys.exit()\\r\\n elif event.key == pygame.K_UP:\\r\\n if self.direction == [0, 1]:\\r\\n self.direction == [0, 1]\\r\\n return self.direction\\r\\n else:\\r\\n self.direction = [dx, dy] = [0, -1]\\r\\n return self.direction\\r\\n elif event.key == pygame.K_DOWN:\\r\\n if self.direction == [0, -1]:\\r\\n self.direction == [0, -1]\\r\\n return self.direction\\r\\n else:\\r\\n self.direction = [dx, dy] = [0, 1]\\r\\n return self.direction\\r\\n elif event.key == pygame.K_LEFT:\\r\\n if self.direction == [1, 0]:\\r\\n self.direction == [1, 0]\\r\\n return self.direction\\r\\n else:\\r\\n self.direction = [dx, dy] = [-1, 0]\\r\\n return self.direction\\r\\n elif event.key == pygame.K_RIGHT:\\r\\n if self.direction == [-1, 0]:\\r\\n self.direction == [-1, 0]\\r\\n return self.direction\\r\\n else:\\r\\n self.direction = [dx, dy] = [1, 0]\\r\\n return self.direction\",\n \"def get_direction(self):\\n is_direction_correct = False\\n while not is_direction_correct:\\n direction = random.randint(0, 2)\\n if direction == 0:\\n self.turtle.left(90)\\n elif direction == 1:\\n self.turtle.right(90)\\n else:\\n self.turtle.right(0)\\n is_direction_correct = self.check_boundary()\",\n \"def update_direction(self, update_data: dict):\\n if self.on_update_direction:\\n self.on_update_direction(self, update_data)\",\n \"def right(self):\\r\\n z = len(direction_tuple)\\r\\n if self.d in direction_tuple:\\r\\n index = direction_tuple.index(self.d)\\r\\n if index == (z-1):\\r\\n self.d = direction_tuple[0]\\r\\n else:\\r\\n self.d = direction_tuple[index + 1]\\r\\n else:\\r\\n print(\\\"NO VALID ROBOT POSITION\\\")\",\n \"def get_desired_joint_position(self):\\n return self._position_joint_desired\",\n \"def get_next_moving_direction(self, nearest_zombie_pos=None):\\n if nearest_zombie_pos is None:\\n nearest_zombie_pos=(uniform(-1.0, 1.0), uniform(-1.0, 1.0))\\n\\n vector = (self.pos[0]-nearest_zombie_pos[0], \\\\\\n self.pos[1]-nearest_zombie_pos[1])\\n magnitude = math.sqrt(vector[0]**2 + vector[1]**2)\\n direction_vector = (vector[0]/magnitude, vector[1]/magnitude)\\n return direction_vector\",\n \"def translate_direction(self):\\n xpart = math.sin(self.direction)\\n ypart = math.cos(self.direction)\\n if ypart > 0:\\n print(\\\"oben \\\", end='')\\n else:\\n print(\\\"unten \\\", end='')\\n if xpart > 0:\\n print(\\\"rechts\\\")\\n else:\\n print(\\\"links\\\")\",\n \"def set_direction(self, dir):\\n if dir == 0:\\n self.direction = [0, -1]\\n elif dir == 1:\\n self.direction = [1, 0]\\n elif dir == 2:\\n self.direction = [0, 1]\\n elif dir == 3:\\n self.direction = [-1, 0]\",\n \"def direction(point0, point1):\\n d = [0, 0, 0]\\n vector = [point1[0] - point0[0], point1[1] - point0[1]]\\n d[1] = math.atan2(vector[1], vector[0])\\n while d[1] <= -np.pi / 2:\\n d[1] += np.pi\\n return d\",\n \"def motor_angles(self):\\n return np.asarray(self._robot_state.position)\",\n \"def getDirection(self):\\n return self.listener.direction\",\n \"def dirToIncrement(direction):\\n # assume upperleft corner is (0,0)\\n if direction == 'up':\\n return (0,-1)\\n elif direction == 'down':\\n return (0,1)\\n elif direction == 'left':\\n return (-1,0)\\n elif direction == 'right':\\n return (1,0)\\n vertical, horizontal = direction.split()\\n v1, h1 = dirToIncrement(vertical)\\n v2, h2 = dirToIncrement(horizontal)\\n return (v1+v2, h1+h2)\",\n \"def coordnav(self,dx,dy,dth): \\n self.cg_ant = np.array(self.cg)\\n self.coord[0] = self.coord[0] - self.cg[0]\\n self.coord[1] = self.coord[1] - self.cg[1] \\n self.Rz = rotation.matrix([0,0,1],dth)\\n self.coord = np.dot(self.Rz,self.coord)\\n \\n self.coord[0] = self.coord[0] + self.cg[0] + dx\\n self.coord[1] = self.coord[1] + self.cg[1] + dy \\n \\n self.px = self.coord[:,self.px_index]\\n self.Bx = self.px-self.cg \\n self.basex = self.Bx/math.sqrt(np.dot(self.Bx,self.Bx))\",\n \"def move(self, twist: Optional[Twist] = None):\\n if twist is None:\\n left = right = 0\\n self.navigation_goal = None\\n else:\\n linear = np.clip(twist.linear.x, -1, 1)\\n angular = np.clip(twist.angular.x, -1, 1)\\n left, right = (linear - angular) / 2, (linear + angular) / 2\\n # # always give a robot the full velocity at least on one side\\n # if (greater := max(abs(left), abs(right))) > 0:\\n # left, right = left / greater, right / greater\\n\\n self.locomotion_lock.acquire()\\n self.v_left = SPEEDUP * left\\n self.v_right = SPEEDUP * right\\n self.locomotion_lock.release()\",\n \"def compute_angle(self, direction):\\n scaled_cosine = self.w1.dot(direction) # ||direction|| cos(theta)\\n scaled_sine = self.w2.dot(direction) # ||direction|| sin(theta)\\n return np.arctan2(scaled_sine, scaled_cosine)\",\n \"def move_in_direction(self, direction):\\n if direction == NORTH:\\n self.__position[y] += 1\\n elif direction == NORTHEAST:\\n self.__position[x] += 1\\n self.__position[y] += 1\\n elif direction == EAST:\\n self.__position[x] += 1\\n elif direction == SOUTHEAST:\\n self.__position[x] += 1\\n self.__position[y] -= 1\\n elif direction == SOUTH:\\n self.__position[y] -= 1\\n elif direction == SOUTHWEST:\\n self.__position[x] -= 1\\n self.__position[y] -= 1\\n elif direction == WEST:\\n self.__position[x] -= 1\\n elif direction == NORTHWEST:\\n self.__position[x] -= 1\\n self.__position[y] += 1\",\n \"def turn_to_endpoint(previous_direction, w_real, d, d_traveled):\\n theta = math.atan((D_ENDPOINT-d_traveled)/w_real)\\n phi = np.pi/2 - theta\\n\\n if previous_direction==0:\\n # drone yaws to the right to the direction of the endpoint\\n yaw(phi)\\n else:\\n # drone yaws to the left to the direction of the endpoint\\n yaw(-phi)\\n phi = -phi\\n\\n return phi\",\n \"def compute_direction(self, feats):\\n if feats.name == \\\"ARNC\\\":\\n if feats[\\\"z-score\\\"] < -1.5:\\n return Directions.long_dir\\n elif feats[\\\"z-score\\\"] > 1.5:\\n return Directions.short_dir\\n elif feats.name == \\\"UNG\\\":\\n if feats[\\\"z-score\\\"] < -1.5:\\n return Directions.short_dir\\n elif feats[\\\"z-score\\\"] > 1.5:\\n return Directions.long_dir\",\n \"def get_relative_direction(self, source, destination, orientation):\\r\\n direction = {}\\r\\n direction['North'] = {}\\r\\n direction['North']['North'] = 'Straight'\\r\\n direction['North']['East'] = 'Right'\\r\\n direction['North']['West'] = 'Left'\\r\\n direction['East'] = {}\\r\\n direction['East']['East'] = 'Straight'\\r\\n direction['East']['North'] = 'Left'\\r\\n direction['East']['South'] = 'Right'\\r\\n direction['South'] = {}\\r\\n direction['South']['South'] = 'Straight'\\r\\n direction['South']['East'] = 'Left'\\r\\n direction['South']['West'] = 'Right'\\r\\n direction['West'] = {}\\r\\n direction['West']['West'] = 'Straight'\\r\\n direction['West']['South'] = 'Left'\\r\\n direction['West']['North'] = 'Right'\\r\\n if type(orientation) != str or orientation not in ['North', 'East', 'South', 'West']:\\r\\n return None\\r\\n final_orientation = self.get_orientation_from_to(source, destination)\\r\\n if orientation == final_orientation:\\r\\n return 'Straight'\\r\\n else:\\r\\n print orientation\\r\\n print final_orientation\\r\\n return direction[orientation][final_orientation]\",\n \"def direction(self,four_dir=False):\\n a = self.angle()\\n if a is None:\\n return None\\n\\n if four_dir:\\n if a >= -45 and a <= 45:\\n return \\\"UP\\\"\\n elif a >= 45 and a <= 135:\\n return \\\"RIGHT\\\"\\n elif a >= 135 or a <= -135:\\n return \\\"DOWN\\\"\\n elif a >= -135 and a <= -45:\\n return \\\"LEFT\\\"\\n else:\\n raise RuntimeError(\\\"Couldn't figure out %f\\\" % a)\\n else:\\n if a >= -22.5 and a <= 22.5:\\n return \\\"UP\\\"\\n elif a >= 22.5 and a <= 67.5:\\n return \\\"UP-RIGHT\\\"\\n elif a >= 67.5 and a <= 112.5:\\n return \\\"RIGHT\\\"\\n elif a >= 112.5 and a <= 157.5:\\n return \\\"DOWN-RIGHT\\\"\\n elif a >= 157.5 or a <= -157.5:\\n return \\\"DOWN\\\"\\n elif a >= -157.5 and a <= -112.5:\\n return \\\"DOWN-LEFT\\\"\\n elif a >= -112.5 and a <= -67.5:\\n return \\\"LEFT\\\"\\n elif a >= -67.5 and a <= -22.5:\\n return \\\"UP-LEFT\\\"\\n else:\\n raise RuntimeError(\\\"Couldn't figure out %f\\\" % a)\",\n \"def update_position(position, direction):\\n\\n\\tif direction == 'left':\\n\\t\\treturn [position[0], position[1] - 1]\\n\\tif direction == 'right':\\n\\t\\treturn [position[0], position[1] + 1]\\n\\tif direction == 'down':\\n\\t\\treturn [position[0] + 1, position[1]]\\n\\tif direction == 'up':\\n\\t\\treturn [position[0] - 1, position[1]]\\n\\treturn [-1, -1]\",\n \"async def direction(self, value) -> str:\\n if value is None:\\n return \\\"N\\\"\\n\\n direction_array = [\\n \\\"N\\\",\\n \\\"NNE\\\",\\n \\\"NE\\\",\\n \\\"ENE\\\",\\n \\\"E\\\",\\n \\\"ESE\\\",\\n \\\"SE\\\",\\n \\\"SSE\\\",\\n \\\"S\\\",\\n \\\"SSW\\\",\\n \\\"SW\\\",\\n \\\"WSW\\\",\\n \\\"W\\\",\\n \\\"WNW\\\",\\n \\\"NW\\\",\\n \\\"NNW\\\",\\n \\\"N\\\",\\n ]\\n direction_str = direction_array[int((value + 11.25) / 22.5)]\\n return self._translations[\\\"wind_dir\\\"][direction_str]\",\n \"def getMovement(self):\\n # store the robot's current location and set the directional movement to 0,0 so that the robot won't move by default\\n currentLocation = (self.me['x'], self.me['y'])\\n directionalMovement = (0,0)\\n\\n # ensure that target location is not none and not equal to the current location\\n if self.targetLocation and not currentLocation == self.targetLocation:\\n\\n # store the direction, directional movement, and the new map location we will trying to move the robot to this round\\n direction = self.getDirection(currentLocation, self.targetLocation)\\n directionalMovement = self.getDirectionalMovement(currentLocation, direction)\\n newLocation = self.getNewLocation(currentLocation, directionalMovement)\\n\\n # store the current direction for use later\\n initialDirection = direction\\n\\n # by default, the robot is ready to move in the event that the new map location is already passable\\n readyToMove = True\\n\\n # while the new map location is not passable\\n while not self.isPassable(newLocation):\\n # if unit is a crusader moving diagonally at their fastest pace, set their directional movement to (1,1)\\n if self.isCrusader and directionalMovement[0] == 2 and directionalMovement[1] == 2:\\n directionalMovement[0] = 1\\n directionalMovement[1] = 1\\n # or if the unit is traveling faster than 1 block East\\n elif directionalMovement[0] > 1:\\n # lower the unit's movement East by 1 block\\n directionalMovement[0] -= 1\\n # or if the unit is traveling faster than 1 block West\\n elif directionalMovement[0] < -1:\\n # lower the unit's movement West by 1 block\\n directionalMovement[0] += 1\\n # or if the unit is traveling faster than 1 block South\\n elif directionalMovement[1] > 1:\\n # lower the unit's movement South by 1 block\\n directionalMovement[1] -= 1\\n # or if the unit is traveling faster than 1 block North\\n elif directionalMovement[1] < -1:\\n # lower the unit's movement North by 1 block\\n directionalMovement[1] += 1\\n # else the unit is already moving the shortest distance they can in the current direction\\n else:\\n # rotate the robots direction clockwise and proceed\\n direction = self.getRotatedDirection(direction, 1)\\n\\n # if we ened up facing the same direction we started in\\n if direction == initialDirection:\\n # let the code know we're not ready to move\\n readyToMove = False\\n # break out of the while loop\\n break\\n\\n # overwrite the directional movement with a new one based on the direction we just got\\n directionalMovement = self.getDirectionalMovement(currentLocation, direction)\\n\\n # overwrite the new location with the location we get from the directional movement we just got\\n newLocation = self.getNewLocation(currentLocation, directionalMovement)\\n\\n # if the robot ended up not being ready to move\\n if not readyToMove:\\n # change the directional movement back to (0,0) so that it doesn't move\\n directionalMovement = (0,0)\\n else :\\n self.targetLocation = self.getRandomPassableLocation()\\n # return the directional movement\\n return directionalMovement\",\n \"def align(self) -> np.ndarray:\\n vel = self.state[:, :, Boids.Attr.VEL]\\n vel_norm = np.linalg.norm(vel, axis=0)\\n orientation = vel / (vel_norm + EPSILON)\\n mut_influence = self._perceive(self.p_range)\\n desired_orientation = np.dot(orientation, mut_influence)\\n desired_orientation = np.multiply(desired_orientation, \\n vel_norm + EPSILON)\\n return desired_orientation - orientation\",\n \"def direction(self) -> int:\\n return self._direction\",\n \"def _step_direction(self, rho, phi, direction_reading, *args, **kwargs):\\r\\n condition = kwargs['obj'] is not None\\\\\\r\\n and rho <= self.range\\\\\\r\\n and phi <= self.aperture #<= 3*np.pi/self.n_sectors\\r\\n if direction_reading is None:\\r\\n direction_reading = 0.\\r\\n # import pdb; pdb.set_trace()\\r\\n if condition and direction_reading == 0.0:\\r\\n my_pos = self.get_position(self.sensors_idx[args[0]]) + np.r_[0, 0, 0.1] #+ np.r_[0, 0, 0.017]\\r\\n tar_post = kwargs['obj'].position + np.r_[0, 0, 0.07] # my_pos[2]]\\r\\n ray_res = p.rayTest(my_pos, tar_post, physicsClientId=self.sensor_owner.physics_client)[0][0]\\r\\n # signal_strength = self.propagation(rho, phi)\\r\\n if ray_res == kwargs['obj'].id:\\r\\n direction_reading = 1.\\r\\n return direction_reading\",\n \"def get_dir_of_obst(cur_pos: Node, cur_heading: float, obst_to_follow: ObstacleSegment) -> Dir:\\n # find closest obstacle boundary point\\n closest_dist = math.inf\\n closest_bp = obst_to_follow.boundary_points[0]\\n for bp in obst_to_follow.get_boundary_points():\\n d = cur_pos.dist_2d(bp)\\n if d <= closest_dist:\\n closest_bp = bp\\n closest_dist = d\\n\\n a = cur_pos.as_node_2d()\\n b = cur_pos + Node.from_array(rotate(np.array([0, 0]), np.array([1, 0]), cur_heading))\\n p = closest_bp.as_node_2d()\\n\\n # make a origin\\n b = b - a\\n p = p - a\\n c_p = np.cross(b.as_ndarray_2d(), p.as_ndarray_2d())\\n if np.all(c_p > 0):\\n return Dir.LEFT\\n else:\\n return Dir.RIGHT\",\n \"def calculate_heading_direction_from_position(positions, smoothing=False, return_as_deg=False):\\n X, Y = positions[:, 0], positions[:, 1]\\n # Smooth diffs instead of speeds directly\\n Xdiff = np.diff(X)\\n Ydiff = np.diff(Y)\\n if smoothing:\\n Xdiff = smooth_signal(Xdiff, smoothing)\\n Ydiff = smooth_signal(Ydiff, smoothing)\\n # Calculate heading direction\\n heading_direction = np.arctan2(Ydiff, Xdiff)\\n heading_direction = np.append(heading_direction, heading_direction[-1])\\n if return_as_deg:\\n heading_direction = heading_direction * (180 / np.pi)\\n\\n return heading_direction\",\n \"def getBotRightVelocity(self):\\n\\t\\tif len(self.prevPositions) < 2:\\n\\t\\t\\tself.velocity = (0,0,0)\\n\\t\\telse:\\n\\t\\t\\ttime = self.position[2] - self.prevPositions[len(self.prevPositions)-1][2]\\n\\t\\t\\txdist = self.position[1][0] - self.prevPositions[len(self.prevPositions)-1][1][0]\\n\\t\\t\\tydist = self.position[1][1] - self.prevPositions[len(self.prevPositions)-1][1][1]\\n\\t\\t\\tself.velocity = (xdist,ydist,time.total_seconds())\\n\\t\\treturn self.velocity\",\n \"def current_direction(self):\\n return self._attributes.get(\\\"current_direction\\\")\",\n \"def get_goal_direction(self, cur_goal):\\n\\t\\trho_robot = math.atan2(cur_goal.y - self.cur_pos.y, cur_goal.x - self.cur_pos.x)\\n\\n\\t\\tyaw_err = rho_robot - self.rotation\\n\\t\\tif yaw_err < 0:\\n\\t\\t\\tself.cur_action = 'tr'\\n\\t\\telse:\\n\\t\\t\\tself.cur_action = 'tl'\\n\\t\\tself.next_action_time = rospy.Time.now() + rospy.Duration(abs(yaw_err) / self.angular_speed)\",\n \"def get_angle_rad_between_joints(joint_a: Joint2D, joint_b: Joint2D) -> float:\\n return math.atan2(joint_a.y - joint_b.y, joint_a.x - joint_b.x)\",\n \"def comp_angle_magnet(self):\\n Rbo = self.get_Rbo()\\n W0 = self.comp_W0m()\\n Harc = self.comp_H_arc()\\n if self.is_outwards():\\n return float(2 * arctan(W0 / (2 * (Rbo + self.H1 - Harc))))\\n else:\\n return float(2 * arctan(W0 / (2 * (Rbo - self.H1 - Harc))))\\n\\n # if self.W0_is_rad:\\n # return self.W0\\n # else: # Convert W0 from m to rad\\n # Rbo = self.get_Rbo()\\n # return float(2 * arcsin(self.W0 / (2 * Rbo)))\",\n \"def get_direction(pi_values):\\n if pi_values == (0, 0, 0, 0):\\n return 'do not move'\\n pi_right, pi_up, pi_left, pi_down = pi_values\\n pi_sum = sum((pi_right, pi_up, pi_left, pi_down))\\n\\n p_right = pi_right / pi_sum\\n p_up = pi_up / pi_sum\\n p_left = pi_left / pi_sum\\n p_down = pi_down / pi_sum\\n\\n return np.random.choice(\\n ('right', 'up', 'left', 'down'),\\n p=[p_right, p_up, p_left, p_down])\",\n \"def GetCGStepDir(self):\\n if self.n_iter <= 1:\\n self.hvec = self.mol.g_total\\n gamma = 0.0\\n else:\\n v1 = self.traj.grad[-1] - self.traj.grad[-2]\\n v1 = v1.reshape((1, const.NUMDIM * self.mol.n_atoms))\\n v2 = self.traj.grad[-1].reshape((const.NUMDIM, self.mol.n_atoms, 1))\\n gamma = numpy.linalg.norm(numpy.dot(v1, v2))\\n gamma *= 1.0 / numpy.linalg.norm(self.traj.grad[-1])**2\\n self.hvec = self.mol.g_total + gamma * self.hvec\\n self.step_dir = self.hvec\"\n]","isConversational":false},"negative_scores":{"kind":"list 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effector position."},"document":{"kind":"string","value":"def inverse_kinematics(self, finger_id, xdes, q0, tol=0.001, max_iter=20):\n iter = 0\n q = self._project_onto_constraints(q0)\n xcurrent = self.forward_kinematics(q)[finger_id]\n error = np.linalg.norm(xdes - xcurrent)\n dt = 1.0\n prev_error = np.inf\n while error > tol and (prev_error - error) > 1e-5 and iter < max_iter:\n dq = self._compute_dq(finger_id, xdes, q)\n # start the line search with a step size a bit larger than the\n # previous step size.\n dt = min(1.0, 2 * dt)\n prev_error = error\n q, error, dt = self._line_search(finger_id, xdes, q, dq, dt=dt)\n iter += 1\n if error > tol:\n return None\n return q"},"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 get_joint_positions(self, joint_angles ): \n\n\n # current angles\n res_joint_angles = joint_angles.copy() \n\n # detect limits\n maskminus= res_joint_angles > self.joint_lims[:,0]\n maskplus = res_joint_angles < self.joint_lims[:,1]\n \n res_joint_angles = res_joint_angles*(maskplus*maskminus) \n res_joint_angles += self.joint_lims[:,0]*(np.logical_not(maskminus) )\n res_joint_angles += self.joint_lims[:,1]*(np.logical_not(maskplus) )\n \n # mirror\n if self.mirror :\n res_joint_angles = -res_joint_angles\n res_joint_angles[0] += np.pi \n \n # calculate x coords of arm edges.\n # the x-axis position of each edge is the \n # sum of its projection on the x-axis\n # and all the projections of the \n # previous edges \n x = np.array([ \n sum([ \n self.segment_lengths[k] *\n np.cos( (res_joint_angles[:(k+1)]).sum() ) \n for k in range(j+1) \n ])\n for j in range(self.number_of_joint) \n ])\n \n # trabslate to the x origin \n x = np.hstack([self.origin[0], x+self.origin[0]])\n\n # calculate y coords of arm edges.\n # the y-axis position of each edge is the \n # sum of its projection on the x-axis\n # and all the projections of the \n # previous edges \n y = np.array([ \n sum([ \n self.segment_lengths[k] *\n np.sin( (res_joint_angles[:(k+1)]).sum() ) \n for k in range(j+1) \n ])\n for j in range(self.number_of_joint) \n ])\n \n # translate to the y origin \n y = np.hstack([self.origin[1], y+self.origin[1]])\n\n pos = np.array([x, y]).T\n \n return (pos, res_joint_angles)","def end_effectors_pos(self):\n def relative_pos_in_egocentric_frame(physics):\n end_effector = physics.bind(self._entity.end_effectors).xpos\n torso = physics.bind(self._entity.root_body).xpos\n xmat = np.reshape(physics.bind(self._entity.root_body).xmat, (3, 3))\n return np.reshape(np.dot(end_effector - torso, xmat), -1)\n return observable.Generic(relative_pos_in_egocentric_frame)","def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n end_effector_nodes = ee_goals.keys()\n J = np.zeros(self.n)\n for (\n ee\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\n ee_path = kinematic_map[ee][\n 1:\n ] # [:-1] # no last node, only phys. joint locations\n t_ee = self.get_pose(joint_angles, ee).trans\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\n p_idx = int(joint_p[1:]) - 1\n for jdx in range(pdx, len(ee_path)):\n node_jdx = ee_path[jdx]\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\n J[p_idx] += (\n 2.0\n * self.a[node_jdx]\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\n )\n\n return J","def fk(arm,base=np.identity(4),joint_num=-1):\n\n pEE = base # Cumulative pose of the End Effector \n # (initially set up as the base of the robot)\n if joint_num==-1:\n for joint in arm:\n pEE=np.dot(pEE, joint.dhMatrix())\n else:\n for i in range(joint_num):\n pEE=np.dot(pEE, arm[i].dhMatrix())\n\n return pEE","def get_unhindered_positions(self, endposition):\n pass","def connect(ends):\n d = np.diff(ends, axis=0)[0]\n j = np.argmax(np.abs(d))\n D = d[j]\n aD = np.abs(D)\n return ends[0] + (np.outer(np.arange(aD + 1), d) + (aD >> 1)) // aD","def _calculate_h(self, end):\n return PATH_COST * (abs(self.x - end.x) + abs(self.y - end.y))","def get_current_joint_position(self) -> list:\n joint_positions = get_joint_positions(self.body, self.joints[:self.DoF])\n for i in range(self.DoF):\n if self.JOINT_TYPES[i] == 'P':\n # get the unscaled joint position\n joint_positions[i] /= self.scaling\n return joint_positions","def joint_variables(self, G: nx.Graph, T_final: dict = None) -> np.ndarray:\n # TODO: make this more readable\n tol = 1e-10\n q_zero = list_to_variable_dict(self.n * [0])\n kinematic_map = self.kinematic_map\n parents = self.parents\n get_pose = self.get_pose\n\n T = {}\n T[\"p0\"] = self.T_base\n theta = {}\n\n for ee in self.end_effectors:\n path = kinematic_map[\"p0\"][ee[0]][1:]\n axis_length = self.axis_length\n for node in path:\n aux_node = f\"q{node[1:]}\"\n pred = [u for u in parents.predecessors(node)]\n\n T_prev = T[pred[0]]\n\n T_prev_0 = get_pose(q_zero, pred[0])\n T_0 = get_pose(q_zero, node)\n T_rel = T_prev_0.inv().dot(T_0)\n T_0_q = get_pose(q_zero, node).dot(trans_axis(axis_length, \"z\"))\n T_rel_q = T_prev_0.inv().dot(T_0_q)\n\n p = G.nodes[node][POS] - T_prev.trans\n q = G.nodes[aux_node][POS] - T_prev.trans\n ps = T_prev.inv().as_matrix()[:3, :3].dot(p)\n qs = T_prev.inv().as_matrix()[:3, :3].dot(q)\n\n zs = skew(np.array([0, 0, 1]))\n cp = (T_rel.trans - ps) + zs.dot(zs).dot(T_rel.trans)\n cq = (T_rel_q.trans - qs) + zs.dot(zs).dot(T_rel_q.trans)\n ap = zs.dot(T_rel.trans)\n aq = zs.dot(T_rel_q.trans)\n bp = zs.dot(zs).dot(T_rel.trans)\n bq = zs.dot(zs).dot(T_rel_q.trans)\n\n c0 = cp.dot(cp) + cq.dot(cq)\n c1 = 2 * (cp.dot(ap) + cq.dot(aq))\n c2 = 2 * (cp.dot(bp) + cq.dot(bq))\n c3 = ap.dot(ap) + aq.dot(aq)\n c4 = bp.dot(bp) + bq.dot(bq)\n c5 = 2 * (ap.dot(bp) + aq.dot(bq))\n\n # poly = [c0 -c2 +c4, 2*c1 - 2*c5, 2*c0 + 4*c3 -2*c4, 2*c1 + 2*c5, c0 + c2 + c4]\n diff = np.array(\n [\n c1 - c5,\n 2 * c2 + 4 * c3 - 4 * c4,\n 3 * c1 + 3 * c5,\n 8 * c2 + 4 * c3 - 4 * c4,\n -4 * c1 + 4 * c5,\n ]\n )\n if all(diff < tol):\n theta[node] = 0\n else:\n sols = np.roots(\n diff\n ) # solutions to the Whaba problem for fixed axis\n\n def error_test(x):\n if abs(x.imag) > 0:\n return 1e6\n x = -2 * arctan2(x.real, 1)\n return (\n c0\n + c1 * sin(x)\n - c2 * cos(x)\n + c3 * sin(x) ** 2\n + c4 * cos(x) ** 2\n - c5 * sin(2 * x) / 2\n )\n\n sol = min(sols, key=error_test)\n theta[node] = -2 * arctan2(sol.real, 1)\n\n T[node] = (T_prev.dot(rot_axis(theta[node], \"z\"))).dot(T_rel)\n\n if T_final is None:\n return theta\n\n if (\n T_final[ee[0]] is not None\n and norm(cross(T_rel.trans, np.array([0, 0, 1]))) < tol\n ):\n T_th = (T[node]).inv().dot(T_final[ee[0]]).as_matrix()\n theta[ee[0]] += np.arctan2(T_th[1, 0], T_th[0, 0])\n\n return theta","def jac_pos(self):\n J = self.sim.data.get_body_jacp(self.end_effector)\n J = J.reshape(3, -1)[:, 0:7].T\n return J","def get_unhindered_positions(self, endposition):\n current_position = self.position\n potential_positions = { \n 'diag1' : [],\n 'diag2' : [],\n 'diag3' : [],\n 'diag4' : []\n }\n space_down = current_position[0]-1\n space_up = self.ncols-current_position[0]\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\n space_left = ord(current_position[1]) - ord('A')\n\n for i in range(1, space_down+1):\n diag1 = (current_position[0]-i, chr(ord(current_position[1])+i))\n diag2 = (current_position[0]-i, chr(ord(current_position[1])-i))\n if self.pos_within_bounds(diag1):\n potential_positions['diag1'].append(diag1)\n if self.pos_within_bounds(diag2):\n potential_positions['diag2'].append(diag2)\n\n for i in range(1, space_up+1):\n diag3 = (current_position[0]+i, chr(ord(current_position[1])+i))\n diag4 = (current_position[0]+i, chr(ord(current_position[1])-i))\n if self.pos_within_bounds(diag3):\n potential_positions['diag3'].append(diag3)\n if self.pos_within_bounds(diag4):\n potential_positions['diag4'].append(diag4)\n \n for direction, square in potential_positions.items():\n if tuple(endposition) in square:\n return potential_positions[direction]","def compute_joint_error_position(self, current_position, target_position):\n \n # helper variables\n tmp_c = []\n tmp_t = [] \n \n for x in range(0,20):\n tmp_c.append(current_position[x].joint_target)\n tmp_t.append(math.radians (target_position[x].joint_target) )\n \n # Compute the norm of the error\n error = numpy.linalg.norm( numpy.array(tmp_c) - numpy.array(tmp_t) )\n \n #print error \n\n return error","def joint_pairs(self):\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]","def jacobian_linear(self, joint_angles: dict, query_frame: str = \"\") -> np.ndarray:\n\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n end_effector_nodes = []\n for ee in self.end_effectors: # get p nodes in end-effectors\n if ee[0][0] == \"p\":\n end_effector_nodes += [ee[0]]\n if ee[1][0] == \"p\":\n end_effector_nodes += [ee[1]]\n\n node_names = [\n name for name in self.structure if name[0] == \"p\"\n ] # list of p node ids\n\n # Ts = self.get_full_pose_fast_lambdify(joint_angles) # all frame poses\n Ts = self.get_all_poses(joint_angles) # all frame poses\n Ts[\"p0\"] = np.eye(4)\n\n J = np.zeros([0, len(node_names) - 1])\n for ee in end_effector_nodes: # iterate through end-effector nodes\n ee_path = kinematic_map[ee][:-1] # no last node, only phys. joint locations\n\n T_0_ee = Ts[ee] # ee frame\n p_ee = T_0_ee[0:3, -1] # ee position\n\n Jp_t = np.zeros([3, len(node_names) - 1]) # translation jac for theta\n Jp_al = np.zeros([3, len(node_names) - 1]) # translation jac alpha\n for joint in ee_path: # algorithm fills Jac per column\n T_0_i = Ts[joint]\n z_hat_i = T_0_i[:3, 2]\n x_hat_i = T_0_i[:3, 0]\n p_i = T_0_i[:3, -1]\n j_idx = node_names.index(joint)\n Jp_t[:, j_idx] = np.cross(z_hat_i, p_ee - p_i)\n Jp_al[:, j_idx] = np.cross(x_hat_i, p_ee - p_i)\n\n J_ee = np.vstack([Jp_t, Jp_al])\n J = np.vstack([J, J_ee]) # stack big jac for multiple ee\n\n return J","def get_end_vertices(self):\n # Note that concatenating two vertices needs to make a\n # vertices for the frame.\n extesion_fraction = self.extesion_fraction\n\n corx = extesion_fraction*2.\n cory = 1./(1. - corx)\n x1, y1, w, h = 0, 0, 1, 1\n x2, y2 = x1 + w, y1 + h\n dw, dh = w*extesion_fraction, h*extesion_fraction*cory\n\n if self.extend in [\"min\", \"both\"]:\n bottom = [(x1, y1),\n (x1+w/2., y1-dh),\n (x2, y1)]\n else:\n bottom = [(x1, y1),\n (x2, y1)]\n\n if self.extend in [\"max\", \"both\"]:\n top = [(x2, y2),\n (x1+w/2., y2+dh),\n (x1, y2)]\n else:\n top = [(x2, y2),\n (x1, y2)]\n\n if self.orientation == \"horizontal\":\n bottom = [(y,x) for (x,y) in bottom]\n top = [(y,x) for (x,y) in top]\n\n return bottom, top","def end_tensor(end_vector, # type: Union[np.ndarray, List[int]]\n center_in, # type: List[int]\n center_out, # type: List[int]\n surround_in, # type: List[int]\n surround_out, # type: List[int]\n attractor_function=__linear_function_generator, # type: Callable[[], Callable[[Real], Real]]\n size=3\n ):\n ndim = len(end_vector)\n assert ndim >= 1\n if not isinstance(end_vector, np.ndarray):\n end_vector = np.asarray(end_vector)\n\n attractor_function = attractor_function()\n\n center_surround = np.zeros(shape=[size for _ in range(ndim)] + [len(center_out), len(center_in)])\n zero_centered = np.ndarray(shape=[size for _ in range(ndim)])\n\n for tup in itertools.product(*[range(size) for _ in range(ndim)]):\n tup_vec = np.asarray(tup) - np.asarray([size / 2 for _ in range(ndim)])\n angle_dist = (m.acos(\n np.dot(tup_vec, end_vector) / (np.linalg.norm(tup_vec) * np.linalg.norm(end_vector))) - m.pi / 2.0) / m.pi\n zero_centered[tup] = attractor_function(angle_dist * m.pi)\n\n __normalize_center_surround(zero_centered)\n\n for tup in itertools.product(*[range(size) for _ in range(ndim)]):\n center_surround[tup] = [[surround_out[o] * surround_in[i] * abs(zero_centered[tup]) if zero_centered[tup] < 0\n else center_surround[tuple(tup + (o, i))]\n for o in range(len(surround_out))] for i in range(len(surround_in))]\n center_surround[tup] = [[center_out[o] * center_in[i] * abs(zero_centered[tup]) if zero_centered[tup] >= 0\n else center_surround[tuple(tup + (i, o))]\n for o in range(len(center_out))] for i in range(len(center_in))]\n\n return center_surround","def p2p_xyz(start_point, end_point, top_left_cor, cellsize, dem):\n start_cell = (int((start_point[0] - top_left_cor[0]) / cellsize[0]),\n int((start_point[1] - top_left_cor[1]) / cellsize[1]))\n end_cell = (int((end_point[0] - top_left_cor[0]) / cellsize[0]),\n int((end_point[1] - top_left_cor[1]) / cellsize[1]))\n cells = misc.get_line(start_cell, end_cell) \n pnts = []\n elev = []\n \n dem_elv = dem[:,1]\n dem_indx = dem[:,2:4]\n\n for cell in cells:\n x = top_left_cor[0] + cell[0] * cellsize[0] + cellsize[0] / 2\n y = top_left_cor[1] + cell[1] * cellsize[1] + cellsize[1] / 2\n #xy_indx=[str(cell[0]),str(cell[1])]\n z_indx=np.logical_and(np.equal(dem_indx[:,0],cell[0]),np.equal(dem_indx[:,1],cell[1]))\n try:\n z=dem_elv[z_indx][0]\n except (np.sum(z_indx)>1):\n print(\"Oops! That was more than one indices in dem matching the query index (in getCellValue)\")\n #z_indx = [i for i,j in enumerate(dem_indx) if j == xy_indx]\n z = float(dem_elv[z_indx])\n pnts.append((x, y))\n elev.append(z)\n return pnts, elev","def enumerate_joint_ask(X, e, P):\n Q = ProbDist(X) ## A probability distribution for X, initially empty\n Y = [v for v in P.variables if v != X and v not in e]\n for xi in P.values(X):\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\n return Q.normalize()","def target_position(self, time):\n # get joint positions and use fk to get end effector position?\n # ar_tag from topic\n\n cur_pos = self.target_velocity(time)*time + self.start_pos\n\n self.points_generated.append(cur_pos)\n #print(self.start_pos)\n # print(cur_pos)\n return cur_pos","def get_desired_joint_position(self):\n return self._position_joint_desired","def moveit_cartesian_path(start_pos, start_quat,\n delta_xyz, moveit_group,\n eef_step, jump_threshold=0.0):\n start_pos = np.array(start_pos).flatten()\n\n delta_xyz = np.array(delta_xyz).flatten()\n end_pos = start_pos + delta_xyz\n moveit_waypoints = []\n wpose = moveit_group.get_current_pose().pose\n wpose.position.x = start_pos[0]\n wpose.position.y = start_pos[1]\n wpose.position.z = start_pos[2]\n wpose.orientation.x = start_quat[0]\n wpose.orientation.y = start_quat[1]\n wpose.orientation.z = start_quat[2]\n wpose.orientation.w = start_quat[3]\n moveit_waypoints.append(copy.deepcopy(wpose))\n\n wpose.position.x = end_pos[0]\n wpose.position.y = end_pos[1]\n wpose.position.z = end_pos[2]\n wpose.orientation.x = start_quat[0]\n wpose.orientation.y = start_quat[1]\n wpose.orientation.z = start_quat[2]\n wpose.orientation.w = start_quat[3]\n moveit_waypoints.append(copy.deepcopy(wpose))\n\n (plan, fraction) = moveit_group.compute_cartesian_path(\n moveit_waypoints, # waypoints to follow\n eef_step, # eef_step\n jump_threshold) # jump_threshold\n return plan","def _solve_joints(self, bicep_angle: float, forearm_angle: float):\n bicep_length_x = self.bicep_length*math.cos(math.radians(bicep_angle))\n bicep_length_y = self.bicep_length*math.sin(math.radians(bicep_angle))\n forearm_length_x = self.forearm_length*math.cos(math.radians(bicep_angle + forearm_angle))\n forearm_length_y = self.forearm_length*math.sin(math.radians(bicep_angle + forearm_angle))\n elbow = (bicep_length_x, bicep_length_y)\n hand = (bicep_length_x + forearm_length_x, bicep_length_y + forearm_length_y)\n return elbow, hand","def _generate_end_position(self):\n end_position = []\n new_row = []\n\n for i in range(1, self.PUZZLE_NUM_ROWS * self.PUZZLE_NUM_COLUMNS + 1):\n new_row.append(i)\n if len(new_row) == self.PUZZLE_NUM_COLUMNS:\n end_position.append(new_row)\n new_row = []\n\n end_position[-1][-1] = 0\n return end_position","def estimate_foe(start, end):\n A = np.zeros(start.shape)\n diff = end - start\n A[:, 0] = diff[:, 1]\n A[:, 1] = -diff[:, 0]\n b = start[:, 0] * diff[:, 1] - start[:, 1] * diff[:, 0]\n foe = np.dot(np.linalg.inv(np.dot(A.T, A)), np.dot(A.T, b))\n foe = (int(foe[0]),int(foe[1]))\n return foe","def compute_fk_position(self, jpos, tgt_frame):\n if isinstance(jpos, list):\n jpos = np.array(jpos)\n jpos = jpos.flatten()\n if jpos.size != self.arm_dof:\n raise ValueError('Length of the joint angles '\n 'does not match the robot DOF')\n assert jpos.size == self.arm_dof\n kdl_jnt_angles = joints_to_kdl(jpos)\n\n kdl_end_frame = kdl.Frame()\n idx = self.arm_link_names.index(tgt_frame) + 1\n fg = self._fk_solver_pos.JntToCart(kdl_jnt_angles,\n kdl_end_frame,\n idx)\n if fg < 0:\n raise ValueError('KDL Pos JntToCart error!')\n pose = kdl_frame_to_numpy(kdl_end_frame)\n pos = pose[:3, 3].flatten()\n rot = pose[:3, :3]\n return pos, rot","def create_joint_trajectory(start_position, end_position,\n duration_of_trajectory, frequency_of_trajectory):\n\n frequency_of_ros_messages = frequency_of_trajectory # in Hz.\n number_of_way_points = duration_of_trajectory * frequency_of_ros_messages\n number_of_joints = start_position.__len__()\n trajectory = np.zeros((number_of_joints, number_of_way_points))\n\n for i in xrange(number_of_joints):\n trajectory[i] = np.linspace(start_position[i], end_position[i],\n number_of_way_points)\n trajectory = trajectory.T.copy()\n vel_lims = np.diff(trajectory, axis=0)\n #Because this is discrete differentiation,\n # the last value is missing: len(vel_lims) = len(trajectory) - 1\n # so we just repeat the last calculated velocity.\n vel_lims = np.append(vel_lims, [[x for x in vel_lims[-1,:]]], axis = 0)\n vel_lims = vel_lims * frequency_of_trajectory\n vel_lims = np.absolute(vel_lims)\n\n if vel_lims.all() > 1.0:\n raise ValueError(\"One or more of the values in the specified velocities\"\n \"Exceed 1 rad / second. The robot won't like this.\"\n \"Adjust the trajectory so that each point can be \"\n \"reached without exceeding this limit.\")\n return trajectory, vel_lims","def hessian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n end_effector_nodes = ee_goals.keys()\n H = np.zeros((self.n, self.n))\n for (\n ee\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\n ee_path = kinematic_map[ee][\n 1:\n ] # [:-1] # no last node, only phys. joint locations\n t_ee = self.get_pose(joint_angles, ee).trans\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Hess per column\n p_idx = int(joint_p[1:]) - 1\n sin_p_term = 0.0\n cos_p_term = 0.0\n for jdx in range(pdx, len(ee_path)):\n node_jdx = ee_path[jdx]\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\n sin_p_term += self.a[node_jdx] * np.sin(theta_jdx)\n cos_p_term += self.a[node_jdx] * np.cos(theta_jdx)\n\n for (qdx, joint_q) in enumerate(\n ee_path[pdx:]\n ): # TODO: check if starting from pdx works\n qdx = qdx + pdx\n q_idx = int(joint_q[1:]) - 1\n sin_q_term = 0.0\n cos_q_term = 0.0\n for kdx in range(qdx, len(ee_path)):\n node_kdx = ee_path[kdx]\n theta_kdx = sum(\n [joint_angles[key] for key in ee_path[0 : kdx + 1]]\n )\n sin_q_term += self.a[node_kdx] * np.sin(theta_kdx)\n cos_q_term += self.a[node_kdx] * np.cos(theta_kdx)\n\n # assert(q_idx >= p_idx)\n H[p_idx, q_idx] += (\n 2.0 * sin_q_term * sin_p_term\n - 2.0 * dg_ee_x * cos_q_term\n + 2.0 * cos_p_term * cos_q_term\n - 2.0 * dg_ee_y * sin_q_term\n )\n\n return H + H.T - np.diag(np.diag(H))","def get_joints(joint_listener): \n if LOCAL_TEST: # dummy\n return np.array([-0.5596, 0.5123, 0.5575, -1.6929, 0.2937, 1.6097, -1.237, 0.04, 0.04])\n else:\n joints = joint_listener.joint_position\n print('robot joints', joints)\n return joints","def get_unhindered_positions(self, endposition):\n current_position = self.position\n potential_positions = potential_positions = {\n 'left' : [], \n 'right' : [],\n 'up' : [], \n 'down' : []\n }\n space_down = current_position[0]-1\n space_up = self.ncols-current_position[0]\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\n space_left = ord(current_position[1]) - ord('A')\n\n for i in range(1, space_down+1):\n pos = (current_position[0]-i, current_position[1])\n if self.pos_within_bounds(pos):\n potential_positions['down'].append(pos)\n\n for i in range(1, space_up+1):\n pos = (current_position[0]+i, current_position[1])\n if self.pos_within_bounds(pos):\n potential_positions['up'].append(pos)\n \n for i in range(1, space_left+1):\n pos = (current_position[0], chr(ord(current_position[1])-i))\n if self.pos_within_bounds(pos):\n potential_positions['left'].append(pos)\n\n for i in range(1, space_right+1):\n pos = (current_position[0], chr(ord(current_position[1])+i))\n if self.pos_within_bounds(pos):\n potential_positions['right'].append(pos)\n\n for direction, square in potential_positions.items():\n if tuple(endposition) in square:\n return potential_positions[direction]","def _get_output_steps_to_beam_indices(self, end_state: Tensor, beam_prev_indices: Tensor) ->List[int]:\n present_position = int(end_state[1])\n beam_index = int(end_state[2])\n beam_indices = torch.jit.annotate(List[int], [])\n while present_position >= 0:\n beam_indices.insert(0, beam_index)\n beam_index = int(beam_prev_indices[present_position][beam_index])\n present_position = present_position - 1\n return beam_indices","def joint_trajectory(theta_start, theta_end, Tf, N, method):\n\n N = int(N)\n timegap = Tf / (N - 1.0) # N points, N-1 line segments\n traj = np.zeros((len(theta_start), N)) # intitialize the trajectory matrix, 1D joint vars, 2D each time instance\n\n # for each line segment, from 0 to T, calculate the corresponding s value (0to1)\n for i in range(N):\n if method == 3:\n s = cubic_time_scaling(Tf, timegap * i)\n else:\n s = quintic_time_scaling(Tf, timegap * i)\n traj[:, i] = s * np.array(theta_end) + (1 - s) * np.array(theta_start) # xi = x_start + (0.whatever fraction s)(x_end-x_start)\n traj = np.array(traj).T\n return traj","def jacobian_linear_symb(\n self, joint_angles: dict, pose_term=False, ee_keys=None\n ) -> dict:\n kinematic_map = self.kinematic_map[\"p0\"] # get map to all nodes from root\n\n if ee_keys is None:\n end_effector_nodes = []\n for ee in self.end_effectors: # get p nodes in end-effectors\n if ee[0][0] == \"p\":\n end_effector_nodes += [ee[0]]\n else:\n end_effector_nodes += [ee[1]]\n else:\n end_effector_nodes = ee_keys\n # Ts = self.get_all_poses_symb(joint_angles) # all frame poses\n Ts = self.get_all_poses(joint_angles) # all frame poses\n J = {} # np.zeros([0, len(node_names) - 1])\n for ee in end_effector_nodes: # iterate through end-effector nodes\n ee_path = kinematic_map[ee][\n 1:\n ] # [:-1] # no last node, only phys. joint locations\n\n T_0_ee = Ts[ee].as_matrix() # ee frame\n if pose_term:\n dZ = np.array([0.0, 0.0, 1.0])\n p_ee = T_0_ee[0:3, 0:3] @ dZ + T_0_ee[0:3, -1]\n else:\n p_ee = T_0_ee[0:3, -1] # ee position\n\n Jp = np.zeros([3, self.n], dtype=object) # translation jac\n for joint in ee_path: # algorithm fills Jac per column\n T_0_i = Ts[list(self.parents.predecessors(joint))[0]].as_matrix()\n z_hat_i = T_0_i[:3, 2]\n if pose_term:\n p_i = T_0_i[0:3, 0:3] @ dZ + T_0_i[0:3, -1]\n else:\n p_i = T_0_i[:3, -1]\n j_idx = int(joint[1:]) - 1 # node_names.index(joint) - 1\n Jp[:, j_idx] = cross_symb(z_hat_i, p_ee - p_i)\n J[ee] = Jp\n return J","def joint_limits_upper_constraint(q,ee_pos):\n return self.max_angles - q","def backtrack_to_start_to_draw_purpose(board, end):\r\n cell = board.at(end)\r\n # print(cell)\r\n path = []\r\n lis = []\r\n while cell != None:\r\n path.append(cell)\r\n cell = cell.path_from\r\n for i in path[-1:]:\r\n for j in i.position:\r\n lis.append(j)\r\n\r\n return path","def get_return_from_grasp_joint_trajectory(self, start_joints, target_pose, n_steps=40):\n assert len(start_joints) == len(self.joint_indices)\n assert target_pose.frame.count('base_link') == 1\n \n # set active manipulator and start joint positions\n self.robot.SetDOFValues(start_joints, self.joint_indices)\n \n # initialize trajopt inputs\n rave_pose = tfx.pose(self.sim.transform_from_to(target_pose.matrix, target_pose.frame, 'world'))\n quat = rave_pose.orientation\n xyz = rave_pose.position\n quat_target = [quat.w, quat.x, quat.y, quat.z]\n xyz_target = [xyz.x, xyz.y, xyz.z]\n rave_mat = rave.matrixFromPose(np.r_[quat_target, xyz_target])\n \n request = self._get_return_from_grasp_trajopt_request(xyz_target, quat_target, n_steps)\n \n # convert dictionary into json-formatted string\n s = json.dumps(request) \n # create object that stores optimization problem\n prob = trajoptpy.ConstructProblem(s, self.sim.env)\n \n tool_link = self.robot.GetLink(self.tool_frame)\n def penalize_low_height(x):\n self.robot.SetDOFValues(x, self.joint_indices, False)\n z = tool_link.GetTransform()[2,3]\n return max(0, 10.0 - z)\n\n for t in xrange(n_steps-2):\n prob.AddErrorCost(penalize_low_height, [(t,j) for j in xrange(len(self.joint_indices))], \"ABS\", \"PENALIZE_LOW_HEIGHT_%i\"%t)\n \n # do optimization\n result = trajoptpy.OptimizeProblem(prob)\n \n self.robot.SetDOFValues(start_joints, self.joint_indices)\n prob.SetRobotActiveDOFs() # set robot DOFs to DOFs in optimization problem\n num_upsampled_collisions = self._num_collisions(result.GetTraj())\n print('Number of collisions: {0}'.format(num_upsampled_collisions))\n self.robot.SetDOFValues(start_joints, self.joint_indices)\n if num_upsampled_collisions > 2:\n return None\n else:\n return result.GetTraj()","def joint_pairs(self):\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))","def compute_end_point(self):\n\n L = self.level\n P = L.prob\n\n # check if Mth node is equal to right point and do_coll_update is false, perform a simple copy\n if self.coll.right_is_node and not self.params.do_coll_update:\n # a copy is sufficient\n L.uend = P.dtype_u(L.u[-1])\n else:\n # start with u0 and add integral over the full interval (using coll.weights)\n L.uend = P.dtype_u(L.u[0])\n for m in range(self.coll.num_nodes):\n L.uend += L.dt * self.coll.weights[m] * L.f[m + 1]\n # add up tau correction of the full interval (last entry)\n if L.tau[-1] is not None:\n L.uend += L.tau[-1]\n\n return None","def test_calcAngles_joint_centers(self):\n _,joint_centers = pycgmCalc.calcAngles(self.motion_data, vsk=self.cal_SM, frame=0,\\\n formatData=False, splitAnglesAxis=False, returnjoints=True)\n #Verify that several the expected joint_centers are returned.\n expected_Front_Head = np.array([ 255.19071198, 406.12081909, 1721.92053223])\n expected_LHip = np.array([182.57097863, 339.43231855, 935.52900126])\n expected_RHand = np.array([ 859.80614366, 517.28239823, 1051.97278944])\n expected_Thorax = np.array([256.149810236564, 364.3090603933987, 1459.6553639290375])\n expected_LKnee = np.array([143.55478579, 279.90370346, 524.78408753])\n expected_result = [expected_Front_Head, expected_LHip, expected_RHand, expected_Thorax, expected_LKnee]\n result = [\n joint_centers[0]['Front_Head'],\n joint_centers[0]['LHip'],\n joint_centers[0]['RHand'],\n joint_centers[0]['Thorax'],\n joint_centers[0]['LKnee']\n ]\n np.testing.assert_almost_equal(result, expected_result, self.rounding_precision)","def _body_coord(self):\r\n cth = np.cos(self.theta)\r\n sth = np.sin(self.theta)\r\n M = self.P - 0.5 * np.diag(self.lengths)\r\n # stores the vector from the center of mass to the nose\r\n c2n = np.array([np.dot(M[self.nose], cth), np.dot(M[self.nose], sth)])\r\n # absolute position of nose\r\n T = -self.pos_cm - c2n - self.goal\r\n # rotating coordinate such that nose is axis-aligned (nose frame)\r\n # (no effect when \\theta_{nose} = 0)\r\n c2n_x = np.array([cth[self.nose], sth[self.nose]])\r\n c2n_y = np.array([-sth[self.nose], cth[self.nose]])\r\n Tcn = np.array([np.sum(T * c2n_x), np.sum(T * c2n_y)])\r\n\r\n # velocity at each joint relative to center of mass velocity\r\n vx = -np.dot(M, sth * self.dtheta)\r\n vy = np.dot(M, cth * self.dtheta)\r\n # velocity at nose (world frame) relative to center of mass velocity\r\n v2n = np.array([vx[self.nose], vy[self.nose]])\r\n # rotating nose velocity to be in nose frame\r\n Vcn = np.array([np.sum((self.v_cm + v2n) * c2n_x),\r\n np.sum((self.v_cm + v2n) * c2n_y)])\r\n # angles should be in [-pi, pi]\r\n ang = np.mod(\r\n self.theta[1:] - self.theta[:-1] + np.pi,\r\n 2 * np.pi) - np.pi\r\n return Tcn, ang, Vcn, self.dtheta","def rel_kin(self, joints): # kinematic term\n order1 = [9, 5, 20, 1, 2]\n order2 = [8, 6, 4, 20, 3] # joints' order\n order3 = [10, 4, 8, 0, 20]\n refer1 = [5, 6, 4, 2, 0] # kinseg's order\n refer2 = [6, 5, 4, 3, 1]\n\n segrel = defaultdict(lambda: int(0))\n result = []\n cnts = np.zeros(21)\n\n for i in xrange(len(order1)):\n A = np.array([joints[order1[i]].Position.x, joints[order1[i]].Position.y, joints[order1[i]].Position.z])\n B = np.array([joints[order2[i]].Position.x, joints[order2[i]].Position.y, joints[order2[i]].Position.z])\n C = np.array([joints[order3[i]].Position.x, joints[order3[i]].Position.y, joints[order3[i]].Position.z])\n\n tmp = min(np.abs(np.linalg.norm(A-B)*100-self.kinseg[refer1[i]])/self.kinseg[refer1[i]], 1)\n segrel[order1[i]] += tmp\n segrel[order2[i]] += tmp\n\n tmp = min(np.abs(np.linalg.norm(A-C)*100-self.kinseg[refer2[i]])/self.kinseg[refer2[i]], 1)\n segrel[order1[i]] += tmp\n segrel[order3[i]] += tmp\n\n cnts[order1[i]] += 2\n cnts[order2[i]] += 1\n cnts[order3[i]] += 1\n\n for i in self.trg_jorder:\n result.append(1-(segrel[i]/cnts[i]))\n\n return result","def compute_position(self, goal: np.ndarray) -> Any:\n return NotImplementedError","def get_vehicle_end_index(self):\n return [len(self.matrix) - 1 for i in range(len(self.vehicles))]","def affected_end(self):\n types = {alt.type for alt in self.ALT} # set!\n BAD_MIX = {INS, SV, BND, SYMBOLIC} # don't mix well with others\n if (BAD_MIX & types) and len(types) == 1 and list(types)[0] == INS:\n # Only insertions, return 0-based position right of first base\n return self.POS # right of first base\n else: # Return 0-based end position, behind last REF base\n return (self.POS - 1) + len(self.REF)","def calculateExteriorElementBoundaryQuadrature(self):\n #\n #get physical locations of element boundary quadrature points\n #\n #assume all components live on the same mesh\n self.u[0].femSpace.elementMaps.getValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['x'])\n #\n #get metric tensor and unit normals\n #\n if self.movingDomain:\n if self.tLast_mesh != None:\n self.ebqe['xt'][:]=self.ebqe['x']\n self.ebqe['xt']-=self.ebqe['x_last']\n alpha = 1.0/(self.t_mesh - self.tLast_mesh)\n self.ebqe['xt']*=alpha\n else:\n self.ebqe['xt'][:]=0.0\n self.ebqe['x_last'][:]=self.ebqe['x']\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace_movingDomain(self.elementBoundaryQuadraturePoints,\n self.ebqe['xt'],\n self.ebqe['inverse(J)'],\n self.ebqe['g'],\n self.ebqe['sqrt(det(g))'],\n self.ebqe['n'])\n else:\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['inverse(J)'],\n self.ebqe['g'],\n self.ebqe['sqrt(det(g))'],\n self.ebqe['n'])\n #now map the physical points back to the reference element\n #assume all components live on same mesh\n self.u[0].femSpace.elementMaps.getInverseValuesGlobalExteriorTrace(self.ebqe['inverse(J)'],self.ebqe['x'],self.ebqe['hat(x)'])\n #\n #since the points on the reference boundary may be reordered on many right element boundaries, we\n #have to use an array of reference boundary points on all element boundaries\n #first copy the left reference element boundary quadrature points from the reference element boundary\n self.testSpace[0].getBasisValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe[('w',0)])\n cfemIntegrals.calculateWeightedShapeGlobalExteriorTrace(self.mesh.exteriorElementBoundariesArray,\n self.mesh.elementBoundaryElementsArray,\n self.mesh.elementBoundaryLocalElementBoundariesArray,\n self.elementBoundaryQuadratureWeights[('f',0)],\n self.ebqe['sqrt(det(g))'],\n self.ebqe[('w',0)],\n self.ebqe[('w*dS_f',0)])\n self.u[0].femSpace.getBasisGradientValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\n self.ebqe['inverse(J)'],\n self.ebqe[('grad(v)',0)])\n #setup flux boundary conditions\n self.fluxBoundaryConditionsObjectsDict = dict([(cj,FluxBoundaryConditions(self.mesh,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.ebqe[('x')],\n self.advectiveFluxBoundaryConditionsSetterDict[cj],\n self.diffusiveFluxBoundaryConditionsSetterDictDict[cj]))\n for cj in self.advectiveFluxBoundaryConditionsSetterDict.keys()])\n self.ebqe['dS'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n cfemIntegrals.calculateIntegrationWeights(self.ebqe['sqrt(det(g))'],\n self.elementBoundaryQuadratureWeights[('u',0)],\n self.ebqe['dS'])\n for ci in range(self.nc): self.ebqe[('dS',ci)] = self.ebqe['dS']\n #\n self.ellamDiscretization.calculateExteriorElementBoundaryQuadrature(self.ebqe)\n #\n self.coefficients.initializeGlobalExteriorElementBoundaryQuadrature(self.timeIntegration.t,self.ebqe)","def _handle_bounds_speedj(self):\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(self._qt_[-1])\n inside_angle_bound = np.all(self._angles_low <= self._qt_[-1, self._joint_indices]) and \\\n np.all(self._qt_[-1, self._joint_indices] <= self._angles_high)\n if inside_bound:\n self.return_point = None\n if inside_angle_bound:\n self.angle_return_point = False\n if not inside_bound:\n if self.return_point is None:\n # we are outside the bounds and return point wasn't computed yet\n print(\"outside box bound\")\n xyz = np.clip(xyz, self._end_effector_low + self._box_bound_buffer,\n self._end_effector_high - self._box_bound_buffer)\n mat[:3, 3] = xyz\n ref_pos = self._q_ref.copy()\n ref_pos[self._joint_indices] = self._q_[-1, self._joint_indices]\n solutions = ur_utils.inverse_near(mat, wrist_desired=self._q_ref[-1], ref_pos=ref_pos,\n params=self._ik_params)\n servoj_q = self._q_ref.copy()\n if len(solutions) == 0:\n servoj_q[self._joint_indices] = self._q_[-1, self._joint_indices]\n else:\n servoj_q[self._joint_indices] = solutions[0][self._joint_indices]\n self.return_point = servoj_q[self._joint_indices]\n # Speed at which arm approaches the boundary. The faster this speed,\n # the larger opposite acceleration we need to apply in order to slow down\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\n # if return point is already computed, keep going to it, no need\n # to recompute it at every time step\n self._cmd_ = self.return_point - self._q_[0][self._joint_indices]\n # Take the direction to return point and normalize it to have norm 0.1\n if np.linalg.norm(self._cmd_) != 0:\n self._cmd_ /= np.linalg.norm(self._cmd_) / 0.1\n\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\n # This acceleration guarantees that we won't move beyond\n # the bounds by more than 0.05 radian on each joint. This\n # follows from kinematics equations.\n accel_to_apply = np.max(np.abs(self._qd_)) * self.init_boundary_speed / 0.05\n\n # self.boundary_packet[1:1 + 6][self.joint_indices] = self.return_point\n # self.actuator_comms['UR5'].actuator_buffer.write(self.reset_packet)\n # time.sleep(1.0)\n self._speedj_packet[-2] = np.clip(accel_to_apply, 2.0, 5.0)\n self._actuation_packet_['UR5'] = self._speedj_packet\n self._cmd_.fill(0.0)\n self._cmd_prev_.fill(0.0)\n self._first_deriv_.fill(0.0)\n\n elif not inside_angle_bound:\n # if return point is already computed, keep going to it, no need\n self.rel_indices = self._joint_indices\n cur_pos = self._q_[0][self._joint_indices]\n clipped_pos = np.clip(cur_pos, self._angles_low + self._angle_bound_buffer,\n self._angles_high - self._angle_bound_buffer)\n # a point within the box to which we will be returning\n affected_joints = np.where(clipped_pos != cur_pos)\n if not self.angle_return_point:\n print(\"outside of angle bound on joints %r\" % (list(affected_joints[0])))\n self.angle_return_point = True\n self._cmd_[affected_joints] = np.sign(clipped_pos - cur_pos)[affected_joints]*np.max(np.abs(self._cmd_))\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\n self._actuation_packet_['UR5'] = self._speedj_packet","def get_neighb_coords(self, i, ci):\n j = self.conn[i][ci]\n rj = self.xyz[j].copy()\n if self.periodic:\n if self.use_pconn:\n img = self.pconn[i][ci]\n rj += np.dot(img, self.cell)\n else:\n all_rj = rj + self.images_cellvec\n all_r = all_rj - self.xyz[i]\n all_d = np.sqrt(np.add.reduce(all_r*all_r,1))\n closest = np.argsort(all_d)[0]\n return all_rj[closest]\n return rj","def compute_observation(self):\n robotPos, robotOrn = p.getBasePositionAndOrientation(self.botId)\n robotEuler = p.getEulerFromQuaternion(robotOrn)\n linear, angular = p.getBaseVelocity(self.botId)\n return (np.array([robotEuler[0],angular[0],self.vt], dtype='float32'))","def end_points(self, origin, destination):\n # origin and destination are components with bounding-boxes\n # direction is a 2 char code representing starting and ending directions\n # 'h' horizontal, 'v' vertical\n o_coords = origin.bounding_coords()\n d_coords = destination.bounding_coords()\n\n start = {\n \"h\": core.Coords(o_coords.x2, o_coords.y1 + origin.height / 2),\n \"v\": core.Coords(origin.x + (o_coords.x2 - o_coords.x1) / 2, o_coords.y2),\n }\n end = {\n \"h\": core.Coords(d_coords.x1, d_coords.y1 + destination.height / 2),\n \"v\": core.Coords(\n destination.x + (d_coords.x2 - d_coords.x1) / 2, d_coords.y1\n ),\n }\n self.start = start[self.direction[0]]\n self.end = end[self.direction[-1]]\n return (self.start, self.end)","def comet_joint_orient(pynodes, aim_axis = None, up_axis = None, up_dir = None, do_auto = False):\n if aim_axis is None:\n aim_axis = [1, 0, 0]\n if up_axis is None:\n up_axis = [0, 0, 1]\n if up_dir is None:\n up_dir = [1, 0, 0]\n # convert to Vector\n aim_axis = pm.dt.Vector(aim_axis)\n up_axis = pm.dt.Vector(up_axis)\n up_dir = pm.dt.Vector(up_dir)\n # Filter supplied pynodes, if equal to 0 then return false\n if len(pynodes) == 0:\n return False\n\n # make sure only joint get passed through here\n pynodes = pm.ls(pynodes, type = 'joint')\n\n # init variable prevUp for later use\n prev_up = pm.dt.Vector()\n\n for i, o in enumerate(pynodes):\n parent_point = None\n # first we need to unparent everything and then store that,\n children = o.getChildren()\n for x in children:\n x.setParent(None)\n\n # find parent for later in case we need it\n parent = o.getParent()\n\n # Now if we have a child joint... aim to that\n aim_tgt = None\n for child in children:\n if child.nodeType() == 'joint':\n aim_tgt = child\n break\n\n if aim_tgt:\n # init variable upVec using upDir variable\n up_vec = pm.dt.Vector(up_dir)\n\n # first off... if doAuto is on, we need to guess the cross axis dir\n if do_auto:\n # now since the first joint we want to match the second orientation\n # we kind of hack the things passed in if it is the first joint\n # ie: if the joint doesnt have a parent... or if the parent it has\n # has the 'same' position as itself... then we use the 'next' joints\n # as the up cross calculations\n jnt_point = o.getRotatePivot(space = 'world')\n if parent:\n parent_point.setValue(parent.getRotatePivot(space = 'world'))\n else:\n parent_point = jnt_point.copy()\n aim_tgt_point = aim_tgt.getRotatePivot(space = 'world')\n\n # how close to we consider 'same'?\n tol = 0.0001\n\n point_cond = jnt_point - parent_point\n pos_cond = [abs(x) for x in point_cond.tolist()]\n if not parent or pos_cond[0] <= tol and pos_cond[1] <= tol and pos_cond[2] <= tol:\n # get aimChild\n aim_child = None\n aim_children = aim_tgt.getChildren(type = 'joint')\n if aim_children:\n aim_child = aim_children[0]\n\n # get aimChild vector\n if aim_child:\n aim_child_point = aim_child.getRotatePivot(space = 'world')\n else:\n aim_child_point = pm.dt.Vector()\n\n # find the up vector using child vector of aim target\n up_vec = (jnt_point - aim_tgt_point).cross(aim_child_point - aim_tgt_point)\n else:\n # find the up vector using the parent vector\n up_vec = (parent_point - jnt_point).cross(aim_tgt_point - jnt_point)\n\n # reorient the current joint\n a_cons = pm.aimConstraint(\n aim_tgt, o, aimVector = aim_axis, upVector = up_axis, worldUpVector = up_vec.tolist(),\n worldUpType = 'vector', weight = 1\n )\n pm.delete(a_cons)\n\n # now compare the up we used to the prev one\n current_up = up_vec.normal()\n # dot product for angle between... store for later\n dot = current_up.dot(prev_up)\n prev_up = up_vec\n\n if i > 0 >= dot:\n # adjust the rotation axis 180 if it looks like we have flopped the wrong way!\n # FIXME: some shit need to fix here\n # pm.xform( o, relative = True, objectSpace = True, rotateAxis = True )\n o.rotateX.set(o.rotateX.get() + (aim_axis.x * 180))\n o.rotateY.set(o.rotateY.get() + (aim_axis.y * 180))\n o.rotateZ.set(o.rotateZ.get() + (aim_axis.z * 180))\n\n prev_up *= -1\n elif parent:\n # otherwise if there is no target, just dup orientation of parent...\n transformation.align(o, parent, mode = 'rotate')\n\n # and now finish clearing out joint axis ...\n pm.joint(o, e = True, zeroScaleOrient = True)\n transformation.freeze_transform(o)\n\n # now that we are done ... reparent\n if len(children) > 0:\n for x in children:\n x.setParent(o)\n\n return True","def startEndPoints(mazz):\n for i in range (len(mazz)):\n for j in range (len(mazz[i])):\n if mazz[i][j] == 6:\n startx = i\n starty = j\n elif mazz[i][j] == 7:\n endx = i\n endy = j\n return startx, starty, endx, endy","def _get_observation_upper_bound(self):\n upper_bound = np.zeros(self._get_observation_dimension())\n num_motors = self.rex.num_motors\n upper_bound[0:num_motors] = math.pi # Joint angle.\n upper_bound[num_motors:2 * num_motors] = motor.MOTOR_SPEED_LIMIT # Joint velocity.\n upper_bound[2 * num_motors:3 * num_motors] = motor.OBSERVED_TORQUE_LIMIT # Joint torque.\n upper_bound[3 * num_motors:-7] = 1.0 # Quaternion of base orientation.\n upper_bound[-7] = 1.0 # ratio in [0,1]\n upper_bound[-6:-2] = [1.0, 1.0, 1.0, 1.0] # sin in [-1, 1]\n upper_bound[-2:] = [self.max_speed, self.max_side_speed]\n\n return upper_bound","def get_fk(self, joints):\n\n header = Header()\n header.frame_id = self.group.get_planning_frame()\n\n robot_state = self.robot.get_current_state()\n robot_state.joint_state.position = joints\n\n links = [self.group.get_end_effector_link()]\n\n return self.fk_solver(header, links, robot_state).pose_stamped[0]","def trigger_points(self, eouts, elens):\n bs, xmax, _ = eouts.size()\n log_probs = torch.log_softmax(self.output(eouts), dim=-1)\n best_paths = log_probs.argmax(-1)\n hyps = []\n for b in range(bs):\n indices = [best_paths[b, t].item() for t in range(elens[b])]\n collapsed_indices = [x[0] for x in groupby(indices)]\n best_hyp = [x for x in filter(lambda x: x != self.blank, collapsed_indices)]\n hyps.append(best_hyp)\n ymax = max([len(h) for h in hyps])\n trigger_points_pred = log_probs.new_zeros((bs, ymax + 1), dtype=torch.int32)\n for b in range(bs):\n n_triggers = 0\n for t in range(elens[b]):\n token_idx = best_paths[b, t]\n if token_idx == self.blank:\n continue\n if not (t == 0 or token_idx != best_paths[b, t - 1]):\n continue\n trigger_points_pred[b, n_triggers] = t\n n_triggers += 1\n return trigger_points_pred","def compute_positions(self):\n return (self.x + DIRECTIONS[self.facing_direction][0]) % (self.image.shape[0] - 1), \\\n (self.y + DIRECTIONS[self.facing_direction][1]) % (self.image.shape[1] - 1)","def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\r\n L = start_scores.shape[0]\r\n assert end_scores.shape[0] == L\r\n assert trans_scores.shape[0] == L\r\n assert trans_scores.shape[1] == L\r\n assert emission_scores.shape[1] == L\r\n N = emission_scores.shape[0]\r\n\r\n y = []\r\n dp_scores = []\r\n back_pointer = []\r\n\r\n for i in xrange(N):\r\n dp_scores.append([])\r\n back_pointer.append([])\r\n for j in xrange(L):\r\n if (i == 0):\r\n score = start_scores[j] + emission_scores[0, j]\r\n back = -1\r\n else:\r\n max = dp_scores[i-1][0] + trans_scores[0, j]\r\n back = 0\r\n for k in xrange(L):\r\n if (dp_scores[i-1][k] + trans_scores[k, j] > max):\r\n max = dp_scores[i-1][k] + trans_scores[k, j]\r\n back = k\r\n score = max + emission_scores[i, j]\r\n dp_scores[i].append(score)\r\n back_pointer[i].append(back)\r\n\r\n s = dp_scores[N-1][0] + end_scores[0]\r\n back = 0\r\n for k in xrange(L):\r\n if (dp_scores[N-1][k] + end_scores[k] > s):\r\n s = dp_scores[N-1][k] + end_scores[k]\r\n back = k\r\n\r\n y.append(back)\r\n for i in range(N-1, 0, -1):\r\n y.append(back_pointer[i][back])\r\n back = back_pointer[i][back]\r\n y.reverse()\r\n\r\n return (s, y)","def get_move_indexes(i, j):\n return (i, j), (j, n - 1 - i), (n - 1 - i, n - 1 - j), (n - 1 - j, i)","def _findExonEnd(self, exonRecs, iBlkStart):\n iBlkEnd = iBlkStart + 1\n while (iBlkEnd < len(exonRecs)) and (self._tGapSize(exonRecs, iBlkEnd) < minIntronSize):\n iBlkEnd += 1\n return iBlkEnd, exonRecs[iBlkEnd - 1].end - exonRecs[iBlkStart].start","def\tbegin_end(env, blc):\n\n\tb_e = np.empty((blc.shape[0], 2))\n\tinf = 0\n\tp = 0\n\twin = env.win_over\n\tb_e[0, 0] = inf\n\tif blc[0] + win <= blc[-1]:\n\t\tb_e[0, 1] = blc[0] + win\n\telse:\n\t\tb_e[0, 1] = blc[-1]\n\tif blc.shape[0] == 1:\n\t\tb_e[0, 1] = blc[0]\n\t\treturn (b_e)\n\tfor k in range(1, blc.shape[0] - 1):\n\t\tinf = blc[k - 1] - win\n\t\tb_e[k, 0] = inf\n\t\tif blc[k] + win <= blc[-1]:\n\t\t\tb_e[k, 1] = blc[k] + win\n\t\telse:\n\t\t\tb_e[k, 1] = blc[-1]\n\tb_e[blc.shape[0] - 1, 0] = blc[-2] - win\n\tb_e[blc.shape[0] - 1, 1] = blc[-1]\n\tneg = np.where(b_e < 0)[0]\n\tif neg.shape[0]:\n\t\tb_e = b_e[neg[-1]:]\n\t\tb_e[0, 0] = 0\n\treturn (b_e)","def get_bend_port_distances(bend: Component) -> Tuple[float64, float64]:\n p0, p1 = bend.ports.values()\n return abs(p0.x - p1.x), abs(p0.y - p1.y)","def end_effectors(self) -> list:\n if not hasattr(self, \"_end_effectors\"):\n S = self.structure\n self._end_effectors = [\n [x, y]\n for x in S\n if S.out_degree(x) == 0\n for y in S.predecessors(x)\n if DIST in S[y][x]\n if S[y][x][DIST] < np.inf\n ]\n\n return self._end_effectors","def end_effectors(self) -> list:\n if not hasattr(self, \"_end_effectors\"):\n S = self.structure\n self._end_effectors = [\n [x, y]\n for x in S\n if S.out_degree(x) == 0\n for y in S.predecessors(x)\n if DIST in S[y][x]\n if S[y][x][DIST] < np.inf\n ]\n\n return self._end_effectors","def getPos(self,len,end,nodes):\n start=end\n if self.count==nodes:\n last=len\n else:\n last=end+(int)(len/(nodes+1))\n self.count+=1\n return (start,last)","def heuristicValueOfPosition(currPositions):\n hVal = 0;\n\n for y in range(1, n+1): #1,2,3\n for x in range(1, n+1):\n val = currPositions[y][x];\n if ((val == 0) or (goalPositions[val] == (y,x))): #val 0 means blank\n continue;\n else:\n hVal += abs(y-goalPositions[val][0]) + abs(x-goalPositions[val][1])\n\n return hVal;","def get_ecc(self):\n mu_mass = G*(self._mm + self._sm)\n h_mom = self.sp_ang_mom()\n vel = self.getvel_xyz()\n pos = self.getpos_xyz()\n e_vec = 1.0/mu_mass*(np.cross(vel, h_mom) -\n mu_mass*pos/np.linalg.norm(pos))\n return e_vec","def __path_to_end(self) -> List[List[int]]:\n predecessors = self.__predecessors_list()\n path = []\n\n row_exit, col_exit = Player.find_exit_position(self.__labyrinth)\n dest = self.__convert_position(row_exit, col_exit)\n\n v = dest\n\n path.append([v // 10, v % 10])\n\n while predecessors[v] != -1:\n path.append(predecessors[v])\n v = self.__convert_position(predecessors[v][0], predecessors[v][1])\n\n return path[::-1]","def end_point(self) -> Vec3:\n v = list(self.vertices([self.dxf.end_angle]))\n return v[0]","def reach_gradient(self):\n step_size = 0.05\n min_step_size = 0.001\n moved_closer = True\n while_loop_counter = 0\n max_steps = 100\n old_total_cost = 10\n epsilon = 0.005\n\n # While moved closer and not reached minimum step size\n while moved_closer and step_size > min_step_size:\n while_loop_counter += 1\n # Set a maximum number of steps per change to see progress - used for testing\n if while_loop_counter > max_steps:\n break\n new_total_cost = 0\n text = \"\"\n i = 0\n\n # Go through each joint within the arm\n for joint_key, joint_value in self.joint_angles.items():\n # Text to show for each joint change\n text += str(self.joint_names[i]) + \" \"\n i += 1\n\n # Old endpoint values\n old_value = joint_value\n\n # Update joints in ROS with current self.joint_angle values\n self.update_angles()\n self.get_current_ee_pose() # Old endpoint\n\n # Determine cost from current end effector to target\n old_cost = self.cost(self.arm_endpoint)\n\n # Gradient of old values\n gradient = self.gradient(joint_key)\n if gradient > 0: # Determine direction of gradient\n direction = 1\n else:\n direction = -1\n\n # Determine new angle value based on gradient\n self.joint_angles[joint_key] = (old_value - direction * step_size)\n\n if self.joint_angles[joint_key] < self.joint_min[joint_key]:\n self.joint_angles[joint_key] = self.joint_min[joint_key]\n elif self.joint_angles[joint_key] > self.joint_max[joint_key]:\n self.joint_angles[joint_key] = self.joint_max[joint_key]\n\n # Update joint angle values within ROS and get new endpoint value\n self.update_angles()\n self.get_current_ee_pose()\n\n # Determine cost from current end effector to target\n new_cost = self.cost(self.arm_endpoint)\n\n # Determine the cost of\n if new_cost > old_cost:\n self.joint_angles[joint_key] = old_value\n new_total_cost += old_cost\n text += \": No change \\n\"\n else:\n text += \": Improved by \" + str(direction * step_size) + \"\\n\"\n new_total_cost += new_cost\n\n # Display change of each joint through text\n print(\"Robot part changes: \\n\", text)\n self.cost_values += [new_total_cost]\n\n # Check if improved from previous position\n if old_total_cost < new_total_cost:\n step_size -= .01\n moved_closer = False\n else:\n moved_closer = True\n\n print(\"abs(old_total_cost - new_total_cost): \", abs(old_total_cost - new_total_cost))\n print(\"new_total_cost: \", new_total_cost)\n # If changes are less than epsilon, we stop\n if abs(old_total_cost - new_total_cost) < epsilon:\n break\n old_total_cost = new_total_cost\n\n # Save new joint angle values\n save_file = \"/OptimizedAngles.csv\"\n print(\"Saving new joint angles at \", save_file)\n self.save_new_joint_angles(save_file)","def at_b (self):\n self.argc = int((len(n.coord[0]))/2)\n self.pts_con = np.array(self.coord[:,self.argc:len(n.coord[0])])\n\n self.xd = self.xdi\n self.zd = self.zdi \n \n for i, x in enumerate(self.xdi):\n self.aux_con = self.pts_con[0] - x \n self.arg1 = np.argmin(abs(self.aux_con)) \n \n if (self.aux_con[self.arg1] < 0 and self.arg1 == 0) or (self.aux_con[self.arg1] > 0 and self.arg1 == len(self.aux_con)-1):\n self.yd[i] = 99999.\n #print(self.yd[i],self.arg1)\n #print(self.aux_con)\n \n elif (self.aux_con[self.arg1] > 0 and self.aux_con[self.arg1+1] > self.aux_con[self.arg1]): #(self.aux_con[self.arg1] < 0 and self.aux_con[self.arg1-1] > self.aux_con[self.arg1]) or \n self.yd[i] = 99999.\n #print(self.yd[i],self.arg1)\n #print(self.aux_con)\n \n elif self.aux_con[self.arg1] < 0:\n #print(self.arg1)\n self.arg1 = self.arg1 + self.argc\n self.arg2 = self.arg1 - 1\n self.yd[i] = self.coord[1,n.arg1] + (x-self.coord[0,n.arg1])*(self.coord[1,n.arg2]-self.coord[1,n.arg1])/(self.coord[0,n.arg2]-self.coord[0,n.arg1])\n #print(self.yd[i],self.arg1,self.arg2)\n #print(self.aux_con)\n\n elif self.aux_con[self.arg1] > 0:\n #print(self.arg1) \n self.arg1 = self.arg1 + self.argc\n self.arg2 = self.arg1 + 1\n self.yd[i] = self.coord[1,n.arg1] + (x-self.coord[0,n.arg1])*(self.coord[1,n.arg2]-self.coord[1,n.arg1])/(self.coord[0,n.arg2]-self.coord[0,n.arg1]) \n #print(self.yd[i],self.arg1,self.arg2)\n #print(self.aux_con)\n \n #print('Defensa {0}\\n{1}: {2}\\n{3}: {4}'.format(i,self.arg1,self.aux_con[self.arg1],self.arg2,self.aux_con[self.arg2])) \n \n #self.yd = self.yd\n self.b = np.array([self.xd,self.yd,self.zd])\n #self.b.loc[:,('y')] = self.b.loc[:,('y')] ","def endtoend(d):\n dists = distance_matrix(d)\n avgdists = np.array([np.diag(dists, i).mean() for i in range(dists.shape[0])])\n return avgdists","def calculate_coefficients(self, start, end):\n A = np.array([\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\n ])\n\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\n c_1 = a_1 + 2 * a_2 * self.deltaT\n c_2 = 2 * a_2\n\n B = np.array([\n end[0] - c_0,\n end[1] - c_1,\n end[2] - c_2\n ])\n\n a_3_4_5 = np.linalg.solve(A, B)\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\n\n return coeff","def lendiag(self):\n if self.y <= self.x:\n return self.y\n else:\n return self.x","def get_frame_joints(self, frame):\n joints = frame[(self.POS_SIZE + self.ROT_SIZE):].copy()\n return joints","def get_end_pos(cls, start_pos, dimensions, left=False, up=False):\n dx = (dimensions[0] - 1) * cls.width\n if left: dx = -dx\n dy = (dimensions[1] -1 ) * cls.height\n if up: dy = -dy\n \n end_pos = start_pos[0] + dx, start_pos[1] + dy\n return end_pos","def find(self, ends):\n\n if self.function(ends[0]) * self.function(ends[1]) > 0:\n raise ValueError('Sign of function at both the end points must be opposite.')\n \n a = min(ends)\n b = max(ends)\n\n while (b - a > self.epsilon):\n mid = (a + b) / 2\n if self.function(mid) * self.function(a) > 0:\n a = mid\n else:\n b = mid\n \n return (a + b) / 2","def evalBottomGuideEndDisp(length: float,\n bottomcurve_radius: float,\n tendonDistFromAxis: float,\n tendonOrientationDF: float)-> np.ndarray:\n horizontalDispAlongCurve = tendonDistFromAxis*sin(tendonOrientationDF)\n return np.array((\n tendonDistFromAxis*cos(tendonOrientationDF),\n tendonDistFromAxis*sin(tendonOrientationDF),\n -sqrt(bottomcurve_radius**2 - horizontalDispAlongCurve**2) + bottomcurve_radius - length/2\n ))","def get_ee_points_velocities(ref_jacobian, ee_points, ref_rot, joint_velocities):\n ref_jacobians_trans = ref_jacobian[:3, :]\n ref_jacobians_rot = ref_jacobian[3:, :]\n ee_velocities_trans = np.dot(ref_jacobians_trans, joint_velocities)\n ee_velocities_rot = np.dot(ref_jacobians_rot, joint_velocities)\n ee_velocities = ee_velocities_trans + np.cross(ee_velocities_rot.reshape(1, 3),\n ref_rot.dot(ee_points.T).T)\n return ee_velocities.reshape(-1)","def joints_torque(self):\r\n return self._arm.joints_torque","def determine_move_position(self):\n green_probs = []\n net_size = len(self.net)\n adjacents = self.net[self.current_pos].adjacents\n #Belief propagation:\n #Analyzes each position's probability of obtaining\n #green when measuring at a time t+1.\n for i in adjacents:\n accum = 0\n for j in range(0, net_size):\n distance = self.__get_distance(i-1, j)\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[0][0]\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[1][0]\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[2][0]\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[3][0]\n else: #Probability of measure green at a distance >= 4 from 'i'.\n accum += self.enemy_net[i-1].value * self.ct[4][0]\n green_probs.append((i, accum))\n #Returns the position in which the probability of\n #obtaining green when measuring is the lowest.\n return min(green_probs, key=itemgetter(1))[0]","def _handle_bounds_servoj(self):\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(self._qt_[-1])\n inside_angle_bound = np.all(self._angles_low <= self._qt_[-1, self._joint_indices]) and \\\n np.all(self._qt_[-1, self._joint_indices] <= self._angles_high)\n\n if inside_bound and inside_angle_bound:\n self.return_point = None\n return\n\n if self.return_point is None:\n # we are outside the bounds and return point wasn't computed yet\n if inside_bound and not inside_angle_bound:\n print(\"outside of angle bound\")\n self.rel_indices = self._joint_indices\n self._cmd_ = self._q_[0][self._joint_indices]\n self._cmd_ = np.clip(self._cmd_, self._angles_low + self._angle_bound_buffer,\n self._angles_high - self._angle_bound_buffer)\n # a point within the box to which we will be returning\n self.return_point = self._cmd_.copy()\n # Speed at which arm approaches the boundary. The faster this speed,\n # the larger opposite acceleration we need to apply in order to slow down\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\n\n else:\n print(\"outside box bound\")\n xyz = np.clip(xyz, self._end_effector_low + self._box_bound_buffer,\n self._end_effector_high - self._box_bound_buffer)\n mat[:3, 3] = xyz\n ref_pos = self._q_ref.copy()\n ref_pos[self._joint_indices] = self._q_[-1, self._joint_indices]\n solutions = ur_utils.inverse_near(mat, wrist_desired=self._q_ref[-1], ref_pos=ref_pos,\n params=self._ik_params)\n servoj_q = self._q_ref.copy()\n if len(solutions) == 0:\n servoj_q[self._joint_indices] = self._q_[-1, self._joint_indices]\n else:\n servoj_q[self._joint_indices] = solutions[0][self._joint_indices]\n self.return_point = servoj_q[self._joint_indices]\n # Speed at which arm approaches the boundary. The faster this speed,\n # the larger opposite acceleration we need to apply in order to slow down\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\n # if return point is already computed, keep going to it, no need\n # to recompute it at every time step\n self._cmd_ = self.return_point - self._q_[0][self._joint_indices]\n # Take the direction to return point and normalize it to have norm 0.1\n if np.linalg.norm(self._cmd_) != 0:\n self._cmd_ /= np.linalg.norm(self._cmd_) / 0.1\n\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\n # This acceleration guarantees that we won't move beyond\n # the bounds by more than 0.05 radian on each joint. This\n # follows from kinematics equations.\n accel_to_apply = np.max(np.abs(self._qd_)) * self.init_boundary_speed / 0.05\n\n # self.boundary_packet[1:1 + 6][self.joint_indices] = self.return_point\n # self.actuator_comms['UR5'].actuator_buffer.write(self.reset_packet)\n # time.sleep(1.0)\n self._speedj_packet[-2] = np.clip(accel_to_apply, 2.0, 5.0)\n self._actuation_packet_['UR5'] = self._speedj_packet\n self._cmd_.fill(0.0)\n self._cmd_prev_.fill(0.0)\n self._first_deriv_.fill(0.0)","def __inverse_kinematics(self, guess, target_point):\n\n error = 1.0\n tolerance = 0.05\n\n # Initial Guess - Joint Angles\n thetas = np.matrix(guess) # thetas is list which is contain all axes theta angles.\n target_point = np.matrix(target_point) # X, Y, Z list to matrix for Target Position\n # print(target_point.shape)\n # Jacobian\n self.__calc_jacobian_matrix()\n tf_matrix_first_to_last = self.tf_matrices_list[-1]\n\n error_grad = []\n\n theta_dict = {}\n\n lr = 0.2\n while error > tolerance:\n for i in range(len(np.array(thetas)[0])):\n theta_dict[self.q[i]] = np.array(thetas)[0][i]\n\n theta_dict[self.q[-1]] = self.q[-1]\n\n calculated_target_point = np.matrix(self.get_coords_from_forward_kinematics(self.__forward_kinematics(np.array(thetas)[0])[-1]))\n logger.debug(f'calculated target point is \\n{calculated_target_point}')\n\n diff_wanted_calculated = target_point - calculated_target_point\n\n jacob_mat = np.matrix(self.jacobian_matrix.evalf(subs=theta_dict, chop=True, maxn=4)).astype(np.float64).T\n logger.debug(f'jacobian matrix is\\n{jacob_mat} \\n\\n diff is \\n {diff_wanted_calculated}')\n\n thetas = thetas + lr * (jacob_mat * diff_wanted_calculated.T)\n # thetas = np.array(thetas)[0] # this line's purpose is changing Q from matrix level to array level.\n\n prev_error = error\n\n error = linalg.norm(diff_wanted_calculated)\n\n if error > 10 * tolerance:\n lr = 0.3\n elif error < 10 * tolerance:\n lr = 0.2\n error_grad.append((error - prev_error))\n\n # print(error)\n return np.array(thetas)[0]","def generate_obstacle_point(start, end):\n top_left = (start[0], start[1] - _OBSTACLE_SIZE)\n top_right = (end[0], end[1] - _OBSTACLE_SIZE)\n return start, end, top_right, top_left","def compute_fk_velocity(self, jpos, jvel, tgt_frame):\n if isinstance(jpos, list):\n jpos = np.array(jpos)\n if isinstance(jvel, list):\n jvel = np.array(jvel)\n kdl_end_frame = kdl.FrameVel()\n kdl_jnt_angles = joints_to_kdl(jpos)\n kdl_jnt_vels = joints_to_kdl(jvel)\n kdl_jnt_qvels = kdl.JntArrayVel(kdl_jnt_angles, kdl_jnt_vels)\n idx = self.arm_link_names.index(tgt_frame) + 1\n fg = self._fk_solver_vel.JntToCart(kdl_jnt_qvels,\n kdl_end_frame,\n idx)\n if fg < 0:\n raise ValueError('KDL Vel JntToCart error!')\n end_twist = kdl_end_frame.GetTwist()\n return np.array([end_twist[0], end_twist[1], end_twist[2],\n end_twist[3], end_twist[4], end_twist[5]])","def _inverse_kinematics(self):\n q_2 = np.arccos(\n (\n (self._x_obj_0 - self._jnt_lengths[2]) ** 2\n + self._y_obj_0 ** 2\n - self._jnt_lengths[0] ** 2\n - self._jnt_lengths[1] ** 2\n )\n / (2 * self._jnt_lengths[0] * self._jnt_lengths[1])\n )\n psi = np.arcsin(\n self._jnt_lengths[1] * np.sin(q_2)\n / np.sqrt(\n (self._x_obj_0 - self._jnt_lengths[2]) ** 2\n + self._y_obj_0 ** 2\n )\n )\n q_1 = (np.arctan2(self._x_obj_0 - self._jnt_lengths[2], self._y_obj_0)\n - psi)\n q_3 = np.pi / 2 - q_1 - q_2\n return np.array([q_1, q_2, q_3])","def _robot_jpos_getter(self):\n return np.array(self.env._joint_positions)","def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\r\n\r\n L = start_scores.shape[0]\r\n assert end_scores.shape[0] == L\r\n assert trans_scores.shape[0] == L\r\n assert trans_scores.shape[1] == L\r\n assert emission_scores.shape[1] == L\r\n N = emission_scores.shape[0]\r\n\r\n #T - Score matrix same as in assignement pdf\r\n T = np.zeros(shape=(L,N))\r\n #Back pointers - to store the previous best tag for word at (i-1)th position\r\n #that resulted into current best tag for (i)th word \r\n back_pointer = np.full((L,N), -1)\r\n\r\n for i in xrange(L):\r\n emission = emission_scores[0][i]\r\n combined = emission + start_scores[i]\r\n T[i][0] = combined\r\n\r\n # Loop over all the words in a sequesnce\r\n for i in xrange(1, N):\r\n # Loop over all the tags for the word at index i \r\n for j in xrange(L):\r\n # Varibale for maximum tag score from previous word (word at i-1)\r\n tmp_max = float('-inf')\r\n tmp_max_idx = -1\r\n #Emission value of word at idx i from state (i.e tag) j\r\n emission = emission_scores[i][j]\r\n #Loop over all the possibile tags for previous word T[tag (1..L), word at i-1]\r\n #and get max among them. Store the corresponding back pointer for there T[tag (1..L), word at i-1]\r\n for k in xrange(L):\r\n transition = trans_scores[k][j]\r\n prev_path = T[k][i-1]\r\n combined = transition + prev_path\r\n if (tmp_max < combined):\r\n tmp_max = combined\r\n tmp_max_idx = k\r\n\r\n back_pointer[j][i] = tmp_max_idx\r\n T[j][i] = tmp_max + emission\r\n\r\n # Doing this step outside because if N == 1 then above loop will not run\r\n # Variable for maximum tag score\r\n tag_max = float('-inf')\r\n # Variable for back pointer(previous T[tag, word])\r\n tag_max_idx = -1\r\n for i in xrange(L):\r\n T[i][N-1] = T[i][N-1] + end_scores[i]\r\n if (tag_max < T[i][N-1]):\r\n tag_max = T[i][N-1]\r\n tag_max_idx = i\r\n # print(\"Max tag -> \" + str(tag_max_idx))\r\n\r\n #Variable to track the path length - should be equal to N\r\n path_length = 0\r\n #Variable to back track on the tags\r\n tag_idx = tag_max_idx\r\n #Varibale to track the word index in N\r\n word_idx = N-1 \r\n #Path strored using backtracking\r\n y = []\r\n\r\n #Getting the best path using backtracking on back_pointers\r\n while path_length != N-1:\r\n y.append(back_pointer[tag_idx][word_idx])\r\n tag_idx = back_pointer[tag_idx][word_idx]\r\n word_idx = word_idx - 1\r\n path_length = path_length + 1\r\n\r\n #Reversing the backtracked path\r\n y = y[::-1]\r\n #Adding the tag for the last word idx in N\r\n y.append(tag_max_idx)\r\n # print(\"Path -> \" + str(y))\r\n\r\n return (tag_max, y)","def getEePointsJacobians(refJacobian, eePoints, refRot, numberOfJoints):\n eePoints = np.asarray(eePoints)\n refJacobiansTrans = refJacobian[:3, :]\n refJacobiansRot = refJacobian[3:, :]\n endEffectorPointsRot = np.expand_dims(refRot.dot(eePoints.T).T, axis=1)\n eePointsJacTrans = np.tile(refJacobiansTrans, (eePoints.shape[0], 1)) + \\\n np.cross(refJacobiansRot.T, endEffectorPointsRot).transpose(\n (0, 2, 1)).reshape(-1, numberOfJoints)\n eePointsJacRot = np.tile(refJacobiansRot, (eePoints.shape[0], 1))\n return eePointsJacTrans, eePointsJacRot","def get_first_quadrant(self):\n num_copies_x = ceil(self.max_x / self.room_x)\n num_copies_x = int(num_copies_x)\n num_copies_y = ceil(self.max_y / self.room_y)\n num_copies_y = int(num_copies_y)\n\n player_exp_x = []\n player_exp_y = []\n guard_exp_x = []\n guard_exp_y = []\n # Loop expands along the x axis\n for i in range(0, num_copies_x + 1, 1):\n temp_player_y_list = []\n temp_guard_y_list = []\n r_x = self.room_x * i\n\n if len(player_exp_x) == 0:\n n_p_p_x = self.player_x\n else:\n n_p_p_x = (r_x - player_exp_x[-1][0]) + r_x\n player_exp_x.append([n_p_p_x, self.player_y, 1])\n\n if len(guard_exp_x) == 0:\n n_g_p_x = self.guard_x\n else:\n n_g_p_x = (r_x - guard_exp_x[-1][0]) + r_x\n guard_exp_x.append([n_g_p_x, self.guard_y, 7])\n\n # Loop expands along the x axis\n for j in range(1, num_copies_y + 1, 1):\n r_y = self.room_y * j\n if len(temp_guard_y_list) == 0:\n n_g_p_y = (r_y - self.guard_y) + r_y\n temp_guard_y_list.append(n_g_p_y)\n else:\n n_g_p_y = (r_y - temp_guard_y_list[-1]) + r_y\n temp_guard_y_list.append(n_g_p_y)\n guard_exp_y.append([n_g_p_x, n_g_p_y, 7])\n\n if len(temp_player_y_list) == 0:\n n_p_p_y = (r_y - self.player_y) + r_y\n temp_player_y_list.append(n_p_p_y)\n else:\n n_p_p_y = (r_y - temp_player_y_list[-1]) + r_y\n temp_player_y_list.append(n_p_p_y)\n player_exp_y.append([n_p_p_x, n_p_p_y, 1])\n\n return player_exp_x + guard_exp_x + player_exp_y + guard_exp_y","def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\r\n L = start_scores.shape[0]\r\n assert end_scores.shape[0] == L\r\n assert trans_scores.shape[0] == L\r\n assert trans_scores.shape[1] == L\r\n assert emission_scores.shape[1] == L\r\n N = emission_scores.shape[0]\n \r\n trans_scores += start_scores\n back_ptrs = np.zeros_like(emission_scores,dtype=np.int32)\n emission_scores += start_scores\r\n em_scores = np.zeros_like(emission_scores)\n em_scores[0] = start_scores+emission_scores[0]\n \n for k in range(1,N):\n transition_plus_score =trans_scores+np.expand_dims(em_scores[k-1],1)\n back_ptrs[k] =np.argmax(transition_plus_score,0)\n em_scores[k] =np.max(transition_plus_score,0)+emission_scores[k]\n \n v = [np.argmax(end_scores+em_scores[-1])]\n v_score = np.max(end_scores+em_scores[-1])\n\n for back_ptr in reversed(back_ptrs[1:]):\n v.append(back_ptr[v[-1]])\n v.reverse()\n return v_score,v","def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\r\n L = start_scores.shape[0]\r\n assert end_scores.shape[0] == L\r\n assert trans_scores.shape[0] == L\r\n assert trans_scores.shape[1] == L\r\n assert emission_scores.shape[1] == L\r\n N = emission_scores.shape[0]\r\n\r\n # SHAPES \r\n # N = 5, L = 3\r\n # emission_scores = (5,3), trans_scores = (3,3)\r\n # start_scores = (3,), end_scores = (3,)\r\n\r\n # Creating the transition DP matrix\r\n T = [[0 for _ in range(N)] for _ in range(L)]\r\n backpointers = [[0 for _ in range(N)] for _ in range(L)]\r\n\r\n # Filling the first column\r\n for row in range(L):\r\n T[row][0] = emission_scores[0][row] + start_scores[row] # emission_scores matrix is (N X L)\r\n \r\n # Filling the rest of the transition matrix\r\n for col in range(1, N):\r\n for row in range(L):\r\n prev_list = []\r\n for prev_label in range(L):\r\n prev_list.append(trans_scores[prev_label, row] + T[prev_label][col-1])\r\n T[row][col] = max(prev_list) + emission_scores[col][row] \r\n backpointers[row][col] = np.argmax(prev_list)\r\n\r\n # Filling the last column\r\n for row in range(L):\r\n T[row][N-1] += end_scores[row]\r\n\r\n # print for debug\r\n # print \"T\"\r\n # for i in T:\r\n # print i\r\n \r\n # print \r\n # print\r\n\r\n # print \"B\"\r\n # for i in backpointers:\r\n # print i\r\n\r\n # Finding max score in last column of T matrix\r\n T = np.array(T)\r\n score = np.asscalar(np.max(T[:,N-1]))\r\n location = np.asscalar(np.argmax(T[:,N-1]))\r\n\r\n # Getting best sequence from right to left using backpointers\r\n y = [location]\r\n for col in range(N-1, 0, -1):\r\n y.insert(0, backpointers[location][col])\r\n location = backpointers[location][col]\r\n\r\n '''\r\n y = []\r\n for i in xrange(N):\r\n # stupid sequence\r\n y.append(i % L)\r\n # score set to 0\r\n return (0.0, y)\r\n '''\r\n return (score, y)","def _get_joint_positions_all(self, abs_input: [list, np.ndarray]):\n return np.copy(abs_input)","def _endx(self, parents):\n ALPHA = (1.-2*0.35**2)**0.5/2.\n BETA = 0.35/(self.n_gene-1)**0.5\n\n child = np.empty(self.n_gene+1)\n\n t1 = (parents[1, :self.n_gene]-parents[0, :self.n_gene]) / 2.\n t2 = np.random.normal(scale=ALPHA) * (\n parents[1, :self.n_gene] - parents[0, :self.n_gene]\n )\n t3 = np.sum(\n np.random.normal(scale=BETA, size=self.n_gene)[:, np.newaxis]\n * (\n parents[2:, :self.n_gene] - (\n np.sum(parents[2:, :self.n_gene], axis=0) / self.n_gene\n )\n ), axis=0\n )\n child[:self.n_gene] = t1 + t2 + t3\n\n return child","def interpolate_to_parent(self, start, end, linspace_count):\n \n v = end - start\n length = norm(v)\n v = v / length # Make v a unit vector\n l = np.linspace(0, length, linspace_count) \n\n return np.array([start[i] + v[i] * l for i in range(3)])","def calc_nearest_ind(self, robot_pose):\n pass","def get_ending_direction_vector(self):\n\n total_length = len(self.pixel_list)\n\n if total_length < 2:\n return None\n elif total_length < 15:\n delta_x = self.pixel_list[-1].x - self.pixel_list[0].x\n delta_y = self.pixel_list[-1].y - self.pixel_list[0].y\n return delta_y, delta_x\n else:\n delta_x = self.pixel_list[-15].x - self.pixel_list[-1].x\n delta_y = self.pixel_list[-15].y - self.pixel_list[-1].y\n return delta_y, delta_x","def joint_angles(self, arm):\n self.main(1, arm.angles)\n # self.distance_service = rospy.Service('distance', Distance, self.distance)\n return Joint_anglesResponse(True)","def get_imin(x1, x2, y, k=1, normalize=None, norm=np.inf):\n\n if normalize:\n y = normalize(y)\n\n y_tree = cKDTree(y)\n\n n = len(y)\n i_spec = np.zeros((2, n))\n\n for jj, x in enumerate([x1, x2]):\n\n if normalize:\n x = normalize(x)\n\n # construct state array for the joint processes:\n xy = np.c_[x,y]\n\n # store data pts in kd-trees for efficient nearest neighbour computations\n # TODO: choose a better leaf size\n x_tree = cKDTree(x)\n xy_tree = cKDTree(xy)\n\n # kth nearest neighbour distances for every state\n # query with k=k+1 to return the nearest neighbour, not counting the data point itself\n # dist, idx = xy_tree.query(xy, k=k+1, p=norm)\n dist, idx = xy_tree.query(xy, k=k+1, p=np.inf)\n epsilon = dist[:, -1]\n\n # for each point, count the number of neighbours\n # whose distance in the x-subspace is strictly < epsilon\n # repeat for the y subspace\n nx = np.empty(n, dtype=np.int)\n ny = np.empty(n, dtype=np.int)\n for ii in xrange(N):\n # nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=norm)) - 1\n # ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=norm)) - 1\n nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\n ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\n\n i_spec[jj] = digamma(k) - digamma(nx+1) + digamma(ny+1) + digamma(n) # version (1)\n\n i_min = np.mean(np.min(i_spec, 0))\n\n return i_min","def inv_kin(self, ee_pos=None):\n\n if ee_pos != None:\n self.ee_pos = ee_pos\n else:\n ee_pos = self.ee_pos\n \n def distance_to_default(q, *args):\n \"\"\"Objective function to minimize\n Calculates the euclidean distance through joint space to the\n default arm configuration. The weight list allows the penalty of\n each joint being away from the resting position to be scaled\n differently, such that the arm tries to stay closer to resting\n state more for higher weighted joints than those with a lower\n weight.\n\n q : np.array\n the list of current joint angles\n\n returns : scalar\n euclidean distance to the default arm position\n \"\"\"\n # weights found with trial and error,\n # get some wrist bend, but not much\n weight = [1, 1, 1.3]\n return np.sqrt(np.sum([(qi - q0i)**2 * wi\n for qi, q0i, wi in zip(q, self.q0, weight)]))\n\n def x_constraint(q,ee_pos):\n \"\"\"Returns the corresponding hand self.ee_pos coordinates for\n a given set of joint angle values [shoulder, elbow, wrist],\n and the above defined arm segment lengths, L\n\n q : np.array\n the list of current joint angles\n self.ee_pos : np.array\n current self.ee_pos position (not used)\n\n returns : np.array\n the difference between current and desired x position\n \"\"\"\n x = (self.length[0]*np.cos(q[0]) + self.length[1]*np.cos(q[0]+q[1]) +\n self.length[2]*np.cos(np.sum(q))) - self.ee_pos[0]\n return x\n\n def y_constraint(q,ee_pos):\n \"\"\"Returns the corresponding hand self.ee_pos coordinates for\n a given set of joint angle values [shoulder, elbow, wrist],\n and the above defined arm segment lengths, L\n\n q : np.array\n the list of current joint angles\n self.ee_pos : np.array\n current self.ee_pos position (not used)\n returns : np.array\n the difference between current and desired y position\n \"\"\"\n y = (self.length[0]*np.sin(q[0]) + self.length[1]*np.sin(q[0]+q[1]) +\n self.length[2]*np.sin(np.sum(q))) - self.ee_pos[1]\n return y\n\n def joint_limits_upper_constraint(q,ee_pos):\n \"\"\"Used in the function minimization such that the output from\n this function must be greater than 0 to be successfully passed.\n\n q : np.array\n the current joint angles\n self.ee_pos : np.array\n current self.ee_pos position (not used)\n\n returns : np.array\n all > 0 if constraint matched\n \"\"\"\n return self.max_angles - q\n\n def joint_limits_lower_constraint(q,ee_pos):\n \"\"\"Used in the function minimization such that the output from\n this function must be greater than 0 to be successfully passed.\n\n q : np.array\n the current joint angles\n self.ee_pos : np.array\n current self.ee_pos position (not used)\n\n returns : np.array\n all > 0 if constraint matched\n \"\"\"\n return q - self.min_angles\n\n return scipy.optimize.fmin_slsqp(\n func=distance_to_default,\n x0=self.q,\n eqcons=[x_constraint,\n y_constraint],\n # uncomment to add in min / max angles for the joints\n # ieqcons=[joint_limits_upper_constraint,\n # joint_limits_lower_constraint],\n args=(self.ee_pos,),\n iprint=0) # iprint=0 suppresses output","def ortho_line_cut(self):\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\n\n ave_grad = self.average_grad()\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\n\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\n\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\n\n\n coordleft = [x_intersect, y_intersect]\n coordright =[x_mid_right, y_mid_right]\n\n dist = self.distance(coordleft, coordright)\n\n return coordleft, coordright, dist","def joint_variables(self, G: nx.Graph, T_final: dict = None) -> np.ndarray:\n R = {\"p0\": SO3.identity()}\n joint_variables = {}\n for u, v, dat in self.structure.edges(data=DIST):\n if dat:\n diff_uv = G.nodes[v][POS] - G.nodes[u][POS]\n len_uv = np.linalg.norm(diff_uv)\n\n sol = np.linalg.lstsq(len_uv * R[u].as_matrix(), diff_uv)\n sol = sol[0]\n\n theta_idx = np.math.atan2(sol[1], sol[0]) + pi / 2\n Rz = SO3.rotz(theta_idx)\n\n alpha_idx = abs(np.math.acos(min(sol[2], 1)))\n Rx = SO3.rotx(alpha_idx)\n\n joint_variables[v] = [wraptopi(theta_idx), alpha_idx]\n R[v] = R[u].dot(Rz.dot(Rx))\n\n return joint_variables","def _get_end_points(self, segmented_instances, i, stats, idx):\n\n end_points=[]\n\n # find all points intersecting the bbox\n #(tl_x, th_y, width, height, area)\n label_num=i+1\n leftmost_x = stats['bbox'][i][cv2.CC_STAT_LEFT]\n topmost_y = stats['bbox'][i][cv2.CC_STAT_TOP]\n width = stats['bbox'][i][cv2.CC_STAT_WIDTH]\n height = stats['bbox'][i][cv2.CC_STAT_HEIGHT]\n bottom_most_y = topmost_y + height-1\n right_most_x = leftmost_x + width-1\n\n segmented_instances_copy=segmented_instances.copy()\n edge_points = np.zeros(segmented_instances.shape).astype(np.uint8)\n segs = np.zeros(segmented_instances.shape).astype(np.uint8)\n segs[segmented_instances==label_num]=255\n cv2.rectangle(segmented_instances_copy,(leftmost_x, topmost_y), (right_most_x, bottom_most_y), 150, 2)\n\n #Get all points for the current stem segment\n label_points = np.argwhere(segmented_instances.copy()==label_num)\n\n # upper points from (tl_x,th_y) to (th_x, th_y) that instersect with the upper edge of the bouding box\n upper_points = [i for i in label_points if i[0]==topmost_y and i[1]>=leftmost_x and i[1]<=right_most_x]\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(upper_points, edge_points, segs, 1)\n center_upper_pts = sorted(self._get_centeroids(x_pts))\n\n # left side points from (tl_x, tl_y) to (tl_x, th_y) that instersect with the left edge of the bouding box\n left_points = [i for i in label_points if i[1]==leftmost_x and i[0]<=bottom_most_y and i[0]>=topmost_y]\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(left_points, edge_points, segs, 0)\n center_left_pts = sorted(self._get_centeroids(x_pts))\n\n #right side points form (th_x, tl_y) to (th_x, th_y) that instersect with the right edge of the bouding box\n right_points = [i for i in label_points if i[1]==right_most_x and i[0]<=bottom_most_y and i[0]>=topmost_y]\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(right_points, edge_points, segs, 0)\n center_right_pts = sorted(self._get_centeroids(x_pts))\n\n #bottom points from (tl_x, tl_y) to (th_x,tl_y)\n bottom_points = [i for i in label_points if i[1]>=leftmost_x and i[1]<=right_most_x and i[0]==bottom_most_y]\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(bottom_points, edge_points, segs, 1)\n center_bottom_pts = sorted(self._get_centeroids(x_pts))\n\n # If there are corner edges, get the centroid of that\n center_x_lb, center_y_lb, center_left_pts, center_bottom_pts = self._get_corner_centers(center_left_pts, \\\n center_bottom_pts, bottom_most_y, leftmost_x)\n if (center_x_lb != None) and (center_y_lb != None):\n end_points.append([center_x_lb, center_y_lb])\n else:\n if len(center_left_pts)>0:\n for pt_idx in range(0, len(center_left_pts)):\n end_points.append([leftmost_x, center_left_pts[pt_idx]])\n if len(center_bottom_pts)>0:\n for pt_idx in range(0, len(center_bottom_pts)):\n end_points.append([center_bottom_pts[pt_idx], bottom_most_y])\n\n # If there are corner edges, get the centroid of that\n center_x_ur, center_y_ur, center_right_pts, center_upper_pts = self._get_corner_centers(center_right_pts, \\\n center_upper_pts, topmost_y, right_most_x)\n if (center_x_ur != None) and (center_y_ur != None):\n end_points.append([center_x_ur, center_y_ur])\n else:\n if len(center_right_pts)>0:\n for pt_idx in range(0, len(center_right_pts)):\n end_points.append([right_most_x, center_right_pts[pt_idx]])\n if len(center_upper_pts)>0:\n for pt_idx in range(0, len(center_upper_pts)):\n end_points.append([center_upper_pts[pt_idx], topmost_y])\n\n # If there are corner edges, get the centroid of that\n center_x_ul, center_y_ul, center_left_pts, center_upper_pts = self._get_corner_centers(center_left_pts, \\\n center_upper_pts, topmost_y, leftmost_x)\n if (center_x_ul != None) and (center_y_ul != None):\n end_points.append([center_x_ul, center_y_ul])\n else:\n if len(center_left_pts)>0:\n for pt_idx in range(0, len(center_left_pts)):\n end_points.append([leftmost_x, center_left_pts[pt_idx]])\n if len(center_upper_pts)>0:\n for pt_idx in range(0, len(center_upper_pts)):\n end_points.append([center_upper_pts[pt_idx], topmost_y])\n\n\n # If there are corner edges, get the centroid of that\n center_x_br, center_y_br, center_right_pts, center_bottom_pts = self._get_corner_centers(center_right_pts, \\\n center_bottom_pts, bottom_most_y, right_most_x)\n if (center_x_br != None) and (center_y_br != None):\n end_points.append([center_x_br, center_y_br])\n else:\n if len(center_right_pts)>0:\n for pt_idx in range(0, len(center_right_pts)):\n end_points.append([right_most_x, center_right_pts[pt_idx]])\n if len(center_bottom_pts)>0:\n for pt_idx in range(0, len(center_bottom_pts)):\n end_points.append([center_bottom_pts[pt_idx], bottom_most_y])\n\n #self.showme(segmented_instances_copy, 'bbox')\n\n return end_points","def get_exterior(self, x, y, x1, x2, bottom, head_y):\n fx1 = x+(x-x1)*8\n fx2 = x+(x-x2)*8\n # compute bounding ellipse; and intersection with body outline\n cv2.ellipse(self.ellipse_finder, ((x/mscale,y/mscale), ((fx1-fx2)/mscale, (2*(bottom-head_y))/mscale), 0), 255,-1 )\n intersection = np.bitwise_and(255-self.ellipse_finder, self.median_finder)\n # find external blobs\n im2, out_contours, out_hierarchy = cv2.findContours(intersection,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n return out_contours, out_hierarchy, fx1-fx2"],"string":"[\n \"def get_joint_positions(self, joint_angles ): \\n\\n\\n # current angles\\n res_joint_angles = joint_angles.copy() \\n\\n # detect limits\\n maskminus= res_joint_angles > self.joint_lims[:,0]\\n maskplus = res_joint_angles < self.joint_lims[:,1]\\n \\n res_joint_angles = res_joint_angles*(maskplus*maskminus) \\n res_joint_angles += self.joint_lims[:,0]*(np.logical_not(maskminus) )\\n res_joint_angles += self.joint_lims[:,1]*(np.logical_not(maskplus) )\\n \\n # mirror\\n if self.mirror :\\n res_joint_angles = -res_joint_angles\\n res_joint_angles[0] += np.pi \\n \\n # calculate x coords of arm edges.\\n # the x-axis position of each edge is the \\n # sum of its projection on the x-axis\\n # and all the projections of the \\n # previous edges \\n x = np.array([ \\n sum([ \\n self.segment_lengths[k] *\\n np.cos( (res_joint_angles[:(k+1)]).sum() ) \\n for k in range(j+1) \\n ])\\n for j in range(self.number_of_joint) \\n ])\\n \\n # trabslate to the x origin \\n x = np.hstack([self.origin[0], x+self.origin[0]])\\n\\n # calculate y coords of arm edges.\\n # the y-axis position of each edge is the \\n # sum of its projection on the x-axis\\n # and all the projections of the \\n # previous edges \\n y = np.array([ \\n sum([ \\n self.segment_lengths[k] *\\n np.sin( (res_joint_angles[:(k+1)]).sum() ) \\n for k in range(j+1) \\n ])\\n for j in range(self.number_of_joint) \\n ])\\n \\n # translate to the y origin \\n y = np.hstack([self.origin[1], y+self.origin[1]])\\n\\n pos = np.array([x, y]).T\\n \\n return (pos, res_joint_angles)\",\n \"def end_effectors_pos(self):\\n def relative_pos_in_egocentric_frame(physics):\\n end_effector = physics.bind(self._entity.end_effectors).xpos\\n torso = physics.bind(self._entity.root_body).xpos\\n xmat = np.reshape(physics.bind(self._entity.root_body).xmat, (3, 3))\\n return np.reshape(np.dot(end_effector - torso, xmat), -1)\\n return observable.Generic(relative_pos_in_egocentric_frame)\",\n \"def jacobian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n end_effector_nodes = ee_goals.keys()\\n J = np.zeros(self.n)\\n for (\\n ee\\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\\n ee_path = kinematic_map[ee][\\n 1:\\n ] # [:-1] # no last node, only phys. joint locations\\n t_ee = self.get_pose(joint_angles, ee).trans\\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Jac per column\\n p_idx = int(joint_p[1:]) - 1\\n for jdx in range(pdx, len(ee_path)):\\n node_jdx = ee_path[jdx]\\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\\n J[p_idx] += (\\n 2.0\\n * self.a[node_jdx]\\n * (-dg_ee_x * np.sin(theta_jdx) + dg_ee_y * np.cos(theta_jdx))\\n )\\n\\n return J\",\n \"def fk(arm,base=np.identity(4),joint_num=-1):\\n\\n pEE = base # Cumulative pose of the End Effector \\n # (initially set up as the base of the robot)\\n if joint_num==-1:\\n for joint in arm:\\n pEE=np.dot(pEE, joint.dhMatrix())\\n else:\\n for i in range(joint_num):\\n pEE=np.dot(pEE, arm[i].dhMatrix())\\n\\n return pEE\",\n \"def get_unhindered_positions(self, endposition):\\n pass\",\n \"def connect(ends):\\n d = np.diff(ends, axis=0)[0]\\n j = np.argmax(np.abs(d))\\n D = d[j]\\n aD = np.abs(D)\\n return ends[0] + (np.outer(np.arange(aD + 1), d) + (aD >> 1)) // aD\",\n \"def _calculate_h(self, end):\\n return PATH_COST * (abs(self.x - end.x) + abs(self.y - end.y))\",\n \"def get_current_joint_position(self) -> list:\\n joint_positions = get_joint_positions(self.body, self.joints[:self.DoF])\\n for i in range(self.DoF):\\n if self.JOINT_TYPES[i] == 'P':\\n # get the unscaled joint position\\n joint_positions[i] /= self.scaling\\n return joint_positions\",\n \"def joint_variables(self, G: nx.Graph, T_final: dict = None) -> np.ndarray:\\n # TODO: make this more readable\\n tol = 1e-10\\n q_zero = list_to_variable_dict(self.n * [0])\\n kinematic_map = self.kinematic_map\\n parents = self.parents\\n get_pose = self.get_pose\\n\\n T = {}\\n T[\\\"p0\\\"] = self.T_base\\n theta = {}\\n\\n for ee in self.end_effectors:\\n path = kinematic_map[\\\"p0\\\"][ee[0]][1:]\\n axis_length = self.axis_length\\n for node in path:\\n aux_node = f\\\"q{node[1:]}\\\"\\n pred = [u for u in parents.predecessors(node)]\\n\\n T_prev = T[pred[0]]\\n\\n T_prev_0 = get_pose(q_zero, pred[0])\\n T_0 = get_pose(q_zero, node)\\n T_rel = T_prev_0.inv().dot(T_0)\\n T_0_q = get_pose(q_zero, node).dot(trans_axis(axis_length, \\\"z\\\"))\\n T_rel_q = T_prev_0.inv().dot(T_0_q)\\n\\n p = G.nodes[node][POS] - T_prev.trans\\n q = G.nodes[aux_node][POS] - T_prev.trans\\n ps = T_prev.inv().as_matrix()[:3, :3].dot(p)\\n qs = T_prev.inv().as_matrix()[:3, :3].dot(q)\\n\\n zs = skew(np.array([0, 0, 1]))\\n cp = (T_rel.trans - ps) + zs.dot(zs).dot(T_rel.trans)\\n cq = (T_rel_q.trans - qs) + zs.dot(zs).dot(T_rel_q.trans)\\n ap = zs.dot(T_rel.trans)\\n aq = zs.dot(T_rel_q.trans)\\n bp = zs.dot(zs).dot(T_rel.trans)\\n bq = zs.dot(zs).dot(T_rel_q.trans)\\n\\n c0 = cp.dot(cp) + cq.dot(cq)\\n c1 = 2 * (cp.dot(ap) + cq.dot(aq))\\n c2 = 2 * (cp.dot(bp) + cq.dot(bq))\\n c3 = ap.dot(ap) + aq.dot(aq)\\n c4 = bp.dot(bp) + bq.dot(bq)\\n c5 = 2 * (ap.dot(bp) + aq.dot(bq))\\n\\n # poly = [c0 -c2 +c4, 2*c1 - 2*c5, 2*c0 + 4*c3 -2*c4, 2*c1 + 2*c5, c0 + c2 + c4]\\n diff = np.array(\\n [\\n c1 - c5,\\n 2 * c2 + 4 * c3 - 4 * c4,\\n 3 * c1 + 3 * c5,\\n 8 * c2 + 4 * c3 - 4 * c4,\\n -4 * c1 + 4 * c5,\\n ]\\n )\\n if all(diff < tol):\\n theta[node] = 0\\n else:\\n sols = np.roots(\\n diff\\n ) # solutions to the Whaba problem for fixed axis\\n\\n def error_test(x):\\n if abs(x.imag) > 0:\\n return 1e6\\n x = -2 * arctan2(x.real, 1)\\n return (\\n c0\\n + c1 * sin(x)\\n - c2 * cos(x)\\n + c3 * sin(x) ** 2\\n + c4 * cos(x) ** 2\\n - c5 * sin(2 * x) / 2\\n )\\n\\n sol = min(sols, key=error_test)\\n theta[node] = -2 * arctan2(sol.real, 1)\\n\\n T[node] = (T_prev.dot(rot_axis(theta[node], \\\"z\\\"))).dot(T_rel)\\n\\n if T_final is None:\\n return theta\\n\\n if (\\n T_final[ee[0]] is not None\\n and norm(cross(T_rel.trans, np.array([0, 0, 1]))) < tol\\n ):\\n T_th = (T[node]).inv().dot(T_final[ee[0]]).as_matrix()\\n theta[ee[0]] += np.arctan2(T_th[1, 0], T_th[0, 0])\\n\\n return theta\",\n \"def jac_pos(self):\\n J = self.sim.data.get_body_jacp(self.end_effector)\\n J = J.reshape(3, -1)[:, 0:7].T\\n return J\",\n \"def get_unhindered_positions(self, endposition):\\n current_position = self.position\\n potential_positions = { \\n 'diag1' : [],\\n 'diag2' : [],\\n 'diag3' : [],\\n 'diag4' : []\\n }\\n space_down = current_position[0]-1\\n space_up = self.ncols-current_position[0]\\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\\n space_left = ord(current_position[1]) - ord('A')\\n\\n for i in range(1, space_down+1):\\n diag1 = (current_position[0]-i, chr(ord(current_position[1])+i))\\n diag2 = (current_position[0]-i, chr(ord(current_position[1])-i))\\n if self.pos_within_bounds(diag1):\\n potential_positions['diag1'].append(diag1)\\n if self.pos_within_bounds(diag2):\\n potential_positions['diag2'].append(diag2)\\n\\n for i in range(1, space_up+1):\\n diag3 = (current_position[0]+i, chr(ord(current_position[1])+i))\\n diag4 = (current_position[0]+i, chr(ord(current_position[1])-i))\\n if self.pos_within_bounds(diag3):\\n potential_positions['diag3'].append(diag3)\\n if self.pos_within_bounds(diag4):\\n potential_positions['diag4'].append(diag4)\\n \\n for direction, square in potential_positions.items():\\n if tuple(endposition) in square:\\n return potential_positions[direction]\",\n \"def compute_joint_error_position(self, current_position, target_position):\\n \\n # helper variables\\n tmp_c = []\\n tmp_t = [] \\n \\n for x in range(0,20):\\n tmp_c.append(current_position[x].joint_target)\\n tmp_t.append(math.radians (target_position[x].joint_target) )\\n \\n # Compute the norm of the error\\n error = numpy.linalg.norm( numpy.array(tmp_c) - numpy.array(tmp_t) )\\n \\n #print error \\n\\n return error\",\n \"def joint_pairs(self):\\n return [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], #17 body keypoints\\n [20-3, 23-3], [21-3, 24-3], [22-3, 25-3], [26-3, 42-3], [27-3, 41-3], [28-3, 40-3], [29-3, 39-3], [30-3, 38-3], \\n [31-3, 37-3], [32-3, 36-3], [33-3, 35-3], [43-3, 52-3], [44-3, 51-3], [45-3, 50-3], [46-3, 49-3], [47-3, 48-3], \\n [62-3, 71-3], [63-3, 70-3], [64-3, 69-3], [65-3, 68-3], [66-3, 73-3], [67-3, 72-3], [57-3, 61-3], [58-3, 60-3],\\n [74-3, 80-3], [75-3, 79-3], [76-3, 78-3], [87-3, 89-3], [93-3, 91-3], [86-3, 90-3], [85-3, 81-3], [84-3, 82-3],\\n [94-3, 115-3], [95-3, 116-3], [96-3, 117-3], [97-3, 118-3], [98-3, 119-3], [99-3, 120-3], [100-3, 121-3],\\n [101-3, 122-3], [102-3, 123-3], [103-3, 124-3], [104-3, 125-3], [105-3, 126-3], [106-3, 127-3], [107-3, 128-3],\\n [108-3, 129-3], [109-3, 130-3], [110-3, 131-3], [111-3, 132-3], [112-3, 133-3], [113-3, 134-3], [114-3, 135-3]]\",\n \"def jacobian_linear(self, joint_angles: dict, query_frame: str = \\\"\\\") -> np.ndarray:\\n\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n end_effector_nodes = []\\n for ee in self.end_effectors: # get p nodes in end-effectors\\n if ee[0][0] == \\\"p\\\":\\n end_effector_nodes += [ee[0]]\\n if ee[1][0] == \\\"p\\\":\\n end_effector_nodes += [ee[1]]\\n\\n node_names = [\\n name for name in self.structure if name[0] == \\\"p\\\"\\n ] # list of p node ids\\n\\n # Ts = self.get_full_pose_fast_lambdify(joint_angles) # all frame poses\\n Ts = self.get_all_poses(joint_angles) # all frame poses\\n Ts[\\\"p0\\\"] = np.eye(4)\\n\\n J = np.zeros([0, len(node_names) - 1])\\n for ee in end_effector_nodes: # iterate through end-effector nodes\\n ee_path = kinematic_map[ee][:-1] # no last node, only phys. joint locations\\n\\n T_0_ee = Ts[ee] # ee frame\\n p_ee = T_0_ee[0:3, -1] # ee position\\n\\n Jp_t = np.zeros([3, len(node_names) - 1]) # translation jac for theta\\n Jp_al = np.zeros([3, len(node_names) - 1]) # translation jac alpha\\n for joint in ee_path: # algorithm fills Jac per column\\n T_0_i = Ts[joint]\\n z_hat_i = T_0_i[:3, 2]\\n x_hat_i = T_0_i[:3, 0]\\n p_i = T_0_i[:3, -1]\\n j_idx = node_names.index(joint)\\n Jp_t[:, j_idx] = np.cross(z_hat_i, p_ee - p_i)\\n Jp_al[:, j_idx] = np.cross(x_hat_i, p_ee - p_i)\\n\\n J_ee = np.vstack([Jp_t, Jp_al])\\n J = np.vstack([J, J_ee]) # stack big jac for multiple ee\\n\\n return J\",\n \"def get_end_vertices(self):\\n # Note that concatenating two vertices needs to make a\\n # vertices for the frame.\\n extesion_fraction = self.extesion_fraction\\n\\n corx = extesion_fraction*2.\\n cory = 1./(1. - corx)\\n x1, y1, w, h = 0, 0, 1, 1\\n x2, y2 = x1 + w, y1 + h\\n dw, dh = w*extesion_fraction, h*extesion_fraction*cory\\n\\n if self.extend in [\\\"min\\\", \\\"both\\\"]:\\n bottom = [(x1, y1),\\n (x1+w/2., y1-dh),\\n (x2, y1)]\\n else:\\n bottom = [(x1, y1),\\n (x2, y1)]\\n\\n if self.extend in [\\\"max\\\", \\\"both\\\"]:\\n top = [(x2, y2),\\n (x1+w/2., y2+dh),\\n (x1, y2)]\\n else:\\n top = [(x2, y2),\\n (x1, y2)]\\n\\n if self.orientation == \\\"horizontal\\\":\\n bottom = [(y,x) for (x,y) in bottom]\\n top = [(y,x) for (x,y) in top]\\n\\n return bottom, top\",\n \"def end_tensor(end_vector, # type: Union[np.ndarray, List[int]]\\n center_in, # type: List[int]\\n center_out, # type: List[int]\\n surround_in, # type: List[int]\\n surround_out, # type: List[int]\\n attractor_function=__linear_function_generator, # type: Callable[[], Callable[[Real], Real]]\\n size=3\\n ):\\n ndim = len(end_vector)\\n assert ndim >= 1\\n if not isinstance(end_vector, np.ndarray):\\n end_vector = np.asarray(end_vector)\\n\\n attractor_function = attractor_function()\\n\\n center_surround = np.zeros(shape=[size for _ in range(ndim)] + [len(center_out), len(center_in)])\\n zero_centered = np.ndarray(shape=[size for _ in range(ndim)])\\n\\n for tup in itertools.product(*[range(size) for _ in range(ndim)]):\\n tup_vec = np.asarray(tup) - np.asarray([size / 2 for _ in range(ndim)])\\n angle_dist = (m.acos(\\n np.dot(tup_vec, end_vector) / (np.linalg.norm(tup_vec) * np.linalg.norm(end_vector))) - m.pi / 2.0) / m.pi\\n zero_centered[tup] = attractor_function(angle_dist * m.pi)\\n\\n __normalize_center_surround(zero_centered)\\n\\n for tup in itertools.product(*[range(size) for _ in range(ndim)]):\\n center_surround[tup] = [[surround_out[o] * surround_in[i] * abs(zero_centered[tup]) if zero_centered[tup] < 0\\n else center_surround[tuple(tup + (o, i))]\\n for o in range(len(surround_out))] for i in range(len(surround_in))]\\n center_surround[tup] = [[center_out[o] * center_in[i] * abs(zero_centered[tup]) if zero_centered[tup] >= 0\\n else center_surround[tuple(tup + (i, o))]\\n for o in range(len(center_out))] for i in range(len(center_in))]\\n\\n return center_surround\",\n \"def p2p_xyz(start_point, end_point, top_left_cor, cellsize, dem):\\n start_cell = (int((start_point[0] - top_left_cor[0]) / cellsize[0]),\\n int((start_point[1] - top_left_cor[1]) / cellsize[1]))\\n end_cell = (int((end_point[0] - top_left_cor[0]) / cellsize[0]),\\n int((end_point[1] - top_left_cor[1]) / cellsize[1]))\\n cells = misc.get_line(start_cell, end_cell) \\n pnts = []\\n elev = []\\n \\n dem_elv = dem[:,1]\\n dem_indx = dem[:,2:4]\\n\\n for cell in cells:\\n x = top_left_cor[0] + cell[0] * cellsize[0] + cellsize[0] / 2\\n y = top_left_cor[1] + cell[1] * cellsize[1] + cellsize[1] / 2\\n #xy_indx=[str(cell[0]),str(cell[1])]\\n z_indx=np.logical_and(np.equal(dem_indx[:,0],cell[0]),np.equal(dem_indx[:,1],cell[1]))\\n try:\\n z=dem_elv[z_indx][0]\\n except (np.sum(z_indx)>1):\\n print(\\\"Oops! That was more than one indices in dem matching the query index (in getCellValue)\\\")\\n #z_indx = [i for i,j in enumerate(dem_indx) if j == xy_indx]\\n z = float(dem_elv[z_indx])\\n pnts.append((x, y))\\n elev.append(z)\\n return pnts, elev\",\n \"def enumerate_joint_ask(X, e, P):\\n Q = ProbDist(X) ## A probability distribution for X, initially empty\\n Y = [v for v in P.variables if v != X and v not in e]\\n for xi in P.values(X):\\n Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)\\n return Q.normalize()\",\n \"def target_position(self, time):\\n # get joint positions and use fk to get end effector position?\\n # ar_tag from topic\\n\\n cur_pos = self.target_velocity(time)*time + self.start_pos\\n\\n self.points_generated.append(cur_pos)\\n #print(self.start_pos)\\n # print(cur_pos)\\n return cur_pos\",\n \"def get_desired_joint_position(self):\\n return self._position_joint_desired\",\n \"def moveit_cartesian_path(start_pos, start_quat,\\n delta_xyz, moveit_group,\\n eef_step, jump_threshold=0.0):\\n start_pos = np.array(start_pos).flatten()\\n\\n delta_xyz = np.array(delta_xyz).flatten()\\n end_pos = start_pos + delta_xyz\\n moveit_waypoints = []\\n wpose = moveit_group.get_current_pose().pose\\n wpose.position.x = start_pos[0]\\n wpose.position.y = start_pos[1]\\n wpose.position.z = start_pos[2]\\n wpose.orientation.x = start_quat[0]\\n wpose.orientation.y = start_quat[1]\\n wpose.orientation.z = start_quat[2]\\n wpose.orientation.w = start_quat[3]\\n moveit_waypoints.append(copy.deepcopy(wpose))\\n\\n wpose.position.x = end_pos[0]\\n wpose.position.y = end_pos[1]\\n wpose.position.z = end_pos[2]\\n wpose.orientation.x = start_quat[0]\\n wpose.orientation.y = start_quat[1]\\n wpose.orientation.z = start_quat[2]\\n wpose.orientation.w = start_quat[3]\\n moveit_waypoints.append(copy.deepcopy(wpose))\\n\\n (plan, fraction) = moveit_group.compute_cartesian_path(\\n moveit_waypoints, # waypoints to follow\\n eef_step, # eef_step\\n jump_threshold) # jump_threshold\\n return plan\",\n \"def _solve_joints(self, bicep_angle: float, forearm_angle: float):\\n bicep_length_x = self.bicep_length*math.cos(math.radians(bicep_angle))\\n bicep_length_y = self.bicep_length*math.sin(math.radians(bicep_angle))\\n forearm_length_x = self.forearm_length*math.cos(math.radians(bicep_angle + forearm_angle))\\n forearm_length_y = self.forearm_length*math.sin(math.radians(bicep_angle + forearm_angle))\\n elbow = (bicep_length_x, bicep_length_y)\\n hand = (bicep_length_x + forearm_length_x, bicep_length_y + forearm_length_y)\\n return elbow, hand\",\n \"def _generate_end_position(self):\\n end_position = []\\n new_row = []\\n\\n for i in range(1, self.PUZZLE_NUM_ROWS * self.PUZZLE_NUM_COLUMNS + 1):\\n new_row.append(i)\\n if len(new_row) == self.PUZZLE_NUM_COLUMNS:\\n end_position.append(new_row)\\n new_row = []\\n\\n end_position[-1][-1] = 0\\n return end_position\",\n \"def estimate_foe(start, end):\\n A = np.zeros(start.shape)\\n diff = end - start\\n A[:, 0] = diff[:, 1]\\n A[:, 1] = -diff[:, 0]\\n b = start[:, 0] * diff[:, 1] - start[:, 1] * diff[:, 0]\\n foe = np.dot(np.linalg.inv(np.dot(A.T, A)), np.dot(A.T, b))\\n foe = (int(foe[0]),int(foe[1]))\\n return foe\",\n \"def compute_fk_position(self, jpos, tgt_frame):\\n if isinstance(jpos, list):\\n jpos = np.array(jpos)\\n jpos = jpos.flatten()\\n if jpos.size != self.arm_dof:\\n raise ValueError('Length of the joint angles '\\n 'does not match the robot DOF')\\n assert jpos.size == self.arm_dof\\n kdl_jnt_angles = joints_to_kdl(jpos)\\n\\n kdl_end_frame = kdl.Frame()\\n idx = self.arm_link_names.index(tgt_frame) + 1\\n fg = self._fk_solver_pos.JntToCart(kdl_jnt_angles,\\n kdl_end_frame,\\n idx)\\n if fg < 0:\\n raise ValueError('KDL Pos JntToCart error!')\\n pose = kdl_frame_to_numpy(kdl_end_frame)\\n pos = pose[:3, 3].flatten()\\n rot = pose[:3, :3]\\n return pos, rot\",\n \"def create_joint_trajectory(start_position, end_position,\\n duration_of_trajectory, frequency_of_trajectory):\\n\\n frequency_of_ros_messages = frequency_of_trajectory # in Hz.\\n number_of_way_points = duration_of_trajectory * frequency_of_ros_messages\\n number_of_joints = start_position.__len__()\\n trajectory = np.zeros((number_of_joints, number_of_way_points))\\n\\n for i in xrange(number_of_joints):\\n trajectory[i] = np.linspace(start_position[i], end_position[i],\\n number_of_way_points)\\n trajectory = trajectory.T.copy()\\n vel_lims = np.diff(trajectory, axis=0)\\n #Because this is discrete differentiation,\\n # the last value is missing: len(vel_lims) = len(trajectory) - 1\\n # so we just repeat the last calculated velocity.\\n vel_lims = np.append(vel_lims, [[x for x in vel_lims[-1,:]]], axis = 0)\\n vel_lims = vel_lims * frequency_of_trajectory\\n vel_lims = np.absolute(vel_lims)\\n\\n if vel_lims.all() > 1.0:\\n raise ValueError(\\\"One or more of the values in the specified velocities\\\"\\n \\\"Exceed 1 rad / second. The robot won't like this.\\\"\\n \\\"Adjust the trajectory so that each point can be \\\"\\n \\\"reached without exceeding this limit.\\\")\\n return trajectory, vel_lims\",\n \"def hessian_cost(self, joint_angles: dict, ee_goals) -> np.ndarray:\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n end_effector_nodes = ee_goals.keys()\\n H = np.zeros((self.n, self.n))\\n for (\\n ee\\n ) in end_effector_nodes: # iterate through end-effector nodes, assumes sorted\\n ee_path = kinematic_map[ee][\\n 1:\\n ] # [:-1] # no last node, only phys. joint locations\\n t_ee = self.get_pose(joint_angles, ee).trans\\n dg_ee_x = t_ee[0] - ee_goals[ee].trans[0]\\n dg_ee_y = t_ee[1] - ee_goals[ee].trans[1]\\n for (pdx, joint_p) in enumerate(ee_path): # algorithm fills Hess per column\\n p_idx = int(joint_p[1:]) - 1\\n sin_p_term = 0.0\\n cos_p_term = 0.0\\n for jdx in range(pdx, len(ee_path)):\\n node_jdx = ee_path[jdx]\\n theta_jdx = sum([joint_angles[key] for key in ee_path[0 : jdx + 1]])\\n sin_p_term += self.a[node_jdx] * np.sin(theta_jdx)\\n cos_p_term += self.a[node_jdx] * np.cos(theta_jdx)\\n\\n for (qdx, joint_q) in enumerate(\\n ee_path[pdx:]\\n ): # TODO: check if starting from pdx works\\n qdx = qdx + pdx\\n q_idx = int(joint_q[1:]) - 1\\n sin_q_term = 0.0\\n cos_q_term = 0.0\\n for kdx in range(qdx, len(ee_path)):\\n node_kdx = ee_path[kdx]\\n theta_kdx = sum(\\n [joint_angles[key] for key in ee_path[0 : kdx + 1]]\\n )\\n sin_q_term += self.a[node_kdx] * np.sin(theta_kdx)\\n cos_q_term += self.a[node_kdx] * np.cos(theta_kdx)\\n\\n # assert(q_idx >= p_idx)\\n H[p_idx, q_idx] += (\\n 2.0 * sin_q_term * sin_p_term\\n - 2.0 * dg_ee_x * cos_q_term\\n + 2.0 * cos_p_term * cos_q_term\\n - 2.0 * dg_ee_y * sin_q_term\\n )\\n\\n return H + H.T - np.diag(np.diag(H))\",\n \"def get_joints(joint_listener): \\n if LOCAL_TEST: # dummy\\n return np.array([-0.5596, 0.5123, 0.5575, -1.6929, 0.2937, 1.6097, -1.237, 0.04, 0.04])\\n else:\\n joints = joint_listener.joint_position\\n print('robot joints', joints)\\n return joints\",\n \"def get_unhindered_positions(self, endposition):\\n current_position = self.position\\n potential_positions = potential_positions = {\\n 'left' : [], \\n 'right' : [],\\n 'up' : [], \\n 'down' : []\\n }\\n space_down = current_position[0]-1\\n space_up = self.ncols-current_position[0]\\n space_right = self.nrows - (ord('H')-ord(current_position[1]))\\n space_left = ord(current_position[1]) - ord('A')\\n\\n for i in range(1, space_down+1):\\n pos = (current_position[0]-i, current_position[1])\\n if self.pos_within_bounds(pos):\\n potential_positions['down'].append(pos)\\n\\n for i in range(1, space_up+1):\\n pos = (current_position[0]+i, current_position[1])\\n if self.pos_within_bounds(pos):\\n potential_positions['up'].append(pos)\\n \\n for i in range(1, space_left+1):\\n pos = (current_position[0], chr(ord(current_position[1])-i))\\n if self.pos_within_bounds(pos):\\n potential_positions['left'].append(pos)\\n\\n for i in range(1, space_right+1):\\n pos = (current_position[0], chr(ord(current_position[1])+i))\\n if self.pos_within_bounds(pos):\\n potential_positions['right'].append(pos)\\n\\n for direction, square in potential_positions.items():\\n if tuple(endposition) in square:\\n return potential_positions[direction]\",\n \"def _get_output_steps_to_beam_indices(self, end_state: Tensor, beam_prev_indices: Tensor) ->List[int]:\\n present_position = int(end_state[1])\\n beam_index = int(end_state[2])\\n beam_indices = torch.jit.annotate(List[int], [])\\n while present_position >= 0:\\n beam_indices.insert(0, beam_index)\\n beam_index = int(beam_prev_indices[present_position][beam_index])\\n present_position = present_position - 1\\n return beam_indices\",\n \"def joint_trajectory(theta_start, theta_end, Tf, N, method):\\n\\n N = int(N)\\n timegap = Tf / (N - 1.0) # N points, N-1 line segments\\n traj = np.zeros((len(theta_start), N)) # intitialize the trajectory matrix, 1D joint vars, 2D each time instance\\n\\n # for each line segment, from 0 to T, calculate the corresponding s value (0to1)\\n for i in range(N):\\n if method == 3:\\n s = cubic_time_scaling(Tf, timegap * i)\\n else:\\n s = quintic_time_scaling(Tf, timegap * i)\\n traj[:, i] = s * np.array(theta_end) + (1 - s) * np.array(theta_start) # xi = x_start + (0.whatever fraction s)(x_end-x_start)\\n traj = np.array(traj).T\\n return traj\",\n \"def jacobian_linear_symb(\\n self, joint_angles: dict, pose_term=False, ee_keys=None\\n ) -> dict:\\n kinematic_map = self.kinematic_map[\\\"p0\\\"] # get map to all nodes from root\\n\\n if ee_keys is None:\\n end_effector_nodes = []\\n for ee in self.end_effectors: # get p nodes in end-effectors\\n if ee[0][0] == \\\"p\\\":\\n end_effector_nodes += [ee[0]]\\n else:\\n end_effector_nodes += [ee[1]]\\n else:\\n end_effector_nodes = ee_keys\\n # Ts = self.get_all_poses_symb(joint_angles) # all frame poses\\n Ts = self.get_all_poses(joint_angles) # all frame poses\\n J = {} # np.zeros([0, len(node_names) - 1])\\n for ee in end_effector_nodes: # iterate through end-effector nodes\\n ee_path = kinematic_map[ee][\\n 1:\\n ] # [:-1] # no last node, only phys. joint locations\\n\\n T_0_ee = Ts[ee].as_matrix() # ee frame\\n if pose_term:\\n dZ = np.array([0.0, 0.0, 1.0])\\n p_ee = T_0_ee[0:3, 0:3] @ dZ + T_0_ee[0:3, -1]\\n else:\\n p_ee = T_0_ee[0:3, -1] # ee position\\n\\n Jp = np.zeros([3, self.n], dtype=object) # translation jac\\n for joint in ee_path: # algorithm fills Jac per column\\n T_0_i = Ts[list(self.parents.predecessors(joint))[0]].as_matrix()\\n z_hat_i = T_0_i[:3, 2]\\n if pose_term:\\n p_i = T_0_i[0:3, 0:3] @ dZ + T_0_i[0:3, -1]\\n else:\\n p_i = T_0_i[:3, -1]\\n j_idx = int(joint[1:]) - 1 # node_names.index(joint) - 1\\n Jp[:, j_idx] = cross_symb(z_hat_i, p_ee - p_i)\\n J[ee] = Jp\\n return J\",\n \"def joint_limits_upper_constraint(q,ee_pos):\\n return self.max_angles - q\",\n \"def backtrack_to_start_to_draw_purpose(board, end):\\r\\n cell = board.at(end)\\r\\n # print(cell)\\r\\n path = []\\r\\n lis = []\\r\\n while cell != None:\\r\\n path.append(cell)\\r\\n cell = cell.path_from\\r\\n for i in path[-1:]:\\r\\n for j in i.position:\\r\\n lis.append(j)\\r\\n\\r\\n return path\",\n \"def get_return_from_grasp_joint_trajectory(self, start_joints, target_pose, n_steps=40):\\n assert len(start_joints) == len(self.joint_indices)\\n assert target_pose.frame.count('base_link') == 1\\n \\n # set active manipulator and start joint positions\\n self.robot.SetDOFValues(start_joints, self.joint_indices)\\n \\n # initialize trajopt inputs\\n rave_pose = tfx.pose(self.sim.transform_from_to(target_pose.matrix, target_pose.frame, 'world'))\\n quat = rave_pose.orientation\\n xyz = rave_pose.position\\n quat_target = [quat.w, quat.x, quat.y, quat.z]\\n xyz_target = [xyz.x, xyz.y, xyz.z]\\n rave_mat = rave.matrixFromPose(np.r_[quat_target, xyz_target])\\n \\n request = self._get_return_from_grasp_trajopt_request(xyz_target, quat_target, n_steps)\\n \\n # convert dictionary into json-formatted string\\n s = json.dumps(request) \\n # create object that stores optimization problem\\n prob = trajoptpy.ConstructProblem(s, self.sim.env)\\n \\n tool_link = self.robot.GetLink(self.tool_frame)\\n def penalize_low_height(x):\\n self.robot.SetDOFValues(x, self.joint_indices, False)\\n z = tool_link.GetTransform()[2,3]\\n return max(0, 10.0 - z)\\n\\n for t in xrange(n_steps-2):\\n prob.AddErrorCost(penalize_low_height, [(t,j) for j in xrange(len(self.joint_indices))], \\\"ABS\\\", \\\"PENALIZE_LOW_HEIGHT_%i\\\"%t)\\n \\n # do optimization\\n result = trajoptpy.OptimizeProblem(prob)\\n \\n self.robot.SetDOFValues(start_joints, self.joint_indices)\\n prob.SetRobotActiveDOFs() # set robot DOFs to DOFs in optimization problem\\n num_upsampled_collisions = self._num_collisions(result.GetTraj())\\n print('Number of collisions: {0}'.format(num_upsampled_collisions))\\n self.robot.SetDOFValues(start_joints, self.joint_indices)\\n if num_upsampled_collisions > 2:\\n return None\\n else:\\n return result.GetTraj()\",\n \"def joint_pairs(self):\\n return ((1, 4), (2, 5), (3, 6), (14, 11), (15, 12), (16, 13))\",\n \"def compute_end_point(self):\\n\\n L = self.level\\n P = L.prob\\n\\n # check if Mth node is equal to right point and do_coll_update is false, perform a simple copy\\n if self.coll.right_is_node and not self.params.do_coll_update:\\n # a copy is sufficient\\n L.uend = P.dtype_u(L.u[-1])\\n else:\\n # start with u0 and add integral over the full interval (using coll.weights)\\n L.uend = P.dtype_u(L.u[0])\\n for m in range(self.coll.num_nodes):\\n L.uend += L.dt * self.coll.weights[m] * L.f[m + 1]\\n # add up tau correction of the full interval (last entry)\\n if L.tau[-1] is not None:\\n L.uend += L.tau[-1]\\n\\n return None\",\n \"def test_calcAngles_joint_centers(self):\\n _,joint_centers = pycgmCalc.calcAngles(self.motion_data, vsk=self.cal_SM, frame=0,\\\\\\n formatData=False, splitAnglesAxis=False, returnjoints=True)\\n #Verify that several the expected joint_centers are returned.\\n expected_Front_Head = np.array([ 255.19071198, 406.12081909, 1721.92053223])\\n expected_LHip = np.array([182.57097863, 339.43231855, 935.52900126])\\n expected_RHand = np.array([ 859.80614366, 517.28239823, 1051.97278944])\\n expected_Thorax = np.array([256.149810236564, 364.3090603933987, 1459.6553639290375])\\n expected_LKnee = np.array([143.55478579, 279.90370346, 524.78408753])\\n expected_result = [expected_Front_Head, expected_LHip, expected_RHand, expected_Thorax, expected_LKnee]\\n result = [\\n joint_centers[0]['Front_Head'],\\n joint_centers[0]['LHip'],\\n joint_centers[0]['RHand'],\\n joint_centers[0]['Thorax'],\\n joint_centers[0]['LKnee']\\n ]\\n np.testing.assert_almost_equal(result, expected_result, self.rounding_precision)\",\n \"def _body_coord(self):\\r\\n cth = np.cos(self.theta)\\r\\n sth = np.sin(self.theta)\\r\\n M = self.P - 0.5 * np.diag(self.lengths)\\r\\n # stores the vector from the center of mass to the nose\\r\\n c2n = np.array([np.dot(M[self.nose], cth), np.dot(M[self.nose], sth)])\\r\\n # absolute position of nose\\r\\n T = -self.pos_cm - c2n - self.goal\\r\\n # rotating coordinate such that nose is axis-aligned (nose frame)\\r\\n # (no effect when \\\\theta_{nose} = 0)\\r\\n c2n_x = np.array([cth[self.nose], sth[self.nose]])\\r\\n c2n_y = np.array([-sth[self.nose], cth[self.nose]])\\r\\n Tcn = np.array([np.sum(T * c2n_x), np.sum(T * c2n_y)])\\r\\n\\r\\n # velocity at each joint relative to center of mass velocity\\r\\n vx = -np.dot(M, sth * self.dtheta)\\r\\n vy = np.dot(M, cth * self.dtheta)\\r\\n # velocity at nose (world frame) relative to center of mass velocity\\r\\n v2n = np.array([vx[self.nose], vy[self.nose]])\\r\\n # rotating nose velocity to be in nose frame\\r\\n Vcn = np.array([np.sum((self.v_cm + v2n) * c2n_x),\\r\\n np.sum((self.v_cm + v2n) * c2n_y)])\\r\\n # angles should be in [-pi, pi]\\r\\n ang = np.mod(\\r\\n self.theta[1:] - self.theta[:-1] + np.pi,\\r\\n 2 * np.pi) - np.pi\\r\\n return Tcn, ang, Vcn, self.dtheta\",\n \"def rel_kin(self, joints): # kinematic term\\n order1 = [9, 5, 20, 1, 2]\\n order2 = [8, 6, 4, 20, 3] # joints' order\\n order3 = [10, 4, 8, 0, 20]\\n refer1 = [5, 6, 4, 2, 0] # kinseg's order\\n refer2 = [6, 5, 4, 3, 1]\\n\\n segrel = defaultdict(lambda: int(0))\\n result = []\\n cnts = np.zeros(21)\\n\\n for i in xrange(len(order1)):\\n A = np.array([joints[order1[i]].Position.x, joints[order1[i]].Position.y, joints[order1[i]].Position.z])\\n B = np.array([joints[order2[i]].Position.x, joints[order2[i]].Position.y, joints[order2[i]].Position.z])\\n C = np.array([joints[order3[i]].Position.x, joints[order3[i]].Position.y, joints[order3[i]].Position.z])\\n\\n tmp = min(np.abs(np.linalg.norm(A-B)*100-self.kinseg[refer1[i]])/self.kinseg[refer1[i]], 1)\\n segrel[order1[i]] += tmp\\n segrel[order2[i]] += tmp\\n\\n tmp = min(np.abs(np.linalg.norm(A-C)*100-self.kinseg[refer2[i]])/self.kinseg[refer2[i]], 1)\\n segrel[order1[i]] += tmp\\n segrel[order3[i]] += tmp\\n\\n cnts[order1[i]] += 2\\n cnts[order2[i]] += 1\\n cnts[order3[i]] += 1\\n\\n for i in self.trg_jorder:\\n result.append(1-(segrel[i]/cnts[i]))\\n\\n return result\",\n \"def compute_position(self, goal: np.ndarray) -> Any:\\n return NotImplementedError\",\n \"def get_vehicle_end_index(self):\\n return [len(self.matrix) - 1 for i in range(len(self.vehicles))]\",\n \"def affected_end(self):\\n types = {alt.type for alt in self.ALT} # set!\\n BAD_MIX = {INS, SV, BND, SYMBOLIC} # don't mix well with others\\n if (BAD_MIX & types) and len(types) == 1 and list(types)[0] == INS:\\n # Only insertions, return 0-based position right of first base\\n return self.POS # right of first base\\n else: # Return 0-based end position, behind last REF base\\n return (self.POS - 1) + len(self.REF)\",\n \"def calculateExteriorElementBoundaryQuadrature(self):\\n #\\n #get physical locations of element boundary quadrature points\\n #\\n #assume all components live on the same mesh\\n self.u[0].femSpace.elementMaps.getValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['x'])\\n #\\n #get metric tensor and unit normals\\n #\\n if self.movingDomain:\\n if self.tLast_mesh != None:\\n self.ebqe['xt'][:]=self.ebqe['x']\\n self.ebqe['xt']-=self.ebqe['x_last']\\n alpha = 1.0/(self.t_mesh - self.tLast_mesh)\\n self.ebqe['xt']*=alpha\\n else:\\n self.ebqe['xt'][:]=0.0\\n self.ebqe['x_last'][:]=self.ebqe['x']\\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace_movingDomain(self.elementBoundaryQuadraturePoints,\\n self.ebqe['xt'],\\n self.ebqe['inverse(J)'],\\n self.ebqe['g'],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe['n'])\\n else:\\n self.u[0].femSpace.elementMaps.getJacobianValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['inverse(J)'],\\n self.ebqe['g'],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe['n'])\\n #now map the physical points back to the reference element\\n #assume all components live on same mesh\\n self.u[0].femSpace.elementMaps.getInverseValuesGlobalExteriorTrace(self.ebqe['inverse(J)'],self.ebqe['x'],self.ebqe['hat(x)'])\\n #\\n #since the points on the reference boundary may be reordered on many right element boundaries, we\\n #have to use an array of reference boundary points on all element boundaries\\n #first copy the left reference element boundary quadrature points from the reference element boundary\\n self.testSpace[0].getBasisValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe[('w',0)])\\n cfemIntegrals.calculateWeightedShapeGlobalExteriorTrace(self.mesh.exteriorElementBoundariesArray,\\n self.mesh.elementBoundaryElementsArray,\\n self.mesh.elementBoundaryLocalElementBoundariesArray,\\n self.elementBoundaryQuadratureWeights[('f',0)],\\n self.ebqe['sqrt(det(g))'],\\n self.ebqe[('w',0)],\\n self.ebqe[('w*dS_f',0)])\\n self.u[0].femSpace.getBasisGradientValuesGlobalExteriorTrace(self.elementBoundaryQuadraturePoints,\\n self.ebqe['inverse(J)'],\\n self.ebqe[('grad(v)',0)])\\n #setup flux boundary conditions\\n self.fluxBoundaryConditionsObjectsDict = dict([(cj,FluxBoundaryConditions(self.mesh,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.ebqe[('x')],\\n self.advectiveFluxBoundaryConditionsSetterDict[cj],\\n self.diffusiveFluxBoundaryConditionsSetterDictDict[cj]))\\n for cj in self.advectiveFluxBoundaryConditionsSetterDict.keys()])\\n self.ebqe['dS'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n cfemIntegrals.calculateIntegrationWeights(self.ebqe['sqrt(det(g))'],\\n self.elementBoundaryQuadratureWeights[('u',0)],\\n self.ebqe['dS'])\\n for ci in range(self.nc): self.ebqe[('dS',ci)] = self.ebqe['dS']\\n #\\n self.ellamDiscretization.calculateExteriorElementBoundaryQuadrature(self.ebqe)\\n #\\n self.coefficients.initializeGlobalExteriorElementBoundaryQuadrature(self.timeIntegration.t,self.ebqe)\",\n \"def _handle_bounds_speedj(self):\\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(self._qt_[-1])\\n inside_angle_bound = np.all(self._angles_low <= self._qt_[-1, self._joint_indices]) and \\\\\\n np.all(self._qt_[-1, self._joint_indices] <= self._angles_high)\\n if inside_bound:\\n self.return_point = None\\n if inside_angle_bound:\\n self.angle_return_point = False\\n if not inside_bound:\\n if self.return_point is None:\\n # we are outside the bounds and return point wasn't computed yet\\n print(\\\"outside box bound\\\")\\n xyz = np.clip(xyz, self._end_effector_low + self._box_bound_buffer,\\n self._end_effector_high - self._box_bound_buffer)\\n mat[:3, 3] = xyz\\n ref_pos = self._q_ref.copy()\\n ref_pos[self._joint_indices] = self._q_[-1, self._joint_indices]\\n solutions = ur_utils.inverse_near(mat, wrist_desired=self._q_ref[-1], ref_pos=ref_pos,\\n params=self._ik_params)\\n servoj_q = self._q_ref.copy()\\n if len(solutions) == 0:\\n servoj_q[self._joint_indices] = self._q_[-1, self._joint_indices]\\n else:\\n servoj_q[self._joint_indices] = solutions[0][self._joint_indices]\\n self.return_point = servoj_q[self._joint_indices]\\n # Speed at which arm approaches the boundary. The faster this speed,\\n # the larger opposite acceleration we need to apply in order to slow down\\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\\n # if return point is already computed, keep going to it, no need\\n # to recompute it at every time step\\n self._cmd_ = self.return_point - self._q_[0][self._joint_indices]\\n # Take the direction to return point and normalize it to have norm 0.1\\n if np.linalg.norm(self._cmd_) != 0:\\n self._cmd_ /= np.linalg.norm(self._cmd_) / 0.1\\n\\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\\n # This acceleration guarantees that we won't move beyond\\n # the bounds by more than 0.05 radian on each joint. This\\n # follows from kinematics equations.\\n accel_to_apply = np.max(np.abs(self._qd_)) * self.init_boundary_speed / 0.05\\n\\n # self.boundary_packet[1:1 + 6][self.joint_indices] = self.return_point\\n # self.actuator_comms['UR5'].actuator_buffer.write(self.reset_packet)\\n # time.sleep(1.0)\\n self._speedj_packet[-2] = np.clip(accel_to_apply, 2.0, 5.0)\\n self._actuation_packet_['UR5'] = self._speedj_packet\\n self._cmd_.fill(0.0)\\n self._cmd_prev_.fill(0.0)\\n self._first_deriv_.fill(0.0)\\n\\n elif not inside_angle_bound:\\n # if return point is already computed, keep going to it, no need\\n self.rel_indices = self._joint_indices\\n cur_pos = self._q_[0][self._joint_indices]\\n clipped_pos = np.clip(cur_pos, self._angles_low + self._angle_bound_buffer,\\n self._angles_high - self._angle_bound_buffer)\\n # a point within the box to which we will be returning\\n affected_joints = np.where(clipped_pos != cur_pos)\\n if not self.angle_return_point:\\n print(\\\"outside of angle bound on joints %r\\\" % (list(affected_joints[0])))\\n self.angle_return_point = True\\n self._cmd_[affected_joints] = np.sign(clipped_pos - cur_pos)[affected_joints]*np.max(np.abs(self._cmd_))\\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\\n self._actuation_packet_['UR5'] = self._speedj_packet\",\n \"def get_neighb_coords(self, i, ci):\\n j = self.conn[i][ci]\\n rj = self.xyz[j].copy()\\n if self.periodic:\\n if self.use_pconn:\\n img = self.pconn[i][ci]\\n rj += np.dot(img, self.cell)\\n else:\\n all_rj = rj + self.images_cellvec\\n all_r = all_rj - self.xyz[i]\\n all_d = np.sqrt(np.add.reduce(all_r*all_r,1))\\n closest = np.argsort(all_d)[0]\\n return all_rj[closest]\\n return rj\",\n \"def compute_observation(self):\\n robotPos, robotOrn = p.getBasePositionAndOrientation(self.botId)\\n robotEuler = p.getEulerFromQuaternion(robotOrn)\\n linear, angular = p.getBaseVelocity(self.botId)\\n return (np.array([robotEuler[0],angular[0],self.vt], dtype='float32'))\",\n \"def end_points(self, origin, destination):\\n # origin and destination are components with bounding-boxes\\n # direction is a 2 char code representing starting and ending directions\\n # 'h' horizontal, 'v' vertical\\n o_coords = origin.bounding_coords()\\n d_coords = destination.bounding_coords()\\n\\n start = {\\n \\\"h\\\": core.Coords(o_coords.x2, o_coords.y1 + origin.height / 2),\\n \\\"v\\\": core.Coords(origin.x + (o_coords.x2 - o_coords.x1) / 2, o_coords.y2),\\n }\\n end = {\\n \\\"h\\\": core.Coords(d_coords.x1, d_coords.y1 + destination.height / 2),\\n \\\"v\\\": core.Coords(\\n destination.x + (d_coords.x2 - d_coords.x1) / 2, d_coords.y1\\n ),\\n }\\n self.start = start[self.direction[0]]\\n self.end = end[self.direction[-1]]\\n return (self.start, self.end)\",\n \"def comet_joint_orient(pynodes, aim_axis = None, up_axis = None, up_dir = None, do_auto = False):\\n if aim_axis is None:\\n aim_axis = [1, 0, 0]\\n if up_axis is None:\\n up_axis = [0, 0, 1]\\n if up_dir is None:\\n up_dir = [1, 0, 0]\\n # convert to Vector\\n aim_axis = pm.dt.Vector(aim_axis)\\n up_axis = pm.dt.Vector(up_axis)\\n up_dir = pm.dt.Vector(up_dir)\\n # Filter supplied pynodes, if equal to 0 then return false\\n if len(pynodes) == 0:\\n return False\\n\\n # make sure only joint get passed through here\\n pynodes = pm.ls(pynodes, type = 'joint')\\n\\n # init variable prevUp for later use\\n prev_up = pm.dt.Vector()\\n\\n for i, o in enumerate(pynodes):\\n parent_point = None\\n # first we need to unparent everything and then store that,\\n children = o.getChildren()\\n for x in children:\\n x.setParent(None)\\n\\n # find parent for later in case we need it\\n parent = o.getParent()\\n\\n # Now if we have a child joint... aim to that\\n aim_tgt = None\\n for child in children:\\n if child.nodeType() == 'joint':\\n aim_tgt = child\\n break\\n\\n if aim_tgt:\\n # init variable upVec using upDir variable\\n up_vec = pm.dt.Vector(up_dir)\\n\\n # first off... if doAuto is on, we need to guess the cross axis dir\\n if do_auto:\\n # now since the first joint we want to match the second orientation\\n # we kind of hack the things passed in if it is the first joint\\n # ie: if the joint doesnt have a parent... or if the parent it has\\n # has the 'same' position as itself... then we use the 'next' joints\\n # as the up cross calculations\\n jnt_point = o.getRotatePivot(space = 'world')\\n if parent:\\n parent_point.setValue(parent.getRotatePivot(space = 'world'))\\n else:\\n parent_point = jnt_point.copy()\\n aim_tgt_point = aim_tgt.getRotatePivot(space = 'world')\\n\\n # how close to we consider 'same'?\\n tol = 0.0001\\n\\n point_cond = jnt_point - parent_point\\n pos_cond = [abs(x) for x in point_cond.tolist()]\\n if not parent or pos_cond[0] <= tol and pos_cond[1] <= tol and pos_cond[2] <= tol:\\n # get aimChild\\n aim_child = None\\n aim_children = aim_tgt.getChildren(type = 'joint')\\n if aim_children:\\n aim_child = aim_children[0]\\n\\n # get aimChild vector\\n if aim_child:\\n aim_child_point = aim_child.getRotatePivot(space = 'world')\\n else:\\n aim_child_point = pm.dt.Vector()\\n\\n # find the up vector using child vector of aim target\\n up_vec = (jnt_point - aim_tgt_point).cross(aim_child_point - aim_tgt_point)\\n else:\\n # find the up vector using the parent vector\\n up_vec = (parent_point - jnt_point).cross(aim_tgt_point - jnt_point)\\n\\n # reorient the current joint\\n a_cons = pm.aimConstraint(\\n aim_tgt, o, aimVector = aim_axis, upVector = up_axis, worldUpVector = up_vec.tolist(),\\n worldUpType = 'vector', weight = 1\\n )\\n pm.delete(a_cons)\\n\\n # now compare the up we used to the prev one\\n current_up = up_vec.normal()\\n # dot product for angle between... store for later\\n dot = current_up.dot(prev_up)\\n prev_up = up_vec\\n\\n if i > 0 >= dot:\\n # adjust the rotation axis 180 if it looks like we have flopped the wrong way!\\n # FIXME: some shit need to fix here\\n # pm.xform( o, relative = True, objectSpace = True, rotateAxis = True )\\n o.rotateX.set(o.rotateX.get() + (aim_axis.x * 180))\\n o.rotateY.set(o.rotateY.get() + (aim_axis.y * 180))\\n o.rotateZ.set(o.rotateZ.get() + (aim_axis.z * 180))\\n\\n prev_up *= -1\\n elif parent:\\n # otherwise if there is no target, just dup orientation of parent...\\n transformation.align(o, parent, mode = 'rotate')\\n\\n # and now finish clearing out joint axis ...\\n pm.joint(o, e = True, zeroScaleOrient = True)\\n transformation.freeze_transform(o)\\n\\n # now that we are done ... reparent\\n if len(children) > 0:\\n for x in children:\\n x.setParent(o)\\n\\n return True\",\n \"def startEndPoints(mazz):\\n for i in range (len(mazz)):\\n for j in range (len(mazz[i])):\\n if mazz[i][j] == 6:\\n startx = i\\n starty = j\\n elif mazz[i][j] == 7:\\n endx = i\\n endy = j\\n return startx, starty, endx, endy\",\n \"def _get_observation_upper_bound(self):\\n upper_bound = np.zeros(self._get_observation_dimension())\\n num_motors = self.rex.num_motors\\n upper_bound[0:num_motors] = math.pi # Joint angle.\\n upper_bound[num_motors:2 * num_motors] = motor.MOTOR_SPEED_LIMIT # Joint velocity.\\n upper_bound[2 * num_motors:3 * num_motors] = motor.OBSERVED_TORQUE_LIMIT # Joint torque.\\n upper_bound[3 * num_motors:-7] = 1.0 # Quaternion of base orientation.\\n upper_bound[-7] = 1.0 # ratio in [0,1]\\n upper_bound[-6:-2] = [1.0, 1.0, 1.0, 1.0] # sin in [-1, 1]\\n upper_bound[-2:] = [self.max_speed, self.max_side_speed]\\n\\n return upper_bound\",\n \"def get_fk(self, joints):\\n\\n header = Header()\\n header.frame_id = self.group.get_planning_frame()\\n\\n robot_state = self.robot.get_current_state()\\n robot_state.joint_state.position = joints\\n\\n links = [self.group.get_end_effector_link()]\\n\\n return self.fk_solver(header, links, robot_state).pose_stamped[0]\",\n \"def trigger_points(self, eouts, elens):\\n bs, xmax, _ = eouts.size()\\n log_probs = torch.log_softmax(self.output(eouts), dim=-1)\\n best_paths = log_probs.argmax(-1)\\n hyps = []\\n for b in range(bs):\\n indices = [best_paths[b, t].item() for t in range(elens[b])]\\n collapsed_indices = [x[0] for x in groupby(indices)]\\n best_hyp = [x for x in filter(lambda x: x != self.blank, collapsed_indices)]\\n hyps.append(best_hyp)\\n ymax = max([len(h) for h in hyps])\\n trigger_points_pred = log_probs.new_zeros((bs, ymax + 1), dtype=torch.int32)\\n for b in range(bs):\\n n_triggers = 0\\n for t in range(elens[b]):\\n token_idx = best_paths[b, t]\\n if token_idx == self.blank:\\n continue\\n if not (t == 0 or token_idx != best_paths[b, t - 1]):\\n continue\\n trigger_points_pred[b, n_triggers] = t\\n n_triggers += 1\\n return trigger_points_pred\",\n \"def compute_positions(self):\\n return (self.x + DIRECTIONS[self.facing_direction][0]) % (self.image.shape[0] - 1), \\\\\\n (self.y + DIRECTIONS[self.facing_direction][1]) % (self.image.shape[1] - 1)\",\n \"def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\\r\\n L = start_scores.shape[0]\\r\\n assert end_scores.shape[0] == L\\r\\n assert trans_scores.shape[0] == L\\r\\n assert trans_scores.shape[1] == L\\r\\n assert emission_scores.shape[1] == L\\r\\n N = emission_scores.shape[0]\\r\\n\\r\\n y = []\\r\\n dp_scores = []\\r\\n back_pointer = []\\r\\n\\r\\n for i in xrange(N):\\r\\n dp_scores.append([])\\r\\n back_pointer.append([])\\r\\n for j in xrange(L):\\r\\n if (i == 0):\\r\\n score = start_scores[j] + emission_scores[0, j]\\r\\n back = -1\\r\\n else:\\r\\n max = dp_scores[i-1][0] + trans_scores[0, j]\\r\\n back = 0\\r\\n for k in xrange(L):\\r\\n if (dp_scores[i-1][k] + trans_scores[k, j] > max):\\r\\n max = dp_scores[i-1][k] + trans_scores[k, j]\\r\\n back = k\\r\\n score = max + emission_scores[i, j]\\r\\n dp_scores[i].append(score)\\r\\n back_pointer[i].append(back)\\r\\n\\r\\n s = dp_scores[N-1][0] + end_scores[0]\\r\\n back = 0\\r\\n for k in xrange(L):\\r\\n if (dp_scores[N-1][k] + end_scores[k] > s):\\r\\n s = dp_scores[N-1][k] + end_scores[k]\\r\\n back = k\\r\\n\\r\\n y.append(back)\\r\\n for i in range(N-1, 0, -1):\\r\\n y.append(back_pointer[i][back])\\r\\n back = back_pointer[i][back]\\r\\n y.reverse()\\r\\n\\r\\n return (s, y)\",\n \"def get_move_indexes(i, j):\\n return (i, j), (j, n - 1 - i), (n - 1 - i, n - 1 - j), (n - 1 - j, i)\",\n \"def _findExonEnd(self, exonRecs, iBlkStart):\\n iBlkEnd = iBlkStart + 1\\n while (iBlkEnd < len(exonRecs)) and (self._tGapSize(exonRecs, iBlkEnd) < minIntronSize):\\n iBlkEnd += 1\\n return iBlkEnd, exonRecs[iBlkEnd - 1].end - exonRecs[iBlkStart].start\",\n \"def\\tbegin_end(env, blc):\\n\\n\\tb_e = np.empty((blc.shape[0], 2))\\n\\tinf = 0\\n\\tp = 0\\n\\twin = env.win_over\\n\\tb_e[0, 0] = inf\\n\\tif blc[0] + win <= blc[-1]:\\n\\t\\tb_e[0, 1] = blc[0] + win\\n\\telse:\\n\\t\\tb_e[0, 1] = blc[-1]\\n\\tif blc.shape[0] == 1:\\n\\t\\tb_e[0, 1] = blc[0]\\n\\t\\treturn (b_e)\\n\\tfor k in range(1, blc.shape[0] - 1):\\n\\t\\tinf = blc[k - 1] - win\\n\\t\\tb_e[k, 0] = inf\\n\\t\\tif blc[k] + win <= blc[-1]:\\n\\t\\t\\tb_e[k, 1] = blc[k] + win\\n\\t\\telse:\\n\\t\\t\\tb_e[k, 1] = blc[-1]\\n\\tb_e[blc.shape[0] - 1, 0] = blc[-2] - win\\n\\tb_e[blc.shape[0] - 1, 1] = blc[-1]\\n\\tneg = np.where(b_e < 0)[0]\\n\\tif neg.shape[0]:\\n\\t\\tb_e = b_e[neg[-1]:]\\n\\t\\tb_e[0, 0] = 0\\n\\treturn (b_e)\",\n \"def get_bend_port_distances(bend: Component) -> Tuple[float64, float64]:\\n p0, p1 = bend.ports.values()\\n return abs(p0.x - p1.x), abs(p0.y - p1.y)\",\n \"def end_effectors(self) -> list:\\n if not hasattr(self, \\\"_end_effectors\\\"):\\n S = self.structure\\n self._end_effectors = [\\n [x, y]\\n for x in S\\n if S.out_degree(x) == 0\\n for y in S.predecessors(x)\\n if DIST in S[y][x]\\n if S[y][x][DIST] < np.inf\\n ]\\n\\n return self._end_effectors\",\n \"def end_effectors(self) -> list:\\n if not hasattr(self, \\\"_end_effectors\\\"):\\n S = self.structure\\n self._end_effectors = [\\n [x, y]\\n for x in S\\n if S.out_degree(x) == 0\\n for y in S.predecessors(x)\\n if DIST in S[y][x]\\n if S[y][x][DIST] < np.inf\\n ]\\n\\n return self._end_effectors\",\n \"def getPos(self,len,end,nodes):\\n start=end\\n if self.count==nodes:\\n last=len\\n else:\\n last=end+(int)(len/(nodes+1))\\n self.count+=1\\n return (start,last)\",\n \"def heuristicValueOfPosition(currPositions):\\n hVal = 0;\\n\\n for y in range(1, n+1): #1,2,3\\n for x in range(1, n+1):\\n val = currPositions[y][x];\\n if ((val == 0) or (goalPositions[val] == (y,x))): #val 0 means blank\\n continue;\\n else:\\n hVal += abs(y-goalPositions[val][0]) + abs(x-goalPositions[val][1])\\n\\n return hVal;\",\n \"def get_ecc(self):\\n mu_mass = G*(self._mm + self._sm)\\n h_mom = self.sp_ang_mom()\\n vel = self.getvel_xyz()\\n pos = self.getpos_xyz()\\n e_vec = 1.0/mu_mass*(np.cross(vel, h_mom) -\\n mu_mass*pos/np.linalg.norm(pos))\\n return e_vec\",\n \"def __path_to_end(self) -> List[List[int]]:\\n predecessors = self.__predecessors_list()\\n path = []\\n\\n row_exit, col_exit = Player.find_exit_position(self.__labyrinth)\\n dest = self.__convert_position(row_exit, col_exit)\\n\\n v = dest\\n\\n path.append([v // 10, v % 10])\\n\\n while predecessors[v] != -1:\\n path.append(predecessors[v])\\n v = self.__convert_position(predecessors[v][0], predecessors[v][1])\\n\\n return path[::-1]\",\n \"def end_point(self) -> Vec3:\\n v = list(self.vertices([self.dxf.end_angle]))\\n return v[0]\",\n \"def reach_gradient(self):\\n step_size = 0.05\\n min_step_size = 0.001\\n moved_closer = True\\n while_loop_counter = 0\\n max_steps = 100\\n old_total_cost = 10\\n epsilon = 0.005\\n\\n # While moved closer and not reached minimum step size\\n while moved_closer and step_size > min_step_size:\\n while_loop_counter += 1\\n # Set a maximum number of steps per change to see progress - used for testing\\n if while_loop_counter > max_steps:\\n break\\n new_total_cost = 0\\n text = \\\"\\\"\\n i = 0\\n\\n # Go through each joint within the arm\\n for joint_key, joint_value in self.joint_angles.items():\\n # Text to show for each joint change\\n text += str(self.joint_names[i]) + \\\" \\\"\\n i += 1\\n\\n # Old endpoint values\\n old_value = joint_value\\n\\n # Update joints in ROS with current self.joint_angle values\\n self.update_angles()\\n self.get_current_ee_pose() # Old endpoint\\n\\n # Determine cost from current end effector to target\\n old_cost = self.cost(self.arm_endpoint)\\n\\n # Gradient of old values\\n gradient = self.gradient(joint_key)\\n if gradient > 0: # Determine direction of gradient\\n direction = 1\\n else:\\n direction = -1\\n\\n # Determine new angle value based on gradient\\n self.joint_angles[joint_key] = (old_value - direction * step_size)\\n\\n if self.joint_angles[joint_key] < self.joint_min[joint_key]:\\n self.joint_angles[joint_key] = self.joint_min[joint_key]\\n elif self.joint_angles[joint_key] > self.joint_max[joint_key]:\\n self.joint_angles[joint_key] = self.joint_max[joint_key]\\n\\n # Update joint angle values within ROS and get new endpoint value\\n self.update_angles()\\n self.get_current_ee_pose()\\n\\n # Determine cost from current end effector to target\\n new_cost = self.cost(self.arm_endpoint)\\n\\n # Determine the cost of\\n if new_cost > old_cost:\\n self.joint_angles[joint_key] = old_value\\n new_total_cost += old_cost\\n text += \\\": No change \\\\n\\\"\\n else:\\n text += \\\": Improved by \\\" + str(direction * step_size) + \\\"\\\\n\\\"\\n new_total_cost += new_cost\\n\\n # Display change of each joint through text\\n print(\\\"Robot part changes: \\\\n\\\", text)\\n self.cost_values += [new_total_cost]\\n\\n # Check if improved from previous position\\n if old_total_cost < new_total_cost:\\n step_size -= .01\\n moved_closer = False\\n else:\\n moved_closer = True\\n\\n print(\\\"abs(old_total_cost - new_total_cost): \\\", abs(old_total_cost - new_total_cost))\\n print(\\\"new_total_cost: \\\", new_total_cost)\\n # If changes are less than epsilon, we stop\\n if abs(old_total_cost - new_total_cost) < epsilon:\\n break\\n old_total_cost = new_total_cost\\n\\n # Save new joint angle values\\n save_file = \\\"/OptimizedAngles.csv\\\"\\n print(\\\"Saving new joint angles at \\\", save_file)\\n self.save_new_joint_angles(save_file)\",\n \"def at_b (self):\\n self.argc = int((len(n.coord[0]))/2)\\n self.pts_con = np.array(self.coord[:,self.argc:len(n.coord[0])])\\n\\n self.xd = self.xdi\\n self.zd = self.zdi \\n \\n for i, x in enumerate(self.xdi):\\n self.aux_con = self.pts_con[0] - x \\n self.arg1 = np.argmin(abs(self.aux_con)) \\n \\n if (self.aux_con[self.arg1] < 0 and self.arg1 == 0) or (self.aux_con[self.arg1] > 0 and self.arg1 == len(self.aux_con)-1):\\n self.yd[i] = 99999.\\n #print(self.yd[i],self.arg1)\\n #print(self.aux_con)\\n \\n elif (self.aux_con[self.arg1] > 0 and self.aux_con[self.arg1+1] > self.aux_con[self.arg1]): #(self.aux_con[self.arg1] < 0 and self.aux_con[self.arg1-1] > self.aux_con[self.arg1]) or \\n self.yd[i] = 99999.\\n #print(self.yd[i],self.arg1)\\n #print(self.aux_con)\\n \\n elif self.aux_con[self.arg1] < 0:\\n #print(self.arg1)\\n self.arg1 = self.arg1 + self.argc\\n self.arg2 = self.arg1 - 1\\n self.yd[i] = self.coord[1,n.arg1] + (x-self.coord[0,n.arg1])*(self.coord[1,n.arg2]-self.coord[1,n.arg1])/(self.coord[0,n.arg2]-self.coord[0,n.arg1])\\n #print(self.yd[i],self.arg1,self.arg2)\\n #print(self.aux_con)\\n\\n elif self.aux_con[self.arg1] > 0:\\n #print(self.arg1) \\n self.arg1 = self.arg1 + self.argc\\n self.arg2 = self.arg1 + 1\\n self.yd[i] = self.coord[1,n.arg1] + (x-self.coord[0,n.arg1])*(self.coord[1,n.arg2]-self.coord[1,n.arg1])/(self.coord[0,n.arg2]-self.coord[0,n.arg1]) \\n #print(self.yd[i],self.arg1,self.arg2)\\n #print(self.aux_con)\\n \\n #print('Defensa {0}\\\\n{1}: {2}\\\\n{3}: {4}'.format(i,self.arg1,self.aux_con[self.arg1],self.arg2,self.aux_con[self.arg2])) \\n \\n #self.yd = self.yd\\n self.b = np.array([self.xd,self.yd,self.zd])\\n #self.b.loc[:,('y')] = self.b.loc[:,('y')] \",\n \"def endtoend(d):\\n dists = distance_matrix(d)\\n avgdists = np.array([np.diag(dists, i).mean() for i in range(dists.shape[0])])\\n return avgdists\",\n \"def calculate_coefficients(self, start, end):\\n A = np.array([\\n [self.deltaT**3, self.deltaT**4, self.deltaT**5],\\n [3 * self.deltaT**2, 4 * self.deltaT**3, 5 * self.deltaT**4],\\n [6 * self.deltaT, 12 * self.deltaT**2, 20 * self.deltaT**3],\\n ])\\n\\n a_0, a_1, a_2 = start[0], start[1], start[2] / 2.0\\n c_0 = a_0 + a_1 * self.deltaT + a_2 * self.deltaT**2\\n c_1 = a_1 + 2 * a_2 * self.deltaT\\n c_2 = 2 * a_2\\n\\n B = np.array([\\n end[0] - c_0,\\n end[1] - c_1,\\n end[2] - c_2\\n ])\\n\\n a_3_4_5 = np.linalg.solve(A, B)\\n coeff = np.concatenate((np.array([a_0, a_1, a_2]), a_3_4_5))\\n\\n return coeff\",\n \"def lendiag(self):\\n if self.y <= self.x:\\n return self.y\\n else:\\n return self.x\",\n \"def get_frame_joints(self, frame):\\n joints = frame[(self.POS_SIZE + self.ROT_SIZE):].copy()\\n return joints\",\n \"def get_end_pos(cls, start_pos, dimensions, left=False, up=False):\\n dx = (dimensions[0] - 1) * cls.width\\n if left: dx = -dx\\n dy = (dimensions[1] -1 ) * cls.height\\n if up: dy = -dy\\n \\n end_pos = start_pos[0] + dx, start_pos[1] + dy\\n return end_pos\",\n \"def find(self, ends):\\n\\n if self.function(ends[0]) * self.function(ends[1]) > 0:\\n raise ValueError('Sign of function at both the end points must be opposite.')\\n \\n a = min(ends)\\n b = max(ends)\\n\\n while (b - a > self.epsilon):\\n mid = (a + b) / 2\\n if self.function(mid) * self.function(a) > 0:\\n a = mid\\n else:\\n b = mid\\n \\n return (a + b) / 2\",\n \"def evalBottomGuideEndDisp(length: float,\\n bottomcurve_radius: float,\\n tendonDistFromAxis: float,\\n tendonOrientationDF: float)-> np.ndarray:\\n horizontalDispAlongCurve = tendonDistFromAxis*sin(tendonOrientationDF)\\n return np.array((\\n tendonDistFromAxis*cos(tendonOrientationDF),\\n tendonDistFromAxis*sin(tendonOrientationDF),\\n -sqrt(bottomcurve_radius**2 - horizontalDispAlongCurve**2) + bottomcurve_radius - length/2\\n ))\",\n \"def get_ee_points_velocities(ref_jacobian, ee_points, ref_rot, joint_velocities):\\n ref_jacobians_trans = ref_jacobian[:3, :]\\n ref_jacobians_rot = ref_jacobian[3:, :]\\n ee_velocities_trans = np.dot(ref_jacobians_trans, joint_velocities)\\n ee_velocities_rot = np.dot(ref_jacobians_rot, joint_velocities)\\n ee_velocities = ee_velocities_trans + np.cross(ee_velocities_rot.reshape(1, 3),\\n ref_rot.dot(ee_points.T).T)\\n return ee_velocities.reshape(-1)\",\n \"def joints_torque(self):\\r\\n return self._arm.joints_torque\",\n \"def determine_move_position(self):\\n green_probs = []\\n net_size = len(self.net)\\n adjacents = self.net[self.current_pos].adjacents\\n #Belief propagation:\\n #Analyzes each position's probability of obtaining\\n #green when measuring at a time t+1.\\n for i in adjacents:\\n accum = 0\\n for j in range(0, net_size):\\n distance = self.__get_distance(i-1, j)\\n if distance == 0: #Probability of measure green at distance 0 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[0][0]\\n elif distance == 1: #Probability of measure green at distance 1 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[1][0]\\n elif distance == 2: #Probability of measure green at distance 2 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[2][0]\\n elif distance == 3: #Probability of measure green at distance 3 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[3][0]\\n else: #Probability of measure green at a distance >= 4 from 'i'.\\n accum += self.enemy_net[i-1].value * self.ct[4][0]\\n green_probs.append((i, accum))\\n #Returns the position in which the probability of\\n #obtaining green when measuring is the lowest.\\n return min(green_probs, key=itemgetter(1))[0]\",\n \"def _handle_bounds_servoj(self):\\n inside_bound, inside_buffer_bound, mat, xyz = self._check_bound(self._qt_[-1])\\n inside_angle_bound = np.all(self._angles_low <= self._qt_[-1, self._joint_indices]) and \\\\\\n np.all(self._qt_[-1, self._joint_indices] <= self._angles_high)\\n\\n if inside_bound and inside_angle_bound:\\n self.return_point = None\\n return\\n\\n if self.return_point is None:\\n # we are outside the bounds and return point wasn't computed yet\\n if inside_bound and not inside_angle_bound:\\n print(\\\"outside of angle bound\\\")\\n self.rel_indices = self._joint_indices\\n self._cmd_ = self._q_[0][self._joint_indices]\\n self._cmd_ = np.clip(self._cmd_, self._angles_low + self._angle_bound_buffer,\\n self._angles_high - self._angle_bound_buffer)\\n # a point within the box to which we will be returning\\n self.return_point = self._cmd_.copy()\\n # Speed at which arm approaches the boundary. The faster this speed,\\n # the larger opposite acceleration we need to apply in order to slow down\\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\\n\\n else:\\n print(\\\"outside box bound\\\")\\n xyz = np.clip(xyz, self._end_effector_low + self._box_bound_buffer,\\n self._end_effector_high - self._box_bound_buffer)\\n mat[:3, 3] = xyz\\n ref_pos = self._q_ref.copy()\\n ref_pos[self._joint_indices] = self._q_[-1, self._joint_indices]\\n solutions = ur_utils.inverse_near(mat, wrist_desired=self._q_ref[-1], ref_pos=ref_pos,\\n params=self._ik_params)\\n servoj_q = self._q_ref.copy()\\n if len(solutions) == 0:\\n servoj_q[self._joint_indices] = self._q_[-1, self._joint_indices]\\n else:\\n servoj_q[self._joint_indices] = solutions[0][self._joint_indices]\\n self.return_point = servoj_q[self._joint_indices]\\n # Speed at which arm approaches the boundary. The faster this speed,\\n # the larger opposite acceleration we need to apply in order to slow down\\n self.init_boundary_speed = np.max(np.abs(self._qd_.copy()))\\n # if return point is already computed, keep going to it, no need\\n # to recompute it at every time step\\n self._cmd_ = self.return_point - self._q_[0][self._joint_indices]\\n # Take the direction to return point and normalize it to have norm 0.1\\n if np.linalg.norm(self._cmd_) != 0:\\n self._cmd_ /= np.linalg.norm(self._cmd_) / 0.1\\n\\n self._speedj_packet[1:1 + 6][self._joint_indices] = self._cmd_\\n # This acceleration guarantees that we won't move beyond\\n # the bounds by more than 0.05 radian on each joint. This\\n # follows from kinematics equations.\\n accel_to_apply = np.max(np.abs(self._qd_)) * self.init_boundary_speed / 0.05\\n\\n # self.boundary_packet[1:1 + 6][self.joint_indices] = self.return_point\\n # self.actuator_comms['UR5'].actuator_buffer.write(self.reset_packet)\\n # time.sleep(1.0)\\n self._speedj_packet[-2] = np.clip(accel_to_apply, 2.0, 5.0)\\n self._actuation_packet_['UR5'] = self._speedj_packet\\n self._cmd_.fill(0.0)\\n self._cmd_prev_.fill(0.0)\\n self._first_deriv_.fill(0.0)\",\n \"def __inverse_kinematics(self, guess, target_point):\\n\\n error = 1.0\\n tolerance = 0.05\\n\\n # Initial Guess - Joint Angles\\n thetas = np.matrix(guess) # thetas is list which is contain all axes theta angles.\\n target_point = np.matrix(target_point) # X, Y, Z list to matrix for Target Position\\n # print(target_point.shape)\\n # Jacobian\\n self.__calc_jacobian_matrix()\\n tf_matrix_first_to_last = self.tf_matrices_list[-1]\\n\\n error_grad = []\\n\\n theta_dict = {}\\n\\n lr = 0.2\\n while error > tolerance:\\n for i in range(len(np.array(thetas)[0])):\\n theta_dict[self.q[i]] = np.array(thetas)[0][i]\\n\\n theta_dict[self.q[-1]] = self.q[-1]\\n\\n calculated_target_point = np.matrix(self.get_coords_from_forward_kinematics(self.__forward_kinematics(np.array(thetas)[0])[-1]))\\n logger.debug(f'calculated target point is \\\\n{calculated_target_point}')\\n\\n diff_wanted_calculated = target_point - calculated_target_point\\n\\n jacob_mat = np.matrix(self.jacobian_matrix.evalf(subs=theta_dict, chop=True, maxn=4)).astype(np.float64).T\\n logger.debug(f'jacobian matrix is\\\\n{jacob_mat} \\\\n\\\\n diff is \\\\n {diff_wanted_calculated}')\\n\\n thetas = thetas + lr * (jacob_mat * diff_wanted_calculated.T)\\n # thetas = np.array(thetas)[0] # this line's purpose is changing Q from matrix level to array level.\\n\\n prev_error = error\\n\\n error = linalg.norm(diff_wanted_calculated)\\n\\n if error > 10 * tolerance:\\n lr = 0.3\\n elif error < 10 * tolerance:\\n lr = 0.2\\n error_grad.append((error - prev_error))\\n\\n # print(error)\\n return np.array(thetas)[0]\",\n \"def generate_obstacle_point(start, end):\\n top_left = (start[0], start[1] - _OBSTACLE_SIZE)\\n top_right = (end[0], end[1] - _OBSTACLE_SIZE)\\n return start, end, top_right, top_left\",\n \"def compute_fk_velocity(self, jpos, jvel, tgt_frame):\\n if isinstance(jpos, list):\\n jpos = np.array(jpos)\\n if isinstance(jvel, list):\\n jvel = np.array(jvel)\\n kdl_end_frame = kdl.FrameVel()\\n kdl_jnt_angles = joints_to_kdl(jpos)\\n kdl_jnt_vels = joints_to_kdl(jvel)\\n kdl_jnt_qvels = kdl.JntArrayVel(kdl_jnt_angles, kdl_jnt_vels)\\n idx = self.arm_link_names.index(tgt_frame) + 1\\n fg = self._fk_solver_vel.JntToCart(kdl_jnt_qvels,\\n kdl_end_frame,\\n idx)\\n if fg < 0:\\n raise ValueError('KDL Vel JntToCart error!')\\n end_twist = kdl_end_frame.GetTwist()\\n return np.array([end_twist[0], end_twist[1], end_twist[2],\\n end_twist[3], end_twist[4], end_twist[5]])\",\n \"def _inverse_kinematics(self):\\n q_2 = np.arccos(\\n (\\n (self._x_obj_0 - self._jnt_lengths[2]) ** 2\\n + self._y_obj_0 ** 2\\n - self._jnt_lengths[0] ** 2\\n - self._jnt_lengths[1] ** 2\\n )\\n / (2 * self._jnt_lengths[0] * self._jnt_lengths[1])\\n )\\n psi = np.arcsin(\\n self._jnt_lengths[1] * np.sin(q_2)\\n / np.sqrt(\\n (self._x_obj_0 - self._jnt_lengths[2]) ** 2\\n + self._y_obj_0 ** 2\\n )\\n )\\n q_1 = (np.arctan2(self._x_obj_0 - self._jnt_lengths[2], self._y_obj_0)\\n - psi)\\n q_3 = np.pi / 2 - q_1 - q_2\\n return np.array([q_1, q_2, q_3])\",\n \"def _robot_jpos_getter(self):\\n return np.array(self.env._joint_positions)\",\n \"def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\\r\\n\\r\\n L = start_scores.shape[0]\\r\\n assert end_scores.shape[0] == L\\r\\n assert trans_scores.shape[0] == L\\r\\n assert trans_scores.shape[1] == L\\r\\n assert emission_scores.shape[1] == L\\r\\n N = emission_scores.shape[0]\\r\\n\\r\\n #T - Score matrix same as in assignement pdf\\r\\n T = np.zeros(shape=(L,N))\\r\\n #Back pointers - to store the previous best tag for word at (i-1)th position\\r\\n #that resulted into current best tag for (i)th word \\r\\n back_pointer = np.full((L,N), -1)\\r\\n\\r\\n for i in xrange(L):\\r\\n emission = emission_scores[0][i]\\r\\n combined = emission + start_scores[i]\\r\\n T[i][0] = combined\\r\\n\\r\\n # Loop over all the words in a sequesnce\\r\\n for i in xrange(1, N):\\r\\n # Loop over all the tags for the word at index i \\r\\n for j in xrange(L):\\r\\n # Varibale for maximum tag score from previous word (word at i-1)\\r\\n tmp_max = float('-inf')\\r\\n tmp_max_idx = -1\\r\\n #Emission value of word at idx i from state (i.e tag) j\\r\\n emission = emission_scores[i][j]\\r\\n #Loop over all the possibile tags for previous word T[tag (1..L), word at i-1]\\r\\n #and get max among them. Store the corresponding back pointer for there T[tag (1..L), word at i-1]\\r\\n for k in xrange(L):\\r\\n transition = trans_scores[k][j]\\r\\n prev_path = T[k][i-1]\\r\\n combined = transition + prev_path\\r\\n if (tmp_max < combined):\\r\\n tmp_max = combined\\r\\n tmp_max_idx = k\\r\\n\\r\\n back_pointer[j][i] = tmp_max_idx\\r\\n T[j][i] = tmp_max + emission\\r\\n\\r\\n # Doing this step outside because if N == 1 then above loop will not run\\r\\n # Variable for maximum tag score\\r\\n tag_max = float('-inf')\\r\\n # Variable for back pointer(previous T[tag, word])\\r\\n tag_max_idx = -1\\r\\n for i in xrange(L):\\r\\n T[i][N-1] = T[i][N-1] + end_scores[i]\\r\\n if (tag_max < T[i][N-1]):\\r\\n tag_max = T[i][N-1]\\r\\n tag_max_idx = i\\r\\n # print(\\\"Max tag -> \\\" + str(tag_max_idx))\\r\\n\\r\\n #Variable to track the path length - should be equal to N\\r\\n path_length = 0\\r\\n #Variable to back track on the tags\\r\\n tag_idx = tag_max_idx\\r\\n #Varibale to track the word index in N\\r\\n word_idx = N-1 \\r\\n #Path strored using backtracking\\r\\n y = []\\r\\n\\r\\n #Getting the best path using backtracking on back_pointers\\r\\n while path_length != N-1:\\r\\n y.append(back_pointer[tag_idx][word_idx])\\r\\n tag_idx = back_pointer[tag_idx][word_idx]\\r\\n word_idx = word_idx - 1\\r\\n path_length = path_length + 1\\r\\n\\r\\n #Reversing the backtracked path\\r\\n y = y[::-1]\\r\\n #Adding the tag for the last word idx in N\\r\\n y.append(tag_max_idx)\\r\\n # print(\\\"Path -> \\\" + str(y))\\r\\n\\r\\n return (tag_max, y)\",\n \"def getEePointsJacobians(refJacobian, eePoints, refRot, numberOfJoints):\\n eePoints = np.asarray(eePoints)\\n refJacobiansTrans = refJacobian[:3, :]\\n refJacobiansRot = refJacobian[3:, :]\\n endEffectorPointsRot = np.expand_dims(refRot.dot(eePoints.T).T, axis=1)\\n eePointsJacTrans = np.tile(refJacobiansTrans, (eePoints.shape[0], 1)) + \\\\\\n np.cross(refJacobiansRot.T, endEffectorPointsRot).transpose(\\n (0, 2, 1)).reshape(-1, numberOfJoints)\\n eePointsJacRot = np.tile(refJacobiansRot, (eePoints.shape[0], 1))\\n return eePointsJacTrans, eePointsJacRot\",\n \"def get_first_quadrant(self):\\n num_copies_x = ceil(self.max_x / self.room_x)\\n num_copies_x = int(num_copies_x)\\n num_copies_y = ceil(self.max_y / self.room_y)\\n num_copies_y = int(num_copies_y)\\n\\n player_exp_x = []\\n player_exp_y = []\\n guard_exp_x = []\\n guard_exp_y = []\\n # Loop expands along the x axis\\n for i in range(0, num_copies_x + 1, 1):\\n temp_player_y_list = []\\n temp_guard_y_list = []\\n r_x = self.room_x * i\\n\\n if len(player_exp_x) == 0:\\n n_p_p_x = self.player_x\\n else:\\n n_p_p_x = (r_x - player_exp_x[-1][0]) + r_x\\n player_exp_x.append([n_p_p_x, self.player_y, 1])\\n\\n if len(guard_exp_x) == 0:\\n n_g_p_x = self.guard_x\\n else:\\n n_g_p_x = (r_x - guard_exp_x[-1][0]) + r_x\\n guard_exp_x.append([n_g_p_x, self.guard_y, 7])\\n\\n # Loop expands along the x axis\\n for j in range(1, num_copies_y + 1, 1):\\n r_y = self.room_y * j\\n if len(temp_guard_y_list) == 0:\\n n_g_p_y = (r_y - self.guard_y) + r_y\\n temp_guard_y_list.append(n_g_p_y)\\n else:\\n n_g_p_y = (r_y - temp_guard_y_list[-1]) + r_y\\n temp_guard_y_list.append(n_g_p_y)\\n guard_exp_y.append([n_g_p_x, n_g_p_y, 7])\\n\\n if len(temp_player_y_list) == 0:\\n n_p_p_y = (r_y - self.player_y) + r_y\\n temp_player_y_list.append(n_p_p_y)\\n else:\\n n_p_p_y = (r_y - temp_player_y_list[-1]) + r_y\\n temp_player_y_list.append(n_p_p_y)\\n player_exp_y.append([n_p_p_x, n_p_p_y, 1])\\n\\n return player_exp_x + guard_exp_x + player_exp_y + guard_exp_y\",\n \"def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\\r\\n L = start_scores.shape[0]\\r\\n assert end_scores.shape[0] == L\\r\\n assert trans_scores.shape[0] == L\\r\\n assert trans_scores.shape[1] == L\\r\\n assert emission_scores.shape[1] == L\\r\\n N = emission_scores.shape[0]\\n \\r\\n trans_scores += start_scores\\n back_ptrs = np.zeros_like(emission_scores,dtype=np.int32)\\n emission_scores += start_scores\\r\\n em_scores = np.zeros_like(emission_scores)\\n em_scores[0] = start_scores+emission_scores[0]\\n \\n for k in range(1,N):\\n transition_plus_score =trans_scores+np.expand_dims(em_scores[k-1],1)\\n back_ptrs[k] =np.argmax(transition_plus_score,0)\\n em_scores[k] =np.max(transition_plus_score,0)+emission_scores[k]\\n \\n v = [np.argmax(end_scores+em_scores[-1])]\\n v_score = np.max(end_scores+em_scores[-1])\\n\\n for back_ptr in reversed(back_ptrs[1:]):\\n v.append(back_ptr[v[-1]])\\n v.reverse()\\n return v_score,v\",\n \"def run_viterbi(emission_scores, trans_scores, start_scores, end_scores):\\r\\n L = start_scores.shape[0]\\r\\n assert end_scores.shape[0] == L\\r\\n assert trans_scores.shape[0] == L\\r\\n assert trans_scores.shape[1] == L\\r\\n assert emission_scores.shape[1] == L\\r\\n N = emission_scores.shape[0]\\r\\n\\r\\n # SHAPES \\r\\n # N = 5, L = 3\\r\\n # emission_scores = (5,3), trans_scores = (3,3)\\r\\n # start_scores = (3,), end_scores = (3,)\\r\\n\\r\\n # Creating the transition DP matrix\\r\\n T = [[0 for _ in range(N)] for _ in range(L)]\\r\\n backpointers = [[0 for _ in range(N)] for _ in range(L)]\\r\\n\\r\\n # Filling the first column\\r\\n for row in range(L):\\r\\n T[row][0] = emission_scores[0][row] + start_scores[row] # emission_scores matrix is (N X L)\\r\\n \\r\\n # Filling the rest of the transition matrix\\r\\n for col in range(1, N):\\r\\n for row in range(L):\\r\\n prev_list = []\\r\\n for prev_label in range(L):\\r\\n prev_list.append(trans_scores[prev_label, row] + T[prev_label][col-1])\\r\\n T[row][col] = max(prev_list) + emission_scores[col][row] \\r\\n backpointers[row][col] = np.argmax(prev_list)\\r\\n\\r\\n # Filling the last column\\r\\n for row in range(L):\\r\\n T[row][N-1] += end_scores[row]\\r\\n\\r\\n # print for debug\\r\\n # print \\\"T\\\"\\r\\n # for i in T:\\r\\n # print i\\r\\n \\r\\n # print \\r\\n # print\\r\\n\\r\\n # print \\\"B\\\"\\r\\n # for i in backpointers:\\r\\n # print i\\r\\n\\r\\n # Finding max score in last column of T matrix\\r\\n T = np.array(T)\\r\\n score = np.asscalar(np.max(T[:,N-1]))\\r\\n location = np.asscalar(np.argmax(T[:,N-1]))\\r\\n\\r\\n # Getting best sequence from right to left using backpointers\\r\\n y = [location]\\r\\n for col in range(N-1, 0, -1):\\r\\n y.insert(0, backpointers[location][col])\\r\\n location = backpointers[location][col]\\r\\n\\r\\n '''\\r\\n y = []\\r\\n for i in xrange(N):\\r\\n # stupid sequence\\r\\n y.append(i % L)\\r\\n # score set to 0\\r\\n return (0.0, y)\\r\\n '''\\r\\n return (score, y)\",\n \"def _get_joint_positions_all(self, abs_input: [list, np.ndarray]):\\n return np.copy(abs_input)\",\n \"def _endx(self, parents):\\n ALPHA = (1.-2*0.35**2)**0.5/2.\\n BETA = 0.35/(self.n_gene-1)**0.5\\n\\n child = np.empty(self.n_gene+1)\\n\\n t1 = (parents[1, :self.n_gene]-parents[0, :self.n_gene]) / 2.\\n t2 = np.random.normal(scale=ALPHA) * (\\n parents[1, :self.n_gene] - parents[0, :self.n_gene]\\n )\\n t3 = np.sum(\\n np.random.normal(scale=BETA, size=self.n_gene)[:, np.newaxis]\\n * (\\n parents[2:, :self.n_gene] - (\\n np.sum(parents[2:, :self.n_gene], axis=0) / self.n_gene\\n )\\n ), axis=0\\n )\\n child[:self.n_gene] = t1 + t2 + t3\\n\\n return child\",\n \"def interpolate_to_parent(self, start, end, linspace_count):\\n \\n v = end - start\\n length = norm(v)\\n v = v / length # Make v a unit vector\\n l = np.linspace(0, length, linspace_count) \\n\\n return np.array([start[i] + v[i] * l for i in range(3)])\",\n \"def calc_nearest_ind(self, robot_pose):\\n pass\",\n \"def get_ending_direction_vector(self):\\n\\n total_length = len(self.pixel_list)\\n\\n if total_length < 2:\\n return None\\n elif total_length < 15:\\n delta_x = self.pixel_list[-1].x - self.pixel_list[0].x\\n delta_y = self.pixel_list[-1].y - self.pixel_list[0].y\\n return delta_y, delta_x\\n else:\\n delta_x = self.pixel_list[-15].x - self.pixel_list[-1].x\\n delta_y = self.pixel_list[-15].y - self.pixel_list[-1].y\\n return delta_y, delta_x\",\n \"def joint_angles(self, arm):\\n self.main(1, arm.angles)\\n # self.distance_service = rospy.Service('distance', Distance, self.distance)\\n return Joint_anglesResponse(True)\",\n \"def get_imin(x1, x2, y, k=1, normalize=None, norm=np.inf):\\n\\n if normalize:\\n y = normalize(y)\\n\\n y_tree = cKDTree(y)\\n\\n n = len(y)\\n i_spec = np.zeros((2, n))\\n\\n for jj, x in enumerate([x1, x2]):\\n\\n if normalize:\\n x = normalize(x)\\n\\n # construct state array for the joint processes:\\n xy = np.c_[x,y]\\n\\n # store data pts in kd-trees for efficient nearest neighbour computations\\n # TODO: choose a better leaf size\\n x_tree = cKDTree(x)\\n xy_tree = cKDTree(xy)\\n\\n # kth nearest neighbour distances for every state\\n # query with k=k+1 to return the nearest neighbour, not counting the data point itself\\n # dist, idx = xy_tree.query(xy, k=k+1, p=norm)\\n dist, idx = xy_tree.query(xy, k=k+1, p=np.inf)\\n epsilon = dist[:, -1]\\n\\n # for each point, count the number of neighbours\\n # whose distance in the x-subspace is strictly < epsilon\\n # repeat for the y subspace\\n nx = np.empty(n, dtype=np.int)\\n ny = np.empty(n, dtype=np.int)\\n for ii in xrange(N):\\n # nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=norm)) - 1\\n # ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=norm)) - 1\\n nx[ii] = len(x_tree.query_ball_point(x_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\\n ny[ii] = len(y_tree.query_ball_point(y_tree.data[ii], r=epsilon[ii], p=np.inf)) - 1\\n\\n i_spec[jj] = digamma(k) - digamma(nx+1) + digamma(ny+1) + digamma(n) # version (1)\\n\\n i_min = np.mean(np.min(i_spec, 0))\\n\\n return i_min\",\n \"def inv_kin(self, ee_pos=None):\\n\\n if ee_pos != None:\\n self.ee_pos = ee_pos\\n else:\\n ee_pos = self.ee_pos\\n \\n def distance_to_default(q, *args):\\n \\\"\\\"\\\"Objective function to minimize\\n Calculates the euclidean distance through joint space to the\\n default arm configuration. The weight list allows the penalty of\\n each joint being away from the resting position to be scaled\\n differently, such that the arm tries to stay closer to resting\\n state more for higher weighted joints than those with a lower\\n weight.\\n\\n q : np.array\\n the list of current joint angles\\n\\n returns : scalar\\n euclidean distance to the default arm position\\n \\\"\\\"\\\"\\n # weights found with trial and error,\\n # get some wrist bend, but not much\\n weight = [1, 1, 1.3]\\n return np.sqrt(np.sum([(qi - q0i)**2 * wi\\n for qi, q0i, wi in zip(q, self.q0, weight)]))\\n\\n def x_constraint(q,ee_pos):\\n \\\"\\\"\\\"Returns the corresponding hand self.ee_pos coordinates for\\n a given set of joint angle values [shoulder, elbow, wrist],\\n and the above defined arm segment lengths, L\\n\\n q : np.array\\n the list of current joint angles\\n self.ee_pos : np.array\\n current self.ee_pos position (not used)\\n\\n returns : np.array\\n the difference between current and desired x position\\n \\\"\\\"\\\"\\n x = (self.length[0]*np.cos(q[0]) + self.length[1]*np.cos(q[0]+q[1]) +\\n self.length[2]*np.cos(np.sum(q))) - self.ee_pos[0]\\n return x\\n\\n def y_constraint(q,ee_pos):\\n \\\"\\\"\\\"Returns the corresponding hand self.ee_pos coordinates for\\n a given set of joint angle values [shoulder, elbow, wrist],\\n and the above defined arm segment lengths, L\\n\\n q : np.array\\n the list of current joint angles\\n self.ee_pos : np.array\\n current self.ee_pos position (not used)\\n returns : np.array\\n the difference between current and desired y position\\n \\\"\\\"\\\"\\n y = (self.length[0]*np.sin(q[0]) + self.length[1]*np.sin(q[0]+q[1]) +\\n self.length[2]*np.sin(np.sum(q))) - self.ee_pos[1]\\n return y\\n\\n def joint_limits_upper_constraint(q,ee_pos):\\n \\\"\\\"\\\"Used in the function minimization such that the output from\\n this function must be greater than 0 to be successfully passed.\\n\\n q : np.array\\n the current joint angles\\n self.ee_pos : np.array\\n current self.ee_pos position (not used)\\n\\n returns : np.array\\n all > 0 if constraint matched\\n \\\"\\\"\\\"\\n return self.max_angles - q\\n\\n def joint_limits_lower_constraint(q,ee_pos):\\n \\\"\\\"\\\"Used in the function minimization such that the output from\\n this function must be greater than 0 to be successfully passed.\\n\\n q : np.array\\n the current joint angles\\n self.ee_pos : np.array\\n current self.ee_pos position (not used)\\n\\n returns : np.array\\n all > 0 if constraint matched\\n \\\"\\\"\\\"\\n return q - self.min_angles\\n\\n return scipy.optimize.fmin_slsqp(\\n func=distance_to_default,\\n x0=self.q,\\n eqcons=[x_constraint,\\n y_constraint],\\n # uncomment to add in min / max angles for the joints\\n # ieqcons=[joint_limits_upper_constraint,\\n # joint_limits_lower_constraint],\\n args=(self.ee_pos,),\\n iprint=0) # iprint=0 suppresses output\",\n \"def ortho_line_cut(self):\\n x_mid_left, y_mid_left = self.midpoint(0,1) # Computes the mid point of the LHS face of the edm cut\\n x_mid_right, y_mid_right = self.midpoint(2,3) # Computes the mid point of the RHS face of the edm cut\\n\\n ave_grad = self.average_grad()\\n m_horizontal = -1/ave_grad #90 degrees rotation of the vertical line average gradient\\n\\n horizontal_eq_c = y_mid_right - m_horizontal*x_mid_right # y offset of horizontal line\\n vertical_eq_left_c = y_mid_left - ave_grad * x_mid_left # y offset of vertical line on left side\\n\\n x_intersect, y_intersect = self.intersect_point(m_horizontal, horizontal_eq_c, ave_grad,vertical_eq_left_c)\\n\\n\\n coordleft = [x_intersect, y_intersect]\\n coordright =[x_mid_right, y_mid_right]\\n\\n dist = self.distance(coordleft, coordright)\\n\\n return coordleft, coordright, dist\",\n \"def joint_variables(self, G: nx.Graph, T_final: dict = None) -> np.ndarray:\\n R = {\\\"p0\\\": SO3.identity()}\\n joint_variables = {}\\n for u, v, dat in self.structure.edges(data=DIST):\\n if dat:\\n diff_uv = G.nodes[v][POS] - G.nodes[u][POS]\\n len_uv = np.linalg.norm(diff_uv)\\n\\n sol = np.linalg.lstsq(len_uv * R[u].as_matrix(), diff_uv)\\n sol = sol[0]\\n\\n theta_idx = np.math.atan2(sol[1], sol[0]) + pi / 2\\n Rz = SO3.rotz(theta_idx)\\n\\n alpha_idx = abs(np.math.acos(min(sol[2], 1)))\\n Rx = SO3.rotx(alpha_idx)\\n\\n joint_variables[v] = [wraptopi(theta_idx), alpha_idx]\\n R[v] = R[u].dot(Rz.dot(Rx))\\n\\n return joint_variables\",\n \"def _get_end_points(self, segmented_instances, i, stats, idx):\\n\\n end_points=[]\\n\\n # find all points intersecting the bbox\\n #(tl_x, th_y, width, height, area)\\n label_num=i+1\\n leftmost_x = stats['bbox'][i][cv2.CC_STAT_LEFT]\\n topmost_y = stats['bbox'][i][cv2.CC_STAT_TOP]\\n width = stats['bbox'][i][cv2.CC_STAT_WIDTH]\\n height = stats['bbox'][i][cv2.CC_STAT_HEIGHT]\\n bottom_most_y = topmost_y + height-1\\n right_most_x = leftmost_x + width-1\\n\\n segmented_instances_copy=segmented_instances.copy()\\n edge_points = np.zeros(segmented_instances.shape).astype(np.uint8)\\n segs = np.zeros(segmented_instances.shape).astype(np.uint8)\\n segs[segmented_instances==label_num]=255\\n cv2.rectangle(segmented_instances_copy,(leftmost_x, topmost_y), (right_most_x, bottom_most_y), 150, 2)\\n\\n #Get all points for the current stem segment\\n label_points = np.argwhere(segmented_instances.copy()==label_num)\\n\\n # upper points from (tl_x,th_y) to (th_x, th_y) that instersect with the upper edge of the bouding box\\n upper_points = [i for i in label_points if i[0]==topmost_y and i[1]>=leftmost_x and i[1]<=right_most_x]\\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(upper_points, edge_points, segs, 1)\\n center_upper_pts = sorted(self._get_centeroids(x_pts))\\n\\n # left side points from (tl_x, tl_y) to (tl_x, th_y) that instersect with the left edge of the bouding box\\n left_points = [i for i in label_points if i[1]==leftmost_x and i[0]<=bottom_most_y and i[0]>=topmost_y]\\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(left_points, edge_points, segs, 0)\\n center_left_pts = sorted(self._get_centeroids(x_pts))\\n\\n #right side points form (th_x, tl_y) to (th_x, th_y) that instersect with the right edge of the bouding box\\n right_points = [i for i in label_points if i[1]==right_most_x and i[0]<=bottom_most_y and i[0]>=topmost_y]\\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(right_points, edge_points, segs, 0)\\n center_right_pts = sorted(self._get_centeroids(x_pts))\\n\\n #bottom points from (tl_x, tl_y) to (th_x,tl_y)\\n bottom_points = [i for i in label_points if i[1]>=leftmost_x and i[1]<=right_most_x and i[0]==bottom_most_y]\\n x_pts, segs, edge_points = self._update_imgs_and_pt_list(bottom_points, edge_points, segs, 1)\\n center_bottom_pts = sorted(self._get_centeroids(x_pts))\\n\\n # If there are corner edges, get the centroid of that\\n center_x_lb, center_y_lb, center_left_pts, center_bottom_pts = self._get_corner_centers(center_left_pts, \\\\\\n center_bottom_pts, bottom_most_y, leftmost_x)\\n if (center_x_lb != None) and (center_y_lb != None):\\n end_points.append([center_x_lb, center_y_lb])\\n else:\\n if len(center_left_pts)>0:\\n for pt_idx in range(0, len(center_left_pts)):\\n end_points.append([leftmost_x, center_left_pts[pt_idx]])\\n if len(center_bottom_pts)>0:\\n for pt_idx in range(0, len(center_bottom_pts)):\\n end_points.append([center_bottom_pts[pt_idx], bottom_most_y])\\n\\n # If there are corner edges, get the centroid of that\\n center_x_ur, center_y_ur, center_right_pts, center_upper_pts = self._get_corner_centers(center_right_pts, \\\\\\n center_upper_pts, topmost_y, right_most_x)\\n if (center_x_ur != None) and (center_y_ur != None):\\n end_points.append([center_x_ur, center_y_ur])\\n else:\\n if len(center_right_pts)>0:\\n for pt_idx in range(0, len(center_right_pts)):\\n end_points.append([right_most_x, center_right_pts[pt_idx]])\\n if len(center_upper_pts)>0:\\n for pt_idx in range(0, len(center_upper_pts)):\\n end_points.append([center_upper_pts[pt_idx], topmost_y])\\n\\n # If there are corner edges, get the centroid of that\\n center_x_ul, center_y_ul, center_left_pts, center_upper_pts = self._get_corner_centers(center_left_pts, \\\\\\n center_upper_pts, topmost_y, leftmost_x)\\n if (center_x_ul != None) and (center_y_ul != None):\\n end_points.append([center_x_ul, center_y_ul])\\n else:\\n if len(center_left_pts)>0:\\n for pt_idx in range(0, len(center_left_pts)):\\n end_points.append([leftmost_x, center_left_pts[pt_idx]])\\n if len(center_upper_pts)>0:\\n for pt_idx in range(0, len(center_upper_pts)):\\n end_points.append([center_upper_pts[pt_idx], topmost_y])\\n\\n\\n # If there are corner edges, get the centroid of that\\n center_x_br, center_y_br, center_right_pts, center_bottom_pts = self._get_corner_centers(center_right_pts, \\\\\\n center_bottom_pts, bottom_most_y, right_most_x)\\n if (center_x_br != None) and (center_y_br != None):\\n end_points.append([center_x_br, center_y_br])\\n else:\\n if len(center_right_pts)>0:\\n for pt_idx in range(0, len(center_right_pts)):\\n end_points.append([right_most_x, center_right_pts[pt_idx]])\\n if len(center_bottom_pts)>0:\\n for pt_idx in range(0, len(center_bottom_pts)):\\n end_points.append([center_bottom_pts[pt_idx], bottom_most_y])\\n\\n #self.showme(segmented_instances_copy, 'bbox')\\n\\n return end_points\",\n \"def get_exterior(self, x, y, x1, x2, bottom, head_y):\\n fx1 = x+(x-x1)*8\\n fx2 = x+(x-x2)*8\\n # compute bounding ellipse; and intersection with body outline\\n cv2.ellipse(self.ellipse_finder, ((x/mscale,y/mscale), ((fx1-fx2)/mscale, (2*(bottom-head_y))/mscale), 0), 255,-1 )\\n intersection = np.bitwise_and(255-self.ellipse_finder, self.median_finder)\\n # find external blobs\\n im2, out_contours, out_hierarchy = cv2.findContours(intersection,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\\n return out_contours, out_hierarchy, fx1-fx2\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6462823","0.63993484","0.5633511","0.5578069","0.5572044","0.55286443","0.549767","0.54915667","0.5486143","0.5462283","0.5461075","0.54595315","0.54163367","0.5408067","0.5399364","0.53867376","0.53730583","0.536113","0.533599","0.5330091","0.5328494","0.5319201","0.53152406","0.5295751","0.5288994","0.52836466","0.52835757","0.52709764","0.526558","0.5255029","0.5250141","0.5206064","0.5205125","0.5193275","0.5190355","0.51846075","0.51632285","0.5141516","0.5126835","0.5121687","0.5116427","0.51126635","0.5078218","0.50752306","0.5065359","0.50528884","0.5047486","0.504582","0.5044543","0.5036272","0.5026307","0.5015012","0.50148976","0.50040954","0.49992245","0.49958342","0.49935743","0.49911764","0.49881643","0.4980434","0.4980434","0.49780747","0.49752676","0.49728394","0.49715993","0.4971583","0.49675456","0.49673098","0.49567753","0.49555266","0.4954187","0.49531457","0.49510938","0.49409878","0.49396205","0.49262637","0.492414","0.49201354","0.4914983","0.49141097","0.49099877","0.48908836","0.48833558","0.48725563","0.48718724","0.4869381","0.48673844","0.48662364","0.48627448","0.48590475","0.4858755","0.48361635","0.48333922","0.4832488","0.48283625","0.48243064","0.4824188","0.48235804","0.48216158","0.48202258","0.4817702"],"string":"[\n \"0.6462823\",\n \"0.63993484\",\n \"0.5633511\",\n \"0.5578069\",\n \"0.5572044\",\n \"0.55286443\",\n \"0.549767\",\n \"0.54915667\",\n \"0.5486143\",\n \"0.5462283\",\n \"0.5461075\",\n \"0.54595315\",\n \"0.54163367\",\n \"0.5408067\",\n \"0.5399364\",\n \"0.53867376\",\n \"0.53730583\",\n \"0.536113\",\n \"0.533599\",\n \"0.5330091\",\n \"0.5328494\",\n \"0.5319201\",\n \"0.53152406\",\n \"0.5295751\",\n \"0.5288994\",\n \"0.52836466\",\n \"0.52835757\",\n \"0.52709764\",\n \"0.526558\",\n \"0.5255029\",\n \"0.5250141\",\n \"0.5206064\",\n \"0.5205125\",\n \"0.5193275\",\n \"0.5190355\",\n \"0.51846075\",\n \"0.51632285\",\n \"0.5141516\",\n \"0.5126835\",\n \"0.5121687\",\n \"0.5116427\",\n \"0.51126635\",\n \"0.5078218\",\n \"0.50752306\",\n \"0.5065359\",\n \"0.50528884\",\n \"0.5047486\",\n \"0.504582\",\n \"0.5044543\",\n \"0.5036272\",\n \"0.5026307\",\n \"0.5015012\",\n \"0.50148976\",\n \"0.50040954\",\n \"0.49992245\",\n \"0.49958342\",\n \"0.49935743\",\n \"0.49911764\",\n \"0.49881643\",\n \"0.4980434\",\n \"0.4980434\",\n \"0.49780747\",\n \"0.49752676\",\n \"0.49728394\",\n \"0.49715993\",\n \"0.4971583\",\n \"0.49675456\",\n \"0.49673098\",\n \"0.49567753\",\n \"0.49555266\",\n \"0.4954187\",\n \"0.49531457\",\n \"0.49510938\",\n \"0.49409878\",\n \"0.49396205\",\n \"0.49262637\",\n \"0.492414\",\n \"0.49201354\",\n \"0.4914983\",\n \"0.49141097\",\n \"0.49099877\",\n \"0.48908836\",\n \"0.48833558\",\n \"0.48725563\",\n \"0.48718724\",\n \"0.4869381\",\n \"0.48673844\",\n \"0.48662364\",\n \"0.48627448\",\n \"0.48590475\",\n \"0.4858755\",\n \"0.48361635\",\n \"0.48333922\",\n \"0.4832488\",\n \"0.48283625\",\n \"0.48243064\",\n \"0.4824188\",\n \"0.48235804\",\n \"0.48216158\",\n \"0.48202258\",\n \"0.4817702\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":4682,"cells":{"query":{"kind":"string","value":"Return the first element of a 2tuple. >>> x([1,2]) 1"},"document":{"kind":"string","value":"def x(a):\n return a[0]"},"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 _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def single_element_tuple():\n single = (1,)\n print(type(single)) # <type 'tuple'>","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def second(xs):\n if not xs:\n return None\n return xs[1]","def second(xs):\n if not xs:\n return None\n return xs[1]","def first(pair):\n\treturn pair[0]","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def single_structure_tuple(x,strucutre_id):\n if isinstance(x,tuple):\n assert len(x)==2;\"tuple input must have two elements\"\n return (x[0][:,strucutre_id,:],x[1][:,strucutre_id,:])\n else:\n return x[:,strucutre_id,:]","def first(xs):\n if not xs:\n return None\n return xs[0]","def first(xs):\n if not xs:\n return None\n return xs[0]","def tuple(x):\n pass","def return_first(x):\r\n if x == []:\r\n return ''\r\n else:\r\n return x[0]","def second(pair):\n\treturn pair[1]","def first(items):\r\n return items[0]","def first(x):\n try:\n x = x.to_series()\n except AttributeError:\n pass\n return list(x)[0]","def _flatten_one(x):\n return x[0] if is_iterable(x) else x","def give_me_a_tuple():\n my_tuple = ('p','e','r','m','i','t')\n return my_tuple\n pass","def _sfn(x):\n if len(x) == 1:\n return x[0]\n return fn(*x)","def _get_first_element(cls, d):\n\n t = np.where(d[:, 2] > 0)[0]\n if len(t):\n return d[t[0], 0], d[t[0], 1], t[0]\n return None, None, None","def getfirst(s):\n return s[0] if isinstance(s, list) else s","def x(self):\n return self[0]","def key(self, x):\r\n return tuple(x)","def take_first(info):\n return info[0]","def tuple_from_sequence(*args):\n return tuple(args)","def tuple_map(x):\n return x * 2","def extract(l):\n if l is None: return None\n if len(l) > 1:\n raise ValueError('More than 1 Value')\n try:\n return l[0]\n except IndexError:\n return None","def first(items):\n return next(iter(items or []), None)","def __tuple_to_scalar(tuple_value):\n if isinstance(tuple_value, tuple) and len(tuple_value)==1:\n return tuple_value[0]\n else:\n return tuple_value","def _tuple_from_one_or_two_ints(self, v):\n try:\n a, b = [int(x) for x in v]\n except TypeError:\n a, b = int(v), int(v)\n return (a,b)","def second(self) -> Element:\n return typing.cast(Element, self[1])","def element_to_tuple(list_of_elements):\n return list(map(lambda x: tuple(x), list_of_elements))","def first(l):\n return next(iter(l), None)","def get_only_element_from_collection(one_element_collection):\n if len(one_element_collection) != 1:\n raise AssertionError(u'Expected a collection with exactly one element, but got: {}'\n .format(one_element_collection))\n return funcy.first(one_element_collection)","def get_first(self):\n return self.A[1][0] if self.n > 0 else None","def get_first_item(checklist):\r\n return checklist['items'][0]","def first(iterable: t.Iterable[T]) -> T:\n return next(iter(iterable))","def first(self) -> Element:\n return typing.cast(Element, self[0])","def _list_to_tuple(v):\n if isinstance(v, list):\n return tuple(v)\n return v","def get_only(seq: Iterable[T]) -> T:\n it = iter(seq)\n try:\n first_element = it.__next__()\n # we use the sentinel approach rather than the usual (evil) Python \"attempt can catch the\n # exception\" approach to avoid raising zillions of spurious exceptions on the expected\n # code path, which makes debugging a pain\n sentinel = object()\n second_element = next(it, sentinel)\n if second_element is sentinel:\n return first_element\n else:\n got_msg: str\n if isinstance(seq, Sized):\n got_msg = str_list_limited(seq, limit=10)\n else:\n got_msg = f\"{first_element!r}, {second_element!r}, and possibly more.\"\n raise ValueError(f\"Expected one item in sequence but got {got_msg}\")\n except StopIteration:\n raise ValueError(\"Expected one item in sequence but got none\")","def _get_first(details: CallableDetails) -> CallableArg:\n return details.args[0]","def first(self):\n if self.is_empty():\n raise Empty('list is empty')\n return self._head._element # front aligned with head of list","def identity(*args):\n return args if len(args) > 1 else args[0]","def hd(lst):\n return lst[0] if lst else None","def firstMove(board):\r\n x = board.size / 2\r\n return (x, x)","def head(array) -> T:\n return array[0]","def get_value_tuple_outer_function(index, tuple_input):\n return (tuple_input[0][index],\n tuple_input[1][index],\n tuple_input[2][index])","def get_first_element(dataset):\n return dataset.first()","def easy_unpack_my(elements):\n try:\n res = tuple(elements[i] for i in [0, 2, -2])\n except IndexError:\n res = 0\n return res","def _first(self) -> Tuple[np.ndarray, np.ndarray, ModelGeneratorBase]:\n pass","def first(seq):\n try: # try iterator interface\n return seq.next()\n except AttributeError:\n pass\n try: # seq is no iterator, try indexed lookup\n return seq[0]\n except IndexError:\n pass\n raise TypeError(\n \"Argument to `first()` method needs to be iterator or sequence.\")","def pair(first, second):\n return [first, second]","def fetchone(cursor):\n\t# type: (Cursor, ) -> Any\n\n\trows = cursor.fetchall()\n\tif len(rows) == 0:\n\t\traise NoResult(\"No result found\")\n\telif len(rows) == 1:\n\t\treturn rows[0]\n\telse:\n\t\traise InconsistentState(\"More than one result found\")","def try_tuple(obj):\n # type: (Union[T, Tuple[T]]) -> Tuple[T]\n if isinstance(obj, tuple):\n return obj\n\n return obj, # NOTE the comma, made into tuple","def pick_first(self, x, y):\n\n return self.cast(x, x.type.combine(y.type))","def head(self, xes):\n return xes[0]","def astuple(self):\n try:\n return tuple([x.astuple() for x in self])\n except Exception:\n pass\n return tuple([x for x in self])","def tup(item, ret_is_single=False):\r\n #return true for iterables, except for strings, which is what we want\r\n if hasattr(item, '__iter__'):\r\n return (item, False) if ret_is_single else item\r\n else:\r\n return ((item,), True) if ret_is_single else (item,)","def _ensure_iterable(x):\n if isinstance(x[0], Iterable):\n if len(x) > 1:\n raise TypeError(\"Either Iterable or variable argument list expected\")\n return x[0]\n else:\n return x","def visit_Tuple(self, node):\n self.generic_visit(node)\n if isinstance(node.ctx, ast.Load):\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\n return node","def flatten(x): # przerobić na lambda?\n if x==[]:\n return None\n else:\n return x[0]","def P_(x, y):\r\n return (x, y)","def first(self):\n if self.is_empty():\n raise Empty('list is empty')\n return self._head._next._element # front aligned with head of list","def first(seq):\n return next(iter(seq))","def get_first_item(videos):\n\n return next(iter(videos or []), None)","def aind(x):\n\treturn tuple(x.T)","def identity_filter(element_tuple):\r\n\treturn element_tuple","def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\n return tuple([l_[x] for x in i])","def __getitem__(self, index):\n if index == 0:\n return self.x\n elif index == 1:\n return self.y\n raise IndexError","def take_second(info):\n return info[1]","def first(self):\n return _(self._[0])","def vec2tuple(x):\n return (x.x, x.y, x.z)","def easy_unpack(elements: Tuple[int]) -> Tuple[int]:\n\n return itemgetter(0, 2, -2)(elements)","def first_last(item):\n # first_element = item[0]\n # last_element = item[len(item) - 1]\n # if type(item) == list:\n # return [first_element, last_element]\n # if type(item) == tuple:\n # return (first_element, last_element)\n # if type(item) == str:\n # return first_element + last_element\n\n # return item[:1] + item[-1:] \n \n return item[0: len(item): len(item)-1]","def first(s):\n assert is_link(s), 'fist only applies to a linked list.'\n assert s != empty, 'empty linked list has no first element.'\n return s[0]","def first2(x, y):\n y = np.asarray(y)\n return y[np.argsort(x)][0]","def x(self):\n return self._arr[0]","def parse(arg: Tuple[str, str, str, str, str]) -> Tuple[str, str, str]:\n return (arg[2], arg[3], arg[4])","def first(s):\n assert is_link(s), 'first only applies to linked lists.'\n assert s != empty, 'empty linked list has no first element.'\n return s[0]","def project(self, x):\n return (x, x) # TODO Your code goes here.","def _get(self, (y, x)):\n return self[y][x]","def tuple(self, arg: SeField[Any]) -> str:\n if is_bare_tuple(arg.type):\n return arg.varname\n elif is_variable_tuple(arg.type):\n earg = arg[0]\n earg.name = \"v\"\n return f\"tuple({self.render(earg)} for v in {arg.varname})\"\n else:\n rvalues = []\n for i, _ in enumerate(type_args(arg.type)):\n r = arg[i]\n r.name = f\"{arg.varname}[{i}]\"\n rvalues.append(self.render(r))\n return f\"({', '.join(rvalues)},)\" # trailing , is required for single element tuples","def first(s):\n assert is_link(s), \"first only applies to linked lists.\"\n assert s != empty, \"empty linked list has no first element.\"\n return s[0]","def popArg(args):\n if len(args) == 0:\n return (None, args)\n nextArg = args[0]\n args = args[1:]\n return (nextArg, args)","def unpack_all_equal_tuple(t):\n if not isinstance(t, tuple):\n return t\n\n assert all(x == t[0] for x in t)\n return t[0]","def __call__ (self, x):\n try:\n return self._lookup[x - 1]\n except IndexError:\n message = '{} not in permutation. Must be between 1 and {}.'\n raise IndexError(message.format(x, len(self)))","def first(collection):\n return next(iter(collection))","def first(collection):\n return next(iter(collection))","def get(s: Iterable[T]) -> T:\n return next(iter(s))","def retrieve_data_tuple(self):\n return ((42,))","def index_to_tuple(self, index):\n if index < 0:\n index = self.num_items + index\n assert index >= 0 and index < self.num_items\n\n return self.indices[index]","def _transform_inputs(x):\n if not isinstance(x, (list, tuple)):\n return x\n assert len(x) > 0\n return x[-1]","def return_first_item(func):\n\n # Define the wrapper function.\n def wrapper(self, *args, **kwargs):\n\n # Execute the decorated method with the provided arguments.\n result = func(self, *args, **kwargs)\n\n # If the function returned a result and that result is a list then\n # return the first item on that list.\n if result and isinstance(result, list):\n result = result[0]\n\n return result\n\n return wrapper","def convert_to_tuple(v):\n if not isinstance(v, tuple):\n return tuple(v)\n else:\n return v","def tuple(self) -> tuple:\n return tuple(self)","def one(self):\n return next(iter(self), None)","def firstElement(self):\n return self.top()","def first(self):\n if self.is_empty():\n raise Empty(\"List is empty!\")\n return self._header._next._element","def pop_first_arg(argv):\n for arg in argv:\n if not arg.startswith('-'):\n argv.remove(arg)\n return (arg, argv)\n\n return (None, argv)"],"string":"[\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def single_element_tuple():\\n single = (1,)\\n print(type(single)) # <type 'tuple'>\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def second(xs):\\n if not xs:\\n return None\\n return xs[1]\",\n \"def second(xs):\\n if not xs:\\n return None\\n return xs[1]\",\n \"def first(pair):\\n\\treturn pair[0]\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def single_structure_tuple(x,strucutre_id):\\n if isinstance(x,tuple):\\n assert len(x)==2;\\\"tuple input must have two elements\\\"\\n return (x[0][:,strucutre_id,:],x[1][:,strucutre_id,:])\\n else:\\n return x[:,strucutre_id,:]\",\n \"def first(xs):\\n if not xs:\\n return None\\n return xs[0]\",\n \"def first(xs):\\n if not xs:\\n return None\\n return xs[0]\",\n \"def tuple(x):\\n pass\",\n \"def return_first(x):\\r\\n if x == []:\\r\\n return ''\\r\\n else:\\r\\n return x[0]\",\n \"def second(pair):\\n\\treturn pair[1]\",\n \"def first(items):\\r\\n return items[0]\",\n \"def first(x):\\n try:\\n x = x.to_series()\\n except AttributeError:\\n pass\\n return list(x)[0]\",\n \"def _flatten_one(x):\\n return x[0] if is_iterable(x) else x\",\n \"def give_me_a_tuple():\\n my_tuple = ('p','e','r','m','i','t')\\n return my_tuple\\n pass\",\n \"def _sfn(x):\\n if len(x) == 1:\\n return x[0]\\n return fn(*x)\",\n \"def _get_first_element(cls, d):\\n\\n t = np.where(d[:, 2] > 0)[0]\\n if len(t):\\n return d[t[0], 0], d[t[0], 1], t[0]\\n return None, None, None\",\n \"def getfirst(s):\\n return s[0] if isinstance(s, list) else s\",\n \"def x(self):\\n return self[0]\",\n \"def key(self, x):\\r\\n return tuple(x)\",\n \"def take_first(info):\\n return info[0]\",\n \"def tuple_from_sequence(*args):\\n return tuple(args)\",\n \"def tuple_map(x):\\n return x * 2\",\n \"def extract(l):\\n if l is None: return None\\n if len(l) > 1:\\n raise ValueError('More than 1 Value')\\n try:\\n return l[0]\\n except IndexError:\\n return None\",\n \"def first(items):\\n return next(iter(items or []), None)\",\n \"def __tuple_to_scalar(tuple_value):\\n if isinstance(tuple_value, tuple) and len(tuple_value)==1:\\n return tuple_value[0]\\n else:\\n return tuple_value\",\n \"def _tuple_from_one_or_two_ints(self, v):\\n try:\\n a, b = [int(x) for x in v]\\n except TypeError:\\n a, b = int(v), int(v)\\n return (a,b)\",\n \"def second(self) -> Element:\\n return typing.cast(Element, self[1])\",\n \"def element_to_tuple(list_of_elements):\\n return list(map(lambda x: tuple(x), list_of_elements))\",\n \"def first(l):\\n return next(iter(l), None)\",\n \"def get_only_element_from_collection(one_element_collection):\\n if len(one_element_collection) != 1:\\n raise AssertionError(u'Expected a collection with exactly one element, but got: {}'\\n .format(one_element_collection))\\n return funcy.first(one_element_collection)\",\n \"def get_first(self):\\n return self.A[1][0] if self.n > 0 else None\",\n \"def get_first_item(checklist):\\r\\n return checklist['items'][0]\",\n \"def first(iterable: t.Iterable[T]) -> T:\\n return next(iter(iterable))\",\n \"def first(self) -> Element:\\n return typing.cast(Element, self[0])\",\n \"def _list_to_tuple(v):\\n if isinstance(v, list):\\n return tuple(v)\\n return v\",\n \"def get_only(seq: Iterable[T]) -> T:\\n it = iter(seq)\\n try:\\n first_element = it.__next__()\\n # we use the sentinel approach rather than the usual (evil) Python \\\"attempt can catch the\\n # exception\\\" approach to avoid raising zillions of spurious exceptions on the expected\\n # code path, which makes debugging a pain\\n sentinel = object()\\n second_element = next(it, sentinel)\\n if second_element is sentinel:\\n return first_element\\n else:\\n got_msg: str\\n if isinstance(seq, Sized):\\n got_msg = str_list_limited(seq, limit=10)\\n else:\\n got_msg = f\\\"{first_element!r}, {second_element!r}, and possibly more.\\\"\\n raise ValueError(f\\\"Expected one item in sequence but got {got_msg}\\\")\\n except StopIteration:\\n raise ValueError(\\\"Expected one item in sequence but got none\\\")\",\n \"def _get_first(details: CallableDetails) -> CallableArg:\\n return details.args[0]\",\n \"def first(self):\\n if self.is_empty():\\n raise Empty('list is empty')\\n return self._head._element # front aligned with head of list\",\n \"def identity(*args):\\n return args if len(args) > 1 else args[0]\",\n \"def hd(lst):\\n return lst[0] if lst else None\",\n \"def firstMove(board):\\r\\n x = board.size / 2\\r\\n return (x, x)\",\n \"def head(array) -> T:\\n return array[0]\",\n \"def get_value_tuple_outer_function(index, tuple_input):\\n return (tuple_input[0][index],\\n tuple_input[1][index],\\n tuple_input[2][index])\",\n \"def get_first_element(dataset):\\n return dataset.first()\",\n \"def easy_unpack_my(elements):\\n try:\\n res = tuple(elements[i] for i in [0, 2, -2])\\n except IndexError:\\n res = 0\\n return res\",\n \"def _first(self) -> Tuple[np.ndarray, np.ndarray, ModelGeneratorBase]:\\n pass\",\n \"def first(seq):\\n try: # try iterator interface\\n return seq.next()\\n except AttributeError:\\n pass\\n try: # seq is no iterator, try indexed lookup\\n return seq[0]\\n except IndexError:\\n pass\\n raise TypeError(\\n \\\"Argument to `first()` method needs to be iterator or sequence.\\\")\",\n \"def pair(first, second):\\n return [first, second]\",\n \"def fetchone(cursor):\\n\\t# type: (Cursor, ) -> Any\\n\\n\\trows = cursor.fetchall()\\n\\tif len(rows) == 0:\\n\\t\\traise NoResult(\\\"No result found\\\")\\n\\telif len(rows) == 1:\\n\\t\\treturn rows[0]\\n\\telse:\\n\\t\\traise InconsistentState(\\\"More than one result found\\\")\",\n \"def try_tuple(obj):\\n # type: (Union[T, Tuple[T]]) -> Tuple[T]\\n if isinstance(obj, tuple):\\n return obj\\n\\n return obj, # NOTE the comma, made into tuple\",\n \"def pick_first(self, x, y):\\n\\n return self.cast(x, x.type.combine(y.type))\",\n \"def head(self, xes):\\n return xes[0]\",\n \"def astuple(self):\\n try:\\n return tuple([x.astuple() for x in self])\\n except Exception:\\n pass\\n return tuple([x for x in self])\",\n \"def tup(item, ret_is_single=False):\\r\\n #return true for iterables, except for strings, which is what we want\\r\\n if hasattr(item, '__iter__'):\\r\\n return (item, False) if ret_is_single else item\\r\\n else:\\r\\n return ((item,), True) if ret_is_single else (item,)\",\n \"def _ensure_iterable(x):\\n if isinstance(x[0], Iterable):\\n if len(x) > 1:\\n raise TypeError(\\\"Either Iterable or variable argument list expected\\\")\\n return x[0]\\n else:\\n return x\",\n \"def visit_Tuple(self, node):\\n self.generic_visit(node)\\n if isinstance(node.ctx, ast.Load):\\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\\n return node\",\n \"def flatten(x): # przerobić na lambda?\\n if x==[]:\\n return None\\n else:\\n return x[0]\",\n \"def P_(x, y):\\r\\n return (x, y)\",\n \"def first(self):\\n if self.is_empty():\\n raise Empty('list is empty')\\n return self._head._next._element # front aligned with head of list\",\n \"def first(seq):\\n return next(iter(seq))\",\n \"def get_first_item(videos):\\n\\n return next(iter(videos or []), None)\",\n \"def aind(x):\\n\\treturn tuple(x.T)\",\n \"def identity_filter(element_tuple):\\r\\n\\treturn element_tuple\",\n \"def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\\n return tuple([l_[x] for x in i])\",\n \"def __getitem__(self, index):\\n if index == 0:\\n return self.x\\n elif index == 1:\\n return self.y\\n raise IndexError\",\n \"def take_second(info):\\n return info[1]\",\n \"def first(self):\\n return _(self._[0])\",\n \"def vec2tuple(x):\\n return (x.x, x.y, x.z)\",\n \"def easy_unpack(elements: Tuple[int]) -> Tuple[int]:\\n\\n return itemgetter(0, 2, -2)(elements)\",\n \"def first_last(item):\\n # first_element = item[0]\\n # last_element = item[len(item) - 1]\\n # if type(item) == list:\\n # return [first_element, last_element]\\n # if type(item) == tuple:\\n # return (first_element, last_element)\\n # if type(item) == str:\\n # return first_element + last_element\\n\\n # return item[:1] + item[-1:] \\n \\n return item[0: len(item): len(item)-1]\",\n \"def first(s):\\n assert is_link(s), 'fist only applies to a linked list.'\\n assert s != empty, 'empty linked list has no first element.'\\n return s[0]\",\n \"def first2(x, y):\\n y = np.asarray(y)\\n return y[np.argsort(x)][0]\",\n \"def x(self):\\n return self._arr[0]\",\n \"def parse(arg: Tuple[str, str, str, str, str]) -> Tuple[str, str, str]:\\n return (arg[2], arg[3], arg[4])\",\n \"def first(s):\\n assert is_link(s), 'first only applies to linked lists.'\\n assert s != empty, 'empty linked list has no first element.'\\n return s[0]\",\n \"def project(self, x):\\n return (x, x) # TODO Your code goes here.\",\n \"def _get(self, (y, x)):\\n return self[y][x]\",\n \"def tuple(self, arg: SeField[Any]) -> str:\\n if is_bare_tuple(arg.type):\\n return arg.varname\\n elif is_variable_tuple(arg.type):\\n earg = arg[0]\\n earg.name = \\\"v\\\"\\n return f\\\"tuple({self.render(earg)} for v in {arg.varname})\\\"\\n else:\\n rvalues = []\\n for i, _ in enumerate(type_args(arg.type)):\\n r = arg[i]\\n r.name = f\\\"{arg.varname}[{i}]\\\"\\n rvalues.append(self.render(r))\\n return f\\\"({', '.join(rvalues)},)\\\" # trailing , is required for single element tuples\",\n \"def first(s):\\n assert is_link(s), \\\"first only applies to linked lists.\\\"\\n assert s != empty, \\\"empty linked list has no first element.\\\"\\n return s[0]\",\n \"def popArg(args):\\n if len(args) == 0:\\n return (None, args)\\n nextArg = args[0]\\n args = args[1:]\\n return (nextArg, args)\",\n \"def unpack_all_equal_tuple(t):\\n if not isinstance(t, tuple):\\n return t\\n\\n assert all(x == t[0] for x in t)\\n return t[0]\",\n \"def __call__ (self, x):\\n try:\\n return self._lookup[x - 1]\\n except IndexError:\\n message = '{} not in permutation. Must be between 1 and {}.'\\n raise IndexError(message.format(x, len(self)))\",\n \"def first(collection):\\n return next(iter(collection))\",\n \"def first(collection):\\n return next(iter(collection))\",\n \"def get(s: Iterable[T]) -> T:\\n return next(iter(s))\",\n \"def retrieve_data_tuple(self):\\n return ((42,))\",\n \"def index_to_tuple(self, index):\\n if index < 0:\\n index = self.num_items + index\\n assert index >= 0 and index < self.num_items\\n\\n return self.indices[index]\",\n \"def _transform_inputs(x):\\n if not isinstance(x, (list, tuple)):\\n return x\\n assert len(x) > 0\\n return x[-1]\",\n \"def return_first_item(func):\\n\\n # Define the wrapper function.\\n def wrapper(self, *args, **kwargs):\\n\\n # Execute the decorated method with the provided arguments.\\n result = func(self, *args, **kwargs)\\n\\n # If the function returned a result and that result is a list then\\n # return the first item on that list.\\n if result and isinstance(result, list):\\n result = result[0]\\n\\n return result\\n\\n return wrapper\",\n \"def convert_to_tuple(v):\\n if not isinstance(v, tuple):\\n return tuple(v)\\n else:\\n return v\",\n \"def tuple(self) -> tuple:\\n return tuple(self)\",\n \"def one(self):\\n return next(iter(self), None)\",\n \"def firstElement(self):\\n return self.top()\",\n \"def first(self):\\n if self.is_empty():\\n raise Empty(\\\"List is empty!\\\")\\n return self._header._next._element\",\n \"def pop_first_arg(argv):\\n for arg in argv:\\n if not arg.startswith('-'):\\n argv.remove(arg)\\n return (arg, argv)\\n\\n return (None, argv)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7628563","0.7628563","0.70851874","0.6855407","0.6824074","0.6824074","0.68147033","0.6742139","0.6742139","0.6360675","0.63022053","0.63022053","0.62847096","0.62811184","0.62070453","0.6096625","0.60256463","0.6006833","0.59457403","0.5892296","0.5847164","0.58175504","0.5778301","0.5728105","0.57054967","0.569324","0.56838566","0.56570625","0.56556493","0.56485033","0.5637585","0.5636039","0.5619627","0.5612777","0.55828965","0.55768","0.5573989","0.5553067","0.5537594","0.55251414","0.55221456","0.5518657","0.54852265","0.5481925","0.5464605","0.5463466","0.54615915","0.5460893","0.54498744","0.54493195","0.54469216","0.5446488","0.54053175","0.54050773","0.5403222","0.54023606","0.53963584","0.53914964","0.53729063","0.53598475","0.53536355","0.5347252","0.5334872","0.5330845","0.5325149","0.5324077","0.5318189","0.5314992","0.53143454","0.5312873","0.5298198","0.52895695","0.52873296","0.52840364","0.52785397","0.52663386","0.52577466","0.5253106","0.5250338","0.52496433","0.52486944","0.52481043","0.52422357","0.5238056","0.5236511","0.5236008","0.5234331","0.5226396","0.5226396","0.52244323","0.5210247","0.5203924","0.52038413","0.51797706","0.5178927","0.5178878","0.51760066","0.51730216","0.5161182","0.5160664"],"string":"[\n \"0.7628563\",\n \"0.7628563\",\n \"0.70851874\",\n \"0.6855407\",\n \"0.6824074\",\n \"0.6824074\",\n \"0.68147033\",\n \"0.6742139\",\n \"0.6742139\",\n \"0.6360675\",\n \"0.63022053\",\n \"0.63022053\",\n \"0.62847096\",\n \"0.62811184\",\n \"0.62070453\",\n \"0.6096625\",\n \"0.60256463\",\n \"0.6006833\",\n \"0.59457403\",\n \"0.5892296\",\n \"0.5847164\",\n \"0.58175504\",\n \"0.5778301\",\n \"0.5728105\",\n \"0.57054967\",\n \"0.569324\",\n \"0.56838566\",\n \"0.56570625\",\n \"0.56556493\",\n \"0.56485033\",\n \"0.5637585\",\n \"0.5636039\",\n \"0.5619627\",\n \"0.5612777\",\n \"0.55828965\",\n \"0.55768\",\n \"0.5573989\",\n \"0.5553067\",\n \"0.5537594\",\n \"0.55251414\",\n \"0.55221456\",\n \"0.5518657\",\n \"0.54852265\",\n \"0.5481925\",\n \"0.5464605\",\n \"0.5463466\",\n \"0.54615915\",\n \"0.5460893\",\n \"0.54498744\",\n \"0.54493195\",\n \"0.54469216\",\n \"0.5446488\",\n \"0.54053175\",\n \"0.54050773\",\n \"0.5403222\",\n \"0.54023606\",\n \"0.53963584\",\n \"0.53914964\",\n \"0.53729063\",\n \"0.53598475\",\n \"0.53536355\",\n \"0.5347252\",\n \"0.5334872\",\n \"0.5330845\",\n \"0.5325149\",\n \"0.5324077\",\n \"0.5318189\",\n \"0.5314992\",\n \"0.53143454\",\n \"0.5312873\",\n \"0.5298198\",\n \"0.52895695\",\n \"0.52873296\",\n \"0.52840364\",\n \"0.52785397\",\n \"0.52663386\",\n \"0.52577466\",\n \"0.5253106\",\n \"0.5250338\",\n \"0.52496433\",\n \"0.52486944\",\n \"0.52481043\",\n \"0.52422357\",\n \"0.5238056\",\n \"0.5236511\",\n \"0.5236008\",\n \"0.5234331\",\n \"0.5226396\",\n \"0.5226396\",\n \"0.52244323\",\n \"0.5210247\",\n \"0.5203924\",\n \"0.52038413\",\n \"0.51797706\",\n \"0.5178927\",\n \"0.5178878\",\n \"0.51760066\",\n \"0.51730216\",\n \"0.5161182\",\n \"0.5160664\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6782777"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":4683,"cells":{"query":{"kind":"string","value":"Return the second element of a 2tuple. >>> y([1,2]) 2"},"document":{"kind":"string","value":"def y(a):\n return a[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 second(xs):\n if not xs:\n return None\n return xs[1]","def second(xs):\n if not xs:\n return None\n return xs[1]","def second(pair):\n\treturn pair[1]","def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x","def _get(self, (y, x)):\n return self[y][x]","def second(self) -> Element:\n return typing.cast(Element, self[1])","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def _item_or_tuple(self, seq):\n t = tuple(seq)\n if self._is_multi:\n return t\n else:\n return t[0]","def pick_second(self, x, y):\n\n return self.cast(y, x.type.combine(y.type))","def take_second(info):\n return info[1]","def get(self, x, y):\n i = self.map[y][x]\n return self.get(i)","def single_element_tuple():\n single = (1,)\n print(type(single)) # <type 'tuple'>","def P_(x, y):\r\n return (x, y)","def x(a):\n return a[0]","def tuple(x):\n pass","def secondMember(x, bsp, i, j=0):\n\ty = source(x)*bsp(x, i=i)\n\treturn y","def __getitem__(self, index):\n if index == 0:\n return self.x\n elif index == 1:\n return self.y\n raise IndexError","def give_me_a_tuple():\n my_tuple = ('p','e','r','m','i','t')\n return my_tuple\n pass","def p2(self):\n return tuple(self.rect[2:])","def single_structure_tuple(x,strucutre_id):\n if isinstance(x,tuple):\n assert len(x)==2;\"tuple input must have two elements\"\n return (x[0][:,strucutre_id,:],x[1][:,strucutre_id,:])\n else:\n return x[:,strucutre_id,:]","def tuple_map(x):\n return x * 2","def first2(x, y):\n y = np.asarray(y)\n return y[np.argsort(x)][0]","def x2(self):\n return self._x2","def pair(first, second):\n return [first, second]","def Rt(X):\n return X[:2,:2], X[:2, 2]","def y2(self):\n return self._y2","def __getitem__(self, index):\n x = self._input_data[index]\n if self._output_data is None:\n return x, x\n else:\n y = self._output_data[index]\n return x, y","def _tuple_from_one_or_two_ints(self, v):\n try:\n a, b = [int(x) for x in v]\n except TypeError:\n a, b = int(v), int(v)\n return (a,b)","def y(self):\n return self[1]","def y(self, x):\n return x","def get_value_tuple_outer_function(index, tuple_input):\n return (tuple_input[0][index],\n tuple_input[1][index],\n tuple_input[2][index])","def first(pair):\n\treturn pair[0]","def retrieve_data_tuple(self):\n return ((42,))","def last2(x, y):\n y = np.asarray(y)\n return y[np.argsort(x)][-1]","def y(self, value=None):\n if isinstance(value, (int, float)):\n self[1] = value\n else:\n if value is not None:\n raise TypeError(\"Cannot be set to {}\".format(type(value)))\n return self[1]","def try_tuple(obj):\n # type: (Union[T, Tuple[T]]) -> Tuple[T]\n if isinstance(obj, tuple):\n return obj\n\n return obj, # NOTE the comma, made into tuple","def get(self):\n return (self.x,self.y);","def easy_unpack_my(elements):\n try:\n res = tuple(elements[i] for i in [0, 2, -2])\n except IndexError:\n res = 0\n return res","def key(self, x):\r\n return tuple(x)","def getPixel (self, x, y):\r\n return self.image [y][x]","def get(self) -> tuple:","def popArg(args):\n if len(args) == 0:\n return (None, args)\n nextArg = args[0]\n args = args[1:]\n return (nextArg, args)","def __getitem__(self, index):\n if isinstance(index, (tuple, list)) and len(index) == 2:\n return self.cells[index[1]][index[0]]\n return self.cells[index]","def y(self):\n return self._arr[1]","def second(self) -> int:\r\n return self._second","def twoaxes(self, y):\r\n X = [y] if np.isscalar(y[0]) else y\r\n N2 = len(X[0]) // 2\r\n f = [1e6 * sum(x[0:N2]**2) + sum(x[N2:]**2) for x in X]\r\n return f if len(f) > 1 else f[0]","def retrieve_result(self, x, y):\n pass","def visit_Tuple(self, node):\n self.generic_visit(node)\n if isinstance(node.ctx, ast.Load):\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\n return node","def get_substr(self, y, x1, x2):\n return self.lines[y][x1 : x2]","def __tuple_to_scalar(tuple_value):\n if isinstance(tuple_value, tuple) and len(tuple_value)==1:\n return tuple_value[0]\n else:\n return tuple_value","def _value_at_axis(value, axis):\n if not isinstance(value, (list, tuple)):\n return value\n if len(value) == 1:\n return value[0]\n else:\n return value[axis]","def get_two_armies(self) -> tuple:\n\n if(len(self.armies) < 2):\n print(\"Could not choose an army. must have more than one army on the list\")\n raise Exception\n\n while(True):\n first = R.randint(0, len(self.armies)-1)\n second = R.randint(0, len(self.armies)-1)\n\n if(first != second):\n break\n\n return (self.armies[first], self.armies[second])","def relay_tuple_getitem(c, t, idx):\n assert idx.is_constant(int)\n return relay.expr.TupleGetItem(c.ref(t), idx.value)","def getAxisTuple(axis):","def extract(l):\n if l is None: return None\n if len(l) > 1:\n raise ValueError('More than 1 Value')\n try:\n return l[0]\n except IndexError:\n return None","def easy_unpack(elements: Tuple[int]) -> Tuple[int]:\n\n return itemgetter(0, 2, -2)(elements)","def _coord_to_tuple(self, coord):\n if isinstance(coord, str):\n return tuple(float(k) for k in coord[1:-1].split(', '))\n else:\n assert len(coord) == 2\n return coord","def pick_first(self, x, y):\n\n return self.cast(x, x.type.combine(y.type))","def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\n return tuple([l_[x] for x in i])","def calc_cell(self, x, y):\n if x < 1 or x > self.length or y < 1 or y > self.length:\n raise IndexError\n n = (x - 1) * self.length + y - 1\n return self.lst[n][1]","def getP2(self):\n return self.points[1]","def _list_to_tuple(v):\n if isinstance(v, list):\n return tuple(v)\n return v","def _get_pt_tuple(pnt1, pnt2):\n return tuple(map(_map_x_dim(tuple(pnt1)), pnt2))","def __getitem__(self, index):\n if isinstance(index, int):\n return list.__getitem__(self, index)\n if isinstance(index, tuple):\n return list.__getitem__(self, index[0])[index[1]]\n raise TypeError, \"Table indices must be int or tuple\"","def get_xy(self, x, y):\r\n\t\treturn self.grid[y, x]","def __getitem__(self, j):\n\t\treturn self._coords[j]","def getPair(self, args):\r\n return self.name, self.getValue(args)","def get(s: Iterable[T]) -> T:\n return next(iter(s))","def __getitem__(self, index: int) -> float:\n if index == 0:\n return self.x\n elif index == 1:\n return self.y\n else:\n raise IndexError","def second(a, b):","def second_path(self):\n\t\treturn self.args[2]","def __getitem__(self, key):\n return tuple(self._mapping[key])","def other_entry(self):\r\n l = self.other_entries()\r\n assert len(l) == 1\r\n return l[0][1]","def x(self):\n return self[0]","def foo_2(x, y):\n\tif x > y:\n\t\treturn x\n\treturn y","def _get_none(self, x, y):\n try:\n return self[x, y]\n except ArrayError:\n return None","def hinted_tuple_hook(obj):\n if '__tuple__' in obj:\n return tuple(obj['items'])\n return obj","def get_cell(self, x, y):\n if y < 0 or y >= len(self.g): return None\n if x < 0 or x >= len(self.g[y]): return None\n return self.g[y][x]","def get(self) -> Tuple[str, Tuple]:\n # TODO:\n pass","def studying_tuple():\r\n tuple_one = ()\r\n tuple_two = tuple()\r\n print(\"Creating empty tuple: \", tuple_one)\r\n print(\"Creating empty tuple: \", tuple_two)\r\n tuple_two = tuple(range(10))\r\n print(\"Tuple created using range: \", tuple_two)\r\n tuple_one = (11, 22, 33, 44, 55, 66, 77)\r\n print(\"Created tuple is: \", tuple_one)\r\n print(\"Accessing element using element index,tuple_one[2]: \", tuple_one[2])\r\n # returns tuple\r\n sliced_elements = tuple_one[2:]\r\n print(\"Sliced elements from the tuple are: \", sliced_elements)\r\n tuple_two = (1, 2, 3, 4, \"tom\", 3.14, 7+4j, True)\r\n print(\"New tuple is: \", tuple_two)\r\n three_tuple = 3, 4.6, \"dog\"\r\n print(\"New tuple is: \", three_tuple)\r\n string_tuple = (\"hello\",)\r\n # <class 'tuple'>\r\n print(\"Type of tuple is: \", type(string_tuple))\r\n data_tuple = (\"mouse\", [8, 4, 6], (1, 2, 3), 110, 134, 167, \"mouse\")\r\n print(\"Original data tuple is: \", data_tuple)\r\n # using nested indexing accessing tuple element\r\n print(\"Element data_tuple[0][3] we got is: \", data_tuple[0][3])\r\n print(\"Element data_tuple[1][1] we got is: \", data_tuple[1][1])\r\n # negative indexing\r\n print(\"Accessing last element from tuple using negative index: \",\r\n data_tuple[-1])\r\n print(\"Use index method to get index of element,data_tuple.index(\\\"mouse\\\"): \",\r\n data_tuple.index(\"mouse\"))\r\n print(\"Use count method to get number of count of element,data_tuple.count(\\\"mouse\\\"): \",\r\n data_tuple.count(\"mouse\"))\r\n # join two tuples\r\n tuple_one = (\"a\", \"b\", \"c\")\r\n tuple_two = (1, 2, 3)\r\n tuple_three = tuple_one+tuple_two\r\n print(\"tuple_one is: \", tuple_one)\r\n print(\"tuple_two is: \", tuple_two)\r\n print(\"Joined tuple of above two tuple is: \", tuple_three)\r\n print(\"Length of tuple_three is,len(tuple_three): \", len(tuple_three))\r\n print(\"We can delete entire tuple using \\\" del data_tuple \\\"\")\r\n return","def add_get_tuple(self, input_name, index, name=None):\n return self._build_op('get_tuple', [input_name], name=name, attr={'index': index})","def _transform_point(self, x, y):\n return (x, y)","def parse(arg: Tuple[str, str, str, str, str]) -> Tuple[str, str, str]:\n return (arg[2], arg[3], arg[4])","def y2(self, y2=None):\n\n if y2 is None:\n return self._y2\n else:\n if not isinstance(y2, int) and not isinstance(y2, float):\n raise TypeError(\"y2 must be numeric, not '%s'\" % y2)\n self._y2 = y2","def tuple_from_sequence(*args):\n return tuple(args)","def get_sample(x, y):\n return noise[x][y]","def room_xy(room, x, y, value=None):\n return room[x][y]","def __getitem__(self, index):\n return index, super().__getitem__(index)","def y2(self):\n return self._y + self._y2","def coord (i, j):\r\n return j, i","def get(self, x, y=None):\n edges = self.edges.setdefault(x,{})\n if y is None:\n return edges\n else:\n return edges.get(y, math.inf)","def case(self, x, y):\n return self.grille[x][y]","def x2(self, x2=None):\n\n if x2 is None:\n return self._x2\n else:\n if not isinstance(x2, int) and not isinstance(x2, float):\n raise TypeError(\"x2 must be numeric, not '%s'\" % x2)\n self._x2 = x2","def __getitem__(self, data):\n i,j = data\n return self._data[i][j]","def getTuple(self):\n return self.position.exportToTuple()","def arg2(self) -> int:\n if self.command_type() not in (\n CommandType.push,\n CommandType.pop,\n CommandType.function,\n CommandType.call,\n ):\n raise RuntimeError('Cannot call arg2 on command type: %s' % self.command_type().value)\n\n return int(self.current_command_split[2])","def getByLable(self, a, b):\n\t\treturn self.matrix[self.access[a]][self.access[b]]","def xy(self) -> Tuple[float, float]:\n return (self.x, self.y)"],"string":"[\n \"def second(xs):\\n if not xs:\\n return None\\n return xs[1]\",\n \"def second(xs):\\n if not xs:\\n return None\\n return xs[1]\",\n \"def second(pair):\\n\\treturn pair[1]\",\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def _unpack_tuple(x):\\n if len(x) == 1:\\n return x[0]\\n else:\\n return x\",\n \"def _get(self, (y, x)):\\n return self[y][x]\",\n \"def second(self) -> Element:\\n return typing.cast(Element, self[1])\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def _item_or_tuple(self, seq):\\n t = tuple(seq)\\n if self._is_multi:\\n return t\\n else:\\n return t[0]\",\n \"def pick_second(self, x, y):\\n\\n return self.cast(y, x.type.combine(y.type))\",\n \"def take_second(info):\\n return info[1]\",\n \"def get(self, x, y):\\n i = self.map[y][x]\\n return self.get(i)\",\n \"def single_element_tuple():\\n single = (1,)\\n print(type(single)) # <type 'tuple'>\",\n \"def P_(x, y):\\r\\n return (x, y)\",\n \"def x(a):\\n return a[0]\",\n \"def tuple(x):\\n pass\",\n \"def secondMember(x, bsp, i, j=0):\\n\\ty = source(x)*bsp(x, i=i)\\n\\treturn y\",\n \"def __getitem__(self, index):\\n if index == 0:\\n return self.x\\n elif index == 1:\\n return self.y\\n raise IndexError\",\n \"def give_me_a_tuple():\\n my_tuple = ('p','e','r','m','i','t')\\n return my_tuple\\n pass\",\n \"def p2(self):\\n return tuple(self.rect[2:])\",\n \"def single_structure_tuple(x,strucutre_id):\\n if isinstance(x,tuple):\\n assert len(x)==2;\\\"tuple input must have two elements\\\"\\n return (x[0][:,strucutre_id,:],x[1][:,strucutre_id,:])\\n else:\\n return x[:,strucutre_id,:]\",\n \"def tuple_map(x):\\n return x * 2\",\n \"def first2(x, y):\\n y = np.asarray(y)\\n return y[np.argsort(x)][0]\",\n \"def x2(self):\\n return self._x2\",\n \"def pair(first, second):\\n return [first, second]\",\n \"def Rt(X):\\n return X[:2,:2], X[:2, 2]\",\n \"def y2(self):\\n return self._y2\",\n \"def __getitem__(self, index):\\n x = self._input_data[index]\\n if self._output_data is None:\\n return x, x\\n else:\\n y = self._output_data[index]\\n return x, y\",\n \"def _tuple_from_one_or_two_ints(self, v):\\n try:\\n a, b = [int(x) for x in v]\\n except TypeError:\\n a, b = int(v), int(v)\\n return (a,b)\",\n \"def y(self):\\n return self[1]\",\n \"def y(self, x):\\n return x\",\n \"def get_value_tuple_outer_function(index, tuple_input):\\n return (tuple_input[0][index],\\n tuple_input[1][index],\\n tuple_input[2][index])\",\n \"def first(pair):\\n\\treturn pair[0]\",\n \"def retrieve_data_tuple(self):\\n return ((42,))\",\n \"def last2(x, y):\\n y = np.asarray(y)\\n return y[np.argsort(x)][-1]\",\n \"def y(self, value=None):\\n if isinstance(value, (int, float)):\\n self[1] = value\\n else:\\n if value is not None:\\n raise TypeError(\\\"Cannot be set to {}\\\".format(type(value)))\\n return self[1]\",\n \"def try_tuple(obj):\\n # type: (Union[T, Tuple[T]]) -> Tuple[T]\\n if isinstance(obj, tuple):\\n return obj\\n\\n return obj, # NOTE the comma, made into tuple\",\n \"def get(self):\\n return (self.x,self.y);\",\n \"def easy_unpack_my(elements):\\n try:\\n res = tuple(elements[i] for i in [0, 2, -2])\\n except IndexError:\\n res = 0\\n return res\",\n \"def key(self, x):\\r\\n return tuple(x)\",\n \"def getPixel (self, x, y):\\r\\n return self.image [y][x]\",\n \"def get(self) -> tuple:\",\n \"def popArg(args):\\n if len(args) == 0:\\n return (None, args)\\n nextArg = args[0]\\n args = args[1:]\\n return (nextArg, args)\",\n \"def __getitem__(self, index):\\n if isinstance(index, (tuple, list)) and len(index) == 2:\\n return self.cells[index[1]][index[0]]\\n return self.cells[index]\",\n \"def y(self):\\n return self._arr[1]\",\n \"def second(self) -> int:\\r\\n return self._second\",\n \"def twoaxes(self, y):\\r\\n X = [y] if np.isscalar(y[0]) else y\\r\\n N2 = len(X[0]) // 2\\r\\n f = [1e6 * sum(x[0:N2]**2) + sum(x[N2:]**2) for x in X]\\r\\n return f if len(f) > 1 else f[0]\",\n \"def retrieve_result(self, x, y):\\n pass\",\n \"def visit_Tuple(self, node):\\n self.generic_visit(node)\\n if isinstance(node.ctx, ast.Load):\\n return to_call(to_attribute(self.operator, '__tuple__'), node.elts)\\n return node\",\n \"def get_substr(self, y, x1, x2):\\n return self.lines[y][x1 : x2]\",\n \"def __tuple_to_scalar(tuple_value):\\n if isinstance(tuple_value, tuple) and len(tuple_value)==1:\\n return tuple_value[0]\\n else:\\n return tuple_value\",\n \"def _value_at_axis(value, axis):\\n if not isinstance(value, (list, tuple)):\\n return value\\n if len(value) == 1:\\n return value[0]\\n else:\\n return value[axis]\",\n \"def get_two_armies(self) -> tuple:\\n\\n if(len(self.armies) < 2):\\n print(\\\"Could not choose an army. must have more than one army on the list\\\")\\n raise Exception\\n\\n while(True):\\n first = R.randint(0, len(self.armies)-1)\\n second = R.randint(0, len(self.armies)-1)\\n\\n if(first != second):\\n break\\n\\n return (self.armies[first], self.armies[second])\",\n \"def relay_tuple_getitem(c, t, idx):\\n assert idx.is_constant(int)\\n return relay.expr.TupleGetItem(c.ref(t), idx.value)\",\n \"def getAxisTuple(axis):\",\n \"def extract(l):\\n if l is None: return None\\n if len(l) > 1:\\n raise ValueError('More than 1 Value')\\n try:\\n return l[0]\\n except IndexError:\\n return None\",\n \"def easy_unpack(elements: Tuple[int]) -> Tuple[int]:\\n\\n return itemgetter(0, 2, -2)(elements)\",\n \"def _coord_to_tuple(self, coord):\\n if isinstance(coord, str):\\n return tuple(float(k) for k in coord[1:-1].split(', '))\\n else:\\n assert len(coord) == 2\\n return coord\",\n \"def pick_first(self, x, y):\\n\\n return self.cast(x, x.type.combine(y.type))\",\n \"def index(l_: List[int], i: Tuple[int, ...]) -> Tuple[int, ...]:\\n return tuple([l_[x] for x in i])\",\n \"def calc_cell(self, x, y):\\n if x < 1 or x > self.length or y < 1 or y > self.length:\\n raise IndexError\\n n = (x - 1) * self.length + y - 1\\n return self.lst[n][1]\",\n \"def getP2(self):\\n return self.points[1]\",\n \"def _list_to_tuple(v):\\n if isinstance(v, list):\\n return tuple(v)\\n return v\",\n \"def _get_pt_tuple(pnt1, pnt2):\\n return tuple(map(_map_x_dim(tuple(pnt1)), pnt2))\",\n \"def __getitem__(self, index):\\n if isinstance(index, int):\\n return list.__getitem__(self, index)\\n if isinstance(index, tuple):\\n return list.__getitem__(self, index[0])[index[1]]\\n raise TypeError, \\\"Table indices must be int or tuple\\\"\",\n \"def get_xy(self, x, y):\\r\\n\\t\\treturn self.grid[y, x]\",\n \"def __getitem__(self, j):\\n\\t\\treturn self._coords[j]\",\n \"def getPair(self, args):\\r\\n return self.name, self.getValue(args)\",\n \"def get(s: Iterable[T]) -> T:\\n return next(iter(s))\",\n \"def __getitem__(self, index: int) -> float:\\n if index == 0:\\n return self.x\\n elif index == 1:\\n return self.y\\n else:\\n raise IndexError\",\n \"def second(a, b):\",\n \"def second_path(self):\\n\\t\\treturn self.args[2]\",\n \"def __getitem__(self, key):\\n return tuple(self._mapping[key])\",\n \"def other_entry(self):\\r\\n l = self.other_entries()\\r\\n assert len(l) == 1\\r\\n return l[0][1]\",\n \"def x(self):\\n return self[0]\",\n \"def foo_2(x, y):\\n\\tif x > y:\\n\\t\\treturn x\\n\\treturn y\",\n \"def _get_none(self, x, y):\\n try:\\n return self[x, y]\\n except ArrayError:\\n return None\",\n \"def hinted_tuple_hook(obj):\\n if '__tuple__' in obj:\\n return tuple(obj['items'])\\n return obj\",\n \"def get_cell(self, x, y):\\n if y < 0 or y >= len(self.g): return None\\n if x < 0 or x >= len(self.g[y]): return None\\n return self.g[y][x]\",\n \"def get(self) -> Tuple[str, Tuple]:\\n # TODO:\\n pass\",\n \"def studying_tuple():\\r\\n tuple_one = ()\\r\\n tuple_two = tuple()\\r\\n print(\\\"Creating empty tuple: \\\", tuple_one)\\r\\n print(\\\"Creating empty tuple: \\\", tuple_two)\\r\\n tuple_two = tuple(range(10))\\r\\n print(\\\"Tuple created using range: \\\", tuple_two)\\r\\n tuple_one = (11, 22, 33, 44, 55, 66, 77)\\r\\n print(\\\"Created tuple is: \\\", tuple_one)\\r\\n print(\\\"Accessing element using element index,tuple_one[2]: \\\", tuple_one[2])\\r\\n # returns tuple\\r\\n sliced_elements = tuple_one[2:]\\r\\n print(\\\"Sliced elements from the tuple are: \\\", sliced_elements)\\r\\n tuple_two = (1, 2, 3, 4, \\\"tom\\\", 3.14, 7+4j, True)\\r\\n print(\\\"New tuple is: \\\", tuple_two)\\r\\n three_tuple = 3, 4.6, \\\"dog\\\"\\r\\n print(\\\"New tuple is: \\\", three_tuple)\\r\\n string_tuple = (\\\"hello\\\",)\\r\\n # <class 'tuple'>\\r\\n print(\\\"Type of tuple is: \\\", type(string_tuple))\\r\\n data_tuple = (\\\"mouse\\\", [8, 4, 6], (1, 2, 3), 110, 134, 167, \\\"mouse\\\")\\r\\n print(\\\"Original data tuple is: \\\", data_tuple)\\r\\n # using nested indexing accessing tuple element\\r\\n print(\\\"Element data_tuple[0][3] we got is: \\\", data_tuple[0][3])\\r\\n print(\\\"Element data_tuple[1][1] we got is: \\\", data_tuple[1][1])\\r\\n # negative indexing\\r\\n print(\\\"Accessing last element from tuple using negative index: \\\",\\r\\n data_tuple[-1])\\r\\n print(\\\"Use index method to get index of element,data_tuple.index(\\\\\\\"mouse\\\\\\\"): \\\",\\r\\n data_tuple.index(\\\"mouse\\\"))\\r\\n print(\\\"Use count method to get number of count of element,data_tuple.count(\\\\\\\"mouse\\\\\\\"): \\\",\\r\\n data_tuple.count(\\\"mouse\\\"))\\r\\n # join two tuples\\r\\n tuple_one = (\\\"a\\\", \\\"b\\\", \\\"c\\\")\\r\\n tuple_two = (1, 2, 3)\\r\\n tuple_three = tuple_one+tuple_two\\r\\n print(\\\"tuple_one is: \\\", tuple_one)\\r\\n print(\\\"tuple_two is: \\\", tuple_two)\\r\\n print(\\\"Joined tuple of above two tuple is: \\\", tuple_three)\\r\\n print(\\\"Length of tuple_three is,len(tuple_three): \\\", len(tuple_three))\\r\\n print(\\\"We can delete entire tuple using \\\\\\\" del data_tuple \\\\\\\"\\\")\\r\\n return\",\n \"def add_get_tuple(self, input_name, index, name=None):\\n return self._build_op('get_tuple', [input_name], name=name, attr={'index': index})\",\n \"def _transform_point(self, x, y):\\n return (x, y)\",\n \"def parse(arg: Tuple[str, str, str, str, str]) -> Tuple[str, str, str]:\\n return (arg[2], arg[3], arg[4])\",\n \"def y2(self, y2=None):\\n\\n if y2 is None:\\n return self._y2\\n else:\\n if not isinstance(y2, int) and not isinstance(y2, float):\\n raise TypeError(\\\"y2 must be numeric, not '%s'\\\" % y2)\\n self._y2 = y2\",\n \"def tuple_from_sequence(*args):\\n return tuple(args)\",\n \"def get_sample(x, y):\\n return noise[x][y]\",\n \"def room_xy(room, x, y, value=None):\\n return room[x][y]\",\n \"def __getitem__(self, index):\\n return index, super().__getitem__(index)\",\n \"def y2(self):\\n return self._y + self._y2\",\n \"def coord (i, j):\\r\\n return j, i\",\n \"def get(self, x, y=None):\\n edges = self.edges.setdefault(x,{})\\n if y is None:\\n return edges\\n else:\\n return edges.get(y, math.inf)\",\n \"def case(self, x, y):\\n return self.grille[x][y]\",\n \"def x2(self, x2=None):\\n\\n if x2 is None:\\n return self._x2\\n else:\\n if not isinstance(x2, int) and not isinstance(x2, float):\\n raise TypeError(\\\"x2 must be numeric, not '%s'\\\" % x2)\\n self._x2 = x2\",\n \"def __getitem__(self, data):\\n i,j = data\\n return self._data[i][j]\",\n \"def getTuple(self):\\n return self.position.exportToTuple()\",\n \"def arg2(self) -> int:\\n if self.command_type() not in (\\n CommandType.push,\\n CommandType.pop,\\n CommandType.function,\\n CommandType.call,\\n ):\\n raise RuntimeError('Cannot call arg2 on command type: %s' % self.command_type().value)\\n\\n return int(self.current_command_split[2])\",\n \"def getByLable(self, a, b):\\n\\t\\treturn self.matrix[self.access[a]][self.access[b]]\",\n \"def xy(self) -> Tuple[float, float]:\\n return (self.x, self.y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68817425","0.68817425","0.6846556","0.67233163","0.67233163","0.6483205","0.62575674","0.6175765","0.60955626","0.60955626","0.60284346","0.59068465","0.57835966","0.57772356","0.57753384","0.5761288","0.570475","0.5695715","0.5689088","0.568156","0.56749314","0.5633721","0.5557852","0.5532588","0.5458863","0.5448019","0.5414589","0.5352322","0.5340934","0.53265786","0.53212565","0.53122145","0.53105396","0.5310256","0.53067535","0.52893555","0.52661616","0.52656734","0.52266437","0.52086437","0.51995254","0.51876235","0.5183957","0.5181199","0.5179069","0.51669544","0.51638526","0.5159012","0.5153408","0.5152366","0.51437855","0.5142978","0.5142834","0.5140046","0.51341766","0.5125775","0.51182455","0.511737","0.51096845","0.510093","0.5099762","0.5096865","0.50936615","0.50908786","0.50899583","0.5081977","0.508174","0.5071511","0.50659704","0.5064966","0.5061577","0.50594354","0.5059427","0.5052633","0.50510675","0.5037052","0.5035394","0.502749","0.50156295","0.5015554","0.5012766","0.5003684","0.4999431","0.49903595","0.49815106","0.4976937","0.4968053","0.49609736","0.49609426","0.49577174","0.49438936","0.49418417","0.49358925","0.49296108","0.49197114","0.49158052","0.49141008","0.49037278","0.48978984","0.48958135"],"string":"[\n \"0.68817425\",\n \"0.68817425\",\n \"0.6846556\",\n \"0.67233163\",\n \"0.67233163\",\n \"0.6483205\",\n \"0.62575674\",\n \"0.6175765\",\n \"0.60955626\",\n \"0.60955626\",\n \"0.60284346\",\n \"0.59068465\",\n \"0.57835966\",\n \"0.57772356\",\n \"0.57753384\",\n \"0.5761288\",\n \"0.570475\",\n \"0.5695715\",\n \"0.5689088\",\n \"0.568156\",\n \"0.56749314\",\n \"0.5633721\",\n \"0.5557852\",\n \"0.5532588\",\n \"0.5458863\",\n \"0.5448019\",\n \"0.5414589\",\n \"0.5352322\",\n \"0.5340934\",\n \"0.53265786\",\n \"0.53212565\",\n \"0.53122145\",\n \"0.53105396\",\n \"0.5310256\",\n \"0.53067535\",\n \"0.52893555\",\n \"0.52661616\",\n \"0.52656734\",\n \"0.52266437\",\n \"0.52086437\",\n \"0.51995254\",\n \"0.51876235\",\n \"0.5183957\",\n \"0.5181199\",\n \"0.5179069\",\n \"0.51669544\",\n \"0.51638526\",\n \"0.5159012\",\n \"0.5153408\",\n \"0.5152366\",\n \"0.51437855\",\n \"0.5142978\",\n \"0.5142834\",\n \"0.5140046\",\n \"0.51341766\",\n \"0.5125775\",\n \"0.51182455\",\n \"0.511737\",\n \"0.51096845\",\n \"0.510093\",\n \"0.5099762\",\n \"0.5096865\",\n \"0.50936615\",\n \"0.50908786\",\n \"0.50899583\",\n \"0.5081977\",\n \"0.508174\",\n \"0.5071511\",\n \"0.50659704\",\n \"0.5064966\",\n \"0.5061577\",\n \"0.50594354\",\n \"0.5059427\",\n \"0.5052633\",\n \"0.50510675\",\n \"0.5037052\",\n \"0.5035394\",\n \"0.502749\",\n \"0.50156295\",\n \"0.5015554\",\n \"0.5012766\",\n \"0.5003684\",\n \"0.4999431\",\n \"0.49903595\",\n \"0.49815106\",\n \"0.4976937\",\n \"0.4968053\",\n \"0.49609736\",\n \"0.49609426\",\n \"0.49577174\",\n \"0.49438936\",\n \"0.49418417\",\n \"0.49358925\",\n \"0.49296108\",\n \"0.49197114\",\n \"0.49158052\",\n \"0.49141008\",\n \"0.49037278\",\n \"0.48978984\",\n \"0.48958135\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6603884"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":4684,"cells":{"query":{"kind":"string","value":"Euclidean distance (in pixels). >>> distance( (1,1),(2,2) ) == math.sqrt(2) True"},"document":{"kind":"string","value":"def distance(a,b):\n return np.sqrt( (x(a)-x(b))**2 + (y(a)-y(b))**2 )"},"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 euclidean_distance(x, y):\n x1, y1 = x\n x2, y2 = y\n return sqrt((x1 - x2)**2 + (y1 - y2)**2)","def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> float:\n return np.sqrt(np.square(x1 - x2).sum())","def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.square(np.subtract(x1, x2))))","def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.power(x1 - x2, 2)))","def euclidean_distance(x1, y1, x2, y2):\n distance = math.sqrt(((x2 - x1) ** 2) + ((y2 - y1) ** 2))\n return distance","def euclideanDistance(x1,y1,x2,y2):\n distance = math.sqrt(abs(math.pow((x2-x1),2)) + abs(math.pow((y2-y1),2)))\n return distance","def euclidean_distance(a, b):\n return np.linalg.norm(a - b)","def euclidean_distance(self,):\n return sqrt(pow((self.pose1.x - self.pose2.x), 2) +\n pow((self.pose1.y - self.pose2.y), 2))","def euclidean_distance(point1, point2):\n return np.linalg.norm(np.array(point1) - np.array(point2))","def euclidean(p1, p2):\n return p1.distance(p2)","def euclidean_distance(a, b):\n return sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)","def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\n\n distance = np.linalg.norm(x - y)\n\n return distance","def EuclideanDistance( self, a, b ):\n return sqrt( self.EuclideanDistanceSq(a,b) )","def euclidean_distance(point_one, point_two):\n return np.linalg.norm(point_one-point_two)","def euclidean_distance(vec1, vec2):\n return numpy.linalg.norm(vec1 - vec2)","def euclidean_distance(x1, x2):\n return (x2[0] - x1[0])**2 + (x2[1] - x1[1])**2","def euclidian_distance(x1, y1, x2, y2):\n distance = sqrt(pow((x1-x2), 2)+(pow((y1-y2), 2)))\n return distance","def distance(a, b):\n return math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2)","def distance(self, c1, c2):\r\n x = (c2.x - c1.x) ** 2\r\n y = (c2.y - c1.y) ** 2\r\n d = int(round(math.sqrt(x + y)))\r\n return d","def euclidean_distance(x1, x2):\n\tdistance = 0\n\t# Squared distance between each coordinate\n\tfor i in range(len(x1)):\n\t\tdistance += pow((x1[i], x2[i]), 2)\n\treturn math.sqrt(distance)","def euclidean_distance(point1, point2):\n\n return math.sqrt(sum([(x - y) ** 2 for x, y in zip(point1, point2)]))","def euclidean_distance(p1, p2):\n dist = np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\n return dist","def distance(x,y):\n return np.sqrt( np.power(np.array(x) - np.array(y), 2).sum() )","def euclidean_distance(x, y):\n return sqrt(sum(pow(a - b, 2) for a, b in zip(x, y)))","def dist_euclidean(line, i1, i2):\n return sqrt((line[i1][0] - line[i2][0]) ** 2 + (line[i1][1] - line[i2][1]) ** 2)","def get_euclidean_distance(p1, p2):\n return np.sqrt(np.power((p2[0] - p1[0]), 2) + np.power((p2[1] - p1[1]), 2))","def euclidean(x,y): \n\treturn np.sqrt(np.sum((x-y)**2))","def compute_euclidean_dist(vec1, vec2):\r\n assert len(vec1) == len(vec2)\r\n vec1 = np.array(vec1)\r\n vec2 = np.array(vec2)\r\n return np.sqrt(np.sum(np.square(vec2 - vec1)))","def dist(x1, x2, distance):\n if distance == 'l2':\n return np.sqrt(np.sum(np.square(x1 - x2)))\n elif distance == 'squared_l2':\n return np.sum(np.square(x1 - x2))\n else:\n raise Exception(\"The distance '%s' is not supported.\" % distance)","def euclideanDistance(loc1, loc2):\n # BEGIN_YOUR_CODE (our solution is 1 line of code, but don't worry if you deviate from this)\n return math.sqrt((loc1[1]-loc2[1])**2+(loc1[0]-loc2[0])**2)\n # END_YOUR_CODE","def euclidean_distance(square_one: tuple, square_two: tuple):\n return math.sqrt((square_one[0] - square_two[0]) ** 2 + (square_one[1] - square_two[1]) ** 2)","def euclidean_distance(start, end):\n\n value = np.sqrt(np.sum(np.square(np.subtract(start, end)), axis=-1))\n return value","def euclideanDistance(loc1, loc2):\n return math.sqrt(sum([(a - b) ** 2 for a, b in zip(loc1, loc2)]))","def test_euclidean_distance_Ndimension(self):\n\n self.assertEqual(15, euclidean_distance([0, 0, 0], [10, 10, 5]))\n self.assertEqual(15, euclidean_distance([0, 0, 0], [-10, -10, -5]))\n\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [10, 10, 8, 5]))\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [-10, -10, -8, -5]))\n\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [5, 1, 1, 1, 6]))\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [-5, -1, -1, -1, -6]))","def _euclidian_distance(self, x1, x2):\n a= x1-x2\n a2 = a**2\n b = np.sum(a2, axis=1)\n c = np.sqrt(b)\n return c","def distance(x1, y1, x2, y2):\n return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5","def euclidean_distance(x, y):\n distance = 0\n for i, j in zip(x, y):\n distance += (i - j) ** 2\n return math.sqrt(distance)","def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\n distance_vector: np.ndarray = x - y\n distance = compute_norm(distance_vector)\n return distance","def euclidean(x, y):\n return np.sqrt(np.sum((x - y) ** 2))","def euclidean_dist(X, y):\n return np.sqrt(np.sum((X - y) ** 2, 1)) # broadcasted calculations","def euclidean_distance(pred, squared=False, eps=1e-12):\n pred_square = pred.pow(2).sum(dim=-1) # (N, )\n prod = torch.mm(pred, pred.t()) # (N, N)\n distance = (pred_square.unsqueeze(1) + pred_square.unsqueeze(0) -\n 2 * prod).clamp(min=eps) # (N, N)\n\n if not squared:\n distance = distance.sqrt()\n\n distance = distance.clone()\n distance[range(len(prod)), range(len(prod))] = 0\n return distance","def square_distance(a, b):\n return np.sum((a-b)**2)","def get_euclidean_distance(self, x_coord_1, x_coord_2, y_coord_1, y_coord_2):\r\n\r\n return math.sqrt(((x_coord_1 - x_coord_2) ** 2) + \\\r\n ((y_coord_1 - y_coord_2) ** 2))","def calculate_euclidean_distance(self, matrix, input, output_neuron):\n result = 0\n\n # Loop over all input data.\n diff = input - matrix[output_neuron]\n return np.sqrt(sum(diff*diff))","def euclidean(x, y):\n ed = np.sqrt(np.sum((x-y)**2))\n # print ed\n return ed","def euclidianDistance(row1, row2):\n\n dist = 0.0\n for i in range(len(row1)-1):\n dist += (row1[i] - row2[i])**2\n \n return sqrt(dist)","def euclidean_distance(self, point: List[int]) -> float:\n return sqrt(point[0] ** 2 + point[1] ** 2)","def euclidean_distance(a: Tuple[float, ...], b: Tuple[float, ...]) -> float:\n assert len(a) == len(b)\n return sqrt(sum(pow(x[0] - x[1], 2) for x in zip(a, b)))","def distance(coord1, coord2):\n \n return sqrt((coord1[0]-coord2[0])**2+\n (coord1[1]-coord2[1])**2+\n (coord1[2]-coord2[2])**2)","def get_distance(x1, y1, x2, y2):\n return math.sqrt((x1 - x2) ** 2 + (y1 * 2.38 - y2 * 2.38) ** 2)","def distance(p1, p2):\n\treturn sqrt((p1[1]-p2[1])**2 + (p1[0]-p2[0])**2)","def euclidean_distance(arr1,arr2):\n distance = np.sqrt(np.sum((arr1 - arr2)**2))\n return distance","def _dist(x, y):\n return np.sqrt(np.mean(np.square(x - y)))","def euclidean_distance(p1, p2):\n distance = 0\n for i in range(len(p1)-1):\n distance += (p1[i]-p2[i])**(2)\n return sqrt(distance)","def distance_checker(xyz1, xyz2):\n return math.sqrt((xyz1[0] - xyz2[0])**2 + (xyz1[1] - xyz2[1])**2 +\n (xyz1[2] - xyz2[2])**2)","def eucl_dist(x_0, y_0, x_1, y_1):\n return sqrt((x_1 - x_0)**2 + (y_1 - y_0)**2)","def euclidean_distance(self, other_point):\n return sqrt((self.x - other_point.x)**2 + (self.y - other_point.y)**2)","def distance(rgb1, rgb2):\n diffs = np.array(rgb1) - np.array(rgb2)\n return math.sqrt(np.sum(diffs**2))","def distance(rgb1: Tuple[int, int, int], rgb2: Tuple[int, int, int]) -> float:\n r = rgb1[0] - rgb2[0]\n g = rgb1[1] - rgb2[1]\n b = rgb1[2] - rgb2[2]\n return math.sqrt(r**2 + g**2 + b**2)","def euclidean_dist(ss1, ss2):\n lat1, lon1 = ss1.centroid\n lat2, lon2 = ss2.centroid\n\n return sqrt((lat1 - lat2)**2 + (lon1 - lon2)**2)","def _euclidean_distance(self, points_a, points_b):\n assert len(points_a.shape) == 2\n assert len(points_b.shape) == 2\n\n transpose_b = points_b.T\n dot = np.dot(points_a, transpose_b)\n\n a_mode_sq = np.tile(\n (points_a ** 2).sum(-1, keepdims=True), (1, points_b.shape[0]))\n b_mode_sq = np.tile((transpose_b ** 2).sum(0, keepdims=True),\n (points_a.shape[0], 1))\n\n distance = np.sqrt(a_mode_sq + b_mode_sq - 2 * dot)\n return distance","def distance(a: Point, b: Point) -> float:\n return math.sqrt(math.pow(b.x - a.x, 2) + math.pow(b.y - a.y, 2))","def euclidean_distance(list1, list2):\n # Make sure we're working with lists\n # Sorry, no other iterables are permitted\n assert isinstance(list1, list)\n assert isinstance(list2, list)\n\n dist = 0\n\n # 'zip' is a Python builtin, documented at\n # <http://www.python.org/doc/lib/built-in-funcs.html>\n for item1, item2 in zip(list1, list2):\n dist += (item2 - item1)**2\n return math.sqrt(dist)","def distance(self, x2, y2):\r\n return math.sqrt((x2 - self.x) ** 2 + (y2 - self.y) ** 2)","def distance(a,b): \r\n return math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)","def euclidian_distance(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 if np.mean(m) < 0:\n return 0, 0\n else:\n return np.mean(d), np.mean(m)","def distance(XYZ1=np.array([0, 0, 0], dtype='float32'),\n XYZ2=np.array([1, 1, 1], dtype='float32')):\n a=XYZ2-XYZ1\n b=a**2\n c=b.sum()\n return np.sqrt(c)","def distance(pt1, pt2):\n\tx1, y1 = pt1\n\tx2, y2 = pt2\n\tx = x2 - x1\n\ty = y2 - y1\n\ts = x**2 + y**2\n\treturn np.sqrt(s)","def _distance(coord1, coord2):\n xdist = coord1[0] - coord2[0]\n ydist = coord1[1] - coord2[1]\n return sqrt(xdist*xdist + ydist*ydist)","def euclidean_distance(self):\n return sqrt(pow((self.goal_pose.x - self.ground_truth_pose.x), 2) +\n pow((self.goal_pose.y - self.ground_truth_pose.y), 2))","def distance((x,y,z),(x0,y0,z0)):\n return sqrt((x-x0)**2+(y-y0)**2+(z-z0)**2)","def distance(p1, p2):\n return sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)","def _nn_euclidean_distance(x, y):\n distances = _pdist(x, y)\n return np.maximum(0.0, distances.min(axis=0))","def _nn_euclidean_distance(x, y):\n distances = _pdist(x, y)\n return np.maximum(0.0, distances.min(axis=0))","def distance(p1,p2):\n return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2)**0.5","def euclideanDistance(a, b):\n vec = [pow(a[i] - b[i], 2) for i in range(len(a)) if None not in [a[i],b[i]]]\n return (sum(vec) / len(vec)) if len(vec) > 0 else NaN","def _distance(point_a: tuple, point_b: tuple):\n # rgb values\n x1, y1, z1 = point_a\n x2, y2, z2 = point_b\n\n # distances\n dx = x1 - x2\n dy = y1 - y2\n dz = z1 - z2\n\n # final distance\n return sqrt(dx**2 + dy**2 + dz**2)","def euclidian_distance(x: np.arrays, y: np.arrays):\r\n diff = x - np.mean(y, axis=0)\r\n return np.sqrt(np.dot(diff.T, diff))","def distance (p1,p2):\n return np.sqrt(np.sum(np.power(p2-p1,2)))","def euclidean_distance(s1,s2): \n tmpsum = 0\n \n for index,value in enumerate(s1):\n tmpsum += (s1[index]-s2[index])**2\n \n return math.sqrt(tmpsum)","def euclidean_distance(vector1, vector2):\n e_dist = [(v1 - v2) ** 2 for v1, v2 in zip(vector1, vector2)]\n e_dist = math.sqrt(sum(e_dist))\n return e_dist","def distance(self, x: int, y: int) -> float:\n return math.sqrt((x - self.x) ** 2 + (y - self.y) ** 2)","def distance(x: int, y: int, a: int, b: int) -> float:\n return ((x - a) ** 2 + (y - b) ** 2) ** .5","def compute_distance(node1, node2):\n return np.linalg.norm(node1 - node2)","def distance(X, Y):\n\n return math.sqrt(np.sum((X-Y)**2))","def distance(self,coord_1, coord_2):\n return np.sqrt(np.sum((np.array(coord_1)-np.array(coord_2))**2))","def distance(x1, y1, x2, y2):\n dist = ((x1-x2)**2 + (y1-y2)**2)**0.5\n return dist","def DISTANCE(x,y,x2=0,y2=0):\n\treturn sqrt((x-x2)*(x-x2)+(y-y2)*(y-y2))","def euclidean_distance(data1, data2):\n #Convert data into numpy array\n array1 = np.array(data1)\n array2 = np.array(data2)\n \n #Create distance array\n dist_array = np.sqrt(np.sum((array2-array1)**2, axis=1))\n \n #Reshape array before return results\n return np.reshape(dist_array, [len(dist_array),1])","def __distance(start_x, start_y, end_x, end_y):\n distance = math.sqrt((start_x - end_x) ** 2 + (start_y - end_y) ** 2)\n return distance","def distance(p1, p2):\n return math.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2 + (p1[2]-p2[2])**2)","def distance(a1, a2):\n [x1, y1, z1] = a1\n [x2, y2, z2] = a2\n\n return math.sqrt((x1 - x2)**2 + (y1 - y2)**2 + (z1 - z2)**2)","def distance(a, b):\n return math.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)","def euclidean_distance(vector_x, vector_y):\n if len(vector_x) != len(vector_y):\n raise Exception('Vectors must be same dimensions')\n return math.sqrt(sum((vector_x[dim] - vector_y[dim]) ** 2 for dim in range(len(vector_x))))","def distance(a, b):\n return (np.sum((a - b)**2))**0.5","def __get_distance(point1: np.ndarray, point2: np.ndarray) -> float:\n return np.sqrt(np.sum(np.square(point1 - point2)))","def distance(p1, p2):\n\n return sqrt(((p2[0] - p1[0])**2) + ((p2[1] - p1[1])**2))","def distance(self,other):\n return math.sqrt((self.x - other.x)**2 +(self.y - other.y)**2)","def euclidian_distance(p):\n return(np.sqrt(sum([(p[0][i]-p[1][i])**2 for i, _ in enumerate(p)])))","def _euclideanDistance(A, B):\n if len(A) != len(B):\n raise ValueError(\"A and B must have the same number of dimensions\")\n sqr_dist = 0\n for i in range(len(A)):\n sqr_dist += (A[i] - B[i])**2\n return np.sqrt(sqr_dist)"],"string":"[\n \"def euclidean_distance(x, y):\\n x1, y1 = x\\n x2, y2 = y\\n return sqrt((x1 - x2)**2 + (y1 - y2)**2)\",\n \"def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> float:\\n return np.sqrt(np.square(x1 - x2).sum())\",\n \"def euclidean_distance(x1, x2):\\n return np.sqrt(np.sum(np.square(np.subtract(x1, x2))))\",\n \"def euclidean_distance(x1, x2):\\n return np.sqrt(np.sum(np.power(x1 - x2, 2)))\",\n \"def euclidean_distance(x1, y1, x2, y2):\\n distance = math.sqrt(((x2 - x1) ** 2) + ((y2 - y1) ** 2))\\n return distance\",\n \"def euclideanDistance(x1,y1,x2,y2):\\n distance = math.sqrt(abs(math.pow((x2-x1),2)) + abs(math.pow((y2-y1),2)))\\n return distance\",\n \"def euclidean_distance(a, b):\\n return np.linalg.norm(a - b)\",\n \"def euclidean_distance(self,):\\n return sqrt(pow((self.pose1.x - self.pose2.x), 2) +\\n pow((self.pose1.y - self.pose2.y), 2))\",\n \"def euclidean_distance(point1, point2):\\n return np.linalg.norm(np.array(point1) - np.array(point2))\",\n \"def euclidean(p1, p2):\\n return p1.distance(p2)\",\n \"def euclidean_distance(a, b):\\n return sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)\",\n \"def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\\n\\n distance = np.linalg.norm(x - y)\\n\\n return distance\",\n \"def EuclideanDistance( self, a, b ):\\n return sqrt( self.EuclideanDistanceSq(a,b) )\",\n \"def euclidean_distance(point_one, point_two):\\n return np.linalg.norm(point_one-point_two)\",\n \"def euclidean_distance(vec1, vec2):\\n return numpy.linalg.norm(vec1 - vec2)\",\n \"def euclidean_distance(x1, x2):\\n return (x2[0] - x1[0])**2 + (x2[1] - x1[1])**2\",\n \"def euclidian_distance(x1, y1, x2, y2):\\n distance = sqrt(pow((x1-x2), 2)+(pow((y1-y2), 2)))\\n return distance\",\n \"def distance(a, b):\\n return math.sqrt((a.x - b.x) ** 2 + (a.y - b.y) ** 2)\",\n \"def distance(self, c1, c2):\\r\\n x = (c2.x - c1.x) ** 2\\r\\n y = (c2.y - c1.y) ** 2\\r\\n d = int(round(math.sqrt(x + y)))\\r\\n return d\",\n \"def euclidean_distance(x1, x2):\\n\\tdistance = 0\\n\\t# Squared distance between each coordinate\\n\\tfor i in range(len(x1)):\\n\\t\\tdistance += pow((x1[i], x2[i]), 2)\\n\\treturn math.sqrt(distance)\",\n \"def euclidean_distance(point1, point2):\\n\\n return math.sqrt(sum([(x - y) ** 2 for x, y in zip(point1, point2)]))\",\n \"def euclidean_distance(p1, p2):\\n dist = np.sqrt((p1[0] - p2[0])**2 + (p1[1] - p2[1])**2)\\n return dist\",\n \"def distance(x,y):\\n return np.sqrt( np.power(np.array(x) - np.array(y), 2).sum() )\",\n \"def euclidean_distance(x, y):\\n return sqrt(sum(pow(a - b, 2) for a, b in zip(x, y)))\",\n \"def dist_euclidean(line, i1, i2):\\n return sqrt((line[i1][0] - line[i2][0]) ** 2 + (line[i1][1] - line[i2][1]) ** 2)\",\n \"def get_euclidean_distance(p1, p2):\\n return np.sqrt(np.power((p2[0] - p1[0]), 2) + np.power((p2[1] - p1[1]), 2))\",\n \"def euclidean(x,y): \\n\\treturn np.sqrt(np.sum((x-y)**2))\",\n \"def compute_euclidean_dist(vec1, vec2):\\r\\n assert len(vec1) == len(vec2)\\r\\n vec1 = np.array(vec1)\\r\\n vec2 = np.array(vec2)\\r\\n return np.sqrt(np.sum(np.square(vec2 - vec1)))\",\n \"def dist(x1, x2, distance):\\n if distance == 'l2':\\n return np.sqrt(np.sum(np.square(x1 - x2)))\\n elif distance == 'squared_l2':\\n return np.sum(np.square(x1 - x2))\\n else:\\n raise Exception(\\\"The distance '%s' is not supported.\\\" % distance)\",\n \"def euclideanDistance(loc1, loc2):\\n # BEGIN_YOUR_CODE (our solution is 1 line of code, but don't worry if you deviate from this)\\n return math.sqrt((loc1[1]-loc2[1])**2+(loc1[0]-loc2[0])**2)\\n # END_YOUR_CODE\",\n \"def euclidean_distance(square_one: tuple, square_two: tuple):\\n return math.sqrt((square_one[0] - square_two[0]) ** 2 + (square_one[1] - square_two[1]) ** 2)\",\n \"def euclidean_distance(start, end):\\n\\n value = np.sqrt(np.sum(np.square(np.subtract(start, end)), axis=-1))\\n return value\",\n \"def euclideanDistance(loc1, loc2):\\n return math.sqrt(sum([(a - b) ** 2 for a, b in zip(loc1, loc2)]))\",\n \"def test_euclidean_distance_Ndimension(self):\\n\\n self.assertEqual(15, euclidean_distance([0, 0, 0], [10, 10, 5]))\\n self.assertEqual(15, euclidean_distance([0, 0, 0], [-10, -10, -5]))\\n\\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [10, 10, 8, 5]))\\n self.assertEqual(17, euclidean_distance([0, 0, 0, 0], [-10, -10, -8, -5]))\\n\\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [5, 1, 1, 1, 6]))\\n self.assertEqual(8, euclidean_distance([0, 0, 0, 0, 0], [-5, -1, -1, -1, -6]))\",\n \"def _euclidian_distance(self, x1, x2):\\n a= x1-x2\\n a2 = a**2\\n b = np.sum(a2, axis=1)\\n c = np.sqrt(b)\\n return c\",\n \"def distance(x1, y1, x2, y2):\\n return ((x2 - x1) ** 2 + (y2 - y1) ** 2) ** 0.5\",\n \"def euclidean_distance(x, y):\\n distance = 0\\n for i, j in zip(x, y):\\n distance += (i - j) ** 2\\n return math.sqrt(distance)\",\n \"def euclidean_distance(x: np.ndarray, y: np.ndarray) -> float:\\n distance_vector: np.ndarray = x - y\\n distance = compute_norm(distance_vector)\\n return distance\",\n \"def euclidean(x, y):\\n return np.sqrt(np.sum((x - y) ** 2))\",\n \"def euclidean_dist(X, y):\\n return np.sqrt(np.sum((X - y) ** 2, 1)) # broadcasted calculations\",\n \"def euclidean_distance(pred, squared=False, eps=1e-12):\\n pred_square = pred.pow(2).sum(dim=-1) # (N, )\\n prod = torch.mm(pred, pred.t()) # (N, N)\\n distance = (pred_square.unsqueeze(1) + pred_square.unsqueeze(0) -\\n 2 * prod).clamp(min=eps) # (N, N)\\n\\n if not squared:\\n distance = distance.sqrt()\\n\\n distance = distance.clone()\\n distance[range(len(prod)), range(len(prod))] = 0\\n return distance\",\n \"def square_distance(a, b):\\n return np.sum((a-b)**2)\",\n \"def get_euclidean_distance(self, x_coord_1, x_coord_2, y_coord_1, y_coord_2):\\r\\n\\r\\n return math.sqrt(((x_coord_1 - x_coord_2) ** 2) + \\\\\\r\\n ((y_coord_1 - y_coord_2) ** 2))\",\n \"def calculate_euclidean_distance(self, matrix, input, output_neuron):\\n result = 0\\n\\n # Loop over all input data.\\n diff = input - matrix[output_neuron]\\n return np.sqrt(sum(diff*diff))\",\n \"def euclidean(x, y):\\n ed = np.sqrt(np.sum((x-y)**2))\\n # print ed\\n return ed\",\n \"def euclidianDistance(row1, row2):\\n\\n dist = 0.0\\n for i in range(len(row1)-1):\\n dist += (row1[i] - row2[i])**2\\n \\n return sqrt(dist)\",\n \"def euclidean_distance(self, point: List[int]) -> float:\\n return sqrt(point[0] ** 2 + point[1] ** 2)\",\n \"def euclidean_distance(a: Tuple[float, ...], b: Tuple[float, ...]) -> float:\\n assert len(a) == len(b)\\n return sqrt(sum(pow(x[0] - x[1], 2) for x in zip(a, b)))\",\n \"def distance(coord1, coord2):\\n \\n return sqrt((coord1[0]-coord2[0])**2+\\n (coord1[1]-coord2[1])**2+\\n (coord1[2]-coord2[2])**2)\",\n \"def get_distance(x1, y1, x2, y2):\\n return math.sqrt((x1 - x2) ** 2 + (y1 * 2.38 - y2 * 2.38) ** 2)\",\n \"def distance(p1, p2):\\n\\treturn sqrt((p1[1]-p2[1])**2 + (p1[0]-p2[0])**2)\",\n \"def euclidean_distance(arr1,arr2):\\n distance = np.sqrt(np.sum((arr1 - arr2)**2))\\n return distance\",\n \"def _dist(x, y):\\n return np.sqrt(np.mean(np.square(x - y)))\",\n \"def euclidean_distance(p1, p2):\\n distance = 0\\n for i in range(len(p1)-1):\\n distance += (p1[i]-p2[i])**(2)\\n return sqrt(distance)\",\n \"def distance_checker(xyz1, xyz2):\\n return math.sqrt((xyz1[0] - xyz2[0])**2 + (xyz1[1] - xyz2[1])**2 +\\n (xyz1[2] - xyz2[2])**2)\",\n \"def eucl_dist(x_0, y_0, x_1, y_1):\\n return sqrt((x_1 - x_0)**2 + (y_1 - y_0)**2)\",\n \"def euclidean_distance(self, other_point):\\n return sqrt((self.x - other_point.x)**2 + (self.y - other_point.y)**2)\",\n \"def distance(rgb1, rgb2):\\n diffs = np.array(rgb1) - np.array(rgb2)\\n return math.sqrt(np.sum(diffs**2))\",\n \"def distance(rgb1: Tuple[int, int, int], rgb2: Tuple[int, int, int]) -> float:\\n r = rgb1[0] - rgb2[0]\\n g = rgb1[1] - rgb2[1]\\n b = rgb1[2] - rgb2[2]\\n return math.sqrt(r**2 + g**2 + b**2)\",\n \"def euclidean_dist(ss1, ss2):\\n lat1, lon1 = ss1.centroid\\n lat2, lon2 = ss2.centroid\\n\\n return sqrt((lat1 - lat2)**2 + (lon1 - lon2)**2)\",\n \"def _euclidean_distance(self, points_a, points_b):\\n assert len(points_a.shape) == 2\\n assert len(points_b.shape) == 2\\n\\n transpose_b = points_b.T\\n dot = np.dot(points_a, transpose_b)\\n\\n a_mode_sq = np.tile(\\n (points_a ** 2).sum(-1, keepdims=True), (1, points_b.shape[0]))\\n b_mode_sq = np.tile((transpose_b ** 2).sum(0, keepdims=True),\\n (points_a.shape[0], 1))\\n\\n distance = np.sqrt(a_mode_sq + b_mode_sq - 2 * dot)\\n return distance\",\n \"def distance(a: Point, b: Point) -> float:\\n return math.sqrt(math.pow(b.x - a.x, 2) + math.pow(b.y - a.y, 2))\",\n \"def euclidean_distance(list1, list2):\\n # Make sure we're working with lists\\n # Sorry, no other iterables are permitted\\n assert isinstance(list1, list)\\n assert isinstance(list2, list)\\n\\n dist = 0\\n\\n # 'zip' is a Python builtin, documented at\\n # <http://www.python.org/doc/lib/built-in-funcs.html>\\n for item1, item2 in zip(list1, list2):\\n dist += (item2 - item1)**2\\n return math.sqrt(dist)\",\n \"def distance(self, x2, y2):\\r\\n return math.sqrt((x2 - self.x) ** 2 + (y2 - self.y) ** 2)\",\n \"def distance(a,b): \\r\\n return math.sqrt((a[0] - b[0])**2 + (a[1] - b[1])**2)\",\n \"def euclidian_distance(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 if np.mean(m) < 0:\\n return 0, 0\\n else:\\n return np.mean(d), np.mean(m)\",\n \"def distance(XYZ1=np.array([0, 0, 0], dtype='float32'),\\n XYZ2=np.array([1, 1, 1], dtype='float32')):\\n a=XYZ2-XYZ1\\n b=a**2\\n c=b.sum()\\n return np.sqrt(c)\",\n \"def distance(pt1, pt2):\\n\\tx1, y1 = pt1\\n\\tx2, y2 = pt2\\n\\tx = x2 - x1\\n\\ty = y2 - y1\\n\\ts = x**2 + y**2\\n\\treturn np.sqrt(s)\",\n \"def _distance(coord1, coord2):\\n xdist = coord1[0] - coord2[0]\\n ydist = coord1[1] - coord2[1]\\n return sqrt(xdist*xdist + ydist*ydist)\",\n \"def euclidean_distance(self):\\n return sqrt(pow((self.goal_pose.x - self.ground_truth_pose.x), 2) +\\n pow((self.goal_pose.y - self.ground_truth_pose.y), 2))\",\n \"def distance((x,y,z),(x0,y0,z0)):\\n return sqrt((x-x0)**2+(y-y0)**2+(z-z0)**2)\",\n \"def distance(p1, p2):\\n return sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2)\",\n \"def _nn_euclidean_distance(x, y):\\n distances = _pdist(x, y)\\n return np.maximum(0.0, distances.min(axis=0))\",\n \"def _nn_euclidean_distance(x, y):\\n distances = _pdist(x, y)\\n return np.maximum(0.0, distances.min(axis=0))\",\n \"def distance(p1,p2):\\n return ((p1.x - p2.x)**2 + (p1.y - p2.y)**2)**0.5\",\n \"def euclideanDistance(a, b):\\n vec = [pow(a[i] - b[i], 2) for i in range(len(a)) if None not in [a[i],b[i]]]\\n return (sum(vec) / len(vec)) if len(vec) > 0 else NaN\",\n \"def _distance(point_a: tuple, point_b: tuple):\\n # rgb values\\n x1, y1, z1 = point_a\\n x2, y2, z2 = point_b\\n\\n # distances\\n dx = x1 - x2\\n dy = y1 - y2\\n dz = z1 - z2\\n\\n # final distance\\n return sqrt(dx**2 + dy**2 + dz**2)\",\n \"def euclidian_distance(x: np.arrays, y: np.arrays):\\r\\n diff = x - np.mean(y, axis=0)\\r\\n return np.sqrt(np.dot(diff.T, diff))\",\n \"def distance (p1,p2):\\n return np.sqrt(np.sum(np.power(p2-p1,2)))\",\n \"def euclidean_distance(s1,s2): \\n tmpsum = 0\\n \\n for index,value in enumerate(s1):\\n tmpsum += (s1[index]-s2[index])**2\\n \\n return math.sqrt(tmpsum)\",\n \"def euclidean_distance(vector1, vector2):\\n e_dist = [(v1 - v2) ** 2 for v1, v2 in zip(vector1, vector2)]\\n e_dist = math.sqrt(sum(e_dist))\\n return e_dist\",\n \"def distance(self, x: int, y: int) -> float:\\n return math.sqrt((x - self.x) ** 2 + (y - self.y) ** 2)\",\n \"def distance(x: int, y: int, a: int, b: int) -> float:\\n return ((x - a) ** 2 + (y - b) ** 2) ** .5\",\n \"def compute_distance(node1, node2):\\n return np.linalg.norm(node1 - node2)\",\n \"def distance(X, Y):\\n\\n return math.sqrt(np.sum((X-Y)**2))\",\n \"def distance(self,coord_1, coord_2):\\n return np.sqrt(np.sum((np.array(coord_1)-np.array(coord_2))**2))\",\n \"def distance(x1, y1, x2, y2):\\n dist = ((x1-x2)**2 + (y1-y2)**2)**0.5\\n return dist\",\n \"def DISTANCE(x,y,x2=0,y2=0):\\n\\treturn sqrt((x-x2)*(x-x2)+(y-y2)*(y-y2))\",\n \"def euclidean_distance(data1, data2):\\n #Convert data into numpy array\\n array1 = np.array(data1)\\n array2 = np.array(data2)\\n \\n #Create distance array\\n dist_array = np.sqrt(np.sum((array2-array1)**2, axis=1))\\n \\n #Reshape array before return results\\n return np.reshape(dist_array, [len(dist_array),1])\",\n \"def __distance(start_x, start_y, end_x, end_y):\\n distance = math.sqrt((start_x - end_x) ** 2 + (start_y - end_y) ** 2)\\n return distance\",\n \"def distance(p1, p2):\\n return math.sqrt((p1[0]-p2[0])**2 + (p1[1]-p2[1])**2 + (p1[2]-p2[2])**2)\",\n \"def distance(a1, a2):\\n [x1, y1, z1] = a1\\n [x2, y2, z2] = a2\\n\\n return math.sqrt((x1 - x2)**2 + (y1 - y2)**2 + (z1 - z2)**2)\",\n \"def distance(a, b):\\n return math.sqrt((a[0]-b[0])**2 + (a[1]-b[1])**2)\",\n \"def euclidean_distance(vector_x, vector_y):\\n if len(vector_x) != len(vector_y):\\n raise Exception('Vectors must be same dimensions')\\n return math.sqrt(sum((vector_x[dim] - vector_y[dim]) ** 2 for dim in range(len(vector_x))))\",\n \"def distance(a, b):\\n return (np.sum((a - b)**2))**0.5\",\n \"def __get_distance(point1: np.ndarray, point2: np.ndarray) -> float:\\n return np.sqrt(np.sum(np.square(point1 - point2)))\",\n \"def distance(p1, p2):\\n\\n return sqrt(((p2[0] - p1[0])**2) + ((p2[1] - p1[1])**2))\",\n \"def distance(self,other):\\n return math.sqrt((self.x - other.x)**2 +(self.y - other.y)**2)\",\n \"def euclidian_distance(p):\\n return(np.sqrt(sum([(p[0][i]-p[1][i])**2 for i, _ in enumerate(p)])))\",\n \"def _euclideanDistance(A, B):\\n if len(A) != len(B):\\n raise ValueError(\\\"A and B must have the same number of dimensions\\\")\\n sqr_dist = 0\\n for i in range(len(A)):\\n sqr_dist += (A[i] - B[i])**2\\n return np.sqrt(sqr_dist)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73406076","0.7278529","0.7243951","0.7199394","0.71825296","0.7171505","0.71208745","0.7119449","0.7014406","0.6984854","0.69682425","0.69490933","0.694145","0.69366956","0.69258237","0.6921075","0.6908294","0.68853986","0.68835557","0.68756497","0.6842449","0.6838545","0.6832998","0.6801225","0.6791674","0.67895573","0.67835706","0.67649865","0.6743123","0.6741848","0.67387414","0.6738642","0.6733243","0.67299277","0.6721555","0.67181486","0.67178416","0.67136693","0.67016584","0.6700291","0.6696712","0.66937786","0.66827035","0.667923","0.66445756","0.6639561","0.66245794","0.66222435","0.65561783","0.6541609","0.6535502","0.6516878","0.6515811","0.6512158","0.65072954","0.6502012","0.6498344","0.6494","0.6483181","0.64785075","0.6472602","0.64686227","0.6467172","0.64664537","0.64651066","0.6455185","0.6449013","0.6447178","0.6447032","0.64436805","0.6438832","0.64329696","0.64317924","0.64317924","0.6427897","0.6417032","0.6416153","0.63987476","0.639824","0.6386913","0.6362629","0.63590485","0.63553023","0.6351933","0.63499737","0.6346408","0.6345497","0.63420963","0.63406944","0.63380146","0.63368154","0.6333405","0.633035","0.6329958","0.6324855","0.63229364","0.6313493","0.63020915","0.6301684","0.62949383"],"string":"[\n \"0.73406076\",\n \"0.7278529\",\n \"0.7243951\",\n \"0.7199394\",\n \"0.71825296\",\n \"0.7171505\",\n \"0.71208745\",\n \"0.7119449\",\n \"0.7014406\",\n \"0.6984854\",\n \"0.69682425\",\n \"0.69490933\",\n \"0.694145\",\n \"0.69366956\",\n \"0.69258237\",\n \"0.6921075\",\n \"0.6908294\",\n \"0.68853986\",\n \"0.68835557\",\n \"0.68756497\",\n \"0.6842449\",\n \"0.6838545\",\n \"0.6832998\",\n \"0.6801225\",\n \"0.6791674\",\n \"0.67895573\",\n \"0.67835706\",\n \"0.67649865\",\n \"0.6743123\",\n \"0.6741848\",\n \"0.67387414\",\n \"0.6738642\",\n \"0.6733243\",\n \"0.67299277\",\n \"0.6721555\",\n \"0.67181486\",\n \"0.67178416\",\n \"0.67136693\",\n \"0.67016584\",\n \"0.6700291\",\n \"0.6696712\",\n \"0.66937786\",\n \"0.66827035\",\n \"0.667923\",\n \"0.66445756\",\n \"0.6639561\",\n \"0.66245794\",\n \"0.66222435\",\n \"0.65561783\",\n \"0.6541609\",\n \"0.6535502\",\n \"0.6516878\",\n \"0.6515811\",\n \"0.6512158\",\n \"0.65072954\",\n \"0.6502012\",\n \"0.6498344\",\n \"0.6494\",\n \"0.6483181\",\n \"0.64785075\",\n \"0.6472602\",\n \"0.64686227\",\n \"0.6467172\",\n \"0.64664537\",\n \"0.64651066\",\n \"0.6455185\",\n \"0.6449013\",\n \"0.6447178\",\n \"0.6447032\",\n \"0.64436805\",\n \"0.6438832\",\n \"0.64329696\",\n \"0.64317924\",\n \"0.64317924\",\n \"0.6427897\",\n \"0.6417032\",\n \"0.6416153\",\n \"0.63987476\",\n \"0.639824\",\n \"0.6386913\",\n \"0.6362629\",\n \"0.63590485\",\n \"0.63553023\",\n \"0.6351933\",\n \"0.63499737\",\n \"0.6346408\",\n \"0.6345497\",\n \"0.63420963\",\n \"0.63406944\",\n \"0.63380146\",\n \"0.63368154\",\n \"0.6333405\",\n \"0.633035\",\n \"0.6329958\",\n \"0.6324855\",\n \"0.63229364\",\n \"0.6313493\",\n \"0.63020915\",\n \"0.6301684\",\n \"0.62949383\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6488884"},"document_rank":{"kind":"string","value":"58"}}},{"rowIdx":4685,"cells":{"query":{"kind":"string","value":"Creates initial design of n_samples drawn from a latin hypercube."},"document":{"kind":"string","value":"def sample_latin_hypercube(low, high, n_samples, rng=None):\n if rng is None:\n rng = np.random.RandomState(np.random.randint(0, 10000))\n\n n_dims = low.shape[0]\n\n samples = []\n for i in range(n_dims):\n if isinstance(low[i], numbers.Integral):\n sample = random.sample(range(low[i], high[i]), n_samples)\n elif isinstance(low[i], numbers.Real):\n lower_bound = low[i]\n upper_bound = high[i]\n sample = lower_bound + rng.uniform(0, 1, n_samples) * (upper_bound - lower_bound)\n else:\n raise ValueError('Latin hypercube sampling can only draw from types int and real,'\n ' got {}!'.format(type(low[i])))\n\n samples.append(sample)\n\n samples = np.array(samples, dtype=object)\n\n for i in range(n_dims):\n rng.shuffle(samples[i, :])\n\n return samples.T"},"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 gen_hypercube(samples, N):\n\n np.random.seed(4654562)\n hypercube = lhs(N, samples=samples)\n\n return hypercube","def latin_hypercube(n_pts, dim):\n X = np.zeros((n_pts, dim))\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\n for i in range(dim): # Shuffle the center locataions for each dimension.\n X[:, i] = centers[np.random.permutation(n_pts)]\n\n # Add some perturbations within each box\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\n X += pert\n return X","def generate_latin_hypercube(samples, param_dict, class_root, seed=10):\n # Set random seed\n random.seed(seed)\n\n # Create dictionary to hold sampled parameter values\n sample_points = {}\n for key in param_dict.keys():\n sample_points[key] = np.zeros(samples)\n Ndim = len(param_dict.keys())\n pnames = [key for key in param_dict.keys()]\n\n # List of indices for each dimension\n l = [range(samples) for j in range(Ndim)]\n\n # Generate samples until there are no indices left to choose\n for i in range(samples):\n\n # Randomly choose index and then remove the number that was chosen\n # (Latin hypercubes require at most one item per row and column)\n for j, p in enumerate(pnames):\n pmin, pmax = param_dict[p]\n idx = random.choice(l[j])\n\n # Get value at this sample point (add 0.5 to idx get bin centroid)\n sample_points[p][i] = pmin + (pmax - pmin) \\\n * (idx + 0.5) / float(samples)\n l[j].remove(idx) # Remove choice from list (sampling w/o replacement)\n\n return sample_points","def generate_samples(self, n_samples):","def generate_samples(self, n_samples):","def latin_hypercube(n_pts, mins, maxs):\n #return a latin_hypercube\n design = lhs(np.size(maxs), samples=n_pts)\n for i in range(2):\n design[:, i] = design[:, i] * (maxs[i]-mins[i]) + mins[i]\n return design","def latin_hypercube_sampler(n=1, indim=1, bounds=None, rng=None):\r\n rng = ensure_rng(rng)\r\n if bounds is None:\r\n bounds = np.zeros((indim, 2))\r\n bounds[:,1] = 1. \r\n # Divide each dimension into `n` equal intervals\r\n hypercubes = np.linspace(bounds[:,0], bounds[:,1], n+1)\r\n \r\n l = hypercubes[:-1,:].reshape(-1,)\r\n u = hypercubes[1:,:].reshape(-1,)\r\n _x = rng.uniform(l,u, (1, indim*n)).reshape(n, indim)\r\n x = _x\r\n for j in range(indim):\r\n x[:,j] = _x[rng.permutation(n), j]\r\n return x","def lhs_start(hyperbounds, n_samples, rng=None):\n low_bounds = []\n high_bounds = []\n for bound in hyperbounds:\n low_bounds.append(bound[0])\n high_bounds.append(bound[1])\n\n low_bounds = np.array(low_bounds, dtype=object)\n high_bounds = np.array(high_bounds, dtype=object)\n\n samples = sample_latin_hypercube(low_bounds, high_bounds, n_samples, rng=rng)\n samples = samples.tolist()\n return samples","def n_cube(self, dim_n):\n if dim_n == 1:\n return Polyhedron(vertices = [[1],[-1]])\n\n pre_cube = polytopes.n_cube(dim_n-1)\n vertices = [];\n for pre_v in pre_cube.vertex_generator():\n vertices.append( [ 1] + [v for v in pre_v] );\n vertices.append( [-1] + [v for v in pre_v] );\n return Polyhedron(vertices = vertices)","def __init__(self, n, prey_cnt=0, predator_cnt=0):\n # print n, prey_cnt, predator_cnt\n self.grid_size = n\n self.grid = []\n for i in range(n):\n row = [0]*n # row is a list of n zeros\n self.grid.append(row)\n self.init_animals(prey_cnt, predator_cnt)","def construct_initial_sample(n):\n sample_normal = np.random.normal(size=(n, 3))\n sample_radius = np.linalg.norm(sample_normal, axis=1, keepdims=True)\n sample_cartesian = sample_normal / sample_radius\n sample_polar = cartesian_to_polar(sample_cartesian)\n return np.reshape(sample_polar[:, 1:3], (-1))","def create_samples(self):\n self._samples = self.load_samples()\n self.modify_samples()","def sample(self, n_samples):\n with torch.no_grad():\n z = torch.randn((n_samples, self.z_dim))\n samples = self.decoder(z)\n im_size = int(np.sqrt(self.input_dim))\n samples = samples.view(-1, 1, im_size, im_size)\n\n return samples","def test_2_1_3D_cube_init(self):\n check = [(0, 0, 0), (1, 1, 1), (1, 0, 0), (1, 1, 0), (1, 0, 1),\n (0, 1, 0), (0, 1, 1), (0, 0, 1), (0.5, 0.5, 0.5)]\n\n nn_checks = {\n (1, 1, 1): [(1, 1, 0), (0, 1, 1), (1, 0, 0), (0, 0, 1), (1, 0, 1),\n (0.5, 0.5, 0.5), (0, 1, 0)],\n (1, 0, 1): [(1, 0, 0), (0, 0, 1), (0, 0, 0), (0.5, 0.5, 0.5),\n (1, 1, 1)],\n (0.5, 0.5, 0.5): [(1, 1, 0), (0, 1, 1), (0, 1, 0), (1, 0, 0),\n (0, 0, 1), (1, 0, 1), (0, 0, 0), (1, 1, 1)]}\n\n init_triangulation(3, 0, check, nn_checks)","def initialiser(N, dimensions = 2):\r\n \r\n #shape for correct dimensions\r\n shape = tuple([N]) * dimensions\r\n \r\n #randomise spins\r\n lattice = np.random.choice([1,-1], size = shape)\r\n \r\n return lattice","def create_grids_structure(self):\n for indices, hypercube in np.ndenumerate(self.hypercubes):\n self.hypercubes[indices] = Hypercube(coords=indices)","def __init__(self, n_features):\n self.W = np.random.randn(n_features)\n self.b = np.random.randn()","def _make_random_matrix(self, n_components, n_features):","def createCube():\n subjects, detections, antigen = getAxes()\n cube = np.full([len(subjects), len(detections), len(antigen)], np.nan)\n\n IGG = importIGG()\n glycan, dfGlycan = importGlycan()\n glyCube = np.full([len(subjects), len(glycan)], np.nan)\n\n for k, curAnti in enumerate(antigen):\n lumx = importLuminex(curAnti)\n\n for _, row in lumx.iterrows():\n i = subjects.index(row[\"subject\"])\n j = detections.index(row[\"variable\"])\n cube[i, j, k] = row[\"value\"]\n\n for _, row in dfGlycan.iterrows():\n i = subjects.index(row[\"subject\"])\n j = glycan.index(row[\"variable\"])\n glyCube[i, j] = row[\"value\"]\n\n # Add IgG data on the end as another detection\n for _, row in IGG.iterrows():\n i = subjects.index(row[\"subject\"])\n k = antigen.index(row[\"variable\"])\n cube[i, -1, k] = row[\"value\"]\n\n # Clip to 0 as there are a few strongly negative outliers\n cube = np.clip(cube, 1.0, None)\n glyCube = np.clip(glyCube, 0.1, None)\n\n cube = np.log10(cube)\n glyCube = np.log10(glyCube)\n\n # Mean center each measurement\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n cube -= np.nanmean(cube, axis=0)\n glyCube -= np.nanmean(glyCube, axis=0)\n\n # Check that there are no slices with completely missing data\n assert ~np.any(np.all(np.isnan(cube), axis=(0, 1)))\n assert ~np.any(np.all(np.isnan(cube), axis=(0, 2)))\n assert ~np.any(np.all(np.isnan(cube), axis=(1, 2)))\n\n glyCube *= np.sqrt(np.nanvar(cube) / np.nanvar(glyCube))\n return cube, glyCube","def generate_samples(self, n_samples=100):\n \t\t\n\t\t#how many times should ancestral sampling be run\n\t\t#n_samples\n prior_samples=[]\n for i in range(0,n_samples):\n prior_sample = self.prior.get_samples(\n n_latent_nodes=self.n_latent_nodes,\n n_gibbs_sampling_steps=100, \n sampling_mode=\"gibbs_ancestral\")\n prior_sample = torch.cat(prior_sample)\n prior_samples.append(prior_sample)\n prior_samples=torch.stack(prior_samples)\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\n output_activations = self.decoder.decode(prior_samples)\n output_activations = output_activations+self._train_bias\n output_distribution = Bernoulli(logit=output_activations)\n output=torch.sigmoid(output_distribution.logits)\n # output_activations[0] = output_activations[0] + self.train_bias\n # output_dist = FactorialBernoulliUtil(output_activations)\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\n # print(\"--- \",\"end VAE::generate_samples()\")\n return output","def __create_sample_data__(npts = 20):\n\t#data function\n\tdef wavy(x, y):\n\t\treturn np.sin(0.2*np.pi*x)*np.cos(0.4*np.pi*y)\n\t\n\t#make grid\n\txs = np.linspace(0, 2*20, 2*npts + 1)\n\tys = np.linspace(0, 20, npts + 1)\n\t(xgrid, ygrid) = np.meshgrid(xs, ys)\n\tzgrid = wavy(xgrid, ygrid)\n\t\n\treturn (xgrid, ygrid, zgrid)","def generate_in(self, n_samples, start_sample=0):\n pass","def random_LatinHypercube(seeds, n, skip_seeds=0):\n Nseeds = len(seeds)\n rndms = np.zeros((Nseeds, n), dtype='float64')\n # define arrays here and re-use them later\n samples = np.zeros(n, dtype='float64')\n perms = np.zeros(n, dtype='float64')\n\n for seed_i in range(skip_seeds, Nseeds, 1):\n # set the seed\n np.random.seed(seeds[seed_i])\n\n # draw the random numbers and re-generate permutations array\n for i in range(n):\n samples[i] = np.random.uniform(0., 1.)\n perms[i] = i + 1\n\n # in-place shuffle permutations\n np.random.shuffle(perms)\n\n for j in range(n):\n rndms[seed_i, j] = (perms[j] - samples[j]) / float(n)\n\n return rndms","def sample(h, seed_ix, n):\n x = np.zeros((vocab_size, 1))\n x[seed_ix] = 1\n ixes = []\n for t in xrange(n):\n h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)\n y = np.dot(Why, h) + by\n p = np.exp(y) / np.sum(np.exp(y))\n ix = np.argmax(p)#np.random.choice(range(vocab_size), p=p.ravel())\n x = np.zeros((vocab_size, 1))\n x[ix] = 1\n ixes.append(ix)\n return ixes","def __init__(self, nonogram_size):\n # create random id\n self.nonogram_id = uuid.uuid4()\n self.row_numbers = [(2), (2), (2)]\n self.column_numbers = [(1, 1), (3), (1)]\n self.nonogram_size = nonogram_size\n self.grid = Nonogram.create_rand_grid(nonogram_size)\n #TODO\n self.fitness = 999","def sample(h, seed_ix, n):\n x = np.zeros((vocab_size,1))\n x[seed_ix] = 1\n ixes = []\n for t in xrange(n):\n h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)\n y = np.dot(Why, h) + by\n p = np.exp(y) / np.sum(np.exp(y))\n ix = np.random.choice(range(vocab_size), p=p.ravel())\n x = np.zeros((vocab_size,1))\n x[ix] = 1\n ixes.append(ix)\n return ixes","def _reconstruct(self, num_samples=None):","def setUp(self):\n self.cube = _create_2d_cube()","def get_raw_seedling(datasource, n):\n matrix = MatrixDict()\n \n for lang, sent in SEEDLING[datasource].sents():\n features = sent2ngrams(sent, n=n)\n matrix.setdefault(lang, Counter()).update(features)\n \n return matrix","def initial_samples(lb, ub, method, numSamp):\r\n if not len(lb) == len(ub):\r\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\r\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\r\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\r\n for case in Switch(method):\r\n if case('random'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = lb + (ub - lb) * rand(len(lb))\r\n\r\n break\r\n if case('rand-wor'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\r\n\r\n break\r\n if case('nolh'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = range(q)\r\n if r != 0:\r\n remove = range(dim - r, dim)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-rp'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = random.sample(range(q), q)\r\n if r != 0:\r\n remove = random.sample(range(q - 1), r)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-cdr'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf, remove = get_cdr_permutations(len(ub))\r\n if remove != []:\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('lhc'):\r\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\r\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\r\n ])\r\n break\r\n if case():\r\n print 'Somehow you evaded my assert statement - good job!',\r\n print ' However, you still need to use a valid method string.'\r\n\r\n return s","def test_1_1_2D_cube_init(self): # TODO: REMOVE FUNC AFTER SPLIT\n check = [(0, 0), (1, 1), (1, 0), (0, 1), (0.5, 0.5)]\n\n nn_checks = {(0.5, 0.5): [(0, 1), (1, 0), (0, 0), (1, 1)],\n (0, 1): [(0, 0), (1, 1), (0.5, 0.5)]}\n\n init_triangulation(2, 0, check, nn_checks)","def generate(net, z, maxlen=50, im=None, init=None, use_end=True):\n caption = lm_tools.sample(net, z['word_dict'], z['index_dict'], num=maxlen, Im=im, initial=init, use_end=use_end)\n print ' '.join(caption)","def create_samples(self):\n for s_id in range(len(self.data[\"sample\"])):\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\n self.data[\"label\"][s_id]))","def __init__(self, n):\n self.rows = [0 for _ in range(n)]\n self.columns = [0 for _ in range(n)]\n # First diagonal x+y, second y-x\n self.diagonal = [0, 0]\n self.score = {1: 1, 2: n+1}\n self.win = {1: n, 2: (n+1)*n}\n self.size = n","def set_up_threshold_cube():\n test_data = 50*np.arange(16).reshape(4, 4)\n grid_x = DimCoord(np.arange(4), standard_name=\"projection_x_coordinate\",\n units=\"km\")\n grid_y = DimCoord(np.arange(4), standard_name=\"projection_y_coordinate\",\n units=\"km\")\n test_cube = iris.cube.Cube(test_data, long_name=\"surface_altitude\",\n units=\"m\",\n dim_coords_and_dims=[(grid_y, 0), (grid_x, 1)])\n return test_cube","def initialize(self, n_samples=1, kl_scaling=None, *args, **kwargs):\n if kl_scaling is None:\n kl_scaling = {}\n if n_samples <= 0:\n raise ValueError(\n \"n_samples should be greater than zero: {}\".format(n_samples))\n\n self.n_samples = n_samples\n self.kl_scaling = kl_scaling\n return super(elbo_optimizer, self).initialize(*args, **kwargs)","def __init__(self, num_prealloc_samples=0):\n self.num_prealloc_samples_ = num_prealloc_samples\n if self.num_prealloc_samples_ > 0:\n self._preallocate_samples()","def initialize(self, numSamples, sampleMethod):\n initSamples = initial_samples(self.lb, self.ub, sampleMethod, numSamples)\n if sum(self.xID) != 0:\n xUB = [\n self.ub[np.where(self.xID == 1)[0][0]]] * len(self.xID)\n xSamples = initial_samples([0] * len(self.xID), xUB, 'rand-wor', numSamples)\n for var in range(len(self.varType)):\n if 'i' in self.varType[var] or 'd' in self.varType[var]:\n initSamples[:, var] = np.rint(initSamples[:, var])\n\n if sum(self.xID) != 0:\n initSamples = initSamples * (self.cID + self.iID + self.dID) + xSamples * self.xID\n return initSamples","def setUp(self):\n # generate lattice\n self.lattice = lattice.Lattice()\n self.lattice.addAtom(\"He\", [0,0,0], 0)\n self.lattice.addAtom(\"He\", [2,0,0], 0)\n self.lattice.addAtom(\"He\", [0,2,0], 0)\n self.lattice.addAtom(\"He\", [0,0,2], 0)\n self.lattice.addAtom(\"He\", [9,9,9], 0)\n self.lattice.addAtom(\"He\", [2,2,0], 0)\n self.lattice.addAtom(\"He\", [2,0,2], 0)\n self.lattice.addAtom(\"He\", [0,2,2], 0)\n self.lattice.addAtom(\"He\", [2,2,2], 0)\n \n # indexes of cluster atoms\n self.bigClusterIndexes = [0,1,2,3,5,6,7,8]\n self.smallClusterIndexes = [4]\n \n # filter\n self.filter = clusterFilter.ClusterFilter(\"Cluster\")","def make_sample_workspace(self, **kwargs):\n function = \"name=LinearBackground, A0=0.1;name=Lorentzian, PeakCentre=0.5, Amplitude=2, FWHM=0.1\"\n sample = self.make_dummy_workspace(function, output_name='sample', **kwargs)\n return sample","def _sample_schechter(x0, alpha, x_min, size=100, max_iter=1000):\n out = []\n n = 0\n num_iter = 0\n while (n<size) & (num_iter<max_iter):\n x = np.random.gamma(scale=x0, shape=alpha+2, size=size)\n x = x[x>x_min]\n u = np.random.uniform(size=x.size)\n x = x[u<x_min/x]\n out.append(x)\n n+=x.size\n num_iter += 1\n\n if num_iter >= max_iter:\n msg = (\"The maximum number of iterations reached.\",\n \"Random variates may not be representitive.\",\n \"Try increasing `max_iter`.\")\n print(msg)\n\n return np.concatenate(out)[:size]","def generate_samples(self, nsamples):\n assert self.trained, \"Model must first be fitted to some data.\"\n logger.debug(f'Generate synthetic dataset of size {nsamples}')\n synthetic_data, _ = self.gm.sample(nsamples)\n return synthetic_data","def __init__(self, initial, size, horizontalChunks, verticalChunks, goal = \"\"):\n\t\tself.initial = initial\n\t\tself.size = size\n\t\tself.horChunks = horizontalChunks\n\t\tself.verChunks = verticalChunks\n\n\t\t# Goal holds the solution, once we find it.\n\t\tself.goal = goal\n\n\t\t# For a puzzle of size n, initializes blank n x n 2d array\n\t\tself.graph = [[0 for x in range(self.size)] for x in range(self.size)] \n\t\tfor i in range (0,self.size):\n\t\t\tfor j in range (0,self.size):\n\t\t\t\tself.graph[i][j] = initial[i*self.size + j] \n\t\tself.initial = \"\"","def generate_cube():\n \n num_voxels = 31\n\n data_x = []\n data_y = []\n data_z = []\n data_intensity = []\n\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\n\n for x in range(num_voxels):\n for y in range(num_voxels):\n for z in range(num_voxels):\n\n if 5 < x < 10 and 5 < y < 10:\n data_x.append(x)\n data_y.append(y)\n data_z.append(z)\n data_intensity.append(200.0)\n\n volume[x,y,z] = 200.0\n\n\n return data_x, data_y, data_z, data_intensity, volume","def sample(h, seed_ix, n):\n x = np.zeros((vocab_size, 1))\n x[seed_ix] = 1\n ixes = []\n H = hidden_size\n\n for t in range(n):\n W = np.dot(Wxh, x) # hidden state, shape(300, 1)\n U = np.dot(Whh, h) # hidden state, shape(300, 100)\n z = sigmoid(W[:H] + U[:H] + bh[:H])\n r = sigmoid(W[H:H * 2] + U[H:H * 2] + bh[H:H * 2])\n r_ = r * U[H * 2:]\n h_ = np.tanh(W[H * 2:] + r_)\n h = z * h + (1 - z) * h_\n\n y = np.dot(Why, h) + by\n p = np.exp(y) / np.sum(np.exp(y))\n ix = np.random.choice(range(vocab_size), p=p.ravel())\n x = np.zeros((vocab_size, 1))\n x[ix] = 1\n ixes.append(ix)\n return ixes","def randomgrid(self, n):\n lam = np.random.random((n, 3))\n return self.normalize(lam)","def _make_test_cube(long_name):\n cs = GeogCS(EARTH_RADIUS)\n data = np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0]])\n cube = Cube(data, long_name=long_name)\n x_coord = DimCoord(\n np.linspace(-45.0, 45.0, 3), \"latitude\", units=\"degrees\", coord_system=cs\n )\n y_coord = DimCoord(\n np.linspace(120, 180, 3), \"longitude\", units=\"degrees\", coord_system=cs\n )\n cube.add_dim_coord(x_coord, 0)\n cube.add_dim_coord(y_coord, 1)\n return cube","def __init__(self, n_rows: int = 2, n_columns: int = 2):\n self.set_uniform(n_rows, n_columns)","def __init__(self, n):\n self.rows = [0] * n\n self.cols = [0] * n\n self.diagonal1 = 0\n self.diagonal2 = 0\n self.n = n","def initial_setup(N,L,dim):\r\n r = np.zeros((3,N))\r\n #n = int(np.rint((N/3)**(1/dim)))\r\n #nz = int(n+n)\r\n x1 = np.linspace(0+L/n/2,L-L/n/2,n)\r\n d0 = 1.0e-10 #intial distance between hydrogen and oxygen\r\n x2 = x1 + d0\r\n x3 = x1 - d0/np.sqrt(2)\r\n z = np.zeros(3*n)\r\n xy = np.zeros(3*n)\r\n for ii in range(n):\r\n z[ii*3+1] = x1[ii]\r\n z[ii*3+2] = x2[ii]\r\n z[ii*3] = x3[ii]\r\n xy[ii*3] = x1[ii]\r\n xy[ii*3+1] = x1[ii]\r\n xy[ii*3+2] = x1[ii]\r\n rx, ry,rz = np.meshgrid(x1,x1,z)\r\n r[0,:] = np.reshape(rx,N)\r\n r[1,:] = np.reshape(ry,N)\r\n r[2,:] = np.reshape(rz,N)\r\n r[0,0::3] += d0/np.sqrt(2) #add additional offset to one Oxygen\r\n return r","def generate_test():\n o = []\n pos = [384, 288]\n note_group_size = GAN_PARAMS[\"note_group_size\"]\n generate_set(begin=3 * note_group_size, start_pos=pos,\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)","def random_sample(self, n):\n indices = random.sample(xrange(np.shape(self.data)[0]), n)\n table = DataTable(self.data[indices], self.dims, self.legends, self.tags.copy())\n return table","def __init__(self, i, h, o):\n self.Wz = np.random.randn(i + h, h)\n self.bz = np.zeros((1, h))\n self.Wr = np.random.randn(i + h, h)\n self.br = np.zeros((1, h))\n self.Wh = np.random.randn(i + h, h)\n self.bh = np.zeros((1, h))\n self.Wy = np.random.randn(h, o)\n self.by = np.zeros((1, o))","def __init__(self, n):\n self.n = n\n self.rows = [0 for _ in range(n)]\n self.colums = [0 for _ in range(n)]\n self.diag = [0,0]","def gen_data(npt, typ, ndim, rstate=None):\n mid = .5 # i'm placing in unit cube\n if typ == 'ball':\n r0 = 0.5\n pts = genball(npt, ndim, rstate=rstate) * r0 + mid\n volume = (np.pi**(ndim / 2) / scipy.special.gamma(ndim / 2 + 1) *\n r0**ndim)\n elif typ == 'pin':\n w = 0.01\n a = 1\n pts = np.zeros((npt, ndim))\n pts[:, 1:] = genball(npt, ndim - 1, rstate=rstate) * w + mid\n pts[:, 0] = (rstate.uniform(size=npt) - 0.5) * a + mid\n volume = (np.pi**((ndim - 1) / 2) /\n scipy.special.gamma((ndim - 1) / 2 + 1) * w**(ndim - 1) * a)\n elif typ == 'torus':\n w = 0.01\n r0 = 0.45\n pts = np.zeros((npt, ndim))\n pts[:, :2] = genshell(r0 - w / 2, r0 + w / 2, npt, 2,\n rstate=rstate) + mid\n pts[:,\n 2:] = (rstate.uniform(size=(npt, ndim - 2)) * 2 - 1) * w / 2 + mid\n volume = w**(ndim - 2) * np.pi * ((r0 + w / 2)**2 - (r0 - w / 2)**2)\n elif typ == 'cylinder':\n w = 0.01\n r0 = 0.45\n a = 1\n pts = np.zeros((npt, ndim))\n pts[:, :2] = genshell(r0 - w / 2, r0 + w / 2, npt, 2,\n rstate=rstate) + mid\n pts[:, 2:] = rstate.uniform(size=(npt, ndim - 2)) * a\n volume = np.pi * ((r0 + w / 2)**2 - (r0 - w / 2)**2)\n elif typ == 'shell':\n r1 = 0.45\n r2 = 0.46\n pts = genshell(r1, r2, npt, ndim, rstate=rstate) + mid\n volume = (np.pi**(ndim / 2) / scipy.special.gamma(ndim / 2 + 1) *\n (r2**ndim - r1**ndim))\n else:\n raise RuntimeError('unknown', typ)\n return pts, volume","def __init__(self, n):\n self.row = [0] * n\n self.col = [0] * n\n self.diagonal = 0\n self.antidiagonal = 0\n self.winning = False","def __init__(self, number_of_cheeses, number_of_stools):\n self.model = TOAHModel(number_of_stools)\n self.model.fill_first_stool(number_of_cheeses)","def pseudo_sample(self):\n return (torch.zeros(1, 1, 28, 28), None)","def generate_initial_sample(pmin, pmax, ntemps, nwalkers):\n\n npl = pmin.npl\n nobs = pmin.nobs\n\n assert npl == pmax.npl, 'Number of planets must agree in prior bounds'\n assert nobs == pmax.nobs, 'Number of observations must agree in prior bounds'\n\n N = pmin.shape[-1]\n\n samps=params.Parameters(arr=np.zeros((ntemps, nwalkers, N)), nobs=nobs, npl=npl)\n\n V=samps.V\n tau=samps.tau\n sigma=samps.sigma\n sigma0=samps.sigma0\n for i in range(nobs):\n V[:,:,i] = nr.uniform(low=pmin.V[i], high=pmax.V[i], size=(ntemps, nwalkers))\n tau[:,:,i] = draw_logarithmic(low=pmin.tau[i], high=pmax.tau[i], size=(ntemps,nwalkers))\n sigma[:,:,i] = draw_logarithmic(low=pmin.sigma[i], high=pmax.sigma[i], size=(ntemps,nwalkers))\n sigma0[:,:,i] = draw_logarithmic(low=pmin.sigma[i], high=pmax.sigma[i], size=(ntemps, nwalkers))\n samps.V=np.squeeze(V)\n samps.tau = np.squeeze(tau)\n samps.sigma = np.squeeze(sigma)\n samps.sigma0 = np.squeeze(sigma0)\n\n if npl >= 1:\n samps.K = np.squeeze(draw_logarithmic(low=pmin.K[0], high=pmax.K[0], size=(ntemps, nwalkers, npl)))\n\n # Make sure that periods are increasing\n samps.n = np.squeeze(np.sort(draw_logarithmic(low=pmin.n, high=pmax.n, size=(ntemps,nwalkers,npl)))[:,:,::-1])\n\n samps.e = np.squeeze(nr.uniform(low=0.0, high=1.0, size=(ntemps, nwalkers,npl)))\n samps.chi = np.squeeze(nr.uniform(low=0.0, high=1.0, size=(ntemps, nwalkers,npl)))\n samps.omega = np.squeeze(nr.uniform(low=0.0, high=2.0*np.pi, size=(ntemps, nwalkers,npl)))\n\n return samps","def random_sample_object_of_size(\n self, n: int, **parameters: int\n ) -> CombinatorialObjectType:","def test_4_1_5D_cube_init(self):\n check = [(0, 0, 0, 0, 0), (1, 1, 1, 1, 1), (1, 0, 0, 0, 0),\n (1, 1, 0, 0, 0),\n (1, 1, 1, 0, 0), (1, 1, 1, 1, 0), (1, 1, 1, 0, 1),\n (1, 1, 0, 1, 0),\n (1, 1, 0, 1, 1), (1, 1, 0, 0, 1), (1, 0, 1, 0, 0),\n (1, 0, 1, 1, 0),\n (1, 0, 1, 1, 1), (1, 0, 1, 0, 1), (1, 0, 0, 1, 0),\n (1, 0, 0, 1, 1),\n (1, 0, 0, 0, 1), (0, 1, 0, 0, 0), (0, 1, 1, 0, 0),\n (0, 1, 1, 1, 0),\n (0, 1, 1, 1, 1), (0, 1, 1, 0, 1), (0, 1, 0, 1, 0),\n (0, 1, 0, 1, 1),\n (0, 1, 0, 0, 1), (0, 0, 1, 0, 0), (0, 0, 1, 1, 0),\n (0, 0, 1, 1, 1),\n (0, 0, 1, 0, 1), (0, 0, 0, 1, 0), (0, 0, 0, 1, 1),\n (0, 0, 0, 0, 1),\n (0.5, 0.5, 0.5, 0.5, 0.5)]\n\n nn_checks = {(0, 1, 0, 1, 1): [(0, 0, 0, 0, 0), (\n 0.5, 0.5, 0.5, 0.5, 0.5), (0, 0, 0, 1, 1), (1, 1, 0, 1, 1),\n (0, 1, 0, 0, 0),\n (0, 1, 0, 0, 1),\n (0, 1, 0, 1, 0),\n (0, 0, 0, 0, 1),\n (1, 1, 1, 1, 1),\n (0, 1, 1, 1, 1),\n (0, 0, 0, 1, 0)]}\n\n init_triangulation(5, 0, check, nn_checks)","def sample(self, n_samples):\n\n z = sample_prior((n_samples,) + self.flow.z_shape)\n ldj = torch.zeros(z.size(0))\n\n z, ldj = self.flow (z, ldj, reverse=True)\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\n\n return z","def __init__(self, dimension, n):\n self.dimension = dimension\n self.n = n\n self.basis = None","def __init__(self, n=1):\n vertices = [Vertex(i) for i in range(n)]\n for vertex in vertices:\n self.add_vertex(vertex)\n self.populate_graph()","def __init__(self, n_samples=1000, n_features=4):\n self.n_samples = 1000\n self.n_features = 4\n self.forest = []","def main(n_samples):\n uso = usolib.uso.uar(N)\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\n return sum(lst) / float(n_samples)","def generate( k, n, scale = 10, prior = \"uniform\" ): \n\n if prior == \"uniform\":\n # Each topic is a multinomial generated from a Dirichlet\n topics = sc.column_stack( [ dirichlet( sc.ones( n ) * scale\n ) for i in xrange( k ) ] )\n # We also draw the weights of each topic\n weights = dirichlet( sc.ones(k) * scale ) \n\n return TopicModel( weights, topics )\n else:\n # TODO: Support the anchor word assumption.\n raise NotImplementedError","def setUp(self):\n self.samples = 5\n self.otus = 10\n seed(0) # this will seed numpy prng at 0 before each test","def setUp(self):\n self.grid = SudukuGrid(BaseCase)\n for i in range(81):\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]","def latent_space(size):\n n = torch.randn(size, 100)\n return n","def __init__(self, n: int):\n self.n = n\n self.rows_1 = [0 for _ in range(n + 1)]\n self.rows_2 = [0 for _ in range(n + 1)]\n self.cols_1 = [0 for _ in range(n + 1)]\n self.cols_2 = [0 for _ in range(n + 1)]\n self.diag1 = [0 for _ in range(n + 1)]\n self.diag2 = [0 for _ in range(n + 1)]","def sample(self, n=1):\n raise NotImplementedError","def test_3_1_4D_cube_init(self):\n check = [(0, 0, 0, 0), (1, 1, 1, 1), (1, 0, 0, 0), (1, 1, 0, 0),\n (1, 1, 1, 0), (1, 1, 0, 1), (1, 0, 1, 0), (1, 0, 1, 1),\n (1, 0, 0, 1), (0, 1, 0, 0), (0, 1, 1, 0), (0, 1, 1, 1),\n (0, 1, 0, 1), (0, 0, 1, 0), (0, 0, 1, 1), (0, 0, 0, 1),\n (0.5, 0.5, 0.5, 0.5)]\n nn_checks = {(0, 1, 1, 0): [(1, 1, 1, 0), (0, 1, 1, 1), (1, 1, 1, 1),\n (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 0),\n (0.5, 0.5, 0.5, 0.5)],\n (0.5, 0.5, 0.5, 0.5): [(1, 1, 0, 1), (1, 0, 1, 1),\n (1, 1, 1, 0), (1, 0, 0, 0),\n (1, 1, 0, 0), (1, 0, 1, 0),\n (0, 1, 1, 1), (0, 0, 0, 1),\n (1, 1, 1, 1), (1, 0, 0, 1),\n (0, 1, 0, 0), (0, 0, 1, 0),\n (0, 0, 0, 0), (0, 1, 1, 0),\n (0, 1, 0, 1), (0, 0, 1, 1)],\n (1, 0, 0, 0): [(1, 1, 0, 1), (1, 0, 1, 1), (1, 1, 1, 0),\n (1, 1, 0, 0), (1, 0, 1, 0), (1, 1, 1, 1),\n (1, 0, 0, 1), (0, 0, 0, 0),\n (0.5, 0.5, 0.5, 0.5)]}\n\n init_triangulation(4, 0, check, nn_checks)","def generate_synth_data(n):","def generaCubo(self):\r\n #Use Panda predefined format for vertex coordinate only\r\n format = GeomVertexFormat.getV3()\r\n \r\n #Build Vertex data using the created format. Vertex will never change so I use Static attribute \r\n vdata = GeomVertexData('CuboData', format, Geom.UHStatic)\r\n \r\n #I will have to write vertex data so I create a writer for these data\r\n vertex = GeomVertexWriter(vdata, 'vertex')\r\n \r\n #I now use the writer to add vertex data\r\n vertex.addData3f(0, 0, 0)\r\n vertex.addData3f(1, 1, 1)\r\n vertex.addData3f(0, 1, 1)\r\n vertex.addData3f(0, 1, 0)\r\n vertex.addData3f(0, 0, 1)\r\n vertex.addData3f(1, 0, 0)\r\n vertex.addData3f(1, 0, 1)\r\n vertex.addData3f(1, 1, 0)\r\n \r\n #I now create 12 triangles\r\n prim = GeomTriangles(Geom.UHStatic)\r\n\r\n #and then I add vertex to them\r\n #Next time use addVertices(0,1,2) !!!\r\n prim.addVertex(7)\r\n prim.addVertex(0)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n \r\n prim.addVertex(3)\r\n prim.addVertex(0)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n \r\n prim.addVertex(2)\r\n prim.addVertex(6)\r\n prim.addVertex(4)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(1)\r\n prim.addVertex(6)\r\n prim.addVertex(2)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(7)\r\n prim.addVertex(2)\r\n prim.addVertex(3)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(1)\r\n prim.addVertex(2)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(3)\r\n prim.addVertex(4)\r\n prim.addVertex(0)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(2)\r\n prim.addVertex(4)\r\n prim.addVertex(3)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(0)\r\n prim.addVertex(6)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(4)\r\n prim.addVertex(6)\r\n prim.addVertex(0)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(5)\r\n prim.addVertex(1)\r\n prim.addVertex(7)\r\n prim.closePrimitive()\r\n\r\n prim.addVertex(6)\r\n prim.addVertex(1)\r\n prim.addVertex(5)\r\n prim.closePrimitive()\r\n\r\n #Create a Geom to bing vertex data to primitives\r\n geom = Geom(vdata)\r\n geom.addPrimitive(prim)\r\n\r\n #Create a node for the Geom in order to be able to render it\r\n node = GeomNode('gnode')\r\n node.addGeom(geom)\r\n\r\n #Adde the node to the scene graph == render it!\r\n nodePath = render.attachNewNode(node)\r\n \r\n #is this needed?\r\n nodePath.setPos( 0, 5, 0)\r\n \r\n self.camera.lookAt(nodePath)\r\n \r\n base.setBackgroundColor( .0, .0, .0 )\r\n \r\n taskMgr.add(self.SpinCameraTask, \"SpinCameraTask\")","def _generate(self, **kwargs):\n N = self.parameter_schema['N']\n parameter_count = len(self._parameter_names)\n common_override_kwargs = {}\n override_kwargs = self._sampler_overrides(common_override_kwargs)\n if kwargs:\n kwargs.update(override_kwargs)\n else:\n kwargs = override_kwargs\n __import__(\"SALib.sample\", fromlist=[self.sampler_class])\n sampler = getattr(SALib.sample, self.sampler_class)\n problem = self.parameter_schema[\"problem\"]\n self._samples = sampler.sample(problem, N, **kwargs)\n self._samples = numpy.unique(self._samples, axis=0)\n super()._generate()","def __init__(self, **kwargs):\n super().__init__(**kwargs)\n self.__iteration_number = kwargs['iteration_number']\n self.__particles = [\n PSOParticle(**kwargs, bit_generator=self._random)\n for _ in range(kwargs['particles'])\n ]\n\n # The library stores particles in the visualizer .... groan\n positions = [particle.position for particle in self.__particles]\n self._visualizer = NoVisualizer(**kwargs)\n self._visualizer.add_data(positions=positions)","def construct_data_LHS(model,num_pts,seed=None):\n num_dims = model.num_dims\n rv_trans = define_random_variable_transformation_hydromad(model)\n pts = latin_hypercube_design( num_pts, num_dims, seed )\n # returns points on [0,1] but need pts on [-1,1]\n pts = 2*pts-1.\n pts = rv_trans.map_from_canonical_distributions( pts )\n vals = model.evaluate_set( pts )\n numpy.savetxt( 'pts.txt', pts, delimiter = ',' )\n numpy.savetxt( 'vals.txt', vals, delimiter = ',' )","def create_samples(self):\n sample_list = []\n genes = []\n for record in range(len(self.data_dict[\"samples\"])):\n sample_id = self.data_dict[\"samples\"][record]\n genes_cols = list(self.data_dict.keys())[2:]\n for gene in genes_cols:\n genes.append(self.data_dict[gene][record])\n label = self.data_dict[\"type\"][record]\n sample_list.append(Sample(sample_id, genes, label))\n genes = []\n return sample_list","def __init__(self, n):\n self._n = n\n self._grid = [[False] * n for _ in range(n)]\n # create sites for n-by-n grid and 2 \"virtual\" sites for top and bottom\n # self._uf = QuickFindUF(n * n + 2)\n self._uf = WeightedQuickUnionUF(n * n + 2) # QuickFindUF(n * n + 2)\n # connect top and bottom virtual sites with respecting sides of grid\n self._top_idx = n * n\n self._bottom_idx = n * n + 1\n for i in range(n):\n self._uf.union(self._top_idx, i)\n self._uf.union(self._bottom_idx, (n - 1) * n + i)","def load_hypercube(fname):\n # Get header\n f = open(fname, 'r')\n hdr = f.readline()[2:-1].split(\" \")\n f.close()\n\n # Load data\n dat = np.loadtxt(fname).T\n\n # Build dict\n sample_points = {}\n for i in range(len(hdr)):\n sample_points[hdr[i]] = dat[i]\n return sample_points","def generate_samples(mu1,cov,number_of_samples):\n samples = np.random.multivariate_normal(mu1, cov,number_of_samples)\n return samples","def __init__(self, cube_size, time_range):\n\n # cubesize is in z,y,x for interactions with tile/image data\n self.zdim, self.ydim, self.xdim = self.cubesize = [cube_size[2], cube_size[1], cube_size[0]]\n self.time_range = time_range\n self._newcube = False","def n_simplex(self, dim_n=3, project = True):\n verts = permutations([0 for i in range(dim_n)] + [1])\n if project: verts = [Polytopes.project_1(x) for x in verts]\n return Polyhedron(vertices = verts)","def __init__(self, n_samples, max_iter=1000, verbose=1, eps=1e-3):\n\n\t\tself.sparam = dict()\n\t\twith open('config.json') as config_file:\n\t\t\tself.sparam = json.load(config_file)\n\n\t\tself.C = self.sparam['C']\n\t\tself.sizePsi = self.sparam['sizePsi']\n\t\tself.num_classes = self.sparam['num_classes']\n\t\t#self.w = np.random.rand(sparam['sizePsi'],1)\n\t\tself.w = np.zeros((self.sparam['sizePsi'],1))\n\t\tself.tempw = np.zeros((self.sparam['sizePsi'],1))\n\t\t#self.tempw = np.random.rand(self.sparam['sizePsi'], 1)\n\t\t#self.tempw = np.random.rand(self.sparam['sizePsi'])\n\t\t#self.tempw[0:self.sizePsi/2] = np.zeros(self.sizePsi/2)\n\t\t#self.tempw = self.tempw.reshape(self.sizePsi, 1)\n\t\t#print np.sum(self.tempw)\n\t\tself.w_changed = False\n\t\tself.n = n_samples\n\t\tself.max_iter = max_iter\n\t\tself.verbose = verbose\n\t\tself.eps = eps\n\t\tself.alphas = []\n\t\tself.losses = []","def generate_samples(self,n_samples=100):\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\n zeta=rnd_input \n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\n # print(rnd_input) \n # output, mu, logvar, zeta=self.forward(rnd_input)\n # mu = self._reparam_layers['mu'](rnd_input)\n # logvar = self._reparam_layers['var'](rnd_input)\n # zeta = self.reparameterize(mu, logvar)\n output = self.decoder.decode(zeta)\n return output","def reconstructions_sample(self, n=()):\n self.assert_sampled()\n return [[j.sample(sample_shape=n, seed=self.randint).numpy()\n for j in i]\n for i in self._reconstructions]","def _generate(self, **kwargs):\n self._samples = numpy.array(list(itertools.product(*self.parameter_schema.values())), dtype=object)\n super()._generate()","def resample(self):\n self.X = np.random.uniform(-1, 1, (100, 2))\n self.generate_y()\n self.phi = None\n self.K = 0\n self.centers = None\n self.cluster_sizes = None\n self.g = None\n self.gamma = None","def __init__(self, n):\n self.n = n\n self.w = np.zeros(self.n)\n self.z = np.zeros(self.n)\n self.F = 0\n self.M = 0","def __init__(self, i, h, o):\n self.Wh = np.random.normal(size=(i+h, h))\n self.Wy = np.random.normal(size=(h, o))\n self.bh = np.zeros((1, h))\n self.by = np.zeros((1, o))","def _init_empty_polyhedron(self):\n self._ambient_dim = 0\n\n self._Vrepresentation = Sequence([])\n self._Vrepresentation.set_immutable()\n \n self._Hrepresentation = Sequence([])\n Equation(self, [-1]);\n self._Hrepresentation.set_immutable()\n\n self._V_adjacency_matrix = matrix(ZZ, 0, 0, 0)\n self._V_adjacency_matrix.set_immutable()\n\n self._H_adjacency_matrix = matrix(ZZ, 1, 1, 0)\n self._H_adjacency_matrix.set_immutable()","def create_start_data(self):\n\t\tdef inputMesh(feature_size):\n\t\t\tc1= np.expand_dims(np.array([0,-0.9]),0)\n\t\t\tc2= np.expand_dims(np.array([-0.9,0.9]),0)\n\t\t\tc3= np.expand_dims(np.array([0.9,0.9]),0)\n\t\t\tx1 = np.expand_dims(np.pad(np.array([0,-0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\n\t\t\tx2 = np.expand_dims(np.pad(np.array([-0.9,0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\n\t\t\tx3 = np.expand_dims(np.pad(np.array([0.9,0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\n\t\t\tedge_index = np.transpose(np.array([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]])) # COO format\n\t\t\treturn np.concatenate((c1,c2,c3),axis=0), np.concatenate((x1,x2,x3),axis=0),edge_index\n\n\t\tc, x, edge_index = inputMesh(self.params.feature_size)# x is c with zeros appended, x=f ..pixel2mesh\n\t\tdata_list_x = []\n\t\tdata_list_c = []\n\t\tdata_list_pid = []\n\t\tfor i in range(self.params.batch_size):\n\t\t\tdata_list_x.append(Data(x=torch.Tensor(x).type(dtypeF), edge_index=torch.Tensor(edge_index).type(dtypeL)))\n\t\t\tdata_list_c.append(Data(x=torch.Tensor(c).type(dtypeF), edge_index=torch.Tensor(edge_index).type(dtypeL)))\n\t\t\tdata_list_pid.append(Data(x=torch.zeros(c.shape[0],1).type(dtypeL).requires_grad_(False)))\n\t\tbatch_x = Batch.from_data_list(data_list_x)\n\t\tbatch_c = Batch.from_data_list(data_list_c)\n\t\tbatch_pid = Batch.from_data_list(data_list_pid)\n\t\treturn batch_x, batch_c, batch_pid","def labelledCube(self, dim=None, sample=None):\n if dim is None:\n dim = self.D\n if sample is None:\n sample = range(1, int(self.poolSize)+1)\n \n all_labels = list(it.product(*(range(self.slices),) * dim))\n self.sample_labels = set(random.sample(all_labels, k= len(sample)))\n labelled_sample = {label : sample for label, sample in zip(self.sample_labels, sample)}\n self.text[\"labelledSamples\"] = labelled_sample\n return labelled_sample","def __init__(self, n_in, n_out):\n self.W = np.random.randn(n_in, n_out) * 0.1\n self.b = np.zeros(n_out)","def view_surface_rec(self, x, n_max=1000, random_state=42, title=None, dataset_name=None):\n if self.comet_exp is not None:\n # If comet_exp is set, use different backend to avoid display errors on clusters\n matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!\n import matplotlib.pyplot as plt\n from grae.data.manifolds import set_axes_equal\n\n np.random.seed(random_state)\n\n x_hat = self.reconstruct(x)\n x, y = x.numpy()\n\n if x.shape[0] > n_max:\n sample_mask = np.random.choice(x.shape[0], size=n_max, replace=False)\n x_hat = x_hat[sample_mask]\n x = x[sample_mask]\n y = y[sample_mask]\n\n scene_dict = dict(SwissRoll=(0, 0), Mammoth=(-15, 90), ToroidalHelices=(30, 0))\n if dataset_name in scene_dict:\n tilt, rotation = scene_dict[dataset_name]\n else:\n tilt, rotation = 0, 0\n\n # set up a figure twice as wide as it is tall\n fig = plt.figure(figsize=plt.figaspect(0.5))\n\n ax = fig.add_subplot(1, 2, 1, projection='3d')\n ax.view_init(tilt, rotation)\n ax.set_title('Input')\n ax.scatter(*x.T, c=y, cmap='jet', edgecolor='k')\n set_axes_equal(ax)\n\n ax = fig.add_subplot(1, 2, 2, projection='3d')\n\n ax.view_init(tilt, rotation)\n ax.set_title('Reconstruction')\n ax.scatter(*x_hat.T, c=y, cmap='jet', edgecolor='k')\n set_axes_equal(ax)\n\n\n if title is not None:\n fig.suptitle(title, fontsize=20)\n\n if self.comet_exp is not None:\n self.comet_exp.log_figure(figure=plt, figure_name=title)\n plt.clf()\n else:\n plt.show()","def __init__(self,nback=1,ntokens_pm=2,ntokens_og=3,stimdim=2,seed=99):\n np.random.seed(seed)\n tr.manual_seed(seed)\n self.nback = nback\n # embedding\n self.ntokens_pm = ntokens_pm\n self.ntokens_og = ntokens_og\n self.stimdim = stimdim\n # emat\n self.randomize_emat()\n return None","def test_gan_qiskit(n, Database):\n mini = np.min(Database)\n maxi = np.max(Database)\n h = (maxi - mini) / (2 ** n)\n bins = [[k for d in Database if mini + h * k < d < mini + h * (k + 1)] for k in range(2 ** n)]\n interv = [mini + h * k for k in range(2 ** n)]\n backend = BasicAer.get_backend('statevector_simulator')\n random_seed = 10598\n\n quantum_instance = QuantumInstance(backend, seed=random_seed, seed_transpiler=random_seed)\n gan_test = QGAN(Database, num_qubits=[n], snapshot_dir=None,\n quantum_instance=quantum_instance, batch_size=int(len(Database) / 20), num_epochs=300)\n gan_test.train()\n samp, bins_var = gan_test.generator.get_output(gan_test.quantum_instance, shots=4096)\n\n compar = [len(b) / len(Database) for b in bins]\n if len(interv) == len(compar):\n plt.plot(interv, compar)\n\n plt.plot(interv, bins_var)\n\n plt.show()","def init_homographies(self, homography_path, n_images):\n with open(homography_path) as f:\n h_data = f.readlines()\n h_scale = h_data[0].rstrip().split(' ')\n self.h_scale_x = int(h_scale[1])\n self.h_scale_y = int(h_scale[2])\n h_bounds = h_data[1].rstrip().split(' ')\n self.h_bounds_x = [float(h_bounds[1]), float(h_bounds[2])]\n self.h_bounds_y = [float(h_bounds[3]), float(h_bounds[4])]\n homographies = h_data[2:2 + n_images]\n homographies = [torch.from_numpy(np.array(line.rstrip().split(' ')).astype(np.float32).reshape(3, 3)) for line\n in\n homographies]\n self.homographies = homographies","def sample(self, n):\n raise NotImplementedError"],"string":"[\n \"def gen_hypercube(samples, N):\\n\\n np.random.seed(4654562)\\n hypercube = lhs(N, samples=samples)\\n\\n return hypercube\",\n \"def latin_hypercube(n_pts, dim):\\n X = np.zeros((n_pts, dim))\\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\\n for i in range(dim): # Shuffle the center locataions for each dimension.\\n X[:, i] = centers[np.random.permutation(n_pts)]\\n\\n # Add some perturbations within each box\\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\\n X += pert\\n return X\",\n \"def generate_latin_hypercube(samples, param_dict, class_root, seed=10):\\n # Set random seed\\n random.seed(seed)\\n\\n # Create dictionary to hold sampled parameter values\\n sample_points = {}\\n for key in param_dict.keys():\\n sample_points[key] = np.zeros(samples)\\n Ndim = len(param_dict.keys())\\n pnames = [key for key in param_dict.keys()]\\n\\n # List of indices for each dimension\\n l = [range(samples) for j in range(Ndim)]\\n\\n # Generate samples until there are no indices left to choose\\n for i in range(samples):\\n\\n # Randomly choose index and then remove the number that was chosen\\n # (Latin hypercubes require at most one item per row and column)\\n for j, p in enumerate(pnames):\\n pmin, pmax = param_dict[p]\\n idx = random.choice(l[j])\\n\\n # Get value at this sample point (add 0.5 to idx get bin centroid)\\n sample_points[p][i] = pmin + (pmax - pmin) \\\\\\n * (idx + 0.5) / float(samples)\\n l[j].remove(idx) # Remove choice from list (sampling w/o replacement)\\n\\n return sample_points\",\n \"def generate_samples(self, n_samples):\",\n \"def generate_samples(self, n_samples):\",\n \"def latin_hypercube(n_pts, mins, maxs):\\n #return a latin_hypercube\\n design = lhs(np.size(maxs), samples=n_pts)\\n for i in range(2):\\n design[:, i] = design[:, i] * (maxs[i]-mins[i]) + mins[i]\\n return design\",\n \"def latin_hypercube_sampler(n=1, indim=1, bounds=None, rng=None):\\r\\n rng = ensure_rng(rng)\\r\\n if bounds is None:\\r\\n bounds = np.zeros((indim, 2))\\r\\n bounds[:,1] = 1. \\r\\n # Divide each dimension into `n` equal intervals\\r\\n hypercubes = np.linspace(bounds[:,0], bounds[:,1], n+1)\\r\\n \\r\\n l = hypercubes[:-1,:].reshape(-1,)\\r\\n u = hypercubes[1:,:].reshape(-1,)\\r\\n _x = rng.uniform(l,u, (1, indim*n)).reshape(n, indim)\\r\\n x = _x\\r\\n for j in range(indim):\\r\\n x[:,j] = _x[rng.permutation(n), j]\\r\\n return x\",\n \"def lhs_start(hyperbounds, n_samples, rng=None):\\n low_bounds = []\\n high_bounds = []\\n for bound in hyperbounds:\\n low_bounds.append(bound[0])\\n high_bounds.append(bound[1])\\n\\n low_bounds = np.array(low_bounds, dtype=object)\\n high_bounds = np.array(high_bounds, dtype=object)\\n\\n samples = sample_latin_hypercube(low_bounds, high_bounds, n_samples, rng=rng)\\n samples = samples.tolist()\\n return samples\",\n \"def n_cube(self, dim_n):\\n if dim_n == 1:\\n return Polyhedron(vertices = [[1],[-1]])\\n\\n pre_cube = polytopes.n_cube(dim_n-1)\\n vertices = [];\\n for pre_v in pre_cube.vertex_generator():\\n vertices.append( [ 1] + [v for v in pre_v] );\\n vertices.append( [-1] + [v for v in pre_v] );\\n return Polyhedron(vertices = vertices)\",\n \"def __init__(self, n, prey_cnt=0, predator_cnt=0):\\n # print n, prey_cnt, predator_cnt\\n self.grid_size = n\\n self.grid = []\\n for i in range(n):\\n row = [0]*n # row is a list of n zeros\\n self.grid.append(row)\\n self.init_animals(prey_cnt, predator_cnt)\",\n \"def construct_initial_sample(n):\\n sample_normal = np.random.normal(size=(n, 3))\\n sample_radius = np.linalg.norm(sample_normal, axis=1, keepdims=True)\\n sample_cartesian = sample_normal / sample_radius\\n sample_polar = cartesian_to_polar(sample_cartesian)\\n return np.reshape(sample_polar[:, 1:3], (-1))\",\n \"def create_samples(self):\\n self._samples = self.load_samples()\\n self.modify_samples()\",\n \"def sample(self, n_samples):\\n with torch.no_grad():\\n z = torch.randn((n_samples, self.z_dim))\\n samples = self.decoder(z)\\n im_size = int(np.sqrt(self.input_dim))\\n samples = samples.view(-1, 1, im_size, im_size)\\n\\n return samples\",\n \"def test_2_1_3D_cube_init(self):\\n check = [(0, 0, 0), (1, 1, 1), (1, 0, 0), (1, 1, 0), (1, 0, 1),\\n (0, 1, 0), (0, 1, 1), (0, 0, 1), (0.5, 0.5, 0.5)]\\n\\n nn_checks = {\\n (1, 1, 1): [(1, 1, 0), (0, 1, 1), (1, 0, 0), (0, 0, 1), (1, 0, 1),\\n (0.5, 0.5, 0.5), (0, 1, 0)],\\n (1, 0, 1): [(1, 0, 0), (0, 0, 1), (0, 0, 0), (0.5, 0.5, 0.5),\\n (1, 1, 1)],\\n (0.5, 0.5, 0.5): [(1, 1, 0), (0, 1, 1), (0, 1, 0), (1, 0, 0),\\n (0, 0, 1), (1, 0, 1), (0, 0, 0), (1, 1, 1)]}\\n\\n init_triangulation(3, 0, check, nn_checks)\",\n \"def initialiser(N, dimensions = 2):\\r\\n \\r\\n #shape for correct dimensions\\r\\n shape = tuple([N]) * dimensions\\r\\n \\r\\n #randomise spins\\r\\n lattice = np.random.choice([1,-1], size = shape)\\r\\n \\r\\n return lattice\",\n \"def create_grids_structure(self):\\n for indices, hypercube in np.ndenumerate(self.hypercubes):\\n self.hypercubes[indices] = Hypercube(coords=indices)\",\n \"def __init__(self, n_features):\\n self.W = np.random.randn(n_features)\\n self.b = np.random.randn()\",\n \"def _make_random_matrix(self, n_components, n_features):\",\n \"def createCube():\\n subjects, detections, antigen = getAxes()\\n cube = np.full([len(subjects), len(detections), len(antigen)], np.nan)\\n\\n IGG = importIGG()\\n glycan, dfGlycan = importGlycan()\\n glyCube = np.full([len(subjects), len(glycan)], np.nan)\\n\\n for k, curAnti in enumerate(antigen):\\n lumx = importLuminex(curAnti)\\n\\n for _, row in lumx.iterrows():\\n i = subjects.index(row[\\\"subject\\\"])\\n j = detections.index(row[\\\"variable\\\"])\\n cube[i, j, k] = row[\\\"value\\\"]\\n\\n for _, row in dfGlycan.iterrows():\\n i = subjects.index(row[\\\"subject\\\"])\\n j = glycan.index(row[\\\"variable\\\"])\\n glyCube[i, j] = row[\\\"value\\\"]\\n\\n # Add IgG data on the end as another detection\\n for _, row in IGG.iterrows():\\n i = subjects.index(row[\\\"subject\\\"])\\n k = antigen.index(row[\\\"variable\\\"])\\n cube[i, -1, k] = row[\\\"value\\\"]\\n\\n # Clip to 0 as there are a few strongly negative outliers\\n cube = np.clip(cube, 1.0, None)\\n glyCube = np.clip(glyCube, 0.1, None)\\n\\n cube = np.log10(cube)\\n glyCube = np.log10(glyCube)\\n\\n # Mean center each measurement\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n cube -= np.nanmean(cube, axis=0)\\n glyCube -= np.nanmean(glyCube, axis=0)\\n\\n # Check that there are no slices with completely missing data\\n assert ~np.any(np.all(np.isnan(cube), axis=(0, 1)))\\n assert ~np.any(np.all(np.isnan(cube), axis=(0, 2)))\\n assert ~np.any(np.all(np.isnan(cube), axis=(1, 2)))\\n\\n glyCube *= np.sqrt(np.nanvar(cube) / np.nanvar(glyCube))\\n return cube, glyCube\",\n \"def generate_samples(self, n_samples=100):\\n \\t\\t\\n\\t\\t#how many times should ancestral sampling be run\\n\\t\\t#n_samples\\n prior_samples=[]\\n for i in range(0,n_samples):\\n prior_sample = self.prior.get_samples(\\n n_latent_nodes=self.n_latent_nodes,\\n n_gibbs_sampling_steps=100, \\n sampling_mode=\\\"gibbs_ancestral\\\")\\n prior_sample = torch.cat(prior_sample)\\n prior_samples.append(prior_sample)\\n prior_samples=torch.stack(prior_samples)\\n # prior_samples = tf.slice(prior_samples, [0, 0], [num_samples, -1])\\n output_activations = self.decoder.decode(prior_samples)\\n output_activations = output_activations+self._train_bias\\n output_distribution = Bernoulli(logit=output_activations)\\n output=torch.sigmoid(output_distribution.logits)\\n # output_activations[0] = output_activations[0] + self.train_bias\\n # output_dist = FactorialBernoulliUtil(output_activations)\\n # output_samples = tf.nn.sigmoid(output_dist.logit_mu)\\n # print(\\\"--- \\\",\\\"end VAE::generate_samples()\\\")\\n return output\",\n \"def __create_sample_data__(npts = 20):\\n\\t#data function\\n\\tdef wavy(x, y):\\n\\t\\treturn np.sin(0.2*np.pi*x)*np.cos(0.4*np.pi*y)\\n\\t\\n\\t#make grid\\n\\txs = np.linspace(0, 2*20, 2*npts + 1)\\n\\tys = np.linspace(0, 20, npts + 1)\\n\\t(xgrid, ygrid) = np.meshgrid(xs, ys)\\n\\tzgrid = wavy(xgrid, ygrid)\\n\\t\\n\\treturn (xgrid, ygrid, zgrid)\",\n \"def generate_in(self, n_samples, start_sample=0):\\n pass\",\n \"def random_LatinHypercube(seeds, n, skip_seeds=0):\\n Nseeds = len(seeds)\\n rndms = np.zeros((Nseeds, n), dtype='float64')\\n # define arrays here and re-use them later\\n samples = np.zeros(n, dtype='float64')\\n perms = np.zeros(n, dtype='float64')\\n\\n for seed_i in range(skip_seeds, Nseeds, 1):\\n # set the seed\\n np.random.seed(seeds[seed_i])\\n\\n # draw the random numbers and re-generate permutations array\\n for i in range(n):\\n samples[i] = np.random.uniform(0., 1.)\\n perms[i] = i + 1\\n\\n # in-place shuffle permutations\\n np.random.shuffle(perms)\\n\\n for j in range(n):\\n rndms[seed_i, j] = (perms[j] - samples[j]) / float(n)\\n\\n return rndms\",\n \"def sample(h, seed_ix, n):\\n x = np.zeros((vocab_size, 1))\\n x[seed_ix] = 1\\n ixes = []\\n for t in xrange(n):\\n h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)\\n y = np.dot(Why, h) + by\\n p = np.exp(y) / np.sum(np.exp(y))\\n ix = np.argmax(p)#np.random.choice(range(vocab_size), p=p.ravel())\\n x = np.zeros((vocab_size, 1))\\n x[ix] = 1\\n ixes.append(ix)\\n return ixes\",\n \"def __init__(self, nonogram_size):\\n # create random id\\n self.nonogram_id = uuid.uuid4()\\n self.row_numbers = [(2), (2), (2)]\\n self.column_numbers = [(1, 1), (3), (1)]\\n self.nonogram_size = nonogram_size\\n self.grid = Nonogram.create_rand_grid(nonogram_size)\\n #TODO\\n self.fitness = 999\",\n \"def sample(h, seed_ix, n):\\n x = np.zeros((vocab_size,1))\\n x[seed_ix] = 1\\n ixes = []\\n for t in xrange(n):\\n h = np.tanh(np.dot(Wxh, x) + np.dot(Whh, h) + bh)\\n y = np.dot(Why, h) + by\\n p = np.exp(y) / np.sum(np.exp(y))\\n ix = np.random.choice(range(vocab_size), p=p.ravel())\\n x = np.zeros((vocab_size,1))\\n x[ix] = 1\\n ixes.append(ix)\\n return ixes\",\n \"def _reconstruct(self, num_samples=None):\",\n \"def setUp(self):\\n self.cube = _create_2d_cube()\",\n \"def get_raw_seedling(datasource, n):\\n matrix = MatrixDict()\\n \\n for lang, sent in SEEDLING[datasource].sents():\\n features = sent2ngrams(sent, n=n)\\n matrix.setdefault(lang, Counter()).update(features)\\n \\n return matrix\",\n \"def initial_samples(lb, ub, method, numSamp):\\r\\n if not len(lb) == len(ub):\\r\\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\\r\\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\\r\\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\\r\\n for case in Switch(method):\\r\\n if case('random'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = lb + (ub - lb) * rand(len(lb))\\r\\n\\r\\n break\\r\\n if case('rand-wor'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\\r\\n\\r\\n break\\r\\n if case('nolh'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = range(q)\\r\\n if r != 0:\\r\\n remove = range(dim - r, dim)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-rp'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = random.sample(range(q), q)\\r\\n if r != 0:\\r\\n remove = random.sample(range(q - 1), r)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-cdr'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf, remove = get_cdr_permutations(len(ub))\\r\\n if remove != []:\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('lhc'):\\r\\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\\r\\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case():\\r\\n print 'Somehow you evaded my assert statement - good job!',\\r\\n print ' However, you still need to use a valid method string.'\\r\\n\\r\\n return s\",\n \"def test_1_1_2D_cube_init(self): # TODO: REMOVE FUNC AFTER SPLIT\\n check = [(0, 0), (1, 1), (1, 0), (0, 1), (0.5, 0.5)]\\n\\n nn_checks = {(0.5, 0.5): [(0, 1), (1, 0), (0, 0), (1, 1)],\\n (0, 1): [(0, 0), (1, 1), (0.5, 0.5)]}\\n\\n init_triangulation(2, 0, check, nn_checks)\",\n \"def generate(net, z, maxlen=50, im=None, init=None, use_end=True):\\n caption = lm_tools.sample(net, z['word_dict'], z['index_dict'], num=maxlen, Im=im, initial=init, use_end=use_end)\\n print ' '.join(caption)\",\n \"def create_samples(self):\\n for s_id in range(len(self.data[\\\"sample\\\"])):\\n self.samples.add(Sample(s_id, [self.data[key][s_id] for key in self.data.keys() if key not in WRONG_KEYS],\\n self.data[\\\"label\\\"][s_id]))\",\n \"def __init__(self, n):\\n self.rows = [0 for _ in range(n)]\\n self.columns = [0 for _ in range(n)]\\n # First diagonal x+y, second y-x\\n self.diagonal = [0, 0]\\n self.score = {1: 1, 2: n+1}\\n self.win = {1: n, 2: (n+1)*n}\\n self.size = n\",\n \"def set_up_threshold_cube():\\n test_data = 50*np.arange(16).reshape(4, 4)\\n grid_x = DimCoord(np.arange(4), standard_name=\\\"projection_x_coordinate\\\",\\n units=\\\"km\\\")\\n grid_y = DimCoord(np.arange(4), standard_name=\\\"projection_y_coordinate\\\",\\n units=\\\"km\\\")\\n test_cube = iris.cube.Cube(test_data, long_name=\\\"surface_altitude\\\",\\n units=\\\"m\\\",\\n dim_coords_and_dims=[(grid_y, 0), (grid_x, 1)])\\n return test_cube\",\n \"def initialize(self, n_samples=1, kl_scaling=None, *args, **kwargs):\\n if kl_scaling is None:\\n kl_scaling = {}\\n if n_samples <= 0:\\n raise ValueError(\\n \\\"n_samples should be greater than zero: {}\\\".format(n_samples))\\n\\n self.n_samples = n_samples\\n self.kl_scaling = kl_scaling\\n return super(elbo_optimizer, self).initialize(*args, **kwargs)\",\n \"def __init__(self, num_prealloc_samples=0):\\n self.num_prealloc_samples_ = num_prealloc_samples\\n if self.num_prealloc_samples_ > 0:\\n self._preallocate_samples()\",\n \"def initialize(self, numSamples, sampleMethod):\\n initSamples = initial_samples(self.lb, self.ub, sampleMethod, numSamples)\\n if sum(self.xID) != 0:\\n xUB = [\\n self.ub[np.where(self.xID == 1)[0][0]]] * len(self.xID)\\n xSamples = initial_samples([0] * len(self.xID), xUB, 'rand-wor', numSamples)\\n for var in range(len(self.varType)):\\n if 'i' in self.varType[var] or 'd' in self.varType[var]:\\n initSamples[:, var] = np.rint(initSamples[:, var])\\n\\n if sum(self.xID) != 0:\\n initSamples = initSamples * (self.cID + self.iID + self.dID) + xSamples * self.xID\\n return initSamples\",\n \"def setUp(self):\\n # generate lattice\\n self.lattice = lattice.Lattice()\\n self.lattice.addAtom(\\\"He\\\", [0,0,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,0,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,2,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,0,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [9,9,9], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,2,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,0,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,2,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,2,2], 0)\\n \\n # indexes of cluster atoms\\n self.bigClusterIndexes = [0,1,2,3,5,6,7,8]\\n self.smallClusterIndexes = [4]\\n \\n # filter\\n self.filter = clusterFilter.ClusterFilter(\\\"Cluster\\\")\",\n \"def make_sample_workspace(self, **kwargs):\\n function = \\\"name=LinearBackground, A0=0.1;name=Lorentzian, PeakCentre=0.5, Amplitude=2, FWHM=0.1\\\"\\n sample = self.make_dummy_workspace(function, output_name='sample', **kwargs)\\n return sample\",\n \"def _sample_schechter(x0, alpha, x_min, size=100, max_iter=1000):\\n out = []\\n n = 0\\n num_iter = 0\\n while (n<size) & (num_iter<max_iter):\\n x = np.random.gamma(scale=x0, shape=alpha+2, size=size)\\n x = x[x>x_min]\\n u = np.random.uniform(size=x.size)\\n x = x[u<x_min/x]\\n out.append(x)\\n n+=x.size\\n num_iter += 1\\n\\n if num_iter >= max_iter:\\n msg = (\\\"The maximum number of iterations reached.\\\",\\n \\\"Random variates may not be representitive.\\\",\\n \\\"Try increasing `max_iter`.\\\")\\n print(msg)\\n\\n return np.concatenate(out)[:size]\",\n \"def generate_samples(self, nsamples):\\n assert self.trained, \\\"Model must first be fitted to some data.\\\"\\n logger.debug(f'Generate synthetic dataset of size {nsamples}')\\n synthetic_data, _ = self.gm.sample(nsamples)\\n return synthetic_data\",\n \"def __init__(self, initial, size, horizontalChunks, verticalChunks, goal = \\\"\\\"):\\n\\t\\tself.initial = initial\\n\\t\\tself.size = size\\n\\t\\tself.horChunks = horizontalChunks\\n\\t\\tself.verChunks = verticalChunks\\n\\n\\t\\t# Goal holds the solution, once we find it.\\n\\t\\tself.goal = goal\\n\\n\\t\\t# For a puzzle of size n, initializes blank n x n 2d array\\n\\t\\tself.graph = [[0 for x in range(self.size)] for x in range(self.size)] \\n\\t\\tfor i in range (0,self.size):\\n\\t\\t\\tfor j in range (0,self.size):\\n\\t\\t\\t\\tself.graph[i][j] = initial[i*self.size + j] \\n\\t\\tself.initial = \\\"\\\"\",\n \"def generate_cube():\\n \\n num_voxels = 31\\n\\n data_x = []\\n data_y = []\\n data_z = []\\n data_intensity = []\\n\\n volume = numpy.zeros((num_voxels, num_voxels, num_voxels))\\n\\n for x in range(num_voxels):\\n for y in range(num_voxels):\\n for z in range(num_voxels):\\n\\n if 5 < x < 10 and 5 < y < 10:\\n data_x.append(x)\\n data_y.append(y)\\n data_z.append(z)\\n data_intensity.append(200.0)\\n\\n volume[x,y,z] = 200.0\\n\\n\\n return data_x, data_y, data_z, data_intensity, volume\",\n \"def sample(h, seed_ix, n):\\n x = np.zeros((vocab_size, 1))\\n x[seed_ix] = 1\\n ixes = []\\n H = hidden_size\\n\\n for t in range(n):\\n W = np.dot(Wxh, x) # hidden state, shape(300, 1)\\n U = np.dot(Whh, h) # hidden state, shape(300, 100)\\n z = sigmoid(W[:H] + U[:H] + bh[:H])\\n r = sigmoid(W[H:H * 2] + U[H:H * 2] + bh[H:H * 2])\\n r_ = r * U[H * 2:]\\n h_ = np.tanh(W[H * 2:] + r_)\\n h = z * h + (1 - z) * h_\\n\\n y = np.dot(Why, h) + by\\n p = np.exp(y) / np.sum(np.exp(y))\\n ix = np.random.choice(range(vocab_size), p=p.ravel())\\n x = np.zeros((vocab_size, 1))\\n x[ix] = 1\\n ixes.append(ix)\\n return ixes\",\n \"def randomgrid(self, n):\\n lam = np.random.random((n, 3))\\n return self.normalize(lam)\",\n \"def _make_test_cube(long_name):\\n cs = GeogCS(EARTH_RADIUS)\\n data = np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0]])\\n cube = Cube(data, long_name=long_name)\\n x_coord = DimCoord(\\n np.linspace(-45.0, 45.0, 3), \\\"latitude\\\", units=\\\"degrees\\\", coord_system=cs\\n )\\n y_coord = DimCoord(\\n np.linspace(120, 180, 3), \\\"longitude\\\", units=\\\"degrees\\\", coord_system=cs\\n )\\n cube.add_dim_coord(x_coord, 0)\\n cube.add_dim_coord(y_coord, 1)\\n return cube\",\n \"def __init__(self, n_rows: int = 2, n_columns: int = 2):\\n self.set_uniform(n_rows, n_columns)\",\n \"def __init__(self, n):\\n self.rows = [0] * n\\n self.cols = [0] * n\\n self.diagonal1 = 0\\n self.diagonal2 = 0\\n self.n = n\",\n \"def initial_setup(N,L,dim):\\r\\n r = np.zeros((3,N))\\r\\n #n = int(np.rint((N/3)**(1/dim)))\\r\\n #nz = int(n+n)\\r\\n x1 = np.linspace(0+L/n/2,L-L/n/2,n)\\r\\n d0 = 1.0e-10 #intial distance between hydrogen and oxygen\\r\\n x2 = x1 + d0\\r\\n x3 = x1 - d0/np.sqrt(2)\\r\\n z = np.zeros(3*n)\\r\\n xy = np.zeros(3*n)\\r\\n for ii in range(n):\\r\\n z[ii*3+1] = x1[ii]\\r\\n z[ii*3+2] = x2[ii]\\r\\n z[ii*3] = x3[ii]\\r\\n xy[ii*3] = x1[ii]\\r\\n xy[ii*3+1] = x1[ii]\\r\\n xy[ii*3+2] = x1[ii]\\r\\n rx, ry,rz = np.meshgrid(x1,x1,z)\\r\\n r[0,:] = np.reshape(rx,N)\\r\\n r[1,:] = np.reshape(ry,N)\\r\\n r[2,:] = np.reshape(rz,N)\\r\\n r[0,0::3] += d0/np.sqrt(2) #add additional offset to one Oxygen\\r\\n return r\",\n \"def generate_test():\\n o = []\\n pos = [384, 288]\\n note_group_size = GAN_PARAMS[\\\"note_group_size\\\"]\\n generate_set(begin=3 * note_group_size, start_pos=pos,\\n length_multiplier=dist_multiplier, group_id=3, plot_map=True)\",\n \"def random_sample(self, n):\\n indices = random.sample(xrange(np.shape(self.data)[0]), n)\\n table = DataTable(self.data[indices], self.dims, self.legends, self.tags.copy())\\n return table\",\n \"def __init__(self, i, h, o):\\n self.Wz = np.random.randn(i + h, h)\\n self.bz = np.zeros((1, h))\\n self.Wr = np.random.randn(i + h, h)\\n self.br = np.zeros((1, h))\\n self.Wh = np.random.randn(i + h, h)\\n self.bh = np.zeros((1, h))\\n self.Wy = np.random.randn(h, o)\\n self.by = np.zeros((1, o))\",\n \"def __init__(self, n):\\n self.n = n\\n self.rows = [0 for _ in range(n)]\\n self.colums = [0 for _ in range(n)]\\n self.diag = [0,0]\",\n \"def gen_data(npt, typ, ndim, rstate=None):\\n mid = .5 # i'm placing in unit cube\\n if typ == 'ball':\\n r0 = 0.5\\n pts = genball(npt, ndim, rstate=rstate) * r0 + mid\\n volume = (np.pi**(ndim / 2) / scipy.special.gamma(ndim / 2 + 1) *\\n r0**ndim)\\n elif typ == 'pin':\\n w = 0.01\\n a = 1\\n pts = np.zeros((npt, ndim))\\n pts[:, 1:] = genball(npt, ndim - 1, rstate=rstate) * w + mid\\n pts[:, 0] = (rstate.uniform(size=npt) - 0.5) * a + mid\\n volume = (np.pi**((ndim - 1) / 2) /\\n scipy.special.gamma((ndim - 1) / 2 + 1) * w**(ndim - 1) * a)\\n elif typ == 'torus':\\n w = 0.01\\n r0 = 0.45\\n pts = np.zeros((npt, ndim))\\n pts[:, :2] = genshell(r0 - w / 2, r0 + w / 2, npt, 2,\\n rstate=rstate) + mid\\n pts[:,\\n 2:] = (rstate.uniform(size=(npt, ndim - 2)) * 2 - 1) * w / 2 + mid\\n volume = w**(ndim - 2) * np.pi * ((r0 + w / 2)**2 - (r0 - w / 2)**2)\\n elif typ == 'cylinder':\\n w = 0.01\\n r0 = 0.45\\n a = 1\\n pts = np.zeros((npt, ndim))\\n pts[:, :2] = genshell(r0 - w / 2, r0 + w / 2, npt, 2,\\n rstate=rstate) + mid\\n pts[:, 2:] = rstate.uniform(size=(npt, ndim - 2)) * a\\n volume = np.pi * ((r0 + w / 2)**2 - (r0 - w / 2)**2)\\n elif typ == 'shell':\\n r1 = 0.45\\n r2 = 0.46\\n pts = genshell(r1, r2, npt, ndim, rstate=rstate) + mid\\n volume = (np.pi**(ndim / 2) / scipy.special.gamma(ndim / 2 + 1) *\\n (r2**ndim - r1**ndim))\\n else:\\n raise RuntimeError('unknown', typ)\\n return pts, volume\",\n \"def __init__(self, n):\\n self.row = [0] * n\\n self.col = [0] * n\\n self.diagonal = 0\\n self.antidiagonal = 0\\n self.winning = False\",\n \"def __init__(self, number_of_cheeses, number_of_stools):\\n self.model = TOAHModel(number_of_stools)\\n self.model.fill_first_stool(number_of_cheeses)\",\n \"def pseudo_sample(self):\\n return (torch.zeros(1, 1, 28, 28), None)\",\n \"def generate_initial_sample(pmin, pmax, ntemps, nwalkers):\\n\\n npl = pmin.npl\\n nobs = pmin.nobs\\n\\n assert npl == pmax.npl, 'Number of planets must agree in prior bounds'\\n assert nobs == pmax.nobs, 'Number of observations must agree in prior bounds'\\n\\n N = pmin.shape[-1]\\n\\n samps=params.Parameters(arr=np.zeros((ntemps, nwalkers, N)), nobs=nobs, npl=npl)\\n\\n V=samps.V\\n tau=samps.tau\\n sigma=samps.sigma\\n sigma0=samps.sigma0\\n for i in range(nobs):\\n V[:,:,i] = nr.uniform(low=pmin.V[i], high=pmax.V[i], size=(ntemps, nwalkers))\\n tau[:,:,i] = draw_logarithmic(low=pmin.tau[i], high=pmax.tau[i], size=(ntemps,nwalkers))\\n sigma[:,:,i] = draw_logarithmic(low=pmin.sigma[i], high=pmax.sigma[i], size=(ntemps,nwalkers))\\n sigma0[:,:,i] = draw_logarithmic(low=pmin.sigma[i], high=pmax.sigma[i], size=(ntemps, nwalkers))\\n samps.V=np.squeeze(V)\\n samps.tau = np.squeeze(tau)\\n samps.sigma = np.squeeze(sigma)\\n samps.sigma0 = np.squeeze(sigma0)\\n\\n if npl >= 1:\\n samps.K = np.squeeze(draw_logarithmic(low=pmin.K[0], high=pmax.K[0], size=(ntemps, nwalkers, npl)))\\n\\n # Make sure that periods are increasing\\n samps.n = np.squeeze(np.sort(draw_logarithmic(low=pmin.n, high=pmax.n, size=(ntemps,nwalkers,npl)))[:,:,::-1])\\n\\n samps.e = np.squeeze(nr.uniform(low=0.0, high=1.0, size=(ntemps, nwalkers,npl)))\\n samps.chi = np.squeeze(nr.uniform(low=0.0, high=1.0, size=(ntemps, nwalkers,npl)))\\n samps.omega = np.squeeze(nr.uniform(low=0.0, high=2.0*np.pi, size=(ntemps, nwalkers,npl)))\\n\\n return samps\",\n \"def random_sample_object_of_size(\\n self, n: int, **parameters: int\\n ) -> CombinatorialObjectType:\",\n \"def test_4_1_5D_cube_init(self):\\n check = [(0, 0, 0, 0, 0), (1, 1, 1, 1, 1), (1, 0, 0, 0, 0),\\n (1, 1, 0, 0, 0),\\n (1, 1, 1, 0, 0), (1, 1, 1, 1, 0), (1, 1, 1, 0, 1),\\n (1, 1, 0, 1, 0),\\n (1, 1, 0, 1, 1), (1, 1, 0, 0, 1), (1, 0, 1, 0, 0),\\n (1, 0, 1, 1, 0),\\n (1, 0, 1, 1, 1), (1, 0, 1, 0, 1), (1, 0, 0, 1, 0),\\n (1, 0, 0, 1, 1),\\n (1, 0, 0, 0, 1), (0, 1, 0, 0, 0), (0, 1, 1, 0, 0),\\n (0, 1, 1, 1, 0),\\n (0, 1, 1, 1, 1), (0, 1, 1, 0, 1), (0, 1, 0, 1, 0),\\n (0, 1, 0, 1, 1),\\n (0, 1, 0, 0, 1), (0, 0, 1, 0, 0), (0, 0, 1, 1, 0),\\n (0, 0, 1, 1, 1),\\n (0, 0, 1, 0, 1), (0, 0, 0, 1, 0), (0, 0, 0, 1, 1),\\n (0, 0, 0, 0, 1),\\n (0.5, 0.5, 0.5, 0.5, 0.5)]\\n\\n nn_checks = {(0, 1, 0, 1, 1): [(0, 0, 0, 0, 0), (\\n 0.5, 0.5, 0.5, 0.5, 0.5), (0, 0, 0, 1, 1), (1, 1, 0, 1, 1),\\n (0, 1, 0, 0, 0),\\n (0, 1, 0, 0, 1),\\n (0, 1, 0, 1, 0),\\n (0, 0, 0, 0, 1),\\n (1, 1, 1, 1, 1),\\n (0, 1, 1, 1, 1),\\n (0, 0, 0, 1, 0)]}\\n\\n init_triangulation(5, 0, check, nn_checks)\",\n \"def sample(self, n_samples):\\n\\n z = sample_prior((n_samples,) + self.flow.z_shape)\\n ldj = torch.zeros(z.size(0))\\n\\n z, ldj = self.flow (z, ldj, reverse=True)\\n z, ldj = self.logit_normalize(z, ldj, reverse=True)\\n\\n return z\",\n \"def __init__(self, dimension, n):\\n self.dimension = dimension\\n self.n = n\\n self.basis = None\",\n \"def __init__(self, n=1):\\n vertices = [Vertex(i) for i in range(n)]\\n for vertex in vertices:\\n self.add_vertex(vertex)\\n self.populate_graph()\",\n \"def __init__(self, n_samples=1000, n_features=4):\\n self.n_samples = 1000\\n self.n_features = 4\\n self.forest = []\",\n \"def main(n_samples):\\n uso = usolib.uso.uar(N)\\n lst = [usolib.randomfacet.randomfacet_sample(uso, N) for i in range(n_samples)]\\n return sum(lst) / float(n_samples)\",\n \"def generate( k, n, scale = 10, prior = \\\"uniform\\\" ): \\n\\n if prior == \\\"uniform\\\":\\n # Each topic is a multinomial generated from a Dirichlet\\n topics = sc.column_stack( [ dirichlet( sc.ones( n ) * scale\\n ) for i in xrange( k ) ] )\\n # We also draw the weights of each topic\\n weights = dirichlet( sc.ones(k) * scale ) \\n\\n return TopicModel( weights, topics )\\n else:\\n # TODO: Support the anchor word assumption.\\n raise NotImplementedError\",\n \"def setUp(self):\\n self.samples = 5\\n self.otus = 10\\n seed(0) # this will seed numpy prng at 0 before each test\",\n \"def setUp(self):\\n self.grid = SudukuGrid(BaseCase)\\n for i in range(81):\\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]\",\n \"def latent_space(size):\\n n = torch.randn(size, 100)\\n return n\",\n \"def __init__(self, n: int):\\n self.n = n\\n self.rows_1 = [0 for _ in range(n + 1)]\\n self.rows_2 = [0 for _ in range(n + 1)]\\n self.cols_1 = [0 for _ in range(n + 1)]\\n self.cols_2 = [0 for _ in range(n + 1)]\\n self.diag1 = [0 for _ in range(n + 1)]\\n self.diag2 = [0 for _ in range(n + 1)]\",\n \"def sample(self, n=1):\\n raise NotImplementedError\",\n \"def test_3_1_4D_cube_init(self):\\n check = [(0, 0, 0, 0), (1, 1, 1, 1), (1, 0, 0, 0), (1, 1, 0, 0),\\n (1, 1, 1, 0), (1, 1, 0, 1), (1, 0, 1, 0), (1, 0, 1, 1),\\n (1, 0, 0, 1), (0, 1, 0, 0), (0, 1, 1, 0), (0, 1, 1, 1),\\n (0, 1, 0, 1), (0, 0, 1, 0), (0, 0, 1, 1), (0, 0, 0, 1),\\n (0.5, 0.5, 0.5, 0.5)]\\n nn_checks = {(0, 1, 1, 0): [(1, 1, 1, 0), (0, 1, 1, 1), (1, 1, 1, 1),\\n (0, 1, 0, 0), (0, 0, 1, 0), (0, 0, 0, 0),\\n (0.5, 0.5, 0.5, 0.5)],\\n (0.5, 0.5, 0.5, 0.5): [(1, 1, 0, 1), (1, 0, 1, 1),\\n (1, 1, 1, 0), (1, 0, 0, 0),\\n (1, 1, 0, 0), (1, 0, 1, 0),\\n (0, 1, 1, 1), (0, 0, 0, 1),\\n (1, 1, 1, 1), (1, 0, 0, 1),\\n (0, 1, 0, 0), (0, 0, 1, 0),\\n (0, 0, 0, 0), (0, 1, 1, 0),\\n (0, 1, 0, 1), (0, 0, 1, 1)],\\n (1, 0, 0, 0): [(1, 1, 0, 1), (1, 0, 1, 1), (1, 1, 1, 0),\\n (1, 1, 0, 0), (1, 0, 1, 0), (1, 1, 1, 1),\\n (1, 0, 0, 1), (0, 0, 0, 0),\\n (0.5, 0.5, 0.5, 0.5)]}\\n\\n init_triangulation(4, 0, check, nn_checks)\",\n \"def generate_synth_data(n):\",\n \"def generaCubo(self):\\r\\n #Use Panda predefined format for vertex coordinate only\\r\\n format = GeomVertexFormat.getV3()\\r\\n \\r\\n #Build Vertex data using the created format. Vertex will never change so I use Static attribute \\r\\n vdata = GeomVertexData('CuboData', format, Geom.UHStatic)\\r\\n \\r\\n #I will have to write vertex data so I create a writer for these data\\r\\n vertex = GeomVertexWriter(vdata, 'vertex')\\r\\n \\r\\n #I now use the writer to add vertex data\\r\\n vertex.addData3f(0, 0, 0)\\r\\n vertex.addData3f(1, 1, 1)\\r\\n vertex.addData3f(0, 1, 1)\\r\\n vertex.addData3f(0, 1, 0)\\r\\n vertex.addData3f(0, 0, 1)\\r\\n vertex.addData3f(1, 0, 0)\\r\\n vertex.addData3f(1, 0, 1)\\r\\n vertex.addData3f(1, 1, 0)\\r\\n \\r\\n #I now create 12 triangles\\r\\n prim = GeomTriangles(Geom.UHStatic)\\r\\n\\r\\n #and then I add vertex to them\\r\\n #Next time use addVertices(0,1,2) !!!\\r\\n prim.addVertex(7)\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n \\r\\n prim.addVertex(3)\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n \\r\\n prim.addVertex(2)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(4)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(2)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(7)\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(3)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(3)\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(0)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(2)\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(3)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(0)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(4)\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(0)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(5)\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(7)\\r\\n prim.closePrimitive()\\r\\n\\r\\n prim.addVertex(6)\\r\\n prim.addVertex(1)\\r\\n prim.addVertex(5)\\r\\n prim.closePrimitive()\\r\\n\\r\\n #Create a Geom to bing vertex data to primitives\\r\\n geom = Geom(vdata)\\r\\n geom.addPrimitive(prim)\\r\\n\\r\\n #Create a node for the Geom in order to be able to render it\\r\\n node = GeomNode('gnode')\\r\\n node.addGeom(geom)\\r\\n\\r\\n #Adde the node to the scene graph == render it!\\r\\n nodePath = render.attachNewNode(node)\\r\\n \\r\\n #is this needed?\\r\\n nodePath.setPos( 0, 5, 0)\\r\\n \\r\\n self.camera.lookAt(nodePath)\\r\\n \\r\\n base.setBackgroundColor( .0, .0, .0 )\\r\\n \\r\\n taskMgr.add(self.SpinCameraTask, \\\"SpinCameraTask\\\")\",\n \"def _generate(self, **kwargs):\\n N = self.parameter_schema['N']\\n parameter_count = len(self._parameter_names)\\n common_override_kwargs = {}\\n override_kwargs = self._sampler_overrides(common_override_kwargs)\\n if kwargs:\\n kwargs.update(override_kwargs)\\n else:\\n kwargs = override_kwargs\\n __import__(\\\"SALib.sample\\\", fromlist=[self.sampler_class])\\n sampler = getattr(SALib.sample, self.sampler_class)\\n problem = self.parameter_schema[\\\"problem\\\"]\\n self._samples = sampler.sample(problem, N, **kwargs)\\n self._samples = numpy.unique(self._samples, axis=0)\\n super()._generate()\",\n \"def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n self.__iteration_number = kwargs['iteration_number']\\n self.__particles = [\\n PSOParticle(**kwargs, bit_generator=self._random)\\n for _ in range(kwargs['particles'])\\n ]\\n\\n # The library stores particles in the visualizer .... groan\\n positions = [particle.position for particle in self.__particles]\\n self._visualizer = NoVisualizer(**kwargs)\\n self._visualizer.add_data(positions=positions)\",\n \"def construct_data_LHS(model,num_pts,seed=None):\\n num_dims = model.num_dims\\n rv_trans = define_random_variable_transformation_hydromad(model)\\n pts = latin_hypercube_design( num_pts, num_dims, seed )\\n # returns points on [0,1] but need pts on [-1,1]\\n pts = 2*pts-1.\\n pts = rv_trans.map_from_canonical_distributions( pts )\\n vals = model.evaluate_set( pts )\\n numpy.savetxt( 'pts.txt', pts, delimiter = ',' )\\n numpy.savetxt( 'vals.txt', vals, delimiter = ',' )\",\n \"def create_samples(self):\\n sample_list = []\\n genes = []\\n for record in range(len(self.data_dict[\\\"samples\\\"])):\\n sample_id = self.data_dict[\\\"samples\\\"][record]\\n genes_cols = list(self.data_dict.keys())[2:]\\n for gene in genes_cols:\\n genes.append(self.data_dict[gene][record])\\n label = self.data_dict[\\\"type\\\"][record]\\n sample_list.append(Sample(sample_id, genes, label))\\n genes = []\\n return sample_list\",\n \"def __init__(self, n):\\n self._n = n\\n self._grid = [[False] * n for _ in range(n)]\\n # create sites for n-by-n grid and 2 \\\"virtual\\\" sites for top and bottom\\n # self._uf = QuickFindUF(n * n + 2)\\n self._uf = WeightedQuickUnionUF(n * n + 2) # QuickFindUF(n * n + 2)\\n # connect top and bottom virtual sites with respecting sides of grid\\n self._top_idx = n * n\\n self._bottom_idx = n * n + 1\\n for i in range(n):\\n self._uf.union(self._top_idx, i)\\n self._uf.union(self._bottom_idx, (n - 1) * n + i)\",\n \"def load_hypercube(fname):\\n # Get header\\n f = open(fname, 'r')\\n hdr = f.readline()[2:-1].split(\\\" \\\")\\n f.close()\\n\\n # Load data\\n dat = np.loadtxt(fname).T\\n\\n # Build dict\\n sample_points = {}\\n for i in range(len(hdr)):\\n sample_points[hdr[i]] = dat[i]\\n return sample_points\",\n \"def generate_samples(mu1,cov,number_of_samples):\\n samples = np.random.multivariate_normal(mu1, cov,number_of_samples)\\n return samples\",\n \"def __init__(self, cube_size, time_range):\\n\\n # cubesize is in z,y,x for interactions with tile/image data\\n self.zdim, self.ydim, self.xdim = self.cubesize = [cube_size[2], cube_size[1], cube_size[0]]\\n self.time_range = time_range\\n self._newcube = False\",\n \"def n_simplex(self, dim_n=3, project = True):\\n verts = permutations([0 for i in range(dim_n)] + [1])\\n if project: verts = [Polytopes.project_1(x) for x in verts]\\n return Polyhedron(vertices = verts)\",\n \"def __init__(self, n_samples, max_iter=1000, verbose=1, eps=1e-3):\\n\\n\\t\\tself.sparam = dict()\\n\\t\\twith open('config.json') as config_file:\\n\\t\\t\\tself.sparam = json.load(config_file)\\n\\n\\t\\tself.C = self.sparam['C']\\n\\t\\tself.sizePsi = self.sparam['sizePsi']\\n\\t\\tself.num_classes = self.sparam['num_classes']\\n\\t\\t#self.w = np.random.rand(sparam['sizePsi'],1)\\n\\t\\tself.w = np.zeros((self.sparam['sizePsi'],1))\\n\\t\\tself.tempw = np.zeros((self.sparam['sizePsi'],1))\\n\\t\\t#self.tempw = np.random.rand(self.sparam['sizePsi'], 1)\\n\\t\\t#self.tempw = np.random.rand(self.sparam['sizePsi'])\\n\\t\\t#self.tempw[0:self.sizePsi/2] = np.zeros(self.sizePsi/2)\\n\\t\\t#self.tempw = self.tempw.reshape(self.sizePsi, 1)\\n\\t\\t#print np.sum(self.tempw)\\n\\t\\tself.w_changed = False\\n\\t\\tself.n = n_samples\\n\\t\\tself.max_iter = max_iter\\n\\t\\tself.verbose = verbose\\n\\t\\tself.eps = eps\\n\\t\\tself.alphas = []\\n\\t\\tself.losses = []\",\n \"def generate_samples(self,n_samples=100):\\n rnd_input=torch.randn((n_samples,self._reparam_nodes[1]))\\n zeta=rnd_input \\n # rnd_input=torch.where((rnd_input>0.5),torch.ones(rnd_input.size()),torch.zeros(rnd_input.size()))\\n # print(rnd_input) \\n # output, mu, logvar, zeta=self.forward(rnd_input)\\n # mu = self._reparam_layers['mu'](rnd_input)\\n # logvar = self._reparam_layers['var'](rnd_input)\\n # zeta = self.reparameterize(mu, logvar)\\n output = self.decoder.decode(zeta)\\n return output\",\n \"def reconstructions_sample(self, n=()):\\n self.assert_sampled()\\n return [[j.sample(sample_shape=n, seed=self.randint).numpy()\\n for j in i]\\n for i in self._reconstructions]\",\n \"def _generate(self, **kwargs):\\n self._samples = numpy.array(list(itertools.product(*self.parameter_schema.values())), dtype=object)\\n super()._generate()\",\n \"def resample(self):\\n self.X = np.random.uniform(-1, 1, (100, 2))\\n self.generate_y()\\n self.phi = None\\n self.K = 0\\n self.centers = None\\n self.cluster_sizes = None\\n self.g = None\\n self.gamma = None\",\n \"def __init__(self, n):\\n self.n = n\\n self.w = np.zeros(self.n)\\n self.z = np.zeros(self.n)\\n self.F = 0\\n self.M = 0\",\n \"def __init__(self, i, h, o):\\n self.Wh = np.random.normal(size=(i+h, h))\\n self.Wy = np.random.normal(size=(h, o))\\n self.bh = np.zeros((1, h))\\n self.by = np.zeros((1, o))\",\n \"def _init_empty_polyhedron(self):\\n self._ambient_dim = 0\\n\\n self._Vrepresentation = Sequence([])\\n self._Vrepresentation.set_immutable()\\n \\n self._Hrepresentation = Sequence([])\\n Equation(self, [-1]);\\n self._Hrepresentation.set_immutable()\\n\\n self._V_adjacency_matrix = matrix(ZZ, 0, 0, 0)\\n self._V_adjacency_matrix.set_immutable()\\n\\n self._H_adjacency_matrix = matrix(ZZ, 1, 1, 0)\\n self._H_adjacency_matrix.set_immutable()\",\n \"def create_start_data(self):\\n\\t\\tdef inputMesh(feature_size):\\n\\t\\t\\tc1= np.expand_dims(np.array([0,-0.9]),0)\\n\\t\\t\\tc2= np.expand_dims(np.array([-0.9,0.9]),0)\\n\\t\\t\\tc3= np.expand_dims(np.array([0.9,0.9]),0)\\n\\t\\t\\tx1 = np.expand_dims(np.pad(np.array([0,-0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\\n\\t\\t\\tx2 = np.expand_dims(np.pad(np.array([-0.9,0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\\n\\t\\t\\tx3 = np.expand_dims(np.pad(np.array([0.9,0.9]),(0,feature_size-2),'constant',constant_values=(0,0)),0)\\n\\t\\t\\tedge_index = np.transpose(np.array([[0, 1], [0, 2], [1, 0], [1, 2], [2, 0], [2, 1]])) # COO format\\n\\t\\t\\treturn np.concatenate((c1,c2,c3),axis=0), np.concatenate((x1,x2,x3),axis=0),edge_index\\n\\n\\t\\tc, x, edge_index = inputMesh(self.params.feature_size)# x is c with zeros appended, x=f ..pixel2mesh\\n\\t\\tdata_list_x = []\\n\\t\\tdata_list_c = []\\n\\t\\tdata_list_pid = []\\n\\t\\tfor i in range(self.params.batch_size):\\n\\t\\t\\tdata_list_x.append(Data(x=torch.Tensor(x).type(dtypeF), edge_index=torch.Tensor(edge_index).type(dtypeL)))\\n\\t\\t\\tdata_list_c.append(Data(x=torch.Tensor(c).type(dtypeF), edge_index=torch.Tensor(edge_index).type(dtypeL)))\\n\\t\\t\\tdata_list_pid.append(Data(x=torch.zeros(c.shape[0],1).type(dtypeL).requires_grad_(False)))\\n\\t\\tbatch_x = Batch.from_data_list(data_list_x)\\n\\t\\tbatch_c = Batch.from_data_list(data_list_c)\\n\\t\\tbatch_pid = Batch.from_data_list(data_list_pid)\\n\\t\\treturn batch_x, batch_c, batch_pid\",\n \"def labelledCube(self, dim=None, sample=None):\\n if dim is None:\\n dim = self.D\\n if sample is None:\\n sample = range(1, int(self.poolSize)+1)\\n \\n all_labels = list(it.product(*(range(self.slices),) * dim))\\n self.sample_labels = set(random.sample(all_labels, k= len(sample)))\\n labelled_sample = {label : sample for label, sample in zip(self.sample_labels, sample)}\\n self.text[\\\"labelledSamples\\\"] = labelled_sample\\n return labelled_sample\",\n \"def __init__(self, n_in, n_out):\\n self.W = np.random.randn(n_in, n_out) * 0.1\\n self.b = np.zeros(n_out)\",\n \"def view_surface_rec(self, x, n_max=1000, random_state=42, title=None, dataset_name=None):\\n if self.comet_exp is not None:\\n # If comet_exp is set, use different backend to avoid display errors on clusters\\n matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!\\n import matplotlib.pyplot as plt\\n from grae.data.manifolds import set_axes_equal\\n\\n np.random.seed(random_state)\\n\\n x_hat = self.reconstruct(x)\\n x, y = x.numpy()\\n\\n if x.shape[0] > n_max:\\n sample_mask = np.random.choice(x.shape[0], size=n_max, replace=False)\\n x_hat = x_hat[sample_mask]\\n x = x[sample_mask]\\n y = y[sample_mask]\\n\\n scene_dict = dict(SwissRoll=(0, 0), Mammoth=(-15, 90), ToroidalHelices=(30, 0))\\n if dataset_name in scene_dict:\\n tilt, rotation = scene_dict[dataset_name]\\n else:\\n tilt, rotation = 0, 0\\n\\n # set up a figure twice as wide as it is tall\\n fig = plt.figure(figsize=plt.figaspect(0.5))\\n\\n ax = fig.add_subplot(1, 2, 1, projection='3d')\\n ax.view_init(tilt, rotation)\\n ax.set_title('Input')\\n ax.scatter(*x.T, c=y, cmap='jet', edgecolor='k')\\n set_axes_equal(ax)\\n\\n ax = fig.add_subplot(1, 2, 2, projection='3d')\\n\\n ax.view_init(tilt, rotation)\\n ax.set_title('Reconstruction')\\n ax.scatter(*x_hat.T, c=y, cmap='jet', edgecolor='k')\\n set_axes_equal(ax)\\n\\n\\n if title is not None:\\n fig.suptitle(title, fontsize=20)\\n\\n if self.comet_exp is not None:\\n self.comet_exp.log_figure(figure=plt, figure_name=title)\\n plt.clf()\\n else:\\n plt.show()\",\n \"def __init__(self,nback=1,ntokens_pm=2,ntokens_og=3,stimdim=2,seed=99):\\n np.random.seed(seed)\\n tr.manual_seed(seed)\\n self.nback = nback\\n # embedding\\n self.ntokens_pm = ntokens_pm\\n self.ntokens_og = ntokens_og\\n self.stimdim = stimdim\\n # emat\\n self.randomize_emat()\\n return None\",\n \"def test_gan_qiskit(n, Database):\\n mini = np.min(Database)\\n maxi = np.max(Database)\\n h = (maxi - mini) / (2 ** n)\\n bins = [[k for d in Database if mini + h * k < d < mini + h * (k + 1)] for k in range(2 ** n)]\\n interv = [mini + h * k for k in range(2 ** n)]\\n backend = BasicAer.get_backend('statevector_simulator')\\n random_seed = 10598\\n\\n quantum_instance = QuantumInstance(backend, seed=random_seed, seed_transpiler=random_seed)\\n gan_test = QGAN(Database, num_qubits=[n], snapshot_dir=None,\\n quantum_instance=quantum_instance, batch_size=int(len(Database) / 20), num_epochs=300)\\n gan_test.train()\\n samp, bins_var = gan_test.generator.get_output(gan_test.quantum_instance, shots=4096)\\n\\n compar = [len(b) / len(Database) for b in bins]\\n if len(interv) == len(compar):\\n plt.plot(interv, compar)\\n\\n plt.plot(interv, bins_var)\\n\\n plt.show()\",\n \"def init_homographies(self, homography_path, n_images):\\n with open(homography_path) as f:\\n h_data = f.readlines()\\n h_scale = h_data[0].rstrip().split(' ')\\n self.h_scale_x = int(h_scale[1])\\n self.h_scale_y = int(h_scale[2])\\n h_bounds = h_data[1].rstrip().split(' ')\\n self.h_bounds_x = [float(h_bounds[1]), float(h_bounds[2])]\\n self.h_bounds_y = [float(h_bounds[3]), float(h_bounds[4])]\\n homographies = h_data[2:2 + n_images]\\n homographies = [torch.from_numpy(np.array(line.rstrip().split(' ')).astype(np.float32).reshape(3, 3)) for line\\n in\\n homographies]\\n self.homographies = homographies\",\n \"def sample(self, n):\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7344228","0.70358026","0.6742598","0.66756856","0.66756856","0.66473216","0.6490043","0.6219821","0.61849946","0.5989805","0.5899154","0.5826263","0.57939756","0.5752896","0.57472134","0.5707989","0.5667523","0.5647223","0.5642459","0.5631831","0.55860597","0.55722594","0.55670935","0.55549943","0.5521048","0.55015755","0.5500793","0.548168","0.54805875","0.54661304","0.5465828","0.54652905","0.54336905","0.53997153","0.53947407","0.53863263","0.53831786","0.53694826","0.536645","0.53449196","0.532726","0.5314865","0.52996874","0.52964103","0.5285389","0.5283856","0.5274502","0.52640295","0.52618","0.52577335","0.5249758","0.52486694","0.5232152","0.5227412","0.5224593","0.5215586","0.5215308","0.52136815","0.5211935","0.52034837","0.5198511","0.51967734","0.5193326","0.51876485","0.518466","0.5181856","0.51705223","0.51680934","0.516793","0.51662","0.51651675","0.51615375","0.51598644","0.51565355","0.5154698","0.51540935","0.51501155","0.51463443","0.5144513","0.51418734","0.5138205","0.5137083","0.51350915","0.5134563","0.5134352","0.5129879","0.51263607","0.5126178","0.5121261","0.5114322","0.5112676","0.5104572","0.5104282","0.5101256","0.50980407","0.50959635","0.5089722","0.5076849","0.5075957","0.50732213"],"string":"[\n \"0.7344228\",\n \"0.70358026\",\n \"0.6742598\",\n \"0.66756856\",\n \"0.66756856\",\n \"0.66473216\",\n \"0.6490043\",\n \"0.6219821\",\n \"0.61849946\",\n \"0.5989805\",\n \"0.5899154\",\n \"0.5826263\",\n \"0.57939756\",\n \"0.5752896\",\n \"0.57472134\",\n \"0.5707989\",\n \"0.5667523\",\n \"0.5647223\",\n \"0.5642459\",\n \"0.5631831\",\n \"0.55860597\",\n \"0.55722594\",\n \"0.55670935\",\n \"0.55549943\",\n \"0.5521048\",\n \"0.55015755\",\n \"0.5500793\",\n \"0.548168\",\n \"0.54805875\",\n \"0.54661304\",\n \"0.5465828\",\n \"0.54652905\",\n \"0.54336905\",\n \"0.53997153\",\n \"0.53947407\",\n \"0.53863263\",\n \"0.53831786\",\n \"0.53694826\",\n \"0.536645\",\n \"0.53449196\",\n \"0.532726\",\n \"0.5314865\",\n \"0.52996874\",\n \"0.52964103\",\n \"0.5285389\",\n \"0.5283856\",\n \"0.5274502\",\n \"0.52640295\",\n \"0.52618\",\n \"0.52577335\",\n \"0.5249758\",\n \"0.52486694\",\n \"0.5232152\",\n \"0.5227412\",\n \"0.5224593\",\n \"0.5215586\",\n \"0.5215308\",\n \"0.52136815\",\n \"0.5211935\",\n \"0.52034837\",\n \"0.5198511\",\n \"0.51967734\",\n \"0.5193326\",\n \"0.51876485\",\n \"0.518466\",\n \"0.5181856\",\n \"0.51705223\",\n \"0.51680934\",\n \"0.516793\",\n \"0.51662\",\n \"0.51651675\",\n \"0.51615375\",\n \"0.51598644\",\n \"0.51565355\",\n \"0.5154698\",\n \"0.51540935\",\n \"0.51501155\",\n \"0.51463443\",\n \"0.5144513\",\n \"0.51418734\",\n \"0.5138205\",\n \"0.5137083\",\n \"0.51350915\",\n \"0.5134563\",\n \"0.5134352\",\n \"0.5129879\",\n \"0.51263607\",\n \"0.5126178\",\n \"0.5121261\",\n \"0.5114322\",\n \"0.5112676\",\n \"0.5104572\",\n \"0.5104282\",\n \"0.5101256\",\n \"0.50980407\",\n \"0.50959635\",\n \"0.5089722\",\n \"0.5076849\",\n \"0.5075957\",\n \"0.50732213\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6458857"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":4686,"cells":{"query":{"kind":"string","value":"Creates the initial search space using latin hypercube sampling."},"document":{"kind":"string","value":"def lhs_start(hyperbounds, n_samples, rng=None):\n low_bounds = []\n high_bounds = []\n for bound in hyperbounds:\n low_bounds.append(bound[0])\n high_bounds.append(bound[1])\n\n low_bounds = np.array(low_bounds, dtype=object)\n high_bounds = np.array(high_bounds, dtype=object)\n\n samples = sample_latin_hypercube(low_bounds, high_bounds, n_samples, rng=rng)\n samples = samples.tolist()\n return samples"},"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 generate_latin_hypercube(samples, param_dict, class_root, seed=10):\n # Set random seed\n random.seed(seed)\n\n # Create dictionary to hold sampled parameter values\n sample_points = {}\n for key in param_dict.keys():\n sample_points[key] = np.zeros(samples)\n Ndim = len(param_dict.keys())\n pnames = [key for key in param_dict.keys()]\n\n # List of indices for each dimension\n l = [range(samples) for j in range(Ndim)]\n\n # Generate samples until there are no indices left to choose\n for i in range(samples):\n\n # Randomly choose index and then remove the number that was chosen\n # (Latin hypercubes require at most one item per row and column)\n for j, p in enumerate(pnames):\n pmin, pmax = param_dict[p]\n idx = random.choice(l[j])\n\n # Get value at this sample point (add 0.5 to idx get bin centroid)\n sample_points[p][i] = pmin + (pmax - pmin) \\\n * (idx + 0.5) / float(samples)\n l[j].remove(idx) # Remove choice from list (sampling w/o replacement)\n\n return sample_points","def latin_hypercube(n_pts, dim):\n X = np.zeros((n_pts, dim))\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\n for i in range(dim): # Shuffle the center locataions for each dimension.\n X[:, i] = centers[np.random.permutation(n_pts)]\n\n # Add some perturbations within each box\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\n X += pert\n return X","def latin_hypercube(n_pts, mins, maxs):\n #return a latin_hypercube\n design = lhs(np.size(maxs), samples=n_pts)\n for i in range(2):\n design[:, i] = design[:, i] * (maxs[i]-mins[i]) + mins[i]\n return design","def localInitialize(self):\n SVL = self.readFromROM()\n self._generateQuadsAndPolys(SVL)\n #print out the setup for each variable.\n msg = self.printTag+' INTERPOLATION INFO:\\n'\n msg += ' Variable | Distribution | Quadrature | Polynomials\\n'\n for v in self.quadDict:\n msg += ' '+' | '.join([v,self.distDict[v].type,self.quadDict[v].type,self.polyDict[v].type])+'\\n'\n msg += ' Polynomial Set Degree: '+str(self.maxPolyOrder)+'\\n'\n msg += ' Polynomial Set Type : '+str(SVL.indexSetType)+'\\n'\n self.raiseADebug(msg)\n\n self.raiseADebug('Starting index set generation...')\n self.indexSet = IndexSets.factory.returnInstance(SVL.indexSetType)\n self.indexSet.initialize(self.features, self.importanceDict, self.maxPolyOrder)\n if self.indexSet.type=='Custom':\n self.indexSet.setPoints(SVL.indexSetVals)\n\n self.sparseGrid = Quadratures.factory.returnInstance(self.sparseGridType)\n self.raiseADebug(f'Starting {self.sparseGridType} sparse grid generation...')\n self.sparseGrid.initialize(self.features, self.indexSet, self.dists, self.quadDict, self.jobHandler)\n\n if self.writeOut is not None:\n msg = self.sparseGrid.__csv__()\n outFile = open(self.writeOut,'w')\n outFile.writelines(msg)\n outFile.close()\n\n self.limit=len(self.sparseGrid)\n self.raiseADebug(f'Size of Sparse Grid: {self.limit}')\n self.raiseADebug('Finished sampler generation.')\n\n self.raiseADebug('indexset:',self.indexSet)\n for SVL in self.ROM.supervisedContainer:\n SVL.initialize({'SG': self.sparseGrid,\n 'dists': self.dists,\n 'quads': self.quadDict,\n 'polys': self.polyDict,\n 'iSet': self.indexSet})","def setUp(self):\n self.grid = SudukuGrid(BaseCase)\n for i in range(81):\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]","def sample_latin_hypercube(low, high, n_samples, rng=None):\n if rng is None:\n rng = np.random.RandomState(np.random.randint(0, 10000))\n\n n_dims = low.shape[0]\n\n samples = []\n for i in range(n_dims):\n if isinstance(low[i], numbers.Integral):\n sample = random.sample(range(low[i], high[i]), n_samples)\n elif isinstance(low[i], numbers.Real):\n lower_bound = low[i]\n upper_bound = high[i]\n sample = lower_bound + rng.uniform(0, 1, n_samples) * (upper_bound - lower_bound)\n else:\n raise ValueError('Latin hypercube sampling can only draw from types int and real,'\n ' got {}!'.format(type(low[i])))\n\n samples.append(sample)\n\n samples = np.array(samples, dtype=object)\n\n for i in range(n_dims):\n rng.shuffle(samples[i, :])\n\n return samples.T","def latin_hypercube_sampler(n=1, indim=1, bounds=None, rng=None):\r\n rng = ensure_rng(rng)\r\n if bounds is None:\r\n bounds = np.zeros((indim, 2))\r\n bounds[:,1] = 1. \r\n # Divide each dimension into `n` equal intervals\r\n hypercubes = np.linspace(bounds[:,0], bounds[:,1], n+1)\r\n \r\n l = hypercubes[:-1,:].reshape(-1,)\r\n u = hypercubes[1:,:].reshape(-1,)\r\n _x = rng.uniform(l,u, (1, indim*n)).reshape(n, indim)\r\n x = _x\r\n for j in range(indim):\r\n x[:,j] = _x[rng.permutation(n), j]\r\n return x","def initialize(self):\n#TODO: choose user defined START position\n values_type = np.dtype(float)\n self.visual_field = np.zeros(self.number_of_locs, dtype=values_type)\n self.weighted_sums = np.zeros(self.number_of_locs, dtype=values_type)\n self.prior_prob = 1.0 / np.prod(self.number_of_locs)\n self.post_probs = np.full(\n self.number_of_locs, self.prior_prob, dtype=values_type\n )\n starting_location = np.array(START)\n self.focus = get_index_of_in(starting_location,self.senzory_map)\n self.target_location = [\n x for x in xrange(self.number_of_locs) if x != self.focus\n ][random.randint(0,self.number_of_locs-2)]","def __init__(self, initial, size, horizontalChunks, verticalChunks, goal = \"\"):\n\t\tself.initial = initial\n\t\tself.size = size\n\t\tself.horChunks = horizontalChunks\n\t\tself.verChunks = verticalChunks\n\n\t\t# Goal holds the solution, once we find it.\n\t\tself.goal = goal\n\n\t\t# For a puzzle of size n, initializes blank n x n 2d array\n\t\tself.graph = [[0 for x in range(self.size)] for x in range(self.size)] \n\t\tfor i in range (0,self.size):\n\t\t\tfor j in range (0,self.size):\n\t\t\t\tself.graph[i][j] = initial[i*self.size + j] \n\t\tself.initial = \"\"","def gen_hypercube(samples, N):\n\n np.random.seed(4654562)\n hypercube = lhs(N, samples=samples)\n\n return hypercube","def __init__(self):\n super().__init__()\n self.type = 'SparseGridCollocationSampler'\n self.printTag = 'SAMPLER '+self.type.upper()\n self.maxPolyOrder = None #L, the relative maximum polynomial order to use in any dimension\n self.indexSetType = None #TP, TD, or HC; the type of index set to use\n self.polyDict = {} #varName-indexed dict of polynomial types\n self.quadDict = {} #varName-indexed dict of quadrature types\n self.importanceDict = {} #varName-indexed dict of importance weights\n self.maxPolyOrder = None #integer, relative maximum polynomial order to be used in any one dimension\n self.lastOutput = None #pointer to output dataObjects object\n self.ROM = None #pointer to ROM\n self.jobHandler = None #pointer to job handler for parallel runs\n self.doInParallel = True #compute sparse grid in parallel flag, recommended True\n self.dists = {} #Contains the instance of the distribution to be used. keys are the variable names\n self.writeOut = None\n self.indexSet = None\n self.sparseGrid = None\n self.features = None\n self.sparseGridType = None\n self.addAssemblerObject('ROM', InputData.Quantity.one)","def initialise():\n _initialiseGlobals()\n for pop in AnadPartOfPerspectiveDb.Iterator():\n _addToKnowledge(pop)\n return","def create_grids_structure(self):\n for indices, hypercube in np.ndenumerate(self.hypercubes):\n self.hypercubes[indices] = Hypercube(coords=indices)","def setUp(self):\n # generate lattice\n self.lattice = lattice.Lattice()\n self.lattice.addAtom(\"He\", [0,0,0], 0)\n self.lattice.addAtom(\"He\", [2,0,0], 0)\n self.lattice.addAtom(\"He\", [0,2,0], 0)\n self.lattice.addAtom(\"He\", [0,0,2], 0)\n self.lattice.addAtom(\"He\", [9,9,9], 0)\n self.lattice.addAtom(\"He\", [2,2,0], 0)\n self.lattice.addAtom(\"He\", [2,0,2], 0)\n self.lattice.addAtom(\"He\", [0,2,2], 0)\n self.lattice.addAtom(\"He\", [2,2,2], 0)\n \n # indexes of cluster atoms\n self.bigClusterIndexes = [0,1,2,3,5,6,7,8]\n self.smallClusterIndexes = [4]\n \n # filter\n self.filter = clusterFilter.ClusterFilter(\"Cluster\")","def main():\n\n parser = argparse.ArgumentParser(description='Create a new Wordsearch')\n parser.add_argument('size', type=grid_size_type,\n help=\"height and width of our wordsearch grid (min: 3)\")\n parser.add_argument('wordfile', type=argparse.FileType('r'),\n help=\"file including words to search for\")\n parser_args = parser.parse_args()\n\n new_matrix = Matrix(parser_args.size)\n\n words_to_find = create_word_list_from_file(parser_args.wordfile, parser_args.size)\n\n words_found = []\n for word in words_to_find:\n if word not in words_found and word in new_matrix:\n words_found.append(word)\n\n print(\"\\n{}\\n\\n{}\\n\".format(new_matrix, \" \".join(sorted(words_found))))","def at_object_creation(self):\r\n with open(\"./commands/CSW15.txt\") as word_file:\r\n self.db.csw15 = set(word.strip().upper() for word in word_file)\r\n self.db.centre = \"\" \r\n self.db.tiledict = {'A' : 9,\r\n 'B' : 2,\r\n 'C' : 2,\r\n 'D' : 4,\r\n 'E' : 12,\r\n 'F' : 2,\r\n 'G' : 3,\r\n 'H' : 2,\r\n 'I' : 9,\r\n 'J' : 1,\r\n 'K' : 1,\r\n 'L' : 4,\r\n 'M' : 2,\r\n 'N' : 6,\r\n 'O' : 8,\r\n 'P' : 2,\r\n 'Q' : 1,\r\n 'R' : 6,\r\n 'S' : 4,\r\n 'T' : 6,\r\n 'U' : 4,\r\n 'V' : 2,\r\n 'W' : 2,\r\n 'X' : 1,\r\n 'Y' : 2,\r\n 'Z' : 1,\r\n '?' : 0\r\n } #removing blanks from play; blanks make it very slow. Change here, in dict\r\n self.db.tilestring = list(''.join([L*self.db.tiledict[L] for L in string.ascii_uppercase+'?']))","def setUp(self):\n np.random.seed(1234)\n\n _TEST_FILE_NAME = 'AHN3.las'\n _TEST_DATA_SOURCE = 'testdata'\n\n _CYLINDER = InfiniteCylinder(4)\n _PC_260807 = load(os.path.join(_TEST_DATA_SOURCE, _TEST_FILE_NAME))\n _PC_1000 = copy_point_cloud(_PC_260807, array_mask=(\n np.random.choice(range(len(_PC_260807[keys.point]['x']['data'])), size=1000, replace=False)))\n _1000_NEIGHBORHOODS_IN_260807 = list(compute_neighbors.compute_neighborhoods(_PC_260807, _PC_1000, _CYLINDER))\n\n self.point_cloud = _PC_260807\n self.neigh = _1000_NEIGHBORHOODS_IN_260807","def convert_searchspace(self, hyperparameter):\n LOG.debug(\"convert input parameter\\n\\n\\t{}\\n\".format(pformat(hyperparameter)))\n searchspace = [[], []]\n for name, param in hyperparameter.items():\n if param[\"domain\"] != \"categorical\" and \"frequency\" not in param.keys():\n param[\"frequency\"] = DEFAULTGRIDFREQUENCY\n warnings.warn(\"No frequency field found, used default gridsearch frequency {}\".format(DEFAULTGRIDFREQUENCY))\n\n if param[\"domain\"] == \"categorical\":\n searchspace[0].append(name)\n searchspace[1].append(param[\"data\"])\n elif param[\"domain\"] == \"uniform\":\n searchspace[0].append(name)\n searchspace[1].append(get_uniform_axis_sample(param[\"data\"][0],\n param[\"data\"][1],\n param[\"frequency\"],\n param[\"type\"]))\n elif param[\"domain\"] == \"normal\":\n searchspace[0].append(name)\n searchspace[1].append(get_gaussian_axis_sample(param[\"data\"][0],\n param[\"data\"][1],\n param[\"frequency\"],\n param[\"type\"]))\n elif param[\"domain\"] == \"loguniform\":\n searchspace[0].append(name)\n searchspace[1].append(get_logarithmic_axis_sample(param[\"data\"][0],\n param[\"data\"][1],\n param[\"frequency\"],\n param[\"type\"]))\n return searchspace","def initialize(self, search_space, names, outer_i=None):\n name = search_space.name\n names = copy.deepcopy(names)\n names.append(name)\n output_dim = self.cells[-1].hidden_size\n\n num_inner = self.search_space.eval_(search_space.num_inner, **locals())\n if len(num_inner) > 1:\n key = f'{\"_\".join(names[:-1])}_{len(num_inner)}_{name}s'\n add_if_doesnt_exist(self.policies, key, nn.Linear(output_dim, len(num_inner)))\n add_if_doesnt_exist(self.values, key, nn.Linear(output_dim, len(num_inner)))\n\n add_increment(self.embedding_index, f'{name}_start')\n add_increment(self.embedding_index, f'{name}_end')\n\n self.adapt(search_space.outer.items(), names, outer_i)\n\n for i in range(max(num_inner)):\n add_increment(self.embedding_index, f'{i+1}_{name}s')\n if isinstance(search_space.inner, (list, tuple)):\n for space in search_space.inner: self.initialize(space, names, i)\n elif isinstance(search_space.inner, SearchSpace):\n self.initialize(search_space.inner, names, i)\n else:\n assert isinstance(search_space.inner, dict), \\\n 'Inner search space must be either list, dict or SearchSpace.'\n self.adapt(search_space.inner.items(), names, outer_i)\n add_increment(self.embedding_index, f'{name}_inner_done')","def initialize(self):\n self.SIZE = self.vectors.shape[0]\n # todo can use max distance to allocation farthest apart points\n self.centroids = self.vectors[[random.randint(1, self.SIZE) for x in range(self.K)], :]","def grid_search(self):\n\t\t''' common settings without grid-search '''\n\t\tbinary_rele, unknown_as_zero = False, False\n\t\tcommon_data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\n\t\t\t\t\t\t\t\tunknown_as_zero=unknown_as_zero, binary_rele=binary_rele)\n\n\t\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\n\t\tcommon_data_dict.update(data_meta)\n\n\t\t''' some settings for grid-search '''\n\t\tchoice_presort = [True] if self.debug else [True]\n\t\tchoice_sample_rankings_per_q = [1] if self.debug else [1] # number of sample rankings per query\n\t\tchoice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(data_id=self.data_id, grid_search=True)\n\n\t\tfor scale_data, scaler_id, scaler_level, presort, sample_rankings_per_q in product(choice_scale_data,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t choice_scaler_id,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t choice_scaler_level,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t choice_presort,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t choice_sample_rankings_per_q):\n\n\t\t\tself.data_dict = dict(presort=presort, sample_rankings_per_q=sample_rankings_per_q,\n\t\t\t\t\t\t\t\t scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\n\t\t\tself.data_dict.update(common_data_dict)\n\t\t\tyield self.data_dict","def initialize( self, layout, numGhostAgents=1000 ):\n self.data.initialize(layout, numGhostAgents) ##self.data is defined in the Grid() class of game.py REF112.It creates an initial game state from a layout array (see layout.py).","def __init__(self, center_words, context_words, neg_samples): \n self.center_words = center_words\n self.context_words = context_words\n self.neg_samples = neg_samples\n # The index of the data the batch should start from. \n self.data_index = 0","def test_init_experiment(self):\n optimizer = \"RandomSearch\"\n name = \"test_init_experiment\"\n param_defs = {\n \"x\": MinMaxNumericParamDef(0, 1),\n \"name\": NominalParamDef([\"A\", \"B\", \"C\"])\n }\n minimization = True\n\n LAss = PrettyLabAssistant()\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\n\n exp_ass = LAss.exp_assistants[name]\n\n assert_equal(exp_ass.optimizer, optimizer)\n assert_is_none(exp_ass.optimizer_arguments, None)\n assert_equal(exp_ass.experiment.minimization_problem, minimization)\n with assert_raises(ValueError):\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)","def __init__(self, hyperparameters, total_dim, num_is):\n self._dim = total_dim # dimension of IS \\times search space\n self._num_is = num_is # Number of information sources, then including 0th IS (truth), size of hyper should be dim * (num_is+1).\n # Note: it's not (dim+1)*(num_is+1) because dimension of search space is (dim-1), plus the multiplication factor param is dim\n self.set_hyperparameters(hyperparameters)","def build_index(self):\n # Init the HNSWLIB index\n self.create_index()\n logger.info(f\"Building HNSWLIB index, max_elements: {len(self.corpus)}\")\n logger.debug(f\"Parameters Required: M: {self.M}\")\n logger.debug(f\"Parameters Required: ef_construction: {self.ef_construction}\")\n logger.debug(f\"Parameters Required: ef(>topn): {self.ef}\")\n\n # Then we train the index to find a suitable clustering\n self.index.add_items(self.corpus_embeddings, list(range(len(self.corpus_embeddings))))","def init_embedding(size=50):\n vector = np.random.normal(0.0, 0.01, size)\n return vector","def __init__(self):\n # better to be a prime number, less collision\n self.key_space = 2069\n self.hash_table = [Bucket() for i in range(self.key_space)]","def create_index(args, client):\n policy = {}\n client.index_geo2dsphere_create(args.nspace, args.set,\n LOCBIN, LOCNDX, policy)\n client.index_integer_create(args.nspace, args.set,\n HSHBIN, HSHNDX, policy)","def initializeDistribution(self):\n self.checkDistParams()\n\n self.lowerBound = min(self.mapping.keys())\n self.upperBound = max(self.mapping.keys())","def __init__(self, lower=True, num_norm=True,\n use_char=True, initial_vocab=None):\n self._num_norm = num_norm\n self._use_char = use_char\n \n # TODO: check how to use this\n self._node_vocab = Vocabulary(lower=False)\n self._word_vocab = Vocabulary(lower=lower)\n self._char_vocab = Vocabulary(lower=False)\n #TODO: check usability\n self._label_vocab = Vocabulary(lower=False, unk_token=False)\n\n if initial_vocab:\n self._word_vocab.add_documents([initial_vocab])\n self._char_vocab.add_documents(initial_vocab)","def init_from_scratch(args, train_exs, dev_exs):\n \n # Build a dictionary from the data sqls+queries (train/dev splits)\n logger.info('-' * 100)\n logger.info('Build vocab')\n \n vocab = build_vocab(train_exs + dev_exs, args)\n logger.info('Num words = %d' % vocab.size())\n \n # Initialize model\n model = CopyNet(args, vocab)\n logger.info('-' * 100)\n logger.info('Model Architecture')\n logger.info(model)\n if args.embedding_file:\n model.load_embeddings(vocab.tokens(), args.embedding_file)\n \n return model, vocab","def setUp(self):\n self._vocab = np.array([\"one\", \"two\", \"three\", \"four\",\n \"five\", \"six\", \"seven\", \"eight\", \"nine\"])\n self._embedding_dim = 2\n\n self._default_config = {\n \"vocab\": self._vocab,\n \"embedding_dim\": self._embedding_dim,\n \"position_encoding\": None\n }","def init(x_in):\n global public_keys, secret_keys, x\n x = func.get_bits(x_in)\n\n public_keys, secret_keys = [], []\n\n elgamal.init_g_p_q()\n for i in range(3):\n create_keys(i)","def main(self):\n grid = self.make_game_grid()\n print(self.grid_size, ' by ', self.grid_size, 'grid')\n trie = self.retrieve_trie()\n if not trie:\n trie = self.read_in_file(self.file)\n self.persist_trie(trie)\n\n all_possible_words = []\n # left to right rows\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid)\n # right to left rows\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=True, transpose=False)\n # left to right columns\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=False, transpose=True)\n # right to left columns\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=True, transpose=True)\n\n # handle all possible sun sets of the array row\n all_possible_words = self.all_words(all_possible_words)\n # get diagonal letters top to bottom\n all_possible_words = all_possible_words + self.get_diagonal_words(grid)\n # get diagonal letters bottom to top\n all_possible_words = all_possible_words + self.get_diagonal_words(grid, reverse=True)\n ans = self.check_words_in_trie(trie, all_possible_words)\n self.sorted_words = sorted(ans, key=len)\n if self.sorted_words:\n print(\"The number of words in the solution is: %s.\" % (len(ans),))\n print(\"The shortest word in the solution is: %s.\" % (self.sorted_words[0],))\n print(\"The longest word in the solution is: %s.\" % (self.sorted_words[-1],))\n print('the possible words in this grid are ', self.sorted_words)\n return self.sorted_words","def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\n raise NotImplementedError","def _initialize(self):\n for doc_index, doc in enumerate(self.document):\n temp_word_topic_matrix = []\n for word in doc:\n if word in self.word2id.keys():\n start_topic_index = np.random.randint(0, self.K)\n temp_word_topic_matrix.append(start_topic_index)\n self.doc_topic_matrix[doc_index, start_topic_index] += 1\n self.topic_word_matrix[start_topic_index, self.word2id[word]] += 1\n self.topic_matrix[start_topic_index] += 1\n self.current_word_topic_matrix.append(temp_word_topic_matrix)","def test_1_1_2D_cube_init(self): # TODO: REMOVE FUNC AFTER SPLIT\n check = [(0, 0), (1, 1), (1, 0), (0, 1), (0.5, 0.5)]\n\n nn_checks = {(0.5, 0.5): [(0, 1), (1, 0), (0, 0), (1, 1)],\n (0, 1): [(0, 0), (1, 1), (0.5, 0.5)]}\n\n init_triangulation(2, 0, check, nn_checks)","def __init__(self, input_n, input_t, input_language, input_epochs, input_embedding_type, input_clusters_num,\n input_training_data, input_evaluation_data, input_hunits_lower, input_hunits_upper,\n input_embedding_dim_lower, input_embedding_dim_upper, input_c, input_iterations):\n self.n = input_n\n self.t = input_t\n self.language = input_language\n self.epochs = input_epochs\n self.embedding_type = input_embedding_type\n self.clusters_num = input_clusters_num\n self.training_data = input_training_data\n self.evaluation_data = input_evaluation_data\n self.hunits_lower = input_hunits_lower\n self.hunits_upper = input_hunits_upper\n self.embedding_dim_lower = input_embedding_dim_lower\n self.embedding_dim_upper = input_embedding_dim_upper\n self.c = input_c\n self.iterations = input_iterations\n\n # Setting self.lambda to the number of the parameters of the largest possible model\n word_segmenter = WordSegmenter(input_name=\"temp\", input_n=50, input_t=10000,\n input_clusters_num=self.clusters_num,\n input_embedding_dim=self.embedding_dim_upper, input_hunits=self.hunits_upper,\n input_dropout_rate=0.2, input_output_dim=4, input_epochs=1,\n input_training_data=self.training_data,\n input_evaluation_data=self.evaluation_data, input_language=self.language,\n input_embedding_type=self.embedding_type)\n word_segmenter.train_model()\n self.lam = 1/word_segmenter.model.count_params()","def init_algorithm(self):\n pass","def create_initial_grid():\n\n\tgrid = {(x, y) : ' + ' for x in range(8) for y in range(8)}\n\n\t# Define initial positions \n\tgrid[(3,3)] = colors.RED + \"[I]\" + colors.STOP\n\tgrid[(4,3)] = colors.GREEN + \"[A]\" + colors.STOP\n\tgrid[(3,4)] = colors.GREEN + \"[A]\" + colors.STOP\n\tgrid[(4,4)] = colors.RED + \"[I]\" + colors.STOP\n\n\treturn grid","def test_2_1_3D_cube_init(self):\n check = [(0, 0, 0), (1, 1, 1), (1, 0, 0), (1, 1, 0), (1, 0, 1),\n (0, 1, 0), (0, 1, 1), (0, 0, 1), (0.5, 0.5, 0.5)]\n\n nn_checks = {\n (1, 1, 1): [(1, 1, 0), (0, 1, 1), (1, 0, 0), (0, 0, 1), (1, 0, 1),\n (0.5, 0.5, 0.5), (0, 1, 0)],\n (1, 0, 1): [(1, 0, 0), (0, 0, 1), (0, 0, 0), (0.5, 0.5, 0.5),\n (1, 1, 1)],\n (0.5, 0.5, 0.5): [(1, 1, 0), (0, 1, 1), (0, 1, 0), (1, 0, 0),\n (0, 0, 1), (1, 0, 1), (0, 0, 0), (1, 1, 1)]}\n\n init_triangulation(3, 0, check, nn_checks)","def __init__( self ):\n self.NQ = 16\n self.Nbranches = 3\n self.NatomsUC = 1\n self.dim = 3\n self.QVectors = np.zeros( ( self.NQ , 3 ) )\n self.MakeQVectors()\n self.EigenVectors = np.zeros( [ self.NQ , \n self.Nbranches ,\n self.NatomsUC , \n self.dim ] )\n self.MakeEigenVectors()","def __init__(self):\n self.space = 1000\n self.hash_table = [Node(-1, -1)] * self.space","def __initCluster(self):\n data_size, cluster_center = self.data_size, self.cluster_center\n self.cluster_temp = np.zeros(data_size, dtype=int)\n self.cluster_upper_bound = np.full(len(cluster_center), float('inf'), dtype=float)\n for center in cluster_center:\n self.cluster_temp[center] = center","def _init_vocab(self):\n self._word2idx = {}\n self._idx2word = {}\n self.freqs = {}\n self.vocab_size = 0\n\n self._add_word(self.pad_word)\n self._add_word(self.start_word)\n self._add_word(self.end_word)\n self._add_word(self.unk_word)\n\n self.start_word_idx = self.stoi(self.start_word)\n self.end_word_idx = self.stoi(self.end_word)\n self.unk_word_idx = self.stoi(self.unk_word)\n self.pad_word_idx = self.stoi(self.pad_word)\n\n self._special_tokens = {\n 'bos_token': self.start_word,\n 'cls_token': self.start_word,\n 'eos_token': self.end_word,\n 'sep_token': self.end_word,\n 'pad_token': self.pad_word,\n 'unk_token': self.unk_word,\n }\n\n self._special_ids = {\n 'bos_token_id': self.start_word_idx,\n 'cls_token_id': self.start_word_idx,\n 'eos_token_id': self.end_word_idx,\n 'sep_token_id': self.end_word_idx,\n 'pad_token_id': self.pad_word_idx,\n 'unk_token_id': self.unk_word_idx,\n }\n\n self.cls_token_id = self.bos_token_id = self.start_word_idx\n self.eos_token_id = self.sep_token_id = self.end_word_idx\n self.pad_token_id = self.pad_word_idx\n self.unk_token_id = self.unk_word_idx\n\n self.cls_token = self.bos_token = self.start_word\n self.eos_token = self.sep_token = self.end_word\n self.pad_token = self.pad_word\n self.unk_token = self.unk_word","def initialize_grid(self):\n self.grid = np.zeros([self.N, self.N, self.N])\n return self.grid","def setup_StandardGCMCSphereSampler():\n # Make variables global so that they can be used\n global std_gcmc_sphere_sampler\n global std_gcmc_sphere_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\n system = ff.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=12 * angstroms,\n constraints=HBonds)\n\n ref_atoms = [{'name': 'CA', 'resname': 'TYR', 'resid': '10'},\n {'name': 'CA', 'resname': 'ASN', 'resid': '43'}]\n\n std_gcmc_sphere_sampler = samplers.StandardGCMCSphereSampler(system=system, topology=pdb.topology,\n temperature=300*kelvin, referenceAtoms=ref_atoms,\n sphereRadius=4*angstroms,\n ghostFile=os.path.join(outdir, 'bpti-ghost-wats.txt'),\n log=os.path.join(outdir, 'stdgcmcspheresampler.log'))\n\n # Define a simulation\n integrator = NonequilibriumLangevinIntegrator(temperature=300*kelvin, collision_rate=1./picosecond, timestep=2.*femtoseconds)\n\n try:\n platform = Platform.getPlatformByName('CUDA')\n except:\n try:\n platform = Platform.getPlatformByName('OpenCL')\n except:\n platform = Platform.getPlatformByName('CPU')\n\n std_gcmc_sphere_simulation = Simulation(pdb.topology, system, integrator, platform)\n std_gcmc_sphere_simulation.context.setPositions(pdb.positions)\n std_gcmc_sphere_simulation.context.setVelocitiesToTemperature(300*kelvin)\n std_gcmc_sphere_simulation.context.setPeriodicBoxVectors(*pdb.topology.getPeriodicBoxVectors())\n\n # Set up the sampler\n std_gcmc_sphere_sampler.initialise(std_gcmc_sphere_simulation.context, [3054, 3055, 3056, 3057, 3058])\n\n return None","def create_sparseDB():\n datas = data.Kmercount_to_matrix()\n datas.run()\n print('***Sparse matrix created***')","def initialize(self):\n self.U = range(self.K)\n self.H = np.identity(self.rank)\n temp = 0\n self.S = np.zeros([self.rank, self.rank, self.K])\n for k in range(self.K):\n self.S[:, :, k] = np.identity(self.rank)\n temp += self.X[k].T.dot(self.X[k])\n [eigval, eigvec] = np.linalg.eig(temp)\n self.V = eigvec[:, range(self.rank)]","def create_index():","def __init__(self, initial, goal=None):\n \n #fill the grid with random numbers\n from random import randint as IA\n \n #in switches we must keep track of what numbers are here at the start\n #to prevent switching them.\n initialNumber = [[1 for x in range(size)] for y in range(size)]\n \n for i in range(size):\n for j in range(size):\n if(initial[i][j] == 0):\n x = IA(1,9)\n initialNumber[i][j] = 0\n while(not isLegalInBox(initial,i,j,x)):\n x = IA(1,9)\n initial[i][j] = x\n \n self.initialNumber = initialNumber\n self.initial = initial","def setUp(self):\n self.cube = _create_2d_cube()","def _train(self):\n return np.zeros(1, 10)","def __init__(self, vocabulary_size=1000):\n self.vocabulary_size = vocabulary_size","def setUp(self):\n problem = setup_house_L(size=(40, 40))\n\n env = MetroLayoutEnv()\n\n costfn = objectives.ConstraintsHeur(problem,\n wmap={'AspectConstraint':0.1,\n 'AreaConstraint': 2\n },\n default=1.)\n\n model = algo.MetropolisHastings(env, costfn)\n\n self.exp = SimpleMH(\n env,\n problem,\n model=model,\n cost_fn=costfn,\n num_iter=1000,\n initializer=PointsInBound(problem, env, size=3, seed=69)\n )","def init(self):\n\t\tfrom splat_to_db import splat_to_db\n\t\tfrom visualize.clustering_test import clustering_test\n\t\tfrom codense.codense2db import codense2db\n\t\tself.splat_to_db_instance = splat_to_db()\n\t\tself.clustering_test_instance = clustering_test()\n\t\tself.codense2db_instance = codense2db()\n\t\t\n\t\tif not os.path.isdir(self.dir_files):\n\t\t\tos.makedirs(self.dir_files)\n\t\telse:\n\t\t\tsys.stderr.write(\"Warning, directory %s already exists.\\n\"%(self.dir_files))\n\t\tself.tmpinfname = os.path.join(self.dir_files, 'input')\n\t\tself.tmpoutfname = os.path.join(self.dir_files, 'output')\n\t\t\n\t\tself.crack_dict = {1: crack_by_modes(self.debug),\n\t\t\t2:crack_by_splat(self.debug)}\n\t\tself.argument1_dict = {1: self.clustering_test_instance,\n\t\t\t2: self.splat_to_db_instance}\n\t\t\n\t\t#two descending tables\n\t\tself.splat_table = '%ss'%self.table\n\t\tself.mcl_table = self.splat_table.replace('splat','mcl')\n\t\tif self.mcl_table == self.splat_table:\n\t\t\tsys.stderr.write(\"Error: new splat and mcl tables have the same name, %s\\n\"%self.splat_table)\n\t\t\tsys.exit(2)","def _init_dataset(self):\n chars = set()\n with open(self.file_path + \"/words.txt\", 'r') as input_file:\n for line in input_file:\n line_split = line.strip().split('\\t')\n file_name = self.file_path+\"/words/\"+line_split[1]\n gt_text = line_split[0]\n chars = chars.union(set(list(gt_text)))\n self.samples.append((file_name, gt_text))\n input_file.close()\n\n self.char_set = sorted(list(chars))","def initialize(cls):\n return cls( *([0.]*cls._parsize) )","def __init__(self):\n self.words = None\n self.letters = None\n self.a = None\n self.nwords = None\n self.nletters = None","def set_up_threshold_cube():\n test_data = 50*np.arange(16).reshape(4, 4)\n grid_x = DimCoord(np.arange(4), standard_name=\"projection_x_coordinate\",\n units=\"km\")\n grid_y = DimCoord(np.arange(4), standard_name=\"projection_y_coordinate\",\n units=\"km\")\n test_cube = iris.cube.Cube(test_data, long_name=\"surface_altitude\",\n units=\"m\",\n dim_coords_and_dims=[(grid_y, 0), (grid_x, 1)])\n return test_cube","def setup_spatial():\n arena_size = \"default\"\n\n output_dict = {\n \"arena_size\": arena_size,\n }\n return output_dict","def __init__(self, neighbourhood, algorithm, iterations, set_up):\n self.input = neighbourhood\n self.algorithm = algorithm\n self.set_up = set_up\n self.iterations = int(iterations)\n self.configs = self.get_configs()\n self.houses = self.load_houses()\n self.big_iterations = -1\n self.small_iterations = 0\n self.caps = []\n self.batteries = {}\n self.lowest = 99999\n self.index = 0\n self.run_algorithm()","def __init__(self):\n self.grid = {}\n for i in range(21):\n self.grid[i] = [' ']*21\n self._len_x = len(self.grid[0])\n self._len_y = len(self.grid)\n self.forbidden_tiles = []\n self.allowed_tiles = []\n self.exit = None\n self.entrance = None","def initial_samples(lb, ub, method, numSamp):\r\n if not len(lb) == len(ub):\r\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\r\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\r\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\r\n for case in Switch(method):\r\n if case('random'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = lb + (ub - lb) * rand(len(lb))\r\n\r\n break\r\n if case('rand-wor'):\r\n s = np.zeros((numSamp, len(lb)))\r\n for i in range(0, numSamp, 1):\r\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\r\n\r\n break\r\n if case('nolh'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = range(q)\r\n if r != 0:\r\n remove = range(dim - r, dim)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-rp'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf = random.sample(range(q), q)\r\n if r != 0:\r\n remove = random.sample(range(q - 1), r)\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('nolh-cdr'):\r\n dim = len(ub)\r\n m, q, r = params(dim)\r\n conf, remove = get_cdr_permutations(len(ub))\r\n if remove != []:\r\n nolh = NOLH(conf, remove)\r\n else:\r\n nolh = NOLH(conf)\r\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\r\n ])\r\n break\r\n if case('lhc'):\r\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\r\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\r\n ])\r\n break\r\n if case():\r\n print 'Somehow you evaded my assert statement - good job!',\r\n print ' However, you still need to use a valid method string.'\r\n\r\n return s","def initialize(self):\n self.n_words = len(self.vocab)\n self.n_docs = len(self.documents)\n\n # Initialize the three count matrices.\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\n self.nmz = np.zeros((self.n_docs, self.n_topics))\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\n self.nzw = np.zeros((self.n_topics, self.n_words))\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\n self.nz = np.zeros(self.n_topics)\n\n # Initialize the topic assignment dictionary.\n self.topics = {} # key-value pairs of form (m,i):z\n\n for m in range(self.n_docs):\n for i in self.documents[m]:\n # Get random topic assignment, i.e. z is a random integer in the range of topics\n z = np.random.randint(self.n_topics)\n # Increment count matrices\n self.nmz[m,z] += 1\n self.nzw[z,self.documents[m][i]] += 1\n self.nz[z] += 1\n # Store topic assignment\n self.topics[(m,i)] = z","def initGrid( self, name, suffix, n, ni, nj, ifields=[],rfields=[], lsize=10):\n #print \"initGrid: initializing %s\"%(name)\n self.name = name\n self.suffix = suffix\n self.n = n\n self.ni = ni\n self.nj = nj\n self.ifields = ifields\n self.rfields = rfields\n #print \"ifields=%s\\nrfields=%s\\nlsize=%d\"%(temp_ifields, temp_rfields, lsize)\n self.lgrid = attributevector.AttributeVector( ifields, rfields, lsize )\n #print \"allocating a temp array...\"\n temp = Numeric.zeros( lsize, Numeric.Float64 )\n #temp = -9999.0\n #print \"Filling real fields with default values...\"\n for f in rfields:\n #print \"\\tFilling field\",f,\":\",\n self.lgrid.importRAttr( f, temp )\n #print \"... OK!\"\n print \"initGrid: Initialized Grid!\"\n # setup av complete\n return","def initialize_beam(self, prefix):\n\n def _from_none():\n \"\"\" Initializes a beam from scratch. \"\"\"\n beam_prefix = np.array([[self.vocab.go_id]] * self.opt.batch_size, dtype=np.int32)\n return beam_prefix\n\n def _from_string(string_prefix):\n \"\"\" Initializes a beam from an existing string prefix. \"\"\"\n idx_prefix = index_sentence(string_prefix, self.vocab, self.opt)\n beam_prefix = np.concatenate(\n [np.array([[self.vocab.eos_id]] * self.opt.batch_size, dtype=np.int32), idx_prefix], 1)\n return beam_prefix\n\n beam = list()\n # Initialize beam entries; the number is determined by the specified beam width\n # Beam entries are tuples of the form (generated sequence, sequence probability)\n for _ in range(self.opt.beam_width):\n if prefix is None:\n beam_init = _from_none()\n else:\n beam_init = _from_string(prefix)\n beam_tpl = (np.array([[1.0]] * self.opt.batch_size, dtype=np.float32), beam_init)\n beam.append(beam_tpl)\n\n return beam","def __init__(self):\n self.bigramCounts = collections.defaultdict(lambda : 0)\n self.trigramCounts = collections.defaultdict(lambda : 0)\n self.unigramCounts = collections.defaultdict(lambda : 1)\n self.continuationCounts = collections.defaultdict(lambda: 0)\n self.followingCounts = collections.defaultdict(lambda: 0)\n self.total = 1\n self.totalBigramCounts = 0\n print \"Training Language Model...\"\n self.train(brown.sents())\n print \"--Training Complete--\"","def GenerateInitialSolution():\n c = random.random()*C\n count = 0\n while np.count_nonzero(alpha) < gamma:\n rand = random.randint(0, len(x_train)-1)\n if y_train[rand] == 1:\n alpha[rand] = c\n L[rand, 1] = c\n # L[count, 0] = rand\n # L[count, 1] = alpha[rand]\n SVs[count] = rand\n count += 1\n while np.count_nonzero(alpha) < 2*gamma:\n rand = random.randint(0, len(x_train)-1)\n if y_train[rand] == -1:\n alpha[rand] = c\n L[rand, 1] = c\n # L[count, 0] = rand\n # L[count, 1] = alpha[rand]\n SVs[count] = rand\n count += 1\n return alpha","def __init__(self, quad_corpus, translations, train_ratio=0.9):\n a = list(quad_corpus)\n random.seed(1337)\n random.shuffle(a)\n t = int(train_ratio * len(a))\n self.a = a[:t]\n self.b = a[t:]\n self.translations = translations\n self.i = 0","def setup_StandardGCMCSystemSampler():\n # Make variables global so that they can be used\n global std_gcmc_system_sampler\n global std_gcmc_system_simulation\n\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\n ff = ForceField('tip3p.xml')\n system = ff.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=12 * angstroms,\n constraints=HBonds)\n\n std_gcmc_system_sampler = samplers.StandardGCMCSystemSampler(system=system, topology=pdb.topology,\n temperature=300*kelvin,\n boxVectors=np.array(pdb.topology.getPeriodicBoxVectors()),\n ghostFile=os.path.join(outdir, 'water-ghost-wats.txt'),\n log=os.path.join(outdir, 'stdgcmcsystemsampler.log'))\n\n # Define a simulation\n integrator = NonequilibriumLangevinIntegrator(temperature=300*kelvin, collision_rate=1./picosecond, timestep=2.*femtoseconds)\n\n try:\n platform = Platform.getPlatformByName('CUDA')\n except:\n try:\n platform = Platform.getPlatformByName('OpenCL')\n except:\n platform = Platform.getPlatformByName('CPU')\n\n std_gcmc_system_simulation = Simulation(pdb.topology, system, integrator, platform)\n std_gcmc_system_simulation.context.setPositions(pdb.positions)\n std_gcmc_system_simulation.context.setVelocitiesToTemperature(300*kelvin)\n std_gcmc_system_simulation.context.setPeriodicBoxVectors(*pdb.topology.getPeriodicBoxVectors())\n\n # Set up the sampler\n std_gcmc_system_sampler.initialise(std_gcmc_system_simulation.context, [2094, 2095, 2096, 2097, 2098])\n\n return None","def __init__(self, Q, initSol, tenure, scaleFactor, timeout):\n this = _tabu_search.new_TabuSearch(Q, initSol, tenure, scaleFactor, timeout)\n try:\n self.this.append(this)\n except __builtin__.Exception:\n self.this = this","def init():\n # analyzer es utilizado para interactuar con el modelo\n analyzer = model.newAnalyzer()\n return analyzer","def _suggest_samples(dataset: Dataset, settings: ZoomOptSettings) -> np.ndarray:\n\n if settings.batch < 1:\n raise ValueError(f\"Use batch size at least 1. (Was {settings.batch}).\") # pragma: no cover\n\n continuous_dict, categorical_dict = dataset.parameter_space\n\n # If any categorical variable is present, we raise an exception. In theory they should be represented by one-hot\n # encodings, but I'm not sure how to retrieve the bounds of this space and do optimization within it (the\n # best way is probably to optimize it in an unconstrained space and map it to one-hot vectors using softmax).\n # Moreover, in BayesOpt there is iteration over contexts.\n if categorical_dict:\n raise NotImplementedError(\"This method doesn't work with categorical inputs right now.\") # pragma: no cover\n\n # It seems that continuous_dict.values() contains pandas series instead of tuples, so we need to map over it\n # to retrieve the parameter space\n original_space: Hypercuboid = [(a, b) for a, b in continuous_dict.values()]\n\n # Find the location of the optimum. We will shrink the space around it\n optimum: np.ndarray = _get_optimum_location(dataset)\n\n # Estimate how many optimization iterations were performed.\n step_number: int = settings.n_step or _estimate_step_number(\n n_points=len(dataset.output_array), batch_size=settings.batch\n )\n\n # Convert to per-batch shrinking factor if a per-iteration shrinking factor supplied\n per_batch_shrinking_factor = (\n settings.shrinking_factor ** settings.batch if settings.shrink_per_iter else settings.shrinking_factor\n )\n\n # Calculate by what factor each dimension of the hypercube should be shrunk\n shrinking_factor_per_dim: float = _calculate_shrinking_factor(\n initial_shrinking_factor=per_batch_shrinking_factor, step_number=step_number, n_dim=len(original_space)\n )\n\n # Shrink the space\n new_space: Hypercuboid = [\n shrink_interval(\n shrinking_factor=shrinking_factor_per_dim, interval=interval, shrinking_anchor=optimum_coordinate\n )\n for interval, optimum_coordinate in zip(original_space, optimum)\n ]\n\n # The shrunk space may be out of the original bounds (e.g. if the maximum was close to the boundary).\n # Translate it.\n new_space = _move_to_original_bounds(new_space=new_space, original_space=original_space)\n\n # Sample the new space to get a batch of new suggestions.\n parameter_space = ParameterSpace([ContinuousParameter(f\"x{i}\", low, upp) for i, (low, upp) in enumerate(new_space)])\n\n return designs.suggest_samples(\n parameter_space=parameter_space, design_type=settings.design, point_count=settings.batch\n )","def create_samples(self):\n self._samples = self.load_samples()\n self.modify_samples()","def __init__(self, settings,study):\n \n # Store the study #\n ###################\n \n self._study = study\n self._parameters_size = self._study.geometry.parameters_size\n \n # Read settings #\n ################# \n if hasattr(settings, 'global_sample_function'):\n # Use given function and ignore bounds\n self._global_sample_function = settings.global_sample_function\n self._global_parameters_bounds = None\n else:\n # If no function, use uniform rand with given boundaries if provided. If not, assume [0,1]\n if hasattr(settings, 'global_parameters_bounds'):\n self._global_parameters_bounds = np.array(settings.global_parameters_bounds)\n else:\n self._global_parameters_bounds = [(0, 1)]*self._parameters_size\n \n self._global_sample_function = lambda: self._global_parameters_bounds[:,0] + (self._global_parameters_bounds[:,1]-self._global_parameters_bounds[:,0])*np.random.rand(1,self._parameters_size).flatten()\n \n\n if hasattr(settings, 'global_result_constraint'):\n self._global_result_constraint = settings.global_result_constraint\n else:\n self._global_result_constraint = None \n \n if hasattr(settings, 'local_result_constraint'):\n self._local_result_constraint = settings.local_result_constraint\n else:\n self._local_result_constraint = None\n \n if hasattr(settings, 'local_max_iterations'):\n self._local_max_iterations = settings.local_max_iterations\n else:\n self._local_max_iterations = 50\n \n if hasattr(settings, 'local_method'):\n self._local_method = settings.local_method\n else:\n self._local_method = 'L-BFGS-B'\n \n if hasattr(settings, 'local_scaling_factor'):\n self._local_scaling_factor = settings.local_scaling_factor\n else:\n self._local_scaling_factor = 1\n \n if hasattr(settings, 'local_ftol'):\n self._local_ftol = settings.local_ftol\n else:\n self._local_ftol = 1e-5\n \n if hasattr(settings, 'local_pgtol'):\n self._local_pgtol = settings.local_pgtol\n else:\n self._local_pgtol = 1e-5\n \n # Wavelength settings for lumopt \n if hasattr(settings, 'local_wavelength_start'):\n self._local_wavelength_start = settings.local_wavelength_start\n else:\n self._local_wavelength_start = 1550e-9\n \n if hasattr(settings, 'local_wavelength_stop'):\n self._local_wavelength_stop = settings.local_wavelength_stop\n else:\n self._local_wavelength_stop = 1550e-9\n \n if hasattr(settings, 'local_wavelength_points'):\n self._local_wavelength_points = settings.local_wavelength_points\n else:\n self._local_wavelength_points = 1\n \n # Keep track of the latest random restart. Run a first simulation with\n # the initial parameters already stored in the geometry\n self._new_param = None","def initialize(self, corpus):\n if self.id2word is None:\n logger.info(\"no word id mapping provided; initializing from corpus, assuming identity\")\n self.id2word = utils.dict_from_corpus(corpus)\n self.num_terms = len(self.id2word)\n elif self.id2word:\n self.num_terms = 1 + max(self.id2word)\n else:\n self.num_terms = 0\n\n shape = self.num_topics, self.num_terms\n logger.info(\"constructing %s random matrix\", str(shape))\n # Now construct the projection matrix itself.\n # Here i use a particular form, derived in \"Achlioptas: Database-friendly random projection\",\n # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).\n randmat = 1 - 2 * np.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1\n # convert from int32 to floats, for faster multiplications\n self.projection = np.asfortranarray(randmat, dtype=np.float32)\n # TODO: check whether the Fortran-order shenanigans still make sense. In the original\n # code (~2010), this made a BIG difference for np BLAS implementations; perhaps now the wrappers\n # are smarter and this is no longer needed?","def create_individual(self):\n self.genes = np.random.rand(self.chromosome_size)\n self.personal_best = self.genes.copy","def __init__(self, name, grid):\n self.name = name\n self.space_dimensions = grid.dimensions\n self.step_dimension = grid.stepping_dim\n self.dtype = grid.dtype\n\n # All known solutions and grids in this context\n self.solutions = []\n self.grids = {}","def __init__(self, corpus, n_grams, min_length):\n self.grams = {}\n self.n_grams = n_grams\n self.corpus = corpus\n self.min_length = min_length\n self.sequences()","def __init__(self, min_freq = 20):\n\n # A dict that maps a word to an index.\n self.word_to_idx = {}\n\n # A dict that maps an index to it's word.\n self.idx_to_word = {}\n\n # Store the min_freq.\n self.min_freq = min_freq","def __init__(self, n, sents, corpus='', gamma=None, addone=True):\n self.n = n\n self.smoothingtechnique = 'Interpolated (Jelinek Mercer) Smoothing'\n self.gamma = gamma\n self.addone = addone\n self.counts = counts = defaultdict(int)\n self.gamma_flag = True\n self.corpus = corpus\n # way more efficient than use set unions\n voc = ['</s>']\n for s in sents:\n voc += s\n self.voc = list(set(voc))\n\n if gamma is None:\n self.gamma_flag = False\n\n # if not gamma given\n if not self.gamma_flag:\n total_sents = len(sents)\n aux = int(total_sents * 90 / 100)\n # 90 per cent for training\n train_sents = sents[:aux]\n # 10 per cent for perplexity (held out data)\n held_out_sents = sents[-total_sents+aux:]\n\n train_sents = list(map((lambda x: ['<s>']*(n-1) + x), train_sents))\n train_sents = list(map((lambda x: x + ['</s>']), train_sents))\n\n for sent in train_sents:\n for j in range(n+1):\n # move along the sent saving all its j-grams\n for i in range(n-j, len(sent) - j + 1):\n ngram = tuple(sent[i: i + j])\n counts[ngram] += 1\n # added by hand\n counts[('</s>',)] = len(train_sents)\n # variable only for tests\n self.tocounts = counts\n # search the gamma that gives lower perplexity\n gamma_candidates = [i*50 for i in range(1, 15)]\n # xs is a list with (gamma, perplexity)\n xs = []\n sents = train_sents\n for aux_gamma in gamma_candidates:\n self.gamma = aux_gamma\n aux_perx = self.perplexity(held_out_sents)\n xs.append((aux_gamma, aux_perx))\n xs.sort(key=lambda x: x[1])\n self.gamma = xs[0][0]\n with open('old-stuff/interpolated_' + str(n) + '_parameters_'+corpus, 'a') as f:\n f.write('Order: {}\\n'.format(self.n))\n f.write('Gamma: {}\\n'.format(self.gamma))\n f.write('AddOne: {}\\n'.format(self.addone))\n f.write('Perplexity observed: {}\\n'.format(xs[0][1]))\n f.write('-------------------------------\\n')\n f.close()\n\n else:\n sents = list(map((lambda x: ['<s>']*(n-1) + x), sents))\n sents = list(map((lambda x: x + ['</s>']), sents))\n\n for sent in sents:\n # counts now holds all k-grams for 0 < k < n + 1\n for j in range(n+1):\n # move along the sent saving all its j-grams\n for i in range(n-j, len(sent) - j + 1):\n ngram = tuple(sent[i: i + j])\n counts[ngram] += 1\n # added by hand\n counts[('</s>',)] = len(sents)","def _sinit(cls):\n SGR = oq.plugin.get('SciGraph')\n IXR = oq.plugin.get('InterLex')\n #sgr.verbose = True\n for rc in (SGR, IXR):\n rc.known_inverses += (\n ('hasPart:', 'partOf:'),\n ('NIFRID:has_proper_part', 'NIFRID:proper_part_of'))\n\n sgr = SGR(apiEndpoint=auth.get('scigraph-api'))\n ixr = IXR(readonly=True)\n ixr.Graph = OntGraph\n cls.query_init(sgr, ixr) # = oq.OntQuery(sgr, ixr, instrumented=OntTerm)\n [cls.repr_level(verbose=False) for _ in range(2)]","def _localGenerateAssembler(self,initDict):\n Grid._localGenerateAssembler(self, initDict)\n self.jobHandler = initDict['internal']['jobHandler']\n self.dists = self.transformDistDict()\n # Do a distributions check for ND\n # This sampler only accept ND distributions with variable transformation defined in this sampler\n for dist in self.dists.values():\n if isinstance(dist, Distributions.NDimensionalDistributions):\n self.raiseAnError(IOError, 'ND Dists contain the variables in the original input space are not supported for this sampler!')","def setUp(self):\n\n self._hash_bins = 10\n self._embedding_dim = 2\n\n self._default_config = {\n \"hash_bins\": self._hash_bins,\n \"embedding_dim\": self._embedding_dim\n }","def create_embedding(self):\n self.embedding = []\n\n for index in range(1,self.args.window_size+1):\n print(\"\\nOptimization round: \" +str(index)+\"/\"+str(self.args.window_size)+\".\")\n print(\"Creating documents.\")\n clean_documents = self.walk_extracts(index)\n print(\"Fitting model.\")\n model = Word2Vec(clean_documents,\n size = self.args.dimensions,\n window = 1,\n min_count = self.args.min_count,\n sg = 1,\n workers = self.args.workers)\n\n new_embedding = self.get_embedding(model)\n self.embedding = self.embedding +[new_embedding]\n self.embedding = np.concatenate(self.embedding, axis = 1)","def main():\n grid = make_grid(3, 3) # change to 3x3\n dictionary = get_dictionary(\"words.txt\")\n words = search(grid, dictionary)\n display_words(words)","def _make_test_cube(long_name):\n cs = GeogCS(EARTH_RADIUS)\n data = np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0]])\n cube = Cube(data, long_name=long_name)\n x_coord = DimCoord(\n np.linspace(-45.0, 45.0, 3), \"latitude\", units=\"degrees\", coord_system=cs\n )\n y_coord = DimCoord(\n np.linspace(120, 180, 3), \"longitude\", units=\"degrees\", coord_system=cs\n )\n cube.add_dim_coord(x_coord, 0)\n cube.add_dim_coord(y_coord, 1)\n return cube","def build_index(path, chunk_size):\n physical_files = set()\n boundaries = []\n examples_needed = 0\n for sgf in find_sgfs(path):\n physical_files.add(sgf.locator.physical_file)\n if examples_needed == 0:\n # The start of this SGF is a chunk boundary.\n boundaries.append(Pointer(sgf.locator, 0))\n examples_needed = chunk_size\n game_record = Sgf_game.from_string(sgf.contents)\n num_positions = len(_sequence(game_record))\n if examples_needed < num_positions:\n # The start of the next chunk is inside this SGF.\n boundaries.append(Pointer(sgf.locator, examples_needed))\n remaining_examples = num_positions - examples_needed\n examples_needed = chunk_size - remaining_examples\n else:\n # This SGF is entirely contained within the current chunk.\n examples_needed -= num_positions\n\n return CorpusIndex(physical_files, chunk_size, boundaries)","def __initialise_smart(self, X, args):\n\t\tcentroids = np.zeros((self.K,self.D))\n\t\tif X.shape[0] > 10*self.K:\n\t\t\tdata = X[:10*self.K,:]\n\t\telse:\n\t\t\tdata = X\n\t\tN = data.shape[0]\n\n\t\t\t#choosing centroids\n\t\t\t#points are chosen from dataset with farhtest point clustering\n\t\tran_index = np.random.choice(N)\n\t\tcentroids[0,:] = data[ran_index]\n\n\t\tfor k in range(1,self.K):\n\t\t\tdistances = np.zeros((N,k)) #(N,K)\n\t\t\tfor k_prime in range(k):\n\t\t\t\tdistances[:,k_prime] = np.sum(np.square(data - centroids[k_prime,:]), axis =1) #(N,K')\n\t\t\tdistances = np.min(distances, axis = 1) #(N,)\n\t\t\tdistances /= np.sum(distances) #normalizing distances to make it a prob vector\n\t\t\tnext_cl_arg = np.random.choice(range(data.shape[0]), p = distances) #chosen argument for the next cluster center\n\t\t\tcentroids[k,:] = data[next_cl_arg,:]\n\n\t\tvar = np.var(X, axis = 0) #(D,)\n\n\t\t\t#computing initial responsibilities\n\t\tr_0 = np.zeros((X.shape[0],self.K))\n\t\tfor k in range(self.K):\n\t\t\tr_0[:,k] = np.sum(np.divide(np.square(X - centroids[k,:]), var), axis = 1) + 1e-5\n\t\tr_0 = np.divide(r_0.T, np.sum(r_0,axis=1)).T\n\n\t\tself.gating.fit(X,r_0, *args)\n\n\t\treturn r_0","def createAllSG():\n\tfor info in conf_HVM:\n\t\tec2 = boto.ec2.connect_to_region(info['region']+'-'+info['zone'])\n\t\tcreateSG(ec2,'SG-Cassandra-'+info['region']+'-'+info['zone'],CASSANDRA_RULES)","def construct_random_initial(self):\n x = np.random.random((self._crv_size, self._bound))\n return x","def __init__(self, n, prey_cnt=0, predator_cnt=0):\n # print n, prey_cnt, predator_cnt\n self.grid_size = n\n self.grid = []\n for i in range(n):\n row = [0]*n # row is a list of n zeros\n self.grid.append(row)\n self.init_animals(prey_cnt, predator_cnt)","def _init_empty_polyhedron(self):\n self._ambient_dim = 0\n\n self._Vrepresentation = Sequence([])\n self._Vrepresentation.set_immutable()\n \n self._Hrepresentation = Sequence([])\n Equation(self, [-1]);\n self._Hrepresentation.set_immutable()\n\n self._V_adjacency_matrix = matrix(ZZ, 0, 0, 0)\n self._V_adjacency_matrix.set_immutable()\n\n self._H_adjacency_matrix = matrix(ZZ, 1, 1, 0)\n self._H_adjacency_matrix.set_immutable()","def _initialize_corpus(self):\n vocab = self.vocab # vocab is the word vector\n theta = self.theta # theta is the model parameter\n corpus = self.corpus\n\n for line in corpus:\n for word in line:\n if word not in vocab:\n vocab[word] = init_vector(self.n)\n theta[word] = init_vector(self.n)\n\n if self.verbose:\n print(f\"{len(vocab)} words have been loaded\")","def init(self):\n self._es.create_index_template(\n name=DATASETS_INDEX_NAME,\n template=DATASETS_INDEX_TEMPLATE,\n force_recreate=True,\n )\n self._es.create_index(DATASETS_INDEX_NAME)","def __init__(self, n):\n self._n = n\n self._grid = [[False] * n for _ in range(n)]\n # create sites for n-by-n grid and 2 \"virtual\" sites for top and bottom\n # self._uf = QuickFindUF(n * n + 2)\n self._uf = WeightedQuickUnionUF(n * n + 2) # QuickFindUF(n * n + 2)\n # connect top and bottom virtual sites with respecting sides of grid\n self._top_idx = n * n\n self._bottom_idx = n * n + 1\n for i in range(n):\n self._uf.union(self._top_idx, i)\n self._uf.union(self._bottom_idx, (n - 1) * n + i)","def test_hunger_grid_create(self):\n self.grid = Hunger_Grid.hunger_grid()\n self.grid.newGrid = Hunger_Grid.hunger_grid().create_hunger_grid(M=30, N=30, P_LAVA = 1.0)\n self.assertTrue(self.grid.newGrid.size == 900, \"Grid size is incorrect\")\n self.assertTrue(self.grid.newGrid[2, 2] == 1, \"Lava chance is not acting correctly\")\n self.assertTrue(self.grid.newGrid[-3, -3] == 1, \"Lava chance is not acting correctly\")","def initialize(self):\n # Initializing the counter and distribution.\n for k in range(0, self.topic_number,1):\n self.topic_term_count_matrix[k]= [0.0] * self.term_number\n self.topic_distribution_over_term[k] = [0.0] * self.term_number\n self.sum_topic_by_term_count[k] = 0.0\n for m in range(0, self.document_number,1):\n self.document_topic_count_matrix[m] = [0.0] * self.topic_number\n self.document_distribution_over_topic[m] = [0.0] * self.topic_number\n self.sum_document_by_topic_count[m] = 0.0\n\n # Initializing topics assigned to all words of all documents.\n for m in range(0, self.document_number, 1):\n N = len(self.documents[m])\n self.word_topic_assignment[m] = [-1] * N\n for n in range(0, N,1):\n topic = int(random.uniform(0,1) * self.topic_number)\n self.document_topic_count_matrix[m][topic] += 1.0\n self.topic_term_count_matrix[topic][self.documents[m][n]] += 1.0\n self.sum_topic_by_term_count[topic] += 1.0\n self.word_topic_assignment[m][n] = topic\n self.sum_document_by_topic_count[m] = N"],"string":"[\n \"def generate_latin_hypercube(samples, param_dict, class_root, seed=10):\\n # Set random seed\\n random.seed(seed)\\n\\n # Create dictionary to hold sampled parameter values\\n sample_points = {}\\n for key in param_dict.keys():\\n sample_points[key] = np.zeros(samples)\\n Ndim = len(param_dict.keys())\\n pnames = [key for key in param_dict.keys()]\\n\\n # List of indices for each dimension\\n l = [range(samples) for j in range(Ndim)]\\n\\n # Generate samples until there are no indices left to choose\\n for i in range(samples):\\n\\n # Randomly choose index and then remove the number that was chosen\\n # (Latin hypercubes require at most one item per row and column)\\n for j, p in enumerate(pnames):\\n pmin, pmax = param_dict[p]\\n idx = random.choice(l[j])\\n\\n # Get value at this sample point (add 0.5 to idx get bin centroid)\\n sample_points[p][i] = pmin + (pmax - pmin) \\\\\\n * (idx + 0.5) / float(samples)\\n l[j].remove(idx) # Remove choice from list (sampling w/o replacement)\\n\\n return sample_points\",\n \"def latin_hypercube(n_pts, dim):\\n X = np.zeros((n_pts, dim))\\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\\n for i in range(dim): # Shuffle the center locataions for each dimension.\\n X[:, i] = centers[np.random.permutation(n_pts)]\\n\\n # Add some perturbations within each box\\n pert = np.random.uniform(-1.0, 1.0, (n_pts, dim)) / float(2 * n_pts)\\n X += pert\\n return X\",\n \"def latin_hypercube(n_pts, mins, maxs):\\n #return a latin_hypercube\\n design = lhs(np.size(maxs), samples=n_pts)\\n for i in range(2):\\n design[:, i] = design[:, i] * (maxs[i]-mins[i]) + mins[i]\\n return design\",\n \"def localInitialize(self):\\n SVL = self.readFromROM()\\n self._generateQuadsAndPolys(SVL)\\n #print out the setup for each variable.\\n msg = self.printTag+' INTERPOLATION INFO:\\\\n'\\n msg += ' Variable | Distribution | Quadrature | Polynomials\\\\n'\\n for v in self.quadDict:\\n msg += ' '+' | '.join([v,self.distDict[v].type,self.quadDict[v].type,self.polyDict[v].type])+'\\\\n'\\n msg += ' Polynomial Set Degree: '+str(self.maxPolyOrder)+'\\\\n'\\n msg += ' Polynomial Set Type : '+str(SVL.indexSetType)+'\\\\n'\\n self.raiseADebug(msg)\\n\\n self.raiseADebug('Starting index set generation...')\\n self.indexSet = IndexSets.factory.returnInstance(SVL.indexSetType)\\n self.indexSet.initialize(self.features, self.importanceDict, self.maxPolyOrder)\\n if self.indexSet.type=='Custom':\\n self.indexSet.setPoints(SVL.indexSetVals)\\n\\n self.sparseGrid = Quadratures.factory.returnInstance(self.sparseGridType)\\n self.raiseADebug(f'Starting {self.sparseGridType} sparse grid generation...')\\n self.sparseGrid.initialize(self.features, self.indexSet, self.dists, self.quadDict, self.jobHandler)\\n\\n if self.writeOut is not None:\\n msg = self.sparseGrid.__csv__()\\n outFile = open(self.writeOut,'w')\\n outFile.writelines(msg)\\n outFile.close()\\n\\n self.limit=len(self.sparseGrid)\\n self.raiseADebug(f'Size of Sparse Grid: {self.limit}')\\n self.raiseADebug('Finished sampler generation.')\\n\\n self.raiseADebug('indexset:',self.indexSet)\\n for SVL in self.ROM.supervisedContainer:\\n SVL.initialize({'SG': self.sparseGrid,\\n 'dists': self.dists,\\n 'quads': self.quadDict,\\n 'polys': self.polyDict,\\n 'iSet': self.indexSet})\",\n \"def setUp(self):\\n self.grid = SudukuGrid(BaseCase)\\n for i in range(81):\\n self.grid[i] = SudukuAlphabet.VALUES[(i+(i//9)*3+i//27)%9]\",\n \"def sample_latin_hypercube(low, high, n_samples, rng=None):\\n if rng is None:\\n rng = np.random.RandomState(np.random.randint(0, 10000))\\n\\n n_dims = low.shape[0]\\n\\n samples = []\\n for i in range(n_dims):\\n if isinstance(low[i], numbers.Integral):\\n sample = random.sample(range(low[i], high[i]), n_samples)\\n elif isinstance(low[i], numbers.Real):\\n lower_bound = low[i]\\n upper_bound = high[i]\\n sample = lower_bound + rng.uniform(0, 1, n_samples) * (upper_bound - lower_bound)\\n else:\\n raise ValueError('Latin hypercube sampling can only draw from types int and real,'\\n ' got {}!'.format(type(low[i])))\\n\\n samples.append(sample)\\n\\n samples = np.array(samples, dtype=object)\\n\\n for i in range(n_dims):\\n rng.shuffle(samples[i, :])\\n\\n return samples.T\",\n \"def latin_hypercube_sampler(n=1, indim=1, bounds=None, rng=None):\\r\\n rng = ensure_rng(rng)\\r\\n if bounds is None:\\r\\n bounds = np.zeros((indim, 2))\\r\\n bounds[:,1] = 1. \\r\\n # Divide each dimension into `n` equal intervals\\r\\n hypercubes = np.linspace(bounds[:,0], bounds[:,1], n+1)\\r\\n \\r\\n l = hypercubes[:-1,:].reshape(-1,)\\r\\n u = hypercubes[1:,:].reshape(-1,)\\r\\n _x = rng.uniform(l,u, (1, indim*n)).reshape(n, indim)\\r\\n x = _x\\r\\n for j in range(indim):\\r\\n x[:,j] = _x[rng.permutation(n), j]\\r\\n return x\",\n \"def initialize(self):\\n#TODO: choose user defined START position\\n values_type = np.dtype(float)\\n self.visual_field = np.zeros(self.number_of_locs, dtype=values_type)\\n self.weighted_sums = np.zeros(self.number_of_locs, dtype=values_type)\\n self.prior_prob = 1.0 / np.prod(self.number_of_locs)\\n self.post_probs = np.full(\\n self.number_of_locs, self.prior_prob, dtype=values_type\\n )\\n starting_location = np.array(START)\\n self.focus = get_index_of_in(starting_location,self.senzory_map)\\n self.target_location = [\\n x for x in xrange(self.number_of_locs) if x != self.focus\\n ][random.randint(0,self.number_of_locs-2)]\",\n \"def __init__(self, initial, size, horizontalChunks, verticalChunks, goal = \\\"\\\"):\\n\\t\\tself.initial = initial\\n\\t\\tself.size = size\\n\\t\\tself.horChunks = horizontalChunks\\n\\t\\tself.verChunks = verticalChunks\\n\\n\\t\\t# Goal holds the solution, once we find it.\\n\\t\\tself.goal = goal\\n\\n\\t\\t# For a puzzle of size n, initializes blank n x n 2d array\\n\\t\\tself.graph = [[0 for x in range(self.size)] for x in range(self.size)] \\n\\t\\tfor i in range (0,self.size):\\n\\t\\t\\tfor j in range (0,self.size):\\n\\t\\t\\t\\tself.graph[i][j] = initial[i*self.size + j] \\n\\t\\tself.initial = \\\"\\\"\",\n \"def gen_hypercube(samples, N):\\n\\n np.random.seed(4654562)\\n hypercube = lhs(N, samples=samples)\\n\\n return hypercube\",\n \"def __init__(self):\\n super().__init__()\\n self.type = 'SparseGridCollocationSampler'\\n self.printTag = 'SAMPLER '+self.type.upper()\\n self.maxPolyOrder = None #L, the relative maximum polynomial order to use in any dimension\\n self.indexSetType = None #TP, TD, or HC; the type of index set to use\\n self.polyDict = {} #varName-indexed dict of polynomial types\\n self.quadDict = {} #varName-indexed dict of quadrature types\\n self.importanceDict = {} #varName-indexed dict of importance weights\\n self.maxPolyOrder = None #integer, relative maximum polynomial order to be used in any one dimension\\n self.lastOutput = None #pointer to output dataObjects object\\n self.ROM = None #pointer to ROM\\n self.jobHandler = None #pointer to job handler for parallel runs\\n self.doInParallel = True #compute sparse grid in parallel flag, recommended True\\n self.dists = {} #Contains the instance of the distribution to be used. keys are the variable names\\n self.writeOut = None\\n self.indexSet = None\\n self.sparseGrid = None\\n self.features = None\\n self.sparseGridType = None\\n self.addAssemblerObject('ROM', InputData.Quantity.one)\",\n \"def initialise():\\n _initialiseGlobals()\\n for pop in AnadPartOfPerspectiveDb.Iterator():\\n _addToKnowledge(pop)\\n return\",\n \"def create_grids_structure(self):\\n for indices, hypercube in np.ndenumerate(self.hypercubes):\\n self.hypercubes[indices] = Hypercube(coords=indices)\",\n \"def setUp(self):\\n # generate lattice\\n self.lattice = lattice.Lattice()\\n self.lattice.addAtom(\\\"He\\\", [0,0,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,0,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,2,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,0,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [9,9,9], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,2,0], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,0,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [0,2,2], 0)\\n self.lattice.addAtom(\\\"He\\\", [2,2,2], 0)\\n \\n # indexes of cluster atoms\\n self.bigClusterIndexes = [0,1,2,3,5,6,7,8]\\n self.smallClusterIndexes = [4]\\n \\n # filter\\n self.filter = clusterFilter.ClusterFilter(\\\"Cluster\\\")\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='Create a new Wordsearch')\\n parser.add_argument('size', type=grid_size_type,\\n help=\\\"height and width of our wordsearch grid (min: 3)\\\")\\n parser.add_argument('wordfile', type=argparse.FileType('r'),\\n help=\\\"file including words to search for\\\")\\n parser_args = parser.parse_args()\\n\\n new_matrix = Matrix(parser_args.size)\\n\\n words_to_find = create_word_list_from_file(parser_args.wordfile, parser_args.size)\\n\\n words_found = []\\n for word in words_to_find:\\n if word not in words_found and word in new_matrix:\\n words_found.append(word)\\n\\n print(\\\"\\\\n{}\\\\n\\\\n{}\\\\n\\\".format(new_matrix, \\\" \\\".join(sorted(words_found))))\",\n \"def at_object_creation(self):\\r\\n with open(\\\"./commands/CSW15.txt\\\") as word_file:\\r\\n self.db.csw15 = set(word.strip().upper() for word in word_file)\\r\\n self.db.centre = \\\"\\\" \\r\\n self.db.tiledict = {'A' : 9,\\r\\n 'B' : 2,\\r\\n 'C' : 2,\\r\\n 'D' : 4,\\r\\n 'E' : 12,\\r\\n 'F' : 2,\\r\\n 'G' : 3,\\r\\n 'H' : 2,\\r\\n 'I' : 9,\\r\\n 'J' : 1,\\r\\n 'K' : 1,\\r\\n 'L' : 4,\\r\\n 'M' : 2,\\r\\n 'N' : 6,\\r\\n 'O' : 8,\\r\\n 'P' : 2,\\r\\n 'Q' : 1,\\r\\n 'R' : 6,\\r\\n 'S' : 4,\\r\\n 'T' : 6,\\r\\n 'U' : 4,\\r\\n 'V' : 2,\\r\\n 'W' : 2,\\r\\n 'X' : 1,\\r\\n 'Y' : 2,\\r\\n 'Z' : 1,\\r\\n '?' : 0\\r\\n } #removing blanks from play; blanks make it very slow. Change here, in dict\\r\\n self.db.tilestring = list(''.join([L*self.db.tiledict[L] for L in string.ascii_uppercase+'?']))\",\n \"def setUp(self):\\n np.random.seed(1234)\\n\\n _TEST_FILE_NAME = 'AHN3.las'\\n _TEST_DATA_SOURCE = 'testdata'\\n\\n _CYLINDER = InfiniteCylinder(4)\\n _PC_260807 = load(os.path.join(_TEST_DATA_SOURCE, _TEST_FILE_NAME))\\n _PC_1000 = copy_point_cloud(_PC_260807, array_mask=(\\n np.random.choice(range(len(_PC_260807[keys.point]['x']['data'])), size=1000, replace=False)))\\n _1000_NEIGHBORHOODS_IN_260807 = list(compute_neighbors.compute_neighborhoods(_PC_260807, _PC_1000, _CYLINDER))\\n\\n self.point_cloud = _PC_260807\\n self.neigh = _1000_NEIGHBORHOODS_IN_260807\",\n \"def convert_searchspace(self, hyperparameter):\\n LOG.debug(\\\"convert input parameter\\\\n\\\\n\\\\t{}\\\\n\\\".format(pformat(hyperparameter)))\\n searchspace = [[], []]\\n for name, param in hyperparameter.items():\\n if param[\\\"domain\\\"] != \\\"categorical\\\" and \\\"frequency\\\" not in param.keys():\\n param[\\\"frequency\\\"] = DEFAULTGRIDFREQUENCY\\n warnings.warn(\\\"No frequency field found, used default gridsearch frequency {}\\\".format(DEFAULTGRIDFREQUENCY))\\n\\n if param[\\\"domain\\\"] == \\\"categorical\\\":\\n searchspace[0].append(name)\\n searchspace[1].append(param[\\\"data\\\"])\\n elif param[\\\"domain\\\"] == \\\"uniform\\\":\\n searchspace[0].append(name)\\n searchspace[1].append(get_uniform_axis_sample(param[\\\"data\\\"][0],\\n param[\\\"data\\\"][1],\\n param[\\\"frequency\\\"],\\n param[\\\"type\\\"]))\\n elif param[\\\"domain\\\"] == \\\"normal\\\":\\n searchspace[0].append(name)\\n searchspace[1].append(get_gaussian_axis_sample(param[\\\"data\\\"][0],\\n param[\\\"data\\\"][1],\\n param[\\\"frequency\\\"],\\n param[\\\"type\\\"]))\\n elif param[\\\"domain\\\"] == \\\"loguniform\\\":\\n searchspace[0].append(name)\\n searchspace[1].append(get_logarithmic_axis_sample(param[\\\"data\\\"][0],\\n param[\\\"data\\\"][1],\\n param[\\\"frequency\\\"],\\n param[\\\"type\\\"]))\\n return searchspace\",\n \"def initialize(self, search_space, names, outer_i=None):\\n name = search_space.name\\n names = copy.deepcopy(names)\\n names.append(name)\\n output_dim = self.cells[-1].hidden_size\\n\\n num_inner = self.search_space.eval_(search_space.num_inner, **locals())\\n if len(num_inner) > 1:\\n key = f'{\\\"_\\\".join(names[:-1])}_{len(num_inner)}_{name}s'\\n add_if_doesnt_exist(self.policies, key, nn.Linear(output_dim, len(num_inner)))\\n add_if_doesnt_exist(self.values, key, nn.Linear(output_dim, len(num_inner)))\\n\\n add_increment(self.embedding_index, f'{name}_start')\\n add_increment(self.embedding_index, f'{name}_end')\\n\\n self.adapt(search_space.outer.items(), names, outer_i)\\n\\n for i in range(max(num_inner)):\\n add_increment(self.embedding_index, f'{i+1}_{name}s')\\n if isinstance(search_space.inner, (list, tuple)):\\n for space in search_space.inner: self.initialize(space, names, i)\\n elif isinstance(search_space.inner, SearchSpace):\\n self.initialize(search_space.inner, names, i)\\n else:\\n assert isinstance(search_space.inner, dict), \\\\\\n 'Inner search space must be either list, dict or SearchSpace.'\\n self.adapt(search_space.inner.items(), names, outer_i)\\n add_increment(self.embedding_index, f'{name}_inner_done')\",\n \"def initialize(self):\\n self.SIZE = self.vectors.shape[0]\\n # todo can use max distance to allocation farthest apart points\\n self.centroids = self.vectors[[random.randint(1, self.SIZE) for x in range(self.K)], :]\",\n \"def grid_search(self):\\n\\t\\t''' common settings without grid-search '''\\n\\t\\tbinary_rele, unknown_as_zero = False, False\\n\\t\\tcommon_data_dict = dict(data_id=self.data_id, dir_data=self.dir_data, min_docs=10, min_rele=1,\\n\\t\\t\\t\\t\\t\\t\\t\\tunknown_as_zero=unknown_as_zero, binary_rele=binary_rele)\\n\\n\\t\\tdata_meta = get_data_meta(data_id=self.data_id) # add meta-information\\n\\t\\tcommon_data_dict.update(data_meta)\\n\\n\\t\\t''' some settings for grid-search '''\\n\\t\\tchoice_presort = [True] if self.debug else [True]\\n\\t\\tchoice_sample_rankings_per_q = [1] if self.debug else [1] # number of sample rankings per query\\n\\t\\tchoice_scale_data, choice_scaler_id, choice_scaler_level = get_default_scaler_setting(data_id=self.data_id, grid_search=True)\\n\\n\\t\\tfor scale_data, scaler_id, scaler_level, presort, sample_rankings_per_q in product(choice_scale_data,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t choice_scaler_id,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t choice_scaler_level,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t choice_presort,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t choice_sample_rankings_per_q):\\n\\n\\t\\t\\tself.data_dict = dict(presort=presort, sample_rankings_per_q=sample_rankings_per_q,\\n\\t\\t\\t\\t\\t\\t\\t\\t scale_data=scale_data, scaler_id=scaler_id, scaler_level=scaler_level)\\n\\t\\t\\tself.data_dict.update(common_data_dict)\\n\\t\\t\\tyield self.data_dict\",\n \"def initialize( self, layout, numGhostAgents=1000 ):\\n self.data.initialize(layout, numGhostAgents) ##self.data is defined in the Grid() class of game.py REF112.It creates an initial game state from a layout array (see layout.py).\",\n \"def __init__(self, center_words, context_words, neg_samples): \\n self.center_words = center_words\\n self.context_words = context_words\\n self.neg_samples = neg_samples\\n # The index of the data the batch should start from. \\n self.data_index = 0\",\n \"def test_init_experiment(self):\\n optimizer = \\\"RandomSearch\\\"\\n name = \\\"test_init_experiment\\\"\\n param_defs = {\\n \\\"x\\\": MinMaxNumericParamDef(0, 1),\\n \\\"name\\\": NominalParamDef([\\\"A\\\", \\\"B\\\", \\\"C\\\"])\\n }\\n minimization = True\\n\\n LAss = PrettyLabAssistant()\\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\\n\\n exp_ass = LAss.exp_assistants[name]\\n\\n assert_equal(exp_ass.optimizer, optimizer)\\n assert_is_none(exp_ass.optimizer_arguments, None)\\n assert_equal(exp_ass.experiment.minimization_problem, minimization)\\n with assert_raises(ValueError):\\n LAss.init_experiment(name, optimizer, param_defs, minimization=minimization)\",\n \"def __init__(self, hyperparameters, total_dim, num_is):\\n self._dim = total_dim # dimension of IS \\\\times search space\\n self._num_is = num_is # Number of information sources, then including 0th IS (truth), size of hyper should be dim * (num_is+1).\\n # Note: it's not (dim+1)*(num_is+1) because dimension of search space is (dim-1), plus the multiplication factor param is dim\\n self.set_hyperparameters(hyperparameters)\",\n \"def build_index(self):\\n # Init the HNSWLIB index\\n self.create_index()\\n logger.info(f\\\"Building HNSWLIB index, max_elements: {len(self.corpus)}\\\")\\n logger.debug(f\\\"Parameters Required: M: {self.M}\\\")\\n logger.debug(f\\\"Parameters Required: ef_construction: {self.ef_construction}\\\")\\n logger.debug(f\\\"Parameters Required: ef(>topn): {self.ef}\\\")\\n\\n # Then we train the index to find a suitable clustering\\n self.index.add_items(self.corpus_embeddings, list(range(len(self.corpus_embeddings))))\",\n \"def init_embedding(size=50):\\n vector = np.random.normal(0.0, 0.01, size)\\n return vector\",\n \"def __init__(self):\\n # better to be a prime number, less collision\\n self.key_space = 2069\\n self.hash_table = [Bucket() for i in range(self.key_space)]\",\n \"def create_index(args, client):\\n policy = {}\\n client.index_geo2dsphere_create(args.nspace, args.set,\\n LOCBIN, LOCNDX, policy)\\n client.index_integer_create(args.nspace, args.set,\\n HSHBIN, HSHNDX, policy)\",\n \"def initializeDistribution(self):\\n self.checkDistParams()\\n\\n self.lowerBound = min(self.mapping.keys())\\n self.upperBound = max(self.mapping.keys())\",\n \"def __init__(self, lower=True, num_norm=True,\\n use_char=True, initial_vocab=None):\\n self._num_norm = num_norm\\n self._use_char = use_char\\n \\n # TODO: check how to use this\\n self._node_vocab = Vocabulary(lower=False)\\n self._word_vocab = Vocabulary(lower=lower)\\n self._char_vocab = Vocabulary(lower=False)\\n #TODO: check usability\\n self._label_vocab = Vocabulary(lower=False, unk_token=False)\\n\\n if initial_vocab:\\n self._word_vocab.add_documents([initial_vocab])\\n self._char_vocab.add_documents(initial_vocab)\",\n \"def init_from_scratch(args, train_exs, dev_exs):\\n \\n # Build a dictionary from the data sqls+queries (train/dev splits)\\n logger.info('-' * 100)\\n logger.info('Build vocab')\\n \\n vocab = build_vocab(train_exs + dev_exs, args)\\n logger.info('Num words = %d' % vocab.size())\\n \\n # Initialize model\\n model = CopyNet(args, vocab)\\n logger.info('-' * 100)\\n logger.info('Model Architecture')\\n logger.info(model)\\n if args.embedding_file:\\n model.load_embeddings(vocab.tokens(), args.embedding_file)\\n \\n return model, vocab\",\n \"def setUp(self):\\n self._vocab = np.array([\\\"one\\\", \\\"two\\\", \\\"three\\\", \\\"four\\\",\\n \\\"five\\\", \\\"six\\\", \\\"seven\\\", \\\"eight\\\", \\\"nine\\\"])\\n self._embedding_dim = 2\\n\\n self._default_config = {\\n \\\"vocab\\\": self._vocab,\\n \\\"embedding_dim\\\": self._embedding_dim,\\n \\\"position_encoding\\\": None\\n }\",\n \"def init(x_in):\\n global public_keys, secret_keys, x\\n x = func.get_bits(x_in)\\n\\n public_keys, secret_keys = [], []\\n\\n elgamal.init_g_p_q()\\n for i in range(3):\\n create_keys(i)\",\n \"def main(self):\\n grid = self.make_game_grid()\\n print(self.grid_size, ' by ', self.grid_size, 'grid')\\n trie = self.retrieve_trie()\\n if not trie:\\n trie = self.read_in_file(self.file)\\n self.persist_trie(trie)\\n\\n all_possible_words = []\\n # left to right rows\\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid)\\n # right to left rows\\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=True, transpose=False)\\n # left to right columns\\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=False, transpose=True)\\n # right to left columns\\n all_possible_words = all_possible_words + self.search_columns_and_rows(grid, reverse=True, transpose=True)\\n\\n # handle all possible sun sets of the array row\\n all_possible_words = self.all_words(all_possible_words)\\n # get diagonal letters top to bottom\\n all_possible_words = all_possible_words + self.get_diagonal_words(grid)\\n # get diagonal letters bottom to top\\n all_possible_words = all_possible_words + self.get_diagonal_words(grid, reverse=True)\\n ans = self.check_words_in_trie(trie, all_possible_words)\\n self.sorted_words = sorted(ans, key=len)\\n if self.sorted_words:\\n print(\\\"The number of words in the solution is: %s.\\\" % (len(ans),))\\n print(\\\"The shortest word in the solution is: %s.\\\" % (self.sorted_words[0],))\\n print(\\\"The longest word in the solution is: %s.\\\" % (self.sorted_words[-1],))\\n print('the possible words in this grid are ', self.sorted_words)\\n return self.sorted_words\",\n \"def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):\\n raise NotImplementedError\",\n \"def _initialize(self):\\n for doc_index, doc in enumerate(self.document):\\n temp_word_topic_matrix = []\\n for word in doc:\\n if word in self.word2id.keys():\\n start_topic_index = np.random.randint(0, self.K)\\n temp_word_topic_matrix.append(start_topic_index)\\n self.doc_topic_matrix[doc_index, start_topic_index] += 1\\n self.topic_word_matrix[start_topic_index, self.word2id[word]] += 1\\n self.topic_matrix[start_topic_index] += 1\\n self.current_word_topic_matrix.append(temp_word_topic_matrix)\",\n \"def test_1_1_2D_cube_init(self): # TODO: REMOVE FUNC AFTER SPLIT\\n check = [(0, 0), (1, 1), (1, 0), (0, 1), (0.5, 0.5)]\\n\\n nn_checks = {(0.5, 0.5): [(0, 1), (1, 0), (0, 0), (1, 1)],\\n (0, 1): [(0, 0), (1, 1), (0.5, 0.5)]}\\n\\n init_triangulation(2, 0, check, nn_checks)\",\n \"def __init__(self, input_n, input_t, input_language, input_epochs, input_embedding_type, input_clusters_num,\\n input_training_data, input_evaluation_data, input_hunits_lower, input_hunits_upper,\\n input_embedding_dim_lower, input_embedding_dim_upper, input_c, input_iterations):\\n self.n = input_n\\n self.t = input_t\\n self.language = input_language\\n self.epochs = input_epochs\\n self.embedding_type = input_embedding_type\\n self.clusters_num = input_clusters_num\\n self.training_data = input_training_data\\n self.evaluation_data = input_evaluation_data\\n self.hunits_lower = input_hunits_lower\\n self.hunits_upper = input_hunits_upper\\n self.embedding_dim_lower = input_embedding_dim_lower\\n self.embedding_dim_upper = input_embedding_dim_upper\\n self.c = input_c\\n self.iterations = input_iterations\\n\\n # Setting self.lambda to the number of the parameters of the largest possible model\\n word_segmenter = WordSegmenter(input_name=\\\"temp\\\", input_n=50, input_t=10000,\\n input_clusters_num=self.clusters_num,\\n input_embedding_dim=self.embedding_dim_upper, input_hunits=self.hunits_upper,\\n input_dropout_rate=0.2, input_output_dim=4, input_epochs=1,\\n input_training_data=self.training_data,\\n input_evaluation_data=self.evaluation_data, input_language=self.language,\\n input_embedding_type=self.embedding_type)\\n word_segmenter.train_model()\\n self.lam = 1/word_segmenter.model.count_params()\",\n \"def init_algorithm(self):\\n pass\",\n \"def create_initial_grid():\\n\\n\\tgrid = {(x, y) : ' + ' for x in range(8) for y in range(8)}\\n\\n\\t# Define initial positions \\n\\tgrid[(3,3)] = colors.RED + \\\"[I]\\\" + colors.STOP\\n\\tgrid[(4,3)] = colors.GREEN + \\\"[A]\\\" + colors.STOP\\n\\tgrid[(3,4)] = colors.GREEN + \\\"[A]\\\" + colors.STOP\\n\\tgrid[(4,4)] = colors.RED + \\\"[I]\\\" + colors.STOP\\n\\n\\treturn grid\",\n \"def test_2_1_3D_cube_init(self):\\n check = [(0, 0, 0), (1, 1, 1), (1, 0, 0), (1, 1, 0), (1, 0, 1),\\n (0, 1, 0), (0, 1, 1), (0, 0, 1), (0.5, 0.5, 0.5)]\\n\\n nn_checks = {\\n (1, 1, 1): [(1, 1, 0), (0, 1, 1), (1, 0, 0), (0, 0, 1), (1, 0, 1),\\n (0.5, 0.5, 0.5), (0, 1, 0)],\\n (1, 0, 1): [(1, 0, 0), (0, 0, 1), (0, 0, 0), (0.5, 0.5, 0.5),\\n (1, 1, 1)],\\n (0.5, 0.5, 0.5): [(1, 1, 0), (0, 1, 1), (0, 1, 0), (1, 0, 0),\\n (0, 0, 1), (1, 0, 1), (0, 0, 0), (1, 1, 1)]}\\n\\n init_triangulation(3, 0, check, nn_checks)\",\n \"def __init__( self ):\\n self.NQ = 16\\n self.Nbranches = 3\\n self.NatomsUC = 1\\n self.dim = 3\\n self.QVectors = np.zeros( ( self.NQ , 3 ) )\\n self.MakeQVectors()\\n self.EigenVectors = np.zeros( [ self.NQ , \\n self.Nbranches ,\\n self.NatomsUC , \\n self.dim ] )\\n self.MakeEigenVectors()\",\n \"def __init__(self):\\n self.space = 1000\\n self.hash_table = [Node(-1, -1)] * self.space\",\n \"def __initCluster(self):\\n data_size, cluster_center = self.data_size, self.cluster_center\\n self.cluster_temp = np.zeros(data_size, dtype=int)\\n self.cluster_upper_bound = np.full(len(cluster_center), float('inf'), dtype=float)\\n for center in cluster_center:\\n self.cluster_temp[center] = center\",\n \"def _init_vocab(self):\\n self._word2idx = {}\\n self._idx2word = {}\\n self.freqs = {}\\n self.vocab_size = 0\\n\\n self._add_word(self.pad_word)\\n self._add_word(self.start_word)\\n self._add_word(self.end_word)\\n self._add_word(self.unk_word)\\n\\n self.start_word_idx = self.stoi(self.start_word)\\n self.end_word_idx = self.stoi(self.end_word)\\n self.unk_word_idx = self.stoi(self.unk_word)\\n self.pad_word_idx = self.stoi(self.pad_word)\\n\\n self._special_tokens = {\\n 'bos_token': self.start_word,\\n 'cls_token': self.start_word,\\n 'eos_token': self.end_word,\\n 'sep_token': self.end_word,\\n 'pad_token': self.pad_word,\\n 'unk_token': self.unk_word,\\n }\\n\\n self._special_ids = {\\n 'bos_token_id': self.start_word_idx,\\n 'cls_token_id': self.start_word_idx,\\n 'eos_token_id': self.end_word_idx,\\n 'sep_token_id': self.end_word_idx,\\n 'pad_token_id': self.pad_word_idx,\\n 'unk_token_id': self.unk_word_idx,\\n }\\n\\n self.cls_token_id = self.bos_token_id = self.start_word_idx\\n self.eos_token_id = self.sep_token_id = self.end_word_idx\\n self.pad_token_id = self.pad_word_idx\\n self.unk_token_id = self.unk_word_idx\\n\\n self.cls_token = self.bos_token = self.start_word\\n self.eos_token = self.sep_token = self.end_word\\n self.pad_token = self.pad_word\\n self.unk_token = self.unk_word\",\n \"def initialize_grid(self):\\n self.grid = np.zeros([self.N, self.N, self.N])\\n return self.grid\",\n \"def setup_StandardGCMCSphereSampler():\\n # Make variables global so that they can be used\\n global std_gcmc_sphere_sampler\\n global std_gcmc_sphere_simulation\\n\\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'bpti-ghosts.pdb')))\\n ff = ForceField('amber14-all.xml', 'amber14/tip3p.xml')\\n system = ff.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=12 * angstroms,\\n constraints=HBonds)\\n\\n ref_atoms = [{'name': 'CA', 'resname': 'TYR', 'resid': '10'},\\n {'name': 'CA', 'resname': 'ASN', 'resid': '43'}]\\n\\n std_gcmc_sphere_sampler = samplers.StandardGCMCSphereSampler(system=system, topology=pdb.topology,\\n temperature=300*kelvin, referenceAtoms=ref_atoms,\\n sphereRadius=4*angstroms,\\n ghostFile=os.path.join(outdir, 'bpti-ghost-wats.txt'),\\n log=os.path.join(outdir, 'stdgcmcspheresampler.log'))\\n\\n # Define a simulation\\n integrator = NonequilibriumLangevinIntegrator(temperature=300*kelvin, collision_rate=1./picosecond, timestep=2.*femtoseconds)\\n\\n try:\\n platform = Platform.getPlatformByName('CUDA')\\n except:\\n try:\\n platform = Platform.getPlatformByName('OpenCL')\\n except:\\n platform = Platform.getPlatformByName('CPU')\\n\\n std_gcmc_sphere_simulation = Simulation(pdb.topology, system, integrator, platform)\\n std_gcmc_sphere_simulation.context.setPositions(pdb.positions)\\n std_gcmc_sphere_simulation.context.setVelocitiesToTemperature(300*kelvin)\\n std_gcmc_sphere_simulation.context.setPeriodicBoxVectors(*pdb.topology.getPeriodicBoxVectors())\\n\\n # Set up the sampler\\n std_gcmc_sphere_sampler.initialise(std_gcmc_sphere_simulation.context, [3054, 3055, 3056, 3057, 3058])\\n\\n return None\",\n \"def create_sparseDB():\\n datas = data.Kmercount_to_matrix()\\n datas.run()\\n print('***Sparse matrix created***')\",\n \"def initialize(self):\\n self.U = range(self.K)\\n self.H = np.identity(self.rank)\\n temp = 0\\n self.S = np.zeros([self.rank, self.rank, self.K])\\n for k in range(self.K):\\n self.S[:, :, k] = np.identity(self.rank)\\n temp += self.X[k].T.dot(self.X[k])\\n [eigval, eigvec] = np.linalg.eig(temp)\\n self.V = eigvec[:, range(self.rank)]\",\n \"def create_index():\",\n \"def __init__(self, initial, goal=None):\\n \\n #fill the grid with random numbers\\n from random import randint as IA\\n \\n #in switches we must keep track of what numbers are here at the start\\n #to prevent switching them.\\n initialNumber = [[1 for x in range(size)] for y in range(size)]\\n \\n for i in range(size):\\n for j in range(size):\\n if(initial[i][j] == 0):\\n x = IA(1,9)\\n initialNumber[i][j] = 0\\n while(not isLegalInBox(initial,i,j,x)):\\n x = IA(1,9)\\n initial[i][j] = x\\n \\n self.initialNumber = initialNumber\\n self.initial = initial\",\n \"def setUp(self):\\n self.cube = _create_2d_cube()\",\n \"def _train(self):\\n return np.zeros(1, 10)\",\n \"def __init__(self, vocabulary_size=1000):\\n self.vocabulary_size = vocabulary_size\",\n \"def setUp(self):\\n problem = setup_house_L(size=(40, 40))\\n\\n env = MetroLayoutEnv()\\n\\n costfn = objectives.ConstraintsHeur(problem,\\n wmap={'AspectConstraint':0.1,\\n 'AreaConstraint': 2\\n },\\n default=1.)\\n\\n model = algo.MetropolisHastings(env, costfn)\\n\\n self.exp = SimpleMH(\\n env,\\n problem,\\n model=model,\\n cost_fn=costfn,\\n num_iter=1000,\\n initializer=PointsInBound(problem, env, size=3, seed=69)\\n )\",\n \"def init(self):\\n\\t\\tfrom splat_to_db import splat_to_db\\n\\t\\tfrom visualize.clustering_test import clustering_test\\n\\t\\tfrom codense.codense2db import codense2db\\n\\t\\tself.splat_to_db_instance = splat_to_db()\\n\\t\\tself.clustering_test_instance = clustering_test()\\n\\t\\tself.codense2db_instance = codense2db()\\n\\t\\t\\n\\t\\tif not os.path.isdir(self.dir_files):\\n\\t\\t\\tos.makedirs(self.dir_files)\\n\\t\\telse:\\n\\t\\t\\tsys.stderr.write(\\\"Warning, directory %s already exists.\\\\n\\\"%(self.dir_files))\\n\\t\\tself.tmpinfname = os.path.join(self.dir_files, 'input')\\n\\t\\tself.tmpoutfname = os.path.join(self.dir_files, 'output')\\n\\t\\t\\n\\t\\tself.crack_dict = {1: crack_by_modes(self.debug),\\n\\t\\t\\t2:crack_by_splat(self.debug)}\\n\\t\\tself.argument1_dict = {1: self.clustering_test_instance,\\n\\t\\t\\t2: self.splat_to_db_instance}\\n\\t\\t\\n\\t\\t#two descending tables\\n\\t\\tself.splat_table = '%ss'%self.table\\n\\t\\tself.mcl_table = self.splat_table.replace('splat','mcl')\\n\\t\\tif self.mcl_table == self.splat_table:\\n\\t\\t\\tsys.stderr.write(\\\"Error: new splat and mcl tables have the same name, %s\\\\n\\\"%self.splat_table)\\n\\t\\t\\tsys.exit(2)\",\n \"def _init_dataset(self):\\n chars = set()\\n with open(self.file_path + \\\"/words.txt\\\", 'r') as input_file:\\n for line in input_file:\\n line_split = line.strip().split('\\\\t')\\n file_name = self.file_path+\\\"/words/\\\"+line_split[1]\\n gt_text = line_split[0]\\n chars = chars.union(set(list(gt_text)))\\n self.samples.append((file_name, gt_text))\\n input_file.close()\\n\\n self.char_set = sorted(list(chars))\",\n \"def initialize(cls):\\n return cls( *([0.]*cls._parsize) )\",\n \"def __init__(self):\\n self.words = None\\n self.letters = None\\n self.a = None\\n self.nwords = None\\n self.nletters = None\",\n \"def set_up_threshold_cube():\\n test_data = 50*np.arange(16).reshape(4, 4)\\n grid_x = DimCoord(np.arange(4), standard_name=\\\"projection_x_coordinate\\\",\\n units=\\\"km\\\")\\n grid_y = DimCoord(np.arange(4), standard_name=\\\"projection_y_coordinate\\\",\\n units=\\\"km\\\")\\n test_cube = iris.cube.Cube(test_data, long_name=\\\"surface_altitude\\\",\\n units=\\\"m\\\",\\n dim_coords_and_dims=[(grid_y, 0), (grid_x, 1)])\\n return test_cube\",\n \"def setup_spatial():\\n arena_size = \\\"default\\\"\\n\\n output_dict = {\\n \\\"arena_size\\\": arena_size,\\n }\\n return output_dict\",\n \"def __init__(self, neighbourhood, algorithm, iterations, set_up):\\n self.input = neighbourhood\\n self.algorithm = algorithm\\n self.set_up = set_up\\n self.iterations = int(iterations)\\n self.configs = self.get_configs()\\n self.houses = self.load_houses()\\n self.big_iterations = -1\\n self.small_iterations = 0\\n self.caps = []\\n self.batteries = {}\\n self.lowest = 99999\\n self.index = 0\\n self.run_algorithm()\",\n \"def __init__(self):\\n self.grid = {}\\n for i in range(21):\\n self.grid[i] = [' ']*21\\n self._len_x = len(self.grid[0])\\n self._len_y = len(self.grid)\\n self.forbidden_tiles = []\\n self.allowed_tiles = []\\n self.exit = None\\n self.entrance = None\",\n \"def initial_samples(lb, ub, method, numSamp):\\r\\n if not len(lb) == len(ub):\\r\\n raise AssertionError('Lower and upper bounds have different #s of design variables in initial_samples function.')\\r\\n assert method == 'random' or method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr' or method == 'lhc' or method == 'rand-wor', 'An invalid method was specified for the initial_samples.'\\r\\n assert (method == 'nolh' or method == 'nolh-rp' or method == 'nolh-cdr') and len(ub) >= 2 and len(ub) <= 29, 'The Phase space dimensions are outside of the bounds for initial_samples.'\\r\\n for case in Switch(method):\\r\\n if case('random'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = lb + (ub - lb) * rand(len(lb))\\r\\n\\r\\n break\\r\\n if case('rand-wor'):\\r\\n s = np.zeros((numSamp, len(lb)))\\r\\n for i in range(0, numSamp, 1):\\r\\n s[i, :] = choice(len(ub), size=len(ub), replace=False)\\r\\n\\r\\n break\\r\\n if case('nolh'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = range(q)\\r\\n if r != 0:\\r\\n remove = range(dim - r, dim)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-rp'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf = random.sample(range(q), q)\\r\\n if r != 0:\\r\\n remove = random.sample(range(q - 1), r)\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('nolh-cdr'):\\r\\n dim = len(ub)\\r\\n m, q, r = params(dim)\\r\\n conf, remove = get_cdr_permutations(len(ub))\\r\\n if remove != []:\\r\\n nolh = NOLH(conf, remove)\\r\\n else:\\r\\n nolh = NOLH(conf)\\r\\n s = np.array([ list(lb + (ub - lb) * nolh[i, :]) for i in range(len(nolh[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case('lhc'):\\r\\n tmp = lhs(len(lb), samples=numSamp, criterion='center')\\r\\n s = np.array([ list(lb + (ub - lb) * tmp[i, :]) for i in range(len(tmp[:, 0]))\\r\\n ])\\r\\n break\\r\\n if case():\\r\\n print 'Somehow you evaded my assert statement - good job!',\\r\\n print ' However, you still need to use a valid method string.'\\r\\n\\r\\n return s\",\n \"def initialize(self):\\n self.n_words = len(self.vocab)\\n self.n_docs = len(self.documents)\\n\\n # Initialize the three count matrices.\\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\\n self.nmz = np.zeros((self.n_docs, self.n_topics))\\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\\n self.nzw = np.zeros((self.n_topics, self.n_words))\\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\\n self.nz = np.zeros(self.n_topics)\\n\\n # Initialize the topic assignment dictionary.\\n self.topics = {} # key-value pairs of form (m,i):z\\n\\n for m in range(self.n_docs):\\n for i in self.documents[m]:\\n # Get random topic assignment, i.e. z is a random integer in the range of topics\\n z = np.random.randint(self.n_topics)\\n # Increment count matrices\\n self.nmz[m,z] += 1\\n self.nzw[z,self.documents[m][i]] += 1\\n self.nz[z] += 1\\n # Store topic assignment\\n self.topics[(m,i)] = z\",\n \"def initGrid( self, name, suffix, n, ni, nj, ifields=[],rfields=[], lsize=10):\\n #print \\\"initGrid: initializing %s\\\"%(name)\\n self.name = name\\n self.suffix = suffix\\n self.n = n\\n self.ni = ni\\n self.nj = nj\\n self.ifields = ifields\\n self.rfields = rfields\\n #print \\\"ifields=%s\\\\nrfields=%s\\\\nlsize=%d\\\"%(temp_ifields, temp_rfields, lsize)\\n self.lgrid = attributevector.AttributeVector( ifields, rfields, lsize )\\n #print \\\"allocating a temp array...\\\"\\n temp = Numeric.zeros( lsize, Numeric.Float64 )\\n #temp = -9999.0\\n #print \\\"Filling real fields with default values...\\\"\\n for f in rfields:\\n #print \\\"\\\\tFilling field\\\",f,\\\":\\\",\\n self.lgrid.importRAttr( f, temp )\\n #print \\\"... OK!\\\"\\n print \\\"initGrid: Initialized Grid!\\\"\\n # setup av complete\\n return\",\n \"def initialize_beam(self, prefix):\\n\\n def _from_none():\\n \\\"\\\"\\\" Initializes a beam from scratch. \\\"\\\"\\\"\\n beam_prefix = np.array([[self.vocab.go_id]] * self.opt.batch_size, dtype=np.int32)\\n return beam_prefix\\n\\n def _from_string(string_prefix):\\n \\\"\\\"\\\" Initializes a beam from an existing string prefix. \\\"\\\"\\\"\\n idx_prefix = index_sentence(string_prefix, self.vocab, self.opt)\\n beam_prefix = np.concatenate(\\n [np.array([[self.vocab.eos_id]] * self.opt.batch_size, dtype=np.int32), idx_prefix], 1)\\n return beam_prefix\\n\\n beam = list()\\n # Initialize beam entries; the number is determined by the specified beam width\\n # Beam entries are tuples of the form (generated sequence, sequence probability)\\n for _ in range(self.opt.beam_width):\\n if prefix is None:\\n beam_init = _from_none()\\n else:\\n beam_init = _from_string(prefix)\\n beam_tpl = (np.array([[1.0]] * self.opt.batch_size, dtype=np.float32), beam_init)\\n beam.append(beam_tpl)\\n\\n return beam\",\n \"def __init__(self):\\n self.bigramCounts = collections.defaultdict(lambda : 0)\\n self.trigramCounts = collections.defaultdict(lambda : 0)\\n self.unigramCounts = collections.defaultdict(lambda : 1)\\n self.continuationCounts = collections.defaultdict(lambda: 0)\\n self.followingCounts = collections.defaultdict(lambda: 0)\\n self.total = 1\\n self.totalBigramCounts = 0\\n print \\\"Training Language Model...\\\"\\n self.train(brown.sents())\\n print \\\"--Training Complete--\\\"\",\n \"def GenerateInitialSolution():\\n c = random.random()*C\\n count = 0\\n while np.count_nonzero(alpha) < gamma:\\n rand = random.randint(0, len(x_train)-1)\\n if y_train[rand] == 1:\\n alpha[rand] = c\\n L[rand, 1] = c\\n # L[count, 0] = rand\\n # L[count, 1] = alpha[rand]\\n SVs[count] = rand\\n count += 1\\n while np.count_nonzero(alpha) < 2*gamma:\\n rand = random.randint(0, len(x_train)-1)\\n if y_train[rand] == -1:\\n alpha[rand] = c\\n L[rand, 1] = c\\n # L[count, 0] = rand\\n # L[count, 1] = alpha[rand]\\n SVs[count] = rand\\n count += 1\\n return alpha\",\n \"def __init__(self, quad_corpus, translations, train_ratio=0.9):\\n a = list(quad_corpus)\\n random.seed(1337)\\n random.shuffle(a)\\n t = int(train_ratio * len(a))\\n self.a = a[:t]\\n self.b = a[t:]\\n self.translations = translations\\n self.i = 0\",\n \"def setup_StandardGCMCSystemSampler():\\n # Make variables global so that they can be used\\n global std_gcmc_system_sampler\\n global std_gcmc_system_simulation\\n\\n pdb = PDBFile(utils.get_data_file(os.path.join('tests', 'water-ghosts.pdb')))\\n ff = ForceField('tip3p.xml')\\n system = ff.createSystem(pdb.topology, nonbondedMethod=PME, nonbondedCutoff=12 * angstroms,\\n constraints=HBonds)\\n\\n std_gcmc_system_sampler = samplers.StandardGCMCSystemSampler(system=system, topology=pdb.topology,\\n temperature=300*kelvin,\\n boxVectors=np.array(pdb.topology.getPeriodicBoxVectors()),\\n ghostFile=os.path.join(outdir, 'water-ghost-wats.txt'),\\n log=os.path.join(outdir, 'stdgcmcsystemsampler.log'))\\n\\n # Define a simulation\\n integrator = NonequilibriumLangevinIntegrator(temperature=300*kelvin, collision_rate=1./picosecond, timestep=2.*femtoseconds)\\n\\n try:\\n platform = Platform.getPlatformByName('CUDA')\\n except:\\n try:\\n platform = Platform.getPlatformByName('OpenCL')\\n except:\\n platform = Platform.getPlatformByName('CPU')\\n\\n std_gcmc_system_simulation = Simulation(pdb.topology, system, integrator, platform)\\n std_gcmc_system_simulation.context.setPositions(pdb.positions)\\n std_gcmc_system_simulation.context.setVelocitiesToTemperature(300*kelvin)\\n std_gcmc_system_simulation.context.setPeriodicBoxVectors(*pdb.topology.getPeriodicBoxVectors())\\n\\n # Set up the sampler\\n std_gcmc_system_sampler.initialise(std_gcmc_system_simulation.context, [2094, 2095, 2096, 2097, 2098])\\n\\n return None\",\n \"def __init__(self, Q, initSol, tenure, scaleFactor, timeout):\\n this = _tabu_search.new_TabuSearch(Q, initSol, tenure, scaleFactor, timeout)\\n try:\\n self.this.append(this)\\n except __builtin__.Exception:\\n self.this = this\",\n \"def init():\\n # analyzer es utilizado para interactuar con el modelo\\n analyzer = model.newAnalyzer()\\n return analyzer\",\n \"def _suggest_samples(dataset: Dataset, settings: ZoomOptSettings) -> np.ndarray:\\n\\n if settings.batch < 1:\\n raise ValueError(f\\\"Use batch size at least 1. (Was {settings.batch}).\\\") # pragma: no cover\\n\\n continuous_dict, categorical_dict = dataset.parameter_space\\n\\n # If any categorical variable is present, we raise an exception. In theory they should be represented by one-hot\\n # encodings, but I'm not sure how to retrieve the bounds of this space and do optimization within it (the\\n # best way is probably to optimize it in an unconstrained space and map it to one-hot vectors using softmax).\\n # Moreover, in BayesOpt there is iteration over contexts.\\n if categorical_dict:\\n raise NotImplementedError(\\\"This method doesn't work with categorical inputs right now.\\\") # pragma: no cover\\n\\n # It seems that continuous_dict.values() contains pandas series instead of tuples, so we need to map over it\\n # to retrieve the parameter space\\n original_space: Hypercuboid = [(a, b) for a, b in continuous_dict.values()]\\n\\n # Find the location of the optimum. We will shrink the space around it\\n optimum: np.ndarray = _get_optimum_location(dataset)\\n\\n # Estimate how many optimization iterations were performed.\\n step_number: int = settings.n_step or _estimate_step_number(\\n n_points=len(dataset.output_array), batch_size=settings.batch\\n )\\n\\n # Convert to per-batch shrinking factor if a per-iteration shrinking factor supplied\\n per_batch_shrinking_factor = (\\n settings.shrinking_factor ** settings.batch if settings.shrink_per_iter else settings.shrinking_factor\\n )\\n\\n # Calculate by what factor each dimension of the hypercube should be shrunk\\n shrinking_factor_per_dim: float = _calculate_shrinking_factor(\\n initial_shrinking_factor=per_batch_shrinking_factor, step_number=step_number, n_dim=len(original_space)\\n )\\n\\n # Shrink the space\\n new_space: Hypercuboid = [\\n shrink_interval(\\n shrinking_factor=shrinking_factor_per_dim, interval=interval, shrinking_anchor=optimum_coordinate\\n )\\n for interval, optimum_coordinate in zip(original_space, optimum)\\n ]\\n\\n # The shrunk space may be out of the original bounds (e.g. if the maximum was close to the boundary).\\n # Translate it.\\n new_space = _move_to_original_bounds(new_space=new_space, original_space=original_space)\\n\\n # Sample the new space to get a batch of new suggestions.\\n parameter_space = ParameterSpace([ContinuousParameter(f\\\"x{i}\\\", low, upp) for i, (low, upp) in enumerate(new_space)])\\n\\n return designs.suggest_samples(\\n parameter_space=parameter_space, design_type=settings.design, point_count=settings.batch\\n )\",\n \"def create_samples(self):\\n self._samples = self.load_samples()\\n self.modify_samples()\",\n \"def __init__(self, settings,study):\\n \\n # Store the study #\\n ###################\\n \\n self._study = study\\n self._parameters_size = self._study.geometry.parameters_size\\n \\n # Read settings #\\n ################# \\n if hasattr(settings, 'global_sample_function'):\\n # Use given function and ignore bounds\\n self._global_sample_function = settings.global_sample_function\\n self._global_parameters_bounds = None\\n else:\\n # If no function, use uniform rand with given boundaries if provided. If not, assume [0,1]\\n if hasattr(settings, 'global_parameters_bounds'):\\n self._global_parameters_bounds = np.array(settings.global_parameters_bounds)\\n else:\\n self._global_parameters_bounds = [(0, 1)]*self._parameters_size\\n \\n self._global_sample_function = lambda: self._global_parameters_bounds[:,0] + (self._global_parameters_bounds[:,1]-self._global_parameters_bounds[:,0])*np.random.rand(1,self._parameters_size).flatten()\\n \\n\\n if hasattr(settings, 'global_result_constraint'):\\n self._global_result_constraint = settings.global_result_constraint\\n else:\\n self._global_result_constraint = None \\n \\n if hasattr(settings, 'local_result_constraint'):\\n self._local_result_constraint = settings.local_result_constraint\\n else:\\n self._local_result_constraint = None\\n \\n if hasattr(settings, 'local_max_iterations'):\\n self._local_max_iterations = settings.local_max_iterations\\n else:\\n self._local_max_iterations = 50\\n \\n if hasattr(settings, 'local_method'):\\n self._local_method = settings.local_method\\n else:\\n self._local_method = 'L-BFGS-B'\\n \\n if hasattr(settings, 'local_scaling_factor'):\\n self._local_scaling_factor = settings.local_scaling_factor\\n else:\\n self._local_scaling_factor = 1\\n \\n if hasattr(settings, 'local_ftol'):\\n self._local_ftol = settings.local_ftol\\n else:\\n self._local_ftol = 1e-5\\n \\n if hasattr(settings, 'local_pgtol'):\\n self._local_pgtol = settings.local_pgtol\\n else:\\n self._local_pgtol = 1e-5\\n \\n # Wavelength settings for lumopt \\n if hasattr(settings, 'local_wavelength_start'):\\n self._local_wavelength_start = settings.local_wavelength_start\\n else:\\n self._local_wavelength_start = 1550e-9\\n \\n if hasattr(settings, 'local_wavelength_stop'):\\n self._local_wavelength_stop = settings.local_wavelength_stop\\n else:\\n self._local_wavelength_stop = 1550e-9\\n \\n if hasattr(settings, 'local_wavelength_points'):\\n self._local_wavelength_points = settings.local_wavelength_points\\n else:\\n self._local_wavelength_points = 1\\n \\n # Keep track of the latest random restart. Run a first simulation with\\n # the initial parameters already stored in the geometry\\n self._new_param = None\",\n \"def initialize(self, corpus):\\n if self.id2word is None:\\n logger.info(\\\"no word id mapping provided; initializing from corpus, assuming identity\\\")\\n self.id2word = utils.dict_from_corpus(corpus)\\n self.num_terms = len(self.id2word)\\n elif self.id2word:\\n self.num_terms = 1 + max(self.id2word)\\n else:\\n self.num_terms = 0\\n\\n shape = self.num_topics, self.num_terms\\n logger.info(\\\"constructing %s random matrix\\\", str(shape))\\n # Now construct the projection matrix itself.\\n # Here i use a particular form, derived in \\\"Achlioptas: Database-friendly random projection\\\",\\n # and his (1) scenario of Theorem 1.1 in particular (all entries are +1/-1).\\n randmat = 1 - 2 * np.random.binomial(1, 0.5, shape) # convert from 0/1 to +1/-1\\n # convert from int32 to floats, for faster multiplications\\n self.projection = np.asfortranarray(randmat, dtype=np.float32)\\n # TODO: check whether the Fortran-order shenanigans still make sense. In the original\\n # code (~2010), this made a BIG difference for np BLAS implementations; perhaps now the wrappers\\n # are smarter and this is no longer needed?\",\n \"def create_individual(self):\\n self.genes = np.random.rand(self.chromosome_size)\\n self.personal_best = self.genes.copy\",\n \"def __init__(self, name, grid):\\n self.name = name\\n self.space_dimensions = grid.dimensions\\n self.step_dimension = grid.stepping_dim\\n self.dtype = grid.dtype\\n\\n # All known solutions and grids in this context\\n self.solutions = []\\n self.grids = {}\",\n \"def __init__(self, corpus, n_grams, min_length):\\n self.grams = {}\\n self.n_grams = n_grams\\n self.corpus = corpus\\n self.min_length = min_length\\n self.sequences()\",\n \"def __init__(self, min_freq = 20):\\n\\n # A dict that maps a word to an index.\\n self.word_to_idx = {}\\n\\n # A dict that maps an index to it's word.\\n self.idx_to_word = {}\\n\\n # Store the min_freq.\\n self.min_freq = min_freq\",\n \"def __init__(self, n, sents, corpus='', gamma=None, addone=True):\\n self.n = n\\n self.smoothingtechnique = 'Interpolated (Jelinek Mercer) Smoothing'\\n self.gamma = gamma\\n self.addone = addone\\n self.counts = counts = defaultdict(int)\\n self.gamma_flag = True\\n self.corpus = corpus\\n # way more efficient than use set unions\\n voc = ['</s>']\\n for s in sents:\\n voc += s\\n self.voc = list(set(voc))\\n\\n if gamma is None:\\n self.gamma_flag = False\\n\\n # if not gamma given\\n if not self.gamma_flag:\\n total_sents = len(sents)\\n aux = int(total_sents * 90 / 100)\\n # 90 per cent for training\\n train_sents = sents[:aux]\\n # 10 per cent for perplexity (held out data)\\n held_out_sents = sents[-total_sents+aux:]\\n\\n train_sents = list(map((lambda x: ['<s>']*(n-1) + x), train_sents))\\n train_sents = list(map((lambda x: x + ['</s>']), train_sents))\\n\\n for sent in train_sents:\\n for j in range(n+1):\\n # move along the sent saving all its j-grams\\n for i in range(n-j, len(sent) - j + 1):\\n ngram = tuple(sent[i: i + j])\\n counts[ngram] += 1\\n # added by hand\\n counts[('</s>',)] = len(train_sents)\\n # variable only for tests\\n self.tocounts = counts\\n # search the gamma that gives lower perplexity\\n gamma_candidates = [i*50 for i in range(1, 15)]\\n # xs is a list with (gamma, perplexity)\\n xs = []\\n sents = train_sents\\n for aux_gamma in gamma_candidates:\\n self.gamma = aux_gamma\\n aux_perx = self.perplexity(held_out_sents)\\n xs.append((aux_gamma, aux_perx))\\n xs.sort(key=lambda x: x[1])\\n self.gamma = xs[0][0]\\n with open('old-stuff/interpolated_' + str(n) + '_parameters_'+corpus, 'a') as f:\\n f.write('Order: {}\\\\n'.format(self.n))\\n f.write('Gamma: {}\\\\n'.format(self.gamma))\\n f.write('AddOne: {}\\\\n'.format(self.addone))\\n f.write('Perplexity observed: {}\\\\n'.format(xs[0][1]))\\n f.write('-------------------------------\\\\n')\\n f.close()\\n\\n else:\\n sents = list(map((lambda x: ['<s>']*(n-1) + x), sents))\\n sents = list(map((lambda x: x + ['</s>']), sents))\\n\\n for sent in sents:\\n # counts now holds all k-grams for 0 < k < n + 1\\n for j in range(n+1):\\n # move along the sent saving all its j-grams\\n for i in range(n-j, len(sent) - j + 1):\\n ngram = tuple(sent[i: i + j])\\n counts[ngram] += 1\\n # added by hand\\n counts[('</s>',)] = len(sents)\",\n \"def _sinit(cls):\\n SGR = oq.plugin.get('SciGraph')\\n IXR = oq.plugin.get('InterLex')\\n #sgr.verbose = True\\n for rc in (SGR, IXR):\\n rc.known_inverses += (\\n ('hasPart:', 'partOf:'),\\n ('NIFRID:has_proper_part', 'NIFRID:proper_part_of'))\\n\\n sgr = SGR(apiEndpoint=auth.get('scigraph-api'))\\n ixr = IXR(readonly=True)\\n ixr.Graph = OntGraph\\n cls.query_init(sgr, ixr) # = oq.OntQuery(sgr, ixr, instrumented=OntTerm)\\n [cls.repr_level(verbose=False) for _ in range(2)]\",\n \"def _localGenerateAssembler(self,initDict):\\n Grid._localGenerateAssembler(self, initDict)\\n self.jobHandler = initDict['internal']['jobHandler']\\n self.dists = self.transformDistDict()\\n # Do a distributions check for ND\\n # This sampler only accept ND distributions with variable transformation defined in this sampler\\n for dist in self.dists.values():\\n if isinstance(dist, Distributions.NDimensionalDistributions):\\n self.raiseAnError(IOError, 'ND Dists contain the variables in the original input space are not supported for this sampler!')\",\n \"def setUp(self):\\n\\n self._hash_bins = 10\\n self._embedding_dim = 2\\n\\n self._default_config = {\\n \\\"hash_bins\\\": self._hash_bins,\\n \\\"embedding_dim\\\": self._embedding_dim\\n }\",\n \"def create_embedding(self):\\n self.embedding = []\\n\\n for index in range(1,self.args.window_size+1):\\n print(\\\"\\\\nOptimization round: \\\" +str(index)+\\\"/\\\"+str(self.args.window_size)+\\\".\\\")\\n print(\\\"Creating documents.\\\")\\n clean_documents = self.walk_extracts(index)\\n print(\\\"Fitting model.\\\")\\n model = Word2Vec(clean_documents,\\n size = self.args.dimensions,\\n window = 1,\\n min_count = self.args.min_count,\\n sg = 1,\\n workers = self.args.workers)\\n\\n new_embedding = self.get_embedding(model)\\n self.embedding = self.embedding +[new_embedding]\\n self.embedding = np.concatenate(self.embedding, axis = 1)\",\n \"def main():\\n grid = make_grid(3, 3) # change to 3x3\\n dictionary = get_dictionary(\\\"words.txt\\\")\\n words = search(grid, dictionary)\\n display_words(words)\",\n \"def _make_test_cube(long_name):\\n cs = GeogCS(EARTH_RADIUS)\\n data = np.array([[1.0, 1.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0]])\\n cube = Cube(data, long_name=long_name)\\n x_coord = DimCoord(\\n np.linspace(-45.0, 45.0, 3), \\\"latitude\\\", units=\\\"degrees\\\", coord_system=cs\\n )\\n y_coord = DimCoord(\\n np.linspace(120, 180, 3), \\\"longitude\\\", units=\\\"degrees\\\", coord_system=cs\\n )\\n cube.add_dim_coord(x_coord, 0)\\n cube.add_dim_coord(y_coord, 1)\\n return cube\",\n \"def build_index(path, chunk_size):\\n physical_files = set()\\n boundaries = []\\n examples_needed = 0\\n for sgf in find_sgfs(path):\\n physical_files.add(sgf.locator.physical_file)\\n if examples_needed == 0:\\n # The start of this SGF is a chunk boundary.\\n boundaries.append(Pointer(sgf.locator, 0))\\n examples_needed = chunk_size\\n game_record = Sgf_game.from_string(sgf.contents)\\n num_positions = len(_sequence(game_record))\\n if examples_needed < num_positions:\\n # The start of the next chunk is inside this SGF.\\n boundaries.append(Pointer(sgf.locator, examples_needed))\\n remaining_examples = num_positions - examples_needed\\n examples_needed = chunk_size - remaining_examples\\n else:\\n # This SGF is entirely contained within the current chunk.\\n examples_needed -= num_positions\\n\\n return CorpusIndex(physical_files, chunk_size, boundaries)\",\n \"def __initialise_smart(self, X, args):\\n\\t\\tcentroids = np.zeros((self.K,self.D))\\n\\t\\tif X.shape[0] > 10*self.K:\\n\\t\\t\\tdata = X[:10*self.K,:]\\n\\t\\telse:\\n\\t\\t\\tdata = X\\n\\t\\tN = data.shape[0]\\n\\n\\t\\t\\t#choosing centroids\\n\\t\\t\\t#points are chosen from dataset with farhtest point clustering\\n\\t\\tran_index = np.random.choice(N)\\n\\t\\tcentroids[0,:] = data[ran_index]\\n\\n\\t\\tfor k in range(1,self.K):\\n\\t\\t\\tdistances = np.zeros((N,k)) #(N,K)\\n\\t\\t\\tfor k_prime in range(k):\\n\\t\\t\\t\\tdistances[:,k_prime] = np.sum(np.square(data - centroids[k_prime,:]), axis =1) #(N,K')\\n\\t\\t\\tdistances = np.min(distances, axis = 1) #(N,)\\n\\t\\t\\tdistances /= np.sum(distances) #normalizing distances to make it a prob vector\\n\\t\\t\\tnext_cl_arg = np.random.choice(range(data.shape[0]), p = distances) #chosen argument for the next cluster center\\n\\t\\t\\tcentroids[k,:] = data[next_cl_arg,:]\\n\\n\\t\\tvar = np.var(X, axis = 0) #(D,)\\n\\n\\t\\t\\t#computing initial responsibilities\\n\\t\\tr_0 = np.zeros((X.shape[0],self.K))\\n\\t\\tfor k in range(self.K):\\n\\t\\t\\tr_0[:,k] = np.sum(np.divide(np.square(X - centroids[k,:]), var), axis = 1) + 1e-5\\n\\t\\tr_0 = np.divide(r_0.T, np.sum(r_0,axis=1)).T\\n\\n\\t\\tself.gating.fit(X,r_0, *args)\\n\\n\\t\\treturn r_0\",\n \"def createAllSG():\\n\\tfor info in conf_HVM:\\n\\t\\tec2 = boto.ec2.connect_to_region(info['region']+'-'+info['zone'])\\n\\t\\tcreateSG(ec2,'SG-Cassandra-'+info['region']+'-'+info['zone'],CASSANDRA_RULES)\",\n \"def construct_random_initial(self):\\n x = np.random.random((self._crv_size, self._bound))\\n return x\",\n \"def __init__(self, n, prey_cnt=0, predator_cnt=0):\\n # print n, prey_cnt, predator_cnt\\n self.grid_size = n\\n self.grid = []\\n for i in range(n):\\n row = [0]*n # row is a list of n zeros\\n self.grid.append(row)\\n self.init_animals(prey_cnt, predator_cnt)\",\n \"def _init_empty_polyhedron(self):\\n self._ambient_dim = 0\\n\\n self._Vrepresentation = Sequence([])\\n self._Vrepresentation.set_immutable()\\n \\n self._Hrepresentation = Sequence([])\\n Equation(self, [-1]);\\n self._Hrepresentation.set_immutable()\\n\\n self._V_adjacency_matrix = matrix(ZZ, 0, 0, 0)\\n self._V_adjacency_matrix.set_immutable()\\n\\n self._H_adjacency_matrix = matrix(ZZ, 1, 1, 0)\\n self._H_adjacency_matrix.set_immutable()\",\n \"def _initialize_corpus(self):\\n vocab = self.vocab # vocab is the word vector\\n theta = self.theta # theta is the model parameter\\n corpus = self.corpus\\n\\n for line in corpus:\\n for word in line:\\n if word not in vocab:\\n vocab[word] = init_vector(self.n)\\n theta[word] = init_vector(self.n)\\n\\n if self.verbose:\\n print(f\\\"{len(vocab)} words have been loaded\\\")\",\n \"def init(self):\\n self._es.create_index_template(\\n name=DATASETS_INDEX_NAME,\\n template=DATASETS_INDEX_TEMPLATE,\\n force_recreate=True,\\n )\\n self._es.create_index(DATASETS_INDEX_NAME)\",\n \"def __init__(self, n):\\n self._n = n\\n self._grid = [[False] * n for _ in range(n)]\\n # create sites for n-by-n grid and 2 \\\"virtual\\\" sites for top and bottom\\n # self._uf = QuickFindUF(n * n + 2)\\n self._uf = WeightedQuickUnionUF(n * n + 2) # QuickFindUF(n * n + 2)\\n # connect top and bottom virtual sites with respecting sides of grid\\n self._top_idx = n * n\\n self._bottom_idx = n * n + 1\\n for i in range(n):\\n self._uf.union(self._top_idx, i)\\n self._uf.union(self._bottom_idx, (n - 1) * n + i)\",\n \"def test_hunger_grid_create(self):\\n self.grid = Hunger_Grid.hunger_grid()\\n self.grid.newGrid = Hunger_Grid.hunger_grid().create_hunger_grid(M=30, N=30, P_LAVA = 1.0)\\n self.assertTrue(self.grid.newGrid.size == 900, \\\"Grid size is incorrect\\\")\\n self.assertTrue(self.grid.newGrid[2, 2] == 1, \\\"Lava chance is not acting correctly\\\")\\n self.assertTrue(self.grid.newGrid[-3, -3] == 1, \\\"Lava chance is not acting correctly\\\")\",\n \"def initialize(self):\\n # Initializing the counter and distribution.\\n for k in range(0, self.topic_number,1):\\n self.topic_term_count_matrix[k]= [0.0] * self.term_number\\n self.topic_distribution_over_term[k] = [0.0] * self.term_number\\n self.sum_topic_by_term_count[k] = 0.0\\n for m in range(0, self.document_number,1):\\n self.document_topic_count_matrix[m] = [0.0] * self.topic_number\\n self.document_distribution_over_topic[m] = [0.0] * self.topic_number\\n self.sum_document_by_topic_count[m] = 0.0\\n\\n # Initializing topics assigned to all words of all documents.\\n for m in range(0, self.document_number, 1):\\n N = len(self.documents[m])\\n self.word_topic_assignment[m] = [-1] * N\\n for n in range(0, N,1):\\n topic = int(random.uniform(0,1) * self.topic_number)\\n self.document_topic_count_matrix[m][topic] += 1.0\\n self.topic_term_count_matrix[topic][self.documents[m][n]] += 1.0\\n self.sum_topic_by_term_count[topic] += 1.0\\n self.word_topic_assignment[m][n] = topic\\n self.sum_document_by_topic_count[m] = N\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6223337","0.59628797","0.58912903","0.5874907","0.5854502","0.5754657","0.5733271","0.56222767","0.5502466","0.54998386","0.54974604","0.5460404","0.5426823","0.5370437","0.53134114","0.5280649","0.52736545","0.5264173","0.52439547","0.52239805","0.52133995","0.5201464","0.5197781","0.5195287","0.5166887","0.51579237","0.5157122","0.5151636","0.5146957","0.5110705","0.51000965","0.5098655","0.509602","0.5092681","0.50834566","0.50803155","0.5068113","0.5067704","0.5064384","0.50620085","0.5060049","0.5052792","0.50522333","0.5037757","0.50341415","0.5033885","0.5029221","0.502805","0.5027424","0.5025511","0.5024078","0.50147194","0.50089437","0.5004268","0.5002548","0.49922487","0.49869925","0.4986569","0.49761832","0.4975849","0.49675873","0.496703","0.4966215","0.49642715","0.49641946","0.49612823","0.49590996","0.49575084","0.4950198","0.4941925","0.49405187","0.49303788","0.4929549","0.49191633","0.49177888","0.49147114","0.49141252","0.491141","0.49017224","0.4901646","0.48998806","0.48997557","0.48994607","0.48976502","0.48962414","0.48951176","0.48930106","0.48927853","0.4889207","0.4889196","0.488467","0.4880462","0.48791718","0.4877827","0.48759046","0.48751605","0.48726475","0.48701224","0.48685074","0.48641506"],"string":"[\n \"0.6223337\",\n \"0.59628797\",\n \"0.58912903\",\n \"0.5874907\",\n \"0.5854502\",\n \"0.5754657\",\n \"0.5733271\",\n \"0.56222767\",\n \"0.5502466\",\n \"0.54998386\",\n \"0.54974604\",\n \"0.5460404\",\n \"0.5426823\",\n \"0.5370437\",\n \"0.53134114\",\n \"0.5280649\",\n \"0.52736545\",\n \"0.5264173\",\n \"0.52439547\",\n \"0.52239805\",\n \"0.52133995\",\n \"0.5201464\",\n \"0.5197781\",\n \"0.5195287\",\n \"0.5166887\",\n \"0.51579237\",\n \"0.5157122\",\n \"0.5151636\",\n \"0.5146957\",\n \"0.5110705\",\n \"0.51000965\",\n \"0.5098655\",\n \"0.509602\",\n \"0.5092681\",\n \"0.50834566\",\n \"0.50803155\",\n \"0.5068113\",\n \"0.5067704\",\n \"0.5064384\",\n \"0.50620085\",\n \"0.5060049\",\n \"0.5052792\",\n \"0.50522333\",\n \"0.5037757\",\n \"0.50341415\",\n \"0.5033885\",\n \"0.5029221\",\n \"0.502805\",\n \"0.5027424\",\n \"0.5025511\",\n \"0.5024078\",\n \"0.50147194\",\n \"0.50089437\",\n \"0.5004268\",\n \"0.5002548\",\n \"0.49922487\",\n \"0.49869925\",\n \"0.4986569\",\n \"0.49761832\",\n \"0.4975849\",\n \"0.49675873\",\n \"0.496703\",\n \"0.4966215\",\n \"0.49642715\",\n \"0.49641946\",\n \"0.49612823\",\n \"0.49590996\",\n \"0.49575084\",\n \"0.4950198\",\n \"0.4941925\",\n \"0.49405187\",\n \"0.49303788\",\n \"0.4929549\",\n \"0.49191633\",\n \"0.49177888\",\n \"0.49147114\",\n \"0.49141252\",\n \"0.491141\",\n \"0.49017224\",\n \"0.4901646\",\n \"0.48998806\",\n \"0.48997557\",\n \"0.48994607\",\n \"0.48976502\",\n \"0.48962414\",\n \"0.48951176\",\n \"0.48930106\",\n \"0.48927853\",\n \"0.4889207\",\n \"0.4889196\",\n \"0.488467\",\n \"0.4880462\",\n \"0.48791718\",\n \"0.4877827\",\n \"0.48759046\",\n \"0.48751605\",\n \"0.48726475\",\n \"0.48701224\",\n \"0.48685074\",\n \"0.48641506\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6189054"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":4687,"cells":{"query":{"kind":"string","value":"merges station letters into single list of station names"},"document":{"kind":"string","value":"def stations(station_let):\n\tstat = ['']*np.size(station_let,0)\n\tfor i in range(len(stat)):\n\t\tfor j in range(4):\n\t\t\tif station_let[i][j] is not np.ma.masked:\n\t\t\t\tstat[i]+=station_let[i][j]\n\treturn stat"},"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 combine_state_names_and_abbreviations():\n lst=[]\n for k,v in us_state_abbrev.items():\n lst.append(v)\n lst = sorted(lst[:10])\n state = sorted(states)\n print(lst+state[-10:])\n return","def combine_state_names_and_abbreviations():\n return sorted(us_state_abbrev.values())[:10] + sorted(states)[-10:]","def get_station_names(self):\n station_names = []\n for wrapper in self.soup.find_all(\"div\", {\"class\": \"stop-wrapper\"}):\n station_name = ' '.join(wrapper.find(\"h3\").text.split(' ')[:-1])\n station_names.append(station_name)\n return np.array(station_names).T","def stations():\n\n return station_list","def getStationsName(self) :\n names = []\n for sts in self._stations :\n names.append(sts.getName())\n\n return names","def stationabbreviation(station):\n stations = {'Utrecht': 'Ut',\n 'Amsterdam Centraal': 'asd'}\n if station in stations:\n return stations[station]","def canon_station_name(s, line):\n s = s.strip()\n s = re.sub('^Heathrow$', 'Heathrow Terminals 1, 2, 3', s)\n s = re.sub('^Olympia$', 'Kensington (Olympia)', s)\n s = re.sub('^Warwick Ave$', 'Warwick Avenue', s)\n s = re.sub('^Camden$', 'Camden Town', s)\n s = re.sub('^Central$', 'Finchley Central', s) # They say \"Between Central and East Finchley\"\n s = re.sub('\\s*Platform \\d$', '', s)\n s = s + ' Station'\n s = s.replace('(Bakerloo)', 'Bakerloo').replace('Earls', 'Earl\\'s') \\\n .replace(' fast ', ' ') \\\n .replace('\\xe2\\x80\\x99', \"'\") \\\n .replace('St ', 'St. ') \\\n .replace('Elephant and Castle', 'Elephant & Castle') \\\n .replace('Lambeth Station', 'Lambeth North Station') \\\n .replace('Chalfont Station', 'Chalfont & Latimer Station') \\\n .replace('West Brompon', 'West Brompton') \\\n .replace('Picadilly Circus', 'Piccadilly Circus') \\\n .replace('High Barent', 'High Barnet') \\\n .replace('Bartnet', 'Barnet') \\\n .replace('Faringdon', 'Farringdon') \\\n .replace('Turnham Greens', 'Turnham Green') \\\n .replace('Ruilsip', 'Ruislip') \\\n .replace('Dagemham', 'Dagenham') \\\n .replace('Edgware Road (H & C)', 'Edgware Road Circle') \\\n .replace('Hammersmith (Circle and H&C)', 'Hammersmith') \\\n .replace('Shepherds Bush (Central Line)', \"Shepherd's Bush\") \\\n .replace('Terminals 123', 'Terminals 1, 2, 3').replace('Terminal 1,2,3', 'Terminals 1, 2, 3') \\\n .replace('Woodford Junction', 'Woodford') \\\n .replace(\"King's Cross Station\", \"King's Cross St. Pancras Station\") \\\n .replace(\"Kings Cross Station\", \"King's Cross St. Pancras Station\") \\\n .replace('Central Finchley', 'Finchley Central').replace('District and Picc', 'D & P') \\\n .replace('South Fields', 'Southfields') \\\n .replace('Regents Park', \"Regent's Park\") \\\n .replace('Bromley-by-Bow', \"Bromley-By-Bow\") \\\n .replace('Brent Oak', 'Burnt Oak') \\\n .replace('St. Johns Wood', \"St. John's Wood\") \\\n .replace('Totteridge and Whetstone', 'Totteridge & Whetstone') \\\n .replace('Newbury Park Loop', 'Newbury Park') \\\n .replace('Harrow-on-the-Hill', 'Harrow on the Hill')\n if s == 'Edgware Road Station' and line == 'B':\n s = 'Edgware Road Bakerloo Station'\n if s == 'Edgware Road Station' and line != 'B':\n s = 'Edgware Road Circle Station'\n return s","def station_list() -> List[Dict]:\n return STATIONS","def train_stations(self) -> List[str]:\n return sorted([train_info['HE'] for train_info in train_api.stations_info.values()])","def parse_station_name (station_name):\n try:\n _,chinese_name,code,full_pinyin,short_pinyin = station_name.split('|')\n except ValueError:\n # print(station_name)\n _,chinese_name,code,full_pinyin,short_pinyin,_ = station_name.split('|')\n return {chinese_name:code,full_pinyin:code,short_pinyin:code}","def stations():\n\n # Query all Stations\n station_results = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n all_station_names = list(np.ravel(station_results))\n\n return jsonify(all_station_names)","def satname(rocketsatname):\n \n # split the rocket and satellite name at the bullet\n names = rocketsatname.split('•')\n \n # remove spaces around satellite name\n namefull = names[1].strip()\n \n # return the satellite's name\n return namefull","def getSecondStrand(sequences):\n compDNA = []\n for dna in sequences:\n compDNAAux = dna.replace('A', 't')\n compDNAAux = compDNAAux.replace('T', 'a')\n compDNAAux = compDNAAux.replace('C', 'g')\n compDNAAux = compDNAAux.replace('G', 'c')\n compDNA.append(compDNAAux.upper())\n\n for i in range(0, len(compDNA)):\n compDNA[i] = compDNA[i][::-1]\n\n return compDNA","def stations():\n # Return a JSON list of stations from the dataset\n session = Session(engine)\n stations = session.query(Station.name).all()\n\n # Convert list of tuples into normal list\n station_names = list(np.ravel(stations))\n\n return jsonify(station_names)","def get_data_list_name(name):\n last = name[-1]\n if last in 'y':\n if last in 'a,e,i,o,u,y':\n name = name[0:-1] + 'ies'\n else:\n name += 's'\n elif last in 'ou':\n name += 'es'\n elif last == 'f':\n name = name[0:-1] + 'ves'\n elif name[-2:-1] == 'fe':\n name = name[0:-2] + 'ves'\n elif last in ['s', 'ss', 'x', 'sh', 'ch']:\n name += 'es'\n else:\n name += 's'\n return name","def extract_names(register):\n names = []\n for i in range(len(register) - 1): # len() -> no of columns\n first_name = str(register.iloc[i][2]).capitalize()\n last_name = str(register.iloc[i][1]).upper()\n name = last_name + ' ' + first_name\n names.append(name)\n names = list(set(names))\n return names","def station_name(f):\n return f.split('/')[1].split('_')[0]","def extract_full_names(people):\n result = []\n \n for lst in names:\n x = ''\n for name in lst.values():\n x += ' ' + name \n x = x[1:] \n result.append(x)\n return result","def miss_station(all_stations,stations):\n\tdiff = len(all_stations)-len(stations)\n k=0\n i=0\n miss_stations = ['']*diff\n a = all_stations[:]\n a.sort()\n s = stations[:]\n s.sort()\n while i < len(stations):\n while a[i] != s[i]:\n miss_stations[k]=a[i]\n del a[i]\n k+=1\n i+=1\n\treturn miss_stations","def name_supply(stems=string.ascii_lowercase, drop_zero=True):\n k = 0\n while 1:\n for a in stems:\n yield a+str(k) if (k or not drop_zero) else a\n k = k+1","def currentAntennaNames(carmaOnly=False) :\n a=s.getAntennaAssignments()\n namelist = []\n for i in a:\n cname = i.carmaAntennaName\n tname = i.typedAntennaName\n if (carmaOnly) :\n names = i.carmaAntennaName\n else :\n names = \"%s(%s)\" %(cname,tname)\n namelist.append(names)\n return namelist","def stations():\n results = session.query(Station.name).all()\n station_names = list(np.ravel(results))\n\n return jsonify(station_names)","def watershedlist():\n opts = watersheds_db()\n return [(opts[opt]['name'] + ' (' + opts[opt]['delineation'] + ')', opt) for opt in opts]","def allstrings2(alphabet, length):\n c = []\n for i in range(length):\n c = [[x]+y for x in alphabet for y in c or [[]]]\n for value in c:\n \tfvalue = ''.join(value)\n \tprint fvalue\n return \"\"","def formatTickers(self):\n availableTickers = []\n for pair in self.allPairs:\n if not pair.upper()[0:3] in availableTickers:\n availableTickers.append(pair.upper()[0:3])\n if not pair.upper()[3:] in availableTickers:\n availableTickers.append(pair.upper()[3:])\n return availableTickers","def formatTickers(self):\n availableTickers = []\n for pair in self.allPairs:\n if not pair.upper()[0:3] in availableTickers:\n availableTickers.append(pair.upper()[0:3])\n if not pair.upper()[3:] in availableTickers:\n availableTickers.append(pair.upper()[3:])\n return availableTickers","def create_station_list(self):\n sorted_station_list = sorted(self.station_dict, key=self.station_dict.get)\n\n return sorted_station_list","def get_uniprot_names(uniprot_result):\n name_lines = [l for l in uniprot_result.split('\\n') if l.startswith('DE')]\n\n names = []\n\n for nm_line in name_lines:\n if 'Full=' in nm_line:\n names.append(nm_line.split('Full=')[-1][:-1])\n elif 'Short=' in nm_line:\n names.append(nm_line.split('Short=')[-1][:-1])\n\n return names","def get_and_clean_student_list(students):\r\n\r\n students = split_by_comma_and_whitespace(students)\r\n students = [unicode(s.strip()) for s in students]\r\n students = [s for s in students if s != '']\r\n students_lc = [x.lower() for x in students]\r\n\r\n return students, students_lc","def impute_station_name(tags):\n\n if not tags.get(\"StationName\"):\n try:\n tags[\"StationName\"] = tags[\"DeviceSerialNumber\"]\n except:\n try:\n tags[\"StationName\"] = tags[\"X-Ray Radiation Dose Report\"][\"Device Observer UID\"]\n except:\n tags[\"StationName\"] = \"Unknown\"\n logger = logging.getLogger(\"DcmSimplify\")\n logger.warning('No station name identified')\n\n return tags","def make_label_names(name_lsit):\n\n hover_label_names = []\n for x in range(len(name_lsit)):\n temp1 = name_lsit[x]\n hover_label_names.append(temp1)\n\n return hover_label_names","def deabbreviate(self, st):\n\t\tabbrs = {'gws': 'greater western sydney giants',\n\t\t\t\t 'gwsg': 'greater western sydney giants',\n\t\t\t\t 'afl': 'australian football league',\n\t\t\t\t 'nrc': 'national rugby championship',\n\t\t\t\t 'nrl': 'national rugby league',\n\t\t\t\t 'syd': 'sydney',\n\t\t\t\t 'mel': 'melbourne',\n\t\t\t\t 'melb': 'melbourne',\n\t\t\t\t 'bris': 'brisbane',\n\t\t\t\t 'brisb': 'brisbane',\n\t\t\t\t 'gc': 'gold coast',\n\t\t\t\t 'adel': 'adelaide',\n\t\t\t\t 'canb': 'canberra',\n\t\t\t\t 'mt': 'mount',\n\t\t\t\t 'utd': 'united',\n\t\t\t\t 'cty': 'city',\n\t\t\t\t 'football club': 'fc',\n\t\t\t\t 'snr': 'senior',\n\t\t\t\t 'jr': 'junion',\n\t\t\t\t 'nsw': 'new south wales' ,\n\t\t\t\t 'vic': 'victoria',\n\t\t\t\t 'tas' : 'tasmania',\n\t\t\t\t 'sa': 'south australia',\n\t\t\t\t 'wa': 'western australia',\n\t\t\t\t 'act': 'australian capital territory',\n\t\t\t\t 'nt': 'northern territory',\n\t\t\t\t 'qld': 'queensland',\n\t\t\t\t 'champs': 'championships', \n\t\t\t\t 'champ': 'championship', \n\t\t\t\t 'soc': 'society',\n\t\t\t\t 'ent': 'entertainment',\n\t\t\t\t 'intl': 'international', \n\t\t\t\t 'int': 'international', \n\t\t\t\t 'aust': 'australian'}\n\n\t\t# first replace full state names by abbreviations;\n\t\tfor ab in abbrs:\n\t\t\tst = re.sub(r'\\b' + ab + r'\\b', abbrs[ab], st)\n\n\t\treturn st","def get_luns(pairs: list) -> list:\n return [pair.split() for pair in pairs]","def typedAntennaNames() :\n a=s.getAntennaAssignments()\n namelist = []\n for i in a:\n namelist.append( i.typedAntennaName )\n return namelist","def clean_plant_list(plant_list_in):\r\n\tfull_plants = [plant for plant in plants if '.' not in plant] #remove abbreviation\r\n\t#print(full_plants)\r\n\r\n\treturn list(set(full_plants)) # return unique names as list\r","def state2stations(state):\r\n state = state[:2].upper()\r\n for row in rows():\r\n if row[5]==state:\r\n yield tuple(row)","def get_aliases_string(trembl_list):\n aliases_list = []\n\n for row in trembl_list:\n psimi_trembl = \"trembl:\" + row[1]\n aliases_list.append(psimi_trembl)\n\n return \"|\".join(aliases_list)","def __lettersToString(self, words):\r\n \r\n li = []\r\n \r\n for word in words:\r\n li.append(\"\".join(word))\r\n \r\n return li","def build_next_stations(stations):\n\n station_0_bikes = stations[0]['bikesAvailable']\n station_1_bikes = stations[1]['bikesAvailable']\n\n return f\"On station {stations[0]['name']} is {station_0_bikes} \" \\\n f\"bike{'s' if station_0_bikes > 1 else ''} available and on station\" \\\n f\"{stations[1]['name']} is {station_1_bikes} \" \\\n f\"bike{'s' if station_1_bikes > 1 else ''} available. Goodbye and happy cycling!\"","def FormatName(X):\n full = [v.split(\"OS\")[0].strip() for v in X.iloc[:, 0]]\n gene = [v.split(\"GN=\")[1].split(\" PE\")[0].strip() for v in X.iloc[:, 0]]\n return full, gene","def merge_speeches(speeches_list):\n\n # Create a new string variable\n speeches_string = ''\n\n # Iterate over speeches in the given list\n for speech in speeches_list:\n # Append the speech and add a single space at the end\n speeches_string += speech + ' '\n\n return speeches_string","def make_market_street(start):\r\n return [start]","def _list_of_availability_strings():\n names = [availability.name for availability in Availability]\n return names","def get_words_from_sysets(synset):\n synlist = []\n for s in synset:\n syns = s.lemmas()[0].name()\n synlist.append(syns)\n return synlist","def nameList(self):\r\n return [self.name.lower(), self.code] + self._otherNames","def __stringToLetters(self, words):\r\n li = []\r\n \r\n for word in words:\r\n li.append(list(word))\r\n \r\n return li","def tapeToList(tape):\n tapelist = []\n if tape[0] != 'b' and tape[1] != 'b':\n tape = 'bbbb' + tape\n for i in range(5):\n tape = tape + 'b'\n for c in tape:\n tapelist.append(c)\n return tapelist","def squeeze(word):\n return ''.join(x[0] for x in groupby(word))","def stations():\n # Query all station names from dataset\n station_list = session.query(Measurement.station).distinct().all()\n all_stations = list(np.ravel(station_list))\n\n return jsonify(all_stations)","def _toStr(toList):\n\n names = [formataddr(i) for i in zip(*toList)]\n return ', '.join(names)","def monomer_names(self):\n output = set()\n for item in self.monomers():\n if item in self.pyranose_fac:\n output.add(self.pyranose_fac[item][\"name\"])\n return list(output)","def allstrings2(alphabet, length):\n\n c = []\n for i in range(length):\n c = [[x]+y for x in alphabet for y in c or [[]]]\n\n return c","def hgvs2single(s):\n _validate_str(s)\n t = re_protein.findall(s)\n return [\"{}{}{}\".format(AA_CODES[m[1]], m[2], AA_CODES[m[3]]) for m in t]","def list_stations(pdbfile):\n try:\n pdb = WriteableParmDB(pdbfile)\n return sorted(set(name.split(\":\")[-1] for name in pdb.getNames()))\n finally:\n pdb = None","def keep_lowercase(str_list):","def get_vos(mappings):\n regex = re.compile(\"^/(\\w+)/\")\n patterns = (m.pattern for m in mappings)\n matches = filter(None, (regex.match(p) for p in patterns))\n vo_groups = set(m.group(1).lower() for m in matches)\n\n return vo_groups","def build_list(city):\n\tcity_text = get_city_text(city)\n\tcity_list = []\n\tcity_text = city_text.split( )\n\tbad_chars = '(){}[]\".,1234567890 '\n\n\tfor word in city_text:\n\t\tword = word.strip(bad_chars)\n\t\tif word not in city_list:\n\t\t\tcity_list.append(word)\n\treturn city_list","def get_station_name(self, station=0):\n return self.statuslist()[station][1]","def getStationName(analyzer,stationId):\n name = model.getStationName(analyzer,stationId)\n return name","def player_names(players):\r\n string = ''\r\n for p in players:\r\n string = string + p.name + ', '\r\n return string","def neuronyms(input_str, k):\n n = len(input_str)\n result = []\n\n for length in range(k, n-k+1):\n for start in range (1, n - length):\n prefix = input_str[:start]\n suffix = input_str[(start+length):]\n res_str = prefix+str(length)+suffix\n result.append(res_str)\n\n return result","def update_city_name(name):\r\n if ', WA' or ',WA' in name:\r\n name = name.rstrip (', WA')\r\n return string.capwords(name)","def eng_with_sub(self, eng: list, subword: list) -> list:\n subwords = subword + eng[0]\n while [] in subwords:\n subwords.remove([])\n out = \" \".join('%s' % id for id in subwords).split()\n return out","def generate(seats: List[str]) -> List[str]:\n m = len(seats)\n n = len(seats[0]) if m else 0\n\n regen = [[\".\" for _ in range(n)] for _ in range(m)]\n\n for i in range(m):\n for j in range(n):\n if seats[i][j] == \"L\":\n regen[i][j] = \"#\" if check_empty(seats, i, j) else \"L\"\n if seats[i][j] == \"#\":\n regen[i][j] = \"L\" if check_occ(seats, i, j) else \"#\"\n \n for i in range(m):\n regen[i] = \"\".join(regen[i])\n\n return regen","def convert_angstroms_nm(d_angstroms):\n return d_angstroms/nm_angstroms","def fix_name_wga(artist):\n comma = artist.find(\",\")\n\n return \" \".join([artist[comma + 1:].strip(), artist[:comma].strip()]) if comma != -1 else artist","def acgt_to_string(s: list[list[str]]) -> list[list[str]]:\r\n s_out = [[\"\"] for i in range(len(s))]\r\n for i in range(len(s) - 1):\r\n h = \"\"\r\n for j in range(len(s[i])):\r\n if s[i][j] == 0:\r\n h += \"00\"\r\n if s[i][j] == 1:\r\n h += \"01\"\r\n if s[i][j] == 2:\r\n h += \"10\"\r\n if s[i][j] == 3:\r\n h += \"11\"\r\n s_out[i][0] = h\r\n return s_out","def parse_streetname(self):\n index = self.index\n \n name = \"\"\n for i in range(4):\n if index + i == self.length:\n break\n if self.words[index+i]['word'] == ',':\n break\n # Hack\n if self.words[index+i]['word'] == 'doctor':\n self.words[index+i]['word'] = 'drive'\n break\n try:\n word = sttype[self.words[index+i]['word']]\n break\n except:\n try:\n word = vocab[self.words[index+i]['word']]\n if Vocabulary.STREET_TYPE in word['tag']:\n break\n if name != '':\n name += ' ' + word['lemma'][0]\n else:\n name = word['lemma'][0]\n except: \n if self.words[index+i]['word'][-2:] in [ 'th', 'st', 'nd', 'rd' ]:\n name = self.words[index+i]['word'][:-2]\n else:\n self.index += i\n _dir, _n = self.parse_streetdir()\n self.index -= i\n if _dir:\n break\n if name != '':\n name += ' ' + self.words[index+i]['word']\n else:\n name = self.words[index+i]['word']\n \n if i == 0 or i == 4:\n return None, 0\n else:\n return name, i","def get_alternate_names(self, alt_list):\n self.alternates = [a.name for a in alt_list if a.raga == self.name]","def get_raga_swaras(self, mapping):\n self.aro_swaras = [mapping[s][0] for s in self.aro]\n self.ava_swaras = [mapping[s][0] for s in self.ava]\n aro = [\"s\\'\" if sw == 's.' else sw for sw in self.aro]\n ava = [\"s\\'\" if sw == 's.' else sw for sw in self.ava]\n self.aro_seq = '|'.join([s.upper().strip() for s in aro])\n self.ava_seq = '|'.join([s.upper().strip() for s in ava])","def merge(decks):\n\ta = (mergesort(decks))\n\treturn ''.join([item[1] for item in a]) #if type(item[1]) == str])","def song_by_word(ans):\r\n songs_list = \"\"\r\n ans = ans.lower()\r\n albums = simple_album_list()\r\n for album in albums:\r\n songs = simple_songs_list(album)\r\n for song in songs:\r\n song = str(song)\r\n if ans in song.lower():\r\n songs_list += song + \", \"\r\n return songs_list[:-2]","def seperate_Loc_Data(data, us_state_abbrev):\n assert data is not None\n dictionary = dict(data)\n keys = dictionary.keys()\n tmp = list(keys)\n values = dictionary.values()\n res = []\n for elem in keys:\n state = elem[1].strip()\n if state in us_state_abbrev:\n res.append(us_state_abbrev[state])\n return res, list(values)","def get_whole_nato_alphabet_string(mappings):\n def tuple_to_string(letter_word_pair):\n \"\"\"Convert a tuple to a mapping string.\"\"\"\n letter, word = letter_word_pair\n return '{letter}: {word}'.format(letter=letter, word=word)\n\n items = mappings.items()\n sorted_items = sorted(mappings.items())\n return '\\n'.join(map(tuple_to_string, sorted_items))","def get_station_features(cls, station_row):\n features = station_row[2].lower(), station_row[7], station_row[8]\n return features","def seperate_City_Data(data, us_state_abbrev):\n assert data is not None\n dictionary = dict(data)\n keys = dictionary.keys()\n tmp = list(keys)\n values = dictionary.values()\n res = []\n for elem in keys:\n state = elem[1].strip()\n city = elem[0].strip()\n# print(city)\n if state in us_state_abbrev:\n res.append(city)\n return res, list(values)","def _repair_names_universal(\n names: Iterable[str], quiet: bool = False, base0_: bool = None\n) -> List[str]:\n min_names = _repair_names_minimal(names)\n neat_names = [re.sub(r\"[^\\w]\", \"_\", name) for name in min_names]\n new_names = _repair_names_unique(\n neat_names,\n quiet=True,\n sanitizer=lambda name: (\n f\"_{name}\"\n if keyword.iskeyword(name) or (name and name[0].isdigit())\n else name\n ),\n base0_=base0_,\n )\n if not quiet:\n changed_names = [\n (orig_name, new_name)\n for orig_name, new_name in zip(names, new_names)\n if orig_name != new_name\n ]\n _log_changed_names(changed_names)\n return new_names","def get_processed_stations(out_dir):\n lista = [ f.split('_')[0] for f in os.listdir(out_dir) if '.nc' in f ]\n #print('Skipping these stations: ' , lista )\n return lista","def encode_word(word: str) -> List[str]:\n inner_letters = word[1:-1]\n inner_letters = shuffle(inner_letters)\n return [word[0], *inner_letters, word[-1]]","def encode_san_dns_names(self, san):\n dns_names = []\n for dns_name in san:\n dns_names.append(x509.DNSName(dns_name))\n return dns_names","def expand_abbrevs(name):\n key = name.upper()\n for abbrev, word in ABBREVS.iteritems():\n key = re.sub(abbrev, word, key)\n \n #Remove (.*) from the street name\n key = re.sub(r'\\(.*?(:?\\)|$)', '', key)\n \n #Unify names\n key = NUMBER_IN_NAMES_REGEX.sub(lambda i: i.group(1) + \" \", key)\n key = re.sub(u\"Ё\", u\"Е\", key)\n key = re.sub(u\"[\\\"'«»№]\", u\" \", key)\n\n # remove \"им\" prefix\n key = re.sub(ur'[^\\s]ИМ[\\.\\s]+', u' ', key)\n\n #Change name parts order\n words = key.split(r\" \")\n words.sort()\n key = \" \".join(words)\n\n key = re.sub(u\"\\s+\", u\" \", key).strip()\n\n logging.debug(\"Street name %s was converted to %s\" % (name, key))\n \n return key","def append_sitename(strs,site):\n strs = [x+' site:'+site for x in strs]\n return strs","def create_word(char_list):","def getNames():\r\n return [\"Server1\", \"Server2\", \"Client1\", \"Client2\"]","def normalize(address):\n replacement = re.sub('\\W+', SEPARATOR, address.lower())\n\n processed = []\n for p in replacement.split(SEPARATOR):\n if not p:\n continue\n\n if p in ABBRS:\n processed.append(ABBRS[p])\n else:\n processed.append(p)\n\n processed.sort()\n\n normalized = SEPARATOR.join(processed)\n return normalized","def format_words(words):\n return sorted(words, key=str.lower)","def get_room_names(soup: bs4.BeautifulSoup) -> Iterable[str]:\n return set(x.string for x in soup.Lecture.find_all(\"RaumBez\"))","def essay_to_wordlist(essay_v, remove_stopwords):\n essay_v = re.sub(\"[^a-zA-Z]\", \" \", essay_v)\n words = essay_v.lower().split()\n if remove_stopwords:\n stops = set(stopwords.words(\"english\"))\n words = [w for w in words if not w in stops]\n return (words)","def metaphlan_krona_string(input):\n s = []\n for f in input:\n name = bn(f).replace(\"_pe.krona\", \"\").replace(\"_se.krona\", \"\")\n s.append(f\"{f},{name}\")\n return \" \".join(s)","def fill_stempool(self):\n tokens = [apply_word_tokenize(x) for x in self.df['name']]\n\n flatten1 = itertools.chain.from_iterable\n flat = list(flatten1(tokens))\n\n stems = [self.stemmer.stem(x) for x in flat]\n\n return set(stems)","def get_stations(nordic_file_names, output_level=0):\n stations = []\n for file in nordic_file_names:\n new_stations = get_event_stations(file, output_level)\n\n if new_stations == -1:\n continue\n\n for x in new_stations:\n if x not in stations:\n stations.append(x)\n\n return sorted(stations)","def list_stations(intent, session):\n stations = location.get_stations(config.bikes_api)\n street_name = intent['slots']['street_name']['value']\n possible = location.matching_station_list(stations,\n street_name,\n exact=True)\n street_name = street_name.capitalize()\n\n if len(possible) == 0:\n return reply.build(\"I didn't find any stations on %s.\" % street_name,\n is_end=True)\n elif len(possible) == 1:\n sta_name = location.text_to_speech(possible[0]['name'])\n return reply.build(\"There's only one: the %s \"\n \"station.\" % sta_name,\n card_title=(\"%s Stations on %s\" %\n (config.network_name, street_name)),\n card_text=(\"One station on %s: %s\" %\n (street_name, possible[0]['name'])),\n is_end=True)\n else:\n last_name = location.text_to_speech(possible[-1]['name'])\n speech = \"There are %d stations on %s: \" % (len(possible),\n street_name)\n speech += (', '.join([location.text_to_speech(p['name'])\n for p in possible[:-1]]) +\n ', and %s' % last_name)\n card_text = (\"The following %d stations are on %s:\\n%s\" %\n (len(possible), street_name,\n '\\n'.join(p['name'] for p in possible)))\n return reply.build(speech,\n card_title=(\"%s Stations on %s\" %\n (config.network_name, street_name)),\n card_text=card_text,\n is_end=True)","def TransformNames(self) -> _n_2_t_0[str]:","def remove_duplicate_chars(w):\n return ''.join(c for c, _ in itertools.groupby(w))","def split_name(fullname):","def get_initials(name):\n\n # your code here\n result = ''\n na = name.find(' NA ')\n if na != -1:\n name = name[:na] + name[na+3:]\n name += ' '\n while name.find(' ') != -1:\n result += name[0] + '.'\n name = name[name.find(' ')+1:]\n print(result)","def build_subway(**lines):\n for key in lines.keys():\n # print key\n value = lines[key]\n lines[key] = value.split()\n\n for key in lines.keys():\n stations.update(set(lines[key]))\n system = {}\n for station in stations:\n next_station = {}\n for key in lines:\n if station in lines[key]:\n line = lines[key]\n idx = line.index(station)\n if idx == 0:\n if next_station.has_key(line[1]): \n temp = next_station[line[1]]\n temp.append(key)\n next_station[line[1]] = temp\n else:\n next_station[line[1]] = [key]\n elif idx == len(line)-1:\n if next_station.has_key(line[idx-1]): \n temp = next_station[line[idx-1]]\n temp.append(key)\n next_station[line[idx-1]] = temp\n else:\n next_station[line[idx-1]] = [key]\n else:\n if next_station.has_key(line[idx-1]): \n temp = next_station[line[idx-1]]\n temp.append(key)\n next_station[line[idx-1]] = temp\n else:\n next_station[line[idx-1]] = [key]\n if next_station.has_key(line[idx+1]): \n temp = next_station[line[idx+1]]\n temp.append(key)\n next_station[line[idx+1]] = temp\n else:\n next_station[line[idx+1]] = [key]\n system[station] = next_station\n return system","def Student_names(l:list)->list:\n result=[]\n for s in l:\n result.append(s.name)\n return result","def meronym(self, sense=0):\n s = self._synset(self.text, sense=sense)\n\n if not s:\n return []\n\n return s.member_meronyms()","def rem_str(prelist,names):\n \n for prefix in prelist:\n names=[name.replace(prefix,'') for name in names]\n \n return names"],"string":"[\n \"def combine_state_names_and_abbreviations():\\n lst=[]\\n for k,v in us_state_abbrev.items():\\n lst.append(v)\\n lst = sorted(lst[:10])\\n state = sorted(states)\\n print(lst+state[-10:])\\n return\",\n \"def combine_state_names_and_abbreviations():\\n return sorted(us_state_abbrev.values())[:10] + sorted(states)[-10:]\",\n \"def get_station_names(self):\\n station_names = []\\n for wrapper in self.soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"stop-wrapper\\\"}):\\n station_name = ' '.join(wrapper.find(\\\"h3\\\").text.split(' ')[:-1])\\n station_names.append(station_name)\\n return np.array(station_names).T\",\n \"def stations():\\n\\n return station_list\",\n \"def getStationsName(self) :\\n names = []\\n for sts in self._stations :\\n names.append(sts.getName())\\n\\n return names\",\n \"def stationabbreviation(station):\\n stations = {'Utrecht': 'Ut',\\n 'Amsterdam Centraal': 'asd'}\\n if station in stations:\\n return stations[station]\",\n \"def canon_station_name(s, line):\\n s = s.strip()\\n s = re.sub('^Heathrow$', 'Heathrow Terminals 1, 2, 3', s)\\n s = re.sub('^Olympia$', 'Kensington (Olympia)', s)\\n s = re.sub('^Warwick Ave$', 'Warwick Avenue', s)\\n s = re.sub('^Camden$', 'Camden Town', s)\\n s = re.sub('^Central$', 'Finchley Central', s) # They say \\\"Between Central and East Finchley\\\"\\n s = re.sub('\\\\s*Platform \\\\d$', '', s)\\n s = s + ' Station'\\n s = s.replace('(Bakerloo)', 'Bakerloo').replace('Earls', 'Earl\\\\'s') \\\\\\n .replace(' fast ', ' ') \\\\\\n .replace('\\\\xe2\\\\x80\\\\x99', \\\"'\\\") \\\\\\n .replace('St ', 'St. ') \\\\\\n .replace('Elephant and Castle', 'Elephant & Castle') \\\\\\n .replace('Lambeth Station', 'Lambeth North Station') \\\\\\n .replace('Chalfont Station', 'Chalfont & Latimer Station') \\\\\\n .replace('West Brompon', 'West Brompton') \\\\\\n .replace('Picadilly Circus', 'Piccadilly Circus') \\\\\\n .replace('High Barent', 'High Barnet') \\\\\\n .replace('Bartnet', 'Barnet') \\\\\\n .replace('Faringdon', 'Farringdon') \\\\\\n .replace('Turnham Greens', 'Turnham Green') \\\\\\n .replace('Ruilsip', 'Ruislip') \\\\\\n .replace('Dagemham', 'Dagenham') \\\\\\n .replace('Edgware Road (H & C)', 'Edgware Road Circle') \\\\\\n .replace('Hammersmith (Circle and H&C)', 'Hammersmith') \\\\\\n .replace('Shepherds Bush (Central Line)', \\\"Shepherd's Bush\\\") \\\\\\n .replace('Terminals 123', 'Terminals 1, 2, 3').replace('Terminal 1,2,3', 'Terminals 1, 2, 3') \\\\\\n .replace('Woodford Junction', 'Woodford') \\\\\\n .replace(\\\"King's Cross Station\\\", \\\"King's Cross St. Pancras Station\\\") \\\\\\n .replace(\\\"Kings Cross Station\\\", \\\"King's Cross St. Pancras Station\\\") \\\\\\n .replace('Central Finchley', 'Finchley Central').replace('District and Picc', 'D & P') \\\\\\n .replace('South Fields', 'Southfields') \\\\\\n .replace('Regents Park', \\\"Regent's Park\\\") \\\\\\n .replace('Bromley-by-Bow', \\\"Bromley-By-Bow\\\") \\\\\\n .replace('Brent Oak', 'Burnt Oak') \\\\\\n .replace('St. Johns Wood', \\\"St. John's Wood\\\") \\\\\\n .replace('Totteridge and Whetstone', 'Totteridge & Whetstone') \\\\\\n .replace('Newbury Park Loop', 'Newbury Park') \\\\\\n .replace('Harrow-on-the-Hill', 'Harrow on the Hill')\\n if s == 'Edgware Road Station' and line == 'B':\\n s = 'Edgware Road Bakerloo Station'\\n if s == 'Edgware Road Station' and line != 'B':\\n s = 'Edgware Road Circle Station'\\n return s\",\n \"def station_list() -> List[Dict]:\\n return STATIONS\",\n \"def train_stations(self) -> List[str]:\\n return sorted([train_info['HE'] for train_info in train_api.stations_info.values()])\",\n \"def parse_station_name (station_name):\\n try:\\n _,chinese_name,code,full_pinyin,short_pinyin = station_name.split('|')\\n except ValueError:\\n # print(station_name)\\n _,chinese_name,code,full_pinyin,short_pinyin,_ = station_name.split('|')\\n return {chinese_name:code,full_pinyin:code,short_pinyin:code}\",\n \"def stations():\\n\\n # Query all Stations\\n station_results = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n all_station_names = list(np.ravel(station_results))\\n\\n return jsonify(all_station_names)\",\n \"def satname(rocketsatname):\\n \\n # split the rocket and satellite name at the bullet\\n names = rocketsatname.split('•')\\n \\n # remove spaces around satellite name\\n namefull = names[1].strip()\\n \\n # return the satellite's name\\n return namefull\",\n \"def getSecondStrand(sequences):\\n compDNA = []\\n for dna in sequences:\\n compDNAAux = dna.replace('A', 't')\\n compDNAAux = compDNAAux.replace('T', 'a')\\n compDNAAux = compDNAAux.replace('C', 'g')\\n compDNAAux = compDNAAux.replace('G', 'c')\\n compDNA.append(compDNAAux.upper())\\n\\n for i in range(0, len(compDNA)):\\n compDNA[i] = compDNA[i][::-1]\\n\\n return compDNA\",\n \"def stations():\\n # Return a JSON list of stations from the dataset\\n session = Session(engine)\\n stations = session.query(Station.name).all()\\n\\n # Convert list of tuples into normal list\\n station_names = list(np.ravel(stations))\\n\\n return jsonify(station_names)\",\n \"def get_data_list_name(name):\\n last = name[-1]\\n if last in 'y':\\n if last in 'a,e,i,o,u,y':\\n name = name[0:-1] + 'ies'\\n else:\\n name += 's'\\n elif last in 'ou':\\n name += 'es'\\n elif last == 'f':\\n name = name[0:-1] + 'ves'\\n elif name[-2:-1] == 'fe':\\n name = name[0:-2] + 'ves'\\n elif last in ['s', 'ss', 'x', 'sh', 'ch']:\\n name += 'es'\\n else:\\n name += 's'\\n return name\",\n \"def extract_names(register):\\n names = []\\n for i in range(len(register) - 1): # len() -> no of columns\\n first_name = str(register.iloc[i][2]).capitalize()\\n last_name = str(register.iloc[i][1]).upper()\\n name = last_name + ' ' + first_name\\n names.append(name)\\n names = list(set(names))\\n return names\",\n \"def station_name(f):\\n return f.split('/')[1].split('_')[0]\",\n \"def extract_full_names(people):\\n result = []\\n \\n for lst in names:\\n x = ''\\n for name in lst.values():\\n x += ' ' + name \\n x = x[1:] \\n result.append(x)\\n return result\",\n \"def miss_station(all_stations,stations):\\n\\tdiff = len(all_stations)-len(stations)\\n k=0\\n i=0\\n miss_stations = ['']*diff\\n a = all_stations[:]\\n a.sort()\\n s = stations[:]\\n s.sort()\\n while i < len(stations):\\n while a[i] != s[i]:\\n miss_stations[k]=a[i]\\n del a[i]\\n k+=1\\n i+=1\\n\\treturn miss_stations\",\n \"def name_supply(stems=string.ascii_lowercase, drop_zero=True):\\n k = 0\\n while 1:\\n for a in stems:\\n yield a+str(k) if (k or not drop_zero) else a\\n k = k+1\",\n \"def currentAntennaNames(carmaOnly=False) :\\n a=s.getAntennaAssignments()\\n namelist = []\\n for i in a:\\n cname = i.carmaAntennaName\\n tname = i.typedAntennaName\\n if (carmaOnly) :\\n names = i.carmaAntennaName\\n else :\\n names = \\\"%s(%s)\\\" %(cname,tname)\\n namelist.append(names)\\n return namelist\",\n \"def stations():\\n results = session.query(Station.name).all()\\n station_names = list(np.ravel(results))\\n\\n return jsonify(station_names)\",\n \"def watershedlist():\\n opts = watersheds_db()\\n return [(opts[opt]['name'] + ' (' + opts[opt]['delineation'] + ')', opt) for opt in opts]\",\n \"def allstrings2(alphabet, length):\\n c = []\\n for i in range(length):\\n c = [[x]+y for x in alphabet for y in c or [[]]]\\n for value in c:\\n \\tfvalue = ''.join(value)\\n \\tprint fvalue\\n return \\\"\\\"\",\n \"def formatTickers(self):\\n availableTickers = []\\n for pair in self.allPairs:\\n if not pair.upper()[0:3] in availableTickers:\\n availableTickers.append(pair.upper()[0:3])\\n if not pair.upper()[3:] in availableTickers:\\n availableTickers.append(pair.upper()[3:])\\n return availableTickers\",\n \"def formatTickers(self):\\n availableTickers = []\\n for pair in self.allPairs:\\n if not pair.upper()[0:3] in availableTickers:\\n availableTickers.append(pair.upper()[0:3])\\n if not pair.upper()[3:] in availableTickers:\\n availableTickers.append(pair.upper()[3:])\\n return availableTickers\",\n \"def create_station_list(self):\\n sorted_station_list = sorted(self.station_dict, key=self.station_dict.get)\\n\\n return sorted_station_list\",\n \"def get_uniprot_names(uniprot_result):\\n name_lines = [l for l in uniprot_result.split('\\\\n') if l.startswith('DE')]\\n\\n names = []\\n\\n for nm_line in name_lines:\\n if 'Full=' in nm_line:\\n names.append(nm_line.split('Full=')[-1][:-1])\\n elif 'Short=' in nm_line:\\n names.append(nm_line.split('Short=')[-1][:-1])\\n\\n return names\",\n \"def get_and_clean_student_list(students):\\r\\n\\r\\n students = split_by_comma_and_whitespace(students)\\r\\n students = [unicode(s.strip()) for s in students]\\r\\n students = [s for s in students if s != '']\\r\\n students_lc = [x.lower() for x in students]\\r\\n\\r\\n return students, students_lc\",\n \"def impute_station_name(tags):\\n\\n if not tags.get(\\\"StationName\\\"):\\n try:\\n tags[\\\"StationName\\\"] = tags[\\\"DeviceSerialNumber\\\"]\\n except:\\n try:\\n tags[\\\"StationName\\\"] = tags[\\\"X-Ray Radiation Dose Report\\\"][\\\"Device Observer UID\\\"]\\n except:\\n tags[\\\"StationName\\\"] = \\\"Unknown\\\"\\n logger = logging.getLogger(\\\"DcmSimplify\\\")\\n logger.warning('No station name identified')\\n\\n return tags\",\n \"def make_label_names(name_lsit):\\n\\n hover_label_names = []\\n for x in range(len(name_lsit)):\\n temp1 = name_lsit[x]\\n hover_label_names.append(temp1)\\n\\n return hover_label_names\",\n \"def deabbreviate(self, st):\\n\\t\\tabbrs = {'gws': 'greater western sydney giants',\\n\\t\\t\\t\\t 'gwsg': 'greater western sydney giants',\\n\\t\\t\\t\\t 'afl': 'australian football league',\\n\\t\\t\\t\\t 'nrc': 'national rugby championship',\\n\\t\\t\\t\\t 'nrl': 'national rugby league',\\n\\t\\t\\t\\t 'syd': 'sydney',\\n\\t\\t\\t\\t 'mel': 'melbourne',\\n\\t\\t\\t\\t 'melb': 'melbourne',\\n\\t\\t\\t\\t 'bris': 'brisbane',\\n\\t\\t\\t\\t 'brisb': 'brisbane',\\n\\t\\t\\t\\t 'gc': 'gold coast',\\n\\t\\t\\t\\t 'adel': 'adelaide',\\n\\t\\t\\t\\t 'canb': 'canberra',\\n\\t\\t\\t\\t 'mt': 'mount',\\n\\t\\t\\t\\t 'utd': 'united',\\n\\t\\t\\t\\t 'cty': 'city',\\n\\t\\t\\t\\t 'football club': 'fc',\\n\\t\\t\\t\\t 'snr': 'senior',\\n\\t\\t\\t\\t 'jr': 'junion',\\n\\t\\t\\t\\t 'nsw': 'new south wales' ,\\n\\t\\t\\t\\t 'vic': 'victoria',\\n\\t\\t\\t\\t 'tas' : 'tasmania',\\n\\t\\t\\t\\t 'sa': 'south australia',\\n\\t\\t\\t\\t 'wa': 'western australia',\\n\\t\\t\\t\\t 'act': 'australian capital territory',\\n\\t\\t\\t\\t 'nt': 'northern territory',\\n\\t\\t\\t\\t 'qld': 'queensland',\\n\\t\\t\\t\\t 'champs': 'championships', \\n\\t\\t\\t\\t 'champ': 'championship', \\n\\t\\t\\t\\t 'soc': 'society',\\n\\t\\t\\t\\t 'ent': 'entertainment',\\n\\t\\t\\t\\t 'intl': 'international', \\n\\t\\t\\t\\t 'int': 'international', \\n\\t\\t\\t\\t 'aust': 'australian'}\\n\\n\\t\\t# first replace full state names by abbreviations;\\n\\t\\tfor ab in abbrs:\\n\\t\\t\\tst = re.sub(r'\\\\b' + ab + r'\\\\b', abbrs[ab], st)\\n\\n\\t\\treturn st\",\n \"def get_luns(pairs: list) -> list:\\n return [pair.split() for pair in pairs]\",\n \"def typedAntennaNames() :\\n a=s.getAntennaAssignments()\\n namelist = []\\n for i in a:\\n namelist.append( i.typedAntennaName )\\n return namelist\",\n \"def clean_plant_list(plant_list_in):\\r\\n\\tfull_plants = [plant for plant in plants if '.' not in plant] #remove abbreviation\\r\\n\\t#print(full_plants)\\r\\n\\r\\n\\treturn list(set(full_plants)) # return unique names as list\\r\",\n \"def state2stations(state):\\r\\n state = state[:2].upper()\\r\\n for row in rows():\\r\\n if row[5]==state:\\r\\n yield tuple(row)\",\n \"def get_aliases_string(trembl_list):\\n aliases_list = []\\n\\n for row in trembl_list:\\n psimi_trembl = \\\"trembl:\\\" + row[1]\\n aliases_list.append(psimi_trembl)\\n\\n return \\\"|\\\".join(aliases_list)\",\n \"def __lettersToString(self, words):\\r\\n \\r\\n li = []\\r\\n \\r\\n for word in words:\\r\\n li.append(\\\"\\\".join(word))\\r\\n \\r\\n return li\",\n \"def build_next_stations(stations):\\n\\n station_0_bikes = stations[0]['bikesAvailable']\\n station_1_bikes = stations[1]['bikesAvailable']\\n\\n return f\\\"On station {stations[0]['name']} is {station_0_bikes} \\\" \\\\\\n f\\\"bike{'s' if station_0_bikes > 1 else ''} available and on station\\\" \\\\\\n f\\\"{stations[1]['name']} is {station_1_bikes} \\\" \\\\\\n f\\\"bike{'s' if station_1_bikes > 1 else ''} available. Goodbye and happy cycling!\\\"\",\n \"def FormatName(X):\\n full = [v.split(\\\"OS\\\")[0].strip() for v in X.iloc[:, 0]]\\n gene = [v.split(\\\"GN=\\\")[1].split(\\\" PE\\\")[0].strip() for v in X.iloc[:, 0]]\\n return full, gene\",\n \"def merge_speeches(speeches_list):\\n\\n # Create a new string variable\\n speeches_string = ''\\n\\n # Iterate over speeches in the given list\\n for speech in speeches_list:\\n # Append the speech and add a single space at the end\\n speeches_string += speech + ' '\\n\\n return speeches_string\",\n \"def make_market_street(start):\\r\\n return [start]\",\n \"def _list_of_availability_strings():\\n names = [availability.name for availability in Availability]\\n return names\",\n \"def get_words_from_sysets(synset):\\n synlist = []\\n for s in synset:\\n syns = s.lemmas()[0].name()\\n synlist.append(syns)\\n return synlist\",\n \"def nameList(self):\\r\\n return [self.name.lower(), self.code] + self._otherNames\",\n \"def __stringToLetters(self, words):\\r\\n li = []\\r\\n \\r\\n for word in words:\\r\\n li.append(list(word))\\r\\n \\r\\n return li\",\n \"def tapeToList(tape):\\n tapelist = []\\n if tape[0] != 'b' and tape[1] != 'b':\\n tape = 'bbbb' + tape\\n for i in range(5):\\n tape = tape + 'b'\\n for c in tape:\\n tapelist.append(c)\\n return tapelist\",\n \"def squeeze(word):\\n return ''.join(x[0] for x in groupby(word))\",\n \"def stations():\\n # Query all station names from dataset\\n station_list = session.query(Measurement.station).distinct().all()\\n all_stations = list(np.ravel(station_list))\\n\\n return jsonify(all_stations)\",\n \"def _toStr(toList):\\n\\n names = [formataddr(i) for i in zip(*toList)]\\n return ', '.join(names)\",\n \"def monomer_names(self):\\n output = set()\\n for item in self.monomers():\\n if item in self.pyranose_fac:\\n output.add(self.pyranose_fac[item][\\\"name\\\"])\\n return list(output)\",\n \"def allstrings2(alphabet, length):\\n\\n c = []\\n for i in range(length):\\n c = [[x]+y for x in alphabet for y in c or [[]]]\\n\\n return c\",\n \"def hgvs2single(s):\\n _validate_str(s)\\n t = re_protein.findall(s)\\n return [\\\"{}{}{}\\\".format(AA_CODES[m[1]], m[2], AA_CODES[m[3]]) for m in t]\",\n \"def list_stations(pdbfile):\\n try:\\n pdb = WriteableParmDB(pdbfile)\\n return sorted(set(name.split(\\\":\\\")[-1] for name in pdb.getNames()))\\n finally:\\n pdb = None\",\n \"def keep_lowercase(str_list):\",\n \"def get_vos(mappings):\\n regex = re.compile(\\\"^/(\\\\w+)/\\\")\\n patterns = (m.pattern for m in mappings)\\n matches = filter(None, (regex.match(p) for p in patterns))\\n vo_groups = set(m.group(1).lower() for m in matches)\\n\\n return vo_groups\",\n \"def build_list(city):\\n\\tcity_text = get_city_text(city)\\n\\tcity_list = []\\n\\tcity_text = city_text.split( )\\n\\tbad_chars = '(){}[]\\\".,1234567890 '\\n\\n\\tfor word in city_text:\\n\\t\\tword = word.strip(bad_chars)\\n\\t\\tif word not in city_list:\\n\\t\\t\\tcity_list.append(word)\\n\\treturn city_list\",\n \"def get_station_name(self, station=0):\\n return self.statuslist()[station][1]\",\n \"def getStationName(analyzer,stationId):\\n name = model.getStationName(analyzer,stationId)\\n return name\",\n \"def player_names(players):\\r\\n string = ''\\r\\n for p in players:\\r\\n string = string + p.name + ', '\\r\\n return string\",\n \"def neuronyms(input_str, k):\\n n = len(input_str)\\n result = []\\n\\n for length in range(k, n-k+1):\\n for start in range (1, n - length):\\n prefix = input_str[:start]\\n suffix = input_str[(start+length):]\\n res_str = prefix+str(length)+suffix\\n result.append(res_str)\\n\\n return result\",\n \"def update_city_name(name):\\r\\n if ', WA' or ',WA' in name:\\r\\n name = name.rstrip (', WA')\\r\\n return string.capwords(name)\",\n \"def eng_with_sub(self, eng: list, subword: list) -> list:\\n subwords = subword + eng[0]\\n while [] in subwords:\\n subwords.remove([])\\n out = \\\" \\\".join('%s' % id for id in subwords).split()\\n return out\",\n \"def generate(seats: List[str]) -> List[str]:\\n m = len(seats)\\n n = len(seats[0]) if m else 0\\n\\n regen = [[\\\".\\\" for _ in range(n)] for _ in range(m)]\\n\\n for i in range(m):\\n for j in range(n):\\n if seats[i][j] == \\\"L\\\":\\n regen[i][j] = \\\"#\\\" if check_empty(seats, i, j) else \\\"L\\\"\\n if seats[i][j] == \\\"#\\\":\\n regen[i][j] = \\\"L\\\" if check_occ(seats, i, j) else \\\"#\\\"\\n \\n for i in range(m):\\n regen[i] = \\\"\\\".join(regen[i])\\n\\n return regen\",\n \"def convert_angstroms_nm(d_angstroms):\\n return d_angstroms/nm_angstroms\",\n \"def fix_name_wga(artist):\\n comma = artist.find(\\\",\\\")\\n\\n return \\\" \\\".join([artist[comma + 1:].strip(), artist[:comma].strip()]) if comma != -1 else artist\",\n \"def acgt_to_string(s: list[list[str]]) -> list[list[str]]:\\r\\n s_out = [[\\\"\\\"] for i in range(len(s))]\\r\\n for i in range(len(s) - 1):\\r\\n h = \\\"\\\"\\r\\n for j in range(len(s[i])):\\r\\n if s[i][j] == 0:\\r\\n h += \\\"00\\\"\\r\\n if s[i][j] == 1:\\r\\n h += \\\"01\\\"\\r\\n if s[i][j] == 2:\\r\\n h += \\\"10\\\"\\r\\n if s[i][j] == 3:\\r\\n h += \\\"11\\\"\\r\\n s_out[i][0] = h\\r\\n return s_out\",\n \"def parse_streetname(self):\\n index = self.index\\n \\n name = \\\"\\\"\\n for i in range(4):\\n if index + i == self.length:\\n break\\n if self.words[index+i]['word'] == ',':\\n break\\n # Hack\\n if self.words[index+i]['word'] == 'doctor':\\n self.words[index+i]['word'] = 'drive'\\n break\\n try:\\n word = sttype[self.words[index+i]['word']]\\n break\\n except:\\n try:\\n word = vocab[self.words[index+i]['word']]\\n if Vocabulary.STREET_TYPE in word['tag']:\\n break\\n if name != '':\\n name += ' ' + word['lemma'][0]\\n else:\\n name = word['lemma'][0]\\n except: \\n if self.words[index+i]['word'][-2:] in [ 'th', 'st', 'nd', 'rd' ]:\\n name = self.words[index+i]['word'][:-2]\\n else:\\n self.index += i\\n _dir, _n = self.parse_streetdir()\\n self.index -= i\\n if _dir:\\n break\\n if name != '':\\n name += ' ' + self.words[index+i]['word']\\n else:\\n name = self.words[index+i]['word']\\n \\n if i == 0 or i == 4:\\n return None, 0\\n else:\\n return name, i\",\n \"def get_alternate_names(self, alt_list):\\n self.alternates = [a.name for a in alt_list if a.raga == self.name]\",\n \"def get_raga_swaras(self, mapping):\\n self.aro_swaras = [mapping[s][0] for s in self.aro]\\n self.ava_swaras = [mapping[s][0] for s in self.ava]\\n aro = [\\\"s\\\\'\\\" if sw == 's.' else sw for sw in self.aro]\\n ava = [\\\"s\\\\'\\\" if sw == 's.' else sw for sw in self.ava]\\n self.aro_seq = '|'.join([s.upper().strip() for s in aro])\\n self.ava_seq = '|'.join([s.upper().strip() for s in ava])\",\n \"def merge(decks):\\n\\ta = (mergesort(decks))\\n\\treturn ''.join([item[1] for item in a]) #if type(item[1]) == str])\",\n \"def song_by_word(ans):\\r\\n songs_list = \\\"\\\"\\r\\n ans = ans.lower()\\r\\n albums = simple_album_list()\\r\\n for album in albums:\\r\\n songs = simple_songs_list(album)\\r\\n for song in songs:\\r\\n song = str(song)\\r\\n if ans in song.lower():\\r\\n songs_list += song + \\\", \\\"\\r\\n return songs_list[:-2]\",\n \"def seperate_Loc_Data(data, us_state_abbrev):\\n assert data is not None\\n dictionary = dict(data)\\n keys = dictionary.keys()\\n tmp = list(keys)\\n values = dictionary.values()\\n res = []\\n for elem in keys:\\n state = elem[1].strip()\\n if state in us_state_abbrev:\\n res.append(us_state_abbrev[state])\\n return res, list(values)\",\n \"def get_whole_nato_alphabet_string(mappings):\\n def tuple_to_string(letter_word_pair):\\n \\\"\\\"\\\"Convert a tuple to a mapping string.\\\"\\\"\\\"\\n letter, word = letter_word_pair\\n return '{letter}: {word}'.format(letter=letter, word=word)\\n\\n items = mappings.items()\\n sorted_items = sorted(mappings.items())\\n return '\\\\n'.join(map(tuple_to_string, sorted_items))\",\n \"def get_station_features(cls, station_row):\\n features = station_row[2].lower(), station_row[7], station_row[8]\\n return features\",\n \"def seperate_City_Data(data, us_state_abbrev):\\n assert data is not None\\n dictionary = dict(data)\\n keys = dictionary.keys()\\n tmp = list(keys)\\n values = dictionary.values()\\n res = []\\n for elem in keys:\\n state = elem[1].strip()\\n city = elem[0].strip()\\n# print(city)\\n if state in us_state_abbrev:\\n res.append(city)\\n return res, list(values)\",\n \"def _repair_names_universal(\\n names: Iterable[str], quiet: bool = False, base0_: bool = None\\n) -> List[str]:\\n min_names = _repair_names_minimal(names)\\n neat_names = [re.sub(r\\\"[^\\\\w]\\\", \\\"_\\\", name) for name in min_names]\\n new_names = _repair_names_unique(\\n neat_names,\\n quiet=True,\\n sanitizer=lambda name: (\\n f\\\"_{name}\\\"\\n if keyword.iskeyword(name) or (name and name[0].isdigit())\\n else name\\n ),\\n base0_=base0_,\\n )\\n if not quiet:\\n changed_names = [\\n (orig_name, new_name)\\n for orig_name, new_name in zip(names, new_names)\\n if orig_name != new_name\\n ]\\n _log_changed_names(changed_names)\\n return new_names\",\n \"def get_processed_stations(out_dir):\\n lista = [ f.split('_')[0] for f in os.listdir(out_dir) if '.nc' in f ]\\n #print('Skipping these stations: ' , lista )\\n return lista\",\n \"def encode_word(word: str) -> List[str]:\\n inner_letters = word[1:-1]\\n inner_letters = shuffle(inner_letters)\\n return [word[0], *inner_letters, word[-1]]\",\n \"def encode_san_dns_names(self, san):\\n dns_names = []\\n for dns_name in san:\\n dns_names.append(x509.DNSName(dns_name))\\n return dns_names\",\n \"def expand_abbrevs(name):\\n key = name.upper()\\n for abbrev, word in ABBREVS.iteritems():\\n key = re.sub(abbrev, word, key)\\n \\n #Remove (.*) from the street name\\n key = re.sub(r'\\\\(.*?(:?\\\\)|$)', '', key)\\n \\n #Unify names\\n key = NUMBER_IN_NAMES_REGEX.sub(lambda i: i.group(1) + \\\" \\\", key)\\n key = re.sub(u\\\"Ё\\\", u\\\"Е\\\", key)\\n key = re.sub(u\\\"[\\\\\\\"'«»№]\\\", u\\\" \\\", key)\\n\\n # remove \\\"им\\\" prefix\\n key = re.sub(ur'[^\\\\s]ИМ[\\\\.\\\\s]+', u' ', key)\\n\\n #Change name parts order\\n words = key.split(r\\\" \\\")\\n words.sort()\\n key = \\\" \\\".join(words)\\n\\n key = re.sub(u\\\"\\\\s+\\\", u\\\" \\\", key).strip()\\n\\n logging.debug(\\\"Street name %s was converted to %s\\\" % (name, key))\\n \\n return key\",\n \"def append_sitename(strs,site):\\n strs = [x+' site:'+site for x in strs]\\n return strs\",\n \"def create_word(char_list):\",\n \"def getNames():\\r\\n return [\\\"Server1\\\", \\\"Server2\\\", \\\"Client1\\\", \\\"Client2\\\"]\",\n \"def normalize(address):\\n replacement = re.sub('\\\\W+', SEPARATOR, address.lower())\\n\\n processed = []\\n for p in replacement.split(SEPARATOR):\\n if not p:\\n continue\\n\\n if p in ABBRS:\\n processed.append(ABBRS[p])\\n else:\\n processed.append(p)\\n\\n processed.sort()\\n\\n normalized = SEPARATOR.join(processed)\\n return normalized\",\n \"def format_words(words):\\n return sorted(words, key=str.lower)\",\n \"def get_room_names(soup: bs4.BeautifulSoup) -> Iterable[str]:\\n return set(x.string for x in soup.Lecture.find_all(\\\"RaumBez\\\"))\",\n \"def essay_to_wordlist(essay_v, remove_stopwords):\\n essay_v = re.sub(\\\"[^a-zA-Z]\\\", \\\" \\\", essay_v)\\n words = essay_v.lower().split()\\n if remove_stopwords:\\n stops = set(stopwords.words(\\\"english\\\"))\\n words = [w for w in words if not w in stops]\\n return (words)\",\n \"def metaphlan_krona_string(input):\\n s = []\\n for f in input:\\n name = bn(f).replace(\\\"_pe.krona\\\", \\\"\\\").replace(\\\"_se.krona\\\", \\\"\\\")\\n s.append(f\\\"{f},{name}\\\")\\n return \\\" \\\".join(s)\",\n \"def fill_stempool(self):\\n tokens = [apply_word_tokenize(x) for x in self.df['name']]\\n\\n flatten1 = itertools.chain.from_iterable\\n flat = list(flatten1(tokens))\\n\\n stems = [self.stemmer.stem(x) for x in flat]\\n\\n return set(stems)\",\n \"def get_stations(nordic_file_names, output_level=0):\\n stations = []\\n for file in nordic_file_names:\\n new_stations = get_event_stations(file, output_level)\\n\\n if new_stations == -1:\\n continue\\n\\n for x in new_stations:\\n if x not in stations:\\n stations.append(x)\\n\\n return sorted(stations)\",\n \"def list_stations(intent, session):\\n stations = location.get_stations(config.bikes_api)\\n street_name = intent['slots']['street_name']['value']\\n possible = location.matching_station_list(stations,\\n street_name,\\n exact=True)\\n street_name = street_name.capitalize()\\n\\n if len(possible) == 0:\\n return reply.build(\\\"I didn't find any stations on %s.\\\" % street_name,\\n is_end=True)\\n elif len(possible) == 1:\\n sta_name = location.text_to_speech(possible[0]['name'])\\n return reply.build(\\\"There's only one: the %s \\\"\\n \\\"station.\\\" % sta_name,\\n card_title=(\\\"%s Stations on %s\\\" %\\n (config.network_name, street_name)),\\n card_text=(\\\"One station on %s: %s\\\" %\\n (street_name, possible[0]['name'])),\\n is_end=True)\\n else:\\n last_name = location.text_to_speech(possible[-1]['name'])\\n speech = \\\"There are %d stations on %s: \\\" % (len(possible),\\n street_name)\\n speech += (', '.join([location.text_to_speech(p['name'])\\n for p in possible[:-1]]) +\\n ', and %s' % last_name)\\n card_text = (\\\"The following %d stations are on %s:\\\\n%s\\\" %\\n (len(possible), street_name,\\n '\\\\n'.join(p['name'] for p in possible)))\\n return reply.build(speech,\\n card_title=(\\\"%s Stations on %s\\\" %\\n (config.network_name, street_name)),\\n card_text=card_text,\\n is_end=True)\",\n \"def TransformNames(self) -> _n_2_t_0[str]:\",\n \"def remove_duplicate_chars(w):\\n return ''.join(c for c, _ in itertools.groupby(w))\",\n \"def split_name(fullname):\",\n \"def get_initials(name):\\n\\n # your code here\\n result = ''\\n na = name.find(' NA ')\\n if na != -1:\\n name = name[:na] + name[na+3:]\\n name += ' '\\n while name.find(' ') != -1:\\n result += name[0] + '.'\\n name = name[name.find(' ')+1:]\\n print(result)\",\n \"def build_subway(**lines):\\n for key in lines.keys():\\n # print key\\n value = lines[key]\\n lines[key] = value.split()\\n\\n for key in lines.keys():\\n stations.update(set(lines[key]))\\n system = {}\\n for station in stations:\\n next_station = {}\\n for key in lines:\\n if station in lines[key]:\\n line = lines[key]\\n idx = line.index(station)\\n if idx == 0:\\n if next_station.has_key(line[1]): \\n temp = next_station[line[1]]\\n temp.append(key)\\n next_station[line[1]] = temp\\n else:\\n next_station[line[1]] = [key]\\n elif idx == len(line)-1:\\n if next_station.has_key(line[idx-1]): \\n temp = next_station[line[idx-1]]\\n temp.append(key)\\n next_station[line[idx-1]] = temp\\n else:\\n next_station[line[idx-1]] = [key]\\n else:\\n if next_station.has_key(line[idx-1]): \\n temp = next_station[line[idx-1]]\\n temp.append(key)\\n next_station[line[idx-1]] = temp\\n else:\\n next_station[line[idx-1]] = [key]\\n if next_station.has_key(line[idx+1]): \\n temp = next_station[line[idx+1]]\\n temp.append(key)\\n next_station[line[idx+1]] = temp\\n else:\\n next_station[line[idx+1]] = [key]\\n system[station] = next_station\\n return system\",\n \"def Student_names(l:list)->list:\\n result=[]\\n for s in l:\\n result.append(s.name)\\n return result\",\n \"def meronym(self, sense=0):\\n s = self._synset(self.text, sense=sense)\\n\\n if not s:\\n return []\\n\\n return s.member_meronyms()\",\n \"def rem_str(prelist,names):\\n \\n for prefix in prelist:\\n names=[name.replace(prefix,'') for name in names]\\n \\n return names\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59636515","0.5915971","0.58542436","0.5853559","0.5729847","0.5717873","0.57039684","0.5570993","0.54327935","0.54008317","0.53579676","0.5210223","0.51921755","0.5177079","0.5170437","0.51636785","0.5162297","0.514331","0.5133783","0.51176363","0.5116946","0.50965136","0.50916845","0.5085611","0.5071022","0.5071022","0.50691146","0.5064639","0.5062359","0.5034298","0.50284106","0.50264263","0.5021686","0.5017494","0.50084084","0.50024664","0.5001962","0.4988385","0.49543414","0.49521515","0.4945231","0.49293977","0.4927092","0.49176168","0.4915139","0.4900497","0.48996523","0.48945412","0.48939404","0.48938602","0.48938423","0.4890943","0.48908943","0.48907763","0.48831102","0.4878899","0.4859294","0.48584524","0.48543245","0.48507634","0.48482257","0.48480356","0.48465312","0.48421136","0.4840037","0.48369643","0.483175","0.4829894","0.48257753","0.4816988","0.4809957","0.48069483","0.48032123","0.47986624","0.4792884","0.47901314","0.47772747","0.47727233","0.47709304","0.47648045","0.47610003","0.47574395","0.4750958","0.4746842","0.47467065","0.47427657","0.47415423","0.47390294","0.47340626","0.4728951","0.4720255","0.47182348","0.47155702","0.47151142","0.47138458","0.47078738","0.47067863","0.4705929","0.47038886","0.47014362"],"string":"[\n \"0.59636515\",\n \"0.5915971\",\n \"0.58542436\",\n \"0.5853559\",\n \"0.5729847\",\n \"0.5717873\",\n \"0.57039684\",\n \"0.5570993\",\n \"0.54327935\",\n \"0.54008317\",\n \"0.53579676\",\n \"0.5210223\",\n \"0.51921755\",\n \"0.5177079\",\n \"0.5170437\",\n \"0.51636785\",\n \"0.5162297\",\n \"0.514331\",\n \"0.5133783\",\n \"0.51176363\",\n \"0.5116946\",\n \"0.50965136\",\n \"0.50916845\",\n \"0.5085611\",\n \"0.5071022\",\n \"0.5071022\",\n \"0.50691146\",\n \"0.5064639\",\n \"0.5062359\",\n \"0.5034298\",\n \"0.50284106\",\n \"0.50264263\",\n \"0.5021686\",\n \"0.5017494\",\n \"0.50084084\",\n \"0.50024664\",\n \"0.5001962\",\n \"0.4988385\",\n \"0.49543414\",\n \"0.49521515\",\n \"0.4945231\",\n \"0.49293977\",\n \"0.4927092\",\n \"0.49176168\",\n \"0.4915139\",\n \"0.4900497\",\n \"0.48996523\",\n \"0.48945412\",\n \"0.48939404\",\n \"0.48938602\",\n \"0.48938423\",\n \"0.4890943\",\n \"0.48908943\",\n \"0.48907763\",\n \"0.48831102\",\n \"0.4878899\",\n \"0.4859294\",\n \"0.48584524\",\n \"0.48543245\",\n \"0.48507634\",\n \"0.48482257\",\n \"0.48480356\",\n \"0.48465312\",\n \"0.48421136\",\n \"0.4840037\",\n \"0.48369643\",\n \"0.483175\",\n \"0.4829894\",\n \"0.48257753\",\n \"0.4816988\",\n \"0.4809957\",\n \"0.48069483\",\n \"0.48032123\",\n \"0.47986624\",\n \"0.4792884\",\n \"0.47901314\",\n \"0.47772747\",\n \"0.47727233\",\n \"0.47709304\",\n \"0.47648045\",\n \"0.47610003\",\n \"0.47574395\",\n \"0.4750958\",\n \"0.4746842\",\n \"0.47467065\",\n \"0.47427657\",\n \"0.47415423\",\n \"0.47390294\",\n \"0.47340626\",\n \"0.4728951\",\n \"0.4720255\",\n \"0.47182348\",\n \"0.47155702\",\n \"0.47151142\",\n \"0.47138458\",\n \"0.47078738\",\n \"0.47067863\",\n \"0.4705929\",\n \"0.47038886\",\n \"0.47014362\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53781"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":4688,"cells":{"query":{"kind":"string","value":"converts time to gmt, appends to list"},"document":{"kind":"string","value":"def gmt(time):\n gmt = [0]*time.size\n for i in range(time.size):\n gmt[i]=datetime.utcfromtimestamp(time[i]).strftime('%Y-%m-%d %H:%M:%S')\n return gmt"},"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 _update_time(self):\r\n\r\n curr_time = datetime.datetime.now()\r\n time = []\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.second)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.minute)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.hour)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.day)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.month)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.year - 2000)])\r\n return time","def _add_time_field(self) -> None:\n self.data[\"time\"] = [datetime(int(yyyy), int(mm), int(dd)) + timedelta(hours=hh) for yyyy, mm, dd, hh in zip(self.data[\"year\"], self.data[\"month\"], self.data[\"day\"], self.data[\"hour\"])]\n for key in [\"year\", \"doy\", \"month\", \"day\", \"hour\"]:\n del self.data[key]","def get_times():\n global times\n global times_list\n base_url = \"http://www.crawleymosque.com/\"\n r = requests.get(base_url)\n soup = BeautifulSoup(r.text, features=\"html.parser\")\n\n times_list = []\n for salah_time in soup.find_all(class_=\"prayer-start\"):\n times_list.append(salah_time.contents[0].strip())\n\n print(times_list)\n times = []\n for i in times_list:\n datetime_object = datetime.strptime(i, \"%I:%M %p\")\n just_time = datetime.time(datetime_object)\n times.append(just_time)\n\n print(times)\n\n # spam = Label(root, text=\"checking for spam\")\n # spam.place(x=460, y=110)","def time_to_hour_and_minute(time):\n return [time // 60, time % 60]","def convert_time(time):\n\n s = time.split()[0]\n s_h = int(s.split(':')[0])\n\n am_pm = s.split(':')[1][-2:]\n if s_h == 12:\n s_h = s_h - 12\n if am_pm == 'PM':\n s_h = s_h + 12\n s_h = s_h + 1\n\n e = time.split()[2]\n e_h = int(e.split(':')[0])\n\n am_pm = e.split(':')[1][-2:]\n if e_h == 12:\n e_h = e_h - 12\n if am_pm == 'PM':\n e_h = e_h + 12\n e_h = e_h + 1\n\n hour_list = range(s_h, e_h + 1)\n return hour_list","def filter_lower_datetime(time, list_time):\n return [t for t in list_time if t <= time]","def convert_time(self, t_variable):\n date_list = []\n times = self.dataset[t_variable].values\n\n for time in times:\n try:\n time = pd.to_datetime(str(time))\n date_list.append(time.strftime('%Y-%m-%dT%H:%M:%SZ'))\n except ValueError as ve:\n print(\"Error parsing and converting '%s' variable object to CovJSON compliant string.\" % (t_variable), ve)\n\n return date_list","def convert_seconds_to_readable(self, time_value):\n time_readable = []\n for value in time_value:\n time_readable_mini = time.strftime('%I:%M:%S%p', time.localtime(value))\n time_readable.append(time_readable_mini)\n mylog.debug('Converting %s to %s' % (value, time_readable_mini))\n return time_readable","def get_time(t):\n return [time.clock()-t[0], time.time()-t[1]]","def list_times(self, start: int = None, end: int = None) -> List:\n return [i.time for i in self.data[start:end]]","def get_timestamped_strings(self):\n ret_list = []\n i = 0\n while i < len(self.__string_list):\n ret_list.append(self.__timestamp_list[i].strftime(\"%Y-%m-%d %H:%M:%S\")+\" \"+self.__string_list[i])\n i += 1\n return ret_list","def get_time_strs(self):\n\n log(\"Getting time strings starting at {}\".format(self._t0))\n tz = dt.timezone.utc\n mkdt = lambda n: dt.datetime.fromtimestamp(\n self._t0 - (self._delta * n),\n tz=tz\n )\n ns = range(self._frames, 0, -1)\n return [mkdt(n).strftime('%Y%m%d%H%M') for n in ns]","def get_times(my_vars):\n base_time = my_vars['base_time'].getValue()\n try:\n times=my_vars['time']\n except KeyError:\n times = my_vars['time_offset']\n\n ts = []\n for time in times:\n temp = datetime.utcfromtimestamp(base_time+time)\n if (temp.minute == 0) :\n ts.append(temp)\n return ts","def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:","async def _timein_list(self):\n\t\t\n\t\tmessage = 'Favourites\\n```Name: Timezones\\n'\n\t\t\n\t\tfor fav in self.favourites:\n\t\t\tmessage += fav + ': '\n\t\t\tmessage += self.favourites[fav].replace(',', ', ').replace('_', ' ') + '\\n'\n\t\t\n\t\tmessage += '```'\n\t\tawait self.bot.say(message)","def add_time(data, t):\n data['year'] = t.year\n data['month'] = t.month\n data['day'] = t.day\n data['hour'] = t.hour\n data['minute'] = t.minute\n data['second'] = t.second","def add_timecard(self,time,name):\n id = self.find_employee_id(name)\n if id in self.timecard:\n self.timecard[id].append(time)\n else:\n self.timecard[id] = [time]\n return self.timecard","def sort_time(self):\n self.entries.sort(key=lambda x: x.date_stamp_utc)","def get_current_time():\n cur_time = datetime.datetime.now() + offset_time\n return [cur_time.year, cur_time.month, cur_time.day, cur_time.hour, cur_time.min, cur_time.second]","def _splitTime(self, time): \n if (time):\n x = re.split(\"[-\\/\\s:]\", time)\n else:\n x = []\n # Pad the list to four elements (year,month,day,hour)\n while (len(x) < 4):\n x.append(None)\n return x","def order_by_ftime(tasks_lst):\n return sorted(tasks_lst, key=lambda task: task[1])","def conv_time(l, h):\n\t# Function modified from post on ActiveState by John Nielsen\n\n\t#converts 64-bit integer specifying the number of 100-nanosecond\n\t#intervals which have passed since January 1, 1601.\n\t#This 64-bit value is split into the\n\t#two 32 bits stored in the structure.\n\td = 116444736000000000L \n\n\t# Some LNK files do not have time field populated \n\tif l + h != 0:\n\t\tnewTime = (((long(h) << 32) + long(l)) - d)/10000000 \n\telse:\n\t\tnewTime = 0\n\n\treturn time.strftime(\"%Y/%m/%d %H:%M:%S %a\", time.localtime(newTime))","def add_times_of_travels(dfs_splited: list, time: datetime.datetime) -> list:\n results = google_api_request(dfs_splited, time)\n logger.debug(\"Ready answer for request\")\n print(results)\n travel_times = [[i.get('duration', {}).get('value')\n for i in result['rows'][0]['elements']]\n for result in results]\n logger.debug(\"Times of travel extracted\")\n\n return [df.assign(time_sec=time_t)\n for df, time_t in zip(dfs_splited, travel_times)]","def time_to_view(view, edit, fmt):\n for s in view.sel():\n if s.empty():\n view.insert(edit, s.a, time.strftime(fmt))\n else:\n view.replace(edit, s, time.strftime(fmt))","def get_timestring_from_int(time_array, format=\"%H:%M:%S\"):\n list = []\n for value in time_array:\n list.append((value, int2dt(value, 1).strftime(format)))\n return list","def add_time(t):\n\n times.append(t)\n\n # update number display to show real time\n number_display.time = t\n number_display.update()\n\n # generate new scramble and update scramble_image\n new_scramble = generate_scramble(int(settings['puzzle']),\n int(settings['scramble-length']))\n scrambles.append(new_scramble)\n scramble_image.clear()\n scramble_image.chars = char(new_scramble)\n\n ao5, ao12 = update_stats()\n\n with open(session_file.string, 'a') as f:\n if len(times) == 1:\n f.write(f'{add_zero(t)}\\t{ao5}\\t{ao12}\\t{new_scramble}')\n else:\n f.write(f'\\n{add_zero(t)}\\t{ao5}\\t{ao12}\\t{new_scramble}')","def get_time(text_time):\n # return Observer.datetime_to_astropy_time(dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M'))\n the_time = dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M')\n return Time(the_time.strftime('%Y-%m-%d %H:%M'))\n #date = [int(i) for i in date.split('/')]","def get_teams_and_schedule():\n start_time = timedelta(hours=19)\n time_to_add = timedelta(minutes=15)\n teams = session.query(Team).all()\n\n for team in teams:\n team.time = str(start_time)\n start_time += time_to_add\n yield team","def gprmc_convert(line):\r\n gps = line.strip().split(',')\r\n #check data\r\n if gps[2] == 'V':\r\n return\r\n raw_date = gps[9]\r\n time = ''\r\n date = raw_date[0:2]\r\n month = raw_date[2:4]\r\n year = raw_date[4:]\r\n #modify year if reaches year 2100\r\n time += date + '/' + month + '/20' + year\r\n return [time]","def datetime_to_list(date):\n return [date.year, date.month, date.day,\n date.hour, date.minute, date.second]","def datetime_to_list(date):\n return [date.year, date.month, date.day,\n date.hour, date.minute, date.second]","def time_calculator(seconds):\n seconds = int(seconds)\n days = seconds // 86400\n hours = seconds % 86400 // 3600\n minutes = seconds % 86400 % 3600 // 60\n seconds = seconds % 86400 % 3600 % 60\n time_list = [days, hours, minutes, seconds] # Created a list that stores these variables in order\n return time_list","def time_convert(intime):\n Nt = intime.shape[0]\n outtime = []\n for t in range(Nt):\n timestr = ''.join([intime[t,:][~intime[t,:].mask].data[i].decode('utf-8') for i in range(len(intime[t,:][~intime[t,:].mask].data))])\n outtime.append(datetime.strptime(timestr, '%Y-%m-%d_%H:%M:%S'))\n return outtime","def time(self, start_time):\n \n TIME_LIST.append((time.time() - start_time))\n print(\"--- %s seconds ---\" % (time.time() - start_time))","def time_settime(currenttime):\r\n\r\n time_query_times.append((getruntime(), currenttime))","def record_time(times, enabled, *args):\n if not enabled:\n yield\n else:\n start = time.time()\n yield\n end = time.time()\n times.append((' '.join(args), start, end))","def generate_time_data(self):\n # generate random dates and append to a list\n sd = self.start_date\n ed = self.end_date\n dates = [random_date(start=sd, end=ed) for d in range(0, self.obs)]\n\n # convert to ISO 8601 format and update \"Local Time\" field\n self.output['Local Time'] = map(lambda x: x.isoformat(), dates)","def start_time():\n t = [time.clock(), time.time()]\n return t","def format_time(self, time):\n hh = time[0:2]\n mm = time[2:4]\n ss = time[4:]\n return \"%s:%s:%s UTC\" % (hh,mm,ss)","def process_timecards(self):\n timecard = open('timecards.txt','r')\n time_temp = []\n time = []\n for line in timecard:\n time_temp.append(line)\n for i in time_temp:\n time.append(i.split(','))\n for i in time:\n for q in range(len(i)):\n if q == 0:\n pass\n else:\n i[q] = float(i[q])\n for i in time:\n for q in range(len(i)):\n self.timecard[i[0]] = i[1:]\n #print(self.timecard)\n return self.timecard","def convert_time_to_seconds(self, time_value):\n time_epoch = []\n mylog.debug('Converting %s to epoch time' % time_value)\n for value in time_value:\n try:\n pattern = ' %I:%M:%S%p'\n time_epoch_mini = int(time.mktime(time.strptime(value, pattern))) \n time_epoch.append(time_epoch_mini)\n except:\n mylog.debug('%s Does not seem to be in format with leading space' % value)\n try:\n pattern = '%I:%M:%S%p'\n time_epoch_mini = int(time.mktime(time.strptime(value, pattern))) \n time_epoch.append(time_epoch_mini)\n except:\n mylog.debug('%s Does not appear to be in format without leading space' % value)\n return time_epoch","def __call__(self, x: Sequence[datetime]) -> Sequence[str]:\n if self.tz is not None:\n x = [d.astimezone(self.tz) for d in x]\n return [d.strftime(self.fmt) for d in x]","def add_gigasecond(time = datetime(1, 1, 1, 0, 0, 0)): # -> datetime() object\n time += timedelta(seconds = 10 ** 9)\n return time","def format_time(self, time):\n hours = time // 3600\n time = time - hours*3600\n minutes = time // 60\n seconds = time - minutes*60\n return ('%d:%d:%d' %(hours, minutes, seconds))","def word_time(word, elapsed_time):\n return [word, elapsed_time]","def sort_time(cls):\n CloudCtx.objCloudCtx.sort(key=lambda x: datetime.strptime(x.modTs, \"%d-%m-%Y %I:%M:%S %p\"), reverse=True)\n for elem in CloudCtx.objCloudCtx:\n print(elem.display_cloud_ctx())","def add_minutes(self):\n r = self.minute + self.value\n x = int((r / 60))\n\n self.hour = self.hour + x\n self.minute = r - (60 * x)\n\n cycles = int(self.hour / 12)\n if cycles > 0:\n if (cycles % 2) == 0:\n pass\n else:\n if self.meridiem == 'AM':\n self.meridiem = 'PM'\n else:\n self.meridiem = 'AM'\n\n self.hour = self.hour - cycles * 12\n if self.hour == 0:\n self.hour = 1\n\n if self.minute < 10:\n self.minute = str(0) + str(self.minute)\n\n new_time: str = str(self.hour) + ':' + str(self.minute) + ' ' + self.meridiem.upper()\n return new_time","def makeChronList(self):\n from operator import itemgetter\n ## make list of msg lists in the format accespted by reconstructLine\n self.outData_temp = [] # this will be in chronological order\n for sens in self.outData:\n if sens is not 'header':\n for meas in self.outData[sens]:\n for time in self.outData[sens][meas]:\n value = self.outData[sens][meas][time]\n thismsg = [time, sens, meas, str(value)] # leave time as float for sorting\n self.outData_temp.append(thismsg)\n self.outData_temp.sort(key=itemgetter(0)) # sort by first index\n for msg in self.outData_temp: # now we can make time a string\n msg[0] = str(msg[0])","def oneTimepoint(timepoint):\n\tt = []\n\tfor vs in timepoint:\n\t\tt.append((timepoint.attrib.get('CollectionTime'), vs[0].text, vs[1].text))\n\treturn(t)","def Time(row):\r\n try:\r\n timeadd = dt.datetime.strptime(row['TickIssueTime'], '%H:%M').time()\r\n except:\r\n timeadd = dt.datetime.strptime('00:00', '%H:%M').time()\r\n\r\n newtime = dt.datetime.combine(dt.datetime.strptime(row['TickIssueDate'], '%Y-%m-%d %H:%M:%S') , timeadd)\r\n return newtime","def add_timedelta_to_time():\n dt = datetime.datetime.combine(datetime.date.today(), datetime.time(12, 30, 5)) + datetime.timedelta(hours=1)\n t = dt.time()\n print(t) # 13:30:05","def _add_time(time_to_add: int):\n store.time += time_to_add","def time_iterator(\n *,\n first_time: datetime.datetime,\n last_time: datetime.datetime,\n resolution: int,\n timezone: datetime.timezone,\n) -> Generator[datetime.datetime, None, None]:\n\n current_time = first_time\n while current_time < last_time:\n yield current_time.replace(tzinfo=timezone)\n current_time += relativedelta(\n hours=resolution // 3600,\n minutes=(resolution // 60) % 60,\n seconds=resolution % 60,\n )","def dump_datetime(value):\n if value is None:\n return\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def get_target_timestamps(self):\n times=[]\n curr = self.begin_ts\n while curr<=self.end_ts:\n times.append(curr)\n curr = curr + 24 * 60 * 60\n return times","def _get_time(self) -> None:\n self.data[\"time\"] = np.zeros(len(self.data[\"yyyymmdd\"]), dtype=object)\n \n for idx, (yyyymmdd, hhmmss) in enumerate(zip(self.data[\"yyyymmdd\"], self.data[\"hhmmss\"])):\n year, month, day = yyyymmdd.split(\"/\")\n hour, minute, second = hhmmss.split(\":\")\n self.data[\"time\"][idx] = datetime(int(year), int(month), int(day), int(hour), int(minute), int(second))\n \n del self.data[\"yyyymmdd\"]\n del self.data[\"hhmmss\"]","def offset(self):\n\n offsetList = ['12 am', '1 am', '2 am', '3 am', '4 am', '5 am', '6 am', '7 am', '8 am', '9 am',\n '10 am', '11 am', '12 pm', '1 pm', '2 pm', '3 pm', '4 pm', '5 pm', '6 pm', '7 pm',\n '8 pm', '9 pm', '10 pm', '11 pm', '12 pm']\n\n firstTimeHour = self.firstTime.time().hour\n print ('First Time Hour:', firstTimeHour)\n\n m2 = str(self.firstTime.time())\n m2 = datetime.datetime.strptime(m2, '%I:%M %p')\n print(m2)","def want_timeframe(self, timeframe):\n self._wanted.append(timeframe)","def format_start_time(self, data):\n return data","def get_fetch_times(last_fetch):\n now = get_now()\n times = list()\n time_format = \"%Y-%m-%dT%H:%M:%SZ\"\n if isinstance(last_fetch, str):\n times.append(last_fetch)\n last_fetch = datetime.strptime(last_fetch, time_format)\n elif isinstance(last_fetch, datetime):\n times.append(last_fetch.strftime(time_format))\n while now - last_fetch > timedelta(minutes=59):\n last_fetch += timedelta(minutes=59)\n times.append(last_fetch.strftime(time_format))\n times.append(now.strftime(time_format))\n return times","def datetime_timeplotxml(self):\n if self.time:\n return self.date.strftime(\"%b %d %Y\") + \" \" + self.time.strftime(\"%H:%M:%S\") + \" GMT\"\n else:\n return self.date.strftime(\"%b %d %Y\") + \" \" + \"00:00:00\" + \" GMT\"","def getTimeStamps():\n\n # Initialize\n results = dict()\n\n # UT time\n ut = utils.getUT(pointing=True).split()\n results['utday'] = ut[0]\n results['ut'] = float(ut[1])\n\n # year/month/day/second\n utStamp = time.gmtime()\n utHour = maybeAddAZero(utStamp[3])\n utMin = maybeAddAZero(utStamp[4])\n utSec = maybeAddAZero(utStamp[5])\n results['timeLab'] = ''.join([commands.yearMonthDay(),'_',utHour,utMin,utSec])\n\n # Done\n return results","def get_ph_time(as_array=False):\n utc = timezone('UTC')\n phtz = timezone('Asia/Manila')\n now = utc.localize(datetime.utcnow())\n now = now.astimezone(phtz)\n if as_array:\n return [now.year, now.month, now.day, now.hour, now.minute, now.second]\n else:\n return datetime(now.year, now.month, now.day, now.hour, now.minute, now.second)","def render_time(dt):\n return dt.strftime('%H:%M:%S')","def addtomemorycollectiontime(self, datetime):\n self._memorycollectiontime.append(datetime)","def createdatv(times):\n t0=datetime.datetime.strptime(times[0].tostring().decode('utf-8'), '%Y-%m-%d_%H:%M:%S')\n t1=datetime.datetime.strptime(times[1].tostring().decode('utf-8'), '%Y-%m-%d_%H:%M:%S')\n ts=(t1-t0).total_seconds()\n datev=[]\n for i in range(0,len(times)):\n datev.append(t0+datetime.timedelta(seconds=i*ts))\n return(datev)","def localize_time(self, apitime):\n return self.feedzone.localize(apitime).astimezone(self.localzone)","def parse_time(text):\n\n # When keyword is 'in' adds values to time\n if text[-3] == 'in':\n remind_time = time.gmtime(int(text[-2]) * int(text[-1]) + time.time())\n # Otherwise try to parse time as written\n else:\n remind_time = text[-1].replace(':', ' ') \\\n + \" \" \\\n + time.strftime(\"%m/%d/%y\", time.gmtime(time.time()))\n remind_time = time.strptime(remind_time, \"%H %M %m/%d/%y\")\n return remind_time","def OPCtimetransform(data, to):\n \n remove_times = []\n outtimes = []\n times = {'ms':[],'SS':[],'MM':[],'HH':[]}\n\n for i in range(0, len(data)):\n times['HH'] = 0\n times['MM'] = 0\n times['SS'] = 0\n times['ms'] = 0\n\n item = data[i]\n \n try:\n if len(item.split('.')[1]) < 2:\n item += '0'\n except IndexError:\n item += '.00'\n if len(item) < 9:\n item = item.zfill(9)\n if int(item[:2]) > 23:\n item = '0' + item\n \n # remove items with extra zero (2319010.00 to 231910)\n if len(item) > 9:\n olditem = item\n newitem = item[:4] + item[5:]\n print( ('Repairing strange value %s into %s')%(olditem, newitem) )\n item = newitem\n else:\n pass\n try:\n md = dt.datetime.strptime(item, \"%H%M%S.%f\")\n \n # round off items which exceed 59 minutes or 59 seconds \n # (i.e. 146001 to 150001.)\n except ValueError:\n \n try:\n times['HH'] = int(item[0:2])\n times['MM'] = int(item[2:4])\n times['SS'] = int(item[4:6])\n times['ms'] = int(item[7:9])\n except ValueError:\n print(i, item)\n\n if times['SS'] > 59:\n times['MM'] += 1\n times['SS'] = 0\n if times['MM'] > 59:\n times['HH'] += 1\n times['MM'] = 0\n # discard items which exceed 23 hours\n if times['HH'] > 23:\n times['HH'] = 23\n print( ('resetting value %s')%(item) )\n \n\n md = dt.datetime(1900,1,1,times['HH'], times['MM'], times['SS']) \n\n \n outtimes.append( dt.datetime.strftime(md, to) )\n\n return outtimes","def change_format_from_input_to_datetime(list_d_t_t):\n data_output = []\n\n for row in list_d_t_t:\n data_output.append([datetime.datetime.strptime(row[0] + \" \" + row[1], \"%Y-%m-%d %H:%M:%S\"),\n datetime.datetime.strptime(row[0] + \" \" + row[2], \"%Y-%m-%d %H:%M:%S\")])\n\n return data_output","def render_date_time_with_relative_into(into, date_time, add_ago):\n into.append(format(date_time, DATETIME_FORMAT_CODE))\n \n into.append(' [*')\n into.append(elapsed_time(date_time))\n if add_ago:\n into.append(' ago')\n into.append('*]')\n \n return into","def constructTimeLineItem(self):\n\t\treturn","def get_hours_and_minutes(time):\n\n # Get the total seconds of the time object\n totalseconds = time.total_seconds()\n \n # Find the total hours in the time object\n totalhours = totalseconds//3600\n # Find the total minutes in the time object\n totalminutes = (totalseconds%3600) // 60 \n\n # Return a list composing of the total hours and minutes\n return [totalhours,totalminutes]","def call_list_timestamp(timestamp):\n return datetime.datetime.utcfromtimestamp(timestamp).isoformat()","def render_todays_listings(request, context, callsigns):\n context['listings_matrix'] = []\n for callsign in callsigns:\n whats_on_today_url = settings.SODOR_ENDPOINT + 'tvss/' + callsign + '/today/'\n data = requests.get(whats_on_today_url, headers={'X-PBSAUTH': settings.TVSS_KEY})\n\n if data.status_code == 200:\n jd = data.json()\n # have to loop through and covert the goofy timestamps into datetime objects\n for f in jd['feeds']:\n for l in f['listings']:\n l['start_time_obj'] = datetime.datetime.strptime(l['start_time'], \"%H%M\")\n l['callsign'] = callsign\n context['listings_matrix'].append(jd)\n return context","def currentTime():\n return strftime(\"%H:%M:%S\", time.localtime())","def _hour_to_time(num: int):\n return datetime.datetime.now().replace(hour=num).strftime(\"%-I %p\")","def time_form(gdf):\n gdf['time'] = gdf['time'].dt.strftime(\"%Y-%m-%dT%H:%M:%S\")\n \n return gdf","def set_ga_timestamp(self, time: int):\n for cl in self:\n cl.tga = time","def _get_time(self): \n\t\t# need to variable-ize the version ??? \n\t\ttime = self.root.find('.//{http://www.opengis.net/kml/2.2}when').text\n\t\t## strip off last 5 chars, ie '.135Z in '2015-08-01T00:06:29.135Z'\n\t\tutc = dateutil.tz.tzutc() \n\t\tcentral = dateutil.tz.gettz('America/Chicago')\n\t\ttime = datetime.datetime.strptime(time[:-5], '%Y-%m-%dT%H:%M:%S')\n\t\ttime = time.replace(tzinfo=utc)\n\t\tself.time = time.astimezone(central)","def get_timestamps(self) -> List[datetime.datetime]:\n return [activity.timestamp for activity in self.activities]","def _update_time_cursor(self):\n for line in self.timeLines:\n line.setValue(self.playbackTime)","def output_format(times_list):\n formatted_free_times = []\n for i in times_list:\n fmt_str = \"{} to {}.\".format(\n i[0].format('ddd, MMM D, h:mm a'),\n i[1].format('ddd, MMM D, h:mm a'))\n formatted_free_times.append(fmt_str)\n return formatted_free_times","def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:\n if self.single_scene:\n return [time_interval[0]] # type: ignore[index, list-item]\n\n timestamps = get_available_timestamps(\n bbox=bbox,\n time_interval=time_interval,\n data_collection=self.data_collection,\n maxcc=self.maxcc,\n config=self.config,\n )\n\n return self.timestamp_filter(timestamps, self.time_difference)","def getTimes():","def getTimes():","def getTimes():","def addTimes(time1, time2):\n t1 = timedelta(hours=time1.hour, minutes=time1.minute, seconds=time1.second)\n t2 = timedelta(hours=time2.hour, minutes=time2.minute, seconds=time2.second)\n t3 = t1 + t2\n return (datetime.min + t3).time()","def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def dump_datetime(value):\n if value is None:\n return None\n return [value.strftime(\"%Y-%m-%d\"), value.strftime(\"%H:%M:%S\")]","def round_trip_time(self):\n ...","def scale_time_to(recs, unit):\n\n for r in recs:\n if unit == 'd':\n r.t = [t / 3600 / 24 for t in r.time]\n elif unit == 'hours':\n r.t = [t / 3600 for t in r.time]\n elif unit == 'min':\n r.t = [t / 60 for t in r.time]\n elif unit in ('s', 'sec'):\n r.t = r.time\n else:\n Exception('Wrong time unit')\n\n Records.time_unit = unit\n Records.time_label = 'Time (' + unit + ')'","def determineTimes():\r\n tm = getLocalTime()\r\n startFadeUpTime = utime.localtime(utime.mktime((tm[0], tm[1], tm[2], WAKEUP_TUPLE[0],\r\n WAKEUP_TUPLE[1] - FADE_TIME, tm[5], tm[6], tm[7])))\r\n startFadeDownTime = utime.localtime(utime.mktime((tm[0], tm[1], tm[2], WAKEUP_TUPLE[0],\r\n WAKEUP_TUPLE[1] + LIT_LENGTH, tm[5], tm[6], tm[7])))\r\n return [startFadeUpTime[3:5], startFadeDownTime[3:5]]","def format_time_sortkey(self, data):\n return self.input['start_time'].time().strftime('%H%M').lstrip('0')","def round_time(self, time):\n hour, mins, _ = time.split(\":\")\n return '{:02d}:00:00'.format(int(hour)+1 ) if int(mins) >= 30 else '{:02d}:00:00'.format(int(hour))","def _groupDate(item):\n return item.time.date()","def get_time_attr_map(t):\n now = datetime.datetime.now()\n if t + datetime.timedelta(hours=3) > now:\n return get_map(\"main_list_white\")\n if t + datetime.timedelta(days=3) > now:\n return get_map(\"main_list_lg\")\n else:\n return get_map(\"main_list_dg\")","def planwatch(self, hours=12):\n post = {'mytime': str(hours)}\n response = self._get_page('planwatch.php', post=post)\n soup = bs4.BeautifulSoup(response.text, 'html5lib')\n results = soup.find('ul', {'id': 'new_plan_list'})\n new_plans = results.findAll('div', {'class': 'newplan'})\n resultlist = []\n for div in new_plans:\n user = div.find('a', {'class': 'planlove'}).contents[0]\n time = div.find('span').contents[0]\n time = parse_plans_date(time, tz_name=self.server_tz)\n resultlist.append((user, time))\n return resultlist"],"string":"[\n \"def _update_time(self):\\r\\n\\r\\n curr_time = datetime.datetime.now()\\r\\n time = []\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.second)])\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.minute)])\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.hour)])\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.day)])\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.month)])\\r\\n time.append([int(x) for x in '{0:06b}'.format(curr_time.year - 2000)])\\r\\n return time\",\n \"def _add_time_field(self) -> None:\\n self.data[\\\"time\\\"] = [datetime(int(yyyy), int(mm), int(dd)) + timedelta(hours=hh) for yyyy, mm, dd, hh in zip(self.data[\\\"year\\\"], self.data[\\\"month\\\"], self.data[\\\"day\\\"], self.data[\\\"hour\\\"])]\\n for key in [\\\"year\\\", \\\"doy\\\", \\\"month\\\", \\\"day\\\", \\\"hour\\\"]:\\n del self.data[key]\",\n \"def get_times():\\n global times\\n global times_list\\n base_url = \\\"http://www.crawleymosque.com/\\\"\\n r = requests.get(base_url)\\n soup = BeautifulSoup(r.text, features=\\\"html.parser\\\")\\n\\n times_list = []\\n for salah_time in soup.find_all(class_=\\\"prayer-start\\\"):\\n times_list.append(salah_time.contents[0].strip())\\n\\n print(times_list)\\n times = []\\n for i in times_list:\\n datetime_object = datetime.strptime(i, \\\"%I:%M %p\\\")\\n just_time = datetime.time(datetime_object)\\n times.append(just_time)\\n\\n print(times)\\n\\n # spam = Label(root, text=\\\"checking for spam\\\")\\n # spam.place(x=460, y=110)\",\n \"def time_to_hour_and_minute(time):\\n return [time // 60, time % 60]\",\n \"def convert_time(time):\\n\\n s = time.split()[0]\\n s_h = int(s.split(':')[0])\\n\\n am_pm = s.split(':')[1][-2:]\\n if s_h == 12:\\n s_h = s_h - 12\\n if am_pm == 'PM':\\n s_h = s_h + 12\\n s_h = s_h + 1\\n\\n e = time.split()[2]\\n e_h = int(e.split(':')[0])\\n\\n am_pm = e.split(':')[1][-2:]\\n if e_h == 12:\\n e_h = e_h - 12\\n if am_pm == 'PM':\\n e_h = e_h + 12\\n e_h = e_h + 1\\n\\n hour_list = range(s_h, e_h + 1)\\n return hour_list\",\n \"def filter_lower_datetime(time, list_time):\\n return [t for t in list_time if t <= time]\",\n \"def convert_time(self, t_variable):\\n date_list = []\\n times = self.dataset[t_variable].values\\n\\n for time in times:\\n try:\\n time = pd.to_datetime(str(time))\\n date_list.append(time.strftime('%Y-%m-%dT%H:%M:%SZ'))\\n except ValueError as ve:\\n print(\\\"Error parsing and converting '%s' variable object to CovJSON compliant string.\\\" % (t_variable), ve)\\n\\n return date_list\",\n \"def convert_seconds_to_readable(self, time_value):\\n time_readable = []\\n for value in time_value:\\n time_readable_mini = time.strftime('%I:%M:%S%p', time.localtime(value))\\n time_readable.append(time_readable_mini)\\n mylog.debug('Converting %s to %s' % (value, time_readable_mini))\\n return time_readable\",\n \"def get_time(t):\\n return [time.clock()-t[0], time.time()-t[1]]\",\n \"def list_times(self, start: int = None, end: int = None) -> List:\\n return [i.time for i in self.data[start:end]]\",\n \"def get_timestamped_strings(self):\\n ret_list = []\\n i = 0\\n while i < len(self.__string_list):\\n ret_list.append(self.__timestamp_list[i].strftime(\\\"%Y-%m-%d %H:%M:%S\\\")+\\\" \\\"+self.__string_list[i])\\n i += 1\\n return ret_list\",\n \"def get_time_strs(self):\\n\\n log(\\\"Getting time strings starting at {}\\\".format(self._t0))\\n tz = dt.timezone.utc\\n mkdt = lambda n: dt.datetime.fromtimestamp(\\n self._t0 - (self._delta * n),\\n tz=tz\\n )\\n ns = range(self._frames, 0, -1)\\n return [mkdt(n).strftime('%Y%m%d%H%M') for n in ns]\",\n \"def get_times(my_vars):\\n base_time = my_vars['base_time'].getValue()\\n try:\\n times=my_vars['time']\\n except KeyError:\\n times = my_vars['time_offset']\\n\\n ts = []\\n for time in times:\\n temp = datetime.utcfromtimestamp(base_time+time)\\n if (temp.minute == 0) :\\n ts.append(temp)\\n return ts\",\n \"def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:\",\n \"async def _timein_list(self):\\n\\t\\t\\n\\t\\tmessage = 'Favourites\\\\n```Name: Timezones\\\\n'\\n\\t\\t\\n\\t\\tfor fav in self.favourites:\\n\\t\\t\\tmessage += fav + ': '\\n\\t\\t\\tmessage += self.favourites[fav].replace(',', ', ').replace('_', ' ') + '\\\\n'\\n\\t\\t\\n\\t\\tmessage += '```'\\n\\t\\tawait self.bot.say(message)\",\n \"def add_time(data, t):\\n data['year'] = t.year\\n data['month'] = t.month\\n data['day'] = t.day\\n data['hour'] = t.hour\\n data['minute'] = t.minute\\n data['second'] = t.second\",\n \"def add_timecard(self,time,name):\\n id = self.find_employee_id(name)\\n if id in self.timecard:\\n self.timecard[id].append(time)\\n else:\\n self.timecard[id] = [time]\\n return self.timecard\",\n \"def sort_time(self):\\n self.entries.sort(key=lambda x: x.date_stamp_utc)\",\n \"def get_current_time():\\n cur_time = datetime.datetime.now() + offset_time\\n return [cur_time.year, cur_time.month, cur_time.day, cur_time.hour, cur_time.min, cur_time.second]\",\n \"def _splitTime(self, time): \\n if (time):\\n x = re.split(\\\"[-\\\\/\\\\s:]\\\", time)\\n else:\\n x = []\\n # Pad the list to four elements (year,month,day,hour)\\n while (len(x) < 4):\\n x.append(None)\\n return x\",\n \"def order_by_ftime(tasks_lst):\\n return sorted(tasks_lst, key=lambda task: task[1])\",\n \"def conv_time(l, h):\\n\\t# Function modified from post on ActiveState by John Nielsen\\n\\n\\t#converts 64-bit integer specifying the number of 100-nanosecond\\n\\t#intervals which have passed since January 1, 1601.\\n\\t#This 64-bit value is split into the\\n\\t#two 32 bits stored in the structure.\\n\\td = 116444736000000000L \\n\\n\\t# Some LNK files do not have time field populated \\n\\tif l + h != 0:\\n\\t\\tnewTime = (((long(h) << 32) + long(l)) - d)/10000000 \\n\\telse:\\n\\t\\tnewTime = 0\\n\\n\\treturn time.strftime(\\\"%Y/%m/%d %H:%M:%S %a\\\", time.localtime(newTime))\",\n \"def add_times_of_travels(dfs_splited: list, time: datetime.datetime) -> list:\\n results = google_api_request(dfs_splited, time)\\n logger.debug(\\\"Ready answer for request\\\")\\n print(results)\\n travel_times = [[i.get('duration', {}).get('value')\\n for i in result['rows'][0]['elements']]\\n for result in results]\\n logger.debug(\\\"Times of travel extracted\\\")\\n\\n return [df.assign(time_sec=time_t)\\n for df, time_t in zip(dfs_splited, travel_times)]\",\n \"def time_to_view(view, edit, fmt):\\n for s in view.sel():\\n if s.empty():\\n view.insert(edit, s.a, time.strftime(fmt))\\n else:\\n view.replace(edit, s, time.strftime(fmt))\",\n \"def get_timestring_from_int(time_array, format=\\\"%H:%M:%S\\\"):\\n list = []\\n for value in time_array:\\n list.append((value, int2dt(value, 1).strftime(format)))\\n return list\",\n \"def add_time(t):\\n\\n times.append(t)\\n\\n # update number display to show real time\\n number_display.time = t\\n number_display.update()\\n\\n # generate new scramble and update scramble_image\\n new_scramble = generate_scramble(int(settings['puzzle']),\\n int(settings['scramble-length']))\\n scrambles.append(new_scramble)\\n scramble_image.clear()\\n scramble_image.chars = char(new_scramble)\\n\\n ao5, ao12 = update_stats()\\n\\n with open(session_file.string, 'a') as f:\\n if len(times) == 1:\\n f.write(f'{add_zero(t)}\\\\t{ao5}\\\\t{ao12}\\\\t{new_scramble}')\\n else:\\n f.write(f'\\\\n{add_zero(t)}\\\\t{ao5}\\\\t{ao12}\\\\t{new_scramble}')\",\n \"def get_time(text_time):\\n # return Observer.datetime_to_astropy_time(dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M'))\\n the_time = dt.datetime.strptime(text_time, '%d/%m/%Y %H:%M')\\n return Time(the_time.strftime('%Y-%m-%d %H:%M'))\\n #date = [int(i) for i in date.split('/')]\",\n \"def get_teams_and_schedule():\\n start_time = timedelta(hours=19)\\n time_to_add = timedelta(minutes=15)\\n teams = session.query(Team).all()\\n\\n for team in teams:\\n team.time = str(start_time)\\n start_time += time_to_add\\n yield team\",\n \"def gprmc_convert(line):\\r\\n gps = line.strip().split(',')\\r\\n #check data\\r\\n if gps[2] == 'V':\\r\\n return\\r\\n raw_date = gps[9]\\r\\n time = ''\\r\\n date = raw_date[0:2]\\r\\n month = raw_date[2:4]\\r\\n year = raw_date[4:]\\r\\n #modify year if reaches year 2100\\r\\n time += date + '/' + month + '/20' + year\\r\\n return [time]\",\n \"def datetime_to_list(date):\\n return [date.year, date.month, date.day,\\n date.hour, date.minute, date.second]\",\n \"def datetime_to_list(date):\\n return [date.year, date.month, date.day,\\n date.hour, date.minute, date.second]\",\n \"def time_calculator(seconds):\\n seconds = int(seconds)\\n days = seconds // 86400\\n hours = seconds % 86400 // 3600\\n minutes = seconds % 86400 % 3600 // 60\\n seconds = seconds % 86400 % 3600 % 60\\n time_list = [days, hours, minutes, seconds] # Created a list that stores these variables in order\\n return time_list\",\n \"def time_convert(intime):\\n Nt = intime.shape[0]\\n outtime = []\\n for t in range(Nt):\\n timestr = ''.join([intime[t,:][~intime[t,:].mask].data[i].decode('utf-8') for i in range(len(intime[t,:][~intime[t,:].mask].data))])\\n outtime.append(datetime.strptime(timestr, '%Y-%m-%d_%H:%M:%S'))\\n return outtime\",\n \"def time(self, start_time):\\n \\n TIME_LIST.append((time.time() - start_time))\\n print(\\\"--- %s seconds ---\\\" % (time.time() - start_time))\",\n \"def time_settime(currenttime):\\r\\n\\r\\n time_query_times.append((getruntime(), currenttime))\",\n \"def record_time(times, enabled, *args):\\n if not enabled:\\n yield\\n else:\\n start = time.time()\\n yield\\n end = time.time()\\n times.append((' '.join(args), start, end))\",\n \"def generate_time_data(self):\\n # generate random dates and append to a list\\n sd = self.start_date\\n ed = self.end_date\\n dates = [random_date(start=sd, end=ed) for d in range(0, self.obs)]\\n\\n # convert to ISO 8601 format and update \\\"Local Time\\\" field\\n self.output['Local Time'] = map(lambda x: x.isoformat(), dates)\",\n \"def start_time():\\n t = [time.clock(), time.time()]\\n return t\",\n \"def format_time(self, time):\\n hh = time[0:2]\\n mm = time[2:4]\\n ss = time[4:]\\n return \\\"%s:%s:%s UTC\\\" % (hh,mm,ss)\",\n \"def process_timecards(self):\\n timecard = open('timecards.txt','r')\\n time_temp = []\\n time = []\\n for line in timecard:\\n time_temp.append(line)\\n for i in time_temp:\\n time.append(i.split(','))\\n for i in time:\\n for q in range(len(i)):\\n if q == 0:\\n pass\\n else:\\n i[q] = float(i[q])\\n for i in time:\\n for q in range(len(i)):\\n self.timecard[i[0]] = i[1:]\\n #print(self.timecard)\\n return self.timecard\",\n \"def convert_time_to_seconds(self, time_value):\\n time_epoch = []\\n mylog.debug('Converting %s to epoch time' % time_value)\\n for value in time_value:\\n try:\\n pattern = ' %I:%M:%S%p'\\n time_epoch_mini = int(time.mktime(time.strptime(value, pattern))) \\n time_epoch.append(time_epoch_mini)\\n except:\\n mylog.debug('%s Does not seem to be in format with leading space' % value)\\n try:\\n pattern = '%I:%M:%S%p'\\n time_epoch_mini = int(time.mktime(time.strptime(value, pattern))) \\n time_epoch.append(time_epoch_mini)\\n except:\\n mylog.debug('%s Does not appear to be in format without leading space' % value)\\n return time_epoch\",\n \"def __call__(self, x: Sequence[datetime]) -> Sequence[str]:\\n if self.tz is not None:\\n x = [d.astimezone(self.tz) for d in x]\\n return [d.strftime(self.fmt) for d in x]\",\n \"def add_gigasecond(time = datetime(1, 1, 1, 0, 0, 0)): # -> datetime() object\\n time += timedelta(seconds = 10 ** 9)\\n return time\",\n \"def format_time(self, time):\\n hours = time // 3600\\n time = time - hours*3600\\n minutes = time // 60\\n seconds = time - minutes*60\\n return ('%d:%d:%d' %(hours, minutes, seconds))\",\n \"def word_time(word, elapsed_time):\\n return [word, elapsed_time]\",\n \"def sort_time(cls):\\n CloudCtx.objCloudCtx.sort(key=lambda x: datetime.strptime(x.modTs, \\\"%d-%m-%Y %I:%M:%S %p\\\"), reverse=True)\\n for elem in CloudCtx.objCloudCtx:\\n print(elem.display_cloud_ctx())\",\n \"def add_minutes(self):\\n r = self.minute + self.value\\n x = int((r / 60))\\n\\n self.hour = self.hour + x\\n self.minute = r - (60 * x)\\n\\n cycles = int(self.hour / 12)\\n if cycles > 0:\\n if (cycles % 2) == 0:\\n pass\\n else:\\n if self.meridiem == 'AM':\\n self.meridiem = 'PM'\\n else:\\n self.meridiem = 'AM'\\n\\n self.hour = self.hour - cycles * 12\\n if self.hour == 0:\\n self.hour = 1\\n\\n if self.minute < 10:\\n self.minute = str(0) + str(self.minute)\\n\\n new_time: str = str(self.hour) + ':' + str(self.minute) + ' ' + self.meridiem.upper()\\n return new_time\",\n \"def makeChronList(self):\\n from operator import itemgetter\\n ## make list of msg lists in the format accespted by reconstructLine\\n self.outData_temp = [] # this will be in chronological order\\n for sens in self.outData:\\n if sens is not 'header':\\n for meas in self.outData[sens]:\\n for time in self.outData[sens][meas]:\\n value = self.outData[sens][meas][time]\\n thismsg = [time, sens, meas, str(value)] # leave time as float for sorting\\n self.outData_temp.append(thismsg)\\n self.outData_temp.sort(key=itemgetter(0)) # sort by first index\\n for msg in self.outData_temp: # now we can make time a string\\n msg[0] = str(msg[0])\",\n \"def oneTimepoint(timepoint):\\n\\tt = []\\n\\tfor vs in timepoint:\\n\\t\\tt.append((timepoint.attrib.get('CollectionTime'), vs[0].text, vs[1].text))\\n\\treturn(t)\",\n \"def Time(row):\\r\\n try:\\r\\n timeadd = dt.datetime.strptime(row['TickIssueTime'], '%H:%M').time()\\r\\n except:\\r\\n timeadd = dt.datetime.strptime('00:00', '%H:%M').time()\\r\\n\\r\\n newtime = dt.datetime.combine(dt.datetime.strptime(row['TickIssueDate'], '%Y-%m-%d %H:%M:%S') , timeadd)\\r\\n return newtime\",\n \"def add_timedelta_to_time():\\n dt = datetime.datetime.combine(datetime.date.today(), datetime.time(12, 30, 5)) + datetime.timedelta(hours=1)\\n t = dt.time()\\n print(t) # 13:30:05\",\n \"def _add_time(time_to_add: int):\\n store.time += time_to_add\",\n \"def time_iterator(\\n *,\\n first_time: datetime.datetime,\\n last_time: datetime.datetime,\\n resolution: int,\\n timezone: datetime.timezone,\\n) -> Generator[datetime.datetime, None, None]:\\n\\n current_time = first_time\\n while current_time < last_time:\\n yield current_time.replace(tzinfo=timezone)\\n current_time += relativedelta(\\n hours=resolution // 3600,\\n minutes=(resolution // 60) % 60,\\n seconds=resolution % 60,\\n )\",\n \"def dump_datetime(value):\\n if value is None:\\n return\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def get_target_timestamps(self):\\n times=[]\\n curr = self.begin_ts\\n while curr<=self.end_ts:\\n times.append(curr)\\n curr = curr + 24 * 60 * 60\\n return times\",\n \"def _get_time(self) -> None:\\n self.data[\\\"time\\\"] = np.zeros(len(self.data[\\\"yyyymmdd\\\"]), dtype=object)\\n \\n for idx, (yyyymmdd, hhmmss) in enumerate(zip(self.data[\\\"yyyymmdd\\\"], self.data[\\\"hhmmss\\\"])):\\n year, month, day = yyyymmdd.split(\\\"/\\\")\\n hour, minute, second = hhmmss.split(\\\":\\\")\\n self.data[\\\"time\\\"][idx] = datetime(int(year), int(month), int(day), int(hour), int(minute), int(second))\\n \\n del self.data[\\\"yyyymmdd\\\"]\\n del self.data[\\\"hhmmss\\\"]\",\n \"def offset(self):\\n\\n offsetList = ['12 am', '1 am', '2 am', '3 am', '4 am', '5 am', '6 am', '7 am', '8 am', '9 am',\\n '10 am', '11 am', '12 pm', '1 pm', '2 pm', '3 pm', '4 pm', '5 pm', '6 pm', '7 pm',\\n '8 pm', '9 pm', '10 pm', '11 pm', '12 pm']\\n\\n firstTimeHour = self.firstTime.time().hour\\n print ('First Time Hour:', firstTimeHour)\\n\\n m2 = str(self.firstTime.time())\\n m2 = datetime.datetime.strptime(m2, '%I:%M %p')\\n print(m2)\",\n \"def want_timeframe(self, timeframe):\\n self._wanted.append(timeframe)\",\n \"def format_start_time(self, data):\\n return data\",\n \"def get_fetch_times(last_fetch):\\n now = get_now()\\n times = list()\\n time_format = \\\"%Y-%m-%dT%H:%M:%SZ\\\"\\n if isinstance(last_fetch, str):\\n times.append(last_fetch)\\n last_fetch = datetime.strptime(last_fetch, time_format)\\n elif isinstance(last_fetch, datetime):\\n times.append(last_fetch.strftime(time_format))\\n while now - last_fetch > timedelta(minutes=59):\\n last_fetch += timedelta(minutes=59)\\n times.append(last_fetch.strftime(time_format))\\n times.append(now.strftime(time_format))\\n return times\",\n \"def datetime_timeplotxml(self):\\n if self.time:\\n return self.date.strftime(\\\"%b %d %Y\\\") + \\\" \\\" + self.time.strftime(\\\"%H:%M:%S\\\") + \\\" GMT\\\"\\n else:\\n return self.date.strftime(\\\"%b %d %Y\\\") + \\\" \\\" + \\\"00:00:00\\\" + \\\" GMT\\\"\",\n \"def getTimeStamps():\\n\\n # Initialize\\n results = dict()\\n\\n # UT time\\n ut = utils.getUT(pointing=True).split()\\n results['utday'] = ut[0]\\n results['ut'] = float(ut[1])\\n\\n # year/month/day/second\\n utStamp = time.gmtime()\\n utHour = maybeAddAZero(utStamp[3])\\n utMin = maybeAddAZero(utStamp[4])\\n utSec = maybeAddAZero(utStamp[5])\\n results['timeLab'] = ''.join([commands.yearMonthDay(),'_',utHour,utMin,utSec])\\n\\n # Done\\n return results\",\n \"def get_ph_time(as_array=False):\\n utc = timezone('UTC')\\n phtz = timezone('Asia/Manila')\\n now = utc.localize(datetime.utcnow())\\n now = now.astimezone(phtz)\\n if as_array:\\n return [now.year, now.month, now.day, now.hour, now.minute, now.second]\\n else:\\n return datetime(now.year, now.month, now.day, now.hour, now.minute, now.second)\",\n \"def render_time(dt):\\n return dt.strftime('%H:%M:%S')\",\n \"def addtomemorycollectiontime(self, datetime):\\n self._memorycollectiontime.append(datetime)\",\n \"def createdatv(times):\\n t0=datetime.datetime.strptime(times[0].tostring().decode('utf-8'), '%Y-%m-%d_%H:%M:%S')\\n t1=datetime.datetime.strptime(times[1].tostring().decode('utf-8'), '%Y-%m-%d_%H:%M:%S')\\n ts=(t1-t0).total_seconds()\\n datev=[]\\n for i in range(0,len(times)):\\n datev.append(t0+datetime.timedelta(seconds=i*ts))\\n return(datev)\",\n \"def localize_time(self, apitime):\\n return self.feedzone.localize(apitime).astimezone(self.localzone)\",\n \"def parse_time(text):\\n\\n # When keyword is 'in' adds values to time\\n if text[-3] == 'in':\\n remind_time = time.gmtime(int(text[-2]) * int(text[-1]) + time.time())\\n # Otherwise try to parse time as written\\n else:\\n remind_time = text[-1].replace(':', ' ') \\\\\\n + \\\" \\\" \\\\\\n + time.strftime(\\\"%m/%d/%y\\\", time.gmtime(time.time()))\\n remind_time = time.strptime(remind_time, \\\"%H %M %m/%d/%y\\\")\\n return remind_time\",\n \"def OPCtimetransform(data, to):\\n \\n remove_times = []\\n outtimes = []\\n times = {'ms':[],'SS':[],'MM':[],'HH':[]}\\n\\n for i in range(0, len(data)):\\n times['HH'] = 0\\n times['MM'] = 0\\n times['SS'] = 0\\n times['ms'] = 0\\n\\n item = data[i]\\n \\n try:\\n if len(item.split('.')[1]) < 2:\\n item += '0'\\n except IndexError:\\n item += '.00'\\n if len(item) < 9:\\n item = item.zfill(9)\\n if int(item[:2]) > 23:\\n item = '0' + item\\n \\n # remove items with extra zero (2319010.00 to 231910)\\n if len(item) > 9:\\n olditem = item\\n newitem = item[:4] + item[5:]\\n print( ('Repairing strange value %s into %s')%(olditem, newitem) )\\n item = newitem\\n else:\\n pass\\n try:\\n md = dt.datetime.strptime(item, \\\"%H%M%S.%f\\\")\\n \\n # round off items which exceed 59 minutes or 59 seconds \\n # (i.e. 146001 to 150001.)\\n except ValueError:\\n \\n try:\\n times['HH'] = int(item[0:2])\\n times['MM'] = int(item[2:4])\\n times['SS'] = int(item[4:6])\\n times['ms'] = int(item[7:9])\\n except ValueError:\\n print(i, item)\\n\\n if times['SS'] > 59:\\n times['MM'] += 1\\n times['SS'] = 0\\n if times['MM'] > 59:\\n times['HH'] += 1\\n times['MM'] = 0\\n # discard items which exceed 23 hours\\n if times['HH'] > 23:\\n times['HH'] = 23\\n print( ('resetting value %s')%(item) )\\n \\n\\n md = dt.datetime(1900,1,1,times['HH'], times['MM'], times['SS']) \\n\\n \\n outtimes.append( dt.datetime.strftime(md, to) )\\n\\n return outtimes\",\n \"def change_format_from_input_to_datetime(list_d_t_t):\\n data_output = []\\n\\n for row in list_d_t_t:\\n data_output.append([datetime.datetime.strptime(row[0] + \\\" \\\" + row[1], \\\"%Y-%m-%d %H:%M:%S\\\"),\\n datetime.datetime.strptime(row[0] + \\\" \\\" + row[2], \\\"%Y-%m-%d %H:%M:%S\\\")])\\n\\n return data_output\",\n \"def render_date_time_with_relative_into(into, date_time, add_ago):\\n into.append(format(date_time, DATETIME_FORMAT_CODE))\\n \\n into.append(' [*')\\n into.append(elapsed_time(date_time))\\n if add_ago:\\n into.append(' ago')\\n into.append('*]')\\n \\n return into\",\n \"def constructTimeLineItem(self):\\n\\t\\treturn\",\n \"def get_hours_and_minutes(time):\\n\\n # Get the total seconds of the time object\\n totalseconds = time.total_seconds()\\n \\n # Find the total hours in the time object\\n totalhours = totalseconds//3600\\n # Find the total minutes in the time object\\n totalminutes = (totalseconds%3600) // 60 \\n\\n # Return a list composing of the total hours and minutes\\n return [totalhours,totalminutes]\",\n \"def call_list_timestamp(timestamp):\\n return datetime.datetime.utcfromtimestamp(timestamp).isoformat()\",\n \"def render_todays_listings(request, context, callsigns):\\n context['listings_matrix'] = []\\n for callsign in callsigns:\\n whats_on_today_url = settings.SODOR_ENDPOINT + 'tvss/' + callsign + '/today/'\\n data = requests.get(whats_on_today_url, headers={'X-PBSAUTH': settings.TVSS_KEY})\\n\\n if data.status_code == 200:\\n jd = data.json()\\n # have to loop through and covert the goofy timestamps into datetime objects\\n for f in jd['feeds']:\\n for l in f['listings']:\\n l['start_time_obj'] = datetime.datetime.strptime(l['start_time'], \\\"%H%M\\\")\\n l['callsign'] = callsign\\n context['listings_matrix'].append(jd)\\n return context\",\n \"def currentTime():\\n return strftime(\\\"%H:%M:%S\\\", time.localtime())\",\n \"def _hour_to_time(num: int):\\n return datetime.datetime.now().replace(hour=num).strftime(\\\"%-I %p\\\")\",\n \"def time_form(gdf):\\n gdf['time'] = gdf['time'].dt.strftime(\\\"%Y-%m-%dT%H:%M:%S\\\")\\n \\n return gdf\",\n \"def set_ga_timestamp(self, time: int):\\n for cl in self:\\n cl.tga = time\",\n \"def _get_time(self): \\n\\t\\t# need to variable-ize the version ??? \\n\\t\\ttime = self.root.find('.//{http://www.opengis.net/kml/2.2}when').text\\n\\t\\t## strip off last 5 chars, ie '.135Z in '2015-08-01T00:06:29.135Z'\\n\\t\\tutc = dateutil.tz.tzutc() \\n\\t\\tcentral = dateutil.tz.gettz('America/Chicago')\\n\\t\\ttime = datetime.datetime.strptime(time[:-5], '%Y-%m-%dT%H:%M:%S')\\n\\t\\ttime = time.replace(tzinfo=utc)\\n\\t\\tself.time = time.astimezone(central)\",\n \"def get_timestamps(self) -> List[datetime.datetime]:\\n return [activity.timestamp for activity in self.activities]\",\n \"def _update_time_cursor(self):\\n for line in self.timeLines:\\n line.setValue(self.playbackTime)\",\n \"def output_format(times_list):\\n formatted_free_times = []\\n for i in times_list:\\n fmt_str = \\\"{} to {}.\\\".format(\\n i[0].format('ddd, MMM D, h:mm a'),\\n i[1].format('ddd, MMM D, h:mm a'))\\n formatted_free_times.append(fmt_str)\\n return formatted_free_times\",\n \"def _get_timestamps(self, time_interval: RawTimeIntervalType | None, bbox: BBox) -> list[dt.datetime]:\\n if self.single_scene:\\n return [time_interval[0]] # type: ignore[index, list-item]\\n\\n timestamps = get_available_timestamps(\\n bbox=bbox,\\n time_interval=time_interval,\\n data_collection=self.data_collection,\\n maxcc=self.maxcc,\\n config=self.config,\\n )\\n\\n return self.timestamp_filter(timestamps, self.time_difference)\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def getTimes():\",\n \"def addTimes(time1, time2):\\n t1 = timedelta(hours=time1.hour, minutes=time1.minute, seconds=time1.second)\\n t2 = timedelta(hours=time2.hour, minutes=time2.minute, seconds=time2.second)\\n t3 = t1 + t2\\n return (datetime.min + t3).time()\",\n \"def dump_datetime(value):\\n if value is None:\\n return None\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def dump_datetime(value):\\n if value is None:\\n return None\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def dump_datetime(value):\\n if value is None:\\n return None\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def dump_datetime(value):\\n if value is None:\\n return None\\n return [value.strftime(\\\"%Y-%m-%d\\\"), value.strftime(\\\"%H:%M:%S\\\")]\",\n \"def round_trip_time(self):\\n ...\",\n \"def scale_time_to(recs, unit):\\n\\n for r in recs:\\n if unit == 'd':\\n r.t = [t / 3600 / 24 for t in r.time]\\n elif unit == 'hours':\\n r.t = [t / 3600 for t in r.time]\\n elif unit == 'min':\\n r.t = [t / 60 for t in r.time]\\n elif unit in ('s', 'sec'):\\n r.t = r.time\\n else:\\n Exception('Wrong time unit')\\n\\n Records.time_unit = unit\\n Records.time_label = 'Time (' + unit + ')'\",\n \"def determineTimes():\\r\\n tm = getLocalTime()\\r\\n startFadeUpTime = utime.localtime(utime.mktime((tm[0], tm[1], tm[2], WAKEUP_TUPLE[0],\\r\\n WAKEUP_TUPLE[1] - FADE_TIME, tm[5], tm[6], tm[7])))\\r\\n startFadeDownTime = utime.localtime(utime.mktime((tm[0], tm[1], tm[2], WAKEUP_TUPLE[0],\\r\\n WAKEUP_TUPLE[1] + LIT_LENGTH, tm[5], tm[6], tm[7])))\\r\\n return [startFadeUpTime[3:5], startFadeDownTime[3:5]]\",\n \"def format_time_sortkey(self, data):\\n return self.input['start_time'].time().strftime('%H%M').lstrip('0')\",\n \"def round_time(self, time):\\n hour, mins, _ = time.split(\\\":\\\")\\n return '{:02d}:00:00'.format(int(hour)+1 ) if int(mins) >= 30 else '{:02d}:00:00'.format(int(hour))\",\n \"def _groupDate(item):\\n return item.time.date()\",\n \"def get_time_attr_map(t):\\n now = datetime.datetime.now()\\n if t + datetime.timedelta(hours=3) > now:\\n return get_map(\\\"main_list_white\\\")\\n if t + datetime.timedelta(days=3) > now:\\n return get_map(\\\"main_list_lg\\\")\\n else:\\n return get_map(\\\"main_list_dg\\\")\",\n \"def planwatch(self, hours=12):\\n post = {'mytime': str(hours)}\\n response = self._get_page('planwatch.php', post=post)\\n soup = bs4.BeautifulSoup(response.text, 'html5lib')\\n results = soup.find('ul', {'id': 'new_plan_list'})\\n new_plans = results.findAll('div', {'class': 'newplan'})\\n resultlist = []\\n for div in new_plans:\\n user = div.find('a', {'class': 'planlove'}).contents[0]\\n time = div.find('span').contents[0]\\n time = parse_plans_date(time, tz_name=self.server_tz)\\n resultlist.append((user, time))\\n return resultlist\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6536344","0.61749583","0.5915965","0.57811874","0.5765255","0.5675775","0.5667193","0.5663627","0.5660941","0.5621869","0.5613156","0.55986845","0.5576497","0.5538767","0.55233485","0.55079854","0.5443805","0.5407144","0.53734505","0.53541595","0.53422564","0.5310349","0.5274767","0.5265787","0.52646846","0.5240537","0.52287173","0.5223971","0.5217923","0.52029425","0.52029425","0.5164896","0.5162162","0.5159103","0.5147681","0.51302767","0.51286936","0.5104627","0.5104495","0.5088883","0.50818616","0.5075457","0.5064261","0.506147","0.5049036","0.504405","0.50338143","0.50313014","0.49972877","0.49830714","0.49816045","0.4980034","0.49789843","0.4977556","0.4967999","0.49594393","0.49569255","0.4954857","0.49540606","0.4950274","0.49422488","0.49363834","0.49332118","0.49319202","0.49293098","0.49247378","0.49205348","0.4915798","0.49111706","0.49042174","0.49003845","0.48878545","0.48783308","0.48723876","0.4871808","0.4868223","0.48548976","0.48515105","0.48377123","0.48303837","0.4826693","0.48263168","0.48211282","0.48169085","0.48163712","0.48163712","0.48163712","0.48123205","0.4810726","0.4810726","0.4810726","0.4810726","0.48086488","0.48051482","0.47955707","0.47931612","0.47929242","0.47883552","0.4782844","0.47748497"],"string":"[\n \"0.6536344\",\n \"0.61749583\",\n \"0.5915965\",\n \"0.57811874\",\n \"0.5765255\",\n \"0.5675775\",\n \"0.5667193\",\n \"0.5663627\",\n \"0.5660941\",\n \"0.5621869\",\n \"0.5613156\",\n \"0.55986845\",\n \"0.5576497\",\n \"0.5538767\",\n \"0.55233485\",\n \"0.55079854\",\n \"0.5443805\",\n \"0.5407144\",\n \"0.53734505\",\n \"0.53541595\",\n \"0.53422564\",\n \"0.5310349\",\n \"0.5274767\",\n \"0.5265787\",\n \"0.52646846\",\n \"0.5240537\",\n \"0.52287173\",\n \"0.5223971\",\n \"0.5217923\",\n \"0.52029425\",\n \"0.52029425\",\n \"0.5164896\",\n \"0.5162162\",\n \"0.5159103\",\n \"0.5147681\",\n \"0.51302767\",\n \"0.51286936\",\n \"0.5104627\",\n \"0.5104495\",\n \"0.5088883\",\n \"0.50818616\",\n \"0.5075457\",\n \"0.5064261\",\n \"0.506147\",\n \"0.5049036\",\n \"0.504405\",\n \"0.50338143\",\n \"0.50313014\",\n \"0.49972877\",\n \"0.49830714\",\n \"0.49816045\",\n \"0.4980034\",\n \"0.49789843\",\n \"0.4977556\",\n \"0.4967999\",\n \"0.49594393\",\n \"0.49569255\",\n \"0.4954857\",\n \"0.49540606\",\n \"0.4950274\",\n \"0.49422488\",\n \"0.49363834\",\n \"0.49332118\",\n \"0.49319202\",\n \"0.49293098\",\n \"0.49247378\",\n \"0.49205348\",\n \"0.4915798\",\n \"0.49111706\",\n \"0.49042174\",\n \"0.49003845\",\n \"0.48878545\",\n \"0.48783308\",\n \"0.48723876\",\n \"0.4871808\",\n \"0.4868223\",\n \"0.48548976\",\n \"0.48515105\",\n \"0.48377123\",\n \"0.48303837\",\n \"0.4826693\",\n \"0.48263168\",\n \"0.48211282\",\n \"0.48169085\",\n \"0.48163712\",\n \"0.48163712\",\n \"0.48163712\",\n \"0.48123205\",\n \"0.4810726\",\n \"0.4810726\",\n \"0.4810726\",\n \"0.4810726\",\n \"0.48086488\",\n \"0.48051482\",\n \"0.47955707\",\n \"0.47931612\",\n \"0.47929242\",\n \"0.47883552\",\n \"0.4782844\",\n \"0.47748497\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72068673"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":4689,"cells":{"query":{"kind":"string","value":"finds stations that don't have predictand data and appends them to a list"},"document":{"kind":"string","value":"def miss_station(all_stations,stations):\n\tdiff = len(all_stations)-len(stations)\n k=0\n i=0\n miss_stations = ['']*diff\n a = all_stations[:]\n a.sort()\n s = stations[:]\n s.sort()\n while i < len(stations):\n while a[i] != s[i]:\n miss_stations[k]=a[i]\n del a[i]\n k+=1\n i+=1\n\treturn miss_stations"},"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 stations():\n\n return station_list","def prep_stations(url):\n stations = []\n _stations = requests.get(url).json()\n\n for _station in _stations['stationBeanList']:\n if _station['statusKey'] == 1:\n stations.append([_station['stationName'], _station['id'],\n _station['availableDocks'], _station['totalDocks'],\n _station['latitude'], _station['longitude']])\n\n return stations","def _get_ogd_stations():\n return {r[\"Station\"] for r in ZamgData.current_observations()}","def get_processed_stations(out_dir):\n lista = [ f.split('_')[0] for f in os.listdir(out_dir) if '.nc' in f ]\n #print('Skipping these stations: ' , lista )\n return lista","def train_stations(self) -> List[str]:\n return sorted([train_info['HE'] for train_info in train_api.stations_info.values()])","def stations(station_let):\n\tstat = ['']*np.size(station_let,0)\n\tfor i in range(len(stat)):\n\t\tfor j in range(4):\n\t\t\tif station_let[i][j] is not np.ma.masked:\n\t\t\t\tstat[i]+=station_let[i][j]\n\treturn stat","def _build_stations(self, stop_list):\n # stations = [] TODO: What is this for\n dists = self._euclidian_distances(stop_list)\n stations = self._calculate_y_lines(dists)\n return stations","async def get_train_stations(self, latitude: float, longitude: float,\n valid_stations=None) -> list:\n params = {\n 'location': '{},{}'.format(latitude, longitude),\n 'key': self.api_key,\n 'type': \"train_station\",\n \"radius\": 1600\n }\n\n logging.info(\"Getting train stations near (%f, %f)\", latitude, longitude)\n\n async with aiohttp.ClientSession() as session:\n async with session.post('https://maps.googleapis.com/maps/api/place/nearbysearch/json',\n params=params) as response:\n if response.status == HTTPStatus.OK:\n payload = await response.json()\n if payload['status'] == 'OK':\n if valid_stations:\n return [result[\"name\"] for result in payload[\"results\"]\n if result[\"name\"] in valid_stations]\n\n return [result[\"name\"] for result in payload[\"results\"]]\n\n return []","def all(self, skip_cache=False):\n now = _time_ms(datetime.datetime.utcnow())\n if skip_cache or now - self._last_updated > CACHE_LIMIT:\n self._process_stations()\n return self._stations_lst","def collect_stations(self):\n # First, iterate provinces and build url's\n site = urllib.request.urlopen(self.base_url)\n\n # Check that the site is still valid or operating by collecting a list of provinces\n print(\"Collecting provinces\")\n provinces = [s[9:11] for s in re.findall('<a href=\"../\">../</a>', site.read())]\n\n # Iterate provinces and collect list of available times\n print(\"Collecting time periods and station ID's\")\n self.stations = defaultdict(dict)\n for prov in provinces:\n site = urllib.request.urlopen(self.build_url(prov))\n expression = '<a href=\"[hd][a-zA-Z]*/\">[hd][a-zA-Z]*/</a>'\n times = [s.split('>')[1].split('<')[0].replace('/', '') for s in re.findall(expression, site.read())]\n\n # Iterate times and collect the station ID's\n for time in times:\n site = urllib.request.urlopen(self.build_url(prov, time))\n expression = '<a href=\"{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv\">{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv</a>'\n expression = expression.format(prov.upper(), time.lower())\n stations = [s.split('_')[1] for s in re.findall(expression, site.read())]\n self.stations[prov][time] = stations","def station_list() -> List[Dict]:\n return STATIONS","def test_get_closest_stations(self):\n\t\tpoint = \"POINT(40.71911552 -74.00666661)\"\n\t\tstations = set(server.get_closest_stations(point))\n\t\t# find the closest stations, make them a set of objects see if sets intersect completely","def create_list() -> List[Optional[float]]:\n return [None] * num_stations","def getStations(self) :\n return self._stations","def get_all_stations(session: Session) -> List[Row]:\n return session.query(PlanningWeatherStation.station_code).all()","def stations(self):\n for stat in sorted(self.station_records):\n yield self.station_records[stat]","def read_noaa_stations(self):\n # wget -c http://weather.noaa.gov/data/nsd_bbsss.txt\n #72;656;KSFD;Winner, Bob Wiley Field Airport;SD;United States;4;43-23-26N;099-50-33W;;;619;;\n #93;246;NZRO;Rotorua Aerodrome;;New Zealand;5;38-07S;176-19E;38-07S;176-19E;285;294;\n #block;synop;icao;name;?;country;??;lat;lon;lat2;lon2;height;?;\n #0 1 2 3 4 5 6 7 8 9 10 11 12\n if not os.path.exists(self.noaa_filename):\n LOGGER.warning('could not find noaa file \"%s\"', self.noaa_filename)\n return self.known_stations\n count = 0\n with open(self.noaa_filename, 'r') as csvfile:\n stationreader = csv.reader(csvfile, delimiter=';')\n for row in stationreader:\n station_id = '{}{}'.format(row[0], row[1])\n station_id_icao = row[2].strip().upper()\n data = noaa_station_data_from_row(row)\n if data is not None:\n count += 1\n self.known_stations[station_id] = data\n if len(station_id_icao) == 4 and station_id_icao.isalpha():\n self.known_stations[station_id_icao] = data\n self.noaa_file_age = os.path.getmtime(self.noaa_filename)\n LOGGER.info(' Loaded %i noaa station records from \"%s\"', count, self.noaa_filename)\n return self.known_stations","def _add_data(self, model_stations: Iterable[model.Station],\n validate_prefix: str = \"\") -> int:\n valid_station_count = 0\n jreast_merged_codes: dict[model.StationID, str] = load_csv_as_mapping(\n DIR_CURATED / \"jreast_merged_codes.csv\",\n itemgetter(\"sta_id\"),\n itemgetter(\"code\")\n )\n\n # Add data from model stations\n for model_sta in model_stations:\n is_invalid = False\n should_validate = model_sta.id.startswith(validate_prefix)\n\n # Find a matching geo_sta\n geo_sta = self.by_id.get(model_sta.id)\n if not geo_sta:\n if should_validate:\n self.logger.critical(f\"{Color.RED}geo.osm is missing station \"\n f\"{Color.MAGENTA}{model_sta.id}{Color.RESET}\")\n self.valid = False\n continue\n\n # Find a name\n name_id = last_part(geo_sta.id)\n geo_sta.name = self.names.get(name_id)\n if geo_sta.name is None and should_validate:\n self.logger.critical(f\"{Color.RED}sta_names.csv is missing name for \"\n f\"{Color.MAGENTA}{name_id}{Color.RESET}\")\n is_invalid = True\n\n # Copy stop_code\n geo_sta.code = model_sta.code\n\n # Check if station was valid\n if is_invalid:\n self.valid = False\n elif should_validate:\n valid_station_count += 1\n\n # Generate codes and names for mother stations\n for sta in self.by_id.values():\n if not sta.children:\n continue\n\n name_id = last_part(sta.id)\n sta.name = self.names.get(name_id)\n if not sta.name:\n self.logger.critical(f\"{Color.RED}sta_names.csv is missing name for \"\n f\"{Color.MAGENTA}{name_id}{Color.RESET}\")\n is_invalid = True\n\n # Get children codes\n children_codes = []\n jreast_merged_code = jreast_merged_codes.get(sta.id)\n if jreast_merged_code:\n children_codes.append(jreast_merged_code)\n\n for child in sta.children:\n # Ignore JR-East child codes if there's a JR-East merged code\n if child.id.startswith(\"JR-East\") and jreast_merged_code:\n continue\n elif child.code:\n children_codes.append(child.code)\n\n sta.code = \"/\".join(children_codes)\n\n return valid_station_count","def stations(self):\n stations = []\n f = self._fetch(Citibike.STATION_URL)\n data = json.load(f)\n if 'stationBeanList' not in data or len(data['stationBeanList']) == 0:\n raise BadResponse('Station Fetch Failed', data)\n for station in data['stationBeanList']:\n stations.append(Station._from_json(station))\n logging.debug(\"Retrieved %d stations\" % len(stations))\n return stations","async def get_stations() -> List[WeatherStation]:\n # Check if we're really using the api, or loading from pre-generated files.\n use_wfwx = config.get('USE_WFWX') == 'True'\n if use_wfwx:\n return await _get_stations_remote()\n return _get_stations_local()","def get_all_stations(engine): \n # Query db\n sql = (\"SELECT DISTINCT a.station_id, \"\n \" a.station_code, \"\n \" a.station_name, \"\n \" c.station_type, \"\n \" d.latitude, \"\n \" d.longitude \"\n \"FROM nivadatabase.projects_stations a, \"\n \" nivadatabase.stations b, \"\n \" nivadatabase.station_types c, \"\n \" niva_geometry.sample_points d \"\n \"WHERE a.station_id = b.station_id \"\n \"AND b.station_type_id = c.station_type_id \"\n \"AND b.geom_ref_id = d.sample_point_id \"\n \"ORDER BY a.station_id\")\n df = pd.read_sql(sql, engine)\n\n return df","def get_stations(self):\n return self.__request('stations')['stations']","def get_stations(base_url, hts, mtype):\n stns1 = ws.site_list(base_url, hts, location='LatLong') # There's a problem with Hilltop that requires running the site list without a measurement first...\n stns1 = ws.site_list(base_url, hts, location='LatLong', measurement=mtype)\n stns2 = stns1[(stns1.lat > -47.5) & (stns1.lat < -34) & (stns1.lon > 166) & (stns1.lon < 179)].dropna().copy()\n stns2.rename(columns={'SiteName': 'ref'}, inplace=True)\n\n return stns2","def create_station_list(self):\n sorted_station_list = sorted(self.station_dict, key=self.station_dict.get)\n\n return sorted_station_list","def stations(self):\n try:\n stations_api = requests.get(self._stations_url)\n stations = {}\n for station in stations_api.json():\n station_id = station['id']\n station_name = station['name']\n stations[station_id] = station_name\n\n return stations\n except (RequestException, KeyError) as exc:\n LOG.error('could not read from api: %s', exc)\n raise SlfError('could not read from api: %s' % exc) from None","def list_stations(intent, session):\n stations = location.get_stations(config.bikes_api)\n street_name = intent['slots']['street_name']['value']\n possible = location.matching_station_list(stations,\n street_name,\n exact=True)\n street_name = street_name.capitalize()\n\n if len(possible) == 0:\n return reply.build(\"I didn't find any stations on %s.\" % street_name,\n is_end=True)\n elif len(possible) == 1:\n sta_name = location.text_to_speech(possible[0]['name'])\n return reply.build(\"There's only one: the %s \"\n \"station.\" % sta_name,\n card_title=(\"%s Stations on %s\" %\n (config.network_name, street_name)),\n card_text=(\"One station on %s: %s\" %\n (street_name, possible[0]['name'])),\n is_end=True)\n else:\n last_name = location.text_to_speech(possible[-1]['name'])\n speech = \"There are %d stations on %s: \" % (len(possible),\n street_name)\n speech += (', '.join([location.text_to_speech(p['name'])\n for p in possible[:-1]]) +\n ', and %s' % last_name)\n card_text = (\"The following %d stations are on %s:\\n%s\" %\n (len(possible), street_name,\n '\\n'.join(p['name'] for p in possible)))\n return reply.build(speech,\n card_title=(\"%s Stations on %s\" %\n (config.network_name, street_name)),\n card_text=card_text,\n is_end=True)","def get_tasks_that_fit_station(self, station: Station) -> TaskList:\n return TaskList([task for task in self._tasks if station.can_fit(task)])","def stations():\n # Query all stations before a given date 2017\n results = session.query(Measurement.date, Measurement.tobs).filter(func.strftime(\"%Y\", Measurement.date) >= \"2017\").all()\n all_results = list(np.ravel(results))\n \n return jsonify(all_results)","def read_table_stations(self):\n if not os.path.exists(self.station_table_filename):\n LOGGER.warning('could not find station.table file \"%s\"', self.station_table_filename)\n return self.known_stations\n count = 0\n with open(self.station_table_filename, 'r') as textfile:\n lines = textfile.read().split(LF)\n for line in lines:\n station_id, data = read_table_station_from_line(line)\n if station_id is not None:\n self.known_stations[station_id] = data\n count += 1\n self.station_file_age = os.path.getmtime(self.station_table_filename)\n LOGGER.info(' Loaded %i station records from \"%s\"', count, self.station_table_filename)\n return self.known_stations","def _get_stations_local() -> List[dict]:\n LOGGER.info('Using pre-generated json to retrieve station list')\n with open(weather_stations_file_path) as weather_stations_file:\n json_data = json.load(weather_stations_file)\n return json_data['weather_stations']","def search_station(st):\n\n res = []\n for key, val in _STATIONS.items():\n score = fuzz.token_set_ratio(st, key)\n res.append(\n {\n 'station': key,\n 'score': score,\n 'station_id': val\n }\n )\n if not res:\n return {}\n else:\n res = sorted(res, key=lambda k: k['score'], reverse=True)\n res = res[0]\n return res","def get_stations():\n response = requests.get('https://api.hh.ru/metro/160')\n todos = json.loads(response.text)\n colors = {'CD0505': 'red'}\n all_stations_one_line = []\n\n for i in todos['lines']:\n all_stations_one_line = []\n\n for j in i['stations']:\n one_station = station.station()\n one_station.set_name(j['name'])\n one_station.set_color(colors.get(i['hex_color']))\n one_station.set_lat(j['lat'])\n one_station.set_lng(j['lng'])\n all_stations_one_line.append(one_station)\n return all_stations_one_line","def stations():\n # Query all station names from dataset\n station_list = session.query(Measurement.station).distinct().all()\n all_stations = list(np.ravel(station_list))\n\n return jsonify(all_stations)","def get_station_graph(start_station_id, end_station_list):\n start_station_graph = []\n for i in range(10):\n if end_station_list[i] is not None:\n start_station_graph.append((start_station_id, end_station_list[i]))\n return start_station_graph","def get_stations(nordic_file_names, output_level=0):\n stations = []\n for file in nordic_file_names:\n new_stations = get_event_stations(file, output_level)\n\n if new_stations == -1:\n continue\n\n for x in new_stations:\n if x not in stations:\n stations.append(x)\n\n return sorted(stations)","def get_all_station_feature(city):\n poi_frequency = np.load(exp_data_path + os.sep + 'poi_frequency' + os.sep + 'poi_frequency_{}.npy'.format(city),\n allow_pickle=True) # .tolist()\n poi_num = np.load(exp_data_path + os.sep + 'poi' + os.sep + 'poi_{}.npy'.format(city), allow_pickle=True)\n poi_entropy = np.load(exp_data_path + os.sep + 'poi_entropy' + os.sep + 'poi_entropy_{}.npy'.format(city),\n allow_pickle=True)\n road = np.load(exp_data_path + os.sep + 'roadnet' + os.sep + 'roadnet_{}.npy'.format(city), allow_pickle=True)\n transportation = np.load(exp_data_path + os.sep + 'transportation' + os.sep + 'transportation_{}.npy'.format(city),\n allow_pickle=True)\n commerce = np.load(exp_data_path + os.sep + 'commerce' + os.sep + 'commerce_{}.npy'.format(city), allow_pickle=True)\n\n file_name = exp_data_path + os.sep + 'station' + os.sep + 'all_demand_{}.npy'.format(city)\n demand_data = np.load(file_name, allow_pickle=True)\n num = demand_data[:, 0, -2, np.newaxis] # todo check meaning here, get quick and slow feature\n\n raw_data = np.concatenate((num, poi_frequency, poi_num, poi_entropy, road, transportation, commerce), axis=1)\n csv_data = pd.DataFrame(raw_data, columns=GENERAL_HEADER)\n\n file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\n if os.path.exists(file_path):\n os.remove(file_path)\n csv_data.to_csv(file_path)\n pass","def get_zipcode_stations(add):\r\n name=get_zipcode_names(add)\r\n engine = get_sql_engine()\r\n neighborhood_stations = text(\r\n \"\"\"\r\n SELECT\r\n \"name\" as name,\r\n \"addressStreet\" as address,\r\n \"bikesAvailable\" as available_bikes,\r\n v.geom as geom,\r\n ST_X(v.geom) as lon, ST_Y(v.geom)as lat\r\n FROM indego_rt1130 as v\r\n JOIN philly_zipcode as n\r\n ON ST_Intersects(v.geom, n.geom)\r\n WHERE n.code = :name\r\n \"\"\"\r\n )\r\n stations = gpd.read_postgis(neighborhood_stations, con=engine, params={\"name\": name})\r\n return stations","def cluster_stations(stations, empty='empty'):\n if empty == 'empty':\n tocluster = [i for i in stations if (i[3] - i[2])/float(i[3]) < .2]\n else:\n tocluster = [i for i in stations if (i[2])/float(i[3]) < .2]\n cl = KMeansClustering([(i[4], i[5]) for i in tocluster])\n clusters = cl.getclusters(4)\n\n # Note that this returns a list of lists of lat/long tuples. We're\n # going to have to re-associate them back to the rest of the stations\n\n clustered = []\n for ix, i in enumerate(clusters):\n for j in i:\n for k in tocluster:\n if (j[0], j[1]) == (k[4], k[5]):\n clustered.append([k[0], k[1], k[2],\n k[3], k[4], k[5], ix+1])\n\n return clustered","def get_station_boroughs(self):\\","def _get_next_available_train_list(self, context) -> Tuple[List, datetime.datetime]:\n try:\n for day in self._next_week:\n trains = list(train_api.get_available_trains(origin_station_id=context.user_data['origin_station_id'],\n dest_station_id=context.user_data['dest_station_id'],\n date=day))\n if len(trains) > 0:\n return trains, day\n\n except (ValueError, AttributeError):\n traceback.print_exc()\n raise RuntimeError(\"general error\")","def getstationaryobslist(self):\n\n stationaryobslist = [self.__tablecm]\n return stationaryobslist","def get_data(self):\n if not self.form.submit():\n return False\n\n parser = XMLParser(huge_tree=True)\n doc = etree.fromstring(self.form.raw_data, parser=parser)\n # There are a few stations with multiple generator id's, separated by '\\n' so\n # capture them and add each as a separate entry.\n for detail in doc.xpath(\"//*[local-name()='Detail']\"):\n stt = Station(detail)\n if '\\n' in stt.generator_id:\n ids = [x.strip() for x in stt.generator_id.split('\\n')]\n stt.generator_id = ids[0]\n for _id in ids[1:]:\n _st = copy.copy(stt)\n _st.generator_id = _id\n self.stations.append(_st)\n self.stations.append(stt)\n return len(self.stations) > 0","async def get_stations_by_codes(station_codes: List[int]) -> List[WeatherStation]:\n use_wfwx = config.get('USE_WFWX') == 'True'\n if use_wfwx:\n return await _get_stations_by_codes_remote(station_codes)\n return _get_stations_by_codes_local(station_codes)","def get_stations(self, limit=250):\n\n endpoint = \"/station/getStations\"\n response = self._send(endpoint, \"POST\", {\"pageSize\": limit})\n stations = response.json()[\"stations\"]\n return stations","def get_neigh_demand(city):\n\n # get station set S with more than 10 charge equipment\n static_file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\n static_feature = pd.read_csv(static_file_path, header=0)\n station_set = set(static_feature[static_feature.num >= 10].index)\n\n # calculate 10 nearest neighborhoods for each station, sort by distance and store their index, get a map\n neighbor_distance_map = {}\n matrix_distance = np.load(exp_data_path + os.sep + 'similarity' + os.sep + 'similarity_distance_{}_numpy.npy'.format(city), allow_pickle=True)\n all_distance_map = {i: [] for i in range(station_count[city])}\n for i in range(station_count[city]):\n if i not in station_set:\n continue\n for j in range(station_count[city]):\n if j not in station_set:\n continue\n all_distance_map[i].append((j, matrix_distance[i][j]))\n all_distance_map[i].sort(key=lambda x : x[1], reverse=True)\n neighbor_distance_map[i] = [idx for idx, distance in all_distance_map[i][:10]]\n\n # 11 times header, get static neighborhood feature for each station(in S), get csv: neighbor_feature_{city}.csv\n ALL_HEADER = ['index']\n ALL_HEADER.extend(GENERAL_HEADER)\n for i in range(10):\n for j in GENERAL_HEADER:\n ALL_HEADER.append('{}_{}'.format(j, i))\n\n raw_data = np.empty((len(neighbor_distance_map), len(ALL_HEADER)))\n for i, idx in enumerate(neighbor_distance_map.keys()):\n raw_data[i][0] = idx\n raw_data[i][1:1+len(GENERAL_HEADER)] = static_feature.iloc[idx]['num':'mall']\n for j in range(10):\n neighbor_idx = neighbor_distance_map[idx][j]\n raw_data[i][1+len(GENERAL_HEADER)*(j+1):1+len(GENERAL_HEADER)*(j+2)] = static_feature.iloc[neighbor_idx]['num':'mall']\n neighbor_feature_data = pd.DataFrame(raw_data, columns=ALL_HEADER)\n print('neighbor feature')\n print(neighbor_feature_data)\n\n neighbor_feature_path = exp_data_path + os.sep + 'static' + os.sep + 'static_neighor_feature_{}.csv'.format(city)\n if os.path.exists(neighbor_feature_path):\n os.remove(neighbor_feature_path)\n neighbor_feature_data.to_csv(neighbor_feature_path)\n\n # create final csv(11 times header with basic info(time_index + time_embed_index))\n # if index in S, fill basic info, neighbor_feature and demand\n\n demand = np.load(exp_data_path + os.sep + 'station' + os.sep + 'demand_{}.npy'.format(city), allow_pickle=True)\n time_count = demand.shape[1]\n\n DEMAND_HEADER = []\n DEMAND_HEADER.extend(ALL_HEADER)\n DEMAND_HEADER.extend(['time_index', 'time_embed', 'demand'])\n neighbor_demand_raw_data = np.empty(((len(neighbor_distance_map)*time_count, len(DEMAND_HEADER))))\n\n # get time map like {\"0800\": 1, \"0830\": 2, ....}\n time_index_map = np.load(exp_data_path + os.sep + 'station_list' + os.sep + 'time_index.npy', allow_pickle=True)\n time_index_map = dict(time_index_map.tolist())\n time_map = {t: i for i, t in enumerate(sorted(set([k[-4:] for k in time_index_map['rev_index'].keys()])))}\n\n cur_idx = 0\n for time_idx in range(time_count):\n time_embed_idx = time_map[time_index_map['index'][time_idx][-4:]]\n for station_idx in station_set:\n neighbor_demand_raw_data[cur_idx][0:len(ALL_HEADER)] = neighbor_feature_data.loc[neighbor_feature_data['index']==station_idx, 'index':'mall_9']\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)] = time_idx\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)+1] = time_embed_idx\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)+2] = demand[station_idx][time_idx][-1]\n # todo add slow demand and quick demand here\n cur_idx = cur_idx + 1\n print(cur_idx, neighbor_demand_raw_data.shape)\n\n neighbor_demand_data = pd.DataFrame(neighbor_demand_raw_data, columns=DEMAND_HEADER)\n print('neighbor demand')\n print(neighbor_demand_data)\n\n neighbor_demand_path = exp_data_path + os.sep + 'static' + os.sep + 'neighbor_demand_{}.csv'.format(city)\n if os.path.exists(neighbor_demand_path):\n os.remove(neighbor_demand_path)\n neighbor_demand_data.to_csv(neighbor_demand_path)","def station_analysis(data):\n unique_stations = list(set(data['start_station_name'].tolist() + data['end_station_name'].tolist()))\n\n station_counter = {station : 0 for station in unique_stations}\n for index, row in data.iterrows():\n station_counter[row['start_station_name']] += 1\n\n print('List of all stations:')\n print(unique_stations)\n\n keys = list(station_counter.keys())\n vals = list(station_counter.values())\n indexArr = np.argsort(list(station_counter.values()))\n popularStations = []\n for i in reversed(indexArr):\n popularStations.append((keys[i], vals[i]))\n\n stations1, journeys = zip(*popularStations[0:10])\n plt.bar(stations1, journeys, 0.1)\n\n plt.xticks(stations1, rotation='vertical')\n plt.title('Popular stations')\n plt.xlabel('Station names')\n plt.ylabel('Journeys')\n\n plt.show()\n return station_counter","def gatherStationData():\n flist = list_files()\n station_dics = {}\n print(\"Reading in csv data...\")\n for f_in in flist:\n start,end = find_timespan(f_in)\n station = station_name(f=f_in)\n print(\"File: {0} Station: {1} {2}--{3}\".format(f_in, \n station, start, end))\n station_dics[station] = read_precip(fname=f_in, \n label=station, start_year=start, end_year=end)\n data_list = []\n for s in station_dics:\n data_list.append(station_dics[s]) \n return pd.concat(data_list,axis=1)","async def _get_stations_remote() -> List[WeatherStation]:\n LOGGER.info('Using WFWX to retrieve station list')\n async with ClientSession() as session:\n # Get the authentication header\n header = await _get_auth_header(session)\n stations = []\n # Iterate through \"raw\" station data.\n async for raw_station in _fetch_raw_stations(session, header, BuildQueryAllStations()):\n # If the station is valid, add it to our list of stations.\n if _is_station_valid(raw_station):\n LOGGER.info('Processing raw_station %d',\n int(raw_station['stationCode']))\n stations.append(_parse_station(raw_station))\n LOGGER.debug('total stations: %d', len(stations))\n return stations","def stations():\n # Query \n results = session.query(Station.station).all()\n \n list = []\n for result in results:\n list.append(result)\n return jsonify(list)","def stations_dict(self):\n return self.__stations_dict","def stations():\n # Create a link to the session\n session = Session(engine)\n \n # Query all station records\n results = session.query(Stations.station, Stations.name).all()\n \n session.close()\n\n # Create a dictionary from the query results\n all_stations = []\n for station, name in results:\n station_dict = {}\n station_dict[\"station\"] = station\n station_dict[\"name\"] = name\n all_stations.append(station_dict)\n \n return jsonify(all_stations)","def stations ():\n # Query all passengers\n Stns= session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station).all()\n\n allStationns = list(np.ravel(Stns))\n\n return jsonify(allStations)","def get_station_entrances(self):\n station_entrances = []\n for wrapper in self.soup.find_all(\"div\", {\"class\": \"stop-wrapper\"}):\n text = wrapper.find(\"span\").text\n if text == '' or text is None:\n entrance = ''\n else:\n entrance = text.split(',')[0].lstrip().rstrip()\n station_entrances.append(entrance)\n return np.array(station_entrances).T","def get_station_lines(self):\n station_lines = []\n for wrapper in self.soup.find_all(\"div\", {\"class\": \"stop-wrapper\"}):\n lines = wrapper.find(\"h3\").text.split(' ')[-1][1:-1]\n station_lines.append(lines)\n return np.array(station_lines).T","def stations():\n list_of_stations = session.query(Station.station, Station.name)\n all_stations = []\n for s, n in list_of_stations:\n station_dict = {}\n station_dict[\"station\"] = s\n station_dict[\"name\"] = n\n all_stations.append(station_dict)\n return jsonify(all_stations)","def stations():\n results = session.query(Measurement.station).\\\n group_by(Measurement.station).all()\n\n return jsonify(results)","def __save_all():\n \n # Use directory listing from stilt-web data. Ignore stations that\n # may be in the queue but are not finished yet.\n allStations = [s for s in os.listdir(CPC.STILTPATH) if os.path.exists(CPC.STILTPATH + s)]\n\n \n # read lis of ICOS stations\n icosStations = cpstation.getIdList()\n icosStations = list(icosStations['id'][icosStations.theme=='AS'])\n \n # dictionary to return\n stations = {}\n\n # fill dictionary with ICOS station id, latitude, longitude and altitude\n for ist in tqdm(sorted(allStations)):\n \n stations[ist] = {}\n # get filename of link (original stiltweb directory structure) and extract location information\n \n loc_ident = os.readlink(CPC.STILTPATH+ist)\n clon = loc_ident[-13:-6]\n lon = float(clon[:-1])\n if clon[-1:] == 'W':\n lon = -lon\n clat = loc_ident[-20:-14]\n lat = float(clat[:-1])\n if clat[-1:] == 'S':\n lat = -lat\n alt = int(loc_ident[-5:])\n\n stations[ist]['lat']=lat\n stations[ist]['lon']=lon\n stations[ist]['alt']=alt\n stations[ist]['locIdent']=os.path.split(loc_ident)[-1]\n \n # set the name and id\n stations[ist]['id'] = ist\n \n # set a flag if it is an ICOS station\n stn = ist[0:3].upper()\n if stn in icosStations:\n stations[ist]['icos'] = cpstation.get(stn).info()\n lat = stations[ist]['icos']['lat']\n lon = stations[ist]['icos']['lon']\n else:\n stations[ist]['icos'] = False \n lat = stations[ist]['lat']\n lon = stations[ist]['lon']\n \n stations[ist]['geoinfo'] = country.get(latlon=[lat,lon])\n \n return stations","def mock_get_all_stations(__):\n return all_station_codes","def stations():\n\t\n\n\tstationquery = session.query(Station.station).all()\n\n\tstationlist = list(np.ravel(stationquery))\n\t\n\treturn jsonify(stationlist)","def Find_nearest_dwd_stations(inpt_data,\r\n date_start='20051201',\r\n date_end='20201231',\r\n dwd_time_format='%Y%m%d%H',\r\n data_category='air_temperature',\r\n temp_resolution='hourly',\r\n no_of_nearest_stations=4,\r\n memory_save=True,\r\n Output='True'):\r\n if isinstance(data_category,list):\r\n if len(list(data_category)) > 1:\r\n print(\r\n 'Currently only one dwd category allowed, please run function multiple times for each category'\r\n )\r\n return None\r\n \r\n #convert time to datetime\r\n dt_start=datetime.strptime(date_start,'%Y%m%d')\r\n dt_end=datetime.strptime(date_end,'%Y%m%d')\r\n print('Start quering data from DWD')\r\n #define the database folder\r\n pypath = os.path.dirname(os.path.abspath(__file__))\r\n table_dir = pypath + '\\\\' + 'tables'\r\n dbase_dir = pypath + '\\\\' + 'dbase' \r\n #%% we check all available stations and create a valid list\r\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\r\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\r\n # delete all stations which do not cover the category\r\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\r\n #correct to datetime\r\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\r\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\r\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\r\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\r\n #make a geodataframe out of it\r\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\r\n \r\n #loop through all rows to get the n closest points\r\n distances=pd.DataFrame()\r\n for _, station in dwd_stations.iterrows():\r\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\r\n \r\n #%% get the n stations with smallest distance and update database\r\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\r\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\r\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\r\n \r\n #update the database\r\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\r\n \r\n #distance of nearest stattions\r\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\r\n #create new columns in the input data\r\n station_col_nm=list()\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_station_'+str(i))\r\n for i in range(0,no_of_nearest_stations):\r\n station_col_nm.append(data_category+'_distance_'+str(i))\r\n #create new dataframe\r\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\r\n distance_data.columns=station_col_nm\r\n #add to main dataset\r\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \r\n \r\n return inpt_data,db_dwd_stations","def getEmpty(self):\n emptyList = []\n for spot in self.parkingSpots:\n if spot.status == 'empty':\n emptyList.append(spot)\n return emptyList","def all_stations(self, provider: ID) -> List[StationInfo]:\n srv_key = self.__stations_key(provider=provider)\n value = self.get(name=srv_key)\n if value is None:\n return []\n js = utf8_decode(data=value)\n array = json_decode(string=js)\n return StationInfo.convert(array=array)","def getSyntheticShiftLists(nmrProject):\n \n #NBNB Filtering on isSimulated might be enough, but is it reliable?\n\n if not nmrProject:\n return []\n \n result = [x for x in nmrProject.sortedMeasurementLists()\n if x.className=='ShiftList'\n and x.findFirstExperiment() is None\n #and x.isSimulated # disabled pending check on how to set it\n ]\n \n return result","def statuslist(self):\n self.getfullstatus()\n response = self.geturl('js')\n if not response:\n return None\n data = response.json()\n states = data[\"sn\"]\n stations = list(zip(range(0, data[\"nstations\"]),\n self.lastfullresponse[\"stations\"][\"snames\"],\n ['ON' if x==1 else 'OFF' for x in states]))\n return stations","def reload(self):\n self.known_stations = {}\n self.read_noaa_stations()\n self.read_table_stations()\n self.last_reload_check_time = datetime.datetime.utcnow()\n LOGGER.info('Have %s known stations', len(self.known_stations.keys()))","def get_wrf_stations(pool):\n\n wrfv3_stations = {}\n\n connection = pool.connection()\n try:\n with connection.cursor() as cursor:\n sql_statement = \"SELECT `id`, `name` FROM `station` WHERE `id` like %s\"\n row_count = cursor.execute(sql_statement, \"11_____\")\n if row_count > 0:\n results = cursor.fetchall()\n for dict in results:\n wrfv3_stations[dict.get(\"name\")] = dict.get(\"id\")\n return wrfv3_stations\n else:\n return None\n except Exception as exception:\n error_message = \"Retrieving wrf stations failed\"\n logger.error(error_message)\n traceback.print_exc()\n raise exception\n finally:\n if connection is not None:\n connection.close()","def get_station(div=0, wd=0, abbrev=None, as_dataframe=False,\n input_file=None, output_file=None, suds_cache=None):\n # ensure div is a list of ints\n if type(div) is not list: div = [div]\n # ensure wd is a list\n if type(wd) is not list: wd = [wd]\n # ensure name is a homogeneous list of str if it exists\n if abbrev is not None and type(abbrev) is not list:\n abbrev = [abbrev]\n assert all(type(a) is str for a in abbrev)\n\n stations = []\n if input_file is None:\n # use the Co DWR SOAP service\n suds_client = _get_client(CODWR_WSDL_URL)\n\n # get the water division/districts\n dists = get_water_district(div, wd, as_dataframe=False, suds_cache=suds_cache)\n if dists is None:\n # no matching division/district(s)\n return None\n\n if abbrev is None:\n for d in dists:\n # get the stations for the division/district\n sites = suds_client.service.GetSMSTransmittingStations(d['div'], d['wd'])\n if sites is None:\n return None\n\n # get the parameters for the stations\n sparms = suds_client.service.GetSMSTransmittingStationVariables(d['div'], d['wd'])\n if sparms is None:\n # hmmm - we have stations but no parameters...\n raise ValueError(\"Service returned no parameters for transmitting station(s).\")\n\n # the SOAP service returns each parameter for a station as a\n # separate row <abbrev, parameter> which we will compact into\n # a dict {abbrev,[parameter,parameter,...]}\n params = {}\n for sp in sparms.StationVariables:\n spd = dict(sp)\n if spd['abbrev'] not in params:\n params[spd['abbrev']] = []\n params[spd['abbrev']].append(spd['variable'])\n\n # build up the complete station description (including the water\n # district name and parameters) and add it to the station list\n # to the station list\n for site in sites.Station:\n sited = dict(site)\n sited['waterDistrictName'] = d['waterDistrictName']\n sited['parameters'] = params[sited['abbrev']]\n stations.append(sited)\n\n else:\n for a in abbrev:\n site = suds_client.service.GetSMSTransmittingStations(0, 0, a)\n if site is not None:\n sited = dict(site)\n\n for d in dists:\n if d['div'] == sited['div'] and d['wd'] == sited['wd']:\n sited['waterDistrictName'] = d['waterDistrictName']\n break\n\n # retrieve the station parameters and attach them to the\n # station information\n sparms = suds_client.service.GetSMSTransmittingStationVariables(sited['div'],\n sited['wd'],\n sited['abbrev'])\n if sparms is None:\n # hmmm - we have stations but no parameters...\n raise ValueError(\"Service returned no parameters for station \"\n + sited['abbrev'])\n for sp in sparms.StationVariables:\n spd = dict(sp)\n if sited['parameters'] is None:\n sited['parameters'] = []\n assert(spd['abbrev'] == sited['abbrev'])\n sited['parameters'].append(spd['variable'])\n\n else:\n # retrieve the list of sites in the specified file\n print(\"Nothing yet\")\n\n if as_dataframe is True:\n stations = pd.DataFrame(stations)\n\n return stations if len(stations) > 0 else None","def stations():\n session = Session(engine)\n # Query all Stations\n stations = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n all_stations = list(np.ravel(stations))\n\n return jsonify(all_stations)","def add_stations(stations, pool):\n\n for station in stations:\n\n print(add_station(pool=pool, name=station.get('name'), latitude=station.get('latitude'),\n longitude=station.get('longitude'), station_type=station.get('station_type'),\n description=station.get('description')))\n print(station.get('name'))","def get_all_stations():\n latest_scraping_time = db.session \\\n .query(func.max(DublinBike.scraping_time)) \\\n .one()[0]\n\n stations = db.session.query(DublinBike) \\\n .filter(DublinBike.scraping_time == latest_scraping_time) \\\n .order_by(DublinBike.number.asc()) \\\n .all()\n\n return jsonify({\n 'data': [station.serialize for station in stations]\n })","def get_station_model_predictions(\n session: Session,\n station_codes: List,\n model: str,\n start_date: datetime.datetime,\n end_date: datetime.datetime) -> List[\n Union[WeatherStationModelPrediction, PredictionModelRunTimestamp, PredictionModel]]:\n query = session.query(WeatherStationModelPrediction, PredictionModelRunTimestamp, PredictionModel).\\\n filter(WeatherStationModelPrediction.station_code.in_(station_codes)).\\\n filter(WeatherStationModelPrediction.prediction_timestamp >= start_date).\\\n filter(WeatherStationModelPrediction.prediction_timestamp <= end_date).\\\n filter(PredictionModelRunTimestamp.id ==\n WeatherStationModelPrediction.prediction_model_run_timestamp_id).\\\n filter(PredictionModelRunTimestamp.prediction_model_id == PredictionModel.id,\n PredictionModel.abbreviation == model).\\\n order_by(WeatherStationModelPrediction.station_code).\\\n order_by(WeatherStationModelPrediction.prediction_timestamp).\\\n order_by(PredictionModelRunTimestamp.prediction_run_timestamp.asc())\n return query","def stations():\n\n # Query all Stations\n station_results = session.query(Station.station).all()\n\n # Convert list of tuples into normal list\n all_station_names = list(np.ravel(station_results))\n\n return jsonify(all_station_names)","def stations(): \n # creating the Docstring\n session = Session(engine)\n\n # creat the Query stations\n\n stations_qu = session.query(measurement.station).group_by(measurement.station).all()\n\n # Converting the list of tuples into a normal list\n stations_qu_dict = list(np.ravel(stations_qu))\n session.close()\n\n return jsonify(stations_qu_dict)","def stations():\n\n station_results = session.query(Stations.station, Stations.name).all()\n\n station_data = []\n for row in station_results:\n station_dict = {}\n station_dict[\"station\"] = row.station\n station_dict[\"name\"] = row.name\n station_data.append(station_dict)\n\n return jsonify(station_data)","def processStationInfo(obs_loc_df, source, st_list=None):\n if not st_list:\n st_list = dict()\n st_data = obs_loc_df['station_id']\n lat_data = obs_loc_df['latitude (degree)']\n lon_data = obs_loc_df['longitude (degree)']\n\n for k, station_name in enumerate(st_data):\n if station_name in st_list:\n pass\n else:\n st_list[station_name] = dict()\n st_list[station_name][\"lat\"] = lat_data[k]\n st_list[station_name][\"source\"] = source\n st_list[station_name][\"lon\"] = lon_data[k]\n print(station_name)\n\n print(\"Number of stations in bbox {}\".format(len(st_list.keys())))\n return st_list","def get_flo2d_output_stations(pool, flo2d_model):\n\n flo2d_output_stations = {}\n\n id_pattern = '{}_____'.format((str(flo2d_model.value)).split('0')[0])\n\n connection = pool.connection()\n try:\n with connection.cursor() as cursor:\n sql_statement = \"SELECT * FROM `station` WHERE `id` like %s\"\n row_count = cursor.execute(sql_statement, id_pattern)\n if row_count > 0:\n results = cursor.fetchall()\n for dict in results:\n station_name = dict.get(\"name\")\n flo2d_output_stations[station_name.split(\"_\")[0]] = [dict.get(\"id\"), dict.get(\"latitude\"),\n dict.get(\"longitude\"),\n '_'.join(station_name.split('_')[1:])]\n return flo2d_output_stations\n else:\n return None\n except Exception as exception:\n error_message = \"Retrieving flo2d output stations failed\"\n logger.error(error_message)\n traceback.print_exc()\n raise exception\n finally:\n if connection is not None:\n connection.close()","def run():\n\n # Build list of tuples of station names and distance \n stations = build_station_list()\n p = (52.2053, 0.1218)\n by_distance = stations_by_distance(stations, p)\n for n in range(10):\n print(by_distance[n])\n for n in range(10):\n i = len(by_distance) - 10 + n\n print(by_distance[i])","def _automatic_training_set(self, n_cutouts=100):\n dstl = Load_DSTL()\n np.random.seed(42)\n for ii in range(n_cutouts):\n # Get region around a shape\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\n if self.radius is not None:\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\n # Add to Feature Matrix\n if ii == 0:\n features = triples\n labels = mask\n else:\n features = np.vstack([features, triples])\n labels = np.hstack([labels, mask])\n return features, labels","def create_training_set_blending():\n stime = time.time()\n indexes = []\n y_train = np.zeros((nb_places * nb_pis,))\n x_train = np.zeros((nb_places * nb_pis, nb_clfs))\n for i, place_id in enumerate(place_ids):\n # 1. Get the relevance ratings for the place (y).\n ratings = get_relevance_ratings(place_id, connection=c_study, cursor=cur_study)\n if len(ratings) == 0: # filter the ratings.\n continue\n r = [np.mean(ratings[pi_id]['ratings']) if pi_id in ratings else 0 for pi_id in pis_ids]\n for k, pi_id in enumerate(pis_ids):\n cl = 0\n if r[k] >= 4:\n cl = 1\n y_train[i * nb_pis + k] = cl # int(np.ceil(r[k]))\n indexes.append((place_id, pi_id))\n\n # 2. Get the predictions from the models for the place (x).\n for j, clf in enumerate(clfs):\n predictions = clf._get_prediction(place_id)\n p = [predictions[pi_id]['score'] if pi_id in predictions else 0 for pi_id in pis_ids]\n for k in range(nb_pis):\n x_train[i * nb_pis + k, j] = p[k]\n print(\"[.] Done with x_train: %s, y_train: %s, indexes: %s (%.2f)\" % (x_train.shape, y_train.shape, len(indexes), time.time()-stime))\n return x_train, y_train, indexes","def get_station_names(self):\n station_names = []\n for wrapper in self.soup.find_all(\"div\", {\"class\": \"stop-wrapper\"}):\n station_name = ' '.join(wrapper.find(\"h3\").text.split(' ')[:-1])\n station_names.append(station_name)\n return np.array(station_names).T","def get_top_station_set(city):\n s = {}\n for file in os.listdir(exp_data_path + os.sep + 'station' + os.sep + city):\n with open(exp_data_path + os.sep + 'station' + os.sep + city + os.sep + file) as f:\n reader = csv.reader(f)\n for row in reader:\n if row[0] not in s:\n s[row[0]] = 1\n else:\n s[row[0]] = s[row[0]] + 1\n\n sort_s = dict(sorted(s.items(), key=lambda x : x[1], reverse=True))\n first = True\n res = []\n for k, v in sort_s.items():\n if first:\n top = v\n first = False\n if top - v <= 30:\n res.append(k)\n print('before', len(sort_s))\n print('after', len(res))\n\n # restore new map [old_index, new_index]\n list_remap = {}\n new_index = 0\n for index in range(0, data_length[city]):\n if str(index) in res:\n list_remap[index] = new_index\n new_index = new_index + 1\n\n # print(list_remap)\n check_path(exp_data_path + os.sep + 'station_list')\n file_name = exp_data_path + os.sep + 'station_list' + os.sep + 'list_remap_{}'.format(city) + '.npy'\n if os.path.exists(file_name):\n os.remove(file_name)\n np.save(file_name, list_remap)","def test_nearest_filter(self):\n for airport, reports, count in (\n (True, True, 6),\n (True, False, 16),\n (False, True, 6),\n (False, False, 30),\n ):\n stations = station.nearest(30, -80, 30, airport, reports, 1.5)\n self.assertEqual(len(stations), count)","def stations():\n \n # Query all the stations\n results = session.query(Station).all()\n\n # Create a dictionary to append the station data\n stations_info = []\n for stations in results:\n stations_dict = {}\n stations_dict[\"Station\"] = stations.station\n stations_dict[\"Station Name\"] = stations.name\n stations_dict[\"Latitude\"] = stations.latitude\n stations_dict[\"Longitude\"] = stations.longitude\n stations_dict[\"Elevation\"] = stations.elevation\n all_stations.append(stations_dict)\n \n return jsonify(stations_info)","def stations():\n \n station_result = session.query(Station.station).all()\n stations = []\n # Convert list of tuples into normal list\n stations = list(np.ravel(station_result))\n return jsonify(stations)","def stations():\n results = session.query(Station.station).all()\n stations = list(np.revel(results))\n return jsonify(stations)","def stations():\n # Return a JSON list of stations from the dataset\n session = Session(engine)\n stations = session.query(Station.name).all()\n\n # Convert list of tuples into normal list\n station_names = list(np.ravel(stations))\n\n return jsonify(station_names)","def inlezen_beginstation(stations):\n beginstation = input(\"Wat is je beginstation? : \")\n\n while beginstation not in stations:\n print(\"Geen correcte invoer.. Probeer opnieuw\")\n\n return beginstation","def rm_duplicate(Sta_all, address):\n \n sta_all = []\n saved = []\n \n for i in Sta_all:\n if i[2] == '--' or i[2] == ' ':\n i[2] = ''\n for j in range(0, len(i)):\n if i[j] != str(i[j]):\n i[j] = str(i[j]) \n if len(i) == 7:\n sta_all.append(str(i[0] + '_' + i[1] + '_' + i[2] + '_' + \\\n i[3] + '_' + i[4] + '_' + i[5] + '_' + i[6]))\n elif len(i) == 8:\n sta_all.append(str(i[0] + '_' + i[1] + '_' + i[2] + '_' + \\\n i[3] + '_' + i[4] + '_' + i[5] + '_' + i[6]\\\n + '_' + i[7]))\n \n sta_ev = read_station_event(address)\n ls_saved_stas = sta_ev[0]\n \n for i in range(0, len(ls_saved_stas)):\n sta_info = ls_saved_stas[i]\n saved.append(sta_info[0] + '_' + sta_info[1] + '_' + \\\n sta_info[2] + '_' + sta_info[3])\n \n Stas_req = sta_all\n \n len_all_sta = len(sta_all)\n num = []\n for i in range(0, len(saved)):\n for j in range(0, len(Stas_req)):\n if saved[i] in Stas_req[j]:\n num.append(j)\n\n num.sort(reverse=True)\n for i in num:\n del Stas_req[i] \n \n for m in range(0, len(Stas_req)):\n Stas_req[m] = Stas_req[m].split('_')\n \n Stas_req.sort()\n \n print '------------------------------------------'\n print 'Info:'\n print 'Number of all saved stations: ' + str(len(saved))\n print 'Number of all available stations: ' + str(len_all_sta)\n print 'Number of stations to update for: ' + str(len(Stas_req))\n print '------------------------------------------'\n \n return Stas_req","def stations():\r\n # Query all passengers\r\n results = session.query(Station.station, \r\n Station.name, \r\n Station.latitude,\r\n Station.longitude,\r\n Station.elevation).all()\r\n\r\n return jsonify(results)","def __len__(self):\n return len(self.stations)","def stations():\n # Create link from Python to db\n session = Session(engine)\n\n # Query stations.\n stations = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\n\n session.close()\n\n # Convert to a dictionary.\n all_stations = []\n for station, name, latitude, longitude, elevation in stations:\n station_dict = {}\n station_dict[\"station\"] = station\n station_dict[\"name\"] = name\n station_dict[\"latitude\"] = latitude\n station_dict[\"longitude\"] = longitude\n station_dict[\"elevation\"] = elevation\n all_stations.append(station_dict)\n\n # Return JSON\n return jsonify(all_stations)","def add_station(self, station):\n self.__stations.append(station)","def _load_stations(self, nodes: List[OSMNode]) -> None:\n # Process OSM nodes into intermediate stations\n grouped_stations: defaultdict[str, list[IntermediateStation]] = defaultdict(list)\n\n # Iterate thru nodes while popping them from the provided list\n # to allow used nodes to bne garbage collected.\n while nodes:\n node = nodes.pop()\n name_id = node.tags[\"name\"]\n grouped_stations[name_id].append(IntermediateStation(\n node.id,\n name_id,\n node.lat,\n node.lon,\n [k for (k, v) in node.tags.items() if \".\" in k and v == \"yes\"],\n node.tags.get(\"merged\") == \"all\",\n ))\n\n # Convert the intermediate representations to GeoStation\n # (again popping from grouped_stations to allow intermediate representation to be gc-ed)\n while grouped_stations:\n name_id, stations = grouped_stations.popitem()\n merged_all_node = get_merged_all_node(stations)\n\n if len(stations) == 1 and len(stations[0].routes) == 1:\n # Case 1 - one station and one line.\n sta = stations[0]\n sta_id = sta.routes[0] + \".\" + name_id\n self.by_id[sta_id] = GeoStation(sta_id, sta.lat, sta.lon)\n\n elif len(stations) == 1:\n # Case 2 - one station and multiple lines.\n # Simple parent-children structure, all in one location.\n sta = stations[0]\n parent = GeoStation(\"Merged.\" + name_id, sta.lat, sta.lon)\n self.by_id[parent.id] = parent\n\n for route in sta.routes:\n child = GeoStation(route + \".\" + name_id, sta.lat, sta.lon, parent=parent)\n self.by_id[child.id] = child\n parent.children.append(child)\n\n elif merged_all_node:\n # Case 3: many nodes, but all under one parent\n parent = GeoStation(\"Merged.\" + name_id, merged_all_node.lat, merged_all_node.lon)\n self.by_id[parent.id] = parent\n\n for ista in stations:\n for route in ista.routes:\n child = GeoStation(route + \".\" + name_id, ista.lat, ista.lon,\n parent=parent)\n self.by_id[child.id] = child\n parent.children.append(child)\n\n else:\n # Case 4: many nodes, no parent-of-all\n needs_merged_no = count_multiple_routes(stations) > 1\n merged_no = 1\n\n for sta in stations:\n if len(sta.routes) == 1:\n # Case 4.1 - single line - behavior as in case 1\n sta_id = sta.routes[0] + \".\" + name_id\n self.by_id[sta_id] = GeoStation(sta_id, sta.lat, sta.lon)\n\n else:\n # Case 4.2 - multiple lines - behavior as in case 2\n parent_prefix = \"Merged.\"\n if needs_merged_no:\n parent_prefix = f\"Merged.{merged_no}.\"\n merged_no += 1\n\n parent = GeoStation(parent_prefix + name_id, sta.lat, sta.lon)\n self.by_id[parent.id] = parent\n\n for route in sta.routes:\n child = GeoStation(route + \".\" + name_id, sta.lat, sta.lon,\n parent=parent)\n self.by_id[child.id] = child\n parent.children.append(child)","def station_from_lat_lon(lat, lon, stations, n_nearest=3):\n lat, lon = float(lat), float(lon)\n distances = [(distance(lat, lon, st['lat'], st['lon']), st)\n for st in stations\n if (st['is_renting'] and st['is_installed'])]\n distances = sorted(distances)\n return [pair[1] for pair in distances[:n_nearest]]","def stations():\n \n session = Session(engine)\n # Query to bring all stations\n results = pd.DataFrame(session.query(S.id.label('ID'),S.station.label('Station'),S.name.label('Name'),\\\n S.latitude.label('Latitude'),S.longitude.label('Longitude'), \\\n S.elevation.label('Elevation')).all())\n \n session.close()\n \n # Create a dictionary from the row data of the dataframe and return it as a JSON\n return jsonify(results.to_dict(orient = 'records'))","def _parse_departures(self, data, stop, servernow):\n servernow.replace(second=0, microsecond=0)\n results = []\n departures = data.findall('./itdDeparture')\n for departure in departures:\n # Get Line Information\n origin, destination, line, ridenum, ridedir, canceled = self._parse_mot(departure.find('./itdServingLine'))\n\n if departure.find('./genAttrList/genAttrElem[value=\"HIGHSPEEDTRAIN\"]') is not None:\n line.linetype = LineType('train.longdistance.highspeed')\n elif departure.find('./genAttrList/genAttrElem[value=\"LONG_DISTANCE_TRAINS\"]') is not None:\n line.linetype = LineType('train.longdistance')\n\n # if ridenum is None:\n # ridedata = departure.find('./itdServingTrip')\n # if ridedata is not None:\n # ridenum = ridedata.attrib.get('tripCode', None)\n # if ridenum is not None:\n # ridenum = ridenum.strip()\n\n # Build Ride Objekt with known stops\n ride = Ride(line, ridenum)\n ride.direction = ridedir\n ride.canceled = canceled\n\n train_line = line.linetype in self.train_station_lines\n\n # todo: take delay and add it to next stops\n mypoint = self._parse_trip_point(departure, train_line=train_line)\n\n before_delay = None\n if mypoint.arrival:\n before_delay = mypoint.arrival.delay\n after_delay = None\n if mypoint.departure:\n after_delay = mypoint.departure.delay\n\n delay = None\n if departure.find('./itdServingLine/itdNoTrain'):\n delay = departure.find('./itdServingLine/itdNoTrain').attrib.get('delay', None)\n if delay is not None:\n delay = timedelta(minutes=delay)\n\n if delay is not None:\n if (mypoint.arrival and servernow < mypoint.arrival.livetime) or (mypoint.departure and servernow < mypoint.departure.livetime):\n before_delay = delay\n else:\n after_delay = delay\n\n prevs = False\n for pointdata in departure.findall('./itdPrevStopSeq/itdPoint'):\n point = self._parse_trip_point(pointdata, train_line=train_line)\n if point is not None:\n if before_delay is not None:\n if point.arrival is not None and point.arrival.delay is None and point.arrival.time + before_delay >= servernow:\n point.arrival.delay = before_delay\n if point.departure is not None and point.departure.delay is None and point.departure.time + before_delay >= servernow:\n point.departure.delay = before_delay\n prevs = True\n ride.append(point)\n\n pointer = ride.append(mypoint)\n\n onwards = False\n for pointdata in departure.findall('./itdOnwardStopSeq/itdPoint'):\n point = self._parse_trip_point(pointdata, train_line=train_line)\n if point is not None:\n if after_delay is not None:\n if point.arrival is not None and point.arrival.delay is None and point.arrival.time + after_delay >= servernow:\n point.arrival.delay = after_delay\n if point.departure is not None and point.departure.delay is None and point.departure.time + after_delay >= servernow:\n point.departure.delay = after_delay\n onwards = True\n ride.append(point)\n\n if not prevs and not onwards:\n ride.prepend(None)\n if origin is not None:\n ride.prepend(TimeAndPlace(Platform(origin)))\n\n ride.append(None)\n if destination is not None:\n ride.append(TimeAndPlace(Platform(destination)))\n\n # Return RideSegment from the Station we depart from on\n results.append(ride[pointer:])\n return Ride.Results(results)","def __getpredictors_distance(self, staname, distance):\n\n distfromsta = distance[staname]\n del distfromsta[staname] # remove the station to be fill from the dataframe\n distfromsta = distfromsta.sort_values()\n\n stations = self.network.getsta(distfromsta.index.values)\n # station = self.network.getsta(staname)\n\n # Only 3 closest stations\n # sel1 = [ (i,e) for i,e in zip(stations[0:2], stations[1:3])] # selction predictors with spacing 1\n # sel2 = [ (i,e) for i,e in zip(stations[0:2], stations[2:4])] # selction predictors with spacing 2\n\n # Use all stations\n sel1 = [(i, e) for i, e in zip(stations[0:-1], stations[1:])] # selction predictors with spacing 1\n sel2 = [(i, e) for i, e in zip(stations[0:-2], stations[2:])] # selction predictors with spacing 2\n\n # sel3 = [ (i,e) for i,e in zip(stations[0:-3], stations[3:])] # selction predictors with spacing 3\n # sel4 = [ (i,e) for i,e in zip(stations[0:-4], stations[4:])] # selction predictors with spacing 4\n\n # Only 3 closest stations\n # sel1names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:2], stations[1:3])] # selction predictors with spacing 1\n # sel2names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:2], stations[2:4])] # selction predictors with spacing 1\n\n # using all stations\n sel1names = [(i.getpara('stanames'), e.getpara('stanames')) for i, e in\n zip(stations[0:-1], stations[1:])] # selction predictors with spacing 1\n sel2names = [(i.getpara('stanames'), e.getpara('stanames')) for i, e in\n zip(stations[0:-2], stations[2:])] # selction predictors with spacing 1\n\n # sel3names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:-3], stations[3:])] # selction predictors with spacing 1\n # sel4names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:-4], stations[4:])] # selction predictors with spacing 1\n\n selection = [x for x in itertools.chain.from_iterable(itertools.izip_longest(sel1, sel2)) if x]\n selectionnames = [x for x in itertools.chain.from_iterable(itertools.izip_longest(sel1names, sel2names)) if x]\n\n return selection, selectionnames","def sort_bike_stations(bike_stations, location):\n\n stations = bike_stations.copy()\n\n for index, station in enumerate(stations):\n station_location = (station[\"lat\"], station[\"lon\"])\n dist = distance.distance(station_location, location).m\n stations[index][\"distance\"] = dist\n\n stations = sorted(stations, key=lambda station: station[\"distance\"])\n stations = list(filter(lambda station: station[\"bikesAvailable\"] > 0, stations))\n\n return stations","def stations():\n # Query all stations\n\n stations = session.query(Station.station).all()\n all_stations = list(np.ravel(stations))\n\n return jsonify(all_stations)","def stations():\n \n # Create our session (link) from Python to the DB\n session = Session(engine)\n\n results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\n\n session.close()\n\n stations = []\n for result in results:\n station_dict = {}\n station_dict[\"station\"] = result.station\n station_dict[\"name\"] = result.name\n station_dict[\"latitude\"] = result.latitude\n station_dict[\"longitude\"] = result.longitude\n station_dict[\"elevation\"] = result.elevation\n stations.append(station_dict)\n \n return jsonify(stations)"],"string":"[\n \"def stations():\\n\\n return station_list\",\n \"def prep_stations(url):\\n stations = []\\n _stations = requests.get(url).json()\\n\\n for _station in _stations['stationBeanList']:\\n if _station['statusKey'] == 1:\\n stations.append([_station['stationName'], _station['id'],\\n _station['availableDocks'], _station['totalDocks'],\\n _station['latitude'], _station['longitude']])\\n\\n return stations\",\n \"def _get_ogd_stations():\\n return {r[\\\"Station\\\"] for r in ZamgData.current_observations()}\",\n \"def get_processed_stations(out_dir):\\n lista = [ f.split('_')[0] for f in os.listdir(out_dir) if '.nc' in f ]\\n #print('Skipping these stations: ' , lista )\\n return lista\",\n \"def train_stations(self) -> List[str]:\\n return sorted([train_info['HE'] for train_info in train_api.stations_info.values()])\",\n \"def stations(station_let):\\n\\tstat = ['']*np.size(station_let,0)\\n\\tfor i in range(len(stat)):\\n\\t\\tfor j in range(4):\\n\\t\\t\\tif station_let[i][j] is not np.ma.masked:\\n\\t\\t\\t\\tstat[i]+=station_let[i][j]\\n\\treturn stat\",\n \"def _build_stations(self, stop_list):\\n # stations = [] TODO: What is this for\\n dists = self._euclidian_distances(stop_list)\\n stations = self._calculate_y_lines(dists)\\n return stations\",\n \"async def get_train_stations(self, latitude: float, longitude: float,\\n valid_stations=None) -> list:\\n params = {\\n 'location': '{},{}'.format(latitude, longitude),\\n 'key': self.api_key,\\n 'type': \\\"train_station\\\",\\n \\\"radius\\\": 1600\\n }\\n\\n logging.info(\\\"Getting train stations near (%f, %f)\\\", latitude, longitude)\\n\\n async with aiohttp.ClientSession() as session:\\n async with session.post('https://maps.googleapis.com/maps/api/place/nearbysearch/json',\\n params=params) as response:\\n if response.status == HTTPStatus.OK:\\n payload = await response.json()\\n if payload['status'] == 'OK':\\n if valid_stations:\\n return [result[\\\"name\\\"] for result in payload[\\\"results\\\"]\\n if result[\\\"name\\\"] in valid_stations]\\n\\n return [result[\\\"name\\\"] for result in payload[\\\"results\\\"]]\\n\\n return []\",\n \"def all(self, skip_cache=False):\\n now = _time_ms(datetime.datetime.utcnow())\\n if skip_cache or now - self._last_updated > CACHE_LIMIT:\\n self._process_stations()\\n return self._stations_lst\",\n \"def collect_stations(self):\\n # First, iterate provinces and build url's\\n site = urllib.request.urlopen(self.base_url)\\n\\n # Check that the site is still valid or operating by collecting a list of provinces\\n print(\\\"Collecting provinces\\\")\\n provinces = [s[9:11] for s in re.findall('<a href=\\\"../\\\">../</a>', site.read())]\\n\\n # Iterate provinces and collect list of available times\\n print(\\\"Collecting time periods and station ID's\\\")\\n self.stations = defaultdict(dict)\\n for prov in provinces:\\n site = urllib.request.urlopen(self.build_url(prov))\\n expression = '<a href=\\\"[hd][a-zA-Z]*/\\\">[hd][a-zA-Z]*/</a>'\\n times = [s.split('>')[1].split('<')[0].replace('/', '') for s in re.findall(expression, site.read())]\\n\\n # Iterate times and collect the station ID's\\n for time in times:\\n site = urllib.request.urlopen(self.build_url(prov, time))\\n expression = '<a href=\\\"{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv\\\">{0}_[a-zA-Z0-9]*_{1}_hydrometric.csv</a>'\\n expression = expression.format(prov.upper(), time.lower())\\n stations = [s.split('_')[1] for s in re.findall(expression, site.read())]\\n self.stations[prov][time] = stations\",\n \"def station_list() -> List[Dict]:\\n return STATIONS\",\n \"def test_get_closest_stations(self):\\n\\t\\tpoint = \\\"POINT(40.71911552 -74.00666661)\\\"\\n\\t\\tstations = set(server.get_closest_stations(point))\\n\\t\\t# find the closest stations, make them a set of objects see if sets intersect completely\",\n \"def create_list() -> List[Optional[float]]:\\n return [None] * num_stations\",\n \"def getStations(self) :\\n return self._stations\",\n \"def get_all_stations(session: Session) -> List[Row]:\\n return session.query(PlanningWeatherStation.station_code).all()\",\n \"def stations(self):\\n for stat in sorted(self.station_records):\\n yield self.station_records[stat]\",\n \"def read_noaa_stations(self):\\n # wget -c http://weather.noaa.gov/data/nsd_bbsss.txt\\n #72;656;KSFD;Winner, Bob Wiley Field Airport;SD;United States;4;43-23-26N;099-50-33W;;;619;;\\n #93;246;NZRO;Rotorua Aerodrome;;New Zealand;5;38-07S;176-19E;38-07S;176-19E;285;294;\\n #block;synop;icao;name;?;country;??;lat;lon;lat2;lon2;height;?;\\n #0 1 2 3 4 5 6 7 8 9 10 11 12\\n if not os.path.exists(self.noaa_filename):\\n LOGGER.warning('could not find noaa file \\\"%s\\\"', self.noaa_filename)\\n return self.known_stations\\n count = 0\\n with open(self.noaa_filename, 'r') as csvfile:\\n stationreader = csv.reader(csvfile, delimiter=';')\\n for row in stationreader:\\n station_id = '{}{}'.format(row[0], row[1])\\n station_id_icao = row[2].strip().upper()\\n data = noaa_station_data_from_row(row)\\n if data is not None:\\n count += 1\\n self.known_stations[station_id] = data\\n if len(station_id_icao) == 4 and station_id_icao.isalpha():\\n self.known_stations[station_id_icao] = data\\n self.noaa_file_age = os.path.getmtime(self.noaa_filename)\\n LOGGER.info(' Loaded %i noaa station records from \\\"%s\\\"', count, self.noaa_filename)\\n return self.known_stations\",\n \"def _add_data(self, model_stations: Iterable[model.Station],\\n validate_prefix: str = \\\"\\\") -> int:\\n valid_station_count = 0\\n jreast_merged_codes: dict[model.StationID, str] = load_csv_as_mapping(\\n DIR_CURATED / \\\"jreast_merged_codes.csv\\\",\\n itemgetter(\\\"sta_id\\\"),\\n itemgetter(\\\"code\\\")\\n )\\n\\n # Add data from model stations\\n for model_sta in model_stations:\\n is_invalid = False\\n should_validate = model_sta.id.startswith(validate_prefix)\\n\\n # Find a matching geo_sta\\n geo_sta = self.by_id.get(model_sta.id)\\n if not geo_sta:\\n if should_validate:\\n self.logger.critical(f\\\"{Color.RED}geo.osm is missing station \\\"\\n f\\\"{Color.MAGENTA}{model_sta.id}{Color.RESET}\\\")\\n self.valid = False\\n continue\\n\\n # Find a name\\n name_id = last_part(geo_sta.id)\\n geo_sta.name = self.names.get(name_id)\\n if geo_sta.name is None and should_validate:\\n self.logger.critical(f\\\"{Color.RED}sta_names.csv is missing name for \\\"\\n f\\\"{Color.MAGENTA}{name_id}{Color.RESET}\\\")\\n is_invalid = True\\n\\n # Copy stop_code\\n geo_sta.code = model_sta.code\\n\\n # Check if station was valid\\n if is_invalid:\\n self.valid = False\\n elif should_validate:\\n valid_station_count += 1\\n\\n # Generate codes and names for mother stations\\n for sta in self.by_id.values():\\n if not sta.children:\\n continue\\n\\n name_id = last_part(sta.id)\\n sta.name = self.names.get(name_id)\\n if not sta.name:\\n self.logger.critical(f\\\"{Color.RED}sta_names.csv is missing name for \\\"\\n f\\\"{Color.MAGENTA}{name_id}{Color.RESET}\\\")\\n is_invalid = True\\n\\n # Get children codes\\n children_codes = []\\n jreast_merged_code = jreast_merged_codes.get(sta.id)\\n if jreast_merged_code:\\n children_codes.append(jreast_merged_code)\\n\\n for child in sta.children:\\n # Ignore JR-East child codes if there's a JR-East merged code\\n if child.id.startswith(\\\"JR-East\\\") and jreast_merged_code:\\n continue\\n elif child.code:\\n children_codes.append(child.code)\\n\\n sta.code = \\\"/\\\".join(children_codes)\\n\\n return valid_station_count\",\n \"def stations(self):\\n stations = []\\n f = self._fetch(Citibike.STATION_URL)\\n data = json.load(f)\\n if 'stationBeanList' not in data or len(data['stationBeanList']) == 0:\\n raise BadResponse('Station Fetch Failed', data)\\n for station in data['stationBeanList']:\\n stations.append(Station._from_json(station))\\n logging.debug(\\\"Retrieved %d stations\\\" % len(stations))\\n return stations\",\n \"async def get_stations() -> List[WeatherStation]:\\n # Check if we're really using the api, or loading from pre-generated files.\\n use_wfwx = config.get('USE_WFWX') == 'True'\\n if use_wfwx:\\n return await _get_stations_remote()\\n return _get_stations_local()\",\n \"def get_all_stations(engine): \\n # Query db\\n sql = (\\\"SELECT DISTINCT a.station_id, \\\"\\n \\\" a.station_code, \\\"\\n \\\" a.station_name, \\\"\\n \\\" c.station_type, \\\"\\n \\\" d.latitude, \\\"\\n \\\" d.longitude \\\"\\n \\\"FROM nivadatabase.projects_stations a, \\\"\\n \\\" nivadatabase.stations b, \\\"\\n \\\" nivadatabase.station_types c, \\\"\\n \\\" niva_geometry.sample_points d \\\"\\n \\\"WHERE a.station_id = b.station_id \\\"\\n \\\"AND b.station_type_id = c.station_type_id \\\"\\n \\\"AND b.geom_ref_id = d.sample_point_id \\\"\\n \\\"ORDER BY a.station_id\\\")\\n df = pd.read_sql(sql, engine)\\n\\n return df\",\n \"def get_stations(self):\\n return self.__request('stations')['stations']\",\n \"def get_stations(base_url, hts, mtype):\\n stns1 = ws.site_list(base_url, hts, location='LatLong') # There's a problem with Hilltop that requires running the site list without a measurement first...\\n stns1 = ws.site_list(base_url, hts, location='LatLong', measurement=mtype)\\n stns2 = stns1[(stns1.lat > -47.5) & (stns1.lat < -34) & (stns1.lon > 166) & (stns1.lon < 179)].dropna().copy()\\n stns2.rename(columns={'SiteName': 'ref'}, inplace=True)\\n\\n return stns2\",\n \"def create_station_list(self):\\n sorted_station_list = sorted(self.station_dict, key=self.station_dict.get)\\n\\n return sorted_station_list\",\n \"def stations(self):\\n try:\\n stations_api = requests.get(self._stations_url)\\n stations = {}\\n for station in stations_api.json():\\n station_id = station['id']\\n station_name = station['name']\\n stations[station_id] = station_name\\n\\n return stations\\n except (RequestException, KeyError) as exc:\\n LOG.error('could not read from api: %s', exc)\\n raise SlfError('could not read from api: %s' % exc) from None\",\n \"def list_stations(intent, session):\\n stations = location.get_stations(config.bikes_api)\\n street_name = intent['slots']['street_name']['value']\\n possible = location.matching_station_list(stations,\\n street_name,\\n exact=True)\\n street_name = street_name.capitalize()\\n\\n if len(possible) == 0:\\n return reply.build(\\\"I didn't find any stations on %s.\\\" % street_name,\\n is_end=True)\\n elif len(possible) == 1:\\n sta_name = location.text_to_speech(possible[0]['name'])\\n return reply.build(\\\"There's only one: the %s \\\"\\n \\\"station.\\\" % sta_name,\\n card_title=(\\\"%s Stations on %s\\\" %\\n (config.network_name, street_name)),\\n card_text=(\\\"One station on %s: %s\\\" %\\n (street_name, possible[0]['name'])),\\n is_end=True)\\n else:\\n last_name = location.text_to_speech(possible[-1]['name'])\\n speech = \\\"There are %d stations on %s: \\\" % (len(possible),\\n street_name)\\n speech += (', '.join([location.text_to_speech(p['name'])\\n for p in possible[:-1]]) +\\n ', and %s' % last_name)\\n card_text = (\\\"The following %d stations are on %s:\\\\n%s\\\" %\\n (len(possible), street_name,\\n '\\\\n'.join(p['name'] for p in possible)))\\n return reply.build(speech,\\n card_title=(\\\"%s Stations on %s\\\" %\\n (config.network_name, street_name)),\\n card_text=card_text,\\n is_end=True)\",\n \"def get_tasks_that_fit_station(self, station: Station) -> TaskList:\\n return TaskList([task for task in self._tasks if station.can_fit(task)])\",\n \"def stations():\\n # Query all stations before a given date 2017\\n results = session.query(Measurement.date, Measurement.tobs).filter(func.strftime(\\\"%Y\\\", Measurement.date) >= \\\"2017\\\").all()\\n all_results = list(np.ravel(results))\\n \\n return jsonify(all_results)\",\n \"def read_table_stations(self):\\n if not os.path.exists(self.station_table_filename):\\n LOGGER.warning('could not find station.table file \\\"%s\\\"', self.station_table_filename)\\n return self.known_stations\\n count = 0\\n with open(self.station_table_filename, 'r') as textfile:\\n lines = textfile.read().split(LF)\\n for line in lines:\\n station_id, data = read_table_station_from_line(line)\\n if station_id is not None:\\n self.known_stations[station_id] = data\\n count += 1\\n self.station_file_age = os.path.getmtime(self.station_table_filename)\\n LOGGER.info(' Loaded %i station records from \\\"%s\\\"', count, self.station_table_filename)\\n return self.known_stations\",\n \"def _get_stations_local() -> List[dict]:\\n LOGGER.info('Using pre-generated json to retrieve station list')\\n with open(weather_stations_file_path) as weather_stations_file:\\n json_data = json.load(weather_stations_file)\\n return json_data['weather_stations']\",\n \"def search_station(st):\\n\\n res = []\\n for key, val in _STATIONS.items():\\n score = fuzz.token_set_ratio(st, key)\\n res.append(\\n {\\n 'station': key,\\n 'score': score,\\n 'station_id': val\\n }\\n )\\n if not res:\\n return {}\\n else:\\n res = sorted(res, key=lambda k: k['score'], reverse=True)\\n res = res[0]\\n return res\",\n \"def get_stations():\\n response = requests.get('https://api.hh.ru/metro/160')\\n todos = json.loads(response.text)\\n colors = {'CD0505': 'red'}\\n all_stations_one_line = []\\n\\n for i in todos['lines']:\\n all_stations_one_line = []\\n\\n for j in i['stations']:\\n one_station = station.station()\\n one_station.set_name(j['name'])\\n one_station.set_color(colors.get(i['hex_color']))\\n one_station.set_lat(j['lat'])\\n one_station.set_lng(j['lng'])\\n all_stations_one_line.append(one_station)\\n return all_stations_one_line\",\n \"def stations():\\n # Query all station names from dataset\\n station_list = session.query(Measurement.station).distinct().all()\\n all_stations = list(np.ravel(station_list))\\n\\n return jsonify(all_stations)\",\n \"def get_station_graph(start_station_id, end_station_list):\\n start_station_graph = []\\n for i in range(10):\\n if end_station_list[i] is not None:\\n start_station_graph.append((start_station_id, end_station_list[i]))\\n return start_station_graph\",\n \"def get_stations(nordic_file_names, output_level=0):\\n stations = []\\n for file in nordic_file_names:\\n new_stations = get_event_stations(file, output_level)\\n\\n if new_stations == -1:\\n continue\\n\\n for x in new_stations:\\n if x not in stations:\\n stations.append(x)\\n\\n return sorted(stations)\",\n \"def get_all_station_feature(city):\\n poi_frequency = np.load(exp_data_path + os.sep + 'poi_frequency' + os.sep + 'poi_frequency_{}.npy'.format(city),\\n allow_pickle=True) # .tolist()\\n poi_num = np.load(exp_data_path + os.sep + 'poi' + os.sep + 'poi_{}.npy'.format(city), allow_pickle=True)\\n poi_entropy = np.load(exp_data_path + os.sep + 'poi_entropy' + os.sep + 'poi_entropy_{}.npy'.format(city),\\n allow_pickle=True)\\n road = np.load(exp_data_path + os.sep + 'roadnet' + os.sep + 'roadnet_{}.npy'.format(city), allow_pickle=True)\\n transportation = np.load(exp_data_path + os.sep + 'transportation' + os.sep + 'transportation_{}.npy'.format(city),\\n allow_pickle=True)\\n commerce = np.load(exp_data_path + os.sep + 'commerce' + os.sep + 'commerce_{}.npy'.format(city), allow_pickle=True)\\n\\n file_name = exp_data_path + os.sep + 'station' + os.sep + 'all_demand_{}.npy'.format(city)\\n demand_data = np.load(file_name, allow_pickle=True)\\n num = demand_data[:, 0, -2, np.newaxis] # todo check meaning here, get quick and slow feature\\n\\n raw_data = np.concatenate((num, poi_frequency, poi_num, poi_entropy, road, transportation, commerce), axis=1)\\n csv_data = pd.DataFrame(raw_data, columns=GENERAL_HEADER)\\n\\n file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\\n if os.path.exists(file_path):\\n os.remove(file_path)\\n csv_data.to_csv(file_path)\\n pass\",\n \"def get_zipcode_stations(add):\\r\\n name=get_zipcode_names(add)\\r\\n engine = get_sql_engine()\\r\\n neighborhood_stations = text(\\r\\n \\\"\\\"\\\"\\r\\n SELECT\\r\\n \\\"name\\\" as name,\\r\\n \\\"addressStreet\\\" as address,\\r\\n \\\"bikesAvailable\\\" as available_bikes,\\r\\n v.geom as geom,\\r\\n ST_X(v.geom) as lon, ST_Y(v.geom)as lat\\r\\n FROM indego_rt1130 as v\\r\\n JOIN philly_zipcode as n\\r\\n ON ST_Intersects(v.geom, n.geom)\\r\\n WHERE n.code = :name\\r\\n \\\"\\\"\\\"\\r\\n )\\r\\n stations = gpd.read_postgis(neighborhood_stations, con=engine, params={\\\"name\\\": name})\\r\\n return stations\",\n \"def cluster_stations(stations, empty='empty'):\\n if empty == 'empty':\\n tocluster = [i for i in stations if (i[3] - i[2])/float(i[3]) < .2]\\n else:\\n tocluster = [i for i in stations if (i[2])/float(i[3]) < .2]\\n cl = KMeansClustering([(i[4], i[5]) for i in tocluster])\\n clusters = cl.getclusters(4)\\n\\n # Note that this returns a list of lists of lat/long tuples. We're\\n # going to have to re-associate them back to the rest of the stations\\n\\n clustered = []\\n for ix, i in enumerate(clusters):\\n for j in i:\\n for k in tocluster:\\n if (j[0], j[1]) == (k[4], k[5]):\\n clustered.append([k[0], k[1], k[2],\\n k[3], k[4], k[5], ix+1])\\n\\n return clustered\",\n \"def get_station_boroughs(self):\\\\\",\n \"def _get_next_available_train_list(self, context) -> Tuple[List, datetime.datetime]:\\n try:\\n for day in self._next_week:\\n trains = list(train_api.get_available_trains(origin_station_id=context.user_data['origin_station_id'],\\n dest_station_id=context.user_data['dest_station_id'],\\n date=day))\\n if len(trains) > 0:\\n return trains, day\\n\\n except (ValueError, AttributeError):\\n traceback.print_exc()\\n raise RuntimeError(\\\"general error\\\")\",\n \"def getstationaryobslist(self):\\n\\n stationaryobslist = [self.__tablecm]\\n return stationaryobslist\",\n \"def get_data(self):\\n if not self.form.submit():\\n return False\\n\\n parser = XMLParser(huge_tree=True)\\n doc = etree.fromstring(self.form.raw_data, parser=parser)\\n # There are a few stations with multiple generator id's, separated by '\\\\n' so\\n # capture them and add each as a separate entry.\\n for detail in doc.xpath(\\\"//*[local-name()='Detail']\\\"):\\n stt = Station(detail)\\n if '\\\\n' in stt.generator_id:\\n ids = [x.strip() for x in stt.generator_id.split('\\\\n')]\\n stt.generator_id = ids[0]\\n for _id in ids[1:]:\\n _st = copy.copy(stt)\\n _st.generator_id = _id\\n self.stations.append(_st)\\n self.stations.append(stt)\\n return len(self.stations) > 0\",\n \"async def get_stations_by_codes(station_codes: List[int]) -> List[WeatherStation]:\\n use_wfwx = config.get('USE_WFWX') == 'True'\\n if use_wfwx:\\n return await _get_stations_by_codes_remote(station_codes)\\n return _get_stations_by_codes_local(station_codes)\",\n \"def get_stations(self, limit=250):\\n\\n endpoint = \\\"/station/getStations\\\"\\n response = self._send(endpoint, \\\"POST\\\", {\\\"pageSize\\\": limit})\\n stations = response.json()[\\\"stations\\\"]\\n return stations\",\n \"def get_neigh_demand(city):\\n\\n # get station set S with more than 10 charge equipment\\n static_file_path = exp_data_path + os.sep + 'static' + os.sep + 'static_feature_{}.csv'.format(city)\\n static_feature = pd.read_csv(static_file_path, header=0)\\n station_set = set(static_feature[static_feature.num >= 10].index)\\n\\n # calculate 10 nearest neighborhoods for each station, sort by distance and store their index, get a map\\n neighbor_distance_map = {}\\n matrix_distance = np.load(exp_data_path + os.sep + 'similarity' + os.sep + 'similarity_distance_{}_numpy.npy'.format(city), allow_pickle=True)\\n all_distance_map = {i: [] for i in range(station_count[city])}\\n for i in range(station_count[city]):\\n if i not in station_set:\\n continue\\n for j in range(station_count[city]):\\n if j not in station_set:\\n continue\\n all_distance_map[i].append((j, matrix_distance[i][j]))\\n all_distance_map[i].sort(key=lambda x : x[1], reverse=True)\\n neighbor_distance_map[i] = [idx for idx, distance in all_distance_map[i][:10]]\\n\\n # 11 times header, get static neighborhood feature for each station(in S), get csv: neighbor_feature_{city}.csv\\n ALL_HEADER = ['index']\\n ALL_HEADER.extend(GENERAL_HEADER)\\n for i in range(10):\\n for j in GENERAL_HEADER:\\n ALL_HEADER.append('{}_{}'.format(j, i))\\n\\n raw_data = np.empty((len(neighbor_distance_map), len(ALL_HEADER)))\\n for i, idx in enumerate(neighbor_distance_map.keys()):\\n raw_data[i][0] = idx\\n raw_data[i][1:1+len(GENERAL_HEADER)] = static_feature.iloc[idx]['num':'mall']\\n for j in range(10):\\n neighbor_idx = neighbor_distance_map[idx][j]\\n raw_data[i][1+len(GENERAL_HEADER)*(j+1):1+len(GENERAL_HEADER)*(j+2)] = static_feature.iloc[neighbor_idx]['num':'mall']\\n neighbor_feature_data = pd.DataFrame(raw_data, columns=ALL_HEADER)\\n print('neighbor feature')\\n print(neighbor_feature_data)\\n\\n neighbor_feature_path = exp_data_path + os.sep + 'static' + os.sep + 'static_neighor_feature_{}.csv'.format(city)\\n if os.path.exists(neighbor_feature_path):\\n os.remove(neighbor_feature_path)\\n neighbor_feature_data.to_csv(neighbor_feature_path)\\n\\n # create final csv(11 times header with basic info(time_index + time_embed_index))\\n # if index in S, fill basic info, neighbor_feature and demand\\n\\n demand = np.load(exp_data_path + os.sep + 'station' + os.sep + 'demand_{}.npy'.format(city), allow_pickle=True)\\n time_count = demand.shape[1]\\n\\n DEMAND_HEADER = []\\n DEMAND_HEADER.extend(ALL_HEADER)\\n DEMAND_HEADER.extend(['time_index', 'time_embed', 'demand'])\\n neighbor_demand_raw_data = np.empty(((len(neighbor_distance_map)*time_count, len(DEMAND_HEADER))))\\n\\n # get time map like {\\\"0800\\\": 1, \\\"0830\\\": 2, ....}\\n time_index_map = np.load(exp_data_path + os.sep + 'station_list' + os.sep + 'time_index.npy', allow_pickle=True)\\n time_index_map = dict(time_index_map.tolist())\\n time_map = {t: i for i, t in enumerate(sorted(set([k[-4:] for k in time_index_map['rev_index'].keys()])))}\\n\\n cur_idx = 0\\n for time_idx in range(time_count):\\n time_embed_idx = time_map[time_index_map['index'][time_idx][-4:]]\\n for station_idx in station_set:\\n neighbor_demand_raw_data[cur_idx][0:len(ALL_HEADER)] = neighbor_feature_data.loc[neighbor_feature_data['index']==station_idx, 'index':'mall_9']\\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)] = time_idx\\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)+1] = time_embed_idx\\n neighbor_demand_raw_data[cur_idx][len(ALL_HEADER)+2] = demand[station_idx][time_idx][-1]\\n # todo add slow demand and quick demand here\\n cur_idx = cur_idx + 1\\n print(cur_idx, neighbor_demand_raw_data.shape)\\n\\n neighbor_demand_data = pd.DataFrame(neighbor_demand_raw_data, columns=DEMAND_HEADER)\\n print('neighbor demand')\\n print(neighbor_demand_data)\\n\\n neighbor_demand_path = exp_data_path + os.sep + 'static' + os.sep + 'neighbor_demand_{}.csv'.format(city)\\n if os.path.exists(neighbor_demand_path):\\n os.remove(neighbor_demand_path)\\n neighbor_demand_data.to_csv(neighbor_demand_path)\",\n \"def station_analysis(data):\\n unique_stations = list(set(data['start_station_name'].tolist() + data['end_station_name'].tolist()))\\n\\n station_counter = {station : 0 for station in unique_stations}\\n for index, row in data.iterrows():\\n station_counter[row['start_station_name']] += 1\\n\\n print('List of all stations:')\\n print(unique_stations)\\n\\n keys = list(station_counter.keys())\\n vals = list(station_counter.values())\\n indexArr = np.argsort(list(station_counter.values()))\\n popularStations = []\\n for i in reversed(indexArr):\\n popularStations.append((keys[i], vals[i]))\\n\\n stations1, journeys = zip(*popularStations[0:10])\\n plt.bar(stations1, journeys, 0.1)\\n\\n plt.xticks(stations1, rotation='vertical')\\n plt.title('Popular stations')\\n plt.xlabel('Station names')\\n plt.ylabel('Journeys')\\n\\n plt.show()\\n return station_counter\",\n \"def gatherStationData():\\n flist = list_files()\\n station_dics = {}\\n print(\\\"Reading in csv data...\\\")\\n for f_in in flist:\\n start,end = find_timespan(f_in)\\n station = station_name(f=f_in)\\n print(\\\"File: {0} Station: {1} {2}--{3}\\\".format(f_in, \\n station, start, end))\\n station_dics[station] = read_precip(fname=f_in, \\n label=station, start_year=start, end_year=end)\\n data_list = []\\n for s in station_dics:\\n data_list.append(station_dics[s]) \\n return pd.concat(data_list,axis=1)\",\n \"async def _get_stations_remote() -> List[WeatherStation]:\\n LOGGER.info('Using WFWX to retrieve station list')\\n async with ClientSession() as session:\\n # Get the authentication header\\n header = await _get_auth_header(session)\\n stations = []\\n # Iterate through \\\"raw\\\" station data.\\n async for raw_station in _fetch_raw_stations(session, header, BuildQueryAllStations()):\\n # If the station is valid, add it to our list of stations.\\n if _is_station_valid(raw_station):\\n LOGGER.info('Processing raw_station %d',\\n int(raw_station['stationCode']))\\n stations.append(_parse_station(raw_station))\\n LOGGER.debug('total stations: %d', len(stations))\\n return stations\",\n \"def stations():\\n # Query \\n results = session.query(Station.station).all()\\n \\n list = []\\n for result in results:\\n list.append(result)\\n return jsonify(list)\",\n \"def stations_dict(self):\\n return self.__stations_dict\",\n \"def stations():\\n # Create a link to the session\\n session = Session(engine)\\n \\n # Query all station records\\n results = session.query(Stations.station, Stations.name).all()\\n \\n session.close()\\n\\n # Create a dictionary from the query results\\n all_stations = []\\n for station, name in results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station\\n station_dict[\\\"name\\\"] = name\\n all_stations.append(station_dict)\\n \\n return jsonify(all_stations)\",\n \"def stations ():\\n # Query all passengers\\n Stns= session.query(Measurement.station, func.count(Measurement.station)).group_by(Measurement.station).all()\\n\\n allStationns = list(np.ravel(Stns))\\n\\n return jsonify(allStations)\",\n \"def get_station_entrances(self):\\n station_entrances = []\\n for wrapper in self.soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"stop-wrapper\\\"}):\\n text = wrapper.find(\\\"span\\\").text\\n if text == '' or text is None:\\n entrance = ''\\n else:\\n entrance = text.split(',')[0].lstrip().rstrip()\\n station_entrances.append(entrance)\\n return np.array(station_entrances).T\",\n \"def get_station_lines(self):\\n station_lines = []\\n for wrapper in self.soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"stop-wrapper\\\"}):\\n lines = wrapper.find(\\\"h3\\\").text.split(' ')[-1][1:-1]\\n station_lines.append(lines)\\n return np.array(station_lines).T\",\n \"def stations():\\n list_of_stations = session.query(Station.station, Station.name)\\n all_stations = []\\n for s, n in list_of_stations:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = s\\n station_dict[\\\"name\\\"] = n\\n all_stations.append(station_dict)\\n return jsonify(all_stations)\",\n \"def stations():\\n results = session.query(Measurement.station).\\\\\\n group_by(Measurement.station).all()\\n\\n return jsonify(results)\",\n \"def __save_all():\\n \\n # Use directory listing from stilt-web data. Ignore stations that\\n # may be in the queue but are not finished yet.\\n allStations = [s for s in os.listdir(CPC.STILTPATH) if os.path.exists(CPC.STILTPATH + s)]\\n\\n \\n # read lis of ICOS stations\\n icosStations = cpstation.getIdList()\\n icosStations = list(icosStations['id'][icosStations.theme=='AS'])\\n \\n # dictionary to return\\n stations = {}\\n\\n # fill dictionary with ICOS station id, latitude, longitude and altitude\\n for ist in tqdm(sorted(allStations)):\\n \\n stations[ist] = {}\\n # get filename of link (original stiltweb directory structure) and extract location information\\n \\n loc_ident = os.readlink(CPC.STILTPATH+ist)\\n clon = loc_ident[-13:-6]\\n lon = float(clon[:-1])\\n if clon[-1:] == 'W':\\n lon = -lon\\n clat = loc_ident[-20:-14]\\n lat = float(clat[:-1])\\n if clat[-1:] == 'S':\\n lat = -lat\\n alt = int(loc_ident[-5:])\\n\\n stations[ist]['lat']=lat\\n stations[ist]['lon']=lon\\n stations[ist]['alt']=alt\\n stations[ist]['locIdent']=os.path.split(loc_ident)[-1]\\n \\n # set the name and id\\n stations[ist]['id'] = ist\\n \\n # set a flag if it is an ICOS station\\n stn = ist[0:3].upper()\\n if stn in icosStations:\\n stations[ist]['icos'] = cpstation.get(stn).info()\\n lat = stations[ist]['icos']['lat']\\n lon = stations[ist]['icos']['lon']\\n else:\\n stations[ist]['icos'] = False \\n lat = stations[ist]['lat']\\n lon = stations[ist]['lon']\\n \\n stations[ist]['geoinfo'] = country.get(latlon=[lat,lon])\\n \\n return stations\",\n \"def mock_get_all_stations(__):\\n return all_station_codes\",\n \"def stations():\\n\\t\\n\\n\\tstationquery = session.query(Station.station).all()\\n\\n\\tstationlist = list(np.ravel(stationquery))\\n\\t\\n\\treturn jsonify(stationlist)\",\n \"def Find_nearest_dwd_stations(inpt_data,\\r\\n date_start='20051201',\\r\\n date_end='20201231',\\r\\n dwd_time_format='%Y%m%d%H',\\r\\n data_category='air_temperature',\\r\\n temp_resolution='hourly',\\r\\n no_of_nearest_stations=4,\\r\\n memory_save=True,\\r\\n Output='True'):\\r\\n if isinstance(data_category,list):\\r\\n if len(list(data_category)) > 1:\\r\\n print(\\r\\n 'Currently only one dwd category allowed, please run function multiple times for each category'\\r\\n )\\r\\n return None\\r\\n \\r\\n #convert time to datetime\\r\\n dt_start=datetime.strptime(date_start,'%Y%m%d')\\r\\n dt_end=datetime.strptime(date_end,'%Y%m%d')\\r\\n print('Start quering data from DWD')\\r\\n #define the database folder\\r\\n pypath = os.path.dirname(os.path.abspath(__file__))\\r\\n table_dir = pypath + '\\\\\\\\' + 'tables'\\r\\n dbase_dir = pypath + '\\\\\\\\' + 'dbase' \\r\\n #%% we check all available stations and create a valid list\\r\\n filename_stations=update_stationlist(time_res='hourly',dbase_dir=table_dir)\\r\\n stations_all=pd.read_csv(filename_stations, dtype={'STATIONS_ID': object})\\r\\n # delete all stations which do not cover the category\\r\\n dwd_stations=stations_all[stations_all[data_category]==True].copy()\\r\\n #correct to datetime\\r\\n dwd_stations['date_end']=pd.to_datetime(stations_all.date_end,format='%Y%m%d')\\r\\n dwd_stations['date_start']=pd.to_datetime(stations_all.date_start,format='%Y%m%d')\\r\\n # clean to stations which cover the campaign time #dt_low <= dt <= dt_high:\\r\\n dwd_stations=dwd_stations[(dwd_stations.date_start<=dt_start) & (dwd_stations.date_end>=dt_end)]\\r\\n #make a geodataframe out of it\\r\\n dwd_stations=gpd.GeoDataFrame(dwd_stations,geometry=gpd.points_from_xy(dwd_stations.geo_lon, dwd_stations.geo_lat))\\r\\n \\r\\n #loop through all rows to get the n closest points\\r\\n distances=pd.DataFrame()\\r\\n for _, station in dwd_stations.iterrows():\\r\\n distances[station.STATIONS_ID]=inpt_data.distance(station.geometry)\\r\\n \\r\\n #%% get the n stations with smallest distance and update database\\r\\n id_nearest_stations=distances.apply(lambda s: s.nsmallest(no_of_nearest_stations).index.tolist(), axis=1).values.tolist() #station ids\\r\\n #get them as unique values by sum a list of lists https://bit.ly/353iZQB\\r\\n id_dwd_stations=list(set(sum(id_nearest_stations,[])))\\r\\n \\r\\n #update the database\\r\\n db_dwd_stations=import_stations(time_res=temp_resolution,time_format=dwd_time_format,campaign_time=[dt_start,dt_end],data_category=data_category,station_ids=id_dwd_stations,dbase_dir=dbase_dir,Output=Output,table_dir=table_dir,memory_save=memory_save)\\r\\n \\r\\n #distance of nearest stattions\\r\\n dist_nearest_stations=pd.DataFrame(np.sort(distances.values)[:,:no_of_nearest_stations]).values.tolist() #distances themself\\r\\n #create new columns in the input data\\r\\n station_col_nm=list()\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_station_'+str(i))\\r\\n for i in range(0,no_of_nearest_stations):\\r\\n station_col_nm.append(data_category+'_distance_'+str(i))\\r\\n #create new dataframe\\r\\n distance_data=pd.concat([pd.DataFrame(id_nearest_stations).astype(int),pd.DataFrame(dist_nearest_stations)],axis=1)\\r\\n distance_data.columns=station_col_nm\\r\\n #add to main dataset\\r\\n inpt_data=pd.concat([inpt_data, distance_data],axis=1) \\r\\n \\r\\n return inpt_data,db_dwd_stations\",\n \"def getEmpty(self):\\n emptyList = []\\n for spot in self.parkingSpots:\\n if spot.status == 'empty':\\n emptyList.append(spot)\\n return emptyList\",\n \"def all_stations(self, provider: ID) -> List[StationInfo]:\\n srv_key = self.__stations_key(provider=provider)\\n value = self.get(name=srv_key)\\n if value is None:\\n return []\\n js = utf8_decode(data=value)\\n array = json_decode(string=js)\\n return StationInfo.convert(array=array)\",\n \"def getSyntheticShiftLists(nmrProject):\\n \\n #NBNB Filtering on isSimulated might be enough, but is it reliable?\\n\\n if not nmrProject:\\n return []\\n \\n result = [x for x in nmrProject.sortedMeasurementLists()\\n if x.className=='ShiftList'\\n and x.findFirstExperiment() is None\\n #and x.isSimulated # disabled pending check on how to set it\\n ]\\n \\n return result\",\n \"def statuslist(self):\\n self.getfullstatus()\\n response = self.geturl('js')\\n if not response:\\n return None\\n data = response.json()\\n states = data[\\\"sn\\\"]\\n stations = list(zip(range(0, data[\\\"nstations\\\"]),\\n self.lastfullresponse[\\\"stations\\\"][\\\"snames\\\"],\\n ['ON' if x==1 else 'OFF' for x in states]))\\n return stations\",\n \"def reload(self):\\n self.known_stations = {}\\n self.read_noaa_stations()\\n self.read_table_stations()\\n self.last_reload_check_time = datetime.datetime.utcnow()\\n LOGGER.info('Have %s known stations', len(self.known_stations.keys()))\",\n \"def get_wrf_stations(pool):\\n\\n wrfv3_stations = {}\\n\\n connection = pool.connection()\\n try:\\n with connection.cursor() as cursor:\\n sql_statement = \\\"SELECT `id`, `name` FROM `station` WHERE `id` like %s\\\"\\n row_count = cursor.execute(sql_statement, \\\"11_____\\\")\\n if row_count > 0:\\n results = cursor.fetchall()\\n for dict in results:\\n wrfv3_stations[dict.get(\\\"name\\\")] = dict.get(\\\"id\\\")\\n return wrfv3_stations\\n else:\\n return None\\n except Exception as exception:\\n error_message = \\\"Retrieving wrf stations failed\\\"\\n logger.error(error_message)\\n traceback.print_exc()\\n raise exception\\n finally:\\n if connection is not None:\\n connection.close()\",\n \"def get_station(div=0, wd=0, abbrev=None, as_dataframe=False,\\n input_file=None, output_file=None, suds_cache=None):\\n # ensure div is a list of ints\\n if type(div) is not list: div = [div]\\n # ensure wd is a list\\n if type(wd) is not list: wd = [wd]\\n # ensure name is a homogeneous list of str if it exists\\n if abbrev is not None and type(abbrev) is not list:\\n abbrev = [abbrev]\\n assert all(type(a) is str for a in abbrev)\\n\\n stations = []\\n if input_file is None:\\n # use the Co DWR SOAP service\\n suds_client = _get_client(CODWR_WSDL_URL)\\n\\n # get the water division/districts\\n dists = get_water_district(div, wd, as_dataframe=False, suds_cache=suds_cache)\\n if dists is None:\\n # no matching division/district(s)\\n return None\\n\\n if abbrev is None:\\n for d in dists:\\n # get the stations for the division/district\\n sites = suds_client.service.GetSMSTransmittingStations(d['div'], d['wd'])\\n if sites is None:\\n return None\\n\\n # get the parameters for the stations\\n sparms = suds_client.service.GetSMSTransmittingStationVariables(d['div'], d['wd'])\\n if sparms is None:\\n # hmmm - we have stations but no parameters...\\n raise ValueError(\\\"Service returned no parameters for transmitting station(s).\\\")\\n\\n # the SOAP service returns each parameter for a station as a\\n # separate row <abbrev, parameter> which we will compact into\\n # a dict {abbrev,[parameter,parameter,...]}\\n params = {}\\n for sp in sparms.StationVariables:\\n spd = dict(sp)\\n if spd['abbrev'] not in params:\\n params[spd['abbrev']] = []\\n params[spd['abbrev']].append(spd['variable'])\\n\\n # build up the complete station description (including the water\\n # district name and parameters) and add it to the station list\\n # to the station list\\n for site in sites.Station:\\n sited = dict(site)\\n sited['waterDistrictName'] = d['waterDistrictName']\\n sited['parameters'] = params[sited['abbrev']]\\n stations.append(sited)\\n\\n else:\\n for a in abbrev:\\n site = suds_client.service.GetSMSTransmittingStations(0, 0, a)\\n if site is not None:\\n sited = dict(site)\\n\\n for d in dists:\\n if d['div'] == sited['div'] and d['wd'] == sited['wd']:\\n sited['waterDistrictName'] = d['waterDistrictName']\\n break\\n\\n # retrieve the station parameters and attach them to the\\n # station information\\n sparms = suds_client.service.GetSMSTransmittingStationVariables(sited['div'],\\n sited['wd'],\\n sited['abbrev'])\\n if sparms is None:\\n # hmmm - we have stations but no parameters...\\n raise ValueError(\\\"Service returned no parameters for station \\\"\\n + sited['abbrev'])\\n for sp in sparms.StationVariables:\\n spd = dict(sp)\\n if sited['parameters'] is None:\\n sited['parameters'] = []\\n assert(spd['abbrev'] == sited['abbrev'])\\n sited['parameters'].append(spd['variable'])\\n\\n else:\\n # retrieve the list of sites in the specified file\\n print(\\\"Nothing yet\\\")\\n\\n if as_dataframe is True:\\n stations = pd.DataFrame(stations)\\n\\n return stations if len(stations) > 0 else None\",\n \"def stations():\\n session = Session(engine)\\n # Query all Stations\\n stations = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n all_stations = list(np.ravel(stations))\\n\\n return jsonify(all_stations)\",\n \"def add_stations(stations, pool):\\n\\n for station in stations:\\n\\n print(add_station(pool=pool, name=station.get('name'), latitude=station.get('latitude'),\\n longitude=station.get('longitude'), station_type=station.get('station_type'),\\n description=station.get('description')))\\n print(station.get('name'))\",\n \"def get_all_stations():\\n latest_scraping_time = db.session \\\\\\n .query(func.max(DublinBike.scraping_time)) \\\\\\n .one()[0]\\n\\n stations = db.session.query(DublinBike) \\\\\\n .filter(DublinBike.scraping_time == latest_scraping_time) \\\\\\n .order_by(DublinBike.number.asc()) \\\\\\n .all()\\n\\n return jsonify({\\n 'data': [station.serialize for station in stations]\\n })\",\n \"def get_station_model_predictions(\\n session: Session,\\n station_codes: List,\\n model: str,\\n start_date: datetime.datetime,\\n end_date: datetime.datetime) -> List[\\n Union[WeatherStationModelPrediction, PredictionModelRunTimestamp, PredictionModel]]:\\n query = session.query(WeatherStationModelPrediction, PredictionModelRunTimestamp, PredictionModel).\\\\\\n filter(WeatherStationModelPrediction.station_code.in_(station_codes)).\\\\\\n filter(WeatherStationModelPrediction.prediction_timestamp >= start_date).\\\\\\n filter(WeatherStationModelPrediction.prediction_timestamp <= end_date).\\\\\\n filter(PredictionModelRunTimestamp.id ==\\n WeatherStationModelPrediction.prediction_model_run_timestamp_id).\\\\\\n filter(PredictionModelRunTimestamp.prediction_model_id == PredictionModel.id,\\n PredictionModel.abbreviation == model).\\\\\\n order_by(WeatherStationModelPrediction.station_code).\\\\\\n order_by(WeatherStationModelPrediction.prediction_timestamp).\\\\\\n order_by(PredictionModelRunTimestamp.prediction_run_timestamp.asc())\\n return query\",\n \"def stations():\\n\\n # Query all Stations\\n station_results = session.query(Station.station).all()\\n\\n # Convert list of tuples into normal list\\n all_station_names = list(np.ravel(station_results))\\n\\n return jsonify(all_station_names)\",\n \"def stations(): \\n # creating the Docstring\\n session = Session(engine)\\n\\n # creat the Query stations\\n\\n stations_qu = session.query(measurement.station).group_by(measurement.station).all()\\n\\n # Converting the list of tuples into a normal list\\n stations_qu_dict = list(np.ravel(stations_qu))\\n session.close()\\n\\n return jsonify(stations_qu_dict)\",\n \"def stations():\\n\\n station_results = session.query(Stations.station, Stations.name).all()\\n\\n station_data = []\\n for row in station_results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = row.station\\n station_dict[\\\"name\\\"] = row.name\\n station_data.append(station_dict)\\n\\n return jsonify(station_data)\",\n \"def processStationInfo(obs_loc_df, source, st_list=None):\\n if not st_list:\\n st_list = dict()\\n st_data = obs_loc_df['station_id']\\n lat_data = obs_loc_df['latitude (degree)']\\n lon_data = obs_loc_df['longitude (degree)']\\n\\n for k, station_name in enumerate(st_data):\\n if station_name in st_list:\\n pass\\n else:\\n st_list[station_name] = dict()\\n st_list[station_name][\\\"lat\\\"] = lat_data[k]\\n st_list[station_name][\\\"source\\\"] = source\\n st_list[station_name][\\\"lon\\\"] = lon_data[k]\\n print(station_name)\\n\\n print(\\\"Number of stations in bbox {}\\\".format(len(st_list.keys())))\\n return st_list\",\n \"def get_flo2d_output_stations(pool, flo2d_model):\\n\\n flo2d_output_stations = {}\\n\\n id_pattern = '{}_____'.format((str(flo2d_model.value)).split('0')[0])\\n\\n connection = pool.connection()\\n try:\\n with connection.cursor() as cursor:\\n sql_statement = \\\"SELECT * FROM `station` WHERE `id` like %s\\\"\\n row_count = cursor.execute(sql_statement, id_pattern)\\n if row_count > 0:\\n results = cursor.fetchall()\\n for dict in results:\\n station_name = dict.get(\\\"name\\\")\\n flo2d_output_stations[station_name.split(\\\"_\\\")[0]] = [dict.get(\\\"id\\\"), dict.get(\\\"latitude\\\"),\\n dict.get(\\\"longitude\\\"),\\n '_'.join(station_name.split('_')[1:])]\\n return flo2d_output_stations\\n else:\\n return None\\n except Exception as exception:\\n error_message = \\\"Retrieving flo2d output stations failed\\\"\\n logger.error(error_message)\\n traceback.print_exc()\\n raise exception\\n finally:\\n if connection is not None:\\n connection.close()\",\n \"def run():\\n\\n # Build list of tuples of station names and distance \\n stations = build_station_list()\\n p = (52.2053, 0.1218)\\n by_distance = stations_by_distance(stations, p)\\n for n in range(10):\\n print(by_distance[n])\\n for n in range(10):\\n i = len(by_distance) - 10 + n\\n print(by_distance[i])\",\n \"def _automatic_training_set(self, n_cutouts=100):\\n dstl = Load_DSTL()\\n np.random.seed(42)\\n for ii in range(n_cutouts):\\n # Get region around a shape\\n triples, mask, ind_shape, img_dim = dstl.extract_region(object_class=self.class_type, image_id='6120_2_2', buffer_size=10)\\n if self.radius is not None:\\n triples, mask = self._sliding_window(triples.reshape(*img_dim, 3), mask.reshape(img_dim), window_radius=self.radius)\\n # Add to Feature Matrix\\n if ii == 0:\\n features = triples\\n labels = mask\\n else:\\n features = np.vstack([features, triples])\\n labels = np.hstack([labels, mask])\\n return features, labels\",\n \"def create_training_set_blending():\\n stime = time.time()\\n indexes = []\\n y_train = np.zeros((nb_places * nb_pis,))\\n x_train = np.zeros((nb_places * nb_pis, nb_clfs))\\n for i, place_id in enumerate(place_ids):\\n # 1. Get the relevance ratings for the place (y).\\n ratings = get_relevance_ratings(place_id, connection=c_study, cursor=cur_study)\\n if len(ratings) == 0: # filter the ratings.\\n continue\\n r = [np.mean(ratings[pi_id]['ratings']) if pi_id in ratings else 0 for pi_id in pis_ids]\\n for k, pi_id in enumerate(pis_ids):\\n cl = 0\\n if r[k] >= 4:\\n cl = 1\\n y_train[i * nb_pis + k] = cl # int(np.ceil(r[k]))\\n indexes.append((place_id, pi_id))\\n\\n # 2. Get the predictions from the models for the place (x).\\n for j, clf in enumerate(clfs):\\n predictions = clf._get_prediction(place_id)\\n p = [predictions[pi_id]['score'] if pi_id in predictions else 0 for pi_id in pis_ids]\\n for k in range(nb_pis):\\n x_train[i * nb_pis + k, j] = p[k]\\n print(\\\"[.] Done with x_train: %s, y_train: %s, indexes: %s (%.2f)\\\" % (x_train.shape, y_train.shape, len(indexes), time.time()-stime))\\n return x_train, y_train, indexes\",\n \"def get_station_names(self):\\n station_names = []\\n for wrapper in self.soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"stop-wrapper\\\"}):\\n station_name = ' '.join(wrapper.find(\\\"h3\\\").text.split(' ')[:-1])\\n station_names.append(station_name)\\n return np.array(station_names).T\",\n \"def get_top_station_set(city):\\n s = {}\\n for file in os.listdir(exp_data_path + os.sep + 'station' + os.sep + city):\\n with open(exp_data_path + os.sep + 'station' + os.sep + city + os.sep + file) as f:\\n reader = csv.reader(f)\\n for row in reader:\\n if row[0] not in s:\\n s[row[0]] = 1\\n else:\\n s[row[0]] = s[row[0]] + 1\\n\\n sort_s = dict(sorted(s.items(), key=lambda x : x[1], reverse=True))\\n first = True\\n res = []\\n for k, v in sort_s.items():\\n if first:\\n top = v\\n first = False\\n if top - v <= 30:\\n res.append(k)\\n print('before', len(sort_s))\\n print('after', len(res))\\n\\n # restore new map [old_index, new_index]\\n list_remap = {}\\n new_index = 0\\n for index in range(0, data_length[city]):\\n if str(index) in res:\\n list_remap[index] = new_index\\n new_index = new_index + 1\\n\\n # print(list_remap)\\n check_path(exp_data_path + os.sep + 'station_list')\\n file_name = exp_data_path + os.sep + 'station_list' + os.sep + 'list_remap_{}'.format(city) + '.npy'\\n if os.path.exists(file_name):\\n os.remove(file_name)\\n np.save(file_name, list_remap)\",\n \"def test_nearest_filter(self):\\n for airport, reports, count in (\\n (True, True, 6),\\n (True, False, 16),\\n (False, True, 6),\\n (False, False, 30),\\n ):\\n stations = station.nearest(30, -80, 30, airport, reports, 1.5)\\n self.assertEqual(len(stations), count)\",\n \"def stations():\\n \\n # Query all the stations\\n results = session.query(Station).all()\\n\\n # Create a dictionary to append the station data\\n stations_info = []\\n for stations in results:\\n stations_dict = {}\\n stations_dict[\\\"Station\\\"] = stations.station\\n stations_dict[\\\"Station Name\\\"] = stations.name\\n stations_dict[\\\"Latitude\\\"] = stations.latitude\\n stations_dict[\\\"Longitude\\\"] = stations.longitude\\n stations_dict[\\\"Elevation\\\"] = stations.elevation\\n all_stations.append(stations_dict)\\n \\n return jsonify(stations_info)\",\n \"def stations():\\n \\n station_result = session.query(Station.station).all()\\n stations = []\\n # Convert list of tuples into normal list\\n stations = list(np.ravel(station_result))\\n return jsonify(stations)\",\n \"def stations():\\n results = session.query(Station.station).all()\\n stations = list(np.revel(results))\\n return jsonify(stations)\",\n \"def stations():\\n # Return a JSON list of stations from the dataset\\n session = Session(engine)\\n stations = session.query(Station.name).all()\\n\\n # Convert list of tuples into normal list\\n station_names = list(np.ravel(stations))\\n\\n return jsonify(station_names)\",\n \"def inlezen_beginstation(stations):\\n beginstation = input(\\\"Wat is je beginstation? : \\\")\\n\\n while beginstation not in stations:\\n print(\\\"Geen correcte invoer.. Probeer opnieuw\\\")\\n\\n return beginstation\",\n \"def rm_duplicate(Sta_all, address):\\n \\n sta_all = []\\n saved = []\\n \\n for i in Sta_all:\\n if i[2] == '--' or i[2] == ' ':\\n i[2] = ''\\n for j in range(0, len(i)):\\n if i[j] != str(i[j]):\\n i[j] = str(i[j]) \\n if len(i) == 7:\\n sta_all.append(str(i[0] + '_' + i[1] + '_' + i[2] + '_' + \\\\\\n i[3] + '_' + i[4] + '_' + i[5] + '_' + i[6]))\\n elif len(i) == 8:\\n sta_all.append(str(i[0] + '_' + i[1] + '_' + i[2] + '_' + \\\\\\n i[3] + '_' + i[4] + '_' + i[5] + '_' + i[6]\\\\\\n + '_' + i[7]))\\n \\n sta_ev = read_station_event(address)\\n ls_saved_stas = sta_ev[0]\\n \\n for i in range(0, len(ls_saved_stas)):\\n sta_info = ls_saved_stas[i]\\n saved.append(sta_info[0] + '_' + sta_info[1] + '_' + \\\\\\n sta_info[2] + '_' + sta_info[3])\\n \\n Stas_req = sta_all\\n \\n len_all_sta = len(sta_all)\\n num = []\\n for i in range(0, len(saved)):\\n for j in range(0, len(Stas_req)):\\n if saved[i] in Stas_req[j]:\\n num.append(j)\\n\\n num.sort(reverse=True)\\n for i in num:\\n del Stas_req[i] \\n \\n for m in range(0, len(Stas_req)):\\n Stas_req[m] = Stas_req[m].split('_')\\n \\n Stas_req.sort()\\n \\n print '------------------------------------------'\\n print 'Info:'\\n print 'Number of all saved stations: ' + str(len(saved))\\n print 'Number of all available stations: ' + str(len_all_sta)\\n print 'Number of stations to update for: ' + str(len(Stas_req))\\n print '------------------------------------------'\\n \\n return Stas_req\",\n \"def stations():\\r\\n # Query all passengers\\r\\n results = session.query(Station.station, \\r\\n Station.name, \\r\\n Station.latitude,\\r\\n Station.longitude,\\r\\n Station.elevation).all()\\r\\n\\r\\n return jsonify(results)\",\n \"def __len__(self):\\n return len(self.stations)\",\n \"def stations():\\n # Create link from Python to db\\n session = Session(engine)\\n\\n # Query stations.\\n stations = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\\n\\n session.close()\\n\\n # Convert to a dictionary.\\n all_stations = []\\n for station, name, latitude, longitude, elevation in stations:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = station\\n station_dict[\\\"name\\\"] = name\\n station_dict[\\\"latitude\\\"] = latitude\\n station_dict[\\\"longitude\\\"] = longitude\\n station_dict[\\\"elevation\\\"] = elevation\\n all_stations.append(station_dict)\\n\\n # Return JSON\\n return jsonify(all_stations)\",\n \"def add_station(self, station):\\n self.__stations.append(station)\",\n \"def _load_stations(self, nodes: List[OSMNode]) -> None:\\n # Process OSM nodes into intermediate stations\\n grouped_stations: defaultdict[str, list[IntermediateStation]] = defaultdict(list)\\n\\n # Iterate thru nodes while popping them from the provided list\\n # to allow used nodes to bne garbage collected.\\n while nodes:\\n node = nodes.pop()\\n name_id = node.tags[\\\"name\\\"]\\n grouped_stations[name_id].append(IntermediateStation(\\n node.id,\\n name_id,\\n node.lat,\\n node.lon,\\n [k for (k, v) in node.tags.items() if \\\".\\\" in k and v == \\\"yes\\\"],\\n node.tags.get(\\\"merged\\\") == \\\"all\\\",\\n ))\\n\\n # Convert the intermediate representations to GeoStation\\n # (again popping from grouped_stations to allow intermediate representation to be gc-ed)\\n while grouped_stations:\\n name_id, stations = grouped_stations.popitem()\\n merged_all_node = get_merged_all_node(stations)\\n\\n if len(stations) == 1 and len(stations[0].routes) == 1:\\n # Case 1 - one station and one line.\\n sta = stations[0]\\n sta_id = sta.routes[0] + \\\".\\\" + name_id\\n self.by_id[sta_id] = GeoStation(sta_id, sta.lat, sta.lon)\\n\\n elif len(stations) == 1:\\n # Case 2 - one station and multiple lines.\\n # Simple parent-children structure, all in one location.\\n sta = stations[0]\\n parent = GeoStation(\\\"Merged.\\\" + name_id, sta.lat, sta.lon)\\n self.by_id[parent.id] = parent\\n\\n for route in sta.routes:\\n child = GeoStation(route + \\\".\\\" + name_id, sta.lat, sta.lon, parent=parent)\\n self.by_id[child.id] = child\\n parent.children.append(child)\\n\\n elif merged_all_node:\\n # Case 3: many nodes, but all under one parent\\n parent = GeoStation(\\\"Merged.\\\" + name_id, merged_all_node.lat, merged_all_node.lon)\\n self.by_id[parent.id] = parent\\n\\n for ista in stations:\\n for route in ista.routes:\\n child = GeoStation(route + \\\".\\\" + name_id, ista.lat, ista.lon,\\n parent=parent)\\n self.by_id[child.id] = child\\n parent.children.append(child)\\n\\n else:\\n # Case 4: many nodes, no parent-of-all\\n needs_merged_no = count_multiple_routes(stations) > 1\\n merged_no = 1\\n\\n for sta in stations:\\n if len(sta.routes) == 1:\\n # Case 4.1 - single line - behavior as in case 1\\n sta_id = sta.routes[0] + \\\".\\\" + name_id\\n self.by_id[sta_id] = GeoStation(sta_id, sta.lat, sta.lon)\\n\\n else:\\n # Case 4.2 - multiple lines - behavior as in case 2\\n parent_prefix = \\\"Merged.\\\"\\n if needs_merged_no:\\n parent_prefix = f\\\"Merged.{merged_no}.\\\"\\n merged_no += 1\\n\\n parent = GeoStation(parent_prefix + name_id, sta.lat, sta.lon)\\n self.by_id[parent.id] = parent\\n\\n for route in sta.routes:\\n child = GeoStation(route + \\\".\\\" + name_id, sta.lat, sta.lon,\\n parent=parent)\\n self.by_id[child.id] = child\\n parent.children.append(child)\",\n \"def station_from_lat_lon(lat, lon, stations, n_nearest=3):\\n lat, lon = float(lat), float(lon)\\n distances = [(distance(lat, lon, st['lat'], st['lon']), st)\\n for st in stations\\n if (st['is_renting'] and st['is_installed'])]\\n distances = sorted(distances)\\n return [pair[1] for pair in distances[:n_nearest]]\",\n \"def stations():\\n \\n session = Session(engine)\\n # Query to bring all stations\\n results = pd.DataFrame(session.query(S.id.label('ID'),S.station.label('Station'),S.name.label('Name'),\\\\\\n S.latitude.label('Latitude'),S.longitude.label('Longitude'), \\\\\\n S.elevation.label('Elevation')).all())\\n \\n session.close()\\n \\n # Create a dictionary from the row data of the dataframe and return it as a JSON\\n return jsonify(results.to_dict(orient = 'records'))\",\n \"def _parse_departures(self, data, stop, servernow):\\n servernow.replace(second=0, microsecond=0)\\n results = []\\n departures = data.findall('./itdDeparture')\\n for departure in departures:\\n # Get Line Information\\n origin, destination, line, ridenum, ridedir, canceled = self._parse_mot(departure.find('./itdServingLine'))\\n\\n if departure.find('./genAttrList/genAttrElem[value=\\\"HIGHSPEEDTRAIN\\\"]') is not None:\\n line.linetype = LineType('train.longdistance.highspeed')\\n elif departure.find('./genAttrList/genAttrElem[value=\\\"LONG_DISTANCE_TRAINS\\\"]') is not None:\\n line.linetype = LineType('train.longdistance')\\n\\n # if ridenum is None:\\n # ridedata = departure.find('./itdServingTrip')\\n # if ridedata is not None:\\n # ridenum = ridedata.attrib.get('tripCode', None)\\n # if ridenum is not None:\\n # ridenum = ridenum.strip()\\n\\n # Build Ride Objekt with known stops\\n ride = Ride(line, ridenum)\\n ride.direction = ridedir\\n ride.canceled = canceled\\n\\n train_line = line.linetype in self.train_station_lines\\n\\n # todo: take delay and add it to next stops\\n mypoint = self._parse_trip_point(departure, train_line=train_line)\\n\\n before_delay = None\\n if mypoint.arrival:\\n before_delay = mypoint.arrival.delay\\n after_delay = None\\n if mypoint.departure:\\n after_delay = mypoint.departure.delay\\n\\n delay = None\\n if departure.find('./itdServingLine/itdNoTrain'):\\n delay = departure.find('./itdServingLine/itdNoTrain').attrib.get('delay', None)\\n if delay is not None:\\n delay = timedelta(minutes=delay)\\n\\n if delay is not None:\\n if (mypoint.arrival and servernow < mypoint.arrival.livetime) or (mypoint.departure and servernow < mypoint.departure.livetime):\\n before_delay = delay\\n else:\\n after_delay = delay\\n\\n prevs = False\\n for pointdata in departure.findall('./itdPrevStopSeq/itdPoint'):\\n point = self._parse_trip_point(pointdata, train_line=train_line)\\n if point is not None:\\n if before_delay is not None:\\n if point.arrival is not None and point.arrival.delay is None and point.arrival.time + before_delay >= servernow:\\n point.arrival.delay = before_delay\\n if point.departure is not None and point.departure.delay is None and point.departure.time + before_delay >= servernow:\\n point.departure.delay = before_delay\\n prevs = True\\n ride.append(point)\\n\\n pointer = ride.append(mypoint)\\n\\n onwards = False\\n for pointdata in departure.findall('./itdOnwardStopSeq/itdPoint'):\\n point = self._parse_trip_point(pointdata, train_line=train_line)\\n if point is not None:\\n if after_delay is not None:\\n if point.arrival is not None and point.arrival.delay is None and point.arrival.time + after_delay >= servernow:\\n point.arrival.delay = after_delay\\n if point.departure is not None and point.departure.delay is None and point.departure.time + after_delay >= servernow:\\n point.departure.delay = after_delay\\n onwards = True\\n ride.append(point)\\n\\n if not prevs and not onwards:\\n ride.prepend(None)\\n if origin is not None:\\n ride.prepend(TimeAndPlace(Platform(origin)))\\n\\n ride.append(None)\\n if destination is not None:\\n ride.append(TimeAndPlace(Platform(destination)))\\n\\n # Return RideSegment from the Station we depart from on\\n results.append(ride[pointer:])\\n return Ride.Results(results)\",\n \"def __getpredictors_distance(self, staname, distance):\\n\\n distfromsta = distance[staname]\\n del distfromsta[staname] # remove the station to be fill from the dataframe\\n distfromsta = distfromsta.sort_values()\\n\\n stations = self.network.getsta(distfromsta.index.values)\\n # station = self.network.getsta(staname)\\n\\n # Only 3 closest stations\\n # sel1 = [ (i,e) for i,e in zip(stations[0:2], stations[1:3])] # selction predictors with spacing 1\\n # sel2 = [ (i,e) for i,e in zip(stations[0:2], stations[2:4])] # selction predictors with spacing 2\\n\\n # Use all stations\\n sel1 = [(i, e) for i, e in zip(stations[0:-1], stations[1:])] # selction predictors with spacing 1\\n sel2 = [(i, e) for i, e in zip(stations[0:-2], stations[2:])] # selction predictors with spacing 2\\n\\n # sel3 = [ (i,e) for i,e in zip(stations[0:-3], stations[3:])] # selction predictors with spacing 3\\n # sel4 = [ (i,e) for i,e in zip(stations[0:-4], stations[4:])] # selction predictors with spacing 4\\n\\n # Only 3 closest stations\\n # sel1names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:2], stations[1:3])] # selction predictors with spacing 1\\n # sel2names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:2], stations[2:4])] # selction predictors with spacing 1\\n\\n # using all stations\\n sel1names = [(i.getpara('stanames'), e.getpara('stanames')) for i, e in\\n zip(stations[0:-1], stations[1:])] # selction predictors with spacing 1\\n sel2names = [(i.getpara('stanames'), e.getpara('stanames')) for i, e in\\n zip(stations[0:-2], stations[2:])] # selction predictors with spacing 1\\n\\n # sel3names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:-3], stations[3:])] # selction predictors with spacing 1\\n # sel4names = [ (i.getpara('stanames'),e.getpara('stanames')) for i,e in zip(stations[0:-4], stations[4:])] # selction predictors with spacing 1\\n\\n selection = [x for x in itertools.chain.from_iterable(itertools.izip_longest(sel1, sel2)) if x]\\n selectionnames = [x for x in itertools.chain.from_iterable(itertools.izip_longest(sel1names, sel2names)) if x]\\n\\n return selection, selectionnames\",\n \"def sort_bike_stations(bike_stations, location):\\n\\n stations = bike_stations.copy()\\n\\n for index, station in enumerate(stations):\\n station_location = (station[\\\"lat\\\"], station[\\\"lon\\\"])\\n dist = distance.distance(station_location, location).m\\n stations[index][\\\"distance\\\"] = dist\\n\\n stations = sorted(stations, key=lambda station: station[\\\"distance\\\"])\\n stations = list(filter(lambda station: station[\\\"bikesAvailable\\\"] > 0, stations))\\n\\n return stations\",\n \"def stations():\\n # Query all stations\\n\\n stations = session.query(Station.station).all()\\n all_stations = list(np.ravel(stations))\\n\\n return jsonify(all_stations)\",\n \"def stations():\\n \\n # Create our session (link) from Python to the DB\\n session = Session(engine)\\n\\n results = session.query(Station.station, Station.name, Station.latitude, Station.longitude, Station.elevation).all()\\n\\n session.close()\\n\\n stations = []\\n for result in results:\\n station_dict = {}\\n station_dict[\\\"station\\\"] = result.station\\n station_dict[\\\"name\\\"] = result.name\\n station_dict[\\\"latitude\\\"] = result.latitude\\n station_dict[\\\"longitude\\\"] = result.longitude\\n station_dict[\\\"elevation\\\"] = result.elevation\\n stations.append(station_dict)\\n \\n return jsonify(stations)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6918483","0.63394576","0.6295055","0.6175655","0.6134076","0.6036043","0.59990704","0.5990439","0.5939404","0.59325945","0.5899833","0.5873434","0.58216053","0.5731162","0.5703947","0.5658584","0.5635071","0.563275","0.56268054","0.5625375","0.56150836","0.5578745","0.5568343","0.5566908","0.5550945","0.5497827","0.5480448","0.54793215","0.5447455","0.54438853","0.54423","0.5408923","0.53940755","0.537816","0.53723323","0.53699684","0.5344327","0.53432655","0.53240126","0.53021026","0.52799815","0.5248857","0.5243314","0.5238324","0.5230706","0.5225993","0.52098584","0.52077234","0.5183783","0.5183574","0.51826656","0.51740634","0.515469","0.5153958","0.5152967","0.5143183","0.514239","0.51363176","0.5127989","0.51132345","0.51124007","0.5106552","0.51044154","0.5102174","0.5086586","0.508632","0.5083396","0.50797653","0.5066607","0.50582135","0.5058194","0.50395805","0.50383264","0.5037706","0.5029194","0.50290066","0.5027434","0.5024481","0.5021352","0.50211394","0.50206435","0.5019944","0.50107074","0.50099236","0.50091255","0.49914712","0.49885646","0.4980024","0.49778178","0.49762672","0.49662644","0.49654043","0.49487618","0.4948441","0.49457708","0.4945759","0.49422902","0.49404457","0.4934721","0.49289748"],"string":"[\n \"0.6918483\",\n \"0.63394576\",\n \"0.6295055\",\n \"0.6175655\",\n \"0.6134076\",\n \"0.6036043\",\n \"0.59990704\",\n \"0.5990439\",\n \"0.5939404\",\n \"0.59325945\",\n \"0.5899833\",\n \"0.5873434\",\n \"0.58216053\",\n \"0.5731162\",\n \"0.5703947\",\n \"0.5658584\",\n \"0.5635071\",\n \"0.563275\",\n \"0.56268054\",\n \"0.5625375\",\n \"0.56150836\",\n \"0.5578745\",\n \"0.5568343\",\n \"0.5566908\",\n \"0.5550945\",\n \"0.5497827\",\n \"0.5480448\",\n \"0.54793215\",\n \"0.5447455\",\n \"0.54438853\",\n \"0.54423\",\n \"0.5408923\",\n \"0.53940755\",\n \"0.537816\",\n \"0.53723323\",\n \"0.53699684\",\n \"0.5344327\",\n \"0.53432655\",\n \"0.53240126\",\n \"0.53021026\",\n \"0.52799815\",\n \"0.5248857\",\n \"0.5243314\",\n \"0.5238324\",\n \"0.5230706\",\n \"0.5225993\",\n \"0.52098584\",\n \"0.52077234\",\n \"0.5183783\",\n \"0.5183574\",\n \"0.51826656\",\n \"0.51740634\",\n \"0.515469\",\n \"0.5153958\",\n \"0.5152967\",\n \"0.5143183\",\n \"0.514239\",\n \"0.51363176\",\n \"0.5127989\",\n \"0.51132345\",\n \"0.51124007\",\n \"0.5106552\",\n \"0.51044154\",\n \"0.5102174\",\n \"0.5086586\",\n \"0.508632\",\n \"0.5083396\",\n \"0.50797653\",\n \"0.5066607\",\n \"0.50582135\",\n \"0.5058194\",\n \"0.50395805\",\n \"0.50383264\",\n \"0.5037706\",\n \"0.5029194\",\n \"0.50290066\",\n \"0.5027434\",\n \"0.5024481\",\n \"0.5021352\",\n \"0.50211394\",\n \"0.50206435\",\n \"0.5019944\",\n \"0.50107074\",\n \"0.50099236\",\n \"0.50091255\",\n \"0.49914712\",\n \"0.49885646\",\n \"0.4980024\",\n \"0.49778178\",\n \"0.49762672\",\n \"0.49662644\",\n \"0.49654043\",\n \"0.49487618\",\n \"0.4948441\",\n \"0.49457708\",\n \"0.4945759\",\n \"0.49422902\",\n \"0.49404457\",\n \"0.4934721\",\n \"0.49289748\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66159266"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":4690,"cells":{"query":{"kind":"string","value":"builds and saves dataframe to be used for graphs"},"document":{"kind":"string","value":"def dataframe():\n\t#allows function to access station, gmt, and miss_station functions\n global stations\n\tglobal gmt\n\tglobal miss_station\n\t\n\t#read predictor file\n\tcontrol = cfg.read_yaml('../registry/graphs.yaml')\n\tpred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))\n\tpredd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))\n\n\t#get file paths and update database\n\tpredictor_file_path = control.predictor_file_path\n\tpredictand_file_path = control.predictand_file_path\n\tpred_file_id = update(predictor_file_path)\n\tpredd_file_id = update(predictand_file_path)\n\t\n\t#store lead time and date range\n\tlead_time = control.lead_time\n\tdate_range = control.date_range\n\n\t#get info for fetch many dates\n\tstart,end,stride = read_pred.parse_range(date_range)\n\tfcst_ref_time = control.date_range[0].split('-')[0][-2:]\n\t\n\t#initialize list of predictors\n\tpred_list = pred_ctrl.predictors\n\tpredictor = []\n\n\t#loops through predictors to build camps data objects\n\tfor entry_dict in pred_list:\n\t\t#formats metadata\n\t\tpred = create.preprocess_entries(entry_dict, fcst_ref_time)\n\t\t\n\t\t#adds info to metadata that's not currently being stored\n\t\tpred.search_metadata['reserved2'] = lead_time*3600\n pred.search_metadata['file_id'] = pred_file_id\n\t\tpred.search_metadata['reserved1'] = 'vector'\n\n\t\t#build camps data objects for each day\n\t\tvariable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)\n\t\t\n\t\t#appends all data to single camps object\n\t\tif variable[0] is not None:\n\t\t\tvar = variable[0]\n\t\t\tarrs = []\n\t\t\tfor i in range(len(variable)):\n\t\t\t\tarrs.append(variable[i].data)\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictor.append(var)\n\n\t#initializes list of predictands\n\tpredd_list = predd_ctrl.predictands\n predictand = []\n\t\n\t#loops through predictands to build camps data objects\n for entry_dict in predd_list:\n\t\t#formats metadata\n \tvertical_coordinate = entry_dict.pop('Vertical_Coordinate')\n\t\tentry_dict['file_id'] = predd_file_id\n\n\t\t#build camps objects for each day\n variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)\n\n\t\t#append all data to single camps object\n var = variable[0]\n arrs = []\n for i in range(len(variable)):\n arrs.append(variable[i].data)\n try:\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictand.append(var)\n\t\texcept:\n\t\t\tprint(\"Can't read \" + variable.name)\n\n\t#getting predictor station and time data\n\tpredr = Dataset(predictor_file_path[0])\n\tpredr_stat = predr.variables['station'][:]\n\tif lead_time == 3:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant'][:]\n\telif lead_time == 6:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant1'][:]\n\telif lead_time == 12:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant2'][:]\n\tpredr.close()\n\n\t#reformatting predictor station and time data\n\tpredr_stations = stations(predr_stat)\n\tpredr_gmt = gmt(predr_time)\n\t\n\t#getting predictand station and time data\n\tpredd = Dataset(predictand_file_path[0])\n\tpredd_stat = predd.variables['station'][:]\n\tpredd_time = predd.variables['OM__resultTime'][:]\n\tpredd.close()\n\t\n\t#reformatting predictand station and time data\n\tpredd_stations = stations(predd_stat)\n\tpredd_gmt = gmt(predd_time)\n\n\t#choosing predictand observations that line up with predictor time\n\thour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time\n\tdays = len(predd_gmt)/24\n\tpredd_hours = [0]*days\n k=0\n for i in range(len(predd_gmt)):\n if i%24 == hour:\n\t\t\tpredd_hours[k]=predd_gmt[i]\n\t\t\tk+=1\n\t\n\t#catches when GFS data doesn't cover the last day of the month\n\tif len(predr_gmt) < len(predd_hours):\n\t\tpredd_hours = predd_hours[:-1]\t\n\t\n\t#find missing stations\n\tmiss_stations = miss_station(predr_stations,predd_stations)\n\tstations = predd_stations\n\t\n\t#station and time array\n\tinfo = [['',''] for k in range(len(predr_gmt)*len(stations))]\n\tfor i in range(len(predr_gmt)):\n\t\tfor j in range(len(stations)):\n\t\t\tk = i*len(stations)+j\n\t\t\tinfo[k][0]=predr_gmt[i]\n\t\t\tinfo[k][1]=stations[j]\n\n\t#create column names\n\tnames = ['']*(len(predictor)+len(predictand)+2)\n\tnames[0]='Time'\n\tnames[1]='Station'\n\n\t#creating array\n\tarr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))\n\t\n\t#adding predictor data\n\tfor i in range(len(predictor)):\n\t\t#remove lead time and forecast reference time from variable name\n\t\t#and add variable name to column list of final dataframe\n\t\tif lead_time == 12:\n\t\t\tnames[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]\n\t\telse:\n\t\t\t names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]\n\n\t\t#create pandas dataframe of data and sort alphabetically by station name\n\t\tpredictor[i].data = np.squeeze(predictor[i].data,axis=2)\n\t\tpredictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)\n\t\tpredictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)\n\t\t\n\t\t#remove stations with no predictand data\n\t\tk=0\n\t\ta=miss_stations[:]\n\t\tfor j in predictor[i].data.columns:\n\t\t\tif not a:\n\t\t\t\tbreak\n\t\t\tif j==a[k]:\n\t\t\t\tpredictor[i].data=predictor[i].data.drop(j,axis=1)\n\t\t\t\tdel a[k]\n\t\t\n\t\t#add data to final dataframe\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tarr[k][i] = predictor[i].data.iloc[b][c]\n\n\t#add predictand data\n\tfor i in range(len(predictand)):\n\t\t#removing extra underscore, adding variable name to column names\n\t\tnames[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]\n\t\n\t\t#resize array and create pandas dataframe\n\t\tpredictand[i].data = np.squeeze(predictand[i].data,axis=2)\n\t\tpredictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)\n\t\tpredictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)\n\t\t\n\t\t#remove extra days of predictand data\n\t\tpredictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]\n\t\t\t\n\t\t#add predictand data to array\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tval = predictand[i].data.iloc[b][c]\n\t\t\t\t\n\t\t\t\t#catch metar fill data\n\t\t\t\tif val == 9999: \n\t\t\t\t\tval = np.nan\n\t\t\t\tarr[k][len(predictor)+i]=val\n\t\n\t#add station and time data to array and save as csv\n\tdata = np.concatenate([info,arr],axis = 1)\n\tto_save = pd.DataFrame(data,columns=names)\n\tto_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv')"},"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 create_dataframe(self):\n\n df = pd.DataFrame({'date': [],\n 'RUN': [],\n 'CLONE': [],\n 'GEN': pd.Series(0, index=[], dtype='int'),\n 'frame': pd.Series([], index=[], dtype='int'),\n 'time (ns)': [] }) # set the index\n df.set_index('date')\n print(df)\n\n # Save the DataFrame to disk\n\n ### create a file handle to store the data in (a dict-like) HDF5 format\n store = pd.HDFStore(self.dataframe_path)\n print(store)\n store.put('df', df)\n return store","def __create_data_frame(self, soup):\n self.__data_frame = pd.read_html(str(soup))[0]\n timestamp = self.__navigate_rows(soup)\n # rename dataframe columns by columns name in sqlite\n self.__data_frame = self.__data_frame.rename(\n columns=self.__columns_name)\n self.__data_frame['time'] = pd.Series(timestamp)\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\n str.replace('%', '')\n self.__data_frame['created_date'] = datetime.now()\n # save_file(self.__name_file, self.__data_frame.to_string())","def make_dataframe(self):\n logging.info('*** Creating the dataframes from the source files ' )\n \n for k in self.datasets_keys:\n #for k in ['igra2' , 'ncar']:\n \n logging.info('*** Creating the dataframe for the dataset: %s ' , k ) \n \n p_levels = self.data[k]['df']['observations_table']['z_coordinate'][:]\n logging.debug(' Loaded the z_coordinate')\n \n z_type = self.data[k]['df']['observations_table']['z_coordinate_type'][:]\n logging.debug(' Loaded the z_coordinate_type')\n \n obs_variable = self.data[k]['df']['observations_table']['observed_variable'][:]\n logging.debug(' Loaded the observed_variable')\n \n obs_values = self.data[k]['df']['observations_table']['observation_value'][:]\n logging.debug(' Loaded the observation_value')\n \n observation_id = self.data[k]['df']['observations_table']['observation_id'][:]\n logging.debug(' Loaded the observation_id')\n \n units = self.data[k]['df']['observations_table']['units'][:].astype(int)\n logging.debug(' Loaded the units') \n \n report_id = self.data[k]['df']['observations_table']['report_id'][:] \n logging.debug(' Loaded the report_id')\n \n date_time = self.data[k]['df']['observations_table']['date_time'][:]\n logging.debug(' Loaded the date_time (deltas)')\n \n lat , lon = self.data[k]['df']['observations_table']['latitude'][:] , self.data[k]['df']['observations_table']['longitude'][:]\n logging.debug(' Loaded the lat,lon ')\n \n \n self.obs_table_columns = list(self.data[k]['df']['observations_table'].keys() )\n \n self.data[k]['df'].close()\n \n \"\"\" Creating a dataframe \"\"\"\n columns = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units']\n logging.info(' Loaded the data, creating dataframe ')\n \n df = pd.DataFrame( list(zip( date_time, p_levels, z_type, obs_variable , obs_values, report_id, observation_id , lat , lon, units ) ) , columns = columns ) \n \n \n \"\"\" Storing the dataframe \"\"\" ### try using xarrays ??? \n logging.debug('Storing the DF ' ) \n self.data[k]['dataframe'] = df\n \n logging.debug(' PD dataframe created !!! ')","def save_dataframe(state: State):\n\n try:\n state.games.df.to_csv(ROOT_PATH + \"/results/data/raw_data.csv\")\n LOGGER.debug(\"Successfully saved data in ../results/data/\")\n\n except Exception as e:\n LOGGER.error(f\"Could not save dataframe file - {e}\")","def data_frame_creator(self):\n sequence_folder = [\n '/SEQ1', '/SEQ2', '/SEQ3', '/SEQ4', '/SEQ5', '/SEQ6'\n ]\n rgb_folder = ['/RGBLeft/', '/RGBRight/']\n depth_folder = ['/DepthLeft/', '/DepthRight/']\n segmentation_folder = ['/GTLeft/', '/GTright/']\n rgb_dir = [\n self.dataset_dir + sequence_f + rgb_f for rgb_f in rgb_folder\n for sequence_f in sequence_folder\n ]\n rgb_data = [\n rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d)\n ]\n\n depth_dir = [\n self.dataset_dir + sequence_f + depth_f\n for depth_f in depth_folder\n for sequence_f in sequence_folder\n ]\n depth_data = [\n depth_d + depth for depth_d in depth_dir\n for depth in os.listdir(depth_d)\n ]\n\n segmentation_dir = [\n self.dataset_dir + sequence_f + segmentation_f\n for segmentation_f in segmentation_folder\n for sequence_f in sequence_folder\n ]\n segmentation_data = [\n segmentation_d + segmentation\n for segmentation_d in segmentation_dir\n for segmentation in os.listdir(segmentation_d)\n ]\n\n dataset = {\n 'RGB': rgb_data,\n 'DEPTH': depth_data,\n 'SEGMENTATION': segmentation_data\n }\n\n if self.shuffle:\n return pd.DataFrame(dataset).sample(frac=1, random_state=123)\n\n return pd.DataFrame(dataset)","def make_output_df(self):\n df = pd.concat([pd.DataFrame(dat) for dat in [self.qdata, self.pdata]], axis=1)\n columns = np.hstack(([['{}{}'.format(x, c) for c in self.actions] for x in ['q', 'p']]))\n df.columns = columns\n df.insert(0, 'trial', np.arange(1, df.shape[0]+1))\n df['choice'] = self.choices\n df['feedback'] = self.feedback\n# r = np.array(self.bandits.rvalues)\n# p = np.array(self.bandits.preward)\n df['optimal'] = self.demand\n df.insert(0, 'agent', 1)\n self.data = df.copy()","def data_frame_creator(self):\n\n rgb_dir = [\n self.dataset_address + sequence_f + rgb_f\n for rgb_f in self.rgb_folder for sequence_f in self.sequence_folder\n ]\n rgb_data = [\n rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d)\n ]\n\n depth_dir = [\n self.dataset_address + sequence_f + depth_f\n for depth_f in self.depth_folder\n for sequence_f in self.sequence_folder\n ]\n depth_data = [\n depth_d + depth for depth_d in depth_dir\n for depth in os.listdir(depth_d)\n ]\n\n segmentation_dir = [\n self.dataset_address + sequence_f + segmentation_f\n for segmentation_f in self.segmentation_folder\n for sequence_f in self.sequence_folder\n ]\n segmentation_data = [\n segmentation_d + segmentation\n for segmentation_d in segmentation_dir\n for segmentation in os.listdir(segmentation_d)\n ]\n\n dataset = {\n 'RGB': rgb_data,\n 'DEPTH': depth_data,\n 'SEGMENTATION': segmentation_data\n }\n\n if self.shuffle:\n return pd.DataFrame(dataset).sample(frac=1)\n\n return pd.DataFrame(dataset)","def df():\n fs.df()","def make_dataset(self, df, **kwargs):\n\t\treturn df","def create_dataframe():\r\n\r\n df = pd.read_csv('data/data.csv', header=0)\r\n return df","def save_to_dataframe(self):\n titles, years, months, days, authors = list(), list(), list(), list(), list()\n for doc in self.results[\"documents\"]:\n titles.append(doc['title'])\n years.append(doc['year'])\n months.append(doc['month'])\n days.append(doc['day'])\n authors.append(doc['authors'])\n return pd.DataFrame({\"title\": titles, \"years\": years, \"months\": months, \"days\": days, \"author\": authors})","def save_df(data_frame, file_path):\r\n data_frame.to_csv(file_path)\r\n return None","def df():\n path, _ = os.path.split(os.path.abspath(__file__))\n project_path = os.path.join(path, os.pardir, os.pardir)\n\n values_path = os.path.join(project_path, \"data\", \"raw\", \"pumps_train_values.csv\")\n labels_path = os.path.join(project_path, \"data\", \"raw\", \"pumps_train_labels.csv\")\n\n train = pd.read_csv(values_path, index_col='id', parse_dates=[\"date_recorded\"])\n labels = pd.read_csv(labels_path, index_col='id')\n\n return train.join(labels)","def build_df(path_orig = r'.\\chest_xray', orig_file_ext = 'jpeg', path_seg = r'.\\segmentation', seg_file_ext = 'png', save_path = '.\\df_all.csv'):\n \n read_df = 'C'\n list_df = [] \n \n if os.path.exists(save_path):\n read_df = input('DataFrame was found, would you like to read it (R) or recreate it (C) (default Read)?\\n') or 'R'\n if read_df == 'R':\n df = pd.read_csv(save_path, index_col = 0)\n return df\n \n if read_df == 'C':\n for dirname, _, filenames in os.walk(path_orig):\n for filename in tqdm(filenames, disable=len(filenames)==0):\n if ('.' + orig_file_ext) in filename:\n list_val = []\n list_val.append('PNEUMONIA' if 'PNEUMONIA' in dirname else 'NORMAL')\n list_val.append(1 if 'PNEUMONIA' in dirname else 0)\n list_val.append('bacteria' if 'bacteria' in filename.lower() else 'virus' if 'virus' in filename.lower() else 'normal')\n list_val.append(1 if 'bacteria' in filename.lower() else 2 if 'virus' in filename.lower() else 0)\n list_val.append(filename)\n list_val.append(os.path.join(dirname, filename)) \n list_val.append(filename.replace(orig_file_ext, seg_file_ext))\n list_val.append(os.path.join(dirname.replace(path_orig, path_seg), filename.replace(orig_file_ext, seg_file_ext)))\n list_df.append(list_val)\n\n df = pd.DataFrame(list_df, columns = ['Label_name', 'Label_int', 'Label_pathology', 'Label_pathology_int', 'Filename_orig', 'Filepath_orig', 'Filename_seg', 'Filepath_seg'])\n df.to_csv(save_path)\n \n print('Done')\n \n return df","def save_data(self) -> None:\n # Construct a grid in physical space\n rvals = np.logspace(start=-3,\n stop=2.5,\n num=21,\n endpoint=True)\n # Compute C, D, K1 and F on that grid\n Cvals = np.array([self.compute_C(r, Suppression.RAW) for r in rvals])\n Dvals = np.array([self.compute_D(r, Suppression.RAW) for r in rvals])\n K1vals = np.array([self.compute_K1(r, Suppression.RAW) for r in rvals])\n Fvals = np.array([self.compute_F(r, Suppression.RAW) for r in rvals])\n # Save them to file\n df = pd.DataFrame([rvals, Cvals[:, 0], Dvals[:, 0], K1vals[:, 0], Fvals[:, 0],\n Cvals[:, 1], Dvals[:, 1], K1vals[:, 1], Fvals[:, 1]]).transpose()\n df.columns = ['r', 'C(r)', 'D(r)', 'K1(r)', 'F(r)', 'dC(r)', 'dD(r)', 'dK1(r)', 'dF(r)']\n df.to_csv(self.file_path(self.filename + '.csv'), index=False)","def build_dataframe(self):\n #Freq 0.0 2.5\n #ElementID NodeID Item\n #6901 6901 angle 0.000000+0.000000j 0.000000+0.000000j\n # sc 13.847674-0.461543j 13.855294-0.462052j\n # sd 0.625892-0.020861j 0.623742-0.020717j\n # se -12.178029+0.405894j -12.185331+0.406381j\n # sf 1.043753-0.034788j 1.046222-0.034953j\n # 6904 angle 0.000000+0.000000j 0.000000+0.000000j\n # sc -1.660571-0.416504j -1.663256-0.416978j\n # sd -2.790551+0.024178j -2.789738+0.024356j\n # se 0.627616+0.450933j 0.629571+0.451455j\n # sf 1.757596+0.010251j 1.756053+0.010121j\n #6902 6901 angle 0.000000+0.000000j 0.000000+0.000000j\n headers = self.headers\n column_names, column_values = self._build_dataframe_transient_header()\n self.data_frame = self._build_pandas_transient_element_node(\n column_values, column_names,\n headers, self.element_node, self.data)","def build_dataframe() -> pd.DataFrame:\n df = pd.DataFrame(\n np.random.randint(0, 1000, size=(1000, 6)), columns=list(\"ABCDEF\")\n )\n\n return df","def construct_data_frame(self) -> pd.DataFrame:\n data_frame = self.base_data_frame[\n [self.name_col, self.description_col]\n ].reset_index()\n data_frame.columns = [\"label_encoder\", \"name\", \"description\"]\n\n return data_frame.set_index(\"label_encoder\")","def make_dataframe(self, dataframe_path, corpus_path):\n directory_list = os.listdir(corpus_path)\n pub_year = []\n pii = []\n doi = []\n title = []\n authors = []\n num_authors = []\n abstract = []\n journal_name = []\n\n for i in trange(len(directory_list)):\n directory = directory_list[i]\n json_dict = self.load_journal_json(f'{corpus_path}/{directory}/{directory}.json')\n\n for year in json_dict:\n for pub in json_dict[year]:\n pub_year.append(year)\n pii.append(json_dict[year][pub]['pii'])\n doi.append(json_dict[year][pub]['doi'])\n title.append(json_dict[year][pub]['title'])\n authors.append(json_dict[year][pub]['authors'])\n num_authors.append(json_dict[year][pub]['num_authors'])\n abstract.append(json_dict[year][pub]['description'])\n journal_name.append(directory)\n\n columns = ['pub_year', 'pii', 'doi', 'title', 'authors', 'num_authors', 'abstract', 'journal_name']\n df = pd.DataFrame(np.array([pub_year, pii, doi, title, authors, num_authors, abstract, journal_name], dtype=object).transpose(), columns=columns)\n df.to_pickle(dataframe_path + '/dataframe_from_CorpusGenerator' +'.pkl')","def to_frame(self) -> DataFrame:\n if not self.is_initialized:\n _logger.info(\"Grid has not been initialized. Ensure to run DataGrid.initialize()\")\n return DataFrame()\n\n return self._post_process()","def create_geneIDsDF():\n datas=data.plfam_to_matrix()\n datas.run()\n print('***Dataframe created***')","def output_df(outdict, out_file):\n\tcols = ['#chrom', 'source', 'feature', 'chromStart', 'chromEnd', 'score', 'strand', 'frame', 'transcript_id']\n\tcolOut = ['#chrom', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'transcript_id']\n\tgtfDF = pd.DataFrame(columns=cols)\n\n\tfor trsp in outdict:\n\t\tgtfDF = gtfDF.append(outdict[trsp], ignore_index=True)\n\t\t\n\tgtfDF.columns = colOut\n\t# print gtfDF.head()\n\tgtfDF.to_csv(out_file, compression='gzip', sep='\\t', index=False)","def construct_df():\n iterable = [['approach', 'contact', 'retract', 'pause'], ['force', 'height']]\n index = pd.MultiIndex.from_product(iterable, names=['segment', 'channel'])\n return pd.DataFrame(columns=index)","def df(self):\n\n # populate dataframe with level data\n columns = {\n \"z\": self.z(),\n \"z_level_qc\": self.z_level_qc(),\n \"z_unc\": self.z_unc(),\n \"t\": self.t(),\n \"t_level_qc\": self.t_level_qc(),\n \"t_unc\": self.t_unc(),\n \"s\": self.s(),\n \"s_level_qc\": self.s_level_qc(),\n \"s_unc\": self.s_unc(),\n \"oxygen\": self.oxygen(),\n \"phosphate\": self.phosphate(),\n \"silicate\": self.silicate(),\n \"pH\": self.pH(),\n \"p\": self.p()\n }\n\n df = pd.DataFrame(columns)\n\n # record profile data in a metadata object on the dataframe\n df.attrs[\"latitude\"] = self.latitude()\n df.attrs[\"latitude_unc\"] = self.latitude_unc()\n df.attrs[\"longitude\"] = self.longitude()\n df.attrs[\"longitude_unc\"] = self.longitude_unc()\n df.attrs[\"uid\"] = self.uid()\n df.attrs[\"n_levels\"] = self.n_levels()\n df.attrs[\"year\"] = self.year()\n df.attrs[\"month\"] = self.month()\n df.attrs[\"day\"] = self.day()\n df.attrs[\"time\"] = self.time()\n df.attrs[\"cruise\"] = self.cruise()\n df.attrs[\"probe_type\"] = self.probe_type()\n df.attrs[\"originator_flag_type\"] = self.originator_flag_type()\n df.attrs[\"PIs\"] = self.PIs()\n df.attrs[\"originator_station\"] = self.originator_station()\n df.attrs[\"originator_cruise\"] = self.originator_cruise()\n df.attrs[\"t_metadata\"] = self.t_metadata()\n df.attrs[\"s_metadata\"] = self.s_metadata()\n\n return df","def save(df, out_file):\n print('------------< save >------------')\n out_path = './data'\n makedirs(out_path, exist_ok=True)\n print(f'path: {out_path}/{out_file}')\n print(f'shape: {df.shape}')\n df.to_csv(f'{out_path}/{out_file}', index=False)\n print('--------------------------------')","def data_visualization_general(data):\n path_to_save = str(Path(__file__).parent.parent) + '/jupyter_notebook/'\n key = list(data.keys())[0] # we do this way because keys() return a dict-keys which is not subscriptable\n df = pd.DataFrame(data[key])\n file_name = key + \".csv\"\n df.to_csv(os.path.join(path_to_save, file_name))\n # we save it in the jupyter_notebook folder so that it will be easier to show data on jupyter notebook","def save_data(df, database_filename):\n engine = create_engine('sqlite:///' +database_filename)\n df.to_sql('Project2', engine, index=False)","def _make_results_dataframe(self):\n LOG.debug(\"Creating Results Dataframes.\")\n results_df = tfs.TfsDataFrame(index=self.twiss_df.index)\n results_df[\"S\"] = self.twiss_df[\"S\"]\n return results_df","def sourceToDataframe(self):\n df = pd.read_excel(self.filename)\n df.columns = df.iloc[10]\n df = df.drop(df.index[:11])\n self.df = df #makes this df accessible to the whole class now\n self.insertODN()\n display(df.head())","def data_frame_creator(self):\n\n return pd.DataFrame()","def dataframe():\n headers = get_headers()\n headers = {'headers': headers}\n headers = pd.DataFrame.from_dict(headers, orient='index')\n headers = headers.replace(r'\\n', ' ', regex=True)\n headers = headers.replace(r'\\r', ' ', regex=True)\n headers = headers.replace(r'\\t', ' ', regex=True)\n headers = headers.replace(r'\\\\t', ' ', regex=True)\n headers = headers.replace(r' ', ' ', regex=True)\n headers = headers.replace(r' ', ' ', regex=True)\n\n paragraphs = get_paragraphs()\n paragraphs = {'paragraphs': paragraphs}\n paragraphs = pd.DataFrame.from_dict(paragraphs, orient='index')\n paragraphs = paragraphs.replace(r'\\n', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\r', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\t', ' ', regex=True)\n paragraphs = paragraphs.replace(r'\\\\t', ' ', regex=True)\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\n\n return headers.to_csv('headers.csv', index=False), paragraphs.to_csv('paragraphs.csv', index=False)","def execute(self):\n try:\n self.data_frame.write.mode('append').format(self.file_format).save(self.location)\n return self.data_frame\n except AnalysisException as exp:\n raise","def glass_pandas(self):\n # pandas.set_option('display.width', 120)\n # TODO timeit (git_implementation) vs (my_implementation)\n # * df = pd.DataFrame(json.loads(r.text))\n # * df = df.set_index('t')\n # * df.index = pd.to_datetime(df.index, unit='s')\n # * df = df.sort_index()\n # * s = df.v\n # * s.name = '_'.join(url.split('/')[-2:])\n # * return s\n # for elem in self.loaded:\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\n # try:\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\n # framed = pandas.DataFrame(\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\n # for k in frame_keys} for iters in range(len(_data))],\n # columns=frame_keys)\n # except KeyError:\n # framed = pandas.DataFrame(_data)\n # framed.set_index('t', inplace=True)\n # framed.index = pandas.to_datetime(\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\n # framed.sort_index(inplace=True)\n # framed.name = _metric\n # print(framed.name)\n # print(framed)","def create_data_frame(self):\n column_names = Annotations.create_columns(self.headers, self.annot_types)\n dtypes = Annotations.get_dtypes_for_group_annots(self.headers, self.annot_types)\n df = self.open_file(\n self.file_path,\n open_as=\"dataframe\",\n # Coerce values in group annotations\n converters=dtypes,\n # Header/column names\n names=self.headers,\n # Prevent pandas from reading first 2 lines in file\n # since they're passed in with param 'names'\n skiprows=2,\n )[0]\n self.file = Annotations.convert_header_to_multi_index(df, column_names)","def createDataFrame(path):\n df = pd.read_csv(path)\n df = df[['planet_name', 'planet_mass', 'orbital_radius', 'host_name', \n 'spectral_type', 'stellar_age', 'stellar_radius', \n 'stellar_mass', 'stellar_temperature', 'stellar_luminosity', \n 'optical_magnitude', 'near_ir_magnitude', \n 'stellar_surface_gravity', 'stellar_metallicity']]\n \n df = df.dropna(subset=['spectral_type'])\n df.spectral_type = df.spectral_type.str[0:1]\n df.spectral_type = df.spectral_type.str.strip()\n classification = np.array(['O','B','A','F','G','K','M'])\n df = df[df.spectral_type.isin(classification)]\n df.insert(4, \"amount_of_planets\", 0)\n df.amount_of_planets = df.groupby('host_name')['host_name'].transform('count')\n \n df.planet_mass = np.log10(df.planet_mass)\n df.orbital_radius = np.log10(df.orbital_radius)\n \n df = df.sort_values(by=['host_name'])\n df = df.reset_index(drop=True) \n \n return df","def create_data_table(df: pd.DataFrame) -> pd.DataFrame:\n\n df = df.copy()\n\n # Normalize times by labeling all of today's data with its future label, 00:00\n # tomorrow (as that's the timestamp marking the end of the 24-hour data collection\n # period). No need to adjust data not from today; it's already been adjusted and is\n # labeled with the date whose 00:00 marked the end of data collection (i.e., data\n # generated on Mar 20 is labeled Mar 21).\n normalized_dates = df[Columns.DATE].dt.normalize()\n is_at_midnight = df[Columns.DATE] == normalized_dates\n df.loc[~is_at_midnight, Columns.DATE] = normalized_dates[\n ~is_at_midnight\n ] + pd.Timedelta(days=1)\n df[Columns.DATE] = df[Columns.DATE].dt.strftime(r\"%Y-%m-%d\")\n\n df = df.drop(\n columns=[\n Columns.IS_STATE,\n Columns.LOCATION_NAME,\n Columns.OUTBREAK_START_DATE_COL,\n Columns.DAYS_SINCE_OUTBREAK,\n Columns.POPULATION,\n Columns.STAGE,\n Columns.COUNT_TYPE,\n ]\n )\n\n df = (\n df.pivot_table(\n index=[\n c\n for c in df.columns\n if c not in [Columns.CASE_TYPE, Columns.CASE_COUNT]\n ],\n columns=Columns.CASE_TYPE,\n values=Columns.CASE_COUNT,\n aggfunc=\"first\",\n )\n .reset_index()\n .sort_values([Columns.COUNTRY, Columns.STATE, Columns.DATE])\n )\n\n for col in CaseInfo.get_info_items_for(\n InfoField.CASE_TYPE, count=Counting.TOTAL_CASES\n ):\n df[col] = pd.to_numeric(df[col], downcast=\"integer\")\n\n # save_path = Paths.DATA / \"data_table.csv\"\n # df.to_csv(save_path, index=False)\n # print(f\"Saved data to {save_path.relative_to(Paths.ROOT)}\")\n\n return df","def save_fitted_dataframe(comp_data_df, filename):\r\n comp_data_df.to_csv('{}.csv'.format(filename))\r\n return 0","def generateDataFrame(self):\n labelArray = []\n\n # At this level ignored files are excluded\n for item in self.added:\n self.folderTree.append(item)\n\n for item in self.folderTree:\n if item in self.modified:\n labelArray.append('modified')\n elif item in self.deleted:\n labelArray.append('deleted')\n elif item in self.ignored:\n labelArray.append('ignored')\n elif item in self.added:\n labelArray.append('added')\n else:\n labelArray.append('baseFile')\n\n df = pd.DataFrame(list(zip(self.folderTree, labelArray)), \\\n columns=['File', 'Type'])\n self.fileDataFrame = df","def _get_outputdf(self):\n keys = self.info_df['Trace'].values.tolist()\n frame = deepcopy([line.df for line in self.info_df['Line'].values.tolist()])\n for i in range(len(frame)):\n df = frame[i]\n num = list(range(len(df)))\n angle_gr = list(map(deg2grad,df['Angle Horizontal'].values))\n df.insert(0,'Number', ['s' + '-'.join(x) + 'gr' for x in zip(map(str,num),map(str,map(int,angle_gr)))])\n df.insert(1, 'Name', keys[i])\n return pd.concat(frame, keys=keys, join='inner', ignore_index=True)","def save_to_csv(self):\n path = partial(os.path.join, 'datasets')\n save_name = self.name.lower().replace(' ', '_')\n self.df['values'].sum(axis=1).to_csv(path('{0}_values.csv'.format(save_name)))\n self.df['allocations'].to_csv(path('{0}_allocations.csv'.format(save_name)))\n self.df['returns'].to_csv(path('{0}_returns.csv'.format(save_name)))\n self.trades.to_csv(path('{0}_trades.csv'.format(save_name)))","def gen_main_df(add_list: list):\r\n # 由Bert 计算得来的 sentiment信息\r\n if 'sentiment' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('sentiment')\r\n sentiment = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'daily_svm_sentiment_6class' , 'csv')[0],\r\n 'date', ['0'], 'sentiment') # 'daily_svm_sentiment_2class' '0', '1', '2', '3', '4', '5'\r\n data_manipulator.add_column(sentiment)\r\n # 中国CPI指数\r\n if 'cpi' in add_list and 'cpi' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('cpi')\r\n cpi = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'CPI', 'csv')[0],\r\n '日期', ['最新值', '涨跌幅', '近3月涨跌幅'], 'CPI')\r\n data_manipulator.add_column(cpi)\r\n # 上海银行间同业拆放利率\r\n if 'shibor' in add_list and 'shibor' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('shibor')\r\n shibor = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'shibor', 'csv')[0],\r\n 'date', ['on', '1w', '2w', '1m', '3m'], 'Shibor')\r\n data_manipulator.add_column(shibor)\r\n # 上证综指\r\n if 'shangzheng' in add_list and 'shangzheng' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('shangzheng')\r\n shangzheng = data_manipulator.read_in_file(\r\n data_manipulator.get_file_names(root, 'datasets', 'ShangZheng', 'csv')[0],\r\n 'trade_date', ['open', 'high', 'low', 'close', 'pct_chg', 'vol', 'amount',\r\n 'total_mv', 'float_mv', 'total_share', 'float_share',\r\n 'free_share', 'turnover_rate', 'turnover_rate_f', 'pe',\r\n 'pe_ttm', 'pb'],\r\n 'ShangZheng')\r\n data_manipulator.add_column(shangzheng)\r\n data_manipulator.shift_columns(['ShangZheng_pct_chg'], (-1,),\r\n add=True) # name has changed to shift-1_ShangZheng_pct_chg\r\n data_manipulator.rank_df_column(['shift-1_ShangZheng_pct_chg'],\r\n rank_list=[-10, -1, -0.5, 0, 0.5, 1, 10]) # rank_list=[-10, 0, 10] [-10, -1, -0.5, 0, 0.5, 1, 10]\r\n shangzheng_30min = data_manipulator.read_in_file(\r\n data_manipulator.get_file_names(root, 'datasets', 'ShangZheng_index_30min', 'csv')[0],\r\n 'trade_time', ['open', 'high', 'low', 'close', 'pct_chg', 'vol', 'amount'],\r\n 'ShangZheng_30min')\r\n data_manipulator.news_df_add_column(shangzheng_30min)\r\n data_manipulator.shift_minute_columns(['ShangZheng_30min_pct_chg'], (-1,),\r\n add=True)\r\n data_manipulator.rank_minute_df_columns(['shift-1_ShangZheng_30min_pct_chg'],\r\n rank_list=[-10, -1, -0.5, 0, 0.5, 1, 10]) # rank_list=[-10, 0, 10] [-10, -1, -0.5, 0, 0.5, 1, 10]\r\n\r\n # M2 广义货币量\r\n if 'm2' in add_list and 'm2' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('m2')\r\n m2 = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'M2', 'csv')[0],\r\n '月份', ['M2数量(亿元)', 'M2同比增长', 'M2环比增长'], 'M2')\r\n m2 = data_manipulator.complement_df(m2, 'date')\r\n data_manipulator.add_column(m2)\r\n\r\n # 人民币美元汇率\r\n if 'rmb_usd' in add_list and 'rmb_usd' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('rmb_usd')\r\n rmb_usd = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'RMB_USD', 'csv')[0],\r\n 'trade_date',\r\n ['bid_open', 'bid_close', 'bid_high', 'bid_low', 'ask_open',\r\n 'ask_close', 'ask_high', 'ask_low', 'tick_qty'], 'exchange')\r\n data_manipulator.add_column(rmb_usd)\r\n\r\n # 沪港通 沪深通 到岸 离岸资金流\r\n if 'fund_flow' in add_list and 'fund_flow' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('fund_flow')\r\n fund_flow = data_manipulator.read_in_file(\r\n data_manipulator.get_file_names(root, 'datasets', 'fund_flow', 'csv')[0],\r\n 'trade_date', ['north_money', 'south_money'], 'fund_flow')\r\n data_manipulator.add_column(fund_flow)\r\n\r\n # 债券回购日行情\r\n if 'repo' in add_list and 'repo' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('repo')\r\n repo = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'repo', 'csv')[0],\r\n 'trade_date', ['repo_maturity', 'open', 'high', 'low', 'close',\r\n 'amount'], 'repo', data_manipulator.cut_time_string,\r\n (0, 10,))\r\n repo = data_manipulator.select_col_group_by(repo, 'repo_repo_maturity', ['GC001', 'GC007', 'GC014', 'GC028'],\r\n 'date')\r\n data_manipulator.add_column(repo)\r\n\r\n # 新浪新闻\r\n if 'sina_news' in add_list and 'sina_news' not in data_manipulator.used_measure_list:\r\n data_manipulator.used_measure_list.append('sina_news')\r\n columns_type = {'create_time': str, 'text': str}\r\n sina_news = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'sina', 'csv')[0],\r\n 'create_time', ['text', ], 'sina', dtypes=columns_type)\r\n data_manipulator.add_change_news('sina', (7, 9), columns_type, sina_news, time_col_name='create_time')\r\n data_manipulator.add_minute_change_news('sina', columns_type, sina_news, time_col_name='create_time')\r\n if 'scale' in add_list:\r\n data_manipulator.scaling_col()\r\n if 'clear' in add_list:\r\n data_manipulator.clear()","def data(self):\n dfdata = pd.concat([self.weights, self.returns, self.category], axis=1)\n dfdata.columns = ['weights', 'returns', self.category_name]\n if self.period is not None:\n dfdata['date'] = self.period\n return dfdata","def create_query_csv(self):\n\n self.query_df.to_csv(self.query_output_file)","def run(self) -> DataFrame:\n with self.create_census_api_session():\n logger.info('Retrieving variables...')\n variables: Variables = self.get_variables()\n logger.info('Retrieving ACS tables...')\n tables = self.get_tables()\n\n # Add geometry\n gazetteer_files: List[GazetteerFile] = []\n shapefiles: List[Shapefile] = []\n if self.geometry == 'points':\n logger.info('Retrieving Gazetteer files...')\n gazetteer_files.extend(self.get_gazetteer_files())\n elif self.geometry == 'polygons':\n logger.info('Retrieving shapefiles...')\n shapefiles.extend(self.get_shapefiles())\n dataframe = self.assemble_dataframe(variables, tables, gazetteer_files, shapefiles)\n return dataframe","def build_graphs(df: pd.DataFrame, ir_dir: Path, graph_dir: Path):\n for _, row in df.iterrows():\n with open(ir_dir / f\"{row['name']}.ll\") as f:\n ir = f.read()\n graph = pg.from_llvm_ir(ir)\n graph.features.feature[\"devmap_label\"].int64_list.value[:] = [row[\"label\"]]\n graph.features.feature[\"wgsize\"].int64_list.value[:] = [row[\"wgsize\"]]\n graph.features.feature[\"transfer_bytes\"].int64_list.value[:] = [\n row[\"transfer_bytes\"]\n ]\n graph.features.feature[\"wgsize_log1p\"].float_list.value[:] = [\n row[\"wgsize_log1p\"]\n ]\n graph.features.feature[\"transfer_bytes_log1p\"].float_list.value[:] = [\n row[\"transfer_bytes_log1p\"]\n ]\n pbutil.ToFile(\n graph, graph_dir / f\"{row['name']}.ProgramGraph.pb\", exist_ok=False\n )","def write(self):\n \n self.df.to_csv('/home/austin/Desktop/Falcon/realestate/Falcon/Datasets/mls.csv')","def to_df(self):\n if self.shape > 1:\n range_str = [s for s in range(self.shape)]\n iterables = [self.columns, range_str]\n multiindex = pd.MultiIndex.from_product(iterables, names=['song', 'frame'])\n # multiindex = [i for i in itertools.product(self.columns, range_str, repeat=1)]\n df = pd.DataFrame(columns=multiindex, index=self.columns, dtype=np.float64)\n\n for c_1 in self.columns:\n for c_2 in self.columns:\n for s in range_str:\n df.loc[c_1][c_2, s] = self.dict_[c_1][c_2][s]\n df = df.T\n else:\n df = pd.DataFrame(columns=self.columns + ['song'], dtype=np.float64)\n df['song'] = self.columns\n df = df.set_index('song')\n\n for c_1 in self.columns:\n for c_2 in self.columns:\n df.loc[c_1, c_2] = self.max_diff(c_1, c_2)\n\n return df","def create_sample_dataframe():\n ax_readings = []\n ay_readings = []\n az_readings = []\n mx_readings = []\n my_readings = []\n mz_readings = []\n gx_readings = []\n gy_readings = []\n gz_readings = []\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\n\n\n for _ in range(SEGMENT_TIME_SIZE):\n ax_readings.append(random.uniform(-10,10))\n ay_readings.append(random.uniform(-10,10))\n az_readings.append(random.uniform(-10,10))\n mx_readings.append(random.uniform(-10,10))\n my_readings.append(random.uniform(-10,10))\n mz_readings.append(random.uniform(-10,10))\n gx_readings.append(random.uniform(-10,10))\n gy_readings.append(random.uniform(-10,10))\n gz_readings.append(random.uniform(-10,10))\n\n data_dict = {\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\n }\n\n df = pd.DataFrame(data=data_dict)\n return df","def export_data(self, pth):\n self.cleanup_allowed = False\n self.train_df.to_csv(os.path.join(pth, \"train.csv\"))\n self.valid_df.to_csv(os.path.join(pth, \"valid.csv\"))\n self.test_df.to_csv(os.path.join(pth, \"test.csv\"))","def save_dataframe(dataframe, filename):\n with open(filename, \"w\", encoding=\"utf8\") as outfile: \n dataframe.to_csv(outfile, sep=\",\")","def create_data_frame_for_figures(\n results_path, save_path, results_folder_name, is_baseline=False\n):\n\n # Columns will be loading in data for\n\n columns = [\n \"filename\",\n \"sim_id\",\n \"virtual_patient_num\",\n \"sensor_num\",\n \"patient_scenario_filename\",\n \"age\",\n \"ylw\",\n \"cir\",\n \"isf\",\n \"sbr\",\n \"starting_bg\",\n \"starting_bg_sensor\",\n \"true_bolus\",\n \"initial_bias\",\n \"bias_norm_factor\",\n \"bias_drift_oscillations\",\n \"bias_drift_range_start\",\n \"bias_drift_range_end\",\n \"noise_coefficient\",\n \"delay\",\n \"bias_drift_type\",\n \"bias_type\",\n \"noise_per_sensor\",\n \"noise\",\n \"bias_factor\",\n \"phi_drift\",\n \"drift_multiplier\",\n \"drift_multiplier_start\",\n \"drift_multiplier_end\",\n \"noise_max\",\n \"mard\",\n \"mbe\",\n \"bg_test_condition\",\n \"analysis_type\",\n \"lbgi\",\n \"lbgi_risk_score\",\n \"dkai\",\n \"dkai_risk_score\",\n \"hbgi\",\n \"bgri\",\n \"percent_lt_54\",\n ]\n\n # Blank list to keep track of scenarios removed because settings are outside of clinical bounds.\n removed_scenarios = []\n\n # Blank list for adding data to\n data = []\n\n # Iterate through each of the files\n for i, filename in enumerate(\n sorted(os.listdir(results_path))\n ): # [0:100])): #(for testing)\n # Identify file is simulation file\n if filename.endswith(\".tsv\"):\n\n print(i, filename)\n\n # Read in that simulation data to a dataframe\n simulation_df = pd.read_csv(os.path.join(results_path, filename), sep=\"\\t\")\n\n # Check that the first two bg values are equal\n assert (\n simulation_df.loc[0][\"bg\"] == simulation_df.loc[1][\"bg\"]\n ), \"First two BG values of simulation are not equal\"\n\n # Find and read in the corresponding json data\n f = open(os.path.join(results_path, filename.replace(\".tsv\", \".json\")), \"r\")\n simulation_characteristics_json_data = json.loads(f.read())\n\n # Get the scenario settings characteristics so can check whether outside clinical bounds\n cir = simulation_characteristics_json_data[\"patient\"][\"config\"][\n \"carb_ratio_schedule\"\n ][\"schedule\"][0][\"setting\"].replace(\" g\", \"\")\n\n isf = simulation_characteristics_json_data[\"patient\"][\"config\"][\n \"insulin_sensitivity_schedule\"\n ][\"schedule\"][0][\"setting\"].replace(\" m\", \"\")\n\n sbr = simulation_characteristics_json_data[\"patient\"][\"config\"][\n \"basal_schedule\"\n ][\"schedule\"][0][\"setting\"].replace(\" U\", \"\")\n\n # If any of the settings are outside clinical bounds, add to removed scnearios list and do not load\n # into aggregated dataframe\n if settings_outside_clinical_bounds(cir, isf, sbr):\n print(filename + \" has settings outside clinical bounds.\")\n removed_scenarios.append([filename, cir, isf, sbr])\n\n # Otherwise add that data to data list\n else:\n\n # Get a row of data for that particular simulation and scenario characteristics\n data_row = get_data(\n filename,\n simulation_df,\n simulation_characteristics_json_data,\n baseline=is_baseline,\n )\n\n # Confirm that the length of the returned data matches the number of columns for\n # ultimate aggregate datafrrame\n assert len(data_row) == len(\n columns\n ), \"length of returned data does not match number of columns\"\n\n data.append(data_row)\n\n # Create and save dataframe of removed scenarios\n removed_scenarios_df = pd.DataFrame(\n removed_scenarios, columns=[\"filename\", \"cir\", \"isf\", \"sbr\"]\n )\n removed_scenarios_filename = \"{}_{}_{}_{}\".format(\n \"removed_scenarios_df\", results_folder_name, utc_string, code_version\n )\n\n removed_scenarios_df.to_csv(\n path_or_buf=os.path.join(save_path, removed_scenarios_filename + \".csv\"),\n index=False,\n )\n\n # Create the results dataframe\n results_df = pd.DataFrame(data, columns=columns)\n\n # Clean up the results dataframe\n results_df = clean_up_results_df(results_df)\n\n # Save the results dataframe to a csv\n\n results_df_filename = \"{}_{}_{}_{}\".format(\n \"results_df\", results_folder_name, utc_string, code_version\n )\n\n results_df.to_csv(\n path_or_buf=os.path.join(save_path, results_df_filename + \".csv\"),\n index=False,\n )\n\n return results_df","def create_model_csv(self):\n\n self.model_df.to_csv(self.model_output_file)","def to_df(self):\n from ..df import DataFrame\n\n return DataFrame(self)","def __generate_data_table__(self):\n # | - __generate_data_table__\n rows_list = []\n for job in self.job_var_lst:\n revisions = self.job_revision_number(job)\n for revision in range(revisions + 1)[1:]:\n # | - FOR LOOP BODY\n entry_param_dict = {}\n for prop in job:\n entry_param_dict[prop[\"property\"]] = prop[\"value\"]\n\n entry_param_dict[\"variable_list\"] = job\n entry_param_dict[\"path\"] = self.var_lst_to_path(job)\n\n entry_param_dict[\"max_revision\"] = revisions\n entry_param_dict[\"revision_number\"] = revision\n\n rows_list.append(entry_param_dict)\n # __|\n\n data_frame = pd.DataFrame(rows_list)\n\n return(data_frame)\n # __|","def generate_graph_feature(self):\n traj_graph_feature = [traj.get_graph_feature() for traj in self.trajectories]\n self.df_graph_feature = pd.DataFrame(traj_graph_feature)\n self.df_graph_feature[\"LABEL\"] = self.df[\"LABEL\"]\n return self.df_graph_feature","def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\n # Get all our data first with async\n # Note that all our pandas work will tax CPU so we wouldn't expect any\n # performance gains from doing the data parsing as a callback\n records = self.to_dict()\n data = []\n for series in records:\n df = pd.DataFrame(series.pop(\"data\"), columns=[\"period\", \"value\"])\n if include_metadata:\n df = df.assign(**series)\n data.append(df)\n return pd.concat(data, ignore_index=True)","def create_df(Varr, Iarr, POA, T, mode):\n df = pd.DataFrame()\n df['voltage'] = Varr\n df['current'] = Iarr\n df['E'] = POA\n df['T'] = T\n df['mode'] = mode\n return df","def create_df(datadir: str, ext: str='txt') -> pd.DataFrame:\n\n datalist = []\n for name in os.listdir(datadir):\n filename = '/'.join([datadir, name])\n if os.path.isfile(filename) and ext in name[-len(ext):]:\n row_data = []\n content = read_file.read_file(filename)\n row_data.append(read_file.extract_name(content))\n row_data.append(read_file.extract_year(content))\n row_data.append(read_file.extract_form_factor(content))\n row_data.append(read_file.extract_max_power(content))\n row_data.append(read_file.extract_min_power(content))\n row_data.append(read_file.extract_cpu_speed(content))\n row_data.append(read_file.extract_core_num(content))\n for ind in range(10, 100, 10):\n row_data.append(read_file.extract_int_power(content, ind))\n datalist.append(row_data)\n\n return pd.DataFrame(data=datalist, columns=[\n 'Name', 'Year', 'FormFac', 'MaxPower', 'IdlePower', 'CPU speed',\n 'NumCores'\n ]+[''.join([str(ind), '%Power']) for ind in range(10, 100, 10)])","def save_data(df, database_filename):\n # Create a database connection \n engine = create_engine('sqlite:///' + database_filename)\n \n # Insert df into DisasterCategories table\n df.to_sql('DisasterCategories', engine, index=False)","def save_data(df: pd.DataFrame, database_filename: str) -> None:\n engine = create_engine(f\"sqlite:///{database_filename}\")\n df.to_sql(Path(database_filename).stem, engine, index=False, if_exists=\"replace\")","def save_data(df, database_filename):\n engine = create_engine('sqlite:///'+database_filename)\n df.to_sql('disasterdata', engine, index=False)","def make_dataframes(self):\n self._data_frame_30days = pd.DataFrame(self._all30_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\n self._data_frame_60days = pd.DataFrame(self._all60_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\n self._data_frame_90days = pd.DataFrame(self._all90_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\n self._data_frame_counts = pd.DataFrame(\n {\n \"Created\": {\"totals\": self._data_frame_30days.count()[\"Created\"]},\n \"Closed\": {\"totals\": self._data_frame_30days.count()[\"Closed\"]},\n \"Owner\": (self._data_frame_30days[\"Owner\"].value_counts().to_dict()),\n \"Resolution\": (self._data_frame_30days[\"Resolution\"].value_counts().to_dict()),\n \"Severity\": (self._data_frame_30days[\"Severity\"].value_counts().to_dict()),\n },\n index=self.counts_frame_INDEX,\n )\n self._data_frame_counts.fillna(0, inplace=True)","def dataFrame(self):\n\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\n')\n reader = DictReader(memory_file)\n\n schema = StructType(\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\n )\n\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\n\n return self.spark.createDataFrame(data, schema)","def run(self, *args, **kw):\n super(GeopandasWriter, self).run(*args, **kw)\n data = self.get_input_data()\n data.to_file(self.file_name, self.format)","def write_df_to_db(df, db_path):\n print \"Writing to 'results' table in db: \", db_path\n conn = sqlite3.connect(db_path)\n df.to_sql(\"results\", con=conn,if_exists='replace')","def create_catalog_dataframe(save_dataframe):\n if not save_dataframe:\n return pd.read_pickle(f'{path_dictionary[\"catalog_dataframe_grouped_path\"]}')\n\n catalog_items = DbHelper.get_all_catalog_items()\n\n # Create dataframe to put all information together\n columns = ['user_id', 'item_id', 'session_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y', 'catalog_item_list', 'user_log_list']\n catalog_items_df = pd.DataFrame(catalog_items, columns=columns)\n\n # Clean 'Catalog Items' that user see during a session\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(clean_objects_listed, axis=1)\n\n # Clean Log Files\n catalog_items_df['user_log_list'] = catalog_items_df.apply(clean_logs, axis=1)\n\n # Get Catalog Items that user hover or has a click action, her/his mouse\n catalog_items_df = get_interacted_catalog_items(catalog_items_df)\n\n # Label the catalog items as 0\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(label_page_type, axis=1)\n\n catalog_items_df_grouped = catalog_items_df.groupby(['user_id', 'session_id'], as_index=False).agg(lambda x: list(x))\n catalog_items_df_grouped.drop(['item_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y'], axis=1, inplace=True)\n\n if save_dataframe:\n catalog_items_df.to_pickle(f'{path_dictionary[\"path_raw_catalog_dataframe\"]}')\n catalog_items_df_grouped.to_pickle(f'{path_dictionary[\"path_catalog_dataframe\"]}')\n catalog_items_df_grouped.to_csv(f'{path_dictionary[\"path_catalog_csv\"]}', index=False, sep='|')\n return catalog_items_df_grouped","def makeDF(csv_path):\n DF = pd.read_csv(csv_path)\n\n DF['height'] = DF.apply(lambda DF: abs(DF['ymax'] - DF['ymin']), axis=1)\n DF['width'] = DF.apply(lambda DF: abs(DF['xmax'] - DF['xmin']), axis=1)\n DF['objArea'] = DF.apply(lambda DF: (DF['width'] * DF['height']), axis=1)\n imageArea = 2704 * 1524\n DF['objPortion'] = DF.apply(lambda DF: (DF['objArea'] / imageArea), axis=1)\n\n # DF.to_csv('/NewDF.csv')\n DF.to_json('json_annot_all.json')\n\n # Looking at the first 5 rows to get the insigt on the data.\n print(DF.head(5))\n print(DF.label.unique())\n return DF","def make_df(self):\n # read in file\n df = pd.read_csv(self.data_file)\n cols_to_drop = [f'view{x}' for x in range(1,4)]+['response']\n # subtract loc3 viewing from location of interest\n df[self.label_key] = df[self.predictor] - df['view3']\n df.drop(cols_to_drop, axis=1, inplace=True)\n df.reset_index(drop=True, inplace=True)\n\n return df","def export_data(self):\r\n stocks = {}\r\n headings = ['Security', 'Price', 'Change', 'Change %', '52 Week', 'Market Cap']\r\n\r\n for data in range(6):\r\n for items in self.root.main.treeview.get_children():\r\n values = self.root.main.treeview.item(items, 'values')\r\n if headings[data] not in stocks:\r\n stocks[headings[data]] = []\r\n stocks.get(headings[data]).append(values[data])\r\n\r\n df = pd.DataFrame(stocks, columns=headings)\r\n path = tk.filedialog.asksaveasfilename(title='Save File As...',\r\n filetypes=((\"CComma-separated values (.csv)\", \"*.csv\"), (\"Text Document(.txt)\", \"*.txt\")))\r\n\r\n if not path:\r\n return\r\n else:\r\n df.to_excel(path, index=False, header=True)","def dataframe(self):\n df = pd.DataFrame({'x':self.x, 'y':self.y, 'd':self.d})\n\n if self.z is not None:\n for k, v in self.z.items():\n df[k] = v\n\n return df","def save(self):\n\t\t# save self.dfAnalysis\n\t\tcsvPath = self._getSavePath()\n\t\tprint('saving:', csvPath)\n\t\tself.dfAnalysis.to_csv(csvPath)","def data_solution_save(sol, column_names, file_name_prefix='vaccination_data_solution'):\n df = pd.DataFrame(sol)\n df.columns = column_names\n string_date = str(datetime.date(datetime.now()))\n file_name = file_name_prefix + string_date + \".pkl\" #\n df.to_pickle(file_name)\n return","def save_data(dataset_path: str,\n graphs: np.ndarray,\n influence_features: np.ndarray,\n labels: np.ndarray,\n distances: np.ndarray,\n embeddings: np.ndarray,\n own_company: np.ndarray,\n data_splits: np.ndarray,\n family_flag: np.ndarray) -> None:\n df_graphs = pd.concat(\n [pd.DataFrame(graph, dtype=int)\n for graph in graphs],\n keys=range(graphs.shape[0]),\n names=['observation id', 'neighbor id'])\n df_graphs.columns.name = 'neighbor id'\n\n df_normalized_embedding = pd.concat(\n [pd.DataFrame(normalized_embedding, dtype=float)\n for normalized_embedding in embeddings],\n keys=range(graphs.shape[0]),\n names=['observation id', 'neighbor id'])\n df_normalized_embedding.columns.name = 'embedding dimension'\n\n df_influence = pd.concat(\n [pd.DataFrame(influence_feature,\n columns=['influence', 'ego id'], dtype=int)\n for influence_feature in influence_features],\n keys=range(influence_features.shape[0]),\n names=['observation id', 'neighbor id'])\n\n df_labels = pd.Series(labels, name='label')\n df_labels.index.name = 'observation id'\n\n df_data_splits = pd.Series(data_splits, name='data_split')\n df_data_splits.index.name = 'observation id'\n\n df_family_flag = pd.Series(family_flag, name='family_flag')\n df_family_flag.index.name = 'observation id'\n\n df_distances = pd.Series(distances, name='distance')\n df_distances.index.name = 'observation id'\n\n df_own_company = pd.Series(own_company, name='own_company')\n df_own_company.index.name = 'observation id'\n\n dataset = os.path.basename(dataset_path[:-1])\n save_path = f'./public/data/{dataset}'\n os.makedirs(save_path, exist_ok=True)\n df_normalized_embedding.to_csv(save_path + '/Public_Normalized_Embedding.csv')\n # np.save(save_path + '/Public_Normalized_Embedding', embeddings)\n # torch.save(embeddings.detach(), save_path + '/Public_Normalized_Embedding_tensor.pt')\n df_influence.to_csv(save_path + '/Public_Influence_Features.csv')\n df_labels.to_csv(save_path + '/Public_Labels.csv')\n df_graphs.to_csv(save_path + '/Public_Graphs.csv')\n df_distances.to_csv(save_path + '/Public_Distances.csv')\n df_own_company.to_csv(save_path + '/Public_Own_Company_flag.csv')\n df_data_splits.to_csv(save_path + '/Public_Dataset_splits.csv')\n df_family_flag.to_csv(save_path + '/Public_Family_flag.csv')","def execute(self):\n try:\n self.data_frame.write.partitionBy(self.partition_by).mode('append') \\\n .format(self.file_format).save(self.location)\n return self.data_frame\n except AnalysisException as exp:\n raise","def return_combine_df_for_graph():\n\n\tkey = '18c95216b1230de68164158aeb02e2c2'\n\t# bade login with key\n\tbase = Dashboard(key)\n\t# get csv with write vars\n\tstart =os.path.dirname(os.path.realpath(sys.argv[0])) \n\tpath = os.path.join(start, 'Fred Graphs')\n\t# this path was used for flask,anothe day to fix this one \n\t#path = '/home/mike/Documents/coding_all/ModelApp/app/Fred Graphs'\n\t#base.write_fred_data_to_csv_from_dict(fred_econ_data, path)\n\n\t# convert csv to dict to use\n\tdf_dict = base.saved_csvs_to_dict(fred_econ_data.keys(), path)\n\n\t# skipped step, drop down, whyich can be typed into bc at some point will have list of all vars\n\t# and display graph indivusal with relevent data (type, seaonailty) displayed liek fed\n\n\t# next combine wanted vars to single df\n\tcombined_df = base.get_full_group_df(df_dict, time_interval='6M', group_unit='mean')\n\t# get spreads for IR rates\n\tcols_against = ['10 YR Treasury','Moody Aaa Yield','Moody Baa Yield','30 Year Mortgage', ]\n\tbase_col = 'Federal Funds Rate'\n\tspread_dict = base.get_yield_spreads_new(combined_df, cols_against, base_col, graph='no')\n\tspread_dict['date'] = combined_df.index\n\tcombined_spread_df = pd.DataFrame.from_dict(spread_dict)\n\tcombined_spread_df = combined_spread_df.set_index('date')\n\tcombined_spread_df.index = pd.to_datetime(combined_spread_df.index)\n\treturn combined_df, combined_spread_df","def fetch_training_df(df):\n\n gen_df = df.copy()\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\n return gen_df","def generate_DataFrame(file_path):\n # print (\"Generating DataFrame\")\n __log(1, 'Generating DataFrame....')\n\n df = pd.read_csv(file_path)\n df = df.rename(columns=lambda x: x.strip())\n df = df.dropna()\n\n for i in list(df.keys()):\n df[i] = df[i].apply(cleaning)\n\n # print (\"DataFrame Generated Successfully\")\n __log(1, 'DataFrame Generated Sucessfully.')\n return df","def createDataframe(filepath_train, filepath_test, domain, type_dataset):\n \n print(\"Reading pickle files...\")\n #read pickle files\n with open(filepath_train, \"rb\") as pkl_file:\n traindata = pickle.load(pkl_file)\n \n with open(filepath_test, \"rb\") as pkl_file:\n testdata = pickle.load(pkl_file)\n \n if domain == 'lopen':\n d = '.D450: Lopen en zich verplaatsen'\n l = 'FAC '\n elif domain == 'stemming':\n d = '.B152: Stemming'\n l = 'STM '\n elif domain == 'beroep':\n d = '.D840-859: Beroep en werk'\n l = 'BER '\n elif domain == 'inspanningstolerantie':\n d = '.B455: Inspanningstolerantie'\n l = 'INS '\n \n df_train, df_list_tr = completeDataframe(traindata)\n df_test, df_list_te = completeDataframe(testdata)\n \n #get note id's from keys in dataframe\n if type_dataset == 'covid':\n ids= []\n list_keys = df_test['key'].tolist()\n for key in list_keys:\n y = key.split('--')[3]\n ids.append(y)\n df_test['note_id'] = ids\n \n if type_dataset == 'noncovid':\n ids = []\n for instance in df_list_te:\n le = len(str(instance[2]))\n if instance[3] == \"['']\":\n ids.append(instance[0].split('---')[1])\n else:\n ids.append(instance[0].split('---')[1][:-le])\n df_test['note_id'] = ids\n \n \n df_train[domain] = 0\n df_train['level'] = 'None'\n df_test[domain] = 0\n df_test['level'] = 'None'\n \n #Add domain labels to a seperate column\n df_train['domain'][df_train['labels'].apply(lambda x: d in x)] = d\n df_train['level'][df_train['labels'].apply(lambda x: l+'0'in x)] = 0\n df_train['level'][df_train['labels'].apply(lambda x: l+'1'in x)] = 1\n df_train['level'][df_train['labels'].apply(lambda x: l+'2'in x)] = 2\n df_train['level'][df_train['labels'].apply(lambda x: l+'3'in x)] = 3\n df_train['level'][df_train['labels'].apply(lambda x: l+'4'in x)] = 4\n df_train['level'][df_train['labels'].apply(lambda x: l+'5'in x)] = 5\n \n df_test['domain'][df_test['labels'].apply(lambda x: d in x)] = d\n df_test['level'][df_test['labels'].apply(lambda x: l+'0'in x)] = 0\n df_test['level'][df_test['labels'].apply(lambda x: l+'1'in x)] = 1\n df_test['level'][df_test['labels'].apply(lambda x: l+'2'in x)] = 2\n df_test['level'][df_test['labels'].apply(lambda x: l+'3'in x)] = 3\n df_test['level'][df_test['labels'].apply(lambda x: l+'4'in x)] = 4\n df_test['level'][df_test['labels'].apply(lambda x: l+'5'in x)] = 5\n\n df_train.loc[df_train['domain'] == d, domain] = 1\n df_test.loc[df_test['domain'] == d, domain] = 1\n \n \n print(\"Filtering dataframes...\")\n #filter dataframe\n del_rows_tr, filtered_df_train = filterDataframe(df_train)\n #only select instances where there is an entry for level\n df_selection_train = filtered_df_train[(filtered_df_train[domain] == 1) & (filtered_df_train['level'] != 'None')]\n\n #test1\n del_rows_te, filtered_df_test = filterDataframe(df_test)\n #gold output\n df_selection_test = filtered_df_test[(df_test[domain] == 1) & (filtered_df_test['level'] != 'None')]\n \n return(df_selection_train, df_selection_test)","def build(self):\n list_of_mafs = []\n maf_generator = self.get_dataframe()\n\n for maf_as_dict in maf_generator:\n list_of_mafs.extend(maf_as_dict)\n\n reporting_path = os.path.join(app.config.get('REPORTING_ROOT_PATH'), app.config.get('REPORTING_PATH'), 'global')\n combined_maf = None\n try:\n combined_maf = pandas.DataFrame(list_of_mafs)\n except Exception as e:\n logger.error(f'Problem creating dataframe from list of dicts: {str(e)}')\n try:\n combined_maf.to_csv(\n os.path.join(reporting_path, f'{self.method}_combined_maf.tsv'),\n sep=\"\\t\",\n encoding='utf-8',\n index='false'\n )\n except Exception as e:\n # bad practice here catching base exception, but the pandas documentation did not reveal what errors or\n # exceptions to expect\n logger.error(f'Problem writing the combined maf file to csv:{str(e)}')\n abort(500)","def save_data(df, database_filepath):\n # create a database connect\n conn = sqlite3.connect(database_filepath)\n # replace .db with empty space for new table name\n table_name = database_filepath.replace('.db', '')\n \n return df.to_sql(table_name, con=conn, if_exists='replace', index=False)","def to_df(self) -> pd.DataFrame:\n data = []\n for action in self.actions:\n data.append(action.to_df())\n df = pd.read_json(json.dumps(data), orient=\"list\")\n return df[self.fields]","def create_dataframe_for_training(data):\n feature_column_name = 'X'\n #data_cp = data[['label']].copy()\n for i, row in tqdm(data.iterrows(), total=len(data)):\n all_features = f'{row.claimant} {row.claim} {row.article_content}'\n data.loc[i, feature_column_name] = all_features\n\n return data[feature_column_name]","def Make_DF(File=\"N1_50\", Period=\"\", Truth=False, Save=False):\n Folder = \"/scratch2/Master_krilangs/Trilepton_Ntuples/Skimslim/\"\n suffix = \"_merged_processed\"\n print(\"\\u0332\".join(File + \" \"))\n\n if Period == \"18\":\n SubFolder = \"data18_mc16e/\"\n elif Period == \"17\":\n SubFolder = \"data17_mc16d/\"\n elif Period == \"1516\":\n SubFolder = \"data1516_mc16a/\"\n else:\n SubFolder = \"\"\n\n \n if File == \"N1_50\":\n file50 = \"../myfile_allevents\"\n tree = uproot.open(Folder+file50+\".root\")[\"mytree\"]\n df = tree.pandas.df(flatten = False)\n del(tree) # Free up memory\n\n elif File == \"N1_150\":\n file150 = \"myfile_VERTEX_LFC_150_200_250\"\n tree = uproot.open(Folder + file150 + \".root\")[\"mytree\"]\n df = tree.pandas.df(flatten = False)\n del(tree) # Free up memory\n df = df.drop([df.index[11071], df.index[23774], df.index[60373], df.index[40743]])\n\n elif File == \"N1_450\":\n file450 = \"myfile_VERTEX_LFC_450_500_550\"\n tree = uproot.open(Folder + file450 + \".root\")[\"mytree\"]\n df = tree.pandas.df(flatten = False)\n del(tree) # Free up memory\n df = df.drop([df.index[14678], df.index[26355], df.index[39870], df.index[76527], df.index[60540], df.index[125862]])\n\n else:\n file = SubFolder + File + suffix\n tree = uproot.open(Folder + file + \".root\")[File + \"_NoSys\"]\n df_tree = tree.pandas.df(flatten = False)\n del(tree) # Free up memory\n df = df_tree.iloc[:,64:74]\n del(df_tree) # Free up memory\n if File == \"diboson3L\":\n df = df.drop([df.index[178412]])\n if File == \"diboson4L\":\n df = df.drop([df.index[3826200]])\n if File == \"topOther\":\n df = df.drop([df.index[3281191]])\n if File == \"ttbar\":\n df = df.drop([df.index[5690459], df.index[3723599]])\n\n newdf = lepaugmentation(df, 4, Truth)\n del(df) # Free up memory\n\n if Save:\n print(\"Save:\")\n newdf.to_hdf(\"Trilepton_ML.h5\", key=File+Period) # Save dataframe to file.\n\n print(newdf.info(verbose=True))\n #print(newdf[\"target\"].value_counts())\n \n del(newdf) # Free up memory","def as_DF(self):\n\n gs_df = pd.DataFrame(self.P, columns=self.xvec, index=self.yvec)\n gs_df.columns.name = 'x'\n gs_df.index.name = 'y'\n\n return gs_df","def save(self):\r\n self.df_app_data = self.df_app_data.to_csv(\"app_data.csv\", index=False)","def df_builder(path):\n\n ###CHANGE FILE ENDING (.csv or .csv.gz)\n all_files = glob.glob(\n os.path.join(path, \"probe_data_I210.201710*.csv\")) # advisable to use os.path.join as this makes concatenation OS independent\n df_from_each_file = (pd.read_csv(f) for f in all_files)\n return pd.concat(df_from_each_file, ignore_index=True)","def set_data(self):\n # take care of samples\n patients = self.samples.iloc[:,1].tolist()\n samples = self.samples.iloc[:,0].tolist()\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\n #\n # take care of expression data\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \n \n new_exp = self.expression.T.ix[1:,:] # transpose\n new_exp.columns = cols\n self.expression = new_exp # add columns\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join","def make_stats_df(self):\n columns = ['DATE', 'TEAM', 'teamId', 'R', 'HR', 'RBI', 'SBN', 'OBP', \n 'K', 'QS', 'SV', 'ERA', 'WHIP', 'MOVES', 'CHANGE']\n trimmed_table = self.parse_soup(self.stats)\n self.df_stats = pd.DataFrame(trimmed_table, columns=columns) \n # load season standings csv from file\n try: # if it already exists\n df = pd.read_csv('2016_stats.csv', index_col=0)\n except OSError:\n df = pd.DataFrame(columns=columns) # if it doesn't already exist\n df = df.append(self.df_stats)\n df.to_csv('2016_stats.csv')","def glue_table(name: str, df: pd.DataFrame, build_path=\"_build\"):\n\n if not os.path.exists(build_path):\n os.mkdir(build_path)\n df.to_excel(os.path.join(build_path, f\"{name}.xlsx\"))\n\n glue(name, df)","def create_dataframe(ids, names, p_links, c_links, cl_links):\n try:\n dict = {'ID':ids, 'Name': names, 'Photo':p_links, 'Flag':c_links, 'Club Logo':cl_links}\n df = pd.DataFrame(dict)\n return df\n except Exception as e:\n print(\"Exception creating or storing the dataframe: \" + str(e))","def __call__(self):\n\n # create dataframes of relevant sections from the INP\n for ix, sect in enumerate(self.config['inp_sections']):\n if ix == 0:\n df = create_dataframeINP(self.inp.path, sect, comment_cols=False)\n else:\n df_other = create_dataframeINP(self.inp.path, sect, comment_cols=False)\n df = df.join(df_other)\n\n if self.rpt:\n for rpt_sect in self.config['rpt_sections']:\n df = df.join(create_dataframeRPT(self.rpt.path, rpt_sect))\n\n # add conduit coordinates\n xys = df.apply(lambda r: get_link_coords(r, self.inp.coordinates, self.inp.vertices), axis=1)\n df = df.assign(coords=xys.map(lambda x: x[0]))\n\n # make inlet/outlet node IDs string type\n df.InletNode = df.InletNode.astype(str)\n df.OutletNode = df.OutletNode.astype(str)\n\n return df","def to_pandas(self):\n pass","def to_pandas(self):\n pass","def save_prediction(self, meta, y_pred, y, filename):\n df = pd.DataFrame(meta)\n df['y_pred'] = y_pred\n df['y'] = y\n print(df)\n df.loc[:, 'id'] = df.index\n self.df_to_csv(df, filename, store_header=False)","def dataframe(self):\n return self.generator.dataframe","def prepare_data(args):\n logger.info('Loading dataframe from %s' % args.newspath)\n df = pd.read_csv(args.newspath, encoding='gb18030')\n logger.info('Dataframe size: %d observations %d features after loaded' % (df.shape[0], df.shape[1]))\n\n # exclude rows with column source == NaN\n logger.info('Data cleansing...')\n df = df[~pd.isna(df['source'])]\n logger.info('Dataframe size: %d observations %d features after data cleansing' % (df.shape[0], df.shape[1]))\n\n # split the dataframe into training set and test set\n logger.info('Making training set & test set...')\n train_set, test_set = split_data(df)\n logger.info('Traning set size: %d' % train_set.shape[0])\n logger.info('Test set size: %d' % test_set.shape[0])\n\n # save the train set and test set to picke files\n logger.info('Save dataframes to files...')\n train_set.to_pickle(args.trainpath)\n test_set.to_pickle(args.testpath)","def save(df, save_preprocessed_dataframe_path, name):\n\n df.to_csv(save_preprocessed_dataframe_path + name + '.csv', index=False)","def get_training_dataframe(dataset_path, two_class=True, get_node_df=False):\n training_df = pd.DataFrame()\n graph_dict = {}\n\n\n rows_for_training_df = []\n \n if get_node_df:\n node_df = pd.DataFrame()\n rows_for_node_df = []\n\n\n # add conspiracy graphs to dataframe\n conspiracy_graphs = []\n for i in range(1,271): # 270 total\n graph_id = 'consp'+str(i)\n conspiracy_path = dataset_path + \"5g_corona_conspiracy/\"\n nodes = pd.read_csv(conspiracy_path + str(i)+ \"/nodes.csv\")\n edges = open(conspiracy_path + str(i)+ \"/edges.txt\")\n\n features, G = prepare_graph(1, nodes, edges, graph_id)\n rows_for_training_df.append(features)\n if get_node_df:\n prep_node_info(rows_for_node_df, nodes, graph_id)\n edges.close()\n conspiracy_graphs.append(G)\n graph_dict['conspiracy_graphs'] = conspiracy_graphs\n\n non_conspiracy_graphs = []\n for i in range(1,1661): # 1660 total\n graph_id = 'non_consp'+str(i)\n path = dataset_path + \"non_conspiracy/\"\n nodes = pd.read_csv(path + str(i)+ \"/nodes.csv\")\n edges = open(path + str(i)+ \"/edges.txt\")\n\n label = 0 if two_class else 3\n features, G = prepare_graph(label, nodes, edges, graph_id)\n rows_for_training_df.append(features)\n if get_node_df:\n prep_node_info(rows_for_node_df, nodes, graph_id)\n edges.close()\n edges.close()\n non_conspiracy_graphs.append(G)\n graph_dict['non_conspiracy_graphs'] = non_conspiracy_graphs\n\n other_conspiracy_graphs = []\n for i in range(1,398): # 397 total\n graph_id = 'other_consp'+str(i)\n path = dataset_path + \"other_conspiracy/\"\n nodes = pd.read_csv(path + str(i)+ \"/nodes.csv\")\n edges = open(path + str(i)+ \"/edges.txt\")\n\n label = 0 if two_class else 2\n features, G = prepare_graph(label, nodes, edges, graph_id)\n rows_for_training_df.append(features)\n if get_node_df:\n prep_node_info(rows_for_node_df, nodes, graph_id)\n edges.close()\n edges.close()\n other_conspiracy_graphs.append(G)\n graph_dict['other_conspiracy_graphs'] = other_conspiracy_graphs\n\n training_df = training_df.append(rows_for_training_df)\n if get_node_df:\n node_df = node_df.append(rows_for_node_df)\n return training_df, graph_dict, node_df\n return training_df, graph_dict","def to_archive(self, filename):\n with pd.HDFStore(filename, mode=\"w\") as store:\n if self.size() > 0:\n df = pd.DataFrame.from_records(self.data.to_records(index=True))\n else:\n df = pd.DataFrame(columns=self.columns, index=self.data.index)\n store.put(ARCH_KEY, df)\n metadata = {\n k: v\n for k, v in self.__dict__.items()\n if k not in [\"data\", \"filelist\", \"conf\", \"cats\"]\n }\n metadata[\"conf\"] = getattr(self.conf, \"to_dict\", lambda: {})()\n store.get_storer(ARCH_KEY).attrs.metadata = metadata\n # Store DataFrame with categorisation data\n if self.is_categorised:\n df_cat = pd.DataFrame.from_records(self.cats.astype(\"uint8\").to_records(index=True))\n store.put(ARCH_KEY_CAT, df_cat)","def create_data_frame(input_filepath):\n df = pd.read_json(input_filepath)\n logger = logging.getLogger(__name__)\n logger.info('Imported dataframe:')\n logger.info(df.info())\n logger.info(df.describe())\n logger.info(df.head())\n return df"],"string":"[\n \"def create_dataframe(self):\\n\\n df = pd.DataFrame({'date': [],\\n 'RUN': [],\\n 'CLONE': [],\\n 'GEN': pd.Series(0, index=[], dtype='int'),\\n 'frame': pd.Series([], index=[], dtype='int'),\\n 'time (ns)': [] }) # set the index\\n df.set_index('date')\\n print(df)\\n\\n # Save the DataFrame to disk\\n\\n ### create a file handle to store the data in (a dict-like) HDF5 format\\n store = pd.HDFStore(self.dataframe_path)\\n print(store)\\n store.put('df', df)\\n return store\",\n \"def __create_data_frame(self, soup):\\n self.__data_frame = pd.read_html(str(soup))[0]\\n timestamp = self.__navigate_rows(soup)\\n # rename dataframe columns by columns name in sqlite\\n self.__data_frame = self.__data_frame.rename(\\n columns=self.__columns_name)\\n self.__data_frame['time'] = pd.Series(timestamp)\\n self.__data_frame['chg_perc'] = self.__data_frame['chg_perc'].\\\\\\n str.replace('%', '')\\n self.__data_frame['created_date'] = datetime.now()\\n # save_file(self.__name_file, self.__data_frame.to_string())\",\n \"def make_dataframe(self):\\n logging.info('*** Creating the dataframes from the source files ' )\\n \\n for k in self.datasets_keys:\\n #for k in ['igra2' , 'ncar']:\\n \\n logging.info('*** Creating the dataframe for the dataset: %s ' , k ) \\n \\n p_levels = self.data[k]['df']['observations_table']['z_coordinate'][:]\\n logging.debug(' Loaded the z_coordinate')\\n \\n z_type = self.data[k]['df']['observations_table']['z_coordinate_type'][:]\\n logging.debug(' Loaded the z_coordinate_type')\\n \\n obs_variable = self.data[k]['df']['observations_table']['observed_variable'][:]\\n logging.debug(' Loaded the observed_variable')\\n \\n obs_values = self.data[k]['df']['observations_table']['observation_value'][:]\\n logging.debug(' Loaded the observation_value')\\n \\n observation_id = self.data[k]['df']['observations_table']['observation_id'][:]\\n logging.debug(' Loaded the observation_id')\\n \\n units = self.data[k]['df']['observations_table']['units'][:].astype(int)\\n logging.debug(' Loaded the units') \\n \\n report_id = self.data[k]['df']['observations_table']['report_id'][:] \\n logging.debug(' Loaded the report_id')\\n \\n date_time = self.data[k]['df']['observations_table']['date_time'][:]\\n logging.debug(' Loaded the date_time (deltas)')\\n \\n lat , lon = self.data[k]['df']['observations_table']['latitude'][:] , self.data[k]['df']['observations_table']['longitude'][:]\\n logging.debug(' Loaded the lat,lon ')\\n \\n \\n self.obs_table_columns = list(self.data[k]['df']['observations_table'].keys() )\\n \\n self.data[k]['df'].close()\\n \\n \\\"\\\"\\\" Creating a dataframe \\\"\\\"\\\"\\n columns = ['date_time', 'z_coordinate' , 'z_coordinate_type', 'observed_variable' , 'observation_value' , 'report_id' , 'observation_id' , 'latitude' , 'longitude', 'units']\\n logging.info(' Loaded the data, creating dataframe ')\\n \\n df = pd.DataFrame( list(zip( date_time, p_levels, z_type, obs_variable , obs_values, report_id, observation_id , lat , lon, units ) ) , columns = columns ) \\n \\n \\n \\\"\\\"\\\" Storing the dataframe \\\"\\\"\\\" ### try using xarrays ??? \\n logging.debug('Storing the DF ' ) \\n self.data[k]['dataframe'] = df\\n \\n logging.debug(' PD dataframe created !!! ')\",\n \"def save_dataframe(state: State):\\n\\n try:\\n state.games.df.to_csv(ROOT_PATH + \\\"/results/data/raw_data.csv\\\")\\n LOGGER.debug(\\\"Successfully saved data in ../results/data/\\\")\\n\\n except Exception as e:\\n LOGGER.error(f\\\"Could not save dataframe file - {e}\\\")\",\n \"def data_frame_creator(self):\\n sequence_folder = [\\n '/SEQ1', '/SEQ2', '/SEQ3', '/SEQ4', '/SEQ5', '/SEQ6'\\n ]\\n rgb_folder = ['/RGBLeft/', '/RGBRight/']\\n depth_folder = ['/DepthLeft/', '/DepthRight/']\\n segmentation_folder = ['/GTLeft/', '/GTright/']\\n rgb_dir = [\\n self.dataset_dir + sequence_f + rgb_f for rgb_f in rgb_folder\\n for sequence_f in sequence_folder\\n ]\\n rgb_data = [\\n rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d)\\n ]\\n\\n depth_dir = [\\n self.dataset_dir + sequence_f + depth_f\\n for depth_f in depth_folder\\n for sequence_f in sequence_folder\\n ]\\n depth_data = [\\n depth_d + depth for depth_d in depth_dir\\n for depth in os.listdir(depth_d)\\n ]\\n\\n segmentation_dir = [\\n self.dataset_dir + sequence_f + segmentation_f\\n for segmentation_f in segmentation_folder\\n for sequence_f in sequence_folder\\n ]\\n segmentation_data = [\\n segmentation_d + segmentation\\n for segmentation_d in segmentation_dir\\n for segmentation in os.listdir(segmentation_d)\\n ]\\n\\n dataset = {\\n 'RGB': rgb_data,\\n 'DEPTH': depth_data,\\n 'SEGMENTATION': segmentation_data\\n }\\n\\n if self.shuffle:\\n return pd.DataFrame(dataset).sample(frac=1, random_state=123)\\n\\n return pd.DataFrame(dataset)\",\n \"def make_output_df(self):\\n df = pd.concat([pd.DataFrame(dat) for dat in [self.qdata, self.pdata]], axis=1)\\n columns = np.hstack(([['{}{}'.format(x, c) for c in self.actions] for x in ['q', 'p']]))\\n df.columns = columns\\n df.insert(0, 'trial', np.arange(1, df.shape[0]+1))\\n df['choice'] = self.choices\\n df['feedback'] = self.feedback\\n# r = np.array(self.bandits.rvalues)\\n# p = np.array(self.bandits.preward)\\n df['optimal'] = self.demand\\n df.insert(0, 'agent', 1)\\n self.data = df.copy()\",\n \"def data_frame_creator(self):\\n\\n rgb_dir = [\\n self.dataset_address + sequence_f + rgb_f\\n for rgb_f in self.rgb_folder for sequence_f in self.sequence_folder\\n ]\\n rgb_data = [\\n rgb_d + rgb for rgb_d in rgb_dir for rgb in os.listdir(rgb_d)\\n ]\\n\\n depth_dir = [\\n self.dataset_address + sequence_f + depth_f\\n for depth_f in self.depth_folder\\n for sequence_f in self.sequence_folder\\n ]\\n depth_data = [\\n depth_d + depth for depth_d in depth_dir\\n for depth in os.listdir(depth_d)\\n ]\\n\\n segmentation_dir = [\\n self.dataset_address + sequence_f + segmentation_f\\n for segmentation_f in self.segmentation_folder\\n for sequence_f in self.sequence_folder\\n ]\\n segmentation_data = [\\n segmentation_d + segmentation\\n for segmentation_d in segmentation_dir\\n for segmentation in os.listdir(segmentation_d)\\n ]\\n\\n dataset = {\\n 'RGB': rgb_data,\\n 'DEPTH': depth_data,\\n 'SEGMENTATION': segmentation_data\\n }\\n\\n if self.shuffle:\\n return pd.DataFrame(dataset).sample(frac=1)\\n\\n return pd.DataFrame(dataset)\",\n \"def df():\\n fs.df()\",\n \"def make_dataset(self, df, **kwargs):\\n\\t\\treturn df\",\n \"def create_dataframe():\\r\\n\\r\\n df = pd.read_csv('data/data.csv', header=0)\\r\\n return df\",\n \"def save_to_dataframe(self):\\n titles, years, months, days, authors = list(), list(), list(), list(), list()\\n for doc in self.results[\\\"documents\\\"]:\\n titles.append(doc['title'])\\n years.append(doc['year'])\\n months.append(doc['month'])\\n days.append(doc['day'])\\n authors.append(doc['authors'])\\n return pd.DataFrame({\\\"title\\\": titles, \\\"years\\\": years, \\\"months\\\": months, \\\"days\\\": days, \\\"author\\\": authors})\",\n \"def save_df(data_frame, file_path):\\r\\n data_frame.to_csv(file_path)\\r\\n return None\",\n \"def df():\\n path, _ = os.path.split(os.path.abspath(__file__))\\n project_path = os.path.join(path, os.pardir, os.pardir)\\n\\n values_path = os.path.join(project_path, \\\"data\\\", \\\"raw\\\", \\\"pumps_train_values.csv\\\")\\n labels_path = os.path.join(project_path, \\\"data\\\", \\\"raw\\\", \\\"pumps_train_labels.csv\\\")\\n\\n train = pd.read_csv(values_path, index_col='id', parse_dates=[\\\"date_recorded\\\"])\\n labels = pd.read_csv(labels_path, index_col='id')\\n\\n return train.join(labels)\",\n \"def build_df(path_orig = r'.\\\\chest_xray', orig_file_ext = 'jpeg', path_seg = r'.\\\\segmentation', seg_file_ext = 'png', save_path = '.\\\\df_all.csv'):\\n \\n read_df = 'C'\\n list_df = [] \\n \\n if os.path.exists(save_path):\\n read_df = input('DataFrame was found, would you like to read it (R) or recreate it (C) (default Read)?\\\\n') or 'R'\\n if read_df == 'R':\\n df = pd.read_csv(save_path, index_col = 0)\\n return df\\n \\n if read_df == 'C':\\n for dirname, _, filenames in os.walk(path_orig):\\n for filename in tqdm(filenames, disable=len(filenames)==0):\\n if ('.' + orig_file_ext) in filename:\\n list_val = []\\n list_val.append('PNEUMONIA' if 'PNEUMONIA' in dirname else 'NORMAL')\\n list_val.append(1 if 'PNEUMONIA' in dirname else 0)\\n list_val.append('bacteria' if 'bacteria' in filename.lower() else 'virus' if 'virus' in filename.lower() else 'normal')\\n list_val.append(1 if 'bacteria' in filename.lower() else 2 if 'virus' in filename.lower() else 0)\\n list_val.append(filename)\\n list_val.append(os.path.join(dirname, filename)) \\n list_val.append(filename.replace(orig_file_ext, seg_file_ext))\\n list_val.append(os.path.join(dirname.replace(path_orig, path_seg), filename.replace(orig_file_ext, seg_file_ext)))\\n list_df.append(list_val)\\n\\n df = pd.DataFrame(list_df, columns = ['Label_name', 'Label_int', 'Label_pathology', 'Label_pathology_int', 'Filename_orig', 'Filepath_orig', 'Filename_seg', 'Filepath_seg'])\\n df.to_csv(save_path)\\n \\n print('Done')\\n \\n return df\",\n \"def save_data(self) -> None:\\n # Construct a grid in physical space\\n rvals = np.logspace(start=-3,\\n stop=2.5,\\n num=21,\\n endpoint=True)\\n # Compute C, D, K1 and F on that grid\\n Cvals = np.array([self.compute_C(r, Suppression.RAW) for r in rvals])\\n Dvals = np.array([self.compute_D(r, Suppression.RAW) for r in rvals])\\n K1vals = np.array([self.compute_K1(r, Suppression.RAW) for r in rvals])\\n Fvals = np.array([self.compute_F(r, Suppression.RAW) for r in rvals])\\n # Save them to file\\n df = pd.DataFrame([rvals, Cvals[:, 0], Dvals[:, 0], K1vals[:, 0], Fvals[:, 0],\\n Cvals[:, 1], Dvals[:, 1], K1vals[:, 1], Fvals[:, 1]]).transpose()\\n df.columns = ['r', 'C(r)', 'D(r)', 'K1(r)', 'F(r)', 'dC(r)', 'dD(r)', 'dK1(r)', 'dF(r)']\\n df.to_csv(self.file_path(self.filename + '.csv'), index=False)\",\n \"def build_dataframe(self):\\n #Freq 0.0 2.5\\n #ElementID NodeID Item\\n #6901 6901 angle 0.000000+0.000000j 0.000000+0.000000j\\n # sc 13.847674-0.461543j 13.855294-0.462052j\\n # sd 0.625892-0.020861j 0.623742-0.020717j\\n # se -12.178029+0.405894j -12.185331+0.406381j\\n # sf 1.043753-0.034788j 1.046222-0.034953j\\n # 6904 angle 0.000000+0.000000j 0.000000+0.000000j\\n # sc -1.660571-0.416504j -1.663256-0.416978j\\n # sd -2.790551+0.024178j -2.789738+0.024356j\\n # se 0.627616+0.450933j 0.629571+0.451455j\\n # sf 1.757596+0.010251j 1.756053+0.010121j\\n #6902 6901 angle 0.000000+0.000000j 0.000000+0.000000j\\n headers = self.headers\\n column_names, column_values = self._build_dataframe_transient_header()\\n self.data_frame = self._build_pandas_transient_element_node(\\n column_values, column_names,\\n headers, self.element_node, self.data)\",\n \"def build_dataframe() -> pd.DataFrame:\\n df = pd.DataFrame(\\n np.random.randint(0, 1000, size=(1000, 6)), columns=list(\\\"ABCDEF\\\")\\n )\\n\\n return df\",\n \"def construct_data_frame(self) -> pd.DataFrame:\\n data_frame = self.base_data_frame[\\n [self.name_col, self.description_col]\\n ].reset_index()\\n data_frame.columns = [\\\"label_encoder\\\", \\\"name\\\", \\\"description\\\"]\\n\\n return data_frame.set_index(\\\"label_encoder\\\")\",\n \"def make_dataframe(self, dataframe_path, corpus_path):\\n directory_list = os.listdir(corpus_path)\\n pub_year = []\\n pii = []\\n doi = []\\n title = []\\n authors = []\\n num_authors = []\\n abstract = []\\n journal_name = []\\n\\n for i in trange(len(directory_list)):\\n directory = directory_list[i]\\n json_dict = self.load_journal_json(f'{corpus_path}/{directory}/{directory}.json')\\n\\n for year in json_dict:\\n for pub in json_dict[year]:\\n pub_year.append(year)\\n pii.append(json_dict[year][pub]['pii'])\\n doi.append(json_dict[year][pub]['doi'])\\n title.append(json_dict[year][pub]['title'])\\n authors.append(json_dict[year][pub]['authors'])\\n num_authors.append(json_dict[year][pub]['num_authors'])\\n abstract.append(json_dict[year][pub]['description'])\\n journal_name.append(directory)\\n\\n columns = ['pub_year', 'pii', 'doi', 'title', 'authors', 'num_authors', 'abstract', 'journal_name']\\n df = pd.DataFrame(np.array([pub_year, pii, doi, title, authors, num_authors, abstract, journal_name], dtype=object).transpose(), columns=columns)\\n df.to_pickle(dataframe_path + '/dataframe_from_CorpusGenerator' +'.pkl')\",\n \"def to_frame(self) -> DataFrame:\\n if not self.is_initialized:\\n _logger.info(\\\"Grid has not been initialized. Ensure to run DataGrid.initialize()\\\")\\n return DataFrame()\\n\\n return self._post_process()\",\n \"def create_geneIDsDF():\\n datas=data.plfam_to_matrix()\\n datas.run()\\n print('***Dataframe created***')\",\n \"def output_df(outdict, out_file):\\n\\tcols = ['#chrom', 'source', 'feature', 'chromStart', 'chromEnd', 'score', 'strand', 'frame', 'transcript_id']\\n\\tcolOut = ['#chrom', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame', 'transcript_id']\\n\\tgtfDF = pd.DataFrame(columns=cols)\\n\\n\\tfor trsp in outdict:\\n\\t\\tgtfDF = gtfDF.append(outdict[trsp], ignore_index=True)\\n\\t\\t\\n\\tgtfDF.columns = colOut\\n\\t# print gtfDF.head()\\n\\tgtfDF.to_csv(out_file, compression='gzip', sep='\\\\t', index=False)\",\n \"def construct_df():\\n iterable = [['approach', 'contact', 'retract', 'pause'], ['force', 'height']]\\n index = pd.MultiIndex.from_product(iterable, names=['segment', 'channel'])\\n return pd.DataFrame(columns=index)\",\n \"def df(self):\\n\\n # populate dataframe with level data\\n columns = {\\n \\\"z\\\": self.z(),\\n \\\"z_level_qc\\\": self.z_level_qc(),\\n \\\"z_unc\\\": self.z_unc(),\\n \\\"t\\\": self.t(),\\n \\\"t_level_qc\\\": self.t_level_qc(),\\n \\\"t_unc\\\": self.t_unc(),\\n \\\"s\\\": self.s(),\\n \\\"s_level_qc\\\": self.s_level_qc(),\\n \\\"s_unc\\\": self.s_unc(),\\n \\\"oxygen\\\": self.oxygen(),\\n \\\"phosphate\\\": self.phosphate(),\\n \\\"silicate\\\": self.silicate(),\\n \\\"pH\\\": self.pH(),\\n \\\"p\\\": self.p()\\n }\\n\\n df = pd.DataFrame(columns)\\n\\n # record profile data in a metadata object on the dataframe\\n df.attrs[\\\"latitude\\\"] = self.latitude()\\n df.attrs[\\\"latitude_unc\\\"] = self.latitude_unc()\\n df.attrs[\\\"longitude\\\"] = self.longitude()\\n df.attrs[\\\"longitude_unc\\\"] = self.longitude_unc()\\n df.attrs[\\\"uid\\\"] = self.uid()\\n df.attrs[\\\"n_levels\\\"] = self.n_levels()\\n df.attrs[\\\"year\\\"] = self.year()\\n df.attrs[\\\"month\\\"] = self.month()\\n df.attrs[\\\"day\\\"] = self.day()\\n df.attrs[\\\"time\\\"] = self.time()\\n df.attrs[\\\"cruise\\\"] = self.cruise()\\n df.attrs[\\\"probe_type\\\"] = self.probe_type()\\n df.attrs[\\\"originator_flag_type\\\"] = self.originator_flag_type()\\n df.attrs[\\\"PIs\\\"] = self.PIs()\\n df.attrs[\\\"originator_station\\\"] = self.originator_station()\\n df.attrs[\\\"originator_cruise\\\"] = self.originator_cruise()\\n df.attrs[\\\"t_metadata\\\"] = self.t_metadata()\\n df.attrs[\\\"s_metadata\\\"] = self.s_metadata()\\n\\n return df\",\n \"def save(df, out_file):\\n print('------------< save >------------')\\n out_path = './data'\\n makedirs(out_path, exist_ok=True)\\n print(f'path: {out_path}/{out_file}')\\n print(f'shape: {df.shape}')\\n df.to_csv(f'{out_path}/{out_file}', index=False)\\n print('--------------------------------')\",\n \"def data_visualization_general(data):\\n path_to_save = str(Path(__file__).parent.parent) + '/jupyter_notebook/'\\n key = list(data.keys())[0] # we do this way because keys() return a dict-keys which is not subscriptable\\n df = pd.DataFrame(data[key])\\n file_name = key + \\\".csv\\\"\\n df.to_csv(os.path.join(path_to_save, file_name))\\n # we save it in the jupyter_notebook folder so that it will be easier to show data on jupyter notebook\",\n \"def save_data(df, database_filename):\\n engine = create_engine('sqlite:///' +database_filename)\\n df.to_sql('Project2', engine, index=False)\",\n \"def _make_results_dataframe(self):\\n LOG.debug(\\\"Creating Results Dataframes.\\\")\\n results_df = tfs.TfsDataFrame(index=self.twiss_df.index)\\n results_df[\\\"S\\\"] = self.twiss_df[\\\"S\\\"]\\n return results_df\",\n \"def sourceToDataframe(self):\\n df = pd.read_excel(self.filename)\\n df.columns = df.iloc[10]\\n df = df.drop(df.index[:11])\\n self.df = df #makes this df accessible to the whole class now\\n self.insertODN()\\n display(df.head())\",\n \"def data_frame_creator(self):\\n\\n return pd.DataFrame()\",\n \"def dataframe():\\n headers = get_headers()\\n headers = {'headers': headers}\\n headers = pd.DataFrame.from_dict(headers, orient='index')\\n headers = headers.replace(r'\\\\n', ' ', regex=True)\\n headers = headers.replace(r'\\\\r', ' ', regex=True)\\n headers = headers.replace(r'\\\\t', ' ', regex=True)\\n headers = headers.replace(r'\\\\\\\\t', ' ', regex=True)\\n headers = headers.replace(r' ', ' ', regex=True)\\n headers = headers.replace(r' ', ' ', regex=True)\\n\\n paragraphs = get_paragraphs()\\n paragraphs = {'paragraphs': paragraphs}\\n paragraphs = pd.DataFrame.from_dict(paragraphs, orient='index')\\n paragraphs = paragraphs.replace(r'\\\\n', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\r', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\t', ' ', regex=True)\\n paragraphs = paragraphs.replace(r'\\\\\\\\t', ' ', regex=True)\\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\\n paragraphs = paragraphs.replace(r' ', ' ', regex=True)\\n\\n return headers.to_csv('headers.csv', index=False), paragraphs.to_csv('paragraphs.csv', index=False)\",\n \"def execute(self):\\n try:\\n self.data_frame.write.mode('append').format(self.file_format).save(self.location)\\n return self.data_frame\\n except AnalysisException as exp:\\n raise\",\n \"def glass_pandas(self):\\n # pandas.set_option('display.width', 120)\\n # TODO timeit (git_implementation) vs (my_implementation)\\n # * df = pd.DataFrame(json.loads(r.text))\\n # * df = df.set_index('t')\\n # * df.index = pd.to_datetime(df.index, unit='s')\\n # * df = df.sort_index()\\n # * s = df.v\\n # * s.name = '_'.join(url.split('/')[-2:])\\n # * return s\\n # for elem in self.loaded:\\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\\n # try:\\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\\n # framed = pandas.DataFrame(\\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\\n # for k in frame_keys} for iters in range(len(_data))],\\n # columns=frame_keys)\\n # except KeyError:\\n # framed = pandas.DataFrame(_data)\\n # framed.set_index('t', inplace=True)\\n # framed.index = pandas.to_datetime(\\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\\n # framed.sort_index(inplace=True)\\n # framed.name = _metric\\n # print(framed.name)\\n # print(framed)\",\n \"def create_data_frame(self):\\n column_names = Annotations.create_columns(self.headers, self.annot_types)\\n dtypes = Annotations.get_dtypes_for_group_annots(self.headers, self.annot_types)\\n df = self.open_file(\\n self.file_path,\\n open_as=\\\"dataframe\\\",\\n # Coerce values in group annotations\\n converters=dtypes,\\n # Header/column names\\n names=self.headers,\\n # Prevent pandas from reading first 2 lines in file\\n # since they're passed in with param 'names'\\n skiprows=2,\\n )[0]\\n self.file = Annotations.convert_header_to_multi_index(df, column_names)\",\n \"def createDataFrame(path):\\n df = pd.read_csv(path)\\n df = df[['planet_name', 'planet_mass', 'orbital_radius', 'host_name', \\n 'spectral_type', 'stellar_age', 'stellar_radius', \\n 'stellar_mass', 'stellar_temperature', 'stellar_luminosity', \\n 'optical_magnitude', 'near_ir_magnitude', \\n 'stellar_surface_gravity', 'stellar_metallicity']]\\n \\n df = df.dropna(subset=['spectral_type'])\\n df.spectral_type = df.spectral_type.str[0:1]\\n df.spectral_type = df.spectral_type.str.strip()\\n classification = np.array(['O','B','A','F','G','K','M'])\\n df = df[df.spectral_type.isin(classification)]\\n df.insert(4, \\\"amount_of_planets\\\", 0)\\n df.amount_of_planets = df.groupby('host_name')['host_name'].transform('count')\\n \\n df.planet_mass = np.log10(df.planet_mass)\\n df.orbital_radius = np.log10(df.orbital_radius)\\n \\n df = df.sort_values(by=['host_name'])\\n df = df.reset_index(drop=True) \\n \\n return df\",\n \"def create_data_table(df: pd.DataFrame) -> pd.DataFrame:\\n\\n df = df.copy()\\n\\n # Normalize times by labeling all of today's data with its future label, 00:00\\n # tomorrow (as that's the timestamp marking the end of the 24-hour data collection\\n # period). No need to adjust data not from today; it's already been adjusted and is\\n # labeled with the date whose 00:00 marked the end of data collection (i.e., data\\n # generated on Mar 20 is labeled Mar 21).\\n normalized_dates = df[Columns.DATE].dt.normalize()\\n is_at_midnight = df[Columns.DATE] == normalized_dates\\n df.loc[~is_at_midnight, Columns.DATE] = normalized_dates[\\n ~is_at_midnight\\n ] + pd.Timedelta(days=1)\\n df[Columns.DATE] = df[Columns.DATE].dt.strftime(r\\\"%Y-%m-%d\\\")\\n\\n df = df.drop(\\n columns=[\\n Columns.IS_STATE,\\n Columns.LOCATION_NAME,\\n Columns.OUTBREAK_START_DATE_COL,\\n Columns.DAYS_SINCE_OUTBREAK,\\n Columns.POPULATION,\\n Columns.STAGE,\\n Columns.COUNT_TYPE,\\n ]\\n )\\n\\n df = (\\n df.pivot_table(\\n index=[\\n c\\n for c in df.columns\\n if c not in [Columns.CASE_TYPE, Columns.CASE_COUNT]\\n ],\\n columns=Columns.CASE_TYPE,\\n values=Columns.CASE_COUNT,\\n aggfunc=\\\"first\\\",\\n )\\n .reset_index()\\n .sort_values([Columns.COUNTRY, Columns.STATE, Columns.DATE])\\n )\\n\\n for col in CaseInfo.get_info_items_for(\\n InfoField.CASE_TYPE, count=Counting.TOTAL_CASES\\n ):\\n df[col] = pd.to_numeric(df[col], downcast=\\\"integer\\\")\\n\\n # save_path = Paths.DATA / \\\"data_table.csv\\\"\\n # df.to_csv(save_path, index=False)\\n # print(f\\\"Saved data to {save_path.relative_to(Paths.ROOT)}\\\")\\n\\n return df\",\n \"def save_fitted_dataframe(comp_data_df, filename):\\r\\n comp_data_df.to_csv('{}.csv'.format(filename))\\r\\n return 0\",\n \"def generateDataFrame(self):\\n labelArray = []\\n\\n # At this level ignored files are excluded\\n for item in self.added:\\n self.folderTree.append(item)\\n\\n for item in self.folderTree:\\n if item in self.modified:\\n labelArray.append('modified')\\n elif item in self.deleted:\\n labelArray.append('deleted')\\n elif item in self.ignored:\\n labelArray.append('ignored')\\n elif item in self.added:\\n labelArray.append('added')\\n else:\\n labelArray.append('baseFile')\\n\\n df = pd.DataFrame(list(zip(self.folderTree, labelArray)), \\\\\\n columns=['File', 'Type'])\\n self.fileDataFrame = df\",\n \"def _get_outputdf(self):\\n keys = self.info_df['Trace'].values.tolist()\\n frame = deepcopy([line.df for line in self.info_df['Line'].values.tolist()])\\n for i in range(len(frame)):\\n df = frame[i]\\n num = list(range(len(df)))\\n angle_gr = list(map(deg2grad,df['Angle Horizontal'].values))\\n df.insert(0,'Number', ['s' + '-'.join(x) + 'gr' for x in zip(map(str,num),map(str,map(int,angle_gr)))])\\n df.insert(1, 'Name', keys[i])\\n return pd.concat(frame, keys=keys, join='inner', ignore_index=True)\",\n \"def save_to_csv(self):\\n path = partial(os.path.join, 'datasets')\\n save_name = self.name.lower().replace(' ', '_')\\n self.df['values'].sum(axis=1).to_csv(path('{0}_values.csv'.format(save_name)))\\n self.df['allocations'].to_csv(path('{0}_allocations.csv'.format(save_name)))\\n self.df['returns'].to_csv(path('{0}_returns.csv'.format(save_name)))\\n self.trades.to_csv(path('{0}_trades.csv'.format(save_name)))\",\n \"def gen_main_df(add_list: list):\\r\\n # 由Bert 计算得来的 sentiment信息\\r\\n if 'sentiment' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('sentiment')\\r\\n sentiment = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'daily_svm_sentiment_6class' , 'csv')[0],\\r\\n 'date', ['0'], 'sentiment') # 'daily_svm_sentiment_2class' '0', '1', '2', '3', '4', '5'\\r\\n data_manipulator.add_column(sentiment)\\r\\n # 中国CPI指数\\r\\n if 'cpi' in add_list and 'cpi' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('cpi')\\r\\n cpi = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'CPI', 'csv')[0],\\r\\n '日期', ['最新值', '涨跌幅', '近3月涨跌幅'], 'CPI')\\r\\n data_manipulator.add_column(cpi)\\r\\n # 上海银行间同业拆放利率\\r\\n if 'shibor' in add_list and 'shibor' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('shibor')\\r\\n shibor = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'shibor', 'csv')[0],\\r\\n 'date', ['on', '1w', '2w', '1m', '3m'], 'Shibor')\\r\\n data_manipulator.add_column(shibor)\\r\\n # 上证综指\\r\\n if 'shangzheng' in add_list and 'shangzheng' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('shangzheng')\\r\\n shangzheng = data_manipulator.read_in_file(\\r\\n data_manipulator.get_file_names(root, 'datasets', 'ShangZheng', 'csv')[0],\\r\\n 'trade_date', ['open', 'high', 'low', 'close', 'pct_chg', 'vol', 'amount',\\r\\n 'total_mv', 'float_mv', 'total_share', 'float_share',\\r\\n 'free_share', 'turnover_rate', 'turnover_rate_f', 'pe',\\r\\n 'pe_ttm', 'pb'],\\r\\n 'ShangZheng')\\r\\n data_manipulator.add_column(shangzheng)\\r\\n data_manipulator.shift_columns(['ShangZheng_pct_chg'], (-1,),\\r\\n add=True) # name has changed to shift-1_ShangZheng_pct_chg\\r\\n data_manipulator.rank_df_column(['shift-1_ShangZheng_pct_chg'],\\r\\n rank_list=[-10, -1, -0.5, 0, 0.5, 1, 10]) # rank_list=[-10, 0, 10] [-10, -1, -0.5, 0, 0.5, 1, 10]\\r\\n shangzheng_30min = data_manipulator.read_in_file(\\r\\n data_manipulator.get_file_names(root, 'datasets', 'ShangZheng_index_30min', 'csv')[0],\\r\\n 'trade_time', ['open', 'high', 'low', 'close', 'pct_chg', 'vol', 'amount'],\\r\\n 'ShangZheng_30min')\\r\\n data_manipulator.news_df_add_column(shangzheng_30min)\\r\\n data_manipulator.shift_minute_columns(['ShangZheng_30min_pct_chg'], (-1,),\\r\\n add=True)\\r\\n data_manipulator.rank_minute_df_columns(['shift-1_ShangZheng_30min_pct_chg'],\\r\\n rank_list=[-10, -1, -0.5, 0, 0.5, 1, 10]) # rank_list=[-10, 0, 10] [-10, -1, -0.5, 0, 0.5, 1, 10]\\r\\n\\r\\n # M2 广义货币量\\r\\n if 'm2' in add_list and 'm2' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('m2')\\r\\n m2 = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'M2', 'csv')[0],\\r\\n '月份', ['M2数量(亿元)', 'M2同比增长', 'M2环比增长'], 'M2')\\r\\n m2 = data_manipulator.complement_df(m2, 'date')\\r\\n data_manipulator.add_column(m2)\\r\\n\\r\\n # 人民币美元汇率\\r\\n if 'rmb_usd' in add_list and 'rmb_usd' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('rmb_usd')\\r\\n rmb_usd = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'RMB_USD', 'csv')[0],\\r\\n 'trade_date',\\r\\n ['bid_open', 'bid_close', 'bid_high', 'bid_low', 'ask_open',\\r\\n 'ask_close', 'ask_high', 'ask_low', 'tick_qty'], 'exchange')\\r\\n data_manipulator.add_column(rmb_usd)\\r\\n\\r\\n # 沪港通 沪深通 到岸 离岸资金流\\r\\n if 'fund_flow' in add_list and 'fund_flow' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('fund_flow')\\r\\n fund_flow = data_manipulator.read_in_file(\\r\\n data_manipulator.get_file_names(root, 'datasets', 'fund_flow', 'csv')[0],\\r\\n 'trade_date', ['north_money', 'south_money'], 'fund_flow')\\r\\n data_manipulator.add_column(fund_flow)\\r\\n\\r\\n # 债券回购日行情\\r\\n if 'repo' in add_list and 'repo' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('repo')\\r\\n repo = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'repo', 'csv')[0],\\r\\n 'trade_date', ['repo_maturity', 'open', 'high', 'low', 'close',\\r\\n 'amount'], 'repo', data_manipulator.cut_time_string,\\r\\n (0, 10,))\\r\\n repo = data_manipulator.select_col_group_by(repo, 'repo_repo_maturity', ['GC001', 'GC007', 'GC014', 'GC028'],\\r\\n 'date')\\r\\n data_manipulator.add_column(repo)\\r\\n\\r\\n # 新浪新闻\\r\\n if 'sina_news' in add_list and 'sina_news' not in data_manipulator.used_measure_list:\\r\\n data_manipulator.used_measure_list.append('sina_news')\\r\\n columns_type = {'create_time': str, 'text': str}\\r\\n sina_news = data_manipulator.read_in_file(data_manipulator.get_file_names(root, 'datasets', 'sina', 'csv')[0],\\r\\n 'create_time', ['text', ], 'sina', dtypes=columns_type)\\r\\n data_manipulator.add_change_news('sina', (7, 9), columns_type, sina_news, time_col_name='create_time')\\r\\n data_manipulator.add_minute_change_news('sina', columns_type, sina_news, time_col_name='create_time')\\r\\n if 'scale' in add_list:\\r\\n data_manipulator.scaling_col()\\r\\n if 'clear' in add_list:\\r\\n data_manipulator.clear()\",\n \"def data(self):\\n dfdata = pd.concat([self.weights, self.returns, self.category], axis=1)\\n dfdata.columns = ['weights', 'returns', self.category_name]\\n if self.period is not None:\\n dfdata['date'] = self.period\\n return dfdata\",\n \"def create_query_csv(self):\\n\\n self.query_df.to_csv(self.query_output_file)\",\n \"def run(self) -> DataFrame:\\n with self.create_census_api_session():\\n logger.info('Retrieving variables...')\\n variables: Variables = self.get_variables()\\n logger.info('Retrieving ACS tables...')\\n tables = self.get_tables()\\n\\n # Add geometry\\n gazetteer_files: List[GazetteerFile] = []\\n shapefiles: List[Shapefile] = []\\n if self.geometry == 'points':\\n logger.info('Retrieving Gazetteer files...')\\n gazetteer_files.extend(self.get_gazetteer_files())\\n elif self.geometry == 'polygons':\\n logger.info('Retrieving shapefiles...')\\n shapefiles.extend(self.get_shapefiles())\\n dataframe = self.assemble_dataframe(variables, tables, gazetteer_files, shapefiles)\\n return dataframe\",\n \"def build_graphs(df: pd.DataFrame, ir_dir: Path, graph_dir: Path):\\n for _, row in df.iterrows():\\n with open(ir_dir / f\\\"{row['name']}.ll\\\") as f:\\n ir = f.read()\\n graph = pg.from_llvm_ir(ir)\\n graph.features.feature[\\\"devmap_label\\\"].int64_list.value[:] = [row[\\\"label\\\"]]\\n graph.features.feature[\\\"wgsize\\\"].int64_list.value[:] = [row[\\\"wgsize\\\"]]\\n graph.features.feature[\\\"transfer_bytes\\\"].int64_list.value[:] = [\\n row[\\\"transfer_bytes\\\"]\\n ]\\n graph.features.feature[\\\"wgsize_log1p\\\"].float_list.value[:] = [\\n row[\\\"wgsize_log1p\\\"]\\n ]\\n graph.features.feature[\\\"transfer_bytes_log1p\\\"].float_list.value[:] = [\\n row[\\\"transfer_bytes_log1p\\\"]\\n ]\\n pbutil.ToFile(\\n graph, graph_dir / f\\\"{row['name']}.ProgramGraph.pb\\\", exist_ok=False\\n )\",\n \"def write(self):\\n \\n self.df.to_csv('/home/austin/Desktop/Falcon/realestate/Falcon/Datasets/mls.csv')\",\n \"def to_df(self):\\n if self.shape > 1:\\n range_str = [s for s in range(self.shape)]\\n iterables = [self.columns, range_str]\\n multiindex = pd.MultiIndex.from_product(iterables, names=['song', 'frame'])\\n # multiindex = [i for i in itertools.product(self.columns, range_str, repeat=1)]\\n df = pd.DataFrame(columns=multiindex, index=self.columns, dtype=np.float64)\\n\\n for c_1 in self.columns:\\n for c_2 in self.columns:\\n for s in range_str:\\n df.loc[c_1][c_2, s] = self.dict_[c_1][c_2][s]\\n df = df.T\\n else:\\n df = pd.DataFrame(columns=self.columns + ['song'], dtype=np.float64)\\n df['song'] = self.columns\\n df = df.set_index('song')\\n\\n for c_1 in self.columns:\\n for c_2 in self.columns:\\n df.loc[c_1, c_2] = self.max_diff(c_1, c_2)\\n\\n return df\",\n \"def create_sample_dataframe():\\n ax_readings = []\\n ay_readings = []\\n az_readings = []\\n mx_readings = []\\n my_readings = []\\n mz_readings = []\\n gx_readings = []\\n gy_readings = []\\n gz_readings = []\\n activity_list = [LABELS_NAMES[0] for _ in range(SEGMENT_TIME_SIZE)]\\n\\n\\n for _ in range(SEGMENT_TIME_SIZE):\\n ax_readings.append(random.uniform(-10,10))\\n ay_readings.append(random.uniform(-10,10))\\n az_readings.append(random.uniform(-10,10))\\n mx_readings.append(random.uniform(-10,10))\\n my_readings.append(random.uniform(-10,10))\\n mz_readings.append(random.uniform(-10,10))\\n gx_readings.append(random.uniform(-10,10))\\n gy_readings.append(random.uniform(-10,10))\\n gz_readings.append(random.uniform(-10,10))\\n\\n data_dict = {\\n COLUMN_NAMES[0]: activity_list, COLUMN_NAMES[1]: ax_readings,\\n COLUMN_NAMES[2]: ay_readings, COLUMN_NAMES[3]: az_readings,\\n COLUMN_NAMES[4]: gx_readings, COLUMN_NAMES[5]: gy_readings,\\n COLUMN_NAMES[6]: gz_readings, COLUMN_NAMES[7]: mx_readings,\\n COLUMN_NAMES[8]: my_readings, COLUMN_NAMES[9]: mz_readings\\n }\\n\\n df = pd.DataFrame(data=data_dict)\\n return df\",\n \"def export_data(self, pth):\\n self.cleanup_allowed = False\\n self.train_df.to_csv(os.path.join(pth, \\\"train.csv\\\"))\\n self.valid_df.to_csv(os.path.join(pth, \\\"valid.csv\\\"))\\n self.test_df.to_csv(os.path.join(pth, \\\"test.csv\\\"))\",\n \"def save_dataframe(dataframe, filename):\\n with open(filename, \\\"w\\\", encoding=\\\"utf8\\\") as outfile: \\n dataframe.to_csv(outfile, sep=\\\",\\\")\",\n \"def create_data_frame_for_figures(\\n results_path, save_path, results_folder_name, is_baseline=False\\n):\\n\\n # Columns will be loading in data for\\n\\n columns = [\\n \\\"filename\\\",\\n \\\"sim_id\\\",\\n \\\"virtual_patient_num\\\",\\n \\\"sensor_num\\\",\\n \\\"patient_scenario_filename\\\",\\n \\\"age\\\",\\n \\\"ylw\\\",\\n \\\"cir\\\",\\n \\\"isf\\\",\\n \\\"sbr\\\",\\n \\\"starting_bg\\\",\\n \\\"starting_bg_sensor\\\",\\n \\\"true_bolus\\\",\\n \\\"initial_bias\\\",\\n \\\"bias_norm_factor\\\",\\n \\\"bias_drift_oscillations\\\",\\n \\\"bias_drift_range_start\\\",\\n \\\"bias_drift_range_end\\\",\\n \\\"noise_coefficient\\\",\\n \\\"delay\\\",\\n \\\"bias_drift_type\\\",\\n \\\"bias_type\\\",\\n \\\"noise_per_sensor\\\",\\n \\\"noise\\\",\\n \\\"bias_factor\\\",\\n \\\"phi_drift\\\",\\n \\\"drift_multiplier\\\",\\n \\\"drift_multiplier_start\\\",\\n \\\"drift_multiplier_end\\\",\\n \\\"noise_max\\\",\\n \\\"mard\\\",\\n \\\"mbe\\\",\\n \\\"bg_test_condition\\\",\\n \\\"analysis_type\\\",\\n \\\"lbgi\\\",\\n \\\"lbgi_risk_score\\\",\\n \\\"dkai\\\",\\n \\\"dkai_risk_score\\\",\\n \\\"hbgi\\\",\\n \\\"bgri\\\",\\n \\\"percent_lt_54\\\",\\n ]\\n\\n # Blank list to keep track of scenarios removed because settings are outside of clinical bounds.\\n removed_scenarios = []\\n\\n # Blank list for adding data to\\n data = []\\n\\n # Iterate through each of the files\\n for i, filename in enumerate(\\n sorted(os.listdir(results_path))\\n ): # [0:100])): #(for testing)\\n # Identify file is simulation file\\n if filename.endswith(\\\".tsv\\\"):\\n\\n print(i, filename)\\n\\n # Read in that simulation data to a dataframe\\n simulation_df = pd.read_csv(os.path.join(results_path, filename), sep=\\\"\\\\t\\\")\\n\\n # Check that the first two bg values are equal\\n assert (\\n simulation_df.loc[0][\\\"bg\\\"] == simulation_df.loc[1][\\\"bg\\\"]\\n ), \\\"First two BG values of simulation are not equal\\\"\\n\\n # Find and read in the corresponding json data\\n f = open(os.path.join(results_path, filename.replace(\\\".tsv\\\", \\\".json\\\")), \\\"r\\\")\\n simulation_characteristics_json_data = json.loads(f.read())\\n\\n # Get the scenario settings characteristics so can check whether outside clinical bounds\\n cir = simulation_characteristics_json_data[\\\"patient\\\"][\\\"config\\\"][\\n \\\"carb_ratio_schedule\\\"\\n ][\\\"schedule\\\"][0][\\\"setting\\\"].replace(\\\" g\\\", \\\"\\\")\\n\\n isf = simulation_characteristics_json_data[\\\"patient\\\"][\\\"config\\\"][\\n \\\"insulin_sensitivity_schedule\\\"\\n ][\\\"schedule\\\"][0][\\\"setting\\\"].replace(\\\" m\\\", \\\"\\\")\\n\\n sbr = simulation_characteristics_json_data[\\\"patient\\\"][\\\"config\\\"][\\n \\\"basal_schedule\\\"\\n ][\\\"schedule\\\"][0][\\\"setting\\\"].replace(\\\" U\\\", \\\"\\\")\\n\\n # If any of the settings are outside clinical bounds, add to removed scnearios list and do not load\\n # into aggregated dataframe\\n if settings_outside_clinical_bounds(cir, isf, sbr):\\n print(filename + \\\" has settings outside clinical bounds.\\\")\\n removed_scenarios.append([filename, cir, isf, sbr])\\n\\n # Otherwise add that data to data list\\n else:\\n\\n # Get a row of data for that particular simulation and scenario characteristics\\n data_row = get_data(\\n filename,\\n simulation_df,\\n simulation_characteristics_json_data,\\n baseline=is_baseline,\\n )\\n\\n # Confirm that the length of the returned data matches the number of columns for\\n # ultimate aggregate datafrrame\\n assert len(data_row) == len(\\n columns\\n ), \\\"length of returned data does not match number of columns\\\"\\n\\n data.append(data_row)\\n\\n # Create and save dataframe of removed scenarios\\n removed_scenarios_df = pd.DataFrame(\\n removed_scenarios, columns=[\\\"filename\\\", \\\"cir\\\", \\\"isf\\\", \\\"sbr\\\"]\\n )\\n removed_scenarios_filename = \\\"{}_{}_{}_{}\\\".format(\\n \\\"removed_scenarios_df\\\", results_folder_name, utc_string, code_version\\n )\\n\\n removed_scenarios_df.to_csv(\\n path_or_buf=os.path.join(save_path, removed_scenarios_filename + \\\".csv\\\"),\\n index=False,\\n )\\n\\n # Create the results dataframe\\n results_df = pd.DataFrame(data, columns=columns)\\n\\n # Clean up the results dataframe\\n results_df = clean_up_results_df(results_df)\\n\\n # Save the results dataframe to a csv\\n\\n results_df_filename = \\\"{}_{}_{}_{}\\\".format(\\n \\\"results_df\\\", results_folder_name, utc_string, code_version\\n )\\n\\n results_df.to_csv(\\n path_or_buf=os.path.join(save_path, results_df_filename + \\\".csv\\\"),\\n index=False,\\n )\\n\\n return results_df\",\n \"def create_model_csv(self):\\n\\n self.model_df.to_csv(self.model_output_file)\",\n \"def to_df(self):\\n from ..df import DataFrame\\n\\n return DataFrame(self)\",\n \"def __generate_data_table__(self):\\n # | - __generate_data_table__\\n rows_list = []\\n for job in self.job_var_lst:\\n revisions = self.job_revision_number(job)\\n for revision in range(revisions + 1)[1:]:\\n # | - FOR LOOP BODY\\n entry_param_dict = {}\\n for prop in job:\\n entry_param_dict[prop[\\\"property\\\"]] = prop[\\\"value\\\"]\\n\\n entry_param_dict[\\\"variable_list\\\"] = job\\n entry_param_dict[\\\"path\\\"] = self.var_lst_to_path(job)\\n\\n entry_param_dict[\\\"max_revision\\\"] = revisions\\n entry_param_dict[\\\"revision_number\\\"] = revision\\n\\n rows_list.append(entry_param_dict)\\n # __|\\n\\n data_frame = pd.DataFrame(rows_list)\\n\\n return(data_frame)\\n # __|\",\n \"def generate_graph_feature(self):\\n traj_graph_feature = [traj.get_graph_feature() for traj in self.trajectories]\\n self.df_graph_feature = pd.DataFrame(traj_graph_feature)\\n self.df_graph_feature[\\\"LABEL\\\"] = self.df[\\\"LABEL\\\"]\\n return self.df_graph_feature\",\n \"def to_dataframe(self, include_metadata: bool = True) -> pd.DataFrame:\\n # Get all our data first with async\\n # Note that all our pandas work will tax CPU so we wouldn't expect any\\n # performance gains from doing the data parsing as a callback\\n records = self.to_dict()\\n data = []\\n for series in records:\\n df = pd.DataFrame(series.pop(\\\"data\\\"), columns=[\\\"period\\\", \\\"value\\\"])\\n if include_metadata:\\n df = df.assign(**series)\\n data.append(df)\\n return pd.concat(data, ignore_index=True)\",\n \"def create_df(Varr, Iarr, POA, T, mode):\\n df = pd.DataFrame()\\n df['voltage'] = Varr\\n df['current'] = Iarr\\n df['E'] = POA\\n df['T'] = T\\n df['mode'] = mode\\n return df\",\n \"def create_df(datadir: str, ext: str='txt') -> pd.DataFrame:\\n\\n datalist = []\\n for name in os.listdir(datadir):\\n filename = '/'.join([datadir, name])\\n if os.path.isfile(filename) and ext in name[-len(ext):]:\\n row_data = []\\n content = read_file.read_file(filename)\\n row_data.append(read_file.extract_name(content))\\n row_data.append(read_file.extract_year(content))\\n row_data.append(read_file.extract_form_factor(content))\\n row_data.append(read_file.extract_max_power(content))\\n row_data.append(read_file.extract_min_power(content))\\n row_data.append(read_file.extract_cpu_speed(content))\\n row_data.append(read_file.extract_core_num(content))\\n for ind in range(10, 100, 10):\\n row_data.append(read_file.extract_int_power(content, ind))\\n datalist.append(row_data)\\n\\n return pd.DataFrame(data=datalist, columns=[\\n 'Name', 'Year', 'FormFac', 'MaxPower', 'IdlePower', 'CPU speed',\\n 'NumCores'\\n ]+[''.join([str(ind), '%Power']) for ind in range(10, 100, 10)])\",\n \"def save_data(df, database_filename):\\n # Create a database connection \\n engine = create_engine('sqlite:///' + database_filename)\\n \\n # Insert df into DisasterCategories table\\n df.to_sql('DisasterCategories', engine, index=False)\",\n \"def save_data(df: pd.DataFrame, database_filename: str) -> None:\\n engine = create_engine(f\\\"sqlite:///{database_filename}\\\")\\n df.to_sql(Path(database_filename).stem, engine, index=False, if_exists=\\\"replace\\\")\",\n \"def save_data(df, database_filename):\\n engine = create_engine('sqlite:///'+database_filename)\\n df.to_sql('disasterdata', engine, index=False)\",\n \"def make_dataframes(self):\\n self._data_frame_30days = pd.DataFrame(self._all30_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\\n self._data_frame_60days = pd.DataFrame(self._all60_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\\n self._data_frame_90days = pd.DataFrame(self._all90_dict, index=SIRPPipeline.data_frame_INDEX).transpose()\\n self._data_frame_counts = pd.DataFrame(\\n {\\n \\\"Created\\\": {\\\"totals\\\": self._data_frame_30days.count()[\\\"Created\\\"]},\\n \\\"Closed\\\": {\\\"totals\\\": self._data_frame_30days.count()[\\\"Closed\\\"]},\\n \\\"Owner\\\": (self._data_frame_30days[\\\"Owner\\\"].value_counts().to_dict()),\\n \\\"Resolution\\\": (self._data_frame_30days[\\\"Resolution\\\"].value_counts().to_dict()),\\n \\\"Severity\\\": (self._data_frame_30days[\\\"Severity\\\"].value_counts().to_dict()),\\n },\\n index=self.counts_frame_INDEX,\\n )\\n self._data_frame_counts.fillna(0, inplace=True)\",\n \"def dataFrame(self):\\n\\n memory_file = StringIO(initial_value=self.sparql_result.decode('utf-8'), newline='\\\\n')\\n reader = DictReader(memory_file)\\n\\n schema = StructType(\\n list(map(lambda f: StructField(f, StringType()), reader.fieldnames))\\n )\\n\\n data = list(map(lambda d: [d[f] for f in reader.fieldnames], list(reader)))\\n\\n return self.spark.createDataFrame(data, schema)\",\n \"def run(self, *args, **kw):\\n super(GeopandasWriter, self).run(*args, **kw)\\n data = self.get_input_data()\\n data.to_file(self.file_name, self.format)\",\n \"def write_df_to_db(df, db_path):\\n print \\\"Writing to 'results' table in db: \\\", db_path\\n conn = sqlite3.connect(db_path)\\n df.to_sql(\\\"results\\\", con=conn,if_exists='replace')\",\n \"def create_catalog_dataframe(save_dataframe):\\n if not save_dataframe:\\n return pd.read_pickle(f'{path_dictionary[\\\"catalog_dataframe_grouped_path\\\"]}')\\n\\n catalog_items = DbHelper.get_all_catalog_items()\\n\\n # Create dataframe to put all information together\\n columns = ['user_id', 'item_id', 'session_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y', 'catalog_item_list', 'user_log_list']\\n catalog_items_df = pd.DataFrame(catalog_items, columns=columns)\\n\\n # Clean 'Catalog Items' that user see during a session\\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(clean_objects_listed, axis=1)\\n\\n # Clean Log Files\\n catalog_items_df['user_log_list'] = catalog_items_df.apply(clean_logs, axis=1)\\n\\n # Get Catalog Items that user hover or has a click action, her/his mouse\\n catalog_items_df = get_interacted_catalog_items(catalog_items_df)\\n\\n # Label the catalog items as 0\\n catalog_items_df['catalog_item_list'] = catalog_items_df.apply(label_page_type, axis=1)\\n\\n catalog_items_df_grouped = catalog_items_df.groupby(['user_id', 'session_id'], as_index=False).agg(lambda x: list(x))\\n catalog_items_df_grouped.drop(['item_id', 'window_size_x', 'window_size_y', 'page_size_x', 'page_size_y'], axis=1, inplace=True)\\n\\n if save_dataframe:\\n catalog_items_df.to_pickle(f'{path_dictionary[\\\"path_raw_catalog_dataframe\\\"]}')\\n catalog_items_df_grouped.to_pickle(f'{path_dictionary[\\\"path_catalog_dataframe\\\"]}')\\n catalog_items_df_grouped.to_csv(f'{path_dictionary[\\\"path_catalog_csv\\\"]}', index=False, sep='|')\\n return catalog_items_df_grouped\",\n \"def makeDF(csv_path):\\n DF = pd.read_csv(csv_path)\\n\\n DF['height'] = DF.apply(lambda DF: abs(DF['ymax'] - DF['ymin']), axis=1)\\n DF['width'] = DF.apply(lambda DF: abs(DF['xmax'] - DF['xmin']), axis=1)\\n DF['objArea'] = DF.apply(lambda DF: (DF['width'] * DF['height']), axis=1)\\n imageArea = 2704 * 1524\\n DF['objPortion'] = DF.apply(lambda DF: (DF['objArea'] / imageArea), axis=1)\\n\\n # DF.to_csv('/NewDF.csv')\\n DF.to_json('json_annot_all.json')\\n\\n # Looking at the first 5 rows to get the insigt on the data.\\n print(DF.head(5))\\n print(DF.label.unique())\\n return DF\",\n \"def make_df(self):\\n # read in file\\n df = pd.read_csv(self.data_file)\\n cols_to_drop = [f'view{x}' for x in range(1,4)]+['response']\\n # subtract loc3 viewing from location of interest\\n df[self.label_key] = df[self.predictor] - df['view3']\\n df.drop(cols_to_drop, axis=1, inplace=True)\\n df.reset_index(drop=True, inplace=True)\\n\\n return df\",\n \"def export_data(self):\\r\\n stocks = {}\\r\\n headings = ['Security', 'Price', 'Change', 'Change %', '52 Week', 'Market Cap']\\r\\n\\r\\n for data in range(6):\\r\\n for items in self.root.main.treeview.get_children():\\r\\n values = self.root.main.treeview.item(items, 'values')\\r\\n if headings[data] not in stocks:\\r\\n stocks[headings[data]] = []\\r\\n stocks.get(headings[data]).append(values[data])\\r\\n\\r\\n df = pd.DataFrame(stocks, columns=headings)\\r\\n path = tk.filedialog.asksaveasfilename(title='Save File As...',\\r\\n filetypes=((\\\"CComma-separated values (.csv)\\\", \\\"*.csv\\\"), (\\\"Text Document(.txt)\\\", \\\"*.txt\\\")))\\r\\n\\r\\n if not path:\\r\\n return\\r\\n else:\\r\\n df.to_excel(path, index=False, header=True)\",\n \"def dataframe(self):\\n df = pd.DataFrame({'x':self.x, 'y':self.y, 'd':self.d})\\n\\n if self.z is not None:\\n for k, v in self.z.items():\\n df[k] = v\\n\\n return df\",\n \"def save(self):\\n\\t\\t# save self.dfAnalysis\\n\\t\\tcsvPath = self._getSavePath()\\n\\t\\tprint('saving:', csvPath)\\n\\t\\tself.dfAnalysis.to_csv(csvPath)\",\n \"def data_solution_save(sol, column_names, file_name_prefix='vaccination_data_solution'):\\n df = pd.DataFrame(sol)\\n df.columns = column_names\\n string_date = str(datetime.date(datetime.now()))\\n file_name = file_name_prefix + string_date + \\\".pkl\\\" #\\n df.to_pickle(file_name)\\n return\",\n \"def save_data(dataset_path: str,\\n graphs: np.ndarray,\\n influence_features: np.ndarray,\\n labels: np.ndarray,\\n distances: np.ndarray,\\n embeddings: np.ndarray,\\n own_company: np.ndarray,\\n data_splits: np.ndarray,\\n family_flag: np.ndarray) -> None:\\n df_graphs = pd.concat(\\n [pd.DataFrame(graph, dtype=int)\\n for graph in graphs],\\n keys=range(graphs.shape[0]),\\n names=['observation id', 'neighbor id'])\\n df_graphs.columns.name = 'neighbor id'\\n\\n df_normalized_embedding = pd.concat(\\n [pd.DataFrame(normalized_embedding, dtype=float)\\n for normalized_embedding in embeddings],\\n keys=range(graphs.shape[0]),\\n names=['observation id', 'neighbor id'])\\n df_normalized_embedding.columns.name = 'embedding dimension'\\n\\n df_influence = pd.concat(\\n [pd.DataFrame(influence_feature,\\n columns=['influence', 'ego id'], dtype=int)\\n for influence_feature in influence_features],\\n keys=range(influence_features.shape[0]),\\n names=['observation id', 'neighbor id'])\\n\\n df_labels = pd.Series(labels, name='label')\\n df_labels.index.name = 'observation id'\\n\\n df_data_splits = pd.Series(data_splits, name='data_split')\\n df_data_splits.index.name = 'observation id'\\n\\n df_family_flag = pd.Series(family_flag, name='family_flag')\\n df_family_flag.index.name = 'observation id'\\n\\n df_distances = pd.Series(distances, name='distance')\\n df_distances.index.name = 'observation id'\\n\\n df_own_company = pd.Series(own_company, name='own_company')\\n df_own_company.index.name = 'observation id'\\n\\n dataset = os.path.basename(dataset_path[:-1])\\n save_path = f'./public/data/{dataset}'\\n os.makedirs(save_path, exist_ok=True)\\n df_normalized_embedding.to_csv(save_path + '/Public_Normalized_Embedding.csv')\\n # np.save(save_path + '/Public_Normalized_Embedding', embeddings)\\n # torch.save(embeddings.detach(), save_path + '/Public_Normalized_Embedding_tensor.pt')\\n df_influence.to_csv(save_path + '/Public_Influence_Features.csv')\\n df_labels.to_csv(save_path + '/Public_Labels.csv')\\n df_graphs.to_csv(save_path + '/Public_Graphs.csv')\\n df_distances.to_csv(save_path + '/Public_Distances.csv')\\n df_own_company.to_csv(save_path + '/Public_Own_Company_flag.csv')\\n df_data_splits.to_csv(save_path + '/Public_Dataset_splits.csv')\\n df_family_flag.to_csv(save_path + '/Public_Family_flag.csv')\",\n \"def execute(self):\\n try:\\n self.data_frame.write.partitionBy(self.partition_by).mode('append') \\\\\\n .format(self.file_format).save(self.location)\\n return self.data_frame\\n except AnalysisException as exp:\\n raise\",\n \"def return_combine_df_for_graph():\\n\\n\\tkey = '18c95216b1230de68164158aeb02e2c2'\\n\\t# bade login with key\\n\\tbase = Dashboard(key)\\n\\t# get csv with write vars\\n\\tstart =os.path.dirname(os.path.realpath(sys.argv[0])) \\n\\tpath = os.path.join(start, 'Fred Graphs')\\n\\t# this path was used for flask,anothe day to fix this one \\n\\t#path = '/home/mike/Documents/coding_all/ModelApp/app/Fred Graphs'\\n\\t#base.write_fred_data_to_csv_from_dict(fred_econ_data, path)\\n\\n\\t# convert csv to dict to use\\n\\tdf_dict = base.saved_csvs_to_dict(fred_econ_data.keys(), path)\\n\\n\\t# skipped step, drop down, whyich can be typed into bc at some point will have list of all vars\\n\\t# and display graph indivusal with relevent data (type, seaonailty) displayed liek fed\\n\\n\\t# next combine wanted vars to single df\\n\\tcombined_df = base.get_full_group_df(df_dict, time_interval='6M', group_unit='mean')\\n\\t# get spreads for IR rates\\n\\tcols_against = ['10 YR Treasury','Moody Aaa Yield','Moody Baa Yield','30 Year Mortgage', ]\\n\\tbase_col = 'Federal Funds Rate'\\n\\tspread_dict = base.get_yield_spreads_new(combined_df, cols_against, base_col, graph='no')\\n\\tspread_dict['date'] = combined_df.index\\n\\tcombined_spread_df = pd.DataFrame.from_dict(spread_dict)\\n\\tcombined_spread_df = combined_spread_df.set_index('date')\\n\\tcombined_spread_df.index = pd.to_datetime(combined_spread_df.index)\\n\\treturn combined_df, combined_spread_df\",\n \"def fetch_training_df(df):\\n\\n gen_df = df.copy()\\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\\n return gen_df\",\n \"def generate_DataFrame(file_path):\\n # print (\\\"Generating DataFrame\\\")\\n __log(1, 'Generating DataFrame....')\\n\\n df = pd.read_csv(file_path)\\n df = df.rename(columns=lambda x: x.strip())\\n df = df.dropna()\\n\\n for i in list(df.keys()):\\n df[i] = df[i].apply(cleaning)\\n\\n # print (\\\"DataFrame Generated Successfully\\\")\\n __log(1, 'DataFrame Generated Sucessfully.')\\n return df\",\n \"def createDataframe(filepath_train, filepath_test, domain, type_dataset):\\n \\n print(\\\"Reading pickle files...\\\")\\n #read pickle files\\n with open(filepath_train, \\\"rb\\\") as pkl_file:\\n traindata = pickle.load(pkl_file)\\n \\n with open(filepath_test, \\\"rb\\\") as pkl_file:\\n testdata = pickle.load(pkl_file)\\n \\n if domain == 'lopen':\\n d = '.D450: Lopen en zich verplaatsen'\\n l = 'FAC '\\n elif domain == 'stemming':\\n d = '.B152: Stemming'\\n l = 'STM '\\n elif domain == 'beroep':\\n d = '.D840-859: Beroep en werk'\\n l = 'BER '\\n elif domain == 'inspanningstolerantie':\\n d = '.B455: Inspanningstolerantie'\\n l = 'INS '\\n \\n df_train, df_list_tr = completeDataframe(traindata)\\n df_test, df_list_te = completeDataframe(testdata)\\n \\n #get note id's from keys in dataframe\\n if type_dataset == 'covid':\\n ids= []\\n list_keys = df_test['key'].tolist()\\n for key in list_keys:\\n y = key.split('--')[3]\\n ids.append(y)\\n df_test['note_id'] = ids\\n \\n if type_dataset == 'noncovid':\\n ids = []\\n for instance in df_list_te:\\n le = len(str(instance[2]))\\n if instance[3] == \\\"['']\\\":\\n ids.append(instance[0].split('---')[1])\\n else:\\n ids.append(instance[0].split('---')[1][:-le])\\n df_test['note_id'] = ids\\n \\n \\n df_train[domain] = 0\\n df_train['level'] = 'None'\\n df_test[domain] = 0\\n df_test['level'] = 'None'\\n \\n #Add domain labels to a seperate column\\n df_train['domain'][df_train['labels'].apply(lambda x: d in x)] = d\\n df_train['level'][df_train['labels'].apply(lambda x: l+'0'in x)] = 0\\n df_train['level'][df_train['labels'].apply(lambda x: l+'1'in x)] = 1\\n df_train['level'][df_train['labels'].apply(lambda x: l+'2'in x)] = 2\\n df_train['level'][df_train['labels'].apply(lambda x: l+'3'in x)] = 3\\n df_train['level'][df_train['labels'].apply(lambda x: l+'4'in x)] = 4\\n df_train['level'][df_train['labels'].apply(lambda x: l+'5'in x)] = 5\\n \\n df_test['domain'][df_test['labels'].apply(lambda x: d in x)] = d\\n df_test['level'][df_test['labels'].apply(lambda x: l+'0'in x)] = 0\\n df_test['level'][df_test['labels'].apply(lambda x: l+'1'in x)] = 1\\n df_test['level'][df_test['labels'].apply(lambda x: l+'2'in x)] = 2\\n df_test['level'][df_test['labels'].apply(lambda x: l+'3'in x)] = 3\\n df_test['level'][df_test['labels'].apply(lambda x: l+'4'in x)] = 4\\n df_test['level'][df_test['labels'].apply(lambda x: l+'5'in x)] = 5\\n\\n df_train.loc[df_train['domain'] == d, domain] = 1\\n df_test.loc[df_test['domain'] == d, domain] = 1\\n \\n \\n print(\\\"Filtering dataframes...\\\")\\n #filter dataframe\\n del_rows_tr, filtered_df_train = filterDataframe(df_train)\\n #only select instances where there is an entry for level\\n df_selection_train = filtered_df_train[(filtered_df_train[domain] == 1) & (filtered_df_train['level'] != 'None')]\\n\\n #test1\\n del_rows_te, filtered_df_test = filterDataframe(df_test)\\n #gold output\\n df_selection_test = filtered_df_test[(df_test[domain] == 1) & (filtered_df_test['level'] != 'None')]\\n \\n return(df_selection_train, df_selection_test)\",\n \"def build(self):\\n list_of_mafs = []\\n maf_generator = self.get_dataframe()\\n\\n for maf_as_dict in maf_generator:\\n list_of_mafs.extend(maf_as_dict)\\n\\n reporting_path = os.path.join(app.config.get('REPORTING_ROOT_PATH'), app.config.get('REPORTING_PATH'), 'global')\\n combined_maf = None\\n try:\\n combined_maf = pandas.DataFrame(list_of_mafs)\\n except Exception as e:\\n logger.error(f'Problem creating dataframe from list of dicts: {str(e)}')\\n try:\\n combined_maf.to_csv(\\n os.path.join(reporting_path, f'{self.method}_combined_maf.tsv'),\\n sep=\\\"\\\\t\\\",\\n encoding='utf-8',\\n index='false'\\n )\\n except Exception as e:\\n # bad practice here catching base exception, but the pandas documentation did not reveal what errors or\\n # exceptions to expect\\n logger.error(f'Problem writing the combined maf file to csv:{str(e)}')\\n abort(500)\",\n \"def save_data(df, database_filepath):\\n # create a database connect\\n conn = sqlite3.connect(database_filepath)\\n # replace .db with empty space for new table name\\n table_name = database_filepath.replace('.db', '')\\n \\n return df.to_sql(table_name, con=conn, if_exists='replace', index=False)\",\n \"def to_df(self) -> pd.DataFrame:\\n data = []\\n for action in self.actions:\\n data.append(action.to_df())\\n df = pd.read_json(json.dumps(data), orient=\\\"list\\\")\\n return df[self.fields]\",\n \"def create_dataframe_for_training(data):\\n feature_column_name = 'X'\\n #data_cp = data[['label']].copy()\\n for i, row in tqdm(data.iterrows(), total=len(data)):\\n all_features = f'{row.claimant} {row.claim} {row.article_content}'\\n data.loc[i, feature_column_name] = all_features\\n\\n return data[feature_column_name]\",\n \"def Make_DF(File=\\\"N1_50\\\", Period=\\\"\\\", Truth=False, Save=False):\\n Folder = \\\"/scratch2/Master_krilangs/Trilepton_Ntuples/Skimslim/\\\"\\n suffix = \\\"_merged_processed\\\"\\n print(\\\"\\\\u0332\\\".join(File + \\\" \\\"))\\n\\n if Period == \\\"18\\\":\\n SubFolder = \\\"data18_mc16e/\\\"\\n elif Period == \\\"17\\\":\\n SubFolder = \\\"data17_mc16d/\\\"\\n elif Period == \\\"1516\\\":\\n SubFolder = \\\"data1516_mc16a/\\\"\\n else:\\n SubFolder = \\\"\\\"\\n\\n \\n if File == \\\"N1_50\\\":\\n file50 = \\\"../myfile_allevents\\\"\\n tree = uproot.open(Folder+file50+\\\".root\\\")[\\\"mytree\\\"]\\n df = tree.pandas.df(flatten = False)\\n del(tree) # Free up memory\\n\\n elif File == \\\"N1_150\\\":\\n file150 = \\\"myfile_VERTEX_LFC_150_200_250\\\"\\n tree = uproot.open(Folder + file150 + \\\".root\\\")[\\\"mytree\\\"]\\n df = tree.pandas.df(flatten = False)\\n del(tree) # Free up memory\\n df = df.drop([df.index[11071], df.index[23774], df.index[60373], df.index[40743]])\\n\\n elif File == \\\"N1_450\\\":\\n file450 = \\\"myfile_VERTEX_LFC_450_500_550\\\"\\n tree = uproot.open(Folder + file450 + \\\".root\\\")[\\\"mytree\\\"]\\n df = tree.pandas.df(flatten = False)\\n del(tree) # Free up memory\\n df = df.drop([df.index[14678], df.index[26355], df.index[39870], df.index[76527], df.index[60540], df.index[125862]])\\n\\n else:\\n file = SubFolder + File + suffix\\n tree = uproot.open(Folder + file + \\\".root\\\")[File + \\\"_NoSys\\\"]\\n df_tree = tree.pandas.df(flatten = False)\\n del(tree) # Free up memory\\n df = df_tree.iloc[:,64:74]\\n del(df_tree) # Free up memory\\n if File == \\\"diboson3L\\\":\\n df = df.drop([df.index[178412]])\\n if File == \\\"diboson4L\\\":\\n df = df.drop([df.index[3826200]])\\n if File == \\\"topOther\\\":\\n df = df.drop([df.index[3281191]])\\n if File == \\\"ttbar\\\":\\n df = df.drop([df.index[5690459], df.index[3723599]])\\n\\n newdf = lepaugmentation(df, 4, Truth)\\n del(df) # Free up memory\\n\\n if Save:\\n print(\\\"Save:\\\")\\n newdf.to_hdf(\\\"Trilepton_ML.h5\\\", key=File+Period) # Save dataframe to file.\\n\\n print(newdf.info(verbose=True))\\n #print(newdf[\\\"target\\\"].value_counts())\\n \\n del(newdf) # Free up memory\",\n \"def as_DF(self):\\n\\n gs_df = pd.DataFrame(self.P, columns=self.xvec, index=self.yvec)\\n gs_df.columns.name = 'x'\\n gs_df.index.name = 'y'\\n\\n return gs_df\",\n \"def save(self):\\r\\n self.df_app_data = self.df_app_data.to_csv(\\\"app_data.csv\\\", index=False)\",\n \"def df_builder(path):\\n\\n ###CHANGE FILE ENDING (.csv or .csv.gz)\\n all_files = glob.glob(\\n os.path.join(path, \\\"probe_data_I210.201710*.csv\\\")) # advisable to use os.path.join as this makes concatenation OS independent\\n df_from_each_file = (pd.read_csv(f) for f in all_files)\\n return pd.concat(df_from_each_file, ignore_index=True)\",\n \"def set_data(self):\\n # take care of samples\\n patients = self.samples.iloc[:,1].tolist()\\n samples = self.samples.iloc[:,0].tolist()\\n self.samples = pd.DataFrame(patients,index = samples,columns = ['patient']) # indexed by sample\\n #\\n # take care of expression data\\n cols = self.expression.SYMBOL.tolist() # set new column names to transposed expression_data \\n \\n new_exp = self.expression.T.ix[1:,:] # transpose\\n new_exp.columns = cols\\n self.expression = new_exp # add columns\\n self.data = pd.merge(self.expression,self.samples,left_index = True,right_index=True) # merged data sets\\n #pd.merge(df1,df2,how = 'left',left_index=True,right_index=True) # do a left join\",\n \"def make_stats_df(self):\\n columns = ['DATE', 'TEAM', 'teamId', 'R', 'HR', 'RBI', 'SBN', 'OBP', \\n 'K', 'QS', 'SV', 'ERA', 'WHIP', 'MOVES', 'CHANGE']\\n trimmed_table = self.parse_soup(self.stats)\\n self.df_stats = pd.DataFrame(trimmed_table, columns=columns) \\n # load season standings csv from file\\n try: # if it already exists\\n df = pd.read_csv('2016_stats.csv', index_col=0)\\n except OSError:\\n df = pd.DataFrame(columns=columns) # if it doesn't already exist\\n df = df.append(self.df_stats)\\n df.to_csv('2016_stats.csv')\",\n \"def glue_table(name: str, df: pd.DataFrame, build_path=\\\"_build\\\"):\\n\\n if not os.path.exists(build_path):\\n os.mkdir(build_path)\\n df.to_excel(os.path.join(build_path, f\\\"{name}.xlsx\\\"))\\n\\n glue(name, df)\",\n \"def create_dataframe(ids, names, p_links, c_links, cl_links):\\n try:\\n dict = {'ID':ids, 'Name': names, 'Photo':p_links, 'Flag':c_links, 'Club Logo':cl_links}\\n df = pd.DataFrame(dict)\\n return df\\n except Exception as e:\\n print(\\\"Exception creating or storing the dataframe: \\\" + str(e))\",\n \"def __call__(self):\\n\\n # create dataframes of relevant sections from the INP\\n for ix, sect in enumerate(self.config['inp_sections']):\\n if ix == 0:\\n df = create_dataframeINP(self.inp.path, sect, comment_cols=False)\\n else:\\n df_other = create_dataframeINP(self.inp.path, sect, comment_cols=False)\\n df = df.join(df_other)\\n\\n if self.rpt:\\n for rpt_sect in self.config['rpt_sections']:\\n df = df.join(create_dataframeRPT(self.rpt.path, rpt_sect))\\n\\n # add conduit coordinates\\n xys = df.apply(lambda r: get_link_coords(r, self.inp.coordinates, self.inp.vertices), axis=1)\\n df = df.assign(coords=xys.map(lambda x: x[0]))\\n\\n # make inlet/outlet node IDs string type\\n df.InletNode = df.InletNode.astype(str)\\n df.OutletNode = df.OutletNode.astype(str)\\n\\n return df\",\n \"def to_pandas(self):\\n pass\",\n \"def to_pandas(self):\\n pass\",\n \"def save_prediction(self, meta, y_pred, y, filename):\\n df = pd.DataFrame(meta)\\n df['y_pred'] = y_pred\\n df['y'] = y\\n print(df)\\n df.loc[:, 'id'] = df.index\\n self.df_to_csv(df, filename, store_header=False)\",\n \"def dataframe(self):\\n return self.generator.dataframe\",\n \"def prepare_data(args):\\n logger.info('Loading dataframe from %s' % args.newspath)\\n df = pd.read_csv(args.newspath, encoding='gb18030')\\n logger.info('Dataframe size: %d observations %d features after loaded' % (df.shape[0], df.shape[1]))\\n\\n # exclude rows with column source == NaN\\n logger.info('Data cleansing...')\\n df = df[~pd.isna(df['source'])]\\n logger.info('Dataframe size: %d observations %d features after data cleansing' % (df.shape[0], df.shape[1]))\\n\\n # split the dataframe into training set and test set\\n logger.info('Making training set & test set...')\\n train_set, test_set = split_data(df)\\n logger.info('Traning set size: %d' % train_set.shape[0])\\n logger.info('Test set size: %d' % test_set.shape[0])\\n\\n # save the train set and test set to picke files\\n logger.info('Save dataframes to files...')\\n train_set.to_pickle(args.trainpath)\\n test_set.to_pickle(args.testpath)\",\n \"def save(df, save_preprocessed_dataframe_path, name):\\n\\n df.to_csv(save_preprocessed_dataframe_path + name + '.csv', index=False)\",\n \"def get_training_dataframe(dataset_path, two_class=True, get_node_df=False):\\n training_df = pd.DataFrame()\\n graph_dict = {}\\n\\n\\n rows_for_training_df = []\\n \\n if get_node_df:\\n node_df = pd.DataFrame()\\n rows_for_node_df = []\\n\\n\\n # add conspiracy graphs to dataframe\\n conspiracy_graphs = []\\n for i in range(1,271): # 270 total\\n graph_id = 'consp'+str(i)\\n conspiracy_path = dataset_path + \\\"5g_corona_conspiracy/\\\"\\n nodes = pd.read_csv(conspiracy_path + str(i)+ \\\"/nodes.csv\\\")\\n edges = open(conspiracy_path + str(i)+ \\\"/edges.txt\\\")\\n\\n features, G = prepare_graph(1, nodes, edges, graph_id)\\n rows_for_training_df.append(features)\\n if get_node_df:\\n prep_node_info(rows_for_node_df, nodes, graph_id)\\n edges.close()\\n conspiracy_graphs.append(G)\\n graph_dict['conspiracy_graphs'] = conspiracy_graphs\\n\\n non_conspiracy_graphs = []\\n for i in range(1,1661): # 1660 total\\n graph_id = 'non_consp'+str(i)\\n path = dataset_path + \\\"non_conspiracy/\\\"\\n nodes = pd.read_csv(path + str(i)+ \\\"/nodes.csv\\\")\\n edges = open(path + str(i)+ \\\"/edges.txt\\\")\\n\\n label = 0 if two_class else 3\\n features, G = prepare_graph(label, nodes, edges, graph_id)\\n rows_for_training_df.append(features)\\n if get_node_df:\\n prep_node_info(rows_for_node_df, nodes, graph_id)\\n edges.close()\\n edges.close()\\n non_conspiracy_graphs.append(G)\\n graph_dict['non_conspiracy_graphs'] = non_conspiracy_graphs\\n\\n other_conspiracy_graphs = []\\n for i in range(1,398): # 397 total\\n graph_id = 'other_consp'+str(i)\\n path = dataset_path + \\\"other_conspiracy/\\\"\\n nodes = pd.read_csv(path + str(i)+ \\\"/nodes.csv\\\")\\n edges = open(path + str(i)+ \\\"/edges.txt\\\")\\n\\n label = 0 if two_class else 2\\n features, G = prepare_graph(label, nodes, edges, graph_id)\\n rows_for_training_df.append(features)\\n if get_node_df:\\n prep_node_info(rows_for_node_df, nodes, graph_id)\\n edges.close()\\n edges.close()\\n other_conspiracy_graphs.append(G)\\n graph_dict['other_conspiracy_graphs'] = other_conspiracy_graphs\\n\\n training_df = training_df.append(rows_for_training_df)\\n if get_node_df:\\n node_df = node_df.append(rows_for_node_df)\\n return training_df, graph_dict, node_df\\n return training_df, graph_dict\",\n \"def to_archive(self, filename):\\n with pd.HDFStore(filename, mode=\\\"w\\\") as store:\\n if self.size() > 0:\\n df = pd.DataFrame.from_records(self.data.to_records(index=True))\\n else:\\n df = pd.DataFrame(columns=self.columns, index=self.data.index)\\n store.put(ARCH_KEY, df)\\n metadata = {\\n k: v\\n for k, v in self.__dict__.items()\\n if k not in [\\\"data\\\", \\\"filelist\\\", \\\"conf\\\", \\\"cats\\\"]\\n }\\n metadata[\\\"conf\\\"] = getattr(self.conf, \\\"to_dict\\\", lambda: {})()\\n store.get_storer(ARCH_KEY).attrs.metadata = metadata\\n # Store DataFrame with categorisation data\\n if self.is_categorised:\\n df_cat = pd.DataFrame.from_records(self.cats.astype(\\\"uint8\\\").to_records(index=True))\\n store.put(ARCH_KEY_CAT, df_cat)\",\n \"def create_data_frame(input_filepath):\\n df = pd.read_json(input_filepath)\\n logger = logging.getLogger(__name__)\\n logger.info('Imported dataframe:')\\n logger.info(df.info())\\n logger.info(df.describe())\\n logger.info(df.head())\\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72918093","0.6938951","0.69203115","0.67623717","0.6709177","0.661987","0.66142595","0.6613234","0.65876186","0.65471715","0.65268254","0.6518715","0.6476695","0.6442566","0.63830763","0.63486266","0.6345487","0.6316515","0.6284121","0.6281206","0.6278438","0.62686926","0.62654364","0.62378913","0.6235741","0.62254065","0.6189013","0.61483115","0.6141178","0.6137117","0.6122946","0.61114436","0.61020136","0.60915416","0.6089382","0.6072203","0.6060114","0.6057092","0.6056945","0.605357","0.6052877","0.6048948","0.6031402","0.6025524","0.6002891","0.59991556","0.59950984","0.5993048","0.5986284","0.59854627","0.5983826","0.59786975","0.5974424","0.5972987","0.5967404","0.59553546","0.5950378","0.59476876","0.59466547","0.5946154","0.5943337","0.5938696","0.5937919","0.5936918","0.5934892","0.5933923","0.5932648","0.59184736","0.59182835","0.5914071","0.59135026","0.5909076","0.5908037","0.5901011","0.5899025","0.5898912","0.58956623","0.58896893","0.58884835","0.58822256","0.5874656","0.58743656","0.587427","0.5873021","0.5872103","0.58699733","0.58693355","0.58660054","0.58625996","0.5859404","0.58514446","0.5850162","0.5850162","0.58459675","0.58406836","0.58389467","0.5832244","0.5831809","0.5830671","0.58274925"],"string":"[\n \"0.72918093\",\n \"0.6938951\",\n \"0.69203115\",\n \"0.67623717\",\n \"0.6709177\",\n \"0.661987\",\n \"0.66142595\",\n \"0.6613234\",\n \"0.65876186\",\n \"0.65471715\",\n \"0.65268254\",\n \"0.6518715\",\n \"0.6476695\",\n \"0.6442566\",\n \"0.63830763\",\n \"0.63486266\",\n \"0.6345487\",\n \"0.6316515\",\n \"0.6284121\",\n \"0.6281206\",\n \"0.6278438\",\n \"0.62686926\",\n \"0.62654364\",\n \"0.62378913\",\n \"0.6235741\",\n \"0.62254065\",\n \"0.6189013\",\n \"0.61483115\",\n \"0.6141178\",\n \"0.6137117\",\n \"0.6122946\",\n \"0.61114436\",\n \"0.61020136\",\n \"0.60915416\",\n \"0.6089382\",\n \"0.6072203\",\n \"0.6060114\",\n \"0.6057092\",\n \"0.6056945\",\n \"0.605357\",\n \"0.6052877\",\n \"0.6048948\",\n \"0.6031402\",\n \"0.6025524\",\n \"0.6002891\",\n \"0.59991556\",\n \"0.59950984\",\n \"0.5993048\",\n \"0.5986284\",\n \"0.59854627\",\n \"0.5983826\",\n \"0.59786975\",\n \"0.5974424\",\n \"0.5972987\",\n \"0.5967404\",\n \"0.59553546\",\n \"0.5950378\",\n \"0.59476876\",\n \"0.59466547\",\n \"0.5946154\",\n \"0.5943337\",\n \"0.5938696\",\n \"0.5937919\",\n \"0.5936918\",\n \"0.5934892\",\n \"0.5933923\",\n \"0.5932648\",\n \"0.59184736\",\n \"0.59182835\",\n \"0.5914071\",\n \"0.59135026\",\n \"0.5909076\",\n \"0.5908037\",\n \"0.5901011\",\n \"0.5899025\",\n \"0.5898912\",\n \"0.58956623\",\n \"0.58896893\",\n \"0.58884835\",\n \"0.58822256\",\n \"0.5874656\",\n \"0.58743656\",\n \"0.587427\",\n \"0.5873021\",\n \"0.5872103\",\n \"0.58699733\",\n \"0.58693355\",\n \"0.58660054\",\n \"0.58625996\",\n \"0.5859404\",\n \"0.58514446\",\n \"0.5850162\",\n \"0.5850162\",\n \"0.58459675\",\n \"0.58406836\",\n \"0.58389467\",\n \"0.5832244\",\n \"0.5831809\",\n \"0.5830671\",\n \"0.58274925\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6171512"},"document_rank":{"kind":"string","value":"27"}}},{"rowIdx":4691,"cells":{"query":{"kind":"string","value":"Hexlify raw text, return hexlified text."},"document":{"kind":"string","value":"def hexlify(text):\n if six.PY3:\n text = text.encode('utf-8')\n\n hexlified = binascii.hexlify(text)\n\n if six.PY3:\n hexlified = hexlified.decode('utf-8')\n\n return hexlified"},"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 unhexlify(text):\n unhexlified = binascii.unhexlify(text)\n\n if six.PY3:\n unhexlified = unhexlified.decode('utf-8')\n\n return unhexlified","def hexify(text):\r\n return ' '.join([hexify_word(word) for word in text.split()])","def normalize(self, text):\n\n return binascii.hexlify(text)","def to_hex(text):\n return ' '.join([hex(ord(char)) for char in unicode(text, 'UTF-8')])","def encrypt(text):\r\n\r\n cipher = fuzz(text)\r\n return hexify(cipher)","def test_unhexlify_not_python():\n assert '' == uflash.unhexlify(\n ':020000040003F7\\n:10E000000000000000000000000000000000000010')","def test_hexlify():\n result = uflash.hexlify(TEST_SCRIPT)\n lines = result.split()\n # The first line should be the extended linear address, ox0003\n assert lines[0] == ':020000040003F7'\n # There should be the expected number of lines.\n assert len(lines) == 5","def hex(self):\n return binascii.hexlify(self.data)","def test_unhexlify():\n hexlified = uflash.hexlify(TEST_SCRIPT)\n unhexlified = uflash.unhexlify(hexlified)\n assert unhexlified == TEST_SCRIPT.decode('utf-8')","def hexlify(self: str, verbose=False):\n nbytes = len(_chunk_bs(self))\n buf = b''\n strlen = ''\n for b in to_bytes(_chunk_bs(self)):\n buf+=b\n# for s in _from_list(_chunk_bs(self)):\n# strlen+=f'{ _bit_length(s): 02d}'\n if verbose:\n for n in range(nbytes):\n strlen += f'{_bit_length(_from_list(_chunk_bs(self))[n])} @[{n}] '\n print(strlen)\n return buf","def _hexlify(data):\n if data is None:\n return None\n elif isinstance(data, bytes) or isinstance(data, bytearray):\n return data.hex()\n elif isinstance(data, list):\n return [_hexlify(item) for item in data]\n elif isinstance(data, dict):\n return {k: _hexlify(v) for k, v in data.items()}\n else:\n return data","def hexify_word(word):\r\n\r\n return ''.join([str(hex(ord(c))[2::]) for c in word])","def sanatize_hex(data: str) -> str:\n return data.replace(\"0x\", \"\").replace(\"0X\", \"\")","def as_hex(self):\n return binascii.hexlify(self.as_bytes()).decode('ascii')","def hex(cls, x):\n return c_hex(x)","def _dehex(s):\n import re\n import binascii\n\n # Remove all non-hexadecimal digits\n s = re.sub(br'[^a-fA-F\\d]', b'', s)\n # binscii.unhexlify works in Python 2 and Python 3 (unlike\n # thing.decode('hex')).\n return binascii.unhexlify(s)","def test_unhexlify_bad_unicode():\n assert '' == uflash.unhexlify(\n ':020000040003F7\\n:10E000004D50FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF')","def hexstring(self):\n if self.current != b\"<\":\n self.on_parser_error(\"Hexadecimal string expected\")\n self.next()\n token = b''\n self.maybe_spaces_or_comments()\n while self.is_hex_digit:\n token += self.next()\n self.maybe_spaces_or_comments()\n\n ch = self.next()\n if ch != b'>':\n self.on_parser_error(\"Wrong hexadecimal string\")\n if len(token) % 2:\n # if there is an odd number of digits - the last one should be assumed 0\n token += b'0'\n return HexString(token.decode(DEFAULT_ENCODING).upper())","def preprocess_hashes(tex):\n blocks = catlist()\n rx = hash_rx\n m = rx.search(tex)\n while m:\n if len(m.group(2)) > 40:\n tex2htm.warn(\"Possible runaway hash: {}\".format(text_sample(m.group(2))))\n raise(None)\n blocks.append(tex[:m.start()])\n blocks.append(re.sub(r'(^|[^\\\\])%', r'\\1\\%', m.group(0)))\n tex = tex[m.end():]\n m = rx.search(tex)\n blocks.append(tex)\n return \"\".join(blocks)","def ascii_to_phred64(c):\r\n return ascii_to_phred(c, 64)","def remove_hex(text): \n return re.sub(r'&.*?;', r'', text)","def test_embed_hex():\n python = uflash.hexlify(TEST_SCRIPT)\n result = uflash.embed_hex(uflash._RUNTIME, python)\n # The resulting hex should be of the expected length.\n assert len(result) == len(python) + len(uflash._RUNTIME) + 1 # +1 for \\n\n # The hex should end with a newline '\\n'\n assert result[-1:] == '\\n'\n # The Python hex should be in the correct location.\n py_list = python.split()\n result_list = result.split()\n start_of_python_from_end = len(py_list) + 5\n start_of_python = len(result_list) - start_of_python_from_end\n assert result_list[start_of_python:-5] == py_list\n # The firmware should enclose the Python correctly.\n firmware_list = uflash._RUNTIME.split()\n assert firmware_list[:-5] == result_list[:-start_of_python_from_end]\n assert firmware_list[-5:] == result_list[-5:]","def hex(string):\n return string.encode('hex')","def tohex(data: str) -> str:\n match = re.fullmatch(r\"^0[x|X][0-9a-fA-F]+\", data)\n if match:\n return data.lower()\n match = re.fullmatch(r\"^[0-9a-fA-F]+[h|H]$\", data)\n if not match:\n raise ValueError(f\"Required hex of the form `0x` or `H` found {data}\")\n match = re.match(r\"^[0-9a-fA-F]+\", data)\n return f\"0x{match.group().lower()}\"","def entity_encode_hex(input, errors='strict'):\n output = ''\n for character in input:\n if character in ('&', '<', '>'):\n output += \"&#x%s;\" % character.encode('hex')\n else:\n output += character\n\n return (output, len(input))","def str_sha(raw_sha):\n return hexlify(raw_sha)[:12]","def hexify(c):\n try:\n s = c.encode(\"utf-8\").encode(\"hex\")\n except UnicodeDecodeError:\n s = 0\n n = len(s)\n if n <= 2: return s\n a = ' - '.join([s[i:i+2] for i in range(0,n,2)])\n return a[:-1]","def hexdigest(self):\n # bytes.hex() is simpler, but not available For Python <= 3.4\n return \"\".join(\"{0:0>2x}\".format(b) for b in self.digest())","def _encode_text(self):\n\n print(f\"Hex encode; received message is {self.message}\")\n return self.message.encode(\"utf-8\").hex()","def toHexa(data):\n\tresult = \"\"\n\tif isBytes(data):\n\t\tdata = data.decode(\"latin-1\")\n\tfor i in data:\n\t\tresult += \"\\\\x%02X\"%ord(i)\n\treturn result","def hexify(buffer):\n return ''.join('%02x' % ord(c) for c in buffer)","def hexdump(data):\n\n def is_hexdump_printable(b):\n return b in b' 0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz`~!@#$%^&*()-_=+[]{}\\\\|\\'\";:/?.,<>'\n\n lines = []\n chunks = (data[i*16:i*16+16] for i in range((len(data) + 15) // 16))\n\n for i, chunk in enumerate(chunks):\n hexblock = ['{:02x}'.format(b) for b in chunk]\n left, right = ' '.join(hexblock[:8]), ' '.join(hexblock[8:])\n asciiblock = ''.join(chr(b) if is_hexdump_printable(b) else '.' for b in chunk)\n lines.append('{:08x} {:23} {:23} |{}|'.format(i*16, left, right, asciiblock))\n\n return '\\n'.join(lines)","def _encode_code(self, text):\r\n replacements = [\r\n # Encode all ampersands; HTML entities are not\r\n # entities within a Markdown code span.\r\n ('&', '&'),\r\n # Do the angle bracket song and dance:\r\n ('<', '<'),\r\n ('>', '>'),\r\n ]\r\n for before, after in replacements:\r\n text = text.replace(before, after)\r\n hashed = _hash_text(text)\r\n self._escape_table[text] = hashed\r\n return hashed","def test_hexlify_empty_script():\n assert uflash.hexlify('') == ''","def hash_coloured_escapes(text):\n ansi_code = int(sha256(text.encode(\"utf-8\")).hexdigest(), 16) % 230\n prefix, suffix = colored(\"SPLIT\", ansi_code=ansi_code).split(\"SPLIT\")\n return prefix, suffix","def _hex2bin(cls, h):\n b = []\n while h:\n if h[0] in ' \\n': # ignore whitespace\n h = h[1:]\n elif h[0] in '0123456789abcdef': # hex byte\n b.append(int(h[:2], 16))\n h = h[2:]\n elif h[0] == '.': # for chunk type\n b.extend(ord(h[i]) for i in range(1,5))\n h = h[5:]\n else: # for PNG magic\n b.append(ord(h[0]))\n h = h[1:]\n return ''.join(map(chr,b))","def __set_has_hexadecimal(text=str):\n reg_ex = constants.HEXADECIMAL_REG_EX_PATTERN\n if reg_ex.search(text) is None:\n return text\n return reg_ex.sub(constants.QUESTION_HAS_HEXADECIMAL_KEY, text)","def sha256_hexoutput(in_str):\r\n return sha256(in_str.encode('ascii')).hexdigest()","def to_ascii(text):\n return re.sub(r'[^\\x00-\\x7F]+', ' ', text)","def test_ascii_to_phred(self):\r\n self.assertEqual(ascii_to_phred('x', 120), 0)\r\n self.assertEqual(ascii_to_phred('x', 119), 1)","def genHexStr(instr: str) -> str:\n\n return hashlib.md5(instr.encode(\"utf-8\")).hexdigest()","def hexdigest(self):\n return \"\".join(\"%02x\" % ord(x)\n for x in MegaCrypto.a32_to_str(self.digest()))","def hex_form(hash):\n final_hash = ''\n for i in range(len(hash)):\n final_hash += format(hash[i], '02x')\n return final_hash","def hexdigest(self):\r\n return ''.join(['%02x' % ord(c) for c in self.digest()])","def entity_decode_hex(input, errors='strict'):\n if _is_unicode(input):\n if '%' not in input:\n return s\n bits = _asciire.split(input)\n res = [bits[0]]\n append = res.append\n for i in range(1, len(bits), 2):\n append(unquote(str(bits[i])).decode('latin1'))\n append(bits[i + 1])\n return (''.join(res), len(input))\n\n preamble_regex = re.compile(r\"&#x\", flags=re.I)\n bits = preamble_regex.split(input)\n # fastpath\n if len(bits) == 1:\n return input\n res = [bits[0]]\n append = res.append\n for item in bits[1:]:\n try:\n append(_hextochr[item[:2]])\n append(item[3:])\n except KeyError:\n append('&#x')\n append(item)\n append(';')\n\n return (''.join(res), len(input))","def handle_hex_stream(data):\n stream = extract_stream(data)\n if stream is not False: # stream can actually be null (HexStream = \\\\x00\\\\x00). so we explicitly check for False\n is_binary = not all(c in string.printable for c in stream)\n return stream.encode(\"base64\") if is_binary else stream\n return data","def hash(self, text):\n hashval = 0\n for i in xrange(0, len(text)):\n hashval += ord(text[i])**i\n return hashval","def hashhex(s):\n h = hashlib.sha1()\n h.update(s)\n return h.hexdigest()","def _convert_hex(self, hex_value):\n if not isinstance(hex_value, str):\n raise TypeError(\"given hex value must be str\")\n m = HEX_RE.match(hex_value)\n if m is None:\n raise ValueError(\"given string does not seem to be Python hex\")\n sign_char, base, exp_sign, exp = [m.group(i) for i in range(1,5)]\n new_sign = \"+\" if sign_char is None else sign_char\n # Line below converts exp to hex value. The \"0x\" prefix is removed \n # with [2:]. The exponent is padded with (too many) zeros (Stata \n # requires 3 digits), and reduced to last 3 digits with [-3:].\n new_exp = (\"000\" + hex(int(exp))[2:])[-3:]\n return \"\".join((new_sign, base, 'X', exp_sign, new_exp))","def hashhex(s):\n h = hashlib.sha1()\n h.update(s.encode('utf-8'))\n return h.hexdigest()","def hex_str (self):\n return \"#%02X%02X%02X\"%(self._intern[0],self._intern[1],self._intern[2])","def convertirHexadecimal(self):\n self.convertir(lambda c: hex(ord(c))[2:], sep=' ')","def hex_hash(s):\n if not s:\n return '0'\n s = s.encode('utf-8')\n return '{:x}'.format(adler32(s) & 0xffffffff)","def _hash_content(data):\n return hashlib.sha512(str(data).encode('utf-8')).hexdigest()","def ascii_convert(the_bytes: bytes):\n return ANSI_ESCAPE_B.sub(rb\"\", the_bytes).decode(\"utf-8\")","def b2a_hex(data):\n\n return binascii.b2a_hex(data)","def unH(s):\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])","def as_hex(self, *, align='left'):\n return self.as_bytes(align=align).hex()","def bytes_to_hexstr(data: bytes) -> str:\n return ''.join(int_to_hexstr(byte) for byte in data)","def from_hex_str(value):\n \n return SHex(value)","def toHex(self):\n return hexlify(self.serialize()).decode(\"utf-8\")","def hashhex(s):\n h = hashlib.sha1()\n h.update(s.encode())\n return h.hexdigest()","def HashForText (text):\n if isinstance(text, six.text_type):\n text = text.encode('utf-8')\n return __Hasher(text).hexdigest()","def test_bytes_to_intel_hex():\n data = [1, 2, 3, 4, 5]\n expected_hex_str = \"\\n\".join([\":050000000102030405EC\", INTEL_HEX_EOF])\n\n result = cmds._bytes_to_intel_hex(data=data)\n\n assert expected_hex_str == result","def hexbyte(string):\n#\treturn repr(string)\n\ts = \"\"\n\tfor i in string:\n\t\tif (ord(i) >= ord('A') and ord(i) <= ord('z')) \\\n\t\t\tor (ord(i) >= ord('0') and ord(i) <= ord('9')) \\\n\t\t\tor (ord(i) == ord(\" \")):\n\t\t\ts += \"%s\" % i\n\t\telse:\n\t\t\ts += \"\\\\x%02x\" % ord(i)\n\n#\t\ts += \" \"\n\treturn s","def hex_str (self):\n return \"#%02X%02X%02X\"%(self.r, self.g, self.b)","def hex(space, w_val):\n return space.hex(w_val)","def hexDump(bytes):\n for i in range(len(bytes)):\n sys.stdout.write(\"%2x \" % (ord(bytes[i])))\n if (i+1) % 8 == 0:\n print repr(bytes[i-7:i+1])\n\n if(len(bytes) % 8 != 0):\n print string.rjust(\"\", 11), repr(bytes[i-7:i+1])","def wkb_hex(self): # -> str:\n ...","def test_phred_to_ascii(self):\r\n self.assertEqual(phred_to_ascii(0, 120), 'x')\r\n self.assertEqual(phred_to_ascii(1, 119), 'x')","def hashhex(s):\n h = hashlib.sha1()\n h.update(s.encode('utf-8'))\n return h.hexdigest()","def encode_hex(b):\n if isinstance(b, str):\n b = bytes(b, \"utf-8\")\n if isinstance(b, bytes):\n return str(hexlify(b), \"utf-8\")\n raise TypeError(\"Value must be an instance of str or bytes\")","def bytes_to_hex(s):\n\n return s.encode(\"hex\")","def _hashsanitize(bytesin):\n # Used for converting raw byte data into a hex string. If the byte isn't a hex digit, use nothing instead.\n return \"\".join([x if x.lower() in 'abcdef0123456789' else '' for x in bytesin])","def text(self, text: str) -> bytes:\n\n buffer = text.encode(\"utf-8\")\n return struct.pack(\">i\", len(buffer)) + buffer","def hx(i):\n a = hex(i)[2:]\n if len(a)<2: a = ''.join(['0',a])\n return a","def dump_hex(output_file, input_file):\n\n with open(input_file, 'rb') as ifile, open(output_file, 'wb') as ofile:\n while True:\n word = ifile.read(4)\n if not word:\n break\n\n ofile.write(binascii.hexlify(word))\n ofile.write(b'\\n')","def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\n\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None","def longtohex(n):\n\n plain=(re.match(r\"0x([0-9A-Fa-f]*)l?$\", hex(n), re.I).group(1)).lower()\n return \"0x\" + plain","def w__format_hex(self, string):\n d = map(None, string)\n d = map(ord, d)\n d = map(lambda x: \"%02x\" % x, d)\n return ' '.join(d)","def to_h(self):\n return str(self).encode('hex')","def hash_coloured(text):\n ansi_code = int(sha256(text.encode(\"utf-8\")).hexdigest(), 16) % 230\n return colored(text, ansi_code=ansi_code)","def codeline_to_hexadecimal(codeline, data):\n # Extract each of the three locations in turn, the first two as data\n # items and the third as an address\n first_op = data[codeline[0]][0]\n second_op = data[codeline[1]][0]\n third_op = codeline[2]\n # Create a binary string from the operands\n binary_string = \"{0}{1}{2}00\".format(\n format(first_op, \"010b\"), format(second_op, \"010b\"),\n format(third_op, \"010b\"))\n # Take the binary string and return it as a 32-bit hexadecimal string.\n return format(int(binary_string, base=2), \"#010x\")","def base64_to_hex(b64_string):\n # Remove hex encoding\n unencoded_string = base64.b64decode(b64_string)\n # Encode base64 and return\n return binascii.hexlify(unencoded_string)","def c_hex(x):\n #print(\"c_hex\", x, type(x))\n h = hex(x & ((1 << INT_BITS) - 1))\n while h[-1:] == \"L\": h = h[:-1] # for python 2\n return h + UINT_SUFFIX","def encodeText(text):\r\n#\treturn repr( quote_plus(text.replace(\"'\", '\"')) )\r\n\ttry:\r\n\t\treturn repr( quote_plus(text.replace(\"'\", '\"').encode('utf-8')) )\r\n\texcept:\r\n\t\tlogError(\"encodeText()\")\r\n\treturn repr(text.replace(\"'\", '\"'))","def print_as_hex(s):\n print(\":\".join(\"{0:x}\".format(ord(c)) for c in s))","def string_raw(self):\n return \"x%x\" % self.encoded","def encodeHex(s):\n encoder = codecs.getencoder('hex_codec')\n hex, length = encoder(s)\n assert len(s) == length, \"Hex encoding incomplete\"\n return hex","def printable_hash(h):\n return int(h).to_bytes(32, byteorder='big', signed=False).hex()","def elf_hash(s):\n h = 0\n for c in s:\n h = (h << 4) + ord(c)\n t = (h & 0xF0000000)\n if t != 0:\n h = h ^ (t >> 24)\n h = h & ~t\n return h","def int2hex(n: int) -> str:","def test_phred_to_ascii64(self):\r\n self.assertEqual(phred_to_ascii64(0), '@')\r\n self.assertEqual(phred_to_ascii64(30), '^')","def test_bytes_to_pretty_hex():\n data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n expected = (\n \"0000 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F 10 \"\n \"|................|\\n\"\n )\n\n result = cmds._bytes_to_pretty_hex(data=data)\n\n assert expected == result","def stringify(self):\n hexcode = \"#\"\n for x in self.value:\n part = hex(x)[2:]\n if len(part) < 2: part = \"0\" + part\n hexcode += part\n return hexcode","def hash(plainString):\n result = plainString\n for i in range(0,12):\n result = hashHelp(result)\n return result","def hex_dump(string):\n return ' '.join([\"%0.2X\" % ord(x) for x in string])","def look(source_path):\r\n hex_data_formated = get(source_path, \"f\")\r\n hex_list = hex_data_formated.split(\" \")\r\n\r\n result = \" 00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F\\n\"\r\n for index, value in enumerate(hex_list):\r\n if index == 0:\r\n result += (\"0x000000 \" + value + \" \")\r\n\r\n elif index % 16 == 15:\r\n address = hex(index + 1)\r\n result += (value + \"\\n\" + address[0:2] + address[2:].zfill(6) + \" \")\r\n\r\n elif index % 16 == 7:\r\n result += (value + \" \")\r\n\r\n else:\r\n result += (value + \" \")\r\n\r\n print(result)","def hexcode(self):\n hexc = \"#%.02X%.02X%.02X\" % (int(self.rgb_255[0]), int(self.rgb_255[1]), int(self.rgb_255[2]))\n return hexc","def ansi_escape(text: object) -> str:\n return str(text).replace(\"\\x1b\", \"?\").replace(\"\\b\", \"?\")"],"string":"[\n \"def unhexlify(text):\\n unhexlified = binascii.unhexlify(text)\\n\\n if six.PY3:\\n unhexlified = unhexlified.decode('utf-8')\\n\\n return unhexlified\",\n \"def hexify(text):\\r\\n return ' '.join([hexify_word(word) for word in text.split()])\",\n \"def normalize(self, text):\\n\\n return binascii.hexlify(text)\",\n \"def to_hex(text):\\n return ' '.join([hex(ord(char)) for char in unicode(text, 'UTF-8')])\",\n \"def encrypt(text):\\r\\n\\r\\n cipher = fuzz(text)\\r\\n return hexify(cipher)\",\n \"def test_unhexlify_not_python():\\n assert '' == uflash.unhexlify(\\n ':020000040003F7\\\\n:10E000000000000000000000000000000000000010')\",\n \"def test_hexlify():\\n result = uflash.hexlify(TEST_SCRIPT)\\n lines = result.split()\\n # The first line should be the extended linear address, ox0003\\n assert lines[0] == ':020000040003F7'\\n # There should be the expected number of lines.\\n assert len(lines) == 5\",\n \"def hex(self):\\n return binascii.hexlify(self.data)\",\n \"def test_unhexlify():\\n hexlified = uflash.hexlify(TEST_SCRIPT)\\n unhexlified = uflash.unhexlify(hexlified)\\n assert unhexlified == TEST_SCRIPT.decode('utf-8')\",\n \"def hexlify(self: str, verbose=False):\\n nbytes = len(_chunk_bs(self))\\n buf = b''\\n strlen = ''\\n for b in to_bytes(_chunk_bs(self)):\\n buf+=b\\n# for s in _from_list(_chunk_bs(self)):\\n# strlen+=f'{ _bit_length(s): 02d}'\\n if verbose:\\n for n in range(nbytes):\\n strlen += f'{_bit_length(_from_list(_chunk_bs(self))[n])} @[{n}] '\\n print(strlen)\\n return buf\",\n \"def _hexlify(data):\\n if data is None:\\n return None\\n elif isinstance(data, bytes) or isinstance(data, bytearray):\\n return data.hex()\\n elif isinstance(data, list):\\n return [_hexlify(item) for item in data]\\n elif isinstance(data, dict):\\n return {k: _hexlify(v) for k, v in data.items()}\\n else:\\n return data\",\n \"def hexify_word(word):\\r\\n\\r\\n return ''.join([str(hex(ord(c))[2::]) for c in word])\",\n \"def sanatize_hex(data: str) -> str:\\n return data.replace(\\\"0x\\\", \\\"\\\").replace(\\\"0X\\\", \\\"\\\")\",\n \"def as_hex(self):\\n return binascii.hexlify(self.as_bytes()).decode('ascii')\",\n \"def hex(cls, x):\\n return c_hex(x)\",\n \"def _dehex(s):\\n import re\\n import binascii\\n\\n # Remove all non-hexadecimal digits\\n s = re.sub(br'[^a-fA-F\\\\d]', b'', s)\\n # binscii.unhexlify works in Python 2 and Python 3 (unlike\\n # thing.decode('hex')).\\n return binascii.unhexlify(s)\",\n \"def test_unhexlify_bad_unicode():\\n assert '' == uflash.unhexlify(\\n ':020000040003F7\\\\n:10E000004D50FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF')\",\n \"def hexstring(self):\\n if self.current != b\\\"<\\\":\\n self.on_parser_error(\\\"Hexadecimal string expected\\\")\\n self.next()\\n token = b''\\n self.maybe_spaces_or_comments()\\n while self.is_hex_digit:\\n token += self.next()\\n self.maybe_spaces_or_comments()\\n\\n ch = self.next()\\n if ch != b'>':\\n self.on_parser_error(\\\"Wrong hexadecimal string\\\")\\n if len(token) % 2:\\n # if there is an odd number of digits - the last one should be assumed 0\\n token += b'0'\\n return HexString(token.decode(DEFAULT_ENCODING).upper())\",\n \"def preprocess_hashes(tex):\\n blocks = catlist()\\n rx = hash_rx\\n m = rx.search(tex)\\n while m:\\n if len(m.group(2)) > 40:\\n tex2htm.warn(\\\"Possible runaway hash: {}\\\".format(text_sample(m.group(2))))\\n raise(None)\\n blocks.append(tex[:m.start()])\\n blocks.append(re.sub(r'(^|[^\\\\\\\\])%', r'\\\\1\\\\%', m.group(0)))\\n tex = tex[m.end():]\\n m = rx.search(tex)\\n blocks.append(tex)\\n return \\\"\\\".join(blocks)\",\n \"def ascii_to_phred64(c):\\r\\n return ascii_to_phred(c, 64)\",\n \"def remove_hex(text): \\n return re.sub(r'&.*?;', r'', text)\",\n \"def test_embed_hex():\\n python = uflash.hexlify(TEST_SCRIPT)\\n result = uflash.embed_hex(uflash._RUNTIME, python)\\n # The resulting hex should be of the expected length.\\n assert len(result) == len(python) + len(uflash._RUNTIME) + 1 # +1 for \\\\n\\n # The hex should end with a newline '\\\\n'\\n assert result[-1:] == '\\\\n'\\n # The Python hex should be in the correct location.\\n py_list = python.split()\\n result_list = result.split()\\n start_of_python_from_end = len(py_list) + 5\\n start_of_python = len(result_list) - start_of_python_from_end\\n assert result_list[start_of_python:-5] == py_list\\n # The firmware should enclose the Python correctly.\\n firmware_list = uflash._RUNTIME.split()\\n assert firmware_list[:-5] == result_list[:-start_of_python_from_end]\\n assert firmware_list[-5:] == result_list[-5:]\",\n \"def hex(string):\\n return string.encode('hex')\",\n \"def tohex(data: str) -> str:\\n match = re.fullmatch(r\\\"^0[x|X][0-9a-fA-F]+\\\", data)\\n if match:\\n return data.lower()\\n match = re.fullmatch(r\\\"^[0-9a-fA-F]+[h|H]$\\\", data)\\n if not match:\\n raise ValueError(f\\\"Required hex of the form `0x` or `H` found {data}\\\")\\n match = re.match(r\\\"^[0-9a-fA-F]+\\\", data)\\n return f\\\"0x{match.group().lower()}\\\"\",\n \"def entity_encode_hex(input, errors='strict'):\\n output = ''\\n for character in input:\\n if character in ('&', '<', '>'):\\n output += \\\"&#x%s;\\\" % character.encode('hex')\\n else:\\n output += character\\n\\n return (output, len(input))\",\n \"def str_sha(raw_sha):\\n return hexlify(raw_sha)[:12]\",\n \"def hexify(c):\\n try:\\n s = c.encode(\\\"utf-8\\\").encode(\\\"hex\\\")\\n except UnicodeDecodeError:\\n s = 0\\n n = len(s)\\n if n <= 2: return s\\n a = ' - '.join([s[i:i+2] for i in range(0,n,2)])\\n return a[:-1]\",\n \"def hexdigest(self):\\n # bytes.hex() is simpler, but not available For Python <= 3.4\\n return \\\"\\\".join(\\\"{0:0>2x}\\\".format(b) for b in self.digest())\",\n \"def _encode_text(self):\\n\\n print(f\\\"Hex encode; received message is {self.message}\\\")\\n return self.message.encode(\\\"utf-8\\\").hex()\",\n \"def toHexa(data):\\n\\tresult = \\\"\\\"\\n\\tif isBytes(data):\\n\\t\\tdata = data.decode(\\\"latin-1\\\")\\n\\tfor i in data:\\n\\t\\tresult += \\\"\\\\\\\\x%02X\\\"%ord(i)\\n\\treturn result\",\n \"def hexify(buffer):\\n return ''.join('%02x' % ord(c) for c in buffer)\",\n \"def hexdump(data):\\n\\n def is_hexdump_printable(b):\\n return b in b' 0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz`~!@#$%^&*()-_=+[]{}\\\\\\\\|\\\\'\\\";:/?.,<>'\\n\\n lines = []\\n chunks = (data[i*16:i*16+16] for i in range((len(data) + 15) // 16))\\n\\n for i, chunk in enumerate(chunks):\\n hexblock = ['{:02x}'.format(b) for b in chunk]\\n left, right = ' '.join(hexblock[:8]), ' '.join(hexblock[8:])\\n asciiblock = ''.join(chr(b) if is_hexdump_printable(b) else '.' for b in chunk)\\n lines.append('{:08x} {:23} {:23} |{}|'.format(i*16, left, right, asciiblock))\\n\\n return '\\\\n'.join(lines)\",\n \"def _encode_code(self, text):\\r\\n replacements = [\\r\\n # Encode all ampersands; HTML entities are not\\r\\n # entities within a Markdown code span.\\r\\n ('&', '&'),\\r\\n # Do the angle bracket song and dance:\\r\\n ('<', '<'),\\r\\n ('>', '>'),\\r\\n ]\\r\\n for before, after in replacements:\\r\\n text = text.replace(before, after)\\r\\n hashed = _hash_text(text)\\r\\n self._escape_table[text] = hashed\\r\\n return hashed\",\n \"def test_hexlify_empty_script():\\n assert uflash.hexlify('') == ''\",\n \"def hash_coloured_escapes(text):\\n ansi_code = int(sha256(text.encode(\\\"utf-8\\\")).hexdigest(), 16) % 230\\n prefix, suffix = colored(\\\"SPLIT\\\", ansi_code=ansi_code).split(\\\"SPLIT\\\")\\n return prefix, suffix\",\n \"def _hex2bin(cls, h):\\n b = []\\n while h:\\n if h[0] in ' \\\\n': # ignore whitespace\\n h = h[1:]\\n elif h[0] in '0123456789abcdef': # hex byte\\n b.append(int(h[:2], 16))\\n h = h[2:]\\n elif h[0] == '.': # for chunk type\\n b.extend(ord(h[i]) for i in range(1,5))\\n h = h[5:]\\n else: # for PNG magic\\n b.append(ord(h[0]))\\n h = h[1:]\\n return ''.join(map(chr,b))\",\n \"def __set_has_hexadecimal(text=str):\\n reg_ex = constants.HEXADECIMAL_REG_EX_PATTERN\\n if reg_ex.search(text) is None:\\n return text\\n return reg_ex.sub(constants.QUESTION_HAS_HEXADECIMAL_KEY, text)\",\n \"def sha256_hexoutput(in_str):\\r\\n return sha256(in_str.encode('ascii')).hexdigest()\",\n \"def to_ascii(text):\\n return re.sub(r'[^\\\\x00-\\\\x7F]+', ' ', text)\",\n \"def test_ascii_to_phred(self):\\r\\n self.assertEqual(ascii_to_phred('x', 120), 0)\\r\\n self.assertEqual(ascii_to_phred('x', 119), 1)\",\n \"def genHexStr(instr: str) -> str:\\n\\n return hashlib.md5(instr.encode(\\\"utf-8\\\")).hexdigest()\",\n \"def hexdigest(self):\\n return \\\"\\\".join(\\\"%02x\\\" % ord(x)\\n for x in MegaCrypto.a32_to_str(self.digest()))\",\n \"def hex_form(hash):\\n final_hash = ''\\n for i in range(len(hash)):\\n final_hash += format(hash[i], '02x')\\n return final_hash\",\n \"def hexdigest(self):\\r\\n return ''.join(['%02x' % ord(c) for c in self.digest()])\",\n \"def entity_decode_hex(input, errors='strict'):\\n if _is_unicode(input):\\n if '%' not in input:\\n return s\\n bits = _asciire.split(input)\\n res = [bits[0]]\\n append = res.append\\n for i in range(1, len(bits), 2):\\n append(unquote(str(bits[i])).decode('latin1'))\\n append(bits[i + 1])\\n return (''.join(res), len(input))\\n\\n preamble_regex = re.compile(r\\\"&#x\\\", flags=re.I)\\n bits = preamble_regex.split(input)\\n # fastpath\\n if len(bits) == 1:\\n return input\\n res = [bits[0]]\\n append = res.append\\n for item in bits[1:]:\\n try:\\n append(_hextochr[item[:2]])\\n append(item[3:])\\n except KeyError:\\n append('&#x')\\n append(item)\\n append(';')\\n\\n return (''.join(res), len(input))\",\n \"def handle_hex_stream(data):\\n stream = extract_stream(data)\\n if stream is not False: # stream can actually be null (HexStream = \\\\\\\\x00\\\\\\\\x00). so we explicitly check for False\\n is_binary = not all(c in string.printable for c in stream)\\n return stream.encode(\\\"base64\\\") if is_binary else stream\\n return data\",\n \"def hash(self, text):\\n hashval = 0\\n for i in xrange(0, len(text)):\\n hashval += ord(text[i])**i\\n return hashval\",\n \"def hashhex(s):\\n h = hashlib.sha1()\\n h.update(s)\\n return h.hexdigest()\",\n \"def _convert_hex(self, hex_value):\\n if not isinstance(hex_value, str):\\n raise TypeError(\\\"given hex value must be str\\\")\\n m = HEX_RE.match(hex_value)\\n if m is None:\\n raise ValueError(\\\"given string does not seem to be Python hex\\\")\\n sign_char, base, exp_sign, exp = [m.group(i) for i in range(1,5)]\\n new_sign = \\\"+\\\" if sign_char is None else sign_char\\n # Line below converts exp to hex value. The \\\"0x\\\" prefix is removed \\n # with [2:]. The exponent is padded with (too many) zeros (Stata \\n # requires 3 digits), and reduced to last 3 digits with [-3:].\\n new_exp = (\\\"000\\\" + hex(int(exp))[2:])[-3:]\\n return \\\"\\\".join((new_sign, base, 'X', exp_sign, new_exp))\",\n \"def hashhex(s):\\n h = hashlib.sha1()\\n h.update(s.encode('utf-8'))\\n return h.hexdigest()\",\n \"def hex_str (self):\\n return \\\"#%02X%02X%02X\\\"%(self._intern[0],self._intern[1],self._intern[2])\",\n \"def convertirHexadecimal(self):\\n self.convertir(lambda c: hex(ord(c))[2:], sep=' ')\",\n \"def hex_hash(s):\\n if not s:\\n return '0'\\n s = s.encode('utf-8')\\n return '{:x}'.format(adler32(s) & 0xffffffff)\",\n \"def _hash_content(data):\\n return hashlib.sha512(str(data).encode('utf-8')).hexdigest()\",\n \"def ascii_convert(the_bytes: bytes):\\n return ANSI_ESCAPE_B.sub(rb\\\"\\\", the_bytes).decode(\\\"utf-8\\\")\",\n \"def b2a_hex(data):\\n\\n return binascii.b2a_hex(data)\",\n \"def unH(s):\\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])\",\n \"def as_hex(self, *, align='left'):\\n return self.as_bytes(align=align).hex()\",\n \"def bytes_to_hexstr(data: bytes) -> str:\\n return ''.join(int_to_hexstr(byte) for byte in data)\",\n \"def from_hex_str(value):\\n \\n return SHex(value)\",\n \"def toHex(self):\\n return hexlify(self.serialize()).decode(\\\"utf-8\\\")\",\n \"def hashhex(s):\\n h = hashlib.sha1()\\n h.update(s.encode())\\n return h.hexdigest()\",\n \"def HashForText (text):\\n if isinstance(text, six.text_type):\\n text = text.encode('utf-8')\\n return __Hasher(text).hexdigest()\",\n \"def test_bytes_to_intel_hex():\\n data = [1, 2, 3, 4, 5]\\n expected_hex_str = \\\"\\\\n\\\".join([\\\":050000000102030405EC\\\", INTEL_HEX_EOF])\\n\\n result = cmds._bytes_to_intel_hex(data=data)\\n\\n assert expected_hex_str == result\",\n \"def hexbyte(string):\\n#\\treturn repr(string)\\n\\ts = \\\"\\\"\\n\\tfor i in string:\\n\\t\\tif (ord(i) >= ord('A') and ord(i) <= ord('z')) \\\\\\n\\t\\t\\tor (ord(i) >= ord('0') and ord(i) <= ord('9')) \\\\\\n\\t\\t\\tor (ord(i) == ord(\\\" \\\")):\\n\\t\\t\\ts += \\\"%s\\\" % i\\n\\t\\telse:\\n\\t\\t\\ts += \\\"\\\\\\\\x%02x\\\" % ord(i)\\n\\n#\\t\\ts += \\\" \\\"\\n\\treturn s\",\n \"def hex_str (self):\\n return \\\"#%02X%02X%02X\\\"%(self.r, self.g, self.b)\",\n \"def hex(space, w_val):\\n return space.hex(w_val)\",\n \"def hexDump(bytes):\\n for i in range(len(bytes)):\\n sys.stdout.write(\\\"%2x \\\" % (ord(bytes[i])))\\n if (i+1) % 8 == 0:\\n print repr(bytes[i-7:i+1])\\n\\n if(len(bytes) % 8 != 0):\\n print string.rjust(\\\"\\\", 11), repr(bytes[i-7:i+1])\",\n \"def wkb_hex(self): # -> str:\\n ...\",\n \"def test_phred_to_ascii(self):\\r\\n self.assertEqual(phred_to_ascii(0, 120), 'x')\\r\\n self.assertEqual(phred_to_ascii(1, 119), 'x')\",\n \"def hashhex(s):\\n h = hashlib.sha1()\\n h.update(s.encode('utf-8'))\\n return h.hexdigest()\",\n \"def encode_hex(b):\\n if isinstance(b, str):\\n b = bytes(b, \\\"utf-8\\\")\\n if isinstance(b, bytes):\\n return str(hexlify(b), \\\"utf-8\\\")\\n raise TypeError(\\\"Value must be an instance of str or bytes\\\")\",\n \"def bytes_to_hex(s):\\n\\n return s.encode(\\\"hex\\\")\",\n \"def _hashsanitize(bytesin):\\n # Used for converting raw byte data into a hex string. If the byte isn't a hex digit, use nothing instead.\\n return \\\"\\\".join([x if x.lower() in 'abcdef0123456789' else '' for x in bytesin])\",\n \"def text(self, text: str) -> bytes:\\n\\n buffer = text.encode(\\\"utf-8\\\")\\n return struct.pack(\\\">i\\\", len(buffer)) + buffer\",\n \"def hx(i):\\n a = hex(i)[2:]\\n if len(a)<2: a = ''.join(['0',a])\\n return a\",\n \"def dump_hex(output_file, input_file):\\n\\n with open(input_file, 'rb') as ifile, open(output_file, 'wb') as ofile:\\n while True:\\n word = ifile.read(4)\\n if not word:\\n break\\n\\n ofile.write(binascii.hexlify(word))\\n ofile.write(b'\\\\n')\",\n \"def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\\n\\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None\",\n \"def longtohex(n):\\n\\n plain=(re.match(r\\\"0x([0-9A-Fa-f]*)l?$\\\", hex(n), re.I).group(1)).lower()\\n return \\\"0x\\\" + plain\",\n \"def w__format_hex(self, string):\\n d = map(None, string)\\n d = map(ord, d)\\n d = map(lambda x: \\\"%02x\\\" % x, d)\\n return ' '.join(d)\",\n \"def to_h(self):\\n return str(self).encode('hex')\",\n \"def hash_coloured(text):\\n ansi_code = int(sha256(text.encode(\\\"utf-8\\\")).hexdigest(), 16) % 230\\n return colored(text, ansi_code=ansi_code)\",\n \"def codeline_to_hexadecimal(codeline, data):\\n # Extract each of the three locations in turn, the first two as data\\n # items and the third as an address\\n first_op = data[codeline[0]][0]\\n second_op = data[codeline[1]][0]\\n third_op = codeline[2]\\n # Create a binary string from the operands\\n binary_string = \\\"{0}{1}{2}00\\\".format(\\n format(first_op, \\\"010b\\\"), format(second_op, \\\"010b\\\"),\\n format(third_op, \\\"010b\\\"))\\n # Take the binary string and return it as a 32-bit hexadecimal string.\\n return format(int(binary_string, base=2), \\\"#010x\\\")\",\n \"def base64_to_hex(b64_string):\\n # Remove hex encoding\\n unencoded_string = base64.b64decode(b64_string)\\n # Encode base64 and return\\n return binascii.hexlify(unencoded_string)\",\n \"def c_hex(x):\\n #print(\\\"c_hex\\\", x, type(x))\\n h = hex(x & ((1 << INT_BITS) - 1))\\n while h[-1:] == \\\"L\\\": h = h[:-1] # for python 2\\n return h + UINT_SUFFIX\",\n \"def encodeText(text):\\r\\n#\\treturn repr( quote_plus(text.replace(\\\"'\\\", '\\\"')) )\\r\\n\\ttry:\\r\\n\\t\\treturn repr( quote_plus(text.replace(\\\"'\\\", '\\\"').encode('utf-8')) )\\r\\n\\texcept:\\r\\n\\t\\tlogError(\\\"encodeText()\\\")\\r\\n\\treturn repr(text.replace(\\\"'\\\", '\\\"'))\",\n \"def print_as_hex(s):\\n print(\\\":\\\".join(\\\"{0:x}\\\".format(ord(c)) for c in s))\",\n \"def string_raw(self):\\n return \\\"x%x\\\" % self.encoded\",\n \"def encodeHex(s):\\n encoder = codecs.getencoder('hex_codec')\\n hex, length = encoder(s)\\n assert len(s) == length, \\\"Hex encoding incomplete\\\"\\n return hex\",\n \"def printable_hash(h):\\n return int(h).to_bytes(32, byteorder='big', signed=False).hex()\",\n \"def elf_hash(s):\\n h = 0\\n for c in s:\\n h = (h << 4) + ord(c)\\n t = (h & 0xF0000000)\\n if t != 0:\\n h = h ^ (t >> 24)\\n h = h & ~t\\n return h\",\n \"def int2hex(n: int) -> str:\",\n \"def test_phred_to_ascii64(self):\\r\\n self.assertEqual(phred_to_ascii64(0), '@')\\r\\n self.assertEqual(phred_to_ascii64(30), '^')\",\n \"def test_bytes_to_pretty_hex():\\n data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\\n expected = (\\n \\\"0000 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F 10 \\\"\\n \\\"|................|\\\\n\\\"\\n )\\n\\n result = cmds._bytes_to_pretty_hex(data=data)\\n\\n assert expected == result\",\n \"def stringify(self):\\n hexcode = \\\"#\\\"\\n for x in self.value:\\n part = hex(x)[2:]\\n if len(part) < 2: part = \\\"0\\\" + part\\n hexcode += part\\n return hexcode\",\n \"def hash(plainString):\\n result = plainString\\n for i in range(0,12):\\n result = hashHelp(result)\\n return result\",\n \"def hex_dump(string):\\n return ' '.join([\\\"%0.2X\\\" % ord(x) for x in string])\",\n \"def look(source_path):\\r\\n hex_data_formated = get(source_path, \\\"f\\\")\\r\\n hex_list = hex_data_formated.split(\\\" \\\")\\r\\n\\r\\n result = \\\" 00 01 02 03 04 05 06 07 08 09 0A 0B 0C 0D 0E 0F\\\\n\\\"\\r\\n for index, value in enumerate(hex_list):\\r\\n if index == 0:\\r\\n result += (\\\"0x000000 \\\" + value + \\\" \\\")\\r\\n\\r\\n elif index % 16 == 15:\\r\\n address = hex(index + 1)\\r\\n result += (value + \\\"\\\\n\\\" + address[0:2] + address[2:].zfill(6) + \\\" \\\")\\r\\n\\r\\n elif index % 16 == 7:\\r\\n result += (value + \\\" \\\")\\r\\n\\r\\n else:\\r\\n result += (value + \\\" \\\")\\r\\n\\r\\n print(result)\",\n \"def hexcode(self):\\n hexc = \\\"#%.02X%.02X%.02X\\\" % (int(self.rgb_255[0]), int(self.rgb_255[1]), int(self.rgb_255[2]))\\n return hexc\",\n \"def ansi_escape(text: object) -> str:\\n return str(text).replace(\\\"\\\\x1b\\\", \\\"?\\\").replace(\\\"\\\\b\\\", \\\"?\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7632533","0.7439492","0.70764095","0.65433866","0.6380229","0.61830765","0.61594963","0.61447966","0.6132453","0.6124988","0.61229116","0.6114684","0.59884095","0.5947459","0.59325373","0.5788667","0.5782058","0.57663274","0.5741124","0.57352805","0.57184196","0.569126","0.5679554","0.56763494","0.5665832","0.56610286","0.5636232","0.56199336","0.5613361","0.56061125","0.55995613","0.5596987","0.5582064","0.5571382","0.55577147","0.55570304","0.5555","0.5551178","0.55373037","0.5524953","0.5517788","0.5492711","0.5448082","0.54381806","0.54351413","0.5428333","0.54275894","0.54261357","0.54196656","0.5413973","0.5410879","0.54056716","0.540348","0.53913975","0.53872806","0.5387104","0.53835076","0.53742814","0.53737146","0.53733015","0.5371563","0.53584415","0.5357567","0.5348881","0.53463143","0.53372604","0.5333345","0.53272647","0.5326326","0.5326077","0.5325715","0.53086215","0.52947664","0.5287426","0.52853036","0.52722824","0.5266037","0.5263579","0.52596605","0.5252476","0.52445513","0.5228706","0.52240354","0.5220531","0.5208807","0.5201498","0.51985955","0.51960015","0.5190079","0.5186943","0.51836264","0.518356","0.5178052","0.51759917","0.51697165","0.5167198","0.51614773","0.51598424","0.5152149","0.5143026"],"string":"[\n \"0.7632533\",\n \"0.7439492\",\n \"0.70764095\",\n \"0.65433866\",\n \"0.6380229\",\n \"0.61830765\",\n \"0.61594963\",\n \"0.61447966\",\n \"0.6132453\",\n \"0.6124988\",\n \"0.61229116\",\n \"0.6114684\",\n \"0.59884095\",\n \"0.5947459\",\n \"0.59325373\",\n \"0.5788667\",\n \"0.5782058\",\n \"0.57663274\",\n \"0.5741124\",\n \"0.57352805\",\n \"0.57184196\",\n \"0.569126\",\n \"0.5679554\",\n \"0.56763494\",\n \"0.5665832\",\n \"0.56610286\",\n \"0.5636232\",\n \"0.56199336\",\n \"0.5613361\",\n \"0.56061125\",\n \"0.55995613\",\n \"0.5596987\",\n \"0.5582064\",\n \"0.5571382\",\n \"0.55577147\",\n \"0.55570304\",\n \"0.5555\",\n \"0.5551178\",\n \"0.55373037\",\n \"0.5524953\",\n \"0.5517788\",\n \"0.5492711\",\n \"0.5448082\",\n \"0.54381806\",\n \"0.54351413\",\n \"0.5428333\",\n \"0.54275894\",\n \"0.54261357\",\n \"0.54196656\",\n \"0.5413973\",\n \"0.5410879\",\n \"0.54056716\",\n \"0.540348\",\n \"0.53913975\",\n \"0.53872806\",\n \"0.5387104\",\n \"0.53835076\",\n \"0.53742814\",\n \"0.53737146\",\n \"0.53733015\",\n \"0.5371563\",\n \"0.53584415\",\n \"0.5357567\",\n \"0.5348881\",\n \"0.53463143\",\n \"0.53372604\",\n \"0.5333345\",\n \"0.53272647\",\n \"0.5326326\",\n \"0.5326077\",\n \"0.5325715\",\n \"0.53086215\",\n \"0.52947664\",\n \"0.5287426\",\n \"0.52853036\",\n \"0.52722824\",\n \"0.5266037\",\n \"0.5263579\",\n \"0.52596605\",\n \"0.5252476\",\n \"0.52445513\",\n \"0.5228706\",\n \"0.52240354\",\n \"0.5220531\",\n \"0.5208807\",\n \"0.5201498\",\n \"0.51985955\",\n \"0.51960015\",\n \"0.5190079\",\n \"0.5186943\",\n \"0.51836264\",\n \"0.518356\",\n \"0.5178052\",\n \"0.51759917\",\n \"0.51697165\",\n \"0.5167198\",\n \"0.51614773\",\n \"0.51598424\",\n \"0.5152149\",\n \"0.5143026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78595716"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":4692,"cells":{"query":{"kind":"string","value":"Unhexlify raw text, return unhexlified text."},"document":{"kind":"string","value":"def unhexlify(text):\n unhexlified = binascii.unhexlify(text)\n\n if six.PY3:\n unhexlified = unhexlified.decode('utf-8')\n\n return unhexlified"},"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 hexlify(text):\n if six.PY3:\n text = text.encode('utf-8')\n\n hexlified = binascii.hexlify(text)\n\n if six.PY3:\n hexlified = hexlified.decode('utf-8')\n\n return hexlified","def test_unhexlify_not_python():\n assert '' == uflash.unhexlify(\n ':020000040003F7\\n:10E000000000000000000000000000000000000010')","def test_unhexlify():\n hexlified = uflash.hexlify(TEST_SCRIPT)\n unhexlified = uflash.unhexlify(hexlified)\n assert unhexlified == TEST_SCRIPT.decode('utf-8')","def normalize(self, text):\n\n return binascii.hexlify(text)","def test_unhexlify_bad_unicode():\n assert '' == uflash.unhexlify(\n ':020000040003F7\\n:10E000004D50FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF')","def _hexlify(data):\n if data is None:\n return None\n elif isinstance(data, bytes) or isinstance(data, bytearray):\n return data.hex()\n elif isinstance(data, list):\n return [_hexlify(item) for item in data]\n elif isinstance(data, dict):\n return {k: _hexlify(v) for k, v in data.items()}\n else:\n return data","def test_hexlify():\n result = uflash.hexlify(TEST_SCRIPT)\n lines = result.split()\n # The first line should be the extended linear address, ox0003\n assert lines[0] == ':020000040003F7'\n # There should be the expected number of lines.\n assert len(lines) == 5","def _dehex(s):\n import re\n import binascii\n\n # Remove all non-hexadecimal digits\n s = re.sub(br'[^a-fA-F\\d]', b'', s)\n # binscii.unhexlify works in Python 2 and Python 3 (unlike\n # thing.decode('hex')).\n return binascii.unhexlify(s)","def hexify(text):\r\n return ' '.join([hexify_word(word) for word in text.split()])","def test_hexlify_empty_script():\n assert uflash.hexlify('') == ''","def CUnescape(text):\n # type: (str) -> bytes\n\n def ReplaceHex(m):\n # Only replace the match if the number of leading back slashes is odd. i.e.\n # the slash itself is not escaped.\n if len(m.group(1)) & 1:\n return m.group(1) + 'x0' + m.group(2)\n return m.group(0)\n\n # This is required because the 'string_escape' encoding doesn't\n # allow single-digit hex escapes (like '\\xf').\n result = _CUNESCAPE_HEX.sub(ReplaceHex, text)\n\n return (result.encode('utf-8') # Make it bytes to allow decode.\n .decode('unicode_escape')\n # Make it bytes again to return the proper type.\n .encode('raw_unicode_escape'))","def unH(s):\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])","def str_sha(raw_sha):\n return hexlify(raw_sha)[:12]","def hexlify(self: str, verbose=False):\n nbytes = len(_chunk_bs(self))\n buf = b''\n strlen = ''\n for b in to_bytes(_chunk_bs(self)):\n buf+=b\n# for s in _from_list(_chunk_bs(self)):\n# strlen+=f'{ _bit_length(s): 02d}'\n if verbose:\n for n in range(nbytes):\n strlen += f'{_bit_length(_from_list(_chunk_bs(self))[n])} @[{n}] '\n print(strlen)\n return buf","def unescape(msg):\n skip = False\n unescaped = bytearray()\n\n for i in range(len(msg)):\n\n if not skip and msg[i] is 0x7D:\n\n if not (i + 1) >= len(msg):\n unescaped.append(msg[i + 1] ^ 0x20)\n skip = True\n\n elif not skip:\n unescaped.append(msg[i])\n else:\n skip = False\n\n return unescaped","def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\n\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None","def _grab_unascii(self):\r\n unascii = \"\"\r\n while self._char != -1 and not self._char in \"\\x00\\t\\r\\n\":\r\n unascii += self._char\r\n self._get_char()\r\n return unascii","def unescape(input):\n output=atpic.cleaner_escape.unescape(input)\n return output","def sanatize_hex(data: str) -> str:\n return data.replace(\"0x\", \"\").replace(\"0X\", \"\")","def bh2u(x: bytes) -> str:\n return hfu(x).decode('ascii')","def _hashsanitize(bytesin):\n # Used for converting raw byte data into a hex string. If the byte isn't a hex digit, use nothing instead.\n return \"\".join([x if x.lower() in 'abcdef0123456789' else '' for x in bytesin])","def entity_decode_hex(input, errors='strict'):\n if _is_unicode(input):\n if '%' not in input:\n return s\n bits = _asciire.split(input)\n res = [bits[0]]\n append = res.append\n for i in range(1, len(bits), 2):\n append(unquote(str(bits[i])).decode('latin1'))\n append(bits[i + 1])\n return (''.join(res), len(input))\n\n preamble_regex = re.compile(r\"&#x\", flags=re.I)\n bits = preamble_regex.split(input)\n # fastpath\n if len(bits) == 1:\n return input\n res = [bits[0]]\n append = res.append\n for item in bits[1:]:\n try:\n append(_hextochr[item[:2]])\n append(item[3:])\n except KeyError:\n append('&#x')\n append(item)\n append(';')\n\n return (''.join(res), len(input))","def unescape(escaped_string):\n\n hex_msg = \"^x not followed by a valid 2-digit hex number\"\n\n token_start = 0\n l = len(escaped_string)\n i = 0\n output = []\n while i < l:\n c = escaped_string[i]\n\n if c in _unprintables:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string, \"unprintable character\",\n token_start, i)\n elif c != \"^\":\n output.append(c)\n else:\n if i == l - 1:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string, \"caret at end of string\",\n token_start, i)\n i += 1\n next_c = escaped_string[i]\n if next_c not in \"'\\\"^x\":\n if next_c in _unprintables:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string,\n \"^ followed by unprintable character\",\n token_start, i)\n else:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string,\n \"^ followed by invalid character %s\" % (next_c,),\n token_start, i)\n if next_c != 'x':\n output.append(next_c)\n else:\n if i >= l - 2:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string, hex_msg, token_start, i)\n i += 1\n hex1 = escaped_string[i]\n i += 1\n hex2 = escaped_string[i]\n if hex1 not in _ALLOWED_SAMPLE_HEX_DIGITS:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string, hex_msg, token_start, i - 1)\n if hex2 not in _ALLOWED_SAMPLE_HEX_DIGITS:\n raise vps.errorhandler.StringUnspecial_characterException(\n escaped_string, hex_msg, token_start, i)\n val = int(hex1 + hex2, 16)\n output.append(chr(val))\n # incrementing i should happen at the end of the loop body for\n # all paths\n i += 1\n return ''.join(output)","def unobscure(obscured: bytes) -> bytes:\n return decompress(b64d(obscured))","def remove_hex(text): \n return re.sub(r'&.*?;', r'', text)","def to_hex(text):\n return ' '.join([hex(ord(char)) for char in unicode(text, 'UTF-8')])","def cook(raw):\n if sys.version_info[0] < 3:\n # python 2\n if isinstance(raw, str):\n try:\n cooked = raw.decode('utf-8')\n except UnicodeDecodeError:\n cooked = raw.decode('ascii', 'ignore')\n else:\n cooked = raw\n else:\n # python 3\n if isinstance(raw, bytes):\n try:\n cooked = raw.decode('utf-8')\n except UnicodeDecodeError:\n cooked = raw.decode('ascii', 'ignore')\n else:\n cooked = raw\n return cooked","def unescape(text):\r\n\r\n def fixup(m):\r\n text = m.group(0)\r\n if text[:2] == '&#':\r\n try:\r\n if text[:3] == '&#x':\r\n return unichr(int(text[3:-1], 16)).encode('utf-8')\r\n return unichr(int(text[2:-1])).encode('utf-8')\r\n except ValueError:\r\n logger.info('error de valor')\r\n\r\n else:\r\n try:\r\n import htmlentitydefs\r\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]]).encode('utf-8')\r\n except KeyError:\r\n logger.info('keyerror')\r\n except:\r\n pass\r\n\r\n return text\r\n\r\n return re.sub('&#?\\\\w+;', fixup, text)","def str_to_raw(s):\n raw_map = {8:r'\\b', 7:r'\\a', 12:r'\\f', 10:r'\\n', 13:r'\\r', 9:r'\\t', 11:r'\\v'}\n return r''.join(i if ord(i) > 32 else raw_map.get(ord(i), i) for i in s)","def hex(self):\n return binascii.hexlify(self.data)","def to_ascii(text):\n return re.sub(r'[^\\x00-\\x7F]+', ' ', text)","def force_ascii(text):\n return \"\".join([c for c in text if ord(c) < 128])","def unescape(text):\n import re, htmlentitydefs\n def fixup(m):\n text = m.group(0)\n if text[:2] == \"&#\":\n # character ref\n try:\n if text[:3] == \"&#x\":\n return unichr(int(text[3:-1],1))\n else:\n return unichr(int(text[2:-1]))\n except ValueError:\n pass\n else:\n #named entity\n try:\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]])\n except KeyError:\n pass\n return text\n return re.sub(\"&#?\\w+;\", fixup, text)","def handle_hex_stream(data):\n stream = extract_stream(data)\n if stream is not False: # stream can actually be null (HexStream = \\\\x00\\\\x00). so we explicitly check for False\n is_binary = not all(c in string.printable for c in stream)\n return stream.encode(\"base64\") if is_binary else stream\n return data","def b2a(b):\n return binascii.hexlify(b)","def hexify_word(word):\r\n\r\n return ''.join([str(hex(ord(c))[2::]) for c in word])","def html_unescape(text):\n return html.unescape(text)","def test_embed_hex():\n python = uflash.hexlify(TEST_SCRIPT)\n result = uflash.embed_hex(uflash._RUNTIME, python)\n # The resulting hex should be of the expected length.\n assert len(result) == len(python) + len(uflash._RUNTIME) + 1 # +1 for \\n\n # The hex should end with a newline '\\n'\n assert result[-1:] == '\\n'\n # The Python hex should be in the correct location.\n py_list = python.split()\n result_list = result.split()\n start_of_python_from_end = len(py_list) + 5\n start_of_python = len(result_list) - start_of_python_from_end\n assert result_list[start_of_python:-5] == py_list\n # The firmware should enclose the Python correctly.\n firmware_list = uflash._RUNTIME.split()\n assert firmware_list[:-5] == result_list[:-start_of_python_from_end]\n assert firmware_list[-5:] == result_list[-5:]","def UnescapeHTMLEntities(self, data):\n if '#39' not in htmlentitydefs.name2codepoint:\n htmlentitydefs.name2codepoint['#39'] = 39\n return re.sub('&(%s);' % '|'.join(htmlentitydefs.name2codepoint),\n lambda m: unichr(htmlentitydefs.name2codepoint[m.group(1)]),\n data)","def ascii_convert(the_bytes: bytes):\n return ANSI_ESCAPE_B.sub(rb\"\", the_bytes).decode(\"utf-8\")","def _unquote(src, encoding=\"utf-8\"):\n return urllib.unquote(src).decode(encoding)","def de_hex(msg):\n try:\n return bytes.fromhex(msg).decode('utf-8')\n except (UnicodeDecodeError, ValueError):\n print('Invalid hexadecimal-encoded string')","def decode_high(self, text):\n h = HTMLParser()\n text = '&#%s;' % text\n return h.unescape(text)","def parse_sysex_string(s):\n return binascii.unhexlify(s.replace(' ', ''))","def normalize_input_shellcode(shellcode):\n shellcode = shellcode.replace(' ', '')\n shellcode = shellcode.replace('\\\\x', '')\n shellcode = shellcode.replace('\\\\X', '')\n return shellcode","def HtmlUnescape(text):\n def fixup(m):\n text = m.group(0)\n if text[:2] == \"&#\":\n # character reference\n try:\n if text[:3] == \"&#x\":\n return unichr(int(text[3:-1], 16))\n else:\n return unichr(int(text[2:-1]))\n except ValueError:\n pass\n else:\n # named entity\n try:\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]])\n except KeyError:\n pass\n return text # leave as is\n return re.sub(\"&#?\\w+;\", fixup, text)","def __unicode_to_ascii(text):\n line = unicodedata.normalize('NFKD', text)\n return ''.join(c for c in line if not unicodedata.combining(c))","def _hex2bin(cls, h):\n b = []\n while h:\n if h[0] in ' \\n': # ignore whitespace\n h = h[1:]\n elif h[0] in '0123456789abcdef': # hex byte\n b.append(int(h[:2], 16))\n h = h[2:]\n elif h[0] == '.': # for chunk type\n b.extend(ord(h[i]) for i in range(1,5))\n h = h[5:]\n else: # for PNG magic\n b.append(ord(h[0]))\n h = h[1:]\n return ''.join(map(chr,b))","def unescape(text):\n if isinstance(text, list):\n for i, t in enumerate(text):\n t = t.replace(r'&', r'\\&')\n t = t.replace(r'<', r'<')\n t = t.replace(r'>', r'>')\n text[i] = t\n else:\n text = text.replace(r'&', r'\\&')\n text = text.replace(r'<', r'<')\n text = text.replace(r'>', r'>')\n return text","def removeNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else '' for i in text])","def unhexchar(c):\n c = c[:1]\n if \"abcdefABCDEF0123456789\".find(c) != -1:\n return int(c,16)\n else:\n return None","def tohex(data: str) -> str:\n match = re.fullmatch(r\"^0[x|X][0-9a-fA-F]+\", data)\n if match:\n return data.lower()\n match = re.fullmatch(r\"^[0-9a-fA-F]+[h|H]$\", data)\n if not match:\n raise ValueError(f\"Required hex of the form `0x` or `H` found {data}\")\n match = re.match(r\"^[0-9a-fA-F]+\", data)\n return f\"0x{match.group().lower()}\"","def strip_escape(data):\n m = excel_escape_re.match(data)\n if m:\n return m.group(1)\n else:\n return data","def _unescape_str(text):\n text = text.decode('utf-8') if isinstance(text, six.binary_type) else text\n tokens = []\n i = 0\n basicstr_re = re.compile(r'[^\"\\\\\\000-\\037]*')\n unicode_re = re.compile(r'[uU]((?<=u)[a-fA-F0-9]{4}|(?<=U)[a-fA-F0-9]{8})')\n escapes = {\n 'b': '\\b',\n 't': '\\t',\n 'n': '\\n',\n 'f': '\\f',\n 'r': '\\r',\n '\\\\': '\\\\',\n '\"': '\"',\n '/': '/',\n \"'\": \"'\"\n }\n while True:\n m = basicstr_re.match(text, i)\n i = m.end()\n tokens.append(m.group())\n if i == len(text) or text[i] != '\\\\':\n break\n else:\n i += 1\n if unicode_re.match(text, i):\n m = unicode_re.match(text, i)\n i = m.end()\n tokens.append(six.unichr(int(m.group(1), 16)))\n else:\n if text[i] not in escapes:\n raise BadEscapeCharacter\n tokens.append(escapes[text[i]])\n i += 1\n return ''.join(tokens)","def clean_txt(txt):\n r = txt.encode(\"utf-8\", errors=\"backslashreplace\").decode('utf-8').replace(\"\\\\u0144\", \"\")\n return r","def preprocess_hashes(tex):\n blocks = catlist()\n rx = hash_rx\n m = rx.search(tex)\n while m:\n if len(m.group(2)) > 40:\n tex2htm.warn(\"Possible runaway hash: {}\".format(text_sample(m.group(2))))\n raise(None)\n blocks.append(tex[:m.start()])\n blocks.append(re.sub(r'(^|[^\\\\])%', r'\\1\\%', m.group(0)))\n tex = tex[m.end():]\n m = rx.search(tex)\n blocks.append(tex)\n return \"\".join(blocks)","def normalizeRawFromHeader(value):\n return value.replace('\\n', '').replace('\\r', '').strip()","def normalize(text):\n text = text.encode('utf-8')\n # Python idiom to remove extraneous spaces\n text = ' '.join(text.split())\n return text.strip('\"')","def _pythonifyString(s):\n\n if \"\\x00\" in s:\n s = s[:s.index(\"\\x00\")]\n return s","def encrypt(text):\r\n\r\n cipher = fuzz(text)\r\n return hexify(cipher)","def toHexa(data):\n\tresult = \"\"\n\tif isBytes(data):\n\t\tdata = data.decode(\"latin-1\")\n\tfor i in data:\n\t\tresult += \"\\\\x%02X\"%ord(i)\n\treturn result","def sanitize(buf,\n backspaces=['\\x08\\x1b[K', '\\x08 \\x08'],\n escape_regex=re.compile(r'\\x1b(\\[|\\]|\\(|\\))[;?0-9]*[0-9A-Za-z](.*\\x07)?')):\n # Filter out control characters\n\n # First, handle the backspaces.\n for backspace in backspaces:\n try:\n while True:\n ind = buf.index(backspace)\n buf = ''.join((buf[0:ind-1],buf[ind+len(backspace):]))\n except:\n pass\n\n strip_escapes = escape_regex.sub('',buf)\n\n # strip non-printable ASCII characters\n\n clean = ''.join([x for x in strip_escapes if is_printable(x)])\n return clean","def unquote(cls, value):\n if six.PY2:\n return unquote(value).decode(\"utf8\")\n else:\n return unquote(value.decode(\"ascii\"))","def base64_to_hex(b64_string):\n # Remove hex encoding\n unencoded_string = base64.b64decode(b64_string)\n # Encode base64 and return\n return binascii.hexlify(unencoded_string)","def unquote_safe(s, unsafe_list):\n # note: this build utf8 raw strings ,then does a .decode('utf8') at the end.\n # as a result it's doing .encode('utf8') on each block of the string as it's processed.\n res = _utf8(s).split('%')\n for i in xrange(1, len(res)):\n item = res[i]\n try:\n raw_chr = _hextochr[item[:2]]\n if raw_chr in unsafe_list or ord(raw_chr) < 20:\n # leave it unescaped (but uppercase the percent escape)\n res[i] = '%' + item[:2].upper() + item[2:]\n else:\n res[i] = raw_chr + item[2:]\n except KeyError:\n res[i] = '%' + item\n except UnicodeDecodeError:\n # note: i'm not sure what this does\n res[i] = unichr(int(item[:2], 16)) + item[2:]\n o = \"\".join(res)\n return _unicode(o)","def a2b(a):\n return binascii.unhexlify(a)","def ascii_to_phred64(c):\r\n return ascii_to_phred(c, 64)","def parse_text(text):\n return str(str(text).encode(\"ascii\", \"ignore\")).replace(\"\\\\n\",\"\\n\").replace(\"b'\",\"\")","def surrogate(text):\n if isinstance(text, bytes):\n return text.decode('utf-8', errors='surrogateescape')\n return text","def prepare_for_hashing(text):\n if not text:\n return ''\n return text.translate(CHARS_TO_DELETE).lower()","def string_raw(self):\n return \"x%x\" % self.encoded","def as_hex(self):\n return binascii.hexlify(self.as_bytes()).decode('ascii')","def clean_string(text: str, ascii_only=False) -> str:\n done = False\n while text and not done:\n done = True\n if ((text[0] == '\"' and text[-1] == '\"') or\n (text[0] == '[' and text[-1] == ']')):\n text = text[1:-1]\n done = False\n if text[:2] == \"u'\" and text[-1] == \"'\":\n text = text[2:-1]\n done = False\n if ascii_only:\n try:\n # Python v3.7\n if text.isascii(): # type: ignore\n return text\n except AttributeError:\n # Python less than v3.7\n pass\n return ''.join(filter(lambda c: ord(c) >= 32 and ord(c) < 0x7F,\n list(text)))\n return text","def removeUnicode(text):\n text = re.sub(r'(\\\\u[0-9A-Fa-f]+)',r'', text) \n text = re.sub(r'[^\\x00-\\x7f]',r'',text)\n return text","def _unascii(s):\n\n # make the fast path fast: if there are no matches in the string, the\n # whole thing is ascii. On python 2, that means we're done. On python 3,\n # we have to turn it into a bytes, which is quickest with encode('utf-8')\n m = _U_ESCAPE.search(s)\n if not m:\n return s if PY2 else s.encode('utf-8')\n\n # appending to a string (or a bytes) is slooow, so we accumulate sections\n # of string result in 'chunks', and join them all together later.\n # (It doesn't seem to make much difference whether we accumulate\n # utf8-encoded bytes, or strings which we utf-8 encode after rejoining)\n #\n chunks = []\n\n # 'pos' tracks the index in 's' that we have processed into 'chunks' so\n # far.\n pos = 0\n\n while m:\n start = m.start()\n end = m.end()\n\n g = m.group(1)\n\n if g is None:\n # escaped backslash: pass it through along with anything before the\n # match\n chunks.append(s[pos:end])\n else:\n # \\uNNNN, but we have to watch out for surrogate pairs.\n #\n # On python 2, str.encode(\"utf-8\") will decode utf-16 surrogates\n # before re-encoding, so it's fine for us to pass the surrogates\n # through. (Indeed we must, to deal with UCS-2 python builds, per\n # https://github.com/matrix-org/python-canonicaljson/issues/12).\n #\n # On python 3, str.encode(\"utf-8\") complains about surrogates, so\n # we have to unpack them.\n c = int(g, 16)\n\n if c < 0x20:\n # leave as a \\uNNNN escape\n chunks.append(s[pos:end])\n else:\n if PY3: # pragma nocover\n if c & 0xfc00 == 0xd800 and s[end:end + 2] == '\\\\u':\n esc2 = s[end + 2:end + 6]\n c2 = int(esc2, 16)\n if c2 & 0xfc00 == 0xdc00:\n c = 0x10000 + (((c - 0xd800) << 10) |\n (c2 - 0xdc00))\n end += 6\n\n chunks.append(s[pos:start])\n chunks.append(unichr(c))\n\n pos = end\n m = _U_ESCAPE.search(s, pos)\n\n # pass through anything after the last match\n chunks.append(s[pos:])\n\n return (''.join(chunks)).encode(\"utf-8\")","def unicode2ascii(_unicrap):\n xlate = {0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\n 0xc6:'Ae', 0xc7:'C',\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\n 0xd0:'Th', 0xd1:'N',\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\n 0xe6:'ae', 0xe7:'c',\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\n 0xf0:'th', 0xf1:'n',\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\n 0xfd:'y', 0xfe:'th', 0xff:'y',\n 0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\n 0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\n 0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\n 0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\n 0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\"'\",\n 0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\n 0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>',\n 0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\n 0xd7:'*', 0xf7:'/'\n }\n\n s = \"\"\n for i in _unicrap:\n ordi = ord(i)\n if ordi in xlate:\n s += xlate[ordi]\n elif ordi >= 0x80:\n pass\n else:\n s += str(i)\n return s","def hex_to_unichr(hex_string):\n if (hex_string is None) or (len(hex_string) < 1):\n return None\n if hex_string.startswith(\"U+\"):\n hex_string = hex_string[2:]\n return int_to_unichr(int(hex_string, base=16))","def unphred_string(phred):\n arr = [(ord(c) - 33) / 30. for c in phred]\n return arr","def tidy_string(s: str\n ) -> str:\n s = s.encode('ascii', errors='ignore').decode(FORMAT)\n s = s.replace(\"\\r\", \"\").replace(\"\\t\", \"\").replace('\\n', '') \n return s","def obscure(data: bytes) -> bytes:\n return b64e(compress(data, 9))","def decode_bytes(data: bytearray) -> str:\n pattern = re.compile('\\r', re.UNICODE)\n res = data.decode('utf-8', 'ignore')\n res = pattern.sub('', res)\n return res","def unescape(s):\n\n\tif s is None:\n\t\treturn \"\"\n\n\t# html entities\n\ts = s.replace(\" \", \"\\r\")\n\n\t# standard html\n\ts = s.replace(\"<\", \"<\")\n\ts = s.replace(\">\", \">\")\n\ts = s.replace(\"&\", \"&\") # this has to be last\n\n\treturn s","def escapeDecode(s: unicode) -> unicode:\n ...","def rl_unescape_prompt(prompt: str) -> str:\n if rl_type == RlType.GNU:\n escape_start = \"\\x01\"\n escape_end = \"\\x02\"\n prompt = prompt.replace(escape_start, \"\").replace(escape_end, \"\")\n\n return prompt","def unicoder(string):\n\treturn \"\\x00\".join(string) + \"\\x00\"","def text2Int(text):\n return reduce(lambda x, y : (x << 8) + y, map(ord, text))","def unescape(t):\r\n return (t\r\n .replace(\"&\", \"&\").replace(\"<\", \"<\").replace(\">\", \">\")\r\n .replace(\"'\", \"´\").replace(\""\", '\"').replace(''',\"'\")\r\n )","def html_unescape(text):\n\n def fixup(m):\n text = m.group(0)\n if text[:2] == \"&#\":\n # character reference\n try:\n if text[:3] == \"&#x\":\n return chr(int(text[3:-1], 16))\n else:\n return chr(int(text[2:-1]))\n except ValueError:\n pass\n else:\n # named entity\n try:\n text = chr(html.entities.name2codepoint[text[1:-1]])\n except KeyError:\n pass\n return text # leave as is\n return re.sub(\"&#?\\w+;\", fixup, text)","def decode(text: str) -> str:\n # Reverse of reverse is original text.\n return encode(text)","def clean_up_text(text):\n text = html.unescape(text)\n return remove_emoji(text)","def value_convert(x):\n try:\n return x.decode(\"ascii\")\n except UnicodeDecodeError:\n return x.hex()","def unquote(uri):\r\n uri = uri.encode('ascii')\r\n unquoted = urllib_unquote(uri)\r\n return unquoted.decode('utf-8')","def unicode_unquote(value):\n return unquote(value).decode('utf-8')","def test_unquote(self):\n fwa = FakeWikiArchivo('abcd <a href=\"/wiki/f%C3%B3u\">FooBar</a> dcba')\n _, r = self.peishranc(fwa)\n self.assertEqual(r, [(u'fóu', SCORE_PEISHRANC)])","def hexify(c):\n try:\n s = c.encode(\"utf-8\").encode(\"hex\")\n except UnicodeDecodeError:\n s = 0\n n = len(s)\n if n <= 2: return s\n a = ' - '.join([s[i:i+2] for i in range(0,n,2)])\n return a[:-1]","def decode_hex(self, s):\n return self.transcode(int(s, 16))","def _decode_html_entities(text: str) -> str:\n return html.unescape(text)","def remove_non_ascii(text):\n return re.sub(r'[^\\x00-\\x7F]', ' ', text)","def unescape_tweet(tweet):\r\n return html.unescape(tweet)","def __html_unescape(self, text):\n\n return re.sub(\"&(%s);\" % \"|\".join(name2codepoint),\n lambda m: unichr(name2codepoint[m.group(1)]),\n text)"],"string":"[\n \"def hexlify(text):\\n if six.PY3:\\n text = text.encode('utf-8')\\n\\n hexlified = binascii.hexlify(text)\\n\\n if six.PY3:\\n hexlified = hexlified.decode('utf-8')\\n\\n return hexlified\",\n \"def test_unhexlify_not_python():\\n assert '' == uflash.unhexlify(\\n ':020000040003F7\\\\n:10E000000000000000000000000000000000000010')\",\n \"def test_unhexlify():\\n hexlified = uflash.hexlify(TEST_SCRIPT)\\n unhexlified = uflash.unhexlify(hexlified)\\n assert unhexlified == TEST_SCRIPT.decode('utf-8')\",\n \"def normalize(self, text):\\n\\n return binascii.hexlify(text)\",\n \"def test_unhexlify_bad_unicode():\\n assert '' == uflash.unhexlify(\\n ':020000040003F7\\\\n:10E000004D50FFFFFFFFFFFFFFFFFFFFFFFFFFFFFF')\",\n \"def _hexlify(data):\\n if data is None:\\n return None\\n elif isinstance(data, bytes) or isinstance(data, bytearray):\\n return data.hex()\\n elif isinstance(data, list):\\n return [_hexlify(item) for item in data]\\n elif isinstance(data, dict):\\n return {k: _hexlify(v) for k, v in data.items()}\\n else:\\n return data\",\n \"def test_hexlify():\\n result = uflash.hexlify(TEST_SCRIPT)\\n lines = result.split()\\n # The first line should be the extended linear address, ox0003\\n assert lines[0] == ':020000040003F7'\\n # There should be the expected number of lines.\\n assert len(lines) == 5\",\n \"def _dehex(s):\\n import re\\n import binascii\\n\\n # Remove all non-hexadecimal digits\\n s = re.sub(br'[^a-fA-F\\\\d]', b'', s)\\n # binscii.unhexlify works in Python 2 and Python 3 (unlike\\n # thing.decode('hex')).\\n return binascii.unhexlify(s)\",\n \"def hexify(text):\\r\\n return ' '.join([hexify_word(word) for word in text.split()])\",\n \"def test_hexlify_empty_script():\\n assert uflash.hexlify('') == ''\",\n \"def CUnescape(text):\\n # type: (str) -> bytes\\n\\n def ReplaceHex(m):\\n # Only replace the match if the number of leading back slashes is odd. i.e.\\n # the slash itself is not escaped.\\n if len(m.group(1)) & 1:\\n return m.group(1) + 'x0' + m.group(2)\\n return m.group(0)\\n\\n # This is required because the 'string_escape' encoding doesn't\\n # allow single-digit hex escapes (like '\\\\xf').\\n result = _CUNESCAPE_HEX.sub(ReplaceHex, text)\\n\\n return (result.encode('utf-8') # Make it bytes to allow decode.\\n .decode('unicode_escape')\\n # Make it bytes again to return the proper type.\\n .encode('raw_unicode_escape'))\",\n \"def unH(s):\\n return ''.join([chr(int(s[i:i+2],16)) for i in range(2, len(s),2)])\",\n \"def str_sha(raw_sha):\\n return hexlify(raw_sha)[:12]\",\n \"def hexlify(self: str, verbose=False):\\n nbytes = len(_chunk_bs(self))\\n buf = b''\\n strlen = ''\\n for b in to_bytes(_chunk_bs(self)):\\n buf+=b\\n# for s in _from_list(_chunk_bs(self)):\\n# strlen+=f'{ _bit_length(s): 02d}'\\n if verbose:\\n for n in range(nbytes):\\n strlen += f'{_bit_length(_from_list(_chunk_bs(self))[n])} @[{n}] '\\n print(strlen)\\n return buf\",\n \"def unescape(msg):\\n skip = False\\n unescaped = bytearray()\\n\\n for i in range(len(msg)):\\n\\n if not skip and msg[i] is 0x7D:\\n\\n if not (i + 1) >= len(msg):\\n unescaped.append(msg[i + 1] ^ 0x20)\\n skip = True\\n\\n elif not skip:\\n unescaped.append(msg[i])\\n else:\\n skip = False\\n\\n return unescaped\",\n \"def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\\n\\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None\",\n \"def _grab_unascii(self):\\r\\n unascii = \\\"\\\"\\r\\n while self._char != -1 and not self._char in \\\"\\\\x00\\\\t\\\\r\\\\n\\\":\\r\\n unascii += self._char\\r\\n self._get_char()\\r\\n return unascii\",\n \"def unescape(input):\\n output=atpic.cleaner_escape.unescape(input)\\n return output\",\n \"def sanatize_hex(data: str) -> str:\\n return data.replace(\\\"0x\\\", \\\"\\\").replace(\\\"0X\\\", \\\"\\\")\",\n \"def bh2u(x: bytes) -> str:\\n return hfu(x).decode('ascii')\",\n \"def _hashsanitize(bytesin):\\n # Used for converting raw byte data into a hex string. If the byte isn't a hex digit, use nothing instead.\\n return \\\"\\\".join([x if x.lower() in 'abcdef0123456789' else '' for x in bytesin])\",\n \"def entity_decode_hex(input, errors='strict'):\\n if _is_unicode(input):\\n if '%' not in input:\\n return s\\n bits = _asciire.split(input)\\n res = [bits[0]]\\n append = res.append\\n for i in range(1, len(bits), 2):\\n append(unquote(str(bits[i])).decode('latin1'))\\n append(bits[i + 1])\\n return (''.join(res), len(input))\\n\\n preamble_regex = re.compile(r\\\"&#x\\\", flags=re.I)\\n bits = preamble_regex.split(input)\\n # fastpath\\n if len(bits) == 1:\\n return input\\n res = [bits[0]]\\n append = res.append\\n for item in bits[1:]:\\n try:\\n append(_hextochr[item[:2]])\\n append(item[3:])\\n except KeyError:\\n append('&#x')\\n append(item)\\n append(';')\\n\\n return (''.join(res), len(input))\",\n \"def unescape(escaped_string):\\n\\n hex_msg = \\\"^x not followed by a valid 2-digit hex number\\\"\\n\\n token_start = 0\\n l = len(escaped_string)\\n i = 0\\n output = []\\n while i < l:\\n c = escaped_string[i]\\n\\n if c in _unprintables:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string, \\\"unprintable character\\\",\\n token_start, i)\\n elif c != \\\"^\\\":\\n output.append(c)\\n else:\\n if i == l - 1:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string, \\\"caret at end of string\\\",\\n token_start, i)\\n i += 1\\n next_c = escaped_string[i]\\n if next_c not in \\\"'\\\\\\\"^x\\\":\\n if next_c in _unprintables:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string,\\n \\\"^ followed by unprintable character\\\",\\n token_start, i)\\n else:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string,\\n \\\"^ followed by invalid character %s\\\" % (next_c,),\\n token_start, i)\\n if next_c != 'x':\\n output.append(next_c)\\n else:\\n if i >= l - 2:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string, hex_msg, token_start, i)\\n i += 1\\n hex1 = escaped_string[i]\\n i += 1\\n hex2 = escaped_string[i]\\n if hex1 not in _ALLOWED_SAMPLE_HEX_DIGITS:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string, hex_msg, token_start, i - 1)\\n if hex2 not in _ALLOWED_SAMPLE_HEX_DIGITS:\\n raise vps.errorhandler.StringUnspecial_characterException(\\n escaped_string, hex_msg, token_start, i)\\n val = int(hex1 + hex2, 16)\\n output.append(chr(val))\\n # incrementing i should happen at the end of the loop body for\\n # all paths\\n i += 1\\n return ''.join(output)\",\n \"def unobscure(obscured: bytes) -> bytes:\\n return decompress(b64d(obscured))\",\n \"def remove_hex(text): \\n return re.sub(r'&.*?;', r'', text)\",\n \"def to_hex(text):\\n return ' '.join([hex(ord(char)) for char in unicode(text, 'UTF-8')])\",\n \"def cook(raw):\\n if sys.version_info[0] < 3:\\n # python 2\\n if isinstance(raw, str):\\n try:\\n cooked = raw.decode('utf-8')\\n except UnicodeDecodeError:\\n cooked = raw.decode('ascii', 'ignore')\\n else:\\n cooked = raw\\n else:\\n # python 3\\n if isinstance(raw, bytes):\\n try:\\n cooked = raw.decode('utf-8')\\n except UnicodeDecodeError:\\n cooked = raw.decode('ascii', 'ignore')\\n else:\\n cooked = raw\\n return cooked\",\n \"def unescape(text):\\r\\n\\r\\n def fixup(m):\\r\\n text = m.group(0)\\r\\n if text[:2] == '&#':\\r\\n try:\\r\\n if text[:3] == '&#x':\\r\\n return unichr(int(text[3:-1], 16)).encode('utf-8')\\r\\n return unichr(int(text[2:-1])).encode('utf-8')\\r\\n except ValueError:\\r\\n logger.info('error de valor')\\r\\n\\r\\n else:\\r\\n try:\\r\\n import htmlentitydefs\\r\\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]]).encode('utf-8')\\r\\n except KeyError:\\r\\n logger.info('keyerror')\\r\\n except:\\r\\n pass\\r\\n\\r\\n return text\\r\\n\\r\\n return re.sub('&#?\\\\\\\\w+;', fixup, text)\",\n \"def str_to_raw(s):\\n raw_map = {8:r'\\\\b', 7:r'\\\\a', 12:r'\\\\f', 10:r'\\\\n', 13:r'\\\\r', 9:r'\\\\t', 11:r'\\\\v'}\\n return r''.join(i if ord(i) > 32 else raw_map.get(ord(i), i) for i in s)\",\n \"def hex(self):\\n return binascii.hexlify(self.data)\",\n \"def to_ascii(text):\\n return re.sub(r'[^\\\\x00-\\\\x7F]+', ' ', text)\",\n \"def force_ascii(text):\\n return \\\"\\\".join([c for c in text if ord(c) < 128])\",\n \"def unescape(text):\\n import re, htmlentitydefs\\n def fixup(m):\\n text = m.group(0)\\n if text[:2] == \\\"&#\\\":\\n # character ref\\n try:\\n if text[:3] == \\\"&#x\\\":\\n return unichr(int(text[3:-1],1))\\n else:\\n return unichr(int(text[2:-1]))\\n except ValueError:\\n pass\\n else:\\n #named entity\\n try:\\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]])\\n except KeyError:\\n pass\\n return text\\n return re.sub(\\\"&#?\\\\w+;\\\", fixup, text)\",\n \"def handle_hex_stream(data):\\n stream = extract_stream(data)\\n if stream is not False: # stream can actually be null (HexStream = \\\\\\\\x00\\\\\\\\x00). so we explicitly check for False\\n is_binary = not all(c in string.printable for c in stream)\\n return stream.encode(\\\"base64\\\") if is_binary else stream\\n return data\",\n \"def b2a(b):\\n return binascii.hexlify(b)\",\n \"def hexify_word(word):\\r\\n\\r\\n return ''.join([str(hex(ord(c))[2::]) for c in word])\",\n \"def html_unescape(text):\\n return html.unescape(text)\",\n \"def test_embed_hex():\\n python = uflash.hexlify(TEST_SCRIPT)\\n result = uflash.embed_hex(uflash._RUNTIME, python)\\n # The resulting hex should be of the expected length.\\n assert len(result) == len(python) + len(uflash._RUNTIME) + 1 # +1 for \\\\n\\n # The hex should end with a newline '\\\\n'\\n assert result[-1:] == '\\\\n'\\n # The Python hex should be in the correct location.\\n py_list = python.split()\\n result_list = result.split()\\n start_of_python_from_end = len(py_list) + 5\\n start_of_python = len(result_list) - start_of_python_from_end\\n assert result_list[start_of_python:-5] == py_list\\n # The firmware should enclose the Python correctly.\\n firmware_list = uflash._RUNTIME.split()\\n assert firmware_list[:-5] == result_list[:-start_of_python_from_end]\\n assert firmware_list[-5:] == result_list[-5:]\",\n \"def UnescapeHTMLEntities(self, data):\\n if '#39' not in htmlentitydefs.name2codepoint:\\n htmlentitydefs.name2codepoint['#39'] = 39\\n return re.sub('&(%s);' % '|'.join(htmlentitydefs.name2codepoint),\\n lambda m: unichr(htmlentitydefs.name2codepoint[m.group(1)]),\\n data)\",\n \"def ascii_convert(the_bytes: bytes):\\n return ANSI_ESCAPE_B.sub(rb\\\"\\\", the_bytes).decode(\\\"utf-8\\\")\",\n \"def _unquote(src, encoding=\\\"utf-8\\\"):\\n return urllib.unquote(src).decode(encoding)\",\n \"def de_hex(msg):\\n try:\\n return bytes.fromhex(msg).decode('utf-8')\\n except (UnicodeDecodeError, ValueError):\\n print('Invalid hexadecimal-encoded string')\",\n \"def decode_high(self, text):\\n h = HTMLParser()\\n text = '&#%s;' % text\\n return h.unescape(text)\",\n \"def parse_sysex_string(s):\\n return binascii.unhexlify(s.replace(' ', ''))\",\n \"def normalize_input_shellcode(shellcode):\\n shellcode = shellcode.replace(' ', '')\\n shellcode = shellcode.replace('\\\\\\\\x', '')\\n shellcode = shellcode.replace('\\\\\\\\X', '')\\n return shellcode\",\n \"def HtmlUnescape(text):\\n def fixup(m):\\n text = m.group(0)\\n if text[:2] == \\\"&#\\\":\\n # character reference\\n try:\\n if text[:3] == \\\"&#x\\\":\\n return unichr(int(text[3:-1], 16))\\n else:\\n return unichr(int(text[2:-1]))\\n except ValueError:\\n pass\\n else:\\n # named entity\\n try:\\n text = unichr(htmlentitydefs.name2codepoint[text[1:-1]])\\n except KeyError:\\n pass\\n return text # leave as is\\n return re.sub(\\\"&#?\\\\w+;\\\", fixup, text)\",\n \"def __unicode_to_ascii(text):\\n line = unicodedata.normalize('NFKD', text)\\n return ''.join(c for c in line if not unicodedata.combining(c))\",\n \"def _hex2bin(cls, h):\\n b = []\\n while h:\\n if h[0] in ' \\\\n': # ignore whitespace\\n h = h[1:]\\n elif h[0] in '0123456789abcdef': # hex byte\\n b.append(int(h[:2], 16))\\n h = h[2:]\\n elif h[0] == '.': # for chunk type\\n b.extend(ord(h[i]) for i in range(1,5))\\n h = h[5:]\\n else: # for PNG magic\\n b.append(ord(h[0]))\\n h = h[1:]\\n return ''.join(map(chr,b))\",\n \"def unescape(text):\\n if isinstance(text, list):\\n for i, t in enumerate(text):\\n t = t.replace(r'&', r'\\\\&')\\n t = t.replace(r'<', r'<')\\n t = t.replace(r'>', r'>')\\n text[i] = t\\n else:\\n text = text.replace(r'&', r'\\\\&')\\n text = text.replace(r'<', r'<')\\n text = text.replace(r'>', r'>')\\n return text\",\n \"def removeNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else '' for i in text])\",\n \"def unhexchar(c):\\n c = c[:1]\\n if \\\"abcdefABCDEF0123456789\\\".find(c) != -1:\\n return int(c,16)\\n else:\\n return None\",\n \"def tohex(data: str) -> str:\\n match = re.fullmatch(r\\\"^0[x|X][0-9a-fA-F]+\\\", data)\\n if match:\\n return data.lower()\\n match = re.fullmatch(r\\\"^[0-9a-fA-F]+[h|H]$\\\", data)\\n if not match:\\n raise ValueError(f\\\"Required hex of the form `0x` or `H` found {data}\\\")\\n match = re.match(r\\\"^[0-9a-fA-F]+\\\", data)\\n return f\\\"0x{match.group().lower()}\\\"\",\n \"def strip_escape(data):\\n m = excel_escape_re.match(data)\\n if m:\\n return m.group(1)\\n else:\\n return data\",\n \"def _unescape_str(text):\\n text = text.decode('utf-8') if isinstance(text, six.binary_type) else text\\n tokens = []\\n i = 0\\n basicstr_re = re.compile(r'[^\\\"\\\\\\\\\\\\000-\\\\037]*')\\n unicode_re = re.compile(r'[uU]((?<=u)[a-fA-F0-9]{4}|(?<=U)[a-fA-F0-9]{8})')\\n escapes = {\\n 'b': '\\\\b',\\n 't': '\\\\t',\\n 'n': '\\\\n',\\n 'f': '\\\\f',\\n 'r': '\\\\r',\\n '\\\\\\\\': '\\\\\\\\',\\n '\\\"': '\\\"',\\n '/': '/',\\n \\\"'\\\": \\\"'\\\"\\n }\\n while True:\\n m = basicstr_re.match(text, i)\\n i = m.end()\\n tokens.append(m.group())\\n if i == len(text) or text[i] != '\\\\\\\\':\\n break\\n else:\\n i += 1\\n if unicode_re.match(text, i):\\n m = unicode_re.match(text, i)\\n i = m.end()\\n tokens.append(six.unichr(int(m.group(1), 16)))\\n else:\\n if text[i] not in escapes:\\n raise BadEscapeCharacter\\n tokens.append(escapes[text[i]])\\n i += 1\\n return ''.join(tokens)\",\n \"def clean_txt(txt):\\n r = txt.encode(\\\"utf-8\\\", errors=\\\"backslashreplace\\\").decode('utf-8').replace(\\\"\\\\\\\\u0144\\\", \\\"\\\")\\n return r\",\n \"def preprocess_hashes(tex):\\n blocks = catlist()\\n rx = hash_rx\\n m = rx.search(tex)\\n while m:\\n if len(m.group(2)) > 40:\\n tex2htm.warn(\\\"Possible runaway hash: {}\\\".format(text_sample(m.group(2))))\\n raise(None)\\n blocks.append(tex[:m.start()])\\n blocks.append(re.sub(r'(^|[^\\\\\\\\])%', r'\\\\1\\\\%', m.group(0)))\\n tex = tex[m.end():]\\n m = rx.search(tex)\\n blocks.append(tex)\\n return \\\"\\\".join(blocks)\",\n \"def normalizeRawFromHeader(value):\\n return value.replace('\\\\n', '').replace('\\\\r', '').strip()\",\n \"def normalize(text):\\n text = text.encode('utf-8')\\n # Python idiom to remove extraneous spaces\\n text = ' '.join(text.split())\\n return text.strip('\\\"')\",\n \"def _pythonifyString(s):\\n\\n if \\\"\\\\x00\\\" in s:\\n s = s[:s.index(\\\"\\\\x00\\\")]\\n return s\",\n \"def encrypt(text):\\r\\n\\r\\n cipher = fuzz(text)\\r\\n return hexify(cipher)\",\n \"def toHexa(data):\\n\\tresult = \\\"\\\"\\n\\tif isBytes(data):\\n\\t\\tdata = data.decode(\\\"latin-1\\\")\\n\\tfor i in data:\\n\\t\\tresult += \\\"\\\\\\\\x%02X\\\"%ord(i)\\n\\treturn result\",\n \"def sanitize(buf,\\n backspaces=['\\\\x08\\\\x1b[K', '\\\\x08 \\\\x08'],\\n escape_regex=re.compile(r'\\\\x1b(\\\\[|\\\\]|\\\\(|\\\\))[;?0-9]*[0-9A-Za-z](.*\\\\x07)?')):\\n # Filter out control characters\\n\\n # First, handle the backspaces.\\n for backspace in backspaces:\\n try:\\n while True:\\n ind = buf.index(backspace)\\n buf = ''.join((buf[0:ind-1],buf[ind+len(backspace):]))\\n except:\\n pass\\n\\n strip_escapes = escape_regex.sub('',buf)\\n\\n # strip non-printable ASCII characters\\n\\n clean = ''.join([x for x in strip_escapes if is_printable(x)])\\n return clean\",\n \"def unquote(cls, value):\\n if six.PY2:\\n return unquote(value).decode(\\\"utf8\\\")\\n else:\\n return unquote(value.decode(\\\"ascii\\\"))\",\n \"def base64_to_hex(b64_string):\\n # Remove hex encoding\\n unencoded_string = base64.b64decode(b64_string)\\n # Encode base64 and return\\n return binascii.hexlify(unencoded_string)\",\n \"def unquote_safe(s, unsafe_list):\\n # note: this build utf8 raw strings ,then does a .decode('utf8') at the end.\\n # as a result it's doing .encode('utf8') on each block of the string as it's processed.\\n res = _utf8(s).split('%')\\n for i in xrange(1, len(res)):\\n item = res[i]\\n try:\\n raw_chr = _hextochr[item[:2]]\\n if raw_chr in unsafe_list or ord(raw_chr) < 20:\\n # leave it unescaped (but uppercase the percent escape)\\n res[i] = '%' + item[:2].upper() + item[2:]\\n else:\\n res[i] = raw_chr + item[2:]\\n except KeyError:\\n res[i] = '%' + item\\n except UnicodeDecodeError:\\n # note: i'm not sure what this does\\n res[i] = unichr(int(item[:2], 16)) + item[2:]\\n o = \\\"\\\".join(res)\\n return _unicode(o)\",\n \"def a2b(a):\\n return binascii.unhexlify(a)\",\n \"def ascii_to_phred64(c):\\r\\n return ascii_to_phred(c, 64)\",\n \"def parse_text(text):\\n return str(str(text).encode(\\\"ascii\\\", \\\"ignore\\\")).replace(\\\"\\\\\\\\n\\\",\\\"\\\\n\\\").replace(\\\"b'\\\",\\\"\\\")\",\n \"def surrogate(text):\\n if isinstance(text, bytes):\\n return text.decode('utf-8', errors='surrogateescape')\\n return text\",\n \"def prepare_for_hashing(text):\\n if not text:\\n return ''\\n return text.translate(CHARS_TO_DELETE).lower()\",\n \"def string_raw(self):\\n return \\\"x%x\\\" % self.encoded\",\n \"def as_hex(self):\\n return binascii.hexlify(self.as_bytes()).decode('ascii')\",\n \"def clean_string(text: str, ascii_only=False) -> str:\\n done = False\\n while text and not done:\\n done = True\\n if ((text[0] == '\\\"' and text[-1] == '\\\"') or\\n (text[0] == '[' and text[-1] == ']')):\\n text = text[1:-1]\\n done = False\\n if text[:2] == \\\"u'\\\" and text[-1] == \\\"'\\\":\\n text = text[2:-1]\\n done = False\\n if ascii_only:\\n try:\\n # Python v3.7\\n if text.isascii(): # type: ignore\\n return text\\n except AttributeError:\\n # Python less than v3.7\\n pass\\n return ''.join(filter(lambda c: ord(c) >= 32 and ord(c) < 0x7F,\\n list(text)))\\n return text\",\n \"def removeUnicode(text):\\n text = re.sub(r'(\\\\\\\\u[0-9A-Fa-f]+)',r'', text) \\n text = re.sub(r'[^\\\\x00-\\\\x7f]',r'',text)\\n return text\",\n \"def _unascii(s):\\n\\n # make the fast path fast: if there are no matches in the string, the\\n # whole thing is ascii. On python 2, that means we're done. On python 3,\\n # we have to turn it into a bytes, which is quickest with encode('utf-8')\\n m = _U_ESCAPE.search(s)\\n if not m:\\n return s if PY2 else s.encode('utf-8')\\n\\n # appending to a string (or a bytes) is slooow, so we accumulate sections\\n # of string result in 'chunks', and join them all together later.\\n # (It doesn't seem to make much difference whether we accumulate\\n # utf8-encoded bytes, or strings which we utf-8 encode after rejoining)\\n #\\n chunks = []\\n\\n # 'pos' tracks the index in 's' that we have processed into 'chunks' so\\n # far.\\n pos = 0\\n\\n while m:\\n start = m.start()\\n end = m.end()\\n\\n g = m.group(1)\\n\\n if g is None:\\n # escaped backslash: pass it through along with anything before the\\n # match\\n chunks.append(s[pos:end])\\n else:\\n # \\\\uNNNN, but we have to watch out for surrogate pairs.\\n #\\n # On python 2, str.encode(\\\"utf-8\\\") will decode utf-16 surrogates\\n # before re-encoding, so it's fine for us to pass the surrogates\\n # through. (Indeed we must, to deal with UCS-2 python builds, per\\n # https://github.com/matrix-org/python-canonicaljson/issues/12).\\n #\\n # On python 3, str.encode(\\\"utf-8\\\") complains about surrogates, so\\n # we have to unpack them.\\n c = int(g, 16)\\n\\n if c < 0x20:\\n # leave as a \\\\uNNNN escape\\n chunks.append(s[pos:end])\\n else:\\n if PY3: # pragma nocover\\n if c & 0xfc00 == 0xd800 and s[end:end + 2] == '\\\\\\\\u':\\n esc2 = s[end + 2:end + 6]\\n c2 = int(esc2, 16)\\n if c2 & 0xfc00 == 0xdc00:\\n c = 0x10000 + (((c - 0xd800) << 10) |\\n (c2 - 0xdc00))\\n end += 6\\n\\n chunks.append(s[pos:start])\\n chunks.append(unichr(c))\\n\\n pos = end\\n m = _U_ESCAPE.search(s, pos)\\n\\n # pass through anything after the last match\\n chunks.append(s[pos:])\\n\\n return (''.join(chunks)).encode(\\\"utf-8\\\")\",\n \"def unicode2ascii(_unicrap):\\n xlate = {0xc0:'A', 0xc1:'A', 0xc2:'A', 0xc3:'A', 0xc4:'A', 0xc5:'A',\\n 0xc6:'Ae', 0xc7:'C',\\n 0xc8:'E', 0xc9:'E', 0xca:'E', 0xcb:'E',\\n 0xcc:'I', 0xcd:'I', 0xce:'I', 0xcf:'I',\\n 0xd0:'Th', 0xd1:'N',\\n 0xd2:'O', 0xd3:'O', 0xd4:'O', 0xd5:'O', 0xd6:'O', 0xd8:'O',\\n 0xd9:'U', 0xda:'U', 0xdb:'U', 0xdc:'U',\\n 0xdd:'Y', 0xde:'th', 0xdf:'ss',\\n 0xe0:'a', 0xe1:'a', 0xe2:'a', 0xe3:'a', 0xe4:'a', 0xe5:'a',\\n 0xe6:'ae', 0xe7:'c',\\n 0xe8:'e', 0xe9:'e', 0xea:'e', 0xeb:'e',\\n 0xec:'i', 0xed:'i', 0xee:'i', 0xef:'i',\\n 0xf0:'th', 0xf1:'n',\\n 0xf2:'o', 0xf3:'o', 0xf4:'o', 0xf5:'o', 0xf6:'o', 0xf8:'o',\\n 0xf9:'u', 0xfa:'u', 0xfb:'u', 0xfc:'u',\\n 0xfd:'y', 0xfe:'th', 0xff:'y',\\n 0xa1:'!', 0xa2:'{cent}', 0xa3:'{pound}', 0xa4:'{currency}',\\n 0xa5:'{yen}', 0xa6:'|', 0xa7:'{section}', 0xa8:'{umlaut}',\\n 0xa9:'{C}', 0xaa:'{^a}', 0xab:'<<', 0xac:'{not}',\\n 0xad:'-', 0xae:'{R}', 0xaf:'_', 0xb0:'{degrees}',\\n 0xb1:'{+/-}', 0xb2:'{^2}', 0xb3:'{^3}', 0xb4:\\\"'\\\",\\n 0xb5:'{micro}', 0xb6:'{paragraph}', 0xb7:'*', 0xb8:'{cedilla}',\\n 0xb9:'{^1}', 0xba:'{^o}', 0xbb:'>>',\\n 0xbc:'{1/4}', 0xbd:'{1/2}', 0xbe:'{3/4}', 0xbf:'?',\\n 0xd7:'*', 0xf7:'/'\\n }\\n\\n s = \\\"\\\"\\n for i in _unicrap:\\n ordi = ord(i)\\n if ordi in xlate:\\n s += xlate[ordi]\\n elif ordi >= 0x80:\\n pass\\n else:\\n s += str(i)\\n return s\",\n \"def hex_to_unichr(hex_string):\\n if (hex_string is None) or (len(hex_string) < 1):\\n return None\\n if hex_string.startswith(\\\"U+\\\"):\\n hex_string = hex_string[2:]\\n return int_to_unichr(int(hex_string, base=16))\",\n \"def unphred_string(phred):\\n arr = [(ord(c) - 33) / 30. for c in phred]\\n return arr\",\n \"def tidy_string(s: str\\n ) -> str:\\n s = s.encode('ascii', errors='ignore').decode(FORMAT)\\n s = s.replace(\\\"\\\\r\\\", \\\"\\\").replace(\\\"\\\\t\\\", \\\"\\\").replace('\\\\n', '') \\n return s\",\n \"def obscure(data: bytes) -> bytes:\\n return b64e(compress(data, 9))\",\n \"def decode_bytes(data: bytearray) -> str:\\n pattern = re.compile('\\\\r', re.UNICODE)\\n res = data.decode('utf-8', 'ignore')\\n res = pattern.sub('', res)\\n return res\",\n \"def unescape(s):\\n\\n\\tif s is None:\\n\\t\\treturn \\\"\\\"\\n\\n\\t# html entities\\n\\ts = s.replace(\\\" \\\", \\\"\\\\r\\\")\\n\\n\\t# standard html\\n\\ts = s.replace(\\\"<\\\", \\\"<\\\")\\n\\ts = s.replace(\\\">\\\", \\\">\\\")\\n\\ts = s.replace(\\\"&\\\", \\\"&\\\") # this has to be last\\n\\n\\treturn s\",\n \"def escapeDecode(s: unicode) -> unicode:\\n ...\",\n \"def rl_unescape_prompt(prompt: str) -> str:\\n if rl_type == RlType.GNU:\\n escape_start = \\\"\\\\x01\\\"\\n escape_end = \\\"\\\\x02\\\"\\n prompt = prompt.replace(escape_start, \\\"\\\").replace(escape_end, \\\"\\\")\\n\\n return prompt\",\n \"def unicoder(string):\\n\\treturn \\\"\\\\x00\\\".join(string) + \\\"\\\\x00\\\"\",\n \"def text2Int(text):\\n return reduce(lambda x, y : (x << 8) + y, map(ord, text))\",\n \"def unescape(t):\\r\\n return (t\\r\\n .replace(\\\"&\\\", \\\"&\\\").replace(\\\"<\\\", \\\"<\\\").replace(\\\">\\\", \\\">\\\")\\r\\n .replace(\\\"'\\\", \\\"´\\\").replace(\\\""\\\", '\\\"').replace(''',\\\"'\\\")\\r\\n )\",\n \"def html_unescape(text):\\n\\n def fixup(m):\\n text = m.group(0)\\n if text[:2] == \\\"&#\\\":\\n # character reference\\n try:\\n if text[:3] == \\\"&#x\\\":\\n return chr(int(text[3:-1], 16))\\n else:\\n return chr(int(text[2:-1]))\\n except ValueError:\\n pass\\n else:\\n # named entity\\n try:\\n text = chr(html.entities.name2codepoint[text[1:-1]])\\n except KeyError:\\n pass\\n return text # leave as is\\n return re.sub(\\\"&#?\\\\w+;\\\", fixup, text)\",\n \"def decode(text: str) -> str:\\n # Reverse of reverse is original text.\\n return encode(text)\",\n \"def clean_up_text(text):\\n text = html.unescape(text)\\n return remove_emoji(text)\",\n \"def value_convert(x):\\n try:\\n return x.decode(\\\"ascii\\\")\\n except UnicodeDecodeError:\\n return x.hex()\",\n \"def unquote(uri):\\r\\n uri = uri.encode('ascii')\\r\\n unquoted = urllib_unquote(uri)\\r\\n return unquoted.decode('utf-8')\",\n \"def unicode_unquote(value):\\n return unquote(value).decode('utf-8')\",\n \"def test_unquote(self):\\n fwa = FakeWikiArchivo('abcd <a href=\\\"/wiki/f%C3%B3u\\\">FooBar</a> dcba')\\n _, r = self.peishranc(fwa)\\n self.assertEqual(r, [(u'fóu', SCORE_PEISHRANC)])\",\n \"def hexify(c):\\n try:\\n s = c.encode(\\\"utf-8\\\").encode(\\\"hex\\\")\\n except UnicodeDecodeError:\\n s = 0\\n n = len(s)\\n if n <= 2: return s\\n a = ' - '.join([s[i:i+2] for i in range(0,n,2)])\\n return a[:-1]\",\n \"def decode_hex(self, s):\\n return self.transcode(int(s, 16))\",\n \"def _decode_html_entities(text: str) -> str:\\n return html.unescape(text)\",\n \"def remove_non_ascii(text):\\n return re.sub(r'[^\\\\x00-\\\\x7F]', ' ', text)\",\n \"def unescape_tweet(tweet):\\r\\n return html.unescape(tweet)\",\n \"def __html_unescape(self, text):\\n\\n return re.sub(\\\"&(%s);\\\" % \\\"|\\\".join(name2codepoint),\\n lambda m: unichr(name2codepoint[m.group(1)]),\\n text)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76805043","0.724818","0.71128637","0.6909091","0.6862392","0.6219935","0.6197366","0.59515","0.5922873","0.5843483","0.57907474","0.5650561","0.56349534","0.56267136","0.55028135","0.550243","0.5497304","0.54252976","0.5407516","0.53313655","0.52876633","0.52724916","0.5268687","0.5219308","0.52061766","0.52057064","0.5189475","0.5174156","0.5166434","0.51478815","0.5142549","0.511327","0.50778574","0.5072602","0.50717604","0.5070542","0.5068844","0.5062356","0.5055262","0.50538003","0.50488925","0.50438184","0.5035686","0.50311035","0.5029688","0.5028371","0.5016538","0.5012723","0.49692258","0.49406317","0.493382","0.49318412","0.49298275","0.49275565","0.49159658","0.4913919","0.49047682","0.49008802","0.4898744","0.48972988","0.48834342","0.48784578","0.48740408","0.4845163","0.4843533","0.48340705","0.48280346","0.482675","0.4822948","0.4820369","0.48159528","0.481389","0.47928926","0.47904122","0.4782441","0.4782262","0.4782021","0.4766663","0.47497153","0.47476533","0.47440577","0.47425053","0.4738442","0.47365698","0.47270638","0.4722092","0.47148982","0.4713723","0.47133064","0.47078067","0.4705975","0.46987578","0.46878996","0.46837682","0.46822342","0.46759978","0.46759155","0.46717483","0.4670215","0.46678558"],"string":"[\n \"0.76805043\",\n \"0.724818\",\n \"0.71128637\",\n \"0.6909091\",\n \"0.6862392\",\n \"0.6219935\",\n \"0.6197366\",\n \"0.59515\",\n \"0.5922873\",\n \"0.5843483\",\n \"0.57907474\",\n \"0.5650561\",\n \"0.56349534\",\n \"0.56267136\",\n \"0.55028135\",\n \"0.550243\",\n \"0.5497304\",\n \"0.54252976\",\n \"0.5407516\",\n \"0.53313655\",\n \"0.52876633\",\n \"0.52724916\",\n \"0.5268687\",\n \"0.5219308\",\n \"0.52061766\",\n \"0.52057064\",\n \"0.5189475\",\n \"0.5174156\",\n \"0.5166434\",\n \"0.51478815\",\n \"0.5142549\",\n \"0.511327\",\n \"0.50778574\",\n \"0.5072602\",\n \"0.50717604\",\n \"0.5070542\",\n \"0.5068844\",\n \"0.5062356\",\n \"0.5055262\",\n \"0.50538003\",\n \"0.50488925\",\n \"0.50438184\",\n \"0.5035686\",\n \"0.50311035\",\n \"0.5029688\",\n \"0.5028371\",\n \"0.5016538\",\n \"0.5012723\",\n \"0.49692258\",\n \"0.49406317\",\n \"0.493382\",\n \"0.49318412\",\n \"0.49298275\",\n \"0.49275565\",\n \"0.49159658\",\n \"0.4913919\",\n \"0.49047682\",\n \"0.49008802\",\n \"0.4898744\",\n \"0.48972988\",\n \"0.48834342\",\n \"0.48784578\",\n \"0.48740408\",\n \"0.4845163\",\n \"0.4843533\",\n \"0.48340705\",\n \"0.48280346\",\n \"0.482675\",\n \"0.4822948\",\n \"0.4820369\",\n \"0.48159528\",\n \"0.481389\",\n \"0.47928926\",\n \"0.47904122\",\n \"0.4782441\",\n \"0.4782262\",\n \"0.4782021\",\n \"0.4766663\",\n \"0.47497153\",\n \"0.47476533\",\n \"0.47440577\",\n \"0.47425053\",\n \"0.4738442\",\n \"0.47365698\",\n \"0.47270638\",\n \"0.4722092\",\n \"0.47148982\",\n \"0.4713723\",\n \"0.47133064\",\n \"0.47078067\",\n \"0.4705975\",\n \"0.46987578\",\n \"0.46878996\",\n \"0.46837682\",\n \"0.46822342\",\n \"0.46759978\",\n \"0.46759155\",\n \"0.46717483\",\n \"0.4670215\",\n \"0.46678558\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8604057"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":4693,"cells":{"query":{"kind":"string","value":"Parse a line of text from the plot_data file."},"document":{"kind":"string","value":"def parse_line(self, line):\n\t\tif line[0] == \"#\":\n\t\t\treturn False\n\t\tparts = [x.strip() for x in line.strip().split(\",\")]\n\t\tself.unix_time = int(parts[0])\n\t\tself.cycles_done = int(parts[1])\n\t\tself.cur_path = int(parts[2])\n\t\tself.paths_total = int(parts[3])\n\t\tself.pending_total = int(parts[4])\n\t\tself.pending_favs = int(parts[5])\n\t\tself.map_size = float(parts[6].replace(\"%\",\"\"))\n\t\tself.unique_crashes = int(parts[7])\n\t\tself.unique_hangs = int(parts[8])\n\t\tself.max_depth = int(parts[9])\n\t\tself.execs_per_sec = float(parts[10])\n\t\treturn True"},"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 parse_plot_cmd(self, line):\n line, any_vars = self.find_vars_in_str(line)\n words = line.split()\n words = self.fix_words(words)\n\n # Parse line\n has_out_var = False\n if len(words) == 6:\n has_out_var = True\n _, plot_type, _, in_data, _, out_data = words\n else: _, plot_type, _, in_data = words\n\n in_data = getattr(self, in_data)\n plot_fnc = f_dicts.plot_fncs[plot_type]\n\n if has_out_var:\n OutVar = plot_fnc(in_data)\n else: plot_fnc(in_data)\n\n if has_out_var:\n self.set_var(out_data,\n {plot_type: OutVar}, {})","def parse_point(self, text_line):\n record_type = self.substr(text_line, sps21point['RECORD_ID'][0], sps21point['RECORD_ID'][1]).strip()\n if record_type not in (SRC_DATA_RECORD, RCV_DATA_RECORD):\n return\n self.set_definition(sps21point)\n return self.parse(text_line)","def read_line(line):\n label = line[0:11]\n text = line[11:]\n y = 1 if label == '__label__2 ' else 0\n return text, y","def parse_line(self, line):\n raise NotImplementedError","def parse_line(self, line):\n success = self.parser.handle_line(line)\n if success:\n self.data.update()\n else:\n self.bot.log(\"didn't handle line: '{}'\".format(line))","def _parseLine(self, line, delimiter = \":\"):\r\n\t\tsplt = line.split(delimiter)\r\n\t\tinVec = self._parseVec(splt[0])\r\n\t\toutVec = self._parseVec(splt[1])\r\n\t\tif (len(splt) == 2):\r\n\t\t\tlabel = \"\"\r\n\t\telse:\r\n\t\t\tlabel = splt[2]\r\n\t\tself.data.append({'in':inVec, 'out':outVec, 'label':label})","def parse_data(fp):\n pass","def parse(cls, line):\r\n raise NotImplementedError","def parse_line(line):\n return parse('#{id_:d} @ {x:d},{y:d}: {w:d}x{h:d}', line)","def parseLine(self, line):\n # Removes surrounding whitespace\n line = self.separateElements(line)\n if len(line) == 0: return\n # Checks if the line is a label declaration\n if line[0].lower() == \"label\":\n # --Validates the line\n if len(line) != 2: raise Exception(\"Invalid Label\")\n if len(line[1]) < 2: raise Exception(\"Invalid Label\") \n if line[1][-1] != ':': raise Exception(\"Invalid Label\")\n # Gets the label name\n labelName = line[1][:-1]\n\n # Creates a new symbol entry for the label, the pointer refers to the memory location of the label\n # It defaults to the location of the label in the instruction sequence\n self.symbolTable.append({ \"type\": \"LABEL\", \"name\": labelName, \"pointer\": len(self.instructionList) * 4})\n # Checks if the line is data declaration\n elif line[0].lower() == \"data\" or (line[0].lower()[:4] == \"data\" and line[0][4] == \"[\"):\n # Removes the DATA tag from the data\n line[0] = line[0][4:]\n # --Validates the line\n if len(line) < 2: raise Exception(\"Invalid DATA\")\n # Gets the data name\n dataName = line[1]\n # Stores the data length\n dataLength = 4 # A word\n # Gets any default data\n defaultData = 0\n # Stores the data type\n dataType = \"int\"\n if len(line) == 3:\n if line[2][0] == \"\\\"\" and line[2][-1] == \"\\\"\":\n dataType = \"string\"\n defaultData = line[2][1:-1]\n dataLength = len(defaultData)\n elif line[2].isnumeric():\n defaultData = line[2]\n elif line[2][-1] == 'f' and line[2][:-1].isnumeric():\n dataType = \"float\"\n defaultData = line[2][0]\n # Checks if a data length was given\n if len(line[0]) > 2 and (line[0][0] == \"[\" and line[0][-1] == \"]\"):\n data = line[0][1:-1]\n if not data.isnumeric(): raise TypeError(\"Invalid data length type\")\n dataLength = int(data)\n\n # Creates a new symbol entry for the data\n self.symbolTable.append({ \"type\": \"DATA\", \"name\": dataName, \"default\": defaultData, \"dataType\": dataType, \"length\": dataLength})\n # The line is most likely an instruction\n else:\n # --Validates the line\n #Stores the control bits\n controlBits = 1 << 5 # Sets it to 0b100000\n # Checks if the first element is control bits\n if line[0][0] == \"{\" and line[0][-1] == \"}\": # First element is control bits\n # Separates the two sections of the control bits\n controlSections = line[0].split(':')\n #Goes through the characters and constructs the control bits for the instruction\n carryBits = controlSections[0].lower()\n carryFlag = int('c' in carryBits)\n zeroFlag = int('z' in carryBits)\n negativeFlag = int('n' in carryBits)\n signedOverflowFlag = int('s' in carryBits)\n #Gets the conditions bits\n if len(controlSections) == 2:\n conditionBits = controlSections[1].lower()\n isAnd = int('x' in conditionBits)\n isOne = int('1' in conditionBits)\n #Sets the last two bits on controlBits to the conditionBits\n controlBits ^= isAnd << 1\n controlBits ^= isOne\n # Constructs the control bits section\n controlBits ^= carryFlag << 5\n controlBits ^= zeroFlag << 4\n controlBits ^= negativeFlag << 3\n controlBits ^= signedOverflowFlag << 2\n # Removes the control bits section from the line\n line.pop(0)\n # Performs this check as the controlbits element gets removed (if it existed) and so the length of the elments could be zerp\n if len(line) == 0: raise Exception(\"Invalid Instruction\")\n # --The first element is the instruction\n # Identifies the instruction from the mnemonic using the lookup table\n if line[0] in self.InstructionLookupTable:\n ins = self.InstructionLookupTable[line[0]]\n insCode = ins[\"code\"]\n insControlBits = ins['controlBits'] if ins['controlBits'] else controlBits\n # Creates a representation of the instruction, this is stored in the instructionList and is assembled later\n instrucitonRepr = {\n \"code\": insCode,\n \"controlBits\": insControlBits,\n }\n # Parses the arguments given and stores the operandStruct returned in the instruciton representation\n if len(line) > 1: instrucitonRepr[\"operand\"] = self.parseArgs(line[1:], insCode)\n self.instructionList.append(instrucitonRepr)","def line_parser(path):\n lines = []\n with open(path, 'r') as input:\n lines = [line.rstrip().split(',') for line in input]\n lines = [\n [[float(x1), float(y1)],\n [float(x2), float(y2)]] \n for x1, y1, x2, y2 in lines]\n return lines","def parse_string_line(self, data_line):\n if data_line:\n data_line = data_line.rstrip()\n if data_line:\n if data_line[0] == '#':\n extraparams = json.loads(data_line[1:])\n if 'glyph_cap_line' in extraparams:\n self.__capline = extraparams['glyph_cap_line']\n if 'glyph_base_line' in extraparams:\n self.__baseline = extraparams['glyph_base_line']\n if 'glyph_bottom_line' in extraparams:\n self.__bottomline = extraparams['glyph_bottom_line']\n elif len(data_line) > 9:\n strokes = []\n xmin = xmax = ymin = ymax = None\n # individual strokes are stored separated by <space>+R\n # starting at col 11\n for s in split(data_line[10:], ' R'):\n if len(s):\n stroke = list(zip(map(self.__char2val, s[::2]), map(self.__char2val, s[1::2])))\n xmin = min(stroke + ([xmin] if xmin else []), key=lambda t: t[0])\n ymin = min(stroke + ([ymin] if ymin else []), key=lambda t: t[1])\n xmax = max(stroke + ([xmax] if xmax else []), key=lambda t: t[0])\n ymax = max(stroke + ([ymax] if ymax else []), key=lambda t: t[1])\n strokes.append(stroke)\n self.__charcode = int(data_line[0:5])\n self.__left_side = self.__char2val(data_line[8])\n self.__right_side = self.__char2val(data_line[9])\n self.__strokes = strokes\n self.__xmin, self.__ymin, self.__xmax, self.__ymax = (xmin[0], ymin[1], xmax[0], ymax[1]) if strokes else (0, 0, 0, 0)\n return True\n return False","def _process_data_file(self):\n \n with open(self.data_file, 'r') as f:\n self.description = f.readline().strip()\n data = np.loadtxt(self.data_file, skiprows=1)\n\n return data","def parse_file(self):\n # read the first line in the file\n line = self._stream_handle.readline()\n\n while line:\n # check for a data line or a dcl logger line we specifically ignore\n data_match = DATA_LINE_MATCHER.match(line)\n ignore_match = IGNORE_LINE_MATCHER.match(line)\n\n if data_match:\n # found a data line, extract this particle\n # DCL controller timestamp is the port_timestamp\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\n\n particle = self._extract_sample(self.particle_class,\n None,\n data_match,\n port_timestamp=port_timestamp,\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\n\n self._record_buffer.append(particle)\n\n elif not ignore_match:\n # we found a line with an unknown format, call an exception\n error_message = 'Found line with unknown format %s' % line\n log.warn(error_message)\n self._exception_callback(SampleException(error_message))\n\n # read the next line\n line = self._stream_handle.readline()","def parse_point(line):\n return json.loads(line)","def parse_line(self, line):\n if self.signal_eof:\n return \"\"\n\n match = re.search(\"^([\\w\\s]+from) ([^:]+):(\\d+)(:|,)$\", line)\n if match:\n return self.parse_line_from(match)\n\n match = re.search(\"^([^:]+):(?:((?:\\d+:)?\\d+):)?(?:(error|warning|note):)?(.+)$\", line)\n if match:\n return self.parse_line_err(match)\n\n return line","def line_to_data(line):\n elems = line.strip().split(\"\\t\")\n assert len(elems) in [1,2]\n text = None\n label = None\n if len(elems) == 1:\n text = elems[0]\n if len(elems) == 2:\n text = elems[0]\n label = elems[1]\n return (text, label)","def __parse_position_data(self):\n self.add_debug('Parse position data ...')\n\n for i in range(len(self._lines)):\n if self.has_errors(): break\n line = self._lines[i]\n if len(line) < 1: continue\n if self.TIMESTAMP_MARKER in line: continue\n if self.RACK_BARCODE_MARKER in line: continue\n\n msg = 'Unexpected content in line %i: %s' % (i + 1, line)\n if not self.SEPARATOR in line: self.add_error(msg)\n tokens = line.split(self.SEPARATOR)\n if not len(tokens) == 2: self.add_error(msg)\n if self.has_errors(): continue\n\n pos_label = tokens[0].strip()\n if self.position_map.has_key(pos_label):\n msg = 'Duplicate position label \"%s\"' % (pos_label)\n self.add_error(msg)\n if self.has_errors(): continue\n\n tube_barcode = tokens[1].strip()\n if tube_barcode == self.NO_TUBE_PLACEHOLDER: tube_barcode = None\n self.position_map[pos_label] = tube_barcode","def parse_dataset(self, data):\n pass","def csv_parser(lines): \n\n data_points = []\n for line in lines:\n items = line.strip().split(\",\")\n try: #will fail on header line in file\n data_points.append(map(float, items[1:])) #first item is the label\n except ValueError: #must be the header\n continue\n return data_points","def _parse_txt(path, n_channels):\n f = open(path)\n lines = f.readlines()\n f.close()\n\n geom = np.zeros((0, 2))\n\n for i, line in zip(range(n_channels), lines):\n line = line.replace('\\r', '')\n line = line.replace('\\n', '')\n row = line.split(' ')\n geom = np.vstack((geom, row[:2])).astype('float')\n\n return geom","def parse(self, line):\n try:\n (year, month, day, hour, minute, second, microseconds, offset_hour, offset_minute, source, process, logentry) = re.match('^(\\d\\d\\d\\d)-(\\d\\d)-(\\d\\d)T(\\d\\d):(\\d\\d):(\\d\\d)\\.([\\d]+)\\+(\\d\\d):(\\d\\d) ([a-z]+)\\[([a-zA-Z0-9_.]+)\\]: ([0-9a-z-A-Z\\-_\\.\\[\\]:\\?\\#\\\",/\\ ={}\\'\\(\\)<>]+)$', line).groups()\n except:\n pass\n \n try:\n parsed_data = dict()\n parsed_data['timestamp'] = \" \".join([\"-\".join([year, month, day]), \":\".join([hour, minute, second])])\n parsed_data['log_time'] = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), int(second))\n parsed_data['log_source'] = source\n parsed_data['log_type'] = process\n except (AttributeError, UnboundLocalError):\n PARSE_ERRORS.append(line)\n return False\n\n #TODO: This still needs work on spaces in values surrounded by \" \" \n if parsed_data['log_source'] == \"heroku\":\n if logentry.__len__() > 1:\n logentry = re.sub(', ', ',', logentry)\n line_chunks = re.split(' ', logentry)\n for chunk in line_chunks:\n line_chunks = re.split('=', chunk)\n if line_chunks.__len__() > 2:\n #fwd and path are a little clunky to parse\n pass\n elif line_chunks.__len__() > 1:\n parsed_data[line_chunks[0]] = line_chunks[1]\n else:\n pass\n else:\n return False\n else:\n # TODO: [app] \n # Needs parsing. Do that here.\n return False\n\n return parsed_data","def parse_text(self):\n text = self.get_data()\n line1 = text[0]\n index_list = [0]\n start_index = 3\n for i in range(1, len(text)):\n\n if line1.startswith('*'):\n index_list, start_index = self.star_parser(index_list, line1)\n elif line1.startswith('.'):\n start_index = self.dot_parser(start_index, line1, text, i)\n else:\n print \"\".rjust(start_index) + line1\n line1 = text[i]\n # Parse the last line\n if text[-1].startswith('*'):\n self.star_parser(index_list, text[-1])\n elif text[-1].startswith('.'):\n print '-'.rjust(start_index) + text[-1].lstrip('.')\n else:\n print \"\".rjust(start_index) + text[-1]","def process_line(line):\n [label, text] = line.split('\\t')\n return text.split()","def parse_line(line: str) -> str:\n return line","def parse_line(line: str) -> str:\n return line","def parse_line(line: str) -> str:\n return line","def parse_line(line: str) -> str:\n return line","def parse_text(self, text):\n self._text_paragraph = text.split(\"\\n\")\n self._render()","def process_line(self, line, data):\n return data","def isLineData(self, line):\n\n if line is None or line.strip().startswith('#'):\n return False, None, 0\n\n dataType = self.getDataType()\n\n if dataType == 'Y':\n # Y with 1 column\n try:\n yValue = float(line)\n\n return True, 'Y', 1\n except:\n pass\n\n # Y with comma 2 to 5 column\n try:\n yValueList = []\n yValueList = line.split(',')\n\n if len(yValueList) > 1 and len(yValueList) <= 5:\n newYValues = []\n for yValue in yValueList:\n try:\n yValue = float(yValue)\n newYValues.append(yValue)\n except ValueError:\n pass\n\n return True, 'Y', len(newYValues)\n except:\n pass\n\n # Y with space 2 to 5 column\n try:\n yValueList = []\n yValueList = line.split()\n\n if len(yValueList) > 1 and len(yValueList) <= 5:\n for yValue in yValueList:\n yValue = float(yValue)\n\n return True, 'Y', len(yValueList)\n except:\n pass\n elif dataType == 'XY':\n # XY with comma\n try:\n (xValue, yValue) = line.split(',')\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n\n # XY with comma\n try:\n xValue, yValue, dummy = line.split(',')\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n\n # XY with space\n try:\n (xValue, yValue) = line.split()\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n else:\n # Y with 1 column\n try:\n yValue = float(line)\n\n return True, 'Y', 1\n except:\n pass\n\n # Y with comma 2 to 5 column\n try:\n yValueList = []\n yValueList = line.split(',')\n\n if len(yValueList) > 1 and len(yValueList) <= 5:\n numberValues = 0\n for yValue in yValueList:\n try:\n yValue = float(yValue)\n numberValues += 1\n except ValueError:\n pass\n\n return True, 'Y', numberValues\n except:\n pass\n\n # Y with space 2 to 5 column\n try:\n yValueList = []\n yValueList = line.split()\n\n if len(yValueList) > 1 and len(yValueList) <= 5:\n for yValue in yValueList:\n yValue = float(yValue)\n\n return True, 'Y', len(yValueList)\n except:\n pass\n\n # XY with comma\n try:\n (xValue, yValue) = line.split(',')\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n\n # XY with comma\n try:\n xValue, yValue, dummy = line.split(',')\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n\n # XY with space\n try:\n (xValue, yValue) = line.split()\n\n xValue = float(xValue)\n yValue = float(yValue)\n\n return True, 'XY', 2\n except:\n pass\n\n return False, None, 0","def parse_string(self, data):\n pass","def _parse(self, lines):\n global VELSCALE\n self.title = lines[0].strip()\n self.time = None\n\n try:\n words = lines[1].split()\n self.natom = int(words[0])\n except (IndexError, ValueError):\n raise TypeError('Unrecognized file type [%s]' % self.filename)\n\n if len(words) >= 2:\n self.time = float(words[1]) * units.picoseconds\n\n if len(lines) == int(ceil(self.natom / 2.0) + 2):\n hasbox = hasvels = False\n self.boxVectors = self.velocities = None\n elif self.natom in (1, 2) and len(lines) == 4:\n # This is the _only_ case where line counting does not work -- there\n # is either 1 or 2 atoms and there are 4 lines. The 1st 3 lines are\n # the title, natom/time, and coordinates. The 4th are almost always\n # velocities since Amber does not make it easy to make a periodic\n # system with only 2 atoms. If natom is 1, the 4th line is either a\n # velocity (3 #'s) or a box (6 #'s). If natom is 2, it is a bit\n # ambiguous. However, velocities (which are scaled by 20.445) have a\n # ~0% chance of being 60+, so we can pretty easily tell if the last\n # line has box dimensions and angles or velocities. I cannot\n # envision a _plausible_ scenario where the detection here will fail\n # in real life.\n line = lines[3]\n if self.natom == 1:\n tmp = [line[i:i+12] for i in range(0, 72, 12) if line[i:i+12]]\n if len(tmp) == 3:\n hasvels = True\n hasbox = False\n self.boxVectors = False\n elif len(tmp) == 6:\n hasbox = True\n hasvels = False\n self.velocities = None\n else:\n raise TypeError('Unrecognized line in restart file %s' %\n self.filename)\n else:\n # Ambiguous case\n tmp = [float(line[i:i+12]) >= 60.0 for i in range(0, 72, 12)]\n if any(tmp):\n hasbox = True\n hasvels = False\n self.velocities = False\n else:\n hasvels = True\n hasbox = False\n self.boxVectors = False\n elif len(lines) == int(ceil(self.natom / 2.0) + 3):\n hasbox = True\n hasvels = False\n self.velocities = None\n elif len(lines) == int(2 * ceil(self.natom / 2.0) + 2):\n hasbox = False\n self.boxVectors = None\n hasvels = True\n elif len(lines) == int(2 * ceil(self.natom / 2.0) + 3):\n hasbox = hasvels = True\n else:\n raise TypeError('Badly formatted restart file. Has %d lines '\n 'for %d atoms.' % (len(self.lines), self.natom))\n\n if self._asNumpy:\n coordinates = np.zeros((self.natom, 3), np.float32)\n if hasvels:\n velocities = np.zeros((self.natom, 3), np.float32)\n else:\n coordinates = [Vec3(0.0, 0.0, 0.0) for i in range(self.natom)]\n if hasvels:\n velocities = [Vec3(0.0, 0.0, 0.0) for i in range(self.natom)]\n\n # Now it's time to parse. Coordinates first\n startline = 2\n endline = startline + int(ceil(self.natom / 2.0))\n idx = 0\n for i in range(startline, endline):\n line = lines[i]\n x = float(line[ 0:12])\n y = float(line[12:24])\n z = float(line[24:36])\n coordinates[idx] = Vec3(x, y, z)\n idx += 1\n if idx < self.natom:\n x = float(line[36:48])\n y = float(line[48:60])\n z = float(line[60:72])\n coordinates[idx] = Vec3(x, y, z)\n idx += 1\n self.coordinates = units.Quantity(coordinates, units.angstroms)\n startline = endline\n # Now it's time to parse velocities if we have them\n if hasvels:\n endline = startline + int(ceil(self.natom / 2.0))\n idx = 0\n for i in range(startline, endline):\n line = lines[i]\n x = float(line[ 0:12]) * VELSCALE\n y = float(line[12:24]) * VELSCALE\n z = float(line[24:36]) * VELSCALE\n velocities[idx] = Vec3(x, y, z)\n idx += 1\n if idx < self.natom:\n x = float(line[36:48]) * VELSCALE\n y = float(line[48:60]) * VELSCALE\n z = float(line[60:72]) * VELSCALE\n velocities[idx] = Vec3(x, y, z)\n idx += 1\n startline = endline\n self.velocities = units.Quantity(velocities,\n units.angstroms/units.picoseconds)\n if hasbox:\n line = lines[startline]\n try:\n tmp = [float(line[i:i+12]) for i in range(0, 72, 12)]\n except (IndexError, ValueError):\n raise ValueError('Could not parse box line in %s' %\n self.filename)\n lengths = tmp[:3] * units.angstroms\n angles = tmp[3:] * units.degrees\n self.boxVectors = computePeriodicBoxVectors(lengths[0], lengths[1],\n lengths[2], angles[0], angles[1], angles[2])","def parse_report_line(self,line):\n\n report = self.new_police_report()\n report['original_text'] = line\n \n #\n # extract month and day\n match_date = REPORT_DATE_REGEXP.search(line)\n assert(match_date)\n start_index=match_date.start('month')\n stop_index=match_date.end('month')\n report['date_month'] = int(line[start_index:stop_index])\n\n start_index=match_date.start('day')\n stop_index=match_date.end('day')\n report['date_day'] = int(line[start_index:stop_index])\n\n my_logger.debug('extracted date (%d/%d)' % (report['date_month'],report['date_day']))\n\n #############################################\n # extract location & scale\n line = line[0:match_date.start('month')-1] # truncate after start of date\n \n #\n # trim off preceding html and trailing comma\n start_index=line.rfind('>')+1\n assert(start_index>0)\n\n stop_index=line.rfind(',',start_index)\n \n if stop_index >= 2:\n #\n # found a comma, \n line = line[start_index:stop_index]\n else:\n #\n # no comma found\n line = line[start_index:]\n my_logger.debug('truncated string: (%s)' % line)\n report['address']=line\n #\n # try to determine which case:\n # a block\n # an exact address\n # an establishment\n # an intersection\n # special cases, like: \"downtown mountain view\"\n # \n\n if (BLOCK_REGEXP.match(line)!=None):\n my_logger.debug('BLOCK detected')\n report['map_scale']=mapscale.BLOCK\n elif (INTERSECTION_REGEXP.match(line)!=None):\n my_logger.debug('INTERSECTION detected')\n report['map_scale']=mapscale.INTERSECTION\n elif (EXACT_REGEXP.match(line)!=None):\n my_logger.debug('EXACT detected')\n report['map_scale']=mapscale.EXACT\n else:\n #\n # must be manually assigned\n report['map_scale']=mapscale.OTHER\n\n\n return report","def parse(self, f):\n lines = []\n for line in f:\n _line = line.split(\"//\")[0].strip()\n if _line.startswith(\"(\"): # is a label\n label_name = _line[1:-1]\n self.labels[label_name] = len(lines) # line number / address of label\n elif _line:\n lines.append(_line)\n # else: it's just a whitespace/comment line (ignore)\n return lines","def parse(text):\n parts = [int(part) for part in text.strip().split(',')]\n point = Point(*parts)\n actual = \"{},{},{},{}\".format(point.x, point.y, point.z, point.t)\n assert actual == text, diff(actual, text)\n return point","def __parse(self):\n lines = self.data.readlines()\n for i in range(0, len(lines)):\n line = lines[i]\n if line[0] == '#':\n continue\n tokens = line.split(\"\\t\")\n time_str = tokens[self.timecol]\n if time_str.find('start:') != -1:\n time_str = time_str.split()[1] + \" \" + time_str.split()[2]\n self.calls.append((0, 0, 0))\n self.durations.append(0.0)\n elif time_str.find('end:') != -1:\n time_str = time_str.split()[1] + \" \" + time_str.split()[2]\n time = datetime.strptime(time_str, \"%Y-%m-%d %H:%M:%S\")\n self.times.append(time)\n self.calls.append((0, 0, 0))\n self.durations.append(0.0)\n break\n else:\n duration = float(tokens[6])\n fms = int(tokens[2])\n hfms = int(tokens[3])\n svs = int(tokens[4])\n self.calls.append((fms, hfms, svs))\n self.durations.append(duration)\n time = datetime.strptime(time_str, \"%Y-%m-%d %H:%M:%S\")\n self.times.append(time)\n self.length = (self.times[len(self.times) - 1] -\\\n self.times[0]).seconds","def plot(self, plotType):\n # Build plotting data\n self.data_x_axis = []\n self.data_y_axis = []\n for i in range(0, self.csv_data_table.rowCount()):\n value = self.csv_data_table.item(i, self.selected_columns[0]).text()\n self.data_x_axis.append(value)\n value = self.csv_data_table.item(i, self.selected_columns[1]).text()\n self.data_y_axis.append(value)\n\n self.label_x_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[0]).text()\n self.label_y_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[1]).text()\n\n # Avoid duplication of resources if already allocated\n if self.figure is None:\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n\n # self.plot_frame_horizontal.addStretch()\n self.plot_frame_horizontal.addWidget(self.canvas)\n # self.plot_frame_horizontal.addStretch()\n\n # Ensures only 2 tabs at max are open at a time - file and plot tabs respectively\n if self.tabWidget.count() == 1:\n self.tabWidget.insertTab(1, self.plot_page_tab, \"Plot\")\n\n self.tabWidget.setCurrentIndex(1)\n\n # Set plot type (1,2,3 => order according to scatter, scatter-line, line)\n self.plotType = plotType\n\n # Convert the data to np arrays if it is purely numerical\n try:\n for i in range(0, len(self.data_x_axis)):\n if self.data_x_axis[i] == '':\n self.data_x_axis[i] = 0\n if self.data_y_axis[i] == '':\n self.data_y_axis[i] = 0\n\n self.data_x_axis[i] = self.coerce_str_to_number(self.data_x_axis[i])\n self.data_y_axis[i] = self.coerce_str_to_number(self.data_y_axis[i])\n\n self.data_x_axis = np.array(self.data_x_axis)\n self.data_y_axis = np.array(self.data_y_axis)\n\n print(self.data_x_axis)\n print(self.data_y_axis)\n\n print(\"In specialized plotting\")\n\n except:\n pass\n # Dont attempt the conversion, directly plot\n print(\"In generic plotting\")\n\n self.draw_plot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)","def check_plot_command(self, line):\n err_msg = \"The plot command takes the syntax:\\n\\n\"\n err_msg += \"\\t'plot <plot type> from <data name> as <plot name>'\"\n err_msg += \"\\n\\n\\t\\t\\tOR\\n\\n\\t'plot <plot type> from <data name>'\"\n\n line, any_vars = self.find_vars_in_str(line)\n\n # Check syntax\n words = line.split()\n words = self.fix_words(words)\n self.E_str = \"check_plot_command\"\n has_out_var = False\n if len(words) != 4:\n if len(words) != 6:\n self.print_error(err_msg)\n else:\n has_out_var = True\n _, plot_type, _, in_data, _, out_data = words\n else:\n _, plot_type, _, in_data = words\n\n # Check we can plot the thing asked for\n if plot_type not in f_dicts.plot_fncs:\n err_msg = f\"I don't know how to plot '{words[1]}'.\"\n err_msg += \"\\n\\nFor a full list of plots that can be done see below:\\n\\t* \"\n err_msg += \"\\n\\t* \".join(list(f_dicts.plot_fncs.keys()))\n self.print_error(err_msg)\n\n if has_out_var:\n metadata = f_dicts.plot_fncs[words[1]].metadata\n self.set_var(out_data, \"^EMPTY^\", metadata)\n return out_data\n\n return None","def readInput(fileName):\n with open(fileName, 'r') as file:\n\n plotArray = []\n for line in file:\n plotArray.append(list(line.strip()))\n\n return plotArray","def parsePoint(line):\n parts = line.split(\",\")\n return LabeledPoint(parts[0], [parts[1], parts[2]])","def read_line(l):\n return [read_float(l[s]) for s in slices['data']]","def parse(self, data):\n raise NotImplementedError","def parse_line(self, line: str) -> None:\n self._count += 1","def parse_datum( self, data ):\n return data","def parse_datum( self, data ):\n return data","def plot(self, plotType):\n # Build plotting data\n self.data_x_axis = []\n self.data_y_axis = []\n for i in range(0, self.csv_data_table.rowCount()):\n value = self.csv_data_table.item(i, self.selected_columns[0]).text()\n self.data_x_axis.append(value)\n value = self.csv_data_table.item(i, self.selected_columns[1]).text()\n self.data_y_axis.append(value)\n\n self.label_x_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[0]).text()\n self.label_y_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[1]).text()\n\n # Avoid duplication of resources if already allocated\n if self.figure is None:\n self.figure = plt.figure()\n self.canvas = FigureCanvas(self.figure)\n\n # self.plot_frame_horizontal.addStretch()\n self.plot_frame_horizontal.addWidget(self.canvas)\n # self.plot_frame_horizontal.addStretch()\n\n # Ensures only 2 tabs at max are open at a time - file and plot tabs respectively\n if self.tabWidget.count() == 1:\n self.tabWidget.insertTab(1, self.plot_page_tab, \"Plot\")\n\n self.tabWidget.setCurrentIndex(1)\n\n self.plotType = plotType\n\n try:\n for i in range(0, len(self.data_x_axis)):\n if self.data_x_axis[i] == '':\n self.data_x_axis[i] = 0\n if self.data_y_axis[i] == '':\n self.data_y_axis[i] = 0\n\n self.data_x_axis[i] = self.strToNumber(self.data_x_axis[i])\n self.data_y_axis[i] = self.strToNumber(self.data_y_axis[i])\n\n self.data_x_axis = np.array(self.data_x_axis)\n self.data_y_axis = np.array(self.data_y_axis)\n\n print(self.data_x_axis)\n print(self.data_y_axis)\n\n except:\n pass\n print(\"In generic plotting\")\n\n self.drawPlot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)","def _find_first_data_point(self, lines):\r\n\r\n for i in range(len(lines)):\r\n if lines[i][0] in ['+', '-']:\r\n return i\r\n\r\n raise DataFormatError(\"No data found in file. Check data format spec.\")","def parse_line(data, line, keyword):\n if '[' in line:\n pos = re.search(r'\\[(.*)\\]', line).group(1).split(':')\n pos = sorted([int(i.strip()) for i in pos])\n for i in range(pos[0], pos[1]):\n for j in re.search(r'\\](.*);', line).group(1).split(','):\n data[keyword].append(j.strip() + f'[{i}]')\n else:\n for i in re.search(rf'{keyword}(.*);', line).group(1).split(','):\n data[keyword].append(i.strip())","def cli_text_scatter_plot(sep, graph_width, graph_height, input_file):\n values = []\n for line in input_file:\n try:\n x, y = line.split(sep)\n values.append((float(x), float(y)))\n except ValueError:\n click.echo(f'Warning: \"{line.strip()}\" could not be parsed', err=True)\n\n click.echo(text_scatter_plot(\n values, graph_width=graph_width, graph_height=graph_height\n ))","def Parse(filename):\n\n f = open(filename, 'r')\n\n metadata = Metadata()\n data = [] # array of dataset\n dataset = None\n\n for num, line in enumerate(f):\n try:\n line = line.strip()\n if not line: continue\n\n if not metadata.complete:\n metadata.Parse(line)\n continue\n\n if re.match('[a-z_]', line):\n continue\n\n if line.startswith('# StopWatch'): # Start of a new dataset\n if dataset:\n if dataset.summary:\n metadata.UpdateWith(dataset)\n else:\n data.append(dataset)\n\n dataset = DataSet(line)\n continue\n\n if line.startswith('#'):\n continue\n\n # must be data at this stage\n try:\n (time, value) = line.split(None, 1)\n except ValueError:\n print 'skipping line %d: %s' % (num, line)\n continue\n\n if dataset and not dataset.summary:\n dataset.Add(float(time), float(value))\n\n except Exception:\n print 'Error parsing line %d' % num, sys.exc_info()[0]\n raise\n data.append(dataset)\n if not metadata.complete:\n print \"\"\"Error missing metadata. Did you mount debugfs?\n [adb shell mount -t debugfs none /sys/kernel/debug]\"\"\"\n sys.exit(1)\n return (metadata, data)","def parse(self):\n try:\n self.open_file()\n lines = list(self._file)\n\n if len(lines) > 0:\n text = ''.join(lines)\n regex = 'Song \\d+\\nStart (\\d+:\\d+:\\d+)\\nEnd (\\d+:\\d+:\\d+)\\nLength (\\d+.\\d+)'\n match = re.findall(regex, text)\n if len(match):\n starts = []\n ends = []\n lengths = []\n\n for i in range(len(match)):\n starts.append(match[i][0])\n ends.append(match[i][1])\n lengths.append(float(match[i][2]))\n\n for i in range(len(match)):\n self.debug_data.append({\n 'start':starts[i],'end':ends[i],'length':lengths[i]})\n\n match = re.search('T\\d_S(\\d{4})_.*.txt', self._filepath)\n if match:\n self._experiment_metadata['session_id'] = int(match.groups()[0])\n else:\n raise EIMParsingError(\"No valid session id found in filename %s\" % self._filepath)\n\n finally:\n if self._file and not self._file.closed:\n self.close_file()","def parseFileLine(self, line):\n c = line.strip().split(\":\")\n return (c[0], c[1], c[2], c[3])","def parse(self, f):\n \n for line in f:\n self.parse_line(line)","def logplot(in_dir, fname, xlim, ylim, title):\n\n with open(in_dir + fname,'r') as logfile:\n lf_lines = logfile.readlines()\n\n traj_x = []\n traj_y = []\n\n for row in lf_lines:\n if row[:4] == 'pose':\n #print(float(row[10:-2]))\n tup = row[7:]\n sep_pos = tup.find(' , ')\n traj_x.append(float(tup[:sep_pos]))\n traj_y.append(float(tup[sep_pos+3:]))\n\n liveplot(traj_x, traj_y, xlim, ylim, title)","def parse_line(line : str) -> Tuple[str, str, int]:\n line = line[:-1] # Strips newline character\n question_end = line.find(';')\n question = line[:question_end]\n\n line = line[question_end+1:]\n answer_end = line.find(';')\n answer = line[:answer_end]\n\n points = int(line[answer_end+1:])\n\n return question, answer, points","def parse_movie(self, line):\n pass","def _parse_data(self):\n for i, val in enumerate(self.values.keys()):\n x_, y_ = [], []\n xy = self.values[val]\n for value in self.values.index:\n x_.append(xy[value][0])\n y_.append(xy[value][1])\n\n self.set_and_get(\"x_\", val, x_)\n self.set_and_get(\"y_\", val, y_)","def retrieve_plot_data(self):\n spec2nexus.specplot.LinePlotter.retrieve_plot_data(self)\n\n if self.signal in self.data:\n # can't plot negative Y on log scale\n # Alternative to raising NotPlottable would be\n # to remove any data where Y <= 0\n if min(self.data[self.signal]) <= 0:\n msg = \"cannot plot Y<0: \" + str(self.scan)\n raise spec2nexus.specplot.NotPlottable(msg)\n\n # in the uascan, a name for the sample is given in `self.scan.comments[0]`\n self.set_y_log(True)\n self.set_plot_subtitle(\n \"#%s uascan: %s\" % (str(self.scan.scanNum), self.scan.comments[0])\n )","def parse_new_mwdata_line(L):\n data = L.split()\n if len(data) != len(column_names['mwdata']):\n print(\"mwdata line {} has only {} fields, skipping\".format(L, len(data)))\n return\n label, record = parse_line_label_cols(L)\n\n def decode_col(col, decoder): # use for columns which may have '?'\n return None if col in ['?', 'None'] else decoder(col)\n\n record['rank'] = decode_col(data[4], int)\n record['rank_bounds'] = decode_col(data[5], decode_int_list)\n record['analytic_rank'] = decode_col(data[6], int)\n record['ngens'] = int(data[7])\n record['gens'] = decode_points_one2many(data[8])\n record['heights'] = data[9]\n #record['reg'] = decode_col(data[10], RealNumber) if record['ngens'] else 1\n record['reg'] = data[10] if record['ngens'] else 1\n record['torsion_order'] = nt = int(data[11])\n record['torsion_primes'] = ZZ(nt).prime_divisors()\n record['torsion_structure'] = decode_int_list(data[12])\n record['torsion_gens'] = decode_points_one2many(data[13])\n if len(data) == 17:\n #record['omega'] = decode_col(data[14], RealNumber)\n #record['Lvalue'] = decode_col(data[15], RealNumber)\n record['sha'] = decode_col(data[16], int)\n record['omega'] = data[14]\n record['Lvalue'] = data[15]\n else:\n record['omega'] = None\n record['Lvalue'] = None\n record['sha'] = None\n return label, record","def tag_parser(file_path: str):\n with open(file_path) as f:\n t = f.read()\n t = t.split(\"Points =\\n\")[1]\n t = t.replace(\" 0.1 1 1 \\\"Marker\\\"\", \"\")\n t = t.replace(\";\", \"\")\n t = t.replace(\" \\n\", \"\\n\")\n t = t[1:]\n t = StringIO(t)\n\n return np.genfromtxt(t, delimiter=' ')","def parse_mwdata_line(L):\n data = L.split()\n # if len(data)!=14:\n # print(\"mwdata line {} does not have 14 fields, skipping\".format(L))\n # return\n label, record = parse_line_label_cols(L)\n\n r = data[4]\n record['rank'] = None if r == '?' else int(r)\n r = data[5]\n record['rank_bounds'] = '?' if r == '?' else [int(rb) for rb in r[1:-1].split(\",\")]\n r = data[6]\n record['analytic_rank'] = None if r == '?' else int(r)\n record['ngens'] = int(data[7])\n gens = data[8]\n record['gens'] = [] if gens == '[]' else gens.replace(\"[[[\", \"[[\").replace(\"]]]\", \"]]\").replace(\"]],[[\", \"]];[[\").split(\";\")\n record['heights'] = data[9]\n record['reg'] = data[10]\n record['torsion_order'] = nt = int(data[11])\n ts = data[12]\n record['torsion_structure'] = [] if ts == '[]' else [int(t) for t in ts[1:-1].split(\",\")]\n record['torsion_primes'] = ZZ(nt).prime_divisors()\n record['torsion_gens'] = decode_points_one2many(data[13])\n\n record['omega'] = None\n record['Lvalue'] = None\n record['sha'] = None\n\n return label, record","def _parse_line(line):\n\n number_pattern = '(\\d+(?:\\.\\d+)?)'\n line_pattern = '^\\s+%s\\s+$' % ('\\s+'.join([number_pattern for x in range(10)]))\n\n match = re.match(line_pattern, line)\n if match:\n print(match.groups())\n return match.groups()\n # if there are no matches\n return None","def parse_line(self, line, time_shift=0.0):\n raise NotImplementedError(\"must be defined by subclass\")","def datareader(self, path):\n\n f = open(path, 'r')\n data = f.read()\n data = data.split('\\n')\n data_tmp = []\n for idx in range(len(data)):\n if str(data[idx]).find('@data') >= 0:\n data_tmp = data[idx + 1:]\n break\n res = []\n for record in data_tmp:\n record = record.split(',')\n record = map(float, record)\n res.append(record)\n return res","def loadLine(self, line):\n self.text.append(nlp(line[0]))\n self.full_text = self.full_text + \" \" + line[0]\n self.rec_paths.append(line[1])\n with contextlib.closing(wave.open(self.rec_paths[-1],'r')) as f:\n frames = f.getnframes()\n rate = f.getframerate()\n self.durations.append(frames / float(rate))\n\n # self.media.append(self.instance.media_new(self.rec_paths[-1]))\n self.total_duration = self.calculateTotalDuration(self.durations)","def ParseWeatherData(parrData):\n ClearDisplay()\n for i in range(0, len(parrData)):\n if i < (len(parrData) - 1):\n DisplayMsg(parrData[i], int(i * 8))\n else:\n FinalLine = parrData[i].split(\": \")\n DisplayMsg(FinalLine[0], 40)\n DisplayMsg('{:^16}'.format(FinalLine[1]), 48)\n DrawHLine(Width, 37)\n display.show()","def _analyzeLine(self, line, date, data):\n user = re.search(': (.*)', line).group().split()[1]\n data[user].add(ConvertTime(date, line))","def parse_file(line, position):\n movement = line.split(\" \")\n if movement[0] == \"SUS\":\n position[\"x\"] += float(movement[1])\n elif movement[0] == \"JOS\":\n position[\"x\"] -= float(movement[1])\n elif movement[0] == \"STANGA\":\n position[\"y\"] -= float(movement[1])\n elif movement[0] == \"DREAPTA\":\n position[\"y\"] += float(movement[1])\n else:\n print(\"Incorrect input\")","def parse(self):\r\n # open the file\r\n # try to find the first line with Time =\r\n # send the file to a recursive residualParser\r\n try:\r\n with open(self.filepath, 'rb') as f:\r\n for line in f:\r\n if line.startswith('Time ='):\r\n self.timestep = self.__getTime(line)\r\n self.__residuals[self.timestep] = {}\r\n self.__parseResiduals(f)\r\n except Exception as e:\r\n raise 'Failed to parse {}:\\n\\t{}'.format(self.filepath, e)","def read_text(self, text):\n if isinstance(text, (str, unicode)):\n lines = text.split('\\n')\n else:\n lines = text\n\n for line in lines:\n l = line.strip()\n\n if line == ST_POS0:\n self._state = ST_POS0\n elif line == ST_TRNS:\n self._state = ST_TRNS\n elif line == ST_POS0:\n self._state = ST_POS0\n else:\n self._parse_line(line)","def parse_position_line(line):\n\n match = Response.regex_position.search(line)\n if match is not None:\n result = dict(\n x=float(match.group(\"x\")),\n y=float(match.group(\"y\")),\n z=float(match.group(\"z\")),\n )\n if match.group(\"e\") is not None:\n # report contains only one E\n result[\"e\"] = float(match.group(\"e\"))\n\n elif match.group(\"es\") is not None:\n # report contains individual entries for multiple extruders (\"E0:... E1:... E2:...\")\n es = match.group(\"es\")\n for m in Response.regex_e_positions.finditer(es):\n result[\"e{}\".format(m.group(\"id\"))] = float(m.group(\"value\"))\n\n else:\n # apparently no E at all, should never happen but let's still handle this\n return None\n\n return result\n\n return None","def _parse_coords(self):\n\n coords = []\n\n while True:\n try:\n _, x, y = self._lines.current.split()\n coords.append((float(x), float(y)))\n except ValueError:\n break\n\n try:\n next(self._lines)\n except StopIteration:\n break\n\n return coords","def _build_data_from_text(self, text):\n try:\n record = json.loads(text)\n except Exception as e:\n logging.error(f\"Exception: {e}\")\n logging.error(f\"datapoint: {text}\")\n raise e\n return record","def parse_line(self, line):\n\n kv_match = self.kv_rex.match(line)\n\n if kv_match:\n kv_dict = kv_match.groupdict()\n kv_key = kv_dict['key']\n kv_value = kv_dict['value']\n\n if 'time_' in kv_key:\n kv_unit = kv_dict['unit']\n\n if not kv_key in self.kv_times:\n self.kv_times[kv_key] = {'unit': kv_unit, 'values': []}\n\n self.kv_times[kv_key]['values'].append(float(kv_value))\n else:\n if not kv_key in self.kv_counts:\n self.kv_counts[kv_key] = 0.0\n\n self.kv_counts[kv_key] += float(kv_value)","def line_to_data( line ):\n data = [ ]\n if '>>>' in line:\n print(line)\n return data\n\n secs = filter( None, line.split(',') )\n for i, x in enumerate( secs ):\n try:\n data.append( float(x.strip()) )\n except Exception as e:\n data = None\n # print( data )\n return data","def populate_plot(self, plot, data):\n\n # Determine which type of line gets which color\n color_map = {\n 'REF': Category20c_20[16],\n 'REF1': Category20c_20[16],\n 'REF2': Category20c_20[16],\n 'REF3': Category20c_20[16],\n 'REF4': Category20c_20[16],\n 'SCRIBE_LINE': Category20c_20[0],\n 'SCRIBE_LINE1': Category20c_20[0],\n 'SCRIBE_LINE2': Category20c_20[1],\n 'SCRIBE_LINE3': Category20c_20[2],\n 'SCRIBE_LINE4': Category20c_20[3],\n 'BUSBAR_LINE': Category20c_20[4],\n 'BUSBAR_LINE1': Category20c_20[4],\n 'BUSBAR_LINE2': Category20c_20[5],\n 'BUSBAR_LINE3': Category20c_20[6],\n 'BUSBAR_LINE4': Category20c_20[7],\n 'EDGEDEL_LINE': Category20c_20[8],\n 'EDGEDEL_LINE1': Category20c_20[8],\n 'EDGEDEL_LINE2': Category20c_20[9],\n 'EDGEDEL_LINE3': Category20c_20[10],\n 'EDGEDEL_LINE4': Category20c_20[11]\n }\n\n # Color of the non cutting line\n radius = 13\n line_width = 3\n\n scatter_points = {}\n for line in data:\n group_name = line.get_line_type() + line.get_recipe()\n sp = line.get_starting_point()\n ep = line.get_endpoint()\n\n # Sort scatter points\n if group_name not in scatter_points:\n scatter_points[group_name] = {\n 'x': [sp[0], ep[0]],\n 'y': [sp[1], ep[1]]\n }\n else:\n scatter_points[group_name]['x'].append(sp[0])\n scatter_points[group_name]['x'].append(ep[0])\n scatter_points[group_name]['y'].append(sp[1])\n scatter_points[group_name]['y'].append(ep[1])\n\n # Cutting line\n plot.line(\n [sp[0], ep[0]],\n [sp[1], ep[1]],\n color=color_map[group_name],\n line_width=line_width\n )\n\n # Add a scatter plot for every group\n for group_name, group in scatter_points.items():\n plot.scatter(\n group['x'],\n group['y'],\n color=color_map[group_name],\n radius=radius,\n legend=group_name\n )\n\n # Add travel lines\n for line in range(len(data) - 1):\n # Get the endpoint of the current line, as well as the starting\n # point of the next line\n ep0 = data[line].get_endpoint()\n sp1 = data[line + 1].get_starting_point()\n\n # Plot the travel line (non-cutting line)\n plot.line(\n [\n ep0[0],\n sp1[0]],\n [\n ep0[1],\n sp1[1]\n ],\n color='black',\n legend='Non Cutting'\n )\n\n return plot","def isDataLine(line):\n if len(line) > 1:\n return line[0] != \"#\"\n return False","def isDataLine(line):\n if len(line) > 1:\n return line[0] != \"#\"\n return False","def _parse(self, line):\n comd, value = cmd.parse(line, SERVER_PREFIX)\n player = self.player\n if comd == 'play':\n audio, seek = cmd.separate(value)\n player.playFile(audio, seek=float(seek))\n newline = 'Player started playing %s' %(value)\n elif comd == 'seek':\n value = float(value)\n player.seekto(value)\n newline = 'Forwared to %d seconds' %(value)\n elif comd == 'pause':\n player.pause()\n newline = 'Player Paused'\n elif comd == 'volume':\n value = float(value)\n player.volume = value\n newline = 'Volume changed to %d' %(value)\n else:\n return line\n return newline","def read_text(filename):\n with open(filename, 'r') as f:\n com = f.readline()[0]\n wavelength, flux = np.loadtxt(filename, unpack=True,\n usecols=(0, 1), comments=com)\n return wavelength, flux","def __parse(self):\n lines = self.file.readlines()\n for i in range(0, len(lines)):\n line = lines[i]\n tokens = line.split()\n if tokens[0] == \"#start\":\n trial_name = tokens[1]\n trial = Trial(trial_name)\n self.trials[trial_name] = trial\n elif tokens[0] == \"#end\":\n continue\n else:\n date_str = tokens[0] + \" \" + tokens[1]\n date = datetime.strptime(date_str, \"%m/%d/%y %H:%M:%S\")\n sound_file = line[18:-1].strip()\n event = Event(date, sound_file, 0)\n trial.addevent(event)","def parse_pdb(self, line):\n if line is not None:\n self.original_text.append(line.rstrip(\"\\r\\n\"))","def make_line_plot(data, x_label=\"Data\", y_label=\"Data Point\"):\n\n y = data\n x = range(len(y))\n\n plt.xlabel(x_label)\n plt.ylabel(y_label)\n plt.plot(x, y)\n plt.show()","def parse(cls, data):\n raise NotImplementedError","def parse_galrep_line(L):\n data = L.split(maxsplit=1)\n record = parse_galrep_data_string(\"\" if len(data) == 1 else data[1])\n record['label'] = label = data[0]\n return label, record","def function2():\r\n with open('data.txt', 'r') as file:\r\n read_data = file.read()\r\n data = read_data.split()\r\n line_chart = pygal.Line()\r\n line_chart.title = data[26]\r\n line_chart.x_labels = map(str, range(2554, 2558))\r\n line_chart.add(data[27], [int(data[28]), int(data[29]), int(data[30]), int(data[31])])\r\n line_chart.add(data[32], [int(data[33]), int(data[34]), int(data[35]), int(data[36])])\r\n line_chart.add(data[37], [int(data[38]), int(data[38]), int(data[40]), int(data[41])])\r\n line_chart.add(data[42], [int(data[43]), int(data[44]), int(data[45]), int(data[46])])\r\n line_chart.add(data[47], [int(data[48]), int(data[49]), int(data[50]), int(data[51])])\r\n line_chart.render_to_file('02.svg')","def parse_txt_file(txtfile):\n array = np.genfromtxt(txtfile)\n return array","def parse_lines(self, start_line=0, end_line=False):\n if end_line is False: end_line = len(self.file_ltxt)\n\n lines = self.file_ltxt\n self.E_str = \"parse_lines\"\n self.line_num = start_line\n\n # Loop over lines and parse\n while self.line_num < end_line:\n line = lines[self.line_num].strip()\n\n if line == \"echo\": print(\"\")\n\n # Parse any variables\n elif self.line_declarations['variable'](line):\n self.parse_variable_line(line)\n\n # Parse any file loading commands\n elif self.line_declarations['load'](line):\n self.parse_load_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['plot'](line):\n self.parse_plot_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['write'](line):\n self.parse_write_cmd(line)\n\n # Parse any math commands\n elif self.line_declarations['math'](line):\n self.parse_math_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['echo'](line):\n self.parse_echo_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['calc'](line):\n self.parse_calc_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['set'](line):\n self.parse_set_cmd(line)\n\n # Parse any shell commands\n elif self.line_declarations['shell'](line):\n self.parse_shell_cmd()\n\n # Parse any for loop commands\n elif self.line_declarations['for'](line):\n self.parse_for_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['script'](line):\n self.parse_script_cmd(line)\n\n elif self.line_declarations['inline_code'](line):\n getattr(self, f\"parse_{line.split()[0]}_cmd\")(line)\n\n elif self.line_declarations['if'](line):\n self.parse_if_cmd(line)\n\n # elif self.line_declarations['splice'](line):\n # self.parse_splice_cmd(line)\n\n elif self.line_declarations['glue'](line):\n self.parse_glue_cmd(line)\n\n elif self.line_declarations['exit'](line):\n print(\"\\n\\nStopped Code -exit was called.\")\n raise SystemExit\n\n # The end of control statements\n elif '}' in line:\n pass\n\n # Print a warning about unknown line\n else:\n self.print_warning(f\"I don't understand a line: '{line}'\")\n\n self.line_num += 1","def get_data(dataf):\n with open(dataf) as f:\n label = []\n e_val = []\n for line in f:\n label.append(float(line.split()[1]))\n e_val.append(-1 * float(line.split()[0]))\n return label, e_val","def parse_line(self, atline: List, list_of_lines: List, part: PART, afix: AFIX, resi: RESI) -> None:\n uvals = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n self.name = atline[0][:4] # Atom names are limited to 4 characters\n for n, u in enumerate(atline[6:12]):\n uvals[n] = float(u)\n self.uvals_orig = uvals[:]\n self.set_uvals(uvals)\n self._line_numbers = list_of_lines\n self.part = part\n self.afix = afix\n self.resi = resi\n self._get_part_and_occupation(atline)\n self.x, self.y, self.z = self._get_atom_coordinates(atline)\n self.xc, self.yc, self.zc = self._cell.o * Array(self.frac_coords)\n if abs(self.uvals[1]) > 0.0 and self.uvals[2] == 0.0 and self.shx.hklf: # qpeaks are always behind hklf\n self.peak_height = uvals[1]\n self.qpeak = True\n if self.shx.end: # After 'END' can only be Q-peaks!\n self.qpeak = True\n self.sfac_num = int(atline[1])\n self.shx.fvars.set_fvar_usage(self.fvar)\n self.Ucif = self.set_ucif(uvals)\n # TODO: I am still unsure if this these are correct values:\n # self.Ustar = self.Ucif * self._cell.N * self._cell.N.T\n # self.Ucart = self.Ustar * self._cell.o * self._cell.o.T\n # self.Ueq = self.set_ueq(uvals)\n # self.Uiso = self.Ueq\n # transformed_u = self.transform_u_by_symmetry(2)\n # print(self.name, [round(x, 6) for x in transformed_u], self.frac_coords)","def parse_relation(self, text_line):\n record_type = self.substr(text_line, sps21point['RECORD_ID'][0], sps21point['RECORD_ID'][1]).strip()\n if record_type not in REL_DATA_RECORD:\n return\n\n self.set_definition(sps21relation)\n return self.parse(text_line)","def read_data(self,data_array):\n value_type = self.config['value_type']\n try:\n curr_value = data_array[self.index]\n except:\n curr_value = 'None'\n \n #if value is none\n if curr_value.strip() == 'None':\n #break line\n curr_value = np.nan\n else:\n value_type = self.str_to_command(value_type)\n curr_value = value_type(curr_value)\n\n # TODO: Read time and not log id\n try:\n time = int(data_array[0])\n except:\n time = self.x[-1] + 1\n\n self.x = np.append(self.x,time)\n self.y = np.append(self.y,curr_value)\n\n if(self.auto_scale and len(self.x) > self.config[\"limit\"]):\n #delete old values\n self.x = np.delete(self.x, 0)\n self.y = np.delete(self.y, 0)\n\n self.set_fig_color(curr_value)","def parse_data(name):\n with open(name) as f:\n lines = f.read().splitlines()\n lines = filter(lambda x: x.split(' ')[0].isdigit(), lines)\n lx = [int(p.split(' ')[1]) for p in lines]\n ly = [int(p.split(' ')[2]) for p in lines]\n return lx, ly","def parser_txt_file(self, content):\n ai_cpu_str = str(content.replace(b'\\n\\x00', b' ___ ').replace(b'\\x00', b' ___ '))[2:-1]\n ai_cpu_lines = ai_cpu_str.split(\" ___ \")\n result_list = list()\n ai_cpu_total_time_summary = 0\n # Node serial number.\n serial_number = 1\n for i in range(len(ai_cpu_lines) - 1):\n node_line = ai_cpu_lines[i]\n thread_line = ai_cpu_lines[i + 1]\n if \"Node\" in node_line and \"Thread\" in thread_line:\n # Get the node data from node_line\n result = self._get_kernel_result(\n serial_number,\n node_line.split(','),\n thread_line.split(',')\n )\n\n if result is None:\n continue\n\n result_list.append(result)\n # Calculate the total time.\n total_time = result[2]\n ai_cpu_total_time_summary += total_time\n # Increase node serial number.\n serial_number += 1\n elif \"Node\" in node_line and \"Thread\" not in thread_line:\n node_type_name = node_line.split(',')[0].split(':')[-1]\n logger.warning(\"The node type:%s cannot find thread data\", node_type_name)\n return ai_cpu_total_time_summary, result_list","def parse_feature(self, feature_key, lines):\n ...","def doomed_parser(line):\n raise exceptions.LineParseException('Error occurred')","def readlogfile(file_name):\n F = open(file_name, \"r\")\n M = F.readlines()\n F.close()\n x = []\n y = []\n \n for i in range(len(M)):\n if find(M[i], \"#\") <0:\n # Since the x,y are recorded as, e.g., '[398:402,300:304]'\n x.append(float(M[i].split('[')[1].split(':')[0]) + 2.0)\n y.append(float(M[i].split(',')[1].split(':')[0]) + 2.0)\n \n # Notice this function allows multiple stars to be chosen. For our purposes here,\n # we need pick only one, and so return the first values in the list.\n return x[0], y[0]","def load_regular_coord_by_line(line):\n elems = line.split('\\t')\n if len(elems) < 4:\n elems = line.split(',')\n if len(elems) < 4:\n elems = line.split(' ')\n\n [X1, Y1, W, H] = elems[0:4]\n coord_regular = [int(X1), int(Y1), int(W), int(H)]\n return coord_regular","def parse(self):\n\t\tfirst = None\n\t\tf = open(self.input_file)\n\t\tfor line in f.readlines():\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tcontinue\n\t\t\ttry:\n\t\t\t\tflow,t,sequence,size = line.split()\n\t\t\texcept:\n\t\t\t\tcontinue\n\t\t\t# append data to a list of tuples\n\t\t\tflow = int(flow)\n\t\t\tt = float(t)\n\t\t\tsequence = int(sequence)\n\t\t\tif size == \"x\":\n\t\t\t\tcontinue\n\t\t\tsize = int(size)\n\t\t\tif not size == 0:\n\t\t\t\tif flow == 1:\n\t\t\t\t\tself.data1.append((t,sequence,size))\n\t\t\t\telif flow == 2:\n\t\t\t\t\tself.data2.append((t,sequence,size))\n\t\t\t\telif flow == 3:\n\t\t\t\t\tself.data3.append((t, sequence, size))\n\t\t\t\telif flow == 4:\n\t\t\t\t\tself.data4.append((t, sequence, size))\n\t\t\t\telif flow == 5:\n\t\t\t\t\tself.data5.append((t, sequence, size))\n\t\t\t\telse:\n\t\t\t\t\tprint \"Erroneous data: \",flow, t, sequence, size\n\t\t\t# Keep track of the minimum and maximum time seen\n\t\t\tif not self.min_time or t < self.min_time:\n\t\t\t\tself.min_time = t\n\t\t\tif not self.max_time or t > self.max_time:\n\t\t\t\tself.max_time = t\n\n\t\t\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)"],"string":"[\n \"def parse_plot_cmd(self, line):\\n line, any_vars = self.find_vars_in_str(line)\\n words = line.split()\\n words = self.fix_words(words)\\n\\n # Parse line\\n has_out_var = False\\n if len(words) == 6:\\n has_out_var = True\\n _, plot_type, _, in_data, _, out_data = words\\n else: _, plot_type, _, in_data = words\\n\\n in_data = getattr(self, in_data)\\n plot_fnc = f_dicts.plot_fncs[plot_type]\\n\\n if has_out_var:\\n OutVar = plot_fnc(in_data)\\n else: plot_fnc(in_data)\\n\\n if has_out_var:\\n self.set_var(out_data,\\n {plot_type: OutVar}, {})\",\n \"def parse_point(self, text_line):\\n record_type = self.substr(text_line, sps21point['RECORD_ID'][0], sps21point['RECORD_ID'][1]).strip()\\n if record_type not in (SRC_DATA_RECORD, RCV_DATA_RECORD):\\n return\\n self.set_definition(sps21point)\\n return self.parse(text_line)\",\n \"def read_line(line):\\n label = line[0:11]\\n text = line[11:]\\n y = 1 if label == '__label__2 ' else 0\\n return text, y\",\n \"def parse_line(self, line):\\n raise NotImplementedError\",\n \"def parse_line(self, line):\\n success = self.parser.handle_line(line)\\n if success:\\n self.data.update()\\n else:\\n self.bot.log(\\\"didn't handle line: '{}'\\\".format(line))\",\n \"def _parseLine(self, line, delimiter = \\\":\\\"):\\r\\n\\t\\tsplt = line.split(delimiter)\\r\\n\\t\\tinVec = self._parseVec(splt[0])\\r\\n\\t\\toutVec = self._parseVec(splt[1])\\r\\n\\t\\tif (len(splt) == 2):\\r\\n\\t\\t\\tlabel = \\\"\\\"\\r\\n\\t\\telse:\\r\\n\\t\\t\\tlabel = splt[2]\\r\\n\\t\\tself.data.append({'in':inVec, 'out':outVec, 'label':label})\",\n \"def parse_data(fp):\\n pass\",\n \"def parse(cls, line):\\r\\n raise NotImplementedError\",\n \"def parse_line(line):\\n return parse('#{id_:d} @ {x:d},{y:d}: {w:d}x{h:d}', line)\",\n \"def parseLine(self, line):\\n # Removes surrounding whitespace\\n line = self.separateElements(line)\\n if len(line) == 0: return\\n # Checks if the line is a label declaration\\n if line[0].lower() == \\\"label\\\":\\n # --Validates the line\\n if len(line) != 2: raise Exception(\\\"Invalid Label\\\")\\n if len(line[1]) < 2: raise Exception(\\\"Invalid Label\\\") \\n if line[1][-1] != ':': raise Exception(\\\"Invalid Label\\\")\\n # Gets the label name\\n labelName = line[1][:-1]\\n\\n # Creates a new symbol entry for the label, the pointer refers to the memory location of the label\\n # It defaults to the location of the label in the instruction sequence\\n self.symbolTable.append({ \\\"type\\\": \\\"LABEL\\\", \\\"name\\\": labelName, \\\"pointer\\\": len(self.instructionList) * 4})\\n # Checks if the line is data declaration\\n elif line[0].lower() == \\\"data\\\" or (line[0].lower()[:4] == \\\"data\\\" and line[0][4] == \\\"[\\\"):\\n # Removes the DATA tag from the data\\n line[0] = line[0][4:]\\n # --Validates the line\\n if len(line) < 2: raise Exception(\\\"Invalid DATA\\\")\\n # Gets the data name\\n dataName = line[1]\\n # Stores the data length\\n dataLength = 4 # A word\\n # Gets any default data\\n defaultData = 0\\n # Stores the data type\\n dataType = \\\"int\\\"\\n if len(line) == 3:\\n if line[2][0] == \\\"\\\\\\\"\\\" and line[2][-1] == \\\"\\\\\\\"\\\":\\n dataType = \\\"string\\\"\\n defaultData = line[2][1:-1]\\n dataLength = len(defaultData)\\n elif line[2].isnumeric():\\n defaultData = line[2]\\n elif line[2][-1] == 'f' and line[2][:-1].isnumeric():\\n dataType = \\\"float\\\"\\n defaultData = line[2][0]\\n # Checks if a data length was given\\n if len(line[0]) > 2 and (line[0][0] == \\\"[\\\" and line[0][-1] == \\\"]\\\"):\\n data = line[0][1:-1]\\n if not data.isnumeric(): raise TypeError(\\\"Invalid data length type\\\")\\n dataLength = int(data)\\n\\n # Creates a new symbol entry for the data\\n self.symbolTable.append({ \\\"type\\\": \\\"DATA\\\", \\\"name\\\": dataName, \\\"default\\\": defaultData, \\\"dataType\\\": dataType, \\\"length\\\": dataLength})\\n # The line is most likely an instruction\\n else:\\n # --Validates the line\\n #Stores the control bits\\n controlBits = 1 << 5 # Sets it to 0b100000\\n # Checks if the first element is control bits\\n if line[0][0] == \\\"{\\\" and line[0][-1] == \\\"}\\\": # First element is control bits\\n # Separates the two sections of the control bits\\n controlSections = line[0].split(':')\\n #Goes through the characters and constructs the control bits for the instruction\\n carryBits = controlSections[0].lower()\\n carryFlag = int('c' in carryBits)\\n zeroFlag = int('z' in carryBits)\\n negativeFlag = int('n' in carryBits)\\n signedOverflowFlag = int('s' in carryBits)\\n #Gets the conditions bits\\n if len(controlSections) == 2:\\n conditionBits = controlSections[1].lower()\\n isAnd = int('x' in conditionBits)\\n isOne = int('1' in conditionBits)\\n #Sets the last two bits on controlBits to the conditionBits\\n controlBits ^= isAnd << 1\\n controlBits ^= isOne\\n # Constructs the control bits section\\n controlBits ^= carryFlag << 5\\n controlBits ^= zeroFlag << 4\\n controlBits ^= negativeFlag << 3\\n controlBits ^= signedOverflowFlag << 2\\n # Removes the control bits section from the line\\n line.pop(0)\\n # Performs this check as the controlbits element gets removed (if it existed) and so the length of the elments could be zerp\\n if len(line) == 0: raise Exception(\\\"Invalid Instruction\\\")\\n # --The first element is the instruction\\n # Identifies the instruction from the mnemonic using the lookup table\\n if line[0] in self.InstructionLookupTable:\\n ins = self.InstructionLookupTable[line[0]]\\n insCode = ins[\\\"code\\\"]\\n insControlBits = ins['controlBits'] if ins['controlBits'] else controlBits\\n # Creates a representation of the instruction, this is stored in the instructionList and is assembled later\\n instrucitonRepr = {\\n \\\"code\\\": insCode,\\n \\\"controlBits\\\": insControlBits,\\n }\\n # Parses the arguments given and stores the operandStruct returned in the instruciton representation\\n if len(line) > 1: instrucitonRepr[\\\"operand\\\"] = self.parseArgs(line[1:], insCode)\\n self.instructionList.append(instrucitonRepr)\",\n \"def line_parser(path):\\n lines = []\\n with open(path, 'r') as input:\\n lines = [line.rstrip().split(',') for line in input]\\n lines = [\\n [[float(x1), float(y1)],\\n [float(x2), float(y2)]] \\n for x1, y1, x2, y2 in lines]\\n return lines\",\n \"def parse_string_line(self, data_line):\\n if data_line:\\n data_line = data_line.rstrip()\\n if data_line:\\n if data_line[0] == '#':\\n extraparams = json.loads(data_line[1:])\\n if 'glyph_cap_line' in extraparams:\\n self.__capline = extraparams['glyph_cap_line']\\n if 'glyph_base_line' in extraparams:\\n self.__baseline = extraparams['glyph_base_line']\\n if 'glyph_bottom_line' in extraparams:\\n self.__bottomline = extraparams['glyph_bottom_line']\\n elif len(data_line) > 9:\\n strokes = []\\n xmin = xmax = ymin = ymax = None\\n # individual strokes are stored separated by <space>+R\\n # starting at col 11\\n for s in split(data_line[10:], ' R'):\\n if len(s):\\n stroke = list(zip(map(self.__char2val, s[::2]), map(self.__char2val, s[1::2])))\\n xmin = min(stroke + ([xmin] if xmin else []), key=lambda t: t[0])\\n ymin = min(stroke + ([ymin] if ymin else []), key=lambda t: t[1])\\n xmax = max(stroke + ([xmax] if xmax else []), key=lambda t: t[0])\\n ymax = max(stroke + ([ymax] if ymax else []), key=lambda t: t[1])\\n strokes.append(stroke)\\n self.__charcode = int(data_line[0:5])\\n self.__left_side = self.__char2val(data_line[8])\\n self.__right_side = self.__char2val(data_line[9])\\n self.__strokes = strokes\\n self.__xmin, self.__ymin, self.__xmax, self.__ymax = (xmin[0], ymin[1], xmax[0], ymax[1]) if strokes else (0, 0, 0, 0)\\n return True\\n return False\",\n \"def _process_data_file(self):\\n \\n with open(self.data_file, 'r') as f:\\n self.description = f.readline().strip()\\n data = np.loadtxt(self.data_file, skiprows=1)\\n\\n return data\",\n \"def parse_file(self):\\n # read the first line in the file\\n line = self._stream_handle.readline()\\n\\n while line:\\n # check for a data line or a dcl logger line we specifically ignore\\n data_match = DATA_LINE_MATCHER.match(line)\\n ignore_match = IGNORE_LINE_MATCHER.match(line)\\n\\n if data_match:\\n # found a data line, extract this particle\\n # DCL controller timestamp is the port_timestamp\\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\\n\\n particle = self._extract_sample(self.particle_class,\\n None,\\n data_match,\\n port_timestamp=port_timestamp,\\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\\n\\n self._record_buffer.append(particle)\\n\\n elif not ignore_match:\\n # we found a line with an unknown format, call an exception\\n error_message = 'Found line with unknown format %s' % line\\n log.warn(error_message)\\n self._exception_callback(SampleException(error_message))\\n\\n # read the next line\\n line = self._stream_handle.readline()\",\n \"def parse_point(line):\\n return json.loads(line)\",\n \"def parse_line(self, line):\\n if self.signal_eof:\\n return \\\"\\\"\\n\\n match = re.search(\\\"^([\\\\w\\\\s]+from) ([^:]+):(\\\\d+)(:|,)$\\\", line)\\n if match:\\n return self.parse_line_from(match)\\n\\n match = re.search(\\\"^([^:]+):(?:((?:\\\\d+:)?\\\\d+):)?(?:(error|warning|note):)?(.+)$\\\", line)\\n if match:\\n return self.parse_line_err(match)\\n\\n return line\",\n \"def line_to_data(line):\\n elems = line.strip().split(\\\"\\\\t\\\")\\n assert len(elems) in [1,2]\\n text = None\\n label = None\\n if len(elems) == 1:\\n text = elems[0]\\n if len(elems) == 2:\\n text = elems[0]\\n label = elems[1]\\n return (text, label)\",\n \"def __parse_position_data(self):\\n self.add_debug('Parse position data ...')\\n\\n for i in range(len(self._lines)):\\n if self.has_errors(): break\\n line = self._lines[i]\\n if len(line) < 1: continue\\n if self.TIMESTAMP_MARKER in line: continue\\n if self.RACK_BARCODE_MARKER in line: continue\\n\\n msg = 'Unexpected content in line %i: %s' % (i + 1, line)\\n if not self.SEPARATOR in line: self.add_error(msg)\\n tokens = line.split(self.SEPARATOR)\\n if not len(tokens) == 2: self.add_error(msg)\\n if self.has_errors(): continue\\n\\n pos_label = tokens[0].strip()\\n if self.position_map.has_key(pos_label):\\n msg = 'Duplicate position label \\\"%s\\\"' % (pos_label)\\n self.add_error(msg)\\n if self.has_errors(): continue\\n\\n tube_barcode = tokens[1].strip()\\n if tube_barcode == self.NO_TUBE_PLACEHOLDER: tube_barcode = None\\n self.position_map[pos_label] = tube_barcode\",\n \"def parse_dataset(self, data):\\n pass\",\n \"def csv_parser(lines): \\n\\n data_points = []\\n for line in lines:\\n items = line.strip().split(\\\",\\\")\\n try: #will fail on header line in file\\n data_points.append(map(float, items[1:])) #first item is the label\\n except ValueError: #must be the header\\n continue\\n return data_points\",\n \"def _parse_txt(path, n_channels):\\n f = open(path)\\n lines = f.readlines()\\n f.close()\\n\\n geom = np.zeros((0, 2))\\n\\n for i, line in zip(range(n_channels), lines):\\n line = line.replace('\\\\r', '')\\n line = line.replace('\\\\n', '')\\n row = line.split(' ')\\n geom = np.vstack((geom, row[:2])).astype('float')\\n\\n return geom\",\n \"def parse(self, line):\\n try:\\n (year, month, day, hour, minute, second, microseconds, offset_hour, offset_minute, source, process, logentry) = re.match('^(\\\\d\\\\d\\\\d\\\\d)-(\\\\d\\\\d)-(\\\\d\\\\d)T(\\\\d\\\\d):(\\\\d\\\\d):(\\\\d\\\\d)\\\\.([\\\\d]+)\\\\+(\\\\d\\\\d):(\\\\d\\\\d) ([a-z]+)\\\\[([a-zA-Z0-9_.]+)\\\\]: ([0-9a-z-A-Z\\\\-_\\\\.\\\\[\\\\]:\\\\?\\\\#\\\\\\\",/\\\\ ={}\\\\'\\\\(\\\\)<>]+)$', line).groups()\\n except:\\n pass\\n \\n try:\\n parsed_data = dict()\\n parsed_data['timestamp'] = \\\" \\\".join([\\\"-\\\".join([year, month, day]), \\\":\\\".join([hour, minute, second])])\\n parsed_data['log_time'] = datetime.datetime(int(year), int(month), int(day), int(hour), int(minute), int(second))\\n parsed_data['log_source'] = source\\n parsed_data['log_type'] = process\\n except (AttributeError, UnboundLocalError):\\n PARSE_ERRORS.append(line)\\n return False\\n\\n #TODO: This still needs work on spaces in values surrounded by \\\" \\\" \\n if parsed_data['log_source'] == \\\"heroku\\\":\\n if logentry.__len__() > 1:\\n logentry = re.sub(', ', ',', logentry)\\n line_chunks = re.split(' ', logentry)\\n for chunk in line_chunks:\\n line_chunks = re.split('=', chunk)\\n if line_chunks.__len__() > 2:\\n #fwd and path are a little clunky to parse\\n pass\\n elif line_chunks.__len__() > 1:\\n parsed_data[line_chunks[0]] = line_chunks[1]\\n else:\\n pass\\n else:\\n return False\\n else:\\n # TODO: [app] \\n # Needs parsing. Do that here.\\n return False\\n\\n return parsed_data\",\n \"def parse_text(self):\\n text = self.get_data()\\n line1 = text[0]\\n index_list = [0]\\n start_index = 3\\n for i in range(1, len(text)):\\n\\n if line1.startswith('*'):\\n index_list, start_index = self.star_parser(index_list, line1)\\n elif line1.startswith('.'):\\n start_index = self.dot_parser(start_index, line1, text, i)\\n else:\\n print \\\"\\\".rjust(start_index) + line1\\n line1 = text[i]\\n # Parse the last line\\n if text[-1].startswith('*'):\\n self.star_parser(index_list, text[-1])\\n elif text[-1].startswith('.'):\\n print '-'.rjust(start_index) + text[-1].lstrip('.')\\n else:\\n print \\\"\\\".rjust(start_index) + text[-1]\",\n \"def process_line(line):\\n [label, text] = line.split('\\\\t')\\n return text.split()\",\n \"def parse_line(line: str) -> str:\\n return line\",\n \"def parse_line(line: str) -> str:\\n return line\",\n \"def parse_line(line: str) -> str:\\n return line\",\n \"def parse_line(line: str) -> str:\\n return line\",\n \"def parse_text(self, text):\\n self._text_paragraph = text.split(\\\"\\\\n\\\")\\n self._render()\",\n \"def process_line(self, line, data):\\n return data\",\n \"def isLineData(self, line):\\n\\n if line is None or line.strip().startswith('#'):\\n return False, None, 0\\n\\n dataType = self.getDataType()\\n\\n if dataType == 'Y':\\n # Y with 1 column\\n try:\\n yValue = float(line)\\n\\n return True, 'Y', 1\\n except:\\n pass\\n\\n # Y with comma 2 to 5 column\\n try:\\n yValueList = []\\n yValueList = line.split(',')\\n\\n if len(yValueList) > 1 and len(yValueList) <= 5:\\n newYValues = []\\n for yValue in yValueList:\\n try:\\n yValue = float(yValue)\\n newYValues.append(yValue)\\n except ValueError:\\n pass\\n\\n return True, 'Y', len(newYValues)\\n except:\\n pass\\n\\n # Y with space 2 to 5 column\\n try:\\n yValueList = []\\n yValueList = line.split()\\n\\n if len(yValueList) > 1 and len(yValueList) <= 5:\\n for yValue in yValueList:\\n yValue = float(yValue)\\n\\n return True, 'Y', len(yValueList)\\n except:\\n pass\\n elif dataType == 'XY':\\n # XY with comma\\n try:\\n (xValue, yValue) = line.split(',')\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n\\n # XY with comma\\n try:\\n xValue, yValue, dummy = line.split(',')\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n\\n # XY with space\\n try:\\n (xValue, yValue) = line.split()\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n else:\\n # Y with 1 column\\n try:\\n yValue = float(line)\\n\\n return True, 'Y', 1\\n except:\\n pass\\n\\n # Y with comma 2 to 5 column\\n try:\\n yValueList = []\\n yValueList = line.split(',')\\n\\n if len(yValueList) > 1 and len(yValueList) <= 5:\\n numberValues = 0\\n for yValue in yValueList:\\n try:\\n yValue = float(yValue)\\n numberValues += 1\\n except ValueError:\\n pass\\n\\n return True, 'Y', numberValues\\n except:\\n pass\\n\\n # Y with space 2 to 5 column\\n try:\\n yValueList = []\\n yValueList = line.split()\\n\\n if len(yValueList) > 1 and len(yValueList) <= 5:\\n for yValue in yValueList:\\n yValue = float(yValue)\\n\\n return True, 'Y', len(yValueList)\\n except:\\n pass\\n\\n # XY with comma\\n try:\\n (xValue, yValue) = line.split(',')\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n\\n # XY with comma\\n try:\\n xValue, yValue, dummy = line.split(',')\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n\\n # XY with space\\n try:\\n (xValue, yValue) = line.split()\\n\\n xValue = float(xValue)\\n yValue = float(yValue)\\n\\n return True, 'XY', 2\\n except:\\n pass\\n\\n return False, None, 0\",\n \"def parse_string(self, data):\\n pass\",\n \"def _parse(self, lines):\\n global VELSCALE\\n self.title = lines[0].strip()\\n self.time = None\\n\\n try:\\n words = lines[1].split()\\n self.natom = int(words[0])\\n except (IndexError, ValueError):\\n raise TypeError('Unrecognized file type [%s]' % self.filename)\\n\\n if len(words) >= 2:\\n self.time = float(words[1]) * units.picoseconds\\n\\n if len(lines) == int(ceil(self.natom / 2.0) + 2):\\n hasbox = hasvels = False\\n self.boxVectors = self.velocities = None\\n elif self.natom in (1, 2) and len(lines) == 4:\\n # This is the _only_ case where line counting does not work -- there\\n # is either 1 or 2 atoms and there are 4 lines. The 1st 3 lines are\\n # the title, natom/time, and coordinates. The 4th are almost always\\n # velocities since Amber does not make it easy to make a periodic\\n # system with only 2 atoms. If natom is 1, the 4th line is either a\\n # velocity (3 #'s) or a box (6 #'s). If natom is 2, it is a bit\\n # ambiguous. However, velocities (which are scaled by 20.445) have a\\n # ~0% chance of being 60+, so we can pretty easily tell if the last\\n # line has box dimensions and angles or velocities. I cannot\\n # envision a _plausible_ scenario where the detection here will fail\\n # in real life.\\n line = lines[3]\\n if self.natom == 1:\\n tmp = [line[i:i+12] for i in range(0, 72, 12) if line[i:i+12]]\\n if len(tmp) == 3:\\n hasvels = True\\n hasbox = False\\n self.boxVectors = False\\n elif len(tmp) == 6:\\n hasbox = True\\n hasvels = False\\n self.velocities = None\\n else:\\n raise TypeError('Unrecognized line in restart file %s' %\\n self.filename)\\n else:\\n # Ambiguous case\\n tmp = [float(line[i:i+12]) >= 60.0 for i in range(0, 72, 12)]\\n if any(tmp):\\n hasbox = True\\n hasvels = False\\n self.velocities = False\\n else:\\n hasvels = True\\n hasbox = False\\n self.boxVectors = False\\n elif len(lines) == int(ceil(self.natom / 2.0) + 3):\\n hasbox = True\\n hasvels = False\\n self.velocities = None\\n elif len(lines) == int(2 * ceil(self.natom / 2.0) + 2):\\n hasbox = False\\n self.boxVectors = None\\n hasvels = True\\n elif len(lines) == int(2 * ceil(self.natom / 2.0) + 3):\\n hasbox = hasvels = True\\n else:\\n raise TypeError('Badly formatted restart file. Has %d lines '\\n 'for %d atoms.' % (len(self.lines), self.natom))\\n\\n if self._asNumpy:\\n coordinates = np.zeros((self.natom, 3), np.float32)\\n if hasvels:\\n velocities = np.zeros((self.natom, 3), np.float32)\\n else:\\n coordinates = [Vec3(0.0, 0.0, 0.0) for i in range(self.natom)]\\n if hasvels:\\n velocities = [Vec3(0.0, 0.0, 0.0) for i in range(self.natom)]\\n\\n # Now it's time to parse. Coordinates first\\n startline = 2\\n endline = startline + int(ceil(self.natom / 2.0))\\n idx = 0\\n for i in range(startline, endline):\\n line = lines[i]\\n x = float(line[ 0:12])\\n y = float(line[12:24])\\n z = float(line[24:36])\\n coordinates[idx] = Vec3(x, y, z)\\n idx += 1\\n if idx < self.natom:\\n x = float(line[36:48])\\n y = float(line[48:60])\\n z = float(line[60:72])\\n coordinates[idx] = Vec3(x, y, z)\\n idx += 1\\n self.coordinates = units.Quantity(coordinates, units.angstroms)\\n startline = endline\\n # Now it's time to parse velocities if we have them\\n if hasvels:\\n endline = startline + int(ceil(self.natom / 2.0))\\n idx = 0\\n for i in range(startline, endline):\\n line = lines[i]\\n x = float(line[ 0:12]) * VELSCALE\\n y = float(line[12:24]) * VELSCALE\\n z = float(line[24:36]) * VELSCALE\\n velocities[idx] = Vec3(x, y, z)\\n idx += 1\\n if idx < self.natom:\\n x = float(line[36:48]) * VELSCALE\\n y = float(line[48:60]) * VELSCALE\\n z = float(line[60:72]) * VELSCALE\\n velocities[idx] = Vec3(x, y, z)\\n idx += 1\\n startline = endline\\n self.velocities = units.Quantity(velocities,\\n units.angstroms/units.picoseconds)\\n if hasbox:\\n line = lines[startline]\\n try:\\n tmp = [float(line[i:i+12]) for i in range(0, 72, 12)]\\n except (IndexError, ValueError):\\n raise ValueError('Could not parse box line in %s' %\\n self.filename)\\n lengths = tmp[:3] * units.angstroms\\n angles = tmp[3:] * units.degrees\\n self.boxVectors = computePeriodicBoxVectors(lengths[0], lengths[1],\\n lengths[2], angles[0], angles[1], angles[2])\",\n \"def parse_report_line(self,line):\\n\\n report = self.new_police_report()\\n report['original_text'] = line\\n \\n #\\n # extract month and day\\n match_date = REPORT_DATE_REGEXP.search(line)\\n assert(match_date)\\n start_index=match_date.start('month')\\n stop_index=match_date.end('month')\\n report['date_month'] = int(line[start_index:stop_index])\\n\\n start_index=match_date.start('day')\\n stop_index=match_date.end('day')\\n report['date_day'] = int(line[start_index:stop_index])\\n\\n my_logger.debug('extracted date (%d/%d)' % (report['date_month'],report['date_day']))\\n\\n #############################################\\n # extract location & scale\\n line = line[0:match_date.start('month')-1] # truncate after start of date\\n \\n #\\n # trim off preceding html and trailing comma\\n start_index=line.rfind('>')+1\\n assert(start_index>0)\\n\\n stop_index=line.rfind(',',start_index)\\n \\n if stop_index >= 2:\\n #\\n # found a comma, \\n line = line[start_index:stop_index]\\n else:\\n #\\n # no comma found\\n line = line[start_index:]\\n my_logger.debug('truncated string: (%s)' % line)\\n report['address']=line\\n #\\n # try to determine which case:\\n # a block\\n # an exact address\\n # an establishment\\n # an intersection\\n # special cases, like: \\\"downtown mountain view\\\"\\n # \\n\\n if (BLOCK_REGEXP.match(line)!=None):\\n my_logger.debug('BLOCK detected')\\n report['map_scale']=mapscale.BLOCK\\n elif (INTERSECTION_REGEXP.match(line)!=None):\\n my_logger.debug('INTERSECTION detected')\\n report['map_scale']=mapscale.INTERSECTION\\n elif (EXACT_REGEXP.match(line)!=None):\\n my_logger.debug('EXACT detected')\\n report['map_scale']=mapscale.EXACT\\n else:\\n #\\n # must be manually assigned\\n report['map_scale']=mapscale.OTHER\\n\\n\\n return report\",\n \"def parse(self, f):\\n lines = []\\n for line in f:\\n _line = line.split(\\\"//\\\")[0].strip()\\n if _line.startswith(\\\"(\\\"): # is a label\\n label_name = _line[1:-1]\\n self.labels[label_name] = len(lines) # line number / address of label\\n elif _line:\\n lines.append(_line)\\n # else: it's just a whitespace/comment line (ignore)\\n return lines\",\n \"def parse(text):\\n parts = [int(part) for part in text.strip().split(',')]\\n point = Point(*parts)\\n actual = \\\"{},{},{},{}\\\".format(point.x, point.y, point.z, point.t)\\n assert actual == text, diff(actual, text)\\n return point\",\n \"def __parse(self):\\n lines = self.data.readlines()\\n for i in range(0, len(lines)):\\n line = lines[i]\\n if line[0] == '#':\\n continue\\n tokens = line.split(\\\"\\\\t\\\")\\n time_str = tokens[self.timecol]\\n if time_str.find('start:') != -1:\\n time_str = time_str.split()[1] + \\\" \\\" + time_str.split()[2]\\n self.calls.append((0, 0, 0))\\n self.durations.append(0.0)\\n elif time_str.find('end:') != -1:\\n time_str = time_str.split()[1] + \\\" \\\" + time_str.split()[2]\\n time = datetime.strptime(time_str, \\\"%Y-%m-%d %H:%M:%S\\\")\\n self.times.append(time)\\n self.calls.append((0, 0, 0))\\n self.durations.append(0.0)\\n break\\n else:\\n duration = float(tokens[6])\\n fms = int(tokens[2])\\n hfms = int(tokens[3])\\n svs = int(tokens[4])\\n self.calls.append((fms, hfms, svs))\\n self.durations.append(duration)\\n time = datetime.strptime(time_str, \\\"%Y-%m-%d %H:%M:%S\\\")\\n self.times.append(time)\\n self.length = (self.times[len(self.times) - 1] -\\\\\\n self.times[0]).seconds\",\n \"def plot(self, plotType):\\n # Build plotting data\\n self.data_x_axis = []\\n self.data_y_axis = []\\n for i in range(0, self.csv_data_table.rowCount()):\\n value = self.csv_data_table.item(i, self.selected_columns[0]).text()\\n self.data_x_axis.append(value)\\n value = self.csv_data_table.item(i, self.selected_columns[1]).text()\\n self.data_y_axis.append(value)\\n\\n self.label_x_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[0]).text()\\n self.label_y_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[1]).text()\\n\\n # Avoid duplication of resources if already allocated\\n if self.figure is None:\\n self.figure = plt.figure()\\n self.canvas = FigureCanvas(self.figure)\\n\\n # self.plot_frame_horizontal.addStretch()\\n self.plot_frame_horizontal.addWidget(self.canvas)\\n # self.plot_frame_horizontal.addStretch()\\n\\n # Ensures only 2 tabs at max are open at a time - file and plot tabs respectively\\n if self.tabWidget.count() == 1:\\n self.tabWidget.insertTab(1, self.plot_page_tab, \\\"Plot\\\")\\n\\n self.tabWidget.setCurrentIndex(1)\\n\\n # Set plot type (1,2,3 => order according to scatter, scatter-line, line)\\n self.plotType = plotType\\n\\n # Convert the data to np arrays if it is purely numerical\\n try:\\n for i in range(0, len(self.data_x_axis)):\\n if self.data_x_axis[i] == '':\\n self.data_x_axis[i] = 0\\n if self.data_y_axis[i] == '':\\n self.data_y_axis[i] = 0\\n\\n self.data_x_axis[i] = self.coerce_str_to_number(self.data_x_axis[i])\\n self.data_y_axis[i] = self.coerce_str_to_number(self.data_y_axis[i])\\n\\n self.data_x_axis = np.array(self.data_x_axis)\\n self.data_y_axis = np.array(self.data_y_axis)\\n\\n print(self.data_x_axis)\\n print(self.data_y_axis)\\n\\n print(\\\"In specialized plotting\\\")\\n\\n except:\\n pass\\n # Dont attempt the conversion, directly plot\\n print(\\\"In generic plotting\\\")\\n\\n self.draw_plot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)\",\n \"def check_plot_command(self, line):\\n err_msg = \\\"The plot command takes the syntax:\\\\n\\\\n\\\"\\n err_msg += \\\"\\\\t'plot <plot type> from <data name> as <plot name>'\\\"\\n err_msg += \\\"\\\\n\\\\n\\\\t\\\\t\\\\tOR\\\\n\\\\n\\\\t'plot <plot type> from <data name>'\\\"\\n\\n line, any_vars = self.find_vars_in_str(line)\\n\\n # Check syntax\\n words = line.split()\\n words = self.fix_words(words)\\n self.E_str = \\\"check_plot_command\\\"\\n has_out_var = False\\n if len(words) != 4:\\n if len(words) != 6:\\n self.print_error(err_msg)\\n else:\\n has_out_var = True\\n _, plot_type, _, in_data, _, out_data = words\\n else:\\n _, plot_type, _, in_data = words\\n\\n # Check we can plot the thing asked for\\n if plot_type not in f_dicts.plot_fncs:\\n err_msg = f\\\"I don't know how to plot '{words[1]}'.\\\"\\n err_msg += \\\"\\\\n\\\\nFor a full list of plots that can be done see below:\\\\n\\\\t* \\\"\\n err_msg += \\\"\\\\n\\\\t* \\\".join(list(f_dicts.plot_fncs.keys()))\\n self.print_error(err_msg)\\n\\n if has_out_var:\\n metadata = f_dicts.plot_fncs[words[1]].metadata\\n self.set_var(out_data, \\\"^EMPTY^\\\", metadata)\\n return out_data\\n\\n return None\",\n \"def readInput(fileName):\\n with open(fileName, 'r') as file:\\n\\n plotArray = []\\n for line in file:\\n plotArray.append(list(line.strip()))\\n\\n return plotArray\",\n \"def parsePoint(line):\\n parts = line.split(\\\",\\\")\\n return LabeledPoint(parts[0], [parts[1], parts[2]])\",\n \"def read_line(l):\\n return [read_float(l[s]) for s in slices['data']]\",\n \"def parse(self, data):\\n raise NotImplementedError\",\n \"def parse_line(self, line: str) -> None:\\n self._count += 1\",\n \"def parse_datum( self, data ):\\n return data\",\n \"def parse_datum( self, data ):\\n return data\",\n \"def plot(self, plotType):\\n # Build plotting data\\n self.data_x_axis = []\\n self.data_y_axis = []\\n for i in range(0, self.csv_data_table.rowCount()):\\n value = self.csv_data_table.item(i, self.selected_columns[0]).text()\\n self.data_x_axis.append(value)\\n value = self.csv_data_table.item(i, self.selected_columns[1]).text()\\n self.data_y_axis.append(value)\\n\\n self.label_x_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[0]).text()\\n self.label_y_axis = self.csv_data_table.horizontalHeaderItem(self.selected_columns[1]).text()\\n\\n # Avoid duplication of resources if already allocated\\n if self.figure is None:\\n self.figure = plt.figure()\\n self.canvas = FigureCanvas(self.figure)\\n\\n # self.plot_frame_horizontal.addStretch()\\n self.plot_frame_horizontal.addWidget(self.canvas)\\n # self.plot_frame_horizontal.addStretch()\\n\\n # Ensures only 2 tabs at max are open at a time - file and plot tabs respectively\\n if self.tabWidget.count() == 1:\\n self.tabWidget.insertTab(1, self.plot_page_tab, \\\"Plot\\\")\\n\\n self.tabWidget.setCurrentIndex(1)\\n\\n self.plotType = plotType\\n\\n try:\\n for i in range(0, len(self.data_x_axis)):\\n if self.data_x_axis[i] == '':\\n self.data_x_axis[i] = 0\\n if self.data_y_axis[i] == '':\\n self.data_y_axis[i] = 0\\n\\n self.data_x_axis[i] = self.strToNumber(self.data_x_axis[i])\\n self.data_y_axis[i] = self.strToNumber(self.data_y_axis[i])\\n\\n self.data_x_axis = np.array(self.data_x_axis)\\n self.data_y_axis = np.array(self.data_y_axis)\\n\\n print(self.data_x_axis)\\n print(self.data_y_axis)\\n\\n except:\\n pass\\n print(\\\"In generic plotting\\\")\\n\\n self.drawPlot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)\",\n \"def _find_first_data_point(self, lines):\\r\\n\\r\\n for i in range(len(lines)):\\r\\n if lines[i][0] in ['+', '-']:\\r\\n return i\\r\\n\\r\\n raise DataFormatError(\\\"No data found in file. Check data format spec.\\\")\",\n \"def parse_line(data, line, keyword):\\n if '[' in line:\\n pos = re.search(r'\\\\[(.*)\\\\]', line).group(1).split(':')\\n pos = sorted([int(i.strip()) for i in pos])\\n for i in range(pos[0], pos[1]):\\n for j in re.search(r'\\\\](.*);', line).group(1).split(','):\\n data[keyword].append(j.strip() + f'[{i}]')\\n else:\\n for i in re.search(rf'{keyword}(.*);', line).group(1).split(','):\\n data[keyword].append(i.strip())\",\n \"def cli_text_scatter_plot(sep, graph_width, graph_height, input_file):\\n values = []\\n for line in input_file:\\n try:\\n x, y = line.split(sep)\\n values.append((float(x), float(y)))\\n except ValueError:\\n click.echo(f'Warning: \\\"{line.strip()}\\\" could not be parsed', err=True)\\n\\n click.echo(text_scatter_plot(\\n values, graph_width=graph_width, graph_height=graph_height\\n ))\",\n \"def Parse(filename):\\n\\n f = open(filename, 'r')\\n\\n metadata = Metadata()\\n data = [] # array of dataset\\n dataset = None\\n\\n for num, line in enumerate(f):\\n try:\\n line = line.strip()\\n if not line: continue\\n\\n if not metadata.complete:\\n metadata.Parse(line)\\n continue\\n\\n if re.match('[a-z_]', line):\\n continue\\n\\n if line.startswith('# StopWatch'): # Start of a new dataset\\n if dataset:\\n if dataset.summary:\\n metadata.UpdateWith(dataset)\\n else:\\n data.append(dataset)\\n\\n dataset = DataSet(line)\\n continue\\n\\n if line.startswith('#'):\\n continue\\n\\n # must be data at this stage\\n try:\\n (time, value) = line.split(None, 1)\\n except ValueError:\\n print 'skipping line %d: %s' % (num, line)\\n continue\\n\\n if dataset and not dataset.summary:\\n dataset.Add(float(time), float(value))\\n\\n except Exception:\\n print 'Error parsing line %d' % num, sys.exc_info()[0]\\n raise\\n data.append(dataset)\\n if not metadata.complete:\\n print \\\"\\\"\\\"Error missing metadata. Did you mount debugfs?\\n [adb shell mount -t debugfs none /sys/kernel/debug]\\\"\\\"\\\"\\n sys.exit(1)\\n return (metadata, data)\",\n \"def parse(self):\\n try:\\n self.open_file()\\n lines = list(self._file)\\n\\n if len(lines) > 0:\\n text = ''.join(lines)\\n regex = 'Song \\\\d+\\\\nStart (\\\\d+:\\\\d+:\\\\d+)\\\\nEnd (\\\\d+:\\\\d+:\\\\d+)\\\\nLength (\\\\d+.\\\\d+)'\\n match = re.findall(regex, text)\\n if len(match):\\n starts = []\\n ends = []\\n lengths = []\\n\\n for i in range(len(match)):\\n starts.append(match[i][0])\\n ends.append(match[i][1])\\n lengths.append(float(match[i][2]))\\n\\n for i in range(len(match)):\\n self.debug_data.append({\\n 'start':starts[i],'end':ends[i],'length':lengths[i]})\\n\\n match = re.search('T\\\\d_S(\\\\d{4})_.*.txt', self._filepath)\\n if match:\\n self._experiment_metadata['session_id'] = int(match.groups()[0])\\n else:\\n raise EIMParsingError(\\\"No valid session id found in filename %s\\\" % self._filepath)\\n\\n finally:\\n if self._file and not self._file.closed:\\n self.close_file()\",\n \"def parseFileLine(self, line):\\n c = line.strip().split(\\\":\\\")\\n return (c[0], c[1], c[2], c[3])\",\n \"def parse(self, f):\\n \\n for line in f:\\n self.parse_line(line)\",\n \"def logplot(in_dir, fname, xlim, ylim, title):\\n\\n with open(in_dir + fname,'r') as logfile:\\n lf_lines = logfile.readlines()\\n\\n traj_x = []\\n traj_y = []\\n\\n for row in lf_lines:\\n if row[:4] == 'pose':\\n #print(float(row[10:-2]))\\n tup = row[7:]\\n sep_pos = tup.find(' , ')\\n traj_x.append(float(tup[:sep_pos]))\\n traj_y.append(float(tup[sep_pos+3:]))\\n\\n liveplot(traj_x, traj_y, xlim, ylim, title)\",\n \"def parse_line(line : str) -> Tuple[str, str, int]:\\n line = line[:-1] # Strips newline character\\n question_end = line.find(';')\\n question = line[:question_end]\\n\\n line = line[question_end+1:]\\n answer_end = line.find(';')\\n answer = line[:answer_end]\\n\\n points = int(line[answer_end+1:])\\n\\n return question, answer, points\",\n \"def parse_movie(self, line):\\n pass\",\n \"def _parse_data(self):\\n for i, val in enumerate(self.values.keys()):\\n x_, y_ = [], []\\n xy = self.values[val]\\n for value in self.values.index:\\n x_.append(xy[value][0])\\n y_.append(xy[value][1])\\n\\n self.set_and_get(\\\"x_\\\", val, x_)\\n self.set_and_get(\\\"y_\\\", val, y_)\",\n \"def retrieve_plot_data(self):\\n spec2nexus.specplot.LinePlotter.retrieve_plot_data(self)\\n\\n if self.signal in self.data:\\n # can't plot negative Y on log scale\\n # Alternative to raising NotPlottable would be\\n # to remove any data where Y <= 0\\n if min(self.data[self.signal]) <= 0:\\n msg = \\\"cannot plot Y<0: \\\" + str(self.scan)\\n raise spec2nexus.specplot.NotPlottable(msg)\\n\\n # in the uascan, a name for the sample is given in `self.scan.comments[0]`\\n self.set_y_log(True)\\n self.set_plot_subtitle(\\n \\\"#%s uascan: %s\\\" % (str(self.scan.scanNum), self.scan.comments[0])\\n )\",\n \"def parse_new_mwdata_line(L):\\n data = L.split()\\n if len(data) != len(column_names['mwdata']):\\n print(\\\"mwdata line {} has only {} fields, skipping\\\".format(L, len(data)))\\n return\\n label, record = parse_line_label_cols(L)\\n\\n def decode_col(col, decoder): # use for columns which may have '?'\\n return None if col in ['?', 'None'] else decoder(col)\\n\\n record['rank'] = decode_col(data[4], int)\\n record['rank_bounds'] = decode_col(data[5], decode_int_list)\\n record['analytic_rank'] = decode_col(data[6], int)\\n record['ngens'] = int(data[7])\\n record['gens'] = decode_points_one2many(data[8])\\n record['heights'] = data[9]\\n #record['reg'] = decode_col(data[10], RealNumber) if record['ngens'] else 1\\n record['reg'] = data[10] if record['ngens'] else 1\\n record['torsion_order'] = nt = int(data[11])\\n record['torsion_primes'] = ZZ(nt).prime_divisors()\\n record['torsion_structure'] = decode_int_list(data[12])\\n record['torsion_gens'] = decode_points_one2many(data[13])\\n if len(data) == 17:\\n #record['omega'] = decode_col(data[14], RealNumber)\\n #record['Lvalue'] = decode_col(data[15], RealNumber)\\n record['sha'] = decode_col(data[16], int)\\n record['omega'] = data[14]\\n record['Lvalue'] = data[15]\\n else:\\n record['omega'] = None\\n record['Lvalue'] = None\\n record['sha'] = None\\n return label, record\",\n \"def tag_parser(file_path: str):\\n with open(file_path) as f:\\n t = f.read()\\n t = t.split(\\\"Points =\\\\n\\\")[1]\\n t = t.replace(\\\" 0.1 1 1 \\\\\\\"Marker\\\\\\\"\\\", \\\"\\\")\\n t = t.replace(\\\";\\\", \\\"\\\")\\n t = t.replace(\\\" \\\\n\\\", \\\"\\\\n\\\")\\n t = t[1:]\\n t = StringIO(t)\\n\\n return np.genfromtxt(t, delimiter=' ')\",\n \"def parse_mwdata_line(L):\\n data = L.split()\\n # if len(data)!=14:\\n # print(\\\"mwdata line {} does not have 14 fields, skipping\\\".format(L))\\n # return\\n label, record = parse_line_label_cols(L)\\n\\n r = data[4]\\n record['rank'] = None if r == '?' else int(r)\\n r = data[5]\\n record['rank_bounds'] = '?' if r == '?' else [int(rb) for rb in r[1:-1].split(\\\",\\\")]\\n r = data[6]\\n record['analytic_rank'] = None if r == '?' else int(r)\\n record['ngens'] = int(data[7])\\n gens = data[8]\\n record['gens'] = [] if gens == '[]' else gens.replace(\\\"[[[\\\", \\\"[[\\\").replace(\\\"]]]\\\", \\\"]]\\\").replace(\\\"]],[[\\\", \\\"]];[[\\\").split(\\\";\\\")\\n record['heights'] = data[9]\\n record['reg'] = data[10]\\n record['torsion_order'] = nt = int(data[11])\\n ts = data[12]\\n record['torsion_structure'] = [] if ts == '[]' else [int(t) for t in ts[1:-1].split(\\\",\\\")]\\n record['torsion_primes'] = ZZ(nt).prime_divisors()\\n record['torsion_gens'] = decode_points_one2many(data[13])\\n\\n record['omega'] = None\\n record['Lvalue'] = None\\n record['sha'] = None\\n\\n return label, record\",\n \"def _parse_line(line):\\n\\n number_pattern = '(\\\\d+(?:\\\\.\\\\d+)?)'\\n line_pattern = '^\\\\s+%s\\\\s+$' % ('\\\\s+'.join([number_pattern for x in range(10)]))\\n\\n match = re.match(line_pattern, line)\\n if match:\\n print(match.groups())\\n return match.groups()\\n # if there are no matches\\n return None\",\n \"def parse_line(self, line, time_shift=0.0):\\n raise NotImplementedError(\\\"must be defined by subclass\\\")\",\n \"def datareader(self, path):\\n\\n f = open(path, 'r')\\n data = f.read()\\n data = data.split('\\\\n')\\n data_tmp = []\\n for idx in range(len(data)):\\n if str(data[idx]).find('@data') >= 0:\\n data_tmp = data[idx + 1:]\\n break\\n res = []\\n for record in data_tmp:\\n record = record.split(',')\\n record = map(float, record)\\n res.append(record)\\n return res\",\n \"def loadLine(self, line):\\n self.text.append(nlp(line[0]))\\n self.full_text = self.full_text + \\\" \\\" + line[0]\\n self.rec_paths.append(line[1])\\n with contextlib.closing(wave.open(self.rec_paths[-1],'r')) as f:\\n frames = f.getnframes()\\n rate = f.getframerate()\\n self.durations.append(frames / float(rate))\\n\\n # self.media.append(self.instance.media_new(self.rec_paths[-1]))\\n self.total_duration = self.calculateTotalDuration(self.durations)\",\n \"def ParseWeatherData(parrData):\\n ClearDisplay()\\n for i in range(0, len(parrData)):\\n if i < (len(parrData) - 1):\\n DisplayMsg(parrData[i], int(i * 8))\\n else:\\n FinalLine = parrData[i].split(\\\": \\\")\\n DisplayMsg(FinalLine[0], 40)\\n DisplayMsg('{:^16}'.format(FinalLine[1]), 48)\\n DrawHLine(Width, 37)\\n display.show()\",\n \"def _analyzeLine(self, line, date, data):\\n user = re.search(': (.*)', line).group().split()[1]\\n data[user].add(ConvertTime(date, line))\",\n \"def parse_file(line, position):\\n movement = line.split(\\\" \\\")\\n if movement[0] == \\\"SUS\\\":\\n position[\\\"x\\\"] += float(movement[1])\\n elif movement[0] == \\\"JOS\\\":\\n position[\\\"x\\\"] -= float(movement[1])\\n elif movement[0] == \\\"STANGA\\\":\\n position[\\\"y\\\"] -= float(movement[1])\\n elif movement[0] == \\\"DREAPTA\\\":\\n position[\\\"y\\\"] += float(movement[1])\\n else:\\n print(\\\"Incorrect input\\\")\",\n \"def parse(self):\\r\\n # open the file\\r\\n # try to find the first line with Time =\\r\\n # send the file to a recursive residualParser\\r\\n try:\\r\\n with open(self.filepath, 'rb') as f:\\r\\n for line in f:\\r\\n if line.startswith('Time ='):\\r\\n self.timestep = self.__getTime(line)\\r\\n self.__residuals[self.timestep] = {}\\r\\n self.__parseResiduals(f)\\r\\n except Exception as e:\\r\\n raise 'Failed to parse {}:\\\\n\\\\t{}'.format(self.filepath, e)\",\n \"def read_text(self, text):\\n if isinstance(text, (str, unicode)):\\n lines = text.split('\\\\n')\\n else:\\n lines = text\\n\\n for line in lines:\\n l = line.strip()\\n\\n if line == ST_POS0:\\n self._state = ST_POS0\\n elif line == ST_TRNS:\\n self._state = ST_TRNS\\n elif line == ST_POS0:\\n self._state = ST_POS0\\n else:\\n self._parse_line(line)\",\n \"def parse_position_line(line):\\n\\n match = Response.regex_position.search(line)\\n if match is not None:\\n result = dict(\\n x=float(match.group(\\\"x\\\")),\\n y=float(match.group(\\\"y\\\")),\\n z=float(match.group(\\\"z\\\")),\\n )\\n if match.group(\\\"e\\\") is not None:\\n # report contains only one E\\n result[\\\"e\\\"] = float(match.group(\\\"e\\\"))\\n\\n elif match.group(\\\"es\\\") is not None:\\n # report contains individual entries for multiple extruders (\\\"E0:... E1:... E2:...\\\")\\n es = match.group(\\\"es\\\")\\n for m in Response.regex_e_positions.finditer(es):\\n result[\\\"e{}\\\".format(m.group(\\\"id\\\"))] = float(m.group(\\\"value\\\"))\\n\\n else:\\n # apparently no E at all, should never happen but let's still handle this\\n return None\\n\\n return result\\n\\n return None\",\n \"def _parse_coords(self):\\n\\n coords = []\\n\\n while True:\\n try:\\n _, x, y = self._lines.current.split()\\n coords.append((float(x), float(y)))\\n except ValueError:\\n break\\n\\n try:\\n next(self._lines)\\n except StopIteration:\\n break\\n\\n return coords\",\n \"def _build_data_from_text(self, text):\\n try:\\n record = json.loads(text)\\n except Exception as e:\\n logging.error(f\\\"Exception: {e}\\\")\\n logging.error(f\\\"datapoint: {text}\\\")\\n raise e\\n return record\",\n \"def parse_line(self, line):\\n\\n kv_match = self.kv_rex.match(line)\\n\\n if kv_match:\\n kv_dict = kv_match.groupdict()\\n kv_key = kv_dict['key']\\n kv_value = kv_dict['value']\\n\\n if 'time_' in kv_key:\\n kv_unit = kv_dict['unit']\\n\\n if not kv_key in self.kv_times:\\n self.kv_times[kv_key] = {'unit': kv_unit, 'values': []}\\n\\n self.kv_times[kv_key]['values'].append(float(kv_value))\\n else:\\n if not kv_key in self.kv_counts:\\n self.kv_counts[kv_key] = 0.0\\n\\n self.kv_counts[kv_key] += float(kv_value)\",\n \"def line_to_data( line ):\\n data = [ ]\\n if '>>>' in line:\\n print(line)\\n return data\\n\\n secs = filter( None, line.split(',') )\\n for i, x in enumerate( secs ):\\n try:\\n data.append( float(x.strip()) )\\n except Exception as e:\\n data = None\\n # print( data )\\n return data\",\n \"def populate_plot(self, plot, data):\\n\\n # Determine which type of line gets which color\\n color_map = {\\n 'REF': Category20c_20[16],\\n 'REF1': Category20c_20[16],\\n 'REF2': Category20c_20[16],\\n 'REF3': Category20c_20[16],\\n 'REF4': Category20c_20[16],\\n 'SCRIBE_LINE': Category20c_20[0],\\n 'SCRIBE_LINE1': Category20c_20[0],\\n 'SCRIBE_LINE2': Category20c_20[1],\\n 'SCRIBE_LINE3': Category20c_20[2],\\n 'SCRIBE_LINE4': Category20c_20[3],\\n 'BUSBAR_LINE': Category20c_20[4],\\n 'BUSBAR_LINE1': Category20c_20[4],\\n 'BUSBAR_LINE2': Category20c_20[5],\\n 'BUSBAR_LINE3': Category20c_20[6],\\n 'BUSBAR_LINE4': Category20c_20[7],\\n 'EDGEDEL_LINE': Category20c_20[8],\\n 'EDGEDEL_LINE1': Category20c_20[8],\\n 'EDGEDEL_LINE2': Category20c_20[9],\\n 'EDGEDEL_LINE3': Category20c_20[10],\\n 'EDGEDEL_LINE4': Category20c_20[11]\\n }\\n\\n # Color of the non cutting line\\n radius = 13\\n line_width = 3\\n\\n scatter_points = {}\\n for line in data:\\n group_name = line.get_line_type() + line.get_recipe()\\n sp = line.get_starting_point()\\n ep = line.get_endpoint()\\n\\n # Sort scatter points\\n if group_name not in scatter_points:\\n scatter_points[group_name] = {\\n 'x': [sp[0], ep[0]],\\n 'y': [sp[1], ep[1]]\\n }\\n else:\\n scatter_points[group_name]['x'].append(sp[0])\\n scatter_points[group_name]['x'].append(ep[0])\\n scatter_points[group_name]['y'].append(sp[1])\\n scatter_points[group_name]['y'].append(ep[1])\\n\\n # Cutting line\\n plot.line(\\n [sp[0], ep[0]],\\n [sp[1], ep[1]],\\n color=color_map[group_name],\\n line_width=line_width\\n )\\n\\n # Add a scatter plot for every group\\n for group_name, group in scatter_points.items():\\n plot.scatter(\\n group['x'],\\n group['y'],\\n color=color_map[group_name],\\n radius=radius,\\n legend=group_name\\n )\\n\\n # Add travel lines\\n for line in range(len(data) - 1):\\n # Get the endpoint of the current line, as well as the starting\\n # point of the next line\\n ep0 = data[line].get_endpoint()\\n sp1 = data[line + 1].get_starting_point()\\n\\n # Plot the travel line (non-cutting line)\\n plot.line(\\n [\\n ep0[0],\\n sp1[0]],\\n [\\n ep0[1],\\n sp1[1]\\n ],\\n color='black',\\n legend='Non Cutting'\\n )\\n\\n return plot\",\n \"def isDataLine(line):\\n if len(line) > 1:\\n return line[0] != \\\"#\\\"\\n return False\",\n \"def isDataLine(line):\\n if len(line) > 1:\\n return line[0] != \\\"#\\\"\\n return False\",\n \"def _parse(self, line):\\n comd, value = cmd.parse(line, SERVER_PREFIX)\\n player = self.player\\n if comd == 'play':\\n audio, seek = cmd.separate(value)\\n player.playFile(audio, seek=float(seek))\\n newline = 'Player started playing %s' %(value)\\n elif comd == 'seek':\\n value = float(value)\\n player.seekto(value)\\n newline = 'Forwared to %d seconds' %(value)\\n elif comd == 'pause':\\n player.pause()\\n newline = 'Player Paused'\\n elif comd == 'volume':\\n value = float(value)\\n player.volume = value\\n newline = 'Volume changed to %d' %(value)\\n else:\\n return line\\n return newline\",\n \"def read_text(filename):\\n with open(filename, 'r') as f:\\n com = f.readline()[0]\\n wavelength, flux = np.loadtxt(filename, unpack=True,\\n usecols=(0, 1), comments=com)\\n return wavelength, flux\",\n \"def __parse(self):\\n lines = self.file.readlines()\\n for i in range(0, len(lines)):\\n line = lines[i]\\n tokens = line.split()\\n if tokens[0] == \\\"#start\\\":\\n trial_name = tokens[1]\\n trial = Trial(trial_name)\\n self.trials[trial_name] = trial\\n elif tokens[0] == \\\"#end\\\":\\n continue\\n else:\\n date_str = tokens[0] + \\\" \\\" + tokens[1]\\n date = datetime.strptime(date_str, \\\"%m/%d/%y %H:%M:%S\\\")\\n sound_file = line[18:-1].strip()\\n event = Event(date, sound_file, 0)\\n trial.addevent(event)\",\n \"def parse_pdb(self, line):\\n if line is not None:\\n self.original_text.append(line.rstrip(\\\"\\\\r\\\\n\\\"))\",\n \"def make_line_plot(data, x_label=\\\"Data\\\", y_label=\\\"Data Point\\\"):\\n\\n y = data\\n x = range(len(y))\\n\\n plt.xlabel(x_label)\\n plt.ylabel(y_label)\\n plt.plot(x, y)\\n plt.show()\",\n \"def parse(cls, data):\\n raise NotImplementedError\",\n \"def parse_galrep_line(L):\\n data = L.split(maxsplit=1)\\n record = parse_galrep_data_string(\\\"\\\" if len(data) == 1 else data[1])\\n record['label'] = label = data[0]\\n return label, record\",\n \"def function2():\\r\\n with open('data.txt', 'r') as file:\\r\\n read_data = file.read()\\r\\n data = read_data.split()\\r\\n line_chart = pygal.Line()\\r\\n line_chart.title = data[26]\\r\\n line_chart.x_labels = map(str, range(2554, 2558))\\r\\n line_chart.add(data[27], [int(data[28]), int(data[29]), int(data[30]), int(data[31])])\\r\\n line_chart.add(data[32], [int(data[33]), int(data[34]), int(data[35]), int(data[36])])\\r\\n line_chart.add(data[37], [int(data[38]), int(data[38]), int(data[40]), int(data[41])])\\r\\n line_chart.add(data[42], [int(data[43]), int(data[44]), int(data[45]), int(data[46])])\\r\\n line_chart.add(data[47], [int(data[48]), int(data[49]), int(data[50]), int(data[51])])\\r\\n line_chart.render_to_file('02.svg')\",\n \"def parse_txt_file(txtfile):\\n array = np.genfromtxt(txtfile)\\n return array\",\n \"def parse_lines(self, start_line=0, end_line=False):\\n if end_line is False: end_line = len(self.file_ltxt)\\n\\n lines = self.file_ltxt\\n self.E_str = \\\"parse_lines\\\"\\n self.line_num = start_line\\n\\n # Loop over lines and parse\\n while self.line_num < end_line:\\n line = lines[self.line_num].strip()\\n\\n if line == \\\"echo\\\": print(\\\"\\\")\\n\\n # Parse any variables\\n elif self.line_declarations['variable'](line):\\n self.parse_variable_line(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['load'](line):\\n self.parse_load_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['plot'](line):\\n self.parse_plot_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['write'](line):\\n self.parse_write_cmd(line)\\n\\n # Parse any math commands\\n elif self.line_declarations['math'](line):\\n self.parse_math_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['echo'](line):\\n self.parse_echo_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['calc'](line):\\n self.parse_calc_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['set'](line):\\n self.parse_set_cmd(line)\\n\\n # Parse any shell commands\\n elif self.line_declarations['shell'](line):\\n self.parse_shell_cmd()\\n\\n # Parse any for loop commands\\n elif self.line_declarations['for'](line):\\n self.parse_for_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['script'](line):\\n self.parse_script_cmd(line)\\n\\n elif self.line_declarations['inline_code'](line):\\n getattr(self, f\\\"parse_{line.split()[0]}_cmd\\\")(line)\\n\\n elif self.line_declarations['if'](line):\\n self.parse_if_cmd(line)\\n\\n # elif self.line_declarations['splice'](line):\\n # self.parse_splice_cmd(line)\\n\\n elif self.line_declarations['glue'](line):\\n self.parse_glue_cmd(line)\\n\\n elif self.line_declarations['exit'](line):\\n print(\\\"\\\\n\\\\nStopped Code -exit was called.\\\")\\n raise SystemExit\\n\\n # The end of control statements\\n elif '}' in line:\\n pass\\n\\n # Print a warning about unknown line\\n else:\\n self.print_warning(f\\\"I don't understand a line: '{line}'\\\")\\n\\n self.line_num += 1\",\n \"def get_data(dataf):\\n with open(dataf) as f:\\n label = []\\n e_val = []\\n for line in f:\\n label.append(float(line.split()[1]))\\n e_val.append(-1 * float(line.split()[0]))\\n return label, e_val\",\n \"def parse_line(self, atline: List, list_of_lines: List, part: PART, afix: AFIX, resi: RESI) -> None:\\n uvals = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\\n self.name = atline[0][:4] # Atom names are limited to 4 characters\\n for n, u in enumerate(atline[6:12]):\\n uvals[n] = float(u)\\n self.uvals_orig = uvals[:]\\n self.set_uvals(uvals)\\n self._line_numbers = list_of_lines\\n self.part = part\\n self.afix = afix\\n self.resi = resi\\n self._get_part_and_occupation(atline)\\n self.x, self.y, self.z = self._get_atom_coordinates(atline)\\n self.xc, self.yc, self.zc = self._cell.o * Array(self.frac_coords)\\n if abs(self.uvals[1]) > 0.0 and self.uvals[2] == 0.0 and self.shx.hklf: # qpeaks are always behind hklf\\n self.peak_height = uvals[1]\\n self.qpeak = True\\n if self.shx.end: # After 'END' can only be Q-peaks!\\n self.qpeak = True\\n self.sfac_num = int(atline[1])\\n self.shx.fvars.set_fvar_usage(self.fvar)\\n self.Ucif = self.set_ucif(uvals)\\n # TODO: I am still unsure if this these are correct values:\\n # self.Ustar = self.Ucif * self._cell.N * self._cell.N.T\\n # self.Ucart = self.Ustar * self._cell.o * self._cell.o.T\\n # self.Ueq = self.set_ueq(uvals)\\n # self.Uiso = self.Ueq\\n # transformed_u = self.transform_u_by_symmetry(2)\\n # print(self.name, [round(x, 6) for x in transformed_u], self.frac_coords)\",\n \"def parse_relation(self, text_line):\\n record_type = self.substr(text_line, sps21point['RECORD_ID'][0], sps21point['RECORD_ID'][1]).strip()\\n if record_type not in REL_DATA_RECORD:\\n return\\n\\n self.set_definition(sps21relation)\\n return self.parse(text_line)\",\n \"def read_data(self,data_array):\\n value_type = self.config['value_type']\\n try:\\n curr_value = data_array[self.index]\\n except:\\n curr_value = 'None'\\n \\n #if value is none\\n if curr_value.strip() == 'None':\\n #break line\\n curr_value = np.nan\\n else:\\n value_type = self.str_to_command(value_type)\\n curr_value = value_type(curr_value)\\n\\n # TODO: Read time and not log id\\n try:\\n time = int(data_array[0])\\n except:\\n time = self.x[-1] + 1\\n\\n self.x = np.append(self.x,time)\\n self.y = np.append(self.y,curr_value)\\n\\n if(self.auto_scale and len(self.x) > self.config[\\\"limit\\\"]):\\n #delete old values\\n self.x = np.delete(self.x, 0)\\n self.y = np.delete(self.y, 0)\\n\\n self.set_fig_color(curr_value)\",\n \"def parse_data(name):\\n with open(name) as f:\\n lines = f.read().splitlines()\\n lines = filter(lambda x: x.split(' ')[0].isdigit(), lines)\\n lx = [int(p.split(' ')[1]) for p in lines]\\n ly = [int(p.split(' ')[2]) for p in lines]\\n return lx, ly\",\n \"def parser_txt_file(self, content):\\n ai_cpu_str = str(content.replace(b'\\\\n\\\\x00', b' ___ ').replace(b'\\\\x00', b' ___ '))[2:-1]\\n ai_cpu_lines = ai_cpu_str.split(\\\" ___ \\\")\\n result_list = list()\\n ai_cpu_total_time_summary = 0\\n # Node serial number.\\n serial_number = 1\\n for i in range(len(ai_cpu_lines) - 1):\\n node_line = ai_cpu_lines[i]\\n thread_line = ai_cpu_lines[i + 1]\\n if \\\"Node\\\" in node_line and \\\"Thread\\\" in thread_line:\\n # Get the node data from node_line\\n result = self._get_kernel_result(\\n serial_number,\\n node_line.split(','),\\n thread_line.split(',')\\n )\\n\\n if result is None:\\n continue\\n\\n result_list.append(result)\\n # Calculate the total time.\\n total_time = result[2]\\n ai_cpu_total_time_summary += total_time\\n # Increase node serial number.\\n serial_number += 1\\n elif \\\"Node\\\" in node_line and \\\"Thread\\\" not in thread_line:\\n node_type_name = node_line.split(',')[0].split(':')[-1]\\n logger.warning(\\\"The node type:%s cannot find thread data\\\", node_type_name)\\n return ai_cpu_total_time_summary, result_list\",\n \"def parse_feature(self, feature_key, lines):\\n ...\",\n \"def doomed_parser(line):\\n raise exceptions.LineParseException('Error occurred')\",\n \"def readlogfile(file_name):\\n F = open(file_name, \\\"r\\\")\\n M = F.readlines()\\n F.close()\\n x = []\\n y = []\\n \\n for i in range(len(M)):\\n if find(M[i], \\\"#\\\") <0:\\n # Since the x,y are recorded as, e.g., '[398:402,300:304]'\\n x.append(float(M[i].split('[')[1].split(':')[0]) + 2.0)\\n y.append(float(M[i].split(',')[1].split(':')[0]) + 2.0)\\n \\n # Notice this function allows multiple stars to be chosen. For our purposes here,\\n # we need pick only one, and so return the first values in the list.\\n return x[0], y[0]\",\n \"def load_regular_coord_by_line(line):\\n elems = line.split('\\\\t')\\n if len(elems) < 4:\\n elems = line.split(',')\\n if len(elems) < 4:\\n elems = line.split(' ')\\n\\n [X1, Y1, W, H] = elems[0:4]\\n coord_regular = [int(X1), int(Y1), int(W), int(H)]\\n return coord_regular\",\n \"def parse(self):\\n\\t\\tfirst = None\\n\\t\\tf = open(self.input_file)\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tflow,t,sequence,size = line.split()\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t# append data to a list of tuples\\n\\t\\t\\tflow = int(flow)\\n\\t\\t\\tt = float(t)\\n\\t\\t\\tsequence = int(sequence)\\n\\t\\t\\tif size == \\\"x\\\":\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tsize = int(size)\\n\\t\\t\\tif not size == 0:\\n\\t\\t\\t\\tif flow == 1:\\n\\t\\t\\t\\t\\tself.data1.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 2:\\n\\t\\t\\t\\t\\tself.data2.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 3:\\n\\t\\t\\t\\t\\tself.data3.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 4:\\n\\t\\t\\t\\t\\tself.data4.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 5:\\n\\t\\t\\t\\t\\tself.data5.append((t, sequence, size))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"Erroneous data: \\\",flow, t, sequence, size\\n\\t\\t\\t# Keep track of the minimum and maximum time seen\\n\\t\\t\\tif not self.min_time or t < self.min_time:\\n\\t\\t\\t\\tself.min_time = t\\n\\t\\t\\tif not self.max_time or t > self.max_time:\\n\\t\\t\\t\\tself.max_time = t\\n\\n\\t\\t\\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6670215","0.6484195","0.6202049","0.61103636","0.5995822","0.5897944","0.5888218","0.58159584","0.57317466","0.5645726","0.56055975","0.55672276","0.55463046","0.55388415","0.55194986","0.55111915","0.5509744","0.55055726","0.5483326","0.54770184","0.5465886","0.54570895","0.54524744","0.5423845","0.5398041","0.5398041","0.5398041","0.5398041","0.5397984","0.53813803","0.5368919","0.53672004","0.535464","0.5333608","0.53215057","0.5316749","0.5315787","0.529568","0.5292059","0.52672243","0.5265506","0.5248204","0.5237473","0.5235293","0.52241886","0.52241886","0.5223019","0.52171373","0.5215269","0.52010906","0.51854026","0.51793504","0.5178843","0.5166506","0.516077","0.51564825","0.51531285","0.51522624","0.5148474","0.51424754","0.51416415","0.51373637","0.5136216","0.5099763","0.5097705","0.5083215","0.50815076","0.5073259","0.5066409","0.5066381","0.5060576","0.50597644","0.5058827","0.5056352","0.5052406","0.5051651","0.5051203","0.50432134","0.50432134","0.5042337","0.5033903","0.5025639","0.50222784","0.50205165","0.50095886","0.5008532","0.5004431","0.50043684","0.500129","0.4996022","0.49944782","0.4993242","0.49883732","0.49874756","0.49865717","0.49836966","0.49739528","0.4973132","0.49689922","0.49523634"],"string":"[\n \"0.6670215\",\n \"0.6484195\",\n \"0.6202049\",\n \"0.61103636\",\n \"0.5995822\",\n \"0.5897944\",\n \"0.5888218\",\n \"0.58159584\",\n \"0.57317466\",\n \"0.5645726\",\n \"0.56055975\",\n \"0.55672276\",\n \"0.55463046\",\n \"0.55388415\",\n \"0.55194986\",\n \"0.55111915\",\n \"0.5509744\",\n \"0.55055726\",\n \"0.5483326\",\n \"0.54770184\",\n \"0.5465886\",\n \"0.54570895\",\n \"0.54524744\",\n \"0.5423845\",\n \"0.5398041\",\n \"0.5398041\",\n \"0.5398041\",\n \"0.5398041\",\n \"0.5397984\",\n \"0.53813803\",\n \"0.5368919\",\n \"0.53672004\",\n \"0.535464\",\n \"0.5333608\",\n \"0.53215057\",\n \"0.5316749\",\n \"0.5315787\",\n \"0.529568\",\n \"0.5292059\",\n \"0.52672243\",\n \"0.5265506\",\n \"0.5248204\",\n \"0.5237473\",\n \"0.5235293\",\n \"0.52241886\",\n \"0.52241886\",\n \"0.5223019\",\n \"0.52171373\",\n \"0.5215269\",\n \"0.52010906\",\n \"0.51854026\",\n \"0.51793504\",\n \"0.5178843\",\n \"0.5166506\",\n \"0.516077\",\n \"0.51564825\",\n \"0.51531285\",\n \"0.51522624\",\n \"0.5148474\",\n \"0.51424754\",\n \"0.51416415\",\n \"0.51373637\",\n \"0.5136216\",\n \"0.5099763\",\n \"0.5097705\",\n \"0.5083215\",\n \"0.50815076\",\n \"0.5073259\",\n \"0.5066409\",\n \"0.5066381\",\n \"0.5060576\",\n \"0.50597644\",\n \"0.5058827\",\n \"0.5056352\",\n \"0.5052406\",\n \"0.5051651\",\n \"0.5051203\",\n \"0.50432134\",\n \"0.50432134\",\n \"0.5042337\",\n \"0.5033903\",\n \"0.5025639\",\n \"0.50222784\",\n \"0.50205165\",\n \"0.50095886\",\n \"0.5008532\",\n \"0.5004431\",\n \"0.50043684\",\n \"0.500129\",\n \"0.4996022\",\n \"0.49944782\",\n \"0.4993242\",\n \"0.49883732\",\n \"0.49874756\",\n \"0.49865717\",\n \"0.49836966\",\n \"0.49739528\",\n \"0.4973132\",\n \"0.49689922\",\n \"0.49523634\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.49969345"},"document_rank":{"kind":"string","value":"89"}}},{"rowIdx":4694,"cells":{"query":{"kind":"string","value":"Obtains the record in the set with the time closest to the given $unix_time. If this record with not $within the correct number of seconds, an exception is raised."},"document":{"kind":"string","value":"def get_record(self, unix_time, within):\n\t\tif len(self.records) <= 0:\n\t\t\traise Exception(\"No records in this set\")\n\t\tr = self.records[0]\n\t\tclosest_record = r\n\t\tclosest_delta = abs(r.unix_time - unix_time)\n\n\t\tfor r in self.records[1:]:\n\t\t\tdelta = abs(r.unix_time - unix_time)\n\t\t\tif delta < closest_delta:\n\t\t\t\tclosest_record = r\n\t\t\t\tclosest_delta = delta\n\t\tif closest_delta > within:\n\t\t\traise Exception(\"Closest record to %d was %d (delta=%d) which exceeds limit of %d\" %\n\t\t\t\t(unix_time, closest_record.unix_time, closest_delta, within))\n\n\t\treturn closest_record"},"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 get_closest_record(self, time):\n dist = 10000000\n record = -1\n # TODO: optimise a bit\n for i, itime in enumerate(self.times):\n if (abs(time-itime)) < dist:\n dist = abs(time-itime)\n record = i\n\n return record","def find_nearest_time(self, time):\n\n idx = np.searchsorted(self.times, time, side=\"left\")\n if idx > 0 and (idx == len(self.times) or math.fabs(time - self.times[idx-1]) < math.fabs(time - self.times[idx])):\n return self.times[idx-1]\n else:\n return self.times[idx]","def closest_time(self, when):\n try:\n return np.argmin(np.abs(self.time.datetime - when))\n except AttributeError:\n self.load_time()\n return np.argmin(np.abs(self.time.datetime - when))","def nearest_time_sql(self, t):\n if self.verbose:\n sys.stderr.write('SQL Time %s' % t)\n self.cursor.execute('SELECT * FROM tracklog WHERE dt <= ? ORDER BY dt DESC LIMIT 1', (t,))\n d = self.cursor.fetchone()\n if d != None:\n t0 = [datetime.strptime(d[0],'%Y-%m-%d %H:%M:%S')]\n t0.extend(d[1:])\n else:\n t0 = None\n self.cursor.fetchall()\n self.cursor.execute('SELECT * FROM tracklog WHERE dt >= ? ORDER BY dt LIMIT 1', (t,))\n d2 = self.cursor.fetchone()\n if d2 != None:\n t1 = [datetime.strptime(d2[0],'%Y-%m-%d %H:%M:%S')]\n t1.extend(d2[1:])\n else:\n t1 = None\n self.cursor.fetchall()\n if self.verbose:\n sys.stderr.write('SQL Resuls %s %s' % (t0,t1))\n if t0 == None or t1 == None:\n return None\n return t0,t1","def nearest():\n\n # Time this functions.\n timer = coils.Timer()\n\n # Parse the URL parameter \"time\".\n errors = list()\n try:\n tstamp_query = flask.request.args.get('time')\n time_query = coils.string2time(tstamp_query)\n assert time_query != None\n except:\n errors.append('Failed to parse \"time\" parameter.')\n\n # Bail on any errors.\n if errors:\n return flask.jsonify(errors=errors)\n \n return flask.jsonify(\n result=getNearestTime(time_query),\n elapsed=timer.get().total_seconds(),\n )","def closest_to(self, a, b):\n diff_a = abs(a.ts - self.ts)\n diff_b = abs(b.ts - self.ts)\n if diff_a < diff_b and diff_a < TIME_THRESHOLD:\n return a\n elif diff_b < TIME_THRESHOLD:\n return b\n return None","def _nearest_datetime(self, datetime_list, target_datetime):\n if not datetime_list:\n raise errors.ParserError(\n \"Input parameter datetime_list length is zero. Required\"\n \" parameters: [datetime.datetime], datetime.datetime\")\n work_list = [entry for entry in datetime_list if entry < target_datetime]\n if not work_list:\n raise errors.ParserError(\n \"work_list length is zero. Entries in datetime_list\"\n \" {} are not < target_datetime {}\".format(datetime_list,\n target_datetime))\n return min(\n work_list,\n key=lambda datetime_entry: abs(datetime_entry - target_datetime))","def findNearestTime(foamCase, time):\n times = list(getTimeFolders(foamCase,returnType=\"float\"))\n strTimes = np.array(getTimeFolders(foamCase,returnType=\"string\"))\n if time in times:\n try:\n intTime = int(strTimes[times.index(time)])\n return int(time)\n except:\n return time\n else:\n nearestTime = times[np.argmin(np.abs(np.array(times)-time))]\n print(\"Time %f is not available, choosing nearest time %f\" % ( time, nearestTime))\n try:\n intTime = int(strTimes[times.index(nearestTime)])\n return int(nearestTime)\n except:\n return nearestTime","def bisect(self, dtime, b=0): # pylint: disable=invalid-name\n return self._collection.bisect(dtime, b)","def getNearestTime(time_query):\n\n # Convert datetime object to string, for lookup in database.\n tstamp_query = coils.time2string(time_query)\n\n # Retrieve image timestamps.\n try:\n tstamp_left = db.session.query(mapping.Image.time).\\\n filter(mapping.Image.time <= tstamp_query).\\\n order_by(mapping.Image.time.desc()).limit(1)\n tstamp_left = tstamp_left[0].time\n delta_left = abs(coils.string2time(tstamp_left) - time_query)\n except:\n tstamp_left = None\n delta_left = dt.timedelta.max\n \n try:\n tstamp_right = db.session.query(mapping.Image.time).\\\n filter(mapping.Image.time >= tstamp_query).\\\n order_by(mapping.Image.time).limit(1)\n tstamp_right = tstamp_right[0].time\n delta_right = abs(coils.string2time(tstamp_right) - time_query)\n except:\n tstamp_right = None\n delta_right = dt.timedelta.max\n \n # The nearest value has the smallest delta from the query.\n result = tstamp_left if (delta_left < delta_right) else tstamp_right\n return result","def mempool_assert_relative_time_exceeds(\n condition: ConditionWithArgs, unspent: CoinRecord, timestamp: uint64\n) -> Optional[Err]:\n try:\n expected_seconds = int_from_bytes(condition.vars[0])\n except ValueError:\n return Err.INVALID_CONDITION\n\n if timestamp is None:\n timestamp = uint64(int(time.time()))\n if timestamp < expected_seconds + unspent.timestamp:\n return Err.ASSERT_SECONDS_RELATIVE_FAILED\n return None","def findontarget(starttime, event_list):\n for r in event_list:\n if r[0]==18 and r[1]>starttime: return r[1]\n return None","def getElemAfterTime(self, stamp):\n newer = [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\n if time >= stamp]\n if not newer:\n return None\n return newer[0]","def locate_nearest_event(self):\n nearest_event_date = ''\n min = 1000000\n today = self.get_today()\n event_array = self.events.keys()\n for event_date in event_array:\n event_date = self.date_to_operate_format(event_date)\n if int(event_date) - int(today) > 0:\n if int(event_date) - int(today) < min:\n min = int(event_date) - int(today)\n nearest_event_date = event_date\n\n nearest_event = '0'\n if len(event_array) > 0:\n nearest_event = self.change_format_to_database_index(nearest_event_date)\n\n return nearest_event","def getElemBeforeTime(self, stamp):\n older = [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\n if time <= stamp]\n if not older:\n return None\n return older[-1]","def mempool_assert_absolute_time_exceeds(condition: ConditionWithArgs, timestamp: uint64) -> Optional[Err]:\n try:\n expected_seconds = int_from_bytes(condition.vars[0])\n except ValueError:\n return Err.INVALID_CONDITION\n\n if timestamp is None:\n timestamp = uint64(int(time.time()))\n if timestamp < expected_seconds:\n return Err.ASSERT_SECONDS_ABSOLUTE_FAILED\n return None","def find_above(self, time, level):\n\n if self.get(time) >= level:\n return time\n ix = self._trace.bisect_right(time)\n for t, lvl in self._trace.items()[ix:]:\n if lvl >= level:\n return t\n return None","def get_prev_time(time, c_type=None, c_pk=None):\n\n # TODO if efficiency is an issue here, make occ_times a generator and\n # keep a queue (collections.deque) of the relevant times; this should\n # work because we're moving through them in sequence so could safely\n # discard those before the current time as we go\n\n if c_pk:\n def filter_func(obj):\n return obj['colloquialism__pk'] == c_pk\n elif c_type:\n def filter_func(obj):\n return obj['colloquialism__type'] == c_type\n else:\n def filter_func(obj):\n return True\n\n # filter out relevant times\n filtered = filter(\n lambda obj: filter_func(obj) and obj['start_exact'] < time,\n occ_times)\n\n if not len(filtered):\n return None\n\n # return last filtered time since they are in order\n return filtered[-1]['start_exact']","def from_unix_sec(self):\n try:\n self.in_unix_sec = dt.utcfromtimestamp(float(unix)).strftime('%Y-%m-%d %H:%M:%S.%f')\n except Exception as e:\n if not args.log:\n pass\n else:\n logging.error(str(type(e)) + \",\" + str(e))\n self.in_unix_sec = False\n return self.in_unix_sec","def cacheFindEntry(cache, cameraID, desiredTime):\n if not cameraID in cache:\n return None\n cameraTimes = cache[cameraID]\n closestEntry = min(cameraTimes, key=lambda x: abs(x['time'] - desiredTime))\n if abs(closestEntry['time'] - desiredTime) < 30:\n # logging.warning('close: %s', str(closestEntry))\n return os.path.join(cache['readDir'], closestEntry['fileName'])\n else:\n # logging.warning('far: %s, %s', str(desiredTime), str(closestEntry))\n return None","def object_at(self, time):\n for event in self._timeline: \n if time >= event.start_time and time <= event.end_time:\n return event.obj\n return self._timeline[-1].obj","def subset_by_time(prediction_dict, desired_times_unix_sec):\n\n error_checking.assert_is_numpy_array(\n desired_times_unix_sec, num_dimensions=1\n )\n error_checking.assert_is_integer_numpy_array(desired_times_unix_sec)\n\n desired_indices = numpy.array([\n numpy.where(prediction_dict[VALID_TIMES_KEY] == t)[0][0]\n for t in desired_times_unix_sec\n ], dtype=int)\n\n prediction_dict = subset_by_index(\n prediction_dict=prediction_dict, desired_indices=desired_indices\n )\n\n return prediction_dict, desired_indices","def find_below(self, time, level):\n\n if self.get(time) <= level:\n return time\n ix = self._trace.bisect_right(time)\n for t, lvl in self._trace.items()[ix:]:\n if lvl <= level:\n return t\n return None","def lookup_time_spent():\n while True:\n search_query = input('Show entries in which time spent '\n '(in minutes) is: ')\n if validate_lookup_time_spent_format(search_query):\n break\n print('** Please enter positive integer **')\n return Entry.select().where(Entry.time_spent == search_query)","def test_4_data_fetching_unix_time_and_insertion(self):\n d1 = date.today()\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\n self.assertTrue(success)\n self.assertEqual(result[0]['Datetime'],self.unix_time)","def GetPriorUniquePoint(lap: gps_pb2.Lap,\n point_c: gps_pb2.Point) -> gps_pb2.Point:\n index = -1\n point = lap.points[-1]\n while point.time.ToNanoseconds() == point_c.time.ToNanoseconds():\n index -= 1\n point = lap.points[index]\n return point","def get_closest_minute(t):\n ts = dt.datetime.utcfromtimestamp(t/1000)\n s = ts.second\n if s < 30:\n return dt.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute)\n else:\n return dt.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute) + dt.timedelta(minutes=1)","def validity_by_time(self):\n conn = psycopg2.connect(self.conn)\n permissable_maximum_age_secs = 600 # 600s = 10mins\n query = \"SELECT time FROM steve_sense_sensor_logs ORDER BY time DESC LIMIT 1\"\n cur = conn.cursor()\n cur.execute(query)\n queryResult = cur.fetchall()\n age_seconds = (datetime.datetime.now(\n timezone.utc) - queryResult[0][0]).seconds\n cur.close()\n conn.close()\n if age_seconds > permissable_maximum_age_secs:\n print(\"Check Sensor, last sample is \"+str(age_seconds)+\" old\")\n return False\n else:\n return True","def fetch_entry(unique_id, time_stamp):\n print('Fetching items with unique_id: {}'.format(unique_id))\n entry_exists = False\n item = None\n try:\n resp = TIME_TABLE.get_item(Key={'uniqueId': unique_id, 'timeStamp': time_stamp})\n print(resp)\n item = resp.get('Item')\n print(item)\n if item:\n entry_exists = True\n except Exception as e:\n print('Unique Item does not exists: {0}. Error: {1}'.format(unique_id, e))\n\n return entry_exists, item","def findFirstHigh(thisStFile):\n with open(thisStFile) as f:\n reader = csv.DictReader(f, delimiter='\\t')\n for row in reader:\n return datetime.datetime.strptime(row['time'], fmt)","def closest_point_in_cloud(point, cloud):\n data = sort_points(point, cloud)\n return data[0]","def subset_by_time(example_dict, first_time_unix_sec, last_time_unix_sec):\n\n error_checking.assert_is_integer(first_time_unix_sec)\n error_checking.assert_is_integer(last_time_unix_sec)\n error_checking.assert_is_geq(last_time_unix_sec, first_time_unix_sec)\n\n good_indices = numpy.where(numpy.logical_and(\n example_dict[VALID_TIMES_KEY] >= first_time_unix_sec,\n example_dict[VALID_TIMES_KEY] <= last_time_unix_sec\n ))[0]\n\n for this_key in ONE_PER_EXAMPLE_KEYS:\n if isinstance(example_dict[this_key], list):\n example_dict[this_key] = [\n example_dict[this_key][k] for k in good_indices\n ]\n else:\n example_dict[this_key] = (\n example_dict[this_key][good_indices, ...]\n )\n\n return example_dict, good_indices","def check_time_since_last_data(device_origin):\n actual_time = time.time()\n sec_since_last_data = actual_time - mon_item.read_device_status_values(device_origin)[1]\n min_since_last_data = sec_since_last_data / 60\n min_since_last_data = int(min_since_last_data)\n latest_data_hr = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(latest_data))\n return min_since_last_data","def test_get_between_datetime_same_microseconds(self):\n now = datetime.datetime.utcnow()\n start_dt = testdata.get_past_datetime(now)\n stop_dt = testdata.get_between_datetime(start_dt, now)\n self.assertGreater(stop_dt, start_dt)","def get(self, target_time=None):\n if target_time is None:\n target_time = Servo.ctime()\n size = len(self.history)\n idx = -1\n with History.lock:\n while idx >= -size:\n data = self.history[idx]\n timestamp, position = data[0], data[1:]\n if timestamp <= target_time:\n return data\n elif idx - 1 > -size:\n prev_timestamp = self.history[idx - 1]\n if prev_timestamp <= target_time:\n return self.history[idx - 1] # It's better to interpolate\n else:\n idx -= 1\n continue\n else:\n return self.history[-size]","def get_time_ceiling(time, data):\n if time >= data.index.max():\n return data.iloc[-1]\n elif time <= data.index.min():\n return data.iloc[0]\n return data[str(time):].iloc[0]","def get_unixtime(self):\n if not self.Complete():\n raise DateTimeError(\"get_unixtime requires complete timepoint\")\n zoffset = self.time.GetZoneOffset()\n if zoffset is None:\n raise DateTimeError(\"get_unixtime requires timezone\")\n elif zoffset == 0:\n zt = self\n else:\n zt = self.ShiftZone(zDirection=0)\n days = zt.date.GetAbsoluteDay() - EPOCH.date.GetAbsoluteDay()\n seconds = zt.time.GetTotalSeconds() - EPOCH.time.GetTotalSeconds()\n return 86400 * days + seconds","def get_index_before_time(messages, time):\n # Getting the timestamp that specifies the cutoff.\n timestamp = messages[0].timestamp + time\n\n for index, message in enumerate(messages):\n if message.timestamp > timestamp:\n return index - 1","def find_nearest_stop(self, gtfs, trip_id, lat, long):\n stop_ids = [stop_times['stop_id'] for stop_times in gtfs[\"stop_times\"][trip_id]]\n min_distance = 10000000\n closest_stop = stop_ids[0]\n for stop_id in stop_ids:\n stop = gtfs[\"stops\"][stop_id]\n distance = geopy.distance.distance((lat, long), (stop['stop_lat'], stop['stop_lon'])).km\n if distance < min_distance:\n closest_stop_id = stop_id\n min_distance = distance\n return closest_stop_id","def _FindNearestAnat(self, acqtime):\n tdiff_min = 1e6\n for anat in self.entry_map['anat']:\n if self.info[anat]['type'] == 'T1High' and \\\n self.info[anat]['InversionTime'] > 0.:\n tdiff = abs(acqtime - self.info[anat]['acqtime'])\n if tdiff < tdiff_min:\n tdiff_min = tdiff\n anat_min = anat\n return anat_min","def takeclosest(takecloselist, takecloseint):\n pos = bisect_left(takecloselist, takecloseint)\n if pos == 0:\n return takecloselist[0]\n if pos == len(takecloselist):\n return takecloselist[-1]\n before = takecloselist[pos - 1]\n after = takecloselist[pos]\n if after - takecloseint < takecloseint - before:\n return after\n else:\n return before","def _matchTime(self, time: float):\n return self._comparator['Time'] < time","def get_booking_at(self, datetime):\n for booking in self.booking_set.all():\n if booking.schedule_start <= datetime < booking.schedule_end and not booking.is_cancelled():\n return booking\n return None","def at_time(self, time):\n return self._collection.at_time(time)","def test_searchSince(self):\n self.assertTrue(\n self.server.search_SINCE(self.earlierQuery, self.seq, self.msg))\n self.assertTrue(\n self.server.search_SINCE(self.sameDateQuery, self.seq, self.msg))\n self.assertFalse(\n self.server.search_SINCE(self.laterQuery, self.seq, self.msg))","def unix_to_timestamp(unix):\n return int(round(unix * 1e6))","def FromUnixTime(cls, unixTime):\n utcTuple = pytime.gmtime(0)\n t, overflow = Time.FromStructTime(utcTuple).Offset(seconds=unixTime)\n d = Date.FromStructTime(utcTuple).Offset(days=overflow)\n return cls(date=d, time=t.WithZone(zDirection=0))","def get_best_time():\n\n # Always remember about db.session.rollback() when debugging\n\n max_timing = db.session.query(func.min(Game.timing)).filter(Game.status == \"won\").first()\n time_user = db.session.query(Game.timing, User.username).join(User).filter(Game.timing == max_timing, Game.status == \"won\").first()\n\n return time_user","def getValueAt(self, time):\n for tvp in self.timeValuePairs:\n if time <= tvp[0]:\n return tvp[1]\n return self.defaultValue","def _parse_timestamp(self, api_time):\n return (\n pendulum.parse(api_time)\n if api_time is not None\n else pendulum.from_timestamp(-1)\n )","def findguidingstop(starttime, event_list):\n for r in event_list:\n if r[0]==6 and r[1]+datetime.timedelta(seconds=0)>starttime: return r[1]\n return None","def olderThan(record, value=Constants.older_than):\n # Check first if the record is valid\n if isRecordNameValid(record):\n # Get the record values and split them - year:month:day:hour:minute\n splitted_record = record.split(':')\n # Get the current date and time from the system and split them\n splitted_date = getTime().split(':')\n # Check if the year, month and day are the same\n if splitted_record[0] == splitted_date[0] \\\n and splitted_record[1] == splitted_date[1] \\\n and splitted_record[2] == splitted_date[2]:\n # Change the record hour in minutes and add the record minutes - how many minutes since the record stored\n record_minutes = int(splitted_record[3]) * 60 + int(splitted_record[4])\n # Change the current hour in minutes and add the current minutes - how many minutes passed today\n minutes = int(splitted_date[3]) * 60 + int(splitted_date[4])\n # Check if the difference is bigger than the constant - the record is too old and must be deleted\n if int(minutes - record_minutes) >= value:\n return True\n else:\n return False\n else:\n # The record is way too old - at least 1 day\n return True\n # The record is not valid and must be deleted\n return True","def test_larger_rhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = datetime(2012, 9, 20, 2, 59)\n rhs = datetime(2012, 9, 20, 3, 00)\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(rhs, result)","def findguidingstart(starttime, event_list):\n for r in event_list:\n if r[0]==5 and r[1]>starttime: return r[1]\n return None","def get_next_available_open_timeset(\n a_timestamp: str, list_of_timesets: list, debug_mode: bool = False\n) -> dict:\n\n results = {\"next_free_timeset\": None, \"reached_end_of_list\": True}\n\n sorted_list_of_timesets = sorted(list_of_timesets, key=lambda k: k[0])\n\n filtered_list_of_timesets = []\n for timeset in sorted_list_of_timesets:\n if datetime.fromisoformat(a_timestamp) <= datetime.fromisoformat(timeset[1]):\n filtered_list_of_timesets.append(timeset)\n\n # get rid of timesets that end before timestamp\n if filtered_list_of_timesets != sorted_list_of_timesets:\n print_time_data(\n \"Next available_timeset: filtering effect from:\",\n sorted_list_of_timesets,\n debug_mode,\n )\n print_time_data(\n \"Next available_timeset: filtering effect to:\",\n filtered_list_of_timesets,\n debug_mode,\n )\n\n # the last timeset triggers some actions. However if the last is also the first\n # i.e. list of 1 timeset, then its too early to set off the trigger\n index_of_last_timeset = (len(filtered_list_of_timesets) - 1) or 1\n\n temp_timestamp = a_timestamp\n\n for timeset_index, timeset in enumerate(filtered_list_of_timesets):\n if datetime.fromisoformat(timeset[0]) > datetime.fromisoformat(temp_timestamp):\n\n results[\"next_free_timeset\"] = [temp_timestamp, timeset[0]]\n if timeset_index != index_of_last_timeset:\n results[\"reached_end_of_list\"] = False\n\n print_time_data(\n \"Next available_timeset: Going to break: current timeset\",\n timeset,\n debug_mode,\n )\n print_time_data(\n \"Next available_timeset: Going to break: timestamp\",\n temp_timestamp,\n debug_mode,\n )\n print_time_data(\n \"Next available_timeset: Going to break: results\", results, debug_mode\n )\n break\n\n temp_timestamp = timeset[1]\n\n # Check if the found timeset has a startTime\n # inside another timeset\n if results[\"next_free_timeset\"]:\n temp_timeset = validate_update_timestamp(\n results[\"next_free_timeset\"], filtered_list_of_timesets, debug_mode\n )\n results[\"next_free_timeset\"] = temp_timeset\n\n print_time_data(\"Next available_timeset: Final results\", results, debug_mode)\n\n return results","def iso_from_unix_time(unix_time: float, precision: int = 9) -> ISOTimestamp:\n frac_part, int_part = math.modf(unix_time)\n\n seconds = int(int_part)\n\n if frac_part < 0:\n seconds -= 1\n frac_part += 1\n\n decimals = f\"{{0:.{precision}f}}\".format(frac_part)[1:].rstrip('0.') # noqa\n\n return _from_unix_time(seconds, decimals)","def time(self,orid_time,window=5):\n #{{{ Function to get possible matches of events for some epoch time.\n\n results = {}\n\n #\n # If running in simple mode we don't have access to the tables we need\n #\n if config.simple:\n return results\n\n orid_time = _isNumber(orid_time)\n\n if not orid_time:\n print \"Not a valid number in function call: %s\" % orid_time\n return\n \n start = float(orid_time)-float(window)\n end = float(orid_time)+float(window)\n\n dbname = self.dbcentral(orid_time)\n\n if not db:\n print \"No match for orid_time in dbcentral object: (%s,%s)\" % (orid_time,self.dbcentral(orid_time))\n return\n\n try: \n db = datascope.dbopen( dbname , 'r' )\n db.lookup( table='origin')\n db.query(datascope.dbTABLE_PRESENT) \n except Exception,e:\n print \"Exception on Events() time(%s): Error on db pointer %s [%s]\" % (orid_time,db,e)\n return\n\n db.subset( 'time >= %f' % start )\n db.subset( 'time <= %f' % end )\n\n try:\n db = datascope.dbopen( dbname , 'r' )\n db.lookup( table='wfdisc' )\n records = db.query(datascope.dbRECORD_COUNT)\n\n except:\n records = 0\n\n if records:\n\n for i in range(records):\n\n db.record = i\n\n (orid,time) = db.getv('orid','time')\n\n orid = _isNumber(orid)\n time = _isNumber(time)\n results[orid] = time\n\n return results","def closest_approach_to_camera(scene, speaker_object) -> (float, int):\n max_dist = sys.float_info.max\n at_time = scene.frame_start\n for frame in range(scene.frame_start, scene.frame_end + 1):\n scene.frame_set(frame)\n rel = speaker_object.matrix_world.to_translation() - scene.camera.matrix_world.to_translation()\n dist = norm(rel)\n\n if dist < max_dist:\n max_dist = dist\n at_time = frame\n\n return max_dist, at_time","def get(self, column, key, time):\n # Check connection\n self._checkInit()\n \n # Split the time string\n (year,month,day,hour) = self._splitTime(time)\n\n # Construct the query\n query = \"SELECT {} FROM {} WHERE {} ORDER BY TIMESTAMP DESC LIMIT 1\".format(\n column,\n self.table,\n self._exactMatchClause(key,year,month,day,hour)) \n\n #logging.debug(\"query: \\\"{}\\\"\".format(query))\n\n # Get Connection\n cnx = self.getConnection()\n cur = cnx.cursor()\n cur.execute(query)\n retval = None\n for fields in cur:\n retval = fields[0]\n break\n cur.close()\n cnx.close() \n return retval","def closest(self, x):\n # http://www.ahinson.com/algorithms_general/Sections/Geometry/PluckerLine.pdf\n # has different equation for moment, the negative\n\n x = arg.getvector(x, 3)\n\n lam = np.dot(x - self.pp, self.uw)\n p = self.point(lam) # is the closest point on the line\n d = np.linalg.norm( x - p)\n \n return namedtuple('closest', 'p d lam')(p, d, lam)","def find_closest_trajectory(cls, **kwargs):\n # if we can find an approximation that works to two\n # decimal places, just return that\n ideal_min_pitch = kwargs[\"pitch\"] - \\\n kwargs.get(\"ideal_min_pitch_differential\", cls.IDEAL_DIFFERENTIAL)\n ideal_max_pitch = kwargs[\"pitch\"] + \\\n kwargs.get(\"ideal_min_pitch_differential\", cls.IDEAL_DIFFERENTIAL)\n\n ideal_min_roll = kwargs[\"roll\"] - \\\n kwargs.get(\"ideal_min_roll_differential\", cls.IDEAL_DIFFERENTIAL)\n ideal_max_roll = kwargs[\"roll\"] + \\\n kwargs.get(\"ideal_min_roll_differential\", cls.IDEAL_DIFFERENTIAL)\n\n # find trajectories that we are good with even if they aren't the absolute\n # best\n ideal_trajectory = SolvedTrajectory.objects.filter(\n pitch__gt=ideal_min_pitch,\n roll__gt=ideal_min_roll\n ).filter(\n pitch__lt=ideal_max_pitch,\n roll__lt=ideal_max_roll)\n ideal_trajectory = ideal_trajectory.first()\n\n # if we found something in the ideal trajectory, just return that!\n if ideal_trajectory:\n best_trajectory = ideal_trajectory\n best_match_score = cls.get_match_score(\n best_trajectory, kwargs[\"pitch\"], kwargs[\"roll\"])\n\n # otherwise, we expand our filter and include more results\n else:\n\n # determine bounds on the pitch and the roll\n # of the trajectory we will return\n min_pitch = kwargs[\"pitch\"] - kwargs[\"min_pitch_differential\"]\n max_pitch = kwargs[\"pitch\"] + kwargs[\"min_pitch_differential\"]\n\n min_roll = kwargs[\"roll\"] - kwargs[\"min_roll_differential\"]\n max_roll = kwargs[\"roll\"] + kwargs[\"min_roll_differential\"]\n\n # determine the candidate trajectories\n candidate_trajectories = SolvedTrajectory.objects.filter(\n pitch__gt=min_pitch,\n roll__gt=min_roll\n ).filter(\n pitch__lt=max_pitch,\n roll__lt=max_roll\n )\n\n # determine the best match from what we have available\n best_trajectory = None\n best_match_score = float(\"inf\")\n\n for trajectory in candidate_trajectories:\n match_score = cls.get_match_score(\n trajectory, kwargs[\"pitch\"], kwargs[\"roll\"])\n\n if match_score < best_match_score:\n best_trajectory = trajectory\n best_match_score = match_score\n\n # calculate the norm of the deviation\n deviation = math.sqrt(best_match_score)\n return best_trajectory.file_name, deviation","def get_data_at_time(self, time=None, tolerance=0.0):\n if time is None:\n # If time is not specified, assume we want the entire time\n # set. Skip all the overhead, don't create a new object, and\n # return self.\n return self\n is_iterable = _is_iterable(time)\n time_iter = iter(time) if is_iterable else (time,)\n indices = []\n # Allocate indices list dynamically to support a general iterator\n # for time. Not sure if this will ever matter...\n for t in time_iter:\n if t in self._time_idx_map:\n idx = self._time_idx_map[t]\n else:\n idx = find_nearest_index(self._time, t, tolerance=tolerance)\n if idx is None:\n raise RuntimeError(\n \"Time point %s is invalid within tolerance %s\" % (t, tolerance)\n )\n indices.append(idx)\n if not is_iterable:\n indices = indices[0]\n return self.get_data_at_time_indices(indices)","def exists_at_time(e, t):\n t0 = 0\n for l, x in e:\n t1 = t0 + l\n if t > t0 and t < t1:\n return x\n t0 = t1\n return x","def since(self, ts):\n spec = {'ts': {'$gt': ts}}\n cursor = self.query(spec)\n while True:\n # todo: trap InvalidDocument errors:\n # except bson.errors.InvalidDocument as e:\n # logging.info(repr(e))\n for doc in cursor:\n yield doc\n if not cursor.alive:\n break\n time.sleep(1)","def test_larger_lhs(self):\n from sosbeacon.utils import get_latest_datetime\n\n lhs = datetime(2012, 9, 20, 3, 45)\n rhs = datetime(2012, 9, 20, 2, 45)\n\n result = get_latest_datetime(lhs, rhs)\n\n self.assertIs(lhs, result)","def test_blind_delete_with_datetime(self):\r\n uid = uuid4()\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n TestTimestampModel.get(id=uid).should.be.ok\r\n\r\n plus_five_seconds = datetime.now() + timedelta(seconds=5)\r\n\r\n TestTimestampModel.objects(id=uid).timestamp(plus_five_seconds).delete()\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)\r\n\r\n tmp = TestTimestampModel.create(id=uid, count=1)\r\n\r\n with self.assertRaises(TestTimestampModel.DoesNotExist):\r\n TestTimestampModel.get(id=uid)","def _get_closest_eth1_voting_period_start_block(\n self, timestamp: Timestamp\n ) -> BlockNumber:\n # Compare with the largest recoreded block timestamp first before querying\n # for the latest block.\n # If timestamp larger than largest block timestamp, request block from eth1 provider.\n if (\n self._largest_block_timestamp is None\n or timestamp > self._largest_block_timestamp\n ):\n try:\n block = self._eth1_data_provider.get_block(\"latest\")\n except BlockNotFound:\n raise Eth1MonitorValidationError(\"Fail to get latest block\")\n if block.timestamp <= timestamp:\n return block.number\n else:\n block_number = block.number\n # Try the latest `self._num_blocks_confirmed` blocks until we give up\n for i in range(1, self._num_blocks_confirmed + 1):\n lookback_number = block_number - i\n if lookback_number < 0:\n break\n else:\n shifted_block = BlockNumber(lookback_number)\n block = self._eth1_data_provider.get_block(shifted_block)\n if block.timestamp <= timestamp:\n return block.number\n raise Eth1BlockNotFound(\n \"Can not find block with timestamp closest\"\n \"to voting period start timestamp: %s\",\n timestamp,\n )\n else:\n # NOTE: It can be done by binary search with web3 queries.\n # Regarding the current block number is around `9000000`, not sure if it is worthwhile\n # to do it through web3 with `log(9000000, 2)` ~= 24 `getBlock` queries.\n # It's quite expensive compared to calculating it by the cached data\n # which involves 0 query.\n\n # Binary search for the right-most timestamp smaller than `timestamp`.\n all_timestamps = tuple(self._block_timestamp_to_number.keys())\n target_timestamp_index = bisect.bisect_right(all_timestamps, timestamp)\n # Though `index < 0` should never happen, check it for safety.\n if target_timestamp_index <= 0:\n raise Eth1BlockNotFound(\n \"Failed to find the closest eth1 voting period start block to \"\n f\"timestamp {timestamp}\"\n )\n else:\n # `bisect.bisect_right` returns the index we should insert `timestamp` into\n # `all_timestamps`, to make `all_timestamps` still in order. The element we are\n # looking for is actually `index - 1`\n index = target_timestamp_index - 1\n target_key = all_timestamps[index]\n return self._block_timestamp_to_number[target_key]","def unix_timestamp_date(unix=1459141485):\n return datetime.datetime.fromtimestamp(int(unix))","def get_closest_rt_match(self, name_rt_dict):\n abs_dic = {}\n for index, name_rt in name_rt_dict.items():\n dup_name = name_rt[0]\n dup_rt = name_rt[1]\n for name, rt in self.std_temp_dict.items():\n if name == dup_name:\n abs_value = abs(dup_rt - rt)\n abs_dic[index] = abs_value\n\n print(abs_dic)\n keep_index = min(abs_dic, key=lambda x: abs_dic.get(x))\n print(\"The keep_index is\", keep_index) # Return this and then use it as below.\n\n return keep_index","def _validate_timestamp(timestamp):\n dts = datetime.datetime.utcnow()\n current_time = round(time.mktime(dts.timetuple()) + dts.microsecond/1e6)\n if (timestamp - current_time) > SYNC_TOLERANCE:\n raise InvalidTransaction(\n 'Timestamp must be less than local time.'\n ' Expected {0} in ({1}-{2}, {1}+{2})'.format(\n timestamp, current_time, SYNC_TOLERANCE))","def filter_lower_datetime(time, list_time):\n return [t for t in list_time if t <= time]","def _closest_opponent_to_object(self, raw_obs, o):\n min_d = None\n closest = None\n for p in raw_obs['right_team']:\n d = self._object_distance(o, p)\n if min_d is None or d < min_d:\n min_d = d\n closest = p\n assert closest is not None\n return closest","def get_time_from_db():\n if not (date := select(date for date in LastestCheck).order_by(lambda date: desc(date.id)).first()):\n date = LastestCheck(timestamp=int(datetime.timestamp(datetime.now())))\n commit()\n return date.timestamp","def to_python_datetime(unix_timestamp):\n return datetime.datetime.fromtimestamp(int(unix_timestamp),\n pytz.timezone(settings.TIME_ZONE))","def get_specific_nc_timeindex(fname,\n time_value,\n time_varname='time'):\n\n assert isinstance(time_value, datetime.date)\n\n nc_fid = netCDF4.Dataset(fname, 'r')\n time_values = netCDF4.num2date(nc_fid.variables[time_varname][:],\n nc_fid.variables[time_varname].units,\n nc_fid.variables[time_varname].calendar)\n nearest_index = 0\n nearest_value = time_values[nearest_index]\n nearest_diff = time_difference(nearest_value, time_value)\n for idx, tvalue in enumerate(time_values):\n this_diff = time_difference(tvalue, time_value)\n if this_diff < nearest_diff:\n nearest_index = idx\n nearest_value = time_values[idx]\n nearest_diff = this_diff\n\n return nearest_index","def _xid_pick_only_closest(self, xid):\n\t\tif len(xid) == 1:\n\t\t\tself._xid_sanity_check(xid)\n\n\t\telif len(xid) > 1: \n\t\t\tprint(\"[hscObj] multiple objects found, picking the closest\")\n\t\t\txid = self._resolve_multiple_sources(xid)\n\t\t\tself._xid_sanity_check(xid)\n\n\t\telif len(xid) < 1:\n\t\t\tprint(\"[hscObj] no object found\")\n\t\t\txid = None\n\n\t\treturn xid","def testTimestamps(self):\n predicate = \"metadata:predicate\"\n subject = \"aff4:/metadata:8\"\n\n # Extend the range of valid timestamps returned from the table to account\n # for potential clock skew.\n start = long(time.time() - 60) * 1e6\n data_store.DB.Set(subject, predicate, \"1\", token=self.token)\n\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\n\n # Check the time is reasonable\n end = long(time.time() + 60) * 1e6\n\n self.assert_(ts >= start and ts <= end)\n self.assertEqual(stored, \"1\")","def get_closest_bus_stop(due_time, stop_src, stop_ids, route_id):\n\n # Get index of where the SRC stop is in the tupple to serve as the high-bound, and store that position in original. Also, store the original due time, as it will be needed\n high_bound = 0\n original = 0\n original_due_time = due_time\n for i in range(0, len(stop_ids)):\n if str(stop_ids[i]) == stop_src:\n high_bound = i\n original = i\n break\n\n # Innitialize pointer to be halfway between the lowbound (set to 0 index) and the highbound (the SRC stop).\n pointer = original//4\n low_bound = 0\n\n # Optimally we want to find the stop where our bus is just 1 minute away, for better accuracy. But sometimes that is not possible, so we will\n # need to look for a bus further away. This variable, arrival_within_minutes, starts with 1 minutes, and will be increased as necessary.\n arrival_within_minutes = 1\n\n # Search until we find where the bus is\n while True:\n last_due_time = 0\n # Search while our due time is not 'Due' or within the specified minutes\n while due_time != 'Due' or int(due_time) > arrival_within_minutes:\n # Once more, get the buses for the stop we are currently looking at\n first_3_buses = get_due_time(str(stop_ids[pointer]), route_id)\n\n # Get just the first bus, since we already have the 3 buses from our SRC stop (this one is just looking for where one of those 3 buses is)\n possible_stop = filter_buses(first_3_buses)\n # Store the new due time, from the bus stop our binary algorithm selected\n new_due_time_due = possible_stop['duetime']\n\n # If the new due_time is the same as the last_due_time it means the algorithm got stuck without finding a better value, and we need to break, and change our\n # arrival_within_minutes for a longer time\n if new_due_time_due == last_due_time:\n break\n\n # If we found a 'Due' or within the arrival_within_minutes, return that index. That is the index of the stop where our bus is at/close to.\n if possible_stop['duetime'] == 'Due' or int(possible_stop['duetime']) <= arrival_within_minutes:\n # ('Found the bus with', new_due_time_due, 'minutes due time.')\n # This for loop is to check if the previous bus stop(s) have the same due time, and find a closer more accurae stop\n # print('Original pointer:', pointer)\n for i in range(pointer - 1, 0, -1):\n if new_due_time_due == (filter_buses(get_due_time(str(stop_ids[i]), route_id))['duetime']):\n pointer = i\n # print('New pointer:', pointer)\n else:\n break\n # Return the pointer, the index of the stop\n return pointer\n else:\n # If the due time at the possible stop is less than the one at SRC, we're on the right path, and need to look for a stop farther from the SRC\n if int(possible_stop['duetime']) < int(due_time):\n # Store the new, better due time\n due_time = possible_stop['duetime']\n # Change the highbound to the pointer and reduce our pointer again to halfway between lowbound and highbound\n high_bound = pointer\n pointer -= ((high_bound - low_bound)//4)\n else:\n # If the due time at the possible stop is bigger than the one at SRC, we've gone too far, and need to look for a stop closer to the SRC\n # The lowbound becomes the pointer and we move the pointer, again, to halfway between the lowbound and the highbound\n low_bound = pointer\n pointer += ((high_bound - low_bound)//4)\n # If we found a better (shortter) due time, we store this one for the next iteration and keep looking for an even better one\n last_due_time = new_due_time_due\n\n # If the algorithm comes to this part, it means we didn't find a stop where our bus was due wihin 1 (or more) minutes. So we need to increase the\n # arrival_within minutes to keep searching.\n arrival_within_minutes += 1\n\n # Reset our lowbound, highbound and pointer to restart the search\n low_bound = 0\n high_bound = original\n pointer = original // 4\n\n # If we start looking for a stop, previous to the SRC, were our bus has MORE duetime, we've gonne too far. Possibly, there are two buses running very close to one another,\n # and they may be due to our SRC stop at the same time (seen before too many times with the 17). In this case, we need to increase the original bound to take the stop where\n # we found the previous bus.\n if arrival_within_minutes > int(original_due_time):\n high_bound += 1\n return high_bound\n\n # Just a token return\n return 0","def test_parse_time_unix_timestamp(self):\n self.assertEqual(\n parse_time(\"1422748800\", None), datetime(2015, 2, 1, 0, 0, 0))\n self.assertEqual(parse_time(\"0\", None), datetime(1970, 1, 1, 0, 0, 0))\n # The following are treated as unix timestamps, not YYYYMMDD strings.\n self.assertEqual(\n parse_time(\"19000101\", None), datetime(1970, 8, 8, 21, 48, 21))\n self.assertEqual(\n parse_time(\"20150132\", None), datetime(1970, 8, 22, 5, 15, 32))\n self.assertEqual(\n parse_time(\"20151301\", None), datetime(1970, 8, 22, 5, 35, 1))","def test_subset_by_time(self):\n\n this_satellite_dict = satellite_io.subset_by_time(\n satellite_dict=copy.deepcopy(SATELLITE_DICT_ALL_EXAMPLES),\n desired_times_unix_sec=DESIRED_TIMES_UNIX_SEC\n )[0]\n\n self.assertTrue(compare_satellite_dicts(\n this_satellite_dict, SATELLITE_DICT_SUBSET_BY_TIME\n ))","def find_nearest_wav(self, wavelength):\n\n idx = np.searchsorted(self.wavelengths, wavelength, side=\"left\")\n if idx > 0 and (idx == len(self.wavelengths) or math.fabs(wavelength - self.wavelengths[idx-1]) < math.fabs(wavelength - self.wavelengths[idx])):\n return self.wavelengths[idx-1]\n else:\n return self.wavelengths[idx]","def closest_element(self, where, cartesian=False, threshold=None, vincenty=False):\n if not vincenty:\n if cartesian:\n x, y = self.grid.xc, self.grid.yc\n else:\n x, y = self.grid.lonc, self.grid.latc\n dist = np.sqrt((x - where[0])**2 + (y - where[1])**2)\n else:\n grid_pts = np.asarray([self.grid.lonc, self.grid.latc]).T\n where_pt_rep = np.tile(np.asarray(where), (len(self.grid.lonc),1))\n dist = np.asarray([vincenty_distance(pt_1, pt_2) for pt_1, pt_2 in zip(grid_pts, where_pt_rep)])*1000\n\n index = np.argmin(dist)\n if threshold:\n if dist.min() < threshold:\n index = np.argmin(dist)\n else:\n index = None\n\n return index","def find_nearest_spot(this_coord, coord_list, scale_z, scale_xy):\n closest_sed = np.inf\n closest_spot = 0\n for test_data in coord_list:\n test_spot_id = test_data[0]\n test_coords = (test_data[1:4])\n sed = sq_euc_distance(test_coords, this_coord, scale_z, scale_xy)\n if (sed < closest_sed):\n closest_sed = sed\n closest_spot = test_spot_id\n closest_spot_coords = test_coords\n return closest_spot, np.sqrt(closest_sed), closest_spot_coords","def _get_offset(self, seconds, default):\n now_nanosec = time.time_ns()\n target_nanosec = now_nanosec - utils.seconds_in_nano(seconds)\n\n query_values = {\n \"target_nanosec\": target_nanosec\n }\n\n self._cursor.execute(f\"\"\"\n SELECT rowid\n FROM {self._table_name}\n WHERE timestamp >= :target_nanosec\n ORDER BY timestamp\n LIMIT 1;\"\"\", query_values)\n\n offset_row = self._cursor.fetchone()\n rowid = offset_row[0] if offset_row else default\n\n return rowid - 1","def _mktime(self):\n epoch = datetime(1970, 1, 1)\n max_fold_seconds = 24 * 3600\n t = (self - epoch) // timedelta(0, 1)\n\n def local(u):\n y, m, d, hh, mm, ss = _time.localtime(u)[:6]\n return (datetime(y, m, d, hh, mm, ss) - epoch) // timedelta(0, 1)\n\n # Our goal is to solve t = local(u) for u.\n a = local(t) - t\n u1 = t - a\n t1 = local(u1)\n if t1 == t:\n # We found one solution, but it may not be the one we need.\n # Look for an earlier solution (if `fold` is 0), or a\n # later one (if `fold` is 1).\n u2 = u1 + (-max_fold_seconds, max_fold_seconds)[self.fold]\n b = local(u2) - u2\n if a == b:\n return u1\n else:\n b = t1 - u1\n assert a != b\n u2 = t - b\n t2 = local(u2)\n if t2 == t:\n return u2\n if t1 == t:\n return u1\n # We have found both offsets a and b, but neither t - a nor t - b is\n # a solution. This means t is in the gap.\n return (max, min)[self.fold](u1, u2)","def from_unix(cls, seconds, milliseconds=0):\n return datetime.datetime.fromtimestamp(seconds + milliseconds * .001)","def __find_nearest_weathers(\n self, timestamp: datetime, weather_list: list,\n ) -> dict:\n if (\n timestamp.tzinfo is None\n ): # If timestamp is naive (tzinfo = None),\n # make it so that it is the same as weather_list timestamp.\n timestamp = timestamp.replace(tzinfo=weather_list[0].date.tzinfo)\n\n beforeWeathers = list(\n filter(\n lambda x: timestamp >= x.date - timedelta(minutes=1),\n weather_list,\n ),\n )\n afterWeathers = list(\n filter(lambda x: timestamp < x.date, weather_list),\n )\n before = None\n beforeSeconds = 999999999999999999999999999\n after = None\n afterSeconds = 999999999999999999999999999\n\n for bw in beforeWeathers:\n if timestamp > bw.date:\n t = timestamp - bw.date\n else:\n t = bw.date - timestamp\n if beforeSeconds > t.seconds:\n before = bw\n beforeSeconds = t.seconds\n for aw in afterWeathers:\n if timestamp > aw.date:\n t = timestamp - aw.date\n else:\n t = aw.date - timestamp\n if afterSeconds > t.seconds:\n after = aw\n afterSeconds = t.seconds\n return {\n 'before': {'weather': before, 'seconds': beforeSeconds},\n 'after': {'weather': after, 'seconds': afterSeconds},\n }","def closest_distance(self, time, other_object, other_time):\n ti = np.where(self.times == time)[0][0]\n oti = np.where(other_object.times == other_time)[0][0]\n xs = self.x[ti].ravel()[self.masks[ti].ravel() == 1]\n xs = xs.reshape(xs.size, 1)\n ys = self.y[ti].ravel()[self.masks[ti].ravel() == 1]\n ys = ys.reshape(ys.size, 1)\n o_xs = other_object.x[oti].ravel()[other_object.masks[oti].ravel() == 1]\n o_xs = o_xs.reshape(1, o_xs.size)\n o_ys = other_object.y[oti].ravel()[other_object.masks[oti].ravel() == 1]\n o_ys = o_ys.reshape(1, o_ys.size)\n distances = (xs - o_xs) ** 2 + (ys - o_ys) ** 2\n return np.sqrt(distances.min())","def find_last_value(self, value, closest=False):\n value = pd.to_datetime(value)\n value = column.as_column(value).as_numerical[0]\n return self.as_numerical.find_last_value(value, closest=closest)","def obtain_time_duration(collection, new_document):\r\n\r\n # Obtain the previously existing two document for the incoming bizLocation\r\n # Sort them in descending order\r\n # The first in the list is the newly inserted document detected by Change Streams\r\n # the second document is of interest\r\n prev_documents = collection.find({'epcList.epc': new_document['epcList'][0]['epc']}).limit(2).sort([(\"eventTime\", DESCENDING)])\r\n\r\n if prev_documents is not None:\r\n # if there is a previous set of documents\r\n prev_doc_list = list(prev_documents)\r\n # print(prev_doc_list)\r\n if len(prev_doc_list) == 1:\r\n logger.info('Only Single entry exists for Product.. It implies it is the a new product with no previous events.')\r\n return None\r\n else:\r\n logger.debug('Previous BizLocation of Product: {}, Present BizLocation of Product: {}'.format(\r\n prev_doc_list[1]['bizLocation']['id'], new_document['bizLocation']['id']))\r\n logger.debug('Time Duration: From {} to {}'.format(prev_doc_list[1]['eventTime'], new_document['eventTime']))\r\n\r\n # make the dictionary to return\r\n duration = {\r\n 'bizLocation': {\r\n 'prev': prev_doc_list[1]['bizLocation']['id'],\r\n 'present': new_document['bizLocation']['id']\r\n },\r\n 'from_time': prev_doc_list[1]['eventTime'].isoformat(timespec='milliseconds') + 'Z',\r\n 'to_time': new_document['eventTime'].isoformat(timespec='milliseconds') + 'Z'\r\n }\r\n # print(duration)\r\n return duration\r\n else:\r\n logger.info('No Previous Information of Event Found')\r\n return None","def filter_times(timestamps, time_difference):\n timestamps = sorted(set(timestamps))\n\n filtered_timestamps = []\n for current_timestamp in timestamps:\n if not filtered_timestamps or current_timestamp - filtered_timestamps[-1] > time_difference:\n filtered_timestamps.append(current_timestamp)\n\n return filtered_timestamps","def _closest_date(target_dt, date_list, before_target=None) -> datetime.date | None:\n\n def time_before(d):\n return target_dt - d if d <= target_dt else datetime.timedelta.max\n\n def time_after(d):\n return d - target_dt if d >= target_dt else datetime.timedelta.max\n\n def any_time(d):\n return target_dt - d if d < target_dt else d - target_dt\n\n if before_target is None:\n return min(date_list, key=any_time).date()\n if before_target:\n return min(date_list, key=time_before).date()\n else:\n return min(date_list, key=time_after).date()","def check_time_borders(self, sam_ev, ):\n mask = np.logical_and(sam_ev['timeMJD'] > self.DataStart,\n sam_ev['timeMJD'] < self.DataEnd)\n return sam_ev[mask]","def findspan(self, u):\n #if u >= self.kv[-self.p-1]:\n # return self.kv.size - self.p - 2 # last interval\n #else:\n # return self.kv.searchsorted(u, side='right') - 1\n return pyx_findspan(self.kv, self.p, u)","def get_last_entry_time():\r\n try:\r\n last_entry_time = list(mongo_coll_tweets.find().sort(\r\n [(\"_id\", -1)]).limit(1))[0][\"_id\"].generation_time\r\n except:\r\n last_entry_time = 0\r\n\r\n return last_entry_time","def get_scan_by_time(self, time):\n scan_ids = tuple(self.index)\n lo = 0\n hi = len(scan_ids)\n while hi != lo:\n mid = (hi + lo) // 2\n sid = scan_ids[mid]\n sid = sid.decode('utf-8')\n scan = self.get_scan_by_id(sid)\n if not self._validate(scan):\n sid = scan_ids[mid - 1]\n scan = self.get_scan_by_id(sid)\n if not self._validate(scan):\n sid = scan_ids[mid - 2]\n scan = self.get_scan_by_id(sid)\n\n scan_time = scan.scan_time\n if scan_time == time:\n return scan\n elif (hi - lo) == 1:\n return scan\n elif scan_time > time:\n hi = mid\n else:\n lo = mid\n if hi == 0 and not self._use_index:\n raise TypeError(\"This method requires the index. Please pass `use_index=True` during initialization\")","def observation_for_closest(\n lat: float, lon: float, lang: str = _DEFAULT_LANG, num_stations_to_try: int = 3\n) -> Tuple[Dict, Dict]:\n assert lang in _SUPPORTED_LANGS\n\n stations = closest_stations(lat, lon, limit=num_stations_to_try)\n for s in stations:\n o = observation_for_station(s[\"id\"], lang=lang)\n if o[\"results\"] and not o[\"results\"][0].get(\"err\") and o[\"results\"][0][\"valid\"]:\n return o, s\n return observation_for_station(stations[0][\"id\"], lang=lang), stations[0]","def check_time():\n times = get_times()\n time_difference = abs((times['local'] - times['target']).total_seconds())\n return time_difference < post_time_tol_seconds","def binary_search_time(values: list[any], low: int, high: int, target: int) -> any:\n if low <= high:\n middle_index = (low+high) // 2\n middle_value = values[middle_index]['time']\n if middle_value < target:\n return binary_search_time(values, middle_index+1, high, target)\n elif middle_value > target:\n return binary_search_time(values, low, middle_index-1, target)\n else:\n return values[middle_index]\n return values[high]","def find_conflict(self, time):\n node = self.root\n wait_time = self.wait_time\n\n while node is not None: \n lower_limit = node.key - wait_time\n upper_limit = node.key + wait_time\n \n if time > lower_limit and time < upper_limit: \n return node\n if time < node.key: \n node = node.left\n else: \n node = node.right\n return None"],"string":"[\n \"def get_closest_record(self, time):\\n dist = 10000000\\n record = -1\\n # TODO: optimise a bit\\n for i, itime in enumerate(self.times):\\n if (abs(time-itime)) < dist:\\n dist = abs(time-itime)\\n record = i\\n\\n return record\",\n \"def find_nearest_time(self, time):\\n\\n idx = np.searchsorted(self.times, time, side=\\\"left\\\")\\n if idx > 0 and (idx == len(self.times) or math.fabs(time - self.times[idx-1]) < math.fabs(time - self.times[idx])):\\n return self.times[idx-1]\\n else:\\n return self.times[idx]\",\n \"def closest_time(self, when):\\n try:\\n return np.argmin(np.abs(self.time.datetime - when))\\n except AttributeError:\\n self.load_time()\\n return np.argmin(np.abs(self.time.datetime - when))\",\n \"def nearest_time_sql(self, t):\\n if self.verbose:\\n sys.stderr.write('SQL Time %s' % t)\\n self.cursor.execute('SELECT * FROM tracklog WHERE dt <= ? ORDER BY dt DESC LIMIT 1', (t,))\\n d = self.cursor.fetchone()\\n if d != None:\\n t0 = [datetime.strptime(d[0],'%Y-%m-%d %H:%M:%S')]\\n t0.extend(d[1:])\\n else:\\n t0 = None\\n self.cursor.fetchall()\\n self.cursor.execute('SELECT * FROM tracklog WHERE dt >= ? ORDER BY dt LIMIT 1', (t,))\\n d2 = self.cursor.fetchone()\\n if d2 != None:\\n t1 = [datetime.strptime(d2[0],'%Y-%m-%d %H:%M:%S')]\\n t1.extend(d2[1:])\\n else:\\n t1 = None\\n self.cursor.fetchall()\\n if self.verbose:\\n sys.stderr.write('SQL Resuls %s %s' % (t0,t1))\\n if t0 == None or t1 == None:\\n return None\\n return t0,t1\",\n \"def nearest():\\n\\n # Time this functions.\\n timer = coils.Timer()\\n\\n # Parse the URL parameter \\\"time\\\".\\n errors = list()\\n try:\\n tstamp_query = flask.request.args.get('time')\\n time_query = coils.string2time(tstamp_query)\\n assert time_query != None\\n except:\\n errors.append('Failed to parse \\\"time\\\" parameter.')\\n\\n # Bail on any errors.\\n if errors:\\n return flask.jsonify(errors=errors)\\n \\n return flask.jsonify(\\n result=getNearestTime(time_query),\\n elapsed=timer.get().total_seconds(),\\n )\",\n \"def closest_to(self, a, b):\\n diff_a = abs(a.ts - self.ts)\\n diff_b = abs(b.ts - self.ts)\\n if diff_a < diff_b and diff_a < TIME_THRESHOLD:\\n return a\\n elif diff_b < TIME_THRESHOLD:\\n return b\\n return None\",\n \"def _nearest_datetime(self, datetime_list, target_datetime):\\n if not datetime_list:\\n raise errors.ParserError(\\n \\\"Input parameter datetime_list length is zero. Required\\\"\\n \\\" parameters: [datetime.datetime], datetime.datetime\\\")\\n work_list = [entry for entry in datetime_list if entry < target_datetime]\\n if not work_list:\\n raise errors.ParserError(\\n \\\"work_list length is zero. Entries in datetime_list\\\"\\n \\\" {} are not < target_datetime {}\\\".format(datetime_list,\\n target_datetime))\\n return min(\\n work_list,\\n key=lambda datetime_entry: abs(datetime_entry - target_datetime))\",\n \"def findNearestTime(foamCase, time):\\n times = list(getTimeFolders(foamCase,returnType=\\\"float\\\"))\\n strTimes = np.array(getTimeFolders(foamCase,returnType=\\\"string\\\"))\\n if time in times:\\n try:\\n intTime = int(strTimes[times.index(time)])\\n return int(time)\\n except:\\n return time\\n else:\\n nearestTime = times[np.argmin(np.abs(np.array(times)-time))]\\n print(\\\"Time %f is not available, choosing nearest time %f\\\" % ( time, nearestTime))\\n try:\\n intTime = int(strTimes[times.index(nearestTime)])\\n return int(nearestTime)\\n except:\\n return nearestTime\",\n \"def bisect(self, dtime, b=0): # pylint: disable=invalid-name\\n return self._collection.bisect(dtime, b)\",\n \"def getNearestTime(time_query):\\n\\n # Convert datetime object to string, for lookup in database.\\n tstamp_query = coils.time2string(time_query)\\n\\n # Retrieve image timestamps.\\n try:\\n tstamp_left = db.session.query(mapping.Image.time).\\\\\\n filter(mapping.Image.time <= tstamp_query).\\\\\\n order_by(mapping.Image.time.desc()).limit(1)\\n tstamp_left = tstamp_left[0].time\\n delta_left = abs(coils.string2time(tstamp_left) - time_query)\\n except:\\n tstamp_left = None\\n delta_left = dt.timedelta.max\\n \\n try:\\n tstamp_right = db.session.query(mapping.Image.time).\\\\\\n filter(mapping.Image.time >= tstamp_query).\\\\\\n order_by(mapping.Image.time).limit(1)\\n tstamp_right = tstamp_right[0].time\\n delta_right = abs(coils.string2time(tstamp_right) - time_query)\\n except:\\n tstamp_right = None\\n delta_right = dt.timedelta.max\\n \\n # The nearest value has the smallest delta from the query.\\n result = tstamp_left if (delta_left < delta_right) else tstamp_right\\n return result\",\n \"def mempool_assert_relative_time_exceeds(\\n condition: ConditionWithArgs, unspent: CoinRecord, timestamp: uint64\\n) -> Optional[Err]:\\n try:\\n expected_seconds = int_from_bytes(condition.vars[0])\\n except ValueError:\\n return Err.INVALID_CONDITION\\n\\n if timestamp is None:\\n timestamp = uint64(int(time.time()))\\n if timestamp < expected_seconds + unspent.timestamp:\\n return Err.ASSERT_SECONDS_RELATIVE_FAILED\\n return None\",\n \"def findontarget(starttime, event_list):\\n for r in event_list:\\n if r[0]==18 and r[1]>starttime: return r[1]\\n return None\",\n \"def getElemAfterTime(self, stamp):\\n newer = [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\\n if time >= stamp]\\n if not newer:\\n return None\\n return newer[0]\",\n \"def locate_nearest_event(self):\\n nearest_event_date = ''\\n min = 1000000\\n today = self.get_today()\\n event_array = self.events.keys()\\n for event_date in event_array:\\n event_date = self.date_to_operate_format(event_date)\\n if int(event_date) - int(today) > 0:\\n if int(event_date) - int(today) < min:\\n min = int(event_date) - int(today)\\n nearest_event_date = event_date\\n\\n nearest_event = '0'\\n if len(event_array) > 0:\\n nearest_event = self.change_format_to_database_index(nearest_event_date)\\n\\n return nearest_event\",\n \"def getElemBeforeTime(self, stamp):\\n older = [msg for (msg, time) in zip(self.cache_msgs, self.cache_times)\\n if time <= stamp]\\n if not older:\\n return None\\n return older[-1]\",\n \"def mempool_assert_absolute_time_exceeds(condition: ConditionWithArgs, timestamp: uint64) -> Optional[Err]:\\n try:\\n expected_seconds = int_from_bytes(condition.vars[0])\\n except ValueError:\\n return Err.INVALID_CONDITION\\n\\n if timestamp is None:\\n timestamp = uint64(int(time.time()))\\n if timestamp < expected_seconds:\\n return Err.ASSERT_SECONDS_ABSOLUTE_FAILED\\n return None\",\n \"def find_above(self, time, level):\\n\\n if self.get(time) >= level:\\n return time\\n ix = self._trace.bisect_right(time)\\n for t, lvl in self._trace.items()[ix:]:\\n if lvl >= level:\\n return t\\n return None\",\n \"def get_prev_time(time, c_type=None, c_pk=None):\\n\\n # TODO if efficiency is an issue here, make occ_times a generator and\\n # keep a queue (collections.deque) of the relevant times; this should\\n # work because we're moving through them in sequence so could safely\\n # discard those before the current time as we go\\n\\n if c_pk:\\n def filter_func(obj):\\n return obj['colloquialism__pk'] == c_pk\\n elif c_type:\\n def filter_func(obj):\\n return obj['colloquialism__type'] == c_type\\n else:\\n def filter_func(obj):\\n return True\\n\\n # filter out relevant times\\n filtered = filter(\\n lambda obj: filter_func(obj) and obj['start_exact'] < time,\\n occ_times)\\n\\n if not len(filtered):\\n return None\\n\\n # return last filtered time since they are in order\\n return filtered[-1]['start_exact']\",\n \"def from_unix_sec(self):\\n try:\\n self.in_unix_sec = dt.utcfromtimestamp(float(unix)).strftime('%Y-%m-%d %H:%M:%S.%f')\\n except Exception as e:\\n if not args.log:\\n pass\\n else:\\n logging.error(str(type(e)) + \\\",\\\" + str(e))\\n self.in_unix_sec = False\\n return self.in_unix_sec\",\n \"def cacheFindEntry(cache, cameraID, desiredTime):\\n if not cameraID in cache:\\n return None\\n cameraTimes = cache[cameraID]\\n closestEntry = min(cameraTimes, key=lambda x: abs(x['time'] - desiredTime))\\n if abs(closestEntry['time'] - desiredTime) < 30:\\n # logging.warning('close: %s', str(closestEntry))\\n return os.path.join(cache['readDir'], closestEntry['fileName'])\\n else:\\n # logging.warning('far: %s, %s', str(desiredTime), str(closestEntry))\\n return None\",\n \"def object_at(self, time):\\n for event in self._timeline: \\n if time >= event.start_time and time <= event.end_time:\\n return event.obj\\n return self._timeline[-1].obj\",\n \"def subset_by_time(prediction_dict, desired_times_unix_sec):\\n\\n error_checking.assert_is_numpy_array(\\n desired_times_unix_sec, num_dimensions=1\\n )\\n error_checking.assert_is_integer_numpy_array(desired_times_unix_sec)\\n\\n desired_indices = numpy.array([\\n numpy.where(prediction_dict[VALID_TIMES_KEY] == t)[0][0]\\n for t in desired_times_unix_sec\\n ], dtype=int)\\n\\n prediction_dict = subset_by_index(\\n prediction_dict=prediction_dict, desired_indices=desired_indices\\n )\\n\\n return prediction_dict, desired_indices\",\n \"def find_below(self, time, level):\\n\\n if self.get(time) <= level:\\n return time\\n ix = self._trace.bisect_right(time)\\n for t, lvl in self._trace.items()[ix:]:\\n if lvl <= level:\\n return t\\n return None\",\n \"def lookup_time_spent():\\n while True:\\n search_query = input('Show entries in which time spent '\\n '(in minutes) is: ')\\n if validate_lookup_time_spent_format(search_query):\\n break\\n print('** Please enter positive integer **')\\n return Entry.select().where(Entry.time_spent == search_query)\",\n \"def test_4_data_fetching_unix_time_and_insertion(self):\\n d1 = date.today()\\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\\n self.assertTrue(success)\\n self.assertEqual(result[0]['Datetime'],self.unix_time)\",\n \"def GetPriorUniquePoint(lap: gps_pb2.Lap,\\n point_c: gps_pb2.Point) -> gps_pb2.Point:\\n index = -1\\n point = lap.points[-1]\\n while point.time.ToNanoseconds() == point_c.time.ToNanoseconds():\\n index -= 1\\n point = lap.points[index]\\n return point\",\n \"def get_closest_minute(t):\\n ts = dt.datetime.utcfromtimestamp(t/1000)\\n s = ts.second\\n if s < 30:\\n return dt.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute)\\n else:\\n return dt.datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute) + dt.timedelta(minutes=1)\",\n \"def validity_by_time(self):\\n conn = psycopg2.connect(self.conn)\\n permissable_maximum_age_secs = 600 # 600s = 10mins\\n query = \\\"SELECT time FROM steve_sense_sensor_logs ORDER BY time DESC LIMIT 1\\\"\\n cur = conn.cursor()\\n cur.execute(query)\\n queryResult = cur.fetchall()\\n age_seconds = (datetime.datetime.now(\\n timezone.utc) - queryResult[0][0]).seconds\\n cur.close()\\n conn.close()\\n if age_seconds > permissable_maximum_age_secs:\\n print(\\\"Check Sensor, last sample is \\\"+str(age_seconds)+\\\" old\\\")\\n return False\\n else:\\n return True\",\n \"def fetch_entry(unique_id, time_stamp):\\n print('Fetching items with unique_id: {}'.format(unique_id))\\n entry_exists = False\\n item = None\\n try:\\n resp = TIME_TABLE.get_item(Key={'uniqueId': unique_id, 'timeStamp': time_stamp})\\n print(resp)\\n item = resp.get('Item')\\n print(item)\\n if item:\\n entry_exists = True\\n except Exception as e:\\n print('Unique Item does not exists: {0}. Error: {1}'.format(unique_id, e))\\n\\n return entry_exists, item\",\n \"def findFirstHigh(thisStFile):\\n with open(thisStFile) as f:\\n reader = csv.DictReader(f, delimiter='\\\\t')\\n for row in reader:\\n return datetime.datetime.strptime(row['time'], fmt)\",\n \"def closest_point_in_cloud(point, cloud):\\n data = sort_points(point, cloud)\\n return data[0]\",\n \"def subset_by_time(example_dict, first_time_unix_sec, last_time_unix_sec):\\n\\n error_checking.assert_is_integer(first_time_unix_sec)\\n error_checking.assert_is_integer(last_time_unix_sec)\\n error_checking.assert_is_geq(last_time_unix_sec, first_time_unix_sec)\\n\\n good_indices = numpy.where(numpy.logical_and(\\n example_dict[VALID_TIMES_KEY] >= first_time_unix_sec,\\n example_dict[VALID_TIMES_KEY] <= last_time_unix_sec\\n ))[0]\\n\\n for this_key in ONE_PER_EXAMPLE_KEYS:\\n if isinstance(example_dict[this_key], list):\\n example_dict[this_key] = [\\n example_dict[this_key][k] for k in good_indices\\n ]\\n else:\\n example_dict[this_key] = (\\n example_dict[this_key][good_indices, ...]\\n )\\n\\n return example_dict, good_indices\",\n \"def check_time_since_last_data(device_origin):\\n actual_time = time.time()\\n sec_since_last_data = actual_time - mon_item.read_device_status_values(device_origin)[1]\\n min_since_last_data = sec_since_last_data / 60\\n min_since_last_data = int(min_since_last_data)\\n latest_data_hr = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(latest_data))\\n return min_since_last_data\",\n \"def test_get_between_datetime_same_microseconds(self):\\n now = datetime.datetime.utcnow()\\n start_dt = testdata.get_past_datetime(now)\\n stop_dt = testdata.get_between_datetime(start_dt, now)\\n self.assertGreater(stop_dt, start_dt)\",\n \"def get(self, target_time=None):\\n if target_time is None:\\n target_time = Servo.ctime()\\n size = len(self.history)\\n idx = -1\\n with History.lock:\\n while idx >= -size:\\n data = self.history[idx]\\n timestamp, position = data[0], data[1:]\\n if timestamp <= target_time:\\n return data\\n elif idx - 1 > -size:\\n prev_timestamp = self.history[idx - 1]\\n if prev_timestamp <= target_time:\\n return self.history[idx - 1] # It's better to interpolate\\n else:\\n idx -= 1\\n continue\\n else:\\n return self.history[-size]\",\n \"def get_time_ceiling(time, data):\\n if time >= data.index.max():\\n return data.iloc[-1]\\n elif time <= data.index.min():\\n return data.iloc[0]\\n return data[str(time):].iloc[0]\",\n \"def get_unixtime(self):\\n if not self.Complete():\\n raise DateTimeError(\\\"get_unixtime requires complete timepoint\\\")\\n zoffset = self.time.GetZoneOffset()\\n if zoffset is None:\\n raise DateTimeError(\\\"get_unixtime requires timezone\\\")\\n elif zoffset == 0:\\n zt = self\\n else:\\n zt = self.ShiftZone(zDirection=0)\\n days = zt.date.GetAbsoluteDay() - EPOCH.date.GetAbsoluteDay()\\n seconds = zt.time.GetTotalSeconds() - EPOCH.time.GetTotalSeconds()\\n return 86400 * days + seconds\",\n \"def get_index_before_time(messages, time):\\n # Getting the timestamp that specifies the cutoff.\\n timestamp = messages[0].timestamp + time\\n\\n for index, message in enumerate(messages):\\n if message.timestamp > timestamp:\\n return index - 1\",\n \"def find_nearest_stop(self, gtfs, trip_id, lat, long):\\n stop_ids = [stop_times['stop_id'] for stop_times in gtfs[\\\"stop_times\\\"][trip_id]]\\n min_distance = 10000000\\n closest_stop = stop_ids[0]\\n for stop_id in stop_ids:\\n stop = gtfs[\\\"stops\\\"][stop_id]\\n distance = geopy.distance.distance((lat, long), (stop['stop_lat'], stop['stop_lon'])).km\\n if distance < min_distance:\\n closest_stop_id = stop_id\\n min_distance = distance\\n return closest_stop_id\",\n \"def _FindNearestAnat(self, acqtime):\\n tdiff_min = 1e6\\n for anat in self.entry_map['anat']:\\n if self.info[anat]['type'] == 'T1High' and \\\\\\n self.info[anat]['InversionTime'] > 0.:\\n tdiff = abs(acqtime - self.info[anat]['acqtime'])\\n if tdiff < tdiff_min:\\n tdiff_min = tdiff\\n anat_min = anat\\n return anat_min\",\n \"def takeclosest(takecloselist, takecloseint):\\n pos = bisect_left(takecloselist, takecloseint)\\n if pos == 0:\\n return takecloselist[0]\\n if pos == len(takecloselist):\\n return takecloselist[-1]\\n before = takecloselist[pos - 1]\\n after = takecloselist[pos]\\n if after - takecloseint < takecloseint - before:\\n return after\\n else:\\n return before\",\n \"def _matchTime(self, time: float):\\n return self._comparator['Time'] < time\",\n \"def get_booking_at(self, datetime):\\n for booking in self.booking_set.all():\\n if booking.schedule_start <= datetime < booking.schedule_end and not booking.is_cancelled():\\n return booking\\n return None\",\n \"def at_time(self, time):\\n return self._collection.at_time(time)\",\n \"def test_searchSince(self):\\n self.assertTrue(\\n self.server.search_SINCE(self.earlierQuery, self.seq, self.msg))\\n self.assertTrue(\\n self.server.search_SINCE(self.sameDateQuery, self.seq, self.msg))\\n self.assertFalse(\\n self.server.search_SINCE(self.laterQuery, self.seq, self.msg))\",\n \"def unix_to_timestamp(unix):\\n return int(round(unix * 1e6))\",\n \"def FromUnixTime(cls, unixTime):\\n utcTuple = pytime.gmtime(0)\\n t, overflow = Time.FromStructTime(utcTuple).Offset(seconds=unixTime)\\n d = Date.FromStructTime(utcTuple).Offset(days=overflow)\\n return cls(date=d, time=t.WithZone(zDirection=0))\",\n \"def get_best_time():\\n\\n # Always remember about db.session.rollback() when debugging\\n\\n max_timing = db.session.query(func.min(Game.timing)).filter(Game.status == \\\"won\\\").first()\\n time_user = db.session.query(Game.timing, User.username).join(User).filter(Game.timing == max_timing, Game.status == \\\"won\\\").first()\\n\\n return time_user\",\n \"def getValueAt(self, time):\\n for tvp in self.timeValuePairs:\\n if time <= tvp[0]:\\n return tvp[1]\\n return self.defaultValue\",\n \"def _parse_timestamp(self, api_time):\\n return (\\n pendulum.parse(api_time)\\n if api_time is not None\\n else pendulum.from_timestamp(-1)\\n )\",\n \"def findguidingstop(starttime, event_list):\\n for r in event_list:\\n if r[0]==6 and r[1]+datetime.timedelta(seconds=0)>starttime: return r[1]\\n return None\",\n \"def olderThan(record, value=Constants.older_than):\\n # Check first if the record is valid\\n if isRecordNameValid(record):\\n # Get the record values and split them - year:month:day:hour:minute\\n splitted_record = record.split(':')\\n # Get the current date and time from the system and split them\\n splitted_date = getTime().split(':')\\n # Check if the year, month and day are the same\\n if splitted_record[0] == splitted_date[0] \\\\\\n and splitted_record[1] == splitted_date[1] \\\\\\n and splitted_record[2] == splitted_date[2]:\\n # Change the record hour in minutes and add the record minutes - how many minutes since the record stored\\n record_minutes = int(splitted_record[3]) * 60 + int(splitted_record[4])\\n # Change the current hour in minutes and add the current minutes - how many minutes passed today\\n minutes = int(splitted_date[3]) * 60 + int(splitted_date[4])\\n # Check if the difference is bigger than the constant - the record is too old and must be deleted\\n if int(minutes - record_minutes) >= value:\\n return True\\n else:\\n return False\\n else:\\n # The record is way too old - at least 1 day\\n return True\\n # The record is not valid and must be deleted\\n return True\",\n \"def test_larger_rhs(self):\\n from sosbeacon.utils import get_latest_datetime\\n\\n lhs = datetime(2012, 9, 20, 2, 59)\\n rhs = datetime(2012, 9, 20, 3, 00)\\n\\n result = get_latest_datetime(lhs, rhs)\\n\\n self.assertIs(rhs, result)\",\n \"def findguidingstart(starttime, event_list):\\n for r in event_list:\\n if r[0]==5 and r[1]>starttime: return r[1]\\n return None\",\n \"def get_next_available_open_timeset(\\n a_timestamp: str, list_of_timesets: list, debug_mode: bool = False\\n) -> dict:\\n\\n results = {\\\"next_free_timeset\\\": None, \\\"reached_end_of_list\\\": True}\\n\\n sorted_list_of_timesets = sorted(list_of_timesets, key=lambda k: k[0])\\n\\n filtered_list_of_timesets = []\\n for timeset in sorted_list_of_timesets:\\n if datetime.fromisoformat(a_timestamp) <= datetime.fromisoformat(timeset[1]):\\n filtered_list_of_timesets.append(timeset)\\n\\n # get rid of timesets that end before timestamp\\n if filtered_list_of_timesets != sorted_list_of_timesets:\\n print_time_data(\\n \\\"Next available_timeset: filtering effect from:\\\",\\n sorted_list_of_timesets,\\n debug_mode,\\n )\\n print_time_data(\\n \\\"Next available_timeset: filtering effect to:\\\",\\n filtered_list_of_timesets,\\n debug_mode,\\n )\\n\\n # the last timeset triggers some actions. However if the last is also the first\\n # i.e. list of 1 timeset, then its too early to set off the trigger\\n index_of_last_timeset = (len(filtered_list_of_timesets) - 1) or 1\\n\\n temp_timestamp = a_timestamp\\n\\n for timeset_index, timeset in enumerate(filtered_list_of_timesets):\\n if datetime.fromisoformat(timeset[0]) > datetime.fromisoformat(temp_timestamp):\\n\\n results[\\\"next_free_timeset\\\"] = [temp_timestamp, timeset[0]]\\n if timeset_index != index_of_last_timeset:\\n results[\\\"reached_end_of_list\\\"] = False\\n\\n print_time_data(\\n \\\"Next available_timeset: Going to break: current timeset\\\",\\n timeset,\\n debug_mode,\\n )\\n print_time_data(\\n \\\"Next available_timeset: Going to break: timestamp\\\",\\n temp_timestamp,\\n debug_mode,\\n )\\n print_time_data(\\n \\\"Next available_timeset: Going to break: results\\\", results, debug_mode\\n )\\n break\\n\\n temp_timestamp = timeset[1]\\n\\n # Check if the found timeset has a startTime\\n # inside another timeset\\n if results[\\\"next_free_timeset\\\"]:\\n temp_timeset = validate_update_timestamp(\\n results[\\\"next_free_timeset\\\"], filtered_list_of_timesets, debug_mode\\n )\\n results[\\\"next_free_timeset\\\"] = temp_timeset\\n\\n print_time_data(\\\"Next available_timeset: Final results\\\", results, debug_mode)\\n\\n return results\",\n \"def iso_from_unix_time(unix_time: float, precision: int = 9) -> ISOTimestamp:\\n frac_part, int_part = math.modf(unix_time)\\n\\n seconds = int(int_part)\\n\\n if frac_part < 0:\\n seconds -= 1\\n frac_part += 1\\n\\n decimals = f\\\"{{0:.{precision}f}}\\\".format(frac_part)[1:].rstrip('0.') # noqa\\n\\n return _from_unix_time(seconds, decimals)\",\n \"def time(self,orid_time,window=5):\\n #{{{ Function to get possible matches of events for some epoch time.\\n\\n results = {}\\n\\n #\\n # If running in simple mode we don't have access to the tables we need\\n #\\n if config.simple:\\n return results\\n\\n orid_time = _isNumber(orid_time)\\n\\n if not orid_time:\\n print \\\"Not a valid number in function call: %s\\\" % orid_time\\n return\\n \\n start = float(orid_time)-float(window)\\n end = float(orid_time)+float(window)\\n\\n dbname = self.dbcentral(orid_time)\\n\\n if not db:\\n print \\\"No match for orid_time in dbcentral object: (%s,%s)\\\" % (orid_time,self.dbcentral(orid_time))\\n return\\n\\n try: \\n db = datascope.dbopen( dbname , 'r' )\\n db.lookup( table='origin')\\n db.query(datascope.dbTABLE_PRESENT) \\n except Exception,e:\\n print \\\"Exception on Events() time(%s): Error on db pointer %s [%s]\\\" % (orid_time,db,e)\\n return\\n\\n db.subset( 'time >= %f' % start )\\n db.subset( 'time <= %f' % end )\\n\\n try:\\n db = datascope.dbopen( dbname , 'r' )\\n db.lookup( table='wfdisc' )\\n records = db.query(datascope.dbRECORD_COUNT)\\n\\n except:\\n records = 0\\n\\n if records:\\n\\n for i in range(records):\\n\\n db.record = i\\n\\n (orid,time) = db.getv('orid','time')\\n\\n orid = _isNumber(orid)\\n time = _isNumber(time)\\n results[orid] = time\\n\\n return results\",\n \"def closest_approach_to_camera(scene, speaker_object) -> (float, int):\\n max_dist = sys.float_info.max\\n at_time = scene.frame_start\\n for frame in range(scene.frame_start, scene.frame_end + 1):\\n scene.frame_set(frame)\\n rel = speaker_object.matrix_world.to_translation() - scene.camera.matrix_world.to_translation()\\n dist = norm(rel)\\n\\n if dist < max_dist:\\n max_dist = dist\\n at_time = frame\\n\\n return max_dist, at_time\",\n \"def get(self, column, key, time):\\n # Check connection\\n self._checkInit()\\n \\n # Split the time string\\n (year,month,day,hour) = self._splitTime(time)\\n\\n # Construct the query\\n query = \\\"SELECT {} FROM {} WHERE {} ORDER BY TIMESTAMP DESC LIMIT 1\\\".format(\\n column,\\n self.table,\\n self._exactMatchClause(key,year,month,day,hour)) \\n\\n #logging.debug(\\\"query: \\\\\\\"{}\\\\\\\"\\\".format(query))\\n\\n # Get Connection\\n cnx = self.getConnection()\\n cur = cnx.cursor()\\n cur.execute(query)\\n retval = None\\n for fields in cur:\\n retval = fields[0]\\n break\\n cur.close()\\n cnx.close() \\n return retval\",\n \"def closest(self, x):\\n # http://www.ahinson.com/algorithms_general/Sections/Geometry/PluckerLine.pdf\\n # has different equation for moment, the negative\\n\\n x = arg.getvector(x, 3)\\n\\n lam = np.dot(x - self.pp, self.uw)\\n p = self.point(lam) # is the closest point on the line\\n d = np.linalg.norm( x - p)\\n \\n return namedtuple('closest', 'p d lam')(p, d, lam)\",\n \"def find_closest_trajectory(cls, **kwargs):\\n # if we can find an approximation that works to two\\n # decimal places, just return that\\n ideal_min_pitch = kwargs[\\\"pitch\\\"] - \\\\\\n kwargs.get(\\\"ideal_min_pitch_differential\\\", cls.IDEAL_DIFFERENTIAL)\\n ideal_max_pitch = kwargs[\\\"pitch\\\"] + \\\\\\n kwargs.get(\\\"ideal_min_pitch_differential\\\", cls.IDEAL_DIFFERENTIAL)\\n\\n ideal_min_roll = kwargs[\\\"roll\\\"] - \\\\\\n kwargs.get(\\\"ideal_min_roll_differential\\\", cls.IDEAL_DIFFERENTIAL)\\n ideal_max_roll = kwargs[\\\"roll\\\"] + \\\\\\n kwargs.get(\\\"ideal_min_roll_differential\\\", cls.IDEAL_DIFFERENTIAL)\\n\\n # find trajectories that we are good with even if they aren't the absolute\\n # best\\n ideal_trajectory = SolvedTrajectory.objects.filter(\\n pitch__gt=ideal_min_pitch,\\n roll__gt=ideal_min_roll\\n ).filter(\\n pitch__lt=ideal_max_pitch,\\n roll__lt=ideal_max_roll)\\n ideal_trajectory = ideal_trajectory.first()\\n\\n # if we found something in the ideal trajectory, just return that!\\n if ideal_trajectory:\\n best_trajectory = ideal_trajectory\\n best_match_score = cls.get_match_score(\\n best_trajectory, kwargs[\\\"pitch\\\"], kwargs[\\\"roll\\\"])\\n\\n # otherwise, we expand our filter and include more results\\n else:\\n\\n # determine bounds on the pitch and the roll\\n # of the trajectory we will return\\n min_pitch = kwargs[\\\"pitch\\\"] - kwargs[\\\"min_pitch_differential\\\"]\\n max_pitch = kwargs[\\\"pitch\\\"] + kwargs[\\\"min_pitch_differential\\\"]\\n\\n min_roll = kwargs[\\\"roll\\\"] - kwargs[\\\"min_roll_differential\\\"]\\n max_roll = kwargs[\\\"roll\\\"] + kwargs[\\\"min_roll_differential\\\"]\\n\\n # determine the candidate trajectories\\n candidate_trajectories = SolvedTrajectory.objects.filter(\\n pitch__gt=min_pitch,\\n roll__gt=min_roll\\n ).filter(\\n pitch__lt=max_pitch,\\n roll__lt=max_roll\\n )\\n\\n # determine the best match from what we have available\\n best_trajectory = None\\n best_match_score = float(\\\"inf\\\")\\n\\n for trajectory in candidate_trajectories:\\n match_score = cls.get_match_score(\\n trajectory, kwargs[\\\"pitch\\\"], kwargs[\\\"roll\\\"])\\n\\n if match_score < best_match_score:\\n best_trajectory = trajectory\\n best_match_score = match_score\\n\\n # calculate the norm of the deviation\\n deviation = math.sqrt(best_match_score)\\n return best_trajectory.file_name, deviation\",\n \"def get_data_at_time(self, time=None, tolerance=0.0):\\n if time is None:\\n # If time is not specified, assume we want the entire time\\n # set. Skip all the overhead, don't create a new object, and\\n # return self.\\n return self\\n is_iterable = _is_iterable(time)\\n time_iter = iter(time) if is_iterable else (time,)\\n indices = []\\n # Allocate indices list dynamically to support a general iterator\\n # for time. Not sure if this will ever matter...\\n for t in time_iter:\\n if t in self._time_idx_map:\\n idx = self._time_idx_map[t]\\n else:\\n idx = find_nearest_index(self._time, t, tolerance=tolerance)\\n if idx is None:\\n raise RuntimeError(\\n \\\"Time point %s is invalid within tolerance %s\\\" % (t, tolerance)\\n )\\n indices.append(idx)\\n if not is_iterable:\\n indices = indices[0]\\n return self.get_data_at_time_indices(indices)\",\n \"def exists_at_time(e, t):\\n t0 = 0\\n for l, x in e:\\n t1 = t0 + l\\n if t > t0 and t < t1:\\n return x\\n t0 = t1\\n return x\",\n \"def since(self, ts):\\n spec = {'ts': {'$gt': ts}}\\n cursor = self.query(spec)\\n while True:\\n # todo: trap InvalidDocument errors:\\n # except bson.errors.InvalidDocument as e:\\n # logging.info(repr(e))\\n for doc in cursor:\\n yield doc\\n if not cursor.alive:\\n break\\n time.sleep(1)\",\n \"def test_larger_lhs(self):\\n from sosbeacon.utils import get_latest_datetime\\n\\n lhs = datetime(2012, 9, 20, 3, 45)\\n rhs = datetime(2012, 9, 20, 2, 45)\\n\\n result = get_latest_datetime(lhs, rhs)\\n\\n self.assertIs(lhs, result)\",\n \"def test_blind_delete_with_datetime(self):\\r\\n uid = uuid4()\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n TestTimestampModel.get(id=uid).should.be.ok\\r\\n\\r\\n plus_five_seconds = datetime.now() + timedelta(seconds=5)\\r\\n\\r\\n TestTimestampModel.objects(id=uid).timestamp(plus_five_seconds).delete()\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\\r\\n\\r\\n tmp = TestTimestampModel.create(id=uid, count=1)\\r\\n\\r\\n with self.assertRaises(TestTimestampModel.DoesNotExist):\\r\\n TestTimestampModel.get(id=uid)\",\n \"def _get_closest_eth1_voting_period_start_block(\\n self, timestamp: Timestamp\\n ) -> BlockNumber:\\n # Compare with the largest recoreded block timestamp first before querying\\n # for the latest block.\\n # If timestamp larger than largest block timestamp, request block from eth1 provider.\\n if (\\n self._largest_block_timestamp is None\\n or timestamp > self._largest_block_timestamp\\n ):\\n try:\\n block = self._eth1_data_provider.get_block(\\\"latest\\\")\\n except BlockNotFound:\\n raise Eth1MonitorValidationError(\\\"Fail to get latest block\\\")\\n if block.timestamp <= timestamp:\\n return block.number\\n else:\\n block_number = block.number\\n # Try the latest `self._num_blocks_confirmed` blocks until we give up\\n for i in range(1, self._num_blocks_confirmed + 1):\\n lookback_number = block_number - i\\n if lookback_number < 0:\\n break\\n else:\\n shifted_block = BlockNumber(lookback_number)\\n block = self._eth1_data_provider.get_block(shifted_block)\\n if block.timestamp <= timestamp:\\n return block.number\\n raise Eth1BlockNotFound(\\n \\\"Can not find block with timestamp closest\\\"\\n \\\"to voting period start timestamp: %s\\\",\\n timestamp,\\n )\\n else:\\n # NOTE: It can be done by binary search with web3 queries.\\n # Regarding the current block number is around `9000000`, not sure if it is worthwhile\\n # to do it through web3 with `log(9000000, 2)` ~= 24 `getBlock` queries.\\n # It's quite expensive compared to calculating it by the cached data\\n # which involves 0 query.\\n\\n # Binary search for the right-most timestamp smaller than `timestamp`.\\n all_timestamps = tuple(self._block_timestamp_to_number.keys())\\n target_timestamp_index = bisect.bisect_right(all_timestamps, timestamp)\\n # Though `index < 0` should never happen, check it for safety.\\n if target_timestamp_index <= 0:\\n raise Eth1BlockNotFound(\\n \\\"Failed to find the closest eth1 voting period start block to \\\"\\n f\\\"timestamp {timestamp}\\\"\\n )\\n else:\\n # `bisect.bisect_right` returns the index we should insert `timestamp` into\\n # `all_timestamps`, to make `all_timestamps` still in order. The element we are\\n # looking for is actually `index - 1`\\n index = target_timestamp_index - 1\\n target_key = all_timestamps[index]\\n return self._block_timestamp_to_number[target_key]\",\n \"def unix_timestamp_date(unix=1459141485):\\n return datetime.datetime.fromtimestamp(int(unix))\",\n \"def get_closest_rt_match(self, name_rt_dict):\\n abs_dic = {}\\n for index, name_rt in name_rt_dict.items():\\n dup_name = name_rt[0]\\n dup_rt = name_rt[1]\\n for name, rt in self.std_temp_dict.items():\\n if name == dup_name:\\n abs_value = abs(dup_rt - rt)\\n abs_dic[index] = abs_value\\n\\n print(abs_dic)\\n keep_index = min(abs_dic, key=lambda x: abs_dic.get(x))\\n print(\\\"The keep_index is\\\", keep_index) # Return this and then use it as below.\\n\\n return keep_index\",\n \"def _validate_timestamp(timestamp):\\n dts = datetime.datetime.utcnow()\\n current_time = round(time.mktime(dts.timetuple()) + dts.microsecond/1e6)\\n if (timestamp - current_time) > SYNC_TOLERANCE:\\n raise InvalidTransaction(\\n 'Timestamp must be less than local time.'\\n ' Expected {0} in ({1}-{2}, {1}+{2})'.format(\\n timestamp, current_time, SYNC_TOLERANCE))\",\n \"def filter_lower_datetime(time, list_time):\\n return [t for t in list_time if t <= time]\",\n \"def _closest_opponent_to_object(self, raw_obs, o):\\n min_d = None\\n closest = None\\n for p in raw_obs['right_team']:\\n d = self._object_distance(o, p)\\n if min_d is None or d < min_d:\\n min_d = d\\n closest = p\\n assert closest is not None\\n return closest\",\n \"def get_time_from_db():\\n if not (date := select(date for date in LastestCheck).order_by(lambda date: desc(date.id)).first()):\\n date = LastestCheck(timestamp=int(datetime.timestamp(datetime.now())))\\n commit()\\n return date.timestamp\",\n \"def to_python_datetime(unix_timestamp):\\n return datetime.datetime.fromtimestamp(int(unix_timestamp),\\n pytz.timezone(settings.TIME_ZONE))\",\n \"def get_specific_nc_timeindex(fname,\\n time_value,\\n time_varname='time'):\\n\\n assert isinstance(time_value, datetime.date)\\n\\n nc_fid = netCDF4.Dataset(fname, 'r')\\n time_values = netCDF4.num2date(nc_fid.variables[time_varname][:],\\n nc_fid.variables[time_varname].units,\\n nc_fid.variables[time_varname].calendar)\\n nearest_index = 0\\n nearest_value = time_values[nearest_index]\\n nearest_diff = time_difference(nearest_value, time_value)\\n for idx, tvalue in enumerate(time_values):\\n this_diff = time_difference(tvalue, time_value)\\n if this_diff < nearest_diff:\\n nearest_index = idx\\n nearest_value = time_values[idx]\\n nearest_diff = this_diff\\n\\n return nearest_index\",\n \"def _xid_pick_only_closest(self, xid):\\n\\t\\tif len(xid) == 1:\\n\\t\\t\\tself._xid_sanity_check(xid)\\n\\n\\t\\telif len(xid) > 1: \\n\\t\\t\\tprint(\\\"[hscObj] multiple objects found, picking the closest\\\")\\n\\t\\t\\txid = self._resolve_multiple_sources(xid)\\n\\t\\t\\tself._xid_sanity_check(xid)\\n\\n\\t\\telif len(xid) < 1:\\n\\t\\t\\tprint(\\\"[hscObj] no object found\\\")\\n\\t\\t\\txid = None\\n\\n\\t\\treturn xid\",\n \"def testTimestamps(self):\\n predicate = \\\"metadata:predicate\\\"\\n subject = \\\"aff4:/metadata:8\\\"\\n\\n # Extend the range of valid timestamps returned from the table to account\\n # for potential clock skew.\\n start = long(time.time() - 60) * 1e6\\n data_store.DB.Set(subject, predicate, \\\"1\\\", token=self.token)\\n\\n (stored, ts) = data_store.DB.Resolve(subject, predicate, token=self.token)\\n\\n # Check the time is reasonable\\n end = long(time.time() + 60) * 1e6\\n\\n self.assert_(ts >= start and ts <= end)\\n self.assertEqual(stored, \\\"1\\\")\",\n \"def get_closest_bus_stop(due_time, stop_src, stop_ids, route_id):\\n\\n # Get index of where the SRC stop is in the tupple to serve as the high-bound, and store that position in original. Also, store the original due time, as it will be needed\\n high_bound = 0\\n original = 0\\n original_due_time = due_time\\n for i in range(0, len(stop_ids)):\\n if str(stop_ids[i]) == stop_src:\\n high_bound = i\\n original = i\\n break\\n\\n # Innitialize pointer to be halfway between the lowbound (set to 0 index) and the highbound (the SRC stop).\\n pointer = original//4\\n low_bound = 0\\n\\n # Optimally we want to find the stop where our bus is just 1 minute away, for better accuracy. But sometimes that is not possible, so we will\\n # need to look for a bus further away. This variable, arrival_within_minutes, starts with 1 minutes, and will be increased as necessary.\\n arrival_within_minutes = 1\\n\\n # Search until we find where the bus is\\n while True:\\n last_due_time = 0\\n # Search while our due time is not 'Due' or within the specified minutes\\n while due_time != 'Due' or int(due_time) > arrival_within_minutes:\\n # Once more, get the buses for the stop we are currently looking at\\n first_3_buses = get_due_time(str(stop_ids[pointer]), route_id)\\n\\n # Get just the first bus, since we already have the 3 buses from our SRC stop (this one is just looking for where one of those 3 buses is)\\n possible_stop = filter_buses(first_3_buses)\\n # Store the new due time, from the bus stop our binary algorithm selected\\n new_due_time_due = possible_stop['duetime']\\n\\n # If the new due_time is the same as the last_due_time it means the algorithm got stuck without finding a better value, and we need to break, and change our\\n # arrival_within_minutes for a longer time\\n if new_due_time_due == last_due_time:\\n break\\n\\n # If we found a 'Due' or within the arrival_within_minutes, return that index. That is the index of the stop where our bus is at/close to.\\n if possible_stop['duetime'] == 'Due' or int(possible_stop['duetime']) <= arrival_within_minutes:\\n # ('Found the bus with', new_due_time_due, 'minutes due time.')\\n # This for loop is to check if the previous bus stop(s) have the same due time, and find a closer more accurae stop\\n # print('Original pointer:', pointer)\\n for i in range(pointer - 1, 0, -1):\\n if new_due_time_due == (filter_buses(get_due_time(str(stop_ids[i]), route_id))['duetime']):\\n pointer = i\\n # print('New pointer:', pointer)\\n else:\\n break\\n # Return the pointer, the index of the stop\\n return pointer\\n else:\\n # If the due time at the possible stop is less than the one at SRC, we're on the right path, and need to look for a stop farther from the SRC\\n if int(possible_stop['duetime']) < int(due_time):\\n # Store the new, better due time\\n due_time = possible_stop['duetime']\\n # Change the highbound to the pointer and reduce our pointer again to halfway between lowbound and highbound\\n high_bound = pointer\\n pointer -= ((high_bound - low_bound)//4)\\n else:\\n # If the due time at the possible stop is bigger than the one at SRC, we've gone too far, and need to look for a stop closer to the SRC\\n # The lowbound becomes the pointer and we move the pointer, again, to halfway between the lowbound and the highbound\\n low_bound = pointer\\n pointer += ((high_bound - low_bound)//4)\\n # If we found a better (shortter) due time, we store this one for the next iteration and keep looking for an even better one\\n last_due_time = new_due_time_due\\n\\n # If the algorithm comes to this part, it means we didn't find a stop where our bus was due wihin 1 (or more) minutes. So we need to increase the\\n # arrival_within minutes to keep searching.\\n arrival_within_minutes += 1\\n\\n # Reset our lowbound, highbound and pointer to restart the search\\n low_bound = 0\\n high_bound = original\\n pointer = original // 4\\n\\n # If we start looking for a stop, previous to the SRC, were our bus has MORE duetime, we've gonne too far. Possibly, there are two buses running very close to one another,\\n # and they may be due to our SRC stop at the same time (seen before too many times with the 17). In this case, we need to increase the original bound to take the stop where\\n # we found the previous bus.\\n if arrival_within_minutes > int(original_due_time):\\n high_bound += 1\\n return high_bound\\n\\n # Just a token return\\n return 0\",\n \"def test_parse_time_unix_timestamp(self):\\n self.assertEqual(\\n parse_time(\\\"1422748800\\\", None), datetime(2015, 2, 1, 0, 0, 0))\\n self.assertEqual(parse_time(\\\"0\\\", None), datetime(1970, 1, 1, 0, 0, 0))\\n # The following are treated as unix timestamps, not YYYYMMDD strings.\\n self.assertEqual(\\n parse_time(\\\"19000101\\\", None), datetime(1970, 8, 8, 21, 48, 21))\\n self.assertEqual(\\n parse_time(\\\"20150132\\\", None), datetime(1970, 8, 22, 5, 15, 32))\\n self.assertEqual(\\n parse_time(\\\"20151301\\\", None), datetime(1970, 8, 22, 5, 35, 1))\",\n \"def test_subset_by_time(self):\\n\\n this_satellite_dict = satellite_io.subset_by_time(\\n satellite_dict=copy.deepcopy(SATELLITE_DICT_ALL_EXAMPLES),\\n desired_times_unix_sec=DESIRED_TIMES_UNIX_SEC\\n )[0]\\n\\n self.assertTrue(compare_satellite_dicts(\\n this_satellite_dict, SATELLITE_DICT_SUBSET_BY_TIME\\n ))\",\n \"def find_nearest_wav(self, wavelength):\\n\\n idx = np.searchsorted(self.wavelengths, wavelength, side=\\\"left\\\")\\n if idx > 0 and (idx == len(self.wavelengths) or math.fabs(wavelength - self.wavelengths[idx-1]) < math.fabs(wavelength - self.wavelengths[idx])):\\n return self.wavelengths[idx-1]\\n else:\\n return self.wavelengths[idx]\",\n \"def closest_element(self, where, cartesian=False, threshold=None, vincenty=False):\\n if not vincenty:\\n if cartesian:\\n x, y = self.grid.xc, self.grid.yc\\n else:\\n x, y = self.grid.lonc, self.grid.latc\\n dist = np.sqrt((x - where[0])**2 + (y - where[1])**2)\\n else:\\n grid_pts = np.asarray([self.grid.lonc, self.grid.latc]).T\\n where_pt_rep = np.tile(np.asarray(where), (len(self.grid.lonc),1))\\n dist = np.asarray([vincenty_distance(pt_1, pt_2) for pt_1, pt_2 in zip(grid_pts, where_pt_rep)])*1000\\n\\n index = np.argmin(dist)\\n if threshold:\\n if dist.min() < threshold:\\n index = np.argmin(dist)\\n else:\\n index = None\\n\\n return index\",\n \"def find_nearest_spot(this_coord, coord_list, scale_z, scale_xy):\\n closest_sed = np.inf\\n closest_spot = 0\\n for test_data in coord_list:\\n test_spot_id = test_data[0]\\n test_coords = (test_data[1:4])\\n sed = sq_euc_distance(test_coords, this_coord, scale_z, scale_xy)\\n if (sed < closest_sed):\\n closest_sed = sed\\n closest_spot = test_spot_id\\n closest_spot_coords = test_coords\\n return closest_spot, np.sqrt(closest_sed), closest_spot_coords\",\n \"def _get_offset(self, seconds, default):\\n now_nanosec = time.time_ns()\\n target_nanosec = now_nanosec - utils.seconds_in_nano(seconds)\\n\\n query_values = {\\n \\\"target_nanosec\\\": target_nanosec\\n }\\n\\n self._cursor.execute(f\\\"\\\"\\\"\\n SELECT rowid\\n FROM {self._table_name}\\n WHERE timestamp >= :target_nanosec\\n ORDER BY timestamp\\n LIMIT 1;\\\"\\\"\\\", query_values)\\n\\n offset_row = self._cursor.fetchone()\\n rowid = offset_row[0] if offset_row else default\\n\\n return rowid - 1\",\n \"def _mktime(self):\\n epoch = datetime(1970, 1, 1)\\n max_fold_seconds = 24 * 3600\\n t = (self - epoch) // timedelta(0, 1)\\n\\n def local(u):\\n y, m, d, hh, mm, ss = _time.localtime(u)[:6]\\n return (datetime(y, m, d, hh, mm, ss) - epoch) // timedelta(0, 1)\\n\\n # Our goal is to solve t = local(u) for u.\\n a = local(t) - t\\n u1 = t - a\\n t1 = local(u1)\\n if t1 == t:\\n # We found one solution, but it may not be the one we need.\\n # Look for an earlier solution (if `fold` is 0), or a\\n # later one (if `fold` is 1).\\n u2 = u1 + (-max_fold_seconds, max_fold_seconds)[self.fold]\\n b = local(u2) - u2\\n if a == b:\\n return u1\\n else:\\n b = t1 - u1\\n assert a != b\\n u2 = t - b\\n t2 = local(u2)\\n if t2 == t:\\n return u2\\n if t1 == t:\\n return u1\\n # We have found both offsets a and b, but neither t - a nor t - b is\\n # a solution. This means t is in the gap.\\n return (max, min)[self.fold](u1, u2)\",\n \"def from_unix(cls, seconds, milliseconds=0):\\n return datetime.datetime.fromtimestamp(seconds + milliseconds * .001)\",\n \"def __find_nearest_weathers(\\n self, timestamp: datetime, weather_list: list,\\n ) -> dict:\\n if (\\n timestamp.tzinfo is None\\n ): # If timestamp is naive (tzinfo = None),\\n # make it so that it is the same as weather_list timestamp.\\n timestamp = timestamp.replace(tzinfo=weather_list[0].date.tzinfo)\\n\\n beforeWeathers = list(\\n filter(\\n lambda x: timestamp >= x.date - timedelta(minutes=1),\\n weather_list,\\n ),\\n )\\n afterWeathers = list(\\n filter(lambda x: timestamp < x.date, weather_list),\\n )\\n before = None\\n beforeSeconds = 999999999999999999999999999\\n after = None\\n afterSeconds = 999999999999999999999999999\\n\\n for bw in beforeWeathers:\\n if timestamp > bw.date:\\n t = timestamp - bw.date\\n else:\\n t = bw.date - timestamp\\n if beforeSeconds > t.seconds:\\n before = bw\\n beforeSeconds = t.seconds\\n for aw in afterWeathers:\\n if timestamp > aw.date:\\n t = timestamp - aw.date\\n else:\\n t = aw.date - timestamp\\n if afterSeconds > t.seconds:\\n after = aw\\n afterSeconds = t.seconds\\n return {\\n 'before': {'weather': before, 'seconds': beforeSeconds},\\n 'after': {'weather': after, 'seconds': afterSeconds},\\n }\",\n \"def closest_distance(self, time, other_object, other_time):\\n ti = np.where(self.times == time)[0][0]\\n oti = np.where(other_object.times == other_time)[0][0]\\n xs = self.x[ti].ravel()[self.masks[ti].ravel() == 1]\\n xs = xs.reshape(xs.size, 1)\\n ys = self.y[ti].ravel()[self.masks[ti].ravel() == 1]\\n ys = ys.reshape(ys.size, 1)\\n o_xs = other_object.x[oti].ravel()[other_object.masks[oti].ravel() == 1]\\n o_xs = o_xs.reshape(1, o_xs.size)\\n o_ys = other_object.y[oti].ravel()[other_object.masks[oti].ravel() == 1]\\n o_ys = o_ys.reshape(1, o_ys.size)\\n distances = (xs - o_xs) ** 2 + (ys - o_ys) ** 2\\n return np.sqrt(distances.min())\",\n \"def find_last_value(self, value, closest=False):\\n value = pd.to_datetime(value)\\n value = column.as_column(value).as_numerical[0]\\n return self.as_numerical.find_last_value(value, closest=closest)\",\n \"def obtain_time_duration(collection, new_document):\\r\\n\\r\\n # Obtain the previously existing two document for the incoming bizLocation\\r\\n # Sort them in descending order\\r\\n # The first in the list is the newly inserted document detected by Change Streams\\r\\n # the second document is of interest\\r\\n prev_documents = collection.find({'epcList.epc': new_document['epcList'][0]['epc']}).limit(2).sort([(\\\"eventTime\\\", DESCENDING)])\\r\\n\\r\\n if prev_documents is not None:\\r\\n # if there is a previous set of documents\\r\\n prev_doc_list = list(prev_documents)\\r\\n # print(prev_doc_list)\\r\\n if len(prev_doc_list) == 1:\\r\\n logger.info('Only Single entry exists for Product.. It implies it is the a new product with no previous events.')\\r\\n return None\\r\\n else:\\r\\n logger.debug('Previous BizLocation of Product: {}, Present BizLocation of Product: {}'.format(\\r\\n prev_doc_list[1]['bizLocation']['id'], new_document['bizLocation']['id']))\\r\\n logger.debug('Time Duration: From {} to {}'.format(prev_doc_list[1]['eventTime'], new_document['eventTime']))\\r\\n\\r\\n # make the dictionary to return\\r\\n duration = {\\r\\n 'bizLocation': {\\r\\n 'prev': prev_doc_list[1]['bizLocation']['id'],\\r\\n 'present': new_document['bizLocation']['id']\\r\\n },\\r\\n 'from_time': prev_doc_list[1]['eventTime'].isoformat(timespec='milliseconds') + 'Z',\\r\\n 'to_time': new_document['eventTime'].isoformat(timespec='milliseconds') + 'Z'\\r\\n }\\r\\n # print(duration)\\r\\n return duration\\r\\n else:\\r\\n logger.info('No Previous Information of Event Found')\\r\\n return None\",\n \"def filter_times(timestamps, time_difference):\\n timestamps = sorted(set(timestamps))\\n\\n filtered_timestamps = []\\n for current_timestamp in timestamps:\\n if not filtered_timestamps or current_timestamp - filtered_timestamps[-1] > time_difference:\\n filtered_timestamps.append(current_timestamp)\\n\\n return filtered_timestamps\",\n \"def _closest_date(target_dt, date_list, before_target=None) -> datetime.date | None:\\n\\n def time_before(d):\\n return target_dt - d if d <= target_dt else datetime.timedelta.max\\n\\n def time_after(d):\\n return d - target_dt if d >= target_dt else datetime.timedelta.max\\n\\n def any_time(d):\\n return target_dt - d if d < target_dt else d - target_dt\\n\\n if before_target is None:\\n return min(date_list, key=any_time).date()\\n if before_target:\\n return min(date_list, key=time_before).date()\\n else:\\n return min(date_list, key=time_after).date()\",\n \"def check_time_borders(self, sam_ev, ):\\n mask = np.logical_and(sam_ev['timeMJD'] > self.DataStart,\\n sam_ev['timeMJD'] < self.DataEnd)\\n return sam_ev[mask]\",\n \"def findspan(self, u):\\n #if u >= self.kv[-self.p-1]:\\n # return self.kv.size - self.p - 2 # last interval\\n #else:\\n # return self.kv.searchsorted(u, side='right') - 1\\n return pyx_findspan(self.kv, self.p, u)\",\n \"def get_last_entry_time():\\r\\n try:\\r\\n last_entry_time = list(mongo_coll_tweets.find().sort(\\r\\n [(\\\"_id\\\", -1)]).limit(1))[0][\\\"_id\\\"].generation_time\\r\\n except:\\r\\n last_entry_time = 0\\r\\n\\r\\n return last_entry_time\",\n \"def get_scan_by_time(self, time):\\n scan_ids = tuple(self.index)\\n lo = 0\\n hi = len(scan_ids)\\n while hi != lo:\\n mid = (hi + lo) // 2\\n sid = scan_ids[mid]\\n sid = sid.decode('utf-8')\\n scan = self.get_scan_by_id(sid)\\n if not self._validate(scan):\\n sid = scan_ids[mid - 1]\\n scan = self.get_scan_by_id(sid)\\n if not self._validate(scan):\\n sid = scan_ids[mid - 2]\\n scan = self.get_scan_by_id(sid)\\n\\n scan_time = scan.scan_time\\n if scan_time == time:\\n return scan\\n elif (hi - lo) == 1:\\n return scan\\n elif scan_time > time:\\n hi = mid\\n else:\\n lo = mid\\n if hi == 0 and not self._use_index:\\n raise TypeError(\\\"This method requires the index. Please pass `use_index=True` during initialization\\\")\",\n \"def observation_for_closest(\\n lat: float, lon: float, lang: str = _DEFAULT_LANG, num_stations_to_try: int = 3\\n) -> Tuple[Dict, Dict]:\\n assert lang in _SUPPORTED_LANGS\\n\\n stations = closest_stations(lat, lon, limit=num_stations_to_try)\\n for s in stations:\\n o = observation_for_station(s[\\\"id\\\"], lang=lang)\\n if o[\\\"results\\\"] and not o[\\\"results\\\"][0].get(\\\"err\\\") and o[\\\"results\\\"][0][\\\"valid\\\"]:\\n return o, s\\n return observation_for_station(stations[0][\\\"id\\\"], lang=lang), stations[0]\",\n \"def check_time():\\n times = get_times()\\n time_difference = abs((times['local'] - times['target']).total_seconds())\\n return time_difference < post_time_tol_seconds\",\n \"def binary_search_time(values: list[any], low: int, high: int, target: int) -> any:\\n if low <= high:\\n middle_index = (low+high) // 2\\n middle_value = values[middle_index]['time']\\n if middle_value < target:\\n return binary_search_time(values, middle_index+1, high, target)\\n elif middle_value > target:\\n return binary_search_time(values, low, middle_index-1, target)\\n else:\\n return values[middle_index]\\n return values[high]\",\n \"def find_conflict(self, time):\\n node = self.root\\n wait_time = self.wait_time\\n\\n while node is not None: \\n lower_limit = node.key - wait_time\\n upper_limit = node.key + wait_time\\n \\n if time > lower_limit and time < upper_limit: \\n return node\\n if time < node.key: \\n node = node.left\\n else: \\n node = node.right\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6383025","0.57725835","0.5726713","0.5603155","0.550198","0.5465888","0.51996434","0.51091826","0.49394408","0.49366295","0.49245772","0.48886275","0.48809275","0.48312008","0.48274845","0.4817998","0.4760067","0.47593623","0.47202304","0.47175002","0.4663312","0.4646897","0.46306196","0.4605473","0.46016923","0.45864683","0.45778677","0.45742598","0.45709598","0.4556572","0.44782287","0.44638187","0.44625032","0.44500652","0.44321182","0.44237286","0.44229278","0.4411701","0.43894556","0.43883225","0.43815115","0.4377511","0.43738","0.43644208","0.4358279","0.43360013","0.43313226","0.43262392","0.43261746","0.43214053","0.43187287","0.42907608","0.42834088","0.42813873","0.42751548","0.42689177","0.4260629","0.4252465","0.42509535","0.424769","0.4241173","0.42321485","0.42314976","0.4228018","0.42275235","0.4227106","0.422132","0.41999498","0.4195137","0.41850537","0.4179975","0.41785333","0.41784263","0.41742566","0.415735","0.41515374","0.4150833","0.41500276","0.41482443","0.4146893","0.41410995","0.41370693","0.4132076","0.41258848","0.4104681","0.41036394","0.40837675","0.40771002","0.40754423","0.4069814","0.4066251","0.4049001","0.40455782","0.4032667","0.4029973","0.4029847","0.40284482","0.4027219","0.40258557","0.40145198"],"string":"[\n \"0.6383025\",\n \"0.57725835\",\n \"0.5726713\",\n \"0.5603155\",\n \"0.550198\",\n \"0.5465888\",\n \"0.51996434\",\n \"0.51091826\",\n \"0.49394408\",\n \"0.49366295\",\n \"0.49245772\",\n \"0.48886275\",\n \"0.48809275\",\n \"0.48312008\",\n \"0.48274845\",\n \"0.4817998\",\n \"0.4760067\",\n \"0.47593623\",\n \"0.47202304\",\n \"0.47175002\",\n \"0.4663312\",\n \"0.4646897\",\n \"0.46306196\",\n \"0.4605473\",\n \"0.46016923\",\n \"0.45864683\",\n \"0.45778677\",\n \"0.45742598\",\n \"0.45709598\",\n \"0.4556572\",\n \"0.44782287\",\n \"0.44638187\",\n \"0.44625032\",\n \"0.44500652\",\n \"0.44321182\",\n \"0.44237286\",\n \"0.44229278\",\n \"0.4411701\",\n \"0.43894556\",\n \"0.43883225\",\n \"0.43815115\",\n \"0.4377511\",\n \"0.43738\",\n \"0.43644208\",\n \"0.4358279\",\n \"0.43360013\",\n \"0.43313226\",\n \"0.43262392\",\n \"0.43261746\",\n \"0.43214053\",\n \"0.43187287\",\n \"0.42907608\",\n \"0.42834088\",\n \"0.42813873\",\n \"0.42751548\",\n \"0.42689177\",\n \"0.4260629\",\n \"0.4252465\",\n \"0.42509535\",\n \"0.424769\",\n \"0.4241173\",\n \"0.42321485\",\n \"0.42314976\",\n \"0.4228018\",\n \"0.42275235\",\n \"0.4227106\",\n \"0.422132\",\n \"0.41999498\",\n \"0.4195137\",\n \"0.41850537\",\n \"0.4179975\",\n \"0.41785333\",\n \"0.41784263\",\n \"0.41742566\",\n \"0.415735\",\n \"0.41515374\",\n \"0.4150833\",\n \"0.41500276\",\n \"0.41482443\",\n \"0.4146893\",\n \"0.41410995\",\n \"0.41370693\",\n \"0.4132076\",\n \"0.41258848\",\n \"0.4104681\",\n \"0.41036394\",\n \"0.40837675\",\n \"0.40771002\",\n \"0.40754423\",\n \"0.4069814\",\n \"0.4066251\",\n \"0.4049001\",\n \"0.40455782\",\n \"0.4032667\",\n \"0.4029973\",\n \"0.4029847\",\n \"0.40284482\",\n \"0.4027219\",\n \"0.40258557\",\n \"0.40145198\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.84365386"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":4695,"cells":{"query":{"kind":"string","value":"Pulls in the records from other into self with the other, but since the timestamps won't match up perfectly, the output will only have a record per $period number of seconds."},"document":{"kind":"string","value":"def merge_with(self, other, period=60):\n\t\tnew_list = []\n\t\tlast_timestamp = 0\n\t\tfor r in self.records:\n\t\t\tif abs(r.unix_time - last_timestamp) > period:\n\t\t\t\t# Accept this record\n\t\t\t\tlast_timestamp = r.unix_time\n\t\t\t\tother_r = other.get_record(r.unix_time, period/2)\n\t\t\t\tr.merge_with(other_r)\n\t\t\t\tnew_list.append(r)\n\t\tself.records = new_list"},"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 __add__ ( self, other, resample_opts=None ):\n result = ObservationStorage (datadir=self.datadir, \\\n resample_opts=resample_opts )\n if self.date[0] > other.date[0]:\n start_date = other.date[0]\n else:\n start_date = self.date[0]\n if self.date[-1] > other.date[-1]:\n end_date = other.date[-1]\n else:\n end_date = self.date[-1]\n \n delta = datetime.timedelta ( days=1 )\n this_date = start_date.date()\n end_date = end_date.date() + delta\n \n this_obs_dates = [ x.date() for x in self.date ]\n other_obs_dates = [ x.date() for x in other.date ]\n \n date = [] ; vza = [] ; vaa = [] ; sza = [] ; saa = []\n emulator = [] ; mask = [] ; data_pntr = [] ; spectral = []\n sensor = []\n \n while this_date < end_date:\n if this_date in this_obs_dates:\n iloc = this_obs_dates.index ( this_date )\n date.append ( self.date[iloc] )\n emulator.append ( self.emulator[iloc] )\n vza.append ( self.vza[iloc] )\n sza.append ( self.sza[iloc] )\n vaa.append ( self.vaa[iloc] )\n saa.append ( self.saa[iloc] )\n spectral.append ( self.spectral )\n mask.append ( ( self.get_mask, [iloc] ) )\n sensor.append ( self.sensor )\n \n data_pntr.append ( self._data_pntr[iloc] )\n if this_date in other_obs_dates:\n iloc = other_obs_dates.index ( this_date )\n date.append ( other.date[iloc] )\n emulator.append ( other.emulator[iloc] )\n vza.append ( other.vza[iloc] )\n sza.append ( other.sza[iloc] )\n vaa.append ( other.vaa[iloc] )\n saa.append ( other.saa[iloc] )\n spectral.append ( other.spectral )\n mask.append ( ( other.get_mask, [iloc] ) )\n sensor.append ( other.sensor )\n data_pntr.append ( other._data_pntr[iloc] )\n this_date += delta\n result.vza = vza\n result.vaa = vaa\n result.sza = sza \n result.saa = saa \n result.date = date\n result.spectral = spectral\n result.masks = mask\n result.sensor = sensor\n result.emulator = emulator\n result._data_pntr = data_pntr\n return result","def merge_new(dfc, pairs, span=None):\n global last_update\n t1 = Timer()\n columns = ['open', 'close', 'trades', 'volume', 'buy_ratio']\n exclude = ['_id','high','low','quote_vol','sell_vol', 'close_time']\n projection = dict(zip(exclude, [False]*len(exclude)))\n idx, data = [], []\n db = app.get_db()\n\n if span is None and last_update:\n # If no span, query/merge db records inserted since last update.\n oid = ObjectId.from_datetime(last_update)\n last_update = now()\n _filter = {'_id':{'$gte':oid}}\n else:\n # Else query/merge all since timespan.\n span = span if span else timedelta(days=7)\n last_update = now()\n _filter = {'pair':{'$in':pairs}, 'close_time':{'$gte':now()-span}}\n\n batches = db.candles.find_raw_batches(_filter, projection)\n\n if batches.count() < 1:\n return dfc\n\n try:\n ndarray = bsonnumpy.sequence_to_ndarray(\n batches,\n dtype,\n db.candles.count()\n )\n except Exception as e:\n log.error(str(e))\n return dfc\n #raise\n\n df = pd.DataFrame(ndarray)\n df['open_time'] = pd.to_datetime(df['open_time'], unit='ms')\n df['freq'] = df['freq'].str.decode('utf-8')\n df['pair'] = df['pair'].str.decode('utf-8')\n\n df['freq'] = df['freq'].replace('1m',60)\n df['freq'] = df['freq'].replace('5m',300)\n df['freq'] = df['freq'].replace('1h',3600)\n df['freq'] = df['freq'].replace('1d',86400)\n df = df.sort_values(by=['pair','freq','open_time'])\n\n df2 = pd.DataFrame(df[columns].values,\n index = pd.MultiIndex.from_arrays(\n [df['pair'], df['freq'], df['open_time']],\n names = ['pair','freq','open_time']),\n columns = columns\n ).sort_index()\n\n df3 = pd.concat([dfc, df2]).drop_duplicates().sort_index()\n\n log.debug(\"{:,} records loaded into numpy. [{:,.1f} ms]\".format(\n len(df3), t1))\n #print(\"Done in %s ms\" % t1)\n return df3","def _fill_results(self,spec,measurements,period,duration):\r\n logging.info(\"Fill measurements for spec {0}\".format(spec))\r\n \r\n if self._verb==mplane.model.VERB_QUERY:\r\n \"\"\"\r\n Query according to the time specified in the specification\r\n \"\"\"\r\n (first_time,last_time) = spec.when().datetimes()\r\n first_time=int(first_time.replace(tzinfo=datetime.timezone.utc).timestamp())\r\n last_time=int(last_time.replace(tzinfo=datetime.timezone.utc).timestamp())\r\n sleep_time = 0\r\n else:\r\n \"\"\"\r\n Query from NOW\r\n \"\"\"\r\n first_time = int(time.time())\r\n if (len(measurements[1])>0 or len(measurements[2])>0) and period<=self._pvsr_default_conf_check_cycle:\r\n #there are newly created or modified measurements\r\n first_time = first_time + self._pvsr_default_conf_check_cycle\r\n if first_time % period > 0:\r\n first_time = first_time - (first_time % period)\r\n last_time = first_time + int(duration / period) * period\r\n sleep_time = duration\r\n\r\n logging.debug(\"From: {0}, To: {1}\".format(datetime.datetime.fromtimestamp(first_time),datetime.datetime.fromtimestamp(last_time)))\r\n \r\n meas_data = {}\r\n\r\n while True:\r\n logging.info(\"Wait {0} seconds\".format(sleep_time))\r\n time.sleep(sleep_time)\r\n sleep_time = 30\r\n \r\n loaded_until=self._pvsr.getLastLoadedDataTimestamp(period)\r\n if int(loaded_until.timestamp())>=last_time or time.time()>last_time+period+300:\r\n for i in (0,1,2):\r\n for j in range(len(measurements[i])):\r\n self._fill_meas_result(measurements[i][j],first_time,last_time,meas_data)\r\n break\r\n else:\r\n logging.debug(\"last loaded is still {0}\".format(loaded_until))\r\n \r\n res = mplane.model.Result(specification=spec)\r\n res.set_when(mplane.model.When(a = datetime.datetime.utcfromtimestamp(first_time+period), b = datetime.datetime.utcfromtimestamp(last_time)))\r\n \r\n tmp_time=first_time+period\r\n row_index=0\r\n while tmp_time<=last_time:\r\n tmp_time2 = datetime.datetime.fromtimestamp(tmp_time)\r\n tmp_time3 = datetime.datetime.utcfromtimestamp(tmp_time)\r\n res.set_result_value(\"time\", tmp_time3, row_index)\r\n if tmp_time2 in meas_data:\r\n for mplane_name in meas_data[tmp_time2]:\r\n value = str(meas_data[tmp_time2][mplane_name])\r\n res.set_result_value(mplane_name, value, row_index)\r\n row_index+=1\r\n tmp_time+=period\r\n \r\n return res","def filter_time_match(file1, file2):\n freq1 = int(file1.split(\".\")[1].split(\"_\")[1].replace(\"M\", \"\"))\n freq2 = int(file2.split(\".\")[1].split(\"_\")[1].replace(\"M\", \"\"))\n df1, df2 = filter_overlapping_files_dfs(file1, file2)\n\n dt1 = pandas.to_datetime(df1[\"date\"] + \" \" + df1[\"hour\"])\n dt2 = pandas.to_datetime(df2[\"date\"] + \" \" + df2[\"hour\"])\n\n dt_delta = datetime.timedelta(minutes=freq2 - freq1)\n time_match_df1 = dt1.copy()\n time_match_df2 = dt2.copy()\n for idx, dt in dt2.items():\n match = dt1[(dt1 >= dt) & (dt1 <= dt + dt_delta)]\n time_match_df1[match.index] = idx\n time_match_df2[idx] = 0\n time_match_df2[idx] = tuple(match.index)\n\n time_match_df2[time_match_df2.apply(len) != 10]\n return time_match_df1, time_match_df2","def _merge_report(self, target, new):\r\n time = None\r\n if 'ts' in new['parsed']:\r\n time = new['parsed']['ts']\r\n\r\n if (target.get('lastSeenDate', None) and\r\n time and\r\n target['lastSeenDate'] < time):\r\n target['lastSeenDate'] = time\r\n\r\n query_millis = int(new['parsed']['stats']['millis'])\r\n target['stats']['totalTimeMillis'] += query_millis\r\n target['stats']['count'] += 1\r\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']","def _merge_report(self, target, new):\n time = None\n if 'ts' in new['parsed']:\n time = new['parsed']['ts']\n\n if (target.get('lastSeenDate', None) and\n time and\n target['lastSeenDate'] < time):\n target['lastSeenDate'] = time\n\n query_millis = int(new['parsed']['stats']['millis'])\n target['stats']['totalTimeMillis'] += query_millis\n target['stats']['count'] += 1\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']","def test_find_df_period(self):\n test_search_df = pd.read_csv(DF_PATH)\n result_1 = find_df_period(test_search_df, 'pickup_datetime', 6)\n p_time_periods_1 = result_1['time_period'].tolist()\n p_intervals_1 = [2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4]\n\n result_2 = find_df_period(test_search_df, 'pickup_datetime', 4)\n p_time_periods_2 = result_2['time_period'].tolist()\n p_intervals_2 = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]\n\n self.assertTrue(p_time_periods_1 == p_intervals_1)\n self.assertTrue(p_time_periods_2 == p_intervals_2)","def make_all_datetime(self):\n \n logging.info('\\n *** Running make_all_datetime ' )\n\n all_uniques = [] # storing a list with all the unique date_times \n which_k_in_dt = {} # list of avilable dataset for each unique date_time, so that when looping over the distinct date_times, only the proper dataset will be read and compared \n\n \"\"\" Loop over all the datasets \n k: name of the dataset\n v: list of file paths, eg 'era5_1':[filepath_1, filepath_2 ]\"\"\"\n\n for k,v in self.datasets.items() :\n self.unique_dates[k] = {}\n for F in v: \n self.unique_dates[k][F] = {}\n \n self.unique_dates[k][F]['indices'] = {} \n self.unique_dates[k][F]['index_offset_next'] = 0 # to be replaced later when slicing \n self.unique_dates[k][F]['index_offset'] = 0 # to be replaced later when slicing \n\n unique_dt = list(data[k][F]['recordtimestamp'])\n \n indices = list(data[k][F]['recordindex'])\n all_uniques += unique_dt # adding to the total unique date_times \n\n \"\"\" Loop over all the date_times of each dataset \"\"\"\n for dt, index_low, count in zip (unique_dt, indices, range(len(unique_dt)) ):\n\n if dt not in which_k_in_dt.keys():\n which_k_in_dt[dt] = {}\n if k not in which_k_in_dt[dt].keys():\n which_k_in_dt[dt][k] = [] \n if F not in which_k_in_dt[dt][k]:\n which_k_in_dt[dt][k].append(F)\n # at this point I have e.g. which_k_in_dt= {1990-01-01-12-00: {era5_1:[file1,file2] , ncar:[file3] } }\n\n self.unique_dates[k][F]['indices'][dt] = {}\n self.unique_dates[k][F]['indices'][dt]['low'] = index_low \n try:\n index_up = indices[ count + 1 ] # works until the last available recordindex\n except: \n index_up = max(indices)+1000000 # dummy large number \n\n self.unique_dates[k][F]['indices'][dt]['up'] = index_up\n self.unique_dates[k][F]['up_to_dt_slice'] = data[k][F]['min_date'] \n \n\n self.dataset_per_dt = which_k_in_dt \n self.merged_unique_dates = np.unique(np.array(all_uniques) ) # storing the set of *ALL* distinct dt values of all datasets and all files \n logging.debug('*** make_all_datetime finished ')","def Merge(self, other):\n\n # Logging just in case\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: before\", %s);'\n %(self.persistant['id'],\n sql.FormatSqlValue('details',\n repr(self.persistant))))\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: deleted\", %s);'\n %(other.persistant['id'], \n sql.FormatSqlValue('details',\n repr(other.persistant))))\n\n # Fields which can be summed\n for f in ['plays', 'skips']:\n self.persistant[f] = (self.persistant.get(f, 0) +\n other.persistant.get(f, 0))\n\n # Date fields where we take the newest\n for f in ['last_played', 'last_skipped', 'last_action']:\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\n if a > b:\n v = a\n else:\n v = b\n if v != datetime.datetime(1970, 1, 1):\n self.persistant[f] = v\n\n # Date fields where we take the oldest\n for f in ['creation_time']:\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\n if a < b:\n v = a\n else:\n v = b\n if v != datetime.datetime(1970, 1, 1):\n self.persistant[f] = v\n\n # Fields where we only clobber ours if we don't have a value\n for f in ['artist', 'album', 'song']:\n if not self.persistant.has_key(f) or not self.persistant[f]:\n self.persistant[f] = other.persistant.get(f, None)\n\n # Sometimes the number is a placeholder\n if self.persistant.has_key('number') and self.persistant['number'] == -1:\n self.persistant['number'] = other.persistant.get('number', -1)\n if not self.persistant.has_key('number'):\n self.persistant['number'] = other.persistant.get('number', -1)\n\n # Update the id in the tags table\n tags = self.db.GetRows('select tag from tags where track_id=%d;'\n % other.persistant['id'])\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: tags: %d\", %s);'\n %(self.persistant['id'], other.persistant['id'],\n sql.FormatSqlValue('details', repr(tags))))\n\n try:\n self.db.ExecuteSql('update tags set track_id=%d where track_id=%d;'\n %(self.persistant['id'], other.persistant['id']))\n self.db.ExecuteSql('commit;')\n except:\n # This can happen if the is already a matching tag for the first track\n pass\n\n # Update the id in the paths table\n paths = self.db.GetRows('select path from paths where track_id=%d;'\n % other.persistant['id'])\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: paths: %d\", %s);'\n %(self.persistant['id'], other.persistant['id'],\n sql.FormatSqlValue('details', repr(paths))))\n \n self.db.ExecuteSql('update paths set track_id=%d where track_id=%d;'\n %(self.persistant['id'], other.persistant['id']))\n self.db.ExecuteSql('commit;')\n\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\n 'details) values (now(), %d, \"merge: after\", %s);'\n %(self.persistant['id'],\n sql.FormatSqlValue('details',\n repr(self.persistant))))\n self.db.ExecuteSql('commit;')","def merge_all_data(self):\n\n logging.info('***** Starting the merging process merge_all_data')\n\n \"\"\" All possible unique_dates to loop on \"\"\"\n date_times = self.merged_unique_dates\n date_times.sort()\n date_times = np.array(date_times) \n\n \"\"\" List storing the indices of the date_index of the merged dataset \"\"\"\n all_combined_obs , all_combined_head, all_combined_era5fb , combined_indices , combined_date_time, = [] , [] , [] , [] , []\n best_ds_list = [] \n source_files = []\n station_configurations = []\n\n \"\"\" The items contained in the lists in the list below can be removed from the list when the record that was previously stored is removed. \"\"\"\n all_list = [all_combined_obs , all_combined_head, all_combined_era5fb , combined_indices , combined_date_time, best_ds_list, source_files , station_configurations ] # holder of all the above lists\n all_list_name = ['all_combined_obs' , 'all_combined_head', 'all_combined_era5fb' , 'combined_indices' , 'combined_date_time' , 'best_ds_list', 'source_files' ] \n \n removed_record, kept_record = [], []\n \n \"\"\" Dictionary that will contain the merged file. \"\"\" \n # rand = datetime.strptime('1981-01-03 12:00:00', '%Y-%m-%d %H:%M:%S') \n #dt_bestds_dic = {} # store the selected best dataset for each dt \n #date_times=date_times[0:30000]\n tot = len(date_times)\n tt=time.time()\n print('*** Merging ' , tot, ' records ***')\n \n early_datasets = True\n \n self.processed_dt = [] \n \n for dt, c in zip(date_times, range(tot) ): # loop over all the possible date_times \n\n if (c+1)%1000==0:\n print('Analize : ', str(c+1) , '/', str(tot) , ' ', dt , ' ',\n now(time.time()),'{:5.3f}'.format(time.time()-tt ))\n\n delete = self.delete_ds(dt) # check if there is a dataset to delete \n \n \"\"\" Finding if this record is the same as the previous one analyzed, according to the given time_shift \"\"\"\n if c == 0:\n is_same_record = False\n else:\n is_same_record = self.is_same_record( time_shift = self.hour_time_delta , dt = dt)\n \n \"\"\" Updating list of processed datetimes \"\"\"\n self.processed_dt.append(dt) # cannot put it before the check_timeshift or it will check itself \n\n \n cleaned_df_container = {} \n all_len = [] # will hold the length of all the obs_tabs \n \n for k in self.dataset_per_dt[dt].keys() : # checking the list of available datasets \n ''' {'era5_2': ['example_stations/0-20000-0-82930_era5_2_harvested_era5.conv._1:82930.gz.nc', \n 'example_stations/0-20000-0-82930_era5_2_harvested_era5.conv._82930.gz.nc']}\n ''' \n for F in self.dataset_per_dt[dt][k]: # checking the list of available files for the dataset\n \n if data[k][F][\"counter\"] %self.slice_size==0 or data[k][F][\"counter\"] == 0: # loading the data only at specific slices \n load = self.load_obstab_feedback_sliced(datetime=dt, dataset=k, file = F)\n \n data[k][F][\"counter\"] = data[k][F][\"counter\"] + 1 \n \n obs_tab, era5fb_tab = self.make_obstab_era5fb_dic(dataset = k , date_time = dt, File = F )\n\n if len(obs_tab['date_time'][:])==0: # go to next file if obs_tab is empty \n #print('ZERO length')\n continue \n\n all_len.append( len(obs_tab['date_time'][:] ) )\n \n if k not in cleaned_df_container.keys():\n cleaned_df_container[k] = {}\n\n cleaned_df_container[k][F] = {}\n cleaned_df_container[k][F]['obs_tab'] = obs_tab # cleaned dataframe \n cleaned_df_container[k][F]['era5fb_tab'] = era5fb_tab # cleaned dataframe \n \n \"\"\" Merging the different records found in the sifferent sources \"\"\"\n if bool(all_len): # skipping empty container dictionary. At this point I certainyl have one valid record \n best_ds, combined_obs_tab, combined_era5fb_tab, combined_head_tab, selected_file, best_file = self.combine_record(dt, container = cleaned_df_container)\n \n if is_same_record: # decide what to keep in case of same record\n temporary_previous = all_combined_obs[-1] # keep the temporary previous record \n\n if best_ds in ['era5_1','era5_2']: # best_ds from era5\n if best_ds_list[-1] not in ['era5_1','era5_2']: # remove previous non era5_1 or era5_2 record \n for lista in all_list:\n lista.pop() \n #removed_record.append(temporary_previous)\n #kept_record.append(combined_obs_tab) \n\n elif best_ds_list[-1] in ['era5_1','era5_2']:\n if len(combined_obs_tab) <= len(all_combined_obs[-1] ):\n #kept_record.append(temporary_previous) \n #removed_record.append(combined_obs_tab)\n continue # nothing to do, will keep the previous records -> go to next dt \n \n else: # case where both the current and previous are from era5_1 and era5_2, but the previous has smaller number of data \n for lista in all_list:\n lista.pop() \n #removed_record.append(temporary_previous)\n #kept_record.append(combined_obs_tab) \n \n else: # best_ds not from era5\n if best_ds_list[-1] in ['era5_1','era5_2']:\n #print('This best ds is ' , best_ds , ' but I will keep ' , best_ds_list[-1] )\n #kept_record.append(temporary_previous) \n #removed_record.append(combined_obs_tab) \n continue \n \n else:\n if len(combined_obs_tab) < len(all_combined_obs[-1] ):\n #kept_record.append(temporary_previous) \n #removed_record.append(combined_obs_tab) \n continue # nothing to do, will keep the previous records -> go to next dt \n \n elif len(combined_obs_tab) > len(all_combined_obs[-1] ): # remove previous, keep current \n for lista in all_list:\n lista.pop() \n #kept_record.append(combined_obs_tab) \n #removed_record.append(temporary_previous)\n \n elif len(combined_obs_tab) == len(all_combined_obs[-1] ): # prefer igra2, otherwise\n if best_ds == 'igra2':\n for lista in all_list:\n lista.pop() \n #removed_record.append(temporary_previous)\n #kept_record.append(combined_obs_tab) \n \n else: # case where data source is not important, I keep the previous and do nothing \n #kept_record.append(temporary_previous) \n #removed_record.append(combined_obs_tab) \n continue \n \n else: # not the same record, nothing special to do, keep both previous and current \n pass \n else:\n print(' Found an empty record / time shifted record ')\n continue\n \n\n \"\"\" Fill the best_ds list \"\"\"\n best_ds_list.append(best_ds)\n\n \"\"\" Storing the selected file for the source_configuration \"\"\"\n source_files.append(selected_file)\n \"\"\" Selecting the station_configuration \"\"\"\n station_configurations.append(self.data[best_ds][best_file]['station_configuration'] )\n \n \"\"\" Storing the combined era5fb, header and observations tables\"\"\"\n all_combined_era5fb.append(combined_era5fb_tab)\n all_combined_obs .append(combined_obs_tab)\n \n primary, name = self.data[best_ds][best_file]['station_configuration']['primary_id'][0] , self.data[best_ds][best_file]['station_configuration']['station_name'][0] \n #combined_head_tab['primary_station_id'] = [ primary ] * len( combined_head_tab ) \n #combined_head_tab['station_name'] = [ name ] * len( combined_head_tab ) \n \n combined_head_tab['primary_station_id'] = np.array( [primary] )\n combined_head_tab['station_name'] = np.array( [name] )\n \n all_combined_head .append(combined_head_tab)\n\n \"\"\" Dictionary to fill the best_ds for duplicates \"\"\"\n #dt_bestds_dic[dt] = {}\n #dt_bestds_dic[dt]['best_ds'] = best_ds\n #dt_bestds_dic[dt]['len'] = len(combined_obs_tab['date_time'])\n\n \"\"\" New merged recordindex and recordtimestamps indices \"\"\"\n combined_indices.append(len(combined_obs_tab['date_time'])) \n combined_date_time.append(dt)\n\n del cleaned_df_container \n \n \n \n #print(blue + 'Memory used after deleting the cleaned_df_container: ', process.memory_info().rss/1000000000 , cend)\n\n \"\"\" Removing remaining loaded df \"\"\"\n for k in self.datasets_keys:\n for F in self.datasets[k]:\n try:\n del data[k][F]['era5fb_tab']\n print('=== removed era5fb ' , k , F )\n except:\n pass\n try:\n del data[k][F]['observations_table']\n print('=== removed obstab ' , k , F ) \n except:\n pass\n \n \n \"\"\" Saving a numpy dictionary \"\"\"\n print(\" === Saving the numpy dictionary of removed and kept records +++ \")\n #dic_records = { 'kept' : kept_record , 'removed': removed_record }\n #np.save(self.station + '_time_shift_removed_kept.npy',dic_records )\n \n \n \"\"\" Storing the merged date_time values and indices \"\"\"\n di=xr.Dataset()\n combined_date_time = np.array(combined_date_time)\n di['recordtimestamp'] = ( {'recordtimestamp' : combined_date_time.shape } , combined_date_time )\n di['recordtimestamp'].attrs['units']='seconds since 1900-01-01 00:00:00'\n\n \"\"\" Creating the merged indices mi \"\"\"\n mi = [] \n mi.append(0)\n for i in range(len(combined_indices)):\n mi.append( combined_indices[i] + mi[-1] )\n mi.pop()\n pop = np.array(mi) # removing last unecessary index \n di['recordindex'] = ( {'recordindex' : pop.shape } , pop )\n\n\n \"\"\" Creating the combined data \"\"\"\n logging.debug('*** Concatenating the observations_table ' ) \n combined_obs = {}\n #### Writing combined observations_table dic\n logging.info(' ***** Writing the observations_table to the netCDF output ***** ' ) \n for k in all_combined_obs[0].keys(): \n a = np.concatenate([all_combined_obs[i][k][:] for i in range(len(all_combined_obs))])\n if k == 'date_time':\n combined_obs[k]= a \n self.tot_records = len(combined_obs[k])\n self.write_merged(content = 'observations_table', table= {k:a})\n #logging.info('*** Written observations table %s: ', k)\n\n\n #self.tot_records = len(combined_obs['date_time'])\n del all_combined_obs\n print(blue + 'Memory used after deleting all_combined_obs dic: ', process.memory_info().rss/1000000000 , cend )\n \n dateindex = combined_obs['date_time']//86400 \n date_times, indices, counts = np.unique(dateindex, return_counts = True, return_index= True) \n di['dateindex'] = ( {'dateindex' : indices.shape } , indices ) # considers the day only \n del combined_obs\n \n combined_era5fb = {}\n #### Writing combined era5fb_table dic \n for k in all_combined_era5fb[0].keys():\n try:\n #combined_era5fb[k]=np.concatenate([all_combined_era5fb[i][k][:] for i in range(len(all_combined_era5fb))])\n #self.write_merged(content = 'era5fb', table= {k:combined_era5fb[k]})\n \"\"\" try replacing , remove combined_era5fb = {} \"\"\"\n a = np.concatenate([all_combined_era5fb[i][k][:] for i in range(len(all_combined_era5fb))])\n self.write_merged(content = 'era5fb', table= {k:a})\n logging.debug('*** Written era5fb %s: ', k)\n except:\n print(\"FAILED feedback variable \" , k)\n\n del all_combined_era5fb\n print(blue + 'Memory used after deleting era5fb_tab dic: ', process.memory_info().rss/1000000000 , cend)\n\n\n #### Writing combined header_table dic \n for k in all_combined_head[0].keys():\n print('head variable is', k )\n if ( k == 'comments' or k == 'history'):\n continue\n try:\n tab=np.concatenate([all_combined_head[i][k][:] for i in range(len(all_combined_head))])\n self.write_merged(content = 'header_table', table= {k: tab}) # { key: np.array([])}\n logging.info('*** Written header table %s: ', k)\n except:\n print('FFF FAILED variable in header table', k )\n\n del all_combined_head\n print(blue + 'Memory used after deleting all_merged head_tab dic: ', process.memory_info().rss/1000000000 , cend)\n \n self.write_merged(content = 'recordindex', table = di) \n self.write_merged(content = 'cdm_tables', table= '')\n\n\n source_conf=xr.Dataset()\n source_files = np.array(source_files).astype(dtype='|S70')\n source_conf['source_file'] = ( {'source_file' : source_files.shape } , source_files )\n self.write_merged(content = 'source_configuration', table= source_conf )\n\n print(0)\n\n\n \"\"\" Concatenation of station_configurations \"\"\"\n station_conf = pd.concat( station_configurations ) \n for k in station_conf.columns:\n try:\n a =np.array( station_conf[k])\n self.write_merged(content = 'station_configuration', table= {k:a})\n logging.debug('*** Written station_configuration %s: ', k)\n except:\n print(\" Failed station_configuration \" , k )\n \n return 0","def get_records(self, since_ts=0, num_rec=0):\n nerr = 0\n while True:\n try:\n fixed_block = self.get_fixed_block(unbuffered=True)\n if fixed_block['read_period'] is None:\n raise weewx.WeeWxIOError('invalid read_period in get_records')\n if fixed_block['data_count'] is None:\n raise weewx.WeeWxIOError('invalid data_count in get_records')\n if since_ts:\n dt = datetime.datetime.utcfromtimestamp(since_ts)\n dt += datetime.timedelta(seconds=fixed_block['read_period']*30)\n else:\n dt = datetime.datetime.min\n max_count = fixed_block['data_count'] - 1\n if num_rec == 0 or num_rec > max_count:\n num_rec = max_count\n log.debug('get %d records since %s' % (num_rec, dt))\n dts, ptr = self.sync(read_period=fixed_block['read_period'])\n count = 0\n records = []\n while dts > dt and count < num_rec:\n raw_data = self.get_raw_data(ptr)\n data = self.decode(raw_data)\n if data['delay'] is None or data['delay'] < 1 or data['delay'] > 30:\n log.error('invalid data in get_records at 0x%04x, %s' %\n (ptr, dts.isoformat()))\n dts -= datetime.timedelta(minutes=fixed_block['read_period'])\n else:\n record = dict()\n record['ptr'] = ptr\n record['datetime'] = dts\n record['data'] = data\n record['raw_data'] = raw_data\n record['interval'] = data['delay']\n records.insert(0, record)\n count += 1\n dts -= datetime.timedelta(minutes=data['delay'])\n ptr = self.dec_ptr(ptr)\n return records\n except (IndexError, usb.USBError, ObservationError) as e:\n log.error('get_records failed: %s' % e)\n nerr += 1\n if nerr > self.max_tries:\n raise weewx.WeeWxIOError(\"Max retries exceeded while fetching records\")\n time.sleep(self.wait_before_retry)","def pull(self, period):\n # Compile the regex expressions we'll use to parse the title text\n self.identity_regex = re.compile(r\"(\\d{4} \\d{3} \\d{3})\\s{2,}(\\S+)\\s{2,}(\\d{3} \\d{3} \\d{3} *\\S*)\")\n self.ats_regex = re.compile(r\"ATS REFERENCE: (\\S*)\")\n self.municipality_regex = re.compile(r\"MUNICIPALITY: (.*)\")\n self.reference_regex = re.compile(r\"REFERENCE NUMBER: (.*?)\\-{80}\", re.DOTALL)\n self.payday_regex = re.compile(r\"(\\-{80}).*(\\-{80})(.*)\", re.DOTALL)\n\n # Filter the dataframe by date and retrieve each title\n df = self.journal\n df = df[df['Registration Date'] >= period]\n\n df.to_pickle('run/{}.journal.pkl'.format(self.runtime))\n\n click.echo('Journal constructed and saved with timestamp {}'.format(self.runtime))\n\n # Set up structure for target DataFrame\n self.dataframe = pd.DataFrame(\n columns=[\n 'linc',\n 'short_legal',\n 'title_number',\n 'ats_reference',\n 'municipality',\n 'registration',\n 'registration_date',\n 'document_type',\n 'sworn_value',\n 'consideration',\n 'condo'\n ], index=df.index\n )\n\n with click.progressbar(df.iterrows(), label='Pulling basic title data', length=len(df)) as d:\n for index, row in d:\n try:\n payload = self.retrieve_title(index)\n self.dataframe.loc[index, 'linc'] = payload['linc']\n self.dataframe.loc[index, 'short_legal'] = payload['short_legal']\n self.dataframe.loc[index, 'title_number'] = payload['title_number']\n self.dataframe.loc[index, 'ats_reference'] = payload['ats_reference']\n self.dataframe.loc[index, 'municipality'] = payload['municipality']\n self.dataframe.loc[index, 'registration'] = payload['registration']\n self.dataframe.loc[index, 'registration_date'] = payload['date']\n self.dataframe.loc[index, 'document_type'] = payload['document_type']\n self.dataframe.loc[index, 'sworn_value'] = payload['value']\n self.dataframe.loc[index, 'consideration'] = payload['consideration']\n self.dataframe.loc[index, 'condo'] = payload['condo']\n except TypeError:\n pass\n\n self.dataframe['registration_date'] = pd.to_datetime(self.dataframe['registration_date'])\n self.dataframe['sworn_value'] = self.dataframe['sworn_value'].astype(float)\n self.dataframe['consideration'] = self.dataframe['consideration'].astype(float)\n self.dataframe['condo'] = self.dataframe['condo'].fillna(False).astype(bool)\n\n self.dataframe.to_pickle('run/{}.dataframe.pkl'.format(self.runtime))\n click.echo('Dataframe constructed and saved with timestamp {}'.format(self.runtime))\n\n return self.dataframe","def merge_logfiles(log1, log2):\n first_in_2 = log2['time'][0]\n keep_from_1 = log1['time'] < first_in_2\n for key in log1.keys():\n log1[key] = log1[key][keep_from_1]\n log1.timeseries_append(log2)\n return log1","def __init__(self, numQueues, rate, start_hour, end_hour, appt_low, appt_high):\n\n self.rate = rate\n self.numQueues = numQueues\n self.start = datetime.datetime.combine(datetime.date.today(), datetime.time(start_hour,0,0))\n self.end = datetime.datetime.combine(datetime.date.today(), datetime.time(end_hour,0,0))\n self.appt_low = appt_low\n self.appt_high = appt_high\n minutes_for_new_items = (end_hour-start_hour)*60 #new patients seen between 9AM and 4PM\n time_between_items = rate #exponential dist. time parameter\n self.expected_count = int(np.ceil(stats.poisson.ppf(.9999, minutes_for_new_items/time_between_items)))\n self.ques = [datetime.datetime.combine(datetime.datetime.today(), datetime.time(start_hour,0,0)) for i in range(0, self.numQueues)]\n cols = ['simulation', 'num_items', 'wait_count', 'avg_wait_time', 'close_time']\n self.results = pd.DataFrame(columns = cols)\n return","def join_domain_time_span(domain_tables, span):\n joined_domain_tables = []\n \n for domain_table in domain_tables:\n #extract the domain concept_id from the table fields. E.g. condition_concept_id from condition_occurrence\n #extract the domain start_date column\n #extract the name of the table\n concept_id_field, date_field, table_domain_field = get_key_fields(domain_table) \n\n domain_table = domain_table.withColumn(\"date\", unix_timestamp(to_date(col(date_field)), \"yyyy-MM-dd\")) \\\n .withColumn(\"lower_bound\", unix_timestamp(date_add(col(date_field), -span), \"yyyy-MM-dd\"))\\\n .withColumn(\"upper_bound\", unix_timestamp(date_add(col(date_field), span), \"yyyy-MM-dd\"))\\\n .withColumn(\"time_window\", lit(1))\n \n #standardize the output columns\n joined_domain_tables.append(\n domain_table \\\n .select(domain_table[\"person_id\"], \n domain_table[concept_id_field].alias(\"standard_concept_id\"),\n domain_table[\"date\"],\n domain_table[\"lower_bound\"],\n domain_table[\"upper_bound\"],\n lit(table_domain_field).alias(\"domain\"))\n )\n \n return joined_domain_tables","def fake_record_data():\n\n user_ids = [4, 4, 4, 4, 5,\n 5, 2, 6, 1, 2,\n 5, 7, 5, 1, 3,\n 3, 1, 4, 2, 3,\n 6, 4, 2, 7, 3,\n 3, 3, 6, 7, 6,\n 6, 7, 1, 7, 1,\n 8, 7, 1, 8, 4]\n\n days = [1519200000, 1519200000, 1519200000, 1519200000, 1519113600,\n 1519113600, 1519113600, 1519027200, 1519027200, 1519027200,\n 1518940800, 1518940800, 1518854400, 1518854400, 1518768000,\n 1518681600, 1518681600, 1518681600, 1518681600, 1518681600,\n 1518595200, 1518595200, 1518595200, 1518595200, 1518508800,\n 1518422400, 1518422400, 1518422400, 1518422400, 1518336000,\n 1518336000, 1518336000, 1518336000, 1518249600, 1518249600,\n 1518163200, 1518163200, 1518076800, 1517990400, 1517904000]\n\n for i, user_id in enumerate(user_ids):\n act_qty = random.randint(5, 13)\n selected_activities = set()\n\n for _ in range(0, act_qty):\n act_id = random.randint(1, 13)\n selected_activities.add(act_id)\n\n day = days[-(i + 1)]\n start = day + 33000\n total_time = 0\n\n for act_id in selected_activities:\n act_time = random.randint(120, 1000)\n\n start_t = start + total_time\n end_t = datetime.fromtimestamp(start_t + act_time)\n start_t = datetime.fromtimestamp(start_t)\n\n total_time += act_time\n\n print (str(user_id) + '|' + str(i + 1) + '|' + str(act_id) + '|' +\n str(start_t) + '|' + str(end_t))","def update_period(self, period):\n\n # Update attribute\n self._period = period\n\n # Create new data and time\n shape = int(round(self._drate) * self._period + 1)\n new_data = OrderedDict([(ch, np.zeros(shape=shape)) for i, ch in enumerate(self.channels)])\n new_time = np.zeros(shape=shape)\n\n # Check whether new time and data hold more or less indices\n decreased = True if self._time.shape[0] >= shape else False\n\n if decreased:\n # Cut time axis\n new_time = self._time[:shape]\n\n # If filled before, go to 0, else go to 0 if currnt index is bigger than new shape\n if self._filled:\n self._idx = 0\n else:\n self._idx = 0 if self._idx >= shape else self._idx\n\n # Set wheter the array is now filled\n self._filled = True if self._idx == 0 else False\n\n else:\n # Extend time axis\n new_time[:self._time.shape[0]] = self._time\n\n # If array was filled before, go to last time, set it as offset and start from last timestamp\n if self._filled:\n self._idx = self._time.shape[0]\n self._start = self._timestamp\n self._offset = self._time[-1]\n\n self._filled = False\n\n # Set new time and data\n for ch in self.channels:\n if decreased:\n new_data[ch] = self._data[ch][:shape]\n else:\n new_data[ch][:self._data[ch].shape[0]] = self._data[ch]\n\n # Update\n self._time = new_time\n self._data = new_data","def copy_many_from_temp(self,\r\n count):\r\n\r\n for counter in range(count):\r\n print(PERIOD,end=EMPTYCHAR)\r\n self.copy_from_temp(self.tempobject)\r\n self.constitute_key_freq_dict()\r\n print()","def _write_to_dataset(parser1, parser2, dset, rundate):\n\n data_all1 = parser1.as_dict()\n data_all2 = parser2.as_dict()\n if parser1.file_path == parser2.file_path:\n collection = [data_all1]\n else:\n collection = [data_all1, data_all2]\n\n # Meta information\n dset.meta[\"tech\"] = \"slr\"\n dset.meta.add(\"file\", parser1.file_path.stem, section=\"input\")\n dset.meta.add(\"file\", parser2.file_path.stem, section=\"input\")\n dset.meta.add(\"type\", config.tech.obs_format.str.upper(), section=\"input\")\n\n # Make new dict \"obs_data\" containing only data in relevant time interval:\n arc_length = config.tech.arc_length.float\n rundate_datetime = datetime(rundate.year, rundate.month, rundate.day)\n obs_data = dict()\n for data_all in collection:\n for i, x in enumerate(data_all[\"meta\"][\"obs_time\"]):\n if rundate_datetime <= x < rundate_datetime + timedelta(days=arc_length):\n for key in (\"meta\", \"obs\", \"obs_str\"):\n for field, val in data_all[key].items():\n obs_data.setdefault(key, dict()).setdefault(field, list()).append(val[i])\n\n data_all.pop(\"meta\")\n data_all.pop(\"obs\")\n data_all.pop(\"obs_str\")\n\n for key in data_all.keys():\n if key.startswith(\"met_\"):\n for key2, val in data_all[key].items():\n obs_data.setdefault(key, dict()).setdefault(key2, list()).append(val)\n elif key.startswith(\"satellite_\"):\n # TODO: Use this information in the future?\n continue\n elif key.startswith(\"station_\"):\n # TODO: Use this information in the future?\n continue\n else:\n log.fatal(f\"Unknown data type{key}\")\n\n obs_date = obs_data[\"meta\"][\"obs_date\"]\n time = [obs_date[i] + timedelta(seconds=obs_data[\"meta\"][\"obs_sec\"][i]) for i in range(0, len(obs_date))]\n dset.num_obs = len(obs_data[\"meta\"][\"obs_time\"])\n dset.add_time(\"time\", val=time, scale=\"utc\", fmt=\"datetime\")\n dset.add_text(val=obs_data[\"meta\"][\"station\"], name=\"station\")\n dset.add_text(val=obs_data[\"meta\"][\"satellite\"], name=\"satellite\")\n dset.add_float(val=obs_data[\"meta\"][\"bin_rms\"], unit=\"picoseconds\", name=\"bin_rms\")\n # Positions\n trf = apriori.get(\"trf\", time=dset.time)\n for station in dset.unique(\"station\"):\n trf_site = trf[station]\n station_pos = trf_site.pos.trs.val\n log.debug(f\"Station position for {station} ({trf_site.name}) is (x,y,z) = {station_pos.mean(axis=0)}\")\n domes = trf_site.meta[\"domes\"]\n obs_data[\"pos_\" + station] = station_pos\n obs_data[\"station-other_\" + station] = dict(domes=domes, cdp=station, site_id=station)\n dset.add_position(\n \"site_pos\",\n time=dset.time,\n system=\"trs\",\n val=np.array([obs_data[\"pos_\" + s][idx] for idx, s in enumerate(dset.station)]),\n )\n # Station data\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"station_\")])\n for field in sta_fields:\n dset.add_float(field, val=np.array([float(obs_data[\"station_\" + s][field]) for s in dset.station]))\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"station-other_\")])\n for field in sta_fields:\n dset.add_text(field, val=[obs_data[\"station-other_\" + s][field] for s in dset.station])\n\n # Station meta\n station_keys = sorted([k for k, v in obs_data.items() if k.startswith(\"station-other_\")])\n pos_keys = sorted([k for k, v in obs_data.items() if k.startswith(\"pos_\")])\n\n for sta_key, pos_key in zip(station_keys, pos_keys):\n sta_name = sta_key.replace(\"station-other_\", \"\")\n cdp = obs_data[sta_key][\"cdp\"]\n dset.meta.add(sta_name, \"site_id\", cdp)\n longitude, latitude, height, _ = sofa.iau_gc2gd(2, obs_data[pos_key][0, :]) # TODO: Reference ellipsoid\n dset.meta[\"station\"].setdefault(sta_name, {})[\"cdp\"] = cdp\n dset.meta[\"station\"].setdefault(sta_name, {})[\"site_id\"] = cdp\n dset.meta[\"station\"].setdefault(sta_name, {})[\"domes\"] = obs_data[sta_key][\"domes\"]\n dset.meta[\"station\"].setdefault(sta_name, {})[\"marker\"] = \" \"\n dset.meta[\"station\"].setdefault(sta_name, {})[\"description\"] = \" \"\n dset.meta[\"station\"].setdefault(sta_name, {})[\"longitude\"] = longitude\n dset.meta[\"station\"].setdefault(sta_name, {})[\"latitude\"] = latitude\n dset.meta[\"station\"].setdefault(sta_name, {})[\"height\"] = height\n\n # Satellite data\n sat_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\"satellite_\")])\n for field in sat_fields:\n dset.add_float(field, val=np.array([float(obs_data[\"satellite_\" + s][field]) for s in dset.satellite]))\n\n # Observations\n # In the dataset, obs_time is seconds since rundate:\n v = [\n (obs_data[\"meta\"][\"obs_date\"][i] - rundate_datetime).total_seconds() + obs_data[\"meta\"][\"obs_sec\"][i]\n for i in range(0, dset.num_obs)\n ]\n\n obs_data[\"obs\"].pop(\"obs_time\")\n dset.add_float(\"obs_time\", val=v)\n for field, values in obs_data[\"obs\"].items():\n dset.add_float(field, val=np.array(values))\n\n for field, values in obs_data[\"obs_str\"].items():\n dset.add_text(field, val=values)\n\n return obs_data","def chunk_periods(start, end):\n\n logging.debug(f'chunking {start} to {end}')\n # convert the strings to datetime objects\n #start = dt.datetime.strptime(''.join(start.rsplit(':', 1)), '%Y-%m-%dT%H:%M:%S-%z')\n start = dt.datetime.strptime(start, '%Y-%m-%dT%H:%M:%S-%z')\n logging.debug(f'start: {start}')\n periods = []\n\n # if the year and month of the period are the same, just return the dates as we got them\n\n\n\n return periods","def stack_ps(ps1, ps2, keep_unique = False, fill_time = False, message = True):\n # create deepcopies to avoid changing original instances\n \n ps1 = copy.deepcopy(ps1)\n ps2 = copy.deepcopy(ps2)\n \n # create datetime information in PS instances\n \n try:\n _ = getattr(ps1, \"datetime\")\n except AttributeError:\n ps1.createTimeDate()\n \n try: \n _ = getattr(ps2, \"datetime\")\n except AttributeError:\n ps2.createTimeDate()\n \n # check time resolutions\n res1 = (dt.datetime.strptime(ps1.datetime['data'][1], ps1.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])).seconds\n res2 = (dt.datetime.strptime(ps2.datetime['data'][1], ps2.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])).seconds\n \n if abs(res1-res2) > 60:\n if message:\n print( (\"warning: resolutions differ %d seconds\")%(abs(res1-res2)) )\n \n # check if ps1 is \"older\" than ps2\n \n reversed_order = False\n cut = None\n \n if dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units']) < dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']):\n # ps2 starts after ps1 ends\n timediff = (dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units'])).total_seconds()\n elif dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units']) < dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']):\n # ps1 starts after ps2 ends (user has inadvertently switched the order of the instances)\n reversed_order = True\n timediff = (dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units'])).total_seconds()\n else:\n # yikes! The particle sizer instances have overlapping data\n # it is assumed that ps2 data replaces ps1 data starting \n # from the overlapping time\n cut, cutdate = tt.findNearestDate(ps1.datetime['data'], ps2.datetime['data'][0]) \n fill_time = False\n \n #print(timediff, 1.5*res1)\n # check if filling is required\n if fill_time is True:\n # check time difference\n if reversed_order:\n # ps1 starts after ps2 ends\n if timediff > 1.5*res2:\n # the time gap between two instances has to be\n # larger than twice the normal resolution\n numdates = int(np.ceil(timediff/res2))\n base = dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])\n date_list = [base - dt.timedelta(seconds=res2*x) for x in range(numdates)]\n date_list = list(reversed(date_list[1:]))# because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\n datetimelist = [dt.datetime.strftime(dl, ps2.datetime['units']) for dl in date_list]\n ps2.datetime['data'] = np.append(ps2.datetime['data'], datetimelist)\n timelist = [dt.datetime.strftime(dl, ps2.time['units']) for dl in date_list]\n ps2.time['data'] = np.append(ps2.time['data'], timelist)\n datelist = [dt.datetime.strftime(dl, ps2.date['units']) for dl in date_list]\n ps2.date['data'] = np.append(ps2.date['data'], datelist)\n else:\n fill_time = False\n else:\n if timediff > 1.5*res1:\n # the time gap between two instances has to be\n # larger than twice the normal resolution\n numdates = int(np.ceil(timediff/res1))\n base = dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])\n date_list = [base - dt.timedelta(seconds=res1*x) for x in range(numdates)]\n date_list = list(reversed(date_list[1:])) # because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\n datetimelist = [dt.datetime.strftime(dl, ps1.datetime['units']) for dl in date_list]\n ps1.datetime['data'] = np.append(ps1.datetime['data'], datetimelist)\n timelist = [dt.datetime.strftime(dl, ps1.time['units']) for dl in date_list]\n ps1.time['data'] = np.append(ps1.time['data'], timelist)\n datelist = [dt.datetime.strftime(dl, ps1.date['units']) for dl in date_list]\n ps1.date['data'] = np.append(ps1.date['data'], datelist)\n else:\n fill_time = False\n \n if message:\n print(\"reversed order:\", reversed_order)\n # check which attributes are similar in both instances\n if reversed_order:\n # ps1 starts after ps2 ends\n new_ps = copy.deepcopy(ps2)\n for attribute in ps1.__dict__.keys():\n if attribute in ps2.__dict__.keys():\n afield = getattr(new_ps, attribute)\n if attribute == 'diameter':\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\n \n for var in new_ps.data['variables']:\n if fill_time is True:\n add = np.ma.zeros((ps2.data[var]['data'].shape[0],len(date_list))) \n add[:] = np.nan\n newdata = np.append(ps2.data[var]['data'],add,axis=1)\n ps2.data[var]['data'] = newdata\n \n sh1 = ps1.data[var]['data'].shape\n sh2 = ps2.data[var]['data'].shape\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\n new_field = np.ma.zeros(newfields)\n new_field[:] = np.ma.masked\n \n new_field[st21:st22, 0:ps2.data[var]['data'][:,:cut].shape[1]] = ps2.data[var]['data'][:,:cut]\n new_field[st11:st12, ps2.data[var]['data'][:,:cut].shape[1]:] = ps1.data[var]['data']\n \n new_ps.data[var]['data'] = new_field\n \n afield['data'] = diamlist\n \n elif attribute == 'data':\n # data has been appended with diameters\n pass\n else:\n try:\n field_ps2 = getattr(ps2, attribute)\n field_ps1 = getattr(ps1, attribute)\n except TypeError:\n if attribute == 'header':\n pass\n else:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n try:\n data_ps2 = field_ps2['data']\n data_ps1 = field_ps1['data']\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\n elif fill_time:\n add = np.ma.zeros(len(date_list))\n add[:] = np.nan\n afield['data'] = np.append(np.append(data_ps2[:cut],add), data_ps1)\n else:\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\n except:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n \n else:\n if keep_unique:\n newattribute = getattr(ps1,attribute)\n newattribute['time'] = ps1['datetime']['data']\n setattr(new_ps, attribute, newattribute)\n else:\n pass\n if keep_unique is False:\n # get rid of attributes which were in ps2 but not in ps1\n for attribute in ps2.__dict__.keys():\n if attribute in ps1.__dict__.keys():\n pass\n else:\n delattr(new_ps, attribute)\n \n \n else:\n # ps2 starts after ps1 ends\n new_ps = copy.deepcopy(ps1)\n for attribute in ps2.__dict__.keys():\n if attribute in ps1.__dict__.keys():\n afield = getattr(new_ps, attribute)\n if attribute == 'diameter':\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\n for var in new_ps.data['variables']:\n if fill_time is True:\n add = np.ma.zeros((ps1.data[var]['data'].shape[0],len(date_list))) \n add[:] = np.nan\n newdata = np.append(ps1.data[var]['data'],add,axis=1)\n ps1.data[var]['data'] = newdata\n \n sh1 = ps1.data[var]['data'].shape\n sh2 = ps2.data[var]['data'].shape\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\n new_field = np.ma.zeros(newfields)\n new_field[:] = np.ma.masked\n \n new_field[st11:st12, 0:ps1.data[var]['data'][:,:cut].shape[1]] = ps1.data[var]['data'][:,:cut]\n new_field[st21:st22, ps1.data[var]['data'][:,:cut].shape[1]:] = ps2.data[var]['data']\n \n new_ps.data[var]['data'] = new_field\n \n afield['data'] = diamlist\n \n elif attribute == 'data':\n # data has been appended with diameters\n pass\n else:\n try:\n field_ps2 = getattr(ps2, attribute)\n field_ps1 = getattr(ps1, attribute)\n except TypeError:\n if attribute == 'header':\n pass\n else:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n try:\n data_ps2 = field_ps2['data']\n data_ps1 = field_ps1['data']\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\n elif fill_time:\n add = np.ma.zeros(len(date_list))\n add[:] = np.nan\n afield['data'] = np.append(np.append(data_ps1[:cut],add), data_ps2)\n else:\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\n except:\n if message:\n print( (\"Could not append %s attribute\")%(attribute) )\n \n else:\n if keep_unique:\n newattribute = getattr(ps2,attribute)\n newattribute['time'] = ps2['datetime']['data']\n setattr(new_ps, attribute,newattribute)\n else:\n pass\n if keep_unique is False:\n # get rid of attributes which were in ps2 but not in ps1\n for attribute in ps1.__dict__.keys():\n if attribute in ps2.__dict__.keys():\n pass\n else:\n delattr(new_ps, attribute)\n \n new_ps.sample['data'] = np.arange(1.0, len(new_ps.datetime['data'])+1)\n new_ps.instrument_type = ps1.instrument_type.split('_')[0] + '_concatenated'\n \n if message:\n print('filltime: ', fill_time)\n \n return new_ps","def _periodically_create_records(self):\n # WINNERS holds the members that have 'won' this cycle\n winners = set()\n\n while True:\n now = time()\n start_climb = int(now / CYCLE_SIZE) * CYCLE_SIZE\n start_create = start_climb + CYCLE_SIZE * 0.5\n start_idle = start_climb + CYCLE_SIZE * 0.9\n start_next = start_climb + CYCLE_SIZE\n\n if start_climb <= now < start_create:\n yield start_create - now\n\n elif start_create <= now < start_idle and len(winners) < self._signature_count:\n logger.debug(\"c%d record creation phase. wait %.2f seconds until record creation\", int(now / CYCLE_SIZE), CYCLE_SIZE * 0.4 / self._signature_count)\n yield (CYCLE_SIZE * 0.4 / self._signature_count) * pythonrandlib.random()\n\n # find the best candidate for this cycle\n score = 0\n winner = None\n for member in self._slope.iterkeys():\n book = self.get_book(member)\n if book.score > score and not member in winners:\n winner = member\n\n if winner:\n logger.debug(\"c%d attempt record creation %s\", int(now / CYCLE_SIZE), winner.mid.encode(\"HEX\"))\n record_candidate = self._slope[winner]\n\n # prevent this winner to 'win' again in this cycle\n winners.add(winner)\n\n # # TODO: this may be and invalid assumption\n # # assume that the peer is online\n # record_candidate.history.set(now)\n\n self._dispersy.callback.unregister(record_candidate.callback_id)\n self.create_barter_record(record_candidate.candidate, winner)\n\n else:\n logger.debug(\"c%d no peers available for record creation (%d peers on slope)\", int(now / CYCLE_SIZE), len(self._slope))\n\n else:\n logger.debug(\"c%d second climbing phase. wait %d seconds until the next phase\", int(now / CYCLE_SIZE), start_next - now)\n assert now >= start_idle or len(winners) >= self._signature_count\n for record_candidate in self._slope.itervalues():\n self._dispersy.callback.unregister(record_candidate.callback_id)\n self._slope = {}\n winners = set()\n yield start_next - now","def two_in_one(obs_file,et,subevent):\r\n \r\n #in this function, the \"original time window\" talked about in the comments\r\n #refers to the start and end times that were input to create the file obs_file,\r\n #which will likely have been created using the database_extraction function\r\n \r\n #opening first output file created by operational_sep_quantities\r\n with open(obs_file, 'r') as o:\r\n out = js.load(o)\r\n \r\n #all events recorded in that output file\r\n ongoing_events = (out['sep_forecast_submission']['triggers'][0]['particle_intensity']\r\n ['ongoing_events'])\r\n \r\n #creating lists for values from each event\r\n end_times = [] \r\n start_times = []\r\n energy_thresholds = []\r\n flux_thresholds = []\r\n out_names = []\r\n \r\n #appending values to lists for each event\r\n for i in range(len(ongoing_events)):\r\n start_times.append(parse(ongoing_events[i]['start_time']))\r\n end_times.append(parse(ongoing_events[i]['end_time']))\r\n energy_thresholds.append(ongoing_events[i]['energy_min'])\r\n flux_thresholds.append(float(ongoing_events[i]['threshold']))\r\n \r\n #checking if there was a second event for each threshold\r\n for i in range(len(end_times)):\r\n end = end_times[i]\r\n #if the end time of an event for any threshold was a day before the last day\r\n #in the original time window given, will check if ONLY THAT THRESHOLD\r\n #had another event after the first one, using the end time of the first\r\n #event of that threshold as the new start time of the event window\r\n if end.date() < et.date():\r\n print('end time to use as new start time: %s' %end)\r\n #figuring out which threshold this end time was for\r\n flux_thresh = int(flux_thresholds[i])\r\n energy_thresh = int(energy_thresholds[i])\r\n print('extracting second event for threshold ' + str(flux_thresh) + ' MeV '\r\n + str(energy_thresh) + ' pfu')\r\n #new start time (2 days in advance bc the database_extraction function\r\n #makes the start time 2 days prior, so will cancel that out)\r\n st = end + timedelta(days=2)\r\n #thresholds in correct format\r\n thresholds = str(energy_thresh) + ',' + str(flux_thresh)\r\n print('thresholds: %s' %thresholds)\r\n #creating observation data for second event for thresholds given\r\n out_names.append(Path(cfg.obs_path) /\r\n database_extraction(st,et,instrument_chosen,subevent,\r\n thresholds = thresholds,\r\n one_thresh = True))\r\n \r\n #returns list of all new files created by this function\r\n return(out_names)","def merge_logs(self):\n ourlog = LogData()\n for l in self.data_set:\n ourlog.entries = ourlog.entries + l.entries\n ourlog.sort_time()\n self.finalized_data = ourlog","def merge_all_data(self):\n \n logging.info('***** Starting the merging process ')\n\n \n \"\"\" All possible unqiue_dates to loop on \"\"\"\n date_times = self.merged_unique_dates\n date_times.sort()\n \n date_times = np.array(date_times) \n \n \"\"\" List storing the indices of the date_index of the merged dataset \"\"\"\n all_merged_obs , all_merged_head, all_merged_fb , merged_indices , merged_date_time, mi= [] , [] , [] , [] , [], []\n \n \"\"\" Dictionary that will contain the merged file. \"\"\" \n # rand = datetime.strptime('1981-01-03 12:00:00', '%Y-%m-%d %H:%M:%S') \n #for dt in date_times[3008:3100]: # loop over all the possible date_times \n \n tot = len(date_times)\n for dt, c in zip(date_times[3008:3100], range(tot) ): # loop over all the possible date_times \n #print('Analize : ', str(c) , '/', str(tot) , ' ', dt , ' ', now(time.time()) )\n \n logging.info('Analize : %s %s /', str(c) , str(tot) )\n \n cleaned_df_container = {} \n chunk = ''\n \n for k in self.dataset_per_dt[dt] : # checking the list of available datasets \n \n index, index_up = self.unique_dates[k]['indices'][dt]['low'] , self.unique_dates[k]['indices'][dt]['up'] # extracting the exact chunk of the dataframe where the data of this are stored \n \n chunk = self.data[k]['dataframe'].iloc[index:index_up]\n \n chunk['date_time'] = dt\n chunk = self.clean_dataframe(chunk) # cleaning from wrong or nan values \n \n if len(chunk)==0:\n continue\n \n cleaned_df_container[k] = {} \n cleaned_df_container[k]['df'] = chunk # cleaned dataframe \n\n \n if all(value == 0 for value in cleaned_df_container.values()):\n logging.debug('No data were found! ')\n continue\n \n merged_observations_table, best_ds, duplicates, header = self.merge_record(dt, container = cleaned_df_container)\n \n merged_observations_table['source_id'] = best_ds # adding extra columns i.e. chosen dataset, other dataset with data, number of pressure levels \n merged_observations_table['z_coordinate_type'] = 1 # only pressure inn [Pa] available at the moment. Check z_coordinate_type table for the correpsonding code \n \n \n \"\"\" Extracting the merged feedback, flagging the advanced_observations_feedback flag = 1\"\"\"\n feedback, merged_obs = self.get_reanalysis_feedback( dt, merged_observations_table , reanalysis='era5fb', best_ds= best_ds)\n all_merged_fb.append(feedback) \n all_merged_obs.append(merged_obs)\n \n \"\"\" Setting the correct report_id in the header table \"\"\"\n merged_report_id = merged_obs['report_id'].values[0] # same report_id as calculated in the observation_table \n header['report_id'] = merged_report_id \n all_merged_head.append(header)\n \n #if len(merged_observations_table) != len(header): \n #print('lengths check best ds: ', best_ds , ' obs_merged: ' , len(merged_observations_table), ' feedback:' , len(feedback) , ' header: ' , len(header) )\n #print( len(merged_observations_table), ' ' , len(feedback) )\n\n \"\"\" New merged recordindex and recordtimestamps indices \"\"\"\n merged_indices.append(len(merged_observations_table)) \n merged_date_time.append(dt)\n\n\n \"\"\" Storing the merged date_time values and indices \"\"\"\n di=xr.Dataset()\n merged_date_time = np.array(merged_date_time)\n di['recordtimestamp'] = ( {'recordtimestamp' : merged_date_time.shape } , merged_date_time )\n \n \n \"\"\" Creating the merged indices \"\"\"\n mi.append(0)\n for i,ind in zip(merged_indices[0:], range(len(merged_indices[0:]) ) ) :\n mi.append(mi[ind] + i )\n mi = np.array(mi) \n di['recordindex'] = ( {'recordindex' : mi.shape } , mi )\n self.MergedRecordIndex = di \n \n \n \"\"\" Creating the merged dataframes \"\"\"\n logging.debug('*** Concatenating the observations_table dataframes' ) \n merged_obs = pd.concat (all_merged_obs)\n \n self.MergedObs = merged_obs \n logging.debug('*** Finished concatenating theobservations_table dataframes' ) \n \n logging.debug('*** Concatenating the header_table dataframes' ) \n merged_hd = pd.concat (all_merged_head)\n self.MergedHead = merged_hd \n logging.debug('*** Finished concatenating the header_table dataframes' ) \n \n logging.debug('*** Concatenating the feedback dataframes' ) \n merged_fb = pd.concat (all_merged_fb)\n self.MergedFeedback = merged_fb \n logging.debug('*** Finished concatenating the feedback dataframes' ) \n\n return 0","def _fill_day_dicts(self):\n today = datetime.date.today()\n for i, record in enumerate(self._dataset):\n if (record[\"createdAt\"] / 1000) > time.mktime((today - datetime.timedelta(days=30)).timetuple()):\n self._add_record(self._all30_dict, record, key=i)\n\n elif (record[\"createdAt\"] / 1000) > time.mktime((today - datetime.timedelta(days=60)).timetuple()):\n self._add_record(self._all60_dict, record, key=i)\n\n else:\n self._add_record(self._all90_dict, record, key=i)","def merge(self, other, gap_method=\"slinear\", new_sample_rate=None):\n if new_sample_rate is not None:\n merge_sample_rate = new_sample_rate\n combine_list = [self.decimate(new_sample_rate).dataset]\n else:\n merge_sample_rate = self.sample_rate\n combine_list = [self.dataset]\n\n ts_filters = self.filters\n if isinstance(other, (list, tuple)):\n for run in other:\n if not isinstance(run, RunTS):\n raise TypeError(f\"Cannot combine {type(run)} with RunTS.\")\n\n if new_sample_rate is not None:\n run = run.decimate(new_sample_rate)\n combine_list.append(run.dataset)\n ts_filters.update(run.filters)\n else:\n if not isinstance(other, RunTS):\n raise TypeError(f\"Cannot combine {type(other)} with RunTS.\")\n\n if new_sample_rate is not None:\n other = other.decimate(new_sample_rate)\n combine_list.append(other.dataset)\n ts_filters.update(other.filters)\n\n # combine into a data set use override to keep attrs from original\n\n combined_ds = xr.combine_by_coords(\n combine_list, combine_attrs=\"override\"\n )\n\n n_samples = (\n merge_sample_rate\n * float(\n combined_ds.time.max().values - combined_ds.time.min().values\n )\n / 1e9\n ) + 1\n\n new_dt_index = make_dt_coordinates(\n combined_ds.time.min().values,\n merge_sample_rate,\n n_samples,\n self.logger,\n )\n\n run_metadata = self.run_metadata.copy()\n run_metadata.sample_rate = merge_sample_rate\n\n new_run = RunTS(\n run_metadata=self.run_metadata,\n station_metadata=self.station_metadata,\n survey_metadata=self.survey_metadata,\n )\n\n ## tried reindex then interpolate_na, but that has issues if the\n ## intial time index does not exactly match up with the new time index\n ## and then get a bunch of Nan, unless use nearest or pad, but then\n ## gaps are not filled correctly, just do a interp seems easier.\n new_run.dataset = combined_ds.interp(\n time=new_dt_index, method=gap_method\n )\n\n # update channel attributes\n for ch in new_run.channels:\n new_run.dataset[ch].attrs[\"time_period.start\"] = new_run.start\n new_run.dataset[ch].attrs[\"time_period.end\"] = new_run.end\n\n new_run.run_metadata.update_time_period()\n new_run.station_metadata.update_time_period()\n new_run.survey_metadata.update_time_period()\n new_run.filters = ts_filters\n\n return new_run","def get_data(self):\n# epoch_from = 1301641200\n# epoch_to = epoch_from+60*60*24\n \"\"\"\n letting runs finish for 2 more hours\n ideally, want to make this a function of time from schedule plus some\n variation, like 1 hour just in case\n \"\"\" \n# epoch_to_adjusted = epoch_to + 7200\n conn = self.connect_to_mongo()\n db = conn.muni\n \n# print \"==== Collecting starting runs from %s to %s ====\"\\\n# % (str(time.ctime(epoch_from)), str(time.ctime(epoch_to)))\n \"\"\"\n > db.location.find({loc:{$within:{$center:[[37.80241, -122.4364],\n 0.01]}}})\n > db.location.find({loc:{$within:{$center:[[37.76048, -122.38895],\n 0.002]}}})\n \"\"\"\n bus_ids = db.location.find({'route':self.route_name}).distinct(\"bus_id\")\n for bus_id in bus_ids:\n c_start = db.location.find({\"bus_id\":bus_id,\n \"loc\":{\"$within\":{\"$center\":[[self.start_lat, self.start_lon],\n self.start_prec]}}\n }).sort(\"cur_time\", DESCENDING)\n self.massage_start_data(c_start)\n \"\"\"\n TODO: the end point seems to be too nice to Muni, need to tighten\n the circle a little\n \"\"\"\n c_end = db.location.find({\"bus_id\":bus_id,\n \"loc\":{\"$within\":{\"$center\":[[self.end_lat, self.end_lon],\n self.end_prec]}}\n }).sort(\"cur_time\", ASCENDING)\n self.massage_end_data(c_end)\n if self.to_log:\n print self.start_bus_ids_to_times\n print self.end_bus_ids_to_times\n \n return self.start_bus_ids_to_times, self.end_bus_ids_to_times","def generate_record(self, data_dictionaries, group_by):\n result = {}\n\n for one_measurement in data_dictionaries:\n time = one_measurement['datetime']\n\n if isinstance(time, str):\n if self.timezone:\n time = arrow.get(time).shift(hours=6) # TODO: fix utc conversion\n else:\n time = arrow.get(time)\n\n record = Record(self.name, self.lat, self.lon, self.height, time)\n\n del one_measurement['datetime']\n\n one_measurement = {k: float(v) for k, v in one_measurement.items()}\n\n record.merge(one_measurement)\n\n key = group_by(time)\n \n if key == '2016-04-01_00':\n break\n\n record_string = record.little_r_report()\n\n try:\n result[key].append(record_string)\n except KeyError:\n result[key] = [record_string]\n\n return result","def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\n logger = getLogger(__name__)\n # sess = conn.get_session()\n das = {}\n all_wz_features = {}\n all_wz_laneconfigs = {}\n\n # collecting daily data\n for prd in periods:\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\n year = prd.start_date.year\n sdate = prd.start_date\n edate = prd.end_date\n if year not in das:\n da_tt = tt.TravelTimeDataAccess(year)\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\n das[year] = (\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent)\n\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\n da_tt_specialevent) = das[year]\n\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \"\"\"\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \"\"\"\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \"\"\"\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \"\"\"\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\n \"\"\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \"\"\"\n traveltimes = da_tt.list_by_period(ttri.id, prd)\n \"\"\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \"\"\"\n\n if not any(weathers):\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\n continue\n\n extras = {\n 'weathers': {_tt.id: [] for _tt in traveltimes},\n 'workzones': {_tt.id: [] for _tt in traveltimes},\n 'incidents': {_tt.id: [] for _tt in traveltimes},\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\n }\n \"\"\":type: dict[str, dict[int, list]]\"\"\"\n\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\n\n for tti in traveltimes:\n _tt_weathers = extras['weathers'][tti.id]\n extdata = ExtData(tti,\n _tt_weathers[0] if _tt_weathers else None,\n extras['incidents'][tti.id],\n extras['workzones'][tti.id],\n extras['specialevents'][tti.id],\n extras['snowmgmts'][tti.id])\n\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\n for ef in filters:\n try:\n ef.check(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\n continue\n else:\n for ef in filters:\n try:\n ef.check_outofrange(extdata)\n except Exception as ex:\n tb.traceback(ex)\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\n continue\n\n del extras\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\n\n # sess.close()","async def fetch_all_periods_raw(self):\n self._logger.info(\"Fetching current period data\")\n await self._client.select_customer(self.account_id, self.customer_id)\n\n params = {'idContrat': '0' + self.contract_id}\n res = await self._client.http_request(CONTRACT_CURRENT_URL_3, \"get\", params=params)\n text_res = await res.text()\n\n headers = {\"Content-Type\": \"application/json\"}\n res = await self._client.http_request(CONTRACT_CURRENT_URL_2, \"get\", headers=headers)\n text_res = await res.text()\n # We can not use res.json() because the response header are not application/json\n json_res = json.loads(text_res)['results']\n\n self._all_periods_raw = json_res","def aggregate_local_and_hist_klines(self, symbol, intervals): \n result = {}\n\n for interval in intervals:\n filename = PATH_HIST_KLINES[interval]\n file_klines = pd.DataFrame()\n\n request_begin = strat_begin = self.start_ts - 300000\n write_mode = 'w'\n\n try:\n file_klines = pd.read_csv(filename, index_col=0, names = [\"Open\",\"High\",\"Low\",\"Close\",\"Volume\",\"TurnOver\",\"Date\"])\n file_klines.loc[:,'Date'] = [datetime.fromtimestamp(i).strftime('%Y-%m-%d %H:%M:%S.%d')[:-3] for i in file_klines.index]\n newest = file_klines.tail(1).index[0]\n oldest = file_klines.head(1).index[0]\n if oldest - strat_begin > interval_bybit_notation(interval) * 60:\n request_begin = strat_begin\n write_mode = 'w'\n file_klines = pd.DataFrame()\n else:\n request_begin = int(newest) + interval_bybit_notation(interval) * 60\n write_mode = 'a'\n except FileNotFoundError as err:\n pass\n\n bybit_klines = pd.DataFrame()\n if request_begin < int(datetime.now().timestamp()):\n output_data = self.bybit.get_hist_klines(symbol, interval_bybit_notation(interval), str(request_begin))\n column_data = [i[1:] for i in output_data]\n index = [int(i[0]) for i in output_data]\n # convert to data frame\n bybit_klines = pd.DataFrame(column_data, index = index, columns=['Open', 'High', 'Low', 'Close', 'Volume', 'TurnOver'])\n bybit_klines.loc[:,'Date'] = [datetime.fromtimestamp(i).strftime('%Y-%m-%d %H:%M:%S.%d')[:-3] for i in bybit_klines.index]\n\n bybit_klines.to_csv(filename, header = False, mode=write_mode)\n\n result[interval] = pd.concat([file_klines, bybit_klines]) if not len(bybit_klines.index) == 0 else file_klines\n\n return result","def records(self, current=False):\n now = datetime.now()\n format = '%Y/%m/%d %H:%M:%S'\n for key in self.keys:\n contents = key.get_contents_as_string()\n (headers, contents) = self._parse_contents(contents)\n # Ignore the first line and any row that is not of type\n # `PayerLineItem`.\n for content in contents[1:]:\n content = dict(itertools.izip(headers, content))\n if content.get('RecordType') != 'PayerLineItem':\n continue\n start_date = datetime.strptime(\n content['BillingPeriodStartDate'], format\n )\n end_date = datetime.strptime(\n content['BillingPeriodEndDate'], format\n )\n if current:\n if start_date <= now <= end_date:\n yield content\n else:\n if start_date <= end_date <= now:\n yield content","def get_all_periods(self, df):\n df_append = pd.DataFrame()\n for index, element in enumerate(self.periods):\n df_temp = self.get_period(df, element)\n df_append = df_append.append(df_temp)\n return(df_append.sort_index())","def get_data(self, date_time):\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\n return_columns = ','.join(self.table_columns)\n with self.con:\n cur = self.con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS temp;\")\n cur.execute(\"DROP TABLE IF EXISTS temp2;\")\n cur.execute(\"DROP TABLE IF EXISTS temp3;\")\n cur.execute(\"DROP TABLE IF EXISTS temp4;\")\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp AS \n SELECT * \n FROM {table} \n WHERE EFFECTIVEDATE <= '{datetime}';\"\"\"\n cur.execute(query.format(table=self.table_name, datetime=date_time))\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp2 AS\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\n FROM temp\n GROUP BY {id}, EFFECTIVEDATE;\"\"\"\n cur.execute(query.format(id=id_columns))\n # For each unique set of ids get the record with the most recent effective date.\n query = \"\"\"CREATE TEMPORARY TABLE temp3 as\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\n FROM temp2\n GROUP BY {id};\"\"\"\n cur.execute(query.format(id=id_columns))\n # Inner join the original table to the set of most recent effective dates and version no.\n query = \"\"\"CREATE TEMPORARY TABLE temp4 AS\n SELECT * \n FROM {table} \n INNER JOIN temp3 \n USING ({id}, VERSIONNO, EFFECTIVEDATE);\"\"\"\n cur.execute(query.format(table=self.table_name, id=id_columns))\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\n query = \"\"\"SELECT {cols} FROM temp4 ;\"\"\"\n query = query.format(cols=return_columns)\n data = pd.read_sql_query(query, con=self.con)\n return data","def combine_dict(self, dict2):\n # iterate through smaller data set\n # base_set will be the larger set and is used for updating\n if len(self.content[\"values\"]) > len(dict2[\"values\"]):\n large_set = self.content[\"values\"]\n small_set = dict2[\"values\"]\n base_set = self.content\n else:\n small_set = self.content[\"values\"]\n large_set = dict2[\"values\"]\n base_set = dict2\n\n subset = {}\n for key in small_set.keys():\n # determine wether to compare keys\n if key in large_set:\n updated_l = large_set[key][\"updated_at\"]\n updated_s = small_set[key][\"updated_at\"]\n if updated_l == 'NULL':\n if updated_s != 'NULL':\n # update to not NULL set\n # if both updated_at are NULL, things\n # are ambiguos. We could defer to created_at\n # but for simplicity we will default to\n # the values in the larger set\n subset[key] = small_set[key]\n else:\n if updated_s == 'NULL':\n # update to not NULL set\n subset[key] = large_set[key]\n else:\n if updated_l > updated_s:\n subset[key] = large_set[key]\n else:\n subset[key] =small_set[key]\n else:\n subset[key] = small_set[key]\n base_set[\"values\"].update(subset)\n new_obj = BackupData()\n new_obj.load_from_dict(base_set)\n return new_obj","def extract(self, extract_from, extract_to):\n # Some API calls do not expect a TZ, so we have to remove the timezone\n # from the dates. We assume that all dates coming from upstream are\n # in UTC TZ.\n extract_from = extract_from.replace(tzinfo=None)\n extract_to = extract_to.replace(tzinfo=None)\n\n # Our records\n self.records = {}\n self.acc_records = {}\n\n # We cannot use just 'changes-since' in the servers.list() API query,\n # as it will only include servers that have changed its status after\n # that date. However we cannot just get all the usages and then query\n # server by server, as deleted servers are not returned by the usages\n # call. Moreover, Nova resets the start_time after performing some\n # actions on the server (rebuild, resize, rescue). If we use that time,\n # we may get a drop in the wall time, as a server that has been resized\n # in the middle of its lifetime will suddenly change its start_time\n #\n # Therefore, what we do is the following (hackish approach)\n #\n # 1.- List all the servers that changed its status after the start time\n # for the reporting period\n # 2.- Build the records for the period [start, end] using those servers\n # 3.- Get all the usages, being aware that the start time may be wrong\n # 4.- Iter over the usages and:\n # 4.1.- get information for servers that are not returned by the query\n # in (1), for instance servers that have not changed it status.\n # We build then the records for those severs\n # 4.2.- For all the servers, adjust the CPU, memory and disk resources\n # as the flavor may not exist, but we can get those resources\n # from the usages API.\n\n # Lets start\n\n # 1.- List all the deleted servers from that period.\n servers = self._get_servers(extract_from)\n # 2.- Build the records for the period. Drop servers outside the period\n # (we do this manually as we cannot limit the query to a period, only\n # changes after start date).\n self._process_servers_for_period(servers, extract_from, extract_to)\n\n # 3.- Get all the usages for the period\n usages = self._get_usages(extract_from, extract_to)\n # 4.- Iter over the results and\n # This one will also generate accelerator records if GPU flavors\n # are found.\n self._process_usages_for_period(usages, extract_from, extract_to)\n\n return list(self.records.values()) + list(self.acc_records.values())","def compare_results_data(result1, result2):\n def average(r1, r2, delta):\n return (r2 - r1) / delta\n\n if (result1 and result2) and (len(result1) and len(result2)):\n results = result1.copy()\n\n time_delta = round(float(result2['timestamp']) - float(result1['timestamp']))\n results['time_delta'] = time_delta\n\n for key in result1['data'].keys():\n if key in result2['data']:\n results['data'][key]['value'] = round(\n average(float(result1['data'][key]['value']), float(result2['data'][key]['value']), time_delta),\n 2)\n\n results['timestamp'] = time.time()\n\n return results\n \n return {}","def since(self, ts):\n while True:\n items = super(TailingOplog, self).since(ts)\n for doc in items:\n yield doc\n ts = doc['ts']","def calculate_new_rating_period(start_datetime, end_datetime):\n # Create the rating period\n rating_period = models.RatingPeriod.objects.create(\n start_datetime=start_datetime, end_datetime=end_datetime\n )\n\n # Grab all games that will be in this rating period\n games = models.Game.objects.filter(\n datetime_played__gte=start_datetime, datetime_played__lte=end_datetime\n )\n\n # Mark all of the above games as belonging in this rating period\n for game in games:\n game.rating_period = rating_period\n game.save()\n\n # For each player, find all their matches, their scores in those\n # matches; then calculate their ratings. The new_ratings dictionary\n # contains players as keys, and dictionaries containing their new\n # rating parameters as the dictionary values.\n new_ratings = {}\n\n for player in models.Player.objects.all():\n # Don't calculate anything if they player's first game is prior\n # to this rating period\n first_game_played = player.get_first_game_played()\n\n if (\n first_game_played is None\n or first_game_played.datetime_played > end_datetime\n ):\n continue\n\n # Get the players rating parameters\n player_rating = player.rating\n player_rating_deviation = player.rating_deviation\n player_rating_volatility = player.rating_volatility\n player_inactivity = player.inactivity\n\n # Build up the per-game rating parameters of opponents\n opponent_ratings = []\n opponent_rating_deviations = []\n scores = []\n\n for won_game in games.filter(winner=player):\n opponent_ratings.append(won_game.loser.rating)\n opponent_rating_deviations.append(won_game.loser.rating_deviation)\n scores.append(1)\n\n for lost_game in games.filter(loser=player):\n opponent_ratings.append(lost_game.winner.rating)\n opponent_rating_deviations.append(\n lost_game.winner.rating_deviation\n )\n scores.append(0)\n\n # Convert empty lists (meaning no matches) to None types\n if not opponent_ratings:\n opponent_ratings = None\n opponent_rating_deviations = None\n scores = None\n\n new_player_rating, new_player_rating_deviation, new_player_rating_volatility = calculate_player_rating(\n r=player_rating,\n RD=player_rating_deviation,\n sigma=player_rating_volatility,\n opponent_rs=opponent_ratings,\n opponent_RDs=opponent_rating_deviations,\n scores=scores,\n )\n\n # Calculate new inactivity\n if opponent_ratings is None:\n new_player_inactivity = player_inactivity + 1\n else:\n new_player_inactivity = 0\n\n # Determine if the player is labelled as active\n new_player_is_active = bool(\n new_player_inactivity\n < settings.NUMBER_OF_RATING_PERIODS_MISSED_TO_BE_INACTIVE\n )\n\n new_ratings[player] = {\n \"player_ranking\": None,\n \"player_ranking_delta\": None,\n \"player_rating\": new_player_rating,\n \"player_rating_deviation\": new_player_rating_deviation,\n \"player_rating_volatility\": new_player_rating_volatility,\n \"player_inactivity\": new_player_inactivity,\n \"player_is_active\": new_player_is_active,\n }\n\n # Filter all active players and sort by rating\n new_active_player_ratings = [\n (player, new_rating[\"player_rating\"])\n for player, new_rating in new_ratings.items()\n if new_rating[\"player_is_active\"]\n ]\n new_active_player_ratings.sort(key=lambda x: x[1], reverse=True)\n\n # Form a tuple of active players where the order is their ranking\n new_active_player_rankings = [\n player for player, _ in new_active_player_ratings\n ]\n\n # Process new rankings and ranking changes\n num_active_players = len(new_active_player_rankings)\n\n for ranking, player in enumerate(new_active_player_rankings, 1):\n # Ranking\n new_ratings[player][\"player_ranking\"] = ranking\n\n # Ranking delta\n if player.ranking is None:\n new_ratings[player][\"player_ranking_delta\"] = (\n num_active_players - ranking + 1\n )\n else:\n new_ratings[player][\"player_ranking_delta\"] = (\n player.ranking - ranking\n )\n\n # Now save all ratings\n for player, ratings_dict in new_ratings.items():\n models.PlayerRatingNode.objects.create(\n player=player,\n rating_period=rating_period,\n ranking=ratings_dict[\"player_ranking\"],\n ranking_delta=ratings_dict[\"player_ranking_delta\"],\n rating=ratings_dict[\"player_rating\"],\n rating_deviation=ratings_dict[\"player_rating_deviation\"],\n rating_volatility=ratings_dict[\"player_rating_volatility\"],\n inactivity=ratings_dict[\"player_inactivity\"],\n is_active=ratings_dict[\"player_is_active\"],\n )","def example_staypoints_merge():\n p1 = Point(8.5067847, 47.4)\n\n t1 = pd.Timestamp(\"1971-01-01 00:00:00\", tz=\"utc\")\n t2 = pd.Timestamp(\"1971-01-02 05:00:00\", tz=\"utc\")\n t3 = pd.Timestamp(\"1971-01-02 06:45:00\", tz=\"utc\")\n t4 = pd.Timestamp(\"1971-01-02 08:55:00\", tz=\"utc\")\n t45 = pd.Timestamp(\"1971-01-02 08:57:00\", tz=\"utc\")\n t5 = pd.Timestamp(\"1971-01-02 09:00:00\", tz=\"utc\")\n t6 = pd.Timestamp(\"1971-01-02 09:20:00\", tz=\"utc\")\n\n list_dict = [\n {\"id\": 1, \"user_id\": 0, \"started_at\": t1, \"finished_at\": t2, \"geom\": p1, \"location_id\": 1},\n {\"id\": 5, \"user_id\": 0, \"started_at\": t2, \"finished_at\": t2, \"geom\": p1, \"location_id\": 2},\n {\"id\": 2, \"user_id\": 0, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 6, \"user_id\": 0, \"started_at\": t4, \"finished_at\": t45, \"geom\": p1, \"location_id\": 2},\n {\"id\": 15, \"user_id\": 0, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 2},\n {\"id\": 7, \"user_id\": 1, \"started_at\": t3, \"finished_at\": t4, \"geom\": p1, \"location_id\": 2},\n {\"id\": 80, \"user_id\": 1, \"started_at\": t45, \"finished_at\": t5, \"geom\": p1, \"location_id\": 2},\n {\"id\": 3, \"user_id\": 1, \"started_at\": t5, \"finished_at\": t6, \"geom\": p1, \"location_id\": 4},\n ]\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\"geom\", crs=\"EPSG:4326\")\n sp = sp.set_index(\"id\")\n sp.as_staypoints\n\n # generate empty triplegs for the merge function\n tpls = pd.DataFrame([], columns=[\"user_id\", \"started_at\", \"finished_at\"])\n return sp, tpls","def newChunk(self, data, sample_period):\n added = False\n found = False\n for r in self._receiving:\n if r[0] == data.timestamp:\n r[1][data.sensor - 1] = data\n found = True\n break\n if found is False:\n self._receiving.append((data.timestamp, [None] * self._sensors))\n self._receiving[-1][1][data.sensor - 1] = data\n self._receiving.sort(key=lambda x: x[0])\n\n # all data for given timestamp received\n while len(self._receiving) > 0:\n r = self._receiving[0]\n if r[1].count(None) > 0:\n break\n dl = len(data.accelerationX)\n timestamps = np.arange(r[0], r[0] + sample_period * dl, sample_period)\n\n def copy_data(start, l):\n self.data[\"timestamp\"][\n self.current_index : self.current_index + l\n ] = timestamps[start : start + l]\n for s in range(1, self._sensors + 1):\n self.data[f\"{s} X\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationX[start : start + l]\n self.data[f\"{s} Y\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationY[start : start + l]\n self.data[f\"{s} Z\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationZ[start : start + l]\n\n l = min(dl, self._size - self.current_index)\n if l > 0:\n copy_data(0, l)\n self.current_index += l\n dl -= l\n\n if dl > 0:\n self.current_index = 0\n self.filled = True\n copy_data(l, dl)\n self.current_index = dl\n\n self._receiving.remove(r)\n added = True\n\n chunk_removed = False\n if len(self._receiving) > self._window:\n self._receiving = self._receiving[1:]\n chunk_removed = True\n return (added, chunk_removed)","def merging_time_periods(time_periods, min_time_between_periods):\n n_periods = len(time_periods)\n merged_time_periods = []\n index = 0\n while index < n_periods:\n time_period = time_periods[index]\n if len(merged_time_periods) == 0:\n merged_time_periods.append([time_period[0], time_period[1]])\n index += 1\n continue\n # we check if the time between both is superior at min_time_between_periods\n last_time_period = time_periods[index - 1]\n beg_time = last_time_period[1]\n end_time = time_period[0]\n if (end_time - beg_time) <= min_time_between_periods:\n # then we merge them\n merged_time_periods[-1][1] = time_period[1]\n index += 1\n continue\n else:\n merged_time_periods.append([time_period[0], time_period[1]])\n index += 1\n return merged_time_periods","def experiment_periods():\n # Temperature readings start March 22 2004, loads start Feb 01 2004.\n # period1_start = pd.Period(\"2004-02-01 00:00\", \"H\")\n # period1_end = pd.Period(\"2005-07-01 00:00\", \"H\")\n # period2_start = pd.Period(\"2005-10-01 00:00\", \"H\")\n # period2_end = pd.Period(\"2006-10-01 00:00\", \"H\")\n period1_start = datetime.datetime.strptime(\"2004-02-01 00:00\", \"%Y-%m-%d %H:%M\")\n period1_end = datetime.datetime.strptime(\"2005-07-01 00:00\", \"%Y-%m-%d %H:%M\")\n period2_start = datetime.datetime.strptime(\"2005-10-01 00:00\", \"%Y-%m-%d %H:%M\")\n period2_end = datetime.datetime.strptime(\"2006-10-01 00:00\", \"%Y-%m-%d %H:%M\")\n return ((period1_start, period1_end), (period2_start, period2_end))","def get_timeseries_data(self, table, datetime_start, datetime_end, timechunk=datetime.timedelta(hours=1)):\n table_schema = LMTDB_TABLES.get(table.upper())\n if table_schema is None:\n raise KeyError(\"Table '%s' is not valid\" % table)\n else:\n result_columns = ['TIMESTAMP'] + table_schema['columns']\n format_dict = {\n 'schema': ', '.join(result_columns).replace(\"TS_ID,\", \"TIMESTAMP_INFO.TS_ID,\"),\n 'table': table,\n }\n\n index0 = len(self.saved_results.get(table, {'rows': []})['rows'])\n chunk_start = datetime_start\n while chunk_start < datetime_end:\n if timechunk is None:\n chunk_end = datetime_end\n else:\n chunk_end = chunk_start + timechunk\n if chunk_end > datetime_end:\n chunk_end = datetime_end\n start_stamp = chunk_start.strftime(\"%Y-%m-%d %H:%M:%S\")\n end_stamp = chunk_end.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n query_str = \"\"\"SELECT\n %(schema)s\n FROM\n %(table)s\n INNER JOIN TIMESTAMP_INFO ON TIMESTAMP_INFO.TS_ID = %(table)s.TS_ID\n WHERE\n TIMESTAMP_INFO.TIMESTAMP >= %%(ps)s\n AND TIMESTAMP_INFO.TIMESTAMP < %%(ps)s\n \"\"\" % format_dict\n self.query(query_str, (start_stamp, end_stamp), table=table, table_schema=table_schema)\n if timechunk is not None:\n chunk_start += timechunk\n\n return self.saved_results[table]['rows'][index0:], result_columns","def get_list_block_time_periods(filename_time_periods):\n\n if not hasattr(get_list_block_time_periods, \"cached\"):\n get_list_block_time_periods.cached = {}\n\n if filename_time_periods not in get_list_block_time_periods.cached:\n with open(filename_time_periods, 'r') as file:\n contents = csv.reader(file)\n next(contents)\n\n time_periods = []\n for row in contents:\n time_beginning = datetime.datetime.strptime(row[0], \"%Y-%m-%d %H:%M:%S\")\n time_ending = datetime.datetime.strptime(row[1], \"%Y-%m-%d %H:%M:%S\")\n\n time_periods.append((time_beginning, time_ending, row[2]))\n #print(\"List of time periods: \", time_periods)\n\n get_list_block_time_periods.cached[filename_time_periods] = time_periods\n\n return get_list_block_time_periods.cached[filename_time_periods]","def _join_on_millisec(dfs: list):\n # Resample to milliseconds befor joining\n for idx, df in enumerate(dfs):\n df[\"sys_time_dt\"] = pd.to_datetime(df[\"sys_time\"], unit=\"ms\")\n df = df.set_index(\"sys_time_dt\")\n df = df.drop(columns=[\"sys_time\"])\n df = df[~df.index.duplicated(keep=\"last\")] # Remove index dups\n dfs[idx] = df.resample(\"10ms\").interpolate(method=\"time\")\n\n # Join resampled sensor data, drop NaNs, that might be generated for\n # start or end of session, because not all sensors start/end at same time\n df_joined = pd.concat(dfs, axis=1).dropna()\n\n # Add datetimeindex as ms\n df_joined[\"sys_time\"] = df_joined.index.astype(\"int64\") // 10 ** 6\n\n # Reset index to save memory\n df_joined = df_joined.reset_index(drop=True)\n\n return df_joined","def _add(self, other):\n if isinstance(other, SeqPer):\n per1, lper1 = self.periodical, self.period\n per2, lper2 = other.periodical, other.period\n\n per_length = lcm(lper1, lper2)\n\n new_per = []\n for x in range(per_length):\n ele1 = per1[x % lper1]\n ele2 = per2[x % lper2]\n new_per.append(ele1 + ele2)\n\n start, stop = self._intersect_interval(other)\n return SeqPer(new_per, (self.variables[0], start, stop))","def _get_unit_records(self, start_time):\r\n\r\n if self.optMTTF.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN \\\r\n ( \\\r\n SELECT DISTINCT MIN(fld_unit, fld_request_date), \\\r\n fld_incident_id, fld_request_date, \\\r\n fld_unit, fld_hardware_id \\\r\n FROM rtk_incident \\\r\n GROUP BY fld_unit \\\r\n ) AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n elif self.optMTBBD.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN \\\r\n ( \\\r\n SELECT fld_incident_id, fld_request_date, fld_unit, \\\r\n fld_hardware_id \\\r\n FROM rtk_incident \\\r\n GROUP BY fld_unit, fld_request_date \\\r\n ) AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n GROUP BY t2.fld_unit, t1.fld_age_at_incident \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n elif self.optMTBF.get_active():\r\n _query = \"SELECT t2.fld_unit, t1.fld_incident_id, \\\r\n t1.fld_age_at_incident, t1.fld_failure, \\\r\n t1.fld_suspension, t1.fld_cnd_nff, \\\r\n t1.fld_occ_fault, t1.fld_initial_installation, \\\r\n t1.fld_interval_censored, t2.fld_request_date, \\\r\n t2.fld_hardware_id \\\r\n FROM rtk_incident_detail AS t1 \\\r\n INNER JOIN rtk_incident AS t2 \\\r\n ON t2.fld_incident_id=t1.fld_incident_id \\\r\n WHERE t1.fld_age_at_incident >= {0:f} \\\r\n ORDER BY t2.fld_unit ASC, \\\r\n t1.fld_age_at_incident ASC, \\\r\n t2.fld_request_date ASC\".format(start_time)\r\n\r\n (_results, _error_code, __) = self._dao.execute(_query, commit=False)\r\n\r\n return(_results, _error_code)","def load_obstab_feedback_sliced(self, dataset='' , file ='' , datetime='' ):\n k = dataset \n F = file \n dt = datetime\n \n if dt != self.unique_dates[k][F]['up_to_dt_slice']:\n print(\"Error! the dit does not correspond to the dt I calculated in the previous loading! \")\n return 0\n \n logging.debug(\" === (Re)Load data for %s file %s counter %s\" , dataset, file, data[k][F][\"counter\"])\n print(blue + 'Memory used before reading data: ', process.memory_info().rss/1000000000 , cend)\n \n slice_size = self.slice_size\n \n file = data[k][F]['h5py_file']\n rts, ri = data[k][F][\"recordtimestamp\"][:] , data[k][F][\"recordindex\"][:]\n\n index_min = self.unique_dates[k][F]['indices'][dt]['low'] # here no offset since I am reading the original data \n ind = np.where(rts==dt)[0][0] # index of specific dt , I need the extremes indices of the next date_time after slicing \n \n try: \n up_to_dt_slice = rts[ind + slice_size ] # \n index_max = self.unique_dates[k][F]['indices'][up_to_dt_slice]['low'] # maximum index in the array of date_time to slice on\n update_index = True\n except:\n \"\"\" If the dt is too large, I take the whole array \"\"\"\n index_max = 1000000000000000\n update_index = False \n \n \n ####################\n # OBSERVATIONS TABLE\n #################### \n logging.debug ('*** Loading observations_table' )\n obs_tab = file['observations_table'] \n\n #print('CHECKING THE INDICES:::: ' , k , ' index_min ', index_min , ' index_max ', index_max )\n obs_dic= {} \n for ov in self.observations_table_vars:\n v = copy.deepcopy( obs_tab[ov][index_min:index_max ] )\n obs_dic[ov] = v \n data[k][F]['observations_table']= obs_dic \n\n ###########\n # ERA5FB\n ###########\n if k == 'era5_1' or k == 'era5_2':\n logging.debug('*** Loading era5fb ' )\n era5fb_tab = file['era5fb']\n fb_dic = {} \n for ov in self.era5fb_columns:\n try:\n v = copy.deepcopy( era5fb_tab[ov][index_min:index_max ] )\n fb_dic[ov] = v \n except:\n continue\n #print(\"CANNOT FIND \", ov ) \n \n data[k][F]['era5fb_tab']= fb_dic\n \n print(blue + 'Memory used after reading data: ', process.memory_info().rss/1000000000 , cend)\n \n \"\"\" Updating the indices \"\"\" \n self.unique_dates[k][F]['index_offset'] = copy.deepcopy( self.unique_dates[k][F]['index_offset_next'] ) \n \n if update_index: \n self.unique_dates[k][F]['index_offset_next'] = index_max \n self.unique_dates[k][F]['up_to_dt_slice'] = up_to_dt_slice\n\n return 0","def reduce_data():\n snapshots = Snapshot.objects.all()\n locations = Location.objects.all()\n lst = []\n for snapshot in snapshots:\n lst.append([snapshot.location.name, snapshot.avail_bikes,\n snapshot.free_stands, snapshot.timestamp])\n cols = ['location', 'avail_bikes', 'free_stands', 'timestamp']\n df = pd.DataFrame(lst, columns=cols)\n df['time'] = df['timestamp'].dt.round('30min').dt.strftime('%H:%M')\n\n group = df.groupby(['location', 'time'])\n means = group.mean()\n sd = group.std()\n today = date.today()\n first = today.replace(day=1)\n last_month = first - timedelta(days=1)\n\n for name, time in means.index:\n subset_mean = means.xs((name, time), level=(0, 1), axis=0)\n subset_sd = sd.xs((name, time), level=(0, 1), axis=0)\n m = Stat.objects.get_or_create(\n location=locations.get(name=name),\n avail_bikes_mean=subset_mean['avail_bikes'],\n free_stands_mean=subset_mean['free_stands'],\n avail_bikes_sd=subset_sd['avail_bikes'],\n free_stands_sd=subset_sd['free_stands'],\n time=time,\n month=last_month\n )\n\n # snaps = Snapshot.objects.all()\n # i = 0\n # length = len(snaps)\n # for s in snaps:\n # i += 1\n # print(i)\n # if i > 35000:\n # s.save()\n # reduce_data()","def reload_period_from_analytics(cls, period):\n counts = googleanalytics.pageviews_by_document(*period_dates(period))\n if counts:\n # Delete and remake the rows:\n # Horribly inefficient until\n # http://code.djangoproject.com/ticket/9519 is fixed.\n cls.objects.filter(period=period).delete()\n for doc_id, visits in counts.iteritems():\n cls.objects.create(document=Document(pk=doc_id), visits=visits,\n period=period)\n else:\n # Don't erase interesting data if there's nothing to replace it:\n log.warning('Google Analytics returned no interesting data,'\n ' so I kept what I had.')","def compound(self, period):\n\t\tif self.calendar:\n\t\t\tperiod = CalendarRangePeriod(period, self.calendar)\n\t\t\n\t\tt = self.daycount.timefreq(period, self.frequency)\n\t\treturn self.compounding(self.rate, t)","def update(start_date, end_date, binning):\n global mean_source1,mean_source,median_source1,median_source, difference_source1,difference_source, difference_source2 , difference_source3, mean_source2, median_source2\n\n # if type of start/end date is date, turn it into a datetime,\n # set time of start/end date time to 12:00\n\n def convert_time(t):\n if type(t) == datetime.date:\n return datetime.datetime(t.year, t.month, t.day, 12, 0, 0, 0)\n else:\n return t.replace(hour=12, minute=0, second=0, microsecond=0)\n\n start_date = convert_time(start_date)\n end_date = convert_time(end_date)\n if binning is None:\n binning = ''\n\n first_timestamp = start_date.timestamp() + time_offset\n second_timestamp = end_date.timestamp() + time_offset\n\n # query data in mysql database\n sql1 = 'SELECT str_to_date(datetime,\"%%Y-%%m-%%d %%H:%%i:%%s\") AS datetime, seeing from seeing ' \\\n ' where datetime >= str_to_date(\"{start_date_}\",\"%%Y-%%m-%%d %%H:%%i:%%s\")' \\\n ' and datetime <= str_to_date(\"{end_date_}\",\"%%Y-%%m-%%d %%H:%%i:%%s\") ' \\\n .format(start_date_=str(start_date), end_date_=str(end_date))\n\n sql2 = 'select _timestamp_,ee50,fwhm,timestamp from tpc_guidance_status__timestamp where timestamp >= {start_date_}' \\\n ' and timestamp<= {end_date_} and guidance_available=\"T\" ' \\\n ' order by _timestamp_' \\\n .format(start_date_=str(first_timestamp), end_date_=str(second_timestamp))\n\n df2 = pd.read_sql(sql2, db.get_engine(app=current_app, bind='els'))\n df1 = pd.read_sql(sql1, db.get_engine(app=current_app, bind='suthweather'))\n\n # setting index time for calculating mean and average\n df2.index = df2[\"_timestamp_\"]\n df1.index = df1['datetime']\n\n # It seems that Pandas doesn't change the index type if the data frame is empty, which means that resampling\n # would fail for an empty data frame. As there will be no row for median or mean , it is safe to just use the\n # original data frame to avoid this problem.\n\n # for external seeing calculating median and mean\n if not df1.empty:\n mean1_all = df1.resample(str(binning) + 'T').mean()\n else:\n mean1_all = df1.copy(deep=True)\n source1 = ColumnDataSource(mean1_all)\n mean_source1.data = source1.data\n\n if not df1.empty:\n median1_all = df1.resample(str(binning) + 'T').median()\n else:\n median1_all = df1.copy(deep=True)\n source = ColumnDataSource(median1_all)\n median_source1.data = source.data\n\n # calculate mean and median for ee50\n if not df2.empty:\n mean_all = df2.resample(str(binning) + 'T').mean()\n else:\n mean_all = df2.copy(deep=True)\n source3 = ColumnDataSource(mean_all)\n mean_source.data = source3.data\n\n if not df2.empty:\n median_all = df2.resample(str(binning) + 'T').median()\n else:\n median_all = df2.copy(deep=True)\n source4 = ColumnDataSource(median_all)\n median_source.data = source4.data\n\n #calculate mean and median for fwhm\n if not df2.empty:\n mean_all1 = df2.resample(str(binning) + 'T').mean()\n else:\n mean_all1 = df2.copy(deep=True)\n source4 = ColumnDataSource(mean_all)\n mean_source2.data = source4.data\n\n if not df2.empty:\n median_all = df2.resample(str(binning) + 'T').median()\n else:\n median_all = df2.copy(deep=True)\n source5 = ColumnDataSource(median_all)\n median_source2.data = source5.data\n\n # calculate difference for external seeing against fwhm and ee50\n dataframes = [mean1_all, mean_all]\n add_dataframes = pd.concat(dataframes, axis=1)\n add_dataframes.index.name = '_timestamp_'\n add_dataframes['difference'] = add_dataframes['seeing'] - add_dataframes['ee50']\n datasource2 = ColumnDataSource(add_dataframes)\n difference_source.data = datasource2.data\n\n dataframes = [mean1_all, mean_all1]\n add_dataframes = pd.concat(dataframes, axis=1)\n add_dataframes.index.name = '_timestamp_'\n add_dataframes['difference1'] = add_dataframes['seeing'] - add_dataframes['fwhm']\n datasource1 = ColumnDataSource(add_dataframes)\n difference_source1.data = datasource1.data\n\n # #difference using the median\n # dataframes2 = [median_all, median1_all]\n # add_dataframes2 = pd.concat(dataframes2, axis=1)\n # add_dataframes2.index.name = '_timestamp_'\n # add_dataframes2['difference2'] = add_dataframes2['seeing'] - add_dataframes2['ee50']\n # datasource2 = ColumnDataSource(add_dataframes2)\n # difference_source2.data = datasource2.data\n #\n # dataframes3 = [median_all, median1_all]\n # add_dataframes3 = pd.concat(dataframes3, axis=1)\n # add_dataframes3.index.name = '_timestamp_'\n # add_dataframes3['difference3'] = add_dataframes3['seeing'] - add_dataframes3['fwhm']\n # datasource3 = ColumnDataSource(add_dataframes3)\n # difference_source3.data = datasource3.data\n\n # plot labels\n p = figure(title=\"external vs internal seeing ({binning} minute bins)\".format(binning=binning), x_axis_type='datetime'\n , x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\n dif=figure(title='difference between average internal and external seeing ({binning} minute bins)'.format(binning=binning), x_axis_type='datetime',\n x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\n\n #plots\n # plots for external seeing\n p.circle(source=mean_source1, x='datetime',y='seeing', legend=\"external average\" ,fill_color=\"white\",color='green')\n p.line(source=median_source1, x='datetime',y='seeing', legend=\"external median\" ,color='blue')\n\n #plots showing median and mean for ee50 and fwhm\n p.circle(source=mean_source, x='_timestamp_', y='ee50', legend='ee50 average')\n p.circle(source=mean_source, x='_timestamp_', y='fwhm', legend='fwhm average', color='red', fill_color='white')\n\n p.line(source=median_source, x='_timestamp_', y='ee50', legend='ee50 median', color='green')\n p.line(source=median_source, x='_timestamp_', y='fwhm', legend='fwhm median', color='orange')\n\n #for difference\n dif.circle(source=difference_source, x='_timestamp_', y='difference', legend='ee50_mean difference', color='red')\n dif.circle(source=difference_source1, x='_timestamp_', y='difference1', legend='fwhm_mean difference', fill_color='green')\n\n #\n # dif.circle(source=difference_source2, x='_timestamp_', y='difference2', legend='ee50_median difference', fill_color='blue')\n # dif.circle(source=difference_source3, x='_timestamp_', y='difference3', legend='fwhm_median difference', color='orange')\n\n p.xaxis.formatter = date_formatter\n p.legend.location = \"top_left\"\n p.legend.click_policy=\"hide\"\n\n dif.xaxis.formatter = date_formatter\n dif.legend.click_policy=\"hide\"\n\n script, div = components(p)\n content1 = '<div>{script}{div}</div>'.format(script=script, div=div)\n\n script, div = components(dif)\n content2 = '<div>{script}{div}</div>'.format(script=script, div=div)\n\n return '{cont} {cont2}'.format(cont=content1,cont2=content2)","def by_time_period(cls, user, time_periods):\n return [cls.by_user(user, p.start, p.end) for p in time_periods]","def history_clones(file, ht_df):\n if os.path.isfile(file):\n # if the file exists, we merge\n print(file + ' found, merging')\n df_file = pd.read_csv(file)\n\n ht_df['timestamp'] = pd.to_datetime(ht_df['timestamp']).dt.date\n\n df_file = pd.concat([df_file, ht_df])\n df_file['timestamp'] = df_file['timestamp'].astype(str)\n\n df_file.sort_values('timestamp', inplace=True)\n print(df_file.to_string())\n # we can't just drop the first instance: for the first day, we'll loose data.\n # so keep max value per date\n\n #df_file.drop_duplicates(subset=['timestamp'], keep='last', inplace=True)\n df_file = df_file.groupby('timestamp')[['uniques', 'count']].agg(['max']).reset_index()\n\n df_file.columns = df_file.columns.droplevel(level=1)\n #print(df_file.to_string())\n #print(df_file.columns)\n df_file.to_csv(file, index=False)\n\n else:\n # otherwise, just dump the df\n print('There is no file to merge, dumping df to ' + file)\n ht_df.to_csv(file, index=False)","def create_time_s(df, medidor, freq='15T'):\n dates_complete = pd.date_range('1/18/2013', '02/09/2014', freq='15T')\n # this dates take them from the file\n my_complete_series = pd.Series(dates_complete)\n frame1 = my_complete_series.to_frame()\n frame1.columns = ['key']\n merged = pd.merge(frame1, df, on='key', how='outer')\n merged = merged.sort('key')\n # fill the merged file with the number of the meter\n merged['medidor'].fillna(medidor, inplace=True)\n\n return merged","def testQueryWithTimestamp(self):\n for i in range(5):\n row_name = \"aff4:/row:query_with_ts\"\n data_store.DB.Set(row_name, \"metadata:5\", \"test\", timestamp=i + 10,\n replace=False, token=self.token)\n data_store.DB.Set(row_name, \"aff4:type\", \"test\", timestamp=i + 10,\n replace=False, token=self.token)\n\n # Read all timestamps.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=data_store.DB.ALL_TIMESTAMPS, token=self.token)]\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n self.assertEqual(len(attributes[\"aff4:type\"]), 5)\n\n # Read latest timestamp.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=data_store.DB.NEWEST_TIMESTAMP, token=self.token)]\n\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n self.assertEqual(len(attributes[\"aff4:type\"]), 1)\n self.assertEqual(attributes[\"aff4:type\"][0][0], \"test\")\n\n # Newest timestamp is 4.\n self.assertEqual(attributes[\"aff4:type\"][0][1], 14)\n\n # Now query for a timestamp range.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=(11, 13), token=self.token)]\n\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n # Now we should have three timestamps.\n self.assertEqual(len(attributes[\"aff4:type\"]), 3)\n\n timestamps = [attribute[1] for attribute in attributes[\"aff4:type\"]]\n self.assertListEqual(sorted(timestamps), [11, 12, 13])","def _tail_profile(self, db, interval):\r\n latest_doc = None\r\n while latest_doc is None:\r\n time.sleep(interval)\r\n latest_doc = db['system.profile'].find_one()\r\n\r\n current_time = latest_doc['ts']\r\n\r\n while True:\r\n time.sleep(interval)\r\n cursor = db['system.profile'].find({'ts': {'$gte': current_time}}).sort('ts', pymongo.ASCENDING)\r\n for doc in cursor:\r\n current_time = doc['ts']\r\n yield doc","def merge_record(self, dt, container = ''): \n record_dataset_legth ={} \n \n \n \"\"\" Combining the ncar_t and ncar_w files.\n If both are present, select the ncar_t data and rename it as 'ncar'. \n If only one is present, simply rename it as 'ncar'. \n \"\"\" \n if ('ncar_t' in list(container.keys()) ):\n container['ncar'] = {} \n container['ncar']['df'] = container['ncar_t']['df'] \n \n elif ( 'ncar_w' in list(container.keys()) and 'ncar_t' not in list(container.keys()) ) :\n container['ncar'] = {} \n container['ncar']['df'] = container['ncar_w']['df'] \n\n \n for k in container.keys():\n if k == 'ncar_t' or k == 'ncar_w': \n continue \n record_dataset_legth[k] = len(container[k]['df'] )\n \n \n \"\"\" For now, choosing the dataset with more records of all or igra2>ncar>rest data if available and with same number of records \"\"\"\n best_ds, all_ds , best_datasets, all_ds_reports = 'dummy' , [] , [], [] # total number of records, name of the chosen dataset , list of other possible dataset with available data \n \n most_records = max( [ v for v in record_dataset_legth.values() ] ) # maximum number of records per date_time \n \n for k, v in record_dataset_legth.items(): \n if v == 0:\n continue\n if v == most_records:\n best_datasets.append(k) \n if v > 0:\n all_ds.append(k) # all other datasets with smaller number of records than the maximum found\n try: \n all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + container[k]['df']['report_id'].values[0] ) # converting the original report id using the same convention as for observation_id\n except:\n all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + int( (container[k]['df']['report_id'].values[0]).tostring() ) ) # converting the original report id using the same convention as for observation_id\n \n \n #all_ds_reports.append(np.nan)\n #print ( type(container[k]['df']['report_id'].values) )\n #all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + float(container[k]['df']['report_id'].values[0].decode('latin1') ))\n \n if len(best_datasets) ==0:\n print('wrong??? please check')\n return 0,0,0,0 \n \n if 'igra2' in best_datasets:\n best_ds = 'igra2'\n elif 'ncar' in best_datasets:\n best_ds = 'ncar'\n elif 'era5_1' in best_datasets:\n best_ds = 'era5_1' \n else:\n best_ds = best_datasets[0]\n \n \"\"\" Extract container \"\"\" \n selected_df = container[best_ds]['df'].copy(deep = True) # might take extra time, dont know how to get rid of this \n\n try:\n merged_report = self.observation_ids_merged[best_ds] * 1000000000 + int( selected_df['report_id'].values[0].tostring() ) \n except:\n merged_report = np.nan \n\n \"\"\" Calculate new unique observation id \"\"\"\n try: \n obs_ids_merged = [ self.observation_ids_merged[best_ds] * 1000000000 + int( i.tostring() ) for i in selected_df['observation_id'] ]\n except:\n obs_ids_merged = [ np.nan for i in selected_df['observation_id'] ]\n \n \n selected_df['observation_id'] = obs_ids_merged\n \n \"\"\" Calculate new unique report id \"\"\" \n selected_df['report_id'] = merged_report\n\n \"\"\" Returning a string with the alternative available datasets data \"\"\"\n if len(all_ds_reports) > 1: \n duplicates = \",\".join( [ str(i) for i in all_ds_reports] )\n else:\n duplicates = str(all_ds_reports[0])\n \n \n \"\"\" Extracting the merged header_table.\n Again, must consider the special case where best_ds == ncar. \n Note that the header table *should* be identical for ncar_w or ncar_t \"\"\" \n if best_ds != 'ncar':\n header = self.get_header_table(dt, ds= best_ds, all_ds = duplicates , length= len(selected_df) )\n \n elif ( best_ds == 'ncar' and 'ncar_t' in list(container.keys()) ) :\n header = self.get_header_table(dt, ds = 'ncar_t', all_ds = duplicates, length= len(selected_df))\n \n elif ( best_ds == 'ncar' and 'ncar_t' not in list(container.keys()) ) :\n header = self.get_header_table(dt, ds = 'ncar_w', all_ds = duplicates, length= len(selected_df) ) \n \n logging.debug('I use %s record since it has more entries: %s but other available datasets are : %s' , best_ds , str(most_records) , all_ds ) \n \n #print ('duplicates are: ', duplicates)\n return selected_df, best_ds , duplicates, header","def compare_displacements(ds1,ds2):\n # Obteniendo los datos para BP\n t1 = ds1['t']\n t1 = t1[:n_im-1]\n t1 = mplt.dates.date2num(t1)\n d1 = ds1['d_t']\n # Obteniendo los datos para RMA\n t2 = ds2['t']\n t2 = t2[:n_im-1]\n t2 = mplt.dates.date2num(t2)\n d2 = ds2['d_t']\n\n # Graficando las 2 curvas juntas\n formatter = DateFormatter(\"%d/%m - %H:%M\")\n for i in range(len(d1)):\n # Hallando el valor promedio final x zona\n mean_bp = d1[i].mean()\n mean_rma = d2[i].mean()\n print(\"Valor promedio BP_zona\"+str(i)+\": \",mean_bp)\n print(\"Valor promedio RMA_zona\"+str(i)+\": \",mean_rma)\n print(\"\")\n # Graficando\n direction = 'desplazamientosPromedios_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'_zona'+str(i)\n\n fig, ax= plt.subplots(figsize=(10,7))\n ax.plot_date(t1,d1[i],'b',marker='',markerfacecolor='b',markeredgecolor='b',label='Back Projection')\n ax.plot_date(t2,d2[i],'r',marker='',markerfacecolor='r',markeredgecolor='r',label='RMA')\n ax.set(xlabel='Tiempo',ylabel='Desplazamiento(mm)',title=\"Desplazamientos promedios\\n(Zona \"+str(i)+')')\n ax.xaxis.set_major_formatter(formatter)\n ax.xaxis.set_tick_params(rotation=20)\n #ax.set_xlim([R.min(),R.max()])\n ax.set_ylim([-c*1000*4/(4*fc),c*1000*4/(4*fc)])\n ax.grid(linestyle='dashed')\n ax.legend()\n plt.show()\n fig.savefig(os.getcwd()+\"/Results/Desplazamientos/\"+direction,orientation='landscape')\n\n return 'Ok'","def __iter__(self):\n if len(self) == 0:\n return\n current = self.first_timestamp\n delta = datetime.timedelta(0, self.interval)\n while current <= self.last_timestamp:\n yield current\n current += delta","def create_person_offer(transcript,portfolio,profile,person_transaction): \n tqdm.pandas()\n to_be_appended = None\n\n # This will not include transaction, so we need another new table for those.\n for (person_id, offer_index), transcript_grouped in tqdm(transcript.dropna(subset=['offer_index']).groupby(['person','offer_index'])):\n this_offer = portfolio.loc[offer_index]\n this_person = profile.loc[person_id]\n to_be_appended = append_one_person_offer(to_be_appended, this_offer, person_id, offer_index, transcript_grouped, this_person)\n \n person_offer_df = pd.DataFrame(to_be_appended)\n\n # TODO, the stuff above and the stuff below was originally made at completly different times and\n # was different files and functions, now I put it into one, however, there's still probably alot\n # that can be done together instead of looping multiple times on the same stuff... but not reealy needed, as it works\n # but could probably make it way faster...\n person_offer_df['before_start'] = 0\n person_offer_df['same_day_start'] = 0\n person_offer_df['after_start'] = 0\n person_offer_df['before_view'] = 0\n person_offer_df['same_day_view'] = 0\n person_offer_df['after_view'] =0\n person_offer_df['before_complete'] = 0\n person_offer_df['same_day_complete']= 0\n person_offer_df['after_complete'] = 0\n person_offer_df['w_before'] = 0\n person_offer_df['sum_during'] = 0\n person_offer_df['mean_during'] = 0\n person_offer_df['w_after'] = 0\n person_offer_df = person_offer_df.progress_apply(get_before_after_mean,person_transaction=person_transaction, axis=1) \n \n \n person_offer_df['viewed_reltime']=np.nan\n person_offer_df['completed_reltime']=np.nan\n\n def absulute2relative_time(x):\n \"\"\"Converts absolute time (hours since start of simulation)\n to hours since offer recieved (start)\n \"\"\" \n if x.viewed:\n x.viewed_reltime=x.viewed_time-x.start\n \n if x.completed:\n x.completed_reltime=x.completed_time-x.start\n \n return x\n\n person_offer_df = person_offer_df.progress_apply(absulute2relative_time, axis=1)\n \n #makes it easier to access these combinations\n person_offer_df['complete_viewed'] = (person_offer_df['completed'] & person_offer_df['viewed']).astype(int)\n person_offer_df['complete_not_viewed'] = (person_offer_df['completed'] & ~person_offer_df['viewed']).astype(int)\n person_offer_df['not_complete_not_viewed'] = (~person_offer_df['completed'] & ~person_offer_df['viewed']).astype(int)\n person_offer_df['not_complete_viewed'] = (~person_offer_df['completed'] & person_offer_df['viewed']).astype(int)\n person_offer_df['completed'] = person_offer_df['completed'].astype(int)\n person_offer_df['viewed'] = person_offer_df['viewed'].astype(int)\n \n #calculates diff in sales before and after an event\n for x in ['start','view','complete']:\n person_offer_df[f'diff_{x}'] = person_offer_df[f'after_{x}'] - person_offer_df[f'before_{x}'] \n \n person_offer_df[f'diff_offer'] = person_offer_df[f'w_after'] - person_offer_df[f'w_before']\n \n #recalculates became_member_on to member_since instead (where newest member is 0) in days\n person_offer_df['became_member_on'] = pd.to_datetime(person_offer_df['became_member_on'], format='%Y-%m-%d')\n person_offer_df['member_since_days'] = (person_offer_df['became_member_on'].max() - person_offer_df['became_member_on']).dt.days\n \n #remve these wrong ages and turn to NaN\n person_offer_df['age']=person_offer_df['age'].apply(lambda x:np.nan if x==118 else x)\n \n return person_offer_df","def _run_records_sporadic(self, run_idxs, run_record_key):\n\n # we loop over the run_idxs in the contig and get the fields\n # and cycle idxs for the whole contig\n fields = None\n cycle_idxs = np.array([], dtype=int)\n # keep a cumulative total of the runs cycle idxs\n prev_run_cycle_total = 0\n for run_idx in run_idxs:\n\n # get all the value columns from the datasets, and convert\n # them to something amenable to a table\n run_fields = self._convert_record_fields_to_table_columns(run_idx, run_record_key)\n\n # we need to concatenate each field to the end of the\n # field in the master dictionary, first we need to\n # initialize it if it isn't already made\n if fields is None:\n # if it isn't initialized we just set it as this first\n # run fields dictionary\n fields = run_fields\n else:\n # if it is already initialized we need to go through\n # each field and concatenate\n for field_name, field_data in run_fields.items():\n # just add it to the list of fields that will be concatenated later\n fields[field_name].extend(field_data)\n\n # get the cycle idxs for this run\n rec_grp = self.records_grp(run_idx, run_record_key)\n run_cycle_idxs = rec_grp[CYCLE_IDXS][:]\n\n # add the total number of cycles that came before this run\n # to each of the cycle idxs to get the cycle_idxs in terms\n # of the full contig\n run_contig_cycle_idxs = run_cycle_idxs + prev_run_cycle_total\n\n # add these cycle indices to the records for the whole contig\n cycle_idxs = np.hstack( (cycle_idxs, run_contig_cycle_idxs) )\n\n # add the total number of cycle_idxs from this run to the\n # running total\n prev_run_cycle_total += self.num_run_cycles(run_idx)\n\n # then make the records from the fields\n records = self._make_records(run_record_key, cycle_idxs, fields)\n\n return records","def pulsEphem(self):\n\n hduMain = fits.open(self.ft1)\n\n # --------------------------------------------------------------------------------------------- #\n # Split the FT1 file every 4000 events\n noEv = 0\n deltEv = 5000\n count = 0\n wfil = open(os.path.dirname(self.ft1) + os.path.basename('tmpFT1.lis'), 'w')\n while noEv <= self.nevents:\n hduCols = []\n for colname, form, uni in zip(hduMain['EVENTS'].columns.names, hduMain['EVENTS'].columns.formats, hduMain['EVENTS'].columns.units):\n hduCols.append( fits.Column(name=colname, array=hduMain['EVENTS'].data[colname][noEv:noEv+deltEv], format=form, unit=uni) )\n # Updte the tstart and tstop in the header in order for tempo2 to work...\n hduMain['EVENTS'].header.set('TSTART', hduMain['EVENTS'].data['TIME'][noEv:noEv+deltEv][0])\n hduMain['EVENTS'].header.set('TSTOP', hduMain['EVENTS'].data['TIME'][noEv:noEv+deltEv][-1])\n newHDU = fits.BinTableHDU.from_columns(hduCols, name='EVENTS', header=hduMain['EVENTS'].header) \n hdulist = fits.HDUList([hduMain['PRIMARY'], newHDU, hduMain['GTI']])\n tmpName = os.path.dirname(self.ft1)+os.path.basename('tempFT1_'+str(count)+'.fits')\n hdulist.writeto(tmpName, clobber=True)\n wfil.write(tmpName + '\\n')\n noEv += deltEv\n count += 1\n if noEv != self.nevents:\n hduCols = []\n noEv -= deltEv\n for colname, form, uni in zip(hduMain['EVENTS'].columns.names, hduMain['EVENTS'].columns.formats, hduMain['EVENTS'].columns.units):\n hduCols.append( fits.Column(name=colname, array=hduMain['EVENTS'].data[colname][noEv:self.nevents], format=form, unit=uni) )\n hduMain['EVENTS'].header.set('TSTART', hduMain['EVENTS'].data['TIME'][noEv:self.nevents][0])\n hduMain['EVENTS'].header.set('TSTOP', hduMain['EVENTS'].data['TIME'][noEv:self.nevents][-1])\n newHDU = fits.BinTableHDU.from_columns(hduCols, name='EVENTS', header=hduMain['EVENTS'].header)\n hdulist = fits.HDUList([hduMain['PRIMARY'], newHDU, hduMain['GTI']])\n tmpName = os.path.dirname(self.ft1)+os.path.basename('tempFT1_'+str(count)+'.fits')\n hdulist.writeto(tmpName, clobber=True)\n wfil.write(tmpName + '\\n')\n wfil.close()\n\n hduMain.close()\n\n # --------------------------------------------------------------------------------------------- #\n # Run tempo2 for each piece of the FT1\n rfil = open(os.path.dirname(self.ft1) + 'tmpFT1.lis', 'r')\n percent = 0\n nbFiles = sum(1 for line in open(os.path.dirname(self.ft1) + 'tmpFT1.lis', 'r'))\n count = 0\n for tmpFil in rfil:\n # Print a progression bar every 5%\n if ( count / np.floor(nbFiles) * 100 ) >= percent:\n self._progressBar(percent, printEvery=5)\n percent += 5\n with open(os.devnull, 'wb') as devnull:\n subprocess.check_call(['/dsm/fermi/fermifast/glast/tempo2-2013.9.1/tempo2',\n '-gr', 'fermi', '-ft1', tmpFil[:-1], '-ft2', self.ft2, '-f', self.ephem,\n '-phase'], stdout=devnull, stderr=subprocess.STDOUT)\n count += 1\n # Replace the old ft1 by the new one with the PULSE_PHASE column\n #os.remove()\n self._gtSelect(data = os.path.dirname(self.ft1) + os.path.basename('tmpFT1.lis'))\n\n\n\n\n #self.nevents\n #J2032+4127_54683_57791_chol_pos.par\n #os.popen(\"tempo2 -gr fermi -ft1 {} -ft2 {} -f {} -phase\".format(self.ft1, self.ft2, self.ephem))","def _copy(source, track, filter_f=lambda x: True, coef=1000):\n for msg in source:\n if filter_f(msg):\n track.append(msg.copy(time=int(msg.time*coef)))","def _mergeKeys(self, other):\n for id in set(other.clock.keys()).difference(set(self.clock.keys())):\n self.clock[id] = 0\n for id in set(self.clock.keys()).difference(set(other.clock.keys())):\n other.clock[id] = 0","def test_get_time_period_search(self):\n obj = CalculateSearchTimes(True, TIME_PERIODS,\n search_path=SEARCH_PATH, df_path=DF_PATH)\n dt1 = dt.datetime(2017, 8, 9, 14, 56, 40, 796658)\n dt2 = dt.datetime(2017, 8, 9, 17, 56, 40, 796658)\n dt3 = dt.datetime(2017, 8, 9, 10, 56, 40, 796658)\n result1 = obj.get_time_period_search(dt1)\n result2 = obj.get_time_period_search(dt2)\n result3 = obj.get_time_period_search(dt3)\n\n self.assertTrue(np.mean(list(result1.values())) == 1666.6666666666667)\n self.assertTrue(np.mean(list(result2.values())) != 1666.6666666666667)\n self.assertTrue(np.mean(list(result3.values())) != 1666.6666666666667)","def run(self):\n # pylint: disable=too-many-locals\n loop_start = time.clock()\n partitions = []\n for item in self._results:\n partitions.append(\n Partition(item.name, item.dataframe, self.PARTITION_SIZE)\n )\n if item.name == self._primary_dataset:\n primary_dataset = partitions[-1]\n\n index = 0\n while index < self.PARTITION_SIZE:\n merge_sets = []\n combination = ['\\033[1m{0}\\033[0m'.format(self._mappings[self._primary_dataset])]\n for partition in partitions:\n if partition.name != self._primary_dataset:\n merge_sets.append({'name': partition.name, 'data': partition.next()})\n combination.append(self._mappings[partition.name])\n\n Logger().info(\n 'Running data extraction. Index {0} of {1} partitions, (combination: {2}, queries: {3})'.format(\n index,\n self.PARTITION_SIZE,\n ''.join(combination),\n len(self._queries)\n )\n )\n\n for size in range(self.PARTITION_SIZE):\n self._running = []\n q_start = time.clock()\n merge_table = self.merge(\n merge_sets + [{'name': primary_dataset.name, 'data': primary_dataset.next()}]\n )\n m_end = '{0:.2f}'.format(float(time.clock() - q_start))\n Logger().debug(\n 'Combination {0} merge table completed in {1} seconds ({2} rows)'.format(\n ''.join(combination),\n m_end,\n len(merge_table.index)\n )\n )\n for query in self._queries:\n self._running.append(\n DataThreader(merge_table, query, self._unique_columns)\n )\n\n self.monitor()\n times = [item.duration for item in self._running]\n message = '{4}\\n Combination {0}, Index {5}/{1}'\n message += ', merge_table size: {2}.\\n Average time per query {3}'\n Logger().debug(\n message.format(\n ''.join(combination),\n index,\n len(merge_table.index),\n times,\n [len(query.results.index) for query in self._queries],\n size\n )\n )\n del times\n del merge_table\n del self._running\n\n end_time = math.floor(time.clock() - loop_start)\n Logger().info('=========================================================================================')\n Logger().info(\n 'Completed partition {0} in {1} seconds. {2} queries, combination: {3}'.format(\n index,\n end_time,\n len(self._queries),\n ''.join(combination)\n )\n )\n Logger().info('=========================================================================================')\n primary_dataset.reset()\n index += 1\n\n Logger().info('Completed data extraction')\n self._complete = True","def getContinuousBookingTimeSeries(span=28):\n data = []\n x = []\n y = []\n users = []\n now = datetime.datetime.now(pytz.utc)\n delta = datetime.timedelta(days=span)\n end = now - delta\n bookings = Booking.objects.filter(start__lte=now, end__gte=end).prefetch_related(\"collaborators\")\n for booking in bookings: # collect data from each booking\n user_list = [u.pk for u in booking.collaborators.all()]\n user_list.append(booking.owner.pk)\n data.append((booking.start, 1, user_list))\n data.append((booking.end, -1, user_list))\n\n # sort based on time\n data.sort(key=lambda i: i[0])\n\n # collect data\n count = 0\n active_users = {}\n for datum in data:\n x.append(str(datum[0])) # time\n count += datum[1] # booking count\n y.append(count)\n for pk in datum[2]: # maintain count of each user's active bookings\n active_users[pk] = active_users.setdefault(pk, 0) + datum[1]\n if active_users[pk] == 0:\n del active_users[pk]\n users.append(len([x for x in active_users.values() if x > 0]))\n\n return {\"booking\": [x, y], \"user\": [x, users]}","def main():\n\n f = open(eventsfile, 'r')\n lines = f.readlines()\n numcounter = 0\n counter = 0\n fullcounter = 0\n movielist = []\n movielists =[]\n timestamp_list = []\n filteredlist = [] \n startdate = \"2020-02-26\"\n \n for line in lines:\n TAPES = line.split('\\t')\n if int(TAPES[2]) == 1 or int(TAPES[2]) == 2:\n filteredlist.append(line)\n \n for newline in filteredlist:\n TAPES = newline.split('\\t')\n fullcounter +=1\n if int(TAPES[2]) == 2:\n timestamp_list.append(0)\n continue\n startdate2 = startdate.split(\"-\")[1] + \"/\" + startdate.split(\"-\")[2] + \"/\" + startdate.split(\"-\")[0]\n dateplustime = startdate2 + TAPES[0][0:len(TAPES[0])]\n thistime = faststrptime(dateplustime)\n unixtimestamp = datetime.datetime.timestamp(thistime)\n timestamp_list.append(int(unixtimestamp))\n\n i = 0 \n for element in timestamp_list:\n\n if i < (len(timestamp_list)-1) and timestamp_list[i+(counter-i)]-timestamp_list[i] >= 3600:\n counter += 1\n i = counter\n movielist.append(counter)\n \n if len(movielist) <= 15:\n numcounter = 0\n j = 0\n for step in movielist:\n movielists[len(movielists)-1].append(movielist[j])\n j += 1\n movielist = []\n continue \n else:\n movielists.append(movielist)\n movielist = []\n numcounter = 0\n continue\n\n if i < (len(timestamp_list)-1) and timestamp_list[i+1]-timestamp_list[i] >= 3600:\n counter += 1\n i = counter\n movielist.append(counter)\n\n if len(movielist) <= 15:\n numcounter = 0\n j = 0\n for step in movielist:\n movielists[len(movielists)-1].append(movielist[j])\n j += 1\n movielist = []\n continue\n else:\n movielists.append(movielist)\n movielist = []\n numcounter = 0\n continue\n\n counter += 1\n numcounter += 1\n if element != 0:\n movielist.append(counter)\n i += 1\n \n if numcounter == 30:\n numcounter = 0\n movielists.append(movielist)\n movielist = []\n \n if i > (len(timestamp_list)-1):\n movielists.append(movielist)\n movielist = []\n numcounter = 0\n \n numendlists = counter - fullcounter\n first = len(movielists)-numendlists\n last = len(movielists)\n del movielists[first:last]\n \n for x in movielists:\n for y in x:\n if int(filenumber) == y:\n movielist = x\n\n modename = str(movielist[0]) + \"to\" + str(movielist[len(movielist)-1])\n modefilename = \"mode_\" + modename + \".png\"\n try:\n imread(modefilename)\n except:\n imageMode(modename,movielist)\n\n e = loadmodeImage(modefilename)\n \n roimask = np.zeros((ydim,xdim))\n f = open(roisfile, 'r')\n lines = f.readlines()\n i = 1\n i2 = 0\n for line in lines:\n try:\n print(int(line.split(' ')[0]))\n except ValueError:\n i2 += 1\n continue\n minx = int(line.split(' ')[0])\n miny = int(line.split(' ')[1])\n maxx = int(line.split(' ')[2])\n maxy = int(line.split(' ')[3])\n roimask[int(miny):int(maxy),int(minx):int(maxx)] = i\n i += 1\n numberofwells = i-1\n numberofcols = int(i2/2)\n numberofrows = int(numberofwells/numberofcols)\n roimaskweights = convertMaskToWeights(roimask)\n\n cap = cv2.VideoCapture(videoStream)\n\n cap.set(3,roimask.shape[1])\n cap.set(4,roimask.shape[0])\n \n ret,frame = cap.read()\n storedImage = np.array(e * 255, dtype = np.uint8)\n storedMode = Blur(storedImage)\n storedFrame = grayBlur(frame)\n cenData = np.zeros([ int(saveFreq), len(np.unique(roimaskweights))*2 -2])\n pixData = np.zeros([ int(saveFreq), len(np.unique(roimaskweights))])\n i = 0;\n totalFrames = 0\n while(cap.isOpened()):\n ret,frame = cap.read()\n if ret == False:\n break\n currentFrame = grayBlur(frame)\n diffpix = diffImage(storedFrame,currentFrame,pixThreshold)\n diff = trackdiffImage(storedMode,currentFrame,pixThreshold)\n diff.dtype = np.uint8\n contours,hierarchy = cv2.findContours(diff, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\n MIN_THRESH = 20.0\n MIN_THRESH_P = 20.0\n roi_dict = {}\n for r in range(0,numberofwells):\n roi_dict[r+1] = []\n for cs in range(0,len(contours)):\n if cv2.contourArea(contours[cs]) < 1.0:\n continue\n if cv2.arcLength(contours[cs],True) < 1.0:\n continue\n if cv2.contourArea(contours[cs]) > MIN_THRESH or cv2.arcLength(contours[cs],True) > MIN_THRESH_P:\n M = cv2.moments(contours[cs])\n cX = int(M[\"m10\"] / M[\"m00\"])\n cY = int(M[\"m01\"] / M[\"m00\"])\n area = cv2.contourArea(contours[cs])\n perim = cv2.arcLength(contours[cs],True)\n if int(roimask[cY,cX]) == 0:\n continue\n if not roi_dict[int(roimask[cY,cX])]:\n roi_dict[int(roimask[cY,cX])].append((area*perim,cX,cY))\n else:\n if roi_dict[int(roimask[cY,cX])][0][0] < area*perim:\n roi_dict[int(roimask[cY,cX])][0] = (area*perim,cX,cY)\n\n pixcounts = []\n pixcounts = np.bincount(roimaskweights, weights=diffpix.ravel())\n pixData[i,:] = np.hstack((pixcounts))\n counts = []\n keys = roi_dict.keys()\n keys = sorted(keys)\n for k in keys:\n x = -10000\n y = -10000\n if roi_dict[k]:\n x = roi_dict[k][0][1]\n y = roi_dict[k][0][2]\n counts.append(x)\n counts.append(y)\n cv2.line(storedImage,(x,y),(x,y),(255,255,255),2)\n if i == 284:\n cv2.imwrite(videoStream + '_trackedimagewithlines_' + str(i) + \".png\", storedImage)\n cenData[i,:] = np.asarray(counts)\n totalFrames += 1\n storedFrame = currentFrame\n i += 1\n\n file = open(videoStream + \".centroid2\",'w')\n for x in range(0,frameRate):\n for y in range(0,numberofwells*2):\n file.write(str(int(cenData[x,:][y])) + '\\n')\n pixData = pixData[:i,:]\n pixData = pixData[:,1:] \n file = open(videoStream + \".motion2\",'w')\n for x in range(0,frameRate):\n for y in range(0,numberofwells):\n file.write(str(int(pixData[x,:][y])) + '\\n')\n\n cap.release()\n cv2.destroyAllWindows()\n \n try:\n image = Image.open('lastframe.png')\n except:\n makenumROIsimage()","def combine_record(self, dt, container = ''):\n \n record_dataset_legth ={} \n other_ds = []\n\n ''' I fill the dic e.g. record_dataset_legth{100:['era5_1','ncar'], 80:['bufr','igra2'] }\n i.e. the keys are the lengths, the entries are the lists of datasets '''\n\n duplicates = []\n\n for k in container.keys(): # loop over the dataset\n if k not in other_ds:\n other_ds.append(k)\n for f in container[k]: # loop over the file per dataset\n num_rec = len(container[k][f]['obs_tab'][\"date_time\"])\n \n \"\"\" Storing all the reports id with the proper prefix (for each different dataset) \"\"\"\n rep_id = b''.join(container[k][f][\"obs_tab\"]['report_id'][0]) \n rep_id = self.observation_ids_merged[k] + rep_id \n duplicates.append( rep_id ) \n \n if num_rec not in record_dataset_legth.keys():\n record_dataset_legth[num_rec] = {}\n record_dataset_legth[num_rec]['best_ds'] = []\n record_dataset_legth[num_rec]['file'] = []\n\n record_dataset_legth[num_rec]['best_ds'].append(k)\n record_dataset_legth[num_rec]['file'].append(f)\n\n max_entries = max(record_dataset_legth.keys())\n \n ''' best_ds is the list of longest datasets, best_datasets the list of all the datasets available including best_ds '''\n best_datasets = record_dataset_legth[max_entries]\n\n \"\"\" Choosing the priority of the datasets:\n - if era5_1 or era5_2 are present, pick them (they cant be both present for the same date_time)\n - else, if igra2 is present, pick it\n - else, one of the remaining ones \"\"\"\n\n if 'era5_2' in best_datasets and 'era5_1' not in best_datasets: # era5_1 and era5_2 should never be both present anyway...\n best_ds = 'era5_2' \n elif 'era5_1' in best_datasets and 'era5_2' not in best_datasets:\n best_ds = 'era5_1'\n elif 'era5_1' not in best_datasets and 'era5_2' not in best_datasets and 'igra2' in best_datasets:\n best_ds = 'igra2'\n elif 'era5_1' not in best_datasets and 'era5_2' not in best_datasets and 'igra2' not in best_datasets:\n best_ds = record_dataset_legth[max_entries]['best_ds'][0] # pick the first of the list \n\n best_file = record_dataset_legth[max_entries]['file'][0]\n\n ''' If more file are available for the same best_ds, pick the first one from the list '''\n selected_obstab, selected_era5fb = container[best_ds][best_file]['obs_tab'] , container[best_ds][best_file]['era5fb_tab']\n\n ''' Creating the correct observations and record ids. \n All the bytes variable are shrunk to a long |S1 byte variable type, otherwise \n writing in h5py will not work. '''\n \n for var in ['observation_id']:\n if type (selected_obstab[var] ) == np.ndarray and type (selected_obstab[var][0] ) == np.bytes_:\n selected_obstab[var] = np.array ([self.observation_ids_merged[best_ds] + b''.join(l) for l in selected_obstab[var] ] )\n elif type (selected_obstab[var] ) == np.ndarray and type (selected_obstab[var][0] ) == np.ndarray:\n selected_obstab[var] = np.array ([self.observation_ids_merged[best_ds] + b''.join(l) for l in selected_obstab[var][:] ] )\n\n for var in ['report_id']:\n val = selected_obstab[var][0]\n if type (selected_obstab[var] ) == np.ndarray and type (val) == np.bytes_:\n value = self.observation_ids_merged[best_ds] + b''.join(val) # it is the same for each row in the table\n elif type (selected_obstab[var] ) == np.ndarray and type (val) == np.ndarray:\n value = self.observation_ids_merged[best_ds] + b''.join(val) \n arr = np.full( (1, len( selected_obstab['date_time']) ) , value )[0] # np.full returns a list of lists\n\n selected_obstab[var] = arr\n\n\n for var in selected_era5fb.keys():\n if type (selected_era5fb[var]) == np.ndarray and type (selected_era5fb[var][0] ) == np.ndarray:\n try:\n selected_era5fb[var] = np.array( [b''.join(l) for l in selected_era5fb[var][:] ] )\n #print('MANAGED FFF', var)\n except:\n value = [b''.join(l) for l in selected_era5fb[var][0] ][0]\n #print('VALUE IS FFF', value)\n selected_era5fb[var] = np.array( (1, len( selected_obstab[var]) ) ).fill(value)\n\n \"\"\" Extracting the header \"\"\"\n selected_head = self.get_header_table(dt, ds = best_ds, File = best_file )\n for var in selected_head.keys():\n if type (selected_head[var] ) == np.ndarray and type (selected_head[var][0] ) == np.bytes_:\n selected_head[var] = np.array( [b''.join(l) for l in selected_head[var][:] ] )\n\n if 'best_ds' == 'era5_1' or best_ds == 'era5_2' :\n selected_obstab['advanced_assimilation_feedback'] = np.array([1]*len(selected_obstab['date_time']) )\n else:\n selected_obstab['advanced_assimilation_feedback'] = np.array([0]*len(selected_obstab['date_time']) )\n\n #best_ds_byte = np.bytes_(best_ds, ndtype = '|S10') # converting to bytes object\n best_ds_byte = np.bytes_(best_ds) # converting to bytes object \n arr = np.full( (1, len( selected_obstab['date_time']) ) , best_ds_byte )[0]\n selected_obstab['source_id'] = arr\n\n duplicate = b','.join(duplicates)\n #selected_head['duplicates'] = np.array(duplicate)\n\n duplicate = np.array(duplicate).astype(dtype='|S70')\n selected_head['duplicates'] = np.array([duplicate])\n selected_head['report_id'] = np.array([selected_obstab['report_id'][0]])\n selected_head['source_id'] = np.array([selected_obstab['source_id'][0]])\n selected_head['record_timestamp'] = np.array([selected_obstab['date_time'][0]])\n\n selected_file = np.bytes_(best_file.split('/')[-1])\n \n return best_ds, selected_obstab, selected_era5fb, selected_head, selected_file, best_file","def append_past_returns(raw, periods):\n data = raw.dropna(subset=['ret']).copy()\n wide = data.pivot(index='time_idx', columns='permno', values='ret')\n wide = wide + 1\n\n rets = []\n for period in periods:\n tmp = wide.rolling(window=period).apply(np.prod, raw=True) - 1\n tmp = tmp.reset_index().melt(id_vars='time_idx', var_name='permno', value_name='prev_ret_' + str(period)).dropna()\n tmp.permno = tmp.permno.astype('int')\n tmp.time_idx = pd.to_datetime(tmp.time_idx)\n rets.append(tmp)\n\n for ret in rets:\n data = pd.merge(data, ret, on=['time_idx', 'permno'], how='left')\n return data # don't drop because we may need to work on other fundamental data too","def gen_records(self, count=None):\n if not count:\n count = self.num_rec\n tt = time.localtime(time.time())\n addr = None\n for i in range(count):\n logdbg(\"reading record %d of %d\" % (i+1, count))\n addr, record = self.get_record(addr, tt.tm_year, tt.tm_mon)\n yield addr, record","def merge_working_sets(self, other):\n\n for dist in other.by_key.values(): self.add(dist)\n return self","def merge(self, otr):\n self._duration = otr.get_start() - self.get_start()\n self._duration += otr.get_duration()\n self._line[3] = self._duration","def extend(self,\n period_data: List[Candle]) -> None:\n self.point_1_moment = period_data[0].moment\n self.point_1_price = period_data[0].open\n self.point_2_moment = period_data[1].moment\n self.point_2_price = period_data[1].open","def make_data_pipeline(self, from_, to_):\n\n # Get the volume over time data.\n r_volume = self.get_data_from_endpoint(from_, to_, 'volume')\n print('There are approximately {} documents.'.format(r_volume.json()['numberOfDocuments']))\n\n # Carve up time into buckets of volume <10k.\n l_dates = self.get_dates_from_timespan(r_volume)\n\n data = []\n for i in range(0, len(l_dates)):\n from_, to_ = l_dates[i]\n\n # Pull posts.\n r_posts = self.get_data_from_endpoint(from_, to_, 'posts')\n if r_posts.ok and (r_posts.json()['status'] != 'error'):\n j_result = json.loads(r_posts.content.decode('utf8'))\n data.extend(j_result['posts'])\n return data","def get_data(self, date_time):\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\n return_columns = ','.join(self.table_columns)\n with self.con:\n cur = self.con.cursor()\n cur.execute(\"DROP TABLE IF EXISTS temp;\")\n cur.execute(\"DROP TABLE IF EXISTS temp2;\")\n cur.execute(\"DROP TABLE IF EXISTS temp3;\")\n cur.execute(\"DROP TABLE IF EXISTS temp4;\")\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp AS \n SELECT * \n FROM {table} \n WHERE EFFECTIVEDATE <= '{datetime}';\"\"\"\n cur.execute(query.format(table=self.table_name, datetime=date_time))\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\n query = \"\"\"CREATE TEMPORARY TABLE temp2 AS\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\n FROM temp\n GROUP BY {id}, EFFECTIVEDATE;\"\"\"\n cur.execute(query.format(id=id_columns))\n # For each unique set of ids get the record with the most recent effective date.\n query = \"\"\"CREATE TEMPORARY TABLE temp3 as\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\n FROM temp2\n GROUP BY {id};\"\"\"\n cur.execute(query.format(id=id_columns))\n # Inner join the original table to the set of most recent effective dates and version no.\n query = \"\"\"CREATE TEMPORARY TABLE temp4 AS\n SELECT * \n FROM {table} \n INNER JOIN temp3 \n USING ({id}, VERSIONNO, EFFECTIVEDATE);\"\"\"\n cur.execute(query.format(table=self.table_name, id=id_columns))\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\n query = \"\"\"SELECT {cols} \n FROM temp4 \n INNER JOIN (SELECT * \n FROM DISPATCHINTERCONNECTORRES \n WHERE SETTLEMENTDATE == '{datetime}') \n USING (INTERCONNECTORID);\"\"\"\n query = query.format(datetime=date_time, id=id_columns, cols=return_columns)\n data = pd.read_sql_query(query, con=self.con)\n return data","def _link_data_and_events(self, ev, data, limit=None, timezone=None):\n if limit is not None:\n data = data.head(limit)\n\n date, time = ev[\"Date\"], ev[\"Time\"]\n\n for dfmt, tfmt in DATE_AND_TIME_FORMATS:\n try:\n ev[\"DateAndTime\"] = [pd.to_datetime(d + \" \" + t, format=dfmt)\n for d, t in zip(date, time)]\n data[\"DateAndTime\"] = [pd.to_datetime(ts, format=tfmt)\n for ts in data[\"Timestamp\"]]\n except ValueError:\n continue\n date_time_format = dfmt\n break\n\n if timezone is not None:\n ev.DateAndTime += timedelta(hours=timezone)\n\n self.log(\"[.] Data filtering...\")\n\n output = list()\n # group events with proper data\n for event in ev.values:\n ts = event[-1]\n one_min_before = ts - timedelta(minutes=1)\n five_min_ahead = ts + timedelta(minutes=5)\n data_in_range = data[(data.DateAndTime >= one_min_before) &\n (data.DateAndTime <= five_min_ahead)]\n\n for d in data_in_range.values:\n output.append(np.hstack((event[:-1], d)))\n\n if not output:\n return pd.DataFrame()\n\n cols = list(ev.columns[:-1]) + list(data.columns)\n dataframe = pd.DataFrame(output, columns=cols)\n\n self.log(\"[.] Timezones synchronization...\")\n\n # synchronize original Date and Time columns with DateAndTime\n if timezone is not None:\n dates = pd.Series(datetime.strptime(d + \" \" + t, date_time_format)\n + timedelta(hours=timezone)\n for d, t in zip(dataframe.Date, dataframe.Time))\n dataframe[\"DateUTC\"] = dates.map(methodcaller('date'))\n dataframe[\"TimeUTC\"] = dates.map(methodcaller('time'))\n\n return dataframe","def data_comparison(observations, records, record):\n for observation in observations:\n if observation != \"_id\":\n try:\n if re.search(observation, f\"{records[record]}\"):\n if not re.search(\n observations[observation], f\"{records[record]}\"\n ):\n records[record] = (\n f\"{records[record]}\"\n + \" --> \"\n + observations[observation]\n )\n except Exception as ex:\n Common.logger.warning(f\"Exception happened in data comparison {ex}\")\n return records","def join_daily_cweeds_wy2_and_wy3(wy2_df, wy3_df):\n assert wy2_df['CWEEDS Format'] == 'WY2'\n assert wy3_df['CWEEDS Format'] == 'WY3'\n assert wy2_df['Time Format'] == wy3_df['Time Format']\n\n time_wy23 = np.hstack([wy2_df['Time'], wy3_df['Time']])\n time_wy23 = np.unique(time_wy23)\n time_wy23 = np.sort(time_wy23)\n\n wy23_df = {}\n wy23_df['Time Format'] = wy3_df['Time Format']\n wy23_df['CWEEDS Format'] = 'WY2+WY3'\n\n # Copy the header info from WY3 dataset :\n\n for key in ['HORZ version', 'Location', 'Province', 'Country',\n 'Station ID', 'Latitude', 'Longitude', 'Time Zone',\n 'Elevation']:\n wy23_df[key] = wy3_df[key]\n\n # Merge the two datasets :\n\n wy23_df['Time'] = time_wy23\n wy23_df['Years'] = np.empty(len(time_wy23)).astype(int)\n wy23_df['Months'] = np.empty(len(time_wy23)).astype(int)\n wy23_df['Days'] = np.empty(len(time_wy23)).astype(int)\n wy23_df['Hours'] = np.empty(len(time_wy23)).astype(int)\n wy23_df['Irradiance'] = np.empty(len(time_wy23)).astype('float64')\n\n for dataset in [wy2_df, wy3_df]:\n indexes = np.digitize(dataset['Time'], time_wy23, right=True)\n for key in ['Years', 'Months', 'Days', 'Hours', 'Irradiance']:\n wy23_df[key][indexes] = dataset[key]\n\n return wy23_df","def run(self):\n\t\tdf_iter = self.file_to_df(50000)\n\t\tdf_airport = self.airport_file_to_df()\n\t\tfor df in df_iter: # type: pd.DataFrame\n\t\t\tdf.drop_duplicates(inplace=True)\n\t\t\tdf = self.transform(df, df_airport)\n\n\t\t\tdf_result = self.get_only_new_records(\n\t\t\t\tdf=df,\n\t\t\t\tdf_columns=self.join_columns,\n\t\t\t\ttable_columns=self.join_columns\n\t\t\t)\n\n\t\t\tif len(df_result) > 0:\n\t\t\t\t# df_result.drop(self.table_columns, axis=1)\n\n\t\t\t\tself.save(\n\t\t\t\t\tdf=df_result,\n\t\t\t\t\ttable_name=\"travel_dimension\",\n\t\t\t\t\tdf_columns=self.table_columns,\n\t\t\t\t\ttable_colums=self.table_columns\n\t\t\t\t)","def concat_and_sort(self):\n for link in self.to_concat:\n \n to_concat = self.to_concat[link]\n df = pd.concat(to_concat,axis=0)\n df=df.sort_values(by=['day','actualtime_arr_from'])\n for d in df['day'].unique():\n self.data[d][link] = {}\n temp = df[df['day']==d]\n \n for r in temp['routeid'].unique(): \n self.data[d][link][r] = temp[temp['routeid']==r][['actualtime_arr_from','actualtime_arr_to','routeid']].values \n del(temp)\n del(df)\n del(self.to_concat)","def period_limit_time_series(self, length, period, use_smalls=False):\n filtered = self._components[:]\n if use_smalls:\n filtered = filter(lambda c: c.period <= period, filtered)\n else:\n filtered = filter(lambda c: c.period >= period, filtered)\n \n maker = r.Recomposer(filtered, self.bias)\n return maker.time_series(length)","def create_query_df(self):\n\n # display output message for timeframe\n print(\n f'{Fore.GREEN}\\nQuerying database for tags between the timeframe: '\n f'{Fore.LIGHTGREEN_EX}{str(self._start)}{Fore.GREEN} and {Fore.LIGHTGREEN_EX}{str(self._end)}'\n f'{Style.RESET_ALL}')\n print(\n f'{Fore.GREEN}\\nTIMESPAN: '\n f'{Fore.LIGHTGREEN_EX}{self.time_span} hours'\n f'{Style.RESET_ALL}')\n\n engine = get_db_engine()\n offset = 0\n chunk_size = 100000\n\n dfs = []\n while True:\n sa_select = sa.select(\n [self.data_table],\n whereclause=sa.and_(\n self.data_table.c._TIMESTAMP > '{}'.format(self._start),\n self.data_table.c._TIMESTAMP <= '{}'.format(self._end)),\n limit=chunk_size,\n offset=offset,\n order_by=self.data_table.c._NUMERICID\n )\n dfs.append(pd.read_sql(sa_select, engine))\n offset += chunk_size\n if len(dfs[-1]) < chunk_size:\n break\n\n self.query_df = pd.concat(dfs)","def collect(self, revisions):\n nr_revisions = len(revisions)\n estimate = TimeEstimator(nr_revisions)\n for index, revision_number in enumerate(revisions):\n last_measurement = self.__get_last_measurement(revision_number)\n self.__write_measurement(last_measurement)\n self.__last_revision.set(revision_number)\n logging.info('Revision: %s, %s/%s, measurement date: %s, time remaining: %s', revision_number, index + 1,\n nr_revisions, self.__get_date(last_measurement), estimate.time_remaining(index))","def overlap(self, other):\r\n self.set_datetime()\r\n other.set_datetime()\r\n return (self.dt_1 - other.dt_0).total_seconds()","def _recompute(self):\n current_date = self.start_date\n self.quarterly_date_list = []\n self.daily_date_list = []\n while current_date <= self.end_date:\n current_quarter = get_quarter(current_date)\n current_year = current_date.year\n next_year, next_quarter = add_quarter(current_year, current_quarter)\n next_start_quarter_date = date(next_year, get_month(next_quarter),\n 1)\n\n days_till_next_quarter = (next_start_quarter_date -\n current_date).days\n days_till_end = (self.end_date - current_date).days\n if days_till_next_quarter <= days_till_end:\n current_start_quarter_date = date(current_year,\n get_month(current_quarter), 1)\n if current_start_quarter_date == current_date:\n self.quarterly_date_list.append(\n (current_year, current_quarter, lambda x: True))\n current_date = next_start_quarter_date\n elif days_till_next_quarter > self.balancing_point:\n self.quarterly_date_list.append(\n (current_year, current_quarter,\n lambda x: date(x['date_filed']) >= self.start_date))\n current_date = next_start_quarter_date\n else:\n while current_date < next_start_quarter_date:\n self.daily_date_list.append(current_date)\n current_date += timedelta(days=1)\n else:\n if days_till_end > self.balancing_point:\n if days_till_next_quarter - 1 == days_till_end:\n self.quarterly_date_list.append(\n (current_year, current_quarter, lambda x: True))\n current_date = next_start_quarter_date\n else:\n self.quarterly_date_list.append(\n (current_year, current_quarter,\n lambda x: date(x['date_filed']) <= self.end_date))\n current_date = self.end_date\n else:\n while current_date <= self.end_date:\n self.daily_date_list.append(current_date)\n current_date += timedelta(days=1)","def read_daily_qualified_report(self):\n from itertools import repeat\n\n self.ID_TOTAL_CANDIDATES = kpi_from_db_config.ID_TOTAL_CANDIDATES\n self.ID_TOTAL_PROCESSED = kpi_from_db_config.ID_TOTAL_PROCESSED\n self.ID_TOTAL_EXPORTED = kpi_from_db_config.ID_TOTAL_EXPORTED\n self.ID_TOTAL_CLASSIFIED = kpi_from_db_config.ID_TOTAL_CLASSIFIED\n self.ID_TOTAL_QUALIFIED = kpi_from_db_config.ID_TOTAL_QUALIFIED\n self.ID_TOTAL_DISQUALIFIED = kpi_from_db_config.ID_TOTAL_DISQUALIFIED\n\n list_id = [self.ID_TOTAL_CANDIDATES, \n self.ID_TOTAL_PROCESSED, \n self.ID_TOTAL_EXPORTED, \n self.ID_TOTAL_CLASSIFIED, \n self.ID_TOTAL_QUALIFIED, \n self.ID_TOTAL_DISQUALIFIED]\n list_result = [[] for i in repeat(None,len(list_id))]\n\n for i in range(len(list_id)):\n self.cursor.execute('''\n SELECT value\n FROM public.kpi_report\n WHERE id = %s\n ORDER BY created_at DESC\n LIMIT 2\n ''', [list_id[i]])\n\n rows_count = self.cursor.rowcount\n if (rows_count == 2):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n elif (rows_count == 1):\n for doc in self.cursor:\n list_result[i].append(int(doc[0]))\n list_result[i] = list_result[i] + [0]\n else:\n list_result[i] = [0] * 2 \n\n# print \"TESTING .... {}\".format(list_result)\n return list_result","def return_trade_history(\n self,\n start: Timestamp,\n end: Timestamp,\n ) -> list[dict[str, Any]]:\n limit = 100\n data: list[dict[str, Any]] = []\n start_ms = start * 1000\n end_ms = end * 1000\n while True:\n new_data = self.api_query_list('/trades', {\n 'startTime': start_ms,\n 'endTime': end_ms,\n 'limit': limit,\n })\n results_length = len(new_data)\n if data == [] and results_length < limit:\n return new_data # simple case - only one query needed\n\n latest_ts_ms = start_ms\n # add results to data and prepare for next query\n existing_ids = {x['id'] for x in data}\n for trade in new_data:\n try:\n timestamp_ms = trade['createTime']\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\n # since we query again from last ts seen make sure no duplicates make it in\n if trade['id'] not in existing_ids:\n data.append(trade)\n except (DeserializationError, KeyError) as e:\n msg = str(e)\n if isinstance(e, KeyError):\n msg = f'Missing key entry for {msg}.'\n self.msg_aggregator.add_warning(\n 'Error deserializing a poloniex trade. Check the logs for details',\n )\n log.error(\n 'Error deserializing poloniex trade',\n trade=trade,\n error=msg,\n )\n continue\n\n if results_length < limit:\n break # last query has less than limit. We are done.\n\n # otherwise we query again from the last ts seen in the last result\n start_ms = latest_ts_ms\n continue\n\n return data","def fill_price_gaps(\n from_date=dt.datetime(1970,1,1),\n to_date=dt.datetime.today().replace(hour=0, minute=0, second=0, microsecond=0)\n ):\n #Create a collection of years\n years = []\n cur_year = from_date.year\n while cur_year <= to_date.year:\n years.append(cur_year)\n cur_year += 1\n #Loop each year\n all_year_dates = pd.DataFrame([])\n for year in tqdm(years, total=len(years), desc=\"Loop through years to find dates\"):\n #establish bounding dates\n year_from_date = None if year != from_date.year else from_date\n year_to_date = None if year != to_date.year else to_date\n #Get filtered year dates\n year_dates = create_filtered_year_dates(year, from_date=year_from_date, to_date=year_to_date, )\n #Add to the full list\n all_year_dates = pd.concat([all_year_dates, year_dates])\n #Order the dates (just in case)\n all_year_dates = all_year_dates.sort_values([\"date\"]) \\\n .reset_index(drop=True)\n #Fetch all the tickers\n tickers = sqlaq_to_df(ticker.fetch())\n #Loop through tickers\n errors = []\n run_time = ProcessTime()\n for _,r in tqdm(tickers[[\"id\",\"ticker\"]].iterrows(), total=tickers.shape[0], desc=\"Filling in gaps\"):\n logger.info(f\"Filling gaps in {r.id} -> {r.ticker}\")\n try:\n #Fetch all prices\n dp = sqlaq_to_df(daily_price.fetch(ticker_ids=[r.id]))\n dp[\"date\"] = dp.date.astype(\"datetime64[ns]\")\n #Identify missing dates\n missing_dates = pd.merge(all_year_dates, dp[[\"date\",\"id\"]], on=[\"date\"], how=\"left\")\n #Identify the start date and remove all missing date before that\n start_date = missing_dates[~missing_dates.id.isnull()].date.min()\n missing_dates = missing_dates[missing_dates.date > start_date]\n #Remove all other items which have dates\n missing_dates = missing_dates[missing_dates.id.isnull()]\n #Order remaining dates\n missing_dates = missing_dates.sort_values(\"date\")\n #Create groupings no larger than max_days (in config)\n st_d = None\n date_groups = []\n missing_dates = missing_dates.date.to_list()\n if len(missing_dates):\n for i,d in enumerate(missing_dates):\n if not st_d:\n st_d = d\n else:\n #Append when group gets too big\n if (d - st_d).days > WEB_SCRAPE_MAX_DAYS:\n date_groups.append([st_d, missing_dates[i-1]])\n #Update the start date\n st_d = d\n #Append the last item\n date_groups.append([st_d, d])\n #Scrape the missing prices\n logger.info('Number of webscrapes to perform -> {}'.format(len(date_groups)))\n #For each time frame perform a scrape\n try: #Try loop so as not to miss all following date groups\n for i,dates in enumerate(date_groups):\n logger.info(f\"Running dates {i} -> {dt.datetime.strptime(str(dates[0])[:10], '%Y-%m-%d')} - {dt.datetime.strptime(str(dates[1])[:10], '%Y-%m-%d')}\")\n process_daily_prices(\n r.ticker,\n r.id,\n st_date=dates[0],\n en_date=dates[1],\n \n )\n except Exception as e:\n logger.error(e)\n errors.append({'ticker_id':r.id, 'ticker':r.ticker, \"error\":e, \"st_date\":dates[0], \"en_dates\":dates[1]})\n #Run an update on th weekly prices\n process_weekly_prices(\n r.id,\n \n )\n except Exception as e:\n logger.error(e)\n errors.append({'ticker_id':r.id, 'ticker':r.ticker, \"error\":e})\n #Lap\n logger.info(run_time.lap())\n logger.info(run_time.show_latest_lap_time(show_time=True))\n logger.info(f\"GAP FILL RUN TIME - {run_time.end()}\")\n\n logger.info(f'\\nGAP FILL ERROR COUNT -> {len(errors)}')\n if len(errors) > 0:\n logger.info('GAP FILL ERRORS ->')\n for e in errors:\n logger.error(e)","def set_period_starters(self, missing_period_starters=MISSING_PERIOD_STARTERS):\n for period in self.Periods:\n period.Starters = {self.HomeTeamId: [], self.VisitorTeamId: []}\n subbed_in_players = {self.HomeTeamId: [], self.VisitorTeamId: []}\n for pbp_event in period.Events:\n player_id = pbp_event.player_id\n if player_id in self.Players[self.HomeTeamId]:\n team_id = self.HomeTeamId\n elif player_id in self.Players[self.VisitorTeamId]:\n team_id = self.VisitorTeamId\n else:\n team_id = None\n\n if team_id is not None and team_id != '0' and player_id != '0':\n player2_id = pbp_event.player2_id\n player3_id = pbp_event.player3_id\n if pbp_event.is_substitution():\n # player_id is player going out, player2_id is playing coming in\n if player2_id not in period.Starters[team_id] and player2_id not in subbed_in_players[team_id]:\n subbed_in_players[team_id].append(player2_id)\n if player_id not in period.Starters[team_id] and player_id not in subbed_in_players[team_id]:\n if player_id in self.Players[self.HomeTeamId] or player_id in self.Players[self.VisitorTeamId]:\n period.Starters[team_id].append(player_id)\n if player_id != '0':\n # player_id 0 is team\n if player_id not in period.Starters[team_id] and player_id not in subbed_in_players[team_id]:\n if player_id in self.Players[self.HomeTeamId] or player_id in self.Players[self.VisitorTeamId]:\n if not (\n pbp_event.is_technical_foul() or\n pbp_event.is_double_technical_foul() or\n pbp_event.is_ejection() or\n (pbp_event.is_technical_ft() and pbp_event.clock_time == '12:00') # ignore technical fts at start of period\n ):\n # ignore all techs because a player could get a technical foul when they aren't in the game\n period.Starters[team_id].append(player_id)\n # need player2_id for players who play full period and never appear in an event as player_id - ex assists\n if (player2_id in self.Players[self.HomeTeamId] or player2_id in self.Players[self.VisitorTeamId]) and not pbp_event.is_substitution():\n if not (\n pbp_event.is_technical_foul() or\n pbp_event.is_double_technical_foul() or\n pbp_event.is_ejection()\n ):\n # ignore all techs because a player could get a technical foul when they aren't in the game\n if player2_id in self.Players[self.HomeTeamId]:\n player2_team_id = self.HomeTeamId\n if player2_id in self.Players[self.VisitorTeamId]:\n player2_team_id = self.VisitorTeamId\n if player2_id not in period.Starters[player2_team_id] and player2_id not in subbed_in_players[player2_team_id]:\n period.Starters[player2_team_id].append(player2_id)\n if (player3_id in self.Players[self.HomeTeamId] or player3_id in self.Players[self.VisitorTeamId]) and not pbp_event.is_substitution():\n if not (\n pbp_event.is_technical_foul() or\n pbp_event.is_double_technical_foul() or\n pbp_event.is_ejection()\n ):\n # ignore all techs because a player could get a technical foul when they aren't in the game\n if player3_id in self.Players[self.HomeTeamId]:\n player3_team_id = self.HomeTeamId\n if player3_id in self.Players[self.VisitorTeamId]:\n player3_team_id = self.VisitorTeamId\n if player3_id not in period.Starters[player3_team_id] and player3_id not in subbed_in_players[player3_team_id]:\n period.Starters[player3_team_id].append(player3_id)\n\n if self.GameId in missing_period_starters.keys() and str(period.Number) in missing_period_starters[self.GameId].keys():\n for team_id in missing_period_starters[self.GameId][str(period.Number)].keys():\n period.Starters[team_id] = missing_period_starters[self.GameId][str(period.Number)][team_id]\n\n for team_id in period.Starters.keys():\n if len(period.Starters[team_id]) != 5:\n raise InvalidNumberOfStartersException(f\"GameId: {self.GameId}, Period: {period}, TeamId: {team_id}, Players: {period.Starters[team_id]}\")","def _get_time_periods(current_time_period=None):\n sorted_time_periods = list(SORTED_TIME_PERIODS)\n if current_time_period and current_time_period in sorted_time_periods:\n # make the current time period the first in the list so that it shows up\n # as selected in project choice drop down\n for time_period in sorted_time_periods:\n if time_period == current_time_period:\n sorted_time_periods.remove(time_period)\n sorted_time_periods.insert(0, time_period)\n break\n\n return sorted_time_periods","def _summarize_period(self):\n print(\"entering _summarize_period()\")\n for i in range(TIME_BETWEEN_FEEDBACK // SONG_OVER_CHECK_TIME): ### Wait 30 seconds\n self._check_completion()\n if self._song_over is True:\n break\n self._set_last_30_sec()\n return","def spending_over_time_test_data():\n for i in range(30):\n # Define some values that are calculated and used multiple times\n transaction_id = i\n award_id = i + 1000\n awarding_agency_id = i + 2000\n toptier_awarding_agency_id = i + 3000\n subtier_awarding_agency_id = i + 4000\n funding_agency_id = i + 5000\n toptier_funding_agency_id = i + 6000\n subtier_funding_agency_id = i + 7000\n federal_action_obligation = i + 8000\n total_obligation = i + 9000\n federal_account_id = i + 10000\n treasury_account_id = i + 11000\n\n action_date = f\"20{i % 10 + 10}-{i % 9 + 1}-{i % 28 + 1}\"\n action_date_obj = datetime.datetime.strptime(action_date, \"%Y-%m-%d\")\n fiscal_month = generate_fiscal_month(action_date_obj)\n fiscal_year = generate_fiscal_year(action_date_obj)\n fiscal_action_date = f\"{fiscal_year}-{fiscal_month}-{i % 28 + 1}\"\n contract_award_type = [\"A\", \"B\", \"C\", \"D\"][i % 4]\n grant_award_type = [\"02\", \"03\", \"04\", \"05\"][i % 4]\n is_fpds = i % 2 == 0\n\n # Award\n baker.make(\n \"search.AwardSearch\",\n award_id=award_id,\n fain=f\"fain_{transaction_id}\" if not is_fpds else None,\n is_fpds=is_fpds,\n latest_transaction_id=transaction_id,\n piid=f\"piid_{transaction_id}\" if is_fpds else None,\n total_obligation=total_obligation,\n type=contract_award_type if is_fpds else grant_award_type,\n action_date=\"2020-01-01\",\n )\n\n # Federal, Treasury, and Financial Accounts\n baker.make(\n \"accounts.FederalAccount\",\n id=federal_account_id,\n parent_toptier_agency_id=toptier_awarding_agency_id,\n account_title=f\"federal_account_title_{transaction_id}\",\n federal_account_code=f\"federal_account_code_{transaction_id}\",\n )\n baker.make(\n \"accounts.TreasuryAppropriationAccount\",\n agency_id=f\"taa_aid_{transaction_id}\",\n allocation_transfer_agency_id=f\"taa_ata_{transaction_id}\",\n availability_type_code=f\"taa_a_{transaction_id}\",\n beginning_period_of_availability=f\"taa_bpoa_{transaction_id}\",\n ending_period_of_availability=f\"taa_epoa_{transaction_id}\",\n federal_account_id=federal_account_id,\n main_account_code=f\"taa_main_{transaction_id}\",\n sub_account_code=f\"taa_sub_{transaction_id}\",\n treasury_account_identifier=treasury_account_id,\n )\n tas_components = [\n f\"aid=taa_aid_{transaction_id}\"\n f\"main=taa_main_{transaction_id}\"\n f\"ata=taa_ata_{transaction_id}\"\n f\"sub=taa_sub_{transaction_id}\"\n f\"bpoa=taa_bpoa_{transaction_id}\"\n f\"epoa=taa_epoa_{transaction_id}\"\n f\"a=taa_a_{transaction_id}\"\n ]\n baker.make(\"awards.FinancialAccountsByAwards\", award_id=award_id, treasury_account_id=treasury_account_id)\n\n # Awarding Agency\n baker.make(\n \"references.Agency\",\n id=awarding_agency_id,\n subtier_agency_id=subtier_awarding_agency_id,\n toptier_agency_id=toptier_awarding_agency_id,\n )\n baker.make(\n \"references.ToptierAgency\",\n abbreviation=f\"toptier_awarding_agency_abbreviation_{transaction_id}\",\n name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n toptier_agency_id=toptier_awarding_agency_id,\n toptier_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n )\n baker.make(\n \"references.SubtierAgency\",\n abbreviation=f\"subtier_awarding_agency_abbreviation_{transaction_id}\",\n name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n subtier_agency_id=subtier_awarding_agency_id,\n subtier_code=f\"subtier_awarding_agency_code_{transaction_id}\",\n )\n\n # Funding Agency\n baker.make(\n \"references.Agency\",\n id=funding_agency_id,\n subtier_agency_id=subtier_funding_agency_id,\n toptier_agency_id=toptier_funding_agency_id,\n )\n baker.make(\n \"references.ToptierAgency\",\n abbreviation=f\"toptier_funding_agency_abbreviation_{transaction_id}\",\n name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n toptier_agency_id=toptier_funding_agency_id,\n toptier_code=f\"toptier_funding_agency_code_{transaction_id}\",\n )\n baker.make(\n \"references.SubtierAgency\",\n abbreviation=f\"subtier_funding_agency_abbreviation_{transaction_id}\",\n name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n subtier_agency_id=subtier_funding_agency_id,\n subtier_code=f\"subtier_funding_agency_code_{transaction_id}\",\n )\n\n # Ref Country Code\n baker.make(\"references.RefCountryCode\", country_code=\"USA\", country_name=\"UNITED STATES\")\n\n # FPDS / FABS\n if is_fpds:\n baker.make(\n \"search.TransactionSearch\",\n transaction_id=transaction_id,\n is_fpds=is_fpds,\n action_date=action_date,\n fiscal_year=fiscal_year,\n fiscal_action_date=fiscal_action_date,\n award_id=award_id,\n awarding_agency_id=awarding_agency_id,\n business_categories=[f\"business_category_1_{transaction_id}\", f\"business_category_2_{transaction_id}\"],\n transaction_description=f\"This is a test description {transaction_id}\"\n if transaction_id % 2 == 0\n else None,\n federal_action_obligation=federal_action_obligation,\n generated_pragmatic_obligation=federal_action_obligation,\n award_amount=total_obligation,\n funding_agency_id=funding_agency_id,\n type=contract_award_type if is_fpds else grant_award_type,\n awarding_agency_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n awarding_toptier_agency_name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n awarding_toptier_agency_abbreviation=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n funding_agency_code=f\"toptier_funding_agency_code_{transaction_id}\",\n funding_toptier_agency_name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n funding_toptier_agency_abbreviation=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n awarding_sub_tier_agency_c=f\"subtier_awarding_agency_code_{transaction_id}\",\n awarding_subtier_agency_name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n funding_sub_tier_agency_co=f\"subtier_funding_agency_code_{transaction_id}\",\n funding_subtier_agency_name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n funding_subtier_agency_abbreviation=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n recipient_name=f\"recipient_name_{transaction_id}\",\n recipient_unique_id=f\"{transaction_id:09d}\",\n recipient_hash=\"c687823d-10af-701b-1bad-650c6e680190\" if transaction_id == 21 else None,\n recipient_levels=[\"R\"] if i == 21 else [],\n extent_competed=f\"extent_competed_{transaction_id}\",\n recipient_location_country_code=\"USA\",\n recipient_location_country_name=\"USA\",\n recipient_location_state_code=f\"LE_STATE_CODE_{transaction_id}\",\n recipient_location_county_code=f\"{transaction_id:03d}\",\n recipient_location_county_name=f\"LE_COUNTY_NAME_{transaction_id}\",\n recipient_location_congressional_code=f\"{transaction_id:02d}\",\n recipient_location_zip5=f\"LE_ZIP5_{transaction_id}\",\n recipient_location_city_name=f\"LE_CITY_NAME_{transaction_id}\",\n naics_code=f\"{transaction_id}{transaction_id}\",\n naics_description=f\"naics_description_{transaction_id}\",\n piid=f\"piid_{transaction_id}\",\n pop_country_code=\"USA\",\n pop_country_name=\"UNITED STATES\",\n pop_state_code=f\"POP_STATE_CODE_{transaction_id}\",\n pop_county_code=f\"{transaction_id:03d}\",\n pop_county_name=f\"POP_COUNTY_NAME_{transaction_id}\",\n pop_zip5=f\"POP_ZIP5_{transaction_id}\",\n pop_congressional_code=f\"{transaction_id:02d}\",\n pop_city_name=f\"POP_CITY_NAME_{transaction_id}\",\n product_or_service_code=str(transaction_id).zfill(4),\n product_or_service_description=f\"psc_description_{transaction_id}\",\n type_of_contract_pricing=f\"type_of_contract_pricing_{transaction_id}\",\n type_set_aside=f\"type_set_aside_{transaction_id}\",\n tas_components=tas_components,\n )\n baker.make(\n \"references.NAICS\",\n code=f\"{transaction_id}\",\n description=f\"naics_description_{transaction_id}\",\n )\n baker.make(\n \"references.PSC\", code=str(transaction_id).zfill(4), description=f\"psc_description_{transaction_id}\"\n )\n else:\n baker.make(\n \"search.TransactionSearch\",\n transaction_id=transaction_id,\n is_fpds=is_fpds,\n action_date=action_date,\n fiscal_year=fiscal_year,\n fiscal_action_date=fiscal_action_date,\n award_id=award_id,\n awarding_agency_id=awarding_agency_id,\n business_categories=[f\"business_category_1_{transaction_id}\", f\"business_category_2_{transaction_id}\"],\n transaction_description=f\"This is a test description {transaction_id}\"\n if transaction_id % 2 == 0\n else None,\n federal_action_obligation=federal_action_obligation,\n generated_pragmatic_obligation=federal_action_obligation,\n award_amount=total_obligation,\n funding_agency_id=funding_agency_id,\n type=contract_award_type if is_fpds else grant_award_type,\n awarding_agency_code=f\"toptier_awarding_agency_code_{transaction_id}\",\n awarding_toptier_agency_name=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n awarding_toptier_agency_abbreviation=f\"toptier_awarding_agency_agency_name_{transaction_id}\",\n funding_agency_code=f\"toptier_funding_agency_code_{transaction_id}\",\n funding_toptier_agency_name=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n funding_toptier_agency_abbreviation=f\"toptier_funding_agency_agency_name_{transaction_id}\",\n awarding_sub_tier_agency_c=f\"subtier_awarding_agency_code_{transaction_id}\",\n awarding_subtier_agency_name=f\"subtier_awarding_agency_agency_name_{transaction_id}\",\n funding_sub_tier_agency_co=f\"subtier_funding_agency_code_{transaction_id}\",\n funding_subtier_agency_name=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n funding_subtier_agency_abbreviation=f\"subtier_funding_agency_agency_name_{transaction_id}\",\n recipient_name=f\"recipient_name_{transaction_id}\",\n recipient_unique_id=f\"{transaction_id:09d}\",\n recipient_hash=\"c687823d-10af-701b-1bad-650c6e680190\" if transaction_id == 21 else None,\n recipient_levels=[\"R\"] if i == 21 else [],\n cfda_number=f\"cfda_number_{transaction_id}\",\n fain=f\"fain_{transaction_id}\",\n recipient_location_country_code=\"USA\",\n recipient_location_country_name=\"USA\",\n recipient_location_state_code=f\"LE_STATE_CODE_{transaction_id}\",\n recipient_location_county_code=f\"{transaction_id:03d}\",\n recipient_location_county_name=f\"LE_COUNTY_NAME_{transaction_id}\",\n recipient_location_congressional_code=f\"{transaction_id:02d}\",\n recipient_location_zip5=f\"LE_ZIP5_{transaction_id}\",\n recipient_location_city_name=f\"LE_CITY_NAME_{transaction_id}\",\n pop_country_code=\"USA\",\n pop_country_name=\"UNITED STATES\",\n pop_state_code=f\"POP_STATE_CODE_{transaction_id}\",\n pop_county_code=f\"{transaction_id:03d}\",\n pop_county_name=f\"POP_COUNTY_NAME_{transaction_id}\",\n pop_zip5=f\"POP_ZIP5_{transaction_id}\",\n pop_congressional_code=f\"{transaction_id:02d}\",\n pop_city_name=f\"POP_CITY_NAME{transaction_id}\",\n tas_components=tas_components,\n )","def print_alerts_stats(now, stats_period_seconds, alerts_df):\n\n if not alerts_df.empty:\n active_alerts = alerts_df.loc[alerts_df[\"date_end\"].isnull()]\n archived_alerts = alerts_df.loc[alerts_df[\"date_end\"].notnull()]\n new_alerts = active_alerts.loc[active_alerts[\"date_start\"] > now - stats_period_seconds]\n recovered_alerts = archived_alerts.loc[archived_alerts[\"date_end\"] > now - stats_period_seconds ]\n\n if len(new_alerts) > 0:\n print(f'### New alerts during the last {stats_period_seconds} seconds###')\n for index, row in new_alerts.iterrows():\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\n print(f\" - {row['type']} generated an alert - {row['value_start']}, triggered at {date_time_start}\")\n\n if len(recovered_alerts) > 0:\n print(f'### Recovered alerts during the last {stats_period_seconds} seconds###')\n for index, row in recovered_alerts.iterrows():\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\n date_time_end = datetime.datetime.fromtimestamp(row['date_end']).strftime('%Y-%m-%d %H:%M:%S')\n print(f\" - {row['type']} triggered at {date_time_start} recovered at {date_time_end} - was {row['value_start']}, now {row['value_end']}\")\n\n if len(active_alerts) > 0:\n print(f'### Active alerts ###')\n for index, row in active_alerts.iterrows():\n date_time = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\n print(f\" - Since {date_time} : {row['type']}\")\n\n if len(archived_alerts) > 0:\n print(f'### Past alerts ###')\n for index, row in archived_alerts.iterrows():\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\n date_time_end = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\n print(f\" - {date_time_start} to {date_time_end} : {row['type']}\")\n else:\n print(f'### No alert ###')","def get_aggregate_periods(all_periods, all_samples, write_to_csv=True):\n null_idx = all_periods['activation_date'].isna()\n all_periods['activation_date'][null_idx] = all_periods['date_from'][null_idx]\n assert (all_periods['date_to'] >= all_periods['date_from']).all()\n # we can do this below since they're all in 2017 and this way is faster\n all_periods['days_to_publish'] = all_periods['date_from'].dt.dayofyear - \\\n all_periods['activation_date'].dt.dayofyear\n all_periods['days_online'] = all_periods['date_to'].dt.dayofyear - \\\n all_periods['date_from'].dt.dayofyear\n for col in ['activation_date', 'date_from', 'date_to']:\n all_periods = featurize_date_col(all_periods, col)\n\n grouped = all_periods.groupby('item_id')\n base = grouped[['item_id']].count().rename(columns={'item_id': 'nlisted'})\n base['sum_days_online'] = grouped[['days_online']].sum()\n base['mean_days_online'] = grouped[['days_online']].mean()\n base['last_days_online'] = grouped[['days_online']].last()\n base['sum_days_to_publish'] = grouped[['days_to_publish']].sum()\n base['mean_days_to_publish'] = grouped[['days_to_publish']].mean()\n base['median_date_to_isholiday'] = grouped[['date_to_isholiday']].median()\n base['median_date_to_wday'] = grouped[['date_to_wday']].median()\n base['median_date_to_yday'] = grouped[['date_to_yday']].median()\n\n base['start_date'] = grouped[['date_from']].min()\n base['end_date'] = grouped[['date_to']].max()\n for col in ['start_date', 'end_date']:\n base = featurize_date_col(base, col, remove_when_done=True)\n if 'item_id' not in all_periods:\n all_periods = all_periods.reset_index()\n if 'item_id' not in base:\n base = base.reset_index()\n all_periods = all_periods.drop_duplicates(['item_id'])\n all_periods = all_periods.merge(base, on='item_id', how='left')\n all_periods = all_periods.merge(all_samples, on='item_id', how='left')\n avg_per_user_periods = all_periods.drop(\n ['item_id', 'activation_date', 'date_from', 'date_to'], axis=1\n ).groupby('user_id').mean()\n avg_per_user_periods['nitems'] = all_periods[\n ['user_id', 'item_id']].groupby('user_id').count().reset_index()['item_id']\n if write_to_csv:\n avg_per_user_periods.to_csv('data/periods_aggregate_features.csv')\n return avg_per_user_periods","def _fill_moment_results(self):\n toprocess = [('stock_tom', self.c_stock, 2),\n ('stock_woody', self.c_stock, 3),\n ('stock_non_woody', self.c_stock, 4),\n ('stock_acid', self.c_stock, 5),\n ('stock_water', self.c_stock, 6),\n ('stock_ethanol', self.c_stock, 7),\n ('stock_non_soluble', self.c_stock, 8),\n ('stock_humus', self.c_stock, 9),\n ('change_tom', self.c_change, 2),\n ('change_woody', self.c_change, 3),\n ('change_non_woody', self.c_change, 4),\n ('change_acid', self.c_change, 5),\n ('change_water', self.c_change, 6),\n ('change_ethanol', self.c_change, 7),\n ('change_non_soluble', self.c_change, 8),\n ('change_humus', self.c_change, 9),\n ('co2', self.co2_yield, 2)]\n for (resto, dataarr, dataind) in toprocess:\n # filter time steps\n ts = numpy.unique(dataarr[:,1])\n # extract data for the timestep\n for timestep in ts:\n ind = numpy.where(dataarr[:,1]==timestep)\n mean = stats.mean(dataarr[ind[0], dataind])\n mode_res = stats.mode(dataarr[ind[0], dataind])\n mode = mode_res[0]\n var = stats.var(dataarr[ind[0], dataind])\n skew = stats.skew(dataarr[ind[0], dataind])\n kurtosis = stats.kurtosis(dataarr[ind[0], dataind])\n if var>0.0:\n sd2 = 2 * math.sqrt(var)\n else:\n sd2 = var\n res = [[timestep, mean, mode[0], var, skew, kurtosis,\n mean - sd2, mean + sd2]]\n if resto=='stock_tom':\n self.md.stock_tom = numpy.append(self.md.stock_tom,\n res, axis=0)\n elif resto=='stock_woody':\n self.md.stock_woody = numpy.append(self.md.stock_woody,\n res, axis=0)\n elif resto=='stock_non_woody':\n self.md.stock_non_woody = numpy.append(\\\n self.md.stock_non_woody, res, axis=0)\n elif resto=='stock_acid':\n self.md.stock_acid = numpy.append(self.md.stock_acid,\n res, axis=0)\n elif resto=='stock_water':\n self.md.stock_water = numpy.append(self.md.stock_water,\n res, axis=0)\n elif resto=='stock_ethanol':\n self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,\n res, axis=0)\n elif resto=='stock_non_soluble':\n self.md.stock_non_soluble= numpy.append(\n self.md.stock_non_soluble, res, axis=0)\n elif resto=='stock_humus':\n self.md.stock_humus = numpy.append(self.md.stock_humus,\n res, axis=0)\n elif resto=='change_tom':\n self.md.change_tom = numpy.append(self.md.change_tom,\n res, axis=0)\n elif resto=='change_woody':\n self.md.change_woody = numpy.append(self.md.change_woody,\n res, axis=0)\n elif resto=='change_non_woody':\n self.md.change_non_woody = numpy.append(\\\n self.md.change_non_woody, res, axis=0)\n elif resto=='change_acid':\n self.md.change_acid = numpy.append(self.md.change_acid,\n res, axis=0)\n elif resto=='change_water':\n self.md.change_water = numpy.append(self.md.change_water,\n res, axis=0)\n elif resto=='change_ethanol':\n self.md.change_ethanol = numpy.append(\n self.md.change_ethanol, res, axis=0)\n elif resto=='change_non_soluble':\n self.md.change_non_soluble=numpy.append(\n self.md.change_non_soluble, res, axis=0)\n elif resto=='change_humus':\n self.md.change_humus = numpy.append(self.md.change_humus,\n res, axis=0)\n elif resto=='co2':\n self.md.co2 = numpy.append(self.md.co2, res, axis=0)","def pair_records():\r\n\r\n study_list = retrieve_ref('study_list')\r\n sensor_list = retrieve_ref('sensor_list')\r\n\r\n # check each study\r\n for study in study_list:\r\n\r\n df_meta = retrieve_meta(study)\r\n recordNames = list(df_meta['recordName'])\r\n\r\n # create column to list wearableName and coregister records\r\n df_meta = add_wearableName(df_meta)\r\n df_meta['coregisterRecords'] = recordNames\r\n\r\n # look for paired records using the unix time stamp for when the record begins\r\n for recordA in recordNames:\r\n\r\n i = df_meta[ df_meta['recordName']== recordA].index.values[0]\r\n recordBeginA = df_meta.loc[i, 'recordBegin' ]\r\n wearableA = df_meta.loc[i, 'wearableName' ]\r\n\r\n for recordB in recordNames:\r\n\r\n j = df_meta[ df_meta['recordName']== recordB].index.values[0]\r\n recordBeginB = df_meta.loc[j, 'recordBegin' ]\r\n wearableB = df_meta.loc[j, 'wearableName' ]\r\n\r\n if abs(recordBeginA - recordBeginB) < 300:\r\n\r\n if recordA != recordB:\r\n\r\n if wearableA != wearableB:\r\n\r\n print('coregister record found for ' + recordA + ' + ' + recordB)\r\n coregisterList = str(recordA + ' ' + recordB)\r\n df_meta.loc[i, 'coregisterRecords' ] = coregisterList\r\n\r\n save_meta(study, df_meta)"],"string":"[\n \"def __add__ ( self, other, resample_opts=None ):\\n result = ObservationStorage (datadir=self.datadir, \\\\\\n resample_opts=resample_opts )\\n if self.date[0] > other.date[0]:\\n start_date = other.date[0]\\n else:\\n start_date = self.date[0]\\n if self.date[-1] > other.date[-1]:\\n end_date = other.date[-1]\\n else:\\n end_date = self.date[-1]\\n \\n delta = datetime.timedelta ( days=1 )\\n this_date = start_date.date()\\n end_date = end_date.date() + delta\\n \\n this_obs_dates = [ x.date() for x in self.date ]\\n other_obs_dates = [ x.date() for x in other.date ]\\n \\n date = [] ; vza = [] ; vaa = [] ; sza = [] ; saa = []\\n emulator = [] ; mask = [] ; data_pntr = [] ; spectral = []\\n sensor = []\\n \\n while this_date < end_date:\\n if this_date in this_obs_dates:\\n iloc = this_obs_dates.index ( this_date )\\n date.append ( self.date[iloc] )\\n emulator.append ( self.emulator[iloc] )\\n vza.append ( self.vza[iloc] )\\n sza.append ( self.sza[iloc] )\\n vaa.append ( self.vaa[iloc] )\\n saa.append ( self.saa[iloc] )\\n spectral.append ( self.spectral )\\n mask.append ( ( self.get_mask, [iloc] ) )\\n sensor.append ( self.sensor )\\n \\n data_pntr.append ( self._data_pntr[iloc] )\\n if this_date in other_obs_dates:\\n iloc = other_obs_dates.index ( this_date )\\n date.append ( other.date[iloc] )\\n emulator.append ( other.emulator[iloc] )\\n vza.append ( other.vza[iloc] )\\n sza.append ( other.sza[iloc] )\\n vaa.append ( other.vaa[iloc] )\\n saa.append ( other.saa[iloc] )\\n spectral.append ( other.spectral )\\n mask.append ( ( other.get_mask, [iloc] ) )\\n sensor.append ( other.sensor )\\n data_pntr.append ( other._data_pntr[iloc] )\\n this_date += delta\\n result.vza = vza\\n result.vaa = vaa\\n result.sza = sza \\n result.saa = saa \\n result.date = date\\n result.spectral = spectral\\n result.masks = mask\\n result.sensor = sensor\\n result.emulator = emulator\\n result._data_pntr = data_pntr\\n return result\",\n \"def merge_new(dfc, pairs, span=None):\\n global last_update\\n t1 = Timer()\\n columns = ['open', 'close', 'trades', 'volume', 'buy_ratio']\\n exclude = ['_id','high','low','quote_vol','sell_vol', 'close_time']\\n projection = dict(zip(exclude, [False]*len(exclude)))\\n idx, data = [], []\\n db = app.get_db()\\n\\n if span is None and last_update:\\n # If no span, query/merge db records inserted since last update.\\n oid = ObjectId.from_datetime(last_update)\\n last_update = now()\\n _filter = {'_id':{'$gte':oid}}\\n else:\\n # Else query/merge all since timespan.\\n span = span if span else timedelta(days=7)\\n last_update = now()\\n _filter = {'pair':{'$in':pairs}, 'close_time':{'$gte':now()-span}}\\n\\n batches = db.candles.find_raw_batches(_filter, projection)\\n\\n if batches.count() < 1:\\n return dfc\\n\\n try:\\n ndarray = bsonnumpy.sequence_to_ndarray(\\n batches,\\n dtype,\\n db.candles.count()\\n )\\n except Exception as e:\\n log.error(str(e))\\n return dfc\\n #raise\\n\\n df = pd.DataFrame(ndarray)\\n df['open_time'] = pd.to_datetime(df['open_time'], unit='ms')\\n df['freq'] = df['freq'].str.decode('utf-8')\\n df['pair'] = df['pair'].str.decode('utf-8')\\n\\n df['freq'] = df['freq'].replace('1m',60)\\n df['freq'] = df['freq'].replace('5m',300)\\n df['freq'] = df['freq'].replace('1h',3600)\\n df['freq'] = df['freq'].replace('1d',86400)\\n df = df.sort_values(by=['pair','freq','open_time'])\\n\\n df2 = pd.DataFrame(df[columns].values,\\n index = pd.MultiIndex.from_arrays(\\n [df['pair'], df['freq'], df['open_time']],\\n names = ['pair','freq','open_time']),\\n columns = columns\\n ).sort_index()\\n\\n df3 = pd.concat([dfc, df2]).drop_duplicates().sort_index()\\n\\n log.debug(\\\"{:,} records loaded into numpy. [{:,.1f} ms]\\\".format(\\n len(df3), t1))\\n #print(\\\"Done in %s ms\\\" % t1)\\n return df3\",\n \"def _fill_results(self,spec,measurements,period,duration):\\r\\n logging.info(\\\"Fill measurements for spec {0}\\\".format(spec))\\r\\n \\r\\n if self._verb==mplane.model.VERB_QUERY:\\r\\n \\\"\\\"\\\"\\r\\n Query according to the time specified in the specification\\r\\n \\\"\\\"\\\"\\r\\n (first_time,last_time) = spec.when().datetimes()\\r\\n first_time=int(first_time.replace(tzinfo=datetime.timezone.utc).timestamp())\\r\\n last_time=int(last_time.replace(tzinfo=datetime.timezone.utc).timestamp())\\r\\n sleep_time = 0\\r\\n else:\\r\\n \\\"\\\"\\\"\\r\\n Query from NOW\\r\\n \\\"\\\"\\\"\\r\\n first_time = int(time.time())\\r\\n if (len(measurements[1])>0 or len(measurements[2])>0) and period<=self._pvsr_default_conf_check_cycle:\\r\\n #there are newly created or modified measurements\\r\\n first_time = first_time + self._pvsr_default_conf_check_cycle\\r\\n if first_time % period > 0:\\r\\n first_time = first_time - (first_time % period)\\r\\n last_time = first_time + int(duration / period) * period\\r\\n sleep_time = duration\\r\\n\\r\\n logging.debug(\\\"From: {0}, To: {1}\\\".format(datetime.datetime.fromtimestamp(first_time),datetime.datetime.fromtimestamp(last_time)))\\r\\n \\r\\n meas_data = {}\\r\\n\\r\\n while True:\\r\\n logging.info(\\\"Wait {0} seconds\\\".format(sleep_time))\\r\\n time.sleep(sleep_time)\\r\\n sleep_time = 30\\r\\n \\r\\n loaded_until=self._pvsr.getLastLoadedDataTimestamp(period)\\r\\n if int(loaded_until.timestamp())>=last_time or time.time()>last_time+period+300:\\r\\n for i in (0,1,2):\\r\\n for j in range(len(measurements[i])):\\r\\n self._fill_meas_result(measurements[i][j],first_time,last_time,meas_data)\\r\\n break\\r\\n else:\\r\\n logging.debug(\\\"last loaded is still {0}\\\".format(loaded_until))\\r\\n \\r\\n res = mplane.model.Result(specification=spec)\\r\\n res.set_when(mplane.model.When(a = datetime.datetime.utcfromtimestamp(first_time+period), b = datetime.datetime.utcfromtimestamp(last_time)))\\r\\n \\r\\n tmp_time=first_time+period\\r\\n row_index=0\\r\\n while tmp_time<=last_time:\\r\\n tmp_time2 = datetime.datetime.fromtimestamp(tmp_time)\\r\\n tmp_time3 = datetime.datetime.utcfromtimestamp(tmp_time)\\r\\n res.set_result_value(\\\"time\\\", tmp_time3, row_index)\\r\\n if tmp_time2 in meas_data:\\r\\n for mplane_name in meas_data[tmp_time2]:\\r\\n value = str(meas_data[tmp_time2][mplane_name])\\r\\n res.set_result_value(mplane_name, value, row_index)\\r\\n row_index+=1\\r\\n tmp_time+=period\\r\\n \\r\\n return res\",\n \"def filter_time_match(file1, file2):\\n freq1 = int(file1.split(\\\".\\\")[1].split(\\\"_\\\")[1].replace(\\\"M\\\", \\\"\\\"))\\n freq2 = int(file2.split(\\\".\\\")[1].split(\\\"_\\\")[1].replace(\\\"M\\\", \\\"\\\"))\\n df1, df2 = filter_overlapping_files_dfs(file1, file2)\\n\\n dt1 = pandas.to_datetime(df1[\\\"date\\\"] + \\\" \\\" + df1[\\\"hour\\\"])\\n dt2 = pandas.to_datetime(df2[\\\"date\\\"] + \\\" \\\" + df2[\\\"hour\\\"])\\n\\n dt_delta = datetime.timedelta(minutes=freq2 - freq1)\\n time_match_df1 = dt1.copy()\\n time_match_df2 = dt2.copy()\\n for idx, dt in dt2.items():\\n match = dt1[(dt1 >= dt) & (dt1 <= dt + dt_delta)]\\n time_match_df1[match.index] = idx\\n time_match_df2[idx] = 0\\n time_match_df2[idx] = tuple(match.index)\\n\\n time_match_df2[time_match_df2.apply(len) != 10]\\n return time_match_df1, time_match_df2\",\n \"def _merge_report(self, target, new):\\r\\n time = None\\r\\n if 'ts' in new['parsed']:\\r\\n time = new['parsed']['ts']\\r\\n\\r\\n if (target.get('lastSeenDate', None) and\\r\\n time and\\r\\n target['lastSeenDate'] < time):\\r\\n target['lastSeenDate'] = time\\r\\n\\r\\n query_millis = int(new['parsed']['stats']['millis'])\\r\\n target['stats']['totalTimeMillis'] += query_millis\\r\\n target['stats']['count'] += 1\\r\\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']\",\n \"def _merge_report(self, target, new):\\n time = None\\n if 'ts' in new['parsed']:\\n time = new['parsed']['ts']\\n\\n if (target.get('lastSeenDate', None) and\\n time and\\n target['lastSeenDate'] < time):\\n target['lastSeenDate'] = time\\n\\n query_millis = int(new['parsed']['stats']['millis'])\\n target['stats']['totalTimeMillis'] += query_millis\\n target['stats']['count'] += 1\\n target['stats']['avgTimeMillis'] = target['stats']['totalTimeMillis'] / target['stats']['count']\",\n \"def test_find_df_period(self):\\n test_search_df = pd.read_csv(DF_PATH)\\n result_1 = find_df_period(test_search_df, 'pickup_datetime', 6)\\n p_time_periods_1 = result_1['time_period'].tolist()\\n p_intervals_1 = [2, 2, 2, 2, 2, 3, 3, 3, 4, 4, 4]\\n\\n result_2 = find_df_period(test_search_df, 'pickup_datetime', 4)\\n p_time_periods_2 = result_2['time_period'].tolist()\\n p_intervals_2 = [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2]\\n\\n self.assertTrue(p_time_periods_1 == p_intervals_1)\\n self.assertTrue(p_time_periods_2 == p_intervals_2)\",\n \"def make_all_datetime(self):\\n \\n logging.info('\\\\n *** Running make_all_datetime ' )\\n\\n all_uniques = [] # storing a list with all the unique date_times \\n which_k_in_dt = {} # list of avilable dataset for each unique date_time, so that when looping over the distinct date_times, only the proper dataset will be read and compared \\n\\n \\\"\\\"\\\" Loop over all the datasets \\n k: name of the dataset\\n v: list of file paths, eg 'era5_1':[filepath_1, filepath_2 ]\\\"\\\"\\\"\\n\\n for k,v in self.datasets.items() :\\n self.unique_dates[k] = {}\\n for F in v: \\n self.unique_dates[k][F] = {}\\n \\n self.unique_dates[k][F]['indices'] = {} \\n self.unique_dates[k][F]['index_offset_next'] = 0 # to be replaced later when slicing \\n self.unique_dates[k][F]['index_offset'] = 0 # to be replaced later when slicing \\n\\n unique_dt = list(data[k][F]['recordtimestamp'])\\n \\n indices = list(data[k][F]['recordindex'])\\n all_uniques += unique_dt # adding to the total unique date_times \\n\\n \\\"\\\"\\\" Loop over all the date_times of each dataset \\\"\\\"\\\"\\n for dt, index_low, count in zip (unique_dt, indices, range(len(unique_dt)) ):\\n\\n if dt not in which_k_in_dt.keys():\\n which_k_in_dt[dt] = {}\\n if k not in which_k_in_dt[dt].keys():\\n which_k_in_dt[dt][k] = [] \\n if F not in which_k_in_dt[dt][k]:\\n which_k_in_dt[dt][k].append(F)\\n # at this point I have e.g. which_k_in_dt= {1990-01-01-12-00: {era5_1:[file1,file2] , ncar:[file3] } }\\n\\n self.unique_dates[k][F]['indices'][dt] = {}\\n self.unique_dates[k][F]['indices'][dt]['low'] = index_low \\n try:\\n index_up = indices[ count + 1 ] # works until the last available recordindex\\n except: \\n index_up = max(indices)+1000000 # dummy large number \\n\\n self.unique_dates[k][F]['indices'][dt]['up'] = index_up\\n self.unique_dates[k][F]['up_to_dt_slice'] = data[k][F]['min_date'] \\n \\n\\n self.dataset_per_dt = which_k_in_dt \\n self.merged_unique_dates = np.unique(np.array(all_uniques) ) # storing the set of *ALL* distinct dt values of all datasets and all files \\n logging.debug('*** make_all_datetime finished ')\",\n \"def Merge(self, other):\\n\\n # Logging just in case\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: before\\\", %s);'\\n %(self.persistant['id'],\\n sql.FormatSqlValue('details',\\n repr(self.persistant))))\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: deleted\\\", %s);'\\n %(other.persistant['id'], \\n sql.FormatSqlValue('details',\\n repr(other.persistant))))\\n\\n # Fields which can be summed\\n for f in ['plays', 'skips']:\\n self.persistant[f] = (self.persistant.get(f, 0) +\\n other.persistant.get(f, 0))\\n\\n # Date fields where we take the newest\\n for f in ['last_played', 'last_skipped', 'last_action']:\\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\\n if a > b:\\n v = a\\n else:\\n v = b\\n if v != datetime.datetime(1970, 1, 1):\\n self.persistant[f] = v\\n\\n # Date fields where we take the oldest\\n for f in ['creation_time']:\\n a = self.persistant.get(f, datetime.datetime(1970, 1, 1))\\n b = other.persistant.get(f, datetime.datetime(1970, 1, 1))\\n if a < b:\\n v = a\\n else:\\n v = b\\n if v != datetime.datetime(1970, 1, 1):\\n self.persistant[f] = v\\n\\n # Fields where we only clobber ours if we don't have a value\\n for f in ['artist', 'album', 'song']:\\n if not self.persistant.has_key(f) or not self.persistant[f]:\\n self.persistant[f] = other.persistant.get(f, None)\\n\\n # Sometimes the number is a placeholder\\n if self.persistant.has_key('number') and self.persistant['number'] == -1:\\n self.persistant['number'] = other.persistant.get('number', -1)\\n if not self.persistant.has_key('number'):\\n self.persistant['number'] = other.persistant.get('number', -1)\\n\\n # Update the id in the tags table\\n tags = self.db.GetRows('select tag from tags where track_id=%d;'\\n % other.persistant['id'])\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: tags: %d\\\", %s);'\\n %(self.persistant['id'], other.persistant['id'],\\n sql.FormatSqlValue('details', repr(tags))))\\n\\n try:\\n self.db.ExecuteSql('update tags set track_id=%d where track_id=%d;'\\n %(self.persistant['id'], other.persistant['id']))\\n self.db.ExecuteSql('commit;')\\n except:\\n # This can happen if the is already a matching tag for the first track\\n pass\\n\\n # Update the id in the paths table\\n paths = self.db.GetRows('select path from paths where track_id=%d;'\\n % other.persistant['id'])\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: paths: %d\\\", %s);'\\n %(self.persistant['id'], other.persistant['id'],\\n sql.FormatSqlValue('details', repr(paths))))\\n \\n self.db.ExecuteSql('update paths set track_id=%d where track_id=%d;'\\n %(self.persistant['id'], other.persistant['id']))\\n self.db.ExecuteSql('commit;')\\n\\n self.db.ExecuteSql('insert into events(timestamp, track_id, event, '\\n 'details) values (now(), %d, \\\"merge: after\\\", %s);'\\n %(self.persistant['id'],\\n sql.FormatSqlValue('details',\\n repr(self.persistant))))\\n self.db.ExecuteSql('commit;')\",\n \"def merge_all_data(self):\\n\\n logging.info('***** Starting the merging process merge_all_data')\\n\\n \\\"\\\"\\\" All possible unique_dates to loop on \\\"\\\"\\\"\\n date_times = self.merged_unique_dates\\n date_times.sort()\\n date_times = np.array(date_times) \\n\\n \\\"\\\"\\\" List storing the indices of the date_index of the merged dataset \\\"\\\"\\\"\\n all_combined_obs , all_combined_head, all_combined_era5fb , combined_indices , combined_date_time, = [] , [] , [] , [] , []\\n best_ds_list = [] \\n source_files = []\\n station_configurations = []\\n\\n \\\"\\\"\\\" The items contained in the lists in the list below can be removed from the list when the record that was previously stored is removed. \\\"\\\"\\\"\\n all_list = [all_combined_obs , all_combined_head, all_combined_era5fb , combined_indices , combined_date_time, best_ds_list, source_files , station_configurations ] # holder of all the above lists\\n all_list_name = ['all_combined_obs' , 'all_combined_head', 'all_combined_era5fb' , 'combined_indices' , 'combined_date_time' , 'best_ds_list', 'source_files' ] \\n \\n removed_record, kept_record = [], []\\n \\n \\\"\\\"\\\" Dictionary that will contain the merged file. \\\"\\\"\\\" \\n # rand = datetime.strptime('1981-01-03 12:00:00', '%Y-%m-%d %H:%M:%S') \\n #dt_bestds_dic = {} # store the selected best dataset for each dt \\n #date_times=date_times[0:30000]\\n tot = len(date_times)\\n tt=time.time()\\n print('*** Merging ' , tot, ' records ***')\\n \\n early_datasets = True\\n \\n self.processed_dt = [] \\n \\n for dt, c in zip(date_times, range(tot) ): # loop over all the possible date_times \\n\\n if (c+1)%1000==0:\\n print('Analize : ', str(c+1) , '/', str(tot) , ' ', dt , ' ',\\n now(time.time()),'{:5.3f}'.format(time.time()-tt ))\\n\\n delete = self.delete_ds(dt) # check if there is a dataset to delete \\n \\n \\\"\\\"\\\" Finding if this record is the same as the previous one analyzed, according to the given time_shift \\\"\\\"\\\"\\n if c == 0:\\n is_same_record = False\\n else:\\n is_same_record = self.is_same_record( time_shift = self.hour_time_delta , dt = dt)\\n \\n \\\"\\\"\\\" Updating list of processed datetimes \\\"\\\"\\\"\\n self.processed_dt.append(dt) # cannot put it before the check_timeshift or it will check itself \\n\\n \\n cleaned_df_container = {} \\n all_len = [] # will hold the length of all the obs_tabs \\n \\n for k in self.dataset_per_dt[dt].keys() : # checking the list of available datasets \\n ''' {'era5_2': ['example_stations/0-20000-0-82930_era5_2_harvested_era5.conv._1:82930.gz.nc', \\n 'example_stations/0-20000-0-82930_era5_2_harvested_era5.conv._82930.gz.nc']}\\n ''' \\n for F in self.dataset_per_dt[dt][k]: # checking the list of available files for the dataset\\n \\n if data[k][F][\\\"counter\\\"] %self.slice_size==0 or data[k][F][\\\"counter\\\"] == 0: # loading the data only at specific slices \\n load = self.load_obstab_feedback_sliced(datetime=dt, dataset=k, file = F)\\n \\n data[k][F][\\\"counter\\\"] = data[k][F][\\\"counter\\\"] + 1 \\n \\n obs_tab, era5fb_tab = self.make_obstab_era5fb_dic(dataset = k , date_time = dt, File = F )\\n\\n if len(obs_tab['date_time'][:])==0: # go to next file if obs_tab is empty \\n #print('ZERO length')\\n continue \\n\\n all_len.append( len(obs_tab['date_time'][:] ) )\\n \\n if k not in cleaned_df_container.keys():\\n cleaned_df_container[k] = {}\\n\\n cleaned_df_container[k][F] = {}\\n cleaned_df_container[k][F]['obs_tab'] = obs_tab # cleaned dataframe \\n cleaned_df_container[k][F]['era5fb_tab'] = era5fb_tab # cleaned dataframe \\n \\n \\\"\\\"\\\" Merging the different records found in the sifferent sources \\\"\\\"\\\"\\n if bool(all_len): # skipping empty container dictionary. At this point I certainyl have one valid record \\n best_ds, combined_obs_tab, combined_era5fb_tab, combined_head_tab, selected_file, best_file = self.combine_record(dt, container = cleaned_df_container)\\n \\n if is_same_record: # decide what to keep in case of same record\\n temporary_previous = all_combined_obs[-1] # keep the temporary previous record \\n\\n if best_ds in ['era5_1','era5_2']: # best_ds from era5\\n if best_ds_list[-1] not in ['era5_1','era5_2']: # remove previous non era5_1 or era5_2 record \\n for lista in all_list:\\n lista.pop() \\n #removed_record.append(temporary_previous)\\n #kept_record.append(combined_obs_tab) \\n\\n elif best_ds_list[-1] in ['era5_1','era5_2']:\\n if len(combined_obs_tab) <= len(all_combined_obs[-1] ):\\n #kept_record.append(temporary_previous) \\n #removed_record.append(combined_obs_tab)\\n continue # nothing to do, will keep the previous records -> go to next dt \\n \\n else: # case where both the current and previous are from era5_1 and era5_2, but the previous has smaller number of data \\n for lista in all_list:\\n lista.pop() \\n #removed_record.append(temporary_previous)\\n #kept_record.append(combined_obs_tab) \\n \\n else: # best_ds not from era5\\n if best_ds_list[-1] in ['era5_1','era5_2']:\\n #print('This best ds is ' , best_ds , ' but I will keep ' , best_ds_list[-1] )\\n #kept_record.append(temporary_previous) \\n #removed_record.append(combined_obs_tab) \\n continue \\n \\n else:\\n if len(combined_obs_tab) < len(all_combined_obs[-1] ):\\n #kept_record.append(temporary_previous) \\n #removed_record.append(combined_obs_tab) \\n continue # nothing to do, will keep the previous records -> go to next dt \\n \\n elif len(combined_obs_tab) > len(all_combined_obs[-1] ): # remove previous, keep current \\n for lista in all_list:\\n lista.pop() \\n #kept_record.append(combined_obs_tab) \\n #removed_record.append(temporary_previous)\\n \\n elif len(combined_obs_tab) == len(all_combined_obs[-1] ): # prefer igra2, otherwise\\n if best_ds == 'igra2':\\n for lista in all_list:\\n lista.pop() \\n #removed_record.append(temporary_previous)\\n #kept_record.append(combined_obs_tab) \\n \\n else: # case where data source is not important, I keep the previous and do nothing \\n #kept_record.append(temporary_previous) \\n #removed_record.append(combined_obs_tab) \\n continue \\n \\n else: # not the same record, nothing special to do, keep both previous and current \\n pass \\n else:\\n print(' Found an empty record / time shifted record ')\\n continue\\n \\n\\n \\\"\\\"\\\" Fill the best_ds list \\\"\\\"\\\"\\n best_ds_list.append(best_ds)\\n\\n \\\"\\\"\\\" Storing the selected file for the source_configuration \\\"\\\"\\\"\\n source_files.append(selected_file)\\n \\\"\\\"\\\" Selecting the station_configuration \\\"\\\"\\\"\\n station_configurations.append(self.data[best_ds][best_file]['station_configuration'] )\\n \\n \\\"\\\"\\\" Storing the combined era5fb, header and observations tables\\\"\\\"\\\"\\n all_combined_era5fb.append(combined_era5fb_tab)\\n all_combined_obs .append(combined_obs_tab)\\n \\n primary, name = self.data[best_ds][best_file]['station_configuration']['primary_id'][0] , self.data[best_ds][best_file]['station_configuration']['station_name'][0] \\n #combined_head_tab['primary_station_id'] = [ primary ] * len( combined_head_tab ) \\n #combined_head_tab['station_name'] = [ name ] * len( combined_head_tab ) \\n \\n combined_head_tab['primary_station_id'] = np.array( [primary] )\\n combined_head_tab['station_name'] = np.array( [name] )\\n \\n all_combined_head .append(combined_head_tab)\\n\\n \\\"\\\"\\\" Dictionary to fill the best_ds for duplicates \\\"\\\"\\\"\\n #dt_bestds_dic[dt] = {}\\n #dt_bestds_dic[dt]['best_ds'] = best_ds\\n #dt_bestds_dic[dt]['len'] = len(combined_obs_tab['date_time'])\\n\\n \\\"\\\"\\\" New merged recordindex and recordtimestamps indices \\\"\\\"\\\"\\n combined_indices.append(len(combined_obs_tab['date_time'])) \\n combined_date_time.append(dt)\\n\\n del cleaned_df_container \\n \\n \\n \\n #print(blue + 'Memory used after deleting the cleaned_df_container: ', process.memory_info().rss/1000000000 , cend)\\n\\n \\\"\\\"\\\" Removing remaining loaded df \\\"\\\"\\\"\\n for k in self.datasets_keys:\\n for F in self.datasets[k]:\\n try:\\n del data[k][F]['era5fb_tab']\\n print('=== removed era5fb ' , k , F )\\n except:\\n pass\\n try:\\n del data[k][F]['observations_table']\\n print('=== removed obstab ' , k , F ) \\n except:\\n pass\\n \\n \\n \\\"\\\"\\\" Saving a numpy dictionary \\\"\\\"\\\"\\n print(\\\" === Saving the numpy dictionary of removed and kept records +++ \\\")\\n #dic_records = { 'kept' : kept_record , 'removed': removed_record }\\n #np.save(self.station + '_time_shift_removed_kept.npy',dic_records )\\n \\n \\n \\\"\\\"\\\" Storing the merged date_time values and indices \\\"\\\"\\\"\\n di=xr.Dataset()\\n combined_date_time = np.array(combined_date_time)\\n di['recordtimestamp'] = ( {'recordtimestamp' : combined_date_time.shape } , combined_date_time )\\n di['recordtimestamp'].attrs['units']='seconds since 1900-01-01 00:00:00'\\n\\n \\\"\\\"\\\" Creating the merged indices mi \\\"\\\"\\\"\\n mi = [] \\n mi.append(0)\\n for i in range(len(combined_indices)):\\n mi.append( combined_indices[i] + mi[-1] )\\n mi.pop()\\n pop = np.array(mi) # removing last unecessary index \\n di['recordindex'] = ( {'recordindex' : pop.shape } , pop )\\n\\n\\n \\\"\\\"\\\" Creating the combined data \\\"\\\"\\\"\\n logging.debug('*** Concatenating the observations_table ' ) \\n combined_obs = {}\\n #### Writing combined observations_table dic\\n logging.info(' ***** Writing the observations_table to the netCDF output ***** ' ) \\n for k in all_combined_obs[0].keys(): \\n a = np.concatenate([all_combined_obs[i][k][:] for i in range(len(all_combined_obs))])\\n if k == 'date_time':\\n combined_obs[k]= a \\n self.tot_records = len(combined_obs[k])\\n self.write_merged(content = 'observations_table', table= {k:a})\\n #logging.info('*** Written observations table %s: ', k)\\n\\n\\n #self.tot_records = len(combined_obs['date_time'])\\n del all_combined_obs\\n print(blue + 'Memory used after deleting all_combined_obs dic: ', process.memory_info().rss/1000000000 , cend )\\n \\n dateindex = combined_obs['date_time']//86400 \\n date_times, indices, counts = np.unique(dateindex, return_counts = True, return_index= True) \\n di['dateindex'] = ( {'dateindex' : indices.shape } , indices ) # considers the day only \\n del combined_obs\\n \\n combined_era5fb = {}\\n #### Writing combined era5fb_table dic \\n for k in all_combined_era5fb[0].keys():\\n try:\\n #combined_era5fb[k]=np.concatenate([all_combined_era5fb[i][k][:] for i in range(len(all_combined_era5fb))])\\n #self.write_merged(content = 'era5fb', table= {k:combined_era5fb[k]})\\n \\\"\\\"\\\" try replacing , remove combined_era5fb = {} \\\"\\\"\\\"\\n a = np.concatenate([all_combined_era5fb[i][k][:] for i in range(len(all_combined_era5fb))])\\n self.write_merged(content = 'era5fb', table= {k:a})\\n logging.debug('*** Written era5fb %s: ', k)\\n except:\\n print(\\\"FAILED feedback variable \\\" , k)\\n\\n del all_combined_era5fb\\n print(blue + 'Memory used after deleting era5fb_tab dic: ', process.memory_info().rss/1000000000 , cend)\\n\\n\\n #### Writing combined header_table dic \\n for k in all_combined_head[0].keys():\\n print('head variable is', k )\\n if ( k == 'comments' or k == 'history'):\\n continue\\n try:\\n tab=np.concatenate([all_combined_head[i][k][:] for i in range(len(all_combined_head))])\\n self.write_merged(content = 'header_table', table= {k: tab}) # { key: np.array([])}\\n logging.info('*** Written header table %s: ', k)\\n except:\\n print('FFF FAILED variable in header table', k )\\n\\n del all_combined_head\\n print(blue + 'Memory used after deleting all_merged head_tab dic: ', process.memory_info().rss/1000000000 , cend)\\n \\n self.write_merged(content = 'recordindex', table = di) \\n self.write_merged(content = 'cdm_tables', table= '')\\n\\n\\n source_conf=xr.Dataset()\\n source_files = np.array(source_files).astype(dtype='|S70')\\n source_conf['source_file'] = ( {'source_file' : source_files.shape } , source_files )\\n self.write_merged(content = 'source_configuration', table= source_conf )\\n\\n print(0)\\n\\n\\n \\\"\\\"\\\" Concatenation of station_configurations \\\"\\\"\\\"\\n station_conf = pd.concat( station_configurations ) \\n for k in station_conf.columns:\\n try:\\n a =np.array( station_conf[k])\\n self.write_merged(content = 'station_configuration', table= {k:a})\\n logging.debug('*** Written station_configuration %s: ', k)\\n except:\\n print(\\\" Failed station_configuration \\\" , k )\\n \\n return 0\",\n \"def get_records(self, since_ts=0, num_rec=0):\\n nerr = 0\\n while True:\\n try:\\n fixed_block = self.get_fixed_block(unbuffered=True)\\n if fixed_block['read_period'] is None:\\n raise weewx.WeeWxIOError('invalid read_period in get_records')\\n if fixed_block['data_count'] is None:\\n raise weewx.WeeWxIOError('invalid data_count in get_records')\\n if since_ts:\\n dt = datetime.datetime.utcfromtimestamp(since_ts)\\n dt += datetime.timedelta(seconds=fixed_block['read_period']*30)\\n else:\\n dt = datetime.datetime.min\\n max_count = fixed_block['data_count'] - 1\\n if num_rec == 0 or num_rec > max_count:\\n num_rec = max_count\\n log.debug('get %d records since %s' % (num_rec, dt))\\n dts, ptr = self.sync(read_period=fixed_block['read_period'])\\n count = 0\\n records = []\\n while dts > dt and count < num_rec:\\n raw_data = self.get_raw_data(ptr)\\n data = self.decode(raw_data)\\n if data['delay'] is None or data['delay'] < 1 or data['delay'] > 30:\\n log.error('invalid data in get_records at 0x%04x, %s' %\\n (ptr, dts.isoformat()))\\n dts -= datetime.timedelta(minutes=fixed_block['read_period'])\\n else:\\n record = dict()\\n record['ptr'] = ptr\\n record['datetime'] = dts\\n record['data'] = data\\n record['raw_data'] = raw_data\\n record['interval'] = data['delay']\\n records.insert(0, record)\\n count += 1\\n dts -= datetime.timedelta(minutes=data['delay'])\\n ptr = self.dec_ptr(ptr)\\n return records\\n except (IndexError, usb.USBError, ObservationError) as e:\\n log.error('get_records failed: %s' % e)\\n nerr += 1\\n if nerr > self.max_tries:\\n raise weewx.WeeWxIOError(\\\"Max retries exceeded while fetching records\\\")\\n time.sleep(self.wait_before_retry)\",\n \"def pull(self, period):\\n # Compile the regex expressions we'll use to parse the title text\\n self.identity_regex = re.compile(r\\\"(\\\\d{4} \\\\d{3} \\\\d{3})\\\\s{2,}(\\\\S+)\\\\s{2,}(\\\\d{3} \\\\d{3} \\\\d{3} *\\\\S*)\\\")\\n self.ats_regex = re.compile(r\\\"ATS REFERENCE: (\\\\S*)\\\")\\n self.municipality_regex = re.compile(r\\\"MUNICIPALITY: (.*)\\\")\\n self.reference_regex = re.compile(r\\\"REFERENCE NUMBER: (.*?)\\\\-{80}\\\", re.DOTALL)\\n self.payday_regex = re.compile(r\\\"(\\\\-{80}).*(\\\\-{80})(.*)\\\", re.DOTALL)\\n\\n # Filter the dataframe by date and retrieve each title\\n df = self.journal\\n df = df[df['Registration Date'] >= period]\\n\\n df.to_pickle('run/{}.journal.pkl'.format(self.runtime))\\n\\n click.echo('Journal constructed and saved with timestamp {}'.format(self.runtime))\\n\\n # Set up structure for target DataFrame\\n self.dataframe = pd.DataFrame(\\n columns=[\\n 'linc',\\n 'short_legal',\\n 'title_number',\\n 'ats_reference',\\n 'municipality',\\n 'registration',\\n 'registration_date',\\n 'document_type',\\n 'sworn_value',\\n 'consideration',\\n 'condo'\\n ], index=df.index\\n )\\n\\n with click.progressbar(df.iterrows(), label='Pulling basic title data', length=len(df)) as d:\\n for index, row in d:\\n try:\\n payload = self.retrieve_title(index)\\n self.dataframe.loc[index, 'linc'] = payload['linc']\\n self.dataframe.loc[index, 'short_legal'] = payload['short_legal']\\n self.dataframe.loc[index, 'title_number'] = payload['title_number']\\n self.dataframe.loc[index, 'ats_reference'] = payload['ats_reference']\\n self.dataframe.loc[index, 'municipality'] = payload['municipality']\\n self.dataframe.loc[index, 'registration'] = payload['registration']\\n self.dataframe.loc[index, 'registration_date'] = payload['date']\\n self.dataframe.loc[index, 'document_type'] = payload['document_type']\\n self.dataframe.loc[index, 'sworn_value'] = payload['value']\\n self.dataframe.loc[index, 'consideration'] = payload['consideration']\\n self.dataframe.loc[index, 'condo'] = payload['condo']\\n except TypeError:\\n pass\\n\\n self.dataframe['registration_date'] = pd.to_datetime(self.dataframe['registration_date'])\\n self.dataframe['sworn_value'] = self.dataframe['sworn_value'].astype(float)\\n self.dataframe['consideration'] = self.dataframe['consideration'].astype(float)\\n self.dataframe['condo'] = self.dataframe['condo'].fillna(False).astype(bool)\\n\\n self.dataframe.to_pickle('run/{}.dataframe.pkl'.format(self.runtime))\\n click.echo('Dataframe constructed and saved with timestamp {}'.format(self.runtime))\\n\\n return self.dataframe\",\n \"def merge_logfiles(log1, log2):\\n first_in_2 = log2['time'][0]\\n keep_from_1 = log1['time'] < first_in_2\\n for key in log1.keys():\\n log1[key] = log1[key][keep_from_1]\\n log1.timeseries_append(log2)\\n return log1\",\n \"def __init__(self, numQueues, rate, start_hour, end_hour, appt_low, appt_high):\\n\\n self.rate = rate\\n self.numQueues = numQueues\\n self.start = datetime.datetime.combine(datetime.date.today(), datetime.time(start_hour,0,0))\\n self.end = datetime.datetime.combine(datetime.date.today(), datetime.time(end_hour,0,0))\\n self.appt_low = appt_low\\n self.appt_high = appt_high\\n minutes_for_new_items = (end_hour-start_hour)*60 #new patients seen between 9AM and 4PM\\n time_between_items = rate #exponential dist. time parameter\\n self.expected_count = int(np.ceil(stats.poisson.ppf(.9999, minutes_for_new_items/time_between_items)))\\n self.ques = [datetime.datetime.combine(datetime.datetime.today(), datetime.time(start_hour,0,0)) for i in range(0, self.numQueues)]\\n cols = ['simulation', 'num_items', 'wait_count', 'avg_wait_time', 'close_time']\\n self.results = pd.DataFrame(columns = cols)\\n return\",\n \"def join_domain_time_span(domain_tables, span):\\n joined_domain_tables = []\\n \\n for domain_table in domain_tables:\\n #extract the domain concept_id from the table fields. E.g. condition_concept_id from condition_occurrence\\n #extract the domain start_date column\\n #extract the name of the table\\n concept_id_field, date_field, table_domain_field = get_key_fields(domain_table) \\n\\n domain_table = domain_table.withColumn(\\\"date\\\", unix_timestamp(to_date(col(date_field)), \\\"yyyy-MM-dd\\\")) \\\\\\n .withColumn(\\\"lower_bound\\\", unix_timestamp(date_add(col(date_field), -span), \\\"yyyy-MM-dd\\\"))\\\\\\n .withColumn(\\\"upper_bound\\\", unix_timestamp(date_add(col(date_field), span), \\\"yyyy-MM-dd\\\"))\\\\\\n .withColumn(\\\"time_window\\\", lit(1))\\n \\n #standardize the output columns\\n joined_domain_tables.append(\\n domain_table \\\\\\n .select(domain_table[\\\"person_id\\\"], \\n domain_table[concept_id_field].alias(\\\"standard_concept_id\\\"),\\n domain_table[\\\"date\\\"],\\n domain_table[\\\"lower_bound\\\"],\\n domain_table[\\\"upper_bound\\\"],\\n lit(table_domain_field).alias(\\\"domain\\\"))\\n )\\n \\n return joined_domain_tables\",\n \"def fake_record_data():\\n\\n user_ids = [4, 4, 4, 4, 5,\\n 5, 2, 6, 1, 2,\\n 5, 7, 5, 1, 3,\\n 3, 1, 4, 2, 3,\\n 6, 4, 2, 7, 3,\\n 3, 3, 6, 7, 6,\\n 6, 7, 1, 7, 1,\\n 8, 7, 1, 8, 4]\\n\\n days = [1519200000, 1519200000, 1519200000, 1519200000, 1519113600,\\n 1519113600, 1519113600, 1519027200, 1519027200, 1519027200,\\n 1518940800, 1518940800, 1518854400, 1518854400, 1518768000,\\n 1518681600, 1518681600, 1518681600, 1518681600, 1518681600,\\n 1518595200, 1518595200, 1518595200, 1518595200, 1518508800,\\n 1518422400, 1518422400, 1518422400, 1518422400, 1518336000,\\n 1518336000, 1518336000, 1518336000, 1518249600, 1518249600,\\n 1518163200, 1518163200, 1518076800, 1517990400, 1517904000]\\n\\n for i, user_id in enumerate(user_ids):\\n act_qty = random.randint(5, 13)\\n selected_activities = set()\\n\\n for _ in range(0, act_qty):\\n act_id = random.randint(1, 13)\\n selected_activities.add(act_id)\\n\\n day = days[-(i + 1)]\\n start = day + 33000\\n total_time = 0\\n\\n for act_id in selected_activities:\\n act_time = random.randint(120, 1000)\\n\\n start_t = start + total_time\\n end_t = datetime.fromtimestamp(start_t + act_time)\\n start_t = datetime.fromtimestamp(start_t)\\n\\n total_time += act_time\\n\\n print (str(user_id) + '|' + str(i + 1) + '|' + str(act_id) + '|' +\\n str(start_t) + '|' + str(end_t))\",\n \"def update_period(self, period):\\n\\n # Update attribute\\n self._period = period\\n\\n # Create new data and time\\n shape = int(round(self._drate) * self._period + 1)\\n new_data = OrderedDict([(ch, np.zeros(shape=shape)) for i, ch in enumerate(self.channels)])\\n new_time = np.zeros(shape=shape)\\n\\n # Check whether new time and data hold more or less indices\\n decreased = True if self._time.shape[0] >= shape else False\\n\\n if decreased:\\n # Cut time axis\\n new_time = self._time[:shape]\\n\\n # If filled before, go to 0, else go to 0 if currnt index is bigger than new shape\\n if self._filled:\\n self._idx = 0\\n else:\\n self._idx = 0 if self._idx >= shape else self._idx\\n\\n # Set wheter the array is now filled\\n self._filled = True if self._idx == 0 else False\\n\\n else:\\n # Extend time axis\\n new_time[:self._time.shape[0]] = self._time\\n\\n # If array was filled before, go to last time, set it as offset and start from last timestamp\\n if self._filled:\\n self._idx = self._time.shape[0]\\n self._start = self._timestamp\\n self._offset = self._time[-1]\\n\\n self._filled = False\\n\\n # Set new time and data\\n for ch in self.channels:\\n if decreased:\\n new_data[ch] = self._data[ch][:shape]\\n else:\\n new_data[ch][:self._data[ch].shape[0]] = self._data[ch]\\n\\n # Update\\n self._time = new_time\\n self._data = new_data\",\n \"def copy_many_from_temp(self,\\r\\n count):\\r\\n\\r\\n for counter in range(count):\\r\\n print(PERIOD,end=EMPTYCHAR)\\r\\n self.copy_from_temp(self.tempobject)\\r\\n self.constitute_key_freq_dict()\\r\\n print()\",\n \"def _write_to_dataset(parser1, parser2, dset, rundate):\\n\\n data_all1 = parser1.as_dict()\\n data_all2 = parser2.as_dict()\\n if parser1.file_path == parser2.file_path:\\n collection = [data_all1]\\n else:\\n collection = [data_all1, data_all2]\\n\\n # Meta information\\n dset.meta[\\\"tech\\\"] = \\\"slr\\\"\\n dset.meta.add(\\\"file\\\", parser1.file_path.stem, section=\\\"input\\\")\\n dset.meta.add(\\\"file\\\", parser2.file_path.stem, section=\\\"input\\\")\\n dset.meta.add(\\\"type\\\", config.tech.obs_format.str.upper(), section=\\\"input\\\")\\n\\n # Make new dict \\\"obs_data\\\" containing only data in relevant time interval:\\n arc_length = config.tech.arc_length.float\\n rundate_datetime = datetime(rundate.year, rundate.month, rundate.day)\\n obs_data = dict()\\n for data_all in collection:\\n for i, x in enumerate(data_all[\\\"meta\\\"][\\\"obs_time\\\"]):\\n if rundate_datetime <= x < rundate_datetime + timedelta(days=arc_length):\\n for key in (\\\"meta\\\", \\\"obs\\\", \\\"obs_str\\\"):\\n for field, val in data_all[key].items():\\n obs_data.setdefault(key, dict()).setdefault(field, list()).append(val[i])\\n\\n data_all.pop(\\\"meta\\\")\\n data_all.pop(\\\"obs\\\")\\n data_all.pop(\\\"obs_str\\\")\\n\\n for key in data_all.keys():\\n if key.startswith(\\\"met_\\\"):\\n for key2, val in data_all[key].items():\\n obs_data.setdefault(key, dict()).setdefault(key2, list()).append(val)\\n elif key.startswith(\\\"satellite_\\\"):\\n # TODO: Use this information in the future?\\n continue\\n elif key.startswith(\\\"station_\\\"):\\n # TODO: Use this information in the future?\\n continue\\n else:\\n log.fatal(f\\\"Unknown data type{key}\\\")\\n\\n obs_date = obs_data[\\\"meta\\\"][\\\"obs_date\\\"]\\n time = [obs_date[i] + timedelta(seconds=obs_data[\\\"meta\\\"][\\\"obs_sec\\\"][i]) for i in range(0, len(obs_date))]\\n dset.num_obs = len(obs_data[\\\"meta\\\"][\\\"obs_time\\\"])\\n dset.add_time(\\\"time\\\", val=time, scale=\\\"utc\\\", fmt=\\\"datetime\\\")\\n dset.add_text(val=obs_data[\\\"meta\\\"][\\\"station\\\"], name=\\\"station\\\")\\n dset.add_text(val=obs_data[\\\"meta\\\"][\\\"satellite\\\"], name=\\\"satellite\\\")\\n dset.add_float(val=obs_data[\\\"meta\\\"][\\\"bin_rms\\\"], unit=\\\"picoseconds\\\", name=\\\"bin_rms\\\")\\n # Positions\\n trf = apriori.get(\\\"trf\\\", time=dset.time)\\n for station in dset.unique(\\\"station\\\"):\\n trf_site = trf[station]\\n station_pos = trf_site.pos.trs.val\\n log.debug(f\\\"Station position for {station} ({trf_site.name}) is (x,y,z) = {station_pos.mean(axis=0)}\\\")\\n domes = trf_site.meta[\\\"domes\\\"]\\n obs_data[\\\"pos_\\\" + station] = station_pos\\n obs_data[\\\"station-other_\\\" + station] = dict(domes=domes, cdp=station, site_id=station)\\n dset.add_position(\\n \\\"site_pos\\\",\\n time=dset.time,\\n system=\\\"trs\\\",\\n val=np.array([obs_data[\\\"pos_\\\" + s][idx] for idx, s in enumerate(dset.station)]),\\n )\\n # Station data\\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"station_\\\")])\\n for field in sta_fields:\\n dset.add_float(field, val=np.array([float(obs_data[\\\"station_\\\" + s][field]) for s in dset.station]))\\n sta_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"station-other_\\\")])\\n for field in sta_fields:\\n dset.add_text(field, val=[obs_data[\\\"station-other_\\\" + s][field] for s in dset.station])\\n\\n # Station meta\\n station_keys = sorted([k for k, v in obs_data.items() if k.startswith(\\\"station-other_\\\")])\\n pos_keys = sorted([k for k, v in obs_data.items() if k.startswith(\\\"pos_\\\")])\\n\\n for sta_key, pos_key in zip(station_keys, pos_keys):\\n sta_name = sta_key.replace(\\\"station-other_\\\", \\\"\\\")\\n cdp = obs_data[sta_key][\\\"cdp\\\"]\\n dset.meta.add(sta_name, \\\"site_id\\\", cdp)\\n longitude, latitude, height, _ = sofa.iau_gc2gd(2, obs_data[pos_key][0, :]) # TODO: Reference ellipsoid\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"cdp\\\"] = cdp\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"site_id\\\"] = cdp\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"domes\\\"] = obs_data[sta_key][\\\"domes\\\"]\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"marker\\\"] = \\\" \\\"\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"description\\\"] = \\\" \\\"\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"longitude\\\"] = longitude\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"latitude\\\"] = latitude\\n dset.meta[\\\"station\\\"].setdefault(sta_name, {})[\\\"height\\\"] = height\\n\\n # Satellite data\\n sat_fields = set().union(*[v.keys() for k, v in obs_data.items() if k.startswith(\\\"satellite_\\\")])\\n for field in sat_fields:\\n dset.add_float(field, val=np.array([float(obs_data[\\\"satellite_\\\" + s][field]) for s in dset.satellite]))\\n\\n # Observations\\n # In the dataset, obs_time is seconds since rundate:\\n v = [\\n (obs_data[\\\"meta\\\"][\\\"obs_date\\\"][i] - rundate_datetime).total_seconds() + obs_data[\\\"meta\\\"][\\\"obs_sec\\\"][i]\\n for i in range(0, dset.num_obs)\\n ]\\n\\n obs_data[\\\"obs\\\"].pop(\\\"obs_time\\\")\\n dset.add_float(\\\"obs_time\\\", val=v)\\n for field, values in obs_data[\\\"obs\\\"].items():\\n dset.add_float(field, val=np.array(values))\\n\\n for field, values in obs_data[\\\"obs_str\\\"].items():\\n dset.add_text(field, val=values)\\n\\n return obs_data\",\n \"def chunk_periods(start, end):\\n\\n logging.debug(f'chunking {start} to {end}')\\n # convert the strings to datetime objects\\n #start = dt.datetime.strptime(''.join(start.rsplit(':', 1)), '%Y-%m-%dT%H:%M:%S-%z')\\n start = dt.datetime.strptime(start, '%Y-%m-%dT%H:%M:%S-%z')\\n logging.debug(f'start: {start}')\\n periods = []\\n\\n # if the year and month of the period are the same, just return the dates as we got them\\n\\n\\n\\n return periods\",\n \"def stack_ps(ps1, ps2, keep_unique = False, fill_time = False, message = True):\\n # create deepcopies to avoid changing original instances\\n \\n ps1 = copy.deepcopy(ps1)\\n ps2 = copy.deepcopy(ps2)\\n \\n # create datetime information in PS instances\\n \\n try:\\n _ = getattr(ps1, \\\"datetime\\\")\\n except AttributeError:\\n ps1.createTimeDate()\\n \\n try: \\n _ = getattr(ps2, \\\"datetime\\\")\\n except AttributeError:\\n ps2.createTimeDate()\\n \\n # check time resolutions\\n res1 = (dt.datetime.strptime(ps1.datetime['data'][1], ps1.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])).seconds\\n res2 = (dt.datetime.strptime(ps2.datetime['data'][1], ps2.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])).seconds\\n \\n if abs(res1-res2) > 60:\\n if message:\\n print( (\\\"warning: resolutions differ %d seconds\\\")%(abs(res1-res2)) )\\n \\n # check if ps1 is \\\"older\\\" than ps2\\n \\n reversed_order = False\\n cut = None\\n \\n if dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units']) < dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']):\\n # ps2 starts after ps1 ends\\n timediff = (dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units']) - dt.datetime.strptime(ps1.datetime['data'][-1], ps1.datetime['units'])).total_seconds()\\n elif dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units']) < dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']):\\n # ps1 starts after ps2 ends (user has inadvertently switched the order of the instances)\\n reversed_order = True\\n timediff = (dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units']) - dt.datetime.strptime(ps2.datetime['data'][-1], ps2.datetime['units'])).total_seconds()\\n else:\\n # yikes! The particle sizer instances have overlapping data\\n # it is assumed that ps2 data replaces ps1 data starting \\n # from the overlapping time\\n cut, cutdate = tt.findNearestDate(ps1.datetime['data'], ps2.datetime['data'][0]) \\n fill_time = False\\n \\n #print(timediff, 1.5*res1)\\n # check if filling is required\\n if fill_time is True:\\n # check time difference\\n if reversed_order:\\n # ps1 starts after ps2 ends\\n if timediff > 1.5*res2:\\n # the time gap between two instances has to be\\n # larger than twice the normal resolution\\n numdates = int(np.ceil(timediff/res2))\\n base = dt.datetime.strptime(ps1.datetime['data'][0], ps1.datetime['units'])\\n date_list = [base - dt.timedelta(seconds=res2*x) for x in range(numdates)]\\n date_list = list(reversed(date_list[1:]))# because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\\n datetimelist = [dt.datetime.strftime(dl, ps2.datetime['units']) for dl in date_list]\\n ps2.datetime['data'] = np.append(ps2.datetime['data'], datetimelist)\\n timelist = [dt.datetime.strftime(dl, ps2.time['units']) for dl in date_list]\\n ps2.time['data'] = np.append(ps2.time['data'], timelist)\\n datelist = [dt.datetime.strftime(dl, ps2.date['units']) for dl in date_list]\\n ps2.date['data'] = np.append(ps2.date['data'], datelist)\\n else:\\n fill_time = False\\n else:\\n if timediff > 1.5*res1:\\n # the time gap between two instances has to be\\n # larger than twice the normal resolution\\n numdates = int(np.ceil(timediff/res1))\\n base = dt.datetime.strptime(ps2.datetime['data'][0], ps2.datetime['units'])\\n date_list = [base - dt.timedelta(seconds=res1*x) for x in range(numdates)]\\n date_list = list(reversed(date_list[1:])) # because numdates starts at 0, first date on date_list is the same as the startdate from the second instance\\n datetimelist = [dt.datetime.strftime(dl, ps1.datetime['units']) for dl in date_list]\\n ps1.datetime['data'] = np.append(ps1.datetime['data'], datetimelist)\\n timelist = [dt.datetime.strftime(dl, ps1.time['units']) for dl in date_list]\\n ps1.time['data'] = np.append(ps1.time['data'], timelist)\\n datelist = [dt.datetime.strftime(dl, ps1.date['units']) for dl in date_list]\\n ps1.date['data'] = np.append(ps1.date['data'], datelist)\\n else:\\n fill_time = False\\n \\n if message:\\n print(\\\"reversed order:\\\", reversed_order)\\n # check which attributes are similar in both instances\\n if reversed_order:\\n # ps1 starts after ps2 ends\\n new_ps = copy.deepcopy(ps2)\\n for attribute in ps1.__dict__.keys():\\n if attribute in ps2.__dict__.keys():\\n afield = getattr(new_ps, attribute)\\n if attribute == 'diameter':\\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\\n \\n for var in new_ps.data['variables']:\\n if fill_time is True:\\n add = np.ma.zeros((ps2.data[var]['data'].shape[0],len(date_list))) \\n add[:] = np.nan\\n newdata = np.append(ps2.data[var]['data'],add,axis=1)\\n ps2.data[var]['data'] = newdata\\n \\n sh1 = ps1.data[var]['data'].shape\\n sh2 = ps2.data[var]['data'].shape\\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\\n new_field = np.ma.zeros(newfields)\\n new_field[:] = np.ma.masked\\n \\n new_field[st21:st22, 0:ps2.data[var]['data'][:,:cut].shape[1]] = ps2.data[var]['data'][:,:cut]\\n new_field[st11:st12, ps2.data[var]['data'][:,:cut].shape[1]:] = ps1.data[var]['data']\\n \\n new_ps.data[var]['data'] = new_field\\n \\n afield['data'] = diamlist\\n \\n elif attribute == 'data':\\n # data has been appended with diameters\\n pass\\n else:\\n try:\\n field_ps2 = getattr(ps2, attribute)\\n field_ps1 = getattr(ps1, attribute)\\n except TypeError:\\n if attribute == 'header':\\n pass\\n else:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n try:\\n data_ps2 = field_ps2['data']\\n data_ps1 = field_ps1['data']\\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\\n elif fill_time:\\n add = np.ma.zeros(len(date_list))\\n add[:] = np.nan\\n afield['data'] = np.append(np.append(data_ps2[:cut],add), data_ps1)\\n else:\\n afield['data'] = np.append(data_ps2[:cut], data_ps1)\\n except:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n \\n else:\\n if keep_unique:\\n newattribute = getattr(ps1,attribute)\\n newattribute['time'] = ps1['datetime']['data']\\n setattr(new_ps, attribute, newattribute)\\n else:\\n pass\\n if keep_unique is False:\\n # get rid of attributes which were in ps2 but not in ps1\\n for attribute in ps2.__dict__.keys():\\n if attribute in ps1.__dict__.keys():\\n pass\\n else:\\n delattr(new_ps, attribute)\\n \\n \\n else:\\n # ps2 starts after ps1 ends\\n new_ps = copy.deepcopy(ps1)\\n for attribute in ps2.__dict__.keys():\\n if attribute in ps1.__dict__.keys():\\n afield = getattr(new_ps, attribute)\\n if attribute == 'diameter':\\n st11, st12, st21, st22, diamlist = check_diameters(ps1.diameter['data'], ps2.diameter['data'], ps1.instrument_type)\\n for var in new_ps.data['variables']:\\n if fill_time is True:\\n add = np.ma.zeros((ps1.data[var]['data'].shape[0],len(date_list))) \\n add[:] = np.nan\\n newdata = np.append(ps1.data[var]['data'],add,axis=1)\\n ps1.data[var]['data'] = newdata\\n \\n sh1 = ps1.data[var]['data'].shape\\n sh2 = ps2.data[var]['data'].shape\\n newfields = (len(diamlist) ,sh1[1] + sh2[1])\\n new_field = np.ma.zeros(newfields)\\n new_field[:] = np.ma.masked\\n \\n new_field[st11:st12, 0:ps1.data[var]['data'][:,:cut].shape[1]] = ps1.data[var]['data'][:,:cut]\\n new_field[st21:st22, ps1.data[var]['data'][:,:cut].shape[1]:] = ps2.data[var]['data']\\n \\n new_ps.data[var]['data'] = new_field\\n \\n afield['data'] = diamlist\\n \\n elif attribute == 'data':\\n # data has been appended with diameters\\n pass\\n else:\\n try:\\n field_ps2 = getattr(ps2, attribute)\\n field_ps1 = getattr(ps1, attribute)\\n except TypeError:\\n if attribute == 'header':\\n pass\\n else:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n try:\\n data_ps2 = field_ps2['data']\\n data_ps1 = field_ps1['data']\\n if attribute in ['date', 'datetime', 'time']: # these have already been extended with the correct data\\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\\n elif fill_time:\\n add = np.ma.zeros(len(date_list))\\n add[:] = np.nan\\n afield['data'] = np.append(np.append(data_ps1[:cut],add), data_ps2)\\n else:\\n afield['data'] = np.append(data_ps1[:cut], data_ps2)\\n except:\\n if message:\\n print( (\\\"Could not append %s attribute\\\")%(attribute) )\\n \\n else:\\n if keep_unique:\\n newattribute = getattr(ps2,attribute)\\n newattribute['time'] = ps2['datetime']['data']\\n setattr(new_ps, attribute,newattribute)\\n else:\\n pass\\n if keep_unique is False:\\n # get rid of attributes which were in ps2 but not in ps1\\n for attribute in ps1.__dict__.keys():\\n if attribute in ps2.__dict__.keys():\\n pass\\n else:\\n delattr(new_ps, attribute)\\n \\n new_ps.sample['data'] = np.arange(1.0, len(new_ps.datetime['data'])+1)\\n new_ps.instrument_type = ps1.instrument_type.split('_')[0] + '_concatenated'\\n \\n if message:\\n print('filltime: ', fill_time)\\n \\n return new_ps\",\n \"def _periodically_create_records(self):\\n # WINNERS holds the members that have 'won' this cycle\\n winners = set()\\n\\n while True:\\n now = time()\\n start_climb = int(now / CYCLE_SIZE) * CYCLE_SIZE\\n start_create = start_climb + CYCLE_SIZE * 0.5\\n start_idle = start_climb + CYCLE_SIZE * 0.9\\n start_next = start_climb + CYCLE_SIZE\\n\\n if start_climb <= now < start_create:\\n yield start_create - now\\n\\n elif start_create <= now < start_idle and len(winners) < self._signature_count:\\n logger.debug(\\\"c%d record creation phase. wait %.2f seconds until record creation\\\", int(now / CYCLE_SIZE), CYCLE_SIZE * 0.4 / self._signature_count)\\n yield (CYCLE_SIZE * 0.4 / self._signature_count) * pythonrandlib.random()\\n\\n # find the best candidate for this cycle\\n score = 0\\n winner = None\\n for member in self._slope.iterkeys():\\n book = self.get_book(member)\\n if book.score > score and not member in winners:\\n winner = member\\n\\n if winner:\\n logger.debug(\\\"c%d attempt record creation %s\\\", int(now / CYCLE_SIZE), winner.mid.encode(\\\"HEX\\\"))\\n record_candidate = self._slope[winner]\\n\\n # prevent this winner to 'win' again in this cycle\\n winners.add(winner)\\n\\n # # TODO: this may be and invalid assumption\\n # # assume that the peer is online\\n # record_candidate.history.set(now)\\n\\n self._dispersy.callback.unregister(record_candidate.callback_id)\\n self.create_barter_record(record_candidate.candidate, winner)\\n\\n else:\\n logger.debug(\\\"c%d no peers available for record creation (%d peers on slope)\\\", int(now / CYCLE_SIZE), len(self._slope))\\n\\n else:\\n logger.debug(\\\"c%d second climbing phase. wait %d seconds until the next phase\\\", int(now / CYCLE_SIZE), start_next - now)\\n assert now >= start_idle or len(winners) >= self._signature_count\\n for record_candidate in self._slope.itervalues():\\n self._dispersy.callback.unregister(record_candidate.callback_id)\\n self._slope = {}\\n winners = set()\\n yield start_next - now\",\n \"def two_in_one(obs_file,et,subevent):\\r\\n \\r\\n #in this function, the \\\"original time window\\\" talked about in the comments\\r\\n #refers to the start and end times that were input to create the file obs_file,\\r\\n #which will likely have been created using the database_extraction function\\r\\n \\r\\n #opening first output file created by operational_sep_quantities\\r\\n with open(obs_file, 'r') as o:\\r\\n out = js.load(o)\\r\\n \\r\\n #all events recorded in that output file\\r\\n ongoing_events = (out['sep_forecast_submission']['triggers'][0]['particle_intensity']\\r\\n ['ongoing_events'])\\r\\n \\r\\n #creating lists for values from each event\\r\\n end_times = [] \\r\\n start_times = []\\r\\n energy_thresholds = []\\r\\n flux_thresholds = []\\r\\n out_names = []\\r\\n \\r\\n #appending values to lists for each event\\r\\n for i in range(len(ongoing_events)):\\r\\n start_times.append(parse(ongoing_events[i]['start_time']))\\r\\n end_times.append(parse(ongoing_events[i]['end_time']))\\r\\n energy_thresholds.append(ongoing_events[i]['energy_min'])\\r\\n flux_thresholds.append(float(ongoing_events[i]['threshold']))\\r\\n \\r\\n #checking if there was a second event for each threshold\\r\\n for i in range(len(end_times)):\\r\\n end = end_times[i]\\r\\n #if the end time of an event for any threshold was a day before the last day\\r\\n #in the original time window given, will check if ONLY THAT THRESHOLD\\r\\n #had another event after the first one, using the end time of the first\\r\\n #event of that threshold as the new start time of the event window\\r\\n if end.date() < et.date():\\r\\n print('end time to use as new start time: %s' %end)\\r\\n #figuring out which threshold this end time was for\\r\\n flux_thresh = int(flux_thresholds[i])\\r\\n energy_thresh = int(energy_thresholds[i])\\r\\n print('extracting second event for threshold ' + str(flux_thresh) + ' MeV '\\r\\n + str(energy_thresh) + ' pfu')\\r\\n #new start time (2 days in advance bc the database_extraction function\\r\\n #makes the start time 2 days prior, so will cancel that out)\\r\\n st = end + timedelta(days=2)\\r\\n #thresholds in correct format\\r\\n thresholds = str(energy_thresh) + ',' + str(flux_thresh)\\r\\n print('thresholds: %s' %thresholds)\\r\\n #creating observation data for second event for thresholds given\\r\\n out_names.append(Path(cfg.obs_path) /\\r\\n database_extraction(st,et,instrument_chosen,subevent,\\r\\n thresholds = thresholds,\\r\\n one_thresh = True))\\r\\n \\r\\n #returns list of all new files created by this function\\r\\n return(out_names)\",\n \"def merge_logs(self):\\n ourlog = LogData()\\n for l in self.data_set:\\n ourlog.entries = ourlog.entries + l.entries\\n ourlog.sort_time()\\n self.finalized_data = ourlog\",\n \"def merge_all_data(self):\\n \\n logging.info('***** Starting the merging process ')\\n\\n \\n \\\"\\\"\\\" All possible unqiue_dates to loop on \\\"\\\"\\\"\\n date_times = self.merged_unique_dates\\n date_times.sort()\\n \\n date_times = np.array(date_times) \\n \\n \\\"\\\"\\\" List storing the indices of the date_index of the merged dataset \\\"\\\"\\\"\\n all_merged_obs , all_merged_head, all_merged_fb , merged_indices , merged_date_time, mi= [] , [] , [] , [] , [], []\\n \\n \\\"\\\"\\\" Dictionary that will contain the merged file. \\\"\\\"\\\" \\n # rand = datetime.strptime('1981-01-03 12:00:00', '%Y-%m-%d %H:%M:%S') \\n #for dt in date_times[3008:3100]: # loop over all the possible date_times \\n \\n tot = len(date_times)\\n for dt, c in zip(date_times[3008:3100], range(tot) ): # loop over all the possible date_times \\n #print('Analize : ', str(c) , '/', str(tot) , ' ', dt , ' ', now(time.time()) )\\n \\n logging.info('Analize : %s %s /', str(c) , str(tot) )\\n \\n cleaned_df_container = {} \\n chunk = ''\\n \\n for k in self.dataset_per_dt[dt] : # checking the list of available datasets \\n \\n index, index_up = self.unique_dates[k]['indices'][dt]['low'] , self.unique_dates[k]['indices'][dt]['up'] # extracting the exact chunk of the dataframe where the data of this are stored \\n \\n chunk = self.data[k]['dataframe'].iloc[index:index_up]\\n \\n chunk['date_time'] = dt\\n chunk = self.clean_dataframe(chunk) # cleaning from wrong or nan values \\n \\n if len(chunk)==0:\\n continue\\n \\n cleaned_df_container[k] = {} \\n cleaned_df_container[k]['df'] = chunk # cleaned dataframe \\n\\n \\n if all(value == 0 for value in cleaned_df_container.values()):\\n logging.debug('No data were found! ')\\n continue\\n \\n merged_observations_table, best_ds, duplicates, header = self.merge_record(dt, container = cleaned_df_container)\\n \\n merged_observations_table['source_id'] = best_ds # adding extra columns i.e. chosen dataset, other dataset with data, number of pressure levels \\n merged_observations_table['z_coordinate_type'] = 1 # only pressure inn [Pa] available at the moment. Check z_coordinate_type table for the correpsonding code \\n \\n \\n \\\"\\\"\\\" Extracting the merged feedback, flagging the advanced_observations_feedback flag = 1\\\"\\\"\\\"\\n feedback, merged_obs = self.get_reanalysis_feedback( dt, merged_observations_table , reanalysis='era5fb', best_ds= best_ds)\\n all_merged_fb.append(feedback) \\n all_merged_obs.append(merged_obs)\\n \\n \\\"\\\"\\\" Setting the correct report_id in the header table \\\"\\\"\\\"\\n merged_report_id = merged_obs['report_id'].values[0] # same report_id as calculated in the observation_table \\n header['report_id'] = merged_report_id \\n all_merged_head.append(header)\\n \\n #if len(merged_observations_table) != len(header): \\n #print('lengths check best ds: ', best_ds , ' obs_merged: ' , len(merged_observations_table), ' feedback:' , len(feedback) , ' header: ' , len(header) )\\n #print( len(merged_observations_table), ' ' , len(feedback) )\\n\\n \\\"\\\"\\\" New merged recordindex and recordtimestamps indices \\\"\\\"\\\"\\n merged_indices.append(len(merged_observations_table)) \\n merged_date_time.append(dt)\\n\\n\\n \\\"\\\"\\\" Storing the merged date_time values and indices \\\"\\\"\\\"\\n di=xr.Dataset()\\n merged_date_time = np.array(merged_date_time)\\n di['recordtimestamp'] = ( {'recordtimestamp' : merged_date_time.shape } , merged_date_time )\\n \\n \\n \\\"\\\"\\\" Creating the merged indices \\\"\\\"\\\"\\n mi.append(0)\\n for i,ind in zip(merged_indices[0:], range(len(merged_indices[0:]) ) ) :\\n mi.append(mi[ind] + i )\\n mi = np.array(mi) \\n di['recordindex'] = ( {'recordindex' : mi.shape } , mi )\\n self.MergedRecordIndex = di \\n \\n \\n \\\"\\\"\\\" Creating the merged dataframes \\\"\\\"\\\"\\n logging.debug('*** Concatenating the observations_table dataframes' ) \\n merged_obs = pd.concat (all_merged_obs)\\n \\n self.MergedObs = merged_obs \\n logging.debug('*** Finished concatenating theobservations_table dataframes' ) \\n \\n logging.debug('*** Concatenating the header_table dataframes' ) \\n merged_hd = pd.concat (all_merged_head)\\n self.MergedHead = merged_hd \\n logging.debug('*** Finished concatenating the header_table dataframes' ) \\n \\n logging.debug('*** Concatenating the feedback dataframes' ) \\n merged_fb = pd.concat (all_merged_fb)\\n self.MergedFeedback = merged_fb \\n logging.debug('*** Finished concatenating the feedback dataframes' ) \\n\\n return 0\",\n \"def _fill_day_dicts(self):\\n today = datetime.date.today()\\n for i, record in enumerate(self._dataset):\\n if (record[\\\"createdAt\\\"] / 1000) > time.mktime((today - datetime.timedelta(days=30)).timetuple()):\\n self._add_record(self._all30_dict, record, key=i)\\n\\n elif (record[\\\"createdAt\\\"] / 1000) > time.mktime((today - datetime.timedelta(days=60)).timetuple()):\\n self._add_record(self._all60_dict, record, key=i)\\n\\n else:\\n self._add_record(self._all90_dict, record, key=i)\",\n \"def merge(self, other, gap_method=\\\"slinear\\\", new_sample_rate=None):\\n if new_sample_rate is not None:\\n merge_sample_rate = new_sample_rate\\n combine_list = [self.decimate(new_sample_rate).dataset]\\n else:\\n merge_sample_rate = self.sample_rate\\n combine_list = [self.dataset]\\n\\n ts_filters = self.filters\\n if isinstance(other, (list, tuple)):\\n for run in other:\\n if not isinstance(run, RunTS):\\n raise TypeError(f\\\"Cannot combine {type(run)} with RunTS.\\\")\\n\\n if new_sample_rate is not None:\\n run = run.decimate(new_sample_rate)\\n combine_list.append(run.dataset)\\n ts_filters.update(run.filters)\\n else:\\n if not isinstance(other, RunTS):\\n raise TypeError(f\\\"Cannot combine {type(other)} with RunTS.\\\")\\n\\n if new_sample_rate is not None:\\n other = other.decimate(new_sample_rate)\\n combine_list.append(other.dataset)\\n ts_filters.update(other.filters)\\n\\n # combine into a data set use override to keep attrs from original\\n\\n combined_ds = xr.combine_by_coords(\\n combine_list, combine_attrs=\\\"override\\\"\\n )\\n\\n n_samples = (\\n merge_sample_rate\\n * float(\\n combined_ds.time.max().values - combined_ds.time.min().values\\n )\\n / 1e9\\n ) + 1\\n\\n new_dt_index = make_dt_coordinates(\\n combined_ds.time.min().values,\\n merge_sample_rate,\\n n_samples,\\n self.logger,\\n )\\n\\n run_metadata = self.run_metadata.copy()\\n run_metadata.sample_rate = merge_sample_rate\\n\\n new_run = RunTS(\\n run_metadata=self.run_metadata,\\n station_metadata=self.station_metadata,\\n survey_metadata=self.survey_metadata,\\n )\\n\\n ## tried reindex then interpolate_na, but that has issues if the\\n ## intial time index does not exactly match up with the new time index\\n ## and then get a bunch of Nan, unless use nearest or pad, but then\\n ## gaps are not filled correctly, just do a interp seems easier.\\n new_run.dataset = combined_ds.interp(\\n time=new_dt_index, method=gap_method\\n )\\n\\n # update channel attributes\\n for ch in new_run.channels:\\n new_run.dataset[ch].attrs[\\\"time_period.start\\\"] = new_run.start\\n new_run.dataset[ch].attrs[\\\"time_period.end\\\"] = new_run.end\\n\\n new_run.run_metadata.update_time_period()\\n new_run.station_metadata.update_time_period()\\n new_run.survey_metadata.update_time_period()\\n new_run.filters = ts_filters\\n\\n return new_run\",\n \"def get_data(self):\\n# epoch_from = 1301641200\\n# epoch_to = epoch_from+60*60*24\\n \\\"\\\"\\\"\\n letting runs finish for 2 more hours\\n ideally, want to make this a function of time from schedule plus some\\n variation, like 1 hour just in case\\n \\\"\\\"\\\" \\n# epoch_to_adjusted = epoch_to + 7200\\n conn = self.connect_to_mongo()\\n db = conn.muni\\n \\n# print \\\"==== Collecting starting runs from %s to %s ====\\\"\\\\\\n# % (str(time.ctime(epoch_from)), str(time.ctime(epoch_to)))\\n \\\"\\\"\\\"\\n > db.location.find({loc:{$within:{$center:[[37.80241, -122.4364],\\n 0.01]}}})\\n > db.location.find({loc:{$within:{$center:[[37.76048, -122.38895],\\n 0.002]}}})\\n \\\"\\\"\\\"\\n bus_ids = db.location.find({'route':self.route_name}).distinct(\\\"bus_id\\\")\\n for bus_id in bus_ids:\\n c_start = db.location.find({\\\"bus_id\\\":bus_id,\\n \\\"loc\\\":{\\\"$within\\\":{\\\"$center\\\":[[self.start_lat, self.start_lon],\\n self.start_prec]}}\\n }).sort(\\\"cur_time\\\", DESCENDING)\\n self.massage_start_data(c_start)\\n \\\"\\\"\\\"\\n TODO: the end point seems to be too nice to Muni, need to tighten\\n the circle a little\\n \\\"\\\"\\\"\\n c_end = db.location.find({\\\"bus_id\\\":bus_id,\\n \\\"loc\\\":{\\\"$within\\\":{\\\"$center\\\":[[self.end_lat, self.end_lon],\\n self.end_prec]}}\\n }).sort(\\\"cur_time\\\", ASCENDING)\\n self.massage_end_data(c_end)\\n if self.to_log:\\n print self.start_bus_ids_to_times\\n print self.end_bus_ids_to_times\\n \\n return self.start_bus_ids_to_times, self.end_bus_ids_to_times\",\n \"def generate_record(self, data_dictionaries, group_by):\\n result = {}\\n\\n for one_measurement in data_dictionaries:\\n time = one_measurement['datetime']\\n\\n if isinstance(time, str):\\n if self.timezone:\\n time = arrow.get(time).shift(hours=6) # TODO: fix utc conversion\\n else:\\n time = arrow.get(time)\\n\\n record = Record(self.name, self.lat, self.lon, self.height, time)\\n\\n del one_measurement['datetime']\\n\\n one_measurement = {k: float(v) for k, v in one_measurement.items()}\\n\\n record.merge(one_measurement)\\n\\n key = group_by(time)\\n \\n if key == '2016-04-01_00':\\n break\\n\\n record_string = record.little_r_report()\\n\\n try:\\n result[key].append(record_string)\\n except KeyError:\\n result[key] = [record_string]\\n\\n return result\",\n \"def extract_tt_by_periods(ttri, periods, start_time, end_time, filters):\\n logger = getLogger(__name__)\\n # sess = conn.get_session()\\n das = {}\\n all_wz_features = {}\\n all_wz_laneconfigs = {}\\n\\n # collecting daily data\\n for prd in periods:\\n logger.debug('>>>> retrieving data for %s' % prd.get_date_string())\\n year = prd.start_date.year\\n sdate = prd.start_date\\n edate = prd.end_date\\n if year not in das:\\n da_tt = tt.TravelTimeDataAccess(year)\\n da_tt_wz = tt_workzone.TTWorkZoneDataAccess(year)\\n da_tt_wz_feature = wz_feature.WZFeatureDataAccess()\\n da_tt_wz_lncfg = wz_laneconfig.WZLaneConfigDataAccess()\\n da_tt_weather = tt_weather.TTWeatherDataAccess(year)\\n da_tt_snowmgmt = tt_snowmgmt.TTSnowManagementDataAccess(year)\\n da_tt_incident = tt_incident.TTIncidentDataAccess(year)\\n da_tt_specialevent = tt_specialevent.TTSpecialeventDataAccess(year)\\n das[year] = (\\n da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent)\\n\\n (da_tt, da_tt_wz, da_tt_wz_feature, da_tt_wz_lncfg, da_tt_weather, da_tt_snowmgmt, da_tt_incident,\\n da_tt_specialevent) = das[year]\\n\\n # traveltimes = da_tt.list_by_period(ttri.id, self.prd)\\n weathers = da_tt_weather.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WeatherInfo] \\\"\\\"\\\"\\n workzones = da_tt_wz.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.WorkZoneInfo] \\\"\\\"\\\"\\n incidents = da_tt_incident.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.IncidentInfo] \\\"\\\"\\\"\\n snowmgmts = da_tt_snowmgmt.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SnowManagementInfo] \\\"\\\"\\\"\\n specialevents = da_tt_specialevent.list(ttri.id, sdate, edate, as_model=True)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.SpecialEventInfo] \\\"\\\"\\\"\\n traveltimes = da_tt.list_by_period(ttri.id, prd)\\n \\\"\\\"\\\":type: list[pyticas_tetres.ttrms_types.TravelTimeInfo] \\\"\\\"\\\"\\n\\n if not any(weathers):\\n logger.debug('>>>> end of retrieving data for %s (no weather data)' % prd.get_date_string())\\n continue\\n\\n extras = {\\n 'weathers': {_tt.id: [] for _tt in traveltimes},\\n 'workzones': {_tt.id: [] for _tt in traveltimes},\\n 'incidents': {_tt.id: [] for _tt in traveltimes},\\n 'specialevents': {_tt.id: [] for _tt in traveltimes},\\n 'snowmgmts': {_tt.id: [] for _tt in traveltimes},\\n }\\n \\\"\\\"\\\":type: dict[str, dict[int, list]]\\\"\\\"\\\"\\n\\n _put_to_bucket(ttri, weathers, extras['weathers'], da_tt_weather, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, workzones, extras['workzones'], da_tt_wz, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, incidents, extras['incidents'], da_tt_incident, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, snowmgmts, extras['snowmgmts'], da_tt_snowmgmt, year, all_wz_features, all_wz_laneconfigs, das)\\n _put_to_bucket(ttri, specialevents, extras['specialevents'], da_tt_specialevent, year, all_wz_features, all_wz_laneconfigs, das)\\n\\n for tti in traveltimes:\\n _tt_weathers = extras['weathers'][tti.id]\\n extdata = ExtData(tti,\\n _tt_weathers[0] if _tt_weathers else None,\\n extras['incidents'][tti.id],\\n extras['workzones'][tti.id],\\n extras['specialevents'][tti.id],\\n extras['snowmgmts'][tti.id])\\n\\n if start_time <= tti.str2datetime(tti.time).time() <= end_time:\\n for ef in filters:\\n try:\\n ef.check(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 1)' % prd.get_date_string())\\n continue\\n else:\\n for ef in filters:\\n try:\\n ef.check_outofrange(extdata)\\n except Exception as ex:\\n tb.traceback(ex)\\n logger.debug('>>>> end of retrieving data for %s (error occured 2)' % prd.get_date_string())\\n continue\\n\\n del extras\\n logger.debug('>>>> end of retrieving data for %s' % prd.get_date_string())\\n\\n # sess.close()\",\n \"async def fetch_all_periods_raw(self):\\n self._logger.info(\\\"Fetching current period data\\\")\\n await self._client.select_customer(self.account_id, self.customer_id)\\n\\n params = {'idContrat': '0' + self.contract_id}\\n res = await self._client.http_request(CONTRACT_CURRENT_URL_3, \\\"get\\\", params=params)\\n text_res = await res.text()\\n\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n res = await self._client.http_request(CONTRACT_CURRENT_URL_2, \\\"get\\\", headers=headers)\\n text_res = await res.text()\\n # We can not use res.json() because the response header are not application/json\\n json_res = json.loads(text_res)['results']\\n\\n self._all_periods_raw = json_res\",\n \"def aggregate_local_and_hist_klines(self, symbol, intervals): \\n result = {}\\n\\n for interval in intervals:\\n filename = PATH_HIST_KLINES[interval]\\n file_klines = pd.DataFrame()\\n\\n request_begin = strat_begin = self.start_ts - 300000\\n write_mode = 'w'\\n\\n try:\\n file_klines = pd.read_csv(filename, index_col=0, names = [\\\"Open\\\",\\\"High\\\",\\\"Low\\\",\\\"Close\\\",\\\"Volume\\\",\\\"TurnOver\\\",\\\"Date\\\"])\\n file_klines.loc[:,'Date'] = [datetime.fromtimestamp(i).strftime('%Y-%m-%d %H:%M:%S.%d')[:-3] for i in file_klines.index]\\n newest = file_klines.tail(1).index[0]\\n oldest = file_klines.head(1).index[0]\\n if oldest - strat_begin > interval_bybit_notation(interval) * 60:\\n request_begin = strat_begin\\n write_mode = 'w'\\n file_klines = pd.DataFrame()\\n else:\\n request_begin = int(newest) + interval_bybit_notation(interval) * 60\\n write_mode = 'a'\\n except FileNotFoundError as err:\\n pass\\n\\n bybit_klines = pd.DataFrame()\\n if request_begin < int(datetime.now().timestamp()):\\n output_data = self.bybit.get_hist_klines(symbol, interval_bybit_notation(interval), str(request_begin))\\n column_data = [i[1:] for i in output_data]\\n index = [int(i[0]) for i in output_data]\\n # convert to data frame\\n bybit_klines = pd.DataFrame(column_data, index = index, columns=['Open', 'High', 'Low', 'Close', 'Volume', 'TurnOver'])\\n bybit_klines.loc[:,'Date'] = [datetime.fromtimestamp(i).strftime('%Y-%m-%d %H:%M:%S.%d')[:-3] for i in bybit_klines.index]\\n\\n bybit_klines.to_csv(filename, header = False, mode=write_mode)\\n\\n result[interval] = pd.concat([file_klines, bybit_klines]) if not len(bybit_klines.index) == 0 else file_klines\\n\\n return result\",\n \"def records(self, current=False):\\n now = datetime.now()\\n format = '%Y/%m/%d %H:%M:%S'\\n for key in self.keys:\\n contents = key.get_contents_as_string()\\n (headers, contents) = self._parse_contents(contents)\\n # Ignore the first line and any row that is not of type\\n # `PayerLineItem`.\\n for content in contents[1:]:\\n content = dict(itertools.izip(headers, content))\\n if content.get('RecordType') != 'PayerLineItem':\\n continue\\n start_date = datetime.strptime(\\n content['BillingPeriodStartDate'], format\\n )\\n end_date = datetime.strptime(\\n content['BillingPeriodEndDate'], format\\n )\\n if current:\\n if start_date <= now <= end_date:\\n yield content\\n else:\\n if start_date <= end_date <= now:\\n yield content\",\n \"def get_all_periods(self, df):\\n df_append = pd.DataFrame()\\n for index, element in enumerate(self.periods):\\n df_temp = self.get_period(df, element)\\n df_append = df_append.append(df_temp)\\n return(df_append.sort_index())\",\n \"def get_data(self, date_time):\\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\\n return_columns = ','.join(self.table_columns)\\n with self.con:\\n cur = self.con.cursor()\\n cur.execute(\\\"DROP TABLE IF EXISTS temp;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp2;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp3;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp4;\\\")\\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp AS \\n SELECT * \\n FROM {table} \\n WHERE EFFECTIVEDATE <= '{datetime}';\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, datetime=date_time))\\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp2 AS\\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\\n FROM temp\\n GROUP BY {id}, EFFECTIVEDATE;\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # For each unique set of ids get the record with the most recent effective date.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp3 as\\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\\n FROM temp2\\n GROUP BY {id};\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # Inner join the original table to the set of most recent effective dates and version no.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp4 AS\\n SELECT * \\n FROM {table} \\n INNER JOIN temp3 \\n USING ({id}, VERSIONNO, EFFECTIVEDATE);\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, id=id_columns))\\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\\n query = \\\"\\\"\\\"SELECT {cols} FROM temp4 ;\\\"\\\"\\\"\\n query = query.format(cols=return_columns)\\n data = pd.read_sql_query(query, con=self.con)\\n return data\",\n \"def combine_dict(self, dict2):\\n # iterate through smaller data set\\n # base_set will be the larger set and is used for updating\\n if len(self.content[\\\"values\\\"]) > len(dict2[\\\"values\\\"]):\\n large_set = self.content[\\\"values\\\"]\\n small_set = dict2[\\\"values\\\"]\\n base_set = self.content\\n else:\\n small_set = self.content[\\\"values\\\"]\\n large_set = dict2[\\\"values\\\"]\\n base_set = dict2\\n\\n subset = {}\\n for key in small_set.keys():\\n # determine wether to compare keys\\n if key in large_set:\\n updated_l = large_set[key][\\\"updated_at\\\"]\\n updated_s = small_set[key][\\\"updated_at\\\"]\\n if updated_l == 'NULL':\\n if updated_s != 'NULL':\\n # update to not NULL set\\n # if both updated_at are NULL, things\\n # are ambiguos. We could defer to created_at\\n # but for simplicity we will default to\\n # the values in the larger set\\n subset[key] = small_set[key]\\n else:\\n if updated_s == 'NULL':\\n # update to not NULL set\\n subset[key] = large_set[key]\\n else:\\n if updated_l > updated_s:\\n subset[key] = large_set[key]\\n else:\\n subset[key] =small_set[key]\\n else:\\n subset[key] = small_set[key]\\n base_set[\\\"values\\\"].update(subset)\\n new_obj = BackupData()\\n new_obj.load_from_dict(base_set)\\n return new_obj\",\n \"def extract(self, extract_from, extract_to):\\n # Some API calls do not expect a TZ, so we have to remove the timezone\\n # from the dates. We assume that all dates coming from upstream are\\n # in UTC TZ.\\n extract_from = extract_from.replace(tzinfo=None)\\n extract_to = extract_to.replace(tzinfo=None)\\n\\n # Our records\\n self.records = {}\\n self.acc_records = {}\\n\\n # We cannot use just 'changes-since' in the servers.list() API query,\\n # as it will only include servers that have changed its status after\\n # that date. However we cannot just get all the usages and then query\\n # server by server, as deleted servers are not returned by the usages\\n # call. Moreover, Nova resets the start_time after performing some\\n # actions on the server (rebuild, resize, rescue). If we use that time,\\n # we may get a drop in the wall time, as a server that has been resized\\n # in the middle of its lifetime will suddenly change its start_time\\n #\\n # Therefore, what we do is the following (hackish approach)\\n #\\n # 1.- List all the servers that changed its status after the start time\\n # for the reporting period\\n # 2.- Build the records for the period [start, end] using those servers\\n # 3.- Get all the usages, being aware that the start time may be wrong\\n # 4.- Iter over the usages and:\\n # 4.1.- get information for servers that are not returned by the query\\n # in (1), for instance servers that have not changed it status.\\n # We build then the records for those severs\\n # 4.2.- For all the servers, adjust the CPU, memory and disk resources\\n # as the flavor may not exist, but we can get those resources\\n # from the usages API.\\n\\n # Lets start\\n\\n # 1.- List all the deleted servers from that period.\\n servers = self._get_servers(extract_from)\\n # 2.- Build the records for the period. Drop servers outside the period\\n # (we do this manually as we cannot limit the query to a period, only\\n # changes after start date).\\n self._process_servers_for_period(servers, extract_from, extract_to)\\n\\n # 3.- Get all the usages for the period\\n usages = self._get_usages(extract_from, extract_to)\\n # 4.- Iter over the results and\\n # This one will also generate accelerator records if GPU flavors\\n # are found.\\n self._process_usages_for_period(usages, extract_from, extract_to)\\n\\n return list(self.records.values()) + list(self.acc_records.values())\",\n \"def compare_results_data(result1, result2):\\n def average(r1, r2, delta):\\n return (r2 - r1) / delta\\n\\n if (result1 and result2) and (len(result1) and len(result2)):\\n results = result1.copy()\\n\\n time_delta = round(float(result2['timestamp']) - float(result1['timestamp']))\\n results['time_delta'] = time_delta\\n\\n for key in result1['data'].keys():\\n if key in result2['data']:\\n results['data'][key]['value'] = round(\\n average(float(result1['data'][key]['value']), float(result2['data'][key]['value']), time_delta),\\n 2)\\n\\n results['timestamp'] = time.time()\\n\\n return results\\n \\n return {}\",\n \"def since(self, ts):\\n while True:\\n items = super(TailingOplog, self).since(ts)\\n for doc in items:\\n yield doc\\n ts = doc['ts']\",\n \"def calculate_new_rating_period(start_datetime, end_datetime):\\n # Create the rating period\\n rating_period = models.RatingPeriod.objects.create(\\n start_datetime=start_datetime, end_datetime=end_datetime\\n )\\n\\n # Grab all games that will be in this rating period\\n games = models.Game.objects.filter(\\n datetime_played__gte=start_datetime, datetime_played__lte=end_datetime\\n )\\n\\n # Mark all of the above games as belonging in this rating period\\n for game in games:\\n game.rating_period = rating_period\\n game.save()\\n\\n # For each player, find all their matches, their scores in those\\n # matches; then calculate their ratings. The new_ratings dictionary\\n # contains players as keys, and dictionaries containing their new\\n # rating parameters as the dictionary values.\\n new_ratings = {}\\n\\n for player in models.Player.objects.all():\\n # Don't calculate anything if they player's first game is prior\\n # to this rating period\\n first_game_played = player.get_first_game_played()\\n\\n if (\\n first_game_played is None\\n or first_game_played.datetime_played > end_datetime\\n ):\\n continue\\n\\n # Get the players rating parameters\\n player_rating = player.rating\\n player_rating_deviation = player.rating_deviation\\n player_rating_volatility = player.rating_volatility\\n player_inactivity = player.inactivity\\n\\n # Build up the per-game rating parameters of opponents\\n opponent_ratings = []\\n opponent_rating_deviations = []\\n scores = []\\n\\n for won_game in games.filter(winner=player):\\n opponent_ratings.append(won_game.loser.rating)\\n opponent_rating_deviations.append(won_game.loser.rating_deviation)\\n scores.append(1)\\n\\n for lost_game in games.filter(loser=player):\\n opponent_ratings.append(lost_game.winner.rating)\\n opponent_rating_deviations.append(\\n lost_game.winner.rating_deviation\\n )\\n scores.append(0)\\n\\n # Convert empty lists (meaning no matches) to None types\\n if not opponent_ratings:\\n opponent_ratings = None\\n opponent_rating_deviations = None\\n scores = None\\n\\n new_player_rating, new_player_rating_deviation, new_player_rating_volatility = calculate_player_rating(\\n r=player_rating,\\n RD=player_rating_deviation,\\n sigma=player_rating_volatility,\\n opponent_rs=opponent_ratings,\\n opponent_RDs=opponent_rating_deviations,\\n scores=scores,\\n )\\n\\n # Calculate new inactivity\\n if opponent_ratings is None:\\n new_player_inactivity = player_inactivity + 1\\n else:\\n new_player_inactivity = 0\\n\\n # Determine if the player is labelled as active\\n new_player_is_active = bool(\\n new_player_inactivity\\n < settings.NUMBER_OF_RATING_PERIODS_MISSED_TO_BE_INACTIVE\\n )\\n\\n new_ratings[player] = {\\n \\\"player_ranking\\\": None,\\n \\\"player_ranking_delta\\\": None,\\n \\\"player_rating\\\": new_player_rating,\\n \\\"player_rating_deviation\\\": new_player_rating_deviation,\\n \\\"player_rating_volatility\\\": new_player_rating_volatility,\\n \\\"player_inactivity\\\": new_player_inactivity,\\n \\\"player_is_active\\\": new_player_is_active,\\n }\\n\\n # Filter all active players and sort by rating\\n new_active_player_ratings = [\\n (player, new_rating[\\\"player_rating\\\"])\\n for player, new_rating in new_ratings.items()\\n if new_rating[\\\"player_is_active\\\"]\\n ]\\n new_active_player_ratings.sort(key=lambda x: x[1], reverse=True)\\n\\n # Form a tuple of active players where the order is their ranking\\n new_active_player_rankings = [\\n player for player, _ in new_active_player_ratings\\n ]\\n\\n # Process new rankings and ranking changes\\n num_active_players = len(new_active_player_rankings)\\n\\n for ranking, player in enumerate(new_active_player_rankings, 1):\\n # Ranking\\n new_ratings[player][\\\"player_ranking\\\"] = ranking\\n\\n # Ranking delta\\n if player.ranking is None:\\n new_ratings[player][\\\"player_ranking_delta\\\"] = (\\n num_active_players - ranking + 1\\n )\\n else:\\n new_ratings[player][\\\"player_ranking_delta\\\"] = (\\n player.ranking - ranking\\n )\\n\\n # Now save all ratings\\n for player, ratings_dict in new_ratings.items():\\n models.PlayerRatingNode.objects.create(\\n player=player,\\n rating_period=rating_period,\\n ranking=ratings_dict[\\\"player_ranking\\\"],\\n ranking_delta=ratings_dict[\\\"player_ranking_delta\\\"],\\n rating=ratings_dict[\\\"player_rating\\\"],\\n rating_deviation=ratings_dict[\\\"player_rating_deviation\\\"],\\n rating_volatility=ratings_dict[\\\"player_rating_volatility\\\"],\\n inactivity=ratings_dict[\\\"player_inactivity\\\"],\\n is_active=ratings_dict[\\\"player_is_active\\\"],\\n )\",\n \"def example_staypoints_merge():\\n p1 = Point(8.5067847, 47.4)\\n\\n t1 = pd.Timestamp(\\\"1971-01-01 00:00:00\\\", tz=\\\"utc\\\")\\n t2 = pd.Timestamp(\\\"1971-01-02 05:00:00\\\", tz=\\\"utc\\\")\\n t3 = pd.Timestamp(\\\"1971-01-02 06:45:00\\\", tz=\\\"utc\\\")\\n t4 = pd.Timestamp(\\\"1971-01-02 08:55:00\\\", tz=\\\"utc\\\")\\n t45 = pd.Timestamp(\\\"1971-01-02 08:57:00\\\", tz=\\\"utc\\\")\\n t5 = pd.Timestamp(\\\"1971-01-02 09:00:00\\\", tz=\\\"utc\\\")\\n t6 = pd.Timestamp(\\\"1971-01-02 09:20:00\\\", tz=\\\"utc\\\")\\n\\n list_dict = [\\n {\\\"id\\\": 1, \\\"user_id\\\": 0, \\\"started_at\\\": t1, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 1},\\n {\\\"id\\\": 5, \\\"user_id\\\": 0, \\\"started_at\\\": t2, \\\"finished_at\\\": t2, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 2, \\\"user_id\\\": 0, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 6, \\\"user_id\\\": 0, \\\"started_at\\\": t4, \\\"finished_at\\\": t45, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 15, \\\"user_id\\\": 0, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 7, \\\"user_id\\\": 1, \\\"started_at\\\": t3, \\\"finished_at\\\": t4, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 80, \\\"user_id\\\": 1, \\\"started_at\\\": t45, \\\"finished_at\\\": t5, \\\"geom\\\": p1, \\\"location_id\\\": 2},\\n {\\\"id\\\": 3, \\\"user_id\\\": 1, \\\"started_at\\\": t5, \\\"finished_at\\\": t6, \\\"geom\\\": p1, \\\"location_id\\\": 4},\\n ]\\n sp = gpd.GeoDataFrame(data=list_dict, geometry=\\\"geom\\\", crs=\\\"EPSG:4326\\\")\\n sp = sp.set_index(\\\"id\\\")\\n sp.as_staypoints\\n\\n # generate empty triplegs for the merge function\\n tpls = pd.DataFrame([], columns=[\\\"user_id\\\", \\\"started_at\\\", \\\"finished_at\\\"])\\n return sp, tpls\",\n \"def newChunk(self, data, sample_period):\\n added = False\\n found = False\\n for r in self._receiving:\\n if r[0] == data.timestamp:\\n r[1][data.sensor - 1] = data\\n found = True\\n break\\n if found is False:\\n self._receiving.append((data.timestamp, [None] * self._sensors))\\n self._receiving[-1][1][data.sensor - 1] = data\\n self._receiving.sort(key=lambda x: x[0])\\n\\n # all data for given timestamp received\\n while len(self._receiving) > 0:\\n r = self._receiving[0]\\n if r[1].count(None) > 0:\\n break\\n dl = len(data.accelerationX)\\n timestamps = np.arange(r[0], r[0] + sample_period * dl, sample_period)\\n\\n def copy_data(start, l):\\n self.data[\\\"timestamp\\\"][\\n self.current_index : self.current_index + l\\n ] = timestamps[start : start + l]\\n for s in range(1, self._sensors + 1):\\n self.data[f\\\"{s} X\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationX[start : start + l]\\n self.data[f\\\"{s} Y\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationY[start : start + l]\\n self.data[f\\\"{s} Z\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationZ[start : start + l]\\n\\n l = min(dl, self._size - self.current_index)\\n if l > 0:\\n copy_data(0, l)\\n self.current_index += l\\n dl -= l\\n\\n if dl > 0:\\n self.current_index = 0\\n self.filled = True\\n copy_data(l, dl)\\n self.current_index = dl\\n\\n self._receiving.remove(r)\\n added = True\\n\\n chunk_removed = False\\n if len(self._receiving) > self._window:\\n self._receiving = self._receiving[1:]\\n chunk_removed = True\\n return (added, chunk_removed)\",\n \"def merging_time_periods(time_periods, min_time_between_periods):\\n n_periods = len(time_periods)\\n merged_time_periods = []\\n index = 0\\n while index < n_periods:\\n time_period = time_periods[index]\\n if len(merged_time_periods) == 0:\\n merged_time_periods.append([time_period[0], time_period[1]])\\n index += 1\\n continue\\n # we check if the time between both is superior at min_time_between_periods\\n last_time_period = time_periods[index - 1]\\n beg_time = last_time_period[1]\\n end_time = time_period[0]\\n if (end_time - beg_time) <= min_time_between_periods:\\n # then we merge them\\n merged_time_periods[-1][1] = time_period[1]\\n index += 1\\n continue\\n else:\\n merged_time_periods.append([time_period[0], time_period[1]])\\n index += 1\\n return merged_time_periods\",\n \"def experiment_periods():\\n # Temperature readings start March 22 2004, loads start Feb 01 2004.\\n # period1_start = pd.Period(\\\"2004-02-01 00:00\\\", \\\"H\\\")\\n # period1_end = pd.Period(\\\"2005-07-01 00:00\\\", \\\"H\\\")\\n # period2_start = pd.Period(\\\"2005-10-01 00:00\\\", \\\"H\\\")\\n # period2_end = pd.Period(\\\"2006-10-01 00:00\\\", \\\"H\\\")\\n period1_start = datetime.datetime.strptime(\\\"2004-02-01 00:00\\\", \\\"%Y-%m-%d %H:%M\\\")\\n period1_end = datetime.datetime.strptime(\\\"2005-07-01 00:00\\\", \\\"%Y-%m-%d %H:%M\\\")\\n period2_start = datetime.datetime.strptime(\\\"2005-10-01 00:00\\\", \\\"%Y-%m-%d %H:%M\\\")\\n period2_end = datetime.datetime.strptime(\\\"2006-10-01 00:00\\\", \\\"%Y-%m-%d %H:%M\\\")\\n return ((period1_start, period1_end), (period2_start, period2_end))\",\n \"def get_timeseries_data(self, table, datetime_start, datetime_end, timechunk=datetime.timedelta(hours=1)):\\n table_schema = LMTDB_TABLES.get(table.upper())\\n if table_schema is None:\\n raise KeyError(\\\"Table '%s' is not valid\\\" % table)\\n else:\\n result_columns = ['TIMESTAMP'] + table_schema['columns']\\n format_dict = {\\n 'schema': ', '.join(result_columns).replace(\\\"TS_ID,\\\", \\\"TIMESTAMP_INFO.TS_ID,\\\"),\\n 'table': table,\\n }\\n\\n index0 = len(self.saved_results.get(table, {'rows': []})['rows'])\\n chunk_start = datetime_start\\n while chunk_start < datetime_end:\\n if timechunk is None:\\n chunk_end = datetime_end\\n else:\\n chunk_end = chunk_start + timechunk\\n if chunk_end > datetime_end:\\n chunk_end = datetime_end\\n start_stamp = chunk_start.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n end_stamp = chunk_end.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n query_str = \\\"\\\"\\\"SELECT\\n %(schema)s\\n FROM\\n %(table)s\\n INNER JOIN TIMESTAMP_INFO ON TIMESTAMP_INFO.TS_ID = %(table)s.TS_ID\\n WHERE\\n TIMESTAMP_INFO.TIMESTAMP >= %%(ps)s\\n AND TIMESTAMP_INFO.TIMESTAMP < %%(ps)s\\n \\\"\\\"\\\" % format_dict\\n self.query(query_str, (start_stamp, end_stamp), table=table, table_schema=table_schema)\\n if timechunk is not None:\\n chunk_start += timechunk\\n\\n return self.saved_results[table]['rows'][index0:], result_columns\",\n \"def get_list_block_time_periods(filename_time_periods):\\n\\n if not hasattr(get_list_block_time_periods, \\\"cached\\\"):\\n get_list_block_time_periods.cached = {}\\n\\n if filename_time_periods not in get_list_block_time_periods.cached:\\n with open(filename_time_periods, 'r') as file:\\n contents = csv.reader(file)\\n next(contents)\\n\\n time_periods = []\\n for row in contents:\\n time_beginning = datetime.datetime.strptime(row[0], \\\"%Y-%m-%d %H:%M:%S\\\")\\n time_ending = datetime.datetime.strptime(row[1], \\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n time_periods.append((time_beginning, time_ending, row[2]))\\n #print(\\\"List of time periods: \\\", time_periods)\\n\\n get_list_block_time_periods.cached[filename_time_periods] = time_periods\\n\\n return get_list_block_time_periods.cached[filename_time_periods]\",\n \"def _join_on_millisec(dfs: list):\\n # Resample to milliseconds befor joining\\n for idx, df in enumerate(dfs):\\n df[\\\"sys_time_dt\\\"] = pd.to_datetime(df[\\\"sys_time\\\"], unit=\\\"ms\\\")\\n df = df.set_index(\\\"sys_time_dt\\\")\\n df = df.drop(columns=[\\\"sys_time\\\"])\\n df = df[~df.index.duplicated(keep=\\\"last\\\")] # Remove index dups\\n dfs[idx] = df.resample(\\\"10ms\\\").interpolate(method=\\\"time\\\")\\n\\n # Join resampled sensor data, drop NaNs, that might be generated for\\n # start or end of session, because not all sensors start/end at same time\\n df_joined = pd.concat(dfs, axis=1).dropna()\\n\\n # Add datetimeindex as ms\\n df_joined[\\\"sys_time\\\"] = df_joined.index.astype(\\\"int64\\\") // 10 ** 6\\n\\n # Reset index to save memory\\n df_joined = df_joined.reset_index(drop=True)\\n\\n return df_joined\",\n \"def _add(self, other):\\n if isinstance(other, SeqPer):\\n per1, lper1 = self.periodical, self.period\\n per2, lper2 = other.periodical, other.period\\n\\n per_length = lcm(lper1, lper2)\\n\\n new_per = []\\n for x in range(per_length):\\n ele1 = per1[x % lper1]\\n ele2 = per2[x % lper2]\\n new_per.append(ele1 + ele2)\\n\\n start, stop = self._intersect_interval(other)\\n return SeqPer(new_per, (self.variables[0], start, stop))\",\n \"def _get_unit_records(self, start_time):\\r\\n\\r\\n if self.optMTTF.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN \\\\\\r\\n ( \\\\\\r\\n SELECT DISTINCT MIN(fld_unit, fld_request_date), \\\\\\r\\n fld_incident_id, fld_request_date, \\\\\\r\\n fld_unit, fld_hardware_id \\\\\\r\\n FROM rtk_incident \\\\\\r\\n GROUP BY fld_unit \\\\\\r\\n ) AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n elif self.optMTBBD.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN \\\\\\r\\n ( \\\\\\r\\n SELECT fld_incident_id, fld_request_date, fld_unit, \\\\\\r\\n fld_hardware_id \\\\\\r\\n FROM rtk_incident \\\\\\r\\n GROUP BY fld_unit, fld_request_date \\\\\\r\\n ) AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n GROUP BY t2.fld_unit, t1.fld_age_at_incident \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n elif self.optMTBF.get_active():\\r\\n _query = \\\"SELECT t2.fld_unit, t1.fld_incident_id, \\\\\\r\\n t1.fld_age_at_incident, t1.fld_failure, \\\\\\r\\n t1.fld_suspension, t1.fld_cnd_nff, \\\\\\r\\n t1.fld_occ_fault, t1.fld_initial_installation, \\\\\\r\\n t1.fld_interval_censored, t2.fld_request_date, \\\\\\r\\n t2.fld_hardware_id \\\\\\r\\n FROM rtk_incident_detail AS t1 \\\\\\r\\n INNER JOIN rtk_incident AS t2 \\\\\\r\\n ON t2.fld_incident_id=t1.fld_incident_id \\\\\\r\\n WHERE t1.fld_age_at_incident >= {0:f} \\\\\\r\\n ORDER BY t2.fld_unit ASC, \\\\\\r\\n t1.fld_age_at_incident ASC, \\\\\\r\\n t2.fld_request_date ASC\\\".format(start_time)\\r\\n\\r\\n (_results, _error_code, __) = self._dao.execute(_query, commit=False)\\r\\n\\r\\n return(_results, _error_code)\",\n \"def load_obstab_feedback_sliced(self, dataset='' , file ='' , datetime='' ):\\n k = dataset \\n F = file \\n dt = datetime\\n \\n if dt != self.unique_dates[k][F]['up_to_dt_slice']:\\n print(\\\"Error! the dit does not correspond to the dt I calculated in the previous loading! \\\")\\n return 0\\n \\n logging.debug(\\\" === (Re)Load data for %s file %s counter %s\\\" , dataset, file, data[k][F][\\\"counter\\\"])\\n print(blue + 'Memory used before reading data: ', process.memory_info().rss/1000000000 , cend)\\n \\n slice_size = self.slice_size\\n \\n file = data[k][F]['h5py_file']\\n rts, ri = data[k][F][\\\"recordtimestamp\\\"][:] , data[k][F][\\\"recordindex\\\"][:]\\n\\n index_min = self.unique_dates[k][F]['indices'][dt]['low'] # here no offset since I am reading the original data \\n ind = np.where(rts==dt)[0][0] # index of specific dt , I need the extremes indices of the next date_time after slicing \\n \\n try: \\n up_to_dt_slice = rts[ind + slice_size ] # \\n index_max = self.unique_dates[k][F]['indices'][up_to_dt_slice]['low'] # maximum index in the array of date_time to slice on\\n update_index = True\\n except:\\n \\\"\\\"\\\" If the dt is too large, I take the whole array \\\"\\\"\\\"\\n index_max = 1000000000000000\\n update_index = False \\n \\n \\n ####################\\n # OBSERVATIONS TABLE\\n #################### \\n logging.debug ('*** Loading observations_table' )\\n obs_tab = file['observations_table'] \\n\\n #print('CHECKING THE INDICES:::: ' , k , ' index_min ', index_min , ' index_max ', index_max )\\n obs_dic= {} \\n for ov in self.observations_table_vars:\\n v = copy.deepcopy( obs_tab[ov][index_min:index_max ] )\\n obs_dic[ov] = v \\n data[k][F]['observations_table']= obs_dic \\n\\n ###########\\n # ERA5FB\\n ###########\\n if k == 'era5_1' or k == 'era5_2':\\n logging.debug('*** Loading era5fb ' )\\n era5fb_tab = file['era5fb']\\n fb_dic = {} \\n for ov in self.era5fb_columns:\\n try:\\n v = copy.deepcopy( era5fb_tab[ov][index_min:index_max ] )\\n fb_dic[ov] = v \\n except:\\n continue\\n #print(\\\"CANNOT FIND \\\", ov ) \\n \\n data[k][F]['era5fb_tab']= fb_dic\\n \\n print(blue + 'Memory used after reading data: ', process.memory_info().rss/1000000000 , cend)\\n \\n \\\"\\\"\\\" Updating the indices \\\"\\\"\\\" \\n self.unique_dates[k][F]['index_offset'] = copy.deepcopy( self.unique_dates[k][F]['index_offset_next'] ) \\n \\n if update_index: \\n self.unique_dates[k][F]['index_offset_next'] = index_max \\n self.unique_dates[k][F]['up_to_dt_slice'] = up_to_dt_slice\\n\\n return 0\",\n \"def reduce_data():\\n snapshots = Snapshot.objects.all()\\n locations = Location.objects.all()\\n lst = []\\n for snapshot in snapshots:\\n lst.append([snapshot.location.name, snapshot.avail_bikes,\\n snapshot.free_stands, snapshot.timestamp])\\n cols = ['location', 'avail_bikes', 'free_stands', 'timestamp']\\n df = pd.DataFrame(lst, columns=cols)\\n df['time'] = df['timestamp'].dt.round('30min').dt.strftime('%H:%M')\\n\\n group = df.groupby(['location', 'time'])\\n means = group.mean()\\n sd = group.std()\\n today = date.today()\\n first = today.replace(day=1)\\n last_month = first - timedelta(days=1)\\n\\n for name, time in means.index:\\n subset_mean = means.xs((name, time), level=(0, 1), axis=0)\\n subset_sd = sd.xs((name, time), level=(0, 1), axis=0)\\n m = Stat.objects.get_or_create(\\n location=locations.get(name=name),\\n avail_bikes_mean=subset_mean['avail_bikes'],\\n free_stands_mean=subset_mean['free_stands'],\\n avail_bikes_sd=subset_sd['avail_bikes'],\\n free_stands_sd=subset_sd['free_stands'],\\n time=time,\\n month=last_month\\n )\\n\\n # snaps = Snapshot.objects.all()\\n # i = 0\\n # length = len(snaps)\\n # for s in snaps:\\n # i += 1\\n # print(i)\\n # if i > 35000:\\n # s.save()\\n # reduce_data()\",\n \"def reload_period_from_analytics(cls, period):\\n counts = googleanalytics.pageviews_by_document(*period_dates(period))\\n if counts:\\n # Delete and remake the rows:\\n # Horribly inefficient until\\n # http://code.djangoproject.com/ticket/9519 is fixed.\\n cls.objects.filter(period=period).delete()\\n for doc_id, visits in counts.iteritems():\\n cls.objects.create(document=Document(pk=doc_id), visits=visits,\\n period=period)\\n else:\\n # Don't erase interesting data if there's nothing to replace it:\\n log.warning('Google Analytics returned no interesting data,'\\n ' so I kept what I had.')\",\n \"def compound(self, period):\\n\\t\\tif self.calendar:\\n\\t\\t\\tperiod = CalendarRangePeriod(period, self.calendar)\\n\\t\\t\\n\\t\\tt = self.daycount.timefreq(period, self.frequency)\\n\\t\\treturn self.compounding(self.rate, t)\",\n \"def update(start_date, end_date, binning):\\n global mean_source1,mean_source,median_source1,median_source, difference_source1,difference_source, difference_source2 , difference_source3, mean_source2, median_source2\\n\\n # if type of start/end date is date, turn it into a datetime,\\n # set time of start/end date time to 12:00\\n\\n def convert_time(t):\\n if type(t) == datetime.date:\\n return datetime.datetime(t.year, t.month, t.day, 12, 0, 0, 0)\\n else:\\n return t.replace(hour=12, minute=0, second=0, microsecond=0)\\n\\n start_date = convert_time(start_date)\\n end_date = convert_time(end_date)\\n if binning is None:\\n binning = ''\\n\\n first_timestamp = start_date.timestamp() + time_offset\\n second_timestamp = end_date.timestamp() + time_offset\\n\\n # query data in mysql database\\n sql1 = 'SELECT str_to_date(datetime,\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\") AS datetime, seeing from seeing ' \\\\\\n ' where datetime >= str_to_date(\\\"{start_date_}\\\",\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\")' \\\\\\n ' and datetime <= str_to_date(\\\"{end_date_}\\\",\\\"%%Y-%%m-%%d %%H:%%i:%%s\\\") ' \\\\\\n .format(start_date_=str(start_date), end_date_=str(end_date))\\n\\n sql2 = 'select _timestamp_,ee50,fwhm,timestamp from tpc_guidance_status__timestamp where timestamp >= {start_date_}' \\\\\\n ' and timestamp<= {end_date_} and guidance_available=\\\"T\\\" ' \\\\\\n ' order by _timestamp_' \\\\\\n .format(start_date_=str(first_timestamp), end_date_=str(second_timestamp))\\n\\n df2 = pd.read_sql(sql2, db.get_engine(app=current_app, bind='els'))\\n df1 = pd.read_sql(sql1, db.get_engine(app=current_app, bind='suthweather'))\\n\\n # setting index time for calculating mean and average\\n df2.index = df2[\\\"_timestamp_\\\"]\\n df1.index = df1['datetime']\\n\\n # It seems that Pandas doesn't change the index type if the data frame is empty, which means that resampling\\n # would fail for an empty data frame. As there will be no row for median or mean , it is safe to just use the\\n # original data frame to avoid this problem.\\n\\n # for external seeing calculating median and mean\\n if not df1.empty:\\n mean1_all = df1.resample(str(binning) + 'T').mean()\\n else:\\n mean1_all = df1.copy(deep=True)\\n source1 = ColumnDataSource(mean1_all)\\n mean_source1.data = source1.data\\n\\n if not df1.empty:\\n median1_all = df1.resample(str(binning) + 'T').median()\\n else:\\n median1_all = df1.copy(deep=True)\\n source = ColumnDataSource(median1_all)\\n median_source1.data = source.data\\n\\n # calculate mean and median for ee50\\n if not df2.empty:\\n mean_all = df2.resample(str(binning) + 'T').mean()\\n else:\\n mean_all = df2.copy(deep=True)\\n source3 = ColumnDataSource(mean_all)\\n mean_source.data = source3.data\\n\\n if not df2.empty:\\n median_all = df2.resample(str(binning) + 'T').median()\\n else:\\n median_all = df2.copy(deep=True)\\n source4 = ColumnDataSource(median_all)\\n median_source.data = source4.data\\n\\n #calculate mean and median for fwhm\\n if not df2.empty:\\n mean_all1 = df2.resample(str(binning) + 'T').mean()\\n else:\\n mean_all1 = df2.copy(deep=True)\\n source4 = ColumnDataSource(mean_all)\\n mean_source2.data = source4.data\\n\\n if not df2.empty:\\n median_all = df2.resample(str(binning) + 'T').median()\\n else:\\n median_all = df2.copy(deep=True)\\n source5 = ColumnDataSource(median_all)\\n median_source2.data = source5.data\\n\\n # calculate difference for external seeing against fwhm and ee50\\n dataframes = [mean1_all, mean_all]\\n add_dataframes = pd.concat(dataframes, axis=1)\\n add_dataframes.index.name = '_timestamp_'\\n add_dataframes['difference'] = add_dataframes['seeing'] - add_dataframes['ee50']\\n datasource2 = ColumnDataSource(add_dataframes)\\n difference_source.data = datasource2.data\\n\\n dataframes = [mean1_all, mean_all1]\\n add_dataframes = pd.concat(dataframes, axis=1)\\n add_dataframes.index.name = '_timestamp_'\\n add_dataframes['difference1'] = add_dataframes['seeing'] - add_dataframes['fwhm']\\n datasource1 = ColumnDataSource(add_dataframes)\\n difference_source1.data = datasource1.data\\n\\n # #difference using the median\\n # dataframes2 = [median_all, median1_all]\\n # add_dataframes2 = pd.concat(dataframes2, axis=1)\\n # add_dataframes2.index.name = '_timestamp_'\\n # add_dataframes2['difference2'] = add_dataframes2['seeing'] - add_dataframes2['ee50']\\n # datasource2 = ColumnDataSource(add_dataframes2)\\n # difference_source2.data = datasource2.data\\n #\\n # dataframes3 = [median_all, median1_all]\\n # add_dataframes3 = pd.concat(dataframes3, axis=1)\\n # add_dataframes3.index.name = '_timestamp_'\\n # add_dataframes3['difference3'] = add_dataframes3['seeing'] - add_dataframes3['fwhm']\\n # datasource3 = ColumnDataSource(add_dataframes3)\\n # difference_source3.data = datasource3.data\\n\\n # plot labels\\n p = figure(title=\\\"external vs internal seeing ({binning} minute bins)\\\".format(binning=binning), x_axis_type='datetime'\\n , x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\\n dif=figure(title='difference between average internal and external seeing ({binning} minute bins)'.format(binning=binning), x_axis_type='datetime',\\n x_axis_label='datetime', y_axis_label='seeing',plot_width=1000, plot_height=500,tools=TOOLS)\\n\\n #plots\\n # plots for external seeing\\n p.circle(source=mean_source1, x='datetime',y='seeing', legend=\\\"external average\\\" ,fill_color=\\\"white\\\",color='green')\\n p.line(source=median_source1, x='datetime',y='seeing', legend=\\\"external median\\\" ,color='blue')\\n\\n #plots showing median and mean for ee50 and fwhm\\n p.circle(source=mean_source, x='_timestamp_', y='ee50', legend='ee50 average')\\n p.circle(source=mean_source, x='_timestamp_', y='fwhm', legend='fwhm average', color='red', fill_color='white')\\n\\n p.line(source=median_source, x='_timestamp_', y='ee50', legend='ee50 median', color='green')\\n p.line(source=median_source, x='_timestamp_', y='fwhm', legend='fwhm median', color='orange')\\n\\n #for difference\\n dif.circle(source=difference_source, x='_timestamp_', y='difference', legend='ee50_mean difference', color='red')\\n dif.circle(source=difference_source1, x='_timestamp_', y='difference1', legend='fwhm_mean difference', fill_color='green')\\n\\n #\\n # dif.circle(source=difference_source2, x='_timestamp_', y='difference2', legend='ee50_median difference', fill_color='blue')\\n # dif.circle(source=difference_source3, x='_timestamp_', y='difference3', legend='fwhm_median difference', color='orange')\\n\\n p.xaxis.formatter = date_formatter\\n p.legend.location = \\\"top_left\\\"\\n p.legend.click_policy=\\\"hide\\\"\\n\\n dif.xaxis.formatter = date_formatter\\n dif.legend.click_policy=\\\"hide\\\"\\n\\n script, div = components(p)\\n content1 = '<div>{script}{div}</div>'.format(script=script, div=div)\\n\\n script, div = components(dif)\\n content2 = '<div>{script}{div}</div>'.format(script=script, div=div)\\n\\n return '{cont} {cont2}'.format(cont=content1,cont2=content2)\",\n \"def by_time_period(cls, user, time_periods):\\n return [cls.by_user(user, p.start, p.end) for p in time_periods]\",\n \"def history_clones(file, ht_df):\\n if os.path.isfile(file):\\n # if the file exists, we merge\\n print(file + ' found, merging')\\n df_file = pd.read_csv(file)\\n\\n ht_df['timestamp'] = pd.to_datetime(ht_df['timestamp']).dt.date\\n\\n df_file = pd.concat([df_file, ht_df])\\n df_file['timestamp'] = df_file['timestamp'].astype(str)\\n\\n df_file.sort_values('timestamp', inplace=True)\\n print(df_file.to_string())\\n # we can't just drop the first instance: for the first day, we'll loose data.\\n # so keep max value per date\\n\\n #df_file.drop_duplicates(subset=['timestamp'], keep='last', inplace=True)\\n df_file = df_file.groupby('timestamp')[['uniques', 'count']].agg(['max']).reset_index()\\n\\n df_file.columns = df_file.columns.droplevel(level=1)\\n #print(df_file.to_string())\\n #print(df_file.columns)\\n df_file.to_csv(file, index=False)\\n\\n else:\\n # otherwise, just dump the df\\n print('There is no file to merge, dumping df to ' + file)\\n ht_df.to_csv(file, index=False)\",\n \"def create_time_s(df, medidor, freq='15T'):\\n dates_complete = pd.date_range('1/18/2013', '02/09/2014', freq='15T')\\n # this dates take them from the file\\n my_complete_series = pd.Series(dates_complete)\\n frame1 = my_complete_series.to_frame()\\n frame1.columns = ['key']\\n merged = pd.merge(frame1, df, on='key', how='outer')\\n merged = merged.sort('key')\\n # fill the merged file with the number of the meter\\n merged['medidor'].fillna(medidor, inplace=True)\\n\\n return merged\",\n \"def testQueryWithTimestamp(self):\\n for i in range(5):\\n row_name = \\\"aff4:/row:query_with_ts\\\"\\n data_store.DB.Set(row_name, \\\"metadata:5\\\", \\\"test\\\", timestamp=i + 10,\\n replace=False, token=self.token)\\n data_store.DB.Set(row_name, \\\"aff4:type\\\", \\\"test\\\", timestamp=i + 10,\\n replace=False, token=self.token)\\n\\n # Read all timestamps.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=data_store.DB.ALL_TIMESTAMPS, token=self.token)]\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 5)\\n\\n # Read latest timestamp.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=data_store.DB.NEWEST_TIMESTAMP, token=self.token)]\\n\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 1)\\n self.assertEqual(attributes[\\\"aff4:type\\\"][0][0], \\\"test\\\")\\n\\n # Newest timestamp is 4.\\n self.assertEqual(attributes[\\\"aff4:type\\\"][0][1], 14)\\n\\n # Now query for a timestamp range.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=(11, 13), token=self.token)]\\n\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n # Now we should have three timestamps.\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 3)\\n\\n timestamps = [attribute[1] for attribute in attributes[\\\"aff4:type\\\"]]\\n self.assertListEqual(sorted(timestamps), [11, 12, 13])\",\n \"def _tail_profile(self, db, interval):\\r\\n latest_doc = None\\r\\n while latest_doc is None:\\r\\n time.sleep(interval)\\r\\n latest_doc = db['system.profile'].find_one()\\r\\n\\r\\n current_time = latest_doc['ts']\\r\\n\\r\\n while True:\\r\\n time.sleep(interval)\\r\\n cursor = db['system.profile'].find({'ts': {'$gte': current_time}}).sort('ts', pymongo.ASCENDING)\\r\\n for doc in cursor:\\r\\n current_time = doc['ts']\\r\\n yield doc\",\n \"def merge_record(self, dt, container = ''): \\n record_dataset_legth ={} \\n \\n \\n \\\"\\\"\\\" Combining the ncar_t and ncar_w files.\\n If both are present, select the ncar_t data and rename it as 'ncar'. \\n If only one is present, simply rename it as 'ncar'. \\n \\\"\\\"\\\" \\n if ('ncar_t' in list(container.keys()) ):\\n container['ncar'] = {} \\n container['ncar']['df'] = container['ncar_t']['df'] \\n \\n elif ( 'ncar_w' in list(container.keys()) and 'ncar_t' not in list(container.keys()) ) :\\n container['ncar'] = {} \\n container['ncar']['df'] = container['ncar_w']['df'] \\n\\n \\n for k in container.keys():\\n if k == 'ncar_t' or k == 'ncar_w': \\n continue \\n record_dataset_legth[k] = len(container[k]['df'] )\\n \\n \\n \\\"\\\"\\\" For now, choosing the dataset with more records of all or igra2>ncar>rest data if available and with same number of records \\\"\\\"\\\"\\n best_ds, all_ds , best_datasets, all_ds_reports = 'dummy' , [] , [], [] # total number of records, name of the chosen dataset , list of other possible dataset with available data \\n \\n most_records = max( [ v for v in record_dataset_legth.values() ] ) # maximum number of records per date_time \\n \\n for k, v in record_dataset_legth.items(): \\n if v == 0:\\n continue\\n if v == most_records:\\n best_datasets.append(k) \\n if v > 0:\\n all_ds.append(k) # all other datasets with smaller number of records than the maximum found\\n try: \\n all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + container[k]['df']['report_id'].values[0] ) # converting the original report id using the same convention as for observation_id\\n except:\\n all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + int( (container[k]['df']['report_id'].values[0]).tostring() ) ) # converting the original report id using the same convention as for observation_id\\n \\n \\n #all_ds_reports.append(np.nan)\\n #print ( type(container[k]['df']['report_id'].values) )\\n #all_ds_reports.append( self.observation_ids_merged[k] * 1000000000 + float(container[k]['df']['report_id'].values[0].decode('latin1') ))\\n \\n if len(best_datasets) ==0:\\n print('wrong??? please check')\\n return 0,0,0,0 \\n \\n if 'igra2' in best_datasets:\\n best_ds = 'igra2'\\n elif 'ncar' in best_datasets:\\n best_ds = 'ncar'\\n elif 'era5_1' in best_datasets:\\n best_ds = 'era5_1' \\n else:\\n best_ds = best_datasets[0]\\n \\n \\\"\\\"\\\" Extract container \\\"\\\"\\\" \\n selected_df = container[best_ds]['df'].copy(deep = True) # might take extra time, dont know how to get rid of this \\n\\n try:\\n merged_report = self.observation_ids_merged[best_ds] * 1000000000 + int( selected_df['report_id'].values[0].tostring() ) \\n except:\\n merged_report = np.nan \\n\\n \\\"\\\"\\\" Calculate new unique observation id \\\"\\\"\\\"\\n try: \\n obs_ids_merged = [ self.observation_ids_merged[best_ds] * 1000000000 + int( i.tostring() ) for i in selected_df['observation_id'] ]\\n except:\\n obs_ids_merged = [ np.nan for i in selected_df['observation_id'] ]\\n \\n \\n selected_df['observation_id'] = obs_ids_merged\\n \\n \\\"\\\"\\\" Calculate new unique report id \\\"\\\"\\\" \\n selected_df['report_id'] = merged_report\\n\\n \\\"\\\"\\\" Returning a string with the alternative available datasets data \\\"\\\"\\\"\\n if len(all_ds_reports) > 1: \\n duplicates = \\\",\\\".join( [ str(i) for i in all_ds_reports] )\\n else:\\n duplicates = str(all_ds_reports[0])\\n \\n \\n \\\"\\\"\\\" Extracting the merged header_table.\\n Again, must consider the special case where best_ds == ncar. \\n Note that the header table *should* be identical for ncar_w or ncar_t \\\"\\\"\\\" \\n if best_ds != 'ncar':\\n header = self.get_header_table(dt, ds= best_ds, all_ds = duplicates , length= len(selected_df) )\\n \\n elif ( best_ds == 'ncar' and 'ncar_t' in list(container.keys()) ) :\\n header = self.get_header_table(dt, ds = 'ncar_t', all_ds = duplicates, length= len(selected_df))\\n \\n elif ( best_ds == 'ncar' and 'ncar_t' not in list(container.keys()) ) :\\n header = self.get_header_table(dt, ds = 'ncar_w', all_ds = duplicates, length= len(selected_df) ) \\n \\n logging.debug('I use %s record since it has more entries: %s but other available datasets are : %s' , best_ds , str(most_records) , all_ds ) \\n \\n #print ('duplicates are: ', duplicates)\\n return selected_df, best_ds , duplicates, header\",\n \"def compare_displacements(ds1,ds2):\\n # Obteniendo los datos para BP\\n t1 = ds1['t']\\n t1 = t1[:n_im-1]\\n t1 = mplt.dates.date2num(t1)\\n d1 = ds1['d_t']\\n # Obteniendo los datos para RMA\\n t2 = ds2['t']\\n t2 = t2[:n_im-1]\\n t2 = mplt.dates.date2num(t2)\\n d2 = ds2['d_t']\\n\\n # Graficando las 2 curvas juntas\\n formatter = DateFormatter(\\\"%d/%m - %H:%M\\\")\\n for i in range(len(d1)):\\n # Hallando el valor promedio final x zona\\n mean_bp = d1[i].mean()\\n mean_rma = d2[i].mean()\\n print(\\\"Valor promedio BP_zona\\\"+str(i)+\\\": \\\",mean_bp)\\n print(\\\"Valor promedio RMA_zona\\\"+str(i)+\\\": \\\",mean_rma)\\n print(\\\"\\\")\\n # Graficando\\n direction = 'desplazamientosPromedios_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'_zona'+str(i)\\n\\n fig, ax= plt.subplots(figsize=(10,7))\\n ax.plot_date(t1,d1[i],'b',marker='',markerfacecolor='b',markeredgecolor='b',label='Back Projection')\\n ax.plot_date(t2,d2[i],'r',marker='',markerfacecolor='r',markeredgecolor='r',label='RMA')\\n ax.set(xlabel='Tiempo',ylabel='Desplazamiento(mm)',title=\\\"Desplazamientos promedios\\\\n(Zona \\\"+str(i)+')')\\n ax.xaxis.set_major_formatter(formatter)\\n ax.xaxis.set_tick_params(rotation=20)\\n #ax.set_xlim([R.min(),R.max()])\\n ax.set_ylim([-c*1000*4/(4*fc),c*1000*4/(4*fc)])\\n ax.grid(linestyle='dashed')\\n ax.legend()\\n plt.show()\\n fig.savefig(os.getcwd()+\\\"/Results/Desplazamientos/\\\"+direction,orientation='landscape')\\n\\n return 'Ok'\",\n \"def __iter__(self):\\n if len(self) == 0:\\n return\\n current = self.first_timestamp\\n delta = datetime.timedelta(0, self.interval)\\n while current <= self.last_timestamp:\\n yield current\\n current += delta\",\n \"def create_person_offer(transcript,portfolio,profile,person_transaction): \\n tqdm.pandas()\\n to_be_appended = None\\n\\n # This will not include transaction, so we need another new table for those.\\n for (person_id, offer_index), transcript_grouped in tqdm(transcript.dropna(subset=['offer_index']).groupby(['person','offer_index'])):\\n this_offer = portfolio.loc[offer_index]\\n this_person = profile.loc[person_id]\\n to_be_appended = append_one_person_offer(to_be_appended, this_offer, person_id, offer_index, transcript_grouped, this_person)\\n \\n person_offer_df = pd.DataFrame(to_be_appended)\\n\\n # TODO, the stuff above and the stuff below was originally made at completly different times and\\n # was different files and functions, now I put it into one, however, there's still probably alot\\n # that can be done together instead of looping multiple times on the same stuff... but not reealy needed, as it works\\n # but could probably make it way faster...\\n person_offer_df['before_start'] = 0\\n person_offer_df['same_day_start'] = 0\\n person_offer_df['after_start'] = 0\\n person_offer_df['before_view'] = 0\\n person_offer_df['same_day_view'] = 0\\n person_offer_df['after_view'] =0\\n person_offer_df['before_complete'] = 0\\n person_offer_df['same_day_complete']= 0\\n person_offer_df['after_complete'] = 0\\n person_offer_df['w_before'] = 0\\n person_offer_df['sum_during'] = 0\\n person_offer_df['mean_during'] = 0\\n person_offer_df['w_after'] = 0\\n person_offer_df = person_offer_df.progress_apply(get_before_after_mean,person_transaction=person_transaction, axis=1) \\n \\n \\n person_offer_df['viewed_reltime']=np.nan\\n person_offer_df['completed_reltime']=np.nan\\n\\n def absulute2relative_time(x):\\n \\\"\\\"\\\"Converts absolute time (hours since start of simulation)\\n to hours since offer recieved (start)\\n \\\"\\\"\\\" \\n if x.viewed:\\n x.viewed_reltime=x.viewed_time-x.start\\n \\n if x.completed:\\n x.completed_reltime=x.completed_time-x.start\\n \\n return x\\n\\n person_offer_df = person_offer_df.progress_apply(absulute2relative_time, axis=1)\\n \\n #makes it easier to access these combinations\\n person_offer_df['complete_viewed'] = (person_offer_df['completed'] & person_offer_df['viewed']).astype(int)\\n person_offer_df['complete_not_viewed'] = (person_offer_df['completed'] & ~person_offer_df['viewed']).astype(int)\\n person_offer_df['not_complete_not_viewed'] = (~person_offer_df['completed'] & ~person_offer_df['viewed']).astype(int)\\n person_offer_df['not_complete_viewed'] = (~person_offer_df['completed'] & person_offer_df['viewed']).astype(int)\\n person_offer_df['completed'] = person_offer_df['completed'].astype(int)\\n person_offer_df['viewed'] = person_offer_df['viewed'].astype(int)\\n \\n #calculates diff in sales before and after an event\\n for x in ['start','view','complete']:\\n person_offer_df[f'diff_{x}'] = person_offer_df[f'after_{x}'] - person_offer_df[f'before_{x}'] \\n \\n person_offer_df[f'diff_offer'] = person_offer_df[f'w_after'] - person_offer_df[f'w_before']\\n \\n #recalculates became_member_on to member_since instead (where newest member is 0) in days\\n person_offer_df['became_member_on'] = pd.to_datetime(person_offer_df['became_member_on'], format='%Y-%m-%d')\\n person_offer_df['member_since_days'] = (person_offer_df['became_member_on'].max() - person_offer_df['became_member_on']).dt.days\\n \\n #remve these wrong ages and turn to NaN\\n person_offer_df['age']=person_offer_df['age'].apply(lambda x:np.nan if x==118 else x)\\n \\n return person_offer_df\",\n \"def _run_records_sporadic(self, run_idxs, run_record_key):\\n\\n # we loop over the run_idxs in the contig and get the fields\\n # and cycle idxs for the whole contig\\n fields = None\\n cycle_idxs = np.array([], dtype=int)\\n # keep a cumulative total of the runs cycle idxs\\n prev_run_cycle_total = 0\\n for run_idx in run_idxs:\\n\\n # get all the value columns from the datasets, and convert\\n # them to something amenable to a table\\n run_fields = self._convert_record_fields_to_table_columns(run_idx, run_record_key)\\n\\n # we need to concatenate each field to the end of the\\n # field in the master dictionary, first we need to\\n # initialize it if it isn't already made\\n if fields is None:\\n # if it isn't initialized we just set it as this first\\n # run fields dictionary\\n fields = run_fields\\n else:\\n # if it is already initialized we need to go through\\n # each field and concatenate\\n for field_name, field_data in run_fields.items():\\n # just add it to the list of fields that will be concatenated later\\n fields[field_name].extend(field_data)\\n\\n # get the cycle idxs for this run\\n rec_grp = self.records_grp(run_idx, run_record_key)\\n run_cycle_idxs = rec_grp[CYCLE_IDXS][:]\\n\\n # add the total number of cycles that came before this run\\n # to each of the cycle idxs to get the cycle_idxs in terms\\n # of the full contig\\n run_contig_cycle_idxs = run_cycle_idxs + prev_run_cycle_total\\n\\n # add these cycle indices to the records for the whole contig\\n cycle_idxs = np.hstack( (cycle_idxs, run_contig_cycle_idxs) )\\n\\n # add the total number of cycle_idxs from this run to the\\n # running total\\n prev_run_cycle_total += self.num_run_cycles(run_idx)\\n\\n # then make the records from the fields\\n records = self._make_records(run_record_key, cycle_idxs, fields)\\n\\n return records\",\n \"def pulsEphem(self):\\n\\n hduMain = fits.open(self.ft1)\\n\\n # --------------------------------------------------------------------------------------------- #\\n # Split the FT1 file every 4000 events\\n noEv = 0\\n deltEv = 5000\\n count = 0\\n wfil = open(os.path.dirname(self.ft1) + os.path.basename('tmpFT1.lis'), 'w')\\n while noEv <= self.nevents:\\n hduCols = []\\n for colname, form, uni in zip(hduMain['EVENTS'].columns.names, hduMain['EVENTS'].columns.formats, hduMain['EVENTS'].columns.units):\\n hduCols.append( fits.Column(name=colname, array=hduMain['EVENTS'].data[colname][noEv:noEv+deltEv], format=form, unit=uni) )\\n # Updte the tstart and tstop in the header in order for tempo2 to work...\\n hduMain['EVENTS'].header.set('TSTART', hduMain['EVENTS'].data['TIME'][noEv:noEv+deltEv][0])\\n hduMain['EVENTS'].header.set('TSTOP', hduMain['EVENTS'].data['TIME'][noEv:noEv+deltEv][-1])\\n newHDU = fits.BinTableHDU.from_columns(hduCols, name='EVENTS', header=hduMain['EVENTS'].header) \\n hdulist = fits.HDUList([hduMain['PRIMARY'], newHDU, hduMain['GTI']])\\n tmpName = os.path.dirname(self.ft1)+os.path.basename('tempFT1_'+str(count)+'.fits')\\n hdulist.writeto(tmpName, clobber=True)\\n wfil.write(tmpName + '\\\\n')\\n noEv += deltEv\\n count += 1\\n if noEv != self.nevents:\\n hduCols = []\\n noEv -= deltEv\\n for colname, form, uni in zip(hduMain['EVENTS'].columns.names, hduMain['EVENTS'].columns.formats, hduMain['EVENTS'].columns.units):\\n hduCols.append( fits.Column(name=colname, array=hduMain['EVENTS'].data[colname][noEv:self.nevents], format=form, unit=uni) )\\n hduMain['EVENTS'].header.set('TSTART', hduMain['EVENTS'].data['TIME'][noEv:self.nevents][0])\\n hduMain['EVENTS'].header.set('TSTOP', hduMain['EVENTS'].data['TIME'][noEv:self.nevents][-1])\\n newHDU = fits.BinTableHDU.from_columns(hduCols, name='EVENTS', header=hduMain['EVENTS'].header)\\n hdulist = fits.HDUList([hduMain['PRIMARY'], newHDU, hduMain['GTI']])\\n tmpName = os.path.dirname(self.ft1)+os.path.basename('tempFT1_'+str(count)+'.fits')\\n hdulist.writeto(tmpName, clobber=True)\\n wfil.write(tmpName + '\\\\n')\\n wfil.close()\\n\\n hduMain.close()\\n\\n # --------------------------------------------------------------------------------------------- #\\n # Run tempo2 for each piece of the FT1\\n rfil = open(os.path.dirname(self.ft1) + 'tmpFT1.lis', 'r')\\n percent = 0\\n nbFiles = sum(1 for line in open(os.path.dirname(self.ft1) + 'tmpFT1.lis', 'r'))\\n count = 0\\n for tmpFil in rfil:\\n # Print a progression bar every 5%\\n if ( count / np.floor(nbFiles) * 100 ) >= percent:\\n self._progressBar(percent, printEvery=5)\\n percent += 5\\n with open(os.devnull, 'wb') as devnull:\\n subprocess.check_call(['/dsm/fermi/fermifast/glast/tempo2-2013.9.1/tempo2',\\n '-gr', 'fermi', '-ft1', tmpFil[:-1], '-ft2', self.ft2, '-f', self.ephem,\\n '-phase'], stdout=devnull, stderr=subprocess.STDOUT)\\n count += 1\\n # Replace the old ft1 by the new one with the PULSE_PHASE column\\n #os.remove()\\n self._gtSelect(data = os.path.dirname(self.ft1) + os.path.basename('tmpFT1.lis'))\\n\\n\\n\\n\\n #self.nevents\\n #J2032+4127_54683_57791_chol_pos.par\\n #os.popen(\\\"tempo2 -gr fermi -ft1 {} -ft2 {} -f {} -phase\\\".format(self.ft1, self.ft2, self.ephem))\",\n \"def _copy(source, track, filter_f=lambda x: True, coef=1000):\\n for msg in source:\\n if filter_f(msg):\\n track.append(msg.copy(time=int(msg.time*coef)))\",\n \"def _mergeKeys(self, other):\\n for id in set(other.clock.keys()).difference(set(self.clock.keys())):\\n self.clock[id] = 0\\n for id in set(self.clock.keys()).difference(set(other.clock.keys())):\\n other.clock[id] = 0\",\n \"def test_get_time_period_search(self):\\n obj = CalculateSearchTimes(True, TIME_PERIODS,\\n search_path=SEARCH_PATH, df_path=DF_PATH)\\n dt1 = dt.datetime(2017, 8, 9, 14, 56, 40, 796658)\\n dt2 = dt.datetime(2017, 8, 9, 17, 56, 40, 796658)\\n dt3 = dt.datetime(2017, 8, 9, 10, 56, 40, 796658)\\n result1 = obj.get_time_period_search(dt1)\\n result2 = obj.get_time_period_search(dt2)\\n result3 = obj.get_time_period_search(dt3)\\n\\n self.assertTrue(np.mean(list(result1.values())) == 1666.6666666666667)\\n self.assertTrue(np.mean(list(result2.values())) != 1666.6666666666667)\\n self.assertTrue(np.mean(list(result3.values())) != 1666.6666666666667)\",\n \"def run(self):\\n # pylint: disable=too-many-locals\\n loop_start = time.clock()\\n partitions = []\\n for item in self._results:\\n partitions.append(\\n Partition(item.name, item.dataframe, self.PARTITION_SIZE)\\n )\\n if item.name == self._primary_dataset:\\n primary_dataset = partitions[-1]\\n\\n index = 0\\n while index < self.PARTITION_SIZE:\\n merge_sets = []\\n combination = ['\\\\033[1m{0}\\\\033[0m'.format(self._mappings[self._primary_dataset])]\\n for partition in partitions:\\n if partition.name != self._primary_dataset:\\n merge_sets.append({'name': partition.name, 'data': partition.next()})\\n combination.append(self._mappings[partition.name])\\n\\n Logger().info(\\n 'Running data extraction. Index {0} of {1} partitions, (combination: {2}, queries: {3})'.format(\\n index,\\n self.PARTITION_SIZE,\\n ''.join(combination),\\n len(self._queries)\\n )\\n )\\n\\n for size in range(self.PARTITION_SIZE):\\n self._running = []\\n q_start = time.clock()\\n merge_table = self.merge(\\n merge_sets + [{'name': primary_dataset.name, 'data': primary_dataset.next()}]\\n )\\n m_end = '{0:.2f}'.format(float(time.clock() - q_start))\\n Logger().debug(\\n 'Combination {0} merge table completed in {1} seconds ({2} rows)'.format(\\n ''.join(combination),\\n m_end,\\n len(merge_table.index)\\n )\\n )\\n for query in self._queries:\\n self._running.append(\\n DataThreader(merge_table, query, self._unique_columns)\\n )\\n\\n self.monitor()\\n times = [item.duration for item in self._running]\\n message = '{4}\\\\n Combination {0}, Index {5}/{1}'\\n message += ', merge_table size: {2}.\\\\n Average time per query {3}'\\n Logger().debug(\\n message.format(\\n ''.join(combination),\\n index,\\n len(merge_table.index),\\n times,\\n [len(query.results.index) for query in self._queries],\\n size\\n )\\n )\\n del times\\n del merge_table\\n del self._running\\n\\n end_time = math.floor(time.clock() - loop_start)\\n Logger().info('=========================================================================================')\\n Logger().info(\\n 'Completed partition {0} in {1} seconds. {2} queries, combination: {3}'.format(\\n index,\\n end_time,\\n len(self._queries),\\n ''.join(combination)\\n )\\n )\\n Logger().info('=========================================================================================')\\n primary_dataset.reset()\\n index += 1\\n\\n Logger().info('Completed data extraction')\\n self._complete = True\",\n \"def getContinuousBookingTimeSeries(span=28):\\n data = []\\n x = []\\n y = []\\n users = []\\n now = datetime.datetime.now(pytz.utc)\\n delta = datetime.timedelta(days=span)\\n end = now - delta\\n bookings = Booking.objects.filter(start__lte=now, end__gte=end).prefetch_related(\\\"collaborators\\\")\\n for booking in bookings: # collect data from each booking\\n user_list = [u.pk for u in booking.collaborators.all()]\\n user_list.append(booking.owner.pk)\\n data.append((booking.start, 1, user_list))\\n data.append((booking.end, -1, user_list))\\n\\n # sort based on time\\n data.sort(key=lambda i: i[0])\\n\\n # collect data\\n count = 0\\n active_users = {}\\n for datum in data:\\n x.append(str(datum[0])) # time\\n count += datum[1] # booking count\\n y.append(count)\\n for pk in datum[2]: # maintain count of each user's active bookings\\n active_users[pk] = active_users.setdefault(pk, 0) + datum[1]\\n if active_users[pk] == 0:\\n del active_users[pk]\\n users.append(len([x for x in active_users.values() if x > 0]))\\n\\n return {\\\"booking\\\": [x, y], \\\"user\\\": [x, users]}\",\n \"def main():\\n\\n f = open(eventsfile, 'r')\\n lines = f.readlines()\\n numcounter = 0\\n counter = 0\\n fullcounter = 0\\n movielist = []\\n movielists =[]\\n timestamp_list = []\\n filteredlist = [] \\n startdate = \\\"2020-02-26\\\"\\n \\n for line in lines:\\n TAPES = line.split('\\\\t')\\n if int(TAPES[2]) == 1 or int(TAPES[2]) == 2:\\n filteredlist.append(line)\\n \\n for newline in filteredlist:\\n TAPES = newline.split('\\\\t')\\n fullcounter +=1\\n if int(TAPES[2]) == 2:\\n timestamp_list.append(0)\\n continue\\n startdate2 = startdate.split(\\\"-\\\")[1] + \\\"/\\\" + startdate.split(\\\"-\\\")[2] + \\\"/\\\" + startdate.split(\\\"-\\\")[0]\\n dateplustime = startdate2 + TAPES[0][0:len(TAPES[0])]\\n thistime = faststrptime(dateplustime)\\n unixtimestamp = datetime.datetime.timestamp(thistime)\\n timestamp_list.append(int(unixtimestamp))\\n\\n i = 0 \\n for element in timestamp_list:\\n\\n if i < (len(timestamp_list)-1) and timestamp_list[i+(counter-i)]-timestamp_list[i] >= 3600:\\n counter += 1\\n i = counter\\n movielist.append(counter)\\n \\n if len(movielist) <= 15:\\n numcounter = 0\\n j = 0\\n for step in movielist:\\n movielists[len(movielists)-1].append(movielist[j])\\n j += 1\\n movielist = []\\n continue \\n else:\\n movielists.append(movielist)\\n movielist = []\\n numcounter = 0\\n continue\\n\\n if i < (len(timestamp_list)-1) and timestamp_list[i+1]-timestamp_list[i] >= 3600:\\n counter += 1\\n i = counter\\n movielist.append(counter)\\n\\n if len(movielist) <= 15:\\n numcounter = 0\\n j = 0\\n for step in movielist:\\n movielists[len(movielists)-1].append(movielist[j])\\n j += 1\\n movielist = []\\n continue\\n else:\\n movielists.append(movielist)\\n movielist = []\\n numcounter = 0\\n continue\\n\\n counter += 1\\n numcounter += 1\\n if element != 0:\\n movielist.append(counter)\\n i += 1\\n \\n if numcounter == 30:\\n numcounter = 0\\n movielists.append(movielist)\\n movielist = []\\n \\n if i > (len(timestamp_list)-1):\\n movielists.append(movielist)\\n movielist = []\\n numcounter = 0\\n \\n numendlists = counter - fullcounter\\n first = len(movielists)-numendlists\\n last = len(movielists)\\n del movielists[first:last]\\n \\n for x in movielists:\\n for y in x:\\n if int(filenumber) == y:\\n movielist = x\\n\\n modename = str(movielist[0]) + \\\"to\\\" + str(movielist[len(movielist)-1])\\n modefilename = \\\"mode_\\\" + modename + \\\".png\\\"\\n try:\\n imread(modefilename)\\n except:\\n imageMode(modename,movielist)\\n\\n e = loadmodeImage(modefilename)\\n \\n roimask = np.zeros((ydim,xdim))\\n f = open(roisfile, 'r')\\n lines = f.readlines()\\n i = 1\\n i2 = 0\\n for line in lines:\\n try:\\n print(int(line.split(' ')[0]))\\n except ValueError:\\n i2 += 1\\n continue\\n minx = int(line.split(' ')[0])\\n miny = int(line.split(' ')[1])\\n maxx = int(line.split(' ')[2])\\n maxy = int(line.split(' ')[3])\\n roimask[int(miny):int(maxy),int(minx):int(maxx)] = i\\n i += 1\\n numberofwells = i-1\\n numberofcols = int(i2/2)\\n numberofrows = int(numberofwells/numberofcols)\\n roimaskweights = convertMaskToWeights(roimask)\\n\\n cap = cv2.VideoCapture(videoStream)\\n\\n cap.set(3,roimask.shape[1])\\n cap.set(4,roimask.shape[0])\\n \\n ret,frame = cap.read()\\n storedImage = np.array(e * 255, dtype = np.uint8)\\n storedMode = Blur(storedImage)\\n storedFrame = grayBlur(frame)\\n cenData = np.zeros([ int(saveFreq), len(np.unique(roimaskweights))*2 -2])\\n pixData = np.zeros([ int(saveFreq), len(np.unique(roimaskweights))])\\n i = 0;\\n totalFrames = 0\\n while(cap.isOpened()):\\n ret,frame = cap.read()\\n if ret == False:\\n break\\n currentFrame = grayBlur(frame)\\n diffpix = diffImage(storedFrame,currentFrame,pixThreshold)\\n diff = trackdiffImage(storedMode,currentFrame,pixThreshold)\\n diff.dtype = np.uint8\\n contours,hierarchy = cv2.findContours(diff, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)\\n MIN_THRESH = 20.0\\n MIN_THRESH_P = 20.0\\n roi_dict = {}\\n for r in range(0,numberofwells):\\n roi_dict[r+1] = []\\n for cs in range(0,len(contours)):\\n if cv2.contourArea(contours[cs]) < 1.0:\\n continue\\n if cv2.arcLength(contours[cs],True) < 1.0:\\n continue\\n if cv2.contourArea(contours[cs]) > MIN_THRESH or cv2.arcLength(contours[cs],True) > MIN_THRESH_P:\\n M = cv2.moments(contours[cs])\\n cX = int(M[\\\"m10\\\"] / M[\\\"m00\\\"])\\n cY = int(M[\\\"m01\\\"] / M[\\\"m00\\\"])\\n area = cv2.contourArea(contours[cs])\\n perim = cv2.arcLength(contours[cs],True)\\n if int(roimask[cY,cX]) == 0:\\n continue\\n if not roi_dict[int(roimask[cY,cX])]:\\n roi_dict[int(roimask[cY,cX])].append((area*perim,cX,cY))\\n else:\\n if roi_dict[int(roimask[cY,cX])][0][0] < area*perim:\\n roi_dict[int(roimask[cY,cX])][0] = (area*perim,cX,cY)\\n\\n pixcounts = []\\n pixcounts = np.bincount(roimaskweights, weights=diffpix.ravel())\\n pixData[i,:] = np.hstack((pixcounts))\\n counts = []\\n keys = roi_dict.keys()\\n keys = sorted(keys)\\n for k in keys:\\n x = -10000\\n y = -10000\\n if roi_dict[k]:\\n x = roi_dict[k][0][1]\\n y = roi_dict[k][0][2]\\n counts.append(x)\\n counts.append(y)\\n cv2.line(storedImage,(x,y),(x,y),(255,255,255),2)\\n if i == 284:\\n cv2.imwrite(videoStream + '_trackedimagewithlines_' + str(i) + \\\".png\\\", storedImage)\\n cenData[i,:] = np.asarray(counts)\\n totalFrames += 1\\n storedFrame = currentFrame\\n i += 1\\n\\n file = open(videoStream + \\\".centroid2\\\",'w')\\n for x in range(0,frameRate):\\n for y in range(0,numberofwells*2):\\n file.write(str(int(cenData[x,:][y])) + '\\\\n')\\n pixData = pixData[:i,:]\\n pixData = pixData[:,1:] \\n file = open(videoStream + \\\".motion2\\\",'w')\\n for x in range(0,frameRate):\\n for y in range(0,numberofwells):\\n file.write(str(int(pixData[x,:][y])) + '\\\\n')\\n\\n cap.release()\\n cv2.destroyAllWindows()\\n \\n try:\\n image = Image.open('lastframe.png')\\n except:\\n makenumROIsimage()\",\n \"def combine_record(self, dt, container = ''):\\n \\n record_dataset_legth ={} \\n other_ds = []\\n\\n ''' I fill the dic e.g. record_dataset_legth{100:['era5_1','ncar'], 80:['bufr','igra2'] }\\n i.e. the keys are the lengths, the entries are the lists of datasets '''\\n\\n duplicates = []\\n\\n for k in container.keys(): # loop over the dataset\\n if k not in other_ds:\\n other_ds.append(k)\\n for f in container[k]: # loop over the file per dataset\\n num_rec = len(container[k][f]['obs_tab'][\\\"date_time\\\"])\\n \\n \\\"\\\"\\\" Storing all the reports id with the proper prefix (for each different dataset) \\\"\\\"\\\"\\n rep_id = b''.join(container[k][f][\\\"obs_tab\\\"]['report_id'][0]) \\n rep_id = self.observation_ids_merged[k] + rep_id \\n duplicates.append( rep_id ) \\n \\n if num_rec not in record_dataset_legth.keys():\\n record_dataset_legth[num_rec] = {}\\n record_dataset_legth[num_rec]['best_ds'] = []\\n record_dataset_legth[num_rec]['file'] = []\\n\\n record_dataset_legth[num_rec]['best_ds'].append(k)\\n record_dataset_legth[num_rec]['file'].append(f)\\n\\n max_entries = max(record_dataset_legth.keys())\\n \\n ''' best_ds is the list of longest datasets, best_datasets the list of all the datasets available including best_ds '''\\n best_datasets = record_dataset_legth[max_entries]\\n\\n \\\"\\\"\\\" Choosing the priority of the datasets:\\n - if era5_1 or era5_2 are present, pick them (they cant be both present for the same date_time)\\n - else, if igra2 is present, pick it\\n - else, one of the remaining ones \\\"\\\"\\\"\\n\\n if 'era5_2' in best_datasets and 'era5_1' not in best_datasets: # era5_1 and era5_2 should never be both present anyway...\\n best_ds = 'era5_2' \\n elif 'era5_1' in best_datasets and 'era5_2' not in best_datasets:\\n best_ds = 'era5_1'\\n elif 'era5_1' not in best_datasets and 'era5_2' not in best_datasets and 'igra2' in best_datasets:\\n best_ds = 'igra2'\\n elif 'era5_1' not in best_datasets and 'era5_2' not in best_datasets and 'igra2' not in best_datasets:\\n best_ds = record_dataset_legth[max_entries]['best_ds'][0] # pick the first of the list \\n\\n best_file = record_dataset_legth[max_entries]['file'][0]\\n\\n ''' If more file are available for the same best_ds, pick the first one from the list '''\\n selected_obstab, selected_era5fb = container[best_ds][best_file]['obs_tab'] , container[best_ds][best_file]['era5fb_tab']\\n\\n ''' Creating the correct observations and record ids. \\n All the bytes variable are shrunk to a long |S1 byte variable type, otherwise \\n writing in h5py will not work. '''\\n \\n for var in ['observation_id']:\\n if type (selected_obstab[var] ) == np.ndarray and type (selected_obstab[var][0] ) == np.bytes_:\\n selected_obstab[var] = np.array ([self.observation_ids_merged[best_ds] + b''.join(l) for l in selected_obstab[var] ] )\\n elif type (selected_obstab[var] ) == np.ndarray and type (selected_obstab[var][0] ) == np.ndarray:\\n selected_obstab[var] = np.array ([self.observation_ids_merged[best_ds] + b''.join(l) for l in selected_obstab[var][:] ] )\\n\\n for var in ['report_id']:\\n val = selected_obstab[var][0]\\n if type (selected_obstab[var] ) == np.ndarray and type (val) == np.bytes_:\\n value = self.observation_ids_merged[best_ds] + b''.join(val) # it is the same for each row in the table\\n elif type (selected_obstab[var] ) == np.ndarray and type (val) == np.ndarray:\\n value = self.observation_ids_merged[best_ds] + b''.join(val) \\n arr = np.full( (1, len( selected_obstab['date_time']) ) , value )[0] # np.full returns a list of lists\\n\\n selected_obstab[var] = arr\\n\\n\\n for var in selected_era5fb.keys():\\n if type (selected_era5fb[var]) == np.ndarray and type (selected_era5fb[var][0] ) == np.ndarray:\\n try:\\n selected_era5fb[var] = np.array( [b''.join(l) for l in selected_era5fb[var][:] ] )\\n #print('MANAGED FFF', var)\\n except:\\n value = [b''.join(l) for l in selected_era5fb[var][0] ][0]\\n #print('VALUE IS FFF', value)\\n selected_era5fb[var] = np.array( (1, len( selected_obstab[var]) ) ).fill(value)\\n\\n \\\"\\\"\\\" Extracting the header \\\"\\\"\\\"\\n selected_head = self.get_header_table(dt, ds = best_ds, File = best_file )\\n for var in selected_head.keys():\\n if type (selected_head[var] ) == np.ndarray and type (selected_head[var][0] ) == np.bytes_:\\n selected_head[var] = np.array( [b''.join(l) for l in selected_head[var][:] ] )\\n\\n if 'best_ds' == 'era5_1' or best_ds == 'era5_2' :\\n selected_obstab['advanced_assimilation_feedback'] = np.array([1]*len(selected_obstab['date_time']) )\\n else:\\n selected_obstab['advanced_assimilation_feedback'] = np.array([0]*len(selected_obstab['date_time']) )\\n\\n #best_ds_byte = np.bytes_(best_ds, ndtype = '|S10') # converting to bytes object\\n best_ds_byte = np.bytes_(best_ds) # converting to bytes object \\n arr = np.full( (1, len( selected_obstab['date_time']) ) , best_ds_byte )[0]\\n selected_obstab['source_id'] = arr\\n\\n duplicate = b','.join(duplicates)\\n #selected_head['duplicates'] = np.array(duplicate)\\n\\n duplicate = np.array(duplicate).astype(dtype='|S70')\\n selected_head['duplicates'] = np.array([duplicate])\\n selected_head['report_id'] = np.array([selected_obstab['report_id'][0]])\\n selected_head['source_id'] = np.array([selected_obstab['source_id'][0]])\\n selected_head['record_timestamp'] = np.array([selected_obstab['date_time'][0]])\\n\\n selected_file = np.bytes_(best_file.split('/')[-1])\\n \\n return best_ds, selected_obstab, selected_era5fb, selected_head, selected_file, best_file\",\n \"def append_past_returns(raw, periods):\\n data = raw.dropna(subset=['ret']).copy()\\n wide = data.pivot(index='time_idx', columns='permno', values='ret')\\n wide = wide + 1\\n\\n rets = []\\n for period in periods:\\n tmp = wide.rolling(window=period).apply(np.prod, raw=True) - 1\\n tmp = tmp.reset_index().melt(id_vars='time_idx', var_name='permno', value_name='prev_ret_' + str(period)).dropna()\\n tmp.permno = tmp.permno.astype('int')\\n tmp.time_idx = pd.to_datetime(tmp.time_idx)\\n rets.append(tmp)\\n\\n for ret in rets:\\n data = pd.merge(data, ret, on=['time_idx', 'permno'], how='left')\\n return data # don't drop because we may need to work on other fundamental data too\",\n \"def gen_records(self, count=None):\\n if not count:\\n count = self.num_rec\\n tt = time.localtime(time.time())\\n addr = None\\n for i in range(count):\\n logdbg(\\\"reading record %d of %d\\\" % (i+1, count))\\n addr, record = self.get_record(addr, tt.tm_year, tt.tm_mon)\\n yield addr, record\",\n \"def merge_working_sets(self, other):\\n\\n for dist in other.by_key.values(): self.add(dist)\\n return self\",\n \"def merge(self, otr):\\n self._duration = otr.get_start() - self.get_start()\\n self._duration += otr.get_duration()\\n self._line[3] = self._duration\",\n \"def extend(self,\\n period_data: List[Candle]) -> None:\\n self.point_1_moment = period_data[0].moment\\n self.point_1_price = period_data[0].open\\n self.point_2_moment = period_data[1].moment\\n self.point_2_price = period_data[1].open\",\n \"def make_data_pipeline(self, from_, to_):\\n\\n # Get the volume over time data.\\n r_volume = self.get_data_from_endpoint(from_, to_, 'volume')\\n print('There are approximately {} documents.'.format(r_volume.json()['numberOfDocuments']))\\n\\n # Carve up time into buckets of volume <10k.\\n l_dates = self.get_dates_from_timespan(r_volume)\\n\\n data = []\\n for i in range(0, len(l_dates)):\\n from_, to_ = l_dates[i]\\n\\n # Pull posts.\\n r_posts = self.get_data_from_endpoint(from_, to_, 'posts')\\n if r_posts.ok and (r_posts.json()['status'] != 'error'):\\n j_result = json.loads(r_posts.content.decode('utf8'))\\n data.extend(j_result['posts'])\\n return data\",\n \"def get_data(self, date_time):\\n id_columns = ','.join([col for col in self.table_primary_keys if col not in ['EFFECTIVEDATE', 'VERSIONNO']])\\n return_columns = ','.join(self.table_columns)\\n with self.con:\\n cur = self.con.cursor()\\n cur.execute(\\\"DROP TABLE IF EXISTS temp;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp2;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp3;\\\")\\n cur.execute(\\\"DROP TABLE IF EXISTS temp4;\\\")\\n # Store just the unique sets of ids that came into effect before the the datetime in a temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp AS \\n SELECT * \\n FROM {table} \\n WHERE EFFECTIVEDATE <= '{datetime}';\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, datetime=date_time))\\n # For each unique set of ids and effective dates get the latest versionno and sore in temporary table.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp2 AS\\n SELECT {id}, EFFECTIVEDATE, MAX(VERSIONNO) AS VERSIONNO\\n FROM temp\\n GROUP BY {id}, EFFECTIVEDATE;\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # For each unique set of ids get the record with the most recent effective date.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp3 as\\n SELECT {id}, VERSIONNO, max(EFFECTIVEDATE) as EFFECTIVEDATE\\n FROM temp2\\n GROUP BY {id};\\\"\\\"\\\"\\n cur.execute(query.format(id=id_columns))\\n # Inner join the original table to the set of most recent effective dates and version no.\\n query = \\\"\\\"\\\"CREATE TEMPORARY TABLE temp4 AS\\n SELECT * \\n FROM {table} \\n INNER JOIN temp3 \\n USING ({id}, VERSIONNO, EFFECTIVEDATE);\\\"\\\"\\\"\\n cur.execute(query.format(table=self.table_name, id=id_columns))\\n # Inner join the most recent data with the interconnectors used in the actual interval of interest.\\n query = \\\"\\\"\\\"SELECT {cols} \\n FROM temp4 \\n INNER JOIN (SELECT * \\n FROM DISPATCHINTERCONNECTORRES \\n WHERE SETTLEMENTDATE == '{datetime}') \\n USING (INTERCONNECTORID);\\\"\\\"\\\"\\n query = query.format(datetime=date_time, id=id_columns, cols=return_columns)\\n data = pd.read_sql_query(query, con=self.con)\\n return data\",\n \"def _link_data_and_events(self, ev, data, limit=None, timezone=None):\\n if limit is not None:\\n data = data.head(limit)\\n\\n date, time = ev[\\\"Date\\\"], ev[\\\"Time\\\"]\\n\\n for dfmt, tfmt in DATE_AND_TIME_FORMATS:\\n try:\\n ev[\\\"DateAndTime\\\"] = [pd.to_datetime(d + \\\" \\\" + t, format=dfmt)\\n for d, t in zip(date, time)]\\n data[\\\"DateAndTime\\\"] = [pd.to_datetime(ts, format=tfmt)\\n for ts in data[\\\"Timestamp\\\"]]\\n except ValueError:\\n continue\\n date_time_format = dfmt\\n break\\n\\n if timezone is not None:\\n ev.DateAndTime += timedelta(hours=timezone)\\n\\n self.log(\\\"[.] Data filtering...\\\")\\n\\n output = list()\\n # group events with proper data\\n for event in ev.values:\\n ts = event[-1]\\n one_min_before = ts - timedelta(minutes=1)\\n five_min_ahead = ts + timedelta(minutes=5)\\n data_in_range = data[(data.DateAndTime >= one_min_before) &\\n (data.DateAndTime <= five_min_ahead)]\\n\\n for d in data_in_range.values:\\n output.append(np.hstack((event[:-1], d)))\\n\\n if not output:\\n return pd.DataFrame()\\n\\n cols = list(ev.columns[:-1]) + list(data.columns)\\n dataframe = pd.DataFrame(output, columns=cols)\\n\\n self.log(\\\"[.] Timezones synchronization...\\\")\\n\\n # synchronize original Date and Time columns with DateAndTime\\n if timezone is not None:\\n dates = pd.Series(datetime.strptime(d + \\\" \\\" + t, date_time_format)\\n + timedelta(hours=timezone)\\n for d, t in zip(dataframe.Date, dataframe.Time))\\n dataframe[\\\"DateUTC\\\"] = dates.map(methodcaller('date'))\\n dataframe[\\\"TimeUTC\\\"] = dates.map(methodcaller('time'))\\n\\n return dataframe\",\n \"def data_comparison(observations, records, record):\\n for observation in observations:\\n if observation != \\\"_id\\\":\\n try:\\n if re.search(observation, f\\\"{records[record]}\\\"):\\n if not re.search(\\n observations[observation], f\\\"{records[record]}\\\"\\n ):\\n records[record] = (\\n f\\\"{records[record]}\\\"\\n + \\\" --> \\\"\\n + observations[observation]\\n )\\n except Exception as ex:\\n Common.logger.warning(f\\\"Exception happened in data comparison {ex}\\\")\\n return records\",\n \"def join_daily_cweeds_wy2_and_wy3(wy2_df, wy3_df):\\n assert wy2_df['CWEEDS Format'] == 'WY2'\\n assert wy3_df['CWEEDS Format'] == 'WY3'\\n assert wy2_df['Time Format'] == wy3_df['Time Format']\\n\\n time_wy23 = np.hstack([wy2_df['Time'], wy3_df['Time']])\\n time_wy23 = np.unique(time_wy23)\\n time_wy23 = np.sort(time_wy23)\\n\\n wy23_df = {}\\n wy23_df['Time Format'] = wy3_df['Time Format']\\n wy23_df['CWEEDS Format'] = 'WY2+WY3'\\n\\n # Copy the header info from WY3 dataset :\\n\\n for key in ['HORZ version', 'Location', 'Province', 'Country',\\n 'Station ID', 'Latitude', 'Longitude', 'Time Zone',\\n 'Elevation']:\\n wy23_df[key] = wy3_df[key]\\n\\n # Merge the two datasets :\\n\\n wy23_df['Time'] = time_wy23\\n wy23_df['Years'] = np.empty(len(time_wy23)).astype(int)\\n wy23_df['Months'] = np.empty(len(time_wy23)).astype(int)\\n wy23_df['Days'] = np.empty(len(time_wy23)).astype(int)\\n wy23_df['Hours'] = np.empty(len(time_wy23)).astype(int)\\n wy23_df['Irradiance'] = np.empty(len(time_wy23)).astype('float64')\\n\\n for dataset in [wy2_df, wy3_df]:\\n indexes = np.digitize(dataset['Time'], time_wy23, right=True)\\n for key in ['Years', 'Months', 'Days', 'Hours', 'Irradiance']:\\n wy23_df[key][indexes] = dataset[key]\\n\\n return wy23_df\",\n \"def run(self):\\n\\t\\tdf_iter = self.file_to_df(50000)\\n\\t\\tdf_airport = self.airport_file_to_df()\\n\\t\\tfor df in df_iter: # type: pd.DataFrame\\n\\t\\t\\tdf.drop_duplicates(inplace=True)\\n\\t\\t\\tdf = self.transform(df, df_airport)\\n\\n\\t\\t\\tdf_result = self.get_only_new_records(\\n\\t\\t\\t\\tdf=df,\\n\\t\\t\\t\\tdf_columns=self.join_columns,\\n\\t\\t\\t\\ttable_columns=self.join_columns\\n\\t\\t\\t)\\n\\n\\t\\t\\tif len(df_result) > 0:\\n\\t\\t\\t\\t# df_result.drop(self.table_columns, axis=1)\\n\\n\\t\\t\\t\\tself.save(\\n\\t\\t\\t\\t\\tdf=df_result,\\n\\t\\t\\t\\t\\ttable_name=\\\"travel_dimension\\\",\\n\\t\\t\\t\\t\\tdf_columns=self.table_columns,\\n\\t\\t\\t\\t\\ttable_colums=self.table_columns\\n\\t\\t\\t\\t)\",\n \"def concat_and_sort(self):\\n for link in self.to_concat:\\n \\n to_concat = self.to_concat[link]\\n df = pd.concat(to_concat,axis=0)\\n df=df.sort_values(by=['day','actualtime_arr_from'])\\n for d in df['day'].unique():\\n self.data[d][link] = {}\\n temp = df[df['day']==d]\\n \\n for r in temp['routeid'].unique(): \\n self.data[d][link][r] = temp[temp['routeid']==r][['actualtime_arr_from','actualtime_arr_to','routeid']].values \\n del(temp)\\n del(df)\\n del(self.to_concat)\",\n \"def period_limit_time_series(self, length, period, use_smalls=False):\\n filtered = self._components[:]\\n if use_smalls:\\n filtered = filter(lambda c: c.period <= period, filtered)\\n else:\\n filtered = filter(lambda c: c.period >= period, filtered)\\n \\n maker = r.Recomposer(filtered, self.bias)\\n return maker.time_series(length)\",\n \"def create_query_df(self):\\n\\n # display output message for timeframe\\n print(\\n f'{Fore.GREEN}\\\\nQuerying database for tags between the timeframe: '\\n f'{Fore.LIGHTGREEN_EX}{str(self._start)}{Fore.GREEN} and {Fore.LIGHTGREEN_EX}{str(self._end)}'\\n f'{Style.RESET_ALL}')\\n print(\\n f'{Fore.GREEN}\\\\nTIMESPAN: '\\n f'{Fore.LIGHTGREEN_EX}{self.time_span} hours'\\n f'{Style.RESET_ALL}')\\n\\n engine = get_db_engine()\\n offset = 0\\n chunk_size = 100000\\n\\n dfs = []\\n while True:\\n sa_select = sa.select(\\n [self.data_table],\\n whereclause=sa.and_(\\n self.data_table.c._TIMESTAMP > '{}'.format(self._start),\\n self.data_table.c._TIMESTAMP <= '{}'.format(self._end)),\\n limit=chunk_size,\\n offset=offset,\\n order_by=self.data_table.c._NUMERICID\\n )\\n dfs.append(pd.read_sql(sa_select, engine))\\n offset += chunk_size\\n if len(dfs[-1]) < chunk_size:\\n break\\n\\n self.query_df = pd.concat(dfs)\",\n \"def collect(self, revisions):\\n nr_revisions = len(revisions)\\n estimate = TimeEstimator(nr_revisions)\\n for index, revision_number in enumerate(revisions):\\n last_measurement = self.__get_last_measurement(revision_number)\\n self.__write_measurement(last_measurement)\\n self.__last_revision.set(revision_number)\\n logging.info('Revision: %s, %s/%s, measurement date: %s, time remaining: %s', revision_number, index + 1,\\n nr_revisions, self.__get_date(last_measurement), estimate.time_remaining(index))\",\n \"def overlap(self, other):\\r\\n self.set_datetime()\\r\\n other.set_datetime()\\r\\n return (self.dt_1 - other.dt_0).total_seconds()\",\n \"def _recompute(self):\\n current_date = self.start_date\\n self.quarterly_date_list = []\\n self.daily_date_list = []\\n while current_date <= self.end_date:\\n current_quarter = get_quarter(current_date)\\n current_year = current_date.year\\n next_year, next_quarter = add_quarter(current_year, current_quarter)\\n next_start_quarter_date = date(next_year, get_month(next_quarter),\\n 1)\\n\\n days_till_next_quarter = (next_start_quarter_date -\\n current_date).days\\n days_till_end = (self.end_date - current_date).days\\n if days_till_next_quarter <= days_till_end:\\n current_start_quarter_date = date(current_year,\\n get_month(current_quarter), 1)\\n if current_start_quarter_date == current_date:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter, lambda x: True))\\n current_date = next_start_quarter_date\\n elif days_till_next_quarter > self.balancing_point:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter,\\n lambda x: date(x['date_filed']) >= self.start_date))\\n current_date = next_start_quarter_date\\n else:\\n while current_date < next_start_quarter_date:\\n self.daily_date_list.append(current_date)\\n current_date += timedelta(days=1)\\n else:\\n if days_till_end > self.balancing_point:\\n if days_till_next_quarter - 1 == days_till_end:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter, lambda x: True))\\n current_date = next_start_quarter_date\\n else:\\n self.quarterly_date_list.append(\\n (current_year, current_quarter,\\n lambda x: date(x['date_filed']) <= self.end_date))\\n current_date = self.end_date\\n else:\\n while current_date <= self.end_date:\\n self.daily_date_list.append(current_date)\\n current_date += timedelta(days=1)\",\n \"def read_daily_qualified_report(self):\\n from itertools import repeat\\n\\n self.ID_TOTAL_CANDIDATES = kpi_from_db_config.ID_TOTAL_CANDIDATES\\n self.ID_TOTAL_PROCESSED = kpi_from_db_config.ID_TOTAL_PROCESSED\\n self.ID_TOTAL_EXPORTED = kpi_from_db_config.ID_TOTAL_EXPORTED\\n self.ID_TOTAL_CLASSIFIED = kpi_from_db_config.ID_TOTAL_CLASSIFIED\\n self.ID_TOTAL_QUALIFIED = kpi_from_db_config.ID_TOTAL_QUALIFIED\\n self.ID_TOTAL_DISQUALIFIED = kpi_from_db_config.ID_TOTAL_DISQUALIFIED\\n\\n list_id = [self.ID_TOTAL_CANDIDATES, \\n self.ID_TOTAL_PROCESSED, \\n self.ID_TOTAL_EXPORTED, \\n self.ID_TOTAL_CLASSIFIED, \\n self.ID_TOTAL_QUALIFIED, \\n self.ID_TOTAL_DISQUALIFIED]\\n list_result = [[] for i in repeat(None,len(list_id))]\\n\\n for i in range(len(list_id)):\\n self.cursor.execute('''\\n SELECT value\\n FROM public.kpi_report\\n WHERE id = %s\\n ORDER BY created_at DESC\\n LIMIT 2\\n ''', [list_id[i]])\\n\\n rows_count = self.cursor.rowcount\\n if (rows_count == 2):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n elif (rows_count == 1):\\n for doc in self.cursor:\\n list_result[i].append(int(doc[0]))\\n list_result[i] = list_result[i] + [0]\\n else:\\n list_result[i] = [0] * 2 \\n\\n# print \\\"TESTING .... {}\\\".format(list_result)\\n return list_result\",\n \"def return_trade_history(\\n self,\\n start: Timestamp,\\n end: Timestamp,\\n ) -> list[dict[str, Any]]:\\n limit = 100\\n data: list[dict[str, Any]] = []\\n start_ms = start * 1000\\n end_ms = end * 1000\\n while True:\\n new_data = self.api_query_list('/trades', {\\n 'startTime': start_ms,\\n 'endTime': end_ms,\\n 'limit': limit,\\n })\\n results_length = len(new_data)\\n if data == [] and results_length < limit:\\n return new_data # simple case - only one query needed\\n\\n latest_ts_ms = start_ms\\n # add results to data and prepare for next query\\n existing_ids = {x['id'] for x in data}\\n for trade in new_data:\\n try:\\n timestamp_ms = trade['createTime']\\n latest_ts_ms = max(latest_ts_ms, timestamp_ms)\\n # since we query again from last ts seen make sure no duplicates make it in\\n if trade['id'] not in existing_ids:\\n data.append(trade)\\n except (DeserializationError, KeyError) as e:\\n msg = str(e)\\n if isinstance(e, KeyError):\\n msg = f'Missing key entry for {msg}.'\\n self.msg_aggregator.add_warning(\\n 'Error deserializing a poloniex trade. Check the logs for details',\\n )\\n log.error(\\n 'Error deserializing poloniex trade',\\n trade=trade,\\n error=msg,\\n )\\n continue\\n\\n if results_length < limit:\\n break # last query has less than limit. We are done.\\n\\n # otherwise we query again from the last ts seen in the last result\\n start_ms = latest_ts_ms\\n continue\\n\\n return data\",\n \"def fill_price_gaps(\\n from_date=dt.datetime(1970,1,1),\\n to_date=dt.datetime.today().replace(hour=0, minute=0, second=0, microsecond=0)\\n ):\\n #Create a collection of years\\n years = []\\n cur_year = from_date.year\\n while cur_year <= to_date.year:\\n years.append(cur_year)\\n cur_year += 1\\n #Loop each year\\n all_year_dates = pd.DataFrame([])\\n for year in tqdm(years, total=len(years), desc=\\\"Loop through years to find dates\\\"):\\n #establish bounding dates\\n year_from_date = None if year != from_date.year else from_date\\n year_to_date = None if year != to_date.year else to_date\\n #Get filtered year dates\\n year_dates = create_filtered_year_dates(year, from_date=year_from_date, to_date=year_to_date, )\\n #Add to the full list\\n all_year_dates = pd.concat([all_year_dates, year_dates])\\n #Order the dates (just in case)\\n all_year_dates = all_year_dates.sort_values([\\\"date\\\"]) \\\\\\n .reset_index(drop=True)\\n #Fetch all the tickers\\n tickers = sqlaq_to_df(ticker.fetch())\\n #Loop through tickers\\n errors = []\\n run_time = ProcessTime()\\n for _,r in tqdm(tickers[[\\\"id\\\",\\\"ticker\\\"]].iterrows(), total=tickers.shape[0], desc=\\\"Filling in gaps\\\"):\\n logger.info(f\\\"Filling gaps in {r.id} -> {r.ticker}\\\")\\n try:\\n #Fetch all prices\\n dp = sqlaq_to_df(daily_price.fetch(ticker_ids=[r.id]))\\n dp[\\\"date\\\"] = dp.date.astype(\\\"datetime64[ns]\\\")\\n #Identify missing dates\\n missing_dates = pd.merge(all_year_dates, dp[[\\\"date\\\",\\\"id\\\"]], on=[\\\"date\\\"], how=\\\"left\\\")\\n #Identify the start date and remove all missing date before that\\n start_date = missing_dates[~missing_dates.id.isnull()].date.min()\\n missing_dates = missing_dates[missing_dates.date > start_date]\\n #Remove all other items which have dates\\n missing_dates = missing_dates[missing_dates.id.isnull()]\\n #Order remaining dates\\n missing_dates = missing_dates.sort_values(\\\"date\\\")\\n #Create groupings no larger than max_days (in config)\\n st_d = None\\n date_groups = []\\n missing_dates = missing_dates.date.to_list()\\n if len(missing_dates):\\n for i,d in enumerate(missing_dates):\\n if not st_d:\\n st_d = d\\n else:\\n #Append when group gets too big\\n if (d - st_d).days > WEB_SCRAPE_MAX_DAYS:\\n date_groups.append([st_d, missing_dates[i-1]])\\n #Update the start date\\n st_d = d\\n #Append the last item\\n date_groups.append([st_d, d])\\n #Scrape the missing prices\\n logger.info('Number of webscrapes to perform -> {}'.format(len(date_groups)))\\n #For each time frame perform a scrape\\n try: #Try loop so as not to miss all following date groups\\n for i,dates in enumerate(date_groups):\\n logger.info(f\\\"Running dates {i} -> {dt.datetime.strptime(str(dates[0])[:10], '%Y-%m-%d')} - {dt.datetime.strptime(str(dates[1])[:10], '%Y-%m-%d')}\\\")\\n process_daily_prices(\\n r.ticker,\\n r.id,\\n st_date=dates[0],\\n en_date=dates[1],\\n \\n )\\n except Exception as e:\\n logger.error(e)\\n errors.append({'ticker_id':r.id, 'ticker':r.ticker, \\\"error\\\":e, \\\"st_date\\\":dates[0], \\\"en_dates\\\":dates[1]})\\n #Run an update on th weekly prices\\n process_weekly_prices(\\n r.id,\\n \\n )\\n except Exception as e:\\n logger.error(e)\\n errors.append({'ticker_id':r.id, 'ticker':r.ticker, \\\"error\\\":e})\\n #Lap\\n logger.info(run_time.lap())\\n logger.info(run_time.show_latest_lap_time(show_time=True))\\n logger.info(f\\\"GAP FILL RUN TIME - {run_time.end()}\\\")\\n\\n logger.info(f'\\\\nGAP FILL ERROR COUNT -> {len(errors)}')\\n if len(errors) > 0:\\n logger.info('GAP FILL ERRORS ->')\\n for e in errors:\\n logger.error(e)\",\n \"def set_period_starters(self, missing_period_starters=MISSING_PERIOD_STARTERS):\\n for period in self.Periods:\\n period.Starters = {self.HomeTeamId: [], self.VisitorTeamId: []}\\n subbed_in_players = {self.HomeTeamId: [], self.VisitorTeamId: []}\\n for pbp_event in period.Events:\\n player_id = pbp_event.player_id\\n if player_id in self.Players[self.HomeTeamId]:\\n team_id = self.HomeTeamId\\n elif player_id in self.Players[self.VisitorTeamId]:\\n team_id = self.VisitorTeamId\\n else:\\n team_id = None\\n\\n if team_id is not None and team_id != '0' and player_id != '0':\\n player2_id = pbp_event.player2_id\\n player3_id = pbp_event.player3_id\\n if pbp_event.is_substitution():\\n # player_id is player going out, player2_id is playing coming in\\n if player2_id not in period.Starters[team_id] and player2_id not in subbed_in_players[team_id]:\\n subbed_in_players[team_id].append(player2_id)\\n if player_id not in period.Starters[team_id] and player_id not in subbed_in_players[team_id]:\\n if player_id in self.Players[self.HomeTeamId] or player_id in self.Players[self.VisitorTeamId]:\\n period.Starters[team_id].append(player_id)\\n if player_id != '0':\\n # player_id 0 is team\\n if player_id not in period.Starters[team_id] and player_id not in subbed_in_players[team_id]:\\n if player_id in self.Players[self.HomeTeamId] or player_id in self.Players[self.VisitorTeamId]:\\n if not (\\n pbp_event.is_technical_foul() or\\n pbp_event.is_double_technical_foul() or\\n pbp_event.is_ejection() or\\n (pbp_event.is_technical_ft() and pbp_event.clock_time == '12:00') # ignore technical fts at start of period\\n ):\\n # ignore all techs because a player could get a technical foul when they aren't in the game\\n period.Starters[team_id].append(player_id)\\n # need player2_id for players who play full period and never appear in an event as player_id - ex assists\\n if (player2_id in self.Players[self.HomeTeamId] or player2_id in self.Players[self.VisitorTeamId]) and not pbp_event.is_substitution():\\n if not (\\n pbp_event.is_technical_foul() or\\n pbp_event.is_double_technical_foul() or\\n pbp_event.is_ejection()\\n ):\\n # ignore all techs because a player could get a technical foul when they aren't in the game\\n if player2_id in self.Players[self.HomeTeamId]:\\n player2_team_id = self.HomeTeamId\\n if player2_id in self.Players[self.VisitorTeamId]:\\n player2_team_id = self.VisitorTeamId\\n if player2_id not in period.Starters[player2_team_id] and player2_id not in subbed_in_players[player2_team_id]:\\n period.Starters[player2_team_id].append(player2_id)\\n if (player3_id in self.Players[self.HomeTeamId] or player3_id in self.Players[self.VisitorTeamId]) and not pbp_event.is_substitution():\\n if not (\\n pbp_event.is_technical_foul() or\\n pbp_event.is_double_technical_foul() or\\n pbp_event.is_ejection()\\n ):\\n # ignore all techs because a player could get a technical foul when they aren't in the game\\n if player3_id in self.Players[self.HomeTeamId]:\\n player3_team_id = self.HomeTeamId\\n if player3_id in self.Players[self.VisitorTeamId]:\\n player3_team_id = self.VisitorTeamId\\n if player3_id not in period.Starters[player3_team_id] and player3_id not in subbed_in_players[player3_team_id]:\\n period.Starters[player3_team_id].append(player3_id)\\n\\n if self.GameId in missing_period_starters.keys() and str(period.Number) in missing_period_starters[self.GameId].keys():\\n for team_id in missing_period_starters[self.GameId][str(period.Number)].keys():\\n period.Starters[team_id] = missing_period_starters[self.GameId][str(period.Number)][team_id]\\n\\n for team_id in period.Starters.keys():\\n if len(period.Starters[team_id]) != 5:\\n raise InvalidNumberOfStartersException(f\\\"GameId: {self.GameId}, Period: {period}, TeamId: {team_id}, Players: {period.Starters[team_id]}\\\")\",\n \"def _get_time_periods(current_time_period=None):\\n sorted_time_periods = list(SORTED_TIME_PERIODS)\\n if current_time_period and current_time_period in sorted_time_periods:\\n # make the current time period the first in the list so that it shows up\\n # as selected in project choice drop down\\n for time_period in sorted_time_periods:\\n if time_period == current_time_period:\\n sorted_time_periods.remove(time_period)\\n sorted_time_periods.insert(0, time_period)\\n break\\n\\n return sorted_time_periods\",\n \"def _summarize_period(self):\\n print(\\\"entering _summarize_period()\\\")\\n for i in range(TIME_BETWEEN_FEEDBACK // SONG_OVER_CHECK_TIME): ### Wait 30 seconds\\n self._check_completion()\\n if self._song_over is True:\\n break\\n self._set_last_30_sec()\\n return\",\n \"def spending_over_time_test_data():\\n for i in range(30):\\n # Define some values that are calculated and used multiple times\\n transaction_id = i\\n award_id = i + 1000\\n awarding_agency_id = i + 2000\\n toptier_awarding_agency_id = i + 3000\\n subtier_awarding_agency_id = i + 4000\\n funding_agency_id = i + 5000\\n toptier_funding_agency_id = i + 6000\\n subtier_funding_agency_id = i + 7000\\n federal_action_obligation = i + 8000\\n total_obligation = i + 9000\\n federal_account_id = i + 10000\\n treasury_account_id = i + 11000\\n\\n action_date = f\\\"20{i % 10 + 10}-{i % 9 + 1}-{i % 28 + 1}\\\"\\n action_date_obj = datetime.datetime.strptime(action_date, \\\"%Y-%m-%d\\\")\\n fiscal_month = generate_fiscal_month(action_date_obj)\\n fiscal_year = generate_fiscal_year(action_date_obj)\\n fiscal_action_date = f\\\"{fiscal_year}-{fiscal_month}-{i % 28 + 1}\\\"\\n contract_award_type = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"][i % 4]\\n grant_award_type = [\\\"02\\\", \\\"03\\\", \\\"04\\\", \\\"05\\\"][i % 4]\\n is_fpds = i % 2 == 0\\n\\n # Award\\n baker.make(\\n \\\"search.AwardSearch\\\",\\n award_id=award_id,\\n fain=f\\\"fain_{transaction_id}\\\" if not is_fpds else None,\\n is_fpds=is_fpds,\\n latest_transaction_id=transaction_id,\\n piid=f\\\"piid_{transaction_id}\\\" if is_fpds else None,\\n total_obligation=total_obligation,\\n type=contract_award_type if is_fpds else grant_award_type,\\n action_date=\\\"2020-01-01\\\",\\n )\\n\\n # Federal, Treasury, and Financial Accounts\\n baker.make(\\n \\\"accounts.FederalAccount\\\",\\n id=federal_account_id,\\n parent_toptier_agency_id=toptier_awarding_agency_id,\\n account_title=f\\\"federal_account_title_{transaction_id}\\\",\\n federal_account_code=f\\\"federal_account_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"accounts.TreasuryAppropriationAccount\\\",\\n agency_id=f\\\"taa_aid_{transaction_id}\\\",\\n allocation_transfer_agency_id=f\\\"taa_ata_{transaction_id}\\\",\\n availability_type_code=f\\\"taa_a_{transaction_id}\\\",\\n beginning_period_of_availability=f\\\"taa_bpoa_{transaction_id}\\\",\\n ending_period_of_availability=f\\\"taa_epoa_{transaction_id}\\\",\\n federal_account_id=federal_account_id,\\n main_account_code=f\\\"taa_main_{transaction_id}\\\",\\n sub_account_code=f\\\"taa_sub_{transaction_id}\\\",\\n treasury_account_identifier=treasury_account_id,\\n )\\n tas_components = [\\n f\\\"aid=taa_aid_{transaction_id}\\\"\\n f\\\"main=taa_main_{transaction_id}\\\"\\n f\\\"ata=taa_ata_{transaction_id}\\\"\\n f\\\"sub=taa_sub_{transaction_id}\\\"\\n f\\\"bpoa=taa_bpoa_{transaction_id}\\\"\\n f\\\"epoa=taa_epoa_{transaction_id}\\\"\\n f\\\"a=taa_a_{transaction_id}\\\"\\n ]\\n baker.make(\\\"awards.FinancialAccountsByAwards\\\", award_id=award_id, treasury_account_id=treasury_account_id)\\n\\n # Awarding Agency\\n baker.make(\\n \\\"references.Agency\\\",\\n id=awarding_agency_id,\\n subtier_agency_id=subtier_awarding_agency_id,\\n toptier_agency_id=toptier_awarding_agency_id,\\n )\\n baker.make(\\n \\\"references.ToptierAgency\\\",\\n abbreviation=f\\\"toptier_awarding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n toptier_agency_id=toptier_awarding_agency_id,\\n toptier_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.SubtierAgency\\\",\\n abbreviation=f\\\"subtier_awarding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n subtier_agency_id=subtier_awarding_agency_id,\\n subtier_code=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n )\\n\\n # Funding Agency\\n baker.make(\\n \\\"references.Agency\\\",\\n id=funding_agency_id,\\n subtier_agency_id=subtier_funding_agency_id,\\n toptier_agency_id=toptier_funding_agency_id,\\n )\\n baker.make(\\n \\\"references.ToptierAgency\\\",\\n abbreviation=f\\\"toptier_funding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n toptier_agency_id=toptier_funding_agency_id,\\n toptier_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.SubtierAgency\\\",\\n abbreviation=f\\\"subtier_funding_agency_abbreviation_{transaction_id}\\\",\\n name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n subtier_agency_id=subtier_funding_agency_id,\\n subtier_code=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n )\\n\\n # Ref Country Code\\n baker.make(\\\"references.RefCountryCode\\\", country_code=\\\"USA\\\", country_name=\\\"UNITED STATES\\\")\\n\\n # FPDS / FABS\\n if is_fpds:\\n baker.make(\\n \\\"search.TransactionSearch\\\",\\n transaction_id=transaction_id,\\n is_fpds=is_fpds,\\n action_date=action_date,\\n fiscal_year=fiscal_year,\\n fiscal_action_date=fiscal_action_date,\\n award_id=award_id,\\n awarding_agency_id=awarding_agency_id,\\n business_categories=[f\\\"business_category_1_{transaction_id}\\\", f\\\"business_category_2_{transaction_id}\\\"],\\n transaction_description=f\\\"This is a test description {transaction_id}\\\"\\n if transaction_id % 2 == 0\\n else None,\\n federal_action_obligation=federal_action_obligation,\\n generated_pragmatic_obligation=federal_action_obligation,\\n award_amount=total_obligation,\\n funding_agency_id=funding_agency_id,\\n type=contract_award_type if is_fpds else grant_award_type,\\n awarding_agency_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n awarding_toptier_agency_name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n awarding_toptier_agency_abbreviation=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_agency_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n funding_toptier_agency_name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_toptier_agency_abbreviation=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n awarding_sub_tier_agency_c=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n awarding_subtier_agency_name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_sub_tier_agency_co=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n funding_subtier_agency_name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_subtier_agency_abbreviation=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n recipient_name=f\\\"recipient_name_{transaction_id}\\\",\\n recipient_unique_id=f\\\"{transaction_id:09d}\\\",\\n recipient_hash=\\\"c687823d-10af-701b-1bad-650c6e680190\\\" if transaction_id == 21 else None,\\n recipient_levels=[\\\"R\\\"] if i == 21 else [],\\n extent_competed=f\\\"extent_competed_{transaction_id}\\\",\\n recipient_location_country_code=\\\"USA\\\",\\n recipient_location_country_name=\\\"USA\\\",\\n recipient_location_state_code=f\\\"LE_STATE_CODE_{transaction_id}\\\",\\n recipient_location_county_code=f\\\"{transaction_id:03d}\\\",\\n recipient_location_county_name=f\\\"LE_COUNTY_NAME_{transaction_id}\\\",\\n recipient_location_congressional_code=f\\\"{transaction_id:02d}\\\",\\n recipient_location_zip5=f\\\"LE_ZIP5_{transaction_id}\\\",\\n recipient_location_city_name=f\\\"LE_CITY_NAME_{transaction_id}\\\",\\n naics_code=f\\\"{transaction_id}{transaction_id}\\\",\\n naics_description=f\\\"naics_description_{transaction_id}\\\",\\n piid=f\\\"piid_{transaction_id}\\\",\\n pop_country_code=\\\"USA\\\",\\n pop_country_name=\\\"UNITED STATES\\\",\\n pop_state_code=f\\\"POP_STATE_CODE_{transaction_id}\\\",\\n pop_county_code=f\\\"{transaction_id:03d}\\\",\\n pop_county_name=f\\\"POP_COUNTY_NAME_{transaction_id}\\\",\\n pop_zip5=f\\\"POP_ZIP5_{transaction_id}\\\",\\n pop_congressional_code=f\\\"{transaction_id:02d}\\\",\\n pop_city_name=f\\\"POP_CITY_NAME_{transaction_id}\\\",\\n product_or_service_code=str(transaction_id).zfill(4),\\n product_or_service_description=f\\\"psc_description_{transaction_id}\\\",\\n type_of_contract_pricing=f\\\"type_of_contract_pricing_{transaction_id}\\\",\\n type_set_aside=f\\\"type_set_aside_{transaction_id}\\\",\\n tas_components=tas_components,\\n )\\n baker.make(\\n \\\"references.NAICS\\\",\\n code=f\\\"{transaction_id}\\\",\\n description=f\\\"naics_description_{transaction_id}\\\",\\n )\\n baker.make(\\n \\\"references.PSC\\\", code=str(transaction_id).zfill(4), description=f\\\"psc_description_{transaction_id}\\\"\\n )\\n else:\\n baker.make(\\n \\\"search.TransactionSearch\\\",\\n transaction_id=transaction_id,\\n is_fpds=is_fpds,\\n action_date=action_date,\\n fiscal_year=fiscal_year,\\n fiscal_action_date=fiscal_action_date,\\n award_id=award_id,\\n awarding_agency_id=awarding_agency_id,\\n business_categories=[f\\\"business_category_1_{transaction_id}\\\", f\\\"business_category_2_{transaction_id}\\\"],\\n transaction_description=f\\\"This is a test description {transaction_id}\\\"\\n if transaction_id % 2 == 0\\n else None,\\n federal_action_obligation=federal_action_obligation,\\n generated_pragmatic_obligation=federal_action_obligation,\\n award_amount=total_obligation,\\n funding_agency_id=funding_agency_id,\\n type=contract_award_type if is_fpds else grant_award_type,\\n awarding_agency_code=f\\\"toptier_awarding_agency_code_{transaction_id}\\\",\\n awarding_toptier_agency_name=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n awarding_toptier_agency_abbreviation=f\\\"toptier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_agency_code=f\\\"toptier_funding_agency_code_{transaction_id}\\\",\\n funding_toptier_agency_name=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_toptier_agency_abbreviation=f\\\"toptier_funding_agency_agency_name_{transaction_id}\\\",\\n awarding_sub_tier_agency_c=f\\\"subtier_awarding_agency_code_{transaction_id}\\\",\\n awarding_subtier_agency_name=f\\\"subtier_awarding_agency_agency_name_{transaction_id}\\\",\\n funding_sub_tier_agency_co=f\\\"subtier_funding_agency_code_{transaction_id}\\\",\\n funding_subtier_agency_name=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n funding_subtier_agency_abbreviation=f\\\"subtier_funding_agency_agency_name_{transaction_id}\\\",\\n recipient_name=f\\\"recipient_name_{transaction_id}\\\",\\n recipient_unique_id=f\\\"{transaction_id:09d}\\\",\\n recipient_hash=\\\"c687823d-10af-701b-1bad-650c6e680190\\\" if transaction_id == 21 else None,\\n recipient_levels=[\\\"R\\\"] if i == 21 else [],\\n cfda_number=f\\\"cfda_number_{transaction_id}\\\",\\n fain=f\\\"fain_{transaction_id}\\\",\\n recipient_location_country_code=\\\"USA\\\",\\n recipient_location_country_name=\\\"USA\\\",\\n recipient_location_state_code=f\\\"LE_STATE_CODE_{transaction_id}\\\",\\n recipient_location_county_code=f\\\"{transaction_id:03d}\\\",\\n recipient_location_county_name=f\\\"LE_COUNTY_NAME_{transaction_id}\\\",\\n recipient_location_congressional_code=f\\\"{transaction_id:02d}\\\",\\n recipient_location_zip5=f\\\"LE_ZIP5_{transaction_id}\\\",\\n recipient_location_city_name=f\\\"LE_CITY_NAME_{transaction_id}\\\",\\n pop_country_code=\\\"USA\\\",\\n pop_country_name=\\\"UNITED STATES\\\",\\n pop_state_code=f\\\"POP_STATE_CODE_{transaction_id}\\\",\\n pop_county_code=f\\\"{transaction_id:03d}\\\",\\n pop_county_name=f\\\"POP_COUNTY_NAME_{transaction_id}\\\",\\n pop_zip5=f\\\"POP_ZIP5_{transaction_id}\\\",\\n pop_congressional_code=f\\\"{transaction_id:02d}\\\",\\n pop_city_name=f\\\"POP_CITY_NAME{transaction_id}\\\",\\n tas_components=tas_components,\\n )\",\n \"def print_alerts_stats(now, stats_period_seconds, alerts_df):\\n\\n if not alerts_df.empty:\\n active_alerts = alerts_df.loc[alerts_df[\\\"date_end\\\"].isnull()]\\n archived_alerts = alerts_df.loc[alerts_df[\\\"date_end\\\"].notnull()]\\n new_alerts = active_alerts.loc[active_alerts[\\\"date_start\\\"] > now - stats_period_seconds]\\n recovered_alerts = archived_alerts.loc[archived_alerts[\\\"date_end\\\"] > now - stats_period_seconds ]\\n\\n if len(new_alerts) > 0:\\n print(f'### New alerts during the last {stats_period_seconds} seconds###')\\n for index, row in new_alerts.iterrows():\\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\\n print(f\\\" - {row['type']} generated an alert - {row['value_start']}, triggered at {date_time_start}\\\")\\n\\n if len(recovered_alerts) > 0:\\n print(f'### Recovered alerts during the last {stats_period_seconds} seconds###')\\n for index, row in recovered_alerts.iterrows():\\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\\n date_time_end = datetime.datetime.fromtimestamp(row['date_end']).strftime('%Y-%m-%d %H:%M:%S')\\n print(f\\\" - {row['type']} triggered at {date_time_start} recovered at {date_time_end} - was {row['value_start']}, now {row['value_end']}\\\")\\n\\n if len(active_alerts) > 0:\\n print(f'### Active alerts ###')\\n for index, row in active_alerts.iterrows():\\n date_time = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\\n print(f\\\" - Since {date_time} : {row['type']}\\\")\\n\\n if len(archived_alerts) > 0:\\n print(f'### Past alerts ###')\\n for index, row in archived_alerts.iterrows():\\n date_time_start = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\\n date_time_end = datetime.datetime.fromtimestamp(row['date_start']).strftime('%Y-%m-%d %H:%M:%S')\\n print(f\\\" - {date_time_start} to {date_time_end} : {row['type']}\\\")\\n else:\\n print(f'### No alert ###')\",\n \"def get_aggregate_periods(all_periods, all_samples, write_to_csv=True):\\n null_idx = all_periods['activation_date'].isna()\\n all_periods['activation_date'][null_idx] = all_periods['date_from'][null_idx]\\n assert (all_periods['date_to'] >= all_periods['date_from']).all()\\n # we can do this below since they're all in 2017 and this way is faster\\n all_periods['days_to_publish'] = all_periods['date_from'].dt.dayofyear - \\\\\\n all_periods['activation_date'].dt.dayofyear\\n all_periods['days_online'] = all_periods['date_to'].dt.dayofyear - \\\\\\n all_periods['date_from'].dt.dayofyear\\n for col in ['activation_date', 'date_from', 'date_to']:\\n all_periods = featurize_date_col(all_periods, col)\\n\\n grouped = all_periods.groupby('item_id')\\n base = grouped[['item_id']].count().rename(columns={'item_id': 'nlisted'})\\n base['sum_days_online'] = grouped[['days_online']].sum()\\n base['mean_days_online'] = grouped[['days_online']].mean()\\n base['last_days_online'] = grouped[['days_online']].last()\\n base['sum_days_to_publish'] = grouped[['days_to_publish']].sum()\\n base['mean_days_to_publish'] = grouped[['days_to_publish']].mean()\\n base['median_date_to_isholiday'] = grouped[['date_to_isholiday']].median()\\n base['median_date_to_wday'] = grouped[['date_to_wday']].median()\\n base['median_date_to_yday'] = grouped[['date_to_yday']].median()\\n\\n base['start_date'] = grouped[['date_from']].min()\\n base['end_date'] = grouped[['date_to']].max()\\n for col in ['start_date', 'end_date']:\\n base = featurize_date_col(base, col, remove_when_done=True)\\n if 'item_id' not in all_periods:\\n all_periods = all_periods.reset_index()\\n if 'item_id' not in base:\\n base = base.reset_index()\\n all_periods = all_periods.drop_duplicates(['item_id'])\\n all_periods = all_periods.merge(base, on='item_id', how='left')\\n all_periods = all_periods.merge(all_samples, on='item_id', how='left')\\n avg_per_user_periods = all_periods.drop(\\n ['item_id', 'activation_date', 'date_from', 'date_to'], axis=1\\n ).groupby('user_id').mean()\\n avg_per_user_periods['nitems'] = all_periods[\\n ['user_id', 'item_id']].groupby('user_id').count().reset_index()['item_id']\\n if write_to_csv:\\n avg_per_user_periods.to_csv('data/periods_aggregate_features.csv')\\n return avg_per_user_periods\",\n \"def _fill_moment_results(self):\\n toprocess = [('stock_tom', self.c_stock, 2),\\n ('stock_woody', self.c_stock, 3),\\n ('stock_non_woody', self.c_stock, 4),\\n ('stock_acid', self.c_stock, 5),\\n ('stock_water', self.c_stock, 6),\\n ('stock_ethanol', self.c_stock, 7),\\n ('stock_non_soluble', self.c_stock, 8),\\n ('stock_humus', self.c_stock, 9),\\n ('change_tom', self.c_change, 2),\\n ('change_woody', self.c_change, 3),\\n ('change_non_woody', self.c_change, 4),\\n ('change_acid', self.c_change, 5),\\n ('change_water', self.c_change, 6),\\n ('change_ethanol', self.c_change, 7),\\n ('change_non_soluble', self.c_change, 8),\\n ('change_humus', self.c_change, 9),\\n ('co2', self.co2_yield, 2)]\\n for (resto, dataarr, dataind) in toprocess:\\n # filter time steps\\n ts = numpy.unique(dataarr[:,1])\\n # extract data for the timestep\\n for timestep in ts:\\n ind = numpy.where(dataarr[:,1]==timestep)\\n mean = stats.mean(dataarr[ind[0], dataind])\\n mode_res = stats.mode(dataarr[ind[0], dataind])\\n mode = mode_res[0]\\n var = stats.var(dataarr[ind[0], dataind])\\n skew = stats.skew(dataarr[ind[0], dataind])\\n kurtosis = stats.kurtosis(dataarr[ind[0], dataind])\\n if var>0.0:\\n sd2 = 2 * math.sqrt(var)\\n else:\\n sd2 = var\\n res = [[timestep, mean, mode[0], var, skew, kurtosis,\\n mean - sd2, mean + sd2]]\\n if resto=='stock_tom':\\n self.md.stock_tom = numpy.append(self.md.stock_tom,\\n res, axis=0)\\n elif resto=='stock_woody':\\n self.md.stock_woody = numpy.append(self.md.stock_woody,\\n res, axis=0)\\n elif resto=='stock_non_woody':\\n self.md.stock_non_woody = numpy.append(\\\\\\n self.md.stock_non_woody, res, axis=0)\\n elif resto=='stock_acid':\\n self.md.stock_acid = numpy.append(self.md.stock_acid,\\n res, axis=0)\\n elif resto=='stock_water':\\n self.md.stock_water = numpy.append(self.md.stock_water,\\n res, axis=0)\\n elif resto=='stock_ethanol':\\n self.md.stock_ethanol = numpy.append(self.md.stock_ethanol,\\n res, axis=0)\\n elif resto=='stock_non_soluble':\\n self.md.stock_non_soluble= numpy.append(\\n self.md.stock_non_soluble, res, axis=0)\\n elif resto=='stock_humus':\\n self.md.stock_humus = numpy.append(self.md.stock_humus,\\n res, axis=0)\\n elif resto=='change_tom':\\n self.md.change_tom = numpy.append(self.md.change_tom,\\n res, axis=0)\\n elif resto=='change_woody':\\n self.md.change_woody = numpy.append(self.md.change_woody,\\n res, axis=0)\\n elif resto=='change_non_woody':\\n self.md.change_non_woody = numpy.append(\\\\\\n self.md.change_non_woody, res, axis=0)\\n elif resto=='change_acid':\\n self.md.change_acid = numpy.append(self.md.change_acid,\\n res, axis=0)\\n elif resto=='change_water':\\n self.md.change_water = numpy.append(self.md.change_water,\\n res, axis=0)\\n elif resto=='change_ethanol':\\n self.md.change_ethanol = numpy.append(\\n self.md.change_ethanol, res, axis=0)\\n elif resto=='change_non_soluble':\\n self.md.change_non_soluble=numpy.append(\\n self.md.change_non_soluble, res, axis=0)\\n elif resto=='change_humus':\\n self.md.change_humus = numpy.append(self.md.change_humus,\\n res, axis=0)\\n elif resto=='co2':\\n self.md.co2 = numpy.append(self.md.co2, res, axis=0)\",\n \"def pair_records():\\r\\n\\r\\n study_list = retrieve_ref('study_list')\\r\\n sensor_list = retrieve_ref('sensor_list')\\r\\n\\r\\n # check each study\\r\\n for study in study_list:\\r\\n\\r\\n df_meta = retrieve_meta(study)\\r\\n recordNames = list(df_meta['recordName'])\\r\\n\\r\\n # create column to list wearableName and coregister records\\r\\n df_meta = add_wearableName(df_meta)\\r\\n df_meta['coregisterRecords'] = recordNames\\r\\n\\r\\n # look for paired records using the unix time stamp for when the record begins\\r\\n for recordA in recordNames:\\r\\n\\r\\n i = df_meta[ df_meta['recordName']== recordA].index.values[0]\\r\\n recordBeginA = df_meta.loc[i, 'recordBegin' ]\\r\\n wearableA = df_meta.loc[i, 'wearableName' ]\\r\\n\\r\\n for recordB in recordNames:\\r\\n\\r\\n j = df_meta[ df_meta['recordName']== recordB].index.values[0]\\r\\n recordBeginB = df_meta.loc[j, 'recordBegin' ]\\r\\n wearableB = df_meta.loc[j, 'wearableName' ]\\r\\n\\r\\n if abs(recordBeginA - recordBeginB) < 300:\\r\\n\\r\\n if recordA != recordB:\\r\\n\\r\\n if wearableA != wearableB:\\r\\n\\r\\n print('coregister record found for ' + recordA + ' + ' + recordB)\\r\\n coregisterList = str(recordA + ' ' + recordB)\\r\\n df_meta.loc[i, 'coregisterRecords' ] = coregisterList\\r\\n\\r\\n save_meta(study, df_meta)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60452324","0.5793913","0.5528706","0.5526547","0.5517215","0.5493142","0.54775965","0.54737484","0.5462078","0.54354554","0.5395026","0.5321515","0.529761","0.5283373","0.5275335","0.5271934","0.52359205","0.51437724","0.51378435","0.513382","0.51214904","0.5115958","0.51156235","0.50994545","0.5098985","0.50937","0.5085141","0.5084859","0.5083243","0.50631934","0.5050032","0.50406504","0.5030587","0.5024467","0.5010877","0.5004063","0.4992973","0.49842623","0.4964456","0.4963334","0.49429554","0.49367234","0.49336576","0.4925689","0.49231765","0.49179956","0.4915248","0.49092072","0.48959056","0.48920822","0.48910353","0.48871446","0.4884426","0.4883723","0.48716518","0.486742","0.4847713","0.48465183","0.48282254","0.48252833","0.48232928","0.48164704","0.48076624","0.48031577","0.4798058","0.47851086","0.47810793","0.4776493","0.47729877","0.4770081","0.47691718","0.476548","0.47651705","0.47644314","0.4754531","0.47516888","0.4750576","0.4739991","0.4735852","0.4735222","0.4735202","0.47345805","0.4731123","0.47231656","0.4719897","0.47168493","0.47109717","0.4709279","0.47080532","0.47059748","0.4697536","0.46955082","0.4690138","0.46901008","0.46873394","0.4684192","0.46790767","0.4678922","0.46744505","0.46708104"],"string":"[\n \"0.60452324\",\n \"0.5793913\",\n \"0.5528706\",\n \"0.5526547\",\n \"0.5517215\",\n \"0.5493142\",\n \"0.54775965\",\n \"0.54737484\",\n \"0.5462078\",\n \"0.54354554\",\n \"0.5395026\",\n \"0.5321515\",\n \"0.529761\",\n \"0.5283373\",\n \"0.5275335\",\n \"0.5271934\",\n \"0.52359205\",\n \"0.51437724\",\n \"0.51378435\",\n \"0.513382\",\n \"0.51214904\",\n \"0.5115958\",\n \"0.51156235\",\n \"0.50994545\",\n \"0.5098985\",\n \"0.50937\",\n \"0.5085141\",\n \"0.5084859\",\n \"0.5083243\",\n \"0.50631934\",\n \"0.5050032\",\n \"0.50406504\",\n \"0.5030587\",\n \"0.5024467\",\n \"0.5010877\",\n \"0.5004063\",\n \"0.4992973\",\n \"0.49842623\",\n \"0.4964456\",\n \"0.4963334\",\n \"0.49429554\",\n \"0.49367234\",\n \"0.49336576\",\n \"0.4925689\",\n \"0.49231765\",\n \"0.49179956\",\n \"0.4915248\",\n \"0.49092072\",\n \"0.48959056\",\n \"0.48920822\",\n \"0.48910353\",\n \"0.48871446\",\n \"0.4884426\",\n \"0.4883723\",\n \"0.48716518\",\n \"0.486742\",\n \"0.4847713\",\n \"0.48465183\",\n \"0.48282254\",\n \"0.48252833\",\n \"0.48232928\",\n \"0.48164704\",\n \"0.48076624\",\n \"0.48031577\",\n \"0.4798058\",\n \"0.47851086\",\n \"0.47810793\",\n \"0.4776493\",\n \"0.47729877\",\n \"0.4770081\",\n \"0.47691718\",\n \"0.476548\",\n \"0.47651705\",\n \"0.47644314\",\n \"0.4754531\",\n \"0.47516888\",\n \"0.4750576\",\n \"0.4739991\",\n \"0.4735852\",\n \"0.4735222\",\n \"0.4735202\",\n \"0.47345805\",\n \"0.4731123\",\n \"0.47231656\",\n \"0.4719897\",\n \"0.47168493\",\n \"0.47109717\",\n \"0.4709279\",\n \"0.47080532\",\n \"0.47059748\",\n \"0.4697536\",\n \"0.46955082\",\n \"0.4690138\",\n \"0.46901008\",\n \"0.46873394\",\n \"0.4684192\",\n \"0.46790767\",\n \"0.4678922\",\n \"0.46744505\",\n \"0.46708104\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7975035"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":4696,"cells":{"query":{"kind":"string","value":"Given a UC480 camera object (instrumental module) and a number indicating the number of trap objects, applies an iterative image analysis to individual trap adjustment in order to achieve a nearly homogeneous intensity profile across traps."},"document":{"kind":"string","value":"def stabilize_intensity(which_cam, cam, verbose=False):\n L = 0.5 # Correction Rate\n mags = np.ones(12) ### !\n ntraps = len(mags)\n iteration = 0\n while iteration < 5:\n iteration += 1\n print(\"Iteration \", iteration)\n\n im = cam.latest_frame()\n try:\n trap_powers = analyze_image(which_cam, im, ntraps, iteration, verbose)\n except (AttributeError, ValueError) as e:\n print(\"No Bueno, error occurred during image analysis:\\n\", e)\n break\n\n mean_power = trap_powers.mean()\n rel_dif = 100 * trap_powers.std() / mean_power\n print(f'Relative Power Difference: {rel_dif:.2f} %')\n if rel_dif < 0.8:\n print(\"WOW\")\n break\n\n deltaP = [mean_power - P for P in trap_powers]\n dmags = [(dP / abs(dP)) * sqrt(abs(dP)) * L for dP in deltaP]\n mags = np.add(mags, dmags)\n print(\"Magnitudes: \", mags)\n break\n # self._update_magnitudes(mags)\n _ = analyze_image(im, ntraps, verbose=verbose)"},"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 analyze_image(which_cam, image, ntraps, iteration=0, verbose=False):\n threshes = [0.5, 0.6]\n margin = 10\n threshold = np.max(image) * threshes[which_cam]\n im = image.transpose()\n\n x_len = len(im)\n peak_locs = np.zeros(x_len)\n peak_vals = np.zeros(x_len)\n\n ## Trap Peak Detection ##\n for i in range(x_len):\n if i < margin or x_len - i < margin:\n peak_locs[i] = 0\n peak_vals[i] = 0\n else:\n peak_locs[i] = np.argmax(im[i])\n peak_vals[i] = max(im[i])\n\n ## Trap Range Detection ##\n first = True\n pos_first, pos_last = 0, 0\n left_pos = 0\n for i, p in enumerate(peak_vals):\n if p > threshold:\n left_pos = i\n elif left_pos != 0:\n if first:\n pos_first = (left_pos + i) // 2\n first = False\n pos_last = (left_pos + i) // 2\n left_pos = 0\n\n ## Separation Value ##\n separation = (pos_last - pos_first) / ntraps # In Pixels\n\n ## Initial Guesses ##\n means0 = np.linspace(pos_first, pos_last, ntraps).tolist()\n waists0 = (separation * np.ones(ntraps) / 2).tolist()\n ampls0 = (max(peak_vals) * 0.7 * np.ones(ntraps)).tolist()\n _params0 = [means0, waists0, ampls0, [0.06]]\n params0 = [item for sublist in _params0 for item in sublist]\n\n ## Fitting ##\n if verbose:\n print(\"Fitting...\")\n xdata = np.arange(x_len)\n popt, pcov = curve_fit(lambda x, *params_0: wrapper_fit_func(x, ntraps, params_0),\n xdata, peak_vals, p0=params0)\n if verbose:\n print(\"Fit!\")\n plt.figure()\n plt.plot(xdata, peak_vals) # Data\n if iteration:\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, params0), '--r') # Initial Guess\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, popt)) # Fit\n plt.title(\"Iteration: %d\" % iteration)\n else:\n plt.title(\"Final Product\")\n\n plt.xlim((pos_first - margin, pos_last + margin))\n plt.legend([\"Data\", \"Guess\", \"Fit\"])\n plt.show(block=False)\n print(\"Fig_Newton\")\n trap_powers = np.frombuffer(popt[2 * ntraps:3 * ntraps])\n return trap_powers","def enumerate_detector(det, thresholds, shot_ok=None, tiles=None, nimages=np.inf, startimg=0, stopimg=np.inf, correction=False, progress=True):\n global terminated\n Ncorrect = 64\n correctionphotonthres = 3000\n if not isinstance(det, h5py.Group):\n raise TypeError('det should be a h5 group')\n if tiles is None:\n tiles = [k for k in det.keys() if 'tile' in k]\n else:\n newtiles = []\n for t in tiles:\n if t in det:\n newtiles.append(t)\n elif f'tile{t}' in det:\n newtiles.append(f'tile{t}')\n else:\n raise KeyError(f'tile {t} not found')\n tiles = newtiles\n datanames = [(f'{det.name}/{t}/data') for t in tiles]\n filename = det.file.filename\n\n nshots = det[f'{tiles[0]}/data'].shape[0]\n startimg = int(np.clip(startimg, 0, nshots))\n stopimg = int(np.clip(stopimg, startimg, nshots))\n tileshape = det[f'{tiles[0]}/data'].shape[1:]\n correctmask = [correctionmask(det[t]['absfft0/mean'], Ncorrect) for t in tiles]\n if shot_ok is None:\n shot_ok = np.ones(nshots, np.bool)\n ind_filtered = 0\n data = np.zeros((len(tiles), *tileshape))\n willread = np.copy(shot_ok)\n willread[:startimg] = False\n willread[stopimg:] = False\n with datasetreader(datanames, filename, sizecache=10, willread=willread) as reader:\n\n for ind_orig in range(startimg, stopimg):\n if not shot_ok[ind_orig]:\n continue\n if ind_filtered >= nimages or terminated != 0:\n return\n if progress and ind_filtered % 10 == 0:\n print(ind_filtered, end=' ', flush=True)\n\n for it, t in enumerate(tiles):\n cdat = np.array(reader[ind_orig, it], dtype=np.float, order='C')\n if correction:\n correct(cdat, correctionphotonthres, Ncorrect, correctmask[it])\n data[it, ...] = cdat\n ev, number, scatter = getstats(data, thresholds)\n\n yield (ind_filtered, ind_orig, data, ev, number, scatter)\n\n ind_filtered += 1","def run(self,workspace):\n image_name = self.image_name.value\n cpimage = workspace.image_set.get_image(image_name)\n image = cpimage.pixel_data\n mask = cpimage.mask\n workspace.display_data.statistics = []\n level = int(self.atrous_level.value)\n\n wavelet = self.a_trous(1.0*image, level+1)\n wlevprod = wavelet[:,:,level-1] * 3.0\n\n spotthresh = wlevprod.mean() + float(self.noise_removal_factor.value) * wlevprod.std()\n tidx = wlevprod < spotthresh\n wlevprod[tidx] = 0\n\n wlevprod = self.circular_average_filter(wlevprod, int(self.smoothing_filter_size.value))\n wlevprod = self.smooth_image(wlevprod, mask)\n\n max_wlevprod = scipy.ndimage.filters.maximum_filter(wlevprod,3)\n maxloc = (wlevprod == max_wlevprod)\n twlevprod = max_wlevprod > float(self.final_spot_threshold.value)\n maxloc[twlevprod == 0] = 0\n \n labeled_image,object_count = scipy.ndimage.label(maxloc,\n np.ones((3,3),bool))\n\n unedited_labels = labeled_image.copy()\n # Filter out objects touching the border or mask\n border_excluded_labeled_image = labeled_image.copy()\n labeled_image = self.filter_on_border(image, labeled_image)\n border_excluded_labeled_image[labeled_image > 0] = 0\n \n # Relabel the image\n labeled_image,object_count = relabel(labeled_image)\n new_labeled_image, new_object_count = self.limit_object_count(\n labeled_image, object_count)\n if new_object_count < object_count:\n # Add the labels that were filtered out into the border\n # image.\n border_excluded_mask = ((border_excluded_labeled_image > 0) |\n ((labeled_image > 0) & \n (new_labeled_image == 0)))\n border_excluded_labeled_image = scipy.ndimage.label(border_excluded_mask,\n np.ones((3,3),bool))[0]\n object_count = new_object_count\n labeled_image = new_labeled_image\n \n # Make an outline image\n outline_image = cellprofiler.cpmath.outline.outline(labeled_image)\n outline_border_excluded_image = cellprofiler.cpmath.outline.outline(border_excluded_labeled_image)\n \n if self.show_window:\n statistics = workspace.display_data.statistics\n statistics.append([\"# of accepted objects\",\n \"%d\"%(object_count)])\n\n workspace.display_data.image = image\n workspace.display_data.labeled_image = labeled_image\n workspace.display_data.border_excluded_labels = border_excluded_labeled_image\n\n # Add image measurements\n objname = self.object_name.value\n measurements = workspace.measurements\n cpmi.add_object_count_measurements(measurements,\n objname, object_count)\n # Add label matrices to the object set\n objects = cellprofiler.objects.Objects()\n objects.segmented = labeled_image\n objects.unedited_segmented = unedited_labels\n objects.parent_image = image\n \n workspace.object_set.add_objects(objects,self.object_name.value)\n cpmi.add_object_location_measurements(workspace.measurements, \n self.object_name.value,\n labeled_image)\n if self.should_save_outlines.value:\n out_img = cpi.Image(outline_image.astype(bool),\n parent_image = image)\n workspace.image_set.add(self.save_outlines.value, out_img)","def simImpacts(blankimage):\n\n # - Initialize variables - #\n count = 0\n unique = .001\n uniquelist = []\n cratersatstep = []\n cratermap = blankimage\n\n # -- Loop until saturation -- #\n while True:\n # - Wait until we have at least 10000 iterations before checking if we\n # have reached saturation - #\n if len(cratersatstep) > 10000:\n # - We calculate average by comparing the average of the last 1000\n # to the average of the last 100 - #\n smallAvg = np.average(cratersatstep[-100:])\n bigAvg = np.average(cratersatstep[-1000:])\n # - If we have reached saturation we can leave the loop - #\n if abs( smallAvg - bigAvg ) < (bigAvg * (1 - 0.99)):\n return cratermap, count, uniquelist, cratersatstep\n\n # - Every 1000 impacts we should save an image so we can compare - #\n if count%1000 == 0:\n pl.imshow(image)\n pl.title('Uniform Craters after '+str(int(count))+' Impactors')\n pl.savefig('../images/Uniform'+str(int(count/1000))+'.png')\n pl.clf()\n\n # --- BEGIN SIMULATION CODE --- #\n # - Increment our impactor count - #\n count += 1\n\n # - Generate the location for the center of the crater - #\n x = int(np.random.rand()*500.)\n y = int(np.random.rand()*500.)\n\n # - All of our impactors are the same size since this is our uniform sim - #\n impactsize = 10\n\n # - Pass our image array, the impact size (divided by 2 for radius)\n # origin of the impact, and a unique color value to drawCircle function - #\n cratermap = drawCircle(cratermap, int(impactsize / 2.), [x,y], unique)\n # - Get all of the unique color values still in cratermap - #\n uniquelist = np.unique(cratermap[:,:,0])\n # - Keep track of how many craters we can see at each step of the loop - #\n cratersatstep.append(len(uniquelist))\n\n # - Add to our unique value to keep it unique! - #\n unique += .001\n \n return cratermap, count , uniquelist, cratersvisible","def test_nirspec_aperture_transforms(verbose=False, siaf=None):\n if siaf is None:\n siaf = Siaf(instrument)\n else:\n siaf = copy.deepcopy(siaf)\n\n labels = ['X', 'Y']\n threshold = 0.2\n\n from_frame = 'sci'\n to_frames = 'det gwa idl tel'.split()\n\n x_sci = np.linspace(-10, 10, 3)\n y_sci = np.linspace(10, -10, 3)\n\n for include_tilt in [False, True]:\n\n for aper_name in siaf.apertures.keys():\n skip = False\n\n # aperture\n aperture = siaf[aper_name]\n # offset slightly from default tilt values\n\n if (aperture.AperType in ['COMPOUND', 'TRANSFORM', 'SLIT']) or ('_FULL' not in aper_name):\n skip = True\n\n if skip is False:\n if(include_tilt is True):\n # set tilt to a representative off nominal value\n gwa_aperture = getattr(aperture, '_CLEAR_GWA_OTE')\n rx0 = getattr(gwa_aperture, 'XSciRef')\n ry0 = getattr(gwa_aperture, 'YSciRef')\n aperture.tilt = (ry0 - 0.002, rx0 - 0.01)\n \n # test transformations\n if verbose:\n print('testing {} {} Tilt={}'.format(siaf.instrument, aper_name, aperture.tilt))\n\n for to_frame in to_frames:\n forward_transform = getattr(aperture, '{}_to_{}'.format(from_frame, to_frame))\n backward_transform = getattr(aperture, '{}_to_{}'.format(to_frame, from_frame))\n\n x_out, y_out = backward_transform(*forward_transform(x_sci, y_sci))\n x_mean_error = np.mean(np.abs(x_sci - x_out))\n y_mean_error = np.mean(np.abs(y_sci - y_out))\n for i, error in enumerate([x_mean_error, y_mean_error]):\n if verbose:\n print('{} {}: Error in {}<->{} {}-transform is {:02.6f})'.format(\n siaf.instrument, aper_name, from_frame, to_frame, labels[i], error))\n assert error < threshold","def enumerate_detector(det, thresholds, shot_ok=None, tiles=None, nimages=np.inf, stats=True, correction=False, progress=True, photonfunction=None):\n Ncorrect = 64\n correctionphotonthres = 3000\n if not isinstance(det, h5py.Group):\n raise TypeError('det should be a h5 group')\n if tiles is None:\n tiles = [k for k in det.keys() if 'tile' in k]\n else:\n newtiles = []\n for t in tiles:\n if t in det:\n newtiles.append(t)\n elif f'tile{t}' in det:\n newtiles.append(f'tile{t}')\n else:\n raise KeyError(f'tile {t} not found')\n tiles = newtiles\n multitiles = not (len(tiles) == 1 and 'data' in det[tiles[0]])\n mincorners = []\n maxcorners = []\n rots = []\n datanames = []\n filename = det.file.filename\n nshots = det[f'{tiles[0]}/data'].shape[0]\n correctmask = []\n for t in tiles:\n d = det[t]\n offset = np.rint(d.attrs['detector_tile_position_in_pixels'])\n rot = int(d.attrs['detector_rotation_steps'][0])\n rots.append(rot)\n n, a, b = d['data'].shape\n if n != nshots:\n raise ValueError('tiles should have same number of shots')\n shape = ((a, b), (-b, a), (-a, -b), (b, -a))[rot % 4]\n corners = (offset, (shape + offset))\n mincorners.append(np.min(corners, axis=0))\n maxcorners.append(np.max(corners, axis=0))\n datanames.append(f'{d.name}/data')\n if correction:\n correctmask.append(correctionmask(det[t]['absfft0/mean'], Ncorrect))\n\n globaloffset = np.floor(np.min(mincorners, axis=0)).astype(int)\n extent = [fastlen(x) for x in (np.ceil(np.max(maxcorners, axis=0)) - globaloffset)]\n startx, starty = [list(s) for s in (np.floor(mincorners - globaloffset).astype(int)).T]\n\n if shot_ok is None:\n shot_ok = np.ones(nshots, np.bool)\n assembled = np.zeros(extent, np.float64)\n global terminated\n ind_filtered = 0\n with datasetreader(datanames, filename, willread=shot_ok) if multitiles else arrayreader(det[tiles[0]]['data']) as reader:\n for ind_orig in range(nshots):\n if not shot_ok[ind_orig]:\n continue\n if ind_filtered >= nimages or terminated != 0:\n return\n if progress and ind_filtered % 100 == 0:\n print(ind_filtered, end=' ', flush=True)\n\n for t in range(len(tiles)):\n if multitiles:\n tile = np.asarray(reader[ind_orig, t], order='C', dtype=np.float64)\n if correction:\n correct(tile, correctionphotonthres, Ncorrect, correctmask[t], rots[t], assembled, startx[t], starty[t])\n else:\n place(tile, rots[t], assembled, startx[t], starty[t])\n else:\n if correction:\n tile = np.asarray(reader[ind_orig], order='C', dtype=np.float64)\n correct(tile, correctionphotonthres, Ncorrect, correctmask[t], rots[t], assembled, startx[t], starty[t])\n else:\n assembled = np.asarray(np.rot90(reader[ind_orig], rots[t]), order='C', dtype=np.float64)\n \n\n \n numberfromfunc = photonfunction(assembled) if photonfunction is not None else None\n if thresholds is not None:\n if stats:\n ev, number, scatter = getstats(assembled, thresholds)\n yield (ind_filtered, ind_orig, np.copy(assembled), ev, number, scatter, numberfromfunc)\n else:\n number = getphotons(assembled, thresholds)\n yield (ind_filtered, ind_orig, np.copy(assembled), None, number, None, numberfromfunc)\n else: \n yield (ind_filtered, ind_orig, np.copy(assembled), None, None, None, numberfromfunc)\n\n \n ind_filtered += 1","def eye_timings(self, nr_dummy_scans = 6, mystery_threshold = 0.05,saccade_duration_threshold = 10):\n\n\t\n\t\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\n\t\t\t# shell()\n\t\t\tniiFile = NiftiImage(self.runFile(stage = 'processed/mri', run = r))\n\t\t\ttr = round(niiFile.rtime*1)/1000.0\n\t\t\twith open (self.runFile(stage = 'processed/eye', run = r, extension = '.msg')) as inputFileHandle:\n\t\t\t\tmsg_file = inputFileHandle.read()\n\n\n\t\t\tsacc_re = 'ESACC\\t(\\S+)[\\s\\t]+(-?\\d*\\.?\\d*)\\t(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)\\s+(-?\\d+.?\\d+)'\n\t\t\tfix_re = 'EFIX\\t(\\S+)\\s+(-?\\d*\\.?\\d*)\\t(-?\\d+\\.?\\d*)\\s+(-?\\d+\\.?\\d*)?\\s+(-?\\d+\\.?\\d*)?\\s+(-?\\d+\\.?\\d*)?\\s+(-?\\d+\\.?\\d*)?'\n\t\t\tblink_re = 'EBLINK\\t(\\S+)\\s+(-?\\d*\\.?\\d*)\\t(-?\\d+\\.?\\d*)\\s+(-?\\d?.?\\d*)?'\n\t\t\tstart_eye = 'START\\t(-?\\d+\\.?\\d*)'\n\n\t\t\t# self.logger.info('reading eyelink events from %s', os.path.split(self.message_file)[-1])\n\t\t\tsaccade_strings = re.findall(re.compile(sacc_re), msg_file)\n\t\t\tfix_strings = re.findall(re.compile(fix_re), msg_file)\n\t\t\tblink_strings = re.findall(re.compile(blink_re), msg_file)\n\t\t\tstart_time_scan = float(re.findall(re.compile(start_eye),msg_file)[0])\n\t\t\t\n\t\t\tif len(saccade_strings) > 0:\n\t\t\t\tself.saccades_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3]),'start_x':float(e[4]),'start_y':float(e[5]),'end_x':float(e[6]),'end_y':float(e[7]), 'mystery_measure':float(e[8]),'peak_velocity':float(e[9])} for e in saccade_strings]\n\t\t\t\tself.fixations_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3]),'x':float(e[4]),'y':float(e[5]),'pupil_size':float(e[6])} for e in fix_strings]\n\t\t\t\tself.blinks_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3])} for e in blink_strings]\n\t\t\t\n\t\t\t\tself.saccade_type_dictionary = np.dtype([(s , np.array(self.saccades_from_message_file[0][s]).dtype) for s in self.saccades_from_message_file[0].keys()])\n\t\t\t\tself.fixation_type_dictionary = np.dtype([(s , np.array(self.fixations_from_message_file[0][s]).dtype) for s in self.fixations_from_message_file[0].keys()])\n\t\t\t\tif len(self.blinks_from_message_file) > 0:\n\t\t\t\t\tself.blink_type_dictionary = np.dtype([(s , np.array(self.blinks_from_message_file[0][s]).dtype) for s in self.blinks_from_message_file[0].keys()])\n\t\t\t\n\t\t\teye_blinks = [[((self.blinks_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.blinks_from_message_file[i]['duration']/1000,1] for i in range(len(self.blinks_from_message_file)) if (self.blinks_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000)]\n\t\t\t\n\t\t\t\n\t\t\tsaccades = [[((self.saccades_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.saccades_from_message_file[i]['duration']/1000,1] for i in range(len(self.saccades_from_message_file)) if np.all([(self.saccades_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000), (self.saccades_from_message_file[i]['duration'] > saccade_duration_threshold)]) ]\n\t\t\tsaccades_thresholded = [[((self.saccades_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.saccades_from_message_file[i]['duration']/1000,1] for i in range(len(self.saccades_from_message_file)) if np.all([(self.saccades_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000), (self.saccades_from_message_file[i]['mystery_measure'] > mystery_threshold), (self.saccades_from_message_file[i]['duration'] > saccade_duration_threshold)]) ]\n\t\t\n\t\t\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['eye_blinks']), np.array(eye_blinks), fmt = '%3.2f', delimiter = '\\t')\n\t\t\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['saccades']), np.array(saccades), fmt = '%3.2f', delimiter = '\\t')\n\t\t\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['saccades_thresholded']), np.array(saccades_thresholded), fmt = '%3.2f', delimiter = '\\t')","def phot_aperture(input_file):\n #set the original directory\n original_path = os.getcwd()\n save_path = input_file['save_path']\n planet = input_file['exoplanet']\n #radii = np.arange(input_file['apertures'][0],input_file['apertures'][1],0.1)\n radii = np.array(input_file['apertures'])\n #change to save data reduction directory\n os.chdir(save_path)\n if not os.path.exists('phot_results'):\n os.makedirs('phot_results')\n tempo = time.time()\n print 'Starting aperture photometry'\n print 'Saving results on: '+save_path+'/phot_results/'\n \n #check the number of objects to make the photometry\n N_obj = len(input_file['pxpositions'])/2.\n print 'Number of objects = ',N_obj\n positions = [] #create the positions variable (X,Y) in pixels unit on the CCD\n for i in range(len(input_file['pxpositions'])):\n if i % 2 == 0: #if the number is a even (or not a odd), the turple is created\n positions.append((input_file['pxpositions'][i],input_file['pxpositions'][i+1]))\n print 'Radius from ',radii[0],' to ',radii[-1],'\\n'\n \n skysection = input_file['skysection']\n skysection[0] = int(skysection[0])\n skysection[1] = int(skysection[1])\n \n images = sorted(glob.glob('AB'+planet+'*.fits'))\n for radius in radii:\n flux_data = []\n for i in range(len(images)):\n im = fits.getdata(images[i],header=False)\n im = array(im,dtype='Float64')\n \n # ERROR\n #Traceback (most recent call last):\n # File \"ExoTRed.py\", line 105, in <module>\n # exotred.phot_aperture(input_file)\n # File \"./sources/ExoTRed_core.py\", line 637, in phot_aperture \n # File \"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\", line 329, in __init__\n # self._calc_bkg_bkgrms()\n # File \"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\", line 686, in _calc_bkg_bkgrms\n # bkg = self._interpolate_meshes(self._bkg1d)\n # File \"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\", line 575, in _interpolate_meshes\n # f = ShepardIDWInterpolator(yx, data)\n # File \"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/utils/interpolation.py\", line 138, in __init__\n # raise ValueError('The number of values must match the number '\n # ValueError: The number of values must match the number of coordinates.\n\n # bkg = background.background_2d.Background2D(im,tuple(skysection))\n # bkg_data = bkg.background\n # bkg_rms = bkg.background_rms\n\n # phot_table = aperture_photometry(im - bkg_data, CircularAperture(positions, radius),\n # error=bkg_rms, method ='center')#,effective_gain=float(input_file['gain']))\n ####### SUBSTITUTE ROUTINE\n window = 100\n sky_size = im.shape\n sky_mean = float(np.median(im[int(skysection[1]-window):int(skysection[1]+window),int(skysection[0]-window):int(skysection[0]+window)]))\n bkg = np.random.poisson(sky_mean,sky_size)\n apertures = CircularAperture(positions, radius)\n phot_table = aperture_photometry(im, apertures, error=bkg)\n #######\n phot_table_flux = np.array([]) #saving results of aperture photometry\n for j in range(len(phot_table['aperture_sum'])):\n phot_table_flux = np.concatenate((phot_table_flux,np.array([phot_table['aperture_sum'][j]])),axis=0)\n phot_table_flux = np.concatenate((phot_table_flux,np.array([phot_table['aperture_sum_err'][j]])),axis=0)\n flux = np.concatenate((phot_table_flux,np.array([images[i]])),axis=0)\n # flux = [phot_table['aperture_sum'][0], phot_table['aperture_sum'][1],phot_table['aperture_sum_err'][0],\n # phot_table['aperture_sum_err'][1],images[i]]\n flux_data.append(flux)\n flux_data = DataFrame(flux_data)#,columns=['hoststar','refstar','hoststar_err','refstar_err','image'])\n flux_data.to_csv('./phot_results/'+planet+'_flux_radius_'+str(radius)+'.csv',index=False)\n use.update_progress((float(np.where(radii == radius)[0])+1.)/len(radii))\n print 'Time total = ',abs(time.time()-tempo)/60.,' minutes'\n os.chdir(original_path)","def update_for_in_trap(self, t, traps): #******\n sources = traps.param['source_locations'] #Of format [(0,0),]\n for trap_num, trap_loc in enumerate(sources):\n dist_vals = distance((self.x_position, self.y_position),trap_loc)\n mask_trapped = dist_vals < traps.param['trap_radius']\n self.mode[mask_trapped] = self.Mode_Trapped\n self.trap_num[mask_trapped] = trap_num\n self.x_trap_loc[mask_trapped] = trap_loc[0]\n self.y_trap_loc[mask_trapped] = trap_loc[1]\n self.x_velocity[mask_trapped] = 0.0\n self.y_velocity[mask_trapped] = 0.0\n\n # Get time stamp for newly trapped flies\n mask_newly_trapped = mask_trapped & (self.t_in_trap == scipy.inf)\n self.t_in_trap[mask_newly_trapped] = t","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 analyze(self):\n try:\n self.options[self.multi_image][1]()\n except:\n raise Exception(\"Multi Image Option not defined.\")\n\n self.image = self.data / self.exposure\n\n background = self.min_val = np.min(self.image[:511,:511])\n self.max_val = np.max(self.image[:511,:511])\n # stats.mode returns modal value = value that occours most often\n #background = stats.mode(im[:50,:50].ravel())[0][0]\n\n intensity = self.image.sum() - background*np.size(self.image)\n\n #results.append((self.index, intensity, background))\n self.index =+ 1","def main():\n\n with its.device.ItsSession() as cam:\n\n props = cam.get_camera_properties()\n its.caps.skip_unless(its.caps.raw16(props) and\n its.caps.manual_sensor(props) and\n its.caps.read_3a(props) and\n its.caps.per_frame_control(props) and\n not its.caps.mono_camera(props))\n debug = its.caps.debug_mode()\n\n # Expose for the scene with min sensitivity\n exp_min, exp_max = props[\"android.sensor.info.exposureTimeRange\"]\n sens_min, _ = props[\"android.sensor.info.sensitivityRange\"]\n # Digital gains might not be visible on RAW data\n sens_max = props[\"android.sensor.maxAnalogSensitivity\"]\n sens_step = (sens_max - sens_min) / NUM_ISO_STEPS\n white_level = float(props[\"android.sensor.info.whiteLevel\"])\n black_levels = [its.image.get_black_level(i,props) for i in range(4)]\n # Get the active array width and height.\n aax = props[\"android.sensor.info.activeArraySize\"][\"left\"]\n aay = props[\"android.sensor.info.activeArraySize\"][\"top\"]\n aaw = props[\"android.sensor.info.activeArraySize\"][\"right\"]-aax\n aah = props[\"android.sensor.info.activeArraySize\"][\"bottom\"]-aay\n raw_stat_fmt = {\"format\": \"rawStats\",\n \"gridWidth\": aaw/IMG_STATS_GRID,\n \"gridHeight\": aah/IMG_STATS_GRID}\n\n e_test = []\n mult = 1.0\n while exp_min*mult < exp_max:\n e_test.append(int(exp_min*mult))\n mult *= EXP_MULT\n if e_test[-1] < exp_max * INCREASING_THR:\n e_test.append(int(exp_max))\n e_test_ms = [e / 1000000.0 for e in e_test]\n\n for s in range(sens_min, sens_max, sens_step):\n means = []\n means.append(black_levels)\n reqs = [its.objects.manual_capture_request(s, e, 0) for e in e_test]\n # Capture raw in debug mode, rawStats otherwise\n caps = []\n for i in range(len(reqs) / SLICE_LEN):\n if debug:\n caps += cam.do_capture(reqs[i*SLICE_LEN:(i+1)*SLICE_LEN], cam.CAP_RAW)\n else:\n caps += cam.do_capture(reqs[i*SLICE_LEN:(i+1)*SLICE_LEN], raw_stat_fmt)\n last_n = len(reqs) % SLICE_LEN\n if last_n == 1:\n if debug:\n caps += [cam.do_capture(reqs[-last_n:], cam.CAP_RAW)]\n else:\n caps += [cam.do_capture(reqs[-last_n:], raw_stat_fmt)]\n elif last_n > 0:\n if debug:\n caps += cam.do_capture(reqs[-last_n:], cam.CAP_RAW)\n else:\n caps += cam.do_capture(reqs[-last_n:], raw_stat_fmt)\n\n # Measure the mean of each channel.\n # Each shot should be brighter (except underexposed/overexposed scene)\n for i,cap in enumerate(caps):\n if debug:\n planes = its.image.convert_capture_to_planes(cap, props)\n tiles = [its.image.get_image_patch(p, 0.445, 0.445, 0.11, 0.11) for p in planes]\n mean = [m * white_level for tile in tiles\n for m in its.image.compute_image_means(tile)]\n img = its.image.convert_capture_to_rgb_image(cap, props=props)\n its.image.write_image(img, \"%s_s=%d_e=%05d.jpg\" % (NAME, s, e_test))\n else:\n mean_image, _ = its.image.unpack_rawstats_capture(cap)\n mean = mean_image[IMG_STATS_GRID/2, IMG_STATS_GRID/2]\n\n print \"ISO=%d, exposure time=%.3fms, mean=%s\" % (\n s, e_test[i] / 1000000.0, str(mean))\n means.append(mean)\n\n\n # means[0] is black level value\n r = [m[0] for m in means[1:]]\n gr = [m[1] for m in means[1:]]\n gb = [m[2] for m in means[1:]]\n b = [m[3] for m in means[1:]]\n\n pylab.plot(e_test_ms, r, \"r.-\")\n pylab.plot(e_test_ms, b, \"b.-\")\n pylab.plot(e_test_ms, gr, \"g.-\")\n pylab.plot(e_test_ms, gb, \"k.-\")\n pylab.xscale('log')\n pylab.yscale('log')\n pylab.title(\"%s ISO=%d\" % (NAME, s))\n pylab.xlabel(\"Exposure time (ms)\")\n pylab.ylabel(\"Center patch pixel mean\")\n matplotlib.pyplot.savefig(\"%s_s=%d.png\" % (NAME, s))\n pylab.clf()\n\n allow_under_saturated = True\n for i in xrange(1, len(means)):\n prev_mean = means[i-1]\n mean = means[i]\n\n if np.isclose(max(mean), white_level, rtol=SATURATION_TOL):\n print \"Saturated: white_level %f, max_mean %f\"% (white_level, max(mean))\n break;\n\n if allow_under_saturated and np.allclose(mean, black_levels, rtol=BLK_LVL_TOL):\n # All channel means are close to black level\n continue\n\n allow_under_saturated = False\n # Check pixel means are increasing (with small tolerance)\n channels = [\"Red\", \"Gr\", \"Gb\", \"Blue\"]\n for chan in range(4):\n err_msg = \"ISO=%d, %s, exptime %3fms mean: %.2f, %s mean: %.2f, TOL=%.f%%\" % (\n s, channels[chan],\n e_test_ms[i-1], mean[chan],\n \"black level\" if i == 1 else \"exptime %3fms\"%e_test_ms[i-2],\n prev_mean[chan],\n INCREASING_THR*100)\n assert mean[chan] > prev_mean[chan] * INCREASING_THR, err_msg","def trapfilt_taps(N, phil, alfa):\n\n\n\n tt = arange(-N/2,N/2 + 1) # Time axis for h(t) \n # ***** Generate impulse response ht here *****\n ht = zeros(len(tt))\n ix = where(tt != 0)[0]\n if alfa != 0:\n ht[ix] = ((sin(2*pi*phil*tt[ix]))/(pi*tt[ix]))*((sin(2*pi*alfa*phil*tt[ix]))/(2*pi*alfa*phil*tt[ix]))\n else:\n ht[ix] = (sin(2*pi*phil*tt[ix]))/(pi*tt[ix])\n ix0 = where(tt == 0)[0]\n ht[ix0] = 2*phil\n ht = ht/sum(power(ht,2))\n\n return ht","def guess_image(which_cam, image, ntraps):\n threshes = [0.5, 0.65]\n ## Image Conditioning ##\n margin = 10\n threshold = np.max(image)*threshes[which_cam]\n im = image.transpose()\n\n x_len = len(im)\n peak_locs = np.zeros(x_len)\n peak_vals = np.zeros(x_len)\n\n ## Trap Peak Detection ##\n for i in range(x_len):\n if i < margin or x_len - i < margin:\n peak_locs[i] = 0\n peak_vals[i] = 0\n else:\n peak_locs[i] = np.argmax(im[i])\n peak_vals[i] = max(im[i])\n\n ## Trap Range Detection ##\n first = True\n pos_first, pos_last = 0, 0\n left_pos = 0\n for i, p in enumerate(peak_vals):\n if p > threshold:\n left_pos = i\n elif p < threshold and left_pos != 0:\n if first:\n pos_first = (left_pos + i) // 2\n first = False\n pos_last = (left_pos + i) // 2\n left_pos = 0\n\n ## Separation Value ##\n separation = (pos_last - pos_first) / ntraps # In Pixels\n\n ## Initial Guesses ##\n means0 = np.linspace(pos_first, pos_last, ntraps).tolist()\n waists0 = (separation * np.ones(ntraps) / 2).tolist()\n ampls0 = (max(peak_vals) * 0.7 * np.ones(ntraps)).tolist()\n _params0 = [means0, waists0, ampls0, [0.06]]\n params0 = [item for sublist in _params0 for item in sublist]\n\n xdata = np.arange(x_len)\n plt.figure()\n plt.plot(xdata, peak_vals)\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, params0), '--r') # Initial Guess\n plt.xlim((pos_first - margin, pos_last + margin))\n plt.legend([\"Data\", \"Guess\", \"Fit\"])\n plt.show(block=False)","def mri_dixon_analysis(data_objects, working_dir, settings):\n\n logger.info(\"Running Dixon analysis Calculation\")\n logger.info(\"Using settings: %s\", settings)\n\n output_objects = []\n\n fat_obj = None\n water_obj = None\n for data_obj in data_objects:\n\n if data_obj.meta_data[\"image_type\"] == \"fat\":\n fat_obj = data_obj\n\n if data_obj.meta_data[\"image_type\"] == \"water\":\n water_obj = data_obj\n\n if fat_obj is None or water_obj is None:\n logger.error(\"Both Fat and Water Images are required\")\n return []\n\n # Read the image series\n fat_load_path = fat_obj.path\n if fat_obj.type == \"DICOM\":\n fat_load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(fat_obj.path)\n fat_img = sitk.ReadImage(fat_load_path)\n\n water_load_path = water_obj.path\n if water_obj.type == \"DICOM\":\n water_load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(water_obj.path)\n water_img = sitk.ReadImage(water_load_path)\n\n # Cast to float for calculation\n fat_img = sitk.Cast(fat_img, sitk.sitkFloat32)\n water_img = sitk.Cast(water_img, sitk.sitkFloat32)\n\n # Let's do the calcuation using NumPy\n fat_arr = sitk.GetArrayFromImage(fat_img)\n water_arr = sitk.GetArrayFromImage(water_img)\n\n # Do the calculation\n divisor = water_arr + fat_arr\n fat_fraction_arr = (fat_arr * 100) / divisor\n fat_fraction_arr[divisor == 0] = 0 # Sets those voxels which were divided by zero to 0\n water_fraction_arr = (water_arr * 100) / divisor\n water_fraction_arr[divisor == 0] = 0 # Sets those voxels which were divided by zero to 0\n\n fat_fraction_img = sitk.GetImageFromArray(fat_fraction_arr)\n water_fraction_img = sitk.GetImageFromArray(water_fraction_arr)\n\n fat_fraction_img.CopyInformation(fat_img)\n water_fraction_img.CopyInformation(water_img)\n\n # Create the output Data Objects and add it to output_ob\n fat_fraction_file = os.path.join(working_dir, \"fat.nii.gz\")\n sitk.WriteImage(fat_fraction_img, fat_fraction_file)\n water_fraction_file = os.path.join(working_dir, \"water.nii.gz\")\n sitk.WriteImage(water_fraction_img, water_fraction_file)\n\n fat_data_object = DataObject(type=\"FILE\", path=fat_fraction_file, parent=fat_obj)\n output_objects.append(fat_data_object)\n\n water_data_object = DataObject(type=\"FILE\", path=water_fraction_file, parent=water_obj)\n output_objects.append(water_data_object)\n\n return output_objects","def imagetest(thetainput,doubleopponencyinput):\n theta = thetainput\n rgcMode = doubleopponencyinput\n\n\n C = retina.sample(img,x,y,coeff[i],loc[i],rgb=True) # CENTRE\n S = retina.sample(img,x,y,dcoeff[i],dloc[i],rgb=True) # SURROUND\n \n if rgcMode == 0:\n \tpV,nV = rgc.opponency(C,S,theta)\n else:\n \tpV,nV = rgc.doubleopponency(C,S,theta)\n cv2.namedWindow(\"Input\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Input\", img)\n rIntensity,cIntensity = showNonOpponency(C,theta)\n cv2.namedWindow(\"Intensity Responses\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Intensity Responses\", rIntensity)\n cv2.namedWindow(\"Intensity Responses Cortex\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Intensity Responses Cortex\", cIntensity)\n cv2.waitKey(0)\n #Generate backprojected images\n if showInverse:\n rOpponent = showBPImg(pV,nV)\n cv2.namedWindow(\"Backprojected Opponent Cells Output\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Backprojected Opponent Cells Output\", rOpponent)\n cv2.waitKey(0)\n # Cortex\n if showCortex:\n cOpponent = showCortexImg(pV,nV)\n cv2.namedWindow(\"Cortex Opponent Cells Output\", cv2.WINDOW_NORMAL)\n cv2.imshow(\"Cortex Opponent Cells Output\", cOpponent)\n cv2.waitKey(0)","def calculate_uip(vpx, raster, weight, neuron, tau):\n\n m = 1\n\n vpx[0] = weight[neuron, raster[0][\"id\"]]\n\n for k, evt in enumerate(raster[1:], 1):\n\n dt = evt[\"time\"] - raster[k - 1][\"time\"]\n\n if not dt:\n m -= 1\n else:\n vpx[m] = vpx[m - 1] * np.exp(-dt / tau)\n\n vpx[m] += weight[neuron, evt[\"id\"]]\n\n m += 1","def stack_tir(scene_urls,cloud_mask_bits,aoi,aoi_crs,\n subtract_median_lst=True,subtract_air_temp=False):\n if subtract_air_temp:\n ceda_password = get_ceda_password()\n at = met_climate.access_ukcp09(cf.ceda_username,ceda_password)\n\n \n # with rasterio.open(scene_bqa) as bqa:\n # with rasterio.open(scene_tir) as tir:\n\n # bqa_data,bqa_trans = ru.read_in_aoi(bqa,**aoi_kwargs)\n # tir_data,tir_trans = ru.read_in_aoi(tir,**aoi_kwargs)\n \n # bqa_data = bqa_data[0,:,:]\n # tir_data = tir_data[0,:,:]\n # tir_data = ma.array(tir_data,dtype=float,\n # mask=ru.mask_qa(bqa_data,bitmask=0b1))\n\n # (ymin,ymax) = (0, tir_data.shape[0])\n # (xmin,xmax) = (0, tir_data.shape[1])\n \n counter=-1\n for scene_url in scene_urls:\n counter+=1\n scene_tir = scene_url\n scene_bqa = scene_url.replace('B'+tirband,'B'+qaband)\n scene_red = scene_url.replace('B'+tirband,'B'+rband)\n scene_nir = scene_url.replace('B'+tirband,'B'+nband)\n scene_metadata = scene_url.replace('B'+tirband+'.TIF','MTL.txt')\n\n print('Reading scene {}'.format(counter+1))\n try:\n with rasterio.open(scene_bqa) as bqa:\n #print(scene_bqa)\n bqa_data,bqa_trans = ru.read_in_aoi(bqa,aoi=aoi,aoi_crs=aoi_crs)\n\n with rasterio.open(scene_tir) as tir:\n #print(scene_tir)\n tir_data,tir_trans = ru.read_in_aoi(tir,aoi=aoi,aoi_crs=aoi_crs)\n tir_crs = tir.crs\n tir_profile = tir.profile\n\n with rasterio.open(scene_red) as red:\n #print(scene_red)\n red_data,red_trans = ru.read_in_aoi(red,aoi=aoi,aoi_crs=aoi_crs)\n red_crs = red.crs\n\n with rasterio.open(scene_nir) as nir:\n #print(scene_nir)\n nir_data,nir_trans = ru.read_in_aoi(nir,aoi=aoi,aoi_crs=aoi_crs)\n \n except OSError as e:\n print('ERROR',e)\n print('skipping scene')\n counter = counter-1\n continue\n \n # Determine size of stack allowing for AoI to extend outside of scene\n if counter == 0:\n aoi_box = rasterio.warp.transform_bounds(aoi_crs,tir_crs,*aoi.values())\n aoi_left, aoi_bottom, aoi_right, aoi_top = aoi_box\n aoi_box = dict(zip(('minx','miny','maxx','maxy'),aoi_box))\n # rowmin,colmin = (bqa.index(aoi_left,aoi_top)) #,op=round))\n # rowmax,colmax = (bqa.index(aoi_right,aoi_bottom)) #,op=round))\n # The above two lines are fine but the following does not \n # require the rasterio dataset to be kept open\n rowmin,colmin = rasterio.transform.rowcol(tir_trans,aoi_left,aoi_top)\n rowmax,colmax = rasterio.transform.rowcol(tir_trans,aoi_right,aoi_bottom)\n stack_height,stack_width = (rowmax-rowmin,colmax-colmin)\n lst_stack = (ma.zeros((len(scene_urls),stack_height,stack_width),\n dtype=np.float,fill_value=np.nan\n )+np.nan) \n \n # Determine size of intersect in THIS scene\n intersect = ru.aoi_scene_intersection(aoi_box,bqa)\n ins_left, ins_bottom, ins_right, ins_top = intersect.bounds\n #rowmin,colmin = (bqa.index(ins_left,ins_top,op=round))\n #rowmax,colmax = (bqa.index(ins_right,ins_bottom,op=round))\n # The above two lines are incorrect now that we read a window:\n # We need to transform the coordinates into the row,col of \n # the window, not the original file.\n rowmin,colmin = rasterio.transform.rowcol(tir_trans,ins_left,ins_top)\n rowmax,colmax = rasterio.transform.rowcol(tir_trans,ins_right,ins_bottom)\n\n try:\n # Subset data \n bqa_data = ma.array(bqa_data[0,rowmin:rowmax,colmin:colmax])\n tir_data = ma.array(tir_data[0,rowmin:rowmax,colmin:colmax])\n red_data = ma.array(red_data[0,rowmin:rowmax,colmin:colmax])\n nir_data = ma.array(nir_data[0,rowmin:rowmax,colmin:colmax])\n assert tir_data.shape == lst_stack.shape[1:]\n except (IndexError,AssertionError) as e:\n print('ERROR:',e)\n print('loop count',counter)\n print(tir_data.shape, lst_stack.shape)\n print(rowmin,rowmax,colmin,colmax)\n import pdb; pdb.set_trace()\n\n lst_data = lst.calculate_land_surface_temperature_NB(\n red_data, nir_data, tir_data,\n red_trans, tir_trans, \n red_crs, tir_crs, scene_metadata\n )\n \n # Masks\n smw = 11\n mask_all = filters.maximum_filter(\n ru.mask_qa(bqa_data,bits=cloud_mask_bits),size=smw\n )\n\n lst_data_mask_all = ma.array(lst_data,\n mask=mask_all,\n dtype=np.float,\n fill_value=np.nan) #.filled()\n\n # After masking, reproject\n # not necessary if they share a CRS\n if counter > 0:\n assert tir_crs == prev_crs\n prev_crs = tir_crs\n\n # Now do some normalisation\n if subtract_air_temp:\n filename = scene_tir.split('/')[-1]\n datestring = filename.split('_')[3]\n\n atscene = met_climate.dummy_scene( \n tir_crs, tir_trans, aoi_box,(stack_height,stack_width))\n\n # import pdb; pdb.set_trace()\n # If the following fails, it may mean there was a problem setting up the session\n atdata = at.grid_temp_over_scene(\n atscene, datestring, interpolation='linear')\n atdata = atdata[rowmin:rowmax,colmin:colmax]\n assert lst_data_mask_all.shape == atdata.shape\n lst_data_mask_all = ma.array(\n lst_data_mask_all - atdata,\n mask=mask_all,\n fill_value=np.nan)\n \n if subtract_median_lst:\n # ALSO subtract median xLST\n medval = ma.median(lst_data_mask_all)\n lst_data_mask_all = ma.array(\n lst_data_mask_all - medval,\n mask=mask_all,\n fill_value=np.nan)\n \n elif subtract_median_lst:\n # Subtract median LST from scene (within QA mask) \n \n medval = ma.median(lst_data_mask_all)\n lst_data_mask_all = ma.array(\n lst_data_mask_all - medval,\n mask=mask_all,\n fill_value=np.nan)\n \n # Then add to stack\n lst_stack[counter,:,:] = lst_data_mask_all\n\n # Make profile for file output\n N_layers = counter+1\n tir_profile.update(\n dtype=rasterio.float64,\n width=stack_width,\n height=stack_height,\n transform=tir_trans,\n count=N_layers,\n compress='lzw'\n )\n\n\n return lst_stack, tir_profile","def tail_cts_per_shot(datapath, lower, TPQI_starts, bin_size = 0.256, normalize = False, correct_for_bg = True, save = 1, pulses_in_sequence = 300):\n\n print 'analyzing tail counts per shot...' \n current_dir = os.getcwd()\n plt.close('all')\n os.chdir(datapath)\n files = os.listdir(datapath)\n\n for k in arange(len(files)):\n right_file = '.npz' in files[k]\n \n if right_file:\n data = numpy.load(datapath+'\\\\'+files[k])\n\n ch1_counts = data['hist_ch1']\n ch0_counts = data['hist_ch0']\n\n time = bin_size*arange(len(ch1_counts))\n \n if correct_for_bg:\n bg_level_ch1 = ch1_counts[int(0.75*len(ch1_counts)):int(0.90*len(ch1_counts))].mean()\n ch1_counts = ch1_counts - bg_level_ch1*ones(len(ch1_counts))\n bg_level_ch0 = ch0_counts[int(0.75*len(ch0_counts)):int(0.90*len(ch0_counts))].mean()\n ch0_counts = ch0_counts - bg_level_ch0*ones(len(ch0_counts))\n\n #print 'measured background level for [ch0,ch1] = ['+num2str(bg_level_ch0,1)+','+num2str(bg_level_ch1,1)+']'\n\n if normalize:\n ch1_counts_normalized = ch1_counts/ch1_counts.max()\n ch0_counts_normalized = ch0_counts/ch0_counts.max()\n \n upper = lower + 40.0\n\n tail_area_time = time[int(lower/bin_size):int(upper/bin_size)]\n tail_area_ch1 = ch1_counts[int(lower/bin_size):int(upper/bin_size)]\n tail_area_ch0 = ch0_counts[int(lower/bin_size):int(upper/bin_size)]\n\n tail_counts_per_shot = (tail_area_ch1.sum()+tail_area_ch0.sum())/float(TPQI_starts*pulses_in_sequence)\n\n figure1 = plt.figure(figsize=(16.0, 12.0))\n plt.subplot(211)\n if not normalize:\n plt.semilogy(time, ch1_counts, '-k')\n plt.plot(array([lower,lower]), array([1E-1,ch1_counts.max()]), 'r', lw = 2.0)\n plt.plot(array([upper,upper]), array([1E-1,ch1_counts.max()]), 'r', lw = 2.0)\n else:\n plt.semilogy(time, ch1_counts_normalized, '-r')\n plt.plot(array([lower,lower]), array([1E-1,ch1_counts_normalized.max()]), 'r', lw = 2.0)\n plt.plot(array([upper,upper]), array([1E-1,ch1_counts_normalized.max()]), 'r', lw = 2.0)\n \n plt.xlabel('Time after sync (ns)')\n plt.ylabel('Counts ch1')\n plt.title('tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4')\n plt.xlim([0,200])\n\n plt.subplot(212)\n if not normalize:\n plt.semilogy(time, ch0_counts, '-k')\n plt.plot(array([lower,lower]), array([1E-1,ch0_counts.max()]), 'r', lw = 2.0)\n plt.plot(array([upper,upper]), array([1E-1,ch0_counts.max()]), 'r', lw = 2.0)\n else:\n plt.semilogy(time, ch0_counts_normalized, '-k')\n plt.plot(array([lower,lower]), array([1E-1,ch0_counts_normalized.max()]), 'r', lw = 2.0)\n plt.plot(array([upper,upper]), array([1E-1,ch0_counts_normalized.max()]), 'r', lw = 2.0)\n \n plt.xlabel('Time after sync (ns)')\n plt.ylabel('Counts ch0')\n plt.title('tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4')\n plt.xlim([0,200])\n if save:\n figure1.savefig('tail_cts_per_shot.pdf')\n\n try:\n data.close()\n except:\n pass\n\n print 'tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4'\n\n return tail_counts_per_shot","def transform(self, dataset, number_of_thresholds=1,\n enable_valley_emphasis=False):\n\n # Initial progress\n self.progress.value = 0\n self.progress.maximum = 100\n\n # Approximate percentage of work completed after each step in the\n # transform\n STEP_PCT = [10, 20, 70, 90, 100]\n\n try:\n import itk\n import itkExtras\n import itkTypes\n from tomviz import itkutils\n except Exception as exc:\n print(\"Could not import necessary module(s)\")\n raise exc\n\n # Return values\n returnValues = None\n\n # Add a try/except around the ITK portion. ITK exceptions are\n # passed up to the Python layer, so we can at least report what\n # went wrong with the script, e.g,, unsupported image type.\n try:\n self.progress.value = STEP_PCT[0]\n self.progress.message = \"Converting data to ITK image\"\n\n # Get the ITK image\n itk_image = itkutils.dataset_to_itk_image(dataset)\n itk_input_image_type = type(itk_image)\n\n # OtsuMultipleThresholdsImageFilter's wrapping requires that the\n # input and output image types be the same.\n itk_threshold_image_type = itk_input_image_type\n\n # Otsu multiple threshold filter\n otsu_filter = itk.OtsuMultipleThresholdsImageFilter[\n itk_input_image_type, itk_threshold_image_type].New()\n otsu_filter.SetNumberOfThresholds(number_of_thresholds)\n otsu_filter.SetValleyEmphasis(enable_valley_emphasis)\n otsu_filter.SetInput(itk_image)\n itkutils.observe_filter_progress(self, otsu_filter,\n STEP_PCT[1], STEP_PCT[2])\n\n try:\n otsu_filter.Update()\n except RuntimeError:\n return\n\n print(\"Otsu threshold(s): %s\" % (otsu_filter.GetThresholds(),))\n\n itk_image_data = otsu_filter.GetOutput()\n\n # Cast threshold output to an integral type if needed.\n py_buffer_type = itk_threshold_image_type\n voxel_type = itkExtras.template(itk_threshold_image_type)[1][0]\n if voxel_type is itkTypes.F or voxel_type is itkTypes.D:\n self.progress.message = \"Casting output to integral type\"\n\n # Unsigned char supports 256 labels, or 255 threshold levels.\n # This should be sufficient for all but the most unusual use\n # cases.\n py_buffer_type = itk.Image.UC3\n caster = itk.CastImageFilter[itk_threshold_image_type,\n py_buffer_type].New()\n caster.SetInput(itk_image_data)\n itkutils.observe_filter_progress(self, caster,\n STEP_PCT[2], STEP_PCT[3])\n\n try:\n caster.Update()\n except RuntimeError:\n return\n\n itk_image_data = caster.GetOutput()\n\n self.progress.value = STEP_PCT[3]\n self.progress.message = \"Saving results\"\n\n label_map_dataset = dataset.create_child_dataset()\n itkutils.set_itk_image_on_dataset(itk_image_data, label_map_dataset,\n dtype=py_buffer_type)\n\n self.progress.value = STEP_PCT[4]\n\n # Set up dictionary to return operator results\n returnValues = {}\n returnValues[\"label_map\"] = label_map_dataset\n\n except Exception as exc:\n print(\"Problem encountered while running %s\" %\n self.__class__.__name__)\n raise exc\n\n return returnValues","def process_observation(self, observation):\n #print(\"start_process_obs\")\n processed_observation = np.zeros((NB_AGENTS, OBSERVATION_SIZE))\n\n goliath_type = getattr(env, 'Terran_Goliath')\n battlecruiser_type = getattr(env, 'Terran_Battlecruiser')\n '''\n goliath and battlecruiser type:\n hp_max: 125\n armor: 1\n cooldown_max: 22\n acceleration: 1\n top_speed: 4.57\n damage_amount: 12\n damage_factor: 1\n weapon_range: 192\n sight_range: 256\n seek_range: 160\n\n hp_max: 500\n energy_max: 200\n armor: 3\n cooldown_max: 30\n acceleration: 27\n top_speed: 2.5\n damage_amount: 25\n damage_factor: 1\n weapon_range: 192\n sight_range: 352\n '''\n #print(\"goliath and battlecruiser type:\")\n #print(goliath_type)\n #print(battlecruiser_type)\n\n for i, agent in enumerate(observation.my_unit):\n if agent.hp <= 0:\n continue\n my_x = agent.pos_x\n my_y = agent.pos_y\n my_type_str = agent.unit_type\n my_type = goliath_type if my_type_str == 'Terran_Goliath' else print(\"error in the my_type\")\n t1 = [agent.hp + agent.shield, agent.cooldown, math.atan2(agent.velocity_y, agent.velocity_x),\n math.sqrt((agent.velocity_x) ** 2 + (agent.velocity_y) ** 2), agent.angle,\n 1 if agent.accelerating else -1 if agent.braking else 0, agent.attacking, agent.is_attack_frame]\n t2 = [self.last_action[i] / (env.action_space[1] - 1)]\n t3 = [i.nearest_obstacle_dist for i in agent.pos_info]\n t4 = []\n t5 = []\n t4_max = []\n t5_max = []\n for idx, enemy in enumerate(observation.en_unit):\n en_type_str = enemy.unit_type\n if en_type_str == 'Terran_Battlecruiser':\n en_type = battlecruiser_type\n else:\n continue \n if enemy.hp <= 0:\n t4.extend([0,0,0,0,0,0,0,0,0,0])\n else:\n t4.extend([math.atan2(enemy.pos_y - my_y, enemy.pos_x - my_x), math.sqrt((enemy.pos_x - my_x) ** 2 + (enemy.pos_y - my_y) ** 2),\n math.atan2(enemy.velocity_y, enemy.velocity_x), math.sqrt((enemy.velocity_x) ** 2 + (enemy.velocity_y) ** 2),\n enemy.cooldown, enemy.hp + enemy.shield, enemy.angle, 1 if agent.accelerating else -1 if agent.braking else 0, agent.attacking, agent.is_attack_frame])\n t4_max.extend([math.pi, 320, math.pi, en_type.top_speed, en_type.cooldown_max, en_type.hp_max + en_type.shield_max, math.pi, 1, 1, 1])\n for idx, ally in enumerate(observation.my_unit):\n if i == idx:\n continue\n if ally.hp <= 0:\n t5.extend([0,0,0,0,0])\n else:\n t5.extend([math.atan2(ally.pos_y - my_y, ally.pos_x - my_x), math.sqrt((ally.pos_x - my_x) ** 2 + (ally.pos_y - my_y) ** 2),\n math.atan2(ally.velocity_y, ally.velocity_x), math.sqrt((ally.velocity_x) ** 2 + (ally.velocity_y) ** 2), ally.hp + ally.shield])\n ally_type = goliath_type\n t5_max.extend([math.pi, 320, math.pi, ally_type.top_speed, ally_type.hp_max + ally_type.shield_max])\n if my_type_str == 'Terran_Goliath':\n t1_max = [my_type.hp_max + my_type.shield_max, 1, math.pi, my_type.top_speed, math.pi, 1, 1, 1]\n else:\n t1_max = [my_type.hp_max + my_type.shield_max, my_type.cooldown_max, math.pi, my_type.top_speed, math.pi, 1, 1, 1]\n #t4_max = [math.pi, 320, math.pi, en_type.top_speed, en_type.cooldown_max, en_type.hp_max + en_type.shield_max, math.pi, 1, 1, 1]\n #t5_max = [math.pi, 320, math.pi, ally_type.top_speed, ally_type.hp_max + ally_type.shield_max]\n\n #t5_max = [32, 32, type.hp_max + type.shield_max, type.cooldown_max,\n #32, 32, type.hp_max + type.shield_max, type.cooldown_max,\n #32, 32, type.hp_max + type.shield_max, type.cooldown_max,\n #32, 32, type.hp_max + type.shield_max, math.pi,\n #32, 32, type.hp_max + type.shield_max, math.pi,\n #32, 32, type.hp_max + type.shield_max, math.pi]\n\n t1 = np.divide(t1, t1_max) # runtime warning\n t2 = np.array(t2) / 320\n t3 = np.array(t3) / 320\n t4 = np.divide(t4, t4_max)\n t5 = np.divide(t5, t5_max)\n\n processed_observation[i] = np.concatenate([t1, t2, t3, t4, t5])\n\n self.last_my_unit_cnt.append(np.sum(np.array([u.hp+u.shield for u in observation.my_unit]) > 0))\n self.last_enemy_unit_cnt.append(np.sum(np.array([u.hp+u.shield for u in observation.en_unit]) > 0))\n self.last_enemy_unit_hp.append(sum([u.hp + u.shield for u in observation.en_unit]))\n self.accumulated_observation.append(processed_observation)\n\n\n return processed_observation","def single_channel_stacking(tifs):\n template_ID=int(len(tifs)/2)\n \n template_raster=gdal_array.LoadFile(tifs[template_ID-1])\n avg_raster=np.zeros_like(template_raster)\n avg_raster=avg_raster+1\n new_raster=np.copy(template_raster)\n # ones=np.full(template_raster.shape, 1)\n for i, tif in enumerate(tifs, start=1):\n if i==template_ID: \n continue\n \n tif_raster=gdal_array.LoadFile(tif)\n # tif_raster=cut_transformed_array_borders(tif_raster)\n result=ird.similarity(template_raster,tif_raster , numiter=1, order=1)\n img_transformed= ird.transform_img(tif_raster, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=2)\n \n img_transformed=cut_transformed_array_borders(img_transformed)\n \n # ones_transformed=ird.transform_img(ones, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\n ones_transformed=np.zeros_like(template_raster)\n ones_transformed[np.where(img_transformed>0)]=1\n print(ones_transformed)\n \n print(np.mean(ones_transformed), np.max(ones_transformed), np.min(ones_transformed))\n print(ones_transformed[np.where(ones_transformed>0)])\n print(np.min(ones_transformed[np.where(ones_transformed>0)]))\n print(np.max(ones_transformed[np.where(ones_transformed>0)]))\n\n plt.imshow(ones_transformed)\n plt.show()\n plt.close()\n \n # ones_transformed=cut_transformed_array_borders(ones_transformed)\n \n avg_raster=avg_raster+ones_transformed\n # ird.imshow(template_raster, tif_raster, img_transformed)\n \n new_raster=new_raster+img_transformed\n \n # new_raster=new_raster+template_raster \n # new_raster=new_raster/len(tifs)\n\n gtz=np.where(avg_raster>0)\n \n\n \n\n \n \n plt.imshow(new_raster)\n plt.show()\n plt.close()\n # gdal_array.SaveArray(new_raster, tifs[0][:-4]+\"_not_abvertaghe_stacked_.tiff\")\n new_raster[gtz]=new_raster[gtz]/avg_raster[gtz] \n gdal_array.SaveArray(new_raster, tifs[0][:-4]+\"_stacked_.tiff\")\n plt.imshow(new_raster)\n plt.savefig(\"test.tif\", dpi=800)\n plt.show()\n plt.close()\n\n def discrete_cmap(N, base_cmap=None):\n \"\"\"Create an N-bin discrete colormap from the specified input map\"\"\"\n \n # Note that if base_cmap is a string or None, you can simply do\n # return plt.cm.get_cmap(base_cmap, N)\n # The following works for string, None, or a colormap instance:\n \n base = plt.cm.get_cmap(base_cmap)\n color_list = base(np.linspace(0, 1, N))\n cmap_name = base.name + str(N)\n return base.from_list(cmap_name, color_list, N)\n\n cmap=discrete_cmap(int(avg_raster.max())+1, base_cmap=\"ocean\") \n \n norm=mpl.colors.BoundaryNorm(np.arange(-0.5,int(avg_raster.max()+1)), cmap.N)\n fig=plt.figure()\n fig.set_size_inches(5,4)\n ax=fig.add_subplot(111)\n data=ax.matshow(avg_raster, cmap=cmap, norm=norm)\n fig.colorbar(data, ticks=np.linspace(0,int(avg_raster.max()),int(avg_raster.max()+1)), drawedges=True)\n\n plt.show()\n plt.close()\n\n\n # gdal_array.SaveArray(new_raster, tifs[0][:-4]+\"_stacked_.tiff\")","def photometry(userinputs, image, catalog, outputname, apertures, annulus='', dannulus='', recenter=False):\n logging.info('Running photometry function on {}'.format(image))\n logging.info('Using {}px apertures'.format(apertures))\n\n #set directory\n target_dir = userinputs['OUTDIR']\n\n #Update passed names to be full paths if they are not\n\n if len(image.split('/'))==1:\n logging.info('Looking for {} in {}.'.format(image,userinputs['DATA']))\n image = glob.glob(userinputs['DATA'] + '/' + image)\n if len(image)==0:\n logging.critical('No {} image found'.format(image))\n filemanagement.shutdown('Selected image does not exist',userinputs)\n else:\n image = image[0]\n logging.debug('Using image: {}'.format(image))\n\n if len(catalog.split('/'))==1:\n catalog = target_dir + '/init/' + catalog\n logging.debug('Input catalog: {}'.format(catalog))\n\n if len(outputname.split('/'))==1:\n output = target_dir + '/photometry/' + outputname\n logging.debug('Output name: {}'.format(output))\n else:\n output = outputname\n outputname = outputname.split('/')[-1]\n logging.debug('Output name: {}'.format(output))\n\n\n #Load zeropoints\n inst_zp, filter_zp, zp_zp = np.loadtxt(target_dir + '/init/Hi-PEEC_zeropoints.tab', unpack=True, dtype='str')\n # print inst_zp, filter_zp, zp_zp\n # Get filter from header\n filter = get_filter(image)\n\n\n # Set the necessary variables for photometry on the reference image\n exptime = fits.getheader(image)['EXPTIME']\n logging.debug('Exposure time from header: {}'.format(exptime))\n inst = fits.getheader(image)['INSTRUME']\n logging.debug('Intrument from header: {}'.format(inst))\n inst = inst.lower()\n\n\n match = (inst_zp == inst) & (filter_zp == filter.lower())\n zp = zp_zp[match]\n\n # zp is a string within an array, so need to turn into a float\n try:\n zp = float(zp[0])\n #If that cannot be done there was no match.\n except IndexError:\n if inst == 'acs':\n logging.debug('Zeropoint not found in file, passing to ACS calculation')\n zp = ACS_zeropoint(image)\n elif inst == 'wfc3':\n logging.debug('Zeropoint not found in file, passing to WFC3 calculation')\n zp = WFC3_zeropoint(image)\n else:\n logging.critical('No matching zeropoint found. Quitting.')\n logging.debug('No zeropoint match found for filter {} with instrument {}'\\\n .format(filter,inst))\n logging.debug('Available filters in zeropoint file : {} for instrument {}'\\\n .format(filter_zp, inst_zp))\n filemanagement.shutdown('No zeropoint was found for filter: {}'.format(filter),userinputs)\n\n logging.debug('Zeropoint from file: {}'.format(zp))\n # Remove output file if it already exists\n filemanagement.remove_if_exists(output)\n\n\n # Run photometry\n #--------------------------------------------------------------------------\n # Set up IRAF params:\n iraf.datapars.epadu = exptime\n\n # !!!!!!!!!!!!!!!!!\n # Only center on reference frame\n if recenter:\n iraf.centerpars.calgorithm = 'centroid'\n else:\n iraf.centerpars.calgorithm = 'none'\n # !!!!!!!!!!!!!!!\n # CHANGE BACKGROUND ESTIMATE IN ANNULUS TO MODE\n\n # Select the annulus depending on whether it is overwritten in the function call or not\n if annulus == '':\n iraf.fitskypars.annulus = userinputs['ANNULUS']\n logging.debug('Using annulus from inputfile ({}px)'.format(userinputs['ANNULUS']))\n else:\n iraf.fitskypars.annulus = annulus\n logging.debug('Using user specified annulus ({}px)'.format(annulus))\n if dannulus == '':\n iraf.fitskypars.dannulus = userinputs['D_ANNULUS']\n logging.debug('Using annulus width from inputfile ({}px)'.format(userinputs['D_ANNULUS']))\n else:\n iraf.fitskypars.dannulus = dannulus\n logging.debug('Using user specified annulus width ({}px)'.format(dannulus))\n\n iraf.photpars.apertures = apertures\n logging.debug('Using aperture(s) of {}px'.format(apertures))\n iraf.photpars.zmag = zp\n logging.debug('Setting zeropoint to {}'.format(zp))\n\n # Do phot\n iraf.phot(image+'[SCI]', catalog, output)\n #--------------------------------------------------------------------------\n\n\n #Depending on the number of apertures used, different methods of saving the\n # results are required\n #--------------------------------------------------------------------------\n\n naper = len(apertures.split(','))\n logging.debug('Number of apertures used {}'.format(naper))\n\n #final output filename\n fullcat_mag_short = target_dir + '/photometry/short_' + outputname\n\n if naper > 1:\n # Removes all outputlines that do not contain the character '*'\n # ensures only phot results are kept\n cmd = 'grep \"*\" ' + output + ' > ' + fullcat_mag_short\n os.system(cmd)\n\n # Replace INDEFS:\n cmd = 'sed -i.bak \"s/INDEF/99.999/g\" ' + fullcat_mag_short\n os.system(cmd)\n\n # Remove .bak files to prevent confusion\n bak_fullcat = fullcat_mag_short + '.bak'\n os.remove(bak_fullcat)\n\n\n else:\n #Dump results into a temp file\n temp = target_dir + '/photometry/phot_dump.mag'\n filemanagement.remove_if_exists(temp)\n iraf.txdump(output, 'XCENTER,YCENTER,FLUX,MAG,MERR,MSKY,ID', 'yes', Stdout = temp)\n\n # Set placeholders for sources outside of FOV and undetected sources\n # For outside of FOV, use 66.666 instead of INDEF\n # For undetected sources, use 99.999 instead of INDEF\n\n # Sources outside of FOV have exactly zero flux\n x, y, flux, mag, merr, msky, id = np.loadtxt(temp, unpack = True,\n dtype = str)\n\n flux = flux.astype(float)\n\n out_fov = (flux == 0.)\n logging.debug('Number of sources outside FOV: {}'.format(len(out_fov)))\n\n mag[out_fov] = 66.666\n merr[out_fov] = 66.666\n msky[out_fov] = 66.666\n\n # Undetected sources, those with negative flux or fluxes so small that mag err\n # is INDEF\n neg_flux = (flux < 0.)\n tiny_flux = (flux > 0.) & (merr == 'INDEF')\n\n mag[neg_flux] = 99.999\n merr[neg_flux] = 99.999\n msky[neg_flux] = 99.999\n\n merr[tiny_flux] = 99.999\n msky[tiny_flux] = 99.999\n\n logging.debug('Nr of undetected sources: {}'.format(len(tiny_flux)+len(neg_flux)))\n # Save results to new file\n x = x.astype(float)\n y = y.astype(float)\n mag = mag.astype(float)\n merr = merr.astype(float)\n msky = msky.astype(float)\n id = id.astype(int)\n\n zip_phot = zip(x, y, mag, merr, msky, id)\n\n np.savetxt(fullcat_mag_short, zip_phot,\n fmt = '%.3f %.3f %.3f %.3f %.9f %i')\n\n #--------------------------------------------------------------------------\n\n return fullcat_mag_short","def main():\n camera = picamera.PiCamera()\n camera.resolution = (RESOLUTIONX, RESOLUTIONY)\n camera.iso = 800\n time.sleep(2)\n while True:\n camera.capture('current-image.jpg')\n adapt_steering(navigation.get_xposition('current-image.jpg'))\n time.sleep(0.4)","def process_tir_image(ds, data_res, t_thresh=-50, min_mcs_size=5000):\n ctt = (ds['tb']).squeeze()-273.15\n min_pix_nb = min_mcs_size / data_res**2\n\n max_pix_nb = 300000 / data_res**2 # this is to capture satellite artefacts that come in large contiguous stripes.\n labels, goodinds = mcs_define(ctt.values, t_thresh, minmax_area=[min_pix_nb, max_pix_nb]) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2\n dic = dictionary()\n #plt.figure()\n #plt.pcolormesh(labels)\n #plt.colorbar()\n #plt.show()\n for g in goodinds:\n\n if g==0:\n continue\n\n pos = np.where(labels==g)\n npos = np.where(labels!=g)\n datestr = str(int(ctt['time.year'].values))+'-'+str(int(ctt['time.month'].values)).zfill(2)+'-'+str(int(ctt['time.day'].values)).zfill(2)+'_'+\\\n str(int(ctt['time.hour'].values)).zfill(2)+':'+str(int(ctt['time.minute'].values)).zfill(2)\n \n dic['date'].append(datestr)\n dic['month'].append(int(ctt['time.month']))\n dic['hour'].append(int(ctt['time.hour']))\n dic['year'].append(int(ctt['time.year']))\n dic['day'].append(int(ctt['time.day']))\n dic['minute'].append(int(ctt['time.minute']))\n\n storm = ctt.copy()\n storm.values[npos] = np.nan\n tmin_pos = np.nanargmin(storm.values)\n tpos_2d = np.unravel_index(tmin_pos, storm.shape)\n \n latmin = np.nanmin(ctt.lat.values[pos[0]])\n latmax = np.nanmax(ctt.lat.values[pos[0]])\n lonmin = np.nanmin(ctt.lon.values[pos[1]])\n lonmax = np.nanmax(ctt.lon.values[pos[1]])\n dic['area'].append(np.sum(np.isfinite(storm.values))*data_res**2)\n dic['70area'].append(np.sum(storm.values<=-70)*data_res**2)\n dic['minlon'].append(lonmin)\n dic['minlat'].append(latmin)\n dic['maxlon'].append(lonmax)\n dic['maxlat'].append(latmax)\n dic['clon'].append(lonmin + (lonmax - lonmin)/2)\n dic['clat'].append(latmin + (latmax - latmin)/2)\n dic['tmin'].append(np.nanmin(storm))\n dic['tminlat'].append(float(ctt.lat[tpos_2d[0]].values))\n dic['tminlon'].append(float(ctt.lon[tpos_2d[1]].values))\n dic['tmean'].append(float(np.nanmean(storm)))\n dic['tp1'].append(float(np.nanpercentile(storm, 1)))\n dic['tp99'].append(float(np.nanpercentile(storm, 99)))\n dic['stormID'].append(datestr + '_' + str(g))\n dic['cloudMask'].append(labels==g)\n dic['tir'].append(storm.values)\n\n # for k in dic.keys():\n # print(k, len(dic[k]))\n return dic","def calculate_thresholds(self):\n \n for group in self.roi_groups:\n for roi in group.rois:\n for image in range(len(roi.counts)):\n # print(roi.autothreshs)\n # print('image',image)\n if roi.autothreshs[image]:\n values = np.fromiter(roi.counts[image].values(), dtype=float)\n roi.thresholds[image] = self.calculate_threshold(values)\n\n for image, im_copy in enumerate(self.copy_im_threshs): # copy values from a different image and set to manual thresh if needed\n if im_copy is not None:\n for group in self.roi_groups:\n for roi in group.rois:\n roi.autothreshs[image] = False\n roi.thresholds[image] = roi.thresholds[im_copy]","def calculate_dark_current(image, i, int_time):\n dark_data_dir = r'F:\\TEMPO\\Data\\GroundTest\\FPS\\Integration_Sweep\\Dark'\n data_path_name_split = image.split('_')\n #print(data_path_name_split)\n all_int_files = [each for each in os.listdir(dark_data_dir) \\\n if each.endswith('_'+data_path_name_split[-1])] \n print(all_int_files)\n \n dark_data_file = os.path.join(dark_data_dir, all_int_files[0])\n IDL_variable = readsav(dark_data_file) \n all_full_frame = IDL_variable.q \n quad = all_full_frame[:, i, :, :]\n active_quad = np.mean(quad[:, 4:1028, 10:1034], axis=0) \n tsoc = np.mean(quad[:, 4:1028, 1034:1056], axis=0)\n bias_subtracted_quad = perform_bias_subtraction_ave(active_quad, tsoc)\n smear_subtracted_quad, smear_signal = perform_smear_subtraction(bias_subtracted_quad[10:1000, :], int_time)\n return smear_subtracted_quad","def process_sample(self, ch, method, properties, body):\n\n # data inside a dictionary\n # {'amplitude':[1.3,4.4,5...],\n # 'angle': [0.04,0.1,...]}\n sample_dict = self.deserialize_vtt_60_processed(body)\n\n with open('sample_test_run.txt', 'a') as f:\n x_arrstr = np.char.mod('%d', sample_dict['amplitude'])\n\n # x_arrstr -> should be 2d array \"frame\".\n raw_data = \",\".join(x_arrstr.flatten())\n f.write(raw_data)\n f.write('\\n')\n\n arr = np.array(sample_dict['amplitude'])\n\n frame = np.reshape(arr, (180,110))\n frame_transposed_flipped = np.flip(np.transpose(frame))\n frame_transposed_flipped = np.flip(np.transpose(frame))\n details_removed = frame_transposed_flipped\n details_removed = np.clip(frame_transposed_flipped, a_min=frame_transposed_flipped.max() - 15, a_max=None)\n self.i = self.i + 1\n if self.i % 10 == 0:\n b = sum(pd.DataFrame(frame).max().diff() / pd.DataFrame(frame).max() > 0.13)\n self.report_people(b)\n\n\n if body is None or sample_dict is None:\n print('Stream stopped.')\n return\n if self.samples_in_total % self.fps == 0:\n endTime = time.time()\n print('FPS: {:.1f}'.format(self.fps/(endTime - self.startTime)))\n self.startTime = endTime\n \n # your code here\n amplitude = sample_dict['amplitude']\n amplitude = np.reshape(amplitude,(180,110))\n amplitude = np.where(amplitude>130,130,amplitude)\n amplitude = np.uint8(255*amplitude/130)\n out = cv2.cvtColor(amplitude,cv2.COLOR_GRAY2BGR)\n out = cv2.applyColorMap(out,cv2.COLORMAP_MAGMA)\n cv2.imshow('junction',out)\n cv2.waitKey(10)","def optimize_trap(dg):\n f_peak = './temp_peak.lh5' # lh5\n f_results = './temp_results.h5' # pandas\n grp_data, grp_grid = '/optimize_data', '/optimize_grid'\n \n # epar, elo, ehi, epb = 'energy', 0, 1e7, 10000 # full range\n epar, elo, ehi, epb = 'energy', 3.88e6, 3.92e6, 500 # K40 peak\n \n show_movie = True\n write_output = True\n n_rows = None # default None\n \n with open('opt_trap.json') as f:\n dsp_config = json.load(f, object_pairs_hook=OrderedDict)\n \n # files to consider. fixme: right now only works with one file\n sto = lh5.Store()\n lh5_dir = os.path.expandvars(dg.config['lh5_dir'])\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\n f_raw = raw_list.values[0] \n tb_raw = 'ORSIS3302DecoderForEnergy/raw/'\n\n # quick check of the energy range\n # ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\n # plt.plot(bins[1:], hist, ds='steps')\n # plt.show()\n # exit()\n \n # set grid parameters\n # TODO: jason's suggestions, knowing the expected shape of the noise curve\n # e_rises = np.linspace(-1, 0, sqrt(sqrt(3))\n # e_rises # make another list which is 10^pwr of this list\n # np.linspace(log_tau_min, log_tau_max) # try this too\n e_rises = np.arange(1, 12, 1)\n e_flats = np.arange(1, 6, 1)\n # rc_consts = np.arange(54, 154, 10) # changing this here messes up DCR\n \n # -- create the grid search file the first time -- \n # NOTE: this makes a linear grid, and is editable by the arrays above.\n # jason also proposed a more active gradient-descent style search\n # like with Brent's method. (https://en.wikipedia.org/wiki/Brent%27s_method)\n \n if True:\n # if not os.path.exists(f_peak):\n print('Recreating grid search file')\n \n # create the grid file\n # NOTE: save it as an lh5 Table just as an example of writing/reading one\n lists = [e_rises, e_flats]#, rc_consts]\n prod = list(itertools.product(*lists)) # clint <3 stackoverflow\n df_grid = pd.DataFrame(prod, columns=['rise', 'flat'])#,'rc']) \n lh5_grid = {}\n for i, dfcol in df_grid.iteritems():\n lh5_grid[dfcol.name] = lh5.Array(dfcol.values)\n tb_grid = lh5.Table(col_dict=lh5_grid)\n sto.write_object(tb_grid, grp_grid, f_peak)\n \n # filter events by onboard energy\n ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\n # plt.plot(bins[1:], hist, ds='steps')\n # plt.show()\n if n_rows is not None:\n ene_raw = ene_raw[:n_rows]\n idx = np.where((ene_raw > elo) & (ene_raw < ehi))\n\n # create a filtered table with correct waveform and attrs\n # TODO: move this into a function in lh5.py which takes idx as an input\n tb_data, wf_tb_data = lh5.Table(), lh5.Table()\n\n # read non-wf cols (lh5 Arrays)\n data_raw = sto.read_object(tb_raw, f_raw, n_rows=n_rows)\n for col in data_raw.keys():\n if col=='waveform': continue\n newcol = lh5.Array(data_raw[col].nda[idx], attrs=data_raw[col].attrs)\n tb_data.add_field(col, newcol)\n \n # handle waveform column (lh5 Table)\n data_wfs = sto.read_object(tb_raw+'/waveform', f_raw, n_rows=n_rows)\n for col in data_wfs.keys():\n attrs = data_wfs[col].attrs\n if isinstance(data_wfs[col], lh5.ArrayOfEqualSizedArrays):\n # idk why i can't put the filtered array into the constructor\n aoesa = lh5.ArrayOfEqualSizedArrays(attrs=attrs, dims=[1,1])\n aoesa.nda = data_wfs[col].nda[idx]\n newcol = aoesa\n else:\n newcol = lh5.Array(data_wfs[col].nda[idx], attrs=attrs)\n wf_tb_data.add_field(col, newcol)\n tb_data.add_field('waveform', wf_tb_data)\n tb_data.attrs = data_raw.attrs\n sto.write_object(tb_data, grp_data, f_peak)\n\n else:\n print('Loading peak file. groups:', sto.ls(f_peak))\n tb_grid = sto.read_object(grp_grid, f_peak)\n tb_data = sto.read_object(grp_data, f_peak) # filtered file\n # tb_data = sto.read_object(tb_raw, f_raw) # orig file\n df_grid = tb_grid.get_dataframe()\n \n # check shape of input table\n print('input table attributes:')\n for key in tb_data.keys():\n obj = tb_data[key]\n if isinstance(obj, lh5.Table):\n for key2 in obj.keys():\n obj2 = obj[key2]\n print(' ', key, key2, obj2.nda.shape, obj2.attrs)\n else:\n print(' ', key, obj.nda.shape, obj.attrs)\n\n # clear new colums if they exist\n new_cols = ['e_fit', 'fwhm_fit', 'rchisq', 'xF_err', 'fwhm_ovr_mean']\n for col in new_cols:\n if col in df_grid.columns:\n df_grid.drop(col, axis=1, inplace=True)\n\n t_start = time.time()\n def run_dsp(dfrow):\n \"\"\"\n run dsp on the test file, editing the processor list\n alternate idea: generate a long list of processors with different names\n \"\"\"\n # adjust dsp config dictionary\n rise, flat = dfrow\n # dsp_config['processors']['wf_pz']['defaults']['db.pz.tau'] = f'{tau}*us'\n dsp_config['processors']['wf_trap']['args'][1] = f'{rise}*us'\n dsp_config['processors']['wf_trap']['args'][2] = f'{flat}*us'\n # pprint(dsp_config)\n \n # run dsp\n pc, tb_out = build_processing_chain(tb_data, dsp_config, verbosity=0)\n pc.execute()\n \n # analyze peak\n e_peak = 1460.\n etype = 'trapEmax'\n elo, ehi, epb = 4000, 4500, 3 # the peak moves around a bunch\n energy = tb_out[etype].nda\n \n # get histogram\n hE, bins, vE = pgh.get_hist(energy, range=(elo, ehi), dx=epb)\n xE = bins[1:]\n \n # should I center the max at 1460?\n\n # simple numerical width\n i_max = np.argmax(hE)\n h_max = hE[i_max]\n upr_half = xE[(xE > xE[i_max]) & (hE <= h_max/2)][0]\n bot_half = xE[(xE < xE[i_max]) & (hE >= h_max/2)][0]\n fwhm = upr_half - bot_half\n sig = fwhm / 2.355\n \n # fit to gaussian: amp, mu, sig, bkg\n fit_func = pgf.gauss_bkg\n amp = h_max * fwhm\n bg0 = np.mean(hE[:20])\n x0 = [amp, xE[i_max], sig, bg0]\n xF, xF_cov = pgf.fit_hist(fit_func, hE, bins, var=vE, guess=x0)\n\n # collect results\n e_fit = xF[0]\n xF_err = np.sqrt(np.diag(xF_cov))\n e_err = xF\n fwhm_fit = xF[1] * 2.355 * 1460. / e_fit\n \n fwhm_err = xF_err[2] * 2.355 * 1460. / e_fit\n \n chisq = []\n for i, h in enumerate(hE):\n model = fit_func(xE[i], *xF)\n diff = (model - h)**2 / model\n chisq.append(abs(diff))\n rchisq = sum(np.array(chisq) / len(hE))\n fwhm_ovr_mean = fwhm_fit / e_fit\n\n if show_movie:\n \n plt.plot(xE, hE, ds='steps', c='b', lw=2, label=f'{etype} {rise}--{flat}')\n\n # peak shape\n plt.plot(xE, fit_func(xE, *x0), '-', c='orange', alpha=0.5,\n label='init. guess')\n plt.plot(xE, fit_func(xE, *xF), '-r', alpha=0.8, label='peakshape fit')\n plt.plot(np.nan, np.nan, '-w', label=f'mu={e_fit:.1f}, fwhm={fwhm_fit:.2f}')\n\n plt.xlabel(etype, ha='right', x=1)\n plt.ylabel('Counts', ha='right', y=1)\n plt.legend(loc=2)\n\n # show a little movie\n plt.show(block=False)\n plt.pause(0.01)\n plt.cla()\n\n # return results\n return pd.Series({'e_fit':e_fit, 'fwhm_fit':fwhm_fit, 'rchisq':rchisq,\n 'fwhm_err':xF_err[0], 'fwhm_ovr_mean': fwhm_ovr_mean})\n \n # df_grid=df_grid[:10]\n df_tmp = df_grid.progress_apply(run_dsp, axis=1)\n df_grid[new_cols] = df_tmp\n # print(df_grid)\n \n if show_movie:\n plt.close()\n \n print('elapsed:', time.time() - t_start)\n if write_output:\n df_grid.to_hdf(f_results, key=grp_grid)\n print(f\"Wrote output file: {f_results}\")","def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.\n suffix = '+orig'\n epifile = '%s%s' % (info['imgfile'], suffix)\n prefix = info['imgfile_m']\n base_entry = info['base_entry']\n if info['base'] == 'start':\n# Use the first frame specified in template file. Defaults\n# to zero.\n base = info['motion_ref_frame']\n else:\n# Use the last frame.\n base = self.info[base_entry]['tdim'] - info['skip']-1\n base = ('%d' % base).replace(' ','')\n\n# Correct for slice-timing.\n self.SliceTimeCorrect(info, epifile)\n\n plane = info['plane']\n anat_tgt = info['anat_tgt']\n# anat_entry = self.anat_entry[plane]\n\n if info['catmats']:\n# Include additonal transformation in motion correction such\n# that final image is in register with the fieldmap, which has\n# been registered to the structural image that will be used for\n# spatial normalization.\n self.MotcorCatenate(info, base, anat_tgt)\n else:\n# Assume fieldmap is in register with the structural.\n self.Motcor(info, base)\n\n if info.get('fmapname', None) is None:\n# No fieldmap correction.\n if self.fsl_flip:\n# Flip the way fslview likes it.\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\n elif info['suffix'] == '.nii':\n# Copy motion-corrected images from /tmp to output directory\n outfile = info['imgfile_final'] + info['suffix']\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\n self.CheckExec(cmd, [outfile], force=True)\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\n self.CheckExec(cmd, [], force=True)","def test_rolling_before_analysis(self):\n cheese = TomoCheese.from_demo_images()\n cheese.analyze()\n original_roi_1 = copy.copy(cheese.module.rois[\"1\"].pixel_value)\n for img in cheese.dicom_stack:\n img.roll(direction=\"x\", amount=20)\n cheese.analyze()\n new_roi_1 = cheese.module.rois[\"1\"].pixel_value\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)","def atmos_worker(srcs, window, ij, args):\n src = srcs[0]\n rgb = src.read(window=window)\n rgb = to_math_type(rgb)\n\n atmos = simple_atmo(rgb, args[\"atmo\"], args[\"contrast\"], args[\"bias\"])\n\n # should be scaled 0 to 1, scale to outtype\n return scale_dtype(atmos, args[\"out_dtype\"])","def make_sim(args):\n flux, nread, iexp = args\n satlevel=65535\n\n #outdir = \"sim_data_flux\" + str(flux)\n #outdir = \"sim_data_flux\" + str(flux) + \"_nonl\"\n outdir = \"sim_\" + \"exposure\" + str(iexp) + \"_flux\" + str(flux)\n pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) \n \n for i in range(1, nread+1):\n logger.info(\"Looping through UTR {}\".format(i))\n #outfileprefix = outdir + \"/exposure\" + str(iexp) + \"_utr\" + str(i)\n outfileprefix = outdir + \"/utr\" + str(i)\n refoutfileprefix = outdir + \"/utr\" + str(i) + \"_ref\"\n\n primary_hdu = fits.PrimaryHDU()\n outhdus = fits.HDUList([primary_hdu])\n outhdus_ref = fits.HDUList([primary_hdu])\n\n ahdu = fits.HDUList([primary_hdu]) #for a coefficients\n bhdu = fits.HDUList([primary_hdu]) #for b coefficients\n\n #loop all 16 detectors\n ndet = 16\n logger.info(\"Looping through all 16 detectors\")\n for idet in range(ndet):\n detid = detids[idet]\n logger.info(\"Looping through the detector {}, {}\".format(idet+1, detid)) \n\n flux_actual = np.zeros((2048, 2048))\n #start with the measured flux, constant spatially but varying temporally\n flux_actual[4:2044,4:2044] = flux * TREAD * i #fill data region with measured flux, (ADU/f) * s = ADU ???\n flux_actual *= Gain #e-??\n logger.info(\"Filled with measured flux\")\n\n #calculate nonlinearity from calibration data, varing spatially but not temporally, unit e-??\n flux_nonlinear = make_nonlinear(flux_actual, outfileprefix, ahdu, bhdu, detid) \n #flux_nonlinear = flux_actual #no non-linearity\n logger.info(\"Made non-linearized flux\")\n\n #add pedestal, constant spatially very varying temporally\n pedestal = random.randint(0, 1000) #set the random pedistal (between 0 - 1000) for this exposure, unit e-??\n flux_nonlinear[:, :] += pedestal #add the pedestal to all of the data including the reference pixels.\n logger.info(\"Added pedestal\")\n\n #add noise (e-)\n readnoise_array = np.random.normal(size=[2048,2048]) * ReadNoise #generate the read noise, the reference pixels should also have noise. \n flux_nonlinear += readnoise_array #add the read noise to the data array\n logger.info(\"Added read noise\")\n\n # set any pixel above the saturation limit to the saturation limit.\n # This can set to 65535 for all pixels for now, but should be able to \n # take a map since this will vary pixel to pixel. \n ind = np.where(flux_nonlinear > satlevel)\n flux_nonlinear[ind] = satlevel\n logger.info(\"Checked saturation level\")\n\n #add this dector to the final simulated data\n hdu = fits.ImageHDU(data=flux_nonlinear, name=detid)\n outhdus.append(hdu)\n\n #do reference pixel correction\n updownCorr(flux_nonlinear)\n leftrightCorr(flux_nonlinear)\n\n #add this dector to another output\n hdu1 = fits.ImageHDU(data=flux_nonlinear, name=detid)\n outhdus_ref.append(hdu1)\n \n\n #write the sim data for this UTR\n outfile = outfileprefix + \".fits\"\n #logger.info(\"Writing sim data to: {}\".format(outfile))\n #outhdus.writeto(outfile)\n\n refoutfile = refoutfileprefix + \".fits\"\n logger.info(\"Writing reference pixel corrected sim data to: {}\".format(refoutfile))\n outhdus_ref.writeto(refoutfile)\n\n #ahdu.writeto(outfileprefix + \"_a_coef.fits\")\n #bhdu.writeto(outfileprefix + \"_b_coef.fits\")","def calibrate(cap, location):\n\n #Poisition and size of sensor\n [x, y, h, w] = location\n\n #show square to user and wait for key\n print(\"please, step away to clear the blue square displayed on screen and press q to continue\")\n while True:\n ret, frame = cap.read()\n cv2.namedWindow('Calibrate',cv2.WINDOW_NORMAL)\n show = cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0) , 5)\n cv2.imshow('Calibrate', show)\n key = cv2.waitKey(1)\n if key == ord('q'):\n break\n\n #get first image, process and define window previous for iteration\n ret, frame = cap.read()\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.GaussianBlur(frame, (7,7), 0)\n previous = frame[y:y+w,x:x+h]\n\n #set parameters for mean value of sensor, kernel of erode function,\n sampleNbMean = 50\n xi = np.empty((0, sampleNbMean))\n kernel = np.ones((5,5), np.uint8)\n\n #iterate over each frame until sample number\n for iteration in range(sampleNbMean):\n\n # Capture frame, draw the window and display to the user\n ret, frame = cap.read()\n # Image operation\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.GaussianBlur(frame, (7,7), 0)\n\n #get present window\n present = frame[y:y+w,x:x+h]\n\n #add sample for mean, add diference of window with prieviuos\n xi = np.append(xi,\n np.sum(\n cv2.erode(\n cv2.bitwise_xor(present,previous), kernel, iterations=1)))\n\n #present image becomes previous before steping into next image\n previous = present\n\n #mean\n mean = np.sum(xi)/len(xi)\n\n #sigma\n sum = 0\n for sample in xi:\n sum += np.power(sample - mean, 2)\n sigma = np.sqrt(sum/len(xi))\n\n #close window\n cv2.destroyWindow('Calibrate')\n\n return mean, sigma","def __call__(self, frame_num):\n quant_sys = self.quant_sys\n quant_sys.propagate(10)\n\n # propagate the wigner function\n self.img_clasical_rho.set_array(\n (quant_sys.D22 + quant_sys.D11).real\n #quant_sys.get_classical_rho()\n )\n\n self.img_Upsilon2.set_array(\n quant_sys.quantum_rho.real\n )\n\n return self.img_clasical_rho, self.img_Upsilon2","def main ():\n\n # interval ends\n a,b = -1.0, +1.0\n\n # List of trapezium counts\n NVals = numpy.logspace (1, 8, base=10.0, num=6, dtype=numpy.int)\n\n # List of implementations\n intFuncList = [ \n (integral.trapintPython, \"python.txt\")\n#\t\t (integral.trapintNumPy, \"numpy.txt\"),\n#\t\t (integral.trapintNumba, \"numba.txt\"),\n#\t\t (cpintegral.trapint,\t \"cython.txt\")\n ]\n\n for intFunc, file in intFuncList:\n\n\t# Open results file...\n\n\tfh = open( file, \"w\" ) \n\n \t# compute Pi \n\tfor N in NVals:\n \tt1 = time.time ()\n \t \tv1 = intFunc (a, b, N)\n\t \te1 = math.fabs (v1 - math.pi)\n \t \tt1 = time.time () - t1\n\t \tfh.write(\"{:d} {:6.4g}\\n\".format(N, t1))\n\tfh.close()","def process_image( self, image ):\n \n # 1. detect cars in image at different scales\n \n # Modify x/y start stop according to scale, cars appear smaller near horizon\n scales = config.scales\n \n box_list = []\n for scale_item in scales:\n scale = scale_item[\"scale\"]\n detects_image, boxes = hog_subsample.find_cars(image, \n scale_item[\"y_start_stop\"][0], scale_item[\"y_start_stop\"][1], \n scale, \n config.settings[\"svc\"], \n config.settings[\"scaler\"], \n config.settings[\"orient\"], \n config.settings[\"pix_per_cell\"], config.settings[\"cell_per_block\"], \n config.settings[\"spatial_size\"], config.settings[\"hist_bins\"],\n scale_item[\"x_start_stop\"][0], scale_item[\"x_start_stop\"][1])\n box_list.extend(boxes)\n \n # Update history\n self.bbox_list_history.append( box_list )\n bbox_list_history_list = sum(self.bbox_list_history.copy(), []) # single list of bbox lists in history\n \n # 2. heat map and threshold\n \n # Make zeros shaped like image\n heat = np.zeros_like(image[:,:,0]).astype(np.float)\n\n # Add heat for each box in box list history\n heat = heatmap_threshold_detection.add_heat(heat, bbox_list_history_list)\n\n # Apply threshold to help remove false positives\n heat_threshold = config.heatmap_threshold\n heat = heatmap_threshold_detection.apply_threshold(heat, heat_threshold)\n\n # Find final boxes from heatmap using label function\n heatmap = np.clip(heat, 0, 255) # only need to clip if there is more than 255 boxes around a point?\n labels = label(heatmap)\n boxed_image = heatmap_threshold_detection.draw_labeled_bboxes(np.copy(image), labels)\n \n # frame image annotation\n font = cv2.FONT_HERSHEY_SIMPLEX\n cv2.putText(boxed_image,\"Frame:{}\".format(config.count), (10,100), font, 1, (255,255,255), 2 ,cv2.LINE_AA )\n \n return boxed_image","def process_plot_mri_with_damaged(paths, params):\n\n\n\t# hdf5 file that contains the original images\n\thdf5_file = os.path.join(paths['hdf5_folder'], params['hdf5_file'])\n\t\n\t# get all patient names from original MRI group\n\tpatients = get_datasets_from_group(group_name = params['group_original_mri'], hdf5_file = hdf5_file)\n\n\t# get list of patients without state\n\tpatients = set([re.search('(.*) (fersk|Tint)', x).group(1) for x in patients])\n\t\n\t# loop over each patient, read data, perform inference\n\tfor i, patient in enumerate(patients):\n\n\t\tlogging.info(f'Processing patient: {patient} {i + 1}/{len(patients)}')\n\n\t\t# parse out treatment, sample, and state from patient name\n\t\ttreatment, _, _ = parse_patientname(patient_name = f'{patient} fersk')\n\n\t\t\"\"\"\n\t\tGet fresh state\n\t\t\"\"\"\n\t\t# read original images\n\t\tfresh_original_images = read_dataset_from_group(dataset = f'{patient} fersk', group_name = params['group_original_mri'], hdf5_file = hdf5_file)\n\t\t# read reconstructed images\n\t\tfresh_reconstructed_images = read_dataset_from_group(dataset = f'{patient} fersk', group_name = params['group_segmented_classification_mri'], hdf5_file = hdf5_file)\n\t\t# only take damaged tissue and set connected tissue\n\t\tfresh_reconstructed_damaged_images = (process_connected_tissue(images = fresh_reconstructed_images.copy(), params = params) == 1)\n\n\t\t\"\"\"\n\t\tGet frozen/thawed\n\t\t\"\"\"\n\t\t# read original images\n\t\tfrozen_original_images = read_dataset_from_group(dataset = f'{patient} Tint', group_name = params['group_original_mri'], hdf5_file = hdf5_file)\n\t\t# read reconstructed images\n\t\tfrozen_reconstructed_images = read_dataset_from_group(dataset = f'{patient} Tint', group_name = params['group_segmented_classification_mri'], hdf5_file = hdf5_file)\n\t\t# only take damaged tissue and set connected tissue\n\t\tfrozen_reconstructed_damaged_images = (process_connected_tissue(images = frozen_reconstructed_images.copy(), params = params) == 1)\n\n\t\t# get total number of slices to process\n\t\ttotal_num_slices = fresh_original_images.shape[0]\n\t\t# loop over each slice\n\t\tfor mri_slice in range(total_num_slices):\n\n\t\t\t# check slice validity of fresh patient\n\t\t\tif check_mri_slice_validity(patient = f'{patient} fersk', mri_slice = mri_slice, total_num_slices = total_num_slices):\n\n\t\t\t\tif check_mri_slice_validity(patient = f'{patient} Tint', mri_slice = mri_slice, total_num_slices = total_num_slices):\n\t\n\t\t\t\t\t# setting up the plot environment\n\t\t\t\t\tfig, axs = plt.subplots(2, 2, figsize = (8, 8))\n\t\t\t\t\taxs = axs.ravel()\n\n\t\t\t\t\t# define the colors we want\n\t\t\t\t\tplot_colors = ['#250463', '#e34a33']\n\t\t\t\t\t# create a custom listed colormap (so we can overwrite the colors of predefined cmaps)\n\t\t\t\t\tcmap = colors.ListedColormap(plot_colors)\n\t\t\t\t\t# subfigure label for example, a, b, c, d etc\n\t\t\t\t\tsf = cycle(['a', 'b', 'c', 'd', 'e', 'f', 'g'])\n\n\t\t\t\t\t\"\"\"\n\t\t\t\t\tPlot fresh state\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t# obtain vmax score so image grayscales are normalized better\n\t\t\t\t\tvmax_percentile = 99.9\n\t\t\t\t\tvmax = np.percentile(fresh_original_images[mri_slice], vmax_percentile)\n\t\t\t\t\t\n\t\t\t\t\t# plot fresh original MRI image\n\t\t\t\t\taxs[0].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\taxs[0].set_title(rf'$\\bf({next(sf)})$ Fresh - Original MRI')\n\t\t\t\t\t\n\t\t\t\t\t# plot fresh reconstucted image overlayed on top of the original image\n\t\t\t\t\t# axs[1].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\t# im = axs[1].imshow(fresh_reconstructed_images[mri_slice],alpha = 0.7, interpolation = 'none')\n\t\t\t\t\t# axs[1].set_title(rf'$\\bf({next(sf)})$ Fresh - Reconstructed')\n\t\t\t\t\t\n\n\t\t\t\t\t# plot fresh reconstucted image overlayed on top of the original image\n\t\t\t\t\taxs[1].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\taxs[1].imshow(fresh_reconstructed_damaged_images[mri_slice], cmap = cmap, alpha = .5, interpolation = 'none')\n\t\t\t\t\taxs[1].set_title(rf'$\\bf({next(sf)})$ Fresh - Reconstructed')\n\t\t\t\t\t\n\t\t\t\t\t\"\"\"\n\t\t\t\t\tPlot frozen/thawed state\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t# plot frozen/thawed original MRI image\n\t\t\t\t\t# obtain vmax score so image grayscales are normalized better\n\t\t\t\t\tvmax = np.percentile(frozen_original_images[mri_slice], vmax_percentile)\n\t\t\t\t\taxs[2].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\taxs[2].set_title(rf'$\\bf({next(sf)})$ {treatment_to_title(treatment)} - Original MRI')\n\n\t\t\t\t\t# plot frozen reconstucted all classes\n\t\t\t\t\t# axs[4].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\t# im = axs[4].imshow(frozen_reconstructed_images[mri_slice], alpha = 0.7, interpolation = 'none')\n\t\t\t\t\t# axs[4].set_title(rf'$\\bf({next(sf)})$ {treatment_to_title(treatment)} - Reconstructed')\n\t\t\t\t\t\n\t\t\t\t\t# # plot frozen/thawed reconstucted image overlayed on top of the original image\n\t\t\t\t\taxs[3].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\n\t\t\t\t\taxs[3].imshow(frozen_reconstructed_damaged_images[mri_slice], cmap = cmap, alpha = .5, interpolation = 'none')\n\t\t\t\t\taxs[3].set_title(rf'$\\bf({next(sf)})$ {treatment_to_title(treatment)} - Reconstructed')\n\n\t\t\t\t\t\"\"\"\n\t\t\t\t\tCreate custom legend\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t# add custom legend\t\t\t\t\n\t\t\t\t\tclass_labels = {0 : 'background', 1 : 'damaged tissue'}\n\t\t\t\t\tclass_values = list(class_labels.keys())\n\t\t\t\t\t# create a patch \n\t\t\t\t\tpatches = [ mpatches.Patch(color = plot_colors[i], label= class_labels[i]) for i in range(len(class_values)) ]\n\t\t\t\t\taxs[1].legend(handles = patches)#, bbox_to_anchor=(1.05, 1), loc = 2, borderaxespad=0. )\n\t\t\t\t\n\t\t\t\t\t# legend for fully reconstructed image\n\t\t\t\t\t# get class labels\n\t\t\t\t\t# class_labels = params['class_labels']\n\t\t\t\t\t# # get class indexes from dictionary\n\t\t\t\t\t# values = class_labels.keys()\n\t\t\t\t\t# # get the colors of the values, according to the \n\t\t\t\t\t# # colormap used by imshow\n\t\t\t\t\t# plt_colors = [ im.cmap(im.norm(value)) for value in values]\n\t\t\t\t\t# # create a patch (proxy artist) for every color \n\t\t\t\t\t# patches = [ mpatches.Patch(color = plt_colors[i], label= class_labels[i]) for i in range(len(values)) ]\n\t\t\t\t\t# # put those patched as legend-handles into the legend\n\t\t\t\t\t# axs[1].legend(handles = patches)#, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0. )\n\t\t\t\t\t\n\t\t\t\t\t\"\"\"\n\t\t\t\t\tAdjust figures\n\t\t\t\t\t\"\"\"\n\t\t\t\t\t# remove axis of all subplots\n\t\t\t\t\t[ax.axis('off') for ax in axs]\n\t\t\t\t\t# define plot subfolder\n\t\t\t\t\tsubfolder = os.path.join(paths['paper_plot_folder'], 'original_vs_reconstructed', patient)\n\t\t\t\t\t# create subfolder\n\t\t\t\t\tcreate_directory(subfolder)\n\t\t\t\t\t# crop white space\n\t\t\t\t\tfig.set_tight_layout(True)\n\t\t\t\t\t# save the figure\n\t\t\t\t\tfig.savefig(os.path.join(subfolder, f'slice_{mri_slice}.pdf'))\n\t\t\t\t\t\n\t\t\t\t\t# close the figure environment\n\t\t\t\t\tplt.close()","def main(input_data_path, output_data_path, window):\n # open data info dataframe\n info_df = pd.read_csv(input_data_path + 'hemorrhage_diagnosis_raw_ct.csv')\n # replace No-Hemorrhage to hemorrange\n info_df['Hemorrhage'] = 1 - info_df.No_Hemorrhage\n info_df.drop(columns='No_Hemorrhage', inplace=True)\n # open patient info dataframe\n patient_df = pd.read_csv(input_data_path + 'Patient_demographics.csv', header=1, skipfooter=2, engine='python') \\\n .rename(columns={'Unnamed: 0':'PatientNumber', 'Unnamed: 1':'Age',\n 'Unnamed: 2':'Gender', 'Unnamed: 8':'Fracture', 'Unnamed: 9':'Note'})\n patient_df[patient_df.columns[3:9]] = patient_df[patient_df.columns[3:9]].fillna(0).astype(int)\n # add columns Hemorrgae (any ICH)\n patient_df['Hemorrhage'] = patient_df[patient_df.columns[3:8]].max(axis=1)\n\n # make patient directory\n if not os.path.exists(output_data_path): os.mkdir(output_data_path)\n if not os.path.exists(output_data_path + 'Patient_CT/'): os.mkdir(output_data_path + 'Patient_CT/')\n # iterate over volume to extract data\n output_info = []\n for n, id in enumerate(info_df.PatientNumber.unique()):\n # read nii volume\n ct_nii = nib.load(input_data_path + f'ct_scans/{id:03}.nii')\n mask_nii = nib.load(input_data_path + f'masks/{id:03}.nii')\n # get np.array\n ct_vol = ct_nii.get_fdata()\n mask_vol = skimage.img_as_bool(mask_nii.get_fdata())\n # rotate 90° counter clockwise for head pointing upward\n ct_vol = np.rot90(ct_vol, axes=(0,1))\n mask_vol = np.rot90(mask_vol, axes=(0,1))\n # window the ct volume to get better contrast of soft tissues\n if window is not None:\n ct_vol = window_ct(ct_vol, win_center=window[0], win_width=window[1], out_range=(0,1))\n\n if mask_vol.shape != ct_vol.shape:\n print(f'>>> Warning! The ct volume of patient {id} does not have '\n f'the same dimension as the ground truth. CT ({ct_vol.shape}) vs Mask ({mask_vol.shape})')\n # make patient directory\n if not os.path.exists(output_data_path + f'Patient_CT/{id:03}/'): os.mkdir(output_data_path + f'Patient_CT/{id:03}/')\n # iterate over slices to save slices\n for i, slice in enumerate(range(ct_vol.shape[2])):\n ct_slice_fn =f'Patient_CT/{id:03}/{slice+1}.tif'\n # save CT slice\n skimage.io.imsave(output_data_path + ct_slice_fn, ct_vol[:,:,slice], check_contrast=False)\n is_low = True if skimage.exposure.is_low_contrast(ct_vol[:,:,slice]) else False\n # save mask if some positive ICH\n if np.any(mask_vol[:,:,slice]):\n mask_slice_fn = f'Patient_CT/{id:03}/{slice+1}_ICH_Seg.bmp'\n skimage.io.imsave(output_data_path + mask_slice_fn, skimage.img_as_ubyte(mask_vol[:,:,slice]), check_contrast=False)\n else:\n mask_slice_fn = 'None'\n # add info to output list\n output_info.append({'PatientNumber':id, 'SliceNumber':slice+1, 'CT_fn':ct_slice_fn, 'mask_fn':mask_slice_fn, 'low_contrast_CT':is_low})\n\n print_progessbar(i, ct_vol.shape[2], Name=f'Patient {id:03} {n+1:03}/{len(info_df.PatientNumber.unique()):03}',\n Size=20, erase=False)\n\n # Make dataframe of outputs\n output_info_df = pd.DataFrame(output_info)\n # Merge with input info\n info_df = pd.merge(info_df, output_info_df, how='inner', on=['PatientNumber', 'SliceNumber'])\n # save df\n info_df.to_csv(output_data_path + 'ct_info.csv')\n print('>>> Data informations saved at ' + output_data_path + 'ct_info.csv')\n # save patient df\n patient_df.to_csv(output_data_path + 'patient_info.csv')\n print('>>> Patient informations saved at ' + output_data_path + 'patient_info.csv')","def ring_int(self, tissue):\n print(\"controller - ring_int!\")\n img_cv2_mask = self.pressure_img.ring_ext\n self.pressure_img.roi_crop(img_cv2_mask, tissue, 2)","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 calc_tau(z_array_reion_allmodels, cosmology_allmodels, helium_allmodels,\n mass_frac_allmodels):\n\n def integrand(z, h, OM):\n H = av.Hubble_Param(z, h, OM) / (av.pc_to_m * 1.0e6 / 1.0e3)\n return (((1 + z)**2) / H)\n\n tau = []\n for model_number in range(len(mass_frac_allmodels)):\n\n # Set up some things for the model cosmology etc.\n model_mass_frac = mass_frac_allmodels[model_number]\n model_helium = helium_allmodels[model_number]\n model_h = cosmology_allmodels[model_number].H(0).value/100.0\n model_OM = cosmology_allmodels[model_number].Om0\n model_OB = cosmology_allmodels[model_number].Ob0\n model_z = z_array_reion_allmodels[model_number]\n\n model_tau = np.zeros(len(model_mass_frac))\n\n # First determine optical depth for redshift 0 to 4.\n tau_04 = integrate.quad(integrand, 0, 4, args=(model_h, model_OM,))[0] \n tau_04 *= (1 + 2*model_helium/(4 * (1-model_helium)))\n\n # Then determine optical depth from z = 4 to lowest z of model.\n tau_46 = integrate.quad(integrand, 4, model_z[-1], args=(model_h, model_OM,))[0]\n tau_46 *= (1 + model_helium/(4* (1-model_helium)))\n\n tau_06 = tau_04 + tau_46\n\n model_tau[-1] = tau_06\n\n # Then loop down through snapshots (low z to high z) and calculate tau.\n for snapnum in np.arange(len(model_mass_frac) - 2, -1, -1):\n\n this_z = model_z[snapnum]\n prev_z = model_z[snapnum + 1]\n\n # Hubble Parameter in Mpc/s/Mpc.\n H = av.Hubble_Param(this_z, model_h, model_OM) / (av.pc_to_m * 1.0e6 / 1.0e3)\n numerator = ((1 + this_z) **2) * (1.0 - model_mass_frac[snapnum])\n \n model_tau[snapnum] = model_tau[snapnum+1] + (( numerator / H) * (this_z - prev_z) * (1 + model_helium/(4 * (1-model_helium)))) \n\n model_tau *= av.n_HI(0, model_h, model_OB, model_helium) * av.c_in_ms * av.Sigmat\n\n tau.append(model_tau)\n\n return tau","def trap_knobs():\n print('Executing trap_knobs...')\n #0) Define parameters and heck to see what scripts have been run\n from project_parameters import save,debug,trapFile,elMap,electrodes,multipoles,name,simulationDirectory,reg,trapType\n #from all_functions import nullspace,plotN\n import pickle\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n if trap.configuration.expand_field!=True:\n return 'You must run expand_field first!'\n if trap.configuration.trap_knobs and not debug.trap_knobs:\n return 'Already executed trap_knobs.'\n #1) redefine parameters with shorthand and run sanity checks\n totE = len(electrodes) # numTotalElectrodes\n totM = len(multipoles) # numTotalMultipoles\n assert totE == trap.numElectrodes # Make sure that the total number of electrodes includes the RF.\n tc = trap.configuration\n mc = tc.multipoleCoefficients # this is the original, maximum-length multipole coefficients matrix (multipoles,electrodes)\n for row in range(totM):\n #row+=1\n if abs(np.sum(mc[row,:])) < 10**-50: # arbitrarily small\n return 'trap_knobs: row {} is all 0, can not solve least square, stopping trap knobs'.format(row)\n #2) Apply electrode mapping by clearing some electrodes and adding them to the new map\n # mapping one to an unused electrode should turn it off as well\n for index in range(totE):\n if index != elMap[index]:\n mc[:,elMap[index]] += mc[:,index] # combine the electrode to its mapping\n electrodes[index] = 0 # clear the mapped electrode, implemented in part 3\n useE = int(np.sum(electrodes)) # numUsedElectrodes\n useM = int(np.sum(multipoles)) # numUsedMultipoles\n eo = np.sqrt(useM)-1 # expansionOrder\n #3) build a reduced array of multipole coefficients to invert\n MC = np.zeros((useM,useE)) # reduced matrix to build up and invert\n ML = 0\n for ml in range(totM):\n if multipoles[ml]:\n EL = 0 # clear the electrode indexing before looping through electrodes again for new multipole\n for el in range(totE):\n if electrodes[el]:\n MC[ML,EL] = mc[ml,el]\n EL += 1\n ML += 1\n print('trap_knobs: with electrode and multipole constraints, the coefficient matrix size is ({0},{1}).'.format(MC.shape[0],MC.shape[1]))\n #4) numerially invert the multipole coefficients to get the multipole controls, one multipole at a time\n # solve the equation MC*A = B, where the matrix made from all A vectors is C\n C = np.zeros((useE,useM)) # multipole controls (electrodes,multipoles) will be the inverse of multipole coefficents \n for mult in range(useM):\n B = np.zeros(useM)\n B[mult] = 1\n A = np.linalg.lstsq(MC,B)[0]\n C[:,mult] = A\n #5) calculate the nullspace and regularize if the coefficients matrix is sufficiently overdetermined\n if useM < useE:\n K = nullspace(MC)\n else:\n print('There is no nullspace because the coefficient matrix is rank deficient.\\nThere can be no regularization.')\n K = None\n reg = False\n if reg:\n for mult in range(useM):\n Cv = C[:,mult].T\n Lambda = np.linalg.lstsq(K,Cv)[0]\n test=np.dot(K,Lambda)\n C[:,mult] = C[:,mult]-test \n #6) expand the matrix back out again, with zero at all unused electrodes and multipoles; same as #3 with everything flipped\n c = np.zeros((totE,totM)) \n EL = 0\n for el in range(totE):\n if electrodes[el]:\n ML = 0 # clear the electrode indexing before looping through electrodes again for new multipole\n for ml in range(totM):\n if multipoles[ml]:\n c[el,ml] = C[EL,ML]\n ML += 1\n EL += 1\n #7) plot the multipole controls in teh trap geometry\n if debug.trap_knobs:\n for ml in range(totM):\n if multipoles[ml]:\n plot = c[:,ml]\n plotN(plot,trap,'Multipole {}'.format(ml)) \n #8) update instance configuration with multipole controls to be used bu dc_voltages in post_process_trap\n tc.multipoleKernel = K\n tc.multipoleControl = c\n tc.trap_knobs = True\n trap.configuration=tc\n #8.5) change the order of the columns of c for labrad\n # originally (after constant) Ez,Ey,Ex,U3,U4,U2,U5,U1,...,Y4-4,...,Y40,...,Y44\n # we want it to be Ex,Ey,Ez,U2,U1,U3,U5,U4,...,Y40 and then end before any of the other 4th order terms\n# cc = c.copy()\n# cc[:,1] = c[:,3] # Ex\n# cc[:,2] = c[:,1] # Ey\n# cc[:,3] = c[:,2] # Ez\n# cc[:,4] = c[:,6] # U2\n# cc[:,5] = c[:,8]\n# cc[:,6] = 0 # U3\n# cc[:,16]= c[:,20]# Y40\n# cc[:,20]= 0\n cc = c\n #9) save trap and save c as a text file (called the \"C\" file for Labrad)\n if save: \n print('Saving '+name+' as a data structure...')\n with open(trapFile,'wb') as f:\n pickle.dump(trap,f)\n #ct = cc[1:totE,1:17] #eliminate the RF electrode and constant multipole; eliminate everything past Y40\n ct = cc[1:totE,1:25] # only eliminate the constant\n text = np.zeros((ct.shape[0])*(ct.shape[1]))\n for j in range(ct.shape[1]):\n for i in range(ct.shape[0]):\n text[j*ct.shape[0]+i] = ct[i,j]\n np.savetxt(simulationDirectory+name+'.txt',text,delimiter=',')\n return 'Completed trap_knobs.'","def rayShooting():\r\n \r\n \r\n if nbRay==1:\r\n maxi=1\r\n mini=1\r\n peaceofAngle=angleMax\r\n #to trace one ray at angleMax\r\n else:\r\n maxi=(nbRay-1)/2\r\n mini=-maxi\r\n peaceofAngle=2*angleMax/(nbRay-1)\r\n #to trace rays at regular intervals between [-angleMax;angleMax] \r\n\r\n tot=0 #to count the number of peace of ray\r\n indice=0 #to browse raysIndex\r\n\r\n raysMatrix=np.empty(shape=(0,5),dtype=np.float64)#will contain all the rays in a row\r\n raysIndex=np.empty(shape=(nbRay,),dtype=np.int16)#indexation of the rays in raysMatrix\r\n \r\n for i in np.arange(mini,maxi+1,1):#put maxi+1 to include maxi in the loop\r\n \r\n rayon=Rayon(source.position,angleToVector(peaceofAngle*i))#rayon is\r\n #the ray we will trace\r\n ray,compt=traceRay(rayon)\r\n tot+=(compt+1)\r\n\r\n \r\n raysIndex[indice]=tot #the rays index contains the indice just above\r\n #of the end of the i th ray\r\n\r\n raysMatrix=np.vstack((raysMatrix,ray))\r\n #the form of the ray matrix is a stack of peace of rays describe by\r\n #a,b,c,x1,reflexion. the polynome of the peace of ray being ax^2+bx+c and the\r\n #abscisses of the limiting point being x1, reflexion indicating if a reflexion happened\r\n #when we meet a 5-uple with a coefficient b or c infinite it means\r\n #a new ray begin\r\n \r\n indice+=1\r\n print(\"ray at indice\",i,\"and at angle\",peaceofAngle*i/np.pi*180,'degree(s)')\r\n \r\n print(\"the total number of peaces of ray is :\", tot)\r\n\r\n return(raysMatrix,raysIndex)","def decode_patches_from_roi(self, nr_patches = 100, roi = 'V1', stim_stat_threshold = 2.0, prf_stat_threshold = 4.0, prf_ecc_threshold = 0.6, run_type = 'WMM', smoothing_for_PRF = 5, scaling = True, sum_TRs = True,summed_TRs = 3):\n\t\t\n\t\t# first determine individual run duration (to make sure that stimulus timings of all runs are correct)\n\t\trun_duration = []\n\t\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\n\t\t\tniiFile = NiftiImage(self.runFile(stage = 'processed/mri', run = r))\n\t\t\ttr, nr_trs = round(niiFile.rtime*1)/1000.0, niiFile.timepoints\n\t\t\trun_duration.append(tr * nr_trs)\n\t\trun_duration = np.r_[0,np.cumsum(np.array(run_duration))]\n\n\t\t# timing information stimuli\n\t\tpatches_all_trials = []\n\t\trun = 0\n\t\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\n\t\t\tstim_events = np.loadtxt(self.runFile(stage = 'processed/behavior', run = r, extension = '.txt', postFix = ['stim' ,'all','task']))\n\t\t\tpatches_run= np.array([[stim_events[i][2],np.ceil((stim_events[i][0]+ run_duration[run])/tr),np.ceil((stim_events[i][1] +run_duration[run])/tr),stim_events[i][3]] for i in range(stim_events.shape[0])])\n\t\t\tpatches_all_trials.append(patches_run)\n\t\t\trun += 1\n\n\t\tpatches_all_trials = np.vstack(patches_all_trials) # array (patches, timing_memory, timing test)\n\t\tunique_patches = np.unique(patches_all_trials[:,0]) # array (unique_patches)\n\n\t\t# numpy array of all patches from stimulus pool (also rotated by 90 degrees to check orientation space of PRF)\n\t\tpatches = np.array(self.rescale_images())\n\t\tpatches_rotated = [patches]\n\t\tfor i in range(3):\n\t\t\tpatches_rotated.append(np.array([np.rot90(p) for p in patches_rotated[-1]]))\n\t\tpatches_rotated = np.array(patches_rotated) \n\t\t\n\t\t# brain data\n\t\tself.hdf5_filename = os.path.join(self.conditionFolder(stage = 'processed/mri', run = self.runList[self.conditionDict[run_type][0]]), run_type + '.hdf5')\n\t\tself.logger.info('data from table file ' + self.hdf5_filename)\n\t\th5file = open_file(self.hdf5_filename, mode = 'r')\t\t\n\n\t\tstim_stats = self.roi_data_from_hdf(h5file, roi, data_type = 'zstat1', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = False)\n\t\tprfs = self.roi_data_from_hdf(h5file, roi, data_type = 'all_coefs', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)\n\t\tprf_stats = self.roi_data_from_hdf(h5file, roi, data_type = 'all_corrs', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)\n\t\t\n\t\tprf_ecc = self.roi_data_from_hdf(h5file, roi, data_type = 'all_results', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)[:,1]\n\n\t\ttimeseries = []\n\t\tfor i in range(len(self.conditionDict[run_type])):\n\t\t\ttimeseries.append(self.roi_data_from_hdf(h5file, roi, data_type = 'tf_psc_data', run = self.runList[self.conditionDict[run_type][i]], postFix = ['mcf','sgtf'], combined = False, prf = False))\n\t\ttimeseries = np.concatenate(timeseries, axis = 1) \n\n\t\t# select voxels based on prf and stimulus contrasts\n\t\tprfs = prfs[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),:] \n\t\ttimeseries = timeseries[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),:] \n\t\tprf_stats = prf_stats[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),1]\n\n\t\t#create stim mask (based on eccentricity mask and atend centre mask)\n\t\tcentre_x, centre_y = patches.shape[1]/2,patches.shape[1]/2\n\n\t\ty,x = np.ogrid[-centre_x:patches.shape[1]-centre_x, -centre_y:patches.shape[1]-centre_y]\n\t\tmask_ecc = x*x + y*y <= (prf_ecc_threshold*patches.shape[1]/2)**2\n\n\t\ty,x = np.ogrid[-centre_x:patches.shape[1]-centre_x, -centre_y:patches.shape[1]-centre_y]\n\t\tmask_centre = x*x + y*y >= 4**2\n\n\t\tmask_stim = (mask_ecc.ravel()*mask_centre.ravel())\t\n\n\t\t# create scaling matrix (identity matrix) of nr_voxels by nr_voxels\n\t\tif scaling:\n\t\t\tscaling_matrix = np.eye(prfs.shape[0])\n\t\t\tfor i in range(len(prf_stats)):\n\t\t\t\tscaling_matrix[i,i] = prf_stats[i]\n\n\n\t\t# based on prfs and scaling factor create brain patches (either for single TRs or for summed TRS)\t\t\n\t\tbrain_patches = np.mat(timeseries.T) * np.mat(scaling_matrix) * np.mat(prfs) \n\t\tif sum_TRs == True:\n\t\t\ttimeseries_summed = np.array([np.sum(timeseries.T[i:i+summed_TRs,:],axis = 0) for i in range(timeseries.shape[1]) if i < timeseries.shape[1] - summed_TRs])\n\t\t\tbrain_patches= np.mat(timeseries_summed) * np.mat(scaling_matrix) * np.mat(prfs)\n \n\t\t# per TR (either summed or individual) calculate correlations between brain patch and stimulus input\n\t\tpatch_correlations = np.zeros((4, brain_patches.shape[0], patches.shape[0]))\n\n\t\tfor k in range(4): # 4 orientations\n\t\t\tfor i, t in enumerate(np.array(brain_patches)): # all brain_patches\n\t\t\t\tpatch_correlations[k, i, :] = np.array([pearsonr(t[mask_stim], patch.ravel()[mask_stim])[0] for patch in patches_rotated[k]])\n\n\t\t# for analysis only look at correlations for all patches that were presented in this subjects session\n\t\tpatch_index_unique = np.array([j for i in patches_all_trials[:,0] for j in range(len( np.unique(patches_all_trials[:,0]))) if i == np.unique(patches_all_trials[:,0])[j]])\t\n\t\tpatch_correlations_unique = patch_correlations[:,:,np.array(unique_patches, dtype = int)]\n\n\t\t# show correlation matrix of patches for all rotations (dots represent stimulus that was present)\n\t\tfig, axes = plt.subplots(2,2)\n\t\tfor i in range(patches_rotated.shape[0]):\n\t\t\tax = axes.flat[i]\n\t\t\tax.imshow(patch_correlations_unique[i,:,:].T,vmin = np.min(patch_correlations_unique), vmax = np.max(patch_correlations_unique))\n\t\t\tax.scatter(patches_all_trials[:,1],patch_index_unique)\n\t\t\tax.set_title(\"rotation_\" + str(i*90))\n\n\t\t# show decoding evidence across runs for all unique patches\t\n\t\tfig, axes = plt.subplots(2,2)\t\n\t\tfor i in range(patches_rotated.shape[0]):\n\t\t\tax = axes.flat[i]\n\t\t\tax.hist(np.argmax(patch_correlations_unique[i,:,:], axis = 1), bins = patch_correlations_unique.shape[2] + 1)\n\t\t\tax.set_ylim([0,600])\n\t\t\tax.set_title(\"rotation_\" + str(i*90))\n\t\t\n\t\t# show brain_patches and stimulus input\t\t\n\t\tmask_stim = np.array([not i for i in mask_stim]).reshape(patches.shape[1],patches.shape[1]) # flip stim mask for graphing purposes\n\n\t\tindex_att_patch = patches_all_trials[:,3] == 0\n\t\tindex_att_center = patches_all_trials[:,3] == 1 \n\n\n\t\t# calculate decoding performance\n\t\tcorrelations_unique_TR_patch = np.array([patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_patch]+ TR][:,:,np.array(unique_patches, dtype = int)] for TR in range(2,5)])\n\t\tcorrelations_unique_TR_center = np.array([patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_center]+ TR][:,:,np.array(unique_patches, dtype = int)] for TR in range(2,5)])\n\n\t\t# check relative position of presented patch compared to other unique patches\n\t\tpatch_dict = {}\n\t\tcenter_dict = {}\n\t\tfor condition in ['patch' ,'center']:\n\t\t\tfor TR in range(correlations_unique_TR_patch.shape[0]):\n\t\t\t\tfor rotation in range(correlations_unique_TR_patch.shape[1]):\n\t\t\t\t\tif condition == 'patch':\n\t\t\t\t\t\tprint 'update'\n\t\t\t\t\t\tdecode = np.array([int(np.where(np.argsort(correlations_unique_TR_patch[TR,rotation,i])==patch_index_unique[index_att_patch][i])[0]) for i in range(correlations_unique_TR_patch.shape[2])])\n\t\t\t\t\t\tpatch_dict.update({'RT_' + str(TR) + '_Rotate_' + str(rotation):[decode, np.mean(decode),np.where(decode == np.max(decode))]})\n\t\t\t\t\telif condition == 'center':\t\n\t\t\t\t\t\tdecode = np.array([int(np.where(np.argsort(correlations_unique_TR_center[TR,rotation,i])==patch_index_unique[index_att_patch][i])[0]) for i in range(correlations_unique_TR_patch.shape[2])])\n\t\t\t\t\t\tcenter_dict.update({'RT_' + str(TR) + '_Rotate_' + str(rotation):[decode, np.mean(decode),np.where(decode == np.max(decode))]})\n\n\t\tshell()\n\t\t# show decoding performance\n\t\tfor TR in [2]:\n\t\t\tcorrelations_unique_TR_patch = patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_patch]+ TR][:,:,np.array(unique_patches, dtype = int)]\n\t\t\tcorrelations_unique_TR_cent = patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_cent]+ TR][:,:,np.array(unique_patches, dtype = int)]\n\n\t\t\tfor cond in ['patch','center']:\n\t\t\t\tfig, axes = plt.subplots(2,2)\n\t\t\t\tfor k in range(4): # 4 rotations\n\t\t\t\t\tax = axes.flat[k]\n\t\t\t\t\tax.set_title(\"rotation_\" + str(k*90))\n\t\t\t\t\tif cond == 'patch':\n\t\t\t\t\t\tax.hist([np.argsort(correlations_unique_TR_patch[k][i])[patch_index_unique[i]] for i in range(len(patch_index_unique)/2)], alpha = 0.3, color = ['r','g','b','k'][k], bins = 44, cumulative = True, histtype = 'step')\n\t\t\t\t\telif cond == 'center':\n\t\t\t\t\t\tax.hist([np.argsort(correlations_unique_TR_cent[k][i])[patch_index_unique[i]] for i in range(len(patch_index_unique)/2)], alpha = 0.3, color = ['r','g','b','k'][k], bins = 44, cumulative = True, histtype = 'step')\n\n\t\tif sum_TRs == True:\n\t\t\tbpr = np.array(brain_patches).reshape((timeseries.shape[1] - summed_TRs,patches.shape[1],patches.shape[1])) # brain patch reshaped to PRF space, length is nr of TRS\n\t\telse:\n\t\t\tbpr = np.array(brain_patches).reshape((timeseries.shape[1],patches.shape[1],patches.shape[1]))\n\t\t\t\n\t\t\n\t\ttr_bpr_patch = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)[index_att_patch]+i] for i in range(2,5)]) #brain patches for different timepoints \n\t\ttr_bpr_cent = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)[index_att_cent]+i] for i in range(2,5)])\n\n\t\t\n\t\t# show decoding attend patches condition\n\t\n\n\n\t\tpatch = 0\n\t\ttest = [i for i in range(index_att_patch.shape[0]) if index_att_patch[i]]\n\t\tfor i in test:\n\t\t\tprint i, patch\n\t\t\tf = pl.figure()\n\t\t\tpatch += 1\n\t\t\t#f = pl.figure()\n\t\t\t#s = f.add_subplot(241, aspect = 'equal')\n\t\t\t\n\t\t\t#tr_bpr_patch[0,patch][mask_stim] = 0\n\t\t\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\t#s = f.add_subplot(242, aspect = 'equal')\n\t\t\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\t#s = f.add_subplot(243, aspect = 'equal')\n\t\t\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\t#s = f.add_subplot(244, aspect = 'equal')\n\t\t\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\t#s = f.add_subplot(245, aspect = 'equal')\n\t\t\t\n\t\t\t#this_patch = patches_rotated[0,int(patches_all_trials[i][0])]\n\t\t\t#this_patch[mask_stim] = 0\n\t\t\t#pl.imshow(this_patch)\n\t\t\t#s = f.add_subplot(246, aspect = 'equal')\n\t\t\t#this_patch = patches_rotated[1,int(patches_all_trials[i][0])]\n\t\t\t#this_patch[mask_stim] = 0\n\t\t\t#pl.imshow(this_patch)\n\t\t\t#s = f.add_subplot(247, aspect = 'equal')\n\t\t\t#this_patch = patches_rotated[2,int(patches_all_trials[i][0])]\n\t\t\t#this_patch[mask_stim] = 0\n\t\t\t#pl.imshow(this_patch)\n\t\t\t#s = f.add_subplot(248, aspect = 'equal')\n\t\t\t#this_patch = patches_rotated[3,int(patches_all_trials[i][0])]\n\t\t\t#this_patch[mask_stim] = 0\n\t\t\t#pl.imshow(this_patch)\n\n\n\n\n\n\n\n\t\ttr_bpr = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)+i] for i in range(2,5)]) #brain patches for different timepoints \n\t\t\n\t\tfor i in []:\n\t\t\tf = pl.figure()\n\t\t\ts = f.add_subplot(241, aspect = 'equal')\n\t\t\ttr_bpr[0,i][mask_stim] = 0\n\t\t\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\ts = f.add_subplot(242, aspect = 'equal')\n\t\t\ttr_bpr[0,i][mask_stim] = 0\n\t\t\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\ts = f.add_subplot(243, aspect = 'equal')\n\t\t\ttr_bpr[0,i][mask_stim] = 0\n\t\t\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\ts = f.add_subplot(244, aspect = 'equal')\n\t\t\ttr_bpr[0,i][mask_stim] = 0\n\t\t\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\n\t\t\ts = f.add_subplot(245, aspect = 'equal')\n\t\t\tpatch = patches_rotated[0,np.array(patches_all_trials[:,0], dtype = int)][i]\n\t\t\tpatch[mask_stim] = 0\n\t\t\tpl.imshow(patch)\n\t\t\ts = f.add_subplot(246, aspect = 'equal')\n\t\t\tpatch = patches_rotated[1,np.array(patches_all_trials[:,0], dtype = int)][i]\n\t\t\tpatch[mask_stim] = 0\n\t\t\tpl.imshow(patch)\n\t\t\ts = f.add_subplot(247, aspect = 'equal')\n\t\t\tpatch = patches_rotated[2,np.array(patches_all_trials[:,0], dtype = int)][i]\n\t\t\tpatch[mask_stim] = 0\n\t\t\tpl.imshow(patch)\n\t\t\ts = f.add_subplot(248, aspect = 'equal')\n\t\t\tpatch = patches_rotated[3,np.array(patches_all_trials[:,0], dtype = int)][i]\n\t\t\tpatch[mask_stim] = 0\n\t\t\tpl.imshow(patch)\n\n\t\tpl.show()","def behaviour03(prior_propia): \r\n count = 0\r\n global stop\r\n while not stop:\r\n results = process_image(cameraData,prior_propia)\r\n if count > 3:\r\n count = 0\r\n print(\"hay estos pixeles verdes\")\r\n print(results)\r\n else:\r\n count = count + 1","def process(self, im, include=True):\n full_im = im - self.bias # remove the bias offset, it's arbitrary\n try:\n self.im_vals = full_im * self.mask # get the ROI\n not_roi = full_im * (1-self.mask)\n except ValueError as e:\n s0 = np.shape(self.mask)\n self.im_vals = np.zeros(s0)\n not_roi = np.zeros(s0)\n include = False\n s1 = np.shape(im)\n error(\"Received image was wrong shape (%s,%s) for analyser's ROI (%s,%s)\"%(\n s1[0],s1[1],s0[0],s0[1]))\n # background statistics: mean count and standard deviation across image\n N = np.sum(1-self.mask)\n self.stats['Mean bg count'].append(np.sum(not_roi) / N)\n self.stats['Bg s.d.'].append(\n np.sqrt(np.sum((not_roi - self.stats['Mean bg count'][-1])**2) / (N - 1)))\n # sum of counts in the ROI of the image gives the signal\n self.stats['Counts'].append(np.sum(self.im_vals)) \n # file ID number should be updated externally before each event\n self.stats['File ID'].append(self.fid)\n # the pixel value at the centre of the ROI\n try:\n self.stats['ROI centre count'].append(full_im[self.xc, self.yc])\n except IndexError as e:\n error('ROI centre (%s, %s) outside of image size (%s, %s)'%(\n self.xc, self.yc, self.pic_width, self.pic_height))\n self.stats['ROI centre count'].append(0)\n # position of the (first) max intensity pixel\n xmax, ymax = np.unravel_index(np.argmax(full_im), full_im.shape)\n self.stats['Max xpos'].append(xmax)\n self.stats['Max ypos'].append(ymax)\n self.stats['Include'].append(include)\n if np.size(self.bins):\n index = np.where(self.bins > self.stats['Counts'][-1])\n try:\n self.occs[index[0]] += 1\n except IndexError:\n self.occs[-1] += 1\n self.ind += 1","def freespaceImageAnalysis( fids, guesses = None, fit=True, bgInput=None, bgPcInput=None, shapes=[None], zeroCorrection=0, zeroCorrectionPC=0,\n keys=None, fitModule=bump, extraPicDictionaries=None, newAnnotation=False, onlyThisPic=None, pltVSize=5, \n plotSigmas=False, plotCounts=False, manualColorRange=None, calcTemperature=False, clearOutput=True, \n dataRange=None, guessTemp=10e-6, trackFitCenter=False, picsPerRep=1, startPic=0, binningParams=None, \n win=pw.PictureWindow(), transferAnalysisOpts=None, tferBinningParams=None, tferWin= pw.PictureWindow(),\n extraTferAnalysisArgs={}, emGainSetting=300, lastConditionIsBackGround=True, showTferAnalysisPlots=True,\n show2dFitsAndResiduals=True, plotFitAmps=False, indvColorRanges=False, fitF2D=gaussian_2d.f_notheta, \n rmHighCounts=True, useBase=True, weightBackgroundByLoading=True, returnPics=False, forceNoAnnotation=False):\n fids = [fids] if type(fids) == int else fids\n keys = [None for _ in fids] if keys is None else keys\n sortedStackedPics = {}\n initThresholds = [None]\n picsForBg = []\n bgWeights = []\n isAnnotatedList = []\n for filenum, fid in enumerate(fids):\n if transferAnalysisOpts is not None:\n res = ta.stage1TransferAnalysis( fid, transferAnalysisOpts, useBase=useBase, **extraTferAnalysisArgs )\n (initAtoms, tferAtoms, initAtomsPs, tferAtomsPs, key, keyName, initPicCounts, tferPicCounts, repetitions, initThresholds,\n avgPics, tferThresholds, initAtomImages, tferAtomImages, basicInfoStr, ensembleHits, groupedPostSelectedPics, isAnnotated) = res\n isAnnotatedList.append(isAnnotated)\n # assumes that you only want to look at the first condition. \n for varPics in groupedPostSelectedPics: # don't remember why 0 works if false...\n picsForBg.append(varPics[-1 if lastConditionIsBackGround else 0])\n bgWeights.append(len(varPics[0]))\n allFSIPics = [ varpics[0][startPic::picsPerRep] for varpics in groupedPostSelectedPics]\n if showTferAnalysisPlots:\n fig, axs = plt.subplots(1,2)\n mp.makeAvgPlts( axs[0], axs[1], avgPics, transferAnalysisOpts, ['r','g','b'] ) \n allFSIPics = [win.window( np.array(pics) ) for pics in allFSIPics]\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\n elif type(fid) == int:\n ### For looking at either PGC imgs or FSI imgs \n with exp.ExpFile(fid) as file:\n # I think this only makes sense if there is a specific bg pic in the rotation\n picsForBg.append(list(file.get_pics()))\n allFSIPics = file.get_pics()[startPic::picsPerRep]\n _, key = file.get_key()\n if len(np.array(key).shape) == 2:\n key = key[:,0]\n file.get_basic_info()\n allFSIPics = win.window( allFSIPics )\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\n allFSIPics = np.reshape( allFSIPics, (len(key), int(allFSIPics.shape[0]/len(key)), allFSIPics.shape[1], allFSIPics.shape[2]) )\n else:\n ### Assumes given pics have the same start pic and increment (picsPerRep).\n # doesn't combine well w/ transfer analysis\n picsForBg.append(fid)\n allFSIPics = fid[startPic::picsPerRep]\n print(\"Assuming input is list of all pics, then splices to get FSI pics. Old code assumed the given were FSI pics.\")\n allFSIPics = win.window( allFSIPics )\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\n allFSIPics = np.reshape( allFSIPics, (len(key), int(allFSIPics.shape[0]/len(key)), allFSIPics.shape[1], allFSIPics.shape[2]) )\n # ##############\n if keys[filenum] is not None:\n key = keys[filenum]\n for i, keyV in enumerate(key):\n keyV = misc.round_sig_str(keyV)\n sortedStackedPics[keyV] = np.append(sortedStackedPics[keyV], allFSIPics[i],axis=0) if (keyV in sortedStackedPics) else allFSIPics[i] \n if lastConditionIsBackGround:\n bgInput, pcBgInput = getBgImgs(picsForBg, startPic = startPic, picsPerRep = picsPerRep, rmHighCounts=rmHighCounts, bgWeights=bgWeights, \n weightBackgrounds=weightBackgroundByLoading)\n elif bgInput == 'lastPic':\n bgInput, pcBgInput = getBgImgs(picsForBg, startPic = picsPerRep-1, picsPerRep = picsPerRep, rmHighCounts=rmHighCounts, bgWeights=bgWeights,\n weightBackgrounds=weightBackgroundByLoading )\n if bgInput is not None: # was broken and not working if not given bg\n bgInput = win.window(bgInput)\n bgInput = ah.softwareBinning(binningParams, bgInput)\n if bgPcInput is not None:\n bgPcInput = win.window(bgPcInput)\n bgPcInput = ah.softwareBinning(binningParams, bgPcInput) \n \n if extraPicDictionaries is not None:\n if type(extraPicDictionaries) == dict:\n extraPicDictionaries = [extraPicDictionaries]\n for dictionary in extraPicDictionaries:\n for keyV, pics in dictionary.items():\n sortedStackedPics[keyV] = (np.append(sortedStackedPics[keyV], pics,axis=0) if keyV in sortedStackedPics else pics) \n sortedStackedKeyFl = [float(keyStr) for keyStr in sortedStackedPics.keys()]\n sortedKey, sortedStackedPics = ah.applyDataRange(dataRange, sortedStackedPics, list(sorted(sortedStackedKeyFl)))\n numVars = len(sortedStackedPics.items())\n if len(np.array(shapes).shape) == 1:\n shapes = [shapes for _ in range(numVars)] \n if guesses is None:\n guesses = [[None for _ in range(4)] for _ in range(numVars)]\n if len(np.array(bgInput).shape) == 2 or bgInput == None:\n bgInput = [bgInput for _ in range(numVars)]\n if len(np.array(bgPcInput).shape) == 2 or bgPcInput == None:\n bgPcInput = [bgPcInput for _ in range(numVars)]\n \n datalen, avgFitSigmas, images, hFitParams, hFitErrs, vFitParams, vFitErrs, fitParams2D, fitErrs2D = [{} for _ in range(9)]\n titles = ['Bare', 'Photon-Count', 'Bare-mbg', 'Photon-Count-mbg']\n assert(len(sortedKey)>0)\n for vari, keyV in enumerate(sortedKey):\n keyV=misc.round_sig_str(keyV)\n if vari==0:\n initKeyv = keyV\n varPics = sortedStackedPics[keyV]\n # 0 is init atom pics for post-selection on atom number... if we wanted to.\n expansionPics = rmHighCountPics(varPics,7000) if rmHighCounts else varPics\n datalen[keyV] = len(expansionPics)\n expPhotonCountImage = photonCounting(expansionPics, 120)[0] / len(expansionPics)\n bgPhotonCountImage = np.zeros(expansionPics[0].shape) if bgPcInput[vari] is None else bgPcInput[vari]\n expAvg = np.mean(expansionPics, 0)\n bgAvg = np.zeros(expansionPics[0].shape) if (bgInput[vari] is None or len(bgInput[vari]) == 1) else bgInput[vari]\n \n if bgPhotonCountImage is None:\n print('no bg photon', expAvg.shape)\n bgPhotonCount = np.zeros(photonCountImage.shape)\n avg_mbg = expAvg - bgAvg\n avg_mbgpc = expPhotonCountImage - bgPhotonCountImage\n images[keyV] = [expAvg, expPhotonCountImage, avg_mbg, avg_mbgpc]\n hFitParams[keyV], hFitErrs[keyV], vFitParams[keyV], vFitErrs[keyV], fitParams2D[keyV], fitErrs2D[keyV] = [[] for _ in range(6)]\n for imnum, (im, guess) in enumerate(zip(images[keyV], guesses[vari])):\n if fit:\n # fancy guess_x and guess_y values use the initial fitted value, typically short time, as a guess.\n _, pictureFitParams2d, pictureFitErrors2d, v_params, v_errs, h_params, h_errs = ah.fitPic(\n im, guessSigma_x=5, guessSigma_y=5, showFit=False, \n guess_x=None if vari==0 else fitParams2D[initKeyv][imnum][1], guess_y=None if vari==0 else fitParams2D[initKeyv][imnum][2],\n fitF=fitF2D)\n fitParams2D[keyV].append(pictureFitParams2d)\n fitErrs2D[keyV].append(pictureFitErrors2d)\n hFitParams[keyV].append(h_params)\n hFitErrs[keyV].append(h_errs)\n vFitParams[keyV].append(v_params)\n vFitErrs[keyV].append(v_errs)\n # conversion from the num of pixels on the camera to microns at the focus of the tweezers\n cf = 16e-6/64\n mins, maxes = [[], []]\n imgs_ = np.array(list(images.values()))\n for imgInc in range(4):\n if indvColorRanges:\n mins.append(None)\n maxes.append(None)\n elif manualColorRange is None:\n mins.append(min(imgs_[:,imgInc].flatten()))\n maxes.append(max(imgs_[:,imgInc].flatten()))\n else:\n mins.append(manualColorRange[0])\n maxes.append(manualColorRange[1])\n numVariations = len(images)\n if onlyThisPic is None:\n fig, axs = plt.subplots(numVariations, 4, figsize=(20, pltVSize*numVariations))\n if numVariations == 1:\n axs = np.array([axs])\n bgFig, bgAxs = plt.subplots(1, 2, figsize=(20, pltVSize))\n else:\n numRows = int(np.ceil((numVariations+3)/4))\n fig, axs = plt.subplots(numRows, 4 if numVariations>1 else 3, figsize=(20, pltVSize*numRows))\n avgPicAx = axs.flatten()[-3]\n avgPicFig = fig\n bgAxs = [axs.flatten()[-1], axs.flatten()[-2]]\n bgFig = fig\n if show2dFitsAndResiduals:\n fig2d, axs2d = plt.subplots(*((2,numVariations) if numVariations>1 else (1,2)))\n keyPlt = np.zeros(len(images))\n (totalSignal, hfitCenter, hFitCenterErrs, hSigmas, hSigmaErrs, h_amp, hAmpErrs, vfitCenter, vFitCenterErrs, vSigmas, vSigmaErrs, v_amp, \n vAmpErrs, hSigma2D, hSigma2dErr, vSigma2D, vSigma2dErr) = [np.zeros((len(images), 4)) for _ in range(17)]\n \n for vari, ((keyV,ims), hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D) in enumerate(zip(\n images.items(), *[dic.values() for dic in [hFitParams, hFitErrs, vFitParams, vFitErrs, fitParams2D, fitErrs2D]])):\n for which in range(4):\n if onlyThisPic is None:\n (im, ax, title, min_, max_, hparams, hErrs, vparams, vErrs, param2d, err2d\n ) = [obj[which] for obj in (ims, axs[vari], titles, mins, maxes, hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D)] \n else:\n which = onlyThisPic\n ax = axs.flatten()[vari]\n (im, title, min_, max_, hparams, hErrs, vparams, vErrs, param2d, err2d\n ) = [obj[which] for obj in (ims, titles, mins, maxes, hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D)] \n h_amp[vari][which], hfitCenter[vari][which], hSigmas[vari][which] = hparams[0], hparams[1], hparams[2]*cf*1e6\n hAmpErrs[vari][which], hFitCenterErrs[vari][which], hSigmaErrs[vari][which] = hErrs[0], hErrs[1], hErrs[2]*cf*1e6\n v_amp[vari][which], vfitCenter[vari][which], vSigmas[vari][which] = vparams[0], vparams[1], vparams[2]*cf*1e6\n vAmpErrs[vari][which], vFitCenterErrs[vari][which], vSigmaErrs[vari][which] = vErrs[0], vErrs[1], vErrs[2]*cf*1e6\n hSigma2D[vari][which], hSigma2dErr[vari][which], vSigma2D[vari][which], vSigma2dErr[vari][which] = [\n val*cf*1e6 for val in [param2d[-3], err2d[-3], param2d[-2], err2d[-2]]]\n \n totalSignal[vari][which] = np.sum(im.flatten())\n keyPlt[vari] = keyV\n res = mp.fancyImshow(fig, ax, im, imageArgs={'cmap':dark_viridis_cmap, 'vmin':min_, 'vmax':max_}, \n hFitParams=hparams, vFitParams=vparams, fitModule=fitModule, flipVAx = True, fitParams2D=param2d)\n ax.set_title(keyV + ': ' + str(datalen[keyV]) + ';\\n' + title + ': ' + misc.errString(hSigmas[vari][which],hSigmaErrs[vari][which]) \n + r'$\\mu m$ sigma, ' + misc.round_sig_str(totalSignal[vari][which],5), fontsize=12) \n if show2dFitsAndResiduals:\n X, Y = np.meshgrid(np.arange(len(im[0])), np.arange(len(im)))\n data_fitted = fitF2D((X,Y), *param2d)\n fitProper = data_fitted.reshape(im.shape[0],im.shape[1])\n ax1 = axs2d[0] if numVariations == 1 else axs2d[0,vari]\n ax2 = axs2d[1] if numVariations == 1 else axs2d[1,vari]\n imr = ax1.imshow(fitProper, vmin=min_, vmax=max_)\n mp.addAxColorbar(fig2d, ax1, imr)\n ax1.contour(np.arange(len(im[0])), np.arange(len(im)), fitProper, 4, colors='w', alpha=0.2)\n imr = ax2.imshow(fitProper-im)\n mp.addAxColorbar(fig2d, ax2, imr)\n ax2.contour(np.arange(len(im[0])), np.arange(len(im)), fitProper, 4, colors='w', alpha=0.2)\n if onlyThisPic is not None:\n break\n \n mp.fancyImshow(avgPicFig, avgPicAx, np.mean([img[onlyThisPic] for img in images.values()],axis=0), imageArgs={'cmap':dark_viridis_cmap},flipVAx = True)\n avgPicAx.set_title('Average Over Variations')\n ### Plotting background and photon counted background\n mp.fancyImshow(bgFig, bgAxs[0], bgAvg, imageArgs={'cmap':dark_viridis_cmap},flipVAx = True) \n bgAxs[0].set_title('Background image (' + str(len(picsForBg)/picsPerRep) + ')')\n mp.fancyImshow(bgFig, bgAxs[1], bgPhotonCountImage, imageArgs={'cmap':dark_viridis_cmap},flipVAx = True) \n bgAxs[1].set_title('Photon counted background image (' + str(len(picsForBg)/picsPerRep) + ')')\n fig.subplots_adjust(left=0,right=1,bottom=0.1, hspace=0.2, **({'top': 0.7, 'wspace': 0.4} if (onlyThisPic is None) else {'top': 0.9, 'wspace': 0.3}))\n \n disp.display(fig)\n temps, tempErrs, tempFitVs, = [],[],[]\n if calcTemperature: \n for sigmas, sigmaerrs in zip([hSigmas, vSigmas, hSigma2D, vSigma2D],[hSigmaErrs, vSigmaErrs, hSigma2dErr, vSigma2dErr]):\n mbgSigmas = np.array([elt[2] for elt in sigmas])\n mbgSigmaErrs = np.array([elt[2] for elt in sigmaerrs])\n myGuess = [0.0, min((mbgSigmas)*1e-6), guessTemp]\n temp, fitV, cov = ah.calcBallisticTemperature(keyPlt*1e-3, (mbgSigmas)*1e-6, guess = myGuess, sizeErrors = mbgSigmaErrs)\n error = np.sqrt(np.diag(cov))\n temps.append(temp)\n tempErrs.append(error[2])\n tempFitVs.append(fitV)\n numAxisCol = int(plotSigmas) + int(plotCounts) + int(trackFitCenter)\n if numAxisCol != 0:\n fig2, axs = plt.subplots(1, numAxisCol, figsize = (15, 5)) \n fig2.subplots_adjust(top=0.75, wspace = 0.4)\n colors = ['b','k','c','purple']\n if plotSigmas:\n ax = (axs if numAxisCol == 1 else axs[0]) \n stdStyle = dict(marker='o',linestyle='',capsize=3)\n if onlyThisPic is not None:\n ax.errorbar(keyPlt, hSigmas[:,onlyThisPic], hSigmaErrs[:,onlyThisPic], color=colors[0], label='h '+titles[onlyThisPic], **stdStyle);\n ax.errorbar(keyPlt, hSigma2D[:,onlyThisPic], hSigma2dErr[:,onlyThisPic], color=colors[1], label='2dh '+titles[onlyThisPic], **stdStyle);\n ax.errorbar(keyPlt, vSigmas[:,onlyThisPic], vSigmaErrs[:,onlyThisPic], color=colors[2], label='v '+titles[onlyThisPic], **stdStyle);\n ax.errorbar(keyPlt, vSigma2D[:,onlyThisPic], vSigma2dErr[:,onlyThisPic], color=colors[3], label='2dv '+titles[onlyThisPic], **stdStyle);\n else:\n for whichPic in range(4):\n ax.errorbar(keyPlt, hSigmas[:,whichPic], hSigmaErrs[:,whichPic], color='b', label='h '+titles[whichPic], **stdStyle);\n ax.errorbar(keyPlt, vSigmas[:,whichPic], vSigmaErrs[:,whichPic], color='c', label='v '+titles[whichPic], **stdStyle);\n ax.set_ylim(max(0,ax.get_ylim()[0]),min([ax.get_ylim()[1],5]))\n ax.set_ylabel(r'Fit Sigma ($\\mu m$)')\n \n if calcTemperature:\n # converting time to s, hSigmas in um \n xPoints = np.linspace(min(keyPlt), max(keyPlt))*1e-3\n for num, fitV in enumerate(tempFitVs):\n #ax.plot(xPoints*1e3, LargeBeamMotExpansion.f(xPoints, *myGuess)*1e6, label = 'guess')\n ax.plot(xPoints*1e3, LargeBeamMotExpansion.f(xPoints, *fitV)*1e6, color=colors[num])\n ax.legend()\n\n if plotFitAmps: \n ax = (axs if numAxisCol == 1 else axs[0])\n ampAx = ax.twinx()\n\n if onlyThisPic is not None:\n ampAx.errorbar(keyPlt, h_amp[:,onlyThisPic], hAmpErrs[:,onlyThisPic], label='h '+titles[onlyThisPic], color = 'orange', **stdStyle);\n ampAx.errorbar(keyPlt, v_amp[:,onlyThisPic], vAmpErrs[:,onlyThisPic], label='v '+titles[onlyThisPic], color = 'r', **stdStyle);\n else:\n for whichPic in range(4):\n ampAx.errorbar(keyPlt, h_amp[:,whichPic], hAmpErrs[:,whichPic], label='h '+titles[whichPic], color = 'orange', **stdStyle);\n ampAx.errorbar(keyPlt, v_amp[:,whichPic], vAmpErrs[:,whichPic], label='v '+titles[whichPic], color = 'r', **stdStyle);\n [tick.set_color('red') for tick in ampAx.yaxis.get_ticklines()]\n [tick.set_color('red') for tick in ampAx.yaxis.get_ticklabels()]\n ampAx.set_ylabel(r'Fit h_amps', color = 'r')\n \n hTotalPhotons, vTotalPhotons = None, None\n if plotCounts:\n # numAxCol = 1: ax = axs\n # numAxCol = 2: plotSigmas + plotCounts -- ax = axs[1]\n # numAxCol = 2: plotCounts + trackFitCenter -- ax = axs[0]\n # numAxCol = 3: ax = axs[1]\n if numAxisCol == 1:\n ax = axs\n elif numAxisCol == 2:\n ax = axs[1 if plotSigmas else 0]\n else:\n ax = axs[1]\n # Create axis to plot photon counts\n ax.set_ylabel(r'Integrated signal')\n photon_axis = ax.twinx()\n # This is not currently doing any correct for e.g. the loading rate.\n countToCameraPhotonEM = 0.018577 / (emGainSetting/200) # the float is is EM200. \n countToScatteredPhotonEM = 0.018577 / 0.07 / (emGainSetting/200)\n\n if onlyThisPic is not None:\n # calculate number of photons\n hamp = h_amp[:,onlyThisPic]*len(expansionPics[0][0]) # Horizontal \"un\"normalization for number of columns begin averaged.\n vamp = v_amp[:,onlyThisPic]*len(expansionPics[0]) \n hsigpx = hSigmas[:,onlyThisPic]/(16/64) # Convert from um back to to pixels.\n vsigpx = vSigmas[:,onlyThisPic]/(16/64)\n htotalCountsPerPic = bump.area_under(hamp, hsigpx)\n vtotalCountsPerPic = bump.area_under(vamp, vsigpx)\n hTotalPhotons = countToScatteredPhotonEM*htotalCountsPerPic\n vTotalPhotons = countToScatteredPhotonEM*vtotalCountsPerPic\n ax.plot(keyPlt, totalSignal[:,onlyThisPic], marker='o', linestyle='', label=titles[onlyThisPic]);\n photon_axis.plot(keyPlt, hTotalPhotons, marker='o', linestyle='', color = 'r', label='Horizontal')\n photon_axis.plot(keyPlt, vTotalPhotons, marker='o', linestyle='', color = 'orange', label='Vertical')\n else:\n for whichPic in range(4):\n # See above comments\n amp = h_amp[:,whichPic]*len(expansionPics[0][0]) \n sig = hSigmas[:,whichPic]/(16/64) \n totalCountsPerPic = bump.area_under(amp, sig)\n hTotalPhotons = countToScatteredPhotonEM*totalCountsPerPic\n ax.plot(keyPlt, totalSignal[:,whichPic], marker='o', linestyle='', label=titles[whichPic]);\n photon_axis.plot(keyPlt, hTotalPhotons, marker='o', linestyle='', color = ['red', 'orange', 'yellow', 'pink'][whichPic]) \n ax.legend()\n photon_axis.legend()\n [tick.set_color('red') for tick in photon_axis.yaxis.get_ticklines()]\n [tick.set_color('red') for tick in photon_axis.yaxis.get_ticklabels()]\n photon_axis.set_ylabel(r'Fit-Based Avg Scattered Photon/Img', color = 'r')\n if trackFitCenter:\n #numaxcol = 1: ax = axs\n #numaxcol = 2: trackfitcenter + plothSigmas: ax = axs[1]\n #numaxcol = 2: trackfitcenter + plotCounts: ax = axs[1]\n #numaxcol = 3: ax = axs[2]\n ax = (axs if numAxisCol == 1 else axs[-1])\n if onlyThisPic is not None:\n #ax.errorbar(keyPlt, hfitCenter[:,onlyThisPic], hFitCenterErrs[:,onlyThisPic], marker='o', linestyle='', capsize=3, label=titles[onlyThisPic]);\n ax.errorbar(keyPlt, vfitCenter[:,onlyThisPic], vFitCenterErrs[:,onlyThisPic], marker='o', linestyle='', capsize=3, label=titles[onlyThisPic]);\n #def accel(t, x0, a):\n # return x0 + 0.5*a*t**2\n #accelFit, AccelCov = opt.curve_fit(accel, keyPlt*1e-3, hfitCenter[:,onlyThisPic], sigma = hFitCenterErrs[:,onlyThisPic])\n #fitx = np.linspace(keyPlt[0], keyPlt[-1])*1e-3\n #fity = accel(fitx, *accelFit)\n #ax.plot(fitx*1e3, fity)\n else:\n for whichPic in range(4):\n ax.errorbar(keyPlt, hfitCenter[:,whichPic], hFitCenterErrs[:,whichPic], marker='o', linestyle='', capsize=3, label=titles[whichPic]);\n #accelErr = np.sqrt(np.diag(AccelCov))\n fig2.legend()\n ax.set_ylabel(r'Fit Centers (pix)')\n ax.set_xlabel('time (ms)')\n \n if numAxisCol != 0:\n disp.display(fig2) \n \n if not forceNoAnnotation:\n for fid, isAnnotated in zip(fids, isAnnotatedList):\n if not isAnnotated:\n if type(fid) == int or type(fid) == type(''):\n if newAnnotation or not exp.checkAnnotation(fid, force=False, quiet=True, useBase=useBase):\n exp.annotate(fid, useBase=useBase)\n if clearOutput:\n disp.clear_output()\n if calcTemperature: \n for temp, err, label in zip(temps, tempErrs, ['Hor', 'Vert', 'Hor2D', 'Vert2D']): \n print(label + ' temperature = ' + misc.errString(temp*1e6, err*1e6) + 'uk')\n\n for fid in fids:\n if type(fid) == int:\n expTitle, _, lev = exp.getAnnotation(fid)\n expTitle = ''.join('#' for _ in range(lev)) + ' File ' + str(fid) + ': ' + expTitle\n disp.display(disp.Markdown(expTitle))\n with exp.ExpFile(fid) as file:\n file.get_basic_info()\n if trackFitCenter:\n pass\n #print('Acceleration in Mpix/s^2 = ' + misc.errString(accelFit[1], accelErr[1]))\n if transferAnalysisOpts is not None and showTferAnalysisPlots:\n colors, colors2 = misc.getColors(len(transferAnalysisOpts.initLocs()) + 2)#, cmStr=dataColor)\n pltShape = (transferAnalysisOpts.initLocsIn[-1], transferAnalysisOpts.initLocsIn[-2])\n # mp.plotThresholdHists([initThresholds[0][0],initThresholds[1][0]], colors, shape=pltShape)\n mp.plotThresholdHists([initThresholds[0][0], initThresholds[0][0]], colors, shape=[1,2])\n returnDictionary = {'images':images, 'fits':hFitParams, 'errs':hFitErrs, 'hSigmas':hSigmas, 'sigmaErrors':hSigmaErrs, 'dataKey':keyPlt, \n 'hTotalPhotons':hTotalPhotons, 'tempCalc':temps, 'tempCalcErr':tempErrs, 'initThresholds':initThresholds[0], \n '2DFit':fitParams2D, '2DErr':fitErrs2D, 'bgPics':picsForBg, 'dataLength':datalen}\n if returnPics: \n returnDictionary['pics'] = sortedStackedPics\n return returnDictionary","def calc_rsi(image):\n\n # roll axes to conventional row,col,depth\n img = np.rollaxis(image, 0, 3)\n\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\n COAST = img[:, :, 0]\n B = img[:, :, 1]\n G = img[:, :, 2]\n Y = img[:, :, 3]\n R = img[:, :, 4]\n RE = img[:, :, 5]\n NIR1 = img[:, :, 6]\n NIR2 = img[:, :, 7]\n\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\n dd = (2 * NIR1 - R) - (G - B)\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\n gndvi = old_div((NIR1 - G), (NIR1 + G))\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\n ndvi = old_div((NIR1 - R), (NIR1 + R))\n ndvi35 = old_div((G - R), (G + R))\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\n nirry = old_div((NIR1), (R + Y))\n normnir = old_div(NIR1, (NIR1 + R + G))\n psri = old_div((R - B), RE)\n rey = old_div((RE - Y), (RE + Y))\n rvi = old_div(NIR1, R)\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\n vire = old_div(NIR1, RE)\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\n gr = old_div(G, R)\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\n\n ###Built-Up indices\n wvbi = old_div((COAST - RE), (COAST + RE))\n wvnhfd = old_div((RE - COAST), (RE + COAST))\n\n ###SIs\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\n rgi = old_div(R, G)\n bri = old_div(B, R)\n\n rsi = np.stack(\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\n axis=2)\n\n return rsi","def apphot(im, yx, rap, subsample=4, **kwargs):\n n, f = anphot(im, yx, rap, subsample=subsample, **kwargs)\n if np.size(rap) > 1:\n return n.cumsum(-1), f.cumsum(-1)\n else:\n return n, f","def run(self, verbose=False):\n if verbose:\n from sage.combinat.rigged_configurations.tensor_product_kr_tableaux_element \\\n import TensorProductOfKirillovReshetikhinTableauxElement\n\n for cur_crystal in reversed(self.tp_krt):\n r = cur_crystal.parent().r()\n\n # Check if it is a spinor\n if r == self.n:\n # Perform the spinor bijection by converting to type A_{2n-1}^{(2)}\n # doing the bijection there and pulling back\n from sage.combinat.rigged_configurations.bij_type_A2_odd import KRTToRCBijectionTypeA2Odd\n from sage.combinat.rigged_configurations.tensor_product_kr_tableaux import TensorProductOfKirillovReshetikhinTableaux\n from sage.combinat.rigged_configurations.rigged_partition import RiggedPartition\n\n if verbose:\n print(\"====================\")\n if len(self.cur_path) == 0:\n print(repr([])) # Special case for displaying when the rightmost factor is a spinor\n else:\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\n print(\"--------------------\")\n print(repr(self.ret_rig_con))\n print(\"--------------------\\n\")\n print(\"Applying doubling map\")\n\n # Convert to a type A_{2n-1}^{(2)} RC\n dims = self.cur_dims[:]\n dims.insert(0, [r, cur_crystal.parent().s()])\n KRT = TensorProductOfKirillovReshetikhinTableaux(['A', 2*self.n-1, 2], dims)\n # Convert the n-th partition into a regular rigged partition\n self.ret_rig_con[-1] = RiggedPartition(self.ret_rig_con[-1]._list,\n self.ret_rig_con[-1].rigging,\n self.ret_rig_con[-1].vacancy_numbers)\n bij = KRTToRCBijectionTypeA2Odd(KRT.module_generators[0]) # Placeholder element\n bij.ret_rig_con = KRT.rigged_configurations()(*self.ret_rig_con, use_vacancy_numbers=True)\n bij.cur_path = self.cur_path\n bij.cur_dims = self.cur_dims\n for i in range(len(self.cur_dims)):\n if bij.cur_dims[i][0] != self.n:\n bij.cur_dims[i][1] *= 2\n for i in range(self.n-1):\n for j in range(len(bij.ret_rig_con[i])):\n bij.ret_rig_con[i]._list[j] *= 2\n bij.ret_rig_con[i].rigging[j] *= 2\n bij.ret_rig_con[i].vacancy_numbers[j] *= 2\n\n # Perform the type A_{2n-1}^{(2)} bijection\n r = cur_crystal.parent().r()\n # Iterate through the columns\n for col_number, cur_column in enumerate(reversed(cur_crystal.to_array(False))):\n bij.cur_path.insert(0, []) # Prepend an empty list\n bij.cur_dims.insert(0, [0, 1])\n\n # Note that we do not need to worry about iterating over columns\n # (see previous note about the data structure).\n for letter in reversed(cur_column):\n bij.cur_dims[0][0] += 1\n val = letter.value # Convert from a CrystalOfLetter to an Integer\n\n if verbose:\n print(\"====================\")\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), bij.cur_path)))\n print(\"--------------------\")\n print(repr(bij.ret_rig_con))\n print(\"--------------------\\n\")\n\n # Build the next state\n bij.cur_path[0].insert(0, [letter]) # Prepend the value\n bij.next_state(val)\n\n # If we've split off a column, we need to merge the current column\n # to the current crystal tableau\n if col_number > 0:\n for i, letter_singleton in enumerate(self.cur_path[0]):\n bij.cur_path[1][i].insert(0, letter_singleton[0])\n bij.cur_dims[1][1] += 1\n bij.cur_path.pop(0)\n bij.cur_dims.pop(0)\n\n # And perform the inverse column splitting map on the RC\n for a in range(self.n):\n bij._update_vacancy_nums(a)\n\n if verbose:\n print(\"====================\")\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), bij.cur_path)))\n print(\"--------------------\")\n print(repr(bij.ret_rig_con))\n print(\"--------------------\\n\")\n print(\"Applying halving map\")\n\n # Convert back to a type B_n^{(1)}\n for i in range(len(self.cur_dims)):\n if bij.cur_dims[i][0] != self.n:\n bij.cur_dims[i][1] //= 2\n for i in range(self.n-1):\n for j in range(len(bij.ret_rig_con[i])):\n bij.ret_rig_con[i]._list[j] //= 2\n bij.ret_rig_con[i].rigging[j] //= 2\n bij.ret_rig_con[i].vacancy_numbers[j] //= 2\n self.ret_rig_con = self.tp_krt.parent().rigged_configurations()(*bij.ret_rig_con, use_vacancy_numbers=True)\n # Make it mutable so we don't have to keep making copies, at the\n # end of the bijection, we will make it immutable again\n self.ret_rig_con._set_mutable()\n else:\n # Perform the regular type B_n^{(1)} bijection\n # Iterate through the columns\n for col_number, cur_column in enumerate(reversed(cur_crystal.to_array(False))):\n self.cur_path.insert(0, []) # Prepend an empty list\n self.cur_dims.insert(0, [0, 1])\n\n # Note that we do not need to worry about iterating over columns\n # (see previous note about the data structure).\n for letter in reversed(cur_column):\n self.cur_dims[0][0] += 1\n val = letter.value # Convert from a CrystalOfLetter to an Integer\n\n if verbose:\n print(\"====================\")\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\n print(\"--------------------\")\n print(repr(self.ret_rig_con))\n print(\"--------------------\\n\")\n\n # Build the next state\n self.cur_path[0].insert(0, [letter]) # Prepend the value\n self.next_state(val)\n\n # If we've split off a column, we need to merge the current column\n # to the current crystal tableau\n if col_number > 0:\n if verbose:\n print(\"====================\")\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\n print(\"--------------------\")\n print(repr(self.ret_rig_con))\n print(\"--------------------\\n\")\n print(\"Applying column merge\")\n\n for i, letter_singleton in enumerate(self.cur_path[0]):\n self.cur_path[1][i].insert(0, letter_singleton[0])\n self.cur_dims[1][1] += 1\n self.cur_path.pop(0)\n self.cur_dims.pop(0)\n\n # And perform the inverse column splitting map on the RC\n for a in range(self.n):\n self._update_vacancy_nums(a)\n self.ret_rig_con.set_immutable() # Return it to immutable\n return self.ret_rig_con","def __getitem__(self, idx):\n R = self.R\n \n image_path = os.path.join(self.image_dir, self.data['ImageId'][idx] + '.jpg')\n image = cv2.imread(image_path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = torch.from_numpy(image)\n H, W, N = image.size() # H, W, 3\n image = image.permute(2, 0, 1).float()\n image = 2*(image/255)-1\n\n car_id, euler_angle, world_coords = from_prediction_string_to_world_coords(self.data['PredictionString'][idx])\n image_coords = from_camera_coords_to_image_coords(world_coords, self.camera_matrix)\n image_coords = torch.from_numpy(image_coords).float()\n \n target_pointmap = torch.zeros(H//R, W//R, dtype=torch.float)\n target_heatmap = None\n target_local_offset = torch.zeros(2, H//R, W//R, dtype=torch.float)\n target_depth = torch.zeros(H//R, W//R, dtype=torch.float)\n target_yaw = torch.zeros(H//R, W//R, dtype=torch.float)\n target_pitch = torch.zeros(H//R, W//R, dtype=torch.float)\n target_roll = torch.zeros(H//R, W//R, dtype=torch.float)\n h = torch.arange(0, H//R, 1, dtype=torch.float).unsqueeze(dim=1).expand(-1, W//R)\n w = torch.arange(0, W//R, 1, dtype=torch.float).unsqueeze(dim=0).expand(H//R, -1)\n p_h = image_coords[:, 1]\n p_w = image_coords[:, 0]\n \n num_object = torch.Tensor([len(car_id)])\n \n for object_id in range(int(num_object.item())):\n point = ((p_h[object_id]/R).int(), (p_w[object_id]/R).int())\n \n if point[0] < 0 or H//R - 1 < point[0] or point[1] < 0 or W//R - 1 < point[1] :\n continue\n target_pointmap[point[0], point[1]] = 1\n target_local_offset[0][point[0], point[1]] = p_h[object_id]/R - point[0].float()\n target_local_offset[1][point[0], point[1]] = p_w[object_id]/R - point[1].float()\n target_depth[point[0], point[1]] = image_coords[object_id, 2]\n target_yaw[point[0], point[1]] = torch.Tensor([euler_angle[object_id][0]])\n target_pitch[point[0], point[1]] = torch.Tensor([euler_angle[object_id][1]])\n target_roll[point[0], point[1]] = torch.Tensor([euler_angle[object_id][2]])\n \n sigma = self.coeff_sigma / target_depth[point[0], point[1]]\n exponent = - ((h-p_h[object_id]/R)**2 + (w-p_w[object_id]/R)**2)/(2 * torch.pow(sigma, 2))\n heatmap = torch.exp(exponent)\n \n if target_heatmap is None:\n target_heatmap = heatmap.unsqueeze(dim=0)\n else:\n target_heatmap = torch.cat((target_heatmap, heatmap.unsqueeze(dim=0)), dim=0)\n\n target_heatmap, _ = torch.max(target_heatmap, dim=0)\n \n if torch.cuda.is_available():\n image = image.cuda().contiguous()\n target = {'num_object': num_object.cuda(), 'pointmap': target_pointmap.cuda(), 'heatmap': target_heatmap.cuda(), 'local_offset': target_local_offset.cuda(), 'depth': target_depth.cuda(), 'yaw': target_yaw.cuda(), 'pitch': target_pitch.cuda(), 'roll': target_roll.cuda()}\n else:\n image = image.contiguous()\n target = {'num_object': num_object, 'pointmap': target_pointmap, 'heatmap': target_heatmap, 'local_offset': target_local_offset, 'depth': target_depth, 'yaw': target_yaw, 'pitch': target_pitch, 'roll': target_roll}\n \n return image, target","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 simanalyze(project='sim', image=True, imagename='default', skymodel='', vis='default', modelimage='', imsize=[0, 0], imdirection='', cell='', interactive=False, niter=0, threshold='0.1mJy', weighting='natural', mask=[], outertaper=[''], pbcor=True, stokes='I', featherimage='', analyze=False, showuv=True, showpsf=True, showmodel=True, showconvolved=False, showclean=True, showresidual=False, showdifference=True, showfidelity=True, graphics='both', verbose=False, overwrite=True, dryrun=False, logfile=''):\n if type(imsize)==int: imsize=[imsize]\n if type(outertaper)==str: outertaper=[outertaper]\n\n#\n# The following is work around to avoid a bug with current python translation\n#\n mytmp = {}\n\n mytmp['project'] = project\n mytmp['image'] = image\n mytmp['imagename'] = imagename\n mytmp['skymodel'] = skymodel\n mytmp['vis'] = vis\n mytmp['modelimage'] = modelimage\n mytmp['imsize'] = imsize\n mytmp['imdirection'] = imdirection\n mytmp['cell'] = cell\n mytmp['interactive'] = interactive\n mytmp['niter'] = niter\n mytmp['threshold'] = threshold\n mytmp['weighting'] = weighting\n mytmp['mask'] = mask\n mytmp['outertaper'] = outertaper\n mytmp['pbcor'] = pbcor\n mytmp['stokes'] = stokes\n mytmp['featherimage'] = featherimage\n mytmp['analyze'] = analyze\n mytmp['showuv'] = showuv\n mytmp['showpsf'] = showpsf\n mytmp['showmodel'] = showmodel\n mytmp['showconvolved'] = showconvolved\n mytmp['showclean'] = showclean\n mytmp['showresidual'] = showresidual\n mytmp['showdifference'] = showdifference\n mytmp['showfidelity'] = showfidelity\n mytmp['graphics'] = graphics\n mytmp['verbose'] = verbose\n mytmp['overwrite'] = overwrite\n mytmp['dryrun'] = dryrun\n mytmp['logfile'] = logfile\n pathname='file://' + xmlpath( ) + '/'\n trec = casac.utils().torecord(pathname+'simanalyze.xml')\n\n casalog.origin('simanalyze')\n if trec.has_key('simanalyze') and casac.utils().verify(mytmp, trec['simanalyze']) :\n result = task_simanalyze.simanalyze(project, image, imagename, skymodel, vis, modelimage, imsize, imdirection, cell, interactive, niter, threshold, weighting, mask, outertaper, pbcor, stokes, featherimage, analyze, showuv, showpsf, showmodel, showconvolved, showclean, showresidual, showdifference, showfidelity, graphics, verbose, overwrite, dryrun, logfile)\n\n else :\n result = False\n return result","def create_azi_to_rad_sequence():\n num_tot = 30\n for i in range(2*num_tot + 1):\n angle_arr = azi_to_rad_transformation(512, i, 30)\n phase_arr = create_flat_phase(512, 0)\n delta_1_arr = create_delta_1(phase_arr, angle_arr)\n delta_2_arr = create_delta_2(angle_arr)\n cv2.imwrite('frame' + str(i) +'.tiff', delta_2_arr)\n print(\"Frame \" + str(i))","def update_header(arr_imgs,obj,filter_i):\n \n for img in arr_imgs:\n warnings.simplefilter('ignore', category=AstropyUserWarning)\n try:\n hdulist = fits.open(img,ignore_missing_end=True)\n #if there is only a primary header get the data from it\n if len(hdulist) == 1:\n data = getdata(img, 0, header=False)\n #if there is more than one header get data from the 'SCI' extension\n else:\n data = getdata(img, 1, header=False)\n #Get value of EXPTIME and PHOTZPT keyword from primary header and \n #set CCDGAIN to a default value of 1\n EXPTIME = hdulist[0].header['EXPTIME']\n PHOTFLAM = hdulist[1].header['PHOTFLAM']\n PHOTZPT = hdulist[1].header['PHOTZPT']\n CCDGAIN = 1.0\n #First pass locating value for gain\n for i in range(2):\n if len(hdulist) == 1:\n break\n #Go through primary and secondary header and ignore the \n #BinTable formatted header\n if not isinstance(hdulist[i],astropy.io.fits.hdu.table.\\\n BinTableHDU):\n if 'CCDGAIN' in hdulist[i].header:\n CCDGAIN = hdulist[i].header['CCDGAIN']\n break\n if 'GAIN' in hdulist[i].header:\n CCDGAIN = hdulist[i].header['GAIN']\n break\n if 'ATODGAIN' in hdulist[i].header:\n CCDGAIN = hdulist[i].header['ATODGAIN']\n break\n \n #Locating units of image\n print('Doing BUNIT check')\n for i in range(2):\n #If there is only one header then this is the only place to \n #check\n if len(hdulist) == 1:\n bunit = hdulist[0].header['D001OUUN']\n print('BUNIT was {0}'.format(bunit))\n if bunit == 'counts':\n ### Rescaling zeropoint\n ZPT_NEW = 30.0\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*EXPTIME) + PHOTZPT\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\n data = (data/EXPTIME)*pixmod\n hdulist[0].header.set('BUNIT','COUNTS/S')\n hdulist[0].header.set('MAGZPT',ZPT_NEW)\n print('BUNIT is {0}'.format(hdulist[0].\\\n header['BUNIT']))\n \n #If there are multiple headers then they all have to be checked\n else:\n if 'BUNIT' in hdulist[i].header:\n bunit = hdulist[i].header['BUNIT']\n print('BUNIT was {0}'.format(bunit))\n if bunit == 'COUNTS':\n ZPT_NEW = 30.0\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*EXPTIME) + PHOTZPT\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\n data = (data/EXPTIME)*pixmod\n if bunit == 'ELECTRONS':\n ZPT_NEW = 30.0\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN*EXPTIME) \\\n + PHOTZPT\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\n data = (data/(CCDGAIN*EXPTIME))*pixmod\n if bunit == 'ELECTRONS/S':\n ZPT_NEW = 30.0\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN) + PHOTZPT\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\n data = (data/CCDGAIN)*pixmod\n if bunit == 'ELECTRONS/SEC':\n ZPT_NEW = 30.0\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN) + PHOTZPT\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\n data = (data/CCDGAIN)*pixmod\n hdulist[i].header['BUNIT'] = 'COUNTS/S'\n hdulist[i].header['MAGZPT'] = ZPT_NEW\n ###\n print('BUNIT is {0}'.format(hdulist[i].\\\n header['BUNIT']))\n print('PHOTZPT is {0}'.format(hdulist[i].\\\n header['MAGZPT']))\n print('Done changing BUNIT')\n \n #Second pass to assign gain and exptime to headers\n for i in range(2):\n if len(hdulist) == 1:\n break\n if not isinstance(hdulist[i],astropy.io.fits.hdu.table.\\\n BinTableHDU):\n if 'CCDGAIN' not in hdulist[i].header:\n hdulist[i].header.set('CCDGAIN',CCDGAIN)\n if 'EXPTIME' not in hdulist[i].header:\n hdulist[i].header.set('EXPTIME',EXPTIME)\n \n #Make new versions of images in interim/obj1 folder\n os.chdir(path_to_interim + obj)\n #Remove .fits extension\n img = os.path.splitext(img)[0]\n #If there was only one header write that header's data to new\n #version of fits image\n if len(hdulist) == 1:\n fits.writeto(img+'_test_'+filter_i+'.fits',data,hdulist[0].\\\n header,output_verify='ignore')\n #Else write the 'SCI' header's data to new version of fits image\n else:\n fits.writeto(img+'_test_'+filter_i+'.fits',data,hdulist[1].\\\n header,output_verify='ignore')\n hdulist.close()\n os.chdir(path_to_raw + obj)\n \n #This is to catch 'empty or corrupt FITS file' or any other IOError\n #and write it to a text file along with the object name and the \n #filter name\n except IOError as e:\n os.chdir('..')\n dir_path = os.getcwd()\n if os.path.basename(dir_path) == 'raw':\n os.chdir(path_to_interim)\n with open('Error_swarp.txt','a') as newfile: \n newfile.write('Object {0} and image {1} raises {2}'.\\\n format(obj,img,e))\n newfile.write('\\n')\n newfile.close()\n os.chdir(path_to_raw + obj)\n \n os.chdir(path_to_interim + obj)\n #For this object and filter combination grab all the new versions made\n arr = glob('*test_'+filter_i+'.fits')\n print(len(arr))\n if len(arr) >= 1: #avoid empty cases where files have been removed earlier\n #or don't exist at all since the dictionary also contains\n #pairs of objects and filters that didn't meet the swarp\n #requirements (didn't pass preliminary exptime or filter\n #checks so those folders/images don't exist)\n \n #If new versions exist then write their names to a text file \n with open(filter_i+'_img_list_testfil.txt','wb') as newfile2:\n for obj in arr:\n newfile2.write(obj)\n newfile2.write('\\n')\n newfile2.close()\n #If text file exists return the name\n return filter_i+'_img_list_testfil.txt'\n #If text file doesn't exist return this string\n return 'error'","def simulator_from_instrument(instrument):\r\n\r\n grid = grid_from_instrument(instrument=instrument)\r\n psf = psf_from_instrument(instrument=instrument)\r\n\r\n if instrument in \"vro\":\r\n return ag.SimulatorImaging(\r\n exposure_time_map=ag.Array2D.full(\r\n fill_value=100.0, shape_native=grid.shape_native\r\n ),\r\n psf=psf,\r\n background_sky_map=ag.Array2D.full(\r\n fill_value=1.0, shape_native=grid.shape_native\r\n ),\r\n add_poisson_noise=True,\r\n )\r\n elif instrument in \"euclid\":\r\n return ag.SimulatorImaging(\r\n exposure_time_map=ag.Array2D.full(\r\n fill_value=2260.0, shape_native=grid.shape_native\r\n ),\r\n psf=psf,\r\n background_sky_map=ag.Array2D.full(\r\n fill_value=1.0, shape_native=grid.shape_native\r\n ),\r\n add_poisson_noise=True,\r\n )\r\n elif instrument in \"hst\":\r\n return ag.SimulatorImaging(\r\n exposure_time_map=ag.Array2D.full(\r\n fill_value=2000.0, shape_native=grid.shape_native\r\n ),\r\n psf=psf,\r\n background_sky_map=ag.Array2D.full(\r\n fill_value=1.0, shape_native=grid.shape_native\r\n ),\r\n add_poisson_noise=True,\r\n )\r\n elif instrument in \"hst_up\":\r\n return ag.SimulatorImaging(\r\n exposure_time_map=ag.Array2D.full(\r\n fill_value=2000.0, shape_native=grid.shape_native\r\n ),\r\n psf=psf,\r\n background_sky_map=ag.Array2D.full(\r\n fill_value=1.0, shape_native=grid.shape_native\r\n ),\r\n add_poisson_noise=True,\r\n )\r\n elif instrument in \"ao\":\r\n return ag.SimulatorImaging(\r\n exposure_time_map=ag.Array2D.full(\r\n fill_value=1000.0, shape_native=grid.shape_native\r\n ),\r\n psf=psf,\r\n background_sky_map=ag.Array2D.full(\r\n fill_value=1.0, shape_native=grid.shape_native\r\n ),\r\n add_poisson_noise=True,\r\n )\r\n else:\r\n raise ValueError(\"An invalid instrument was entered - \", instrument)","def FluorescenceAnalysis(self, folder, round_num, save_mask = True):\r\n RoundNumberList, CoordinatesList, fileNameList = self.retrive_scanning_scheme(folder, file_keyword = 'Zmax')\r\n # RoundNumberList, CoordinatesList, fileNameList = self.retrive_scanning_scheme(folder, file_keyword = 'Zfocus')\r\n \r\n if not os.path.exists(os.path.join(folder, 'MLimages_{}'.format(round_num))):\r\n # If the folder is not there, create the folder\r\n os.mkdir(os.path.join(folder, 'MLimages_{}'.format(round_num))) \r\n \r\n for EachRound in RoundNumberList:\r\n \r\n cells_counted_in_round = 0\r\n \r\n if EachRound == round_num:\r\n \r\n # Start numbering cells at each round\r\n self.cell_counted_inRound = 0 \r\n \r\n for EachCoord in CoordinatesList:\r\n \r\n # =============================================================================\r\n # For tag fluorescence:\r\n # ============================================================================= \r\n print(EachCoord)\r\n #-------------- readin image---------------\r\n for Eachfilename in enumerate(fileNameList):\r\n if EachCoord in Eachfilename[1] and EachRound in Eachfilename[1]:\r\n if '0Zmax' in Eachfilename[1]:\r\n ImgNameInfor = Eachfilename[1][0:len(Eachfilename[1])-14] # get rid of '_PMT_0Zmax.tif' in the name.\r\n elif '0Zfocus' in Eachfilename[1]:\r\n ImgNameInfor = Eachfilename[1][0:len(Eachfilename[1])-16] # get rid of '_PMT_0Zfocus.tif' in the name.\r\n _imagefilename = os.path.join(folder, Eachfilename[1])\r\n #------------------------------------------\r\n \r\n # =========================================================================\r\n # USING MASKRCNN...\r\n # =========================================================================\r\n # Imagepath = self.Detector._fixPathName(_imagefilename)\r\n Rawimage = imread(_imagefilename)\r\n \r\n# if ClearImgBef == True:\r\n# # Clear out junk parts to make it esaier for ML detection.\r\n# RawimageCleared = self.preProcessMLimg(Rawimage, smallest_size=300, lowest_region_intensity=0.16)\r\n# else:\r\n# RawimageCleared = Rawimage.copy()\r\n \r\n image = ProcessImage.convert_for_MaskRCNN(Rawimage)\r\n \r\n # Run the detection on input image.\r\n results = self.Detector.detect([image])\r\n \r\n MLresults = results[0]\r\n \r\n if save_mask == True:\r\n fig, ax = plt.subplots()\r\n # Set class_names = [None,None,None,None] to mute class name display.\r\n visualize.display_instances(image, MLresults['rois'], MLresults['masks'], MLresults['class_ids'],\r\n class_names = [None,None,None,None], ax=ax,\r\n centre_coors = MLresults['Centre_coor'], Centre_coor_radius = 2, \r\n WhiteSpace = (0, 0))#MLresults['class_ids'],MLresults['scores'], \r\n # ax.imshow(fig)\r\n fig.tight_layout()\r\n # Save the detection image\r\n fig_name = os.path.join(folder, 'MLimages_{}\\{}.tif'.format(round_num, ImgNameInfor))\r\n plt.savefig(fname = fig_name, dpi=200, pad_inches=0.0, bbox_inches='tight')\r\n \r\n # segmentationImg = Image.fromarray(fig) #generate an image object\r\n # segmentationImg.save(os.path.join(folder, 'MLimages_{}\\{}.tif'.format(round_num, ImgNameInfor)))#save as tif\r\n \r\n if self.cell_counted_inRound == 0:\r\n cell_Data, self.cell_counted_inRound, total_cells_counted_in_coord = \\\r\n ProcessImage.retrieveDataFromML(Rawimage, MLresults, str(ImgNameInfor), self.cell_counted_inRound)\r\n else: \r\n Cell_Data_new, self.cell_counted_inRound, total_cells_counted_in_coord = \\\r\n ProcessImage.retrieveDataFromML(Rawimage, MLresults, str(ImgNameInfor), self.cell_counted_inRound)\r\n if len(Cell_Data_new) > 0:\r\n cell_Data = cell_Data.append(Cell_Data_new)\r\n \r\n # Count in total how many flat and round cells are identified.\r\n cells_counted_in_round += total_cells_counted_in_coord\r\n \r\n print(\"Number of round/flat cells in this round: {}\".format(cells_counted_in_round))\r\n \r\n # Save to excel\r\n cell_Data.to_excel(os.path.join(os.path.join(folder, round_num + '_' + datetime.now().strftime('%Y-%m-%d_%H-%M-%S')+'_CellsProperties.xlsx')))\r\n \r\n return cell_Data","def CalcAtmTransmissionForImage(img, header='', chanInfo='', airmass=1.5,pwv=-1, \n spectralaxis=-1, \n value='transmission', P=-1, H=-1, \n T=-1, altitude=-1):\n if (header == ''):\n print \"imhead\", # the comma prevents the newline so that ...10...20 will be on same line\n header = imhead(img,mode='list')\n if (type(header) != dict):\n # Input was a spectrum rather than an image\n if (chanInfo[1] < 60e9):\n telescopeName = 'ALMA'\n else:\n telescopeName = 'VLA'\n else:\n telescopeName = header['telescope']\n # this will not match up with the plot, which uses numberOfChannelsInCube\n# freqs = getFreqsForImage(img, header, spectralaxis)\n freqs = np.linspace(chanInfo[1]*1e-9,chanInfo[2]*1e-9,chanInfo[0])\n# print \"freqs: %f-%f\" % (freqs[0], freqs[-1])\n numchan = len(freqs)\n lsrkwidth = (chanInfo[2] - chanInfo[1])/(numchan-1)\n result = cubeLSRKToTopo(img, nchan=numchan, f0=chanInfo[1], f1=chanInfo[2], chanwidth=lsrkwidth)\n if (result is None):\n topofreqs = freqs\n else:\n topoWidth = (result[1]-result[0])/(numchan-1)\n topofreqs = np.linspace(result[0], result[1], chanInfo[0]) * 1e-9\n casalogPost(\"Converted LSRK range (%f-%f) to TOPO (%f-%f) over %d channels\" % (chanInfo[1]*1e-9, chanInfo[2]*1e-9,topofreqs[0],topofreqs[-1],numchan))\n P0 = 1000.0 # mbar\n H0 = 20.0 # percent\n T0 = 273.0 # Kelvin\n if (telescopeName.find('ALMA') >= 0 or telescopeName.find('ACA') >= 0):\n pwv0 = 1.0 \n P0 = 563.0\n H0 = 20.0\n T0 = 273.0\n altitude0 = 5059\n elif (telescopeName.find('VLA') >= 0):\n P0 = 786.0\n pwv0 = 5.0 \n altitude0 = 2124\n else:\n pwv0 = 10.0 \n altitude0 = 0\n if (pwv < 0):\n pwv = pwv0\n if (T < 0):\n T = T0\n if (H < 0):\n H = H0\n if (P < 0):\n P = P0\n if (altitude < 0):\n altitude = altitude0\n tropical = 1\n midLatitudeSummer = 2\n midLatitudeWinter = 3\n# print \"image bandwidth = %f GHz\" % (np.max(freqs)-np.min(freqs))\n reffreq = np.mean(topofreqs)\n numchanModel = numchan*1\n chansepModel = (topofreqs[-1]-topofreqs[0])/(numchanModel-1)\n# print \"regridded bandwidth=%f GHz, chansep=%f, reffreq=%f\" % (np.max(topofreqs)-np.min(topofreqs), chansepModel, reffreq)\n nbands = 1\n myqa = createCasaTool(qatool)\n fCenter = create_casa_quantity(myqa, reffreq, 'GHz')\n fResolution = create_casa_quantity(myqa, chansepModel, 'GHz')\n fWidth = create_casa_quantity(myqa, numchanModel*chansepModel, 'GHz')\n myat = casac.atmosphere()\n myat.initAtmProfile(humidity=H, temperature=create_casa_quantity(myqa,T,\"K\"),\n altitude=create_casa_quantity(myqa,altitude,\"m\"),\n pressure=create_casa_quantity(myqa,P,'mbar'),atmType=midLatitudeWinter)\n myat.initSpectralWindow(nbands, fCenter, fWidth, fResolution)\n myat.setUserWH2O(create_casa_quantity(myqa, pwv, 'mm'))\n# myat.setAirMass() # This does not affect the opacity, but it does effect TebbSky, so do it manually.\n myqa.done()\n\n dry = np.array(myat.getDryOpacitySpec(0)[1])\n wet = np.array(myat.getWetOpacitySpec(0)[1]['value'])\n TebbSky = myat.getTebbSkySpec(spwid=0)[1]['value']\n # readback the values to be sure they got set\n \n rf = myat.getRefFreq()['value']\n cs = myat.getChanSep()['value']\n if (myat.getRefFreq()['unit'] != 'GHz'):\n casalogPost(\"There is a unit mismatch for refFreq in the atm code.\")\n if (myat.getChanSep()['unit'] != 'MHz'):\n casalogPost(\"There is a unit mismatch for chanSep in the atm code.\")\n numchanModel = myat.getNumChan()\n freq0 = myat.getChanFreq(0)['value']\n freq1 = myat.getChanFreq(numchanModel-1)['value']\n# print \"atm returned bandwidth = %f GHz = %f to %f \" % (freq1-freq0, freq0, freq1)\n newfreqs = np.linspace(freqs[0], freqs[-1], numchanModel) # fix for SCOPS-4815\n# print \"freqs: %f-%f newfreqs: %f-%f\" % (freqs[0], freqs[-1], newfreqs[0], newfreqs[-1])\n transmission = np.exp(-airmass*(wet+dry))\n TebbSky *= (1-np.exp(-airmass*(wet+dry)))/(1-np.exp(-wet-dry))\n if value=='transmission':\n values = transmission\n else:\n values = TebbSky\n del myat\n return(newfreqs, values)","def analyze(self, options, target):\r\n\r\n target = 0\r\n\r\n upf = None\r\n\r\n dwnf = None\r\n\r\n if options.upfile is not None:\r\n\r\n upf = basepath + options.upfile + '.ma'\r\n\r\n if options.downfile is not None:\r\n\r\n dwnf = basepath + options.downfile + '.ma'\r\n\r\n\r\n\r\n for filename in (upf, dwnf):\r\n\r\n # if options.upfile is not None and options.downfile is not None:\r\n\r\n if filename is None:\r\n\r\n break\r\n\r\n im=[]\r\n\r\n self.imageData = []\r\n\r\n print (\"Loading data from %s\" % filename)\r\n\r\n try:\r\n\r\n im = MetaArray(file = filename, subset=(slice(0,2), slice(64,128), slice(64,128)))\r\n\r\n except:\r\n\r\n print(' Error loading upfile: %s' % filename)\r\n\r\n return\r\n\r\n print(' Data loaded')\r\n\r\n target = target + 1\r\n\r\n self.times = im.axisValues('Time').astype('float32')\r\n\r\n self.imageData = im.view(np.ndarray).astype('float32')\r\n\r\n im=[]\r\n\r\n self.analysis_fourier_map(period=self.period, target=target, bins=binsize,)\r\n\r\n if target > 0:\r\n\r\n self.plot_maps(mode = 1, target = target, gfilter = self.gfilter)","def addAllTasselCapIndices(self,img):\n\t\t\n\t\tdef getTasseledCap(img):\n\t\t\t\t\"\"\"Function to compute the Tasseled Cap transformation and return an image\"\"\"\n\t\t\t\t\n\t\t\tcoefficients = ee.Array([\n\t\t\t\t[0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863],\n\t\t\t\t[-0.2848, -0.2435, -0.5436, 0.7243, 0.0840, -0.1800],\n\t\t\t\t[0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572],\n\t\t\t\t[-0.8242, 0.0849, 0.4392, -0.0580, 0.2012, -0.2768],\n\t\t\t\t[-0.3280, 0.0549, 0.1075, 0.1855, -0.4357, 0.8085],\n\t\t\t\t[0.1084, -0.9022, 0.4120, 0.0573, -0.0251, 0.0238]\n\t\t\t]);\n\t\t\t\n\t\t\tbands=ee.List(['blue','green','red','nir','swir1','swir2'])\n\t\t\t\t\n\t\t\t# Make an Array Image, with a 1-D Array per pixel.\n\t\t\tarrayImage1D = img.select(bands).toArray()\n\t\t\t\n\t\t\t# Make an Array Image with a 2-D Array per pixel, 6x1.\n\t\t\tarrayImage2D = arrayImage1D.toArray(1)\n\t\t\t\n\t\t\tcomponentsImage = ee.Image(coefficients).matrixMultiply(arrayImage2D).arrayProject([0]).arrayFlatten([['brightness', 'greenness', 'wetness', 'fourth', 'fifth', 'sixth']]).float();\n\t\t \n\t\t\t# Get a multi-band image with TC-named bands.\n\t\t\treturn img.addBands(componentsImage);\t\n\t\t\t\t\n\t\t\t\t\n\t\tdef addTCAngles(img):\n\n\t\t\t\"\"\" Function to add Tasseled Cap angles and distances to an image. Assumes image has bands: 'brightness', 'greenness', and 'wetness'.\"\"\"\n\t\t\t\t\t\n\t\t\t# Select brightness, greenness, and wetness bands\t\n\t\t\tbrightness = img.select('brightness');\n\t\t\tgreenness = img.select('greenness');\n\t\t\twetness = img.select('wetness');\n\t \n\t\t\t# Calculate Tasseled Cap angles and distances\n\t\t\ttcAngleBG = brightness.atan2(greenness).divide(math.pi).rename(['tcAngleBG']);\n\t\t\ttcAngleGW = greenness.atan2(wetness).divide(math.pi).rename(['tcAngleGW']);\n\t\t\ttcAngleBW = brightness.atan2(wetness).divide(math.pi).rename(['tcAngleBW']);\n\t\t\ttcDistBG = brightness.hypot(greenness).rename(['tcDistBG']);\n\t\t\ttcDistGW = greenness.hypot(wetness).rename(['tcDistGW']);\n\t\t\ttcDistBW = brightness.hypot(wetness).rename(['tcDistBW']);\n\t\t\timg = img.addBands(tcAngleBG).addBands(tcAngleGW).addBands(tcAngleBW).addBands(tcDistBG).addBands(tcDistGW).addBands(tcDistBW);\n\t\t\t\t\n\t\t\treturn img;\n\t\t\n\t\t\n\t\timg = getTasseledCap(img)\n\t\timg = addTCAngles(img)\n\t\treturn img","def regrid_in_miriad(taskid, image_name, hdu_image, b, c):\n\n\t# Change the reference pixel of beam model to reference pixel of image to correct\n\tcb_model = beam_lookup.model_lookup2(taskid, b)\n\thdulist_cb = pyfits.open(cb_model)\n\thdulist_cb[0].header['CRVAL1'] = hdu_image[0].header['CRVAL1']\n\thdulist_cb[0].header['CRVAL2'] = hdu_image[0].header['CRVAL2']\n\n\t# Rescale to appropriate frequency. This should work for either drift scans or Gaussian regression (only tested on latter):\n\tavg_cube_freq = (hdu_image[0].header['CRVAL3'] + hdu_image[0].header['CDELT3'] * hdu_image[0].data.shape[0]) * u.Hz\n\thdulist_cb[0].header['CDELT1'] = (hdulist_cb[0].header['CDELT1'] * get_cb_model_freq().to(u.Hz) / avg_cube_freq).value\n\thdulist_cb[0].header['CDELT2'] = (hdulist_cb[0].header['CDELT2'] * get_cb_model_freq().to(u.Hz) / avg_cube_freq).value\n\n\tcb2d_name = 'temp_b{}_c{}_cb-2d.fits'.format(b, c)\n\thdulist_cb.writeto(cb2d_name)\n\thdulist_cb.close()\n\n\tprint('\\tRegridding in miriad using model {}'.format(cb_model))\n\n\tfits = lib.miriad('fits')\n\tregrid = lib.miriad('regrid')\n\n\t# Convert files to miriad:\n\tfits.in_ = image_name\n\tfits.out = '{}.mir'.format(image_name[:-5])\n\tfits.op = 'xyin'\n\tfits.go()\n\n\tfits.in_ = cb2d_name\n\tfits.out = '{}.mir'.format(cb2d_name[:-5])\n\tfits.op = 'xyin'\n\tfits.go()\n\n\t# Regrid beam image\n\tregrid.in_ = '{}.mir'.format(cb2d_name[:-5])\n\tregrid.out = '{}_rgrid.mir'.format(cb2d_name[:-5])\n\tregrid.tin = '{}.mir'.format(image_name[:-5])\n\tregrid.axes = '1,2'\n\tregrid.go()\n\n\t# Convert regrided beam image to fits\n\tfits.in_ = '{}_rgrid.mir'.format(cb2d_name[:-5])\n\tfits.out = '{}_rgrid.fits'.format(cb2d_name[:-5])\n\tfits.op = 'xyout'\n\tfits.go()\n\n\t# Make cb 3D and save as FITS:\n\thdu_cb = pyfits.open('{}_rgrid.fits'.format(cb2d_name[:-5]))\n\td_new = np.ones((hdu_image[0].header['NAXIS3'], hdu_cb[0].header['NAXIS2'], hdu_cb[0].header['NAXIS2']))\n\td_beam_cube = d_new * hdu_cb[0].data\n\thdu_cb[0].data = np.float32(d_beam_cube)\n\n\tprint('\\tWriting compound beam cube.')\n\thdu_cb.writeto('{}_cb.fits'.format(image_name[:-5]))\n\n\thdu_cb.close()\n\n\t# Clean up the extra Miriad & 2D cb files\n\tos.system('rm -rf {}*.mir'.format(image_name[:-5]))\n\tos.system('rm -rf {}*'.format(cb2d_name[:-5]))","def ptc_acquisition(self, explow=0.1, exphigh=2, expdelta=0.1, laserchannel = 2, lasercurrent=45.0):\n\n #\n self.laser.select(laserchannel)\n self.laser.setCurrent(laserchannel, lasercurrent)\n self.laser.enable()\n\n #self.powerup_CCD()\n self.reb.set_testtype('PTC')\n\n #self.DKD.setup_current_measurements(DKD_range)\n self.PhD.setup_current_measurements(2e-8)\n\n # Create the logging summary file\n summaryfile = os.path.join(eodir, 'summary.log')\n f = open(summaryfile, 'a')\n\n print >>f, \"# power\\t exposure time\\t file name\"\n\n effpow = self.laser.getPower(laserchannel)\n # First take bias frames\n self.log(\"Taking bias\")\n m = self.execute_reb_sequence('ClearBias', 0, 20, True )\n #to have only useful channels:\n fname = \"%s_ptc_bias_%s.fits\" % (serno, self.reb.reb.imgtag)\n i = self.conv_to_fits(channels=validamps)\n # to save FITS HDU with headers\n self.save_to_fits(i, m, fitsname=os.path.join(eodir, fname))\n\n print >>f, effpow, 0, fname\n\n for t in np.arange(explow, exphigh+expdelta, expdelta):\n # pair of flats\n for numpair in [1, 2]:\n effpow = self.laser.getPower(laserchannel)\n m = self.execute_reb_sequence('Acquisition', t)\n #to have only useful channels:\n fname = \"%s_ptc_flat%d_%05d_%s.fits\" % (serno, numpair, int(t*100), self.reb.reb.imgtag)\n i = self.conv_to_fits(channels=validamps)\n # to save FITS HDU with headers\n self.save_to_fits(i, m, fitsname=os.path.join(eodir, fname))\n\n print >>f, effpow, t, fname\n\n f.close()\n\n # Shutting down (not the lamp by default)\n self.laser.disable()\n #self.shutdown_CCD()\n # p = self.reb.start_waiting_sequence()","def countmap(band,skypos,tranges,skyrange,width=False,height=False,\n\t\t\t verbose=0,tscale=1000.,memlight=False,hdu=False,retries=20):\n\timsz = gxt.deg2pix(skypos,skyrange)\n\tcount = np.zeros(imsz)\n\tfor trange in tranges:\n\t\t# If memlight is requested, break the integration into\n\t\t# smaller chunks.\n\t\tstep = memlight if memlight else trange[1]-trange[0]\n\t\tfor i in np.arange(trange[0],trange[1],step):\n\t\t\tt0,t1=i,i+step\n\t\t\tif verbose:\n\t\t\t\tprint_inline('Coadding '+str(t0)+' to '+str(t1))\n\t\t\tevents = gQuery.getArray(gQuery.rect(band,skypos[0],skypos[1],t0,t1,\n\t\t\t\t\t\t\t\t\t\t\t\t skyrange[0],skyrange[1]),\n\t\t\t\t\t\t\t\t\t verbose=verbose,retries=retries)\n\n\t\t\t# Check that there is actually data here.\n\t\t\tif not events:\n\t\t\t\tif verbose>1:\n\t\t\t\t\tprint \"No data in \"+str([t0,t1])\n\t\t\t\tcontinue\n\n\t\t\ttimes = np.array(events,dtype='float64')[:,0 ]/tscale\n\t\t\tcoo =\tnp.array(events,dtype='float64')[:,1:]\n\n\t\t\t# If there's no data, return a blank image.\n\t\t\tif len(coo)==0:\n\t\t\t\tif verbose:\n\t\t\t\t\tprint 'No data in this frame: '+str([t0,t1])\n\t\t\t\tcontinue\n\n\t\t\t# Define World Coordinate System (WCS)\n\t\t\twcs = define_wcs(skypos,skyrange,width=False,height=False)\n\n\t\t\t# Map the sky coordinates onto the focal plane\n\t\t\tfoc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)\n\n\t\t\t# Bin the events into actual image pixels\n\t\t\tH,xedges,yedges=np.histogram2d(foc[:,1]-0.5,foc[:,0]-0.5,\n\t\t\t\t\t\t\t\tbins=imsz,range=([ [0,imsz[0]],[0,imsz[1]] ]))\n\t\t\tcount += H\n\n\treturn count","def process_event(self, evt):\n det_data = {}\n for det, thisDetDict in zip(self.dets, self.targetVarsXtc):\n try:\n det.getData(evt)\n det.processFuncs()\n thisDetDataDict = getUserData(det)\n img = None\n for key in thisDetDataDict.keys():\n if key=='full_area':\n img = thisDetDataDict[key].astype(float) # needed for detectors whose data are uint16 (Rayonix)\n elif key.find('ROI')>=0:\n img = thisDetDataDict[key].astype(float)\n if img is None:\n print('Problem with getting detector area data.')\n continue\n if 'thresADU' in thisDetDict:\n img[img<thisDetDict['thresADU']] = 0\n elif 'thresRms' in thisDetDict:\n img[img<thisDetDict['thresRms']*det.rms] = 0\n\n det_data[det._name] = img # can onky handle full area ROIFunc for now\n \n # calculate variance (see ... for ref)\n \n# if not (key=='full_area' or key.find('ROI')>=0 or key.find('photon_img')>=0):\n# continue\n# if (key=='full_area' or key.find('ROI')>=0):\n# if 'thresADU' in thisDetDict:\n# thisDetDataDict[key][thisDetDataDict[key]<thisDetDict['thresADU']]=0\n# elif 'thresRms' in thisDetDict:\n# thisDetDataDict[key][thisDetDataDict[key]<thisDetDict['thresRms']*det.rms]=0\n# dArray[ib%bins_per_job]=dArray[ib%bins_per_job]+thisDetDataDict[key]\n# else: #if key.find('photon_img')\n# dIArray[ib%bins_per_job]=dIArray[ib%bins_per_job]+thisDetDataDict[key]\n\n# x = thisDetDataDict[key]\n# oldM = dMArray\n# dMArray = dMArray + (x-dMArray)/(ievt+1)\n# dSArray = dSArray + (x-dMArray)*(x-oldM)\n except Exception as e:\n print('Failed to get data for this event for det {}.\\n{}'.format(det._name, e))\n det_data[det._name] = None\n return det_data","def addAllTasselCapIndices(self,img):\n\t\t\n\t\tdef getTasseledCap(img):\n\t\t\t\"\"\"Function to compute the Tasseled Cap transformation and return an image\"\"\"\n\t\t\t\n\t\t\tcoefficients = ee.Array([\n\t\t\t\t[0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863],\n\t\t\t\t[-0.2848, -0.2435, -0.5436, 0.7243, 0.0840, -0.1800],\n\t\t\t\t[0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572],\n\t\t\t\t[-0.8242, 0.0849, 0.4392, -0.0580, 0.2012, -0.2768],\n\t\t\t\t[-0.3280, 0.0549, 0.1075, 0.1855, -0.4357, 0.8085],\n\t\t\t\t[0.1084, -0.9022, 0.4120, 0.0573, -0.0251, 0.0238]\n\t\t\t]);\n\t\t\n\t\t\tbands=ee.List(['blue','green','red','nir','swir1','swir2'])\n\t\t\t\n\t\t\t# Make an Array Image, with a 1-D Array per pixel.\n\t\t\tarrayImage1D = img.select(bands).toArray()\n\t\t\n\t\t\t# Make an Array Image with a 2-D Array per pixel, 6x1.\n\t\t\tarrayImage2D = arrayImage1D.toArray(1)\n\t\t\n\t\t\tcomponentsImage = ee.Image(coefficients).matrixMultiply(arrayImage2D).arrayProject([0]).arrayFlatten([['brightness', 'greenness', 'wetness', 'fourth', 'fifth', 'sixth']]).float();\n\t \n\t\t\t# Get a multi-band image with TC-named bands.\n\t\t\treturn img.addBands(componentsImage);\t\n\t\t\t\n\t\t\t\n\t\tdef addTCAngles(img):\n\n\t\t\t\"\"\" Function to add Tasseled Cap angles and distances to an image. Assumes image has bands: 'brightness', 'greenness', and 'wetness'.\"\"\"\n\t\t\t\n\t\t\t# Select brightness, greenness, and wetness bands\t\n\t\t\tbrightness = img.select('brightness');\n\t\t\tgreenness = img.select('greenness');\n\t\t\twetness = img.select('wetness');\n\t \n\t\t\t# Calculate Tasseled Cap angles and distances\n\t\t\ttcAngleBG = brightness.atan2(greenness).divide(math.pi).rename(['tcAngleBG']);\n\t\t\ttcAngleGW = greenness.atan2(wetness).divide(math.pi).rename(['tcAngleGW']);\n\t\t\ttcAngleBW = brightness.atan2(wetness).divide(math.pi).rename(['tcAngleBW']);\n\t\t\ttcDistBG = brightness.hypot(greenness).rename(['tcDistBG']);\n\t\t\ttcDistGW = greenness.hypot(wetness).rename(['tcDistGW']);\n\t\t\ttcDistBW = brightness.hypot(wetness).rename(['tcDistBW']);\n\t\t\timg = img.addBands(tcAngleBG).addBands(tcAngleGW).addBands(tcAngleBW).addBands(tcDistBG).addBands(tcDistGW).addBands(tcDistBW);\n\t\t\t\n\t\t\treturn img;\n\t\n\t\timg = getTasseledCap(img)\n\t\timg = addTCAngles(img)\n\t\treturn img","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 PETImageProcess(PET_Scan):\n PET_Scan = normalise(PET_Scan)\n return PET_Scan","def step(self):\n\n for t, z, data_igm, data_cgm, RC_igm, RC_cgm in self.medium.step():\n\n Jc = np.atleast_1d(self._f_Jc(z))\n Ji = np.atleast_1d(self._f_Ji(z))\n Jlw = np.atleast_1d(self._f_Jlw(z))\n Ja = Jc + Ji\n\n # Compute spin temperature\n n_H = self.medium.parcel_igm.grid.cosm.nH(z)\n Ts = self.medium.parcel_igm.grid.hydr.Ts(z,\n data_igm['Tk'], Ja, data_igm['h_2'], data_igm['e'] * n_H)\n\n if self.pf['floor_Ts'] is not None:\n Ts = max(Ts, self.medium.parcel_igm.grid.hydr.Ts_floor(z=z))\n\n # Compute volume-averaged ionized fraction\n if self.pf['include_cgm']:\n xavg = data_cgm['h_2'] + (1. - data_cgm['h_2']) * data_igm['h_2']\n else:\n xavg = data_igm['h_2']\n\n # Derive brightness temperature\n dTb = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts, xavg)\n dTb_b = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts)\n\n # Add derived fields to data\n data_igm.update({'Ts': Ts, 'dTb': dTb, #'dTb_bulk': dTb_b,\n 'Jc': Jc, 'Ji': Ji, 'Ja': Ja, 'Jlw': Jlw})\n\n # Yield!\n yield t, z, data_igm, data_cgm, RC_igm, RC_cgm","def manipulations(path):\r\n\r\n print (\"\\n Working on %s\\n\" %(path))\r\n\r\n # Creates a folder with the results for the current image\r\n if not os.path.exists(\"Results\\\\%s\" %(path)):\r\n os.makedirs(\"Results\\\\%s\" %(path))\r\n\r\n # The variations made of the image\r\n func.pixelImage(path, 10, 10)\r\n func.animate(path)\r\n func.colorScale(path, 0)\r\n func.colorScale(path, 1)\r\n func.colorScale(path, 2)\r\n func.scan(path, 280)\r\n func.greyImage(path)\r\n func.colorSteps(path, 1)\r\n func.inverted(path)","def calculate_psf_tilts():\n for order in [1, 2]:\n\n # Get the file\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\n psf_file = resource_filename('awesimsoss', path)\n\n # Dimensions\n subarray = 'SUBSTRIP256'\n X = range(2048)\n Y = range(256)\n\n # Get the wave map\n wave_map = utils.wave_solutions(subarray, order).astype(float)\n\n # Get the y-coordinate of the trace polynomial in this column\n # (center of the trace)\n coeffs = trace_polynomials(subarray=subarray, order=order)\n trace = np.polyval(coeffs, X)\n\n # Interpolate to get the wavelength value at the center\n wave = interp2d(X, Y, wave_map)\n\n # Get the wavelength of the trace center in each column\n trace_wave = []\n for x, y in zip(X, trace):\n trace_wave.append(wave(x, y)[0])\n\n # For each column wavelength (defined by the wavelength at\n # the trace center) define an isowavelength contour\n angles = []\n for n, x in enumerate(X):\n\n w = trace_wave[x]\n\n # Edge cases\n try:\n w0 = trace_wave[x-1]\n except IndexError:\n w0 = 0\n\n try:\n w1 = trace_wave[x+1]\n except IndexError:\n w1 = 10\n\n # Define the width of the wavelength bin as half-way\n # between neighboring points\n dw0 = np.mean([w0, w])\n dw1 = np.mean([w1, w])\n\n # Get the coordinates of all the pixels in that range\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\n\n # Find the angle between the vertical and the tilted wavelength bin\n if len(xx) >= 1:\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\n else:\n angle = 0\n\n # Don't flip them upside down\n angle = angle % 180\n\n # Add to the array\n angles.append(angle)\n\n # Save the file\n np.save(psf_file, np.array(angles))\n print('Angles saved to', psf_file)","def analyze(self):\n # turn off all indicator lights\n self._stop_all()\n \n # run, but catch exceptions and abort if necessary\n try:\n # setup\n self.analysis_led[1].blink\n ims_left = self.num_images\n fluid_left = True\n \n data_session = Data(self.data_path)\n \n # run motor & imaging\n while self.power.update() and ims_left > 0:\n # run pump\n self.motor.run(self.pump_runtime)\n \n if not self.power.update():\n break\n \n # image\n time.sleep(self.rest_time)\n self.cam_led.on\n self.camera.capture()\n data_session.fetch_data()\n self.cam_led.off\n \n # subtract from remaining images every cycle\n # if the fluid sensor turns off, set remaining\n # images to the maximum possible remaining\n ims_left -= 1\n if fluid_left and \\\n not self.fluid.update() and \\\n ims_left > self.samps_after_sensor_off:\n fluid_left = False\n ims_left = self.samps_after_sensor_off\n \n # change indicator lights, given complete or power off\n if ims_left == 0:\n # set analysis to green\n self.analysis_led[1].off\n self.analysis_led[0].on\n else:\n # set analysis to solid red\n self.analysis_led[1].on\n \n # transmit data whether or not power switched off\n self.data_led.blink\n data = data_session.prepare_broadcast()\n broadcast_session = Broadcast(self.peer_ip)\n broadcast_session.broadcast_data(data)\n self.data_led.off\n \n except:\n # turn on error indicator and turn off all else\n # do not transmit data\n self._stop_all()\n self.error.on","def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min, ttl, mom_alpha=0.95, exp_zoom=1.05, min_area=0):\n\n tracks_active = []\n\n for frame_num, detections_frame in enumerate(detections, start=1):\n # apply low threshold to detections\n dets = [det for det in detections_frame if det['score'] >= sigma_l]\n\n updated_tracks = []\n for track in tracks_active:\n f_dets = list(filter(lambda det: area(det['bbox']) > min_area, dets))\n if len(f_dets) > 0:\n # get det with highest iou and similarity score\n best_match = max(f_dets, key=lambda x: iou(predict_bbox(exp_zoom, track), x['bbox']))\n if iou(track['bboxes'][-1], best_match['bbox']) >= sigma_iou:\n track['bboxes'].append(best_match['bbox'])\n track['max_score'] = max(track['max_score'], best_match['score'])\n if track['inactive'] > 0:\n track['inactive'] = 0\n elif len(track['bboxes']) > 1:\n mom = track['momentum']\n prev_bbox = np.array(track['bboxes'][-2]).reshape(2, 2)\n bbox = np.array(track['bboxes'][-1]).reshape(2, 2)\n\n track['momentum'] = mom * mom_alpha + (bbox - prev_bbox) * (1 - mom_alpha)\n\n updated_tracks.append(track)\n\n # remove from best matching detection from detections\n del dets[dets.index(best_match)]\n\n # if track was not updated, use momentum for 'ttl' frames\n if len(updated_tracks) == 0 or track is not updated_tracks[-1]:\n # if the track's ttl is over\n if track['inactive'] > ttl:\n # finish track when the conditions are met\n if track['max_score'] >= sigma_h and len(track['bboxes']) - track['inactive'] >= t_min:\n if track['inactive'] > 0:\n del track['bboxes'][-track['inactive']:]\n yield track\n else:\n # else use momentum\n # move the bbox and zoom it\n moved_bbox = predict_bbox(exp_zoom, track)\n\n track['bboxes'].append(moved_bbox)\n track['inactive'] += 1\n\n updated_tracks.append(track)\n\n # create new tracks if box large enough\n new_tracks = [\n {\n 'bboxes': [det['bbox']],\n 'max_score': det['score'],\n 'start_frame': frame_num,\n 'momentum': np.zeros((2, 2)),\n 'inactive': 0,\n }\n for det in dets if area(det['bbox']) > min_area\n ]\n tracks_active = updated_tracks + new_tracks\n\n # finish all remaining active tracks\n for track in tracks_active:\n if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min:\n yield track","def system_6(in_dir, out_dir, threshold, num_frames, num_prev_frames, blend_coef, blur=(3,3), as_numeric=True, stretched=True):\n pass","def trajectory_error_correcter(trajectories):\r\n\r\n n_birds, n_paramaters, n_time_steps = np.shape(trajectories)\r\n\r\n for i in range(n_birds):\r\n if squared_distance_calculator(trajectories[i, :, 1],\r\n trajectories[i, :, 0]) > 1.5 * min(squared_distance_calculator(\r\n trajectories[i, :, 1], trajectories[i, :, 2]), squared_distance_calculator(\r\n trajectories[i, :, 2], trajectories[i, :, 3]), squared_distance_calculator(\r\n trajectories[i, :, 3], trajectories[i, :, 4])):\r\n for l in range(n_birds):\r\n if squared_distance_calculator(trajectories[i, :, 0],\r\n trajectories[l, :, 1]) < 1.5 * min(squared_distance_calculator(\r\n trajectories[i, :, 1], trajectories[i, :, 2]), squared_distance_calculator(\r\n trajectories[i, :, 2], trajectories[i, :, 3]), squared_distance_calculator(\r\n trajectories[i, :, 3], trajectories[i, :, 4])):\r\n trajectories[i, :, :], trajectories[l, :, :] = trajectory_switcher(trajectories[i, :, :],\r\n trajectories[l, :, :], 1)\r\n break\r\n for j in range(2, n_time_steps):\r\n if squared_distance_calculator(trajectories[i, :, j - 1],\r\n trajectories[i, :, j]) > 1.5 * squared_distance_calculator(\r\n trajectories[i, :, j - 1], trajectories[i, :, j - 2]):\r\n for l in range(n_birds):\r\n if squared_distance_calculator(trajectories[i, :, j - 1],\r\n trajectories[l, :, j]) < 2 * squared_distance_calculator(\r\n trajectories[i, :, j - 1], trajectories[i, :, j - 2]):\r\n trajectories[i, :, :], trajectories[l, :, :] = trajectory_switcher(trajectories[i, :, :],\r\n trajectories[l, :, :], j)\r\n break\r\n return trajectories","def build_r_map(input_file: str, output_file: str, threshold: float):\n\n DataSiPM = db.DataSiPMsim_only('petalo', 0) # full body PET\n DataSiPM_idx = DataSiPM.set_index('SensorID')\n\n try:\n sns_response = pd.read_hdf(input_file, 'MC/sns_response')\n except ValueError:\n print(f'File {input_file} not found')\n exit()\n except OSError:\n print(f'File {input_file} not found')\n exit()\n except KeyError:\n print(f'No object named MC/sns_response in file {input_file}')\n exit()\n print(f'Analyzing file {input_file}')\n\n sel_df = rf.find_SiPMs_over_threshold(sns_response, threshold)\n\n particles = pd.read_hdf(input_file, 'MC/particles')\n hits = pd.read_hdf(input_file, 'MC/hits')\n events = particles.event_id.unique()\n\n true_r1, true_r2 = [], []\n var_phi1, var_phi2 = [], []\n var_z1, var_z2 = [], []\n\n touched_sipms1, touched_sipms2 = [], []\n\n for evt in events:\n\n ### Select photoelectric events only\n evt_parts = particles[particles.event_id == evt]\n evt_hits = hits [hits .event_id == evt]\n select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)\n if not select: continue\n\n sns_resp = sel_df[sel_df.event_id == evt]\n if len(sns_resp) == 0: continue\n\n _, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(sns_resp, true_pos, DataSiPM_idx)\n\n if len(pos1) > 0:\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:,1]\n _, var_phi = rf.phi_mean_var(pos_phi, q1)\n\n pos_z = np.array(pos1)[:,2]\n mean_z = np.average(pos_z, weights=q1)\n var_z = np.average((pos_z-mean_z)**2, weights=q1)\n r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)\n\n var_phi1 .append(var_phi)\n var_z1 .append(var_z)\n touched_sipms1.append(len(pos1))\n true_r1 .append(r)\n\n else:\n var_phi1 .append(1.e9)\n var_z1 .append(1.e9)\n touched_sipms1.append(1.e9)\n true_r1 .append(1.e9)\n\n if len(pos2) > 0:\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos2))[:,1]\n _, var_phi = rf.phi_mean_var(pos_phi, q2)\n\n pos_z = np.array(pos2)[:,2]\n mean_z = np.average(pos_z, weights=q2)\n var_z = np.average((pos_z-mean_z)**2, weights=q2)\n r = np.sqrt(true_pos[1][0]**2 + true_pos[1][1]**2)\n\n var_phi2 .append(var_phi)\n var_z2 .append(var_z)\n touched_sipms2.append(len(pos2))\n true_r2 .append(r)\n\n else:\n var_phi2 .append(1.e9)\n var_z2 .append(1.e9)\n touched_sipms2.append(1.e9)\n true_r2 .append(1.e9)\n\n a_true_r1 = np.array(true_r1)\n a_true_r2 = np.array(true_r2)\n a_var_phi1 = np.array(var_phi1)\n a_var_phi2 = np.array(var_phi2)\n a_var_z1 = np.array(var_z1)\n a_var_z2 = np.array(var_z2)\n\n a_touched_sipms1 = np.array(touched_sipms1)\n a_touched_sipms2 = np.array(touched_sipms2)\n\n\n np.savez(output_file, a_true_r1=a_true_r1, a_true_r2=a_true_r2, a_var_phi1=a_var_phi1, a_var_phi2=a_var_phi2, a_var_z1=a_var_z1, a_var_z2=a_var_z2, a_touched_sipms1=a_touched_sipms1, a_touched_sipms2=a_touched_sipms2)","def mic_of_simulation(trajectories):\n\n avpvipsol = trajectories[:, 1:(160+1)]\n navsol = trajectories[:, (160+1):]\n\n per2 = np.hstack([avpvipsol[:, ::4], navsol[:, ::3]])\n numcells = per2.shape[1]\n\n # set up mic calculator\n mic = mp.MINE(alpha=0.6, c=15, est='mic_approx')\n mic_values = []\n for combo in combinations(range(numcells), 2):\n mic.compute_score(per2[:, combo[0]], per2[:, combo[1]])\n mic_values.append(mic.mic())\n\n return mic_values","def post_process_trap(): \n #################### 0) assign internal values #################### \n from project_parameters import trapType,debug,trapFile,name,driveAmplitude,driveFrequency,Omega,dcplot,weightElectrodes,coefs,ax,az,phi,save,scale\n #from all_functions import find_saddle,plot_potential,dc_potential,set_voltages,exact_saddle,spher_harm_bas,spher_harm_exp,pfit,plotN\n import pickle\n\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n\n qe = trap.configuration.charge\n mass = trap.configuration.mass\n Zval = trap.configuration.position\n r0 = trap.configuration.r0\n RFampl = driveAmplitude \n V0 = mass*(2*np.pi*Omega)**2*(r0*10**-3)**2/qe\n X,Y,Z=trap.instance.X,trap.instance.Y,trap.instance.Z \n data = trap.configuration\n dcVoltages = set_voltages()\n ne = len(weightElectrodes)\n E = trap.instance.E\n out = trap.configuration\n if debug.post_process_trap:\n print dcVoltages,np.max(dcVoltages)#np.sum(abs(dcVoltages))\n plotN(dcVoltages,trap,'set DC voltages') \n Vdc = dc_potential(trap,dcVoltages,E)\n #[IDC,JDC,KDC] = find_saddle(Vdc,X,Y,Z,3,Zval) \n #[XDC,YDC,ZDC] = exact_saddle(Vdc,X,Y,Z,3,Zval)\n #XDC,YDC,ZDC = X[IDC],150/scale,Z[KDC]\n #print XDC,YDC,ZDC,IDC,JDC,KDC\n #dcbasis,dcscale= spher_harm_bas(XDC,YDC,ZDC,X,Y,Z,4)\n #QQ = spher_harm_exp(Vdc,dcbasis,dcscale) \n #print QQ[0:9].T\n #1) RF Analysis\n print('RF Analysis') \n Vrf = RFampl*data.EL_RF\n [Irf,Jrf,Krf] = find_saddle(Vrf,X,Y,Z,2,Zval)\n if debug.post_process_trap:\n plot_potential(Vrf,X,Y,Z,dcplot,'weighted RF potential','V_{rf} (eV)',[Irf,Jrf,Krf])\n #2) DC Analysis\n print('DC Analysis')\n trap = dc_potential(trap,dcVoltages,E,update=None)\n Vdc = trap.instance.DC\n [Idc,Jdc,Kdc] = find_saddle(Vdc,X,Y,Z,3,Zval) # only used to calculate error at end\n if debug.post_process_trap:\n plot_potential(Vdc,X,Y,Z,'1D plots','full DC potential')\n #3) determine the exact saddles of the RF and DC\n trap = dc_potential(trap,dcVoltages,E)\n Vdc = trap.instance.DC\n print('Determining exact RF saddle...')\n [Xrf,Yrf,Zrf] = exact_saddle(Vrf,X,Y,Z,2,Zval) \n print('Determining exact DC saddle...')\n [Xdc,Ydc,Zdc] = exact_saddle(Vdc,X,Y,Z,3,Zval)\n #4) determine stray field (beginning of justAnalyzeTrap)\n print('Determining compensation due to E field...')\n nx,ny,nz=X.shape[0],Y.shape[0],Z.shape[0]\n x,y,z = np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz))\n for i in range(nx):\n for j in range(ny):\n for k in range(nz):\n x[i,j,k] = X[i]\n y[i,j,k] = Y[j]\n z[i,j,k] = Z[k]\n VlessE = Vdc-E[0]*x-E[1]*y-E[2]*z\n [Xdc,Ydc,Zdc] = exact_saddle(VlessE,X,Y,Z,3) \n dist = np.sqrt((Xrf-Xdc)**2+(Yrf-Ydc)**2+(Zrf-Zdc)**2) \n #5) call pfit to built teh total field and determine the trap characteristics\n [fx,fy,fz,theta,Depth,Xe,Ye,Ze] = pfit(Vrf,Vdc,X,Y,Z,Irf,Jrf,Krf)#pfit(trap,E,Freq,RFampl)\n print('Stray field is ({0},{1},{2}) V/m.'.format(scale*E[0],scale*E[1],scale*E[2]))\n print('With this field, the compensation is optimized to {} micron.'.format(scale*dist))\n print('RF saddle: ({0},{1},{2})\\nDC saddle ({3},{4},{5}).'.format(Xrf,Yrf,Zrf,Xdc,Ydc,Zdc)) \n if debug.trap_depth:\n print('The trap escape position is at ({0},{1},{2}) microns, for a trap depth of {3} mV'.format(Xe*scale,Ye*scale,Ze*scale,Depth*scale))\n print('The trap frequencies are fx = {0} MHz, fy = {1} MHz, and fz = {2} MHz'.format(fx*10**-6,fy*10**-6,fz*10**-6))\n #6) Sanity testing; quality check no longer used\n if debug.post_process_trap:\n rfbasis,rfscale= spher_harm_bas(Xrf,Yrf,Zrf,X,Y,Z,2)\n Qrf = spher_harm_exp(Vrf,rfbasis,rfscale) \n if np.sqrt((Xrf-Xdc)**2+(Yrf-Ydc)**2+(Zrf-Zdc)**2)>0.008: \n print('Expanding DC with RF for saniy checking.')\n Qdc = spher_harm_exp(Vdc,rfbasis,rfscale) \n else:\n print('Expanding DC without RF for sanity checking.')\n dcbasis,dcscale= spher_harm_bas(Xdc,Ydc,Zdc,X,Y,Z,2)\n Qdc = spher_harm_exp(Vdc,dcbasis,dcscale) \n Arf = 2*np.sqrt( (3*Qrf[7])**2+(3*Qrf[8])**2 )\n Thetarf = 45*(Qrf[8]/abs(Qrf[8]))-90*np.arctan((3*Qrf[7])/(3*Qrf[8]))/np.pi\n Adc = 2*np.sqrt( (3*Qdc[7])**2+(3*Qdc[8])**2 )\n Thetadc = 45*(Qrf[8]/abs(Qrf[8]))-90*np.arctan((3*Qdc[7])/(3*Qdc[8]))/np.pi\n out.E = E\n out.miscompensation = dist\n out.ionpos = [Xrf,Yrf,Zdc]\n out.ionposIndex = [Irf,Jrf,Krf]\n out.frequency = [fx,fy,fz]\n out.theta = theta\n out.trap_depth = Depth/qe \n out.escapepos = [Xe,Ye,Ze]\n out.Quadrf = 2*np.array([Qrf[7]*3,Qrf[4]/2,Qrf[8]*6,-Qrf[6]*3,-Qrf[5]*3])\n out.Quaddc = 2*np.array([Qdc[7]*3,Qdc[4]/2,Qdc[8]*6,-Qdc[6]*3,-Qdc[5]*3])\n out.Arf = Arf\n out.Thetarf = Thetarf\n out.Adc = Adc\n out.Thetadc = Thetadc\n T = np.array([[2,-2,0,0,0],[-2,-2,0,0,0],[0,4,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0, 0,0,0,1]])\n Qdrf = out.Quadrf.T\n Qddc = out.Quaddc.T\n out.q = (1/V0)*T*Qdrf\n out.alpha = (2/V0)*T*Qddc\n out.Error = [X[Idc]-Xdc,Y[Jdc]-Ydc,Z[Kdc]-Zdc]\n #7) update the trapping field data structure with instance attributes\n trap.configuration=out\n trap.instance.driveAmplitude = driveAmplitude\n trap.instance.driveFrequency = driveFrequency\n trap.instance.coefs = coefs\n trap.instance.ax = ax\n trap.instance.az = az\n trap.instance.phi = phi\n trap.instance.ppt = True\n trap.instance.out = out\n if save==True:\n print('Saving '+trapFile+' as a data structure...')\n with open(trapFile,'wb') as f:\n pickle.dump(trap,f)\n return 'post_proccess_trap complete' #out # no output needed really","def qc_illumina(args):\n clarity_epp.qc.illumina.set_avg_q30(lims, args.process_id)","def filter_and_correct_expression_and_image_features(tissue, model, aggregation, patch_size, M, k, pc_correction=False, tf_correction=False):\n\n\n\n\n # Filter expression\n Y, X, dIDs, tIDs, tfs, ths, t_idx = extract_final_layer_data(tissue, model, aggregation, patch_size)\n filt_X, filt_tIDs, final_exp_idx = filter_expression(X, tIDs, M, k)\n\n\n\n if pc_correction:\n print ('Correcting with {} expression PCs'.format(pc_correction))\n pca = PCA(n_components=pc_correction)\n\n\n pca_predictors = pca.fit_transform(filt_X)\n\n # Correct Y\n lr = LinearRegression()\n lr.fit(pca_predictors, Y)\n predicted_Y = lr.predict(pca_predictors)\n corrected_Y = Y - predicted_Y\n\n # Correct X\n projected_filt_X = np.dot(pca_predictors,pca.components_)\n corrected_filt_X = filt_X - projected_filt_X\n\n # Set as return variables\n final_X = corrected_filt_X\n final_Y = corrected_Y\n\n elif tf_correction:\n print('Correcting with all technical factors')\n tf_Y = Y[t_idx,:]\n tf_filt_X = filt_X[t_idx,:]\n\n tfs[list(ths).index('SMTSISCH')] = np.log2(tfs[list(ths).index('SMTSISCH')] + 1)\n tf_predictors = tfs\n\n #Correct Y\n lr_Y = LinearRegression()\n lr_Y.fit(tf_predictors, tf_Y)\n tf_Y_predicted = lr_Y.predict(tf_predictors)\n corrected_tf_Y = tf_Y - tf_Y_predicted\n\n #Correct X\n lr_X = LinearRegression()\n lr_X.fit(tf_predictors, tf_filt_X)\n tf_filt_X_predicted = lr_X.predict(tf_predictors)\n corrected_tf_filt_X = tf_filt_X - tf_filt_X_predicted\n\n # Set as return variables\n final_X = corrected_tf_filt_X\n final_Y = corrected_tf_Y\n else:\n # Set unmodified values as return variables\n final_X = filt_X\n final_Y = Y\n\n return final_Y, final_X, dIDs, filt_tIDs, tfs, ths, t_idx","def main():\n print(\"Program version: 1.5\")\n StartTime = datetime.now()\n args = parseArguments()\n\n verbose = args.verbose\n images = args.images\n ignore_warnings = args.ignore_warnings\n if(args.silent):\n verbose = False\n images = False\n ignore_warnings = True\n\n if(args.images):\n plt.ioff()\n\n if(args.ignore_warnings):\n warnings.simplefilter('ignore', UserWarning)\n\n #sample header keywords\n # OBJECT = 'P016+03_P1_JKdeep' / Original target\n # RA = ' 01:06:37.759' / 01:06:37.7 RA (J2000) pointing\n # DEC = ' 03:32:36.096' / 03:32:36.0 DEC (J2000) pointing\n # EQUINOX = 2000. / Standard FK5 (years)\n # RADECSYS= 'FK5 ' / Coordinate reference frame\n # CRVAL1 = 16.65733 / 01:06:37.7, RA at ref pixel\n # CRVAL2 = 3.54336 / 03:32:36.0, DEC at ref pixel\n # CRPIX1 = 447. /Ref pixel in X\n # CRPIX2 = 452. / Ref pixel in Y\n # CDELT1 = -8.0000000000000E-5 / SS arcsec per pixel in RA\n # CDELT2 = 8.00000000000003E-5 / SS arcsec per pixel in DEC\n # CTYPE1 = 'RA---TAN' / pixel coordinate system\n # CTYPE2 = 'DEC--TAN' / pixel coordinate system\n # PC1_1 = 0.000000 / Translation matrix element\n # PC1_2 = 1.000000 / Translation matrix element\n # PC2_1 = -1.000000 / Translation matrix element\n # PC2_2 = 0.000000 / Translation matrix element\n\n fits_image_filenames = args.input\n\n #if directory given search for appropriate fits files\n\n if(os.path.isdir(fits_image_filenames[0])):\n print(\"detected a directory. Will search for fits files in it\")\n path = fits_image_filenames[0]\n fits_image_filenames = []\n for file in os.listdir(path):\n if file.endswith(\".fits\") and \"_astro\" not in file:\n fits_image_filenames.append(path+\"/\"+file)\n print(fits_image_filenames)\n\n multiple = False\n if(len(fits_image_filenames)>1):\n multiple = True\n not_converged = []\n converged_counter = 0\n for fits_image_filename in fits_image_filenames:\n\n result,_ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=0, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent =args.silent, sigma_threshold_for_source_detection= args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\n\n if((not result) and args.rotation_scaling):\n print(\"Did not converge. Will try again with full rotation and scaling\")\n result, _ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=args.rotation_scaling, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent=args.silent, sigma_threshold_for_source_detection=args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\n\n if(result):\n print(\"Astrometry was determined to be good.\")\n converged_counter = converged_counter+1\n else:\n print(\"Astrometry was determined to be bad.\")\n not_converged.append(fits_image_filename)\n if(args.save_bad_result):\n print(\"Result was saved anyway\")\n else:\n print(\"Result was not saved.\")\n # print(\"\")\n # print(\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n # print(\"> Astrometry for {} \".format(fits_image_filename))\n #\n # with fits.open(fits_image_filename) as hdul:\n # #print(hdul.info())\n # if(args.verbose):\n # print(\"if image is not at first position in the fits file the program will break later on\")\n # #print(hdul[0].header)\n #\n # hdu = hdul[0]\n # #hdu.verify('fix')\n # hdr = hdu.header\n #\n #\n # image_or = hdul[0].data.astype(float)\n # median = np.nanmedian(image_or)\n # image_or[np.isnan(image_or)]=median\n # image = image_or - median\n #\n # observation = find_sources(image, args.vignette)\n # #print(observation)\n #\n # positions = (observation['xcenter'], observation['ycenter'])\n # apertures = CircularAperture(positions, r=4.)\n #\n #\n # #world coordinates\n # print(\">Info found in the file -- (CRVAl: position of central pixel (CRPIX) on the sky)\")\n # print(WCS(hdr))\n #\n # hdr[\"NAXIS1\"] = image.shape[0]\n # hdr[\"NAXIS2\"] = image.shape[1]\n #\n # #wcsprm = Wcsprm(hdr.tostring().encode('utf-8')) #everything else gave me errors with python 3, seemed to make problems with pc conversios, so i wwitched to the form below\n # wcsprm = WCS(hdr).wcs\n # wcsprm_original = WCS(hdr).wcs\n # if(args.verbose):\n # print(WCS(wcsprm.to_header()))\n # wcsprm, fov_radius, INCREASE_FOV_FLAG, PIXSCALE_UNCLEAR = read_additional_info_from_header(wcsprm, hdr, args.ra, args.dec, args.projection_ra, args.projection_dec)\n # if(args.verbose):\n # print(WCS(wcsprm.to_header()))\n #\n # #print(wcsprm)\n # #wcsprm.pc = [[2, 0],[0,1]]\n #\n #\n # #Possibly usefull examples of how to use wcsprm:\n # #print(wcsprm.set())\n # #print(wcsprm.get_pc())\n # #pc = wcsprm.get_pc()\n # #print(np.linalg.det(pc))\n # #print(wcsprm.get_cdelt())\n # #wcs.fix()\n # #print(wcsprm.print_contents())\n # #print(repr(hdr.update(wcsprm.to_header().encode('utf-8')))) #not working\n #\n # #hdu.verify(\"fix\")\n # #print(repr(hdr))\n # #wcs.wcs_pix2world(pixcrd, 1)\n # #wcs.wcs_world2pix(world, 1)\n # #wcs.wcs.crpix = [-234.75, 8.3393]\n # # wcs.wcs.cdelt = np.array([-0.066667, 0.066667])\n # # wcs.wcs.crval = [0, -90]\n # # wcs.wcs.ctype = [\"RA---AIR\", \"DEC--AIR\"]\n # # wcs.wcs.set_pv([(2, 1, 45.0)])\n # # For historical compatibility, three alternate specifications of the linear transformations\n # # are available in wcslib. The canonical PCi_ja with CDELTia, CDi_ja, and the deprecated CROTAia\n # # keywords. Although the latter may not formally co-exist with PCi_ja,\n # # the approach here is simply to ignore them if given in conjunction with PCi_ja.\n # # has_pc, has_cd and has_crota can be used to determine which of these alternatives are present in the header.\n # # These alternate specifications of the linear transformation matrix are translated immediately to PCi_ja by set\n # # and are nowhere visible to the lower-level routines. In particular, set resets cdelt to unity if CDi_ja is present\n # # (and no PCi_ja). If no CROTAia is associated with the latitude axis, set reverts to a unity PCi_ja matrix.\n #\n #\n #\n #\n #\n # #get rough coordinates\n # #print(hdr[\"RA\"])\n # #coord = SkyCoord(hdr[\"RA\"], hdr[\"DEC\"], unit=(u.hourangle, u.deg), frame=\"icrs\")\n # coord = SkyCoord(wcsprm.crval[0], wcsprm.crval[1], unit=(u.deg, u.deg), frame=\"icrs\")\n # if(not PIXSCALE_UNCLEAR):\n # if(wcsprm.crpix[0] < 0 or wcsprm.crpix[1] < 0 or wcsprm.crpix[0] > image.shape[0] or wcsprm.crpix[1] > image.shape[1] ):\n # print(\"central value outside of the image, moving it to the center\")\n # coord_radec = wcsprm.p2s([[image.shape[0]/2, image.shape[1]/2]], 0)[\"world\"][0]\n # coord = SkyCoord(coord_radec[0], coord_radec[1], unit=(u.deg, u.deg), frame=\"icrs\")\n # #print(wcsprm)\n #\n #\n #\n # #better: put in nice wrapper! with repeated tries and maybe try synchron!\n # print(\">Dowloading catalog data\")\n # radius = u.Quantity(fov_radius, u.arcmin)#will prob need more\n # catalog_data = query.get_data(coord, radius, args.catalog)\n # #reference = reference.query(\"mag <20\")\n # max_sources = 500\n # if(INCREASE_FOV_FLAG):\n # max_sources= max_sources*2.25 #1.5 times the radius, so 2.25 the area\n # if(catalog_data.shape[0]>max_sources):\n # catalog_data = catalog_data.nsmallest(400, \"mag\")\n #\n # if(args.catalog == \"GAIA\" and catalog_data.shape[0] < 5):\n # print(\"GAIA seems to not have enough objects, will enhance with PS1\")\n # catalog_data2 = query.get_data(coord, radius, \"PS\")\n # catalog_data = pd.concat([catalog_data, catalog_data2])\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\"ra\", \"dec\"]], 1), r=5.)\n # print(\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\".format(catalog_data.shape[0]))\n # elif(args.catalog == \"PS\" and (catalog_data is None or catalog_data.shape[0] < 5)):\n # print(\"We seem to be outside the PS footprint, enhance with GAIA data\")\n # catalog_data2 = query.get_data(coord, radius, \"GAIA\")\n # catalog_data = pd.concat([catalog_data, catalog_data2])\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\"ra\", \"dec\"]], 1), r=5.)\n # print(\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\".format(catalog_data.shape[0]))\n #\n # #remove duplicates in catalog?\n #\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\"ra\", \"dec\"]], 1)['pixcrd'], r=5.)\n #\n #\n # #plotting what we have, I keep it in the detector field, world coordinates are more painfull to plot\n # if(args.images):\n # fig = plt.figure()\n # fig.canvas.set_window_title('Input for {}'.format(fits_image_filename))\n # plt.xlabel(\"pixel x direction\")\n # plt.ylabel(\"pixel y direction\")\n # plt.title(\"Input - red: catalog sources, blue: detected sources in img\")\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\n #\n # plt.xlim(-200,image.shape[0]+200)\n # plt.ylim(-200,image.shape[1]+200)\n # if(args.save_images):\n # name_parts = fits_image_filename.rsplit('.', 1)\n # plt.savefig(name_parts[0]+\"_image_before.pdf\")\n #\n # ###tranforming to match the sources\n # print(\"---------------------------------\")\n # print(\">Finding the transformation\")\n # if(args.rotation_scaling):\n # print(\"Finding scaling and rotation\")\n # wcsprm = register.get_scaling_and_rotation(observation, catalog_data, wcsprm, scale_guessed=PIXSCALE_UNCLEAR, verbose=args.verbose)\n # if(args.xy_transformation):\n # print(\"Finding offset\")\n # wcsprm,_,_ = register.offset_with_orientation(observation, catalog_data, wcsprm, fast=False , INCREASE_FOV_FLAG=INCREASE_FOV_FLAG, verbose= args.verbose)\n #\n # #correct subpixel error\n # obs_x, obs_y, cat_x, cat_y, distances = register.find_matches(observation, catalog_data, wcsprm, threshold=3)\n # rms = np.sqrt(np.mean(np.square(distances)))\n # best_score = len(obs_x)/(rms+10) #start with current best score\n # fine_transformation = False\n # if(args.fine_transformation):\n # for i in [2,3,5,8,10,6,4, 20,2,1,0.5]:\n # wcsprm_new, score = register.fine_transformation(observation, catalog_data, wcsprm, threshold=i)\n # if(score> best_score):\n # wcsprm = wcsprm_new\n # best_score = score\n # fine_transformation = True\n # if not fine_transformation:\n # print(\"Fine transformation did not improve result so will be discarded.\")\n # else:\n # print(\"Fine transformation applied to improve result\")\n # #register.calculate_rms(observation, catalog_data,wcs)\n #\n # #make wcsprim more physical by moving scaling to cdelt, out of the pc matrix\n # wcs =WCS(wcsprm.to_header())\n # if(args.verbose):\n # print(wcs)\n #\n # from astropy.wcs import utils\n # scales = utils.proj_plane_pixel_scales(wcs)\n # print(scales)\n # cdelt = wcsprm.get_cdelt()\n # print(cdelt)\n # scale_ratio = scales/cdelt\n # #print(scale_ratio)\n # pc = np.array(wcsprm.get_pc())\n # pc[0,0] = pc[0,0]/scale_ratio[0]\n # pc[1,0] = pc[1,0]/scale_ratio[1]\n # pc[0,1] = pc[0,1]/scale_ratio[0]\n # pc[1,1] = pc[1,1]/scale_ratio[1]\n # wcsprm.pc = pc\n # wcsprm.cdelt = scales\n # if(args.verbose):\n # print(\"moved scaling info to CDelt\")\n # print(WCS(wcsprm.to_header()))\n #\n # #WCS difference before and after\n # print(\"> Compared to the input the Wcs was changed by: \")\n # scales_original = utils.proj_plane_pixel_scales(WCS(hdr))\n # print(\"WCS got scaled by {} in x direction and {} in y direction\".format(scales[0]/scales_original[0], scales[1]/scales_original[1]))\n # #sources:\n # #https://math.stackexchange.com/questions/2113634/comparing-two-rotation-matrices\n # #https://stackoverflow.com/questions/2827393/angles-between-two-n-dimensional-vectors-in-python/13849249#13849249\n # def unit_vector(vector):\n # \"\"\" Returns the unit vector of the vector. \"\"\"\n # return vector / max(np.linalg.norm(vector), 1e-10)\n # def matrix_angle( B, A ):\n # \"\"\" comment cos between vectors or matrices \"\"\"\n # Aflat = A.reshape(-1)\n # Aflat = unit_vector(Aflat)\n # Bflat = B.reshape(-1)\n # Bflat = unit_vector(Bflat)\n # #return np.arccos((np.dot( Aflat, Bflat ) / max( np.linalg.norm(Aflat) * np.linalg.norm(Bflat), 1e-10 )))\n # return np.arccos(np.clip(np.dot(Aflat, Bflat), -1.0, 1.0))\n # #print(matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360)\n # rotation_angle = matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360\n # if((wcsprm.get_pc() @ wcsprm_original.get_pc() )[0,1] > 0):\n # text = \"counterclockwise\"\n # else:\n # text = \"clockwise\"\n # print(\"Rotation of WCS by an angle of {} deg \".format(rotation_angle)+text)\n # old_central_pixel = wcsprm_original.s2p([wcsprm.crval], 0)[\"pixcrd\"][0]\n # print(\"x offset: {} px, y offset: {} px \".format(wcsprm.crpix[0]- old_central_pixel[0], wcsprm.crpix[1]- old_central_pixel[1]))\n #\n #\n # #check final figure\n # if(args.images):\n # fig = plt.figure()\n # fig.canvas.set_window_title('Result for {}'.format(fits_image_filename))\n # plt.xlabel(\"pixel x direction\")\n # plt.ylabel(\"pixel y direction\")\n # plt.title(\"Result - red: catalog sources, blue: detected sources in img\")\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\"ra\", \"dec\"]], 1), r=5.)\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\"ra\", \"dec\"]], 1)['pixcrd'], r=5.)\n #\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\n # if(args.save_images):\n # name_parts = fits_image_filename.rsplit('.', 1)\n # plt.savefig(name_parts[0]+\"_image_after.pdf\")\n #\n # print(\"--- Evaluate how good the transformation is ----\")\n # register.calculate_rms(observation, catalog_data,wcsprm)\n #\n #\n # #updating file\n # write_wcs_to_hdr(fits_image_filename, wcsprm)\n #\n #\n # print(\"overall time taken\")\n # print(datetime.now()-StartTime)\n # if(args.images):\n # plt.show()\n if(multiple):\n print(\">> Final report:\")\n print(\"Processed {} files, {} of them did converge. The following files failed:\".format(len(fits_image_filenames), converged_counter))\n print(not_converged)\n print(\"-- finished --\")","def analyze(ctx, filename, trigger, threshold, eyecandy, ignore_extra=False,\n fix_missing=False, window_height=None, window_width=None, output=None, notebook=None,\n calibration=None, distance=None, verbose=False, debug=False,processes=None,\n by_channel=False, integrity_filter=0.0): \n #### FILEPATHS\n if not os.path.isfile(filename):\n filename = match_filename(filename)\n data_directory, data_name = os.path.split(filename)\n name, extension = os.path.splitext(data_name)\n analog_file = os.path.join(data_directory, name +'.analog')\n stimulus_file = os.path.join(data_directory, name + \".stimulus\")\n ctx.obj = {\"filename\": os.path.join(data_directory,name)}\n\n if not notebook:\n notebook = find_notebook(data_directory)\n\n #### LOGGING CONFIGURATION\n fh = logging.FileHandler(os.path.join(data_directory,name + '.log'))\n fh.setLevel(logging.DEBUG)\n ch = logging.StreamHandler()\n if verbose:\n ch.setLevel(logging.INFO)\n # tracemalloc.start()\n elif debug:\n ch.setLevel(logging.DEBUG)\n\n else:\n ch.setLevel(logging.WARNING)\n if processes!=None:\n config.processes = processes\n formatter = logging.Formatter('%(asctime)s %(levelname)s - %(message)s', '%H:%M:%S')\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n logger.addHandler(fh)\n logger.addHandler(ch)\n logger.info(\"Verbose logging on\")\n\n lab_notebook = glia.open_lab_notebook(notebook)\n experiment_protocol = glia.get_experiment_protocol(lab_notebook, name)\n flicker_version = experiment_protocol[\"flickerVersion\"]\n\n\n #### LOAD STIMULUS\n try:\n ctx.obj[\"stimulus_list\"] = glia.load_stimulus(stimulus_file)\n except OSError:\n print(\"No .stimulus file found. Attempting to create from .analog file.\".format(trigger))\n if flicker_version==0.3:\n ctx.obj[\"stimulus_list\"] = glia.create_stimulus_list(\n analog_file, stimulus_file, notebook, name, eyecandy, ignore_extra,\n calibration, distance, threshold)\n print('finished creating stimulus list')\n elif trigger == \"ttl\":\n raise ValueError('not implemented')\n else:\n raise ValueError(\"invalid trigger: {}\".format(trigger))\n\n #### LOAD SPIKES\n spyking_regex = re.compile('.*\\.result.hdf5$')\n eye = experiment_protocol['eye']\n experiment_n = experiment_protocol['experimentNumber']\n\n date = experiment_protocol['date'].date().strftime(\"%y%m%d\")\n\n retina_id = date+'_R'+eye+'_E'+experiment_n\n if extension == \".txt\":\n ctx.obj[\"units\"] = glia.read_plexon_txt_file(filename,retina_id, channel_map)\n elif re.match(spyking_regex, filename):\n ctx.obj[\"units\"] = glia.read_spyking_results(filename)\n else:\n raise ValueError('could not read {}. Is it a plexon or spyking circus file?')\n\n #### DATA MUNGING OPTIONS\n if integrity_filter>0.0:\n good_units = solid.filter_units_by_accuracy(\n ctx.obj[\"units\"], ctx.obj['stimulus_list'], integrity_filter)\n filter_good_units = glia.f_filter(lambda u,v: u in good_units)\n ctx.obj[\"units\"] = filter_good_units(ctx.obj[\"units\"])\n\n if by_channel:\n ctx.obj[\"units\"] = glia.combine_units_by_channel(ctx.obj[\"units\"])\n\n\n # prepare_output\n plot_directory = os.path.join(data_directory, name+\"-plots\")\n config.plot_directory = plot_directory\n\n os.makedirs(plot_directory, exist_ok=True)\n os.chmod(plot_directory, 0o777)\n\n if output == \"pdf\":\n logger.debug(\"Outputting pdf\")\n ctx.obj[\"retina_pdf\"] = PdfPages(glia.plot_pdf_path(plot_directory, \"retina\"))\n ctx.obj[\"unit_pdfs\"] = glia.open_pdfs(plot_directory, list(ctx.obj[\"units\"].keys()), Unit.name_lookup())\n # c connotes 'continuation'\n ctx.obj[\"c_unit_fig\"] = partial(glia.add_to_unit_pdfs,\n unit_pdfs=ctx.obj[\"unit_pdfs\"])\n ctx.obj[\"c_retina_fig\"] = lambda x: ctx.obj[\"retina_pdf\"].savefig(x)\n \n elif output == \"png\":\n logger.debug(\"Outputting png\")\n ctx.obj[\"c_unit_fig\"] = glia.save_unit_fig\n ctx.obj[\"c_retina_fig\"] = glia.save_retina_fig\n os.makedirs(os.path.join(plot_directory,\"00-all\"), exist_ok=True)\n\n for unit_id in ctx.obj[\"units\"].keys():\n name = unit_id\n os.makedirs(os.path.join(plot_directory,name), exist_ok=True)","def system_5(in_dir, out_dir, threshold, num_frames=150, num_prev_frames=10, blur=(3,3), as_numeric=True, stretched=True):\n filenames = _prepare_filenames(in_dir, num_frames=150)\n initial_background_model = np.array([cv2.imread(f) for f in filenames[0:num_prev_frames]])\n seed_img = mode(initial_background_model)\n previous_frames = deque(initial_background_model, maxlen=num_prev_frames)\n\n for i, f in tqdm(enumerate(filenames[num_prev_frames:])):\n img = lm(cv2.imread(f))","def check_cti(image, CTI, verbose=0):\n\n#\n# Initialize ctiDict\n#\n ctiDict = {'isCTI': False}\n ctiDict['expnum'] = image['EXPNUM']\n\n # Also create the BAND and NITE keywords if they are not present\n try:\n image['BAND']\n except:\n image['BAND'] = decaminfo.get_band(image['FILTER'])\n try:\n image['NITE']\n except:\n image['NITE'] = decaminfo.get_nite(image['DATE-OBS'])\n\n band = image['BAND'].strip()\n sec = section2slice(image['DATASEC' + CTI['amp']])\n#\n# This could become useful if it is necessary to start examining the opposite amplifier in\n# conjunction with the amplifier that is having a problem\n#\n# if (CTI['amp']==\"A\"):\n# osec = section2slice(image['DATASEC'+'B'])\n# else:\n# osec = section2slice(image['DATASEC'+'A'])\n\n maxiter = 10\n converge_num = 0.0001\n clipsig = 3.0\n\n clip_avg, clip_med, clip_std = lb.medclip(image.data[sec], clipsig, maxiter, converge_num, verbose=0)\n logger.info(' CTI: Global(clipped): median = {:.3f}, stddev = {:.3f} '.format(clip_med, clip_std))\n ctiDict['cmed'] = float(clip_med)\n ctiDict['cstd'] = float(clip_std)\n clow = clip_med - (3.0 * clip_std)\n ctiDict['clow'] = float(clow)\n\n# oclip_avg,oclip_med,oclip_std=medclip(image.data[osec],clipsig,maxiter,converge_num,verbose)\n# print(\" Global(oclipped): median = {:.3f}, stddev = {:.3f} \".format(oclip_med,oclip_std))\n# oclow=oclip_med-(3.0*oclip_std)\n\n#\n# Obtain row-by-row median to look for horizontal striping (also needed to check/reject edgebleeds)\n#\n row_med = np.median(image.data[sec], axis=1)\n wsm = np.where(row_med < clow)\n nrow_low = row_med[wsm].size\n#\n# Hacky attempt to check for edge-bleed\n#\n iedge = [4, 4091]\n while row_med[iedge[0]] < clow:\n iedge[0] = iedge[0] + 1\n while row_med[iedge[1]] < clow:\n iedge[1] = iedge[1] - 1\n if iedge[0] == 4:\n iedge[0] = 0\n if iedge[1] == 4091:\n iedge[1] = 4095\n nrow_edge = 4096 - (iedge[1] - iedge[0] + 1)\n logger.info(' CTI: Number of low rows: {:d} (nrow_edge={:d}) '.format(nrow_low, nrow_edge))\n\n#\n# Blank out pixels that are below the 3-sigma level with respect to median\n# This removes power from vertical stripes\n#\n wsm = np.where(image.data[sec] < clow)\n npix_low = image.data[sec][wsm].size\n logger.info(' CTI: Number of low pixels: {:d} '.format(npix_low))\n u = image.data[sec] - clip_med\n u[wsm] = 0.0\n#\n# Harder cut currently not needed. If used this would get rid of all pixels below the median level\n# (effectively this reduces the amount that noise suppresses contrast of the auto-correlation signal from CTI)\n#\n# wsm=np.where(u<0.)\n# npix_zero=u[wsm].size\n# logger.info(' CTI: Number of sub-zero pixels: {:d} '.format(npix_zero))\n# u[wsm]=0.0\n\n#\n# Calculate a set of auto-correlations by sampling lags in the x-direction and\n# then two diaganol sets at PA=+/-45 degrees\n# Note: y-direction lags would be succeptible to both bad columns and bleeds.\n# These are normalized by the auto-correlation with lag 0 (defined as 'a' below).\n# Take a maximum lag that will be calculated and use that to trim the image.\n# Note: This both gets rid of most edge-effects automatically but also removes the need to calculate an effective normalization for higher lags\n#\n maxlag = 100\n lagList = [0, 1, 3, 5, 7, 11, 15, 19, 23, 31, 37, 45]\n\n a = np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag:-maxlag, maxlag:-maxlag])\n# b=np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag:-maxlag,maxlag:-maxlag])\n x = [1.0]\n d1 = [1.0]\n d2 = [1.0]\n# vx=[1.0]\n# vd1=[1.0]\n# vd2=[1.0]\n#\n# More lags than those sampled are needed because the diagonal (PA=+/-45) measures will need to be interpolated\n# for comaparison to lags in the x-direction.\n#\n\n for lag in lagList:\n if lag != 0:\n x.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag:-maxlag, maxlag - lag:-maxlag - lag]) / a)\n d1.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag-lag:-maxlag - lag, maxlag - lag:-maxlag - lag]) / a)\n d2.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag-lag:-maxlag - lag, maxlag + lag:-maxlag + lag]) / a)\n# vx.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag:-maxlag,maxlag-lag:-maxlag-lag])/b)\n# vd1.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag-lag:-maxlag-lag,maxlag-lag:-maxlag-lag])/b)\n# vd2.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag-lag:-maxlag-lag,maxlag+lag:-maxlag+lag])/b)\n\n data = {'lag': np.array(lagList),\n 'x': np.array(x),\n 'd1': np.array(d1),\n 'd2': np.array(d2)\n# 'vx':np.array(vx),\n# 'vd1':np.array(vd1),\n# 'vd2':np.array(vd2)\n }\n\n r2 = np.sqrt(2.0)\n l1 = data['lag']\n l2 = data['lag'] * r2\n x1 = data['x']\n d1i = np.interp(data['lag'], l2, data['d1'])\n d2i = np.interp(data['lag'], l2, data['d2'])\n rd1 = data['x'] / d1i\n rd2 = data['x'] / d2i\n\n# vx1=data['vx']\n# vd1i=np.interp(data['lag'],l2,data['vd1'])\n# vd2i=np.interp(data['lag'],l2,data['vd2'])\n# vrd1=data['vx']/vd1i\n# vrd2=data['vx']/vd2i\n## vdx=data['x']/data['vx']\n# vdx=(rd1+rd2)/(vrd1+vrd2)\n\n logger.info(' CTI: lags {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(l1[3], l1[4], l1[6], l1[8], l1[10]))\n logger.info(' CTI: lx {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(x1[3], x1[4], x1[6], x1[8], x1[10]))\n logger.info(' CTI: d1i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(d1i[3], d1i[4], d1i[6], d1i[8], d1i[10]))\n logger.info(' CTI: d2i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(d2i[3], d2i[4], d2i[6], d2i[8], d2i[10]))\n logger.info(' CTI: ld1 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(rd1[3], rd1[4], rd1[6], rd1[8], rd1[10]))\n logger.info(' CTI: ld2 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(rd2[3], rd2[4], rd2[6], rd2[8], rd2[10]))\n# logger.info(' CTI: lvx {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vx1[3],vx1[4],vx1[6],vx1[8],vx1[10]))\n# logger.info(' CTI:vd1i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vd1i[3],vd1i[4],vd1i[6],vd1i[8],vd1i[10]))\n# logger.info(' CTI:vd2i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vd2i[3],vd2i[4],vd2i[6],vd2i[8],vd2i[10]))\n# logger.info(' CTI:vld1 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vrd1[3],vrd1[4],vrd1[6],vrd1[8],vrd1[10]))\n# logger.info(' CTI:vld2 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vrd2[3],vrd2[4],vrd2[6],vrd2[8],vrd2[10]))\n# logger.info(' CTI:vdx0 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vdx[3],vdx[4],vdx[6],vdx[8],vdx[10]))\n\n#\n# Set band dependent thresholds...\n# Note the criteria used are based on an empirical study of the one example we currently have (CCD=41, Y6)\n#\n nrow_lim = 5\n if band != \"Y\":\n cclim = 0.9\n else:\n cclim = 1.15\n#\n# Now check and set flag based on empirical critera.\n# First are the horizontal streaks that can appear...\n# Second are the comparison of the auto-correlation in the x and average of the diaganol directrions\n#\n flag_cti = False\n if nrow_low - nrow_edge >= nrow_lim:\n flag_cti = True\n\n avg_rd = (rd1 + rd2) / 2.0\n if avg_rd[3] > cclim and avg_rd[4] > cclim and avg_rd[6] > cclim:\n flag_cti = True\n\n if flag_cti:\n ctiDict['isCTI'] = True\n\n return ctiDict","def integrateObservation(self, img,action):\n self.States.append(img)\n self.Actions.append(action) \n #self.printLevelScene()","def motionCorrection(baseName, outDir, rawDataFile, granularity ='plane',\n maxDisplacement=[40, 40],\n nCpu=(multiprocessing.cpu_count() - 1), \n exportFrames=True):\n try:\n # instead of os.makedirs use os.mkdir\n # the former creates also intermediate dirs\n os.mkdir(outDir + '/' + baseName)\n except OSError:\n print('Either directory exists or \\\n the intermedite directories do not exist!')\n raise\n print(\"--Motion correction started with %s...\" % baseName)\n # Checking if there are two cycles of images. Important to know otherwise \n # you get merged motion corrected images.\n allImages = (glob.glob(rawDataFile))\n tseries_names = [os.path.basename(imageName).split('_')[0] for imageName in allImages]\n uniqueTSeries = Counter(tseries_names).keys()\n tseries_lengths = Counter(tseries_names).values()\n \n if len(uniqueTSeries) > 1:\n print('---More than 1 T-Series found. Aligning them together...')\n\n match = [int(re.search(r'Cycle(.*?)_',imageName).group(1)) for imageName in allImages]\n uniqueCycles = numpy.unique(numpy.asarray(match))\n \n if len(uniqueCycles) > 1:\n warnstr='More than 1 image cycle detected. Aborting alignment.'\n warnings.warn(warnstr)\n return \n sequence = [sima.Sequence.create('TIFFs', [[rawDataFile]])]\n print(\"Creating sima dataset of non-aligned images\")\n nonAlignedDatasetDir = outDir + '/' + baseName + '/' + 'TIFFs.sima'\n sima.ImagingDataset(sequence, nonAlignedDatasetDir)\n\n print(\"Running motion correction.\")\n \n mc_approach = sima.motion.HiddenMarkov2D(granularity=granularity,\n max_displacement=maxDisplacement,\n verbose=True,\n n_processes=nCpu)\n print(\"Creating sima dataset of aligned images\")\n motCorrDir = outDir + '/' + baseName + '/' + 'motCorr.sima'\n dataset = mc_approach.correct(sequence, motCorrDir)\n\n if exportFrames:\n print(\"Exporting motion-corrected movies.\")\n \n for iTSeries, curr_T_series in enumerate(uniqueTSeries):\n \n start_frame = tseries_names.index(curr_T_series)\n end_frame = start_frame+tseries_lengths[iTSeries]\n dataset[0,start_frame:end_frame].export_frames([[[os.path.join(motCorrDir,\n '{t}_motCorr.tif'.format(t = curr_T_series))]]],\n fill_gaps=True)\n \n\n print(\"--Motion correction done with %s...\" % baseName)\n\n return uniqueTSeries","def trajectory_error_correcter_improved(trajectories):\r\n\r\n n_birds, n_paramaters, n_time_steps = np.shape(trajectories)\r\n\r\n conditional_squared_distance = 3 * min(squared_distance_calculator(\r\n trajectories[0, :, 1], trajectories[0, :, 2]), squared_distance_calculator(\r\n trajectories[0, :, 2], trajectories[0, :, 3]), squared_distance_calculator(\r\n trajectories[0, :, 3], trajectories[0, :, 4]))\r\n\r\n difference_array = trajectories[:, :, 1:] - trajectories[:, :, :-1]\r\n squared_distance_array = np.sum(difference_array ** 2, axis=1) # creates array with shape (n_birds, n_time_steps-1)\r\n splits_array = squared_distance_array > conditional_squared_distance # Creates boolean array with True at location of splits\r\n splits_indices = np.array(np.nonzero(splits_array)) # Returns array with shape (n_axes, n_splits)\r\n\r\n counter = 0\r\n limit = 510000\r\n while len(splits_indices[0, :]) != 0 and counter < limit:\r\n counter += 1\r\n indices_of_birds_with_same_split = list(np.nonzero(splits_indices[1, :] == splits_indices[1, 0]))[0]\r\n position_of_first_bird = trajectories[splits_indices[0, 0], :, splits_indices[1, 0]]\r\n for count, i in enumerate(indices_of_birds_with_same_split):\r\n position_of_second_bird = trajectories[splits_indices[0, i], :, splits_indices[1, i] + 1]\r\n if squared_distance_calculator(position_of_first_bird,\r\n position_of_second_bird) < conditional_squared_distance:\r\n trajectories[splits_indices[0, 0], :, :], trajectories[splits_indices[0, i], :,\r\n :] = trajectory_switcher(\r\n trajectories[splits_indices[0, 0], :, :],\r\n trajectories[splits_indices[0, i], :, :], splits_indices[1, i] + 1)\r\n splits_array[splits_indices[0, 0], :], splits_array[splits_indices[0, i], :] = splits_array_switcher(\r\n splits_array[splits_indices[0, 0], :],\r\n splits_array[splits_indices[0, i], :], splits_indices[1, i])\r\n\r\n splits_array[splits_indices[0, 0], splits_indices[\r\n 1, 0]] = False # CHANGE SPLITS_ARRAY AT LOCATION OF SPLIT MANUALLY.\r\n # splits_array[splits_indices[0, i], splits_indices[1, i]] = False\r\n splits_indices = np.array(np.nonzero(splits_array))\r\n break\r\n if counter%4000 == 0:\r\n print(f\"{counter} - Corrections left: {len(splits_indices[0, :])}\")\r\n if counter == limit:\r\n print(\"The trajectory correction failed\")\r\n return trajectories, False\r\n # print(f\"The number of corrections left is {len(splits_indices[0, :])}\")\r\n # trajectory_plotter(trajectories)\r\n return trajectories, True","def trap_depth(V,X,Y,Z,Im,Jm,Km): \n from project_parameters import debug,scale,position\n #from all_functions import sum_of_e_field,spher_harm_bas,spher_harm_exp,spher_harm_cmp,find_saddle\n def a(a,N):\n \"\"\"Shortcut function to convert array x into a row vector.\"\"\" \n a=np.ravel(a, order='F') # Same order\n return a\n N1,N2,N3=V.shape\n N=N1*N2*N3\n [Ex,Ey,Ez]=np.gradient(V,abs(X[1]-X[0])/scale,abs(Y[1]-Y[0])/scale,abs(Z[1]-Z[0])/scale)\n E=np.sqrt(Ex**2+Ey**2+Ez**2)\n # identify the escape position and height by checking each point\n minElectricField=np.max(E) # initialize as maximum E field magnitude\n distance=0\n escapeHeight=1\n escapePosition=[0,0,0]\n [Im,Jm,Km] = find_saddle(V,X,Y,Z,3)\n Vm = V[Im,Jm,Km]\n for i in range(N1):\n for j in range(N2):\n for k in range(N3):\n if E[i,j,k]<minElectricField:\n distance=abs(np.sqrt((Im-i)**2+(Jm-j)**2+(Km-k)**2)) \n if distance > 6:\n minElectricField=E[i,j,k]\n escapeHeight=V[i,j,k]\n escapePosition=[i,j,k]\n if debug.trap_depth: \n print E[i,j,k],V[i,j,k],[i,j,k],distance\n check=1 \n if debug.trap_depth: \n print minElectricField,escapeHeight,escapePosition,distance \n if distance<check:\n print('trap_depth.py: Escape point too close to trap minimum. Improve grid resolution or extend grid.')\n if escapeHeight>0.2:\n print('trap_depth.py: Escape point parameter too high. Improve grid resolution or extend grid.')\n D=escapeHeight-Vm\n [Ie,Je,Ke]=escapePosition\n [Xe,Ye,Ze]=[X[Ie],Y[Je],Z[Ke]] \n return [D,Xe,Ye,Ze]","def pan_corr(file):\n\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\flatfield\\\\NHDBflat_1D'\n # # infile = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Jure_naloga_banje_raw_pyt\\\\NHDRGoreMorje_3D'\n #\n # # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\n # in_ref = in_path + in_pan_ref_file\n #\n # inreffil = gdal.Open(in_ref)\n # image_ref = inreffil.ReadAsArray()\n # # size_ref = image_ref.shape\n # # pix_count = size_ref[0]*size_ref[1]\n #\n # image_ref = image_ref[800:930, 1420:1640]\n # size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n #\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # raz_g1 = 1\n # raz_g2 = g1_avg/g2_avg\n # raz_r1 = g1_avg/r1_avg\n # raz_b1 = g1_avg/b1_avg\n #\n # avg = (g1+g2+r1+b1)/pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n raz_g1 = 1\n raz_g2 = 1.0245196396115988\n raz_r1 = 1.0131841989689434\n raz_b1 = 1.0517113199247086\n\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Slo_PAN\\_26_30\\\\'\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\"nhd\") and filename.lower().endswith(\"tif\")]\n\n \n\n \n\n # print('image', i)\n in_ref=file\n inreffil = gdal.Open(in_ref)\n image_ref = inreffil.ReadAsArray()\n size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n # pix_count = np.count_nonzero(image_ref)\n # pix_count = 3664*650\n\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # avg = (g1 + g2 + r1 + b1) / pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n # popravek\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\n\n\n for i in range(size_ref[0]):\n for j in range(size_ref[1]):\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\n \n _,_,_,_,P=return_flatfield_set_path(2)\n P_flat=gdal_array.LoadFile(P)\n \n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \n \n # outout\n \n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\n \n out=os.path.abspath(file)+\"/corr/\"+os.path.basename(file)[:-4] + \"_pop_flat_corr.tif\"\n\n \n # out = in_ref[:-4] + \"_pop_flat_corr.tif\"\n\n driver = gdal.GetDriverByName('GTiff')\n\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\n\n outband = outRaster.GetRasterBand(1)\n outband.WriteArray(im_p_pop)\n outband.FlushCache()","def light_measure_rn_weit(data, weit_data, pix_size, cx, cy, z0, R_low, R_up):\n\tDa0 = Test_model.angular_diameter_distance(z0).value\n\tR_pix_low = (R_low * 1e-3 * rad2arcsec / Da0) / pix_size\n\tR_pix_up = (R_up * 1e-3 * rad2arcsec / Da0) / pix_size\n\n\tNx = data.shape[1]\n\tNy = data.shape[0]\n\tx0 = np.linspace(0, Nx-1, Nx)\n\ty0 = np.linspace(0, Ny-1, Ny)\n\tpix_id = np.array(np.meshgrid(x0,y0))\n\n\t#..center pixel point\n\tdev_05_x = cx - np.int( cx )\n\tdev_05_y = cy - np.int( cy )\n\n\tif dev_05_x > 0.5:\n\t\txn = np.int( cx ) + 1\n\telse:\n\t\txn = np.int( cx )\n\n\tif dev_05_y > 0.5:\n\t\tyn = np.int( cy ) + 1\n\telse:\n\t\tyn = np.int( cy )\n\n\tdr = np.sqrt(((2*pix_id[0] + 1) / 2 - (2*xn + 1) / 2)**2 + ((2*pix_id[1] + 1) / 2 - (2*yn + 1) / 2)**2)\n\tidu = (dr >= R_pix_low) & (dr <= R_pix_up)\n\n\ttheta = np.arctan2((pix_id[1,:] - yn), (pix_id[0,:] - xn))\n\tchi = theta * 180 / np.pi\n\n\tsamp_chi = chi[idu]\n\tsamp_flux = data[idu]\n\tweit_arr = weit_data[idu]\n\tIntns = np.nansum( samp_flux * weit_arr ) / np.nansum( weit_arr )\n\n\tid_nn = np.isnan(samp_flux)\n\tN_pix = np.sum( id_nn == False )\n\tnsum_ratio = np.nansum(weit_arr) / np.sum( id_nn == False )\n\n\tcdr = R_up - R_low\n\td_phi = ( cdr / (0.5 * (R_low + R_up) ) ) * 180 / np.pi\n\tN_phi = np.int(360 / d_phi) + 1\n\tphi = np.linspace(0, 360, N_phi)\n\tphi = phi - 180.\n\n\ttmpf = []\n\tfor tt in range(len(phi) - 1):\n\t\tidv = (samp_chi >= phi[tt]) & (samp_chi <= phi[tt + 1])\n\n\t\tset_samp = samp_flux[idv]\n\t\tset_weit = weit_arr[idv]\n\n\t\tttf = np.nansum(set_samp * set_weit) / np.nansum( set_weit )\n\t\ttmpf.append(ttf)\n\n\t# rms of flux\n\ttmpf = np.array(tmpf)\n\tid_inf = np.isnan(tmpf)\n\ttmpf[id_inf] = np.nan\n\tid_zero = tmpf == 0\n\ttmpf[id_zero] = np.nan\n\n\tid_nan = np.isnan(tmpf)\n\tid_fals = id_nan == False\n\tTmpf = tmpf[id_fals]\n\n\tRMS = np.std(Tmpf)\n\tif len(Tmpf) > 1:\n\t\tIntns_err = RMS / np.sqrt(len(Tmpf) - 1)\n\telse:\n\t\tIntns_err = RMS\n\n\t#Intns_r = (0.5 * (R_low + R_up) )\n\tcen_r = np.nansum(dr[idu] * weit_arr) / np.nansum( weit_arr ) * pix_size\n\tIntns_r = cen_r * Da0 * 1e3 / rad2arcsec\n\n\tIntns, Intns_err = Intns / pix_size**2, Intns_err / pix_size**2\n\n\treturn Intns, Intns_r, Intns_err, N_pix, nsum_ratio","def preprocess_RSofia(img):\n for i in range(480):\n for j in range(200):\n img[i,j]=0\n for i in range(150):\n for j in range(500):\n img[i,j]=0 \n for i in range(475):\n for j in range(90):\n img[i,img.shape[1]-1-j]=0\n for i in range(90):\n for j in range(465):\n img[i,img.shape[1]-1-j]=0\n return img","def continue_with_exposure(self):\r\n # Allocate space to give to scan_until_abort, and name the two\r\n # rows appropriately.\r\n self.data_pair = self.cam.get_new_array(n_images=2)\r\n self.pump_probe_data = self.data_pair[0]\r\n self.probe_only_data = self.data_pair[1]\r\n # Keep track of which image will be updated next\r\n self.next_data_has_pump = True\r\n\r\n # Tell self.thread what to do when the camera has new images\r\n self.cam.new_images.connect(self.send_new_images)\r\n\r\n # Get the current array of wavelengths from cam\r\n self.wavelen_arr = self.cam.get_wavelen_array()\r\n\r\n # Queue a call to cam.scan_until_abort\r\n self.startAcq.emit(self.data_pair)\r\n\r\n # Tell listeners (plotting widgets) to start displaying data too\r\n self.startDisplay.emit()","def main():\n\n rules = parse_input(get_input())\n for part in [5, 18]:\n image = np.array(START_PATTERN).astype(bool)\n for i in range(part):\n image = enlarge(image, rules)\n count = sum(sum(ch for ch in row) for row in image)\n\n print(\"Number of # in the final matrix after {} iterations is {}.\".format(part, count))\n return","def image_processing(eye_frame, threshold):\n kernel = np.ones((3, 3), np.uint8)\n new_frame = cv2.bilateralFilter(eye_frame, 10, 15, 15)\n new_frame = cv2.erode(new_frame, kernel, iterations=3)\n new_frame = cv2.threshold(new_frame, threshold, 255, cv2.THRESH_BINARY)[1]\n\n return new_frame","def process_image_for_heuristic_af(self, tile_key):\n\n # image provided as numpy array from dictionary,\n # already cropped to 512x512\n img = self.img[tile_key]\n mean = int(np.mean(img))\n # recast as int16 before mean subtraction\n img = img.astype(np.int16)\n img -= mean\n # Autocorrelation:\n norm = np.sum(img ** 2)\n autocorr = fftconvolve(img, img[::-1, ::-1])/norm\n height, width = autocorr.shape[0], autocorr.shape[1]\n # Crop to 64-pixel central region:\n autocorr = autocorr[int(height/2 - 32):int(height/2 + 32),\n int(width/2 - 32):int(width/2 + 32)]\n # Calculate coefficients:\n fi = self.muliply_with_mask(autocorr, self.fi_mask)\n fo = self.muliply_with_mask(autocorr, self.fo_mask)\n apx = self.muliply_with_mask(autocorr, self.apx_mask)\n amx = self.muliply_with_mask(autocorr, self.amx_mask)\n apy = self.muliply_with_mask(autocorr, self.apy_mask)\n amy = self.muliply_with_mask(autocorr, self.amy_mask)\n # Check if tile_key already in dictionary:\n if not (tile_key in self.foc_est):\n self.foc_est[tile_key] = []\n if not (tile_key in self.astgx_est):\n self.astgx_est[tile_key] = []\n if not (tile_key in self.astgy_est):\n self.astgy_est[tile_key] = []\n # Calculate single-image estimators for current tile key:\n if len(self.foc_est[tile_key]) > 1:\n self.foc_est[tile_key].pop(0)\n self.foc_est[tile_key].append(\n (fi - fo) / (fi + fo))\n if len(self.astgx_est[tile_key]) > 1:\n self.astgx_est[tile_key].pop(0)\n self.astgx_est[tile_key].append(\n (apx - amx) / (apx + amx))\n if len(self.astgy_est[tile_key]) > 1:\n self.astgy_est[tile_key].pop(0)\n self.astgy_est[tile_key].append(\n (apy - amy) / (apy + amy))","def imageProcessing(filepath):\n imagedata = []\n for img in glob.glob(filepath):\n edit_image = cv2.imread(img, cv2.IMREAD_GRAYSCALE)\n edit_image = cv2.resize(255-edit_image, (28, 28))\n\n cv2.imwrite(img, edit_image)\n (thresh, edit_image) = cv2.threshold(edit_image, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n while np.sum(edit_image[0]) == 0:\n edit_image = edit_image[1:]\n\n while np.sum(edit_image[:,0]) == 0:\n edit_image = np.delete(edit_image,0,1)\n\n while np.sum(edit_image[-1]) == 0:\n edit_image = edit_image[:-1]\n\n while np.sum(edit_image[:,-1]) == 0:\n edit_image = np.delete(edit_image,-1,1)\n\n rows,cols = edit_image.shape\n if rows > cols:\n factor = 20.0/rows\n rows = 20\n cols = int(round(cols*factor))\n edit_image = cv2.resize(edit_image, (cols,rows))\n else:\n factor = 20.0/cols\n cols = 20\n rows = int(round(rows*factor))\n edit_image = cv2.resize(edit_image, (cols, rows))\n\n colsPadding = (int(math.ceil((28-cols)/2.0)),int(math.floor((28-cols)/2.0)))\n rowsPadding = (int(math.ceil((28-rows)/2.0)),int(math.floor((28-rows)/2.0)))\n edit_image = np.lib.pad(edit_image,(rowsPadding,colsPadding),'constant')\n shiftx,shifty = getBestShift(edit_image)\n shifted = shift(edit_image,shiftx,shifty)\n edit_image = shifted\n edit_image = edit_image.flatten()\n imagedata.append(edit_image)\n return imagedata","def system_7(seed_img_file, in_dir, out_dir, threshold, num_frames, num_prev_frames, blend_coef, blur=(3,3), as_numeric=True, stretched=True):\n pass","def autoAnalyze(self):\n print(\"Perfoming full automatic analysis...\")\n t1=time.perf_counter()\n self.cleanUp()\n self.figure_rois()\n self.figure_roi_inspect_all()\n self.figure_dGoR_roi(showEach=False,saveAs=self.folderSave+\"/avg.png\")\n self.figure_dGoR_roi(showEach=True,saveAs=self.folderSave+\"/each.png\")\n self.index()\n print(\"analysis completed in %.02f sec\"%(time.perf_counter()-t1))","def recompute_exit_pupil(self):\r\n\r\n rearZ = self.rear_z()\r\n if rearZ <= 0.0:\r\n print('Not focus')\r\n rearRadius = self.rear_aperture()\r\n samples = 1024 * 1024\r\n half = 2.0 * rearRadius\r\n proj_bmin, proj_bmax = ti.Vector([-half, -half]), ti.Vector([half, half])\r\n for i in range(pupil_interval_count):\r\n r0 = ti.cast(i, ti.f32) / pupil_interval_count * self.film_diagnal / 2.0\r\n r1 = ti.cast(i + 1, ti.f32) / pupil_interval_count * self.film_diagnal / 2.0\r\n bmin, bmax = make_bound2()\r\n count = 0\r\n for j in range(samples):\r\n u, v= ti.random(), ti.random()\r\n film_pos = ti.Vector([lerp(ti.cast(j, ti.f32)/samples, r0, r1), 0.0, 0.0])\r\n x, y = lerp(u, -half, half), lerp(v, -half, half)\r\n lens_pos = ti.Vector([x, y, rearZ])\r\n if inside_aabb(bmin, bmax, ti.Vector([x, y])):\r\n ti.atomic_add(count, 1)\r\n else:\r\n ok, _, _ = self.gen_ray_from_film(film_pos, (lens_pos - film_pos).normalized())\r\n if ok:\r\n bmin, bmax = bound_union_with(bmin,bmax, ti.Vector([x, y]))\r\n ti.atomic_add(count, 1)\r\n\r\n if count == 0:\r\n bmin, bmax = proj_bmin, proj_bmax\r\n\r\n # extents pupil bound\r\n delta = 2 * (proj_bmax - proj_bmin).norm() / ti.sqrt(samples)\r\n bmin -= delta\r\n bmax += delta\r\n\r\n self.exitPupilBoundMin[i] = bmin\r\n self.exitPupilBoundMax[i] = bmax","def step_run(cls, image, config):\n logger.info('Gain correction will be applied to %s', image)\n\n ret_code = cls.__call__(image)\n return ret_code"],"string":"[\n \"def analyze_image(which_cam, image, ntraps, iteration=0, verbose=False):\\n threshes = [0.5, 0.6]\\n margin = 10\\n threshold = np.max(image) * threshes[which_cam]\\n im = image.transpose()\\n\\n x_len = len(im)\\n peak_locs = np.zeros(x_len)\\n peak_vals = np.zeros(x_len)\\n\\n ## Trap Peak Detection ##\\n for i in range(x_len):\\n if i < margin or x_len - i < margin:\\n peak_locs[i] = 0\\n peak_vals[i] = 0\\n else:\\n peak_locs[i] = np.argmax(im[i])\\n peak_vals[i] = max(im[i])\\n\\n ## Trap Range Detection ##\\n first = True\\n pos_first, pos_last = 0, 0\\n left_pos = 0\\n for i, p in enumerate(peak_vals):\\n if p > threshold:\\n left_pos = i\\n elif left_pos != 0:\\n if first:\\n pos_first = (left_pos + i) // 2\\n first = False\\n pos_last = (left_pos + i) // 2\\n left_pos = 0\\n\\n ## Separation Value ##\\n separation = (pos_last - pos_first) / ntraps # In Pixels\\n\\n ## Initial Guesses ##\\n means0 = np.linspace(pos_first, pos_last, ntraps).tolist()\\n waists0 = (separation * np.ones(ntraps) / 2).tolist()\\n ampls0 = (max(peak_vals) * 0.7 * np.ones(ntraps)).tolist()\\n _params0 = [means0, waists0, ampls0, [0.06]]\\n params0 = [item for sublist in _params0 for item in sublist]\\n\\n ## Fitting ##\\n if verbose:\\n print(\\\"Fitting...\\\")\\n xdata = np.arange(x_len)\\n popt, pcov = curve_fit(lambda x, *params_0: wrapper_fit_func(x, ntraps, params_0),\\n xdata, peak_vals, p0=params0)\\n if verbose:\\n print(\\\"Fit!\\\")\\n plt.figure()\\n plt.plot(xdata, peak_vals) # Data\\n if iteration:\\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, params0), '--r') # Initial Guess\\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, popt)) # Fit\\n plt.title(\\\"Iteration: %d\\\" % iteration)\\n else:\\n plt.title(\\\"Final Product\\\")\\n\\n plt.xlim((pos_first - margin, pos_last + margin))\\n plt.legend([\\\"Data\\\", \\\"Guess\\\", \\\"Fit\\\"])\\n plt.show(block=False)\\n print(\\\"Fig_Newton\\\")\\n trap_powers = np.frombuffer(popt[2 * ntraps:3 * ntraps])\\n return trap_powers\",\n \"def enumerate_detector(det, thresholds, shot_ok=None, tiles=None, nimages=np.inf, startimg=0, stopimg=np.inf, correction=False, progress=True):\\n global terminated\\n Ncorrect = 64\\n correctionphotonthres = 3000\\n if not isinstance(det, h5py.Group):\\n raise TypeError('det should be a h5 group')\\n if tiles is None:\\n tiles = [k for k in det.keys() if 'tile' in k]\\n else:\\n newtiles = []\\n for t in tiles:\\n if t in det:\\n newtiles.append(t)\\n elif f'tile{t}' in det:\\n newtiles.append(f'tile{t}')\\n else:\\n raise KeyError(f'tile {t} not found')\\n tiles = newtiles\\n datanames = [(f'{det.name}/{t}/data') for t in tiles]\\n filename = det.file.filename\\n\\n nshots = det[f'{tiles[0]}/data'].shape[0]\\n startimg = int(np.clip(startimg, 0, nshots))\\n stopimg = int(np.clip(stopimg, startimg, nshots))\\n tileshape = det[f'{tiles[0]}/data'].shape[1:]\\n correctmask = [correctionmask(det[t]['absfft0/mean'], Ncorrect) for t in tiles]\\n if shot_ok is None:\\n shot_ok = np.ones(nshots, np.bool)\\n ind_filtered = 0\\n data = np.zeros((len(tiles), *tileshape))\\n willread = np.copy(shot_ok)\\n willread[:startimg] = False\\n willread[stopimg:] = False\\n with datasetreader(datanames, filename, sizecache=10, willread=willread) as reader:\\n\\n for ind_orig in range(startimg, stopimg):\\n if not shot_ok[ind_orig]:\\n continue\\n if ind_filtered >= nimages or terminated != 0:\\n return\\n if progress and ind_filtered % 10 == 0:\\n print(ind_filtered, end=' ', flush=True)\\n\\n for it, t in enumerate(tiles):\\n cdat = np.array(reader[ind_orig, it], dtype=np.float, order='C')\\n if correction:\\n correct(cdat, correctionphotonthres, Ncorrect, correctmask[it])\\n data[it, ...] = cdat\\n ev, number, scatter = getstats(data, thresholds)\\n\\n yield (ind_filtered, ind_orig, data, ev, number, scatter)\\n\\n ind_filtered += 1\",\n \"def run(self,workspace):\\n image_name = self.image_name.value\\n cpimage = workspace.image_set.get_image(image_name)\\n image = cpimage.pixel_data\\n mask = cpimage.mask\\n workspace.display_data.statistics = []\\n level = int(self.atrous_level.value)\\n\\n wavelet = self.a_trous(1.0*image, level+1)\\n wlevprod = wavelet[:,:,level-1] * 3.0\\n\\n spotthresh = wlevprod.mean() + float(self.noise_removal_factor.value) * wlevprod.std()\\n tidx = wlevprod < spotthresh\\n wlevprod[tidx] = 0\\n\\n wlevprod = self.circular_average_filter(wlevprod, int(self.smoothing_filter_size.value))\\n wlevprod = self.smooth_image(wlevprod, mask)\\n\\n max_wlevprod = scipy.ndimage.filters.maximum_filter(wlevprod,3)\\n maxloc = (wlevprod == max_wlevprod)\\n twlevprod = max_wlevprod > float(self.final_spot_threshold.value)\\n maxloc[twlevprod == 0] = 0\\n \\n labeled_image,object_count = scipy.ndimage.label(maxloc,\\n np.ones((3,3),bool))\\n\\n unedited_labels = labeled_image.copy()\\n # Filter out objects touching the border or mask\\n border_excluded_labeled_image = labeled_image.copy()\\n labeled_image = self.filter_on_border(image, labeled_image)\\n border_excluded_labeled_image[labeled_image > 0] = 0\\n \\n # Relabel the image\\n labeled_image,object_count = relabel(labeled_image)\\n new_labeled_image, new_object_count = self.limit_object_count(\\n labeled_image, object_count)\\n if new_object_count < object_count:\\n # Add the labels that were filtered out into the border\\n # image.\\n border_excluded_mask = ((border_excluded_labeled_image > 0) |\\n ((labeled_image > 0) & \\n (new_labeled_image == 0)))\\n border_excluded_labeled_image = scipy.ndimage.label(border_excluded_mask,\\n np.ones((3,3),bool))[0]\\n object_count = new_object_count\\n labeled_image = new_labeled_image\\n \\n # Make an outline image\\n outline_image = cellprofiler.cpmath.outline.outline(labeled_image)\\n outline_border_excluded_image = cellprofiler.cpmath.outline.outline(border_excluded_labeled_image)\\n \\n if self.show_window:\\n statistics = workspace.display_data.statistics\\n statistics.append([\\\"# of accepted objects\\\",\\n \\\"%d\\\"%(object_count)])\\n\\n workspace.display_data.image = image\\n workspace.display_data.labeled_image = labeled_image\\n workspace.display_data.border_excluded_labels = border_excluded_labeled_image\\n\\n # Add image measurements\\n objname = self.object_name.value\\n measurements = workspace.measurements\\n cpmi.add_object_count_measurements(measurements,\\n objname, object_count)\\n # Add label matrices to the object set\\n objects = cellprofiler.objects.Objects()\\n objects.segmented = labeled_image\\n objects.unedited_segmented = unedited_labels\\n objects.parent_image = image\\n \\n workspace.object_set.add_objects(objects,self.object_name.value)\\n cpmi.add_object_location_measurements(workspace.measurements, \\n self.object_name.value,\\n labeled_image)\\n if self.should_save_outlines.value:\\n out_img = cpi.Image(outline_image.astype(bool),\\n parent_image = image)\\n workspace.image_set.add(self.save_outlines.value, out_img)\",\n \"def simImpacts(blankimage):\\n\\n # - Initialize variables - #\\n count = 0\\n unique = .001\\n uniquelist = []\\n cratersatstep = []\\n cratermap = blankimage\\n\\n # -- Loop until saturation -- #\\n while True:\\n # - Wait until we have at least 10000 iterations before checking if we\\n # have reached saturation - #\\n if len(cratersatstep) > 10000:\\n # - We calculate average by comparing the average of the last 1000\\n # to the average of the last 100 - #\\n smallAvg = np.average(cratersatstep[-100:])\\n bigAvg = np.average(cratersatstep[-1000:])\\n # - If we have reached saturation we can leave the loop - #\\n if abs( smallAvg - bigAvg ) < (bigAvg * (1 - 0.99)):\\n return cratermap, count, uniquelist, cratersatstep\\n\\n # - Every 1000 impacts we should save an image so we can compare - #\\n if count%1000 == 0:\\n pl.imshow(image)\\n pl.title('Uniform Craters after '+str(int(count))+' Impactors')\\n pl.savefig('../images/Uniform'+str(int(count/1000))+'.png')\\n pl.clf()\\n\\n # --- BEGIN SIMULATION CODE --- #\\n # - Increment our impactor count - #\\n count += 1\\n\\n # - Generate the location for the center of the crater - #\\n x = int(np.random.rand()*500.)\\n y = int(np.random.rand()*500.)\\n\\n # - All of our impactors are the same size since this is our uniform sim - #\\n impactsize = 10\\n\\n # - Pass our image array, the impact size (divided by 2 for radius)\\n # origin of the impact, and a unique color value to drawCircle function - #\\n cratermap = drawCircle(cratermap, int(impactsize / 2.), [x,y], unique)\\n # - Get all of the unique color values still in cratermap - #\\n uniquelist = np.unique(cratermap[:,:,0])\\n # - Keep track of how many craters we can see at each step of the loop - #\\n cratersatstep.append(len(uniquelist))\\n\\n # - Add to our unique value to keep it unique! - #\\n unique += .001\\n \\n return cratermap, count , uniquelist, cratersvisible\",\n \"def test_nirspec_aperture_transforms(verbose=False, siaf=None):\\n if siaf is None:\\n siaf = Siaf(instrument)\\n else:\\n siaf = copy.deepcopy(siaf)\\n\\n labels = ['X', 'Y']\\n threshold = 0.2\\n\\n from_frame = 'sci'\\n to_frames = 'det gwa idl tel'.split()\\n\\n x_sci = np.linspace(-10, 10, 3)\\n y_sci = np.linspace(10, -10, 3)\\n\\n for include_tilt in [False, True]:\\n\\n for aper_name in siaf.apertures.keys():\\n skip = False\\n\\n # aperture\\n aperture = siaf[aper_name]\\n # offset slightly from default tilt values\\n\\n if (aperture.AperType in ['COMPOUND', 'TRANSFORM', 'SLIT']) or ('_FULL' not in aper_name):\\n skip = True\\n\\n if skip is False:\\n if(include_tilt is True):\\n # set tilt to a representative off nominal value\\n gwa_aperture = getattr(aperture, '_CLEAR_GWA_OTE')\\n rx0 = getattr(gwa_aperture, 'XSciRef')\\n ry0 = getattr(gwa_aperture, 'YSciRef')\\n aperture.tilt = (ry0 - 0.002, rx0 - 0.01)\\n \\n # test transformations\\n if verbose:\\n print('testing {} {} Tilt={}'.format(siaf.instrument, aper_name, aperture.tilt))\\n\\n for to_frame in to_frames:\\n forward_transform = getattr(aperture, '{}_to_{}'.format(from_frame, to_frame))\\n backward_transform = getattr(aperture, '{}_to_{}'.format(to_frame, from_frame))\\n\\n x_out, y_out = backward_transform(*forward_transform(x_sci, y_sci))\\n x_mean_error = np.mean(np.abs(x_sci - x_out))\\n y_mean_error = np.mean(np.abs(y_sci - y_out))\\n for i, error in enumerate([x_mean_error, y_mean_error]):\\n if verbose:\\n print('{} {}: Error in {}<->{} {}-transform is {:02.6f})'.format(\\n siaf.instrument, aper_name, from_frame, to_frame, labels[i], error))\\n assert error < threshold\",\n \"def enumerate_detector(det, thresholds, shot_ok=None, tiles=None, nimages=np.inf, stats=True, correction=False, progress=True, photonfunction=None):\\n Ncorrect = 64\\n correctionphotonthres = 3000\\n if not isinstance(det, h5py.Group):\\n raise TypeError('det should be a h5 group')\\n if tiles is None:\\n tiles = [k for k in det.keys() if 'tile' in k]\\n else:\\n newtiles = []\\n for t in tiles:\\n if t in det:\\n newtiles.append(t)\\n elif f'tile{t}' in det:\\n newtiles.append(f'tile{t}')\\n else:\\n raise KeyError(f'tile {t} not found')\\n tiles = newtiles\\n multitiles = not (len(tiles) == 1 and 'data' in det[tiles[0]])\\n mincorners = []\\n maxcorners = []\\n rots = []\\n datanames = []\\n filename = det.file.filename\\n nshots = det[f'{tiles[0]}/data'].shape[0]\\n correctmask = []\\n for t in tiles:\\n d = det[t]\\n offset = np.rint(d.attrs['detector_tile_position_in_pixels'])\\n rot = int(d.attrs['detector_rotation_steps'][0])\\n rots.append(rot)\\n n, a, b = d['data'].shape\\n if n != nshots:\\n raise ValueError('tiles should have same number of shots')\\n shape = ((a, b), (-b, a), (-a, -b), (b, -a))[rot % 4]\\n corners = (offset, (shape + offset))\\n mincorners.append(np.min(corners, axis=0))\\n maxcorners.append(np.max(corners, axis=0))\\n datanames.append(f'{d.name}/data')\\n if correction:\\n correctmask.append(correctionmask(det[t]['absfft0/mean'], Ncorrect))\\n\\n globaloffset = np.floor(np.min(mincorners, axis=0)).astype(int)\\n extent = [fastlen(x) for x in (np.ceil(np.max(maxcorners, axis=0)) - globaloffset)]\\n startx, starty = [list(s) for s in (np.floor(mincorners - globaloffset).astype(int)).T]\\n\\n if shot_ok is None:\\n shot_ok = np.ones(nshots, np.bool)\\n assembled = np.zeros(extent, np.float64)\\n global terminated\\n ind_filtered = 0\\n with datasetreader(datanames, filename, willread=shot_ok) if multitiles else arrayreader(det[tiles[0]]['data']) as reader:\\n for ind_orig in range(nshots):\\n if not shot_ok[ind_orig]:\\n continue\\n if ind_filtered >= nimages or terminated != 0:\\n return\\n if progress and ind_filtered % 100 == 0:\\n print(ind_filtered, end=' ', flush=True)\\n\\n for t in range(len(tiles)):\\n if multitiles:\\n tile = np.asarray(reader[ind_orig, t], order='C', dtype=np.float64)\\n if correction:\\n correct(tile, correctionphotonthres, Ncorrect, correctmask[t], rots[t], assembled, startx[t], starty[t])\\n else:\\n place(tile, rots[t], assembled, startx[t], starty[t])\\n else:\\n if correction:\\n tile = np.asarray(reader[ind_orig], order='C', dtype=np.float64)\\n correct(tile, correctionphotonthres, Ncorrect, correctmask[t], rots[t], assembled, startx[t], starty[t])\\n else:\\n assembled = np.asarray(np.rot90(reader[ind_orig], rots[t]), order='C', dtype=np.float64)\\n \\n\\n \\n numberfromfunc = photonfunction(assembled) if photonfunction is not None else None\\n if thresholds is not None:\\n if stats:\\n ev, number, scatter = getstats(assembled, thresholds)\\n yield (ind_filtered, ind_orig, np.copy(assembled), ev, number, scatter, numberfromfunc)\\n else:\\n number = getphotons(assembled, thresholds)\\n yield (ind_filtered, ind_orig, np.copy(assembled), None, number, None, numberfromfunc)\\n else: \\n yield (ind_filtered, ind_orig, np.copy(assembled), None, None, None, numberfromfunc)\\n\\n \\n ind_filtered += 1\",\n \"def eye_timings(self, nr_dummy_scans = 6, mystery_threshold = 0.05,saccade_duration_threshold = 10):\\n\\n\\t\\n\\t\\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\\n\\t\\t\\t# shell()\\n\\t\\t\\tniiFile = NiftiImage(self.runFile(stage = 'processed/mri', run = r))\\n\\t\\t\\ttr = round(niiFile.rtime*1)/1000.0\\n\\t\\t\\twith open (self.runFile(stage = 'processed/eye', run = r, extension = '.msg')) as inputFileHandle:\\n\\t\\t\\t\\tmsg_file = inputFileHandle.read()\\n\\n\\n\\t\\t\\tsacc_re = 'ESACC\\\\t(\\\\S+)[\\\\s\\\\t]+(-?\\\\d*\\\\.?\\\\d*)\\\\t(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+.?\\\\d+)'\\n\\t\\t\\tfix_re = 'EFIX\\\\t(\\\\S+)\\\\s+(-?\\\\d*\\\\.?\\\\d*)\\\\t(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d+\\\\.?\\\\d*)?\\\\s+(-?\\\\d+\\\\.?\\\\d*)?\\\\s+(-?\\\\d+\\\\.?\\\\d*)?\\\\s+(-?\\\\d+\\\\.?\\\\d*)?'\\n\\t\\t\\tblink_re = 'EBLINK\\\\t(\\\\S+)\\\\s+(-?\\\\d*\\\\.?\\\\d*)\\\\t(-?\\\\d+\\\\.?\\\\d*)\\\\s+(-?\\\\d?.?\\\\d*)?'\\n\\t\\t\\tstart_eye = 'START\\\\t(-?\\\\d+\\\\.?\\\\d*)'\\n\\n\\t\\t\\t# self.logger.info('reading eyelink events from %s', os.path.split(self.message_file)[-1])\\n\\t\\t\\tsaccade_strings = re.findall(re.compile(sacc_re), msg_file)\\n\\t\\t\\tfix_strings = re.findall(re.compile(fix_re), msg_file)\\n\\t\\t\\tblink_strings = re.findall(re.compile(blink_re), msg_file)\\n\\t\\t\\tstart_time_scan = float(re.findall(re.compile(start_eye),msg_file)[0])\\n\\t\\t\\t\\n\\t\\t\\tif len(saccade_strings) > 0:\\n\\t\\t\\t\\tself.saccades_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3]),'start_x':float(e[4]),'start_y':float(e[5]),'end_x':float(e[6]),'end_y':float(e[7]), 'mystery_measure':float(e[8]),'peak_velocity':float(e[9])} for e in saccade_strings]\\n\\t\\t\\t\\tself.fixations_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3]),'x':float(e[4]),'y':float(e[5]),'pupil_size':float(e[6])} for e in fix_strings]\\n\\t\\t\\t\\tself.blinks_from_message_file = [{'eye':e[0],'start_timestamp':float(e[1]),'end_timestamp':float(e[2]),'duration':float(e[3])} for e in blink_strings]\\n\\t\\t\\t\\n\\t\\t\\t\\tself.saccade_type_dictionary = np.dtype([(s , np.array(self.saccades_from_message_file[0][s]).dtype) for s in self.saccades_from_message_file[0].keys()])\\n\\t\\t\\t\\tself.fixation_type_dictionary = np.dtype([(s , np.array(self.fixations_from_message_file[0][s]).dtype) for s in self.fixations_from_message_file[0].keys()])\\n\\t\\t\\t\\tif len(self.blinks_from_message_file) > 0:\\n\\t\\t\\t\\t\\tself.blink_type_dictionary = np.dtype([(s , np.array(self.blinks_from_message_file[0][s]).dtype) for s in self.blinks_from_message_file[0].keys()])\\n\\t\\t\\t\\n\\t\\t\\teye_blinks = [[((self.blinks_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.blinks_from_message_file[i]['duration']/1000,1] for i in range(len(self.blinks_from_message_file)) if (self.blinks_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000)]\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\tsaccades = [[((self.saccades_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.saccades_from_message_file[i]['duration']/1000,1] for i in range(len(self.saccades_from_message_file)) if np.all([(self.saccades_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000), (self.saccades_from_message_file[i]['duration'] > saccade_duration_threshold)]) ]\\n\\t\\t\\tsaccades_thresholded = [[((self.saccades_from_message_file[i]['start_timestamp']- start_time_scan)/1000) - nr_dummy_scans*tr, self.saccades_from_message_file[i]['duration']/1000,1] for i in range(len(self.saccades_from_message_file)) if np.all([(self.saccades_from_message_file[i]['start_timestamp']- start_time_scan) > (nr_dummy_scans*tr*1000), (self.saccades_from_message_file[i]['mystery_measure'] > mystery_threshold), (self.saccades_from_message_file[i]['duration'] > saccade_duration_threshold)]) ]\\n\\t\\t\\n\\t\\t\\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['eye_blinks']), np.array(eye_blinks), fmt = '%3.2f', delimiter = '\\\\t')\\n\\t\\t\\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['saccades']), np.array(saccades), fmt = '%3.2f', delimiter = '\\\\t')\\n\\t\\t\\tnp.savetxt(self.runFile(stage = 'processed/eye', run = r, extension = '.txt', postFix = ['saccades_thresholded']), np.array(saccades_thresholded), fmt = '%3.2f', delimiter = '\\\\t')\",\n \"def phot_aperture(input_file):\\n #set the original directory\\n original_path = os.getcwd()\\n save_path = input_file['save_path']\\n planet = input_file['exoplanet']\\n #radii = np.arange(input_file['apertures'][0],input_file['apertures'][1],0.1)\\n radii = np.array(input_file['apertures'])\\n #change to save data reduction directory\\n os.chdir(save_path)\\n if not os.path.exists('phot_results'):\\n os.makedirs('phot_results')\\n tempo = time.time()\\n print 'Starting aperture photometry'\\n print 'Saving results on: '+save_path+'/phot_results/'\\n \\n #check the number of objects to make the photometry\\n N_obj = len(input_file['pxpositions'])/2.\\n print 'Number of objects = ',N_obj\\n positions = [] #create the positions variable (X,Y) in pixels unit on the CCD\\n for i in range(len(input_file['pxpositions'])):\\n if i % 2 == 0: #if the number is a even (or not a odd), the turple is created\\n positions.append((input_file['pxpositions'][i],input_file['pxpositions'][i+1]))\\n print 'Radius from ',radii[0],' to ',radii[-1],'\\\\n'\\n \\n skysection = input_file['skysection']\\n skysection[0] = int(skysection[0])\\n skysection[1] = int(skysection[1])\\n \\n images = sorted(glob.glob('AB'+planet+'*.fits'))\\n for radius in radii:\\n flux_data = []\\n for i in range(len(images)):\\n im = fits.getdata(images[i],header=False)\\n im = array(im,dtype='Float64')\\n \\n # ERROR\\n #Traceback (most recent call last):\\n # File \\\"ExoTRed.py\\\", line 105, in <module>\\n # exotred.phot_aperture(input_file)\\n # File \\\"./sources/ExoTRed_core.py\\\", line 637, in phot_aperture \\n # File \\\"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\\\", line 329, in __init__\\n # self._calc_bkg_bkgrms()\\n # File \\\"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\\\", line 686, in _calc_bkg_bkgrms\\n # bkg = self._interpolate_meshes(self._bkg1d)\\n # File \\\"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/background/background_2d.py\\\", line 575, in _interpolate_meshes\\n # f = ShepardIDWInterpolator(yx, data)\\n # File \\\"/home/walter/bin/anaconda3/envs/iraf27/lib/python2.7/site-packages/photutils/utils/interpolation.py\\\", line 138, in __init__\\n # raise ValueError('The number of values must match the number '\\n # ValueError: The number of values must match the number of coordinates.\\n\\n # bkg = background.background_2d.Background2D(im,tuple(skysection))\\n # bkg_data = bkg.background\\n # bkg_rms = bkg.background_rms\\n\\n # phot_table = aperture_photometry(im - bkg_data, CircularAperture(positions, radius),\\n # error=bkg_rms, method ='center')#,effective_gain=float(input_file['gain']))\\n ####### SUBSTITUTE ROUTINE\\n window = 100\\n sky_size = im.shape\\n sky_mean = float(np.median(im[int(skysection[1]-window):int(skysection[1]+window),int(skysection[0]-window):int(skysection[0]+window)]))\\n bkg = np.random.poisson(sky_mean,sky_size)\\n apertures = CircularAperture(positions, radius)\\n phot_table = aperture_photometry(im, apertures, error=bkg)\\n #######\\n phot_table_flux = np.array([]) #saving results of aperture photometry\\n for j in range(len(phot_table['aperture_sum'])):\\n phot_table_flux = np.concatenate((phot_table_flux,np.array([phot_table['aperture_sum'][j]])),axis=0)\\n phot_table_flux = np.concatenate((phot_table_flux,np.array([phot_table['aperture_sum_err'][j]])),axis=0)\\n flux = np.concatenate((phot_table_flux,np.array([images[i]])),axis=0)\\n # flux = [phot_table['aperture_sum'][0], phot_table['aperture_sum'][1],phot_table['aperture_sum_err'][0],\\n # phot_table['aperture_sum_err'][1],images[i]]\\n flux_data.append(flux)\\n flux_data = DataFrame(flux_data)#,columns=['hoststar','refstar','hoststar_err','refstar_err','image'])\\n flux_data.to_csv('./phot_results/'+planet+'_flux_radius_'+str(radius)+'.csv',index=False)\\n use.update_progress((float(np.where(radii == radius)[0])+1.)/len(radii))\\n print 'Time total = ',abs(time.time()-tempo)/60.,' minutes'\\n os.chdir(original_path)\",\n \"def update_for_in_trap(self, t, traps): #******\\n sources = traps.param['source_locations'] #Of format [(0,0),]\\n for trap_num, trap_loc in enumerate(sources):\\n dist_vals = distance((self.x_position, self.y_position),trap_loc)\\n mask_trapped = dist_vals < traps.param['trap_radius']\\n self.mode[mask_trapped] = self.Mode_Trapped\\n self.trap_num[mask_trapped] = trap_num\\n self.x_trap_loc[mask_trapped] = trap_loc[0]\\n self.y_trap_loc[mask_trapped] = trap_loc[1]\\n self.x_velocity[mask_trapped] = 0.0\\n self.y_velocity[mask_trapped] = 0.0\\n\\n # Get time stamp for newly trapped flies\\n mask_newly_trapped = mask_trapped & (self.t_in_trap == scipy.inf)\\n self.t_in_trap[mask_newly_trapped] = t\",\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 analyze(self):\\n try:\\n self.options[self.multi_image][1]()\\n except:\\n raise Exception(\\\"Multi Image Option not defined.\\\")\\n\\n self.image = self.data / self.exposure\\n\\n background = self.min_val = np.min(self.image[:511,:511])\\n self.max_val = np.max(self.image[:511,:511])\\n # stats.mode returns modal value = value that occours most often\\n #background = stats.mode(im[:50,:50].ravel())[0][0]\\n\\n intensity = self.image.sum() - background*np.size(self.image)\\n\\n #results.append((self.index, intensity, background))\\n self.index =+ 1\",\n \"def main():\\n\\n with its.device.ItsSession() as cam:\\n\\n props = cam.get_camera_properties()\\n its.caps.skip_unless(its.caps.raw16(props) and\\n its.caps.manual_sensor(props) and\\n its.caps.read_3a(props) and\\n its.caps.per_frame_control(props) and\\n not its.caps.mono_camera(props))\\n debug = its.caps.debug_mode()\\n\\n # Expose for the scene with min sensitivity\\n exp_min, exp_max = props[\\\"android.sensor.info.exposureTimeRange\\\"]\\n sens_min, _ = props[\\\"android.sensor.info.sensitivityRange\\\"]\\n # Digital gains might not be visible on RAW data\\n sens_max = props[\\\"android.sensor.maxAnalogSensitivity\\\"]\\n sens_step = (sens_max - sens_min) / NUM_ISO_STEPS\\n white_level = float(props[\\\"android.sensor.info.whiteLevel\\\"])\\n black_levels = [its.image.get_black_level(i,props) for i in range(4)]\\n # Get the active array width and height.\\n aax = props[\\\"android.sensor.info.activeArraySize\\\"][\\\"left\\\"]\\n aay = props[\\\"android.sensor.info.activeArraySize\\\"][\\\"top\\\"]\\n aaw = props[\\\"android.sensor.info.activeArraySize\\\"][\\\"right\\\"]-aax\\n aah = props[\\\"android.sensor.info.activeArraySize\\\"][\\\"bottom\\\"]-aay\\n raw_stat_fmt = {\\\"format\\\": \\\"rawStats\\\",\\n \\\"gridWidth\\\": aaw/IMG_STATS_GRID,\\n \\\"gridHeight\\\": aah/IMG_STATS_GRID}\\n\\n e_test = []\\n mult = 1.0\\n while exp_min*mult < exp_max:\\n e_test.append(int(exp_min*mult))\\n mult *= EXP_MULT\\n if e_test[-1] < exp_max * INCREASING_THR:\\n e_test.append(int(exp_max))\\n e_test_ms = [e / 1000000.0 for e in e_test]\\n\\n for s in range(sens_min, sens_max, sens_step):\\n means = []\\n means.append(black_levels)\\n reqs = [its.objects.manual_capture_request(s, e, 0) for e in e_test]\\n # Capture raw in debug mode, rawStats otherwise\\n caps = []\\n for i in range(len(reqs) / SLICE_LEN):\\n if debug:\\n caps += cam.do_capture(reqs[i*SLICE_LEN:(i+1)*SLICE_LEN], cam.CAP_RAW)\\n else:\\n caps += cam.do_capture(reqs[i*SLICE_LEN:(i+1)*SLICE_LEN], raw_stat_fmt)\\n last_n = len(reqs) % SLICE_LEN\\n if last_n == 1:\\n if debug:\\n caps += [cam.do_capture(reqs[-last_n:], cam.CAP_RAW)]\\n else:\\n caps += [cam.do_capture(reqs[-last_n:], raw_stat_fmt)]\\n elif last_n > 0:\\n if debug:\\n caps += cam.do_capture(reqs[-last_n:], cam.CAP_RAW)\\n else:\\n caps += cam.do_capture(reqs[-last_n:], raw_stat_fmt)\\n\\n # Measure the mean of each channel.\\n # Each shot should be brighter (except underexposed/overexposed scene)\\n for i,cap in enumerate(caps):\\n if debug:\\n planes = its.image.convert_capture_to_planes(cap, props)\\n tiles = [its.image.get_image_patch(p, 0.445, 0.445, 0.11, 0.11) for p in planes]\\n mean = [m * white_level for tile in tiles\\n for m in its.image.compute_image_means(tile)]\\n img = its.image.convert_capture_to_rgb_image(cap, props=props)\\n its.image.write_image(img, \\\"%s_s=%d_e=%05d.jpg\\\" % (NAME, s, e_test))\\n else:\\n mean_image, _ = its.image.unpack_rawstats_capture(cap)\\n mean = mean_image[IMG_STATS_GRID/2, IMG_STATS_GRID/2]\\n\\n print \\\"ISO=%d, exposure time=%.3fms, mean=%s\\\" % (\\n s, e_test[i] / 1000000.0, str(mean))\\n means.append(mean)\\n\\n\\n # means[0] is black level value\\n r = [m[0] for m in means[1:]]\\n gr = [m[1] for m in means[1:]]\\n gb = [m[2] for m in means[1:]]\\n b = [m[3] for m in means[1:]]\\n\\n pylab.plot(e_test_ms, r, \\\"r.-\\\")\\n pylab.plot(e_test_ms, b, \\\"b.-\\\")\\n pylab.plot(e_test_ms, gr, \\\"g.-\\\")\\n pylab.plot(e_test_ms, gb, \\\"k.-\\\")\\n pylab.xscale('log')\\n pylab.yscale('log')\\n pylab.title(\\\"%s ISO=%d\\\" % (NAME, s))\\n pylab.xlabel(\\\"Exposure time (ms)\\\")\\n pylab.ylabel(\\\"Center patch pixel mean\\\")\\n matplotlib.pyplot.savefig(\\\"%s_s=%d.png\\\" % (NAME, s))\\n pylab.clf()\\n\\n allow_under_saturated = True\\n for i in xrange(1, len(means)):\\n prev_mean = means[i-1]\\n mean = means[i]\\n\\n if np.isclose(max(mean), white_level, rtol=SATURATION_TOL):\\n print \\\"Saturated: white_level %f, max_mean %f\\\"% (white_level, max(mean))\\n break;\\n\\n if allow_under_saturated and np.allclose(mean, black_levels, rtol=BLK_LVL_TOL):\\n # All channel means are close to black level\\n continue\\n\\n allow_under_saturated = False\\n # Check pixel means are increasing (with small tolerance)\\n channels = [\\\"Red\\\", \\\"Gr\\\", \\\"Gb\\\", \\\"Blue\\\"]\\n for chan in range(4):\\n err_msg = \\\"ISO=%d, %s, exptime %3fms mean: %.2f, %s mean: %.2f, TOL=%.f%%\\\" % (\\n s, channels[chan],\\n e_test_ms[i-1], mean[chan],\\n \\\"black level\\\" if i == 1 else \\\"exptime %3fms\\\"%e_test_ms[i-2],\\n prev_mean[chan],\\n INCREASING_THR*100)\\n assert mean[chan] > prev_mean[chan] * INCREASING_THR, err_msg\",\n \"def trapfilt_taps(N, phil, alfa):\\n\\n\\n\\n tt = arange(-N/2,N/2 + 1) # Time axis for h(t) \\n # ***** Generate impulse response ht here *****\\n ht = zeros(len(tt))\\n ix = where(tt != 0)[0]\\n if alfa != 0:\\n ht[ix] = ((sin(2*pi*phil*tt[ix]))/(pi*tt[ix]))*((sin(2*pi*alfa*phil*tt[ix]))/(2*pi*alfa*phil*tt[ix]))\\n else:\\n ht[ix] = (sin(2*pi*phil*tt[ix]))/(pi*tt[ix])\\n ix0 = where(tt == 0)[0]\\n ht[ix0] = 2*phil\\n ht = ht/sum(power(ht,2))\\n\\n return ht\",\n \"def guess_image(which_cam, image, ntraps):\\n threshes = [0.5, 0.65]\\n ## Image Conditioning ##\\n margin = 10\\n threshold = np.max(image)*threshes[which_cam]\\n im = image.transpose()\\n\\n x_len = len(im)\\n peak_locs = np.zeros(x_len)\\n peak_vals = np.zeros(x_len)\\n\\n ## Trap Peak Detection ##\\n for i in range(x_len):\\n if i < margin or x_len - i < margin:\\n peak_locs[i] = 0\\n peak_vals[i] = 0\\n else:\\n peak_locs[i] = np.argmax(im[i])\\n peak_vals[i] = max(im[i])\\n\\n ## Trap Range Detection ##\\n first = True\\n pos_first, pos_last = 0, 0\\n left_pos = 0\\n for i, p in enumerate(peak_vals):\\n if p > threshold:\\n left_pos = i\\n elif p < threshold and left_pos != 0:\\n if first:\\n pos_first = (left_pos + i) // 2\\n first = False\\n pos_last = (left_pos + i) // 2\\n left_pos = 0\\n\\n ## Separation Value ##\\n separation = (pos_last - pos_first) / ntraps # In Pixels\\n\\n ## Initial Guesses ##\\n means0 = np.linspace(pos_first, pos_last, ntraps).tolist()\\n waists0 = (separation * np.ones(ntraps) / 2).tolist()\\n ampls0 = (max(peak_vals) * 0.7 * np.ones(ntraps)).tolist()\\n _params0 = [means0, waists0, ampls0, [0.06]]\\n params0 = [item for sublist in _params0 for item in sublist]\\n\\n xdata = np.arange(x_len)\\n plt.figure()\\n plt.plot(xdata, peak_vals)\\n plt.plot(xdata, wrapper_fit_func(xdata, ntraps, params0), '--r') # Initial Guess\\n plt.xlim((pos_first - margin, pos_last + margin))\\n plt.legend([\\\"Data\\\", \\\"Guess\\\", \\\"Fit\\\"])\\n plt.show(block=False)\",\n \"def mri_dixon_analysis(data_objects, working_dir, settings):\\n\\n logger.info(\\\"Running Dixon analysis Calculation\\\")\\n logger.info(\\\"Using settings: %s\\\", settings)\\n\\n output_objects = []\\n\\n fat_obj = None\\n water_obj = None\\n for data_obj in data_objects:\\n\\n if data_obj.meta_data[\\\"image_type\\\"] == \\\"fat\\\":\\n fat_obj = data_obj\\n\\n if data_obj.meta_data[\\\"image_type\\\"] == \\\"water\\\":\\n water_obj = data_obj\\n\\n if fat_obj is None or water_obj is None:\\n logger.error(\\\"Both Fat and Water Images are required\\\")\\n return []\\n\\n # Read the image series\\n fat_load_path = fat_obj.path\\n if fat_obj.type == \\\"DICOM\\\":\\n fat_load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(fat_obj.path)\\n fat_img = sitk.ReadImage(fat_load_path)\\n\\n water_load_path = water_obj.path\\n if water_obj.type == \\\"DICOM\\\":\\n water_load_path = sitk.ImageSeriesReader().GetGDCMSeriesFileNames(water_obj.path)\\n water_img = sitk.ReadImage(water_load_path)\\n\\n # Cast to float for calculation\\n fat_img = sitk.Cast(fat_img, sitk.sitkFloat32)\\n water_img = sitk.Cast(water_img, sitk.sitkFloat32)\\n\\n # Let's do the calcuation using NumPy\\n fat_arr = sitk.GetArrayFromImage(fat_img)\\n water_arr = sitk.GetArrayFromImage(water_img)\\n\\n # Do the calculation\\n divisor = water_arr + fat_arr\\n fat_fraction_arr = (fat_arr * 100) / divisor\\n fat_fraction_arr[divisor == 0] = 0 # Sets those voxels which were divided by zero to 0\\n water_fraction_arr = (water_arr * 100) / divisor\\n water_fraction_arr[divisor == 0] = 0 # Sets those voxels which were divided by zero to 0\\n\\n fat_fraction_img = sitk.GetImageFromArray(fat_fraction_arr)\\n water_fraction_img = sitk.GetImageFromArray(water_fraction_arr)\\n\\n fat_fraction_img.CopyInformation(fat_img)\\n water_fraction_img.CopyInformation(water_img)\\n\\n # Create the output Data Objects and add it to output_ob\\n fat_fraction_file = os.path.join(working_dir, \\\"fat.nii.gz\\\")\\n sitk.WriteImage(fat_fraction_img, fat_fraction_file)\\n water_fraction_file = os.path.join(working_dir, \\\"water.nii.gz\\\")\\n sitk.WriteImage(water_fraction_img, water_fraction_file)\\n\\n fat_data_object = DataObject(type=\\\"FILE\\\", path=fat_fraction_file, parent=fat_obj)\\n output_objects.append(fat_data_object)\\n\\n water_data_object = DataObject(type=\\\"FILE\\\", path=water_fraction_file, parent=water_obj)\\n output_objects.append(water_data_object)\\n\\n return output_objects\",\n \"def imagetest(thetainput,doubleopponencyinput):\\n theta = thetainput\\n rgcMode = doubleopponencyinput\\n\\n\\n C = retina.sample(img,x,y,coeff[i],loc[i],rgb=True) # CENTRE\\n S = retina.sample(img,x,y,dcoeff[i],dloc[i],rgb=True) # SURROUND\\n \\n if rgcMode == 0:\\n \\tpV,nV = rgc.opponency(C,S,theta)\\n else:\\n \\tpV,nV = rgc.doubleopponency(C,S,theta)\\n cv2.namedWindow(\\\"Input\\\", cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"Input\\\", img)\\n rIntensity,cIntensity = showNonOpponency(C,theta)\\n cv2.namedWindow(\\\"Intensity Responses\\\", cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"Intensity Responses\\\", rIntensity)\\n cv2.namedWindow(\\\"Intensity Responses Cortex\\\", cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"Intensity Responses Cortex\\\", cIntensity)\\n cv2.waitKey(0)\\n #Generate backprojected images\\n if showInverse:\\n rOpponent = showBPImg(pV,nV)\\n cv2.namedWindow(\\\"Backprojected Opponent Cells Output\\\", cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"Backprojected Opponent Cells Output\\\", rOpponent)\\n cv2.waitKey(0)\\n # Cortex\\n if showCortex:\\n cOpponent = showCortexImg(pV,nV)\\n cv2.namedWindow(\\\"Cortex Opponent Cells Output\\\", cv2.WINDOW_NORMAL)\\n cv2.imshow(\\\"Cortex Opponent Cells Output\\\", cOpponent)\\n cv2.waitKey(0)\",\n \"def calculate_uip(vpx, raster, weight, neuron, tau):\\n\\n m = 1\\n\\n vpx[0] = weight[neuron, raster[0][\\\"id\\\"]]\\n\\n for k, evt in enumerate(raster[1:], 1):\\n\\n dt = evt[\\\"time\\\"] - raster[k - 1][\\\"time\\\"]\\n\\n if not dt:\\n m -= 1\\n else:\\n vpx[m] = vpx[m - 1] * np.exp(-dt / tau)\\n\\n vpx[m] += weight[neuron, evt[\\\"id\\\"]]\\n\\n m += 1\",\n \"def stack_tir(scene_urls,cloud_mask_bits,aoi,aoi_crs,\\n subtract_median_lst=True,subtract_air_temp=False):\\n if subtract_air_temp:\\n ceda_password = get_ceda_password()\\n at = met_climate.access_ukcp09(cf.ceda_username,ceda_password)\\n\\n \\n # with rasterio.open(scene_bqa) as bqa:\\n # with rasterio.open(scene_tir) as tir:\\n\\n # bqa_data,bqa_trans = ru.read_in_aoi(bqa,**aoi_kwargs)\\n # tir_data,tir_trans = ru.read_in_aoi(tir,**aoi_kwargs)\\n \\n # bqa_data = bqa_data[0,:,:]\\n # tir_data = tir_data[0,:,:]\\n # tir_data = ma.array(tir_data,dtype=float,\\n # mask=ru.mask_qa(bqa_data,bitmask=0b1))\\n\\n # (ymin,ymax) = (0, tir_data.shape[0])\\n # (xmin,xmax) = (0, tir_data.shape[1])\\n \\n counter=-1\\n for scene_url in scene_urls:\\n counter+=1\\n scene_tir = scene_url\\n scene_bqa = scene_url.replace('B'+tirband,'B'+qaband)\\n scene_red = scene_url.replace('B'+tirband,'B'+rband)\\n scene_nir = scene_url.replace('B'+tirband,'B'+nband)\\n scene_metadata = scene_url.replace('B'+tirband+'.TIF','MTL.txt')\\n\\n print('Reading scene {}'.format(counter+1))\\n try:\\n with rasterio.open(scene_bqa) as bqa:\\n #print(scene_bqa)\\n bqa_data,bqa_trans = ru.read_in_aoi(bqa,aoi=aoi,aoi_crs=aoi_crs)\\n\\n with rasterio.open(scene_tir) as tir:\\n #print(scene_tir)\\n tir_data,tir_trans = ru.read_in_aoi(tir,aoi=aoi,aoi_crs=aoi_crs)\\n tir_crs = tir.crs\\n tir_profile = tir.profile\\n\\n with rasterio.open(scene_red) as red:\\n #print(scene_red)\\n red_data,red_trans = ru.read_in_aoi(red,aoi=aoi,aoi_crs=aoi_crs)\\n red_crs = red.crs\\n\\n with rasterio.open(scene_nir) as nir:\\n #print(scene_nir)\\n nir_data,nir_trans = ru.read_in_aoi(nir,aoi=aoi,aoi_crs=aoi_crs)\\n \\n except OSError as e:\\n print('ERROR',e)\\n print('skipping scene')\\n counter = counter-1\\n continue\\n \\n # Determine size of stack allowing for AoI to extend outside of scene\\n if counter == 0:\\n aoi_box = rasterio.warp.transform_bounds(aoi_crs,tir_crs,*aoi.values())\\n aoi_left, aoi_bottom, aoi_right, aoi_top = aoi_box\\n aoi_box = dict(zip(('minx','miny','maxx','maxy'),aoi_box))\\n # rowmin,colmin = (bqa.index(aoi_left,aoi_top)) #,op=round))\\n # rowmax,colmax = (bqa.index(aoi_right,aoi_bottom)) #,op=round))\\n # The above two lines are fine but the following does not \\n # require the rasterio dataset to be kept open\\n rowmin,colmin = rasterio.transform.rowcol(tir_trans,aoi_left,aoi_top)\\n rowmax,colmax = rasterio.transform.rowcol(tir_trans,aoi_right,aoi_bottom)\\n stack_height,stack_width = (rowmax-rowmin,colmax-colmin)\\n lst_stack = (ma.zeros((len(scene_urls),stack_height,stack_width),\\n dtype=np.float,fill_value=np.nan\\n )+np.nan) \\n \\n # Determine size of intersect in THIS scene\\n intersect = ru.aoi_scene_intersection(aoi_box,bqa)\\n ins_left, ins_bottom, ins_right, ins_top = intersect.bounds\\n #rowmin,colmin = (bqa.index(ins_left,ins_top,op=round))\\n #rowmax,colmax = (bqa.index(ins_right,ins_bottom,op=round))\\n # The above two lines are incorrect now that we read a window:\\n # We need to transform the coordinates into the row,col of \\n # the window, not the original file.\\n rowmin,colmin = rasterio.transform.rowcol(tir_trans,ins_left,ins_top)\\n rowmax,colmax = rasterio.transform.rowcol(tir_trans,ins_right,ins_bottom)\\n\\n try:\\n # Subset data \\n bqa_data = ma.array(bqa_data[0,rowmin:rowmax,colmin:colmax])\\n tir_data = ma.array(tir_data[0,rowmin:rowmax,colmin:colmax])\\n red_data = ma.array(red_data[0,rowmin:rowmax,colmin:colmax])\\n nir_data = ma.array(nir_data[0,rowmin:rowmax,colmin:colmax])\\n assert tir_data.shape == lst_stack.shape[1:]\\n except (IndexError,AssertionError) as e:\\n print('ERROR:',e)\\n print('loop count',counter)\\n print(tir_data.shape, lst_stack.shape)\\n print(rowmin,rowmax,colmin,colmax)\\n import pdb; pdb.set_trace()\\n\\n lst_data = lst.calculate_land_surface_temperature_NB(\\n red_data, nir_data, tir_data,\\n red_trans, tir_trans, \\n red_crs, tir_crs, scene_metadata\\n )\\n \\n # Masks\\n smw = 11\\n mask_all = filters.maximum_filter(\\n ru.mask_qa(bqa_data,bits=cloud_mask_bits),size=smw\\n )\\n\\n lst_data_mask_all = ma.array(lst_data,\\n mask=mask_all,\\n dtype=np.float,\\n fill_value=np.nan) #.filled()\\n\\n # After masking, reproject\\n # not necessary if they share a CRS\\n if counter > 0:\\n assert tir_crs == prev_crs\\n prev_crs = tir_crs\\n\\n # Now do some normalisation\\n if subtract_air_temp:\\n filename = scene_tir.split('/')[-1]\\n datestring = filename.split('_')[3]\\n\\n atscene = met_climate.dummy_scene( \\n tir_crs, tir_trans, aoi_box,(stack_height,stack_width))\\n\\n # import pdb; pdb.set_trace()\\n # If the following fails, it may mean there was a problem setting up the session\\n atdata = at.grid_temp_over_scene(\\n atscene, datestring, interpolation='linear')\\n atdata = atdata[rowmin:rowmax,colmin:colmax]\\n assert lst_data_mask_all.shape == atdata.shape\\n lst_data_mask_all = ma.array(\\n lst_data_mask_all - atdata,\\n mask=mask_all,\\n fill_value=np.nan)\\n \\n if subtract_median_lst:\\n # ALSO subtract median xLST\\n medval = ma.median(lst_data_mask_all)\\n lst_data_mask_all = ma.array(\\n lst_data_mask_all - medval,\\n mask=mask_all,\\n fill_value=np.nan)\\n \\n elif subtract_median_lst:\\n # Subtract median LST from scene (within QA mask) \\n \\n medval = ma.median(lst_data_mask_all)\\n lst_data_mask_all = ma.array(\\n lst_data_mask_all - medval,\\n mask=mask_all,\\n fill_value=np.nan)\\n \\n # Then add to stack\\n lst_stack[counter,:,:] = lst_data_mask_all\\n\\n # Make profile for file output\\n N_layers = counter+1\\n tir_profile.update(\\n dtype=rasterio.float64,\\n width=stack_width,\\n height=stack_height,\\n transform=tir_trans,\\n count=N_layers,\\n compress='lzw'\\n )\\n\\n\\n return lst_stack, tir_profile\",\n \"def tail_cts_per_shot(datapath, lower, TPQI_starts, bin_size = 0.256, normalize = False, correct_for_bg = True, save = 1, pulses_in_sequence = 300):\\n\\n print 'analyzing tail counts per shot...' \\n current_dir = os.getcwd()\\n plt.close('all')\\n os.chdir(datapath)\\n files = os.listdir(datapath)\\n\\n for k in arange(len(files)):\\n right_file = '.npz' in files[k]\\n \\n if right_file:\\n data = numpy.load(datapath+'\\\\\\\\'+files[k])\\n\\n ch1_counts = data['hist_ch1']\\n ch0_counts = data['hist_ch0']\\n\\n time = bin_size*arange(len(ch1_counts))\\n \\n if correct_for_bg:\\n bg_level_ch1 = ch1_counts[int(0.75*len(ch1_counts)):int(0.90*len(ch1_counts))].mean()\\n ch1_counts = ch1_counts - bg_level_ch1*ones(len(ch1_counts))\\n bg_level_ch0 = ch0_counts[int(0.75*len(ch0_counts)):int(0.90*len(ch0_counts))].mean()\\n ch0_counts = ch0_counts - bg_level_ch0*ones(len(ch0_counts))\\n\\n #print 'measured background level for [ch0,ch1] = ['+num2str(bg_level_ch0,1)+','+num2str(bg_level_ch1,1)+']'\\n\\n if normalize:\\n ch1_counts_normalized = ch1_counts/ch1_counts.max()\\n ch0_counts_normalized = ch0_counts/ch0_counts.max()\\n \\n upper = lower + 40.0\\n\\n tail_area_time = time[int(lower/bin_size):int(upper/bin_size)]\\n tail_area_ch1 = ch1_counts[int(lower/bin_size):int(upper/bin_size)]\\n tail_area_ch0 = ch0_counts[int(lower/bin_size):int(upper/bin_size)]\\n\\n tail_counts_per_shot = (tail_area_ch1.sum()+tail_area_ch0.sum())/float(TPQI_starts*pulses_in_sequence)\\n\\n figure1 = plt.figure(figsize=(16.0, 12.0))\\n plt.subplot(211)\\n if not normalize:\\n plt.semilogy(time, ch1_counts, '-k')\\n plt.plot(array([lower,lower]), array([1E-1,ch1_counts.max()]), 'r', lw = 2.0)\\n plt.plot(array([upper,upper]), array([1E-1,ch1_counts.max()]), 'r', lw = 2.0)\\n else:\\n plt.semilogy(time, ch1_counts_normalized, '-r')\\n plt.plot(array([lower,lower]), array([1E-1,ch1_counts_normalized.max()]), 'r', lw = 2.0)\\n plt.plot(array([upper,upper]), array([1E-1,ch1_counts_normalized.max()]), 'r', lw = 2.0)\\n \\n plt.xlabel('Time after sync (ns)')\\n plt.ylabel('Counts ch1')\\n plt.title('tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4')\\n plt.xlim([0,200])\\n\\n plt.subplot(212)\\n if not normalize:\\n plt.semilogy(time, ch0_counts, '-k')\\n plt.plot(array([lower,lower]), array([1E-1,ch0_counts.max()]), 'r', lw = 2.0)\\n plt.plot(array([upper,upper]), array([1E-1,ch0_counts.max()]), 'r', lw = 2.0)\\n else:\\n plt.semilogy(time, ch0_counts_normalized, '-k')\\n plt.plot(array([lower,lower]), array([1E-1,ch0_counts_normalized.max()]), 'r', lw = 2.0)\\n plt.plot(array([upper,upper]), array([1E-1,ch0_counts_normalized.max()]), 'r', lw = 2.0)\\n \\n plt.xlabel('Time after sync (ns)')\\n plt.ylabel('Counts ch0')\\n plt.title('tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4')\\n plt.xlim([0,200])\\n if save:\\n figure1.savefig('tail_cts_per_shot.pdf')\\n\\n try:\\n data.close()\\n except:\\n pass\\n\\n print 'tail counts per shot = '+num2str(tail_counts_per_shot*1e4,1)+'E-4'\\n\\n return tail_counts_per_shot\",\n \"def transform(self, dataset, number_of_thresholds=1,\\n enable_valley_emphasis=False):\\n\\n # Initial progress\\n self.progress.value = 0\\n self.progress.maximum = 100\\n\\n # Approximate percentage of work completed after each step in the\\n # transform\\n STEP_PCT = [10, 20, 70, 90, 100]\\n\\n try:\\n import itk\\n import itkExtras\\n import itkTypes\\n from tomviz import itkutils\\n except Exception as exc:\\n print(\\\"Could not import necessary module(s)\\\")\\n raise exc\\n\\n # Return values\\n returnValues = None\\n\\n # Add a try/except around the ITK portion. ITK exceptions are\\n # passed up to the Python layer, so we can at least report what\\n # went wrong with the script, e.g,, unsupported image type.\\n try:\\n self.progress.value = STEP_PCT[0]\\n self.progress.message = \\\"Converting data to ITK image\\\"\\n\\n # Get the ITK image\\n itk_image = itkutils.dataset_to_itk_image(dataset)\\n itk_input_image_type = type(itk_image)\\n\\n # OtsuMultipleThresholdsImageFilter's wrapping requires that the\\n # input and output image types be the same.\\n itk_threshold_image_type = itk_input_image_type\\n\\n # Otsu multiple threshold filter\\n otsu_filter = itk.OtsuMultipleThresholdsImageFilter[\\n itk_input_image_type, itk_threshold_image_type].New()\\n otsu_filter.SetNumberOfThresholds(number_of_thresholds)\\n otsu_filter.SetValleyEmphasis(enable_valley_emphasis)\\n otsu_filter.SetInput(itk_image)\\n itkutils.observe_filter_progress(self, otsu_filter,\\n STEP_PCT[1], STEP_PCT[2])\\n\\n try:\\n otsu_filter.Update()\\n except RuntimeError:\\n return\\n\\n print(\\\"Otsu threshold(s): %s\\\" % (otsu_filter.GetThresholds(),))\\n\\n itk_image_data = otsu_filter.GetOutput()\\n\\n # Cast threshold output to an integral type if needed.\\n py_buffer_type = itk_threshold_image_type\\n voxel_type = itkExtras.template(itk_threshold_image_type)[1][0]\\n if voxel_type is itkTypes.F or voxel_type is itkTypes.D:\\n self.progress.message = \\\"Casting output to integral type\\\"\\n\\n # Unsigned char supports 256 labels, or 255 threshold levels.\\n # This should be sufficient for all but the most unusual use\\n # cases.\\n py_buffer_type = itk.Image.UC3\\n caster = itk.CastImageFilter[itk_threshold_image_type,\\n py_buffer_type].New()\\n caster.SetInput(itk_image_data)\\n itkutils.observe_filter_progress(self, caster,\\n STEP_PCT[2], STEP_PCT[3])\\n\\n try:\\n caster.Update()\\n except RuntimeError:\\n return\\n\\n itk_image_data = caster.GetOutput()\\n\\n self.progress.value = STEP_PCT[3]\\n self.progress.message = \\\"Saving results\\\"\\n\\n label_map_dataset = dataset.create_child_dataset()\\n itkutils.set_itk_image_on_dataset(itk_image_data, label_map_dataset,\\n dtype=py_buffer_type)\\n\\n self.progress.value = STEP_PCT[4]\\n\\n # Set up dictionary to return operator results\\n returnValues = {}\\n returnValues[\\\"label_map\\\"] = label_map_dataset\\n\\n except Exception as exc:\\n print(\\\"Problem encountered while running %s\\\" %\\n self.__class__.__name__)\\n raise exc\\n\\n return returnValues\",\n \"def process_observation(self, observation):\\n #print(\\\"start_process_obs\\\")\\n processed_observation = np.zeros((NB_AGENTS, OBSERVATION_SIZE))\\n\\n goliath_type = getattr(env, 'Terran_Goliath')\\n battlecruiser_type = getattr(env, 'Terran_Battlecruiser')\\n '''\\n goliath and battlecruiser type:\\n hp_max: 125\\n armor: 1\\n cooldown_max: 22\\n acceleration: 1\\n top_speed: 4.57\\n damage_amount: 12\\n damage_factor: 1\\n weapon_range: 192\\n sight_range: 256\\n seek_range: 160\\n\\n hp_max: 500\\n energy_max: 200\\n armor: 3\\n cooldown_max: 30\\n acceleration: 27\\n top_speed: 2.5\\n damage_amount: 25\\n damage_factor: 1\\n weapon_range: 192\\n sight_range: 352\\n '''\\n #print(\\\"goliath and battlecruiser type:\\\")\\n #print(goliath_type)\\n #print(battlecruiser_type)\\n\\n for i, agent in enumerate(observation.my_unit):\\n if agent.hp <= 0:\\n continue\\n my_x = agent.pos_x\\n my_y = agent.pos_y\\n my_type_str = agent.unit_type\\n my_type = goliath_type if my_type_str == 'Terran_Goliath' else print(\\\"error in the my_type\\\")\\n t1 = [agent.hp + agent.shield, agent.cooldown, math.atan2(agent.velocity_y, agent.velocity_x),\\n math.sqrt((agent.velocity_x) ** 2 + (agent.velocity_y) ** 2), agent.angle,\\n 1 if agent.accelerating else -1 if agent.braking else 0, agent.attacking, agent.is_attack_frame]\\n t2 = [self.last_action[i] / (env.action_space[1] - 1)]\\n t3 = [i.nearest_obstacle_dist for i in agent.pos_info]\\n t4 = []\\n t5 = []\\n t4_max = []\\n t5_max = []\\n for idx, enemy in enumerate(observation.en_unit):\\n en_type_str = enemy.unit_type\\n if en_type_str == 'Terran_Battlecruiser':\\n en_type = battlecruiser_type\\n else:\\n continue \\n if enemy.hp <= 0:\\n t4.extend([0,0,0,0,0,0,0,0,0,0])\\n else:\\n t4.extend([math.atan2(enemy.pos_y - my_y, enemy.pos_x - my_x), math.sqrt((enemy.pos_x - my_x) ** 2 + (enemy.pos_y - my_y) ** 2),\\n math.atan2(enemy.velocity_y, enemy.velocity_x), math.sqrt((enemy.velocity_x) ** 2 + (enemy.velocity_y) ** 2),\\n enemy.cooldown, enemy.hp + enemy.shield, enemy.angle, 1 if agent.accelerating else -1 if agent.braking else 0, agent.attacking, agent.is_attack_frame])\\n t4_max.extend([math.pi, 320, math.pi, en_type.top_speed, en_type.cooldown_max, en_type.hp_max + en_type.shield_max, math.pi, 1, 1, 1])\\n for idx, ally in enumerate(observation.my_unit):\\n if i == idx:\\n continue\\n if ally.hp <= 0:\\n t5.extend([0,0,0,0,0])\\n else:\\n t5.extend([math.atan2(ally.pos_y - my_y, ally.pos_x - my_x), math.sqrt((ally.pos_x - my_x) ** 2 + (ally.pos_y - my_y) ** 2),\\n math.atan2(ally.velocity_y, ally.velocity_x), math.sqrt((ally.velocity_x) ** 2 + (ally.velocity_y) ** 2), ally.hp + ally.shield])\\n ally_type = goliath_type\\n t5_max.extend([math.pi, 320, math.pi, ally_type.top_speed, ally_type.hp_max + ally_type.shield_max])\\n if my_type_str == 'Terran_Goliath':\\n t1_max = [my_type.hp_max + my_type.shield_max, 1, math.pi, my_type.top_speed, math.pi, 1, 1, 1]\\n else:\\n t1_max = [my_type.hp_max + my_type.shield_max, my_type.cooldown_max, math.pi, my_type.top_speed, math.pi, 1, 1, 1]\\n #t4_max = [math.pi, 320, math.pi, en_type.top_speed, en_type.cooldown_max, en_type.hp_max + en_type.shield_max, math.pi, 1, 1, 1]\\n #t5_max = [math.pi, 320, math.pi, ally_type.top_speed, ally_type.hp_max + ally_type.shield_max]\\n\\n #t5_max = [32, 32, type.hp_max + type.shield_max, type.cooldown_max,\\n #32, 32, type.hp_max + type.shield_max, type.cooldown_max,\\n #32, 32, type.hp_max + type.shield_max, type.cooldown_max,\\n #32, 32, type.hp_max + type.shield_max, math.pi,\\n #32, 32, type.hp_max + type.shield_max, math.pi,\\n #32, 32, type.hp_max + type.shield_max, math.pi]\\n\\n t1 = np.divide(t1, t1_max) # runtime warning\\n t2 = np.array(t2) / 320\\n t3 = np.array(t3) / 320\\n t4 = np.divide(t4, t4_max)\\n t5 = np.divide(t5, t5_max)\\n\\n processed_observation[i] = np.concatenate([t1, t2, t3, t4, t5])\\n\\n self.last_my_unit_cnt.append(np.sum(np.array([u.hp+u.shield for u in observation.my_unit]) > 0))\\n self.last_enemy_unit_cnt.append(np.sum(np.array([u.hp+u.shield for u in observation.en_unit]) > 0))\\n self.last_enemy_unit_hp.append(sum([u.hp + u.shield for u in observation.en_unit]))\\n self.accumulated_observation.append(processed_observation)\\n\\n\\n return processed_observation\",\n \"def single_channel_stacking(tifs):\\n template_ID=int(len(tifs)/2)\\n \\n template_raster=gdal_array.LoadFile(tifs[template_ID-1])\\n avg_raster=np.zeros_like(template_raster)\\n avg_raster=avg_raster+1\\n new_raster=np.copy(template_raster)\\n # ones=np.full(template_raster.shape, 1)\\n for i, tif in enumerate(tifs, start=1):\\n if i==template_ID: \\n continue\\n \\n tif_raster=gdal_array.LoadFile(tif)\\n # tif_raster=cut_transformed_array_borders(tif_raster)\\n result=ird.similarity(template_raster,tif_raster , numiter=1, order=1)\\n img_transformed= ird.transform_img(tif_raster, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=2)\\n \\n img_transformed=cut_transformed_array_borders(img_transformed)\\n \\n # ones_transformed=ird.transform_img(ones, scale=result['scale'], angle=result['angle'], tvec=result['tvec'], mode='constant', bgval=0, order=1)\\n ones_transformed=np.zeros_like(template_raster)\\n ones_transformed[np.where(img_transformed>0)]=1\\n print(ones_transformed)\\n \\n print(np.mean(ones_transformed), np.max(ones_transformed), np.min(ones_transformed))\\n print(ones_transformed[np.where(ones_transformed>0)])\\n print(np.min(ones_transformed[np.where(ones_transformed>0)]))\\n print(np.max(ones_transformed[np.where(ones_transformed>0)]))\\n\\n plt.imshow(ones_transformed)\\n plt.show()\\n plt.close()\\n \\n # ones_transformed=cut_transformed_array_borders(ones_transformed)\\n \\n avg_raster=avg_raster+ones_transformed\\n # ird.imshow(template_raster, tif_raster, img_transformed)\\n \\n new_raster=new_raster+img_transformed\\n \\n # new_raster=new_raster+template_raster \\n # new_raster=new_raster/len(tifs)\\n\\n gtz=np.where(avg_raster>0)\\n \\n\\n \\n\\n \\n \\n plt.imshow(new_raster)\\n plt.show()\\n plt.close()\\n # gdal_array.SaveArray(new_raster, tifs[0][:-4]+\\\"_not_abvertaghe_stacked_.tiff\\\")\\n new_raster[gtz]=new_raster[gtz]/avg_raster[gtz] \\n gdal_array.SaveArray(new_raster, tifs[0][:-4]+\\\"_stacked_.tiff\\\")\\n plt.imshow(new_raster)\\n plt.savefig(\\\"test.tif\\\", dpi=800)\\n plt.show()\\n plt.close()\\n\\n def discrete_cmap(N, base_cmap=None):\\n \\\"\\\"\\\"Create an N-bin discrete colormap from the specified input map\\\"\\\"\\\"\\n \\n # Note that if base_cmap is a string or None, you can simply do\\n # return plt.cm.get_cmap(base_cmap, N)\\n # The following works for string, None, or a colormap instance:\\n \\n base = plt.cm.get_cmap(base_cmap)\\n color_list = base(np.linspace(0, 1, N))\\n cmap_name = base.name + str(N)\\n return base.from_list(cmap_name, color_list, N)\\n\\n cmap=discrete_cmap(int(avg_raster.max())+1, base_cmap=\\\"ocean\\\") \\n \\n norm=mpl.colors.BoundaryNorm(np.arange(-0.5,int(avg_raster.max()+1)), cmap.N)\\n fig=plt.figure()\\n fig.set_size_inches(5,4)\\n ax=fig.add_subplot(111)\\n data=ax.matshow(avg_raster, cmap=cmap, norm=norm)\\n fig.colorbar(data, ticks=np.linspace(0,int(avg_raster.max()),int(avg_raster.max()+1)), drawedges=True)\\n\\n plt.show()\\n plt.close()\\n\\n\\n # gdal_array.SaveArray(new_raster, tifs[0][:-4]+\\\"_stacked_.tiff\\\")\",\n \"def photometry(userinputs, image, catalog, outputname, apertures, annulus='', dannulus='', recenter=False):\\n logging.info('Running photometry function on {}'.format(image))\\n logging.info('Using {}px apertures'.format(apertures))\\n\\n #set directory\\n target_dir = userinputs['OUTDIR']\\n\\n #Update passed names to be full paths if they are not\\n\\n if len(image.split('/'))==1:\\n logging.info('Looking for {} in {}.'.format(image,userinputs['DATA']))\\n image = glob.glob(userinputs['DATA'] + '/' + image)\\n if len(image)==0:\\n logging.critical('No {} image found'.format(image))\\n filemanagement.shutdown('Selected image does not exist',userinputs)\\n else:\\n image = image[0]\\n logging.debug('Using image: {}'.format(image))\\n\\n if len(catalog.split('/'))==1:\\n catalog = target_dir + '/init/' + catalog\\n logging.debug('Input catalog: {}'.format(catalog))\\n\\n if len(outputname.split('/'))==1:\\n output = target_dir + '/photometry/' + outputname\\n logging.debug('Output name: {}'.format(output))\\n else:\\n output = outputname\\n outputname = outputname.split('/')[-1]\\n logging.debug('Output name: {}'.format(output))\\n\\n\\n #Load zeropoints\\n inst_zp, filter_zp, zp_zp = np.loadtxt(target_dir + '/init/Hi-PEEC_zeropoints.tab', unpack=True, dtype='str')\\n # print inst_zp, filter_zp, zp_zp\\n # Get filter from header\\n filter = get_filter(image)\\n\\n\\n # Set the necessary variables for photometry on the reference image\\n exptime = fits.getheader(image)['EXPTIME']\\n logging.debug('Exposure time from header: {}'.format(exptime))\\n inst = fits.getheader(image)['INSTRUME']\\n logging.debug('Intrument from header: {}'.format(inst))\\n inst = inst.lower()\\n\\n\\n match = (inst_zp == inst) & (filter_zp == filter.lower())\\n zp = zp_zp[match]\\n\\n # zp is a string within an array, so need to turn into a float\\n try:\\n zp = float(zp[0])\\n #If that cannot be done there was no match.\\n except IndexError:\\n if inst == 'acs':\\n logging.debug('Zeropoint not found in file, passing to ACS calculation')\\n zp = ACS_zeropoint(image)\\n elif inst == 'wfc3':\\n logging.debug('Zeropoint not found in file, passing to WFC3 calculation')\\n zp = WFC3_zeropoint(image)\\n else:\\n logging.critical('No matching zeropoint found. Quitting.')\\n logging.debug('No zeropoint match found for filter {} with instrument {}'\\\\\\n .format(filter,inst))\\n logging.debug('Available filters in zeropoint file : {} for instrument {}'\\\\\\n .format(filter_zp, inst_zp))\\n filemanagement.shutdown('No zeropoint was found for filter: {}'.format(filter),userinputs)\\n\\n logging.debug('Zeropoint from file: {}'.format(zp))\\n # Remove output file if it already exists\\n filemanagement.remove_if_exists(output)\\n\\n\\n # Run photometry\\n #--------------------------------------------------------------------------\\n # Set up IRAF params:\\n iraf.datapars.epadu = exptime\\n\\n # !!!!!!!!!!!!!!!!!\\n # Only center on reference frame\\n if recenter:\\n iraf.centerpars.calgorithm = 'centroid'\\n else:\\n iraf.centerpars.calgorithm = 'none'\\n # !!!!!!!!!!!!!!!\\n # CHANGE BACKGROUND ESTIMATE IN ANNULUS TO MODE\\n\\n # Select the annulus depending on whether it is overwritten in the function call or not\\n if annulus == '':\\n iraf.fitskypars.annulus = userinputs['ANNULUS']\\n logging.debug('Using annulus from inputfile ({}px)'.format(userinputs['ANNULUS']))\\n else:\\n iraf.fitskypars.annulus = annulus\\n logging.debug('Using user specified annulus ({}px)'.format(annulus))\\n if dannulus == '':\\n iraf.fitskypars.dannulus = userinputs['D_ANNULUS']\\n logging.debug('Using annulus width from inputfile ({}px)'.format(userinputs['D_ANNULUS']))\\n else:\\n iraf.fitskypars.dannulus = dannulus\\n logging.debug('Using user specified annulus width ({}px)'.format(dannulus))\\n\\n iraf.photpars.apertures = apertures\\n logging.debug('Using aperture(s) of {}px'.format(apertures))\\n iraf.photpars.zmag = zp\\n logging.debug('Setting zeropoint to {}'.format(zp))\\n\\n # Do phot\\n iraf.phot(image+'[SCI]', catalog, output)\\n #--------------------------------------------------------------------------\\n\\n\\n #Depending on the number of apertures used, different methods of saving the\\n # results are required\\n #--------------------------------------------------------------------------\\n\\n naper = len(apertures.split(','))\\n logging.debug('Number of apertures used {}'.format(naper))\\n\\n #final output filename\\n fullcat_mag_short = target_dir + '/photometry/short_' + outputname\\n\\n if naper > 1:\\n # Removes all outputlines that do not contain the character '*'\\n # ensures only phot results are kept\\n cmd = 'grep \\\"*\\\" ' + output + ' > ' + fullcat_mag_short\\n os.system(cmd)\\n\\n # Replace INDEFS:\\n cmd = 'sed -i.bak \\\"s/INDEF/99.999/g\\\" ' + fullcat_mag_short\\n os.system(cmd)\\n\\n # Remove .bak files to prevent confusion\\n bak_fullcat = fullcat_mag_short + '.bak'\\n os.remove(bak_fullcat)\\n\\n\\n else:\\n #Dump results into a temp file\\n temp = target_dir + '/photometry/phot_dump.mag'\\n filemanagement.remove_if_exists(temp)\\n iraf.txdump(output, 'XCENTER,YCENTER,FLUX,MAG,MERR,MSKY,ID', 'yes', Stdout = temp)\\n\\n # Set placeholders for sources outside of FOV and undetected sources\\n # For outside of FOV, use 66.666 instead of INDEF\\n # For undetected sources, use 99.999 instead of INDEF\\n\\n # Sources outside of FOV have exactly zero flux\\n x, y, flux, mag, merr, msky, id = np.loadtxt(temp, unpack = True,\\n dtype = str)\\n\\n flux = flux.astype(float)\\n\\n out_fov = (flux == 0.)\\n logging.debug('Number of sources outside FOV: {}'.format(len(out_fov)))\\n\\n mag[out_fov] = 66.666\\n merr[out_fov] = 66.666\\n msky[out_fov] = 66.666\\n\\n # Undetected sources, those with negative flux or fluxes so small that mag err\\n # is INDEF\\n neg_flux = (flux < 0.)\\n tiny_flux = (flux > 0.) & (merr == 'INDEF')\\n\\n mag[neg_flux] = 99.999\\n merr[neg_flux] = 99.999\\n msky[neg_flux] = 99.999\\n\\n merr[tiny_flux] = 99.999\\n msky[tiny_flux] = 99.999\\n\\n logging.debug('Nr of undetected sources: {}'.format(len(tiny_flux)+len(neg_flux)))\\n # Save results to new file\\n x = x.astype(float)\\n y = y.astype(float)\\n mag = mag.astype(float)\\n merr = merr.astype(float)\\n msky = msky.astype(float)\\n id = id.astype(int)\\n\\n zip_phot = zip(x, y, mag, merr, msky, id)\\n\\n np.savetxt(fullcat_mag_short, zip_phot,\\n fmt = '%.3f %.3f %.3f %.3f %.9f %i')\\n\\n #--------------------------------------------------------------------------\\n\\n return fullcat_mag_short\",\n \"def main():\\n camera = picamera.PiCamera()\\n camera.resolution = (RESOLUTIONX, RESOLUTIONY)\\n camera.iso = 800\\n time.sleep(2)\\n while True:\\n camera.capture('current-image.jpg')\\n adapt_steering(navigation.get_xposition('current-image.jpg'))\\n time.sleep(0.4)\",\n \"def process_tir_image(ds, data_res, t_thresh=-50, min_mcs_size=5000):\\n ctt = (ds['tb']).squeeze()-273.15\\n min_pix_nb = min_mcs_size / data_res**2\\n\\n max_pix_nb = 300000 / data_res**2 # this is to capture satellite artefacts that come in large contiguous stripes.\\n labels, goodinds = mcs_define(ctt.values, t_thresh, minmax_area=[min_pix_nb, max_pix_nb]) # 7.7x7.7km = 64km2 per pix in gridsat? 83 pix is 5000km2\\n dic = dictionary()\\n #plt.figure()\\n #plt.pcolormesh(labels)\\n #plt.colorbar()\\n #plt.show()\\n for g in goodinds:\\n\\n if g==0:\\n continue\\n\\n pos = np.where(labels==g)\\n npos = np.where(labels!=g)\\n datestr = str(int(ctt['time.year'].values))+'-'+str(int(ctt['time.month'].values)).zfill(2)+'-'+str(int(ctt['time.day'].values)).zfill(2)+'_'+\\\\\\n str(int(ctt['time.hour'].values)).zfill(2)+':'+str(int(ctt['time.minute'].values)).zfill(2)\\n \\n dic['date'].append(datestr)\\n dic['month'].append(int(ctt['time.month']))\\n dic['hour'].append(int(ctt['time.hour']))\\n dic['year'].append(int(ctt['time.year']))\\n dic['day'].append(int(ctt['time.day']))\\n dic['minute'].append(int(ctt['time.minute']))\\n\\n storm = ctt.copy()\\n storm.values[npos] = np.nan\\n tmin_pos = np.nanargmin(storm.values)\\n tpos_2d = np.unravel_index(tmin_pos, storm.shape)\\n \\n latmin = np.nanmin(ctt.lat.values[pos[0]])\\n latmax = np.nanmax(ctt.lat.values[pos[0]])\\n lonmin = np.nanmin(ctt.lon.values[pos[1]])\\n lonmax = np.nanmax(ctt.lon.values[pos[1]])\\n dic['area'].append(np.sum(np.isfinite(storm.values))*data_res**2)\\n dic['70area'].append(np.sum(storm.values<=-70)*data_res**2)\\n dic['minlon'].append(lonmin)\\n dic['minlat'].append(latmin)\\n dic['maxlon'].append(lonmax)\\n dic['maxlat'].append(latmax)\\n dic['clon'].append(lonmin + (lonmax - lonmin)/2)\\n dic['clat'].append(latmin + (latmax - latmin)/2)\\n dic['tmin'].append(np.nanmin(storm))\\n dic['tminlat'].append(float(ctt.lat[tpos_2d[0]].values))\\n dic['tminlon'].append(float(ctt.lon[tpos_2d[1]].values))\\n dic['tmean'].append(float(np.nanmean(storm)))\\n dic['tp1'].append(float(np.nanpercentile(storm, 1)))\\n dic['tp99'].append(float(np.nanpercentile(storm, 99)))\\n dic['stormID'].append(datestr + '_' + str(g))\\n dic['cloudMask'].append(labels==g)\\n dic['tir'].append(storm.values)\\n\\n # for k in dic.keys():\\n # print(k, len(dic[k]))\\n return dic\",\n \"def calculate_thresholds(self):\\n \\n for group in self.roi_groups:\\n for roi in group.rois:\\n for image in range(len(roi.counts)):\\n # print(roi.autothreshs)\\n # print('image',image)\\n if roi.autothreshs[image]:\\n values = np.fromiter(roi.counts[image].values(), dtype=float)\\n roi.thresholds[image] = self.calculate_threshold(values)\\n\\n for image, im_copy in enumerate(self.copy_im_threshs): # copy values from a different image and set to manual thresh if needed\\n if im_copy is not None:\\n for group in self.roi_groups:\\n for roi in group.rois:\\n roi.autothreshs[image] = False\\n roi.thresholds[image] = roi.thresholds[im_copy]\",\n \"def calculate_dark_current(image, i, int_time):\\n dark_data_dir = r'F:\\\\TEMPO\\\\Data\\\\GroundTest\\\\FPS\\\\Integration_Sweep\\\\Dark'\\n data_path_name_split = image.split('_')\\n #print(data_path_name_split)\\n all_int_files = [each for each in os.listdir(dark_data_dir) \\\\\\n if each.endswith('_'+data_path_name_split[-1])] \\n print(all_int_files)\\n \\n dark_data_file = os.path.join(dark_data_dir, all_int_files[0])\\n IDL_variable = readsav(dark_data_file) \\n all_full_frame = IDL_variable.q \\n quad = all_full_frame[:, i, :, :]\\n active_quad = np.mean(quad[:, 4:1028, 10:1034], axis=0) \\n tsoc = np.mean(quad[:, 4:1028, 1034:1056], axis=0)\\n bias_subtracted_quad = perform_bias_subtraction_ave(active_quad, tsoc)\\n smear_subtracted_quad, smear_signal = perform_smear_subtraction(bias_subtracted_quad[10:1000, :], int_time)\\n return smear_subtracted_quad\",\n \"def process_sample(self, ch, method, properties, body):\\n\\n # data inside a dictionary\\n # {'amplitude':[1.3,4.4,5...],\\n # 'angle': [0.04,0.1,...]}\\n sample_dict = self.deserialize_vtt_60_processed(body)\\n\\n with open('sample_test_run.txt', 'a') as f:\\n x_arrstr = np.char.mod('%d', sample_dict['amplitude'])\\n\\n # x_arrstr -> should be 2d array \\\"frame\\\".\\n raw_data = \\\",\\\".join(x_arrstr.flatten())\\n f.write(raw_data)\\n f.write('\\\\n')\\n\\n arr = np.array(sample_dict['amplitude'])\\n\\n frame = np.reshape(arr, (180,110))\\n frame_transposed_flipped = np.flip(np.transpose(frame))\\n frame_transposed_flipped = np.flip(np.transpose(frame))\\n details_removed = frame_transposed_flipped\\n details_removed = np.clip(frame_transposed_flipped, a_min=frame_transposed_flipped.max() - 15, a_max=None)\\n self.i = self.i + 1\\n if self.i % 10 == 0:\\n b = sum(pd.DataFrame(frame).max().diff() / pd.DataFrame(frame).max() > 0.13)\\n self.report_people(b)\\n\\n\\n if body is None or sample_dict is None:\\n print('Stream stopped.')\\n return\\n if self.samples_in_total % self.fps == 0:\\n endTime = time.time()\\n print('FPS: {:.1f}'.format(self.fps/(endTime - self.startTime)))\\n self.startTime = endTime\\n \\n # your code here\\n amplitude = sample_dict['amplitude']\\n amplitude = np.reshape(amplitude,(180,110))\\n amplitude = np.where(amplitude>130,130,amplitude)\\n amplitude = np.uint8(255*amplitude/130)\\n out = cv2.cvtColor(amplitude,cv2.COLOR_GRAY2BGR)\\n out = cv2.applyColorMap(out,cv2.COLORMAP_MAGMA)\\n cv2.imshow('junction',out)\\n cv2.waitKey(10)\",\n \"def optimize_trap(dg):\\n f_peak = './temp_peak.lh5' # lh5\\n f_results = './temp_results.h5' # pandas\\n grp_data, grp_grid = '/optimize_data', '/optimize_grid'\\n \\n # epar, elo, ehi, epb = 'energy', 0, 1e7, 10000 # full range\\n epar, elo, ehi, epb = 'energy', 3.88e6, 3.92e6, 500 # K40 peak\\n \\n show_movie = True\\n write_output = True\\n n_rows = None # default None\\n \\n with open('opt_trap.json') as f:\\n dsp_config = json.load(f, object_pairs_hook=OrderedDict)\\n \\n # files to consider. fixme: right now only works with one file\\n sto = lh5.Store()\\n lh5_dir = os.path.expandvars(dg.config['lh5_dir'])\\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\\n f_raw = raw_list.values[0] \\n tb_raw = 'ORSIS3302DecoderForEnergy/raw/'\\n\\n # quick check of the energy range\\n # ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\\n # plt.plot(bins[1:], hist, ds='steps')\\n # plt.show()\\n # exit()\\n \\n # set grid parameters\\n # TODO: jason's suggestions, knowing the expected shape of the noise curve\\n # e_rises = np.linspace(-1, 0, sqrt(sqrt(3))\\n # e_rises # make another list which is 10^pwr of this list\\n # np.linspace(log_tau_min, log_tau_max) # try this too\\n e_rises = np.arange(1, 12, 1)\\n e_flats = np.arange(1, 6, 1)\\n # rc_consts = np.arange(54, 154, 10) # changing this here messes up DCR\\n \\n # -- create the grid search file the first time -- \\n # NOTE: this makes a linear grid, and is editable by the arrays above.\\n # jason also proposed a more active gradient-descent style search\\n # like with Brent's method. (https://en.wikipedia.org/wiki/Brent%27s_method)\\n \\n if True:\\n # if not os.path.exists(f_peak):\\n print('Recreating grid search file')\\n \\n # create the grid file\\n # NOTE: save it as an lh5 Table just as an example of writing/reading one\\n lists = [e_rises, e_flats]#, rc_consts]\\n prod = list(itertools.product(*lists)) # clint <3 stackoverflow\\n df_grid = pd.DataFrame(prod, columns=['rise', 'flat'])#,'rc']) \\n lh5_grid = {}\\n for i, dfcol in df_grid.iteritems():\\n lh5_grid[dfcol.name] = lh5.Array(dfcol.values)\\n tb_grid = lh5.Table(col_dict=lh5_grid)\\n sto.write_object(tb_grid, grp_grid, f_peak)\\n \\n # filter events by onboard energy\\n ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\\n # plt.plot(bins[1:], hist, ds='steps')\\n # plt.show()\\n if n_rows is not None:\\n ene_raw = ene_raw[:n_rows]\\n idx = np.where((ene_raw > elo) & (ene_raw < ehi))\\n\\n # create a filtered table with correct waveform and attrs\\n # TODO: move this into a function in lh5.py which takes idx as an input\\n tb_data, wf_tb_data = lh5.Table(), lh5.Table()\\n\\n # read non-wf cols (lh5 Arrays)\\n data_raw = sto.read_object(tb_raw, f_raw, n_rows=n_rows)\\n for col in data_raw.keys():\\n if col=='waveform': continue\\n newcol = lh5.Array(data_raw[col].nda[idx], attrs=data_raw[col].attrs)\\n tb_data.add_field(col, newcol)\\n \\n # handle waveform column (lh5 Table)\\n data_wfs = sto.read_object(tb_raw+'/waveform', f_raw, n_rows=n_rows)\\n for col in data_wfs.keys():\\n attrs = data_wfs[col].attrs\\n if isinstance(data_wfs[col], lh5.ArrayOfEqualSizedArrays):\\n # idk why i can't put the filtered array into the constructor\\n aoesa = lh5.ArrayOfEqualSizedArrays(attrs=attrs, dims=[1,1])\\n aoesa.nda = data_wfs[col].nda[idx]\\n newcol = aoesa\\n else:\\n newcol = lh5.Array(data_wfs[col].nda[idx], attrs=attrs)\\n wf_tb_data.add_field(col, newcol)\\n tb_data.add_field('waveform', wf_tb_data)\\n tb_data.attrs = data_raw.attrs\\n sto.write_object(tb_data, grp_data, f_peak)\\n\\n else:\\n print('Loading peak file. groups:', sto.ls(f_peak))\\n tb_grid = sto.read_object(grp_grid, f_peak)\\n tb_data = sto.read_object(grp_data, f_peak) # filtered file\\n # tb_data = sto.read_object(tb_raw, f_raw) # orig file\\n df_grid = tb_grid.get_dataframe()\\n \\n # check shape of input table\\n print('input table attributes:')\\n for key in tb_data.keys():\\n obj = tb_data[key]\\n if isinstance(obj, lh5.Table):\\n for key2 in obj.keys():\\n obj2 = obj[key2]\\n print(' ', key, key2, obj2.nda.shape, obj2.attrs)\\n else:\\n print(' ', key, obj.nda.shape, obj.attrs)\\n\\n # clear new colums if they exist\\n new_cols = ['e_fit', 'fwhm_fit', 'rchisq', 'xF_err', 'fwhm_ovr_mean']\\n for col in new_cols:\\n if col in df_grid.columns:\\n df_grid.drop(col, axis=1, inplace=True)\\n\\n t_start = time.time()\\n def run_dsp(dfrow):\\n \\\"\\\"\\\"\\n run dsp on the test file, editing the processor list\\n alternate idea: generate a long list of processors with different names\\n \\\"\\\"\\\"\\n # adjust dsp config dictionary\\n rise, flat = dfrow\\n # dsp_config['processors']['wf_pz']['defaults']['db.pz.tau'] = f'{tau}*us'\\n dsp_config['processors']['wf_trap']['args'][1] = f'{rise}*us'\\n dsp_config['processors']['wf_trap']['args'][2] = f'{flat}*us'\\n # pprint(dsp_config)\\n \\n # run dsp\\n pc, tb_out = build_processing_chain(tb_data, dsp_config, verbosity=0)\\n pc.execute()\\n \\n # analyze peak\\n e_peak = 1460.\\n etype = 'trapEmax'\\n elo, ehi, epb = 4000, 4500, 3 # the peak moves around a bunch\\n energy = tb_out[etype].nda\\n \\n # get histogram\\n hE, bins, vE = pgh.get_hist(energy, range=(elo, ehi), dx=epb)\\n xE = bins[1:]\\n \\n # should I center the max at 1460?\\n\\n # simple numerical width\\n i_max = np.argmax(hE)\\n h_max = hE[i_max]\\n upr_half = xE[(xE > xE[i_max]) & (hE <= h_max/2)][0]\\n bot_half = xE[(xE < xE[i_max]) & (hE >= h_max/2)][0]\\n fwhm = upr_half - bot_half\\n sig = fwhm / 2.355\\n \\n # fit to gaussian: amp, mu, sig, bkg\\n fit_func = pgf.gauss_bkg\\n amp = h_max * fwhm\\n bg0 = np.mean(hE[:20])\\n x0 = [amp, xE[i_max], sig, bg0]\\n xF, xF_cov = pgf.fit_hist(fit_func, hE, bins, var=vE, guess=x0)\\n\\n # collect results\\n e_fit = xF[0]\\n xF_err = np.sqrt(np.diag(xF_cov))\\n e_err = xF\\n fwhm_fit = xF[1] * 2.355 * 1460. / e_fit\\n \\n fwhm_err = xF_err[2] * 2.355 * 1460. / e_fit\\n \\n chisq = []\\n for i, h in enumerate(hE):\\n model = fit_func(xE[i], *xF)\\n diff = (model - h)**2 / model\\n chisq.append(abs(diff))\\n rchisq = sum(np.array(chisq) / len(hE))\\n fwhm_ovr_mean = fwhm_fit / e_fit\\n\\n if show_movie:\\n \\n plt.plot(xE, hE, ds='steps', c='b', lw=2, label=f'{etype} {rise}--{flat}')\\n\\n # peak shape\\n plt.plot(xE, fit_func(xE, *x0), '-', c='orange', alpha=0.5,\\n label='init. guess')\\n plt.plot(xE, fit_func(xE, *xF), '-r', alpha=0.8, label='peakshape fit')\\n plt.plot(np.nan, np.nan, '-w', label=f'mu={e_fit:.1f}, fwhm={fwhm_fit:.2f}')\\n\\n plt.xlabel(etype, ha='right', x=1)\\n plt.ylabel('Counts', ha='right', y=1)\\n plt.legend(loc=2)\\n\\n # show a little movie\\n plt.show(block=False)\\n plt.pause(0.01)\\n plt.cla()\\n\\n # return results\\n return pd.Series({'e_fit':e_fit, 'fwhm_fit':fwhm_fit, 'rchisq':rchisq,\\n 'fwhm_err':xF_err[0], 'fwhm_ovr_mean': fwhm_ovr_mean})\\n \\n # df_grid=df_grid[:10]\\n df_tmp = df_grid.progress_apply(run_dsp, axis=1)\\n df_grid[new_cols] = df_tmp\\n # print(df_grid)\\n \\n if show_movie:\\n plt.close()\\n \\n print('elapsed:', time.time() - t_start)\\n if write_output:\\n df_grid.to_hdf(f_results, key=grp_grid)\\n print(f\\\"Wrote output file: {f_results}\\\")\",\n \"def CorrectMotion(self):\\n if self.verbose:\\n print \\\"Correct for motion\\\"\\n for entry in self.entry_map['epi']:\\n info = self.info[entry]\\n\\n if os.path.exists(info['imgfile_m'] + info['suffix']):\\n return\\n# Always use brik for 3dDeconvolve.\\n suffix = '+orig'\\n epifile = '%s%s' % (info['imgfile'], suffix)\\n prefix = info['imgfile_m']\\n base_entry = info['base_entry']\\n if info['base'] == 'start':\\n# Use the first frame specified in template file. Defaults\\n# to zero.\\n base = info['motion_ref_frame']\\n else:\\n# Use the last frame.\\n base = self.info[base_entry]['tdim'] - info['skip']-1\\n base = ('%d' % base).replace(' ','')\\n\\n# Correct for slice-timing.\\n self.SliceTimeCorrect(info, epifile)\\n\\n plane = info['plane']\\n anat_tgt = info['anat_tgt']\\n# anat_entry = self.anat_entry[plane]\\n\\n if info['catmats']:\\n# Include additonal transformation in motion correction such\\n# that final image is in register with the fieldmap, which has\\n# been registered to the structural image that will be used for\\n# spatial normalization.\\n self.MotcorCatenate(info, base, anat_tgt)\\n else:\\n# Assume fieldmap is in register with the structural.\\n self.Motcor(info, base)\\n\\n if info.get('fmapname', None) is None:\\n# No fieldmap correction.\\n if self.fsl_flip:\\n# Flip the way fslview likes it.\\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\\n elif info['suffix'] == '.nii':\\n# Copy motion-corrected images from /tmp to output directory\\n outfile = info['imgfile_final'] + info['suffix']\\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\\n self.CheckExec(cmd, [outfile], force=True)\\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\\n self.CheckExec(cmd, [], force=True)\",\n \"def test_rolling_before_analysis(self):\\n cheese = TomoCheese.from_demo_images()\\n cheese.analyze()\\n original_roi_1 = copy.copy(cheese.module.rois[\\\"1\\\"].pixel_value)\\n for img in cheese.dicom_stack:\\n img.roll(direction=\\\"x\\\", amount=20)\\n cheese.analyze()\\n new_roi_1 = cheese.module.rois[\\\"1\\\"].pixel_value\\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)\",\n \"def atmos_worker(srcs, window, ij, args):\\n src = srcs[0]\\n rgb = src.read(window=window)\\n rgb = to_math_type(rgb)\\n\\n atmos = simple_atmo(rgb, args[\\\"atmo\\\"], args[\\\"contrast\\\"], args[\\\"bias\\\"])\\n\\n # should be scaled 0 to 1, scale to outtype\\n return scale_dtype(atmos, args[\\\"out_dtype\\\"])\",\n \"def make_sim(args):\\n flux, nread, iexp = args\\n satlevel=65535\\n\\n #outdir = \\\"sim_data_flux\\\" + str(flux)\\n #outdir = \\\"sim_data_flux\\\" + str(flux) + \\\"_nonl\\\"\\n outdir = \\\"sim_\\\" + \\\"exposure\\\" + str(iexp) + \\\"_flux\\\" + str(flux)\\n pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) \\n \\n for i in range(1, nread+1):\\n logger.info(\\\"Looping through UTR {}\\\".format(i))\\n #outfileprefix = outdir + \\\"/exposure\\\" + str(iexp) + \\\"_utr\\\" + str(i)\\n outfileprefix = outdir + \\\"/utr\\\" + str(i)\\n refoutfileprefix = outdir + \\\"/utr\\\" + str(i) + \\\"_ref\\\"\\n\\n primary_hdu = fits.PrimaryHDU()\\n outhdus = fits.HDUList([primary_hdu])\\n outhdus_ref = fits.HDUList([primary_hdu])\\n\\n ahdu = fits.HDUList([primary_hdu]) #for a coefficients\\n bhdu = fits.HDUList([primary_hdu]) #for b coefficients\\n\\n #loop all 16 detectors\\n ndet = 16\\n logger.info(\\\"Looping through all 16 detectors\\\")\\n for idet in range(ndet):\\n detid = detids[idet]\\n logger.info(\\\"Looping through the detector {}, {}\\\".format(idet+1, detid)) \\n\\n flux_actual = np.zeros((2048, 2048))\\n #start with the measured flux, constant spatially but varying temporally\\n flux_actual[4:2044,4:2044] = flux * TREAD * i #fill data region with measured flux, (ADU/f) * s = ADU ???\\n flux_actual *= Gain #e-??\\n logger.info(\\\"Filled with measured flux\\\")\\n\\n #calculate nonlinearity from calibration data, varing spatially but not temporally, unit e-??\\n flux_nonlinear = make_nonlinear(flux_actual, outfileprefix, ahdu, bhdu, detid) \\n #flux_nonlinear = flux_actual #no non-linearity\\n logger.info(\\\"Made non-linearized flux\\\")\\n\\n #add pedestal, constant spatially very varying temporally\\n pedestal = random.randint(0, 1000) #set the random pedistal (between 0 - 1000) for this exposure, unit e-??\\n flux_nonlinear[:, :] += pedestal #add the pedestal to all of the data including the reference pixels.\\n logger.info(\\\"Added pedestal\\\")\\n\\n #add noise (e-)\\n readnoise_array = np.random.normal(size=[2048,2048]) * ReadNoise #generate the read noise, the reference pixels should also have noise. \\n flux_nonlinear += readnoise_array #add the read noise to the data array\\n logger.info(\\\"Added read noise\\\")\\n\\n # set any pixel above the saturation limit to the saturation limit.\\n # This can set to 65535 for all pixels for now, but should be able to \\n # take a map since this will vary pixel to pixel. \\n ind = np.where(flux_nonlinear > satlevel)\\n flux_nonlinear[ind] = satlevel\\n logger.info(\\\"Checked saturation level\\\")\\n\\n #add this dector to the final simulated data\\n hdu = fits.ImageHDU(data=flux_nonlinear, name=detid)\\n outhdus.append(hdu)\\n\\n #do reference pixel correction\\n updownCorr(flux_nonlinear)\\n leftrightCorr(flux_nonlinear)\\n\\n #add this dector to another output\\n hdu1 = fits.ImageHDU(data=flux_nonlinear, name=detid)\\n outhdus_ref.append(hdu1)\\n \\n\\n #write the sim data for this UTR\\n outfile = outfileprefix + \\\".fits\\\"\\n #logger.info(\\\"Writing sim data to: {}\\\".format(outfile))\\n #outhdus.writeto(outfile)\\n\\n refoutfile = refoutfileprefix + \\\".fits\\\"\\n logger.info(\\\"Writing reference pixel corrected sim data to: {}\\\".format(refoutfile))\\n outhdus_ref.writeto(refoutfile)\\n\\n #ahdu.writeto(outfileprefix + \\\"_a_coef.fits\\\")\\n #bhdu.writeto(outfileprefix + \\\"_b_coef.fits\\\")\",\n \"def calibrate(cap, location):\\n\\n #Poisition and size of sensor\\n [x, y, h, w] = location\\n\\n #show square to user and wait for key\\n print(\\\"please, step away to clear the blue square displayed on screen and press q to continue\\\")\\n while True:\\n ret, frame = cap.read()\\n cv2.namedWindow('Calibrate',cv2.WINDOW_NORMAL)\\n show = cv2.rectangle(frame, (x,y), (x+w,y+h), (255, 0, 0) , 5)\\n cv2.imshow('Calibrate', show)\\n key = cv2.waitKey(1)\\n if key == ord('q'):\\n break\\n\\n #get first image, process and define window previous for iteration\\n ret, frame = cap.read()\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n frame = cv2.GaussianBlur(frame, (7,7), 0)\\n previous = frame[y:y+w,x:x+h]\\n\\n #set parameters for mean value of sensor, kernel of erode function,\\n sampleNbMean = 50\\n xi = np.empty((0, sampleNbMean))\\n kernel = np.ones((5,5), np.uint8)\\n\\n #iterate over each frame until sample number\\n for iteration in range(sampleNbMean):\\n\\n # Capture frame, draw the window and display to the user\\n ret, frame = cap.read()\\n # Image operation\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n frame = cv2.GaussianBlur(frame, (7,7), 0)\\n\\n #get present window\\n present = frame[y:y+w,x:x+h]\\n\\n #add sample for mean, add diference of window with prieviuos\\n xi = np.append(xi,\\n np.sum(\\n cv2.erode(\\n cv2.bitwise_xor(present,previous), kernel, iterations=1)))\\n\\n #present image becomes previous before steping into next image\\n previous = present\\n\\n #mean\\n mean = np.sum(xi)/len(xi)\\n\\n #sigma\\n sum = 0\\n for sample in xi:\\n sum += np.power(sample - mean, 2)\\n sigma = np.sqrt(sum/len(xi))\\n\\n #close window\\n cv2.destroyWindow('Calibrate')\\n\\n return mean, sigma\",\n \"def __call__(self, frame_num):\\n quant_sys = self.quant_sys\\n quant_sys.propagate(10)\\n\\n # propagate the wigner function\\n self.img_clasical_rho.set_array(\\n (quant_sys.D22 + quant_sys.D11).real\\n #quant_sys.get_classical_rho()\\n )\\n\\n self.img_Upsilon2.set_array(\\n quant_sys.quantum_rho.real\\n )\\n\\n return self.img_clasical_rho, self.img_Upsilon2\",\n \"def main ():\\n\\n # interval ends\\n a,b = -1.0, +1.0\\n\\n # List of trapezium counts\\n NVals = numpy.logspace (1, 8, base=10.0, num=6, dtype=numpy.int)\\n\\n # List of implementations\\n intFuncList = [ \\n (integral.trapintPython, \\\"python.txt\\\")\\n#\\t\\t (integral.trapintNumPy, \\\"numpy.txt\\\"),\\n#\\t\\t (integral.trapintNumba, \\\"numba.txt\\\"),\\n#\\t\\t (cpintegral.trapint,\\t \\\"cython.txt\\\")\\n ]\\n\\n for intFunc, file in intFuncList:\\n\\n\\t# Open results file...\\n\\n\\tfh = open( file, \\\"w\\\" ) \\n\\n \\t# compute Pi \\n\\tfor N in NVals:\\n \\tt1 = time.time ()\\n \\t \\tv1 = intFunc (a, b, N)\\n\\t \\te1 = math.fabs (v1 - math.pi)\\n \\t \\tt1 = time.time () - t1\\n\\t \\tfh.write(\\\"{:d} {:6.4g}\\\\n\\\".format(N, t1))\\n\\tfh.close()\",\n \"def process_image( self, image ):\\n \\n # 1. detect cars in image at different scales\\n \\n # Modify x/y start stop according to scale, cars appear smaller near horizon\\n scales = config.scales\\n \\n box_list = []\\n for scale_item in scales:\\n scale = scale_item[\\\"scale\\\"]\\n detects_image, boxes = hog_subsample.find_cars(image, \\n scale_item[\\\"y_start_stop\\\"][0], scale_item[\\\"y_start_stop\\\"][1], \\n scale, \\n config.settings[\\\"svc\\\"], \\n config.settings[\\\"scaler\\\"], \\n config.settings[\\\"orient\\\"], \\n config.settings[\\\"pix_per_cell\\\"], config.settings[\\\"cell_per_block\\\"], \\n config.settings[\\\"spatial_size\\\"], config.settings[\\\"hist_bins\\\"],\\n scale_item[\\\"x_start_stop\\\"][0], scale_item[\\\"x_start_stop\\\"][1])\\n box_list.extend(boxes)\\n \\n # Update history\\n self.bbox_list_history.append( box_list )\\n bbox_list_history_list = sum(self.bbox_list_history.copy(), []) # single list of bbox lists in history\\n \\n # 2. heat map and threshold\\n \\n # Make zeros shaped like image\\n heat = np.zeros_like(image[:,:,0]).astype(np.float)\\n\\n # Add heat for each box in box list history\\n heat = heatmap_threshold_detection.add_heat(heat, bbox_list_history_list)\\n\\n # Apply threshold to help remove false positives\\n heat_threshold = config.heatmap_threshold\\n heat = heatmap_threshold_detection.apply_threshold(heat, heat_threshold)\\n\\n # Find final boxes from heatmap using label function\\n heatmap = np.clip(heat, 0, 255) # only need to clip if there is more than 255 boxes around a point?\\n labels = label(heatmap)\\n boxed_image = heatmap_threshold_detection.draw_labeled_bboxes(np.copy(image), labels)\\n \\n # frame image annotation\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n cv2.putText(boxed_image,\\\"Frame:{}\\\".format(config.count), (10,100), font, 1, (255,255,255), 2 ,cv2.LINE_AA )\\n \\n return boxed_image\",\n \"def process_plot_mri_with_damaged(paths, params):\\n\\n\\n\\t# hdf5 file that contains the original images\\n\\thdf5_file = os.path.join(paths['hdf5_folder'], params['hdf5_file'])\\n\\t\\n\\t# get all patient names from original MRI group\\n\\tpatients = get_datasets_from_group(group_name = params['group_original_mri'], hdf5_file = hdf5_file)\\n\\n\\t# get list of patients without state\\n\\tpatients = set([re.search('(.*) (fersk|Tint)', x).group(1) for x in patients])\\n\\t\\n\\t# loop over each patient, read data, perform inference\\n\\tfor i, patient in enumerate(patients):\\n\\n\\t\\tlogging.info(f'Processing patient: {patient} {i + 1}/{len(patients)}')\\n\\n\\t\\t# parse out treatment, sample, and state from patient name\\n\\t\\ttreatment, _, _ = parse_patientname(patient_name = f'{patient} fersk')\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tGet fresh state\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t# read original images\\n\\t\\tfresh_original_images = read_dataset_from_group(dataset = f'{patient} fersk', group_name = params['group_original_mri'], hdf5_file = hdf5_file)\\n\\t\\t# read reconstructed images\\n\\t\\tfresh_reconstructed_images = read_dataset_from_group(dataset = f'{patient} fersk', group_name = params['group_segmented_classification_mri'], hdf5_file = hdf5_file)\\n\\t\\t# only take damaged tissue and set connected tissue\\n\\t\\tfresh_reconstructed_damaged_images = (process_connected_tissue(images = fresh_reconstructed_images.copy(), params = params) == 1)\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tGet frozen/thawed\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t# read original images\\n\\t\\tfrozen_original_images = read_dataset_from_group(dataset = f'{patient} Tint', group_name = params['group_original_mri'], hdf5_file = hdf5_file)\\n\\t\\t# read reconstructed images\\n\\t\\tfrozen_reconstructed_images = read_dataset_from_group(dataset = f'{patient} Tint', group_name = params['group_segmented_classification_mri'], hdf5_file = hdf5_file)\\n\\t\\t# only take damaged tissue and set connected tissue\\n\\t\\tfrozen_reconstructed_damaged_images = (process_connected_tissue(images = frozen_reconstructed_images.copy(), params = params) == 1)\\n\\n\\t\\t# get total number of slices to process\\n\\t\\ttotal_num_slices = fresh_original_images.shape[0]\\n\\t\\t# loop over each slice\\n\\t\\tfor mri_slice in range(total_num_slices):\\n\\n\\t\\t\\t# check slice validity of fresh patient\\n\\t\\t\\tif check_mri_slice_validity(patient = f'{patient} fersk', mri_slice = mri_slice, total_num_slices = total_num_slices):\\n\\n\\t\\t\\t\\tif check_mri_slice_validity(patient = f'{patient} Tint', mri_slice = mri_slice, total_num_slices = total_num_slices):\\n\\t\\n\\t\\t\\t\\t\\t# setting up the plot environment\\n\\t\\t\\t\\t\\tfig, axs = plt.subplots(2, 2, figsize = (8, 8))\\n\\t\\t\\t\\t\\taxs = axs.ravel()\\n\\n\\t\\t\\t\\t\\t# define the colors we want\\n\\t\\t\\t\\t\\tplot_colors = ['#250463', '#e34a33']\\n\\t\\t\\t\\t\\t# create a custom listed colormap (so we can overwrite the colors of predefined cmaps)\\n\\t\\t\\t\\t\\tcmap = colors.ListedColormap(plot_colors)\\n\\t\\t\\t\\t\\t# subfigure label for example, a, b, c, d etc\\n\\t\\t\\t\\t\\tsf = cycle(['a', 'b', 'c', 'd', 'e', 'f', 'g'])\\n\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\tPlot fresh state\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t# obtain vmax score so image grayscales are normalized better\\n\\t\\t\\t\\t\\tvmax_percentile = 99.9\\n\\t\\t\\t\\t\\tvmax = np.percentile(fresh_original_images[mri_slice], vmax_percentile)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# plot fresh original MRI image\\n\\t\\t\\t\\t\\taxs[0].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\taxs[0].set_title(rf'$\\\\bf({next(sf)})$ Fresh - Original MRI')\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# plot fresh reconstucted image overlayed on top of the original image\\n\\t\\t\\t\\t\\t# axs[1].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\t# im = axs[1].imshow(fresh_reconstructed_images[mri_slice],alpha = 0.7, interpolation = 'none')\\n\\t\\t\\t\\t\\t# axs[1].set_title(rf'$\\\\bf({next(sf)})$ Fresh - Reconstructed')\\n\\t\\t\\t\\t\\t\\n\\n\\t\\t\\t\\t\\t# plot fresh reconstucted image overlayed on top of the original image\\n\\t\\t\\t\\t\\taxs[1].imshow(fresh_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\taxs[1].imshow(fresh_reconstructed_damaged_images[mri_slice], cmap = cmap, alpha = .5, interpolation = 'none')\\n\\t\\t\\t\\t\\taxs[1].set_title(rf'$\\\\bf({next(sf)})$ Fresh - Reconstructed')\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\tPlot frozen/thawed state\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t# plot frozen/thawed original MRI image\\n\\t\\t\\t\\t\\t# obtain vmax score so image grayscales are normalized better\\n\\t\\t\\t\\t\\tvmax = np.percentile(frozen_original_images[mri_slice], vmax_percentile)\\n\\t\\t\\t\\t\\taxs[2].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\taxs[2].set_title(rf'$\\\\bf({next(sf)})$ {treatment_to_title(treatment)} - Original MRI')\\n\\n\\t\\t\\t\\t\\t# plot frozen reconstucted all classes\\n\\t\\t\\t\\t\\t# axs[4].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\t# im = axs[4].imshow(frozen_reconstructed_images[mri_slice], alpha = 0.7, interpolation = 'none')\\n\\t\\t\\t\\t\\t# axs[4].set_title(rf'$\\\\bf({next(sf)})$ {treatment_to_title(treatment)} - Reconstructed')\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# # plot frozen/thawed reconstucted image overlayed on top of the original image\\n\\t\\t\\t\\t\\taxs[3].imshow(frozen_original_images[mri_slice], cmap = 'gray', vmin = 0, vmax = vmax)\\n\\t\\t\\t\\t\\taxs[3].imshow(frozen_reconstructed_damaged_images[mri_slice], cmap = cmap, alpha = .5, interpolation = 'none')\\n\\t\\t\\t\\t\\taxs[3].set_title(rf'$\\\\bf({next(sf)})$ {treatment_to_title(treatment)} - Reconstructed')\\n\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\tCreate custom legend\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t# add custom legend\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tclass_labels = {0 : 'background', 1 : 'damaged tissue'}\\n\\t\\t\\t\\t\\tclass_values = list(class_labels.keys())\\n\\t\\t\\t\\t\\t# create a patch \\n\\t\\t\\t\\t\\tpatches = [ mpatches.Patch(color = plot_colors[i], label= class_labels[i]) for i in range(len(class_values)) ]\\n\\t\\t\\t\\t\\taxs[1].legend(handles = patches)#, bbox_to_anchor=(1.05, 1), loc = 2, borderaxespad=0. )\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# legend for fully reconstructed image\\n\\t\\t\\t\\t\\t# get class labels\\n\\t\\t\\t\\t\\t# class_labels = params['class_labels']\\n\\t\\t\\t\\t\\t# # get class indexes from dictionary\\n\\t\\t\\t\\t\\t# values = class_labels.keys()\\n\\t\\t\\t\\t\\t# # get the colors of the values, according to the \\n\\t\\t\\t\\t\\t# # colormap used by imshow\\n\\t\\t\\t\\t\\t# plt_colors = [ im.cmap(im.norm(value)) for value in values]\\n\\t\\t\\t\\t\\t# # create a patch (proxy artist) for every color \\n\\t\\t\\t\\t\\t# patches = [ mpatches.Patch(color = plt_colors[i], label= class_labels[i]) for i in range(len(values)) ]\\n\\t\\t\\t\\t\\t# # put those patched as legend-handles into the legend\\n\\t\\t\\t\\t\\t# axs[1].legend(handles = patches)#, bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0. )\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\tAdjust figures\\n\\t\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t# remove axis of all subplots\\n\\t\\t\\t\\t\\t[ax.axis('off') for ax in axs]\\n\\t\\t\\t\\t\\t# define plot subfolder\\n\\t\\t\\t\\t\\tsubfolder = os.path.join(paths['paper_plot_folder'], 'original_vs_reconstructed', patient)\\n\\t\\t\\t\\t\\t# create subfolder\\n\\t\\t\\t\\t\\tcreate_directory(subfolder)\\n\\t\\t\\t\\t\\t# crop white space\\n\\t\\t\\t\\t\\tfig.set_tight_layout(True)\\n\\t\\t\\t\\t\\t# save the figure\\n\\t\\t\\t\\t\\tfig.savefig(os.path.join(subfolder, f'slice_{mri_slice}.pdf'))\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# close the figure environment\\n\\t\\t\\t\\t\\tplt.close()\",\n \"def main(input_data_path, output_data_path, window):\\n # open data info dataframe\\n info_df = pd.read_csv(input_data_path + 'hemorrhage_diagnosis_raw_ct.csv')\\n # replace No-Hemorrhage to hemorrange\\n info_df['Hemorrhage'] = 1 - info_df.No_Hemorrhage\\n info_df.drop(columns='No_Hemorrhage', inplace=True)\\n # open patient info dataframe\\n patient_df = pd.read_csv(input_data_path + 'Patient_demographics.csv', header=1, skipfooter=2, engine='python') \\\\\\n .rename(columns={'Unnamed: 0':'PatientNumber', 'Unnamed: 1':'Age',\\n 'Unnamed: 2':'Gender', 'Unnamed: 8':'Fracture', 'Unnamed: 9':'Note'})\\n patient_df[patient_df.columns[3:9]] = patient_df[patient_df.columns[3:9]].fillna(0).astype(int)\\n # add columns Hemorrgae (any ICH)\\n patient_df['Hemorrhage'] = patient_df[patient_df.columns[3:8]].max(axis=1)\\n\\n # make patient directory\\n if not os.path.exists(output_data_path): os.mkdir(output_data_path)\\n if not os.path.exists(output_data_path + 'Patient_CT/'): os.mkdir(output_data_path + 'Patient_CT/')\\n # iterate over volume to extract data\\n output_info = []\\n for n, id in enumerate(info_df.PatientNumber.unique()):\\n # read nii volume\\n ct_nii = nib.load(input_data_path + f'ct_scans/{id:03}.nii')\\n mask_nii = nib.load(input_data_path + f'masks/{id:03}.nii')\\n # get np.array\\n ct_vol = ct_nii.get_fdata()\\n mask_vol = skimage.img_as_bool(mask_nii.get_fdata())\\n # rotate 90° counter clockwise for head pointing upward\\n ct_vol = np.rot90(ct_vol, axes=(0,1))\\n mask_vol = np.rot90(mask_vol, axes=(0,1))\\n # window the ct volume to get better contrast of soft tissues\\n if window is not None:\\n ct_vol = window_ct(ct_vol, win_center=window[0], win_width=window[1], out_range=(0,1))\\n\\n if mask_vol.shape != ct_vol.shape:\\n print(f'>>> Warning! The ct volume of patient {id} does not have '\\n f'the same dimension as the ground truth. CT ({ct_vol.shape}) vs Mask ({mask_vol.shape})')\\n # make patient directory\\n if not os.path.exists(output_data_path + f'Patient_CT/{id:03}/'): os.mkdir(output_data_path + f'Patient_CT/{id:03}/')\\n # iterate over slices to save slices\\n for i, slice in enumerate(range(ct_vol.shape[2])):\\n ct_slice_fn =f'Patient_CT/{id:03}/{slice+1}.tif'\\n # save CT slice\\n skimage.io.imsave(output_data_path + ct_slice_fn, ct_vol[:,:,slice], check_contrast=False)\\n is_low = True if skimage.exposure.is_low_contrast(ct_vol[:,:,slice]) else False\\n # save mask if some positive ICH\\n if np.any(mask_vol[:,:,slice]):\\n mask_slice_fn = f'Patient_CT/{id:03}/{slice+1}_ICH_Seg.bmp'\\n skimage.io.imsave(output_data_path + mask_slice_fn, skimage.img_as_ubyte(mask_vol[:,:,slice]), check_contrast=False)\\n else:\\n mask_slice_fn = 'None'\\n # add info to output list\\n output_info.append({'PatientNumber':id, 'SliceNumber':slice+1, 'CT_fn':ct_slice_fn, 'mask_fn':mask_slice_fn, 'low_contrast_CT':is_low})\\n\\n print_progessbar(i, ct_vol.shape[2], Name=f'Patient {id:03} {n+1:03}/{len(info_df.PatientNumber.unique()):03}',\\n Size=20, erase=False)\\n\\n # Make dataframe of outputs\\n output_info_df = pd.DataFrame(output_info)\\n # Merge with input info\\n info_df = pd.merge(info_df, output_info_df, how='inner', on=['PatientNumber', 'SliceNumber'])\\n # save df\\n info_df.to_csv(output_data_path + 'ct_info.csv')\\n print('>>> Data informations saved at ' + output_data_path + 'ct_info.csv')\\n # save patient df\\n patient_df.to_csv(output_data_path + 'patient_info.csv')\\n print('>>> Patient informations saved at ' + output_data_path + 'patient_info.csv')\",\n \"def ring_int(self, tissue):\\n print(\\\"controller - ring_int!\\\")\\n img_cv2_mask = self.pressure_img.ring_ext\\n self.pressure_img.roi_crop(img_cv2_mask, tissue, 2)\",\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 calc_tau(z_array_reion_allmodels, cosmology_allmodels, helium_allmodels,\\n mass_frac_allmodels):\\n\\n def integrand(z, h, OM):\\n H = av.Hubble_Param(z, h, OM) / (av.pc_to_m * 1.0e6 / 1.0e3)\\n return (((1 + z)**2) / H)\\n\\n tau = []\\n for model_number in range(len(mass_frac_allmodels)):\\n\\n # Set up some things for the model cosmology etc.\\n model_mass_frac = mass_frac_allmodels[model_number]\\n model_helium = helium_allmodels[model_number]\\n model_h = cosmology_allmodels[model_number].H(0).value/100.0\\n model_OM = cosmology_allmodels[model_number].Om0\\n model_OB = cosmology_allmodels[model_number].Ob0\\n model_z = z_array_reion_allmodels[model_number]\\n\\n model_tau = np.zeros(len(model_mass_frac))\\n\\n # First determine optical depth for redshift 0 to 4.\\n tau_04 = integrate.quad(integrand, 0, 4, args=(model_h, model_OM,))[0] \\n tau_04 *= (1 + 2*model_helium/(4 * (1-model_helium)))\\n\\n # Then determine optical depth from z = 4 to lowest z of model.\\n tau_46 = integrate.quad(integrand, 4, model_z[-1], args=(model_h, model_OM,))[0]\\n tau_46 *= (1 + model_helium/(4* (1-model_helium)))\\n\\n tau_06 = tau_04 + tau_46\\n\\n model_tau[-1] = tau_06\\n\\n # Then loop down through snapshots (low z to high z) and calculate tau.\\n for snapnum in np.arange(len(model_mass_frac) - 2, -1, -1):\\n\\n this_z = model_z[snapnum]\\n prev_z = model_z[snapnum + 1]\\n\\n # Hubble Parameter in Mpc/s/Mpc.\\n H = av.Hubble_Param(this_z, model_h, model_OM) / (av.pc_to_m * 1.0e6 / 1.0e3)\\n numerator = ((1 + this_z) **2) * (1.0 - model_mass_frac[snapnum])\\n \\n model_tau[snapnum] = model_tau[snapnum+1] + (( numerator / H) * (this_z - prev_z) * (1 + model_helium/(4 * (1-model_helium)))) \\n\\n model_tau *= av.n_HI(0, model_h, model_OB, model_helium) * av.c_in_ms * av.Sigmat\\n\\n tau.append(model_tau)\\n\\n return tau\",\n \"def trap_knobs():\\n print('Executing trap_knobs...')\\n #0) Define parameters and heck to see what scripts have been run\\n from project_parameters import save,debug,trapFile,elMap,electrodes,multipoles,name,simulationDirectory,reg,trapType\\n #from all_functions import nullspace,plotN\\n import pickle\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n if trap.configuration.expand_field!=True:\\n return 'You must run expand_field first!'\\n if trap.configuration.trap_knobs and not debug.trap_knobs:\\n return 'Already executed trap_knobs.'\\n #1) redefine parameters with shorthand and run sanity checks\\n totE = len(electrodes) # numTotalElectrodes\\n totM = len(multipoles) # numTotalMultipoles\\n assert totE == trap.numElectrodes # Make sure that the total number of electrodes includes the RF.\\n tc = trap.configuration\\n mc = tc.multipoleCoefficients # this is the original, maximum-length multipole coefficients matrix (multipoles,electrodes)\\n for row in range(totM):\\n #row+=1\\n if abs(np.sum(mc[row,:])) < 10**-50: # arbitrarily small\\n return 'trap_knobs: row {} is all 0, can not solve least square, stopping trap knobs'.format(row)\\n #2) Apply electrode mapping by clearing some electrodes and adding them to the new map\\n # mapping one to an unused electrode should turn it off as well\\n for index in range(totE):\\n if index != elMap[index]:\\n mc[:,elMap[index]] += mc[:,index] # combine the electrode to its mapping\\n electrodes[index] = 0 # clear the mapped electrode, implemented in part 3\\n useE = int(np.sum(electrodes)) # numUsedElectrodes\\n useM = int(np.sum(multipoles)) # numUsedMultipoles\\n eo = np.sqrt(useM)-1 # expansionOrder\\n #3) build a reduced array of multipole coefficients to invert\\n MC = np.zeros((useM,useE)) # reduced matrix to build up and invert\\n ML = 0\\n for ml in range(totM):\\n if multipoles[ml]:\\n EL = 0 # clear the electrode indexing before looping through electrodes again for new multipole\\n for el in range(totE):\\n if electrodes[el]:\\n MC[ML,EL] = mc[ml,el]\\n EL += 1\\n ML += 1\\n print('trap_knobs: with electrode and multipole constraints, the coefficient matrix size is ({0},{1}).'.format(MC.shape[0],MC.shape[1]))\\n #4) numerially invert the multipole coefficients to get the multipole controls, one multipole at a time\\n # solve the equation MC*A = B, where the matrix made from all A vectors is C\\n C = np.zeros((useE,useM)) # multipole controls (electrodes,multipoles) will be the inverse of multipole coefficents \\n for mult in range(useM):\\n B = np.zeros(useM)\\n B[mult] = 1\\n A = np.linalg.lstsq(MC,B)[0]\\n C[:,mult] = A\\n #5) calculate the nullspace and regularize if the coefficients matrix is sufficiently overdetermined\\n if useM < useE:\\n K = nullspace(MC)\\n else:\\n print('There is no nullspace because the coefficient matrix is rank deficient.\\\\nThere can be no regularization.')\\n K = None\\n reg = False\\n if reg:\\n for mult in range(useM):\\n Cv = C[:,mult].T\\n Lambda = np.linalg.lstsq(K,Cv)[0]\\n test=np.dot(K,Lambda)\\n C[:,mult] = C[:,mult]-test \\n #6) expand the matrix back out again, with zero at all unused electrodes and multipoles; same as #3 with everything flipped\\n c = np.zeros((totE,totM)) \\n EL = 0\\n for el in range(totE):\\n if electrodes[el]:\\n ML = 0 # clear the electrode indexing before looping through electrodes again for new multipole\\n for ml in range(totM):\\n if multipoles[ml]:\\n c[el,ml] = C[EL,ML]\\n ML += 1\\n EL += 1\\n #7) plot the multipole controls in teh trap geometry\\n if debug.trap_knobs:\\n for ml in range(totM):\\n if multipoles[ml]:\\n plot = c[:,ml]\\n plotN(plot,trap,'Multipole {}'.format(ml)) \\n #8) update instance configuration with multipole controls to be used bu dc_voltages in post_process_trap\\n tc.multipoleKernel = K\\n tc.multipoleControl = c\\n tc.trap_knobs = True\\n trap.configuration=tc\\n #8.5) change the order of the columns of c for labrad\\n # originally (after constant) Ez,Ey,Ex,U3,U4,U2,U5,U1,...,Y4-4,...,Y40,...,Y44\\n # we want it to be Ex,Ey,Ez,U2,U1,U3,U5,U4,...,Y40 and then end before any of the other 4th order terms\\n# cc = c.copy()\\n# cc[:,1] = c[:,3] # Ex\\n# cc[:,2] = c[:,1] # Ey\\n# cc[:,3] = c[:,2] # Ez\\n# cc[:,4] = c[:,6] # U2\\n# cc[:,5] = c[:,8]\\n# cc[:,6] = 0 # U3\\n# cc[:,16]= c[:,20]# Y40\\n# cc[:,20]= 0\\n cc = c\\n #9) save trap and save c as a text file (called the \\\"C\\\" file for Labrad)\\n if save: \\n print('Saving '+name+' as a data structure...')\\n with open(trapFile,'wb') as f:\\n pickle.dump(trap,f)\\n #ct = cc[1:totE,1:17] #eliminate the RF electrode and constant multipole; eliminate everything past Y40\\n ct = cc[1:totE,1:25] # only eliminate the constant\\n text = np.zeros((ct.shape[0])*(ct.shape[1]))\\n for j in range(ct.shape[1]):\\n for i in range(ct.shape[0]):\\n text[j*ct.shape[0]+i] = ct[i,j]\\n np.savetxt(simulationDirectory+name+'.txt',text,delimiter=',')\\n return 'Completed trap_knobs.'\",\n \"def rayShooting():\\r\\n \\r\\n \\r\\n if nbRay==1:\\r\\n maxi=1\\r\\n mini=1\\r\\n peaceofAngle=angleMax\\r\\n #to trace one ray at angleMax\\r\\n else:\\r\\n maxi=(nbRay-1)/2\\r\\n mini=-maxi\\r\\n peaceofAngle=2*angleMax/(nbRay-1)\\r\\n #to trace rays at regular intervals between [-angleMax;angleMax] \\r\\n\\r\\n tot=0 #to count the number of peace of ray\\r\\n indice=0 #to browse raysIndex\\r\\n\\r\\n raysMatrix=np.empty(shape=(0,5),dtype=np.float64)#will contain all the rays in a row\\r\\n raysIndex=np.empty(shape=(nbRay,),dtype=np.int16)#indexation of the rays in raysMatrix\\r\\n \\r\\n for i in np.arange(mini,maxi+1,1):#put maxi+1 to include maxi in the loop\\r\\n \\r\\n rayon=Rayon(source.position,angleToVector(peaceofAngle*i))#rayon is\\r\\n #the ray we will trace\\r\\n ray,compt=traceRay(rayon)\\r\\n tot+=(compt+1)\\r\\n\\r\\n \\r\\n raysIndex[indice]=tot #the rays index contains the indice just above\\r\\n #of the end of the i th ray\\r\\n\\r\\n raysMatrix=np.vstack((raysMatrix,ray))\\r\\n #the form of the ray matrix is a stack of peace of rays describe by\\r\\n #a,b,c,x1,reflexion. the polynome of the peace of ray being ax^2+bx+c and the\\r\\n #abscisses of the limiting point being x1, reflexion indicating if a reflexion happened\\r\\n #when we meet a 5-uple with a coefficient b or c infinite it means\\r\\n #a new ray begin\\r\\n \\r\\n indice+=1\\r\\n print(\\\"ray at indice\\\",i,\\\"and at angle\\\",peaceofAngle*i/np.pi*180,'degree(s)')\\r\\n \\r\\n print(\\\"the total number of peaces of ray is :\\\", tot)\\r\\n\\r\\n return(raysMatrix,raysIndex)\",\n \"def decode_patches_from_roi(self, nr_patches = 100, roi = 'V1', stim_stat_threshold = 2.0, prf_stat_threshold = 4.0, prf_ecc_threshold = 0.6, run_type = 'WMM', smoothing_for_PRF = 5, scaling = True, sum_TRs = True,summed_TRs = 3):\\n\\t\\t\\n\\t\\t# first determine individual run duration (to make sure that stimulus timings of all runs are correct)\\n\\t\\trun_duration = []\\n\\t\\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\\n\\t\\t\\tniiFile = NiftiImage(self.runFile(stage = 'processed/mri', run = r))\\n\\t\\t\\ttr, nr_trs = round(niiFile.rtime*1)/1000.0, niiFile.timepoints\\n\\t\\t\\trun_duration.append(tr * nr_trs)\\n\\t\\trun_duration = np.r_[0,np.cumsum(np.array(run_duration))]\\n\\n\\t\\t# timing information stimuli\\n\\t\\tpatches_all_trials = []\\n\\t\\trun = 0\\n\\t\\tfor r in [self.runList[i] for i in self.conditionDict['WMM']]:\\n\\t\\t\\tstim_events = np.loadtxt(self.runFile(stage = 'processed/behavior', run = r, extension = '.txt', postFix = ['stim' ,'all','task']))\\n\\t\\t\\tpatches_run= np.array([[stim_events[i][2],np.ceil((stim_events[i][0]+ run_duration[run])/tr),np.ceil((stim_events[i][1] +run_duration[run])/tr),stim_events[i][3]] for i in range(stim_events.shape[0])])\\n\\t\\t\\tpatches_all_trials.append(patches_run)\\n\\t\\t\\trun += 1\\n\\n\\t\\tpatches_all_trials = np.vstack(patches_all_trials) # array (patches, timing_memory, timing test)\\n\\t\\tunique_patches = np.unique(patches_all_trials[:,0]) # array (unique_patches)\\n\\n\\t\\t# numpy array of all patches from stimulus pool (also rotated by 90 degrees to check orientation space of PRF)\\n\\t\\tpatches = np.array(self.rescale_images())\\n\\t\\tpatches_rotated = [patches]\\n\\t\\tfor i in range(3):\\n\\t\\t\\tpatches_rotated.append(np.array([np.rot90(p) for p in patches_rotated[-1]]))\\n\\t\\tpatches_rotated = np.array(patches_rotated) \\n\\t\\t\\n\\t\\t# brain data\\n\\t\\tself.hdf5_filename = os.path.join(self.conditionFolder(stage = 'processed/mri', run = self.runList[self.conditionDict[run_type][0]]), run_type + '.hdf5')\\n\\t\\tself.logger.info('data from table file ' + self.hdf5_filename)\\n\\t\\th5file = open_file(self.hdf5_filename, mode = 'r')\\t\\t\\n\\n\\t\\tstim_stats = self.roi_data_from_hdf(h5file, roi, data_type = 'zstat1', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = False)\\n\\t\\tprfs = self.roi_data_from_hdf(h5file, roi, data_type = 'all_coefs', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)\\n\\t\\tprf_stats = self.roi_data_from_hdf(h5file, roi, data_type = 'all_corrs', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)\\n\\t\\t\\n\\t\\tprf_ecc = self.roi_data_from_hdf(h5file, roi, data_type = 'all_results', run = self.runList[self.conditionDict[run_type][0]], postFix = ['mcf','sgtf'], combined = False, prf = True)[:,1]\\n\\n\\t\\ttimeseries = []\\n\\t\\tfor i in range(len(self.conditionDict[run_type])):\\n\\t\\t\\ttimeseries.append(self.roi_data_from_hdf(h5file, roi, data_type = 'tf_psc_data', run = self.runList[self.conditionDict[run_type][i]], postFix = ['mcf','sgtf'], combined = False, prf = False))\\n\\t\\ttimeseries = np.concatenate(timeseries, axis = 1) \\n\\n\\t\\t# select voxels based on prf and stimulus contrasts\\n\\t\\tprfs = prfs[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),:] \\n\\t\\ttimeseries = timeseries[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),:] \\n\\t\\tprf_stats = prf_stats[((stim_stats.squeeze() > stim_stat_threshold) * (prf_stats[:,1] > prf_stat_threshold)*(prf_ecc < prf_ecc_threshold)).squeeze(),1]\\n\\n\\t\\t#create stim mask (based on eccentricity mask and atend centre mask)\\n\\t\\tcentre_x, centre_y = patches.shape[1]/2,patches.shape[1]/2\\n\\n\\t\\ty,x = np.ogrid[-centre_x:patches.shape[1]-centre_x, -centre_y:patches.shape[1]-centre_y]\\n\\t\\tmask_ecc = x*x + y*y <= (prf_ecc_threshold*patches.shape[1]/2)**2\\n\\n\\t\\ty,x = np.ogrid[-centre_x:patches.shape[1]-centre_x, -centre_y:patches.shape[1]-centre_y]\\n\\t\\tmask_centre = x*x + y*y >= 4**2\\n\\n\\t\\tmask_stim = (mask_ecc.ravel()*mask_centre.ravel())\\t\\n\\n\\t\\t# create scaling matrix (identity matrix) of nr_voxels by nr_voxels\\n\\t\\tif scaling:\\n\\t\\t\\tscaling_matrix = np.eye(prfs.shape[0])\\n\\t\\t\\tfor i in range(len(prf_stats)):\\n\\t\\t\\t\\tscaling_matrix[i,i] = prf_stats[i]\\n\\n\\n\\t\\t# based on prfs and scaling factor create brain patches (either for single TRs or for summed TRS)\\t\\t\\n\\t\\tbrain_patches = np.mat(timeseries.T) * np.mat(scaling_matrix) * np.mat(prfs) \\n\\t\\tif sum_TRs == True:\\n\\t\\t\\ttimeseries_summed = np.array([np.sum(timeseries.T[i:i+summed_TRs,:],axis = 0) for i in range(timeseries.shape[1]) if i < timeseries.shape[1] - summed_TRs])\\n\\t\\t\\tbrain_patches= np.mat(timeseries_summed) * np.mat(scaling_matrix) * np.mat(prfs)\\n \\n\\t\\t# per TR (either summed or individual) calculate correlations between brain patch and stimulus input\\n\\t\\tpatch_correlations = np.zeros((4, brain_patches.shape[0], patches.shape[0]))\\n\\n\\t\\tfor k in range(4): # 4 orientations\\n\\t\\t\\tfor i, t in enumerate(np.array(brain_patches)): # all brain_patches\\n\\t\\t\\t\\tpatch_correlations[k, i, :] = np.array([pearsonr(t[mask_stim], patch.ravel()[mask_stim])[0] for patch in patches_rotated[k]])\\n\\n\\t\\t# for analysis only look at correlations for all patches that were presented in this subjects session\\n\\t\\tpatch_index_unique = np.array([j for i in patches_all_trials[:,0] for j in range(len( np.unique(patches_all_trials[:,0]))) if i == np.unique(patches_all_trials[:,0])[j]])\\t\\n\\t\\tpatch_correlations_unique = patch_correlations[:,:,np.array(unique_patches, dtype = int)]\\n\\n\\t\\t# show correlation matrix of patches for all rotations (dots represent stimulus that was present)\\n\\t\\tfig, axes = plt.subplots(2,2)\\n\\t\\tfor i in range(patches_rotated.shape[0]):\\n\\t\\t\\tax = axes.flat[i]\\n\\t\\t\\tax.imshow(patch_correlations_unique[i,:,:].T,vmin = np.min(patch_correlations_unique), vmax = np.max(patch_correlations_unique))\\n\\t\\t\\tax.scatter(patches_all_trials[:,1],patch_index_unique)\\n\\t\\t\\tax.set_title(\\\"rotation_\\\" + str(i*90))\\n\\n\\t\\t# show decoding evidence across runs for all unique patches\\t\\n\\t\\tfig, axes = plt.subplots(2,2)\\t\\n\\t\\tfor i in range(patches_rotated.shape[0]):\\n\\t\\t\\tax = axes.flat[i]\\n\\t\\t\\tax.hist(np.argmax(patch_correlations_unique[i,:,:], axis = 1), bins = patch_correlations_unique.shape[2] + 1)\\n\\t\\t\\tax.set_ylim([0,600])\\n\\t\\t\\tax.set_title(\\\"rotation_\\\" + str(i*90))\\n\\t\\t\\n\\t\\t# show brain_patches and stimulus input\\t\\t\\n\\t\\tmask_stim = np.array([not i for i in mask_stim]).reshape(patches.shape[1],patches.shape[1]) # flip stim mask for graphing purposes\\n\\n\\t\\tindex_att_patch = patches_all_trials[:,3] == 0\\n\\t\\tindex_att_center = patches_all_trials[:,3] == 1 \\n\\n\\n\\t\\t# calculate decoding performance\\n\\t\\tcorrelations_unique_TR_patch = np.array([patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_patch]+ TR][:,:,np.array(unique_patches, dtype = int)] for TR in range(2,5)])\\n\\t\\tcorrelations_unique_TR_center = np.array([patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_center]+ TR][:,:,np.array(unique_patches, dtype = int)] for TR in range(2,5)])\\n\\n\\t\\t# check relative position of presented patch compared to other unique patches\\n\\t\\tpatch_dict = {}\\n\\t\\tcenter_dict = {}\\n\\t\\tfor condition in ['patch' ,'center']:\\n\\t\\t\\tfor TR in range(correlations_unique_TR_patch.shape[0]):\\n\\t\\t\\t\\tfor rotation in range(correlations_unique_TR_patch.shape[1]):\\n\\t\\t\\t\\t\\tif condition == 'patch':\\n\\t\\t\\t\\t\\t\\tprint 'update'\\n\\t\\t\\t\\t\\t\\tdecode = np.array([int(np.where(np.argsort(correlations_unique_TR_patch[TR,rotation,i])==patch_index_unique[index_att_patch][i])[0]) for i in range(correlations_unique_TR_patch.shape[2])])\\n\\t\\t\\t\\t\\t\\tpatch_dict.update({'RT_' + str(TR) + '_Rotate_' + str(rotation):[decode, np.mean(decode),np.where(decode == np.max(decode))]})\\n\\t\\t\\t\\t\\telif condition == 'center':\\t\\n\\t\\t\\t\\t\\t\\tdecode = np.array([int(np.where(np.argsort(correlations_unique_TR_center[TR,rotation,i])==patch_index_unique[index_att_patch][i])[0]) for i in range(correlations_unique_TR_patch.shape[2])])\\n\\t\\t\\t\\t\\t\\tcenter_dict.update({'RT_' + str(TR) + '_Rotate_' + str(rotation):[decode, np.mean(decode),np.where(decode == np.max(decode))]})\\n\\n\\t\\tshell()\\n\\t\\t# show decoding performance\\n\\t\\tfor TR in [2]:\\n\\t\\t\\tcorrelations_unique_TR_patch = patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_patch]+ TR][:,:,np.array(unique_patches, dtype = int)]\\n\\t\\t\\tcorrelations_unique_TR_cent = patch_correlations[:,np.array(patches_all_trials[:,1],dtype = int)[index_att_cent]+ TR][:,:,np.array(unique_patches, dtype = int)]\\n\\n\\t\\t\\tfor cond in ['patch','center']:\\n\\t\\t\\t\\tfig, axes = plt.subplots(2,2)\\n\\t\\t\\t\\tfor k in range(4): # 4 rotations\\n\\t\\t\\t\\t\\tax = axes.flat[k]\\n\\t\\t\\t\\t\\tax.set_title(\\\"rotation_\\\" + str(k*90))\\n\\t\\t\\t\\t\\tif cond == 'patch':\\n\\t\\t\\t\\t\\t\\tax.hist([np.argsort(correlations_unique_TR_patch[k][i])[patch_index_unique[i]] for i in range(len(patch_index_unique)/2)], alpha = 0.3, color = ['r','g','b','k'][k], bins = 44, cumulative = True, histtype = 'step')\\n\\t\\t\\t\\t\\telif cond == 'center':\\n\\t\\t\\t\\t\\t\\tax.hist([np.argsort(correlations_unique_TR_cent[k][i])[patch_index_unique[i]] for i in range(len(patch_index_unique)/2)], alpha = 0.3, color = ['r','g','b','k'][k], bins = 44, cumulative = True, histtype = 'step')\\n\\n\\t\\tif sum_TRs == True:\\n\\t\\t\\tbpr = np.array(brain_patches).reshape((timeseries.shape[1] - summed_TRs,patches.shape[1],patches.shape[1])) # brain patch reshaped to PRF space, length is nr of TRS\\n\\t\\telse:\\n\\t\\t\\tbpr = np.array(brain_patches).reshape((timeseries.shape[1],patches.shape[1],patches.shape[1]))\\n\\t\\t\\t\\n\\t\\t\\n\\t\\ttr_bpr_patch = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)[index_att_patch]+i] for i in range(2,5)]) #brain patches for different timepoints \\n\\t\\ttr_bpr_cent = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)[index_att_cent]+i] for i in range(2,5)])\\n\\n\\t\\t\\n\\t\\t# show decoding attend patches condition\\n\\t\\n\\n\\n\\t\\tpatch = 0\\n\\t\\ttest = [i for i in range(index_att_patch.shape[0]) if index_att_patch[i]]\\n\\t\\tfor i in test:\\n\\t\\t\\tprint i, patch\\n\\t\\t\\tf = pl.figure()\\n\\t\\t\\tpatch += 1\\n\\t\\t\\t#f = pl.figure()\\n\\t\\t\\t#s = f.add_subplot(241, aspect = 'equal')\\n\\t\\t\\t\\n\\t\\t\\t#tr_bpr_patch[0,patch][mask_stim] = 0\\n\\t\\t\\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\t#s = f.add_subplot(242, aspect = 'equal')\\n\\t\\t\\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\t#s = f.add_subplot(243, aspect = 'equal')\\n\\t\\t\\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\t#s = f.add_subplot(244, aspect = 'equal')\\n\\t\\t\\t#pl.imshow(tr_bpr_patch[0,patch],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\t#s = f.add_subplot(245, aspect = 'equal')\\n\\t\\t\\t\\n\\t\\t\\t#this_patch = patches_rotated[0,int(patches_all_trials[i][0])]\\n\\t\\t\\t#this_patch[mask_stim] = 0\\n\\t\\t\\t#pl.imshow(this_patch)\\n\\t\\t\\t#s = f.add_subplot(246, aspect = 'equal')\\n\\t\\t\\t#this_patch = patches_rotated[1,int(patches_all_trials[i][0])]\\n\\t\\t\\t#this_patch[mask_stim] = 0\\n\\t\\t\\t#pl.imshow(this_patch)\\n\\t\\t\\t#s = f.add_subplot(247, aspect = 'equal')\\n\\t\\t\\t#this_patch = patches_rotated[2,int(patches_all_trials[i][0])]\\n\\t\\t\\t#this_patch[mask_stim] = 0\\n\\t\\t\\t#pl.imshow(this_patch)\\n\\t\\t\\t#s = f.add_subplot(248, aspect = 'equal')\\n\\t\\t\\t#this_patch = patches_rotated[3,int(patches_all_trials[i][0])]\\n\\t\\t\\t#this_patch[mask_stim] = 0\\n\\t\\t\\t#pl.imshow(this_patch)\\n\\n\\n\\n\\n\\n\\n\\n\\t\\ttr_bpr = np.array([bpr[np.array(patches_all_trials[:,1], dtype = int)+i] for i in range(2,5)]) #brain patches for different timepoints \\n\\t\\t\\n\\t\\tfor i in []:\\n\\t\\t\\tf = pl.figure()\\n\\t\\t\\ts = f.add_subplot(241, aspect = 'equal')\\n\\t\\t\\ttr_bpr[0,i][mask_stim] = 0\\n\\t\\t\\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\ts = f.add_subplot(242, aspect = 'equal')\\n\\t\\t\\ttr_bpr[0,i][mask_stim] = 0\\n\\t\\t\\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\ts = f.add_subplot(243, aspect = 'equal')\\n\\t\\t\\ttr_bpr[0,i][mask_stim] = 0\\n\\t\\t\\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\ts = f.add_subplot(244, aspect = 'equal')\\n\\t\\t\\ttr_bpr[0,i][mask_stim] = 0\\n\\t\\t\\tpl.imshow(tr_bpr[0,i],vmin = np.min(tr_bpr), vmax = np.max(tr_bpr))\\n\\t\\t\\ts = f.add_subplot(245, aspect = 'equal')\\n\\t\\t\\tpatch = patches_rotated[0,np.array(patches_all_trials[:,0], dtype = int)][i]\\n\\t\\t\\tpatch[mask_stim] = 0\\n\\t\\t\\tpl.imshow(patch)\\n\\t\\t\\ts = f.add_subplot(246, aspect = 'equal')\\n\\t\\t\\tpatch = patches_rotated[1,np.array(patches_all_trials[:,0], dtype = int)][i]\\n\\t\\t\\tpatch[mask_stim] = 0\\n\\t\\t\\tpl.imshow(patch)\\n\\t\\t\\ts = f.add_subplot(247, aspect = 'equal')\\n\\t\\t\\tpatch = patches_rotated[2,np.array(patches_all_trials[:,0], dtype = int)][i]\\n\\t\\t\\tpatch[mask_stim] = 0\\n\\t\\t\\tpl.imshow(patch)\\n\\t\\t\\ts = f.add_subplot(248, aspect = 'equal')\\n\\t\\t\\tpatch = patches_rotated[3,np.array(patches_all_trials[:,0], dtype = int)][i]\\n\\t\\t\\tpatch[mask_stim] = 0\\n\\t\\t\\tpl.imshow(patch)\\n\\n\\t\\tpl.show()\",\n \"def behaviour03(prior_propia): \\r\\n count = 0\\r\\n global stop\\r\\n while not stop:\\r\\n results = process_image(cameraData,prior_propia)\\r\\n if count > 3:\\r\\n count = 0\\r\\n print(\\\"hay estos pixeles verdes\\\")\\r\\n print(results)\\r\\n else:\\r\\n count = count + 1\",\n \"def process(self, im, include=True):\\n full_im = im - self.bias # remove the bias offset, it's arbitrary\\n try:\\n self.im_vals = full_im * self.mask # get the ROI\\n not_roi = full_im * (1-self.mask)\\n except ValueError as e:\\n s0 = np.shape(self.mask)\\n self.im_vals = np.zeros(s0)\\n not_roi = np.zeros(s0)\\n include = False\\n s1 = np.shape(im)\\n error(\\\"Received image was wrong shape (%s,%s) for analyser's ROI (%s,%s)\\\"%(\\n s1[0],s1[1],s0[0],s0[1]))\\n # background statistics: mean count and standard deviation across image\\n N = np.sum(1-self.mask)\\n self.stats['Mean bg count'].append(np.sum(not_roi) / N)\\n self.stats['Bg s.d.'].append(\\n np.sqrt(np.sum((not_roi - self.stats['Mean bg count'][-1])**2) / (N - 1)))\\n # sum of counts in the ROI of the image gives the signal\\n self.stats['Counts'].append(np.sum(self.im_vals)) \\n # file ID number should be updated externally before each event\\n self.stats['File ID'].append(self.fid)\\n # the pixel value at the centre of the ROI\\n try:\\n self.stats['ROI centre count'].append(full_im[self.xc, self.yc])\\n except IndexError as e:\\n error('ROI centre (%s, %s) outside of image size (%s, %s)'%(\\n self.xc, self.yc, self.pic_width, self.pic_height))\\n self.stats['ROI centre count'].append(0)\\n # position of the (first) max intensity pixel\\n xmax, ymax = np.unravel_index(np.argmax(full_im), full_im.shape)\\n self.stats['Max xpos'].append(xmax)\\n self.stats['Max ypos'].append(ymax)\\n self.stats['Include'].append(include)\\n if np.size(self.bins):\\n index = np.where(self.bins > self.stats['Counts'][-1])\\n try:\\n self.occs[index[0]] += 1\\n except IndexError:\\n self.occs[-1] += 1\\n self.ind += 1\",\n \"def freespaceImageAnalysis( fids, guesses = None, fit=True, bgInput=None, bgPcInput=None, shapes=[None], zeroCorrection=0, zeroCorrectionPC=0,\\n keys=None, fitModule=bump, extraPicDictionaries=None, newAnnotation=False, onlyThisPic=None, pltVSize=5, \\n plotSigmas=False, plotCounts=False, manualColorRange=None, calcTemperature=False, clearOutput=True, \\n dataRange=None, guessTemp=10e-6, trackFitCenter=False, picsPerRep=1, startPic=0, binningParams=None, \\n win=pw.PictureWindow(), transferAnalysisOpts=None, tferBinningParams=None, tferWin= pw.PictureWindow(),\\n extraTferAnalysisArgs={}, emGainSetting=300, lastConditionIsBackGround=True, showTferAnalysisPlots=True,\\n show2dFitsAndResiduals=True, plotFitAmps=False, indvColorRanges=False, fitF2D=gaussian_2d.f_notheta, \\n rmHighCounts=True, useBase=True, weightBackgroundByLoading=True, returnPics=False, forceNoAnnotation=False):\\n fids = [fids] if type(fids) == int else fids\\n keys = [None for _ in fids] if keys is None else keys\\n sortedStackedPics = {}\\n initThresholds = [None]\\n picsForBg = []\\n bgWeights = []\\n isAnnotatedList = []\\n for filenum, fid in enumerate(fids):\\n if transferAnalysisOpts is not None:\\n res = ta.stage1TransferAnalysis( fid, transferAnalysisOpts, useBase=useBase, **extraTferAnalysisArgs )\\n (initAtoms, tferAtoms, initAtomsPs, tferAtomsPs, key, keyName, initPicCounts, tferPicCounts, repetitions, initThresholds,\\n avgPics, tferThresholds, initAtomImages, tferAtomImages, basicInfoStr, ensembleHits, groupedPostSelectedPics, isAnnotated) = res\\n isAnnotatedList.append(isAnnotated)\\n # assumes that you only want to look at the first condition. \\n for varPics in groupedPostSelectedPics: # don't remember why 0 works if false...\\n picsForBg.append(varPics[-1 if lastConditionIsBackGround else 0])\\n bgWeights.append(len(varPics[0]))\\n allFSIPics = [ varpics[0][startPic::picsPerRep] for varpics in groupedPostSelectedPics]\\n if showTferAnalysisPlots:\\n fig, axs = plt.subplots(1,2)\\n mp.makeAvgPlts( axs[0], axs[1], avgPics, transferAnalysisOpts, ['r','g','b'] ) \\n allFSIPics = [win.window( np.array(pics) ) for pics in allFSIPics]\\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\\n elif type(fid) == int:\\n ### For looking at either PGC imgs or FSI imgs \\n with exp.ExpFile(fid) as file:\\n # I think this only makes sense if there is a specific bg pic in the rotation\\n picsForBg.append(list(file.get_pics()))\\n allFSIPics = file.get_pics()[startPic::picsPerRep]\\n _, key = file.get_key()\\n if len(np.array(key).shape) == 2:\\n key = key[:,0]\\n file.get_basic_info()\\n allFSIPics = win.window( allFSIPics )\\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\\n allFSIPics = np.reshape( allFSIPics, (len(key), int(allFSIPics.shape[0]/len(key)), allFSIPics.shape[1], allFSIPics.shape[2]) )\\n else:\\n ### Assumes given pics have the same start pic and increment (picsPerRep).\\n # doesn't combine well w/ transfer analysis\\n picsForBg.append(fid)\\n allFSIPics = fid[startPic::picsPerRep]\\n print(\\\"Assuming input is list of all pics, then splices to get FSI pics. Old code assumed the given were FSI pics.\\\")\\n allFSIPics = win.window( allFSIPics )\\n allFSIPics = ah.softwareBinning( binningParams, allFSIPics )\\n allFSIPics = np.reshape( allFSIPics, (len(key), int(allFSIPics.shape[0]/len(key)), allFSIPics.shape[1], allFSIPics.shape[2]) )\\n # ##############\\n if keys[filenum] is not None:\\n key = keys[filenum]\\n for i, keyV in enumerate(key):\\n keyV = misc.round_sig_str(keyV)\\n sortedStackedPics[keyV] = np.append(sortedStackedPics[keyV], allFSIPics[i],axis=0) if (keyV in sortedStackedPics) else allFSIPics[i] \\n if lastConditionIsBackGround:\\n bgInput, pcBgInput = getBgImgs(picsForBg, startPic = startPic, picsPerRep = picsPerRep, rmHighCounts=rmHighCounts, bgWeights=bgWeights, \\n weightBackgrounds=weightBackgroundByLoading)\\n elif bgInput == 'lastPic':\\n bgInput, pcBgInput = getBgImgs(picsForBg, startPic = picsPerRep-1, picsPerRep = picsPerRep, rmHighCounts=rmHighCounts, bgWeights=bgWeights,\\n weightBackgrounds=weightBackgroundByLoading )\\n if bgInput is not None: # was broken and not working if not given bg\\n bgInput = win.window(bgInput)\\n bgInput = ah.softwareBinning(binningParams, bgInput)\\n if bgPcInput is not None:\\n bgPcInput = win.window(bgPcInput)\\n bgPcInput = ah.softwareBinning(binningParams, bgPcInput) \\n \\n if extraPicDictionaries is not None:\\n if type(extraPicDictionaries) == dict:\\n extraPicDictionaries = [extraPicDictionaries]\\n for dictionary in extraPicDictionaries:\\n for keyV, pics in dictionary.items():\\n sortedStackedPics[keyV] = (np.append(sortedStackedPics[keyV], pics,axis=0) if keyV in sortedStackedPics else pics) \\n sortedStackedKeyFl = [float(keyStr) for keyStr in sortedStackedPics.keys()]\\n sortedKey, sortedStackedPics = ah.applyDataRange(dataRange, sortedStackedPics, list(sorted(sortedStackedKeyFl)))\\n numVars = len(sortedStackedPics.items())\\n if len(np.array(shapes).shape) == 1:\\n shapes = [shapes for _ in range(numVars)] \\n if guesses is None:\\n guesses = [[None for _ in range(4)] for _ in range(numVars)]\\n if len(np.array(bgInput).shape) == 2 or bgInput == None:\\n bgInput = [bgInput for _ in range(numVars)]\\n if len(np.array(bgPcInput).shape) == 2 or bgPcInput == None:\\n bgPcInput = [bgPcInput for _ in range(numVars)]\\n \\n datalen, avgFitSigmas, images, hFitParams, hFitErrs, vFitParams, vFitErrs, fitParams2D, fitErrs2D = [{} for _ in range(9)]\\n titles = ['Bare', 'Photon-Count', 'Bare-mbg', 'Photon-Count-mbg']\\n assert(len(sortedKey)>0)\\n for vari, keyV in enumerate(sortedKey):\\n keyV=misc.round_sig_str(keyV)\\n if vari==0:\\n initKeyv = keyV\\n varPics = sortedStackedPics[keyV]\\n # 0 is init atom pics for post-selection on atom number... if we wanted to.\\n expansionPics = rmHighCountPics(varPics,7000) if rmHighCounts else varPics\\n datalen[keyV] = len(expansionPics)\\n expPhotonCountImage = photonCounting(expansionPics, 120)[0] / len(expansionPics)\\n bgPhotonCountImage = np.zeros(expansionPics[0].shape) if bgPcInput[vari] is None else bgPcInput[vari]\\n expAvg = np.mean(expansionPics, 0)\\n bgAvg = np.zeros(expansionPics[0].shape) if (bgInput[vari] is None or len(bgInput[vari]) == 1) else bgInput[vari]\\n \\n if bgPhotonCountImage is None:\\n print('no bg photon', expAvg.shape)\\n bgPhotonCount = np.zeros(photonCountImage.shape)\\n avg_mbg = expAvg - bgAvg\\n avg_mbgpc = expPhotonCountImage - bgPhotonCountImage\\n images[keyV] = [expAvg, expPhotonCountImage, avg_mbg, avg_mbgpc]\\n hFitParams[keyV], hFitErrs[keyV], vFitParams[keyV], vFitErrs[keyV], fitParams2D[keyV], fitErrs2D[keyV] = [[] for _ in range(6)]\\n for imnum, (im, guess) in enumerate(zip(images[keyV], guesses[vari])):\\n if fit:\\n # fancy guess_x and guess_y values use the initial fitted value, typically short time, as a guess.\\n _, pictureFitParams2d, pictureFitErrors2d, v_params, v_errs, h_params, h_errs = ah.fitPic(\\n im, guessSigma_x=5, guessSigma_y=5, showFit=False, \\n guess_x=None if vari==0 else fitParams2D[initKeyv][imnum][1], guess_y=None if vari==0 else fitParams2D[initKeyv][imnum][2],\\n fitF=fitF2D)\\n fitParams2D[keyV].append(pictureFitParams2d)\\n fitErrs2D[keyV].append(pictureFitErrors2d)\\n hFitParams[keyV].append(h_params)\\n hFitErrs[keyV].append(h_errs)\\n vFitParams[keyV].append(v_params)\\n vFitErrs[keyV].append(v_errs)\\n # conversion from the num of pixels on the camera to microns at the focus of the tweezers\\n cf = 16e-6/64\\n mins, maxes = [[], []]\\n imgs_ = np.array(list(images.values()))\\n for imgInc in range(4):\\n if indvColorRanges:\\n mins.append(None)\\n maxes.append(None)\\n elif manualColorRange is None:\\n mins.append(min(imgs_[:,imgInc].flatten()))\\n maxes.append(max(imgs_[:,imgInc].flatten()))\\n else:\\n mins.append(manualColorRange[0])\\n maxes.append(manualColorRange[1])\\n numVariations = len(images)\\n if onlyThisPic is None:\\n fig, axs = plt.subplots(numVariations, 4, figsize=(20, pltVSize*numVariations))\\n if numVariations == 1:\\n axs = np.array([axs])\\n bgFig, bgAxs = plt.subplots(1, 2, figsize=(20, pltVSize))\\n else:\\n numRows = int(np.ceil((numVariations+3)/4))\\n fig, axs = plt.subplots(numRows, 4 if numVariations>1 else 3, figsize=(20, pltVSize*numRows))\\n avgPicAx = axs.flatten()[-3]\\n avgPicFig = fig\\n bgAxs = [axs.flatten()[-1], axs.flatten()[-2]]\\n bgFig = fig\\n if show2dFitsAndResiduals:\\n fig2d, axs2d = plt.subplots(*((2,numVariations) if numVariations>1 else (1,2)))\\n keyPlt = np.zeros(len(images))\\n (totalSignal, hfitCenter, hFitCenterErrs, hSigmas, hSigmaErrs, h_amp, hAmpErrs, vfitCenter, vFitCenterErrs, vSigmas, vSigmaErrs, v_amp, \\n vAmpErrs, hSigma2D, hSigma2dErr, vSigma2D, vSigma2dErr) = [np.zeros((len(images), 4)) for _ in range(17)]\\n \\n for vari, ((keyV,ims), hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D) in enumerate(zip(\\n images.items(), *[dic.values() for dic in [hFitParams, hFitErrs, vFitParams, vFitErrs, fitParams2D, fitErrs2D]])):\\n for which in range(4):\\n if onlyThisPic is None:\\n (im, ax, title, min_, max_, hparams, hErrs, vparams, vErrs, param2d, err2d\\n ) = [obj[which] for obj in (ims, axs[vari], titles, mins, maxes, hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D)] \\n else:\\n which = onlyThisPic\\n ax = axs.flatten()[vari]\\n (im, title, min_, max_, hparams, hErrs, vparams, vErrs, param2d, err2d\\n ) = [obj[which] for obj in (ims, titles, mins, maxes, hParamSet, hErr_set, vParamSet, vErr_set, paramSet2D, errSet2D)] \\n h_amp[vari][which], hfitCenter[vari][which], hSigmas[vari][which] = hparams[0], hparams[1], hparams[2]*cf*1e6\\n hAmpErrs[vari][which], hFitCenterErrs[vari][which], hSigmaErrs[vari][which] = hErrs[0], hErrs[1], hErrs[2]*cf*1e6\\n v_amp[vari][which], vfitCenter[vari][which], vSigmas[vari][which] = vparams[0], vparams[1], vparams[2]*cf*1e6\\n vAmpErrs[vari][which], vFitCenterErrs[vari][which], vSigmaErrs[vari][which] = vErrs[0], vErrs[1], vErrs[2]*cf*1e6\\n hSigma2D[vari][which], hSigma2dErr[vari][which], vSigma2D[vari][which], vSigma2dErr[vari][which] = [\\n val*cf*1e6 for val in [param2d[-3], err2d[-3], param2d[-2], err2d[-2]]]\\n \\n totalSignal[vari][which] = np.sum(im.flatten())\\n keyPlt[vari] = keyV\\n res = mp.fancyImshow(fig, ax, im, imageArgs={'cmap':dark_viridis_cmap, 'vmin':min_, 'vmax':max_}, \\n hFitParams=hparams, vFitParams=vparams, fitModule=fitModule, flipVAx = True, fitParams2D=param2d)\\n ax.set_title(keyV + ': ' + str(datalen[keyV]) + ';\\\\n' + title + ': ' + misc.errString(hSigmas[vari][which],hSigmaErrs[vari][which]) \\n + r'$\\\\mu m$ sigma, ' + misc.round_sig_str(totalSignal[vari][which],5), fontsize=12) \\n if show2dFitsAndResiduals:\\n X, Y = np.meshgrid(np.arange(len(im[0])), np.arange(len(im)))\\n data_fitted = fitF2D((X,Y), *param2d)\\n fitProper = data_fitted.reshape(im.shape[0],im.shape[1])\\n ax1 = axs2d[0] if numVariations == 1 else axs2d[0,vari]\\n ax2 = axs2d[1] if numVariations == 1 else axs2d[1,vari]\\n imr = ax1.imshow(fitProper, vmin=min_, vmax=max_)\\n mp.addAxColorbar(fig2d, ax1, imr)\\n ax1.contour(np.arange(len(im[0])), np.arange(len(im)), fitProper, 4, colors='w', alpha=0.2)\\n imr = ax2.imshow(fitProper-im)\\n mp.addAxColorbar(fig2d, ax2, imr)\\n ax2.contour(np.arange(len(im[0])), np.arange(len(im)), fitProper, 4, colors='w', alpha=0.2)\\n if onlyThisPic is not None:\\n break\\n \\n mp.fancyImshow(avgPicFig, avgPicAx, np.mean([img[onlyThisPic] for img in images.values()],axis=0), imageArgs={'cmap':dark_viridis_cmap},flipVAx = True)\\n avgPicAx.set_title('Average Over Variations')\\n ### Plotting background and photon counted background\\n mp.fancyImshow(bgFig, bgAxs[0], bgAvg, imageArgs={'cmap':dark_viridis_cmap},flipVAx = True) \\n bgAxs[0].set_title('Background image (' + str(len(picsForBg)/picsPerRep) + ')')\\n mp.fancyImshow(bgFig, bgAxs[1], bgPhotonCountImage, imageArgs={'cmap':dark_viridis_cmap},flipVAx = True) \\n bgAxs[1].set_title('Photon counted background image (' + str(len(picsForBg)/picsPerRep) + ')')\\n fig.subplots_adjust(left=0,right=1,bottom=0.1, hspace=0.2, **({'top': 0.7, 'wspace': 0.4} if (onlyThisPic is None) else {'top': 0.9, 'wspace': 0.3}))\\n \\n disp.display(fig)\\n temps, tempErrs, tempFitVs, = [],[],[]\\n if calcTemperature: \\n for sigmas, sigmaerrs in zip([hSigmas, vSigmas, hSigma2D, vSigma2D],[hSigmaErrs, vSigmaErrs, hSigma2dErr, vSigma2dErr]):\\n mbgSigmas = np.array([elt[2] for elt in sigmas])\\n mbgSigmaErrs = np.array([elt[2] for elt in sigmaerrs])\\n myGuess = [0.0, min((mbgSigmas)*1e-6), guessTemp]\\n temp, fitV, cov = ah.calcBallisticTemperature(keyPlt*1e-3, (mbgSigmas)*1e-6, guess = myGuess, sizeErrors = mbgSigmaErrs)\\n error = np.sqrt(np.diag(cov))\\n temps.append(temp)\\n tempErrs.append(error[2])\\n tempFitVs.append(fitV)\\n numAxisCol = int(plotSigmas) + int(plotCounts) + int(trackFitCenter)\\n if numAxisCol != 0:\\n fig2, axs = plt.subplots(1, numAxisCol, figsize = (15, 5)) \\n fig2.subplots_adjust(top=0.75, wspace = 0.4)\\n colors = ['b','k','c','purple']\\n if plotSigmas:\\n ax = (axs if numAxisCol == 1 else axs[0]) \\n stdStyle = dict(marker='o',linestyle='',capsize=3)\\n if onlyThisPic is not None:\\n ax.errorbar(keyPlt, hSigmas[:,onlyThisPic], hSigmaErrs[:,onlyThisPic], color=colors[0], label='h '+titles[onlyThisPic], **stdStyle);\\n ax.errorbar(keyPlt, hSigma2D[:,onlyThisPic], hSigma2dErr[:,onlyThisPic], color=colors[1], label='2dh '+titles[onlyThisPic], **stdStyle);\\n ax.errorbar(keyPlt, vSigmas[:,onlyThisPic], vSigmaErrs[:,onlyThisPic], color=colors[2], label='v '+titles[onlyThisPic], **stdStyle);\\n ax.errorbar(keyPlt, vSigma2D[:,onlyThisPic], vSigma2dErr[:,onlyThisPic], color=colors[3], label='2dv '+titles[onlyThisPic], **stdStyle);\\n else:\\n for whichPic in range(4):\\n ax.errorbar(keyPlt, hSigmas[:,whichPic], hSigmaErrs[:,whichPic], color='b', label='h '+titles[whichPic], **stdStyle);\\n ax.errorbar(keyPlt, vSigmas[:,whichPic], vSigmaErrs[:,whichPic], color='c', label='v '+titles[whichPic], **stdStyle);\\n ax.set_ylim(max(0,ax.get_ylim()[0]),min([ax.get_ylim()[1],5]))\\n ax.set_ylabel(r'Fit Sigma ($\\\\mu m$)')\\n \\n if calcTemperature:\\n # converting time to s, hSigmas in um \\n xPoints = np.linspace(min(keyPlt), max(keyPlt))*1e-3\\n for num, fitV in enumerate(tempFitVs):\\n #ax.plot(xPoints*1e3, LargeBeamMotExpansion.f(xPoints, *myGuess)*1e6, label = 'guess')\\n ax.plot(xPoints*1e3, LargeBeamMotExpansion.f(xPoints, *fitV)*1e6, color=colors[num])\\n ax.legend()\\n\\n if plotFitAmps: \\n ax = (axs if numAxisCol == 1 else axs[0])\\n ampAx = ax.twinx()\\n\\n if onlyThisPic is not None:\\n ampAx.errorbar(keyPlt, h_amp[:,onlyThisPic], hAmpErrs[:,onlyThisPic], label='h '+titles[onlyThisPic], color = 'orange', **stdStyle);\\n ampAx.errorbar(keyPlt, v_amp[:,onlyThisPic], vAmpErrs[:,onlyThisPic], label='v '+titles[onlyThisPic], color = 'r', **stdStyle);\\n else:\\n for whichPic in range(4):\\n ampAx.errorbar(keyPlt, h_amp[:,whichPic], hAmpErrs[:,whichPic], label='h '+titles[whichPic], color = 'orange', **stdStyle);\\n ampAx.errorbar(keyPlt, v_amp[:,whichPic], vAmpErrs[:,whichPic], label='v '+titles[whichPic], color = 'r', **stdStyle);\\n [tick.set_color('red') for tick in ampAx.yaxis.get_ticklines()]\\n [tick.set_color('red') for tick in ampAx.yaxis.get_ticklabels()]\\n ampAx.set_ylabel(r'Fit h_amps', color = 'r')\\n \\n hTotalPhotons, vTotalPhotons = None, None\\n if plotCounts:\\n # numAxCol = 1: ax = axs\\n # numAxCol = 2: plotSigmas + plotCounts -- ax = axs[1]\\n # numAxCol = 2: plotCounts + trackFitCenter -- ax = axs[0]\\n # numAxCol = 3: ax = axs[1]\\n if numAxisCol == 1:\\n ax = axs\\n elif numAxisCol == 2:\\n ax = axs[1 if plotSigmas else 0]\\n else:\\n ax = axs[1]\\n # Create axis to plot photon counts\\n ax.set_ylabel(r'Integrated signal')\\n photon_axis = ax.twinx()\\n # This is not currently doing any correct for e.g. the loading rate.\\n countToCameraPhotonEM = 0.018577 / (emGainSetting/200) # the float is is EM200. \\n countToScatteredPhotonEM = 0.018577 / 0.07 / (emGainSetting/200)\\n\\n if onlyThisPic is not None:\\n # calculate number of photons\\n hamp = h_amp[:,onlyThisPic]*len(expansionPics[0][0]) # Horizontal \\\"un\\\"normalization for number of columns begin averaged.\\n vamp = v_amp[:,onlyThisPic]*len(expansionPics[0]) \\n hsigpx = hSigmas[:,onlyThisPic]/(16/64) # Convert from um back to to pixels.\\n vsigpx = vSigmas[:,onlyThisPic]/(16/64)\\n htotalCountsPerPic = bump.area_under(hamp, hsigpx)\\n vtotalCountsPerPic = bump.area_under(vamp, vsigpx)\\n hTotalPhotons = countToScatteredPhotonEM*htotalCountsPerPic\\n vTotalPhotons = countToScatteredPhotonEM*vtotalCountsPerPic\\n ax.plot(keyPlt, totalSignal[:,onlyThisPic], marker='o', linestyle='', label=titles[onlyThisPic]);\\n photon_axis.plot(keyPlt, hTotalPhotons, marker='o', linestyle='', color = 'r', label='Horizontal')\\n photon_axis.plot(keyPlt, vTotalPhotons, marker='o', linestyle='', color = 'orange', label='Vertical')\\n else:\\n for whichPic in range(4):\\n # See above comments\\n amp = h_amp[:,whichPic]*len(expansionPics[0][0]) \\n sig = hSigmas[:,whichPic]/(16/64) \\n totalCountsPerPic = bump.area_under(amp, sig)\\n hTotalPhotons = countToScatteredPhotonEM*totalCountsPerPic\\n ax.plot(keyPlt, totalSignal[:,whichPic], marker='o', linestyle='', label=titles[whichPic]);\\n photon_axis.plot(keyPlt, hTotalPhotons, marker='o', linestyle='', color = ['red', 'orange', 'yellow', 'pink'][whichPic]) \\n ax.legend()\\n photon_axis.legend()\\n [tick.set_color('red') for tick in photon_axis.yaxis.get_ticklines()]\\n [tick.set_color('red') for tick in photon_axis.yaxis.get_ticklabels()]\\n photon_axis.set_ylabel(r'Fit-Based Avg Scattered Photon/Img', color = 'r')\\n if trackFitCenter:\\n #numaxcol = 1: ax = axs\\n #numaxcol = 2: trackfitcenter + plothSigmas: ax = axs[1]\\n #numaxcol = 2: trackfitcenter + plotCounts: ax = axs[1]\\n #numaxcol = 3: ax = axs[2]\\n ax = (axs if numAxisCol == 1 else axs[-1])\\n if onlyThisPic is not None:\\n #ax.errorbar(keyPlt, hfitCenter[:,onlyThisPic], hFitCenterErrs[:,onlyThisPic], marker='o', linestyle='', capsize=3, label=titles[onlyThisPic]);\\n ax.errorbar(keyPlt, vfitCenter[:,onlyThisPic], vFitCenterErrs[:,onlyThisPic], marker='o', linestyle='', capsize=3, label=titles[onlyThisPic]);\\n #def accel(t, x0, a):\\n # return x0 + 0.5*a*t**2\\n #accelFit, AccelCov = opt.curve_fit(accel, keyPlt*1e-3, hfitCenter[:,onlyThisPic], sigma = hFitCenterErrs[:,onlyThisPic])\\n #fitx = np.linspace(keyPlt[0], keyPlt[-1])*1e-3\\n #fity = accel(fitx, *accelFit)\\n #ax.plot(fitx*1e3, fity)\\n else:\\n for whichPic in range(4):\\n ax.errorbar(keyPlt, hfitCenter[:,whichPic], hFitCenterErrs[:,whichPic], marker='o', linestyle='', capsize=3, label=titles[whichPic]);\\n #accelErr = np.sqrt(np.diag(AccelCov))\\n fig2.legend()\\n ax.set_ylabel(r'Fit Centers (pix)')\\n ax.set_xlabel('time (ms)')\\n \\n if numAxisCol != 0:\\n disp.display(fig2) \\n \\n if not forceNoAnnotation:\\n for fid, isAnnotated in zip(fids, isAnnotatedList):\\n if not isAnnotated:\\n if type(fid) == int or type(fid) == type(''):\\n if newAnnotation or not exp.checkAnnotation(fid, force=False, quiet=True, useBase=useBase):\\n exp.annotate(fid, useBase=useBase)\\n if clearOutput:\\n disp.clear_output()\\n if calcTemperature: \\n for temp, err, label in zip(temps, tempErrs, ['Hor', 'Vert', 'Hor2D', 'Vert2D']): \\n print(label + ' temperature = ' + misc.errString(temp*1e6, err*1e6) + 'uk')\\n\\n for fid in fids:\\n if type(fid) == int:\\n expTitle, _, lev = exp.getAnnotation(fid)\\n expTitle = ''.join('#' for _ in range(lev)) + ' File ' + str(fid) + ': ' + expTitle\\n disp.display(disp.Markdown(expTitle))\\n with exp.ExpFile(fid) as file:\\n file.get_basic_info()\\n if trackFitCenter:\\n pass\\n #print('Acceleration in Mpix/s^2 = ' + misc.errString(accelFit[1], accelErr[1]))\\n if transferAnalysisOpts is not None and showTferAnalysisPlots:\\n colors, colors2 = misc.getColors(len(transferAnalysisOpts.initLocs()) + 2)#, cmStr=dataColor)\\n pltShape = (transferAnalysisOpts.initLocsIn[-1], transferAnalysisOpts.initLocsIn[-2])\\n # mp.plotThresholdHists([initThresholds[0][0],initThresholds[1][0]], colors, shape=pltShape)\\n mp.plotThresholdHists([initThresholds[0][0], initThresholds[0][0]], colors, shape=[1,2])\\n returnDictionary = {'images':images, 'fits':hFitParams, 'errs':hFitErrs, 'hSigmas':hSigmas, 'sigmaErrors':hSigmaErrs, 'dataKey':keyPlt, \\n 'hTotalPhotons':hTotalPhotons, 'tempCalc':temps, 'tempCalcErr':tempErrs, 'initThresholds':initThresholds[0], \\n '2DFit':fitParams2D, '2DErr':fitErrs2D, 'bgPics':picsForBg, 'dataLength':datalen}\\n if returnPics: \\n returnDictionary['pics'] = sortedStackedPics\\n return returnDictionary\",\n \"def calc_rsi(image):\\n\\n # roll axes to conventional row,col,depth\\n img = np.rollaxis(image, 0, 3)\\n\\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\\n COAST = img[:, :, 0]\\n B = img[:, :, 1]\\n G = img[:, :, 2]\\n Y = img[:, :, 3]\\n R = img[:, :, 4]\\n RE = img[:, :, 5]\\n NIR1 = img[:, :, 6]\\n NIR2 = img[:, :, 7]\\n\\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\\n dd = (2 * NIR1 - R) - (G - B)\\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\\n gndvi = old_div((NIR1 - G), (NIR1 + G))\\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\\n ndvi = old_div((NIR1 - R), (NIR1 + R))\\n ndvi35 = old_div((G - R), (G + R))\\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\\n nirry = old_div((NIR1), (R + Y))\\n normnir = old_div(NIR1, (NIR1 + R + G))\\n psri = old_div((R - B), RE)\\n rey = old_div((RE - Y), (RE + Y))\\n rvi = old_div(NIR1, R)\\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\\n vire = old_div(NIR1, RE)\\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\\n gr = old_div(G, R)\\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\\n\\n ###Built-Up indices\\n wvbi = old_div((COAST - RE), (COAST + RE))\\n wvnhfd = old_div((RE - COAST), (RE + COAST))\\n\\n ###SIs\\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\\n rgi = old_div(R, G)\\n bri = old_div(B, R)\\n\\n rsi = np.stack(\\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\\n axis=2)\\n\\n return rsi\",\n \"def apphot(im, yx, rap, subsample=4, **kwargs):\\n n, f = anphot(im, yx, rap, subsample=subsample, **kwargs)\\n if np.size(rap) > 1:\\n return n.cumsum(-1), f.cumsum(-1)\\n else:\\n return n, f\",\n \"def run(self, verbose=False):\\n if verbose:\\n from sage.combinat.rigged_configurations.tensor_product_kr_tableaux_element \\\\\\n import TensorProductOfKirillovReshetikhinTableauxElement\\n\\n for cur_crystal in reversed(self.tp_krt):\\n r = cur_crystal.parent().r()\\n\\n # Check if it is a spinor\\n if r == self.n:\\n # Perform the spinor bijection by converting to type A_{2n-1}^{(2)}\\n # doing the bijection there and pulling back\\n from sage.combinat.rigged_configurations.bij_type_A2_odd import KRTToRCBijectionTypeA2Odd\\n from sage.combinat.rigged_configurations.tensor_product_kr_tableaux import TensorProductOfKirillovReshetikhinTableaux\\n from sage.combinat.rigged_configurations.rigged_partition import RiggedPartition\\n\\n if verbose:\\n print(\\\"====================\\\")\\n if len(self.cur_path) == 0:\\n print(repr([])) # Special case for displaying when the rightmost factor is a spinor\\n else:\\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\\n print(\\\"--------------------\\\")\\n print(repr(self.ret_rig_con))\\n print(\\\"--------------------\\\\n\\\")\\n print(\\\"Applying doubling map\\\")\\n\\n # Convert to a type A_{2n-1}^{(2)} RC\\n dims = self.cur_dims[:]\\n dims.insert(0, [r, cur_crystal.parent().s()])\\n KRT = TensorProductOfKirillovReshetikhinTableaux(['A', 2*self.n-1, 2], dims)\\n # Convert the n-th partition into a regular rigged partition\\n self.ret_rig_con[-1] = RiggedPartition(self.ret_rig_con[-1]._list,\\n self.ret_rig_con[-1].rigging,\\n self.ret_rig_con[-1].vacancy_numbers)\\n bij = KRTToRCBijectionTypeA2Odd(KRT.module_generators[0]) # Placeholder element\\n bij.ret_rig_con = KRT.rigged_configurations()(*self.ret_rig_con, use_vacancy_numbers=True)\\n bij.cur_path = self.cur_path\\n bij.cur_dims = self.cur_dims\\n for i in range(len(self.cur_dims)):\\n if bij.cur_dims[i][0] != self.n:\\n bij.cur_dims[i][1] *= 2\\n for i in range(self.n-1):\\n for j in range(len(bij.ret_rig_con[i])):\\n bij.ret_rig_con[i]._list[j] *= 2\\n bij.ret_rig_con[i].rigging[j] *= 2\\n bij.ret_rig_con[i].vacancy_numbers[j] *= 2\\n\\n # Perform the type A_{2n-1}^{(2)} bijection\\n r = cur_crystal.parent().r()\\n # Iterate through the columns\\n for col_number, cur_column in enumerate(reversed(cur_crystal.to_array(False))):\\n bij.cur_path.insert(0, []) # Prepend an empty list\\n bij.cur_dims.insert(0, [0, 1])\\n\\n # Note that we do not need to worry about iterating over columns\\n # (see previous note about the data structure).\\n for letter in reversed(cur_column):\\n bij.cur_dims[0][0] += 1\\n val = letter.value # Convert from a CrystalOfLetter to an Integer\\n\\n if verbose:\\n print(\\\"====================\\\")\\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), bij.cur_path)))\\n print(\\\"--------------------\\\")\\n print(repr(bij.ret_rig_con))\\n print(\\\"--------------------\\\\n\\\")\\n\\n # Build the next state\\n bij.cur_path[0].insert(0, [letter]) # Prepend the value\\n bij.next_state(val)\\n\\n # If we've split off a column, we need to merge the current column\\n # to the current crystal tableau\\n if col_number > 0:\\n for i, letter_singleton in enumerate(self.cur_path[0]):\\n bij.cur_path[1][i].insert(0, letter_singleton[0])\\n bij.cur_dims[1][1] += 1\\n bij.cur_path.pop(0)\\n bij.cur_dims.pop(0)\\n\\n # And perform the inverse column splitting map on the RC\\n for a in range(self.n):\\n bij._update_vacancy_nums(a)\\n\\n if verbose:\\n print(\\\"====================\\\")\\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), bij.cur_path)))\\n print(\\\"--------------------\\\")\\n print(repr(bij.ret_rig_con))\\n print(\\\"--------------------\\\\n\\\")\\n print(\\\"Applying halving map\\\")\\n\\n # Convert back to a type B_n^{(1)}\\n for i in range(len(self.cur_dims)):\\n if bij.cur_dims[i][0] != self.n:\\n bij.cur_dims[i][1] //= 2\\n for i in range(self.n-1):\\n for j in range(len(bij.ret_rig_con[i])):\\n bij.ret_rig_con[i]._list[j] //= 2\\n bij.ret_rig_con[i].rigging[j] //= 2\\n bij.ret_rig_con[i].vacancy_numbers[j] //= 2\\n self.ret_rig_con = self.tp_krt.parent().rigged_configurations()(*bij.ret_rig_con, use_vacancy_numbers=True)\\n # Make it mutable so we don't have to keep making copies, at the\\n # end of the bijection, we will make it immutable again\\n self.ret_rig_con._set_mutable()\\n else:\\n # Perform the regular type B_n^{(1)} bijection\\n # Iterate through the columns\\n for col_number, cur_column in enumerate(reversed(cur_crystal.to_array(False))):\\n self.cur_path.insert(0, []) # Prepend an empty list\\n self.cur_dims.insert(0, [0, 1])\\n\\n # Note that we do not need to worry about iterating over columns\\n # (see previous note about the data structure).\\n for letter in reversed(cur_column):\\n self.cur_dims[0][0] += 1\\n val = letter.value # Convert from a CrystalOfLetter to an Integer\\n\\n if verbose:\\n print(\\\"====================\\\")\\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\\n print(\\\"--------------------\\\")\\n print(repr(self.ret_rig_con))\\n print(\\\"--------------------\\\\n\\\")\\n\\n # Build the next state\\n self.cur_path[0].insert(0, [letter]) # Prepend the value\\n self.next_state(val)\\n\\n # If we've split off a column, we need to merge the current column\\n # to the current crystal tableau\\n if col_number > 0:\\n if verbose:\\n print(\\\"====================\\\")\\n print(repr(TensorProductOfKirillovReshetikhinTableauxElement(self.tp_krt.parent(), self.cur_path)))\\n print(\\\"--------------------\\\")\\n print(repr(self.ret_rig_con))\\n print(\\\"--------------------\\\\n\\\")\\n print(\\\"Applying column merge\\\")\\n\\n for i, letter_singleton in enumerate(self.cur_path[0]):\\n self.cur_path[1][i].insert(0, letter_singleton[0])\\n self.cur_dims[1][1] += 1\\n self.cur_path.pop(0)\\n self.cur_dims.pop(0)\\n\\n # And perform the inverse column splitting map on the RC\\n for a in range(self.n):\\n self._update_vacancy_nums(a)\\n self.ret_rig_con.set_immutable() # Return it to immutable\\n return self.ret_rig_con\",\n \"def __getitem__(self, idx):\\n R = self.R\\n \\n image_path = os.path.join(self.image_dir, self.data['ImageId'][idx] + '.jpg')\\n image = cv2.imread(image_path)\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n image = torch.from_numpy(image)\\n H, W, N = image.size() # H, W, 3\\n image = image.permute(2, 0, 1).float()\\n image = 2*(image/255)-1\\n\\n car_id, euler_angle, world_coords = from_prediction_string_to_world_coords(self.data['PredictionString'][idx])\\n image_coords = from_camera_coords_to_image_coords(world_coords, self.camera_matrix)\\n image_coords = torch.from_numpy(image_coords).float()\\n \\n target_pointmap = torch.zeros(H//R, W//R, dtype=torch.float)\\n target_heatmap = None\\n target_local_offset = torch.zeros(2, H//R, W//R, dtype=torch.float)\\n target_depth = torch.zeros(H//R, W//R, dtype=torch.float)\\n target_yaw = torch.zeros(H//R, W//R, dtype=torch.float)\\n target_pitch = torch.zeros(H//R, W//R, dtype=torch.float)\\n target_roll = torch.zeros(H//R, W//R, dtype=torch.float)\\n h = torch.arange(0, H//R, 1, dtype=torch.float).unsqueeze(dim=1).expand(-1, W//R)\\n w = torch.arange(0, W//R, 1, dtype=torch.float).unsqueeze(dim=0).expand(H//R, -1)\\n p_h = image_coords[:, 1]\\n p_w = image_coords[:, 0]\\n \\n num_object = torch.Tensor([len(car_id)])\\n \\n for object_id in range(int(num_object.item())):\\n point = ((p_h[object_id]/R).int(), (p_w[object_id]/R).int())\\n \\n if point[0] < 0 or H//R - 1 < point[0] or point[1] < 0 or W//R - 1 < point[1] :\\n continue\\n target_pointmap[point[0], point[1]] = 1\\n target_local_offset[0][point[0], point[1]] = p_h[object_id]/R - point[0].float()\\n target_local_offset[1][point[0], point[1]] = p_w[object_id]/R - point[1].float()\\n target_depth[point[0], point[1]] = image_coords[object_id, 2]\\n target_yaw[point[0], point[1]] = torch.Tensor([euler_angle[object_id][0]])\\n target_pitch[point[0], point[1]] = torch.Tensor([euler_angle[object_id][1]])\\n target_roll[point[0], point[1]] = torch.Tensor([euler_angle[object_id][2]])\\n \\n sigma = self.coeff_sigma / target_depth[point[0], point[1]]\\n exponent = - ((h-p_h[object_id]/R)**2 + (w-p_w[object_id]/R)**2)/(2 * torch.pow(sigma, 2))\\n heatmap = torch.exp(exponent)\\n \\n if target_heatmap is None:\\n target_heatmap = heatmap.unsqueeze(dim=0)\\n else:\\n target_heatmap = torch.cat((target_heatmap, heatmap.unsqueeze(dim=0)), dim=0)\\n\\n target_heatmap, _ = torch.max(target_heatmap, dim=0)\\n \\n if torch.cuda.is_available():\\n image = image.cuda().contiguous()\\n target = {'num_object': num_object.cuda(), 'pointmap': target_pointmap.cuda(), 'heatmap': target_heatmap.cuda(), 'local_offset': target_local_offset.cuda(), 'depth': target_depth.cuda(), 'yaw': target_yaw.cuda(), 'pitch': target_pitch.cuda(), 'roll': target_roll.cuda()}\\n else:\\n image = image.contiguous()\\n target = {'num_object': num_object, 'pointmap': target_pointmap, 'heatmap': target_heatmap, 'local_offset': target_local_offset, 'depth': target_depth, 'yaw': target_yaw, 'pitch': target_pitch, 'roll': target_roll}\\n \\n return image, target\",\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 simanalyze(project='sim', image=True, imagename='default', skymodel='', vis='default', modelimage='', imsize=[0, 0], imdirection='', cell='', interactive=False, niter=0, threshold='0.1mJy', weighting='natural', mask=[], outertaper=[''], pbcor=True, stokes='I', featherimage='', analyze=False, showuv=True, showpsf=True, showmodel=True, showconvolved=False, showclean=True, showresidual=False, showdifference=True, showfidelity=True, graphics='both', verbose=False, overwrite=True, dryrun=False, logfile=''):\\n if type(imsize)==int: imsize=[imsize]\\n if type(outertaper)==str: outertaper=[outertaper]\\n\\n#\\n# The following is work around to avoid a bug with current python translation\\n#\\n mytmp = {}\\n\\n mytmp['project'] = project\\n mytmp['image'] = image\\n mytmp['imagename'] = imagename\\n mytmp['skymodel'] = skymodel\\n mytmp['vis'] = vis\\n mytmp['modelimage'] = modelimage\\n mytmp['imsize'] = imsize\\n mytmp['imdirection'] = imdirection\\n mytmp['cell'] = cell\\n mytmp['interactive'] = interactive\\n mytmp['niter'] = niter\\n mytmp['threshold'] = threshold\\n mytmp['weighting'] = weighting\\n mytmp['mask'] = mask\\n mytmp['outertaper'] = outertaper\\n mytmp['pbcor'] = pbcor\\n mytmp['stokes'] = stokes\\n mytmp['featherimage'] = featherimage\\n mytmp['analyze'] = analyze\\n mytmp['showuv'] = showuv\\n mytmp['showpsf'] = showpsf\\n mytmp['showmodel'] = showmodel\\n mytmp['showconvolved'] = showconvolved\\n mytmp['showclean'] = showclean\\n mytmp['showresidual'] = showresidual\\n mytmp['showdifference'] = showdifference\\n mytmp['showfidelity'] = showfidelity\\n mytmp['graphics'] = graphics\\n mytmp['verbose'] = verbose\\n mytmp['overwrite'] = overwrite\\n mytmp['dryrun'] = dryrun\\n mytmp['logfile'] = logfile\\n pathname='file://' + xmlpath( ) + '/'\\n trec = casac.utils().torecord(pathname+'simanalyze.xml')\\n\\n casalog.origin('simanalyze')\\n if trec.has_key('simanalyze') and casac.utils().verify(mytmp, trec['simanalyze']) :\\n result = task_simanalyze.simanalyze(project, image, imagename, skymodel, vis, modelimage, imsize, imdirection, cell, interactive, niter, threshold, weighting, mask, outertaper, pbcor, stokes, featherimage, analyze, showuv, showpsf, showmodel, showconvolved, showclean, showresidual, showdifference, showfidelity, graphics, verbose, overwrite, dryrun, logfile)\\n\\n else :\\n result = False\\n return result\",\n \"def create_azi_to_rad_sequence():\\n num_tot = 30\\n for i in range(2*num_tot + 1):\\n angle_arr = azi_to_rad_transformation(512, i, 30)\\n phase_arr = create_flat_phase(512, 0)\\n delta_1_arr = create_delta_1(phase_arr, angle_arr)\\n delta_2_arr = create_delta_2(angle_arr)\\n cv2.imwrite('frame' + str(i) +'.tiff', delta_2_arr)\\n print(\\\"Frame \\\" + str(i))\",\n \"def update_header(arr_imgs,obj,filter_i):\\n \\n for img in arr_imgs:\\n warnings.simplefilter('ignore', category=AstropyUserWarning)\\n try:\\n hdulist = fits.open(img,ignore_missing_end=True)\\n #if there is only a primary header get the data from it\\n if len(hdulist) == 1:\\n data = getdata(img, 0, header=False)\\n #if there is more than one header get data from the 'SCI' extension\\n else:\\n data = getdata(img, 1, header=False)\\n #Get value of EXPTIME and PHOTZPT keyword from primary header and \\n #set CCDGAIN to a default value of 1\\n EXPTIME = hdulist[0].header['EXPTIME']\\n PHOTFLAM = hdulist[1].header['PHOTFLAM']\\n PHOTZPT = hdulist[1].header['PHOTZPT']\\n CCDGAIN = 1.0\\n #First pass locating value for gain\\n for i in range(2):\\n if len(hdulist) == 1:\\n break\\n #Go through primary and secondary header and ignore the \\n #BinTable formatted header\\n if not isinstance(hdulist[i],astropy.io.fits.hdu.table.\\\\\\n BinTableHDU):\\n if 'CCDGAIN' in hdulist[i].header:\\n CCDGAIN = hdulist[i].header['CCDGAIN']\\n break\\n if 'GAIN' in hdulist[i].header:\\n CCDGAIN = hdulist[i].header['GAIN']\\n break\\n if 'ATODGAIN' in hdulist[i].header:\\n CCDGAIN = hdulist[i].header['ATODGAIN']\\n break\\n \\n #Locating units of image\\n print('Doing BUNIT check')\\n for i in range(2):\\n #If there is only one header then this is the only place to \\n #check\\n if len(hdulist) == 1:\\n bunit = hdulist[0].header['D001OUUN']\\n print('BUNIT was {0}'.format(bunit))\\n if bunit == 'counts':\\n ### Rescaling zeropoint\\n ZPT_NEW = 30.0\\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*EXPTIME) + PHOTZPT\\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\\n data = (data/EXPTIME)*pixmod\\n hdulist[0].header.set('BUNIT','COUNTS/S')\\n hdulist[0].header.set('MAGZPT',ZPT_NEW)\\n print('BUNIT is {0}'.format(hdulist[0].\\\\\\n header['BUNIT']))\\n \\n #If there are multiple headers then they all have to be checked\\n else:\\n if 'BUNIT' in hdulist[i].header:\\n bunit = hdulist[i].header['BUNIT']\\n print('BUNIT was {0}'.format(bunit))\\n if bunit == 'COUNTS':\\n ZPT_NEW = 30.0\\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*EXPTIME) + PHOTZPT\\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\\n data = (data/EXPTIME)*pixmod\\n if bunit == 'ELECTRONS':\\n ZPT_NEW = 30.0\\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN*EXPTIME) \\\\\\n + PHOTZPT\\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\\n data = (data/(CCDGAIN*EXPTIME))*pixmod\\n if bunit == 'ELECTRONS/S':\\n ZPT_NEW = 30.0\\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN) + PHOTZPT\\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\\n data = (data/CCDGAIN)*pixmod\\n if bunit == 'ELECTRONS/SEC':\\n ZPT_NEW = 30.0\\n ZPT_OLD = -2.5*np.log10(PHOTFLAM*CCDGAIN) + PHOTZPT\\n pixmod = 10**(-0.4*(ZPT_OLD-ZPT_NEW))\\n data = (data/CCDGAIN)*pixmod\\n hdulist[i].header['BUNIT'] = 'COUNTS/S'\\n hdulist[i].header['MAGZPT'] = ZPT_NEW\\n ###\\n print('BUNIT is {0}'.format(hdulist[i].\\\\\\n header['BUNIT']))\\n print('PHOTZPT is {0}'.format(hdulist[i].\\\\\\n header['MAGZPT']))\\n print('Done changing BUNIT')\\n \\n #Second pass to assign gain and exptime to headers\\n for i in range(2):\\n if len(hdulist) == 1:\\n break\\n if not isinstance(hdulist[i],astropy.io.fits.hdu.table.\\\\\\n BinTableHDU):\\n if 'CCDGAIN' not in hdulist[i].header:\\n hdulist[i].header.set('CCDGAIN',CCDGAIN)\\n if 'EXPTIME' not in hdulist[i].header:\\n hdulist[i].header.set('EXPTIME',EXPTIME)\\n \\n #Make new versions of images in interim/obj1 folder\\n os.chdir(path_to_interim + obj)\\n #Remove .fits extension\\n img = os.path.splitext(img)[0]\\n #If there was only one header write that header's data to new\\n #version of fits image\\n if len(hdulist) == 1:\\n fits.writeto(img+'_test_'+filter_i+'.fits',data,hdulist[0].\\\\\\n header,output_verify='ignore')\\n #Else write the 'SCI' header's data to new version of fits image\\n else:\\n fits.writeto(img+'_test_'+filter_i+'.fits',data,hdulist[1].\\\\\\n header,output_verify='ignore')\\n hdulist.close()\\n os.chdir(path_to_raw + obj)\\n \\n #This is to catch 'empty or corrupt FITS file' or any other IOError\\n #and write it to a text file along with the object name and the \\n #filter name\\n except IOError as e:\\n os.chdir('..')\\n dir_path = os.getcwd()\\n if os.path.basename(dir_path) == 'raw':\\n os.chdir(path_to_interim)\\n with open('Error_swarp.txt','a') as newfile: \\n newfile.write('Object {0} and image {1} raises {2}'.\\\\\\n format(obj,img,e))\\n newfile.write('\\\\n')\\n newfile.close()\\n os.chdir(path_to_raw + obj)\\n \\n os.chdir(path_to_interim + obj)\\n #For this object and filter combination grab all the new versions made\\n arr = glob('*test_'+filter_i+'.fits')\\n print(len(arr))\\n if len(arr) >= 1: #avoid empty cases where files have been removed earlier\\n #or don't exist at all since the dictionary also contains\\n #pairs of objects and filters that didn't meet the swarp\\n #requirements (didn't pass preliminary exptime or filter\\n #checks so those folders/images don't exist)\\n \\n #If new versions exist then write their names to a text file \\n with open(filter_i+'_img_list_testfil.txt','wb') as newfile2:\\n for obj in arr:\\n newfile2.write(obj)\\n newfile2.write('\\\\n')\\n newfile2.close()\\n #If text file exists return the name\\n return filter_i+'_img_list_testfil.txt'\\n #If text file doesn't exist return this string\\n return 'error'\",\n \"def simulator_from_instrument(instrument):\\r\\n\\r\\n grid = grid_from_instrument(instrument=instrument)\\r\\n psf = psf_from_instrument(instrument=instrument)\\r\\n\\r\\n if instrument in \\\"vro\\\":\\r\\n return ag.SimulatorImaging(\\r\\n exposure_time_map=ag.Array2D.full(\\r\\n fill_value=100.0, shape_native=grid.shape_native\\r\\n ),\\r\\n psf=psf,\\r\\n background_sky_map=ag.Array2D.full(\\r\\n fill_value=1.0, shape_native=grid.shape_native\\r\\n ),\\r\\n add_poisson_noise=True,\\r\\n )\\r\\n elif instrument in \\\"euclid\\\":\\r\\n return ag.SimulatorImaging(\\r\\n exposure_time_map=ag.Array2D.full(\\r\\n fill_value=2260.0, shape_native=grid.shape_native\\r\\n ),\\r\\n psf=psf,\\r\\n background_sky_map=ag.Array2D.full(\\r\\n fill_value=1.0, shape_native=grid.shape_native\\r\\n ),\\r\\n add_poisson_noise=True,\\r\\n )\\r\\n elif instrument in \\\"hst\\\":\\r\\n return ag.SimulatorImaging(\\r\\n exposure_time_map=ag.Array2D.full(\\r\\n fill_value=2000.0, shape_native=grid.shape_native\\r\\n ),\\r\\n psf=psf,\\r\\n background_sky_map=ag.Array2D.full(\\r\\n fill_value=1.0, shape_native=grid.shape_native\\r\\n ),\\r\\n add_poisson_noise=True,\\r\\n )\\r\\n elif instrument in \\\"hst_up\\\":\\r\\n return ag.SimulatorImaging(\\r\\n exposure_time_map=ag.Array2D.full(\\r\\n fill_value=2000.0, shape_native=grid.shape_native\\r\\n ),\\r\\n psf=psf,\\r\\n background_sky_map=ag.Array2D.full(\\r\\n fill_value=1.0, shape_native=grid.shape_native\\r\\n ),\\r\\n add_poisson_noise=True,\\r\\n )\\r\\n elif instrument in \\\"ao\\\":\\r\\n return ag.SimulatorImaging(\\r\\n exposure_time_map=ag.Array2D.full(\\r\\n fill_value=1000.0, shape_native=grid.shape_native\\r\\n ),\\r\\n psf=psf,\\r\\n background_sky_map=ag.Array2D.full(\\r\\n fill_value=1.0, shape_native=grid.shape_native\\r\\n ),\\r\\n add_poisson_noise=True,\\r\\n )\\r\\n else:\\r\\n raise ValueError(\\\"An invalid instrument was entered - \\\", instrument)\",\n \"def FluorescenceAnalysis(self, folder, round_num, save_mask = True):\\r\\n RoundNumberList, CoordinatesList, fileNameList = self.retrive_scanning_scheme(folder, file_keyword = 'Zmax')\\r\\n # RoundNumberList, CoordinatesList, fileNameList = self.retrive_scanning_scheme(folder, file_keyword = 'Zfocus')\\r\\n \\r\\n if not os.path.exists(os.path.join(folder, 'MLimages_{}'.format(round_num))):\\r\\n # If the folder is not there, create the folder\\r\\n os.mkdir(os.path.join(folder, 'MLimages_{}'.format(round_num))) \\r\\n \\r\\n for EachRound in RoundNumberList:\\r\\n \\r\\n cells_counted_in_round = 0\\r\\n \\r\\n if EachRound == round_num:\\r\\n \\r\\n # Start numbering cells at each round\\r\\n self.cell_counted_inRound = 0 \\r\\n \\r\\n for EachCoord in CoordinatesList:\\r\\n \\r\\n # =============================================================================\\r\\n # For tag fluorescence:\\r\\n # ============================================================================= \\r\\n print(EachCoord)\\r\\n #-------------- readin image---------------\\r\\n for Eachfilename in enumerate(fileNameList):\\r\\n if EachCoord in Eachfilename[1] and EachRound in Eachfilename[1]:\\r\\n if '0Zmax' in Eachfilename[1]:\\r\\n ImgNameInfor = Eachfilename[1][0:len(Eachfilename[1])-14] # get rid of '_PMT_0Zmax.tif' in the name.\\r\\n elif '0Zfocus' in Eachfilename[1]:\\r\\n ImgNameInfor = Eachfilename[1][0:len(Eachfilename[1])-16] # get rid of '_PMT_0Zfocus.tif' in the name.\\r\\n _imagefilename = os.path.join(folder, Eachfilename[1])\\r\\n #------------------------------------------\\r\\n \\r\\n # =========================================================================\\r\\n # USING MASKRCNN...\\r\\n # =========================================================================\\r\\n # Imagepath = self.Detector._fixPathName(_imagefilename)\\r\\n Rawimage = imread(_imagefilename)\\r\\n \\r\\n# if ClearImgBef == True:\\r\\n# # Clear out junk parts to make it esaier for ML detection.\\r\\n# RawimageCleared = self.preProcessMLimg(Rawimage, smallest_size=300, lowest_region_intensity=0.16)\\r\\n# else:\\r\\n# RawimageCleared = Rawimage.copy()\\r\\n \\r\\n image = ProcessImage.convert_for_MaskRCNN(Rawimage)\\r\\n \\r\\n # Run the detection on input image.\\r\\n results = self.Detector.detect([image])\\r\\n \\r\\n MLresults = results[0]\\r\\n \\r\\n if save_mask == True:\\r\\n fig, ax = plt.subplots()\\r\\n # Set class_names = [None,None,None,None] to mute class name display.\\r\\n visualize.display_instances(image, MLresults['rois'], MLresults['masks'], MLresults['class_ids'],\\r\\n class_names = [None,None,None,None], ax=ax,\\r\\n centre_coors = MLresults['Centre_coor'], Centre_coor_radius = 2, \\r\\n WhiteSpace = (0, 0))#MLresults['class_ids'],MLresults['scores'], \\r\\n # ax.imshow(fig)\\r\\n fig.tight_layout()\\r\\n # Save the detection image\\r\\n fig_name = os.path.join(folder, 'MLimages_{}\\\\{}.tif'.format(round_num, ImgNameInfor))\\r\\n plt.savefig(fname = fig_name, dpi=200, pad_inches=0.0, bbox_inches='tight')\\r\\n \\r\\n # segmentationImg = Image.fromarray(fig) #generate an image object\\r\\n # segmentationImg.save(os.path.join(folder, 'MLimages_{}\\\\{}.tif'.format(round_num, ImgNameInfor)))#save as tif\\r\\n \\r\\n if self.cell_counted_inRound == 0:\\r\\n cell_Data, self.cell_counted_inRound, total_cells_counted_in_coord = \\\\\\r\\n ProcessImage.retrieveDataFromML(Rawimage, MLresults, str(ImgNameInfor), self.cell_counted_inRound)\\r\\n else: \\r\\n Cell_Data_new, self.cell_counted_inRound, total_cells_counted_in_coord = \\\\\\r\\n ProcessImage.retrieveDataFromML(Rawimage, MLresults, str(ImgNameInfor), self.cell_counted_inRound)\\r\\n if len(Cell_Data_new) > 0:\\r\\n cell_Data = cell_Data.append(Cell_Data_new)\\r\\n \\r\\n # Count in total how many flat and round cells are identified.\\r\\n cells_counted_in_round += total_cells_counted_in_coord\\r\\n \\r\\n print(\\\"Number of round/flat cells in this round: {}\\\".format(cells_counted_in_round))\\r\\n \\r\\n # Save to excel\\r\\n cell_Data.to_excel(os.path.join(os.path.join(folder, round_num + '_' + datetime.now().strftime('%Y-%m-%d_%H-%M-%S')+'_CellsProperties.xlsx')))\\r\\n \\r\\n return cell_Data\",\n \"def CalcAtmTransmissionForImage(img, header='', chanInfo='', airmass=1.5,pwv=-1, \\n spectralaxis=-1, \\n value='transmission', P=-1, H=-1, \\n T=-1, altitude=-1):\\n if (header == ''):\\n print \\\"imhead\\\", # the comma prevents the newline so that ...10...20 will be on same line\\n header = imhead(img,mode='list')\\n if (type(header) != dict):\\n # Input was a spectrum rather than an image\\n if (chanInfo[1] < 60e9):\\n telescopeName = 'ALMA'\\n else:\\n telescopeName = 'VLA'\\n else:\\n telescopeName = header['telescope']\\n # this will not match up with the plot, which uses numberOfChannelsInCube\\n# freqs = getFreqsForImage(img, header, spectralaxis)\\n freqs = np.linspace(chanInfo[1]*1e-9,chanInfo[2]*1e-9,chanInfo[0])\\n# print \\\"freqs: %f-%f\\\" % (freqs[0], freqs[-1])\\n numchan = len(freqs)\\n lsrkwidth = (chanInfo[2] - chanInfo[1])/(numchan-1)\\n result = cubeLSRKToTopo(img, nchan=numchan, f0=chanInfo[1], f1=chanInfo[2], chanwidth=lsrkwidth)\\n if (result is None):\\n topofreqs = freqs\\n else:\\n topoWidth = (result[1]-result[0])/(numchan-1)\\n topofreqs = np.linspace(result[0], result[1], chanInfo[0]) * 1e-9\\n casalogPost(\\\"Converted LSRK range (%f-%f) to TOPO (%f-%f) over %d channels\\\" % (chanInfo[1]*1e-9, chanInfo[2]*1e-9,topofreqs[0],topofreqs[-1],numchan))\\n P0 = 1000.0 # mbar\\n H0 = 20.0 # percent\\n T0 = 273.0 # Kelvin\\n if (telescopeName.find('ALMA') >= 0 or telescopeName.find('ACA') >= 0):\\n pwv0 = 1.0 \\n P0 = 563.0\\n H0 = 20.0\\n T0 = 273.0\\n altitude0 = 5059\\n elif (telescopeName.find('VLA') >= 0):\\n P0 = 786.0\\n pwv0 = 5.0 \\n altitude0 = 2124\\n else:\\n pwv0 = 10.0 \\n altitude0 = 0\\n if (pwv < 0):\\n pwv = pwv0\\n if (T < 0):\\n T = T0\\n if (H < 0):\\n H = H0\\n if (P < 0):\\n P = P0\\n if (altitude < 0):\\n altitude = altitude0\\n tropical = 1\\n midLatitudeSummer = 2\\n midLatitudeWinter = 3\\n# print \\\"image bandwidth = %f GHz\\\" % (np.max(freqs)-np.min(freqs))\\n reffreq = np.mean(topofreqs)\\n numchanModel = numchan*1\\n chansepModel = (topofreqs[-1]-topofreqs[0])/(numchanModel-1)\\n# print \\\"regridded bandwidth=%f GHz, chansep=%f, reffreq=%f\\\" % (np.max(topofreqs)-np.min(topofreqs), chansepModel, reffreq)\\n nbands = 1\\n myqa = createCasaTool(qatool)\\n fCenter = create_casa_quantity(myqa, reffreq, 'GHz')\\n fResolution = create_casa_quantity(myqa, chansepModel, 'GHz')\\n fWidth = create_casa_quantity(myqa, numchanModel*chansepModel, 'GHz')\\n myat = casac.atmosphere()\\n myat.initAtmProfile(humidity=H, temperature=create_casa_quantity(myqa,T,\\\"K\\\"),\\n altitude=create_casa_quantity(myqa,altitude,\\\"m\\\"),\\n pressure=create_casa_quantity(myqa,P,'mbar'),atmType=midLatitudeWinter)\\n myat.initSpectralWindow(nbands, fCenter, fWidth, fResolution)\\n myat.setUserWH2O(create_casa_quantity(myqa, pwv, 'mm'))\\n# myat.setAirMass() # This does not affect the opacity, but it does effect TebbSky, so do it manually.\\n myqa.done()\\n\\n dry = np.array(myat.getDryOpacitySpec(0)[1])\\n wet = np.array(myat.getWetOpacitySpec(0)[1]['value'])\\n TebbSky = myat.getTebbSkySpec(spwid=0)[1]['value']\\n # readback the values to be sure they got set\\n \\n rf = myat.getRefFreq()['value']\\n cs = myat.getChanSep()['value']\\n if (myat.getRefFreq()['unit'] != 'GHz'):\\n casalogPost(\\\"There is a unit mismatch for refFreq in the atm code.\\\")\\n if (myat.getChanSep()['unit'] != 'MHz'):\\n casalogPost(\\\"There is a unit mismatch for chanSep in the atm code.\\\")\\n numchanModel = myat.getNumChan()\\n freq0 = myat.getChanFreq(0)['value']\\n freq1 = myat.getChanFreq(numchanModel-1)['value']\\n# print \\\"atm returned bandwidth = %f GHz = %f to %f \\\" % (freq1-freq0, freq0, freq1)\\n newfreqs = np.linspace(freqs[0], freqs[-1], numchanModel) # fix for SCOPS-4815\\n# print \\\"freqs: %f-%f newfreqs: %f-%f\\\" % (freqs[0], freqs[-1], newfreqs[0], newfreqs[-1])\\n transmission = np.exp(-airmass*(wet+dry))\\n TebbSky *= (1-np.exp(-airmass*(wet+dry)))/(1-np.exp(-wet-dry))\\n if value=='transmission':\\n values = transmission\\n else:\\n values = TebbSky\\n del myat\\n return(newfreqs, values)\",\n \"def analyze(self, options, target):\\r\\n\\r\\n target = 0\\r\\n\\r\\n upf = None\\r\\n\\r\\n dwnf = None\\r\\n\\r\\n if options.upfile is not None:\\r\\n\\r\\n upf = basepath + options.upfile + '.ma'\\r\\n\\r\\n if options.downfile is not None:\\r\\n\\r\\n dwnf = basepath + options.downfile + '.ma'\\r\\n\\r\\n\\r\\n\\r\\n for filename in (upf, dwnf):\\r\\n\\r\\n # if options.upfile is not None and options.downfile is not None:\\r\\n\\r\\n if filename is None:\\r\\n\\r\\n break\\r\\n\\r\\n im=[]\\r\\n\\r\\n self.imageData = []\\r\\n\\r\\n print (\\\"Loading data from %s\\\" % filename)\\r\\n\\r\\n try:\\r\\n\\r\\n im = MetaArray(file = filename, subset=(slice(0,2), slice(64,128), slice(64,128)))\\r\\n\\r\\n except:\\r\\n\\r\\n print(' Error loading upfile: %s' % filename)\\r\\n\\r\\n return\\r\\n\\r\\n print(' Data loaded')\\r\\n\\r\\n target = target + 1\\r\\n\\r\\n self.times = im.axisValues('Time').astype('float32')\\r\\n\\r\\n self.imageData = im.view(np.ndarray).astype('float32')\\r\\n\\r\\n im=[]\\r\\n\\r\\n self.analysis_fourier_map(period=self.period, target=target, bins=binsize,)\\r\\n\\r\\n if target > 0:\\r\\n\\r\\n self.plot_maps(mode = 1, target = target, gfilter = self.gfilter)\",\n \"def addAllTasselCapIndices(self,img):\\n\\t\\t\\n\\t\\tdef getTasseledCap(img):\\n\\t\\t\\t\\t\\\"\\\"\\\"Function to compute the Tasseled Cap transformation and return an image\\\"\\\"\\\"\\n\\t\\t\\t\\t\\n\\t\\t\\tcoefficients = ee.Array([\\n\\t\\t\\t\\t[0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863],\\n\\t\\t\\t\\t[-0.2848, -0.2435, -0.5436, 0.7243, 0.0840, -0.1800],\\n\\t\\t\\t\\t[0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572],\\n\\t\\t\\t\\t[-0.8242, 0.0849, 0.4392, -0.0580, 0.2012, -0.2768],\\n\\t\\t\\t\\t[-0.3280, 0.0549, 0.1075, 0.1855, -0.4357, 0.8085],\\n\\t\\t\\t\\t[0.1084, -0.9022, 0.4120, 0.0573, -0.0251, 0.0238]\\n\\t\\t\\t]);\\n\\t\\t\\t\\n\\t\\t\\tbands=ee.List(['blue','green','red','nir','swir1','swir2'])\\n\\t\\t\\t\\t\\n\\t\\t\\t# Make an Array Image, with a 1-D Array per pixel.\\n\\t\\t\\tarrayImage1D = img.select(bands).toArray()\\n\\t\\t\\t\\n\\t\\t\\t# Make an Array Image with a 2-D Array per pixel, 6x1.\\n\\t\\t\\tarrayImage2D = arrayImage1D.toArray(1)\\n\\t\\t\\t\\n\\t\\t\\tcomponentsImage = ee.Image(coefficients).matrixMultiply(arrayImage2D).arrayProject([0]).arrayFlatten([['brightness', 'greenness', 'wetness', 'fourth', 'fifth', 'sixth']]).float();\\n\\t\\t \\n\\t\\t\\t# Get a multi-band image with TC-named bands.\\n\\t\\t\\treturn img.addBands(componentsImage);\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\tdef addTCAngles(img):\\n\\n\\t\\t\\t\\\"\\\"\\\" Function to add Tasseled Cap angles and distances to an image. Assumes image has bands: 'brightness', 'greenness', and 'wetness'.\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t# Select brightness, greenness, and wetness bands\\t\\n\\t\\t\\tbrightness = img.select('brightness');\\n\\t\\t\\tgreenness = img.select('greenness');\\n\\t\\t\\twetness = img.select('wetness');\\n\\t \\n\\t\\t\\t# Calculate Tasseled Cap angles and distances\\n\\t\\t\\ttcAngleBG = brightness.atan2(greenness).divide(math.pi).rename(['tcAngleBG']);\\n\\t\\t\\ttcAngleGW = greenness.atan2(wetness).divide(math.pi).rename(['tcAngleGW']);\\n\\t\\t\\ttcAngleBW = brightness.atan2(wetness).divide(math.pi).rename(['tcAngleBW']);\\n\\t\\t\\ttcDistBG = brightness.hypot(greenness).rename(['tcDistBG']);\\n\\t\\t\\ttcDistGW = greenness.hypot(wetness).rename(['tcDistGW']);\\n\\t\\t\\ttcDistBW = brightness.hypot(wetness).rename(['tcDistBW']);\\n\\t\\t\\timg = img.addBands(tcAngleBG).addBands(tcAngleGW).addBands(tcAngleBW).addBands(tcDistBG).addBands(tcDistGW).addBands(tcDistBW);\\n\\t\\t\\t\\t\\n\\t\\t\\treturn img;\\n\\t\\t\\n\\t\\t\\n\\t\\timg = getTasseledCap(img)\\n\\t\\timg = addTCAngles(img)\\n\\t\\treturn img\",\n \"def regrid_in_miriad(taskid, image_name, hdu_image, b, c):\\n\\n\\t# Change the reference pixel of beam model to reference pixel of image to correct\\n\\tcb_model = beam_lookup.model_lookup2(taskid, b)\\n\\thdulist_cb = pyfits.open(cb_model)\\n\\thdulist_cb[0].header['CRVAL1'] = hdu_image[0].header['CRVAL1']\\n\\thdulist_cb[0].header['CRVAL2'] = hdu_image[0].header['CRVAL2']\\n\\n\\t# Rescale to appropriate frequency. This should work for either drift scans or Gaussian regression (only tested on latter):\\n\\tavg_cube_freq = (hdu_image[0].header['CRVAL3'] + hdu_image[0].header['CDELT3'] * hdu_image[0].data.shape[0]) * u.Hz\\n\\thdulist_cb[0].header['CDELT1'] = (hdulist_cb[0].header['CDELT1'] * get_cb_model_freq().to(u.Hz) / avg_cube_freq).value\\n\\thdulist_cb[0].header['CDELT2'] = (hdulist_cb[0].header['CDELT2'] * get_cb_model_freq().to(u.Hz) / avg_cube_freq).value\\n\\n\\tcb2d_name = 'temp_b{}_c{}_cb-2d.fits'.format(b, c)\\n\\thdulist_cb.writeto(cb2d_name)\\n\\thdulist_cb.close()\\n\\n\\tprint('\\\\tRegridding in miriad using model {}'.format(cb_model))\\n\\n\\tfits = lib.miriad('fits')\\n\\tregrid = lib.miriad('regrid')\\n\\n\\t# Convert files to miriad:\\n\\tfits.in_ = image_name\\n\\tfits.out = '{}.mir'.format(image_name[:-5])\\n\\tfits.op = 'xyin'\\n\\tfits.go()\\n\\n\\tfits.in_ = cb2d_name\\n\\tfits.out = '{}.mir'.format(cb2d_name[:-5])\\n\\tfits.op = 'xyin'\\n\\tfits.go()\\n\\n\\t# Regrid beam image\\n\\tregrid.in_ = '{}.mir'.format(cb2d_name[:-5])\\n\\tregrid.out = '{}_rgrid.mir'.format(cb2d_name[:-5])\\n\\tregrid.tin = '{}.mir'.format(image_name[:-5])\\n\\tregrid.axes = '1,2'\\n\\tregrid.go()\\n\\n\\t# Convert regrided beam image to fits\\n\\tfits.in_ = '{}_rgrid.mir'.format(cb2d_name[:-5])\\n\\tfits.out = '{}_rgrid.fits'.format(cb2d_name[:-5])\\n\\tfits.op = 'xyout'\\n\\tfits.go()\\n\\n\\t# Make cb 3D and save as FITS:\\n\\thdu_cb = pyfits.open('{}_rgrid.fits'.format(cb2d_name[:-5]))\\n\\td_new = np.ones((hdu_image[0].header['NAXIS3'], hdu_cb[0].header['NAXIS2'], hdu_cb[0].header['NAXIS2']))\\n\\td_beam_cube = d_new * hdu_cb[0].data\\n\\thdu_cb[0].data = np.float32(d_beam_cube)\\n\\n\\tprint('\\\\tWriting compound beam cube.')\\n\\thdu_cb.writeto('{}_cb.fits'.format(image_name[:-5]))\\n\\n\\thdu_cb.close()\\n\\n\\t# Clean up the extra Miriad & 2D cb files\\n\\tos.system('rm -rf {}*.mir'.format(image_name[:-5]))\\n\\tos.system('rm -rf {}*'.format(cb2d_name[:-5]))\",\n \"def ptc_acquisition(self, explow=0.1, exphigh=2, expdelta=0.1, laserchannel = 2, lasercurrent=45.0):\\n\\n #\\n self.laser.select(laserchannel)\\n self.laser.setCurrent(laserchannel, lasercurrent)\\n self.laser.enable()\\n\\n #self.powerup_CCD()\\n self.reb.set_testtype('PTC')\\n\\n #self.DKD.setup_current_measurements(DKD_range)\\n self.PhD.setup_current_measurements(2e-8)\\n\\n # Create the logging summary file\\n summaryfile = os.path.join(eodir, 'summary.log')\\n f = open(summaryfile, 'a')\\n\\n print >>f, \\\"# power\\\\t exposure time\\\\t file name\\\"\\n\\n effpow = self.laser.getPower(laserchannel)\\n # First take bias frames\\n self.log(\\\"Taking bias\\\")\\n m = self.execute_reb_sequence('ClearBias', 0, 20, True )\\n #to have only useful channels:\\n fname = \\\"%s_ptc_bias_%s.fits\\\" % (serno, self.reb.reb.imgtag)\\n i = self.conv_to_fits(channels=validamps)\\n # to save FITS HDU with headers\\n self.save_to_fits(i, m, fitsname=os.path.join(eodir, fname))\\n\\n print >>f, effpow, 0, fname\\n\\n for t in np.arange(explow, exphigh+expdelta, expdelta):\\n # pair of flats\\n for numpair in [1, 2]:\\n effpow = self.laser.getPower(laserchannel)\\n m = self.execute_reb_sequence('Acquisition', t)\\n #to have only useful channels:\\n fname = \\\"%s_ptc_flat%d_%05d_%s.fits\\\" % (serno, numpair, int(t*100), self.reb.reb.imgtag)\\n i = self.conv_to_fits(channels=validamps)\\n # to save FITS HDU with headers\\n self.save_to_fits(i, m, fitsname=os.path.join(eodir, fname))\\n\\n print >>f, effpow, t, fname\\n\\n f.close()\\n\\n # Shutting down (not the lamp by default)\\n self.laser.disable()\\n #self.shutdown_CCD()\\n # p = self.reb.start_waiting_sequence()\",\n \"def countmap(band,skypos,tranges,skyrange,width=False,height=False,\\n\\t\\t\\t verbose=0,tscale=1000.,memlight=False,hdu=False,retries=20):\\n\\timsz = gxt.deg2pix(skypos,skyrange)\\n\\tcount = np.zeros(imsz)\\n\\tfor trange in tranges:\\n\\t\\t# If memlight is requested, break the integration into\\n\\t\\t# smaller chunks.\\n\\t\\tstep = memlight if memlight else trange[1]-trange[0]\\n\\t\\tfor i in np.arange(trange[0],trange[1],step):\\n\\t\\t\\tt0,t1=i,i+step\\n\\t\\t\\tif verbose:\\n\\t\\t\\t\\tprint_inline('Coadding '+str(t0)+' to '+str(t1))\\n\\t\\t\\tevents = gQuery.getArray(gQuery.rect(band,skypos[0],skypos[1],t0,t1,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t skyrange[0],skyrange[1]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t verbose=verbose,retries=retries)\\n\\n\\t\\t\\t# Check that there is actually data here.\\n\\t\\t\\tif not events:\\n\\t\\t\\t\\tif verbose>1:\\n\\t\\t\\t\\t\\tprint \\\"No data in \\\"+str([t0,t1])\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\ttimes = np.array(events,dtype='float64')[:,0 ]/tscale\\n\\t\\t\\tcoo =\\tnp.array(events,dtype='float64')[:,1:]\\n\\n\\t\\t\\t# If there's no data, return a blank image.\\n\\t\\t\\tif len(coo)==0:\\n\\t\\t\\t\\tif verbose:\\n\\t\\t\\t\\t\\tprint 'No data in this frame: '+str([t0,t1])\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t# Define World Coordinate System (WCS)\\n\\t\\t\\twcs = define_wcs(skypos,skyrange,width=False,height=False)\\n\\n\\t\\t\\t# Map the sky coordinates onto the focal plane\\n\\t\\t\\tfoc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)\\n\\n\\t\\t\\t# Bin the events into actual image pixels\\n\\t\\t\\tH,xedges,yedges=np.histogram2d(foc[:,1]-0.5,foc[:,0]-0.5,\\n\\t\\t\\t\\t\\t\\t\\t\\tbins=imsz,range=([ [0,imsz[0]],[0,imsz[1]] ]))\\n\\t\\t\\tcount += H\\n\\n\\treturn count\",\n \"def process_event(self, evt):\\n det_data = {}\\n for det, thisDetDict in zip(self.dets, self.targetVarsXtc):\\n try:\\n det.getData(evt)\\n det.processFuncs()\\n thisDetDataDict = getUserData(det)\\n img = None\\n for key in thisDetDataDict.keys():\\n if key=='full_area':\\n img = thisDetDataDict[key].astype(float) # needed for detectors whose data are uint16 (Rayonix)\\n elif key.find('ROI')>=0:\\n img = thisDetDataDict[key].astype(float)\\n if img is None:\\n print('Problem with getting detector area data.')\\n continue\\n if 'thresADU' in thisDetDict:\\n img[img<thisDetDict['thresADU']] = 0\\n elif 'thresRms' in thisDetDict:\\n img[img<thisDetDict['thresRms']*det.rms] = 0\\n\\n det_data[det._name] = img # can onky handle full area ROIFunc for now\\n \\n # calculate variance (see ... for ref)\\n \\n# if not (key=='full_area' or key.find('ROI')>=0 or key.find('photon_img')>=0):\\n# continue\\n# if (key=='full_area' or key.find('ROI')>=0):\\n# if 'thresADU' in thisDetDict:\\n# thisDetDataDict[key][thisDetDataDict[key]<thisDetDict['thresADU']]=0\\n# elif 'thresRms' in thisDetDict:\\n# thisDetDataDict[key][thisDetDataDict[key]<thisDetDict['thresRms']*det.rms]=0\\n# dArray[ib%bins_per_job]=dArray[ib%bins_per_job]+thisDetDataDict[key]\\n# else: #if key.find('photon_img')\\n# dIArray[ib%bins_per_job]=dIArray[ib%bins_per_job]+thisDetDataDict[key]\\n\\n# x = thisDetDataDict[key]\\n# oldM = dMArray\\n# dMArray = dMArray + (x-dMArray)/(ievt+1)\\n# dSArray = dSArray + (x-dMArray)*(x-oldM)\\n except Exception as e:\\n print('Failed to get data for this event for det {}.\\\\n{}'.format(det._name, e))\\n det_data[det._name] = None\\n return det_data\",\n \"def addAllTasselCapIndices(self,img):\\n\\t\\t\\n\\t\\tdef getTasseledCap(img):\\n\\t\\t\\t\\\"\\\"\\\"Function to compute the Tasseled Cap transformation and return an image\\\"\\\"\\\"\\n\\t\\t\\t\\n\\t\\t\\tcoefficients = ee.Array([\\n\\t\\t\\t\\t[0.3037, 0.2793, 0.4743, 0.5585, 0.5082, 0.1863],\\n\\t\\t\\t\\t[-0.2848, -0.2435, -0.5436, 0.7243, 0.0840, -0.1800],\\n\\t\\t\\t\\t[0.1509, 0.1973, 0.3279, 0.3406, -0.7112, -0.4572],\\n\\t\\t\\t\\t[-0.8242, 0.0849, 0.4392, -0.0580, 0.2012, -0.2768],\\n\\t\\t\\t\\t[-0.3280, 0.0549, 0.1075, 0.1855, -0.4357, 0.8085],\\n\\t\\t\\t\\t[0.1084, -0.9022, 0.4120, 0.0573, -0.0251, 0.0238]\\n\\t\\t\\t]);\\n\\t\\t\\n\\t\\t\\tbands=ee.List(['blue','green','red','nir','swir1','swir2'])\\n\\t\\t\\t\\n\\t\\t\\t# Make an Array Image, with a 1-D Array per pixel.\\n\\t\\t\\tarrayImage1D = img.select(bands).toArray()\\n\\t\\t\\n\\t\\t\\t# Make an Array Image with a 2-D Array per pixel, 6x1.\\n\\t\\t\\tarrayImage2D = arrayImage1D.toArray(1)\\n\\t\\t\\n\\t\\t\\tcomponentsImage = ee.Image(coefficients).matrixMultiply(arrayImage2D).arrayProject([0]).arrayFlatten([['brightness', 'greenness', 'wetness', 'fourth', 'fifth', 'sixth']]).float();\\n\\t \\n\\t\\t\\t# Get a multi-band image with TC-named bands.\\n\\t\\t\\treturn img.addBands(componentsImage);\\t\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\tdef addTCAngles(img):\\n\\n\\t\\t\\t\\\"\\\"\\\" Function to add Tasseled Cap angles and distances to an image. Assumes image has bands: 'brightness', 'greenness', and 'wetness'.\\\"\\\"\\\"\\n\\t\\t\\t\\n\\t\\t\\t# Select brightness, greenness, and wetness bands\\t\\n\\t\\t\\tbrightness = img.select('brightness');\\n\\t\\t\\tgreenness = img.select('greenness');\\n\\t\\t\\twetness = img.select('wetness');\\n\\t \\n\\t\\t\\t# Calculate Tasseled Cap angles and distances\\n\\t\\t\\ttcAngleBG = brightness.atan2(greenness).divide(math.pi).rename(['tcAngleBG']);\\n\\t\\t\\ttcAngleGW = greenness.atan2(wetness).divide(math.pi).rename(['tcAngleGW']);\\n\\t\\t\\ttcAngleBW = brightness.atan2(wetness).divide(math.pi).rename(['tcAngleBW']);\\n\\t\\t\\ttcDistBG = brightness.hypot(greenness).rename(['tcDistBG']);\\n\\t\\t\\ttcDistGW = greenness.hypot(wetness).rename(['tcDistGW']);\\n\\t\\t\\ttcDistBW = brightness.hypot(wetness).rename(['tcDistBW']);\\n\\t\\t\\timg = img.addBands(tcAngleBG).addBands(tcAngleGW).addBands(tcAngleBW).addBands(tcDistBG).addBands(tcDistGW).addBands(tcDistBW);\\n\\t\\t\\t\\n\\t\\t\\treturn img;\\n\\t\\n\\t\\timg = getTasseledCap(img)\\n\\t\\timg = addTCAngles(img)\\n\\t\\treturn img\",\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 PETImageProcess(PET_Scan):\\n PET_Scan = normalise(PET_Scan)\\n return PET_Scan\",\n \"def step(self):\\n\\n for t, z, data_igm, data_cgm, RC_igm, RC_cgm in self.medium.step():\\n\\n Jc = np.atleast_1d(self._f_Jc(z))\\n Ji = np.atleast_1d(self._f_Ji(z))\\n Jlw = np.atleast_1d(self._f_Jlw(z))\\n Ja = Jc + Ji\\n\\n # Compute spin temperature\\n n_H = self.medium.parcel_igm.grid.cosm.nH(z)\\n Ts = self.medium.parcel_igm.grid.hydr.Ts(z,\\n data_igm['Tk'], Ja, data_igm['h_2'], data_igm['e'] * n_H)\\n\\n if self.pf['floor_Ts'] is not None:\\n Ts = max(Ts, self.medium.parcel_igm.grid.hydr.Ts_floor(z=z))\\n\\n # Compute volume-averaged ionized fraction\\n if self.pf['include_cgm']:\\n xavg = data_cgm['h_2'] + (1. - data_cgm['h_2']) * data_igm['h_2']\\n else:\\n xavg = data_igm['h_2']\\n\\n # Derive brightness temperature\\n dTb = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts, xavg)\\n dTb_b = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts)\\n\\n # Add derived fields to data\\n data_igm.update({'Ts': Ts, 'dTb': dTb, #'dTb_bulk': dTb_b,\\n 'Jc': Jc, 'Ji': Ji, 'Ja': Ja, 'Jlw': Jlw})\\n\\n # Yield!\\n yield t, z, data_igm, data_cgm, RC_igm, RC_cgm\",\n \"def manipulations(path):\\r\\n\\r\\n print (\\\"\\\\n Working on %s\\\\n\\\" %(path))\\r\\n\\r\\n # Creates a folder with the results for the current image\\r\\n if not os.path.exists(\\\"Results\\\\\\\\%s\\\" %(path)):\\r\\n os.makedirs(\\\"Results\\\\\\\\%s\\\" %(path))\\r\\n\\r\\n # The variations made of the image\\r\\n func.pixelImage(path, 10, 10)\\r\\n func.animate(path)\\r\\n func.colorScale(path, 0)\\r\\n func.colorScale(path, 1)\\r\\n func.colorScale(path, 2)\\r\\n func.scan(path, 280)\\r\\n func.greyImage(path)\\r\\n func.colorSteps(path, 1)\\r\\n func.inverted(path)\",\n \"def calculate_psf_tilts():\\n for order in [1, 2]:\\n\\n # Get the file\\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\\n psf_file = resource_filename('awesimsoss', path)\\n\\n # Dimensions\\n subarray = 'SUBSTRIP256'\\n X = range(2048)\\n Y = range(256)\\n\\n # Get the wave map\\n wave_map = utils.wave_solutions(subarray, order).astype(float)\\n\\n # Get the y-coordinate of the trace polynomial in this column\\n # (center of the trace)\\n coeffs = trace_polynomials(subarray=subarray, order=order)\\n trace = np.polyval(coeffs, X)\\n\\n # Interpolate to get the wavelength value at the center\\n wave = interp2d(X, Y, wave_map)\\n\\n # Get the wavelength of the trace center in each column\\n trace_wave = []\\n for x, y in zip(X, trace):\\n trace_wave.append(wave(x, y)[0])\\n\\n # For each column wavelength (defined by the wavelength at\\n # the trace center) define an isowavelength contour\\n angles = []\\n for n, x in enumerate(X):\\n\\n w = trace_wave[x]\\n\\n # Edge cases\\n try:\\n w0 = trace_wave[x-1]\\n except IndexError:\\n w0 = 0\\n\\n try:\\n w1 = trace_wave[x+1]\\n except IndexError:\\n w1 = 10\\n\\n # Define the width of the wavelength bin as half-way\\n # between neighboring points\\n dw0 = np.mean([w0, w])\\n dw1 = np.mean([w1, w])\\n\\n # Get the coordinates of all the pixels in that range\\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\\n\\n # Find the angle between the vertical and the tilted wavelength bin\\n if len(xx) >= 1:\\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\\n else:\\n angle = 0\\n\\n # Don't flip them upside down\\n angle = angle % 180\\n\\n # Add to the array\\n angles.append(angle)\\n\\n # Save the file\\n np.save(psf_file, np.array(angles))\\n print('Angles saved to', psf_file)\",\n \"def analyze(self):\\n # turn off all indicator lights\\n self._stop_all()\\n \\n # run, but catch exceptions and abort if necessary\\n try:\\n # setup\\n self.analysis_led[1].blink\\n ims_left = self.num_images\\n fluid_left = True\\n \\n data_session = Data(self.data_path)\\n \\n # run motor & imaging\\n while self.power.update() and ims_left > 0:\\n # run pump\\n self.motor.run(self.pump_runtime)\\n \\n if not self.power.update():\\n break\\n \\n # image\\n time.sleep(self.rest_time)\\n self.cam_led.on\\n self.camera.capture()\\n data_session.fetch_data()\\n self.cam_led.off\\n \\n # subtract from remaining images every cycle\\n # if the fluid sensor turns off, set remaining\\n # images to the maximum possible remaining\\n ims_left -= 1\\n if fluid_left and \\\\\\n not self.fluid.update() and \\\\\\n ims_left > self.samps_after_sensor_off:\\n fluid_left = False\\n ims_left = self.samps_after_sensor_off\\n \\n # change indicator lights, given complete or power off\\n if ims_left == 0:\\n # set analysis to green\\n self.analysis_led[1].off\\n self.analysis_led[0].on\\n else:\\n # set analysis to solid red\\n self.analysis_led[1].on\\n \\n # transmit data whether or not power switched off\\n self.data_led.blink\\n data = data_session.prepare_broadcast()\\n broadcast_session = Broadcast(self.peer_ip)\\n broadcast_session.broadcast_data(data)\\n self.data_led.off\\n \\n except:\\n # turn on error indicator and turn off all else\\n # do not transmit data\\n self._stop_all()\\n self.error.on\",\n \"def track_iou(detections, sigma_l, sigma_h, sigma_iou, t_min, ttl, mom_alpha=0.95, exp_zoom=1.05, min_area=0):\\n\\n tracks_active = []\\n\\n for frame_num, detections_frame in enumerate(detections, start=1):\\n # apply low threshold to detections\\n dets = [det for det in detections_frame if det['score'] >= sigma_l]\\n\\n updated_tracks = []\\n for track in tracks_active:\\n f_dets = list(filter(lambda det: area(det['bbox']) > min_area, dets))\\n if len(f_dets) > 0:\\n # get det with highest iou and similarity score\\n best_match = max(f_dets, key=lambda x: iou(predict_bbox(exp_zoom, track), x['bbox']))\\n if iou(track['bboxes'][-1], best_match['bbox']) >= sigma_iou:\\n track['bboxes'].append(best_match['bbox'])\\n track['max_score'] = max(track['max_score'], best_match['score'])\\n if track['inactive'] > 0:\\n track['inactive'] = 0\\n elif len(track['bboxes']) > 1:\\n mom = track['momentum']\\n prev_bbox = np.array(track['bboxes'][-2]).reshape(2, 2)\\n bbox = np.array(track['bboxes'][-1]).reshape(2, 2)\\n\\n track['momentum'] = mom * mom_alpha + (bbox - prev_bbox) * (1 - mom_alpha)\\n\\n updated_tracks.append(track)\\n\\n # remove from best matching detection from detections\\n del dets[dets.index(best_match)]\\n\\n # if track was not updated, use momentum for 'ttl' frames\\n if len(updated_tracks) == 0 or track is not updated_tracks[-1]:\\n # if the track's ttl is over\\n if track['inactive'] > ttl:\\n # finish track when the conditions are met\\n if track['max_score'] >= sigma_h and len(track['bboxes']) - track['inactive'] >= t_min:\\n if track['inactive'] > 0:\\n del track['bboxes'][-track['inactive']:]\\n yield track\\n else:\\n # else use momentum\\n # move the bbox and zoom it\\n moved_bbox = predict_bbox(exp_zoom, track)\\n\\n track['bboxes'].append(moved_bbox)\\n track['inactive'] += 1\\n\\n updated_tracks.append(track)\\n\\n # create new tracks if box large enough\\n new_tracks = [\\n {\\n 'bboxes': [det['bbox']],\\n 'max_score': det['score'],\\n 'start_frame': frame_num,\\n 'momentum': np.zeros((2, 2)),\\n 'inactive': 0,\\n }\\n for det in dets if area(det['bbox']) > min_area\\n ]\\n tracks_active = updated_tracks + new_tracks\\n\\n # finish all remaining active tracks\\n for track in tracks_active:\\n if track['max_score'] >= sigma_h and len(track['bboxes']) >= t_min:\\n yield track\",\n \"def system_6(in_dir, out_dir, threshold, num_frames, num_prev_frames, blend_coef, blur=(3,3), as_numeric=True, stretched=True):\\n pass\",\n \"def trajectory_error_correcter(trajectories):\\r\\n\\r\\n n_birds, n_paramaters, n_time_steps = np.shape(trajectories)\\r\\n\\r\\n for i in range(n_birds):\\r\\n if squared_distance_calculator(trajectories[i, :, 1],\\r\\n trajectories[i, :, 0]) > 1.5 * min(squared_distance_calculator(\\r\\n trajectories[i, :, 1], trajectories[i, :, 2]), squared_distance_calculator(\\r\\n trajectories[i, :, 2], trajectories[i, :, 3]), squared_distance_calculator(\\r\\n trajectories[i, :, 3], trajectories[i, :, 4])):\\r\\n for l in range(n_birds):\\r\\n if squared_distance_calculator(trajectories[i, :, 0],\\r\\n trajectories[l, :, 1]) < 1.5 * min(squared_distance_calculator(\\r\\n trajectories[i, :, 1], trajectories[i, :, 2]), squared_distance_calculator(\\r\\n trajectories[i, :, 2], trajectories[i, :, 3]), squared_distance_calculator(\\r\\n trajectories[i, :, 3], trajectories[i, :, 4])):\\r\\n trajectories[i, :, :], trajectories[l, :, :] = trajectory_switcher(trajectories[i, :, :],\\r\\n trajectories[l, :, :], 1)\\r\\n break\\r\\n for j in range(2, n_time_steps):\\r\\n if squared_distance_calculator(trajectories[i, :, j - 1],\\r\\n trajectories[i, :, j]) > 1.5 * squared_distance_calculator(\\r\\n trajectories[i, :, j - 1], trajectories[i, :, j - 2]):\\r\\n for l in range(n_birds):\\r\\n if squared_distance_calculator(trajectories[i, :, j - 1],\\r\\n trajectories[l, :, j]) < 2 * squared_distance_calculator(\\r\\n trajectories[i, :, j - 1], trajectories[i, :, j - 2]):\\r\\n trajectories[i, :, :], trajectories[l, :, :] = trajectory_switcher(trajectories[i, :, :],\\r\\n trajectories[l, :, :], j)\\r\\n break\\r\\n return trajectories\",\n \"def build_r_map(input_file: str, output_file: str, threshold: float):\\n\\n DataSiPM = db.DataSiPMsim_only('petalo', 0) # full body PET\\n DataSiPM_idx = DataSiPM.set_index('SensorID')\\n\\n try:\\n sns_response = pd.read_hdf(input_file, 'MC/sns_response')\\n except ValueError:\\n print(f'File {input_file} not found')\\n exit()\\n except OSError:\\n print(f'File {input_file} not found')\\n exit()\\n except KeyError:\\n print(f'No object named MC/sns_response in file {input_file}')\\n exit()\\n print(f'Analyzing file {input_file}')\\n\\n sel_df = rf.find_SiPMs_over_threshold(sns_response, threshold)\\n\\n particles = pd.read_hdf(input_file, 'MC/particles')\\n hits = pd.read_hdf(input_file, 'MC/hits')\\n events = particles.event_id.unique()\\n\\n true_r1, true_r2 = [], []\\n var_phi1, var_phi2 = [], []\\n var_z1, var_z2 = [], []\\n\\n touched_sipms1, touched_sipms2 = [], []\\n\\n for evt in events:\\n\\n ### Select photoelectric events only\\n evt_parts = particles[particles.event_id == evt]\\n evt_hits = hits [hits .event_id == evt]\\n select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)\\n if not select: continue\\n\\n sns_resp = sel_df[sel_df.event_id == evt]\\n if len(sns_resp) == 0: continue\\n\\n _, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(sns_resp, true_pos, DataSiPM_idx)\\n\\n if len(pos1) > 0:\\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:,1]\\n _, var_phi = rf.phi_mean_var(pos_phi, q1)\\n\\n pos_z = np.array(pos1)[:,2]\\n mean_z = np.average(pos_z, weights=q1)\\n var_z = np.average((pos_z-mean_z)**2, weights=q1)\\n r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)\\n\\n var_phi1 .append(var_phi)\\n var_z1 .append(var_z)\\n touched_sipms1.append(len(pos1))\\n true_r1 .append(r)\\n\\n else:\\n var_phi1 .append(1.e9)\\n var_z1 .append(1.e9)\\n touched_sipms1.append(1.e9)\\n true_r1 .append(1.e9)\\n\\n if len(pos2) > 0:\\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos2))[:,1]\\n _, var_phi = rf.phi_mean_var(pos_phi, q2)\\n\\n pos_z = np.array(pos2)[:,2]\\n mean_z = np.average(pos_z, weights=q2)\\n var_z = np.average((pos_z-mean_z)**2, weights=q2)\\n r = np.sqrt(true_pos[1][0]**2 + true_pos[1][1]**2)\\n\\n var_phi2 .append(var_phi)\\n var_z2 .append(var_z)\\n touched_sipms2.append(len(pos2))\\n true_r2 .append(r)\\n\\n else:\\n var_phi2 .append(1.e9)\\n var_z2 .append(1.e9)\\n touched_sipms2.append(1.e9)\\n true_r2 .append(1.e9)\\n\\n a_true_r1 = np.array(true_r1)\\n a_true_r2 = np.array(true_r2)\\n a_var_phi1 = np.array(var_phi1)\\n a_var_phi2 = np.array(var_phi2)\\n a_var_z1 = np.array(var_z1)\\n a_var_z2 = np.array(var_z2)\\n\\n a_touched_sipms1 = np.array(touched_sipms1)\\n a_touched_sipms2 = np.array(touched_sipms2)\\n\\n\\n np.savez(output_file, a_true_r1=a_true_r1, a_true_r2=a_true_r2, a_var_phi1=a_var_phi1, a_var_phi2=a_var_phi2, a_var_z1=a_var_z1, a_var_z2=a_var_z2, a_touched_sipms1=a_touched_sipms1, a_touched_sipms2=a_touched_sipms2)\",\n \"def mic_of_simulation(trajectories):\\n\\n avpvipsol = trajectories[:, 1:(160+1)]\\n navsol = trajectories[:, (160+1):]\\n\\n per2 = np.hstack([avpvipsol[:, ::4], navsol[:, ::3]])\\n numcells = per2.shape[1]\\n\\n # set up mic calculator\\n mic = mp.MINE(alpha=0.6, c=15, est='mic_approx')\\n mic_values = []\\n for combo in combinations(range(numcells), 2):\\n mic.compute_score(per2[:, combo[0]], per2[:, combo[1]])\\n mic_values.append(mic.mic())\\n\\n return mic_values\",\n \"def post_process_trap(): \\n #################### 0) assign internal values #################### \\n from project_parameters import trapType,debug,trapFile,name,driveAmplitude,driveFrequency,Omega,dcplot,weightElectrodes,coefs,ax,az,phi,save,scale\\n #from all_functions import find_saddle,plot_potential,dc_potential,set_voltages,exact_saddle,spher_harm_bas,spher_harm_exp,pfit,plotN\\n import pickle\\n\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n\\n qe = trap.configuration.charge\\n mass = trap.configuration.mass\\n Zval = trap.configuration.position\\n r0 = trap.configuration.r0\\n RFampl = driveAmplitude \\n V0 = mass*(2*np.pi*Omega)**2*(r0*10**-3)**2/qe\\n X,Y,Z=trap.instance.X,trap.instance.Y,trap.instance.Z \\n data = trap.configuration\\n dcVoltages = set_voltages()\\n ne = len(weightElectrodes)\\n E = trap.instance.E\\n out = trap.configuration\\n if debug.post_process_trap:\\n print dcVoltages,np.max(dcVoltages)#np.sum(abs(dcVoltages))\\n plotN(dcVoltages,trap,'set DC voltages') \\n Vdc = dc_potential(trap,dcVoltages,E)\\n #[IDC,JDC,KDC] = find_saddle(Vdc,X,Y,Z,3,Zval) \\n #[XDC,YDC,ZDC] = exact_saddle(Vdc,X,Y,Z,3,Zval)\\n #XDC,YDC,ZDC = X[IDC],150/scale,Z[KDC]\\n #print XDC,YDC,ZDC,IDC,JDC,KDC\\n #dcbasis,dcscale= spher_harm_bas(XDC,YDC,ZDC,X,Y,Z,4)\\n #QQ = spher_harm_exp(Vdc,dcbasis,dcscale) \\n #print QQ[0:9].T\\n #1) RF Analysis\\n print('RF Analysis') \\n Vrf = RFampl*data.EL_RF\\n [Irf,Jrf,Krf] = find_saddle(Vrf,X,Y,Z,2,Zval)\\n if debug.post_process_trap:\\n plot_potential(Vrf,X,Y,Z,dcplot,'weighted RF potential','V_{rf} (eV)',[Irf,Jrf,Krf])\\n #2) DC Analysis\\n print('DC Analysis')\\n trap = dc_potential(trap,dcVoltages,E,update=None)\\n Vdc = trap.instance.DC\\n [Idc,Jdc,Kdc] = find_saddle(Vdc,X,Y,Z,3,Zval) # only used to calculate error at end\\n if debug.post_process_trap:\\n plot_potential(Vdc,X,Y,Z,'1D plots','full DC potential')\\n #3) determine the exact saddles of the RF and DC\\n trap = dc_potential(trap,dcVoltages,E)\\n Vdc = trap.instance.DC\\n print('Determining exact RF saddle...')\\n [Xrf,Yrf,Zrf] = exact_saddle(Vrf,X,Y,Z,2,Zval) \\n print('Determining exact DC saddle...')\\n [Xdc,Ydc,Zdc] = exact_saddle(Vdc,X,Y,Z,3,Zval)\\n #4) determine stray field (beginning of justAnalyzeTrap)\\n print('Determining compensation due to E field...')\\n nx,ny,nz=X.shape[0],Y.shape[0],Z.shape[0]\\n x,y,z = np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz)),np.zeros((nx,ny,nz))\\n for i in range(nx):\\n for j in range(ny):\\n for k in range(nz):\\n x[i,j,k] = X[i]\\n y[i,j,k] = Y[j]\\n z[i,j,k] = Z[k]\\n VlessE = Vdc-E[0]*x-E[1]*y-E[2]*z\\n [Xdc,Ydc,Zdc] = exact_saddle(VlessE,X,Y,Z,3) \\n dist = np.sqrt((Xrf-Xdc)**2+(Yrf-Ydc)**2+(Zrf-Zdc)**2) \\n #5) call pfit to built teh total field and determine the trap characteristics\\n [fx,fy,fz,theta,Depth,Xe,Ye,Ze] = pfit(Vrf,Vdc,X,Y,Z,Irf,Jrf,Krf)#pfit(trap,E,Freq,RFampl)\\n print('Stray field is ({0},{1},{2}) V/m.'.format(scale*E[0],scale*E[1],scale*E[2]))\\n print('With this field, the compensation is optimized to {} micron.'.format(scale*dist))\\n print('RF saddle: ({0},{1},{2})\\\\nDC saddle ({3},{4},{5}).'.format(Xrf,Yrf,Zrf,Xdc,Ydc,Zdc)) \\n if debug.trap_depth:\\n print('The trap escape position is at ({0},{1},{2}) microns, for a trap depth of {3} mV'.format(Xe*scale,Ye*scale,Ze*scale,Depth*scale))\\n print('The trap frequencies are fx = {0} MHz, fy = {1} MHz, and fz = {2} MHz'.format(fx*10**-6,fy*10**-6,fz*10**-6))\\n #6) Sanity testing; quality check no longer used\\n if debug.post_process_trap:\\n rfbasis,rfscale= spher_harm_bas(Xrf,Yrf,Zrf,X,Y,Z,2)\\n Qrf = spher_harm_exp(Vrf,rfbasis,rfscale) \\n if np.sqrt((Xrf-Xdc)**2+(Yrf-Ydc)**2+(Zrf-Zdc)**2)>0.008: \\n print('Expanding DC with RF for saniy checking.')\\n Qdc = spher_harm_exp(Vdc,rfbasis,rfscale) \\n else:\\n print('Expanding DC without RF for sanity checking.')\\n dcbasis,dcscale= spher_harm_bas(Xdc,Ydc,Zdc,X,Y,Z,2)\\n Qdc = spher_harm_exp(Vdc,dcbasis,dcscale) \\n Arf = 2*np.sqrt( (3*Qrf[7])**2+(3*Qrf[8])**2 )\\n Thetarf = 45*(Qrf[8]/abs(Qrf[8]))-90*np.arctan((3*Qrf[7])/(3*Qrf[8]))/np.pi\\n Adc = 2*np.sqrt( (3*Qdc[7])**2+(3*Qdc[8])**2 )\\n Thetadc = 45*(Qrf[8]/abs(Qrf[8]))-90*np.arctan((3*Qdc[7])/(3*Qdc[8]))/np.pi\\n out.E = E\\n out.miscompensation = dist\\n out.ionpos = [Xrf,Yrf,Zdc]\\n out.ionposIndex = [Irf,Jrf,Krf]\\n out.frequency = [fx,fy,fz]\\n out.theta = theta\\n out.trap_depth = Depth/qe \\n out.escapepos = [Xe,Ye,Ze]\\n out.Quadrf = 2*np.array([Qrf[7]*3,Qrf[4]/2,Qrf[8]*6,-Qrf[6]*3,-Qrf[5]*3])\\n out.Quaddc = 2*np.array([Qdc[7]*3,Qdc[4]/2,Qdc[8]*6,-Qdc[6]*3,-Qdc[5]*3])\\n out.Arf = Arf\\n out.Thetarf = Thetarf\\n out.Adc = Adc\\n out.Thetadc = Thetadc\\n T = np.array([[2,-2,0,0,0],[-2,-2,0,0,0],[0,4,0,0,0],[0,0,1,0,0],[0,0,0,1,0],[0, 0,0,0,1]])\\n Qdrf = out.Quadrf.T\\n Qddc = out.Quaddc.T\\n out.q = (1/V0)*T*Qdrf\\n out.alpha = (2/V0)*T*Qddc\\n out.Error = [X[Idc]-Xdc,Y[Jdc]-Ydc,Z[Kdc]-Zdc]\\n #7) update the trapping field data structure with instance attributes\\n trap.configuration=out\\n trap.instance.driveAmplitude = driveAmplitude\\n trap.instance.driveFrequency = driveFrequency\\n trap.instance.coefs = coefs\\n trap.instance.ax = ax\\n trap.instance.az = az\\n trap.instance.phi = phi\\n trap.instance.ppt = True\\n trap.instance.out = out\\n if save==True:\\n print('Saving '+trapFile+' as a data structure...')\\n with open(trapFile,'wb') as f:\\n pickle.dump(trap,f)\\n return 'post_proccess_trap complete' #out # no output needed really\",\n \"def qc_illumina(args):\\n clarity_epp.qc.illumina.set_avg_q30(lims, args.process_id)\",\n \"def filter_and_correct_expression_and_image_features(tissue, model, aggregation, patch_size, M, k, pc_correction=False, tf_correction=False):\\n\\n\\n\\n\\n # Filter expression\\n Y, X, dIDs, tIDs, tfs, ths, t_idx = extract_final_layer_data(tissue, model, aggregation, patch_size)\\n filt_X, filt_tIDs, final_exp_idx = filter_expression(X, tIDs, M, k)\\n\\n\\n\\n if pc_correction:\\n print ('Correcting with {} expression PCs'.format(pc_correction))\\n pca = PCA(n_components=pc_correction)\\n\\n\\n pca_predictors = pca.fit_transform(filt_X)\\n\\n # Correct Y\\n lr = LinearRegression()\\n lr.fit(pca_predictors, Y)\\n predicted_Y = lr.predict(pca_predictors)\\n corrected_Y = Y - predicted_Y\\n\\n # Correct X\\n projected_filt_X = np.dot(pca_predictors,pca.components_)\\n corrected_filt_X = filt_X - projected_filt_X\\n\\n # Set as return variables\\n final_X = corrected_filt_X\\n final_Y = corrected_Y\\n\\n elif tf_correction:\\n print('Correcting with all technical factors')\\n tf_Y = Y[t_idx,:]\\n tf_filt_X = filt_X[t_idx,:]\\n\\n tfs[list(ths).index('SMTSISCH')] = np.log2(tfs[list(ths).index('SMTSISCH')] + 1)\\n tf_predictors = tfs\\n\\n #Correct Y\\n lr_Y = LinearRegression()\\n lr_Y.fit(tf_predictors, tf_Y)\\n tf_Y_predicted = lr_Y.predict(tf_predictors)\\n corrected_tf_Y = tf_Y - tf_Y_predicted\\n\\n #Correct X\\n lr_X = LinearRegression()\\n lr_X.fit(tf_predictors, tf_filt_X)\\n tf_filt_X_predicted = lr_X.predict(tf_predictors)\\n corrected_tf_filt_X = tf_filt_X - tf_filt_X_predicted\\n\\n # Set as return variables\\n final_X = corrected_tf_filt_X\\n final_Y = corrected_tf_Y\\n else:\\n # Set unmodified values as return variables\\n final_X = filt_X\\n final_Y = Y\\n\\n return final_Y, final_X, dIDs, filt_tIDs, tfs, ths, t_idx\",\n \"def main():\\n print(\\\"Program version: 1.5\\\")\\n StartTime = datetime.now()\\n args = parseArguments()\\n\\n verbose = args.verbose\\n images = args.images\\n ignore_warnings = args.ignore_warnings\\n if(args.silent):\\n verbose = False\\n images = False\\n ignore_warnings = True\\n\\n if(args.images):\\n plt.ioff()\\n\\n if(args.ignore_warnings):\\n warnings.simplefilter('ignore', UserWarning)\\n\\n #sample header keywords\\n # OBJECT = 'P016+03_P1_JKdeep' / Original target\\n # RA = ' 01:06:37.759' / 01:06:37.7 RA (J2000) pointing\\n # DEC = ' 03:32:36.096' / 03:32:36.0 DEC (J2000) pointing\\n # EQUINOX = 2000. / Standard FK5 (years)\\n # RADECSYS= 'FK5 ' / Coordinate reference frame\\n # CRVAL1 = 16.65733 / 01:06:37.7, RA at ref pixel\\n # CRVAL2 = 3.54336 / 03:32:36.0, DEC at ref pixel\\n # CRPIX1 = 447. /Ref pixel in X\\n # CRPIX2 = 452. / Ref pixel in Y\\n # CDELT1 = -8.0000000000000E-5 / SS arcsec per pixel in RA\\n # CDELT2 = 8.00000000000003E-5 / SS arcsec per pixel in DEC\\n # CTYPE1 = 'RA---TAN' / pixel coordinate system\\n # CTYPE2 = 'DEC--TAN' / pixel coordinate system\\n # PC1_1 = 0.000000 / Translation matrix element\\n # PC1_2 = 1.000000 / Translation matrix element\\n # PC2_1 = -1.000000 / Translation matrix element\\n # PC2_2 = 0.000000 / Translation matrix element\\n\\n fits_image_filenames = args.input\\n\\n #if directory given search for appropriate fits files\\n\\n if(os.path.isdir(fits_image_filenames[0])):\\n print(\\\"detected a directory. Will search for fits files in it\\\")\\n path = fits_image_filenames[0]\\n fits_image_filenames = []\\n for file in os.listdir(path):\\n if file.endswith(\\\".fits\\\") and \\\"_astro\\\" not in file:\\n fits_image_filenames.append(path+\\\"/\\\"+file)\\n print(fits_image_filenames)\\n\\n multiple = False\\n if(len(fits_image_filenames)>1):\\n multiple = True\\n not_converged = []\\n converged_counter = 0\\n for fits_image_filename in fits_image_filenames:\\n\\n result,_ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=0, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent =args.silent, sigma_threshold_for_source_detection= args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\\n\\n if((not result) and args.rotation_scaling):\\n print(\\\"Did not converge. Will try again with full rotation and scaling\\\")\\n result, _ = astrometry_script(fits_image_filename, catalog=args.catalog, rotation_scaling=args.rotation_scaling, xy_transformation=args.xy_transformation, fine_transformation=args.fine_transformation,\\n images=images, vignette=args.vignette,vignette_rectangular=args.vignette_rectangular, cutouts=args.cutout, ra=args.ra, dec=args.dec, projection_ra=args.projection_ra, projection_dec=args.projection_dec, verbose=verbose, save_images=args.save_images, ignore_header_rot=args.ignore_header_rot, radius = args.radius, save_bad_result=args.save_bad_result, silent=args.silent, sigma_threshold_for_source_detection=args.sigma_threshold_for_source_detection, high_res=args.high_resolution, hdul_idx=args.hdul_idx, filename_for_sources=args.filename_for_sources, FWHM=args.seeing)\\n\\n if(result):\\n print(\\\"Astrometry was determined to be good.\\\")\\n converged_counter = converged_counter+1\\n else:\\n print(\\\"Astrometry was determined to be bad.\\\")\\n not_converged.append(fits_image_filename)\\n if(args.save_bad_result):\\n print(\\\"Result was saved anyway\\\")\\n else:\\n print(\\\"Result was not saved.\\\")\\n # print(\\\"\\\")\\n # print(\\\">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\\\")\\n # print(\\\"> Astrometry for {} \\\".format(fits_image_filename))\\n #\\n # with fits.open(fits_image_filename) as hdul:\\n # #print(hdul.info())\\n # if(args.verbose):\\n # print(\\\"if image is not at first position in the fits file the program will break later on\\\")\\n # #print(hdul[0].header)\\n #\\n # hdu = hdul[0]\\n # #hdu.verify('fix')\\n # hdr = hdu.header\\n #\\n #\\n # image_or = hdul[0].data.astype(float)\\n # median = np.nanmedian(image_or)\\n # image_or[np.isnan(image_or)]=median\\n # image = image_or - median\\n #\\n # observation = find_sources(image, args.vignette)\\n # #print(observation)\\n #\\n # positions = (observation['xcenter'], observation['ycenter'])\\n # apertures = CircularAperture(positions, r=4.)\\n #\\n #\\n # #world coordinates\\n # print(\\\">Info found in the file -- (CRVAl: position of central pixel (CRPIX) on the sky)\\\")\\n # print(WCS(hdr))\\n #\\n # hdr[\\\"NAXIS1\\\"] = image.shape[0]\\n # hdr[\\\"NAXIS2\\\"] = image.shape[1]\\n #\\n # #wcsprm = Wcsprm(hdr.tostring().encode('utf-8')) #everything else gave me errors with python 3, seemed to make problems with pc conversios, so i wwitched to the form below\\n # wcsprm = WCS(hdr).wcs\\n # wcsprm_original = WCS(hdr).wcs\\n # if(args.verbose):\\n # print(WCS(wcsprm.to_header()))\\n # wcsprm, fov_radius, INCREASE_FOV_FLAG, PIXSCALE_UNCLEAR = read_additional_info_from_header(wcsprm, hdr, args.ra, args.dec, args.projection_ra, args.projection_dec)\\n # if(args.verbose):\\n # print(WCS(wcsprm.to_header()))\\n #\\n # #print(wcsprm)\\n # #wcsprm.pc = [[2, 0],[0,1]]\\n #\\n #\\n # #Possibly usefull examples of how to use wcsprm:\\n # #print(wcsprm.set())\\n # #print(wcsprm.get_pc())\\n # #pc = wcsprm.get_pc()\\n # #print(np.linalg.det(pc))\\n # #print(wcsprm.get_cdelt())\\n # #wcs.fix()\\n # #print(wcsprm.print_contents())\\n # #print(repr(hdr.update(wcsprm.to_header().encode('utf-8')))) #not working\\n #\\n # #hdu.verify(\\\"fix\\\")\\n # #print(repr(hdr))\\n # #wcs.wcs_pix2world(pixcrd, 1)\\n # #wcs.wcs_world2pix(world, 1)\\n # #wcs.wcs.crpix = [-234.75, 8.3393]\\n # # wcs.wcs.cdelt = np.array([-0.066667, 0.066667])\\n # # wcs.wcs.crval = [0, -90]\\n # # wcs.wcs.ctype = [\\\"RA---AIR\\\", \\\"DEC--AIR\\\"]\\n # # wcs.wcs.set_pv([(2, 1, 45.0)])\\n # # For historical compatibility, three alternate specifications of the linear transformations\\n # # are available in wcslib. The canonical PCi_ja with CDELTia, CDi_ja, and the deprecated CROTAia\\n # # keywords. Although the latter may not formally co-exist with PCi_ja,\\n # # the approach here is simply to ignore them if given in conjunction with PCi_ja.\\n # # has_pc, has_cd and has_crota can be used to determine which of these alternatives are present in the header.\\n # # These alternate specifications of the linear transformation matrix are translated immediately to PCi_ja by set\\n # # and are nowhere visible to the lower-level routines. In particular, set resets cdelt to unity if CDi_ja is present\\n # # (and no PCi_ja). If no CROTAia is associated with the latitude axis, set reverts to a unity PCi_ja matrix.\\n #\\n #\\n #\\n #\\n #\\n # #get rough coordinates\\n # #print(hdr[\\\"RA\\\"])\\n # #coord = SkyCoord(hdr[\\\"RA\\\"], hdr[\\\"DEC\\\"], unit=(u.hourangle, u.deg), frame=\\\"icrs\\\")\\n # coord = SkyCoord(wcsprm.crval[0], wcsprm.crval[1], unit=(u.deg, u.deg), frame=\\\"icrs\\\")\\n # if(not PIXSCALE_UNCLEAR):\\n # if(wcsprm.crpix[0] < 0 or wcsprm.crpix[1] < 0 or wcsprm.crpix[0] > image.shape[0] or wcsprm.crpix[1] > image.shape[1] ):\\n # print(\\\"central value outside of the image, moving it to the center\\\")\\n # coord_radec = wcsprm.p2s([[image.shape[0]/2, image.shape[1]/2]], 0)[\\\"world\\\"][0]\\n # coord = SkyCoord(coord_radec[0], coord_radec[1], unit=(u.deg, u.deg), frame=\\\"icrs\\\")\\n # #print(wcsprm)\\n #\\n #\\n #\\n # #better: put in nice wrapper! with repeated tries and maybe try synchron!\\n # print(\\\">Dowloading catalog data\\\")\\n # radius = u.Quantity(fov_radius, u.arcmin)#will prob need more\\n # catalog_data = query.get_data(coord, radius, args.catalog)\\n # #reference = reference.query(\\\"mag <20\\\")\\n # max_sources = 500\\n # if(INCREASE_FOV_FLAG):\\n # max_sources= max_sources*2.25 #1.5 times the radius, so 2.25 the area\\n # if(catalog_data.shape[0]>max_sources):\\n # catalog_data = catalog_data.nsmallest(400, \\\"mag\\\")\\n #\\n # if(args.catalog == \\\"GAIA\\\" and catalog_data.shape[0] < 5):\\n # print(\\\"GAIA seems to not have enough objects, will enhance with PS1\\\")\\n # catalog_data2 = query.get_data(coord, radius, \\\"PS\\\")\\n # catalog_data = pd.concat([catalog_data, catalog_data2])\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # print(\\\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\\\".format(catalog_data.shape[0]))\\n # elif(args.catalog == \\\"PS\\\" and (catalog_data is None or catalog_data.shape[0] < 5)):\\n # print(\\\"We seem to be outside the PS footprint, enhance with GAIA data\\\")\\n # catalog_data2 = query.get_data(coord, radius, \\\"GAIA\\\")\\n # catalog_data = pd.concat([catalog_data, catalog_data2])\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # print(\\\"Now we have a total of {} sources. Keep in mind that there might be duplicates now since we combined 2 catalogs\\\".format(catalog_data.shape[0]))\\n #\\n # #remove duplicates in catalog?\\n #\\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1)['pixcrd'], r=5.)\\n #\\n #\\n # #plotting what we have, I keep it in the detector field, world coordinates are more painfull to plot\\n # if(args.images):\\n # fig = plt.figure()\\n # fig.canvas.set_window_title('Input for {}'.format(fits_image_filename))\\n # plt.xlabel(\\\"pixel x direction\\\")\\n # plt.ylabel(\\\"pixel y direction\\\")\\n # plt.title(\\\"Input - red: catalog sources, blue: detected sources in img\\\")\\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\\n #\\n # plt.xlim(-200,image.shape[0]+200)\\n # plt.ylim(-200,image.shape[1]+200)\\n # if(args.save_images):\\n # name_parts = fits_image_filename.rsplit('.', 1)\\n # plt.savefig(name_parts[0]+\\\"_image_before.pdf\\\")\\n #\\n # ###tranforming to match the sources\\n # print(\\\"---------------------------------\\\")\\n # print(\\\">Finding the transformation\\\")\\n # if(args.rotation_scaling):\\n # print(\\\"Finding scaling and rotation\\\")\\n # wcsprm = register.get_scaling_and_rotation(observation, catalog_data, wcsprm, scale_guessed=PIXSCALE_UNCLEAR, verbose=args.verbose)\\n # if(args.xy_transformation):\\n # print(\\\"Finding offset\\\")\\n # wcsprm,_,_ = register.offset_with_orientation(observation, catalog_data, wcsprm, fast=False , INCREASE_FOV_FLAG=INCREASE_FOV_FLAG, verbose= args.verbose)\\n #\\n # #correct subpixel error\\n # obs_x, obs_y, cat_x, cat_y, distances = register.find_matches(observation, catalog_data, wcsprm, threshold=3)\\n # rms = np.sqrt(np.mean(np.square(distances)))\\n # best_score = len(obs_x)/(rms+10) #start with current best score\\n # fine_transformation = False\\n # if(args.fine_transformation):\\n # for i in [2,3,5,8,10,6,4, 20,2,1,0.5]:\\n # wcsprm_new, score = register.fine_transformation(observation, catalog_data, wcsprm, threshold=i)\\n # if(score> best_score):\\n # wcsprm = wcsprm_new\\n # best_score = score\\n # fine_transformation = True\\n # if not fine_transformation:\\n # print(\\\"Fine transformation did not improve result so will be discarded.\\\")\\n # else:\\n # print(\\\"Fine transformation applied to improve result\\\")\\n # #register.calculate_rms(observation, catalog_data,wcs)\\n #\\n # #make wcsprim more physical by moving scaling to cdelt, out of the pc matrix\\n # wcs =WCS(wcsprm.to_header())\\n # if(args.verbose):\\n # print(wcs)\\n #\\n # from astropy.wcs import utils\\n # scales = utils.proj_plane_pixel_scales(wcs)\\n # print(scales)\\n # cdelt = wcsprm.get_cdelt()\\n # print(cdelt)\\n # scale_ratio = scales/cdelt\\n # #print(scale_ratio)\\n # pc = np.array(wcsprm.get_pc())\\n # pc[0,0] = pc[0,0]/scale_ratio[0]\\n # pc[1,0] = pc[1,0]/scale_ratio[1]\\n # pc[0,1] = pc[0,1]/scale_ratio[0]\\n # pc[1,1] = pc[1,1]/scale_ratio[1]\\n # wcsprm.pc = pc\\n # wcsprm.cdelt = scales\\n # if(args.verbose):\\n # print(\\\"moved scaling info to CDelt\\\")\\n # print(WCS(wcsprm.to_header()))\\n #\\n # #WCS difference before and after\\n # print(\\\"> Compared to the input the Wcs was changed by: \\\")\\n # scales_original = utils.proj_plane_pixel_scales(WCS(hdr))\\n # print(\\\"WCS got scaled by {} in x direction and {} in y direction\\\".format(scales[0]/scales_original[0], scales[1]/scales_original[1]))\\n # #sources:\\n # #https://math.stackexchange.com/questions/2113634/comparing-two-rotation-matrices\\n # #https://stackoverflow.com/questions/2827393/angles-between-two-n-dimensional-vectors-in-python/13849249#13849249\\n # def unit_vector(vector):\\n # \\\"\\\"\\\" Returns the unit vector of the vector. \\\"\\\"\\\"\\n # return vector / max(np.linalg.norm(vector), 1e-10)\\n # def matrix_angle( B, A ):\\n # \\\"\\\"\\\" comment cos between vectors or matrices \\\"\\\"\\\"\\n # Aflat = A.reshape(-1)\\n # Aflat = unit_vector(Aflat)\\n # Bflat = B.reshape(-1)\\n # Bflat = unit_vector(Bflat)\\n # #return np.arccos((np.dot( Aflat, Bflat ) / max( np.linalg.norm(Aflat) * np.linalg.norm(Bflat), 1e-10 )))\\n # return np.arccos(np.clip(np.dot(Aflat, Bflat), -1.0, 1.0))\\n # #print(matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360)\\n # rotation_angle = matrix_angle(wcsprm.get_pc(), wcsprm_original.get_pc()) /2/np.pi*360\\n # if((wcsprm.get_pc() @ wcsprm_original.get_pc() )[0,1] > 0):\\n # text = \\\"counterclockwise\\\"\\n # else:\\n # text = \\\"clockwise\\\"\\n # print(\\\"Rotation of WCS by an angle of {} deg \\\".format(rotation_angle)+text)\\n # old_central_pixel = wcsprm_original.s2p([wcsprm.crval], 0)[\\\"pixcrd\\\"][0]\\n # print(\\\"x offset: {} px, y offset: {} px \\\".format(wcsprm.crpix[0]- old_central_pixel[0], wcsprm.crpix[1]- old_central_pixel[1]))\\n #\\n #\\n # #check final figure\\n # if(args.images):\\n # fig = plt.figure()\\n # fig.canvas.set_window_title('Result for {}'.format(fits_image_filename))\\n # plt.xlabel(\\\"pixel x direction\\\")\\n # plt.ylabel(\\\"pixel y direction\\\")\\n # plt.title(\\\"Result - red: catalog sources, blue: detected sources in img\\\")\\n # plt.imshow(image,cmap='Greys', origin='lower', norm=LogNorm())\\n # apertures.plot(color='blue', lw=1.5, alpha=0.5)\\n # #apertures_catalog = CircularAperture(wcs.wcs_world2pix(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1), r=5.)\\n # apertures_catalog = CircularAperture(wcsprm.s2p(catalog_data[[\\\"ra\\\", \\\"dec\\\"]], 1)['pixcrd'], r=5.)\\n #\\n # apertures_catalog.plot(color='red', lw=1.5, alpha=0.5)\\n # if(args.save_images):\\n # name_parts = fits_image_filename.rsplit('.', 1)\\n # plt.savefig(name_parts[0]+\\\"_image_after.pdf\\\")\\n #\\n # print(\\\"--- Evaluate how good the transformation is ----\\\")\\n # register.calculate_rms(observation, catalog_data,wcsprm)\\n #\\n #\\n # #updating file\\n # write_wcs_to_hdr(fits_image_filename, wcsprm)\\n #\\n #\\n # print(\\\"overall time taken\\\")\\n # print(datetime.now()-StartTime)\\n # if(args.images):\\n # plt.show()\\n if(multiple):\\n print(\\\">> Final report:\\\")\\n print(\\\"Processed {} files, {} of them did converge. The following files failed:\\\".format(len(fits_image_filenames), converged_counter))\\n print(not_converged)\\n print(\\\"-- finished --\\\")\",\n \"def analyze(ctx, filename, trigger, threshold, eyecandy, ignore_extra=False,\\n fix_missing=False, window_height=None, window_width=None, output=None, notebook=None,\\n calibration=None, distance=None, verbose=False, debug=False,processes=None,\\n by_channel=False, integrity_filter=0.0): \\n #### FILEPATHS\\n if not os.path.isfile(filename):\\n filename = match_filename(filename)\\n data_directory, data_name = os.path.split(filename)\\n name, extension = os.path.splitext(data_name)\\n analog_file = os.path.join(data_directory, name +'.analog')\\n stimulus_file = os.path.join(data_directory, name + \\\".stimulus\\\")\\n ctx.obj = {\\\"filename\\\": os.path.join(data_directory,name)}\\n\\n if not notebook:\\n notebook = find_notebook(data_directory)\\n\\n #### LOGGING CONFIGURATION\\n fh = logging.FileHandler(os.path.join(data_directory,name + '.log'))\\n fh.setLevel(logging.DEBUG)\\n ch = logging.StreamHandler()\\n if verbose:\\n ch.setLevel(logging.INFO)\\n # tracemalloc.start()\\n elif debug:\\n ch.setLevel(logging.DEBUG)\\n\\n else:\\n ch.setLevel(logging.WARNING)\\n if processes!=None:\\n config.processes = processes\\n formatter = logging.Formatter('%(asctime)s %(levelname)s - %(message)s', '%H:%M:%S')\\n fh.setFormatter(formatter)\\n ch.setFormatter(formatter)\\n logger.addHandler(fh)\\n logger.addHandler(ch)\\n logger.info(\\\"Verbose logging on\\\")\\n\\n lab_notebook = glia.open_lab_notebook(notebook)\\n experiment_protocol = glia.get_experiment_protocol(lab_notebook, name)\\n flicker_version = experiment_protocol[\\\"flickerVersion\\\"]\\n\\n\\n #### LOAD STIMULUS\\n try:\\n ctx.obj[\\\"stimulus_list\\\"] = glia.load_stimulus(stimulus_file)\\n except OSError:\\n print(\\\"No .stimulus file found. Attempting to create from .analog file.\\\".format(trigger))\\n if flicker_version==0.3:\\n ctx.obj[\\\"stimulus_list\\\"] = glia.create_stimulus_list(\\n analog_file, stimulus_file, notebook, name, eyecandy, ignore_extra,\\n calibration, distance, threshold)\\n print('finished creating stimulus list')\\n elif trigger == \\\"ttl\\\":\\n raise ValueError('not implemented')\\n else:\\n raise ValueError(\\\"invalid trigger: {}\\\".format(trigger))\\n\\n #### LOAD SPIKES\\n spyking_regex = re.compile('.*\\\\.result.hdf5$')\\n eye = experiment_protocol['eye']\\n experiment_n = experiment_protocol['experimentNumber']\\n\\n date = experiment_protocol['date'].date().strftime(\\\"%y%m%d\\\")\\n\\n retina_id = date+'_R'+eye+'_E'+experiment_n\\n if extension == \\\".txt\\\":\\n ctx.obj[\\\"units\\\"] = glia.read_plexon_txt_file(filename,retina_id, channel_map)\\n elif re.match(spyking_regex, filename):\\n ctx.obj[\\\"units\\\"] = glia.read_spyking_results(filename)\\n else:\\n raise ValueError('could not read {}. Is it a plexon or spyking circus file?')\\n\\n #### DATA MUNGING OPTIONS\\n if integrity_filter>0.0:\\n good_units = solid.filter_units_by_accuracy(\\n ctx.obj[\\\"units\\\"], ctx.obj['stimulus_list'], integrity_filter)\\n filter_good_units = glia.f_filter(lambda u,v: u in good_units)\\n ctx.obj[\\\"units\\\"] = filter_good_units(ctx.obj[\\\"units\\\"])\\n\\n if by_channel:\\n ctx.obj[\\\"units\\\"] = glia.combine_units_by_channel(ctx.obj[\\\"units\\\"])\\n\\n\\n # prepare_output\\n plot_directory = os.path.join(data_directory, name+\\\"-plots\\\")\\n config.plot_directory = plot_directory\\n\\n os.makedirs(plot_directory, exist_ok=True)\\n os.chmod(plot_directory, 0o777)\\n\\n if output == \\\"pdf\\\":\\n logger.debug(\\\"Outputting pdf\\\")\\n ctx.obj[\\\"retina_pdf\\\"] = PdfPages(glia.plot_pdf_path(plot_directory, \\\"retina\\\"))\\n ctx.obj[\\\"unit_pdfs\\\"] = glia.open_pdfs(plot_directory, list(ctx.obj[\\\"units\\\"].keys()), Unit.name_lookup())\\n # c connotes 'continuation'\\n ctx.obj[\\\"c_unit_fig\\\"] = partial(glia.add_to_unit_pdfs,\\n unit_pdfs=ctx.obj[\\\"unit_pdfs\\\"])\\n ctx.obj[\\\"c_retina_fig\\\"] = lambda x: ctx.obj[\\\"retina_pdf\\\"].savefig(x)\\n \\n elif output == \\\"png\\\":\\n logger.debug(\\\"Outputting png\\\")\\n ctx.obj[\\\"c_unit_fig\\\"] = glia.save_unit_fig\\n ctx.obj[\\\"c_retina_fig\\\"] = glia.save_retina_fig\\n os.makedirs(os.path.join(plot_directory,\\\"00-all\\\"), exist_ok=True)\\n\\n for unit_id in ctx.obj[\\\"units\\\"].keys():\\n name = unit_id\\n os.makedirs(os.path.join(plot_directory,name), exist_ok=True)\",\n \"def system_5(in_dir, out_dir, threshold, num_frames=150, num_prev_frames=10, blur=(3,3), as_numeric=True, stretched=True):\\n filenames = _prepare_filenames(in_dir, num_frames=150)\\n initial_background_model = np.array([cv2.imread(f) for f in filenames[0:num_prev_frames]])\\n seed_img = mode(initial_background_model)\\n previous_frames = deque(initial_background_model, maxlen=num_prev_frames)\\n\\n for i, f in tqdm(enumerate(filenames[num_prev_frames:])):\\n img = lm(cv2.imread(f))\",\n \"def check_cti(image, CTI, verbose=0):\\n\\n#\\n# Initialize ctiDict\\n#\\n ctiDict = {'isCTI': False}\\n ctiDict['expnum'] = image['EXPNUM']\\n\\n # Also create the BAND and NITE keywords if they are not present\\n try:\\n image['BAND']\\n except:\\n image['BAND'] = decaminfo.get_band(image['FILTER'])\\n try:\\n image['NITE']\\n except:\\n image['NITE'] = decaminfo.get_nite(image['DATE-OBS'])\\n\\n band = image['BAND'].strip()\\n sec = section2slice(image['DATASEC' + CTI['amp']])\\n#\\n# This could become useful if it is necessary to start examining the opposite amplifier in\\n# conjunction with the amplifier that is having a problem\\n#\\n# if (CTI['amp']==\\\"A\\\"):\\n# osec = section2slice(image['DATASEC'+'B'])\\n# else:\\n# osec = section2slice(image['DATASEC'+'A'])\\n\\n maxiter = 10\\n converge_num = 0.0001\\n clipsig = 3.0\\n\\n clip_avg, clip_med, clip_std = lb.medclip(image.data[sec], clipsig, maxiter, converge_num, verbose=0)\\n logger.info(' CTI: Global(clipped): median = {:.3f}, stddev = {:.3f} '.format(clip_med, clip_std))\\n ctiDict['cmed'] = float(clip_med)\\n ctiDict['cstd'] = float(clip_std)\\n clow = clip_med - (3.0 * clip_std)\\n ctiDict['clow'] = float(clow)\\n\\n# oclip_avg,oclip_med,oclip_std=medclip(image.data[osec],clipsig,maxiter,converge_num,verbose)\\n# print(\\\" Global(oclipped): median = {:.3f}, stddev = {:.3f} \\\".format(oclip_med,oclip_std))\\n# oclow=oclip_med-(3.0*oclip_std)\\n\\n#\\n# Obtain row-by-row median to look for horizontal striping (also needed to check/reject edgebleeds)\\n#\\n row_med = np.median(image.data[sec], axis=1)\\n wsm = np.where(row_med < clow)\\n nrow_low = row_med[wsm].size\\n#\\n# Hacky attempt to check for edge-bleed\\n#\\n iedge = [4, 4091]\\n while row_med[iedge[0]] < clow:\\n iedge[0] = iedge[0] + 1\\n while row_med[iedge[1]] < clow:\\n iedge[1] = iedge[1] - 1\\n if iedge[0] == 4:\\n iedge[0] = 0\\n if iedge[1] == 4091:\\n iedge[1] = 4095\\n nrow_edge = 4096 - (iedge[1] - iedge[0] + 1)\\n logger.info(' CTI: Number of low rows: {:d} (nrow_edge={:d}) '.format(nrow_low, nrow_edge))\\n\\n#\\n# Blank out pixels that are below the 3-sigma level with respect to median\\n# This removes power from vertical stripes\\n#\\n wsm = np.where(image.data[sec] < clow)\\n npix_low = image.data[sec][wsm].size\\n logger.info(' CTI: Number of low pixels: {:d} '.format(npix_low))\\n u = image.data[sec] - clip_med\\n u[wsm] = 0.0\\n#\\n# Harder cut currently not needed. If used this would get rid of all pixels below the median level\\n# (effectively this reduces the amount that noise suppresses contrast of the auto-correlation signal from CTI)\\n#\\n# wsm=np.where(u<0.)\\n# npix_zero=u[wsm].size\\n# logger.info(' CTI: Number of sub-zero pixels: {:d} '.format(npix_zero))\\n# u[wsm]=0.0\\n\\n#\\n# Calculate a set of auto-correlations by sampling lags in the x-direction and\\n# then two diaganol sets at PA=+/-45 degrees\\n# Note: y-direction lags would be succeptible to both bad columns and bleeds.\\n# These are normalized by the auto-correlation with lag 0 (defined as 'a' below).\\n# Take a maximum lag that will be calculated and use that to trim the image.\\n# Note: This both gets rid of most edge-effects automatically but also removes the need to calculate an effective normalization for higher lags\\n#\\n maxlag = 100\\n lagList = [0, 1, 3, 5, 7, 11, 15, 19, 23, 31, 37, 45]\\n\\n a = np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag:-maxlag, maxlag:-maxlag])\\n# b=np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag:-maxlag,maxlag:-maxlag])\\n x = [1.0]\\n d1 = [1.0]\\n d2 = [1.0]\\n# vx=[1.0]\\n# vd1=[1.0]\\n# vd2=[1.0]\\n#\\n# More lags than those sampled are needed because the diagonal (PA=+/-45) measures will need to be interpolated\\n# for comaparison to lags in the x-direction.\\n#\\n\\n for lag in lagList:\\n if lag != 0:\\n x.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag:-maxlag, maxlag - lag:-maxlag - lag]) / a)\\n d1.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag-lag:-maxlag - lag, maxlag - lag:-maxlag - lag]) / a)\\n d2.append(np.sum(u[maxlag:-maxlag, maxlag:-maxlag] * u[maxlag-lag:-maxlag - lag, maxlag + lag:-maxlag + lag]) / a)\\n# vx.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag:-maxlag,maxlag-lag:-maxlag-lag])/b)\\n# vd1.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag-lag:-maxlag-lag,maxlag-lag:-maxlag-lag])/b)\\n# vd2.append(np.sum(v[maxlag:-maxlag,maxlag:-maxlag]*v[maxlag-lag:-maxlag-lag,maxlag+lag:-maxlag+lag])/b)\\n\\n data = {'lag': np.array(lagList),\\n 'x': np.array(x),\\n 'd1': np.array(d1),\\n 'd2': np.array(d2)\\n# 'vx':np.array(vx),\\n# 'vd1':np.array(vd1),\\n# 'vd2':np.array(vd2)\\n }\\n\\n r2 = np.sqrt(2.0)\\n l1 = data['lag']\\n l2 = data['lag'] * r2\\n x1 = data['x']\\n d1i = np.interp(data['lag'], l2, data['d1'])\\n d2i = np.interp(data['lag'], l2, data['d2'])\\n rd1 = data['x'] / d1i\\n rd2 = data['x'] / d2i\\n\\n# vx1=data['vx']\\n# vd1i=np.interp(data['lag'],l2,data['vd1'])\\n# vd2i=np.interp(data['lag'],l2,data['vd2'])\\n# vrd1=data['vx']/vd1i\\n# vrd2=data['vx']/vd2i\\n## vdx=data['x']/data['vx']\\n# vdx=(rd1+rd2)/(vrd1+vrd2)\\n\\n logger.info(' CTI: lags {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(l1[3], l1[4], l1[6], l1[8], l1[10]))\\n logger.info(' CTI: lx {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(x1[3], x1[4], x1[6], x1[8], x1[10]))\\n logger.info(' CTI: d1i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(d1i[3], d1i[4], d1i[6], d1i[8], d1i[10]))\\n logger.info(' CTI: d2i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(d2i[3], d2i[4], d2i[6], d2i[8], d2i[10]))\\n logger.info(' CTI: ld1 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(rd1[3], rd1[4], rd1[6], rd1[8], rd1[10]))\\n logger.info(' CTI: ld2 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(rd2[3], rd2[4], rd2[6], rd2[8], rd2[10]))\\n# logger.info(' CTI: lvx {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vx1[3],vx1[4],vx1[6],vx1[8],vx1[10]))\\n# logger.info(' CTI:vd1i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vd1i[3],vd1i[4],vd1i[6],vd1i[8],vd1i[10]))\\n# logger.info(' CTI:vd2i {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vd2i[3],vd2i[4],vd2i[6],vd2i[8],vd2i[10]))\\n# logger.info(' CTI:vld1 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vrd1[3],vrd1[4],vrd1[6],vrd1[8],vrd1[10]))\\n# logger.info(' CTI:vld2 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vrd2[3],vrd2[4],vrd2[6],vrd2[8],vrd2[10]))\\n# logger.info(' CTI:vdx0 {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} '.format(vdx[3],vdx[4],vdx[6],vdx[8],vdx[10]))\\n\\n#\\n# Set band dependent thresholds...\\n# Note the criteria used are based on an empirical study of the one example we currently have (CCD=41, Y6)\\n#\\n nrow_lim = 5\\n if band != \\\"Y\\\":\\n cclim = 0.9\\n else:\\n cclim = 1.15\\n#\\n# Now check and set flag based on empirical critera.\\n# First are the horizontal streaks that can appear...\\n# Second are the comparison of the auto-correlation in the x and average of the diaganol directrions\\n#\\n flag_cti = False\\n if nrow_low - nrow_edge >= nrow_lim:\\n flag_cti = True\\n\\n avg_rd = (rd1 + rd2) / 2.0\\n if avg_rd[3] > cclim and avg_rd[4] > cclim and avg_rd[6] > cclim:\\n flag_cti = True\\n\\n if flag_cti:\\n ctiDict['isCTI'] = True\\n\\n return ctiDict\",\n \"def integrateObservation(self, img,action):\\n self.States.append(img)\\n self.Actions.append(action) \\n #self.printLevelScene()\",\n \"def motionCorrection(baseName, outDir, rawDataFile, granularity ='plane',\\n maxDisplacement=[40, 40],\\n nCpu=(multiprocessing.cpu_count() - 1), \\n exportFrames=True):\\n try:\\n # instead of os.makedirs use os.mkdir\\n # the former creates also intermediate dirs\\n os.mkdir(outDir + '/' + baseName)\\n except OSError:\\n print('Either directory exists or \\\\\\n the intermedite directories do not exist!')\\n raise\\n print(\\\"--Motion correction started with %s...\\\" % baseName)\\n # Checking if there are two cycles of images. Important to know otherwise \\n # you get merged motion corrected images.\\n allImages = (glob.glob(rawDataFile))\\n tseries_names = [os.path.basename(imageName).split('_')[0] for imageName in allImages]\\n uniqueTSeries = Counter(tseries_names).keys()\\n tseries_lengths = Counter(tseries_names).values()\\n \\n if len(uniqueTSeries) > 1:\\n print('---More than 1 T-Series found. Aligning them together...')\\n\\n match = [int(re.search(r'Cycle(.*?)_',imageName).group(1)) for imageName in allImages]\\n uniqueCycles = numpy.unique(numpy.asarray(match))\\n \\n if len(uniqueCycles) > 1:\\n warnstr='More than 1 image cycle detected. Aborting alignment.'\\n warnings.warn(warnstr)\\n return \\n sequence = [sima.Sequence.create('TIFFs', [[rawDataFile]])]\\n print(\\\"Creating sima dataset of non-aligned images\\\")\\n nonAlignedDatasetDir = outDir + '/' + baseName + '/' + 'TIFFs.sima'\\n sima.ImagingDataset(sequence, nonAlignedDatasetDir)\\n\\n print(\\\"Running motion correction.\\\")\\n \\n mc_approach = sima.motion.HiddenMarkov2D(granularity=granularity,\\n max_displacement=maxDisplacement,\\n verbose=True,\\n n_processes=nCpu)\\n print(\\\"Creating sima dataset of aligned images\\\")\\n motCorrDir = outDir + '/' + baseName + '/' + 'motCorr.sima'\\n dataset = mc_approach.correct(sequence, motCorrDir)\\n\\n if exportFrames:\\n print(\\\"Exporting motion-corrected movies.\\\")\\n \\n for iTSeries, curr_T_series in enumerate(uniqueTSeries):\\n \\n start_frame = tseries_names.index(curr_T_series)\\n end_frame = start_frame+tseries_lengths[iTSeries]\\n dataset[0,start_frame:end_frame].export_frames([[[os.path.join(motCorrDir,\\n '{t}_motCorr.tif'.format(t = curr_T_series))]]],\\n fill_gaps=True)\\n \\n\\n print(\\\"--Motion correction done with %s...\\\" % baseName)\\n\\n return uniqueTSeries\",\n \"def trajectory_error_correcter_improved(trajectories):\\r\\n\\r\\n n_birds, n_paramaters, n_time_steps = np.shape(trajectories)\\r\\n\\r\\n conditional_squared_distance = 3 * min(squared_distance_calculator(\\r\\n trajectories[0, :, 1], trajectories[0, :, 2]), squared_distance_calculator(\\r\\n trajectories[0, :, 2], trajectories[0, :, 3]), squared_distance_calculator(\\r\\n trajectories[0, :, 3], trajectories[0, :, 4]))\\r\\n\\r\\n difference_array = trajectories[:, :, 1:] - trajectories[:, :, :-1]\\r\\n squared_distance_array = np.sum(difference_array ** 2, axis=1) # creates array with shape (n_birds, n_time_steps-1)\\r\\n splits_array = squared_distance_array > conditional_squared_distance # Creates boolean array with True at location of splits\\r\\n splits_indices = np.array(np.nonzero(splits_array)) # Returns array with shape (n_axes, n_splits)\\r\\n\\r\\n counter = 0\\r\\n limit = 510000\\r\\n while len(splits_indices[0, :]) != 0 and counter < limit:\\r\\n counter += 1\\r\\n indices_of_birds_with_same_split = list(np.nonzero(splits_indices[1, :] == splits_indices[1, 0]))[0]\\r\\n position_of_first_bird = trajectories[splits_indices[0, 0], :, splits_indices[1, 0]]\\r\\n for count, i in enumerate(indices_of_birds_with_same_split):\\r\\n position_of_second_bird = trajectories[splits_indices[0, i], :, splits_indices[1, i] + 1]\\r\\n if squared_distance_calculator(position_of_first_bird,\\r\\n position_of_second_bird) < conditional_squared_distance:\\r\\n trajectories[splits_indices[0, 0], :, :], trajectories[splits_indices[0, i], :,\\r\\n :] = trajectory_switcher(\\r\\n trajectories[splits_indices[0, 0], :, :],\\r\\n trajectories[splits_indices[0, i], :, :], splits_indices[1, i] + 1)\\r\\n splits_array[splits_indices[0, 0], :], splits_array[splits_indices[0, i], :] = splits_array_switcher(\\r\\n splits_array[splits_indices[0, 0], :],\\r\\n splits_array[splits_indices[0, i], :], splits_indices[1, i])\\r\\n\\r\\n splits_array[splits_indices[0, 0], splits_indices[\\r\\n 1, 0]] = False # CHANGE SPLITS_ARRAY AT LOCATION OF SPLIT MANUALLY.\\r\\n # splits_array[splits_indices[0, i], splits_indices[1, i]] = False\\r\\n splits_indices = np.array(np.nonzero(splits_array))\\r\\n break\\r\\n if counter%4000 == 0:\\r\\n print(f\\\"{counter} - Corrections left: {len(splits_indices[0, :])}\\\")\\r\\n if counter == limit:\\r\\n print(\\\"The trajectory correction failed\\\")\\r\\n return trajectories, False\\r\\n # print(f\\\"The number of corrections left is {len(splits_indices[0, :])}\\\")\\r\\n # trajectory_plotter(trajectories)\\r\\n return trajectories, True\",\n \"def trap_depth(V,X,Y,Z,Im,Jm,Km): \\n from project_parameters import debug,scale,position\\n #from all_functions import sum_of_e_field,spher_harm_bas,spher_harm_exp,spher_harm_cmp,find_saddle\\n def a(a,N):\\n \\\"\\\"\\\"Shortcut function to convert array x into a row vector.\\\"\\\"\\\" \\n a=np.ravel(a, order='F') # Same order\\n return a\\n N1,N2,N3=V.shape\\n N=N1*N2*N3\\n [Ex,Ey,Ez]=np.gradient(V,abs(X[1]-X[0])/scale,abs(Y[1]-Y[0])/scale,abs(Z[1]-Z[0])/scale)\\n E=np.sqrt(Ex**2+Ey**2+Ez**2)\\n # identify the escape position and height by checking each point\\n minElectricField=np.max(E) # initialize as maximum E field magnitude\\n distance=0\\n escapeHeight=1\\n escapePosition=[0,0,0]\\n [Im,Jm,Km] = find_saddle(V,X,Y,Z,3)\\n Vm = V[Im,Jm,Km]\\n for i in range(N1):\\n for j in range(N2):\\n for k in range(N3):\\n if E[i,j,k]<minElectricField:\\n distance=abs(np.sqrt((Im-i)**2+(Jm-j)**2+(Km-k)**2)) \\n if distance > 6:\\n minElectricField=E[i,j,k]\\n escapeHeight=V[i,j,k]\\n escapePosition=[i,j,k]\\n if debug.trap_depth: \\n print E[i,j,k],V[i,j,k],[i,j,k],distance\\n check=1 \\n if debug.trap_depth: \\n print minElectricField,escapeHeight,escapePosition,distance \\n if distance<check:\\n print('trap_depth.py: Escape point too close to trap minimum. Improve grid resolution or extend grid.')\\n if escapeHeight>0.2:\\n print('trap_depth.py: Escape point parameter too high. Improve grid resolution or extend grid.')\\n D=escapeHeight-Vm\\n [Ie,Je,Ke]=escapePosition\\n [Xe,Ye,Ze]=[X[Ie],Y[Je],Z[Ke]] \\n return [D,Xe,Ye,Ze]\",\n \"def pan_corr(file):\\n\\n # # infile = 'd:\\\\\\\\Projekti\\\\\\\\Satelit\\\\\\\\CO\\\\\\\\Razpis\\\\\\\\Flat field images_new2020\\\\\\\\flatfield\\\\\\\\NHDBflat_1D'\\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Jure_naloga_banje_raw_pyt\\\\\\\\NHDRGoreMorje_3D'\\n #\\n # # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\\n # in_ref = in_path + in_pan_ref_file\\n #\\n # inreffil = gdal.Open(in_ref)\\n # image_ref = inreffil.ReadAsArray()\\n # # size_ref = image_ref.shape\\n # # pix_count = size_ref[0]*size_ref[1]\\n #\\n # image_ref = image_ref[800:930, 1420:1640]\\n # size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n #\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # raz_g1 = 1\\n # raz_g2 = g1_avg/g2_avg\\n # raz_r1 = g1_avg/r1_avg\\n # raz_b1 = g1_avg/b1_avg\\n #\\n # avg = (g1+g2+r1+b1)/pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n raz_g1 = 1\\n raz_g2 = 1.0245196396115988\\n raz_r1 = 1.0131841989689434\\n raz_b1 = 1.0517113199247086\\n\\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Slo_PAN\\\\_26_30\\\\\\\\'\\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\\\"nhd\\\") and filename.lower().endswith(\\\"tif\\\")]\\n\\n \\n\\n \\n\\n # print('image', i)\\n in_ref=file\\n inreffil = gdal.Open(in_ref)\\n image_ref = inreffil.ReadAsArray()\\n size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n # pix_count = np.count_nonzero(image_ref)\\n # pix_count = 3664*650\\n\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # avg = (g1 + g2 + r1 + b1) / pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n # popravek\\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\\n\\n\\n for i in range(size_ref[0]):\\n for j in range(size_ref[1]):\\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\\n \\n _,_,_,_,P=return_flatfield_set_path(2)\\n P_flat=gdal_array.LoadFile(P)\\n \\n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \\n \\n # outout\\n \\n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\\n \\n out=os.path.abspath(file)+\\\"/corr/\\\"+os.path.basename(file)[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n \\n # out = in_ref[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\\n\\n outband = outRaster.GetRasterBand(1)\\n outband.WriteArray(im_p_pop)\\n outband.FlushCache()\",\n \"def light_measure_rn_weit(data, weit_data, pix_size, cx, cy, z0, R_low, R_up):\\n\\tDa0 = Test_model.angular_diameter_distance(z0).value\\n\\tR_pix_low = (R_low * 1e-3 * rad2arcsec / Da0) / pix_size\\n\\tR_pix_up = (R_up * 1e-3 * rad2arcsec / Da0) / pix_size\\n\\n\\tNx = data.shape[1]\\n\\tNy = data.shape[0]\\n\\tx0 = np.linspace(0, Nx-1, Nx)\\n\\ty0 = np.linspace(0, Ny-1, Ny)\\n\\tpix_id = np.array(np.meshgrid(x0,y0))\\n\\n\\t#..center pixel point\\n\\tdev_05_x = cx - np.int( cx )\\n\\tdev_05_y = cy - np.int( cy )\\n\\n\\tif dev_05_x > 0.5:\\n\\t\\txn = np.int( cx ) + 1\\n\\telse:\\n\\t\\txn = np.int( cx )\\n\\n\\tif dev_05_y > 0.5:\\n\\t\\tyn = np.int( cy ) + 1\\n\\telse:\\n\\t\\tyn = np.int( cy )\\n\\n\\tdr = np.sqrt(((2*pix_id[0] + 1) / 2 - (2*xn + 1) / 2)**2 + ((2*pix_id[1] + 1) / 2 - (2*yn + 1) / 2)**2)\\n\\tidu = (dr >= R_pix_low) & (dr <= R_pix_up)\\n\\n\\ttheta = np.arctan2((pix_id[1,:] - yn), (pix_id[0,:] - xn))\\n\\tchi = theta * 180 / np.pi\\n\\n\\tsamp_chi = chi[idu]\\n\\tsamp_flux = data[idu]\\n\\tweit_arr = weit_data[idu]\\n\\tIntns = np.nansum( samp_flux * weit_arr ) / np.nansum( weit_arr )\\n\\n\\tid_nn = np.isnan(samp_flux)\\n\\tN_pix = np.sum( id_nn == False )\\n\\tnsum_ratio = np.nansum(weit_arr) / np.sum( id_nn == False )\\n\\n\\tcdr = R_up - R_low\\n\\td_phi = ( cdr / (0.5 * (R_low + R_up) ) ) * 180 / np.pi\\n\\tN_phi = np.int(360 / d_phi) + 1\\n\\tphi = np.linspace(0, 360, N_phi)\\n\\tphi = phi - 180.\\n\\n\\ttmpf = []\\n\\tfor tt in range(len(phi) - 1):\\n\\t\\tidv = (samp_chi >= phi[tt]) & (samp_chi <= phi[tt + 1])\\n\\n\\t\\tset_samp = samp_flux[idv]\\n\\t\\tset_weit = weit_arr[idv]\\n\\n\\t\\tttf = np.nansum(set_samp * set_weit) / np.nansum( set_weit )\\n\\t\\ttmpf.append(ttf)\\n\\n\\t# rms of flux\\n\\ttmpf = np.array(tmpf)\\n\\tid_inf = np.isnan(tmpf)\\n\\ttmpf[id_inf] = np.nan\\n\\tid_zero = tmpf == 0\\n\\ttmpf[id_zero] = np.nan\\n\\n\\tid_nan = np.isnan(tmpf)\\n\\tid_fals = id_nan == False\\n\\tTmpf = tmpf[id_fals]\\n\\n\\tRMS = np.std(Tmpf)\\n\\tif len(Tmpf) > 1:\\n\\t\\tIntns_err = RMS / np.sqrt(len(Tmpf) - 1)\\n\\telse:\\n\\t\\tIntns_err = RMS\\n\\n\\t#Intns_r = (0.5 * (R_low + R_up) )\\n\\tcen_r = np.nansum(dr[idu] * weit_arr) / np.nansum( weit_arr ) * pix_size\\n\\tIntns_r = cen_r * Da0 * 1e3 / rad2arcsec\\n\\n\\tIntns, Intns_err = Intns / pix_size**2, Intns_err / pix_size**2\\n\\n\\treturn Intns, Intns_r, Intns_err, N_pix, nsum_ratio\",\n \"def preprocess_RSofia(img):\\n for i in range(480):\\n for j in range(200):\\n img[i,j]=0\\n for i in range(150):\\n for j in range(500):\\n img[i,j]=0 \\n for i in range(475):\\n for j in range(90):\\n img[i,img.shape[1]-1-j]=0\\n for i in range(90):\\n for j in range(465):\\n img[i,img.shape[1]-1-j]=0\\n return img\",\n \"def continue_with_exposure(self):\\r\\n # Allocate space to give to scan_until_abort, and name the two\\r\\n # rows appropriately.\\r\\n self.data_pair = self.cam.get_new_array(n_images=2)\\r\\n self.pump_probe_data = self.data_pair[0]\\r\\n self.probe_only_data = self.data_pair[1]\\r\\n # Keep track of which image will be updated next\\r\\n self.next_data_has_pump = True\\r\\n\\r\\n # Tell self.thread what to do when the camera has new images\\r\\n self.cam.new_images.connect(self.send_new_images)\\r\\n\\r\\n # Get the current array of wavelengths from cam\\r\\n self.wavelen_arr = self.cam.get_wavelen_array()\\r\\n\\r\\n # Queue a call to cam.scan_until_abort\\r\\n self.startAcq.emit(self.data_pair)\\r\\n\\r\\n # Tell listeners (plotting widgets) to start displaying data too\\r\\n self.startDisplay.emit()\",\n \"def main():\\n\\n rules = parse_input(get_input())\\n for part in [5, 18]:\\n image = np.array(START_PATTERN).astype(bool)\\n for i in range(part):\\n image = enlarge(image, rules)\\n count = sum(sum(ch for ch in row) for row in image)\\n\\n print(\\\"Number of # in the final matrix after {} iterations is {}.\\\".format(part, count))\\n return\",\n \"def image_processing(eye_frame, threshold):\\n kernel = np.ones((3, 3), np.uint8)\\n new_frame = cv2.bilateralFilter(eye_frame, 10, 15, 15)\\n new_frame = cv2.erode(new_frame, kernel, iterations=3)\\n new_frame = cv2.threshold(new_frame, threshold, 255, cv2.THRESH_BINARY)[1]\\n\\n return new_frame\",\n \"def process_image_for_heuristic_af(self, tile_key):\\n\\n # image provided as numpy array from dictionary,\\n # already cropped to 512x512\\n img = self.img[tile_key]\\n mean = int(np.mean(img))\\n # recast as int16 before mean subtraction\\n img = img.astype(np.int16)\\n img -= mean\\n # Autocorrelation:\\n norm = np.sum(img ** 2)\\n autocorr = fftconvolve(img, img[::-1, ::-1])/norm\\n height, width = autocorr.shape[0], autocorr.shape[1]\\n # Crop to 64-pixel central region:\\n autocorr = autocorr[int(height/2 - 32):int(height/2 + 32),\\n int(width/2 - 32):int(width/2 + 32)]\\n # Calculate coefficients:\\n fi = self.muliply_with_mask(autocorr, self.fi_mask)\\n fo = self.muliply_with_mask(autocorr, self.fo_mask)\\n apx = self.muliply_with_mask(autocorr, self.apx_mask)\\n amx = self.muliply_with_mask(autocorr, self.amx_mask)\\n apy = self.muliply_with_mask(autocorr, self.apy_mask)\\n amy = self.muliply_with_mask(autocorr, self.amy_mask)\\n # Check if tile_key already in dictionary:\\n if not (tile_key in self.foc_est):\\n self.foc_est[tile_key] = []\\n if not (tile_key in self.astgx_est):\\n self.astgx_est[tile_key] = []\\n if not (tile_key in self.astgy_est):\\n self.astgy_est[tile_key] = []\\n # Calculate single-image estimators for current tile key:\\n if len(self.foc_est[tile_key]) > 1:\\n self.foc_est[tile_key].pop(0)\\n self.foc_est[tile_key].append(\\n (fi - fo) / (fi + fo))\\n if len(self.astgx_est[tile_key]) > 1:\\n self.astgx_est[tile_key].pop(0)\\n self.astgx_est[tile_key].append(\\n (apx - amx) / (apx + amx))\\n if len(self.astgy_est[tile_key]) > 1:\\n self.astgy_est[tile_key].pop(0)\\n self.astgy_est[tile_key].append(\\n (apy - amy) / (apy + amy))\",\n \"def imageProcessing(filepath):\\n imagedata = []\\n for img in glob.glob(filepath):\\n edit_image = cv2.imread(img, cv2.IMREAD_GRAYSCALE)\\n edit_image = cv2.resize(255-edit_image, (28, 28))\\n\\n cv2.imwrite(img, edit_image)\\n (thresh, edit_image) = cv2.threshold(edit_image, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\\n while np.sum(edit_image[0]) == 0:\\n edit_image = edit_image[1:]\\n\\n while np.sum(edit_image[:,0]) == 0:\\n edit_image = np.delete(edit_image,0,1)\\n\\n while np.sum(edit_image[-1]) == 0:\\n edit_image = edit_image[:-1]\\n\\n while np.sum(edit_image[:,-1]) == 0:\\n edit_image = np.delete(edit_image,-1,1)\\n\\n rows,cols = edit_image.shape\\n if rows > cols:\\n factor = 20.0/rows\\n rows = 20\\n cols = int(round(cols*factor))\\n edit_image = cv2.resize(edit_image, (cols,rows))\\n else:\\n factor = 20.0/cols\\n cols = 20\\n rows = int(round(rows*factor))\\n edit_image = cv2.resize(edit_image, (cols, rows))\\n\\n colsPadding = (int(math.ceil((28-cols)/2.0)),int(math.floor((28-cols)/2.0)))\\n rowsPadding = (int(math.ceil((28-rows)/2.0)),int(math.floor((28-rows)/2.0)))\\n edit_image = np.lib.pad(edit_image,(rowsPadding,colsPadding),'constant')\\n shiftx,shifty = getBestShift(edit_image)\\n shifted = shift(edit_image,shiftx,shifty)\\n edit_image = shifted\\n edit_image = edit_image.flatten()\\n imagedata.append(edit_image)\\n return imagedata\",\n \"def system_7(seed_img_file, in_dir, out_dir, threshold, num_frames, num_prev_frames, blend_coef, blur=(3,3), as_numeric=True, stretched=True):\\n pass\",\n \"def autoAnalyze(self):\\n print(\\\"Perfoming full automatic analysis...\\\")\\n t1=time.perf_counter()\\n self.cleanUp()\\n self.figure_rois()\\n self.figure_roi_inspect_all()\\n self.figure_dGoR_roi(showEach=False,saveAs=self.folderSave+\\\"/avg.png\\\")\\n self.figure_dGoR_roi(showEach=True,saveAs=self.folderSave+\\\"/each.png\\\")\\n self.index()\\n print(\\\"analysis completed in %.02f sec\\\"%(time.perf_counter()-t1))\",\n \"def recompute_exit_pupil(self):\\r\\n\\r\\n rearZ = self.rear_z()\\r\\n if rearZ <= 0.0:\\r\\n print('Not focus')\\r\\n rearRadius = self.rear_aperture()\\r\\n samples = 1024 * 1024\\r\\n half = 2.0 * rearRadius\\r\\n proj_bmin, proj_bmax = ti.Vector([-half, -half]), ti.Vector([half, half])\\r\\n for i in range(pupil_interval_count):\\r\\n r0 = ti.cast(i, ti.f32) / pupil_interval_count * self.film_diagnal / 2.0\\r\\n r1 = ti.cast(i + 1, ti.f32) / pupil_interval_count * self.film_diagnal / 2.0\\r\\n bmin, bmax = make_bound2()\\r\\n count = 0\\r\\n for j in range(samples):\\r\\n u, v= ti.random(), ti.random()\\r\\n film_pos = ti.Vector([lerp(ti.cast(j, ti.f32)/samples, r0, r1), 0.0, 0.0])\\r\\n x, y = lerp(u, -half, half), lerp(v, -half, half)\\r\\n lens_pos = ti.Vector([x, y, rearZ])\\r\\n if inside_aabb(bmin, bmax, ti.Vector([x, y])):\\r\\n ti.atomic_add(count, 1)\\r\\n else:\\r\\n ok, _, _ = self.gen_ray_from_film(film_pos, (lens_pos - film_pos).normalized())\\r\\n if ok:\\r\\n bmin, bmax = bound_union_with(bmin,bmax, ti.Vector([x, y]))\\r\\n ti.atomic_add(count, 1)\\r\\n\\r\\n if count == 0:\\r\\n bmin, bmax = proj_bmin, proj_bmax\\r\\n\\r\\n # extents pupil bound\\r\\n delta = 2 * (proj_bmax - proj_bmin).norm() / ti.sqrt(samples)\\r\\n bmin -= delta\\r\\n bmax += delta\\r\\n\\r\\n self.exitPupilBoundMin[i] = bmin\\r\\n self.exitPupilBoundMax[i] = bmax\",\n \"def step_run(cls, image, config):\\n logger.info('Gain correction will be applied to %s', image)\\n\\n ret_code = cls.__call__(image)\\n return ret_code\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5652331","0.5335536","0.52658623","0.5259198","0.5201728","0.51909906","0.515129","0.5142113","0.5076965","0.5063984","0.5055916","0.49459288","0.4884683","0.48668435","0.4824002","0.482143","0.47895026","0.47749937","0.47587273","0.4741739","0.4710989","0.46998623","0.46991298","0.46979156","0.4692717","0.4690254","0.46848482","0.46795285","0.46666843","0.46552742","0.46539572","0.46506596","0.46461558","0.46368298","0.46339366","0.46324965","0.46317604","0.46298322","0.46297243","0.46284553","0.4627619","0.46249053","0.4621805","0.46157008","0.4607761","0.46051344","0.46003705","0.45951575","0.45918143","0.45905232","0.45862117","0.4585815","0.4582318","0.4579147","0.45787397","0.45777598","0.45760685","0.45703727","0.4568256","0.45583394","0.45569658","0.45555323","0.45550525","0.45546493","0.45470822","0.4547025","0.45453024","0.4544413","0.4542224","0.4541217","0.4540933","0.45391735","0.453897","0.453802","0.45259672","0.4519855","0.45186555","0.4518203","0.4516783","0.4515055","0.4507584","0.45060053","0.45056754","0.45049143","0.4497676","0.4496176","0.44961554","0.44916448","0.44911525","0.4489983","0.44887206","0.44873115","0.44793153","0.44784293","0.4476427","0.4475186","0.44735175","0.4470664","0.44679096","0.44641295"],"string":"[\n \"0.5652331\",\n \"0.5335536\",\n \"0.52658623\",\n \"0.5259198\",\n \"0.5201728\",\n \"0.51909906\",\n \"0.515129\",\n \"0.5142113\",\n \"0.5076965\",\n \"0.5063984\",\n \"0.5055916\",\n \"0.49459288\",\n \"0.4884683\",\n \"0.48668435\",\n \"0.4824002\",\n \"0.482143\",\n \"0.47895026\",\n \"0.47749937\",\n \"0.47587273\",\n \"0.4741739\",\n \"0.4710989\",\n \"0.46998623\",\n \"0.46991298\",\n \"0.46979156\",\n \"0.4692717\",\n \"0.4690254\",\n \"0.46848482\",\n \"0.46795285\",\n \"0.46666843\",\n \"0.46552742\",\n \"0.46539572\",\n \"0.46506596\",\n \"0.46461558\",\n \"0.46368298\",\n \"0.46339366\",\n \"0.46324965\",\n \"0.46317604\",\n \"0.46298322\",\n \"0.46297243\",\n \"0.46284553\",\n \"0.4627619\",\n \"0.46249053\",\n \"0.4621805\",\n \"0.46157008\",\n \"0.4607761\",\n \"0.46051344\",\n \"0.46003705\",\n \"0.45951575\",\n \"0.45918143\",\n \"0.45905232\",\n \"0.45862117\",\n \"0.4585815\",\n \"0.4582318\",\n \"0.4579147\",\n \"0.45787397\",\n \"0.45777598\",\n \"0.45760685\",\n \"0.45703727\",\n \"0.4568256\",\n \"0.45583394\",\n \"0.45569658\",\n \"0.45555323\",\n \"0.45550525\",\n \"0.45546493\",\n \"0.45470822\",\n \"0.4547025\",\n \"0.45453024\",\n \"0.4544413\",\n \"0.4542224\",\n \"0.4541217\",\n \"0.4540933\",\n \"0.45391735\",\n \"0.453897\",\n \"0.453802\",\n \"0.45259672\",\n \"0.4519855\",\n \"0.45186555\",\n \"0.4518203\",\n \"0.4516783\",\n \"0.4515055\",\n \"0.4507584\",\n \"0.45060053\",\n \"0.45056754\",\n \"0.45049143\",\n \"0.4497676\",\n \"0.4496176\",\n \"0.44961554\",\n \"0.44916448\",\n \"0.44911525\",\n \"0.4489983\",\n \"0.44887206\",\n \"0.44873115\",\n \"0.44793153\",\n \"0.44784293\",\n \"0.4476427\",\n \"0.4475186\",\n \"0.44735175\",\n \"0.4470664\",\n \"0.44679096\",\n \"0.44641295\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5542998"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":4697,"cells":{"query":{"kind":"string","value":"Returns intensity profile of 1d gaussian beam"},"document":{"kind":"string","value":"def gaussian1d(x, x0, w0, A, offset):\n if w0 == 0:\n return 0\n return A * np.exp(-2 * (x - x0) ** 2 / (w0 ** 2)) + offset"},"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 pvalue_gaussian(self):\n \n pv = 2 * stats.norm.sf(abs(self.TS_prime_obs), loc=0, scale=1)\n return(pv)","def gaussian(amp, fwhm, mean, x):\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def estimate_uni_gaussian(X):\n mu = mean(X, axis=0)\n sigma2 = var(X, axis=0)\n return mu, sigma2","def parzen_windowing_gaussian(self, img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n img = torch.clamp(img, 0, 1)\n img = img.reshape(img.shape[0], -1, 1) # (batch, num_sample, 1)\n weight = torch.exp(\n -self.preterm.to(img) * (img - self.bin_centers.to(img)) ** 2\n ) # (batch, num_sample, num_bin)\n weight = weight / torch.sum(weight, dim=-1, keepdim=True) # (batch, num_sample, num_bin)\n probability = torch.mean(weight, dim=-2, keepdim=True) # (batch, 1, num_bin)\n return weight, probability","def intensity(self):\r\n return np.power(prb.amplitude, 2)","def intensity(self) -> int:","def gaussian(amp, fwhm, mean):\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)","def generate_gaussian():\n amp = 10 * numpy.random.chisquare(3)\n width = numpy.random.chisquare(3)\n mean = numpy.random.uniform(-10 + width, 10 - width)\n x = numpy.linspace(-10, 10, 500)\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\n add_noise(y, 0.1)\n return x, y","def intensityLogGauss(sample,bins,beta):\r\n\r\n width=1/bins\r\n data,edges=np.histogram(sample,bins=bins,range=(0,1))\r\n distMat=width*np.array([[np.abs(i-j) for i in range(bins)] for j in range(bins)])\r\n\r\n model=pm.Model()\r\n\r\n print('building model')\r\n with model:\r\n\r\n beta=beta\r\n sigmaSq=pm.HalfNormal('sigmaSq',5)\r\n\r\n chol=np.sqrt(sigmaSq)*cholesky(pm.math.exp(-1.*distMat**2/(2.*beta**2))+1e-6*np.eye(bins))\r\n y=pm.Normal('gaussfield',mu=0,sigma=1,shape=bins)\r\n\r\n lam=pm.Deterministic('intensity',width*pm.math.exp(pm.math.dot(chol,y)))\r\n k=pm.Poisson('points',mu=lam,observed=data)\r\n\r\n print('model built, start sampling')\r\n trace=pm.sample(draws=1000, tune=500,chains=1)\r\n\r\n #pm.traceplot(trace,varnames=['intensity'])\r\n #plt.show()\r\n\r\n return trace","def uni_gaussian(X, mu, sigma2):\n p = (1 / sqrt(2 * pi * sigma2))\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\n\n def prod(x, y):\n return x * y\n p = array([[reduce(prod, el)] for el in p])\n\n return p","def estimate_gaussian_params(X):\n mu = X.mean(axis=0)\n var = X.std(axis=0)**2.0\n return mu,var","def gaussian(centre, k, intensity, xpos):\r\n\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))","def _FWHMGauss(sigma, pixel=12):\n return sigma*2*np.sqrt(2*np.log(2))*pixel","def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\n -np.power(\n (x - mu) / sigma, 2) / 2)","def calc_psf_fwhm_inpix_gaussian(arr):\n\tmodel = fit_gaussian(arr)\n\n\tsigma = max(model.y_stddev, model.x_stddev)\n\tfwhm = 2.355 * sigma\n\n\treturn fwhm","def gaussian_highpass(image):\n lowpass = ndimage.gaussian_filter(image, 2)\n highpass = image - lowpass\n return highpass","def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\n return blured","def gaussian_hp(image, sigma):\n row, col = image.shape\n H = np.zeros((row, col))\n for y in range(row):\n for x in range(col):\n D = np.sqrt((y-int(row/2))**2 + (x-int(col/2))**2)\n H[y,x] = np.exp(-D**2/(2*sigma**2))\n H_hp = (1-H)\n X = np.fft.fftshift(np.fft.fft2(image))\n Y = np.fft.fftshift((1+H_hp)*X)\n y = np.fft.ifft2(Y)\n return np.abs(y)","def smooth_gauss(image, variance=2, kernel_size=(9, 9)):\n return cv2.GaussianBlur(image, kernel_size, variance)","def EstimateIlluminationProfile(image, kernel_sigma): \n \n illumination_profile = Denoising(image, kernel_sigma);\n \n map_corr = np.ones(image.shape);\n map_corr = Denoising(map_corr, kernel_sigma);\n \n illumination_profile = illumination_profile / map_corr;\n \n return illumination_profile;","def gaus(x, A, mu, sigma):\n return A * np.exp(-(x - mu) ** 2 / (2. * sigma ** 2))","def gauss(x, gamma):\n return 1 / np.sqrt(2*np.pi) / gamma * np.exp(-(x/gamma)**2 / 2)","def gauss(x, *p):\n mu, sigma = p\n return (1 / (sigma * np.sqrt(2 * np.pi)) *\n np.exp(-(x - mu) ** 2 / (2. * sigma ** 2)))","def get_intensity_transformer(aug):\n\n def gamma_tansform(img):\n gamma_range = aug['aug']['gamma_range']\n if isinstance(gamma_range, tuple):\n gamma = np.random.rand() * (gamma_range[1] - gamma_range[0]) + gamma_range[0]\n cmin = img.min()\n irange = (img.max() - cmin + 1e-5)\n\n img = img - cmin + 1e-5\n img = irange * np.power(img * 1.0 / irange, gamma)\n img = img + cmin\n\n elif gamma_range == False:\n pass\n else:\n raise ValueError(\"Cannot identify gamma transform range {}\".format(gamma_range))\n return img\n\n return gamma_tansform","def gauss(self, X, xm, amp, w):\n return amp * np.exp(-((X - xm) / w) ** 2)","def gaussian(x, *parameters):\n position, sigma, amplitude, background = parameters\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background","def differenceOfGausssians(image,sigma0, sigma1,window_size, roi, out = None):\n return (vigra.filters.gaussianSmoothing(image,sigma0,window_size=window_size,roi = roi)-vigra.filters.gaussianSmoothing(image,sigma1,window_size=window_size,roi = roi))","def gaussian(p, x):\n #2008-09-11 15:11 IJC: Created for LINEPROFILE\n # 2011-05-18 11:46 IJC: Moved to analysis.\n # 2013-04-11 12:03 IJMC: Tried to speed things up slightly via copy=False\n # 2013-05-06 21:42 IJMC: Tried to speed things up a little more.\n\n if not isinstance(x, np.ndarray):\n x = array(x, dtype=float, copy=False)\n\n if len(p)==3:\n p = array(p, copy=True)\n p = concatenate((p, [0]))\n #elif len(p)==4:\n # p = array(p, copy=False)\n\n return p[3] + p[0]/(p[1]*sqrt(2*pi)) * exp(-(x-p[2])**2 / (2*p[1]**2))","def gaussian(x, mean, sigma):\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))","def gauss_seeing(npix = None,fwhm=None,e1=None,e2=None,scale=scale):\n fwhm = fwhm/scale\n M20 = 2.*(fwhm/2.35482)**2\n row,col = np.mgrid[-npix/2:npix/2,-npix/2:npix/2]\n rowc = row.mean()\n colc = col.mean()\n Mcc = 0.5*M20*(1+e1)\n Mrc = 0.5*e2*M20\n Mrr = 0.5*M20*(1-e1)\n rho = Mrc/np.sqrt(Mcc*Mrr)\n img = np.exp(-0.5/(1-rho**2)*(row**2/Mrr + col**2/Mcc - 2*rho*row*col/np.sqrt(Mrr*Mcc)))\n res = img/img.sum()\n return res","def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\n return 1 - gaussian_black(z, mu, sigma)","def gauss(x, *p):\n A, mu, sigma = p\n\n return A*np.exp(-(x-mu)**2/(2.*sigma**2))","def gaussian(x, amp, wid, cen):\n return amp*np.exp(-(x-cen)**2/(2*wid**2))","def gaussed_value(self):\n from random import gauss\n return sorted([0, int(gauss(self.value, self.sigma)), \\\n (self.size*8)-1])[1]","def grd_posterior_gaussian(self, ) -> Tuple[np.ndarray, np.ndarray]:\n xmin, xmax = self.x_range\n ymin, ymax = self.y_range\n\n mu = np.array([0, 0])\n sigma = np.zeros((2, 2))\n\n _sample = self._sample\n _prior = self.prior\n\n def mean_x(x: float, y: float):\n return x * _sample(x, y) * _prior.eval(x, y)\n\n def mean_y(x: float, y: float):\n return y * _sample(x, y) * _prior.eval(x, y)\n\n def var_x(x: float, y: float):\n return x * mean_x(x, y)\n\n def var_y(x: float, y: float):\n return y * mean_y(x, y)\n\n # def var_xy(x: float, y: float):\n # return x * mean_y(x, y)\n\n # First moment\n (mu[0], mu[1]) = (integrate.dblquad(mean_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\n integrate.dblquad(mean_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0])\n (sigma[0, 0], sigma[1, 1]) = \\\n (integrate.dblquad(var_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\n integrate.dblquad(var_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\n # integrate.dblquad(var_xy, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\n return mu, sigma","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 Gausian_response(img,sigma=1):\n \n # Gausian response\n img_sigma = zeros(img.shape)\n filters.gaussian_filter(img, (sigma,sigma), (0,0), img_sigma)\n \n return img_sigma","def __measurement_prob(angle, measurement, noise):\n return ParticleFilter.Gaussian(angle, noise, measurement)","def gaussian(x, mu, sigma):\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\n np.sqrt(2.0 * np.pi) / sigma)","def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\n U, V = fourier_meshgrid(image)\n D = fourier_distance(U, V)\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\n \n if kind == 'high' or kind == 'highpass':\n H = 1.0 - H\n \n return H","def estimateGaussian(X):\n\tmu = np.mean(X, axis=0)\n\tsigma2 = np.std(X, axis=0) ** 2\n\treturn mu, sigma2","def _prior_gaussian(self, x_start):\n batch_size = x_start.shape[0]\n t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)\n qt_mean, _, qt_log_variance = self.gaussian_q_mean_variance(x_start, t)\n kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)\n return mean_flat(kl_prior) / np.log(2.0)","def __call__( self, X, Y, Z):\n xb,yb,zb = self.transform( X,Y,Z)\n \n gauss = beam( xb,yb,zb, self.w[0], self.w[1], self.l)\n intensity = (2/np.pi)* self.mW/1000. /self.w[0]/self.w[1] *gauss # W um^-2\n \n return uL(self.l)*intensity","def gaussian(x, amp, cen, wid):\n return amp * exp (-(x-cen)**2/(2*wid**2))","def gauss(x,p):\n return np.exp((-(x - p[0])**2) / (2 * p[1]**2))","def get_pca_mean_beam(self):\n return self.get_pca_images()[0]","def gaussianPSF(shape, sigma):\n psf = dg.drawGaussiansXY(shape,\n numpy.array([0.5*shape[0]]),\n numpy.array([0.5*shape[1]]),\n sigma = sigma)\n return psf/numpy.sum(psf)","def gaussian_prior(self):\n self.prior = sps.multivariate_normal(self.m0,self.S0)","def test_estimate_parameters():\n # a red image\n image = numpy.zeros((3, 11, 11))\n image[0, :, :] = 255\n skin_filter.estimate_gaussian_parameters(image)\n assert (skin_filter.mean == [1.0, 0.0]).all(), \"mean for a red image is not OK\"\n assert (skin_filter.covariance == [[0.0, 0.0], [0.0, 0.0]]).all(), \"covariance for red image is not OK\"\n\n # a green image\n image = numpy.zeros((3, 11, 11))\n image[1, :, :] = 255\n skin_filter.estimate_gaussian_parameters(image)\n assert (skin_filter.mean == [0.0, 1.0]).all(), \"mean for a green image is not OK\"\n assert (skin_filter.covariance == [[0.0, 0.0], [0.0, 0.0]]).all(), \"covariance for green image is not OK\"","def pdf(x):\n return - np.exp(self.ks_gaussian.score_samples(x.reshape(1, -1)))","def gaussian_blur(self,img):\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)","def pseudo_flatfield(img_plane, sigma=5):\n filtered_img = gaussian_filter(img_plane, sigma)\n return img_plane / (filtered_img + 1)","def MyBaseMoments(p,q,img,gauss_sigma,gauss_centroid=None, gauss_g1=0., gauss_g2=0.):\n weight = galsim.Image(np.zeros_like(img.array))\n gauss = galsim.Gaussian(sigma=gauss_sigma*pixel_scale).shear(g1=gauss_g1,g2=gauss_g2)\n if gauss_centroid is None:\n gauss_centroid = img.true_center\n weight = gauss.drawImage(image=weight, scale=pixel_scale, method='no_pixel', use_true_center=True, offset=(gauss_centroid-img.true_center)*(1))\n x = np.linspace(img.xmin-img.center.x*0-gauss_centroid.x*1, img.xmax-img.center.x*0-gauss_centroid.x*1, img.xmax-img.xmin+1)+0.*0.5\n y = np.linspace(img.ymin-img.center.y*0-gauss_centroid.y*1, img.ymax-img.center.y*0-gauss_centroid.y*1, img.ymax-img.ymin+1)+0.*0.5\n X, Y = np.meshgrid(x,y)\n\n Q00 = np.sum(weight.array*img.array)\n Q10 = gauss_centroid.x + np.sum(X*weight.array*img.array)/Q00\n Q01 = gauss_centroid.y + np.sum(Y*weight.array*img.array)/Q00\n Q20 = np.sum((X**2)*weight.array*img.array)\n Q02 = np.sum((Y**2)*weight.array*img.array)\n\n monomial = 1.\n for pp in xrange(p):\n monomial *= X\n for qq in xrange(q):\n monomial *= Y\n Qpq = np.sum(monomial*weight.array*img.array) #/Q00\n\n return Qpq","def gaussian_kernel(training_ex, landmark, sigma=0.1):\n return np.exp(-(np.linalg.norm(training_ex - landmark) ** 2 / (2 * (sigma ** 2))))","def gaussian(mu, wid, x):\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)","def taper_visibility_gaussian(vis: Visibility, beam=None) -> Visibility:\n assert isinstance(vis, Visibility), vis\n \n if beam is None:\n raise ValueError(\"Beam size not specified for Gaussian taper\")\n uvdistsq = vis.u ** 2 + vis.v ** 2\n # See http://mathworld.wolfram.com/FourierTransformGaussian.html\n scale_factor = numpy.pi ** 2 * beam ** 2 / (4.0 * numpy.log(2.0))\n prior = vis.flagged_imaging_weight[:, :]\n wt = numpy.exp(-scale_factor * uvdistsq)\n vis.data['imaging_weight'][:, :] = vis.flagged_imaging_weight[:, :] * wt[:, numpy.newaxis]\n \n return vis","def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)","def get_input_vector(img):\n parts = partition_matrix(blurmap(img), 10)\n return numpy.array([numpy.mean(part) for part in parts],\n dtype=numpy.float32)","def gauss_pert(N,a):\n x = np.arange(0,N,1,float)\n y = x[:,np.newaxis]\n\n # Choose the location of the peak\n x0 = y0 = int(0.4*N)\n\n # Choose the fwhm, 'width' of the perturbation\n fwhm = N/15\n\n return a*np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)","def gaussian_filter(x):\n return _gaussian_filter(x, 3)","def gaussian_blur(self, img):\n kernel_size = self.gaussian_blur_params[\"kernel_size\"]\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)","def gaussian_proba_map(img):\n method = 'cv2.TM_CCOEFF_NORMED'\n sigmas = [41,31,21,11]\n out = np.zeros(img.shape)\n for sigma in sigmas:\n size=3*sigma\n template = gaussian(size,sigma)\n template/=template.max()\n template*=255\n template = template.astype(np.uint8)\n \n img2 = img.copy()\n meth = eval(method)\n # Apply template Matching\n res = cv2.matchTemplate(img2,template,meth)\n res = np.pad(res,size/2,mode='constant')\n to_replace = res>out\n out[to_replace] = res[to_replace]\n return out","def _gauss_pix(x, mean=0.0, sigma=1.0):\n x = (np.asarray(x, dtype=float) - mean) / (sigma*np.sqrt(2))\n dx = x[1]-x[0]\n if not np.allclose(np.diff(x), dx):\n raise ValueError('all pixels must have the same size')\n\n edges = np.concatenate([x-dx/2, x[-1:]+dx/2])\n assert len(edges) == len(x)+1\n\n y = scipy.special.erf(edges)\n return (y[1:] - y[:-1])/2","def sphere_l_intensity(img):\n pixels = []\n for j in range(0, img.shape[0]):\n for i in range(1, 40):\n pixels.append(img[j, i])\n\n return np.mean(pixels)","def estimateGaussian(X):\n mu = X.mean(0, keepdims=True).T\n sigma2 = X.var(0, keepdims=True).T\n return mu, sigma2","def gauss(x, x0, gamma):\n sigma = gamma / sqrt(2.0)\n \n A = 1/ (sigma * sqrt(2*pi))\n return (A * exp (-0.5 * (x-x0)**2/sigma**2))","def gaussint(x, mean=0.0, sigma=1.0):\n z = (x - mean) / (math.sqrt(2) * sigma)\n return (erf(z) + 1.0) / 2.0","def test_gaussian_filter():\n\n def rgb2gray(rgb):\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n\n return gray\n\n img = rgb2gray(np.array(Image.open('data/graf.png')))\n gx, x = gauss_module.gauss(4)\n gx = gx.reshape(1, gx.shape[0])\n gy = gx.reshape(gx.shape[1], gx.shape[0])\n smooth_img = conv2(img, gx * np.array(gy))\n\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\n\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))","def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])","def psfVal(ix, iy, x, y, sigma1, sigma2, b):\n return (math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma1**2) +\n b*math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma2**2))/(1 + b)","def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \n import scipy.ndimage.filters as flt\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)","def gaussian(self, amp_step, sigma_step):\n l = len(self.overlaid_x_axis)\n x = np.linspace(0, l, l) - l/2 # centre of data\n\n # This is new code to 'guess' the size of the Gaussian from the\n # existing data rather than from hard-coded numbers.\n # TODO: test this! Possibly link up to the get_windowed_data function\n # as it uses a lot of the same functionality\n trigger = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][0]\n trace = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][1]\n amplitude = max(trace) + amp_step\n diff = np.diff(trigger)\n stepvalue = 0.5\n if min(diff) > -1 * stepvalue or max(diff) < stepvalue:\n raise RangeError\n else:\n maxtrig = next(x for x in diff if x > stepvalue)\n mintrig = next(x for x in diff if x < -1 * stepvalue)\n edges = [np.where(diff == maxtrig)[0][0],\n np.where(diff == mintrig)[0][0]]\n half_trigger_length = (edges[1]-edges[0])\n sigma = half_trigger_length/4 + sigma_step\n\n gauss = self.ax2.plot(amplitude * np.exp(-x**2 / (2 * sigma**2)), 'r')\n self.overlaid_lines.append(gauss)\n self.draw()","def enhance(self, image):\n\n if self.random.uniform(0, 1) < self.p:\n return self.intensity_enhance(image)\n else:\n return image","def getIntensityS(self):\n return self._Esigma.intensity()","def features_sigma(img,\n sigma,\n intensity=True,\n edges=True,\n texture=True):\n\n features = []\n\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\n # print(gx.shape)\n #features.append(gx)\n gx = filters.gaussian(gx, sigma)\n gy = filters.gaussian(gy, sigma)\n\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\n\n del gx, gy\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Location features extracted using sigma= %f' % (sigma))\n\n img_blur = filters.gaussian(img, sigma)\n\n if intensity:\n features.append(img_blur)\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\n\n if edges:\n features.append(filters.sobel(img_blur))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Edge features extracted using sigma= %f' % (sigma))\n\n if texture:\n H_elems = [\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\n ]\n\n eigvals = feature.hessian_matrix_eigvals(H_elems)\n del H_elems\n\n for eigval_mat in eigvals:\n features.append(eigval_mat)\n del eigval_mat\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Texture features extracted using sigma= %f' % (sigma))\n\n logging.info(datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\"))\n logging.info('Image features extracted using sigma= %f' % (sigma))\n\n return features","def get_intensity_normalization(self):\n return self.intensity_normalize_image","def gaussian(mu, sigma, start, end):\r\n \r\n val = np.linspace(start, end, 100)\r\n a = 1/(sigma*np.pi)\r\n b = - 0.5 * np.power((mu - val)/sigma, 2)\r\n return a*np.exp(b)","def gausspix(x, mean=0.0, sigma=1.0):\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\n integrals = gaussint(edges, mean=mean, sigma=sigma)\n return integrals[1:] - integrals[0:-1]","def _fspecial_gauss_1d(self, size, sigma):\n coords = torch.arange(size).to(dtype=torch.float)\n coords -= size // 2\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\n g /= g.sum()\n return g.reshape(-1)","def phi_gauss(self,x,i):\n s = 0.1\n return np.exp(-(x-self.mu[i])**2/(2*s))","def area_of_gaussian(amp, fwhm):\n return amp * fwhm / 0.93943727869965132","def create_gaussian_array(self):\n\n # Fill array of size l x w with Gaussian Noise.\n terrain_length = int(ceil(self.length/self.resolution))\n terrain_width = int(ceil(self.width/self.resolution))\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\n\n # Filter the array to smoothen the variation of the noise\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\n\n return gaussian_array","def _fspecial_gauss_1d(self, size, sigma):\n coords = torch.arange(size).to(dtype=torch.float)\n coords -= size // 2\n\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\n g /= g.sum()\n\n return g.unsqueeze(0).unsqueeze(0)","def gaussian(window_size, sigma):\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\n return gauss/gauss.sum()","def gauss(x, mu, A, sigma):\n mu, A, sigma = np.atleast_2d(mu), np.atleast_2d(A), np.atleast_2d(sigma)\n val = (A / (sigma * np.sqrt(np.pi * 2)) *\n np.exp(-(x[:, None] - mu)**2 / (2 * sigma**2)))\n return val.sum(axis=-1)","def cf_profile(self):\n x = np.abs(self.gen_profile() / self.sam_sys_inputs['system_capacity'])\n return x","def gaussian_likelihood(input_, mu_, log_std):\n pre_sum = -0.5 * (((input_ - mu_) / (\n tf.exp(log_std) + EPS)) ** 2 + 2 * log_std + np.log(\n 2 * np.pi))\n return tf.reduce_sum(pre_sum, axis=1)","def prob(x):\n\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\n\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)","def gaussian_filter(stddev, array):\n\n return astropy.convolution.convolve(\n array, astropy.convolution.Gaussian2DKernel(stddev))","def getIntensity(self):\n return self.getIntensityS() + self.getIntensityP()","def flat_top_gaussian(a1_val, a2_val, sigma1, sigma2, w1_val, w2_val, w_val):\n gauss1 = a1_val * np.exp(-(w_val - w1_val)**4/(2 * sigma1**2))\n gauss2 = a2_val * np.exp(-(w_val - w2_val)**4/(2 * sigma2**4))\n sum_gauss = gauss1 + gauss2\n return sum_gauss","def prewitt(img):\n kernel1 = numpy.array([1.0, 0.0, -1.0])\n kernel2 = numpy.array([1.0, 1.0, 1.0])\n Gx1 = convRows(img, kernel1)\n Gx = convCols(Gx1, kernel2)\n Gy1 = convCols(img, kernel1)\n Gy = convRows(Gy1, kernel2)\n \n G = numpy.sqrt(Gx*Gx + Gy*Gy)\n \n return G","def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\r\n #width = sigma*(2*np.sqrt(2*np.log(2)))\r\n b = 1/(sigma*np.sqrt(2*np.pi))\r\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\r\n return A*f + y0","def profile(c_mat, mat_rows):\n\n profile_mat = c_mat / mat_rows\n\n return profile_mat","def get_map_gaussian_fit(self, map_type, i_seq):\n map_gaussians = self._map_gaussian_fits.get(map_type, None)\n if (map_gaussians is not None):\n return map_gaussians[i_seq]\n return None","def param_gauss(xdata_, *params_):\n scale_, mean_, cov_ = params_to_scale_mean_cov(params_)\n return scale_ * gaussian(xdata_, mean=mean_, cov=cov_)","def sphere_r_intensity(img):\n pixels = []\n for j in range(0, img.shape[0]):\n for i in range(1, 40):\n pixels.append(img[j, img.shape[1] - i])\n\n return np.mean(pixels)","def get_kernel_values(self, i, j, abscissa_array):\n x = np.zeros_like(abscissa_array)\n for m in range(self.n_gaussians):\n x += self.amplitudes[i, j, m] * \\\n norm.pdf((abscissa_array - self.means_gaussians[m]) \\\n / self.std_gaussian) / self.std_gaussian\n return x","def getIntensityP(self):\n return self._Epi.intensity()","def GaussianKernel(radius, std):\n size = 2 * radius + 1\n weight = torch.ones(size, size)\n weight.requires_grad = False\n for i in range(-radius, radius+1):\n for j in range(-radius, radius+1):\n dis = (i * i) + (j * j)\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\n weight = weight / weight.sum()\n return weight","def gauss_sample(mean, covariance):\n\n return None"],"string":"[\n \"def pvalue_gaussian(self):\\n \\n pv = 2 * stats.norm.sf(abs(self.TS_prime_obs), loc=0, scale=1)\\n return(pv)\",\n \"def gaussian(amp, fwhm, mean, x):\\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def estimate_uni_gaussian(X):\\n mu = mean(X, axis=0)\\n sigma2 = var(X, axis=0)\\n return mu, sigma2\",\n \"def parzen_windowing_gaussian(self, img: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\\n img = torch.clamp(img, 0, 1)\\n img = img.reshape(img.shape[0], -1, 1) # (batch, num_sample, 1)\\n weight = torch.exp(\\n -self.preterm.to(img) * (img - self.bin_centers.to(img)) ** 2\\n ) # (batch, num_sample, num_bin)\\n weight = weight / torch.sum(weight, dim=-1, keepdim=True) # (batch, num_sample, num_bin)\\n probability = torch.mean(weight, dim=-2, keepdim=True) # (batch, 1, num_bin)\\n return weight, probability\",\n \"def intensity(self):\\r\\n return np.power(prb.amplitude, 2)\",\n \"def intensity(self) -> int:\",\n \"def gaussian(amp, fwhm, mean):\\n return lambda x: amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)\",\n \"def generate_gaussian():\\n amp = 10 * numpy.random.chisquare(3)\\n width = numpy.random.chisquare(3)\\n mean = numpy.random.uniform(-10 + width, 10 - width)\\n x = numpy.linspace(-10, 10, 500)\\n y = amp * numpy.exp(- (x - mean) ** 2 / width ** 2)\\n add_noise(y, 0.1)\\n return x, y\",\n \"def intensityLogGauss(sample,bins,beta):\\r\\n\\r\\n width=1/bins\\r\\n data,edges=np.histogram(sample,bins=bins,range=(0,1))\\r\\n distMat=width*np.array([[np.abs(i-j) for i in range(bins)] for j in range(bins)])\\r\\n\\r\\n model=pm.Model()\\r\\n\\r\\n print('building model')\\r\\n with model:\\r\\n\\r\\n beta=beta\\r\\n sigmaSq=pm.HalfNormal('sigmaSq',5)\\r\\n\\r\\n chol=np.sqrt(sigmaSq)*cholesky(pm.math.exp(-1.*distMat**2/(2.*beta**2))+1e-6*np.eye(bins))\\r\\n y=pm.Normal('gaussfield',mu=0,sigma=1,shape=bins)\\r\\n\\r\\n lam=pm.Deterministic('intensity',width*pm.math.exp(pm.math.dot(chol,y)))\\r\\n k=pm.Poisson('points',mu=lam,observed=data)\\r\\n\\r\\n print('model built, start sampling')\\r\\n trace=pm.sample(draws=1000, tune=500,chains=1)\\r\\n\\r\\n #pm.traceplot(trace,varnames=['intensity'])\\r\\n #plt.show()\\r\\n\\r\\n return trace\",\n \"def uni_gaussian(X, mu, sigma2):\\n p = (1 / sqrt(2 * pi * sigma2))\\n p = p * exp(-power(X - mu, 2) / (2 * sigma2))\\n\\n def prod(x, y):\\n return x * y\\n p = array([[reduce(prod, el)] for el in p])\\n\\n return p\",\n \"def estimate_gaussian_params(X):\\n mu = X.mean(axis=0)\\n var = X.std(axis=0)**2.0\\n return mu,var\",\n \"def gaussian(centre, k, intensity, xpos):\\r\\n\\treturn intensity * np.exp(- np.power(k * (xpos - centre), 2))\",\n \"def _FWHMGauss(sigma, pixel=12):\\n return sigma*2*np.sqrt(2*np.log(2))*pixel\",\n \"def _gaussian_distribution(self, x: ndarray, mu: float, sigma: float) -> ndarray:\\n return 1 / (np.sqrt(2 * np.pi) * sigma) * np.exp(\\n -np.power(\\n (x - mu) / sigma, 2) / 2)\",\n \"def calc_psf_fwhm_inpix_gaussian(arr):\\n\\tmodel = fit_gaussian(arr)\\n\\n\\tsigma = max(model.y_stddev, model.x_stddev)\\n\\tfwhm = 2.355 * sigma\\n\\n\\treturn fwhm\",\n \"def gaussian_highpass(image):\\n lowpass = ndimage.gaussian_filter(image, 2)\\n highpass = image - lowpass\\n return highpass\",\n \"def fake_gaussian(img, vertical_horizontal_sigma, iter=3):\\n sigma_vertical, sigma_horizontal = vertical_horizontal_sigma\\n h_blured = box_filter1d(img, sigma_horizontal, horizontal=True, iter=iter)\\n blured = box_filter1d(h_blured, sigma_vertical, horizontal=False, iter=iter)\\n return blured\",\n \"def gaussian_hp(image, sigma):\\n row, col = image.shape\\n H = np.zeros((row, col))\\n for y in range(row):\\n for x in range(col):\\n D = np.sqrt((y-int(row/2))**2 + (x-int(col/2))**2)\\n H[y,x] = np.exp(-D**2/(2*sigma**2))\\n H_hp = (1-H)\\n X = np.fft.fftshift(np.fft.fft2(image))\\n Y = np.fft.fftshift((1+H_hp)*X)\\n y = np.fft.ifft2(Y)\\n return np.abs(y)\",\n \"def smooth_gauss(image, variance=2, kernel_size=(9, 9)):\\n return cv2.GaussianBlur(image, kernel_size, variance)\",\n \"def EstimateIlluminationProfile(image, kernel_sigma): \\n \\n illumination_profile = Denoising(image, kernel_sigma);\\n \\n map_corr = np.ones(image.shape);\\n map_corr = Denoising(map_corr, kernel_sigma);\\n \\n illumination_profile = illumination_profile / map_corr;\\n \\n return illumination_profile;\",\n \"def gaus(x, A, mu, sigma):\\n return A * np.exp(-(x - mu) ** 2 / (2. * sigma ** 2))\",\n \"def gauss(x, gamma):\\n return 1 / np.sqrt(2*np.pi) / gamma * np.exp(-(x/gamma)**2 / 2)\",\n \"def gauss(x, *p):\\n mu, sigma = p\\n return (1 / (sigma * np.sqrt(2 * np.pi)) *\\n np.exp(-(x - mu) ** 2 / (2. * sigma ** 2)))\",\n \"def get_intensity_transformer(aug):\\n\\n def gamma_tansform(img):\\n gamma_range = aug['aug']['gamma_range']\\n if isinstance(gamma_range, tuple):\\n gamma = np.random.rand() * (gamma_range[1] - gamma_range[0]) + gamma_range[0]\\n cmin = img.min()\\n irange = (img.max() - cmin + 1e-5)\\n\\n img = img - cmin + 1e-5\\n img = irange * np.power(img * 1.0 / irange, gamma)\\n img = img + cmin\\n\\n elif gamma_range == False:\\n pass\\n else:\\n raise ValueError(\\\"Cannot identify gamma transform range {}\\\".format(gamma_range))\\n return img\\n\\n return gamma_tansform\",\n \"def gauss(self, X, xm, amp, w):\\n return amp * np.exp(-((X - xm) / w) ** 2)\",\n \"def gaussian(x, *parameters):\\n position, sigma, amplitude, background = parameters\\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background\",\n \"def differenceOfGausssians(image,sigma0, sigma1,window_size, roi, out = None):\\n return (vigra.filters.gaussianSmoothing(image,sigma0,window_size=window_size,roi = roi)-vigra.filters.gaussianSmoothing(image,sigma1,window_size=window_size,roi = roi))\",\n \"def gaussian(p, x):\\n #2008-09-11 15:11 IJC: Created for LINEPROFILE\\n # 2011-05-18 11:46 IJC: Moved to analysis.\\n # 2013-04-11 12:03 IJMC: Tried to speed things up slightly via copy=False\\n # 2013-05-06 21:42 IJMC: Tried to speed things up a little more.\\n\\n if not isinstance(x, np.ndarray):\\n x = array(x, dtype=float, copy=False)\\n\\n if len(p)==3:\\n p = array(p, copy=True)\\n p = concatenate((p, [0]))\\n #elif len(p)==4:\\n # p = array(p, copy=False)\\n\\n return p[3] + p[0]/(p[1]*sqrt(2*pi)) * exp(-(x-p[2])**2 / (2*p[1]**2))\",\n \"def gaussian(x, mean, sigma):\\n return np.exp(-np.square(x-mean)/(2*np.square(sigma))) / (np.sqrt(2*np.pi*sigma**2))\",\n \"def gauss_seeing(npix = None,fwhm=None,e1=None,e2=None,scale=scale):\\n fwhm = fwhm/scale\\n M20 = 2.*(fwhm/2.35482)**2\\n row,col = np.mgrid[-npix/2:npix/2,-npix/2:npix/2]\\n rowc = row.mean()\\n colc = col.mean()\\n Mcc = 0.5*M20*(1+e1)\\n Mrc = 0.5*e2*M20\\n Mrr = 0.5*M20*(1-e1)\\n rho = Mrc/np.sqrt(Mcc*Mrr)\\n img = np.exp(-0.5/(1-rho**2)*(row**2/Mrr + col**2/Mcc - 2*rho*row*col/np.sqrt(Mrr*Mcc)))\\n res = img/img.sum()\\n return res\",\n \"def gaussian_white(z, mu: 'normal' = 0, sigma: (0.4, 1) = 0.7):\\n return 1 - gaussian_black(z, mu, sigma)\",\n \"def gauss(x, *p):\\n A, mu, sigma = p\\n\\n return A*np.exp(-(x-mu)**2/(2.*sigma**2))\",\n \"def gaussian(x, amp, wid, cen):\\n return amp*np.exp(-(x-cen)**2/(2*wid**2))\",\n \"def gaussed_value(self):\\n from random import gauss\\n return sorted([0, int(gauss(self.value, self.sigma)), \\\\\\n (self.size*8)-1])[1]\",\n \"def grd_posterior_gaussian(self, ) -> Tuple[np.ndarray, np.ndarray]:\\n xmin, xmax = self.x_range\\n ymin, ymax = self.y_range\\n\\n mu = np.array([0, 0])\\n sigma = np.zeros((2, 2))\\n\\n _sample = self._sample\\n _prior = self.prior\\n\\n def mean_x(x: float, y: float):\\n return x * _sample(x, y) * _prior.eval(x, y)\\n\\n def mean_y(x: float, y: float):\\n return y * _sample(x, y) * _prior.eval(x, y)\\n\\n def var_x(x: float, y: float):\\n return x * mean_x(x, y)\\n\\n def var_y(x: float, y: float):\\n return y * mean_y(x, y)\\n\\n # def var_xy(x: float, y: float):\\n # return x * mean_y(x, y)\\n\\n # First moment\\n (mu[0], mu[1]) = (integrate.dblquad(mean_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\\n integrate.dblquad(mean_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0])\\n (sigma[0, 0], sigma[1, 1]) = \\\\\\n (integrate.dblquad(var_x, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],\\n integrate.dblquad(var_y, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\\n # integrate.dblquad(var_xy, xmin, xmax, lambda x: ymin, lambda x: ymax)[0],)\\n return mu, sigma\",\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 Gausian_response(img,sigma=1):\\n \\n # Gausian response\\n img_sigma = zeros(img.shape)\\n filters.gaussian_filter(img, (sigma,sigma), (0,0), img_sigma)\\n \\n return img_sigma\",\n \"def __measurement_prob(angle, measurement, noise):\\n return ParticleFilter.Gaussian(angle, noise, measurement)\",\n \"def gaussian(x, mu, sigma):\\n return (np.exp(-(x - mu)**2 / 2.0 / sigma**2) /\\n np.sqrt(2.0 * np.pi) / sigma)\",\n \"def kernel_gaussiano(image: np.ndarray, sigma: float, kind: str = 'low') -> np.ndarray:\\n U, V = fourier_meshgrid(image)\\n D = fourier_distance(U, V)\\n H = np.exp( (-1.0 * D) / (2.0 * sigma**2) )\\n \\n if kind == 'high' or kind == 'highpass':\\n H = 1.0 - H\\n \\n return H\",\n \"def estimateGaussian(X):\\n\\tmu = np.mean(X, axis=0)\\n\\tsigma2 = np.std(X, axis=0) ** 2\\n\\treturn mu, sigma2\",\n \"def _prior_gaussian(self, x_start):\\n batch_size = x_start.shape[0]\\n t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)\\n qt_mean, _, qt_log_variance = self.gaussian_q_mean_variance(x_start, t)\\n kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)\\n return mean_flat(kl_prior) / np.log(2.0)\",\n \"def __call__( self, X, Y, Z):\\n xb,yb,zb = self.transform( X,Y,Z)\\n \\n gauss = beam( xb,yb,zb, self.w[0], self.w[1], self.l)\\n intensity = (2/np.pi)* self.mW/1000. /self.w[0]/self.w[1] *gauss # W um^-2\\n \\n return uL(self.l)*intensity\",\n \"def gaussian(x, amp, cen, wid):\\n return amp * exp (-(x-cen)**2/(2*wid**2))\",\n \"def gauss(x,p):\\n return np.exp((-(x - p[0])**2) / (2 * p[1]**2))\",\n \"def get_pca_mean_beam(self):\\n return self.get_pca_images()[0]\",\n \"def gaussianPSF(shape, sigma):\\n psf = dg.drawGaussiansXY(shape,\\n numpy.array([0.5*shape[0]]),\\n numpy.array([0.5*shape[1]]),\\n sigma = sigma)\\n return psf/numpy.sum(psf)\",\n \"def gaussian_prior(self):\\n self.prior = sps.multivariate_normal(self.m0,self.S0)\",\n \"def test_estimate_parameters():\\n # a red image\\n image = numpy.zeros((3, 11, 11))\\n image[0, :, :] = 255\\n skin_filter.estimate_gaussian_parameters(image)\\n assert (skin_filter.mean == [1.0, 0.0]).all(), \\\"mean for a red image is not OK\\\"\\n assert (skin_filter.covariance == [[0.0, 0.0], [0.0, 0.0]]).all(), \\\"covariance for red image is not OK\\\"\\n\\n # a green image\\n image = numpy.zeros((3, 11, 11))\\n image[1, :, :] = 255\\n skin_filter.estimate_gaussian_parameters(image)\\n assert (skin_filter.mean == [0.0, 1.0]).all(), \\\"mean for a green image is not OK\\\"\\n assert (skin_filter.covariance == [[0.0, 0.0], [0.0, 0.0]]).all(), \\\"covariance for green image is not OK\\\"\",\n \"def pdf(x):\\n return - np.exp(self.ks_gaussian.score_samples(x.reshape(1, -1)))\",\n \"def gaussian_blur(self,img):\\n return cv2.GaussianBlur(img, (self.kernel_size, self.kernel_size), 0)\",\n \"def pseudo_flatfield(img_plane, sigma=5):\\n filtered_img = gaussian_filter(img_plane, sigma)\\n return img_plane / (filtered_img + 1)\",\n \"def MyBaseMoments(p,q,img,gauss_sigma,gauss_centroid=None, gauss_g1=0., gauss_g2=0.):\\n weight = galsim.Image(np.zeros_like(img.array))\\n gauss = galsim.Gaussian(sigma=gauss_sigma*pixel_scale).shear(g1=gauss_g1,g2=gauss_g2)\\n if gauss_centroid is None:\\n gauss_centroid = img.true_center\\n weight = gauss.drawImage(image=weight, scale=pixel_scale, method='no_pixel', use_true_center=True, offset=(gauss_centroid-img.true_center)*(1))\\n x = np.linspace(img.xmin-img.center.x*0-gauss_centroid.x*1, img.xmax-img.center.x*0-gauss_centroid.x*1, img.xmax-img.xmin+1)+0.*0.5\\n y = np.linspace(img.ymin-img.center.y*0-gauss_centroid.y*1, img.ymax-img.center.y*0-gauss_centroid.y*1, img.ymax-img.ymin+1)+0.*0.5\\n X, Y = np.meshgrid(x,y)\\n\\n Q00 = np.sum(weight.array*img.array)\\n Q10 = gauss_centroid.x + np.sum(X*weight.array*img.array)/Q00\\n Q01 = gauss_centroid.y + np.sum(Y*weight.array*img.array)/Q00\\n Q20 = np.sum((X**2)*weight.array*img.array)\\n Q02 = np.sum((Y**2)*weight.array*img.array)\\n\\n monomial = 1.\\n for pp in xrange(p):\\n monomial *= X\\n for qq in xrange(q):\\n monomial *= Y\\n Qpq = np.sum(monomial*weight.array*img.array) #/Q00\\n\\n return Qpq\",\n \"def gaussian_kernel(training_ex, landmark, sigma=0.1):\\n return np.exp(-(np.linalg.norm(training_ex - landmark) ** 2 / (2 * (sigma ** 2))))\",\n \"def gaussian(mu, wid, x):\\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)\",\n \"def taper_visibility_gaussian(vis: Visibility, beam=None) -> Visibility:\\n assert isinstance(vis, Visibility), vis\\n \\n if beam is None:\\n raise ValueError(\\\"Beam size not specified for Gaussian taper\\\")\\n uvdistsq = vis.u ** 2 + vis.v ** 2\\n # See http://mathworld.wolfram.com/FourierTransformGaussian.html\\n scale_factor = numpy.pi ** 2 * beam ** 2 / (4.0 * numpy.log(2.0))\\n prior = vis.flagged_imaging_weight[:, :]\\n wt = numpy.exp(-scale_factor * uvdistsq)\\n vis.data['imaging_weight'][:, :] = vis.flagged_imaging_weight[:, :] * wt[:, numpy.newaxis]\\n \\n return vis\",\n \"def gaussian_black(z, mu: 'normal' = 0, sigma: (0.4,1) = 0.7):\\n return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-np.power((z - mu)/sigma, 2)/2)\",\n \"def get_input_vector(img):\\n parts = partition_matrix(blurmap(img), 10)\\n return numpy.array([numpy.mean(part) for part in parts],\\n dtype=numpy.float32)\",\n \"def gauss_pert(N,a):\\n x = np.arange(0,N,1,float)\\n y = x[:,np.newaxis]\\n\\n # Choose the location of the peak\\n x0 = y0 = int(0.4*N)\\n\\n # Choose the fwhm, 'width' of the perturbation\\n fwhm = N/15\\n\\n return a*np.exp(-4*np.log(2) * ((x-x0)**2 + (y-y0)**2) / fwhm**2)\",\n \"def gaussian_filter(x):\\n return _gaussian_filter(x, 3)\",\n \"def gaussian_blur(self, img):\\n kernel_size = self.gaussian_blur_params[\\\"kernel_size\\\"]\\n return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)\",\n \"def gaussian_proba_map(img):\\n method = 'cv2.TM_CCOEFF_NORMED'\\n sigmas = [41,31,21,11]\\n out = np.zeros(img.shape)\\n for sigma in sigmas:\\n size=3*sigma\\n template = gaussian(size,sigma)\\n template/=template.max()\\n template*=255\\n template = template.astype(np.uint8)\\n \\n img2 = img.copy()\\n meth = eval(method)\\n # Apply template Matching\\n res = cv2.matchTemplate(img2,template,meth)\\n res = np.pad(res,size/2,mode='constant')\\n to_replace = res>out\\n out[to_replace] = res[to_replace]\\n return out\",\n \"def _gauss_pix(x, mean=0.0, sigma=1.0):\\n x = (np.asarray(x, dtype=float) - mean) / (sigma*np.sqrt(2))\\n dx = x[1]-x[0]\\n if not np.allclose(np.diff(x), dx):\\n raise ValueError('all pixels must have the same size')\\n\\n edges = np.concatenate([x-dx/2, x[-1:]+dx/2])\\n assert len(edges) == len(x)+1\\n\\n y = scipy.special.erf(edges)\\n return (y[1:] - y[:-1])/2\",\n \"def sphere_l_intensity(img):\\n pixels = []\\n for j in range(0, img.shape[0]):\\n for i in range(1, 40):\\n pixels.append(img[j, i])\\n\\n return np.mean(pixels)\",\n \"def estimateGaussian(X):\\n mu = X.mean(0, keepdims=True).T\\n sigma2 = X.var(0, keepdims=True).T\\n return mu, sigma2\",\n \"def gauss(x, x0, gamma):\\n sigma = gamma / sqrt(2.0)\\n \\n A = 1/ (sigma * sqrt(2*pi))\\n return (A * exp (-0.5 * (x-x0)**2/sigma**2))\",\n \"def gaussint(x, mean=0.0, sigma=1.0):\\n z = (x - mean) / (math.sqrt(2) * sigma)\\n return (erf(z) + 1.0) / 2.0\",\n \"def test_gaussian_filter():\\n\\n def rgb2gray(rgb):\\n r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\\n gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\\n\\n return gray\\n\\n img = rgb2gray(np.array(Image.open('data/graf.png')))\\n gx, x = gauss_module.gauss(4)\\n gx = gx.reshape(1, gx.shape[0])\\n gy = gx.reshape(gx.shape[1], gx.shape[0])\\n smooth_img = conv2(img, gx * np.array(gy))\\n\\n test_smooth_img = gauss_module.gaussianfilter(img, 4)\\n\\n assert np.all(smooth_img.round(5) == test_smooth_img.round(5))\",\n \"def apply_gaussian(X, sigma):\\n return np.array([ndimage.gaussian_filter(x, sigma) for x in X])\",\n \"def psfVal(ix, iy, x, y, sigma1, sigma2, b):\\n return (math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma1**2) +\\n b*math.exp (-0.5*((ix - x)**2 + (iy - y)**2)/sigma2**2))/(1 + b)\",\n \"def Gauss_filter(data, sigma=(0,2,2), mode='wrap'): \\n import scipy.ndimage.filters as flt\\n return flt.gaussian_filter(data, sigma=sigma, mode=mode)\",\n \"def gaussian(self, amp_step, sigma_step):\\n l = len(self.overlaid_x_axis)\\n x = np.linspace(0, l, l) - l/2 # centre of data\\n\\n # This is new code to 'guess' the size of the Gaussian from the\\n # existing data rather than from hard-coded numbers.\\n # TODO: test this! Possibly link up to the get_windowed_data function\\n # as it uses a lot of the same functionality\\n trigger = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][0]\\n trace = self.pv_monitor.arrays[self.controls.Arrays.WAVEFORMS][1]\\n amplitude = max(trace) + amp_step\\n diff = np.diff(trigger)\\n stepvalue = 0.5\\n if min(diff) > -1 * stepvalue or max(diff) < stepvalue:\\n raise RangeError\\n else:\\n maxtrig = next(x for x in diff if x > stepvalue)\\n mintrig = next(x for x in diff if x < -1 * stepvalue)\\n edges = [np.where(diff == maxtrig)[0][0],\\n np.where(diff == mintrig)[0][0]]\\n half_trigger_length = (edges[1]-edges[0])\\n sigma = half_trigger_length/4 + sigma_step\\n\\n gauss = self.ax2.plot(amplitude * np.exp(-x**2 / (2 * sigma**2)), 'r')\\n self.overlaid_lines.append(gauss)\\n self.draw()\",\n \"def enhance(self, image):\\n\\n if self.random.uniform(0, 1) < self.p:\\n return self.intensity_enhance(image)\\n else:\\n return image\",\n \"def getIntensityS(self):\\n return self._Esigma.intensity()\",\n \"def features_sigma(img,\\n sigma,\\n intensity=True,\\n edges=True,\\n texture=True):\\n\\n features = []\\n\\n gx,gy = np.meshgrid(np.arange(img.shape[1]), np.arange(img.shape[0]))\\n # print(gx.shape)\\n #features.append(gx)\\n gx = filters.gaussian(gx, sigma)\\n gy = filters.gaussian(gy, sigma)\\n\\n features.append(np.sqrt(gx**2 + gy**2)) #use polar radius of pixel locations as cartesian coordinates\\n\\n del gx, gy\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Location features extracted using sigma= %f' % (sigma))\\n\\n img_blur = filters.gaussian(img, sigma)\\n\\n if intensity:\\n features.append(img_blur)\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Intensity features extracted using sigma= %f' % (sigma))\\n\\n if edges:\\n features.append(filters.sobel(img_blur))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Edge features extracted using sigma= %f' % (sigma))\\n\\n if texture:\\n H_elems = [\\n np.gradient(np.gradient(img_blur)[ax0], axis=ax1)\\n for ax0, ax1 in itertools.combinations_with_replacement(range(img.ndim), 2)\\n ]\\n\\n eigvals = feature.hessian_matrix_eigvals(H_elems)\\n del H_elems\\n\\n for eigval_mat in eigvals:\\n features.append(eigval_mat)\\n del eigval_mat\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Texture features extracted using sigma= %f' % (sigma))\\n\\n logging.info(datetime.now().strftime(\\\"%Y-%m-%d-%H-%M-%S\\\"))\\n logging.info('Image features extracted using sigma= %f' % (sigma))\\n\\n return features\",\n \"def get_intensity_normalization(self):\\n return self.intensity_normalize_image\",\n \"def gaussian(mu, sigma, start, end):\\r\\n \\r\\n val = np.linspace(start, end, 100)\\r\\n a = 1/(sigma*np.pi)\\r\\n b = - 0.5 * np.power((mu - val)/sigma, 2)\\r\\n return a*np.exp(b)\",\n \"def gausspix(x, mean=0.0, sigma=1.0):\\n edges = np.concatenate((x-0.5, x[-1:]+0.5))\\n integrals = gaussint(edges, mean=mean, sigma=sigma)\\n return integrals[1:] - integrals[0:-1]\",\n \"def _fspecial_gauss_1d(self, size, sigma):\\n coords = torch.arange(size).to(dtype=torch.float)\\n coords -= size // 2\\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\\n g /= g.sum()\\n return g.reshape(-1)\",\n \"def phi_gauss(self,x,i):\\n s = 0.1\\n return np.exp(-(x-self.mu[i])**2/(2*s))\",\n \"def area_of_gaussian(amp, fwhm):\\n return amp * fwhm / 0.93943727869965132\",\n \"def create_gaussian_array(self):\\n\\n # Fill array of size l x w with Gaussian Noise.\\n terrain_length = int(ceil(self.length/self.resolution))\\n terrain_width = int(ceil(self.width/self.resolution))\\n gaussian_array = np.random.normal(self.mu, self.sigma, (terrain_length,terrain_width))\\n\\n # Filter the array to smoothen the variation of the noise\\n gaussian_array = gaussian_filter(gaussian_array, self.sigma_filter)\\n\\n return gaussian_array\",\n \"def _fspecial_gauss_1d(self, size, sigma):\\n coords = torch.arange(size).to(dtype=torch.float)\\n coords -= size // 2\\n\\n g = torch.exp(-(coords ** 2) / (2 * sigma ** 2))\\n g /= g.sum()\\n\\n return g.unsqueeze(0).unsqueeze(0)\",\n \"def gaussian(window_size, sigma):\\n gauss = torch.Tensor([math.exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])\\n return gauss/gauss.sum()\",\n \"def gauss(x, mu, A, sigma):\\n mu, A, sigma = np.atleast_2d(mu), np.atleast_2d(A), np.atleast_2d(sigma)\\n val = (A / (sigma * np.sqrt(np.pi * 2)) *\\n np.exp(-(x[:, None] - mu)**2 / (2 * sigma**2)))\\n return val.sum(axis=-1)\",\n \"def cf_profile(self):\\n x = np.abs(self.gen_profile() / self.sam_sys_inputs['system_capacity'])\\n return x\",\n \"def gaussian_likelihood(input_, mu_, log_std):\\n pre_sum = -0.5 * (((input_ - mu_) / (\\n tf.exp(log_std) + EPS)) ** 2 + 2 * log_std + np.log(\\n 2 * np.pi))\\n return tf.reduce_sum(pre_sum, axis=1)\",\n \"def prob(x):\\n\\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\\\\n\\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)\",\n \"def gaussian_filter(stddev, array):\\n\\n return astropy.convolution.convolve(\\n array, astropy.convolution.Gaussian2DKernel(stddev))\",\n \"def getIntensity(self):\\n return self.getIntensityS() + self.getIntensityP()\",\n \"def flat_top_gaussian(a1_val, a2_val, sigma1, sigma2, w1_val, w2_val, w_val):\\n gauss1 = a1_val * np.exp(-(w_val - w1_val)**4/(2 * sigma1**2))\\n gauss2 = a2_val * np.exp(-(w_val - w2_val)**4/(2 * sigma2**4))\\n sum_gauss = gauss1 + gauss2\\n return sum_gauss\",\n \"def prewitt(img):\\n kernel1 = numpy.array([1.0, 0.0, -1.0])\\n kernel2 = numpy.array([1.0, 1.0, 1.0])\\n Gx1 = convRows(img, kernel1)\\n Gx = convCols(Gx1, kernel2)\\n Gy1 = convCols(img, kernel1)\\n Gy = convRows(Gy1, kernel2)\\n \\n G = numpy.sqrt(Gx*Gx + Gy*Gy)\\n \\n return G\",\n \"def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\\r\\n #width = sigma*(2*np.sqrt(2*np.log(2)))\\r\\n b = 1/(sigma*np.sqrt(2*np.pi))\\r\\n f = b*np.power(np.e, -(((x-mu)**2)/(2*sigma**2)))\\r\\n return A*f + y0\",\n \"def profile(c_mat, mat_rows):\\n\\n profile_mat = c_mat / mat_rows\\n\\n return profile_mat\",\n \"def get_map_gaussian_fit(self, map_type, i_seq):\\n map_gaussians = self._map_gaussian_fits.get(map_type, None)\\n if (map_gaussians is not None):\\n return map_gaussians[i_seq]\\n return None\",\n \"def param_gauss(xdata_, *params_):\\n scale_, mean_, cov_ = params_to_scale_mean_cov(params_)\\n return scale_ * gaussian(xdata_, mean=mean_, cov=cov_)\",\n \"def sphere_r_intensity(img):\\n pixels = []\\n for j in range(0, img.shape[0]):\\n for i in range(1, 40):\\n pixels.append(img[j, img.shape[1] - i])\\n\\n return np.mean(pixels)\",\n \"def get_kernel_values(self, i, j, abscissa_array):\\n x = np.zeros_like(abscissa_array)\\n for m in range(self.n_gaussians):\\n x += self.amplitudes[i, j, m] * \\\\\\n norm.pdf((abscissa_array - self.means_gaussians[m]) \\\\\\n / self.std_gaussian) / self.std_gaussian\\n return x\",\n \"def getIntensityP(self):\\n return self._Epi.intensity()\",\n \"def GaussianKernel(radius, std):\\n size = 2 * radius + 1\\n weight = torch.ones(size, size)\\n weight.requires_grad = False\\n for i in range(-radius, radius+1):\\n for j in range(-radius, radius+1):\\n dis = (i * i) + (j * j)\\n weight[i+radius][j+radius] = np.exp(-dis / (2 * std * std))\\n weight = weight / weight.sum()\\n return weight\",\n \"def gauss_sample(mean, covariance):\\n\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6619526","0.63317794","0.62996733","0.627856","0.62739635","0.61623496","0.6124203","0.610553","0.60432166","0.60368556","0.60006434","0.59681493","0.5952793","0.59485036","0.5927189","0.5926995","0.5875386","0.5873252","0.5861136","0.58427656","0.5829406","0.58169806","0.5813704","0.58073467","0.5805789","0.57963747","0.5794637","0.57767224","0.5776591","0.57571816","0.57551694","0.5740491","0.57103753","0.5709734","0.5705603","0.5700343","0.5700127","0.5698982","0.56879747","0.5684806","0.5680259","0.56728595","0.565731","0.56439245","0.5642952","0.5639772","0.56325006","0.5625263","0.56243724","0.56167006","0.56114584","0.5610747","0.56047875","0.559979","0.55844843","0.5560884","0.55590236","0.5557906","0.554965","0.55335665","0.5529015","0.5525451","0.55235046","0.55230856","0.55230325","0.552173","0.5521207","0.55200744","0.5508664","0.55042887","0.5497192","0.5490256","0.5464166","0.54619056","0.5459805","0.54595727","0.54559493","0.5446888","0.54463387","0.544419","0.54405904","0.5419804","0.5419641","0.54192597","0.54179","0.54170334","0.5415881","0.5414582","0.540571","0.5403416","0.53972316","0.5391768","0.53890115","0.5388656","0.5388268","0.53880835","0.53854966","0.5379961","0.53759253","0.53732103","0.53727823"],"string":"[\n \"0.6619526\",\n \"0.63317794\",\n \"0.62996733\",\n \"0.627856\",\n \"0.62739635\",\n \"0.61623496\",\n \"0.6124203\",\n \"0.610553\",\n \"0.60432166\",\n \"0.60368556\",\n \"0.60006434\",\n \"0.59681493\",\n \"0.5952793\",\n \"0.59485036\",\n \"0.5927189\",\n \"0.5926995\",\n \"0.5875386\",\n \"0.5873252\",\n \"0.5861136\",\n \"0.58427656\",\n \"0.5829406\",\n \"0.58169806\",\n \"0.5813704\",\n \"0.58073467\",\n \"0.5805789\",\n \"0.57963747\",\n \"0.5794637\",\n \"0.57767224\",\n \"0.5776591\",\n \"0.57571816\",\n \"0.57551694\",\n \"0.5740491\",\n \"0.57103753\",\n \"0.5709734\",\n \"0.5705603\",\n \"0.5700343\",\n \"0.5700127\",\n \"0.5698982\",\n \"0.56879747\",\n \"0.5684806\",\n \"0.5680259\",\n \"0.56728595\",\n \"0.565731\",\n \"0.56439245\",\n \"0.5642952\",\n \"0.5639772\",\n \"0.56325006\",\n \"0.5625263\",\n \"0.56243724\",\n \"0.56167006\",\n \"0.56114584\",\n \"0.5610747\",\n \"0.56047875\",\n \"0.559979\",\n \"0.55844843\",\n \"0.5560884\",\n \"0.55590236\",\n \"0.5557906\",\n \"0.554965\",\n \"0.55335665\",\n \"0.5529015\",\n \"0.5525451\",\n \"0.55235046\",\n \"0.55230856\",\n \"0.55230325\",\n \"0.552173\",\n \"0.5521207\",\n \"0.55200744\",\n \"0.5508664\",\n \"0.55042887\",\n \"0.5497192\",\n \"0.5490256\",\n \"0.5464166\",\n \"0.54619056\",\n \"0.5459805\",\n \"0.54595727\",\n \"0.54559493\",\n \"0.5446888\",\n \"0.54463387\",\n \"0.544419\",\n \"0.54405904\",\n \"0.5419804\",\n \"0.5419641\",\n \"0.54192597\",\n \"0.54179\",\n \"0.54170334\",\n \"0.5415881\",\n \"0.5414582\",\n \"0.540571\",\n \"0.5403416\",\n \"0.53972316\",\n \"0.5391768\",\n \"0.53890115\",\n \"0.5388656\",\n \"0.5388268\",\n \"0.53880835\",\n \"0.53854966\",\n \"0.5379961\",\n \"0.53759253\",\n \"0.53732103\",\n \"0.53727823\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":4698,"cells":{"query":{"kind":"string","value":"Returns intensity profile of trap array"},"document":{"kind":"string","value":"def gaussianarray1d(x, x0_vec, wx_vec, A_vec, offset, ntraps):\n array = np.zeros(np.shape(x))\n for k in range(ntraps):\n array = array + gaussian1d(x, x0_vec[k], wx_vec[k], A_vec[k], 0)\n return array + offset"},"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 intensity(self) -> int:","def intensity(self):\r\n return np.power(prb.amplitude, 2)","def getIntensity(self):\n return self.getIntensityS() + self.getIntensityP()","def estimate_skin_tone(face_roi):\n return [int(face_roi[:, :, i].mean()) for i in range(face_roi.shape[-1])]","def getIntensity(self):\n return self.__intensity","def turbulence_intensity(self):\n return self.flow_field.turbulence_intensity","def getIntensityP(self):\n return self._Epi.intensity()","def detector_intensity(val_addr):\n \n return(np.concatenate((np.unique(val_addr, return_counts=True)[1][:13],[0],np.unique(val_addr, return_counts=True)[1][13:]),axis=0))","def getstats(img, thresholds):\n number = np.zeros(img.shape, np.float64)\n ev = np.zeros(img.shape, np.float64)\n scatter = np.zeros(img.shape, np.float64)\n for n, s, low, high, evs in thresholds:\n for i in numba.prange(img.shape[0]):\n for j in numba.prange(img.shape[1]):\n if (low < img[i, j]) and (img[i, j] < high):\n scatter[i, j] = s\n number[i, j] = n\n ev[i, j] = img[i, j] - evs\n return ev, number, scatter","def extract_intensity(seg, fluo_im):\n\n # Get the region props of the fluorescence image using the segmentation\n # mask.\n props = skimage.measure.regionprops(seg, intensity_image=fluo_im)\n cell_ints = []\n for prop in props:\n cell_ints.append(prop.mean_intensity)\n\n # Convert the cell_ints to an array and return.\n return np.array(cell_ints)","def intensity(self, value: int, /) -> None:","def apphot(im, yx, rap, subsample=4, **kwargs):\n n, f = anphot(im, yx, rap, subsample=subsample, **kwargs)\n if np.size(rap) > 1:\n return n.cumsum(-1), f.cumsum(-1)\n else:\n return n, f","def OF1_CalculateRawHistogram(image):\n h = np.zeros(256, np.float_)\n for i in np.nditer(image):\n h[i - 1] = h[i - 1] + 1\n\n return h","def compute_intensity(self, data):\n # Compute RMS intensity (as float to prevent overflow).\n data = self.audio_resample(data.astype(np.float32)**2)**0.5\n\n # Apply dynamic range compression.\n return data**self._exponent","def confint(arr):\n res=[[],[],[]]\n #r=hpd(arr)\n r=(sap(arr,2.5),sap(arr,97.5))\n res[0]=r[0]\n res[1]=arr.mean(0)\n res[2]=r[1]\n return np.array(res)","def EstimateIlluminationProfile(image, kernel_sigma): \n \n illumination_profile = Denoising(image, kernel_sigma);\n \n map_corr = np.ones(image.shape);\n map_corr = Denoising(map_corr, kernel_sigma);\n \n illumination_profile = illumination_profile / map_corr;\n \n return illumination_profile;","def profile_from_xvalues(self, xvalues):\r\n\r\n transformed_xvalues = xvalues - self.centre\r\n\r\n return np.multiply(\r\n np.divide(self.intensity, self.sigma * np.sqrt(2.0 * np.pi)),\r\n np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),\r\n )","def getIntensityS(self):\n return self._Esigma.intensity()","def measure_amplitude(self, ipx, ipy):\r\n\r\n intensity = np.mean(np.mean(self.imageData[:, ipx-1:ipx+1, ipy-1:ipy+1], axis=2), axis=1)\r\n\r\n # fold over repetitions\r\n\r\n remapped = intensity.reshape( self.nrepetitions, intensity.shape[0]/self.nrepetitions).mean(axis=0)\r\n\r\n return remapped","def get_fthreshold(img,factor=1.):\n import noiselevel\n # sigma=Table.read('noiselevel.csv',format='csv')['sigma'][0]\n sigma=noiselevel.getnoiselevel(img,ranges=(-30,30),toplot=False)\n \n thres= sigma*factor\n return thres,sigma","def detectable_intensity(self):\n return self.disease.detectable_intensity()","def tempProfile_hocuk(self,dist,draine=1):\n radius = np.linspace(0,self.size * self.dist * au2cm,100)\n density = self.denProfile(radius)\n dr = radius[1] - radius[0]\n tdust = np.zeros(len(dist))\n for i in range(len(dist)):\n av = self.compute_av(dist[i],radius,density,dr)\n tdust[i] =(11 + 5.7*np.tanh(0.61 - np.log10(av)))*draine**(1/5.9)\n return tdust","def addNoise_amp(array,counts):\r\n if array.dtype == 'complex' :\r\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\r\n else :\r\n if np.float64(counts) == 0.0e0 :\r\n arrayout = np.copy(array)\r\n elif np.float64(counts) < 0.0e0 :\r\n print 'bg.addNoise : warning counts < 0'\r\n else :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = np.square(normalise(array))\r\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\r\n arrayout = np.sqrt(arrayout)\r\n tot = np.sum(np.abs(array)**2)\r\n arrayout = normalise(arrayout,tot)\r\n return arrayout","def get_brightness(arr):\n\tR,G,B = arr[:,:,0], arr[:,:,1], arr[:,:,2]\n\tY = 0.299*R + 0.587*G + 0.144*B\n\treturn Y.mean()","def observation(self, img):\r\n img = img[25:200]\r\n img = cv2.resize(img, self.img_size[1:])\r\n if not self.color:\r\n img = img.mean(-1, keepdims=True)\r\n\r\n return img.transpose([2, 0, 1]) / 255","def infectious_intensity(self):\n return InfectiousIntensity(self)","def extract_intensity(mask, yfp_image):\n # Get the region properties for the image.\n props = skimage.measure.regionprops(mask, intensity_image=yfp_image)\n\n # Make a vector to store the mean intensities\n mean_int = []\n for prop in props:\n intensity = prop.mean_intensity\n mean_int.append(intensity)\n\n return mean_int","def gen_profile(self):\n return np.array(self['gen'], dtype=np.float32)","def gen_profile(self):\n return np.array(self['gen'], dtype=np.float32)","def calculate_absorbance(intensity):\n blanco = intensity[0]\n intensity_np_arr = np.array(intensity)\n transmittance = intensity_np_arr / blanco\n absorbance = - np.log10(transmittance)\n return absorbance","def cf_profile(self):\n x = np.abs(self.gen_profile() / self.sam_sys_inputs['system_capacity'])\n return x","def scalarInfo(img, cnt):\n\tm = cntInfo(img, cnt)\n\td = {\"perimeter\":m[\"perimeter\"], \"oreientation\":m[\"orientation\"], \"solidity\":m[\"solidity\"],\"height\":m[\"height\"], \"extent\":m[\"extent\"], \"aspect ratio\":m[\"aspect ratio\"], \"area\":m[\"area\"], \"sum intensity\":m[\"sum intensity\"], \"width\":m[\"width\"], \"equivalent diameter\": m[\"equivalent diameter\"], \"mean intensity\": m[\"mean intensity\"]}\n\treturn d","def profile(c_mat, mat_rows):\n\n profile_mat = c_mat / mat_rows\n\n return profile_mat","def row_uncertainty(a):\n try:\n return sum(safe_p_log_p(a), 1)\n except ValueError:\n raise ValueError(\"Array has to be two-dimensional\")","def trapfilt_taps(N, phil, alfa):\n\n\n\n tt = arange(-N/2,N/2 + 1) # Time axis for h(t) \n # ***** Generate impulse response ht here *****\n ht = zeros(len(tt))\n ix = where(tt != 0)[0]\n if alfa != 0:\n ht[ix] = ((sin(2*pi*phil*tt[ix]))/(pi*tt[ix]))*((sin(2*pi*alfa*phil*tt[ix]))/(2*pi*alfa*phil*tt[ix]))\n else:\n ht[ix] = (sin(2*pi*phil*tt[ix]))/(pi*tt[ix])\n ix0 = where(tt == 0)[0]\n ht[ix0] = 2*phil\n ht = ht/sum(power(ht,2))\n\n return ht","def get_uthreshold(img):\n import noiselevel\n # sigma=Table.read('noiselevel.csv',format='csv')['sigma'][0]\n sigma = noiselevel.getnoiselevel(img,ranges=(-30,30),toplot=False)\n \n thres = sigma*np.sqrt(2*np.log(img.size))\n return thres, sigma","def get_intensity(self, step_index):\n\n if step_index < 4 or step_index >= self.intensity_step_count - 4:\n raise ValueError('step index {0} is out of bounds [4, {1})'.format(\n step_index, self.intensity_step_count - 4))\n\n # Checking if we already computed required intensity value.\n\n if self.intensity_list[step_index] != None:\n return self.intensity_list[step_index]\n\n # No, we haven't, so we are going to compute it.\n\n window_list, window_sum = get_kaiser_window(self.intensity_half_window_size)\n\n sample_array = self.intensity_sound.get_array_of_samples()\n sample_sum = 0.0\n\n channel_count = self.intensity_sound.channels\n amplitude_limit = self.intensity_sound.max_possible_amplitude\n\n # We sum squared normalized amplitudes of all samples of all channels in the window.\n\n sample_from = (step_index - 4) * self.intensity_step_size * channel_count\n\n for i in range(self.intensity_window_size):\n for j in range(channel_count):\n sample = sample_array[sample_from + i * channel_count + j] / amplitude_limit\n sample_sum += sample ** 2 * window_list[i]\n\n # Multiplication by 2.5e9 is taken directly from Praat source code, where it is performed via\n # division by 4e-10.\n\n intensity_ratio = sample_sum / (channel_count * window_sum) * 2.5e9\n intensity = -300 if intensity_ratio < 1e-30 else 10 * math.log10(intensity_ratio)\n\n # Saving computed intensity value for reuse, and returning it.\n\n self.intensity_list[step_index] = intensity\n return intensity","def wav_to_intensity(path, sr=16000, offset=10):\n sound = parselmouth.Sound(path)\n intensity = sound.to_intensity()\n\n features = []\n\n max_time = sound.get_total_duration()\n\n for time in np.arange(0, max_time, 0.001):\n int_db = intensity.get_value(time)\n if np.isnan(int_db):\n int_db = 0\n\n features.append([int_db])\n\n array_feats = np.array(features).T\n\n print(\"SHAPE OF THE FEATURES:\", array_feats.shape)\n assert(not np.any(np.isnan(array_feats)))\n\n return array_feats, max_time","def profile(self):\n return NumericStatsMixin.profile(self)","def plot_intensity_prop(stack, wavelengths_arr, colors_arr):\n\n # for the electric fields profile\n for i, wl in enumerate(wavelengths_arr):\n electric_tot_te, electric_tot_tm, reflectivity_te, reflectivity_tm, transmission_te, transmission_tm, index_tot, L_tot, theta_tot = transfer_matrix_method(\n stack, 1, 0, wl, 0)\n intensity = np.abs(electric_tot_te[::-1]) ** 2\n plt.plot(L_tot * 1e6, intensity / max(intensity) * 2, color=colors_arr[i])\n # for the indexes profile\n ax.plot(L_tot * 1e6, index_tot[::-1], color='black')\n ax.fill_between(L_tot * 1e6, index_tot[::-1], color='azure')","def intensity(self):\n LP = 1/np.sin(self.theta)**2/np.cos(self.theta)\n P = 1 + np.cos(2*self.theta)**2\n I = (np.abs(self.F))**2*LP*P\n self.xrd_intensity = I\n self.theta2 = 2*self.theta\n rank = np.argsort(self.theta2)\n self.theta2 = self.theta2[rank]\n self.hkl_list = self.hkl_list[rank]\n self.d_hkl = self.d_hkl[rank]\n self.xrd_intensity = self.xrd_intensity[rank]","def metric_iaf(self, x):\n data = np.asarray(x['data'])\n iaf = [10.0] * data.shape[0]\n for ch, ch_data in enumerate(data):\n pxx, freqs = mlab.psd(ch_data, Fs=128.0, NFFT=256)\n alpha_mask = np.abs(freqs - 10) <= 2.0\n alpha_pxx = 10*np.log10(pxx[alpha_mask])\n alpha_pxx = scipy.signal.detrend(alpha_pxx)\n # iaf[ch] = alpha_pxx.shape\n iaf[ch] = freqs[alpha_mask][np.argmax(alpha_pxx)]\n return iaf","def depth2intensity(depth, interval=300):\n return depth * 3600 / interval","def info_np(img):\n import numpy as np\n\n print ('Dimensions: ' + str(np.shape(img)))\n print ('Min value: ' + str(np.min(img)))\n print ('Avg value: ' + str(np.average(img)))\n print ('Med value: ' + str(np.median(img)))\n print ('Max value: ' + str(np.max(img)))\n print ('Std dev: ' + str(np.std(img)))\n print ('Sum: ' + str(np.sum(img)))","def get_intensity_normalization(self):\n return self.intensity_normalize_image","def sphere_r_intensity(img):\n pixels = []\n for j in range(0, img.shape[0]):\n for i in range(1, 40):\n pixels.append(img[j, img.shape[1] - i])\n\n return np.mean(pixels)","def statistics_from_array(x: numpy.ndarray):\n try:\n return x.mean(), x.std(), x.max(), x.min()\n except AttributeError:\n return numpy.nan, numpy.nan, numpy.nan, numpy.nan","def analyze(self):\n try:\n self.options[self.multi_image][1]()\n except:\n raise Exception(\"Multi Image Option not defined.\")\n\n self.image = self.data / self.exposure\n\n background = self.min_val = np.min(self.image[:511,:511])\n self.max_val = np.max(self.image[:511,:511])\n # stats.mode returns modal value = value that occours most often\n #background = stats.mode(im[:50,:50].ravel())[0][0]\n\n intensity = self.image.sum() - background*np.size(self.image)\n\n #results.append((self.index, intensity, background))\n self.index =+ 1","def extract_temp_integral_2_to_100(batch,index):\n from scipy import integrate\n integral = []\n for ind in index:\n cell_no = list(batch.keys())[ind]\n integrate_ = integrate.simps(batch[cell_no]['summary']['Tavg'][1:100])\n # integral.append(integrate_)\n integral.append(log(abs(integrate_),10))\n integral = np.reshape(integral,(-1,1))\n return integral\n pass","def make_lineprofile(npix,rstar,xc,vgrid,A,veq,linewidth):\n vc=(np.arange(npix)-xc)/rstar*veq\n vs=vgrid[np.newaxis,:]-vc[:,np.newaxis]\n profile=1.-A*np.exp( -(vs*vs)/2./linewidth**2)\n return profile","def createdog(self,imagearr):\n re = [0,1,2,3]\n re[0] = self.diff(self.gs_blur(self.sigma,imagearr))\n for i in range(1,4):\n base = self.sampling(re[i-1][2])\n re[i] = self.diff(self.gs_blur(self.sigma, base))\n return re","def get_threshold_data(self):\n return [roi.get_threshold_data() for roi in self.rois]","def fiber_profile(x, y, r0, blur=0.1):\n r = np.sqrt(x ** 2 + y ** 2)\n return 0.5 + 0.5 * scipy.special.erf((r0 - r) / (np.sqrt(2) * blur))","def getNoiseVar(img,fraction=0.95):\n last_val = np.percentile(img,fraction)\n #si(img<last_val,title=\"Pixel values considered as noise\")\n return np.var(img[img<last_val])","def profile(x):\n return x","def intensityPSF_Fire(N=1000):\n col_seq = [ ( 0/255., 0/255., 0/255.),\n ( 0/255., 0/255., 22/255.),\n ( 0/255., 0/255., 45/255.),\n ( 0/255., 0/255., 65/255.),\n ( 0/255., 0/255., 78/255.),\n ( 0/255., 0/255., 91/255.),\n ( 7/255., 0/255., 104/255.),\n ( 16/255., 0/255., 117/255.),\n ( 25/255., 0/255., 130/255.),\n ( 34/255., 0/255., 143/255.),\n ( 43/255., 0/255., 156/255.),\n ( 52/255., 0/255., 168/255.),\n ( 55/255., 0/255., 171/255.),\n ( 58/255., 0/255., 175/255.),\n ( 61/255., 0/255., 178/255.),\n ( 64/255., 0/255., 181/255.),\n ( 67/255., 0/255., 185/255.),\n ( 70/255., 0/255., 188/255.),\n ( 73/255., 0/255., 192/255.),\n ( 76/255., 0/255., 195/255.),\n ( 79/255., 0/255., 199/255.),\n ( 82/255., 0/255., 202/255.),\n ( 85/255., 0/255., 206/255.),\n ( 88/255., 0/255., 209/255.),\n ( 91/255., 0/255., 213/255.),\n ( 94/255., 0/255., 216/255.),\n ( 98/255., 0/255., 220/255.),\n (101/255., 0/255., 220/255.),\n (104/255., 0/255., 221/255.),\n (107/255., 0/255., 222/255.),\n (110/255., 0/255., 223/255.),\n (113/255., 0/255., 224/255.),\n (116/255., 0/255., 225/255.),\n (119/255., 0/255., 226/255.),\n (122/255., 0/255., 227/255.),\n (125/255., 0/255., 224/255.),\n (128/255., 0/255., 222/255.),\n (131/255., 0/255., 220/255.),\n (134/255., 0/255., 218/255.),\n (137/255., 0/255., 216/255.),\n (140/255., 0/255., 214/255.),\n (143/255., 0/255., 212/255.),\n (146/255., 0/255., 210/255.),\n (148/255., 0/255., 206/255.),\n (150/255., 0/255., 202/255.),\n (152/255., 0/255., 199/255.),\n (154/255., 0/255., 195/255.),\n (156/255., 0/255., 191/255.),\n (158/255., 0/255., 188/255.),\n (160/255., 0/255., 184/255.),\n (162/255., 0/255., 181/255.),\n (163/255., 0/255., 177/255.),\n (164/255., 0/255., 173/255.),\n (166/255., 0/255., 169/255.),\n (167/255., 0/255., 166/255.),\n (168/255., 0/255., 162/255.),\n (170/255., 0/255., 158/255.),\n (171/255., 0/255., 154/255.),\n (173/255., 0/255., 151/255.),\n (174/255., 0/255., 147/255.),\n (175/255., 0/255., 143/255.),\n (177/255., 0/255., 140/255.),\n (178/255., 0/255., 136/255.),\n (179/255., 0/255., 132/255.),\n (181/255., 0/255., 129/255.),\n (182/255., 0/255., 125/255.),\n (184/255., 0/255., 122/255.),\n (185/255., 0/255., 118/255.),\n (186/255., 0/255., 114/255.),\n (188/255., 0/255., 111/255.),\n (189/255., 0/255., 107/255.),\n (190/255., 0/255., 103/255.),\n (192/255., 0/255., 100/255.),\n (193/255., 0/255., 96/255.),\n (195/255., 0/255., 93/255.),\n (196/255., 1/255., 89/255.),\n (198/255., 3/255., 85/255.),\n (199/255., 5/255., 82/255.),\n (201/255., 7/255., 78/255.),\n (202/255., 8/255., 74/255.),\n (204/255., 10/255., 71/255.),\n (205/255., 12/255., 67/255.),\n (207/255., 14/255., 64/255.),\n (208/255., 16/255., 60/255.),\n (209/255., 19/255., 56/255.),\n (210/255., 21/255., 53/255.),\n (211/255., 22/255., 51/255.),\n (212/255., 24/255., 49/255.),\n (213/255., 27/255., 45/255.),\n (214/255., 29/255., 42/255.),\n (215/255., 32/255., 38/255.),\n (217/255., 35/255., 35/255.),\n (218/255., 37/255., 31/255.),\n (219/255., 39/255., 29/255.),\n (220/255., 40/255., 27/255.),\n (220/255., 41/255., 25/255.),\n (221/255., 43/255., 23/255.),\n (222/255., 44/255., 21/255.),\n (223/255., 46/255., 20/255.),\n (224/255., 48/255., 16/255.),\n (225/255., 49/255., 14/255.),\n (226/255., 51/255., 12/255.),\n (227/255., 54/255., 8/255.),\n (228/255., 55/255., 6/255.),\n (229/255., 57/255., 5/255.),\n (230/255., 59/255., 4/255.),\n (231/255., 62/255., 3/255.),\n (233/255., 65/255., 3/255.),\n (234/255., 68/255., 2/255.),\n (235/255., 70/255., 1/255.),\n (237/255., 73/255., 1/255.),\n (238/255., 76/255., 0/255.),\n (240/255., 79/255., 0/255.),\n (241/255., 81/255., 0/255.),\n (243/255., 84/255., 0/255.),\n (244/255., 87/255., 0/255.),\n (246/255., 90/255., 0/255.),\n (247/255., 92/255., 0/255.),\n (249/255., 95/255., 0/255.),\n (250/255., 98/255., 0/255.),\n (252/255., 101/255., 0/255.),\n (252/255., 103/255., 0/255.),\n (252/255., 105/255., 0/255.),\n (253/255., 107/255., 0/255.),\n (253/255., 109/255., 0/255.),\n (253/255., 111/255., 0/255.),\n (254/255., 113/255., 0/255.),\n (254/255., 115/255., 0/255.),\n (255/255., 117/255., 0/255.),\n (255/255., 119/255., 0/255.),\n (255/255., 121/255., 0/255.),\n (255/255., 123/255., 0/255.),\n (255/255., 125/255., 0/255.),\n (255/255., 127/255., 0/255.),\n (255/255., 129/255., 0/255.),\n (255/255., 131/255., 0/255.),\n (255/255., 133/255., 0/255.),\n (255/255., 134/255., 0/255.),\n (255/255., 136/255., 0/255.),\n (255/255., 138/255., 0/255.),\n (255/255., 140/255., 0/255.),\n (255/255., 141/255., 0/255.),\n (255/255., 143/255., 0/255.),\n (255/255., 145/255., 0/255.),\n (255/255., 147/255., 0/255.),\n (255/255., 148/255., 0/255.),\n (255/255., 150/255., 0/255.),\n (255/255., 152/255., 0/255.),\n (255/255., 154/255., 0/255.),\n (255/255., 155/255., 0/255.),\n (255/255., 157/255., 0/255.),\n (255/255., 159/255., 0/255.),\n (255/255., 161/255., 0/255.),\n (255/255., 162/255., 0/255.),\n (255/255., 164/255., 0/255.),\n (255/255., 166/255., 0/255.),\n (255/255., 168/255., 0/255.),\n (255/255., 169/255., 0/255.),\n (255/255., 171/255., 0/255.),\n (255/255., 173/255., 0/255.),\n (255/255., 175/255., 0/255.),\n (255/255., 176/255., 0/255.),\n (255/255., 178/255., 0/255.),\n (255/255., 180/255., 0/255.),\n (255/255., 182/255., 0/255.),\n (255/255., 184/255., 0/255.),\n (255/255., 186/255., 0/255.),\n (255/255., 188/255., 0/255.),\n (255/255., 190/255., 0/255.),\n (255/255., 191/255., 0/255.),\n (255/255., 193/255., 0/255.),\n (255/255., 195/255., 0/255.),\n (255/255., 197/255., 0/255.),\n (255/255., 199/255., 0/255.),\n (255/255., 201/255., 0/255.),\n (255/255., 203/255., 0/255.),\n (255/255., 205/255., 0/255.),\n (255/255., 206/255., 0/255.),\n (255/255., 208/255., 0/255.),\n (255/255., 210/255., 0/255.),\n (255/255., 212/255., 0/255.),\n (255/255., 213/255., 0/255.),\n (255/255., 215/255., 0/255.),\n (255/255., 217/255., 0/255.),\n (255/255., 219/255., 0/255.),\n (255/255., 220/255., 0/255.),\n (255/255., 222/255., 0/255.),\n (255/255., 224/255., 0/255.),\n (255/255., 226/255., 0/255.),\n (255/255., 228/255., 0/255.),\n (255/255., 230/255., 0/255.),\n (255/255., 232/255., 0/255.),\n (255/255., 234/255., 0/255.),\n (255/255., 235/255., 4/255.),\n (255/255., 237/255., 8/255.),\n (255/255., 239/255., 13/255.),\n (255/255., 241/255., 17/255.),\n (255/255., 242/255., 21/255.),\n (255/255., 244/255., 26/255.),\n (255/255., 245/255., 28/255.),\n (255/255., 246/255., 30/255.),\n (255/255., 247/255., 33/255.),\n (255/255., 248/255., 35/255.),\n (255/255., 248/255., 42/255.),\n (255/255., 249/255., 50/255.),\n (255/255., 250/255., 58/255.),\n (255/255., 251/255., 66/255.),\n (255/255., 252/255., 74/255.),\n (255/255., 253/255., 82/255.),\n (255/255., 254/255., 90/255.),\n (255/255., 254/255., 95/255.),\n (255/255., 255/255., 98/255.),\n (255/255., 255/255., 101/255.),\n (255/255., 255/255., 103/255.),\n (255/255., 255/255., 105/255.),\n (255/255., 255/255., 108/255.),\n (255/255., 255/255., 113/255.),\n (255/255., 255/255., 117/255.),\n (255/255., 255/255., 121/255.),\n (255/255., 255/255., 129/255.),\n (255/255., 255/255., 132/255.),\n (255/255., 255/255., 136/255.),\n (255/255., 255/255., 144/255.),\n (255/255., 255/255., 149/255.),\n (255/255., 255/255., 152/255.),\n (255/255., 255/255., 160/255.),\n (255/255., 255/255., 163/255.),\n (255/255., 255/255., 167/255.),\n (255/255., 255/255., 170/255.),\n (255/255., 255/255., 175/255.),\n (255/255., 255/255., 179/255.),\n (255/255., 255/255., 183/255.),\n (255/255., 255/255., 187/255.),\n (255/255., 255/255., 191/255.),\n (255/255., 255/255., 194/255.),\n (255/255., 255/255., 199/255.),\n (255/255., 255/255., 203/255.),\n (255/255., 255/255., 207/255.),\n (255/255., 255/255., 211/255.),\n (255/255., 255/255., 215/255.),\n (255/255., 255/255., 223/255.),\n (255/255., 255/255., 227/255.),\n (255/255., 255/255., 231/255.),\n (255/255., 255/255., 235/255.),\n (255/255., 255/255., 239/255.),\n (255/255., 255/255., 243/255.),\n (255/255., 255/255., 245/255.),\n (255/255., 255/255., 246/255.),\n (255/255., 255/255., 247/255.),\n (255/255., 255/255., 251/255.),\n (255/255., 255/255., 255/255.),\n (255/255., 255/255., 255/255.),\n (255/255., 255/255., 255/255.),\n (255/255., 255/255., 255/255.),\n (255/255., 255/255., 255/255.),\n (255/255., 255/255., 255/255.) ]\n\n seqLen = len(col_seq)\n delta = 1.0/(seqLen - 1)\n r_tuple = ((i*delta, col_seq[i][0], col_seq[i][0]) for i in range(seqLen))\n g_tuple = ((i*delta, col_seq[i][1], col_seq[i][1]) for i in range(seqLen))\n b_tuple = ((i*delta, col_seq[i][2], col_seq[i][2]) for i in range(seqLen))\n cdict = {'red': tuple(r_tuple),\n 'green': tuple(g_tuple),\n 'blue': tuple(b_tuple)}\n firecm = _mplb.colors.LinearSegmentedColormap('psffire', cdict, N)\n return firecm","def get_temp(self) -> float:\n return np.round(np.mean(self.temp_data), 1)","def profile(self):\n\n def _flatten(f):\n return [coeffifient for value in f.values()\\\n for coeffifient in value.coefficients()]\n\n elements = _flatten(self.domain().j) +\\\n _flatten(self.codomain().j) +\\\n _flatten(self)\n\n\n profile = enveloping_profile_elements(elements)\n\n # Avoid returning the zero profile because it triggers a corner case\n # in FP_Module_class.resolution().\n # \n # XXX: Fix FP_Module_class.resolution().\n #\n return (1,) if profile == (0,) else profile","def s2profile(r,r0,A,B):\n x = r/r0\n res = A*4./(np.exp(x)+np.exp(-x))**2 + B\n return res","def addNoise(array,counts):\r\n if array.dtype == 'complex' :\r\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\r\n else :\r\n if np.float64(counts) == 0.0e0 :\r\n arrayout = np.zeros(array.shape, dtype=arrayout.dtype)\r\n elif np.float64(counts) < 0.0e0 :\r\n print 'bg.addNoise : warning counts < 0'\r\n elif np.float64(counts) > 1.0e9 :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = normaliseInt(array)\r\n arrayout = np.random.normal(arrayout*np.float64(counts),np.sqrt(arrayout*np.float64(counts)))/np.float64(counts)\r\n tot = np.sum(array)\r\n arrayout = normaliseInt(arrayout,tot)\r\n else :\r\n arrayout = np.zeros(array.shape)\r\n arrayout = normaliseInt(array)\r\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\r\n tot = np.sum(array)\r\n arrayout = normaliseInt(arrayout,tot)\r\n return arrayout","def calcthresh(self, img):\n mu = np.mean(img.ravel())\n sd = np.std(img.ravel())\n return mu + 3 * sd","def observation(self, img):\r\n img = img.transpose(1, 2, 0)\r\n return img","def scipyTheilSen(array):\n\ttry:\n\t\tslope, intercept, _, _ = stats.mstats.theilslopes(array)\n\t\treturn slope #, intercept\n\texcept Exception as e:\n\t \tprint(e) \n\t \tipdb.set_trace()\n\t \treturn np.NAN","def getIntensitySpectum(self,wavelengths,intensities = 1.0):\n angles,peaks,widths = self.getSpectrum(wavelengths,intensities)\n\n # Get arnge of angles to calulate over\n minField = np.min(angles) - 10*np.max(widths)\n maxField = np.max(angles) + 10*np.max(widths)\n # Sample finely enough to make peak widths visible, but least 400points\n npoints = max(int((maxField - minField)/np.min(widths)),400)\n\n # Make the two two array for the output data\n fieldAngle = np.linspace(minField,maxField,npoints)\n spectralOutput = np.zeros(fieldAngle.size)\n\n # Add each peak in turn\n for a,p,w in zip(angles,peaks,widths):\n\n for i,af in enumerate(fieldAngle):\n s = self.lineShape(a,w,af) # Add the spectrometer lineshape\n spectralOutput[i] += p*s\n\n # Return the two numpy arrays as a list\n return fieldAngle,spectralOutput","def autothreshold(gray_im, method=\"otsu\"):\n if method == \"otsu\":\n t = otsu(gray_im)\n elif method == \"kmeans\":\n t = ave(kmeans(list(gray_im.getdata())))\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?","def get_statistical_information(mat, percentile=(0, 100), denoise=False):\n if denoise is True:\n mat = ndi.gaussian_filter(mat, 2)\n gmin = np.min(mat)\n gmax = np.max(mat)\n min_percent = np.percentile(mat, percentile[0])\n max_percent = np.percentile(mat, percentile[-1])\n median = np.median(mat)\n mean = np.mean(mat)\n variance = np.var(mat)\n return gmin, gmax, min_percent, max_percent, mean, median, variance","def get_rain_values(self):\n return float(self.data[2]) / 10, float(self.data[4]) / 10","def weights(self):\n return np.array(self.intensity[self.idx])","def avg_temps(self):\r\n average_temp = 0\r\n for j in range(len(self.trip)):\r\n average_temp += self.trip[j].get_temperature(j)\r\n average_temp /= len(self.trip)\r\n return average_temp","def switchy_score(array):\n array = np.array(array)\n variance = 1 - np.std(np.sin(array[~np.isnan(array)] * np.pi))\n mean_value = -np.mean(np.cos(array[~np.isnan(array)] * np.pi))\n return variance * mean_value","def calculate_incoherent_scattered_intensity(element, q):\n return current_calculator.get_incoherent_intensity(element, q)","def har_mean(array):\n return ((sum([1/x for x in array]))**(-1))*len(array)","def intensity_histogram_measures(regionmask, intensity):\n feat = Intensity_Histogram_Measures(\n [\n np.percentile(intensity[regionmask], 0),\n np.percentile(intensity[regionmask], 25),\n np.percentile(intensity[regionmask], 50),\n np.percentile(intensity[regionmask], 75),\n np.percentile(intensity[regionmask], 100),\n np.mean(intensity[regionmask]),\n stats.mode(intensity[regionmask], axis=None)[0][0],\n np.std(intensity[regionmask]),\n ]\n )\n return feat","def magerr2Ivar(flux, magErr):\n fluxErr = flux * ((10.0 ** (magErr/2.5)) - 1.0)\n\n return 1.0 / (fluxErr ** 2.0)","def calc_profile(self, phases):\n self._profile = self._generator(phases)\n self._Amax = self.Amax if hasattr(self, '_Amax') else np.max(self.profile)\n return self.profile / self.Amax","def iou_stats(pred, target, num_classes=6, background=5):\n # Set redundant classes to background.\n locs = np.logical_and(target > -1, target < num_classes)\n\n # true positive + false negative\n tp_fn, _ = np.histogram(target[locs],\n bins=np.arange(num_classes+1))\n # true positive + false positive\n tp_fp, _ = np.histogram(pred[locs],\n bins=np.arange(num_classes+1))\n # true positive\n tp_locs = np.logical_and(locs, pred == target)\n tp, _ = np.histogram(target[tp_locs],\n bins=np.arange(num_classes+1))\n\n return tp_fn, tp_fp, tp","def calc_flux_array(self):\n \n # First determine the associated spectrum\n self.compute_template_spectrum()\n\n # Calculate baseline counts to normalise fluxes we scan over\n # Go from 10**(bin_min)*mean up to 10**(bin_max)*mean in nbins steps\n b = self.setup_b_instance(0,add_ps_mask=True)\n mean = np.sum(b.CTB_masked_compressed[0])/len(b.CTB_masked_compressed[0])\n A_array = mean*10**np.linspace(self.bin_min,self.bin_max,self.nbins)\n\n # Array to get LLs when no profile likelihood run\n norun = np.array([1.0, 1.0, 1.0, 1.0])\n\n # Now setup and compute the arrays\n LL_array = np.array([]) \n A_array_short = np.array([])\n spect_array = np.array([])\n\n for i in range(len(A_array)):\n print \"on i =\",i\n # Calculate LL\n if i == 0:\n b1 = self.setup_b_instance(A_array[i],add_ps_mask=True)\n else:\n for key in b1.fixed_template_dict_nested.keys():\n b1.fixed_template_dict_nested[key] = b1.fixed_template_dict_nested[key]*A_array[i]/A_array[i-1]\n ll_val = b1.ll(norun,4,4)\n # Make triangle\n\n # Append to arrays\n LL_array = np.append(LL_array,ll_val)\n A_array_short = np.append(A_array_short,A_array[i])\n spect_array = self.spectrum*np.array(A_array_short)\n\n # Save output\n np.save(work_dir+'ScanOutput/'+self.tag+'/En_array-'+str(self.flux_array_ebin)+'.npy',self.En_center)\n np.save(work_dir+'ScanOutput/'+self.tag+'/LL_array-'+str(self.flux_array_ebin)+'.npy',LL_array)\n np.save(work_dir+'ScanOutput/'+self.tag+'/Flux_array-'+str(self.flux_array_ebin)+'.npy',spect_array)","def get_specific_heat() -> float:\n return 1006.0","def t_measure_estimate(self):\n ho = self.humidity_oversampling\n to = self.temperature_oversampling\n po = self.pressure_oversampling\n typ = 1. + 2.*to + (2.*po + 0.5)*bool(po) + (2.*ho +0.5)*bool(ho)\n mx = 1.25 + 2.3*to + (2.3*po + 0.575)*bool(po) + (2.3*ho +0.575)*bool(ho)\n return typ, mx","def tsz_profile_highacc(self, nu=None):\n bb = np.linspace(0.0, self.bmax, 100) # Range of impact parameters\n rr = bb * self.r500\n ysz = map(lambda b: scipy.integrate.quad(self._ig_tsz, b+0.0001,\n self.bmaxc, args=(b,))[0], bb)\n if nu == None:\n g_nu = 1. # Factor-out spectral dependence\n else:\n g_nu = self.tsz_spectrum(nu)\n fac_ysz = (2. * 2. * 2.051 / 511.) * self.r500\n ysz = g_nu * fac_ysz * np.array(ysz)\n interp = scipy.interpolate.interp1d( rr, ysz, kind='linear', \n bounds_error=False, fill_value=0.0 )\n return interp","def impute_cumulative_array(array):\n array = np.array(array).copy()\n array = convert_non_monotonic_to_nan(array)\n array = log_interpolate(array)\n return array","def intensity( rgb ):\n return int( (rgb[0] + rgb[1] + rgb[2])/3 )","def GetGrayArray(self, p_int):\n ...","def profile(self) -> Optional[Any]:\n\n def get_profile_attribute(numpy_data, attr_name):\n if isinstance(numpy_data, dict):\n return {key: getattr(array, attr_name) for key, array in numpy_data.items()}\n else:\n return getattr(numpy_data, attr_name)\n\n profile = {\n \"features_shape\": get_profile_attribute(self._features, \"shape\"),\n \"features_size\": get_profile_attribute(self._features, \"size\"),\n \"features_nbytes\": get_profile_attribute(self._features, \"nbytes\"),\n }\n if self._targets is not None:\n profile.update(\n {\n \"targets_shape\": get_profile_attribute(self._targets, \"shape\"),\n \"targets_size\": get_profile_attribute(self._targets, \"size\"),\n \"targets_nbytes\": get_profile_attribute(self._targets, \"nbytes\"),\n }\n )\n\n return profile","def get_incoherent_intensity(self, element: str, q):\n fs_coherent = self.get_coherent_scattering_factor(element, q)\n intensity_coherent = fs_coherent ** 2\n s = q / (4 * np.pi)\n Z = float(self.incoherent_param['Z'][element])\n M = float(self.incoherent_param['M'][element])\n K = float(self.incoherent_param['K'][element])\n L = float(self.incoherent_param['L'][element])\n intensity_incoherent = (Z - intensity_coherent / Z) * (1 - M * (np.exp(-K * s) - np.exp(-L * s)))\n return intensity_incoherent","def get_mean(self):\r\n for i in range(1,len(self.data[0])):\r\n self.prom.append(np.mean(self.data[:,i]))","def sphere_l_intensity(img):\n pixels = []\n for j in range(0, img.shape[0]):\n for i in range(1, 40):\n pixels.append(img[j, i])\n\n return np.mean(pixels)","def geo_mean(array):\n logsum = sum([np.log(each) for each in array])/len(array)\n return np.exp(logsum)","def getIntensity(self, pos):\n #Camera doesnt have position so im just using the position of the followed object (of 1st camera)\n camPos = glad.renderer.cameraList[0].objectFollowed.getPos()\n\n r=(pos-camPos)#separation vector\n if r.isNullVector(): #if the vector is null, sound will be max anyways\n sin = 1\n cos = 1\n else:\n #calculate angles to determine where sound is coming from\n cos = dotProduct(r.getNormalized(),Vector(-1,0))\n sin = dotProduct(r.getNormalized(), Vector(0,1))\n #Calculate intensity for left and right channels\n #when sound is directly to the side have 80 percent come from that side speaker\n #hopefully this will give some directional sounds\n k = 130000 #arbitrary constant to calculate sound intensity\n if r.isNullVector():\n intensity = k #removes division by zero error\n else:\n intensity = k/r.getMagnitude()**2\n #major is the percent of the sound intensity from the side with the greater intensity\n a=0.68 #max percent of the intensity coming from one side\n major = (a*0.5)/((0.5*cos)**2+(a*sin)**2)**0.5 #equation for an ellipse\n if r[0] <= 0:\n right = major\n left = 1-major\n else:\n left = major\n right = 1-major\n right *= intensity\n left *= intensity\n if right > 1: right = 1\n if left > 1: left = 1\n return left,right","def _find_saturated_profiles(self) -> np.ndarray:\n n_gates, var_lim, _, _ = self.noise_params\n var = np.var(self.data['backscatter'][:, -n_gates:], axis=1)\n return var < var_lim","def get_metrics(self) -> np.ndarray:\n dice = np.mean(self.dice_scores)\n iou = np.mean(self.iou_scores)\n sens = np.mean(self.sens_scores)\n spec = np.mean(self.spec_scores)\n accu = np.mean(self.accu_scores)\n return dice, iou, sens, spec, accu","def normalize_profile(ph):\n return ph/np.abs(ph[1])","def areaFraction(nb_probes, I):\n P = np.random.randint(I.shape[0], size=(nb_probes, 2))\n\n # count the number of probes in phase\n count = np.sum(I[P[:, 0], P[:, 1]])\n\n# fig=plt.figure();\n# plt.imshow(I);\n# plt.plot(P[:,0], P[:,1], '+');\n# plt.show();\n# fig.savefig('areaFrac.pdf', bbox_inches='tight');\n return float(count) / nb_probes","def _calc_npixels_fired(self, r0, z, pwm):\n N0 = 500 * self.npix * _pwm_multiplier(pwm)\n Nraw = N0 * z / (r0**2 + z**2)**1.5\n return self.npix * (1 - np.exp(-Nraw / self.npix))","def find_backstats(f_arr, sigma, niter):\n ave = f_arr.mean()\n std = f_arr.std()\n for i in range(niter):\n mask = (abs(f_arr - ave) < sigma * std)\n ave = f_arr[mask].mean()\n std = f_arr[mask].std()\n return ave, std","def MeanPixArr(PixArr, binary=True):\n \n import numpy as np\n \n F, R, C = PixArr.shape\n \n # Initialise MeanPixArr:\n MeanPixArr = np.zeros((1, R, C), dtype='uint')\n \n \n result = np.mean(PixArr, axis=0)\n \n if binary:\n MeanPixArr[0] = (result >= 0.5) * result\n else:\n MeanPixArr[0] = result\n \n return MeanPixArr","def result_array(self) -> np.ndarray:\n return np.array([r[\"time\"] for r in self.profile_result])","def getScalarFlux(self):\n totScalarFlux = []\n for cell in self.cells:\n totScalarFlux.append(cell.getTotScalarFlux())\n totScalarFlux = np.array(totScalarFlux)\n #return totScalarFlux / np.sum(totScalarFlux) # norm flux to 1.\n return totScalarFlux","def average_profile_in_bins(Redges, R, prof):\n if np.min(Redges) < np.min(R):\n raise Exception(\"Minimum edge must be >= minimum R\")\n if np.max(Redges) > np.max(R):\n raise Exception(\"Maximum edge must be <= maximum R\")\n ave_prof = np.zeros(len(Redges)-1)\n cluster_toolkit._lib.average_profile_in_bins(_dcast(Redges), len(Redges), _dcast(R), len(R), _dcast(prof), _dcast(ave_prof))\n return ave_prof","def findRMpeaks(self, pix, threshold):\n\t\tsigma = np.std(self.getz(pix))\n\t\tdetections = []\n\t\tfor i, phi in enumerate(self.getz(pix)):\n \t\t \tif phi > threshold*sigma: detections.append(i)\n \t \treturn detections","def skystats(stamp):\n\t\n\tif isinstance(stamp, galsim.Image):\n\t\ta = stamp.array\n\t\t# Normally there should be a .transpose() here, to get the orientation right.\n\t\t# But in the present case it doesn't change anything, and we can skip it.\n\telse:\n\t\ta = stamp # Then we assume that it's simply a numpy array.\n\t\n\tedgepixels = np.concatenate([\n\t\t\ta[0,1:], # left\n\t\t\ta[-1,1:], # right\n\t\t\ta[:,0], # bottom\n\t\t\ta[1:-1,-1] # top\n\t\t\t])\n\tassert len(edgepixels) == 2*(a.shape[0]-1) + 2*(a.shape[0]-1)\n\n\t# And we convert the mad into an estimate of the Gaussian std:\n\treturn {\n\t\t\"std\":np.std(edgepixels), \"mad\": 1.4826 * mad(edgepixels),\n\t\t\"mean\":np.mean(edgepixels), \"med\":np.median(edgepixels),\n\t\t\"stampsum\":np.sum(a)\n\t\t}"],"string":"[\n \"def intensity(self) -> int:\",\n \"def intensity(self):\\r\\n return np.power(prb.amplitude, 2)\",\n \"def getIntensity(self):\\n return self.getIntensityS() + self.getIntensityP()\",\n \"def estimate_skin_tone(face_roi):\\n return [int(face_roi[:, :, i].mean()) for i in range(face_roi.shape[-1])]\",\n \"def getIntensity(self):\\n return self.__intensity\",\n \"def turbulence_intensity(self):\\n return self.flow_field.turbulence_intensity\",\n \"def getIntensityP(self):\\n return self._Epi.intensity()\",\n \"def detector_intensity(val_addr):\\n \\n return(np.concatenate((np.unique(val_addr, return_counts=True)[1][:13],[0],np.unique(val_addr, return_counts=True)[1][13:]),axis=0))\",\n \"def getstats(img, thresholds):\\n number = np.zeros(img.shape, np.float64)\\n ev = np.zeros(img.shape, np.float64)\\n scatter = np.zeros(img.shape, np.float64)\\n for n, s, low, high, evs in thresholds:\\n for i in numba.prange(img.shape[0]):\\n for j in numba.prange(img.shape[1]):\\n if (low < img[i, j]) and (img[i, j] < high):\\n scatter[i, j] = s\\n number[i, j] = n\\n ev[i, j] = img[i, j] - evs\\n return ev, number, scatter\",\n \"def extract_intensity(seg, fluo_im):\\n\\n # Get the region props of the fluorescence image using the segmentation\\n # mask.\\n props = skimage.measure.regionprops(seg, intensity_image=fluo_im)\\n cell_ints = []\\n for prop in props:\\n cell_ints.append(prop.mean_intensity)\\n\\n # Convert the cell_ints to an array and return.\\n return np.array(cell_ints)\",\n \"def intensity(self, value: int, /) -> None:\",\n \"def apphot(im, yx, rap, subsample=4, **kwargs):\\n n, f = anphot(im, yx, rap, subsample=subsample, **kwargs)\\n if np.size(rap) > 1:\\n return n.cumsum(-1), f.cumsum(-1)\\n else:\\n return n, f\",\n \"def OF1_CalculateRawHistogram(image):\\n h = np.zeros(256, np.float_)\\n for i in np.nditer(image):\\n h[i - 1] = h[i - 1] + 1\\n\\n return h\",\n \"def compute_intensity(self, data):\\n # Compute RMS intensity (as float to prevent overflow).\\n data = self.audio_resample(data.astype(np.float32)**2)**0.5\\n\\n # Apply dynamic range compression.\\n return data**self._exponent\",\n \"def confint(arr):\\n res=[[],[],[]]\\n #r=hpd(arr)\\n r=(sap(arr,2.5),sap(arr,97.5))\\n res[0]=r[0]\\n res[1]=arr.mean(0)\\n res[2]=r[1]\\n return np.array(res)\",\n \"def EstimateIlluminationProfile(image, kernel_sigma): \\n \\n illumination_profile = Denoising(image, kernel_sigma);\\n \\n map_corr = np.ones(image.shape);\\n map_corr = Denoising(map_corr, kernel_sigma);\\n \\n illumination_profile = illumination_profile / map_corr;\\n \\n return illumination_profile;\",\n \"def profile_from_xvalues(self, xvalues):\\r\\n\\r\\n transformed_xvalues = xvalues - self.centre\\r\\n\\r\\n return np.multiply(\\r\\n np.divide(self.intensity, self.sigma * np.sqrt(2.0 * np.pi)),\\r\\n np.exp(-0.5 * np.square(np.divide(transformed_xvalues, self.sigma))),\\r\\n )\",\n \"def getIntensityS(self):\\n return self._Esigma.intensity()\",\n \"def measure_amplitude(self, ipx, ipy):\\r\\n\\r\\n intensity = np.mean(np.mean(self.imageData[:, ipx-1:ipx+1, ipy-1:ipy+1], axis=2), axis=1)\\r\\n\\r\\n # fold over repetitions\\r\\n\\r\\n remapped = intensity.reshape( self.nrepetitions, intensity.shape[0]/self.nrepetitions).mean(axis=0)\\r\\n\\r\\n return remapped\",\n \"def get_fthreshold(img,factor=1.):\\n import noiselevel\\n # sigma=Table.read('noiselevel.csv',format='csv')['sigma'][0]\\n sigma=noiselevel.getnoiselevel(img,ranges=(-30,30),toplot=False)\\n \\n thres= sigma*factor\\n return thres,sigma\",\n \"def detectable_intensity(self):\\n return self.disease.detectable_intensity()\",\n \"def tempProfile_hocuk(self,dist,draine=1):\\n radius = np.linspace(0,self.size * self.dist * au2cm,100)\\n density = self.denProfile(radius)\\n dr = radius[1] - radius[0]\\n tdust = np.zeros(len(dist))\\n for i in range(len(dist)):\\n av = self.compute_av(dist[i],radius,density,dr)\\n tdust[i] =(11 + 5.7*np.tanh(0.61 - np.log10(av)))*draine**(1/5.9)\\n return tdust\",\n \"def addNoise_amp(array,counts):\\r\\n if array.dtype == 'complex' :\\r\\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\\r\\n else :\\r\\n if np.float64(counts) == 0.0e0 :\\r\\n arrayout = np.copy(array)\\r\\n elif np.float64(counts) < 0.0e0 :\\r\\n print 'bg.addNoise : warning counts < 0'\\r\\n else :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = np.square(normalise(array))\\r\\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\\r\\n arrayout = np.sqrt(arrayout)\\r\\n tot = np.sum(np.abs(array)**2)\\r\\n arrayout = normalise(arrayout,tot)\\r\\n return arrayout\",\n \"def get_brightness(arr):\\n\\tR,G,B = arr[:,:,0], arr[:,:,1], arr[:,:,2]\\n\\tY = 0.299*R + 0.587*G + 0.144*B\\n\\treturn Y.mean()\",\n \"def observation(self, img):\\r\\n img = img[25:200]\\r\\n img = cv2.resize(img, self.img_size[1:])\\r\\n if not self.color:\\r\\n img = img.mean(-1, keepdims=True)\\r\\n\\r\\n return img.transpose([2, 0, 1]) / 255\",\n \"def infectious_intensity(self):\\n return InfectiousIntensity(self)\",\n \"def extract_intensity(mask, yfp_image):\\n # Get the region properties for the image.\\n props = skimage.measure.regionprops(mask, intensity_image=yfp_image)\\n\\n # Make a vector to store the mean intensities\\n mean_int = []\\n for prop in props:\\n intensity = prop.mean_intensity\\n mean_int.append(intensity)\\n\\n return mean_int\",\n \"def gen_profile(self):\\n return np.array(self['gen'], dtype=np.float32)\",\n \"def gen_profile(self):\\n return np.array(self['gen'], dtype=np.float32)\",\n \"def calculate_absorbance(intensity):\\n blanco = intensity[0]\\n intensity_np_arr = np.array(intensity)\\n transmittance = intensity_np_arr / blanco\\n absorbance = - np.log10(transmittance)\\n return absorbance\",\n \"def cf_profile(self):\\n x = np.abs(self.gen_profile() / self.sam_sys_inputs['system_capacity'])\\n return x\",\n \"def scalarInfo(img, cnt):\\n\\tm = cntInfo(img, cnt)\\n\\td = {\\\"perimeter\\\":m[\\\"perimeter\\\"], \\\"oreientation\\\":m[\\\"orientation\\\"], \\\"solidity\\\":m[\\\"solidity\\\"],\\\"height\\\":m[\\\"height\\\"], \\\"extent\\\":m[\\\"extent\\\"], \\\"aspect ratio\\\":m[\\\"aspect ratio\\\"], \\\"area\\\":m[\\\"area\\\"], \\\"sum intensity\\\":m[\\\"sum intensity\\\"], \\\"width\\\":m[\\\"width\\\"], \\\"equivalent diameter\\\": m[\\\"equivalent diameter\\\"], \\\"mean intensity\\\": m[\\\"mean intensity\\\"]}\\n\\treturn d\",\n \"def profile(c_mat, mat_rows):\\n\\n profile_mat = c_mat / mat_rows\\n\\n return profile_mat\",\n \"def row_uncertainty(a):\\n try:\\n return sum(safe_p_log_p(a), 1)\\n except ValueError:\\n raise ValueError(\\\"Array has to be two-dimensional\\\")\",\n \"def trapfilt_taps(N, phil, alfa):\\n\\n\\n\\n tt = arange(-N/2,N/2 + 1) # Time axis for h(t) \\n # ***** Generate impulse response ht here *****\\n ht = zeros(len(tt))\\n ix = where(tt != 0)[0]\\n if alfa != 0:\\n ht[ix] = ((sin(2*pi*phil*tt[ix]))/(pi*tt[ix]))*((sin(2*pi*alfa*phil*tt[ix]))/(2*pi*alfa*phil*tt[ix]))\\n else:\\n ht[ix] = (sin(2*pi*phil*tt[ix]))/(pi*tt[ix])\\n ix0 = where(tt == 0)[0]\\n ht[ix0] = 2*phil\\n ht = ht/sum(power(ht,2))\\n\\n return ht\",\n \"def get_uthreshold(img):\\n import noiselevel\\n # sigma=Table.read('noiselevel.csv',format='csv')['sigma'][0]\\n sigma = noiselevel.getnoiselevel(img,ranges=(-30,30),toplot=False)\\n \\n thres = sigma*np.sqrt(2*np.log(img.size))\\n return thres, sigma\",\n \"def get_intensity(self, step_index):\\n\\n if step_index < 4 or step_index >= self.intensity_step_count - 4:\\n raise ValueError('step index {0} is out of bounds [4, {1})'.format(\\n step_index, self.intensity_step_count - 4))\\n\\n # Checking if we already computed required intensity value.\\n\\n if self.intensity_list[step_index] != None:\\n return self.intensity_list[step_index]\\n\\n # No, we haven't, so we are going to compute it.\\n\\n window_list, window_sum = get_kaiser_window(self.intensity_half_window_size)\\n\\n sample_array = self.intensity_sound.get_array_of_samples()\\n sample_sum = 0.0\\n\\n channel_count = self.intensity_sound.channels\\n amplitude_limit = self.intensity_sound.max_possible_amplitude\\n\\n # We sum squared normalized amplitudes of all samples of all channels in the window.\\n\\n sample_from = (step_index - 4) * self.intensity_step_size * channel_count\\n\\n for i in range(self.intensity_window_size):\\n for j in range(channel_count):\\n sample = sample_array[sample_from + i * channel_count + j] / amplitude_limit\\n sample_sum += sample ** 2 * window_list[i]\\n\\n # Multiplication by 2.5e9 is taken directly from Praat source code, where it is performed via\\n # division by 4e-10.\\n\\n intensity_ratio = sample_sum / (channel_count * window_sum) * 2.5e9\\n intensity = -300 if intensity_ratio < 1e-30 else 10 * math.log10(intensity_ratio)\\n\\n # Saving computed intensity value for reuse, and returning it.\\n\\n self.intensity_list[step_index] = intensity\\n return intensity\",\n \"def wav_to_intensity(path, sr=16000, offset=10):\\n sound = parselmouth.Sound(path)\\n intensity = sound.to_intensity()\\n\\n features = []\\n\\n max_time = sound.get_total_duration()\\n\\n for time in np.arange(0, max_time, 0.001):\\n int_db = intensity.get_value(time)\\n if np.isnan(int_db):\\n int_db = 0\\n\\n features.append([int_db])\\n\\n array_feats = np.array(features).T\\n\\n print(\\\"SHAPE OF THE FEATURES:\\\", array_feats.shape)\\n assert(not np.any(np.isnan(array_feats)))\\n\\n return array_feats, max_time\",\n \"def profile(self):\\n return NumericStatsMixin.profile(self)\",\n \"def plot_intensity_prop(stack, wavelengths_arr, colors_arr):\\n\\n # for the electric fields profile\\n for i, wl in enumerate(wavelengths_arr):\\n electric_tot_te, electric_tot_tm, reflectivity_te, reflectivity_tm, transmission_te, transmission_tm, index_tot, L_tot, theta_tot = transfer_matrix_method(\\n stack, 1, 0, wl, 0)\\n intensity = np.abs(electric_tot_te[::-1]) ** 2\\n plt.plot(L_tot * 1e6, intensity / max(intensity) * 2, color=colors_arr[i])\\n # for the indexes profile\\n ax.plot(L_tot * 1e6, index_tot[::-1], color='black')\\n ax.fill_between(L_tot * 1e6, index_tot[::-1], color='azure')\",\n \"def intensity(self):\\n LP = 1/np.sin(self.theta)**2/np.cos(self.theta)\\n P = 1 + np.cos(2*self.theta)**2\\n I = (np.abs(self.F))**2*LP*P\\n self.xrd_intensity = I\\n self.theta2 = 2*self.theta\\n rank = np.argsort(self.theta2)\\n self.theta2 = self.theta2[rank]\\n self.hkl_list = self.hkl_list[rank]\\n self.d_hkl = self.d_hkl[rank]\\n self.xrd_intensity = self.xrd_intensity[rank]\",\n \"def metric_iaf(self, x):\\n data = np.asarray(x['data'])\\n iaf = [10.0] * data.shape[0]\\n for ch, ch_data in enumerate(data):\\n pxx, freqs = mlab.psd(ch_data, Fs=128.0, NFFT=256)\\n alpha_mask = np.abs(freqs - 10) <= 2.0\\n alpha_pxx = 10*np.log10(pxx[alpha_mask])\\n alpha_pxx = scipy.signal.detrend(alpha_pxx)\\n # iaf[ch] = alpha_pxx.shape\\n iaf[ch] = freqs[alpha_mask][np.argmax(alpha_pxx)]\\n return iaf\",\n \"def depth2intensity(depth, interval=300):\\n return depth * 3600 / interval\",\n \"def info_np(img):\\n import numpy as np\\n\\n print ('Dimensions: ' + str(np.shape(img)))\\n print ('Min value: ' + str(np.min(img)))\\n print ('Avg value: ' + str(np.average(img)))\\n print ('Med value: ' + str(np.median(img)))\\n print ('Max value: ' + str(np.max(img)))\\n print ('Std dev: ' + str(np.std(img)))\\n print ('Sum: ' + str(np.sum(img)))\",\n \"def get_intensity_normalization(self):\\n return self.intensity_normalize_image\",\n \"def sphere_r_intensity(img):\\n pixels = []\\n for j in range(0, img.shape[0]):\\n for i in range(1, 40):\\n pixels.append(img[j, img.shape[1] - i])\\n\\n return np.mean(pixels)\",\n \"def statistics_from_array(x: numpy.ndarray):\\n try:\\n return x.mean(), x.std(), x.max(), x.min()\\n except AttributeError:\\n return numpy.nan, numpy.nan, numpy.nan, numpy.nan\",\n \"def analyze(self):\\n try:\\n self.options[self.multi_image][1]()\\n except:\\n raise Exception(\\\"Multi Image Option not defined.\\\")\\n\\n self.image = self.data / self.exposure\\n\\n background = self.min_val = np.min(self.image[:511,:511])\\n self.max_val = np.max(self.image[:511,:511])\\n # stats.mode returns modal value = value that occours most often\\n #background = stats.mode(im[:50,:50].ravel())[0][0]\\n\\n intensity = self.image.sum() - background*np.size(self.image)\\n\\n #results.append((self.index, intensity, background))\\n self.index =+ 1\",\n \"def extract_temp_integral_2_to_100(batch,index):\\n from scipy import integrate\\n integral = []\\n for ind in index:\\n cell_no = list(batch.keys())[ind]\\n integrate_ = integrate.simps(batch[cell_no]['summary']['Tavg'][1:100])\\n # integral.append(integrate_)\\n integral.append(log(abs(integrate_),10))\\n integral = np.reshape(integral,(-1,1))\\n return integral\\n pass\",\n \"def make_lineprofile(npix,rstar,xc,vgrid,A,veq,linewidth):\\n vc=(np.arange(npix)-xc)/rstar*veq\\n vs=vgrid[np.newaxis,:]-vc[:,np.newaxis]\\n profile=1.-A*np.exp( -(vs*vs)/2./linewidth**2)\\n return profile\",\n \"def createdog(self,imagearr):\\n re = [0,1,2,3]\\n re[0] = self.diff(self.gs_blur(self.sigma,imagearr))\\n for i in range(1,4):\\n base = self.sampling(re[i-1][2])\\n re[i] = self.diff(self.gs_blur(self.sigma, base))\\n return re\",\n \"def get_threshold_data(self):\\n return [roi.get_threshold_data() for roi in self.rois]\",\n \"def fiber_profile(x, y, r0, blur=0.1):\\n r = np.sqrt(x ** 2 + y ** 2)\\n return 0.5 + 0.5 * scipy.special.erf((r0 - r) / (np.sqrt(2) * blur))\",\n \"def getNoiseVar(img,fraction=0.95):\\n last_val = np.percentile(img,fraction)\\n #si(img<last_val,title=\\\"Pixel values considered as noise\\\")\\n return np.var(img[img<last_val])\",\n \"def profile(x):\\n return x\",\n \"def intensityPSF_Fire(N=1000):\\n col_seq = [ ( 0/255., 0/255., 0/255.),\\n ( 0/255., 0/255., 22/255.),\\n ( 0/255., 0/255., 45/255.),\\n ( 0/255., 0/255., 65/255.),\\n ( 0/255., 0/255., 78/255.),\\n ( 0/255., 0/255., 91/255.),\\n ( 7/255., 0/255., 104/255.),\\n ( 16/255., 0/255., 117/255.),\\n ( 25/255., 0/255., 130/255.),\\n ( 34/255., 0/255., 143/255.),\\n ( 43/255., 0/255., 156/255.),\\n ( 52/255., 0/255., 168/255.),\\n ( 55/255., 0/255., 171/255.),\\n ( 58/255., 0/255., 175/255.),\\n ( 61/255., 0/255., 178/255.),\\n ( 64/255., 0/255., 181/255.),\\n ( 67/255., 0/255., 185/255.),\\n ( 70/255., 0/255., 188/255.),\\n ( 73/255., 0/255., 192/255.),\\n ( 76/255., 0/255., 195/255.),\\n ( 79/255., 0/255., 199/255.),\\n ( 82/255., 0/255., 202/255.),\\n ( 85/255., 0/255., 206/255.),\\n ( 88/255., 0/255., 209/255.),\\n ( 91/255., 0/255., 213/255.),\\n ( 94/255., 0/255., 216/255.),\\n ( 98/255., 0/255., 220/255.),\\n (101/255., 0/255., 220/255.),\\n (104/255., 0/255., 221/255.),\\n (107/255., 0/255., 222/255.),\\n (110/255., 0/255., 223/255.),\\n (113/255., 0/255., 224/255.),\\n (116/255., 0/255., 225/255.),\\n (119/255., 0/255., 226/255.),\\n (122/255., 0/255., 227/255.),\\n (125/255., 0/255., 224/255.),\\n (128/255., 0/255., 222/255.),\\n (131/255., 0/255., 220/255.),\\n (134/255., 0/255., 218/255.),\\n (137/255., 0/255., 216/255.),\\n (140/255., 0/255., 214/255.),\\n (143/255., 0/255., 212/255.),\\n (146/255., 0/255., 210/255.),\\n (148/255., 0/255., 206/255.),\\n (150/255., 0/255., 202/255.),\\n (152/255., 0/255., 199/255.),\\n (154/255., 0/255., 195/255.),\\n (156/255., 0/255., 191/255.),\\n (158/255., 0/255., 188/255.),\\n (160/255., 0/255., 184/255.),\\n (162/255., 0/255., 181/255.),\\n (163/255., 0/255., 177/255.),\\n (164/255., 0/255., 173/255.),\\n (166/255., 0/255., 169/255.),\\n (167/255., 0/255., 166/255.),\\n (168/255., 0/255., 162/255.),\\n (170/255., 0/255., 158/255.),\\n (171/255., 0/255., 154/255.),\\n (173/255., 0/255., 151/255.),\\n (174/255., 0/255., 147/255.),\\n (175/255., 0/255., 143/255.),\\n (177/255., 0/255., 140/255.),\\n (178/255., 0/255., 136/255.),\\n (179/255., 0/255., 132/255.),\\n (181/255., 0/255., 129/255.),\\n (182/255., 0/255., 125/255.),\\n (184/255., 0/255., 122/255.),\\n (185/255., 0/255., 118/255.),\\n (186/255., 0/255., 114/255.),\\n (188/255., 0/255., 111/255.),\\n (189/255., 0/255., 107/255.),\\n (190/255., 0/255., 103/255.),\\n (192/255., 0/255., 100/255.),\\n (193/255., 0/255., 96/255.),\\n (195/255., 0/255., 93/255.),\\n (196/255., 1/255., 89/255.),\\n (198/255., 3/255., 85/255.),\\n (199/255., 5/255., 82/255.),\\n (201/255., 7/255., 78/255.),\\n (202/255., 8/255., 74/255.),\\n (204/255., 10/255., 71/255.),\\n (205/255., 12/255., 67/255.),\\n (207/255., 14/255., 64/255.),\\n (208/255., 16/255., 60/255.),\\n (209/255., 19/255., 56/255.),\\n (210/255., 21/255., 53/255.),\\n (211/255., 22/255., 51/255.),\\n (212/255., 24/255., 49/255.),\\n (213/255., 27/255., 45/255.),\\n (214/255., 29/255., 42/255.),\\n 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211/255.),\\n (255/255., 255/255., 215/255.),\\n (255/255., 255/255., 223/255.),\\n (255/255., 255/255., 227/255.),\\n (255/255., 255/255., 231/255.),\\n (255/255., 255/255., 235/255.),\\n (255/255., 255/255., 239/255.),\\n (255/255., 255/255., 243/255.),\\n (255/255., 255/255., 245/255.),\\n (255/255., 255/255., 246/255.),\\n (255/255., 255/255., 247/255.),\\n (255/255., 255/255., 251/255.),\\n (255/255., 255/255., 255/255.),\\n (255/255., 255/255., 255/255.),\\n (255/255., 255/255., 255/255.),\\n (255/255., 255/255., 255/255.),\\n (255/255., 255/255., 255/255.),\\n (255/255., 255/255., 255/255.) ]\\n\\n seqLen = len(col_seq)\\n delta = 1.0/(seqLen - 1)\\n r_tuple = ((i*delta, col_seq[i][0], col_seq[i][0]) for i in range(seqLen))\\n g_tuple = ((i*delta, col_seq[i][1], col_seq[i][1]) for i in range(seqLen))\\n b_tuple = ((i*delta, col_seq[i][2], col_seq[i][2]) for i in range(seqLen))\\n cdict = {'red': tuple(r_tuple),\\n 'green': tuple(g_tuple),\\n 'blue': tuple(b_tuple)}\\n firecm = _mplb.colors.LinearSegmentedColormap('psffire', cdict, N)\\n return firecm\",\n \"def get_temp(self) -> float:\\n return np.round(np.mean(self.temp_data), 1)\",\n \"def profile(self):\\n\\n def _flatten(f):\\n return [coeffifient for value in f.values()\\\\\\n for coeffifient in value.coefficients()]\\n\\n elements = _flatten(self.domain().j) +\\\\\\n _flatten(self.codomain().j) +\\\\\\n _flatten(self)\\n\\n\\n profile = enveloping_profile_elements(elements)\\n\\n # Avoid returning the zero profile because it triggers a corner case\\n # in FP_Module_class.resolution().\\n # \\n # XXX: Fix FP_Module_class.resolution().\\n #\\n return (1,) if profile == (0,) else profile\",\n \"def s2profile(r,r0,A,B):\\n x = r/r0\\n res = A*4./(np.exp(x)+np.exp(-x))**2 + B\\n return res\",\n \"def addNoise(array,counts):\\r\\n if array.dtype == 'complex' :\\r\\n arrayout = addNoise(np.real(array),counts) + 1.0J * addNoise(np.imag(array),counts)\\r\\n else :\\r\\n if np.float64(counts) == 0.0e0 :\\r\\n arrayout = np.zeros(array.shape, dtype=arrayout.dtype)\\r\\n elif np.float64(counts) < 0.0e0 :\\r\\n print 'bg.addNoise : warning counts < 0'\\r\\n elif np.float64(counts) > 1.0e9 :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = normaliseInt(array)\\r\\n arrayout = np.random.normal(arrayout*np.float64(counts),np.sqrt(arrayout*np.float64(counts)))/np.float64(counts)\\r\\n tot = np.sum(array)\\r\\n arrayout = normaliseInt(arrayout,tot)\\r\\n else :\\r\\n arrayout = np.zeros(array.shape)\\r\\n arrayout = normaliseInt(array)\\r\\n arrayout = np.random.poisson(arrayout*np.float64(counts))/np.float64(counts)\\r\\n tot = np.sum(array)\\r\\n arrayout = normaliseInt(arrayout,tot)\\r\\n return arrayout\",\n \"def calcthresh(self, img):\\n mu = np.mean(img.ravel())\\n sd = np.std(img.ravel())\\n return mu + 3 * sd\",\n \"def observation(self, img):\\r\\n img = img.transpose(1, 2, 0)\\r\\n return img\",\n \"def scipyTheilSen(array):\\n\\ttry:\\n\\t\\tslope, intercept, _, _ = stats.mstats.theilslopes(array)\\n\\t\\treturn slope #, intercept\\n\\texcept Exception as e:\\n\\t \\tprint(e) \\n\\t \\tipdb.set_trace()\\n\\t \\treturn np.NAN\",\n \"def getIntensitySpectum(self,wavelengths,intensities = 1.0):\\n angles,peaks,widths = self.getSpectrum(wavelengths,intensities)\\n\\n # Get arnge of angles to calulate over\\n minField = np.min(angles) - 10*np.max(widths)\\n maxField = np.max(angles) + 10*np.max(widths)\\n # Sample finely enough to make peak widths visible, but least 400points\\n npoints = max(int((maxField - minField)/np.min(widths)),400)\\n\\n # Make the two two array for the output data\\n fieldAngle = np.linspace(minField,maxField,npoints)\\n spectralOutput = np.zeros(fieldAngle.size)\\n\\n # Add each peak in turn\\n for a,p,w in zip(angles,peaks,widths):\\n\\n for i,af in enumerate(fieldAngle):\\n s = self.lineShape(a,w,af) # Add the spectrometer lineshape\\n spectralOutput[i] += p*s\\n\\n # Return the two numpy arrays as a list\\n return fieldAngle,spectralOutput\",\n \"def autothreshold(gray_im, method=\\\"otsu\\\"):\\n if method == \\\"otsu\\\":\\n t = otsu(gray_im)\\n elif method == \\\"kmeans\\\":\\n t = ave(kmeans(list(gray_im.getdata())))\\n return gray_im.point(lambda x: 0 if x < t else 255) # < or <= ?\",\n \"def get_statistical_information(mat, percentile=(0, 100), denoise=False):\\n if denoise is True:\\n mat = ndi.gaussian_filter(mat, 2)\\n gmin = np.min(mat)\\n gmax = np.max(mat)\\n min_percent = np.percentile(mat, percentile[0])\\n max_percent = np.percentile(mat, percentile[-1])\\n median = np.median(mat)\\n mean = np.mean(mat)\\n variance = np.var(mat)\\n return gmin, gmax, min_percent, max_percent, mean, median, variance\",\n \"def get_rain_values(self):\\n return float(self.data[2]) / 10, float(self.data[4]) / 10\",\n \"def weights(self):\\n return np.array(self.intensity[self.idx])\",\n \"def avg_temps(self):\\r\\n average_temp = 0\\r\\n for j in range(len(self.trip)):\\r\\n average_temp += self.trip[j].get_temperature(j)\\r\\n average_temp /= len(self.trip)\\r\\n return average_temp\",\n \"def switchy_score(array):\\n array = np.array(array)\\n variance = 1 - np.std(np.sin(array[~np.isnan(array)] * np.pi))\\n mean_value = -np.mean(np.cos(array[~np.isnan(array)] * np.pi))\\n return variance * mean_value\",\n \"def calculate_incoherent_scattered_intensity(element, q):\\n return current_calculator.get_incoherent_intensity(element, q)\",\n \"def har_mean(array):\\n return ((sum([1/x for x in array]))**(-1))*len(array)\",\n \"def intensity_histogram_measures(regionmask, intensity):\\n feat = Intensity_Histogram_Measures(\\n [\\n np.percentile(intensity[regionmask], 0),\\n np.percentile(intensity[regionmask], 25),\\n np.percentile(intensity[regionmask], 50),\\n np.percentile(intensity[regionmask], 75),\\n np.percentile(intensity[regionmask], 100),\\n np.mean(intensity[regionmask]),\\n stats.mode(intensity[regionmask], axis=None)[0][0],\\n np.std(intensity[regionmask]),\\n ]\\n )\\n return feat\",\n \"def magerr2Ivar(flux, magErr):\\n fluxErr = flux * ((10.0 ** (magErr/2.5)) - 1.0)\\n\\n return 1.0 / (fluxErr ** 2.0)\",\n \"def calc_profile(self, phases):\\n self._profile = self._generator(phases)\\n self._Amax = self.Amax if hasattr(self, '_Amax') else np.max(self.profile)\\n return self.profile / self.Amax\",\n \"def iou_stats(pred, target, num_classes=6, background=5):\\n # Set redundant classes to background.\\n locs = np.logical_and(target > -1, target < num_classes)\\n\\n # true positive + false negative\\n tp_fn, _ = np.histogram(target[locs],\\n bins=np.arange(num_classes+1))\\n # true positive + false positive\\n tp_fp, _ = np.histogram(pred[locs],\\n bins=np.arange(num_classes+1))\\n # true positive\\n tp_locs = np.logical_and(locs, pred == target)\\n tp, _ = np.histogram(target[tp_locs],\\n bins=np.arange(num_classes+1))\\n\\n return tp_fn, tp_fp, tp\",\n \"def calc_flux_array(self):\\n \\n # First determine the associated spectrum\\n self.compute_template_spectrum()\\n\\n # Calculate baseline counts to normalise fluxes we scan over\\n # Go from 10**(bin_min)*mean up to 10**(bin_max)*mean in nbins steps\\n b = self.setup_b_instance(0,add_ps_mask=True)\\n mean = np.sum(b.CTB_masked_compressed[0])/len(b.CTB_masked_compressed[0])\\n A_array = mean*10**np.linspace(self.bin_min,self.bin_max,self.nbins)\\n\\n # Array to get LLs when no profile likelihood run\\n norun = np.array([1.0, 1.0, 1.0, 1.0])\\n\\n # Now setup and compute the arrays\\n LL_array = np.array([]) \\n A_array_short = np.array([])\\n spect_array = np.array([])\\n\\n for i in range(len(A_array)):\\n print \\\"on i =\\\",i\\n # Calculate LL\\n if i == 0:\\n b1 = self.setup_b_instance(A_array[i],add_ps_mask=True)\\n else:\\n for key in b1.fixed_template_dict_nested.keys():\\n b1.fixed_template_dict_nested[key] = b1.fixed_template_dict_nested[key]*A_array[i]/A_array[i-1]\\n ll_val = b1.ll(norun,4,4)\\n # Make triangle\\n\\n # Append to arrays\\n LL_array = np.append(LL_array,ll_val)\\n A_array_short = np.append(A_array_short,A_array[i])\\n spect_array = self.spectrum*np.array(A_array_short)\\n\\n # Save output\\n np.save(work_dir+'ScanOutput/'+self.tag+'/En_array-'+str(self.flux_array_ebin)+'.npy',self.En_center)\\n np.save(work_dir+'ScanOutput/'+self.tag+'/LL_array-'+str(self.flux_array_ebin)+'.npy',LL_array)\\n np.save(work_dir+'ScanOutput/'+self.tag+'/Flux_array-'+str(self.flux_array_ebin)+'.npy',spect_array)\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def t_measure_estimate(self):\\n ho = self.humidity_oversampling\\n to = self.temperature_oversampling\\n po = self.pressure_oversampling\\n typ = 1. + 2.*to + (2.*po + 0.5)*bool(po) + (2.*ho +0.5)*bool(ho)\\n mx = 1.25 + 2.3*to + (2.3*po + 0.575)*bool(po) + (2.3*ho +0.575)*bool(ho)\\n return typ, mx\",\n \"def tsz_profile_highacc(self, nu=None):\\n bb = np.linspace(0.0, self.bmax, 100) # Range of impact parameters\\n rr = bb * self.r500\\n ysz = map(lambda b: scipy.integrate.quad(self._ig_tsz, b+0.0001,\\n self.bmaxc, args=(b,))[0], bb)\\n if nu == None:\\n g_nu = 1. # Factor-out spectral dependence\\n else:\\n g_nu = self.tsz_spectrum(nu)\\n fac_ysz = (2. * 2. * 2.051 / 511.) * self.r500\\n ysz = g_nu * fac_ysz * np.array(ysz)\\n interp = scipy.interpolate.interp1d( rr, ysz, kind='linear', \\n bounds_error=False, fill_value=0.0 )\\n return interp\",\n \"def impute_cumulative_array(array):\\n array = np.array(array).copy()\\n array = convert_non_monotonic_to_nan(array)\\n array = log_interpolate(array)\\n return array\",\n \"def intensity( rgb ):\\n return int( (rgb[0] + rgb[1] + rgb[2])/3 )\",\n \"def GetGrayArray(self, p_int):\\n ...\",\n \"def profile(self) -> Optional[Any]:\\n\\n def get_profile_attribute(numpy_data, attr_name):\\n if isinstance(numpy_data, dict):\\n return {key: getattr(array, attr_name) for key, array in numpy_data.items()}\\n else:\\n return getattr(numpy_data, attr_name)\\n\\n profile = {\\n \\\"features_shape\\\": get_profile_attribute(self._features, \\\"shape\\\"),\\n \\\"features_size\\\": get_profile_attribute(self._features, \\\"size\\\"),\\n \\\"features_nbytes\\\": get_profile_attribute(self._features, \\\"nbytes\\\"),\\n }\\n if self._targets is not None:\\n profile.update(\\n {\\n \\\"targets_shape\\\": get_profile_attribute(self._targets, \\\"shape\\\"),\\n \\\"targets_size\\\": get_profile_attribute(self._targets, \\\"size\\\"),\\n \\\"targets_nbytes\\\": get_profile_attribute(self._targets, \\\"nbytes\\\"),\\n }\\n )\\n\\n return profile\",\n \"def get_incoherent_intensity(self, element: str, q):\\n fs_coherent = self.get_coherent_scattering_factor(element, q)\\n intensity_coherent = fs_coherent ** 2\\n s = q / (4 * np.pi)\\n Z = float(self.incoherent_param['Z'][element])\\n M = float(self.incoherent_param['M'][element])\\n K = float(self.incoherent_param['K'][element])\\n L = float(self.incoherent_param['L'][element])\\n intensity_incoherent = (Z - intensity_coherent / Z) * (1 - M * (np.exp(-K * s) - np.exp(-L * s)))\\n return intensity_incoherent\",\n \"def get_mean(self):\\r\\n for i in range(1,len(self.data[0])):\\r\\n self.prom.append(np.mean(self.data[:,i]))\",\n \"def sphere_l_intensity(img):\\n pixels = []\\n for j in range(0, img.shape[0]):\\n for i in range(1, 40):\\n pixels.append(img[j, i])\\n\\n return np.mean(pixels)\",\n \"def geo_mean(array):\\n logsum = sum([np.log(each) for each in array])/len(array)\\n return np.exp(logsum)\",\n \"def getIntensity(self, pos):\\n #Camera doesnt have position so im just using the position of the followed object (of 1st camera)\\n camPos = glad.renderer.cameraList[0].objectFollowed.getPos()\\n\\n r=(pos-camPos)#separation vector\\n if r.isNullVector(): #if the vector is null, sound will be max anyways\\n sin = 1\\n cos = 1\\n else:\\n #calculate angles to determine where sound is coming from\\n cos = dotProduct(r.getNormalized(),Vector(-1,0))\\n sin = dotProduct(r.getNormalized(), Vector(0,1))\\n #Calculate intensity for left and right channels\\n #when sound is directly to the side have 80 percent come from that side speaker\\n #hopefully this will give some directional sounds\\n k = 130000 #arbitrary constant to calculate sound intensity\\n if r.isNullVector():\\n intensity = k #removes division by zero error\\n else:\\n intensity = k/r.getMagnitude()**2\\n #major is the percent of the sound intensity from the side with the greater intensity\\n a=0.68 #max percent of the intensity coming from one side\\n major = (a*0.5)/((0.5*cos)**2+(a*sin)**2)**0.5 #equation for an ellipse\\n if r[0] <= 0:\\n right = major\\n left = 1-major\\n else:\\n left = major\\n right = 1-major\\n right *= intensity\\n left *= intensity\\n if right > 1: right = 1\\n if left > 1: left = 1\\n return left,right\",\n \"def _find_saturated_profiles(self) -> np.ndarray:\\n n_gates, var_lim, _, _ = self.noise_params\\n var = np.var(self.data['backscatter'][:, -n_gates:], axis=1)\\n return var < var_lim\",\n \"def get_metrics(self) -> np.ndarray:\\n dice = np.mean(self.dice_scores)\\n iou = np.mean(self.iou_scores)\\n sens = np.mean(self.sens_scores)\\n spec = np.mean(self.spec_scores)\\n accu = np.mean(self.accu_scores)\\n return dice, iou, sens, spec, accu\",\n \"def normalize_profile(ph):\\n return ph/np.abs(ph[1])\",\n \"def areaFraction(nb_probes, I):\\n P = np.random.randint(I.shape[0], size=(nb_probes, 2))\\n\\n # count the number of probes in phase\\n count = np.sum(I[P[:, 0], P[:, 1]])\\n\\n# fig=plt.figure();\\n# plt.imshow(I);\\n# plt.plot(P[:,0], P[:,1], '+');\\n# plt.show();\\n# fig.savefig('areaFrac.pdf', bbox_inches='tight');\\n return float(count) / nb_probes\",\n \"def _calc_npixels_fired(self, r0, z, pwm):\\n N0 = 500 * self.npix * _pwm_multiplier(pwm)\\n Nraw = N0 * z / (r0**2 + z**2)**1.5\\n return self.npix * (1 - np.exp(-Nraw / self.npix))\",\n \"def find_backstats(f_arr, sigma, niter):\\n ave = f_arr.mean()\\n std = f_arr.std()\\n for i in range(niter):\\n mask = (abs(f_arr - ave) < sigma * std)\\n ave = f_arr[mask].mean()\\n std = f_arr[mask].std()\\n return ave, std\",\n \"def MeanPixArr(PixArr, binary=True):\\n \\n import numpy as np\\n \\n F, R, C = PixArr.shape\\n \\n # Initialise MeanPixArr:\\n MeanPixArr = np.zeros((1, R, C), dtype='uint')\\n \\n \\n result = np.mean(PixArr, axis=0)\\n \\n if binary:\\n MeanPixArr[0] = (result >= 0.5) * result\\n else:\\n MeanPixArr[0] = result\\n \\n return MeanPixArr\",\n \"def result_array(self) -> np.ndarray:\\n return np.array([r[\\\"time\\\"] for r in self.profile_result])\",\n \"def getScalarFlux(self):\\n totScalarFlux = []\\n for cell in self.cells:\\n totScalarFlux.append(cell.getTotScalarFlux())\\n totScalarFlux = np.array(totScalarFlux)\\n #return totScalarFlux / np.sum(totScalarFlux) # norm flux to 1.\\n return totScalarFlux\",\n \"def average_profile_in_bins(Redges, R, prof):\\n if np.min(Redges) < np.min(R):\\n raise Exception(\\\"Minimum edge must be >= minimum R\\\")\\n if np.max(Redges) > np.max(R):\\n raise Exception(\\\"Maximum edge must be <= maximum R\\\")\\n ave_prof = np.zeros(len(Redges)-1)\\n cluster_toolkit._lib.average_profile_in_bins(_dcast(Redges), len(Redges), _dcast(R), len(R), _dcast(prof), _dcast(ave_prof))\\n return ave_prof\",\n \"def findRMpeaks(self, pix, threshold):\\n\\t\\tsigma = np.std(self.getz(pix))\\n\\t\\tdetections = []\\n\\t\\tfor i, phi in enumerate(self.getz(pix)):\\n \\t\\t \\tif phi > threshold*sigma: detections.append(i)\\n \\t \\treturn detections\",\n \"def skystats(stamp):\\n\\t\\n\\tif isinstance(stamp, galsim.Image):\\n\\t\\ta = stamp.array\\n\\t\\t# Normally there should be a .transpose() here, to get the orientation right.\\n\\t\\t# But in the present case it doesn't change anything, and we can skip it.\\n\\telse:\\n\\t\\ta = stamp # Then we assume that it's simply a numpy array.\\n\\t\\n\\tedgepixels = np.concatenate([\\n\\t\\t\\ta[0,1:], # left\\n\\t\\t\\ta[-1,1:], # right\\n\\t\\t\\ta[:,0], # bottom\\n\\t\\t\\ta[1:-1,-1] # top\\n\\t\\t\\t])\\n\\tassert len(edgepixels) == 2*(a.shape[0]-1) + 2*(a.shape[0]-1)\\n\\n\\t# And we convert the mad into an estimate of the Gaussian std:\\n\\treturn {\\n\\t\\t\\\"std\\\":np.std(edgepixels), \\\"mad\\\": 1.4826 * mad(edgepixels),\\n\\t\\t\\\"mean\\\":np.mean(edgepixels), \\\"med\\\":np.median(edgepixels),\\n\\t\\t\\\"stampsum\\\":np.sum(a)\\n\\t\\t}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62673634","0.593113","0.5869846","0.58618975","0.5649231","0.5581468","0.5578077","0.5490693","0.5435854","0.5350697","0.5301182","0.5300452","0.52878165","0.5265786","0.5254433","0.52346134","0.5218291","0.5211029","0.5208299","0.5205285","0.5173795","0.51410526","0.51351696","0.5130163","0.5101332","0.50918484","0.50865495","0.5083497","0.5083497","0.5079967","0.5077461","0.50650233","0.5060322","0.50473046","0.5047266","0.5039288","0.5033676","0.50262785","0.50054467","0.4974781","0.49624556","0.49617594","0.49397102","0.49305668","0.49176168","0.49158955","0.49019435","0.4897411","0.48893467","0.4883825","0.48665455","0.48657554","0.4865579","0.48634455","0.4855761","0.48501727","0.48474345","0.48458728","0.4844118","0.4835661","0.48253068","0.4820242","0.48197436","0.4815872","0.48111862","0.48078427","0.48047692","0.48041874","0.48010805","0.4788903","0.47836968","0.47653654","0.47526765","0.47477773","0.47471777","0.47461048","0.47434095","0.47428262","0.47420502","0.47388285","0.47343117","0.47324792","0.4725646","0.47255906","0.47159833","0.4707207","0.47069904","0.47069877","0.4704628","0.47021878","0.47016653","0.47015992","0.46995717","0.46992505","0.46919","0.46912","0.46909282","0.46896383","0.46836764","0.46828902","0.46812937"],"string":"[\n \"0.62673634\",\n \"0.593113\",\n \"0.5869846\",\n \"0.58618975\",\n \"0.5649231\",\n \"0.5581468\",\n \"0.5578077\",\n \"0.5490693\",\n \"0.5435854\",\n \"0.5350697\",\n \"0.5301182\",\n \"0.5300452\",\n \"0.52878165\",\n \"0.5265786\",\n \"0.5254433\",\n \"0.52346134\",\n \"0.5218291\",\n \"0.5211029\",\n \"0.5208299\",\n \"0.5205285\",\n \"0.5173795\",\n \"0.51410526\",\n \"0.51351696\",\n \"0.5130163\",\n \"0.5101332\",\n \"0.50918484\",\n \"0.50865495\",\n \"0.5083497\",\n \"0.5083497\",\n \"0.5079967\",\n \"0.5077461\",\n \"0.50650233\",\n \"0.5060322\",\n \"0.50473046\",\n \"0.5047266\",\n \"0.5039288\",\n \"0.5033676\",\n \"0.50262785\",\n \"0.50054467\",\n \"0.4974781\",\n \"0.49624556\",\n \"0.49617594\",\n \"0.49397102\",\n \"0.49305668\",\n \"0.49176168\",\n \"0.49158955\",\n \"0.49019435\",\n \"0.4897411\",\n \"0.48893467\",\n \"0.4883825\",\n \"0.48665455\",\n \"0.48657554\",\n \"0.4865579\",\n \"0.48634455\",\n \"0.4855761\",\n \"0.48501727\",\n \"0.48474345\",\n \"0.48458728\",\n \"0.4844118\",\n \"0.4835661\",\n \"0.48253068\",\n \"0.4820242\",\n \"0.48197436\",\n \"0.4815872\",\n \"0.48111862\",\n \"0.48078427\",\n \"0.48047692\",\n \"0.48041874\",\n \"0.48010805\",\n \"0.4788903\",\n \"0.47836968\",\n \"0.47653654\",\n \"0.47526765\",\n \"0.47477773\",\n \"0.47471777\",\n \"0.47461048\",\n \"0.47434095\",\n \"0.47428262\",\n \"0.47420502\",\n \"0.47388285\",\n \"0.47343117\",\n \"0.47324792\",\n \"0.4725646\",\n \"0.47255906\",\n \"0.47159833\",\n \"0.4707207\",\n \"0.47069904\",\n \"0.47069877\",\n \"0.4704628\",\n \"0.47021878\",\n \"0.47016653\",\n \"0.47015992\",\n \"0.46995717\",\n \"0.46992505\",\n \"0.46919\",\n \"0.46912\",\n \"0.46909282\",\n \"0.46896383\",\n \"0.46836764\",\n \"0.46828902\",\n \"0.46812937\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":4699,"cells":{"query":{"kind":"string","value":"Juggles parameters in order to be able to fit a list of parameters"},"document":{"kind":"string","value":"def wrapper_fit_func(x, ntraps, *args):\n a, b, c = list(args[0][:ntraps]), list(args[0][ntraps:2 * ntraps]), list(args[0][2 * ntraps:3 * ntraps])\n offset = args[0][-1]\n return gaussianarray1d(x, a, b, c, offset, ntraps)"},"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 doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",\"rAfb,rA0\")\n self.modelBuilder.factory_('expr::mAfb(\"@0*@1\",eAfb,rAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0*@1)\",eA0,rA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"mAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"mA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"eAfb,mAfb\")\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"dAfb[0.,-0.75,0.75]\");\n self.modelBuilder.doVar(\"dA0[0.0, -1.0, 1.0]\");\n #self.modelBuilder.doSet(\"POI\",\"dAfb,dA0\")\n self.modelBuilder.doSet(\"POI\",\"dAfb\")\n self.modelBuilder.factory_('expr::mAfb(\"@0+@1\",eAfb,dAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0+@1)\",eA0,dA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"kappa_W[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_Z[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_tau[1,0.0,3.0]\")\n self.modelBuilder.doVar(\"kappa_mu[1,0.0,5.0]\") \n self.modelBuilder.factory_(\"expr::kappa_mu_expr(\\\"@0*@1+(1-@0)*@2\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\")\n self.modelBuilder.doVar(\"kappa_t[1,0.0,4.0]\")\n # additional kappa for the anomalous coupling\n self.modelBuilder.doVar(\"kappa_tilde_t[0.0,0.0,4.0]\")\n self.modelBuilder.doVar(\"kappa_b[1,0.0,3.0]\")\n if not self.resolved:\n self.modelBuilder.doVar(\"kappa_g[1,0.0,2.0]\")\n self.modelBuilder.doVar(\"kappa_gam[1,0.0,2.5]\")\n\tself.modelBuilder.doVar(\"BRinv[0,0,1]\")\n self.modelBuilder.out.var(\"BRinv\").setConstant(True)\n # adding additional kappa to list of parameters of interest\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\n if not self.resolved:\n pois += ',kappa_g,kappa_gam'\n self.doMH()\n self.modelBuilder.doSet(\"POI\",pois)\n # use modified Higgs Builder\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\n self.setup()","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.70,0.70]\");\n self.modelBuilder.doSet(\"POI\",\"Afb\")\n\n # ss templates\n self.modelBuilder.doVar(\"Rdy_mumu_ss[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_ee_ss[1.0,0.0,10.0]\");\n \n self.modelBuilder.factory_('expr::Rpl(\"(1.+@0)\",Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(1.-@0)\",Afb)')","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"mu[1,0,100]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu\")\n self.modelBuilder.factory_('expr::ggH_s_func(\"@0-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::ggH_b_func(\"1-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\"sqrt(@0)\", mu)')","def parameter_tuning(D, param_grid):\n grid = ParameterGrid(param_grid)\n\n for params in grid:\n model_file = 'Theshpairs1_Ind_5' + '_emb_' + str(params['embedding_size']) + '_nr_' + str(\n params['negative_ratio']) + \\\n '_batch_' + str(params['batch_size']) + '_epochs_' \\\n + str(params['nb_epochs']) + '_classification_' + str(params['classification'])\n\n print(model_file)\n\n # Train Model\n Prio = NNEmbeddings(D, embedding_size=params['embedding_size'], negative_ratio=params['negative_ratio'],\n nb_epochs=params['nb_epochs'], batch_size=params['batch_size'],\n classification=params['classification'], save=True,\n model_file='Models/' + model_file + '.h5')\n\n # New Predicitons\n df_metrics = Prio.predict(pickle_file=None)\n plot_single(df_metrics)\n plot_metric(df_metrics, name='Plot_Metrics/' + model_file + '.png')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.7,0.7]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n # ss templates\n self.modelBuilder.doVar(\"R_ee_os_fakes[0.6,0.0,1.0]\");\n self.modelBuilder.doVar(\"ee16_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee17_fakes_norm[1.0, 0.01, 10.]\");\n self.modelBuilder.doVar(\"ee18_fakes_norm[1.0, 0.01, 10.]\");\n #Remember, cant use spaces in these formulas!\n #self.modelBuilder.options.verbose = 10\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\"@0*@1\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\"@0*@1\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\"@0*@1\",ee18_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\"@0*(1.0-@1)\",ee16_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\"@0*(1.0-@1)\",ee17_fakes_norm,R_ee_os_fakes)')\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\"@0*(1.0-@1)\",ee18_fakes_norm,R_ee_os_fakes)')\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"mu[0,0,1000]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu\")\n self.modelBuilder.factory_('expr::vbfH_s_func(\"@0-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\"1-sqrt(@0)\", mu)')\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\"sqrt(@0)\", mu)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doSet(\"POI\",\"Afb\")\n\n # ss templates\n self.modelBuilder.doVar(\"Dilu_ratio[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_mumu_ss[1.0,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rdy_ee_ss[1.0,0.0,10.0]\");\n \n self.modelBuilder.factory_('expr::Rpl(\"0.5*(1.+@0*@1)\",Afb, Dilu_ratio)')\n self.modelBuilder.factory_('expr::Rmn(\"0.5*(1.-@0*@1)\",Afb, Dilu_ratio)')","def get_parameter_estimation_parameters(self, friendly=True):\n #Get the sensitivities task:\n fitTask=self._getTask('parameterFitting')\n fitProblem = fitTask.find(xmlns + 'Problem')\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n global_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n global_string_re = re.compile(global_string)\n global_match = re.match(global_string_re, name)\n \n if global_match:\n name = global_match.group('name')\n \n #else check for a local match.\n #Vector=Reactions[Reaction] Parameter=k1\n local_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n local_string_re = re.compile(local_string)\n local_match = re.match(local_string_re, name)\n \n if local_match:\n reaction = local_match.group('reaction')\n parameter = local_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def test_joint_parameter(self):\n assert_allclose(self.jf.fitparams[0], self.g1.parameters[0])\n assert_allclose(self.jf.fitparams[0], self.g2.parameters[0])","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rdy,Rqcd\")","def suggest_parameters(trial, config):\n\n # Get parameters from config\n parameters = config['params_' + config['model_name']]\n # Init parameters for optuna\n optuna_parameters = dict()\n for key in parameters.keys():\n if parameters[key][0] == 'int':\n optuna_parameters[key] = trial.suggest_int(key, parameters[key][1], parameters[key][2])\n elif parameters[key][0] == 'uniform':\n optuna_parameters[key] = trial.suggest_uniform(key, parameters[key][1], parameters[key][2])\n elif parameters[key][0] == 'categorical':\n optuna_parameters[key] = trial.suggest_categorical(key, parameters[key][1])\n elif parameters[key][0] == 'loguniform':\n optuna_parameters[key] = trial.suggest_loguniform(key, parameters[key][1], parameters[key][2])\n return optuna_parameters","def optimize_parameters(self):\n pass","def optimize_parameters(self):\n pass","def optimize_parameters(self):\n pass","def doParametersOfInterest(self):\n ''' ref : physicsmodel -> rvf\n self.modelBuilder.out.var(\"MH\").setRange(float(self.mHRange[0]),float(self.mHRange[1]))\n self.modelBuilder.out.var(\"MH\").setConstant(False)\n '''\n\n self.modelBuilder.doVar(\"mu[0,0,1000]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doVar(\"Fvbf[0,0,1]\") ##mu is what we want to return (in string) name[starting_value,min,max] \n self.modelBuilder.doSet(\"POI\",\"mu,Fvbf\")\n self.modelBuilder.doVar(\"\")\n self.modelBuilder.factory_('expr::ggH_s_func(\"(@0-sqrt(@0))*(1.-@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::ggH_b_func(\"(1-sqrt(@0))*(1.-@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\"sqrt(@0)*(1.-@1)\", mu,Fvbf)')\n\n self.modelBuilder.factory_('expr::vbfH_s_func(\"(@0-sqrt(@0))*(@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\"(1-sqrt(@0))*(@1)\", mu,Fvbf)')\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\"sqrt(@0)*(@1)\", mu,Fvbf)')","def set_parameters(targeted_flag='true',\r\n tv_flag='false',\r\n hinge_flag='true',\r\n cos_flag='false',\r\n interpolation='bilinear',\r\n model_type='small',\r\n loss_type='center',\r\n dataset_type='vgg',\r\n attack='CW',\r\n norm='2',\r\n epsilon=0.1,\r\n iterations=100,\r\n binary_steps=8,\r\n learning_rate=0.01,\r\n epsilon_steps=0.01,\r\n init_const=0.3,\r\n mean_loss='embeddingmean',\r\n batch_size=-1,\r\n margin=5.0,\r\n amplification=2.0):\r\n params = {}\r\n\r\n params['model_type'] = model_type\r\n params['loss_type'] = loss_type\r\n params['dataset_type'] = dataset_type\r\n params['attack'] = attack\r\n params['norm'] = norm\r\n params['epsilon'] = epsilon\r\n params['iterations'] = iterations\r\n params['binary_steps'] = binary_steps\r\n params['learning_rate'] = learning_rate\r\n params['epsilon_steps'] = epsilon_steps\r\n params['init_const'] = init_const\r\n params['mean_loss'] = mean_loss\r\n params['batch_size'] = batch_size\r\n params['targeted_flag'] = string_to_bool(targeted_flag)\r\n params['tv_flag'] = string_to_bool(tv_flag)\r\n params['hinge_flag'] = string_to_bool(hinge_flag)\r\n params['cos_flag'] = string_to_bool(cos_flag)\r\n params['margin'] = margin\r\n params['amp'] = amplification\r\n\r\n if model_type == 'small' and loss_type == 'center':\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = -1.0\r\n else:\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = 0.0\r\n\r\n if (dataset_type == 'vggsmall'):\r\n params['align_dir'] = VGG_ALIGN_160_DIR\r\n params['test_dir'] = VGG_TEST_DIR\r\n elif model_type == 'large' or dataset_type == 'casia':\r\n params['align_dir'] = ALIGN_160_DIR\r\n elif model_type == 'small':\r\n params['align_dir'] = ALIGN_96_DIR\r\n else:\r\n ValueError('ValueError: Argument must be either \"small\" or \"large\".')\r\n \r\n if interpolation == 'nearest':\r\n params['interpolation'] = cv2.INTER_NEAREST\r\n elif interpolation == 'bilinear':\r\n params['interpolation'] = cv2.INTER_LINEAR\r\n elif interpolation == 'bicubic':\r\n params['interpolation'] = cv2.INTER_CUBIC\r\n elif interpolation == 'lanczos':\r\n params['interpolation'] = cv2.INTER_LANCZOS4\r\n elif interpolation == 'super':\r\n print('finish later')\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\r\n\r\n if params['hinge_flag']:\r\n params['attack_loss'] = 'hinge'\r\n else:\r\n params['attack_loss'] = 'target'\r\n if not params['targeted_flag']:\r\n params['attack_loss'] = 'target'\r\n if norm == 'inf':\r\n norm_name = 'i'\r\n else:\r\n norm_name = '2'\r\n if params['tv_flag']:\r\n tv_name = '_tv'\r\n else:\r\n tv_name = ''\r\n if params['cos_flag']:\r\n cos_name = '_cos'\r\n else:\r\n cos_name = ''\r\n\r\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\r\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\r\n params['model_name'] = dataset_type\r\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\r\n\r\n return params","def experiment_params():\n exp = {\n 'lr': [1e-3],\n 'loss_function': ['cce'],\n 'optimizer': ['nadam'],\n 'dataset': [\n # 'curv_contour_length_9',\n 'curv_contour_length_14',\n # 'curv_baseline',\n ]\n }\n exp['data_augmentations'] = [\n [\n 'grayscale',\n 'left_right',\n 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'resize',\n # 'per_image_standardization',\n 'zero_one'\n ]]\n exp['val_augmentations'] = exp['data_augmentations']\n exp['batch_size'] = 32 # Train/val batch size.\n exp['epochs'] = 16\n exp['exp_name'] = 'hgru_bn_pathfinder_14'\n exp['model_name'] = 'hgru'\n # exp['clip_gradients'] = 7.\n exp['save_weights'] = True\n exp['validation_iters'] = 1000\n exp['num_validation_evals'] = 50\n exp['shuffle_val'] = True # Shuffle val data.\n exp['shuffle_train'] = True\n return exp","def generative_parameters(self):\n params = nn.ParameterList()\n if 'parameters' in dir(self.generative_model):\n params.extend(list(self.generative_model.parameters()))\n params.extend(list(self.latent.generative_parameters()))\n return params","def obtain_training_parameters(para, x, y, alg = 'LR'):\n \n \n global omega\n \n # Iterate to find the optimal parameters\n if alg == 'LR': # logistic regression\n omega = np.zeros((3, 1))\n alpha = para.step_size # step size\n for i in range(para.iteration):\n grad = np.zeros((3, 1))\n for i in range(len(x[:, 0])):\n grad += np.reshape(x[i, :], (3, 1)) * (-y[i] + 1 / (1 + np.exp(-np.dot(x[i, :], omega))))\n omega -= alpha * grad \n \n elif alg == 'GNB': # Gaussian Naive Bayes\n # get counts for each class\n itszero = 0\n itsone = 0\n for i in range(len(y)):\n if y[i] == 1:\n itsone += 1\n else:\n itszero += 1\n \n # probability of see y\n theta0 = itszero / len(y)\n theta1 = 1 - theta0\n \n # mean of omega\n mew00 = 0\n mew01 = 0\n mew02 = 0\n mew10 = 0\n mew11 = 0\n mew12 = 0\n for i in range(len(y)):\n if y[i] == 0:\n mew00 += x[i, 0] / itszero\n mew01 += x[i, 1] / itszero\n mew02 += x[i, 2] / itszero\n else:\n mew10 += x[i, 0] / itsone\n mew11 += x[i, 1] / itsone\n mew12 += x[i, 2] / itsone\n \n # variance of omega \n sigma00 = 0\n sigma01 = 0\n sigma02 = 0\n sigma10 = 0\n sigma11 = 0\n sigma12 = 0\n for i in range(len(y)):\n if y[i] == 0:\n sigma00 += (x[i, 0] - mew00)**2 / itszero\n sigma01 += (x[i, 1] - mew01)**2 / itszero\n sigma02 += (x[i, 2] - mew02)**2 / itszero\n else:\n sigma10 += (x[i, 0] - mew10)**2 / itsone\n sigma11 += (x[i, 1] - mew11)**2 / itsone\n sigma12 += (x[i, 2] - mew12)**2 / itsone\n \n # store these parameters into the name \"omage\"\n omega = [theta0, theta1, mew00, mew01, mew02, mew10, mew11, mew12,\n sigma00, sigma01, sigma02, sigma10, sigma11, sigma12] \n \n else: # Gaussian Mixture\n pass\n \n return omega","def config_params0(data,parameter):\n model = []\n #Range of value of p\n acf = sm.graphics.tsa.acf(data.diff().dropna())\n for i in range(len(acf)):\n acf[i] = abs(acf[i]*10)\n if (ceil(acf[i])) <= 2:\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\n break\n\n #range of value of q\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\n for i in range(len(pacf)):\n pacf[i] = abs(pacf[i]*10)\n if (ceil(pacf[i])) <= 2:\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\n break\n\n\t# define config lists\n p_params = p\n d_params = parameter['d']\n q_params = q\n m_params = parameter['m']\n #P_params = p\n #D_params = [0, 1]\n #Q_params = q\n \n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\n return params","def get_optimal_param(data_desc, ml_model_desc):\n if ml_model_desc == 'ANN': \n # return [<num_layers>, <momentum>, <learn rate>]\n if data_desc == 'young_students_ti_courses':\n return [100, 0.5, 0.001]\n elif data_desc == 'young_students_lic_courses':\n return [36, 0.9, 1.0]\n elif data_desc == 'young_students_comp_courses':\n return [36, 0.6, 0.001]\n elif data_desc == 'old_students':\n return [24, 0.5, 0.7]\n else:\n exit('can not get optimal parameters for the combination passed!')\n elif ml_model_desc == 'naive_bayes':\n if data_desc == 'young_students_ti_courses':\n return [GaussianNB()]\n elif data_desc == 'young_students_lic_courses':\n return [BernoulliNB()]\n elif data_desc == 'young_students_comp_courses':\n return [MultinomialNB()]\n elif data_desc == 'old_students':\n return [GaussianNB()]\n else:\n exit('can not get optimal parameters for the combination passed!')\n elif ml_model_desc == 'SVR': \n if data_desc == 'young_students_ti_courses':\n return ['linear', 1.0]\n elif data_desc == 'young_students_lic_courses':\n return ['linear', 1.0]\n elif data_desc == 'young_students_comp_courses':\n return ['rbf', 1.0]\n elif data_desc == 'old_students':\n return ['linear', 1.0]\n else:\n exit('can not get optimal parameters for the combination passed!')\n else: \n exit('can not get optimal parameters for the combination passed!')","def test_michaelis_menten_fit(self):\n res = michaelis_menten_fit([22])\n self.assertFloatEqual(res,1.0,eps=.01)\n res = michaelis_menten_fit([42])\n self.assertFloatEqual(res,1.0,eps=.01)\n res = michaelis_menten_fit([34],num_repeats=3,params_guess=[13,13])\n self.assertFloatEqual(res,1.0,eps=.01)\n res = michaelis_menten_fit([70,70],num_repeats=5)\n self.assertFloatEqual(res,2.0,eps=.01)","def update_parameters(parameters, grads, learning_rate):\n pass","def __init__(self):\n self.param_names = []\n self.param_values = []\n self.param_settings = []\n self.result = []\n self.best_params = None\n self.best_score = None\n self.max_reps = 5\n self.num_values = False\n self.algorithm_done = False","def update_parameters(parameters, grads, learning_rate):\n L = len(parameters) // 2\n\n for i in range(L):\n parameters[\"W\"+str(i+1)] = parameters[\"W\"+str(i+1)] - learning_rate * grads[\"dW\"+str(i+1)]\n parameters[\"b\"+str(i+1)] = parameters[\"b\"+str(i+1)] - learning_rate * grads[\"db\"+str(i+1)]\n\n return parameters","def get_optimization_parameters(self, friendly=True):\n #Get the sensitivities task:\n sensTask=self._getTask('optimization')\n sensProblem = sensTask.find(xmlns + 'Problem')\n optimizationItems = sensProblem.find(xmlns + 'ParameterGroup')\n parameters = []\n for subGroup in optimizationItems:\n name = None\n lowerBound = None\n upperBound = None\n startValue = None\n \n for item in subGroup:\n if item.attrib['name'] == 'ObjectCN':\n name = item.attrib['value']\n elif item.attrib['name'] == 'UpperBound':\n upperBound = item.attrib['value']\n elif item.attrib['name'] == 'LowerBound':\n lowerBound = item.attrib['value']\n elif item.attrib['name'] == 'StartValue':\n startValue = item.attrib['value']\n assert name !=None\n assert lowerBound != None\n assert upperBound != None\n assert startValue != None\n \n if friendly:\n #Construct a user-friendly name for the parameter name using regexs\n #Look for a match for global parameters: Vector=Values[Test parameter],\n values_string = r'.*Vector=Values\\[(?P<name>.*)\\].*'\n values_string_re = re.compile(values_string)\n values_match = re.match(values_string_re, name)\n \n if values_match:\n name = 'Values[' + values_match.group('name') + ']'\n \n else:\n #else check for a parameter match.\n #Vector=Reactions[Reaction] Parameter=k1\n parameter_string = r'.*Vector=Reactions\\[(?P<reaction>.*)\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\n parameter_string_re = re.compile(parameter_string)\n parameter_match = re.match(parameter_string_re, name)\n \n if parameter_match:\n reaction = parameter_match.group('reaction')\n parameter = parameter_match.group('parameter')\n name = '(%s).%s'%(reaction, parameter)\n \n else:\n #Try again, this time looking for a string like: Vector=Metabolites[blah]\n metabolites_string = r'.*Vector=Metabolites\\[(?P<name>.*)\\].*'\n metabolites_string_re = re.compile(metabolites_string)\n metabolites_match = re.match(metabolites_string_re, name)\n if metabolites_match:\n name = 'Metabolites[' + metabolites_match.group('name') + ']'\n\n parameters.append((name, lowerBound, upperBound, startValue))\n\n return parameters","def trainable_params(model, feature_extract):\n params_to_update = model.parameters()\n print(\"Params to learn:\")\n if feature_extract:\n params_to_update = []\n for name, param in model.named_parameters():\n if param.requires_grad == True:\n params_to_update.append(param)\n print(\"\\t\", name)\n else:\n for name, param in model.named_parameters():\n if param.requires_grad == True:\n print(\"\\t\", name)\n return params_to_update","def parameters(self):","def hyperparams():\n H = 6\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)","def get_param_scenario3():\n nb_carre_x = 42\n nb_carre_y = 45\n largeur_x = 0.825\n largeur_y = 0.645\n z1 = np.array([[11], [32], [17.26]])\n z2 = np.array([[12], [32], [18.43]])\n z3 = np.array([[13], [32], [23.2]])\n z4 = np.array([[15], [31], [23.4]])\n z5 = np.array([[16], [30], [23.29]])\n z6 = np.array([[18], [29], [22.28]])\n z7 = np.array([[19], [28], [35.79]])\n z8 = np.array([[20], [27], [36.87]])\n z9 = np.array([[21], [26], [33.92]])\n z10 = np.array([[23], [24], [38.11]])\n z11 = np.array([[24], [23], [37.76]])\n z12 = np.array([[25], [22], [45.6]])\n z13 = np.array([[26], [20], [56.4]])\n z14 = np.array([[27], [18], [55.2]])\n z15 = np.array([[28], [16], [57.53]])\n z16 = np.array([[29], [14], [58.64]])\n z17 = np.array([[30], [13], [66.04]])\n z18 = np.array([[30], [11], [70.02]])\n z19 = np.array([[31], [9], [69.64]])\n z20 = np.array([[31], [6], [68.82]])\n\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\n\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\n for x in meas]\n\n # We take a measure every second\n thymio_speed_to_mms = 0.4753\n Ts = 1\n\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\n\n return delta_sr_test, delta_sl_test, z","def def_paramt():\n Zeff = 1.0\n amu = 2.0\n mf = mp*amu\n return Zeff, amu,mf","def fit():\n pass","def create_parameters(X, parameters):\n\n parameters['Car_Constant'] = X[:, 0].tolist()\n parameters['Walk_Constant'] = X[:, 1].tolist()\n parameters['PT_Constant'] = X[:, 2].tolist()\n\n return parameters","def experiment_params():\n exp = {\n 'lr': [1e-3],\n 'loss_function': ['cce'],\n 'optimizer': ['nadam'],\n 'dataset': [\n # 'curv_contour_length_9',\n 'cluttered_nist_ix1',\n # 'curv_baseline',\n ]\n }\n exp['data_augmentations'] = [\n [\n 'grayscale',\n 'center_crop',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n exp['val_augmentations'] = [\n [\n 'grayscale',\n 'center_crop',\n # 'left_right',\n # 'up_down',\n 'uint8_rescale',\n 'singleton',\n 'zero_one'\n ]]\n exp['batch_size'] = 32 # Train/val batch size.\n exp['epochs'] = 4\n exp['model_name'] = 'unet'\n exp['exp_name'] = exp['model_name'] + '_' + exp['dataset'][0]\n exp['save_weights'] = True\n exp['validation_iters'] = 1000\n exp['num_validation_evals'] = 200\n exp['shuffle_val'] = True # Shuffle val data.\n exp['shuffle_train'] = True\n return exp","def set_parameters(api_name='',\r\n targeted_flag='true',\r\n tv_flag='false',\r\n hinge_flag='true',\r\n cos_flag='false',\r\n interpolation='bilinear',\r\n model_type='large',\r\n loss_type='triplet',\r\n dataset_type='vgg',\r\n target_model='large',\r\n target_loss='center',\r\n target_dataset='VGG',\r\n attack='CW',\r\n norm='2',\r\n epsilon=0.1,\r\n iterations=20,\r\n binary_steps=5,\r\n learning_rate=0.01,\r\n epsilon_steps=0.01,\r\n init_const=0.3,\r\n mean_loss='embeddingmean',\r\n batch_size=-1,\r\n margin=15.0,\r\n amplification=6.0,\r\n granularity='normal',\r\n whitebox_target=False,\r\n pair_flag='false'):\r\n \r\n params = {}\r\n params['model_type'] = model_type\r\n params['loss_type'] = loss_type\r\n params['dataset_type'] = dataset_type\r\n params['target_model'] = target_model\r\n params['target_loss'] = target_loss\r\n params['target_dataset'] = target_dataset\r\n params['attack'] = attack\r\n params['norm'] = norm\r\n params['epsilon'] = epsilon\r\n params['iterations'] = iterations\r\n params['binary_steps'] = binary_steps\r\n params['learning_rate'] = learning_rate\r\n params['epsilon_steps'] = epsilon_steps\r\n params['init_const'] = init_const\r\n params['mean_loss'] = mean_loss\r\n params['batch_size'] = batch_size\r\n params['test_dir'] = TEST_DIR\r\n params['full_dir'] = FULL_DIR\r\n params['whitebox_target'] = whitebox_target\r\n params['targeted_flag'] = string_to_bool(targeted_flag)\r\n params['tv_flag'] = string_to_bool(tv_flag)\r\n params['hinge_flag'] = string_to_bool(hinge_flag)\r\n params['cos_flag'] = string_to_bool(cos_flag)\r\n params['pair_flag'] = string_to_bool(pair_flag)\r\n params['api_name'] = api_name\r\n\r\n if model_type == 'small' and loss_type == 'center':\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = -1.0\r\n else:\r\n params['pixel_max'] = 1.0\r\n params['pixel_min'] = 0.0\r\n\r\n if dataset_type == 'vggsmall' and not whitebox_target:\r\n params['align_dir'] = VGG_ALIGN_160_DIR\r\n params['test_dir'] = VGG_TEST_DIR\r\n elif model_type == 'large' or dataset_type == 'casia':\r\n params['align_dir'] = ALIGN_160_DIR\r\n elif model_type == 'small':\r\n params['align_dir'] = ALIGN_96_DIR\r\n else:\r\n ValueError('ValueError: Argument must be either \"small\" or \"large\".')\r\n \r\n if interpolation == 'nearest':\r\n params['interpolation'] = cv2.INTER_NEAREST\r\n elif interpolation == 'bilinear':\r\n params['interpolation'] = cv2.INTER_LINEAR\r\n elif interpolation == 'bicubic':\r\n params['interpolation'] = cv2.INTER_CUBIC\r\n elif interpolation == 'lanczos':\r\n params['interpolation'] = cv2.INTER_LANCZOS4\r\n elif interpolation == 'super':\r\n ValueError('ValueError: Super interpolation not yet implemented.')\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\r\n\r\n if granularity == 'fine':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 20.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'normal':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 10.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.5)\r\n elif granularity == 'coarse':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 5.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\r\n elif granularity == 'coarser':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'coarsest':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\r\n elif granularity == 'single':\r\n params['margin_list'] = np.array([margin])\r\n params['amp_list'] = np.array([amplification])\r\n elif granularity == 'fine-tuned':\r\n params['margin_list'] = np.arange(10.0, margin, 1.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\r\n elif granularity == 'coarse-single':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.array([1.0])\r\n elif granularity == 'api-eval':\r\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\r\n params['amp_list'] = np.arange(1.0, amplification, 0.8)\r\n else:\r\n raise ValueError('ValueError: Argument must be of the following, [fine, normal, coarse, coarser, single].')\r\n\r\n if params['hinge_flag']:\r\n params['attack_loss'] = 'hinge'\r\n else:\r\n params['attack_loss'] = 'target'\r\n if not params['targeted_flag']:\r\n params['attack_loss'] = 'target'\r\n if norm == 'inf':\r\n norm_name = 'i'\r\n else:\r\n norm_name = '2'\r\n if params['tv_flag']:\r\n tv_name = '_tv'\r\n else:\r\n tv_name = ''\r\n if params['cos_flag']:\r\n cos_name = '_cos'\r\n else:\r\n cos_name = ''\r\n\r\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\r\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\r\n params['model_name'] = dataset_type\r\n params['target_model_name'] = '{}_{}_{}'.format(target_model, target_loss, target_dataset)\r\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\r\n params['directory_path'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/full'.format(params['attack_loss']))\r\n params['directory_path_crop'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/crop'.format(params['attack_loss']))\r\n params['directory_path_npz'] = os.path.join(ROOT,\r\n OUT_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/npz'.format(params['attack_loss']))\r\n params['api_path'] = os.path.join(ROOT,\r\n API_DIR,\r\n params['attack_name'],\r\n params['model_name'],\r\n '{}_loss/npz'.format(params['attack_loss']))\r\n if params['mean_loss'] == 'embedding':\r\n params['directory_path'] += '_mean'\r\n params['directory_path_crop'] += '_mean'\r\n params['directory_path_npz'] += '_mean'\r\n params['api_path'] += '_mean'\r\n\r\n return params","def fit(self, inputs: list) -> 'BasePreprocessor':","def set_parameters(pars):\n y0=[]\n fun=None \n state_evol=None\n if pars[\"state_law\"]==0:\n state_evol=state_evol_d\n elif pars[\"state_law\"]==1:\n state_evol=state_evol_r\n elif pars[\"state_law\"]==2:\n state_evol=state_evol_p\n elif pars[\"state_law\"]==3:\n state_evol=state_evol_n\n \n if pars[\"model\"]==0:\n y0 = [pars[\"Vpl\"]*0.9,0.1,pars[\"sigma1\"]]\n fun = fun_qds\n damping = pars[\"nu\"]\n \n if pars[\"model\"]==1:\n y0 = [pars[\"Vpl\"]*0.9, 0.1,pars[\"sigma1\"],pars[\"sigma1\"]*pars[\"f0\"]]\n fun = fun_fds\n damping = pars[\"m\"]\n\n if pars[\"model\"]==2:\n y0 = [pars[\"Vpl\"]*0.99,pars[\"Vpl\"], pars[\"Vpl\"],0.1,pars[\"sigma1\"],pars[\"sigma2\"]]\n fun= fun_qdc\n damping = pars[\"nu\"]\n\n if pars[\"model\"]==3:\n y0 = [pars[\"Vpl\"]*1.1,pars[\"Vpl\"], pars[\"Vpl\"],0.0,pars[\"sigma1\"],pars[\"sigma2\"],pars[\"sigma1\"]*pars[\"f0\"]]\n fun = fun_fdc\n damping = pars[\"m\"]\n\n return (np.array(y0), state_evol, fun, damping)","def fit(self, X):","def expand_params(params):\n cv_params = []\n param_pool = unpack_cv_parameters(params)\n\n for i in list(itertools.product(*param_pool)):\n d = copy.deepcopy(params)\n name = d['name']\n for j in i:\n dict_set_nested(d, j[0].split(\".\"), j[1])\n name += \"_\" + j[0] + \"_\" + str(j[1])\n d['name'] = name.replace('.args.', \"_\")\n d = convert_tuples_2_list(d)\n cv_params.append(d)\n if not cv_params:\n return [params] * params['num_runs']\n\n gs_params = []\n for p in cv_params:\n gs_params += [p] * p['num_runs']\n return gs_params","def get_1x_lr_params(model):\n b = [model.xception_features]\n for i in range(len(b)):\n for k in b[i].parameters():\n if k.requires_grad:\n yield k","def optimiseParameters(path_to_aug, species, path_to_training):\n\n\ttraining = {}\n\tcmd = \"{}/scripts/optimize_augustus.pl --species={} --metapars={}/config/species/{}/{}_metapars.cfg --aug_exec_dir={}/bin --AUGUSTUS_CONFIG_PATH={}/config {}/output/trainingSet.gb\" \\\n\t.format(path_to_aug, species, path_to_aug, species, species, \\\n\tpath_to_aug, path_to_aug, path_to_training)\n\te = subprocess.check_call(cmd, shell=True)","def config_params1(parameter):\n\n p = parameter['p']\n q = parameter['q']\n d = parameter['d']\n m = parameter['m']\n pdq_m = list(itertools.product(p, d, q,m)) #Generate all different combinations of p, q and q triplets\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\n return params","def _get_fitted_params(self):\n return {}","def model(data_x, parameters):\n return data_x @ parameters","def initialize_parameters():\n\n W1 = tf.get_variable('W1', [3,3,3,64], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W2 = tf.get_variable('W2', [3,3,64,128], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W3 = tf.get_variable('W3', [3,3,128,256], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W4 = tf.get_variable('W4', [3,3,256,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n W5 = tf.get_variable('W5', [3,3,512,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\n\n ### END CODE HERE ###\n\n parameters = {\"W1\": W1,\n \"W2\": W2,\n \"W3\": W3,\n \"W4\": W4,\n \"W5\": W5\n }\n\n return parameters","def test_param(self):\n Y, T, X, _ = ihdp_surface_B()\n est = AutomatedLinearDML(model_y=automl_model_reg(),\n model_t=GradientBoostingClassifier(),\n featurizer=None,\n discrete_treatment=True)\n est.fit(Y, T, X=X)\n _ = est.effect(X)","def BestFit(self,initialParameterValues=None, method=None , fixedParams=None):\n\n if fixedParams:\n if not isinstance(fixedParams, list):\n fixedParams=[fixedParams]\n #Check now if the name is correct\n l_index=[]\n for index, par in enumerate(fixedParams):\n pName, pValue = par\n if pName not in self.theory.parameterNameList0:\n print \"%s is not a valid name. Ignored\" %pName\n l_index.append(index)\n if l_index:\n for i in l_index:\n fixedParams.pop(i)\n self.SetFixedParams(fixedParams)\n\n if initialParameterValues is None:\n initialParameterValues = self.theory.initialParameterValues\n #d = numpy.ones(len(initialParameterValues))\n start_time = time.time()\n if method is None or method == 'lm':\n out = minpack.leastsq(self.Residual,initialParameterValues,full_output=1, ftol=1.e-16)\n elif method == 'boldAccel':\n initialParameterValues=numpy.array(initialParameterValues)\n out = BoldAccel.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\n elif method == 'bold':\n initialParameterValues = numpy.array(initialParameterValues)\n out = Bold.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\n #out = minpack.leastsq(self.Residual,self.AnalyJac,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,Cgoal=4.e04)\n elif method == 'lm_accel':\n initialParameterValues=numpy.array(initialParameterValues)\n out = numrec.leastsq(self.Residual,self.AnalyJac,initialParameterValues,full_output=1,verbose=True, flags=[],maxfev=500)\n else:\n print \"fitting method is not included\"\n end_time = time.time()\n print \"fitting took time (mins): \", (end_time-start_time)/60.\n print \"number of function_calls:\", f_counter\n \n if fixedParams:\n outputParameterValues = self.MergeFixedAndVariableParams(fixedParams,out[0])\n else:\n outputParameterValues = out[0]\n\n return outputParameterValues, out","def build_parameters(pobj):\n ViscosityWilke.build_parameters(pobj)","def _get_trainable_params(model):\n trainable_params = []\n for x in model.parameters():\n if x.requires_grad:\n trainable_params.append(x)\n return trainable_params","def set_params(self, params_):\n x_start, x_end = params_[\"lim_fit\"]\n self.find_idx_of_fit_limit(x_start, x_end)\n self.is_error_bar_for_fit = params_[\"use_error_bar\"]\n self.fitting_method1 = params_[\"method1\"]\n self.fitting_method2 = params_[\"method2\"]\n self.qty_to_min = params_[\"qty_to_min\"]\n\n for i, key in enumerate(self.params):\n # self.params[key].set(value=params_[\"val\"][i], min=params_[\"min\"][i], max=params_[\"max\"][i], vary=bool(params_[\"hold\"][i]), brute_step=params_[\"brute_step\"][i])\n if self.params[key].user_data is not None:\n if \"dontGenerate\" in self.params[key].user_data:\n continue\n self.params[key].set(value=params_[key][\"value\"], min=params_[key][\"min\"], max=params_[key][\"max\"], vary=params_[key][\"vary\"], brute_step=params_[key][\"b_step\"])","def get_1x_lr_params(model):\r\n\r\n for name, param in model.named_parameters():\r\n if 'weight' in name and param.requires_grad:\r\n # print (name, param.data)\r\n\r\n yield param","def one_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 = 'overfit_' + 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\n # define the changing parameter and its value\n changing_param_name = 'class_weights'\n changing_param_value = [{0: 15, 1: 85}]\n # {0:15, 1:85}]#, {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 features_to_use = ['user', 'countries', 'session', 'format', 'token']\n # set constant parameters\n set_params(use_word_emb=1)\n set_params(epochs=40)\n set_params(features_to_use=features_to_use)\n\n # save constant parameters to a new \"experiment_..\" filgithx+P@2ub\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(class_weights_1=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\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,\n new_model_id, changing_param_name, value,))\n oneExperiment.start()\n oneExperiment.join()","def get_optimization_parameters(self):\n pass","def tune_parameters(self, model, param_set, train, predictor_var, target_var):\n \n grid_search = GridSearchCV(estimator = model, param_grid = param_set,n_jobs=-1, cv=5)\n grid_search.fit(train[predictor_var],train[target_var])\n \n print(grid_search.best_params_, grid_search.best_score_)\n \n return grid_search.best_params_","def BestFit(self,initialParameterValues = None, method = None, fixedParams=None):\n\n if fixedParams:\n if not isinstance(fixedParams, list):\n fixedParams=[fixedParams]\n #Check now if the name is correct\n l_index=[]\n for index, par in enumerate(fixedParams):\n pName, pValue = par\n if pName not in self.theory.parameterNameList0:\n print \"%s is not a valid name. Ignored\" %pName\n l_index.append(index)\n if l_index:\n for i in l_index:\n fixedParams.pop(i)\n\n self.theory.SetFixedParams(fixedParams)\n\n if initialParameterValues is None:\n initialParameterValues = self.theory.initialParameterValues\n #d = numpy.ones(len(initialParamaeterValues))\n start_time = time.time()\n if method is None or method == 'lm':\n out = scipy.optimize.minpack.leastsq(self.Residual,initialParameterValues,full_output=1, ftol=1.e-16)\n elif method == 'boldAccel':\n initialParameterValues=numpy.array(initialParameterValues)\n out = BoldAccel.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\n elif method == 'bold':\n initialParameterValues = numpy.array(initialParameterValues)\n out = Bold.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\n #out = minpack.leastsq(self.Residual,self.AnalyJac,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,Cgoal=4.e04)\n elif method == 'lm_accel':\n initialParameterValues=numpy.array(initialParameterValues)\n out = numrec.leastsq(self.Residual,self.AnalyJac,initialParameterValues,full_output=1,verbose=True, flags=[],maxfev=500)\n else:\n print \"fitting method is not included\"\n out = None\n end_time = time.time()\n print \"fitting took (mins)\", (end_time-start_time)/60.\n print \"number of function evals:\", f_counter\n \n if fixedParams:\n outputParameterValues = self.MergeFixedAndVariableParams(fixedParams,out[0])\n self.theory.SetFixedParams()\n else:\n outputParameterValues = out[0]\n\n\n return outputParameterValues, out","def define_parameters(self):","def params_refactoring(_params):\n _params['wavelength'] = 1e-9 * 299792458 / _params['ms_nu']\n\n return _params","def potential_parameters(cls):\n raise NotImplementedError()","def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)","def _get_fitted_param_names(self):\n return self._fitted_param_names","def params():\n raise NotImplementedError","def stimulus_params(**kwargs):\n pars = {'rate' : 1.,\n 'twin' : [0.,10.],\n 'stimulus' : 'poisson',\n 'spikes_pre': None,\n 'Nsyn' : 1,\n 'Nvar' : 2,\n # GRE parameters\n 'rate_gtr' : None,\n 'twin_gtr' : None,\n 'stimulus_gtr' : None,\n 'pre_gtr' : None\n }\n\n ## User-defined parameters\n pars = gu.varargin(pars,**kwargs)\n # Parameters must be floats\n for k,item in pars.iteritems():\n if k in ['Nsyn','Nvar']:\n pars[k] = int(item)\n elif k=='rate':\n if np.isscalar(item):\n pars[k] = float(item)\n else:\n pars[k] = np.array(item,dtype=float)\n elif k=='twin':\n if not np.isscalar(pars['rate']):\n assert len(pars['rate'])==len(item), \"twin must be of the same size of rate\"\n pars[k] = np.array(item, dtype=float)\n elif (k=='rate_gtr') and (item!=None):\n if np.isscalar(item):\n pars[k] = float(item)\n else:\n pars[k] = np.array(item,dtype=float)\n elif (k=='twin_gtr') and (item!=None):\n if (not pars['rate_gtr']) and (not np.isscalar(pars['rate_gtr'])):\n assert len(pars['rate_gtr'])==len(item), \"twin_gtr must be of the same size of rate_gtr\"\n pars[k] = np.array(item, dtype=float)\n return pars","def set_parameters(self):\n params = {}\n if self.modelname == 'SI':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after splot\n # Ts: Time from split to present, in 2*Na generation units\n names = ['N1', 'N2', 'Ts']\n values = [1, 1, 1]\n upper_bounds = [20, 20, 10]\n lower_bounds = [0.01, 0.01, 0]\n elif self.modelname == 'IM':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Ts: Time from split to present, in 2*Na generations\n names = ['N1', 'N2', 'm21', 'm12', 'Ts']\n values = [1, 1, 1, 1, 1]\n upper_bounds = [20, 20, 20, 20, 10]\n lower_bounds = [0.01, 0.01, 0, 0, 0]\n elif self.modelname == 'AM':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Tam: Time from end of anc migration to split, in 2*Na gens\n # Ts: Time from split to present, in 2*Na generations\n names = ['N1', 'N2', 'm21', 'm12', 'Tam', 'Ts']\n values = [1, 1, 1, 1, 0.1, 1]\n upper_bounds = [20, 20, 20, 20, 2, 10]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\n elif self.modelname == 'SC':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Ts: Time from split to secondary contact, in 2*Na generations\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\n names = ['N1', 'N2', 'm21', 'm12', 'Ts', 'Tsc']\n values = [1, 1, 1, 1, 1, 0.1]\n upper_bounds = [20, 20, 20, 20, 10, 2]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\n elif self.modelname == 'IM2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Ts: Time from split to present, in 2*Na generations\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0.05]\n elif self.modelname == 'AM2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Tam: Time from end of anc migration to split, in 2*Na gens\n # Ts: Time from split to present, in 2*Na generations\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Tam', 'Ts', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 0.1, 1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 2, 10, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\n elif self.modelname == 'SC2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Ts: Time from split to secondary contact, in 2*Na generations\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'Tsc', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 2, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\n params['Names'] = names\n params['Values'] = values\n params['Upper'] = upper_bounds\n params['Lower'] = lower_bounds\n return params","def select_params( model, contains=[]):\n names = []\n for name, param in model.named_parameters():\n param.requires_grad = False\n if any(name.find(c) != -1 for c in contains):\n param.requires_grad = True\n names.append([name,param.numel()])\n\n total = sum(p.numel() for p in model.parameters()) \n p_str = \"\\nParameters: \\n\\nTotal: {} \\n\\nTrained: {} \\n\"\n print(p_str.format(total,sum([p for n,p in names])))\n for n,t in names: print(n,t)\n print(\"\\n\")","def add_params(traj):\n\n # We set the BrianParameter to be the standard parameter\n traj.v_standard_parameter=Brian2Parameter\n traj.v_fast_access=True\n\n # Add parameters we need for our network\n traj.f_add_parameter('Net.C',281*pF)\n traj.f_add_parameter('Net.gL',30*nS)\n traj.f_add_parameter('Net.EL',-70.6*mV)\n traj.f_add_parameter('Net.VT',-50.4*mV)\n traj.f_add_parameter('Net.DeltaT',2*mV)\n traj.f_add_parameter('Net.tauw',40*ms)\n traj.f_add_parameter('Net.a',4*nS)\n traj.f_add_parameter('Net.b',0.08*nA)\n traj.f_add_parameter('Net.I',.8*nA)\n traj.f_add_parameter('Net.Vcut','vm > 0*mV') # practical threshold condition\n traj.f_add_parameter('Net.N',50)\n\n eqs='''\n dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt\n dw/dt=(a*(vm-EL)-w)/tauw : amp\n Vr:volt\n '''\n traj.f_add_parameter('Net.eqs', eqs)\n traj.f_add_parameter('reset', 'vm=Vr;w+=b')","def _define_SDSS_fit_params(self):\n\t\tself.a = 1.4335\n\t\tself.b = 0.3150 \n\t\tself.c = -8.8979\n\t\tself.intrinsic_scatter = 0.0578\n\t\t#self.delta_a = 0.02\n\t\t#self.delta_b = 0.01","def learn_params(self, measurements, true_ranges):\n z_hit,z_short,z_max,z_rand,var_hit,lambda_short= self.params\n pre_params=[z_hit,z_short,z_max,z_rand,var_hit,lambda_short]\n updated_params=[-1,-1,-1,-1,-1,-1]\n while np.max(np.abs(np.array(updated_params) - np.array(pre_params))) > 1e-6:\n\n e_hit, e_short, e_max, e_rand = [], [], [], []\n for i in range(len(measurements)):\n true_range, measurement = true_ranges[i], measurements[i]\n p_hit = self.PHit(true_range, measurement,var_hit)\n p_short = self.PShort(true_range, measurement,lambda_short)\n p_max = self.PMax(measurement)\n p_rand = self.PRand(measurement)\n normalizer = 1.0 / (p_hit + p_short + p_max + p_rand)\n e_hit.append(normalizer * p_hit)\n e_short.append(normalizer * p_short)\n e_max.append(normalizer * p_max)\n e_rand.append(normalizer * p_rand)\n e_hit, e_short, e_max, e_rand = np.array(e_hit), np.array(e_short), np.array(e_max), np.array(e_rand)\n\n # perform M step\n pre_params = [z_hit, z_short, z_max, z_rand, var_hit,lambda_short]\n z_hit = sum(e_hit) / len(measurements)\n z_short = sum(e_short) / len(measurements)\n z_max = sum(e_max)/ len(measurements)\n z_rand = sum(e_rand) / len(measurements)\n var_hit = np.sqrt(1.0 / np.sum(e_hit) * np.sum(e_hit * (np.array(measurements)-np.array(true_ranges))**2)).item()\n lambda_short = (np.sum(e_short) / np.sum(e_short * np.array(measurements))).item()\n updated_params = [z_hit, z_short, z_max, z_rand, var_hit, lambda_short]\n print('origin',self.params)\n print('updated',updated_params)\n return updated_params","def model_2_parameters(num_features, num_classes):\n parameters = {}\n parameters['num_features'] = num_features\n parameters['num_classes'] = num_classes\n \n return parameters","def check_parameters():\r\n for par in PARAM:\r\n if isinstance(par, ExperimentFrame):\r\n EXP.change_variable(**par())\r\n else:\r\n EXP.change_variable(**par)","def mes_fabolas_helper(X, parameters, output_file):\n\n fidelity_feature = parameters['Fidelity_Feature']\n chosen_fidelity = X[:, -1]\n\n if fidelity_feature == 'Population':\n parameters['Population_Fraction'] = chosen_fidelity.tolist()\n else:\n parameters['Iterations'] = chosen_fidelity.tolist()\n\n parameters = create_parameters(X, parameters)\n\n return parameters","def put_trainable_parameters(net,X):\n trainable=filter(lambda p: p.requires_grad, net.parameters())\n paramlist=list(trainable)\n offset=0\n for params in paramlist:\n numel=params.numel()\n with torch.no_grad():\n params.data.copy_(X[offset:offset+numel].data.view_as(params.data))\n offset+=numel","def parameters(self):\n return [i.parameter for i in self.joints.values()]","def generate_parameter_list(self) -> None:\n\n # simulation parameters from model\n model_parameter_ids = np.array(self.amici_model.getParameterIds())\n write_string_array(self.f, \"/parameters/modelParameterNames\",\n model_parameter_ids)\n print(Fore.CYAN + \"Number of model parameters:\",\n len(model_parameter_ids))\n\n print(Fore.CYAN + \"Number of optimization parameters:\",\n len(self.parameter_df))\n write_string_array(self.f, \"/parameters/parameterNames\",\n self.parameter_df.index.values[\n (self.parameter_df.estimate == 1)\n & ~self.parameter_df.index.isin(\n self.amici_model.getFixedParameterIds())])\n\n self.generate_simulation_to_optimization_parameter_mapping()\n\n self.f.flush()","def _training_params(self):\n if isinstance(\n self.lyapunov_hybrid_system.system,\n feedback_system.FeedbackSystem) and self.search_controller:\n # For a feedback system, we train both the Lyapunov network\n # parameters and the controller network parameters.\n training_params = list(\n self.lyapunov_hybrid_system.lyapunov_relu.parameters(\n )) + self.lyapunov_hybrid_system.system.controller_variables(\n ) + self.R_options.variables()\n else:\n training_params = \\\n list(self.lyapunov_hybrid_system.lyapunov_relu.parameters()) +\\\n self.R_options.variables()\n return training_params","def get_model_parameter_names():\n params = ['mu', 'rho']\n return params","def parameter_list(self):\n return [\n [encut, kpoint_mesh]\n for encut, kpoint_mesh in zip(\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\n )\n ]","def params(self):\n return [p for sublist in [o.params for o in self.obs] for p in sublist]","def update_model_parameters(parameters, grads, learning_rate):\n L = len(parameters) /2 # number of layers in the neural network\n\n for l in range(int(L)):\n parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n return parameters\n # raise NotImplementedError","def __init__(self, population=25, initSampling='lhc', fracMutation=0.2, fracElite=0.2, fracLevy=1.0, alpha=0.5, gamma=1, n=1, scalingFactor=10.0, penalty=0.0, maxGens=20000, maxFevals=200000, convTol=1e-06, stallLimit=10000, optConvTol=0.01, **kwargs):\n ProblemParameters_multi.__init__(self, **kwargs)\n self.population = population\n self.initSampling = initSampling\n self.fracMutation = fracMutation\n assert self.fracMutation >= 0 and self.fracMutation <= 1, 'The probability of discovery must exist on (0,1]'\n self.fracElite = fracElite\n assert self.fracElite >= 0 and self.fracElite <= 1, 'The elitism fraction must exist on (0,1]'\n self.fracLevy = fracLevy\n assert self.fracLevy >= 0 and self.fracLevy <= 1, 'The probability that a Levy flight is performed must exist on (0,1]'\n self.alpha = alpha\n self.gamma = gamma\n self.n = n\n self.scalingFactor = scalingFactor\n self.penalty = penalty\n self.maxGens = maxGens\n self.maxFevals = maxFevals\n self.convTol = convTol\n self.stallLimit = stallLimit\n self.optConvTol = optConvTol","def source_individual_params(targ_ind):\n # # Note - Table S6 lists beta1, beta2, pi1, pi2 rather than beta_T, beta_S, pi_T, pi_S.\n # # This seems to be a typo since I can reproduce their curves.\n # all_params = [[21.45e-6, 0.86, 3.68, 0.17e-7, 2.2, 10.89, 14.7, 8.21, 0.06, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [1.31e-6, 1.82, 15.53, 0.8e-7, 2.18, 2.46, 15, 8.44, 0.18, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [13.35e-6, 1.16, 11.61, 2.63e-7, 4.17, 1.67, 6.5, 7.92, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [2.4e-6, 3.55, 11.53, 1.35e-7, 1.6, 1.7, 15.7, 10.99, 2.4, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [1.41e-6, 1.42, 12.47, 1.06e-7, 2.17, 1.08, 22, 8.21, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [6.94e-6, 0.76, 5.89, 0.17e-7, 3.33, 10.34, 17.3, 8.79, 0.15, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [18.21e-6, 0.38, 8.74, 9.19e-7, 0.41, 0.15, 17.85, 9, 0.22, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [5.12e-6, 3.53, 4.5, 4.9e-7, 2.04, 1.64, 8.3, 6.89, 1.89, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # [1.53e-6, 4.06, 9.65, 0.29e-7, 3.96, 8.15, 17.11, 9.47, 0.66, 4e6, 4.8e8, 1, 10, 4, 0.001],\n # ]\n\n # I've modified it so that I10 is instead VT0, the initial (swabbable) virions from the URT.\n all_params = [[21.45e-6, 0.86, 3.68, 0.17e-7, 2.2, 10.89, 14.7, 8.21, 0.06, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [1.31e-6, 1.82, 15.53, 0.8e-7, 2.18, 2.46, 15, 8.44, 0.18, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [13.35e-6, 1.16, 11.61, 2.63e-7, 4.17, 1.67, 6.5, 7.92, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [2.4e-6, 3.55, 11.53, 1.35e-7, 1.6, 1.7, 15.7, 10.99, 2.4, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [1.41e-6, 1.42, 12.47, 1.06e-7, 2.17, 1.08, 22, 8.21, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [6.94e-6, 0.76, 5.89, 0.17e-7, 3.33, 10.34, 17.3, 8.79, 0.15, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [18.21e-6, 0.38, 8.74, 9.19e-7, 0.41, 0.15, 17.85, 9, 0.22, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [5.12e-6, 3.53, 4.5, 4.9e-7, 2.04, 1.64, 8.3, 6.89, 1.89, 4e6, 4.8e8, 1, 10, 4, 0.001],\n [1.53e-6, 4.06, 9.65, 0.29e-7, 3.96, 8.15, 17.11, 9.47, 0.66, 4e6, 4.8e8, 1, 10, 4, 0.001],\n ]\n currparam = all_params[targ_ind]\n return currparam","def fit(train_data, train_target):\r\n for name in models.keys():\r\n est = models[name]\r\n est_params = params2[name]\r\n gscv = GridSearchCV(estimator=est, param_grid=est_params, cv=5)\r\n gscv.fit(train_data, train_target)\r\n print(\"best parameters are: {}\".format(gscv.best_estimator_))\r\n print(\"Where we selected the parameters: {}\" .format(gscv.cv_results_['params'][gscv.best_index_]))\r\n print(\"with mean cross-validated score: {}\" .format(gscv.best_score_))","def generate_grid_search(model: KerasClassifier, model_params: dict,\n fit_params: dict) -> Tuple[List[Model], List[dict]]:\n\n models = []\n fit_parameters = []\n keys = []\n values = []\n is_for_fit = []\n\n # fill keys' and values' lists with model parameters\n for key_model, value_model in model_params.items():\n keys.append(key_model)\n values.append(value_model)\n is_for_fit.append(False)\n\n # fill keys' and values' lists with fit parameters\n for key_fit, value_fit in fit_params.items():\n keys.append(key_fit)\n values.append(value_fit)\n is_for_fit.append(True)\n\n # generate all possible combinations of parameters\n for values_list in product(*values):\n\n learner = copy.deepcopy(model)\n\n # uniquely define model structure\n model_params_values = [values_list[i] for i in range(0, len(values_list)) if is_for_fit[i] is False]\n model_params_keys = [keys[i] for i in range(0, len(keys)) if is_for_fit[i] is False]\n model_params_dict = dict(zip(model_params_keys, model_params_values))\n learner = learner.build_fn(**model_params_dict)\n\n # uniquely define fit function\n fit_params_values = [values_list[i] for i in range(0, len(values_list)) if is_for_fit[i] is True]\n fit_params_keys = [keys[i] for i in range(0, len(keys)) if is_for_fit[i] is True]\n fit_params_dict = dict(zip(fit_params_keys, fit_params_values))\n functools.partial(learner.fit, **fit_params_dict)\n\n models.append(learner)\n fit_parameters.append(fit_params_dict)\n\n return models, fit_parameters","def gen_params(no_cultures):\n # Plate level\n kn = 0.1 # Nutrient diffusion\n ks = 0.1 # Signal diffusion\n b = 0.05 # Signal on cells effect constant\n a = 0.05 # Signal secretion constant\n # Culture level\n # Growth rate constant\n r_mean = 1.0\n r_var = 1.0\n r_params = [max(0.0, gauss(r_mean, r_var)) for i in range(no_cultures)]\n params = np.array([kn, ks, b, a] + r_params)\n return params","def __init__(self, params: Iterable[nn.Parameter]):\n self.params = params\n self.param_states = [p.requires_grad for p in self.params]","def parameter_grid_search(y, tx, fit_function, score_function, ff_params={}, seed=1, k_fold=5,\n verbose=False, **ff_fixed_params):\n\n combinations = product(*ff_params.values())\n results = []\n\n for i, params in enumerate(combinations):\n kwargs = {param: value for param, value in zip(ff_params.keys(), params)}\n score = cross_validation(y, tx, k_fold, fit_function, score_function, seed=seed,\n **kwargs, **ff_fixed_params)\n\n results.append({\"params\": kwargs, \"score\": score})\n\n if verbose:\n print(f\"Parameter combination {i}:\")\n print(f\"\\tParams: {kwargs}\")\n print(f\"\\tScore: {score}\")\n\n return sorted(results, key=lambda x: x[\"score\"])","def model_1_parameters(num_features, num_classes, image_info):\n parameters = {}\n parameters['n1'] = num_features\n parameters['k1'] = int(math.floor(parameters['n1'] / 9))\n parameters['n2'] = parameters['n1'] - parameters['k1'] + 1\n if image_info['key'][:5] == \"pavia\":\n parameters['n3'] = 30\n parameters['k2'] = 3\n else:\n parameters['n3'] = 40\n parameters['k2'] = 5\n parameters['n4'] = 100\n parameters['n5'] = num_classes\n \n return parameters","def step6_set_gan_params(params):\n global GAN_PARAMS\n GAN_PARAMS = {**GAN_PARAMS, **params}","def fit(\n self, parameters: NDArrays, config: Dict[str, Scalar]\n ) -> Tuple[NDArrays, int, Dict[str, Scalar]]:\n _ = (self, parameters, config)\n return [], 0, {}","def update_parameters(self):\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\n self.update_gamma()\n self.update_gamma0()\n self.update_lambda()\n self.update_nu()\n if self.sample_xi:\n self.update_xi()","def _define_SLACS_fit_params(self):\n\t\t# Fit params from R_eff\n\t\tself.a = -0.41\n\t\tself.b = 0.39\n\t\t#self.delta_a = 0.12\n\t\t#self.delta_b = 0.10\n\t\tself.intrinsic_scatter = 0.14\n\t\t# Fit params from vel_disp\n\t\tself.a_v = 0.07\n\t\tself.b_v = -0.12\n\t\tself.int_v = 0.17","def __init__(self, params, fitness, population_size=20, generations=20, temperature_factor=0.9):\n self.params = params\n self.fitness = fitness\n self.population_size = population_size\n self.generations = generations\n self.temperature_factor = temperature_factor\n\n self.population = []","def update_parameters_with_gd(parameters, grads, learning_rate):\n\n L = len(parameters) // 2 # number of layers in the neural networks\n\n # Update rule for each parameter\n for l in range(L):\n ### START CODE HERE ### (approx. 2 lines)\n parameters[\"W\" + str(l+1)] = parameters[\"W\" + str(l+1)]-learning_rate* grads[\"dW\" + str(l+1)]\n parameters[\"b\" + str(l+1)] = parameters[\"b\" + str(l+1)]-learning_rate* grads[\"db\" + str(l+1)]\n ### END CODE HERE ###\n \n return parameters","def hyper_parameter_tuning(X, y, classifier, models, sntypes_map, feature_names, fig_dir='.', remove_models=(), name=''):\n\n # Hyperparameter grid\n n_estimators = [int(x) for x in np.linspace(start=200, stop=2000, num=10)]\n max_features = ['auto', 'sqrt']\n max_depth = [int(x) for x in np.linspace(10, 110, num=11)]\n max_depth.append(None)\n min_samples_split = [2, 5, 10]\n min_samples_leaf = [1, 2, 4]\n bootstrap = [True, False]\n random_grid = {'n_estimators': n_estimators,\n 'max_features': max_features,\n 'max_depth': max_depth,\n 'min_samples_split': min_samples_split,\n 'min_samples_leaf': min_samples_leaf,\n 'bootstrap': bootstrap}\n\n # Get data\n num_features = X.shape[1]\n model_names = [sntypes_map[model] for model in models]\n X, y, models, remove_models = remove_redundant_classes(X, y, models, remove_models)\n\n # Get best features\n n = 50\n num_features, feature_names, X = get_n_best_features(n, X, y, classifier, feature_names, num_features, fig_dir, name, models, model_names)\n\n # Randomised Search\n clf_random = RandomizedSearchCV(estimator=classifier, param_distributions=random_grid, n_iter=7, cv=3, verbose=2,\n random_state=42, n_jobs=2)\n clf_random.fit(X, y)\n print(clf_random.best_params_)\n\n def evaluate(model, test_features, test_labels):\n predictions = model.predict(test_features)\n errors = abs(predictions - test_labels)\n mape = 100 * np.mean(errors / test_labels)\n accuracy = 100 - mape\n print('Model Performance')\n print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\n print('Accuracy = {:0.2f}%.'.format(accuracy))\n\n return accuracy\n\n best_random = clf_random.best_estimator_\n # random_accuracy = evaluate(best_random, test_features, test_labels)","def setup_parameters(self):\n structure = self.ctx.structure_initial_primitive\n ecutwfc = []\n ecutrho = []\n\n for kind in structure.get_kind_names():\n try:\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\n cutrho = dual * cutoff\n ecutwfc.append(cutoff)\n ecutrho.append(cutrho)\n except KeyError as exception:\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\n\n natoms = len(structure.sites)\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\n\n self.ctx.inputs['parameters'] = {\n 'CONTROL': {\n 'restart_mode': 'from_scratch',\n 'tstress': self.ctx.protocol['tstress'],\n },\n 'SYSTEM': {\n 'ecutwfc': max(ecutwfc),\n 'ecutrho': max(ecutrho),\n 'smearing': self.ctx.protocol['smearing'],\n 'degauss': self.ctx.protocol['degauss'],\n 'occupations': self.ctx.protocol['occupations'],\n },\n 'ELECTRONS': {\n 'conv_thr': conv_thr,\n }\n }","def model_3_parameters(num_features, num_classes, image_info):\n parameters = {}\n parameters['num_features'] = num_features\n parameters['num_classes'] = num_classes\n parameters['n_estimators'] = image_info['n_estimators']\n min_child_samples = image_info['min_child_samples']\n parameters['min_child_samples'] = min_child_samples\n \n # Parameters message\n with open(OUTPUT_FILE, 'a') as f:\n f.write(\"min_child_samples: {}\\n\\n\".format(min_child_samples))\n \n return parameters"],"string":"[\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"rAfb[1.0,-5.0, 5.0]\\\");\\n self.modelBuilder.doVar(\\\"rA0[1.0, -5.0, 5.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"rAfb,rA0\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0*@1\\\",eAfb,rAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0*@1)\\\",eA0,rA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"mAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"mA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"eAfb,mAfb\\\")\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"dAfb[0.,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"dA0[0.0, -1.0, 1.0]\\\");\\n #self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb,dA0\\\")\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"dAfb\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0+@1\\\",eAfb,dAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0+@1)\\\",eA0,dA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"kappa_W[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_Z[1,0.0,2.0]\\\") \\n self.modelBuilder.doVar(\\\"kappa_tau[1,0.0,3.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_mu[1,0.0,5.0]\\\") \\n self.modelBuilder.factory_(\\\"expr::kappa_mu_expr(\\\\\\\"@0*@1+(1-@0)*@2\\\\\\\", CMS_use_kmu[0], kappa_mu, kappa_tau)\\\")\\n self.modelBuilder.doVar(\\\"kappa_t[1,0.0,4.0]\\\")\\n # additional kappa for the anomalous coupling\\n self.modelBuilder.doVar(\\\"kappa_tilde_t[0.0,0.0,4.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_b[1,0.0,3.0]\\\")\\n if not self.resolved:\\n self.modelBuilder.doVar(\\\"kappa_g[1,0.0,2.0]\\\")\\n self.modelBuilder.doVar(\\\"kappa_gam[1,0.0,2.5]\\\")\\n\\tself.modelBuilder.doVar(\\\"BRinv[0,0,1]\\\")\\n self.modelBuilder.out.var(\\\"BRinv\\\").setConstant(True)\\n # adding additional kappa to list of parameters of interest\\n pois = 'kappa_W,kappa_Z,kappa_tau,kappa_t,kappa_tilde_t,kappa_b'\\n if not self.resolved:\\n pois += ',kappa_g,kappa_gam'\\n self.doMH()\\n self.modelBuilder.doSet(\\\"POI\\\",pois)\\n # use modified Higgs Builder\\n self.SMH = AnomalousTopHiggsBuilder(self.modelBuilder)\\n self.setup()\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.70,0.70]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"Rdy_mumu_ss[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_ee_ss[1.0,0.0,10.0]\\\");\\n \\n self.modelBuilder.factory_('expr::Rpl(\\\"(1.+@0)\\\",Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(1.-@0)\\\",Afb)')\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"mu[1,0,100]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu\\\")\\n self.modelBuilder.factory_('expr::ggH_s_func(\\\"@0-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::ggH_b_func(\\\"1-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\\\"sqrt(@0)\\\", mu)')\",\n \"def parameter_tuning(D, param_grid):\\n grid = ParameterGrid(param_grid)\\n\\n for params in grid:\\n model_file = 'Theshpairs1_Ind_5' + '_emb_' + str(params['embedding_size']) + '_nr_' + str(\\n params['negative_ratio']) + \\\\\\n '_batch_' + str(params['batch_size']) + '_epochs_' \\\\\\n + str(params['nb_epochs']) + '_classification_' + str(params['classification'])\\n\\n print(model_file)\\n\\n # Train Model\\n Prio = NNEmbeddings(D, embedding_size=params['embedding_size'], negative_ratio=params['negative_ratio'],\\n nb_epochs=params['nb_epochs'], batch_size=params['batch_size'],\\n classification=params['classification'], save=True,\\n model_file='Models/' + model_file + '.h5')\\n\\n # New Predicitons\\n df_metrics = Prio.predict(pickle_file=None)\\n plot_single(df_metrics)\\n plot_metric(df_metrics, name='Plot_Metrics/' + model_file + '.png')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.7,0.7]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"R_ee_os_fakes[0.6,0.0,1.0]\\\");\\n self.modelBuilder.doVar(\\\"ee16_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee17_fakes_norm[1.0, 0.01, 10.]\\\");\\n self.modelBuilder.doVar(\\\"ee18_fakes_norm[1.0, 0.01, 10.]\\\");\\n #Remember, cant use spaces in these formulas!\\n #self.modelBuilder.options.verbose = 10\\n self.modelBuilder.factory_('expr::R_ee16_qcd_os(\\\"@0*@1\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_os(\\\"@0*@1\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_os(\\\"@0*@1\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee16_qcd_ss(\\\"@0*(1.0-@1)\\\",ee16_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee17_qcd_ss(\\\"@0*(1.0-@1)\\\",ee17_fakes_norm,R_ee_os_fakes)')\\n self.modelBuilder.factory_('expr::R_ee18_qcd_ss(\\\"@0*(1.0-@1)\\\",ee18_fakes_norm,R_ee_os_fakes)')\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def doParametersOfInterest(self):\\n self.modelBuilder.doVar(\\\"mu[0,0,1000]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu\\\")\\n self.modelBuilder.factory_('expr::vbfH_s_func(\\\"@0-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\\\"1-sqrt(@0)\\\", mu)')\\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\\\"sqrt(@0)\\\", mu)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb\\\")\\n\\n # ss templates\\n self.modelBuilder.doVar(\\\"Dilu_ratio[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_mumu_ss[1.0,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rdy_ee_ss[1.0,0.0,10.0]\\\");\\n \\n self.modelBuilder.factory_('expr::Rpl(\\\"0.5*(1.+@0*@1)\\\",Afb, Dilu_ratio)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"0.5*(1.-@0*@1)\\\",Afb, Dilu_ratio)')\",\n \"def get_parameter_estimation_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n fitTask=self._getTask('parameterFitting')\\n fitProblem = fitTask.find(xmlns + 'Problem')\\n optimizationItems = fitProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n global_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n global_string_re = re.compile(global_string)\\n global_match = re.match(global_string_re, name)\\n \\n if global_match:\\n name = global_match.group('name')\\n \\n #else check for a local match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n local_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n local_string_re = re.compile(local_string)\\n local_match = re.match(local_string_re, name)\\n \\n if local_match:\\n reaction = local_match.group('reaction')\\n parameter = local_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def test_joint_parameter(self):\\n assert_allclose(self.jf.fitparams[0], self.g1.parameters[0])\\n assert_allclose(self.jf.fitparams[0], self.g2.parameters[0])\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rdy,Rqcd\\\")\",\n \"def suggest_parameters(trial, config):\\n\\n # Get parameters from config\\n parameters = config['params_' + config['model_name']]\\n # Init parameters for optuna\\n optuna_parameters = dict()\\n for key in parameters.keys():\\n if parameters[key][0] == 'int':\\n optuna_parameters[key] = trial.suggest_int(key, parameters[key][1], parameters[key][2])\\n elif parameters[key][0] == 'uniform':\\n optuna_parameters[key] = trial.suggest_uniform(key, parameters[key][1], parameters[key][2])\\n elif parameters[key][0] == 'categorical':\\n optuna_parameters[key] = trial.suggest_categorical(key, parameters[key][1])\\n elif parameters[key][0] == 'loguniform':\\n optuna_parameters[key] = trial.suggest_loguniform(key, parameters[key][1], parameters[key][2])\\n return optuna_parameters\",\n \"def optimize_parameters(self):\\n pass\",\n \"def optimize_parameters(self):\\n pass\",\n \"def optimize_parameters(self):\\n pass\",\n \"def doParametersOfInterest(self):\\n ''' ref : physicsmodel -> rvf\\n self.modelBuilder.out.var(\\\"MH\\\").setRange(float(self.mHRange[0]),float(self.mHRange[1]))\\n self.modelBuilder.out.var(\\\"MH\\\").setConstant(False)\\n '''\\n\\n self.modelBuilder.doVar(\\\"mu[0,0,1000]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doVar(\\\"Fvbf[0,0,1]\\\") ##mu is what we want to return (in string) name[starting_value,min,max] \\n self.modelBuilder.doSet(\\\"POI\\\",\\\"mu,Fvbf\\\")\\n self.modelBuilder.doVar(\\\"\\\")\\n self.modelBuilder.factory_('expr::ggH_s_func(\\\"(@0-sqrt(@0))*(1.-@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::ggH_b_func(\\\"(1-sqrt(@0))*(1.-@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::ggH_sbi_func(\\\"sqrt(@0)*(1.-@1)\\\", mu,Fvbf)')\\n\\n self.modelBuilder.factory_('expr::vbfH_s_func(\\\"(@0-sqrt(@0))*(@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::vbfH_b_func(\\\"(1-sqrt(@0))*(@1)\\\", mu,Fvbf)')\\n self.modelBuilder.factory_( 'expr::vbfH_sbi_func(\\\"sqrt(@0)*(@1)\\\", mu,Fvbf)')\",\n \"def set_parameters(targeted_flag='true',\\r\\n tv_flag='false',\\r\\n hinge_flag='true',\\r\\n cos_flag='false',\\r\\n interpolation='bilinear',\\r\\n model_type='small',\\r\\n loss_type='center',\\r\\n dataset_type='vgg',\\r\\n attack='CW',\\r\\n norm='2',\\r\\n epsilon=0.1,\\r\\n iterations=100,\\r\\n binary_steps=8,\\r\\n learning_rate=0.01,\\r\\n epsilon_steps=0.01,\\r\\n init_const=0.3,\\r\\n mean_loss='embeddingmean',\\r\\n batch_size=-1,\\r\\n margin=5.0,\\r\\n amplification=2.0):\\r\\n params = {}\\r\\n\\r\\n params['model_type'] = model_type\\r\\n params['loss_type'] = loss_type\\r\\n params['dataset_type'] = dataset_type\\r\\n params['attack'] = attack\\r\\n params['norm'] = norm\\r\\n params['epsilon'] = epsilon\\r\\n params['iterations'] = iterations\\r\\n params['binary_steps'] = binary_steps\\r\\n params['learning_rate'] = learning_rate\\r\\n params['epsilon_steps'] = epsilon_steps\\r\\n params['init_const'] = init_const\\r\\n params['mean_loss'] = mean_loss\\r\\n params['batch_size'] = batch_size\\r\\n params['targeted_flag'] = string_to_bool(targeted_flag)\\r\\n params['tv_flag'] = string_to_bool(tv_flag)\\r\\n params['hinge_flag'] = string_to_bool(hinge_flag)\\r\\n params['cos_flag'] = string_to_bool(cos_flag)\\r\\n params['margin'] = margin\\r\\n params['amp'] = amplification\\r\\n\\r\\n if model_type == 'small' and loss_type == 'center':\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = -1.0\\r\\n else:\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = 0.0\\r\\n\\r\\n if (dataset_type == 'vggsmall'):\\r\\n params['align_dir'] = VGG_ALIGN_160_DIR\\r\\n params['test_dir'] = VGG_TEST_DIR\\r\\n elif model_type == 'large' or dataset_type == 'casia':\\r\\n params['align_dir'] = ALIGN_160_DIR\\r\\n elif model_type == 'small':\\r\\n params['align_dir'] = ALIGN_96_DIR\\r\\n else:\\r\\n ValueError('ValueError: Argument must be either \\\"small\\\" or \\\"large\\\".')\\r\\n \\r\\n if interpolation == 'nearest':\\r\\n params['interpolation'] = cv2.INTER_NEAREST\\r\\n elif interpolation == 'bilinear':\\r\\n params['interpolation'] = cv2.INTER_LINEAR\\r\\n elif interpolation == 'bicubic':\\r\\n params['interpolation'] = cv2.INTER_CUBIC\\r\\n elif interpolation == 'lanczos':\\r\\n params['interpolation'] = cv2.INTER_LANCZOS4\\r\\n elif interpolation == 'super':\\r\\n print('finish later')\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\\r\\n\\r\\n if params['hinge_flag']:\\r\\n params['attack_loss'] = 'hinge'\\r\\n else:\\r\\n params['attack_loss'] = 'target'\\r\\n if not params['targeted_flag']:\\r\\n params['attack_loss'] = 'target'\\r\\n if norm == 'inf':\\r\\n norm_name = 'i'\\r\\n else:\\r\\n norm_name = '2'\\r\\n if params['tv_flag']:\\r\\n tv_name = '_tv'\\r\\n else:\\r\\n tv_name = ''\\r\\n if params['cos_flag']:\\r\\n cos_name = '_cos'\\r\\n else:\\r\\n cos_name = ''\\r\\n\\r\\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\\r\\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\\r\\n params['model_name'] = dataset_type\\r\\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\\r\\n\\r\\n return params\",\n \"def experiment_params():\\n exp = {\\n 'lr': [1e-3],\\n 'loss_function': ['cce'],\\n 'optimizer': ['nadam'],\\n 'dataset': [\\n # 'curv_contour_length_9',\\n 'curv_contour_length_14',\\n # 'curv_baseline',\\n ]\\n }\\n exp['data_augmentations'] = [\\n [\\n 'grayscale',\\n 'left_right',\\n 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'resize',\\n # 'per_image_standardization',\\n 'zero_one'\\n ]]\\n exp['val_augmentations'] = exp['data_augmentations']\\n exp['batch_size'] = 32 # Train/val batch size.\\n exp['epochs'] = 16\\n exp['exp_name'] = 'hgru_bn_pathfinder_14'\\n exp['model_name'] = 'hgru'\\n # exp['clip_gradients'] = 7.\\n exp['save_weights'] = True\\n exp['validation_iters'] = 1000\\n exp['num_validation_evals'] = 50\\n exp['shuffle_val'] = True # Shuffle val data.\\n exp['shuffle_train'] = True\\n return exp\",\n \"def generative_parameters(self):\\n params = nn.ParameterList()\\n if 'parameters' in dir(self.generative_model):\\n params.extend(list(self.generative_model.parameters()))\\n params.extend(list(self.latent.generative_parameters()))\\n return params\",\n \"def obtain_training_parameters(para, x, y, alg = 'LR'):\\n \\n \\n global omega\\n \\n # Iterate to find the optimal parameters\\n if alg == 'LR': # logistic regression\\n omega = np.zeros((3, 1))\\n alpha = para.step_size # step size\\n for i in range(para.iteration):\\n grad = np.zeros((3, 1))\\n for i in range(len(x[:, 0])):\\n grad += np.reshape(x[i, :], (3, 1)) * (-y[i] + 1 / (1 + np.exp(-np.dot(x[i, :], omega))))\\n omega -= alpha * grad \\n \\n elif alg == 'GNB': # Gaussian Naive Bayes\\n # get counts for each class\\n itszero = 0\\n itsone = 0\\n for i in range(len(y)):\\n if y[i] == 1:\\n itsone += 1\\n else:\\n itszero += 1\\n \\n # probability of see y\\n theta0 = itszero / len(y)\\n theta1 = 1 - theta0\\n \\n # mean of omega\\n mew00 = 0\\n mew01 = 0\\n mew02 = 0\\n mew10 = 0\\n mew11 = 0\\n mew12 = 0\\n for i in range(len(y)):\\n if y[i] == 0:\\n mew00 += x[i, 0] / itszero\\n mew01 += x[i, 1] / itszero\\n mew02 += x[i, 2] / itszero\\n else:\\n mew10 += x[i, 0] / itsone\\n mew11 += x[i, 1] / itsone\\n mew12 += x[i, 2] / itsone\\n \\n # variance of omega \\n sigma00 = 0\\n sigma01 = 0\\n sigma02 = 0\\n sigma10 = 0\\n sigma11 = 0\\n sigma12 = 0\\n for i in range(len(y)):\\n if y[i] == 0:\\n sigma00 += (x[i, 0] - mew00)**2 / itszero\\n sigma01 += (x[i, 1] - mew01)**2 / itszero\\n sigma02 += (x[i, 2] - mew02)**2 / itszero\\n else:\\n sigma10 += (x[i, 0] - mew10)**2 / itsone\\n sigma11 += (x[i, 1] - mew11)**2 / itsone\\n sigma12 += (x[i, 2] - mew12)**2 / itsone\\n \\n # store these parameters into the name \\\"omage\\\"\\n omega = [theta0, theta1, mew00, mew01, mew02, mew10, mew11, mew12,\\n sigma00, sigma01, sigma02, sigma10, sigma11, sigma12] \\n \\n else: # Gaussian Mixture\\n pass\\n \\n return omega\",\n \"def config_params0(data,parameter):\\n model = []\\n #Range of value of p\\n acf = sm.graphics.tsa.acf(data.diff().dropna())\\n for i in range(len(acf)):\\n acf[i] = abs(acf[i]*10)\\n if (ceil(acf[i])) <= 2:\\n p = range(ceil(acf[i])-1,ceil(acf[i])+2)\\n break\\n\\n #range of value of q\\n pacf = sm.graphics.tsa.pacf(data.diff().dropna())\\n for i in range(len(pacf)):\\n pacf[i] = abs(pacf[i]*10)\\n if (ceil(pacf[i])) <= 2:\\n q = range(ceil(pacf[i])-1,ceil(pacf[i])+2)\\n break\\n\\n\\t# define config lists\\n p_params = p\\n d_params = parameter['d']\\n q_params = q\\n m_params = parameter['m']\\n #P_params = p\\n #D_params = [0, 1]\\n #Q_params = q\\n \\n pdq_m = list(itertools.product(p_params, d_params, q_params,m_params)) #Generate all different combinations of p, q and q triplets\\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\\n return params\",\n \"def get_optimal_param(data_desc, ml_model_desc):\\n if ml_model_desc == 'ANN': \\n # return [<num_layers>, <momentum>, <learn rate>]\\n if data_desc == 'young_students_ti_courses':\\n return [100, 0.5, 0.001]\\n elif data_desc == 'young_students_lic_courses':\\n return [36, 0.9, 1.0]\\n elif data_desc == 'young_students_comp_courses':\\n return [36, 0.6, 0.001]\\n elif data_desc == 'old_students':\\n return [24, 0.5, 0.7]\\n else:\\n exit('can not get optimal parameters for the combination passed!')\\n elif ml_model_desc == 'naive_bayes':\\n if data_desc == 'young_students_ti_courses':\\n return [GaussianNB()]\\n elif data_desc == 'young_students_lic_courses':\\n return [BernoulliNB()]\\n elif data_desc == 'young_students_comp_courses':\\n return [MultinomialNB()]\\n elif data_desc == 'old_students':\\n return [GaussianNB()]\\n else:\\n exit('can not get optimal parameters for the combination passed!')\\n elif ml_model_desc == 'SVR': \\n if data_desc == 'young_students_ti_courses':\\n return ['linear', 1.0]\\n elif data_desc == 'young_students_lic_courses':\\n return ['linear', 1.0]\\n elif data_desc == 'young_students_comp_courses':\\n return ['rbf', 1.0]\\n elif data_desc == 'old_students':\\n return ['linear', 1.0]\\n else:\\n exit('can not get optimal parameters for the combination passed!')\\n else: \\n exit('can not get optimal parameters for the combination passed!')\",\n \"def test_michaelis_menten_fit(self):\\n res = michaelis_menten_fit([22])\\n self.assertFloatEqual(res,1.0,eps=.01)\\n res = michaelis_menten_fit([42])\\n self.assertFloatEqual(res,1.0,eps=.01)\\n res = michaelis_menten_fit([34],num_repeats=3,params_guess=[13,13])\\n self.assertFloatEqual(res,1.0,eps=.01)\\n res = michaelis_menten_fit([70,70],num_repeats=5)\\n self.assertFloatEqual(res,2.0,eps=.01)\",\n \"def update_parameters(parameters, grads, learning_rate):\\n pass\",\n \"def __init__(self):\\n self.param_names = []\\n self.param_values = []\\n self.param_settings = []\\n self.result = []\\n self.best_params = None\\n self.best_score = None\\n self.max_reps = 5\\n self.num_values = False\\n self.algorithm_done = False\",\n \"def update_parameters(parameters, grads, learning_rate):\\n L = len(parameters) // 2\\n\\n for i in range(L):\\n parameters[\\\"W\\\"+str(i+1)] = parameters[\\\"W\\\"+str(i+1)] - learning_rate * grads[\\\"dW\\\"+str(i+1)]\\n parameters[\\\"b\\\"+str(i+1)] = parameters[\\\"b\\\"+str(i+1)] - learning_rate * grads[\\\"db\\\"+str(i+1)]\\n\\n return parameters\",\n \"def get_optimization_parameters(self, friendly=True):\\n #Get the sensitivities task:\\n sensTask=self._getTask('optimization')\\n sensProblem = sensTask.find(xmlns + 'Problem')\\n optimizationItems = sensProblem.find(xmlns + 'ParameterGroup')\\n parameters = []\\n for subGroup in optimizationItems:\\n name = None\\n lowerBound = None\\n upperBound = None\\n startValue = None\\n \\n for item in subGroup:\\n if item.attrib['name'] == 'ObjectCN':\\n name = item.attrib['value']\\n elif item.attrib['name'] == 'UpperBound':\\n upperBound = item.attrib['value']\\n elif item.attrib['name'] == 'LowerBound':\\n lowerBound = item.attrib['value']\\n elif item.attrib['name'] == 'StartValue':\\n startValue = item.attrib['value']\\n assert name !=None\\n assert lowerBound != None\\n assert upperBound != None\\n assert startValue != None\\n \\n if friendly:\\n #Construct a user-friendly name for the parameter name using regexs\\n #Look for a match for global parameters: Vector=Values[Test parameter],\\n values_string = r'.*Vector=Values\\\\[(?P<name>.*)\\\\].*'\\n values_string_re = re.compile(values_string)\\n values_match = re.match(values_string_re, name)\\n \\n if values_match:\\n name = 'Values[' + values_match.group('name') + ']'\\n \\n else:\\n #else check for a parameter match.\\n #Vector=Reactions[Reaction] Parameter=k1\\n parameter_string = r'.*Vector=Reactions\\\\[(?P<reaction>.*)\\\\].*Parameter=(?P<parameter>.*),Reference=Value.*'\\n parameter_string_re = re.compile(parameter_string)\\n parameter_match = re.match(parameter_string_re, name)\\n \\n if parameter_match:\\n reaction = parameter_match.group('reaction')\\n parameter = parameter_match.group('parameter')\\n name = '(%s).%s'%(reaction, parameter)\\n \\n else:\\n #Try again, this time looking for a string like: Vector=Metabolites[blah]\\n metabolites_string = r'.*Vector=Metabolites\\\\[(?P<name>.*)\\\\].*'\\n metabolites_string_re = re.compile(metabolites_string)\\n metabolites_match = re.match(metabolites_string_re, name)\\n if metabolites_match:\\n name = 'Metabolites[' + metabolites_match.group('name') + ']'\\n\\n parameters.append((name, lowerBound, upperBound, startValue))\\n\\n return parameters\",\n \"def trainable_params(model, feature_extract):\\n params_to_update = model.parameters()\\n print(\\\"Params to learn:\\\")\\n if feature_extract:\\n params_to_update = []\\n for name, param in model.named_parameters():\\n if param.requires_grad == True:\\n params_to_update.append(param)\\n print(\\\"\\\\t\\\", name)\\n else:\\n for name, param in model.named_parameters():\\n if param.requires_grad == True:\\n print(\\\"\\\\t\\\", name)\\n return params_to_update\",\n \"def parameters(self):\",\n \"def hyperparams():\\n H = 6\\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)\",\n \"def get_param_scenario3():\\n nb_carre_x = 42\\n nb_carre_y = 45\\n largeur_x = 0.825\\n largeur_y = 0.645\\n z1 = np.array([[11], [32], [17.26]])\\n z2 = np.array([[12], [32], [18.43]])\\n z3 = np.array([[13], [32], [23.2]])\\n z4 = np.array([[15], [31], [23.4]])\\n z5 = np.array([[16], [30], [23.29]])\\n z6 = np.array([[18], [29], [22.28]])\\n z7 = np.array([[19], [28], [35.79]])\\n z8 = np.array([[20], [27], [36.87]])\\n z9 = np.array([[21], [26], [33.92]])\\n z10 = np.array([[23], [24], [38.11]])\\n z11 = np.array([[24], [23], [37.76]])\\n z12 = np.array([[25], [22], [45.6]])\\n z13 = np.array([[26], [20], [56.4]])\\n z14 = np.array([[27], [18], [55.2]])\\n z15 = np.array([[28], [16], [57.53]])\\n z16 = np.array([[29], [14], [58.64]])\\n z17 = np.array([[30], [13], [66.04]])\\n z18 = np.array([[30], [11], [70.02]])\\n z19 = np.array([[31], [9], [69.64]])\\n z20 = np.array([[31], [6], [68.82]])\\n\\n meas = [z1, z2, z3, z4, z5, z6, z7, z8, z9, z10, z11, z12, z13, z14, z15, z16, z17, z18, z19, z20]\\n\\n z = [np.array([[x[0][0] * largeur_x / nb_carre_x], [x[1][0] * largeur_y / nb_carre_y], [-x[2][0] * np.pi / 180]])\\n for x in meas]\\n\\n # We take a measure every second\\n thymio_speed_to_mms = 0.4753\\n Ts = 1\\n\\n Thymio_speed_left = [73, 92, 94, 102, 100, 89, 92, 103, 97, 100, 100, 104, 95, 97, 103, 101, 100, 101, 92, 98]\\n Thymio_speed_right = [67, 96, 102, 89, 105, -45, 102, 101, 104, 71, 100, 93, 94, 105, 96, -45, 95, 103, 88, 94]\\n delta_sr_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_right]\\n delta_sl_test = [x * Ts / thymio_speed_to_mms / 1000 for x in Thymio_speed_left]\\n\\n return delta_sr_test, delta_sl_test, z\",\n \"def def_paramt():\\n Zeff = 1.0\\n amu = 2.0\\n mf = mp*amu\\n return Zeff, amu,mf\",\n \"def fit():\\n pass\",\n \"def create_parameters(X, parameters):\\n\\n parameters['Car_Constant'] = X[:, 0].tolist()\\n parameters['Walk_Constant'] = X[:, 1].tolist()\\n parameters['PT_Constant'] = X[:, 2].tolist()\\n\\n return parameters\",\n \"def experiment_params():\\n exp = {\\n 'lr': [1e-3],\\n 'loss_function': ['cce'],\\n 'optimizer': ['nadam'],\\n 'dataset': [\\n # 'curv_contour_length_9',\\n 'cluttered_nist_ix1',\\n # 'curv_baseline',\\n ]\\n }\\n exp['data_augmentations'] = [\\n [\\n 'grayscale',\\n 'center_crop',\\n # 'left_right',\\n # 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'zero_one'\\n ]]\\n exp['val_augmentations'] = [\\n [\\n 'grayscale',\\n 'center_crop',\\n # 'left_right',\\n # 'up_down',\\n 'uint8_rescale',\\n 'singleton',\\n 'zero_one'\\n ]]\\n exp['batch_size'] = 32 # Train/val batch size.\\n exp['epochs'] = 4\\n exp['model_name'] = 'unet'\\n exp['exp_name'] = exp['model_name'] + '_' + exp['dataset'][0]\\n exp['save_weights'] = True\\n exp['validation_iters'] = 1000\\n exp['num_validation_evals'] = 200\\n exp['shuffle_val'] = True # Shuffle val data.\\n exp['shuffle_train'] = True\\n return exp\",\n \"def set_parameters(api_name='',\\r\\n targeted_flag='true',\\r\\n tv_flag='false',\\r\\n hinge_flag='true',\\r\\n cos_flag='false',\\r\\n interpolation='bilinear',\\r\\n model_type='large',\\r\\n loss_type='triplet',\\r\\n dataset_type='vgg',\\r\\n target_model='large',\\r\\n target_loss='center',\\r\\n target_dataset='VGG',\\r\\n attack='CW',\\r\\n norm='2',\\r\\n epsilon=0.1,\\r\\n iterations=20,\\r\\n binary_steps=5,\\r\\n learning_rate=0.01,\\r\\n epsilon_steps=0.01,\\r\\n init_const=0.3,\\r\\n mean_loss='embeddingmean',\\r\\n batch_size=-1,\\r\\n margin=15.0,\\r\\n amplification=6.0,\\r\\n granularity='normal',\\r\\n whitebox_target=False,\\r\\n pair_flag='false'):\\r\\n \\r\\n params = {}\\r\\n params['model_type'] = model_type\\r\\n params['loss_type'] = loss_type\\r\\n params['dataset_type'] = dataset_type\\r\\n params['target_model'] = target_model\\r\\n params['target_loss'] = target_loss\\r\\n params['target_dataset'] = target_dataset\\r\\n params['attack'] = attack\\r\\n params['norm'] = norm\\r\\n params['epsilon'] = epsilon\\r\\n params['iterations'] = iterations\\r\\n params['binary_steps'] = binary_steps\\r\\n params['learning_rate'] = learning_rate\\r\\n params['epsilon_steps'] = epsilon_steps\\r\\n params['init_const'] = init_const\\r\\n params['mean_loss'] = mean_loss\\r\\n params['batch_size'] = batch_size\\r\\n params['test_dir'] = TEST_DIR\\r\\n params['full_dir'] = FULL_DIR\\r\\n params['whitebox_target'] = whitebox_target\\r\\n params['targeted_flag'] = string_to_bool(targeted_flag)\\r\\n params['tv_flag'] = string_to_bool(tv_flag)\\r\\n params['hinge_flag'] = string_to_bool(hinge_flag)\\r\\n params['cos_flag'] = string_to_bool(cos_flag)\\r\\n params['pair_flag'] = string_to_bool(pair_flag)\\r\\n params['api_name'] = api_name\\r\\n\\r\\n if model_type == 'small' and loss_type == 'center':\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = -1.0\\r\\n else:\\r\\n params['pixel_max'] = 1.0\\r\\n params['pixel_min'] = 0.0\\r\\n\\r\\n if dataset_type == 'vggsmall' and not whitebox_target:\\r\\n params['align_dir'] = VGG_ALIGN_160_DIR\\r\\n params['test_dir'] = VGG_TEST_DIR\\r\\n elif model_type == 'large' or dataset_type == 'casia':\\r\\n params['align_dir'] = ALIGN_160_DIR\\r\\n elif model_type == 'small':\\r\\n params['align_dir'] = ALIGN_96_DIR\\r\\n else:\\r\\n ValueError('ValueError: Argument must be either \\\"small\\\" or \\\"large\\\".')\\r\\n \\r\\n if interpolation == 'nearest':\\r\\n params['interpolation'] = cv2.INTER_NEAREST\\r\\n elif interpolation == 'bilinear':\\r\\n params['interpolation'] = cv2.INTER_LINEAR\\r\\n elif interpolation == 'bicubic':\\r\\n params['interpolation'] = cv2.INTER_CUBIC\\r\\n elif interpolation == 'lanczos':\\r\\n params['interpolation'] = cv2.INTER_LANCZOS4\\r\\n elif interpolation == 'super':\\r\\n ValueError('ValueError: Super interpolation not yet implemented.')\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [nearest, bilinear, bicubic, lanczos, super].')\\r\\n\\r\\n if granularity == 'fine':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 20.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'normal':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 10.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.5)\\r\\n elif granularity == 'coarse':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 5.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\\r\\n elif granularity == 'coarser':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'coarsest':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 1.0)\\r\\n elif granularity == 'single':\\r\\n params['margin_list'] = np.array([margin])\\r\\n params['amp_list'] = np.array([amplification])\\r\\n elif granularity == 'fine-tuned':\\r\\n params['margin_list'] = np.arange(10.0, margin, 1.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.2)\\r\\n elif granularity == 'coarse-single':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.array([1.0])\\r\\n elif granularity == 'api-eval':\\r\\n params['margin_list'] = np.arange(0.0, margin, margin / 3.0)\\r\\n params['amp_list'] = np.arange(1.0, amplification, 0.8)\\r\\n else:\\r\\n raise ValueError('ValueError: Argument must be of the following, [fine, normal, coarse, coarser, single].')\\r\\n\\r\\n if params['hinge_flag']:\\r\\n params['attack_loss'] = 'hinge'\\r\\n else:\\r\\n params['attack_loss'] = 'target'\\r\\n if not params['targeted_flag']:\\r\\n params['attack_loss'] = 'target'\\r\\n if norm == 'inf':\\r\\n norm_name = 'i'\\r\\n else:\\r\\n norm_name = '2'\\r\\n if params['tv_flag']:\\r\\n tv_name = '_tv'\\r\\n else:\\r\\n tv_name = ''\\r\\n if params['cos_flag']:\\r\\n cos_name = '_cos'\\r\\n else:\\r\\n cos_name = ''\\r\\n\\r\\n params['model_name'] = '{}_{}'.format(model_type, loss_type)\\r\\n if dataset_type == 'casia' or dataset_type == 'vggsmall':\\r\\n params['model_name'] = dataset_type\\r\\n params['target_model_name'] = '{}_{}_{}'.format(target_model, target_loss, target_dataset)\\r\\n params['attack_name'] = '{}_l{}{}{}'.format(attack.lower(), norm_name, tv_name, cos_name)\\r\\n params['directory_path'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/full'.format(params['attack_loss']))\\r\\n params['directory_path_crop'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/crop'.format(params['attack_loss']))\\r\\n params['directory_path_npz'] = os.path.join(ROOT,\\r\\n OUT_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/npz'.format(params['attack_loss']))\\r\\n params['api_path'] = os.path.join(ROOT,\\r\\n API_DIR,\\r\\n params['attack_name'],\\r\\n params['model_name'],\\r\\n '{}_loss/npz'.format(params['attack_loss']))\\r\\n if params['mean_loss'] == 'embedding':\\r\\n params['directory_path'] += '_mean'\\r\\n params['directory_path_crop'] += '_mean'\\r\\n params['directory_path_npz'] += '_mean'\\r\\n params['api_path'] += '_mean'\\r\\n\\r\\n return params\",\n \"def fit(self, inputs: list) -> 'BasePreprocessor':\",\n \"def set_parameters(pars):\\n y0=[]\\n fun=None \\n state_evol=None\\n if pars[\\\"state_law\\\"]==0:\\n state_evol=state_evol_d\\n elif pars[\\\"state_law\\\"]==1:\\n state_evol=state_evol_r\\n elif pars[\\\"state_law\\\"]==2:\\n state_evol=state_evol_p\\n elif pars[\\\"state_law\\\"]==3:\\n state_evol=state_evol_n\\n \\n if pars[\\\"model\\\"]==0:\\n y0 = [pars[\\\"Vpl\\\"]*0.9,0.1,pars[\\\"sigma1\\\"]]\\n fun = fun_qds\\n damping = pars[\\\"nu\\\"]\\n \\n if pars[\\\"model\\\"]==1:\\n y0 = [pars[\\\"Vpl\\\"]*0.9, 0.1,pars[\\\"sigma1\\\"],pars[\\\"sigma1\\\"]*pars[\\\"f0\\\"]]\\n fun = fun_fds\\n damping = pars[\\\"m\\\"]\\n\\n if pars[\\\"model\\\"]==2:\\n y0 = [pars[\\\"Vpl\\\"]*0.99,pars[\\\"Vpl\\\"], pars[\\\"Vpl\\\"],0.1,pars[\\\"sigma1\\\"],pars[\\\"sigma2\\\"]]\\n fun= fun_qdc\\n damping = pars[\\\"nu\\\"]\\n\\n if pars[\\\"model\\\"]==3:\\n y0 = [pars[\\\"Vpl\\\"]*1.1,pars[\\\"Vpl\\\"], pars[\\\"Vpl\\\"],0.0,pars[\\\"sigma1\\\"],pars[\\\"sigma2\\\"],pars[\\\"sigma1\\\"]*pars[\\\"f0\\\"]]\\n fun = fun_fdc\\n damping = pars[\\\"m\\\"]\\n\\n return (np.array(y0), state_evol, fun, damping)\",\n \"def fit(self, X):\",\n \"def expand_params(params):\\n cv_params = []\\n param_pool = unpack_cv_parameters(params)\\n\\n for i in list(itertools.product(*param_pool)):\\n d = copy.deepcopy(params)\\n name = d['name']\\n for j in i:\\n dict_set_nested(d, j[0].split(\\\".\\\"), j[1])\\n name += \\\"_\\\" + j[0] + \\\"_\\\" + str(j[1])\\n d['name'] = name.replace('.args.', \\\"_\\\")\\n d = convert_tuples_2_list(d)\\n cv_params.append(d)\\n if not cv_params:\\n return [params] * params['num_runs']\\n\\n gs_params = []\\n for p in cv_params:\\n gs_params += [p] * p['num_runs']\\n return gs_params\",\n \"def get_1x_lr_params(model):\\n b = [model.xception_features]\\n for i in range(len(b)):\\n for k in b[i].parameters():\\n if k.requires_grad:\\n yield k\",\n \"def optimiseParameters(path_to_aug, species, path_to_training):\\n\\n\\ttraining = {}\\n\\tcmd = \\\"{}/scripts/optimize_augustus.pl --species={} --metapars={}/config/species/{}/{}_metapars.cfg --aug_exec_dir={}/bin --AUGUSTUS_CONFIG_PATH={}/config {}/output/trainingSet.gb\\\" \\\\\\n\\t.format(path_to_aug, species, path_to_aug, species, species, \\\\\\n\\tpath_to_aug, path_to_aug, path_to_training)\\n\\te = subprocess.check_call(cmd, shell=True)\",\n \"def config_params1(parameter):\\n\\n p = parameter['p']\\n q = parameter['q']\\n d = parameter['d']\\n m = parameter['m']\\n pdq_m = list(itertools.product(p, d, q,m)) #Generate all different combinations of p, q and q triplets\\n params = [[(x[0], x[1], x[2]),(x[0], x[1], x[2], x[3])] for x in pdq_m]\\n return params\",\n \"def _get_fitted_params(self):\\n return {}\",\n \"def model(data_x, parameters):\\n return data_x @ parameters\",\n \"def initialize_parameters():\\n\\n W1 = tf.get_variable('W1', [3,3,3,64], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W2 = tf.get_variable('W2', [3,3,64,128], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W3 = tf.get_variable('W3', [3,3,128,256], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W4 = tf.get_variable('W4', [3,3,256,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n W5 = tf.get_variable('W5', [3,3,512,512], initializer=tf.contrib.layers.xavier_initializer(seed = 0))\\n\\n ### END CODE HERE ###\\n\\n parameters = {\\\"W1\\\": W1,\\n \\\"W2\\\": W2,\\n \\\"W3\\\": W3,\\n \\\"W4\\\": W4,\\n \\\"W5\\\": W5\\n }\\n\\n return parameters\",\n \"def test_param(self):\\n Y, T, X, _ = ihdp_surface_B()\\n est = AutomatedLinearDML(model_y=automl_model_reg(),\\n model_t=GradientBoostingClassifier(),\\n featurizer=None,\\n discrete_treatment=True)\\n est.fit(Y, T, X=X)\\n _ = est.effect(X)\",\n \"def BestFit(self,initialParameterValues=None, method=None , fixedParams=None):\\n\\n if fixedParams:\\n if not isinstance(fixedParams, list):\\n fixedParams=[fixedParams]\\n #Check now if the name is correct\\n l_index=[]\\n for index, par in enumerate(fixedParams):\\n pName, pValue = par\\n if pName not in self.theory.parameterNameList0:\\n print \\\"%s is not a valid name. Ignored\\\" %pName\\n l_index.append(index)\\n if l_index:\\n for i in l_index:\\n fixedParams.pop(i)\\n self.SetFixedParams(fixedParams)\\n\\n if initialParameterValues is None:\\n initialParameterValues = self.theory.initialParameterValues\\n #d = numpy.ones(len(initialParameterValues))\\n start_time = time.time()\\n if method is None or method == 'lm':\\n out = minpack.leastsq(self.Residual,initialParameterValues,full_output=1, ftol=1.e-16)\\n elif method == 'boldAccel':\\n initialParameterValues=numpy.array(initialParameterValues)\\n out = BoldAccel.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\\n elif method == 'bold':\\n initialParameterValues = numpy.array(initialParameterValues)\\n out = Bold.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\\n #out = minpack.leastsq(self.Residual,self.AnalyJac,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,Cgoal=4.e04)\\n elif method == 'lm_accel':\\n initialParameterValues=numpy.array(initialParameterValues)\\n out = numrec.leastsq(self.Residual,self.AnalyJac,initialParameterValues,full_output=1,verbose=True, flags=[],maxfev=500)\\n else:\\n print \\\"fitting method is not included\\\"\\n end_time = time.time()\\n print \\\"fitting took time (mins): \\\", (end_time-start_time)/60.\\n print \\\"number of function_calls:\\\", f_counter\\n \\n if fixedParams:\\n outputParameterValues = self.MergeFixedAndVariableParams(fixedParams,out[0])\\n else:\\n outputParameterValues = out[0]\\n\\n return outputParameterValues, out\",\n \"def build_parameters(pobj):\\n ViscosityWilke.build_parameters(pobj)\",\n \"def _get_trainable_params(model):\\n trainable_params = []\\n for x in model.parameters():\\n if x.requires_grad:\\n trainable_params.append(x)\\n return trainable_params\",\n \"def set_params(self, params_):\\n x_start, x_end = params_[\\\"lim_fit\\\"]\\n self.find_idx_of_fit_limit(x_start, x_end)\\n self.is_error_bar_for_fit = params_[\\\"use_error_bar\\\"]\\n self.fitting_method1 = params_[\\\"method1\\\"]\\n self.fitting_method2 = params_[\\\"method2\\\"]\\n self.qty_to_min = params_[\\\"qty_to_min\\\"]\\n\\n for i, key in enumerate(self.params):\\n # self.params[key].set(value=params_[\\\"val\\\"][i], min=params_[\\\"min\\\"][i], max=params_[\\\"max\\\"][i], vary=bool(params_[\\\"hold\\\"][i]), brute_step=params_[\\\"brute_step\\\"][i])\\n if self.params[key].user_data is not None:\\n if \\\"dontGenerate\\\" in self.params[key].user_data:\\n continue\\n self.params[key].set(value=params_[key][\\\"value\\\"], min=params_[key][\\\"min\\\"], max=params_[key][\\\"max\\\"], vary=params_[key][\\\"vary\\\"], brute_step=params_[key][\\\"b_step\\\"])\",\n \"def get_1x_lr_params(model):\\r\\n\\r\\n for name, param in model.named_parameters():\\r\\n if 'weight' in name and param.requires_grad:\\r\\n # print (name, param.data)\\r\\n\\r\\n yield param\",\n \"def one_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 = 'overfit_' + 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\\n # define the changing parameter and its value\\n changing_param_name = 'class_weights'\\n changing_param_value = [{0: 15, 1: 85}]\\n # {0:15, 1:85}]#, {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 features_to_use = ['user', 'countries', 'session', 'format', 'token']\\n # set constant parameters\\n set_params(use_word_emb=1)\\n set_params(epochs=40)\\n set_params(features_to_use=features_to_use)\\n\\n # save constant parameters to a new \\\"experiment_..\\\" filgithx+P@2ub\\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(class_weights_1=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\\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,\\n new_model_id, changing_param_name, value,))\\n oneExperiment.start()\\n oneExperiment.join()\",\n \"def get_optimization_parameters(self):\\n pass\",\n \"def tune_parameters(self, model, param_set, train, predictor_var, target_var):\\n \\n grid_search = GridSearchCV(estimator = model, param_grid = param_set,n_jobs=-1, cv=5)\\n grid_search.fit(train[predictor_var],train[target_var])\\n \\n print(grid_search.best_params_, grid_search.best_score_)\\n \\n return grid_search.best_params_\",\n \"def BestFit(self,initialParameterValues = None, method = None, fixedParams=None):\\n\\n if fixedParams:\\n if not isinstance(fixedParams, list):\\n fixedParams=[fixedParams]\\n #Check now if the name is correct\\n l_index=[]\\n for index, par in enumerate(fixedParams):\\n pName, pValue = par\\n if pName not in self.theory.parameterNameList0:\\n print \\\"%s is not a valid name. Ignored\\\" %pName\\n l_index.append(index)\\n if l_index:\\n for i in l_index:\\n fixedParams.pop(i)\\n\\n self.theory.SetFixedParams(fixedParams)\\n\\n if initialParameterValues is None:\\n initialParameterValues = self.theory.initialParameterValues\\n #d = numpy.ones(len(initialParamaeterValues))\\n start_time = time.time()\\n if method is None or method == 'lm':\\n out = scipy.optimize.minpack.leastsq(self.Residual,initialParameterValues,full_output=1, ftol=1.e-16)\\n elif method == 'boldAccel':\\n initialParameterValues=numpy.array(initialParameterValues)\\n out = BoldAccel.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\\n elif method == 'bold':\\n initialParameterValues = numpy.array(initialParameterValues)\\n out = Bold.leastsq(self.Residual,None,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,ibold=0,verbose=True)\\n #out = minpack.leastsq(self.Residual,self.AnalyJac,initialParameterValues,gtol=1e-8,xtol=1.49e-8,ftol=1e-16,full_output=1,Cgoal=4.e04)\\n elif method == 'lm_accel':\\n initialParameterValues=numpy.array(initialParameterValues)\\n out = numrec.leastsq(self.Residual,self.AnalyJac,initialParameterValues,full_output=1,verbose=True, flags=[],maxfev=500)\\n else:\\n print \\\"fitting method is not included\\\"\\n out = None\\n end_time = time.time()\\n print \\\"fitting took (mins)\\\", (end_time-start_time)/60.\\n print \\\"number of function evals:\\\", f_counter\\n \\n if fixedParams:\\n outputParameterValues = self.MergeFixedAndVariableParams(fixedParams,out[0])\\n self.theory.SetFixedParams()\\n else:\\n outputParameterValues = out[0]\\n\\n\\n return outputParameterValues, out\",\n \"def define_parameters(self):\",\n \"def params_refactoring(_params):\\n _params['wavelength'] = 1e-9 * 299792458 / _params['ms_nu']\\n\\n return _params\",\n \"def potential_parameters(cls):\\n raise NotImplementedError()\",\n \"def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)\",\n \"def _get_fitted_param_names(self):\\n return self._fitted_param_names\",\n \"def params():\\n raise NotImplementedError\",\n \"def stimulus_params(**kwargs):\\n pars = {'rate' : 1.,\\n 'twin' : [0.,10.],\\n 'stimulus' : 'poisson',\\n 'spikes_pre': None,\\n 'Nsyn' : 1,\\n 'Nvar' : 2,\\n # GRE parameters\\n 'rate_gtr' : None,\\n 'twin_gtr' : None,\\n 'stimulus_gtr' : None,\\n 'pre_gtr' : None\\n }\\n\\n ## User-defined parameters\\n pars = gu.varargin(pars,**kwargs)\\n # Parameters must be floats\\n for k,item in pars.iteritems():\\n if k in ['Nsyn','Nvar']:\\n pars[k] = int(item)\\n elif k=='rate':\\n if np.isscalar(item):\\n pars[k] = float(item)\\n else:\\n pars[k] = np.array(item,dtype=float)\\n elif k=='twin':\\n if not np.isscalar(pars['rate']):\\n assert len(pars['rate'])==len(item), \\\"twin must be of the same size of rate\\\"\\n pars[k] = np.array(item, dtype=float)\\n elif (k=='rate_gtr') and (item!=None):\\n if np.isscalar(item):\\n pars[k] = float(item)\\n else:\\n pars[k] = np.array(item,dtype=float)\\n elif (k=='twin_gtr') and (item!=None):\\n if (not pars['rate_gtr']) and (not np.isscalar(pars['rate_gtr'])):\\n assert len(pars['rate_gtr'])==len(item), \\\"twin_gtr must be of the same size of rate_gtr\\\"\\n pars[k] = np.array(item, dtype=float)\\n return pars\",\n \"def set_parameters(self):\\n params = {}\\n if self.modelname == 'SI':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after splot\\n # Ts: Time from split to present, in 2*Na generation units\\n names = ['N1', 'N2', 'Ts']\\n values = [1, 1, 1]\\n upper_bounds = [20, 20, 10]\\n lower_bounds = [0.01, 0.01, 0]\\n elif self.modelname == 'IM':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Ts: Time from split to present, in 2*Na generations\\n names = ['N1', 'N2', 'm21', 'm12', 'Ts']\\n values = [1, 1, 1, 1, 1]\\n upper_bounds = [20, 20, 20, 20, 10]\\n lower_bounds = [0.01, 0.01, 0, 0, 0]\\n elif self.modelname == 'AM':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Tam: Time from end of anc migration to split, in 2*Na gens\\n # Ts: Time from split to present, in 2*Na generations\\n names = ['N1', 'N2', 'm21', 'm12', 'Tam', 'Ts']\\n values = [1, 1, 1, 1, 0.1, 1]\\n upper_bounds = [20, 20, 20, 20, 2, 10]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\\n elif self.modelname == 'SC':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Ts: Time from split to secondary contact, in 2*Na generations\\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\\n names = ['N1', 'N2', 'm21', 'm12', 'Ts', 'Tsc']\\n values = [1, 1, 1, 1, 1, 0.1]\\n upper_bounds = [20, 20, 20, 20, 10, 2]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\\n elif self.modelname == 'IM2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Ts: Time from split to present, in 2*Na generations\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0.05]\\n elif self.modelname == 'AM2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Tam: Time from end of anc migration to split, in 2*Na gens\\n # Ts: Time from split to present, in 2*Na generations\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Tam', 'Ts', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 0.1, 1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 2, 10, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\\n elif self.modelname == 'SC2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Ts: Time from split to secondary contact, in 2*Na generations\\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'Tsc', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 2, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\\n params['Names'] = names\\n params['Values'] = values\\n params['Upper'] = upper_bounds\\n params['Lower'] = lower_bounds\\n return params\",\n \"def select_params( model, contains=[]):\\n names = []\\n for name, param in model.named_parameters():\\n param.requires_grad = False\\n if any(name.find(c) != -1 for c in contains):\\n param.requires_grad = True\\n names.append([name,param.numel()])\\n\\n total = sum(p.numel() for p in model.parameters()) \\n p_str = \\\"\\\\nParameters: \\\\n\\\\nTotal: {} \\\\n\\\\nTrained: {} \\\\n\\\"\\n print(p_str.format(total,sum([p for n,p in names])))\\n for n,t in names: print(n,t)\\n print(\\\"\\\\n\\\")\",\n \"def add_params(traj):\\n\\n # We set the BrianParameter to be the standard parameter\\n traj.v_standard_parameter=Brian2Parameter\\n traj.v_fast_access=True\\n\\n # Add parameters we need for our network\\n traj.f_add_parameter('Net.C',281*pF)\\n traj.f_add_parameter('Net.gL',30*nS)\\n traj.f_add_parameter('Net.EL',-70.6*mV)\\n traj.f_add_parameter('Net.VT',-50.4*mV)\\n traj.f_add_parameter('Net.DeltaT',2*mV)\\n traj.f_add_parameter('Net.tauw',40*ms)\\n traj.f_add_parameter('Net.a',4*nS)\\n traj.f_add_parameter('Net.b',0.08*nA)\\n traj.f_add_parameter('Net.I',.8*nA)\\n traj.f_add_parameter('Net.Vcut','vm > 0*mV') # practical threshold condition\\n traj.f_add_parameter('Net.N',50)\\n\\n eqs='''\\n dvm/dt=(gL*(EL-vm)+gL*DeltaT*exp((vm-VT)/DeltaT)+I-w)/C : volt\\n dw/dt=(a*(vm-EL)-w)/tauw : amp\\n Vr:volt\\n '''\\n traj.f_add_parameter('Net.eqs', eqs)\\n traj.f_add_parameter('reset', 'vm=Vr;w+=b')\",\n \"def _define_SDSS_fit_params(self):\\n\\t\\tself.a = 1.4335\\n\\t\\tself.b = 0.3150 \\n\\t\\tself.c = -8.8979\\n\\t\\tself.intrinsic_scatter = 0.0578\\n\\t\\t#self.delta_a = 0.02\\n\\t\\t#self.delta_b = 0.01\",\n \"def learn_params(self, measurements, true_ranges):\\n z_hit,z_short,z_max,z_rand,var_hit,lambda_short= self.params\\n pre_params=[z_hit,z_short,z_max,z_rand,var_hit,lambda_short]\\n updated_params=[-1,-1,-1,-1,-1,-1]\\n while np.max(np.abs(np.array(updated_params) - np.array(pre_params))) > 1e-6:\\n\\n e_hit, e_short, e_max, e_rand = [], [], [], []\\n for i in range(len(measurements)):\\n true_range, measurement = true_ranges[i], measurements[i]\\n p_hit = self.PHit(true_range, measurement,var_hit)\\n p_short = self.PShort(true_range, measurement,lambda_short)\\n p_max = self.PMax(measurement)\\n p_rand = self.PRand(measurement)\\n normalizer = 1.0 / (p_hit + p_short + p_max + p_rand)\\n e_hit.append(normalizer * p_hit)\\n e_short.append(normalizer * p_short)\\n e_max.append(normalizer * p_max)\\n e_rand.append(normalizer * p_rand)\\n e_hit, e_short, e_max, e_rand = np.array(e_hit), np.array(e_short), np.array(e_max), np.array(e_rand)\\n\\n # perform M step\\n pre_params = [z_hit, z_short, z_max, z_rand, var_hit,lambda_short]\\n z_hit = sum(e_hit) / len(measurements)\\n z_short = sum(e_short) / len(measurements)\\n z_max = sum(e_max)/ len(measurements)\\n z_rand = sum(e_rand) / len(measurements)\\n var_hit = np.sqrt(1.0 / np.sum(e_hit) * np.sum(e_hit * (np.array(measurements)-np.array(true_ranges))**2)).item()\\n lambda_short = (np.sum(e_short) / np.sum(e_short * np.array(measurements))).item()\\n updated_params = [z_hit, z_short, z_max, z_rand, var_hit, lambda_short]\\n print('origin',self.params)\\n print('updated',updated_params)\\n return updated_params\",\n \"def model_2_parameters(num_features, num_classes):\\n parameters = {}\\n parameters['num_features'] = num_features\\n parameters['num_classes'] = num_classes\\n \\n return parameters\",\n \"def check_parameters():\\r\\n for par in PARAM:\\r\\n if isinstance(par, ExperimentFrame):\\r\\n EXP.change_variable(**par())\\r\\n else:\\r\\n EXP.change_variable(**par)\",\n \"def mes_fabolas_helper(X, parameters, output_file):\\n\\n fidelity_feature = parameters['Fidelity_Feature']\\n chosen_fidelity = X[:, -1]\\n\\n if fidelity_feature == 'Population':\\n parameters['Population_Fraction'] = chosen_fidelity.tolist()\\n else:\\n parameters['Iterations'] = chosen_fidelity.tolist()\\n\\n parameters = create_parameters(X, parameters)\\n\\n return parameters\",\n \"def put_trainable_parameters(net,X):\\n trainable=filter(lambda p: p.requires_grad, net.parameters())\\n paramlist=list(trainable)\\n offset=0\\n for params in paramlist:\\n numel=params.numel()\\n with torch.no_grad():\\n params.data.copy_(X[offset:offset+numel].data.view_as(params.data))\\n offset+=numel\",\n \"def parameters(self):\\n return [i.parameter for i in self.joints.values()]\",\n \"def generate_parameter_list(self) -> None:\\n\\n # simulation parameters from model\\n model_parameter_ids = np.array(self.amici_model.getParameterIds())\\n write_string_array(self.f, \\\"/parameters/modelParameterNames\\\",\\n model_parameter_ids)\\n print(Fore.CYAN + \\\"Number of model parameters:\\\",\\n len(model_parameter_ids))\\n\\n print(Fore.CYAN + \\\"Number of optimization parameters:\\\",\\n len(self.parameter_df))\\n write_string_array(self.f, \\\"/parameters/parameterNames\\\",\\n self.parameter_df.index.values[\\n (self.parameter_df.estimate == 1)\\n & ~self.parameter_df.index.isin(\\n self.amici_model.getFixedParameterIds())])\\n\\n self.generate_simulation_to_optimization_parameter_mapping()\\n\\n self.f.flush()\",\n \"def _training_params(self):\\n if isinstance(\\n self.lyapunov_hybrid_system.system,\\n feedback_system.FeedbackSystem) and self.search_controller:\\n # For a feedback system, we train both the Lyapunov network\\n # parameters and the controller network parameters.\\n training_params = list(\\n self.lyapunov_hybrid_system.lyapunov_relu.parameters(\\n )) + self.lyapunov_hybrid_system.system.controller_variables(\\n ) + self.R_options.variables()\\n else:\\n training_params = \\\\\\n list(self.lyapunov_hybrid_system.lyapunov_relu.parameters()) +\\\\\\n self.R_options.variables()\\n return training_params\",\n \"def get_model_parameter_names():\\n params = ['mu', 'rho']\\n return params\",\n \"def parameter_list(self):\\n return [\\n [encut, kpoint_mesh]\\n for encut, kpoint_mesh in zip(\\n self._job.iteration_frame.ENCUT, self._job.iteration_frame.KPOINT_MESH\\n )\\n ]\",\n \"def params(self):\\n return [p for sublist in [o.params for o in self.obs] for p in sublist]\",\n \"def update_model_parameters(parameters, grads, learning_rate):\\n L = len(parameters) /2 # number of layers in the neural network\\n\\n for l in range(int(L)):\\n parameters[\\\"W\\\" + str(l + 1)] = parameters[\\\"W\\\" + str(l + 1)] - learning_rate * grads[\\\"dW\\\" + str(l + 1)]\\n parameters[\\\"b\\\" + str(l + 1)] = parameters[\\\"b\\\" + str(l + 1)] - learning_rate * grads[\\\"db\\\" + str(l + 1)]\\n return parameters\\n # raise NotImplementedError\",\n \"def __init__(self, population=25, initSampling='lhc', fracMutation=0.2, fracElite=0.2, fracLevy=1.0, alpha=0.5, gamma=1, n=1, scalingFactor=10.0, penalty=0.0, maxGens=20000, maxFevals=200000, convTol=1e-06, stallLimit=10000, optConvTol=0.01, **kwargs):\\n ProblemParameters_multi.__init__(self, **kwargs)\\n self.population = population\\n self.initSampling = initSampling\\n self.fracMutation = fracMutation\\n assert self.fracMutation >= 0 and self.fracMutation <= 1, 'The probability of discovery must exist on (0,1]'\\n self.fracElite = fracElite\\n assert self.fracElite >= 0 and self.fracElite <= 1, 'The elitism fraction must exist on (0,1]'\\n self.fracLevy = fracLevy\\n assert self.fracLevy >= 0 and self.fracLevy <= 1, 'The probability that a Levy flight is performed must exist on (0,1]'\\n self.alpha = alpha\\n self.gamma = gamma\\n self.n = n\\n self.scalingFactor = scalingFactor\\n self.penalty = penalty\\n self.maxGens = maxGens\\n self.maxFevals = maxFevals\\n self.convTol = convTol\\n self.stallLimit = stallLimit\\n self.optConvTol = optConvTol\",\n \"def source_individual_params(targ_ind):\\n # # Note - Table S6 lists beta1, beta2, pi1, pi2 rather than beta_T, beta_S, pi_T, pi_S.\\n # # This seems to be a typo since I can reproduce their curves.\\n # all_params = [[21.45e-6, 0.86, 3.68, 0.17e-7, 2.2, 10.89, 14.7, 8.21, 0.06, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [1.31e-6, 1.82, 15.53, 0.8e-7, 2.18, 2.46, 15, 8.44, 0.18, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [13.35e-6, 1.16, 11.61, 2.63e-7, 4.17, 1.67, 6.5, 7.92, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [2.4e-6, 3.55, 11.53, 1.35e-7, 1.6, 1.7, 15.7, 10.99, 2.4, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [1.41e-6, 1.42, 12.47, 1.06e-7, 2.17, 1.08, 22, 8.21, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [6.94e-6, 0.76, 5.89, 0.17e-7, 3.33, 10.34, 17.3, 8.79, 0.15, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [18.21e-6, 0.38, 8.74, 9.19e-7, 0.41, 0.15, 17.85, 9, 0.22, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [5.12e-6, 3.53, 4.5, 4.9e-7, 2.04, 1.64, 8.3, 6.89, 1.89, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # [1.53e-6, 4.06, 9.65, 0.29e-7, 3.96, 8.15, 17.11, 9.47, 0.66, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n # ]\\n\\n # I've modified it so that I10 is instead VT0, the initial (swabbable) virions from the URT.\\n all_params = [[21.45e-6, 0.86, 3.68, 0.17e-7, 2.2, 10.89, 14.7, 8.21, 0.06, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [1.31e-6, 1.82, 15.53, 0.8e-7, 2.18, 2.46, 15, 8.44, 0.18, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [13.35e-6, 1.16, 11.61, 2.63e-7, 4.17, 1.67, 6.5, 7.92, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [2.4e-6, 3.55, 11.53, 1.35e-7, 1.6, 1.7, 15.7, 10.99, 2.4, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [1.41e-6, 1.42, 12.47, 1.06e-7, 2.17, 1.08, 22, 8.21, 0, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [6.94e-6, 0.76, 5.89, 0.17e-7, 3.33, 10.34, 17.3, 8.79, 0.15, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [18.21e-6, 0.38, 8.74, 9.19e-7, 0.41, 0.15, 17.85, 9, 0.22, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [5.12e-6, 3.53, 4.5, 4.9e-7, 2.04, 1.64, 8.3, 6.89, 1.89, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n [1.53e-6, 4.06, 9.65, 0.29e-7, 3.96, 8.15, 17.11, 9.47, 0.66, 4e6, 4.8e8, 1, 10, 4, 0.001],\\n ]\\n currparam = all_params[targ_ind]\\n return currparam\",\n \"def fit(train_data, train_target):\\r\\n for name in models.keys():\\r\\n est = models[name]\\r\\n est_params = params2[name]\\r\\n gscv = GridSearchCV(estimator=est, param_grid=est_params, cv=5)\\r\\n gscv.fit(train_data, train_target)\\r\\n print(\\\"best parameters are: {}\\\".format(gscv.best_estimator_))\\r\\n print(\\\"Where we selected the parameters: {}\\\" .format(gscv.cv_results_['params'][gscv.best_index_]))\\r\\n print(\\\"with mean cross-validated score: {}\\\" .format(gscv.best_score_))\",\n \"def generate_grid_search(model: KerasClassifier, model_params: dict,\\n fit_params: dict) -> Tuple[List[Model], List[dict]]:\\n\\n models = []\\n fit_parameters = []\\n keys = []\\n values = []\\n is_for_fit = []\\n\\n # fill keys' and values' lists with model parameters\\n for key_model, value_model in model_params.items():\\n keys.append(key_model)\\n values.append(value_model)\\n is_for_fit.append(False)\\n\\n # fill keys' and values' lists with fit parameters\\n for key_fit, value_fit in fit_params.items():\\n keys.append(key_fit)\\n values.append(value_fit)\\n is_for_fit.append(True)\\n\\n # generate all possible combinations of parameters\\n for values_list in product(*values):\\n\\n learner = copy.deepcopy(model)\\n\\n # uniquely define model structure\\n model_params_values = [values_list[i] for i in range(0, len(values_list)) if is_for_fit[i] is False]\\n model_params_keys = [keys[i] for i in range(0, len(keys)) if is_for_fit[i] is False]\\n model_params_dict = dict(zip(model_params_keys, model_params_values))\\n learner = learner.build_fn(**model_params_dict)\\n\\n # uniquely define fit function\\n fit_params_values = [values_list[i] for i in range(0, len(values_list)) if is_for_fit[i] is True]\\n fit_params_keys = [keys[i] for i in range(0, len(keys)) if is_for_fit[i] is True]\\n fit_params_dict = dict(zip(fit_params_keys, fit_params_values))\\n functools.partial(learner.fit, **fit_params_dict)\\n\\n models.append(learner)\\n fit_parameters.append(fit_params_dict)\\n\\n return models, fit_parameters\",\n \"def gen_params(no_cultures):\\n # Plate level\\n kn = 0.1 # Nutrient diffusion\\n ks = 0.1 # Signal diffusion\\n b = 0.05 # Signal on cells effect constant\\n a = 0.05 # Signal secretion constant\\n # Culture level\\n # Growth rate constant\\n r_mean = 1.0\\n r_var = 1.0\\n r_params = [max(0.0, gauss(r_mean, r_var)) for i in range(no_cultures)]\\n params = np.array([kn, ks, b, a] + r_params)\\n return params\",\n \"def __init__(self, params: Iterable[nn.Parameter]):\\n self.params = params\\n self.param_states = [p.requires_grad for p in self.params]\",\n \"def parameter_grid_search(y, tx, fit_function, score_function, ff_params={}, seed=1, k_fold=5,\\n verbose=False, **ff_fixed_params):\\n\\n combinations = product(*ff_params.values())\\n results = []\\n\\n for i, params in enumerate(combinations):\\n kwargs = {param: value for param, value in zip(ff_params.keys(), params)}\\n score = cross_validation(y, tx, k_fold, fit_function, score_function, seed=seed,\\n **kwargs, **ff_fixed_params)\\n\\n results.append({\\\"params\\\": kwargs, \\\"score\\\": score})\\n\\n if verbose:\\n print(f\\\"Parameter combination {i}:\\\")\\n print(f\\\"\\\\tParams: {kwargs}\\\")\\n print(f\\\"\\\\tScore: {score}\\\")\\n\\n return sorted(results, key=lambda x: x[\\\"score\\\"])\",\n \"def model_1_parameters(num_features, num_classes, image_info):\\n parameters = {}\\n parameters['n1'] = num_features\\n parameters['k1'] = int(math.floor(parameters['n1'] / 9))\\n parameters['n2'] = parameters['n1'] - parameters['k1'] + 1\\n if image_info['key'][:5] == \\\"pavia\\\":\\n parameters['n3'] = 30\\n parameters['k2'] = 3\\n else:\\n parameters['n3'] = 40\\n parameters['k2'] = 5\\n parameters['n4'] = 100\\n parameters['n5'] = num_classes\\n \\n return parameters\",\n \"def step6_set_gan_params(params):\\n global GAN_PARAMS\\n GAN_PARAMS = {**GAN_PARAMS, **params}\",\n \"def fit(\\n self, parameters: NDArrays, config: Dict[str, Scalar]\\n ) -> Tuple[NDArrays, int, Dict[str, Scalar]]:\\n _ = (self, parameters, config)\\n return [], 0, {}\",\n \"def update_parameters(self):\\n # We update gamma, gamma0, lambda and nu in turn (Bottolo et al, 2011)\\n self.update_gamma()\\n self.update_gamma0()\\n self.update_lambda()\\n self.update_nu()\\n if self.sample_xi:\\n self.update_xi()\",\n \"def _define_SLACS_fit_params(self):\\n\\t\\t# Fit params from R_eff\\n\\t\\tself.a = -0.41\\n\\t\\tself.b = 0.39\\n\\t\\t#self.delta_a = 0.12\\n\\t\\t#self.delta_b = 0.10\\n\\t\\tself.intrinsic_scatter = 0.14\\n\\t\\t# Fit params from vel_disp\\n\\t\\tself.a_v = 0.07\\n\\t\\tself.b_v = -0.12\\n\\t\\tself.int_v = 0.17\",\n \"def __init__(self, params, fitness, population_size=20, generations=20, temperature_factor=0.9):\\n self.params = params\\n self.fitness = fitness\\n self.population_size = population_size\\n self.generations = generations\\n self.temperature_factor = temperature_factor\\n\\n self.population = []\",\n \"def update_parameters_with_gd(parameters, grads, learning_rate):\\n\\n L = len(parameters) // 2 # number of layers in the neural networks\\n\\n # Update rule for each parameter\\n for l in range(L):\\n ### START CODE HERE ### (approx. 2 lines)\\n parameters[\\\"W\\\" + str(l+1)] = parameters[\\\"W\\\" + str(l+1)]-learning_rate* grads[\\\"dW\\\" + str(l+1)]\\n parameters[\\\"b\\\" + str(l+1)] = parameters[\\\"b\\\" + str(l+1)]-learning_rate* grads[\\\"db\\\" + str(l+1)]\\n ### END CODE HERE ###\\n \\n return parameters\",\n \"def hyper_parameter_tuning(X, y, classifier, models, sntypes_map, feature_names, fig_dir='.', remove_models=(), name=''):\\n\\n # Hyperparameter grid\\n n_estimators = [int(x) for x in np.linspace(start=200, stop=2000, num=10)]\\n max_features = ['auto', 'sqrt']\\n max_depth = [int(x) for x in np.linspace(10, 110, num=11)]\\n max_depth.append(None)\\n min_samples_split = [2, 5, 10]\\n min_samples_leaf = [1, 2, 4]\\n bootstrap = [True, False]\\n random_grid = {'n_estimators': n_estimators,\\n 'max_features': max_features,\\n 'max_depth': max_depth,\\n 'min_samples_split': min_samples_split,\\n 'min_samples_leaf': min_samples_leaf,\\n 'bootstrap': bootstrap}\\n\\n # Get data\\n num_features = X.shape[1]\\n model_names = [sntypes_map[model] for model in models]\\n X, y, models, remove_models = remove_redundant_classes(X, y, models, remove_models)\\n\\n # Get best features\\n n = 50\\n num_features, feature_names, X = get_n_best_features(n, X, y, classifier, feature_names, num_features, fig_dir, name, models, model_names)\\n\\n # Randomised Search\\n clf_random = RandomizedSearchCV(estimator=classifier, param_distributions=random_grid, n_iter=7, cv=3, verbose=2,\\n random_state=42, n_jobs=2)\\n clf_random.fit(X, y)\\n print(clf_random.best_params_)\\n\\n def evaluate(model, test_features, test_labels):\\n predictions = model.predict(test_features)\\n errors = abs(predictions - test_labels)\\n mape = 100 * np.mean(errors / test_labels)\\n accuracy = 100 - mape\\n print('Model Performance')\\n print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))\\n print('Accuracy = {:0.2f}%.'.format(accuracy))\\n\\n return accuracy\\n\\n best_random = clf_random.best_estimator_\\n # random_accuracy = evaluate(best_random, test_features, test_labels)\",\n \"def setup_parameters(self):\\n structure = self.ctx.structure_initial_primitive\\n ecutwfc = []\\n ecutrho = []\\n\\n for kind in structure.get_kind_names():\\n try:\\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\\n cutrho = dual * cutoff\\n ecutwfc.append(cutoff)\\n ecutrho.append(cutrho)\\n except KeyError as exception:\\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\\n\\n natoms = len(structure.sites)\\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\\n\\n self.ctx.inputs['parameters'] = {\\n 'CONTROL': {\\n 'restart_mode': 'from_scratch',\\n 'tstress': self.ctx.protocol['tstress'],\\n },\\n 'SYSTEM': {\\n 'ecutwfc': max(ecutwfc),\\n 'ecutrho': max(ecutrho),\\n 'smearing': self.ctx.protocol['smearing'],\\n 'degauss': self.ctx.protocol['degauss'],\\n 'occupations': self.ctx.protocol['occupations'],\\n },\\n 'ELECTRONS': {\\n 'conv_thr': conv_thr,\\n }\\n }\",\n \"def model_3_parameters(num_features, num_classes, image_info):\\n parameters = {}\\n parameters['num_features'] = num_features\\n parameters['num_classes'] = num_classes\\n parameters['n_estimators'] = image_info['n_estimators']\\n min_child_samples = image_info['min_child_samples']\\n parameters['min_child_samples'] = min_child_samples\\n \\n # Parameters message\\n with open(OUTPUT_FILE, 'a') as f:\\n f.write(\\\"min_child_samples: {}\\\\n\\\\n\\\".format(min_child_samples))\\n \\n return parameters\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6610045","0.65775573","0.6563652","0.6521646","0.646521","0.6455579","0.63229144","0.63210595","0.63088965","0.629318","0.6284352","0.6191725","0.61914194","0.60860085","0.6085613","0.60815185","0.60616034","0.6052334","0.6052334","0.6052334","0.6023257","0.5992335","0.5957211","0.5929541","0.5894867","0.5884702","0.58454865","0.584223","0.58362436","0.58165574","0.58023673","0.580047","0.5797893","0.5795321","0.5779361","0.5770755","0.57674474","0.5755759","0.5735632","0.573522","0.57270527","0.5722954","0.57111424","0.5705349","0.56825477","0.56725425","0.5666446","0.56642413","0.5663388","0.56615645","0.56569636","0.56552285","0.5651863","0.56473035","0.5634126","0.5633792","0.5631065","0.562488","0.56127614","0.5604299","0.560395","0.5601653","0.5596862","0.55955386","0.5593043","0.5585504","0.5578342","0.5577254","0.55726296","0.5556531","0.5553852","0.5552027","0.55493003","0.5548012","0.55374944","0.55313116","0.55280876","0.551912","0.5514505","0.5512423","0.5507359","0.5505671","0.55036616","0.5489682","0.5484106","0.547881","0.54785514","0.5468003","0.54571664","0.54556334","0.54517466","0.5448485","0.54473436","0.54464704","0.54456705","0.5445578","0.54374987","0.5433507","0.54256684","0.5421985","0.5414501"],"string":"[\n \"0.6610045\",\n \"0.65775573\",\n \"0.6563652\",\n \"0.6521646\",\n \"0.646521\",\n \"0.6455579\",\n \"0.63229144\",\n \"0.63210595\",\n \"0.63088965\",\n \"0.629318\",\n \"0.6284352\",\n \"0.6191725\",\n \"0.61914194\",\n \"0.60860085\",\n \"0.6085613\",\n \"0.60815185\",\n \"0.60616034\",\n \"0.6052334\",\n \"0.6052334\",\n \"0.6052334\",\n \"0.6023257\",\n \"0.5992335\",\n \"0.5957211\",\n \"0.5929541\",\n \"0.5894867\",\n \"0.5884702\",\n \"0.58454865\",\n \"0.584223\",\n \"0.58362436\",\n \"0.58165574\",\n \"0.58023673\",\n \"0.580047\",\n \"0.5797893\",\n \"0.5795321\",\n \"0.5779361\",\n \"0.5770755\",\n \"0.57674474\",\n \"0.5755759\",\n \"0.5735632\",\n \"0.573522\",\n \"0.57270527\",\n \"0.5722954\",\n \"0.57111424\",\n \"0.5705349\",\n \"0.56825477\",\n \"0.56725425\",\n \"0.5666446\",\n \"0.56642413\",\n \"0.5663388\",\n \"0.56615645\",\n \"0.56569636\",\n \"0.56552285\",\n \"0.5651863\",\n \"0.56473035\",\n \"0.5634126\",\n \"0.5633792\",\n \"0.5631065\",\n \"0.562488\",\n \"0.56127614\",\n \"0.5604299\",\n \"0.560395\",\n \"0.5601653\",\n \"0.5596862\",\n \"0.55955386\",\n \"0.5593043\",\n \"0.5585504\",\n \"0.5578342\",\n \"0.5577254\",\n \"0.55726296\",\n \"0.5556531\",\n \"0.5553852\",\n \"0.5552027\",\n \"0.55493003\",\n \"0.5548012\",\n \"0.55374944\",\n \"0.55313116\",\n \"0.55280876\",\n \"0.551912\",\n \"0.5514505\",\n \"0.5512423\",\n \"0.5507359\",\n \"0.5505671\",\n \"0.55036616\",\n \"0.5489682\",\n \"0.5484106\",\n \"0.547881\",\n \"0.54785514\",\n \"0.5468003\",\n \"0.54571664\",\n \"0.54556334\",\n \"0.54517466\",\n \"0.5448485\",\n \"0.54473436\",\n \"0.54464704\",\n \"0.54456705\",\n \"0.5445578\",\n \"0.54374987\",\n \"0.5433507\",\n \"0.54256684\",\n \"0.5421985\",\n \"0.5414501\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":46,"numItemsPerPage":100,"numTotalItems":95772,"offset":4600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzgyNzEyNywic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM4MzA3MjcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.uRmDTMhsfVzHDhHN2X8dIIh1ydE3xEY2fs1_2JUIpUck1-A9yl5EzUyn6Zarsc0Cw6HKGZoS-pvpKGjwgJPYBg","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 |
|---|---|---|---|---|---|---|
Setter for the number at tile position pos | def set_number(self, row, col, value):
self._grid[row][col] = value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_number(self, row, col, value):\r\n self._grid[row][col] = value",
"def set_number(self, row, col, value):\r\n self._grid[row][col] = value",
"def set_number(self, row, col, value):\r\n self._grid[row][col] = value",
"def set_tile(self, row, col, value):\n # replace with yo... | [
"0.7453924",
"0.7453924",
"0.7453924",
"0.69439274",
"0.690476",
"0.690476",
"0.6867873",
"0.6846585",
"0.6842772",
"0.68179584",
"0.67959946",
"0.67660105",
"0.67594844",
"0.67594844",
"0.66707844",
"0.66694415",
"0.66609603",
"0.66590446",
"0.66233623",
"0.6552477",
"0.6444... | 0.74432904 | 6 |
Make a copy of the puzzle to update during solving Returns a Puzzle object | def clone(self):
new_puzzle = Puzzle(self._height, self._width, self._grid)
return new_puzzle | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve(self):\n new_puzzle = self._puzzle.clone()\n self._solution = new_puzzle.solve_puzzle()\n del new_puzzle\n pass",
"def clone(self):\r\n new_puzzle = Puzzle(self._height, self._width, self._grid)\r\n return new_puzzle",
"def clone(self):\r\n new_puzzle ... | [
"0.8077919",
"0.7812692",
"0.7812692",
"0.7812692",
"0.7204438",
"0.70470625",
"0.68246186",
"0.6803633",
"0.67532885",
"0.6707239",
"0.6599824",
"0.65672135",
"0.65080994",
"0.65023094",
"0.64462394",
"0.6381703",
"0.63737535",
"0.62523",
"0.6221241",
"0.62164736",
"0.619480... | 0.7704075 | 7 |
Locate the current position of the tile that will be at position (solved_row, solved_col) when the puzzle is solved Returns a tuple of two integers | def current_position(self, solved_row, solved_col):
solved_value = (solved_col + self._width * solved_row)
for row in range(self._height):
for col in range(self._width):
if self._grid[row][col] == solved_value:
return (row, col)
assert False, "Value " + str(solved_value) + " not found" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def current_position(self, solved_row, solved_col):\r\n solved_value = (solved_col + self._width * solved_row)\r\n\r\n for row in range(self._height):\r\n for col in range(self._width):\r\n if self._grid[row][col] == solved_value:\r\n return (row, col)\r\n... | [
"0.84899926",
"0.84899926",
"0.84899926",
"0.7324289",
"0.707103",
"0.69124496",
"0.6876786",
"0.68116784",
"0.67074156",
"0.6705732",
"0.6690645",
"0.66753584",
"0.6621211",
"0.66135",
"0.66128737",
"0.66079515",
"0.6600574",
"0.65944666",
"0.65453476",
"0.65348464",
"0.6522... | 0.8510771 | 3 |
Updates the puzzle state based on the provided move string | def update_puzzle(self, move_string):
zero_row, zero_col = self.current_position(0, 0)
for direction in move_string:
if direction == "l":
assert zero_col > 0, "move off grid: " + direction
self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col - 1]
self._grid[zero_row][zero_col - 1] = 0
zero_col -= 1
elif direction == "r":
assert zero_col < self._width - 1, "move off grid: " + direction
self._grid[zero_row][zero_col] = self._grid[zero_row][zero_col + 1]
self._grid[zero_row][zero_col + 1] = 0
zero_col += 1
elif direction == "u":
assert zero_row > 0, "move off grid: " + direction
self._grid[zero_row][zero_col] = self._grid[zero_row - 1][zero_col]
self._grid[zero_row - 1][zero_col] = 0
zero_row -= 1
elif direction == "d":
assert zero_row < self._height - 1, "move off grid: " + direction
self._grid[zero_row][zero_col] = self._grid[zero_row + 1][zero_col]
self._grid[zero_row + 1][zero_col] = 0
zero_row += 1
else:
assert False, "invalid direction: " + direction | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_puzzle(self, move_string):\r\n zero_row, zero_col = self.current_position(0, 0)\r\n for direction in move_string:\r\n if direction == \"l\":\r\n assert zero_col > 0, \"move off grid: \" + direction\r\n self._grid[zero_row][zero_col] = self._grid[zer... | [
"0.8120185",
"0.8120185",
"0.8120185",
"0.8101825",
"0.8077781",
"0.7200039",
"0.70454454",
"0.6661715",
"0.6583404",
"0.6582128",
"0.65785265",
"0.65703756",
"0.6560121",
"0.6289467",
"0.6268087",
"0.61335135",
"0.61134064",
"0.60638404",
"0.605805",
"0.6051816",
"0.60436565... | 0.81526875 | 1 |
Check whether the puzzle satisfies the specified invariant at the given position in the bottom rows of the puzzle (target_row > 1) Returns a boolean | def lower_row_invariant(self, target_row, target_col):
assert target_row > 1, 'target_row invalid'
result = True
if self._grid[target_row][target_col] != 0:
result = False
for row in range(target_row+1, self._height):
for col in range(self._width):
solved_value = (col + self._width * row)
if solved_value != self._grid[row][col]:
result = False
for col in range(target_col+1, self._width):
solved_value = (col + self._width * target_row)
if solved_value != self._grid[target_row][col]:
result = False
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row1_invariant(self, target_col):\r\n # replace with your code\r\n conditions = 0\r\n current = self._grid[1][target_col] == 0\r\n if current:\r\n conditions +=1\r\n else:\r\n # print 'Tile ZERO is not at (0, %s) position' %(target_col)\r\n re... | [
"0.71912694",
"0.7152272",
"0.70571184",
"0.70388037",
"0.70372164",
"0.6987461",
"0.6711312",
"0.67097217",
"0.6679228",
"0.6654584",
"0.66055614",
"0.6557503",
"0.6554002",
"0.65482295",
"0.64755046",
"0.6440227",
"0.6397061",
"0.6329067",
"0.6262952",
"0.62180895",
"0.6097... | 0.6862375 | 6 |
helper function for solve_interior_tile and solve_col0_tile | def position_tile(self, target_row, target_col, cur_row, cur_col, need_ld=True):
move_str = ''
if cur_row == target_row:
if cur_col < target_col:
move_str += 'l' * (target_col - cur_col)
if target_col - cur_col > 1:
move_str += 'ur'
move_str += 'druldru' * (target_col - cur_col - 1)
else:
move_str += 'ur' if not need_ld else ''
need_ld = False
else:
move_str += 'r' * (cur_col - target_col)
if cur_col - target_col > 1:
move_str += 'ul'
move_str += 'dlurdlu' * (cur_col - target_col - 1)
else:
need_ld = False
else:
move_str += 'u' * (target_row - cur_row)
if cur_col < target_col:
move_str += ('l' * (target_col - cur_col) + 'dru')
move_str += 'druldru' * (target_col - cur_col - 1)
move_str += 'lddru' * (target_row - cur_row - 1)
elif cur_col > target_col:
move_str += ('r' * (cur_col - target_col) + 'dlu')
move_str += 'dlurdlu' * (cur_col - target_col - 1)
move_str += 'lddru' * (target_row - cur_row - 1)
else:
move_str += 'lddru' * (target_row - cur_row - 1)
if need_ld:
move_str += 'ld'
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_interior_tile(self, target_row, target_col):\r\n whole_move = ''\r\n # replace with your code\r\n if self._grid[target_row][target_col] != 0:\r\n # print \"DEBUG CASE WHEN ZERO IN JOPA\"\r\n \r\n # print self\r\n # print 'Solwing tile', sel... | [
"0.6987945",
"0.69417804",
"0.65950215",
"0.657609",
"0.64476514",
"0.6431955",
"0.6417518",
"0.6369899",
"0.6368723",
"0.6354411",
"0.6283423",
"0.6279517",
"0.625173",
"0.61543477",
"0.61364645",
"0.612347",
"0.606173",
"0.60402596",
"0.6014027",
"0.6010626",
"0.6002269",
... | 0.0 | -1 |
Place correct tile at target position Updates puzzle and returns a move string | def solve_interior_tile(self, target_row, target_col):
cur_row, cur_col = self.current_position(target_row, target_col)
move_str = self.position_tile(target_row, target_col, cur_row, cur_col)
self.update_puzzle(move_str)
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_tile(self, target_row, target_col, val):\n # a little bit twisted here for the use of both solve_interior_tile and solve_col0_tile\n solved_row, solved_col = self.current_position(0, val)\n movements = \"\"\n if solved_row == target_row and solved_col == target_col:\n ... | [
"0.74114114",
"0.7296464",
"0.7176235",
"0.7139627",
"0.7137439",
"0.7097358",
"0.7088615",
"0.7081109",
"0.7047905",
"0.703492",
"0.69906497",
"0.6971217",
"0.6968847",
"0.6915314",
"0.6911569",
"0.68654275",
"0.6853786",
"0.68512785",
"0.68391025",
"0.6836436",
"0.68200254"... | 0.69137377 | 14 |
Solve tile in column zero on specified row (> 1) Updates puzzle and returns a move string | def solve_col0_tile(self, target_row):
move_str = 'ur'
self.update_puzzle(move_str)
cur_row, cur_col = self.current_position(target_row, 0)
if cur_row == target_row and cur_col == 0:
move_str += 'r' * (self._width - 2)
else:
move_str += self.position_tile(target_row-1, 1, cur_row, cur_col)
move_str += 'ruldrdlurdluurddlur'
move_str += 'r' * (self._width - 2)
self.update_puzzle(move_str[2:])
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n... | [
"0.80090946",
"0.8002715",
"0.79543203",
"0.7940283",
"0.7870043",
"0.7847743",
"0.7818097",
"0.77486104",
"0.774018",
"0.7731879",
"0.77268267",
"0.7707487",
"0.77027786",
"0.76879483",
"0.7634697",
"0.76258874",
"0.7590558",
"0.7560754",
"0.7522215",
"0.75079787",
"0.750233... | 0.777891 | 7 |
Check whether the puzzle satisfies the row zero invariant at the given column (col > 1) Returns a boolean | def row0_invariant(self, target_col):
result = True
if self._grid[0][target_col] != 0:
result = False
if self._grid[1][target_col] != (target_col + self._width * 1):
result = False
for row in range(2, self._height):
for col in range(self._width):
solved_value = (col + self._width * row)
if solved_value != self._grid[row][col]:
result = False
for row in (0, 1):
for col in range(target_col+1, self._width):
solved_value = (col + self._width * row)
if solved_value != self._grid[row][col]:
result = False
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row1_invariant(self, target_col):\r\n # replace with your code\r\n conditions = 0\r\n current = self._grid[1][target_col] == 0\r\n if current:\r\n conditions +=1\r\n else:\r\n # print 'Tile ZERO is not at (0, %s) position' %(target_col)\r\n re... | [
"0.7548881",
"0.752049",
"0.75068325",
"0.74679226",
"0.742282",
"0.7359223",
"0.7341293",
"0.73344994",
"0.7321091",
"0.73030216",
"0.7264205",
"0.7223349",
"0.7205626",
"0.7195732",
"0.7169341",
"0.7164632",
"0.71609145",
"0.71587044",
"0.71222657",
"0.7111231",
"0.7105816"... | 0.76055276 | 0 |
Check whether the puzzle satisfies the row one invariant at the given column (col > 1) Returns a boolean | def row1_invariant(self, target_col):
result = True
if self._grid[1][target_col] != 0:
result = False
for row in range(2, self._height):
for col in range(self._width):
solved_value = (col + self._width * row)
if solved_value != self._grid[row][col]:
result = False
for row in (0, 1):
for col in range(target_col+1, self._width):
solved_value = (col + self._width * row)
if solved_value != self._grid[row][col]:
result = False
return result | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row1_invariant(self, target_col):\r\n # assert that row 1 is solved\r\n if not self.lower_row_invariant(1, target_col):\r\n return False\r\n # asserts that tile proceeded to (1,j), the grid below (1,j) and to the right is solved\r\n for dummy_j in range(0, self.get_width(... | [
"0.7744749",
"0.7643967",
"0.7540741",
"0.7405464",
"0.7363063",
"0.7331644",
"0.73298305",
"0.72720087",
"0.7253807",
"0.71994525",
"0.7157826",
"0.71566737",
"0.71124315",
"0.7103953",
"0.7086645",
"0.70836926",
"0.7064152",
"0.7060724",
"0.7032539",
"0.7031373",
"0.7020518... | 0.77575016 | 0 |
Solve the tile in row zero at the specified column Updates puzzle and returns a move string | def solve_row0_tile(self, target_col):
move_str = 'ld'
self.update_puzzle(move_str)
cur_row, cur_col = self.current_position(0, target_col)
if cur_row == 0 and cur_col == target_col:
return move_str
else:
move_str += self.position_tile(1, target_col-1, cur_row, cur_col)
move_str += 'urdlurrdluldrruld'
self.update_puzzle(move_str[2:])
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_col0_tile(self, target_row):\r\n moves_str = \"\"\r\n # move the zero tile from (i,0) to (i−1,1) \r\n # using the move string \"ur\"\r\n moves_str += \"ur\"\r\n temp_grid = Puzzle(self._height, self._width, self._grid)\r\n temp_grid.update_puzzle(moves_str)\r\n ... | [
"0.7689822",
"0.76361793",
"0.7602665",
"0.759841",
"0.7547982",
"0.75370145",
"0.75116783",
"0.7487752",
"0.7478812",
"0.74497175",
"0.7435299",
"0.7407118",
"0.7397174",
"0.73776317",
"0.73732615",
"0.7324699",
"0.72756827",
"0.72559106",
"0.7206573",
"0.71551996",
"0.71403... | 0.745573 | 9 |
Solve the tile in row one at the specified column Updates puzzle and returns a move string | def solve_row1_tile(self, target_col):
cur_row, cur_col = self.current_position(1, target_col)
move_str = self.position_tile(1, target_col, cur_row, cur_col, need_ld=False)
self.update_puzzle(move_str)
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_row1_tile(self, target_col):\r\n moves_str = \"\"\r\n current_row, current_col = self.current_position(1, target_col)\r\n zero_row, zero_col = self.current_position(0, 0)\r\n moves_str += self.position_tile(zero_row, zero_col, current_row, current_col)\r\n moves_str += ... | [
"0.7611342",
"0.7530595",
"0.7489633",
"0.74769557",
"0.7346029",
"0.7312759",
"0.72872084",
"0.72763294",
"0.72613305",
"0.72458375",
"0.72417307",
"0.7236731",
"0.72223794",
"0.7210395",
"0.71957016",
"0.71925163",
"0.7142041",
"0.71206504",
"0.70840454",
"0.7064091",
"0.70... | 0.74047565 | 4 |
Solve the upper left 2x2 part of the puzzle Updates the puzzle and returns a move string | def solve_2x2(self):
cur_row, cur_col = self.current_position(0, 0)
move_str = 'u' * cur_row + 'l' * cur_col
self.update_puzzle(move_str)
if self.check_2x2_solved():
return move_str
else:
while not self.check_2x2_solved():
move_str += 'rdlu'
self.update_puzzle('rdlu')
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n... | [
"0.86358064",
"0.84391785",
"0.820359",
"0.81158245",
"0.8079695",
"0.7876614",
"0.7855013",
"0.77959996",
"0.7629816",
"0.75706357",
"0.7446708",
"0.71919894",
"0.70876247",
"0.6987711",
"0.6876523",
"0.686447",
"0.686447",
"0.686447",
"0.6857152",
"0.6857152",
"0.6825898",
... | 0.8153259 | 3 |
check if the top left 22 puzzle is solved | def check_2x2_solved(self):
return self._grid[0][0] == 0 and self._grid[0][1] == 1 \
and self._grid[1][0] == self._width*1 and self._grid[1][1] == (1 + self._width * 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkPuzzle(self):\n print('Got to checkPuzzle')",
"def solve_puzzle(self):\r\n \r\n counter = 0\r\n rows = self._height-1\r\n cols = self._width-1\r\n # print rows, cols\r\n # print 'The greed has %s rows and %s coloumn indexes' %(rows, cols) \r\n solu... | [
"0.7565178",
"0.73852265",
"0.7355203",
"0.73358935",
"0.7311697",
"0.7294157",
"0.7242904",
"0.7190867",
"0.7131681",
"0.71189845",
"0.70927525",
"0.70451087",
"0.70317227",
"0.70084786",
"0.6971756",
"0.6908844",
"0.69036686",
"0.6901154",
"0.68942595",
"0.68830353",
"0.685... | 0.6677703 | 31 |
Generate a solution string for a puzzle Updates the puzzle and returns a move string | def solve_puzzle(self):
cur0_row, cur0_col = self.current_position(0, 0)
move_str = 'd' * (self._height - cur0_row - 1) + 'r' * (self._width - cur0_col - 1)
self.update_puzzle(move_str)
for row in range(self._height-1, 1, -1):
for col in range(self._width-1, -1, -1):
assert self.lower_row_invariant(row, col)
if col != 0:
move_str += self.solve_interior_tile(row, col)
else:
move_str += self.solve_col0_tile(row)
for col in range(self._width-1, 1, -1):
assert self.row1_invariant(col)
move_str += self.solve_row1_tile(col)
assert self.row0_invariant(col)
move_str += self.solve_row0_tile(col)
move_str += self.solve_2x2()
return move_str | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_puzzle(self):\n\n move_str = \"\"\n \n # Move zero tile to bottom right corner tile of puzzle.\n zero_pos = self.current_position(0,0) \n vert_dist = (self.get_height() - 1) - zero_pos[0]\n horiz_dist = (self.get_width() - 1) - zero_pos[1]\n... | [
"0.7559481",
"0.73133874",
"0.69816166",
"0.66144603",
"0.6516676",
"0.63399726",
"0.6325148",
"0.6325148",
"0.6299051",
"0.6299051",
"0.6299051",
"0.6297025",
"0.62952787",
"0.6252764",
"0.6244446",
"0.6211515",
"0.61357105",
"0.6083045",
"0.6063561",
"0.6049944",
"0.602346"... | 0.7348655 | 1 |
Clear properties. Mapping properties show up in CXSMILES make validation less readable. | def _getProductCXSMILES(product):
for a in product.GetAtoms():
for k in a.GetPropsAsDict():
a.ClearProp(k)
return Chem.MolToCXSmiles(product) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_properties(self):\n self.properties.clear()",
"def clearProperties(*args):",
"def clearProperties(*args):",
"def clearProperties(*args):",
"def clearProperties(*args):",
"def reset(self):\n self.valid_passes = set()\n self.property_set.clear()",
"def reset_properties(self):\n... | [
"0.8134951",
"0.7525838",
"0.7525838",
"0.7525838",
"0.7525838",
"0.6747215",
"0.6691966",
"0.66214865",
"0.66214865",
"0.66214865",
"0.66214865",
"0.65654373",
"0.6476267",
"0.64220273",
"0.64089143",
"0.6397436",
"0.6378762",
"0.6357874",
"0.62982965",
"0.6284592",
"0.62551... | 0.0 | -1 |
Run a reaction and combine the products in a single string. Makes errors readable ish | def _reactAndSummarize(rxn_smarts, *smiles):
rxn = rdChemReactions.ReactionFromSmarts(rxn_smarts)
mols = [Chem.MolFromSmiles(s) for s in smiles]
products = []
for prods in rxn.RunReactants(mols):
products.append(' + '.join(map(_getProductCXSMILES, prods)))
products = ' OR '.join(products)
return products | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reaction_str(self):\n\n def format(number):\n return str(number).rstrip(\".0\") + \" \"\n\n reactant_bits = []\n product_bits = []\n for met in sorted(self._metabolites, key=attrgetter(\"id\")):\n coefficient = self._metabolites[met]\n if coefficient... | [
"0.6317691",
"0.59467554",
"0.57581663",
"0.5728929",
"0.5590028",
"0.5556531",
"0.5528526",
"0.55182624",
"0.5469046",
"0.54264945",
"0.5413973",
"0.5307061",
"0.5297744",
"0.5281674",
"0.5246843",
"0.52160555",
"0.51447445",
"0.51412046",
"0.5139743",
"0.511896",
"0.5064106... | 0.5951013 | 1 |
StereoGroup atoms are in the reaction, but the reaction doesn't affect the chirality at the stereo centers > preserve stereo group | def test_reaction_preserves_stereo(self):
reaction = '[C@:1]>>[C@:1]'
reactants = ['F[C@H](Cl)Br |o1:1|', 'F[C@@H](Cl)Br |&1:1|', 'FC(Cl)Br']
for reactant in reactants:
products = _reactAndSummarize(reaction, reactant)
self.assertEqual(products, reactant) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.75458586",
"0.7168434",
"0.7091707",
"0.6843433",
"0.6454386",
"0.60892975",
"0.5650636",
"0.55933136",
"0.55537635",
"0.5514306",
"0.54825073",
"0.5466172",
"0.54264325",
"0.5393186",
"0.5328103",
"0.5303823",
"0.5297103",
"0.52903897",
"0.5283487",
"0.5206587",
"0.504461... | 0.5968604 | 6 |
StereoGroup atoms are in the reaction, but the reaction doesn't specify the chirality at the stereo centers > preserve stereo group | def test_reaction_ignores_stereo(self):
reaction = '[C:1]>>[C:1]'
reactants = ['F[C@H](Cl)Br |o1:1|', 'F[C@@H](Cl)Br |&1:1|', 'FC(Cl)Br']
for reactant in reactants:
products = _reactAndSummarize(reaction, reactant)
self.assertEqual(products, reactant) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.7502093",
"0.7083151",
"0.696663",
"0.6748644",
"0.6419238",
"0.5837475",
"0.576132",
"0.5678274",
"0.5618676",
"0.55914426",
"0.5525545",
"0.5486366",
"0.54649514",
"0.54351145",
"0.54303616",
"0.5415109",
"0.5333785",
"0.5333203",
"0.5273259",
"0.5203159",
"0.51855856",
... | 0.6020158 | 5 |
StereoGroup atoms are in the reaction, and the reaction inverts the specified chirality at the stereo centers. > preserve stereo group | def test_reaction_inverts_stereo(self):
reaction = '[C@:1]>>[C@@:1]'
products = _reactAndSummarize(reaction, 'F[C@H](Cl)Br |o1:1|')
self.assertEqual(products, 'F[C@@H](Cl)Br |o1:1|')
products = _reactAndSummarize(reaction, 'F[C@@H](Cl)Br |&1:1|')
self.assertEqual(products, 'F[C@H](Cl)Br |&1:1|')
products = _reactAndSummarize(reaction, 'FC(Cl)Br')
self.assertEqual(products, 'FC(Cl)Br') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.65880185",
"0.6446109",
"0.593325",
"0.5791387",
"0.5475662",
"0.53331393",
"0.52869034",
"0.52321607",
"0.51858443",
"0.5148949",
"0.5144954",
"0.5143506",
"0.51018935",
"0.5073538",
"0.5006511",
"0.49255782",
"0.4913669",
"0.4903975",
"0.48686603",
"0.47758043",
"0.47285... | 0.5284103 | 7 |
StereoGroup atoms are in the reaction, and the reaction destroys the specified chirality at the stereo centers > invalidate stereo center, preserve the rest of the stereo group. | def test_reaction_destroys_stereo(self):
reaction = '[C@:1]>>[C:1]'
products = _reactAndSummarize(reaction, 'F[C@H](Cl)Br |o1:1|')
self.assertEqual(products, 'FC(Cl)Br')
products = _reactAndSummarize(reaction, 'F[C@@H](Cl)Br |&1:1|')
self.assertEqual(products, 'FC(Cl)Br')
products = _reactAndSummarize(reaction, 'FC(Cl)Br')
self.assertEqual(products, 'FC(Cl)Br')
reaction = '[C@:1]F>>[C:1]F'
# Reaction destroys stereo (but preserves unaffected group
products = _reactAndSummarize(reaction,
'F[C@H](Cl)[C@@H](Cl)Br |o1:1,&2:3|')
self.assertEqual(products, 'FC(Cl)[C@@H](Cl)Br |&1:3|')
# Reaction destroys stereo (but preserves the rest of the group
products = _reactAndSummarize(reaction, 'F[C@H](Cl)[C@@H](Cl)Br |&1:1,3|')
self.assertEqual(products, 'FC(Cl)[C@@H](Cl)Br |&1:3|') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.6586952",
"0.58681905",
"0.5560465",
"0.5431528",
"0.5087718",
"0.49389872",
"0.49223432",
"0.4907192",
"0.48812112",
"0.4859121",
"0.48425356",
"0.48171473",
"0.48001352",
"0.47328418",
"0.46946502",
"0.46811792",
"0.46665424",
"0.46157676",
"0.4614911",
"0.4604623",
"0.4... | 0.6826048 | 0 |
StereoGroup atoms are in the reaction, and the reaction creates the specified chirality at the stereo centers > remove the stereo center from > invalidate stereo group | def test_reaction_defines_stereo(self):
products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')
self.assertEqual(products, 'F[C@@H](Cl)Br')
products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')
self.assertEqual(products, 'F[C@@H](Cl)Br')
products = _reactAndSummarize('[C:1]>>[C@@:1]', 'FC(Cl)Br')
self.assertEqual(products, 'F[C@@H](Cl)Br')
# Remove group with defined stereo
products = _reactAndSummarize('[C:1]F>>[C@@:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:1,&2:3|')
self.assertEqual(products, 'F[C@@H](Cl)[C@@H](Cl)Br |&1:3|')
# Remove atoms with defined stereo from group
products = _reactAndSummarize('[C:1]F>>[C@@:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:1,3|')
self.assertEqual(products, 'F[C@@H](Cl)[C@@H](Cl)Br |o1:3|') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_destroys_stereo(self):\n reaction = '[C@:1]>>[C:1]'\n products = _reactAndSummarize(reaction, 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'FC(Cl)Br')\n products = _reactAndSummarize(reaction, 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'FC(Cl)Br')\n products = _... | [
"0.6699158",
"0.599879",
"0.5703354",
"0.54769295",
"0.5319069",
"0.51858544",
"0.49389228",
"0.49315634",
"0.4910987",
"0.48884475",
"0.4821768",
"0.4819886",
"0.4803131",
"0.48022223",
"0.48019797",
"0.47844344",
"0.47762632",
"0.47756767",
"0.47696617",
"0.47598013",
"0.46... | 0.6745221 | 0 |
StereoGroup atoms are not in the reaction > stereo group is unaffected | def test_stereogroup_is_spectator_to_reaction(self):
# 5a. Reaction preserves unrelated stereo
products = _reactAndSummarize('[C@:1]F>>[C@:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
self.assertEqual(products, 'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
# 5b. Reaction ignores unrelated stereo'
products = _reactAndSummarize('[C:1]F>>[C:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
self.assertEqual(products, 'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
# 5c. Reaction inverts unrelated stereo'
products = _reactAndSummarize('[C@:1]F>>[C@@:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
self.assertEqual(products, 'F[C@@H](Cl)[C@@H](Cl)Br |o1:3|')
# 5d. Reaction destroys unrelated stereo' 1:3|
products = _reactAndSummarize('[C@:1]F>>[C:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
self.assertEqual(products, 'FC(Cl)[C@@H](Cl)Br |o1:3|')
# 5e. Reaction assigns unrelated stereo'
products = _reactAndSummarize('[C:1]F>>[C@@:1]F',
'F[C@H](Cl)[C@@H](Cl)Br |o1:3|')
self.assertEqual(products, 'F[C@@H](Cl)[C@@H](Cl)Br |o1:3|') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.72588325",
"0.678643",
"0.66508996",
"0.6132741",
"0.60709405",
"0.5719462",
"0.5535332",
"0.552644",
"0.5486801",
"0.54643136",
"0.53574395",
"0.53247046",
"0.52777433",
"0.52353007",
"0.5227962",
"0.52224547",
"0.5211532",
"0.5198085",
"0.51577014",
"0.51439506",
"0.5077... | 0.6698314 | 2 |
StereoGroup atoms are split into two products by the reaction > Should the group be invalidated or trimmed? | def test_reaction_splits_stereogroup(self):
products = _reactAndSummarize('[C:1]OO[C:2]>>[C:2]O.O[C:1]',
'F[C@H](Cl)OO[C@@H](Cl)Br |o1:1,5|')
# Two product sets, each with two mols:
self.assertEqual(products.count('|o1:1|'), 4) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_reaction_defines_stereo(self):\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@H](Cl)Br |o1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAndSummarize('[C:1]>>[C@@:1]', 'F[C@@H](Cl)Br |&1:1|')\n self.assertEqual(products, 'F[C@@H](Cl)Br')\n products = _reactAn... | [
"0.723399",
"0.7145059",
"0.7093139",
"0.6711873",
"0.5570379",
"0.5568875",
"0.54186857",
"0.5407642",
"0.53835154",
"0.5234586",
"0.5234586",
"0.523138",
"0.52302647",
"0.5206368",
"0.51776206",
"0.5163662",
"0.5118267",
"0.5115464",
"0.51110584",
"0.510641",
"0.5056884",
... | 0.7006468 | 3 |
If multiple copies of an atom in StereoGroup show up in the product, they should all be part of the same product StereoGroup. | def test_reaction_copies_stereogroup(self):
# Stereogroup atoms are in the reaction with multiple copies in the product
products = _reactAndSummarize('[O:1].[C:2]=O>>[O:1][C:2][O:1]',
'Cl[C@@H](Br)C[C@H](Br)CCO |&1:1,4|',
'CC(=O)C')
# stereogroup manually checked, product SMILES assumed correct.
self.assertEqual(
products,
'CC(C)(OCC[C@@H](Br)C[C@@H](Cl)Br)OCC[C@@H](Br)C[C@@H](Cl)Br |&1:6,9,15,18|'
)
# Stereogroup atoms are not in the reaction, but have multiple copies in the
# product.
products = _reactAndSummarize('[O:1].[C:2]=O>>[O:1][C:2][O:1]',
'Cl[C@@H](Br)C[C@H](Br)CCO |&1:1,4|',
'CC(=O)C')
# stereogroup manually checked, product SMILES assumed correct.
self.assertEqual(
products,
'CC(C)(OCC[C@@H](Br)C[C@@H](Cl)Br)OCC[C@@H](Br)C[C@@H](Cl)Br |&1:6,9,15,18|'
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_grouping(self):\n n = self.create(NodeItem, UML.Node)\n a = self.create(ArtifactItem, UML.Artifact)\n\n self.group(n, a)\n\n assert 1 == len(n.subject.deployment)\n assert n.subject.deployment[0].deployedArtifact[0] is a.subject",
"def test_reaction_splits_stereogroup(... | [
"0.57665616",
"0.5714965",
"0.56087047",
"0.5555653",
"0.53248274",
"0.5304287",
"0.5222042",
"0.520931",
"0.5198424",
"0.5198424",
"0.5195729",
"0.5162648",
"0.5140849",
"0.51147324",
"0.5112582",
"0.5106029",
"0.50989425",
"0.50513166",
"0.5048671",
"0.50138116",
"0.5004784... | 0.693719 | 0 |
initializes a new instance of the class | def __init__(self, infos, logger):
super().__init__(
infos,
'Delete Green Cloudformation Stack',
logger,
infos.green_infos
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new(self):\n self._init()",
"def initialize(cls):",
"def init(self) -> None:",
"def __init__ (self):\n pass",
"def __init__(self) -> None:\n # TODO: Provide the complete constructor for this object",
"def init(self) -> None:\n ...",
"def __init__():",
"def init(self):\... | [
"0.81939584",
"0.81770974",
"0.78722274",
"0.7813727",
"0.7803766",
"0.77187806",
"0.75613856",
"0.752962",
"0.752962",
"0.752962",
"0.752962",
"0.752962",
"0.752962",
"0.752962",
"0.752962",
"0.7493935",
"0.7488739",
"0.7488739",
"0.7488739",
"0.7488739",
"0.7477393",
"0.7... | 0.0 | -1 |
Always print out typedefs for syntactic reasons in case of more passes. | def visit_Typedef(self, node):
return str_node(node) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPrettyType(self):\n s = self.sym\n if self.sym == None:\n s = self.define\n return \"Typedef (alias type: %s)\" % s.getType()",
"def typedefs(self):\n raise exceptions.NotImplementedError()",
"def typedef(typedefs):\n\n\n for d in typedefs:\n\n\n type = m... | [
"0.6419347",
"0.6250635",
"0.6137227",
"0.6129789",
"0.6000314",
"0.5863036",
"0.58271354",
"0.57932997",
"0.5779045",
"0.5737586",
"0.5731451",
"0.5704002",
"0.5692695",
"0.5642191",
"0.5641333",
"0.56264526",
"0.55807245",
"0.55783653",
"0.5577333",
"0.556277",
"0.55400676"... | 0.5954348 | 5 |
Always print out struct declarations for syntactic reasons in case of more passes. | def visit_Struct(self, node):
return str_node(node) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_out_struct(self):\n args ={\n \"name\": self.python_madz_types + (\"OUTSTRUCT\" if self.namespace == \"\" else self._namespace_mangle(self.namespace)),\n \"fields\":\"\"\n }\n\n res = \\\n\"\"\"class {name}(Structure):\n _fields_ = [{fields}]\n\"\"\"\n ... | [
"0.59062266",
"0.5870518",
"0.57124054",
"0.56050116",
"0.55951077",
"0.55576754",
"0.55329996",
"0.550045",
"0.546557",
"0.5464527",
"0.5442078",
"0.5435242",
"0.5433921",
"0.5400498",
"0.53979063",
"0.5391494",
"0.5334889",
"0.5334051",
"0.53326344",
"0.53083193",
"0.530733... | 0.5306634 | 21 |
Generation from a statement node. This method exists as a wrapper for individual visit_ methods to handle different treatment of some statements in this context. | def _generate_stmt(self, n, add_indent=False):
typ = type(n)
if add_indent: self.indent_level += 2
indent = self._make_indent()
if add_indent: self.indent_level -= 2
if typ in (
c_ast.Decl, c_ast.Assignment, c_ast.Cast, c_ast.UnaryOp,
c_ast.BinaryOp, c_ast.TernaryOp, c_ast.FuncCall, c_ast.ArrayRef,
c_ast.StructRef, c_ast.Constant, c_ast.ID, c_ast.Typedef,
c_ast.ExprList):
# These can also appear in an expression context so no semicolon
# is added to them automatically
#
# Only print out expression if they are part of slice
if n.sliced:
return indent + self.visit(n) + ';\n'
else:
return indent + '{}\n'
elif typ in (c_ast.Compound,):
# No extra indentation required before the opening brace of a
# compound - because it consists of multiple lines it has to
# compute its own indentation.
#
return self.visit(n)
else:
if n.sliced:
return indent + self.visit(n) + '\n'
else:
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_stmt(self, statement):\n fn_map = {\n statements.If: self.gen_if,\n statements.While: self.gen_while,\n statements.DoWhile: self.gen_do_while,\n statements.For: self.gen_for,\n statements.Switch: self.gen_switch,\n statements.Goto: se... | [
"0.6982189",
"0.6952451",
"0.6714583",
"0.6317924",
"0.6317603",
"0.6093038",
"0.6031703",
"0.6027475",
"0.5918272",
"0.5912852",
"0.58984697",
"0.58264",
"0.58131385",
"0.5812323",
"0.5779691",
"0.5761809",
"0.57449067",
"0.57259613",
"0.567997",
"0.5676137",
"0.5661649",
... | 0.64626193 | 3 |
Test the fast upate code against a for loop. | def test_update():
learner = optlearner.VolatilityLearner()
for reward in [0, 1]:
slow_pIk = slow_update(learner, reward)
learner._update(reward)
yield npt.assert_array_equal, slow_pIk, learner.pIk
learner.reset() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testHasForLoop(self):\n no_foreach = build_code(['x=1'], [], ['x=3'], concise=False)\n foreach = build_code(['x=1'], ['x=2'], ['x=3'], concise=False)\n self.assertNotIn('for', no_foreach)\n self.assertIn('for', foreach)",
"def test_run_loop_success(self):\n found = False\n ... | [
"0.627312",
"0.62058866",
"0.61405146",
"0.6024515",
"0.5982735",
"0.59628016",
"0.5885802",
"0.58731663",
"0.57872266",
"0.5761439",
"0.5710765",
"0.56505996",
"0.5533442",
"0.5478586",
"0.54322",
"0.5400814",
"0.53748524",
"0.5355801",
"0.52825946",
"0.5275815",
"0.5266114"... | 0.48848116 | 81 |
This is a more or less literal translation of the original code. | def slow_update(learner, reward):
pIk = learner.pIk.copy()
k_grid = learner.k_grid
I_grid = learner.I_grid
p_grid = learner.p_grid
Ip1gIk = learner._I_trans
pp1gpIp1 = learner._p_trans
for k in xrange(k_grid.size):
# 1) Multiply pIk by Ip1gIk and integrate out I. This will give pIp1k
pIp1k = np.zeros((p_grid.size, I_grid.size))
for Ip1 in xrange(I_grid.size):
for p in xrange(p_grid.size):
pIp1k[p, Ip1] = np.sum(Ip1gIk[Ip1, :, k] * pIk[p, :, k])
# 2) Multiply pIp1k by pp1gpIp1 and integrate out p.
pp1Ip1k = np.zeros((p_grid.size, I_grid.size))
for Ip1 in xrange(I_grid.size):
for pp1 in xrange(p_grid.size):
pp1Ip1k[pp1, Ip1] = np.sum(pIp1k[:, Ip1] *
pp1gpIp1[pp1, :, Ip1].T)
# 3) Place pp1Ip1k into pIk (belief that is carried to the next trial)
pIk[:, :, k] = pp1Ip1k
if reward:
for k in xrange(k_grid.size):
for p in xrange(p_grid.size):
pIk[p, :, k] *= p_grid[p]
else:
for k in xrange(k_grid.size):
for p in xrange(p_grid.size):
pIk[p, :, k] *= 1 - p_grid[p]
# Normalization
pIk /= pIk.sum()
return pIk | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def code():",
"def _fix_up(self, cls, code_name):",
"def exercise_b2_106():\r\n pass",
"def retranslate(self):\r\n pass",
"def retranslate(self):\r\n pass",
"def exercise_b2_107():\r\n pass",
"def exercise_b2_113():\r\n pass",
"def test_fix_code_typical_code():\r\n\r\n pass"... | [
"0.6567136",
"0.62829435",
"0.5988698",
"0.59373957",
"0.59373957",
"0.5923645",
"0.5897334",
"0.58716434",
"0.58613706",
"0.58475685",
"0.5832422",
"0.5757656",
"0.57205373",
"0.56826997",
"0.5669326",
"0.5593282",
"0.55611753",
"0.556074",
"0.5530139",
"0.54967827",
"0.5487... | 0.0 | -1 |
Tests the install workflow using the built in workflows. | def tests_pull_workflow(self):
daemon_client = {}
client = self.get_client(daemon_client)
for container in client.containers(all=True):
if 'test-container' in \
''.join([name for name in container.get('Names')]):
client.remove_container('test-container')
if ['{0}:latest'.format(TEST_IMAGE)] in \
[i.get('RepoTags') for i in client.images()]:
client.remove_image(TEST_IMAGE, force=True)
# execute install workflow
self.env.execute('install', task_retries=0)
container_instance = {}
for instance in self.env.storage.get_node_instances():
if 'container_id' in instance.runtime_properties.keys():
container_instance = instance
self.assertTrue(container_instance is not None,
'Failed getting container.')
container_id = container_instance.runtime_properties.get(
'container_id')
containers = client.containers(all=True)
self.assertTrue(container_id in [c.get('Id') for c in containers])
self.env.execute('uninstall', task_retries=3)
repotags = []
for i in client.images():
repotags.append(i.get('RepoTags'))
self.assertFalse(TEST_IMAGE in [tag for tag in repotags])
if ['{0}:latest'.format(TEST_IMAGE)] in \
[i.get('RepoTags') for i in client.images()]:
client.remove_image(TEST_IMAGE, force=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_install(self):\n pass",
"def test_deploy_workflow_definition(self):\n pass",
"def test_installed(self):\n # OSA script should have been installed in setUp function\n self.assertTrue(self.run_function(\"assistive.installed\", [OSA_SCRIPT]))\n # Clean up install\n ... | [
"0.7448684",
"0.6557498",
"0.65304166",
"0.65078044",
"0.6446686",
"0.63318056",
"0.6329271",
"0.6329271",
"0.63074094",
"0.63003325",
"0.62722313",
"0.62695414",
"0.6268539",
"0.62517077",
"0.62472796",
"0.62345684",
"0.62330306",
"0.62122643",
"0.62017137",
"0.61228365",
"0... | 0.0 | -1 |
Rename a file or folder | def rename_file(self, file_id, new_name):
func = f"setRenameFile(Token: $Token, FileRenames: $FileRenames)"
query = f"mutation SetRenameFile($Token: String!, $FileRenames: [FileRenameInfo]!) {{ {func} }}"
request = {"operationName": "SetRenameFile",
"variables": {
"Token": self.KEYS["Token"],
"FileRenames": [{
"ID": file_id,
"NewName": new_name
}]
},
"query": query
}
header = {"x-api-key": self.KEYS["x-api-key"]}
response = requests.post(URL_API, headers=header, data=json.dumps(request))
if response.ok:
rd = json.loads(response.text)
if "errors" in rd:
messages = []
for error in rd["errors"]:
messages.append(error["message"])
message = '\n'.join(messages)
raise DegooError(f"getUserInfo failed with: {message}")
else:
return rd["data"]['setRenameFile']
else:
raise DegooError(f"renameFile failed with: {response}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RenameFile(self, oldname: str, newname: str) -> None:\n ...",
"def rename(path, new_path):\n fs.rename(path, new_path)",
"def rename(self, src, dst):\n os.rename(src, dst)",
"def rename_file(path, old_name, new_name):\n \n old_file = os.path.join(path, old_name)\n new_file = os.... | [
"0.7906396",
"0.7777854",
"0.77087426",
"0.763373",
"0.755172",
"0.752285",
"0.74998015",
"0.74556607",
"0.73914254",
"0.7262698",
"0.71969265",
"0.7161511",
"0.70347023",
"0.70347023",
"0.70059544",
"0.699431",
"0.69706184",
"0.69470656",
"0.6944914",
"0.69139314",
"0.690678... | 0.6451277 | 46 |
Move a file or folder to new destination | def mv(self, file_id, new_parent_id):
func = f"setMoveFile(Token: $Token, Copy: $Copy, NewParentID: $NewParentID, FileIDs: $FileIDs)"
query = f"mutation SetMoveFile($Token: String!, $Copy: Boolean, $NewParentID: String!, $FileIDs: [String]!) {{ {func} }}"
request = {"operationName": "SetMoveFile",
"variables": {
"Token": self.KEYS["Token"],
"NewParentID": new_parent_id,
"FileIDs": [
file_id
]
},
"query": query
}
header = {"x-api-key": self.KEYS["x-api-key"]}
response = requests.post(URL_API, headers=header, data=json.dumps(request))
if response.ok:
rd = json.loads(response.text)
if "errors" in rd:
messages = []
for error in rd["errors"]:
messages.append(error["message"])
message = '\n'.join(messages)
raise DegooError(f"getUserInfo failed with: {message}")
else:
return rd["data"]['setMoveFile']
else:
raise DegooError(f"renameFile failed with: {response}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_file(source, destination):\n shutil.move(source, destination)",
"def moveFile(source, dest):\n try:\n shutil.move(source, dest) \n except IOError as e:\n print (\"Unable to move file. %s\" %(e))",
"def mv(self, src_path, dst_path):\n try:\n postdata = codecs.en... | [
"0.836858",
"0.77318025",
"0.7662355",
"0.7631933",
"0.7586252",
"0.7535129",
"0.7519395",
"0.75076956",
"0.74930936",
"0.74675715",
"0.74577117",
"0.74045736",
"0.7384608",
"0.7289181",
"0.7248294",
"0.72342104",
"0.72236645",
"0.71997166",
"0.7152438",
"0.71472096",
"0.7135... | 0.62955916 | 80 |
Rename a file or folder | def rename(path_file_folder, new_name):
old_name = path_file_folder[path_file_folder.rfind('/') + 1:] if '/' in path_file_folder else path_file_folder
if old_name == new_name:
raise DegooError(f"rename: Old name and new name \"{new_name}\" cannot be the same")
if isinstance(path_file_folder, int):
file_id = path_file_folder
elif isinstance(path_file_folder, str):
file_id = path_id(path_file_folder)
else:
raise DegooError(f"rm: Illegal file: {path_file_folder}")
return api.rename_file(file_id, new_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def RenameFile(self, oldname: str, newname: str) -> None:\n ...",
"def rename(path, new_path):\n fs.rename(path, new_path)",
"def rename(self, src, dst):\n os.rename(src, dst)",
"def rename_file(path, old_name, new_name):\n \n old_file = os.path.join(path, old_name)\n new_file = os.... | [
"0.7906396",
"0.7777854",
"0.77087426",
"0.763373",
"0.752285",
"0.74998015",
"0.74556607",
"0.73914254",
"0.7262698",
"0.71969265",
"0.7161511",
"0.70347023",
"0.70347023",
"0.70059544",
"0.699431",
"0.69706184",
"0.69470656",
"0.6944914",
"0.69139314",
"0.690678",
"0.689538... | 0.755172 | 4 |
Move a file or folder | def mv(path_file_folder, new_path):
if not is_folder(new_path):
raise DegooError(f"mv: The target path is not a folder")
source_path = path_file_folder if is_folder(path_file_folder) else path_file_folder[:path_file_folder.rfind('/')]
if source_path == new_path:
raise DegooError(f"mv: The target path cannot be the same as the source path")
if isinstance(path_file_folder, int):
file_id = path_file_folder
elif isinstance(path_file_folder, str):
file_id = path_id(path_file_folder)
else:
raise DegooError(f"rm: Illegal file: {path_file_folder}")
if isinstance(new_path, int):
new_parent_id = new_path
elif isinstance(new_path, str):
new_parent_id = path_id(new_path)
else:
raise DegooError(f"rm: Illegal destination folder: {new_path}")
return api.mv(file_id, new_parent_id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move_file(source, destination):\n shutil.move(source, destination)",
"def moveFile(source, dest):\n try:\n shutil.move(source, dest) \n except IOError as e:\n print (\"Unable to move file. %s\" %(e))",
"def move_file(self, path: PathLike, dest: PathLike, force: bool = False):",
"de... | [
"0.83455",
"0.7957065",
"0.7644527",
"0.76268214",
"0.75139415",
"0.74997723",
"0.74345535",
"0.74257374",
"0.73716736",
"0.7324468",
"0.7222057",
"0.7205844",
"0.72037476",
"0.71912545",
"0.7157873",
"0.7147585",
"0.7131787",
"0.7130521",
"0.71214455",
"0.71214455",
"0.71021... | 0.69655865 | 27 |
Get the versions from GitHub tags | def get_versions(self):
# They randomly use and don't use 'r' prefix so we have to sort
# versions manually
versions = list(self._get_github_tags())
versions.sort(
key=operator.attrgetter('base_version'),
reverse=True,
)
return versions | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_github_chandra_models_version_info():\n with urlopen('https://api.github.com/repos/sot/chandra_models/tags') as url:\n response = url.read()\n tags = json.loads(response.decode('utf-8'))\n\n with urlopen('https://api.github.com/repos/sot/chandra_models/branches') as url:\n respon... | [
"0.6975342",
"0.67174757",
"0.66325366",
"0.6590542",
"0.6541449",
"0.64341235",
"0.64160585",
"0.6401173",
"0.6249824",
"0.61892974",
"0.61882895",
"0.618634",
"0.61683893",
"0.61543375",
"0.6109142",
"0.60987633",
"0.608993",
"0.6088227",
"0.6088083",
"0.6067755",
"0.604825... | 0.7527406 | 0 |
Get all attributs values. | def __str__(self):
status = "height = {}\n".format(self.height)
status += "width = {}\n".format(self.width)
status += "channels = {}\n".format(self.channels)
status += "classes = {}\n".format(self.classes)
status += "batch_size = {}\n".format(self.batch_size)
status += "epochs = {}\n".format(self.epochs)
status += "save_step = {}\n".format(self.save_step)
status += "learning_rate = {}\n".format(self.learning_rate)
status += "momentum = {}\n".format(self.momentum)
return status | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_attribute(self):\n for attr, value in self.__dict__.items():\n print(attr, value)",
"def values(self):\n return self.attrs.values()",
"def values(self):\n return [getattr(self, a.name) for a in self.__attrs_attrs__]",
"def getAttributes(self):\n pass",
"de... | [
"0.7782514",
"0.7658524",
"0.75126153",
"0.7339408",
"0.7190724",
"0.7190724",
"0.71009326",
"0.7089321",
"0.7067784",
"0.7013646",
"0.7010554",
"0.6959679",
"0.6932175",
"0.687121",
"0.6866942",
"0.6840013",
"0.6839982",
"0.68385345",
"0.6838188",
"0.67684263",
"0.67606723",... | 0.0 | -1 |
Get all attributs values. | def __str__(self):
status = "height = {}\n".format(self.height)
status += "width = {}\n".format(self.width)
status += "channels = {}\n".format(self.channels)
status += "input_dim = {}\n".format(self.input_dim)
status += "architecture = {}\n".format(self.architecture)
status += "activations = {}\n".format(self.activations)
status += "batch_size = {}\n".format(self.batch_size)
status += "epochs = {}\n".format(self.epochs)
status += "save_step = {}\n".format(self.save_step)
status += "learning_rate = {}\n".format(self.learning_rate)
status += "momentum = {}\n".format(self.momentum)
return status | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_attribute(self):\n for attr, value in self.__dict__.items():\n print(attr, value)",
"def values(self):\n return self.attrs.values()",
"def values(self):\n return [getattr(self, a.name) for a in self.__attrs_attrs__]",
"def getAttributes(self):\n pass",
"de... | [
"0.7782514",
"0.7658524",
"0.75126153",
"0.7339408",
"0.7190724",
"0.7190724",
"0.71009326",
"0.7089321",
"0.7067784",
"0.7013646",
"0.7010554",
"0.6959679",
"0.6932175",
"0.687121",
"0.6866942",
"0.6840013",
"0.6839982",
"0.68385345",
"0.6838188",
"0.67684263",
"0.67606723",... | 0.0 | -1 |
Get all attributs values. | def __str__(self):
status = "height = {}\n".format(self.height)
status += "width = {}\n".format(self.width)
status += "channels = {}\n".format(self.channels)
status += "architecture = {}\n".format(self.architecture)
status += "activations = {}\n".format(self.activations)
status += "conv_activations = {}\n".format(self.conv_activations)
status += "conv_architecture = {}\n".format(self.conv_architecture)
status += "kernel_sizes = {}\n".format(self.kernel_sizes)
status += "pool_kernel = {}\n".format(self.pool_kernel)
status += "batch_size = {}\n".format(self.batch_size)
status += "epochs = {}\n".format(self.epochs)
status += "save_step = {}\n".format(self.save_step)
status += "learning_rate = {}\n".format(self.learning_rate)
status += "momentum = {}\n".format(self.momentum)
return status | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_attribute(self):\n for attr, value in self.__dict__.items():\n print(attr, value)",
"def values(self):\n return self.attrs.values()",
"def values(self):\n return [getattr(self, a.name) for a in self.__attrs_attrs__]",
"def getAttributes(self):\n pass",
"de... | [
"0.7782514",
"0.7658524",
"0.75126153",
"0.7339408",
"0.7190724",
"0.7190724",
"0.71009326",
"0.7089321",
"0.7067784",
"0.7013646",
"0.7010554",
"0.6959679",
"0.6932175",
"0.687121",
"0.6866942",
"0.6840013",
"0.6839982",
"0.68385345",
"0.6838188",
"0.67684263",
"0.67606723",... | 0.0 | -1 |
Address for the xbee, where the response originated from. | def address(self):
return self._address | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAddress(self):\r\n return self._endpoint.getAddress()",
"def addr(self):\r\n return self._addr",
"def getAddress(self):\r\n raise NotImplementedError('Endpoint can not be used directly.')",
"def address(self):\n ...",
"def address(self) -> str:\n return self._backe... | [
"0.7066411",
"0.6962745",
"0.6875606",
"0.68373436",
"0.67819583",
"0.67610395",
"0.6751729",
"0.6742783",
"0.6728146",
"0.6693614",
"0.665993",
"0.665993",
"0.6601143",
"0.6547315",
"0.65288603",
"0.65288603",
"0.65288603",
"0.6516686",
"0.6515416",
"0.6487318",
"0.6476641",... | 0.6983551 | 5 |
Fill the packets data properties. | def fill_data(self, data):
self._data = data
self._data_length = data[1:3]
self._frame_id = data[4]
self._address = XbeeAddress(data[5:9], data[9:13], data[13:15])
self._at_command = data[15:17]
self._command_status = data[17]
try:
self._command_data = data[18:21]
self._checksum = data[22]
except IndexError:
self._command_data = None
self._checksum = data[18] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initAttributes(self):\n CCSDS.DU.DataUnit.initAttributes(self)\n self.dataFieldHeaderFlag = 0\n self.setPacketLength()",
"def _init_data(self) -> None:\n self.dtype = dict()\n self.shape = dict()\n self.size = dict()\n self.attrs = dict()\n self.data_p... | [
"0.6564761",
"0.6411371",
"0.62188",
"0.6214954",
"0.61160105",
"0.60499305",
"0.592144",
"0.5917901",
"0.58758026",
"0.5839473",
"0.58239967",
"0.58188593",
"0.5807109",
"0.57820135",
"0.57779455",
"0.5756668",
"0.57558346",
"0.57392687",
"0.57246864",
"0.57196575",
"0.57140... | 0.6753045 | 0 |
Call me before using any of the tables or classes in the model. | def init_model(connection):
db = connection
for obj in common.__dict__.itervalues():
if type(obj) == type and issubclass(obj, common.Model) and hasattr(obj, '__tablename__'):
tablename = getattr(obj, '__tablename__')
obj._object_store = Domain(db, tablename)
collection_to_class[obj._object_store] = obj | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_model(self):\n pass",
"def prepare_model(self, **kwargs):\n pass",
"def initialize_model(self):\n pass",
"def setUp(self):\n create_table(self.DATABASE_PATH)\n self.model = model.CodeReviewDatabase(self.DATABASE_PATH)",
"def _before_execute(self, db):\n pa... | [
"0.67817503",
"0.6613017",
"0.6608557",
"0.6572093",
"0.6526641",
"0.6456574",
"0.6391493",
"0.63803416",
"0.63562936",
"0.63505673",
"0.63026184",
"0.62730545",
"0.624931",
"0.6238614",
"0.62380886",
"0.6188223",
"0.6165031",
"0.6163278",
"0.61397433",
"0.61384445",
"0.61249... | 0.0 | -1 |
Sets up the database session | def setup():
global connection
connection = MySQLdb.connect(host=config.get('mysql.host'),
user=config.get('mysql.user'),
passwd=config.get('mysql.password'),
db=config.get('mysql.db'),
ssl={'ca' : config.get('mysql.cert')})
init_model(connection) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_db_session():\n g.s = database.db_session()",
"def setup_session():\n print(\"Setting up session\")\n engine = setup_engine()\n Base.metadata.bin = engine\n\n DBSession = sessionmaker(bind=engine)\n session = DBSession()\n\n return session",
"def init_database(self):\n engin... | [
"0.7948405",
"0.7780912",
"0.74744767",
"0.7390211",
"0.7389746",
"0.7370456",
"0.73018765",
"0.7287071",
"0.7270587",
"0.72254795",
"0.721942",
"0.7173008",
"0.71630186",
"0.7160225",
"0.7144433",
"0.7144433",
"0.71353585",
"0.7134453",
"0.7132723",
"0.71059585",
"0.7102454"... | 0.66753596 | 46 |
Order the results. type should be ASC or DESC | def order(self, column, type):
self._order = (column, type)
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort_results(self):\n pass",
"def orderby():\n pass",
"def order_by(self, results, key_, direction=\"ASC\"):\n\n return sorted(results, key=lambda x: x.get(key_), reverse=direction==\"DESC\")",
"def sort_results(results_list, sorting_type):\n if sorting_type == \"Oldest\":\n re... | [
"0.7437501",
"0.6612537",
"0.6586749",
"0.65457374",
"0.65006554",
"0.6475219",
"0.6381626",
"0.6325047",
"0.6201697",
"0.6086321",
"0.60466444",
"0.59852207",
"0.59614545",
"0.59498024",
"0.59177685",
"0.5870425",
"0.58585554",
"0.58449817",
"0.5832564",
"0.57793987",
"0.575... | 0.6021243 | 11 |
Transform an object retrieved from the database | def transform_outgoing(self, son):
if 'type' in son:
klass = common.classify(son['type'])
return klass.demongofy(son)
else:
try:
return collection_to_class[self.domain].demongofy(son)
except KeyError:
return son | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform(self):",
"def transform():",
"def transform_one(self, obj: Any):\n return obj",
"def transform(self, data):",
"def Transform(self, record):\n pass",
"def _from_db_object(boar, db_boar):\n foreign_key = ['category', 'dormitory', 'source']\n for field in boar.fields:\n... | [
"0.65609854",
"0.64150685",
"0.6350461",
"0.6347081",
"0.6238227",
"0.5997735",
"0.598158",
"0.59786004",
"0.59712017",
"0.58984053",
"0.58658886",
"0.5845334",
"0.58016354",
"0.58016354",
"0.58016354",
"0.58016354",
"0.58016354",
"0.58016354",
"0.58016354",
"0.5780222",
"0.5... | 0.0 | -1 |
test if the stations are sorted correctly by distance | def test_stations_by_distance():
station_list = build_station_list()
#test for stations closest to cambridge city coordinates
station_list_sort = stations_by_distance(station_list, (52.2053, 0.1218))
output = [(station.name, distance) for (station, distance) in station_list_sort]
for n in range(1, len(station_list)):
#make sure that the distance of the previous station to the point is less than the next one in the list
assert output[n-1][1] <= output[n][1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_nearest_filter(self):\n for airport, reports, count in (\n (True, True, 6),\n (True, False, 16),\n (False, True, 6),\n (False, False, 30),\n ):\n stations = station.nearest(30, -80, 30, airport, reports, 1.5)\n self.assertEqua... | [
"0.66526514",
"0.64656",
"0.6275362",
"0.60979766",
"0.6097272",
"0.6059003",
"0.6043562",
"0.59792054",
"0.5845956",
"0.5807009",
"0.5800586",
"0.57856506",
"0.5729412",
"0.5716074",
"0.5695116",
"0.5680469",
"0.5653806",
"0.5639628",
"0.5623877",
"0.5608037",
"0.55967736",
... | 0.8008606 | 0 |
Finds the minimum value of SequenceMatcher.ratio() for two strings such that Differ considers them as 'changed'. | def check_difflib_ratio():
import difflib
import random
import string
def random_modify_string(input_string, change_word=0.5, change_char=0.3):
word_list = input_string.split()
for i, word in enumerate(word_list):
if random.random() < change_word:
for j in range(len(word)):
if random.random() < change_char:
word = word[:j] + random.choice(string.printable) + word[j + 1:]
word_list[i] = word
return ' '.join(word_list)
differ = difflib.Differ()
min_ratio = 1.0
for count in range(1000):
length = random.randint(5, 100)
s1 = ''.join(random.SystemRandom().choice(string.printable) for _ in range(length))
s2 = random_modify_string(s1)
sm = difflib.SequenceMatcher(None, s1, s2)
ratio = sm.ratio()
result = list(differ.compare([s1], [s2]))
for line in result:
if line.startswith('?'):
if ratio < min_ratio:
min_ratio = ratio
break
print('Minimum ratio which difflib considers as "change" is: {}'.format(min_ratio)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_equal_rate(str1, str2):\r\n\treturn difflib.SequenceMatcher(None, str1, str2).quick_ratio()",
"def string_match_ratio(str1, str2):\n sm = edit_distance.SequenceMatcher(a=str1, b=str2)\n return sm.ratio()",
"def compare_strings(string1: str, string2: str) -> float:\n return SequenceMatcher(None... | [
"0.76466227",
"0.75796056",
"0.7486614",
"0.7058355",
"0.7058355",
"0.70151675",
"0.6979458",
"0.6847581",
"0.67930263",
"0.6693175",
"0.66115886",
"0.6577811",
"0.6511473",
"0.6479723",
"0.64503026",
"0.64290327",
"0.63911766",
"0.63698953",
"0.6221948",
"0.60449994",
"0.602... | 0.7495494 | 2 |
Function to instanciate the instrument. | def connect_instrument(self):
for instrument in self.rm.list_resources():
try:
k2400 = self.init_inst(instrument)
k2400.timeout = 5000
if k2400.query('*IDN?')[:8] == 'KEITHLEY':
return k2400
except AttributeError as f:
logger.warning(f'Unknown error - {f}')
except errors.VisaIOError as e:
logger.warning(f'Not possible to connect the port - {k2400}.') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_instrument(self, entry=\"entry\", instrument_name=\"id00\",):\n if not isinstance(entry, h5py.Group):\n entry = self.new_entry(entry)\n return self.new_class(entry, instrument_name, \"NXinstrument\")",
"def new_instrument(self, instrument_type):\r\n return self.instrument_... | [
"0.67939395",
"0.6719433",
"0.6655605",
"0.645817",
"0.6346724",
"0.6167076",
"0.6164952",
"0.5983404",
"0.59604347",
"0.5957381",
"0.58981216",
"0.58949465",
"0.5830187",
"0.58211017",
"0.5807674",
"0.580686",
"0.57869667",
"0.5773858",
"0.57293445",
"0.5729042",
"0.5718009"... | 0.0 | -1 |
Function to reset instrument commands. | def reset_instrument(self):
return self.inst.write('*RST') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _doReset(self):\n self._cmdReset()",
"def reset():\n pass",
"def reset():\n pass",
"def reset():",
"def reset():",
"def reset():",
"def reset(*args):",
"def reset(*args):",
"def reset(*args):",
"def reset(self):\r\n _debug('simq03b_api.reset')\r\n self.write('*RST')... | [
"0.6838344",
"0.6738413",
"0.6738413",
"0.671417",
"0.671417",
"0.671417",
"0.66459775",
"0.66459775",
"0.66459775",
"0.6641422",
"0.6612196",
"0.6542922",
"0.6525732",
"0.65187824",
"0.6515516",
"0.6514655",
"0.649084",
"0.646241",
"0.646241",
"0.646241",
"0.646241",
"0.64... | 0.728747 | 0 |
Function to get the instrument ID. | def get_id(self):
try:
return self.inst.query('*IDN?')[:36]
except errors.VisaIOError as e:
logger.warning(e)
return 'Device not connected.' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_instrument(instrument_id=\"ncnr.refl\"):\n instrument = lookup_instrument(instrument_id)\n return instrument.get_definition()",
"def instrID(self):\n return self.query('*IDN?')",
"def getIdent (self) :\n return self.id",
"def instrumentLookup(self):\n try:\n retu... | [
"0.7232644",
"0.7094874",
"0.6803308",
"0.6676362",
"0.66161686",
"0.6494307",
"0.64633137",
"0.64486635",
"0.6435335",
"0.64340276",
"0.6428906",
"0.6420143",
"0.6411098",
"0.6404091",
"0.63868725",
"0.63842493",
"0.63842493",
"0.63842493",
"0.63842493",
"0.63842493",
"0.638... | 0.6533145 | 5 |
Function to turn keithley on. | def power_on(self):
return self.inst.write(':OUTP ON') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def turn_eht_on(self):\n raise NotImplementedError",
"def friewallOn():\n pass",
"def turn_on(self, **kwargs) -> None:\n self.heater.turn_on()",
"def turn_on(self):\n self._interrupt_flash()\n if not self.on:\n GPIO.output(self.pin, GPIO.HIGH)\n self.on = ... | [
"0.7135671",
"0.663822",
"0.6409372",
"0.6337713",
"0.6281222",
"0.6265482",
"0.6240381",
"0.6208941",
"0.61929655",
"0.6179498",
"0.61754537",
"0.6157566",
"0.6145475",
"0.6067093",
"0.60128975",
"0.5974138",
"0.59354514",
"0.58733046",
"0.5868544",
"0.5858084",
"0.58495027"... | 0.0 | -1 |
Function to turn keithley off. | def power_off(self):
return self.inst.write(':OUTP OFF') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def turn_off(self, **kwargs) -> None:\n self.wink.set_state(False)",
"def turn_eht_off(self):\n raise NotImplementedError",
"def turn_off(self, **kwargs: Any) -> None:\n with self._wemo_call_wrapper(\"turn off\"):\n self.wemo.off()",
"def turn_off(self):\n GPIO.output(s... | [
"0.712389",
"0.68947697",
"0.67406696",
"0.6689545",
"0.66849595",
"0.6672334",
"0.66197157",
"0.6614438",
"0.6604366",
"0.6602539",
"0.65976703",
"0.6577364",
"0.6537614",
"0.6469053",
"0.6457736",
"0.64300936",
"0.6429341",
"0.6427579",
"0.64250124",
"0.6411914",
"0.6404472... | 0.0 | -1 |
Function to select panel. | def select_panel(self):
radio_btn = self.sender()
if radio_btn.isChecked():
term = radio_btn.text()[:-9]
return self.inst.write(f':ROUT:TERM {term.upper()}') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_panel(self, panel_id):\n return self.panels.get(panel_id, None)",
"def get_active_panel(cls):\n active_panel = None\n panel_list = pm.getPanel(type='modelPanel')\n for panel in panel_list:\n if pm.modelEditor(panel, q=1, av=1):\n active_panel = panel\... | [
"0.6231665",
"0.60948694",
"0.58209896",
"0.57440066",
"0.55831444",
"0.55661774",
"0.5561857",
"0.55333793",
"0.5477269",
"0.5407169",
"0.53396136",
"0.5338085",
"0.5338085",
"0.53237325",
"0.5315146",
"0.53150046",
"0.5298998",
"0.52901876",
"0.5276756",
"0.5271008",
"0.525... | 0.6054565 | 2 |
Preprocess xml files, including extract main info from xml, and load category info. | def load_data_to_json(root_path, extract=True, decode="utf-8"):
category_map = {}
for file in os.listdir(root_path):
# 行业分类文件夹
path = os.path.join(root_path, file)
if os.path.isdir(path):
for xml_file in os.listdir(path):
# 读取每个行业分类的文件
xml_file_path = os.path.join(path, xml_file)
try:
if xml_file_path.endswith(".xml"):
doc = read_main_info(xml_file_path, extract=extract, decode=decode)
if file in category_map.keys():
category_map[file].append(doc)
else:
category_map[file] = list()
category_map[file].append(doc)
except UnicodeDecodeError:
print("UnicodeDecodeError:%s" % xml_file_path)
continue
with open(JSON_FILE, "w") as f:
json.dump(category_map, f) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_xml(self):\n self.process_gpx_file(str(self.filename))",
"def extract_titles():\n \"\"\"\n The final data has the mapping post_title -> cat.\n This requires three relations:\n (pid, id) -> feed_url, feed_url -> blog_url, blog_url -> cat.\n Each file contains one raw feed with se... | [
"0.6510495",
"0.58586115",
"0.5815168",
"0.5765223",
"0.5643835",
"0.5622581",
"0.5620029",
"0.55302376",
"0.5526815",
"0.5474749",
"0.54636383",
"0.54632366",
"0.54615885",
"0.5434923",
"0.53978896",
"0.53765374",
"0.53668106",
"0.53492767",
"0.5326623",
"0.52779377",
"0.527... | 0.0 | -1 |
load training set and testing set from json | def load_data_set_from_json(json_path, ratio=0.7):
train_doc_list = []
train_category_list = []
test_doc_list = []
test_category_list = []
if os.path.exists(json_path):
with open(json_path, "r") as f:
category_map = json.load(f)
categories = category_map.keys()
for category in categories:
all_doc_list = category_map.get(category)
length = len(all_doc_list)
train_set_length = int(length * ratio)
for i in range(length):
if i < train_set_length:
train_doc_list.append(all_doc_list[i])
train_category_list.append(category)
else:
test_doc_list.append(all_doc_list[i])
test_category_list.append(category)
else:
print("File doesn't exist, please run load_file_to_json first")
return train_doc_list, train_category_list, test_doc_list, test_category_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_training_data(\n self,\n train_data_file=\"datasets/train_data.json\",\n test_data_file=\"datasets/test_data.json\",\n ):\n train_data = pd.read_json(train_data_file)\n test_data = pd.read_json(test_data_file)\n return train_data, test_data",
"def load_traini... | [
"0.74476916",
"0.7354245",
"0.7169759",
"0.6705753",
"0.6644966",
"0.66320914",
"0.6515724",
"0.64992905",
"0.6398464",
"0.63905",
"0.62948185",
"0.6278425",
"0.6241316",
"0.62365764",
"0.62238926",
"0.6203974",
"0.6202843",
"0.6183581",
"0.6180504",
"0.6172601",
"0.6147451",... | 0.6676436 | 4 |
Sends a message to all listeners of the topic | def _send(self, topic, message):
body = {'message': encode(message)}
result = requests.post('{0}/topics/{1}'.format(self.apiUrl, topic), json=body)
return result.json() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listen(self, topics):\n logging.debug(f'Listen to {list(map(lambda x: x.name, topics))}')\n\n for topic in map(lambda x: x.name, topics):\n try:\n self.subscribe(topic)\n logging.debug(f'Subscribed the {topic} topic')\n except Exception:\n ... | [
"0.6755926",
"0.647603",
"0.6460375",
"0.6401803",
"0.6373115",
"0.6307153",
"0.6262211",
"0.6215271",
"0.613345",
"0.6130038",
"0.6114487",
"0.60955477",
"0.6017219",
"0.600299",
"0.5941551",
"0.588395",
"0.58836484",
"0.5870817",
"0.58618623",
"0.5843349",
"0.5838989",
"0... | 0.0 | -1 |
Starts listening for new messages on this topic | def _listen_on(self, topic, transform=None):
task = self.topic_task_map.get(topic)
if task is None:
task = StreamingRequestTask(self.apiUrl, topic, transform)
self.topic_task_map[topic] = task
task.start()
listener = task.create_listener(transform)
self.listener_task_map[listener] = task
return listener | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listen(self):\n self.channel.start_consuming()",
"def listen(self):\n self._client.listen(self._default_subscribe_to_dest)",
"def listen(self):\n\n # It's ideal to start listening before the game starts, but the\n # down-side\n # is that object construction may not be don... | [
"0.7356364",
"0.70570236",
"0.7030769",
"0.7006789",
"0.68794745",
"0.6866184",
"0.685871",
"0.6839579",
"0.6831507",
"0.67023295",
"0.6645298",
"0.6638821",
"0.6618311",
"0.6550533",
"0.65046924",
"0.64672434",
"0.6445343",
"0.6389064",
"0.63380456",
"0.63122493",
"0.6299043... | 0.644124 | 17 |
Sends a message to all listeners of the special topic broadcast | def broadcast(self, message):
self._send('broadcast', message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def broadcast(self, msg):\n for client in self.clients.values():\n send_data(client.socket, msg)",
"def broadcast(msg):\r\n for user in clients:\r\n msg_client(msg, user)",
"def broadcast(msg):\n\n for sock in clients:\n sock.send(bytes(msg, \"utf-8\"))",
"def broadcast(msg, prefi... | [
"0.68770134",
"0.6842449",
"0.6777803",
"0.6613753",
"0.6587547",
"0.6495968",
"0.6483176",
"0.6476947",
"0.64376646",
"0.64327085",
"0.63729095",
"0.6357374",
"0.63375884",
"0.6321455",
"0.63169193",
"0.6260771",
"0.6246585",
"0.62057936",
"0.6156581",
"0.6138631",
"0.613646... | 0.54191 | 70 |
Identity,Account/Astakos. Test ~okeanos authentication credentials | def check_user_credentials(token, auth_url='https://accounts.okeanos.grnet.gr'
'/identity/v2.0'):
logging.info(' Test the credentials')
try:
auth = AstakosClient(auth_url, token)
auth.authenticate()
logging.info(' Authentication verified')
return AUTHENTICATED
except ClientError:
logging.error('Authentication failed with url %s and token %s' % (
auth_url, token))
return NOT_AUTHENTICATED | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aio_can_login_to_web_portal(aio):",
"def test_oms_credentials(*args, **kwargs):\n\treturn {'status':'success'}",
"def test_basic_login(self):\n c = Client()\n c.login(username='a', password='123456')",
"def test_01_authenticated(self):\r\n res = self.signin(email=self.email_addr... | [
"0.6723792",
"0.671743",
"0.6590624",
"0.657539",
"0.65334004",
"0.643523",
"0.64110607",
"0.6394348",
"0.6331037",
"0.63070375",
"0.6304327",
"0.6270155",
"0.62404686",
"0.62375134",
"0.6210401",
"0.6204399",
"0.61970514",
"0.618015",
"0.6161113",
"0.61508554",
"0.61445206",... | 0.5886558 | 66 |
queries the database for a specific character takes a name returns a json with the lines | def lines_from_char(character):
query = f"""
SELECT script_l FROM script
JOIN characters
ON characters.char_id = script.characters_char_id
WHERE name = '{character}'
"""
data = pd.read_sql_query(query,engine)
return data.to_json(orient="records") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lines_from_char_ep(character,ep):\n query = f\"\"\"\nSELECT script_l FROM script\nJOIN characters \nON characters.char_id = script.characters_char_id\nINNER JOIN episodes\nON episodes.ep_id = script.episodes_ep_id\nWHERE name = '{character}' and episode = '{ep}'\n\"\"\"\n data = pd.read_sql_query(query,e... | [
"0.6725487",
"0.60146636",
"0.5816583",
"0.5747994",
"0.5689336",
"0.5673698",
"0.56607604",
"0.5536652",
"0.5442061",
"0.5321022",
"0.5247645",
"0.52152646",
"0.5201513",
"0.5195355",
"0.5165863",
"0.51640224",
"0.5157612",
"0.5139213",
"0.5121536",
"0.5115688",
"0.5093197",... | 0.7432127 | 0 |
queries the database for a specific character and episode takes a name and episode returns a json with the filtered lines | def lines_from_char_ep(character,ep):
query = f"""
SELECT script_l FROM script
JOIN characters
ON characters.char_id = script.characters_char_id
INNER JOIN episodes
ON episodes.ep_id = script.episodes_ep_id
WHERE name = '{character}' and episode = '{ep}'
"""
data = pd.read_sql_query(query,engine)
return data.to_json(orient="records") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lines_():\n query = f\"\"\"\nSELECT script_l, `name`, episode\nFROM script\nINNER JOIN characters\nON characters.char_id = script.characters_char_id\nINNER JOIN episodes\nON episodes.ep_id = script.episodes_ep_id\n\"\"\"\n data = pd.read_sql_query(query, engine)\n return data.to_json(orient=\"records\... | [
"0.6161546",
"0.61566335",
"0.58697116",
"0.531941",
"0.51938283",
"0.5191531",
"0.51225615",
"0.50665915",
"0.49034274",
"0.48820502",
"0.48670354",
"0.48493454",
"0.48400128",
"0.48004526",
"0.47973293",
"0.47908667",
"0.47580832",
"0.47565988",
"0.47521466",
"0.47492182",
... | 0.7129047 | 0 |
queries the database for all lines takes no arguments returns a json with all the lines | def lines_():
query = f"""
SELECT script_l, `name`, episode
FROM script
INNER JOIN characters
ON characters.char_id = script.characters_char_id
INNER JOIN episodes
ON episodes.ep_id = script.episodes_ep_id
"""
data = pd.read_sql_query(query, engine)
return data.to_json(orient="records") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def get_all_record():\n # X_new = item.to_df()\n # item_str = item.to_string()\n # project_code = int(item_str[item_str.find('=')+1:])\n pg = PostgreSQL()\n return_json = pg.fetch_all_records()\n return return_json",
"def select_all_lines(conn):\n\n cur = conn.cursor()\n cur.execute... | [
"0.70531267",
"0.6787454",
"0.6718839",
"0.66217417",
"0.62935805",
"0.62533355",
"0.6238542",
"0.6206922",
"0.611652",
"0.608391",
"0.6056544",
"0.59862447",
"0.5970311",
"0.59272057",
"0.5872251",
"0.58481425",
"0.58452594",
"0.5820792",
"0.58157414",
"0.5807491",
"0.578491... | 0.6937443 | 1 |
queries the database to insert a line from a character takes a name , character and episode returns a confirmation message | def new_line(script_l, character, episode):
if up.check("characters", character):
char_id = up.giveId("characters", character)
else:
up.insertCharacter(character)
char_id = up.giveId("characters", character)
if up.check("episodes", episode):
ep_id = up.giveId("episodes", episode)
else:
up.insertEpisode(episode)
ep_id = up.giveId("episodes", episode)
if up.check("script", script_l) and up.check("characters", character) and up.check("episodes", episode):
return "line exists"
else:
engine.execute(f"""
INSERT INTO script (script_l, characters_char_id, episodes_ep_id) VALUES
("{script_l}", "{char_id}", "{ep_id}");
""")
return f"successfully loaded: {character},{script_l},{episode}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insertCharacter(string):\n if check(\"character\", string):\n return \"character exists\"\n else:\n engine.execute(f\"INSERT INTO characters (name) VALUES ('{string}');\")",
"def insertLine(row):\n if check(\"script\", row[\"dialogue\"]) and check(\"characters\", row[\"character\"]) an... | [
"0.68971574",
"0.6643312",
"0.6609034",
"0.63286656",
"0.62008613",
"0.596312",
"0.5952589",
"0.5870909",
"0.58442897",
"0.5829052",
"0.58106846",
"0.58059555",
"0.57868826",
"0.57820517",
"0.5769277",
"0.56395286",
"0.5630925",
"0.56198794",
"0.5592354",
"0.55757797",
"0.551... | 0.6655293 | 1 |
Creates a new SnakemakeRule instance from a dict representation | def __init__(
self, rule_id, parent_id, input, output, local=False, template=None, **kwargs
):
self.rule_id = rule_id
self.parent_id = parent_id
self.input = input
self.output = output
self.local = local
self.template = template
self.params = kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dictionary: Dict[str, Any]):\n return cls(**dictionary)",
"def from_dict(cls, data: Dict[str, any]):\n return cls(**data)",
"def from_dict(self, d):\r\n options = dict(d)\r\n task_id = options['task_id']\r\n del options['task_id']\r\n return SubtaskS... | [
"0.61375326",
"0.60902405",
"0.60286725",
"0.60125345",
"0.597105",
"0.5911886",
"0.5895749",
"0.5895749",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
... | 0.0 | -1 |
Prints a string representation of SnakemakeRule instance | def __repr__(self):
template = """
SnakemakeRule ({})
- parent_id : {}
- input : {}
- output : {}
- local : {}
- template : {}
- params : {}
"""
return template.format(
self.rule_id,
self.parent_id,
self.input,
self.output,
self.local,
self.template,
self.params,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return \"[ %s ]\" % str(self.__rule)",
"def __str__(self):\n return \"{ %s }\" % str(self.__rule)",
"def __str__(self):\n return \"{ %s }1\" % str(self.__rule)",
"def print_rules(self):\n for idx, r in enumerate(self.rules):\n print(idx, \"=>\", r._... | [
"0.7497269",
"0.74336165",
"0.73365",
"0.7002294",
"0.6986714",
"0.6474687",
"0.6371976",
"0.63269794",
"0.62451273",
"0.620827",
"0.6196005",
"0.6190379",
"0.6182506",
"0.6179493",
"0.6122271",
"0.60830194",
"0.60090804",
"0.5991629",
"0.59799564",
"0.5888777",
"0.5873028",
... | 0.7577809 | 0 |
Creates a new SnakemakeRule instance from a dict representation | def __init__(
self,
rule_id,
parent_id,
input,
output,
inline=True,
local=False,
template=None,
**kwargs
):
super().__init__(rule_id, parent_id, input, output, local, template, **kwargs)
self.inline = inline
self.groupped = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dictionary: Dict[str, Any]):\n return cls(**dictionary)",
"def from_dict(cls, data: Dict[str, any]):\n return cls(**data)",
"def from_dict(self, d):\r\n options = dict(d)\r\n task_id = options['task_id']\r\n del options['task_id']\r\n return SubtaskS... | [
"0.61388683",
"0.60924554",
"0.603016",
"0.60145026",
"0.59718156",
"0.5911789",
"0.5897906",
"0.5897906",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
"0.5892087",
... | 0.0 | -1 |
Prints a string representation of SnakemakeRule instance | def __repr__(self):
template = """
- inline : {}
"""
return super().__repr__() + template.format(self.inline) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __repr__(self):\n template = \"\"\"\n SnakemakeRule ({})\n \n - parent_id : {}\n - input : {}\n - output : {}\n - local : {}\n - template : {}\n - params : {}\n \"\"\"\n return template.format(\n ... | [
"0.7577809",
"0.7497269",
"0.74336165",
"0.73365",
"0.7002294",
"0.6986714",
"0.6474687",
"0.6371976",
"0.63269794",
"0.62451273",
"0.620827",
"0.6196005",
"0.6190379",
"0.6182506",
"0.6179493",
"0.6122271",
"0.60830194",
"0.60090804",
"0.5991629",
"0.59799564",
"0.5888777",
... | 0.0 | -1 |
Creates a new SnakemakeRule instance from a dict representation | def __init__(self, rule_id, input, output, local=False, template=None, **kwargs):
super().__init__(rule_id, None, input, output, local, template, **kwargs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dictionary: Dict[str, Any]):\n return cls(**dictionary)",
"def from_dict(cls, data: Dict[str, any]):\n return cls(**data)",
"def from_dict(self, d):\r\n options = dict(d)\r\n task_id = options['task_id']\r\n del options['task_id']\r\n return SubtaskS... | [
"0.61375326",
"0.60902405",
"0.60286725",
"0.60125345",
"0.597105",
"0.5911886",
"0.5895749",
"0.5895749",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
"0.5891623",
... | 0.0 | -1 |
Creates a new SnakemakeRule instance from a dict representation | def __init__(self, input, output, options, local=False):
super().__init__(
"create_training_set",
None,
input,
output,
local,
"multi_training_set.snakefile",
)
self.options = options | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, dictionary: Dict[str, Any]):\n return cls(**dictionary)",
"def from_dict(cls, data: Dict[str, any]):\n return cls(**data)",
"def from_dict(self, d):\r\n options = dict(d)\r\n task_id = options['task_id']\r\n del options['task_id']\r\n return SubtaskS... | [
"0.61378634",
"0.60913914",
"0.6031257",
"0.6013804",
"0.59718573",
"0.591209",
"0.5898094",
"0.5898094",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
"0.5891009",
... | 0.0 | -1 |
Creates a new ReportRule instance from a dict representation | def __init__(self, rule_id, input, output, rmd, title, name, metadata, styles, theme, local=False, **kwargs):
super().__init__(rule_id, None, input, output, local, **kwargs)
self.rmd = rmd
self.title = title
self.name = name
self.metadata = metadata
self.styles = styles
self.theme = theme
# include rmd in params as well (expected by snakemake)
self.params["rmd"] = rmd
# other parameters used for report generation
self.params["title"] = title
self.params["name"] = name
self.params["metadata"] = metadata
self.params["styles"] = styles
self.params["theme"] = theme | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dictionary(cls,\r\n dictionary):\r\n if dictionary is None:\r\n return None\r\n\r\n # Extract variables from the dictionary\r\n id = dictionary.get('id')\r\n consumer_id = dictionary.get('consumerId')\r\n consumer_ssn = dictionary.get('c... | [
"0.61548984",
"0.6064839",
"0.5945463",
"0.59221816",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0.591966",
"0... | 0.0 | -1 |
Creates a new SnakemakeRuleGroup instance from a dict representation | def __init__(
self, rule_id, parent_id, group_actions, input, output, local=False, **kwargs
):
self.rule_id = rule_id
self.parent_id = parent_id
self.input = input
self.output = output
self.local = local
self.params = kwargs
self.groupped = True
# load sub-actions
self.actions = OrderedDict()
for action in group_actions:
# get action name
action_name = action["action_name"]
del action["action_name"]
# determine template filepath
action_type = action_name.split("_")[0]
template = "actions/{}/{}.snakefile".format(action_type, action_name)
# create new SnakemakeRule instance
self.actions[action_name] = ActionRule(
rule_id=None,
parent_id=None,
input=None,
output=None,
template=template,
**action
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_json(value):\r\n for key in ('id', 'name', 'version'):\r\n if key not in value:\r\n raise TypeError(\"Group dict {0} missing value key '{1}'\".format(\r\n value, key))\r\n\r\n if value[\"version\"] != Group.VERSION:\r\n raise TypeError(... | [
"0.6249006",
"0.60356647",
"0.59878504",
"0.58986735",
"0.5808315",
"0.57978857",
"0.56254345",
"0.55351543",
"0.54451144",
"0.5398016",
"0.53898287",
"0.5336041",
"0.5296309",
"0.5290205",
"0.52836525",
"0.5264334",
"0.52494353",
"0.52434814",
"0.52236265",
"0.5214074",
"0.5... | 0.50452054 | 83 |
Creates a new DataIntegrationRule instance from a dict representation | def __init__(self, rule_id, inputs, output, local=False, template=None, **kwargs):
self.rule_id = rule_id
self.inputs = inputs
self.output = output
self.local = local
self.template = template
self.params = kwargs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_dict(cls, data):\n return cls(**data)",
"def from_dict(cls, data: Dict[str, any]):\n return cls(**data)",
"def _from_dict(cls, d):\n confidence = d.get(\"confidence\", None)\n constant = d.get(\"constant\", False)\n tags = d.get(\"tags\", None)\n return cls(\n... | [
"0.6742197",
"0.65646493",
"0.6552889",
"0.648437",
"0.63919514",
"0.6375292",
"0.6365639",
"0.62918556",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0.62024546",
"0... | 0.0 | -1 |
Prints a string representation of DataIntegrationRule instance | def __repr__(self):
template = """
DataIntegrationRule ({})
- inputs : {}
- output : {}
- local : {}
- template : {}
- params : {}
"""
return template.format(
self.rule_id,
self.inputs,
self.output,
self.local,
self.template,
self.params
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return \"[ %s ]\" % str(self.__rule)",
"def __str__(self):\n return \"{ %s }\" % str(self.__rule)",
"def __str__(self):\n return \"{ %s }1\" % str(self.__rule)",
"def __str__ (self) :\n\t\ttext_rule = \"\"\n\t\t\n\t\tfor key, rules in self.production_rules.items() :\... | [
"0.68674856",
"0.68624336",
"0.6645672",
"0.6532725",
"0.6322785",
"0.628434",
"0.62689245",
"0.6190423",
"0.604574",
"0.6039333",
"0.59951794",
"0.5989062",
"0.5976397",
"0.59762686",
"0.5963851",
"0.5931985",
"0.59197044",
"0.59166557",
"0.590007",
"0.5897591",
"0.5883885",... | 0.7635887 | 0 |
Syncs an account by the account_name | def test_sync_account(self):
runner = CliRunner()
LOG.info("Testing 'calm sync account {}".format(ACCOUNT_NAME))
result = runner.invoke(
cli,
["sync", "account", ACCOUNT_NAME],
)
if result.exit_code:
cli_res_dict = {"Output": result.output, "Exception": str(result.exception)}
LOG.debug(
"Cli Response: {}".format(
json.dumps(cli_res_dict, indent=4, separators=(",", ": "))
)
)
LOG.debug(
"Traceback: \n{}".format(
"".join(traceback.format_tb(result.exc_info[2]))
)
)
pytest.fail("Account sync failed")
LOG.info("Success") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sync_account(account):\n stripe_account = stripe.Account.retrieve(id=account.stripe_id)\n return sync_account_from_stripe_data(stripe_account)",
"def put_account(self, account):\n \n pass",
"def change_account(self, account):\r\n check_account = Account(account, steem_instance=se... | [
"0.7384746",
"0.7075692",
"0.68083966",
"0.6631079",
"0.6615135",
"0.6615135",
"0.657953",
"0.6560432",
"0.6560432",
"0.65222824",
"0.63499165",
"0.6179354",
"0.6179354",
"0.6179354",
"0.6179354",
"0.60058033",
"0.6002847",
"0.5868163",
"0.5854182",
"0.5799399",
"0.57433146",... | 0.6851966 | 2 |
Send a password reset email to the suer | def deliever_password_reset_mail(user_id, reset_password_url):
user = User.query.get(user_id)
if user is not None:
try:
url = f"{celery.conf.get('EMAIL_SERVICE_HOST')}/api/email/"
payload = {
"sender": celery.conf.get("MAIL_DEFAULT_SENDER"),
"receiver": user.email,
"subject": "Password reset from snake eyes",
"template_id": 2,
"request_id": uuid4().hex,
"template_params": {"username": user.username, "reset_password_url": reset_password_url}
}
response = post(url, json=payload, headers={"Accept": "application/json"})
except RequestException as e:
print(f"[********] UNABLE TO DELIEVER MAIL {e}") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_password_reset_email():\n aaa.send_password_reset_email(\n username=post_get('username'),\n email_addr=post_get('email_address')\n )\n return 'Please check your mailbox.'",
"def send_pw_reset_email(user):\n token = user.get_token()\n message = Message(\n 'Reset Your P... | [
"0.8450898",
"0.8287654",
"0.8170258",
"0.81437016",
"0.8022583",
"0.79395986",
"0.79303086",
"0.7930095",
"0.78969234",
"0.7764277",
"0.77437997",
"0.7633127",
"0.75886834",
"0.7538881",
"0.7430109",
"0.74129903",
"0.7399443",
"0.73773956",
"0.7365607",
"0.7308838",
"0.73015... | 0.76305157 | 12 |
Initializes the finger model on which control's to be performed. | def __init__(self):
self.urdf_path = '/opt/blmc_ei/src/robot_properties_fingers/urdf/pro/trifingerpro.urdf'
self.tip_link_names = [
"finger_tip_link_0",
"finger_tip_link_120",
"finger_tip_link_240",
]
self.robot_model = pinocchio.buildModelFromUrdf(self.urdf_path)
self.data = self.robot_model.createData()
self.tip_link_ids = [
self.robot_model.getFrameId(link_name)
for link_name in self.tip_link_names
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def controls_setup(self):\n pass",
"def _initialize(self):\n \n self.view.lineEdit_3.setText(\"C,H,N,O,P,S\")\n self.view.spin_hit.setValue(20)\n self.view.lineEdit_2.setValue(10.)\n self.view.checkBox_8.setChecked(True)",
"def initialize(self):\n self.Update()\... | [
"0.63043123",
"0.60653013",
"0.6031488",
"0.59517133",
"0.5951652",
"0.5938752",
"0.5925825",
"0.5837696",
"0.583645",
"0.5820525",
"0.5815757",
"0.57706904",
"0.5763885",
"0.5759453",
"0.5727885",
"0.57115877",
"0.57033086",
"0.5632936",
"0.5617571",
"0.55889696",
"0.5574349... | 0.0 | -1 |
Compute end effector positions for the given joint configuration. | def forward_kinematics(self, joint_positions):
pinocchio.framesForwardKinematics(
self.robot_model, self.data, joint_positions,
)
return [
np.asarray(self.data.oMf[link_id].translation).reshape(-1).tolist()
for link_id in self.tip_link_ids
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def end_effectors_pos(self):\n def relative_pos_in_egocentric_frame(physics):\n end_effector = physics.bind(self._entity.end_effectors).xpos\n torso = physics.bind(self._entity.root_body).xpos\n xmat = np.reshape(physics.bind(self._entity.root_body).xmat, (3, 3))\n return np.reshape(np.dot(e... | [
"0.66042507",
"0.5623347",
"0.5479054",
"0.5479054",
"0.53170127",
"0.5305009",
"0.517255",
"0.515755",
"0.51083076",
"0.5084015",
"0.5083615",
"0.503888",
"0.4970291",
"0.49539378",
"0.4946133",
"0.4942832",
"0.49085337",
"0.49002567",
"0.48989028",
"0.48899674",
"0.48782575... | 0.0 | -1 |
Compute the jacobian of a finger at configuration q0. | def compute_jacobian(self, finger_id, q0):
frame_id = self.tip_link_ids[finger_id]
return pinocchio.computeFrameJacobian(
self.robot_model,
self.data,
q0,
frame_id,
pinocchio.ReferenceFrame.LOCAL_WORLD_ALIGNED,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def jacobian(self, dt):\n raise NotImplementedError",
"def jacobian(self, x):\n pass",
"def jacobian_ur5(q, delta=0.0001):\n # Alocacion de memoria\n J = np.zeros((3,6))\n # Transformacion homogenea inicial (usando q)\n T = fkine_ur5(q)\n # Iteracion para la derivada de cada column... | [
"0.7174676",
"0.7125965",
"0.71125233",
"0.702315",
"0.6786565",
"0.6759826",
"0.6759339",
"0.66214025",
"0.6568994",
"0.653416",
"0.6530263",
"0.6495432",
"0.64650875",
"0.64634913",
"0.64042807",
"0.6402122",
"0.6401185",
"0.6370891",
"0.6329893",
"0.63101774",
"0.63079286"... | 0.7850823 | 0 |
Performs a line search, reducing the step size until the end effector moves closer to the desired position. | def _line_search(self, finger_id, xdes, q0, dq, max_iter=10, dt=1.0):
xcurrent = self.forward_kinematics(q0)[finger_id]
original_error = np.linalg.norm(xdes - xcurrent)
error = np.inf
q = q0
iter = 0
while error >= original_error:
q = pinocchio.integrate(self.robot_model, q0, dt * dq)
q = self._project_onto_constraints(q)
xcurrent = self.forward_kinematics(q)[finger_id]
error = np.linalg.norm(xdes - xcurrent)
dt /= 2
iter += 1
if iter == max_iter:
# Likely at a local minimum
return q0, original_error, 0
return q, error, 2 * dt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def LineSearch(Pos, Dir, dx, EFracTol, M, L, Cut,\n Accel = 1.5, MaxInc = 10., MaxIter = 10000):\n #start the iteration counter\n Iter = 0\n\n #find the normalized direction\n NormDir = Dir / np.sqrt(np.sum(Dir * Dir))\n\n #take the first two steps and compute energies\n Dists = [0.... | [
"0.68932176",
"0.6578148",
"0.6477977",
"0.6415695",
"0.63791597",
"0.63132477",
"0.6291235",
"0.62559795",
"0.61942357",
"0.6165324",
"0.59852433",
"0.59499186",
"0.58552325",
"0.58447623",
"0.58173835",
"0.57838714",
"0.5747636",
"0.5531475",
"0.5473468",
"0.546951",
"0.546... | 0.6214263 | 8 |
Computes the direction in which to update joint positions. | def _compute_dq(self, finger_id, xdes, q0):
Ji = self.compute_jacobian(finger_id, q0)[:3, :]
frame_id = self.tip_link_ids[finger_id]
xcurrent = self.data.oMf[frame_id].translation
Jinv = np.linalg.pinv(Ji)
return Jinv.dot(xdes - xcurrent) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def junction_direction(start_junction: Cell, end_junction: Cell) -> Direction:\n dx = end_junction.column - start_junction.column\n dy = end_junction.row - start_junction.row\n if dy == 0:\n return Direction.E if dx > 0 else Direction.W\n return Direction.S if dy > 0 else Dir... | [
"0.66263056",
"0.64241725",
"0.6403347",
"0.6384829",
"0.635541",
"0.6334753",
"0.62950736",
"0.6242469",
"0.6156684",
"0.61190027",
"0.6118653",
"0.6086555",
"0.60604405",
"0.59984165",
"0.5993906",
"0.5980935",
"0.59729487",
"0.59629905",
"0.59500116",
"0.5931701",
"0.59066... | 0.0 | -1 |
Compute the joint positions which approximately result in a given end effector position. | def inverse_kinematics(self, finger_id, xdes, q0, tol=0.001, max_iter=20):
iter = 0
q = self._project_onto_constraints(q0)
xcurrent = self.forward_kinematics(q)[finger_id]
error = np.linalg.norm(xdes - xcurrent)
dt = 1.0
prev_error = np.inf
while error > tol and (prev_error - error) > 1e-5 and iter < max_iter:
dq = self._compute_dq(finger_id, xdes, q)
# start the line search with a step size a bit larger than the
# previous step size.
dt = min(1.0, 2 * dt)
prev_error = error
q, error, dt = self._line_search(finger_id, xdes, q, dq, dt=dt)
iter += 1
if error > tol:
return None
return q | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_joint_positions(self, joint_angles ): \n\n\n # current angles\n res_joint_angles = joint_angles.copy() \n\n # detect limits\n maskminus= res_joint_angles > self.joint_lims[:,0]\n maskplus = res_joint_angles < self.joint_lims[:,1]\n \n res_joint_angles = res_joi... | [
"0.6462823",
"0.63993484",
"0.5633511",
"0.5578069",
"0.5572044",
"0.55286443",
"0.549767",
"0.54915667",
"0.5486143",
"0.5462283",
"0.5461075",
"0.54595315",
"0.54163367",
"0.5408067",
"0.5399364",
"0.53867376",
"0.53730583",
"0.536113",
"0.533599",
"0.5330091",
"0.5328494",... | 0.0 | -1 |
Return the first element of a 2tuple. >>> x([1,2]) 1 | def x(a):
return a[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x",
"def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x",
"def single_element_tuple():\n single = (1,)\n print(type(single)) # <type 'tuple'>",
"def _item_or_tuple(self, ... | [
"0.7628563",
"0.7628563",
"0.70851874",
"0.6855407",
"0.6824074",
"0.6824074",
"0.68147033",
"0.6742139",
"0.6742139",
"0.6360675",
"0.63022053",
"0.63022053",
"0.62847096",
"0.62811184",
"0.62070453",
"0.6096625",
"0.60256463",
"0.6006833",
"0.59457403",
"0.5892296",
"0.5847... | 0.6782777 | 7 |
Return the second element of a 2tuple. >>> y([1,2]) 2 | def y(a):
return a[1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def second(xs):\n if not xs:\n return None\n return xs[1]",
"def second(xs):\n if not xs:\n return None\n return xs[1]",
"def second(pair):\n\treturn pair[1]",
"def _unpack_tuple(x):\n if len(x) == 1:\n return x[0]\n else:\n return x",
"def _unpack_tuple(x):\n ... | [
"0.68817425",
"0.68817425",
"0.6846556",
"0.67233163",
"0.67233163",
"0.6483205",
"0.62575674",
"0.6175765",
"0.60955626",
"0.60955626",
"0.60284346",
"0.59068465",
"0.57835966",
"0.57772356",
"0.57753384",
"0.5761288",
"0.570475",
"0.5695715",
"0.5689088",
"0.568156",
"0.567... | 0.6603884 | 5 |
Euclidean distance (in pixels). >>> distance( (1,1),(2,2) ) == math.sqrt(2) True | def distance(a,b):
return np.sqrt( (x(a)-x(b))**2 + (y(a)-y(b))**2 ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def euclidean_distance(x, y):\n x1, y1 = x\n x2, y2 = y\n return sqrt((x1 - x2)**2 + (y1 - y2)**2)",
"def euclidean_distance(x1: np.ndarray, x2: np.ndarray) -> float:\n return np.sqrt(np.square(x1 - x2).sum())",
"def euclidean_distance(x1, x2):\n return np.sqrt(np.sum(np.square(np.subtract(x1, x... | [
"0.73406076",
"0.7278529",
"0.7243951",
"0.7199394",
"0.71825296",
"0.7171505",
"0.71208745",
"0.7119449",
"0.7014406",
"0.6984854",
"0.69682425",
"0.69490933",
"0.694145",
"0.69366956",
"0.69258237",
"0.6921075",
"0.6908294",
"0.68853986",
"0.68835557",
"0.68756497",
"0.6842... | 0.6488884 | 58 |
Creates initial design of n_samples drawn from a latin hypercube. | def sample_latin_hypercube(low, high, n_samples, rng=None):
if rng is None:
rng = np.random.RandomState(np.random.randint(0, 10000))
n_dims = low.shape[0]
samples = []
for i in range(n_dims):
if isinstance(low[i], numbers.Integral):
sample = random.sample(range(low[i], high[i]), n_samples)
elif isinstance(low[i], numbers.Real):
lower_bound = low[i]
upper_bound = high[i]
sample = lower_bound + rng.uniform(0, 1, n_samples) * (upper_bound - lower_bound)
else:
raise ValueError('Latin hypercube sampling can only draw from types int and real,'
' got {}!'.format(type(low[i])))
samples.append(sample)
samples = np.array(samples, dtype=object)
for i in range(n_dims):
rng.shuffle(samples[i, :])
return samples.T | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_hypercube(samples, N):\n\n np.random.seed(4654562)\n hypercube = lhs(N, samples=samples)\n\n return hypercube",
"def latin_hypercube(n_pts, dim):\n X = np.zeros((n_pts, dim))\n centers = (1.0 + 2.0 * np.arange(0.0, n_pts)) / float(2 * n_pts)\n for i in range(dim): # Shuffle the center ... | [
"0.7344228",
"0.70358026",
"0.6742598",
"0.66756856",
"0.66756856",
"0.66473216",
"0.6490043",
"0.6219821",
"0.61849946",
"0.5989805",
"0.5899154",
"0.5826263",
"0.57939756",
"0.5752896",
"0.57472134",
"0.5707989",
"0.5667523",
"0.5647223",
"0.5642459",
"0.5631831",
"0.558605... | 0.6458857 | 7 |
Creates the initial search space using latin hypercube sampling. | def lhs_start(hyperbounds, n_samples, rng=None):
low_bounds = []
high_bounds = []
for bound in hyperbounds:
low_bounds.append(bound[0])
high_bounds.append(bound[1])
low_bounds = np.array(low_bounds, dtype=object)
high_bounds = np.array(high_bounds, dtype=object)
samples = sample_latin_hypercube(low_bounds, high_bounds, n_samples, rng=rng)
samples = samples.tolist()
return samples | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_latin_hypercube(samples, param_dict, class_root, seed=10):\n # Set random seed\n random.seed(seed)\n\n # Create dictionary to hold sampled parameter values\n sample_points = {}\n for key in param_dict.keys():\n sample_points[key] = np.zeros(samples)\n Ndim = len(param_dict.key... | [
"0.6223337",
"0.59628797",
"0.58912903",
"0.5874907",
"0.5854502",
"0.5754657",
"0.5733271",
"0.56222767",
"0.5502466",
"0.54998386",
"0.54974604",
"0.5460404",
"0.5426823",
"0.5370437",
"0.53134114",
"0.5280649",
"0.52736545",
"0.5264173",
"0.52439547",
"0.52239805",
"0.5213... | 0.6189054 | 1 |
merges station letters into single list of station names | def stations(station_let):
stat = ['']*np.size(station_let,0)
for i in range(len(stat)):
for j in range(4):
if station_let[i][j] is not np.ma.masked:
stat[i]+=station_let[i][j]
return stat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def combine_state_names_and_abbreviations():\n lst=[]\n for k,v in us_state_abbrev.items():\n lst.append(v)\n lst = sorted(lst[:10])\n state = sorted(states)\n print(lst+state[-10:])\n return",
"def combine_state_names_and_abbreviations():\n return sorted(us_state_abbrev.values())[:10... | [
"0.59636515",
"0.5915971",
"0.58542436",
"0.5853559",
"0.5729847",
"0.5717873",
"0.57039684",
"0.5570993",
"0.54327935",
"0.54008317",
"0.53579676",
"0.5210223",
"0.51921755",
"0.5177079",
"0.5170437",
"0.51636785",
"0.5162297",
"0.514331",
"0.5133783",
"0.51176363",
"0.51169... | 0.53781 | 10 |
converts time to gmt, appends to list | def gmt(time):
gmt = [0]*time.size
for i in range(time.size):
gmt[i]=datetime.utcfromtimestamp(time[i]).strftime('%Y-%m-%d %H:%M:%S')
return gmt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_time(self):\r\n\r\n curr_time = datetime.datetime.now()\r\n time = []\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.second)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_time.minute)])\r\n time.append([int(x) for x in '{0:06b}'.format(curr_tim... | [
"0.6536344",
"0.61749583",
"0.5915965",
"0.57811874",
"0.5765255",
"0.5675775",
"0.5667193",
"0.5663627",
"0.5660941",
"0.5621869",
"0.5613156",
"0.55986845",
"0.5576497",
"0.5538767",
"0.55233485",
"0.55079854",
"0.5443805",
"0.5407144",
"0.53734505",
"0.53541595",
"0.534225... | 0.72068673 | 0 |
finds stations that don't have predictand data and appends them to a list | def miss_station(all_stations,stations):
diff = len(all_stations)-len(stations)
k=0
i=0
miss_stations = ['']*diff
a = all_stations[:]
a.sort()
s = stations[:]
s.sort()
while i < len(stations):
while a[i] != s[i]:
miss_stations[k]=a[i]
del a[i]
k+=1
i+=1
return miss_stations | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stations():\n\n return station_list",
"def prep_stations(url):\n stations = []\n _stations = requests.get(url).json()\n\n for _station in _stations['stationBeanList']:\n if _station['statusKey'] == 1:\n stations.append([_station['stationName'], _station['id'],\n ... | [
"0.6918483",
"0.63394576",
"0.6295055",
"0.6175655",
"0.6134076",
"0.6036043",
"0.59990704",
"0.5990439",
"0.5939404",
"0.59325945",
"0.5899833",
"0.5873434",
"0.58216053",
"0.5731162",
"0.5703947",
"0.5658584",
"0.5635071",
"0.563275",
"0.56268054",
"0.5625375",
"0.56150836"... | 0.66159266 | 1 |
builds and saves dataframe to be used for graphs | def dataframe():
#allows function to access station, gmt, and miss_station functions
global stations
global gmt
global miss_station
#read predictor file
control = cfg.read_yaml('../registry/graphs.yaml')
pred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))
predd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))
#get file paths and update database
predictor_file_path = control.predictor_file_path
predictand_file_path = control.predictand_file_path
pred_file_id = update(predictor_file_path)
predd_file_id = update(predictand_file_path)
#store lead time and date range
lead_time = control.lead_time
date_range = control.date_range
#get info for fetch many dates
start,end,stride = read_pred.parse_range(date_range)
fcst_ref_time = control.date_range[0].split('-')[0][-2:]
#initialize list of predictors
pred_list = pred_ctrl.predictors
predictor = []
#loops through predictors to build camps data objects
for entry_dict in pred_list:
#formats metadata
pred = create.preprocess_entries(entry_dict, fcst_ref_time)
#adds info to metadata that's not currently being stored
pred.search_metadata['reserved2'] = lead_time*3600
pred.search_metadata['file_id'] = pred_file_id
pred.search_metadata['reserved1'] = 'vector'
#build camps data objects for each day
variable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)
#appends all data to single camps object
if variable[0] is not None:
var = variable[0]
arrs = []
for i in range(len(variable)):
arrs.append(variable[i].data)
var.data = np.stack(arrs)
predictor.append(var)
#initializes list of predictands
predd_list = predd_ctrl.predictands
predictand = []
#loops through predictands to build camps data objects
for entry_dict in predd_list:
#formats metadata
vertical_coordinate = entry_dict.pop('Vertical_Coordinate')
entry_dict['file_id'] = predd_file_id
#build camps objects for each day
variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)
#append all data to single camps object
var = variable[0]
arrs = []
for i in range(len(variable)):
arrs.append(variable[i].data)
try:
var.data = np.stack(arrs)
predictand.append(var)
except:
print("Can't read " + variable.name)
#getting predictor station and time data
predr = Dataset(predictor_file_path[0])
predr_stat = predr.variables['station'][:]
if lead_time == 3:
predr_time = predr.variables['OM__phenomenonTimeInstant'][:]
elif lead_time == 6:
predr_time = predr.variables['OM__phenomenonTimeInstant1'][:]
elif lead_time == 12:
predr_time = predr.variables['OM__phenomenonTimeInstant2'][:]
predr.close()
#reformatting predictor station and time data
predr_stations = stations(predr_stat)
predr_gmt = gmt(predr_time)
#getting predictand station and time data
predd = Dataset(predictand_file_path[0])
predd_stat = predd.variables['station'][:]
predd_time = predd.variables['OM__resultTime'][:]
predd.close()
#reformatting predictand station and time data
predd_stations = stations(predd_stat)
predd_gmt = gmt(predd_time)
#choosing predictand observations that line up with predictor time
hour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time
days = len(predd_gmt)/24
predd_hours = [0]*days
k=0
for i in range(len(predd_gmt)):
if i%24 == hour:
predd_hours[k]=predd_gmt[i]
k+=1
#catches when GFS data doesn't cover the last day of the month
if len(predr_gmt) < len(predd_hours):
predd_hours = predd_hours[:-1]
#find missing stations
miss_stations = miss_station(predr_stations,predd_stations)
stations = predd_stations
#station and time array
info = [['',''] for k in range(len(predr_gmt)*len(stations))]
for i in range(len(predr_gmt)):
for j in range(len(stations)):
k = i*len(stations)+j
info[k][0]=predr_gmt[i]
info[k][1]=stations[j]
#create column names
names = ['']*(len(predictor)+len(predictand)+2)
names[0]='Time'
names[1]='Station'
#creating array
arr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))
#adding predictor data
for i in range(len(predictor)):
#remove lead time and forecast reference time from variable name
#and add variable name to column list of final dataframe
if lead_time == 12:
names[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]
else:
names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]
#create pandas dataframe of data and sort alphabetically by station name
predictor[i].data = np.squeeze(predictor[i].data,axis=2)
predictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)
predictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)
#remove stations with no predictand data
k=0
a=miss_stations[:]
for j in predictor[i].data.columns:
if not a:
break
if j==a[k]:
predictor[i].data=predictor[i].data.drop(j,axis=1)
del a[k]
#add data to final dataframe
for b in range(len(predr_gmt)):
for c in range(len(stations)):
k = b*len(stations)+c
arr[k][i] = predictor[i].data.iloc[b][c]
#add predictand data
for i in range(len(predictand)):
#removing extra underscore, adding variable name to column names
names[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]
#resize array and create pandas dataframe
predictand[i].data = np.squeeze(predictand[i].data,axis=2)
predictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)
predictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)
#remove extra days of predictand data
predictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]
#add predictand data to array
for b in range(len(predr_gmt)):
for c in range(len(stations)):
k = b*len(stations)+c
val = predictand[i].data.iloc[b][c]
#catch metar fill data
if val == 9999:
val = np.nan
arr[k][len(predictor)+i]=val
#add station and time data to array and save as csv
data = np.concatenate([info,arr],axis = 1)
to_save = pd.DataFrame(data,columns=names)
to_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dataframe(self):\n\n df = pd.DataFrame({'date': [],\n 'RUN': [],\n 'CLONE': [],\n 'GEN': pd.Series(0, index=[], dtype='int'),\n 'frame': pd.Series([], index=[], dtype='int'),\n ... | [
"0.72918093",
"0.6938951",
"0.69203115",
"0.67623717",
"0.6709177",
"0.661987",
"0.66142595",
"0.6613234",
"0.65876186",
"0.65471715",
"0.65268254",
"0.6518715",
"0.6476695",
"0.6442566",
"0.63830763",
"0.63486266",
"0.6345487",
"0.6316515",
"0.6284121",
"0.6281206",
"0.62784... | 0.6171512 | 27 |
Hexlify raw text, return hexlified text. | def hexlify(text):
if six.PY3:
text = text.encode('utf-8')
hexlified = binascii.hexlify(text)
if six.PY3:
hexlified = hexlified.decode('utf-8')
return hexlified | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unhexlify(text):\n unhexlified = binascii.unhexlify(text)\n\n if six.PY3:\n unhexlified = unhexlified.decode('utf-8')\n\n return unhexlified",
"def hexify(text):\r\n return ' '.join([hexify_word(word) for word in text.split()])",
"def normalize(self, text):\n\n return binascii.hexli... | [
"0.7632533",
"0.7439492",
"0.70764095",
"0.65433866",
"0.6380229",
"0.61830765",
"0.61594963",
"0.61447966",
"0.6132453",
"0.6124988",
"0.61229116",
"0.6114684",
"0.59884095",
"0.5947459",
"0.59325373",
"0.5788667",
"0.5782058",
"0.57663274",
"0.5741124",
"0.57352805",
"0.571... | 0.78595716 | 0 |
Unhexlify raw text, return unhexlified text. | def unhexlify(text):
unhexlified = binascii.unhexlify(text)
if six.PY3:
unhexlified = unhexlified.decode('utf-8')
return unhexlified | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hexlify(text):\n if six.PY3:\n text = text.encode('utf-8')\n\n hexlified = binascii.hexlify(text)\n\n if six.PY3:\n hexlified = hexlified.decode('utf-8')\n\n return hexlified",
"def test_unhexlify_not_python():\n assert '' == uflash.unhexlify(\n ':020000040003F7\\n:10E0... | [
"0.76805043",
"0.724818",
"0.71128637",
"0.6909091",
"0.6862392",
"0.6219935",
"0.6197366",
"0.59515",
"0.5922873",
"0.5843483",
"0.57907474",
"0.5650561",
"0.56349534",
"0.56267136",
"0.55028135",
"0.550243",
"0.5497304",
"0.54252976",
"0.5407516",
"0.53313655",
"0.52876633"... | 0.8604057 | 0 |
Parse a line of text from the plot_data file. | def parse_line(self, line):
if line[0] == "#":
return False
parts = [x.strip() for x in line.strip().split(",")]
self.unix_time = int(parts[0])
self.cycles_done = int(parts[1])
self.cur_path = int(parts[2])
self.paths_total = int(parts[3])
self.pending_total = int(parts[4])
self.pending_favs = int(parts[5])
self.map_size = float(parts[6].replace("%",""))
self.unique_crashes = int(parts[7])
self.unique_hangs = int(parts[8])
self.max_depth = int(parts[9])
self.execs_per_sec = float(parts[10])
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_plot_cmd(self, line):\n line, any_vars = self.find_vars_in_str(line)\n words = line.split()\n words = self.fix_words(words)\n\n # Parse line\n has_out_var = False\n if len(words) == 6:\n has_out_var = True\n _, plot_type, _, in_data, _, out_... | [
"0.6670215",
"0.6484195",
"0.6202049",
"0.61103636",
"0.5995822",
"0.5897944",
"0.5888218",
"0.58159584",
"0.57317466",
"0.5645726",
"0.56055975",
"0.55672276",
"0.55463046",
"0.55388415",
"0.55194986",
"0.55111915",
"0.5509744",
"0.55055726",
"0.5483326",
"0.54770184",
"0.54... | 0.49969345 | 89 |
Obtains the record in the set with the time closest to the given $unix_time. If this record with not $within the correct number of seconds, an exception is raised. | def get_record(self, unix_time, within):
if len(self.records) <= 0:
raise Exception("No records in this set")
r = self.records[0]
closest_record = r
closest_delta = abs(r.unix_time - unix_time)
for r in self.records[1:]:
delta = abs(r.unix_time - unix_time)
if delta < closest_delta:
closest_record = r
closest_delta = delta
if closest_delta > within:
raise Exception("Closest record to %d was %d (delta=%d) which exceeds limit of %d" %
(unix_time, closest_record.unix_time, closest_delta, within))
return closest_record | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_closest_record(self, time):\n dist = 10000000\n record = -1\n # TODO: optimise a bit\n for i, itime in enumerate(self.times):\n if (abs(time-itime)) < dist:\n dist = abs(time-itime)\n record = i\n\n return record",
"def find_near... | [
"0.6383025",
"0.57725835",
"0.5726713",
"0.5603155",
"0.550198",
"0.5465888",
"0.51996434",
"0.51091826",
"0.49394408",
"0.49366295",
"0.49245772",
"0.48886275",
"0.48809275",
"0.48312008",
"0.48274845",
"0.4817998",
"0.4760067",
"0.47593623",
"0.47202304",
"0.47175002",
"0.4... | 0.84365386 | 0 |
Pulls in the records from other into self with the other, but since the timestamps won't match up perfectly, the output will only have a record per $period number of seconds. | def merge_with(self, other, period=60):
new_list = []
last_timestamp = 0
for r in self.records:
if abs(r.unix_time - last_timestamp) > period:
# Accept this record
last_timestamp = r.unix_time
other_r = other.get_record(r.unix_time, period/2)
r.merge_with(other_r)
new_list.append(r)
self.records = new_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __add__ ( self, other, resample_opts=None ):\n result = ObservationStorage (datadir=self.datadir, \\\n resample_opts=resample_opts )\n if self.date[0] > other.date[0]:\n start_date = other.date[0]\n else:\n start_date = self.date[0]\n if self.date[-1... | [
"0.60452324",
"0.5793913",
"0.5528706",
"0.5526547",
"0.5517215",
"0.5493142",
"0.54775965",
"0.54737484",
"0.5462078",
"0.54354554",
"0.5395026",
"0.5321515",
"0.529761",
"0.5283373",
"0.5275335",
"0.5271934",
"0.52359205",
"0.51437724",
"0.51378435",
"0.513382",
"0.51214904... | 0.7975035 | 0 |
Given a UC480 camera object (instrumental module) and a number indicating the number of trap objects, applies an iterative image analysis to individual trap adjustment in order to achieve a nearly homogeneous intensity profile across traps. | def stabilize_intensity(which_cam, cam, verbose=False):
L = 0.5 # Correction Rate
mags = np.ones(12) ### !
ntraps = len(mags)
iteration = 0
while iteration < 5:
iteration += 1
print("Iteration ", iteration)
im = cam.latest_frame()
try:
trap_powers = analyze_image(which_cam, im, ntraps, iteration, verbose)
except (AttributeError, ValueError) as e:
print("No Bueno, error occurred during image analysis:\n", e)
break
mean_power = trap_powers.mean()
rel_dif = 100 * trap_powers.std() / mean_power
print(f'Relative Power Difference: {rel_dif:.2f} %')
if rel_dif < 0.8:
print("WOW")
break
deltaP = [mean_power - P for P in trap_powers]
dmags = [(dP / abs(dP)) * sqrt(abs(dP)) * L for dP in deltaP]
mags = np.add(mags, dmags)
print("Magnitudes: ", mags)
break
# self._update_magnitudes(mags)
_ = analyze_image(im, ntraps, verbose=verbose) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def analyze_image(which_cam, image, ntraps, iteration=0, verbose=False):\n threshes = [0.5, 0.6]\n margin = 10\n threshold = np.max(image) * threshes[which_cam]\n im = image.transpose()\n\n x_len = len(im)\n peak_locs = np.zeros(x_len)\n peak_vals = np.zeros(x_len)\n\n ## Trap Peak Detectio... | [
"0.5652331",
"0.5335536",
"0.52658623",
"0.5259198",
"0.5201728",
"0.51909906",
"0.515129",
"0.5142113",
"0.5076965",
"0.5063984",
"0.5055916",
"0.49459288",
"0.4884683",
"0.48668435",
"0.4824002",
"0.482143",
"0.47895026",
"0.47749937",
"0.47587273",
"0.4741739",
"0.4710989"... | 0.5542998 | 1 |
Returns intensity profile of 1d gaussian beam | def gaussian1d(x, x0, w0, A, offset):
if w0 == 0:
return 0
return A * np.exp(-2 * (x - x0) ** 2 / (w0 ** 2)) + offset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pvalue_gaussian(self):\n \n pv = 2 * stats.norm.sf(abs(self.TS_prime_obs), loc=0, scale=1)\n return(pv)",
"def gaussian(amp, fwhm, mean, x):\n return amp * np.exp(-4. * np.log(2) * (x-mean)**2 / fwhm**2)",
"def estimate_uni_gaussian(X):\n mu = mean(X, axis=0)\n sigma2 = va... | [
"0.6619526",
"0.63317794",
"0.62996733",
"0.627856",
"0.62739635",
"0.61623496",
"0.6124203",
"0.610553",
"0.60432166",
"0.60368556",
"0.60006434",
"0.59681493",
"0.5952793",
"0.59485036",
"0.5927189",
"0.5926995",
"0.5875386",
"0.5873252",
"0.5861136",
"0.58427656",
"0.58294... | 0.0 | -1 |
Returns intensity profile of trap array | def gaussianarray1d(x, x0_vec, wx_vec, A_vec, offset, ntraps):
array = np.zeros(np.shape(x))
for k in range(ntraps):
array = array + gaussian1d(x, x0_vec[k], wx_vec[k], A_vec[k], 0)
return array + offset | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intensity(self) -> int:",
"def intensity(self):\r\n return np.power(prb.amplitude, 2)",
"def getIntensity(self):\n return self.getIntensityS() + self.getIntensityP()",
"def estimate_skin_tone(face_roi):\n return [int(face_roi[:, :, i].mean()) for i in range(face_roi.shape[-1])]",
"def ... | [
"0.62673634",
"0.593113",
"0.5869846",
"0.58618975",
"0.5649231",
"0.5581468",
"0.5578077",
"0.5490693",
"0.5435854",
"0.5350697",
"0.5301182",
"0.5300452",
"0.52878165",
"0.5265786",
"0.5254433",
"0.52346134",
"0.5218291",
"0.5211029",
"0.5208299",
"0.5205285",
"0.5173795",
... | 0.0 | -1 |
Juggles parameters in order to be able to fit a list of parameters | def wrapper_fit_func(x, ntraps, *args):
a, b, c = list(args[0][:ntraps]), list(args[0][ntraps:2 * ntraps]), list(args[0][2 * ntraps:3 * ntraps])
offset = args[0][-1]
return gaussianarray1d(x, a, b, c, offset, ntraps) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",... | [
"0.6610045",
"0.65775573",
"0.6563652",
"0.6521646",
"0.646521",
"0.6455579",
"0.63229144",
"0.63210595",
"0.63088965",
"0.629318",
"0.6284352",
"0.6191725",
"0.61914194",
"0.60860085",
"0.6085613",
"0.60815185",
"0.60616034",
"0.6052334",
"0.6052334",
"0.6052334",
"0.6023257... | 0.0 | -1 |
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