def __init__(self, param1, param2, param3):
self.radar_data = unpack(param1)
self.platform_position_data = param2
self.given_object = param3
self.meters = 0
self.size = 0
self.eyeballing_start_time = 0
self.eyeballing_end_time = 0
self.time_offset = 0
self.range_offset = 0
'''
#Import required modules
from pulson440_unpack import unpack
from read_files import read_radar_data, read_motion_data
from rcs import rcs
from data_align import align_data
from GeneralAlignedRangeTimeGraph import AlignedGraph
from FastLinInt import FastBackProjection
from LinInt import BackProjection
from Deconvolution import deconvolute
from plotRTI import plotRTI
from helper_functions import linear_interp_nan
import pickle
#Converts motion and RADAR data to the right data structures
unpack("../Raw_Data/uavsar1flight7")
#Loads data from the RADAR
motion_data = read_motion_data("../Raw_Data/UAVSAR1Flight7.csv", "UAVSAR1")
motion_data = linear_interp_nan(motion_data[1], motion_data[0])
radar_data = read_radar_data("data.pkl", 580, 0.3) #580, 0.30 is probably good
#Performs RCS correction
radar_data = rcs(radar_data)
#Plot RTI for radar_data
plotRTI(radar_data)
#Set parameters
#Take motion_start and motion_end from .tak file video
#Estimate radar_start from the plotRTI above, initially comment out code below
from pulson440_unpack import unpack
from backprojection import interp_approach
import matplotlib.pyplot as plt
import numpy as np
import pandas
import math
radar_data = unpack('UASSAR3_Final_1')
platform_position_data = 'uassar3_final_2.csv'
given_object = 'Right_Ref_Refl-FinalEvent.csv'
meters = 3
eyeballing_start_time = 160
eyeballing_end_time = 750
time_offset = 12
range_offset = 0.2
def extract_platform_position():
array = list()
new_array = list()
position_array = list()
final_result = list()
data = pandas.read_csv(platform_position_data,
skiprows=2,
low_memory=False)
for elements in data:
if "Rigid Body" in elements and "Marker" not in elements:
array.append(elements)
for contents in array:
for col in data[contents]:
if type(col) == str and "Position" in col:
@author: Mason + David
'''
#Import required modules
from pulson440_unpack import unpack
from read_files import read_radar_data, read_motion_data
from radar_movement import find_point_one_radar, find_i_of_first_motion, find_i_of_last_motion
from data_align import align_data
from GeneralAlignedRangeTimeGraph import AlignedGraph
from FastLinInt import BackProjection
from read_intensity import read_intensity
from Deconvolution import deconvolute
from backprojection import interp_approach, main #Ramu's backprojection
#Converts motion and RADAR data to the right data structures
unpack("../Raw_Data/uavsar1flight1") #Currently commented out because unused
#Loads data from the RADAR
motion_data = read_motion_data("../Raw_Data/UAVSAR1Flight1.csv","UAVSAR1")
radar_data = read_radar_data("../Raw_Data/data.pkl")
#Finds the first point of the motion data that it starts to move
#motion_start = find_i_of_first_motion(motion_data)
#Finds the last point of the motion data that it moves
#motion_end = find_i_of_last_motion(motion_data)
#Finds the first point in the radar data that it starts to move
#radar_start = find_point_one_radar(radar_data)
radar_start = 600
data = pandas.read_csv(platform_position_data, skiprows=6)
for elements in data:
if "Time" in elements:
array.append(elements)
for contents2 in array:
time_array.append(data[contents2][0:].values)
time_array = np.array(time_array).astype(np.float)
return time_array
def get_range(radar_xpos, radar_ypos, radar_zpos, img_x, img_y, img_z):
return math.sqrt((radar_xpos - img_x)**2 + (radar_ypos - img_y)**2 +
(radar_zpos - img_z)**2)
radar_data = unpack('UASSAR3_Saturday_pseudoflight_1')
platform_position_data = 'uassar3_saturday_pseudoflight_1.csv'
given_object = 'uassar3 triangle 3.csv'
meters = 3
size = 500
eyeballing_start_time = 160
eyeballing_end_time = 750
time_offset = 12
range_offset = 0.2
time_stamps = radar_data['time_stamp']
scan_data = radar_data['scan_data']
range_bins = radar_data['range_bins']
scan_data = np.array(scan_data).astype(float)
#optional rcs need button for this
def extract_time_stamp():
array = list()
time_array = list()
data = pandas.read_csv(platform_position_data, skiprows=6)
for elements in data:
if "Time" in elements:
array.append(elements)
for contents2 in array:
time_array.append(data[contents2][0:].values)
time_array = np.array(time_array).astype(np.float)
return time_array
def get_range(radar_xpos, radar_ypos, radar_zpos, img_x, img_y, img_z):
return math.sqrt((radar_xpos - img_x)**2 + (radar_ypos - img_y)**2 + (radar_zpos - img_z)**2)
radar_data = unpack('UASSAR3_Saturday_test1_scan')
platform_position_data = 'UASSAR_3_saturday_test_1_scan.csv'
given_object = 'triangle.csv'
meters = 7
size = 500
eyeballing_start_time = 200
eyeballing_end_time = 700
time_offset = 19.5
range_offset = 0.6
time_stamps = radar_data['time_stamp']
scan_data = radar_data['scan_data']
range_bins = radar_data['range_bins']
scan_data = np.array(scan_data).astype(float)
'''
#Import required modules
from pulson440_unpack import unpack
from read_files import read_radar_data, read_motion_data
from rcs import rcs
from data_align import align_data
from GeneralAlignedRangeTimeGraph import AlignedGraph
from LinInt import BackProjection
from Deconvolution import deconvolute
from plotRTI import plotRTI
from FastLinInt import FastBackProjection
from helper_functions import linear_interp_nan
import matplotlib.pyplot as plt
#Converts motion and RADAR data to the right data structures
unpack("../Raw_Data/uavsar3flight5")
#Loads data from the RADAR
motion_data = read_motion_data("../Raw_Data/UAVSAR3Flight5.csv", "UAVSAR3")
motion_data = linear_interp_nan(motion_data[1], motion_data[0])
radar_data = read_radar_data("data.pkl", 580, 2) #580, 0.25 is probably good
#Performs RCS correction
radar_data = rcs(radar_data)
#Plot RTI for radar_data
plotRTI(radar_data)
#Set parameters
#Take motion_start and motion_end from .tak file video
#Estimate radar_start from the plotRTI above, initially comment out code below